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
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
A novel siphon-based divide-and-conquer(SbDaC)policy is presented in this paper for the synthesis of Petri net(PN)based liveness-enforcing supervisors(LES)for flexible manufacturing systems(FMS)prone to deadlocks or l...A novel siphon-based divide-and-conquer(SbDaC)policy is presented in this paper for the synthesis of Petri net(PN)based liveness-enforcing supervisors(LES)for flexible manufacturing systems(FMS)prone to deadlocks or livelocks.The proposed method takes an uncontrolled and bounded PN model(UPNM)of the FMS.Firstly,the reduced PNM(RPNM)is obtained from the UPNM by using PN reduction rules to reduce the computation burden.Then,the set of strict minimal siphons(SMSs)of the RPNM is computed.Next,the complementary set of SMSs is computed from the set of SMSs.By the union of these two sets,the superset of SMSs is computed.Finally,the set of subnets of the RPNM is obtained by applying the PN reduction rules to the superset of SMSs.All these subnets suffer from deadlocks.These subnets are then ordered from the smallest one to the largest one based on a criterion.To enforce liveness on these subnets,a set of control places(CPs)is computed starting from the smallest subnet to the largest one.Once all subnets are live,this process provides the LES,consisting of a set of CPs to be used for the UPNM.The live controlled PN model(CPNM)is constructed by merging the LES with the UPNM.The SbDaC policy is applicable to all classes of PNs related to FMS prone to deadlocks or livelocks.Several FMS examples are considered from the literature to highlight the applicability of the SbDaC policy.In particular,three examples are utilized to emphasize the importance,applicability and effectiveness of the SbDaC policy to realistic FMS with very large state spaces.展开更多
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
This research presents a detailed ab initio density functional theory(DFT)analysis on magnetic,thermoelectric,and optoelectronic properties of CaPr_(2)(S/Se)_(4) executed by Wien2k and Boltztrap2 packages for spintron...This research presents a detailed ab initio density functional theory(DFT)analysis on magnetic,thermoelectric,and optoelectronic properties of CaPr_(2)(S/Se)_(4) executed by Wien2k and Boltztrap2 packages for spintronic energy applications.The density of states,optimization energy,and negative formation energy all support the stability of the ferromagnetic state.The spin polarization density and Curie temperature(310 and 289 K)are also reported.In addition,the double exchange model,hybridization,density of states,band structures,exchange constants,exchange energies,and crystal field energies are addressed to ensure ferromagnetism by the spin of electrons.The magnetic moment of Pr shifts to Ca and S/Se sites,revealing that ferromagnetism is due to electron spin,not clustering of Pr magnetic ions.Thermoelectrics were evaluated by electrical conductivity(σ),thermal conductivity(k_(e)),Seebeck coefficient(S),power factor(S^(2)),and figures of merit(ZT).The room tempe rature values of S(0.169,0.183 mV/K)and ZT(0.76,0.90)increase their thermoelectric performance.Furthermore,dielectric function,refractive index,absorption coefficientα(ω),reflectivity R(ω),and other parameters are demonstrated in detail.Therefore,researchers can develop materials with the potential for spintronic and energy harvesting.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 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
Therapeutic experiments are commonly performed on laboratory animals to inves-tigate the possible mechanism(s)of action of toxic agents as well as drugs or sub-stances under consideration.The use of toxins in laborato...Therapeutic experiments are commonly performed on laboratory animals to inves-tigate the possible mechanism(s)of action of toxic agents as well as drugs or sub-stances under consideration.The use of toxins in laboratory animal models,including rats,is intended to cause toxicity.This study aimed to investigate different models of hepatotoxicity and nephrotoxicity in laboratory animals to help researchers advance their research goals.The current narrative review used databases such as Medline,Web of Science,Scopus,and Embase and appropriate keywords until June 2021.Nephrotoxicity and hepatotoxicity models derived from some toxic agents such as cisplatin,acetaminophen,doxorubicin,some anticancer drugs,and other materials through various signaling pathways are investigated.To understand the models of renal or hepatotoxicity in laboratory animals,we have provided a list of toxic agents and their toxicity procedures in this review.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
