Search Results (513)

High-voltage diodes, as key devices in power electronic systems, have important significance for system reliability and preventive maintenance in terms of storage life prediction. In this paper, we propose a hybrid modeling framework that integrates the Long Short-Term Memory Network (LSTM) and Transformer structure, and is hyper-parameter optimized by the Improved Artificial Bee Colony Algorithm (IABC), aiming to realize the high-precision modeling and prediction of high-voltage diode storage life. The framework combines the advantages of LSTM in time-dependent modeling with the global feature extraction capability of Transformer’s self-attention mechanism, and improves the feature learning effect under small-sample conditions through a deep fusion strategy. Meanwhile, the parameter type-aware IABC search mechanism is introduced to efficiently optimize the model hyperparameters. The experimental results show that, compared with the unoptimized model, the average mean square error (MSE) of the proposed model is reduced by 33.7% (from 0.00574 to 0.00402) and the coefficient of determination (R²) is improved by 3.6% (from 0.892 to 0.924) in 10-fold cross-validation. The average predicted lifetime of the sample was 39,403.3 h, and the mean relative uncertainty of prediction was 12.57%. This study provides an efficient tool for power electronics reliability engineering and has important applications for smart grid and new energy system health management. Full article

Metaheuristics are powerful optimization techniques that are well-suited for addressing complex combinatorial problems across diverse scientific and industrial domains. However, their application to computationally expensive problems remains challenging due to the high cost and significant number of fitness evaluations required during the search process. Surrogate modeling has recently emerged as an effective solution to reduce these computational demands by approximating the true, time-intensive fitness function. While surrogate-assisted metaheuristics have gained attention in recent years, their application to complex scheduling problems such as the Permutation Flowshop Scheduling Problem (PFSP) under learning, deterioration, and maintenance effects remains largely unexplored. To the best of our knowledge, this study is the first to investigate the integration of surrogate modeling within the artificial bee colony (ABC) framework specifically tailored to this problem context. We develop and evaluate two distinct strategies for integrating surrogate modeling into the optimization process, leveraging the ABC algorithm. The first strategy uses a Kriging model to dynamically guide the selection of the most effective search operator at each stage of the employed bee phase. The second strategy introduces three variants, each incorporating a Q-learning-based operator in the selection mechanism and a different evolution control mechanism, where the Kriging model is employed to approximate the fitness of generated offspring. Through extensive computational experiments and performance analysis, using Taillard’s well-known standard benchmarks, we assess solution quality, convergence, and the number of exact fitness evaluations, demonstrating that these approaches achieve competitive results. Full article

This paper examines the intersection of global climate governance, carbon markets, and Indigenous Peoples’ rights under the United Nations Framework Convention on Climate Change. It critically analyses how Indigenous Peoples have contested the Article 6 market mechanisms of the Paris Agreement at the height of their negotiation during COP25 and COP26 by drawing attention to their role in perpetuating “carbon colonialism,” thereby revealing deeper power dynamics in global climate governance. Utilising a political ecology framework, this study explores these power dynamics at play during the climate negotiations, focusing on the instrumental, structural, and discursive forms of power that enable or limit Indigenous participation. Through a qualitative case study approach, the research reveals that while Indigenous Peoples have successfully used discursive strategies to challenge market-based solutions, their influence remains limited due to entrenched structural and instrumental power imbalances within the UNFCCC process. This study highlights the need for equitable policies that integrate human rights safeguards and prioritise Indigenous-led, non-market-based approaches to ecological restoration. Full article

The making of a land patrimony for the benefit of religious missions is profoundly linked to territorial construction in the colonies but is rarely examined from the angle of ecclesiastical governmentality over the ceded lands. This analysis highlights three complementary processes for understanding the role of religious mission land heritage in territorial reconfigurations. First, this article examines the process of “land sanctuarization”, which materializes territorial anchoring through the crystallization of land rights granted to religious missions over customary lands previously presumed to be “vacant”. Next, it explores the formation of an “ecclesiastical dominium”, manifested in the dismantling of state missions and their free transfer, explicit or tacit, to religious missions under concession or agreement regimes. This reveals the exercise of state power over the land heritage of religious missions, positioning them as structuring axes and administrative intermediaries for public services, thus giving rise to an ecclesiastical governmentality that drives territorial production and reconfiguration. Finally, postcolonial dynamics reveal the resurgence of new spatial polarities shaped by the complexity of evolving religious actors along the center–periphery axis of a recomposing territorialization. This study underscores the importance of a transversal approach to better govern the land legacies of religious missions, fostering a pluralistic reterritorialization of postcolonial societies in central Africa. Full article

