Search Results (54)

Inspired by the hovering, diving, and cooperative hunting behaviors of the pied kingfisher, the Pied Kingfisher Optimizer (PKO) has demonstrated competitive performance in optimization tasks. However, it exhibits several phase-specific limitations, including uneven population distribution caused by random initialization, insufficient use of historical information during exploration, over-reliance on the global best during exploitation, and weakly guided perturbation in the symbiosis phase. To address these issues, this study proposes an Improved Pied Kingfisher Optimizer (IPKO), which incorporates biologically inspired adaptive strategies. Drawing inspiration from the kingfisher’s diverse perching, gaze adjustment during hovering, evasive diving after failed strikes, and territory shifting based on flock position, four mechanisms are developed. Specifically, sine chaotic opposition-based initialization enhances population diversity; adaptive directional search regulates the exploration–exploitation balance; stochastic perturbation-based information fusion improves the ability to escape local optima; and centroid-based adaptive boundary handling strengthens constraint adaptability. The performance of IPKO is evaluated on the CEC2017 benchmark suite (10, 30, 50, and 100 dimensions) and two real-world engineering problems. Experimental results show that IPKO achieves superior overall performance compared with eleven state-of-the-art algorithms, with statistical significance confirmed by the Friedman test and Holm’s post-hoc procedure. Ablation studies further verify the contribution of each strategy. In engineering applications such as cold chain logistics and dynamic multi-UAV cooperative path planning, the IPKO algorithm demonstrates superior solution quality, robustness, and constraint-handling capability compared with competing algorithms. These results demonstrate that IPKO is a robust and effective bio-inspired optimization approach for solving complex, high-dimensional constrained engineering problems. Full article

This paper proposes a multi-strategy improved pied kingfisher optimizer (MSIPKO), a novel metaheuristic algorithm designed to address constrained optimization problems (COPs). COPs are widely encountered in engineering and industrial applications and are characterized by complex constraints that restrict the feasible solution space and often lead to multiple local optima. To enhance the performance of the original pied kingfisher optimizer (PKO), three strategies are incorporated: (i) a reverse differential crossover mechanism to improve global exploration and maintain population diversity; (ii) an enhanced diving-fishing operator to strengthen local exploitation; and (iii) an improved commensalism phase to enrich search directions and increase robustness. The performance of MSIPKO is evaluated on 12 benchmark functions from the IEEE Congress on Evolutionary Computation 2006 (CEC 2006) test suite and six classical engineering optimization problems. Experimental results demonstrate that MSIPKO outperforms several state-of-the-art algorithms in terms of optimization accuracy, convergence speed, and stability, particularly for high-dimensional, nonlinear, and multi-constrained problems. Moreover, MSIPKO achieves superior or comparable solutions with fewer function evaluations, indicating its high efficiency and adaptability. These results confirm that MSIPKO is a promising tool for solving complex real-world constrained optimization problems. Future work will focus on extending the proposed algorithm to multi-objective and large-scale optimization scenarios. Full article

This paper proposes a new fuzzified multi-objective wireless communication optimization model that maximizes the quantity and placement of Reconfigurable Intelligent Surfaces (RISs). In order to meet realistic deployment constraints like non-overlapping and acceptable location, the model aims to decrease the number of deployed RISs while raising the achievable rate. The Modified Pied Kingfisher Optimization Algorithm (MPKOA) is suggested as a solution to this intricate optimization issue. MPKOA features many significant improvements over the traditional Pied Kingfisher Optimization Algorithm (PKOA), such as energy-based motion control, adaptive subgrouping, flock cooperation, and memory-driven re-perching. These techniques speed up convergence, improve solution precision, reduce computation time, and balance exploration and exploitation. MPKOA performs better than standard PKOA, Enhanced version of PKOA (EPKO), Differential Evolution (DE), Grey Wolf Optimizer (GWO), and other existing algorithms, according to extensive comparisons. MPKOA can achieve up to 20% higher optimization values and 30% faster convergence, according to simulation data. In addition, the proposed MPKOA reduces computational complexity and runtime by about 50% when compared to standard PKOA-based approaches since it only requires single fitness evaluation per iteration. This enables the deployment of fewer RISs while still achieving higher communication rates. In multiuser wireless systems, MPKOA offers a robust and effective approach to RIS placement optimization, which helps to boost capacity and provide more energy-efficient 6G communication networks. Full article

