Search Results (2,600)

Studies conceptualize planning support systems (PSS) outcomes as post-implementation use (limited or continuous) in the planning process. This paper presents another perspective on PSS implementation outcomes—its institutionalization in the planning process. It combines the sociology of translation (SoT) and actor–network theory (ANT) as an analytic framework to investigate and explain a country’s PSS institutionalization in the planning process over 8 years. Ethnographic methods aid qualitative data collection and analysis. Results provide insight in the following three ways: (1) how heterogeneous actors create networks for PSS use, (2) to what extent the network(s) shape PSS institutionalization, and (3) why PSS institutionalization in planning processes does or does not happen. This paper argues that if PSS research investigates and documents these three ways, it will provide additional insights into the decisions, actions, and agencies of PSS institutionalization compared to studies that conceptualize PSS outcomes with use. It contributes to PSS research and practice by demonstrating the value of ANT in enhancing our understanding of PSS institutionalization in planning processes. It recommends further studies to validate this research regarding both retrospective understanding of and prospective management for PSS institutionalization. Full article

Polycyclic aromatic hydrocarbons (PAHs) are persistent organic pollutants with significant ecological and public health implications, particularly in urban wetlands exposed to chronic anthropogenic stress. This study evaluates the occurrence, spatial distribution, seasonal variability, and ecological risk of three low molecular weight PAHs—naphthalene (NAP), phenanthrene (PHEN), and anthracene (ANT)—in surface waters of Caño La Malaria, the main freshwater source of Cucharillas Marsh, Puerto Rico’s largest urban wetland. Surface water samples were collected at four locations during both wet- and dry-season campaigns. Samples were extracted and quantified by GC-MS. NAP was the dominant compound, Σ3PAHs concentrations ranging from 7.4 to 2198.8 ng/L, with higher wet-season levels (mean = 745.79 ng/L) than dry-season levels (mean = 186.71 ng/L); most wet-season samples fell within the mild-to-moderate contamination category. Compositional shifts indicated increased levels of PHEN and ANT during the wet season. No significant spatial differences were found (p = 0.753), and high correlations between sites (r = 0.96) suggest uniform input sources. Diagnostic ratios, inter-species correlations, and principal component analysis (PCA) consistently indicated a predominant pyrogenic origin, with robust PHEN–ANT correlation (r = 0.824) confirming shared combustion-related sources. PCA revealed a clear separation between dry- and wet-season samples, with the latter showing greater variability and stronger associations with NAP and ANT. Ecological risk assessment using hazard quotients (HQ_water) indicated negligible acute toxicity risk across all sites and seasons (<0.01); the highest HQ_water (0.0095), observed upstream during the wet season, remained within this range. However, benchmark exceedances by PHEN and ANT suggest potential chronic risks not captured by the acute ERA framework. These findings support integrated watershed management practices to mitigate PAH pollution and strengthen long-term ecological health in tropical urban wetlands. Full article

Background/Objectives: The global dissemination of tet(X) variants critically threatens tigecycline efficacy as a last-resort antibiotic. The aim of this study was to characterize a tet(X6)-carrying integrative and conjugative element (ICE) in a multidrug-resistant Chryseobacterium lecithinasegens strain, SHZ29, isolated from Shanghai, China. Methods: Minimum inhibitory concentrations (MICs) were determined by broth microdilution for SHZ29. Whole genomic sequencing and bioinformatic analysis were performed to depict the structure of the novel tet(X6)-carrying ICE. Inverse PCR and conjugation experiments were conducted to investigate the transfer ability of the ICE. Results: We depicted a novel tet(X6)-carrying ICE, named ICECleSHZ29, which is 74,906 bp in size and inserted into the 3′ end of tRNA-Met-CAT gene of the C. lecithinasegens strain SHZ29, with 17 bp direct repeats (5′-tcccgtcttcgctacaa-3′). This ICE possesses a 38 kb conserved backbone and four variable regions (VR1-4), with VR3 aggregating multiple resistance genes, including tet(X6), tet(X2), erm(F), ere(D), floR, catB, sul2, ant(6)-I and bla_OXA-1327. NCBI database searching identified 13 additional ICEs sharing a similar backbone to ICECleSHZ29. These ICECleSHZ29-like ICEs could be classified into two types based on their distinct insertion sites: Type I, inserted at the tRNA-Met-CAT gene; and Type II, inserted at the tRNA-Glu-TTC gene. Phylogenetic analysis indicated that differences in integrases may result in differences in the insertion site among these ICEs. A circular intermediate form of ICECleSHZ29 was detected by inverse PCR. However, the conjugation experiments using Escherichia coli EC600 as recipients failed. Conclusions: To our knowledge, this study provides the first report of tet(X6) in C. lecithinasegens and characterizes its carrier, a novel ICE: ICECleSHZ29. Full article

