Evaluating the performance and influencing factors of three portable black carbon monitors for field measurement

Black carbon (BC) is associated with adverse human health and climate change. Mapping BC spatial distribution imperatively requires low-cost and portable devices. Several portable BC monitors are commercially available, but their accuracy and reliability are not always satisfactory during continuous field observation. This study evaluated three models of portable black carbon monitors, C12, MA350 and DST, and investigates the factors that affect their performance. The monitors were tested in urban Beijing, where portable devices running for one month alongside a regular-size reference aethalometer AE33. The study considers several factors that could influence the monitors' performance, including ambient weather, aerosol composition, loading artifacts, and built-in algorithms. The results show that MA350 and DST present considerable discrepancies to the reference instrument, mainly occurring at lower concentrations (0-500 ng/m3) and higher concentrations (2500-8000 ng/m3), respectively. These discrepancies were likely caused by the anomalous noise of MA350 and the loading artifacts of DST. The study also suggests that the ambient environment has limited influence on the monitors' performance, but loading artifacts and accompanying compensation algorithms can result in unrealistic data. Based on the evaluation, the study suggests that C12 is the best choice for unsupervised field measurement, DST should be used in scenarios where frequent maintenance is available, and MA350 is suitable for research purposes with post-processing applicable. The study highlights the importance of assigning portable BC monitors to appropriate applications and the need for optimized real-time compensation algorithms.

A novel IFN-CSM-CoCoSo approach for multiple-attribute group decision-making with intuitionistic fuzzy sets: An application in assessing corporate social responsibility performance

With the rapid growth of the economy, enterprises have encountered a series of problems while pursuing economic benefits, such as food safety and environmental pollution issues, resource shortages and energy consumption issues, which affect the sustainable development of enterprises. Establishing a corporate performance evaluation system from the perspective of social responsibility, based on stakeholder theory and the importance of overall goals reflected in the weight of social responsibility indicators, is a very effective measure to achieve corporate social responsibility (CSR) goals through CSR motivation and stakeholders. The performance evaluation of CSR from the perspective of environmental accounting is a MAGDM. Recently, the CoCoSo technique and cosine similarity measure (CSM) technique was utilized to conduct the MAGDM. The intuitionistic fuzzy sets (IFSs) are utilized as a technique for conducting uncertain information during the performance evaluation of CSR from the perspective of environmental accounting. In this study, the intuitionistic fuzzy CoCoSo based on the CSM (IFN-CSM-CoCoSo) technique is built for MAGDM with IFSs. Finally, a numerical example for performance evaluation of CSR from the perspective of environmental accounting is conducted to verify the IFN-CSM-CoCoSo technique.

Performance and application of the total-body PET/CT scanner: a literature review

BACKGROUND

The total-body positron emission tomography/computed tomography (PET/CT) system, with a long axial field of view, represents the state-of-the-art PET imaging technique. Recently, the total-body PET/CT system has been commercially available. The total-body PET/CT system enables high-resolution whole-body imaging, even under extreme conditions such as ultra-low dose, extremely fast imaging speed, delayed imaging more than 10 h after tracer injection, and total-body dynamic scan. The total-body PET/CT system provides a real-time picture of the tracers of all organs across the body, which not only helps to explain normal human physiological process, but also facilitates the comprehensive assessment of systemic diseases. In addition, the total-body PET/CT system may play critical roles in other medical fields, including cancer imaging, drug development and immunology.

MAIN BODY

Therefore, it is of significance to summarize the existing studies of the total-body PET/CT systems and point out its future direction. This review collected research literatures from the PubMed database since the advent of commercially available total-body PET/CT systems to the present, and was divided into the following sections: Firstly, a brief introduction to the total-body PET/CT system was presented, followed by a summary of the literature on the performance evaluation of the total-body PET/CT. Then, the research and clinical applications of the total-body PET/CT were discussed. Fourthly, deep learning studies based on total-body PET imaging was reviewed. At last, the shortcomings of existing research and future directions for the total-body PET/CT were discussed.

CONCLUSION

Due to its technical advantages, the total-body PET/CT system is bound to play a greater role in clinical practice in the future.

Development and performance evaluation of self-assembled pH-responsive curcumin-bacterial exopolysaccharide micellar conjugates as bioactive delivery system

The present study reports the synthesis of micellar conjugates, wherein curcumin (Cur), a bioactive compound with poor bioavailability, was covalently bonded to a bacterial exopolysaccharide (EPS). These conjugates were synthesized by utilizing succinic acid that linked Cur to the pyranosyl moiety of the EPS. The Cur-EPS conjugates appeared as spherical micelles in aqueous solution and were found to have an average hydrodynamic diameter of 254 ± 2.7 nm. The micellar conjugates showed superior stability than Cur as evident from their negative surface charge (-27 ± 1.8 mV) and low polydispersity index (PDI) (0.33 ± 0.04). The in vitro studies on release kinetics helped elucidate the pH-responsive characteristics of the Cur-EPS conjugate, as 87.50 ± 1.45 % of Cur was released at an acidic pH of 5.6, in contrast to 30.15 ± 2.61 % at systemic pH of 7.4 at 150 h. The conjugates were hemocompatible and exhibited cytotoxic effect against the osteosarcoma cell line (MG-63) after 48 h treatment. They also demonstrated superior antibacterial, antibiofilm, and antioxidant activities in comparison to free Cur. Therefore, the Cur-EPS conjugates have potential pharmaceutical applications as therapeutic biomaterial that can be applied as a drug delivery system.

FedHealthFog: A federated learning-enabled approach towards healthcare analytics over fog computing platform

The emergence of federated learning (FL) technique in fog-enabled healthcare system has leveraged enhanced privacy towards safeguarding sensitive patient information over heterogeneous computing platforms. In this paper, we introduce the FedHealthFog framework, which was meticulously developed to overcome the difficulties of distributed learning in resource-constrained IoT-enabled healthcare systems, particularly those sensitive to delays and energy efficiency. Conventional federated learning approaches face challenges stemming from substantial compute requirements and significant communication costs. This is primarily due to their reliance on a singular server for the aggregation of global data, which results in inefficient training models. We present a transformational approach to address these problems by elevating strategically placed fog nodes to the position of local aggregators within the federated learning architecture. A sophisticated greedy heuristic technique is used to optimize the choice of a fog node as the global aggregator in each communication cycle between edge devices and the cloud. The FedHealthFog system notably accounts for drop in communication latency of 87.01%, 26.90%, and 71.74%, and energy consumption of 57.98%, 34.36%, and 35.37% respectively, for three benchmark algorithms analyzed in this study. The effectiveness of FedHealthFog is strongly supported by outcomes of our experiments compared to cutting-edge alternatives while simultaneously reducing number of global aggregation cycles. These findings highlight FedHealthFog's potential to transform federated learning in resource-constrained IoT environments for delay-sensitive applications.

Efficient Edge-AI Models for Robust ECG Abnormality Detection on Resource-Constrained Hardware

This study introduces two models, ConvLSTM2D-liquid time-constant network (CLTC) and ConvLSTM2D-closed-form continuous-time neural network (CCfC), designed for abnormality identification using electrocardiogram (ECG) data. Trained on the Telehealth Network of Minas Gerais (TNMG) subset dataset, both models were evaluated for their performance, generalizability capacity, and resilience. They demonstrated comparable results in terms of F1 scores and AUROC values. The CCfC model achieved slightly higher accuracy, while the CLTC model showed better handling of empty channels. Remarkably, the models were successfully deployed on a resource-constrained microcontroller, proving their suitability for edge device applications. Generalization capabilities were confirmed through the evaluation on the China Physiological Signal Challenge 2018 (CPSC) dataset. The models' efficient resource utilization, occupying 70.6% of memory and 9.4% of flash memory, makes them promising candidates for real-world healthcare applications. Overall, this research advances abnormality identification in ECG data, contributing to the progress of AI in healthcare.

Modest performance of text mining to extract health outcomes may be almost sufficient for high-quality prognostic model development

BACKGROUND

Across medicine, prognostic models are used to estimate patient risk of certain future health outcomes (e.g., cardiovascular or mortality risk). To develop (or train) prognostic models, historic patient-level training data is needed containing both the predictive factors (i.e., features) and the relevant health outcomes (i.e., labels). Sometimes, when the health outcomes are not recorded in structured data, these are first extracted from textual notes using text mining techniques. Because there exist many studies utilizing text mining to obtain outcome data for prognostic model development, our aim is to study the impact of the text mining quality on downstream prognostic model performance.

METHODS

We conducted a simulation study charting the relationship between text mining quality and prognostic model performance using an illustrative case study about in-hospital mortality prediction in intensive care unit patients. We repeatedly developed and evaluated a prognostic model for in-hospital mortality, using outcome data extracted by multiple text mining models of varying quality.

RESULTS

Interestingly, we found in our case study that a relatively low-quality text mining model (F1 score ≈ 0.50) could already be used to train a prognostic model with quite good discrimination (area under the receiver operating characteristic curve of around 0.80). The calibration of the risks estimated by the prognostic model seemed unreliable across the majority of settings, even when text mining models were of relatively high quality (F1 ≈ 0.80).

DISCUSSION

Developing prognostic models on text-extracted outcomes using imperfect text mining models seems promising. However, it is likely that prognostic models developed using this approach may not produce well-calibrated risk estimates, and require recalibration in (possibly a smaller amount of) manually extracted outcome data.