This study proposes a new approach for optimizing the penetration level of photovoltaic units (PVUs) to achieve both engineering and economic benefits in a standard distribution power system. The Mirage Search Optimization (MSO) and the Pied Kingfisher Optimizer (PKO) are applied to minimize the total active power loss (TRPL) in the IEEE 69-node system. Two cases are considered: Case 1, where PVUs inject only active power, and Case 2, where PVUs inject both active and reactive power. The results demonstrate that PKO outperforms MSO and several metaheuristic algorithms reported in the literature. In Case 2, the optimal PVU penetration level of 67.17% significantly reduces TRPL compared with Case 1. The effectiveness of this optimized penetration level is further evaluated by comparing it with four other penetration levels: 25%, 50%, 75%, and 100%. PKO is then used to optimize the 24 h energy cost considering load variation and dynamic PV generation during four months of the year, including December, September, June, and March, which are ordered by increasing solar radiation across seasons under Vietnam’s climatic conditions. The results show that although the 75% penetration level slightly reduces the energy purchasing cost compared with the optimal level, it requires higher power capacity. Therefore, the optimized penetration level of 67.17% provides a balanced solution for reducing power losses while maintaining economic efficiency. Full article

Under the combined pressures of dual carbon policy constraints, the integration of intelligent construction technologies, and intensifying market competition, the development of a scientific and robust performance evaluation system has become essential for building construction enterprises seeking to enhance their core competitiveness. Traditional evaluation methods, however, often suffer from incomplete indicator systems and limited capability in addressing high-dimensional and nonlinear problems, rendering them inadequate for the evolving demands of the industry. To address these challenges, this study proposes a performance evaluation model for building construction enterprises based on the least squares support vector machine (LSSVM), optimized by an improved Pied Kingfisher Optimizer (IPKO). Drawing on environment–behavior theory, the model incorporates three environmental and ten behavioral factors. To overcome the limitations of the original PKO algorithm—namely, insufficient exploration capability and weak local search—the exploration phase of PKO is integrated with that of the Marine Predators Algorithm. Empirical results demonstrate that: (1) the proposed IPKO outperforms Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Sparrow Search Algorithm (SSA), Dung Beetle Optimizer (DBO), Ospery Optimization Algorithm (OOA), and the original PKO in most benchmark functions; (2) the ReliefF feature selection algorithm improves the model’s test set accuracy by approximately 2.18%; and (3) the IPKO-LSSVM model achieves 6.53%, 4.16%, and 6.74% higher prediction accuracy than Backpropagation Neural Networks (BPNN), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost), respectively. These findings highlight the model’s effectiveness in addressing small-sample, high-dimensional, and nonlinear problems, offering a scientifically sound and practical tool for performance evaluation in building construction enterprises. Full article

The Pacific sleeper shark (Somniosus pacificus) is a long-lived, deep-water, sub-polar species that exhibits flexible foraging strategies, likely combining scavenging with active predation on a broad range of prey, yet their role in marine food webs and impact on commercial species remain undetermined. Tracking the location of Pacific sleeper sharks in Alaskan coastal waters is extremely challenging given the predominantly aphotic depths that these sharks occupy, often in spatially constrained and critically under-sampled regions: deep, steep-flanked, convoluted fjords of Prince William Sound (PWS). From the first ever, year-long depth and temperature records recovered from archiving pop-up satellite-linked transmitters (n = 7), we characterized the residence distributions, depth, and thermal habitat for sharks within the PWS fjords and identified seasonal and temporal variation in habitat use. Depths recorded from the seven sharks ranged from 3 to 572 m, and pop-up tag locations suggested a high degree intra-annual residency within western PWS. Ambient water temperatures ranged from 2.65 to 11.1 °C, with little deviation from the median of 5.9 °C. Seasonal patterns emerged within and across individuals relative to the variation in vertical movements, ambient temperatures, and horizontal movements that could reflect resource-oriented strategies. The high degree of residency combined with extensive use of the water column facilitates the use of physically recoverable, high-resolution behavioral and environmental samplers on Pacific sleeper sharks. This adaptive sampling using Pacific sleeper sharks as platforms of opportunity may in turn enable the use of Pacific sleeper sharks as climate and ecosystem sentinels. Full article

