Physical Module Research Articles

Design and Investigation of a Passive-Type Microfluidics Micromixer Integrated with an Archimedes Screw for Enhanced Mixing Performance

In recent years, microfluidics has emerged as an interdisciplinary field, receiving significant attention across various biomedical applications. Achieving a noticeable mixing of biofluids and biochemicals at laminar flow conditions is essential in numerous microfluidics systems. In this research work, a new kind of micromixer design integrated with an Archimedes screw is designed and investigated using numerical simulation and experimental approaches. First, the geometrical parameters such as screw length (l), screw pitch (p) and gap (s) are optimized using the Design of Expert (DoE) approach and the Central Composite Design (CCD) method. The experimental designs generated by DoE are then numerically simulated aiming to determine Mixing Index (MI) and Performance Index (PI). For this purpose, COMSOL Multiphysics with two physics modules—laminar and transport diluted species—is used. The results revealed a significant influence of screw length, screw pitch and gap on mixing performance. The optimal design achieved is then scaled up and fabricated using a 3D additive manufacturing technique. In addition, the optimal micromixer design is numerically and experimentally investigated at diverse Reynolds numbers, ranging from 2 to 16. The findings revealed the optimal geometrical parameters that produce the best result compared to other designs are a screw length of 0.5 mm, screw pitch of 0.23409 mm and a 0.004 mm gap. The obtained values of the mixing index and the performance index are 98.47% and 20.15 Pa−1, respectively. In addition, a higher mixing performance is achieved at the lower Reynolds number of 2, while a lower mixing performance is observed at the higher Reynolds number of 16. This study can be very beneficial for understanding the impact of geometrical parameters and their interaction with mixing performance.

STEM-Based Physics Modules with CK-12 Simulations for High School Students: Development and Implementation

Research has been conducted to develop STEM-based physics modules with CK-12 simulations for high school students. This study evaluates the STEM-based physics module’s validity, practicality, and effectiveness with CK-12 simulations for grade X students. The research employed research and development (R&D), utilizing the ADDIE model, which includes the stages of analysis, design, development, implementation, and evaluation. The study involved three expert validators and students from two schools to assess the module’s validity, practicality, and effectiveness. The participants comprised grades X MIA students from SMAN 1 Alu and X IPA students from MAN 2 Polewali Mandar. The validity of the developed physics modules, presented as a practical module, was assessed through expert validation, while practicality was evaluated using response questionnaires completed by students and teachers. Effectiveness was determined based on student learning outcome tests. The results indicate that the physics module was highly valid, with an overall validity score of 87.78%. Practicality was also rated highly, with scores of 90.29% from students and 99.00% from teachers. Furthermore, the module demonstrated effectiveness, as evidenced by student learning outcomes meeting the established criteria, with an effectiveness score of 79.17%. These findings suggest that STEM-based physics modules with CK-12 simulations can significantly enhance the quality of physics teaching and learning for high school students. In addition, this research should be explored for other topics.

Bright NIR-II emissive cyanine dye-loaded lipoprotein-mimicking nanoparticles for fluorescence imaging-guided and targeted NIR-II photothermal therapy of subcutaneous glioblastoma

Cyanine dye-containing nanoparticles have widely been used in “all-in-one” NIR fluorescence imaging (FI)-guided photothermal therapy (PTT) because of their intrinsically large extinction coefficient and available physical and chemical modulation methods to tune absorption and emission wavelengths. The combination of good brightness and excellent tumor-targeting capacity is the key to realize efficient NIR-II FI-guided PTT. In this study, by covalently decorating NIR-II absorptive cyanine dyes with bulky AIE motify, we demonstrate how steric hindrance suppresses π-π stacking-induced fluorescence quenching and contributes to the good brightness of NIR-II FI of subcutaneous glioblastoma. The resulting cyanine dye (C12-TPAE) is 5 times brighter than the original cyanine dye in the formulated liposomal nanoparticles and C12-TPAE-AL has a high photothermal conversion efficiency of 62.4%, with good colloidal and light stability. Importantly, the ApoE peptide is absorbed on the liposomal surface, yielding lipoprotein-mimicking nanoparticles, which achieve active targeting of glioblastoma and efficient FI-guided PTT without tumor recurrence without any side effects on normal organs (heart, kidneys, liver, spleen, or lung). This research highlights a facile design route for bright NIR-II emissive and NIR-II photothermal cyanine dyes and indicates that cyanine dye-containing biomimetic theranostic nanoplatforms are promising candidates for future precision therapy.

