This study compared the acute effects of electrical energy transfer(TECAR) and transcutaneous electrical stimulation(TENS) on pain and flexibility after a hamstring injury. Young athletes received either a 20 min TECA...This study compared the acute effects of electrical energy transfer(TECAR) and transcutaneous electrical stimulation(TENS) on pain and flexibility after a hamstring injury. Young athletes received either a 20 min TECAR(n = 24) or TENS(n = 26) session within 5 days following a hamstring injury, while the control(CON, n = 25)group was instructed to rest. Visual analogue scale(VAS), functional Assessment Scale for Acute Hamstring Injuries(FASH), straight leg raise test(SLR), and sit-and-reach scores(STR) were obtained prior to, immediately,24, and 48 h after therapy. Group differences were detected after therapy in VAS and FASH scores(p < 0.05).Compared to pre-therapy measurements, VAS scores showed a greater decrease in the TECAR group(-38.75% to-63.33%) than in the TENS group(-16.67% to-25.00%) and both were greater than in the CON group(-2.81%to-9.81%)(p < 0.05). The TECAR group improved FASH scores(28.57%–48.21%) more than the TENS group(15.89%–27.79%) and both groups more than the CON group(0%–8.33%)(p < 0.05). The increase in SLR and STR was greater in the TECAR group(6.26%–13.96%) than in the TENS(1.72%–9.53%) and CON groups(0%–3.03%). These results suggest that in the acute phase of hamstring injury, the use of TECAR and, to a lesser extent, TENS may relieve pain symptoms and bring some improvements in flexibility more than instructing patients to rest.展开更多
Sustainable energy systems will entail a change in the carbon intensity projections,which should be carried out in a proper manner to facilitate the smooth running of the grid and reduce greenhouse emissions.The prese...Sustainable energy systems will entail a change in the carbon intensity projections,which should be carried out in a proper manner to facilitate the smooth running of the grid and reduce greenhouse emissions.The present article outlines the TransCarbonNet,a novel hybrid deep learning framework with self-attention characteristics added to the bidirectional Long Short-Term Memory(Bi-LSTM)network to forecast the carbon intensity of the grid several days.The proposed temporal fusion model not only learns the local temporal interactions but also the long-term patterns of the carbon emission data;hence,it is able to give suitable forecasts over a period of seven days.TransCarbonNet takes advantage of a multi-head self-attention element to identify significant temporal connections,which means the Bi-LSTM element calculates sequential dependencies in both directions.Massive tests on two actual data sets indicate much improved results in comparison with the existing results,with mean relative errors of 15.3 percent and 12.7 percent,respectively.The framework has given explicable weights of attention that reveal critical periods that influence carbon intensity alterations,and informed decisions on the management of carbon sustainability.The effectiveness of the proposed solution has been validated in numerous cases of operations,and TransCarbonNet is established to be an effective tool when it comes to carbon-friendly optimization of the grid.展开更多
In this work,a computational modelling and analysis framework is developed to investigate the thermal buckling behavior of doubly-curved composite shells reinforced with graphene-origami(G-Ori)auxetic metamaterials.A ...In this work,a computational modelling and analysis framework is developed to investigate the thermal buckling behavior of doubly-curved composite shells reinforced with graphene-origami(G-Ori)auxetic metamaterials.A semi-analytical formulation based on the First-Order Shear Deformation Theory(FSDT)and the principle of virtual displacements is established,and closed-form solutions are derived via Navier’s method for simply supported boundary conditions.The G-Ori metamaterial reinforcements are treated as programmable constructs whose effective thermo-mechanical properties are obtained via micromechanical homogenization and incorporated into the shell model.A comprehensive parametric study examines the influence of folding geometry,dispersion arrangement,reinforcement weight fraction,curvature parameters,and elastic foundation support on the critical buckling temperature(CBT).The results reveal that,under optimal folding geometry and reinforcement alignment with principal stress trajectories,the CBT can increase by more than 150%.Furthermore,the combined effect of G-Ori reinforcement and elastic foundation substantially enhances thermal buckling resistance.These findings establish design guidelines for architected composite shells in applications such as aerospace thermal skins,morphing structures,and thermally-responsive systems,and illustrate the potential of auxetic graphene metamaterials for multifunctional,lightweight,and thermally robust structural components.展开更多
Background:Locally advanced laryngeal squamous cell carcinoma(LA-LSCC)presents clinical challenges due to the lack of reliable non-invasive biomarkers.This study aimed to evaluate miR-449a as a diagnostic and prognost...Background:Locally advanced laryngeal squamous cell carcinoma(LA-LSCC)presents clinical challenges due to the lack of reliable non-invasive biomarkers.This study aimed to evaluate miR-449a as a diagnostic and prognostic biomarker in LA-LSCC.Methods:miR-449a expression was analyzed in tumor tissues,adjacent normal tissues,and serum from 81 LA-LSCC patients and 50 controls using quantitative real-time reverse transcription polymerase chain reaction(qRT-PCR).We assessed the diagnostic accuracy by Receiver Operating Characteristic curve(ROC curves),clinicopathological associations,survival outcomes(Kaplan-Meier),and treatment response dynamics.Results:miR-449a was significantly downregulated in LA-LSCC tissues(p<0.0001)and serum(p<0.0001),with a strong tissue-serum correlation(R^(2)=0.988).Tissue miR-449a demonstrated a diagnostic accuracy(Area Under the Curve,AUC=0.857),while serum showed moderate accuracy(AUC=0.734).High miR-449a expression correlated with favorable clinicopathological features and improved survival(median overall survival:67.82 vs.23.74 months;p=0.0012).Multivariate analysis confirmed miR-449a as an independent prognostic factor(p<0.001).miR-449a levels increased post-treatment,particularly in responders to chemotherapy/radiation(p<0.0001).Conclusion:miR-449a serves as a non-invasive biomarker for LA-LSCC diagnosis,prognosis,and treatment monitoring.Its dynamic expression highlights potential for risk stratification and therapy response prediction,warranting further validation in larger cohorts.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
