A novel optimal identification of various solar PV cell parameters by using MRDT controller

At present, Renewable Energy Sources (RES) utilization keeps on increasing because of their merits are more availability in the atmosphere, easy energy harvesting, less maintenance expenses, plus more reliability. Here, the solar power generation systems are utilized for supplying the energy to the local consumers. The accurate, and efficient solar power supply to the customers is a very important factor to meet the peak load demand. The accurate power generation of the sunlight system completely depends on its accurate parameters extraction. In this work, a Modified Rao-based Dichotomy Technique (MRAODT) is introduced to identify the actual parameters of the different PV cells which are PWP 201 polycrystalline, plus RTC France. The proposed MRAODT method is compared with the other existing algorithms which are the teaching and learning algorithm, African vultures, plus tuna intelligence algorithm. Finally, from the simulation results, the MRAODT gives superior performance when associated with the other controllers in terms of parameters extraction time, accuracy in the PV cells parameters identification, plus convergence time of the algorithm.

Development of a Measurement System Using Infrared Spectroscopy-Attenuated Total Reflectance, Principal Component Analysis and Artificial Intelligence for the Safe Quantification of the Nucleating Agent Sorbitol in Food Packaging

Sorbitol derivatives and other additives are commonly used in various products, such as packaging or food packaging, to improve their mechanical, physical, and optical properties. To accurately and precisely evaluate the efficacy of adding sorbitol-type nucleating agents to these articles, their quantitative determination is essential. This study systematically investigated the quantification of sorbitol-type nucleating agents in food packaging made from impact copolymers of polypropylene (PP) and polyethylene (PE) using attenuated total reflectance infrared spectroscopy (ATR-FTIR) together with analysis of principal components (PCA) and machine learning algorithms. The absorption spectra revealed characteristic bands corresponding to the C-O-C bond and hydroxyl groups attached to the cyclohexane ring of the molecular structure of sorbitol, providing crucial information for identifying and quantifying sorbitol derivatives. PCA analysis showed that with the selected FTIR spectrum range and only the first two components, 99.5% of the variance could be explained. The resulting score plot showed a clear pattern distinguishing different concentrations of the nucleating agent, affirming the predictability of concentrations based on an impact copolymer. The study then employed machine learning algorithms (NN, SVR) to establish prediction models, evaluating their quality using metrics such as RMSE, R2, and RMSECV. Hyperparameter optimization was performed, and SVR showed superior performance, achieving near-perfect predictions (R2 = 0.9999) with an RMSE of 0.100 for both calibration and prediction. The chosen SVR model features two hidden layers with 15 neurons each and uses the Adam algorithm, balanced precision, and computational efficiency. The innovative ATR-FTIR coupled SVR model presented a novel and rapid approach to accurately quantify sorbitol-type nucleating agents in polymer production processes for polymer research and in the analysis of nucleating agent derivatives. The analytical performance of this method surpassed traditional methods (PCR, NN).

Estimation of treatment effects in early-phase randomized clinical trials involving external control data

There are good reasons to perform a randomized controlled trial (RCT) even in early phases of clinical development. However, the low sample sizes in those settings lead to high variability of the treatment effect estimate. The variability could be reduced by adding external control data if available. For the common setting of suitable subject-level control group data only available from one external (clinical trial or real-world) data source, we evaluate different analysis options for estimating the treatment effect via hazard ratios. The impact of the external control data is usually guided by the level of similarity with the current RCT data. Such level of similarity can be determined via outcome and/or baseline covariate data comparisons. We provide an overview over existing methods, propose a novel option for a combined assessment of outcome and baseline data, and compare a selected set of approaches in a simulation study under varying assumptions regarding observable and unobservable confounder distributions using a time-to-event model. Our various simulation scenarios also reflect the differences between external clinical trial and real-world data. Data combinations via simple outcome-based borrowing or simple propensity score weighting with baseline covariate data are not recommended. Analysis options which conflate outcome and baseline covariate data perform best in our simulation study.

CardioSim: a PC-based cardiac signal simulator using segmental modeling of electrocardiogram

Background: ECG modeling has wide application in signal representation, compression and synthetic ECG generation. Method: CardioSim generates synthetic ECG waveform in real-time using PC-based system. It provides dual facility of interface-based visualization with hardware-based waveform generation. It has two stages viz., development of reference model parameter database using Fourier model and generation of synthetic ECG waveform based on user defined parameters using normal and abnormal records (H, APC, PVC, LBBB, RBBB, P) from mitdb under PhysioNet. Result: It generates ten various ECG waveforms including one healthy and nine diseased rhythms from a single dynamic model with flexible user defined parameters. It gives higher reconstruction performance in terms of SNR and MSE. The mean SNR for different beat morphology is 89.2(H), 88.37(V), 86.32(A), 85.35(L), 97.22(P) and 83.3(R) and mean MSE is 2.45 × 10-6(H), 3.14 × 10-6(V), 8.98 × 10-6(A), 5.82 × 10-6(L), 0.43 × 10-6(P) and 0.25 × 10-6(R). Conclusion: It improves the performance parameters over published research work on ECG modeling and simulation. It can be used as a self-learning tool for entry level medical students.

Community-Based Matrix Factorization (CBMF) Approach for Enhancing Quality of Recommendations

Matrix factorization is a long-established method employed for analyzing and extracting valuable insight recommendations from complex networks containing user ratings. The execution time and computational resources demanded by these algorithms pose limitations when confronted with large datasets. Community detection algorithms play a crucial role in identifying groups and communities within intricate networks. To overcome the challenge of extensive computing resources with matrix factorization techniques, we present a novel framework that utilizes the inherent community information of the rating network. Our proposed approach, named Community-Based Matrix Factorization (CBMF), has the following steps: (1) Model the rating network as a complex bipartite network. (2) Divide the network into communities. (3) Extract the rating matrices pertaining only to those communities and apply MF on these matrices in parallel. (4) Merge the predicted rating matrices belonging to communities and evaluate the root mean square error (RMSE). In our experimentation, we use basic MF, SVD++, and FANMF for matrix factorization, and the Louvain algorithm is used for community division. The experimental evaluation on six datasets shows that the proposed CBMF enhances the quality of recommendations in each case. In the MovieLens 100K dataset, RMSE has been reduced to 0.21 from 1.26 using SVD++ by dividing the network into 25 communities. A similar reduction in RMSE is observed for the datasets of FilmTrust, Jester, Wikilens, Good Books, and Cell Phone.

