Search Results (6,283)

Rising production costs have increased interest in lower-cost, non-inversion crop establishment systems in Ireland, yet uptake remains relatively limited. Growers’ perceptions of relative performance of innovations compared to current practice are key determinants of adoption. We surveyed 154 Irish arable growers (77 plough-based, 59 min-till, 18 direct drill) to assess perceived performance of min-till and direct drill across multiple parameters relative to ploughing to identify potential barriers to adoption. Respondents rated impacts on Likert scales; analyses summarized response distributions and between-system differences. For example: >30% of min-till growers believed min-till winter cereal yields exceed ploughing, compared with 0% of plough and <10% of direct drill growers. Growers generally favoured their own establishment system, consistent with adoption theory. Potential barriers to non-inversion adoption included perceived lower establishment reliability, crop performance concerns (especially spring crops), and anticipated increases in weed pressure, herbicide reliance, and herbicide resistance development risk. Several perceptions diverged from the Ireland-relevant literature, revealing both knowledge gaps (notably establishment stability and winter/spring crop performance of establishment systems) and misperceptions (including establishment system on soil structure). Targeted research to address knowledge gaps, combined with focused, grower-centred knowledge exchange, is required to support evidence-based evaluation and sustainable adoption of establishment systems in Ireland. Full article

Background/Objectives: Colorectal cancer (CRC) is a leading cause of cancer-related mortality, with colorectal liver metastasis (CRLM) being the major determinant of poor prognosis. Tumor metabolic reprogramming and spatial heterogeneity complicate biomarker discovery and clinical management. This study aimed to characterize the spatial metabolomic landscape of CRC and identify progression-associated metabolic alterations and potential metabolic signatures for liver metastasis. Methods: A total of 23 tissue samples were collected from patients with CRC, with and without liver metastasis. Air flow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI) was used to map the spatial metabolite distributions. Region-of-interest analysis guided by histopathology enabled comparative metabolomic profiling across different tissue types. Multivariate statistical analysis, pathway enrichment, and receiver operating characteristic (ROC) curve analyses were performed to identify key metabolic alterations and evaluate potential biomarker performance. Results: Distinct spatial metabolomic profiles were observed across normal mucosa, primary tumors, liver metastases, and normal liver tissues. In primary colorectal tumors, amino acid, purine, and choline metabolism were significantly upregulated, whereas liver metastases were characterized by elevated levels of triglycerides, diglycerides, cholesteryl esters, and acylcarnitines, indicating enhanced lipid synthesis, incomplete fatty acid oxidation, and/or mitochondrial dysfunction. Progression-associated analyses across tissue types revealed consistently increasing trends in glycerides and acylcarnitines, along with heterogeneous alterations in amino acids and phospholipids. Furthermore, 122 differential metabolites were identified between metastatic and non-metastatic CRC, and a four-lipid panel demonstrated strong discriminatory performance. Conclusions: This study provides a spatially resolved characterization of metabolic reprogramming during CRC progression and liver metastasis, highlighting lipid and amino acid metabolism as key features and revealing the metabolic signatures of CRLM. Full article

In the context of China’s rapid urbanization and rural land marketization reforms, the entry of rural collectively owned commercial construction land into the market (ERCCCLM) coexists with the traditional government-led land expropriation, forming a dual land supply system. China’s dual-structure land ownership system—where urban land belongs to the state and rural land to rural collectives—aims to balance land market allocation efficiency with government regulation for public interests. However, significant differences exist between the two patterns in terms of revenue distribution, risk-bearing, and institutional constraints. Consequently, stakeholders including rural collective economic organizations, farmers, local governments, and development companies face dilemmas in selecting land supply patterns, thereby limiting land resource allocation efficiency. The research employs multidimensional economic analysis to systematically compare the ERCCCLM and land expropriation patterns, establishing a land supply pattern selection mechanism with land market price and compensation for expropriation as key variables. First, the expenditure and revenue of stakeholders in both patterns were clarified based on relevant documents, and investment revenue models were constructed. Second, through comparative analysis of revenue formation mechanisms across land supply patterns and sensitivity analysis of multi-scenario calculations, the land market price and compensation for expropriation are identified as key variables determining economic revenue. The findings indicate that when the land market price exceeds compensation for expropriation, ERCCCLM generates higher economic revenue for the rural collective economic organization and farmer. Conversely, when the land market price is equal to or lower than the compensation for expropriation, land expropriation provides more stable revenue. The land expropriation and ERCCCLM examined in this research represent a unique land expropriation and utilization system exclusive to China. The proposed selection mechanism improves land market distribution efficiency and informs policy discussions on optimizing land supply patterns, ensuring a balance between market efficiency and stakeholder equity. Full article

