Search Results (96)

The basic Salp Swarm Algorithm (SSA) offers advantages such as a simple structure and few parameters. However, it is prone to falling into local optima and remains inadequate for seed classification tasks that involve hyperparameter optimization of machine learning classifiers such as Support Vector Machines (SVMs). To overcome these limitations, an Enhanced Knowledge-based Salp Swarm Algorithm (EKSSA) is proposed. The EKSSA incorporates three key strategies: Adaptive adjustment mechanisms for parameters $c_1$ and $\alpha$ to better balance exploration and exploitation within the salp population; a Gaussian walk-based position update strategy after the initial update phase, enhancing the global search ability of individuals; and a dynamic mirror learning strategy that expands the search domain through solution mirroring, thereby strengthening local search capability. The proposed algorithm was evaluated on thirty-two CEC benchmark functions, where it demonstrated superior performance compared to eight state-of-the-art algorithms, including Randomized Particle Swarm Optimizer (RPSO), Grey Wolf Optimizer (GWO), Archimedes Optimization Algorithm (AOA), Hybrid Particle Swarm Butterfly Algorithm (HPSBA), Aquila Optimizer (AO), Honey Badger Algorithm (HBA), Salp Swarm Algorithm (SSA), and Sine–Cosine Quantum Salp Swarm Algorithm (SCQSSA). Furthermore, an EKSSA-SVM hybrid classifier was developed for seed classification, achieving higher classification accuracy. Full article

Mount Falakro in Northern Greece historically hosted populations of the Balkan-endemic butterfly Polyommatus orphicus and its larval host plant Onobrychis alba. In this study, we surveyed six historically confirmed localities during the peak flight period of P. orphicus in 2024, but neither the butterfly nor the host plant were detected. While the historical data on both species are scarce and often imprecise, our field observations indicate severe habitat degradation, dominated by overgrazing and suspected climate-driven shifts. Habitat conditions were assessed qualitatively, with special attention to limestone substrates previously known to support O. alba. Although definitive absence cannot be statistically confirmed, the lack of detection in previously occupied sites raises urgent concerns about possible local extinction. Our findings suggest that both species may already be extirpated from parts of their former range. This case study underscores the conservation relevance of absence data and highlights the importance of site-based monitoring in mountainous ecosystems undergoing rapid environmental change. Long-term surveys, regulated grazing, and post-disturbance habitat restoration are urgently needed to clarify the conservation status of these species and guide future management strategies. Full article

Background: Vanishing twin syndrome (VTS) represents a complex and under-recognized phenomenon in multifetal pregnancies, associated with both clinical uncertainty and significant psychosocial impact. Despite its frequency, gaps remain in diagnostic clarity, international guidelines, and communication strategies with patients and families. Materials and Methods: This hybrid review integrates narrative and systematic elements to assess the diagnostic, clinical, and psychosocial gaps in VTS. A systematic literature search was conducted across Medline/PubMed, CINAHL, PsycINFO, EBM Reviews, and Scopus using terms such as “vanishing twin syndrome,” “patient-provider communicat*,” and “bereave* care.” Sources included systematic reviews, randomized controlled trials, cohort studies, and qualitative studies. Exclusion criteria were outdated publications (>10 years old). Results: Evidence revealed multiple domains of concern. Clinical risks and diagnostics remain poorly defined, with inconsistent recognition of maternal and neonatal complications. Psychosocial impacts were prominent, encompassing grief, identity disruption, and unmet support needs. Patient–provider communication was frequently inadequate, with insufficient training and lack of standardized language. International guidelines varied widely in scope, with only a few of them providing clear recommendations for bereavement care in multifetal loss contexts. Discussion: Emerging discourse highlights the limitations of the traditional fission model and alternative conceptual frameworks, such as Herranz’s model, for understanding VTS. These theoretical differences underscore the need for precise terminology and consistent diagnostic practices. Clinical implications extend to prenatal screening, obstetric management, and the integration of psychosocial support. Patient-centered communication and structured support initiatives (e.g., the Butterfly Project) demonstrate the potential to bridge communication gaps and improve care experiences. Conclusions: VTS requires recognition as both a medical and psychosocial condition. Improved clinical definitions, harmonized international guidelines, and emphasis on empathetic communication are essential to address the current gaps. Integrating these elements into practice may enhance patient outcomes and provide families with validation and support following multifetal loss. Full article