Exact correction factor for estimating the OR in the presence of sparse data with a zero cell in 2 × 2 tables

In case-control studies, odds ratios (OR) are calculated from 2 × 2 tables and in some instances, we observe small cell counts or zero counts in one of the cells. The corrections to calculate the ORs in the presence of empty cells are available in literature. Some of these include Yates continuity correction and Agresti and Coull correction. However, the available methods provided different corrections and the situations where each could be applied are not very apparent. Therefore, the current research proposes an iterative algorithm of estimating an exact (optimum) correction factor for the respective sample size. This was evaluated by simulating data with varying proportions and sample sizes. The estimated correction factor was considered after obtaining the bias, standard error of odds ratio, root mean square error and the coverage probability. Also, we have presented a linear function to identify the exact correction factor using sample size and proportion.

Effect of various printing parameters on the accuracy (trueness and precision) of 3D-printed partial denture framework

OBJECTIVES

To measure and compare the accuracy of 3D-printed materials used for RPD production to improve workflow and eliminate errors in manufacturing.

METHODS

A partially edentulous maxilla (Kennedy Class III, modification 1) was prepared and designed with proximal plates, rest seats and clasps in one first premolar, one canine and two second molars. A total of 540 3D printed RPD frameworks were 3D printed with three different types of resin (DentaCAST (Asiga, Australia), SuperCAST (Asiga, Australia) and NextDent (3D Systems, Netherlands)). To evaluate the trueness of the printing materials, they were printed with three types of layer thickness: 50 μm, 75 μm and 100 μm, using two types of build angles: 0° and 45° and three types of plate locations: side, middle and corner. After production, all specimens were scanned and superimposed with a control sample that was digitally designed. Using the initial alignment and best-fit alignment method, the root mean square error (RMSE) was calculated. To capture region specific discrepancy, 10 points of XYZ internal discrepancy within RPDs were measured and Euclidean error was calculated. Data was statistically analysed using Shapiro-Wilk and Kruskal-Wallis tests, one-way ANOVA and T-test (SPSS Version 29) and MATLAB (R2022b).

RESULTS

Optimal results were found using 45°, middle of the build plate and layer thicknesses of 100 μm (115 ± 19 μm, DentaCAST), 75 μm (143 ± 14 μm, NextDent), 50 μm (98 ± 35 μm, SuperCAST), which were clinically acceptable. Results were statistically significant when comparing layer thickness in each testing group (p < 0.001). Layer thickness was a primary parameter in the determination of print accuracy among all materials (p < 0.001). Higher discrepancies and failures were observed in 0° prints. No statistically significant difference was found in material usage between build angles or layer thickness (p > 0.005).

CONCLUSIONS

All three 3D printing materials exhibited clinically acceptable RMSE results with a build angle of 45° with a printing layer thickness of 50 μm for SuperCAST, 75 μm NextDent and 100 μm for DentaCAST. The highest discrepancies were mostly found in posterior clasps, while the lowest discrepancy was found in palatal straps. Despite unoptimized spacing of prints, frameworks configured to print in the middle of the build plate result in the least printing failures.

Can human activities enhance the trade-off intensity of ecosystem services in arid inland river basins? Taking the Taolai River asin as an example

Driven by economic and social factors, more and more human beings intervene in nature to promote rapid economic and social development at the expense of ecosystem services (ES), which inevitably leads to the occurrence and even aggravation of ES trade-offs. Especially in the arid inland river basin is more serious. Therefore, this paper takes the Taolai River Basin as an example and uses the InVEST model to evaluate the spatial distribution of four typical ES, including carbon sequestration, oxygen release, windbreak and sand fixation, and water production, under the potential-actual states of the watershed. And use the Pearson correlation coefficient and the root mean square error (RMSE) to analyze the trade-off relationship between services from qualitative and quantitative aspects, respectively. Finally, the spatial matching types of trade-offs in the potential-actual states are discussed using Bivariate Local Indicators of Spatial Association, and the degree and scope of the impact of human activities on trade-offs are analyzed. The results show that the spatial distribution of the four ES has obvious heterogeneity in the potential-actual states, and the service volume of most services in the potential state is much higher than in the actual state. Secondly, there is a significant trade-offs relationship between Water production and Carbon sequestration and Oxygen release services under the potential state, while the actual state under the impact of human activities shows a significant synergistic relationship, which shows that human activities will not only increase the probability of trade-off will also increase the probability of synergy between ES. Finally, through the analysis of the meaning and causes of "high and low space dislocation" and "low and high space dislocation", it is shown that human activities will not only increase but also weaken the trade-off intensity of ES. The results of this study can provide a certain scientific basis for regional ecological environment planning and promote regional people to share ecological well-being.

Improvement in the Tracking Performance of a Maneuvering Target in the Presence of Clutter

The proposed work uses fixed lag smoothing on the interactive multiple model-integrated probabilistic data association algorithm (IMM-IPDA) to enhance its performance. This approach makes use of the advantages of the fixed lag smoothing algorithm to track the motion of a maneuvering target while it is surrounded by clutter. The suggested method provides a new mathematical foundation in terms of smoothing for mode probabilities in addition to the target trajectory state and target existence state by including the smoothing advantages. The suggested fixed lag smoothing IMM-IPDA (FLs IMM-IPDA) method's root mean square error (RMSE), true track rate (TTR), and mode probabilities are compared to those of other recent algorithms in the literature in this study. The results clearly show that the proposed algorithm outperformed the already-known methods in the literature in terms of these above parameters of interest.

