Search Results (3,700)

To address the challenges of time-dependent error divergence in Strapdown Inertial Navigation Systems (SINS) and the insufficient accuracy of traditional terrain matching algorithms in feature-sparse flat terrain environments, this paper proposes an intelligent terrain-aided navigation method integrating an Improved Osprey Optimization Algorithm (IOOA), Distribution Estimation, and Q-learning. Utilizing terrain information entropy as a robust matching metric, the algorithm establishes a two-phase evolutionary framework comprising Lévy flight-based random search (exploration phase) and elite-guided Gaussian Estimation of Distribution (exploitation phase). By introducing a Q-learning mechanism to adaptively regulate exploration parameters, an intelligent balance between population diversity and convergence speed is achieved. Under a unified computational benchmark, systematic multi-scenario simulations were conducted using datasets from simulated moderately undulating foothill terrain, the Libyan Sahara, and the real Digital Elevation Model (DEM) of the Junggar Basin in Xinjiang, China. Experimental results demonstrate that, compared to traditional TERCOM and mainstream swarm intelligence algorithms, the proposed algorithm drastically reduces positioning errors in the aforementioned complex terrains and significantly enhances matching accuracy. Robustness and real-time performance tests indicate that the algorithm achieves an average single-match processing time of only 0.08 s and maintains error variability as low as ±0.83 m under random perturbations. Furthermore, an ablation study confirms the necessity of the multi-strategy fusion mechanism in suppressing local optima entrapment and non-convergent oscillations. This study validates the engineering feasibility of the algorithm under conditions of low computational dependency, providing an effective technical approach for high-precision autonomous navigation in GPS-denied environments. Full article

Efficient exploitation of evolutionary knowledge while preserving population diversity remains a central challenge in optimization. Existing knowledge-learning evolutionary algorithms primarily rely on successful experiences, overlooking structural information embedded in failed search attempts. This asymmetric learning limits adaptability and may cause premature convergence in high-dimensional landscapes. To address this issue, a failure-aware bidirectional evolutionary knowledge assimilation framework is developed within the honey badger optimization algorithm. Unsuccessful offspring are treated as negative knowledge carriers and transformed through symmetric adversarial reflection, enabling simultaneous extraction of positive and negative structural information. A time-dependent regulation mechanism dynamically adjusts knowledge assimilation intensity across evolutionary phases to balance exploration and exploitation. In addition, a continuous mutation spectrum transition strategy adaptively integrates Cauchy and Gaussian perturbations, facilitating smooth migration from global exploration to local refinement. Comprehensive experiments conducted on the CEC 2017 benchmark suite across 10, 30, and 50 dimensions validate the proposed framework, establishing a novel failure-aware bidirectional evolutionary learning paradigm for knowledge-driven optimization. The results demonstrate that our method achieves statistically significant and consistent performance improvements over classical baseline algorithms. Furthermore, its robustness and cross-domain adaptability are corroborated through successful application to a real-world constrained engineering problem: welded beam design. Full article

The development of stable and efficient essential oil delivery systems remains a persistent challenge in active food packaging applications. This research aimed to develop a multi-functional soy protein isolate (SPI)-based composite gel film integrating a tea tree oil micro emulsion (TME) via a microemulsion-in-gel approach, featuring sustained antioxidant release. The TME was first optimized using pseudo-ternary phase diagrams and exhibited excellent physicochemical stability. It maintained a droplet size ranging from 10 to 13 nm, with a polydispersity index (PDI) less than 0.2 under diverse stress situations (such as dilution, heat treatment, pH change, centrifugation, and 30-day storage). Afterward, TME-SPI composite gel films containing 1 to 3% TME were fabricated through solution casting and subsequent gelation of the protein matrix. The incorporation of TME markedly improved the properties of the gel film network. It raised the opacity by around 2.5 times, boosted the elongation at break to 144% (which is three times that of the control), and distinctively enhanced both water solubility and the water vapor barrier. Importantly, the 2% TME-SPI gel film exhibited sustained antioxidant activity from within the gel matrix, retaining more than 50% of its original 1,1-diphenyl-2-picrylhydrazyl (DPPH) scavenging activity after 72 h, significantly outperforming films containing free TTO. The microemulsion-in-gel approach was shown to be effective in creating SPI-based gel films that possess combined light-barrier characteristics, adjustable moisture resistance, improved flexibility, and extended antioxidant release. This offers a promising framework for the next generation of active food packaging. Furthermore, the composite gel films exhibited concentration-dependent antibacterial activity against Staphylococcus aureus, with the 3% TME-SPI film achieving an 82% inhibition rate, thus experimentally validating its active packaging potential. Full article

