Soft robots, inspired by the flexibility and versatility of biological organisms, have potential in a variety of applications. Recent advancements in magneto-soft robots have demonstrated their abilities to achieve pr...Soft robots, inspired by the flexibility and versatility of biological organisms, have potential in a variety of applications. Recent advancements in magneto-soft robots have demonstrated their abilities to achieve precise remote control through magnetic fields, enabling multi-modal locomotion and complex manipulation tasks. Nonetheless, two main hurdles must be overcome to advance the field: developing a multi-component substrate with embedded magnetic particles to ensure the requisite flexibility and responsiveness, and devising a cost-effective,straightforward method to program three-dimensional distributed magnetic domains without complex processing and expensive machinery. Here, we introduce a cost-effective and simple heat-assisted in-situ integrated molding fabrication method for creating magnetically driven soft robots with three-dimensional programmable magnetic domains. By synthesizing a composite material with neodymium-iron-boron(NdFeB) particles embedded in a polydimethylsiloxane(PDMS) and Ecoflex matrix(PDMS:Ecoflex = 1:2 mass ratio, 50% magnetic particle concentration), we achieved an optimized balance of flexibility, strength, and magnetic responsiveness. The proposed heat-assisted in-situ magnetic domains programming technique,performed at an experimentally optimized temperature of 120℃, resulted in a 2 times magnetization strength(9.5 mT) compared to that at 20℃(4.8 m T), reaching a saturation level comparable to a commercial magnetizer. We demonstrated the versatility of our approach through the fabrication of six kinds of robots, including two kinds of two-dimensional patterned soft robots(2D-PSR), a circular six-pole domain distribution magnetic robot(2D-CSPDMR), a quadrupedal walking magnetic soft robot(QWMSR), an object manipulation robot(OMR), and a hollow thin-walled spherical magneto-soft robot(HTWSMSR). The proposed method provides a practical solution to create highly responsive and adaptable magneto-soft robots.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
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
Aim To obtain an optimizing range of the main configuration parameters of double swirls combustion system (DSCS) Methods To analyze the influence of DS combustion cham-ber configuration parameters on fuel spray and mi...Aim To obtain an optimizing range of the main configuration parameters of double swirls combustion system (DSCS) Methods To analyze the influence of DS combustion cham-ber configuration parameters on fuel spray and mixing by means of the fuel jet developmentperiphery charts obtained by the high speed photography with a modeling test device deve-loped by authors,and to examine it by the tests on a single cylinder diesel engine.Resultsand Conclusion The mixing process can be divided into four phases.The optimizing range of the ration of the inner chamber diameter to the cylinder bore,d2/D,is 0.4-0.7; and the outerchamber diameter,d1 the height of the circular ridge to the piston top face,h1,the radius of outer/inner chamber circle,R1,R2 ,the max depth of outer/inner chamber bowl,H1,H2,etc. are also important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
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
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 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
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
Understanding competition between trees is essential for sustainable forest management as interactions between trees in uneven-aged mixed forests play a key role in growth dynamics. This study investigated nine compet...Understanding competition between trees is essential for sustainable forest management as interactions between trees in uneven-aged mixed forests play a key role in growth dynamics. This study investigated nine competition indices(CIs) for their suitability to model the effects of neighboring trees on silver fir(Abies alba) growth in Dinaric silver fir-European beech(Fagus sylvatica) forests. Although numerous competition indices have been developed, there is still limited consensus on their applicability in different forest types, especially in mature, structurally complex forest stands. The indices were evaluated using the adjusted coefficient of determination in a linear model wherein the volume growth of the last five years for 60 dominant silver fir trees was modeled as a function of tree volume and competition index. The results demonstrated that distance-dependent indices(e.g., the Hegyi height-distance competition and Rouvinen-Kuuluvainen diameter-distance competition indices), which consider the distance to competitors and their size, perform better than distance-independent indices. Using the optimization procedure in calculating the competition indices, only neighboring trees at a distance of up to 26-fold the diameter at breast height(DBH) of the selected tree(optimal search radius) and with a DBH of at least 20% of that of the target tree(optimal DBH) were considered competitors. Therefore, competition significantly influences the growth of dominant silver firs even in older age classes. The model based solely on tree volume explained 32.5% of the variability in volume growth, while the model that accounted for competition explained 64%. Optimizing the optimal search radius had a greater impact on model performance than optimizing the DBH threshold. This emphasizes the importance of balancing stand density and competition in silvicultural practice.展开更多