Forecasting adversities of COVID-19 waves in India using intelligent computing

The second wave of the COVID-19 pandemic outburst triggered enormously all over India. This ill-fated and fatal brawl affected millions of Indian citizens, with many active and infected Indians struggling to recover from this deadly disease to date, leading to a grief situation. The present situation warrants developing a robust and sound forecasting model to evaluate the adversities of the epidemic with reasonable accuracy to assist officials in curbing this hazard. Consequently, we employed Auto-ARIMA, Auto-ETS, Auto-MLP, Auto-ELM, AM, MLP and proposed ELM methods for assessing accumulative infected COVID-19 individuals by the end of July 2021. We made 90 days of advanced forecasting, i.e., up to 24 July 2021, for the number of cumulative infected COVID-19 cases of India using all seven methods in 15 days' intervals. We fine-tuned the hyper-parameters to enhance the prediction performance of these models and observed that the proposed ELM model offers satisfactory accuracy with MAPE of 5.01, and it rendered better accuracy than the other six models. To comprehend the dataset's nature, five features are extracted. The resulting feature values encouraged further investigation of the models for an updated dataset, where the proposed model provides encouraging results.

Grid Search for Lowest Root Mean Squared Error in Predicting Optimal Sensor Location in Protected Cultivation Systems

Irregular changes in the internal climates of protected cultivation systems can prevent attainment of optimal yield when the environmental conditions are not adequately monitored and controlled. Key to indoor environment monitoring and control and potentially reducing operational costs are the strategic placement of an optimal number of sensors using a robust method. A multi-objective approach based on supervised machine learning was used to determine the optimal number of sensors and installation positions in a protected cultivation system. Specifically, a gradient boosting algorithm, a form of a tree-based model, was fitted to measured (temperature and humidity) and derived conditions (dew point temperature, humidity ratio, enthalpy, and specific volume). Feature variables were forecasted in a time-series manner. Training and validation data were categorized without randomizing the observations to ensure the features remained time-dependent. Evaluations of the variations in the number and location of sensors by day, week, and month were done to observe the impact of environmental fluctuations on the optimal number and location of placement of sensors. Results showed that less than 32% of the 56 sensors considered in this study were needed to optimally monitor the protected cultivation system's internal environment with the highest occurring in May. In May, an average change of -0.041% in consecutive RMSE values ranged from the 1st sensor location (0.027°C) to the 17th sensor location (0.013°C). The derived properties better described the ambient condition of the indoor air than the directly measured, leading to a better performing machine learning model. A machine learning model was developed and proposed to determine the optimal sensors number and positions in a protected cultivation system.

Searching for the urine osmolality surrogate: an automated machine learning approach

OBJECTIVES

Automated machine learning (AutoML) tools can help clinical laboratory professionals to develop machine learning models. The objective of this study was to develop a novel formula for the estimation of urine osmolality using an AutoML tool and to determine the efficiency of AutoML tools in a clinical laboratory setting.

METHODS

Three hundred routine urinalysis samples were used for reference osmolality and urine clinical chemistry analysis. The H2O AutoML engine completed the machine learning development steps with minimum human intervention. Four feature groups were created, which include different urinalysis measurements according to the Boruta feature selection algorithm. Method comparison statistics including Spearman correlation, Passing-Bablok regression analysis were performed, and Bland Altman plots were created to compare model predictions with the reference method. The minimum allowable bias (24.17%) from biological variation data was used as the limit of agreement.

RESULTS

The AutoML engine developed a total of 183 ML models. Conductivity and specific gravity had the highest variable importance. Models that include conductivity, specific gravity, and other urinalysis parameters had the highest R² (0.70-0.83), and 70-84% of results were within the limit of agreement.

CONCLUSIONS

Combining urinary conductivity with other urinalysis parameters using validated machine learning models can yield a promising surrogate. Additionally, AutoML tools facilitate the machine learning development cycle and should be considered for developing ML models in clinical laboratories.

Accuracy assessment of UAV-post-processing kinematic (PPK) and UAV-traditional (with ground control points) georeferencing methods

The use of unmanned aerial vehicles (UAV) in photogrammetric mapping/surveying facilities has increased recently due to the developments on photogrammetric instruments and algorithms that enhance high-quality final products (orthoimages, digital surface model-DSM, etc.) in fast, accurate, and economical way. The aim of this study was to assess the accuracy of a UAV-based post-processing kinematic (PPK) solution. To do that, two methods were implemented with PPK solution and georeferencing with ground control points (GCPs). According to the statistical results, root mean square error (RMSE) values obtained from the GCPs and PPK solutions in the horizontal component are 6.5 cm and 5.4 cm, respectively. The RMSE values in the vertical component (ellipsoidal heights) were obtained as 4.8 cm (GCPs) and 5.2 cm (PPK), respectively. The results show that UAV-PPK method can also be used to produce photogrammetric products where high accuracy (≤ 10 cm) is required without GCPs. In addition, the results obtained regarding the use of this method clearly show that it can be applied in many different fields such as agriculture, forestry, natural disasters, and geomatics.

Modeling and prediction of KSE - 100 index closing based on news sentiments: an applications of machine learning model and ARMA (p, q) model

The main financial markets of every country are stock exchange and consider as an imperative cause for the corporations to increase capital. The novelty of this study to explore machine learning techniques when applied to financial stock market data, and to understand how machine learning algorithms can be applied and compare the result with time series analysis to real lifetime series data and helpful for any investor. Investors are constantly reviewing past pricing history and using it to influence their future investment decisions. The another novelty of this study, using news sentiments, the values will be processed into lists displaying and representing the stock and predicting the future rates to describe the market, and to compare investments, which will help to avoid uncertainty amongst the investors regarding the stock index. Using artificial neural network technique for prediction for KSE 100 index data on closing day. In this regard, six months' data cycle trained the data and apply the statistical interference using a ARMA (p, q) model to calculate numerical result. The novelty of this study to find the relation between them either they are strongly correlated or not, using machine learning techniques and ARMA (p, q) process to forecast the behavior KSE 100 index cycles. The adequacy of model describes via least values Akaike information criterion (AIC), Bayesian Schwarz information criterion (SIC) and Hannan Quinn information criterion (HIC). Durbin- Watson (DW) test is also applied. DW values (< 2) shows that all cycles are strongly correlated. Most of the KSE-100 index cycles expresses that the appropriate model is ARMA (2,1). Cycle's 2nd,3rd,4th and 5th shows that ARMA (3,1) is best fitted. Cycle 8th is shows ARMA (1,1) best fit and cycle 12th shows that the most appropriate model is ARMA (4,1). Diagnostic checking tests like Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Theil's U-Statistics are used to predict KSE-100 index cycles. Theil's U-Statistics demonstrate that each cycle is strongly correlated to previous one.

