BACKGROUND:Maxillofacial trauma represents a significant challenge in emergency medicine,requiring both diagnostic accuracy and prompt intervention while balancing immediate life-saving interventions with preservation...BACKGROUND:Maxillofacial trauma represents a significant challenge in emergency medicine,requiring both diagnostic accuracy and prompt intervention while balancing immediate life-saving interventions with preservation of function and aesthetics.The complex anatomy of this region,with its proximity to critical structures,demands a thorough understanding of assessment and management principles.This narrative review aims to provide evidence-based guidelines for emergency physicians managing maxillofacial trauma,with particular emphasis on early recognition of critical injuries,airway management strategies,and special population considerations.METHODS:A narrative review was conducted via a comprehensive literature search of the PubMed and Scopus databases,which focused on maxillofacial trauma management in emergency settings.Articles were selected based on relevance to clinical practice,methodological quality,and current management guidelines.The review synthesized evidence from multiple study types,including original research,systematic reviews,and clinical practice guidelines,to provide practical guidance for emergency physicians.RESULTS:Initial assessment following Advanced Trauma Life Support(ATLS)principles is crucial,with airway management being a primary concern due to the risk of dynamic obstruction.Critical time-sensitive emergencies include orbital compartment syndrome,trapdoor fractures(in pediatric patients),and facial nerve injuries.Computed tomography(CT)imaging remains the gold standard for diagnosis.Special considerations are required for pediatric patients,who present unique anatomical challenges and injury patterns,and for elderly patients,who often have complex medical comorbidities and increased complication risks.Management strategies range from conservative treatment to urgent surgical intervention,with decisions based on the injury pattern and associated complications.CONCLUSION:Emergency physicians must maintain a structured yet fl exible approach to maxillofacial trauma,focusing on early recognition of critical injuries,appropriate airway management,and timely specialist consultation.Understanding injury patterns and their potential complications allows for eff ective risk stratifi cation and treatment planning,ultimately improving patient outcomes.展开更多
As large,room-scale environments become increasingly common,their spatial complexity increases due to variable,unstructured elements.Consequently,demand for room-scale service robots is surging,yet most technologies r...As large,room-scale environments become increasingly common,their spatial complexity increases due to variable,unstructured elements.Consequently,demand for room-scale service robots is surging,yet most technologies remain corridor-centric,and autonomous navigation in expansive rooms becomes unstable even around static obstacles.Existing approaches face several structural limitations.These include the labor-intensive requirement for large-scale object annotation and continual retraining,as well as the vulnerability of vanishing point or linebased methods when geometric cues are insufficient.In addition,the high cost of LiDAR and 3D perception errors caused by limited wall cues and dense interior clutter further limit their effectiveness.To address these challenges,we propose a zero-shot vision-based algorithm for robust 3D map reconstruction in geometry-deficient room-scale environments.The algorithm operates in three layers:Layer 1 performs dimension-wise boundary detection;Layer 2 estimates vanishing points,refines the precise perspective space,and extracts a floor mask;and Layer 3 conducts 3D spatial mapping and obstacle recognition.The proposed method was experimentally validated across various geometric-deficient room-scale environments,including lobbies,seminar rooms,conference rooms,cafeterias,and museums—demonstrating its ability to reliably reconstruct 3D maps and accurately recognize obstacles.Experimental results show that the proposed algorithm achieved an F1 score of 0.959 in precision perspective space detection and 0.965 in floor mask extraction.For obstacle recognition and classification,it obtained F1 scores of 0.980 in obstacle absent areas,0.913 in solid obstacle environments,and 0.939 in skeleton-type sparse obstacle environments,confirming its high precision and reliability in geometric-deficient room-scale environments.展开更多
Classifying job offers into occupational categories is a fundamental task in human resource information systems,as it improves and streamlines indexing,search,and matching between openings and job seekers.Comprehensiv...Classifying job offers into occupational categories is a fundamental task in human resource information systems,as it improves and streamlines indexing,search,and matching between openings and job seekers.Comprehensive occupational databases such as O∗NET or ESCO provide detailed taxonomies of interrelated positions that can be leveraged to align the textual content of postings with occupational categories,thereby facilitating standardization,cross-system interoperability,and access to metadata for each occupation(e.g.,tasks,knowledge,skills,and abilities).In this work,we explore the effectiveness of fine-tuning existing language models(LMs)to classify job offers with occupational descriptors from O∗NET.This enables a more precise assessment of candidate suitability by identifying the specific knowledge and skills required for each position,and helps automate recruitment processes by mitigating human bias and subjectivity in candidate selection.We evaluate three representative BERT-like models:BERT,RoBERTa,and DeBERTa.BERT serves as the baseline encoder-only architecture;RoBERTa incorporates advances in pretraining objectives and data scale;and DeBERTa introduces architectural improvements through disentangled attention mechanisms.The best performance was achieved with the DeBERTa model,although the other models also produced strong results,and no statistically significant differences were observed acrossmodels.We also find that these models typically reach optimal performance after only a few training epochs,and that training with smaller,balanced datasets is effective.Consequently,comparable results can be obtained with models that require fewer computational resources and less training time,facilitating deployment and practical use.展开更多
The rapidly evolving cybersecurity threat landscape exposes a critical flaw in traditional educational programs where static curricula cannot adapt swiftly to novel attack vectors.This creates a significant gap betwee...The rapidly evolving cybersecurity threat landscape exposes a critical flaw in traditional educational programs where static curricula cannot adapt swiftly to novel attack vectors.This creates a significant gap between theoretical knowledge and the practical defensive capabilities needed in the field.To address this,we propose TeachSecure-CTI,a novel framework for adaptive cybersecurity curriculumgeneration that integrates real-time Cyber Threat Intelligence(CTI)with AI-driven personalization.Our framework employs a layered architecture featuring a CTI ingestion and clusteringmodule,natural language processing for semantic concept extraction,and a reinforcement learning agent for adaptive content sequencing.Bydynamically aligning learningmaterialswithboththe evolving threat environment and individual learner profiles,TeachSecure-CTI ensures content remains current,relevant,and tailored.A 12-week study with 150 students across three institutions demonstrated that the framework improves learning gains by 34%,significantly exceeding the 12%–21%reported in recent literature.The system achieved 84.8%personalization accuracy,85.9%recognition accuracy for MITRE ATT&CK tactics,and a 31%faster competency development rate compared to static curricula.These findings have implications beyond academia,extending to workforce development,cyber range training,and certification programs.By bridging the gap between dynamic threats and static educational materials,TeachSecure-CTI offers an empirically validated,scalable solution for cultivating cybersecurity professionals capable of responding to modern threats.展开更多