In the shift toward sustainable and resource-efficient manufacturing, Artificial Intelligence (AI) is playing a transformative role in overcoming the limitations of traditional production scheduling methods. This study, based on a Systematic Literature Review (SLR), explores how AI techniques enhance Advanced Planning and Scheduling (APS) systems, particularly under finite-capacity constraints. Traditional scheduling models often overlook real-time resource limitations, leading to inefficiencies in complex and dynamic production environments. AI, with its capabilities in data fusion, pattern recognition, and adaptive learning, enables the development of intelligent, flexible scheduling solutions. The integration of metaheuristic algorithms—especially Ant Colony Optimization (ACO) and hybrid models like GA-ACO—further improves optimization performance by offering high-quality, near-optimal solutions without requiring extensive structural modeling. These AI-powered APS systems enhance scheduling accuracy, reduce lead times, improve resource utilization, and enable the proactive identification of production bottlenecks. Especially relevant in high-variability sectors like fashion, these approaches support Industry 5.0 goals by enabling agile, sustainable, and human-centered manufacturing systems. The findings have been highlighted in a structured framework for AI-based APS systems supported by metaheuristics that compares the Industry 4.0 and Industry 5.0 perspectives. The study offers valuable implications for both academia and industry: academics can gain a synthesized understanding of emerging trends, while practitioners are provided with actionable insights for deploying intelligent planning systems that align with sustainability goals and operational efficiency in modern supply chains. Full article

Multistatic sonar networks (MSNs) have emerged as a powerful approach for enhancing underwater surveillance capabilities. Different from monostatic sonar systems which use collocated sources and receivers, MSNs consist of spatially distributed and independent sources and receivers. In this work, we address the problem of determining the optimal route for a mobile multistatic active sonar source to maximize area coverage, assuming all receiver locations are known in advance. For this purpose, we first develop a Mixed Integer Linear Program (MILP) formulation that determines the route for a single source within a field discretized using a hexagonal grid structure. Next, we propose an Ant Colony Optimization (ACO) heuristic to efficiently solve large problem instances. We perform a series of numerical experiments and compare the performance of the exact MILP solution with that of the proposed ACO heuristic. Full article

The North American Great Lakes offer a dynamic case study of inundated cultural landscapes. These bodies of water and the life around them have never been static. While submerged lands offer avenues for archaeological research, it is essential to first understand that these cultural landscapes have also been flooded with invasive power dynamics through settler colonialism. For example, the land and water systems in Anishinaabe Akiing (the northern Great Lakes) have fundamentally shifted from flourishing life systems to poisoned areas and now struggle to deal with invasive species. When seeking to learn from or otherwise engage Indigenous knowledge, it is essential to work from a perspective that takes all these changes into consideration. There are Indigenous communities who are interested in these inundated landscapes, and in this research, but a pause, naandamo, is needed to ethically consider the ongoing process of settler colonialism and Indigenous perspectives. Here we address ethical considerations for researchers participating in, or interested in participating in, submerged site research. By incorporating settler colonialism as a methodology of understanding, we will provide an ethical starting place for working with Indigenous communities and inundated landscapes. Full article

