Proton exchange membrane(PEM)is an integral component in fuel cells which enables proton transport for efficient energy conversion.Sulfonated Polyether Ether Ketone(SPEEK)has emerged as a cost-effective option with no...Proton exchange membrane(PEM)is an integral component in fuel cells which enables proton transport for efficient energy conversion.Sulfonated Polyether Ether Ketone(SPEEK)has emerged as a cost-effective option with non-fluorinated aromatic backbones for Proton Exchange Membrane Fuel Cell(PEMFC)applications,even though it exhibits lower proton conductivity compared to Nafion.This work aims to study the influence of Sulfonated Chitosan(SCS)concentrations on proton conductivity of SPEEK-based PEM at room temperature.SPEEK was synthesized using a sulfonation process with concentrated sulfuric acid at room temperature.SCS was synthesized via reflux of CS and 1.2 M H2SO4 with a ratio of 1:35(w/v)at 90℃ for 30 min.The composite membranes of SPEEK-SCS were formed with four different SCS concentrations,using the solution castingmethod,andDimethyl Sulfoxide(DMSO)was used as a solvent.The composite membranes synthesized include pure SPEEK(S0),SPEEK with 1%SCS(S1),SPEEK with 2%SCS(S2),and SPEEK with 3%SCS(S3).Fourier transform infrared spectroscopy(FTIR),X-ray diffraction(XRD),water uptake,degree of swelling,Ionic exchange capacity(IEC)with Electrochemical impedance spectroscopy(EIS)were used to characterize the composite membranes in terms of composition,crystallinity,water absorption,dimensional changes,number of exchangeable ions in membranes,and proton conductivity,respectively.Notably,S3 had the highest water uptake and the lowest degree of swelling.S2 had the highest proton conductivity among the SPEEK-SCS composite membranes at room temperature with 3.44×10^(−2) Scm^(-1).展开更多
There is a need for accurate prediction of heat and mass transfer in aerodynamically designed,non-Newtonian nanofluids across aerodynamically designed,high-flux biomedical micro-devices for thermal management and reac...There is a need for accurate prediction of heat and mass transfer in aerodynamically designed,non-Newtonian nanofluids across aerodynamically designed,high-flux biomedical micro-devices for thermal management and reactive coating processes,but existing work is not uncharacteristically remiss regarding viscoelasticity,radiative heating,viscous dissipation,and homogeneous–heterogeneous reactions within a single scheme that is calibrated.This research investigates the flow of Williamson nanofluid across a dynamically wedged surface under conditions that include viscous dissipation,thermal radiation,and homogeneous-heterogeneous reactions.The paper develops a detailed mathematical approach that utilizes boundary layers to transform partial differential equations into ordinary differential equations using similarity transformations.RK4 is the technique for gaining numerical solutions,but with the addition of ANNs,there is an improvement in prediction accuracy and computational efficiency.The study investigates the influence of wedge angle parameter,along with Weissenberg number,thermal radiation parameter and Brownian motion parameter,and Schmidt number,on velocity distribution,temperature distribution,and concentra-tion distribution.Enhanced Weissenberg numbers enhance viscoelastic responses that modify velocity patterns,but radiation parameters and thermophoresis have key impacts on thermal transfer phenomena.This research develops findings that are of enormous application in aerospace,biomedical(artificial hearts and drug delivery),and industrial cooling technology applications.New findings on non-Newtonian nanofluids under full flow systems are included in this work to enhance heat transfer methods in novel fluid-based systems.展开更多
Objective:To systematically review the effects of administering metformin and glutathione alone and in coformulation with other compounds on the fertility and reproductive health of diabetic male rodents.Methods:The g...Objective:To systematically review the effects of administering metformin and glutathione alone and in coformulation with other compounds on the fertility and reproductive health of diabetic male rodents.Methods:The guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-analyses(PRISMA)were followed to conduct this systematic review.Studies published until August 2024 in PubMed,Web of Science,and Scopus databases were searched,identified,screened,and selected for a detailed review.The keywords included metformin,diabetes,reproduction,glutathione,and rodent models.Results:A total of 166 studies were identified,of which 11 met the inclusion criteria and were included in the qualitative synthesis.One additional study was identified through snowballing and citation tracking,bringing the total to 12 studies.The findings indicate that metformin and glutathione,administered alone or in combination with other compounds,improved sperm count,motility,and morphology;restored reproductive hormone levels;reduced oxidative stress markers;and improved testicular histopathology in diabetic male rodents.Conclusions:Coformulation of metformin and glutathione with other compounds was found to be more effective in improving fertility and reproductive parameters in diabetic male rodents compared to mono-administration.However,further studies on the coformulation of metformin and glutathione are needed to confirm their efficacy and elucidate the underlying mechanisms.Study registration:The study protocol was registered in the International Prospective Register of Systematic Reviews(PROSPERO)with registration number CRD42024561820.展开更多
A novel vibration isolation system designed for superior performance in low-frequency environments is proposed in this work.The isolator is based on a unique hexagonal arrangement of linear springs,allowing for an adj...A novel vibration isolation system designed for superior performance in low-frequency environments is proposed in this work.The isolator is based on a unique hexagonal arrangement of linear springs,allowing for an adjustable geometric configuration via the initial inclination angle.Based on the principle of Lagrangian mechanics,the equation of motion governing the structural dynamics is rigorously derived.The system is modeled as a strongly nonlinear single-degree-of-freedom dynamical system,loaded with a normalized payload and subject to harmonic base excitation.To analyze the steady-state response,the harmonic balance method is employed,providing accurate predictions of the payload's vibration amplitude and displacement transmissibility as functions of both the base excitation amplitude and frequency.The analysis reveals a direct relationship between the isolator's geometric and stiffness parameters and its load-bearing capacity,leading to the identification of three distinct operational regimes.Depending on the unloaded initial inclination angle,the equivalent stiffness ratio,and the payload design configuration,the system can exhibit one of three vibration isolation modes:(i)the quasizero stiffness(QZS)isolation mode,(ii)the zero linear stiffness with controllable nonlinear stiffness,and(iii)the full-band perfect zero stiffness.The vibration isolation performance of the proposed structure is thoroughly discussed for all three oscillation modes in terms of frequency response curves,displacement transmissibility,and time-domain responses.The key novel finding is that this structure can operate as a full-band,high-performance vibration isolator when the initial inclination angle is designed to be a right angle,enabling full isolation of the maximum possible payload.Moreover,the analytical results and numerical simulations demonstrate that the isolator's displacement transmissibility T with the unit dB tends to-∞as the air-damping coefficient approaches zero,enabling ideal vibration isolation across the entire excitation frequency range.These analytical insights are validated through comprehensive numerical simulations,which show excellent agreement with the theoretical predictions.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
