Miracle-dz/UniFAR-FTUs huggingface dataset:
Association of Eccentric Quadriceps Torque With Knee Pain, Physical Function and Extension Lag in Women With Grade ≤ II Knee Osteoarthritis-A Cross-Sectional Study

Background Knee osteoarthritis (OA) is a prevalent disabling disease among women. Quadriceps weakness is attributed to one of the causes of knee pain (KP) and disability. The study aimed to test the correlation of eccentric quadriceps torque (EQT) with 2 subscales of reduced WOMAC questionnaire (KP and physical function) and extension lag range of motion (ROM) at the knee joint in osteoarthritic women. Methods A cross-sectional design was used. A total of 70 (age 41.1 ± 7.1) female patients having grade ≤ II knee OA participated in the study. Pearson’s correlation coefficient was used to test the correlation between the independent variable (EQT) and dependent variables (2 subscales of reduced WOMAC questionnaire and extension lag in the knee). Results EQT presented a significant moderate negative correlation with the reduced WOMAC subscales (pain r = -0.489, p < 0.01 and physical function r = -0.425, p < 0.01), and low positive correlation with available ROM (r = 0.349, p < 0.01). Conclusions The self-reported symptoms of KP, physical function, and extension lag in the early stages of knee OA in women are associated with EQT. Thus designing a rehabilitation program having eccentric quadriceps strengthening exercises may improve KP and physical activities but further randomized controlled trials are needed to verify this.
Work Motivation Profiles and Work Performance in a Group of Corporate Employees: A Two-Step Cluster Analysis

Few studies have applied a person-centered approach to work motivation using cluster or profile analyses. Thus, little is known about which configurations of work motivations characterize professionals. The aim of this study is to establish the structure of work motivation profiles under the framework of self-determination theory and to examine the relationship between work motivation and subjective work performance. The study involved 147 corporate employees who completed the Multidimensional Work Motivation Scale and self-rated their work performance. The two-step cluster analysis was applied, followed by a one-way ANOVA and a post-hoc Bonferroni test. The findings revealed differences between the five profiles for all forms of motivational regulation on the self-determination continuum (large effect sizes). Further examination revealed that the employees’ current work performances differed across motivational profiles (medium effect size). Strongly and poorly motivated as well as autonomously motivated employees reported better subjective work performance compared to unmotivated individuals. In line with self-determination
Coordinated Multi-Agent Deep Reinforcement Learning for Energy-Aware UAV-Based Big-Data Platforms

This paper proposes a novel coordinated multi-agent deep reinforcement learning (MADRL) algorithm for energy sharing among multiple unmanned aerial vehicles (UAVs) in order to conduct big-data processing in a distributed manner. For realizing UAV-assisted aerial surveillance or flexible mobile cellular services, robust wireless charging mechanisms are essential for delivering energy sources from charging towers (i.e., charging infrastructure) to their associated UAVs for seamless operations of autonomous UAVs in the sky. In order to actively and intelligently manage the energy resources in charging towers, a MADRL-based coordinated energy management system is desired and proposed for energy resource sharing among charging towers. When the required energy for charging UAVs is not enough in charging towers, the energy purchase from utility company (i.e., energy source provider in local energy market) is desired, which takes high costs. Therefore, the main objective of our proposed coordinated MADRL-based energy sharing learning algorithm is minimizing energy purchase from external utility companies to minimize system-operational costs. Finally, our performance evaluation results verify that the proposed coordinated MADRL-based algorithm achieves desired performance improvements.
Damage‐sensitive feature extraction with stacked autoencoders for unsupervised damage detection

