Search Results (35,087)

The literature emphasizes the importance of perceived social media marketing activities (SMMAs) in shaping various brand-related outcomes. However, their importance in brand attachment formation remains underexplored. Grounded in the Stimulus–Organism–Response (S-O-R) framework and Attachment Theory, this study examines the relationship between SMMAs and brand attachment, and the impact of brand attachment on brand loyalty and consumer engagement with brand social media (CEBSM). A questionnaire survey was conducted with 502 consumers of outdoor and sports brands in Algeria. Data analysis was performed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings unveil that SMMAs, including interactivity, informativeness, personalization, trendiness, and WOM, are positively associated with brand attachment. Furthermore, brand attachment is significantly associated with both brand loyalty and CEBSM. This study makes several theoretical contributions by being among the early studies to examine the individual effects of social media marketing dimensions, the role of SMMA in brand attachment formation, and brand-related outcomes alongside in-platform outcomes. This study offers recommendations to guide community managers and brand managers in clarifying the roles and capabilities of social media marketing in evoking and reinforcing brand attachment. Full article

Transcatheter aortic valve implantation (TAVI) has transformed the management of severe aortic stenosis (AS), evolving from a therapy reserved for inoperable patients to a viable treatment across the spectrum of surgical risk. This success has stimulated innovation in transcatheter therapies for other valvular heart diseases, including aortic regurgitation (AR). In contrast to AS, AR is characterised by heterogeneous aetiologies, absence of annular calcification, larger and more elliptical annular dimensions, and concomitant aortopathy. These challenges have limited the efficacy and safety of conventional transcatheter aortic valves (TAVs), use of which in pure native AR is associated with high rates of valve embolisation, significant residual regurgitation, permanent pacemaker implantation, and mortality. The development of dedicated TAVs designed specifically for the treatment of AR has addressed many of these anatomical challenges. The JenaValve Trilogy and J-Valve systems incorporate leaflet-grasping mechanisms that enable secure anchoring independent of calcification, resulting in transformation of procedural and clinical outcomes. Recent prospective registry data, including the landmark ALIGN-AR trial, demonstrate high technical and procedural success rates, low residual regurgitation, acceptable safety profiles, and meaningful improvements in functional status and ventricular remodelling. These data have informed contemporary guideline updates, with the 2025 European Society of Cardiology (ESC)/European Association of Cardiothoracic Surgery (EACTS) Guidelines for the management of valvular heart disease issuing the first conditional recommendation for TAVI in selected patients with severe AR and the National Institute for Health and Care Excellence (NICE) recommending TAVI for native AR in patients for whom surgical AVR is not available or is high risk. This review summarises the clinical implications of AR, examines current guideline recommendations for management, and critically appraises the evidence supporting transcatheter treatment strategies. Full article

Background/Objectives: Hypoalbuminaemia is a consistent predictor of mortality in sepsis; however, the temporal dynamics of albumin decline and its relationship with fluid exposure and early norepinephrine therapy remain incompletely characterised. Determining whether early norepinephrine use is associated with attenuation of albumin loss could inform fluid management strategies and identify therapeutic windows for combined vasopressor–albumin interventions. The study aimed to assess whether serum albumin trajectories in sepsis are associated with fluid exposure, modulated by early norepinephrine therapy, and related to 30-day mortality. Methods: We conducted a prospective longitudinal study of patients admitted to an intermediate care unit (IMCU) with community-acquired sepsis. Serum albumin concentrations, cumulative fluid balance (CFB), and vasopressor use were recorded during the first 5 days of hospitalisation. Longitudinal mixed-effects and segmented linear models assessed the association of CFB and vasopressor therapy with albumin trajectories. Lagged mediation modelling explored the potential mediating role of albumin in the association between fluid exposure and 30-day mortality. Results: A total of 389 patients with community-acquired sepsis were included. Thirty-day mortality was 18%. Mean serum albumin at baseline was 2.58 g/dL and declined early to 2.24 g/dL at 72 h. Serum albumin was inversely correlated with cumulative fluid balance over time (r ranging from −0.235 to −0.348; p < 0.001). In longitudinal models, each 1% increase in ΔCFB was associated with a −0.029 g/dL decrease in serum albumin (p < 0.001), supporting an independent effect of fluid exposure. Before norepinephrine initiation, the albumin slope was −0.043 g/dL per interval and was −0.008 g/dL after vasopressor initiation (interaction p = 0.012). Lower albumin concentrations at 72 h predicted 30-day mortality (OR 1.49 per 0.5 g/dL decrease), and serum albumin mediated 18.6% of the association between fluid exposure and mortality. Conclusions: Cumulative fluid exposure was associated with a progressive decline in serum albumin in patients with community-acquired sepsis. Early norepinephrine initiation was associated with attenuation of this trajectory, consistent with the hypothesis that vasopressor-guided haemodynamic stabilisation may limit fluid-associated albumin loss. Full article

