With the increasing growth of online news,fake electronic news detection has become one of the most important paradigms of modern research.Traditional electronic news detection techniques are generally based on contex...With the increasing growth of online news,fake electronic news detection has become one of the most important paradigms of modern research.Traditional electronic news detection techniques are generally based on contextual understanding,sequential dependencies,and/or data imbalance.This makes distinction between genuine and fabricated news a challenging task.To address this problem,we propose a novel hybrid architecture,T5-SA-LSTM,which synergistically integrates the T5 Transformer for semantically rich contextual embedding with the Self-Attentionenhanced(SA)Long Short-Term Memory(LSTM).The LSTM is trained using the Adam optimizer,which provides faster and more stable convergence compared to the Stochastic Gradient Descend(SGD)and Root Mean Square Propagation(RMSProp).The WELFake and FakeNewsPrediction datasets are used,which consist of labeled news articles having fake and real news samples.Tokenization and Synthetic Minority Over-sampling Technique(SMOTE)methods are used for data preprocessing to ensure linguistic normalization and class imbalance.The incorporation of the Self-Attention(SA)mechanism enables the model to highlight critical words and phrases,thereby enhancing predictive accuracy.The proposed model is evaluated using accuracy,precision,recall(sensitivity),and F1-score as performance metrics.The model achieved 99%accuracy on the WELFake dataset and 96.5%accuracy on the FakeNewsPrediction dataset.It outperformed the competitive schemes such as T5-SA-LSTM(RMSProp),T5-SA-LSTM(SGD)and some other models.展开更多
The evolution of cities into digitally managed environments requires computational systems that can operate in real time while supporting predictive and adaptive infrastructure management.Earlier approaches have often...The evolution of cities into digitally managed environments requires computational systems that can operate in real time while supporting predictive and adaptive infrastructure management.Earlier approaches have often advanced one dimension—such as Internet of Things(IoT)-based data acquisition,Artificial Intelligence(AI)-driven analytics,or digital twin visualization—without fully integrating these strands into a single operational loop.As a result,many existing solutions encounter bottlenecks in responsiveness,interoperability,and scalability,while also leaving concerns about data privacy unresolved.This research introduces a hybrid AI–IoT–Digital Twin framework that combines continuous sensing,distributed intelligence,and simulation-based decision support.The design incorporates multi-source sensor data,lightweight edge inference through Convolutional Neural Networks(CNN)and Long ShortTerm Memory(LSTM)models,and federated learning enhanced with secure aggregation and differential privacy to maintain confidentiality.A digital twin layer extends these capabilities by simulating city assets such as traffic flows and water networks,generating what-if scenarios,and issuing actionable control signals.Complementary modules,including model compression and synchronization protocols,are embedded to ensure reliability in bandwidth-constrained and heterogeneous urban environments.The framework is validated in two urban domains:traffic management,where it adapts signal cycles based on real-time congestion patterns,and pipeline monitoring,where it anticipates leaks through pressure and vibration data.Experimental results show a 28%reduction in response time,a 35%decrease in maintenance costs,and a marked reduction in false positives relative to conventional baselines.The architecture also demonstrates stability across 50+edge devices under federated training and resilience to uneven node participation.The proposed system provides a scalable and privacy-aware foundation for predictive urban infrastructure management.By closing the loop between sensing,learning,and control,it reduces operator dependence,enhances resource efficiency,and supports transparent governance models for emerging smart cities.展开更多
Conventional error cancellation approaches separate molecules into smaller fragments and sum the errors of all fragments to counteract the overall computational error of the parent molecules.However,these approaches m...Conventional error cancellation approaches separate molecules into smaller fragments and sum the errors of all fragments to counteract the overall computational error of the parent molecules.However,these approaches may be ineffective for systems with strong localized chemical effects,as fragmenting specific substructures into simpler chemical bonds can introduce additional errors instead of mitigating them.To address this issue,we propose the Substructure-Preserved Connection-Based Hierarchy(SCBH),a method that automatically identifies and freezes substructures with significant local chemical effects prior to molecular fragmentation.The SCBH is validated by the gas-phase enthalpy of formation calculation of CHNO molecules.Therein,based on the atomization scheme,the reference and test values are derived at the levels of Gaussian-4(G4)and M062X/6-31+G(2df,p),respectively.Compared to commonly used approaches,SCBH reduces the average computational error by half and requires only15%of the computational cost of G4 to achieve comparable accuracy.Since different types of local effect structures have differentiated influences on gas-phase enthalpy of formation,substituents with strong electronic effects should be retained preferentially.SCBH can be readily extended to diverse classes of organic compounds.Its workflow and source code allow flexible customization of molecular moieties,including azide,carboxyl,trinitromethyl,phenyl,and others.This strategy facilitates accurate,rapid,and automated computations and corrections,making it well-suited for high-throughput molecular screening and dataset construction for gas-phase enthalpy of formation.展开更多
Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using d...Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using deep learning algorithms further enhances performance;nevertheless,it is challenging due to the nature of small-scale and imbalanced PD datasets.This paper proposed a convolutional neural network-based deep support vector machine(CNN-DSVM)to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets.A customized kernel function reduces the impact of biased classification towards the majority class(healthy candidates in our consideration).An improved generative adversarial network(IGAN)was designed to generate additional training data to enhance the model’s performance.For performance evaluation,the proposed algorithm achieves a sensitivity of 97.6%and a specificity of 97.3%.The performance comparison is evaluated from five perspectives,including comparisons with different data generation algorithms,feature extraction techniques,kernel functions,and existing works.Results reveal the effectiveness of the IGAN algorithm,which improves the sensitivity and specificity by 4.05%–4.72%and 4.96%–5.86%,respectively;and the effectiveness of the CNN-DSVM algorithm,which improves the sensitivity by 1.24%–57.4%and specificity by 1.04%–163%and reduces biased detection towards the majority class.The ablation experiments confirm the effectiveness of individual components.Two future research directions have also been suggested.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
Background:Stomach cancer(SC)is one of the most lethal malignancies worldwide due to late-stage diagnosis and limited treatment.The transcriptomic,epigenomic,and proteomic,etc.,omics datasets generated by high-through...Background:Stomach cancer(SC)is one of the most lethal malignancies worldwide due to late-stage diagnosis and limited treatment.The transcriptomic,epigenomic,and proteomic,etc.,omics datasets generated by high-throughput sequencing technology have become prominent in biomedical research,and they reveal molecular aspects of cancer diagnosis and therapy.Despite the development of advanced sequencing technology,the presence of high-dimensionality in multi-omics data makes it challenging to interpret the data.Methods:In this study,we introduce RankXLAN,an explainable ensemble-based multi-omics framework that integrates feature selection(FS),ensemble learning,bioinformatics,and in-silico validation for robust biomarker detection,potential therapeutic drug-repurposing candidates’identification,and classification of SC.To enhance the interpretability of the model,we incorporated explainable artificial intelligence(SHapley Additive exPlanations analysis),as well as accuracy,precision,F1-score,recall,cross-validation,specificity,likelihood ratio(LR)+,LR−,and Youden index results.Results:The experimental results showed that the top four FS algorithms achieved improved results when applied to the ensemble learning classification model.The proposed ensemble model produced an area under the curve(AUC)score of 0.994 for gene expression,0.97 for methylation,and 0.96 for miRNA expression data.Through the integration of bioinformatics and ML approach of the transcriptomic and epigenomic multi-omics dataset,we identified potential marker genes,namely,UBE2D2,HPCAL4,IGHA1,DPT,and FN3K.In-silico molecular docking revealed a strong binding affinity between ANKRD13C and the FDA-approved drug Everolimus(binding affinity−10.1 kcal/mol),identifying ANKRD13C as a potential therapeutic drug-repurposing target for SC.Conclusion:The proposed framework RankXLAN outperforms other existing frameworks for serum biomarker identification,therapeutic target identification,and SC classification with multi-omics datasets.展开更多
