In recent years,Blockchain Technology has become a paradigm shift,providing Transparent,Secure,and Decentralized platforms for diverse applications,ranging from Cryptocurrency to supply chain management.Nevertheless,t...In recent years,Blockchain Technology has become a paradigm shift,providing Transparent,Secure,and Decentralized platforms for diverse applications,ranging from Cryptocurrency to supply chain management.Nevertheless,the optimization of blockchain networks remains a critical challenge due to persistent issues such as latency,scalability,and energy consumption.This study proposes an innovative approach to Blockchain network optimization,drawing inspiration from principles of biological evolution and natural selection through evolutionary algorithms.Specifically,we explore the application of genetic algorithms,particle swarm optimization,and related evolutionary techniques to enhance the performance of blockchain networks.The proposed methodologies aim to optimize consensus mechanisms,improve transaction throughput,and reduce resource consumption.Through extensive simulations and real-world experiments,our findings demonstrate significant improvements in network efficiency,scalability,and stability.This research offers a thorough analysis of existing optimization techniques,introduces novel strategies,and assesses their efficacy based on empirical outputs.展开更多
Next-GenerationNetworks(NGNs)demand high resilience,dynamic adaptability,and efficient resource utilization to enable ubiquitous connectivity.In this context,the Space-Air-Ground Integrated Network(SAGIN)architecture ...Next-GenerationNetworks(NGNs)demand high resilience,dynamic adaptability,and efficient resource utilization to enable ubiquitous connectivity.In this context,the Space-Air-Ground Integrated Network(SAGIN)architecture is uniquely positioned to meet these requirements.However,conventional NGN routing algorithms often fail to account for SAGIN’s intrinsic characteristics,such as its heterogeneous structure,dynamic topology,and constrained resources,leading to suboptimal performance under disruptions such as node failures or cyberattacks.To meet these demands for SAGIN,this study proposes a resilience-oriented routing optimization framework featuring dynamic weighting and multi-objective evaluation.Methodologically,we define three core routing performance metrics,quantified through a four-dimensionalmodel,encompassing robustness Rd,resilience Rr,adaptability Ra,and resource utilization efficiency Ru,and integrate them into a comprehensive evaluation metric.In simulated SAGIN environments,the proposed Multi-Indicator Weighted Resilience Evaluation Algorithm(MIW-REA)demonstrates significant improvements in resilience enhancement,recovery acceleration,and resource optimization.It maintains 82.3%service availability even with a 30%node failure rate,reduces Distributed Denial of Service(DDoS)attack recovery time by 43%,decreases bandwidth waste by 23.4%,and lowers energy consumption by 18.9%.By addressing challenges unique to the SAGIN network,this research provides a flexible real-time solution for NGN routing optimization that balances resilience,efficiency,and adaptability,advancing the field.展开更多
Advanced technologies like Cyber-Physical Systems(CPS)and the Internet of Things(IoT)have supported modernizing and automating the transportation region through the introduction of Intelligent Transportation Systems(I...Advanced technologies like Cyber-Physical Systems(CPS)and the Internet of Things(IoT)have supported modernizing and automating the transportation region through the introduction of Intelligent Transportation Systems(ITS).Integrating CPS-ITS and IoT provides real-time Vehicle-to-Infrastructure(V2I)communication,supporting better traffic management,safety,and efficiency.These technological innovations generate complex problems that need to be addressed,uniquely about data routing and Task Scheduling(TS)in ITS.Attempts to solve those problems were primarily based on traditional and experimental methods,and the solutions were not so successful due to the dynamic nature of ITS.This is where the scope of Machine learning(ML)and Swarm Intelligence(SI)has significantly impacted dealing with these challenges;in this line,this research paper presents a novel method for TS and data routing in the CPS-ITS.This paper proposes using a cutting-edge ML algorithm for data transmission from CPS-ITS.This ML has Gated Linear Unit-approximated Reinforcement Learning(GLRL).Greedy Iterative-Particle Swarm Optimization(GI-PSO)has been recommended to develop the Particle Swarm Optimization(PSO)for TS.The primary objective of this study is to enhance the security and effectiveness of ITS systems that utilize CPS-ITS.This study trained and validated the models using a network simulation dataset of 50 nodes from numerous ITS environments.The experiments demonstrate that the proposed GLRL reduces End-toEnd Delay(EED)by 12%,enhances data size use from 83.6%to 88.6%,and achieves higher bandwidth allocation,particularly in high-demand scenarios such as multimedia data streams where adherence improved to 98.15%.Furthermore,the GLRL reduced Network Congestion(NC)by 5.5%,demonstrating its efficiency in managing complex traffic conditions across several environments.The model passed simulation tests in three different environments:urban(UE),suburban(SE),and rural(RE).It met the high bandwidth requirements,made task scheduling more efficient,and increased network throughput(NT).This proved that it was robust and flexible enough for scalable ITS applications.These innovations provide robust,scalable solutions for real-time traffic management,ultimately improving safety,reducing NC,and increasing overall NT.This study can affect ITS by developing it to be more responsive,safe,and effective and by creating a perfect method to set up UE,SE,and RE.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
OBJECTIVE:To investigate the effects of optimizing Qinggan Jieyu decoction(清肝解郁方)on purinergic receptor P2X ligand-gated ion channel 7(P2X7R)and autophagy in migraine model rats based on molecular biology and his...OBJECTIVE:To investigate the effects of optimizing Qinggan Jieyu decoction(清肝解郁方)on purinergic receptor P2X ligand-gated ion channel 7(P2X7R)and autophagy in migraine model rats based on molecular biology and histopathology.METHODS:A migraine rat model was established by a single subcutaneous nitroglycerin(NTG)injection into the posterior neck.QGJY was administered via gavage for 7 d prior to NTG induction.Behavioral changes,central sensitization biomarkers,and inflammatory cytokine levels were analyzed to evaluate migraine severity.Western blot,immunofluorescence,quantitative real-time PCR,and transmission electron microscopy were employed to assess P2X7R expression and autophagy activity in trigeminal nucleus caudalis(TNC)tissues.The P2X7R agonist 2'(3')-O-(4-Benzoylbenzoyl)adenosine-5'-triphosphate(Bz ATP)was further utilized to validate QGJY's regulatory effects.RESULTS:QGJY significantly reduced cage-climbing and head-scratching frequencies in NTG-induced migraine rats,downregulated serum and TNC levels of interleukin-1 beta,interleukin-6,and tumor necrosis factor-alpha,and suppressed central sensitization markers(substance P;calcitonin gene-related peptide;and c-fos induced growth factor)in TNC tissues(P<0.05).QGJY markedly decreased microglial cell counts and average immunofluorescence intensity in TNC tissues and promoted elongation of microglial protrusions(P<0.05).Concurrently,QGJY downregulated P2X7R protein and m RNA expression,reduced the light chain 3(LC3)-II/LC3-I ratio,elevated ubiquitin-binding protein p62 levels,and diminished autophagosome numbers in TNC tissues(P<0.05).Furthermore,QGJY reversed Bz ATP-induced P2X7R upregulation(P<0.05).CONCLUSIONS:QGJY alleviates migraine and inhibits central sensitization in rats,potentially by downregulating P2X7R expression,concomitantly suppressing autophagy,attenuating microglial activation,and reducing pro-inflammatory cytokine release.展开更多
The belief rule-based(BRB)system has been popular in complexity system modeling due to its good interpretability.However,the current mainstream optimization methods of the BRB systems only focus on modeling accuracy b...The belief rule-based(BRB)system has been popular in complexity system modeling due to its good interpretability.However,the current mainstream optimization methods of the BRB systems only focus on modeling accuracy but ignore the interpretability.The single-objective optimization strategy has been applied in the interpretability-accuracy trade-off by inte-grating accuracy and interpretability into an optimization objec-tive.But the integration has a greater impact on optimization results with strong subjectivity.Thus,a multi-objective optimiza-tion framework in the modeling of BRB systems with inter-pretability-accuracy trade-off is proposed in this paper.Firstly,complexity and accuracy are taken as two independent opti-mization goals,and uniformity as a constraint to give the mathe-matical description.Secondly,a classical multi-objective opti-mization algorithm,nondominated sorting genetic algorithm II(NSGA-II),is utilized as an optimization tool to give a set of BRB systems with different accuracy and complexity.Finally,a pipeline leakage detection case is studied to verify the feasibility and effectiveness of the developed multi-objective optimization.The comparison illustrates that the proposed multi-objective optimization framework can effectively avoid the subjectivity of single-objective optimization,and has capability of joint optimiz-ing the structure and parameters of BRB systems with inter-pretability-accuracy trade-off.展开更多