Longan originates from southern China and has high nutritional and health value.Recent phytochemistry and pharmacology studies have shown that polysaccharides are a main bioactive component of longan.Longan polysaccha...Longan originates from southern China and has high nutritional and health value.Recent phytochemistry and pharmacology studies have shown that polysaccharides are a main bioactive component of longan.Longan polysaccharides possess antioxidant,anti-aging,anti-tumor,immunomodulatory,and other bioactivities.Hot-water extraction,ethanol precipitation,and ultrasonic extraction are generally used to extract water-soluble longan polysaccharides.However,the relationship between the structure and bioactivity of longan polysaccharides remains unclear,requiring further investigation.The aim of this review is to evaluate the current literature focusing on the extraction,purification,structural characterization,and biological activity of longan polysaccharides.We believe that this review would provide a useful bibliography for further innovation and a basis for using longan polysaccharides in functional food.展开更多
Cloud computing is a dynamic and rapidly evolving field,where the demand for resources fluctuates continuously.This paper delves into the imperative need for adaptability in the allocation of resources to applications...Cloud computing is a dynamic and rapidly evolving field,where the demand for resources fluctuates continuously.This paper delves into the imperative need for adaptability in the allocation of resources to applications and services within cloud computing environments.The motivation stems from the pressing issue of accommodating fluctuating levels of user demand efficiently.By adhering to the proposed resource allocation method,we aim to achieve a substantial reduction in energy consumption.This reduction hinges on the precise and efficient allocation of resources to the tasks that require those most,aligning with the broader goal of sustainable and eco-friendly cloud computing systems.To enhance the resource allocation process,we introduce a novel knowledge-based optimization algorithm.In this study,we rigorously evaluate its efficacy by comparing it to existing algorithms,including the Flower Pollination Algorithm(FPA),Spark Lion Whale Optimization(SLWO),and Firefly Algo-rithm.Our findings reveal that our proposed algorithm,Knowledge Based Flower Pollination Algorithm(KB-FPA),consistently outperforms these conventional methods in both resource allocation efficiency and energy consumption reduction.This paper underscores the profound significance of resource allocation in the realm of cloud computing.By addressing the critical issue of adaptability and energy efficiency,it lays the groundwork for a more sustainable future in cloud computing systems.Our contribution to the field lies in the introduction of a new resource allocation strategy,offering the potential for significantly improved efficiency and sustainability within cloud computing infrastructures.展开更多
As cloud computing usage grows,cloud data centers play an increasingly important role.To maximize resource utilization,ensure service quality,and enhance system performance,it is crucial to allocate tasks and manage p...As cloud computing usage grows,cloud data centers play an increasingly important role.To maximize resource utilization,ensure service quality,and enhance system performance,it is crucial to allocate tasks and manage performance effectively.The purpose of this study is to provide an extensive analysis of task allocation and performance management techniques employed in cloud data centers.The aim is to systematically categorize and organize previous research by identifying the cloud computing methodologies,categories,and gaps.A literature review was conducted,which included the analysis of 463 task allocations and 480 performance management papers.The review revealed three task allocation research topics and seven performance management methods.Task allocation research areas are resource allocation,load-Balancing,and scheduling.Performance management includes monitoring and control,power and energy management,resource utilization optimization,quality of service management,fault management,virtual machine management,and network management.The study proposes new techniques to enhance cloud computing work allocation and performance management.Short-comings in each approach can guide future research.The research’s findings on cloud data center task allocation and performance management can assist academics,practitioners,and cloud service providers in optimizing their systems for dependability,cost-effectiveness,and scalability.Innovative methodologies can steer future research to fill gaps in the literature.展开更多
