In the quest to enhance energy efficiency and reduce environmental impact in the transportation sector,the recovery of waste heat from diesel engines has become a critical area of focus.This study provided an exhausti...In the quest to enhance energy efficiency and reduce environmental impact in the transportation sector,the recovery of waste heat from diesel engines has become a critical area of focus.This study provided an exhaustive thermodynamic analysis optimizing Organic Rankine Cycle(ORC)systems forwaste heat recovery fromdiesel engines.Thestudy assessed the performance of five candidateworking fluids—R11,R123,R113,R245fa,and R141b—under a range of operating conditions,specifically varying overheat temperatures and evaporation pressures.The results indicated that the choice of working fluid substantially influences the system’s exergetic efficiency,net output power,and thermal efficiency.R245fa showed an outstanding net output power of 30.39 kW at high overheat conditions,outperforming R11,which is significant for high-temperature waste heat recovery.At lower temperatures,R11 and R113 demonstrated higher exergetic efficiencies,with R11 reaching a peak exergetic efficiency of 7.4%at an evaporation pressure of 10 bar and an overheat of 10℃.The study also revealed that controlling the overheat and optimizing the evaporation pressure are crucial for enhancing the net output power of the ORC system.Specifically,at an evaporation pressure of 30 bar and an overheat of 0℃,R113 exhibited the lowest exergetic destruction of 544.5 kJ/kg,making it a suitable choice for minimizing irreversible losses.These findings are instrumental for understanding the performance of ORC systems in waste heat recovery applications and offer valuable insights for the design and operation of more efficient and environmentally friendly diesel engine systems.展开更多
The rapid development of brain-like neural networks and secure data transmission technologies has placed greater demands on highly complex neural network systems and highly secure encryption methods.To this end,the pa...The rapid development of brain-like neural networks and secure data transmission technologies has placed greater demands on highly complex neural network systems and highly secure encryption methods.To this end,the paper proposes a novel high-dimensional memristor synapse-coupled hyperchaotic neural network by using the designed memristor as the synapse to connect an inertial neuron(IN)and a Hopfield neural network(HNN).By using numerical tools including bifurcation plots,phase plots,and basins of attraction,it is found that the dynamics of this system are closely related to the memristor coupling strength,self-connection synaptic weights,and inter-connection synaptic weights,and it can exhibit excellent hyperchaotic behaviors and coexisting multi-stable patterns.Through PSIM circuit simulations,the complex dynamics of the coupled IN-HNN system are verified.Furthermore,a DNA-encoded encryption algorithm is given,which utilizes generated hyperchaotic sequences to achieve encoding,operation,and decoding of DNA.The results show that this algorithm possesses strong robustness against statistical attacks,differential attacks,and noise interference,and can effectively resist known/selected plaintext attacks.This work will provide new ideas for the modeling of large-scale brainlike neural networks and high-security image encryption.展开更多
Sudden wildfires cause significant global ecological damage.While satellite imagery has advanced early fire detection and mitigation,image-based systems face limitations including high false alarm rates,visual obstruc...Sudden wildfires cause significant global ecological damage.While satellite imagery has advanced early fire detection and mitigation,image-based systems face limitations including high false alarm rates,visual obstructions,and substantial computational demands,especially in complex forest terrains.To address these challenges,this study proposes a novel forest fire detection model utilizing audio classification and machine learning.We developed an audio-based pipeline using real-world environmental sound recordings.Sounds were converted into Mel-spectrograms and classified via a Convolutional Neural Network(CNN),enabling the capture of distinctive fire acoustic signatures(e.g.,crackling,roaring)that are minimally impacted by visual or weather conditions.Internet of Things(IoT)sound sensors were crucial for generating complex environmental parameters to optimize feature extraction.The CNN model achieved high performance in stratified 5-fold cross-validation(92.4%±1.6 accuracy,91.2%±1.8 F1-score)and on test data(94.93%accuracy,93.04%F1-score),with 98.44%precision and 88.32%recall,demonstrating reliability across environmental conditions.These results indicate that the audio-based approach not only improves detection reliability but also markedly reduces computational overhead compared to traditional image-based methods.The findings suggest that acoustic sensing integrated with machine learning offers a powerful,low-cost,and efficient solution for real-time forest fire monitoring in complex,dynamic environments.展开更多
Unconfined Compressive Strength(UCS)is a key parameter for the assessment of the stability and performance of stabilized soils,yet traditional laboratory testing is both time and resource intensive.In this study,an in...Unconfined Compressive Strength(UCS)is a key parameter for the assessment of the stability and performance of stabilized soils,yet traditional laboratory testing is both time and resource intensive.In this study,an interpretable machine learning approach to UCS prediction is presented,pairing five models(Random Forest(RF),Gradient Boosting(GB),Extreme Gradient Boosting(XGB),CatBoost,and K-Nearest Neighbors(KNN))with SHapley Additive exPlanations(SHAP)for enhanced interpretability and to guide feature removal.A complete dataset of 12 geotechnical and chemical parameters,i.e.,Atterberg limits,compaction properties,stabilizer chemistry,dosage,curing time,was used to train and test the models.R2,RMSE,MSE,and MAE were used to assess performance.Initial results with all 12 features indicated that boosting-based models(GB,XGB,CatBoost)exhibited the highest predictive accuracy(R^(2)=0.93)with satisfactory generalization on test data,followed by RF and KNN.SHAP analysis consistently picked CaO content,curing time,stabilizer dosage,and compaction parameters as the most important features,aligning with established soil stabilization mechanisms.Models were then re-trained on the top 8 and top 5 SHAP-ranked features.Interestingly,GB,XGB,and CatBoost maintained comparable accuracy with reduced input sets,while RF was moderately sensitive and KNN was somewhat better owing to reduced dimensionality.The findings confirm that feature reduction through SHAP enables cost-effective UCS prediction through the reduction of laboratory test requirements without significant accuracy loss.The suggested hybrid approach offers an explainable,interpretable,and cost-effective tool for geotechnical engineering practice.展开更多
This paper proposes to study the impacts of electrical line losses due to the connection of distributed generators (DG) to 22kV distribution system of Provincial Electricity Authority (PEA). Data of geographic informa...This paper proposes to study the impacts of electrical line losses due to the connection of distributed generators (DG) to 22kV distribution system of Provincial Electricity Authority (PEA). Data of geographic information systems (GIS) including the distance of distribution line and location of load being key parameter of PEA is simulated using digital simulation and electrical network calculation program (DIgSILENT) to analyze power loss of the distribution system. In addition, the capacity and location of DG installed into the distribution system is considered. The results are shown that, when DG is installed close to the substation, the electrical line losses are reduced. However, if DG capacity becomes larger and the distance between DG and load is longer, the electrical line losses tend to increase. The results of this paper can be used to create the suitability and fairness of the fee for both DG and utility.展开更多
