Search Results (8,798)

Supplier selection in the medical device manufacturing (MDM) industry significantly affects quality, operational efficiency, and overall organizational performance. Due to the industry’s dependence on advanced technologies and rigorous regulatory standards, identifying critical success factors (CSF) for selecting suppliers is essential. This study aims to analyze relationships among critical success factors (CSF) influencing supplier selection and their influence on supplier quality and the performance outcomes of MDM companies. A structured survey was conducted among MDM companies in Mexico, and the collected data were analyzed through exploratory and confirmatory factor analysis. Structural equation modeling (SEM) was used to quantify the relationships identified. Results indicate that information technology, reliable delivery, Industry 4.0 adoption, resilience, and environmental and social responsibility positively influence supplier quality, which subsequently enhances MDM firm performance. Supplier quality emerges as a critical mediator between supplier selection factors and company performance. Findings emphasize that prioritizing supplier quality, reinforced through Industry 4.0 technologies and resilient practices, ensures operational continuity, enhances competitive advantage, and supports sustainability. Companies incorporating these critical success factors into their supplier selection processes are better equipped to manage supply disruptions, achieve consistent quality, and sustain performance in highly regulated environments. Full article

The limited endowment of initial traffic creates fierce competition among merchants for securing scarce resources in e-commerce platforms. Optimal pricing mechanisms are imperative to maximize transaction volumes and attract sustained traffic support from platforms. This study investigates merchant pricing mechanisms under two advertising strategies—broadband advertising and targeted advertising—by constructing differentiated pricing models that account for consumer preference heterogeneity. The findings reveal that, in most cases where merchants prioritize profit maximization, targeted advertising-driven pricing mechanisms outperform broadband advertising strategies. However, when merchants prioritize early-stage sales volume, broadband advertising proves to be more advantageous. Furthermore, the study shows that merchant pricing strategies evolve over their lifecycle, transitioning from offering lower prices to high-type consumers toward progressively higher prices, thus validating the underlying mechanism of “big data price discrimination” (the phenomenon of “killing loyal customers”). Additionally, this research emphasizes the importance of accurately understanding consumer preferences, as the sensitivity differential between price and advertising responses plays a crucial moderating role. When the sensitivity gap becomes excessively large, the price differential in differentiated pricing mechanisms should be proportionally reduced to maintain effectiveness. In conclusion, by integrating consumer utility, merchant profit, and platform incentives, pricing mechanisms based on targeted advertising exhibit superior capabilities in screening consumer information. When combined with advertising effectiveness and consumer preference heterogeneity, these mechanisms represent a relatively optimal strategy. However, this conclusion holds only when the proportion of high-type consumers in the market is moderate, not excessively low. This study contributes to the literature by providing a comprehensive framework for merchants to select appropriate pricing strategies under varying advertising environments and consumer structures. Full article

We modeled the water level variations in a protected playa-lake system (La Ratosa Natural Reserve, S Spain) comprising two adjacent playa-lakes: La Ratosa and Herriza de los Ladrones. For this purpose, daily water balances were applied to reconstruct the water level. Model results were validated using actual water level monitoring over the past 20 years. We surveyed post-Pliocene geological structures in the endorheic watershed to investigate lake nucleation and to improve the hydrogeological model. Additionally, we investigated the groundwater level evolution in nearby aquifers, which have been profusely affected by groundwater exploitation for domestic and agricultural use. Then, the RCP 4.5 and RCP 8.5 climate change scenarios were applied to forecast the future of this lake system. We found that the playa-lake hydroperiod will shorten, causing the system to shift from seasonal to ephemeral, which appears to be a general trend in this area. However, the impact on the La Ratosa-Herriza de los Ladrones system would be likely more severe due to local stressors, such as groundwater withdrawal for urban demand and agriculture, driving the system to complete desiccation for extended periods. These results highlight the sensitivity of these protected ecosystems to changes in the watershed’s water balance and underscore the urgent need to preserve watersheds from any form of water use, other than ecological purposes. This approach aims to support informed decision-making to mitigate adverse impacts on these fragile ecosystems, ensuring their ecological integrity in the context of climate change and increasing water demand for various uses. Full article

