Search Results (697)

Management for pheasant shooting is a widespread land use in the UK, with potential implications for forest and hedgerow habitats. This study evaluates whether sites managed for pheasant shooting differ ecologically from similar sites not used for shooting. A systematic evidence evaluation of comparative studies was combined with a spatial analysis using remote sensing data (2010–2024). The literature review identified only 32 studies meeting strict criteria for comparability, revealing inconsistent and often weak evidence, with few studies reporting detailed forest management or statistically robust outcomes. While some studies noted increased or decreased biodiversity associated with pheasant shooting, the evidence base was generally of low quality. Remote sensing assessed forest structural and spectral diversity, intactness, and hedgerow density across 1131 pheasant-managed and 1131 matched control sites. Biodiversity data for birds, plants, and butterflies were sourced from GBIF records. Structural diversity and hedgerow density were significantly higher on pheasant-managed sites, while no significant differences were found in forest spectral diversity, intactness, or biodiversity indicators. Pheasant management may shape certain habitat features but has limited demonstrable effects on overall biodiversity. Further field-based, controlled studies are required to understand causal mechanisms and inform ecologically sustainable shooting practices. Full article

Adapting Large Language Models (LLMs) to specialized domains like geriatric care remains a significant challenge due to the limited availability of domain-specific data and the difficulty of achieving efficient yet effective fine-tuning. Current methods often fail to effectively harness domain-specific causal insights, which are crucial for understanding and solving complex problems in low-resource domains.To address these challenges, we propose Causality-Aware, Parameter-Efficient Learning (CPEL), a novel framework that leverages domain-specific causal relationships to guide a multi-layer, parameter-efficient fine-tuning process for more effective domain adaptation. By embedding causal reasoning into the model’s adaptation pipeline, CPEL enables efficient specialization in the target domain while maintaining strong task-specific performance. Specifically, the Causal Prompt Generator of CPEL extracts and applies domain-specific causal structures, generating adaptive prompts that effectively guide the model’s learning process. Complementing this, the MPEFT module employs a dual-adapter mechanism to balance domain-level adaptation with downstream task optimization. This cohesive design ensures that CPEL achieves resource efficiency while capturing domain knowledge in a structured and interpretable manner. Based on this framework, we delved into its application in the field of geriatric care and trained a specialized large language model (Geriatric Care LLaMA) tailored for the aged-care domain, leveraging its capacity to efficiently integrate domain expertise. Experimental results from question-answering tasks demonstrate that CPEL improves ROUGE scores by 9–14% compared to mainstream LLMs and outperforms frontier models by 1–2 points in auto-scoring tasks. In summary, CPEL demonstrates robust generalization and cross-domain adaptability, highlighting its scalability and effectiveness as a transformative solution for domain adaptation in specialized, resource-constrained fields. Full article

Oregano (Origanum vulgare) cultivation is of great economic importance in Peru. Tacna stands out as its main producer. However, the presence of phytopathogenic fungi represents a challenge for its production. This study aimed to characterize both the morphological and molecular levels of the causal agent of crown and root rot in a crop field in the Camilaca district, Candarave, Tacna. To this end, systematic sampling was carried out using the five-gold method, collecting plants with typical symptoms. Fungi were isolated from diseased roots and characterized using macroscopic and microscopic morphological analysis as well as sequencing and multilocus phylogenetic analysis (ITS, 28S, HIS3, TEF1, TUB2). In addition, pathogenicity tests were performed on healthy plants to confirm the infectivity of the isolates. The results demonstrated that root rot was caused by a complex of phytopathogenic fungi through phylogenetic analysis of Dactylonectria torresensis, Fusarium oxysporum, F. iranicum, and F. redolens. These findings represent the first report of these species as causal agents of oregano root rot in Peru, highlighting the need for integrated management strategies that reduce the economic impact of these diseases and contribute to the sustainability of the crop in key producing regions such as Tacna. Full article

