Search Results (68)

In Network-on-Chip (NoC) research, the task mapping problem has attracted considerable attention as a core issue influencing system performance. As an NP-hard problem, it remains challenging, and existing algorithms exhibit limitations in both mapping quality and computational efficiency. To address this, a method named ET (Enhanced Coati Optimization Algorithm) is proposed, which leverages the nature-inspired Coati Optimization Algorithm (COA) for task mapping. An incremental hill-climbing strategy is integrated to improve local search capabilities, and a dynamic mechanism for adjusting the exploration–exploitation ratio is designed to better balance global and local searches. Additionally, an initial mapping strategy based on spectral clustering is introduced, which utilizes inter-task communication strength to cluster tasks, thereby improving the quality of the initial population. To evaluate the effectiveness of the proposed algorithm, the performance of the ET algorithm is compared and analyzed against various existing algorithms in terms of communication cost, energy consumption, and latency, using both real benchmark task maps and randomly generated task maps. Experimental results demonstrate that the ET algorithm consistently outperforms the compared algorithms across all performance metrics, thereby confirming its superiority in addressing the NoC task mapping problem. Full article

Background/Objectives: To investigate associations between Follistatin (FST) gene polymorphisms (SNPs) and baseline musculoskeletal traits, and their interactions with 16-week exercise interventions. Methods: A cohort of 470 untrained Northern Han Chinese adults (208 males, 262 females), sourced from the “Research on Key Technologies for an Exercise and Fitness Expert Guidance System” project, was analyzed. These participants were previously randomly assigned to one of four exercise groups (Hill, Running, Cycling, Combined) or a non-exercising Control group, and completed their respective 16-week protocols. Body composition, bone mineral content (BMC), bone mineral density (BMD), and serum follistatin levels were all assessed pre- and post-intervention. Dual-energy X-ray absorptiometry (DXA) was utilized for the body composition, BMC, and BMD measurements. FST SNPs (rs3797296, rs3797297) were genotyped using matrix assisted laser desorption/ionization time-of-flight mass spectrometer (MALDI-TOF MS) or microarrays. To elucidate the biological mechanisms, we performed in silico functional analyses for rs3797296 and rs3797297. Results: Baseline: In females only, the rs3797297 T allele was associated with higher muscle mass (β = 1.159, 95% confidence interval (CI): 0.202–2.116, P_{_adj} = 0.034) and BMC (β = 0.127, 95% CI: 0.039–0.215, P_{_adj} = 0.009), with the BMC effect significantly mediated by muscle mass. Exercise Response: Interventions improved body composition, particularly in females. Gene-Exercise Interaction: A significant interaction occurred exclusively in women undertaking hill climbing: the rs3797296 G allele was associated with attenuated muscle mass gains (β = −1.126 kg, 95% CI: −1.767 to −0.485, P_{_adj} = 0.034). Baseline follistatin correlated with body composition (stronger in males) and increased post-exercise (primarily in males, Hill/Running groups) but did not mediate SNP effects on exercise adaptation. Functional annotation revealed that rs3797297 is a likely causal variant, acting as a skeletal muscle eQTL for the mitochondrial gene NDUFS4, suggesting a mechanism involving muscle bioenergetics. Conclusions: Findings indicate that FST polymorphisms associate with musculoskeletal traits in Northern Han Chinese. Mechanistic insights from functional annotation reveal potential pathways for these associations, highlighting the potential utility of these genetic markers for optimizing training program design. Full article

Machine Learning-assisted metaheuristics is a new and promising research topic, combining the advantages of both method families. Metaheuristics are widely used general problem solvers that can be fine-tuned by prior knowledge about the search space; however, this adaptation can be a very time-consuming and complex task. This paper proposes a hybrid variation of the Hill Climbing method using a Machine Learning model to learn this domain-specific knowledge in advance to help determine the optimal step size of each iteration. A Deep Feedforward Neural Network was trained on the steps of thousands of Hill Climbing runs. This model was used in a novel alternating method (using traditional and Machine Learning-based steps) to predict the optimal step size for each iteration. This hybrid algorithm was compared to the already-known variants. The results show that the novel hybrid method is able to find slightly better results than the original Hill Climbing method, requiring significantly fewer fitness calculations. Full article