A novel siphon-based divide-and-conquer(SbDaC)policy is presented in this paper for the synthesis of Petri net(PN)based liveness-enforcing supervisors(LES)for flexible manufacturing systems(FMS)prone to deadlocks or l...A novel siphon-based divide-and-conquer(SbDaC)policy is presented in this paper for the synthesis of Petri net(PN)based liveness-enforcing supervisors(LES)for flexible manufacturing systems(FMS)prone to deadlocks or livelocks.The proposed method takes an uncontrolled and bounded PN model(UPNM)of the FMS.Firstly,the reduced PNM(RPNM)is obtained from the UPNM by using PN reduction rules to reduce the computation burden.Then,the set of strict minimal siphons(SMSs)of the RPNM is computed.Next,the complementary set of SMSs is computed from the set of SMSs.By the union of these two sets,the superset of SMSs is computed.Finally,the set of subnets of the RPNM is obtained by applying the PN reduction rules to the superset of SMSs.All these subnets suffer from deadlocks.These subnets are then ordered from the smallest one to the largest one based on a criterion.To enforce liveness on these subnets,a set of control places(CPs)is computed starting from the smallest subnet to the largest one.Once all subnets are live,this process provides the LES,consisting of a set of CPs to be used for the UPNM.The live controlled PN model(CPNM)is constructed by merging the LES with the UPNM.The SbDaC policy is applicable to all classes of PNs related to FMS prone to deadlocks or livelocks.Several FMS examples are considered from the literature to highlight the applicability of the SbDaC policy.In particular,three examples are utilized to emphasize the importance,applicability and effectiveness of the SbDaC policy to realistic FMS with very large state spaces.展开更多
This study developed a novel heterogeneous Vis-Photo+Fenton-like system by integrating visible-light-responsive Co_(3)O_(4)/TiO_(2) photocatalysis with peroxymonosulfate(PMS)activation for efficient atrazine(ATZ)degra...This study developed a novel heterogeneous Vis-Photo+Fenton-like system by integrating visible-light-responsive Co_(3)O_(4)/TiO_(2) photocatalysis with peroxymonosulfate(PMS)activation for efficient atrazine(ATZ)degradation.The synergistic process achieved complete ATZ removal within 60 min under near-neutral pH(6.9),outperform-ing individual Fenton-like(39%)and photocatalytic(24%)processes.Key factors influencing the degradation efficiency included light sources(UV>visible),pH(optimal at 6.9),catalyst dosage(0.01 g Co_(3)O_(4)/TiO_(2)),and PMS:ATZ molar ratio(1:2).The system exhibited a synergistic coefficient of 5.03(degradation)and 1.97(miner-alization),attributed to enhanced radical generation and accelerated Co^(3+)/Co^(2+)redox cycling through photoin-duced electron transfer.Intermediate analysis revealed dealkylation,dechlorination,and oxidation pathways,with reduced toxicity of by-products(e.g.,CEAT,CIAT)confirmed by ecotoxicity assessments.The mineralization efficiency(Vis-Photo+Fenton-like)reached 83.1%,significantly higher than that of standalone processes(Fenton-like:43.2%;photocatalysis:30.5%).The catalyst demonstrated excellent stability(nearly 90%recov-ery,<1μg/L Co leaching)and practical applicability.This study provides an efficient,sludge-free,and solar-compatible strategy for eliminating persistent herbicides in water treatment.展开更多
The growing global energy demand and worsening climate change highlight the urgent need for clean,efficient and sustainable energy solutions.Among emerging technologies,atomically thin two-dimensional(2D)materials off...The growing global energy demand and worsening climate change highlight the urgent need for clean,efficient and sustainable energy solutions.Among emerging technologies,atomically thin two-dimensional(2D)materials offer unique advantages in photovoltaics due to their tunable optoelectronic properties,high surface area and efficient charge transport capabilities.This review explores recent progress in photovoltaics incorporating 2D materials,focusing on their application as hole and electron transport layers to optimize bandgap alignment,enhance carrier mobility and improve chemical stability.A comprehensive analysis is presented on perovskite solar cells utilizing 2D materials,with a particular focus on strategies to enhance crystallization,passivate defects and improve overall cell efficiency.Additionally,the application of 2D materials in organic solar cells is examined,particularly for reducing recombination losses and enhancing charge extraction through work function modification.Their impact on dye-sensitized solar cells,including catalytic activity and counter electrode performance,is also explored.Finally,the review outlines key challenges,material limitations and performance metrics,offering insight into the future development of nextgeneration photovoltaic devices encouraged by 2D materials.展开更多
This study prepared and characterized amphiphilic carboxymethyl cellulose stearate(CMCS)recycled from sugarcane bagasse agro-waste(SB).The Fourier-transform infrared(FTIR)analysis confirmed cellulose,carboxymethyl cel...This study prepared and characterized amphiphilic carboxymethyl cellulose stearate(CMCS)recycled from sugarcane bagasse agro-waste(SB).The Fourier-transform infrared(FTIR)analysis confirmed cellulose,carboxymethyl cellulose(CMC),and CMCS structures,with CMCS showing increased H-bonding.X-ray diffraction analysis(XRD)revealed reduced crystallinity in CMC and CMCS.CMCS exhibited a hydrophobic nature but dispersed in water,enabling nanoemulsion formation.Optimal nanoemulsion was achieved with CMCS1,showing a particle size of 99 nm.Transmission electron microscopy(TEM)images revealed CMC’s honeycomb structure,transforming into spherical particles in CMCS1.Antimicrobial tests demonstrated strong activity of CMCS formulations against Escherichia coli and Staphylococcus aureus,with CMCS3 exhibiting the highest efficacy.These findings highlight the potential of CMCS-based nanoemulsions for antimicrobial applications and nanoemulsification.展开更多
The rapid advancement of nanotechnology has sparked much interest in applying nanoscale perovskite materials for photodetection applications.These materials are promising candidates for next-generation photodetectors(...The rapid advancement of nanotechnology has sparked much interest in applying nanoscale perovskite materials for photodetection applications.These materials are promising candidates for next-generation photodetectors(PDs)due to their unique optoelectronic properties and flexible synthesis routes.This review explores the approaches used in the development and use of optoelectronic devices made of different nanoscale perovskite architectures,including quantum dots,nanosheets,nanorods,nanowires,and nanocrystals.Through a thorough analysis of recent literature,the review also addresses common issues like the mechanisms underlying the degradation of perovskite PDs and offers perspectives on potential solutions to improve stability and scalability that impede widespread implementation.In addition,it highlights that photodetection encompasses the detection of light fields in dimensions other than light intensity and suggests potential avenues for future research to overcome these obstacles and fully realize the potential of nanoscale perovskite materials in state-of-the-art photodetection systems.This review provides a comprehensive overview of nanoscale perovskite PDs and guides future research efforts towards improved performance and wider applicability,making it a valuable resource for researchers.展开更多
Friction stir welding(FSW)is a relatively new welding technique that has significant advantages compared to the fusion welding techniques in joining non weld able alloys by fusion,such as aluminum alloys.Three FSW sea...Friction stir welding(FSW)is a relatively new welding technique that has significant advantages compared to the fusion welding techniques in joining non weld able alloys by fusion,such as aluminum alloys.Three FSW seams of AA6061-T6 plates were made us-ing different FSW parameters.The structure of the FSW seams was investigated using X-ray diffraction(XRD),scanning electron mi-croscope(SEM)and non destructive testing(NDT)techniques and their hardness was also measured.The dominated phase in the AA6061-T6 alloy and the FSW seams was theα-Al.The FSW seam had lower content of the secondary phases than the AA6061-T6 al-loy.The hardness of the FSW seams was decreased by about 30%compared to the AA6061-T6 alloy.The temperature distributions in the weld seams were also studied experimentally and numerically modeled and the results were in a good agreement.展开更多