Over the past years,many efforts have been accomplished to achieve fast and accurate meta-heuristic algorithms to optimize a variety of real-world problems.This study presents a new optimization method based on an unu...Over the past years,many efforts have been accomplished to achieve fast and accurate meta-heuristic algorithms to optimize a variety of real-world problems.This study presents a new optimization method based on an unusual geological phenomenon in nature,named Geyser inspired Algorithm(GEA).The mathematical modeling of this geological phenomenon is carried out to have a better understanding of the optimization process.The efficiency and accuracy of GEA are verified using statistical examination and convergence rate comparison on numerous CEC 2005,CEC 2014,CEC 2017,and real-parameter benchmark functions.Moreover,GEA has been applied to several real-parameter engineering optimization problems to evaluate its effectiveness.In addition,to demonstrate the applicability and robustness of GEA,a comprehensive investigation is performed for a fair comparison with other standard optimization methods.The results demonstrate that GEA is noticeably prosperous in reaching the optimal solutions with a high convergence rate in comparison with other well-known nature-inspired algorithms,including ABC,BBO,PSO,and RCGA.Note that the source code of the GEA is publicly available at https://www.optim-app.com/projects/gea.展开更多
The Firefly Algorithm(FA)is a highly efficient population-based optimization technique developed by mimicking the flashing behavior of fireflies when mating.This article proposes a method based on Differential Evoluti...The Firefly Algorithm(FA)is a highly efficient population-based optimization technique developed by mimicking the flashing behavior of fireflies when mating.This article proposes a method based on Differential Evolution(DE)/current-to-best/1 for enhancing the FA's movement process.The proposed modification increases the global search ability and the convergence rates while maintaining a balance between exploration and exploitation by deploying the global best solution.However,employing the best solution can lead to premature algorithm convergence,but this study handles this issue using a loop adjacent to the algorithm's main loop.Additionally,the suggested algorithm’s sensitivity to the alpha parameter is reduced compared to the original FA.The GbFA surpasses both the original and five-version of enhanced FAs in finding the optimal solution to 30 CEC2014 real parameter benchmark problems with all selected alpha values.Additionally,the CEC 2017 benchmark functions and the eight engineering optimization challenges are also utilized to evaluate GbFA’s efficacy and robustness on real-world problems against several enhanced algorithms.In all cases,GbFA provides the optimal result compared to other methods.Note that the source code of the GbFA algorithm is publicly available at https://www.optim-app.com/projects/gbfa.展开更多
The research aimed to propose a non-destructive technology to control subterranean termites Coptotermes curvignathus Holmgren infestation based on electromagnetic waves. A portable apparatus for this technology has be...The research aimed to propose a non-destructive technology to control subterranean termites Coptotermes curvignathus Holmgren infestation based on electromagnetic waves. A portable apparatus for this technology has been built and its experiment is presented in this paper. Some electrical parameters were measured and analyzed along with their effects to the termites. The experiment using frequency range between 30 Hz - 600 kHz has been done. The average error of the apparatus by comparing the result with the direct measurement using oscilloscope was also measured. The highest error value appeared at 600 kHz with frequency error 6.05 kHz. The highest error of voltage (i.e. 0.186 Volt) appeared at 100 kHz. For safetiness, the highest magnetic field at 300 kHz was 0.1815 μT and at 500 kHz was 0.00725 μT which were safe for human. The average value of termites mortality was higher on irradiation time 120 minutes than 60 minutes respectively in all test frequency: 300 kHz, 400 kHz, 500 kHz and 600 kHz. This paper presents an important information of the electromagnatic-based technology for environmental friendly termites control in spite of using the insecticides.展开更多
This paper presented the simulation results of the three phase electrical systems supplied by four wires with power quality problems, to which the parallel 3-leg APF （active power filters） are connected. The purpose...This paper presented the simulation results of the three phase electrical systems supplied by four wires with power quality problems, to which the parallel 3-leg APF （active power filters） are connected. The purpose of this study is to analyze the results obtained in these conditions in order to observe the limits of the 3-leg active power filters and to form a foundation for the future studies of the 4-leg active power filters. For a complete analysis, the APF will be controlled by four control methods： synchronous reference system control, indirect control, instantaneous p-q theory control, and positive sequence control. The analysis will watch the power quality indicators： THD （total harmonic distortion factor）, PF （power factor）, Iunb （unbalance factor）.展开更多
Objective:To assess the effects of turmeric extract and its compounds on oxidative stress,inflammation,and apoptosis in acetaminophen-induced liver injury.Methods:HepG2 cells were administered with acetaminophen(40 mM...Objective:To assess the effects of turmeric extract and its compounds on oxidative stress,inflammation,and apoptosis in acetaminophen-induced liver injury.Methods:HepG2 cells were administered with acetaminophen(40 mM)to induce hepatotoxicity,followed by treatment with turmeric extract and its isolated compounds including curcumin,demethoxycurcumin,bis-demethoxycurcumin and ar-turmerone at 5,25,and 125μg/mL.IL-1β,IL-6,and IL-10 levels were quantified with ELISA kits.Further,qRT-PCR was used to analyze the mRNA expression of JNK,Casp-9,and Casp-3.Meanwhile,the levels of nitric oxide and lactate dehydrogenase were analyzed using colorimetric assay.Results:Acetaminophen administration caused an increase in the levels of lactate dehydrogenase,nitric oxide,IL-1β,IL-6,and the mRNA expression of JNK,Casp-9,and Casp-3 in HepG2 cells while reducing IL-10 levels.Treatment with turmeric extract,curcumin,demethoxycurcumin,bis-demethoxycurcumin,and ar-turmerone lowered IL-1β,IL-6,nitric oxide,and lactate dehydrogenase levels,downregulated the mRNA expression of JNK,Casp-9,and Casp-3,and increased IL-10 levels.Conclusions:Turmeric extract and its compounds have significant hepatoprotective activity and could be further explored for the treatment of liver damage.展开更多