Against the backdrop of deepening globalization and digital integration,emerging international markets,characterized by large populations,rapidly growing educational demands,and progressively upgraded digital infrastr...Against the backdrop of deepening globalization and digital integration,emerging international markets,characterized by large populations,rapidly growing educational demands,and progressively upgraded digital infrastructure,have become pivotal hubs for educational technology(EdTech)enterprises to expand their global presence.However,the unique characteristics of these markets,including cultural diversity,divergent consumer behaviors,and uneven digital maturity,pose challenges to traditional digital marketing strategies.This results in EdTech products facing issues such as inefficient user acquisition,insufficient brand awareness,and suboptimal conversion rates.To address these challenges,this paper focuses on optimizing digital marketing strategies for EdTech product sales in emerging international markets.This paper focuses on the optimization of digital marketing strategies for Ed Tech product sales in emerging international markets.Through analyzing the pain points in the application of current strategies,this paper proposes a systematic optimization path from four dimensions:localized content construction,multi-channel coordination and integration,user life cycle operation,and data-driven decision making.展开更多
This paper focuses on the optimization of the evaluation index system for the value of transportation infrastructure assets.It analyzes the shortcomings of the current system and explores the directions for optimizing...This paper focuses on the optimization of the evaluation index system for the value of transportation infrastructure assets.It analyzes the shortcomings of the current system and explores the directions for optimizing the index system from the perspectives of functionality,economy,social impact,environmental impact,and sustainability.The paper also discusses the application of the optimized index system in practical evaluation and the measures to ensure its effectiveness.The research aims to enhance the evaluation mechanism for the value of transportation infrastructure assets,providing a more scientific basis for decision-making,addressing challenges in asset management,improving the level of asset management in transportation infrastructure,and meeting the demands of high-quality development in the transportation sector in the new era.展开更多
As Internet of Vehicles(IoV)technology continues to advance,edge computing has become an important tool for assisting vehicles in handling complex tasks.However,the process of offloading tasks to edge servers may expo...As Internet of Vehicles(IoV)technology continues to advance,edge computing has become an important tool for assisting vehicles in handling complex tasks.However,the process of offloading tasks to edge servers may expose vehicles to malicious external attacks,resulting in information loss or even tampering,thereby creating serious security vulnerabilities.Blockchain technology can maintain a shared ledger among servers.In the Raft consensus mechanism,as long as more than half of the nodes remain operational,the system will not collapse,effectively maintaining the system’s robustness and security.To protect vehicle information,we propose a security framework that integrates the Raft consensus mechanism from blockchain technology with edge computing.To address the additional latency introduced by blockchain,we derived a theoretical formula for system delay and proposed a convex optimization solution to minimize the system latency,ensuring that the system meets the requirements for low latency and high reliability.Simulation results demonstrate that the optimized data extraction rate significantly reduces systemdelay,with relatively stable variations in latency.Moreover,the proposed optimization solution based on this model can provide valuable insights for enhancing security and efficiency in future network environments,such as 5G and next-generation smart city systems.展开更多
Recent advancements in computational and database technologies have led to the exponential growth of large-scale medical datasets,significantly increasing data complexity and dimensionality in medical diagnostics.Effi...Recent advancements in computational and database technologies have led to the exponential growth of large-scale medical datasets,significantly increasing data complexity and dimensionality in medical diagnostics.Efficient feature selection methods are critical for improving diagnostic accuracy,reducing computational costs,and enhancing the interpretability of predictive models.Particle Swarm Optimization(PSO),a widely used metaheuristic inspired by swarm intelligence,has shown considerable promise in feature selection tasks.However,conventional PSO often suffers from premature convergence