The efficacy of deep learning based LSTM model in forecasting the outbreak of contagious diseases

The coronavirus disease that outbreak in 2019 has caused various health issues. According to the WHO, the first positive case was detected in Bangladesh on 7th March 2020, but while writing this paper in June 2021, the total confirmed, recovered, and death cases were 826922, 766266 and 13118, respectively. Due to the emergence of COVID-19 in Bangladesh, the country is facing a major public health crisis. Unfortunately, the country does not have a comprehensive health policy to address this issue. This makes it hard to predict how the pandemic will affect the population. Machine learning techniques can help us detect the disease's spread. To predict the trend, parameters, risks, and to take preventive measure in Bangladesh; this work utilized the Recurrent Neural Networks based Deep Learning methodologies like LongShort-Term Memory. Here, we aim to predict the epidemic's progression for a period of more than a year under various scenarios in Bangladesh. We extracted the data for daily confirmed, recovered, and death cases from March 2020 to August 2021. The obtained Root Mean Square Error (RMSE) values of confirmed, recovered, and death cases indicates that our result is more accurate than other contemporary techniques. This study indicates that the LSTM model could be used effectively in predicting contagious diseases. The obtained results could help in explaining the seriousness of the situation, also mayhelp the authorities to take precautionary steps to control the situation.

Monitoring of Oxygen in Simulated Electrolytic Reduction Salt of Pyroprocessing Using Laser-Induced Breakdown Spectroscopy

Laser-induced breakdown spectroscopy (LIBS) was explored as a method of monitoring oxygen (O) concentration in electrolytic reduction salt of pyroprocessing. Simulated salt samples were fabricated, and each sample was put in a transparent and sealed vial filled with argon gas. An neodymium-doped yttrium aluminum garnet (Nd:YAG) laser pulse was applied to the sample through the vial surface, and the optical emission spectrum was measured. O(I) 777.2 nm lines were clearly identified in the spectrum of a sample containing Li₂O, and the intensity of the O peak and the intensity ratio of O and lithium (Li) peaks, in which Li was used as the normalization, increased linearly as the O concentration in the salt sample was increased. The limit of detection and root mean square error were calculated for the cases of O peak area, O peak height, peak area ratio of O-Li, and the peak height ratio of O-Li, and all the cases could indicate that the O concentration in the electrolytic reduction salt was out of normal range. Our result shows that LIBS has the possibility to be used as a method for monitoring of O in electrolytic reduction salt.

Impact of Distracting Emotional Stimuli on the Characteristics of Movement Performance: A Kinematic Study

It is well-documented that emotional stimuli impact both the cognitive and motor aspects of “goal-directed” behavior. However, how emotional distractors impact motor performance remains unclear. This study aimed to characterize how movement quality was impacted during emotional distractors. We used a modified oddball paradigm and documented the performance of pure movement. Participants were designated to draw a triangle or a polygon, while an emotional stimulus was presented. Speed was assessed using reaction time and movement time. The quality and precision of movement were assessed by calculating the accuracy and root-mean-square error (RMSE). Compared to drawings of triangles, polygons had higher accuracy under negative stimuli, but lower RMSE under positive stimuli. The results indicate that distracting emotional stimuli impact different aspects of movement quality, with movement complexity influencing accuracy under negative distractors and precision under positive distractors. This study provides further evidence that movement precision is an important feature of emotional embodiment that should be incorporated in future studies.

Genetic particle filter improved fuzzy-AEEMD for ECG signal de-noising

With the aid of ensemble empirical mode decomposition (EEMD), de-noising of the electrocardiogram (ECG) signal based on the genetic particle filter and fuzzy thresholding is proposed in this paper, which effectively eliminates noise from the ECG signal. This paper proposes a two-phase scheme for removing noise from ECG signal. In the first phase, noisy signal is decomposed into true intrinsic mode functions (IMFs) with the help of EEMD. Adaptive EEMD (AEEMD) is better than EMD because it removes the mode-mixing effect. In the second phase, IMFs which are corrupted by noise are obtained by using spectral flatness of each IMF and fuzzy thresholding. Corrupted IMFs are filtered using genetic particle filter to remove the noise. Finally, the signal is reconstructed with the processed IMFs to get the de-noised ECG. The proposed algorithm is analyzed for different databases and it gives better signal-to-noise ratio and root mean square error than other existing techniques.

Evaluation of Performance of an LR and SVR models to predict COVID-19 Pandemic

Recently, in December 2019 the Coronavirus disease surprisingly influenced the lives of millions of people in the world with its swift spread. To support medical experts/doctors with the overpowering challenge of prediction of total cases in India, a machine-learning algorithm was developed. In this research article, the author describes the possibility of predicting the COVID-19 total, active cases, death and cured cases in India up to 25th June 2020 by applying linear regression and support vector machine. It is extremely tricky to manage the occurrence of corona virus since it is expanding exponentially day to day and is difficult to handle with a limited number of doctors and beds to treat the infected individuals with limited time. Hence, it is essential to develop a machine learning based computerized predicting model. The development effort in this article is based on publicly available data that is downloaded from KAGGLE to estimate the spread of the disease within a short period. We have calculated the RMSE, R2, MAE of LR and SVR models and concluded that the RMSE of linear regression is less than the SVR. Therefore, the LR will help doctors to forecast for the next few days.