Epilepsy is a long-term neurological condition marked by recurrent seizures,which result from abnormal electrical activity in the brain that disrupts its normal functioning.Traditional methods for detecting epilepsy t...Epilepsy is a long-term neurological condition marked by recurrent seizures,which result from abnormal electrical activity in the brain that disrupts its normal functioning.Traditional methods for detecting epilepsy through machine learning typically utilize discrete-time models,which inadequately represent the continuous dynamics of electroencephalogram(EEG)signals.To overcome this limitation,we introduce an innovative approach that employs Neural Ordinary Differential Equations(NODEs)to model EEG signals as continuous-time systems.This allows for effective management of irregular sampling and intricate temporal patterns.In contrast to conventional techniques,such as Convolutional Neural Networks(CNNs)and Recurrent Neural Networks(RNNs),which necessitate fixedlength inputs and often struggle with long-term dependencies,our framework incorporates:(1)a NODE block to capture continuous-time EEG dynamics,(2)a feature extraction module tailored for seizure-specific patterns,and(3)an attention-based fusion mechanism to enhance interpretability in classification.When evaluated on three publicly accessible EEG datasets,including those from Boston Children’s Hospital and the Massachusetts Institute of Technology(CHB-MIT)and the Temple University Hospital(TUH)EEG Corpus,the model demonstrated an average accuracy of 98.2%,a sensitivity of 97.8%,a specificity of 98.3%,and an F1-score of 97.9%.Additionally,the inference latency was reduced by approximately 30%compared to standard CNN and Long Short-Term Memory(LSTM)architectures,making it well-suited for real-time applications.The method’s resilience to noise and its adaptability to irregular sampling enhance its potential for clinical use in real-time settings.展开更多
We demonstrated a new type of MAX phase material,chromium titanium aluminum carbide(Cr_(2)TiAlC_(2)) polymer film,to generate a passively Q-switched erbium-doped fiber laser(EDFL).The film thickness was measured to be...We demonstrated a new type of MAX phase material,chromium titanium aluminum carbide(Cr_(2)TiAlC_(2)) polymer film,to generate a passively Q-switched erbium-doped fiber laser(EDFL).The film thickness was measured to be around 45 μm,which was fabricated using the embedding method with polyvinyl alcohol(PVA) polymer as hoster.The saturable absorber(SA) film demonstrates a dual-wavelength passively Q-switched EDFL which operates at 1 531 nm and 1 560.19 nm,respectively.The Q-switching pulse duration could be varied from 2.46 μs to 770 ns,while the repetition rate varied from 92.76 kHz to 106.6 kHz with an increasing input pumping range from 154 mW to 300 mW.The maximum output power and pulse energy of 15.05 mW and 141.18 nJ were obtained at the maximum input power of 300 mW,respectively.展开更多
Permanent Magnet Synchronous Motors(PMSMs)are widely employed in high-performance drive applications due to their superior efficiency and dynamic capabilities.However,their control remains challenging owing to nonline...Permanent Magnet Synchronous Motors(PMSMs)are widely employed in high-performance drive applications due to their superior efficiency and dynamic capabilities.However,their control remains challenging owing to nonlinear dynamics,parameter variations,and unmeasurable external disturbances,particularly load torquefluctuations.This study proposes an enhanced Interconnection and Damp-ing Assignment Passivity-Based Control(IDA-PBC)scheme,formulated within the port-controlled Hamiltonian(PCH)framework,to address these limitations.A nonlinear disturbance observer is embedded to estimate and compensate,in real time,for lumped mis-matched disturbances arising from parameter uncertainties and external loads.Additionally,aflatness-based control strategy is employed to generate the desired current references within the nonlinear drive system,ensuring accurate tracking of time-varying speed commands.This integrated approach preserves the system’s energy-based structure,enabling systematic stability analysis while enhancing robustness.The proposed control architecture also maintains low complexity with a limited number of tunable parameters,facilitating practical implementation.Simulation and experimental results under various operating conditions demonstrate the effectiveness and robustness of the proposed method.Comparative analysis with conventional proportional-integral(PI)control and standard IDA-PBC strategies confirms its capability to handle disturbances and maintain dynamic performance.展开更多
This study presents a detailed comparative analysis of three electron transport layer(ETL)materials for perovskite solar cells(PSCs),namely titanium dioxide(TiO_(2)),barium titanate(BaTiO_(3)or BTO),and strontium-dope...This study presents a detailed comparative analysis of three electron transport layer(ETL)materials for perovskite solar cells(PSCs),namely titanium dioxide(TiO_(2)),barium titanate(BaTiO_(3)or BTO),and strontium-doped barium titan-ate(Ba_(1−x)Sr_(x)TiO_(3)or BST),and their impact on the quantum efficiency(QE)and power conversion efficiency(PCE)of CH_(3)NH_(3)PbI_(3)(MAPbI_(3))PSCs.The optimized structure demonstrates that devices utilizing BST as an ETL achieved the highest PCE of 29.85%,exhibiting superior thermal stability with the lowest temperature coefficient of−0.43%/K.This temperature-induced degradation is comparable to that of commercially available silicon cells.Furthermore,BST-based ETLs show 29.50%and 26.48%higher PCE than those of TiO_(2)-based and BTO-based ETLs.The enhanced internal QE and favorable current density–voltage(J–V)characteristics of BST compared with those of TiO_(2)and BTO are attributed to its improved charge carrier separation,reduced recombination rates,and robust electrical characteristics under varied environmental conditions.Furthermore,the electric field and generation rate of the BST-based ETLs show a more favorable distribution than those of the TiO_(2)-based and BTO-based ETLs.These findings provide significant insights into the role of different ETLs in enhancing QE,indicating that BST is a superior ETL that enhances both the efficiency and stability of PSCs.This study contributes to the understanding of how perovskite-structured ETLs can be used to design and optimize highly efficient and stable photovoltaic devices.展开更多
Ensuring the reliability of power systems in microgrids is critical,particularly under contingency conditions that can disrupt power flow and system stability.This study investigates the application of Security-Constr...Ensuring the reliability of power systems in microgrids is critical,particularly under contingency conditions that can disrupt power flow and system stability.This study investigates the application of Security-Constrained Optimal Power Flow(SCOPF)using the Line Outage Distribution Factor(LODF)to enhance resilience in a renewable energy-integrated microgrid.The research examines a 30-bus system with 14 generators and an 8669 MW load demand,optimizing both single-objective and multi-objective scenarios.The single-objective opti-mization achieves a total generation cost of$47,738,while the multi-objective approach reduces costs to$47,614 and minimizes battery power output to 165.02 kW.Under contingency conditions,failures in transmission lines 1,22,and 35 lead to complete power loss in those lines,requiring a redistribution strategy.Implementing SCOPF mitigates these disruptions by adjusting power flows,ensuring no line exceeds its capacity.Specifically,in contingency 1,power in channel 4 is reduced from 59 to 32 kW,while overall load shedding is minimized to 0.278 MW.These results demonstrate the effectiveness of SCOPF in maintaining stability and reducing economic losses.Unlike prior studies,this work integrates LODF into SCOPF for large-scale microgrid applications,offering a computationally efficient contingency management framework that enhances grid resilience and supports renewable energy adoption.展开更多
