Search Results (1,372)

Avocado (Persea americana Miller), a crop of major economic importance in Mexico, is threatened by several quarantine pests, and recent reports have suggested that the lance fly Neosilba batesi (Diptera: Lonchaeidae) may be responsible for significant yield losses. To clarify the role of this species, we surveyed avocados from six localities in Veracruz State on the Gulf coast of Mexico and identified lance flies using both morphological and molecular tools. None of the symptoms previously attributed to N. batesi infestation in Hass avocado were observed in any of the fruits inspected across the six localities. However, 90 fruits displayed clear signs of borer attack by Conotrachelus spp. or other primary pests, and 64 of these damaged fruits (60%) yielded lance flies. Hass avocados were rarely infested and hosted only N. batesi, whereas creole avocados (P. americana var. drymifolia) were hosts to N. batesi, N. glaberrima, N. recurva, and N. flavitarsis and an undescribed species (Neosilba sp.3) that was detected by analysis of the COI gene sequences of males. Additionally, Lonchaea cristula was reported for the first time emerging from creole avocado. Each avocado yielded an average of between 2.3 and 21.0 adult lance flies. Infestation was more frequent and numerous in fruits collected from the ground than in those harvested directly from trees, supporting the idea that lance flies preferentially exploit pre-damaged or fallen fruits. Indeed, lonchaeid eggs were frequently observed deposited on the periphery or inside oviposition holes created by other pests. Overall, our results indicate that Neosilba spp. act as secondary invaders in Veracruz, with no evidence of N. batesi behaving as a primary pest in this region. None of the avocados were infested by species of Tephritidae and none of the Neosilba species we identified appear to pose a threat to avocado production in Mexico. This study highlights the value of combining morphological and molecular tools for species identification and underscores the importance of differentiating between primary and secondary invaders in the context of avocado pest management. Full article

This study proposes an efficient method for selecting bi-directional ground motions in compliance with the KDS 41 criteria. The proposed method sequentially selects the required number of ground motions from available libraries without exhaustively evaluating all possible combinations, thereby improving computational efficiency. To validate the method, nonlinear response history analyses were performed on multi-degree-of-freedom (MDF) structures using the selected ground motions. The results demonstrate that the proposed method successfully identifies ground motions that closely match the target response spectrum with minimal variance. When selecting between 3 and 6 ground motions, maximum interstory drift ratio (MIDR) is generally overestimated, with errors increasing as the number of motions increases. However, when 7 or more ground motions are used, the mean MIDR errors remain within 20% for most structural models. This improves the accuracy of seismic demand predictions compared to relying on maximum responses from fewer motions. The results confirm the importance of using a sufficient number of ground motions to ensure reliable and efficient analysis. Full article

With rapid urbanization and intensifying climate change impacts, the thermal comfort performance of semi-outdoor spaces has emerged as a critical issue in sustainable urban design and housing development. However, the unique void decks of residential environments remain underexplored in the existing literature. This study addresses the knowledge gap by investigating how morphological characteristics influence microclimatic conditions and user satisfaction in high-density subtropical residential environments. Field measurements and questionnaire surveys were conducted across 18 void decks in four representative Shenzhen communities during summer 2024, examining air temperature, relative humidity, wind velocity, mean radiant temperature, and UTCI alongside users’ thermal perceptions. Hierarchical cluster analysis identified three distinct typologies based on spatial attributes: North–South-Ventilated (NS-VD), Single-Directional (SD-VD), and Oblique-Oriented (OO-VD). Ridge regression analysis revealed seven critical configuration variables—height-to-depth ratio, orientation, angle with wind, number of open sides, sky view factor, green view factor, and height from ground—collectively explaining 51.2% of UTCI variation. The results were as follows: (1) we identified morphological typologies and quantify microclimate variations across spatial configurations; (2) established quantitative relationships between objective thermal metrics and subjective thermal perceptions; and (3) developed evidence-based design recommendations for enhancing thermal environments in subtropical residential contexts. The findings support climate-responsive design for high-density residential environments by providing a scientific basis for optimizing microclimates and enhancing community vitality. Full article