Modeling and simulation of dielectrophoretic sorting of tenogenically differentiating mesenchymal stem cells for high throughput

Mesenchymal stem cell (MSC)-based regenerative therapies are promising for healing tendon injuries and tears, due to their potential to differentiate into tenogenic cells. However, generating homogeneous populations of tenogenically differentiated stem cells remains a big challenge, as non-differentiated cells can lead to post-transplantation complications. Therefore, a homogenous sample of tenogenically differentiated MSCs is critical for advancing tendon therapies and avoiding uncontrolled cell growth or non-tendon tissue formation (e.g., ectopic bone). This work is focused on designing and simulating a dielectrophoretic (DEP)-based label-free, microfluidic platform to selectively sort and enrich tenogenically differentiated MSCs (tMSCs) from undifferentiated MSCs. Using particle tracing, creeping flow (transport of diluted species model), and electric current physics modules in the COMSOL Multiphysics simulation software package, the sorting was simulated within a two-stage microfluidic device operating at a sinusoidal frequency of 160 kHz. The optimal separation efficiency and purity are achieved at an inlet velocity of 400–1000 μm/s, with specific voltage configurations, enabling recovery of one million tMSCs in ∼3 h. Results demonstrate a near-linear relation between recovery time and particle count at the outlet boundaries and selected surfaces, indicating consistent throughput across varying conditions. This study demonstrates that DEP can offer a scalable, efficient, and label-free method for enriching tMSC populations with high selectivity, enhancing more prospects for MSC-based tendon therapies and advancing the development of microfluidic sorting devices for regenerative medicine applications.

Enhancing adapted physical activity training for community organizations: co-construction and evaluation of training modules.

Community-based physical activity programmes benefit persons with disabilities. However, there is a lack of evidence-based tools to support kinesiologists' training in such programmes. This study aimed to co-create and evaluate physical activity training modules for community-based adapted physical activity (APA) programmes. In Phase 1, a working group (n = 8) consisting of staff, kinesiologists from two community-based APA programmes, and researchers met over four online meetings to discuss needs, co-create training modules, and assess usability. In Phase 2, a pre-post quasi-experimental design evaluated changes in capability, opportunity, and motivation of kinesiologists (n = 14) after completing the training modules, which included standardized mock client assessments and participant ratings of module feasibility. Means and standard deviations were computed for feasibility, followed by paired-samples t-tests, along with Hedge's correction effect size. Mock client sessions underwent coding and reliability assessment. The working group meetings generated two main themes: training in (i) motivational interviewing and behaviour change techniques and (ii) optimizing APA prescription. Nine online training modules were created. In Phase 2, medium to large effects of training modules were observed in capability (Hedge's g = 0.67-1.19) for 8/9 modules, opportunity (Hedge's g = 0.77-1.38) for 9/9 modules, and motivation (Hedge's g = 0.58-1.03) for 6/9 modules. In mock client assessments, over 78% of participants appropriately used five behaviour change techniques and, on average, participants demonstrated good use of motivational interviewing strategies. The findings indicate that training kinesiologists was feasible and has the potential to enhance community-based physical activity programmes for persons with disabilities.

A single-blind, randomised control trial on the effectiveness of a structured multi component training module for family caregiver of persons with Parkinson's disease: A study protocol.

