Objective:To determine the safety and the role of modulating cytokines and proteases in the immune response to intravesical Bacillus Calmette-Guérin(BCG)when primed with systemic intradermal BCG.Methods:Phase 1 a...Objective:To determine the safety and the role of modulating cytokines and proteases in the immune response to intravesical Bacillus Calmette-Guérin(BCG)when primed with systemic intradermal BCG.Methods:Phase 1 and mechanistic longitudinal,prospective,single-blind randomized study(NCT04806178).Twenty-one non-muscle invasive urothelial bladder cancer patients undergoing intravesical adjuvant BCG after transurethral resection of bladder tumor(TURBT)in a teaching hospital between September 2021 and April 2023 were randomized to 0.1 mL of intradermal BCG vaccine or placebo(0.9%saline)administered 15 days before the start of intravesical BCG therapy.Blood samples were evaluated mechanistically regarding eight cytokines serum levels interferon-induced transmembrane protein 3 Gene(IFITM3),Interleukin 1 beta(IL1-BETA),interleukin-2 receptor alpha chain(IL2 RA),Interleukin 6(IL 6),Interleukin 10(IL 10),Tumor necrosis factor alpha(TNF-α),Interferon-β,AXL,and one protease CASPASE 8.Results:After 1 exclusion,twenty patients were randomized to intradermal BCG(n=11)and intradermal placebo(n=9).There was no difference in adverse effects emerging from the intravesical Onco-BCG therapy,and no difference in the expression of the cytokines and proteases analyzed between control and intervention,and over time.Conclusions:Intradermal BCG administration before intravesical application was safe,with no increase in adverse effects.It also does not seem to change the analyzed targets during the intravesical induction-phase BCG.Other immune targets should be explored in the future.The Brazilian tuberculosis-endemic status,where BCG vaccination is mandatory,might have affected the results.展开更多
Parkinson’s disease(PD)is a progressive age-related neurodegenerative disorder clinically defined by motor symptoms and pathologically by the loss of dopaminergic(DA)neurons in the substantia nigra pars compacta.Thes...Parkinson’s disease(PD)is a progressive age-related neurodegenerative disorder clinically defined by motor symptoms and pathologically by the loss of dopaminergic(DA)neurons in the substantia nigra pars compacta.These neurons are characterized by the presence of the cytoplasmic pigment neuromelanin(NM),and their degeneration is closely associated with the accumulation ofα-synuclein(α-syn)into intraneuronal inclusions known as Lewy bodies(LBs),which represent a neuropathological hallmark of PD.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
The development of clinical candidates that modify the natural progression of sporadic Parkinson's disease and related synucleinopathies is a praiseworthy endeavor,but extremely challenging.Therapeutic candidates ...The development of clinical candidates that modify the natural progression of sporadic Parkinson's disease and related synucleinopathies is a praiseworthy endeavor,but extremely challenging.Therapeutic candidates that were successful in preclinical Parkinson's disease animal models have repeatedly failed when tested in clinical trials.While these failures have many possible explanations,it is perhaps time to recognize that the problem lies with the animal models rather than the putative candidate.In other words,the lack of adequate animal models of Parkinson's disease currently represents the main barrier to preclinical identification of potential disease-modifying therapies likely to succeed in clinical trials.However,this barrier may be overcome by the recent introduction of novel generations of viral vectors coding for different forms of alpha-synuclein species and related genes.Although still facing several limitations,these models have managed to mimic the known neuropathological hallmarks of Parkinson's disease with unprecedented accuracy,delineating a more optimistic scenario for the near future.展开更多