Safeguarding modern networks from cyber intrusions has become increasingly challenging as attackers continually refine their evasion tactics.Although numerousmachine-learning-based intrusion detection systems(IDS)have...Safeguarding modern networks from cyber intrusions has become increasingly challenging as attackers continually refine their evasion tactics.Although numerousmachine-learning-based intrusion detection systems(IDS)have been developed,their effectiveness is often constrained by high dimensionality and redundant features that degrade both accuracy and efficiency.This study introduces a hybrid feature-selection framework that integrates the exploration capability of Prairie Dog Optimization(PDO)with the exploitation behavior of Ant Colony Optimization(ACO).The proposed PDO–ACO algorithm identifies a concise yet discriminative subset of features from the NSLKDD dataset and evaluates them using a Support Vector Machine(SVM)classifier.Experimental analyses reveal that the PDO–ACO model achieves superior detection accuracy of 98%while significantly lowering false alarms and computational overhead.Further validation on the CEC2017 benchmark suite confirms the robustness and adaptability of the hybrid model across diverse optimization landscapes,positioning PDO–ACO as an efficient and scalable approach for intelligent intrusion detection.展开更多
Utility-scale PV plants increasingly operate under partial shading,soiling,temperature swings,and rapid irradiance ramps that depress yield and challenge stability on weak grids.This critical review addresses those co...Utility-scale PV plants increasingly operate under partial shading,soiling,temperature swings,and rapid irradiance ramps that depress yield and challenge stability on weak grids.This critical review addresses those conditions by(i)unifying a stressor-to-method taxonomy that links field stressors to global intelligent MPPT(metaheuristics and learning-based trackers)and to advanced inverter controls(adaptive/MPC and grid-forming),(ii)standardizing metrics and reporting aligned with IEC 61724-1 and IEEE 1547/1547.1 to enable fair,reproducible comparisons,and(iii)framing MPPT and grid support as a co-design problem with a DT→HIL→Field validation pathway and seedable scenarios.We identify persistent gaps—fragmented partial-shading benchmarks,limited low-SCR testing,and scarce field-grade validation—and compile a quantitative synthesis:global soiling typically reduces annual production by≈3%–5%,and hybrid/learning MPPT frequently report≈99%tracking efficiency under PSC in simulation/HIL studies.To demonstrate practical relevance,we validate the framework on a seeded scenario library:DRL trackers achieve medianηMPPT≈0.996 with t95≈0.19 s and Hybrid trackers≈0.992/0.26 s,outperforming Metaheuristics(≈0.984/0.42 s);at SCR=2.5,grid-forming control raises VRI from~0.78(tuned GFL)to~0.95 while keeping THD within 2.5%–3.2%,with all stacks meeting IEEE-1547.1 Category-II ride-through.The resulting taxonomy,standards-aligned reporting,and open seeds provide a replicable basis for comparable,grid-relevant benchmarking and clear guidance for real-world design and operations.展开更多
Object detection,a major challenge in computer vision and pattern recognition,plays a significant part in many applications,crossing artificial intelligence,face recognition,and autonomous driving.It involves focusing...Object detection,a major challenge in computer vision and pattern recognition,plays a significant part in many applications,crossing artificial intelligence,face recognition,and autonomous driving.It involves focusing on identifying the detection,localization,and categorization of targets in images.A particularly important emerging task is distinguishing real animals from toy replicas in real-time,mostly for smart camera systems in both urban and natural environments.However,that difficult task is affected by factors such as showing angle,occlusion,light intensity,variations,and texture differences.To tackle these challenges,this paper recommends Group Sparse YOLOv8(You Only Look Once version 8),an improved real-time object detection algorithm that improves YOLOv8 by integrating group sparsity regularization.This adjustment improves efficiency and accuracy while utilizing the computational costs and power consumption,including a frame selection approach.And a hybrid parallel processing method that merges pipelining with dataflow strategies to improve the performance.Established using a custom dataset of toy and real animal images along with well-known datasets,namely ImageNet,MSCOCO,and CIFAR-10/100.The combination of Group Sparsity with YOLOv8 shows high detection accuracy with lower latency.Here provides a real and resource-efficient solution for intelligent camera systems and improves real-time object detection and classification in environments,differentiating between real and toy animals.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
Autonomous connected vehicles(ACV)involve advanced control strategies to effectively balance safety,efficiency,energy consumption,and passenger comfort.This research introduces a deep reinforcement learning(DRL)-based...Autonomous connected vehicles(ACV)involve advanced control strategies to effectively balance safety,efficiency,energy consumption,and passenger comfort.This research introduces a deep reinforcement learning(DRL)-based car-following(CF)framework employing the Deep Deterministic Policy Gradient(DDPG)algorithm,which integrates a multi-objective reward function that balances the four goals while maintaining safe policy learning.Utilizing real-world driving data from the highD dataset,the proposed model learns adaptive speed control policies suitable for dynamic traffic scenarios.The performance of the DRL-based model is evaluated against a traditional model predictive control-adaptive cruise control(MPC-ACC)controller.Results show that theDRLmodel significantly enhances safety,achieving zero collisions and a higher average time-to-collision(TTC)of 8.45 s,compared to 5.67 s for MPC and 6.12 s for human drivers.For efficiency,the model demonstrates 89.2% headway compliance and maintains speed tracking errors below 1.2 m/s in 90% of cases.In terms of energy optimization,the proposed approach reduces fuel consumption by 5.4% relative to MPC.Additionally,it enhances passenger comfort by lowering jerk values by 65%,achieving 0.12 m/s3 vs.0.34 m/s3 for human drivers.A multi-objective reward function is integrated to ensure stable policy convergence while simultaneously balancing the four key performance metrics.Moreover,the findings underscore the potential of DRL in advancing autonomous vehicle control,offering a robust and sustainable solution for safer,more efficient,and more comfortable transportation systems.展开更多
