Search Results (4,467)

Bothriochloa ischaemum is a key forage species with strong grazing tolerance and high nutritional value, making precise quantification of spike and seed traits essential for germplasm evaluation and yield prediction. However, the compact architecture and minute seed size in natural field conditions render manual counting inefficient and labor-intensive. To address this limitation, this study presents a non-destructive and automated quantification framework integrating advanced object detection and regression analysis for accurate in situ estimation of spikes and seed numbers. To further address the challenges of dense spike detection caused by occlusion and small object sizes, this study developed a modified model named YOLOv12-DAN by integrating DySample dynamic upsampling, ASFF feature fusion, and NWD loss, which achieved a mean average precision (mAP) of 91.6%. Meanwhile, for the detection of dense kernels on compact spikes, an improved YOLOv12 architecture incorporating an Explicit Visual Center (EVC) module was proposed to enhance multi-scale feature representation. The optimized model attained a bounding box precision of 96.5%, a recall rate of 86.4%, an mAP50 of 94.3%, and an mAP50-95 of 73.9%. Furthermore, a univariate linear regression model based on 132 spike samples verified the reliable consistency between the predicted and actual seed counts, with a mean absolute error (MAE) of 6.30, a mean absolute percentage error (MAPE) of 9.35, and an R-squared (R²) value of 0.808. Finally, the model was deployed through a lightweight end-to-end web application, enabling real-time field operation and promoting its applicability in breeding programs and agronomic decision-making. This study provides a robust technical pathway for automated phenotyping and precision forage improvement. Full article

When it comes to inspiring and sustaining action for the environment, identity matters. This review examines environment-related identity terms to clarify terminology and support discourse. A literature search was conducted in Scopus for peer-reviewed articles published from 2020 through to 31 July 2025. Articles were included if they discussed one of 15 environment-related identity terms in the title, abstract, or keywords and engaged conceptually with the term. Articles were excluded if the term appeared only in searchable fields, was used in a non-individual context, or was not substantively engaged with. Drawing on 919 articles, the review maps how identity terms are defined in the literature. The result is a three-dimensional framework encompassing connection to nature, pro-environmental orientation in everyday life, and public/political environmental engagement. Findings highlight that identity terms are often inconsistently defined, with substantial overlap. Results are limited to articles with identity terms in searchable fields and explicit definitions, potentially omitting implicit or operationalised uses. To address inconsistencies, we propose three identity terms, ecological identity, environmental steward, and environmental activist, each corresponding to one of the identified dimensions above. Clarifying this language can strengthen academic discourse and help individuals locate themselves within it, keeping identities motivating amid accelerating environmental degradation. Full article

This study presents a comparative analysis of classroom engagement effects on the academic achievement of senior secondary school physics students, focusing on the replication of prior research and contrasting the impacts of guided and minimally guided constructivist instructional approaches. Drawing on established frameworks of inquiry-based instruction, particularly Cognitively Guided Instruction (CGIS) and Cubing Instruction (CIS), the research investigates their relative efficacy in enhancing student learning outcomes. The clustered quasi-experimental pretest–posttest design, involving the Cognitively Guided Instructional Strategy (CGIS) and the Cubing Instructional Strategy (CIS), was adopted by the study. The intact classroom groups of schools purposively selected participated in the study. An achievement test was administered before and after instruction, and the Analysis of Covariance (ANCOVA) and t-tests were used to determine the effects of the intervention while controlling for baseline achievement and mathematical ability. The findings show that the treatment had a significant effect on the students’ achievement (p = 0.030). The t-test result demonstrated that students exposed to the CGIS recorded higher posttest mean scores than those in the CIS group. These outcomes suggests that guided inquiry may offer pedagogical advantages in supporting classroom and conceptual learning. However, the evidence should be cautiously interpreted. The study contributes to the literature as a conceptual replication by providing evidence regarding the effects of guided and minimally guided constructivist approaches in a different instructional setting. The outcomes underscore the importance of balancing instructional guidance and learner autonomy in physics classrooms, as well as the need for further research involving larger samples and diverse contexts to strengthen causal inference. Full article