COVID-19 Pandemic: ARIMA and Regression Model-Based Worldwide Death Cases Predictions

COVID-19 has now taken a frightening form. As the days pass, it is becoming more and more widespread and now it has become an epidemic. The death rate, which was earlier in the hundreds, changed to thousands and then progressed to millions. If the same situation persists over time, the day is not far when the humanity of all the countries on the globe will be endangered and we yearn for breath. From January 2020 till now, many scientists, researchers and doctors have been trying to solve this complex problem so that proper arrangements can be made by the governments in the hospitals and the death rate can be reduced. The presented research article shows the estimated mortality rate by the ARIMA model and the regression model. This dataset has been collected precisely from DataHub-Novel Coronavirus 2019-Dataset from 22nd January to 29th June 2020. To show the current mortality rate of the entire subject, the correlation coefficients of attributes (MAE, MSE, RMSE and MAPE) were used, where the average absolute percentage error validated the model by 99.09%. The ARIMA model is used to generate auto_arima SARIMAX results, auto_arima residual plots, ARIMA model results, and corresponding prediction plots on the training dataset. These data indicate a continuous decline in death cases. By applying a regression model, the coefficients generated by the regression model are estimated, and the actual death cases and expected death cases are compared and analyzed. It is found that the predicted mortality rate has decreased after May 2, 2020. It will help the government and doctors prepare for the forthcoming plans. Based on short-period predictions, these methods can be used to forecast the mortality rate for a long period.

Validation of an IMU Suit for Military-Based Tasks

Investigating the effects of load carriage on military soldiers using optical motion capture is challenging. However, inertial measurement units (IMUs) provide a promising alternative. Our purpose was to compare optical motion capture with an Xsens IMU system in terms of movement reconstruction using principal component analysis (PCA) using correlation coefficients and joint kinematics using root mean squared error (RMSE). Eighteen civilians performed military-type movements while their motion was recorded using both optical and IMU-based systems. Tasks included walking, running, and transitioning between running, kneeling, and prone positions. PCA was applied to both the optical and virtual IMU markers, and the correlations between the principal component (PC) scores were assessed. Full-body joint angles were calculated and compared using RMSE between optical markers, IMU data, and virtual markers generated from IMU data with and without coordinate system alignment. There was good agreement in movement reconstruction using PCA; the average correlation coefficient was 0.81 ± 0.14. RMSE values between the optical markers and IMU data for flexion-extension were less than 9°, and 15° for the lower and upper limbs, respectively, across all tasks. The underlying biomechanical model and associated coordinate systems appear to influence RMSE values the most. The IMU system appears appropriate for capturing and reconstructing full-body motion variability for military-based movements.

Analysis of the variability of airborne particulate matter with prevailing meteorological conditions across a semi-urban environment using a network of low-cost air quality sensors

The concentrations of fine and coarse fractions of airborne particulate matter (PM) and meteorological variables (wind speed, wind direction, temperature and relative humidity) were measured at six selected locations in Ile Ife, a prominent university town in Nigeria using a network of low-cost air quality (AQ) sensor units. The objective of the deployment was to collate baseline air quality data and assess the impact of prevailing meteorological conditions on PM concentrations in selected residential communities downwind of an iron smelting facility. The raw data obtained from OPC-N2 of the AQ sensor units was corrected using the RH correction factor developed based k-Kohler theory. This PM (corrected) fast time resolution data (20 s) from the AQ sensor units were used to create daily averages. The overall mean mass concentrations for PM_2.5 and PM₁₀ were 213.3, 44.1, 23.8, 27.7, 20.2 and 41.5 μg/m³ and; 439.9, 107.1, 55.0, 72.4, 45.5 and 112.0 μg/m³ for Fasina (Iron-Steel Smelting Factory, ISSF), Modomo, Eleweran, Fire Service, O.A.U. staff quarters and Obafemi Awolowo University Teaching and Research Farm (OAUTRF), respectively. PM concentration and wind speed showed a negative exponential distribution curve with the lowest exponential fit coefficient of determination (R²) values of 0.08 for PM_2.5 and 0.03 for PM₁₀ during nighttime periods at Eleweran and Fire service sites, respectively. The relationship between PM concentration and temperature gave a decay curve indicating that higher PM concentrations were observed at lower temperatures. The exponential distribution curve for the relationship between PM concentration and relative humidity (RH) showed that PM concentrations do not vary for RH < 80 % while stronger relationship was noticed with higher PM concentration for RH > 80 % for both day and nighttime. The performances of the MLR model were slightly poor and as such not too reliable for predicting the concentration but useful for improving predictive model accuracy when other variables contributing to the variability of PM is considered. The study concluded that the anthropogenic and industrial activities at the smelting factory contribute significantly to the elevated PM mass concentration measured at the study locations.

Data analysis of COVID-2019 epidemic using machine learning methods: a case study of India

At this time, COVID-2019 is spreading its foot in the form of a huge epidemic for the world. This epidemic is spreading its foot very fast in India too. One of the World Health Organization states that COVID-2019 is a serious disease that spreads from one person to another at very fast speed through contact routes and respiratory drops. On this day, India and the world should rise to an effective step to analyze this disease and eliminate the effects of this epidemic. In this paper presented, the growing database of COVID-2019 has been analyzed from March 1, 2020, to April 11, 2020, and the next one is predicted for the number of patients suffering from the rising COVID-2019. Different regression analysis models have been utilized for data analysis of COVID-2019 of India based on data stored by Kaggle in between 1 March 2020 to 11 April 2020. In this study, we have been utilized six regression analysis based models namely quadratic, third degree, fourth degree, fifth degree, sixth degree, and exponential polynomial respectively for the COVID-2019 dataset. We have calculated the root mean square of these six regression analysis models. In these six models, the root mean square error of sixth degree polynomial is very less in compared other like quadratic, third degree, fourth degree, fifth degree, and exponential polynomial. Therefore the sixth degree polynomial regression model is very good models for forecasting the next 6 days for COVID-2019 data analysis in India. In this study, we have found that the sixth degree polynomial regression models will help Indian doctors and the Government in preparing their plans in the next 7 days. Based on further regression analysis study, this model can be tuned for forecasting over long term intervals.

