Search Results (2,293)

Global policy narratives on livestock development increasingly emphasize environmental concerns, often overlooking the social dimensions of the sector. In the case of dairy, the world’s most valuable agricultural commodity, its role in social and economic development remains poorly quantified. Our study contributes to a more balanced vision of the UN SDGs thanks to the inclusion of a socio-economic dimension. Here we present a novel empirical approach to assess the socio-economic impacts of dairy development using a new global dataset and non-parametric modelling techniques (local polynomial regressions), with yield as a proxy for sectoral performance. We find that as dairy systems intensify, the number of farm households engaged in production declines, yet household incomes rise. On-farm labour productivity also increases, accompanied by a reduction in employment but higher wages. In dairy processing, employment initially grows, peaks, and then contracts, again with rising wages. The most substantial impact is observed among consumers: an increased milk supply leads to lower prices and improved affordability, expanding the access to dairy products. Additionally, dairy development is associated with greater agricultural value added, an expanding tax base, and the increased formalization of the economy. These findings suggest that dairy development, beyond its environmental footprint, plays a significant and largely positive role in social transformation, yet is having to adapt sustainably while tackling labour force relocation, and that dairy development’s social impacts mimic the general agricultural sector. These results might be of interest for the assessment of policies regarding dairy development. Full article

The integration of artificial intelligence (AI), big data analytics, and blockchain technologies within the digital economy presents transformative opportunities for promoting low-carbon urban development. However, a systematic understanding of how these digital innovations influence urban carbon mitigation remains limited. This study addresses this gap by proposing two research questions (RQs): (1) What are the key success factors for artificial intelligence, big data, and blockchain in urban carbon emission reduction? (2) How do these technologies interact and support the transition to low-carbon cities? To answer these questions, the study employs a hybrid methodological framework combining the decision-making trial and evaluation laboratory (DEMATEL) and interpretive structural modeling (ISM) techniques. The data were collected through structured expert questionnaires, enabling the identification and hierarchical analysis of twelve critical success factors (CSFs). Grounded in sustainability transitions theory and institutional theory, the CSFs are categorized into three dimensions: (1) digital infrastructure and technological applications; (2) digital transformation of industry and economy; (3) sustainable urban governance. The results reveal that e-commerce and sustainable logistics, the adoption of the circular economy, and cross-sector collaboration are the most influential drivers of digital-enabled decarbonization, while foundational elements such as smart energy systems and digital infrastructure act as key enablers. The DEMATEL-ISM approach facilitates a system-level understanding of the causal relationships and strategic priorities among the CSFs, offering actionable insights for urban planners, policymakers, and stakeholders committed to sustainable digital transformation and carbon neutrality. Full article

Students, particularly those in the medical field, are exposed to various stressors that can affect their health-related behaviors, including smoking habits, with implications for oral health and quality of life. Background and Objectives: to analyze the relationship between smoking, oral health, perceived stress level, and self-assessed quality of life in a sample of dental students. Materials and Methods: The cross-sectional study included 338 students, who completed validated questionnaires and were clinically examined. Lifestyle was assessed using a smoking behavior questionnaire, stress levels were measured with the Student Stress Inventory (SSI), and quality of life was evaluated using the EQ-5D-5L instrument. The DMFT index was calculated to determine oral health status. Results: Among the 338 participating students, 53.8% were smokers. The lifestyle analysis revealed slightly higher average scores among non-smokers across all domains—social (3.26 vs. 3.09), attitudinal (2.75 vs. 2.97), and behavioral (3.82 vs. 3.49), but without statistically significant differences (p > 0.25). The mean DMFT score was 12.48, with no significant differences between smokers and non-smokers (p = 0.554). The SSI total score averaged 83.15, indicating a moderate level of perceived stress, again with no statistically significant differences between the groups (p > 0.05). However, slightly higher average stress scores among smokers may suggest the use of smoking as a coping mechanism. In contrast, quality of life as measured by EQ-5D-5L showed significantly worse outcomes for smokers across all five dimensions, including mobility (78.6% vs. 95.5%, p = 0.000) and self-care (93.4% vs. 100%, p = 0.001). Multivariable logistic regression identified smoking (OR = 1.935; p = 0.047) and moderate stress levels (OR = 0.258; p < 0.001) as independent predictors of oral health status. Conclusions: The results obtained suggest that smoking may function as a stress management strategy among students, supporting the relevance of integrating specific psychobehavioral interventions that address stress reduction and oral health promotion among student populations. Full article