In an era of rapid urbanisation and climate challenges, understanding how urban land patterns contribute to resilience is crucial for sustainable development. This theoretical review introduces a novel framework positing that greater heterogeneity in plot sizes and land uses enhances urban resilience by promoting the long-term preservation of built environments, multifunctional spaces, and socio-cultural adaptability. Drawing on urban morphology, assemblage theory, and resilience science, we argue that fragmented ownership in small-plot fabrics acts as a barrier to large-scale redevelopment, fostering diversity that buffers against shocks. Through comparative case studies of Venice (Italy), Tokyo (Japan), Hong Kong, Mexico City (Mexico), and York (UK), we illustrate how historical small-plot subdivisions have endured centuries, supporting ecological, economic, and social sustainability. The analysis reveals common patterns: ownership fragmentation preserves fine-grained urban forms, enabling adaptive reuse (exaptation) and inclusivity. The five case studies serve an illustrative function, demonstrating how the theoretical linkages between plot heterogeneity, institutional friction, incremental transformation, and long-term resilience outcomes can plausibly operate in real-world historic urban fabrics. This paper addresses a gap in the literature by synthesising plot-level heterogeneity with broader resilience outcomes, offering policy implications for protecting such fabrics amid global urbanisation pressures. The findings align with land system science, emphasising multifunctionality for regenerative urbanism. Full article

Despite widespread corporate endorsement of the Sustainable Development Goals (SDGs), systematic evidence on how top management in emerging economies prioritizes and frames SDG-related issues over time remains limited. Existing studies are often based on manual or single-year analyses, restricting comparability, scalability, and longitudinal insight. This study examines how corporate managerial communication aligns with and emphasizes SDGs across sectors and over time in two major emerging economies, India and South Africa. Using an AI-driven natural language processing (NLP) pipeline, we analyse 2400 annual reports from 600 publicly listed companies covering the period 2020–2023. A fine-tuned SDG-BERT multi-label classification model is applied to extract and classify SDG-related content from top management communications, enabling sectoral, temporal, and cross-country comparison of SDG relevance. The results reveal a strong and persistent emphasis on SDG 12 (Responsible Consumption and Production) across both countries, alongside sector-specific variation and differing patterns of SDG diversity over time. South African firms exhibit greater variation in SDG emphasis across years, while Indian firms display more concentrated and stable SDG framing. Overall, the findings highlight systematic imbalances in SDG-related managerial communication and persistent underrepresentation of several social SDGs. The study contributes methodologically by demonstrating the value of validated AI-assisted longitudinal text analysis for large-scale SDG research and empirically by providing comparative insights into how corporate SDG narratives evolve in emerging market contexts. Full article

As modern power systems grow increasingly complex, there is a pressing need for stability analysis methods capable of handling nonlinear dynamics while providing physically meaningful and reliable stability indices. Port-Hamiltonian (PH) frameworks have emerged as strong candidates in this regard, offering inherently stable formulations, energy-consistent representations, and modular plug-and-play scalability. However, the practical deployment of PH-based stability analysis remains hindered by the absence of reliable, high-fidelity parameter identification methods that rely on sensor measurements to capture system dynamics while remaining compatible with PH model structures. This paper addresses that gap by proposing a comprehensive three-stage data-driven identification framework for PH modeling of synchronous generators—the central dynamic component of any power system. While the IEEE Standard 115 provides established procedures for transient parameter identification, it exhibits fundamental limitations when applied to PH modeling, including single-scenario identifiability constraints, noise-sensitive derivative-based formulations that amplify sensor measurement errors, and the inability to decouple generator-internal damping from grid contributions. The proposed framework resolves these limitations through multi-scenario excitation using sensor-acquired voltage and current signals, derivative-free state consistency optimization, and physics-based regularization that enforces PH structure preservation. Complete identification of eight key parameters (H, D, $X_d$, $X_q$, $X_d^{\prime }$, $X_q^{\prime }$, $T_{do}^{\prime }$, $T_{qo}^{\prime }$) is achieved with errors ranging from 1.26% to 9.10%, and validation confirms RMS rotor angle errors below 1.2° and speed errors below 0.15%, demonstrating suitability for transient stability analysis, passivity-based control design, and oscillation damping assessment. Full article

