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
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
Phytophthora blight is a devastating disease of pigeon pea(Cajanus cajan)that severely impacts plant growth and productivity.This study investigates the morphological,anatomical,and biochemical responses of a suscepti...Phytophthora blight is a devastating disease of pigeon pea(Cajanus cajan)that severely impacts plant growth and productivity.This study investigates the morphological,anatomical,and biochemical responses of a susceptible variety(ICPL 11260)and a resistant variety(IPAC-02)following infection by Phytophthora.Morphological analyses showed that infection caused a drastic reduction in root length,shoot length,leaf number,fresh weight,and dry weight in the susceptible ICPL 11260 variety,with reductions ranging from 0.5-to 2-fold compared to non-infected controls.Anatomical observations revealed pronounced cellular damage and mycelial invasion in infected ICPL 11260 plants by 30 days after infection,whereas infected IPAC-02 plants exhibited no fungal colonization.Biochemical analyses further demonstrated that the resistant IPAC-02 variety accumulated higher levels of total soluble sugars,proteins,phenols,and flavonoids,along with increased activities of defense-related enzymes(chitinase andβ-1,3-glucanase),compared with the susceptible ICPL 11260.Under P.cajani stress,IPAC-02 maintained significantly elevated osmolyte concentrations(total sugars 153.7 mg g^(−1)FW;proteins 25.4 mg g^(−1)FW),secondary metabolites(phenols 51.7 mg g^(−1)FW;flavonoids 33.1 mg g^(−1)FW),and PR-enzyme activities(chitinase 11.4 U mg^(−1)protein;β-1,3-glucanase 9.1 U mg^(−1)protein).These responses support a lignification-mediated defense mechanism in IPAC-02 and highlight its potential value for breeding Phytophthora-resistant pigeon pea cultivars.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
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
Possessing excellent mechanical properties,a high-coverage slide-ring conductive gel is constructed by in situ polymerization ofα-cyclodextrin(α-CD)polyrotaxane(PR)and 1-vinyl-3-ethylimidazolium bromide([VEIM]Br)ion...Possessing excellent mechanical properties,a high-coverage slide-ring conductive gel is constructed by in situ polymerization ofα-cyclodextrin(α-CD)polyrotaxane(PR)and 1-vinyl-3-ethylimidazolium bromide([VEIM]Br)ionic liquid(IL),using 1-ethyl-3-methylimidazolium bromide([EMIM]Br)IL as solvent.Benefiting from the compatibility of ILs and alkene-PR,the cross-linked network slide-ring gel not only maintains excellent conductivity(1.52×10^(−2) S/m),but also has effectively improved mechanical properties(513%fracture strain,0.713 MPa fracture stress,211 kPa elastic modulus and 1366 kJ/m^(3) toughness)and adhesive properties(472.3±25.9 kPa).The supramolecular gel can be used as a strain sensor to efficiently monitor deformation signals in real-time at least 200 times.Especially,the slide-ring gel can self-power generated by triboelectric effect and electrostatic induction between the skin layer and the polydimethylsiloxane(PDMS)layer that encapsulates the gel,achieving reversible and durable motion sensing,which provides a convenient pathway for constructing supramolecular self-powered flexible electronic materials.展开更多
Covert timing channels(CTC)exploit network resources to establish hidden communication pathways,posing signi cant risks to data security and policy compliance.erefore,detecting such hidden and dangerous threats remain...Covert timing channels(CTC)exploit network resources to establish hidden communication pathways,posing signi cant risks to data security and policy compliance.erefore,detecting such hidden and dangerous threats remains one of the security challenges. is paper proposes LinguTimeX,a new framework that combines natural language processing with arti cial intelligence,along with explainable Arti cial Intelligence(AI)not only to detect CTC but also to provide insights into the decision process.LinguTimeX performs multidimensional feature extraction by fusing linguistic attributes with temporal network patterns to identify covert channels precisely.LinguTimeX demonstrates strong e ectiveness in detecting CTC across multiple languages;namely English,Arabic,and Chinese.Speci cally,the LSTM and RNN models achieved F1 scores of 90%on the English dataset,89%on the Arabic dataset,and 88%on the Chinese dataset,showcasing their superior performance and ability to generalize across multiple languages. is highlights their robustness in detecting CTCs within security systems,regardless of the language or cultural context of the data.In contrast,the DeepForest model produced F1-scores ranging from 86%to 87%across the same datasets,further con rming its e ectiveness in CTC detection.Although other algorithms also showed reasonable accuracy,the LSTM and RNN models consistently outperformed them in multilingual settings,suggesting that deep learning models might be better suited for this particular problem.展开更多