Homret Ghannam alkali feldspar granite(HGAFG)in the central Eastern Desert(CED)of Egypt represents a distinctive example of late Neoproterozoic magmatism in the Arabian-Nubian Shield(ANS).This study integrates field o...Homret Ghannam alkali feldspar granite(HGAFG)in the central Eastern Desert(CED)of Egypt represents a distinctive example of late Neoproterozoic magmatism in the Arabian-Nubian Shield(ANS).This study integrates field observations,petrography,mineral chemistry(EMPA),and whole-rock geochemistry to investigate its petrogenesis,geodynamic evolution,and rare-metal potential.HGAFG comprises two cogenetic varieties,alkali feldspar granite and riebeckite-bearing granite,hosting rare-metal minerals such as zircon,fluorite,columbite and apatite.HGAFG exhibits diagnostic A-type geochemical characteristics,including high SiO₂contents(73.81-77.86 wt%),metaluminous to mildly peralkaline composition(ASI:0.92-1.03),enrichment in HFSE(Zr≈791.80 ppm,Nb≈68.12 ppm,Y≈90.81 ppm)andΣREE(103.40-475.57 ppm),and pronounced negative Eu anomalies(Eu/Eu^(*)=0.07-0.20).Zircon saturation thermometry yields high crystallization temperatures(TZr≈908.87℃)and low emplacement pressures(1.46 kbar)under reducing conditions(ƒO_(2)≈−11.5).The mineralogical and geochemical results reveal that HGAFG originated from a hybrid,fluorine-rich magma generated by anatexis of lower crust,followed by extensive fractional crystallization,during late post-collisional extension associated with lithospheric delamination.The reduced nature and fluorine enrichment of HGAFG magma promoted the mineralization of Nb-Ta-REE phases,highlighting its significance as a fertile,high-temperature product of the terminal magmatic stage in ANS evolution.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
Drought,as the most catastrophic abiotic stress,poses a significant threat to the growth and development of plants.Among the mechanisms employed by plants to cope with drought-induced stress,abscisic acid(ABA)which is...Drought,as the most catastrophic abiotic stress,poses a significant threat to the growth and development of plants.Among the mechanisms employed by plants to cope with drought-induced stress,abscisic acid(ABA)which is the sesquiterpene hormone,occupies a pivotal role.A hypothesis has emerged that the exogenous application of ABA can positively influence the terpenoid content of Lavandula angustifolia cv Hidcote essential oil(EO),thereby conferring enhanced resilience to drought stress.A randomized complete block design experiment was conducted with three replicationsandfour irrigation regimes,including I4[30%-40%of field capacity(FC)],I3(50%-60%FC),I2(70%-80%FC),andI1(90%-100%FC)as control.Application of ABAspraying included three concentrations,A3(30μmol·L^(-1)ABA),A2(15μmol·L^(-1)ABA),and A1 as control(distilled water).Results revealed that drought significantly affected all studied traits except for relative water content(RWC)and shoot dry mass.The ABA impact application on the observed traits was found to be dependent upon the level of drought to which the plants were exposed.Specifically,the highest levels of flavonoid content,total antioxidant activity,peroxidase(POX)activity,and EO percentage were observed under I4A2 conditions.Conversely,the highest levels of superoxide dismutase(SOD)and catalase(CAT)activity,and proline were recorded under I4A3 conditions,while the highest EO yield was obtained under I3A2 conditions.Analysis of the EO revealed that there were common indicative compounds across the varying levels of droughtandABAapplication,including linalool,camphor,borneol,bornyl formate,andcaryophyllene oxide.Theproduction pattern ofmonoterpene and sesquiterpene compounds demonstrated a distinct trend,with the highest concentration of monoterpene hydrocarbon compounds(average of 12.92%)being observed in the I2A3 treatment group,andthe highest concentration of oxygenatedmonoterpenecompounds(average of 64.76%)being recorded in the I1A1 group.Conversely,the most significant levels of sesquiterpene hydrocarboncompounds(14.98%)andoxygenated sesquiterpene compounds(10.46%)were observed in the I4A3 and I4A1 groups,respectively,showing the efficacy of monoterpenes and sesquiterpenes from the action of ABA under drought conditions.The observed results indicated that the concentration of oxygenated monoterpene compounds decreases with an increase in drought level.Conversely,the application of ABA at any given drought level appears to resulted in increased concentrations of oxygenated monoterpene compounds in the same conditions.It may be concluded that plants under high-stress drought conditions allocate more terpene precursors to the production of sesquiterpene hydrocarbon compounds,aided by ABA with the same properties.展开更多
Sport-related concussion(SRC)and its potential neurological sequela represent an emerging global health concern,requiring improved recovery management and strategies for return-to-play(RTP)to enhance brain health in a...Sport-related concussion(SRC)and its potential neurological sequela represent an emerging global health concern,requiring improved recovery management and strategies for return-to-play(RTP)to enhance brain health in athletes.Given the dynamic and multifaceted nature of SRC recovery,the purpose of this review is to synthesize existing literature on post-SRC outcomes in adult athletes,and to outline the temporal trajectories of key recovery indicators(symptoms,cognitive function,blood biomarkers)across distinct recovery phases until resolution.In the acute phase of SRC(first 48 h),symptom scores and brain damage markers peaked immediately,while cognitive impairments and neuroinflammation emerged with a slight delay.Following the initial rise,brain damage marker concentrations rapidly dropped below baseline levels at approximately 48 h following SRC injury.During the early recovery phase,neuroinflammation and most cognitive alterations resolved after 3–5 days,though symptom burden and attention deficits persisted for up to 7 days.Despite prolonged alterations reported in some individuals,recovery markers typically returned to pre-injury levels in the transition phase(≤2 weeks),though mild attention deficits were detected up to 3 weeks,and TNF-α concentrations remained elevated throughout late recovery(>2 weeks).These results reveal distinct temporal discrepancies across recovery markers and emphasize that physiological disturbances can outlast symptom resolution,underscoring the need for both multimodal assessments and appropriately timed evaluations to accurately track recovery progression.Incorporating structured follow-ups at key time points,particularly beyond symptom resolution,may improve RTP decision-making and reduce the risk of premature return and long-term neurological consequences.展开更多
Replicating the chaotic characteristics inherent in nonlinear dynamical systems via machine learning(ML)is a key challenge in this rapidly advancing interdisciplinary field.In this work,we explore the potential of var...Replicating the chaotic characteristics inherent in nonlinear dynamical systems via machine learning(ML)is a key challenge in this rapidly advancing interdisciplinary field.In this work,we explore the potential of variational quantum circuits(VQC)for learning the stochastic properties of classical nonlinear dynamical systems.Specifically,we focus on the one-and two-dimensional logistic maps,which,while simple,remain under-explored in the context of learning dynamical characteristics.Our findings reveal that,even for such simple dynamical systems,accurately replicating longterm characteristics is hindered by a pronounced sensitivity to overfitting.While increasing the parameter complexity of the ML model typically enhances short-term prediction accuracy,it also leads to a degradation in the model’s ability to replicate long-term characteristics,primarily due to the detrimental effects of overfitting on generalization power.By comparing the VQC with two widely recognized classical ML techniques,which are long short-term memory(LSTM)networks for timeseries processing and reservoir computing,we demonstrate that VQC outperforms these methods in terms of replicating long-term characteristics.Our results suggest that for the ML of dynamics,it is demanded to develop more compact and efficient models(such as VQC)rather than more complicated and large-scale ones.展开更多