The Arno River Basin(Central Italy)is affected by a considerable anthropogenic pressure due to the presence of large cities and widespread industrial and agricultural practices.In this work,26 water samples from the A...The Arno River Basin(Central Italy)is affected by a considerable anthropogenic pressure due to the presence of large cities and widespread industrial and agricultural practices.In this work,26 water samples from the Arno River and its main tributaries were analyzed to assess the water pollution status.The geochemical composition of the Arno River changes from the source(dominated by a Ca-HCO_(3) facies)to the mouth(where a Na-Cl(SO4)chemistry prevails)with an increasing quality deterioration,as suggested by the Chemical Water Quality Index,due to anthropogenic contributions and seawater intrusion before flowing into the Ligurian Sea.The Ombrone and Usciana tributaries introduce anthropogenic pollutants into the Arno River,whilst Elsa tributary supplies significant contents of geogenic sulfate.The concentrations of dissolved nitrate and nitrite(up to 63 and 9 mg/L,respectively)and the respective isotopic values of𝛿15N and𝛿18O were also determined to understand origin and fate of the N-species in the Arno River Basin surface waters.The combined application of𝛿15N-NO_(3) and𝛿18O-NO_(3) and N-source apportionment modelling allowed the identification of soil organic nitrogen and sewage and domestic wastes as primary sources for dissolved NO_(3)-.The𝛿15N-NO_(2) and𝛿18O-NO_(2) values suggest that the nitrification process affects the ARB waters,thus controlling the abundances and proportion of the N-species.Our work indicates that additional efforts are needed to improve management strategies to reduce the release of nitrogenated species to the surface waters of the Arno River Basin,since little progress has been made from the early 2000s.展开更多
The present investigation inspects the unsteady,incompressible MHD-induced flow of a ternary hybrid nanofluid made of SiO_(2)(silicon dioxide),ZnO(zinc oxide),and MWCNT(multi-walled carbon nanotubes)suspended in a wat...The present investigation inspects the unsteady,incompressible MHD-induced flow of a ternary hybrid nanofluid made of SiO_(2)(silicon dioxide),ZnO(zinc oxide),and MWCNT(multi-walled carbon nanotubes)suspended in a water-ethylene glycol base fluid between two perforated squeezing Riga plates.This problem is important because it helps us understand the complicated connections between magnetic fields,nanofluid dynamics,and heat transport,all of which are critical for designing thermal management systems.These findings are especially useful for improving the design of innovative cooling technologies in electronics,energy systems,and healthcare applications.No prior study has been done on the theoretical study of the flow of ternary nanofluid(SiO_(2)+ZnO+MWCNT/Water−EthylGl ycol,(60∶40))past a pierced squeezed Riga plates using the boundary value problem solver 4th-order collocation(BVP4C)numerical approach to date.So,the current work has been carried out to fill this gap,and the core purpose of this study is to explore the aspects that enhance the heat transfer of base fluids(H_(2)O/EG)suspended with three nanomaterials SiO_(2),ZnO,and MWCNT.The Riga plates introduce electromagnetic forcing through an embedded array of magnets and electrodes,generating Lorentz forces to regulate the flow.The squeezing effect introduces dynamic boundary movement,which enhances mixing;however,permeability,due to porosity,replicates the true material limits.Similarity transformations of the Navier-Stokes and energy equations result in a highly nonlinear set of ordinary differential equations that govern momentum and thermal energy transport.The subsequent boundary value problem is solved utilizing the BVP4C numerical approach.The study observes the impact of magnetic parameters,squeezing velocity,solid volume percentages of the three nanoparticles,and porous medium factors on velocity and temperature fields.Results show that magnetic fields reduce the velocity profile by 6.75%due to increased squeezing and medium effects.Tri-hybrid nanofluids notice a 9%rise in temperature with higher thermal radiation.展开更多
Photovoltaic(PV)systems in the field operate under complex,uncertain conditions rapid irradiance ramps,partial shading,temperature swings,surface soiling,and weak-grid disturbances including off-nominal frequency and ...Photovoltaic(PV)systems in the field operate under complex,uncertain conditions rapid irradiance ramps,partial shading,temperature swings,surface soiling,and weak-grid disturbances including off-nominal frequency and voltage distortion that degrade energy yield and power quality.We propose a drift-aware,power-quality-constrained MPPT framework that co-optimizes MPPT,PLL,and current-loop gains under stochastic frequency drift,while enforcing IEEE-519 limits(per-order Ih/IL and TDD)during optimization.Unlike energy-only or THD-only methods,the design target integrates PQ constraints into the objective and is validated across calibrated drift scenarios with explicit per-order and TDD reporting.Operating scenarios are calibrated to Cameroon’s Southern Interconnected Grid and city-specific profiles(Douala/Yaoundé),combining measured-style irradiance/temperature traces,partial-shading patterns,and stochastic frequency drift up to±0.8 Hz with synthetic contingencies.Across a 30-scenario campaign,the proposed controller achievesηMPPT=99.3%–99.6%(vs.98.6%Incremental Conductance and 97.8%Perturb-and-Observe),lowers DC-link ripple by 35%–48%,reduces oscillatory PCC power by≈41%,maintains THD≤2.5%(5%limit)and PF≥0.99,and shortens irradiance-step settling from 85–110 ms to 50–65 ms.Sensitivity to PLL bandwidth shows a broad optimum(≈60–90 Hz)with minimum THD/ripple,and ablations confirm that explicit drift weighting is pivotal to ripple and THD suppression without sacrificing yield.The approach is controller-agnostic,firmware-deployable,and generalizes to other converter-interfaced renewables;we outline a short hardware-/HIL-validation path for adoption in Sub-Saharan grids.展开更多
In this article, two relaxation time limits, namely, the momentum relaxation time limit and the energy relaxation time limit are considered. By the compactness argument, it is obtained that the smooth solutions of the...In this article, two relaxation time limits, namely, the momentum relaxation time limit and the energy relaxation time limit are considered. By the compactness argument, it is obtained that the smooth solutions of the multidimensional nonisentropic Euler-Poisson problem converge to the solutions of an energy transport model or a drift diffusion model, respectively, with respect to different time scales.展开更多
In this paper, a theory on the determination of the diffusion coefficient of excess minority carriers in the base of a silicon solar cell is presented. The diffusion coefficient expression has been established and is ...In this paper, a theory on the determination of the diffusion coefficient of excess minority carriers in the base of a silicon solar cell is presented. The diffusion coefficient expression has been established and is related to both frequency modulation and applied magnetic field;the study is then carried out using the impedance spectroscopy method and Bode diagrams. From the diffusion coefficient, we deduced the diffusion length and the minority carriers’ mobility. Electric parameters were derived from the diffusion coefficient equivalent circuits.展开更多
