Remembrance activities can support the Culture of Care(CoC)in Laboratory Animal Science(LAS)not only by promoting a culture of respect,gratitude and thankfulness for animal life but also by helping the emotional proce...Remembrance activities can support the Culture of Care(CoC)in Laboratory Animal Science(LAS)not only by promoting a culture of respect,gratitude and thankfulness for animal life but also by helping the emotional processing and healing of lab animal researchers and animal facility staff.Even though remembrance activities are practiced in many parts of the world,we did not come across any reported cases in Sri Lanka before 2022.Therefore,here,we report on the various remembrance activities and practices observed within our local scientific community.展开更多
A key pathological feature of Parkinson’s disease(PD)is that lysosomes are overwhelmed with cellular materials that need to be degraded and cleared.While the build-up of protein is characteristic of neurodegenerative...A key pathological feature of Parkinson’s disease(PD)is that lysosomes are overwhelmed with cellular materials that need to be degraded and cleared.While the build-up of protein is characteristic of neurodegenerative diseases such as PD and Alzheimer’s disease(AD)and is thought to reflect lysosome dysfunction,lipid accumulation may also contribute to and be indicative of severe lysosomal dysfunction.Much is known about the detrimental effects of glucosylceramide accumulation in PD lysosomes.展开更多
The surge in smishing attacks underscores the urgent need for robust,real-time detection systems powered by advanced deep learning models.This paper introduces PhishNet,a novel ensemble learning framework that integra...The surge in smishing attacks underscores the urgent need for robust,real-time detection systems powered by advanced deep learning models.This paper introduces PhishNet,a novel ensemble learning framework that integrates transformer-based models(RoBERTa)and large language models(LLMs)(GPT-OSS 120B,LLaMA3.370B,and Qwen332B)to enhance smishing detection performance significantly.To mitigate class imbalance,we apply synthetic data augmentation using T5 and leverage various text preprocessing techniques.Our system employs a duallayer voting mechanism:weighted majority voting among LLMs and a final ensemble vote to classify messages as ham,spam,or smishing.Experimental results show an average accuracy improvement from 96%to 98.5%compared to the best standalone transformer,and from 93%to 98.5%when compared to LLMs across datasets.Furthermore,we present a real-time,user-friendly application to operationalize our detection model for practical use.PhishNet demonstrates superior scalability,usability,and detection accuracy,filling critical gaps in current smishing detection methodologies.展开更多
Soil bacteria are integral to ecosystem functioning,significantly contributing to nutrients cycling and organic matter decomposition,and enhancing soil structure.This research considered the composition and dynamics o...Soil bacteria are integral to ecosystem functioning,significantly contributing to nutrients cycling and organic matter decomposition,and enhancing soil structure.This research considered the composition and dynamics of soil bacterial communities under different vegetation types(native Quercus brantii Lindl.and Amygdalus scoparia Spach,and non-native Pinus eldarica Medw.and Cupressus arizonica Greene.)in Zagros mountain area of Iran.This study involved a comparative analysis of soil culturable heterotrophic bacterial communities in spring(wet season)and summer(dry season)to clarify the effects of seasonal changes and vegetation on the dynamics of soil microorganisms.Soil samples were randomly collected under the canopies of various tree species and a control area,yielding a total of 48 composite samples analyzed for bacterial composition.Results indicated that 11 Gram-negative(e.g.,Citrobacter freundii,Enterobacter cloacae,Escherichia coli,Klebsiella oxytoca,Klebsiella pneumoniae,etc.)and 2 Gram-positive(Staphylococcus epidermidis and Staphylococcus aureus)bacteria were identified,showing significant seasonal variation.Specifically,53.85%of bacterial species were common to both seasons,with notable shifts in community composition observed between spring and summer,highlighting a higher abundance of Gram-negative species in spring.Bacterial community structure was significantly influenced by vegetation type,with various tree species shaping distinct microbial assemblages.Moreover,Pearson's correlations revealed that soil properties,particularly pH,phosphorus,and moisture content,were critical drivers of bacterial diversity and abundance.Our findings underscore the dynamic nature of soil bacterial communities in response to seasonal and vegetation changes,emphasizing the importance of repeated temporal sampling for accurate assessments of microbial diversity.Understanding these microbial dynamics is essential for improving soil management strategies and enhancing ecosystem resilience,particularly in arid and semi-arid areas where environmental fluctuations play a pivotal role.This research not only confirms our hypotheses but also enhances our understanding of soil biogeochemical processes and informs future vegetation management practices.展开更多
Central nervous system(CNS) axons fail to regenerate following brain or spinal cord injury(SCI),which typically leads to permanent neurological deficits.Peripheral nervous system axons,howeve r,can regenerate followin...Central nervous system(CNS) axons fail to regenerate following brain or spinal cord injury(SCI),which typically leads to permanent neurological deficits.Peripheral nervous system axons,howeve r,can regenerate following injury.Understanding the mechanisms that underlie this difference is key to developing treatments for CNS neurological diseases and injuries characterized by axonal damage.To initiate repair after peripheral nerve injury,dorsal root ganglion(DRG) neurons mobilize a pro-regenerative gene expression program,which facilitates axon outgrowth.展开更多
The pseudo-two-dimensional(P2D)model plays an important role in exploring physicochemical mechanisms,predicting the state of health,and improving the fast charge capability for Li-ion batteries(LIBs).However,the fast ...The pseudo-two-dimensional(P2D)model plays an important role in exploring physicochemical mechanisms,predicting the state of health,and improving the fast charge capability for Li-ion batteries(LIBs).However,the fast charge leads to the lithium concentration gradient in the solid and electrolyte phases and the non-uniform electrochemical reaction at the solid/electrolyte interface.In order to decouple charge transfer reactions in LIBs under dynamic conditions,understanding the spatio-temporal resolution of the P2D model is urgently required.Till now,the study of this aspect is still insufficient.This work studies the spatio-temporal resolution for dynamic/static electrochemical impedance spectroscopy(DEIS/SEIS)on multiple scales.In detail,DEIS and SEIS with spatio-temporal resolutions are used to decouple charge transfer reactions in LIBs based on the numerical solution of the P2D model in the frequency domain.The calculated results indicate that decoupling solid diffusion requires a high spatial resolution along the r-direction in particles,decoupling electrolyte diffusion and interfacial transfer reaction requires a high spatial resolution along the x-direction,and decoupling charge transfer reactions in LIBs at an extremely low state of charge(SOC)requires an extremely high temporal resolution along the t-direction.Finally,the optimal range of spatio-temporal resolutions for DEIS/SEIS is derived,and the method to decouple charge transfer reactions with spatio-temporal resolutions is developed.展开更多