The Barcelona Metropolitan Area (AMB) has implemented various policies to reduce car use and promote more sustainable mobility. Initiatives such as superblocks, Low Emission Zones (LEZs), and the Bicivia network aim to transform the urban model in response to environmental and congestion challenges. However, the high reliance on private vehicles for intermunicipal travel, uneven infrastructure, and social resistance to certain changes remain significant issues. This study examines the evolution of mobility patterns and assesses the effectiveness of the above policies in fostering real and sustainable change. A mixed-methods approach was adopted, which combined an exploratory factor analysis (EFA) of 2011–2024 data, trend linear regression, and a comparative international analysis. The EFA identified four key structural dimensions: traditional transport infrastructure, active mobility and bus lines, public bicycles and mixed use, and transport efficiency and punctuality. The findings reveal a clear reduction in private car use and an increase in sustainable modes of transport. This indicates that there are prospects for future transformation. Nonetheless, challenges persist in intermunicipal mobility and the public acceptance of the measures. This study provides empirical and comparative evidence and emphasizes the need for integrated metropolitan governance to achieve a resilient and sustainable urban model. Full article

Single-cell RNA sequencing (scRNA-seq) has revolutionized molecular biology and genomics by enabling the profiling of individual cell types, providing insights into cellular heterogeneity. Deep learning methods have become popular in single cell analysis for tasks such as dimension reduction, cell clustering, and data imputation. In this work, we introduce DropDAE, a denoising autoencoder (DAE) model enhanced with contrastive learning, to specifically address the dropout events in scRNA-seq data, where certain genes show very low or even zero expression levels due to technical limitations. DropDAE uses the architecture of a denoising autoencoder to recover the underlying data patterns while leveraging contrastive learning to enhance group separation. Our extensive evaluations across multiple simulation settings based on synthetic data and a real-world dataset demonstrate that DropDAE not only reconstructs data effectively but also further improves clustering performance, outperforming existing methods in terms of accuracy and robustness. Full article

Horizontal well fracturing is vital for low-permeability tight oil reservoirs, but multi-fracture effectiveness is hampered by stress shadowing and fluid-rock interactions, particuarly in optimizing fracture geometry and conductivity under different sequencing strategies. While previous studies have addressed aspects of pore pressure and stress effects, a comprehensive comparison of sequencing strategies using fully coupled models capturing the intricate seepage–stress–damage interactions remains limited. This study employs a novel 2D fully coupled XFEM model to quantitatively evaluate three fracturing approaches: simultaneous, sequential, and alternating. Numerical results demonstrate that sequential and alternating strategies alleviate stress interference, increasing cumulative fracture length by 20.6% and 26.1%, respectively, versus conventional simultaneous fracturing. Based on the research findings, fracture width reductions are 30.44% (simultaneous), 18.78% (sequential), and 7.21% (alternating). As fracture width directly governs conductivity—the critical parameter determining hydrocarbon flow efficiency—the alternating strategy’s superior width preservation (92.79% retention) enables optimal conductivity design. These findings provide critical insights for designing fracture networks with targeted dimensions and conductivity in tight reservoirs and offer a practical basis to optimize fracture sequencing design. Full article

Cultivated land is fundamental to agricultural production, and the eco-efficiency of cultivated land utilization is widely acknowledged as a crucial indicator for assessing rational land use. Accordingly, this study applies a Super-SBM model with undesirable outputs to evaluate the eco-efficiency of cultivated land utilization (ECLU) across 31 provinces in China utilizing provincial panel data from 2005 to 2023 and further employs dynamic fuzzy-set qualitative comparative analysis to investigate, across spatial and temporal dimensions, how government policy, agricultural technology, socioeconomic conditions, and natural conditions interact to achieve a high ECLU and to elucidate the diverse configurational pathways through which these factors converge to deliver a high ECLU. Our findings demonstrate that the ECLU originates from the joint influence of several factors, and no single factor alone can provide a high level of eco-efficiency. In particular, a high GDP per capita and strong government agricultural expenditure intensity are pivotal for achieving a high ECLU, whereas a low GDP per capita and weak government agricultural expenditure intensity are the core conditions associated with poor eco-efficiency outcomes. We identify three distinct driving pathways that foster a high ECLU: the Economy–Technology–Government Synergistic Pathway, Nature–Economy Dual-Driver Pathway, and Government-Supported Land–Economy Pathway. Between-configuration consistency (BECONS) exhibits no significant temporal effect; however, a constellation of external factors triggered a pronounced, collective reduction in configurational consistency from 2008 to 2014. Regional analysis reveals pronounced heterogeneity: Spatially, the Economy–Technology–Government Synergistic Pathway is concentrated in China’s central and eastern provinces, the Nature–Economy Dual-Driver Pathway clusters mainly in the central belt, and the Government-Supported Land–Economy Pathway predominates in the west. Full article