To achieve an optimal trade-off between production efficiency and energy benefits in complex manufacturing environments, this paper addresses the Green Flexible Job Shop Scheduling Problem (GFJSP) by establishing a multi-objective mathematical model that minimizes both makespan and total energy consumption. An Improved Non-dominated Sorting Genetic Algorithm II (INSGA-II) is proposed to solve this model. In the population initialization phase, chaotic mapping is integrated with multiple heuristic rules to generate a high-quality and uniformly distributed initial population. Furthermore, an enhanced elite selection mechanism is employed to effectively prevent premature convergence. Subsequently, adaptive crossover and mutation operators are designed to enable differentiated evolution across sub-populations, effectively coordinating global exploration and local exploitation. Finally, experimental results on the Brandimarte and Hurink benchmark datasets demonstrate the superiority of the proposed algorithm in terms of convergence and diversity, providing a robust solution for optimizing green industrial production scheduling. Full article

Research on trauma in autistic individuals has proliferated in recent years. This scoping review aims to (1) provide a comprehensive overview of the literature on trauma and autism, (2) identify and synthesize key themes, and (3) highlight gaps to inform future research. Following Arksey and O’Malley’s (2005) methodological framework and the PRISMA-ScR guideline and checklist (Tricco et al. 2018), we included articles published after 2000 in French or English that explicitly addressed trauma in autistic individuals. Four databases were searched: PsycINFO, Medline, ERIC, and Web of Science. A two-phase selection process yielded 199 eligible studies. Descriptive analyses and collaborative theme development were conducted to map the field. Findings show that most studies were published between 2018 and 2024, with the United States contributing the largest proportion. Four major themes were identified: (1) the relationship between autism and trauma, including prevalence, vulnerability, and consequences; (2) trauma-related symptoms and clinical manifestations; (3) assessment practices; and (4) intervention strategies. This review offers a critical synthesis of current knowledge, emphasizing the need for approaches that use broader definitions of trauma and reflect the diversity and lived experiences of autistic individuals. It also identifies significant methodological and conceptual gaps, calling for future research that addresses subgroup diversity and promotes equitable, trauma-informed practices for autistic individuals. Full article

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

Peptide therapeutics have emerged as a versatile class of biomolecules bridging the gap between small-molecule drugs and large biologics. Advantages of such molecules include high target specificity, potent bioactivity and reduced off-target toxicity. Despite these, broader clinical translation remains constrained by inherent limitations like poor metabolic stability, rapid renal clearance, limited membrane permeability and scalable synthesis. This review aims to systematically integrate advances in peptide science across natural discovery, synthetic methodologies, structural engineering, and translational delivery systems, while identifying critical research gaps hindering clinical adoption. We highlight diverse natural sources of bioactive peptides, including plant- (lunasin), animal- (Val-Pro-Pro (VPP) and Ile-Pro-Pro (IPP)), microbial- (nisin and cyclosporine), marine- (dolastatins) and venom-derived (chlorotoxin and ω-conotoxin MVIIA (ziconotide)) agents. Advances in solid-phase peptide synthesis (SPPS), green chemistry, and catalytic strategies are discussed alongside emerging in silico approaches, including artificial intelligence-driven sequence design and molecular modeling. Structural modifications such as cyclization, hydrocarbon stapling, PEGylation, and lipidation are critically evaluated for their role in enhancing pharmacokinetic and pharmacodynamic properties. Furthermore, nanoformulation strategies, including self-assembling peptides and cell-penetrating systems, are examined for their potential to overcome biological barriers. Importantly, this review identifies key unresolved challenges, including the lack of predictive models for peptide delivery systems, safety concerns associated with long-term modifications, and limited in vivo validation of naturally derived peptides. Addressing these gaps through integrated computational and experimental approaches will be essential for advancing next-generation peptide therapeutics. Collectively, this work provides a comprehensive framework for the rational design and translation of peptide-based precision medicines. Full article