The potential distribution of the pulse blue butterfly, Lampides boeticus (Lepidoptera: Lycaenidae), was determined using MaxEnt, random forest, and ensemble models. The results indicate that most tropical, subtropical, and some temperate regions are suitable habitats. Climate change is projected to expand the species’ habitat northward in the Northern Hemisphere. Predicted distributions aligned well with the known occurrence records for the species. The minimum temperature of the coldest month was the climatic variable that most strongly influenced the distribution of L. boeticus. As a tropical and subtropical species, it is assumed that cold temperatures are the main factor limiting its habitat range. Because the potential distribution of this pest covers major pulse cultivation areas under both current and future climate scenarios, these findings highlight the urgent need for developing a sustainable pest management strategy. Full article

Under the dual challenges of global energy crisis and climate change, the building sector, as a major carbon emitter consuming 33% of global primary energy, has seen its energy efficiency optimization become a critical pathway towards achieving carbon neutrality goals. The Window-to-Wall Ratio (WWR), serving as a core parameter in building envelope design, directly influences building energy consumption, with its optimized design playing a decisive role in balancing natural daylighting, ventilation efficiency, and thermal comfort. This study focuses on the traditional One-Seal dwellings (Yikeyin) in Kunming, China, establishing a dynamic wind field-thermal environment coupled analysis framework to investigate the impact mechanism of window dimensions (WWR and aspect ratio) on indoor thermal comfort under natural wind conditions in transitional climate zones. Utilizing the Grasshopper platform integrated with Ladybug, Honeybee, and Butterfly plugins, we developed parametric models incorporating Kunming’s Energy Plus Weather meteorological data. EnergyPlus and OpenFOAM were employed, respectively, for building heat-moisture balance calculations and Computational Fluid Dynamic (CFD) simulations, with particular emphasis on analyzing the effects of varying WWR (0.05–0.20) on temperature-humidity, air velocity, and ventilation efficiency during typical winter and summer weeks. Key findings include, (1) in summer, the baseline scenario with WWR = 0.1 achieves a dynamic thermal-humidity balance (20.89–24.27 °C, 65.35–74.22%) through a “air-permeable but non-ventilative” strategy, though wing rooms show humidity-heat accumulation risks; increasing WWR to 0.15–0.2 enhances ventilation efficiency (2–3 times higher air changes) but causes a 4.5% humidity surge; (2) winter conditions with WWR ≥ 0.15 reduce wing room temperatures to 17.32 °C, approaching cold thresholds, while WWR = 0.05 mitigates heat loss but exacerbates humidity accumulation; (3) a symmetrical layout structurally constrains central ventilation, maintaining main halls air changes below one Air Change per Hour (ACH). The study proposes an optimized WWR range of 0.1–0.15 combined with asymmetric window opening strategies, providing quantitative guidance for validating the scientific value of vernacular architectural wisdom in low-energy design. Full article

Wearable laser Doppler devices are susceptible to complex noise interferences, such as Gaussian white noise, baseline drift, and motion artifacts, with motion artifacts significantly impacting clinical diagnostic accuracy. Addressing the limitations of existing denoising methods—including traditional adaptive filtering that relies on prior noise information, modal decomposition techniques that depend on empirical parameter optimization and are prone to modal aliasing, wavelet threshold functions that struggle to balance signal preservation with smoothness, and the high computational complexity of deep learning approaches—this paper proposes an ISSA-VMD-AWPTD denoising algorithm. This innovative approach integrates an improved sparrow search algorithm (ISSA), variational mode decomposition (VMD), and adaptive wavelet packet threshold denoising (AWPTD). The ISSA is enhanced through cubic chaotic mapping, butterfly optimization, and sine–cosine search strategies, targeting the minimization of the envelope entropy of modal components for adaptive optimization of VMD’s decomposition levels and penalty factors. A correlation coefficient-based selection mechanism is employed to separate target and mixed modes effectively, allowing for the efficient removal of noise components. Additionally, an exponential adaptive threshold function is introduced, combining wavelet packet node energy proportion analysis to achieve efficient signal reconstruction. By leveraging the rapid convergence property of ISSA (completing parameter optimization within five iterations), the computational load of traditional VMD is reduced while maintaining the denoising accuracy. Experimental results demonstrate that for a 200 Hz test signal, the proposed algorithm achieves a signal-to-noise ratio (SNR) of 24.47 dB, an improvement of 18.8% over the VMD method (20.63 dB), and a root-mean-square-error (RMSE) of 0.0023, a reduction of 69.3% compared to the VMD method (0.0075). The processing results for measured human blood flow signals achieve an SNR of 24.11 dB, a RMSE of 0.0023, and a correlation coefficient (R) of 0.92, all outperforming other algorithms, such as VMD and WPTD. This study effectively addresses issues related to parameter sensitivity and incomplete noise separation in traditional methods, providing a high-precision and low-complexity real-time signal processing solution for wearable devices. However, the parameter optimization still needs improvement when dealing with large datasets. Full article

