Search Results (247)

The optimal reconfiguration of electrical distribution feeders is a fundamental strategy for reducing active power losses and improving voltage profiles, yet it remains a challenging mixed-integer nonlinear programming (MINLP) problem due to the combinatorial explosion of radial topologies and the nonlinearities introduced by power flow equations. This paper proposes a novel master–slave methodology that integrates a Chu and Beasley genetic algorithm (CBGA) with a minimum spanning tree (MST)-based repair mechanism to address these challenges. In the master stage, the CBGA explores the binary space of switching decisions via steady-state population management, duplicate elimination, and stagnation restart policies. A key contribution lies in the MST-based repair procedure, which ensures that every individual generated by crossover and mutation is projected onto a feasible radial and connected configuration, effectively confining the search to the constrained solution space without recourse to penalty functions. A systematic weight-design rule preserves the Hamming distance between infeasible offspring and repaired solutions, minimizing the distortion of genetic information. The slave stage evaluates each candidate topology using a successive approximations power flow solver, assessing electrical feasibility and computing active power losses. The proposed methodology is validated on multiple test feeders, ranging from small 9- and 24-bus networks to large-scale benchmarks including 33-, 69-, 84-, 136-, and 415-bus systems. A comparison against the deterministic sequential switch opening method (SSOM) and a specialized tabu search demonstrates that the CBGA-MST consistently matches the best-known optima in the literature, achieving loss reductions of up to 9.63% compared to SSOM on the 415-bus system. A statistical analysis over 100 independent runs confirms the algorithm’s robustness, with zero standard deviation for networks of up to 69 buses and a standard deviation of only 2.99 kW (0.51%) for the 415-bus system. The findings confirm that the proposed approach offers superior scalability, robustness, and solution quality, positioning it as a practical and effective tool for distribution system operators seeking to enhance network efficiency under peak load conditions. Full article

In the analysis of complex engineering systems, managing uncertainty and optimizing information processing structures are critical for reliable state prediction. The Belief Rule Base (BRB) provides a powerful machine learning approach for integrating expert knowledge with uncertain information. However, mitigating the combinatorial complexity of BRBs through conventional structure simplification often causes unintended information loss, introducing systematic prediction biases that undermine reliability. To address the trade-off between system complexity and modeling accuracy, this study proposes an adaptive belief rule base framework integrating sensitivity analysis with posterior consistency calibration (BRB-ARR). First, an information-driven rule screening mechanism is developed to dynamically determine the pruning threshold based on optimized Mean Square Error (MSE) fluctuations. This method effectively filters redundant rules while avoiding the cognitive biases associated with fixed empirical values. Second, a low-dimensional optimization process is employed to readjust the parameter vector, significantly enhancing computational efficiency. Finally, a posterior calibration module is introduced to compensate for the systematic biases caused by dimensionality reduction, strictly preserving the interpretability of the core inference architecture. To validate the effectiveness of the proposed framework, experimental evaluations are conducted on petroleum pipeline networks and liquid propellant launch vehicles. In the petroleum pipeline scenario, the rule base scale is reduced by over 60 percent from 56 to approximately 20 rules, while the parameter dimensionality decreases from 338 to 122. Compared to the conventional model, the mean squared error is reduced from 0.5291 to 0.3619. Furthermore, in the liquid propellant launch vehicle case, the model achieves a prediction accuracy of 98.57 percent with a mean squared error of 0.00029 while reducing the rule scale from 441 to 109. These results demonstrate that the BRB-ARR model effectively balances structural compactness with high precision prediction, offering a novel approach to uncertainty modeling in intelligent systems. Full article