Neuronanomedicine is a promising interdisciplinary field combining two critical fields,neuroscience and nanotechnology.This study focuses on the engineering of magnetized nanoparticles(MNPs)in diagnosing and treating ...Neuronanomedicine is a promising interdisciplinary field combining two critical fields,neuroscience and nanotechnology.This study focuses on the engineering of magnetized nanoparticles(MNPs)in diagnosing and treating neurological disorders and brain cancer.Additionally,this mechanism enhances the effectiveness of magnetic-guided drug delivery.The alternating magnetic field is applied to control the directions of the MNPs to target the tumor cells.This study approaches the radiotherapy techniques of magnetic hyperthermia therapy(MHT),wherein the thermal radiative heat transfer effect is applied to achieve homogenous heating to destroy cancer cells.MNPs are injected through the cerebrospinal fluid(CSF)transport in the glymphatic system.The elastic properties of the cerebral arteries cause peristaltic propulsion for the resulting nanofluid.Therefore,the effective Maxwell model for the nanofluid thermal conductivity is selected.The nanofluid governing equations are solved using the perturbation technique under small wavelength number and long wavelength approximation with small Reynolds number.Additionally,the effects of thermal slip and elastic properties boundary conditions are incorporated.The graphical results for the streamwise velocity,pressure,and temperature distributions are plotted using MATLAB package considering the different effects of the magnetic flux intensity,thermal radiation parameter,thermal slipping at boundaries,elastic wall properties,and nanoparticle concentration.The results demonstrate the strong impact of the magnetic field and radiation heating in terms of enhancing the nanofluid CSF flow behavior and destroying cancer.展开更多
BACKGROUND Despite societal guidelines recommending targeted screening for Barrett’s esophagus(BE)and esophageal adenocarcinoma(EAC)in individuals with gastroesophageal reflux symptoms(GERS),screening adherence is su...BACKGROUND Despite societal guidelines recommending targeted screening for Barrett’s esophagus(BE)and esophageal adenocarcinoma(EAC)in individuals with gastroesophageal reflux symptoms(GERS),screening adherence is suboptimal.Current screening approaches fail to identify individuals not seeking medical consultation for GERS or whose GERS are managed with‘over-the-counter’(OTC)acid suppressant therapies.AIM To assess patients’self-management and help-seeking behavior for GERS.METHODS This cross-sectional study collected data from the Dutch general population aged 18-75 years between January and April 2023 using a web-based survey.The survey included questions regarding self-management(e.g.,use of acid suppressant therapy with or without prescription)and help-seeking behavior(e.g.,consulting a primary care provider)for GERS.Simple random sampling was performed to select individuals within the target age group.In total,18156 randomly selected individuals were invited to participate.The study protocol was registered in ClinicalTrials.gov(identifier:NCT05689918).RESULTS Of the 18156 invited individuals,3214 participants(17.7%)completed the survey,of which 1572 participants(48.9%)reported GERS.Of these,904 participants(57.5%)had never consulted a primary care provider for these symptoms,of which 331 participants(36.6%)reported taking OTC acid suppressant therapy in the past six months and 100 participants(11.1%)fulfilled the screening criteria for BE and EAC according to the European Society of Gastrointestinal Endoscopy Guideline.CONCLUSION The population fulfilling the screening criteria for BE and EAC is incompletely identified,suggesting potential underutilization of medical consultation.Raising public awareness of GERS as a risk factor for EAC is needed.展开更多
Colorectal cancer is the third most diagnosed cancer worldwide,and immune checkpoint inhibitors have shown promising therapeutic outcomes in selected patient groups.This study performed a comprehensive analysis of mul...Colorectal cancer is the third most diagnosed cancer worldwide,and immune checkpoint inhibitors have shown promising therapeutic outcomes in selected patient groups.This study performed a comprehensive analysis of multi-omics data from The Cancer Genome Atlas colorectal adenocarcinoma cohort(TCGA-COADREAD),accessed through cBioPortal,to develop machine learning models for predicting progression-free survival(PFS)following immunotherapy.The dataset included clinical variables,genomic alterations in Kirsten Rat Sarcoma Viral Oncogene Homolog(KRAS),B-Raf Proto-Oncogene(BRAF),and Neuroblastoma RAS Viral Oncogene Homolog(NRAS),microsatellite instability(MSI)status,tumor mutation burden(TMB),and expression of immune checkpoint genes.Kaplan–Meier analysis showed that KRAS mutations were significantly associated with reduced PFS,while BRAF and NRAS mutations had no significant impact.MSI-high tumors exhibited elevated TMB and increased immune checkpoint expression,reflecting their immunologically active phenotype.We developed both survival and classification models,with the Extra Trees classifier achieving the best performance(accuracy=0.86,precision=0.67,recall=0.70,F1-score=0.68,AUC=0.84).These findings highlight the potential of combining genomic and immune biomarkers with machine learning to improve patient stratification and guide personalized immunotherapy decisions.An interactive web application was also developed to enable clinicians to input patient-specific molecular and clinical data and visualize individualized PFS predictions,supporting timely,data-driven treatment planning.展开更多
Background:Bullying during adolescence is shaped by numerous psychosocial factors such as family dynamics,attachment,and peer relationships.This study aims to examine parental acceptance-rejection,attachment styles,an...Background:Bullying during adolescence is shaped by numerous psychosocial factors such as family dynamics,attachment,and peer relationships.This study aims to examine parental acceptance-rejection,attachment styles,and social exclusion factors as key psychosocial variables predicting bullying behavior in adolescents.Methods:In a cross-sectional study conducted with 349 high school students in Hakkari,Türkiye.Data were collected using the Olweus Bullying Scale,the Parental Acceptance-Rejection Scale,the Social Exclusion Scale,and the Three-Dimensional Attachment Styles Scale.Independent samples t-tests,one-way ANOVAs,Pearson correlations,and hierarchical regression analyses were performed.Results:Research findings reveal that peer bullying varies significantly according to gender,class level,parents’educational level,and socio-economic status.Furthermore,our findings indicate that social exclusion(β=0.506,p<0.01)and avoidant attachment(β=0.162,p<0.01)positively predict peer bullying,while secure attachment(β=−0.205,p<0.01),maternal(β=−0.385,p<0.01)and paternal(β=−0.217,p<0.01)acceptance/rejection negatively predict bullying.The final regression model explains approximately 55%of the variance in bullying.Conclusion:Our findings indicate that social exclusion,parental acceptance/rejection,and secure or avoidant attachment patterns may be associated with bullying behaviour in adolescents.These findings emphasise the necessity of family-and peer-focused interventions to combat bullying.展开更多