AIM： To determine the prevalence of Helicobacter pylori （H pylori） among dyspeptic patients and to assess the relationship between Hpylori infection, blood group, HIV infection and life style of the patients. METH...AIM： To determine the prevalence of Helicobacter pylori （H pylori） among dyspeptic patients and to assess the relationship between Hpylori infection, blood group, HIV infection and life style of the patients. METHODS： In a hospital-based cross-sectional study, patients attending Outpatient Department of University of Gondar Hospital were enrolled. Socio-demographic information was collected using questionnaires. Serum was analyzed for anti-H pylori IgG antibodies using a commercial kit. HIV serostatus was determined by enzyme-linked immunosorbent assay （ELISA）. Blood grouping was performed by slide agglutination tests. RESULTS： A total of 215 dyspeptic patients were included in the study. One hundred and sixteen patients （54%） were females and 99 （46%） were males. Anti-H pylori IgG antibodies were detected in sera of 184 （85.6%） patients. The prevalence was significantly higher in patients aged 50 years and above. Twenty point five percent of the patients were found to be seropositive for HIV. No significant association was found between sex, ABO blood groups, consumption of spicy diets, socioeconomic status and seropositivity for Hpylori. However,alcohol consumption was significantly associated with H pylori serology. CONCLUSION： The prevalence of H pylori infection is associated with a history of alcohol intake and older age. The effect of different diet, alcohol and socioeconomic status as risk factors for H pylori infection needs further study.展开更多
Photovoltaic(PV)power forecasting is essential for balancing energy supply and demand in renewable energy systems.However,the performance of PV panels varies across different technologies due to differences in efficie...Photovoltaic(PV)power forecasting is essential for balancing energy supply and demand in renewable energy systems.However,the performance of PV panels varies across different technologies due to differences in efficiency and how they process solar radiation.This study evaluates the effectiveness of deep learning models in predicting PV power generation for three panel technologies:Hybrid-Si,Mono-Si,and Poly-Si,across three forecasting horizons:1-step,12-step,and 24-step.Among the tested models,the Convolutional Neural Network—Long Short-Term Memory(CNN-LSTM)architecture exhibited superior performance,particularly for the 24-step horizon,achieving R^(2)=0.9793 and MAE 0.0162 for the Poly-Si array,followed by Mono-Si(R^(2)=0.9768)and Hybrid-Si arrays(R^(2)=0.9769).These findings demonstrate that the CNN-LSTM model can provide accurate and reliable PV power predictions for all studied technologies.By identifying the most suitable predictive model for each panel technology,this study contributes to optimizing PV power forecasting and improving energy management strategies.展开更多
Background:Fever is characterized by an upregulation of the thermoregulatory set-point after the body encounters any pathological challenge.It is accompanied by uncomfortable sickness behaviors and may be harmful in p...Background:Fever is characterized by an upregulation of the thermoregulatory set-point after the body encounters any pathological challenge.It is accompanied by uncomfortable sickness behaviors and may be harmful in patients with other comor-bidities.We have explored the impact of an Ayurvedic medicine,Fevogrit,in an endo-toxin(lipopolysaccharide)-induced fever model in Wistar rats.Methods:Active phytoconstituents of Fevogrit were identified and quantified using ultra-high-performance liquid chromatography(UHPLC)platform.For the in-vivo study,fever was induced in male Wistar rats by the intraperitoneal administration of lipopolysaccharide(LPS),obtained from Escherichia coli.The animals were allocated to normal control,disease control,Paracetamol treated and Fevogrit treated groups.The rectal temperature of animals was recorded at different time points using a digital thermometer.At the 6-h time point,levels of TNF-α,IL-1βand IL-6 cytokines were analyzed in serum.Additionally,the mRNA expression of these cytokines was deter-mined in hypothalamus,24 h post-LPS administration.Results:UHPLC analysis of Fevogrit revealed the presence of picroside I,picroside II,vanillic acid,cinnamic acid,magnoflorine and cordifolioside A,as bioactive constitu-ents with known anti-inflammatory properties.Fevogrit treatment efficiently reduces the LPS-induced rise in the rectal temperature of animals.The levels and gene ex-pression of TNF-α,IL-1βand IL-6 in serum and hypothalamus,respectively,was also significantly reduced by Fevogrit treatment.Conclusion:The findings of the study demonstrated that Fevogrit can suppress LPS-induced fever by inhibiting peripheral or central inflammatory signaling pathways and could well be a viable treatment for infection-induced increase in body temperatures.展开更多
The incidence of large bone defects caused by traumatic injury is increasing worldwide,and the tissue regeneration process requires a long recovery time due to limited self-healing capability.Endogenous bioelectrical ...The incidence of large bone defects caused by traumatic injury is increasing worldwide,and the tissue regeneration process requires a long recovery time due to limited self-healing capability.Endogenous bioelectrical phenomena have been well recognized as critical biophysical factors in bone remodeling and regeneration.Inspired by bioelectricity,electrical stimulation has been widely considered an external intervention to induce the osteogenic lineage of cells and enhance the synthesis of the extracellular matrix,thereby accelerating bone regeneration.With ongoing advances in biomaterials and energy-harvesting techniques,electroactive biomaterials and self-powered systems have been considered biomimetic approaches to ensure functional recovery by recapitulating the natural electrophysiological microenvironment of healthy bone tissue.In this review,we first introduce the role of bioelectricity and the endogenous electric field in bone tissue and summarize different techniques to electrically stimulate cells and tissue.Next,we highlight the latest progress in exploring electroactive hybrid biomaterials as well as self-powered systems such as triboelectric and piezoelectric-based nanogenerators and photovoltaic cell-based devices and their implementation in bone tissue engineering.Finally,we emphasize the significance of simulating the target tissue’s electrophysiological microenvironment and propose the opportunities and challenges faced by electroactive hybrid biomaterials and self-powered bioelectronics for bone repair strategies.展开更多
Current research primarily focuses on emerging organic pollutants,with limited attention to emerging inorganic pollutants (EIPs).However,due to advances in detection technology and the escalating environmental and hea...Current research primarily focuses on emerging organic pollutants,with limited attention to emerging inorganic pollutants (EIPs).However,due to advances in detection technology and the escalating environmental and health challenges posed by pollution,there is a growing interest in treating waters contaminated with EIPs.This paper explores biochar characteristics and modification methods,encompassing physical,chemical,and biological approaches for adsorbing EIPs.It offers a comprehensive review of research advancements in employing biochar for EIPs remediation in water,outlines the adsorption mechanisms of EIPs by biochar,and presents an environmental and economic analysis.It can be concluded that using biochar for the adsorption of EIPs in wastewater exhibits promising potential.Nonetheless,it is noteworthy that certain EIPs like Au(III),Rh(III),Ir(III),Ru(III),Os(III),Sc(III),and Y(III),have not been extensively investigated regarding their adsorption onto biochar.This comprehensive review will catalyze further inquiry into the biochar-based adsorption of EIPs,addressing current research deficiencies and advancing the practical implementation of biochar as a potent substrate for EIP removal from wastewater streams.展开更多