and limited exploration capabilities,particularly in high-dimensional spaces.To overcome these limitations,this study proposes an enhanced PSO framework incorporating Orthogonal Initializa-tion and a Crossover Operator(OrPSOC).Orthogonal Initialization ensures a diverse and uniformly distributed initial particle population,substantially improving the algorithm’s exploration capability.The Crossover Operator,inspired by genetic algorithms,introduces additional diversity during the search process,effectively mitigating premature convergence and enhancing global search performance.The effectiveness of OrPSOC was rigorously evaluated on three benchmark medical datasets—Colon,Leukemia,and Prostate Tumor.Comparative analyses were conducted against traditional filter-based methods,including Fast Clustering-Based Feature Selection Technique(Fast-C),Minimum Redundancy Maximum Relevance(MinRedMaxRel),and Five-Way Joint Mutual Information(FJMI),as well as prominent metaheuristic algorithms such as standard PSO,Ant Colony Optimization(ACO),Comprehensive Learning Gravitational Search Algorithm(CLGSA),and Fuzzy-Based CLGSA(FCLGSA).Experimental results demonstrated that OrPSOC consistently outperformed these existing methods in terms of classification accuracy,computational efficiency,and result stability,achieving significant improvements even with fewer selected features.Additionally,a sensitivity analysis of the crossover parameter provided valuable insights into parameter tuning and its impact on model performance.These findings highlight the superiority and robustness of the proposed OrPSOC approach for feature selection in medical diagnostic applications and underscore its potential for broader adoption in various high-dimensional,data-driven fields.展开更多
Rocking the drillstring at the surface during slide drilling is a common method for reducing drag when drilling horizontal wells.However,the current methods for determining the parameters for rocking are insufficient,...Rocking the drillstring at the surface during slide drilling is a common method for reducing drag when drilling horizontal wells.However,the current methods for determining the parameters for rocking are insufficient,limiting the widespread use of this technology.In this study,the influence of rocking parameters on the friction-reduction effect was investigated using an axialetorsional dynamic model of the drillstring and an experimental apparatus for rocking-assisted slide drilling in a simulated horizontal well.The research shows that increasing the rocking speed is beneficial improving the friction-reduction effect,but there is a diminishing marginal effect.A method was proposed to optimize the rocking speed using the equivalent axial drag coefficienterocking speed curve.Under the influence of rocking,the downhole weight on bit(WOB)exhibits a sinusoidal-like variation,with the predominant frequency being twice the rocking frequency.The fluctuation amplitude of the WOB in the horizontal section has a linear relationship with the rocking-affected depth.Based on this,a method was proposed to estimate the rockingaffected depth using the fluctuation amplitude of the standpipe pressure difference.Application of this method in the drilling field has improved the rate of penetration and toolface stability,demonstrating the reliability and effectiveness of the methods proposed in this paper.展开更多
The efficient implementation of the Advanced Encryption Standard(AES)is crucial for network data security.This paper presents novel hardware implementations of the AES S-box,a core component,using tower field represen...The efficient implementation of the Advanced Encryption Standard(AES)is crucial for network data security.This paper presents novel hardware implementations of the AES S-box,a core component,using tower field representations and Boolean Satisfiability(SAT)solvers.Our research makes several significant contri-butions to the field.Firstly,we have optimized the GF(24)inversion,achieving a remarkable 31.35%area reduction(15.33 GE)compared to the best known implementations.Secondly,we have enhanced multiplication implementa-tions for transformation matrices using a SAT-method based on local solutions.This approach has yielded notable improvements,such as a 22.22%reduction in area(42.00 GE)for the top transformation matrix in GF((24)2)-type S-box implementation.Furthermore,we have proposed new implementations of GF(((22)2)2)-type and GF((24)2)-type S-boxes,with the GF(((22)2)2)-type demonstrating superior performance.This implementation offers two variants:a small area variant that sets new area records,and a fast variant that establishes new benchmarks in Area-Execution-Time(AET)and energy consumption.Our approach significantly improves upon existing S-box implementations,offering advancements in area,speed,and energy consumption.These optimizations contribute to more efficient and secure AES implementations,potentially enhancing various cryptographic applications in the field of network security.展开更多