This review comprehensively summarized the potential of artificial intelligence(AI)in the management of esophageal cancer.It highlighted the significance of AI-assisted endoscopy in Japan where endoscopy is central to...This review comprehensively summarized the potential of artificial intelligence(AI)in the management of esophageal cancer.It highlighted the significance of AI-assisted endoscopy in Japan where endoscopy is central to both screening and diagnosis.For the clinical adaptation of AI,several challenges remain for its effective translation.The establishment of high-quality clinical databases,such as the National Clinical Database and Japan Endoscopy Database in Japan,which covers almost all cases of esophageal cancer,is essential for validating multimodal AI models.This requires rigorous external validation using diverse datasets,including those from different endoscope manufacturers and image qualities.Furthermore,endoscopists’skills significantly affect diagnostic accuracy,suggesting that AI should serve as a supportive tool rather than a replacement.Addressing these challenges,along with country-specific legal and ethical considerations,will facilitate the successful integration of multimodal AI into the management of esophageal cancer,particularly in endoscopic diagnosis,and contribute to improved patient outcomes.Although this review focused on Japan as a case study,the challenges and solutions described are broadly applicable to other high-incidence regions.展开更多
This study explores the thin-layer convective solar drying of Marrubium vulgare L.leaves under conditions typical of sun-rich semi-arid climates.Drying experiments were conducted at three inlet-air temperatures(40℃,5...This study explores the thin-layer convective solar drying of Marrubium vulgare L.leaves under conditions typical of sun-rich semi-arid climates.Drying experiments were conducted at three inlet-air temperatures(40℃,50℃,60℃)and two air velocities(1.5 and 2.5 m·s^(-1))using an indirect solar dryer with auxiliary temperature control.Moisture-ratio data were fitted with eight widely used thin-layer models and evaluated using correlation coefficient(r),root-mean-square error(RMSE),and Akaike information criterion(AIC).A complementary heattransfer analysis based on Reynolds and Prandtl numbers with appropriate Nusselt correlations was used to relate flow regime to drying performance,and an energy balance quantified the relative contributions of solar and auxiliary heat.The logarithmic model consistently achieved the lowest RMSE/AIC with r>0.99 across all conditions.Higher temperature and air velocity significantly reduced drying time during the decreasing-rate period,with no constantrate stage observed.On average,solar input supplied the large majority of the thermal demand,while the auxiliary heater compensated short irradiance drops to maintain setpoints.These findings provide a reproducible dataset and a modelling benchmark for M.vulgare leaves,and they support energy-aware design of hybrid solar dryers formedicinal plants in sun-rich regions.展开更多
The Financial Technology(FinTech)sector has witnessed rapid growth,resulting in increasingly complex and high-volume digital transactions.Although this expansion improves efficiency and accessibility,it also introduce...The Financial Technology(FinTech)sector has witnessed rapid growth,resulting in increasingly complex and high-volume digital transactions.Although this expansion improves efficiency and accessibility,it also introduces significant vulnerabilities,including fraud,money laundering,and market manipulation.Traditional anomaly detection techniques often fail to capture the relational and dynamic characteristics of financial data.Graph Neural Networks(GNNs),capable of modeling intricate interdependencies among entities,have emerged as a powerful framework for detecting subtle and sophisticated anomalies.However,the high-dimensionality and inherent noise of FinTech datasets demand robust feature selection strategies to improve model scalability,performance,and interpretability.This paper presents a comprehensive survey of GNN-based approaches for anomaly detection in FinTech,with an emphasis on the synergistic role of feature selection.We examine the theoretical foundations of GNNs,review state-of-the-art feature selection techniques,analyze their integration with GNNs,and categorize prevalent anomaly types in FinTech applications.In addition,we discuss practical implementation challenges,highlight representative case studies,and propose future research directions to advance the field of graph-based anomaly detection in financial systems.展开更多
Large-volume presses(LVPs)are widely utilized in diverse research fields—including high-pressure physics,chemistry,materials science,and Earth and planetary sciences—to investigate the physical and chemical properti...Large-volume presses(LVPs)are widely utilized in diverse research fields—including high-pressure physics,chemistry,materials science,and Earth and planetary sciences—to investigate the physical and chemical properties of materials under extreme high-pressure and hightemperature conditions.A prerequisite for achieving reproducible property measurements is the determination and control of pressure within experimental setups.However,the lack of precise pressure calibration in LVPs hinders the broader application of such devices in ultrahigh-pressure studies.This study employs a suite of standard phase transition-based pressure markers—comprising metallic conductors,semiconductors,and minerals—through both in situ and ex situ identification approaches,to establish pressure calibration curves ranging from 0.4 to>30 GPa for various types of LVP installed at the Center for High Pressure Science and Technology Advanced Research(HPSTAR),Beijing,including piston–cylinder,cubic,and multi-anvil presses.The results provide a unified and traceable pressure reference for highpressure experiments conducted at HPSTAR,while also offering technical guidance and calibration standards for other researchers utilizing similar LVP systems,thereby enabling more consistent comparison between different laboratories.This work facilitates the advancement of LVP research toward broader applications in higher-pressure regimes.展开更多
Background:Diabetic foot,a severe complication of diabetes,is characterized by chronic refractory wounds.Sanhuang Oil,a topical herbal formula,demonstrates significant therapeutic effects including antibacterial,anti-...Background:Diabetic foot,a severe complication of diabetes,is characterized by chronic refractory wounds.Sanhuang Oil,a topical herbal formula,demonstrates significant therapeutic effects including antibacterial,anti-inflammatory,and immunomodulatory activities.However,its active constituents and mechanisms of action against diabetic foot remain to be elucidated.Methods:In this study,the chemical constituents of Sanhuang Oil were identified using UPLC-QE-Orbitrap-MS.Subsequently,the mechanism by which Sanhuang Oil promotes diabetic foot ulcer healing was predicted by integrating network pharmacology and molecular docking.Additionally,diabetic mouse model was established in ICR mice using a combination of a high-fat diet(HFD)and streptozotocin(STZ)chemical induction.A full-thickness skin defect was created on the dorsum of the mice.Wound healing and the healing rate were observed following Sanhuang Oil intervention.The mechanism underlying Sanhuang Oil’s promotion of diabetic ulcer healing was further investigated using transcriptomics and histopathological examination(H&E staining).Results:A total of 97 active ingredients were identified from Sanhuang Oil.Network pharmacology analysis predicted 543 common targets,and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analysis identified 203 relevant pathways.Molecular docking further confirmed high binding affinity(binding energy≤−5.0 kcal/mol)between specific active components in Sanhuang Oil(e.g.,coptisine,phellodendrine,baicalein)and key targets associated with diabetic foot ulcers(e.g.,EGFR,AKT1,STAT3).In vivo experimental results demonstrated that the wound healing rate was significantly higher in Sanhuang Oil-treated groups compared to the model group(P<0.001).HE staining revealed that the high-dose Sanhuang Oil group exhibited more pronounced epithelial tissue coverage over the wound,reduced inflammatory cell infiltration,and increased collagen deposition and fibroblast proliferation.transcriptomic analysis identified Pdk4,Ttn,Csrp3,Actn2,Myoz2,Tnnc2,Myod1,Myog,Myot,and Myf6 as key regulatory proteins involved in promoting wound healing.Conclusion:Sanhuang Oil promotes wound healing in diabetic ulcer mice,potentially by mitigating inflammation and regulating key targets such as Pdk4 to enhance fibroblast function.These findings provide novel insights into the multi-target,multi-pathway mechanism of Sanhuang Oil for treating diabetic foot ulcers.展开更多