文摘Proton exchange membrane(PEM)is an integral component in fuel cells which enables proton transport for efficient energy conversion.Sulfonated Polyether Ether Ketone(SPEEK)has emerged as a cost-effective option with non-fluorinated aromatic backbones for Proton Exchange Membrane Fuel Cell(PEMFC)applications,even though it exhibits lower proton conductivity compared to Nafion.This work aims to study the influence of Sulfonated Chitosan(SCS)concentrations on proton conductivity of SPEEK-based PEM at room temperature.SPEEK was synthesized using a sulfonation process with concentrated sulfuric acid at room temperature.SCS was synthesized via reflux of CS and 1.2 M H2SO4 with a ratio of 1:35(w/v)at 90℃ for 30 min.The composite membranes of SPEEK-SCS were formed with four different SCS concentrations,using the solution castingmethod,andDimethyl Sulfoxide(DMSO)was used as a solvent.The composite membranes synthesized include pure SPEEK(S0),SPEEK with 1%SCS(S1),SPEEK with 2%SCS(S2),and SPEEK with 3%SCS(S3).Fourier transform infrared spectroscopy(FTIR),X-ray diffraction(XRD),water uptake,degree of swelling,Ionic exchange capacity(IEC)with Electrochemical impedance spectroscopy(EIS)were used to characterize the composite membranes in terms of composition,crystallinity,water absorption,dimensional changes,number of exchangeable ions in membranes,and proton conductivity,respectively.Notably,S3 had the highest water uptake and the lowest degree of swelling.S2 had the highest proton conductivity among the SPEEK-SCS composite membranes at room temperature with 3.44×10^(−2) Scm^(-1).
基金supported by the Korea Institute of Energy Technology Evaluation and Planning(KETEP)and the Ministry of Trade,Industry&Energy(MOTIE)of the Republic of Korea(No.RS-2025-02315209).
文摘There is a need for accurate prediction of heat and mass transfer in aerodynamically designed,non-Newtonian nanofluids across aerodynamically designed,high-flux biomedical micro-devices for thermal management and reactive coating processes,but existing work is not uncharacteristically remiss regarding viscoelasticity,radiative heating,viscous dissipation,and homogeneous–heterogeneous reactions within a single scheme that is calibrated.This research investigates the flow of Williamson nanofluid across a dynamically wedged surface under conditions that include viscous dissipation,thermal radiation,and homogeneous-heterogeneous reactions.The paper develops a detailed mathematical approach that utilizes boundary layers to transform partial differential equations into ordinary differential equations using similarity transformations.RK4 is the technique for gaining numerical solutions,but with the addition of ANNs,there is an improvement in prediction accuracy and computational efficiency.The study investigates the influence of wedge angle parameter,along with Weissenberg number,thermal radiation parameter and Brownian motion parameter,and Schmidt number,on velocity distribution,temperature distribution,and concentra-tion distribution.Enhanced Weissenberg numbers enhance viscoelastic responses that modify velocity patterns,but radiation parameters and thermophoresis have key impacts on thermal transfer phenomena.This research develops findings that are of enormous application in aerospace,biomedical(artificial hearts and drug delivery),and industrial cooling technology applications.New findings on non-Newtonian nanofluids under full flow systems are included in this work to enhance heat transfer methods in novel fluid-based systems.
基金supported by University Technology Mara(UiTM)under grant number 600-IRMI/FRGS 5/3(273/2019).
文摘Objective:To systematically review the effects of administering metformin and glutathione alone and in coformulation with other compounds on the fertility and reproductive health of diabetic male rodents.Methods:The guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-analyses(PRISMA)were followed to conduct this systematic review.Studies published until August 2024 in PubMed,Web of Science,and Scopus databases were searched,identified,screened,and selected for a detailed review.The keywords included metformin,diabetes,reproduction,glutathione,and rodent models.Results:A total of 166 studies were identified,of which 11 met the inclusion criteria and were included in the qualitative synthesis.One additional study was identified through snowballing and citation tracking,bringing the total to 12 studies.The findings indicate that metformin and glutathione,administered alone or in combination with other compounds,improved sperm count,motility,and morphology;restored reproductive hormone levels;reduced oxidative stress markers;and improved testicular histopathology in diabetic male rodents.Conclusions:Coformulation of metformin and glutathione with other compounds was found to be more effective in improving fertility and reproductive parameters in diabetic male rodents compared to mono-administration.However,further studies on the coformulation of metformin and glutathione are needed to confirm their efficacy and elucidate the underlying mechanisms.Study registration:The study protocol was registered in the International Prospective Register of Systematic Reviews(PROSPERO)with registration number CRD42024561820.
基金Project supported by the National Key R&D Program of China(No.2023YFE0125900)。
文摘A novel vibration isolation system designed for superior performance in low-frequency environments is proposed in this work.The isolator is based on a unique hexagonal arrangement of linear springs,allowing for an adjustable geometric configuration via the initial inclination angle.Based on the principle of Lagrangian mechanics,the equation of motion governing the structural dynamics is rigorously derived.The system is modeled as a strongly nonlinear single-degree-of-freedom dynamical system,loaded with a normalized payload and subject to harmonic base excitation.To analyze the steady-state response,the harmonic balance method is employed,providing accurate predictions of the payload's vibration amplitude and displacement transmissibility as functions of both the base excitation amplitude and frequency.The analysis reveals a direct relationship between the isolator's geometric and stiffness parameters and its load-bearing capacity,leading to the identification of three distinct operational regimes.Depending on the unloaded initial inclination angle,the equivalent stiffness ratio,and the payload design configuration,the system can exhibit one of three vibration isolation modes:(i)the quasizero stiffness(QZS)isolation mode,(ii)the zero linear stiffness with controllable nonlinear stiffness,and(iii)the full-band perfect zero stiffness.The vibration isolation performance of the proposed structure is thoroughly discussed for all three oscillation modes in terms of frequency response curves,displacement transmissibility,and time-domain responses.The key novel finding is that this structure can operate as a full-band,high-performance vibration isolator when the initial inclination angle is designed to be a right angle,enabling full isolation of the maximum possible payload.Moreover,the analytical results and numerical simulations demonstrate that the isolator's displacement transmissibility T with the unit dB tends to-∞as the air-damping coefficient approaches zero,enabling ideal vibration isolation across the entire excitation frequency range.These analytical insights are validated through comprehensive numerical simulations,which show excellent agreement with the theoretical predictions.