We present a computer-modeling framework for photovoltaic(PV)source emulation that preserves the exact single-diode physics while enabling iteration-free,real-time evaluation.We derive two closed-form explicit solvers...We present a computer-modeling framework for photovoltaic(PV)source emulation that preserves the exact single-diode physics while enabling iteration-free,real-time evaluation.We derive two closed-form explicit solvers based on the Lambert W function:a voltage-driven V-Lambert solver for high-fidelity I–V computation and a resistance-driven R-Lambert solver designed for seamless integration in a closed-loop PV emulator.Unlike Taylor-linearized explicit models,our proposed formulation retains the exponential nonlinearity of the PV equations.It employs a numerically stable analytical evaluation that eliminates the need for lookup tables and root-finding,all while maintaining limited computational costs and a small memory footprint.The R-Lambert model is integrated into a buck-converter emulator equipped with a discrete PI regulator,which generates current references directly from sensed operating points,thus supporting hardware-constrained implementation.Comprehensive numerical experiments conducted on six commercial modules from various technologies(mono,poly,and multicrystalline)demonstrate significant accuracy improvements under the IEC EN 50530 near-MPP criterion:the V-Lambert solver reduces the±10%Vmpp band error by up to 61 times compared to an explicit-model baseline.Dynamic simulations under varying irradiance,temperature,and load conditions achieve millisecond-scale settling with accurate trajectory tracking.Additionally,processor-in-the-loop experimental validation on an embedded microcontroller supports the simulation results.By unifying exact analytical modeling with embedded realization,this work advances computer modeling for PV emulation,MPPT benchmarking,and controller verification in integrated renewable energy systems.展开更多
Five samples of LiMgPO_(4):Gd were prepared via five different production processes using a solid-state reaction method.The effects of the preparation process on optically stimulated luminescence(OSL)and thermolumines...Five samples of LiMgPO_(4):Gd were prepared via five different production processes using a solid-state reaction method.The effects of the preparation process on optically stimulated luminescence(OSL)and thermoluminescence(TL)were investigated.Considering its high sensitivity,low fading,and minimum detectable dose(MDD),the LiMgPO_(4):Gd phosphor heated to 900℃for 15 h is concluded to be optimal.The effects of annealing on the OSL sensitivity,relative residual OSL signals measured after 24 h of irradiation,and MDD of LiMgPO_(4):Gd phosphors heated to 900℃for 15 h were also investigated.Considering its high sensitivity,low fading,and MDD,annealing at 350℃for 1 h is concluded to be optimal.The OSL signal of LiMgPO_(4):Gd was derived from the principal TL glow peak.For a maximum integration time of 5 s,the OSL signal was stable,with no fading 30 days after irradiation.LiMgPO_(4):Gd eliminated approximately 2.2%of the OSL signal at each readout for a readout time of 0.1 s,which is sufficient for fast and multiple OSL readout.The sensitivity of LiMgPO_(4):Gd phosphor,annealed for 1 h at 350℃with a reading time of 0.1 s,was found to be approximately 98%of that observed forα-Al_(2)O_(3):C(TLD-500k),which should be sufficient for low-dose measurements in personal,workplace,and environmental dosimetry.展开更多
One of the main issues in designing optimum tapered cascades for uranium enrichment for annual fuel production in a power reactor is whether to employ large(fat)or small(thin)cascades.What will be the permissible and ...One of the main issues in designing optimum tapered cascades for uranium enrichment for annual fuel production in a power reactor is whether to employ large(fat)or small(thin)cascades.What will be the permissible and optimal ranges of the number of machines that can be used in a cascade?For the first time,the permissible and optimal ranges of the number of gas centrifuges that can be utilized in a cascade were investigated using two types of centrifuges,and the performance of small and large tapered cascades was discussed.The particle swarm optimization algorithm(PSO)has been used to optimize tapered cascades.The results show:(1)For the first centrifuge,41 cascades(91≤n≤4897)and for the second centrifuge,49 cascades(18≤n≤3839)with small and large sizes can be used in enrichment facilities,and the best cascade for them has 530(with 23 stages)and 39(with 7 stages)centrifuges,respectively.(2)For both centrifuges,when 600≤n(number of centrifuges=n),the large cascade performance changes are relatively insignificant.(3)For both types of gas centrifuges,the annual los s of separation power in enrichment facilities is approximately 1.25%-4.82%of the total separation work required.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
The aim of this study is to design,build,and evaluate an indirect forced convection solar dryer adapted to semi-arid climate,such as that of Bechar situated in the west south region of Algeria.The tested drying system...The aim of this study is to design,build,and evaluate an indirect forced convection solar dryer adapted to semi-arid climate,such as that of Bechar situated in the west south region of Algeria.The tested drying system consists of a flat-plate solar collector,an insulated two-chamber drying unit,and an Arduino-controlled device that ensures uniformtemperature distribution and real-timemonitoring using DHT22 sensors.Drying testswere conducted on locally grown beet slices at air temperatures of 45℃,60℃,and 80℃,with a constant air velocity of 1.2 m/s and a mass flow rate of 0.0027 kg/s.The collector reached a maximum temperature of 65℃,with thermal efficiencies ranging from 20%to 35%.In these conditions,the drying times were cut down to 200–300 min,and the beet’s moisture content dropped to 0.47,0.27,and 0.24 g/g dry matter,respectively.The experimental data were fitted to several empirical models,including the logarithmic model.The modelled results showed strong agreement with the experimental ones(correlation coefficients r=0.9919–0.9989;standard errors SE=0.017–0.043;root-mean-square errors RMSE=0.016–0.027).The results demonstrate that the system operates efficiently and consistently,making it suitable for the sustainable drying of agricultural and medicinal products in arid climates.展开更多
The biological stabilization of soil using microbially induced carbonate precipitation(MICP)employs ureolytic bacteria to precipitate calcium carbonate(CaCO3),which binds soil particles,enhancing strength,stiffness,an...The biological stabilization of soil using microbially induced carbonate precipitation(MICP)employs ureolytic bacteria to precipitate calcium carbonate(CaCO3),which binds soil particles,enhancing strength,stiffness,and erosion resistance.The unconfinedcompressive strength(UCS),a key measure of soil strength,is critical in geotechnical engineering as it directly reflectsthe mechanical stability of treated soils.This study integrates explainable artificialintelligence(XAI)with geotechnical insights to model the UCS of MICP-treated sands.Using 517 experimental data points and a combination of various input variables—including median grain size(D50),coefficientof uniformity(Cu),void ratio(e),urea concentration(Mu),calcium concentration(Mc),optical density(OD)of bacterial solution,pH,and total injection volume(Vt)—fivemachine learning(ML)models,including eXtreme gradient boosting(XGBoost),Light gradient boosting machine(LightGBM),random forest(RF),gene expression programming(GEP),and multivariate adaptive regression splines(MARS),were developed and optimized.The ensemble models(XGBoost,LightGBM,and RF)were optimized using the Chernobyl disaster optimizer(CDO),a recently developed metaheuristic algorithm.Of these,LightGBM-CDO achieved the highest accuracy for UCS prediction.XAI techniques like feature importance analysis(FIA),SHapley additive exPlanations(SHAP),and partial dependence plots(PDPs)were also used to investigate the complex non-linear relationships between the input and output variables.The results obtained have demonstrated that the XAI-driven models can enhance the predictive accuracy and interpretability of MICP processes,offering a sustainable pathway for optimizing geotechnical applications.展开更多