Sudden wildfires cause significant global ecological damage.While satellite imagery has advanced early fire detection and mitigation,image-based systems face limitations including high false alarm rates,visual obstruc...Sudden wildfires cause significant global ecological damage.While satellite imagery has advanced early fire detection and mitigation,image-based systems face limitations including high false alarm rates,visual obstructions,and substantial computational demands,especially in complex forest terrains.To address these challenges,this study proposes a novel forest fire detection model utilizing audio classification and machine learning.We developed an audio-based pipeline using real-world environmental sound recordings.Sounds were converted into Mel-spectrograms and classified via a Convolutional Neural Network(CNN),enabling the capture of distinctive fire acoustic signatures(e.g.,crackling,roaring)that are minimally impacted by visual or weather conditions.Internet of Things(IoT)sound sensors were crucial for generating complex environmental parameters to optimize feature extraction.The CNN model achieved high performance in stratified 5-fold cross-validation(92.4%±1.6 accuracy,91.2%±1.8 F1-score)and on test data(94.93%accuracy,93.04%F1-score),with 98.44%precision and 88.32%recall,demonstrating reliability across environmental conditions.These results indicate that the audio-based approach not only improves detection reliability but also markedly reduces computational overhead compared to traditional image-based methods.The findings suggest that acoustic sensing integrated with machine learning offers a powerful,low-cost,and efficient solution for real-time forest fire monitoring in complex,dynamic environments.展开更多
Drug development for Alzheimer’s disease is extremely challenging,as demonstrated by the repeated failures of amyloid-β-targeted therapeutics and the controversies surrounding the amyloid-βcascade hypothesis.More r...Drug development for Alzheimer’s disease is extremely challenging,as demonstrated by the repeated failures of amyloid-β-targeted therapeutics and the controversies surrounding the amyloid-βcascade hypothesis.More recently,advances in the development of Lecanemab,an anti-amyloid-βmonoclonal antibody,have shown positive results in reducing brain A burden and slowing cognitive decline in patients with early-stage Alzheimer’s disease in the Phase Ⅲ clinical trial(Clarity Alzheimer’s disease).Despite these promising results,side effects such as amyloid-related imaging abnormalities(ARIA)may limit its usage.ARIA can manifest as ARIA-E(cerebral edema or effusions)and ARIA-H(microhemorrhages or superficial siderosis)and is thought to be caused by increased vascular permeability due to inflammatory responses,leading to leakages of blood products and protein-rich fluid into brain parenchyma.Endothelial dysfunction is an early pathological feature of Alzheimer’s disease,and the blood-brain barrier becomes increasingly leaky as the disease progresses.In addition,APOE4,the strongest genetic risk factor for Alzheimer’s disease,is associated with higher vascular amyloid burden,increased ARIA incidence,and accelerated blood-brain barrier disruptions.These interconnected vascular abnormalities highlight the importance of vascular contributions to the pathophysiology of Alzheimer’s disease.Here,we will closely examine recent research evaluating the heterogeneity of brain endothelial cells in the microvasculature of different brain regions and their relationships with Alzheimer’s disease progression.展开更多
Reliable detection of traffic signs and lights(TSLs)at long range and under varying illumination is essen-tial for improving the perception and safety of autonomous driving systems(ADS).Traditional object detection mo...Reliable detection of traffic signs and lights(TSLs)at long range and under varying illumination is essen-tial for improving the perception and safety of autonomous driving systems(ADS).Traditional object detection models often exhibit significant performance degradation in real-world environments characterized by high dynamic range and complex lighting conditions.To overcome these limitations,this research presents FED-YOLOv10s,an improved and lightweight object detection framework based on You Only look Once v10(YOLOv10).The proposed model integrates a C2f-Faster block derived from FasterNet to reduce parameters and floating-point operations,an Efficient Multiscale Attention(EMA)mechanism to improve TSL-invariant feature extraction,and a deformable Convolution Networks v4(DCNv4)module to enhance multiscale spatial adaptability.Experimental findings demonstrate that the proposed architecture achieves an optimal balance between computational efficiency and detection accuracy,attaining an F1-score of 91.8%,and mAP@0.5 of 95.1%,while reducing parameters to 8.13 million.Comparative analyses across multiple traffic sign detection benchmarks demonstrate that FED-YOLOv10s outperforms state-of-the-art models in precision,recall,and mAP.These results highlight FED-YOLOv10s as a robust,efficient,and deployable solution for intelligent traffic perception in ADS.展开更多
Wireless Sensor Networks(WSNs)have become foundational in numerous real-world applications,ranging from environmental monitoring and industrial automation to healthcare systems and smart city development.As these netw...Wireless Sensor Networks(WSNs)have become foundational in numerous real-world applications,ranging from environmental monitoring and industrial automation to healthcare systems and smart city development.As these networks continue to grow in scale and complexity,the need for energy-efficient,scalable,and robust communication protocols becomes more critical than ever.Metaheuristic algorithms have shown significant promise in addressing these challenges,offering flexible and effective solutions for optimizing WSN performance.Among them,the Grey Wolf Optimizer(GWO)algorithm has attracted growing attention due to its simplicity,fast convergence,and strong global search capabilities.Accordingly,this survey provides an in-depth review of the applications of GWO and its variants for clustering,multi-hop routing,and hybrid cluster-based routing in WSNs.We categorize and analyze the existing GWO-based approaches across these key network optimization tasks,discussing the different problem formulations,decision variables,objective functions,and performance metrics used.In doing so,we examine standard GWO,multi-objective GWO,and hybrid GWO models that incorporate other computational intelligence techniques.Each method is evaluated based on how effectively it addresses the core constraints of WSNs,including energy consumption,communication overhead,and network lifetime.Finally,this survey outlines existing gaps in the literature and proposes potential future research directions aimed at enhancing the effectiveness and real-world applicability of GWO-based techniques for WSN clustering and routing.Our goal is to provide researchers and practitioners with a clear,structured understanding of the current state of GWO in WSNs and inspire further innovation in this evolving field.展开更多
Background:Tandem gene repeats naturally occur as important genomic features and determine many traits in living organisms,like human diseases and microbial productivities of target bioproducts.Methods:Here,we develop...Background:Tandem gene repeats naturally occur as important genomic features and determine many traits in living organisms,like human diseases and microbial productivities of target bioproducts.Methods:Here,we developed a bacterial type-II toxin-antitoxin-mediated method to manipulate genomic integration of tandem gene repeats in Saccharomyces cerevisiae and further visualised the evolutionary trajectories of gene repeats.We designed a tri-vector system to introduce toxin-antitoxin-driven gene amplification modules.Results:This system delivered multi-copy gene integration in the form of tandem gene repeats spontaneously and independently from toxin-antitoxin-mediated selection.Inducing the toxin(RelE)expressing via a copper(II)-inducible CUP1 promoter successfully drove the in-situ gene amplification of the antitoxin(RelB)module,resulting in~40 copies of a green fluorescence reporter gene per copy of genome.Copy-number changes,copy-number increase and copy-number decrease,and stable maintenance were visualised using the green fluorescence protein and blue chromoprotein AeBlue as reporters.Copy-number increases happened spontaneously and independent on a selection pressure.Increased copy number was quickly enriched through toxin-antitoxin-mediated selection.Conclusion:In summary,the bacterial toxin-antitoxin systems provide a flexible mechanism to manipulate gene copy number in eukaryotic cells and can be exploited for synthetic biology and metabolic engineering applications.展开更多