In May of 2025, four men were sentenced in a Kenyan court for the attempted smuggling of ants. This case underscores a largely overlooked dimension of global wildlife crime: the trafficking of insects. This article aims to discuss the nature of insect trafficking in legal, criminological, and conservation discourses and to argue for its inclusion in broader debates within environmental justice discourse. Exploring legal and policy dimensions of wildlife trafficking through a green criminological lens, this paper underscores the anthropocentric bias in wildlife protection, which marginalises noncharismatic species despite their ecological importance. It concludes that a shift toward ecological and species justice is necessary, advocating for more inclusive legal definitions, improved enforcement mechanisms, and interdisciplinary research. Recognising insects as victims of environmental harm is essential for developing holistic responses to wildlife crime and advancing the goals of green criminology. Full article

Unmanned surface vehicles (USVs) rely on multi-sensor perception, such as radar, LiDAR, GPS, and vision, to ensure safe and efficient navigation in complex maritime environments. Traditional ant colony optimization (ACO) for path planning, however, suffers from premature convergence, slow adaptation, and poor smoothness in cluttered waters, while the dynamic window approach (DWA) without global guidance can become trapped in local obstacle configurations. This paper presents a sensor-oriented hybrid method that couples an improved ACO for global route planning with an enhanced DWA for local, real-time obstacle avoidance. In the global stage, the ACO state–transition rule integrates path length, obstacle clearance, and trajectory smoothness heuristics, while a cosine-annealed schedule adaptively balances exploration and exploitation. Pheromone updating combines local and global mechanisms under bounded limits, with a stagnation detector to restore diversity. In the local stage, the DWA cost function is redesigned under USV kinematics to integrate velocity adaptability, trajectory smoothness, and goal-deviation, using obstacle data that would typically originate from onboard sensors. Simulation studies, where obstacle maps emulate sensor-detected environments, show that the proposed method achieves shorter paths, faster convergence, smoother trajectories, larger safety margins, and higher success rates against dynamic obstacles compared with standalone ACO or DWA. These results demonstrate the method’s potential for sensor-based, real-time USV navigation and collision avoidance in complex maritime scenarios. Full article

Faced with complex, ever-changing battlefield environments and diverse attacks, enabling warship combat systems to recover rapidly and effectively after damage is key to enhancing resilience and sustained combat capability. We construct a representative naval battle scenario and propose an integrated Attack-Defense-Recovery Strategy (ADRS) grounded in warship system models for different attack types. To address high parameter sensitivity, weak initial pheromone feedback, suboptimal solution quality, and premature convergence in traditional ant colony optimization (ACO), we introduce three improvements: (i) grid-search calibration of key ACO parameters to enhance global exploration, (ii) a non-uniform initial pheromone mechanism based on the wartime importance of equipment to guide early solutions, and (iii) an ADRS-consistent state-transition rule with group-based starting points to prioritize high-value equipment during the search. Simulation results show that the improved ACO (IACO) outperforms classical ACO in convergence speed and solution optimality. Across torpedo, aircraft/missile, and UAV scenarios, ADRS-ACO improves over GRS-ACO by 7.2%, 0.3%, and 5.5%, while ADRS-IACO achieves gains of 34.9%, 17.1%, and 16.7% over GRS-ACO and 25.9%, 16.7%, and 10.6% over ADRS-ACO. Overall, ADRS-IACO consistently delivers the best solutions. In high-intensity, high-damage torpedo conditions, ADRS-IACO demonstrates superior path planning and repair scheduling, more effectively identifying critical equipment and allocating resources. Moreover, under multi-wave combat, coupling with ADRS effectively reduces cumulative damage and substantially improves overall warship-system resilience. Full article