Evaluation and experience from routine use of chemiluminescence assays for serological screening of blood and plasma donations on the Alinity s system and the Alinity i system, two new fully-automated immunoassay systems in Poland

In Poland, independent evaluations under the auspices of the Institute of Hematology and Transfusion Medicine (IHTM) are mandated for any new device, assay, systems for screening samples from whole blood and plasma donors prior to implementation by Blood Transfusion Center (BTC). In last 5 years, two new systems were introduced to the market by Abbott GmbH, namely the Alinity s and the Alinity i. The evaluations performed for these two systems included the assessment of sensitivity, specificity and precision for each of the four mandatory serological screening markers in Poland: Hepatitis B Surface Antigen (HBsAg), Hepatitis C virus antibodies (Anti-HCV), HIV antibodies (anti-HIV) and Syphilis antibodies (anti-Treponema pallidum, anti-TP). Sensitivity was assessed by testing seroconversion panels, HBsAg international reference standard, well characterized local samples, and dilution panels. Specificity was assessed by testing routine donor samples. The results from Alinity i assays were compared to the results from Abbott ARCHITECT i2000SR and Ortho VITROS 3600 assays, while the results from Alinity s assays were compared to the results of ARCHITECT i2000SR assays. The evaluation of the Alinity s and Alinity i assays for sensitivity (100 %), specificity (99,92-100 %) and precision generated results that were as good as or better than generated by routinely used systems, were within acceptance criteria, and met all requirements for screening blood donor samples in accordance with Polish regulations. The specificity of the assays in routine use by BTCs, analyzed after approximately 150,000 donations on both systems, was comparable to the specificity observed during the evaluations at IHTM.

Enhanced LogTODIM-TOPSIS framework for interval-valued intuitionistic fuzzy MAGDM and applications to intangible assets operational management performance evaluation of commercial sporting events

The effective operation of intangible assets in commercial sports events can bring long-term, sustainable, and large-scale economic benefits to sports events. However, currently, in the operation of commercial sports events, the lack of experience in hosting events often hinders the development of intangible assets that should have created higher profits, and cannot achieve the expected value. The intangible assets operational management performance (IAOMP) evaluation of commercial sporting events is the multiple-attribute group decision-making (MAGDM). The Logarithmic TODIM (LogTODIM) and TOPSIS technique was brought forward the MAGDM. The interval-valued intuitionistic fuzzy sets (IVIFSs) are brought forward as the useful technique for coping with uncertain and fuzzy information during the IAOMP evaluation of commercial sporting events. In this study, the interval-valued intuitionistic fuzzy number Logarithmic TODIM-TOPSIS (IVIFN-LogTODIM-TOPSIS) technique is brought forward the MAGDM under IVIFSs circumstances. Finally, the numerical example for IAOMP evaluation of commercial sporting events is brought forward to verify the IVIFN-LogTODIM-TOPSIS technique. The main contribution of this study is brought forward: (1) the LogTODIM-TOPSIS was extended to IVIFSs in light with MEREC model; (2) the MEREC model is brought forward to derive weight in light with score information values under IVIFSs circumstances. (3) the IVIFN-LogTODIM-TOPSIS is brought forward for MAGDM under IVIFSs circumstances; (4) the numerical example for IAOMP evaluation of commercial sporting events and several different comparative analysis is brought forward to verify the IVIFN-LogTODIM-TOPSIS model.

A model for evaluating the performance of compulsory education inputs in ethnic areas in China

A scientific performance evaluation model is necessary to establish a performance evaluation index system for compulsory education in ethnic areas and to conduct objective and impartial evaluations. After conducting theoretical analysis and reviewing literature, it was determined that existing educational input performance evaluation models are general and fail to reflect the unique characteristics of compulsory education development in ethnic areas of China. Therefore, this study intends to improve their self-adaptability and degree of fit. Based on the features of China's ethnic areas and the current situation of compulsory education development, a trinity evaluation model of compulsory education input performance in ethnic regions was constructed using the classical performance evaluation theoretical framework. This model includes the "implementation topic - target concept - performance dimension." The government is the main organization responsible for organizing and implementing the entire performance evaluation, with publicness and responsiveness as the value idea of evaluation. The "4E″ of enough, equity, efficiency, and effectiveness are the evaluation objectives, and input, allocation, output, and effect are the dimensions of the building of the performance evaluation index system. The "4E″ evaluation objectives are integrated into the performance evaluation dimensions and index system. The reconstructed theoretical model of performance evaluation combines universality and specificity, highlights the dual attributes of "tool-value," realizes the organic combination of internal and external performance evaluation, illustrates the overall performance evaluation process and ensures objective, fair, and accurate performance evaluation results. It provides useful guidelines for further optimizing compulsory education investment policies and promoting high-quality and well-balanced compulsory education in China's ethnic areas.

Selecting a sleep tracker from EEG-based, iteratively improved, low-cost multisensor, and actigraphy-only devices

AIMS

Evaluate the performance of 6 wearable sleep trackers across 4 classes (EEG-based headband, research-grade actigraphy, iteratively improved consumer tracker, low-cost consumer tracker).

FOCUS TECHNOLOGY

Dreem 3 headband, Actigraph GT9X, Oura Ring Gen3, Fitbit Sense, Xiaomi Mi Band 7, Axtro Fit3.

REFERENCE TECHNOLOGY

In-lab polysomnography with 3-reader, consensus sleep scoring.

SAMPLE

Sixty participants (26 males) across 3 age groups (18-30, 31-50, and 51-70years).

DESIGN

Overnight in a sleep laboratory from habitual sleep time to wake time.

CORE ANALYTICS

Discrepancy and epoch-by-epoch analyses for sleep/wake (2-stage) and sleep-stage (4-stage; wake/light/deep/rapid eye movement) classification (devices vs. polysomnography).

CORE OUTCOMES

EEG-based Dreem performed the best (2-stage kappa=0.76, 4-stage kappa=0.76-0.86) with the lowest total sleep time, sleep efficiency, sleep onset latency, and wake after sleep onset discrepancies vs. polysomnography. This was followed by the iteratively improved consumer trackers: Oura (2-stage kappa=0.64, 4-stage kappa=0.55-0.70) and Fitbit (2-stage kappa=0.58, 4-stage kappa=0.45-0.60) which had comparable total sleep time and sleep efficiency discrepancies that outperformed accelerometry-only Actigraph (2-stage kappa=0.47). The low-cost consumer trackers had poorest overall performance (2-stage kappa<0.31, 4-stage kappa<0.33).

IMPORTANT ADDITIONAL OUTCOMES

Proportional biases were driven by nights with poorer sleep (longer sleep onset latencies and/or wake after sleep onset).

CORE CONCLUSION

EEG-based Dreem is recommended when evaluating poor quality sleep or when highest accuracy sleep-staging is required. Iteratively improved non-EEG sleep trackers (Oura, Fitbit) balance classification accuracy with well-tolerated, and economic deployment at-scale, and are recommended for studies involving mostly healthy sleepers. The low-cost trackers, can log time in bed but are not recommended for research use.

An integrated approach for evaluating and improving the performance of hospital ICUs based on ergonomic and work-motivational factors

The intensive care unit (ICU) holds significant importance in hospitals. Primarily concerned with monitoring and providing care to critically ill patients, the ICU has proven effective in reducing mortality rates and minimizing complications of diseases, thanks to the highly complex and specific measures taken within this department. Considering the unique contributions made by the staff in this unit, its performance assessment can help improve patient care and satisfaction. This study presents a framework that utilizes ergonomic and work-motivational factors (WMFs) to assess the performance of various ICUs. Upon the identification of these indicators, a standard questionnaire is developed to collect the required data. The mean efficiency score of the units is then determined using the data envelopment analysis (DEA). The model is validated using the principal component analysis (PCA). Ultimately, the SWOT (strengths, weaknesses, opportunities, and threats) matrix is employed to formulate an appropriate strategy and offer improvement measures to the managerial team to enhance their ICU performance. The proposed framework can be applied to evaluate the performance of other healthcare departments. Among the studied ICU centers, including general ICU, isolation ICU catering to individuals with infectious diseases, cardiac care unit (CCU), and neonatal ICU (NICU). NICU and general ICU have the best and worst performance in terms of macro- and micro-ergonomic and motivational indicators, which are on average 0.826% more elevated and 0.659% lower, respectively. According to the performed sensitivity analysis, the ICUs in question demonstrate the most appropriate and inappropriate performance about the indicators of "knowledge, situation assessment, and situation analysis" and "work stress", respectively.

Multi-criteria assessment framework for evaluation of Green Deal performance

The European Green Deal (EGD) is a long-term and important policy to combat climate change. It contains comprehensive regulations that concern the European continent beyond individuals, companies, and cities. Moreover, achieving environmental sustainability depends on the whole world taking responsibility and concrete steps quickly. Although successful and rapid change requires collective effort and high performance across all topics, involving all countries, communities, and sectors, leaving no one behind, since the economic conditions and infrastructures of countries are different, the process of adaptation to green transformation varies. In this study, the multi-criteria decision-making approach has been used to evaluate the performance of the EU member states in terms of many different criteria under the protecting, reducing, and enabling dimensions in an integrated and comprehensive manner. In this context, the data of the EU member states obtained from Eurostat on 3 main and 15 sub-criteria were used, criterion weights were determined by the MEREC method, and then the EGD performances of the countries were revealed by the MAIRCA method. According to the results, "the primary energy consumption" and "freight transport by mode" were selected as the most significant criteria in terms of EGD compliance performance. As a result of the evaluation of the performances of the alternatives, the Netherlands had the best EGD performance which was followed by Sweden while Ireland had the worst ranking and Cyprus was the other underperforming member. Comparative analyses were conducted with the aim of testing the consistency of the proposed methodology.