Next-generation vaccines are being developed to elicit durable and cross-protective immune responses against diverse pathogens, particularly those targeting the respiratory and enteric systems. By strategically engaging T cell-centric antigen design, mucosal immune engagement, and induction of trained innate immunity, these innovative platforms are expected to reshape the paradigm of immunoprophylaxis and to offer promising avenues for enhanced protection against complex infectious diseases. Conventional antibody-based vaccines, though effective against many infections, often lack the capacity to induce durable or cross-protective immunity at mucosal surfaces. Advances in antigen design, delivery platforms, and adjuvant technologies now facilitate precise activation of tissue-resident memory T cells and enhancement of mucosal secretory IgA responses, thereby achieving sterilizing immunity at barrier surfaces while reinforcing systemic immune protection. Advanced delivery platforms, including lipid nanoparticles, viral vectors, and nano or liposomal carriers, further refine antigen presentation, enhancing stability, targeting, and overall immunogenicity. Concurrently, progress in understanding trained innate immunity highlights opportunities to induce broad, non-antigen-specific protection through epigenetic and metabolic reprogramming of innate cells. The integration of these adaptive and innate mechanisms may enhance early pathogen control, limits transmission, and strengthens defense against variant and antimicrobial-resistant pathogens across diverse populations. However, translating these immunological insights into safe, scalable, and globally accessible vaccines remains a major challenge. This review explores the emerging conceptual framework of next-generation vaccines that demonstrate partial integration of these axes in preclinical models, though human translation and functional synergy require Phase II validation. It highlights progress toward next-generation vaccines leveraging integrated adaptive and innate immune reprogramming for superior protection against respiratory and enteric pathogens. Full article

Depression and clinical anxiety (also known as ‘internalising disorders’) are commonly experienced by autistic children. Parents play an important role in reducing their child’s risk of developing internalising disorders, and existing technology-assisted parenting programs have shown promise in empowering parents in this role. Yet, existing interventions do not currently meet the unique needs of parents of autistic children. This study aimed to co-design adaptations to an existing technology-assisted parenting program (Partners in Parenting Kids) to enhance its relevance and acceptability for parents of school-aged autistic children. An iterative two-phase co-design study was conducted with parents of autistic children (n = 5) and service providers (n = 5). In Phase 1, semi-structured interviews explored participant experiences and needs in the context of parenting support, as well as perspectives on parenting programs. In Phase 2, eight co-design workshops were conducted with parents and service providers to build on the findings from Phase 1 and to collaboratively adapt the program content, delivery, and design features. Workshops involved participatory design activities to foster collaborative sharing of ideas and decision-making. Transcripts from both phases were analysed using reflexive thematic analysis. Themes identified in Phase 1 included: (1) Day-to-day challenges of parenting an autistic child; (2) Unique parent knowledge base and skill set; and (3) Desired qualities of parenting programs. Themes from Phase 2 of the study included: (1) Meaningful connections with others in the community; (2) Acceptance of autism; and (3) Diversity within the community. These themes are described in terms of their design implications for the resultant parenting program (Partners in Parenting Kids-Autism). The findings provide critical insights into desired qualities of parenting programs for parents of autistic children. Importantly, they also shed light on key design recommendations for future work focused on empowering parents to support their child’s mental health through interventions. Full article