Exploring the action mechanisms and enhancement pathways of the resilience of agricultural product green supply chains is conducive to strengthening the system’s risk resistance capacity and providing decision support for achieving the “dual carbon” goals. Based on theories such as dynamic capability theory and complex adaptive systems, this paper constructs a resilience framework covering the three stages of “steady-state maintenance–dynamic adjustment–continuous evolution” from both single and multiple perspectives. Combined with 768 units of multi-agent questionnaire data, it adopts Structural Equation Modeling (SEM) and fuzzy-set Qualitative Comparative Analysis (fsQCA) to analyze the influencing factors of resilience and reveal the nonlinear mechanisms of resilience formation. Secondly, by integrating configurational analysis with machine learning, it innovatively constructs a resilience level prediction model based on fsQCA-XGBoost. The research findings are as follows: (1) fsQCA identifies a total of four high-resilience pathways, verifying the core proposition of “multiple conjunctural causality” in complex adaptive system theory; (2) compared with single algorithms such as Random Forest, Decision Tree, AdaBoost, ExtraTrees, and XGBoost, the fsQCA-XGBoost prediction method proposed in this paper achieves an optimization of 66% and over 150% in recall rate and positive sample identification, respectively. It reduces false negative risk omission by 50% and improves the ability to capture high-risk samples by three times, which verifies the feasibility and applicability of the fsQCA-XGBoost prediction method in the field of resilience prediction for agricultural product green supply chains. This research provides a risk prevention and control paradigm with both theoretical explanatory power and practical operability for agricultural product green supply chains, and promotes collaborative realization of the “carbon reduction–supply stability–efficiency improvement” goals, transforming them from policy vision to operational reality. Full article

In this cross-disciplinary investigation, we uncover a suite of previously unexamined factors and their intricate interplay that hold causal relationships with the distribution of Trachurus japonicus in the northern reaches of the South China Sea, thereby extending the existing research paradigms. Leveraging advanced machine learning algorithms and causal inference, our robust experimental design uncovered nine key global and regional factors affecting the distribution of T. japonicus density. A robust experimental design identified nine key factors significantly influencing this density: mean sea-level pressure (msl-0, msl-4), surface pressure (sp-0, sp-4), Summit ozone concentration (Ozone_sum), F10.7 solar flux index (F10.7_index), nitrate concentration at 20 m depth (N3M20), sonar-detected effective vertical range beneath the surface (Height), and survey month (Month). Crucially, stable causal relationships were identified among Ozone_sum, F10.7_index, Height, and N3M20. Variations in Ozone_sum likely impact surface UV radiation levels, influencing plankton dynamics (a primary food source) and potentially larval/juvenile fish survival. The F10.7_index, reflecting solar activity, may affect geomagnetic fields, potentially influencing the migration and orientation behavior of T. japonicus. N3M20 directly modulates primary productivity by limiting phytoplankton growth, thereby shaping the availability and distribution of prey organisms throughout the food web. Height defines the vertical habitat range acoustically detectable, intrinsically linking directly to the vertical distribution and availability of the fish stock itself. Surface pressures (msl-0/sp-0) and their lagged effects (msl-4/sp-4) significantly influence sea surface temperature profiles, ocean currents, and stratification, all critical determinants of suitable habitats and prey aggregation. The strong influence of Month predominantly reflects seasonal changes in water temperature, reproductive cycles, and associated shifts in nutrient supply and plankton blooms. Rigorous robustness checks (Data Subset and Random Common Cause Refutation) confirmed the reliability and consistency of these causal findings. This elucidation of the distinct biological and physical pathways linking these diverse factors leading to T. japonicus density provides a significantly improved foundation for predicting distribution patterns globally and offers concrete scientific insights for sustainable fishery management strategies. Full article

Suicidality, encompassing suicidal ideation, attempts, and completed suicide, continues to be a significant public health concern globally. Traditional research has emphasized genetic, neurobiological, and psychosocial factors; however, recent findings suggest that gut microbiota may play a crucial role in influencing suicidal behavior. The gut microbiota impacts neuroinflammation, neurotransmitter metabolism, and the hypothalamic–pituitary–adrenal (HPA) axis, all of which are associated with psychiatric disorders linked to suicidality. This review gathers current evidence on the gut–brain axis, investigating the role of microbiota in suicidality through mechanisms such as immune system modulation, serotonin regulation, and the stress response. We also consider the potential of microbiota-targeted interventions, such as probiotics and dietary changes, as innovative therapeutic strategies. Despite the accumulating evidence, research in this field remains limited, emphasizing the urgent need for further investigation to clarify the causal relationship between gut microbiota and suicidality. Full article

