A Highly Robust and Conducting Ultramicroporous 3D Fe(II)-Based Metal-Organic Framework for Efficient Energy Storage

Exploitation of metal-organic framework (MOF) materials as active electrodes for energy storage or conversion is reasonably challenging owing to their poor robustness against various acidic/basic conditions and conventionally low electric conductivity. Keeping this in perspective, herein, a 3D ultramicroporous triazolate Fe-MOF (abbreviated as Fe-MET) is judiciously employed using cheap and commercially available starting materials. Fe-MET possesses ultra-stability against various chemical environments (pH-1 to pH-14 with varied organic solvents) and is highly electrically conductive (σ = 0.19 S m-1) in one fell swoop. By taking advantage of the properties mentioned above, Fe-MET electrodes give prominence to electrochemical capacitor (EC) performance by delivering an astounding gravimetric (304 F g-1) and areal (181 mF cm-2) capacitance at 0.5 A g-1 current density with exceptionally high cycling stability. Implementation of Fe-MET as an exclusive (by not using any conductive additives) EC electrode in solid-state energy storage devices outperforms most of the reported MOF-based EC materials and even surpasses certain porous carbon and graphene materials, showcasing superior capabilities and great promise compared to various other alternatives as energy storage materials.

Robust, planning-based targeted locoregional tumour heating in small animals

Objective.To improve hyperthermia in clinical practice, pre-clinical hyperthermia research is essential to investigate hyperthermia effects and assess novel treatment strategies. Translating pre-clinical hyperthermia findings into clinically viable protocols requires laboratory animal treatment techniques similar to clinical hyperthermia techniques. The ALBA micro8 electromagnetic heating system (Med-logix SRL, Rome, Italy) has recently been developed to provide the targeted locoregional tumour heating currently lacking for pre-clinical research. This study evaluates the heat focusing properties of this device and its ability to induce robust locoregional tumour heating under realistic physiological conditions using simulations.Approach.Simulations were performed using the Plan2Heat treatment planning package (Amsterdam UMC, the Netherlands). First, the specific absorption rate (SAR) focus was characterised using a homogeneous phantom. Hereafter, a digital mouse model was used for the characterisation of heating robustness in a mouse. Device settings were optimised for treatment of a pancreas tumour and tested for varying circumstances. The impact of uncertainties in tissue property and perfusion values was evaluated using polynomial chaos expansion. Treatment quality and robustness were evaluated based on SAR and temperature distributions.Main results.The SAR distributions within the phantom are well-focused and can be adjusted to target any specific location. The focus size (full-width half-maximum) is a spheroid with diameters 9 mm (radially) and 20 mm (axially). The mouse model simulations show strong robustness against respiratory motion and intestine and stomach filling (∆T90≤0.14°C).Mouse positioning errors in the cranial-caudal direction lead to∆T90≤0.23°C. Uncertainties in tissue property and perfusion values were found to impact the treatment plan up to 0.56 °C (SD), with a variation onT90of 0.32 °C (1 SD).Significance.Our work shows that the pre-clinical phased-array system can provide adequate and robust locoregional heating of deep-seated target regions in mice. Using our software, robust treatment plans can be generated for pre-clinical hyperthermia research.

Highly Elastic, Robust, and Efficient Hydrogel Solar Absorber against Harsh Environmental Impacts

Solar distillation is a promising approach for addressing water scarcity, but relentless stress/strain perturbations induced by wind and waves would inevitably cause structural damage to solar absorbers. Despite notable advances in efficient solar absorbers, there have been no reports of compliant and robust solar absorbers withstanding practical mechanical impacts. Herein, an elastic and robust hydrogel absorber that exhibited a high level of evaporation performance was fabricated by introducing ion-coordinated MXene nanosheets as photothermal conversion units and mechanically enhanced fillers. The ion-coordinated MXene nanosheets acting as strong cross-linking points provided excellent elasticity and robustness to the hydrogel absorber. As a result, the evaporation rate of hydrogel absorber, with a high initial value of 2.61 kg m-2 h-1 under one sun irradiation, remained at 2.15 kg m-2 h-1 under a 100% tensile strain state and 2.40 kg m-2 h-1 after 10 000 stretching-releasing cycles. This continuous and stable water desalination approach provides a promising device for actual seawater distillation.

Robust and Corrosion-Resistant Overall Water Splitting Electrode Enabled by Additive Manufacturing

Electrolysis of water has emerged as a prominent area of research in recent years. As a promising catalyst support, copper foam is widely investigated for electrolytic water, yet the insufficient mechanical strength and corrosion resistance render it less suitable for harsh working conditions. To exploit high-performance catalyst supports, various metal supports are comprehensively evaluated, and Ti6 Al4 V (Ti64) support exhibited outstanding compression and corrosion resistance. With this in mind, a 3D porous Ti64 catalyst support is fabricated using the selective laser sintering (SLM) 3D printing technology, and a conductive layer of nickel (Ni) is coated to increase the electrical conductivity and facilitate the deposition of catalysts. Subsequently, Co0.8 Ni0.2 (CO3 )0.5 (OH)·0.11H2 O (CoNiCH) nanoneedles are deposited. The resulting porous Ti64/Ni/CoNiCH electrode displayed an impressive performance in the oxygen evolution reaction (OER) and reached 30 mA cm-2 at an overpotential of only 200 mV. Remarkably, even after being compressed at 15.04 MPa, no obvious structural deformation is observed, and the attenuation of its catalytic efficiency is negligible. Based on the computational analysis, the CoNiCH catalyst demonstrated superior catalytic activity at the Ni site in comparison to the Co site. Furthermore, the electrode reached 30 mA cm-2 at 1.75 V in full water splitting conditions and showed no significant performance degradation even after 60 h of continuous operation. This study presents an innovative approach to robust and corrosion-resistant catalyst design.

Robust Multi-Dimensional Time Series Forecasting

Large-scale and high-dimensional time series data are widely generated in modern applications such as intelligent transportation and environmental monitoring. However, such data contains much noise, outliers, and missing values due to interference during measurement or transmission. Directly forecasting such types of data (i.e., anomalous data) can be extremely challenging. The traditional method to deal with anomalies is to cut out the time series with anomalous value entries or replace the data. Both methods may lose important knowledge from the original data. In this paper, we propose a multidimensional time series forecasting framework that can better handle anomalous values: the robust temporal nonnegative matrix factorization forecasting model (RTNMFFM) for multi-dimensional time series. RTNMFFM integrates the autoregressive regularizer into nonnegative matrix factorization (NMF) with the application of the L2,1 norm in NMF. This approach improves robustness and alleviates overfitting compared to standard methods. In addition, to improve the accuracy of model forecasts on severely missing data, we propose a periodic smoothing penalty that keeps the sparse time slices as close as possible to the time slice with high confidence. Finally, we train the model using the alternating gradient descent algorithm. Numerous experiments demonstrate that RTNMFFM provides better robustness and better prediction accuracy.

Well-controlled disease activity with drug treatment will not improve the frailty status of RA patients to robust state: A multicenter observational study (T-FLAG)

OBJECTIVES

To investigate a plateau in treatment enhancement for improving the frailty status of rheumatoid arthritis (RA) patients.

METHODS

A total of 345 RA patients who were not robust in 2021 were assigned to the improved ("robust 2022," n = 51) and non-improved ("pre-frailty/frailty 2022," n = 294) groups. Factors associated with "robust 2022" were examined by logistic regression analysis. Patients were assigned to the stable (Follow-up mean DAS28-ESR in 2020 and 2021 < 3.2, n = 225) and unstable (≥3.2, n = 120) groups, which were further divided into the non-improved (stable: n = 180, unstable: n = 114) and improved (stable: n = 45, unstable: n = 6) groups. Factors influencing Japanese Cardiovascular Health Study (J-CHS) score were examined by multiple regression analysis. Changes over 2 years were compared between the non-improved and improved groups of the stable group.

RESULTS

The associated factor of "robust 2022" was the follow-up meanDAS28-ESR in 2020 and 2021 < 3.2 (i.e., stable state) (OR: 4.01). Follow-up mean DAS28-ESR in 2020 and 2021 was associated with J-CHS score (T = 2.536, p = .013) only in the unstable group. In the stable group, HAQ-DI was lower (2020: 0.32 vs. 0.16; 2021: 0.32 vs. 0.17; 2022: 0.32 vs. 0.21), and the proportion of J-CHS: Q4 (weakness) was lower (2020: 48.4 vs. 17.8%; 2021: 55.0 vs. 29.2%; 2022: 50.4 vs. 0%), in the improved group than in the non-improved group, whereas both groups maintained clinical and functional remission over 2 years.