Ion diffusion, the precipitation of hydration products, and interactions between different reactive particles are critical for optimizing the design of low-carbon cementitious systems. However, at the sub-micron scale, the complex spatial and chemical interactions among diverse components at an early age remain challenging to quantify. In this study, a machine learning-assisted BSE-EDS analytical method was applied to quantify both the phase assemblage and the spatial element features of cement–fly ash–slag ternary systems. The equidistant strip delineation of single-particle and rectangular inter-particle path methods were employed to quantify ionic diffusion gradients in the ternary systems. Single-particle strip analysis quantified the hydration front of clinker, slag and fly ash, while inter-particle analysis identified a persistent calcium-starvation zone at slag–fly ash interfaces. This region is characterized by exceptionally high Si/Ca ratios and a lower average atomic number and material density due to ionic diffusion limitations. These findings identify the slag–fly ash interface as the primary microstructural weak link, providing a robust methodology for capturing the chemical heterogeneities and optimizing the design of sustainable cementitious materials. Full article

This study investigates the volatile composition of twelve medicinal plant species belonging to the Lamiaceae family, which are widely recognized for their diverse biological activities, including antioxidant, antibacterial, and antifungal properties. Despite extensive studies on essential oils, comparative analyses using solvent-free techniques under different microclimatic conditions remain limited. This study investigates the volatile compounds in twelve medicinal plants from the Lamiaceae family using headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry (HS-SPME/GC-MS). Lamiaceae plants are recognized for their diverse medicinal properties, including antioxidative, antibacterial, and antifungal effects. A total of 74 volatile compounds were identified, encompassing terpenes, alcohols, esters, aldehydes, and ketones. Notably, Lavandula spica L. exhibited the highest number of unique volatiles (28), while Melissa officinalis L. had the fewest (16). Key compounds included Citral (65.48%) in Melissa officinalis L., Menthol (33.37%) and Menthyl acetate (30.53%) in Mentha piperita L., Carvone (45.86%) in Mentha spicata L., and Eucalyptol (54.71%) in Origanum syriacum L. Plants from Adana Botanic Park were rich in terpenes and ketones, whereas those from Osmaniye contained higher levels of alcohols, aldehydes, and esters. The findings emphasize the impact of geographic location on volatile profiles and suggest avenues for further research into medicinal efficacy and optimal dosage. This study supports the sustainable use of plant biodiversity (SDG 15) and highlights the importance of bioactive compounds for human health and well-being (SDG 3). Full article

Non-timber forest products (NTFPs) constitute an important component of forest-based production systems and biomass supply chains in Türkiye. Despite their growing economic and ecological significance, the long-term structural dynamics of NTFP production remain insufficiently understood. This study examines temporal and structural changes in NTFP production in Türkiye during the period 1988–2024 using official production statistics and production support data. The analysis applies a quantitative framework that combines linear trend analysis, Shannon diversity and Herfindahl–Hirschman concentration indices, volatility measures based on the coefficient of variation, and regression models to evaluate production trends, structural transformations, stabilization patterns, and the effectiveness of production support mechanisms. The findings reveal a non-linear and multi-phase development pattern characterized by diversification and production growth after 2000, followed by increasing concentration and greater production volatility after 2018. Although total production volume increased substantially, portfolio diversity declined over time, and dependence on a limited number of high-volume products intensified, indicating growing structural vulnerability within the system. In addition, production support mechanisms showed a weak and heterogeneous relationship with production outcomes. A limited contextual comparison with Lithuania’s multifunctional NTFP system is also included to position the findings within a broader European context. Overall, the results suggest that increasing production alone is insufficient to ensure long-term system stability. Instead, diversification-oriented and risk-sensitive resource management strategies that account for production risks, regional disparities, and product heterogeneity are essential for developing sustainable and resilient NTFP production systems. Full article