A new image steganography method with optimum pixel similarity for data hiding in medical images

Steganography is one of the approaches used in data hiding. Image steganography, is a type of steganography that the image is used as a covering object. Data hiding capacity and image quality of the cover object are important factors in image steganography. Because the deterioration of image quality can be noticed by the human vision system, it attracts the attention of attackers. Therefore, the purpose of this study is increasing the amount of data to be hidden and stego image is to ensure high image quality. In the study, a new optimization-based method has been proposed by making use of the similarities of the pixels. In order to test the performance of the proposed method has been used visual quality analysis metrics such as MSE, RMSE, PSNR, SSIM and UQI. As a cover object; different sizes medical images have been used that obtained from the open access Dicom library database. Doctor comments in different capacities have been hidden to the medical images. Experimental results show that the average PSNR value is 66.5374, 59.4420 and 56.3936, respectively, when 1000 characters, 5000 characters and 10,000 characters data is hidden in 512 × 512 images. In addition, the average PSNR value is 60.4308, 53.3529 and 47.4113, respectively, when 1000 characters, 5000 characters and 10,000 characters data is hidden in 256 × 256 images. 10,000 characters of data have not been hidden in 256 × 256 images without using data compression techniques with classical similarity based LSB method. In the proposed method, 10,000 characters of data have been hidden in 256 × 256 size images without using data compression techniques.

Real-time forecasting of epidemic trajectories using computational dynamic ensembles

Forecasting the trajectory of social dynamic processes, such as the spread of infectious diseases, poses significant challenges that call for methods that account for data and model uncertainty. Here we introduce an ensemble model for sequential forecasting that weights a set of plausible models and use a frequentist computational bootstrap approach to evaluate its uncertainty. We demonstrate the feasibility of our approach using simple dynamic differential-equation models and the trajectory of outbreak scenarios of the Ebola Forecasting Challenge. Specifically, we generate sequential short-term forecasts of epidemic outbreaks by combining phenomenological models that incorporate flexible epidemic growth scaling, namely the Generalized-Growth Model (GGM) and the Generalized Logistic Model (GLM). We rely on the root-mean-square error (RMSE) to quantify the quality of the models' fits during the calibration periods for weighting their contribution to the ensemble model while forecasting performance was evaluated using the RMSE of the forecasts. For a given forecasting horizon (1-4 weeks), we report the performance for each model as the percentage of the number of times each model outperforms the other models. The overall mean RMSE performance of the GLM and the GGM-GLM ensemble models outcompeted that of participant models of the Ebola Forecasting Challenge. We also found that the ensemble model provided more accurate forecasts with higher frequency than the GGM and GLM models, but its performance varied across forecasting horizons. For instance, across all of the Ebola Challenge Scenarios, the ensemble model outperformed the other models at horizons of 2 and 3 weeks while the GLM outperformed other models at horizons of 1 and 4 weeks.

Prediction of Skin Disease with Three Different Feature Selection Techniques Using Stacking Ensemble Method

Skin disease is the most common problem between people. Due to pollution and deployment of ozone layer, harmful UV rays of sun burn the skin and develop various types of skin diseases. Nowadays, machine learning and deep learning algorithms are generally used for diagnosis for various kinds of diseases. In this study, we have applied three feature extraction techniques univariate feature selection, feature importance, and correlation matrix with heat map to find the optimum data subset of erythemato-squamous disease. Four classification techniques Gaussian Naïve Bayesian (NB), decision tree (DT), support vector machine (SVM), and random forest are used for measuring the performance of model. Stacking ensemble technique is then applied to enhance the prediction performance of the model. The proposed method used for measuring the performance of the model. It is finding that the optimal subset of the erythemato-squamous disease is performed well in the case of correlation and heat map feature selection techniques. The mean value, slandered deviation, root mean square error, kappa statistical error, and area under receiver operating characteristics and accuracy are calculated for demonstrating the effectiveness of the proposed model. The feature selection techniques applied with staking ensemble technique gives the better result as compared to individual machine learning techniques. The obtained results show that the performance of proposed model is higher than previous results obtained by researchers.

An improved edge detection algorithm for X-Ray images based on the statistical range

Edge detection is the prior stage to object recognition and considered as a pillar for image processing task. It is a process to detect such locations from images in terms of pixels where their intensity changing is abruptly. There are many types of images such as medical images, satellite images, articular images, industrial images, general purpose images etc. X-Ray is a type of medical image in which electronic radiation is passed into the human body to capture image of inner parts for better disease diagnoses by orthopaedics or radiologist. In this research paper, we have proposed an improved method to detect edges from human being's X-Ray images based on Gaussian filter and statistical range. Gaussian filter is used for image preprocessing and enhancement. Whereas, Statistical range is used to calculate difference between maximum and minimum pixels from every 3X3 image matrix partition. These two can work to detect edges from X-Ray images. We have also presented a comprehensive comparison of our proposed method with four existing latest methods/algorithms of edge detection. Apart from X-Ray images, experiments have also been conducted on human X-Ray images to detect edges. Further, we have found that our proposed method is superior in terms of MSE, RMSE, PSNR and computation time to detect edges from X-Ray images of human being.

To Generate an Ensemble Model for Women Thyroid Prediction Using Data Mining Techniques

Objective:

The main objective of this paper is to easily identify thyroid symptom for treatment.

Methods:

In this paper two main techniques are proposed for mining the hidden pattern in the dataset. Ensemble-I and Ensemble-II both are machine learning techniques. Ensemble-I generated from decision tree, over fitting and neural network and Ensemble-II generated from combinations of Bagging and Boosting techniques. Finally proposed experiment is conducted by Ensemble-I vs. Ensemble-II.

Results:

In the entire experimental setup find an ensemble –II generated model is the higher compare to other ensemble-I model. In each experiment observe and compare the value of all the performance of ROC, MAE, RMSE, RAE and RRSE. Stacking (ensemble-I) ensemble model estimate the weights for input with output model by thyroid dataset. After the measurement find out the results ROC=(98.80), MAE= (0.89), 6RMSE=(0.21), RAE= (52.78), RRSE=(83.71)and in the ensemble-II observe thyroid dataset and measure all performance of the model ROC=(98.79), MAE= (0.31), RMSE=(0.05) and RAE= (35.89) and RRSE=(52.67). Finally concluded that (Bagging+ Boosting) ensemble-II model is the best compare to other.