CONCLUSIONS

Drug treatment to maintain well-controlled disease activity alone is insufficient for improving patients' frailty status after achieving treat-to-target goals, suggesting the need for multifaceted approaches.

Robust and Transparent Silver Oxide Coating Fabricated at Room Temperature Kills Clostridioides difficile Spores, MRSA, and Pseudomonas aeruginosa

Antimicrobial coatings can inhibit the transmission of infectious diseases when they provide a quick kill that is achieved long after the coating application. Here, we describe the fabrication and testing of a glass coating containing Ag2O microparticles that was prepared from sodium silicate at room temperature. The half-lives of both methicillin-resistant Staphylococcus aureus (MRSA) and Pseudomonas aeruginosa on this coating are only 2-4 min. The half-life of Clostridioides difficile spores is about 9-12 min, which is extremely short for a spore. Additional tests on MRSA demonstrate that the coating retains its antimicrobial activity after abrasion and that an increased loading of Ag2O leads to a shorter half-life. This coating combines the properties of optical transparency, robustness, fast kill, and room temperature preparation that are highly desirable for an antimicrobial coating.

Zero-Inflated Count Regression Models in Solving Challenges Posed by Outlier-Prone Data; an Application to Length of Hospital Stay

Introduction

Ignoring outliers in data may lead to misleading results. Length of stay (LOS) is often considered a count variable with a high frequency of outliers. This study exemplifies the potential of robust methodologies in enhancing the accuracy and reliability of analyses conducted on skewed and outlier-prone count data of LOS.

Methods

The application of Zero-Inflated Poisson (ZIP) and robust Zero-Inflated Poisson (RZIP) models in solving challenges posed by outlier LOS data were evaluated. The ZIP model incorporates two components, tackling excess zeros with a zero-inflation component and modeling positive counts with a Poisson component. The RZIP model introduces the Robust Expectation-Solution (RES) algorithm to enhance parameter estimation and address the impact of outliers on the model's performance.

Results

Data from 254 intensive care unit patients were analyzed (62.2% male). Patients aged 65 or older accounted for 58.3% of the sample. Notably, 38.6% of patients exhibited zero LOS. The overall mean LOS was 5.89 (± 9.81) days, and 9.45% of cases displayed outliers. Our analysis using the RZIP model revealed significant predictors of LOS, including age, underlying comorbidities (p<0.001), and insurance status (p=0.013). Model comparison demonstrated the RZIP model's superiority over ZIP, as evidenced by lower Akaike information criteria (AIC) and Bayesians information criteria (BIC) values.

Conclusions

The application of the RZIP model allowed us to uncover meaningful insights into the factors influencing LOS, paving the way for more informed decision-making in hospital management.

An economic and robust TMT labeling approach for high throughput proteomic and metaproteomic analysis

Multiplexed quantitative proteomics using tandem mass tag (TMT) is increasingly used in -omic study of complex samples. While TMT-based proteomics has the advantages of the higher quantitative accuracy, fewer missing values, and reduced instrument analysis time, it is limited by the additional reagent cost. In addition, current TMT labeling workflows involve repeated small volume pipetting of reagents in volatile solvents, which may increase the sample-to-sample variations and is not readily suitable for high throughput applications. In this study, we demonstrated that the TMT labeling procedures could be streamlined by using pre-aliquoted dry TMT reagents in a 96 well plate or 12-tube strip. As little as 50 μg dry TMT per channel was used to label 6-12 μg peptides, yielding high TMT labeling efficiency (∼99%) in both microbiome and mammalian cell line samples. We applied this workflow to analyze 97 samples in a study to evaluate whether ice recrystallization inhibitors improve the cultivability and activity of frozen microbiota. The results demonstrated tight sample clustering corresponding to groups and consistent microbiome responses to prebiotic treatments. This study supports the use of TMT reagents that are pre-aliquoted, dried, and stored for robust quantitative proteomics and metaproteomics in high throughput applications.

Instrumentation development, improvement, simplification, and miniaturization: The multifunctional plate source for use in mass spectrometry

In remembrance of Prof. Dr Przybylski, we are presenting a vision towards his beloved mass spectrometry (MS) and its far-reaching promises outside of the academic laboratory. Sub-atmospheric pressure (AP) ionization MS is well positioned to make a step-change in direct ionization, a concept that allows sublimation/evaporation ionization and mass analyses of volatile and nonvolatile molecules from clean or dirty samples, directly, accurately, sensitively, and in a straightforward manner that has the potential to expand the field of MS into unchartered application areas. Contrary to ambient ionization MS, ionization commences in the sub-AP region of the mass spectrometer, important for practical and safety reasons, and offers inter alia, simplicity, speed, sensitivity, and robustness directly from real-world samples without cleanup. The plate source concept, presented here, provides an easy to use, rapid, and direct sample introduction from AP into the sub-AP of a mass spectrometer. Utilizing sub-AP ionization MS based on the plate source concept, small to large molecules from various environments that would be deemed too dirty for some direct MS methods are demonstrated. The new source concept can be expanded to include multiple ionization methods using the same plate source "front end" without the need to vent the mass spectrometer between the different methods, thus allowing ionization of more compounds on the same mass spectrometer for which any one ionization method may be insufficient. Examples such as fentanyl, gamma-hydroxybutyric acid, clozapine, 1-propionyllysergic acid, hydrocodone angiotensin I and II, myoglobin, and carbonic anhydrase are included.

Robust control for a tracked mobile robot based on a finite-time convergence zeroing neural network

Introduction

Since tracked mobile robot is a typical non-linear system, it has been a challenge to achieve the trajectory tracking of tracked mobile robots. A zeroing neural network is employed to control a tracked mobile robot to track the desired trajectory.

Methods

A new fractional exponential activation function is designed in this study, and the implicit derivative dynamic model of the tracked mobile robot is presented, termed finite-time convergence zeroing neural network. The proposed model is analyzed based on the Lyapunov stability theory, and the upper bound of the convergence time is given. In addition, the robustness of the finite-time convergence zeroing neural network model is investigated under different error disturbances.

Results and discussion

Numerical experiments of tracking an eight-shaped trajectory are conducted successfully, validating the proposed model for the trajectory tracking problem of tracked mobile robots. Comparative results validate the effectiveness and superiority of the proposed model for the kinematical resolution of tracked mobile robots even in a disturbance environment.

Exquisitely Constructing a Robust MOF with Dual Pore Sizes for Efficient CO2 Capture

Developing metal-organic framework (MOF) adsorbents with excellent performance and robust stability is of critical importance to reduce CO2 emissions yet challenging. Herein, a robust ultra-microporous MOF, Cu(bpfb)(bdc), with mixed ligands of N, N'-(1,4-phenylene)diisonicotinamide (bpfb), and 1,4-dicarboxybenzene (bdc) was delicately constructed. Structurally, this material possesses double-interpenetrated frameworks formed by two staggered, independent frameworks, resulting in two types of narrow ultra-micropores of 3.4 × 5.0 and 4.2 × 12.8 Å2, respectively. The above structural properties make its highly selective separation at 273~298 K with a CO2 capacity of 71.0~86.2 mg/g. Its adsorption heat over CO2 and IAST selectivity were calculated to be 27 kJ/mol and 52.2, respectively. Remarkably, cyclic breakthrough experiments corroborate its impressive performance in CO2/N2 separation in not only dry but also 75% RH humid conditions. Molecular simulation reveals that C-H···OCO2 in the pores plays a pivotal role in the high selectivity of CO2 adsorption. These results point out the huge potential application of this material for CO2/N2 separation.

Machine learning of spectra-property relationship for imperfect and small chemistry data

Machine learning (ML) is causing profound changes to chemical research through its powerful statistical and mathematical methodological capabilities. However, the nature of chemistry experiments often sets very high hurdles to collect high-quality data that are deficiency free, contradicting the need of ML to learn from big data. Even worse, the black-box nature of most ML methods requires more abundant data to ensure good transferability. Herein, we combine physics-based spectral descriptors with a symbolic regression method to establish interpretable spectra-property relationship. Using the machine-learned mathematical formulas, we have predicted the adsorption energy and charge transfer of the CO-adsorbed Cu-based MOF systems from their infrared and Raman spectra. The explicit prediction models are robust, allowing them to be transferrable to small and low-quality dataset containing partial errors. Surprisingly, they can be used to identify and clean error data, which are common data scenarios in real experiments. Such robust learning protocol will significantly enhance the applicability of machine-learned spectroscopy for chemical science.