Adult-born oligodendrocytes are continuously produced in the brains of rodents.The functional role of these cells has been linked to the motor-related activities of healthy animals and is vital for acquiring new motor...Adult-born oligodendrocytes are continuously produced in the brains of rodents.The functional role of these cells has been linked to the motor-related activities of healthy animals and is vital for acquiring new motor skills.However,the relationship between these cells and the control of motor-related activities has not been investigated in pathological conditions.Therefore,the aim of this study is to investigate the role of oligodendrocytes in depression-related motor deficits and the effects of training.Psychomotor retardation is a key symptom of depression.Consistent with the impairments observed in rodent motor performance,the proliferation and activation of adult-born oligodendrocytes are altered in a corticosterone-induced stress paradigm.Therapeutic rotarod training can alleviate these symptoms by reversing the aforementioned changes.Notably,these alterations are particularly pronounced in layer I of the motor cortex.Thus,this study provides evidence of the potential functional involvement of adult-born oligodendrocytes in the motor impairments observed in the depressed animals.Additionally,it offers preliminary results for further investigation into layer I of the motor cortex in relation to these pathological conditions.展开更多
Fig.8e in our paper(Groves et al.,2018)was incorrectly ascribed to Caddey et al.(1995).It is actually taken from Figure 3 in Morelli et al.(2010).In turn,this was derived from Bell(2013).The authors apologise for this...Fig.8e in our paper(Groves et al.,2018)was incorrectly ascribed to Caddey et al.(1995).It is actually taken from Figure 3 in Morelli et al.(2010).In turn,this was derived from Bell(2013).The authors apologise for this unintentional error.展开更多
Background:Insufficient physical activity and prolonged sedentary behavior have emerged as major global public health challenges.Short bouts(≤10 min)of accumulated exercise(SBAE)throughout the day may be a promising ...Background:Insufficient physical activity and prolonged sedentary behavior have emerged as major global public health challenges.Short bouts(≤10 min)of accumulated exercise(SBAE)throughout the day may be a promising strategy to mitigate the adverse effects of prolonged sitting and promote physical activity,ultimately promoting overall health.However,previous ambiguity in defining this concept has resulted in a fragmented and inconsistent evidence base,impeding practical applications,the development of guidelines,and policymaking.The purpose of this study is to establish an operational definition of SBAE by synthesizing systematic reviews and research trials alongside an expert consensus.Additionally,it seeks to evaluate acute and long-term efficacy and feasibility,providing evidence-based recommendations for practice and future research directions.Methods:A literature search was performed across PubMed and Web of Science,followed by systematic screening and summarization of eligible studies based on predefined inclusion criteria.Inclusion criteria encompassed various modes/types of SBAE(bouts lasting≤10 min,performed multiple times daily with≥30 min intervals);both aerobic and resistance exercise were considered.Relevant systematic reviews and research trials were included.Methodological quality,risk of bias,and evidence certainty were assessed.Expert consensus was obtained through a survey to evaluate recommendations and agreement levels on findings.Results:After analyzing 27 systematic reviews,135 research studies,and an expert consensus involving 48 researchers from 11 countries,SBAE is defined as any exercise mode of activity,regardless of intensity,that is accumulated in either continuous or intermittent bouts lasting≤10 min per session(including multiple intermittent sets)that are performed multiple times(≥2 sessions/day)per day,with intervals of≥30 min between bouts or otherwise sufficient time for recovery.When used to interrupt prolonged periods of sedentary time,SBAE mitigates the acute adverse effects of sedentary behavior on more than 10 clinical biomarkers of endocrine,cardiovascular,and brain health/function among adults of diverse ages and conditions.Moreover,SBAE was superior for improving acute glycemic control compared to a single continuous exercise session.As a long-term intervention(average of 11 weeks),SBAE can improve over 20 health outcomes,including peak oxygen uptake,resting blood pressure,and metabolic health.Additionally,SBAE might be more effective than continuous exercise for improving longer-term glycemic control and body composition.Long-term completion rates for SBAE interventions are generally high(95%),with low dropout rates(12%)and high adherence rates even without supervision(85%),and its safety has been preliminarily validated.Conclusion:An operational definition of SBAE is provided along with its classification and acute and long-term efficacy.Practical exercise prescription recommendations and evidence-based strategies for various populations and contexts are provided.Future research should focus on generating high-quality evidence for SBAE in 5 key areas:quantification and monitoring,population-specific responses,optimization of exercise prescriptions,intervention efficacy,and practical implementation.Additionally,addressing policy,environmental,and promotional barriers is crucial for transitioning from expert consensus to public consensus,and for facilitating the application of this strategy in real-world environments.展开更多
Field training is the backbone of the teacher-preparation process.Its importance stems from the goals that colleges of education aim to achieve,which include bridging the gap between theory and practice and aligning w...Field training is the backbone of the teacher-preparation process.Its importance stems from the goals that colleges of education aim to achieve,which include bridging the gap between theory and practice and aligning with contemporary educational trends during teacher training.Currently,trainee students attendance in field training is recordedmanually through signatures on attendance sheets.However,thismethod is prone to impersonation,time wastage,and misplacement.Additionally,traditional methods of evaluating trainee students are often susceptible to human errors during the evaluation and scoring processes.Field training also lacks modern technology that the supervisor can use in case of his absence from school to monitor the trainee students’implementation of the required activities and tasks.These shortcomings do not meet the needs of the digital era that universities are currently experiencing.As a result,this paper presents a smart management system for field training based on Internet of Things(IoT)and mobile technology.It includes three subsystems:attendance,monitoring,and evaluation.The attendance subsystem uses an R307 fingerprint sensor to record trainee students’attendance.The Arduino Nano microcontroller transmits attendance data to the proposed Android application via an ESP-12F Wi-Fi module,which then forwards it to the Firebase database for storage.The monitoring subsystem utilizes Global Positioning System(GPS)technology to continually track trainee students’locations,ensuring they remain at the school during training.It also enables remote communication between trainee students and supervisors via audio,video,or text by integrating video call and chat technologies.The evaluation subsystem is based on three items:an online exam,attendance,and implementation of required activities and tasks.Experimental results have demonstrated the accuracy and efficiency of the proposed management system in recording attendance,as well as in monitoring and evaluating trainee students during field traiing.展开更多
文摘Remembrance activities can support the Culture of Care(CoC)in Laboratory Animal Science(LAS)not only by promoting a culture of respect,gratitude and thankfulness for animal life but also by helping the emotional processing and healing of lab animal researchers and animal facility staff.Even though remembrance activities are practiced in many parts of the world,we did not come across any reported cases in Sri Lanka before 2022.Therefore,here,we report on the various remembrance activities and practices observed within our local scientific community.