The increasing sophistication of cyberattacks makes Intrusion Detection Systems (IDSs) essential, yet the high dimensionality of modern network traffic hinders accuracy and efficiency. We conduct a comparative study of multi-objective feature selection for IDS using four bio-inspired metaheuristics—Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO)—on the X-IIoTID dataset. GA achieved the highest accuracy (99.60%) with the lowest FPR (0.39%) using 34 features. GWO offered the best accuracy–subset balance, reaching 99.50% accuracy with 22 features (65.08% reduction) within 0.10 percentage points of GA while using ~35% fewer features. PSO delivered competitive performance with 99.58% accuracy, 32 features (49.21% reduction), FPR 0.40%, and FNR 0.44%. ACO was the fastest (total training time 3001 s) and produced the smallest subset (7 features; 88.89% reduction), at an accuracy of 97.65% (FPR 2.30%, FNR 2.40%). These results delineate clear trade-off regions of high accuracy (GA/PSO/GWO), balanced (GWO), and efficiency-oriented (ACO) and underscore that algorithm choice should align with deployment constraints (e.g., edge vs. enterprise vs. cloud). We selected this quartet because it spans distinct search paradigms (hierarchical hunting, evolutionary recombination, social swarming, pheromone-guided foraging) commonly used in IDS feature selection, aiming for a representative, reproducible comparison rather than exhaustiveness; extending to additional bio-inspired and hybrid methods is left for future work. Full article

The integration of renewable energy sources has become a strategic necessity for sustainable energy management and supply security. This study evaluates the performance of eight metaheuristic optimization algorithms in scheduling a renewable-based smart grid system that integrates solar, wind, and battery storage for a factory in İzmir, Türkiye. The algorithms considered include classical approaches—Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), the Whale Optimization Algorithm (WOA), Krill Herd Optimization (KOA), and the Ivy Algorithm (IVY)—alongside hybrid methods, namely KOA–WOA, WOA–PSO, and Gradient-Assisted PSO (GD-PSO). The optimization objectives were minimizing operational energy cost, maximizing renewable utilization, and reducing dependence on grid power, evaluated over a 7-day dataset in MATLAB. The results showed that hybrid algorithms, particularly GD-PSO and WOA–PSO, consistently achieved the lowest average costs with strong stability, while classical methods such as ACO and IVY exhibited higher costs and variability. Statistical analyses confirmed the robustness of these findings, highlighting the effectiveness of hybridization in improving smart grid energy optimization. Full article

Online peer support platforms are vital, scalable resources for mental health, yet their effectiveness is frequently undermined by inefficient user matching, severe participation inequality, and subsequent “super-helper” burnout. This study introduces TrailMap, a novel peer-matching algorithm inspired by the decentralised foraging strategies of ant colonies. By treating user interactions as paths that gain or lose “pheromone” based on helpfulness ratings, the system enables the community to collectively and adaptively identify its most effective helpers. A two-phase validation study was conducted. First, an agent-based simulation demonstrated that TrailMap reduced the mean time to a helpful response by over 70% and improved workload equity compared to random routing. Second, a four-week randomised controlled pilot study with human participants confirmed these gains, showing a 76% reduction in median wait time and significantly higher perceived helpfulness ratings. The findings suggest that by balancing the workload, TrailMap enhances not only the efficiency but also the socio-technical sustainability of online support communities. TrailMap provides a practical, nature-inspired method for building more resilient and equitable online support communities, enhancing access to effective mental health support. Full article

The finite element model (FEM) for induction motors (IM) was developed and validated through experimental testing. The validated FEM provides a reliable basis for further optimization of the electric machine. A strong sliding mode technique, in conjunction with field-oriented control (FOC), is proposed for speed control of the IM. The sliding mode controller ensures steady functioning in the face of ambiguities and disruptions, while FOC enables precise control of the motor’s magnetic field. This combination enhances both the efficiency and accuracy of speed control in IM, making it a valuable tool for industrial applications. The proposed sliding mode control (SMC) was fine-tuned using the advantages produced by the ant colony optimization algorithm. This approach aids in resolving issues and delivers optimal speed and field responses. Simulation and experimental results demonstrate the effectiveness of the proposed approach. The optimized induction motor achieved a 28% reduction in rotor Joule losses, resulting in improved energy efficiency. Additionally, using Ant Colony Optimization to adjust the SMC parameters led to a 99.74% reduction in speed tracking error and a 99.59% reduction in flux error compared to traditional manual tuning. These substantial improvements confirm the superiority of the proposed method for high-performance and energy-efficient electric vehicle applications. Full article

Maize is crucial for food, feed, and industrial materials. The seed purity directly affects yield and quality. Advancements in automation have led to the lightweight unmanned maize detasseling vehicle (UDV). To boost UDV’s efficiency, this paper proposes a dual-route and dual-mode adaptive ant colony optimization (DRDM-AACO) for the detasseling route planning in maize seed production fields with hybrid spatial constraints. A mathematical model is established based on a proposed projection method for male flower nodes. To improve the performance of the ACO, four innovative mechanisms are proposed: a dual-route preference based on the dynamic selection strategy to ensure the integrity of the route topology; a dynamic candidate set with the variable neighborhood search strategy to balance exploration and exploitation; a non-uniform initial pheromone allocation based on the principle of intra-row priority and inter-row inhibition, and direction-constrained adaptive dual-mode pheromone regulation through local penalty and global evaporation strategies to reduce intra-row turnback routes. Comparative experiments showed DRDM-AACO reduced the route by 6.2% compared to ACO variants, verifying its effectiveness. Finally, experiments with various sizes and actual farmland compared DRDM-AACO to other various algorithms. The route was shortened by 32%, confirming its practicality and superiority. Full article