The increasing number of interconnected devices and the incorporation of smart technology into contemporary healthcare systems have significantly raised the attack surface of cyber threats.The early detection of threa...The increasing number of interconnected devices and the incorporation of smart technology into contemporary healthcare systems have significantly raised the attack surface of cyber threats.The early detection of threats is both necessary and complex,yet these interconnected healthcare settings generate enormous amounts of heterogeneous data.Traditional Intrusion Detection Systems(IDS),which are generally centralized and machine learning-based,often fail to address the rapidly changing nature of cyberattacks and are challenged by ethical concerns related to patient data privacy.Moreover,traditional AI-driven IDS usually face challenges in handling large-scale,heterogeneous healthcare data while ensuring data privacy and operational efficiency.To address these issues,emerging technologies such as Big Data Analytics(BDA)and Federated Learning(FL)provide a hybrid framework for scalable,adaptive intrusion detection in IoT-driven healthcare systems.Big data techniques enable processing large-scale,highdimensional healthcare data,and FL can be used to train a model in a decentralized manner without transferring raw data,thereby maintaining privacy between institutions.This research proposes a privacy-preserving Federated Learning–based model that efficiently detects cyber threats in connected healthcare systems while ensuring distributed big data processing,privacy,and compliance with ethical regulations.To strengthen the reliability of the reported findings,the resultswere validated using cross-dataset testing and 95%confidence intervals derived frombootstrap analysis,confirming consistent performance across heterogeneous healthcare data distributions.This solution takes a significant step toward securing next-generation healthcare infrastructure by combining scalability,privacy,adaptability,and earlydetection capabilities.The proposed global model achieves a test accuracy of 99.93%±0.03(95%CI)and amiss-rate of only 0.07%±0.02,representing state-of-the-art performance in privacy-preserving intrusion detection.The proposed FL-driven IDS framework offers an efficient,privacy-preserving,and scalable solution for securing next-generation healthcare infrastructures by combining adaptability,early detection,and ethical data management.展开更多
Origanum elongatum(OE)is an aromatic,medicinal plant endemic to Morocco that is widely used in traditional medicine due to its biological properties.This study aimed to elucidate the chemical composition of the essent...Origanum elongatum(OE)is an aromatic,medicinal plant endemic to Morocco that is widely used in traditional medicine due to its biological properties.This study aimed to elucidate the chemical composition of the essential oil(EO)obtained from O.elongatum(OEEO)at three stages of its life cycle,including vegetative stage(OEEO-VS),flowering stage(OEEO-FS),and post-flowering(OEEO-PFS),as well as to evaluate its biological and antiradical characteristics.The chemical analysis of the essential oil was conducted using gas chromatography-mass spectrometry(GC-MS).The antibacterial activity was evaluated in vitro through distinct methodologies,namely,disc diffusion and microatmosphere assay;subsequently,the minimum inhibitory concentration(MIC)was then determined.The antioxidant potential was also measured by using the DPPH and FRAP assays.The GC-MS revealed the predominant of p-cymene(26.83%_31.45%),γ-terpinene(8.46%_26.95%),thymol(13%_29.54%),and carvacrol(20.25%_37.26%),in all three samples,with notable variations according to the phenological stage of the samples.The EOs extracted at three phenological stages demonstrated notable antibacterial efficacy against all the phytopathogen tested.The MICs for Erwinia amylovora exhibited a range of 6.25 and 250μg/mL.However,for Agrobacterium tumefaciens C58 and Allorhizobium vitis S4,the MICs spanned 125 and 250μg/mL.In the DPPH test,the IC50 values were 168.25±1.14,147.01±0.78,and 132.01±2.06μg/mL for EOs derived from the vegetative,flowering,and post-flowering period,respectively.In the FRAP test,the EC50 values were 164.22±1.04,215.73±1.48,and 184.06±0.95μg/mL for the same stages.The findings offer promising prospects for the phytochemical development,demonstrating how the phenological stage significantly influences the therapeutic and biotechnological potential of O.elongatum.This has the potential to open up new avenues of research in the pharmaceutical,agronomic,and environmental fields.展开更多
Objective:Deep learning is employed increasingly in Gastroenterology(GI)endoscopy computer-aided diagnostics for polyp segmentation and multi-class disease detection.In the real world,implementation requires high accu...Objective:Deep learning is employed increasingly in Gastroenterology(GI)endoscopy computer-aided diagnostics for polyp segmentation and multi-class disease detection.In the real world,implementation requires high accuracy,therapeutically relevant explanations,strong calibration,domain generalization,and efficiency.Current Convolutional Neural Network(CNN)and transformer models compromise border precision and global context,generate attention maps that fail to align with expert reasoning,deteriorate during cross-center changes,and exhibit inadequate calibration,hence diminishing clinical trust.Methods:HMA-DER is a hierarchical multi-attention architecture that uses dilation-enhanced residual blocks and an explainability-aware Cognitive Alignment Score(CAS)regularizer to directly align attribution maps with reasoning signals from experts.The framework has additions that make it more resilient and a way to test for accuracy,macro-averaged F1 score,Area Under the Receiver Operating Characteristic Curve(AUROC),calibration(Expected Calibration Error(ECE),Brier Score),explainability(CAS,insertion/deletion AUC),cross-dataset transfer,and throughput.Results:HMA-DER gets Dice Similarity Coefficient scores of 89.5%and 86.0%on Kvasir-SEG and CVC-ClinicDB,beating the strongest baseline by+1.9 and+1.7 points.It gets 86.4%and 85.3%macro-F1 and 94.0%and 93.4%AUROC on HyperKvasir and GastroVision,which is better than the baseline by+1.4/+1.6macro-F1 and+1.2/+1.1AUROC.Ablation study shows that hierarchical attention gives the highest(+3.0),followed by CAS regularization(+2–3),dilatation(+1.5–2.0),and residual connections(+2–3).Cross-dataset validation demonstrates competitive zero-shot transfer(e.g.,KS→CVC Dice 82.7%),whereas multi-dataset training diminishes the domain gap,yielding an 88.1%primary-metric average.HMA-DER’s mixed-precision inference can handle 155 pictures per second,which helps with calibration.Conclusion:HMA-DER strikes a compromise between accuracy,explainability,robustness,and efficiency for the use of reliable GI computer-aided diagnosis in real-world clinical settings.展开更多