We discuss different quench protocols for Ising and XY spin chains in a transverse magnetic field. With a sudden local magnetic field quench as a starting point, we generalize our approach to a large class of local non-sudden quenches. Using finite time path field theory (FTPFT) perturbative methods, we show that the difference between the sudden quench and a class of quenches with non-sudden switching on the perturbation vanishes exponentially with time, apart from non-substantial modifications that are systematically accounted for. As the consequence of causality and analytic properties of functions describing the discussed class of quenches, this is true at any order of perturbation expansion and thus for the resummed perturbation series. The only requirements on functions describing the perturbation strength switched on at a finite time $t=0$ are as follows: (1) their Fourier transform $f\left(p\right)$ is a function that is analytic everywhere in the lower complex semiplane, except at the simple pole at $p=0$ and possibly others with $\Im \left(p\right)<0$; and (2) $f\left(p\right)/p$ converges to zero at infinity in the lower complex semiplane. A prototypical function of this class is $tanh\left(\eta t\right)$, to which the perturbation strength is proportional after the switching at time $t=0$. In the limit of large $\eta$, such a perturbation approaches the case of a sudden quench. It is shown that, because of this new type of universal behavior of Loschmidt echo (LE) that emerges in an exponentially short time scale, our previous results for the sudden local magnetic field quench of Ising and XY chains, obtained by the resummation of the perturbative expansion, extend in the long-time limit to all non-sudden quench protocols in this class, with non-substantial modifications systematically taken into account. We also show that analogous universal behavior exists in disorder quenches, and ultimately global ones. LE is directly connected to the work probability distribution, and the described universal behavior is therefore appropriate in potential concepts of quantum technology related to spin chains. Full article

The available scientific evidence on padel injuries is scarce and inconclusive. For this reason, the main aim was to analyze the differences in injury incidence in padel according to specific factors of the sport, as well as to the level of physical activity, adherence to the Mediterranean diet, and the psychological state of the players. A sample of 216 padel players (mean age: 30.05 ± 9.50 years old) participated in this study. The participants completed a sociodemographic questionnaire that included padel-specific variables, a sports injury questionnaire, the IPAQ, the MEDAS, and the CPRD. A higher incidence of injuries was observed in players with more experience (p < 0.001), more hours of play (p < 0.001) and at amateur or professional levels (p < 0.001). Mild and moderate injuries were common with mixed or herringbone soles; severe (p = 0.031), muscle, tendon and ligament injuries were common with herringbone soles (p = 0.023). Muscle and ligament injuries occurred more frequently on sand courts (p = 0.037), and with 350–370 g racquets (p = 0.029). Tendon injuries were associated with less mental ability (p = 0.014). There were no significant differences with the Mediterranean diet or level of physical activity. Injury in padel is related to sport-specific factors and psychological state but does not seem to be related to level of physical activity or diet. However, due to the cross-sectional design, causal relationships cannot be established, so future research in this field is needed. Full article

We present an analytic continuation of the central charge c in two-dimensional conformal field theory (2D CFT), modeled as a Nevanlinna function—an analytic map from the upper half-plane to itself. Motivated by the structure of vacuum energies arising from the quantization of spin-j conformal fields on the circle, we derive a discrete spectrum of central charges $c\left(j\right)=1+6j(j+1)$ and extend it continuously via $c\left(z\right)=1+6z$. The Möbius-inverted form $f\left(z\right)=1-6/z$ satisfies the conditions of a Nevanlinna function, providing a physically consistent analytic structure that captures both the unitarity of minimal models ($c<1$) and the continuous spectrum for $c\ge 1$. This unified framework highlights the connection between spectral theory, analyticity, and conformal symmetry in quantum field theory. Full article