Cross-institutional collaborative fraud detection is essential for combating increasingly sophisticated financial fraud, yet privacy regulations and data silos severely constrain knowledge sharing among institutions. This study aims to develop a privacy-preserving framework that enables effective collaborative fraud detection while protecting raw data, with particular emphasis on exploiting symmetry properties in federated architectures and graph topology analysis. We propose an Adaptive Federated Graph Attention Network (FedGAT), which employs spatio-temporal graph attention mechanisms to capture topological structures and dynamic fraud patterns within institutional transaction networks. The framework introduces a symmetric similarity matrix derived from graph topological features, where the symmetry property ($s_{ij}=s_{ji}$) ensures consistent and unbiased measurement of structural relationships between any pair of institutions. Based on this symmetric similarity metric, an adaptive weighted aggregation mechanism is designed for cross-institutional parameter fusion, enabling balanced knowledge transfer that respects the symmetric collaborative relationship among participating institutions. The symmetric information exchange protocol between local institutions and the central server further guarantees equitable contribution and benefit distribution throughout the federated learning process. The framework is evaluated on the Elliptic Bitcoin transaction dataset and the IEEE-CIS fraud detection dataset, with recall rate and false positive rate as primary performance metrics. Results show that FedGAT achieves a recall of 0.85 and a false-positive rate of 0.038 in single-institution detection, representing approximately 40% and 70% improvements over existing methods, respectively. In collaborative detection across five virtual institutions, the symmetry-aware adaptive aggregation mechanism enables all participants to achieve performance gains exceeding 15% while completely eliminating negative transfer effects observed in simple averaging approaches. This work contributes a novel symmetry-based federated learning framework that balances privacy protection with detection performance, advancing the literature on cross-institutional financial risk management. Full article

This study provides sedimentological and stratigraphic insights into the Ediacaran fluviolacustrine successions of the Amane-n’Tourhart and Tifernine basins. The Amane-n’Tourhart Basin developed in a post-caldera volcanic setting along the margin of the Oued Dar’a Caldera, whereas the Tifernine Basin formed in a pre-caldera tectono-volcanic context associated with caldera development. The successions provide valuable information about the sedimentary processes operating in late Ediacaran continental environments. Field observations, facies analysis, and petrography reveal a variety of siliciclastic, carbonate, mixed siliciclastic–carbonate, and volcaniclastic facies. These facies form associations indicative of alluvial fan, floodplain, and shallow-water lacustrine settings. Alluvial fan deposits are dominated by conglomerates and sandstones forming braided systems. Fluviolacustrine successions show a transition from clay-rich siltstones with calcareous nodules to nodular and massive limestones, marking a gradual shift from fluvial to lacustrine conditions. Laminated limestones and stromatolites indicate intermittent microbial activity that contributed to carbonate precipitation. Sedimentation was strongly influenced by volcanic inputs and climatic fluctuations, alternating between humid and arid conditions. These factors drove cycles of channel incision, sediment infill, and lake expansion–contraction, illustrating the dynamic interplay of volcanism and climate that modulated deposition in these Ediacaran continental basins, with broad relevance to our understanding of this critical window in the Earth’s history. Full article

Complementary feeding influences infant growth and health. Māori and Pacific infants in Aotearoa New Zealand experience disproportionate nutrition-related disease, yet complementary feeding data are limited. Caregivers of 625 infants (7–10 months) completed a questionnaire on timing of introduction, baby-led weaning (BLW), and baby food pouch use. Ethnicity was total response; infants not Māori or Pacific were classified as ‘other’. Complementary foods were introduced at around six months for 56.5% of Māori, 62.2% of Pacific, and 80.9% of others; before five months for 40.5%, 34.2%, and 17.3%. BLW prevalence was 29.2% (Māori), 17.1% (Pacific), and 27.3% (other). Although pouches were uncommon when complementary feeding began, by 7–10 months about two-thirds of Māori and Pacific infants were fed pouches sometimes or frequently. Frequent pouch use with mostly or always nozzle feeding occurred in 12.2% of Māori infants, 12.2% of Pacific infants, and 2.7% of other infants. Vegetables and purée were the most common first food and texture. By six months, over half consumed red meat and about half consumed iron-fortified baby rice. These feeding practices have implications for nutrition-related health inequities among Māori and Pacific infants, highlighting the need for culturally centered public health approaches to support whānau with feeding. Full article