The process of designing reinforced concrete (RC) buildings has traditionally relied on manually evaluating a limited number of layout alternatives—a time-intensive process that may not always yield the most functionally efficient solution. This research introduces a parametric algorithmic model for the automated optimization of RC buildings with solid slab systems. The model automates and optimizes the layout process, yielding measurable improvements in spatial efficiency while maintaining compliance with structural performance criteria. Unlike prior models that address structural or architectural parameters separately, the proposed framework integrates both domains through a unified generative design approach within a BIM environment, enabling automated evaluation of structurally viable and architecturally coherent slab layouts. Developed within the parametric visual programming environment in Dynamo for Revit, the model employs a generative design (GD) engine to explore and refine various design alternatives while adhering to structural constraints. By leveraging a BIM-based framework, this method enhances efficiency, optimizes resource utilization, and systematically balances structural and architectural requirements. The model was validated through three case studies, demonstrating cost reductions between 2.7% and 17%, with material savings of up to 13.38% in concrete and 20.87% in reinforcement, achieved within computational times ranging from 120 to 930 s. Despite the current development being limited to vertical load scenarios and being most suitable for regular slab-based configurations, the results demonstrated the model’s effectiveness in optimizing grid dimensions and reducing material quantities and costs, and highlighted its ability to streamline early-stage design processes. Full article

Objectives: This study aimed to evaluate the effects of AMCOP^® elastodontic appliances on cephalometric parameters of skeletal divergence and upper airway dimensions in growing patients, comparing treated individuals with an untreated control group. Methods: A total of 60 subjects (30 treated with AMCOP^® devices and 30 controls) were selected, with mean ages of 8.67 ± 1.3 and 9.19 ± 0.8 years, respectively. The AMCOP^® appliances, designed for mixed dentition, were worn for 1 h during the day and throughout the night for 6–8 months. Cephalometric analyses were conducted at the beginning (T0) and end (T1) of treatment. Statistical analyses were performed using multivariable linear regression models to assess changes in skeletal and airway parameters, with significance set at p < 0.05. Results: Significant reductions were observed in Ans-Snp^Go-Gn (p = 0.0351), SN^Go-Gn (p = 0.0091), and FMA (p < 0.001) in the treated group compared to controls, indicating improved mandibular rotation. Upper airway spaces (SPAS, MAS, IAS) increased significantly, suggesting enhanced airway patency. Regression models confirmed the positive impact of AMCOP^® therapy on skeletal and airway outcomes, particularly in subjects with pronounced vertical discrepancies. Conclusions: AMCOP^® elastodontic devices effectively promote anterior mandibular rotation and reduce mandibular plane inclination in hyperdivergent patients, contributing to balanced craniofacial growth. The expansion of pharyngeal spaces indicates potential respiratory benefits. Future research is needed to confirm long-term stability and address variability in treatment response. Full article

Medical image registration is an indispensable preprocessing step to align medical images to a common coordinate system before in-depth analysis. The registration precision is critical to the following analysis. In addition to representative image features, the initial pose settings and multiple poses in images will significantly affect the registration precision, which is largely neglected in state-of-the-art works. To address this, the paper proposes a dual-pose medical image registration algorithm based on improved differential evolution. More specifically, the proposed algorithm defines a composite similarity measurement based on contour points and utilizes this measurement to calculate the similarity between frontal–lateral positional DRR (Digitally Reconstructed Radiograph) images and X-ray images. In order to ensure the accuracy of the registration algorithm in particular dimensions, the algorithm implements a dual-pose registration strategy. A PDE (Phased Differential Evolution) algorithm is proposed for iterative optimization, enhancing the optimization algorithm’s ability to globally search in low-dimensional space, aiding in the discovery of global optimal solutions. Extensive experimental results demonstrate that the proposed algorithm provides more accurate similarity metrics compared to conventional registration algorithms; the dual-pose registration strategy largely reduces errors in specific dimensions, resulting in reductions of 67.04% and 71.84%, respectively, in rotation and translation errors. Additionally, the algorithm is more suitable for clinical applications due to its lower complexity. Full article