Despite the convenience of solar potential and the magnitude of energy received by the Earth from the sun, solar photovoltaic systems have failed to meet the growing energy demand. This can be attributed to various factors such as low cell efficiency, environmental conditions, and improper tracking of operating points, which further worsen the system’s performance. Various advanced metaheuristic-based Maximum Power Point Tracking (MPPT) techniques were reported in the literature. Most available techniques were designed and tested in subscription-based/paid software such as MATLAB/Simulink, PSIM simulator, etc. Due to this, the simulation and analysis of these MPPT algorithms for developing and underdeveloped countries added an extra economic burden. Many open-source PV libraries are computationally intensive, lack active support, and prove impractical for MPPT testing on resource-constrained hardware. Their complexity and absence of optimization for edge devices limit their viability for the edge device. This issue is addressed in this research by designing a robust framework using an open-source programming language i.e., Python. For demonstration purposes, we simulated and analyzed a solar PV system and benchmarked its performance against the JAP6 solar panel. We implemented multiple metaheuristic MPPT algorithms including Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO), evaluating their efficacy under both Standard Test Conditions (STC) and complex partial shading scenarios. The results obtained validate the feasibility of the implementation in Python. Therefore, this research provides a comprehensive framework that can be utilized to implement sophisticated designs in a cost-effective manner for developing and underdeveloped nations. Full article

Virtual machine (VM) placement in cloud datacenters is a complex multi-objective challenge involving trade-offs among energy efficiency, carbon emissions, and network performance. This paper proposes NCRA-DP-ACO (Network-, Cost-, and Renewable-Aware Ant Colony Optimization with Dynamic Power Usage Effectiveness (PUE)), a bio-inspired metaheuristic that optimizes VM placement across geographically distributed datacenters. The approach integrates real-time solar energy availability, dynamic PUE modeling, and multi-criteria decision-making to enable environmentally and cost-efficient resource allocation. The experimental results show that NCRA-DP-ACO reduces power consumption by 13.7%, carbon emissions by 6.9%, and live VM migrations by 48.2% compared to state-of-the-art methods while maintaining Service Level Agreement (SLA) compliance. These results indicate the algorithm’s potential to support more environmentally and cost-efficient cloud management across dynamic infrastructure scenarios. Full article

Interactions between First Nations and non-Indigenous Australians have long been shaped by notions of Western authority and First Nations inferiority, both culturally and biologically. From invasion to the present day, forced removals and intergenerational trauma have deeply affected First Nations Australians, particularly through the operations of interacting colonial systems, including child removals and placements. Throughout the 20th century, systematic child removals led to the Stolen Generations, a tragic example of power imbalances, paternalism, and Western ideals, perpetuating trauma across generations. This article examines the context of First Nations removals by the state under the lies of benevolence, exposing the evolution of the colonial system and the systematic dislocation of culture and identity. It highlights the social, legal, and political factors that enabled removal practices and their enduring consequences, including the legacy of forced child separations and cultural erasure. This article argues that policies of absorption and assimilation served to further isolate children from their families, communities, and kinship networks. In doing so, it contends that the systematic disruption of First Nations communities is part of an ongoing process of subjugation, continuing the colonial agenda of cultural and familial disintegration. Full article

Ant colony optimization (ACO) has garnered significant attention because of its wide application in route planning problems. Nevertheless, ACO requires a long time to calculate when tackling complex issues. Parallelization emerges as an effective strategy to improve algorithm execution efficiency, and especially in large-scale computations, parallelization technology can significantly reduce execution time. In this study, we propose an ant colony algorithm (Sunway ant colony optimization, SWACO) based on a second-level parallel strategy and tailored to the hardware characteristics of Sunway many-core processors. The first level involves process-level parallelism, in which the initial ant colony is divided into multiple child ant colonies according to the number of processors, with each child ant colony independently performing computations on each island. The second level is thread-level parallelism, utilizing the computing power of the slave core to accelerate path selection and pheromone updates of the ants, thereby effectively improving algorithm execution efficiency. The experimental results demonstrate that, across multiple TSP datasets, the SWACO algorithm significantly reduces computation time, achieving an overall speedup ratio by 3–6 times, and maintains the gap within 5%. A substantial acceleration effect was achieved. Full article