Background With the development of the Internet,the topology optimization of wireless sensor networks has received increasing attention.However,traditional optimization methods often overlook the energy imbalance caus...Background With the development of the Internet,the topology optimization of wireless sensor networks has received increasing attention.However,traditional optimization methods often overlook the energy imbalance caused by node loads,which affects network performance.Methods To improve the overall performance and efficiency of wireless sensor networks,a new method for optimizing the wireless sensor network topology based on K-means clustering and firefly algorithms is proposed.The K-means clustering algorithm partitions nodes by minimizing the within-cluster variance,while the firefly algorithm is an optimization algorithm based on swarm intelligence that simulates the flashing interaction between fireflies to guide the search process.The proposed method first introduces the K-means clustering algorithm to cluster nodes and then introduces a firefly algorithm to dynamically adjust the nodes.Results The results showed that the average clustering accuracies in the Wine and Iris data sets were 86.59%and 94.55%,respectively,demonstrating good clustering performance.When calculating the node mortality rate and network load balancing standard deviation,the proposed algorithm showed dead nodes at approximately 50 iterations,with an average load balancing standard deviation of 1.7×10^(4),proving its contribution to extending the network lifespan.Conclusions This demonstrates the superiority of the proposed algorithm in significantly improving the energy efficiency and load balancing of wireless sensor networks to extend the network lifespan.The research results indicate that wireless sensor networks have theoretical and practical significance in fields such as monitoring,healthcare,and agriculture.展开更多
Kirsten rat sarcoma viral oncogene homolog(KRAS)protein inhibitors are a promising class of therapeutics,but research on molecules that effectively penetrate the blood-brain barrier(BBB)remains limited,which is crucia...Kirsten rat sarcoma viral oncogene homolog(KRAS)protein inhibitors are a promising class of therapeutics,but research on molecules that effectively penetrate the blood-brain barrier(BBB)remains limited,which is crucial for treating central nervous system(CNS)malignancies.Although molecular generation models have recently advanced drug discovery,they often overlook the complexity of biological and chemical factors,leaving room for improvement.In this study,we present a structureconstrained molecular generation workflow designed to optimize lead compounds for both drug efficacy and drug absorption properties.Our approach utilizes a variational autoencoder(VAE)generative model integrated with reinforcement learning for multi-objective optimization.This method specifically aims to enhance BBB permeability(BBBp)while maintaining high-affinity substructures of KRAS inhibitors.To support this,we incorporate a specialized KRAS BBB predictor based on active learning and an affinity predictor employing comparative learning models.Additionally,we introduce two novel metrics,the knowledge-integrated reproduction score(KIRS)and the composite diversity score(CDS),to assess structural performance and biological relevance.Retrospective validation with KRAS inhibitors,AMG510 and MRTX849,demonstrates the framework’s effectiveness in optimizing BBBp and highlights its potential for real-world drug development applications.This study provides a robust framework for accelerating the structural enhancement of lead compounds,advancing the drug development process across diverse targets.展开更多
To the Editor:The recent study by Rochdi et al[1]comparing the effects of two coenzyme Q10(CoQ10)doses(100 mg/day vs.200 mg/day)on male fertility parameters represents a significant advancement in evidence-based treat...To the Editor:The recent study by Rochdi et al[1]comparing the effects of two coenzyme Q10(CoQ10)doses(100 mg/day vs.200 mg/day)on male fertility parameters represents a significant advancement in evidence-based treatment of idiopathic oligoasthenozoospermia.The findings demonstrate a clear dose-response relationship with profound clinical implications that deserve focused attention.The most compelling finding is the 53%relative increase in pregnancy rates between the higher and lower dose groups(23%vs.15%,P<0.001).This 8-percentage point absolute difference translates to a number needed to treat(NNT)of 12.5,meaning that for every 12-13 men treated with the higher dose instead of the lower dose,one additional pregnancy would be achieved.Given that male factor infertility contributes to approximately 50%of all infertility cases globally,this dose-response relationship has immediate clinical relevance.展开更多
基金supported by National Natural Science Foundation of China(Grant Nos.62473277,62473275,62133004,52105072,and 62073230)Jiangsu Provincial Outstanding Youth Program(Grant No.BK20230072)+5 种基金National Key R&D Program of China(Grant Nos.2022YFC3802302 and 2023YFB4705600)Suzhou Industrial Foresight and Key Core Technology Project(Grant No.SYC2022044)Zhejiang Provincial Natural Science Foundation of China(Grant No.LZ24E050004)Shenzhen Polytechnic High-level Talent Start-up Project(Grant No.6023330006K)Shenzhen Science and Technology Program(Grant No.JCYJ20210324132810026)a Grant from Open Foundation of the State Key Laboratory of Fluid Power and Mechatronic Systems,Grants from Jiangsu QingLan Project and Jiangsu 333 high-level talents.