Multivariate time series data (MTSD) anomaly detection due to complex spatio-temporal dependencies among sensors and pervasive environmental noise. The existing methods struggle to balance anomaly detection accuracy with robustness against data contamination. Hence, this paper proposes a robust multivariate temporal data anomaly detection method based on graph attention for training convolutional neural networks (PGAT-BiGRU-NRA). Firstly, the parallel graph attention (PGAT) mechanism extracts the time-dependent and spatially related features of MTSD to realize the MTSD fusion. Then, a bidirectional gate recurrent unit (BiGRU) is utilized to extract the contextual information of the data to avoid information loss. In addition, reconstructing the noise for adversarial training aims to achieve a more robust anomaly detection of MTSD. The experiments conducted on real industrial equipment datasets evaluate the effectiveness of the method in the task of MTSD, and the comparative experiments verify that the proposed method outperforms the mainstream baseline model. The proposed method achieves anomaly detection and robust performance in noise interference, which provides feasible technical support for the stable operation of industrial equipment in complex environments. Full article

Accurate preoperative image processing in markerless image-guided surgeries is an important task. However, preoperative planning highly depends on the quality of medical imaging data. In this study, a novel algorithm for outer skin layer extraction from head computed tomography (CT) scans is presented and evaluated. Axial, sagittal, and coronal slices are processed separately to generate spatial data. Each slice is binarized using manually defined Hounsfield unit (HU) range thresholding to create binary images from which valid contours are extracted. The individual points of each contour are then projected into three-dimensional (3D) space using slice spacing and origin information, resulting in uniplanar point clouds. These point clouds are then fused through geometric addition into a single enriched triplanar point cloud. A two-step downsampling process is applied, first at the uniplanar level and then after merging, using a voxel size of 1 mm. Across two independent datasets with a total of 83 individuals, the merged cloud approach yielded an average of 11.61% more unique points compared to the axial cloud. The validity of the triplanar point cloud reconstruction was confirmed by a root mean square (RMS) registration error of 0.848 ± 0.035 mm relative to the ground truth models. These results establish the proposed algorithm as robust and accurate across different CT scanners and acquisition parameters, supporting its potential integration into patient registration for markerless image-guided surgeries. Full article

Medical assistance in dying (MAID) remains a sensitive and evolving issue in Europe, frequently linked with discussions about human freedom, life dignity, and healthcare policy. While national consensus in Italy is absent, the Region of Tuscany has enacted Law No. 16/2025, which establishes a MAID procedure based on recent Constitutional Court rulings. The commentary aims to provide a preliminary analysis of the new law, addressing ethical, medico-legal, and social issues that emerge in relation to the Italian and global debate on the topic. The law establishes a three-stage process based on four eligibility criteria: irreversible disease, psycho-physical suffering, life-support dependence, and informed consent. However, Tuscany’s model poses medico-legal and ethical concerns, particularly about the boundaries of regional legislative competence, the duties of healthcare professionals, and the possibility of intra-national inequity or “health migration.” In addition, critical organisational implications derived from informed consent and lethal drug self-administration impede clinical implementation in some individuals with mental or neurological disorders. The lack of clarity in the different steps of the procedure, the uncertain supervision system, and the potential consequences for specific categories of vulnerable people underline the need for comprehensive national regulation. A future regulatory framework must balance procedural clarity with individual autonomy and equitable access, bringing Italy in line with larger European context for end-of-life care. Full article