Enhancing Strategic Learning Through the Implementation of Robust Process Improvement in a Specialized Tertiary Care Hospital

Introduction

Performance improvement (PI) in healthcare is essential to improve health, patient experience, and reduce costs. PI projects became low, inconsistent and weakly-sustained in our hospital. The low number and low sustainability were scarcely in alignment with our strategic goal to become a high reliability organization (HRO). This was due to lack of standardized knowledge and ability to initiate and sustain PI projects. Therefore, a structured framework was developed, followed by the building of capacity and capability in the use of robust process improvement (RPI) amidst the COVID-19 pandemic.

Methods

A team of healthcare quality professionals collaborated with Hospital Performance Improvement-Press Ganey for hospital-wide quality improvement project. The team received training on RPI from Press Ganey and created the framework to use . This framework is based on the Institute for Healthcare Improvement Model for Improvement, Lean, Six Sigma, and FOCUS-PDSA (Find-Organize-Clarify-Understand-Select-Plan-Do-Study-Act). Thereafter, the team of internal coaches organized an RPI training course that consisted of 6 sessions, for clinical and nonclinical staff , using classroom and virtual sessions during the pandemic. This course was increased to eight sessions to avoid information overload. Process measures were collected using a survey to obtain feedback, whereas outcome measures were from the number of completed projects and their effects related to costs, access to care, waiting time, number of harms, and compliance.

Results

Participation and submission improved after three PDSA (Plan-Do-Study-Act) cycles. This resulted in an increased number of completed and sustained projects from 50 in 2019 to 94 in 2020 and continued to rise to 109 in 2021. There were 140 and 122 certified RPI coaches in 2020 and 2021, respectively. Although there was a decrease in the number of certified coaches in 2021, the number of completed projects was higher than in 2020. The overall effect of these completed projects by the third quarter of 2021 showed improvement in access to care by 39%, compliance to standards of care by 48%, satisfaction by 8%, and reduction in costs by 47,010 SAR, in waiting time of 170 hours, and in the number of harms by 89.

Conclusion

This quality improvement project led to enhanced capacity of staff as seen in the increased number of certified RPI coaches, thereby increasing the submission and completion of projects in 1 year. Its sustainability during the 2 succeeding years continued to enhance project completion and maintenance, bringing quality improvement benefits to the organization and the patients.

Protocol of a Multicenter Prospective Trial of Office-Based Carpal Tunnel Release With Ultrasound Guidance (ROBUST)

Background Carpal tunnel release (CTR) is a common surgical procedure for patients with severe or refractory carpal tunnel syndrome (CTS) symptoms. Historically, CTR procedures have been performed in a hospital or an ambulatory surgery center (ASC). However, due to advancements in techniques, greater patient demand, and concerns about growing healthcare costs, there is a distinct trend toward performing CTR procedures in an office-based setting. Several small studies with limited follow-up duration have demonstrated the feasibility of CTR with ultrasound guidance (CTR-US) when performed in an office-based setting. The objective of this study is to evaluate the safety and effectiveness of office-based CTR-US in a large cohort of patients (n=140) with symptomatic CTS followed for two years post-treatment. Design and methods ROBUST is a prospective multicenter observational study in which 140 subjects at up to 12 sites in the United States will be treated with CTR-US in an office-based setting. The primary endpoint of the study is the change in the Boston Carpal Tunnel Questionnaire Symptom Severity Scale score. Secondary endpoints include time to return to normal daily activities, time to return to work among employed subjects, change in the Boston Carpal Tunnel Questionnaire Functional Status Scale score, change in the Michigan Hand Questionnaire overall and domain scores, change in the Numeric Pain Scale score, change in the EuroQoL-5 Dimension 5-Level score, global satisfaction scores, and the incidence of device or procedure-related adverse events. The primary analysis of study endpoints will occur three months post-treatment. Patient follow-up in this study will continue for two years. Conclusions A central institutional review board approved the study protocol, and a data safety monitoring board will provide study oversight. The authors plan to report study results at medical conferences and in peer-reviewed medical journals. The outcomes of ROBUST will provide physicians, patients, and payors with important safety and effectiveness data regarding the clinical utility of CTR-US when performed in an office setting.

The impact of conventional versus robust norming on cognitive characterization and clinical classification of MCI and dementia

We examined the impact of conventional versus robust normative approaches on cognitive characterization and clinical classification of MCI versus dementia. The sample included participants from the National Alzheimer's Coordinating Center Uniform Data Set. Separate demographically adjusted z-scores for cognitive tests were derived from conventional (n = 4273) and robust (n = 602) normative groups. To assess the impact of deriving scores from a conventional versus robust normative group on cognitive characterization, we examined likelihood of having a low score on each neuropsychological test. Next, we created receiver operating characteristic (ROC) curves for the ability of normed scores derived from each normative group to differentiate between MCI (n = 3570) and dementia (n = 1564). We examined the impact of choice of normative group on classification accuracy by comparing sensitivity and specificity values and areas under the curves (AUC). Compared with using a conventional normative group, using a robust normative group resulted in a higher likelihood of low cognitive scores for individuals classified with MCI and dementia. Comparison of the classification accuracy for distinguishing MCI from dementia did not suggest a statistically significant advantage for either normative approach (Z = -0.29, p = .77; AUC = 0.86 for conventional and AUC = 0.86 for robust). In summary, these results indicate that using a robust normative group increases the likelihood of characterizing cognitive performance as low. However, there is not a clear advantage of using a robust over a conventional normative group when differentiating between MCI and dementia.

Arterial spin labeling using spatio-temporal encoding readout for robust perfusion imaging in inhomogenous magnetic fields

PURPOSE

To evaluate the feasibility of spatio-temporal encoding (SPEN) readout for pseudo-continuous ASL (pCASL) in brain, and its robustness to susceptibility artifacts as introduced by aneurysm clips.

METHODS

A 2D self-refocused T₂ *-compensated hybrid SPEN scheme, with super-resolution reconstruction was implemented on a 1.5T Philips system. Q (=BW_chirp *T_chirp ) was varied and, the aneurysm clip-induced artifact was evaluated in phantom (label-images) as well as in vivo (perfusion-weighted signal (PWS)-maps and temporal SNR (tSNR)). In vivo results were compared to gradient-echo EPI (GE-EPI) and spin-echo EPI (SE-EPI). The dependence of tSNR on TR was evaluated separately for SPEN and SE-EPI. SPEN with Q ˜ 75 encodes with the same off-resonance robustness as EPI.

RESULTS

The clip-induced artifact with SPEN decreased with increase in Q, and was smaller compared to SE-EPI and GE-EPI in vivo. tSNR decreased with Q and the tSNR of GE-EPI and SE-EPI corresponded to SPEN with a Q-value of approximately ˜85 and ˜108, respectively. In addition, SPEN perfusion images showed a higher tSNR (p < 0.05) for TR = 4000 ms compared to TR = 2100 ms, while SE-EPI did not. tSNR remained relatively stable when the time between SPEN-excitation and start of the next labeling-module was more than ˜1000 ms.

CONCLUSION

Feasibility of combining SPEN with pCASL imaging was demonstrated, enabling cerebral perfusion measurements with a higher robustness to field inhomogeneity (Q > 75) compared to SE-EPI and GE-EPI. However, the SPEN chirp-pulse saturates incoming blood, thereby reducing pCASL labeling efficiency of the next acquisition for short TRs. Future developments are needed to enable 3D scanning.

BitBrain and Sparse Binary Coincidence (SBC) memories: Fast, robust learning and inference for neuromorphic architectures

We present an innovative working mechanism (the SBC memory) and surrounding infrastructure (BitBrain) based upon a novel synthesis of ideas from sparse coding, computational neuroscience and information theory that enables fast and adaptive learning and accurate, robust inference. The mechanism is designed to be implemented efficiently on current and future neuromorphic devices as well as on more conventional CPU and memory architectures. An example implementation on the SpiNNaker neuromorphic platform has been developed and initial results are presented. The SBC memory stores coincidences between features detected in class examples in a training set, and infers the class of a previously unseen test example by identifying the class with which it shares the highest number of feature coincidences. A number of SBC memories may be combined in a BitBrain to increase the diversity of the contributing feature coincidences. The resulting inference mechanism is shown to have excellent classification performance on benchmarks such as MNIST and EMNIST, achieving classification accuracy with single-pass learning approaching that of state-of-the-art deep networks with much larger tuneable parameter spaces and much higher training costs. It can also be made very robust to noise. BitBrain is designed to be very efficient in training and inference on both conventional and neuromorphic architectures. It provides a unique combination of single-pass, single-shot and continuous supervised learning; following a very simple unsupervised phase. Accurate classification inference that is very robust against imperfect inputs has been demonstrated. These contributions make it uniquely well-suited for edge and IoT applications.