Knowledge of how wind turbines interact with vertebrate animals is growing rapidly; however, less is known about plants and insects. Turbines produce infrasound (≤20 Hz), and these vibrations decrease with distance from turbines. We measured seed set and pollinators at six sites 0 to 28 km from turbines. We measured the number and mass of seeds produced by self-pollination, insect pollination, and when pollen was not limiting for nine native plants. We assessed pollinators by target netting bees and butterflies during transects, and by using blue vane traps (bees only). Most plants produced fewer or lighter developed seeds through self-pollination. Seed set did not vary between the open- and hand-pollinated treatments, indicating that the pollen was not limiting. The number and mass of seeds in the self-pollination treatment decreased with distance from the turbines. Bees and butterflies were more abundant near the wind facility, based on transects. The vane traps collected the fewest insects within the wind facility, likely due to bees being attracted to the turbine bases. The pollinator assemblage at the wind facility was distinct compared to other sites. Infrasound produced by the turbines appeared to enhance self-pollination, and the turbine bases attracted pollinators. We provide data on a seldom studied yet critical topic to inform land management and agricultural decisions, and to promote new strategies as wind energy development grows. Full article

Research on bionic superhydrophobic surfaces draws inspiration from the microstructures and wetting mechanisms of natural organisms such as lotus leaves, water striders, and butterfly wings, offering innovative approaches for developing artificial functional surfaces. By synergistically combining micro/nano hierarchical structures with low surface energy chemical modifications, researchers have devised various fabrication strategies—including laser etching, sol-gel processes, electrochemical deposition, and molecular self-assembly—to achieve superhydrophobic surfaces characterized by contact angles exceeding 150° and sliding angles below 5°. These technologies have found widespread applications in self-cleaning architectural coatings, efficient oil–water separation membranes, anti-icing materials for aviation, and anti-biofouling medical devices. This article begins by examining natural organisms exhibiting superhydrophobic properties, elucidating the principles underlying their surface structures and the wetting states of droplets on solid surfaces. Subsequently, it categorizes and highlights key fabrication methods and application domains of superhydrophobic surfaces, providing an in-depth and comprehensive discussion. Full article

Butterflies have nectar-feeding preferences based on various floral characteristics, including flower shape, size, color, fragrance, and nectar composition, which in turn affect their survival, reproduction, and roles in pollination. The National Botanical Garden (NBG) in Lalitpur, Nepal, holds a variety of flowering plants and butterfly populations, providing a suitable study site to test the hypotheses on floral preferences of butterflies. This study assessed the floral preferences of the butterfly community in the NBG based on flower color, the origin of flowering plants (native and alien), and the type of plants (herbs and shrubs). It also tested the association between butterfly proboscis lengths and corolla tube lengths of flowers. Data were collected from 10 blocks (each 5 × 5 m²) through direct observation during the spring and autumn seasons, from March to October 2022. A total of 24 species of butterflies were recorded during the study period, with the chocolate pansy (Junonia iphita) being the most abundant. The relative abundance of pink flowers was higher in the NBG, but the butterflies’ visitation frequency was significantly higher on yellow flowers (p < 0.05) than on other colors. The visitation frequencies of butterflies significantly varied with the flowers’ origin and types. Butterflies visited flowers of alien origin more frequently than native ones (p < 0.05) and those of herbs over shrubs (p < 0.05). Flowers from alien plants, such as Calluna vulgaris and Viola tricolor, were among the most frequently visited. The proboscis length of butterflies showed a significantly strong positive correlation with the corolla tube length of flowers (τ = 0.74, p < 0.001). These results can inform conservation practices and garden management strategies aimed at supporting butterfly diversity through the intentional selection of floral resources. Full article