Given the growing number of vehicle accidents caused by unintended acceleration and braking failure,verifying Sudden Unintended Acceleration(SUA)incidents has become a persistent challenge.A central issue of debate is...Given the growing number of vehicle accidents caused by unintended acceleration and braking failure,verifying Sudden Unintended Acceleration(SUA)incidents has become a persistent challenge.A central issue of debate is whether such events stem frommechanical malfunctions or driver pedalmisapplications.However,existing verification procedures implemented by vehiclemanufacturers often involve closed tests after vehicle recalls;thus raising ongoing concerns about reliability and transparency.Consequently,there is a growing need for a user-driven framework that enables independent data acquisition and verification.Although previous studies have addressed SUA detection using deep learning,few have explored howclass granularity optimization affects power efficiency and inference performance in real-time Edge AI systems.To address this problem,this work presents a cloud-assisted artificial intelligence(AI)solution for the reliable verification of SUA occurrences.The proposed system integrates multimodal sensor streams including camera-based foot images,On-Board Diagnostics II(OBD-II)signals,and six-axismeasurements to determine whether the brake pedal was actually engaged at themoment of a suspected SUA.Beyond image acquisition,convolutional neural network(CNN)models perform real-time inference to classify the driver’s pedal operation states with the resulting outputs transmitted and archived in the cloud.A dedicated dataset of brake and accelerator pedal images was collected from 15 vehicles produced by 6 domestic and international manufacturers.Using this dataset,transfer learning techniques were applied to compare and analyze model performance and generalization as the CNN class granularity varied from coarse to fine levels.Furthermore,classification performance was evaluated in terms of latency and power efficiency under different class configurations.The experimental results demonstrated that the proposed solution identified the driver’s pedal behavior accurately and promptly,with the two-class model achieving the highest F1-score and accuracy among all granularity settings.展开更多
This study presents a methodology to enhance energy management systems(EMS)in hybrid electric vehicles(HEVs)to reduce fuel consumption and greenhouse gas emissions.A novel surrogate-assisted optimization framework is ...This study presents a methodology to enhance energy management systems(EMS)in hybrid electric vehicles(HEVs)to reduce fuel consumption and greenhouse gas emissions.A novel surrogate-assisted optimization framework is employed,incorporating key performance metrics such as fuel efficiency and emissions to develop data-driven surrogate models of the EMS.These models are optimized using various algorithms targeting parameters such as engine idle speed,thermostat temperature fraction,regeneration load factor,and battery stateof-charge thresholds.Correlation analysis highlights the significant impact of the lower state-of-charge threshold and thermostat temperature fraction on fuel efficiency and emissions.Among the optimization methods,the combination of a backpropagation neural network(BPNN)and a multi-objective genetic algorithm(MOGA)proves most effective,achieving fuel consumption reductions of 5.26%and 5.01%in charge-sustaining and charge-depletion modes,respectively.Additionally,the BPNN-based MOGA demonstrates notable improvements in emission reduction.These findings suggest that optimizing rule-based EMS parameters without altering underlying management rules can significantly enhance performance under diverse and unanticipated driving conditions.展开更多
Multiple tuned mass dampers(MTMDs)reduce dynamic response with multiple specified frequencies of building structures.Many optimization algorithms for placement design exist,though they rarely conform to code-based ver...Multiple tuned mass dampers(MTMDs)reduce dynamic response with multiple specified frequencies of building structures.Many optimization algorithms for placement design exist,though they rarely conform to code-based verification nor produce high quality solutions without high computational effort and high complexity.This study proposes an inverse element exchange method(IEEM)with multi-level programming and compares it to a single tuned mass damper(STMD)and uniform distribution of multiple tuned mass dampers in the frequency and time domains.A ten-story shear building is used for the numerical case study.The results show that the proposed method can offer improvement over the STMD,uniform distribution of multiple tuned mass dampers,and distribution optimized by genetic algorithms(GA)with regard to minimizing the interstory drift ratio(IDR)in both the frequency and time domains and the time consumption for optimization.展开更多
With the development of science and technology,there is an increasing demand for energy storage batteries.Aqueous zinc-ion batteries(AZIBs)are expected to become the next generation of commercialized energy storage de...With the development of science and technology,there is an increasing demand for energy storage batteries.Aqueous zinc-ion batteries(AZIBs)are expected to become the next generation of commercialized energy storage devices due to their advantages.The aqueous zinc ion battery is generally composed of zinc metal as the anode,active material as the cathode,and aqueous electrolyte.However,there are still many problems with the cathode/anode material and voltage window of the battery,which limit its use.This review introduces the recent research progress of zinc-ion batteries,including the advantages and disadvantages,energy storage mechanisms,and common cathode/anode materials,electrolytes,etc.It also gives a summary of the current research status of each material and provides solutions to the problems they face.Finally,it looks at the future direction and methods to optimize the performance of zinc-ion full batteries.展开更多
In the new phase of sustainable development,agriculture is seeking sustainable management of the water-land-energy-economy-environment-food nexus.At present,there are few studies on optimizing crop planting structure ...In the new phase of sustainable development,agriculture is seeking sustainable management of the water-land-energy-economy-environment-food nexus.At present,there are few studies on optimizing crop planting structure and analyzing its spatial layout with consideration of natural and socio-economic factors.Herein,we proposed a framework for addressing this issue.In this framework,the NSGA-II algorithm was used to construct the multi-objective optimization model of crop planting structures with consideration of water and energy consumption,greenhouse gas(GHG)emissions,economic benefits,as well as food,land,and water security constraints,while the model for planting spatial layout optimization was established with consideration of crop suitability using the MaxEnt model and the improved Hungarian algorithm.This framework was further applied in the Black Soil Region of Northeast China(BSRNC)for analyzing optimized crop planting structures and spatial layouts of three main crops(rice,maize,and soybean)under various scenarios.This study showed that the sown area of rice in the BSRNC decreased by up to 40.73%and 35.30%in the environmental priority scenario and economic-environmental balance scenario,respectively,whereas that of soybean increased by up to 112.44%and 63.31%,respectively.In the economic priority scenario,the sown area of rice increased by up to 93.98%.Expanding the sown area of soybean was effective in reducing GHG emissions.On the contrary,rice production led to greater environmental costs though it provided higher economic returns.Among the three crops,maize exhibited an advantage in balancing environmental and economic benefits.Hegang-Jixi area in the northeast of the BSRNC was identified as the key area with the most intense crop planting transfer among different scenarios.Overall,this framework provides a new methodology for optimizing crop planting structures and spatial layouts with con-sideration of the nexus of various factors.Moreover,the case study demonstrates the applicability and expansion potential of the framework in the fields of sustainable agricultural development and food security assurance.展开更多