With the increasing penetration of wind power, enhancing the renewable energy accommodation rate and reducing the carbon footprint of the IES, this study proposes a comprehensive evaluation method to assess the impact of a novel dynamic Green Certificate Trading (GCT) and Green Hydrogen Certificate Trading (GHCT) joint mechanism. First, considering the integration of the IES into the carbon trading market, a coupled dynamic GCT-GHCT framework is established. This framework links dynamic green electricity certificate revenues with green hydrogen certificate revenues, leveraging cross-subsidization to incentivize renewable energy consumption. Subsequently, an optimal operation model for the IES is formulated with the objective of minimizing comprehensive costs, which encompass energy procurement, green certificates, carbon trading, and wind curtailment penalties. A piecewise linearization approach is applied to transform the optimization model into a Mixed-Integer Linear Programming problem for efficient solving. Furthermore, based on the dispatch results, a multidimensional evaluation index system is constructed, extracting key indicators from economic, technical, and environmental perspectives. To ensure the rationality of the evaluation, a dynamic reward–penalty asymmetric cloud matter-element (ACME) comprehensive evaluation method based on game theory combinatorial weighting is introduced to calculate the index weights and the final comprehensive evaluation value. Finally, multi-scenario simulations are conducted to verify the superiority of the integrated GCT-GHCT trading framework. The results reveal that the proposed approach not only maximizes renewable energy integration but also provides a robust decision-making tool for the low-carbon transition of multi-energy systems. Full article

With the expansion of offshore oil and gas exploration into deep-water regions, the efficient scheduling of platform supply vessels (PSVs) is critical to offshore operations. The platform supply vessel routing and scheduling problem (PSVRSP) is an NP-hard combinatorial optimization problem, which is further complicated by uncertainty in offshore demand. Existing studies reveal a methodological gap: exact optimization algorithms have rarely been applied to this problem, as most prior research relies on heuristic methods that cannot guarantee optimality. To address this gap, this study proposes a novel enhanced branch-and-price (B&P) algorithm for the platform supply vessel routing and scheduling problem with uncertain demand (PSVRSP-UD). The proposed approach integrates NG-route labeling, a group-representative label mechanism, and a two-level branching strategy to efficiently obtain globally optimal solutions under demand uncertainty. A scenario-based mixed-integer linear programming (MILP) model is formulated, in which demand uncertainty is captured using Latin hypercube sampling (LHS) combined with Cholesky decomposition and sample-based reduction (SBR). Based on Dantzig–Wolfe decomposition, the proposed B&P algorithm integrates NG-route labeling and a two-level branching strategy to achieve global optimization. Computational experiments show that the B&P algorithm outperforms CPLEX in both computational efficiency and solution quality. Sensitivity analyses examine the impacts of scenario number, demand fluctuation, time window tightness, and weight coefficients on the results. The new results in this study can provide a practical decision-support tool for offshore logistics operations. Full article

Male infertility is an escalating global health issue, frequently associated with metabolic problems like obesity, diabetes, and age. Recent evidence designates AMP-activated protein kinase (AMPK) as a pivotal regulator linking energy balance to male reproductive function. AMPK regulates essential activities such as spermatogenesis, metabolic support of Sertoli cells, and steroidogenesis in Leydig cells, as well as sperm motility, capacitation, and the acrosome reaction. At the molecular level, AMPK coordinates signaling networks that include mTOR, SIRT1, PGC-1α, and FOXO to modulate mitochondrial function, oxidative stress, and autophagy-related quality control. Dysregulation of AMPK during metabolic and environmental stress results in compromised spermatogenesis, diminished sperm quality, mitochondrial malfunction, and reduced testosterone synthesis. Targeting AMPK signaling constitutes a possible therapeutic approach for enhancing male reproductive health. Pharmacological agents like metformin and AICAR, together with natural bioactive substances, lifestyle modifications, and exercise mimetics, have shown promise in reestablishing metabolic equilibrium and improving reproductive results. Moreover, combinatorial strategies that integrate antioxidants and autophagy modulators may yield synergistic advantages. Nonetheless, obstacles concerning tissue selectivity, optimum dose, and clinical translation persist. Future perspectives highlight precision medicine, biomarker-directed therapies, and the incorporation of metabolic health into fertility treatment. AMPK-targeted treatments collectively provide a novel and mechanistically sound method for addressing male infertility. Full article

This study investigates the Container Consolidation Problem (CCP), a critical operational challenge in container terminals where containers with specific attributes must be relocated during yard crane idle periods. The primary objective is to maximize yard space availability for incoming vessels by strategically grouping containers, thereby alleviating storage pressure and enhancing throughput. A mixed-integer programming model is formulated to minimize the total handling time, incorporating complex constraints related to crane availability, relocation sequencing, and slot assignment. Due to the combinatorial complexity inherent in large-scale yard operations, a comprehensive optimization framework is proposed. This framework balances computational efficiency with solution quality, offering a robust approach to solve large-scale instances within practical time limits. Computational experiments demonstrate that the proposed methodology consistently yields high-quality solutions, effectively resolving the trade-off between solution speed and optimality. The research provides not only a novel methodological perspective for solving this NP-hard problem but also offers significant practical value. By optimizing crane scheduling, the model directly contributes to reducing operational costs, improving the turnover rate of yard space, and strengthening the overall efficiency of the maritime supply chain. Full article