Background/Objectives: Obsessive–compulsive disorder (OCD) is a complex psychiatric condition often associated with alterations in cognitive processes, including memory. Although memory dysfunction has been proposed as a contributing factor to the onset and maintenance of OCD symptoms, it remains debated whether these deficits reflect genuine cognitive impairments or maladaptive metacognitive processes, such as pathological doubt and memory distrust. This review aims to synthesize current findings on memory functioning in OCD, focusing on distinct memory systems and the role of metacognition. Methods: A comprehensive literature search was conducted across five databases (PubMed, Scopus, Embase, PsycINFO, and Google Scholar), covering studies up to April 2025. Search terms included “Obsessive-compulsive disorder”; “OCD”; “Memory dysfunction”; “Episodic memory”; “Working memory impairment”; “Prospective memory deficits”; “Checking compulsions”; “Memory confidence”; “Cognitive biases”. Results: Short-term memory appears generally preserved in OCD. Working memory deficits are consistently reported, especially in the visuospatial domain, and they are associated with difficulties in updating and clearing irrelevant information. Episodic memory impairments are common and often linked to inefficient encoding strategies and heightened cognitive self-consciousness. Prospective memory is frequently compromised under neutral conditions. Individuals with checking symptoms tend to show intact objective memory performance, despite reporting low memory confidence, supporting the concept of memory distrust. Conclusions: Memory dysfunction in OCD is multifaceted, involving both cognitive and metacognitive alterations. The evidence supports a model in which executive dysfunctions and memory-related beliefs contribute to compulsive behaviors more than objective memory failure. These insights highlight the need for integrative assessment protocols and personalized interventions targeting both cognitive performance and metacognitive appraisals. Full article

The accurate prediction of river discharge is essential in water resource management, particularly under variability due to climate change. Traditional hydrological models commonly struggle to capture the complex, nonlinear relationships between climate variables and river discharge, leading to uncertainties in long-term projections. To mitigate these challenges, this research integrates machine learning (ML) and deep learning (DL) techniques to predict discharge in the Subernarekha River Basin (India) under future climate scenarios. Global climate models (GCMs) from the Coupled Model Intercomparison Project 6 (CMIP6) are assessed for their ability to reproduce historical discharge trends. The selected CNRM-M6-1 model is bias-corrected and downscaled before being used to simulate future discharge patterns under SSP585 (a high-emission scenario). Various AI-driven models, such as a temporal convolutional network (TCN), a gated recurrent unit (GRU), a support vector regressor (SVR), and a novel DL network named the Temporal Enhanced Attention Network (TeaNet), are implemented by integrating the maximum and minimum daily temperatures and precipitation as key input parameters. The performance of the models is evaluated using the mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and coefficient of determination (R²). Among the evaluated models, TeaNet demonstrates the best performance, with the lowest error rates (RMSE: 2.34–3.04; MAE: 1.13–1.52 during training) and highest R² (0.87–0.95), outperforming the TCN (R²: 0.79–0.88), GRU (R²: 0.75–0.84), SVR (R²: 0.68–0.80), and RF (R²: 0.72–0.82) by 8–15% in accuracy across four gauge stations. The efficacy of the proposed model lies in its enhanced attention mechanism, which successfully identifies temporal relationships in hydrological information. In determining the most relevant predictors of river discharge, the feature importance is analyzed using the proposed TeaNet model. The findings of this research strengthen the role of DL architectures in improving long-term discharge prediction, providing valuable knowledge for climate adaptation and strategic planning in the Subernarekha region. Full article

A PROM is a measure of patient needs and therapeutic progress. This paper outlines the validation of the CARe Burn Scale: Parent/Caregiver Form, a PROM that measures quality of life in parents/caregivers supporting a child with a burn injury. A literature review and interviews with sixteen parents and six burns health professionals informed the development of the PROM conceptual framework/draft form. Cognitive debriefing interviews with five parents and seven burns-specialist health professionals provided feedback to ascertain content validity, and two-hundred and four parents/caregivers took part in the field testing. Rasch measurement theory (RMT) analyses and internal consistency tests were conducted to create a shortened version and for psychometric validation. The final conceptual framework included eight domains/individual scales: Physical Well-being, Confidence with Managing Burn Wound/Scar Treatments, Social Situations, Partner Relationship, Self-worth, Negative Mood, Parent Concerns about the Appearance of their Child’s Burn Wounds/Scars, and Positive Growth. Seven scales had solutions from RMT analyses and passed internal consistency criteria. Confidence with Managing Burn Wound/Scar Treatments did not fit the Rasch model but was retained as a checklist based on theoretical insight. The CARe Burn Scale: Parent/Caregiver Form is the first and only burn-specific PROM that assesses parents’ own health needs when caring for a child with a burn. Full article