Agricultural practices significantly contribute to greenhouse gas(GHG)emissions,necessitating cleaner production technologies to reduce environmental pressure and achieve sustainable maize production.Plastic film mulc...Agricultural practices significantly contribute to greenhouse gas(GHG)emissions,necessitating cleaner production technologies to reduce environmental pressure and achieve sustainable maize production.Plastic film mulching is commonly used in the Loess Plateau region.Incorporating slow-release fertilizers as a replacement for urea within this practice can reduce nitrogen losses and enhance crop productivity.Combining these techniques represents a novel agricultural approach in semi-arid areas.However,the impact of this integration on soil carbon storage(SOCS),carbon footprint(CF),and economic benefits has received limited research attention.Therefore,we conducted an eight-year study(2015-2022)in the semi-arid northwestern region to quantify the effects of four treatments[urea supplied without plastic film mulching(CK-U),slow-release fertilizer supplied without plastic film mulching(CK-S),urea supplied with plastic film mulching(PM-U),and slow-release fertilizer supplied with plastic film mulching(PM-S)]on soil fertility,economic and environmental benefits.The results revealed that nitrogen fertilizer was the primary contributor to total GHG emissions(≥71.97%).Compared to other treatments,PM-S increased average grain yield by 12.01%-37.89%,water use efficiency by 9.19%-23.33%,nitrogen accumulation by 27.07%-66.19%,and net return by 6.21%-29.57%.Furthermore,PM-S decreased CF by 12.87%-44.31%and CF per net return by 14.25%-41.16%.After eight years,PM-S increased SOCS(0-40 cm)by 2.46%,while PM-U decreased it by 7.09%.These findings highlight the positive effects of PM-S on surface soil fertility,economic gains,and environmental benefits in spring maize production on the Loess Plateau,underscoring its potential for widespread adoption and application.展开更多
Population growth leads to increased utilization of water resources.One of these resources is groundwater,which has steadily declined each year.The depletion of these resources brings about various environmental chall...Population growth leads to increased utilization of water resources.One of these resources is groundwater,which has steadily declined each year.The depletion of these resources brings about various environmental challenges.The present study aimed to explore the relationship between groundwater fluctuations and land subsidence in the Malayer Plain,Iran,focusing on quantifying subsidence resulting from groundwater extraction.Using Sentinel-1 satellite data(2014–2019)and monthly piezometric measurements(1996–2018),the analysis revealed an average deformation velocity of–6.3 cm yr–1,with accumulated subsidence of–32 cm over the 2014–2019 period.The maximum subsidence rate reached 10.3 cm yr–1 in areas of intensive agricultural activity.A wavelet-PCA spatiotemporal analysis of groundwater fluctuations identified critical multi-scale patterns strongly correlated with subsidence trends.Regression analysis between subsidence rates and groundwater fluctuations at various wavelet decomposition levels explained 75%of the variance(R2=0.75),indicating that intermediate-scale groundwater declines were the primary drivers of subsidence.Furthermore,land use analysis using Landsat data(1999–2021)revealed a 6230-ha increase in irrigated farmland,contributing to heightened groundwater extraction and subsidence rates.These findings highlight the critical need for sustainable groundwater management to mitigate the risks of continued subsidence in the region.展开更多
BACKGROUND： Neural stem cell （NSC） survival is closely associated with cell apoptosis in ischemic-hypoxic regions following transplantation. Numerous studies have revealed that X-box binding protein 1 （XBP1） is a...BACKGROUND： Neural stem cell （NSC） survival is closely associated with cell apoptosis in ischemic-hypoxic regions following transplantation. Numerous studies have revealed that X-box binding protein 1 （XBP1） is a transcription factor during endoplasmic reticulum unfolded protein response and is essential for cell survival, differentiation, and anti-apoptotic effects. OBJECTIVE： To determine the effects of the XBP1 gene on NSC proliferation and apoptosis under hypoxic conditions following XBP1 gene transfection into rat embryonic hippocampal NSCs using recombinant adenovirus vector. DESIGN, TIME AND SETTING： In vitro experiments were performed at the Laboratory of Cell Biology of Jilin University and Laboratory of Proteomics, Department of Neurology, Jilin University China from September 2008 to November 2009. MATERIALS： Recombinant adenovirus package XBP1 gene and Ad-XBPl-enhanced green fluorescent protein plasmid （Guangzhou Easywin BioMed Technology, China）, rabbit anti-XBP1 and its target gene estrogen receptor degradation-enhancing a-mannosidase-like protein （EDEM） glucose-regulated protein 78 （GRP78）, anti-apoptotic molecule Bcl-2 and proapoptotic molecule Bax polyclonal antibody （Santa Cruz Biotechnology, Inc., Santa Cruz, CA, USA）, and COCI2 （Sigma, St. Louis, MO, USA） were used in the present study. METHODS： Hippocampi from embryonic, Sprague Dawley rats on gestational day 16 were harvested for NSC isolation and cloning, followed by immunofluorescence for Nestin and sub-culturing. The recombinant adenovirus Ad-XBPl-enhanced green fluorescent protein plasmid was transfected into rat embryonic hippocampal NSCs, and then CoCl2 was applied to induce hypoxia. MAIN OUTCOME MEASURES： Cell quantification and 3-（4, 5-dimethylthiazol-2-yl）-2, 5-diphenyltetrazolium bromide colorimetric assay were utilized to detect proliferation in XBPl-transfected NSCs for 7 consecutive days. Western blot assay was utilized to quantify XBP1 GRP78, EDEM, Bcl-2, and Bax expression. Flow cytometry was used to measure apoptosis. RESULTS： NSC proliferation was significantly enhanced following XBP1 gene transfection （P 〈 0.05）. Under hypoxic conditions, GRP78, EDEM, and Bcl-2 levels increased, but Bax levels decreased. In addition, NSC apoptosis decreased following transfection （P 〈 0.05）. CONCLUSION： The XBP1 gene was successfully transfected into rat embryonic hippocampal NSCs using a recombinant adenovirus vector. NSC proliferation following transfection, as well as anti-apoptotic effects under hypoxia, was significantly increased.展开更多
For the first time,the linear and nonlinear vibrations of composite rectangular sandwich plates with various geometric patterns of lattice core have been analytically examined in this work.The plate comprises a lattic...For the first time,the linear and nonlinear vibrations of composite rectangular sandwich plates with various geometric patterns of lattice core have been analytically examined in this work.The plate comprises a lattice core located in the middle and several homogeneous orthotropic layers that are symmetrical relative to it.For this purpose,the partial differential equations of motion have been derived based on the first-order shear deformation theory,employing Hamilton’s principle and Von Kármán’s nonlinear displacement-strain relations.Then,the nonlinear partial differential equations of the plate are converted into a time-dependent nonlinear ordinary differential equation(Duffing equation)by applying the Galerkin method.From the solution of this equation,the natural frequencies are extracted.Then,to calculate the non-linear frequencies of the plate,the non-linear equation of the plate has been solved analytically using the method of multiple scales.Finally,the effect of some critical parameters of the system,such as the thickness,height,and different angles of the stiffeners on the linear and nonlinear frequencies,has been analyzed in detail.To confirmthe solution method,the results of this research have been compared with the reported results in the literature and finite elements in ABAQUS,and a perfect match is observed.The results reveal that the geometry and configuration of core ribs strongly affect the natural frequencies of the plate.展开更多