Detecting changepoints in time series is a fundamental task in statistical modeling and data-driven decision-making. We introduce a novel set partitioning-based model for changepoint detection that leverages combinatorial optimization to identify an optimal set of segments explaining the observed data. Unlike conventional dynamic programming approaches, which rely on restrictive structural assumptions on the cost function to ensure tractability, our formulation is based on Integer Linear Programming. While the standard additivity assumption on segment-wise costs is retained, the proposed framework departs from existing methods in its ability to incorporate both local and global structural constraints directly within the optimization model. In particular, it supports a broad class of constraints, ranging from simple segment-level restrictions to complex global conditions coupling multiple segments, without requiring modifications to the underlying solution scheme. This enhanced modeling capability constitutes the main contribution of the work, significantly increasing the expressiveness of the framework while preserving the tractability of additive cost structures. The model’s design enables high adaptability to different application domains, including finance, bioinformatics, and industrial monitoring. The efficiency of modern MILP solvers, combined with tailored dominance rules, enables the solution of instances with several hundreds of observations in practical time. Computational results indicate that the approach extends tractability beyond previously studied settings, effectively handling classes of instances whose structural constraints could not be accommodated by existing methods, while retaining robustness and interpretability. Full article

The Traveling Salesman Problem (TSP) is a fundamental challenge in combinatorial optimization, with wide-ranging applications in logistics, manufacturing, and network design. In addition to the classical formulation, recent years have witnessed the emergence of complex variants, such as the TSP with Drones (TSP-D), TSP with Time Windows, and Prize-Collecting TSP, that incorporate novel constraints reflecting real-world requirements like last-mile delivery and multimodal logistics. This review presents a comprehensive survey of metaheuristic approaches for solving both the classical TSP and its emerging extensions, with particular emphasis on metaheuristic, hybrid methods, and machine learning-enhanced strategies. Recent algorithmic developments, benchmark datasets, and evaluation metrics are investigated, and critical challenges in addressing drone coordination, synchronization, and uncertainty are identified, as well. Bibliometric analysis is further provided to map research trends and the evolution of the field. By synthesizing foundational techniques and state-of-the-art innovations, this review outlines current progress and proposes future directions for metaheuristic optimization in increasingly dynamic and heterogeneous routing scenarios. Full article

Nature-inspired metaheuristic algorithms are commonly applied to complex combinatorial optimization problems where exact methods are computationally impractical. Tourist route optimization is a representative multi-objective problem characterized by realistic constraints such as travel time, cost, opening hours, and transportation modes. Although Mushroom Reproduction Optimization is computationally efficient, it often experiences premature convergence in complex search spaces. This paper proposes a novel hybrid algorithm, Mushroom–Weed Hybrid Reproduction Optimization (MWHRO), which integrates the colony-based local search of the Mushroom Reproduction algorithm with the fitness-proportional reproduction and competitive elimination mechanisms of Invasive Weed Optimization. Hybridization enhances population diversity and global exploration while preserving fast convergence. The proposed algorithm is evaluated based on a realistic tourist route optimization problem using real-world data from Zagreb, Croatia, across multiple transportation modes and objective-weight scenarios. Performance is compared against Ant Colony Optimization, Invasive Weed Optimization, Particle Swarm Optimization, and standard Mushroom Reproduction Optimization under equal evaluation budgets. Experimental results demonstrate that the proposed MWHRO algorithm consistently achieves high-quality solutions with significantly lower execution times, particularly in constrained and multimodal scenarios. Statistical analysis confirms the robustness and practical suitability of the proposed approach for real-world route optimization. Full article