Background/Objectives: Glucagon-like peptide-1 receptor agonists (GLP-1RAs) are increasingly used for the management of obesity and type 2 diabetes, particularly among women of reproductive age. Emerging evidence suggests potential effects on ovulation, fertility, and pregnancy outcomes. This narrative review aims to synthesize current evidence on the reproductive safety of GLP-1RAs, with a focus on their implications for conception, unintended pregnancy, and maternal–fetal outcomes. Methods: A narrative literature review was conducted using PubMed and relevant bibliographic sources to identify studies published between 2020 and 2025. The search included clinical trials, observational studies, registry data, case reports, and selected preclinical evidence. Studies addressing reproductive outcomes, including ovulation, fertility, pregnancy exposure, and fetal safety, were included. Evidence was synthesized descriptively in accordance with recommended approaches for narrative reviews. Results: Available evidence indicates that GLP-1RAs may improve ovulatory function and menstrual regularity, particularly in women with obesity or polycystic ovary syndrome, potentially increasing the likelihood of conception. However, human data on pregnancy exposure remain limited. While current evidence does not consistently demonstrate a strong teratogenic signal, findings are based on small samples and heterogeneous study designs. Concerns persist regarding unintended pregnancies due to improved fertility and the absence of robust safety data during early gestation. Conclusions: GLP-1RAs present a complex clinical scenario in women of reproductive age, with potential benefits for metabolic and reproductive health but uncertain safety during pregnancy. Clinicians should exercise caution, provide appropriate contraceptive counseling, and carefully weigh the risks and benefits when prescribing these agents. Further large-scale, prospective studies are needed to clarify reproductive safety and inform evidence-based clinical guidelines. Full article

In the digital era, online attention has become a key indicator of tourism competitiveness and destination visibility. This study proposes a two-dimensional framework to evaluate the competitive state of online attention by combining its current magnitude and growth dynamics. Using Baidu Index data, the study applies the Boston Consulting Group (BCG) matrix and the coefficient of variation to analyze online attention patterns of Beijing’s 5A–level tourist attractions from 2011 to 2025. The results show clear polarization in online attention. A small number of iconic attractions consistently dominate digital visibility, while many other sites exhibit unstable and uneven attention trajectories. These patterns reflect the cumulative effects of consumer behavior, information-seeking preferences, and algorithmically mediated content environments, which reinforce attention concentration and competitive inequality over time. External shocks, particularly the COVID–19 pandemic, caused sharp declines in online attention in 2020, followed by an uneven recovery in subsequent years, highlighting the volatility of digital attention systems. The study also demonstrates the managerial value of the proposed framework. By classifying attractions according to attention levels and growth potential, the framework supports differentiated marketing and demand–redistribution strategies. For instance, increasing the visibility of high-potential but under-visited attractions can help redirect visitors away from overcrowded “Star/GC” sites and encourage more balanced spatial and temporal visitation. Overall, this study proposes a quantitative and replicable framework that integrates digital attention dynamics, algorithmic filtering, and consumer behavior into destination competitiveness analysis. The framework supports evidence-based and sustainability-oriented destination management by informing adaptive marketing and demand management strategies that can help alleviate overtourism and balance visitor flows. However, the study relies on a single digital platform and lacks direct sustainability indicators. Future research should integrate multi-platform data and link online attention metrics to measurable environmental, social, and economic sustainability outcomes. Full article