The ratooning system enhances agricultural efficiency by reducing secondary sowing and resource input while maintaining rice yield parity with double cropping.However,the prolonged growth duration of the rice ratoonin...The ratooning system enhances agricultural efficiency by reducing secondary sowing and resource input while maintaining rice yield parity with double cropping.However,the prolonged growth duration of the rice ratooning system extends the exposure window to Magnaporthe oryzae infection,thereby elevating the probability of disease incidence.展开更多
Burundi faces major agricultural constraints, including land fragmentation, soil erosion, limited access to inputs, inadequate infrastructure and demographic pressures that exacerbate food insecurity. In order to addr...Burundi faces major agricultural constraints, including land fragmentation, soil erosion, limited access to inputs, inadequate infrastructure and demographic pressures that exacerbate food insecurity. In order to address the multiple challenges faced by farmers in rural areas, a study on improving agricultural productivity and food security in Burundi through optimized land use and diversified farming practices in agroforestry systems has been carried out. The study area is the communes of Giheta and Rutegama, all located in Burundi’s humid plateau livelihood zone, and involved 164 households grouped in coffee growing cooperatives supervised by the cooperative consortium COCOCA. The study uses a mathematical programming model to determine optimal crop selection based on factors such as production costs, yields and market demand. The findings of the study revealed significant insights into the demographic and socio-economic characteristics of the sampled population. Notably, 98.8% of respondents were engaged in agriculture, confirming the predominantly agricultural nature of Burundi. The results indicated that maize is the most important crop, occupying 33.9% of the average total cultivated area, followed by cassava at 26.5% and bananas at 19.4%. Together, these three crops accounted for a substantial portion of the total cultivated area, highlighting their significance in local agriculture. Beans and potatoes also play a role, occupying 14.4% and smaller areas, respectively. In terms of profitability, the study provides a detailed analysis of profit margins by crop. Bananas emerges as the most profitable crop, with a profit margin of 97.3%, followed closely by cassava at 96.1% and rice at 90.5%. These crops not only offered substantial yields relative to their production costs but also benefited from strong market demand. Other crops, such as beans (71.3%), coffee (70.3%), and vegetables (54.5%), also demonstrated considerable profitability, although they occupied smaller cultivated areas. Conversely, crops like pigeon peas (4.1%), potatoes (7.6%), and sweet potatoes (7.6%) exhibited the lowest profit margins, which may discourage farmers from investing in them unless other incentives, such as ecological benefits or local consumption needs, are present. Regarding the results, we therefore recommend to promote policies supporting agroforestry, improve market access and develop infrastructure to exploit these benefits.展开更多
Dynamic soaring,inspired by the wind-riding flight of birds such as albatrosses,is a biomimetic technique which leverages wind fields to enhance the endurance of unmanned aerial vehicles(UAVs).Achieving a precise soar...Dynamic soaring,inspired by the wind-riding flight of birds such as albatrosses,is a biomimetic technique which leverages wind fields to enhance the endurance of unmanned aerial vehicles(UAVs).Achieving a precise soaring trajectory is crucial for maximizing energy efficiency during flight.Existing nonlinear programming methods are heavily dependent on the choice of initial values which is hard to determine.Therefore,this paper introduces a deep reinforcement learning method based on a differentially flat model for dynamic soaring trajectory planning and optimization.Initially,the gliding trajectory is parameterized using Fourier basis functions,achieving a flexible trajectory representation with a minimal number of hyperparameters.Subsequently,the trajectory optimization problem is formulated as a dynamic interactive process of Markov decision-making.The hyperparameters of the trajectory are optimized using the Proximal Policy Optimization(PPO2)algorithm from deep reinforcement learning(DRL),reducing the strong reliance on initial value settings in the optimization process.Finally,a comparison between the proposed method and the nonlinear programming method reveals that the trajectory generated by the proposed approach is smoother while meeting the same performance requirements.Specifically,the proposed method achieves a 34%reduction in maximum thrust,a 39.4%decrease in maximum thrust difference,and a 33%reduction in maximum airspeed difference.展开更多
As legal cases grow in complexity and volume worldwide,integrating machine learning and artificial intelligence into judicial systems has become a pivotal research focus.This study introduces a comprehensive framework...As legal cases grow in complexity and volume worldwide,integrating machine learning and artificial intelligence into judicial systems has become a pivotal research focus.This study introduces a comprehensive framework for verdict recommendation that synergizes rule-based methods with deep learning techniques specifically tailored to the legal domain.The proposed framework comprises three core modules:legal feature extraction,semantic similarity assessment,and verdict recommendation.For legal feature extraction,a rule-based approach leverages Black’s Law Dictionary and WordNet Synsets to construct feature vectors from judicial texts.Semantic similarity between cases is evaluated using a hybrid method that combines rule-based logic with an LSTM model,analyzing the feature vectors of query cases against a legal knowledge base.Verdicts are then recommended through a rule-based retrieval system,enhanced by predefined legal statutes and regulations.By merging rule-based methodologies with deep learning,this framework addresses the interpretability challenges often associated with contemporary AImodels,thereby enhancing both transparency and generalizability across diverse legal contexts.The system was rigorously tested using a legal corpus of 43,000 case laws across six categories:Criminal,Revenue,Service,Corporate,Constitutional,and Civil law,ensuring its adaptability across a wide range of judicial scenarios.Performance evaluation showed that the feature extraction module achieved an average accuracy of 91.6%with an F-Score of 95%.The semantic similarity module,tested using Manhattan,Euclidean,and Cosine distance metrics,achieved 88%accuracy and a 93%F-Score for short queries(Manhattan),89%accuracy and a 93.7%F-Score for medium-length queries(Euclidean),and 87%accuracy with a 92.5%F-Score for longer queries(Cosine).The verdict recommendation module outperformed existing methods,achieving 90%accuracy and a 93.75%F-Score.This study highlights the potential of hybrid AI frameworks to improve judicial decision-making and streamline legal processes,offering a robust,interpretable,and adaptable solution for the evolving demands of modern legal systems.展开更多
In response to the increasing global energy demand and environmental pollution,microgrids have emerged as an innovative solution by integrating distributed energy resources(DERs),energy storage systems,and loads to im...In response to the increasing global energy demand and environmental pollution,microgrids have emerged as an innovative solution by integrating distributed energy resources(DERs),energy storage systems,and loads to improve energy efficiency and reliability.This study proposes a novel hybrid optimization algorithm,DE-HHO,combining differential evolution(DE)and Harris Hawks optimization(HHO)to address microgrid scheduling issues.The proposed method adopts a multi-objective optimization framework that simultaneously minimizes operational costs and environmental impacts.The DE-HHO algorithm demonstrates significant advantages in convergence speed and global search capability through the analysis of wind,solar,micro-gas turbine,and battery models.Comprehensive simulation tests show that DE-HHO converges rapidly within 10 iterations and achieves a 4.5%reduction in total cost compared to PSO and a 5.4%reduction compared to HHO.Specifically,DE-HHO attains an optimal total cost of$20,221.37,outperforming PSO($21,184.45)and HHO($21,372.24).The maximum cost obtained by DE-HHO is$23,420.55,with a mean of$21,615.77,indicating stability and cost control capabilities.These results highlight the effectiveness of DE-HHO in reducing operational costs and enhancing system stability for efficient and sustainable microgrid operation.展开更多