Joint User Scheduling and Hybrid Beamforming Design for Massive MIMO LEO Satellite Multigroup Multicast Communication Systems

In the satellite multigroup multicast communication systems based on the DVB-S2X standard, due to the limitation of the DVB-S2X frame structure, user scheduling and beamforming design have become the focus of academic research. In this work, we take the massive multi-input multi-output (MIMO) low earth orbit (LEO) satellite communication system adopting the DVB-S2X standard as the research scenario, and the LEO satellite adopts a uniform planar array (UPA) based on the fully connected hybrid structure. We focus on the coupling design of user scheduling and beamforming; meanwhile, the scheme design takes the influence of residual Doppler shift and phase disturbance on channel errors into account. Under the constraints of total transmission power and quality of service (QoS), we study the robust joint user scheduling and hybrid beamforming design aimed at maximizing the energy efficiency (EE). For this problem, we first adopt the hierarchical clustering algorithm to group users. Then, the semidefinite programming (SDP) algorithm and the concave convex process (CCCP) framework are applied to tackle the optimization of user scheduling and hybrid beamforming design. To handle the rank-one matrix constraint, the penalty iteration algorithm is proposed. To balance the performance and complexity of the algorithm, the user preselected step is added before joint design. Finally, to obtain the digital beamforming matrix and the analog beamforming matrix in a hybrid beamformer, the alternative optimization algorithm based on the majorization-minimization framework (MM-AltOpt) is proposed. Numerical simulation results show that the EE of the proposed joint user scheduling and beamforming design algorithm is higher than that of the traditional decoupling design algorithms.

Robust Superhydrophobic Surfaces via the Sand-In Method

Superhydrophobic surfaces have gained sustained attention because of their extensive applications in the fields of self-cleaning, anti-icing, and drag reduction systems. Water droplets must have large apparent contact angle (CA) (>150°) and small CA hysteresis (<10°) on these surfaces. However, previous research usually involves complex fabrication strategies to modify the surface wettability. It is also challenging to maintain the temporal and mechanical stability of the delicate surface textures. Here, we develop a one-step solvent-free sand-in method to fabricate robust superhydrophobic surfaces directly atop various substrates with an apparent CA up to ∼163.8° and hysteresis less than 5°. The water repellency can withstand 100 Scotch tape peeling tests and remain stable after being stored under ambient humid conditions in Houston, Texas, for 18 months or being heated at 130 °C in air for 24 h. The superhydrophobic surfaces have excellent anti-icing ability, including a ∼2.6× longer water freezing time and ∼40% smaller ice adhesion strength with the temperature as low as -35 °C. Since the surface layers are fabricated by sanding the substrates with the powder additives, the surface damage can be repaired by a direct re-sanding treatment with the same powder additives. Further sand-in condition screenings broaden surface wettability from hydrophilic to superhydrophobic. The sand-in method induces the surface modification and the formation of the tribofilm. Surface and materials characterizations reveal that both microstructures and nanoscale asperities of the tribofilms contribute to the robust superhydrophobic features of sanded surfaces.

Synergistic Regulation of the Microstructure for Multifunctional Graphene Aerogels by a Dual Template Method

The performance of graphene aerogels (GAs) is based on the microstructure. However, GAs face a challenge of simultaneously controlling the size and alignment of pores strategically. Herein, we initially proposed a simple strategy to construct GAs with an adjustable structure based on the emulsion and ice dual template methods. Specifically, GAs with a honeycomb structure prepared by conventional freezing (CGAs) exhibited a high specific surface of 176 m²/g, superelasticity with a compressive strain of 95%, isotropic compression and thermal insulation performances, as well as an excellent absorption capacity of 150-550 g/g. Instead, the GAs with a bamboo-like network frozen by unidirectional freezing (UGAs) showed anisotropy in compression and thermal insulation behavior. Furthermore, UGAs exhibited incredible special stress (7.9 kPa cm³/mg) along the axial direction twice than that of the radial direction. Meanwhile, the apparent temperature of UGAs was only 45.6 °C when placed on a 120 °C hot stage along the radial direction. Remarkably, the properties of CGAs and UGAs were significantly improved with the adjustment of the microstructure.

Infrared Small Target Detection Based on Weighted Local Coefficient of Variation Measure

Robust infrared (IR) small target detection is critical for infrared search and track (IRST) systems and is a challenging task for complicated backgrounds. Current algorithms have poor performance on complex backgrounds, and there is a high false alarm rate or even missed detection. To address this problem, a weighted local coefficient of variation (WLCV) is proposed for IR small target detection. This method consists of three stages. First, the preprocessing stage can enhance the original IR image and extract potential targets. Second, the detection stage consists of a background suppression module (BSM) and a local coefficient of variation (LCV) module. BSM uses a special three-layer window that combines the anisotropy of the target and differences in the grayscale distribution. LCV exploits the discrete statistical properties of the target grayscale. The weighted advantages of the two modules complement each other and greatly improve the effect of small target enhancement and background suppression. Finally, the weighted saliency map is subjected to adaptive threshold segmentation to extract the true target for detection. The experimental results show that the proposed method is more robust to different target sizes and background types than other methods and has a higher detection accuracy.

Redefining and mapping global irreplaceability

Irreplaceability is a concept used to describe how close a site is to being essential for achieving conservation targets. Current methods for measuring irreplaceability are based on representative combinations of sites, giving them an extrinsic nature and exponential computational requirements. Surrogate measures based on efficiency (complementarity) are often used as alternatives, but they were never intended for this purpose and do not measure irreplaceability. Current approaches used to estimate irreplaceability have key limitations. Some of these are a result of the tools used, but some are due to the nature of the current definition of irreplaceability. For irreplaceability to be stable and useful for conservation purposes and to resolve limitations, irreplaceability measures should adhere to five axioms; baseline coherence, monotonic responsiveness, proportional responsiveness, intrinsic stability, and bounded outputs. We designed a robust method for measuring a site's proximity to irreplaceability that adheres to these requirements and used it to develop the first systematic global map of irreplaceability based on data for terrestrial vertebrates (n = 29,837 species, >1 million grid cells). At least 3.5% of land surface was highly irreplaceable, and 47.6% of highly irreplaceable cells were contained in 12 countries. More generous thresholds of irreplaceability flag greater portions of land surface that would still be realistic to protect under current global objectives. Irreplaceable sites should form a critical component of any global conservation plan and should be part of the UN Convention on Biological Diversity's post2020 Global Biodiversity Framework strategy, forming part of the 30% protection by 2030 target that is gaining support. The reliable identification of irreplaceable sites will be crucial to halting extinctions.

ROSIE: RObust Sparse ensemble for outlIEr detection and gene selection in cancer omics data

The extraction of novel information from omics data is a challenging task, in particular, since the number of features (e.g. genes) often far exceeds the number of samples. In such a setting, conventional parameter estimation leads to ill-posed optimization problems, and regularization may be required. In addition, outliers can largely impact classification accuracy.

Here we introduce ROSIE, an ensemble classification approach, which combines three sparse and robust classification methods for outlier detection and feature selection and further performs a bootstrap-based validity check. Outliers of ROSIE are determined by the rank product test using outlier rankings of all three methods, and important features are selected as features commonly selected by all methods.

We apply ROSIE to RNA-Seq data from The Cancer Genome Atlas (TCGA) to classify observations into Triple-Negative Breast Cancer (TNBC) and non-TNBC tissue samples. The pre-processed dataset consists of 16,600 genes and more than 1,000 samples. We demonstrate that ROSIE selects important features and outliers in a robust way. Identified outliers are concordant with the distribution of the commonly selected genes by the three methods, and results are in line with other independent studies. Furthermore, we discuss the association of some of the selected genes with the TNBC subtype in other investigations. In summary, ROSIE constitutes a robust and sparse procedure to identify outliers and important genes through binary classification. Our approach is ad hoc applicable to other datasets, fulfilling the overall goal of simultaneously identifying outliers and candidate disease biomarkers to the targeted in therapy research and personalized medicine frameworks.