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 Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2026R500)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Safeguarding modern networks from cyber intrusions has become increasingly challenging as attackers continually refine their evasion tactics.Although numerousmachine-learning-based intrusion detection systems(IDS)have been developed,their effectiveness is often constrained by high dimensionality and redundant features that degrade both accuracy and efficiency.This study introduces a hybrid feature-selection framework that integrates the exploration capability of Prairie Dog Optimization(PDO)with the exploitation behavior of Ant Colony Optimization(ACO).The proposed PDO–ACO algorithm identifies a concise yet discriminative subset of features from the NSLKDD dataset and evaluates them using a Support Vector Machine(SVM)classifier.Experimental analyses reveal that the PDO–ACO model achieves superior detection accuracy of 98%while significantly lowering false alarms and computational overhead.Further validation on the CEC2017 benchmark suite confirms the robustness and adaptability of the hybrid model across diverse optimization landscapes,positioning PDO–ACO as an efficient and scalable approach for intelligent intrusion detection.
文摘Utility-scale PV plants increasingly operate under partial shading,soiling,temperature swings,and rapid irradiance ramps that depress yield and challenge stability on weak grids.This critical review addresses those conditions by(i)unifying a stressor-to-method taxonomy that links field stressors to global intelligent MPPT(metaheuristics and learning-based trackers)and to advanced inverter controls(adaptive/MPC and grid-forming),(ii)standardizing metrics and reporting aligned with IEC 61724-1 and IEEE 1547/1547.1 to enable fair,reproducible comparisons,and(iii)framing MPPT and grid support as a co-design problem with a DT→HIL→Field validation pathway and seedable scenarios.We identify persistent gaps—fragmented partial-shading benchmarks,limited low-SCR testing,and scarce field-grade validation—and compile a quantitative synthesis:global soiling typically reduces annual production by≈3%–5%,and hybrid/learning MPPT frequently report≈99%tracking efficiency under PSC in simulation/HIL studies.To demonstrate practical relevance,we validate the framework on a seeded scenario library:DRL trackers achieve medianηMPPT≈0.996 with t95≈0.19 s and Hybrid trackers≈0.992/0.26 s,outperforming Metaheuristics(≈0.984/0.42 s);at SCR=2.5,grid-forming control raises VRI from~0.78(tuned GFL)to~0.95 while keeping THD within 2.5%–3.2%,with all stacks meeting IEEE-1547.1 Category-II ride-through.The resulting taxonomy,standards-aligned reporting,and open seeds provide a replicable basis for comparable,grid-relevant benchmarking and clear guidance for real-world design and operations.
文摘Object detection,a major challenge in computer vision and pattern recognition,plays a significant part in many applications,crossing artificial intelligence,face recognition,and autonomous driving.It involves focusing on identifying the detection,localization,and categorization of targets in images.A particularly important emerging task is distinguishing real animals from toy replicas in real-time,mostly for smart camera systems in both urban and natural environments.However,that difficult task is affected by factors such as showing angle,occlusion,light intensity,variations,and texture differences.To tackle these challenges,this paper recommends Group Sparse YOLOv8(You Only Look Once version 8),an improved real-time object detection algorithm that improves YOLOv8 by integrating group sparsity regularization.This adjustment improves efficiency and accuracy while utilizing the computational costs and power consumption,including a frame selection approach.And a hybrid parallel processing method that merges pipelining with dataflow strategies to improve the performance.Established using a custom dataset of toy and real animal images along with well-known datasets,namely ImageNet,MSCOCO,and CIFAR-10/100.The combination of Group Sparsity with YOLOv8 shows high detection accuracy with lower latency.Here provides a real and resource-efficient solution for intelligent camera systems and improves real-time object detection and classification in environments,differentiating between real and toy animals.
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.
基金the Hebei Province Science and Technology Plan Project(19221909D)rincess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R308),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Autonomous connected vehicles(ACV)involve advanced control strategies to effectively balance safety,efficiency,energy consumption,and passenger comfort.This research introduces a deep reinforcement learning(DRL)-based car-following(CF)framework employing the Deep Deterministic Policy Gradient(DDPG)algorithm,which integrates a multi-objective reward function that balances the four goals while maintaining safe policy learning.Utilizing real-world driving data from the highD dataset,the proposed model learns adaptive speed control policies suitable for dynamic traffic scenarios.The performance of the DRL-based model is evaluated against a traditional model predictive control-adaptive cruise control(MPC-ACC)controller.Results show that theDRLmodel significantly enhances safety,achieving zero collisions and a higher average time-to-collision(TTC)of 8.45 s,compared to 5.67 s for MPC and 6.12 s for human drivers.For efficiency,the model demonstrates 89.2% headway compliance and maintains speed tracking errors below 1.2 m/s in 90% of cases.In terms of energy optimization,the proposed approach reduces fuel consumption by 5.4% relative to MPC.Additionally,it enhances passenger comfort by lowering jerk values by 65%,achieving 0.12 m/s3 vs.0.34 m/s3 for human drivers.A multi-objective reward function is integrated to ensure stable policy convergence while simultaneously balancing the four key performance metrics.Moreover,the findings underscore the potential of DRL in advancing autonomous vehicle control,offering a robust and sustainable solution for safer,more efficient,and more comfortable transportation systems.