This study introduces a comprehensive and automated framework that leverages data-driven method-ologies to address various challenges in shale gas development and production.Specifically,it harnesses the power of Auto...This study introduces a comprehensive and automated framework that leverages data-driven method-ologies to address various challenges in shale gas development and production.Specifically,it harnesses the power of Automated Machine Learning(AutoML)to construct an ensemble model to predict the estimated ultimate recovery(EUR)of shale gas wells.To demystify the“black-box”nature of the ensemble model,KernelSHAP,a kernel-based approach to compute Shapley values,is utilized for elucidating the influential factors that affect shale gas production at both global and local scales.Furthermore,a bi-objective optimization algorithm named NSGA-Ⅱ is seamlessly incorporated to opti-mize hydraulic fracturing designs for production boost and cost control.This innovative framework addresses critical limitations often encountered in applying machine learning(ML)to shale gas pro-duction:the challenge of achieving sufficient model accuracy with limited samples,the multidisciplinary expertise required for developing robust ML models,and the need for interpretability in“black-box”models.Validation with field data from the Fuling shale gas field in the Sichuan Basin substantiates the framework's efficacy in enhancing the precision and applicability of data-driven techniques.The test accuracy of the ensemble ML model reached 83%compared to a maximum of 72%of single ML models.The contribution of each geological and engineering factor to the overall production was quantitatively evaluated.Fracturing design optimization raised EUR by 7%-34%under different production and cost tradeoff scenarios.The results empower domain experts to conduct more precise and objective data-driven analyses and optimizations for shale gas production with minimal expertise in data science.展开更多
Water power is one of the key renewable energy resources,whose efficiency is often hampered due to inefficient water flow management,turbine performance,and environmental variations.Most existing optimization techniqu...Water power is one of the key renewable energy resources,whose efficiency is often hampered due to inefficient water flow management,turbine performance,and environmental variations.Most existing optimization techniques lack the real-time adaptability to sufficiently allocate resources in terms of location and time.Hence,a novel Scalable Tas-manian Devil Optimization(STDO)algorithm is introduced to optimize hydropower generation for maximum power efficiency.Using the STDO to model important system characteristics including water flow,turbine changes,and energy conversion efficiency is part of the process.In the final analysis,optimizing these settings in would help reduce inefficiencies and maximize power generation output.Following that,simulations based on actual hydroelectric data are used to analyze the algorithm's effectiveness.The simulation results provide evidence that the STDO algorithm can enhance hydropower plant efficiency tremendously translating to considerable energy output augmentation compared to conven-tional optimization methods.STDO achieves the reliability(92.5),resiliency(74.3),and reduced vulnerability(9.3).To guarantee increased efficiency towards ecologically friendly power generation,the STDO algorithm may thus offer efficient resource optimization for hydropower.A clear route is made available for expanding the efficiency of current hydropower facilities while tackling the long-term objectives of reducing the environmental impact and increasing the energy output of energy produced from renewable sources.展开更多
This study investigates the mechanical behavior of polyurethane(PU)nanocomposites reinforced with nanodiamonds(NDs)and proposes an integrated optimization-prediction framework that combines the Taguchi method with mac...This study investigates the mechanical behavior of polyurethane(PU)nanocomposites reinforced with nanodiamonds(NDs)and proposes an integrated optimization-prediction framework that combines the Taguchi method with machine learning(ML).The Taguchi design of experiments(DOE),based on an L9 orthogonal array,was applied to investigate the influence of composite type(pure PU,0.1 wt.%ND,0.5 wt.%ND),temperature(145℃-165℃),screw speed(50-70 rpm),and pressure(40-60 bar).The mechanical tests included tensile,hardness,and modulus measurements,performed under varying process parameters.Results showed that the addition of 0.5 wt.%ND substantially improved PU performance,with tensile strength increasing by 117%,Young’s modulus by 10%,and hardness by 21%at optimal conditions of 145℃,70 rpm,and 50 bar.SEM analysis revealed ductile fracture in pure PU and brittle fracture in the optimized PU/ND composite.ANOVA confirmed that composite type was the most influential factor,contributing 70.27%,87.14%,and 74.16%to tensile strength,modulus,and hardness,respectively.Regression modeling demonstrated a deviation of less than 10%between predicted and experimental values,validating the framework.To further strengthen predictive capability,computational modeling and analytical procedureswere employed throughmachine learning frameworks.RandomForest achieved R2/MSE values of 0.95/0.53(tensile),0.95/4.03(modulus),and 0.94/2.44(hardness).XGBoost performed better,with 0.98/0.12,0.98/0.77,and 0.98/0.60,while Gradient Boosting provided the highest accuracy with 0.99/0.03,0.99/0.02,and 0.99/0.01.Residual plots supported these results,showing wide fluctuations for RF and tightly clustered residuals near zero for GB and XGB,highlighting their superior accuracy,precision,and generalization.Overall,the integrated Taguchi-ML framework demonstrates a robust and efficient strategy for optimizing processing parameters and accurately predicting the performance of high-strength PU-ND nanocomposites.展开更多
The rapid expansion of the Internet of Things(IoT)has led to the widespread adoption of sensor networks,with Long-Range Wide-Area Networks(LoRaWANs)emerging as a key technology due to their ability to support long-ran...The rapid expansion of the Internet of Things(IoT)has led to the widespread adoption of sensor networks,with Long-Range Wide-Area Networks(LoRaWANs)emerging as a key technology due to their ability to support long-range communication while minimizing power consumption.However,optimizing network performance and energy efficiency in dynamic,large-scale IoT environments remains a significant challenge.Traditional methods,such as the Adaptive Data Rate(ADR)algorithm,often fail to adapt effectively to rapidly changing network conditions and environmental factors.This study introduces a hybrid approach that leverages Deep Learning(DL)techniques,namely Long Short-Term Memory(LSTM)networks,and Machine Learning(ML)techniques,namely Artificial Neural Networks(ANNs),to optimize key network parameters such as Signal-to-Noise Ratio(SNR)and Received Signal Strength Indicator(RSSI).LSTM-ANN model trained on the“LoRaWAN Path Loss Dataset including Environmental Variables”from Medellín,Colombia,and the model demonstrated exceptional predictive accuracy,achieving an R2 score of 0.999,Mean Squared Error(MSE)of 0.041,Root Mean Squared Error(RMSE)of 0.203,and Mean Absolute Error(MAE)of 0.167,significantly outperforming traditional regression-based approaches.These findings highlight the potential of combining advanced ML and DL techniques to address the limitations of traditional optimization strategies in LoRaWAN.By providing a scalable and adaptive solution for large-scale IoT deployments,this work lays the foundation for real-world implementation,emphasizing the need for continuous learning frameworks to further enhance energy efficiency and network resilience in dynamic environments.展开更多
文摘In recent years,Blockchain Technology has become a paradigm shift,providing Transparent,Secure,and Decentralized platforms for diverse applications,ranging from Cryptocurrency to supply chain management.Nevertheless,the optimization of blockchain networks remains a critical challenge due to persistent issues such as latency,scalability,and energy consumption.This study proposes an innovative approach to Blockchain network optimization,drawing inspiration from principles of biological evolution and natural selection through evolutionary algorithms.Specifically,we explore the application of genetic algorithms,particle swarm optimization,and related evolutionary techniques to enhance the performance of blockchain networks.The proposed methodologies aim to optimize consensus mechanisms,improve transaction throughput,and reduce resource consumption.Through extensive simulations and real-world experiments,our findings demonstrate significant improvements in network efficiency,scalability,and stability.This research offers a thorough analysis of existing optimization techniques,introduces novel strategies,and assesses their efficacy based on empirical outputs.