Strawberry cultivation generates substantial amounts of agricultural by-products, including spent substrates and plant residues, particularly in hydroponic production systems. However, information on the occurrence and management of these by-products remains limited. This study investigated the generation, disposal practices, and chemical characteristics of by-products from hydroponic strawberry cultivation in two major strawberry-producing regions of Republic of Korea, Nonsan and Jinju. Based on national statistics and field surveys, annual by-product generation was estimated at 605,400 m³ of spent substrates and approximately 25,729 t fresh weight and 6003 t dry weight of plant residues. Disposal practices varied regionally: in Jinju, over 80% of by-products were recycled as compost or feed, whereas in Nonsan, recycling rates were lower and a considerable portion remained untreated or were improperly disposed of. Analyses of 463 pesticides and seven heavy metals (Zn, Cu, Ni, Pb, As, Cd, and Hg) confirmed concentrations below the permissible limits, supporting their chemical suitability for potential recycling use. Inorganic analyses revealed high levels of N, Ca, P, and K, suggesting their potential as alternative nutrient sources and as raw materials for recycled fertilizer or soil amendment. Because strawberry by-products are generated continuously throughout the cultivation cycle, their management requires decentralized and long-term strategies. These results provide the first comprehensive assessment of the generation scale, disposal practices, and chemical characteristics of strawberry by-products in Republic of Korea, suggesting their potential as alternative nutrient resources or raw materials for recycled fertilizer or soil amendment under appropriate pretreatment and management. Full article

Fine-scale crop type information is essential for agricultural monitoring, irrigation management, and food security assessment. This study mapped three major crops—wheat, corn, and sunflower—in the Hetao Irrigation District, China, using multi-temporal Sentinel-2 optical imagery and Sentinel-1 SAR observations at the parcel scale. A multi-source feature set, including spectral bands, vegetation and red-edge indices, moisture-related variables, radar backscatter coefficients, and derived radar features, was constructed from the full growing season. An LSTM network was used to learn temporal representations of crop phenological dynamics, and the resulting embeddings were then combined with traditional machine learning classifiers, including Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost), for final classification. The results show that the hybrid framework substantially improves classification performance compared with the corresponding non-LSTM classifiers. Among all tested models, XGBoost + LSTM achieved the best performance, with an overall accuracy of 93.61%, a Kappa coefficient of 91.66%, and a mean IoU of 87.41%. The class-wise F1-scores were 85.61% for wheat, 97.22% for corn, and 87.27% for sunflower. Additional experiments further confirmed the advantages of parcel-based aggregation in improving spatial consistency and reducing mixed-field noise. The proposed framework provides a promising parcel-scale workflow for crop type mapping in fragmented irrigation districts, while its transferability across years and regions still requires further validation. Full article

Surface water systems are increasingly exposed to multiple pressures generated by climate variability, intensified water resource exploitation, and evolving geopolitical dynamics. This study provides a novel contribution by identifying critical threshold effects and non-linear interactions that influence nitrate concentrations through an integrated information systems framework. It develops an integrated information-system-based analytical framework that combines hydrological, climatic, geopolitical, and strategic indicators to shape the broader contextual framework within which hydrological and climatic pressures operate, rather than serving as direct predictors. Considering the nitrate concentration in rivers as a key parameter of water quality, the paper goes beyond univariate analysis of nitrite concentration, examining its relationship with four explanatory variables: the Water Exploitation Index Plus (WEI+), the number of heat stress days (Heat_Stress), the Geopolitical Risk Index (GPR), and a proxy variable representing the presence of strategic infrastructure (Nuclear_State) using a Reduced Error Pruning Tree (REPTree) decision tree algorithm with 10-fold cross-validation. The results indicate that climatic stress emerges as the primary predictor, with a critical threshold of approximately 7.83 heat stress days, beyond which nitrate concentrations increase significantly. Under conditions of high climatic stress and intensive water exploitation (WEI+ ≥ 67.39), predicted nitrate levels exceed 20 mg/L and can reach extreme values of up to 58.82 mg/L. In contrast, low hydrological pressure (WEI+ < 0.39) combined with moderate climatic stress is associated with very low nitrate concentrations, around 2.75 mg/L. The model demonstrates strong predictive performance, with a correlation coefficient of 0.976, a Mean Absolute Error (MAE) of 0.593, a Root Mean Squared Error (RMSE) of 2.046, and a Receiver Operating Characteristic (ROC) area exceeding 0.94 for classification tasks. While geopolitical and strategic variables do not act as direct predictors, they contribute to shaping the contextual framework influencing water resource management and environmental vulnerability. Overall, the study highlights the non-linear and systemic nature of water quality dynamics and demonstrates the effectiveness of decision tree-based models within integrated information systems for supporting environmental monitoring and decision-making under conditions of climate stress and geopolitical uncertainty. Full article