Leaf water content (LWC) and chlorophyll content (CHL) are pivotal physiological indicators for assessing citrus growth and stress responses. However, conventional measurement techniques—such as fresh-to-dry weight ratio and spectrophotometry—are destructive, time-consuming, and limited in spatial and temporal resolution, making them unsuitable for large-scale monitoring. To achieve efficient large-scale monitoring, this study proposes a synergistic inversion framework integrating UAV multispectral remote sensing with intelligent optimization algorithms. Field experiments during the 2024 growing season (April–October) in western Hubei collected 263 ground measurements paired with multispectral images. Sensitive spectral bands and vegetation indices for LWC and CHL were identified through Pearson correlation analysis. Five modeling approaches—Partial Least Squares Regression (PLS); Extreme Learning Machine (ELM); and ELM optimized by Particle Swarm Optimization (PSO-ELM), Artificial Hummingbird Algorithm (AHA-ELM), and Grey Wolf Optimizer (GWO-ELM)—were evaluated. Results demonstrated that (1) VI-based models outperformed raw spectral band models; (2) the PSO-ELM synergistic inversion model using sensitive VIs achieved optimal accuracy (validation R²: 0.790 for LWC, 0.672 for CHL), surpassing PLS by 15.16% (LWC) and 53.78% (CHL), and standard ELM by 20.80% (LWC) and 25.84% (CHL), respectively; and (3) AHA-ELM and GWO-ELM also showed significant enhancements. This research provides a robust technical foundation for precision management of citrus orchards in drought-prone regions. Full article

Radiomics has emerged as a promising tool for non-invasive tumour phenotyping in breast cancer, providing valuable insights into tumour heterogeneity, response prediction, and risk stratification. However, traditional radiomic approaches often rely on correlative patterns of image analysis to clinical data and lack direct biological interpretability. Combining information provided by radiomics with genomics or other multi-omics data can be important to personalise diagnostic and therapeutic work up in breast cancer management. This review aims to explore the current progress in integrating radiomics with multi-omics data—genomics and transcriptomics—to establish biologically grounded, multidimensional models for precision management of breast cancer. We will review recent advances in integrative radiomics and radiogenomics, highlight the synergy between imaging and molecular profiling, and discuss emerging machine learning methodologies that facilitate the integration of high-dimensional data. Applications of radiogenomics, including breast cancer subtype and molecular mutation prediction, radiogenomic mapping of the tumour immune microenvironment, and response forecasting to immunotherapy and targeted therapies, as well as lymph nodes involvement, will be evaluated. Challenges in technical limitations including imaging modalities harmonization, interpretability, and advancing machine learning methodologies will be addressed. This review positions integrative radiogenomics as a driving force for next-generation breast cancer care. Full article

This paper presents a probabilistic methodology for generating fragility surfaces for low- to mid-rise steel moment-resisting frames (MRFs) under fire-following-earthquake (FFE). The framework integrates nonlinear dynamic seismic analysis, residual deformation transfer, and temperature-dependent fire simulations within a Monte Carlo environment, while explicitly accounting for uncertainties in structural properties, ground motions, and fire simulation. A fiber-based modeling strategy is employed, combining temperature-sensitive steel materials with fatigue and fracture wrappers to capture cyclic deterioration and abrupt failure. This formulation yields earthquake-only and fire-only fragility curves along the surface boundaries, while interior points quantify the joint fragility response under sequential hazards. The methodology is benchmarked against a machine learning (ML) synthesis framework originally developed for earthquake–tsunami applications and extended here to FFE. Numerical results for a three-story steel MRF show excellent agreement (R² > 0.95, RMSE < 0.02) between simulated and ML-generated surfaces, demonstrating both the efficiency and hazard-neutral adaptability of the ML framework for multi-hazard resilience assessment. Full article