Plug-in hybrid electric vehicles (PHEVs) typically employ batteries with relatively small capacities due to constraints on chassis space and vehicle cost. Consequently, under conditions such as acceleration and hill climbing, these vehicles often experience high-current battery discharges, which can significantly compromise the battery’s lifespan. To address this issue, this paper focuses on a plug-in hybrid passenger vehicle, introducing supercapacitors to form a hybrid energy storage system (HESS) in conjunction with the PHEV battery, and it proposes a dual-layer energy management strategy for PHEVs. First, a PHEV model is developed, and a rule-based energy management strategy is designed. By conducting simulation comparisons of the CLTC under three control rules with different thresholds, the strategy yielding the lowest fuel consumption is selected, and its battery discharge characteristics are analyzed. Subsequently, the required power parameters of the supercapacitor are calculated, and, taking chassis space constraints into account, the number and specifications of the supercapacitors are determined. Subsequently, a dual-layer energy distribution strategy for PHEVs equipped with supercapacitors is proposed. In the upper layer, an equivalent fuel consumption minimization method is applied to optimize the torque distribution between the engine and the motor, while the lower layer employs a rule-based strategy for power allocation between the battery and the supercapacitor. A proportional feedback factor is introduced for the real-time adjustment of the engine and motor torque distribution, and simulations under the CLTC are conducted to evaluate the PHEV’s torque distribution and fuel consumption. The results indicate that the dual-layer energy management strategy reduces the duration of high-current battery discharge in the supercapacitor-equipped PHEV by 73.61%, decreases the peak current by 30.76%, and lowers the overall vehicle fuel consumption by 5%. Unlike other studies, this paper analyzes and calculates the specifications, dimensions, and quantity of supercapacitors based on the available chassis space of the PHEV passenger car. The results demonstrate that the designed supercapacitor array effectively mitigates the high-current discharge of the PHEV battery, and the proposed dual-layer energy management strategy is capable of reducing the overall fuel consumption of the vehicle. Full article

A software-defined network (SDN) is a new architecture approach for constructing and maintaining networks with the main goal of making the network open and programmable. This allows the achievement of specific network behavior by updating and installing software, instead of making physical changes to the network. Thus, SDNs allow far more flexibility and maintainability compared to conventional device-dependent architectures. Unfortunately, like their predecessors, SDNs are prone to distributed denial of service (DDoS) attacks. These attack paralyze networks by flooding the controller with bogus requests. The answer to this problem is to ignore machines in the network sending these requests. This can be achieved by incorporating classification algorithms that can distinguish between genuine and bogus requests. There is abundant literature on the application of such algorithms on conventional networks. However, because SDNs are relatively new, they lack such abundance both in terms of novel algorithms and effective datasets when it comes to DDoS attack detection. To address these issues, the present study analyzes several variants of the decision tree algorithm for detection of DDoS attacks while using two recently proposed datasets for SDNs. The study finds that a decision tree constructed with a hill climbing approach, termed the greedy decision tree, iteratively adds features on the basis of model performance and provides a simpler and more effective strategy for the detection of DDoS attacks in SDNs when compared with recently proposed schemes in the literature. Furthermore, stability analysis of the greedy decision tree provides useful insights about the performance of the algorithm. One edge that greedy decision tree has over several other methods is its enhanced interpretability in conjunction with higher accuracy. Full article

The structure learning of a Bayesian network (BN) is a crucial process that aims to unravel the complex dependencies relationships among variables using a given dataset. This paper proposes a new BN structure learning method for data with continuous attribute values. As a non-parametric distribution-free method, kernel density estimation (KDE) is applied in the conditional independence (CI) test. The skeleton of the BN is constructed utilizing the test based on mutual information and conditional mutual information, delineating potential relational connections between parents and children without imposing any distributional assumptions. In the searching stage of BN structure learning, the causal relationships between variables are achieved by using the conditional entropy scoring function and hill-climbing strategy. To further enhance the computational efficiency of our method, we incorporate a locality sensitive hashing (LSH) function into the KDE process. The method speeds up the calculations of KDE while maintaining the precision of the estimates, leading to a notable decrease in the time required for computing mutual information, conditional mutual information, and conditional entropy. A BN classifier (BNC) is established by using the computationally efficient BN learning method. Our experiments demonstrated that KDE using LSH has greatly improved the speed compared to traditional KDE without losing fitting accuracy. This achievement underscores the effectiveness of our method in balancing speed and accuracy. By giving the benchmark networks, the network structure learning accuracy with the proposed method is superior to other traditional structure learning methods. The BNC also demonstrates better accuracy with stronger interpretability compared to conventional classifiers on public datasets. Full article

Simulation is a cornerstone of planning and facilitating the transition towards electric mobility in sub-Saharan Africa’s informal public transport. The primary objective of this study is to validate and refine the electro-kinetic model used to simulate electric versions of the sector’s minibuses. A systematic simulation methodology is also developed to correct the simulation parameters and improve the high-frequency GPS data used with the model. A retrofitted electric minibus was used to capture high-frequency GPS mobility data and power draw from the battery. The method incorporates key refinements such as corrections for gross vehicle mass, elevation and speed smoothing, radial drag, hill-climb forces, and the calibration of propulsion and regenerative braking parameters. The refined simulation demonstrates improved alignment with measured power draw and trip energy usage, reducing error margins and enhancing model reliability. Factors such as trip characteristics and environmental conditions, including wind resistance, are identified as potential contributors to observed discrepancies. These findings highlight the importance of precise data handling and model calibration for accurate energy simulation and decision making in the transition to electric public transport. This work provides a robust framework for future studies and practical implementations, offering insights into the technical and operational challenges of electrifying informal public transport systems in resource-constrained regions. Full article