The focus of modern livestock production is increasingly shifting toward improving animal health, welfare, and product quality through the use of natural feed ingredients. Pumpkin (Cucurbita spp.) and its seeds are of interest because they contain biologically active compounds, including tocopherols and phenolic antioxidants. This study evaluated the effects of pumpkin seed cake (PSC) in rabbit diets on blood parameters, oxidative status, and trace element distribution in tissues. Sixty Popielno White rabbits were initially assigned to three dietary groups: control (0% PSC), 5% PSC, and 10% PSC. At 90 days of age, samples from 30 rabbits (10 per group) were collected and analysed. PSC supplementation significantly increased red blood cell count, haemoglobin, haematocrit, and platelet indices (p ≤ 0.05), indicating affected haematological status. It also reduced (p ≤ 0.05) urea, triglycerides, total cholesterol, and LDL cholesterol. Antioxidant status significantly improved, as indicated by higher superoxide dismutase activity and ferric-reducing antioxidant power, together with lower malondialdehyde levels (p ≤ 0.05). Mineral analysis showed lower manganese concentrations in muscle and kidney tissues; cadmium remained low, and lead was below the detection limit in muscle and liver samples. Overall, PSC may be considered a promising feed ingredient that supports haematological status, antioxidant protection, and metabolic balance under the conditions of the present study. Full article

A first study analyzed the effect of the presence of a highly frequented tourist trail on the size and reproductive capacity of Globularia alypum, a Mediterranean shrub of conservation interest. In Sardinia, this species is a typical example of a plant with Extremely Small Populations (PSESPs), restricted to a natural area embedded within an urban matrix, which makes it particularly vulnerable to ecological pressures. In this second contribution, the investigation expands to the entire population of the species distributed across different habitats. The possible correlations between vegetative and reproductive traits of the plant are examined, along with the influence exerted by both habitat type and varying levels of human disturbance. To evaluate potential drivers of its persistence, morphological (H, diameter and plant volume) and reproductive traits (number of flowers, number of fruits and number of seed per plant) were recorded at the individual level. Additionally, to assess human disturbance (consisting mainly of trampling), the presence of trails was used as a proxy and, accordingly, each plant was categorized following its relative position to the nearest path according to three categories: Near Trail (NT), Mid-Trail Distance (MTD), or Far from Trail (FT). A total of 114 individuals distributed across four habitat types were measured. Statistical analyses revealed only marginal associations between habitat type and vegetative or reproductive traits. While trail proximity did not influence flower and fruit production, plant volume tended to be greater in individuals located farther from trails, suggesting a potential, albeit limited, effect of reduced human pressure on plant growth. These findings highlight the importance of understanding subtle ecological interactions that shape the persistence of PSESPs in urban close to nature area and provide valuable insights for developing targeted conservation and management strategies. Full article

Media coverage of disasters frequently frames self-interested behavior in contrast to collective responsibility and coordinated response. This study aims to explore how such behavior is emotionally constructed in disaster-related media, using a carefully selected corpus of 12 text-centered news articles focusing on selfish behavior. The analysis combines transformer-based sentence-level emotion classification using the tweetnlp RoBERTa model, which predicts 11 emotion categories, with Latent Dirichlet Allocation topic modeling across single-sentence and three-sentence windows in a small purposively selected corpus. Emotion–topic relationships are quantified by weighting emotion probabilities by topic distributions and visualized using bar charts, network graphs, and heatmaps. The findings suggest that fear and disgust dominate portrayals of self-interested behavior, while anticipation appears in projections of harm and anger is linked to inequality and institutional accountability. Two discursive configurations emerge: Responsibility Across Individuals and Institutions, emphasizing public accountability and authority, and Collective Fear and Self-Protective Practices, reflecting affect-driven responses under uncertainty. Although negative emotions predominate, optimism appears conditionally, signaling coordination and recovery. Overall, disaster reporting constructs selfishness through integrated emotional–semantic patterns that position individual actions within broader social risk and collective responsibility. Full article