Cancer stem cells(CSCs)are a major challenge in cancer therapy.Stem cell-like cells form a unique subpopulation within many tumors,which govern the degree of malignancy by promoting metastasis,recurrence,heterogeneity...Cancer stem cells(CSCs)are a major challenge in cancer therapy.Stem cell-like cells form a unique subpopulation within many tumors,which govern the degree of malignancy by promoting metastasis,recurrence,heterogeneity,and resistance to drug and radiation.Furthermore,these cells can persist in patients even after undergoing multiple cycles of conventional cancer therapy via dormancy,where they no longer dividing but remain active.These may cause cancer recurrence at any time,even years after a supposed cure,and remain invisible to the immune system.Targeting specific surface markers,signaling pathways and tumor microenvironment,which all have a significant effect on CSC function and maintenance,could help to eradicate CSCs and improve patient survival.Combinations of traditional therapies with nano-based drug delivery systems can efficiently target CSCs.Considering the biology and properties of CSCs,we classify recent approaches involving nanoparticle engineering,extracellular matrix modulation,cocktail strategies,multi-stage therapy,CSC defanging,Trojan horse systems,targeted therapy and organelle targeting.We highlight the most recent advances in nanocarrier design and drug delivery technologies to target CSCs,combined with conventional treatment in preclinical and clinical trials.The prospects of these approaches for CSCs elimination and recurrent cancer treatment are discussed.展开更多
Carbon capture and storage(CCS)is an advanced environmental technology for mitigating CO_(2) emissions and addressing climate change.Among the various approaches,adsorption has emerged as a promising method for CO_(2)...Carbon capture and storage(CCS)is an advanced environmental technology for mitigating CO_(2) emissions and addressing climate change.Among the various approaches,adsorption has emerged as a promising method for CO_(2) capture due to its effiectiveness and practicality.This review explores the potential of clay minerals as adsorbents for CO_(2) capture,providing an in-depth analysis of their inherent properties and the mechanisms involved in adsorption process.The review begins with an introduction to CCS and the concept of adsorption,followed by a detailed examination of various clay minerals,including sepiolite,montmorillonite,bentonite,kaolinite,saponite,halloysite,and illite.Each mineral’s suitability for CO_(2) adsorption is assessed,highlighting the specific properties that contribute to their performance.The mechanisms of CO_(2) adsorption including physisorption,chemisorption,ion exchange,pore diffusion,intraparticle diffusion,surface complexation,and competitive adsorption are thoroughly discussed.The review also covers the modification of clay minerals through physical and chemical treatments,amine functionalization,and composite formation to enhance their CO_(2) adsorption capacity.Additionally,regeneration methods such as temperature-swing adsorption(TSA),pressure-swing adsorption(PSA),and purging are discussed,along with CO_(2) recovery and storage techniques for improving energy efficiency.The review concludes with an overview of characterization methods for clay-based adsorbents and potential applications,while addressing the challenges and future trends in thefield.This work emphasizes the promising role of clay-based adsorbents in advancing CCS technology.展开更多
Greenhouse gas(GHG)emssions from fossil fuel consumption are driving global climate change.This study applied the fully modified ordinary least squares(FMOLS)model and pairwise panel Granger causality test to explore ...Greenhouse gas(GHG)emssions from fossil fuel consumption are driving global climate change.This study applied the fully modified ordinary least squares(FMOLS)model and pairwise panel Granger causality test to explore the relationships of GHG emissions with gross domestic product(GDP),population,urbanization,natural resource rents,foreign direct investment(FDI),and renewable energy consumption in 12 Middle East and North Africa(MENA)countries(Algeria,Bahrain,Comoros,Djibouti,Egypt,Qatar,Somalia,Saudi Arabia,Syria,the United Arab Emirates,Tunisia,and Yemen)from 1990 to 2023.Due to the limited data on renewable energy after 2020,the coverage of renewable energy consumption is from 1990 to 2021.Findings showed that Saudi Arabia,Egypt,Algeria,the United Arab Emirates,and Qatar are the top 5 GHG emitters in the MENA region,with the GHG emissions of the energy