Background/Objectives: Military identity serves as a foundational lens through which service members navigate the events of everyday military and civilian life. However, the very process that cultivates a sense of unity and purpose can be a double-edged sword in civilian life. Although the prevalence and transition needs are known, few studies have explored how chronic pain specifically disrupts military identity in depth. This qualitative study explores three distinct trajectories through which Veterans with chronic pain experience identity change. Methods: This study used narrative inquiry involving two sets of in-depth interviews with 20 Veterans. Reflexive thematic analysis was employed to describe and differentiate three distinct trajectories of chronic pain. Results: Veterans with chronic pain experience identity change through three overlapping pain trajectories: (1) traumatic injury -> immediate discharge; (2) misdiagnosed/non-traumatic injury -> delayed discharge; and (3) cumulative wear and tear -> gradual discharge. Regardless of trajectory, chronic pain consistently disrupted military identity and forced Veterans to confront tensions between institutional expectations of stoicism and combat readiness and the physical realities of chronic pain during military service. Those interviewed described experiencing fragmented institutional support, uneven access to care, and the systemic invalidation of pain that did not conform to military ideals. Conclusions: These findings underscore the need for Veteran-centred approaches, including responsive services, comprehensive pain science education throughout military careers, early detection of conditions that can lead to chronic pain, and flexible care pathways tailored to the nuances of each pain trajectory and grounded in military culture and lifestyle. Full article

This study explores how sustainability leadership and Green Human Resource Management (Green HRM) practices interplay to cultivate an environmentally responsible culture in organizations based in Jeddah. Through thematic analysis of participant interviews, the research identifies key leadership behaviors, such as visionary communication, role modeling, and operational integration, that align culturally grounded ethical values to drive sustainability. Green HRM practices, including green recruitment, targeted training, eco-friendly performance appraisals, and recognition systems, further reinforce these leadership efforts. The study highlights the importance of authentic alignment between leadership values and HRM policies to avoid perceptions of greenwashing and to institutionalize sustainable practices effectively. Findings emphasize that embedding sustainability within organizational culture requires a synergistic approach integrating leadership vision, HRM systems, and cultural context, fostering employee motivation and long-term environmental commitment. The implications provide valuable insights for organizations seeking to implement meaningful sustainability strategies aligned with both global goals and local values. Full article

This case study examines the experience of solastalgia among individuals residing in gold mining towns in Zimbabwe, particularly as environmental degradation intensified, to illustrate the experience of solastalgia through the expressions and reflections of individuals who have been impacted by it. Ecological degradation has intensified, and national forest cover has been declining at an average rate of 327,000 hectares per year between 2000 and 2010. Meanwhile, artisanal gold mining releases more than 24 tons of mercury into ecosystems annually, contaminating water bodies. The research employed a qualitative design grounded in the interpretivist paradigm and utilized an inductive methodology. Data were obtained through in-depth interviews with 11 individuals who had resided in areas impacted by mining for an extended period. Thematic analysis was employed to examine individuals’ emotions, thoughts, and behaviours in response to environmental changes, with an emphasis on feelings of loss, sadness, frustration, and disconnection from place. Participants exhibited intense emotional reactions to environmental changes, including profound sadness, anger, feelings of helplessness, and a longing for the past. Of the participants, 70% reported experiencing profound sorrow, while 60% indicated that they employed community support as a coping strategy. The degradation of forests, water sources, and biodiversity contributed to a sense of alienation regarding their environment and identity. Nevertheless, some demonstrated resilience through their faith, community support, and efforts to safeguard the environment. However, their ability to heal and adapt was hindered by persistent systematic neglect and unfulfilled commitments. This case study contributes to the growing literature on solastalgia by presenting specific instances from Zimbabwe, a region where the phenomenon remains inadequately comprehended. This expands the concept of solastalgia to encompass the ecological degradation resulting from mining activities in Africa. Full article

The advent of Large Language Models (LLMs) has revolutionised natural language processing, providing unprecedented capabilities in text generation and analysis. This paper examines the utility of Artificial-Intelligence-assisted (AI-assisted) content analysis (CA), supported by LLMs, as a methodological tool for research in Information Science (IS) and Cyber Security. It reviews current applications, methodological practices, and challenges, illustrating how LLMs can augment traditional approaches to qualitative data analysis. Key distinctions between CA and other qualitative methods are outlined, alongside the traditional steps involved in CA. To demonstrate relevance, examples from Information Science and Cyber Security are highlighted, along with a new example detailing the steps involved. A hybrid workflow is proposed that integrates human oversight with AI capabilities, grounded in the principles of Responsible AI. Within this model, human researchers remain central to guiding research design, interpretation, and ethical decision-making, while LLMs support efficiency and scalability. Both deductive and inductive AI-assisted frameworks are introduced. Overall, AI-assisted CA is presented as a valuable approach for advancing rigorous, replicable, and ethical scholarship in Information Science and Cyber Security. This paper contributes to prior LLM-assisted coding work, proposing that this hybrid model is preferred over a fully manual content analysis. Full article

