Search Results (9,133)

The rapid digitalization of healthcare has led to the widespread availability of mobile health (mHealth) applications, including those aimed at mental health and well-being. The present study followed the PRISMA guidelines and systematically reviewed English and/or German mental health apps available in the Google Play Store to evaluate their functional quality and behavior-change potential. It utilized the Mobile App Rating Scale (MARS) to assess app quality, including engagement, functionality, esthetics, and information quality, and the App Behavior Change Scale (ABACUS) to evaluate the potential for behavior change by inclusion of behavior change techniques (BCTs). A total of 77 apps were reviewed, with findings indicating an average functional quality and moderate behavior-change potential, as the reviewed apps only utilized a limited amount of BCTs. Notably, only a small fraction of apps had been evaluated in randomized controlled trials (RCTs). Further analysis showed that MARS and ABACUS scores had limited predictive power regarding app popularity as measured by stars awarded by users and number of user ratings in the Google Play Store. The study highlights the need for more rigorous testing of mHealth apps and suggests that factors beyond those measured by MARS and ABACUS may influence app popularity. In addition to the scientific value, this review provides insights for both users interested in mental health support via apps and developers aiming to enhance the quality and impact of mental health applications. Full article

Interventions on Heritage Buildings (HBs) involve significant challenges due to their tangible (embodied in the material, architectural, physical and technical integrity of the cultural asset), and intangible values (linked to socio-historical–cultural and collective identity, memory, customs and symbols meanings), which must be preserved while also adapting to current sustainability and circular economy goals. However, current conservation and management practices often lack systematic tools to trace, assess, and organise material and component information, hindering the implementation of circular strategies. In line with the European Union’s objectives for climate neutrality and resource efficiency and sufficiency, Material and Product Passports (MPPs) have emerged as digital tools that enhance data traceability, interoperability and transparency throughout a building’s lifecycle. This paper examines the potential of MPPs to support circular management of HBs by analysing the structure of MPPs and outlining the information flows generated by rehabilitation, maintenance and adaptive reuse strategies. A mixed methods approach, combining literature review and data structure analysis, is adopted to identify how the different categories of data produced during maintenance, rehabilitation and adaptive reuse processes can be integrated into MPP modules. The research highlights the conceptual opportunities of MPPs to document and interlink historical, cultural, and technical data, thereby improving decision-making and transparency across intervention stages. The analysis suggests that adapting MPPs to the specificities of historic contexts, such as authenticity preservation, reversibility, and contextual sensitivity, can foster innovative, sustainable, and circular practices in the conservation and management of HBs. Full article

Background/Objectives: Cancer pain affects 55–95% of patients with advanced malignancy, representing a complex syndrome involving nociceptive, neuropathic and nociplastic mechanisms. Despite therapeutic advances, two-thirds of patients with metastatic cancer experience inadequate pain control. This scoping review synthesizes recent advances in cancer pain pathophysiology and management, focusing on molecular and cellular mechanisms, emerging pharmacological, interventional and technological therapies and key evidence gaps to inform future precision-based pain management strategies. Methods: Following PRISMA-ScR methodology, we searched PubMed, Embase, Scopus, and Web of Science for studies published between January 2022 and September 2025. After screening 3412 records, 278 studies were included and analyzed across different domains: biological mechanisms, pharmacological management, interventional and neuromodulatory approaches, radiotherapy developments, and digital health innovations. Results: Recent mechanistic research reveals cancer pain arises from tumor–neuron–immune crosstalk, with malignant cells secreting neurotrophic factors that promote axonal sprouting and nociceptor sensitization. Genetic polymorphisms and epigenetic modifications contribute to inter-individual pain variability. Management strategies are evolving toward multimodal precision medicine: NSAIDs and opioids remain foundational, complemented by adjuvant agents and interventional procedures including nerve blocks, intrathecal delivery, and neuromodulation (spinal cord and dorsal root ganglion stimulation). Stereotactic body radiotherapy demonstrates superior analgesic durability versus conventional approaches. Digital health innovations, such as mobile applications, remote monitoring, wearables, and AI-enabled predictive models, enable continuous assessment and personalized treatment optimization. Conclusions: Cancer pain management is transitioning toward mechanism-based precision medicine integrating biological insights, advanced interventional techniques, and digital technologies. However, implementation challenges persist, including limited randomized trials for interventional approaches, the incomplete external validation of AI tools, and digital health equity concerns. Future research must prioritize prospective controlled studies and equitable integration into routine care. Full article