Robustness of ventilation systems in the control of walking-induced indoor fluctuations: Method development and case study

Walking-induced fluctuations have a significant influence on indoor airflow and pollutant dispersion. This study developed a method to quantify the robustness of ventilation systems in the control of walking-induced fluctuation control. Experiments were conducted in a full-scale chamber with four different kinds of ventilation systems: ceiling supply and side return (CS), ceiling supply and ceiling return (CC), side supply and ceiling return (SC), and side supply and side return (SS). The measured temperature, flow and pollutant field data was (1) denoised by FFT filtering or wavelet transform; (2) fitted by a Gaussian function; (3) feature-extracted for the range and time scale disturbance; and then (4) used to calculate the range scale and time scale robustness for different ventilation systems with dimensionless equations developed in this study. The selection processes for FFT filtering and wavelet transform, FFT filter cut-off frequency, wavelet function, and decomposition layers are also discussed, as well as the threshold for wavelet denoising, which can be adjusted accordingly if the walking frequency or sampling frequency differs from that in other studies. The results show that for the flow and pollutant fields, the use of a ventilation system can increase the range scale robustness by 19.7%-39.4% and 10.0%-38.8%, respectively; and the SS system was 7.0%-25.7% more robust than the other three ventilation systems. However, all four kinds of ventilation systems had a very limited effect in controlling the time scale disturbance.

A bi-objective blood supply chain model under uncertain donation, demand, capacity and cost: a robust possibilistic-necessity approach

This paper addresses a multi-objective blood supply chain network design, considering economic and environmental aspects. The objective of this model is to simultaneously minimize a blood supply chain operational cost and its logistical carbon footprint. In order to embed the uncertainty of transportation costs, blood demand, capacity of facilities and carbon emission, a novel robust possibilistic-necessity optimization used regarding a hybrid optimistic-pessimistic form. For solving our bi-objective model, three multi-objective decision making approaches including LP-metric, Goal-Programming and Torabi- Hassini methods are examined. These approaches are assessed and ranked with respect to several attributes using a statistical test and TOPSIS method. Our proposed model can accommodate a wide range of decision-makers’ viewpoints with the normalized objective weights, both at the operational or strategic level. The trade-offs between the cost and carbon emission for each method has been depicted in our analyses and a Pareto frontier is determined, using a real case study data of 21 cities in the North-West of Iran considering a 12-month implementation time window.

Assessment of incident frailty hazard associated with depressive symptoms in a Taiwanese longitudinal study

OBJECTIVES

To estimate the risks of depressive symptoms for developing frailty, accounting for baseline robust or pre-frailty status.

DESIGN

An incident cohort study design.

SETTING

Community dwellers aged 55 years and above from urban and rural areas in seven regions in Taiwan.

PARTICIPANTS

A total of 2,717 participants from the Healthy Aging Longitudinal Study in Taiwan (HALST) were included. Subjects with frailty at baseline were excluded. The average follow-up period was 5.9 years.

MEASUREMENTS

Depressive symptoms were measured by the 20-item Center for Epidemiological Studies Depression (CES-D) Scale. Frailty was assessed using the Fried frailty measurement. Participants were stratified by baseline robust or pre-frailty status to reduce the confounding effects of the shared criteria between depressive symptoms and frailty. Overall and stratified survival analyses were conducted to assess risks of developing frailty as a result of baseline depressive symptoms.

RESULTS

One hundred individuals (3.7%) had depressive symptoms at baseline. Twenty-seven individuals (27.0%) with depressive symptoms developed frailty, whereas only 305 out of the 2,617 participants (11.7%) without depressive symptoms developed frailty during the follow-up period. After adjusting for covariates, depressive symptoms were associated with a 2.6-fold (95% CI 1.6, 4.2) increased hazard of incident frailty. The patterns of increased hazard were also observed when further stratified by baseline robust or pre-frailty status.

CONCLUSIONS

Depressive symptoms increased the risk of developing frailty among the older Asian population. The impact of late-life depressive symptoms on physical health was notable. These findings also replicated results from Western populations. Future policies on geriatric public health need to focus more on treatment and intervention against geriatric depressive symptoms to prevent incident frailty among older population.

Diseases Detection of Occlusion and Overlapping Tomato Leaves Based on Deep Learning

Background: In view of the existence of light shadow, branches occlusion, and leaves overlapping conditions in the real natural environment, problems such as slow detection speed, low detection accuracy, high missed detection rate, and poor robustness in plant diseases and pests detection technology arise. Results: Based on YOLOv3-tiny network architecture, to reduce layer-by-layer loss of information during network transmission, and to learn from the idea of inverse-residual block, this study proposes a YOLOv3-tiny-IRB algorithm to optimize its feature extraction network, improve the gradient disappearance phenomenon during network deepening, avoid feature information loss, and realize network multilayer feature multiplexing and fusion. The network is trained by the methods of expanding datasets and multiscale strategies to obtain the optimal weight model. Conclusion: The experimental results show that when the method is tested on the self-built tomato diseases and pests dataset, and while ensuring the detection speed (206 frame rate per second), the mean Average precision (mAP) under three conditions: (a) deep separation, (b) debris occlusion, and (c) leaves overlapping are 98.3, 92.1, and 90.2%, respectively. Compared with the current mainstream object detection methods, the proposed method improves the detection accuracy of tomato diseases and pests under conditions of occlusion and overlapping in real natural environment.

Robust Superlubric Interface across Nano- and Micro-Scales Enabled by Fluoroalkylsilane Self-Assembled Monolayers and Atomically Thin Graphene

Robust superlubrication across nano- and microscales is highly desirable at the interface with asperities of different sizes in durable micro/nanoelectromechanical systems under a harsh environment. A novel method to fabricate superlubric interfaces across nano- and microscales is developed by combining a batch of surface modification with atomically thin graphene. The robust superlubric interface across nano- and microscales between hydrophobic 1H,1H,2H,2H-perfluorodecyltrichlorosilane (FDTS) self-assembly monolayers (SAMs) and graphene was achieved under high relative humidity, sliding speed, and contact pressure. The superlubric mechanisms at the interface of FDTS/graphene could be attributed to the following at different scales: the hydrophobicity of FDTS SAMs and graphene preventing the capillary interaction of the interfacial friction under high relative humidity; the high elastic modulus of graphene leading to small interfacial contact area; the compressing and orientating of FDTS SAMs decreasing interfacial shear strength under high contact pressure; the surface modification of FDTS molecules reducing the interfacial potential barriers when sliding on the atomically thin graphene. The robust superlubric interface across nano- and microscales reducing the friction at the complicated interfaces with asperities at different scales and improving the performance and durability have great potentials in the field of micro/nano mechanical systems.

A robust method for the assessment of average bioequivalence in the presence of outliers and skewness

PURPOSE

In this paper, we propose a robust Bayesian method for the assessment of average bioequivalence based on data from conventional crossover studies. We evaluate and motivate empirically the need for robust methods in bioequivalence studies by comparing the results of robust and conventional statistical methods in a large data pool of bioequivalence studies.

METHODS

Robustness of the statistical methodology is achieved by replacing the normal distributions for residuals in the linear mixed model with skew-t distributions. In this way, the statistical model can accommodate skew and heavy-tailed data, particularly outliers, yielding robust statistical inference without the need for excluding outliers from the analysis. We performed a simulation study to investigate and compare the performance of the robust and conventional models.

RESULTS

Our study shows that in some trials, the distribution of residuals is skew and heavy-tailed. In the presence of outliers, the 90% confidence intervals for the ratio of geometric means tend to be narrower for the robust methods than for the conventional method. Our simulation study shows that the robust method has suitable frequentist properties and yields more precise confidence intervals and higher statistical power than the conventional maximum likelihood method when outliers are present in the data.

CONCLUSIONS

As a sensitivity analysis, we recommend the fit of robust models for handling outliers that are occasionally encountered in crossover design bioequivalence data.

Objectively measured patterns of daily physical activity and phenotypic frailty

BACKGROUND

Self-reported low physical activity is a defining feature of phenotypic frailty but does not adequately capture physical activity performed throughout the day. This study examined associations between accelerometer-derived patterns of routine daily physical activity and frailty.

METHODS

Wrist accelerometer and frailty data from 638 participants (mean age 77 (SD=5.5) years; 44% women) were used to derive five physical activity metrics: active minutes/day, sedentary minutes/day, total activity counts/day, activity fragmentation (reciprocal of the average active bout length) and sedentary fragmentation (reciprocal of the average sedentary bout length). Robust, pre-frail and frail statuses were identified using the physical frailty phenotype defined as having 0, 1-2, or ≥3 of the following criterion: weight loss, exhaustion, slowness, self-reported low activity, and weakness. Frailty was collapsed into not frail (robust and prefrail) and frail, and each frailty criteria was dichotomized. Multiple logistic regression was used to model each accelerometer metric. Separate frailty criteria and interactions with age and sex were also examined.

RESULTS

With higher amounts and intensity of daily activity (more active minutes, fewer sedentary minutes, higher activity counts) and lower activity fragmentation, the odds of frailty were lower compared to robust/prefrail states (p<0.02 for all). For interactions, only an age by sedentary fragmentation interaction on the odds of frailty was observed (p=0.01). For each separate criteria, accelerometer metrics were associated with odds of slowness, low activity, and weakness.