Accurate prediction of concrete compressive strength is fundamental for optimizing mix designs,improving material utilization,and ensuring structural safety in modern construction.Traditional empirical methods often f...Accurate prediction of concrete compressive strength is fundamental for optimizing mix designs,improving material utilization,and ensuring structural safety in modern construction.Traditional empirical methods often fail to capture the non-linear relationships among concrete constituents,especially with the growing use of supple-mentary cementitious materials and recycled aggregates.This study presents an integrated machine learning framework for concrete strength prediction,combining advanced regression models—namely CatBoost—with metaheuristic optimization algorithms,with a particular focus on the Somersaulting Spider Optimizer(SSO).A comprehensive dataset encompassing diverse mix proportions and material types was used to evaluate baseline machine learning models,including CatBoost,XGBoost,ExtraTrees,and RandomForest.Among these,CatBoost demonstrated superior accuracy across multiple performance metrics.To further enhance predictive capability,several bio-inspired optimizers were employed for hyperparameter tuning.The SSO-CatBoost hybrid achieved the lowest mean squared error and highest correlation coefficients,outperforming other metaheuristic approaches such as Genetic Algorithm,Particle Swarm Optimization,and Grey Wolf Optimizer.Statistical significance was established through Analysis of Variance and Wilcoxon signed-rank testing,confirming the robustness of the optimized models.The proposed methodology not only delivers improved predictive performance but also offers a transparent framework for mix design optimization,supporting data-driven decision making in sustainable and resilient infrastructure development.展开更多
Concrete-filled steel tubes(CFST)are widely utilized in civil engineering due to their superior load-bearing capacity,ductility,and seismic resistance.However,existing design codes,such as AISC and Eurocode 4,tend to ...Concrete-filled steel tubes(CFST)are widely utilized in civil engineering due to their superior load-bearing capacity,ductility,and seismic resistance.However,existing design codes,such as AISC and Eurocode 4,tend to be excessively conservative as they fail to account for the composite action between the steel tube and the concrete core.To address this limitation,this study proposes a hybrid model that integrates XGBoost with the Pied Kingfisher Optimizer(PKO),a nature-inspired algorithm,to enhance the accuracy of shear strength prediction for CFST columns.Additionally,quantile regression is employed to construct prediction intervals for the ultimate shear force,while the Asymmetric Squared Error Loss(ASEL)function is incorporated to mitigate overestimation errors.The computational results demonstrate that the PKO-XGBoost model delivers superior predictive accuracy,achieving a Mean Absolute Percentage Error(MAPE)of 4.431%and R2 of 0.9925 on the test set.Furthermore,the ASEL-PKO-XGBoost model substantially reduces overestimation errors to 28.26%,with negligible impact on predictive performance.Additionally,based on the Genetic Algorithm(GA)and existing equation models,a strength equation model is developed,achieving markedly higher accuracy than existing models(R^(2)=0.934).Lastly,web-based Graphical User Interfaces(GUIs)were developed to enable real-time prediction.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
Cu_(x)Ta_(2-x)O_(5) compositions were investigated for advanced thermoelectric and optical applications,using both simulations and experimental approaches.Density functional theory calculations were performed before t...Cu_(x)Ta_(2-x)O_(5) compositions were investigated for advanced thermoelectric and optical applications,using both simulations and experimental approaches.Density functional theory calculations were performed before the experimental observations to predict the trends of various parameters.Crystal structure analysis confirmed the presence of the orthorhombic Ta_(2)O_(5) phase in all the compositions.The composition and morphology demonstrated impurity-free contents with uniform and crack-free surfaces.Thermoelectric analysis depicted a decrease in Seebeck coefficient from 3.66??V·K^(-1)to 1.91??V·K^(-1)and an increase in the value of specific heat from 0.73 J·K^(-1)·kg^(-1)to 11.6 J·K^(-1)·kg^(-1)upon Cu incorporation in structure.The bandgap was found to reduce from 2.61 to 1.38 e V with Cu-induced electronic states.The real epsilon and static refractive index increased from 3.75 to 4.57 and from 1.93 to 2.11,respectively,with increment in Cu content.The enhanced parameters,focusing on the thermoelectric and optical responses,make these compositions potential candidates for advanced optoelectronic applications.展开更多
In the present study,researchers examined a solar off-grid-connected photovoltaic system for a family house in the city of Baghdad.The design was created with the help of the“How to Design PV Program”and the“Renewa...In the present study,researchers examined a solar off-grid-connected photovoltaic system for a family house in the city of Baghdad.The design was created with the help of the“How to Design PV Program”and the“Renewable Energy Investment Calculator(REICAL)”software(Version 1.1).In Iraq,the national grid provides around 71%of the overall electricity demand,though this drops to nearly 50%during extremely hot and cold months,where the supply alternates between four hours on and four hours off.During the off periods,power is generated by local generators at high costs.To promote the adoption of photovoltaic solar systems among Iraqi citizens through loans,three options for meeting 100% of electricity needs have been proposed:an on-grid solution,a hybrid system that supplies 24 h,and an off-grid solution for a 24-h supply.The 12-h off-grid system(hybrid)is both economical and efficient for delivering electricity.Findings reveal that,over 20 years,the system’s output will amount to 141,176.71 kWh,with a payback period of 5.85 years and a performance ratio of 86.2%.Investment outcome data showed a net present value of $6445,and the profitability index was 6.16,indicating the project’s profitability.Additionally,the system could result in a net reduction of CO_(2) emissions totaling 132,810.24 kg.展开更多