The following study presents the results obtained from a comparative analysis of dry (powder and hard snow) and wet snow based on satellite data and in situ data methods for monitoring in the high mountain belt of Bulgaria. The aim of the study is to analyze the effectiveness of different spectral indices based on satellite data from Synthetic Aperture Radar (SAR), high-resolution (HR) imagery, and spectrometer data for assessing the state and dynamics of the snow cover. The methods studied and the results obtained were validated by instrument-based field observations, with instruments using thermal imaging cameras, spectrometer measurements, ground control points, and HR imagery. Satellite data offer an ever-widening view of trends in snow distribution over time. All these data combined provide a detailed picture of surface temperature and snow properties, which are crucial for understanding snowmelt processes and the energy balance in the high-altitude belt. The findings suggest that a multi-method approach, utilizing the combined advantages of SAR satellite data, offers the most comprehensive and accurate framework for satellite-based snow cover monitoring in the high mountain regions of Bulgaria, such as Rila Mountain. This integrative strategy not only improves the precision of snow cover estimates but can also support many water resource-related studies, such as snowmelt runoff studies, snow avalanche modeling, and better-informed decisions in the management and maintenance of winter tourism resorts. Full article

Efficient contract management is essential for ensuring sustainable and reliable supply chains; yet, traditional methods remain manual, error-prone, and inefficient, leading to delays, financial risks, and compliance challenges. AI and blockchain technology offer a transformative alternative, enabling the establishment of automated, transparent, and self-executing smart contracts that enhance efficiency and sustainability. As part of AI-driven smart contract automation, we previously implemented contractual clause extraction using question answering (QA) and named entity recognition (NER). This paper presents the next step in the information extraction process, relation extraction (RE), which aims to identify relationships between key legal entities and convert them into structured business rules for smart contract execution. To address RE in legal contracts, we present a novel hierarchical transformer model that captures sentence- and document-level dependencies. It incorporates global and segment-based attention mechanisms to extract complex legal relationships spanning multiple sentences. Given the scarcity of publicly available contractual datasets, we also introduce the contractual relation extraction (ContRE) dataset, specifically curated to support relation extraction tasks in legal contracts, that we use to evaluate the proposed model. Together, these contributions enable the structured automation of legal rules from unstructured contract text, advancing the development of AI-powered smart contracts. Full article

Demographic growth in Kazakhstan over the past decades has had a significant impact on the entire education system, particularly at the preschool and primary levels. High birth rates have led to an increasing number of children requiring enrollment in kindergartens and first-grade classes. This often results in a shortage of available places, increased workload for teaching staff, and a decline in the quality of educational services. This paper examines the application of Business Intelligence (BI) tools and Geographic Information Systems (GIS) for forecasting potential shortages of educational places and identifying regional priorities in infrastructure development. A predictive model is presented, based on birth rate indicators and age cohorts, which enables the estimation of future demand for preschool and primary school capacity across the regions of Kazakhstan. The study highlights the urgent need for proactive planning and targeted investment to prevent critical shortages and to ensure equitable access to quality education. The findings can serve as a foundation for the development of effective public education policies and support the formulation of regional strategies that reflect current demographic trends. Full article

The chemical interaction of salicylic acid, formaldehyde, and sulfuric acid produced a disalicylic ligand (3,3′-dicarboxy-2,2′-dihydroxydiphenylmethane, DCM), which was then allowed to coordinate with copper (II) ions. The solid compounds’ chemical structures were determined using elemental analysis, UV-Vis, FT-IR, MS, ¹H-NMR, PXRD, SEM, TEM, magnetic studies, as well as molecular modeling based on DFT (density functional theory) calculations. It was proposed that the ligand coordinates in a tetradentate fashion with the copper ion to give a square-planar binuclear complex. A significant difference in the diffraction patterns between Cu(II)–DCM (amorphous) and DCM (crystalline) was displayed using an X-ray diffraction analysis. Spherical granules were identified throughout through morphology analysis using SEM and TEM. UV-Vis spectra were used to quantify the optical characteristics such as the energy gap, optical conductivity, refractive index, and penetration depth. The band gap values that lie within the semiconductor region suggested that the compounds could be used for electronic applications. The optimized structure of the synthesized Cu(II)–DCM complex was investigated using DFT and TD-DFT (time-dependent density functional theory) at the B3LYP/6-31G(d, p) level, with the LANL2DZ basis set for Cu in an ethanol solvent and the gas environment modeled by CPCM. The experimental data suggest a square-planar geometry of the Cu(II) binuclear complex. The theoretical calculations support the proposed structure of the compound. The cytotoxicity of the DCM against HCT–116 (human colon cancer) cells was tested, and the outcome exhibited good inhibitions of growth. A molecular docking (MD) examination was carried out to illustrate the binding mode/affinity of the prepared compounds (DCM and Cu(II)–DCM) in the active site of the receptor protein [CDK2 enzyme, PDB ID: 6GUE]. The compounds formed hydrogen bonds with the amino acid residues of the protein, increasing the binding affinity from −7.2 to −9.3 kcal/mol through the coordination process. The information from this current study, particularly the copper complex, is beneficial for exploring new compounds that have anticancer potential. Full article