CONCLUSION

Less favorable patterns of objectively measured daily physical activity are associated with frailty and the components of slowness, low self-reported activity, and weakness.

Tunable fabrication of biomimetic polypropylene nanopillars with robust superhydrophobicity and antireflectivity

The fine nanopillars on the natural cicada wing, which exhibits outstanding superhydrophobicity and anti-reflectivity, are carefully observed and analyzed. Here, a promising strategy by combining anodic aluminum oxide template and hot embossing is proposed for rapidly and efficiently mimicking the orderly and densely arranged nanopillars on the cicada wing surface to polypropylene (PP) surfaces. By adjusting the compression pressure, the nanostructures on the PP replica surface gradually evolve from nanoprotrusion-like features to nanopillar-like features so that a gradient wetting behavior from hydrophilicity to hydrophobicity and further to superhydrophobicity appears on the PP replica surfaces. Specifically, the biomimetic PP replica surface exhibits a contact angle of 159 ± 3° and a rolling angle of 8 ± 3° at a compression pressure of 15 MPa. Moreover, the biomimetic PP replica surface can stabilize its superhydrophobic state under a 1.96 kPa external pressure during the dynamic droplet impact. Besides robust dynamic superhydrophobicity, the biomimetic PP replica surface also demonstrated excellent anti-reflectivity because of the gradually changed effective refractive index. Therefore, the biomimetic PP replica inherits both the superhydrophobicity and anti-reflectivity of the natural cicada wing, which makes the products can effectively reduce the external damage when applied to agricultural films, dustproof films, and packaging materials.

Fabrication of a Robust In2O3 Nanolines FET Device as a Biosensor Platform

Field-effect transistors (FETs) are attractive biosensor platforms for rapid and accurate detection of various analytes through surface immobilization of specific bio-receptors. Since it is difficult to maintain the electrical stability of semiconductors of sensing channel under physiological conditions for long periods, passivation by a stable metal oxide dielectric layer, such as Al₂O₃ or HfO₂, is currently used as a common method to prevent damage. However, protecting the sensing channel by passivation has the disadvantage that the distance between the target and the conductive channel increases, and the sensing signal will be degraded by Debye shielding. Even though many efforts use semiconductor materials directly as channels for biosensors, the electrical stability of semiconductors in the physiological environments has rarely been studied. In this work, an In₂O₃ nanolines FET device with high robustness in artificial physiological solution of phosphate buffered saline (PBS) was fabricated and used as a platform for biosensors without employing passivation on the sensing channel. The FET device demonstrated reproducibility with an average threshold voltage (V_TH) of 5.235 V and a standard deviation (SD) of 0.382 V. We tested the robustness of the In₂O₃ nanolines FET device in PBS solution and found that the device had a long-term electrical stability in PBS with more than 9 days’ exposure. Finally, we demonstrated its applicability as a biosensor platform by testing the biosensing performance towards miR-21 targets after immobilizing the phosphonic acid terminated DNA probes. Since the surface immobilization of multiple bioreceptors is feasible, we demonstrate that the robust In₂O₃ FET device can be an excellent biosensor platform for biosensors.

Improving performance and generalizability in radiogenomics: a pilot study for prediction of IDH1/2 mutation status in gliomas with multicentric data

Purpose: Radiogenomics offers a potential virtual and noninvasive biopsy. However, radiogenomics models often suffer from generalizability issues, which cause a performance degradation on unseen data. In MRI, differences in the sequence parameters, manufacturers, and scanners make this generalizability issue worse. Such image acquisition information may be used to define different environments and select robust and invariant radiomic features associated with the clinical outcome that should be included in radiomics/radiogenomics models. Approach: We assessed 77 low-grade gliomas and glioblastomas multiform patients publicly available in TCGA and TCIA. Radiomics features were extracted from multiparametric MRI images (T1-weighted, contrast-enhanced T1-weighted, T2-weighted, and fluid-attenuated inversion recovery) and different regions-of-interest (enhancing tumor, nonenhancing tumor/necrosis, and edema). A method developed to find variables that are part of causal structures was used for feature selection and compared with an embedded feature selection approach commonly used in radiomics/radiogenomics studies, across two different scenarios: (1) leaving data from a center as an independent held-out test set and tuning the model with the data from the remaining centers and (2) use stratified partitioning to obtain the training and the held-out test sets. Results: In scenario (1), the performance of the proposed methodology and the traditional embedded method was AUC: 0.75 [0.25; 1.00] versus 0.83 [0.50; 1.00], Sens.: 0.67 [0.20; 0.93] versus 0.67 [0.20; 0.93], Spec.: 0.75 [0.30; 0.95] versus 0.75 [0.30; 0.95], and MCC: 0.42 [0.19; 0.68] versus 0.42 [0.19; 0.68] for center 1 as the held-out test set. The performance of both methods for center 2 as the held-out test set was AUC: 0.64 [0.36; 0.91] versus 0.55 [0.27; 0.82], Sens.: 0.00 [0.00; 0.73] versus 0.00 [0.00; 0.73], Spec.: 0.82 [0.52; 0.94] versus 0.91 [0.62; 0.98], and MCC: - 0.13 [ - 0.38 ; - 0.04 ] versus - 0.09 [ - 0.38 ; - 0.02 ] , whereas for center 3 was AUC: 0.80 [0.62; 0.95] versus 0.89 [0.56; 0.96], Sens.: 0.86 [0.48; 0.97] versus 0.86 [0.48; 0.97], Spec.: 0.72 [0.54; 0.85] versus 0.79 [0.61; 0.90], and MCC: 0.47 [0.41; 0.53] versus 0.55 [0.48; 0.60]. For center 4, the performance of both methods was AUC: 0.77 [0.51; 1.00] versus 0.75 [0.47; 0.97], Sens.: 0.53 [0.30; 0.75] versus 0.00 [0.00; 0.15], Spec.: 0.71 [0.35; 0.91] versus 0.86 [0.48; 0.97], and MCC: 0.23 [0.16; 0.31] versus. - 0.32 [ - 0.46 ; - 0.20 ] . In scenario (2), the performance of these methods was AUC: 0.89 [0.71; 1.00] versus 0.79 [0.58; 0.94], Sens.: 0.86 [0.80; 0.92] versus 0.43 [0.15; 0.74], Spec.: 0.87 [0.62; 0.96] versus 0.87 [0.62; 0.96], and MCC: 0.70 [0.60; 0.77] versus 0.33 [0.24; 0.42]. Conclusions: This proof-of-concept study demonstrated good performance by the proposed feature selection method in the majority of the studied scenarios, as it promotes robustness of features included in the models and the models' generalizability by making used imaging data of different scanners or with sequence parameters.

Development and Psychometric Validation of a New Scale for Assessment and Screening of Frailty Among Older Indians

Background

Frailty is a major challenge for healthcare systems in ageing societies. This dynamic state of health is a reflection of reduced reserve in various organ systems and enhanced vulnerability to stressors. Research in this area of geriatrics and gerontology is limited in low- and middle-income countries (LMICs) like India. This study is directed at development of a culturally appropriate and validated assessment scale for frailty among older Indians.

Methods

After extensive review of the literature on existing scales, a preliminary draft scale was formed. This draft was pre- and pilot-tested to check feasibility and modified accordingly. The final scale was validated on 107 older adults by confirmatory factor analysis and was named the Frailty Assessment and Screening Tool (FAST). The Fried’s frailty phenotype was also administered on the same 107 older adults and scores of both were co-related. Suitable cut-off scores were found for frail and pre-frail older adults.

Results

The final version of the FAST consisted of 14 questions pertaining to 10 domains. It has good reliability. Cronbach’s alpha co-efficient was 0.99; test–retest reliability was 0.97 and validity by confirmatory factor analysis was adequate. The Kaiser–CMeyer–Olkin (KMO) of sampling adequacy was 0.699, and Bartlett’s test of sphericity was significant (χ² = 353.471, p < 0.001). FAST scores had a cut-off of ≥ 7/14 for frail and ≥ 5/14 for pre-frail elderly.

Conclusion

The FAST is a validated tool with good psychometric properties. It is expected that it will be helpful in screening pre-frail and frail older adults in India and other LMICs and guide in clinical decision making for intervention.