基金supported by the Beijing Natural Science Foundation under Grant 9242003partially supported by the Natural Science Foundation of Chongqing,China under Grant CSTB2023NSCQ-MSX0391+3 种基金partially supported by the National Natural Science Foundation of China under Grant 62471493partially supported by the Natural Science Foundation of Shandong Province under Grants ZR2023LZH017,ZR2024MF066supported by the Key Laboratory of Public Opinion Governance and Computational Communication under Grant YQKFYB202501The Research Project on the Development of Social Sciences in Hebei Province in 2024(No.202403150).
文摘Next-GenerationNetworks(NGNs)demand high resilience,dynamic adaptability,and efficient resource utilization to enable ubiquitous connectivity.In this context,the Space-Air-Ground Integrated Network(SAGIN)architecture is uniquely positioned to meet these requirements.However,conventional NGN routing algorithms often fail to account for SAGIN’s intrinsic characteristics,such as its heterogeneous structure,dynamic topology,and constrained resources,leading to suboptimal performance under disruptions such as node failures or cyberattacks.To meet these demands for SAGIN,this study proposes a resilience-oriented routing optimization framework featuring dynamic weighting and multi-objective evaluation.Methodologically,we define three core routing performance metrics,quantified through a four-dimensionalmodel,encompassing robustness Rd,resilience Rr,adaptability Ra,and resource utilization efficiency Ru,and integrate them into a comprehensive evaluation metric.In simulated SAGIN environments,the proposed Multi-Indicator Weighted Resilience Evaluation Algorithm(MIW-REA)demonstrates significant improvements in resilience enhancement,recovery acceleration,and resource optimization.It maintains 82.3%service availability even with a 30%node failure rate,reduces Distributed Denial of Service(DDoS)attack recovery time by 43%,decreases bandwidth waste by 23.4%,and lowers energy consumption by 18.9%.By addressing challenges unique to the SAGIN network,this research provides a flexible real-time solution for NGN routing optimization that balances resilience,efficiency,and adaptability,advancing the field.
基金funded by Taif University,Taif,Saudi Arabia,project number(TU-DSPP-2024-17)。
文摘Advanced technologies like Cyber-Physical Systems(CPS)and the Internet of Things(IoT)have supported modernizing and automating the transportation region through the introduction of Intelligent Transportation Systems(ITS).Integrating CPS-ITS and IoT provides real-time Vehicle-to-Infrastructure(V2I)communication,supporting better traffic management,safety,and efficiency.These technological innovations generate complex problems that need to be addressed,uniquely about data routing and Task Scheduling(TS)in ITS.Attempts to solve those problems were primarily based on traditional and experimental methods,and the solutions were not so successful due to the dynamic nature of ITS.This is where the scope of Machine learning(ML)and Swarm Intelligence(SI)has significantly impacted dealing with these challenges;in this line,this research paper presents a novel method for TS and data routing in the CPS-ITS.This paper proposes using a cutting-edge ML algorithm for data transmission from CPS-ITS.This ML has Gated Linear Unit-approximated Reinforcement Learning(GLRL).Greedy Iterative-Particle Swarm Optimization(GI-PSO)has been recommended to develop the Particle Swarm Optimization(PSO)for TS.The primary objective of this study is to enhance the security and effectiveness of ITS systems that utilize CPS-ITS.This study trained and validated the models using a network simulation dataset of 50 nodes from numerous ITS environments.The experiments demonstrate that the proposed GLRL reduces End-toEnd Delay(EED)by 12%,enhances data size use from 83.6%to 88.6%,and achieves higher bandwidth allocation,particularly in high-demand scenarios such as multimedia data streams where adherence improved to 98.15%.Furthermore,the GLRL reduced Network Congestion(NC)by 5.5%,demonstrating its efficiency in managing complex traffic conditions across several environments.The model passed simulation tests in three different environments:urban(UE),suburban(SE),and rural(RE).It met the high bandwidth requirements,made task scheduling more efficient,and increased network throughput(NT).This proved that it was robust and flexible enough for scalable ITS applications.These innovations provide robust,scalable solutions for real-time traffic management,ultimately improving safety,reducing NC,and increasing overall NT.This study can affect ITS by developing it to be more responsive,safe,and effective and by creating a perfect method to set up UE,SE,and RE.
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Supported by the China Academy of Chinese Medical Sciences Innovation Fund:Multicenter Randomized Controlled Study on the Intervention of Yiqi Huoxue Huatan Tongluo decoction in Post-Stent Restenosis of Vertebral Arteries(No.CI2021A01308)In-Hospital Mentorship Program of Xiyuan Hospital,China Academy of Chinese Medical Sciences-Zhou Shaohua(No.0203055)。
文摘OBJECTIVE:To investigate the effects of optimizing Qinggan Jieyu decoction(清肝解郁方)on purinergic receptor P2X ligand-gated ion channel 7(P2X7R)and autophagy in migraine model rats based on molecular biology and histopathology.METHODS:A migraine rat model was established by a single subcutaneous nitroglycerin(NTG)injection into the posterior neck.QGJY was administered via gavage for 7 d prior to NTG induction.Behavioral changes,central sensitization biomarkers,and inflammatory cytokine levels were analyzed to evaluate migraine severity.Western blot,immunofluorescence,quantitative real-time PCR,and transmission electron microscopy were employed to assess P2X7R expression and autophagy activity in trigeminal nucleus caudalis(TNC)tissues.The P2X7R agonist 2'(3')-O-(4-Benzoylbenzoyl)adenosine-5'-triphosphate(Bz ATP)was further utilized to validate QGJY's regulatory effects.RESULTS:QGJY significantly reduced cage-climbing and head-scratching frequencies in NTG-induced migraine rats,downregulated serum and TNC levels of interleukin-1 beta,interleukin-6,and tumor necrosis factor-alpha,and suppressed central sensitization markers(substance P;calcitonin gene-related peptide;and c-fos induced growth factor)in TNC tissues(P<0.05).QGJY markedly decreased microglial cell counts and average immunofluorescence intensity in TNC tissues and promoted elongation of microglial protrusions(P<0.05).Concurrently,QGJY downregulated P2X7R protein and m RNA expression,reduced the light chain 3(LC3)-II/LC3-I ratio,elevated ubiquitin-binding protein p62 levels,and diminished autophagosome numbers in TNC tissues(P<0.05).Furthermore,QGJY reversed Bz ATP-induced P2X7R upregulation(P<0.05).CONCLUSIONS:QGJY alleviates migraine and inhibits central sensitization in rats,potentially by downregulating P2X7R expression,concomitantly suppressing autophagy,attenuating microglial activation,and reducing pro-inflammatory cytokine release.