Generative Adversarial Networks(GANs)have become valuable tools in medical imaging,enabling realistic image synthesis for enhancement,augmentation,and restoration.However,their integration into clinical workflows rais...Generative Adversarial Networks(GANs)have become valuable tools in medical imaging,enabling realistic image synthesis for enhancement,augmentation,and restoration.However,their integration into clinical workflows raises concerns,particularly the risk of subtle distortions or hallucinations that may undermine diagnostic accuracy and weaken trust in AI-assisted decision-making.To address this challenge,we propose a hybrid deep learning framework designed to detect GAN-induced artifacts in medical images,thereby reinforcing the reliability of AI-driven diagnostics.The framework integrates low-level statistical descriptors,including high-frequency residuals and Gray-Level Co-occurrence Matrix(GLCM)texture features,with high-level semantic representations extracted from a pre-trained ResNet18.This dual-stream approach enables detection of both pixel-level anomalies and structural inconsistencies introduced by GAN-based manipulation.We validated the framework on a curated dataset of 10,000 medical images,evenly split between authentic and GAN-generated samples across four modalities:MRI,CT,X-ray,and fundus photography.To improve generalizability to real-world clinical settings,we incorporated domain adaptation strategies such as adversarial training and style transfer,reducing domain shift by 15%.Experimental results demonstrate robust performance,achieving 92.6%accuracy and an F1-score of 0.91 on synthetic test data,and maintaining strong performance on real-world GAN-modified images with 87.3%accuracy and an F1-score of 0.85.Additionally,the model attained an AUC of 0.96 and an average precision of 0.92,outperforming conventional GAN detection pipelines and baseline Convolutional Neural Network(CNN)architectures.These findings establish the proposed framework as an effective and reliable solution for detecting GAN-induced hallucinations in medical imaging,representing an important step toward building trustworthy and clinically deployable AI systems.展开更多
Improving device efficiency is fundamental for advancing energy harvesting technology,particularly in systems designed to convert light energy into electrical output.In our previous studies,we developed a basic struct...Improving device efficiency is fundamental for advancing energy harvesting technology,particularly in systems designed to convert light energy into electrical output.In our previous studies,we developed a basic structure light pressure electric generator(Basic-LPEG),which utilized a layered configuration of Ag/Pb(Zr,Ti)O_(3)(PZT)/Pt/GaAs to generate electricity based on light-induced pressure on the PZT.In this study,we sought to enhance the performance of this Basic-LPEG by introducing Ag nanoparticles/graphene oxide(AgNPs/GO)composite units(NP-LPEG),creating upgraded harvesting device.Specifically,by depositing the AgNPs/GO units twice onto the Basic-LPEG,we observed an increase in output voltage and current from 241 mV and 3.1μA to 310 mV and 9.3μA,respectively,under a solar simulator.The increase in electrical output directly correlated with the intensity of the light pressure impacting the PZT,as well as matched the Raman measurements,finite-difference time-domain simulations,and COMSOL Multiphysics Simulation.Experimental data revealed that the enhancement in electrical output was proportional to the number of hot spots generated between Ag nanoparticles,where the electric field experienced substantial amplification.These results underline the effectiveness of AgNPs/GO units in boosting the light-induced electric generation capacity,thereby providing a promising pathway for high-efficiency energy harvesting devices.展开更多
In Human–Robot Interaction(HRI),generating robot trajectories that accurately reflect user intentions while ensuring physical realism remains challenging,especially in unstructured environments.In this study,we devel...In Human–Robot Interaction(HRI),generating robot trajectories that accurately reflect user intentions while ensuring physical realism remains challenging,especially in unstructured environments.In this study,we develop a multimodal framework that integrates symbolic task reasoning with continuous trajectory generation.The approach employs transformer models and adversarial training to map high-level intent to robotic motion.Information from multiple data sources,such as voice traits,hand and body keypoints,visual observations,and recorded paths,is integrated simultaneously.These signals are mapped into a shared representation that supports interpretable reasoning while enabling smooth and realistic motion generation.Based on this design,two different learning strategies are investigated.In the first step,grammar-constrained Linear Temporal Logic(LTL)expressions are created from multimodal human inputs.These expressions are subsequently decoded into robot trajectories.The second