Background:Social media plays an important role in shaping body image and self-perception,particularly among appearance-sensitive groups such as athletes.Although problematic social media use has been linked to body i...Background:Social media plays an important role in shaping body image and self-perception,particularly among appearance-sensitive groups such as athletes.Although problematic social media use has been linked to body image outcomes through processes such as social comparison,self-presentation,and evaluation sensitivity,these mechanisms remain underexplored among athletes with physical disabilities.This study aimed to examine the associations between social media use,addictive use patterns,and body image perception in this population,with a focus on these underlying psychological mechanisms.Methods:A total of 165 athletes with physical disability participated in this quantitative cross-sectional study.Data were collected through online surveys,including demographic questions,the Athlete Social Media Use Scale(content creation,usage frequency,and social media addiction subdimensions),and the Body Image Scale(negative perception,evaluation sensitivity,positive perception,and body modification).Parametric tests,correlation analyses,and group comparisons were performed to assess relationships between social media behaviors and body image dimensions.Results:Problematic social media use was moderately associated with higher negative body image and lower positive body image among athletes with physical disabilities(r=0.32–0.41,all p<0.001).Regression analysis indicated that overall social media use was a significant predictor of body image perception after controlling for demographic variables(β≈0.45,p<0.001),explaining approximately 19.5%of the variance.Mediation analyses using bootstrapping revealed that these psychological mechanisms partially mediated the relationship between problematic social media use and body image perceptions,with small-to-moderate indirect effects,indicating both statistical and practical significance.Conclusion:The findings indicate that not only general social media use but also addictive and problematic usage patterns are linked to vulnerable aspects of body image among athletes with physical disabilities.Increased exposure to idealized digital representations and upward social comparison processes may heighten sensitivity to external evaluation and undermine positive body perception.These results highlight the need for digital literacy initiatives,psychoeducational interventions,and supportive online environments that promote healthier social media engagement and body image among disabled athletes.展开更多
Smart cities,as a typical application in the field of the Internet of Things,can combine cloud computing to realize the intelligent control of objects and process massive data.While cloud computing brings convenience ...Smart cities,as a typical application in the field of the Internet of Things,can combine cloud computing to realize the intelligent control of objects and process massive data.While cloud computing brings convenience to smart city services,a serious problem is ensuring that confidential data cannot be leaked to malicious adversaries.Considering the security and privacy of data,data owners transmit sensitive data in its encrypted form to cloud server,which seriously hinders the improvements of potential utilization and efficient sharing.Public key searchable encryption ensures that users can securely retrieve the encrypted data without decryption.However,most existing schemes cannot resist keyword guessing attacks or the size of trapdoors linearly increases with the number of data owners.In this work,by utilizing certificateless encryption and proxy re-encryption,we design an authenticated searchable encryption scheme with constant trapdoors.The designed scheme preserves the privacy of index ciphertexts and keyword trapdoors,and can resist keyword guessing attacks.In addition,data users can generate and upload trapdoors with lower computation and communication overheads.We show that the proposed scheme is suitable for smart city implementations and applications by experimentally evaluating its performance.展开更多
The technological advancement of the vehicular Internet ofThings(IoT)has revolutionized Intelligent Transportation Systems(ITS)into next-generation ITS.The connectivity of IoT nodes enables improved data availability ...The technological advancement of the vehicular Internet ofThings(IoT)has revolutionized Intelligent Transportation Systems(ITS)into next-generation ITS.The connectivity of IoT nodes enables improved data availability and facilitates automatic control in the ITS environment.The exponential increase in IoT nodes has significantly increased the demand for an energy-efficient,mobility-aware,and secure system for distributed intelligence.This article presents a mobility-aware Deep Reinforcement Learning based Federated Learning(DRL-FL)approach to design an energy-efficient and threat-resilient ITS.In this approach,a Policy Proximal Optimization(PPO)-based DRL agent is first employed for adaptive client selection.Second,an autoencoder-based anomaly detectionmodule is considered for malicious node detection.Results reveal that the proposed framework achieved an 8%higher accuracy increase,and 15%lower energy consumption.Themodel also demonstrates greater resilience under adversarial conditions compared to the state of the art in federated learning.The adaptability of the proposed approach makes it a compelling choice for next-generation vehicular networks.展开更多
Regulating the microenvironment of the support enables precise control of electronic metal-support interactions(EMSI),boosting better catalytic activity of the metal species.However,the fundamental relationship betwee...Regulating the microenvironment of the support enables precise control of electronic metal-support interactions(EMSI),boosting better catalytic activity of the metal species.However,the fundamental relationship between support defect-induced EMSI modulation and the resulting catalytic performance enhancement still needs further elucidation.Herein,a nonequilibrium high-temperature shock(HTS)method,which combines rapid high-temperature heating at 1273 K for 30 s with liquid nitrogen quenching,was adopted to load uniform Pt nanoparticles onto the nitrogen vacancy-rich TiN support(Pt@TiNVN).The catalyst demonstrates a high mass activity of 15.99 A mgPt^(-1)at an overpotential of 100 mV for the hydrogen evolution reaction(HER)in acidic solution and exhibits long-term stability for 60 h at 200 mA cm^(-2).Detailed spectroscopic characterizations and theoretical calculations reveal that the generated nitrogen vacancies can effectively modulate the charge transfer between Pt nanoparticles and the TiN-VN support,leading to a downshifted d-band center of metallic Pt and optimized Pt-H bond strength.This nonequilibrium HTS approach offers new and valuable insights into designing advanced electrocatalysts by harnessing substrate defects to modulate the electronic states of loaded noble metals.展开更多
Nitrogen use efficiency in rice is lower than in upland crops,likely due to differences in soil nitrogen dynamics and crop nitrogen preferences.However,the specific nitrogen dynamics in paddy and upland systems and th...Nitrogen use efficiency in rice is lower than in upland crops,likely due to differences in soil nitrogen dynamics and crop nitrogen preferences.However,the specific nitrogen dynamics in paddy and upland systems and their impact on crop nitrogen uptake remain poorly understood.The N dynamics and impact on crop N uptake determine the downstream environmental pollution from nitrogen fertilizer.To address this poor understanding,we analyzed 2,044 observations of gross nitrogen transformation rates in soils from 136 studies to examine nitrogen dynamics in both systems and their effects on nitrogen uptake in rice and upland crops.Our findings revealed that nitrogen mineralization and autotrophic nitrification rates are lower in paddies than in upland soil,while dissimilatory nitrate reduction to ammonium is higher in paddies,these differences being driven by flooding and lower total nitrogen content in paddies.Rice exhibited higher ammonium uptake,while upland crops had over twice the nitrate uptake.Autotrophic nitrification stimulated by p H reduced rice nitrogen uptake,while heterotrophic nitrification enhanced nitrogen uptake of upland crops.Autotrophic nitrification played a key role in regulating the ammonium-to-nitrate ratio in soils,which further affected the balance of plant nitrogen uptake.These results highlight the need to align soil nitrogen dynamics with crop nitrogen preferences to maximize plant maximize productivity and reduce reactive nitrogen pollution.展开更多