Transcranial magnetic stimulation (TMS) combined with electroencephalography (EEG) provides a powerful framework to probe and modulate human cortical and corticospinal excitability. In recent years, brain state-dependent EEG–TMS paradigms have gained increasing interest by synchronizing stimulation to ongoing neural activity. However, traditional approaches relying on single oscillatory features or fixed thresholds have yielded heterogeneous and often inconsistent results, motivating the adoption of machine learning (ML) and artificial intelligence (AI) methods to model brain state in a multivariate, data-driven manner. This review synthesizes current ML and deep learning (DL) approaches aimed at predicting cortical and corticospinal excitability from pre-stimulus EEG. We contextualize these methods within brain state-dependent EEG–TMS frameworks based on oscillatory phase, power, and network-level features, and within evolving definitions of brain state that move beyond local biomarkers toward distributed, large-scale, and dynamically evolving neural representations. The reviewed studies span feature-engineered models, data-driven decoding approaches, and emerging adaptive closed-loop frameworks. Finally, we discuss key methodological challenges, translational barriers, and future directions toward personalized, interpretable, and fully closed-loop neuromodulation systems. Full article

Accurately identifying urban functional zones and revealing their spatial interaction characteristics is crucial for understanding urban operational mechanisms and optimizing spatial layouts. Addressing the limitations of traditional research in simultaneously capturing static functional attributes and dynamic resident travel behaviors, this study takes the central urban area of Lhasa as the research object, integrating ride-hailing trajectory data with Point of Interest (POI) data to conduct research on urban functional zone identification and spatial interaction characteristics. First, Thiessen polygons were used to quantify the spatial influence range of POIs, and an address matching algorithm was employed to associate ride-hailing origins and destinations (ODs) with POIs. A weighted land use intensity index was constructed, and functional zones were precisely identified using information entropy and K-Means clustering. Secondly, with basic research units as nodes and OD flows as edges, a directed weighted spatial interaction network was constructed. Complex-network indicators and the Infomap community detection algorithm were utilized to analyze network characteristics, node importance, and community interaction patterns. The results show that: (1) The functional mixing degree in the study area exhibits a pattern of “highly composite core, relatively differentiated periphery.” Eight functional zone types, including commercial–residential mixed, science–education–culture, and transportation service zones, were ultimately identified. Residential areas form the base, while the core area features multi-functional agglomeration. (2) The spatial interaction network exhibits typical small-world effects, while its degree distribution is better characterized by a lognormal distribution rather than a power law. Node importance is dominated by betweenness centrality, with Lhasa Station, the Potala Palace, and core commercial areas constituting key hubs. (3) The network can be divided into four functionally coupled communities: the core multi-functional area, the western industry–residence integrated area, the eastern science–education-dominated area, and the southern transportation hub area, forming a “core leading, two wings supporting” center–subcenter spatial organization pattern. This study verifies the effectiveness of integrating trajectory and POI data for identifying urban functional zones and provides a new perspective for understanding the spatial structure and planning of plateau cities. Full article

The urban remoteness of warehouses and distribution centres, known as logistics sprawl, has been observed for several decades. According to some, this increase in distances between logistics facilities and hypercentres contributes to the environmental worsening of transport operations, especially in densely populated places such as the Paris metropolitan area. Therefore, the question of logistics tightening—the opposite phenomenon—arises in the context of reducing pollutant emissions in the territories concerned. The objective of this work is to clarify the “hidden” mechanisms of freight transport services. It evaluates, through a simulation, the carbon footprint and operational efficiency of logistics tightening in the city of Paris. The input data we use comes from a large courier service company that can be regarded as an interesting case study when it comes to the Paris region. In our scenario, the ecological consistency of the journeys and the logistical requirements of the transport chain may be contested. Indeed, the inner resettlement of hubs for greener deliveries suggests the actual scheme of the company gets closer to optimum and ironically illustrates the relevance of the current locations. Logistics tightening mainly focuses on the last mile, but such a problem is complex, as each link of the chain has its own peculiarities, meaning the sustainability of one can undermine that of another. Full article