A survey on few-shot class-incremental learning

Large deep learning models are impressive, but they struggle when real-time data is not available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for deep neural networks to learn new tasks from just a few labeled samples without forgetting the previously learned ones. This setup can easily leads to catastrophic forgetting and overfitting problems, severely affecting model performance. Studying FSCIL helps overcome deep learning model limitations on data volume and acquisition time, while improving practicality and adaptability of machine learning models. This paper provides a comprehensive survey on FSCIL. Unlike previous surveys, we aim to synthesize few-shot learning and incremental learning, focusing on introducing FSCIL from two perspectives, while reviewing over 30 theoretical research studies and more than 20 applied research studies. From the theoretical perspective, we provide a novel categorization approach that divides the field into five subcategories, including traditional machine learning methods, meta learning-based methods, feature and feature space-based methods, replay-based methods, and dynamic network structure-based methods. We also evaluate the performance of recent theoretical research on benchmark datasets of FSCIL. From the application perspective, FSCIL has achieved impressive achievements in various fields of computer vision such as image classification, object detection, and image segmentation, as well as in natural language processing and graph. We summarize the important applications. Finally, we point out potential future research directions, including applications, problem setups, and theory development. Overall, this paper offers a comprehensive analysis of the latest advances in FSCIL from a methodological, performance, and application perspective.

Recycling of the waste battery: Effect of waste battery on property of asphalt and environmental impact evaluation

A waste battery is a kind of hazardous solid waste, and traditional recycling methods can cause serious environmental pollution. In this paper, a pilot study was conducted to reduce the leaching of heavy metals in waste battery power (WBP) by using the wrapping effect of asphalt and explored the feasibility of adding waste battery as a modifier to asphalt. The main components of WBP are determined through microscopic experiments, and its compatibility with asphalt and microscopic mechanism are analyzed; The influence of WBP on asphalt properties are analyzed through routine tests and mixture tests; The leaching test of toxicity is used to analyze the impact of WBP and WBP modified asphalt on the environment. The experimental results indicate that WBP is mainly composed of MnO2, C, and ZnO; There are many wrinkles and grooves on the surface of WBP, which can effectively adsorb asphalt during the modification process, produce anchoring effect, and have good compatibility with asphalt; The components of waste battery adsorb the aging light components in asphalt through their folds and swelling, so that the proportion of heavy components is relatively increased, improving the property indicators of asphalt; From the perspective of engineering property, WBP modified asphalt mixture has strong resistance to deformation and water damage. The leaching concentration of heavy metal ions from bare WBP in soil seriously exceeded the standard. In contrast, when WBP was added to asphalt, the cumulative leaching concentration of heavy metal ions was significantly reduced due to the wrapping effect of asphalt, and the WBP leaching toxicity was greatly suppressed; The method of recycling waste battery and adding it to asphalt as a modifier can prevent the release of heavy metal ions from waste battery into the environment and reduce the risk of the total environmental harm to soil, groundwater and human health.

Phenotypic characterization and seed viability test in ex-situ conserved Ethiopian cultivated barley (Hordeum vulgare L.) landraces

BACKGROUND

Nowadays, most of the Ethiopian barley landraces had been lost from farmer's field and exclusively found ex-situ conserved at the Ethiopian Biodiversity Institute (EBI). Those ex-situ conserved are generally believed to be representative of the original population and possess high genetic diversity and important unique genes that are useful for tackling the various biotic and abiotic stresses in the face of the current climate change. Thus, this research was aimed at testing the performance of 150 ex-situ conserved landraces that had been collected from Arsi and Bale highlands, Southeastern Ethiopia. The landraces were tested at multiple test locations over two years (2021 and 2022).

RESULTS

All the tested landraces showed a good germination rate regardless of their long storage duration. In addition, performance of all the qualitative traits revealed a varying frequency for each character state. For example, most of the accessions (51.3%) had six kernel row numbers (KRN). All the remaining accessions had two rows (28.7%) and irregular KRN with variable lateral florets (20%). Likewise, some of the quantitative traits considered showed a significant variation among the landraces. However, there observed a significant variation for all the interaction effects in some of the traits considered signifying the importance of considering environment effects while targeting genetic selection and improvement of ex-situ conserved germplasms. The phenotypic coefficients of variation (PCV) were considerably high to medium in most of the traits considered including seed yield per hectare (SYPH) but with no associated higher genotypic coefficients of variation (GCV). Moreover, all the traits showed a far greater phenotypic coefficient of variation (PCV) to that of genotypic coefficients of variation (GCV) once again suggesting the pronounced effect of environmental factors to the variation. This was far supported by the significantly higher absolute magnitudes in phenotypic correlation compared to their corresponding genotypic correlation in most of the traits. Low estimates of heritability and genetic advance observed in all the traits considered except seed yield per hectare indicate importance of the trait for selection in Ethiopian barley improvement programs. Clustering patterns of the accessions, in narrow sense, revealed the existence of low divergence among the samples.

CONCLUSION

Ethiopian barley landraces are promising candidates for further yield improvement and conservation. However, further regular testing and screening should be conducted for the ex-situ conserved landraces because of the current erratic climate change. In addition, more robust molecular marker systems could be used to clearly reveal the extents of genetic diversity and to facilitate the breeding and conservation of Ethiopian barley landraces.

Performance evaluation of the open-source Yet Another Spindle Algorithm sleep staging algorithm against gold standard manual evaluation of polysomnographic records in adolescence

GOAL AND AIMS

To evaluate an automatic sleep scoring algorithm against manual polysomnography sleep scoring.

FOCUS METHOD/TECHNOLOGY

Yet Another Spindle Algorithm automatic sleep staging algorithm.

REFERENCE METHOD/TECHNOLOGY

Manual sleep scoring.

SAMPLE

327 nights (151 healthy adolescents), from the NCANDA study.

DESIGN

Participants underwent one-to-three overnight polysomnography recordings, one consisting of an event-related-potential paradigm.

CORE ANALYTICS

Epoch by Epoch and discrepancy analyses (Bland Altman plots) were conducted on the overall sample.

ADDITIONAL ANALYTICS AND EXPLORATORY ANALYSES

Epoch by Epoch and discrepancy analysis were repeated separately on standard polysomnography nights and event-related potential nights. Regression models were estimated on age, sex, scorer, and site of recording, separately on standard polysomnography nights and event-related potential nights.

CORE OUTCOMES

The Yet Another Spindle Algorithm sleep scoring algorithm's average sensitivity of 93.04% for Wake, 87.67% for N2, 84.46% for N3, 86.02% for rapid-eye-movement, and 40.39% for N1. Specificity was 96.75% for Wake, 97.31% for N1, 88.87% for N2, 97.99% for N3, and 97.70% for rapid-eye-movement. The Matthews Correlation Coefficient was highest in rapid-eye-movement sleep (0.85) while lowest in N1 (0.39). Cohen's Kappa mirrored Matthews Correlation Coefficient results. In Bland-Altman plots, the bias between Yet Another Spindle Algorithm and human scoring showed proportionality to the manual scoring measurement size.

IMPORTANT ADDITIONAL OUTCOMES

Yet Another Spindle Algorithm performance was reduced in event-related-potential/polysomnography nights for N3 and rapid-eye-movement. According to the Matthews Correlation Coefficient, the Yet Another Spindle Algorithm performance was affected by younger age, male sex, recording sites, and scorers.

CORE CONCLUSION

Results support the use of Yet Another Spindle Algorithm to score adolescents' polysomnography sleep records, possibly with classification outcomes supervised by an expert scorer.

The Quartet Data Portal: integration of community-wide resources for multiomics quality control

The Quartet Data Portal facilitates community access to well-characterized reference materials, reference datasets, and related resources established based on a family of four individuals with identical twins from the Quartet Project. Users can request DNA, RNA, protein, and metabolite reference materials, as well as datasets generated across omics, platforms, labs, protocols, and batches. Reproducible analysis tools allow for objective performance assessment of user-submitted data, while interactive visualization tools support rapid exploration of reference datasets. A closed-loop "distribution-collection-evaluation-integration" workflow enables updates and integration of community-contributed multiomics data. Ultimately, this portal helps promote the advancement of reference datasets and multiomics quality control.

Examining the Potential of ChatGPT on Biomedical Information Retrieval: Fact-Checking Drug-Disease Associations

Large language models (LLMs) such as ChatGPT have recently attracted significant attention due to their impressive performance on many real-world tasks. These models have also demonstrated the potential in facilitating various biomedical tasks. However, little is known of their potential in biomedical information retrieval, especially identifying drug-disease associations. This study aims to explore the potential of ChatGPT, a popular LLM, in discerning drug-disease associations. We collected 2694 true drug-disease associations and 5662 false drug-disease pairs. Our approach involved creating various prompts to instruct ChatGPT in identifying these associations. Under varying prompt designs, ChatGPT's capability to identify drug-disease associations with an accuracy of 74.6-83.5% and 96.2-97.6% for the true and false pairs, respectively. This study shows that ChatGPT has the potential in identifying drug-disease associations and may serve as a helpful tool in searching pharmacy-related information. However, the accuracy of its insights warrants comprehensive examination before its implementation in medical practice.