For individuals with mental illness who experience multidimensional marginalization, the risks of encountering discrimination and receiving inadequate care are compounded. Artificial intelligence (AI) systems have propelled the provision of mental healthcare through the creation of digital mental health applications (DMHAs). DMHAs can be trained to identify specific markers of distress and resilience by incorporating community knowledge in machine learning algorithms. However, DMHAs that use rule-based systems and large language models (LLMs) may generate algorithmic bias. At-risk populations face challenges in accessing culturally and linguistically competent care, often exacerbating existing inequities. Creating equitable solutions in digital mental health requires AI training models that adequately represent the complex realities of marginalized people. This narrative review analyzes the current literature on digital mental health through an intersectional framework. Using an intersectional framework considers the nuanced experiences of individuals whose identities lie at the intersection of multiple stigmatized social groups. By assessing the disproportionate mental health challenges faced by these individuals, we highlight several culturally responsive strategies to improve community outcomes. Culturally responsive strategies include digital mental health technologies that incorporate the lived experience of individuals with intersecting identities while reducing the incidence of bias, harm, and exclusion. Full article

Federated Graph Neural Networks (FedGNNs) have emerged as a promising paradigm for decentralized graph learning across distributed data silos. However, the influence of underlying communication topologies on model accuracy and efficiency remains underexplored. This study presents a topology-aware benchmarking framework for federated GNNs, systematically evaluating the impact of network structure and aggregation strategy on performance and communication overhead. The proposed framework functions as a synthetic, communication-level digital twin that emulates Federated Learning interactions and topology-dependent dynamics under controlled conditions. Four learning schemes—Centralized, Local, FedAvg, and FedAvg-Fedadam—were assessed across three representative topologies: Barabási–Albert (BA), Watts–Strogatz (WS), and Erdős–Rényi (ER). Results demonstrate that centralized training achieved the highest mean ROC-AUC (0.63), while FedAvg-Fedadam attained the best F1-score (0.038), balancing local adaptation and global convergence. Among topologies, BA and WS yielded higher average AUC values (approximately 0.57 and 0.56, respectively) than ER (approximately 0.39). Communication analysis revealed FedAvg as the most efficient strategy, requiring only approximately 3.8 × 10⁵ bytes cumulatively. These findings highlight key trade-offs between accuracy, robustness, and communication efficiency in federated graph learning and provide empirical guidance for topology-aware optimization of distributed GNNs. While the experiments rely on representative synthetic topologies, the insights offer indicative relevance and potential applicability to Internet-of-Things (IoT), vehicular, and cyber-physical networks, where communication structure and bandwidth constraints critically influence collaborative intelligence. By modeling canonical connectivity patterns and releasing our code and data, the proposed benchmarking framework offers a reproducible basis for comparing emerging federated graph architectures under constrained communication conditions. Full article

This study examines how digital transformational leadership (DTL) drives superior and enduring organizational performance through the mediating roles of organizational agility (OA) and digital transformation (DT) while assessing the contingent moderating role of digital culture (DC). Anchored in the Resource-Based View (RBV), the study conceptualizes DTL as a strategic intangible capability that enables the orchestration of digital and agile resources into sustained performance outcomes in digitally turbulent environments. Data were collected from 284 senior and middle managers across 13 Palestinian commercial banks—a highly regulated sector undergoing intensive digital pressure in an emerging-economy context—using an online survey. The proposed relationships were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4.0. The results reveal that DTL significantly enhances both OA and DT, which in turn contribute positively to organizational performance. OA and DT operate as both independent and sequential mediators, uncovering a multistage capability-building pathway through which leadership fosters long-term adaptability and resilience. The findings further indicate that digital culture conditions the effectiveness of leadership-driven transformation, shaping how digital initiatives consolidate into enduring organizational routines rather than short-term efficiency gains. By reframing sustainable transformation as the continuity of organizational performance through agility, digital renewal, and cultural alignment—rather than as an ESG outcome alone—this study refines RBV boundary conditions in digital contexts. The study contributes theoretically by clarifying how leadership-enabled capabilities generate sustainable competitive advantage and offers actionable managerial insights for cultivating agility, embedding digital transformation, and strengthening cultural readiness to support long-term organizational resilience. Full article