In modern construction,Lightweight Aggregate Concrete(LWAC)has been recognized as a vital material of concern because of its unique properties,such as reduced density and improved thermal insulation.Despite the extens...In modern construction,Lightweight Aggregate Concrete(LWAC)has been recognized as a vital material of concern because of its unique properties,such as reduced density and improved thermal insulation.Despite the extensive knowledge regarding its macroscopic properties,there is a wide knowledge gap in understanding the influence of microscale parameters like aggregate porosity and volume ratio on the mechanical response of LWAC.This study aims to bridge this knowledge gap,spurred by the need to enhance the predictability and applicability of LWAC in various construction environments.With the help of advanced numerical methods,including the finite element method and a random circular aggregate model,this study critically evaluates the role played by these microscale factors.We found that an increase in the aggregate porosity from 23.5%to 48.5%leads to a drastic change of weakness from the bonding interface to the aggregate,reducing compressive strength by up to 24.2%and tensile strength by 27.8%.Similarly,the increase in the volume ratio of lightweight aggregate from 25%to 40%leads to a reduction in compressive strength by 13.0%and tensile strength by 9.23%.These results highlight the imperative role of microscale properties on the mechanical properties of LWAC.By supplying precise quantitative details on the effect of porosity and aggregate volume ratio,this research makes significant contributions to construction materials science by providing useful recommendations for the creation and optimization of LWAC with improved performance and sustainability in construction.展开更多
文摘This study compared the acute effects of electrical energy transfer(TECAR) and transcutaneous electrical stimulation(TENS) on pain and flexibility after a hamstring injury. Young athletes received either a 20 min TECAR(n = 24) or TENS(n = 26) session within 5 days following a hamstring injury, while the control(CON, n = 25)group was instructed to rest. Visual analogue scale(VAS), functional Assessment Scale for Acute Hamstring Injuries(FASH), straight leg raise test(SLR), and sit-and-reach scores(STR) were obtained prior to, immediately,24, and 48 h after therapy. Group differences were detected after therapy in VAS and FASH scores(p < 0.05).Compared to pre-therapy measurements, VAS scores showed a greater decrease in the TECAR group(-38.75% to-63.33%) than in the TENS group(-16.67% to-25.00%) and both were greater than in the CON group(-2.81%to-9.81%)(p < 0.05). The TECAR group improved FASH scores(28.57%–48.21%) more than the TENS group(15.89%–27.79%) and both groups more than the CON group(0%–8.33%)(p < 0.05). The increase in SLR and STR was greater in the TECAR group(6.26%–13.96%) than in the TENS(1.72%–9.53%) and CON groups(0%–3.03%). These results suggest that in the acute phase of hamstring injury, the use of TECAR and, to a lesser extent, TENS may relieve pain symptoms and bring some improvements in flexibility more than instructing patients to rest.
基金funded by the Deanship of Scientific Research and Libraries at Princess Nourah bint Abdulrahman University,through the“Nafea”Program,Grant No.(NP-45-082).
文摘Sustainable energy systems will entail a change in the carbon intensity projections,which should be carried out in a proper manner to facilitate the smooth running of the grid and reduce greenhouse emissions.The present article outlines the TransCarbonNet,a novel hybrid deep learning framework with self-attention characteristics added to the bidirectional Long Short-Term Memory(Bi-LSTM)network to forecast the carbon intensity of the grid several days.The proposed temporal fusion model not only learns the local temporal interactions but also the long-term patterns of the carbon emission data;hence,it is able to give suitable forecasts over a period of seven days.TransCarbonNet takes advantage of a multi-head self-attention element to identify significant temporal connections,which means the Bi-LSTM element calculates sequential dependencies in both directions.Massive tests on two actual data sets indicate much improved results in comparison with the existing results,with mean relative errors of 15.3 percent and 12.7 percent,respectively.The framework has given explicable weights of attention that reveal critical periods that influence carbon intensity alterations,and informed decisions on the management of carbon sustainability.The effectiveness of the proposed solution has been validated in numerous cases of operations,and TransCarbonNet is established to be an effective tool when it comes to carbon-friendly optimization of the grid.
文摘In this work,a computational modelling and analysis framework is developed to investigate the thermal buckling behavior of doubly-curved composite shells reinforced with graphene-origami(G-Ori)auxetic metamaterials.A semi-analytical formulation based on the First-Order Shear Deformation Theory(FSDT)and the principle of virtual displacements is established,and closed-form solutions are derived via Navier’s method for simply supported boundary conditions.The G-Ori metamaterial reinforcements are treated as programmable constructs whose effective thermo-mechanical properties are obtained via micromechanical homogenization and incorporated into the shell model.A comprehensive parametric study examines the influence of folding geometry,dispersion arrangement,reinforcement weight fraction,curvature parameters,and elastic foundation support on the critical buckling temperature(CBT).The results reveal that,under optimal folding geometry and reinforcement alignment with principal stress trajectories,the CBT can increase by more than 150%.Furthermore,the combined effect of G-Ori reinforcement and elastic foundation substantially enhances thermal buckling resistance.These findings establish design guidelines for architected composite shells in applications such as aerospace thermal skins,morphing structures,and thermally-responsive systems,and illustrate the potential of auxetic graphene metamaterials for multifunctional,lightweight,and thermally robust structural components.
基金The authors extend their appreciation to Taif University,Saudi Arabia,for supporting this work through project No.(TU-DSPP-2024-54).
文摘Background:Locally advanced laryngeal squamous cell carcinoma(LA-LSCC)presents clinical challenges due to the lack of reliable non-invasive biomarkers.This study aimed to evaluate miR-449a as a diagnostic and prognostic biomarker in LA-LSCC.Methods:miR-449a expression was analyzed in tumor tissues,adjacent normal tissues,and serum from 81 LA-LSCC patients and 50 controls using quantitative real-time reverse transcription polymerase chain reaction(qRT-PCR).We assessed the diagnostic accuracy by Receiver Operating Characteristic curve(ROC curves),clinicopathological associations,survival outcomes(Kaplan-Meier),and treatment response dynamics.Results:miR-449a was significantly downregulated in LA-LSCC tissues(p<0.0001)and serum(p<0.0001),with a strong tissue-serum correlation(R^(2)=0.988).Tissue miR-449a demonstrated a diagnostic accuracy(Area Under the Curve,AUC=0.857),while serum showed moderate accuracy(AUC=0.734).High miR-449a expression correlated with favorable clinicopathological features and improved survival(median overall survival:67.82 vs.23.74 months;p=0.0012).Multivariate analysis confirmed miR-449a as an independent prognostic factor(p<0.001).miR-449a levels increased post-treatment,particularly in responders to chemotherapy/radiation(p<0.0001).Conclusion:miR-449a serves as a non-invasive biomarker for LA-LSCC diagnosis,prognosis,and treatment monitoring.Its dynamic expression highlights potential for risk stratification and therapy response prediction,warranting further validation in larger cohorts.
