Search Results (74)

This study introduces an innovative Honey Badger Optimization (HBO) designed to address the Optimal Power Flow (OPF) challenge in electrical power systems. HBO is a unique population-based searching method inspired by the resourceful foraging behavior of honey badgers when hunting for food. In this algorithm, the dynamic search process of honey badgers, characterized by digging and honey-seeking tactics, is divided into two distinct stages, exploration and exploitation. The OPF problem is formulated with objectives including fuel cost minimization and voltage deviation reduction, alongside operational constraints such as generator limits, transformer settings, and line power flows. HBO is applied to the IEEE 30-bus test system, outperforming existing methods such as Particle Swarm Optimization (PSO) and Gray Wolf Optimization (GWO) in both fuel cost reduction and voltage profile enhancement. Results indicate significant improvements in system performance, achieving 38.5% and 22.78% better voltage deviations compared to GWO and PSO, respectively. This demonstrates HBO’s efficacy as a robust optimization tool for modern power systems. In addition to the single-objective studies, a multi-objective OPF formulation was investigated to produce the complete Pareto front between fuel cost and voltage deviation objectives. The proposed HBO successfully generated a well-distributed set of trade-off solutions, revealing a clear conflict between economic efficiency and voltage quality. The Pareto analysis demonstrated HBO’s strong capability to balance these competing objectives, identify knee-point operating conditions, and provide flexible decision-making options for system operators. Full article

Although pastures cover nearly half of Brazil’s agricultural land and form the backbone of national livestock production, they have historically received limited attention regarding management and fertilization, resulting in widespread degradation. Sustainable intensification of these pasture-based systems is therefore essential to meet growing global demand for animal products while minimizing environmental impacts. This review highlights recent technological innovations in pasture fertilization in Brazil, with a particular focus on alternative phosphorus sources such as natural reactive phosphates, which offer slow-release nutrients at lower costs compared to conventional fertilizers. Efforts to enhance nitrogen use efficiency through nitrification and urease inhibitors show promise in reducing nutrient losses and greenhouse gas emissions, despite current cost constraints limiting adoption. The integration of grass-legume intercropping, especially with Arachis pintoi, has been shown to enhance forage quality and system persistence when appropriately managed. Moreover, plant growth-promoting microorganisms emerge as sustainable biotechnological tools for restoring degraded pastures and boosting forage productivity without adverse environmental consequences. Properly treated agro-industrial residues also present a viable nutrient source for pastures, provided environmental regulations are strictly followed to prevent pollution. Together, these innovations offer a comprehensive framework for enhancing the productivity and sustainability of Brazilian livestock systems, highlighting the pressing need for continued research and the adoption of advanced fertilization strategies. Full article

Agrivoltaics represents a crucial technology and an innovative solution to promote sustainability. After a cropping season in an agrivoltaic system in Northern Italy, this study investigated the floristic composition and biomass of weed communities across three crops, evaluating their variation under shaded and full light conditions. In addition, the research assessed the role of uncultivated grassland areas in agrivoltaic-shaded conditions by quantifying their biomass and evaluating their potential feed value. Weed floristic diversity and biomass were surveyed at three different times. Soil and canopy parameters were analyzed in relation to photosynthetically active radiation (PAR). Grassland biomass was assessed after four different cuts and its suitability as a feed source was evaluated by the pastoral value and near infrared (NIR) spectroscopic analysis. Results showed that tomato had the lowest weed presence, and Setaria italica and Sorghum halepense were predominant in rice, while in durum wheat, higher nutrient availability favored Echinochloa crus-galli and Cirsium arvense. In weed community composition and biomass, no significant differences were observed for the effect of different light conditions (sun/shadow), and this may be attributed to their high environmental plasticity. PAR was strongly correlated with both soil and canopy temperatures. The analysis of floristic composition, biomass yield, pastoral value and nutritional quality of grassland vegetation indicated that spring cuts can be effectively used as forage, including for grazing. These findings suggest that integrating livestock activities could offer a win–win strategy for managing uncultivated areas within agrivoltaic systems, thereby enhancing their sustainability under organic farming practices. Full article

