Search Results (5,394)

This study examines how tourism platform ecosystems have evolved into mechanisms supporting sustainable, resilient, and digitally transformed tourism systems amid rapid advances in artificial intelligence (AI), immersive technologies, and data-driven platform governance. While previous research has extensively explored smart tourism technologies and digital transformation, limited attention has been devoted to understanding how tourism platforms have evolved from digital infrastructures into ecosystem-level coordination mechanisms shaping sustainability, resilience, adaptive governance, and digitally mediated stakeholder interactions in tourism marketing. To address this gap, the study develops a longitudinal evolutionary analysis of tourism platform ecosystems by combining a bibliometric analysis of 447 Web of Science-indexed publications (2015–2026) with an exploratory discourse analysis of Booking.com corporate communications. The findings reveal a progressive transformation of tourism platforms from digital infrastructure ecosystems (2015–2019) to adaptive coordination systems (2020–2022) and, ultimately, to AI-driven intelligent ecosystem orchestrators (2023–2026). Across these stages, tourism research increasingly integrates AI-enabled personalization, conversational interaction, immersive technologies, ecosystem governance, sustainability coordination, destination resilience, predictive analytics, and customer engagement. The study proposes a longitudinal evolutionary framework explaining how tourism platforms transition toward intelligent ecosystem orchestration supporting sustainability-oriented governance, adaptive destination management, personalized visitor experiences, and long-term competitiveness. The framework advances an ecosystem intelligence perspective that extends existing digitalization and technology-adoption narratives within tourism research. Full article

Power quality disturbance (PQD) classifiers can lose 30–50 percentage points of accuracy when the signal-to-noise ratio (SNR) drops to 5 dB. This study finds that the degradation arises primarily not from architectural limitations but from hyperparameter configurations that fail to adapt across noise levels, within the IEEE 1159 disturbance set and additive-noise conditions studied here. Building on this insight, we propose a one-dimensional convolutional neural network (1D-CNN) with channel attention, whose three key hyperparameters—the learning rate, the first-layer convolutional kernel size, and the attention reduction ratio—are automatically optimized using the Phong Optimization Algorithm (POA). A 12-class synthetic dataset constructed to IEEE 1159-2019, with noise levels from noise-free to 5 dB SNR, is used for training and evaluation. Under the extreme condition of 5 dB SNR, the proposed method achieves 83.4% accuracy, outperforming a support vector machine (SVM) with discrete wavelet transform features (66.30%), a hybrid CNN–long short-term memory network (CNN-LSTM; 43.32%), and plain 1D-CNN (38.39%) under their commonly reported configurations. When every deep-learning baseline receives the same POA hyperparameter optimization under a fair per-SNR protocol, this advantage largely disappears: at 5 dB SNR all POA-optimized deep methods fall within roughly 5 percentage points (82.31–87.13%), and the attention module’s own contribution shrinks to within run-to-run variation (the same proposed model scoring 82.77% with attention vs. 82.48% without), showing that systematic hyperparameter optimization, not architectural novelty, drives the noise robustness. A single-set ablation under the original 10 dB-optimized configuration points the same way (POA optimization alone raising 5 dB accuracy from 41.6% to 76.4%, with channel attention adding a further 7 points). A comparison against classical threshold-index classifiers on the identical dataset shows the same pattern at the level of hand-crafted features: with fixed clean-calibrated thresholds, the index classifier collapses from 95.7% to 17.5% at 5 dB SNR, while per-noise-level re-calibration of the same indices recovers 83.8%. Furthermore, the POA-optimized hyperparameters were validated on an independent public PQD dataset, achieving 90.00% accuracy when training a fresh model from scratch. It also stays robust under more realistic complex noise (80.9% at 5 dB SNR) and, on a two-class real-measured probe, transfers to field signals with only light calibration. These findings suggest that for noise-robust PQD classification, hyperparameter optimization deserves as much attention as the architectural design itself, rather than being treated as a final tuning step. Full article