Hand Tracking and Gesture Recognition Using Lensless Smart Sensors

The Lensless Smart Sensor (LSS) developed by Rambus, Inc. is a low-power, low-cost visual sensing technology that captures information-rich optical data in a tiny form factor using a novel approach to optical sensing. The spiral gratings of LSS diffractive grating, coupled with sophisticated computational algorithms, allow point tracking down to millimeter-level accuracy. This work is focused on developing novel algorithms for the detection of multiple points and thereby enabling hand tracking and gesture recognition using the LSS. The algorithms are formulated based on geometrical and mathematical constraints around the placement of infrared light-emitting diodes (LEDs) on the hand. The developed techniques dynamically adapt the recognition and orientation of the hand and associated gestures. A detailed accuracy analysis for both hand tracking and gesture classification as a function of LED positions is conducted to validate the performance of the system. Our results indicate that the technology is a promising approach, as the current state-of-the-art focuses on human motion tracking that requires highly complex and expensive systems. A wearable, low-power, low-cost system could make a significant impact in this field, as it does not require complex hardware or additional sensors on the tracked segments.

Interval and point estimation in adaptive Phase II trials with binary endpoint

Phase II clinical trials are concerned with making decision of whether a treatment is sufficiently efficacious to be worth further investigations in late large scale Phase III trials. In oncology Phase II trials, frequentist single-arm two-stage group-sequential designs with a binary endpoint are commonly used. To allow for more flexibility, adaptive versions of these designs have been proposed. In this paper, we propose point and interval estimation for adaptive designs in which the second stage sample size is a pre-specified function of first stage's number of responses. Our approach is based on sample space orderings, from which we derive p-values, and point and interval estimates. Simulation studies show that our proposed methods perform better, in terms of bias and root mean square error, than the fixed-sample maximum likelihood estimator.

Information theoretic multiscale truncated SVD for multilead electrocardiogram

BACKGROUND AND OBJECTIVE

In this paper an information theory based multiscale singular value decomposition (SVD) is proposed for multilead electrocardiogram (ECG) signal processing. The shrinkage of singular values for different multivariate multiscale matrices at wavelet scales is based on information content. It aims to capture and preserve the information of clinically important local waves like P-waves, Q-waves, T-waves and QRS-complexes.

METHODS

The information is derived through clinically relevant multivariate multiscale entropy in SVD domain modifying Shannon's entropy. This optimizes the approximate ranks for matrices to capture the clinical components of ECG signals appearing at different scales. A newly introduced multivariate clinical distortion (MCD) metric is computed and compared with existing subjective and objective signal distortion measures. The proposed method is tested with records from CSE multilead measurement library and PTB diagnostic ECG database for various pathological cases.

RESULTS

It gives average percentage root mean square difference (PRD), average normalized root mean square error (NRMSE), average wavelet energy based diagnostic distortion measure (WEDD) values 5.8879%, 0.0059 and 1.0760% respectively for myocarditis pathology. The corresponding MCD value is 1.9429%. The highest average PRD and average WEDD values are 11.4053% and 5.5194% for cardiomyopathy with the corresponding MCD value 1.4003%.

CONCLUSIONS

Based on WEDD values and mean opinion scores (MOS), the quality group of all processed signals fall under excellent category.

Land use regression models for estimating individual NOx and NO₂ exposures in a metropolis with a high density of traffic roads and population

This study is conducted to characterize the intra-urban distribution of NOx and NO2; develop land use regression (LUR) models to assess outdoor NOx and NO2 concentrations, using the ESCAPE modeling approach with locally specific land use data; and compare NOx and NO2 exposures for children in the Taipei Metropolis by the LUR models, the nearest monitoring station, and kriging methods based on data collected at the measurement sites. NOx and NO2 were measured for 2 weeks during 3 seasons at 40 sampling sites by Ogawa passive badges to represent their concentrations at urban backgrounds and streets from October 2009 to September 2010. Land use data and traffic-related information in different buffer zones were combined with measured concentrations to derive LUR models using supervised forward stepwise multiple regressions. The annual average concentrations of NOx and NO2 in Taipei were 72.4 ± 22.5 and 48.9 ± 12.2 μg/m(3), respectively, which were at the high end of all 36 European areas in the ESCAPE project. Spatial contrasts in Taipei were lower than those of the European areas in the ESCAPE project. The NOx LUR model included 6 land use variables, which were lengths of major roads within 25 m, 25-50 m, and 50-500 m, urban green areas within 300 m and 300-5,000 m, and semi-natural and forested areas within 500 m, with R(2)=0.81. The NO2 LUR model included 4 land use variables, which were lengths of major roads within 25 m, urban green areas within 100 m, semi-natural and forested areas within 500 m, and low-density residential area within 500 m, with R(2)=0.74. The LUR models gave a wider variation in estimating NOx and NO2 exposures than either the ordinary kriging method or the nearest measurement site did for the children of Taiwan Birth Cohort Study (TBCS) in Taipei.

Relief of depression and pain improves daily functioning and quality of life in patients with major depressive disorder

OBJECTIVE

The objective of this study was to investigate the effects of depression relief and pain relief on the improvement in daily functioning and quality of life (QOL) for depressed patients receiving a 6-week treatment of fluoxetine.

METHOD

A total of 131 acutely ill inpatients with major depressive disorder (MDD) were enrolled to receive 20mg of fluoxetine daily for 6 weeks. Depression severity, pain severity, daily functioning, and health-related QOL were assessed at baseline and again at week 6. Depression severity, pain severity, and daily functioning were assessed using the 17-item Hamilton Depression Rating Scale, the Short-Form 36 (SF-36) Body Pain Index, and the Work and Social Adjustment Scale. Health-related QOL was assessed by three primary domains of the SF-36, including social functioning, vitality, and general health perceptions. Pearson's correlation and structural equation modeling were used to examine relationships among the study variables. Five models were proposed. In model 1, depression relief alone improved daily functioning and QOL. In model 2, pain relief alone improved daily functioning and QOL. In model 3, depression relief, mediated by pain relief, improved daily functioning and QOL. In model 4, pain relief, mediated by depression relief, improved daily functioning and QOL. In model 5, both depression relief and pain relief improved daily functioning and QOL.

RESULTS

One hundred and six patients completed all the measures at baseline and at week 6. Model 5 was the most fitted structural equation model (χ(2) = 8.62, df = 8, p = 0.376, GFI = 0.975, AGFI = 0.935, TLI = 0.992, CFI = 0.996, RMSEA = 0.027).