After injury,bone tissue initiates a reparative response to restore its structure and function.The failure to initiate or delay this response could result in fracture nonunion.The molecular mechanisms underlying the o...After injury,bone tissue initiates a reparative response to restore its structure and function.The failure to initiate or delay this response could result in fracture nonunion.The molecular mechanisms underlying the occurrence of fracture nonunion are not yet established.We propose that hypoxia-triggered signaling pathways,mediated by reactive oxygen species(ROS)homeostasis,control Bmp2 expression and fracture healing initiation.The excessive ROS leads to oxidative stress and,ultimately,fracture nonunion.In this study,we silenced Apex1,the final ROS signaling transducer that mediates the activation of key transcription factors by their cysteines oxidoreduction,evaluating its role during endochondral ossification and fracture repair.Silencing Apex1 in limb bud mesenchyme results in transient metaphyseal dysplasia derived from impaired chondrocyte differentiation.During bone regeneration,Apex1 silencing induces a fracture nonunion phenotype,characterized by delayed fracture repair initiation,impaired periosteal response,and reduced chondrocyte and osteoblast differentiation.This compromised chondrocyte differentiation hampers callus vascularization and healing progression.Our findings highlight a critical mechanism where hypoxia-driven ROS signaling in mesenchymal progenitors through APEX1 is essential for fracture healing initiation.展开更多
The intelligent transportation systems require secure,low-latency,and reliable communication architectures to enable the real-time vehicular application.This paper proposes an edge-intelligent semantic aggregation(EIS...The intelligent transportation systems require secure,low-latency,and reliable communication architectures to enable the real-time vehicular application.This paper proposes an edge-intelligent semantic aggregation(EISA)framework for 6G unmanned aerial vehicle(UAV)-assisted Internet of vehicles(IoV)networks that integrates task-driven semantic communication,deep reinforcement learning(DRL)-based edge intelligence,and blockchain-based semantic validation across 6G terahertz(THz)links.UAVs in the proposed architecture serve as adaptive edge nodes that receive semantically vital information about the vehicle at any given stage,optimize aggregation and transmission parameters dynamically,and guarantee data integrity through a structured,lightweight consortium blockchain that signs semantically detailed representations rather than raw packets.Simulation results from a hybrid NS-3,MATLAB,and Python environment indicate that the proposed framework can achieve up to 45%reduction in end-to-end latency,an approximately 70%increase in throughput,and semantic efficiency with blockchain verification delays of less than 20 ms(more than 98%).These findings support the effectiveness of the proposed co-design for achieving context-aware,energy-efficient,and reliable communication under heavy-traffic conditions.The proposed framework provides a flexible and scalable foundation for next-generation 6G-enabled automotive networks,with subsequent growth toward federated learning-based collaborative intelligence,digital-twinassisted traffic modeling,and quantum-safe blockchain mechanisms to enhance scalability,intelligence,and long-term security.展开更多
Background:Recent evidence suggests continuous bouts of physical activity(PA)are associated with longevity.We hypothesized the risk of mortality would be lower when the most active minutes of the day were in a continu...Background:Recent evidence suggests continuous bouts of physical activity(PA)are associated with longevity.We hypothesized the risk of mortality would be lower when the most active minutes of the day were in a continuous bout.Methods:PA was assessed using accelerometery in UK Biobank participants.The intensity of the most active continuous(MXCONT)and accumulated(MX)X min of the day,and their ratio(MXRATIO=MXCONT/MX),were determined.MXRATIO indicates how the most active minutes of the day are accumulated,ranging from a single continuous bout through to sporadic accumulation spread across the day.Durations(X)considered ranged from 1 to 20 min.The outcome was mortality.Results:In total,94,541 participants(56.5% female)were included.Over a median(interquartile range)follow-up of 6.9(6.3,7.4)years,2649(2.8%)deaths occurred.Intensity moderated the association between how the most active minutes of the day were accumulated and mortality risk,expressed relative to sporadically accumulated moderate PA.If the most active minutes were of moderate intensity,the risk of mortality was halved for continuous compared to sporadic accumulation,irrespective of duration;if the most active minutes were of vigorous intensity,a continuous bout was associated with the lowest risk for durations under 5 min(e.g.,3 min:hazard ratio(HR)=0.27,95% confidence interval(95%CI):0.21-0.34),while sporadic accumulation was associated with the lowest risk for durations beyond 5 min(HR=0.11,95%CI:0.08-0.15 for the most active 20 min).Conclusion:Optimal PA patterns for reducing mortality differ by intensity and duration.For moderate-intensity PA,a lower mortality risk may be optimized by prioritizing continuous PA for up to 20 min.However,for vigorous-intensity PA,multiple short bouts(<5 min)may be optimal.This suggests tailored PA recommendations may enhance longevity benefits.展开更多
The advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-gener...The advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-generation(5G)networks transformed mobile broadband and machine-type communications at massive scales,their properties of scaling,interference management,and latency remain a limitation in dense high mobility settings.To overcome these limitations,artificial intelligence(AI)and unmanned aerial vehicles(UAVs)have emerged as potential solutions to develop versatile,dynamic,and energy-efficient communication systems.The study proposes an AI-based UAV architecture that utilizes cooperative reinforcement learning(CoRL)to manage an autonomous network.The UAVs collaborate by sharing local observations and real-time state exchanges to optimize user connectivity,movement directions,allocate power,and resource distribution.Unlike conventional centralized or autonomous methods,CoRL involves joint state sharing and conflict-sensitive reward shaping,which ensures fair coverage,less interference,and enhanced adaptability in a dynamic urban environment.Simulations conducted in smart city scenarios with 10 UAVs and 50 ground users demonstrate that the proposed CoRL-based UAV system increases user coverage by up to 10%,achieves convergence 40%faster,and reduces latency and energy consumption by 30%compared with centralized and decentralized baselines.Furthermore,the distributed nature of the algorithm ensures scalability and flexibility,making it well-suited for future large-scale 6G deployments.The results highlighted that AI-enabled UAV systems enhance connectivity,support ultra-reliable low-latency communications(URLLC),and improve 6G network efficiency.Future work will extend the framework with adaptive modulation,beamforming-aware positioning,and real-world testbed deployment.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
Wireless Sensor Networks(WSNs)have become foundational in numerous real-world applications,ranging from environmental monitoring and industrial automation to healthcare systems and smart city development.As these netw...Wireless Sensor Networks(WSNs)have become foundational in numerous real-world applications,ranging from environmental monitoring and industrial automation to healthcare systems and smart city development.As these networks continue to grow in scale and complexity,the need for energy-efficient,scalable,and robust communication protocols becomes more critical than ever.Metaheuristic algorithms have shown significant promise in addressing these challenges,offering flexible and effective solutions for optimizing WSN performance.Among them,the Grey Wolf Optimizer(GWO)algorithm has attracted growing attention due to its simplicity,fast convergence,and strong global search capabilities.Accordingly,this survey provides an in-depth review of the applications of GWO and its variants for clustering,multi-hop routing,and hybrid cluster-based routing in WSNs.We categorize and analyze the existing GWO-based approaches across these key network optimization tasks,discussing the different problem formulations,decision variables,objective functions,and performance metrics used.In doing so,we examine standard GWO,multi-objective GWO,and hybrid GWO models that incorporate other computational intelligence techniques.Each method is evaluated based on how effectively it addresses the core constraints of WSNs,including energy consumption,communication overhead,and network lifetime.Finally,this survey outlines existing gaps in the literature and proposes potential future research directions aimed at enhancing the effectiveness and real-world applicability of GWO-based techniques for WSN clustering and routing.Our goal is to provide researchers and practitioners with a clear,structured understanding of the current state of GWO in WSNs and inspire further innovation in this evolving field.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
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
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
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