In most real‐world monitoring scenarios, the lack of measurements from damaged conditions requires the application of unsupervised approaches, mainly the ones based on modal features estimated from raw vibration data through traditional system identification methods. Although numerous successful applications using modal parameters have been reported, they have demonstrated to be insufficient to estimate a robust set of damage‐sensitive features. Inspired by the idea of compressed sensing and deep learning, an intelligent two‐level feature extraction approach using stacked autoencoders over pre‐processed vibration data is proposed. This procedure can improve the performance of traditional damage detection classifiers by compressing modal parameters into a smaller set of highly informative features when considering information entropy metrics. The proposed technique demonstrates significant improvement in the performance of damage detection and classification approaches when evaluated on the well‐known monitoring data sets from the Z‐24 Bridge, where several damage scenarios were carried out under rigorous operational and environmental effects.
Observations of Ice Nucleating Particles in the Free Troposphere From Western US Wildfires

Wildfires in the western United States are large sources of particulate matter, and the area burned by wildfires is predicted to increase in the future. Some particles released from wildfires can affect cloud formation by serving as ice‐nucleating particles (INPs). INPs have numerous impacts on cloud radiative properties and precipitation development. Wildfires are potentially important sources of INPs, as indicated from previous measurements, but their abundance in the free troposphere has not been quantified. The Western Wildfire Experiment for Cloud Chemistry, Aerosol Absorption, and Nitrogen campaign sampled free tropospheric immersion‐freezing INPs from smoke plumes near their source and downwind, along with widespread aged smoke. The results indicate an enhancement of INPs in smoke plumes relative to out‐of‐plume background air, but the magnitude of enhancement was both temperature and fire dependent. The majority of INPs were inferred to be predominately organic in composition with some contribution from biological sources at modest super cooling, and contributions from minerals at deeper super cooling. A fire involving primarily sagebrush shrub land and aspen forest fuels had the highest INP concentrations measured in the campaign, which is partially attributed to the INP characteristics of lofted, uncombusted plant material. Electron microscopy analysis of INPs also indicated tar balls present in this fire. Parameterization of the plume INP data on a per‐unit‐aerosol surface area basis confirmed that smoke is not an efficient source of INPs. Nevertheless, the high numbers of particles released from, and ubiquity of western US wildfires in summertime, regionally elevate INP concentrations in the free troposphere.
Analysis and Design of General Printed Circuit Board Metagratings With an Equivalent Circuit Model Approach

An accurate equivalent circuit model (ECM) for general multiwire multiorder printed circuit board (PCB) metagratings (MGs) is developed. The effective impedances of the grating wires are modeled as lumped loads in the ECM. The numerous propagating Floquet harmonics supported by the grating are modeled as circuit ports. Based on the desired field transformation for the MG, a set of required scattering parameters for its ECM is derived. The equivalent lumped loads are subsequently optimized to realize these parameters, which is analogous to shaping the diffraction pattern of the MG to match the desired functionality. Within this framework, it is possible to encode multiple functions into a single device by simultaneously optimizing all relevant entries of the ECM scattering matrix. Using the proposed technique, an angle-multiplexed multifunctional MG is designed and investigated.
Ten little jurors in the training camp: a genealogy of audience simulation

ABSTRACT This paper argues that material valuation devices used in consumer research rely on moralized cultural techniques. The paper focuses in detail on one such valuation device, namely audience simulators, and recovers the deeply ascetic and disciplinary nature of this set of techniques. Audience simulation, and in particular the Continuous Response Measurement of media audiences (CRM) allows estimating audience reactions to movies and commercials by simulating the response these offerings would receive in ‘real’ life. The paper traces this simulation method and the material valuation devices it is made up of, namely push-buttons, dials, and polygraphs, back to interwar prediction systems for the success of radio shows. The simulation and valuation practices that perform CRM reveal a genealogy that links audience research to an ethics of religious training. While CRM settings ostensibly aim at audience simulation and programme valuation, they also rely on essentially pre-modern cultural techniques such as monastic pace-setting, congregational judgement, and confession.
Ultracold Atomic Physics