文摘BACKGROUND:Maxillofacial trauma represents a significant challenge in emergency medicine,requiring both diagnostic accuracy and prompt intervention while balancing immediate life-saving interventions with preservation of function and aesthetics.The complex anatomy of this region,with its proximity to critical structures,demands a thorough understanding of assessment and management principles.This narrative review aims to provide evidence-based guidelines for emergency physicians managing maxillofacial trauma,with particular emphasis on early recognition of critical injuries,airway management strategies,and special population considerations.METHODS:A narrative review was conducted via a comprehensive literature search of the PubMed and Scopus databases,which focused on maxillofacial trauma management in emergency settings.Articles were selected based on relevance to clinical practice,methodological quality,and current management guidelines.The review synthesized evidence from multiple study types,including original research,systematic reviews,and clinical practice guidelines,to provide practical guidance for emergency physicians.RESULTS:Initial assessment following Advanced Trauma Life Support(ATLS)principles is crucial,with airway management being a primary concern due to the risk of dynamic obstruction.Critical time-sensitive emergencies include orbital compartment syndrome,trapdoor fractures(in pediatric patients),and facial nerve injuries.Computed tomography(CT)imaging remains the gold standard for diagnosis.Special considerations are required for pediatric patients,who present unique anatomical challenges and injury patterns,and for elderly patients,who often have complex medical comorbidities and increased complication risks.Management strategies range from conservative treatment to urgent surgical intervention,with decisions based on the injury pattern and associated complications.CONCLUSION:Emergency physicians must maintain a structured yet fl exible approach to maxillofacial trauma,focusing on early recognition of critical injuries,appropriate airway management,and timely specialist consultation.Understanding injury patterns and their potential complications allows for eff ective risk stratifi cation and treatment planning,ultimately improving patient outcomes.
基金supported by Kyonggi University Research Grant 2025.
文摘As large,room-scale environments become increasingly common,their spatial complexity increases due to variable,unstructured elements.Consequently,demand for room-scale service robots is surging,yet most technologies remain corridor-centric,and autonomous navigation in expansive rooms becomes unstable even around static obstacles.Existing approaches face several structural limitations.These include the labor-intensive requirement for large-scale object annotation and continual retraining,as well as the vulnerability of vanishing point or linebased methods when geometric cues are insufficient.In addition,the high cost of LiDAR and 3D perception errors caused by limited wall cues and dense interior clutter further limit their effectiveness.To address these challenges,we propose a zero-shot vision-based algorithm for robust 3D map reconstruction in geometry-deficient room-scale environments.The algorithm operates in three layers:Layer 1 performs dimension-wise boundary detection;Layer 2 estimates vanishing points,refines the precise perspective space,and extracts a floor mask;and Layer 3 conducts 3D spatial mapping and obstacle recognition.The proposed method was experimentally validated across various geometric-deficient room-scale environments,including lobbies,seminar rooms,conference rooms,cafeterias,and museums—demonstrating its ability to reliably reconstruct 3D maps and accurately recognize obstacles.Experimental results show that the proposed algorithm achieved an F1 score of 0.959 in precision perspective space detection and 0.965 in floor mask extraction.For obstacle recognition and classification,it obtained F1 scores of 0.980 in obstacle absent areas,0.913 in solid obstacle environments,and 0.939 in skeleton-type sparse obstacle environments,confirming its high precision and reliability in geometric-deficient room-scale environments.
文摘Classifying job offers into occupational categories is a fundamental task in human resource information systems,as it improves and streamlines indexing,search,and matching between openings and job seekers.Comprehensive occupational databases such as O∗NET or ESCO provide detailed taxonomies of interrelated positions that can be leveraged to align the textual content of postings with occupational categories,thereby facilitating standardization,cross-system interoperability,and access to metadata for each occupation(e.g.,tasks,knowledge,skills,and abilities).In this work,we explore the effectiveness of fine-tuning existing language models(LMs)to classify job offers with occupational descriptors from O∗NET.This enables a more precise assessment of candidate suitability by identifying the specific knowledge and skills required for each position,and helps automate recruitment processes by mitigating human bias and subjectivity in candidate selection.We evaluate three representative BERT-like models:BERT,RoBERTa,and DeBERTa.BERT serves as the baseline encoder-only architecture;RoBERTa incorporates advances in pretraining objectives and data scale;and DeBERTa introduces architectural improvements through disentangled attention mechanisms.The best performance was achieved with the DeBERTa model,although the other models also produced strong results,and no statistically significant differences were observed acrossmodels.We also find that these models typically reach optimal performance after only a few training epochs,and that training with smaller,balanced datasets is effective.Consequently,comparable results can be obtained with models that require fewer computational resources and less training time,facilitating deployment and practical use.
文摘The rapidly evolving cybersecurity threat landscape exposes a critical flaw in traditional educational programs where static curricula cannot adapt swiftly to novel attack vectors.This creates a significant gap between theoretical knowledge and the practical defensive capabilities needed in the field.To address this,we propose TeachSecure-CTI,a novel framework for adaptive cybersecurity curriculumgeneration that integrates real-time Cyber Threat Intelligence(CTI)with AI-driven personalization.Our framework employs a layered architecture featuring a CTI ingestion and clusteringmodule,natural language processing for semantic concept extraction,and a reinforcement learning agent for adaptive content sequencing.Bydynamically aligning learningmaterialswithboththe evolving threat environment and individual learner profiles,TeachSecure-CTI ensures content remains current,relevant,and tailored.A 12-week study with 150 students across three institutions demonstrated that the framework improves learning gains by 34%,significantly exceeding the 12%–21%reported in recent literature.The system achieved 84.8%personalization accuracy,85.9%recognition accuracy for MITRE ATT&CK tactics,and a 31%faster competency development rate compared to static curricula.These findings have implications beyond academia,extending to workforce development,cyber range training,and certification programs.By bridging the gap between dynamic threats and static educational materials,TeachSecure-CTI offers an empirically validated,scalable solution for cultivating cybersecurity professionals capable of responding to modern threats.