Parkinson disease (PD), a neurodegenerative disorder that progresses over time, is steadily growing in number and prevalence worldwide. PD in Malaysia is expected to increase five-fold by 2040 from the existing estimate of 20,000 patients in 2018. Treatment program of PD in Malaysia is rather unstructured, and there is no known comprehensive PD family caregiver training program available to date. To ensure the quality of a program, it must be tested for feasibility, effectiveness and sustainability. This paper describes the protocol of a study that evaluates the effectiveness of a structured, comprehensive training program of family caregiver to persons with PD in comparison to usual care. A total of 60 pairs of persons with PD of stage II and III, and their primary family caregiver will be recruited and allocated into either an experimental or a control group for 12 weeks of intervention. The experimental group will undergo initial training from multi-disciplinary healthcare providers and will be given a physical module containing weekly tasks that must be practised at home. While the control group will receive a usual care. Both groups will be assessed in terms of physical functions, functional mobility, quality of life (QoL), caregiver burden and knowledge using standardised assessment tools namely Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS), Timed Up and Go (TUG) test, Parkinson's Disease Questionnaire (PDQ-39), European Quality of Life five-dimensions (EQ-5D), Malay version of Zarit Burden Interview (MZBI) and Knowledge of Parkinson Disease Questionnaire (KPDQ). In addition, the feasibility and sustainability of the interventions will be evaluated, alongside its cost-effectiveness based on the average and incremental cost effectiveness ratio. All data will be analysed using descriptive and inferential statistics, particularly mixed model ANOVA. There is a significant gap in the literature pertaining family caregiver training programs for people with PD. Documented programs are lacking in term of comprehensiveness of content, application approach and the measurement of training outcomes including the program cost-effectiveness. The feasibility and effectiveness of such training program in a Malaysian setting also requires investigation due to differences in living environment, support system and population's perception. This study will assist to fulfil the existing literature gap and demonstrate the potential benefit of caregiver involvement in mediating the care and therapy for PD in the home setting. Optimum knowledge and skills gained through the training are expected to enhance the confidence and ability of the family caregivers and may possibly reduce their perceived caregiving burden. The protocol of this study is registered in the Australian-New Zealand Clinical Trial Registry (ANZCTR) with a registration number ACTRN12623000336684.

Momentary achievement goal profiles: Associations with instructional activities, interest, and anxiety

BackgroundStudies have traditionally assessed students’ achievement goals as stable individual orientations, thereby missing moment-to-moment fluctuations across situations. Furthermore, students may pursue more than one goal at a given moment. AimsTo capture the dynamic nature of achievement goal pursuit, this study combined real-time assessments and pattern-oriented analyses to identify students' momentary achievement goal profiles. It also examined how these profiles varied across instructional activities and related to students’ task interest and anxiety. SampleA total of 3611 responses were collected from 345 upper secondary school students (aged 16) in a physics module. MethodsUsing the experience sampling method, this study collected real-time data on students’ achievement goals and affective experiences in various science classroom activities, including teacher-led instruction, individual work, and group work. ResultsLatent class analyses revealed three distinct momentary goal profiles: Moderate Multiple Goals, High Multiple Goals, and Moderate Mastery Goals. Most students exhibited changes in their momentary goal profiles during the physics module, highlighting the dynamic nature of these profiles. Individual work increased the likelihood of students adopting the High Multiple Goals profile. Additionally, the High Multiple Goals profile was linked to increased situational interest, whereas the Moderate Mastery Goals profile was associated with reduced anxiety. ConclusionThe findings underscore the value of investigating momentary goal states in addition to stable goal orientations.

Advancing IoMT security: A two-factor authentication model employing PUF and Fuzzy logic techniques

The rapid integration of Internet of Things technologies in healthcare has catalyzed the development of the Internet of Medical Things, markedly enhanced patient care while posing significant security risks. This paper introduces a comprehensive computational framework to safeguard Internet of Medical Things devices and healthcare providers through a sophisticated registration and authentication process. Our model incorporates cryptographic technologies such as Physical Unclonable Functions, fuzzy extractors, and hash functions to bolster the security during the registration and authentication processes for Internet of Medical Things devices and healthcare providers. The Physical Unclonable Function module enhances device security by producing unique, non-replicable responses for device authentication, significantly reinforcing the system's defense against physical and cloning attacks. Furthermore, the model leverages fuzzy logic for the real-time classification of patient health states, enhancing the decision-making accuracy. A comparative analysis confirms that our model exceeds existing models in communication cost, computational efficiency and security. The proposed scheme has been rigorously tested against various attacks using the Scyther tool. By employing a unique identifier generation method through Physical Unclonable Function and utilizing fuzzy logic for secure data transmission and patient classification, our framework addresses vulnerabilities such as man-in-the-middle, denial of service, impersonation, identity guessing, password guessing and replay attacks, which are prevalent in current Internet of Medical Things frameworks.