Solvent-Free Fabrication of Robust Superhydrophobic Powder Coatings

Superhydrophobicity originating from the "lotus effect" enables novel applications such as self-cleaning, anti-fouling, anti-icing, anti-corrosion, and oil-water separation. However, their real-world applications are hindered by some main shortcomings, especially the organic solvent problem, complex chemical modification of nanoparticles, and poor mechanical stability of obtained surfaces. Here, we report for the first time the solvent-free, chemical modification-free, and mechanically, chemically, and UV robust superhydrophobic powder coatings. The coatings were fabricated by adding commercially available polytetrafluoroethylene (PTFE) particles into powder coatings and by following the regular powder-coating processing route. The formation of such superhydrophobic surfaces was attributed to PTFE particles, which hindered the microscale leveling of powder coatings during curing. Through adjusting the dosage of PTFE, the hydrophobicity of obtained coatings can be tuned in a large range (water contact angle from 92 to 162°). The superhydrophobic coatings exhibited remarkable mechanical robustness against abrasion because of the unique hierarchical micro/nanoscale roughness and low surface energy throughout the coating and the solid lubrication effect of PTFE particles. The coatings also have robustness against chemical corrosion and UV irradiation owing to high bonding energy and chemical inertness of PTFE. Moreover, the coatings show attractive performances including self-cleaning, anti-rain, anti-snow, and anti-icing. With these multifaceted features, such superhydrophobic coatings are promising for outdoor applications. This study also contributes to the preparation of robust superhydrophobic surfaces in an environmentally friendly way.

Experimental Evaluation of Machine Learning Methods for Robust Received Signal Strength-Based Visible Light Positioning

In this work, the use of Machine Learning methods for robust Received Signal Strength (RSS)-based Visible Light Positioning (VLP) is experimentally evaluated. The performance of Multilayer Perceptron (MLP) models and Gaussian processes (GP) is investigated when using relative RSS input features. The experimental set-up for the RSS-based VLP technology uses light-emitting diodes (LEDs) transmitting intensity modulated light and a single photodiode (PD) as a receiver. The experiments focus on achieving robustness to cope with unknown received signal strength modifications over time. Therefore, several datasets were collected, where per dataset either the LEDs transmitting power is modified or the PD aperture is partly obfuscated by dust particles. Two relative RSS schemes are investigated. The first scheme uses the maximum received light intensity to normalize the received RSS vector, while the second approach obtains RSS ratios by combining all possible unique pairs of received intensities. The Machine Learning (ML) methods are compared to a relative multilateration implementation. It is demonstrated that the adopted MLP and GP models exhibit superior performance and higher robustness when compared to the multilateration strategies. Furthermore, when comparing the investigated ML models, the GP model is proven to be more robust than the MLP for the considered scenarios.

Robust Waterborne Superhydrophobic Coatings with Reinforced Composite Interfaces

Waterborne superhydrophobic coatings have attracted tremendous attention recently, but their practical applications are severely limited by hydrophobic instability and poor mechanical durability. Herein, a novel robust waterborne PTFE-CP&MgO-AOP superhydrophobic coating was successfully fabricated by reinforcing composite interfaces. Combined with the self-polymerization of dopamine and the in situ grown MgO, CNTs-polydopamine&MgO (CP&MgO) particles with improved interfacial compatibility were obtained. Through the cross-linking and hydrogen bonding interactions, phosphate networks (CP&MgO-AOP) with the aluminum orthophosphate (AOP) binder were formed during dehydration polymerization. The phosphate networks not only enhanced the interfacial interaction among CP&MgO to form coral-like structures but also strengthened the interfacial binding force between the waterborne polytetrafluoroethylene (PTFE) coating and the substrate. With the enhanced composite interfacial strength, the waterborne PTFE-CP&MgO-AOP coating exhibited excellent wear-resistance, which can withstand more than 1.27 × 10⁵ abrasion cycles. Moreover, the chemical bonding between the functional groups of phosphate networks and metal substrate improved the adhesion strength from grade 5 to 1. Furthermore, the prepared coating surface with the reticular/coral-like composite structures can lock the stable gas layer to maintain excellent hydrophobic stability, even under the conditions of strong acidic/alkaline, high-temperature, xenon lamp irradiation, and mechanical wear. Thus, this study is expected to open new insights into interfacial enhancement of robust waterborne superhydrophobic coatings.

Robust methods to correct for measurement error when evaluating a surrogate marker

The identification of valid surrogate markers of disease or disease progression has the potential to decrease the length and costs of future studies. Most available methods that assess the value of a surrogate marker ignore the fact that surrogates are often measured with error. Failing to adjust for measurement error can erroneously identify a useful surrogate marker as not useful or vice versa. We investigate and propose robust methods to correct for the effect of measurement error when evaluating a surrogate marker using multiple estimators developed for parametric and nonparametric estimates of the proportion of treatment effect explained by the surrogate marker. In addition, we quantify the attenuation bias induced by measurement error and develop inference procedures to allow for variance and confidence interval estimation. Through a simulation study, we show that our proposed estimators correct for measurement error in the surrogate marker and that our inference procedures perform well in finite samples. We illustrate these methods by examining a potential surrogate marker that is measured with error, hemoglobin A1c, using data from the Diabetes Prevention Program clinical trial.

Mechanisms and impacts of an incentive-based conservation program with evidence from a randomized control trial

Conservation science needs more high-quality impact evaluations, especially ones that explore mechanisms of success or failure. Randomized control trials (RCTs) provide particularly robust evidence of the effectiveness of interventions (although they have been criticized as reductionist and unable to provide insights into mechanisms), but there have been few such experiments investigating conservation at the landscape scale. We explored the impact of Watershared, an incentive-based conservation program in the Bolivian Andes, with one of the few RCTs of landscape-scale conservation in existence. There is strong interest in such incentive-based conservation approaches as some argue they can avoid negative social impacts sometimes associated with protected areas. We focused on social and environmental outcomes based on responses from a household survey in 129 communities randomly allocated to control or treatment (conducted both at the baseline in 2010 and repeated in 2015-2016). We controlled for incomplete program uptake by combining standard RCT analysis with matching methods and investigated mechanisms by exploring intermediate and ultimate outcomes according to the underlying theory of change. Previous analyses, focused on single biophysical outcomes, showed that over its first 5 years Watershared did not slow deforestation or improve water quality at the landscape scale. We found that Watershared influenced some outcomes measured using the survey, but the effects were complex, and some were unexpected. We thus demonstrated how RCTs can provide insights into the pathways of impact, as well as whether an intervention has impact. This paper, one of the first registered reports in conservation science, demonstrates how preregistration can help make complex research designs more transparent, avoid cherry picking, and reduce publication bias.

Quantifying the bias due to observed individual confounders in causal treatment effect estimates

It is often of interest to use observational data to estimate the causal effect of a target exposure or treatment on an outcome. When estimating the treatment effect, it is essential to appropriately adjust for selection bias due to observed confounders using, for example, propensity score weighting. Selection bias due to confounders occurs when individuals who are treated are substantially different from those who are untreated with respect to covariates that are also associated with the outcome. A comparison of the unadjusted, naive treatment effect estimate with the propensity score adjusted treatment effect estimate provides an estimate of the selection bias due to these observed confounders. In this article, we propose methods to identify the observed covariate that explains the largest proportion of the estimated selection bias. Identification of the most influential observed covariate or covariates is important in resource-sensitive settings where the number of covariates obtained from individuals needs to be minimized due to cost and/or patient burden and in settings where this covariate can provide actionable information to healthcare agencies, providers, and stakeholders. We propose straightforward parametric and nonparametric procedures to examine the role of observed covariates and quantify the proportion of the observed selection bias explained by each covariate. We demonstrate good finite sample performance of our proposed estimates using a simulation study and use our procedures to identify the most influential covariates that explain the observed selection bias in estimating the causal effect of alcohol use on progression of Huntington's disease, a rare neurological disease.

Suggestions for improving the visualization of magnetic resonance spectroscopy voxels and spectra

Magnetic resonance spectroscopy (MRS) has seen an increase in popularity as a method for studying the human brain. This approach is dependent on voxel localization and spectral quality, knowledge of which are essential for judging the validity and robustness of any analysis. As such, visualization plays a central role in appropriately communicating MRS studies. The quality of data visualization has been shown to be poor in a number of biomedical fields and so we sought to appraise this in MRS papers. To do this, we conducted a survey of the psychiatric single-voxel MRS literature. This revealed a generally low standard, with a significant proportion of papers not providing the voxel location and spectral quality information required to judge their validity or replicate the experiment. Based on this, we then present a series of suggestions for a minimal standard for MRS data visualization. The primary point of these is that both voxel location and MRS spectra be presented from all participants. Participant group membership should be indicated where more than one is included in the experiment (e.g. patients and controls). A set of suggested figure layouts that fulfil these requirements are presented with sample code provided to produce these (github.com/nwd2918/MRS-voxel-plot).