Understanding the evolutionary and geographic trajectories of crop wild relatives is vital for enhancing agro-biodiversity and advancing climate-resilient agriculture. This study focuses on ten Vicia L. taxa—comprising five species, four varieties, and one subspecies—of significant agricultural importance in Türkiye. An integrative molecular framework was applied, incorporating nuclear ITS sequence data, ITS2 secondary structure modeling, phylogenetic network analysis, and time-calibrated biogeographic reconstruction. This approach revealed well-supported clades, conserved secondary structural elements, and signatures of reticulate evolution, particularly within the Vicia sativa L. and V. villosa Roth. complexes, where high genetic similarity suggests recent divergence and possible hybridization. Anatolia was identified as both a center of origin and a dispersal corridor, with divergence events estimated to have occurred during the Late Miocene–Pliocene epochs. Inferred migration routes extended toward the Balkans, the Caucasus, and Central Asia, corresponding to paleoenvironmental events such as the uplift of the Anatolian Plateau and the Messinian Salinity Crisis. Phylogeographic patterns indicated genetic affiliations between Turkish taxa and drought-adapted Irano-Turanian lineages, offering valuable potential for climate-resilient breeding strategies. The results establish a molecularly informed foundation for conservation and varietal development, supporting sustainability-oriented innovation in forage crop systems and contributing to regional food security. Full article

The Honey Badger Algorithm (HBA) is a recently proposed metaheuristic optimization algorithm inspired by the foraging behavior of honey badgers. The search mechanism of this algorithm is divided into two phases: a mining phase and a honey-seeking phase, effectively emulating the processes of exploration and exploitation within the search space. Despite its innovative approach, the Honey Badger Algorithm (HBA) faces challenges such as slow convergence rates, an imbalanced trade-off between exploration and exploitation, and a tendency to become trapped in local optima. To address these issues, we propose an enhanced version of the Honey Badger Algorithm (HBA), namely the Multi-Strategy Honey Badger Algorithm (MSHBA), which incorporates a Cubic Chaotic Mapping mechanism for population initialization. This integration aims to enhance the uniformity and diversity of the initial population distribution. In the mining and honey-seeking stages, the position of the honey badger is updated based on the best fitness value within the population. This strategy may lead to premature convergence due to population aggregation around the fittest individual. To counteract this tendency and enhance the algorithm’s global optimization capability, we introduce a random search strategy. Furthermore, an elite tangential search and a differential mutation strategy are employed after three iterations without detecting a new best value in the population, thereby enhancing the algorithm’s efficacy. A comprehensive performance evaluation, conducted across a suite of established benchmark functions, reveals that the MSHBA excels in 26 out of 29 IEEE CEC 2017 benchmarks. Subsequent statistical analysis corroborates the superior performance of the MSHBA. Moreover, the MSHBA has been successfully applied to four engineering design problems, highlighting its capability for addressing constrained engineering design challenges and outperforming other optimization algorithms in this domain. Full article

Insect pests impose major economic, agricultural, and public health burdens, damaging crops and transmitting pathogens such as dengue, malaria, and Zika. Conventional chemical control is increasingly ineffective due to insecticide resistance and environmental concerns, prompting a search for innovative strategies. The insect microbiome—comprising both obligate symbionts and environmentally acquired microbes—emerges as a key driver of host physiology and behavior. Microbes influence nutrient acquisition, immunity, reproduction, and chemosensory processing, often to promote their own transmission. By modulating olfactory and gustatory pathways, microbiota can alter host-seeking, mate choice, foraging, and oviposition patterns, reshaping ecological interactions and vector dynamics. These effects are shaped by microbial acquisition routes, habitat conditions, and anthropogenic pressures such as pesticide use, pollution, and climate change. Understanding these multi-directional interactions offers opportunities to design highly specific, microbe-based insect control strategies, from deploying microbial metabolites that disrupt host sensory systems to restoring beneficial symbionts in threatened pollinators. Integrating microbiome ecology with insect physiology and behavior not only deepens our understanding of host–microbe coevolution but also enables the development of sustainable, targeted alternatives to chemical insecticides. This review synthesizes current evidence linking microbiomes to insect biology and explores their potential as tools for pest and vector management. Full article

