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
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
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
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
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
Palladium-catalyzed carboxylative Suzuki coupling reactions of benzyl chlorides with allyl pinacol-borate were successfully conducted in the absence of any extra ligand to produce β,γ-unsaturated esters in satisfact...Palladium-catalyzed carboxylative Suzuki coupling reactions of benzyl chlorides with allyl pinacol-borate were successfully conducted in the absence of any extra ligand to produce β,γ-unsaturated esters in satisfactory to good yields. The carboxylative Suzuki coupling reaction proceeded smooth-ly under mild conditions in the presence of palladium nanoparticles generated in situ through the formation of a π-benzylpalladium chloride intermediate.展开更多
α-Acyloxycarboxamides are synthesized from three-component Passerini reaction between indane-1,2,3-trione, isocyanides, and thiophenecarboxylic acids in quantitative yields. The structures of the final products were ...α-Acyloxycarboxamides are synthesized from three-component Passerini reaction between indane-1,2,3-trione, isocyanides, and thiophenecarboxylic acids in quantitative yields. The structures of the final products were confirmed by IR, 1H and 13C NMR spectroscopy, mass spectrometry, and elemental analysis. The B3LYP/HF calculations for computation of 1H and 13C NMR chemical shifts have been carried out for the title compounds at the 6-311＋G＊＊ and 6-311＋＋G＊＊ basis set levels within GIAO and CSGT approaches. Predicted 1H and 13C NMR che-mical shifts have been assigned and compared with experimental 1H and 13C NMR spectra and they are supported each other.展开更多
2-（Sulfooxy）propane-1,2,3-tricarboxylic acid（supported on silica gel） has been introduced as novel and green catalyst for the formylation of alcohols and amines with ethyl formate,as mild formylation agent,under n...2-（Sulfooxy）propane-1,2,3-tricarboxylic acid（supported on silica gel） has been introduced as novel and green catalyst for the formylation of alcohols and amines with ethyl formate,as mild formylation agent,under neat conditions at room temperature.展开更多
A clean,fast,and facile oxidation of multiwalled carbon nanotubes（MWCNTs） by H_2O_2/heteropolyacid（H_3PW_（12）O_（40）） gave highly carboxylated MWCNTs under mild conditions,at a conveniently accessible temperatu...A clean,fast,and facile oxidation of multiwalled carbon nanotubes（MWCNTs） by H_2O_2/heteropolyacid（H_3PW_（12）O_（40）） gave highly carboxylated MWCNTs under mild conditions,at a conveniently accessible temperature.After an easy workup,the product was characterized by SEM,XRD,and FT-IR.展开更多
Three-component reaction of arylsulfonamides,dialkyl acetylenedicarboxylates,and ethyl chlorooxoacetate promoted by triphenylphosphine and triethylamine provides a sufficient route for the synthesis of dialkyl N-（ary...Three-component reaction of arylsulfonamides,dialkyl acetylenedicarboxylates,and ethyl chlorooxoacetate promoted by triphenylphosphine and triethylamine provides a sufficient route for the synthesis of dialkyl N-（arylsulfonyl）-4-ethoxy-5-oxo-2,5-dihydro -1H-pyrolle-2,3-dicarboxylates in good yields.展开更多
Securing digital image data is a key concern in today’s information-driven society.Effective encryption techniques are required to protect sensitive image data,with the Substitution-box(S-box)often playing a pivotal ...Securing digital image data is a key concern in today’s information-driven society.Effective encryption techniques are required to protect sensitive image data,with the Substitution-box(S-box)often playing a pivotal role in many symmetric encryption systems.This study introduces an innovative approach to creating S-boxes for encryption algorithms.The proposed S-boxes are tested for validity and non-linearity by incorporating them into an image encryption scheme.The nonlinearity measure of the proposed S-boxes is 112.These qualities significantly enhance its resistance to common cryptographic attacks,ensuring high image data security.Furthermore,to assess the robustness of the S-boxes,an encryption system has also been proposed and the proposed S-boxes have been integrated into the designed encryption system.To validate the effectiveness of the proposed encryption system,a comprehensive security analysis including brute force attack and histogram analysis has been performed.In addition,to determine the level of security during the transmission and storage of digital content,the encryption system’s Number of Pixel Change Rate(NPCR),and Unified Averaged Changed Intensity(UACI)are calculated.The results indicate a 99.71%NPCR and 33.51%UACI.These results demonstrate that the proposed S-boxes offer a significant level of security for digital content throughout its transmission and storage.展开更多
The modeling of PV （photovoltaic） systems is very crucial for embedded power system applications and maximum power point tracking. This paper presents a PV array model using Matlab/Simulink with the assistance of Si...The modeling of PV （photovoltaic） systems is very crucial for embedded power system applications and maximum power point tracking. This paper presents a PV array model using Matlab/Simulink with the assistance of SimPowerSystem toolbox. The PV cell is considered as the main building block for simulating and monitoring the PV array performance. The PV model has been developed and used as Simulink subsystems where the effect of solar insolation and PV array temperature on commercial PV modules have been studied throughout the simulated I-V and P-V output characteristics. The proposed model facilitates simulating the dynamic performance of PV-based power systems. The effect of different partial shading patterns of PV arrays under different configurations has been studied.展开更多
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 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Palladium-catalyzed carboxylative Suzuki coupling reactions of benzyl chlorides with allyl pinacol-borate were successfully conducted in the absence of any extra ligand to produce β,γ-unsaturated esters in satisfactory to good yields. The carboxylative Suzuki coupling reaction proceeded smooth-ly under mild conditions in the presence of palladium nanoparticles generated in situ through the formation of a π-benzylpalladium chloride intermediate.