Rubber tree (Hevea brasiliensis Muell. Arg.) is a major tropical cash crop in southern China, with Hainan and Yunnan provinces being the main planting areas. In July 2023, bark cracking and gumming were observed on the trunks of mature rubber trees in Haikou City, Hainan Province, leading to xylem rot, which severely impacted the healthy growth of the rubber trees. The present study was conducted to confirm the pathogenicity of the patho-gen associated with stem gummosis disease, characterize it using morphological and mo-lecular tools, and devise field management strategies. Pathogenicity testing showed that this strain induced symptoms similar to those of natural outdoor infestation. Based on morphological study and molecular analyses of internal transcribed spacer (ITS), transla-tion elongation factor 1 alpha (TEF1-α), and β-tubulin 2 (TUB2) sequences, the causal agent was identified as Lasiodiplodia theobromae. Field trials demonstrated that an inte-grated fungicide approach—combining trunk application of Bordeaux mixture with root irrigation using citric acid–copper 6.4% + chelated copper-ammonium 15% at both 0.1% and 0.2% concentration—effectively suppressed stem gummosis disease incidence in rub-ber trees. To the best of our knowledge, this is the first report of L. theobromae causing stem gummosis on rubber tree in China. The findings of this study can provide valuable infor-mation for the management strategies and understanding of this disease. Full article

The continuous evolution of technology compels higher education leaders to adapt to VUCA (volatile, uncertain, complex, and ambiguous) and BANI (brittle, anxious, non-linear, and incomprehensible) environments through innovative strategies that ensure institutional relevance. While VUCA emphasizes the challenges posed by rapid change and uncertain decision-making, BANI underscores the fragility of systems, heightened anxiety, unpredictable causality, and the collapse of established patterns. Navigating these complexities requires agility, resilience, and visionary leadership to ensure that institutions remain adaptable and future ready. This study presents a bibliometric analysis of digital academic leadership in higher education transformation, examining empirical studies, reviews, book chapters, and proceeding papers published from 2014 to 2024 (11-year period) in the Web of Science—Science Citation Index Expanded (SCIE) and Social Science Citation Index (SSCI). Using CiteSpace software (version 6.3. R1-64 bit), we analyzed 5837 documents, identifying 24 key publications that formed a network of 90 nodes and 256 links. The reduction to 24 publications occurred as part of a structured bibliometric analysis using CiteSpace, which employs algorithmic thresholds to identify the most influential and structurally significant publications within a large corpus. These 24 documents form the core co-citation network, which serves as a conceptual backbone for further thematic interpretation. This was the result of a multi-step refinement process using CiteSpace’s default thresholds and clustering algorithms to detect the most influential nodes based on centrality, citation burst, and network clustering. Our findings reveal six primary research clusters: “Enhancing Academic Performance”, “Digital Leadership Scale Adaptation”, “Construction Industry”, “Innovative Work Behavior”, “Development Business Strategy”, and “Education.” The analysis demonstrates a significant increase in publications over the decade, with the highest concentration in 2024, reflecting growing scholarly interest in this field. Keywords analysis shows “digital leadership”, “digital transformation”, “performance”, and “innovation” as dominant terms, highlighting the field’s evolution from technology-focused approaches to holistic leadership frameworks. Geographical analysis reveals significant contributions from Pakistan, Ireland, and India, indicating valuable insights emerging from diverse global contexts. These findings suggest that effective digital academic leadership requires not only technical competencies but also transformational capabilities, communication skills, and innovation management to enhance student outcomes and institutional performance in an increasingly digitalized educational landscape. Full article

Fungal diseases severely threaten global agriculture, while conventional chemical fungicides face increasing restrictions due to environmental and safety concerns. In this study, we isolated a soil-derived Bacillus stercoris strain, DXQ-1, exhibiting strong antagonistic activity against plant pathogenic fungi, notably Magnaporthe oryzae, the causal agent of rice blast. Scanning electron microscopy revealed that DXQ-1 disrupts fungal hyphae and inhibits conidial germination, with a 24 h crude broth treatment reducing germination to 83.33% and completely blocking appressoria formation. LC-MS-based metabolomic analysis identified key antifungal components, including lipids (35.83%), organic acid derivatives (22.15%), and small bioactive molecules (e.g., Leu-Pro, LPE 15:0). After optimizing fermentation conditions (LB medium, pH 7.0, 28 °C, 48 h), the broth showed >90% inhibition against M. oryzae and Nigrospora oryzae and retained high thermal (68 °C, 1 h) and UV (4 h) stability. Field trials demonstrated effective disease control and significant promotion of rice growth, increasing plant height (17.7%), fresh weight (53.3%), and dry weight (33.3%). These findings highlight DXQ-1 as a promising biocontrol agent, offering a sustainable and effective alternative for managing fungal diseases in crops. Full article