The field of ultracold atomic physics has developed rapidly during the last two decades. It currently encompasses a broad range of topics in physics, with a variety of important applications in topics ranging from quantum computing and simulation to quantum metrology, and can be used to probe fundamental many-body effects, such as superconductivity and superfluidity. Beginning with the underlying and including the most cutting-edge experimental developments, this textbook covers essential topics, such as Bose–Einstein condensation of alkali atoms, studies of BEC-BCS crossover in degenerate Fermi gas, synthetic gauge fields and Hubbard models, and many-body localization and dynamical gauge fields. Key physical concepts, such as symmetry and universality, highlight the connections between different systems, and theory is developed with plain derivations supported by experimental results. This self-contained and modern text will be invaluable for researchers, graduate students, and advanced undergraduates studying cold atom physics, from both a theoretical and experimental perspective.
Assessment of Activity Pacing in Relation to Physical Activity and Health-Related Quality of Life in Adults with Multiple Sclerosis: A Foundation for Further Intervention Development.

Background Activity pacing is a behavioral strategy for coping with fatigue, optimizing physical activity (PA) levels, and achieving a paced approach to lifestyle and sustainable self-regulated exercise practice to optimize health and well-being. Yet little is known about how activity pacing affects PA and health-related quality of life (HRQOL) while controlling for fatigue and demographic characteristics over time in adults with multiple sclerosis (MS). This study examined the natural use of activity pacing and how it is associated with PA and HRQOL over time in adults with MS. Methods Sixty-eight adults with MS (mean ± SD age, 45.2 ± 10.9 years) completed questionnaires on their activity pacing, fatigue, PA, and HRQOL 14, 33, and 52 weeks after rehabilitation. Associations between the variables were examined using multilevel models. Results No associations were found between activity pacing and PA (β = -0.01, P = .89) or between activity pacing and HRQOL (β = -0.15, P = .09). Conclusions This study provides an initial understanding of how activity pacing relates to PA and HRQOL in people with MS over time and indicates that there is no clear strategy among adults with MS that is successful in improving PA and HRQOL in the short or long term. Persons with MS may benefit from goal-directed activity pacing interventions to improve longitudinal engagement in PA, and the present study provides a foundation for further intervention development.
Common mode disturbance tolerant broadband differential SPDT switch for Ka-band radar

The proposed broadband fully diﬀerential single-pole double- throw (SPDT) switch for Ka-band radar with high 1-dB power compression point (P (cid:0) 1 dB ) and isolation is demonstrated using 0.13 (cid:22) m SiGe BiCMOS technology. In contrast to traditional circuit, SPDT switch utilizes fully diﬀerential topology with virtual grounding, so it also oﬀers cancellation to common mode disturbance. As a result, this SPDT switch will help in improving radar range due to its high P (cid:0) 1 dB , high range-resolution and accuracy due to its broad-bandwidth. Further, this paper also introduces the use of asymmetrical tapered inductor in SPDT switch to improve layout area of SPDT switch with normal spiral inductor. Measurement results of fabricated diﬀerential SPDT switch with conventional layout has demon- strated a minimum insertion loss of 2.9 dB and an isolation of (cid:0) 39 dB in 25 GHz to 40 GHz frequency-band, and input P (cid:0) 1 dB of 12.6 dBm at 34 GHz with 0.47 mm 2 area. EM simulation results of compact diﬀerential SPDT switch with symmetrically tapered inductor in layout has demon- strated minimum insertion loss of 1.8 dB and isolation of -39 dB in same frequency-band, input P (cid:0) 1 dB of 14.1 dBm at 34 GHz with 0.11 mm 2 area.
Agreement and reliability of magnetic, angular rate, and gravity (MARG) sensors to assess multiple body segment’s external loads during off-road running