Underwater acoustic sensor networks (UASNs) have emerged as a pivotal technology for ocean exploration, tactical surveillance, and environmental monitoring. However, the underwater acoustic channel poses severe challenges, including high propagation delay, limited bandwidth, and rapid time-varying multipath fading, which significantly degrade communication reliability. Cooperative communication, which exploits spatial diversity via relay nodes, offers a promising solution to these impairments. In this paper, we investigate the joint optimization of relay selection and power allocation in UASNs to maximize the long-term system energy efficiency and throughput. This problem is inherently complex due to the hybrid action space, which couples the discrete selection of relay nodes with the continuous allocation of transmission power, and the absence of real-time, perfect channel state information (CSI). To address these challenges, we propose a novel deep hybrid reinforcement learning (DHRL) framework utilizing a parameterized deep Q-Network (P-DQN) architecture. Unlike traditional approaches that discretize power levels or relax discrete constraints, our approach seamlessly integrates a deterministic policy network for continuous power control and a value-based network for discrete relay evaluation. Furthermore, we incorporate a prioritized experience replay (PER) mechanism to improve sample efficiency by focusing on rare but significant channel transition events. We provide a comprehensive theoretical analysis of the algorithm’s complexity and convergence properties. Extensive simulation results demonstrate that the proposed DHRL algorithm outperforms state-of-the-art combinatorial bandit algorithms and conventional deep reinforcement learning baselines in terms of system energy efficiency, and also exhibits superior robustness against channel estimation errors. Full article

Trichothecene mycotoxins, especially T-2 toxin, represent a significant threat to food safety and public health. Although the enzymatic degradation of deoxynivalenol has been extensively investigated, there are few reports of enzymes capable of efficiently degrading T-2 toxin. This study identified that the aldo-keto reductase AKR13B3 from Devosia A6-243 exhibits 3-keto-DON-degrading and a little T-2 toxin-degrading activity. To address this limitation, a rational design strategy targeting the substrate-binding pocket was employed to enhance its activity. Utilizing site-directed and combinatorial mutagenesis, a double mutant R134F/D217A was successfully screened. R134F/D217A retains catalytic activity towards 3-keto-DON while significantly enhancing its catalytic capacity for T-2. Specifically, the R134F/D217A variant exhibited a 2.88-fold increase in catalytic activity and a 3.15-fold enhancement in catalytic efficiency (k_cat/K_m) relative to the wild type enzyme. Notably, a substantial improvement in thermal stability was also observed. After incubation at 55 °C, the residual activity of the R134F/D217A mutant was 2.63 times that of the wild type. Molecular dynamics (MD) simulations and three-dimensional structural modeling suggested the mechanistic basis for the enhanced performance of the R134F/D217A double mutant. Catalytic enhancement stems from a shortened nucleophilic attack distance, a positively biased electrostatic environment, combined with an enlarged pocket and reduced binding free energy. Concurrently, the increased thermal stability results from decreased flexibility and a more rigid structural architecture. This work presents the first report of AKR13B3 as an effective enzyme for T-2 toxin transformation, and its catalytic activity was significantly enhanced through rational design. Thus, a novel enzymatic strategy was proposed, and could inform future approaches to study issues related to T-2 toxin contamination. Full article

Malaria, caused by Plasmodium parasites, remains a global health crisis, necessitating novel therapeutic strategies targeting host–parasite interactions. During liver-stage infection, parasites exploit host vesicular trafficking machinery, particularly SNARE (soluble N-ethylmaleimide-sensitive factor attachment protein receptor) proteins that mediate membrane fusion. Using a CRISPR/Cas9 knockout system in HeLa cells combined with advanced microscopy of Plasmodium berghei-infected HeLa cells, we identified specific endolysosomal SNAREs including Vesicle-Associated Membrane Protein 7 (VAMP7), Vesicle-Associated Membrane Protein 8 (VAMP8), Vesicle Transport Through Interaction With T-SNAREs 1B (Vti1B), and Syntaxin 7 (Stx7) to be recruited to the parasitophorous vacuole membrane (PVM) with distinct temporal profiles. This demonstrates the parasite’s precise manipulation of host endolysosomal trafficking pathways. VAMP7 and Vti1B were localized to the PVM within 30 min post-infection, suggesting potential roles during invasion, while VAMP8 and Stx7 appeared later around 24 h post infection (hpi), coinciding with increased nutrient acquisition. Single gene deletions showed minimal impact, but combinatorial knockouts (KO) revealed critical redundancy. VAMP7-VAMP8 as well as VAMP7–Vti1B double KO significantly reduced parasite infection and growth, with Vti1B playing a dominant role. Triple KO phenotypes mirrored VAMP7-Vti1B disruption, underscoring Vti1B’s dominant role. SNARE depletion also impaired the lysosome–PVM association and LAMP1 positive vesicle recruitment. Our findings indicate Plasmodium hijacks a coordinated host SNARE network to fuse lysosomes with the PVM for nutrient uptake. Targeting Vti1B-containing complexes disrupts this pathway without host cell toxicity, offering a promising host-directed antimalarial approach. Full article