文摘A key pathological feature of Parkinson’s disease(PD)is that lysosomes are overwhelmed with cellular materials that need to be degraded and cleared.While the build-up of protein is characteristic of neurodegenerative diseases such as PD and Alzheimer’s disease(AD)and is thought to reflect lysosome dysfunction,lipid accumulation may also contribute to and be indicative of severe lysosomal dysfunction.Much is known about the detrimental effects of glucosylceramide accumulation in PD lysosomes.
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,under Grant No.(GPIP:1074-612-2024).
文摘The surge in smishing attacks underscores the urgent need for robust,real-time detection systems powered by advanced deep learning models.This paper introduces PhishNet,a novel ensemble learning framework that integrates transformer-based models(RoBERTa)and large language models(LLMs)(GPT-OSS 120B,LLaMA3.370B,and Qwen332B)to enhance smishing detection performance significantly.To mitigate class imbalance,we apply synthetic data augmentation using T5 and leverage various text preprocessing techniques.Our system employs a duallayer voting mechanism:weighted majority voting among LLMs and a final ensemble vote to classify messages as ham,spam,or smishing.Experimental results show an average accuracy improvement from 96%to 98.5%compared to the best standalone transformer,and from 93%to 98.5%when compared to LLMs across datasets.Furthermore,we present a real-time,user-friendly application to operationalize our detection model for practical use.PhishNet demonstrates superior scalability,usability,and detection accuracy,filling critical gaps in current smishing detection methodologies.
文摘Soil bacteria are integral to ecosystem functioning,significantly contributing to nutrients cycling and organic matter decomposition,and enhancing soil structure.This research considered the composition and dynamics of soil bacterial communities under different vegetation types(native Quercus brantii Lindl.and Amygdalus scoparia Spach,and non-native Pinus eldarica Medw.and Cupressus arizonica Greene.)in Zagros mountain area of Iran.This study involved a comparative analysis of soil culturable heterotrophic bacterial communities in spring(wet season)and summer(dry season)to clarify the effects of seasonal changes and vegetation on the dynamics of soil microorganisms.Soil samples were randomly collected under the canopies of various tree species and a control area,yielding a total of 48 composite samples analyzed for bacterial composition.Results indicated that 11 Gram-negative(e.g.,Citrobacter freundii,Enterobacter cloacae,Escherichia coli,Klebsiella oxytoca,Klebsiella pneumoniae,etc.)and 2 Gram-positive(Staphylococcus epidermidis and Staphylococcus aureus)bacteria were identified,showing significant seasonal variation.Specifically,53.85%of bacterial species were common to both seasons,with notable shifts in community composition observed between spring and summer,highlighting a higher abundance of Gram-negative species in spring.Bacterial community structure was significantly influenced by vegetation type,with various tree species shaping distinct microbial assemblages.Moreover,Pearson's correlations revealed that soil properties,particularly pH,phosphorus,and moisture content,were critical drivers of bacterial diversity and abundance.Our findings underscore the dynamic nature of soil bacterial communities in response to seasonal and vegetation changes,emphasizing the importance of repeated temporal sampling for accurate assessments of microbial diversity.Understanding these microbial dynamics is essential for improving soil management strategies and enhancing ecosystem resilience,particularly in arid and semi-arid areas where environmental fluctuations play a pivotal role.This research not only confirms our hypotheses but also enhances our understanding of soil biogeochemical processes and informs future vegetation management practices.
基金supported by the Canada Foundation for Innovation (Project#44220)the Natural Sciences and Engineering Research Council of Canada (RGPIN-2024-03986)+3 种基金the Michael Smith Foundation for Health Research BCthe financial support of Health Canada,through the Canada Brain Research Fund,an innovative partnership between the Government of Canada (through Health Canada),Brain Canada Foundationthe Azrieli Foundationsupported by a Canadian Institutes of Health Research (CIHR) Canada Graduate Scholarship–Master’s Award。
文摘Central nervous system(CNS) axons fail to regenerate following brain or spinal cord injury(SCI),which typically leads to permanent neurological deficits.Peripheral nervous system axons,howeve r,can regenerate following injury.Understanding the mechanisms that underlie this difference is key to developing treatments for CNS neurological diseases and injuries characterized by axonal damage.To initiate repair after peripheral nerve injury,dorsal root ganglion(DRG) neurons mobilize a pro-regenerative gene expression program,which facilitates axon outgrowth.
基金supported by the National Natural Science Foundation of China(Nos.22479092 and 22078190)。
文摘The pseudo-two-dimensional(P2D)model plays an important role in exploring physicochemical mechanisms,predicting the state of health,and improving the fast charge capability for Li-ion batteries(LIBs).However,the fast charge leads to the lithium concentration gradient in the solid and electrolyte phases and the non-uniform electrochemical reaction at the solid/electrolyte interface.In order to decouple charge transfer reactions in LIBs under dynamic conditions,understanding the spatio-temporal resolution of the P2D model is urgently required.Till now,the study of this aspect is still insufficient.This work studies the spatio-temporal resolution for dynamic/static electrochemical impedance spectroscopy(DEIS/SEIS)on multiple scales.In detail,DEIS and SEIS with spatio-temporal resolutions are used to decouple charge transfer reactions in LIBs based on the numerical solution of the P2D model in the frequency domain.The calculated results indicate that decoupling solid diffusion requires a high spatial resolution along the r-direction in particles,decoupling electrolyte diffusion and interfacial transfer reaction requires a high spatial resolution along the x-direction,and decoupling charge transfer reactions in LIBs at an extremely low state of charge(SOC)requires an extremely high temporal resolution along the t-direction.Finally,the optimal range of spatio-temporal resolutions for DEIS/SEIS is derived,and the method to decouple charge transfer reactions with spatio-temporal resolutions is developed.
基金Supported by Japan Society for the Promotion of Science,No.24K11935.