sector rising fastest among all sectors.Results also indicated that a 1.00%increase in GDP,population,urbanization,natural resource rents,and FDI raises GHG emissions by 0.48%,0.61%,0.86%,0.29%,and 0.11%,respectively.Conversely,a 1.00%increase in renewable energy consumption reduces GHG emissions by 0.13%.Effective policies promoting renewable energy investment and the adoption of renewable energy could significantly reduce electricity costs and GHG emissions,contributing to achieving climate goals,such as net-zero emissions and environmental sustainability.Additionally,the increase of renewable energy consumption and technology development would improve energy efficiency,create jobs,and stimulate economic growth in the MENA region.This study recommends tailored policy instruments to support the transition to low-emission technologies and strategies.展开更多
In recent years,hepatology has undergone a transformative evolution driven by significant advancements in diagnostic and therapeutic technologies.The expanding integration of endoscopic modalities into hepatology has ...In recent years,hepatology has undergone a transformative evolution driven by significant advancements in diagnostic and therapeutic technologies.The expanding integration of endoscopic modalities into hepatology has enforced the diagnosis,staging,management of liver diseases beside integration into transplantation.This review highlights the evolving discipline of“endo-hepatology”,where endoscopic ultrasound,endoscopic retrograde cholangiopancreatography,and novel interventional tools are employed to address the critical challenges in chronic liver disease.The review provides a comprehensive synthesis of current evidence and different clinical applications,while also exploring future directions including revolution of artificial intelligence-assisted endoscopies and enhanced imaging endoscopies.By bridging the anatomical and functional interface between the gastrointestinal lumen and the liver,endo-hepatology is not only improving diagnostic accuracy and therapeutic precision but also reshaping multidisciplinary paradigms in hepatology practice.展开更多
In this paper,we use the Riemann-Hilbert(RH)method to investigate the Cauchy problem of the reverse space-time nonlocal Hirota equation with step-like initial data:q(z,0)=o(1)as z→-∞and q(z,0)=δ+o(1)as z→∞,where...In this paper,we use the Riemann-Hilbert(RH)method to investigate the Cauchy problem of the reverse space-time nonlocal Hirota equation with step-like initial data:q(z,0)=o(1)as z→-∞and q(z,0)=δ+o(1)as z→∞,whereδis an arbitrary positive constant.We show that the solution of the Cauchy problem can be determined by the solution of the corresponding matrix RH problem established on the plane of complex spectral parameterλ.As an example,we construct an exact solution of the reverse space-time nonlocal Hirota equation in a special case via this RH problem.展开更多
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The authors extend their appreciation to King Saud University,Saudi Arabia for funding this work through the Ongoing Research Funding Program(ORF-2025-704),King Saud University,Riyadh,Saudi Arabia.
文摘A novel siphon-based divide-and-conquer(SbDaC)policy is presented in this paper for the synthesis of Petri net(PN)based liveness-enforcing supervisors(LES)for flexible manufacturing systems(FMS)prone to deadlocks or livelocks.The proposed method takes an uncontrolled and bounded PN model(UPNM)of the FMS.Firstly,the reduced PNM(RPNM)is obtained from the UPNM by using PN reduction rules to reduce the computation burden.Then,the set of strict minimal siphons(SMSs)of the RPNM is computed.Next,the complementary set of SMSs is computed from the set of SMSs.By the union of these two sets,the superset of SMSs is computed.Finally,the set of subnets of the RPNM is obtained by applying the PN reduction rules to the superset of SMSs.All these subnets suffer from deadlocks.These subnets are then ordered from the smallest one to the largest one based on a criterion.To enforce liveness on these subnets,a set of control places(CPs)is computed starting from the smallest subnet to the largest one.Once all subnets are live,this process provides the LES,consisting of a set of CPs to be used for the UPNM.The live controlled PN model(CPNM)is constructed by merging the LES with the UPNM.The SbDaC policy is applicable to all classes of PNs related to FMS prone to deadlocks or livelocks.Several FMS examples are considered from the literature to highlight the applicability of the SbDaC policy.In particular,three examples are utilized to emphasize the importance,applicability and effectiveness of the SbDaC policy to realistic FMS with very large state spaces.