基金supported by the National Natural Science Foundation of China(71901212)the Science and Technology Innovation Program of Hunan Province(2020RC4046).
文摘The belief rule-based(BRB)system has been popular in complexity system modeling due to its good interpretability.However,the current mainstream optimization methods of the BRB systems only focus on modeling accuracy but ignore the interpretability.The single-objective optimization strategy has been applied in the interpretability-accuracy trade-off by inte-grating accuracy and interpretability into an optimization objec-tive.But the integration has a greater impact on optimization results with strong subjectivity.Thus,a multi-objective optimiza-tion framework in the modeling of BRB systems with inter-pretability-accuracy trade-off is proposed in this paper.Firstly,complexity and accuracy are taken as two independent opti-mization goals,and uniformity as a constraint to give the mathe-matical description.Secondly,a classical multi-objective opti-mization algorithm,nondominated sorting genetic algorithm II(NSGA-II),is utilized as an optimization tool to give a set of BRB systems with different accuracy and complexity.Finally,a pipeline leakage detection case is studied to verify the feasibility and effectiveness of the developed multi-objective optimization.The comparison illustrates that the proposed multi-objective optimization framework can effectively avoid the subjectivity of single-objective optimization,and has capability of joint optimiz-ing the structure and parameters of BRB systems with inter-pretability-accuracy trade-off.
基金supported by Basic Science Research Program to Research Institute for Basic Sciences(RIBS)of Jeju National University through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(RS-2019-NR040080)This research was also carried out with the support of the Jeju RISE Center,funded by the Ministry of Education and Jeju Special Self-Governing Province in 2025,as part of the“Regional Innovation System&Education(RISE):Glocal University 30”initiative.
文摘Given the growing number of vehicle accidents caused by unintended acceleration and braking failure,verifying Sudden Unintended Acceleration(SUA)incidents has become a persistent challenge.A central issue of debate is whether such events stem frommechanical malfunctions or driver pedalmisapplications.However,existing verification procedures implemented by vehiclemanufacturers often involve closed tests after vehicle recalls;thus raising ongoing concerns about reliability and transparency.Consequently,there is a growing need for a user-driven framework that enables independent data acquisition and verification.Although previous studies have addressed SUA detection using deep learning,few have explored howclass granularity optimization affects power efficiency and inference performance in real-time Edge AI systems.To address this problem,this work presents a cloud-assisted artificial intelligence(AI)solution for the reliable verification of SUA occurrences.The proposed system integrates multimodal sensor streams including camera-based foot images,On-Board Diagnostics II(OBD-II)signals,and six-axismeasurements to determine whether the brake pedal was actually engaged at themoment of a suspected SUA.Beyond image acquisition,convolutional neural network(CNN)models perform real-time inference to classify the driver’s pedal operation states with the resulting outputs transmitted and archived in the cloud.A dedicated dataset of brake and accelerator pedal images was collected from 15 vehicles produced by 6 domestic and international manufacturers.Using this dataset,transfer learning techniques were applied to compare and analyze model performance and generalization as the CNN class granularity varied from coarse to fine levels.Furthermore,classification performance was evaluated in terms of latency and power efficiency under different class configurations.The experimental results demonstrated that the proposed solution identified the driver’s pedal behavior accurately and promptly,with the two-class model achieving the highest F1-score and accuracy among all granularity settings.
文摘This study presents a methodology to enhance energy management systems(EMS)in hybrid electric vehicles(HEVs)to reduce fuel consumption and greenhouse gas emissions.A novel surrogate-assisted optimization framework is employed,incorporating key performance metrics such as fuel efficiency and emissions to develop data-driven surrogate models of the EMS.These models are optimized using various algorithms targeting parameters such as engine idle speed,thermostat temperature fraction,regeneration load factor,and battery stateof-charge thresholds.Correlation analysis highlights the significant impact of the lower state-of-charge threshold and thermostat temperature fraction on fuel efficiency and emissions.Among the optimization methods,the combination of a backpropagation neural network(BPNN)and a multi-objective genetic algorithm(MOGA)proves most effective,achieving fuel consumption reductions of 5.26%and 5.01%in charge-sustaining and charge-depletion modes,respectively.Additionally,the BPNN-based MOGA demonstrates notable improvements in emission reduction.These findings suggest that optimizing rule-based EMS parameters without altering underlying management rules can significantly enhance performance under diverse and unanticipated driving conditions.
文摘Multiple tuned mass dampers(MTMDs)reduce dynamic response with multiple specified frequencies of building structures.Many optimization algorithms for placement design exist,though they rarely conform to code-based verification nor produce high quality solutions without high computational effort and high complexity.This study proposes an inverse element exchange method(IEEM)with multi-level programming and compares it to a single tuned mass damper(STMD)and uniform distribution of multiple tuned mass dampers in the frequency and time domains.A ten-story shear building is used for the numerical case study.The results show that the proposed method can offer improvement over the STMD,uniform distribution of multiple tuned mass dampers,and distribution optimized by genetic algorithms(GA)with regard to minimizing the interstory drift ratio(IDR)in both the frequency and time domains and the time consumption for optimization.
基金supported by the National Natural Science Foundation of China(No.U22A20140)the Jinan City-School Integration Development Strategy Project(No.JNSX2023015)+3 种基金Independent Cultivation Program of Innovation Team of Ji’nan City(No.202333042)the University of Jinan Disciplinary Cross-Convergence Construction Project 2023(No.XKJC-202309)the Youth Innovation Group Plan of Shandong Province(No.2022KJ095)Project supported by State Key Laboratory of Powder Metallurgy,Central South University,Changsha,China。
文摘With the development of science and technology,there is an increasing demand for energy storage batteries.Aqueous zinc-ion batteries(AZIBs)are expected to become the next generation of commercialized energy storage devices due to their advantages.The aqueous zinc ion battery is generally composed of zinc metal as the anode,active material as the cathode,and aqueous electrolyte.However,there are still many problems with the cathode/anode material and voltage window of the battery,which limit its use.This review introduces the recent research progress of zinc-ion batteries,including the advantages and disadvantages,energy storage mechanisms,and common cathode/anode materials,electrolytes,etc.It also gives a summary of the current research status of each material and provides solutions to the problems they face.Finally,it looks at the future direction and methods to optimize the performance of zinc-ion full batteries.
基金funded by the Foundation for Innovative Research Groups of the National Natural Science Foundation of China(Grant No.72221002)the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDA28060200)National Natural Science Foundation of Youth Project(Grant No.72303087).