Tunable optical properties of BAs/ZnO vdW heterostructure

Stacked heterostructures is an effective strategy for physical property modulation and application of novel two-dimensional materials. In this study, a heterostructure consisting of two-dimensional III-V group hexagonal BAs and monolayer of ZnO is presented. The minimum value of binding and cohesive energies screened the BB’ configuration. Phonon spectra and ab initio molecular dynamics (AIMD) simulations further demonstrated the kinetic and thermodynamic stability of the selected model. Most notably, the formation of the heterostructure greatly improves the optical absorption performance of the monolayer, especially in the infrared (IR) regions. At a compressive strain of −6%, the band alignment shifts from type I to type II, while the bandgap becomes dramatically smaller. Refraction and reflection coefficients in the IR region under compressive strain (−2% and −4%) modulation were enhanced significantly. Our results provide theoretical guidance for the design of high-performance photovoltaic devices and solar cells based on BAs/ZnO heterostructures.

Slow-Paced Breathing Intervention in Healthcare Workers Affected by Long COVID: Effects on Systemic and Dysfunctional Breathing Symptoms, Manual Dexterity and HRV.

Many COVID-19 survivors still experience long-term effects of an acute infection, most often characterised by neurological, cognitive and psychiatric sequelae. The treatment of this condition is challenging, and many hypotheses have been proposed. Non-invasive vagus nerve stimulation using slow-paced breathing (SPB) could stimulate both central nervous system areas and parasympathetic autonomic pathways, leading to neuromodulation and a reduction in inflammation. The aim of the present study was to evaluate physical, cognitive, emotional symptoms, executive functions and autonomic cardiac modulation after one month of at-home slow breathing intervention. 6655 healthcare workers (HCWs) were contacted via a company email in November 2022, of which N = 58 HCWs were enrolled as long COVID (cases) and N = 53 HCWs as controls. A baseline comparison of the two groups was performed. Subsequently each case was instructed on how to perform a resonant SPB using visual heart rate variability (HRV) biofeedback. They were then given a mobile video tutorial breathing protocol and asked to perform it three times a day (morning, early afternoon and before sleep). N = 33 cases completed the FU. At T0 and T1, each subject underwent COVID-related, psychosomatic and dysfunctional breathing questionnaires coupled with heart rate variability and manual dexterity assessments. After one month of home intervention, an overall improvement in long-COVID symptoms was observed: confusion/cognitive impairment, chest pain, asthenia, headache and dizziness decreased significantly, while only a small increase in manual dexterity was found, and no relevant changes in cardiac parasympathetic modulation were observed.

Digital Physics Module for 21st Century Education on New and Renewable Energy Based on the Dilemma-STEAM Learning Model

Abstract This study aims to develop a Dilemma-STEAM electronic module on renewable energy materials to be used as a physics teaching resource for 10th-grade high school students using the ADDIE (Analyze, Design, Development, Implementation, and Evaluation) development model. The Dilemma-STEAM based e-module is a digital physics module based on the Dilemma-STEAM (Science, Technology, Engineering, Arts, Mathematics) model of teaching. In the context of 21st-century education, the application of technology and interdisciplinary approaches is emphasized to prepare students for future challenges. This module is designed to encourage critical thinking, collaboration, communication, and creativity in students through real dilemmas story integrated with STEAM. The product is a digital module design presented through the website, it easily accessible to students using compatible electronic devices. Based on the validation results conducted by experts, an overall percentage of 87.50% was obtained from material experts, 84.44% from media experts, and 89.41% from learning experts. Additionally, the product trial by physics teachers showed a final percentage of 95.63% with an interpretation of “Very Good,” while students who had studied the material on physical quantities and measurements gave a percentage of 87.51% with an interpretation of “Very Good.” From this research, it can be concluded that the Dilemma-STEAM electronic module on renewable energy materials is deemed valid and suitable for use as a physics learning medium for 10th-grade high school students.

Progress of research in the application of ultrasound technology for the treatment of Alzheimer's disease.