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This research presents a detailed ab initio density functional theory(DFT)analysis on magnetic,thermoelectric,and optoelectronic properties of CaPr_(2)(S/Se)_(4) executed by Wien2k and Boltztrap2 packages for spintronic energy applications.The density of states,optimization energy,and negative formation energy all support the stability of the ferromagnetic state.The spin polarization density and Curie temperature(310 and 289 K)are also reported.In addition,the double exchange model,hybridization,density of states,band structures,exchange constants,exchange energies,and crystal field energies are addressed to ensure ferromagnetism by the spin of electrons.The magnetic moment of Pr shifts to Ca and S/Se sites,revealing that ferromagnetism is due to electron spin,not clustering of Pr magnetic ions.Thermoelectrics were evaluated by electrical conductivity(σ),thermal conductivity(k_(e)),Seebeck coefficient(S),power factor(S^(2)),and figures of merit(ZT).The room tempe rature values of S(0.169,0.183 mV/K)and ZT(0.76,0.90)increase their thermoelectric performance.Furthermore,dielectric function,refractive index,absorption coefficientα(ω),reflectivity R(ω),and other parameters are demonstrated in detail.Therefore,researchers can develop materials with the potential for spintronic and energy harvesting.
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.
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.
文摘Therapeutic experiments are commonly performed on laboratory animals to inves-tigate the possible mechanism(s)of action of toxic agents as well as drugs or sub-stances under consideration.The use of toxins in laboratory animal models,including rats,is intended to cause toxicity.This study aimed to investigate different models of hepatotoxicity and nephrotoxicity in laboratory animals to help researchers advance their research goals.The current narrative review used databases such as Medline,Web of Science,Scopus,and Embase and appropriate keywords until June 2021.Nephrotoxicity and hepatotoxicity models derived from some toxic agents such as cisplatin,acetaminophen,doxorubicin,some anticancer drugs,and other materials through various signaling pathways are investigated.To understand the models of renal or hepatotoxicity in laboratory animals,we have provided a list of toxic agents and their toxicity procedures in this review.
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the National Natural Science Foundation of China(32201969,82204668)Natural Science Foundation of Henan Province(212300410297)+3 种基金Hebei Natural Science Foundation(H2022423376)Basic Research Plan of Higher Education School Key Scientific Research Project of Henan Province(21A550014)Doctoral Research Foundation of Zhengzhou University of Light Industry(2020BSJJ015)Science and Technology Research Project of Higher Education in Hebei Province(QN2020233).
文摘Longan originates from southern China and has high nutritional and health value.Recent phytochemistry and pharmacology studies have shown that polysaccharides are a main bioactive component of longan.Longan polysaccharides possess antioxidant,anti-aging,anti-tumor,immunomodulatory,and other bioactivities.Hot-water extraction,ethanol precipitation,and ultrasonic extraction are generally used to extract water-soluble longan polysaccharides.However,the relationship between the structure and bioactivity of longan polysaccharides remains unclear,requiring further investigation.The aim of this review is to evaluate the current literature focusing on the extraction,purification,structural characterization,and biological activity of longan polysaccharides.We believe that this review would provide a useful bibliography for further innovation and a basis for using longan polysaccharides in functional food.
基金supported by the Ministerio Espanol de Ciencia e Innovación under Project Number PID2020-115570GB-C22 MCIN/AEI/10.13039/501100011033 and by the Cátedra de Empresa Tecnología para las Personas(UGR-Fujitsu).
文摘Cloud computing is a dynamic and rapidly evolving field,where the demand for resources fluctuates continuously.This paper delves into the imperative need for adaptability in the allocation of resources to applications and services within cloud computing environments.The motivation stems from the pressing issue of accommodating fluctuating levels of user demand efficiently.By adhering to the proposed resource allocation method,we aim to achieve a substantial reduction in energy consumption.This reduction hinges on the precise and efficient allocation of resources to the tasks that require those most,aligning with the broader goal of sustainable and eco-friendly cloud computing systems.To enhance the resource allocation process,we introduce a novel knowledge-based optimization algorithm.In this study,we rigorously evaluate its efficacy by comparing it to existing algorithms,including the Flower Pollination Algorithm(FPA),Spark Lion Whale Optimization(SLWO),and Firefly Algo-rithm.Our findings reveal that our proposed algorithm,Knowledge Based Flower Pollination Algorithm(KB-FPA),consistently outperforms these conventional methods in both resource allocation efficiency and energy consumption reduction.This paper underscores the profound significance of resource allocation in the realm of cloud computing.By addressing the critical issue of adaptability and energy efficiency,it lays the groundwork for a more sustainable future in cloud computing systems.Our contribution to the field lies in the introduction of a new resource allocation strategy,offering the potential for significantly improved efficiency and sustainability within cloud computing infrastructures.