The shortest path problem is algorithmic and involves finding the least costly path (in terms of distance, time, cost, or other criteria) between two nodes in a graph. This problem is fundamental in graph theory and has applications in logistics, networks, mapping, and more. Several methods have been used to solve this problem. In this paper, we applied the ant colony algorithm to optimize the travel path of product quality technicians in a textile company specializing in washing and dyeing denim items. The company aims to minimize distances and costs between its subcontractors. The method has demonstrated a significant impact on distance and cost reduction while contributing to the reduction of the environmental effects. Full article

This study addresses the core challenges of precise marigold corollas recognition and efficient continuous path planning under complex natural conditions (strong illumination, occlusion, adhesion) by proposing an integrated lightweight visual recognition and real-time path planning framework. We introduce MPD-YOLO, an optimized model based on YOLOv11n, incorporating (1) a Multi-scale Information Enhancement Module (MSEE) to boost feature extraction; (2) structured pruning for significant model compression (final size: 2.1 MB, 39.6% of original); and (3) knowledge distillation to recover accuracy loss post-pruning. The resulting model achieves high precision (P: 89.8%, mAP@0.5: 95.1%) with reduced computational load (3.2 GFLOPs) while demonstrating enhanced robustness in challenging scenarios—recall significantly increased by 6.8% versus YOLOv11n. Leveraging these recognition outputs, an adaptive ant colony algorithm featuring dynamic parameter adjustment and an improved pheromone strategy reduces average path planning time to 2.2 s—a 68.6% speedup over benchmark methods. This integrated approach significantly enhances perception accuracy and operational efficiency for automated marigold harvesting in unstructured environments, providing robust technical support for continuous automated operations. Full article

The soft scale Toumeyella martinezae infests the arborescent cactus Myrtillocactus geometrizans. This scale is, in turn, parasitized by the wasp Mexidalgus toumeyellus and forms a mutualistic relationship with the ant Liometopum apiculatum. This study assessed how ant and/or parasitoid presence influenced parasitism rates and the population growth of the scale insect. Experimental treatments included scale populations with ant access (control) or ant exclusion, and parasitoid exclusion with ant access. Scale population growth rates were estimated using Lefkovitch projection matrices, built based on the individual monitoring of approximately 5400 scales. The average parasitism rate was higher in the “with ants” treatment (18.66%) than under ant exclusion (5.42%). In the absence of parasitoids, the scale population growth rate (λ = 1.532) was 8% higher than in the control treatment (λ = 1.423). Population growth was negative (λ = 0.636) when ants were excluded. These results indicate that interaction with the mutualistic ant is the primary factor sustaining a positive scale population growth. In contrast, the impact of the parasitoid alone is insufficient for effectively controlling the soft scale pest. Full article

Virtual power plants (VPPs) integrate distributed energy sources and demand-side resources, but their efficient intelligent resource decision-making faces challenges such as high-dimensional constraints, output volatility of renewable energy, and insufficient adaptability of traditional optimization algorithms. To address these issues, an innovative intelligent decision-making framework based on the Ant Colony Algorithm–Simulated Annealing (ACO-SA) is first proposed in this paper, aiming to realize intelligent collaborative decision-making for the economy and operational stability of VPP in complex scenarios. This framework combines the global path-searching capability of the Ant Colony Algorithm (ACO) with the probabilistic jumping characteristic of the Simulated Annealing Algorithm (SA) and designs a dynamic parameter collaborative adjustment mechanism, which effectively overcomes the defects of traditional algorithms such as slow convergence and easy trapping in local optimal solutions. Secondly, a resource intelligent decision-making cost model under the VPP framework is constructed. To verify algorithm performance, comparative experiments covering multiple scenarios (agricultural parks, industrial parks, and industrial parks with energy storage equipment) are designed and conducted. Finally, the simulation results show that compared with ACO, SA, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), ACO-SA exhibits significant advantages in terms of scheduling cost and convergence speed; the average scheduling cost of ACO-SA is 2.31%, 0.23%, 3.57%, and 1.97% lower than that of GA, PSO, ACO, and SA, respectively, and it can maintain excellent stability even in high-dimensional constraint scenarios with energy storage systems. Full article