V. S. Naipaul’s 1975 novel Guerrillas is the earliest example of Caribbean fiction that purports to provide a realistic depiction of Trinidad’s brief but historically significant Black Power movement. Naipaul was an Indo-Trinidadian expatriate who immigrated to the U.K. in 1950 and remained there until his death in 2018. He was famously Anglophilic; and given his notorious insistence that culturally the West Indies are derivative, not creative, it is unsurprising that Naipaul depicts Black Power as an empty form that Trinidad and Great Britain import to their detriment from the U.S. In its fictionalization of the story of a real-life figure on the periphery of Black Power, Guerrillas presents Black Power’s presence in Trinidad and the UK as a failure and a sham. My article traces Naipaul’s transformation of what was originally a journalistic account into his novel Guerrillas in order to highlight the tendentiousness of his representation of Trinidadian Black Power. The plot of the novel repurposes the crux of Naipaul’s essay “The Killings in Trinidad” in which he reports how a Trinidadian Black Power poseur known as “Michael X” conspired in the January 1972 murder of a white woman named Gale Ann Benson. Crucial to Naipaul’s dismissal of Black Power as a derivative fiction, this article argues, is the fraudulent Michael X, himself a mimic man par excellence in his embodiment of Black Power as an empty and parodic form devoid of original content. I demonstrate how Naipaul’s marginalization of Caribbean Black Power depends on formal mimicry and on his selection of this marginal player/mimic man as representative of the movement in Trinidad. Full article

This document explores short-term hydrothermal economic dispatch (HTED) while explicitly modeling the valve-point effect of thermal units as a factor that adds complexity to power system optimization. Two nature-inspired optimizers, the Bat Algorithm (BAT) and the Artificial Bee Colony (ABC) algorithm, were used on a 24 h horizon for nine unit power plants (five thermal, four hydro). After 30 independent runs, BAT produced the lowest daily operating cost at USD 307,952.44, whereas ABC obtained USD 311,457.48, a 1.14% saving (USD 3.5 k) in favour of BAT. However, ABC converged almost twice as fast, stabilizing after around 40 iterations, while BAT required around 80 iterations. The results demonstrate that BAT offers a modest but measurable economic advantage, whereas ABC provides faster convergence, which is important when real-time computational limits dominate. These quantitative findings confirm that meta-heuristic techniques are practical tools for HTED and highlight the trade-off between cost minimization and computational speed. Full article

This study presents an advanced hybrid energy management system (EMS) designed for isolated microgrids, aiming to optimize the integration of renewable energy sources with backup systems to enhance energy efficiency and ensure a stable power supply. The proposed EMS incorporates solar photovoltaic (PV) and wind turbine (WT) generation systems, coupled with a battery energy storage system (BESS) for energy storage and management and a microturbine (MT) as a backup solution during low generation or peak demand periods. Maximum power point tracking (MPPT) is implemented for the PV and WT systems, with additional control mechanisms such as pitch angle, tip speed ratio (TSR) for wind power, and a proportional-integral (PI) controller for battery and microturbine management. To optimize EMS operations, a novel hybrid optimization algorithm, the JSO-GJO (Jellyfish Search and Golden Jackal hybrid Optimization), is applied and benchmarked against Particle Swarm Optimization (PSO), Bacterial Foraging Optimization (BFO), Artificial Bee Colony (ABC), Grey Wolf Optimization (GWO), and Whale Optimization Algorithm (WOA). Comparative analysis indicates that the JSO-GJO algorithm achieves the highest energy efficiency of 99.20%, minimizes power losses to 0.116 kW, maximizes annual energy production at 421,847.82 kWh, and reduces total annual costs to USD 50,617,477.51. These findings demonstrate the superiority of the JSO-GJO algorithm, establishing it as a highly effective solution for optimizing hybrid isolated EMS in renewable energy applications. Full article

Switch mode power supplies (SMPSs) are prone to various faults under complex operating environments and variable load conditions. To improve the accuracy and reliability of fault diagnosis, this paper proposes an intelligent diagnosis method based on Dynamic Wavelet Packet Transform (DWPT) and Improved Artificial Bee Colony Optimized Support Vector Machine (APABC-SVM). First, an adaptive wavelet packet decomposition mechanism is used to refine the time–frequency feature extraction of the signal to improve the feature differentiation. Then, a dynamic window statistics method is introduced to construct comprehensive dynamic feature vectors to capture the transient changes in fault signals. Finally, the APABC is used to optimize the SVM classifier parameters to improve the classification performance and avoid the local optimum problem. The experimental results show that the method achieves an average accuracy of 99.091% in the complex fault diagnosis of switching power supplies, which is 21.8 percentage points higher than that of the traditional spectrum analysis method (77.273%). This study provides an efficient solution for the accurate diagnosis of complex fault modes in switching power supplies. Full article