Background: Combinatorial optimization problems (COPs) are central to Logistics and Supply Chain decision making, yet their NP-hardness prevents exact optimal solutions in reasonable time. Methods: This work addresses that limitation by developing a novel ternary network flow linear programming (LP) model of the assignment problem (AP) polytope. The model is very large scale (with $\mathsf{\Theta }\left(m^9\right)$ variables and $\mathsf{\Theta }\left(m^8\right)$ constraints, where m is the number of assignments). Although not intended to compete with conventional two-dimensional formulations of the AP with respect to solution procedures, it enables hard COPs to be solved exactly as “strict” (integrality requirements-free) LPs through simple transformations of their cost functions. Illustrations are given for the quadratic assignment problem (QAP) and the traveling salesman problem (TSP). Results: Because the proposed LP model is polynomial-sized and there exist polynomial-time algorithms for solving LPs, it affirms “$P=NP$.” A separable substructure of the model shows promise for practical-scale instances due to its suitability for large-scale optimization techniques such as Dantzig–Wolfe Decomposition, Column Generation, and Lagrangian Relaxation. The formulation also has greater robustness relative to standard network flow models. Conclusions: Overall, the approach provides a systematic, modeling-barrier-free framework for representing NP-complete problems as polynomial-sized LPs, with clear theoretical interest and practical potential for medium to large-scale Logistics and other COP-intensive applications. Full article

We introduce cumulative tic-tac-toe, a novel variant of the classic $3\times 3$ tic-tac-toe game in which play continues until the board is completely filled. Each player’s final score is determined by the total number of three-in-a-row sequences they form. Using combinatorial game theory (CGT), we establish that under optimal play, the game is a draw, and we characterize its theoretical properties. To empirically validate and optimize practical play, we develop a reinforcement learning (RL) framework based on temporal-difference (TD) learning, which is enhanced with a domain-informed evaluation function to accelerate convergence. The experimental results show that our triplet-coverage difference (TCD) evaluation function reduces the average number of training episodes by approximately 23.1% compared with a random-initialization baseline, a statistically significant improvement at the 5% significance level. These results demonstrate the efficiency of our CGT–RL approach for cumulative tic-tac-toe and suggest that similar methods may be useful for analyzing related combinatorial games. We also discuss potential analogies in domains such as competitive resource allocation and coalition formation, illustrating how cumulative-scoring games connect abstract game-theoretic ideas to practical sequential decision problems. Full article

The melon fly, Zeugodacus cucurbitae (Coquillett), is recognized as a globally significant quarantine pest, and it ranks among the most destructive insect species infesting cucurbit and solanaceous crops. However, the molecular mechanisms governing reproductive regulation in female Z. cucurbitae remain poorly characterized, particularly those underlying the reproductive processes mediated by microRNAs (miRNAs). In this study, we firstly identified the ovary-specific gene ZcCTL-S1 in Z. cucurbitae via transcriptomic analysis, and subsequently predicted its targeted miRNAs using bioinformatics approaches. Among these miRNAs, overexpression or inhibition of miR-971-1 and miR-let-7 led to corresponding inverse changes in the transcriptional level of ZcCTL-S1. Notably, only miR-let-7 displayed markedly elevated expression levels in Z. cucurbitae ovaries. Further analyses confirmed that miR-let-7 exhibited a direct targeting relationship with ZcCTL-S1, via a combinatorial approach involving in vivo RNA immunoprecipitation, in vitro dual-luciferase reporter assays, and site-directed mutagenesis techniques. Phenotypic analyses showed that both knockdown of ZcCTL-S1 and overexpression of miR-let-7 significantly inhibited egg hatchability, ultimately compromising the female reproductive capacity of Z. cucurbitae. Collectively, these findings identify a novel miRNA-gene regulatory module in the reproductive development of Z. cucurbitae, and provide novel insights for the development of gene- or miRNA-based pest control strategies. Full article