Transportation asset management (TAM) has traditionally centered on technical performance and economic efficiency. In recent years, however, there has been increasing recognition of the environmental and social impacts of maintenance and rehabilitation (M&R) activities. This paper presents a systematic review of how Environmental, Social, and Governance (ESG) metrics are being incorporated into TAM. Using PRISMA 2020, four major databases were searched, identifying 75 studies since 2010. Environmental metrics were the most developed, especially those measuring emissions, energy use, and material consumption. Social metrics appeared less frequently and are typically used descriptively, including indicators of income inequality, user costs, and equity-focused metrics such as the Benefit Distribution Ratio and Social Return on Investment. Governance was the least explored pillar and is generally addressed through fiscal transparency, risk management, or institutional practices rather than explicit measurable indicators. Overall, the review shows growing interest in integrating ESG into TAM, but the adoption of social and governance metrics remains limited. In particular, governance indicators are rarely operationalized as measurable variables within TAM decision-making, highlighting a critical gap in the literature. This study synthesizes ESG-related indicators used in TAM and provides a structured foundation for future research and more comprehensive sustainability-oriented decision frameworks. Full article

In precision apiculture, the portable digital camera is a cost-effective sensor for capturing hive images or videos used to quantify different colony variables. Openly accessible, well-annotated, interoperable cell-level image datasets are still the exception rather than the norm. This shortage constitutes a major barrier to AI-driven approaches aimed at automating image-based comb analysis. In this article, we present FAIRHiveFrames-1K, a publicly available dataset of 1265 annotated hive frame images (1920 × 1080 PNG) designed to facilitate research in AI-intensive image-based comb analysis automation. The dataset, derived from a 2013–2022 U.S. Department of Agriculture–Agricultural Research Service multi-sensor research reservoir, includes 124,669 annotated regions of interest for seven biologically meaningful categories consistent with comb analysis literature and standard hive inspection protocols. FAIRHiveFrames-1K is curated according to FAIR principles (Findable, Accessible, Interoperable, Reusable) and distributed under CC-BY 4.0 with standard annotation formats, fixed training and validation splits, and reproducible benchmarking artifacts. To establish preliminary baseline performance, we iteratively tuned four YOLO architectures (YOLOv8n, YOLOv8s, YOLOv11n, YOLOv11s) under a shared tuning protocol over the period of dataset growth. Full article

Old-vine vineyards often contain dozens of grapevine varieties intermingled and irregularly distributed, making plant-level varietal identification slow and expensive when based on ampelography or molecular approaches. This paper proposes a field-oriented computer-vision pipeline for Vitis vinifera variety identification using images with a natural background from the historic “Vinha Maria Teresa” parcel (Quinta do Crasto, Portugal). A single-class YOLO11 detector is trained to localize the vine leaf and generate standardized crops, and a YOLO11 classifier is then fine-tuned on leaf regions of interest (ROIs) for eight selected varieties in the Douro UNESCO region. We annotated 2015 vineyard images for classification and supplemented detection training with 2648 additional leaf images; detectors (YOLO11n/s/m) were benchmarked under four augmentation regimes and evaluated on a fixed 48-image subset, including runtime on CPU and GPU. The best detector reached mAP@50–95 of 0.918 on the benchmark, while YOLO11n achieved ∼27 FPS on CPU for fast cropping. On a 303-image test set, the best classifier (YOLO11s with mixed augmentations) achieved 94.06% Top-1 accuracy, 93.92% macro-F1, and 100% Top-5 accuracy with remaining errors concentrated among morphologically similar varieties. To assess deployment-oriented performance, classifiers trained under three input settings (manual crops, detector-generated crops, and full images) were evaluated on a held-out 48-image benchmark subset; removing the detection step reduced Top-1 accuracy from 75.00% to 68.75%, while the gap between manual and automatic crops was only 2.44 pp on successfully detected images with detection failures (14.6%) representing the primary operational bottleneck. Repeated retraining of the best manual-crop YOLO11s configuration across multiple random seeds showed stable performance with low variability in Top-1 accuracy and macro-F1. Under identical training conditions, ResNet50 and EfficientNet-B0 provided competitive baselines, but YOLO11s remained the strongest overall model on the held-out field benchmark. These results indicate that lightweight leaf detection plus crop-based classification can support scalable varietal identification in old vineyards under realistic acquisition conditions. Full article