This study quantifies the influence of soil–structure interaction (SSI) on key parameters of performance-based seismic design (PBSD) for steel moment-resisting frames. Specifically, PBSD is extended as a methodology in which explicit structural performance levels, such as immediate occupancy, damage limitation, life safety, and collapse prevention, serve as the basis for sizing and detailing structural members under specified seismic hazard levels, instead of traditional force-based design. The PBSD framework is further developed to incorporate SSI by adopting a beam on a nonlinear Winkler foundation model. This model captures the nonlinear soil response and its interaction with the structure, enabling a more realistic design framework within a performance-based context. To evaluate and quantify the influence of SSI in the PBSD method, an extensive parametric study is performed using 100 far-field ground motions, categorized into four groups (25 records each) corresponding to EC8 soil types A, B, C, and D. Nonlinear time history analyses reveal consistent trends across the examined frames. When SSI is neglected, the fundamental natural period (T) is systematically underestimated by approximately up to 3.5% on EC8 soil type C and up to 15% on soil type D. As a result, the base shear and the mean values of maximum interstorey drift ratios (IDRs) are overestimated compared to cases accounting for soil flexibility, with the largest drift discrepancies observed in frames with eight or more storeys on soil D. The analyses further reveal that softer soils (e.g., Soil D) lead to significantly higher q values, particularly for moderate-to-long period structures, whereas stiffer soils (e.g., Soil B) cause only minor deviations, remaining close to fixed-base values. A complementary machine learning module, trained on the same dataset, is employed to predict base shear, maximum IDR, and the behavior factor q. It successfully reproduces the deterministic SSI trends, achieving coefficients of determination (R²) ranging from 0.986 to 0.992 for maximum IDR, 0.947 to 0.948 for base shear, and 0.944 to 0.952 for q. Feature importance analysis highlights beam and column ductility, soil class, and performance level as the most influential predictors of structural response. Full article

The use of digital technologies can break down information barriers in rural areas, thereby creating crucial conditions for the widespread adoption of green agricultural technologies (GATs) among farmers. To explore the relationship between digital technology use (DTU) and farmers’ adoption of GATs, this study draws on 18 in-depth interviews and 608 survey responses collected from rice farmers in Sichuan Province, China. By adopting a mixed-methods design, it offers a comprehensive examination of the mechanisms through which digital technology use (DTU) promotes the adoption of green agricultural technologies (GATs) among farmers. Grounded theory analysis reveals that the DTU–GATs adoption pathway can be conceptualized within a “condition–process–outcome” framework. Specifically, digital infrastructure, farmers’ capital endowment, and practical needs constitute the foundational conditions, while technology perception and the regional soft environment act as key mediating processes. The ultimate outcomes include improvements in economic performance, social well-being, and ecological sustainability. Empirical evidence confirms that DTU significantly promotes the adoption of GATs, primarily by enhancing farmers’ perceptions of technology and improving the agricultural soft environment at the regional level. Moreover, the effects of DTU display substantial heterogeneity across different types of green technologies and among various farmer groups. These findings highlight the importance of strengthening digital infrastructure in rural areas, enhancing farmers’ digital literacy and capacity, and leveraging digital tools to tailor the dissemination and guidance of GATs. Such efforts are essential to raise farmers’ awareness, foster a supportive soft environment for sustainable agriculture, and ultimately advance the adoption of GATs. Full article