The extensive use of wearable sensors in sport medicine, exercise medicine, and health has increased the interest in their study. That is why it is necessary to test these technologies’ efficiency, effectiveness, agreement, and reliability in different settings. Consequently, the purpose of this article was to analyze the magnetic, angular rate, and gravity (MARG) sensor’s test-retest agreement and reliability when assessing multiple body segments’ external loads during off-road running. A total of 18 off-road runners (38.78 ± 10.38 years, 73.24 ± 12.6 kg, 172.17 ± 9.48 cm) ran two laps (1st and 2nd Lap) of a 12 km circuit wearing six MARG sensors. The sensors were attached to six different body segments: left (MPLeft) and right (MPRight) malleolus peroneus, left (VLLeft) and right (VLRight) vastus lateralis, lumbar (L1-L3), and thorax (T2-T4) using a special neoprene suit. After a principal component analysis (PCA) was performed, the total data set variance of all body segments was represented by 44.08%–70.64% for the 1st PCA factor considering two variables, Player LoadRT and Impacts, on L1-L3, respectively. These two variables were chosen among three total accelerometry-based external load indicators (ABELIs) to perform the agreement and reliability tests due to their relevance based on PCAs for each body segment. There were no significant differences between laps in the Player LoadRT or Impacts (p > 0.05, trivial). The intraclass correlation and lineal correlation showed a substantial to almost perfect over-time test consistency assessed via reliability in both Player LoadRT and Impacts. Bias and t-test assessments showed good agreement between Laps. It can be concluded that MARGs sensors offer significant test re-test reliability and good agreement when assessing off-road kinematics in the six different body segments.
Ultrastructure of cytopathological responses in Solanum tuberosum cv. Spounta infected with necrotic strain of potato virus Y

Abstract Ultrastructural responses of plant cell organelles in leaf tissues of potato cv. Spounta towards potato virus Y (PVY-necrotic and ordinary strains) infection were investigated using an electron microscope. Numerous cytopathological alterations in cell wall, middle lamella, nucleus, chloroplast, mitochondria, endoplasmic reticulum and peroxisomes have been observed compared to healthy tissues. Splitting, destroying, joining to neighboring protoplasts and necrosis in cell wall were observed. Cell nucleus was changed to lobe-like shape; damage in envelope and enlargement of pore complexes were observed. Mitochondria were increased in number, elongated and destroyed. Many changes in chloroplasts such as deformation, reduction in size and number, damage of envelope and grana thylakoids were observed. Virus particles and cytoplasmic inclusions were observed in ultra-thin sections. To our knowledge, obtained results showed that only peroxisomes could be responsible organelles for induction of necrotic symptoms, because they were increased in number and frequently existed only in plant cells infected with PVY-necrotic strain.
Driver fatigue detection based on convolutional neural network and face alignment for edge computing device

Most current vision-based fatigue detection methods don’t have high-performance and robust face detector. They detect driver fatigue using single detection feature and cannot achieve real-time efficiency on edge computing devices. Aimed at solving these problems, this paper proposes a driver fatigue detection system based on convolutional neural network that can run in real-time on edge computing devices. The system firstly uses the proposed face detection network LittleFace to locate the face and classify the face into two states: small yaw angle state “normal” and large yaw angle state “distract. ” Secondly, the speed-optimized SDM algorithm is conducted only in the face region of the “normal” state to deal with the problem that the face alignment accuracy decreases at large angle profile, and the “distract” state is used to detect driver distraction. Finally, feature parameters EAR, MAR and head pitch angle are calculated from the obtained landmarks and used to detect driver fatigue respectively. Comprehensive experiments are conducted to evaluate the proposed system and the results show its practicality and superiority. Our face detection network LittleFace can achieve 88.53% mAP on AFLW test set at 58 FPS on the edge computing device Nvidia Jetson Nano. Evaluation results on YawDD, 300 W, and DriverEyes show the average detection accuracy of the proposed system can reach 89.55%.
Interference coordination and throughput maximisation in an unmanned aerial vehicle-assisted cellular: User association and three-dimensional trajectory optimisation