Background/Objectives: Human papillomavirus (HPV) infection is a major concern in public health, given its role as a persistent sexually transmitted infection and a causative agent of non-cancerous and cancerous lesions (neoplasms). The increasing infection rates observed in recent years underscore the need for effective public health measures to address this issue. The objective of this study is to describe the challenges and the results of conducting vaccination campaigns within a university setting and its impact on the HPV vaccination rate. Methods: A multifaceted approach was adopted, entailing the implementation of two distinct interventions. Following the promotional and educational online campaign (described elsewhere), vaccination delivery took place from November 2024 to July 2025 in the university campus and in three university hospitals in Milan. Overall and covariate-specific drop-out rate is calculated; significance is tested through a chi-square test of homogeneity between the population that completed less than three doses vs. those who completed the full cycle. Overall and vaccine-specific vaccination proportion is reported. Results: The vaccination rate for first doses reached 92% of available appointments, with a slight female majority (50.9%) and the 23–26 age as the most represented group (47%). The most represented nationality was Italian (58.4%), followed by Iranian (26.5%). Regarding the vaccination sites, the university venue recorded the highest rates in terms of both vaccines booked (56.4%) and vaccines administered (64.7%). With a net loss in follow up, consistent with WHO data, the three-dose HPV vaccination campaign was completed by 82.5% of participants. A chi-squared test of homogeneity revealed significant differences in age distribution between vaccination groups, χ² (3) = 347.78, p < 0.001, Cramér’s V = 0.457. Participants who received only one dose were predominantly younger (17–22 years: 71.1% vs. 19.0%, difference = 52.1 percentage points, 95% CI [46.6, 57.7]). Meanwhile, a catch-up strategy raised interest on other crucial vaccinations. Conclusions: The findings pertaining to the vaccination rate underscore the heightened awareness among young adults concerning the HPV vaccine. They further substantiate the efficacy of the integrated strategy encompassing advisory and educational site-based campaigns as an initial measure to attain the WHO-endorsed vaccination rates. Full article

With advancements in Earth observation capabilities, the demand for large-scale mapping using remote sensing images has increased significantly. However, selecting an optimal image set for the area of interest (AOI) from a large collection of remote sensing images remains challenging. On the one hand, it is crucial to select images with minimal redundancy and low cloud cover to enhance production efficiency and the effective coverage of mapping products. On the other hand, adjacent selected images should transition naturally so that the resulting mapping products appear visually cohesive. Unfortunately, most existing remote sensing image selection algorithms focus only on the former, with little attention to visual consistency. Meanwhile, images from the same swath inherently offer advantages in both redundancy reduction and visual consistency. However, a larger coverage area also carries the potential for greater variation in cloud cover, and cloud distribution within a swath can be highly complex. Managing the relationships among swaths, images, and cloud cover is also challenging. To address these issues, this paper proposes a novel image selection model, SwathSel. Candidate images are grouped through a composite grouping strategy based on swaths, cloud cover, and topological connectivity, thereby expanding the fundamental unit for image selection from individual scenes to connected image subsets. A dynamic adjustment mechanism is introduced to enhance grouping flexibility. Additionally, local and global swath consistency constraints are designed to strengthen visual consistency among images, and a subset evaluation module is used to comprehensively assess swath consistency, coverage, cloud cover, and metadata information. Through a greedy strategy combined with a rapid refinement technique, the final selected image set is obtained. Experiments were conducted on four datasets, and four quantitative metrics were designed to evaluate the visual consistency of the results. Compared with baseline models, SwathSel achieves lower redundancy and cloud cover while delivering superior visual consistency. Full article

This paper examines whether government debt financing crowds out private consumption in South Africa or whether household behaviour is consistent with Ricardian equivalence. Using quarterly data from 1960Q1 to 2025Q1, the study employs a Bayesian time-varying parameter framework that accommodates non-stationarity, structural change, and evolving fiscal transmission mechanisms, and is complemented by a Markov-switching Bayesian VAR as a robustness check. All variables are expressed relative to GDP to avoid scale effects, and inference is based on posterior distributions. The results reveal pronounced state dependence in the debt–consumption relationship. In earlier decades, increases in the debt-to-GDP ratio are associated with statistically meaningful declines in the private consumption share, consistent with crowding-out or precautionary behaviour under weaker fiscal credibility. Over time, however, this negative association weakens and converges toward neutrality, with post-2010 estimates indicating no significant effect of debt on consumption. Conditioning on fiscal stance and financial conditions shows that debt does not exert an independent influence on consumption once government expenditure, tax revenue, and interest rates are taken into account. A constant-parameter Bayesian benchmark masks these dynamics, producing an average effect close to zero. Evidence from a Markov-switching Bayesian VAR similarly finds no persistent regime-specific crowding-out effects. Overall, the findings suggest that observed debt–consumption linkages in South Africa operate primarily through broader fiscal and macroeconomic conditions rather than debt accumulation itself, highlighting the importance of fiscal credibility and policy composition. Full article