In this study,a new analytical technique was developed for the identification and quantification of multifunctional compounds containing simultaneously at least one hydroxyl or one carboxylic group,or both.This techni...In this study,a new analytical technique was developed for the identification and quantification of multifunctional compounds containing simultaneously at least one hydroxyl or one carboxylic group,or both.This technique is based on derivatizing first the carboxylic group(s) of the multifunctional compound using an alcohol (e.g.,methanol,1-butanol) in the presence of a relatively strong Lewisacid (BF3) as a catalyst.This esterification reaction quickly and quantitatively converts carboxylic acids to their ester forms.The second step is based on silylation of the ester compounds using bis(trimethylsilyl) trifluoroacetamide (BSTFA) as the derivatizing agent.For compounds bearing ketone groups in addition to carboxylic and hydroxyl groups,a third step was used based on PFBHA derivatizationof the carbonyls.Different parameters including temperature,reaction time,and effect due to artifacts were optimized.A GC/MS in EI and in methane-CI mode was used for the analysis of these compounds.The new approach was tested on a number of multifunctional compounds.The interpretation of their EI (70 eV) and CI mass spectra shows that critical information is gained leading to unambiguous identification of unknown compounds.For example,when derivatized only with BF3-methanol,their mass spectra comprise primary ions at m/z M ·+ +1,M ·+ +29,and M ·+ - 31 for compounds bearing only carboxylic groups and M ·+ +1,M ·+ +29,M ·+ -31,and M ·+ -17 for those bearing hydroxyl andcarboxylic groups.However,when a second derivatization (BSTFA) was used,compounds bearing hydroxyl and carboxylic groups simultaneously show,in addition to the ions observed before,ions at m/z M ·+ +73,M ·+ -15,M ·+ -59,M ·+ -75,M ·+ -89,and 73.To the best of our knowledge,this technique describes systematically for the first time a method for identifying multifunctional oxygenated compounds containing simultaneously one or more hydroxyl and carboxylic acid groups.展开更多
The increasing frequency and intensity of drought caused by climate change necessitate the implementation of effective ways to increase the ability of wheat to withstand drought, with humic acid being a promising appr...The increasing frequency and intensity of drought caused by climate change necessitate the implementation of effective ways to increase the ability of wheat to withstand drought, with humic acid being a promising approach. Therefore, a pot experiment was conducted to determine the efficacy of exogenous humic acid on wheat under water deficit stress via a completely randomized design (CRD) with three replications. The impacts of four growing conditions, i.e., well water (65% field capacity), water deficit stress (35% field capacity), soil application of humic acid (44 mg kg−1 soil) under water deficit stress and foliar feeding of humic acid (200 ppm) under water deficit stress, were investigated on two wheat varieties (BWMRI Gom 1 and BWMRI Gom 3). The results demonstrated that water deficit stress substantially decreased the studied morphological and physiological traits, yield components and yield, in both genotypes, with the exception of the proline content of flag leaves. Compared with soil application, foliar feeding of humic acid promoted the ability of wheat to overcome stress conditions better. In the present study, humic acid as a soil application increased the grain yield by 9.13% and 13.86% and the biological yield by 9.94% and 5.19%, whereas foliar treatment increased the grain output by 24.76% and 25.19% and the biological yield by 19.23% and 6.50% in BWMRI Gom 1 and BWMRI Gom 3, respectively, under water deficit stress. Therefore, exogenous foliar humic acid treatment was more effective than soil application in alleviating the effects of drought stress on wheat.展开更多
One important step in binary modeling of environmental problems is the generation of absence-datasets that are traditionally generated by random sampling and can undermine the quality of outputs.To solve this problem,...One important step in binary modeling of environmental problems is the generation of absence-datasets that are traditionally generated by random sampling and can undermine the quality of outputs.To solve this problem,this study develops the Absence Point Generation(APG)toolbox which is a Python-based ArcGIS toolbox for automated construction of absence-datasets for geospatial studies.The APG employs a frequency ratio analysis of four commonly used and important driving factors such as altitude,slope degree,topographic wetness index,and distance from rivers,and considers the presence locations buffer and density layers to define the low potential or susceptibility zones where absence-datasets are generated.To test the APG toolbox,we applied two benchmark algorithms of random forest(RF)and boosted regression trees(BRT)in a case study to investigate groundwater potential using three absence datasets i.e.,the APG,random,and selection of absence samples(SAS)toolbox.The BRT-APG and RF-APG had the area under receiver operating curve(AUC)values of 0.947 and 0.942,while BRT and RF had weaker performances with the SAS and Random datasets.This effect resulted in AUC improvements for BRT and RF by 7.2,and 9.7%from the Random dataset,and AUC improvements for BRT and RF by 6.1,and 5.4%from the SAS dataset,respectively.The APG also impacted the importance of the input factors and the pattern of the groundwater potential maps,which proves the importance of absence points in environmental binary issues.The proposed APG toolbox could be easily applied in other environmental hazards such as landslides,floods,and gully erosion,and land subsidence.展开更多
Lung cancer continues to be a leading cause of cancer-related deaths worldwide,emphasizing the critical need for improved diagnostic techniques.Early detection of lung tumors significantly increases the chances of suc...Lung cancer continues to be a leading cause of cancer-related deaths worldwide,emphasizing the critical need for improved diagnostic techniques.Early detection of lung tumors significantly increases the chances of successful treatment and survival.However,current diagnostic methods often fail to detect tumors at an early stage or to accurately pinpoint their location within the lung tissue.Single-model deep learning technologies for lung cancer detection,while beneficial,cannot capture the full range of features present in medical imaging data,leading to incomplete or inaccurate detection.Furthermore,it may not be robust enough to handle the wide variability in medical images due to different imaging conditions,patient anatomy,and tumor characteristics.To overcome these disadvantages,dual-model or multi-model approaches can be employed.This research focuses on enhancing the detection of lung cancer by utilizing a combination of two learning models:a Convolutional Neural Network(CNN)for categorization and the You Only Look Once(YOLOv8)architecture for real-time identification and pinpointing of tumors.CNNs automatically learn to extract hierarchical features from raw image data,capturing patterns such as edges,textures,and complex structures that are crucial for identifying lung cancer.YOLOv8 incorporates multiscale feature extraction,enabling the detection of tumors of varying sizes and scales within a single image.This is particularly beneficial for identifying small or irregularly shaped tumors that may be challenging to detect.Furthermore,through the utilization of cutting-edge data augmentation methods,such as Deep Convolutional Generative Adversarial Networks(DCGAN),the suggested approach can handle the issue of limited data and boost the models’ability to learn from diverse and comprehensive datasets.The combined method not only improved accuracy and localization but also ensured efficient real-time processing,which is crucial for practical clinical applications.The CNN achieved an accuracy of 97.67%in classifying lung tissues into healthy and cancerous categories.The YOLOv8 model achieved an Intersection over Union(IoU)score of 0.85 for tumor localization,reflecting high precision in detecting and marking tumor boundaries within the images.Finally,the incorporation of synthetic images generated by DCGAN led to a 10%improvement in both the CNN classification accuracy and YOLOv8 detection performance.展开更多
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R195)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘With the increasing growth of online news,fake electronic news detection has become one of the most important paradigms of modern research.Traditional electronic news detection techniques are generally based on contextual understanding,sequential dependencies,and/or data imbalance.This makes distinction between genuine and fabricated news a challenging task.To address this problem,we propose a novel hybrid architecture,T5-SA-LSTM,which synergistically integrates the T5 Transformer for semantically rich contextual embedding with the Self-Attentionenhanced(SA)Long Short-Term Memory(LSTM).The LSTM is trained using the Adam optimizer,which provides faster and more stable convergence compared to the Stochastic Gradient Descend(SGD)and Root Mean Square Propagation(RMSProp).The WELFake and FakeNewsPrediction datasets are used,which consist of labeled news articles having fake and real news samples.Tokenization and Synthetic Minority Over-sampling Technique(SMOTE)methods are used for data preprocessing to ensure linguistic normalization and class imbalance.The incorporation of the Self-Attention(SA)mechanism enables the model to highlight critical words and phrases,thereby enhancing predictive accuracy.The proposed model is evaluated using accuracy,precision,recall(sensitivity),and F1-score as performance metrics.The model achieved 99%accuracy on the WELFake dataset and 96.5%accuracy on the FakeNewsPrediction dataset.It outperformed the competitive schemes such as T5-SA-LSTM(RMSProp),T5-SA-LSTM(SGD)and some other models.