The transition toward Industry 5.0 in sustainable construction demands a radical reconceptualisation of workforce development, moving beyond purely technical training to embrace human-centricity, digitalisation, green competencies, and socio-cognitive resilience. Traditional vocational and higher education systems have largely failed to bridge the gap between emerging construction industry demands and the competencies possessed by current and future professionals. This systematic review investigates how Learning Factories’ 5.0 immersive, experiential, and technology-rich educational environments can address these gaps in sustainable construction contexts. Drawing on a synthesis of 71 peer-reviewed publications spanning 2015–2026 and supplemented by targeted construction-domain literature, this study pursues three objectives: (1) identifying core competencies for Industry 5.0 readiness in sustainable construction, (2) examining how Learning Factories 5.0 support the development of these competencies, and (3) proposing a competency-driven framework for integrating Learning Factories 5.0 into sustainable construction education and training. Seven transdisciplinary competency clusters are identified—Attitude toward Digitalisation, Technical–Green Proficiency, Information and Data Literacy, Digital Security, Collaborative Systems Thinking, Adaptive Problem-Solving, and Reflective Sustainability Practice—and a theoretically derived, eight-phase Construction Learning Factory 5.0 (CLF5.0) Framework is proposed as a conceptual architecture for future empirical development and institutional adaptation. The framework is presented as a generative starting point rather than a prescriptive model, and its effectiveness in diverse construction education contexts requires empirical validation through future implementation studies. Findings reveal that while Learning Factories offer transformative potential, critical barriers remain in terms of economic feasibility, faculty development, industry–academia alignment, and empirical validation. This paper contributes a construction-specific competency architecture and implementation pathway to support the industry’s transition toward a sustainable, human-centric, and Industry 5.0-aligned future. Full article

RNA N⁶-methyladenosine (m⁶A) is a prevalent epitranscriptomic modification that governs plant growth, development, and environmental adaptation. This review synthesizes recent advances in understanding the molecular mechanisms and biological functions of m⁶A in plants. The m⁶A landscape is dynamically regulated by methyltransferases (writers), demethylases (erasers), and m⁶A-binding proteins (readers), which collectively influence mRNA stability, translation efficiency, alternative polyadenylation (APA), and chromatin crosstalk. Functionally, m⁶A integrates diverse developmental processes—including embryogenesis, organogenesis, flowering, fruit ripening, and leaf senescence—with abiotic stress responses such as salt, drought, cold, and heat. Notably, m⁶A modification exhibits remarkable species-, cultivar-, and tissue-specific plasticity, enabling precise spatiotemporal gene regulation. Recent breakthroughs have revealed bidirectional crosstalk between m⁶A and histone modifications, forming a multi-layered regulatory network, while emerging concepts including phase separation, RNA structure dynamics, and stress memory further expand the functional repertoire of m⁶A. Despite significant progress, plant epitranscriptomics remains mechanistically underexplored, with critical gaps persisting in our understanding of translation initiation mechanisms, upstream regulatory signals controlling writers/erasers activities, and the functional significance of individual m⁶A sites. This review provided systematic insights into the complexity and specificity of m⁶A regulation in plants, offering a theoretical foundation for future efforts to decipher and ultimately manipulate this epitranscriptional layer for crop improvement. Full article

Bergamot essential oil (BEO) has demonstrated antidepressant potential, but its oral application is limited by poor water solubility and undesirable organoleptic properties. In this study, a BEO-loaded beverage was developed based on a whey protein-stabilized oil-in-water emulsion system. The optimal formulation, determined via single-factor experiments combined with orthogonal optimization, consisted of inulin (0.5 g/50 g), milk powder (2.0 g/50 g), sucralose (0.008 g/50 g), and sodium carboxymethyl cellulose (0.04 g/50 g). The resulting beverage remained stable without visible phase separation during 4 months of storage at 4 °C. In a chronic corticosterone treatment (CCT)-induced mouse model of depression, oral administration of the BEO beverage increased activity in the central area of the open field test and exploratory behavior in the elevated plus maze, while reducing repetitive stereotyped behaviors in the marble burying test. At the molecular level, the BEO beverage was associated with reduced levels of interleukin-1β (IL-1β), tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), and corticosteroid (CORT), and increased levels of corticotropin-releasing hormone (CRH), adrenocorticotropic hormone (ACTH), serotonin (5-HT), dopamine (DA), and norepinephrine (NE). Additionally, the BEO beverage was associated with observed alleviation of neuronal damage in the hippocampal CA3 region, upregulation of brain-derived neurotrophic factor (BDNF), improved gut microbial diversity, and altered host metabolic profiles. Collectively, these findings suggest that the BEO emulsion beverage is a feasible intervention for alleviating depression-like behaviors in the mouse model, and provide initial associative evidence supporting its potential as a functional food for mood management. Full article