Fruit growing is a traditional component of Bulgarian agricultural production. According to the latest statistical data, the share of areas planted with cherries is 10.5% of the total orchard area, and with apples, 7.2%, totaling 67,800 ha. This article presents the results of ground and remote (satellite) measurements and observations of cherry and apple orchards, along with the methods for their processing and interpretation, to define the current state and forecast their expected development. This research aims to combine the capabilities of the two approaches by improving and expanding observation and forecasting activities. Ground-based measurements and observations consider the dates of a permanent transition in air temperature above 5 °C and several cardinal phenological stages, based on the idea that a certain temperature sum (CU, GDH, GDD) must accumulate to move from one phenological stage to another. The obtained data were statistically analyzed, and by means of classification with the Random Forest algorithm, the dates for the occurrence of the stages of bud break, flowering, and fruit ripening in the development of cherry and apple orchards were predicted with an accuracy of −6 to +2 days. Satellite studies include creating a database of Sentinel-2 digital images across different spectral bands for the studied orchards, investigating various post-processing approaches, and deriving indicators of developmental phenostages. Ground data from the 2021–2023 experiment in Kyustendil and Plovdiv were used to determine the phases of fruit bursting, flowering, and ripening through satellite images. An assessment of the two approaches to predicting the development of the accuracy of the models was carried out by comparing their predictions for bud swelling and bursting (BBCH 57), flowering (BBCH 65), and fruit ripening (BBCH 87/89) of the observed phenological events in the two selected orchard types, representatives of stone and pome fruit species. Full article

Background: Hemodialysis access dysfunction can lead to missed treatments and increased mortality. Traditional monitoring methods, such as physical examination and ultrasound, have limitations, emphasizing the need for a more efficient approach. This study investigates the use of digitized acoustic data to identify and monitor vascular access dysfunction. Methods: This prospective study involved patients undergoing hemodialysis with either arteriovenous fistulas (AVF) or arteriovenous grafts (AVG) between June 2023 and February 2025. All patients underwent vascular imaging (either angiography or ultrasound) to confirm the degree of stenosis. Acoustic data were recorded using a standardized procedure at various puncture sites. Pre- and post-angioplasty data were also collected to assess the effects of vascular intervention. The digitized acoustic data were analyzed for changes in relative loudness, peak-to-valley ratios, and frequency distribution. Results: A total of 157 patients with 236 audio recordings (mean age: 67 ± 11 years; 58% male) were included. Significant acoustic differences were found at the arterial puncture and anastomosis sites in AVF patients with dysfunction, particularly in venous site dysfunction, which exhibited a more pronounced reduction in sound volume and an increased peak-to-valley ratio. After angioplasty, acoustic changes were observed in both arterial and venous sites, with values moving toward normal levels. However, no significant acoustic changes were observed in AVG patients. Additionally, frequency distribution ratios showed minimal clinical relevance. Conclusions: Digitized acoustic data, particularly from the arterial puncture and anastomosis sites, can be an effective tool for detecting and monitoring hemodialysis access dysfunction. These findings suggest potential for acoustic analysis in clinical practice, especially when integrated with AI models for better diagnostics. Full article

Small and medium-sized enterprises (SMEs) play a central role in employment and regional economic development, yet they are highly vulnerable to shocks such as pandemics, energy price spikes, and supply chain disruptions. Scenario modelling, stress testing, and digital twins are used to assess resilience, yet most applications focus on large firms in single-country settings. This article develops a model to stress test the resilience of Slovenian SMEs, calibrated with parameters and mechanisms derived from South African SME resilience studies. A system dynamics model with stocks for cash, inventory, and productive capacity is specified and subjected to demand, supply, financial, and compound shock scenarios, with and without resilience measures such as liquidity buffers, customer and supplier diversification, and basic digital planning capabilities. Results indicate non-linear tipping points where small reductions in liquidity sharply increase the likelihood of distress, and show that combinations of liquidity, diversification, and collaborative supply chain practices reduce the depth and duration of output losses. The study demonstrates how evidence from an African context can inform resilience strategies in a small European economy and provides a transparent, portable modelling architecture that can be adapted to other settings. Implications are discussed for SME managers and for policies supporting sustainable, resilient enterprise ecosystems. Full article