文摘The Arno River Basin(Central Italy)is affected by a considerable anthropogenic pressure due to the presence of large cities and widespread industrial and agricultural practices.In this work,26 water samples from the Arno River and its main tributaries were analyzed to assess the water pollution status.The geochemical composition of the Arno River changes from the source(dominated by a Ca-HCO_(3) facies)to the mouth(where a Na-Cl(SO4)chemistry prevails)with an increasing quality deterioration,as suggested by the Chemical Water Quality Index,due to anthropogenic contributions and seawater intrusion before flowing into the Ligurian Sea.The Ombrone and Usciana tributaries introduce anthropogenic pollutants into the Arno River,whilst Elsa tributary supplies significant contents of geogenic sulfate.The concentrations of dissolved nitrate and nitrite(up to 63 and 9 mg/L,respectively)and the respective isotopic values of𝛿15N and𝛿18O were also determined to understand origin and fate of the N-species in the Arno River Basin surface waters.The combined application of𝛿15N-NO_(3) and𝛿18O-NO_(3) and N-source apportionment modelling allowed the identification of soil organic nitrogen and sewage and domestic wastes as primary sources for dissolved NO_(3)-.The𝛿15N-NO_(2) and𝛿18O-NO_(2) values suggest that the nitrification process affects the ARB waters,thus controlling the abundances and proportion of the N-species.Our work indicates that additional efforts are needed to improve management strategies to reduce the release of nitrogenated species to the surface waters of the Arno River Basin,since little progress has been made from the early 2000s.
基金funded by King Saud University,Riyadh,Saudi Arabia,through the Ongo-ing Research Funding program—Research Chairs(ORF-RC-2025-0127)funded via Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R443).
文摘The present investigation inspects the unsteady,incompressible MHD-induced flow of a ternary hybrid nanofluid made of SiO_(2)(silicon dioxide),ZnO(zinc oxide),and MWCNT(multi-walled carbon nanotubes)suspended in a water-ethylene glycol base fluid between two perforated squeezing Riga plates.This problem is important because it helps us understand the complicated connections between magnetic fields,nanofluid dynamics,and heat transport,all of which are critical for designing thermal management systems.These findings are especially useful for improving the design of innovative cooling technologies in electronics,energy systems,and healthcare applications.No prior study has been done on the theoretical study of the flow of ternary nanofluid(SiO_(2)+ZnO+MWCNT/Water−EthylGl ycol,(60∶40))past a pierced squeezed Riga plates using the boundary value problem solver 4th-order collocation(BVP4C)numerical approach to date.So,the current work has been carried out to fill this gap,and the core purpose of this study is to explore the aspects that enhance the heat transfer of base fluids(H_(2)O/EG)suspended with three nanomaterials SiO_(2),ZnO,and MWCNT.The Riga plates introduce electromagnetic forcing through an embedded array of magnets and electrodes,generating Lorentz forces to regulate the flow.The squeezing effect introduces dynamic boundary movement,which enhances mixing;however,permeability,due to porosity,replicates the true material limits.Similarity transformations of the Navier-Stokes and energy equations result in a highly nonlinear set of ordinary differential equations that govern momentum and thermal energy transport.The subsequent boundary value problem is solved utilizing the BVP4C numerical approach.The study observes the impact of magnetic parameters,squeezing velocity,solid volume percentages of the three nanoparticles,and porous medium factors on velocity and temperature fields.Results show that magnetic fields reduce the velocity profile by 6.75%due to increased squeezing and medium effects.Tri-hybrid nanofluids notice a 9%rise in temperature with higher thermal radiation.
基金Deanship of Research and Graduate Studies at King Khalid University for funding this work through the Large Research Project(Grant No.RGP2/587/46).
文摘Photovoltaic(PV)systems in the field operate under complex,uncertain conditions rapid irradiance ramps,partial shading,temperature swings,surface soiling,and weak-grid disturbances including off-nominal frequency and voltage distortion that degrade energy yield and power quality.We propose a drift-aware,power-quality-constrained MPPT framework that co-optimizes MPPT,PLL,and current-loop gains under stochastic frequency drift,while enforcing IEEE-519 limits(per-order Ih/IL and TDD)during optimization.Unlike energy-only or THD-only methods,the design target integrates PQ constraints into the objective and is validated across calibrated drift scenarios with explicit per-order and TDD reporting.Operating scenarios are calibrated to Cameroon’s Southern Interconnected Grid and city-specific profiles(Douala/Yaoundé),combining measured-style irradiance/temperature traces,partial-shading patterns,and stochastic frequency drift up to±0.8 Hz with synthetic contingencies.Across a 30-scenario campaign,the proposed controller achievesηMPPT=99.3%–99.6%(vs.98.6%Incremental Conductance and 97.8%Perturb-and-Observe),lowers DC-link ripple by 35%–48%,reduces oscillatory PCC power by≈41%,maintains THD≤2.5%(5%limit)and PF≥0.99,and shortens irradiance-step settling from 85–110 ms to 50–65 ms.Sensitivity to PLL bandwidth shows a broad optimum(≈60–90 Hz)with minimum THD/ripple,and ablations confirm that explicit drift weighting is pivotal to ripple and THD suppression without sacrificing yield.The approach is controller-agnostic,firmware-deployable,and generalizes to other converter-interfaced renewables;we outline a short hardware-/HIL-validation path for adoption in Sub-Saharan grids.
基金Supported by the Chinese Postdoctoral Science Foundation, the Young Scientists Funds of NSF of China (10401019)the Tsinghua Basic Research Foundation.
文摘In this article, two relaxation time limits, namely, the momentum relaxation time limit and the energy relaxation time limit are considered. By the compactness argument, it is obtained that the smooth solutions of the multidimensional nonisentropic Euler-Poisson problem converge to the solutions of an energy transport model or a drift diffusion model, respectively, with respect to different time scales.
文摘In this paper, a theory on the determination of the diffusion coefficient of excess minority carriers in the base of a silicon solar cell is presented. The diffusion coefficient expression has been established and is related to both frequency modulation and applied magnetic field;the study is then carried out using the impedance spectroscopy method and Bode diagrams. From the diffusion coefficient, we deduced the diffusion length and the minority carriers’ mobility. Electric parameters were derived from the diffusion coefficient equivalent circuits.