Unmanned aerial vehicles in wireless communications have caught great attention in unmanned aerial vehicle assisted communications and cellular-connected unmanned aerial vehicles. However, the unmanned aerial vehicle’s advantage of strong line-of-sight channels also brings about severe and extensive interference between unmanned aerial vehicle and the terrestrial communication networks and between multiple unmanned aerial vehicles. This paper jointly optimises the unmanned aerial vehicle’s three-dimensional trajectory and ground users’ association schemes with the unmanned aerial vehicle and ground base station to reduce interference. In consideration of fairness, the authors formulate the optimisation problem as maximising the minimum average throughput among all ground users, a mixed-integer non-convex problem that is challenging to tackle. To ease the difﬁculties, the authors have the optimisation problem relaxed and propose a suboptimal solution in the framework of the block successive upper-bound minimisation algorithm. Further-more, an improved block successive upper-bound minimisation based penalty algorithm is obtained to avoid the local maximum point problem. Numerical simulations have validated our schemes.
Medical image fusion using Transfer Learning and L‐BFGS optimization algorithm

Medical image fusion can effectively combine different images with different parts and complements each other, which is a hotspot in image processing. However, the medical image acquisition equipment usually obtains single image, so it is unable to use dataset to train convolutional neural network to extract stable image features. This paper proposes an approach using the Transfer Learning combined with the Limited Broyden, Fletcher, Goldfarb, and Shanno (L‐BFGS) optimization algorithm to achieve single medical image fusion. The Transfer Learning which includes pre‐trained model and parameters can be used as a feature extractor to extract or compress features in medical images. Firstly, different single medical images were input into the trained VGG16 Transfer Learning parameter model to extract image features, and then the maximum features between different images were obtained through feature comparison. Finally, L‐BFGS optimization algorithm is used to approximate the initial image features to the maximum features, to achieve the fusion effect (the initial image can be any image of the same size and depth as the source image). The experimental results show that the algorithm is effective and very suitable for medical image fusion.
Application of Marginal Models to Identify Risk Factors of Childhood Stunting in Ethiopia

Background: Child malnutrition is an underlying cause of almost half (45%) of child deaths, particularly in low socioeconomic communities of developing countries. In Ethiopia, the prevalence of stunting decreased from 47% in 2005 to 39% in 2016. Objective: The main objective of the study was identifying risk factors of stunting among under-five children in Ethiopia using marginal models.Methods: Data was taken from the EDHS 2016, which is a nationally representative survey of children in the 0-59 month age groups. Generalized estimating equation and alternating logistic regression models from marginal models family were used for the analysis. Results: The result of the analysis revealed that the variables child’s age, mother’s education level, mother’s body mass index, place of residence, wealth index and previous birth interval were found to be significant determinants of childhood stunting in Ethiopia and from the result it is revealed that children born with lower previous birth interval (less than 24 months) were more likely to be stunted than those born within higher birth interval. Children of rural Ethiopia were more likely to be stunted than children of urban Ethiopia. Conclusion: It is suggested that for reducing childhood malnutrition, due emphasis should be given in improving the knowledge and practice of parents on appropriate young child feeding practice and frequent growth monitoring together with appropriate and timely interventions.
Modeling of a chain of three plasma accelerator stages with the WarpX electromagnetic PIC code on GPUs

The fully electromagnetic particle-in-cell code WarpX is being developed by a team of the U.S. DOE Exascale Computing Project (with additional non-U.S. collaborators on part of the code) to enable the modeling of chains of tens to hundreds of plasma accelerator stages on exascale supercomputers, for future collider designs. The code is combining the latest algorithmic advances (e.g., Lorentz boosted frame and pseudo-spectral Maxwell solvers) with mesh refinement and runs on the latest computer processing unit and graphical processing unit (GPU) architectures. In this paper, we summarize the strategy that was adopted to port WarpX to GPUs, report on the weak parallel scaling of the pseudo-spectral electromagnetic solver, and then present solutions for decreasing the time spent in data exchanges from guard regions between subdomains. In Sec. IV, we demonstrate the simulations of a chain of three consecutive multi-GeV laser-driven plasma accelerator stages.