基金supported by the Ministerio Espanol de Ciencia e Innovación under Project Number PID2020-115570GB-C22,MCIN/AEI/10.13039/501100011033by the Cátedra de Empresa Tecnología para las Personas(UGR-Fujitsu).
文摘As cloud computing usage grows,cloud data centers play an increasingly important role.To maximize resource utilization,ensure service quality,and enhance system performance,it is crucial to allocate tasks and manage performance effectively.The purpose of this study is to provide an extensive analysis of task allocation and performance management techniques employed in cloud data centers.The aim is to systematically categorize and organize previous research by identifying the cloud computing methodologies,categories,and gaps.A literature review was conducted,which included the analysis of 463 task allocations and 480 performance management papers.The review revealed three task allocation research topics and seven performance management methods.Task allocation research areas are resource allocation,load-Balancing,and scheduling.Performance management includes monitoring and control,power and energy management,resource utilization optimization,quality of service management,fault management,virtual machine management,and network management.The study proposes new techniques to enhance cloud computing work allocation and performance management.Short-comings in each approach can guide future research.The research’s findings on cloud data center task allocation and performance management can assist academics,practitioners,and cloud service providers in optimizing their systems for dependability,cost-effectiveness,and scalability.Innovative methodologies can steer future research to fill gaps in the literature.
基金supported by Tabriz University of Medical Sciences,grant number 65364.
文摘Cancer stem cells(CSCs)are a major challenge in cancer therapy.Stem cell-like cells form a unique subpopulation within many tumors,which govern the degree of malignancy by promoting metastasis,recurrence,heterogeneity,and resistance to drug and radiation.Furthermore,these cells can persist in patients even after undergoing multiple cycles of conventional cancer therapy via dormancy,where they no longer dividing but remain active.These may cause cancer recurrence at any time,even years after a supposed cure,and remain invisible to the immune system.Targeting specific surface markers,signaling pathways and tumor microenvironment,which all have a significant effect on CSC function and maintenance,could help to eradicate CSCs and improve patient survival.Combinations of traditional therapies with nano-based drug delivery systems can efficiently target CSCs.Considering the biology and properties of CSCs,we classify recent approaches involving nanoparticle engineering,extracellular matrix modulation,cocktail strategies,multi-stage therapy,CSC defanging,Trojan horse systems,targeted therapy and organelle targeting.We highlight the most recent advances in nanocarrier design and drug delivery technologies to target CSCs,combined with conventional treatment in preclinical and clinical trials.The prospects of these approaches for CSCs elimination and recurrent cancer treatment are discussed.
文摘Carbon capture and storage(CCS)is an advanced environmental technology for mitigating CO_(2) emissions and addressing climate change.Among the various approaches,adsorption has emerged as a promising method for CO_(2) capture due to its effiectiveness and practicality.This review explores the potential of clay minerals as adsorbents for CO_(2) capture,providing an in-depth analysis of their inherent properties and the mechanisms involved in adsorption process.The review begins with an introduction to CCS and the concept of adsorption,followed by a detailed examination of various clay minerals,including sepiolite,montmorillonite,bentonite,kaolinite,saponite,halloysite,and illite.Each mineral’s suitability for CO_(2) adsorption is assessed,highlighting the specific properties that contribute to their performance.The mechanisms of CO_(2) adsorption including physisorption,chemisorption,ion exchange,pore diffusion,intraparticle diffusion,surface complexation,and competitive adsorption are thoroughly discussed.The review also covers the modification of clay minerals through physical and chemical treatments,amine functionalization,and composite formation to enhance their CO_(2) adsorption capacity.Additionally,regeneration methods such as temperature-swing adsorption(TSA),pressure-swing adsorption(PSA),and purging are discussed,along with CO_(2) recovery and storage techniques for improving energy efficiency.The review concludes with an overview of characterization methods for clay-based adsorbents and potential applications,while addressing the challenges and future trends in thefield.This work emphasizes the promising role of clay-based adsorbents in advancing CCS technology.