Background: Resistant hypertension poses a significant therapeutic challenge, often leading to severe polypharmacy. Renal denervation (RDN) has re-emerged as a valuable adjunctive intervention for blood pressure control. Case Presentation: We report the case of a 64-year-old man (body mass index [BMI] 34 kg/m²) with long-standing resistant hypertension (RH), after previous percutaneous coronary intervention (PCI) to the left anterior descending artery, heart failure with preserved ejection fraction (HFpEF), and prior nephron-sparing surgery for clear cell renal carcinoma. Despite treatment with an extensive antihypertensive regimen encompassing nine pharmacological classes including diuretic therapy (angiotensin-converting enzyme inhibitor; calcium channel blocker, thiazide diuretic, β-blocker, α1-blocker, central α2-agonist, mineralocorticoid receptor antagonist, loop diuretic, long-acting nitrates), blood pressure remained severely uncontrolled on both home and office measurements. Persistent hypertension was accompanied by exertional dyspnoea and episodes of exertional chest discomfort. Following comprehensive evaluation and exclusion of secondary causes of hypertension, the patient underwent catheter-based renal denervation using the SymplicitySpyral™ (Medtronic) multi-electrode radiofrequency system. The procedure was associated with substantial and sustained improvement in blood pressure control, with mean 24 h ambulatory blood pressure measurements decreasing to 130/80 mmHg at six-month follow-up. Importantly, successful blood pressure reduction enabled major simplification of pharmacotherapy, including complete discontinuation of clonidine, loop diuretic therapy, and long-acting nitrates, together with marked dose reduction in doxazosin. Conclusions: This case illustrates the potential clinical utility of renal denervation in carefully selected patients with true resistant hypertension and pronounced sympathetic overactivity. Beyond achieving satisfactory blood pressure control, RDN may facilitate meaningful reduction in medication burden, potentially improving treatment adherence, quality of life, and long-term cardiovascular risk. Written informed consent was obtained from the patient for both the procedure and the publication of this case report. Full article

Occupational burnout has become a major topic in occupational health psychology because of its implications for employee well-being, mental health, and organizational functioning. Although the scientific literature has grown steadily, the field still requires an integrated picture of its development, collaboration patterns, and thematic structure. This study examined the evolution, collaboration patterns and thematic structure of occupational burnout research through a bibliometric analysis of publications indexed in the Web of Science Core Collection between 2000 and 2025. A total of 3227 publications were retrieved using a topic-based search focused specifically on occupational burnout terminology. Bibliometric analyses used VOSviewer to map and visualize trends in scientific production, co-authorship networks, country/region collaboration, keyword co-occurrence, and journal citation patterns. The findings indicate an accelerating increase in publication output, from 17 publications in 2000 to 454 in 2025. The period 2020–2025 alone accounted for 2142 publications, representing 66.4% of the corpus. Collaboration analyses revealed an increasingly international field involving 122 countries/regions, with the United States occupying a highly connected position and collaboration clusters emerging in Europe and Asia. Spain also appeared to connect European and Latin American research communities. Thematic mapping showed that burnout is closely associated with organizational variables, psychological distress, healthcare settings, and measurement-related issues. Overall, occupational burnout research has become an internationalized and multidisciplinary field, shaped by the growing relevance of psychosocial risks, mental health, and work-related well-being. Full article

Background/Objectives: Problematic digital technology use may be associated with unhealthy dietary behaviors, but different forms of digital dependence may not relate to diet quality in the same way. This study examined the association between Mediterranean diet adherence and problematic smartphone, Internet, and video game use in young adults. Methods: A cross-sectional study was conducted in 496 adults aged 18–34 years participating in the EVA-Adic study. Mediterranean diet adherence was assessed using the 14-item Mediterranean Diet Adherence Screener (MEDAS). Problematic digital use was evaluated using the EDAS-18 for smartphone dependence, the Compulsive Internet Use Scale (CIUS), and the Questionnaire of Experiences Related to Video Games (CERV). Multivariable regression models were adjusted for sociodemographic, anthropometric, and lifestyle factors. Results: Of the participants, 339 had low-to-moderate Mediterranean diet adherence, and 157 had high adherence. In fully adjusted models, higher EDAS-18 scores were associated with lower MEDAS scores (β = −0.022; 95% CI: −0.041 to −0.003), whereas higher CIUS scores showed a small positive association with MEDAS scores (β = 0.025; 95% CI: 0.002 to 0.049). CERV scores and the global digital addiction index were not associated with overall Mediterranean diet adherence. At the individual dietary-component level, higher smartphone dependence was associated with lower compliance with fruit and sugar-sweetened beverage recommendations. Conclusions: In this cross-sectional sample of young adults, smartphone dependence and compulsive Internet use showed small associations with Mediterranean diet adherence in opposite directions, whereas problematic video-game use and the exploratory global composite were not associated with the overall MEDAS-14 score. The magnitude of the observed associations was modest; their clinical and public-health relevance remains uncertain, and the component-level findings should be considered exploratory in the context of multiple testing. These results do not establish causality and require confirmation in longitudinal, multicenter studies using objective and context-specific measures of digital use. Full article

Satellite altimetry has become an important tool for monitoring inland water dynamics and has been widely used for water-level retrieval, hydrological simulation, and flood–drought risk assessment. To review research progress and development trends over the past three decades, this study combines bibliometric analysis with a traditional review approach. A total of 4764 publications indexed in the Web of Science Core Collection from 1991 to 2025 were analyzed. Using VOSviewer_1.6.20 and CiteSpace_6.4, we constructed knowledge maps of publication trends, disciplinary intersections, author collaboration, keyword clustering, and burst evolution. Representative studies were further synthesized qualitatively. The results show that remote sensing, geology, and imaging science form the core disciplinary framework of this field. Research hotspots have shifted from single water-level observation to multi-parameter retrieval and integration with hydrological models. New missions, including Surface Water and Ocean Topography (SWOT) and Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), have improved spatial resolution and coverage, accelerating the development of this field. However, agricultural water management and the integration of artificial intelligence with hydrological models remain limited. Key challenges include monitoring small and complex water bodies, multi-source data fusion, uncertainty quantification, physics-informed artificial intelligence, and operational applications. Full article