Animal populations, even among common species, often exhibit demographic heterogeneity. This is particularly evident in species with metapopulation structures, where geographically distinct subpopulations experience varying degrees of inbreeding due to limited interbreeding. A phenomenon termed ‘negative ecology’ highlights cases where species, despite suitable habitat availability, display fragmented distributions within small geographic areas (<20 km²). These subpopulations, however, do not conform to classic metapopulation dynamics, as even vagile species such as birds can theoretically move freely between them. Similar patterns emerge at larger scales (>100 km²) in birds and butterflies, suggesting ecological constraints—either abiotic or biotic—underpin these structured distributions. Understanding these constraints is essential, particularly in conservation efforts. The principles of positive (extant) and negative (locally absent/extinct) ecology have profound implications for species re-introductions, as historical occupancy does not guarantee successful re-establishment. Abiotic and biotic factors may hinder the recolonization of previously inhabited or entirely new habitats. This article primarily explores the ecological forces shaping such fragmented distributions in birds and butterflies, two especially well-studied taxa in the UK and mainland Europe, in the hope that such a new perspective may thereby contribute to broader conservation and ecological restoration strategies. Full article

Selecting appropriate locations of emergency centers is an important issue in avoiding probable damages by natural disasters. Emergency rescue sites are constructed to provide emergency supplies swiftly for people in affected areas. Factors of transportation fluency and road damage degrees should be considered, which largely affect rescue efficiency. In order to find appropriate sites accurately, we proposed a redesigned method Variable Butterfly Optimization Algorithm (VBOA), based on the Butterfly Optimization Algorithm, by adding the Variation Operator mechanism to avoid the limitations of local optimum problems present in other optimization algorithms. The Variation Operator effectively combines both global and local search strategies to improve the performance of global searching, and it accelerates the convergence speed of the algorithm. We conducted our experiment on selected candidate sites with multiple optimization methods; the experiment results demonstrate that our proposed method maintains the balance between conditions of coverage area and expenditure. Our proposed method relieved the reliance of local optimum results and achieved better convergence accuracy in our selected samples in comparison with other methods both in initial and later siting phases. Full article

Treating ankle fractures in athletes, commonly resulting from training injuries, remains a significant challenge. Current approaches to managing both non-surgical and postoperative foot and ankle disorders have focused on integrating sensory systems into orthotic devices. Recent analyses have identified several gaps in rehabilitation strategies, especially regarding gait pattern reformation during recovery. This work aims to enhance rehabilitation effectiveness for patients with ankle injuries by controlling load distribution and monitoring joint flexion/extension angles, as well as the reactive forces during therapeutic exercises and walking. We developed an exoskeleton device model using SolidWorks 2024 software, based on data from two patients: one healthy and one with an ankle fracture. Pressure measurements in the posterior limb region were taken using the F-Socket system and a custom electromechanical sensor designed by the authors. The collected data were analyzed using the butterfly parameterization method. This research led to the development of an adaptive exoskeleton device that provided pressure distribution data, gait cycle graphs, and a diagram correlating foot angles with the duration of exoskeleton use. The device demonstrated improvement in the patients’ conditions, facilitating a more normalized gait pattern. A reduction in the load applied to the ankle joint was also observed, with the butterfly parameter confirming the device’s correct operation. Full article