This study presents the optimization of the friction stir welding (FSW) process using polynomial regression to predict the maximum tensile load (MTL) of welded joints. The experimental design included varying spindle speeds from 600 to 2200 rpm and welding speeds from 100 to 350 mm/min over 28 experimental points. The resulting MTL values ranged from 1912 to 15,336 N. A fifth-degree polynomial regression model was developed to fit the experimental data. Diagnostic tests, including the Shapiro–Wilk test and kurtosis analysis, indicated a non-normal distribution of the MTL data. Model validation showed that fifth-degree polynomial regression provided a robust fit with high fitted and predicted R² values, indicating strong predictive power. Hill-climbing optimization was used to fine-tune the welding parameters, identifying an optimal spindle speed of 1100 rpm and a welding speed of 332 mm/min, which was predicted to achieve an MTL of 16,852 N. Response surface analysis confirmed the effectiveness of the identified parameters and demonstrated their significant influence on the MTL. These results suggest that the applied polynomial regression model and optimization approach are effective tools for improving the performance and reliability of the FSW process. Full article

This paper introduces the hybrid population-based hill-climbing (HPHC) algorithm, a novel approach for generating cryptographically strong S-boxes that combines the efficiency of hill climbing with the exploration capabilities of population-based methods. The algorithm achieves consistent generation of 8-bit S-boxes with a nonlinearity of 104, a critical threshold for cryptographic applications. Our approach demonstrates remarkable efficiency, requiring only 49,277 evaluations on average to generate such S-boxes, representing a 600-fold improvement over traditional simulated annealing methods and a 15-fold improvement over recent genetic algorithm variants. We present comprehensive experimental results from extensive parameter space exploration, revealing that minimal populations (often single-individual) combined with moderate mutation rates achieve optimal performance. This paper provides detailed analysis of algorithm behavior, parameter sensitivity, and performance characteristics, supported by rigorous statistical evaluation. We demonstrate that population size should approximate available thread count for optimal parallel execution despite smaller populations being theoretically more efficient. The HPHC algorithm maintains high reliability across diverse parameter settings while requiring minimal computational resources, making it particularly suitable for practical cryptographic applications. Full article

For electrically driven commercial vehicles equipped with three-speed automatic mechanical transmission (AMT), the transmission control unit (TCU) without vehicle mass and road gradient estimation function will lead to frequent shifting and insufficient power during vehicle full-load or grade climbing. Therefore, it is necessary to estimate the mass and road gradient for the electrically driven commercial vehicles equipped with the three-speed AMT, and to adjust the shift rule according to the estimation results. Given the above problems, this paper focuses on the control and development of the electrically driven three-speed AMT and takes the shift controller with the vehicle mass and road gradient estimation as the research goal. The mathematical model and simulation model of vehicle dynamics are established to verify the shift function of TCU. The least squares method and calibration techniques are applied to estimate the vehicle mass and road gradient. According to the estimation results, the existing shift strategy is optimized, and the software-in-the-loop simulation of the transmission controller is carried out to verify the function of the control algorithm software. The hardware-in-the-loop test model is established to verify the shift strategy’s optimization effect, which shortens the controller’s forward development cycle. According to the estimation results of mass and gradient, the error result of the proposed method is controlled within 4.5% for mass and 8.6% for gradient. The experiment verifies that the optimized shift strategy can effectively improve the dynamic performance of the vehicle. The HIL experimental results show that the vehicle can maintain low gear while climbing the hill, and the vehicle speed does not decrease significantly. Full article

Deep learning methods have significantly advanced profiling side-channel attacks. Finding the optimal set of hyperparameters for these models remains challenging. Effective hyperparameter optimization is crucial for training accurate neural networks. In this work, we introduce a novel hill climbing optimization algorithm that is specifically designed for deep learning in profiled side-channel analysis. This algorithm iteratively explores hyperparameter space using gradient-based techniques to make precise, localized adjustments. By incorporating performance feedback at each iteration, our approach efficiently converges on optimal hyperparameters, surpassing traditional Random Search methods. Extensive experiments—covering protected implementations, leakage models, and various neural network architectures—demonstrate that our hill climbing method consistently achieves superior performance in over 80% of test cases, predicting the secret key with fewer attack traces and outperforming both Random Search and state-of-the-art techniques. Full article