Alzheimer's disease is a common neurodegenerative disorder defined by decreased reasoning abilities, memory loss, and cognitive deterioration. The presence of the blood-brain barrier presents a major obstacle to the development of effective drug therapies for Alzheimer's disease. The use of ultrasound as a novel physical modulation approach has garnered widespread attention in recent years. As a safe and feasible therapeutic and drug-delivery method, ultrasound has shown promise in improving cognitive deficits. This article provides a summary of the application of ultrasound technology for treating Alzheimer's disease over the past 5 years, including standalone ultrasound treatment, ultrasound combined with microbubbles or drug therapy, and magnetic resonance imaging-guided focused ultrasound therapy. Emphasis is placed on the benefits of introducing these treatment methods and their potential mechanisms. We found that several ultrasound methods can open the blood-brain barrier and effectively alleviate amyloid-β plaque deposition. We believe that ultrasound is an effective therapy for Alzheimer's disease, and this review provides a theoretical basis for future ultrasound treatment methods.

Development and Delivery of an Integrated Digital Health Care Approach for Children With Juvenile Idiopathic Arthritis: Usability Study.

Juvenile idiopathic arthritis (JIA) is a chronic inflammatory disorder with no cure. Most children are prescribed several medications aimed at controlling disease activity, managing symptoms, and reducing pain. Physical activity is also encouraged to retain musculoskeletal function. The primary determinants of treatment success are maintaining long-term adherence, ongoing monitoring by a pediatric rheumatologist, and involvement of an interdisciplinary team. To support these goals, a new digital intervention was developed, InteractiveClinics, which aimed to prompt children to take their medications, report pain levels, and increase their physical activity. This study aims to evaluate the usability of InteractiveClinics among children with JIA. As part of this pediatric cross-sectional study, 12 children were asked to wear a smartwatch for 2 weeks, which was synchronized to the InteractiveClinics phone app and web-based platform. Personalized notifications were sent daily to the watch and phone, to prompt and record medication adherence and pain level assessment. Physical activity was automatically recorded by the watch. At the end of the study, all children and parents completed a postintervention survey. Written comments were also encouraged to gain further feedback. Descriptive statistics were used to summarize the survey results, and all qualitative data underwent thematic analysis. Twelve children aged 10 to 18 years (mean 14.2, SD 3.1 years; female: n=8, 66.7%) and 1 parent for each child (n=12; female: n=8, 66.7%) were enrolled in the study. Based on the highest and lowest agreement areas of the survey, most children and parents liked the smartwatch and web-based platform; they found it easy to learn and simple to use. They were also satisfied with the pain and physical activity module. However, usability and acceptability barriers that hindered uptake were identified in the phone app and medication module. Children required a unique in-app experience, and their suggestive improvements included more personalization within the app; simplification by removing all links not relevant to antirheumatic medications; flexibility in response times; improved conferment through gamification; additional comment fields for the input of more data, such as medication side effects or pain-related symptoms; more detailed graphical illustrations of the physical activity module, including a breakdown of metrics; and importantly, interconnections between modules, because medication adherence, pain levels, and physical activity can each influence the other. They were, overall, improving usefulness for children and parents. The usability of InteractiveClinics was positive. Children and parents liked the watch and web-based platform and were satisfied with the pain and physical activity module. However, children wanted a unique in-app experience through more personalization, simplification, flexibility, conferment, comment fields, graphical illustrations, a breakdown of metrics, and interconnections. Certainly, inclusions are needed to promote user adoption and advancement of new validated digital health interventions in pediatric rheumatology, to support the delivery of integrated care.

Physics-informed probabilistic deep network with interpretable mechanism for trustworthy mechanical fault diagnosis

The application of data-driven models based on the neural network is pivotal to developing an intelligent fault diagnostic flowchart. However, the reliability and interpretability of these models for fault prediction have presented challenges in neural network-based diagnostic approach. Another challenge is data availability, which has also been a limiting factor in using artificial Intelligence for condition monitoring and fault assessment in industrial mechanical systems. Consequently, this study proposes a Physics Informed Probabilistic Deep Network (PIPDN) framework to overcome these challenges. The PIPDN comprises two main components: the physical labelling module, designed to enhance physical labels with mechanical failure information, and the main body of PIPDN, responsible for learning fault representative features and generating smart data guided by conditional and physical labels. Furthermore, a multi-scale PIPDN model is developed to integrate the proposed uncertainty quantification (UQ) with decision-fusion module for accurate interpretation and enhanced fault diagnosis. The applicability, effectiveness, and superiority of the proposed framework and approach are validated using an experimental bearing dataset. The results indicate that integrating physical labels significantly assists the PIPDN model in capturing more accurate fault characteristics. This increases the importance of latent space features for subsequent fault diagnosis and also enhances the diagnostic interpretability. Furthermore, the addition of UQ-based decision-making module improves the reliability of the MS-PIPDN model by reducing epistemic uncertainty in the predictions.