Longevity evaluation of non-pumping reactive wells for control of groundwater contamination: Application of upscaling methods

Non-pumping reactive wells (NPRWs) are subsurface structures used for the passive treatment of contaminated groundwater using wells containing reactive media. In the vicinity of NPRWs, a combination of hydrogeological and chemical processes makes it difficult to predict their longevity. In this study, we evaluated the longevity of NPRWs using the upscaling methods. A horizontal two-dimensional sandbox was constructed to mimic the hydrogeological and chemical processes in a single unit of NPRW (unit NPRW). The groundwater flow and solute transport were simulated numerically to validate the processes of contaminant spreading prevention in the sandbox. Dye tracing and arsenic transport tests showed different performance of NPRW due to induced flow and uneven consumption of reactivity, which is dependent on the pathway length and residence time of the coal waste. Through numerical modeling of the experiments, the fate-related processes of contamination around NPRW were described in detail in both spatial and temporal terms. The stepwise approach of the upscaling methods was used to predict the contamination-blocking performance of the entire facility based on the reactivity of the materials and the contamination removal of the unit NPRW.

Rapid diagnostic testing for syphilis in Arctic communities (the STAR study): a multisite prospective field diagnostic accuracy study in an intended-use setting

OBJECTIVES

We evaluated the field diagnostic accuracy of a syphilis rapid test (RDT), using serum and whole blood by non-laboratorians in two Canadian Arctic communities.

METHODS

We implemented a multisite prospective field evaluation wherein patients were screened by an RDT containing treponemal and non-treponemal components (Chembio DPP® Syphilis Screen & Confirm) between January 2020 and December 2021. Venous whole blood and serum were collected for rapid testing and compared with laboratory-based serology reference testing using a reverse sequence algorithm of treponemal and rapid plasma reagin (RPR) testing.

RESULTS

Overall, 135 whole blood and 139 serum specimens were collected from 161 participants during clinical encounters. Treponemal-RDT sensitivity against a treponemal-reference standard (38/161 confirmed cases) was similar for serum (78% [95% CI: 61-90%]) and whole blood (81% [95% CI: 63-93%]). In those with RPR titres ≥1:8 (i.e. suggestive of recent/active infection), sensitivity increased to 93% (95% CI: 77-99%) for serum and 92% (95% CI: 73-99%) for whole blood. Treponemal-RDT specificity was excellent (99% [95% CI: 95-100%]) for both specimen types. Non-treponemal-RDT sensitivity against RPR was 94% (95% CI: 80-99%) for serum and 79% (95% CI: 60-92%) for whole blood. Sensitivity increased to 100% (95% CI: 88-100%) for serum and 92% (95% CI: 73-99%) for whole blood when RPR titres ≥1:8. RDT performance with whole blood was similar to that with serum.

DISCUSSION

Non-laboratorians using the RDT accurately identified individuals with infectious syphilis under real-world conditions in an intended-use setting at the point of care. Implementing the RDT can eliminate treatment delays and may enhance disease control.

Performance and profiling data of plane-wave calculations in quantum ESPRESSO simulation on three supercomputing centres

This dataset reflects the parallel execution profiles of five Quantum ESPRESSO simulation (QE) versions in finding the total energy of the Cerium Oxide lattice using the self-consistent field (SCF) method. The data analysis used a strong scale setting to identify the optimal parameters and computing resources needed to complete a single SCF loop for one specific material efficiently. This analysis notably contributed to achieving the Best Performance Award at the 5th APAC HPC-AI Competition. The data comprises three sets. The first set features the parallel execution traces captured via the Extrae performance profiling tool, offering a broad view of the QE's model execution behaviour and how it used computational resources. The second set records how long QE's model ran on a single node at three HPC centres: ThaiSC TARA in Thailand, NSCC ASPIRE-1 in Singapore, and NCI Gadi in Australia. This set focuses on the impact of adjusting three parameters for K-point parallelisation. The final set presents benchmarking data generated by scaling out the QE's model across 32 nodes (1,536 CPU cores) on the NCI Gadi supercomputer. Despite its focus on a single material, the dataset serves as a roadmap for researchers to estimate required computational resources and understand scalability bottlenecks, offering general guidelines adaptable across different HPC systems.

Medical students' perceptions of factors that Impact their performance in human physiology course: suggestions for improving course presentation

OBJECTIVES

The study aims to examine students' perceptions of factors that impact students' performance in the Human Physiology course at HU's College of Applied Health Sciences and their suggestions for improvement.

METHOD

A cross sectional study was conducted between March 2022 and April 2022. A self-administered online questionnaire was distributed to undergraduate students in Physiology courses (online and blended) via Microsoft Teams. Data were analyzed descriptively and inferentially, and thematic analysis was employed based on the most frequent statements for the open-ended question.

RESULTS

In total, 435 students participated in the study. Results indicated that students had high levels of agreement (M = 4.39) regarding faculty teaching style compared to (M = 4.24) towards course content and (M = 3.49) moderate levels towards technological aspects. In terms of the statistically significant differences at (α = 0.05) in students' perceptions of factors that influence their performance due to the variables (gender, GPA, college, and teaching methods: online or blended), results showed that course content was not affected by any variables. The technological aspects were affected by GPA and gender. In terms of faculty teaching style, it was affected by all variables (GPA, college, and teaching method) except gender. One open-ended question regarding suggested improvements revealed four main themes: assessment and evaluation, technical issues, teaching methods and tools, and Arabic language support.

CONCLUSION

The study findings recommend greater use of assessment for learning methods and provision of interactive materials to help medical students overcome the challenges that might impact their performance.

Performance of different colorectal cancer screening strategies: a long-term passive follow-up population-based screening program in Beijing, China

BACKGROUND

We aimed to assess the performance of the risk assessment questionnaire and fecal immunochemical test (FIT) in a population-based colorectal cancer (CRC) screening program to provide timely evidence for tailored screening strategies in China.

METHODS

This analysis was conducted using data from Beijing Cancer Screening Prospective Cohort Study (BCSPCS). A risk assessment questionnaire and FIT were selected as the primary screening methods, and participants with any positive results were referred to undergo a diagnostic colonoscopy.

RESULTS

From 2015 to 2020, 148,636 Beijing residents aged 40-69 years were invited from designated communities, with 147,807 finishing the risk assessment questionnaire and 115,606 (78.2%) completing the FIT. Among the 42,969 (29.1%) high-risk CRC participants, 23,824 (55.4%) underwent colonoscopy. One year after enrollment, all subjects were linked to the Beijing Cancer Registry (BCR) database and 241 cases of CRC were confirmed. The CRC incidence rate was 58.2/100,000 for the low-risk arm and 418.9/100,000 for the high-risk arm. For participants who underwent colonoscopy, 91 CRC cases were detected, with a detection rate of 91.9% and 63.7% of them were early-stage cases. Furthermore, the sensitivities of utilizing the risk assessment questionnaire alone, FIT alone, combined risk assessment questionnaire and FIT were 75.7%, 50.1%, and 95.1%, and the specificities were 75.3%, 87.3%, and 70.7%, respectively.

CONCLUSION

The Beijing CRC screening program can effectively detect early-onset CRC; however, the compliance with colonoscopy still needs to be improved.

polysomnography: Sensitivity, specificity, and reliability across participants and nights

GOAL AND AIMS

Compare the accuracy and reliability of sleep/wake classification between the Fitbit Charge 3 and the Micro Motionlogger actigraph when applying either the Cole-Kripke or Sadeh scoring algorithms. Accuracy was established relative to simultaneous Polysomnography recording. Focus technology: Fitbit Charge 3 and actigraphy. Reference technology: Polysomnography.

SAMPLE

Twenty-one university students (10 females).

DESIGN

Simultaneous Fitbit Charge 3, actigraphy, and polysomnography were recorded over 3 nights at the participants' homes.

CORE ANALYTICS

Total sleep time, wake after sleep onset, sensitivity, specificity, positive predictive value, and negative predictive value.

ADDITIONAL ANALYTICS AND EXPLORATORY ANALYSES

Variability of specificity and negative predictive value across subjects and across nights.

CORE OUTCOMES

Fitbit Charge 3 and actigraphy using the Cole-Kripke or Sadeh algorithms exhibited similar sensitivity in classifying sleep segments relative to polysomnography (sensitivity of 0.95, 0.96, and 0.95, respectively). Fitbit Charge 3 was significantly more accurate in classifying wake segments (specificity of 0.69, 0.33, and 0.29, respectively). Fitbit Charge 3 also exhibited significantly higher positive predictive value than actigraphy (0.99 vs. 0.97 and 0.97, respectively) and a negative predictive value that was significantly higher only relative to the Sadeh algorithm (0.41 vs. 0.25, respectively).

IMPORTANT ADDITIONAL OUTCOMES

Fitbit Charge 3 exhibited significantly lower standard deviation in specificity values across subjects and negative predictive value across nights.

CORE CONCLUSION

This study demonstrates that Fitbit Charge 3 is more accurate and reliable in identifying wake segments than the examined FDA-approved Micro Motionlogger actigraphy device. The results also highlight the need to create devices that record and save raw multi-sensor data, which are necessary for developing open-source sleep or wake classification algorithms.