文摘This review comprehensively summarized the potential of artificial intelligence(AI)in the management of esophageal cancer.It highlighted the significance of AI-assisted endoscopy in Japan where endoscopy is central to both screening and diagnosis.For the clinical adaptation of AI,several challenges remain for its effective translation.The establishment of high-quality clinical databases,such as the National Clinical Database and Japan Endoscopy Database in Japan,which covers almost all cases of esophageal cancer,is essential for validating multimodal AI models.This requires rigorous external validation using diverse datasets,including those from different endoscope manufacturers and image qualities.Furthermore,endoscopists’skills significantly affect diagnostic accuracy,suggesting that AI should serve as a supportive tool rather than a replacement.Addressing these challenges,along with country-specific legal and ethical considerations,will facilitate the successful integration of multimodal AI into the management of esophageal cancer,particularly in endoscopic diagnosis,and contribute to improved patient outcomes.Although this review focused on Japan as a case study,the challenges and solutions described are broadly applicable to other high-incidence regions.
文摘This study explores the thin-layer convective solar drying of Marrubium vulgare L.leaves under conditions typical of sun-rich semi-arid climates.Drying experiments were conducted at three inlet-air temperatures(40℃,50℃,60℃)and two air velocities(1.5 and 2.5 m·s^(-1))using an indirect solar dryer with auxiliary temperature control.Moisture-ratio data were fitted with eight widely used thin-layer models and evaluated using correlation coefficient(r),root-mean-square error(RMSE),and Akaike information criterion(AIC).A complementary heattransfer analysis based on Reynolds and Prandtl numbers with appropriate Nusselt correlations was used to relate flow regime to drying performance,and an energy balance quantified the relative contributions of solar and auxiliary heat.The logarithmic model consistently achieved the lowest RMSE/AIC with r>0.99 across all conditions.Higher temperature and air velocity significantly reduced drying time during the decreasing-rate period,with no constantrate stage observed.On average,solar input supplied the large majority of the thermal demand,while the auxiliary heater compensated short irradiance drops to maintain setpoints.These findings provide a reproducible dataset and a modelling benchmark for M.vulgare leaves,and they support energy-aware design of hybrid solar dryers formedicinal plants in sun-rich regions.
基金supported by Ho Chi Minh City Open University,Vietnam under grant number E2024.02.1CD and Suan Sunandha Rajabhat University,Thailand.
文摘The Financial Technology(FinTech)sector has witnessed rapid growth,resulting in increasingly complex and high-volume digital transactions.Although this expansion improves efficiency and accessibility,it also introduces significant vulnerabilities,including fraud,money laundering,and market manipulation.Traditional anomaly detection techniques often fail to capture the relational and dynamic characteristics of financial data.Graph Neural Networks(GNNs),capable of modeling intricate interdependencies among entities,have emerged as a powerful framework for detecting subtle and sophisticated anomalies.However,the high-dimensionality and inherent noise of FinTech datasets demand robust feature selection strategies to improve model scalability,performance,and interpretability.This paper presents a comprehensive survey of GNN-based approaches for anomaly detection in FinTech,with an emphasis on the synergistic role of feature selection.We examine the theoretical foundations of GNNs,review state-of-the-art feature selection techniques,analyze their integration with GNNs,and categorize prevalent anomaly types in FinTech applications.In addition,we discuss practical implementation challenges,highlight representative case studies,and propose future research directions to advance the field of graph-based anomaly detection in financial systems.
基金supported by the National Science Foundation of China(Grant Nos.U1530402 and U1930401).
文摘Large-volume presses(LVPs)are widely utilized in diverse research fields—including high-pressure physics,chemistry,materials science,and Earth and planetary sciences—to investigate the physical and chemical properties of materials under extreme high-pressure and hightemperature conditions.A prerequisite for achieving reproducible property measurements is the determination and control of pressure within experimental setups.However,the lack of precise pressure calibration in LVPs hinders the broader application of such devices in ultrahigh-pressure studies.This study employs a suite of standard phase transition-based pressure markers—comprising metallic conductors,semiconductors,and minerals—through both in situ and ex situ identification approaches,to establish pressure calibration curves ranging from 0.4 to>30 GPa for various types of LVP installed at the Center for High Pressure Science and Technology Advanced Research(HPSTAR),Beijing,including piston–cylinder,cubic,and multi-anvil presses.The results provide a unified and traceable pressure reference for highpressure experiments conducted at HPSTAR,while also offering technical guidance and calibration standards for other researchers utilizing similar LVP systems,thereby enabling more consistent comparison between different laboratories.This work facilitates the advancement of LVP research toward broader applications in higher-pressure regimes.
基金supported by the Natural Science Foundation of Hubei Provincial Department of Education(D20232101)Shandong Second Medical University 2024 Affiliated Hospital(Teaching Hospital)Scientific Research Development Fund Project(2024FYQ026)+3 种基金the innovative Research Programme of Xiangyang No.1 People’s Hospital(XYY2023ZY01)Faculty Development Grants of Xiangyang No.1 People’s Hospital Affiliated to Hubei University of Medicine(XYY2023D05)Joint supported by Hubei Provincial Natural Science Foundation and Xiangyang of China(2025AFD091)Traditional Chinese Medicine Scientific Research Project of Hubei Provincial Administration of Traditional Chinese Medicine(ZY2025D019).
文摘Background:Diabetic foot,a severe complication of diabetes,is characterized by chronic refractory wounds.Sanhuang Oil,a topical herbal formula,demonstrates significant therapeutic effects including antibacterial,anti-inflammatory,and immunomodulatory activities.However,its active constituents and mechanisms of action against diabetic foot remain to be elucidated.Methods:In this study,the chemical constituents of Sanhuang Oil were identified using UPLC-QE-Orbitrap-MS.Subsequently,the mechanism by which Sanhuang Oil promotes diabetic foot ulcer healing was predicted by integrating network pharmacology and molecular docking.Additionally,diabetic mouse model was established in ICR mice using a combination of a high-fat diet(HFD)and streptozotocin(STZ)chemical induction.A full-thickness skin defect was created on the dorsum of the mice.Wound healing and the healing rate were observed following Sanhuang Oil intervention.The mechanism underlying Sanhuang Oil’s promotion of diabetic ulcer healing was further investigated using transcriptomics and histopathological examination(H&E staining).Results:A total of 97 active ingredients were identified from Sanhuang Oil.Network pharmacology analysis predicted 543 common targets,and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analysis identified 203 relevant pathways.Molecular docking further confirmed high binding affinity(binding energy≤−5.0 kcal/mol)between specific active components in Sanhuang Oil(e.g.,coptisine,phellodendrine,baicalein)and key targets associated with diabetic foot ulcers(e.g.,EGFR,AKT1,STAT3).In vivo experimental results demonstrated that the wound healing rate was significantly higher in Sanhuang Oil-treated groups compared to the model group(P<0.001).HE staining revealed that the high-dose Sanhuang Oil group exhibited more pronounced epithelial tissue coverage over the wound,reduced inflammatory cell infiltration,and increased collagen deposition and fibroblast proliferation.transcriptomic analysis identified Pdk4,Ttn,Csrp3,Actn2,Myoz2,Tnnc2,Myod1,Myog,Myot,and Myf6 as key regulatory proteins involved in promoting wound healing.Conclusion:Sanhuang Oil promotes wound healing in diabetic ulcer mice,potentially by mitigating inflammation and regulating key targets such as Pdk4 to enhance fibroblast function.These findings provide novel insights into the multi-target,multi-pathway mechanism of Sanhuang Oil for treating diabetic foot ulcers.