Sympatric species are closely related taxa that coexist within the same habitat through niche partitioning, and kingfishers serve as an ideal group for studying such ecological mechanisms. The present study examined the perch height in relation to foraging behaviour and hunting success of five kingfisher species: Common Kingfisher (Alcedo atthis), White-throated Kingfisher (Halcyon smyrnensis), Pied Kingfisher (Ceryle rudis), Stork-billed Kingfisher (Pelargopsis capensis), and Black-capped Kingfisher (Halcyon pileata). The study was conducted between 2021 and 2023, across seven habitat types in Kerala, India (Kadalundi–Vallikkunnu Community Reserve (KVCR) mangroves, Kallampara mangroves, Vadakkumpad mangroves, Vazhakkad agroecosystem, Mavoor wetland, Sanketham wetland, and Elathur beach). A generalized linear mixed model (GLMM) with a binomial distribution and logit link function was used to analyze hunting success across species. The model indicated that the effect of perch height on hunting success varied among species, though neither perch height nor species identity alone had a significant effect. Most species favored mid-height perches (2–5 m) for foraging, with the Common Kingfisher exhibiting moderate success across all heights and habitats. The Pied Kingfisher showed significantly reduced success at higher perches, while the Stork-billed Kingfisher achieved the highest success at mid-heights (2–5 m). The White-throated Kingfisher showed a non-significant negative association with capture success, whereas the Black-capped Kingfisher exhibited a neutral to positive relationship across perch heights. Among all variables tested, prey availability emerged as the sole significant predictor of hunting success, indicating that prey abundance is the principal determinant of foraging efficiency in tropical wetlands, rather than environmental conditions. Our findings confirm a pattern of vertical stratification in resource partitioning among sympatric kingfisher species and underscore the importance of conserving habitats that retain natural perch sites of varying heights. Full article

Aim: To assess the prevalence of constipation, dietary fiber intake, and their association with QoL among HD patients. Methods: A cross-sectional study was conducted among 35 adults undergoing maintenance HD at King Abdulaziz University Hospital, Jeddah, Saudi Arabia. Constipation was assessed using the Rome IV criteria and Bristol Stool Form Scale (BSFS). QoL was evaluated using the Kidney Disease Quality of Life Short Form (KDQOL-36), and dietary fiber intake was estimated from three 24-h dietary recalls and a validated Dietary Fiber Intake Short Food Frequency Questionnaire (DFI-FFQ). Data were analyzed using Fisher’s exact test, the Mann–Whitney U test, and correlation coefficients (p < 0.05). Results: Constipation prevalence was 32%. Stool frequency was <3 per week in 14% of participants, 3–4 per week in 23%, and 7 per week in 63%. Mean fiber intake was 9.8 ± 4.8 g/day (24-h recall) and 8.6 ± 4.3 g/day (DFI-FFQ), with all participants classified as low fiber consumers. KDQOL-36 domain scores were highest for “effect of kidney disease” (80.4 ± 16.1) and lowest for “physical health” (40.4 ± 12.4). No significant associations were found between constipation, fiber intake, demographic or clinical variables, or any QoL domain (p > 0.05). Conclusions: Constipation is common among Saudi HD patients, yet not significantly linked to fiber intake or QoL in this small cohort. Larger multicenter studies are needed to identify determinants and guide evidence-based interventions. Full article

The high demand and economic value of kingfish (Scomberomorus commerson) have led to intensive fishing of this species in the Omani waters. The increased fishing pressure has made the fishery vulnerable; hence, information on the current stock status is essential for the sustainability of the kingfish stock. Three length-based stock assessment approaches (TropFishR, spawning potential ratio, and Bayesian biomass method) were used to estimate growth, mortality, exploitation, spawning potential capacity, and relative biomass in relation to maximum sustainable yield (MSY). Asymptotic length (L_∞) was 186.31 cm, and the growth coefficient (K) was 0.15 yr⁻¹ for S. commerson. Fishing mortality was 0.45 yr⁻¹, which was higher than natural mortality (M = 0.18 yr⁻¹) and optimal fishing mortalities (F40% = 0.15 yr⁻¹). The exploitation rate (E) was found to be 0.71 yr⁻¹, higher than the optimum exploitation (E = 0.50), indicating a total overfishing of 42% of the S. commerson in Oman waters. The current length at first capture (Lc50 = 74.38 cm) was significantly smaller than the length at first maturity (Lm50 = 91.25 cm), indicating growth overfishing. The current spawning potential ratio (SPR) was 10%, which was significantly below the reference point (SPR = 20%), indicating that the stock was severely overfished. Biomass was critically low (B/Bo = 0.17), and lower than the reference point of 0.20. Additionally, the current biomass was 44% of Bmsy (B/Bmsy = 0.44), which is significantly lower than the reference point of 1, indicating that the stock biomass was below the maximum sustainable yield level, suggesting recruitment overfishing. Stock indicators revealed that the fishery was primarily targeting immature/juvenile fish, as well as older and larger fish, which indicated stocks were both growth- and recruitment-overfished. Therefore, carrying out commercial fishing for an optimum size range (118 to 144 cm) and reducing fishing pressure to a sustainable level (F = M, 0.18 ^yr−1) would sustain a healthy stock biomass of kingfish in Omani waters. Full article