Lossy Image Compression in a Preclinical Multimodal Imaging Study

The growing use of multimodal high-resolution volumetric data in pre-clinical studies leads to challenges related to the management and handling of the large amount of these datasets. Contrarily to the clinical context, currently there are no standard guidelines to regulate the use of image compression in pre-clinical contexts as a potential alleviation of this problem. In this work, the authors study the application of lossy image coding to compress high-resolution volumetric biomedical data. The impact of compression on the metrics and interpretation of volumetric data was quantified for a correlated multimodal imaging study to characterize murine tumor vasculature, using volumetric high-resolution episcopic microscopy (HREM), micro-computed tomography (µCT), and micro-magnetic resonance imaging (µMRI). The effects of compression were assessed by measuring task-specific performances of several biomedical experts who interpreted and labeled multiple data volumes compressed at different degrees. We defined trade-offs between data volume reduction and preservation of visual information, which ensured the preservation of relevant vasculature morphology at maximum compression efficiency across scales. Using the Jaccard Index (JI) and the average Hausdorff Distance (HD) after vasculature segmentation, we could demonstrate that, in this study, compression that yields to a 256-fold reduction of the data size allowed to keep the error induced by compression below the inter-observer variability, with minimal impact on the assessment of the tumor vasculature across scales.

Field evaluations of four SARS-CoV-2 rapid antigen tests during SARS-CoV-2 Delta variant wave in South Africa

BACKGROUND

Rapid antigen tests detecting SARS-CoV-2 were shown to be a useful tool in managing the COVID-19 pandemic. Here, we report on the results of a prospective diagnostic accuracy study of four SARS-CoV-2 rapid antigen tests in a South African setting.

METHODS

Rapid antigen test evaluations were performed through drive-through testing centres in Durban, South Africa, from July to December 2021. Two evaluation studies were performed: nasal Panbio COVID-19 Ag Rapid Test Device (Abbott) was evaluated in parallel with the nasopharyngeal Espline SARS-CoV-2 Ag test (Fujirebio), followed by the evaluation of nasal RightSign COVID-19 Antigen Rapid test Cassette (Hangzhou Biotest Biotech) in parallel with the nasopharyngeal STANDARD Q COVID-19 Ag test (SD Biosensor). The Abbott RealTime SARS-CoV-2 assay was used as a reference test.

RESULTS

Evaluation of Panbio and Espline Ag tests was performed on 494 samples (31% positivity), while the evaluation of Standard Q and RightTest Ag tests was performed on 539 samples (13.17% positivity). The overall sensitivity for all four tests ranged between 60 and 72% with excellent specificity values (> 98%). Sensitivity increased to > 80% in all tests in samples with cycle number value < 20. All four tests performed best in samples from patients presenting within the first week of symptom onset.

CONCLUSIONS

All four evaluated tests detected a majority of the cases within the first week of symptom onset with high viral load.

Modelling, simulation and performance evaluation of the IEEE 802.11e protocol with station mobility

In this article, we present a parameterized Colored Petri Net (CPN) model of the IEEE 802.11e protocol for wireless communications with mobile stations. CPNs provide a graphical model for the modeling and analysis of concurrent systems, which can be parameterized by the use of constants, and thus they allow us to create more flexible models. Our CPN model captures the protocol's behavior, and the specific parameters used for the 802.11e protocol and the scenarios to be evaluated are captured by the CPN parameters. The model presented is flexible enough to cover full customization of traffic types, user mobility and collision avoidance protocols. In this model, there is an access point (AP) which is visible to all the stations, and we assume that due to physical restrictions, there are two range groups. All the stations in the same range group are visible to each other. The impact of mobility is then analyzed by studying a situation in which the stations move in a controlled way to the same range group. The simulation results demonstrate the impact on network performance for sensitive and insensitive traffic types, as well as the role of the RTS/CTS protocol in collision avoidance, especially when users are located in different regions. Specifically, we show how the performance improves in the different scenarios when the stations move to the same area, where they can see each other, and we also study the impact on the performance for each type of traffic.

Performance evaluation of CMIP6 climate models for selecting a suitable GCM for future precipitation at different places of Tamil Nadu

Climate change refers to long-term variations in climate parameters. Future climate information can be projected using a GCM (General Circulation Model). Identifying a particular GCM is crucial for climate impact studies. Researchers are perplexed about selecting a suitable GCM for downscaling to predict future climate parameters. Recent updates to CMIP6 global climate models have included shared socioeconomic pathways based on the IPCC (Intergovernmental Panel on Climate Change) Sixth Assessment Report (AR6). The performance of 24 CMIP6 GCMs in precipitation with a multi-model ensemble filter was compared to IMD (India Meteorological Department) 0.25 × 0.25 degrees rainfall data in Tamil Nadu. The performance was evaluated with the help of Compromise Programming (CP), which involves metrics such as R2 (Pearson correlation co-efficient), PBIAS (Percentage Bias), NRMSE (Normalized Root Mean Square Error), and NSE (Nash-Sutcliffe Efficiency). The GCM ranking was performed through Compromise programming by comparing the IMD data and GCM data. The results of the CP analyses of the statistical metrics suggest that the suitable GCMs for the North-East monsoon are CESM2 for Chennai, CAN-ESM5 for Vellore, MIROC6 for Salem, BCC-CSM2-MR for Thiruvannamalai, MPI-ESM-1-2-HAM for Erode, MPI-ESM1-2-LR for Tiruppur, MPI-ESM1-2-LR for Trichy, MPI-ESM1-2-LR for Pondicherry, MPI-ESM1-2-LR for Dindigul, CNRM-CM6-HR for Thanjavur, MPI-ESM1-2-LR for Thirunelveli and UKESM1-0-LL for Thoothukudi. The appropriate suitable GCMs for South-West monsoon as CESM2 is appropriate for Chennai, IPSL-CM6A-LR for Vellore, CESM2-WACCM-FV2 for Salem, CAMS-CSM1-0 for Thiruvannamalai, MPI-ESM-1-2-HR for Erode, MPI-ESM-1-2-HR for Tiruppur, EC- EARTH3 for Trichy, EC- EARTH3 for Pondicherry, MPI-ESM-1-2-HR for Dindigul, CESM2-FV2 for Thanjavur, ACCESS-CM2 for Thirunelveli and ACCESS-CM2 for Thoothukudi respectively. This study emphasizes the importance of selecting an appropriate GCM. Selecting a suitable GCM will be useful in climate change impact studies and there by suggesting necessary adaptation and mitigation strategies.

Comprehensive performance evaluation of coordinated development of industrial economy and its air pollution control

Exploring coordinated pathways that can promote not only the sustainable development of the industrial economy but also air quality is of great significance for the prevention and control of air pollution in China. Currently, the joint development pathways of the industrial economy-environment nexus remain unclear and poorly evaluated. In this study, we proposed a comprehensive performance evaluation combining objective and subjective weighting to identify industrial enterprises' economic-environment nexus benefits. It would be one of the most important steps to explore the coordinated pathways. Based on data envelopment analysis (DEA), the proposed method integrated with the index integration was used to evaluate the comprehensive performances of 41 industrial sectors in China's 13th five-year plan (2016-2020). Evaluation results showed that the comprehensive performances of the economy-environment nexus of the industrial sectors varied significantly, with the five-year average comprehensive technical efficiency (TE) of 0.11-1. Overall, the best two performances were realized by the industries of equipment manufacturing and living consumption, whereas the worst one belonged to the industry of bulk raw materials, with average comprehensive TE values of 0.50, 0.43, and 0.19, respectively. The results of the quantitative evaluation were consistent with those of the qualitative analysis in terms of the developmental status of the industrial sectors. According to the analyses of pure technical efficiency and scale effect, the proposed method identified the industrial sectors with the highest developmental value and with the highest need to control air pollution. Compared with those of the original DEA model, the results of the proposed method showed pronounced differences in terms of the performances of industrial sectors with high energy consumption and high particulate matter (PM) emissions and with low energy consumption and low PM emissions. The proposed evaluation method combining the weighting was suitable for identifying the comprehensive performance of the industrial economy-environment nexus and provides the basis for the prevention and control of air pollution.

Quantitative prediction of the hydraulic performance of free water surface constructed wetlands by integrating numerical simulation and machine learning

Quantitative prediction of the design parameter-influenced hydraulic performance is significant for optimizing free water surface constructed wetlands (FWS CWs) to reduce point and non-point source pollution and improve land utilization. However, owing to limitations of the test conditions and data scale, a quantitative prediction model of the hydraulic performance under multiple design parameters has not yet been established. In this study, we integrated field test data, mechanism model, statistical regression, and machine learning (ML) to construct such quantitative prediction models. A FWS CW numerical model was established by integrating 13 groups of trace data from field tests. Subsequently, training, test and extension datasets comprising 125 (5^3), 25 (L₂₅(5⁶)) and 16 (L₁₆(4⁴)) data points, respectively, were generated via numerical simulation of multi-level value combination of three quantitative design parameters, namely, water depth, hydraulic loading rate (HLR), and aspect ratio. The short circuit index (φ₁₀), Morrill dispersion index (MDI), hydraulic efficiency (λ) and moment index (MI) were used as representative hydraulic performance indicators. Training set with large samples were analyzed to determine the variation rules of different hydraulic indicators. Based on the control variable method, φ₁₀, λ, and MI grew exponentially with increasing aspect ratio whereas MDI showed a decreasing trend; with increasing water depth, φ₁₀, λ, and MI showed polynomial decreases whereas MDI increased; with increasing HLR, φ₁₀, λ, and MI slowly increased linearly whereas MDI showed the opposite trend. Finally, we constructed models based on multivariate nonlinear regression (MNLR) and ML (random forest (RF), multilayer perceptron (MLP), and support vector regression. The coefficients of determination (R²) of the MNLR and ML models fitting the training and test sets were all greater than 0.9; however, the generalization abilities of different models in the extension set were different. The most robust MLP, MNLR without interaction term, and RF models were recommended as the preferred models to hydraulic performance prediction. The extreme importance of aspect ratio in hydraulic performance was revealed. Thus, gaps in the current understanding of multivariate quantitative prediction of the hydraulic performance of FWS CWs are addressed while providing an avenue for researching FWS CWs in different regions according to local conditions.