This study examines the social determinants of the digital divide in pre-service teacher education through the design and validation of the Digital Hospitality Scale (DSBD-HD-FID). The instrument was developed to diagnose social inequalities across six key dimensions: socioeconomic status, geographic location, gender, age, disability status, and interculturality. These dimensions are understood as structural factors shaping access to, use of, and participation in digital environments within teacher education. The research followed a non-experimental, quantitative, and cross-sectional design, including content validation through expert judgment and statistical analysis based on a pilot sample of education students from Chile and Portugal. An exploratory factor analysis was conducted, and internal consistency was assessed using Cronbach’s alpha coefficient. The results confirm strong content and construct validity, as well as high reliability (α = 0.93). Empirical findings indicate that socioeconomic status and geographic location significantly condition access to connectivity and digital literacy, while gender differences emerge mainly in recreational uses and frequency of digital training. Beyond these results, the study highlights the relevance of addressing digital inequalities in teacher education through inclusive and equity-oriented training policies. The findings support the integration of digital hospitality, human rights education, and the Sustainable Development Goals into initial teacher training curricula as measurable and evaluable dimensions, providing an evidence-based framework to inform future teacher education policies aimed at reducing digital divides and promoting social cohesion. Full article

Background/Objectives: The growing integration of Artificial Intelligence (AI) and chatbots in health professional education offers innovative methods to enhance learning and clinical preparedness. This study aimed to evaluate the educational impact and perceptions in university students of Human Nutrition and Dietetics, regarding the utility, usability, and design of the E+DIEting_Lab chatbot platform when implemented in clinical nutrition training. Methods: The platform was piloted from December 2023 to April 2025 involving 475 students from multiple European universities. While all 475 students completed the initial survey, 305 finished the follow-up evaluation, representing a 36% attrition rate. Participants completed surveys before and after interacting with the chatbots, assessing prior experience, knowledge, skills, and attitudes. Data were analyzed using descriptive statistics and independent samples t-tests to compare pre- and post-intervention perceptions. Results: A total of 475 university students completed the initial survey and 305 the final evaluation. Most university students were females (75.4%), with representation from six languages and diverse institutions. Students reported clear perceived learning gains: 79.7% reported updated practical skills in clinical dietetics and communication were updated, 90% felt that new digital tools improved classroom practice, and 73.9% reported enhanced interpersonal skills. Self-rated competence in using chatbots as learning tools increased significantly, with mean knowledge scores rising from 2.32 to 2.66 and skills from 2.39 to 2.79 on a 0–5 Likert scale (p < 0.001 for both). Perceived effectiveness and usefulness of chatbots as self-learning tools remained positive but showed a small decline after use (effectiveness from 3.63 to 3.42; usefulness from 3.63 to 3.45), suggesting that hands-on experience refined, but did not diminish, students’ overall favorable views of the platform. Conclusions: The implementation and pilot evaluation of the E+DIEting_Lab self-learning virtual patient chatbot platform demonstrate that structured digital simulation tools can significantly improve perceived clinical nutrition competences. These findings support chatbot adoption in dietetics curricula and inform future digital education innovations. Full article

Understanding how global climate drivers manifest at regional scales is critical for designing targeted adaptation strategies, particularly in vulnerable mountainous countries. This study provides an integrated assessment of atmospheric CO₂ concentrations, surface temperature, and precipitation trends at both global and Armenian levels from the early 20th century to 2024. Using long-term observational datasets and ordinary least squares regression models with HAC-robust errors, this study quantifies the magnitude and statistical significance of historical climate shifts. Results confirm pronounced global warming (+0.021 °C/year) alongside a moderate rise in global precipitation (+1.13 mm/year). Armenia, however, exhibits substantially accelerated warming (+0.052 °C/year) coupled with a non-significant and spatially heterogeneous precipitation trend, including notable declines in humid regions. CO₂ emissions per capita strongly predict temperature change both globally (0.59 °C/ton) and, even more prominently, in Armenia (1.33 °C/ton), indicating heightened regional climate sensitivity. These findings align closely with Armenia’s Fourth National Communication to the UNFCCC, reinforcing the robustness of the analysis. By revealing how global climate forcings translate into region-specific outcomes—and by discussing the emerging thermal contribution of digital infrastructure—this study underscores the urgency of localized climate adaptation, water resource planning, and agricultural resilience measures. Full article