文摘α-Acyloxycarboxamides are synthesized from three-component Passerini reaction between indane-1,2,3-trione, isocyanides, and thiophenecarboxylic acids in quantitative yields. The structures of the final products were confirmed by IR, 1H and 13C NMR spectroscopy, mass spectrometry, and elemental analysis. The B3LYP/HF calculations for computation of 1H and 13C NMR chemical shifts have been carried out for the title compounds at the 6-311＋G＊＊ and 6-311＋＋G＊＊ basis set levels within GIAO and CSGT approaches. Predicted 1H and 13C NMR che-mical shifts have been assigned and compared with experimental 1H and 13C NMR spectra and they are supported each other.
基金Financial support for this work by the Ilam University,Ilam,Iran is gratefully acknowledged
文摘2-（Sulfooxy）propane-1,2,3-tricarboxylic acid（supported on silica gel） has been introduced as novel and green catalyst for the formylation of alcohols and amines with ethyl formate,as mild formylation agent,under neat conditions at room temperature.
文摘A clean,fast,and facile oxidation of multiwalled carbon nanotubes（MWCNTs） by H_2O_2/heteropolyacid（H_3PW_（12）O_（40）） gave highly carboxylated MWCNTs under mild conditions,at a conveniently accessible temperature.After an easy workup,the product was characterized by SEM,XRD,and FT-IR.
基金the financial support from the Research Council of Islamic Azad University,Mahshahr Branch
文摘Three-component reaction of arylsulfonamides,dialkyl acetylenedicarboxylates,and ethyl chlorooxoacetate promoted by triphenylphosphine and triethylamine provides a sufficient route for the synthesis of dialkyl N-（arylsulfonyl）-4-ethoxy-5-oxo-2,5-dihydro -1H-pyrolle-2,3-dicarboxylates in good yields.
基金funded by Deanship of Scientific Research at Najran University under the Research Groups Funding Program Grant Code(NU/RG/SERC/12/3)also by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2023R333)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Securing digital image data is a key concern in today’s information-driven society.Effective encryption techniques are required to protect sensitive image data,with the Substitution-box(S-box)often playing a pivotal role in many symmetric encryption systems.This study introduces an innovative approach to creating S-boxes for encryption algorithms.The proposed S-boxes are tested for validity and non-linearity by incorporating them into an image encryption scheme.The nonlinearity measure of the proposed S-boxes is 112.These qualities significantly enhance its resistance to common cryptographic attacks,ensuring high image data security.Furthermore,to assess the robustness of the S-boxes,an encryption system has also been proposed and the proposed S-boxes have been integrated into the designed encryption system.To validate the effectiveness of the proposed encryption system,a comprehensive security analysis including brute force attack and histogram analysis has been performed.In addition,to determine the level of security during the transmission and storage of digital content,the encryption system’s Number of Pixel Change Rate(NPCR),and Unified Averaged Changed Intensity(UACI)are calculated.The results indicate a 99.71%NPCR and 33.51%UACI.These results demonstrate that the proposed S-boxes offer a significant level of security for digital content throughout its transmission and storage.
文摘The modeling of PV （photovoltaic） systems is very crucial for embedded power system applications and maximum power point tracking. This paper presents a PV array model using Matlab/Simulink with the assistance of SimPowerSystem toolbox. The PV cell is considered as the main building block for simulating and monitoring the PV array performance. The PV model has been developed and used as Simulink subsystems where the effect of solar insolation and PV array temperature on commercial PV modules have been studied throughout the simulated I-V and P-V output characteristics. The proposed model facilitates simulating the dynamic performance of PV-based power systems. The effect of different partial shading patterns of PV arrays under different configurations has been studied.