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funded by Scientific Research Deanship at University of Ha’il-Saudi Arabia through project number(RG-24014).
文摘We present a computer-modeling framework for photovoltaic(PV)source emulation that preserves the exact single-diode physics while enabling iteration-free,real-time evaluation.We derive two closed-form explicit solvers based on the Lambert W function:a voltage-driven V-Lambert solver for high-fidelity I–V computation and a resistance-driven R-Lambert solver designed for seamless integration in a closed-loop PV emulator.Unlike Taylor-linearized explicit models,our proposed formulation retains the exponential nonlinearity of the PV equations.It employs a numerically stable analytical evaluation that eliminates the need for lookup tables and root-finding,all while maintaining limited computational costs and a small memory footprint.The R-Lambert model is integrated into a buck-converter emulator equipped with a discrete PI regulator,which generates current references directly from sensed operating points,thus supporting hardware-constrained implementation.Comprehensive numerical experiments conducted on six commercial modules from various technologies(mono,poly,and multicrystalline)demonstrate significant accuracy improvements under the IEC EN 50530 near-MPP criterion:the V-Lambert solver reduces the±10%Vmpp band error by up to 61 times compared to an explicit-model baseline.Dynamic simulations under varying irradiance,temperature,and load conditions achieve millisecond-scale settling with accurate trajectory tracking.Additionally,processor-in-the-loop experimental validation on an embedded microcontroller supports the simulation results.By unifying exact analytical modeling with embedded realization,this work advances computer modeling for PV emulation,MPPT benchmarking,and controller verification in integrated renewable energy systems.
文摘Five samples of LiMgPO_(4):Gd were prepared via five different production processes using a solid-state reaction method.The effects of the preparation process on optically stimulated luminescence(OSL)and thermoluminescence(TL)were investigated.Considering its high sensitivity,low fading,and minimum detectable dose(MDD),the LiMgPO_(4):Gd phosphor heated to 900℃for 15 h is concluded to be optimal.The effects of annealing on the OSL sensitivity,relative residual OSL signals measured after 24 h of irradiation,and MDD of LiMgPO_(4):Gd phosphors heated to 900℃for 15 h were also investigated.Considering its high sensitivity,low fading,and MDD,annealing at 350℃for 1 h is concluded to be optimal.The OSL signal of LiMgPO_(4):Gd was derived from the principal TL glow peak.For a maximum integration time of 5 s,the OSL signal was stable,with no fading 30 days after irradiation.LiMgPO_(4):Gd eliminated approximately 2.2%of the OSL signal at each readout for a readout time of 0.1 s,which is sufficient for fast and multiple OSL readout.The sensitivity of LiMgPO_(4):Gd phosphor,annealed for 1 h at 350℃with a reading time of 0.1 s,was found to be approximately 98%of that observed forα-Al_(2)O_(3):C(TLD-500k),which should be sufficient for low-dose measurements in personal,workplace,and environmental dosimetry.
文摘One of the main issues in designing optimum tapered cascades for uranium enrichment for annual fuel production in a power reactor is whether to employ large(fat)or small(thin)cascades.What will be the permissible and optimal ranges of the number of machines that can be used in a cascade?For the first time,the permissible and optimal ranges of the number of gas centrifuges that can be utilized in a cascade were investigated using two types of centrifuges,and the performance of small and large tapered cascades was discussed.The particle swarm optimization algorithm(PSO)has been used to optimize tapered cascades.The results show:(1)For the first centrifuge,41 cascades(91≤n≤4897)and for the second centrifuge,49 cascades(18≤n≤3839)with small and large sizes can be used in enrichment facilities,and the best cascade for them has 530(with 23 stages)and 39(with 7 stages)centrifuges,respectively.(2)For both centrifuges,when 600≤n(number of centrifuges=n),the large cascade performance changes are relatively insignificant.(3)For both types of gas centrifuges,the annual los s of separation power in enrichment facilities is approximately 1.25%-4.82%of the total separation work required.
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.
文摘The aim of this study is to design,build,and evaluate an indirect forced convection solar dryer adapted to semi-arid climate,such as that of Bechar situated in the west south region of Algeria.The tested drying system consists of a flat-plate solar collector,an insulated two-chamber drying unit,and an Arduino-controlled device that ensures uniformtemperature distribution and real-timemonitoring using DHT22 sensors.Drying testswere conducted on locally grown beet slices at air temperatures of 45℃,60℃,and 80℃,with a constant air velocity of 1.2 m/s and a mass flow rate of 0.0027 kg/s.The collector reached a maximum temperature of 65℃,with thermal efficiencies ranging from 20%to 35%.In these conditions,the drying times were cut down to 200–300 min,and the beet’s moisture content dropped to 0.47,0.27,and 0.24 g/g dry matter,respectively.The experimental data were fitted to several empirical models,including the logarithmic model.The modelled results showed strong agreement with the experimental ones(correlation coefficients r=0.9919–0.9989;standard errors SE=0.017–0.043;root-mean-square errors RMSE=0.016–0.027).The results demonstrate that the system operates efficiently and consistently,making it suitable for the sustainable drying of agricultural and medicinal products in arid climates.
文摘The biological stabilization of soil using microbially induced carbonate precipitation(MICP)employs ureolytic bacteria to precipitate calcium carbonate(CaCO3),which binds soil particles,enhancing strength,stiffness,and erosion resistance.The unconfinedcompressive strength(UCS),a key measure of soil strength,is critical in geotechnical engineering as it directly reflectsthe mechanical stability of treated soils.This study integrates explainable artificialintelligence(XAI)with geotechnical insights to model the UCS of MICP-treated sands.Using 517 experimental data points and a combination of various input variables—including median grain size(D50),coefficientof uniformity(Cu),void ratio(e),urea concentration(Mu),calcium concentration(Mc),optical density(OD)of bacterial solution,pH,and total injection volume(Vt)—fivemachine learning(ML)models,including eXtreme gradient boosting(XGBoost),Light gradient boosting machine(LightGBM),random forest(RF),gene expression programming(GEP),and multivariate adaptive regression splines(MARS),were developed and optimized.The ensemble models(XGBoost,LightGBM,and RF)were optimized using the Chernobyl disaster optimizer(CDO),a recently developed metaheuristic algorithm.Of these,LightGBM-CDO achieved the highest accuracy for UCS prediction.XAI techniques like feature importance analysis(FIA),SHapley additive exPlanations(SHAP),and partial dependence plots(PDPs)were also used to investigate the complex non-linear relationships between the input and output variables.The results obtained have demonstrated that the XAI-driven models can enhance the predictive accuracy and interpretability of MICP processes,offering a sustainable pathway for optimizing geotechnical applications.