The Parrot Optimization Algorithm (PO) represents a contemporary nature-inspired metaheuristic technique formulated through observations of Pyrrhura Molinae parrot behavioral patterns. PO exhibits effective optimization capabilities by achieving equilibrium between exploration and exploitation phases through mimicking foraging behaviors and social interactions. Nevertheless, during iterative progression, the algorithm encounters significant obstacles in preserving population diversity and experiences declining search effectiveness, resulting in early convergence and diminished capacity to identify optimal solutions within intricate optimization landscapes. To overcome these constraints, this work presents the Adaptive Differentiated Parrot Optimization Algorithm (ADPO), which constitutes a substantial enhancement over baseline PO through the implementation of three innovative mechanisms: Mean Differential Variation (MDV), Dimension Learning-Based Hunting (DLH), and Enhanced Adaptive Mutualism (EAM). The MDV mechanism strengthens the exploration capabilities by implementing dual-phase mutation strategies that facilitate extensive search during initial iterations while promoting intensive exploitation near promising solutions during later phases. Additionally, the DLH mechanism prevents premature convergence by enabling dimension-wise adaptive learning from spatial neighbors, expanding search diversity while maintaining coordinated optimization behavior. Finally, the EAM mechanism replaces rigid cooperation with fitness-guided interactions using flexible reference solutions, ensuring optimal balance between intensification and diversification throughout the optimization process. Collectively, these mechanisms significantly improve the algorithm’s exploration, exploitation, and convergence capabilities. Furthermore, ADPO’s effectiveness was comprehensively assessed using benchmark functions from the CEC2017 and CEC2022 suites, comparing performance against 12 advanced algorithms. The results demonstrate ADPO’s exceptional convergence speed, search efficiency, and solution precision. Additionally, ADPO was applied to wind power forecasting through integration with Long Short-Term Memory (LSTM) networks, achieving remarkable improvements over conventional approaches in real-world renewable energy prediction scenarios. Specifically, ADPO outperformed competing algorithms across multiple evaluation metrics, achieving average R² values of 0.9726 in testing phases with exceptional prediction stability. Moreover, ADPO obtained superior Friedman rankings across all comparative evaluations, with values ranging from 1.42 to 2.78, demonstrating clear superiority over classical, contemporary, and recent algorithms. These outcomes validate the proposed enhancements and establish ADPO’s robustness and effectiveness in addressing complex optimization challenges. Full article

In the Sahelian context characterized by the increasing scarcity of forage resources, this study investigated how climate change perceptions influence the adoption of cottonseed cake in pastoral and agro-pastoral systems in the Hauts-Bassins region of Burkina Faso. Drawing on the Subjective Expected Utility (SEU) theory and using a logit model estimated from survey data collected from 366 livestock farms, the analysis reveals that the perceived degradation of rangelands due to climate change is a key determinant of adoption. Over 40% of surveyed herders believed that climate change is negatively affecting the availability of natural forage. This heightened awareness is significantly associated with a greater likelihood of adopting cottonseed cake as a feed supplementation strategy. This study highlights the crucial role of cognitive factors in shaping adaptation decisions, beyond traditional economic and structural determinants. It underscores the importance of incorporating environmental perceptions into public policies supporting livestock systems and technological innovations in pastoral. Full article

The germin-like protein (GLP) family plays vital roles for plant growth, stress adaptation, and defense; however, its evolutionary dynamics and functional diversity in halophytes remain poorly characterized. Here, we present the genome-wide analysis of the GLP family in the halophytic forage alkaligrass (Puccinellia tenuiflora), which identified 54 PutGLPs with a significant expansion compared to other plant species. Phylogenetic analysis revealed monocot-specific clustering, with 41.5% of PutGLPs densely localized to chromosome 7, suggesting tandem duplication as a key driver of family expansion. Collinearity analysis confirmed evolutionary conservation with monocot GLPs. Integrated gene structure and motif analysis revealed conserved cupin domains (BoxB and BoxC). Promoter cis-acting elements analysis revealed stress-responsive architectures dominated by ABRE, STRE, and G-box motifs. Tissue-/organ-specific expression profiling identified root- and flower-enriched PutGLPs, implying specialized roles in stress adaptation. Dynamic expression patterns under salt-dominated stresses revealed distinct regulatory pathways governing ionic and alkaline stress responses. Functional characterization of PutGLP37 demonstrated its cell wall localization, dual superoxide dismutase (SOD) and oxalate oxidase (OXO) enzymatic activities, and salt stress tolerance in Escherichia coli, yeast (Saccharomyces cerevisiae INVSc1), and transgenic Arabidopsis. This study provides critical insights into the evolutionary innovation and stress adaptive roles of GLPs in halophytes. Full article