Multi-focus image fusion (MFIF) is an image-processing method that aims to generate fully focused images by integrating source images from different focal planes. However, the defocus spread effect (DSE) often leads to blurred or jagged focus/defocus boundaries in fused images, which affects the quality of the image. To address this issue, this paper proposes a novel model that embeds the Kolmogorov–Arnold network with convolutional layers in parallel within the U-Net architecture (KCUNet). This model keeps the spatial dimensions of the feature map constant to maintain high-resolution details while progressively increasing the number of channels to capture multi-level features at the encoding stage. In addition, KCUNet incorporates a content-guided attention mechanism to enhance edge information processing, which is crucial for DSE reduction and edge preservation. The model’s performance is optimized through a hybrid loss function that evaluates in several aspects, including edge alignment, mask prediction, and image quality. Finally, comparative evaluations against 15 state-of-the-art methods demonstrate KCUNet’s superior performance in both qualitative and quantitative analyses. Full article

This study investigates the mechanisms by which green finance mitigates non-point source pollution. Based on provincial panel data from China spanning 2005 to 2023, this study conducts an empirical analysis that yields several key findings: (1) The development of green finance significantly reduces the intensity of agricultural non-point source pollution. (2) Green finance indirectly contributes to pollution reduction by incentivizing farmers to adopt environmentally sustainable production practices. (3) The pollution control effects of green finance are amplified in regions with advanced digital infrastructure. (4) The impact of green finance on agricultural pollution demonstrates a threshold effect associated with regional innovation capacity—only when innovation capability exceeds a certain threshold does the emission reduction effect of green finance become evident. Theoretically, this study broadens the research dimensions of green finance by integrating farmer behavioral factors and revealing boundary conditions related to technology and innovation. Policy implications include the need to tailor green financial products for agriculture, accelerate the development of rural digital infrastructure, and implement innovation-driven differentiated policies to enhance precision. Full article

Background: Physical exercise during pregnancy is strongly recommended due to its well-established benefits for both mother and child. However, its impact on the pelvic floor remains insufficiently studied. This study aimed to evaluate pelvic floor adaptations to a structured prenatal exercise program using transperineal ultrasound, and to assess associations with the duration of the second stage of labor and mode of delivery. Methods: This is a planned secondary analysis of a randomized controlled clinical trial (RCT) (NCT04563065) including women with singleton pregnancies at 12–14 weeks of gestation. Participants were randomized to either an exercise group, which followed a supervised physical exercise program three times per week, or a control group, which received standard antenatal care. Transperineal ultrasound was used at the second trimester of pregnancy and six months postpartum to measure urogenital hiatus dimensions at rest, during maximal pelvic floor contraction, and during the Valsalva maneuver, to calculate hiatal contractility and distensibility and to evaluate levator ani muscle insertion. Regression analyses were performed to assess the relationship between urogenital hiatus measurements and both duration of the second stage of labor and mode of delivery. Results: A total of 78 participants were included in the final analysis: 41 in the control group and 37 in the exercise group. The anteroposterior diameter of the urogenital hiatus at rest was significantly smaller in the exercise group compared to controls (4.60 mm [SD 0.62] vs. 4.91 mm [SD 0.76]; p = 0.049). No other statistically significant differences were observed in static measurements. However, contractility was significantly reduced in the exercise group for both the latero-lateral diameter (8.54% vs. 4.04%; p = 0.012) and hiatus area (20.15% vs. 12.55%; p = 0.020). Distensibility was similar between groups. There were no significant differences in the duration of the second stage of labor or mode of delivery. Six months after delivery, there was an absolute risk reduction of 32.5% of levator ani muscle avulsion in the exercise group compared to the control group (53.3% and 20.8%, respectively; p = 0.009). Conclusions: A supervised exercise program during pregnancy appears to modify pelvic floor morphology and function, reducing the incidence of levator ani muscle avulsion without affecting the type or duration of delivery. These findings support the safety and potential protective role of prenatal exercise in maintaining pelvic floor integrity. Full article