文摘AIM： To determine the prevalence of Helicobacter pylori （H pylori） among dyspeptic patients and to assess the relationship between Hpylori infection, blood group, HIV infection and life style of the patients. METHODS： In a hospital-based cross-sectional study, patients attending Outpatient Department of University of Gondar Hospital were enrolled. Socio-demographic information was collected using questionnaires. Serum was analyzed for anti-H pylori IgG antibodies using a commercial kit. HIV serostatus was determined by enzyme-linked immunosorbent assay （ELISA）. Blood grouping was performed by slide agglutination tests. RESULTS： A total of 215 dyspeptic patients were included in the study. One hundred and sixteen patients （54%） were females and 99 （46%） were males. Anti-H pylori IgG antibodies were detected in sera of 184 （85.6%） patients. The prevalence was significantly higher in patients aged 50 years and above. Twenty point five percent of the patients were found to be seropositive for HIV. No significant association was found between sex, ABO blood groups, consumption of spicy diets, socioeconomic status and seropositivity for Hpylori. However,alcohol consumption was significantly associated with H pylori serology. CONCLUSION： The prevalence of H pylori infection is associated with a history of alcohol intake and older age. The effect of different diet, alcohol and socioeconomic status as risk factors for H pylori infection needs further study.
文摘Photovoltaic(PV)power forecasting is essential for balancing energy supply and demand in renewable energy systems.However,the performance of PV panels varies across different technologies due to differences in efficiency and how they process solar radiation.This study evaluates the effectiveness of deep learning models in predicting PV power generation for three panel technologies:Hybrid-Si,Mono-Si,and Poly-Si,across three forecasting horizons:1-step,12-step,and 24-step.Among the tested models,the Convolutional Neural Network—Long Short-Term Memory(CNN-LSTM)architecture exhibited superior performance,particularly for the 24-step horizon,achieving R^(2)=0.9793 and MAE 0.0162 for the Poly-Si array,followed by Mono-Si(R^(2)=0.9768)and Hybrid-Si arrays(R^(2)=0.9769).These findings demonstrate that the CNN-LSTM model can provide accurate and reliable PV power predictions for all studied technologies.By identifying the most suitable predictive model for each panel technology,this study contributes to optimizing PV power forecasting and improving energy management strategies.
基金This study was supported by internal funds from Patanjali Research Foundation Trust,Haridwar,India。
文摘Background:Fever is characterized by an upregulation of the thermoregulatory set-point after the body encounters any pathological challenge.It is accompanied by uncomfortable sickness behaviors and may be harmful in patients with other comor-bidities.We have explored the impact of an Ayurvedic medicine,Fevogrit,in an endo-toxin(lipopolysaccharide)-induced fever model in Wistar rats.Methods:Active phytoconstituents of Fevogrit were identified and quantified using ultra-high-performance liquid chromatography(UHPLC)platform.For the in-vivo study,fever was induced in male Wistar rats by the intraperitoneal administration of lipopolysaccharide(LPS),obtained from Escherichia coli.The animals were allocated to normal control,disease control,Paracetamol treated and Fevogrit treated groups.The rectal temperature of animals was recorded at different time points using a digital thermometer.At the 6-h time point,levels of TNF-α,IL-1βand IL-6 cytokines were analyzed in serum.Additionally,the mRNA expression of these cytokines was deter-mined in hypothalamus,24 h post-LPS administration.Results:UHPLC analysis of Fevogrit revealed the presence of picroside I,picroside II,vanillic acid,cinnamic acid,magnoflorine and cordifolioside A,as bioactive constitu-ents with known anti-inflammatory properties.Fevogrit treatment efficiently reduces the LPS-induced rise in the rectal temperature of animals.The levels and gene ex-pression of TNF-α,IL-1βand IL-6 in serum and hypothalamus,respectively,was also significantly reduced by Fevogrit treatment.Conclusion:The findings of the study demonstrated that Fevogrit can suppress LPS-induced fever by inhibiting peripheral or central inflammatory signaling pathways and could well be a viable treatment for infection-induced increase in body temperatures.
基金support of the National Natural Science Foundation of China(Grant No.52205593)Shaanxi Natural Science Foundation Project(2024JC-YBMS-711).
文摘The incidence of large bone defects caused by traumatic injury is increasing worldwide,and the tissue regeneration process requires a long recovery time due to limited self-healing capability.Endogenous bioelectrical phenomena have been well recognized as critical biophysical factors in bone remodeling and regeneration.Inspired by bioelectricity,electrical stimulation has been widely considered an external intervention to induce the osteogenic lineage of cells and enhance the synthesis of the extracellular matrix,thereby accelerating bone regeneration.With ongoing advances in biomaterials and energy-harvesting techniques,electroactive biomaterials and self-powered systems have been considered biomimetic approaches to ensure functional recovery by recapitulating the natural electrophysiological microenvironment of healthy bone tissue.In this review,we first introduce the role of bioelectricity and the endogenous electric field in bone tissue and summarize different techniques to electrically stimulate cells and tissue.Next,we highlight the latest progress in exploring electroactive hybrid biomaterials as well as self-powered systems such as triboelectric and piezoelectric-based nanogenerators and photovoltaic cell-based devices and their implementation in bone tissue engineering.Finally,we emphasize the significance of simulating the target tissue’s electrophysiological microenvironment and propose the opportunities and challenges faced by electroactive hybrid biomaterials and self-powered bioelectronics for bone repair strategies.
基金support from the earmarked fund for XJARS(No.XJARS-06)the Bingtuan Science and Technology Program(Nos.2021DB019,2022CB001-01)+1 种基金the National Natural Science Foundation of China(No.42275014)the Guangdong Foundation for Program of Science and Technology Research,China(No.2023B1212060044)。
文摘Current research primarily focuses on emerging organic pollutants,with limited attention to emerging inorganic pollutants (EIPs).However,due to advances in detection technology and the escalating environmental and health challenges posed by pollution,there is a growing interest in treating waters contaminated with EIPs.This paper explores biochar characteristics and modification methods,encompassing physical,chemical,and biological approaches for adsorbing EIPs.It offers a comprehensive review of research advancements in employing biochar for EIPs remediation in water,outlines the adsorption mechanisms of EIPs by biochar,and presents an environmental and economic analysis.It can be concluded that using biochar for the adsorption of EIPs in wastewater exhibits promising potential.Nonetheless,it is noteworthy that certain EIPs like Au(III),Rh(III),Ir(III),Ru(III),Os(III),Sc(III),and Y(III),have not been extensively investigated regarding their adsorption onto biochar.This comprehensive review will catalyze further inquiry into the biochar-based adsorption of EIPs,addressing current research deficiencies and advancing the practical implementation of biochar as a potent substrate for EIP removal from wastewater streams.