文摘Soft robots, inspired by the flexibility and versatility of biological organisms, have potential in a variety of applications. Recent advancements in magneto-soft robots have demonstrated their abilities to achieve precise remote control through magnetic fields, enabling multi-modal locomotion and complex manipulation tasks. Nonetheless, two main hurdles must be overcome to advance the field: developing a multi-component substrate with embedded magnetic particles to ensure the requisite flexibility and responsiveness, and devising a cost-effective,straightforward method to program three-dimensional distributed magnetic domains without complex processing and expensive machinery. Here, we introduce a cost-effective and simple heat-assisted in-situ integrated molding fabrication method for creating magnetically driven soft robots with three-dimensional programmable magnetic domains. By synthesizing a composite material with neodymium-iron-boron(NdFeB) particles embedded in a polydimethylsiloxane(PDMS) and Ecoflex matrix(PDMS:Ecoflex = 1:2 mass ratio, 50% magnetic particle concentration), we achieved an optimized balance of flexibility, strength, and magnetic responsiveness. The proposed heat-assisted in-situ magnetic domains programming technique,performed at an experimentally optimized temperature of 120℃, resulted in a 2 times magnetization strength(9.5 mT) compared to that at 20℃(4.8 m T), reaching a saturation level comparable to a commercial magnetizer. We demonstrated the versatility of our approach through the fabrication of six kinds of robots, including two kinds of two-dimensional patterned soft robots(2D-PSR), a circular six-pole domain distribution magnetic robot(2D-CSPDMR), a quadrupedal walking magnetic soft robot(QWMSR), an object manipulation robot(OMR), and a hollow thin-walled spherical magneto-soft robot(HTWSMSR). The proposed method provides a practical solution to create highly responsive and adaptable magneto-soft robots.
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.
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Aim To obtain an optimizing range of the main configuration parameters of double swirls combustion system (DSCS) Methods To analyze the influence of DS combustion cham-ber configuration parameters on fuel spray and mixing by means of the fuel jet developmentperiphery charts obtained by the high speed photography with a modeling test device deve-loped by authors,and to examine it by the tests on a single cylinder diesel engine.Resultsand Conclusion The mixing process can be divided into four phases.The optimizing range of the ration of the inner chamber diameter to the cylinder bore,d2/D,is 0.4-0.7; and the outerchamber diameter,d1 the height of the circular ridge to the piston top face,h1,the radius of outer/inner chamber circle,R1,R2 ,the max depth of outer/inner chamber bowl,H1,H2,etc. are also important
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.
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.
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.
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funded by the Slovenian Research and Innovation Agency(https://www.aris-rs.si/sl/)ProgramResearch Core Fund-ing No.P4-0107(TL)and No.P4-0059(MK)+1 种基金Young Researcher Program Grant(MK)funded by the Slovenian Forestry Institute(P4-0107).
文摘Understanding competition between trees is essential for sustainable forest management as interactions between trees in uneven-aged mixed forests play a key role in growth dynamics. This study investigated nine competition indices(CIs) for their suitability to model the effects of neighboring trees on silver fir(Abies alba) growth in Dinaric silver fir-European beech(Fagus sylvatica) forests. Although numerous competition indices have been developed, there is still limited consensus on their applicability in different forest types, especially in mature, structurally complex forest stands. The indices were evaluated using the adjusted coefficient of determination in a linear model wherein the volume growth of the last five years for 60 dominant silver fir trees was modeled as a function of tree volume and competition index. The results demonstrated that distance-dependent indices(e.g., the Hegyi height-distance competition and Rouvinen-Kuuluvainen diameter-distance competition indices), which consider the distance to competitors and their size, perform better than distance-independent indices. Using the optimization procedure in calculating the competition indices, only neighboring trees at a distance of up to 26-fold the diameter at breast height(DBH) of the selected tree(optimal search radius) and with a DBH of at least 20% of that of the target tree(optimal DBH) were considered competitors. Therefore, competition significantly influences the growth of dominant silver firs even in older age classes. The model based solely on tree volume explained 32.5% of the variability in volume growth, while the model that accounted for competition explained 64%. Optimizing the optimal search radius had a greater impact on model performance than optimizing the DBH threshold. This emphasizes the importance of balancing stand density and competition in silvicultural practice.