As a vital part of urban public transportation system, subway passenger flow prediction plays a crucial role in alleviating traffic congestion, improving transportation infrastructure, and optimizing travel experience. Existing subway passenger flow prediction mainly focuses on short-term predictions of inbound/outbound passenger flow and origin-destination (O-D) demand. Subway section passenger flow prediction can provide a more direct reflection of passenger fluctuations across different line segments, and offer robust support for management and resource allocation. We propose a subway section passenger flow generation model and a prediction method based on LTiT (LSTM-TSSA-iTransformer). This model is based on the overall architecture of the iTransformer encoder, and an LSTM (Long Short-Term Memory) network is employed to capture the temporal characteristics of subway section passenger flow. This is combined with the TSSA (Token Statistics Self-Attention) to adaptively weight the information at key time points. Efficient performance of the model was evaluated by comparing its predictions with other models, including SARIMA (Seasonal Auto-Regressive integrated moving average), BP neural networks, LightGBM (Light Gradient Boosting Machine) and LSTM (Long Short-Term Memory). Experimental results show that the proposed model outperforms traditional baseline models in evaluation metrics such as R², MAE, MSE, and MAPE. Finally, we further investigate the selection of input window length and prediction step size, and perform robustness analysis under different noise conditions. Full article

Coastal erosion poses a growing threat to natural systems and critical infrastructures, particularly in touristic coastal areas where beaches represent both ecological assets and economic resources. Beyond shoreline retreat, erosion processes progressively reduce emerged beach surfaces and increase the exposure and vulnerability of coastal roads, railways, and urban settlements, with cascading socio-economic consequences. This study presents an integrated geomorphological and economic assessment of coastal erosion impacts. The Coastal Degradation Calculator (CoDeC) is applied along the Tyrrhenian coast of southern Italy, focusing on the municipality of San Lucido. Shoreline variations are quantified to reconstruct long-term changes in the Surface of the Emerged Beach (SEB) before and after major coastal defense interventions using multi-temporal remote sensing data (1954–2018). Simple, science-based box models are implemented to estimate sediment deficits, restoration needs, and associated economic damages, expressed in both €/m² and €/year. Results highlight a reduction in SEB area exceeding 60%, significant downdrift erosion linked to hard defenses and additional losses caused by coastal urbanization. The methodology proved effective in supporting damage quantification and informed the resolution of a long-standing legal dispute between public authorities. Owing to its transparency and reproducibility, the proposed framework offers a transferable tool for coastal risk assessment and management under increasing climate-driven pressures. Full article

Sustainable precision agriculture is crucial for optimizing resource utilization, reducing chemical inputs, and ensuring global food security. High-precision automatic recognition and monitoring of key crop organs (e.g., wheat heads and flower clusters) serve as the technological foundation for sustainable agricultural management decisions. However, visual perception in natural field environments is highly susceptible to external conditions. To address the challenges of severe background interference and feature dilution in crop small object detection within complex agricultural scenarios, this paper proposes an enhanced detection network, ACF-YOLO, based on YOLO11. First, an Aggregated Multi-scale Local-Global Attention (AMLGA) module is designed to enhance the feature representation of weak targets by fusing local details with global semantics. Second, a Context-Guided Fusion Module (CGFM) and a Soft-Neighbor Interpolation (SNI) strategy are introduced. Their synergy alleviates feature aliasing effects and ensures the precise alignment of deep semantic information with shallow spatial details. Furthermore, the Inner-MPDIoU loss function is employed to optimize the bounding box regression accuracy for non-rigid targets by incorporating geometric constraints and auxiliary scale factors. To verify the detection capability of the proposed method, we constructed a UAV Wheat Head Dataset (UWHD) and conducted extensive experiments on the UWHD, GWHD2021, and RFRB datasets. The experimental results demonstrate that ACF-YOLO outperforms other comparative methods, confirming its stable detection performance and contributing to the sustainable development of agriculture. Full article

Accurate fold models are of great significance for mineralization control, resource exploration, and underground engineering. However, existing automated modeling methods show difficulty in quantitatively describing fold development patterns and lack the available reference models required for multiple-point statistics and intelligent modeling techniques. This study proposes a novel three-dimensional (3D) fold-modeling method that integrates multiple-point-statistics-based pattern library construction with a long short-term memory (LSTM) network-based modeling framework. The multiple-point geostatistic is employed to quantify spatial distributions and correlations in geological data, thereby identifying the intrinsic structural patterns of folds. The extracted patterns are transformed into a training library that effectively represents the geological semantics and morphological diversity of folds, providing a reliable dataset for LSTM-based model training. An optimized ConvLSTM network is designed to ensure robust representation of fold complexity and variability. Based on the network, 3D models can be rapidly generated from geological profiles. Multiple experiments demonstrate that the proposed method can automatically produce 3D models that conform to realistic geological conditions and accurately reflect true fold geometries. The approach significantly improves modeling efficiency and geological feature representation, providing a reliable tool for geological engineering applications. Full article