Establishing minimal clinically important differences for the quality of life scale QLICD-SLE for patients with systemic lupus erythematosus based on ROC curve

The minimal clinically important difference (MCID) is an important concept with big appeal in a field struggling to interpret quality of life (QOL) and other patient-reported outcomes (PRO), is also a bridge between statistics and clinical medicine. This study uses the ROC curve to formulate the MCID value of the Quality of Life Instruments for Chronic Diseases of Systemic lupus erythematosus (QLICD-SLE V2.0) scale. Using the representative item “In general, would you say your health is” of the MOS item short form health survey(SF-36) as an anchor, the questionnaire of QLICD-SLE V2.0 and the anchor item were used to investigate the patients on the first day of hospitalization, and the day before the patient was discharged. 279 patients with lupus erythematosus were participated in this longitudinal follow-up study. The ROC curve was constructed by using the classification based on the anchor item as the gold standard and the difference score of the scale as the test variable. The cut-off point corresponding to the maximum value of the Youden index in the ROC curve is taken as the minimum clinical importance difference (MCID) value of the QLICD-SLE (V2.0) scale. The Results showed that the MCID of physical domain, psychological domain, social domain, general module, specific module and QLICD-SLE (V2.0) total scale are 8.3, 2.3, 2.5, 2.7, 9.2 and 3.2, respectively. Area under the ROC curve of QLICD-SLE (V2.0) is 0.898, P (Area = 0.5) < 0.001, the sensitivity is 100%, the specificity is 66.9%. It concluded that if the total scores after treatments changes at least 3.2 points positively, the treatment intervention can be considered as clinically significant. It is more convincing to use the corresponding cut-off point as the MCID for ROC curve method can visualize the sensitivity and specificity.

Spiral-curricular blended learning for the mathematics education in physics teacher training courses

A good physics education depends on a good, transferable mathematics education. A concept for a cross-modular blended learning math course developed for the first two semesters of our physics teacher trainee study program is introduced. The course covers the important new mathematics required for the development of the conceptual understanding of the classical experimental physics mechanics and electrodynamics. It is based on three pillars: parallelism of the math topics to the physics lectures, spiral-curricularity to prior knowledge and high quality digitally available interactive materials such as interactive videos, formative tests and exercises to foster a self-regulated learning of the students in online as well as face-to-face learning environments. The blended learning math course and its face-to-face math seminars are integrated into the experimental physics modules. Results of surveys among the first two cohorts of course participants indicate that the design of the math course is well accepted and the interactive videos are very well received by the students. Initial tests on the learning effectiveness indicate a sufficient development of long term knowledge by the students.

First results of the polar regional climate model RACMO2.4

Abstract. The next version of the polar Regional Atmospheric Climate Model (referred to as RACMO2.4p1) is presented in this study. The principal update includes embedding of the package of physical parameterizations of the Integrated Forecast System (IFS) cycle 47r1. This constitutes changes in the precipitation, convection, turbulence, aerosol and surface schemes and includes a new cloud scheme with more prognostic variables and a dedicated lake model. Furthermore, the standalone IFS radiation physics module ecRad is incorporated into RACMO, and a multilayer snow module for non-glaciated regions is introduced. Other updates involve the introduction of a fractional land–ice mask, new and updated climatological data sets (such as aerosol concentrations and leaf area index), and the revision of several parameterizations specific to glaciated regions. As a proof of concept, we show first results for Greenland, Antarctica and a region encompassing the Arctic. By comparing the results with observations and the output from the previous model version (RACMO2.3p3), we show that the model performs well regarding the surface mass balance, surface energy balance, temperature, wind speed, cloud content and snow depth. The advection of snow hydrometeors strongly impacts the ice sheet's local surface mass balance, particularly in high-accumulation regions such as southeast Greenland and the Antarctic Peninsula. We critically assess the model output and identify some processes that would benefit from further model development.