基金funded by the Directorate of Research and Community Service,Directorate General of Research and Development,Ministry of Higher Education,Science and Technologyin accordance with the Implementation Contract for the Operational Assistance Program for State Universities,Research Program Number:109/C3/DT.05.00/PL/2025.
文摘Sudden wildfires cause significant global ecological damage.While satellite imagery has advanced early fire detection and mitigation,image-based systems face limitations including high false alarm rates,visual obstructions,and substantial computational demands,especially in complex forest terrains.To address these challenges,this study proposes a novel forest fire detection model utilizing audio classification and machine learning.We developed an audio-based pipeline using real-world environmental sound recordings.Sounds were converted into Mel-spectrograms and classified via a Convolutional Neural Network(CNN),enabling the capture of distinctive fire acoustic signatures(e.g.,crackling,roaring)that are minimally impacted by visual or weather conditions.Internet of Things(IoT)sound sensors were crucial for generating complex environmental parameters to optimize feature extraction.The CNN model achieved high performance in stratified 5-fold cross-validation(92.4%±1.6 accuracy,91.2%±1.8 F1-score)and on test data(94.93%accuracy,93.04%F1-score),with 98.44%precision and 88.32%recall,demonstrating reliability across environmental conditions.These results indicate that the audio-based approach not only improves detection reliability but also markedly reduces computational overhead compared to traditional image-based methods.The findings suggest that acoustic sensing integrated with machine learning offers a powerful,low-cost,and efficient solution for real-time forest fire monitoring in complex,dynamic environments.
基金supported by the National Natural Science Foundation of China,Nos.82404892(to QY),82061160374(to ZZ)the Science and Technology Development Fund,Macao Special Administrative Region,China,Nos.0023/2020/AFJ,0035/2020/AGJ+2 种基金the University of Macao Research Grant,Nos.MYRG2022-00248-ICMS,MYRG-CRG2022-00010-ICMS(to MPMH)the Natural Science Foundation of Guangdong Province,No.2024A1515012818(to ZZ)the Fundamental Research Funds for the Central Universities,No.21623114(to ZZ).
文摘Drug development for Alzheimer’s disease is extremely challenging,as demonstrated by the repeated failures of amyloid-β-targeted therapeutics and the controversies surrounding the amyloid-βcascade hypothesis.More recently,advances in the development of Lecanemab,an anti-amyloid-βmonoclonal antibody,have shown positive results in reducing brain A burden and slowing cognitive decline in patients with early-stage Alzheimer’s disease in the Phase Ⅲ clinical trial(Clarity Alzheimer’s disease).Despite these promising results,side effects such as amyloid-related imaging abnormalities(ARIA)may limit its usage.ARIA can manifest as ARIA-E(cerebral edema or effusions)and ARIA-H(microhemorrhages or superficial siderosis)and is thought to be caused by increased vascular permeability due to inflammatory responses,leading to leakages of blood products and protein-rich fluid into brain parenchyma.Endothelial dysfunction is an early pathological feature of Alzheimer’s disease,and the blood-brain barrier becomes increasingly leaky as the disease progresses.In addition,APOE4,the strongest genetic risk factor for Alzheimer’s disease,is associated with higher vascular amyloid burden,increased ARIA incidence,and accelerated blood-brain barrier disruptions.These interconnected vascular abnormalities highlight the importance of vascular contributions to the pathophysiology of Alzheimer’s disease.Here,we will closely examine recent research evaluating the heterogeneity of brain endothelial cells in the microvasculature of different brain regions and their relationships with Alzheimer’s disease progression.
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,Saudi Arabia under Grant No.IPP:172-830-2025.
文摘Reliable detection of traffic signs and lights(TSLs)at long range and under varying illumination is essen-tial for improving the perception and safety of autonomous driving systems(ADS).Traditional object detection models often exhibit significant performance degradation in real-world environments characterized by high dynamic range and complex lighting conditions.To overcome these limitations,this research presents FED-YOLOv10s,an improved and lightweight object detection framework based on You Only look Once v10(YOLOv10).The proposed model integrates a C2f-Faster block derived from FasterNet to reduce parameters and floating-point operations,an Efficient Multiscale Attention(EMA)mechanism to improve TSL-invariant feature extraction,and a deformable Convolution Networks v4(DCNv4)module to enhance multiscale spatial adaptability.Experimental findings demonstrate that the proposed architecture achieves an optimal balance between computational efficiency and detection accuracy,attaining an F1-score of 91.8%,and mAP@0.5 of 95.1%,while reducing parameters to 8.13 million.Comparative analyses across multiple traffic sign detection benchmarks demonstrate that FED-YOLOv10s outperforms state-of-the-art models in precision,recall,and mAP.These results highlight FED-YOLOv10s as a robust,efficient,and deployable solution for intelligent traffic perception in ADS.
文摘Wireless Sensor Networks(WSNs)have become foundational in numerous real-world applications,ranging from environmental monitoring and industrial automation to healthcare systems and smart city development.As these networks continue to grow in scale and complexity,the need for energy-efficient,scalable,and robust communication protocols becomes more critical than ever.Metaheuristic algorithms have shown significant promise in addressing these challenges,offering flexible and effective solutions for optimizing WSN performance.Among them,the Grey Wolf Optimizer(GWO)algorithm has attracted growing attention due to its simplicity,fast convergence,and strong global search capabilities.Accordingly,this survey provides an in-depth review of the applications of GWO and its variants for clustering,multi-hop routing,and hybrid cluster-based routing in WSNs.We categorize and analyze the existing GWO-based approaches across these key network optimization tasks,discussing the different problem formulations,decision variables,objective functions,and performance metrics used.In doing so,we examine standard GWO,multi-objective GWO,and hybrid GWO models that incorporate other computational intelligence techniques.Each method is evaluated based on how effectively it addresses the core constraints of WSNs,including energy consumption,communication overhead,and network lifetime.Finally,this survey outlines existing gaps in the literature and proposes potential future research directions aimed at enhancing the effectiveness and real-world applicability of GWO-based techniques for WSN clustering and routing.Our goal is to provide researchers and practitioners with a clear,structured understanding of the current state of GWO in WSNs and inspire further innovation in this evolving field.