method generates trajectories directly from symbolic intent and linguistic data,bypassing an intermediate logical representation.Transformer encoders combine multiple types of information,and autoregressive transformer decoders generate motion sequences.Adding smoothness and speed limits during training increases the likelihood of physical feasibility.To improve the realism and stability of the generated trajectories during training,an adversarial discriminator is also included to guide them toward the distribution of actual robot motion.Tests on the NATSGLD dataset indicate that the complete system exhibits stable training behaviour and performance.In normalised coordinates,the logic-based pipeline has an Average Displacement Error(ADE)of 0.040 and a Final Displacement Error(FDE)of 0.036.The adversarial generator makes substantially more progress,reducing ADE to 0.021 and FDE to 0.018.Visual examination confirms that the generated trajectories closely align with observed motion patterns while preserving smooth temporal dynamics.展开更多
In recent years,fog computing has become an important environment for dealing with the Internet of Things.Fog computing was developed to handle large-scale big data by scheduling tasks via cloud computing.Task schedul...In recent years,fog computing has become an important environment for dealing with the Internet of Things.Fog computing was developed to handle large-scale big data by scheduling tasks via cloud computing.Task scheduling is crucial for efficiently handling IoT user requests,thereby improving system performance,cost,and energy consumption across nodes in cloud computing.With the large amount of data and user requests,achieving the optimal solution to the task scheduling problem is challenging,particularly in terms of cost and energy efficiency.In this paper,we develop novel strategies to save energy consumption across nodes in fog computing when users execute tasks through the least-cost paths.Task scheduling is developed using modified artificial ecosystem optimization(AEO),combined with negative swarm operators,Salp Swarm Algorithm(SSA),in order to competitively optimize their capabilities during the exploitation phase of the optimal search process.In addition,the proposed strategy,Enhancement Artificial Ecosystem Optimization Salp Swarm Algorithm(EAEOSSA),attempts to find the most suitable solution.The optimization that combines cost and energy for multi-objective task scheduling optimization problems.The backpack problem is also added to improve both cost and energy in the iFogSim implementation as well.A comparison was made between the proposed strategy and other strategies in terms of time,cost,energy,and productivity.Experimental results showed that the proposed strategy improved energy consumption,cost,and time over other algorithms.Simulation results demonstrate that the proposed algorithm increases the average cost,average energy consumption,and mean service time in most scenarios,with average reductions of up to 21.15%in cost and 25.8%in energy consumption.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.
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supported and funded by the Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia,grant number(KFU252909).
文摘Phytophthora blight is a devastating disease of pigeon pea(Cajanus cajan)that severely impacts plant growth and productivity.This study investigates the morphological,anatomical,and biochemical responses of a susceptible variety(ICPL 11260)and a resistant variety(IPAC-02)following infection by Phytophthora.Morphological analyses showed that infection caused a drastic reduction in root length,shoot length,leaf number,fresh weight,and dry weight in the susceptible ICPL 11260 variety,with reductions ranging from 0.5-to 2-fold compared to non-infected controls.Anatomical observations revealed pronounced cellular damage and mycelial invasion in infected ICPL 11260 plants by 30 days after infection,whereas infected IPAC-02 plants exhibited no fungal colonization.Biochemical analyses further demonstrated that the resistant IPAC-02 variety accumulated higher levels of total soluble sugars,proteins,phenols,and flavonoids,along with increased activities of defense-related enzymes(chitinase andβ-1,3-glucanase),compared with the susceptible ICPL 11260.Under P.cajani stress,IPAC-02 maintained significantly elevated osmolyte concentrations(total sugars 153.7 mg g^(−1)FW;proteins 25.4 mg g^(−1)FW),secondary metabolites(phenols 51.7 mg g^(−1)FW;flavonoids 33.1 mg g^(−1)FW),and PR-enzyme activities(chitinase 11.4 U mg^(−1)protein;β-1,3-glucanase 9.1 U mg^(−1)protein).These responses support a lignification-mediated defense mechanism in IPAC-02 and highlight its potential value for breeding Phytophthora-resistant pigeon pea cultivars.
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.
基金Natural Science Foundation of China(NSFC,No.22131008)Natural Science Foundation of Tianjin(No.22JCYBJC00500)the Haihe Laboratory of Sustainable Chemical Transformations for financial support.