文摘Homret Ghannam alkali feldspar granite(HGAFG)in the central Eastern Desert(CED)of Egypt represents a distinctive example of late Neoproterozoic magmatism in the Arabian-Nubian Shield(ANS).This study integrates field observations,petrography,mineral chemistry(EMPA),and whole-rock geochemistry to investigate its petrogenesis,geodynamic evolution,and rare-metal potential.HGAFG comprises two cogenetic varieties,alkali feldspar granite and riebeckite-bearing granite,hosting rare-metal minerals such as zircon,fluorite,columbite and apatite.HGAFG exhibits diagnostic A-type geochemical characteristics,including high SiO₂contents(73.81-77.86 wt%),metaluminous to mildly peralkaline composition(ASI:0.92-1.03),enrichment in HFSE(Zr≈791.80 ppm,Nb≈68.12 ppm,Y≈90.81 ppm)andΣREE(103.40-475.57 ppm),and pronounced negative Eu anomalies(Eu/Eu^(*)=0.07-0.20).Zircon saturation thermometry yields high crystallization temperatures(TZr≈908.87℃)and low emplacement pressures(1.46 kbar)under reducing conditions(ƒO_(2)≈−11.5).The mineralogical and geochemical results reveal that HGAFG originated from a hybrid,fluorine-rich magma generated by anatexis of lower crust,followed by extensive fractional crystallization,during late post-collisional extension associated with lithospheric delamination.The reduced nature and fluorine enrichment of HGAFG magma promoted the mineralization of Nb-Ta-REE phases,highlighting its significance as a fertile,high-temperature product of the terminal magmatic stage in ANS evolution.
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.
基金We appreciate the financial support of this work by Gorgan University of Agricultural Sciences and Natural Resources from Golestan Province(Grant No.9413184180).
文摘Drought,as the most catastrophic abiotic stress,poses a significant threat to the growth and development of plants.Among the mechanisms employed by plants to cope with drought-induced stress,abscisic acid(ABA)which is the sesquiterpene hormone,occupies a pivotal role.A hypothesis has emerged that the exogenous application of ABA can positively influence the terpenoid content of Lavandula angustifolia cv Hidcote essential oil(EO),thereby conferring enhanced resilience to drought stress.A randomized complete block design experiment was conducted with three replicationsandfour irrigation regimes,including I4[30%-40%of field capacity(FC)],I3(50%-60%FC),I2(70%-80%FC),andI1(90%-100%FC)as control.Application of ABAspraying included three concentrations,A3(30μmol·L^(-1)ABA),A2(15μmol·L^(-1)ABA),and A1 as control(distilled water).Results revealed that drought significantly affected all studied traits except for relative water content(RWC)and shoot dry mass.The ABA impact application on the observed traits was found to be dependent upon the level of drought to which the plants were exposed.Specifically,the highest levels of flavonoid content,total antioxidant activity,peroxidase(POX)activity,and EO percentage were observed under I4A2 conditions.Conversely,the highest levels of superoxide dismutase(SOD)and catalase(CAT)activity,and proline were recorded under I4A3 conditions,while the highest EO yield was obtained under I3A2 conditions.Analysis of the EO revealed that there were common indicative compounds across the varying levels of droughtandABAapplication,including linalool,camphor,borneol,bornyl formate,andcaryophyllene oxide.Theproduction pattern ofmonoterpene and sesquiterpene compounds demonstrated a distinct trend,with the highest concentration of monoterpene hydrocarbon compounds(average of 12.92%)being observed in the I2A3 treatment group,andthe highest concentration of oxygenatedmonoterpenecompounds(average of 64.76%)being recorded in the I1A1 group.Conversely,the most significant levels of sesquiterpene hydrocarboncompounds(14.98%)andoxygenated sesquiterpene compounds(10.46%)were observed in the I4A3 and I4A1 groups,respectively,showing the efficacy of monoterpenes and sesquiterpenes from the action of ABA under drought conditions.The observed results indicated that the concentration of oxygenated monoterpene compounds decreases with an increase in drought level.Conversely,the application of ABA at any given drought level appears to resulted in increased concentrations of oxygenated monoterpene compounds in the same conditions.It may be concluded that plants under high-stress drought conditions allocate more terpene precursors to the production of sesquiterpene hydrocarbon compounds,aided by ABA with the same properties.
文摘Sport-related concussion(SRC)and its potential neurological sequela represent an emerging global health concern,requiring improved recovery management and strategies for return-to-play(RTP)to enhance brain health in athletes.Given the dynamic and multifaceted nature of SRC recovery,the purpose of this review is to synthesize existing literature on post-SRC outcomes in adult athletes,and to outline the temporal trajectories of key recovery indicators(symptoms,cognitive function,blood biomarkers)across distinct recovery phases until resolution.In the acute phase of SRC(first 48 h),symptom scores and brain damage markers peaked immediately,while cognitive impairments and neuroinflammation emerged with a slight delay.Following the initial rise,brain damage marker concentrations rapidly dropped below baseline levels at approximately 48 h following SRC injury.During the early recovery phase,neuroinflammation and most cognitive alterations resolved after 3–5 days,though symptom burden and attention deficits persisted for up to 7 days.Despite prolonged alterations reported in some individuals,recovery markers typically returned to pre-injury levels in the transition phase(≤2 weeks),though mild attention deficits were detected up to 3 weeks,and TNF-α concentrations remained elevated throughout late recovery(>2 weeks).These results reveal distinct temporal discrepancies across recovery markers and emphasize that physiological disturbances can outlast symptom resolution,underscoring the need for both multimodal assessments and appropriately timed evaluations to accurately track recovery progression.Incorporating structured follow-ups at key time points,particularly beyond symptom resolution,may improve RTP decision-making and reduce the risk of premature return and long-term neurological consequences.
基金Project supported in part by Beijing Natural Science Foundation(Grant No.1232025)Peng Huanwu Visiting Pro-fessor Program,and Academy for Multidisciplinary Studies,Capital Normal University.
文摘Replicating the chaotic characteristics inherent in nonlinear dynamical systems via machine learning(ML)is a key challenge in this rapidly advancing interdisciplinary field.In this work,we explore the potential of variational quantum circuits(VQC)for learning the stochastic properties of classical nonlinear dynamical systems.Specifically,we focus on the one-and two-dimensional logistic maps,which,while simple,remain under-explored in the context of learning dynamical characteristics.Our findings reveal that,even for such simple dynamical systems,accurately replicating longterm characteristics is hindered by a pronounced sensitivity to overfitting.While increasing the parameter complexity of the ML model typically enhances short-term prediction accuracy,it also leads to a degradation in the model’s ability to replicate long-term characteristics,primarily due to the detrimental effects of overfitting on generalization power.By comparing the VQC with two widely recognized classical ML techniques,which are long short-term memory(LSTM)networks for timeseries processing and reservoir computing,we demonstrate that VQC outperforms these methods in terms of replicating long-term characteristics.Our results suggest that for the ML of dynamics,it is demanded to develop more compact and efficient models(such as VQC)rather than more complicated and large-scale ones.