For the chip integration of MEMS(micro-electromechanical system) safety and arming device, a miniature detonator needs to be developed to reduce the weight and volume of explosive train. A Si-based micro-detonator is ...For the chip integration of MEMS(micro-electromechanical system) safety and arming device, a miniature detonator needs to be developed to reduce the weight and volume of explosive train. A Si-based micro-detonator is designed and fabricated, which meets the requirement of MEMS safety and arming device. The firing sensitivity of micro-detonator is tested according to GJB/z377A-94 sensitivity test methods:Langlie. The function time of micro-detonator is measured using wire probe and photoelectric transducer. The result shows the average firing voltage is 6.4 V when the discharge capacitance of firing electro-circuit is 33 mF. And the average function time is 5.48 ms. The firing energy actually utilized by Si-based micro-detonator is explored.展开更多
Colorectal cancer is the third most common cancer and is highly fatal. During the last several years, research has been primarily based on the study of expression profiles using microarray technology. But now, investi...Colorectal cancer is the third most common cancer and is highly fatal. During the last several years, research has been primarily based on the study of expression profiles using microarray technology. But now, investigators are putting into practice proteomic analyses of cancer tissues and cells to identify new diagnostic or therapeutic biomarkers for this cancer. Because the proteome reflects the state of a cell, tissue or organism more accurately, much is expected from proteomics to yield better tumor markers for disease diagnosis and therapy monitoring. This review summarizes the most relevant applications of proteomics the biomarker discovery for colorectal cancer.展开更多
This paper summarizes the results of the researches on the middle and upper atmosphere obtained by Chinese scientists in 2008-2010.The focuses are specifically placed on the researches being associated with ground-bas...This paper summarizes the results of the researches on the middle and upper atmosphere obtained by Chinese scientists in 2008-2010.The focuses are specifically placed on the researches being associated with ground-based observation capability development,dynamical processes,the property of atmospheric circulation and the chemistry-climate coupling of the middle atmospheric layers.展开更多
基金National Council for Scientific and Technological Development,CNPq,Research Productivity,grant numbers:304747/2018-1/310135/2022-2(Reis LO).
文摘Objective:To determine the safety and the role of modulating cytokines and proteases in the immune response to intravesical Bacillus Calmette-Guérin(BCG)when primed with systemic intradermal BCG.Methods:Phase 1 and mechanistic longitudinal,prospective,single-blind randomized study(NCT04806178).Twenty-one non-muscle invasive urothelial bladder cancer patients undergoing intravesical adjuvant BCG after transurethral resection of bladder tumor(TURBT)in a teaching hospital between September 2021 and April 2023 were randomized to 0.1 mL of intradermal BCG vaccine or placebo(0.9%saline)administered 15 days before the start of intravesical BCG therapy.Blood samples were evaluated mechanistically regarding eight cytokines serum levels interferon-induced transmembrane protein 3 Gene(IFITM3),Interleukin 1 beta(IL1-BETA),interleukin-2 receptor alpha chain(IL2 RA),Interleukin 6(IL 6),Interleukin 10(IL 10),Tumor necrosis factor alpha(TNF-α),Interferon-β,AXL,and one protease CASPASE 8.Results:After 1 exclusion,twenty patients were randomized to intradermal BCG(n=11)and intradermal placebo(n=9).There was no difference in adverse effects emerging from the intravesical Onco-BCG therapy,and no difference in the expression of the cytokines and proteases analyzed between control and intervention,and over time.Conclusions:Intradermal BCG administration before intravesical application was safe,with no increase in adverse effects.It also does not seem to change the analyzed targets during the intravesical induction-phase BCG.Other immune targets should be explored in the future.The Brazilian tuberculosis-endemic status,where BCG vaccination is mandatory,might have affected the results.
基金This work was supported by PID2022-138285OB-I00financiado por MCIN/AEI/10.13039/501100011033/FEDER,UE to AGO and MCTAsociación de Amigos fellowship grant to MGG.
文摘Parkinson’s disease(PD)is a progressive age-related neurodegenerative disorder clinically defined by motor symptoms and pathologically by the loss of dopaminergic(DA)neurons in the substantia nigra pars compacta.These neurons are characterized by the presence of the cytoplasmic pigment neuromelanin(NM),and their degeneration is closely associated with the accumulation ofα-synuclein(α-syn)into intraneuronal inclusions known as Lewy bodies(LBs),which represent a neuropathological hallmark of PD.