基金The researchers would like to thank the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support(QU-APC-2025)。
文摘The evolution of cities into digitally managed environments requires computational systems that can operate in real time while supporting predictive and adaptive infrastructure management.Earlier approaches have often advanced one dimension—such as Internet of Things(IoT)-based data acquisition,Artificial Intelligence(AI)-driven analytics,or digital twin visualization—without fully integrating these strands into a single operational loop.As a result,many existing solutions encounter bottlenecks in responsiveness,interoperability,and scalability,while also leaving concerns about data privacy unresolved.This research introduces a hybrid AI–IoT–Digital Twin framework that combines continuous sensing,distributed intelligence,and simulation-based decision support.The design incorporates multi-source sensor data,lightweight edge inference through Convolutional Neural Networks(CNN)and Long ShortTerm Memory(LSTM)models,and federated learning enhanced with secure aggregation and differential privacy to maintain confidentiality.A digital twin layer extends these capabilities by simulating city assets such as traffic flows and water networks,generating what-if scenarios,and issuing actionable control signals.Complementary modules,including model compression and synchronization protocols,are embedded to ensure reliability in bandwidth-constrained and heterogeneous urban environments.The framework is validated in two urban domains:traffic management,where it adapts signal cycles based on real-time congestion patterns,and pipeline monitoring,where it anticipates leaks through pressure and vibration data.Experimental results show a 28%reduction in response time,a 35%decrease in maintenance costs,and a marked reduction in false positives relative to conventional baselines.The architecture also demonstrates stability across 50+edge devices under federated training and resilience to uneven node participation.The proposed system provides a scalable and privacy-aware foundation for predictive urban infrastructure management.By closing the loop between sensing,learning,and control,it reduces operator dependence,enhances resource efficiency,and supports transparent governance models for emerging smart cities.
基金the support of the National Natural Science Foundation of China(22575230)。
文摘Conventional error cancellation approaches separate molecules into smaller fragments and sum the errors of all fragments to counteract the overall computational error of the parent molecules.However,these approaches may be ineffective for systems with strong localized chemical effects,as fragmenting specific substructures into simpler chemical bonds can introduce additional errors instead of mitigating them.To address this issue,we propose the Substructure-Preserved Connection-Based Hierarchy(SCBH),a method that automatically identifies and freezes substructures with significant local chemical effects prior to molecular fragmentation.The SCBH is validated by the gas-phase enthalpy of formation calculation of CHNO molecules.Therein,based on the atomization scheme,the reference and test values are derived at the levels of Gaussian-4(G4)and M062X/6-31+G(2df,p),respectively.Compared to commonly used approaches,SCBH reduces the average computational error by half and requires only15%of the computational cost of G4 to achieve comparable accuracy.Since different types of local effect structures have differentiated influences on gas-phase enthalpy of formation,substituents with strong electronic effects should be retained preferentially.SCBH can be readily extended to diverse classes of organic compounds.Its workflow and source code allow flexible customization of molecular moieties,including azide,carboxyl,trinitromethyl,phenyl,and others.This strategy facilitates accurate,rapid,and automated computations and corrections,making it well-suited for high-throughput molecular screening and dataset construction for gas-phase enthalpy of formation.
基金The work described in this paper was fully supported by a grant from Hong Kong Metropolitan University(RIF/2021/05).
文摘Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using deep learning algorithms further enhances performance;nevertheless,it is challenging due to the nature of small-scale and imbalanced PD datasets.This paper proposed a convolutional neural network-based deep support vector machine(CNN-DSVM)to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets.A customized kernel function reduces the impact of biased classification towards the majority class(healthy candidates in our consideration).An improved generative adversarial network(IGAN)was designed to generate additional training data to enhance the model’s performance.For performance evaluation,the proposed algorithm achieves a sensitivity of 97.6%and a specificity of 97.3%.The performance comparison is evaluated from five perspectives,including comparisons with different data generation algorithms,feature extraction techniques,kernel functions,and existing works.Results reveal the effectiveness of the IGAN algorithm,which improves the sensitivity and specificity by 4.05%–4.72%and 4.96%–5.86%,respectively;and the effectiveness of the CNN-DSVM algorithm,which improves the sensitivity by 1.24%–57.4%and specificity by 1.04%–163%and reduces biased detection towards the majority class.The ablation experiments confirm the effectiveness of individual components.Two future research directions have also been suggested.
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the Deanship of Research and Graduate Studies at King Khalid University,KSA,for funding this work through the Large Research Project under grant number RGP2/164/46.
文摘Background:Stomach cancer(SC)is one of the most lethal malignancies worldwide due to late-stage diagnosis and limited treatment.The transcriptomic,epigenomic,and proteomic,etc.,omics datasets generated by high-throughput sequencing technology have become prominent in biomedical research,and they reveal molecular aspects of cancer diagnosis and therapy.Despite the development of advanced sequencing technology,the presence of high-dimensionality in multi-omics data makes it challenging to interpret the data.Methods:In this study,we introduce RankXLAN,an explainable ensemble-based multi-omics framework that integrates feature selection(FS),ensemble learning,bioinformatics,and in-silico validation for robust biomarker detection,potential therapeutic drug-repurposing candidates’identification,and classification of SC.To enhance the interpretability of the model,we incorporated explainable artificial intelligence(SHapley Additive exPlanations analysis),as well as accuracy,precision,F1-score,recall,cross-validation,specificity,likelihood ratio(LR)+,LR−,and Youden index results.Results:The experimental results showed that the top four FS algorithms achieved improved results when applied to the ensemble learning classification model.The proposed ensemble model produced an area under the curve(AUC)score of 0.994 for gene expression,0.97 for methylation,and 0.96 for miRNA expression data.Through the integration of bioinformatics and ML approach of the transcriptomic and epigenomic multi-omics dataset,we identified potential marker genes,namely,UBE2D2,HPCAL4,IGHA1,DPT,and FN3K.In-silico molecular docking revealed a strong binding affinity between ANKRD13C and the FDA-approved drug Everolimus(binding affinity−10.1 kcal/mol),identifying ANKRD13C as a potential therapeutic drug-repurposing target for SC.Conclusion:The proposed framework RankXLAN outperforms other existing frameworks for serum biomarker identification,therapeutic target identification,and SC classification with multi-omics datasets.