文摘Against the backdrop of deepening globalization and digital integration,emerging international markets,characterized by large populations,rapidly growing educational demands,and progressively upgraded digital infrastructure,have become pivotal hubs for educational technology(EdTech)enterprises to expand their global presence.However,the unique characteristics of these markets,including cultural diversity,divergent consumer behaviors,and uneven digital maturity,pose challenges to traditional digital marketing strategies.This results in EdTech products facing issues such as inefficient user acquisition,insufficient brand awareness,and suboptimal conversion rates.To address these challenges,this paper focuses on optimizing digital marketing strategies for EdTech product sales in emerging international markets.This paper focuses on the optimization of digital marketing strategies for Ed Tech product sales in emerging international markets.Through analyzing the pain points in the application of current strategies,this paper proposes a systematic optimization path from four dimensions:localized content construction,multi-channel coordination and integration,user life cycle operation,and data-driven decision making.
文摘This paper focuses on the optimization of the evaluation index system for the value of transportation infrastructure assets.It analyzes the shortcomings of the current system and explores the directions for optimizing the index system from the perspectives of functionality,economy,social impact,environmental impact,and sustainability.The paper also discusses the application of the optimized index system in practical evaluation and the measures to ensure its effectiveness.The research aims to enhance the evaluation mechanism for the value of transportation infrastructure assets,providing a more scientific basis for decision-making,addressing challenges in asset management,improving the level of asset management in transportation infrastructure,and meeting the demands of high-quality development in the transportation sector in the new era.
基金supported in part by the National Natural Science Foundation of China under Grant No.61701197in part by the National Key Research and Development Program of China under Grant No.2021YFA1000500(4)in part by the 111 project under Grant No.B23008.
文摘As Internet of Vehicles(IoV)technology continues to advance,edge computing has become an important tool for assisting vehicles in handling complex tasks.However,the process of offloading tasks to edge servers may expose vehicles to malicious external attacks,resulting in information loss or even tampering,thereby creating serious security vulnerabilities.Blockchain technology can maintain a shared ledger among servers.In the Raft consensus mechanism,as long as more than half of the nodes remain operational,the system will not collapse,effectively maintaining the system’s robustness and security.To protect vehicle information,we propose a security framework that integrates the Raft consensus mechanism from blockchain technology with edge computing.To address the additional latency introduced by blockchain,we derived a theoretical formula for system delay and proposed a convex optimization solution to minimize the system latency,ensuring that the system meets the requirements for low latency and high reliability.Simulation results demonstrate that the optimized data extraction rate significantly reduces systemdelay,with relatively stable variations in latency.Moreover,the proposed optimization solution based on this model can provide valuable insights for enhancing security and efficiency in future network environments,such as 5G and next-generation smart city systems.