The forthcoming 6G wireless networks have great potential for establishing AI-based networks that can enhance end-to-end connection and manage massive data of real-time networks.Artificial Intelligence(AI)advancements...The forthcoming 6G wireless networks have great potential for establishing AI-based networks that can enhance end-to-end connection and manage massive data of real-time networks.Artificial Intelligence(AI)advancements have contributed to the development of several innovative technologies by providing sophisticated specific AI mathematical models such as machine learning models,deep learning models,and hybrid models.Furthermore,intelligent resource management allows for self-configuration and autonomous decision-making capabilities of AI methods,which in turn improves the performance of 6G networks.Hence,6G networks rely substantially on AI methods to manage resources.This paper comprehensively surveys the recent work of AI methods-based resource management for 6G networks.Firstly,the AI methods are categorized into Deep Learning(DL),Federated Learning(FL),Reinforcement Learning(RL),and Evolutionary Learning(EL).Then,we analyze the AI approaches according to optimization issues such as user association,channel allocation,power allocation,and mode selection.Thereafter,we provide appropriate solutions to the most significant problems with the existing approaches of AI-based resource management.Finally,various open issues and potential trends related to AI-based resource management applications are presented.In summary,this survey enables researchers to understand these advancements thoroughly and quickly identify remaining challenges that need further investigation.展开更多
Following publication of the original article[1],the authors found that they pasted the same data when drawing XRD for sample NCO-1 and NCO-2 in Fig.2a,however,the XRD of all four samples in the manuscript was tested,...Following publication of the original article[1],the authors found that they pasted the same data when drawing XRD for sample NCO-1 and NCO-2 in Fig.2a,however,the XRD of all four samples in the manuscript was tested,and XRD raw data were kept and can be offered.The correct Fig.2 has been provided in this Correction.展开更多
We propose a theoretical framework,based on the two-component Gross-Pitaevskii equation(GPE),for the investigation of vortex solitons(VSs)in hybrid atomic-molecular Bose-Einstein condensates under the action of the st...We propose a theoretical framework,based on the two-component Gross-Pitaevskii equation(GPE),for the investigation of vortex solitons(VSs)in hybrid atomic-molecular Bose-Einstein condensates under the action of the stimulated Raman-induced photoassociation and square-optical-lattice potential.Stationary solutions of the coupled GPE system are obtained by means of the imaginary-time integration,while the temporal dynamics are simulated using the fourth-order Runge-Kutta algorithm.The analysis reveals stable rhombus-shaped VS shapes with topological charges m=1 and 2 of the atomic component.The stability domains and spatial structure of these VSs are governed by three key parameters:the parametric-coupling strength(χ),atomicmolecular interaction strength(g_(12)),and the optical-lattice potential depth(V_(0)).By varyingχand g_(12),we demonstrate a structural transition where four-core rhombus-shaped VSs evolve into eight-core square-shaped modes,highlighting the nontrivial nonlinear dynamics of the system.This work establishes a connection between interactions of cold atoms and topologically structured matter waves in hybrid quantum systems.展开更多
Email communication plays a crucial role in both personal and professional contexts;however,it is frequently compromised by the ongoing challenge of spam,which detracts from productivity and introduces considerable se...Email communication plays a crucial role in both personal and professional contexts;however,it is frequently compromised by the ongoing challenge of spam,which detracts from productivity and introduces considerable security risks.Current spam detection techniques often struggle to keep pace with the evolving tactics employed by spammers,resulting in user dissatisfaction and potential data breaches.To address this issue,we introduce the Divide and Conquer-Generative Adversarial Network Squeeze and Excitation-Based Framework(DaC-GANSAEBF),an innovative deep-learning model designed to identify spam emails.This framework incorporates cutting-edge technologies,such as Generative Adversarial Networks(GAN),Squeeze and Excitation(SAE)modules,and a newly formulated Light Dual Attention(LDA)mechanism,which effectively utilizes both global and local attention to discern intricate patterns within textual data.This approach significantly improves efficiency and accuracy by segmenting scanned email content into smaller,independently evaluated components.The model underwent training and validation using four publicly available benchmark datasets,achieving an impressive average accuracy of 98.87%,outperforming leading methods in the field.These findings underscore the resilience and scalability of DaC-GANSAEBF,positioning it as a viable solution for contemporary spam detection systems.The framework can be easily integrated into existing technologies to enhance user security and reduce the risks associated with spam.展开更多
The growing integration of nondispatchable renewable energy sources(PV,wind)and the need to cut CO_(2) emissions make energy management crucial.Microgrids provide a framework for RES integration but face challenges fr...The growing integration of nondispatchable renewable energy sources(PV,wind)and the need to cut CO_(2) emissions make energy management crucial.Microgrids provide a framework for RES integration but face challenges from intermittency,fluctuating loads,cost optimization,and uncertainty in real-time balancing.Accurate short-term forecasting of solar generation and demand is vital for reliable and sustainable operation.While stochastic and machine learning methods are used,they struggle with limited data,complex temporal patterns,and scalability.Key challenges include capturing seasonal to weekly variations and modeling sudden fluctuations in generation and consumption.To address these issues,this paper presents a novel three-stage centralized EMS for interconnected microgrids.The first stage involves comprehensive data analysis to extract meaningful patterns.The second stage introduces a hybrid forecasting framework that integrates stochastic(Prophet)with machine learning(BiLSTM)techniques to improve prediction accuracy under uncertainty.In the third stage,a modified linear programming approach leverages the improved short-term forecasts to optimize energy sharing between microgrids,with the aim of reducing operational costs,minimizing carbon emissions,and improving system stability under climate variability.The proposed EMS is designed to accommodate diverse microgrid configurations while maintaining computational efficiency.Four scenarios are considered to evaluate the proposed energy management strategy.The obtained results demonstrate that the proposed EMS significantly improves both forecasting accuracy and operational performance.The combined methods achieve the best performance among all tested models,with an RMSE of 0.0070,MAE of 0.0043,and R^(2) of 0.9988,corresponding to improvements of ΔRMSE=−0.2122 and ΔR^(2)=+0.7126 relative to Prophet.These substantial gains in predictive accuracy translate into more precise battery scheduling,reduced grid dependency,and optimized power dispatching,thereby significantly enhancing system efficiency,reliability,and sustainability.Overall,the results highlight the effectiveness of integrating hybrid forecasting with optimization-based EMS,providing a viable pathway toward high penetration of renewable energy sources in future power systems.展开更多
基金funded by the Huaiyin Institute of Technology—Institute of Smart Energy.