基金The authors extend their appreciation to King Saud University,Saudi Arabia for funding this work through the Ongoing Research Funding Program(ORF-2025-704),King Saud University,Riyadh,Saudi Arabia.
文摘A novel siphon-based divide-and-conquer(SbDaC)policy is presented in this paper for the synthesis of Petri net(PN)based liveness-enforcing supervisors(LES)for flexible manufacturing systems(FMS)prone to deadlocks or livelocks.The proposed method takes an uncontrolled and bounded PN model(UPNM)of the FMS.Firstly,the reduced PNM(RPNM)is obtained from the UPNM by using PN reduction rules to reduce the computation burden.Then,the set of strict minimal siphons(SMSs)of the RPNM is computed.Next,the complementary set of SMSs is computed from the set of SMSs.By the union of these two sets,the superset of SMSs is computed.Finally,the set of subnets of the RPNM is obtained by applying the PN reduction rules to the superset of SMSs.All these subnets suffer from deadlocks.These subnets are then ordered from the smallest one to the largest one based on a criterion.To enforce liveness on these subnets,a set of control places(CPs)is computed starting from the smallest subnet to the largest one.Once all subnets are live,this process provides the LES,consisting of a set of CPs to be used for the UPNM.The live controlled PN model(CPNM)is constructed by merging the LES with the UPNM.The SbDaC policy is applicable to all classes of PNs related to FMS prone to deadlocks or livelocks.Several FMS examples are considered from the literature to highlight the applicability of the SbDaC policy.In particular,three examples are utilized to emphasize the importance,applicability and effectiveness of the SbDaC policy to realistic FMS with very large state spaces.
基金supported by the Financial Supports of the National Natural Science Foundation of China(Nos.51508056,52370030 and 42007352)the Chongqing Postgraduate Joint Training Base Project(No.JDLHPYJD2022005)the special fund of Henan Key Labora-tory of Water Pollution Control and Rehabilitation Technology(No.CJSZ2024001).
文摘This study developed a novel heterogeneous Vis-Photo+Fenton-like system by integrating visible-light-responsive Co_(3)O_(4)/TiO_(2) photocatalysis with peroxymonosulfate(PMS)activation for efficient atrazine(ATZ)degradation.The synergistic process achieved complete ATZ removal within 60 min under near-neutral pH(6.9),outperform-ing individual Fenton-like(39%)and photocatalytic(24%)processes.Key factors influencing the degradation efficiency included light sources(UV>visible),pH(optimal at 6.9),catalyst dosage(0.01 g Co_(3)O_(4)/TiO_(2)),and PMS:ATZ molar ratio(1:2).The system exhibited a synergistic coefficient of 5.03(degradation)and 1.97(miner-alization),attributed to enhanced radical generation and accelerated Co^(3+)/Co^(2+)redox cycling through photoin-duced electron transfer.Intermediate analysis revealed dealkylation,dechlorination,and oxidation pathways,with reduced toxicity of by-products(e.g.,CEAT,CIAT)confirmed by ecotoxicity assessments.The mineralization efficiency(Vis-Photo+Fenton-like)reached 83.1%,significantly higher than that of standalone processes(Fenton-like:43.2%;photocatalysis:30.5%).The catalyst demonstrated excellent stability(nearly 90%recov-ery,<1μg/L Co leaching)and practical applicability.This study provides an efficient,sludge-free,and solar-compatible strategy for eliminating persistent herbicides in water treatment.
基金supported by the IITP(Institute of Information & Communications Technology Planning & Evaluation)-ITRC(Information Technology Research Center) grant funded by the Korea government(Ministry of Science and ICT) (IITP-2025-RS-2024-00437191, and RS-2025-02303505)partly supported by the Korea Basic Science Institute (National Research Facilities and Equipment Center) grant funded by the Ministry of Education. (No. 2022R1A6C101A774)the Deanship of Research and Graduate Studies at King Khalid University, Saudi Arabia, through Large Research Project under grant number RGP-2/527/46
文摘The growing global energy demand and worsening climate change highlight the urgent need for clean,efficient and sustainable energy solutions.Among emerging technologies,atomically thin two-dimensional(2D)materials offer unique advantages in photovoltaics due to their tunable optoelectronic properties,high surface area and efficient charge transport capabilities.This review explores recent progress in photovoltaics incorporating 2D materials,focusing on their application as hole and electron transport layers to optimize bandgap alignment,enhance carrier mobility and improve chemical stability.A comprehensive analysis is presented on perovskite solar cells utilizing 2D materials,with a particular focus on strategies to enhance crystallization,passivate defects and improve overall cell efficiency.Additionally,the application of 2D materials in organic solar cells is examined,particularly for reducing recombination losses and enhancing charge extraction through work function modification.Their impact on dye-sensitized solar cells,including catalytic activity and counter electrode performance,is also explored.Finally,the review outlines key challenges,material limitations and performance metrics,offering insight into the future development of nextgeneration photovoltaic devices encouraged by 2D materials.
文摘This study prepared and characterized amphiphilic carboxymethyl cellulose stearate(CMCS)recycled from sugarcane bagasse agro-waste(SB).The Fourier-transform infrared(FTIR)analysis confirmed cellulose,carboxymethyl cellulose(CMC),and CMCS structures,with CMCS showing increased H-bonding.X-ray diffraction analysis(XRD)revealed reduced crystallinity in CMC and CMCS.CMCS exhibited a hydrophobic nature but dispersed in water,enabling nanoemulsion formation.Optimal nanoemulsion was achieved with CMCS1,showing a particle size of 99 nm.Transmission electron microscopy(TEM)images revealed CMC’s honeycomb structure,transforming into spherical particles in CMCS1.Antimicrobial tests demonstrated strong activity of CMCS formulations against Escherichia coli and Staphylococcus aureus,with CMCS3 exhibiting the highest efficacy.These findings highlight the potential of CMCS-based nanoemulsions for antimicrobial applications and nanoemulsification.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)(No.RS-2022–00165798)Anhui Natural Science Foundation(No.2308085MF211)The authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Research Project under Grant Number(R.G.P.2/491/45).
文摘The rapid advancement of nanotechnology has sparked much interest in applying nanoscale perovskite materials for photodetection applications.These materials are promising candidates for next-generation photodetectors(PDs)due to their unique optoelectronic properties and flexible synthesis routes.This review explores the approaches used in the development and use of optoelectronic devices made of different nanoscale perovskite architectures,including quantum dots,nanosheets,nanorods,nanowires,and nanocrystals.Through a thorough analysis of recent literature,the review also addresses common issues like the mechanisms underlying the degradation of perovskite PDs and offers perspectives on potential solutions to improve stability and scalability that impede widespread implementation.In addition,it highlights that photodetection encompasses the detection of light fields in dimensions other than light intensity and suggests potential avenues for future research to overcome these obstacles and fully realize the potential of nanoscale perovskite materials in state-of-the-art photodetection systems.This review provides a comprehensive overview of nanoscale perovskite PDs and guides future research efforts towards improved performance and wider applicability,making it a valuable resource for researchers.