Spectral volume index creation and performance evaluation: A preliminary test for tree species identification

To fully mine information regarding differences among various tree species from remote sensing data and improve the accuracy of tree species recognition, this study utilized the spectral reflection value, wavelength, and time as parameters and employed three algorithms to create an expression for the spectral volume index (SVI). Then, data were obtained by applying RedEdge-MX to four phases, SVI features were extracted, and a mixed feature set of spectral band + texture + digital surface model + SVI was constructed. A random forest algorithm was employed to determine the importance of the SVI features and derive the optimal feature set for tree species classification. The additional objectives were to determine if the SVI features have an active role in tree species classification and which algorithm is more conducive for extracting useful SVI features. The SVI features extracted with volume constraints exhibit better performance in tree species recognition than those extracted without volume constraints. Moreover, the SVI features extracted using a variable-constrained volume were better than those extracted using a constant-constrained volume. The combination of SVI features could improve the accuracy of tree species recognition (the highest overall accuracy was 92.76%), but the improvement effect was limited (the value was 92.16% when SVI features were not combined). These findings show that the SVI obtained using this method could be used to mine the difference information of tree species in images to a certain extent and hence, could be used in tree species identification.

Nowcasting of fecal coliform presence using an artificial neural network

At least 2 billion people worldwide use drinking water sources that are contaminated with feces, causing waterborne diseases; poor sanitation, poor hygiene, and unsafe drinking water result in a daily death rate of more than 800 children under 5 years of age from diarrheal diseases. This study shows the feasibility of a novel method to nowcast fecal coliforms' (FC) presence in drinking water sources by applying a multilayer perceptron artificial neuron network (MLP-ANN) model. The model gives a binary answer for FC presence or absence in drinking water sources using a minimum of water quality and geographical parameters, which can be monitored in real-time as predictors with low-cost and in-situ equipment. Using 51,400 samples to train, validate and test the model with temperature, pH, electrical conductivity, turbidity, dissolved oxygen, and total dissolved solids (TDS) as water-quality inputs and the water source type and location (as districts in India) as geographical inputs. The model achieved a total accuracy of 92.8% and a sensitivity of 98.2%, meaning that most FC-contaminated samples were classified correctly. In addition, precision reached 93.1%, meaning that most FC-contamination classifications were actually contaminated. The MLP-ANN performed better than the Linear Regression and K-Nearest Neighbors models, with lower accuracies of 90.2% and 91.0%, respectively. The MLP-ANN model could characterize the water quality geospatially, learn from the parameters whether the water is contaminated by FC, and predict with high accuracy on new testing data. This method can be used as a part of a sensor for FC monitoring and management in water, reducing the time gaps between routine lab testing and thus improving drinking water quality and addressing the SDG 6 targets.

Rapid RT-PCR identification of SARS-CoV-2 in screening donors of fecal microbiota transplantation

Since its first appearance in late 2019 in Wuhan, China, severe acute respiratory syndrome caused by Coronavirus 2 (SARS-CoV-2) has had a major impact on healthcare facilities around the world. Although in the past year, mass vaccination and the development of monoclonal antibody treatments have reduced the number of deaths and severe cases, the circulation of SARS-CoV-2 remains high. Over the past two years, diagnostics have played a crucial role in virus containment both in health care facilities and at the community level. For SARS-CoV-2 detection, the commonly used specimen type is the nasopharyngeal swab, although the virus can be identified in other matrices such as feces. Since fecal microbiota transplantation (FMT) assumes significant importance in the treatment of chronic gut infections and that feces may be a potential vehicle for transmission of SARS-CoV-2, in this study we have evaluated the performance of the rapid cartridge-based RT-PCR test STANDARD™ M10 SARS-CoV-2 (SD Biosensor Inc., Suwon, South Korea) using fecal samples. The results obtained indicates that STANDARD™ M10 SARS-CoV-2 can detect SARS-CoV-2 in stool samples even at low concentration. For this reason, STANDARD™ M10 SARS-CoV-2 could be used as reliable methods for the detection of SARS-CoV-2 in fecal samples and for the screening of FMT donors.

Performance evaluation of in vitro diagnostic kits for hepatitis B virus infection using the regional reference panel of Japan

BACKGROUND

Hepatitis B virus (HBV) infection is a global public health concern. Precise and sensitive detection of viral markers, including HBV DNA and HBs antigen (Ag), is essential to determine HBV infection.

METHODS

The sensitivities and specificities of 5 HBV DNA and 14 HBsAg kits were evaluated using World Health Organization International Standards (WHO IS) and the Regional Reference Panel (RRP) consisting of 64 HBsAg-negative and 80 HBsAg-positive specimens.

RESULTS

All 5 HBV DNA kits detected HBV DNA in the WHO IS at a concentration of 10 IU/mL. The sensitivity and specificity to the RRP were 98.8-100% and 96.9-100%, respectively. HBV DNA titers were well correlated among the 5 kits regardless of HBV genotype. However, discordance of the HBV DNA titer was found in 5 specimens measured by CAP/CTM HBV v2.0. Among 12 automated HBsAg kits, the minimum detectable concentrations in the WHO IS varied from 0.01 to 0.1 IU/mL. Two lateral flow assays were positive for WHO IS concentrations greater than or equal to 1.0 and 0.1 IU/mL, respectively. When analyzed by the RRP, 12 automated kits exhibited a sensitivity of 98.8-100%, and 2 lateral flow assays showed sensitivities of 93.8% and 100%. The specificities of HBsAg kits were 100%. In the quantification of HBsAg, some kits showed a poor correlation of measurements with each other and showed up to a 1.7-fold difference in the regression coefficient of HBsAg titers. There were variations in the correlations of measurements among HBsAg kits when analyzed by genotype.

CONCLUSIONS

Five HBV DNA kits showed sufficient sensitivity and specificity to determine HBV infection. HBV DNA titers were compatible with each other irrespective of HBV genotypes. HBsAg kits had enough sensitivity and specificity to screen for HBV infection. One of the lateral flow assays had a nearly equivalent sensitivity to that of the automated HBsAg kit. HBsAg titers quantified by the evaluated kits were not compatible across the kits. Genotype-dependent amino acid variations might affect the quantification of HBsAg titers.

Combination of UC-3500 and UF-5000 as a quick and effective method to exclude bacterial urinary tract infection

BACKGROUND

Our study aims to evaluate the performance of the combination of Sysmex urine dry chemistry analyzer UC-3500 and urine particle analyzer UF-5000 in screening bacterial urinary tract infection (UTI).

METHODS

We analyzed 2000 urine specimens from patients with suspected UTI by using a urine dry chemistry analyzer (UC-3500) and a fully automated sediment analyzer (UF-5000). After being tested by the instrument, all specimens were sent to our clinical microbiology laboratory for culture. In addition, 600 urine specimens were selected to evaluate the accuracy of the six screening strategies established in this study.

RESULTS

The consistency of UF-5000 bacterial classification and bacterial culture was fair (Kappa = 0.339). The counts of WBC and BACT elevated with sequential group designs (P < 0.001). The cut-off value of WBC was 32.20/μL for males (AUC, 0.942, 95%CI, 0.930-0.955) and 39.15/μL for females (AUC, 0.931, 95%CI, 0.914-0.948). The sensitivity and specificity of WBC were relatively higher than those of BACT. Strategy④ and Strategy⑥ in all six strategies had a good negative predictive value (NPV) which was 98.73%.

CONCLUSION

UF-5000 bacterial classification cannot be used as a practical reference. 32.20/μL (male) and 39.15/μL (female) for WBC as well as 22.35/μL (male) and 127.25/μL (female) for BACT were used as cut-off values to effectively determine whether UTI occurs. WBC, BACT and LEU joint screening programs were suitable to rapidly and effectively exclude bacterial UTI.

Controlled air exchange rate method to evaluate reduction of volatile organic compounds by indoor air cleaners

Air cleaning technologies are needed to reduce indoor concentrations and exposure to volatile organic compounds (VOCs). Currently, air cleaning technologies lack an accepted test standard to evaluate their VOC removal performance. A protocol to evaluate the VOC removal performance of air cleaning devices was developed and piloted with two devices. This method injects a VOC mixture and carbon dioxide into a test chamber, supplies outdoor air at a standard building ventilation rate, periodically measures the VOC concentrations in the chamber using solid phase microextraction-gas chromatography-mass spectrometry over a 3-h decay period, and compares the decay rate of VOCs to carbon dioxide to measure the VOC removal air cleaning performance. The method was demonstrated with both a hydroxyl radical generator and an activated carbon air cleaner. It was shown that the activated carbon air cleaner device tested had a clean air delivery rate an order of magnitude greater than the hydroxyl radical generator device (72.10 vs 6.32 m3/h).