文摘In the quest to enhance energy efficiency and reduce environmental impact in the transportation sector,the recovery of waste heat from diesel engines has become a critical area of focus.This study provided an exhaustive thermodynamic analysis optimizing Organic Rankine Cycle(ORC)systems forwaste heat recovery fromdiesel engines.Thestudy assessed the performance of five candidateworking fluids—R11,R123,R113,R245fa,and R141b—under a range of operating conditions,specifically varying overheat temperatures and evaporation pressures.The results indicated that the choice of working fluid substantially influences the system’s exergetic efficiency,net output power,and thermal efficiency.R245fa showed an outstanding net output power of 30.39 kW at high overheat conditions,outperforming R11,which is significant for high-temperature waste heat recovery.At lower temperatures,R11 and R113 demonstrated higher exergetic efficiencies,with R11 reaching a peak exergetic efficiency of 7.4%at an evaporation pressure of 10 bar and an overheat of 10℃.The study also revealed that controlling the overheat and optimizing the evaporation pressure are crucial for enhancing the net output power of the ORC system.Specifically,at an evaporation pressure of 30 bar and an overheat of 0℃,R113 exhibited the lowest exergetic destruction of 544.5 kJ/kg,making it a suitable choice for minimizing irreversible losses.These findings are instrumental for understanding the performance of ORC systems in waste heat recovery applications and offer valuable insights for the design and operation of more efficient and environmentally friendly diesel engine systems.
基金Project supported by the Training Plan of Young Backbone Teachers in Universities of Henan Province(Grant No.2023GGJS142)the Key Scientific Research of Colleges and Universities in Henan Province,China(Grant No.25A120009)+1 种基金Changzhou Leading Innovative Talent Introduction and Cultivation Project(Grant No.CQ20240102)Changzhou Applied Basic Research Program(Grant No.CJ20253065)。
文摘The rapid development of brain-like neural networks and secure data transmission technologies has placed greater demands on highly complex neural network systems and highly secure encryption methods.To this end,the paper proposes a novel high-dimensional memristor synapse-coupled hyperchaotic neural network by using the designed memristor as the synapse to connect an inertial neuron(IN)and a Hopfield neural network(HNN).By using numerical tools including bifurcation plots,phase plots,and basins of attraction,it is found that the dynamics of this system are closely related to the memristor coupling strength,self-connection synaptic weights,and inter-connection synaptic weights,and it can exhibit excellent hyperchaotic behaviors and coexisting multi-stable patterns.Through PSIM circuit simulations,the complex dynamics of the coupled IN-HNN system are verified.Furthermore,a DNA-encoded encryption algorithm is given,which utilizes generated hyperchaotic sequences to achieve encoding,operation,and decoding of DNA.The results show that this algorithm possesses strong robustness against statistical attacks,differential attacks,and noise interference,and can effectively resist known/selected plaintext attacks.This work will provide new ideas for the modeling of large-scale brainlike neural networks and high-security image encryption.
基金funded by the Directorate of Research and Community Service,Directorate General of Research and Development,Ministry of Higher Education,Science and Technologyin accordance with the Implementation Contract for the Operational Assistance Program for State Universities,Research Program Number:109/C3/DT.05.00/PL/2025.
文摘Sudden wildfires cause significant global ecological damage.While satellite imagery has advanced early fire detection and mitigation,image-based systems face limitations including high false alarm rates,visual obstructions,and substantial computational demands,especially in complex forest terrains.To address these challenges,this study proposes a novel forest fire detection model utilizing audio classification and machine learning.We developed an audio-based pipeline using real-world environmental sound recordings.Sounds were converted into Mel-spectrograms and classified via a Convolutional Neural Network(CNN),enabling the capture of distinctive fire acoustic signatures(e.g.,crackling,roaring)that are minimally impacted by visual or weather conditions.Internet of Things(IoT)sound sensors were crucial for generating complex environmental parameters to optimize feature extraction.The CNN model achieved high performance in stratified 5-fold cross-validation(92.4%±1.6 accuracy,91.2%±1.8 F1-score)and on test data(94.93%accuracy,93.04%F1-score),with 98.44%precision and 88.32%recall,demonstrating reliability across environmental conditions.These results indicate that the audio-based approach not only improves detection reliability but also markedly reduces computational overhead compared to traditional image-based methods.The findings suggest that acoustic sensing integrated with machine learning offers a powerful,low-cost,and efficient solution for real-time forest fire monitoring in complex,dynamic environments.
文摘Unconfined Compressive Strength(UCS)is a key parameter for the assessment of the stability and performance of stabilized soils,yet traditional laboratory testing is both time and resource intensive.In this study,an interpretable machine learning approach to UCS prediction is presented,pairing five models(Random Forest(RF),Gradient Boosting(GB),Extreme Gradient Boosting(XGB),CatBoost,and K-Nearest Neighbors(KNN))with SHapley Additive exPlanations(SHAP)for enhanced interpretability and to guide feature removal.A complete dataset of 12 geotechnical and chemical parameters,i.e.,Atterberg limits,compaction properties,stabilizer chemistry,dosage,curing time,was used to train and test the models.R2,RMSE,MSE,and MAE were used to assess performance.Initial results with all 12 features indicated that boosting-based models(GB,XGB,CatBoost)exhibited the highest predictive accuracy(R^(2)=0.93)with satisfactory generalization on test data,followed by RF and KNN.SHAP analysis consistently picked CaO content,curing time,stabilizer dosage,and compaction parameters as the most important features,aligning with established soil stabilization mechanisms.Models were then re-trained on the top 8 and top 5 SHAP-ranked features.Interestingly,GB,XGB,and CatBoost maintained comparable accuracy with reduced input sets,while RF was moderately sensitive and KNN was somewhat better owing to reduced dimensionality.The findings confirm that feature reduction through SHAP enables cost-effective UCS prediction through the reduction of laboratory test requirements without significant accuracy loss.The suggested hybrid approach offers an explainable,interpretable,and cost-effective tool for geotechnical engineering practice.