文摘Possessing excellent mechanical properties,a high-coverage slide-ring conductive gel is constructed by in situ polymerization ofα-cyclodextrin(α-CD)polyrotaxane(PR)and 1-vinyl-3-ethylimidazolium bromide([VEIM]Br)ionic liquid(IL),using 1-ethyl-3-methylimidazolium bromide([EMIM]Br)IL as solvent.Benefiting from the compatibility of ILs and alkene-PR,the cross-linked network slide-ring gel not only maintains excellent conductivity(1.52×10^(−2) S/m),but also has effectively improved mechanical properties(513%fracture strain,0.713 MPa fracture stress,211 kPa elastic modulus and 1366 kJ/m^(3) toughness)and adhesive properties(472.3±25.9 kPa).The supramolecular gel can be used as a strain sensor to efficiently monitor deformation signals in real-time at least 200 times.Especially,the slide-ring gel can self-power generated by triboelectric effect and electrostatic induction between the skin layer and the polydimethylsiloxane(PDMS)layer that encapsulates the gel,achieving reversible and durable motion sensing,which provides a convenient pathway for constructing supramolecular self-powered flexible electronic materials.
基金This study is financed by the European Union-NextGenerationEU,through the National Recovery and Resilience Plan of the Republic of Bulgaria,Project No.BG-RRP-2.013-0001.
文摘Covert timing channels(CTC)exploit network resources to establish hidden communication pathways,posing signi cant risks to data security and policy compliance.erefore,detecting such hidden and dangerous threats remains one of the security challenges. is paper proposes LinguTimeX,a new framework that combines natural language processing with arti cial intelligence,along with explainable Arti cial Intelligence(AI)not only to detect CTC but also to provide insights into the decision process.LinguTimeX performs multidimensional feature extraction by fusing linguistic attributes with temporal network patterns to identify covert channels precisely.LinguTimeX demonstrates strong e ectiveness in detecting CTC across multiple languages;namely English,Arabic,and Chinese.Speci cally,the LSTM and RNN models achieved F1 scores of 90%on the English dataset,89%on the Arabic dataset,and 88%on the Chinese dataset,showcasing their superior performance and ability to generalize across multiple languages. is highlights their robustness in detecting CTCs within security systems,regardless of the language or cultural context of the data.In contrast,the DeepForest model produced F1-scores ranging from 86%to 87%across the same datasets,further con rming its e ectiveness in CTC detection.Although other algorithms also showed reasonable accuracy,the LSTM and RNN models consistently outperformed them in multilingual settings,suggesting that deep learning models might be better suited for this particular problem.
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2601).
文摘Generative Adversarial Networks(GANs)have become valuable tools in medical imaging,enabling realistic image synthesis for enhancement,augmentation,and restoration.However,their integration into clinical workflows raises concerns,particularly the risk of subtle distortions or hallucinations that may undermine diagnostic accuracy and weaken trust in AI-assisted decision-making.To address this challenge,we propose a hybrid deep learning framework designed to detect GAN-induced artifacts in medical images,thereby reinforcing the reliability of AI-driven diagnostics.The framework integrates low-level statistical descriptors,including high-frequency residuals and Gray-Level Co-occurrence Matrix(GLCM)texture features,with high-level semantic representations extracted from a pre-trained ResNet18.This dual-stream approach enables detection of both pixel-level anomalies and structural inconsistencies introduced by GAN-based manipulation.We validated the framework on a curated dataset of 10,000 medical images,evenly split between authentic and GAN-generated samples across four modalities:MRI,CT,X-ray,and fundus photography.To improve generalizability to real-world clinical settings,we incorporated domain adaptation strategies such as adversarial training and style transfer,reducing domain shift by 15%.Experimental results demonstrate robust performance,achieving 92.6%accuracy and an F1-score of 0.91 on synthetic test data,and maintaining strong performance on real-world GAN-modified images with 87.3%accuracy and an F1-score of 0.85.Additionally,the model attained an AUC of 0.96 and an average precision of 0.92,outperforming conventional GAN detection pipelines and baseline Convolutional Neural Network(CNN)architectures.These findings establish the proposed framework as an effective and reliable solution for detecting GAN-induced hallucinations in medical imaging,representing an important step toward building trustworthy and clinically deployable AI systems.
基金supported by Korea Evaluation Institute of Industrial Technology(KEIT)grant funded by the Korea Government(MOTIE)(RS-2022-00154720,Technology Innovation Program Development of next-generation power semiconductor based on Si-on-SiC structure)the National Research Foundation of Korea(NRF)by the Korea government(RS-2023-NR076826)Global-Learning&Academic Research Institution for Master's·PhD students,and Postdocs(LAMP)Program of the National Research Foundation of Korea(NRF)by the Ministry of Education(No.RS-2024-00443714).