Precipitation forecasting is essential for disaster prevention, water resource management, and socio-economic resilience. The field has evolved from numerical weather prediction (NWP) and optical-flow-based methods toward data-driven deep learning approaches that can exploit larger observational datasets and model complex nonlinear relationships. Against this background, this study evaluates multi-station temporal forecasting models within a single-year, station-based proof-of-concept benchmark under unified data conditions. We adapt the Transformer and Informer architectures to this meteorological setting, rigorously preprocess the AWS dataset to avoid data leakage, and select predictive variables using complementary linear and nonlinear relevance criteria. Model performance is assessed using continuous and categorical precipitation metrics, including the Critical Success Index (CSI). The results show that the Informer outperforms the recurrent neural network (RNN) baselines and achieves the lowest mean MAE and RMSE together with the highest mean CSI among the evaluated models while using substantially fewer parameters than the standard Transformer. However, its sample-wise absolute error distribution remains statistically comparable to that of the standard Transformer. Overall, this study establishes a single-year, station-based proof-of-concept benchmark for comparing architectures in very-short-term (1–5 h ahead) precipitation forecasting. Full article

Recently, poultry production has increased worldwide to address the increasing demand of affordable animal-sourced protein. To meet this requirement, poultry production operations have become more concentrated, introducing management challenges related to disease control, productivity, and animal welfare. However, manual flock monitoring and management have become impractical in such cases, creating a need for automatic data-driven management approaches. In this context, the Internet of Things (IoT) has emerged as a potential technological solution for continuous flock monitoring, data sharing, and decision-making. Despite this, its adoption in poultry production is limited compared with its widespread use in crop production, transportation, and manufacturing industrial sectors. Furthermore, advanced analytical techniques such as artificial intelligence (AI), applied to data gathered by IoT-enabled devices, have shown promising results by generating actionable information. Existing literature suggests that the integration of IoT and AI can address the major challenges associated with modern large-scale poultry production systems. While most applications remain at the research scale, such technologies have the potential for improving flock monitoring, enhancing productivity, and ensuring proper animal welfare. This narrative review examines the current state of IoT and AI based technologies, together or in part identifies the limitations, research gaps, and opportunities for future development. Full article

Accurate prediction of effluent water quality is essential for the intelligent and sustainable operation of wastewater treatment plants (WWTPs). However, this task remains challenging due to the strong nonlinearity, long-term temporal dependencies, and severe fluctuations inherent in influent characteristics. In this study, a novel data-driven framework termed the Multi-Scale Temporal Representation-Enhanced Informer (MTRE-Informer), is proposed to predict key effluent quality indicators, including total nitrogen (TN), total phosphorus (TP), and chemical oxygen demand (COD). To ensure data quality and computational efficiency, a generative recurrent learning framework is first employed for anomaly detection and correction, followed by variance inflation factor (VIF)-based feature selection to mitigate multicollinearity. Furthermore, feature contribution analysis is conducted to improve model interpretability. Subsequently, the core MTRE-Informer architecture utilizes hierarchical multi-scale temporal representation learning to simultaneously capture local patterns and long-term dependencies within the complex dynamics of the wastewater treatment process. Experimental results demonstrate that the MTRE-Informer achieves robust and stable predictive performance across diverse operational datasets. For TN prediction, the proposed framework attains a coefficient of determination () of 0.9637 and a mean absolute percentage error (MAPE) of 3.39%. Compared with baseline approaches, the improvement ranges from 3.8% to 14.2%, validating its superior capability. To further enhance model robustness, an anomaly detection and correction strategy based on a generative recurrent learning framework is employed. In addition, feature contribution analysis and VIF-based feature selection are conducted to improve interpretability, mitigate multicollinearity, and enhance computational efficiency. Overall, this framework provides a reliable and practical solution for real-time effluent quality prediction, facilitating the intelligent management of WWTPs. Full article