文摘This paper proposes to study the impacts of electrical line losses due to the connection of distributed generators (DG) to 22kV distribution system of Provincial Electricity Authority (PEA). Data of geographic information systems (GIS) including the distance of distribution line and location of load being key parameter of PEA is simulated using digital simulation and electrical network calculation program (DIgSILENT) to analyze power loss of the distribution system. In addition, the capacity and location of DG installed into the distribution system is considered. The results are shown that, when DG is installed close to the substation, the electrical line losses are reduced. However, if DG capacity becomes larger and the distance between DG and load is longer, the electrical line losses tend to increase. The results of this paper can be used to create the suitability and fairness of the fee for both DG and utility.
文摘Over the past years,many efforts have been accomplished to achieve fast and accurate meta-heuristic algorithms to optimize a variety of real-world problems.This study presents a new optimization method based on an unusual geological phenomenon in nature,named Geyser inspired Algorithm(GEA).The mathematical modeling of this geological phenomenon is carried out to have a better understanding of the optimization process.The efficiency and accuracy of GEA are verified using statistical examination and convergence rate comparison on numerous CEC 2005,CEC 2014,CEC 2017,and real-parameter benchmark functions.Moreover,GEA has been applied to several real-parameter engineering optimization problems to evaluate its effectiveness.In addition,to demonstrate the applicability and robustness of GEA,a comprehensive investigation is performed for a fair comparison with other standard optimization methods.The results demonstrate that GEA is noticeably prosperous in reaching the optimal solutions with a high convergence rate in comparison with other well-known nature-inspired algorithms,including ABC,BBO,PSO,and RCGA.Note that the source code of the GEA is publicly available at https://www.optim-app.com/projects/gea.
文摘The Firefly Algorithm(FA)is a highly efficient population-based optimization technique developed by mimicking the flashing behavior of fireflies when mating.This article proposes a method based on Differential Evolution(DE)/current-to-best/1 for enhancing the FA's movement process.The proposed modification increases the global search ability and the convergence rates while maintaining a balance between exploration and exploitation by deploying the global best solution.However,employing the best solution can lead to premature algorithm convergence,but this study handles this issue using a loop adjacent to the algorithm's main loop.Additionally,the suggested algorithm’s sensitivity to the alpha parameter is reduced compared to the original FA.The GbFA surpasses both the original and five-version of enhanced FAs in finding the optimal solution to 30 CEC2014 real parameter benchmark problems with all selected alpha values.Additionally,the CEC 2017 benchmark functions and the eight engineering optimization challenges are also utilized to evaluate GbFA’s efficacy and robustness on real-world problems against several enhanced algorithms.In all cases,GbFA provides the optimal result compared to other methods.Note that the source code of the GbFA algorithm is publicly available at https://www.optim-app.com/projects/gbfa.
文摘The research aimed to propose a non-destructive technology to control subterranean termites Coptotermes curvignathus Holmgren infestation based on electromagnetic waves. A portable apparatus for this technology has been built and its experiment is presented in this paper. Some electrical parameters were measured and analyzed along with their effects to the termites. The experiment using frequency range between 30 Hz - 600 kHz has been done. The average error of the apparatus by comparing the result with the direct measurement using oscilloscope was also measured. The highest error value appeared at 600 kHz with frequency error 6.05 kHz. The highest error of voltage (i.e. 0.186 Volt) appeared at 100 kHz. For safetiness, the highest magnetic field at 300 kHz was 0.1815 μT and at 500 kHz was 0.00725 μT which were safe for human. The average value of termites mortality was higher on irradiation time 120 minutes than 60 minutes respectively in all test frequency: 300 kHz, 400 kHz, 500 kHz and 600 kHz. This paper presents an important information of the electromagnatic-based technology for environmental friendly termites control in spite of using the insecticides.
文摘This paper presented the simulation results of the three phase electrical systems supplied by four wires with power quality problems, to which the parallel 3-leg APF （active power filters） are connected. The purpose of this study is to analyze the results obtained in these conditions in order to observe the limits of the 3-leg active power filters and to form a foundation for the future studies of the 4-leg active power filters. For a complete analysis, the APF will be controlled by four control methods： synchronous reference system control, indirect control, instantaneous p-q theory control, and positive sequence control. The analysis will watch the power quality indicators： THD （total harmonic distortion factor）, PF （power factor）, Iunb （unbalance factor）.
基金funded by Maranatha Christian University,Bandung,Indonesia for Productive Lecturer Research under grant number:011/SK/ADD/UKM/IV/2024.
文摘Objective:To assess the effects of turmeric extract and its compounds on oxidative stress,inflammation,and apoptosis in acetaminophen-induced liver injury.Methods:HepG2 cells were administered with acetaminophen(40 mM)to induce hepatotoxicity,followed by treatment with turmeric extract and its isolated compounds including curcumin,demethoxycurcumin,bis-demethoxycurcumin and ar-turmerone at 5,25,and 125μg/mL.IL-1β,IL-6,and IL-10 levels were quantified with ELISA kits.Further,qRT-PCR was used to analyze the mRNA expression of JNK,Casp-9,and Casp-3.Meanwhile,the levels of nitric oxide and lactate dehydrogenase were analyzed using colorimetric assay.Results:Acetaminophen administration caused an increase in the levels of lactate dehydrogenase,nitric oxide,IL-1β,IL-6,and the mRNA expression of JNK,Casp-9,and Casp-3 in HepG2 cells while reducing IL-10 levels.Treatment with turmeric extract,curcumin,demethoxycurcumin,bis-demethoxycurcumin,and ar-turmerone lowered IL-1β,IL-6,nitric oxide,and lactate dehydrogenase levels,downregulated the mRNA expression of JNK,Casp-9,and Casp-3,and increased IL-10 levels.Conclusions:Turmeric extract and its compounds have significant hepatoprotective activity and could be further explored for the treatment of liver damage.
基金extend their appreciation to the King Salman Center for Disability Research for funding this work through Research Group No.KSRG-2024-223.