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 grants PID2020-120308RB-I00 and PID2023-147802OB-I00 funded by MICIU/AEI/10.13039/501100011033FEDER,UE,by Aligning Science Across Parkinson’s(ref.ASAP-020505)through the Michael J.Fox Foundation for Parkinson’s Research+1 种基金by CiberNed Intramural Collaborative Projects(ref.PI2020/09)by the Spanish Fundación Mutua Madrile?a de Investigación Médica(to JLL)。
文摘The development of clinical candidates that modify the natural progression of sporadic Parkinson's disease and related synucleinopathies is a praiseworthy endeavor,but extremely challenging.Therapeutic candidates that were successful in preclinical Parkinson's disease animal models have repeatedly failed when tested in clinical trials.While these failures have many possible explanations,it is perhaps time to recognize that the problem lies with the animal models rather than the putative candidate.In other words,the lack of adequate animal models of Parkinson's disease currently represents the main barrier to preclinical identification of potential disease-modifying therapies likely to succeed in clinical trials.However,this barrier may be overcome by the recent introduction of novel generations of viral vectors coding for different forms of alpha-synuclein species and related genes.Although still facing several limitations,these models have managed to mimic the known neuropathological hallmarks of Parkinson's disease with unprecedented accuracy,delineating a more optimistic scenario for the near future.
基金supported by funds of the Ministerio de Ciencia, Innovación y Universidadesco-financed by European Regional Development Fund-FEDER “A way to make Europe” (Project Ref:PID2023-153309OB-I00) supported by MCIN/AEl/ 10.13039/501100011033/ FEDER, UE+10 种基金Ministerio de Ciencia, Innovación y Universidades through Instituto de Salud Carlos Ⅲ and European Regional Development Funds “A way to make Europe” (PI17/00136, PI20/00076)European Union Horizon 2020 program (grant agreement #874889, HEALIKICK) to F. Granero-MoltóNext Generation EU, Plan de Recuperación, Transformación y Resiliencia RICORS TERAV ISCIII (RD21/0017/0009)H2020-MSCA-RISE-2019 (grant agreement #872648, MEPHOS) to F. Próspersupported by a fellowship from “Asociación de Amigos de la Universidad de Navarra”supported by a fellowship CIMA AC from “Fundación para la Investigación Médica Aplicada”funded by grants PID2022-104776RB-100 and CB16/11/00399 (CIBER CV) from MCIN/AEI/10.13039/501100011033La Caixa Research Health Foundation (Ref. HR23-00084)supported by a fellowship of “Asociación de Amigos de la Universidad de Navarra” and “Obra Social La Caixa”the research leading to these results has received funding from “la Caixa” Banking Foundationsupported by a Sara Borrell grant (CD22/00027) from the Instituto Carlos Ⅲ and Next Generation EU。
文摘After injury,bone tissue initiates a reparative response to restore its structure and function.The failure to initiate or delay this response could result in fracture nonunion.The molecular mechanisms underlying the occurrence of fracture nonunion are not yet established.We propose that hypoxia-triggered signaling pathways,mediated by reactive oxygen species(ROS)homeostasis,control Bmp2 expression and fracture healing initiation.The excessive ROS leads to oxidative stress and,ultimately,fracture nonunion.In this study,we silenced Apex1,the final ROS signaling transducer that mediates the activation of key transcription factors by their cysteines oxidoreduction,evaluating its role during endochondral ossification and fracture repair.Silencing Apex1 in limb bud mesenchyme results in transient metaphyseal dysplasia derived from impaired chondrocyte differentiation.During bone regeneration,Apex1 silencing induces a fracture nonunion phenotype,characterized by delayed fracture repair initiation,impaired periosteal response,and reduced chondrocyte and osteoblast differentiation.This compromised chondrocyte differentiation hampers callus vascularization and healing progression.Our findings highlight a critical mechanism where hypoxia-driven ROS signaling in mesenchymal progenitors through APEX1 is essential for fracture healing initiation.
基金supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)-Information Technology Research Center(ITRC)under Grant No.IITP-2025-RS-2023-00259004the Basic Science Research Program through the National Research Foundation of Korea(NRF)under Grant No.RS-2025-25434261.