To address the issues of poor environmental adaptability and high costs associated with traditional methods of measuring water body boundaries, this paper proposes an innovative path planning approach for water body boundary measurement based on Unmanned Surface Vehicles (USVs)—the Chemotactic Search Traversal (CST) algorithm. This method incorporates the chemotaxis operation mechanism of the Bacterial Foraging Optimization algorithm, integrating it with high-resolution grid maps to enable efficient traversal and accurate measurement of water body boundaries within large-scale grid environments. Simulation experiments demonstrate that the CST algorithm outperforms the Brute Force Algorithm (BFA), Roberts operator, Canny operator, Log operator, Prewitt operator, and Sobel operator in terms of optimal pathfinding, stability, and path smoothness. The feasibility and reliability of this algorithm in real water environments are validated through experiments conducted with actual USVs. These findings suggest that the CST algorithm not only enhances the accuracy and efficiency of water body boundary measurement but also offers a cost-effective and practical solution for measuring water body areas. Full article

Climate change and overgrazing significantly constrain the sustainability of meadow land and vegetation in the livestock industry on the Tibetan–Plateau ecosystem. In context of climate change mitigation, grassland soil C sequestration and forage sustainability, it is important to understand how manure regimes influence SOC stability, grassland soil, forage structure and nutritional quality. However, the responses of SOC fractions, soil and forage structure and quality to the influence of manure gradient practices remain unclear, particularly at Tianzhu belt, and require further investigation. A field study was undertaken to evaluate the soil bulk density, aggregate fractions and dynamics in SOC concentration, permanganate oxidizable SOC fractions, SOC stabilization and soil nutrients at the soil aggregate level under manure gradient practices. Moreover, the forage biodiversity, aboveground biomass and nutritional quality of alpine meadow plant communities were also explored. Four treatments, i.e., control (CK), sole sheep manure (SM), cow dung alone (CD) and a mixture of sheep manure and cow dung (SMCD) under five input rates, i.e., 0.54, 1.08, 1.62, 2.16 and 2.70 kg m⁻², were employed under randomized complete block design with four replications. Our analysis confirmed the maximum soil bulk density (BD) (0.80 ± 0.05 g cm⁻³) and micro-aggregate fraction (45.27 ± 0.77%) under CK, whilst the maximum macro-aggregate fraction (40.12 ± 0.54%) was documented under 2.70 kg m⁻² of SMCD. The SOC, very-labile C fraction (Cfrac₁), labile C fraction (Cfrac₂) and non-labile/recalcitrant C fraction (Cfrac₄) increased with manure input levels, being the highest in 2.16 kg m⁻² and 2.70 kg m⁻² applications of sole SM and the integration of 50% SM and 50% CD (SMCD), whereas the less-labile fraction (Cfrac₃) was highest under CK across aggregate fractions. However, manures under varying gradients improved SOC pools and stabilization for both macro- and micro-aggregates. A negative response of the carbon management index (CMI) in macro-aggregates was observed, whilst CMI in the micro-aggregate fraction depicted a positive response to manure addition with input rates, being the maximum under sole SM addition averaged across gradients. Higher SOC pools and CMI under the SM, CD and SMCD might be owing to the higher level of soil organic matter inputs under higher doses of manures. Moreover, the highest accumulation of soil nutrients,, for instance, TN, AN, TP, AP, TK, AK, DTPA extractable Zn, Cu, Fe and Mn, was recorded in SM, CD and SMCD under varying gradients over CK at both aggregate fractions. More nutrient accumulation was found in macro-aggregates over micro-aggregates, which might be credited to the physical protection of macro-aggregates. Overall, manure addition under varying input rates improved the plant community structure and enhanced meadow yield, plant community diversity and nutritional quality more than CK. Therefore, alpine meadows should be managed sustainably via the adoption of sole SM practice under a 2.16 kg m⁻² input rate for the ecological utilization of the meadow ecosystem. The results of this study deliver an innovative perspective in understanding the response of alpine meadows’ SOC pools, SOC stabilization and nutrients at the aggregate level, as well as vegetation structure, productivity and forage nutritional quality to manure input rate practices. Moreover, this research offers valuable information for ensuring climate change mitigation and the clean production of alpine meadows in the Qinghai–Tibetan Plateau area of China. Full article