CONCLUSION

Interventions which relieve depression and pain improve daily functioning and QOL among patients with MDD. The proposed model can provide quantitative estimates of improvement in treating patients with MDD.

Modelling bioaccumulation of oil constituents in aquatic species

Crude oil poses a risk to marine ecosystems due to its toxicity and tendency to accumulate in biota. The present study evaluated the applicability of the OMEGA model for estimating oil accumulation in aquatic species by comparing model predictions of kinetic rates (absorption and elimination) and bioconcentration factors (BCF) with measured values. The model was a better predictor than the means of the measurements for absorption and elimination rate constants, but did not outperform the mean measured BCF. Model estimates and measurements differed less than one order of magnitude for 91%, 80% and 61% of the absorption and elimination rates and BCFs of all oil constituents, respectively. Of the "potentially modifying" factors: exposure duration, biotransformation, molecular mass, and water temperature, the last two tended to influence the performance of the model. Inclusion of more explanatory variables in the bioaccumulation model, like the molecular mass, is expected to improve model performance.

Comparison of Multiple Linear Regressions and Neural Networks based QSAR models for the design of new antitubercular compounds

The performance of two QSAR methodologies, namely Multiple Linear Regressions (MLR) and Neural Networks (NN), towards the modeling and prediction of antitubercular activity was evaluated and compared. A data set of 173 potentially active compounds belonging to the hydrazide family and represented by 96 descriptors was analyzed. Models were built with Multiple Linear Regressions (MLR), single Feed-Forward Neural Networks (FFNNs), ensembles of FFNNs and Associative Neural Networks (AsNNs) using four different data sets and different types of descriptors. The predictive ability of the different techniques used were assessed and discussed on the basis of different validation criteria and results show in general a better performance of AsNNs in terms of learning ability and prediction of antitubercular behaviors when compared with all other methods. MLR have, however, the advantage of pinpointing the most relevant molecular characteristics responsible for the behavior of these compounds against Mycobacterium tuberculosis. The best results for the larger data set (94 compounds in training set and 18 in test set) were obtained with AsNNs using seven descriptors (R(2) of 0.874 and RMSE of 0.437 against R(2) of 0.845 and RMSE of 0.472 in MLRs, for test set). Counter-Propagation Neural Networks (CPNNs) were trained with the same data sets and descriptors. From the scrutiny of the weight levels in each CPNN and the information retrieved from MLRs, a rational design of potentially active compounds was attempted. Two new compounds were synthesized and tested against M. tuberculosis showing an activity close to that predicted by the majority of the models.

The role of neuroticism, perfectionism and depression in chronic fatigue syndrome. A structural equation modeling approach

OBJECTIVE

Previous studies have reported consistent associations between Neuroticism, maladaptive perfectionism and depression with severity of fatigue in Chronic Fatigue Syndrome (CFS). Depression has been considered a mediator factor between maladaptive perfectionism and fatigue severity, but no studies have explored the role of neuroticism in a comparable theoretical framework. This study aims to examine for the first time, the role of neuroticism, maladaptive perfectionism and depression on the severity of CFS, analyzing several explanation models.

METHODS

A sample of 229 CFS patients were studied comparing four structural equation models, testing the role of mediation effect of depression severity in the association of Neuroticism and/or Maladaptive perfectionism on fatigue severity.

RESULTS

The model considering depression severity as mediator factor between Neuroticism and fatigue severity is the only one of the explored models where all the structural modeling indexes have fitted satisfactorily (Chi square=27.01, p=0.079; RMSE=0.047, CFI=0.994; SRMR=0.033). Neuroticism is associated with CFS by the mediation effect of depression severity. This personality variable constitutes a more consistent factor than maladaptive perfectionism in the conceptualization of CFS severity.

Blind prediction of charged ligand binding affinities in a model binding site

Predicting absolute protein-ligand binding affinities remains a frontier challenge in ligand discovery and design. This becomes more difficult when ionic interactions are involved because of the large opposing solvation and electrostatic attraction energies. In a blind test, we examined whether alchemical free-energy calculations could predict binding affinities of 14 charged and 5 neutral compounds previously untested as ligands for a cavity binding site in cytochrome c peroxidase. In this simplified site, polar and cationic ligands compete with solvent to interact with a buried aspartate. Predictions were tested by calorimetry, spectroscopy, and crystallography. Of the 15 compounds predicted to bind, 13 were experimentally confirmed, while 4 compounds were false negative predictions. Predictions had a root-mean-square error of 1.95 kcal/mol to the experimental affinities, and predicted poses had an average RMSD of 1.7Å to the crystallographic poses. This test serves as a benchmark for these thermodynamically rigorous calculations at predicting binding affinities for charged compounds and gives insights into the existing sources of error, which are primarily electrostatic interactions inside proteins. Our experiments also provide a useful set of ionic binding affinities in a simplified system for testing new affinity prediction methods.

Are clinical measurements linked to the gait deviation index in cerebral palsy patients?

OBJECTIVE

From a dataset of clinical assessments and gait analysis, this study was designed to determine which of the assessments or their combinations would most influence a low gait index (i.e., severe gait deviations) for individuals with cerebral palsy.

DESIGN

A retrospective search, including clinical and gait assessments, was conducted from August 2005 to September 2009.

POPULATION

One hundred and fifty-five individuals with a clinical diagnosis of cerebral palsy (CP) (mean age (SD): 11 (5.3) years) were selected for the study.

METHOD

Quinlan's Interactive Dichotomizer 3 algorithm for decision-tree induction, adapted to fuzzy data coding, was employed to predict a Gait Deviation Index (GDI) from a dataset of clinical assessments (i.e., range of motion, muscle strength, and level of spasticity).

RESULTS

Seven rules that could explain severe gait deviation (a fuzzy GDI low class) were induced. Overall, the fuzzy decision-tree method was highly accurate and permitted us to correctly classify GDI classes 9 out of 10 times using our clinical assessments.

CONCLUSION

There is an important relationship between clinical parameters and gait analysis. We have identified the main clinical parameters and combinations of these parameters that lead to severe gait deviations. The strength of the hip extensor, the level of spasticity and the strength of the tibialis posterior were the most important clinical parameters for predicting a severe gait deviation.