This study evaluated the feasibility of culturing Seriola lalandi in a low-cost recirculating aquaculture system (RAS) in an arid region of northern Chile, aiming to establish strategies for broodstock farming and diversify national aquaculture. The system was designed as a low-cost recirculating aquaculture system (RAS) built with locally available materials, such as galvanized corrugated steel panels and flexible plastic liners, instead of specialized aquaculture tanks. Its modular configuration, based on gravity-fed filtration using sedimentation, sand, and disc filters, allows efficient water reuse with minimal energy consumption and a daily water turnover of 12 times the total volume. This design significantly reduced construction and operational costs, making it a feasible option for aquaculture development in arid regions with limited water resources. Over an 8-month period, 46 S. lalandi individuals were used, and the results showed successful physiological adaptation of the specimens to confinement, as evidenced by low mortality, progressive acceptance of formulated feed, and sustained growth. Individual weights progressively increased, with averages ranging from 675 to 1435 g, and the specific growth rate (SGR) fluctuated between 0.14 and 0.43% per day. Fulton’s condition factor (K) remained in an adequate range between 2.4 and 2.8, suggesting good physical condition of the sampled individuals. Water quality within the RAS system was maintained within acceptable parameters, although a strong negative correlation between temperature and dissolved oxygen was recorded (Spearman coefficient = −0.71, p < 0.001), highlighting the importance of monitoring these factors in warm environments. The lack of adequate protocols for the adaptation of marine species in arid areas, such as northern Chile, has limited aquaculture development in these regions. This study addresses this problem by assessing the feasibility of a low-cost recirculating system (RAS) for the cultivation of S. lalandi under conditions of water scarcity, with the aim of diversifying the national aquaculture in arid zones. Full article

Background: Traumatic Brain Injury (TBI) in children is a major cause of morbidity and mortality worldwide. Ocular manifestations are common but often overlooked, despite their potential to cause long-term visual impairment. This study aimed to evaluate the prevalence and characteristics of ocular findings in pediatric TBI patients admitted to the intensive care unit (ICU). Method: We prospectively reviewed records of pediatric patients (≤16 years) with TBI admitted to the Neurosurgery ICU at King Abdullah University Hospital (January 2022–December 2024). TBI was defined using U.S. CDC criteria and confirmed by clinical and radiological findings. Ocular manifestations were identified from ophthalmology consultations, neurosurgical notes, and bedside examinations. Demographics, injury details, and clinical outcomes were recorded. Statistical analyses included Chi-square, Fisher’s exact, and Mann–Whitney U tests, with significance set at p ≤ 0.05. Results: Thirty-eight patients (median age: 8 years; 55.3% male) were included. Ocular findings were present in 20 patients (52.6%). These patients were significantly older (median age 10 vs. 6 years, p = 0.007) and had lower admission GCS scores (11 vs. 14, p = 0.016). Male predominance was higher in the ocular group (75.0% vs. 33.3%, p = 0.030). Ocular findings were significantly associated with surgical intervention (60.0% vs. 22.2%, p = 0.025), orbital fractures (40.0% vs. 5.6%, p = 0.021), basal skull fracture signs (p = 0.036), and extraocular muscle limitation (p = 0.048). On multivariable analysis, orbital fracture remained the only independent predictor of ocular findings (aOR 2.22, 95% CI 1.17–3.57, p = 0.02). Conclusion: Over half of pediatric ICU TBI patients demonstrated ocular manifestations, closely linked to greater injury severity and craniofacial trauma. Routine, comprehensive ophthalmological evaluation should be integrated into the multidisciplinary management of severe pediatric TBI to optimize visual and functional outcomes. Full article