文摘Recent advancements in computational and database technologies have led to the exponential growth of large-scale medical datasets,significantly increasing data complexity and dimensionality in medical diagnostics.Efficient feature selection methods are critical for improving diagnostic accuracy,reducing computational costs,and enhancing the interpretability of predictive models.Particle Swarm Optimization(PSO),a widely used metaheuristic inspired by swarm intelligence,has shown considerable promise in feature selection tasks.However,conventional PSO often suffers from premature convergence and limited exploration capabilities,particularly in high-dimensional spaces.To overcome these limitations,this study proposes an enhanced PSO framework incorporating Orthogonal Initializa-tion and a Crossover Operator(OrPSOC).Orthogonal Initialization ensures a diverse and uniformly distributed initial particle population,substantially improving the algorithm’s exploration capability.The Crossover Operator,inspired by genetic algorithms,introduces additional diversity during the search process,effectively mitigating premature convergence and enhancing global search performance.The effectiveness of OrPSOC was rigorously evaluated on three benchmark medical datasets—Colon,Leukemia,and Prostate Tumor.Comparative analyses were conducted against traditional filter-based methods,including Fast Clustering-Based Feature Selection Technique(Fast-C),Minimum Redundancy Maximum Relevance(MinRedMaxRel),and Five-Way Joint Mutual Information(FJMI),as well as prominent metaheuristic algorithms such as standard PSO,Ant Colony Optimization(ACO),Comprehensive Learning Gravitational Search Algorithm(CLGSA),and Fuzzy-Based CLGSA(FCLGSA).Experimental results demonstrated that OrPSOC consistently outperformed these existing methods in terms of classification accuracy,computational efficiency,and result stability,achieving significant improvements even with fewer selected features.Additionally,a sensitivity analysis of the crossover parameter provided valuable insights into parameter tuning and its impact on model performance.These findings highlight the superiority and robustness of the proposed OrPSOC approach for feature selection in medical diagnostic applications and underscore its potential for broader adoption in various high-dimensional,data-driven fields.
基金sponsored by the National Natural Science Foundation of China,China(No.52304002).
文摘Rocking the drillstring at the surface during slide drilling is a common method for reducing drag when drilling horizontal wells.However,the current methods for determining the parameters for rocking are insufficient,limiting the widespread use of this technology.In this study,the influence of rocking parameters on the friction-reduction effect was investigated using an axialetorsional dynamic model of the drillstring and an experimental apparatus for rocking-assisted slide drilling in a simulated horizontal well.The research shows that increasing the rocking speed is beneficial improving the friction-reduction effect,but there is a diminishing marginal effect.A method was proposed to optimize the rocking speed using the equivalent axial drag coefficienterocking speed curve.Under the influence of rocking,the downhole weight on bit(WOB)exhibits a sinusoidal-like variation,with the predominant frequency being twice the rocking frequency.The fluctuation amplitude of the WOB in the horizontal section has a linear relationship with the rocking-affected depth.Based on this,a method was proposed to estimate the rockingaffected depth using the fluctuation amplitude of the standpipe pressure difference.Application of this method in the drilling field has improved the rate of penetration and toolface stability,demonstrating the reliability and effectiveness of the methods proposed in this paper.
基金supported in part by the National Natural Science Foundation of China(No.62162016)in part by the Innovation Project of Guangxi Graduate Education(Nos.YCBZ2023132 and YCSW2023304).
文摘The efficient implementation of the Advanced Encryption Standard(AES)is crucial for network data security.This paper presents novel hardware implementations of the AES S-box,a core component,using tower field representations and Boolean Satisfiability(SAT)solvers.Our research makes several significant contri-butions to the field.Firstly,we have optimized the GF(24)inversion,achieving a remarkable 31.35%area reduction(15.33 GE)compared to the best known implementations.Secondly,we have enhanced multiplication implementa-tions for transformation matrices using a SAT-method based on local solutions.This approach has yielded notable improvements,such as a 22.22%reduction in area(42.00 GE)for the top transformation matrix in GF((24)2)-type S-box implementation.Furthermore,we have proposed new implementations of GF(((22)2)2)-type and GF((24)2)-type S-boxes,with the GF(((22)2)2)-type demonstrating superior performance.This implementation offers two variants:a small area variant that sets new area records,and a fast variant that establishes new benchmarks in Area-Execution-Time(AET)and energy consumption.Our approach significantly improves upon existing S-box implementations,offering advancements in area,speed,and energy consumption.These optimizations contribute to more efficient and secure AES implementations,potentially enhancing various cryptographic applications in the field of network security.
基金Supported by 2021 Zhanjiang University of Science and Technology"Brand Enhancement Plan"Project:Network Series Course Teaching Team(PPJH202102JXTD)2022 Zhanjiang University of Science and Technology"Brand Enhancement Plan"Project:Network Engineering(PPJHKCSZ-2022301)+1 种基金2023 Zhanjiang Science and Technology Bureau Project:Design and Simulation of Zhanjiang Mangrove Wetland Monitoring Network System(2023B01017)2022 Zhanjiang University of Science and Technology Quality Engineering Project:Audiovisual Language Teaching and Research Office(ZLGC202203).