基金Fundación Mujeres por Africa for supporting this work through the fellowship awarded to her。
文摘Neuronanomedicine is a promising interdisciplinary field combining two critical fields,neuroscience and nanotechnology.This study focuses on the engineering of magnetized nanoparticles(MNPs)in diagnosing and treating neurological disorders and brain cancer.Additionally,this mechanism enhances the effectiveness of magnetic-guided drug delivery.The alternating magnetic field is applied to control the directions of the MNPs to target the tumor cells.This study approaches the radiotherapy techniques of magnetic hyperthermia therapy(MHT),wherein the thermal radiative heat transfer effect is applied to achieve homogenous heating to destroy cancer cells.MNPs are injected through the cerebrospinal fluid(CSF)transport in the glymphatic system.The elastic properties of the cerebral arteries cause peristaltic propulsion for the resulting nanofluid.Therefore,the effective Maxwell model for the nanofluid thermal conductivity is selected.The nanofluid governing equations are solved using the perturbation technique under small wavelength number and long wavelength approximation with small Reynolds number.Additionally,the effects of thermal slip and elastic properties boundary conditions are incorporated.The graphical results for the streamwise velocity,pressure,and temperature distributions are plotted using MATLAB package considering the different effects of the magnetic flux intensity,thermal radiation parameter,thermal slipping at boundaries,elastic wall properties,and nanoparticle concentration.The results demonstrate the strong impact of the magnetic field and radiation heating in terms of enhancing the nanofluid CSF flow behavior and destroying cancer.
文摘BACKGROUND Despite societal guidelines recommending targeted screening for Barrett’s esophagus(BE)and esophageal adenocarcinoma(EAC)in individuals with gastroesophageal reflux symptoms(GERS),screening adherence is suboptimal.Current screening approaches fail to identify individuals not seeking medical consultation for GERS or whose GERS are managed with‘over-the-counter’(OTC)acid suppressant therapies.AIM To assess patients’self-management and help-seeking behavior for GERS.METHODS This cross-sectional study collected data from the Dutch general population aged 18-75 years between January and April 2023 using a web-based survey.The survey included questions regarding self-management(e.g.,use of acid suppressant therapy with or without prescription)and help-seeking behavior(e.g.,consulting a primary care provider)for GERS.Simple random sampling was performed to select individuals within the target age group.In total,18156 randomly selected individuals were invited to participate.The study protocol was registered in ClinicalTrials.gov(identifier:NCT05689918).RESULTS Of the 18156 invited individuals,3214 participants(17.7%)completed the survey,of which 1572 participants(48.9%)reported GERS.Of these,904 participants(57.5%)had never consulted a primary care provider for these symptoms,of which 331 participants(36.6%)reported taking OTC acid suppressant therapy in the past six months and 100 participants(11.1%)fulfilled the screening criteria for BE and EAC according to the European Society of Gastrointestinal Endoscopy Guideline.CONCLUSION The population fulfilling the screening criteria for BE and EAC is incompletely identified,suggesting potential underutilization of medical consultation.Raising public awareness of GERS as a risk factor for EAC is needed.
基金funded by the Research,Development,and Innovation Authority(RDIA)—Kingdom of Saudi Arabia(Grant No.13292-psu-2023-PSNU-R-3-1-EF-).
文摘Colorectal cancer is the third most diagnosed cancer worldwide,and immune checkpoint inhibitors have shown promising therapeutic outcomes in selected patient groups.This study performed a comprehensive analysis of multi-omics data from The Cancer Genome Atlas colorectal adenocarcinoma cohort(TCGA-COADREAD),accessed through cBioPortal,to develop machine learning models for predicting progression-free survival(PFS)following immunotherapy.The dataset included clinical variables,genomic alterations in Kirsten Rat Sarcoma Viral Oncogene Homolog(KRAS),B-Raf Proto-Oncogene(BRAF),and Neuroblastoma RAS Viral Oncogene Homolog(NRAS),microsatellite instability(MSI)status,tumor mutation burden(TMB),and expression of immune checkpoint genes.Kaplan–Meier analysis showed that KRAS mutations were significantly associated with reduced PFS,while BRAF and NRAS mutations had no significant impact.MSI-high tumors exhibited elevated TMB and increased immune checkpoint expression,reflecting their immunologically active phenotype.We developed both survival and classification models,with the Extra Trees classifier achieving the best performance(accuracy=0.86,precision=0.67,recall=0.70,F1-score=0.68,AUC=0.84).These findings highlight the potential of combining genomic and immune biomarkers with machine learning to improve patient stratification and guide personalized immunotherapy decisions.An interactive web application was also developed to enable clinicians to input patient-specific molecular and clinical data and visualize individualized PFS predictions,supporting timely,data-driven treatment planning.
文摘Background:Bullying during adolescence is shaped by numerous psychosocial factors such as family dynamics,attachment,and peer relationships.This study aims to examine parental acceptance-rejection,attachment styles,and social exclusion factors as key psychosocial variables predicting bullying behavior in adolescents.Methods:In a cross-sectional study conducted with 349 high school students in Hakkari,Türkiye.Data were collected using the Olweus Bullying Scale,the Parental Acceptance-Rejection Scale,the Social Exclusion Scale,and the Three-Dimensional Attachment Styles Scale.Independent samples t-tests,one-way ANOVAs,Pearson correlations,and hierarchical regression analyses were performed.Results:Research findings reveal that peer bullying varies significantly according to gender,class level,parents’educational level,and socio-economic status.Furthermore,our findings indicate that social exclusion(β=0.506,p<0.01)and avoidant attachment(β=0.162,p<0.01)positively predict peer bullying,while secure attachment(β=−0.205,p<0.01),maternal(β=−0.385,p<0.01)and paternal(β=−0.217,p<0.01)acceptance/rejection negatively predict bullying.The final regression model explains approximately 55%of the variance in bullying.Conclusion:Our findings indicate that social exclusion,parental acceptance/rejection,and secure or avoidant attachment patterns may be associated with bullying behaviour in adolescents.These findings emphasise the necessity of family-and peer-focused interventions to combat bullying.
基金supported by the İnonu University Scientific Research Projects Unit(SBA-2026-4657),Türkiye.