基金the support provided by the King Fahd University of Petroleum & Minerals (KFUPM) for facilitating this research
文摘Greenhouse gas(GHG)emssions from fossil fuel consumption are driving global climate change.This study applied the fully modified ordinary least squares(FMOLS)model and pairwise panel Granger causality test to explore the relationships of GHG emissions with gross domestic product(GDP),population,urbanization,natural resource rents,foreign direct investment(FDI),and renewable energy consumption in 12 Middle East and North Africa(MENA)countries(Algeria,Bahrain,Comoros,Djibouti,Egypt,Qatar,Somalia,Saudi Arabia,Syria,the United Arab Emirates,Tunisia,and Yemen)from 1990 to 2023.Due to the limited data on renewable energy after 2020,the coverage of renewable energy consumption is from 1990 to 2021.Findings showed that Saudi Arabia,Egypt,Algeria,the United Arab Emirates,and Qatar are the top 5 GHG emitters in the MENA region,with the GHG emissions of the energy sector rising fastest among all sectors.Results also indicated that a 1.00%increase in GDP,population,urbanization,natural resource rents,and FDI raises GHG emissions by 0.48%,0.61%,0.86%,0.29%,and 0.11%,respectively.Conversely,a 1.00%increase in renewable energy consumption reduces GHG emissions by 0.13%.Effective policies promoting renewable energy investment and the adoption of renewable energy could significantly reduce electricity costs and GHG emissions,contributing to achieving climate goals,such as net-zero emissions and environmental sustainability.Additionally,the increase of renewable energy consumption and technology development would improve energy efficiency,create jobs,and stimulate economic growth in the MENA region.This study recommends tailored policy instruments to support the transition to low-emission technologies and strategies.
文摘In recent years,hepatology has undergone a transformative evolution driven by significant advancements in diagnostic and therapeutic technologies.The expanding integration of endoscopic modalities into hepatology has enforced the diagnosis,staging,management of liver diseases beside integration into transplantation.This review highlights the evolving discipline of“endo-hepatology”,where endoscopic ultrasound,endoscopic retrograde cholangiopancreatography,and novel interventional tools are employed to address the critical challenges in chronic liver disease.The review provides a comprehensive synthesis of current evidence and different clinical applications,while also exploring future directions including revolution of artificial intelligence-assisted endoscopies and enhanced imaging endoscopies.By bridging the anatomical and functional interface between the gastrointestinal lumen and the liver,endo-hepatology is not only improving diagnostic accuracy and therapeutic precision but also reshaping multidisciplinary paradigms in hepatology practice.
基金supported by the National Natural Science Foundation of China under Grant No.12147115the Discipline(Subject)Leader Cultivation Project of Universities in Anhui Province under Grant Nos.DTR2023052 and DTR2024046+2 种基金the Natural Science Research Project of Universities in Anhui Province under Grant No.2024AH040202the Young Top Notch Talents and Young Scholars of High End Talent Introduction and Cultivation Action Project in Anhui Provincethe Scientific Research Foundation Funded Project of Chuzhou University under Grant Nos.2022qd022 and 2022qd038。
文摘In this paper,we use the Riemann-Hilbert(RH)method to investigate the Cauchy problem of the reverse space-time nonlocal Hirota equation with step-like initial data:q(z,0)=o(1)as z→-∞and q(z,0)=δ+o(1)as z→∞,whereδis an arbitrary positive constant.We show that the solution of the Cauchy problem can be determined by the solution of the corresponding matrix RH problem established on the plane of complex spectral parameterλ.As an example,we construct an exact solution of the reverse space-time nonlocal Hirota equation in a special case via this RH problem.