Efficiency has always been one of the most critical indicators for evaluating wireless power transfer (WPT) systems. To achieve fast maximum energy efficiency tracking (MEET), this paper provides an innovative control method utilizing the hill climbing-golden section search (HC-GSS) method of an LCC-S compensated WPT system. The receiver side includes a buck-boost converter that regulates the output current or voltage to meet output requirements. In the meantime, the buck-boost converter on the transmitter side is managed by the HC-GSS approach for MEET by minimizing the input power under the premise of output stability. Compared with the conventional P&O method, the HC-GSS method can eliminate the trade-off between the oscillation and convergence rate because it is designed for different system stages. In this WPT system, there is no need for direct communication between the transmitter and receiver. Therefore, the system is potentially cheaper to implement and does not suffer from annoying communication delays, which are prevalent in underwater environments for unmanned underwater vehicles’ (UUV) WPT systems. Both the simulation and experiment results show that this method can improve the efficiency of the WPT system without communication. The proposed method remains valid with coupler displacement as it does not include the mutual inductance of the system. Full article

In the deep-sea environment, the volume available for an in-situ gene sequencer is severely limited. In addition, optical imaging systems are subject to real-time, large-scale defocusing problems caused by ambient temperature fluctuations and vibrational perturbations. To address these challenges, we propose an edge detection algorithm for defocused images based on grayscale gradients and establish a defocus state detection model with nanometer resolution capabilities by relying on the inherent critical illumination light field. The model has been applied to a prototype deep-sea gene sequencing microscope with a 20× objective. It has demonstrated the ability to focus within a dynamic range of ±40 μm with an accuracy of 200 nm by a single iteration within 160 ms. By increasing the number of iterations and exposures, the focusing accuracy can be refined to 78 nm within a dynamic range of ±100 μm within 1.2 s. Notably, unlike conventional photoelectric hill-climbing, this method requires no additional hardware and meets the wide dynamic range, speed, and high-accuracy autofocusing requirements of deep-sea gene sequencing in a compact form factor. Full article

The efficiency of heuristic search algorithms is a critical factor in the realm of cryptographic primitive construction, particularly in the generation of highly nonlinear bijective permutations, known as substitution boxes (S-boxes). The vast search space of 256! (256 factorial) permutations for 8-bit sequences poses a significant challenge in isolating S-boxes with optimal nonlinearity, a crucial property for enhancing the resilience of symmetric ciphers against cryptanalytic attacks. Existing approaches to this problem suffer from high computational costs and limited success rates, necessitating the development of more efficient and effective methods. This study introduces a novel approach that addresses these limitations by dynamically adjusting the cost function parameters within the hill-climbing heuristic search algorithm. By incorporating principles from dynamic programming, our methodology leverages feedback from previous iterations to adaptively refine the search trajectory, leading to a significant reduction in the number of iterations required to converge on optimal solutions. Through extensive comparative analyses with state-of-the-art techniques, we demonstrate that our approach achieves a remarkable 100% success rate in locating 8-bit bijective S-boxes with maximal nonlinearity, while requiring only 50,000 iterations on average—a substantial improvement over existing methods. The proposed dynamic parameter adaptation mechanism not only enhances the computational efficiency of the search process, but also showcases the potential for interdisciplinary collaboration between the fields of heuristic optimization and cryptography. The practical implications of our findings are significant, as the ability to efficiently generate highly nonlinear S-boxes directly contributes to the development of more secure and robust symmetric encryption systems. Furthermore, the dynamic parameter adaptation concept introduced in this study opens up new avenues for future research in the broader context of heuristic optimization and its applications across various domains. Full article

A Bayesian network (BN) is a probabilistic graphical model that can model complex and nonlinear relationships. Its structural learning from data is an NP-hard problem because of its search-space size. One method to perform structural learning is a search and score approach, which uses a search algorithm and structural score. A study comparing 15 algorithms showed that hill climbing (HC) and tabu search (TABU) performed the best overall on the tests. This work performs a deeper analysis of the application of the adaptive genetic algorithm with varying population size (AGAVaPS) on the BN structural learning problem, which a preliminary test showed that it had the potential to perform well on. AGAVaPS is a genetic algorithm that uses the concept of life, where each solution is in the population for a number of iterations. Each individual also has its own mutation rate, and there is a small probability of undergoing mutation twice. Parameter analysis of AGAVaPS in BN structural leaning was performed. Also, AGAVaPS was compared to HC and TABU for six literature datasets considering F1 score, structural Hamming distance (SHD), balanced scoring function (BSF), Bayesian information criterion (BIC), and execution time. HC and TABU performed basically the same for all the tests made. AGAVaPS performed better than the other algorithms for F1 score, SHD, and BIC, showing that it can perform well and is a good choice for BN structural learning. Full article