文摘Epilepsy is a long-term neurological condition marked by recurrent seizures,which result from abnormal electrical activity in the brain that disrupts its normal functioning.Traditional methods for detecting epilepsy through machine learning typically utilize discrete-time models,which inadequately represent the continuous dynamics of electroencephalogram(EEG)signals.To overcome this limitation,we introduce an innovative approach that employs Neural Ordinary Differential Equations(NODEs)to model EEG signals as continuous-time systems.This allows for effective management of irregular sampling and intricate temporal patterns.In contrast to conventional techniques,such as Convolutional Neural Networks(CNNs)and Recurrent Neural Networks(RNNs),which necessitate fixedlength inputs and often struggle with long-term dependencies,our framework incorporates:(1)a NODE block to capture continuous-time EEG dynamics,(2)a feature extraction module tailored for seizure-specific patterns,and(3)an attention-based fusion mechanism to enhance interpretability in classification.When evaluated on three publicly accessible EEG datasets,including those from Boston Children’s Hospital and the Massachusetts Institute of Technology(CHB-MIT)and the Temple University Hospital(TUH)EEG Corpus,the model demonstrated an average accuracy of 98.2%,a sensitivity of 97.8%,a specificity of 98.3%,and an F1-score of 97.9%.Additionally,the inference latency was reduced by approximately 30%compared to standard CNN and Long Short-Term Memory(LSTM)architectures,making it well-suited for real-time applications.The method’s resilience to noise and its adaptability to irregular sampling enhance its potential for clinical use in real-time settings.
文摘We demonstrated a new type of MAX phase material,chromium titanium aluminum carbide(Cr_(2)TiAlC_(2)) polymer film,to generate a passively Q-switched erbium-doped fiber laser(EDFL).The film thickness was measured to be around 45 μm,which was fabricated using the embedding method with polyvinyl alcohol(PVA) polymer as hoster.The saturable absorber(SA) film demonstrates a dual-wavelength passively Q-switched EDFL which operates at 1 531 nm and 1 560.19 nm,respectively.The Q-switching pulse duration could be varied from 2.46 μs to 770 ns,while the repetition rate varied from 92.76 kHz to 106.6 kHz with an increasing input pumping range from 154 mW to 300 mW.The maximum output power and pulse energy of 15.05 mW and 141.18 nJ were obtained at the maximum input power of 300 mW,respectively.
基金supported in part by an International Research Partnership“Electrical Engineering-Thai French Research Center(EE-TFRC)”under the project framework of the Lorraine Universite´d’Excellence(LUE)in cooperation between Universite´de Lorraine(France)and King Mongkut’s University of Technology North Bangkok(year 2021-2024/2025-28)by the National Research Council of Thailand(NRCT)under Research Team Promotion Grant(Senior Research Scholar Program)under Grant No.N42A 680561by the NSRF via the Program Management Unit for Human Resources&Institutional Development,Research and Innovation under Research project Grant No.B41G680025.
文摘Permanent Magnet Synchronous Motors(PMSMs)are widely employed in high-performance drive applications due to their superior efficiency and dynamic capabilities.However,their control remains challenging owing to nonlinear dynamics,parameter variations,and unmeasurable external disturbances,particularly load torquefluctuations.This study proposes an enhanced Interconnection and Damp-ing Assignment Passivity-Based Control(IDA-PBC)scheme,formulated within the port-controlled Hamiltonian(PCH)framework,to address these limitations.A nonlinear disturbance observer is embedded to estimate and compensate,in real time,for lumped mis-matched disturbances arising from parameter uncertainties and external loads.Additionally,aflatness-based control strategy is employed to generate the desired current references within the nonlinear drive system,ensuring accurate tracking of time-varying speed commands.This integrated approach preserves the system’s energy-based structure,enabling systematic stability analysis while enhancing robustness.The proposed control architecture also maintains low complexity with a limited number of tunable parameters,facilitating practical implementation.Simulation and experimental results under various operating conditions demonstrate the effectiveness and robustness of the proposed method.Comparative analysis with conventional proportional-integral(PI)control and standard IDA-PBC strategies confirms its capability to handle disturbances and maintain dynamic performance.
基金funded by the Geran Universiti Penyelidikan(GUP),under the grant number GUP-2022-011 funded by the Universiti Kebangsaan Malaysia。
文摘This study presents a detailed comparative analysis of three electron transport layer(ETL)materials for perovskite solar cells(PSCs),namely titanium dioxide(TiO_(2)),barium titanate(BaTiO_(3)or BTO),and strontium-doped barium titan-ate(Ba_(1−x)Sr_(x)TiO_(3)or BST),and their impact on the quantum efficiency(QE)and power conversion efficiency(PCE)of CH_(3)NH_(3)PbI_(3)(MAPbI_(3))PSCs.The optimized structure demonstrates that devices utilizing BST as an ETL achieved the highest PCE of 29.85%,exhibiting superior thermal stability with the lowest temperature coefficient of−0.43%/K.This temperature-induced degradation is comparable to that of commercially available silicon cells.Furthermore,BST-based ETLs show 29.50%and 26.48%higher PCE than those of TiO_(2)-based and BTO-based ETLs.The enhanced internal QE and favorable current density–voltage(J–V)characteristics of BST compared with those of TiO_(2)and BTO are attributed to its improved charge carrier separation,reduced recombination rates,and robust electrical characteristics under varied environmental conditions.Furthermore,the electric field and generation rate of the BST-based ETLs show a more favorable distribution than those of the TiO_(2)-based and BTO-based ETLs.These findings provide significant insights into the role of different ETLs in enhancing QE,indicating that BST is a superior ETL that enhances both the efficiency and stability of PSCs.This study contributes to the understanding of how perovskite-structured ETLs can be used to design and optimize highly efficient and stable photovoltaic devices.
文摘Ensuring the reliability of power systems in microgrids is critical,particularly under contingency conditions that can disrupt power flow and system stability.This study investigates the application of Security-Constrained Optimal Power Flow(SCOPF)using the Line Outage Distribution Factor(LODF)to enhance resilience in a renewable energy-integrated microgrid.The research examines a 30-bus system with 14 generators and an 8669 MW load demand,optimizing both single-objective and multi-objective scenarios.The single-objective opti-mization achieves a total generation cost of$47,738,while the multi-objective approach reduces costs to$47,614 and minimizes battery power output to 165.02 kW.Under contingency conditions,failures in transmission lines 1,22,and 35 lead to complete power loss in those lines,requiring a redistribution strategy.Implementing SCOPF mitigates these disruptions by adjusting power flows,ensuring no line exceeds its capacity.Specifically,in contingency 1,power in channel 4 is reduced from 59 to 32 kW,while overall load shedding is minimized to 0.278 MW.These results demonstrate the effectiveness of SCOPF in maintaining stability and reducing economic losses.Unlike prior studies,this work integrates LODF into SCOPF for large-scale microgrid applications,offering a computationally efficient contingency management framework that enhances grid resilience and supports renewable energy adoption.