文摘The intelligent transportation systems require secure,low-latency,and reliable communication architectures to enable the real-time vehicular application.This paper proposes an edge-intelligent semantic aggregation(EISA)framework for 6G unmanned aerial vehicle(UAV)-assisted Internet of vehicles(IoV)networks that integrates task-driven semantic communication,deep reinforcement learning(DRL)-based edge intelligence,and blockchain-based semantic validation across 6G terahertz(THz)links.UAVs in the proposed architecture serve as adaptive edge nodes that receive semantically vital information about the vehicle at any given stage,optimize aggregation and transmission parameters dynamically,and guarantee data integrity through a structured,lightweight consortium blockchain that signs semantically detailed representations rather than raw packets.Simulation results from a hybrid NS-3,MATLAB,and Python environment indicate that the proposed framework can achieve up to 45%reduction in end-to-end latency,an approximately 70%increase in throughput,and semantic efficiency with blockchain verification delays of less than 20 ms(more than 98%).These findings support the effectiveness of the proposed co-design for achieving context-aware,energy-efficient,and reliable communication under heavy-traffic conditions.The proposed framework provides a flexible and scalable foundation for next-generation 6G-enabled automotive networks,with subsequent growth toward federated learning-based collaborative intelligence,digital-twinassisted traffic modeling,and quantum-safe blockchain mechanisms to enhance scalability,intelligence,and long-term security.
基金funded by UK Research and Innovation(research which commenced between October 1,2020-March 31,2021,Grant ref MC_PC_20029April 1,2021-September 31,2022,Grant ref MC_PC_20058)supported by the National Institute for Health Research(NIHR)Leicester Biomedical Research Centre and NIHR Applied Research CollaborationEast Midlands(ARC-EM)。
文摘Background:Recent evidence suggests continuous bouts of physical activity(PA)are associated with longevity.We hypothesized the risk of mortality would be lower when the most active minutes of the day were in a continuous bout.Methods:PA was assessed using accelerometery in UK Biobank participants.The intensity of the most active continuous(MXCONT)and accumulated(MX)X min of the day,and their ratio(MXRATIO=MXCONT/MX),were determined.MXRATIO indicates how the most active minutes of the day are accumulated,ranging from a single continuous bout through to sporadic accumulation spread across the day.Durations(X)considered ranged from 1 to 20 min.The outcome was mortality.Results:In total,94,541 participants(56.5% female)were included.Over a median(interquartile range)follow-up of 6.9(6.3,7.4)years,2649(2.8%)deaths occurred.Intensity moderated the association between how the most active minutes of the day were accumulated and mortality risk,expressed relative to sporadically accumulated moderate PA.If the most active minutes were of moderate intensity,the risk of mortality was halved for continuous compared to sporadic accumulation,irrespective of duration;if the most active minutes were of vigorous intensity,a continuous bout was associated with the lowest risk for durations under 5 min(e.g.,3 min:hazard ratio(HR)=0.27,95% confidence interval(95%CI):0.21-0.34),while sporadic accumulation was associated with the lowest risk for durations beyond 5 min(HR=0.11,95%CI:0.08-0.15 for the most active 20 min).Conclusion:Optimal PA patterns for reducing mortality differ by intensity and duration.For moderate-intensity PA,a lower mortality risk may be optimized by prioritizing continuous PA for up to 20 min.However,for vigorous-intensity PA,multiple short bouts(<5 min)may be optimal.This suggests tailored PA recommendations may enhance longevity benefits.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(RS-2025-00559546)supported by the IITP(Institute of Information&Coummunications Technology Planning&Evaluation)-ITRC(Information Technology Research Center)grant funded by the Korea government(Ministry of Science and ICT)(IITP-2025-RS-2023-00259004).