文摘Friction stir welding(FSW)is a relatively new welding technique that has significant advantages compared to the fusion welding techniques in joining non weld able alloys by fusion,such as aluminum alloys.Three FSW seams of AA6061-T6 plates were made us-ing different FSW parameters.The structure of the FSW seams was investigated using X-ray diffraction(XRD),scanning electron mi-croscope(SEM)and non destructive testing(NDT)techniques and their hardness was also measured.The dominated phase in the AA6061-T6 alloy and the FSW seams was theα-Al.The FSW seam had lower content of the secondary phases than the AA6061-T6 al-loy.The hardness of the FSW seams was decreased by about 30%compared to the AA6061-T6 alloy.The temperature distributions in the weld seams were also studied experimentally and numerically modeled and the results were in a good agreement.
基金supported by the National Natural Science Foundation of China(No.32071980)the Key Projects of Shaanxi Agricultural Collaborative Innovation and Extension Alliance(No.LMZD202201)+1 种基金the Key R&D Project in Shaanxi Province(No.2021LLRH-07)Shaanxi Natural Scientific Basic Research Program project(No.2022JQ-157).
文摘Agricultural practices significantly contribute to greenhouse gas(GHG)emissions,necessitating cleaner production technologies to reduce environmental pressure and achieve sustainable maize production.Plastic film mulching is commonly used in the Loess Plateau region.Incorporating slow-release fertilizers as a replacement for urea within this practice can reduce nitrogen losses and enhance crop productivity.Combining these techniques represents a novel agricultural approach in semi-arid areas.However,the impact of this integration on soil carbon storage(SOCS),carbon footprint(CF),and economic benefits has received limited research attention.Therefore,we conducted an eight-year study(2015-2022)in the semi-arid northwestern region to quantify the effects of four treatments[urea supplied without plastic film mulching(CK-U),slow-release fertilizer supplied without plastic film mulching(CK-S),urea supplied with plastic film mulching(PM-U),and slow-release fertilizer supplied with plastic film mulching(PM-S)]on soil fertility,economic and environmental benefits.The results revealed that nitrogen fertilizer was the primary contributor to total GHG emissions(≥71.97%).Compared to other treatments,PM-S increased average grain yield by 12.01%-37.89%,water use efficiency by 9.19%-23.33%,nitrogen accumulation by 27.07%-66.19%,and net return by 6.21%-29.57%.Furthermore,PM-S decreased CF by 12.87%-44.31%and CF per net return by 14.25%-41.16%.After eight years,PM-S increased SOCS(0-40 cm)by 2.46%,while PM-U decreased it by 7.09%.These findings highlight the positive effects of PM-S on surface soil fertility,economic gains,and environmental benefits in spring maize production on the Loess Plateau,underscoring its potential for widespread adoption and application.
文摘Population growth leads to increased utilization of water resources.One of these resources is groundwater,which has steadily declined each year.The depletion of these resources brings about various environmental challenges.The present study aimed to explore the relationship between groundwater fluctuations and land subsidence in the Malayer Plain,Iran,focusing on quantifying subsidence resulting from groundwater extraction.Using Sentinel-1 satellite data(2014–2019)and monthly piezometric measurements(1996–2018),the analysis revealed an average deformation velocity of–6.3 cm yr–1,with accumulated subsidence of–32 cm over the 2014–2019 period.The maximum subsidence rate reached 10.3 cm yr–1 in areas of intensive agricultural activity.A wavelet-PCA spatiotemporal analysis of groundwater fluctuations identified critical multi-scale patterns strongly correlated with subsidence trends.Regression analysis between subsidence rates and groundwater fluctuations at various wavelet decomposition levels explained 75%of the variance(R2=0.75),indicating that intermediate-scale groundwater declines were the primary drivers of subsidence.Furthermore,land use analysis using Landsat data(1999–2021)revealed a 6230-ha increase in irrigated farmland,contributing to heightened groundwater extraction and subsidence rates.These findings highlight the critical need for sustainable groundwater management to mitigate the risks of continued subsidence in the region.
文摘BACKGROUND： Neural stem cell （NSC） survival is closely associated with cell apoptosis in ischemic-hypoxic regions following transplantation. Numerous studies have revealed that X-box binding protein 1 （XBP1） is a transcription factor during endoplasmic reticulum unfolded protein response and is essential for cell survival, differentiation, and anti-apoptotic effects. OBJECTIVE： To determine the effects of the XBP1 gene on NSC proliferation and apoptosis under hypoxic conditions following XBP1 gene transfection into rat embryonic hippocampal NSCs using recombinant adenovirus vector. DESIGN, TIME AND SETTING： In vitro experiments were performed at the Laboratory of Cell Biology of Jilin University and Laboratory of Proteomics, Department of Neurology, Jilin University China from September 2008 to November 2009. MATERIALS： Recombinant adenovirus package XBP1 gene and Ad-XBPl-enhanced green fluorescent protein plasmid （Guangzhou Easywin BioMed Technology, China）, rabbit anti-XBP1 and its target gene estrogen receptor degradation-enhancing a-mannosidase-like protein （EDEM） glucose-regulated protein 78 （GRP78）, anti-apoptotic molecule Bcl-2 and proapoptotic molecule Bax polyclonal antibody （Santa Cruz Biotechnology, Inc., Santa Cruz, CA, USA）, and COCI2 （Sigma, St. Louis, MO, USA） were used in the present study. METHODS： Hippocampi from embryonic, Sprague Dawley rats on gestational day 16 were harvested for NSC isolation and cloning, followed by immunofluorescence for Nestin and sub-culturing. The recombinant adenovirus Ad-XBPl-enhanced green fluorescent protein plasmid was transfected into rat embryonic hippocampal NSCs, and then CoCl2 was applied to induce hypoxia. MAIN OUTCOME MEASURES： Cell quantification and 3-（4, 5-dimethylthiazol-2-yl）-2, 5-diphenyltetrazolium bromide colorimetric assay were utilized to detect proliferation in XBPl-transfected NSCs for 7 consecutive days. Western blot assay was utilized to quantify XBP1 GRP78, EDEM, Bcl-2, and Bax expression. Flow cytometry was used to measure apoptosis. RESULTS： NSC proliferation was significantly enhanced following XBP1 gene transfection （P 〈 0.05）. Under hypoxic conditions, GRP78, EDEM, and Bcl-2 levels increased, but Bax levels decreased. In addition, NSC apoptosis decreased following transfection （P 〈 0.05）. CONCLUSION： The XBP1 gene was successfully transfected into rat embryonic hippocampal NSCs using a recombinant adenovirus vector. NSC proliferation following transfection, as well as anti-apoptotic effects under hypoxia, was significantly increased.