In reinforced concrete structures, the quality of rebar sleeve connections directly impacts the structure’s safety reserve and durability. However, quality inspection is complicated by the periodic distribution of stirrups, concrete obstruction, and noise interference, presenting challenges for assessing sleeve connection integrity. This paper proposes a training-free, interpretable framework for automated rebar sleeve connection quality inspection, leveraging point cloud semantic filtering and geometric a priori segmentation. The method constructs a polar-cylindrical framework, employing hierarchical semantic filtering to eliminate stirrup layers. Geometric a priori instance segmentation techniques are then applied, integrating θ histograms, Kasa circle fitting, and axial bridging domain constraints to reconstruct each longitudinal rebar. Sleeve detection occurs within the rebar coordinate system via radial profile analysis of length, angular coverage, and stability tests, subsequently stratified into two layers and parameterised. Sleeve projections onto column axes calculate spacing and overlap area percentages. Experiments using 18 BIM-TLS paired datasets demonstrate that this method achieves zero residual error in stirrup detection, with sleeve parameter accuracy reaching 98.9% in TLS data and recall at 57.5%, alongside stable runtime transferability. All TLS datasets meet the quality requirements of rebar sleeve connection spacing ≥35d and percentage of overlap area ≤50%. This framework enhances on-site quality inspection efficiency and consistency, providing a viable pathway for digital verification of rebar sleeve connection quality. Full article

Background: We explored a new approach for increasing participation in daily life for individuals with severe movement impairments. The core of the approach is an application designed to Translate Intentions into Action (TIA) as a motivational tool for both leisure and clinical training sessions. Methods: The TIA application was developed to enable users to activate motivational feedback, like sounds, music, or videos, through movement measured with an IMU (Inertial Measurement Unit). IMUs were calibrated to user-specific thresholds based on individual movement potential. TIA was tested in two different age groups to assess applicability throughout lifespan and across different motor capacities. Results: The results indicated that TIA can be used for improved participation when positive feedback is provided during the intervention sessions. Observations demonstrated that regardless of age and motor capabilities, increased participation was achieved. TIA demonstrated the far-reaching potential to enhance the engagement and motivation of individuals with different levels of severe disabilities. Conclusions: By providing personalized, positive feedback through movement-activated outputs, TIA can be used by a wide range of people, with or without motor disabilities, to control digital outputs, such as video and audio. These findings suggest that TIA can be a valuable tool in both clinical and leisure settings to promote meaningful participation in activities. Full article

With the rapid advancement of technology in recent years, signatures on contracts and documents have increasingly shifted from traditional handwritten forms on paper to digital handwritten signatures executed on devices (hereafter referred to as digital tablets). This transition introduces new challenges for forensic document examination due to the differences in writing instruments. According to the European Network of Forensic Science Institutes (ENFSI), a Digital Capture Signature (DCS) refers to data points captured during the writing process on digital devices such as tablets, smartphones, or signature pads. In addition to retaining the visual image of the signature, DCS provides more information previously unavailable, including pen pressure, stroke order, and writing speed. These features possess potential forensic value and warrant further study and evaluation. This study employs three devices—Samsung Galaxy Tab S10, Apple iPad Pro, and Apple iPad Mini—together with their respective styluses as experimental tools. Using custom-developed handwriting capture software for both Android and iOS platforms, we simulated signature-writing scenarios common in the financial and insurance industries. Thirty participants were asked to provide samples of horizontal Chinese, English, and number writings (FUJ-IRB NO: C113187), which were subsequently normalized and segmented into characters. For analysis, we adopted distance-based time-series alignment algorithms (FastDTW and SC-DTW) to match writing data across different instances (intra- and inter-writer). The accumulated distances between corresponding data points, such as coordinates and pressure, were used to assess handwriting stability and to study the differences between same-writer and different-writer samples. The findings indicate that preprocessing through character centroid alignment, followed by the analysis, substantially reduces the average accumulated distance of handwriting. This procedure quantifies the stability of an individual’s handwriting and enables differentiation between same-writer and different-writer scenarios based on the distribution of DCS distances. Furthermore, the use of styluses provides more precise distinctions between same- and different-writer samples compared with direct finger-based writing. In the context of rapid advancements in artificial intelligence and emerging technologies, this preliminary study aims to contribute foundational insights into the forensic application of digital signature examination. Full article