Economic performance evaluation of community health service centers: a DEA-based cross-efficiency study

Health is a basic need for human survival and development. To achieve the goal of access to basic medical and health services for everyone, the operational performance of the health industry should be improved, and the allocation of resources in the health industry should be optimized. Because of this, we conduct an empirical analysis to evaluate the performance of community health service centers using cross-evaluation. In this study, we apply the data envelopment analysis (DEA) cross-efficiency model to empirically analyze the performance of 9 local community health service centers in Hefei, China. Through the empirical study, we obtain the following research results. (1) Haitang has the best performance among all community health service centers, whereas Sanxiaokou is the worst. (2) The performance of more than half of the community health service centers is significantly imbalanced. (3) Bozhoulu and Lindian do not perform well in all dimensions, that is, they have an imbalanced development. (4) Shuanggang is balanced but does not perform well in all dimensions. We conduct an empirical analysis with real-world data from 9 local community health service centers using the classical DEA cross-efficiency model and compare the results of the cross-efficiency (cross-evaluation) and CCR efficiency (self-evaluation) to better understand each community health service center's performance.

PToPI: A Comprehensive Review, Analysis, and Knowledge Representation of Binary Classification Performance Measures/Metrics

Although few performance evaluation instruments have been used conventionally in different machine learning-based classification problem domains, there are numerous ones defined in the literature. This study reviews and describes performance instruments via formally defined novel concepts and clarifies the terminology. The study first highlights the issues in performance evaluation via a survey of 78 mobile-malware classification studies and reviews terminology. Based on three research questions, it proposes novel concepts to identify characteristics, similarities, and differences of instruments that are categorized into 'performance measures' and 'performance metrics' in the classification context for the first time. The concepts reflecting the intrinsic properties of instruments such as canonical form, geometry, duality, complementation, dependency, and leveling, aim to reveal similarities and differences of numerous instruments, such as redundancy and ground-truth versus prediction focuses. As an application of knowledge representation, we introduced a new exploratory table called PToPI (Periodic Table of Performance Instruments) for 29 measures and 28 metrics (69 instruments including variant and parametric ones). Visualizing proposed concepts, PToPI provides a new relational structure for the instruments including graphical, probabilistic, and entropic ones to see their properties and dependencies all in one place. Applications of the exploratory table in six examples from different domains in the literature have shown that PToPI aids overall instrument analysis and selection of the proper performance metrics according to the specific requirements of a classification problem. We expect that the proposed concepts and PToPI will help researchers comprehend and use the instruments and follow a systematic approach to classification performance evaluation and publication.

Practice, pathways, and performance for resource utilization of crop straw: A case study of Xinyang City in China

The open-air burning of the massive generated crop straw in rural areas has been a troubling problem in China in recent decades, and the utilization and valorization of crop straw have provided promising solutions to it. However, due to the limitations in straw resources, economic, social, and technological conditions, different regions vary greatly with each other in the technological pathways and performance of straw utilization. By taking ten counties and districts of Xinyang City in Henan Province of China as examples, it analyzed the spatial distribution of straw yields and the adoption of five pathways of crop straw utilization in Xinyang City, and summarized three typical combination modes. In addition, it also provided the integrated model of FBWM-TOPSIS-PROMETHEE to evaluate the performance of crop straw utilization in the ten counties and districts, to provide local decision-makers with the pathways for promoting the utilization and valorization of crop straw. The results indicated that: (1) The endowment of straw resources and the straw utilization pathways in the ten counties and districts of Xinyang City vary greatly with each other; (2) Returning crop straw to the fields as fertilizer after being crushed is the mainstream mode of resource utilization of crop straw, although five technological modes have been adopted in Xinyang City; (3) Among the 10 counties, Huaibin and Xixian have the best performance of resource utilization of crop straw, and Shangcheng is the worst performer.

Evaluation of the performance of a high-resolution time-of-flight PET system dedicated to the head and breast according to NEMA NU 2-2012 standard

BACKGROUND

This study evaluated the physical performance of a positron emission tomography (PET) system dedicated to the head and breast according to the National Electrical Manufacturers Association (NEMA) NU2-2012 standard.

METHODS

The spatial resolution, sensitivity, scatter fraction, count rate characteristics, corrections for count losses and randoms, and image quality of the system were determined. All measurements were performed according to the NEMA NU2-2012 acquisition protocols, but image quality was assessed using a brain-sized phantom. Furthermore, scans of the three-dimensional (3D) Hoffmann brain phantom and mini-Derenzo phantom were acquired to allow visual evaluation of the imaging performance for small structures.

RESULTS

The tangential, radial, and axial full width at half maximum (FWHM) at a 10-mm offset in half the axial field of view were measured as 2.3, 2.5, and 2.9 mm, respectively. The average system sensitivity at the center of the field of view and at a 10-cm radial offset was 7.18 and 8.65 cps/kBq, respectively. The peak noise-equivalent counting rate was 35.2 kcps at 4.8 kBq/ml. The corresponding scatter fraction at the peak noise-equivalent counting rate was 46.8%. The peak true rate and scatter fraction at 8.6 kBq/ml were 127.8 kcps and 54.3%, respectively. The percent contrast value for a 10-mm sphere was approximately 50%. On the 3D Hoffman brain phantom image, the structures of the thin layers composing the phantom were visualized on the sagittal and coronal images. On the mini-Derenzo phantom, each of the 1.6-mm rods was clearly visualized.

CONCLUSION

Taken together, these results indicate that the head- and breast-dedicated PET system has high resolution and is well suited for clinical PET imaging.

Automatic social distance estimation for photographic studies: Performance evaluation, test benchmark, and algorithm

The social distancing regulations introduced to slow down the spread of COVID-19 virus directly affect a basic form of non-verbal communication, and there may be longer term impacts on human behavior and culture that remain to be analyzed in proxemics studies. To obtain quantitative results for such studies, large media and/or personal photo collections must be analyzed. Several social distance monitoring methods have been proposed for safety purposes, but they are not directly applicable to general photo collections with large variations in the imaging setup. In such studies, the interest shifts from safety to analyzing subtle differences in social distances. Currently, there is no suitable benchmark for developing such algorithms. Collecting images with measured ground-truth pair-wise distances using different camera settings is cumbersome. Moreover, performance evaluation for these algorithms is not straightforward, and there is no widely accepted evaluation protocol. In this paper, we provide an image dataset with measured pair-wise social distances under different camera positions and settings. We suggest a performance evaluation protocol and provide a benchmark to easily evaluate such algorithms. We also propose an automatic social distance estimation method that can be applied on general photo collections. Our method is a hybrid method that combines deep learning-based object detection and human pose estimation with projective geometry. The method can be applied on uncalibrated single images with known focal length and sensor size. The results on our benchmark are encouraging with 91% human detection rate and only 38.24% average relative distance estimation error among the detected people.

Understanding quality data correctly: a randomized comparison of presentation formats among cancer patients and relatives

Background: Patient representatives are increasingly engaged in quality in health care, and even though quality data are publicly available, correct interpretation may be challenging. We designed a randomized study with the primary aim to examine the association between preferred data presentation format and the interpretation of quality data among cancer patients and relatives.Material and methods: Surveys were distributed to the Danish Cancer Society Citizens' Panel between 31 March and 14 April 2019 and 55% completed the survey (N = 464) including six storyboards that presented authentic quality data in table format, league table and point estimates. The storyboards were randomized to expose participants to the data in the three different formats and in varying presentation order. Logistic regression models were used to calculate Odds Ratios (ORs) and 95% confidence intervals (CIs) for the association between preferred presentation format, health literacy, education and cohabitation status as exposures and interpretation of quality data as outcome.Results: The majority of participants (97%) had high literacy and 57% had a medium or long higher education. A total of 60% found the questions difficult or very difficult and 33% were not able to correctly interpret at least one format. Correct interpretation was associated with preferred league table (OR = 1.62; 95% CI = 1.04-5.52) and if the data was presented in the preferred format. Medium and long education were associated with correct interpretation of at least one format (OR = 1.93; 95% CI = 1.16-3.21 and OR = 3.89; 95% CI = 1.90-7.95, respectively) while health literacy and cohabitation status were not.Conclusions: More than one third of the participants were not able to correctly interpret the data and the understanding of quality data improved with longer education and if the data was presented in the preferred format. Decision-makers should carefully consider displaying quality data according to preferred presentation format and to guide interpretation for individuals with short education.

Simulation of performance evaluation model for medical-elderly care integrated institutions based on system dynamics

BACKGROUND

Based on the Chinese model of medical-elderly care integration, this paper aims to explore the impact of different investment levels on the performance of the medical-elderly care integrated institutions.

METHODS

Using the method of system dynamics, this paper establishes the performance evaluation model of medical-elderly care integrated institutions, sets the system element input, service level, and policy support as the key factors, and uses Vensim PLE software for simulation.

RESULTS

The three key factors have different degrees of positive impact on the performance of medical-elderly care integrated institutions. On the whole, policy support has the most significant impact on the performance of institutions, followed by the level of medical-elderly care integrated services. Institutional input mainly has a great impact on the performance of institutions in the early stage. In addition, the model simulation results also show the emergence effect: the improvement rate of institutional performance under the comprehensive simulation is higher than the sum of the improvement rates under the separate action of single factor.

CONCLUSION

Government policies have played an important role in promoting the development of medical-elderly care integrated institutions. The service level and resource input can effectively promote the performance of medical-elderly care integrated institutions. Institutions should formulate development strategies from a systematic perspective, and pay attention to the integration of "medical" and "elderly care" resources.