基金funded by Universiti Kebangsaan Malaysia,Fundamental Research Grant Scheme having Grant number FRGS/1/2023/ICT07/UKM/02/1Universiti Kebangsaan Malaysia Geran Universiti Penyelidikan having Grant number GUP-2024-009.
文摘The forthcoming 6G wireless networks have great potential for establishing AI-based networks that can enhance end-to-end connection and manage massive data of real-time networks.Artificial Intelligence(AI)advancements have contributed to the development of several innovative technologies by providing sophisticated specific AI mathematical models such as machine learning models,deep learning models,and hybrid models.Furthermore,intelligent resource management allows for self-configuration and autonomous decision-making capabilities of AI methods,which in turn improves the performance of 6G networks.Hence,6G networks rely substantially on AI methods to manage resources.This paper comprehensively surveys the recent work of AI methods-based resource management for 6G networks.Firstly,the AI methods are categorized into Deep Learning(DL),Federated Learning(FL),Reinforcement Learning(RL),and Evolutionary Learning(EL).Then,we analyze the AI approaches according to optimization issues such as user association,channel allocation,power allocation,and mode selection.Thereafter,we provide appropriate solutions to the most significant problems with the existing approaches of AI-based resource management.Finally,various open issues and potential trends related to AI-based resource management applications are presented.In summary,this survey enables researchers to understand these advancements thoroughly and quickly identify remaining challenges that need further investigation.
文摘Following publication of the original article[1],the authors found that they pasted the same data when drawing XRD for sample NCO-1 and NCO-2 in Fig.2a,however,the XRD of all four samples in the manuscript was tested,and XRD raw data were kept and can be offered.The correct Fig.2 has been provided in this Correction.
基金supported by the National Natural Science Foundation of China(Grant No.62275075)the Natural Science Foundation of Hubei Soliton Research Association(Grant No.2025HBSRA09)+1 种基金joint supported by Hubei Provincial Natural Science Foundation and Xianning of China(Grant Nos.2025AFD401 and 2025AFD405)Israel Science Foundation(Grant No.1695/22).
文摘We propose a theoretical framework,based on the two-component Gross-Pitaevskii equation(GPE),for the investigation of vortex solitons(VSs)in hybrid atomic-molecular Bose-Einstein condensates under the action of the stimulated Raman-induced photoassociation and square-optical-lattice potential.Stationary solutions of the coupled GPE system are obtained by means of the imaginary-time integration,while the temporal dynamics are simulated using the fourth-order Runge-Kutta algorithm.The analysis reveals stable rhombus-shaped VS shapes with topological charges m=1 and 2 of the atomic component.The stability domains and spatial structure of these VSs are governed by three key parameters:the parametric-coupling strength(χ),atomicmolecular interaction strength(g_(12)),and the optical-lattice potential depth(V_(0)).By varyingχand g_(12),we demonstrate a structural transition where four-core rhombus-shaped VSs evolve into eight-core square-shaped modes,highlighting the nontrivial nonlinear dynamics of the system.This work establishes a connection between interactions of cold atoms and topologically structured matter waves in hybrid quantum systems.
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,Saudi Arabia under Grant No.(GPIP:71-829-2024).
文摘Email communication plays a crucial role in both personal and professional contexts;however,it is frequently compromised by the ongoing challenge of spam,which detracts from productivity and introduces considerable security risks.Current spam detection techniques often struggle to keep pace with the evolving tactics employed by spammers,resulting in user dissatisfaction and potential data breaches.To address this issue,we introduce the Divide and Conquer-Generative Adversarial Network Squeeze and Excitation-Based Framework(DaC-GANSAEBF),an innovative deep-learning model designed to identify spam emails.This framework incorporates cutting-edge technologies,such as Generative Adversarial Networks(GAN),Squeeze and Excitation(SAE)modules,and a newly formulated Light Dual Attention(LDA)mechanism,which effectively utilizes both global and local attention to discern intricate patterns within textual data.This approach significantly improves efficiency and accuracy by segmenting scanned email content into smaller,independently evaluated components.The model underwent training and validation using four publicly available benchmark datasets,achieving an impressive average accuracy of 98.87%,outperforming leading methods in the field.These findings underscore the resilience and scalability of DaC-GANSAEBF,positioning it as a viable solution for contemporary spam detection systems.The framework can be easily integrated into existing technologies to enhance user security and reduce the risks associated with spam.
基金Prince Sattambin AbdulazizUniversity for funding their research work through the project number PSAU/2024/01/31821.
文摘The growing integration of nondispatchable renewable energy sources(PV,wind)and the need to cut CO_(2) emissions make energy management crucial.Microgrids provide a framework for RES integration but face challenges from intermittency,fluctuating loads,cost optimization,and uncertainty in real-time balancing.Accurate short-term forecasting of solar generation and demand is vital for reliable and sustainable operation.While stochastic and machine learning methods are used,they struggle with limited data,complex temporal patterns,and scalability.Key challenges include capturing seasonal to weekly variations and modeling sudden fluctuations in generation and consumption.To address these issues,this paper presents a novel three-stage centralized EMS for interconnected microgrids.The first stage involves comprehensive data analysis to extract meaningful patterns.The second stage introduces a hybrid forecasting framework that integrates stochastic(Prophet)with machine learning(BiLSTM)techniques to improve prediction accuracy under uncertainty.In the third stage,a modified linear programming approach leverages the improved short-term forecasts to optimize energy sharing between microgrids,with the aim of reducing operational costs,minimizing carbon emissions,and improving system stability under climate variability.The proposed EMS is designed to accommodate diverse microgrid configurations while maintaining computational efficiency.Four scenarios are considered to evaluate the proposed energy management strategy.The obtained results demonstrate that the proposed EMS significantly improves both forecasting accuracy and operational performance.The combined methods achieve the best performance among all tested models,with an RMSE of 0.0070,MAE of 0.0043,and R^(2) of 0.9988,corresponding to improvements of ΔRMSE=−0.2122 and ΔR^(2)=+0.7126 relative to Prophet.These substantial gains in predictive accuracy translate into more precise battery scheduling,reduced grid dependency,and optimized power dispatching,thereby significantly enhancing system efficiency,reliability,and sustainability.Overall,the results highlight the effectiveness of integrating hybrid forecasting with optimization-based EMS,providing a viable pathway toward high penetration of renewable energy sources in future power systems.