Heat stress is a significant problem in dairy production that has detrimental effects on milk production, animal well-being and reproductive function. These effects are predicted to worsen due to climate change. With a focus on South African production systems, this review assesses the potential of combining precision livestock farming (PLF) and genomic selection (GS) technology to identify, measure and reduce heat stress in dairy cattle. In addition to PLF tools like wearable sensors, rumen boluses, infrared thermography, GPS- and weather-based decision-support systems, pertinent literature was reviewed to evaluate genomic approaches such as heritability estimates and genome-wide association studies identifying selection signatures for thermotolerance. While advances in genomic techniques have improved the identification of thermotolerance markers and the accuracy of breeding values for heat tolerance, evidence from recent studies shows that PLF technologies can accurately detect early physiological and behavioural indicators of heat stress in real time. The ability to select climate-resilient animals under realistic farm conditions is improved by combining high-resolution phenotypic data from PLF systems with genetic data. Overall, the review concludes that combining PLF and GS provides a useful and complementary approach to enhance the detection of heat stress, facilitate well-informed management choices and hasten the development of thermotolerant dairy cattle, all of which contribute to more sustainable dairy production under rising temperatures. Full article

Flood risk maps play a critical role in land-use regulation, infrastructure planning, and community preparedness, which are key components of sustainable and climate-resilient urban development. Their production, however, remains costly, labor-intensive, and time-demanding as it relies on simulation-driven workflows that combine hydrodynamic modeling with expert interpretation and extensive validation. To address this issue from a sustainability perspective, we develop a novel, practical, and near-real-time large language model (LLM)-based framework to support property-level flood risk assessment. This framework, which synthesizes geospatial, hydrological, infrastructural, and historical flood information, extends existing research and explores novel risk estimation methods for use in planning practice. Using Brisbane, Australia, as a case study, we develop Flood-LLM, a multi-agent system that transforms multi-source urban datasets into structured textual representations, models diverse neighborhood conditions, and fine-tunes a reasoning model using expert-assessed risk classifications. The results show that Flood-LLM can reproduce official flood risk labels for creek, river, storm tide, and overland-flow hazards with reasonable accuracy, outperforming classical machine learning, deep learning, and untuned LLM baselines. Visual and quantitative analyses indicate that the framework demonstrates a qualitatively nuanced capability to capture salient spatial patterns present in the official maps, while generating a textual chain-of-thought providing a transparent audit trail for its labeling decisions. These findings suggest that such LLM-based approaches can produce potential complementary tools to expert-reviewed planning classifications and support more sustainable, adaptive flood risk management by enabling timely map production and updates that facilitate informed decision-making in rapidly changing environmental conditions. Full article

The Cinnamomum camphora seed kernel (CCSK) shows great promise as a natural source of bioactive alkaloids. However, there is little data about recovering alkaloids from CCSK by-products after oil extraction using an aqueous method. This study aimed to establish an efficient technology for enriching CCSK alkaloids (including magnoflorine, lindoldhamine and N,N-methyldomesticinium) using macroporous resin technology. The results showed that XR918C resin was the most suitable adsorbent due to its high adsorption/desorption capacity for CCSK alkaloids. The adsorption process was best described by Langmuir isotherm models and pseudo-second-order kinetics; it was spontaneous and physical in nature. The optimum procedure for CCSK alkaloids enrichment using XR918C resin was as follows: for adsorption, the injection flow rate and sample volume were 2.0 BV/h and 7.0 BV, respectively; for desorption, the eluent type, elution flow rate and volume were 80% ethanol, 2.0 BV/h and 6.0 BV, respectively. Furthermore, the scale-up of the CCSK alkaloid enrichment process was performed under optimal conditions. Following the 10-fold scale-up enrichment, the content of CCSK alkaloids was raised 4.41-fold, with a recovery rate of 89.19 ± 0.01%. After nine regeneration cycles, the efficiency of the XR918C resin remained stable, indicating its good reusability. In addition, CCSK alkaloids exhibited strong in vitro antioxidant activity. This study provides a useful reference for the industrial-scale enrichment of CCSK alkaloids. Full article