文摘In the new phase of sustainable development,agriculture is seeking sustainable management of the water-land-energy-economy-environment-food nexus.At present,there are few studies on optimizing crop planting structure and analyzing its spatial layout with consideration of natural and socio-economic factors.Herein,we proposed a framework for addressing this issue.In this framework,the NSGA-II algorithm was used to construct the multi-objective optimization model of crop planting structures with consideration of water and energy consumption,greenhouse gas(GHG)emissions,economic benefits,as well as food,land,and water security constraints,while the model for planting spatial layout optimization was established with consideration of crop suitability using the MaxEnt model and the improved Hungarian algorithm.This framework was further applied in the Black Soil Region of Northeast China(BSRNC)for analyzing optimized crop planting structures and spatial layouts of three main crops(rice,maize,and soybean)under various scenarios.This study showed that the sown area of rice in the BSRNC decreased by up to 40.73%and 35.30%in the environmental priority scenario and economic-environmental balance scenario,respectively,whereas that of soybean increased by up to 112.44%and 63.31%,respectively.In the economic priority scenario,the sown area of rice increased by up to 93.98%.Expanding the sown area of soybean was effective in reducing GHG emissions.On the contrary,rice production led to greater environmental costs though it provided higher economic returns.Among the three crops,maize exhibited an advantage in balancing environmental and economic benefits.Hegang-Jixi area in the northeast of the BSRNC was identified as the key area with the most intense crop planting transfer among different scenarios.Overall,this framework provides a new methodology for optimizing crop planting structures and spatial layouts with con-sideration of the nexus of various factors.Moreover,the case study demonstrates the applicability and expansion potential of the framework in the fields of sustainable agricultural development and food security assurance.
基金supported by the Key Research and Development Program Project of Hunan Province, China (Grant No. 2023NK2003)the National Key Research and Development Program of China (Grant No. 2022YFD2301001-03)the National Key Research and Development Program of China (Grant No. 2022YFD2301003)
文摘The ratooning system enhances agricultural efficiency by reducing secondary sowing and resource input while maintaining rice yield parity with double cropping.However,the prolonged growth duration of the rice ratooning system extends the exposure window to Magnaporthe oryzae infection,thereby elevating the probability of disease incidence.
文摘Burundi faces major agricultural constraints, including land fragmentation, soil erosion, limited access to inputs, inadequate infrastructure and demographic pressures that exacerbate food insecurity. In order to address the multiple challenges faced by farmers in rural areas, a study on improving agricultural productivity and food security in Burundi through optimized land use and diversified farming practices in agroforestry systems has been carried out. The study area is the communes of Giheta and Rutegama, all located in Burundi’s humid plateau livelihood zone, and involved 164 households grouped in coffee growing cooperatives supervised by the cooperative consortium COCOCA. The study uses a mathematical programming model to determine optimal crop selection based on factors such as production costs, yields and market demand. The findings of the study revealed significant insights into the demographic and socio-economic characteristics of the sampled population. Notably, 98.8% of respondents were engaged in agriculture, confirming the predominantly agricultural nature of Burundi. The results indicated that maize is the most important crop, occupying 33.9% of the average total cultivated area, followed by cassava at 26.5% and bananas at 19.4%. Together, these three crops accounted for a substantial portion of the total cultivated area, highlighting their significance in local agriculture. Beans and potatoes also play a role, occupying 14.4% and smaller areas, respectively. In terms of profitability, the study provides a detailed analysis of profit margins by crop. Bananas emerges as the most profitable crop, with a profit margin of 97.3%, followed closely by cassava at 96.1% and rice at 90.5%. These crops not only offered substantial yields relative to their production costs but also benefited from strong market demand. Other crops, such as beans (71.3%), coffee (70.3%), and vegetables (54.5%), also demonstrated considerable profitability, although they occupied smaller cultivated areas. Conversely, crops like pigeon peas (4.1%), potatoes (7.6%), and sweet potatoes (7.6%) exhibited the lowest profit margins, which may discourage farmers from investing in them unless other incentives, such as ecological benefits or local consumption needs, are present. Regarding the results, we therefore recommend to promote policies supporting agroforestry, improve market access and develop infrastructure to exploit these benefits.
基金support received by the National Natural Science Foundation of China(Grant Nos.52372398&62003272).
文摘Dynamic soaring,inspired by the wind-riding flight of birds such as albatrosses,is a biomimetic technique which leverages wind fields to enhance the endurance of unmanned aerial vehicles(UAVs).Achieving a precise soaring trajectory is crucial for maximizing energy efficiency during flight.Existing nonlinear programming methods are heavily dependent on the choice of initial values which is hard to determine.Therefore,this paper introduces a deep reinforcement learning method based on a differentially flat model for dynamic soaring trajectory planning and optimization.Initially,the gliding trajectory is parameterized using Fourier basis functions,achieving a flexible trajectory representation with a minimal number of hyperparameters.Subsequently,the trajectory optimization problem is formulated as a dynamic interactive process of Markov decision-making.The hyperparameters of the trajectory are optimized using the Proximal Policy Optimization(PPO2)algorithm from deep reinforcement learning(DRL),reducing the strong reliance on initial value settings in the optimization process.Finally,a comparison between the proposed method and the nonlinear programming method reveals that the trajectory generated by the proposed approach is smoother while meeting the same performance requirements.Specifically,the proposed method achieves a 34%reduction in maximum thrust,a 39.4%decrease in maximum thrust difference,and a 33%reduction in maximum airspeed difference.
基金funded by the Deanship of Scientific Research at Jouf University under Grant number DSR-2022-RG-0101。
文摘As legal cases grow in complexity and volume worldwide,integrating machine learning and artificial intelligence into judicial systems has become a pivotal research focus.This study introduces a comprehensive framework for verdict recommendation that synergizes rule-based methods with deep learning techniques specifically tailored to the legal domain.The proposed framework comprises three core modules:legal feature extraction,semantic similarity assessment,and verdict recommendation.For legal feature extraction,a rule-based approach leverages Black’s Law Dictionary and WordNet Synsets to construct feature vectors from judicial texts.Semantic similarity between cases is evaluated using a hybrid method that combines rule-based logic with an LSTM model,analyzing the feature vectors of query cases against a legal knowledge base.Verdicts are then recommended through a rule-based retrieval system,enhanced by predefined legal statutes and regulations.By merging rule-based methodologies with deep learning,this framework addresses the interpretability challenges often associated with contemporary AImodels,thereby enhancing both transparency and generalizability across diverse legal contexts.The system was rigorously tested using a legal corpus of 43,000 case laws across six categories:Criminal,Revenue,Service,Corporate,Constitutional,and Civil law,ensuring its adaptability across a wide range of judicial scenarios.Performance evaluation showed that the feature extraction module achieved an average accuracy of 91.6%with an F-Score of 95%.The semantic similarity module,tested using Manhattan,Euclidean,and Cosine distance metrics,achieved 88%accuracy and a 93%F-Score for short queries(Manhattan),89%accuracy and a 93.7%F-Score for medium-length queries(Euclidean),and 87%accuracy with a 92.5%F-Score for longer queries(Cosine).The verdict recommendation module outperformed existing methods,achieving 90%accuracy and a 93.75%F-Score.This study highlights the potential of hybrid AI frameworks to improve judicial decision-making and streamline legal processes,offering a robust,interpretable,and adaptable solution for the evolving demands of modern legal systems.