文摘The increasing number of interconnected devices and the incorporation of smart technology into contemporary healthcare systems have significantly raised the attack surface of cyber threats.The early detection of threats is both necessary and complex,yet these interconnected healthcare settings generate enormous amounts of heterogeneous data.Traditional Intrusion Detection Systems(IDS),which are generally centralized and machine learning-based,often fail to address the rapidly changing nature of cyberattacks and are challenged by ethical concerns related to patient data privacy.Moreover,traditional AI-driven IDS usually face challenges in handling large-scale,heterogeneous healthcare data while ensuring data privacy and operational efficiency.To address these issues,emerging technologies such as Big Data Analytics(BDA)and Federated Learning(FL)provide a hybrid framework for scalable,adaptive intrusion detection in IoT-driven healthcare systems.Big data techniques enable processing large-scale,highdimensional healthcare data,and FL can be used to train a model in a decentralized manner without transferring raw data,thereby maintaining privacy between institutions.This research proposes a privacy-preserving Federated Learning–based model that efficiently detects cyber threats in connected healthcare systems while ensuring distributed big data processing,privacy,and compliance with ethical regulations.To strengthen the reliability of the reported findings,the resultswere validated using cross-dataset testing and 95%confidence intervals derived frombootstrap analysis,confirming consistent performance across heterogeneous healthcare data distributions.This solution takes a significant step toward securing next-generation healthcare infrastructure by combining scalability,privacy,adaptability,and earlydetection capabilities.The proposed global model achieves a test accuracy of 99.93%±0.03(95%CI)and amiss-rate of only 0.07%±0.02,representing state-of-the-art performance in privacy-preserving intrusion detection.The proposed FL-driven IDS framework offers an efficient,privacy-preserving,and scalable solution for securing next-generation healthcare infrastructures by combining adaptability,early detection,and ethical data management.
文摘Origanum elongatum(OE)is an aromatic,medicinal plant endemic to Morocco that is widely used in traditional medicine due to its biological properties.This study aimed to elucidate the chemical composition of the essential oil(EO)obtained from O.elongatum(OEEO)at three stages of its life cycle,including vegetative stage(OEEO-VS),flowering stage(OEEO-FS),and post-flowering(OEEO-PFS),as well as to evaluate its biological and antiradical characteristics.The chemical analysis of the essential oil was conducted using gas chromatography-mass spectrometry(GC-MS).The antibacterial activity was evaluated in vitro through distinct methodologies,namely,disc diffusion and microatmosphere assay;subsequently,the minimum inhibitory concentration(MIC)was then determined.The antioxidant potential was also measured by using the DPPH and FRAP assays.The GC-MS revealed the predominant of p-cymene(26.83%_31.45%),γ-terpinene(8.46%_26.95%),thymol(13%_29.54%),and carvacrol(20.25%_37.26%),in all three samples,with notable variations according to the phenological stage of the samples.The EOs extracted at three phenological stages demonstrated notable antibacterial efficacy against all the phytopathogen tested.The MICs for Erwinia amylovora exhibited a range of 6.25 and 250μg/mL.However,for Agrobacterium tumefaciens C58 and Allorhizobium vitis S4,the MICs spanned 125 and 250μg/mL.In the DPPH test,the IC50 values were 168.25±1.14,147.01±0.78,and 132.01±2.06μg/mL for EOs derived from the vegetative,flowering,and post-flowering period,respectively.In the FRAP test,the EC50 values were 164.22±1.04,215.73±1.48,and 184.06±0.95μg/mL for the same stages.The findings offer promising prospects for the phytochemical development,demonstrating how the phenological stage significantly influences the therapeutic and biotechnological potential of O.elongatum.This has the potential to open up new avenues of research in the pharmaceutical,agronomic,and environmental fields.
文摘Objective:Deep learning is employed increasingly in Gastroenterology(GI)endoscopy computer-aided diagnostics for polyp segmentation and multi-class disease detection.In the real world,implementation requires high accuracy,therapeutically relevant explanations,strong calibration,domain generalization,and efficiency.Current Convolutional Neural Network(CNN)and transformer models compromise border precision and global context,generate attention maps that fail to align with expert reasoning,deteriorate during cross-center changes,and exhibit inadequate calibration,hence diminishing clinical trust.Methods:HMA-DER is a hierarchical multi-attention architecture that uses dilation-enhanced residual blocks and an explainability-aware Cognitive Alignment Score(CAS)regularizer to directly align attribution maps with reasoning signals from experts.The framework has additions that make it more resilient and a way to test for accuracy,macro-averaged F1 score,Area Under the Receiver Operating Characteristic Curve(AUROC),calibration(Expected Calibration Error(ECE),Brier Score),explainability(CAS,insertion/deletion AUC),cross-dataset transfer,and throughput.Results:HMA-DER gets Dice Similarity Coefficient scores of 89.5%and 86.0%on Kvasir-SEG and CVC-ClinicDB,beating the strongest baseline by+1.9 and+1.7 points.It gets 86.4%and 85.3%macro-F1 and 94.0%and 93.4%AUROC on HyperKvasir and GastroVision,which is better than the baseline by+1.4/+1.6macro-F1 and+1.2/+1.1AUROC.Ablation study shows that hierarchical attention gives the highest(+3.0),followed by CAS regularization(+2–3),dilatation(+1.5–2.0),and residual connections(+2–3).Cross-dataset validation demonstrates competitive zero-shot transfer(e.g.,KS→CVC Dice 82.7%),whereas multi-dataset training diminishes the domain gap,yielding an 88.1%primary-metric average.HMA-DER’s mixed-precision inference can handle 155 pictures per second,which helps with calibration.Conclusion:HMA-DER strikes a compromise between accuracy,explainability,robustness,and efficiency for the use of reliable GI computer-aided diagnosis in real-world clinical settings.