Background: Assessing aortic dissection (AD) in its early stages is crucial for cardiovascular surgeons to improve patient outcomes and avoid complications associated with surgical intervention for type A aortic dissection. Initial evaluations rely on patient referrals for computed tomography (CT) scans, which involve measuring the maximum aortic diameter. Objective: This study aimed to improve current diagnostic thresholds for type A aortic dissection by using computational fluid dynamics (CFD) modeling to correlate hemodynamic factors related to the wall shear stress with maximum aortic diameter growth rate, offering insights into predicting AD progression and reassessing current diameter-based diagnostic criteria. Methods: The pre- and post-AD scan data, with an average duration of three and a half years for the 15 patients, were converted into 3D geometries. These geometries were analyzed using the transitional-turbulent CFD model. Wall shear stress (WSS), its derivatives, and the pressure gradient from the pre-AD CT scans were compared across 15 patients, grouped according to the aortic diameter growth per year. Results: For patients in group 1 (nine patients with normal diagnosis), pre-AD time-average wall shear stress (TAWSS) was mostly 2–4 Pa, above physiologic levels. Post-AD, values dropped below 1.5 Pa (stagnant, thrombus-prone), with oscillatory shear index (OSI) elevated (0.24–0.32). In group 2 (n = 6, abnormal diagnosis), post-AD TAWSS was <3 Pa (thrombosis risk), with OSI 0.1–0.31 near tear sites. These findings confirm a dual-risk profile: low TAWSS promotes thrombosis, while high TAWSS drives dissection progression. Conclusions: WSS parameters, such as TAWSS and OSI, can be utilized to assess the development of a dilated ascending aorta, especially for extreme maximum aortic diameter. Pre-AD analysis for some patients revealed a strong negative correlation, indicating that high shear stress in the true lumen (TL) results in a drop in diastolic pressure post-AD at the upward-going section of the aorta. Full article

Electric vehicle charging stations (EVCSs) are a critical infrastructure in urban areas. However, because they depend on power grids and digital networks, they are prone to disruptions from grid failures, extreme weather, and cyber threats. Ensuring resilience is therefore essential to minimise service disruptions and ensure reliable transportation in urban areas. While interest in EVCS resilience is growing, current studies are dispersed across technical, environmental, and spatial domains, limiting a consolidated understanding of how resilience is conceptualised and assessed in urban areas. Despite this growing body of research, no prior systematic review has comprehensively synthesised resilience-specific evidence for EVCSs in urban areas. Thus, the objective of the study was to systematically synthesise empirical research on resilient EVCSs in urban areas to identify key factors influencing resilience and how resilience is assessed. A systematic literature review was conducted on 52 empirical articles from Web of Science and Scopus published between 2015 and 2025, following the PRISMA protocol. The review revealed an increasing trend in publications over time, with research geographically concentrated in Asia, the United States of America, and Europe. Results also showed that the resilience of EVCSs in urban areas is influenced by context-related factors (such as location, environment, and governance) and system-related factors (such as operational, technical, and financial), with location and technical issues being the most studied. The resilience of EVCSs is mainly assessed through accessibility, capacity, availability, and vulnerability, using tools such as indices, curves, scenarios, and optimisation models. However, gaps remain in governance, environment, modular design, predictive maintenance, social aspects, and developing economies. Future research should focus on integrating governance and equity into EVCS planning and developing modular, renewable-powered charging systems supported by smart technologies to enhance resilience in urban areas, particularly in developing economies. This review proposes a Factors-Dimensions Implementation framework that operationalises established resilience concepts by linking context- and system-related factors to measurable resilience dimensions of EVCSs in urban areas. Full article

Gout is the most common form of inflammatory arthritis in men, driven by hyperuricemia and the deposition of monosodium urate (MSU) crystals. The innate immune response to these crystals leads to acute inflammatory episodes, called flares, characterized by intense joint pain, swelling, and temporary disability. Although gout flares are self-limiting, they impose a considerable burden on patients’ quality of life and contribute to increased healthcare utilization. A detailed understanding of the inflammatory processes triggered by MSU crystals is critical for developing targeted therapies to prevent and manage flares effectively. This review provides an overview of experimental models used to study the inflammatory phase of gout, with a focus on both in vivo and in vitro models of MSU crystal-induced inflammation. We concentrate on models that reproduce the acute inflammatory response following MSU crystal deposition, including the air pouch, intraarticular injection, and peritonitis rodent models, alongside the larval zebrafish model. In addition, we discuss in vitro approaches using primary immune cells and cell lines. We discuss the strengths, limitations, and translational relevance of these models and highlight some examples of how they have contributed to our understanding of the etiology of gout. Of note, models of hyperuricemia are not included here as these have been extensively reviewed elsewhere. Full article