Background/Objectives: Physical activity (PA) is a key determinant of mental and physical health, yet its relationship with affective states remains insufficiently explored. Emotional factors, such as depression, anxiety, and motivation levels, can significantly impact PA engagement. This study aims to validate the Affective State and Physical Activity Questionnaire (ASPAQ), a novel 15-item instrument designed to assess the interplay between affective states and PA in adults. Methods: A cross-sectional study was conducted with 412 adults (145 males, 267 females, aged 18–65 years). Participants completed the ASPAQ alongside the International Physical Activity Questionnaire—Short Form (IPAQ-SF) and the Patient Health Questionnaire-9 (PHQ-9) on an online platform, with the support of trained operators. The psychometric properties of the ASPAQ were evaluated using reliability tests (Cronbach’s alpha, McDonald’s omega), exploratory factor analysis (EFA), and correlational analyses to assess convergent validity. Results: The ASPAQ demonstrated excellent reliability (Cronbach’s alpha = 0.973; McDonald’s omega = 0.973) and a unidimensional structure. Convergent validity was supported by significant correlations between ASPAQ scores and established measures of PA (IPAQ-SF) and depression (PHQ-9). EFA confirmed a single-factor model, reinforcing its conceptual integrity. Conclusions: The ASPAQ is a reliable and valid instrument for assessing the relationship between affective states and PA. Its integration with established measures offers a comprehensive tool for evaluating emotional barriers to PA. Future studies should explore its predictive validity and potential applications in clinical and public health settings to inform personalized interventions promoting PA among individuals with affective challenges. Full article

Background/Objectives: Liver cirrhosis is a critical chronic condition with increasing global mortality and morbidity rates, emphasizing the necessity for early and accurate diagnosis. This study proposes a comprehensive deep-learning framework for the automatic diagnosis and staging of liver cirrhosis using T2-weighted MRI images. Methods: The methodology integrates stacked ensemble learning, multi-task learning (MTL), and transfer learning within an explainable artificial intelligence (XAI) context to improve diagnostic accuracy, reliability, and transparency. A hybrid model combining multiple pre-trained convolutional neural networks (VGG16, MobileNet, and DenseNet121) with XGBoost as a meta-classifier demonstrated robust performance in binary classification between healthy and cirrhotic cases. Results: The model achieved a mean accuracy of 96.92%, precision of 95.12%, recall of 98.93%, and F1-score of 96.98% across 10-fold cross-validation. For staging (mild, moderate, and severe), the MTL framework reached a main task accuracy of 96.71% and an average AUC of 99.81%, with a powerful performance in identifying severe cases. Grad-CAM visualizations reveal class-specific activation regions, enhancing the transparency and trust in the model’s decision-making. The proposed system was validated using the CirrMRI600+ dataset with a 10-fold cross-validation strategy, achieving high accuracy (AUC: 99.7%) and consistent results across folds. Conclusions: This research not only advances State-of-the-Art diagnostic methods but also addresses the black-box nature of deep learning in clinical applications. The framework offers potential as a decision-support system for radiologists, contributing to early detection, effective staging, personalized treatment planning, and better-informed treatment planning for liver cirrhosis. Full article