Background/Objectives: Muscle dysmorphia (MD), a subtype of body dysmorphic disorder, is prevalent among males who engage in the non-medical use of anabolic–androgenic steroids (AASs) and performance-enhancing drugs (PEDs). These individuals often experience severe psychopathology, including mood instability, compulsivity, and a distorted body image. Despite its clinical severity, no randomized controlled trials (RCTs) have evaluated structured psychological treatments in this subgroup. This study aimed to assess the efficacy of a manualized cognitive behavioral therapy (CBT) protocol in reducing MD symptoms and associated psychological distress among male steroid users. Results: Participants in the CBT group showed significant reductions in MD symptoms from the baseline to post-treatment (MDDI: p < 0.001, d = 1.12), with gains sustained at follow-up. Large effect sizes were also observed in secondary outcomes including depressive symptoms (PHQ-9: d = 0.98), psychological distress (K10: d = 0.93), disordered eating (EDE-Q: d = 0.74), and exercise addiction (EAI: d = 1.07). No significant changes were observed in the control group. Significant group × time interactions were found for all outcomes (all p < 0.01), indicating CBT’s specific efficacy. Discussion: This study provides the first RCT evidence that CBT significantly reduces both core MD symptoms and steroid-related psychopathology in men engaged in AAS/PED misuse. Improvements extended to mood, body image perception, and compulsive exercise behaviors. These findings support CBT’s transdiagnostic applicability in addressing both the cognitive–behavioral and affective dimensions of MD. Materials and Methods: In this parallel-group, open-label RCT, 59 male gym-goers with DSM-5-TR diagnoses of MD and a history of AAS/PED use were randomized to either a 12-week CBT intervention (n = 30) or a waitlist control group (n = 29). CBT sessions were delivered weekly online and targeted distorted muscularity beliefs, compulsive behaviors, and emotional dysregulation. Primary and secondary outcomes—Muscle Dysmorphic Disorder Inventory (MDDI), PHQ-9, K10, EDE-Q, EAI, and BIG—were assessed at the baseline, post-treatment, and 3-month follow-up. A repeated-measures ANOVA and paired t-tests were used to analyze time × group interactions. Conclusions: CBT offers an effective, scalable intervention for individuals with muscle dysmorphia complicated by anabolic steroid use. It promotes broad psychological improvement and may serve as a first-line treatment option in high-risk male fitness populations. Future studies should examine long-term outcomes and investigate implementation in diverse clinical and cultural contexts. Full article

Increasing temperatures associated with climate change will lead to (periodic) temperature-induced reductions in food intake in human and other mammal populations. Human adults, however, are both tolerant and resilient to periodic nutritional deficits, and the associated health effects should be limited. Intermittent nutritional deficits may also cause growth restriction in developing foetuses and young children, which potentially affects their food intake in later life. Therefore, temperature-induced hypophagia can be hypothesised to manifest as later compensatory responses with multiple concomitant (or extended) lags of varying temporal dimensions. We examined the relationship between calorie intake and ambient outdoor temperatures for a time series covering past decades (FAO data for 1961–2013) in 80 countries to determine if humans alter their food intake in response to elevated temperatures. We included eleven different temporal “windows of exposure” of varying lag. These windows considered current and recent exposure, just as lagged effects allowed for a consideration of past effects on mothers, their children, and childhood exposure. It was hypothesised that one of these could provide a basis for predicting future changes in human calorie intake in response to climate change. Our analyses showed no apparent association with temperatures in ten of the eleven hypotheses/models. The remaining hypothesis suggests that current calorie intake is linked to decadal mean temperatures with a lag of approximately three decades, pointing to an impact on mothers and their (developing) children. The impact of an increase in mean temperature varies with temperature amplitudes, and negative impacts are only found in countries with low temperature amplitudes (warmer countries), albeit the impact on calorie intake caused by a 2–3 °C change in temperatures or temperature amplitudes is generally modest. However, in considering calorie intake, we only address quantities of food (with unspecified quality), which insufficiently reflect the full range of nutritional challenges associated with increasing temperatures. Understanding climate-driven changes in human food intake requires global interdisciplinary collaboration across public health, environmental science, and policy. Full article