This paper presents a novel Hybrid Artificial Protozoa Optimizer with Differential Evolution (HPDE), combining the biologically inspired principles of the Artificial Protozoa Optimizer (APO) with the powerful optimization strategies of Differential Evolution (DE) to address complex and engineering design challenges. The HPDE algorithm is designed to balance exploration and exploitation features, utilizing innovative features such as autotrophic and heterotrophic foraging behaviors, dormancy, and reproduction processes alongside the DE strategy. The performance of HPDE was evaluated on the CEC2014 benchmark functions, and it was compared against two sets of state-of-the-art optimizers comprising 23 different algorithms. The results demonstrate HPDE’s good performance, outperforming competitors in 24 functions out of 30 from the first set and 23 functions from the second set. Additionally, HPDE has been successfully applied to a range of complex engineering design problems, including robot gripper optimization, welded beam design optimization, pressure vessel design optimization, spring design optimization, speed reducer design optimization, cantilever beam design optimization, and three-bar truss design optimization. The results consistently showcase HPDE’s good performance in solving these engineering problems when compared with the competing algorithms. Full article

This study presents an innovative hybrid evolutionary algorithm that combines the Arctic Puffin Optimization (APO) algorithm with the JADE dynamic differential evolution framework. The APO algorithm, inspired by the foraging patterns of Arctic puffins, demonstrates certain challenges, including a tendency to converge prematurely at local minima, a slow rate of convergence, and an insufficient equilibrium between the exploration and exploitation processes. To mitigate these drawbacks, the proposed hybrid approach incorporates the dynamic features of JADE, which enhances the exploration–exploitation trade-off through adaptive parameter control and the use of an external archive. By synergizing the effective search mechanisms modeled after the foraging behavior of Arctic puffins with JADE’s advanced dynamic strategies, this integration significantly improves global search efficiency and accelerates the convergence process. The effectiveness of APO-JADE is demonstrated through benchmark tests against well-known IEEE CEC 2022 unimodal and multimodal functions, showing superior performance over 32 compared optimization algorithms. Additionally, APO-JADE is applied to complex engineering design problems, including the optimization of engineering structures and mechanisms, revealing its practical utility in navigating challenging, multi-dimensional search spaces typically encountered in real-world engineering problems. The results confirm that APO-JADE outperformed all of the compared optimizers, effectively addressing the challenges of unknown and complex search areas in engineering design optimization. Full article

Climate change poses significant challenges for countries in Northern Africa such as Morocco. Smallholder farmers are especially vulnerable to climate change because they experience several challenges in the adoption of climate-resilient practices. The sustainable and well-managed cultivation of the cactus pear (Opuntia ficus-indica) could contribute to conservation agriculture (CA) in dry climates threatened by climate change. Due to its high-water-use efficiency and ability to withstand extremely dry conditions, the cactus pear is increasingly being recognised as a more sustainable alternative to traditional livestock foraging in dryland areas. Compared to many other common crops and fodder, the cactus pear is easy to establish, maintain, and has a wealth of uses. Two innovative cultivation techniques are being developed: (1) the use of mixed inoculum formulations containing AMF (Arbuscular Mycorrhizal Fungi) and PGPB (Plant Growth-Promoting Bacteria) in the soil; and (2) intercropping between perennial (cactus pear) and short-term species (field crops). We propose to identify factors that could facilitate farmers’ innovation adoption. We conducted face-to-face interviews with 24 smallholder cactus pear farmers in Morocco. We concluded that farmers do not yet have a comprehensive understanding of the principles of the innovations. The main aim of farmers was to increase production and income. Farmers, in general, pay little attention to the environment. The recommendations that are given in relation to these issues are that training and social networks are essential in innovation transfer, adoption needs to be facilitated by providing resources, an innovation transfer needs to be adapted to the current and future needs of farmers, and we need patience so that farmers can slowly learn the innovations. Full article

This paper presents an innovative approach that integrates a fixed-time control (FTC) algorithm with an improved sparrow search algorithm (ISSA) to enhance the trajectory tracking accuracy of a two-degree-of-freedom (two-DOF) robotic arm. The FTC algorithm, which incorporates barrier Lyapunov function (BLF) and adaptive neural network strategies, ensures rapid convergence, effective vibration suppression, and the robust handling of system uncertainties and input saturation. The ISSA, inspired by the foraging behavior of sparrows, improves search efficiency through dynamic weight adjustments and chaotic mapping, balancing global and local search capabilities. By optimizing control parameters, ISSA minimizes tracking errors. Simulation results demonstrate that the combined FTC and ISSA approach significantly reduces tracking errors and improves response speed compared to the use of FTC alone, underscoring its potential for achieving high-precision control in robotic arms and offering a promising direction for precise robotic control applications. Full article