文摘For the first time,the linear and nonlinear vibrations of composite rectangular sandwich plates with various geometric patterns of lattice core have been analytically examined in this work.The plate comprises a lattice core located in the middle and several homogeneous orthotropic layers that are symmetrical relative to it.For this purpose,the partial differential equations of motion have been derived based on the first-order shear deformation theory,employing Hamilton’s principle and Von Kármán’s nonlinear displacement-strain relations.Then,the nonlinear partial differential equations of the plate are converted into a time-dependent nonlinear ordinary differential equation(Duffing equation)by applying the Galerkin method.From the solution of this equation,the natural frequencies are extracted.Then,to calculate the non-linear frequencies of the plate,the non-linear equation of the plate has been solved analytically using the method of multiple scales.Finally,the effect of some critical parameters of the system,such as the thickness,height,and different angles of the stiffeners on the linear and nonlinear frequencies,has been analyzed in detail.To confirmthe solution method,the results of this research have been compared with the reported results in the literature and finite elements in ABAQUS,and a perfect match is observed.The results reveal that the geometry and configuration of core ribs strongly affect the natural frequencies of the plate.
文摘In this study,a new analytical technique was developed for the identification and quantification of multifunctional compounds containing simultaneously at least one hydroxyl or one carboxylic group,or both.This technique is based on derivatizing first the carboxylic group(s) of the multifunctional compound using an alcohol (e.g.,methanol,1-butanol) in the presence of a relatively strong Lewisacid (BF3) as a catalyst.This esterification reaction quickly and quantitatively converts carboxylic acids to their ester forms.The second step is based on silylation of the ester compounds using bis(trimethylsilyl) trifluoroacetamide (BSTFA) as the derivatizing agent.For compounds bearing ketone groups in addition to carboxylic and hydroxyl groups,a third step was used based on PFBHA derivatizationof the carbonyls.Different parameters including temperature,reaction time,and effect due to artifacts were optimized.A GC/MS in EI and in methane-CI mode was used for the analysis of these compounds.The new approach was tested on a number of multifunctional compounds.The interpretation of their EI (70 eV) and CI mass spectra shows that critical information is gained leading to unambiguous identification of unknown compounds.For example,when derivatized only with BF3-methanol,their mass spectra comprise primary ions at m/z M ·+ +1,M ·+ +29,and M ·+ - 31 for compounds bearing only carboxylic groups and M ·+ +1,M ·+ +29,M ·+ -31,and M ·+ -17 for those bearing hydroxyl andcarboxylic groups.However,when a second derivatization (BSTFA) was used,compounds bearing hydroxyl and carboxylic groups simultaneously show,in addition to the ions observed before,ions at m/z M ·+ +73,M ·+ -15,M ·+ -59,M ·+ -75,M ·+ -89,and 73.To the best of our knowledge,this technique describes systematically for the first time a method for identifying multifunctional oxygenated compounds containing simultaneously one or more hydroxyl and carboxylic acid groups.
基金funded byDepartment of Crop Physiology and Ecology,HajeeMohammad Danesh Science and Technology University,Dinajpur 5200 Bangladesh and Taif University,Saudi Arabia,Project No.TU-DSPP-2024-07.
文摘The increasing frequency and intensity of drought caused by climate change necessitate the implementation of effective ways to increase the ability of wheat to withstand drought, with humic acid being a promising approach. Therefore, a pot experiment was conducted to determine the efficacy of exogenous humic acid on wheat under water deficit stress via a completely randomized design (CRD) with three replications. The impacts of four growing conditions, i.e., well water (65% field capacity), water deficit stress (35% field capacity), soil application of humic acid (44 mg kg−1 soil) under water deficit stress and foliar feeding of humic acid (200 ppm) under water deficit stress, were investigated on two wheat varieties (BWMRI Gom 1 and BWMRI Gom 3). The results demonstrated that water deficit stress substantially decreased the studied morphological and physiological traits, yield components and yield, in both genotypes, with the exception of the proline content of flag leaves. Compared with soil application, foliar feeding of humic acid promoted the ability of wheat to overcome stress conditions better. In the present study, humic acid as a soil application increased the grain yield by 9.13% and 13.86% and the biological yield by 9.94% and 5.19%, whereas foliar treatment increased the grain output by 24.76% and 25.19% and the biological yield by 19.23% and 6.50% in BWMRI Gom 1 and BWMRI Gom 3, respectively, under water deficit stress. Therefore, exogenous foliar humic acid treatment was more effective than soil application in alleviating the effects of drought stress on wheat.
基金This research is supported by the MECW research programthe Centre for Advanced Middle Eastern Studies,Lund University.
文摘One important step in binary modeling of environmental problems is the generation of absence-datasets that are traditionally generated by random sampling and can undermine the quality of outputs.To solve this problem,this study develops the Absence Point Generation(APG)toolbox which is a Python-based ArcGIS toolbox for automated construction of absence-datasets for geospatial studies.The APG employs a frequency ratio analysis of four commonly used and important driving factors such as altitude,slope degree,topographic wetness index,and distance from rivers,and considers the presence locations buffer and density layers to define the low potential or susceptibility zones where absence-datasets are generated.To test the APG toolbox,we applied two benchmark algorithms of random forest(RF)and boosted regression trees(BRT)in a case study to investigate groundwater potential using three absence datasets i.e.,the APG,random,and selection of absence samples(SAS)toolbox.The BRT-APG and RF-APG had the area under receiver operating curve(AUC)values of 0.947 and 0.942,while BRT and RF had weaker performances with the SAS and Random datasets.This effect resulted in AUC improvements for BRT and RF by 7.2,and 9.7%from the Random dataset,and AUC improvements for BRT and RF by 6.1,and 5.4%from the SAS dataset,respectively.The APG also impacted the importance of the input factors and the pattern of the groundwater potential maps,which proves the importance of absence points in environmental binary issues.The proposed APG toolbox could be easily applied in other environmental hazards such as landslides,floods,and gully erosion,and land subsidence.
文摘Lung cancer continues to be a leading cause of cancer-related deaths worldwide,emphasizing the critical need for improved diagnostic techniques.Early detection of lung tumors significantly increases the chances of successful treatment and survival.However,current diagnostic methods often fail to detect tumors at an early stage or to accurately pinpoint their location within the lung tissue.Single-model deep learning technologies for lung cancer detection,while beneficial,cannot capture the full range of features present in medical imaging data,leading to incomplete or inaccurate detection.Furthermore,it may not be robust enough to handle the wide variability in medical images due to different imaging conditions,patient anatomy,and tumor characteristics.To overcome these disadvantages,dual-model or multi-model approaches can be employed.This research focuses on enhancing the detection of lung cancer by utilizing a combination of two learning models:a Convolutional Neural Network(CNN)for categorization and the You Only Look Once(YOLOv8)architecture for real-time identification and pinpointing of tumors.CNNs automatically learn to extract hierarchical features from raw image data,capturing patterns such as edges,textures,and complex structures that are crucial for identifying lung cancer.YOLOv8 incorporates multiscale feature extraction,enabling the detection of tumors of varying sizes and scales within a single image.This is particularly beneficial for identifying small or irregularly shaped tumors that may be challenging to detect.Furthermore,through the utilization of cutting-edge data augmentation methods,such as Deep Convolutional Generative Adversarial Networks(DCGAN),the suggested approach can handle the issue of limited data and boost the models’ability to learn from diverse and comprehensive datasets.The combined method not only improved accuracy and localization but also ensured efficient real-time processing,which is crucial for practical clinical applications.The CNN achieved an accuracy of 97.67%in classifying lung tissues into healthy and cancerous categories.The YOLOv8 model achieved an Intersection over Union(IoU)score of 0.85 for tumor localization,reflecting high precision in detecting and marking tumor boundaries within the images.Finally,the incorporation of synthetic images generated by DCGAN led to a 10%improvement in both the CNN classification accuracy and YOLOv8 detection performance.