Current methods of detecting bogie gearbox faults mainly depend on manual judgment, which leads to inaccurate fault identification. In this study, a fault diagnosis model is proposed based on a pied kingfisher optimizer-improved complete ensemble empirical mode decomposition with adaptive noise (PKO-ICEEMDAN), improved multi-scale weighted permutation entropy (IMWPE), and a starfish optimization algorithm optimizing a least-squares support vector machine (SFOA-LSSVM). Firstly, the acceleration signals of a bogie gearbox under six different working conditions were extracted through experiments. Secondly, the acceleration signals were decomposed by ICEEMDAN optimized by PKO to obtain the intrinsic mode function (IMF). Thirdly, IMFs with rich fault information were selected to reconstruct the signals according to the double screening criteria of both the correlation coefficient and variance contribution rate, and the IMWPE of the reconstructed signals was extracted. Finally, IMWPE as a feature vector was input into LSSVM optimized by the SFOA for fault diagnosis and compared with various models. The results show that the average accuracy of the training data of the proposed model was 99.13%, and the standard deviation was 0.09, while the average accuracy of the testing data was 99.44%, and the standard deviation was 0.12. Thus, the effectiveness of the proposed fault diagnosis model for the bogie gearbox was verified. Full article

The increasing adoption of artificial intelligence across domains presents new opportunities to enhance access to justice. In this paper, we introduce a human-centric AI tool that utilises advances in Automatic Speech Recognition (ASR) and Large Language Models (LLMs) to facilitate semantic linking between written UK Supreme Court (SC) judgements and their corresponding hearing videos. The motivation stems from the critical role UK SC hearings play in shaping landmark legal decisions, which often span several hours and remain difficult to navigate manually. Our approach involves two key components: (1) a customised ASR system fine-tuned on 139 h of manually edited SC hearing transcripts and legal documents and (2) a semantic linking module powered by GPT-based text embeddings adapted to the legal domain. The ASR system addresses domain-specific transcription challenges by incorporating a custom language model and legal phrase extraction techniques. The semantic linking module uses fine-tuned embeddings to match judgement paragraphs with relevant spans in the hearing transcripts. Quantitative evaluation shows that our customised ASR system improves transcription accuracy by 9% compared to generic ASR baselines. Furthermore, our adapted GPT embeddings achieve an F1 score of 0.85 in classifying relevant links between judgement text and hearing transcript segments. These results demonstrate the effectiveness of our system in streamlining access to critical legal information and supporting legal professionals in interpreting complex judicial decisions. Full article

In this study, the pied kingfisher optimizer (PKO) algorithm is adapted to the uncapacitated facility location problem (UFLP), and its performance is evaluated. The PKO algorithm is binarized with fourteen different transfer functions (TF), and each variant is tested on a total of fifteen different Cap problems. In addition, performance improvement was realized by adding the Levy flight strategy to BinPKO, and this improved method was named BinIPKO. The experimental results show that the TF1 transfer function for BinIPKO performs very well on all problems in terms of both best and mean solution values. The TF2 transfer function performed efficiently on most Cap problems, ranking second only to TF1. Although the other transfer functions provided competitive solutions in some Cap problems, they lagged behind TF1 and TF2 in terms of overall performance. In addition, the performance of BinIPKO was also compared with the well-known PSO and GWO algorithms in the literature, as well as the recently proposed APO and EEFO algorithms, and it was found that BinIPKO performs well overall. In line with this information, it is seen that the IPKO algorithm, especially when used with the TF1 transfer function, provides an effective alternative for UFLP. Full article

The yellowtail kingfish (Seriola aureovittata), a commercially important marine species, encounters significant survival challenges under low-temperature conditions during winter aquaculture. To elucidate its molecular adaptation mechanisms, this study employed RNA-Seq to analyze transcriptional responses in juvenile muscle tissues under acute cold stress (10 °C) at 0, 6, 12, and 24 h. Differential gene expression analysis revealed time-dependent patterns, with 269, 863, and 984 differentially expressed genes (DEGs) identified at 6, 12, and 24 h, respectively. Key pathways were identified, including lipid metabolism, carbohydrate metabolism, and stress response. Sestrin3 upregulation implicated AMPK-mediated energy homeostasis in cold adaptation. These findings provide novel insights into the molecular strategies underlying cold adaptation in yellowtail kingfish, offering potential targets for breeding cold-resistant strains and improving aquaculture resilience. Full article