Urbanization has posed significant challenges to cities globally, including urban sprawl, traffic congestion, reduced livability, and poor walkability. In Doha, Qatar’s capital, these issues are particularly pronounced in the West Bay Central Business District (CBD). Transit-Oriented Development (TOD) is widely recognized as a key strategy to advance sustainable urbanism and mitigate such challenges. This study employs the Integrated Modification Methodology (IMM) to systematically assess the urban design and spatial configuration of West Bay through observational analysis. The research aims to reassess the urban form and enhance transit integration through a multi-stage, iterative process, focusing on critical determinants such as compactness, complexity, and connectivity. The analysis is structured around five essential design dimensions: (i) walkability, (ii) ground-level land use balance, (iii) mixed-use and public spaces, (iv) inter-modality and transport hubs, and (v) the public transportation network. Findings reveal key urban design deficiencies, including limited intermodal connectivity, insufficient green open spaces, and a lack of diverse land use around the metro station. To address these gaps, the study proposes a set of context-sensitive policy and design guidelines to support TOD-based regeneration. This research contributes directly to SDG 11: Sustainable Cities and Communities, and supports SDG 9 and SDG 13 through its emphasis on infrastructure integration and climate-responsive planning. The findings offer practical insights for urban planners, developers, and policymakers engaged in sustainable urban transformation. Full article

Low Earth orbit (LEO) satellites, characterized by low altitudes, high velocities, and strong ground signal reception, have become an essential and dynamic component of modern global navigation satellite systems (GNSS). However, orbit decay induced by atmospheric drag poses persistent challenges to maintaining stable trajectories. Frequent orbit maneuvers, though necessary to sustain nominal orbits, introduce significant difficulties for precise orbit determination (POD) and navigation augmentation, especially under complex operational conditions. Unlike most existing methods that rely on Two-Line Element (TLE) data—often affected by noise and limited accuracy—this study directly utilizes onboard GNSS observations in combination with real-time precise ephemerides. A novel time-series indicator is proposed, defined as the geometric root-mean-square (RMS) distance between reduced-dynamic and kinematic orbit solutions, which is highly responsive to orbit disturbances. To further enhance robustness, a sliding window-based adaptive thresholding mechanism is developed to dynamically adjust detection thresholds, maintaining sensitivity to maneuvers while suppressing false alarms. The proposed method was validated using eight representative maneuver events from the GRACE-FO satellites (May 2018–June 2022), successfully detecting seven of them. One extremely short-duration maneuver was missed due to the limited number of usable GNSS observations after quality-control filtering. To examine altitude-related applicability, two Sentinel-3A maneuvers were also analyzed, both successfully detected, confirming the method’s effectiveness at higher LEO altitudes. Since the thrust magnitudes and durations of the Sentinel-3A maneuvers are not publicly available, these cases primarily serve to verify applicability rather than to quantify sensitivity. Experimental results show that for GRACE-FO maneuvers, the proposed method achieves near-real-time responsiveness under long-duration, high-thrust conditions, with an average detection delay below 90 s. For Sentinel-3A, detections occurred approximately 7 s earlier than the reported maneuver epochs, a discrepancy attributed to the 30 s observation sampling interval rather than methodological bias. Comparative analysis with representative existing methods, presented in the discussion section, further demonstrates the advantages of the proposed approach in terms of sensitivity, timeliness, and adaptability. Overall, this study presents a practical, efficient, and scalable solution for real-time maneuver detection in LEO satellite missions, contributing to improved GNSS augmentation, space situational awareness, and autonomous orbit control. Full article

Environmental literacy is essential for preparing students with the knowledge, skills, and dispositions to address pressing environmental challenges. This systematic literature review examines how pedagogical approaches used in secondary education foster students’ environmental literacy. The review enriches the current literature by shifting attention away from the predominant focus on higher education and providing new empirically grounded insights into the effectiveness of classroom practices in enhancing students’ environmental literacy at the secondary education level. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 22 peer-reviewed studies published between 2010 and 2024 were identified through Web of Science, Scopus and ERIC. The analysis is guided by Joyce and Calhoun’s taxonomy of teaching models and the conceptualization of environmental literacy developed by the North American Association for Environmental Education (NAAEE). Findings show that strategies grounded in the social family and information-processing models of teaching were most frequently used, reflecting a pedagogical shift toward collaboration, critical thinking, and active engagement, yet a significant gap remains in cultivating environmentally responsible behavior (ERB). The review highlights the need for pedagogical designs that support the integration of every dimension of environmental literacy. It further emphasizes culturally responsive approaches and systematic investment in teacher professional development as critical conditions for pedagogical success. Full article