Physics-guided interpretable CNN for SAR target recognition

Deep Learning (DL) model has been widely used in the field of Synthetic Aperture Radar Automatic Target Recognition (SAR-ATR) and has achieved excellent performance. However, the black-box nature of DL models has been the focus of criticism, especially in the application of SAR-ATR, which is closely associated with the national defense and security domain. To address these issues, a new interpretable recognition model Physics-Guided BagNet (PGBN) is proposed in this article. The model adopts an interpretable convolutional neural network framework and uses time–frequency analysis to extract physical scattering features in SAR images. Based on the physical scattering features, an unsupervised segmentation method is proposed to distinguish targets from the background in SAR images. On the basis of the segmentation result, a structure is designed, which constrains the model’s spatial attention to focus more on the targets themselves rather than the background, thereby making the model’s decision-making more in line with physical principles. In contrast to previous interpretable research methods, this model combines interpretable structure with physical interpretability, further reducing the model’s risk of error recognition. Experiments on the MSTAR dataset verify that the PGBN model exhibits excellent interpretability and recognition performance, and comparative experiments with heatmaps indicate that the physical feature guidance module presented in this article can constrain the model to focus more on the target itself rather than the background.

Implementation of Physical Activity in the Curricula of Medical Schools and Healthcare Professions Across Europe: The VANGUARD Project Study Protocol.

The 2018 published World Health Organisation (WHO) Europe physical activity factsheet reports, specify agreed targets for physical activity and articulate the need to improve the education of medical doctors and healthcare practitioners in order to increase physical activity and reduce sedentary time in people at risk and/or living with Noncommunicable Diseases (NCDs). Given the dearth of relevant initiatives and the continuous need to increase physical activity participation towards better health management of NCDs, the aim of this study is to embed physical activity in the undergraduate curricula of future frontline healthcare professionals (medical doctors and allied health professions) in European countries. The Virtual Advice, Nurturing, Guidance on Universal Action, Research and Development for physical activity and sport engagement (VANGUARD) project consists of a collaborative partnership Consortium between six European Universities, WHO Europe and Ministry representatives that has been developed to implement physical activity in the curricula of medical schools and healthcare professions. The methodology of the VANGUARD project is informed by the WHO implementation guidance and the Reach, Effectiveness, Adoption, Implementation and Maintenance (RE-AIM) framework. Through a carefully planned implementation process and via using established appropriate implementation evaluation tools, the end result of the VANGUARD project will be the a) implementation of a physical activity module in six different European Universities (five medical schools and one physiotherapy department) and b) development of a toolkit/guide, in order to assist other healthcare systems and European Universities to develop relevant grass-root innovations for addressing the decline in physical activity levels.

Synergizing AI and Physical Expertise to Close the Water Budget from Satellite Data

Abstract A multitude of Earth observation (EO) products are available for monitoring the terrestrial water cycle. These EO datasets have resulted in a multiplicity of datasets for the same geophysical variable. Furthermore, inconsistencies between the water components prevent the water budget closure. A maximum a posteriori (MAP) estimator has been used in the past to optimally combine EO datasets. This framework has many advantages, but it can only be utilized when all four water components are available (precipitation P, evapotranspiration E, total water storage change dS, and river discharge R) and solely at the basin scale. By combining physical expertise with the statistical inference of neural networks (NNs), we designed a custom deep learning scheme to optimize EO data. This hybrid approach benefits from the optimization capabilities of NNs to estimate the parameters of interconnected physical modules. The NN is trained using basin-scale data (from MAP results) over 38 basins to obtain optimized EOs globally. The NN integration offers several enhancements compared to MAP: Independent calibration/mixing models are obtained with imbalance reduction and optimization at the pixel level, and environmental variables can be used to extrapolate results to unmonitored regions. The NN integration enables combining EO estimates of individual water components (P, E, dS, and R) in a hydrologically coherent manner, resulting in a significant decrease in the water budget imbalance at the global scale. Mean imbalance errors can be significant on raw EOs, but they become negligible when EOs are integrated. The standard deviation (STD) of the imbalance is around 26 mm month−1 for raw EOs, and they decrease to 21 when combined and 19 when mixed.