文摘The advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-generation(5G)networks transformed mobile broadband and machine-type communications at massive scales,their properties of scaling,interference management,and latency remain a limitation in dense high mobility settings.To overcome these limitations,artificial intelligence(AI)and unmanned aerial vehicles(UAVs)have emerged as potential solutions to develop versatile,dynamic,and energy-efficient communication systems.The study proposes an AI-based UAV architecture that utilizes cooperative reinforcement learning(CoRL)to manage an autonomous network.The UAVs collaborate by sharing local observations and real-time state exchanges to optimize user connectivity,movement directions,allocate power,and resource distribution.Unlike conventional centralized or autonomous methods,CoRL involves joint state sharing and conflict-sensitive reward shaping,which ensures fair coverage,less interference,and enhanced adaptability in a dynamic urban environment.Simulations conducted in smart city scenarios with 10 UAVs and 50 ground users demonstrate that the proposed CoRL-based UAV system increases user coverage by up to 10%,achieves convergence 40%faster,and reduces latency and energy consumption by 30%compared with centralized and decentralized baselines.Furthermore,the distributed nature of the algorithm ensures scalability and flexibility,making it well-suited for future large-scale 6G deployments.The results highlighted that AI-enabled UAV systems enhance connectivity,support ultra-reliable low-latency communications(URLLC),and improve 6G network efficiency.Future work will extend the framework with adaptive modulation,beamforming-aware positioning,and real-world testbed deployment.
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Wireless Sensor Networks(WSNs)have become foundational in numerous real-world applications,ranging from environmental monitoring and industrial automation to healthcare systems and smart city development.As these networks continue to grow in scale and complexity,the need for energy-efficient,scalable,and robust communication protocols becomes more critical than ever.Metaheuristic algorithms have shown significant promise in addressing these challenges,offering flexible and effective solutions for optimizing WSN performance.Among them,the Grey Wolf Optimizer(GWO)algorithm has attracted growing attention due to its simplicity,fast convergence,and strong global search capabilities.Accordingly,this survey provides an in-depth review of the applications of GWO and its variants for clustering,multi-hop routing,and hybrid cluster-based routing in WSNs.We categorize and analyze the existing GWO-based approaches across these key network optimization tasks,discussing the different problem formulations,decision variables,objective functions,and performance metrics used.In doing so,we examine standard GWO,multi-objective GWO,and hybrid GWO models that incorporate other computational intelligence techniques.Each method is evaluated based on how effectively it addresses the core constraints of WSNs,including energy consumption,communication overhead,and network lifetime.Finally,this survey outlines existing gaps in the literature and proposes potential future research directions aimed at enhancing the effectiveness and real-world applicability of GWO-based techniques for WSN clustering and routing.Our goal is to provide researchers and practitioners with a clear,structured understanding of the current state of GWO in WSNs and inspire further innovation in this evolving field.
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For the chip integration of MEMS(micro-electromechanical system) safety and arming device, a miniature detonator needs to be developed to reduce the weight and volume of explosive train. A Si-based micro-detonator is designed and fabricated, which meets the requirement of MEMS safety and arming device. The firing sensitivity of micro-detonator is tested according to GJB/z377A-94 sensitivity test methods:Langlie. The function time of micro-detonator is measured using wire probe and photoelectric transducer. The result shows the average firing voltage is 6.4 V when the discharge capacitance of firing electro-circuit is 33 mF. And the average function time is 5.48 ms. The firing energy actually utilized by Si-based micro-detonator is explored.
文摘Colorectal cancer is the third most common cancer and is highly fatal. During the last several years, research has been primarily based on the study of expression profiles using microarray technology. But now, investigators are putting into practice proteomic analyses of cancer tissues and cells to identify new diagnostic or therapeutic biomarkers for this cancer. Because the proteome reflects the state of a cell, tissue or organism more accurately, much is expected from proteomics to yield better tumor markers for disease diagnosis and therapy monitoring. This review summarizes the most relevant applications of proteomics the biomarker discovery for colorectal cancer.
基金Supported by the National Natural Sciences Foundation of China under grant (40830102,40333034)the Knowledge Innovation Project of Chinese Academy of Sciences under Grant (KZCX2-YW-123)
文摘This paper summarizes the results of the researches on the middle and upper atmosphere obtained by Chinese scientists in 2008-2010.The focuses are specifically placed on the researches being associated with ground-based observation capability development,dynamical processes,the property of atmospheric circulation and the chemistry-climate coupling of the middle atmospheric layers.