Deep learning artificial intelligence framework for multiclass coronary artery disease prediction using combination of conventional risk factors, carotid ultrasound, and intraplaque neovascularization

OBJECTIVE

Cardiovascular disease (CVD) is a major healthcare challenge and therefore early risk assessment is vital. Previous assessment techniques use either "conventional CVD risk calculators (CCVRC)" or machine learning (ML) paradigms. These techniques are ad-hoc, unreliable, not fully automated, and have variabilities. We, therefore, introduce AtheroEdge-MCDLAI (AE3.0DL) windows-based platform using multiclass Deep Learning (DL) system.

METHODS

Data was collected on 500 patients having both carotid ultrasound and corresponding coronary angiography scores (CAS), measured as stenosis in coronary arteries and considered as the gold standard. A total of 39 covariates were used, clubbed into three clusters, namely (i) Office-based: age, gender, body mass index, smoker, hypertension, systolic blood pressure, and diastolic blood pressure; (ii) Laboratory-based: Hyperlipidemia, hemoglobin A1c, and estimated glomerular filtration rate; and (iii) Carotid ultrasound image phenotypes: maximum plaque height, total plaque area, and intra-plaque neovascularization. Baseline characteristics for four classes (target labels) having significant (p < 0.0001) values were calculated using Chi-square and ANOVA. For handling the cohort's imbalance in the risk classes, AE3.0DL used the synthetic minority over-sampling technique (SMOTE). AE3.0DL used Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) DL models and the performance (accuracy and area-under-the-curve) was computed using 10-fold cross-validation (90% training, 10% testing) frameworks. AE3.0DL was validated and benchmarked.

RESULTS

The AE3.0DL using RNN and LSTM showed an accuracy and AUC (p < 0.0001) pairs as (95.00% and 0.98), and (95.34% and 0.99), respectively, and showed an improvement of 32.93% and 9.94% against CCVRC and ML, respectively. AE3.0DL runs in <1 s.

CONCLUSION

DL algorithms are a powerful paradigm for coronary artery disease (CAD) risk prediction and CVD risk stratification.

Influences of a novel cylindrical solar dryer on farmer's income and its impact on environment

Poor grain drying facilities, along with the burden of drying cost, bound the farmers to market their produce soon after harvest. Thus, this research paper intends to study the influences of a novel cylindrical solar-assisted dryer on farmers' income and its impact on the environment. The paper also presents the design and fabrication of a drying system for maize cobs using non-conventional solar energy. The performance of the solar-assisted drying system was also investigated for drying of yellow dent maize cobs. As a very energy-intensive post-harvest process, drying consumes a lot of electricity, which is usually provided by conventional energy. Here, solar dryers are the perfect solution in terms of efficiency, uniform drying of agricultural food products, less drying time, increased marketability of agricultural products, and reducing the load on farmer's pocket for drying. With a high internal rate of return of 66 percent, the designed dryer proved to be technically and economically viable. Compared to open sun drying, the solar drying system produced better quality and drying time results. Compared to other models, the Midilli model fits the experimental maize drying data better, with a coefficient of determination of R² = 0.89729. Energy analysis inferred savings of 1352.97 kWh electrical energy and 128.18 liters of diesel fuel plus a reduction of 1.22 t CO₂ per annum can be achieved by using this dryer.

Efficiency evaluation of sewage treatment plants in Delhi, India, using tolerance-based data envelope analysis

Correct and effective performance evaluation of wastewater treatment plants is a tough task because of the complex biological, physico-chemical, and biochemical processes and associated variables affecting their performance. Conventionally, the efficiency of sewage treatment plants (STPs) are obtained using some index relating pollutant removal efficiency with energy used or costs. These indicators consider only one variable at a time. This leads to incorrect assessment of efficiency, which in turn could adversely affect decision-making of the regulatory authorities. The data envelope analysis (DEA) method utilizes a Linear programming technique which can handle multiple input/output variables without requiring the cost function. This makes it an appropriate tool for assessing the relative efficiency of treatment plants. The present study assess the efficiency of 30 STPs in Delhi, India, using the tolerance-based DEA model utilizing the variable return of scale (VRS). The uncertainty was incorporated into the model using the tolerance measure. The model is solved using the "Add on" option in spreadsheet toolbox of excel solver. Results reveal that out of the 30 plants considered for the study, 6 are performing well (20%). Further, it was observed that a slight change in the input data leads to instability of the efficiency results. Lastly, the ranking is used to determine the treatment plant with best efficiency under all scenarios for the larger period of the year. Such studies will help in chalking out the best management practices that could be adopted by other regulatory authorities.

A unified scheme for the benchmarking of upper limb functions in neurological disorders

BACKGROUND

In neurorehabilitation, we are witnessing a growing awareness of the importance of standardized quantitative assessment of limb functions. Detailed assessments of the sensorimotor deficits following neurological disorders are crucial. So far, this assessment has relied mainly on clinical scales, which showed several drawbacks. Different technologies could provide more objective and repeatable measurements. However, the current literature lacks practical guidelines for this purpose. Nowadays, the integration of available metrics, protocols, and algorithms into one harmonized benchmarking ecosystem for clinical and research practice is necessary.

METHODS

This work presents a benchmarking framework for upper limb capacity. The scheme resulted from a multidisciplinary and iterative discussion among several partners with previous experience in benchmarking methodology, robotics, and clinical neurorehabilitation. We merged previous knowledge in benchmarking methodologies for human locomotion and direct clinical and engineering experience in upper limb rehabilitation. The scheme was designed to enable an instrumented evaluation of arm capacity and to assess the effectiveness of rehabilitative interventions with high reproducibility and resolution. It includes four elements: (1) a taxonomy for motor skills and abilities, (2) a list of performance indicators, (3) a list of required sensor modalities, and (4) a set of reproducible experimental protocols.

RESULTS

We proposed six motor primitives as building blocks of most upper-limb daily-life activities and combined them into a set of functional motor skills. We identified the main aspects to be considered during clinical evaluation, and grouped them into ten motor abilities categories. For each ability, we proposed a set of performance indicators to quantify the proposed ability on a quantitative and high-resolution scale. Finally, we defined the procedures to be followed to perform the benchmarking assessment in a reproducible and reliable way, including the definition of the kinematic models and the target muscles.

CONCLUSIONS

This work represents the first unified scheme for the benchmarking of upper limb capacity. To reach a consensus, this scheme should be validated with real experiments across clinical conditions and motor skills. This validation phase is expected to create a shared database of human performance, necessary to have realistic comparisons of treatments and drive the development of new personalized technologies.

MCNN: a multi-level CNN model for the classification of brain tumors in IoT-healthcare system

The classification of brain tumors is significantly important for diagnosing and treating brain tumors in IoT healthcare systems. In this work, we have proposed a robust classification model for brain tumors employing deep learning techniques. In the design of the proposed method, an improved Convolutional neural network is used to classify Meningioma, Glioma, and Pituitary types of brain tumors. To test the multi-level convolutional neural network model, brain magnetic resonance image data is utilized. The MCNN model classification results were improved using data augmentation and transfer learning methods. In addition, hold-out and performance evaluation metrics have been employed in the proposed MCNN model. The experimental results show that the proposed model obtained higher outcomes than the state-of-the-art techniques and achieved 99.89% classification accuracy. Due to the higher results of the proposed approach, we recommend it for the identification of brain cancer in IoT-healthcare systems.

Development and integration of VGG and dense transfer-learning systems supported with diverse lung images for discovery of the Coronavirus identity

The contagious SARS-CoV-2 has had a tremendous impact on the life and health of many communities. It was first rampant in early 2019 and so far, 539 million cases of COVID-19 have been reported worldwide. This is reminiscent of the 1918 influenza pandemic. However, we can detect the infected cases of COVID-19 by analysing either X-rays or CT, which are presumably considered the least expensive methods. In the existence of state-of-the-art convolutional neural networks (CNNs), which integrate image pre-processing techniques with fully connected layers, we can develop a sophisticated AI system contingent on various pre-trained models. Each pre-trained model we involved in our study assumed its role in extracting some specific features from different chest image datasets in many verified sources, such as (Mendeley, Kaggle, and GitHub). First, for CXR datasets associated with the CNN trained model from the beginning, whereby is comprised of four layers beginning with the Conv2D layer, which comprises 32 filters, followed by the MaxPooling and afterwards, we reiterated similarly. We used two techniques to avoid overgeneralization, the early stopping and the Dropout techniques. After all, the output was one neuron to classify both cases of 0 or 1, followed by a sigmoid function; in addition, we used the Adam optimizer owing to the more improved outcomes than what other optimizers conducted; ultimately, we referred to our findings by using a confusion matrix, classification report (Recall & Precision), sensitivity and specificity; in this approach, we achieved a classification accuracy of 96%. Our three integrated pre-trained models (VGG16, DenseNet201, and DenseNet121) yielded a remarkable test accuracy of 98.81%. Besides, our merged models (VGG16, DenseNet201) trained on CT images with the utmost effort; this model held an accurate test of 99.73% for binary classification with the (Normal/Covid-19) scenario. Comparing our results with related studies shows that our proposed models were superior to the previous CNN machine learning models in terms of various performance metrics. Our pre-trained model associated with the CT dataset achieved 100% of the F1score and the loss value was approximately 0.00268.