Deep learning has demonstrated significant success in hyperspectral image (HSI) classification by effectively leveraging spatial–spectral feature learning. However, current approaches encounter three challenges: (1) high spectral redundancy and the presence of noisy bands, which impair the extraction of discriminative features; (2) limited spatial receptive fields inherent in convolutional operations; and (3) unidirectional context modeling that inadequately captures bidirectional dependencies in non-causal HSI data. To address these challenges, this paper proposes HG-Mamba, a novel hybrid geometry-aware bidirectional Mamba network for HSI classification. The proposed HG-Mamba synergistically integrates convolutional operations, geometry-aware filtering, and bidirectional state-space models (SSMs) to achieve robust spectral–spatial representation learning. The proposed framework comprises two stages. The first stage, termed spectral compression and discrimination enhancement, employs multi-scale spectral convolutions alongside a spectral bidirectional Mamba (SeBM) module to suppress redundant bands while modeling long-range spectral dependencies. The second stage, designated spatial structure perception and context modeling, incorporates a Gaussian Distance Decay (GDD) mechanism to adaptively reweight spatial neighbors based on geometric distances, coupled with a spatial bidirectional Mamba (SaBM) module for comprehensive global context modeling. The GDD mechanism facilitates boundary-aware feature extraction by prioritizing spatially proximate pixels, while the bidirectional SSMs mitigate unidirectional bias through parallel forward–backward state transitions. Extensiveexperiments on the Indian Pines, Houston2013, and WHU-Hi-LongKou datasets demonstrate the superior performance of HG-Mamba, achieving overall accuracies of 94.91%, 98.41%, and 98.67%, respectively. Full article

This study addresses the urgent challenge of safeguarding sensitive health data in today’s digital age by proposing a novel, integrated data protection framework that synthesises six critical pillars—technology, policy, cybersecurity, legal frameworks, governance, and risk assessment—into a unified socio-technical model. Unlike existing piecemeal approaches, this framework is designed to bridge the gap between regulatory requirements and practical implementation through measurable, engineering-based solutions. Healthcare organisations face persistent difficulties in aligning innovation with secure and compliant practices due to fragmented governance and reactive cybersecurity measures. This paper aims to empirically validate the effectiveness of the proposed framework by quantitatively analysing causal relationships between its components (such as between governance and compliance) using advanced statistical methods, including exploratory factor analysis (EFA) and Partial Least Squares Structural Equation Modelling (PLS-SEM). A survey of healthcare professionals across multiple countries revealed significant gaps between regulatory expectations and operational realities, underscoring the need for harmonised strategies. The results demonstrate strong causal linkages between governance, cybersecurity practices, and compliance, validating the framework’s robustness. This research contributes to the fields of digital health, information systems, industrial engineering, and electronic governance by offering a scalable, empirically tested model for socio-technical data protection. The findings provide actionable strategies for policymakers, system architects, and digital infrastructure designers. Full article

Complex question decomposition is an important research topic in the field of natural language processing (NLP). It refers to the decomposition of a compound question containing multiple ontologies and classes into a simple question containing only a single attribute or entity. Most previous studies focus on how to generate simple questions using a single attribute or entity but pay little attention to the generation order of simple questions, which may lead to an inaccurate decomposition or longer execution time. In this study, we propose a new method based on causal reinforcement learning, which combines the advantages of the current optimal performance reinforcement learning method and the causal inference method. Compared with previous methods, causal reinforcement learning can find the generation order of sub-questions more accurately, so as to better decompose complex questions. In particular, the prior knowledge is extracted using the counterfactual method in causal reasoning and is integrated into the policy network of the reinforcement learning model, and the reward rules of reinforcement learning are designed from the perspective of symmetry (positive reward and negative punishment), thus the intelligent body is guided to choose the sub-question with a greater benefit and less risk of decomposing. We compare the proposed method with the baseline method on three datasets. The experimental results show that the performance of our method is improved by 5–10% compared with the baseline method on Hits@n (n = 1, 3, 10), which proves the effectiveness of our proposed method. Full article