Strain for Welan Gum Production via the Expression of Global Transcriptional Regulator IrrE

Welan gum is a widely used microbial polysaccharide produced by Sphingomonas sp. However, an important factor hindering the expansion of its production is the maladaptation of strain to fermentation conditions. In this work, the global transcriptional regulator gene irre was selected as a stress-resistant element. And it was integrated into the site of the genomic carotene synthesis key enzyme gene crtB to construct a robust carotenoid-free welan gum producing strain. Fermentation with the recombinant strain effectively reduced the ethanol consumption and pigment content in the product. The tolerance temperature increased by 10°C without the need for controlling the pH. Under this fermentation condition, welan gum concentration could still reach 20.26 ± 0.25 g/L, which was 187.38% higher than that of the wild-type strain (7.05 ± 0.15 g/L). Transcriptome analysis showed that with the control of IrrE, more than 1000 genes that are involved in multiple pathways, including two-component system, bacterial chemotaxis, flagellar assembly, and cell cycle, exhibited changes at the transcriptional level and jointly allowed the strain to protect against environmental stresses.

Mechanically Robust, Softening Shape Memory Polymer Probes for Intracortical Recording

While intracortical microelectrode arrays (MEAs) may be useful in a variety of basic and clinical scenarios, their implementation is hindered by a variety of factors, many of which are related to the stiff material composition of the device. MEAs are often fabricated from high modulus materials such as silicon, leaving devices vulnerable to brittle fracture and thus complicating device fabrication and handling. For this reason, polymer-based devices are being heavily investigated; however, their implementation is often difficult due to mechanical instability that requires insertion aids during implantation. In this study, we design and fabricate intracortical MEAs from a shape memory polymer (SMP) substrate that remains stiff at room temperature but softens to 20 MPa after implantation, therefore allowing the device to be implanted without aids. We demonstrate chronic recordings and electrochemical measurements for 16 weeks in rat cortex and show that the devices are robust to physical deformation, therefore making them advantageous for surgical implementation.

Fabrication of Robust and Transparent Slippery Coating with Hot Water Repellency, Antifouling Property, and Corrosion Resistance

Water-repellent coatings with low sliding angles for aqueous liquids are of great significance for practical applications. However, these coatings are susceptible to various types of damage during service and lose their effect. Herein, a robust and transparent slippery coating with extremely low water sliding angle was fabricated by covalently grafting polydimethylsiloxane (PDMS) brushes in a cross-linked skeleton of epoxy resin. Polyamidoamine G5.0 with 128 NH₂ end groups was used as curing agent to induce the high cross-linking of the coating and the abundant PDMS brushes being grafted into it. Because of low surface energy and high mobility of PDMS brushes, the obtained coating exhibited a slippery performance for aqueous liquids (10 μL) with a sliding angle lower than 3° and a sliding speed as high as 1.16 mm/s. Even a 10 μL water droplet with temperature of 80 °C can slide off the coating at a low sliding angle (<5°). The strong intermolecular interactions of epoxy cross-linked skeleton endowed the coating with excellent physical and chemical stability. The sliding angle of the coating had no obvious change after heating at 120 °C for 100 h and placing outdoors for 7 months. The slippery performance was not affected by thumb press, knife scratching, high-speed friction, and water of different pH values. Furthermore, because of the excellent stability, antifouling performance, and corrosion resistance, the slippery coating can be applied to a variety of substrates, which makes the robust slippery coating have real potential for practical applications.

A robust VMAT delivery solution for single-fraction lung SABR utilizing FFF beams minimizing dosimetric compromise

Peripheral lung lesions treated with a single fraction of stereotactic ablative body radiotherapy (SABR) utilizing volumetric modulated arc therapy (VMAT) delivery and flattening filter‐free (FFF) beams represent a potentially high‐risk scenario for clinically significant dose blurring effects due to interplay between the respiratory motion of the lesion and dynamic multi‐leaf collimators (MLCs). The aim of this study was to determine an efficient means of developing low‐modulation VMAT plans in the Eclipse treatment planning system (v15.5, Varian Medical Systems, Palo Alto, USA) in order to minimize this risk, while maintaining dosimetric quality. The study involved 19 patients where an internal target volume (ITV) was contoured to encompass the entire range of tumor motion, and a planning target volume (PTV) created using a 5‐mm isotropic expansion of this contour. Each patient had seven plan variations created, with each rescaled to achieve the clinical planning goal for PTV coverage. All plan variations used the same field arrangement, and consisted of one dynamic conformal arc therapy (DCAT) plan, and six VMAT plans with varying degrees of modulation restriction, achieved through utilizing different combinations of the aperture shape controller (ASC) in the calculation parameters, and monitor unit (MU) objective during optimization. The dosimetric quality was assessed based on RTOG conformity indices (CI100/CI50), as well as adherence to dose–volume metrics used clinically at our institution. Plan complexity was assessed based on the modulation factor (MU/cGy) and the field edge metric. While VMAT plans with the least modulation restriction achieved the best dosimetry, it was found that there was no clinically significant trade‐off in terms of dose to organs at risk and conformity by reducing complexity. Furthermore, it was found that utilizing the ASC and MU objective could reduce plan complexity to near‐DCAT levels with improved dosimetry, which may be sufficiently robust to overcome the interplay effect.

Encoding Membrane-Potential-Based Memory within a Microbial Community

Cellular membrane potential plays a key role in the formation and retrieval of memories in the metazoan brain, but it remains unclear whether such memory can also be encoded in simpler organisms like bacteria. Here, we show that single-cell-level memory patterns can be imprinted in bacterial biofilms by light-induced changes in the membrane potential. We demonstrate that transient optical perturbations generate a persistent and robust potassium-channel-mediated change in the membrane potential of bacteria within the biofilm. The light-exposed cells respond in an anti-phase manner, relative to unexposed cells, to both natural and induced oscillations in extracellular ion concentrations. This anti-phase response, which persists for hours following the transient optical stimulus, enables a direct single-cell resolution visualization of spatial memory patterns within the biofilm. The ability to encode robust and persistent membrane-potential-based memory patterns could enable computations within prokaryotic communities and suggests a parallel between neurons and bacteria.

Interval estimation for linear functions of medians in within-subjects and mixed designs

The currently available distribution-free confidence interval for a difference of medians in a within-subjects design requires an unrealistic assumption of identical distribution shapes. A confidence interval for a general linear function of medians is proposed for within-subjects designs that do not assume identical distribution shapes. The proposed method can be combined with a method for linear functions of independent medians to provide a confidence interval for a linear function of medians in mixed designs. Simulation results show that the proposed methods have good small-sample properties under a wide range of conditions. The proposed methods are illustrated with examples, and R functions that implement the new methods are provided.

Robust Physically Linked Double-Network Ionogel as a Flexible Bimodal Sensor

To date, ionogel sensors have aroused the extensive interest as an alternative to hydrogel sensors, as they are promising materials to solve the problems of easy drying and easy freezing. However, the weak mechanical properties of ionogels have seriously hindered their large-scale application. Herein, a robust physically linked double-network ionogel (DN ionogel) was fabricated via interpenetrating a poly(hydroxyethyl acrylate) network into an agarose network in 1-ethyl-3-methylimidazolium chloride. The DN ionogel possessed good mechanical properties, high transparency, extreme temperature tolerance, and excellent self-adhesion. The superior electromechanical properties render the DN ionogel as a perfect candidate to be utilized as a strain sensor to monitor various human activities. In addition, the DN ionogel exhibited reasonably high sensitivity to temperature. Therefore, it is believed that this high performance strain-temperature bimodal sensor offers a promising prospect in flexible intelligent electronics.

Identification of the optimal treatment regimen in the presence of missing covariates

Covariates associated with treatment-effect heterogeneity can potentially be used to make personalized treatment recommendations towards best clinical outcomes. Methods for treatment-selection rule development that directly maximize treatment-selection benefits have attracted much interest in recent years, due to the robustness of these methods to outcome modeling. In practice, the task of treatment-selection rule development can be further complicated by missingness in data. Here, we consider the identification of optimal treatment-selection rules for a binary disease outcome when measurements of an important covariate from study participants are partly missing. Under the missing at random assumption, we develop a robust estimator of treatment-selection rules under the direct-optimization paradigm. This estimator targets the maximum selection benefits to the population under correct specification of at least one mechanism from each of the two sets-missing data or conditional covariate distribution, and treatment assignment or disease outcome model. We evaluate and compare performance of the proposed estimator with alternative direct-optimization estimators through extensive simulation studies. We demonstrate the application of the proposed method through a real data example from an Alzheimer's disease study for developing covariate combinations to guide the treatment of Alzheimer's disease.