Background: Knee osteoarthritis is a prevalent condition that significantly impacts patients’ quality of life. Effective management typically involves a combination of pharmacological and non-pharmacological treatments. However, establishing a consensus on the optimal treatment strategy is crucial for standardizing care. The present study is the result of a rigorous process that combines artificial intelligence with human expertise to improve the reliability of medical recommendations. Methods: A new software platform (Butterfly Decisions, 2021, Italy) was employed to leverage AI-assisted decision-making, facilitating the digitalization of the entire consensus process. The process started with data collection through an online survey including simulated clinical cases of knee osteoarthritis collected by 30 orthopedic surgeons; artificial intelligence (AI) analyzed the collected clinical data and identified the key concepts and relevant patterns. Subsequently, AI generated detailed statements summarizing key concepts extracted from the data and proposed a reformulation of the statements to be discussed during the discussion session of the advisory board. The advisory board, composed of four qualified, experienced specialists of knee osteoarthritis, evaluated statements, providing their agreement levels, confidence, and supporting evidence. The AI tools calculated the degree of certainty and contradiction for each statement based on these evaluations. The literature was critically evaluated to ensure that there was an evidence-based evaluation of the proposed treatment statements. Finally, revised versions were proposed to address the feedback, evidence was collected to refine the scientific report, and the board members evaluated the AI performance too. Results: The consensus analysis revealed a high level of agreement in the need for a multimodal approach to treating knee osteoarthritis. The feedback highlighted the importance of integrating physical therapy and weight management, non-pharmacological methods, with Symptomatic Slow-Acting Drug for Osteoarthritis (SYSADOAs) and pharmacological treatments, such as anti-inflammatory drugs and intra-articular knee injections. The board members found that AI was easy to use and understand and each statement was structured clearly and concisely. Conclusions: The expert consensus about knee osteoarthritis conservative management being facilitated with AI met with unanimous agreement. AI-assisted decision-making was shown to have excellent analytical capabilities, but algorithms needs to be trained by orthopaedic experts with the correct inputs. Future additional efforts are still required to evaluate the incorporation of AI in clinical workflows. Full article

This study evaluated the habitat quality of pollinators in La Cantera Park, a recently renovated urban area in Mexico City. First, we analyzed the presence and preferences of three main pollinators (bees, butterflies, and hummingbirds) with respect to the vegetation composition of the park. Secondly, we assessed the theoretical habitat quality for the pollinators across the zones of the park. Through systematic sampling, we recorded the following species: four hummingbirds, 20 butterflies, and 21 bees, among which we observed a strong preference for native plants such as Lantana camara and Salvia leucantha. While some exotic plants also attracted pollinators, native plants played a central role in supporting diverse pollinator populations. Areas with greater floral diversity and a higher proportion of native species consistently exhibited better habitat quality scores, underscoring the critical link between native flora and pollinator activity. These findings highlight the importance of landscape management practices that strategically combine native and exotic plants to maximize resource availability, improving urban parks’ capacity to sustain pollinator biodiversity. The study suggests that urban green space design strategies should incorporate both ecological infrastructure elements, such as water sources, and a careful selection of plant species to create suitable habitats for pollinators. This approach can contribute to the conservation of pollinators in densely populated urban environments, providing valuable ecosystem services and supporting urban resilience by promoting biodiversity. Full article

Petrol, a vital energy source for residents’ consumption and economically sustainable operation, generates substantial distribution demand. To reduce distribution costs, we propose a fuel replenishment problem using a heterogeneous fleet based on the initiative distribution mode. In this mode, the distribution center determines both the delivery orders of customers and the distribution plan. We develop a mathematical model with minimal operational costs, including transport, employment, and penalty costs. A Two-stage heuristic algorithm K-IBKA based on time-space clustering is proposed, which also combines the advantages of the butterfly optimization algorithm in quick convergence and hierarchical mutation strategy in population diversity. The results demonstrate that: (1) Heterogeneous truck distribution exhibits better cost advantages compared to homogeneous distribution, reducing total costs by 13.07%; (2) Compared to passive distribution mode, the total cost of the initiative distribution mode is reduced by 11.03% and 41.80%, respectively, through small and large-scale instances. (3) Compared with the unimproved BKA, ALNS, and GA, the total cost calculated by K-IBKA is reduced by 37.68%, 35.30%, and 27.26%, respectively, thus demonstrating the contribution of this work to reducing the cost of petrol distribution and achieving sustainable development of distribution. Full article