文摘Accurate prediction of concrete compressive strength is fundamental for optimizing mix designs,improving material utilization,and ensuring structural safety in modern construction.Traditional empirical methods often fail to capture the non-linear relationships among concrete constituents,especially with the growing use of supple-mentary cementitious materials and recycled aggregates.This study presents an integrated machine learning framework for concrete strength prediction,combining advanced regression models—namely CatBoost—with metaheuristic optimization algorithms,with a particular focus on the Somersaulting Spider Optimizer(SSO).A comprehensive dataset encompassing diverse mix proportions and material types was used to evaluate baseline machine learning models,including CatBoost,XGBoost,ExtraTrees,and RandomForest.Among these,CatBoost demonstrated superior accuracy across multiple performance metrics.To further enhance predictive capability,several bio-inspired optimizers were employed for hyperparameter tuning.The SSO-CatBoost hybrid achieved the lowest mean squared error and highest correlation coefficients,outperforming other metaheuristic approaches such as Genetic Algorithm,Particle Swarm Optimization,and Grey Wolf Optimizer.Statistical significance was established through Analysis of Variance and Wilcoxon signed-rank testing,confirming the robustness of the optimized models.The proposed methodology not only delivers improved predictive performance but also offers a transparent framework for mix design optimization,supporting data-driven decision making in sustainable and resilient infrastructure development.
基金funded by United Arab Emirates University(UAEU)under the UAEU-AUA grant number G00004577(12N145)with the corresponding grant at Universiti Malaya(UM)under grant number IF019-2024.
文摘Concrete-filled steel tubes(CFST)are widely utilized in civil engineering due to their superior load-bearing capacity,ductility,and seismic resistance.However,existing design codes,such as AISC and Eurocode 4,tend to be excessively conservative as they fail to account for the composite action between the steel tube and the concrete core.To address this limitation,this study proposes a hybrid model that integrates XGBoost with the Pied Kingfisher Optimizer(PKO),a nature-inspired algorithm,to enhance the accuracy of shear strength prediction for CFST columns.Additionally,quantile regression is employed to construct prediction intervals for the ultimate shear force,while the Asymmetric Squared Error Loss(ASEL)function is incorporated to mitigate overestimation errors.The computational results demonstrate that the PKO-XGBoost model delivers superior predictive accuracy,achieving a Mean Absolute Percentage Error(MAPE)of 4.431%and R2 of 0.9925 on the test set.Furthermore,the ASEL-PKO-XGBoost model substantially reduces overestimation errors to 28.26%,with negligible impact on predictive performance.Additionally,based on the Genetic Algorithm(GA)and existing equation models,a strength equation model is developed,achieving markedly higher accuracy than existing models(R^(2)=0.934).Lastly,web-based Graphical User Interfaces(GUIs)were developed to enable real-time prediction.
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 Deanship of Research and Graduate Studies at King Khalid University,Saudi Arabia,for funding this study through the Large Groups Project(Grant No.RGP2/2/47)the Deanship of Scientific Research at Northern Border University,Arar,KSA for funding this research work through project number NBU-FFR-2025-1902-02。
文摘Cu_(x)Ta_(2-x)O_(5) compositions were investigated for advanced thermoelectric and optical applications,using both simulations and experimental approaches.Density functional theory calculations were performed before the experimental observations to predict the trends of various parameters.Crystal structure analysis confirmed the presence of the orthorhombic Ta_(2)O_(5) phase in all the compositions.The composition and morphology demonstrated impurity-free contents with uniform and crack-free surfaces.Thermoelectric analysis depicted a decrease in Seebeck coefficient from 3.66??V·K^(-1)to 1.91??V·K^(-1)and an increase in the value of specific heat from 0.73 J·K^(-1)·kg^(-1)to 11.6 J·K^(-1)·kg^(-1)upon Cu incorporation in structure.The bandgap was found to reduce from 2.61 to 1.38 e V with Cu-induced electronic states.The real epsilon and static refractive index increased from 3.75 to 4.57 and from 1.93 to 2.11,respectively,with increment in Cu content.The enhanced parameters,focusing on the thermoelectric and optical responses,make these compositions potential candidates for advanced optoelectronic applications.
文摘In the present study,researchers examined a solar off-grid-connected photovoltaic system for a family house in the city of Baghdad.The design was created with the help of the“How to Design PV Program”and the“Renewable Energy Investment Calculator(REICAL)”software(Version 1.1).In Iraq,the national grid provides around 71%of the overall electricity demand,though this drops to nearly 50%during extremely hot and cold months,where the supply alternates between four hours on and four hours off.During the off periods,power is generated by local generators at high costs.To promote the adoption of photovoltaic solar systems among Iraqi citizens through loans,three options for meeting 100% of electricity needs have been proposed:an on-grid solution,a hybrid system that supplies 24 h,and an off-grid solution for a 24-h supply.The 12-h off-grid system(hybrid)is both economical and efficient for delivering electricity.Findings reveal that,over 20 years,the system’s output will amount to 141,176.71 kWh,with a payback period of 5.85 years and a performance ratio of 86.2%.Investment outcome data showed a net present value of $6445,and the profitability index was 6.16,indicating the project’s profitability.Additionally,the system could result in a net reduction of CO_(2) emissions totaling 132,810.24 kg.
基金supported by National Science Foundation of China(10972015,11172015)the Beijing Natural Science Foundation(8162008).
文摘In modern construction,Lightweight Aggregate Concrete(LWAC)has been recognized as a vital material of concern because of its unique properties,such as reduced density and improved thermal insulation.Despite the extensive knowledge regarding its macroscopic properties,there is a wide knowledge gap in understanding the influence of microscale parameters like aggregate porosity and volume ratio on the mechanical response of LWAC.This study aims to bridge this knowledge gap,spurred by the need to enhance the predictability and applicability of LWAC in various construction environments.With the help of advanced numerical methods,including the finite element method and a random circular aggregate model,this study critically evaluates the role played by these microscale factors.We found that an increase in the aggregate porosity from 23.5%to 48.5%leads to a drastic change of weakness from the bonding interface to the aggregate,reducing compressive strength by up to 24.2%and tensile strength by 27.8%.Similarly,the increase in the volume ratio of lightweight aggregate from 25%to 40%leads to a reduction in compressive strength by 13.0%and tensile strength by 9.23%.These results highlight the imperative role of microscale properties on the mechanical properties of LWAC.By supplying precise quantitative details on the effect of porosity and aggregate volume ratio,this research makes significant contributions to construction materials science by providing useful recommendations for the creation and optimization of LWAC with improved performance and sustainability in construction.