文摘Improving device efficiency is fundamental for advancing energy harvesting technology,particularly in systems designed to convert light energy into electrical output.In our previous studies,we developed a basic structure light pressure electric generator(Basic-LPEG),which utilized a layered configuration of Ag/Pb(Zr,Ti)O_(3)(PZT)/Pt/GaAs to generate electricity based on light-induced pressure on the PZT.In this study,we sought to enhance the performance of this Basic-LPEG by introducing Ag nanoparticles/graphene oxide(AgNPs/GO)composite units(NP-LPEG),creating upgraded harvesting device.Specifically,by depositing the AgNPs/GO units twice onto the Basic-LPEG,we observed an increase in output voltage and current from 241 mV and 3.1μA to 310 mV and 9.3μA,respectively,under a solar simulator.The increase in electrical output directly correlated with the intensity of the light pressure impacting the PZT,as well as matched the Raman measurements,finite-difference time-domain simulations,and COMSOL Multiphysics Simulation.Experimental data revealed that the enhancement in electrical output was proportional to the number of hot spots generated between Ag nanoparticles,where the electric field experienced substantial amplification.These results underline the effectiveness of AgNPs/GO units in boosting the light-induced electric generation capacity,thereby providing a promising pathway for high-efficiency energy harvesting devices.
基金The authors extend their appreciation to Prince Sattam bin Abdulaziz University for funding this research work through the project number(PSAU/2024/01/32082).
文摘In Human–Robot Interaction(HRI),generating robot trajectories that accurately reflect user intentions while ensuring physical realism remains challenging,especially in unstructured environments.In this study,we develop a multimodal framework that integrates symbolic task reasoning with continuous trajectory generation.The approach employs transformer models and adversarial training to map high-level intent to robotic motion.Information from multiple data sources,such as voice traits,hand and body keypoints,visual observations,and recorded paths,is integrated simultaneously.These signals are mapped into a shared representation that supports interpretable reasoning while enabling smooth and realistic motion generation.Based on this design,two different learning strategies are investigated.In the first step,grammar-constrained Linear Temporal Logic(LTL)expressions are created from multimodal human inputs.These expressions are subsequently decoded into robot trajectories.The second method generates trajectories directly from symbolic intent and linguistic data,bypassing an intermediate logical representation.Transformer encoders combine multiple types of information,and autoregressive transformer decoders generate motion sequences.Adding smoothness and speed limits during training increases the likelihood of physical feasibility.To improve the realism and stability of the generated trajectories during training,an adversarial discriminator is also included to guide them toward the distribution of actual robot motion.Tests on the NATSGLD dataset indicate that the complete system exhibits stable training behaviour and performance.In normalised coordinates,the logic-based pipeline has an Average Displacement Error(ADE)of 0.040 and a Final Displacement Error(FDE)of 0.036.The adversarial generator makes substantially more progress,reducing ADE to 0.021 and FDE to 0.018.Visual examination confirms that the generated trajectories closely align with observed motion patterns while preserving smooth temporal dynamics.
基金supported and funded by theDeanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2503).
文摘In recent years,fog computing has become an important environment for dealing with the Internet of Things.Fog computing was developed to handle large-scale big data by scheduling tasks via cloud computing.Task scheduling is crucial for efficiently handling IoT user requests,thereby improving system performance,cost,and energy consumption across nodes in cloud computing.With the large amount of data and user requests,achieving the optimal solution to the task scheduling problem is challenging,particularly in terms of cost and energy efficiency.In this paper,we develop novel strategies to save energy consumption across nodes in fog computing when users execute tasks through the least-cost paths.Task scheduling is developed using modified artificial ecosystem optimization(AEO),combined with negative swarm operators,Salp Swarm Algorithm(SSA),in order to competitively optimize their capabilities during the exploitation phase of the optimal search process.In addition,the proposed strategy,Enhancement Artificial Ecosystem Optimization Salp Swarm Algorithm(EAEOSSA),attempts to find the most suitable solution.The optimization that combines cost and energy for multi-objective task scheduling optimization problems.The backpack problem is also added to improve both cost and energy in the iFogSim implementation as well.A comparison was made between the proposed strategy and other strategies in terms of time,cost,energy,and productivity.Experimental results showed that the proposed strategy improved energy consumption,cost,and time over other algorithms.Simulation results demonstrate that the proposed algorithm increases the average cost,average energy consumption,and mean service time in most scenarios,with average reductions of up to 21.15%in cost and 25.8%in energy consumption.