文摘Background:Social media plays an important role in shaping body image and self-perception,particularly among appearance-sensitive groups such as athletes.Although problematic social media use has been linked to body image outcomes through processes such as social comparison,self-presentation,and evaluation sensitivity,these mechanisms remain underexplored among athletes with physical disabilities.This study aimed to examine the associations between social media use,addictive use patterns,and body image perception in this population,with a focus on these underlying psychological mechanisms.Methods:A total of 165 athletes with physical disability participated in this quantitative cross-sectional study.Data were collected through online surveys,including demographic questions,the Athlete Social Media Use Scale(content creation,usage frequency,and social media addiction subdimensions),and the Body Image Scale(negative perception,evaluation sensitivity,positive perception,and body modification).Parametric tests,correlation analyses,and group comparisons were performed to assess relationships between social media behaviors and body image dimensions.Results:Problematic social media use was moderately associated with higher negative body image and lower positive body image among athletes with physical disabilities(r=0.32–0.41,all p<0.001).Regression analysis indicated that overall social media use was a significant predictor of body image perception after controlling for demographic variables(β≈0.45,p<0.001),explaining approximately 19.5%of the variance.Mediation analyses using bootstrapping revealed that these psychological mechanisms partially mediated the relationship between problematic social media use and body image perceptions,with small-to-moderate indirect effects,indicating both statistical and practical significance.Conclusion:The findings indicate that not only general social media use but also addictive and problematic usage patterns are linked to vulnerable aspects of body image among athletes with physical disabilities.Increased exposure to idealized digital representations and upward social comparison processes may heighten sensitivity to external evaluation and undermine positive body perception.These results highlight the need for digital literacy initiatives,psychoeducational interventions,and supportive online environments that promote healthier social media engagement and body image among disabled athletes.
基金supported by the Shandong Provincial Key Research and Development Program(No.2021CXGC010107)the National Natural Science Foundation of China(Nos.U21A20466,62325209)+3 种基金the New 20 Project of Higher Education of Jinan(No.202228017)the Special Project on Science and Technology Program of Hubei Province(No.2021BAA025)the Fundamental Research Funds for the Central Universities(Nos.2042023kf0203,20420241013)the Researchers Supporting Project Number(RSP2024R509),King Saud University,Riyadh,Saudi Arabia。
文摘Smart cities,as a typical application in the field of the Internet of Things,can combine cloud computing to realize the intelligent control of objects and process massive data.While cloud computing brings convenience to smart city services,a serious problem is ensuring that confidential data cannot be leaked to malicious adversaries.Considering the security and privacy of data,data owners transmit sensitive data in its encrypted form to cloud server,which seriously hinders the improvements of potential utilization and efficient sharing.Public key searchable encryption ensures that users can securely retrieve the encrypted data without decryption.However,most existing schemes cannot resist keyword guessing attacks or the size of trapdoors linearly increases with the number of data owners.In this work,by utilizing certificateless encryption and proxy re-encryption,we design an authenticated searchable encryption scheme with constant trapdoors.The designed scheme preserves the privacy of index ciphertexts and keyword trapdoors,and can resist keyword guessing attacks.In addition,data users can generate and upload trapdoors with lower computation and communication overheads.We show that the proposed scheme is suitable for smart city implementations and applications by experimentally evaluating its performance.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project No.PNURSP2025R510Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The technological advancement of the vehicular Internet ofThings(IoT)has revolutionized Intelligent Transportation Systems(ITS)into next-generation ITS.The connectivity of IoT nodes enables improved data availability and facilitates automatic control in the ITS environment.The exponential increase in IoT nodes has significantly increased the demand for an energy-efficient,mobility-aware,and secure system for distributed intelligence.This article presents a mobility-aware Deep Reinforcement Learning based Federated Learning(DRL-FL)approach to design an energy-efficient and threat-resilient ITS.In this approach,a Policy Proximal Optimization(PPO)-based DRL agent is first employed for adaptive client selection.Second,an autoencoder-based anomaly detectionmodule is considered for malicious node detection.Results reveal that the proposed framework achieved an 8%higher accuracy increase,and 15%lower energy consumption.Themodel also demonstrates greater resilience under adversarial conditions compared to the state of the art in federated learning.The adaptability of the proposed approach makes it a compelling choice for next-generation vehicular networks.
基金supported by the National Natural Science Foundation of China(Nos.22209088,22472082,and 22075159)Taishan Scholar Program(Nos.tsqn202103058 and tsqn202306173)Qingdao New Energy Shandong Laboratory Open Project(QNESLOP202302)。
文摘Regulating the microenvironment of the support enables precise control of electronic metal-support interactions(EMSI),boosting better catalytic activity of the metal species.However,the fundamental relationship between support defect-induced EMSI modulation and the resulting catalytic performance enhancement still needs further elucidation.Herein,a nonequilibrium high-temperature shock(HTS)method,which combines rapid high-temperature heating at 1273 K for 30 s with liquid nitrogen quenching,was adopted to load uniform Pt nanoparticles onto the nitrogen vacancy-rich TiN support(Pt@TiNVN).The catalyst demonstrates a high mass activity of 15.99 A mgPt^(-1)at an overpotential of 100 mV for the hydrogen evolution reaction(HER)in acidic solution and exhibits long-term stability for 60 h at 200 mA cm^(-2).Detailed spectroscopic characterizations and theoretical calculations reveal that the generated nitrogen vacancies can effectively modulate the charge transfer between Pt nanoparticles and the TiN-VN support,leading to a downshifted d-band center of metallic Pt and optimized Pt-H bond strength.This nonequilibrium HTS approach offers new and valuable insights into designing advanced electrocatalysts by harnessing substrate defects to modulate the electronic states of loaded noble metals.
基金funded by the National Key Research and Development Program of China(2024YFD1501602)the National Natural Science Foundation of China(42407437)conducted as part of the Coordinated Research Project D1.50.16,implemented by the Soil and Water Management and Crop Nutrition Section of the Joint FAO/IAEA Division of Nuclear Techniques in Food and Agriculture,Department of Nuclear Sciences and Applications,Vienna,Austria。
文摘Nitrogen use efficiency in rice is lower than in upland crops,likely due to differences in soil nitrogen dynamics and crop nitrogen preferences.However,the specific nitrogen dynamics in paddy and upland systems and their impact on crop nitrogen uptake remain poorly understood.The N dynamics and impact on crop N uptake determine the downstream environmental pollution from nitrogen fertilizer.To address this poor understanding,we analyzed 2,044 observations of gross nitrogen transformation rates in soils from 136 studies to examine nitrogen dynamics in both systems and their effects on nitrogen uptake in rice and upland crops.Our findings revealed that nitrogen mineralization and autotrophic nitrification rates are lower in paddies than in upland soil,while dissimilatory nitrate reduction to ammonium is higher in paddies,these differences being driven by flooding and lower total nitrogen content in paddies.Rice exhibited higher ammonium uptake,while upland crops had over twice the nitrate uptake.Autotrophic nitrification stimulated by p H reduced rice nitrogen uptake,while heterotrophic nitrification enhanced nitrogen uptake of upland crops.Autotrophic nitrification played a key role in regulating the ammonium-to-nitrate ratio in soils,which further affected the balance of plant nitrogen uptake.These results highlight the need to align soil nitrogen dynamics with crop nitrogen preferences to maximize plant maximize productivity and reduce reactive nitrogen pollution.