文摘In response to the increasing global energy demand and environmental pollution,microgrids have emerged as an innovative solution by integrating distributed energy resources(DERs),energy storage systems,and loads to improve energy efficiency and reliability.This study proposes a novel hybrid optimization algorithm,DE-HHO,combining differential evolution(DE)and Harris Hawks optimization(HHO)to address microgrid scheduling issues.The proposed method adopts a multi-objective optimization framework that simultaneously minimizes operational costs and environmental impacts.The DE-HHO algorithm demonstrates significant advantages in convergence speed and global search capability through the analysis of wind,solar,micro-gas turbine,and battery models.Comprehensive simulation tests show that DE-HHO converges rapidly within 10 iterations and achieves a 4.5%reduction in total cost compared to PSO and a 5.4%reduction compared to HHO.Specifically,DE-HHO attains an optimal total cost of$20,221.37,outperforming PSO($21,184.45)and HHO($21,372.24).The maximum cost obtained by DE-HHO is$23,420.55,with a mean of$21,615.77,indicating stability and cost control capabilities.These results highlight the effectiveness of DE-HHO in reducing operational costs and enhancing system stability for efficient and sustainable microgrid operation.
基金funded by the National Natural Science Foundation of China(42050104).
文摘This study introduces a comprehensive and automated framework that leverages data-driven method-ologies to address various challenges in shale gas development and production.Specifically,it harnesses the power of Automated Machine Learning(AutoML)to construct an ensemble model to predict the estimated ultimate recovery(EUR)of shale gas wells.To demystify the“black-box”nature of the ensemble model,KernelSHAP,a kernel-based approach to compute Shapley values,is utilized for elucidating the influential factors that affect shale gas production at both global and local scales.Furthermore,a bi-objective optimization algorithm named NSGA-Ⅱ is seamlessly incorporated to opti-mize hydraulic fracturing designs for production boost and cost control.This innovative framework addresses critical limitations often encountered in applying machine learning(ML)to shale gas pro-duction:the challenge of achieving sufficient model accuracy with limited samples,the multidisciplinary expertise required for developing robust ML models,and the need for interpretability in“black-box”models.Validation with field data from the Fuling shale gas field in the Sichuan Basin substantiates the framework's efficacy in enhancing the precision and applicability of data-driven techniques.The test accuracy of the ensemble ML model reached 83%compared to a maximum of 72%of single ML models.The contribution of each geological and engineering factor to the overall production was quantitatively evaluated.Fracturing design optimization raised EUR by 7%-34%under different production and cost tradeoff scenarios.The results empower domain experts to conduct more precise and objective data-driven analyses and optimizations for shale gas production with minimal expertise in data science.
文摘Water power is one of the key renewable energy resources,whose efficiency is often hampered due to inefficient water flow management,turbine performance,and environmental variations.Most existing optimization techniques lack the real-time adaptability to sufficiently allocate resources in terms of location and time.Hence,a novel Scalable Tas-manian Devil Optimization(STDO)algorithm is introduced to optimize hydropower generation for maximum power efficiency.Using the STDO to model important system characteristics including water flow,turbine changes,and energy conversion efficiency is part of the process.In the final analysis,optimizing these settings in would help reduce inefficiencies and maximize power generation output.Following that,simulations based on actual hydroelectric data are used to analyze the algorithm's effectiveness.The simulation results provide evidence that the STDO algorithm can enhance hydropower plant efficiency tremendously translating to considerable energy output augmentation compared to conven-tional optimization methods.STDO achieves the reliability(92.5),resiliency(74.3),and reduced vulnerability(9.3).To guarantee increased efficiency towards ecologically friendly power generation,the STDO algorithm may thus offer efficient resource optimization for hydropower.A clear route is made available for expanding the efficiency of current hydropower facilities while tackling the long-term objectives of reducing the environmental impact and increasing the energy output of energy produced from renewable sources.
基金supported and funded by theDeanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2503).
文摘This study investigates the mechanical behavior of polyurethane(PU)nanocomposites reinforced with nanodiamonds(NDs)and proposes an integrated optimization-prediction framework that combines the Taguchi method with machine learning(ML).The Taguchi design of experiments(DOE),based on an L9 orthogonal array,was applied to investigate the influence of composite type(pure PU,0.1 wt.%ND,0.5 wt.%ND),temperature(145℃-165℃),screw speed(50-70 rpm),and pressure(40-60 bar).The mechanical tests included tensile,hardness,and modulus measurements,performed under varying process parameters.Results showed that the addition of 0.5 wt.%ND substantially improved PU performance,with tensile strength increasing by 117%,Young’s modulus by 10%,and hardness by 21%at optimal conditions of 145℃,70 rpm,and 50 bar.SEM analysis revealed ductile fracture in pure PU and brittle fracture in the optimized PU/ND composite.ANOVA confirmed that composite type was the most influential factor,contributing 70.27%,87.14%,and 74.16%to tensile strength,modulus,and hardness,respectively.Regression modeling demonstrated a deviation of less than 10%between predicted and experimental values,validating the framework.To further strengthen predictive capability,computational modeling and analytical procedureswere employed throughmachine learning frameworks.RandomForest achieved R2/MSE values of 0.95/0.53(tensile),0.95/4.03(modulus),and 0.94/2.44(hardness).XGBoost performed better,with 0.98/0.12,0.98/0.77,and 0.98/0.60,while Gradient Boosting provided the highest accuracy with 0.99/0.03,0.99/0.02,and 0.99/0.01.Residual plots supported these results,showing wide fluctuations for RF and tightly clustered residuals near zero for GB and XGB,highlighting their superior accuracy,precision,and generalization.Overall,the integrated Taguchi-ML framework demonstrates a robust and efficient strategy for optimizing processing parameters and accurately predicting the performance of high-strength PU-ND nanocomposites.
基金funded by King Saud University Researchers Supporting Project Number(RSPD2025R1007),King Saud University,Riyadh,Saudi Arabia.
文摘The rapid expansion of the Internet of Things(IoT)has led to the widespread adoption of sensor networks,with Long-Range Wide-Area Networks(LoRaWANs)emerging as a key technology due to their ability to support long-range communication while minimizing power consumption.However,optimizing network performance and energy efficiency in dynamic,large-scale IoT environments remains a significant challenge.Traditional methods,such as the Adaptive Data Rate(ADR)algorithm,often fail to adapt effectively to rapidly changing network conditions and environmental factors.This study introduces a hybrid approach that leverages Deep Learning(DL)techniques,namely Long Short-Term Memory(LSTM)networks,and Machine Learning(ML)techniques,namely Artificial Neural Networks(ANNs),to optimize key network parameters such as Signal-to-Noise Ratio(SNR)and Received Signal Strength Indicator(RSSI).LSTM-ANN model trained on the“LoRaWAN Path Loss Dataset including Environmental Variables”from Medellín,Colombia,and the model demonstrated exceptional predictive accuracy,achieving an R2 score of 0.999,Mean Squared Error(MSE)of 0.041,Root Mean Squared Error(RMSE)of 0.203,and Mean Absolute Error(MAE)of 0.167,significantly outperforming traditional regression-based approaches.These findings highlight the potential of combining advanced ML and DL techniques to address the limitations of traditional optimization strategies in LoRaWAN.By providing a scalable and adaptive solution for large-scale IoT deployments,this work lays the foundation for real-world implementation,emphasizing the need for continuous learning frameworks to further enhance energy efficiency and network resilience in dynamic environments.