基金supported partially by the Australian Government through the Australian Research Council Centres of Excellence funding scheme(project CE200100029)。
文摘Background:Tandem gene repeats naturally occur as important genomic features and determine many traits in living organisms,like human diseases and microbial productivities of target bioproducts.Methods:Here,we developed a bacterial type-II toxin-antitoxin-mediated method to manipulate genomic integration of tandem gene repeats in Saccharomyces cerevisiae and further visualised the evolutionary trajectories of gene repeats.We designed a tri-vector system to introduce toxin-antitoxin-driven gene amplification modules.Results:This system delivered multi-copy gene integration in the form of tandem gene repeats spontaneously and independently from toxin-antitoxin-mediated selection.Inducing the toxin(RelE)expressing via a copper(II)-inducible CUP1 promoter successfully drove the in-situ gene amplification of the antitoxin(RelB)module,resulting in~40 copies of a green fluorescence reporter gene per copy of genome.Copy-number changes,copy-number increase and copy-number decrease,and stable maintenance were visualised using the green fluorescence protein and blue chromoprotein AeBlue as reporters.Copy-number increases happened spontaneously and independent on a selection pressure.Increased copy number was quickly enriched through toxin-antitoxin-mediated selection.Conclusion:In summary,the bacterial toxin-antitoxin systems provide a flexible mechanism to manipulate gene copy number in eukaryotic cells and can be exploited for synthetic biology and metabolic engineering applications.
基金supported by Hong Kong Health and Medical Research Fund,No.02133206(to KFS).
文摘Adult-born oligodendrocytes are continuously produced in the brains of rodents.The functional role of these cells has been linked to the motor-related activities of healthy animals and is vital for acquiring new motor skills.However,the relationship between these cells and the control of motor-related activities has not been investigated in pathological conditions.Therefore,the aim of this study is to investigate the role of oligodendrocytes in depression-related motor deficits and the effects of training.Psychomotor retardation is a key symptom of depression.Consistent with the impairments observed in rodent motor performance,the proliferation and activation of adult-born oligodendrocytes are altered in a corticosterone-induced stress paradigm.Therapeutic rotarod training can alleviate these symptoms by reversing the aforementioned changes.Notably,these alterations are particularly pronounced in layer I of the motor cortex.Thus,this study provides evidence of the potential functional involvement of adult-born oligodendrocytes in the motor impairments observed in the depressed animals.Additionally,it offers preliminary results for further investigation into layer I of the motor cortex in relation to these pathological conditions.
文摘Fig.8e in our paper(Groves et al.,2018)was incorrectly ascribed to Caddey et al.(1995).It is actually taken from Figure 3 in Morelli et al.(2010).In turn,this was derived from Bell(2013).The authors apologise for this unintentional error.
文摘Background:Insufficient physical activity and prolonged sedentary behavior have emerged as major global public health challenges.Short bouts(≤10 min)of accumulated exercise(SBAE)throughout the day may be a promising strategy to mitigate the adverse effects of prolonged sitting and promote physical activity,ultimately promoting overall health.However,previous ambiguity in defining this concept has resulted in a fragmented and inconsistent evidence base,impeding practical applications,the development of guidelines,and policymaking.The purpose of this study is to establish an operational definition of SBAE by synthesizing systematic reviews and research trials alongside an expert consensus.Additionally,it seeks to evaluate acute and long-term efficacy and feasibility,providing evidence-based recommendations for practice and future research directions.Methods:A literature search was performed across PubMed and Web of Science,followed by systematic screening and summarization of eligible studies based on predefined inclusion criteria.Inclusion criteria encompassed various modes/types of SBAE(bouts lasting≤10 min,performed multiple times daily with≥30 min intervals);both aerobic and resistance exercise were considered.Relevant systematic reviews and research trials were included.Methodological quality,risk of bias,and evidence certainty were assessed.Expert consensus was obtained through a survey to evaluate recommendations and agreement levels on findings.Results:After analyzing 27 systematic reviews,135 research studies,and an expert consensus involving 48 researchers from 11 countries,SBAE is defined as any exercise mode of activity,regardless of intensity,that is accumulated in either continuous or intermittent bouts lasting≤10 min per session(including multiple intermittent sets)that are performed multiple times(≥2 sessions/day)per day,with intervals of≥30 min between bouts or otherwise sufficient time for recovery.When used to interrupt prolonged periods of sedentary time,SBAE mitigates the acute adverse effects of sedentary behavior on more than 10 clinical biomarkers of endocrine,cardiovascular,and brain health/function among adults of diverse ages and conditions.Moreover,SBAE was superior for improving acute glycemic control compared to a single continuous exercise session.As a long-term intervention(average of 11 weeks),SBAE can improve over 20 health outcomes,including peak oxygen uptake,resting blood pressure,and metabolic health.Additionally,SBAE might be more effective than continuous exercise for improving longer-term glycemic control and body composition.Long-term completion rates for SBAE interventions are generally high(95%),with low dropout rates(12%)and high adherence rates even without supervision(85%),and its safety has been preliminarily validated.Conclusion:An operational definition of SBAE is provided along with its classification and acute and long-term efficacy.Practical exercise prescription recommendations and evidence-based strategies for various populations and contexts are provided.Future research should focus on generating high-quality evidence for SBAE in 5 key areas:quantification and monitoring,population-specific responses,optimization of exercise prescriptions,intervention efficacy,and practical implementation.Additionally,addressing policy,environmental,and promotional barriers is crucial for transitioning from expert consensus to public consensus,and for facilitating the application of this strategy in real-world environments.
文摘Field training is the backbone of the teacher-preparation process.Its importance stems from the goals that colleges of education aim to achieve,which include bridging the gap between theory and practice and aligning with contemporary educational trends during teacher training.Currently,trainee students attendance in field training is recordedmanually through signatures on attendance sheets.However,thismethod is prone to impersonation,time wastage,and misplacement.Additionally,traditional methods of evaluating trainee students are often susceptible to human errors during the evaluation and scoring processes.Field training also lacks modern technology that the supervisor can use in case of his absence from school to monitor the trainee students’implementation of the required activities and tasks.These shortcomings do not meet the needs of the digital era that universities are currently experiencing.As a result,this paper presents a smart management system for field training based on Internet of Things(IoT)and mobile technology.It includes three subsystems:attendance,monitoring,and evaluation.The attendance subsystem uses an R307 fingerprint sensor to record trainee students’attendance.The Arduino Nano microcontroller transmits attendance data to the proposed Android application via an ESP-12F Wi-Fi module,which then forwards it to the Firebase database for storage.The monitoring subsystem utilizes Global Positioning System(GPS)technology to continually track trainee students’locations,ensuring they remain at the school during training.It also enables remote communication between trainee students and supervisors via audio,video,or text by integrating video call and chat technologies.The evaluation subsystem is based on three items:an online exam,attendance,and implementation of required activities and tasks.Experimental results have demonstrated the accuracy and efficiency of the proposed management system in recording attendance,as well as in monitoring and evaluating trainee students during field traiing.