文摘Background With the development of the Internet,the topology optimization of wireless sensor networks has received increasing attention.However,traditional optimization methods often overlook the energy imbalance caused by node loads,which affects network performance.Methods To improve the overall performance and efficiency of wireless sensor networks,a new method for optimizing the wireless sensor network topology based on K-means clustering and firefly algorithms is proposed.The K-means clustering algorithm partitions nodes by minimizing the within-cluster variance,while the firefly algorithm is an optimization algorithm based on swarm intelligence that simulates the flashing interaction between fireflies to guide the search process.The proposed method first introduces the K-means clustering algorithm to cluster nodes and then introduces a firefly algorithm to dynamically adjust the nodes.Results The results showed that the average clustering accuracies in the Wine and Iris data sets were 86.59%and 94.55%,respectively,demonstrating good clustering performance.When calculating the node mortality rate and network load balancing standard deviation,the proposed algorithm showed dead nodes at approximately 50 iterations,with an average load balancing standard deviation of 1.7×10^(4),proving its contribution to extending the network lifespan.Conclusions This demonstrates the superiority of the proposed algorithm in significantly improving the energy efficiency and load balancing of wireless sensor networks to extend the network lifespan.The research results indicate that wireless sensor networks have theoretical and practical significance in fields such as monitoring,healthcare,and agriculture.
基金supported by National Key Research and Development Program of China(Grant Nos.:2022YFC3400504 and 2023YFC2305904)the Strategic Priority Research Program of the Chinese Academy of Sciences,China(Grant Nos.:XDB0830203 and XDB0830200)+2 种基金the National Natural Science Foundation of China(Grant Nos.:82204278,31960198,T2225002,and 82273855)SIMM-SHUTCM Traditional Chinese Medicine Innovation Joint Research Program,China(Grant No.:E2G805H)Shanghai Municipal Science and Technology Major Project,China,and Key Technologies R&D Program of Guangdong Province,China(Grant No.:2023B1111030004).
文摘Kirsten rat sarcoma viral oncogene homolog(KRAS)protein inhibitors are a promising class of therapeutics,but research on molecules that effectively penetrate the blood-brain barrier(BBB)remains limited,which is crucial for treating central nervous system(CNS)malignancies.Although molecular generation models have recently advanced drug discovery,they often overlook the complexity of biological and chemical factors,leaving room for improvement.In this study,we present a structureconstrained molecular generation workflow designed to optimize lead compounds for both drug efficacy and drug absorption properties.Our approach utilizes a variational autoencoder(VAE)generative model integrated with reinforcement learning for multi-objective optimization.This method specifically aims to enhance BBB permeability(BBBp)while maintaining high-affinity substructures of KRAS inhibitors.To support this,we incorporate a specialized KRAS BBB predictor based on active learning and an affinity predictor employing comparative learning models.Additionally,we introduce two novel metrics,the knowledge-integrated reproduction score(KIRS)and the composite diversity score(CDS),to assess structural performance and biological relevance.Retrospective validation with KRAS inhibitors,AMG510 and MRTX849,demonstrates the framework’s effectiveness in optimizing BBBp and highlights its potential for real-world drug development applications.This study provides a robust framework for accelerating the structural enhancement of lead compounds,advancing the drug development process across diverse targets.
文摘To the Editor:The recent study by Rochdi et al[1]comparing the effects of two coenzyme Q10(CoQ10)doses(100 mg/day vs.200 mg/day)on male fertility parameters represents a significant advancement in evidence-based treatment of idiopathic oligoasthenozoospermia.The findings demonstrate a clear dose-response relationship with profound clinical implications that deserve focused attention.The most compelling finding is the 53%relative increase in pregnancy rates between the higher and lower dose groups(23%vs.15%,P<0.001).This 8-percentage point absolute difference translates to a number needed to treat(NNT)of 12.5,meaning that for every 12-13 men treated with the higher dose instead of the lower dose,one additional pregnancy would be achieved.Given that male factor infertility contributes to approximately 50%of all infertility cases globally,this dose-response relationship has immediate clinical relevance.