Food insecurity is a major public health concern among university students. This cross-sectional study examined factors associated with food insecurity during and after the COVID-19 pandemic among 1864 students (mean age: 20.6 ± 2.9 years) from selected public universities in Northwest Mexico. Data were collected in two cross-sectional survey waves: during the COVID-19 pandemic (n = 1188) and after the pandemic (n = 676). Data were collected through online surveys during the pandemic and face-to-face interviews in the post-pandemic period. Food insecurity was assessed using the Food Insecurity Experience Scale (FIES). Multiple linear regression models were used to identify factors associated with food insecurity and to examine its association with body mass index, perceived stress, sleep quality, and academic performance. Overall, 62.2% of students experienced food insecurity. Food insecurity was higher among students surveyed after the COVID-19 pandemic than among those surveyed during the pandemic (73.7% vs. 55.7%, p < 0.001). During the pandemic, higher food insecurity scores were associated with older age, female sex, indigenous self-identification, living alone, academic discipline, study hours, educational financing, and poorer nutritional behaviors. In the post-pandemic period, indigenous self-identification, living arrangements, academic discipline, and poor nutritional behaviors remained associated with food insecurity. In both periods, food insecurity was associated with higher perceived stress, poorer sleep quality, and lower academic performance (p < 0.05). These findings suggest that food insecurity remains a major concern among university students and indicate the importance of developing culturally appropriate strategies to support vulnerable student populations. Full article

Objectives: To evaluate bile culture microbiology and factors associated with resistance patterns in patients taken to laparoscopic cholecystectomy to better guide empiric antibiotic therapy. Methods: Prospective cohort study with a logistic regression model in a middle-income public hospital network of two institutions. Inclusion criteria were patients taken to laparoscopic cholecystectomy over 18 years of age due to benign biliary disease without other concomitant surgical procedures taken to bile culture and antibiogram testing to evaluate bile culture positivity considered as a “resistant pattern”. Results: 226 cultures tested positive for at least one microorganism, and 218 were included in the study. Overall, bile cultures classified as resistant were associated with age, comorbidities, acute signs of cholecystitis, and longer antibiotic therapy. Bile microorganisms found consisted mostly of Enterobacteriaceae. In the logistic regression model, previous ERCP, Charlson comorbidity index and duration of antibiotic therapy yielded as statistically significant (p 0.03, p 0.003 and 0.004, respectively) for presenting resistant patterns. Conclusions: Patients taken to laparoscopic cholecystectomy have a higher probability of being resistant to empirical therapy for managing acute cholecystitis if they had a higher Charlson comorbidity index, previous ERCP and longer preoperative antibiotic therapy, and thus intraoperative cultures should be considered for guided antibiotic therapy. Trial registration number: NCT06314399. Full article

In parallel CNC machining systems, machine deterioration can simultaneously increase energy-related operating costs, affect delivery performance, and change the timing of preventive maintenance. This study develops a reliability- and energy-aware decision-support framework for production-maintenance scheduling in a two-machine parallel CNC cell. The model integrates priority sequencing, reliability-based machine assignment during decoding, degradation-dependent energy cost, tardiness penalties, and threshold-triggered preventive maintenance in a unified cost-minimization formulation. A normalized reliability index is updated by a short-horizon exponential degradation function, and the energy term is amplified when machines operate in degraded states. Preventive maintenance is triggered when post-job reliability falls below a specified threshold, restoring the machine’s condition for subsequent production or the next planning horizon. The computational study combines application-inspired machining instances, decoder-space full enumeration for small cases, repeated GA/PSO comparisons, a new algorithm-budget sensitivity experiment, and adapted OR-Library weighted-tardiness benchmarks. Across 270 paired budget-sensitivity runs, GA obtained a lower total cost in 265 cases, whereas PSO retained a shorter average runtime in the matched-budget experiments. Across 90 adapted public-benchmark comparisons, GA obtained a lower total cost in 86 cases. These results show that the framework generates feasible schedules and reveals energy–maintenance–tardiness trade-offs. The algorithmic findings are interpreted as a quality–time trade-off under the tested scalarized cost model, not as a claim of universal algorithmic superiority. Full article

Background: Glucagon-like peptide-1 receptor agonists (GLP-1RAs) and GLP-1/glucose-dependent insulinotropic polypeptide (GIP) dual agonists have revolutionized the treatment of type 2 diabetes and obesity. The rapid growth in public interest, off-label use, and the emergence of counterfeit drugs underscores the need for timely monitoring of information demand. Objective: The objective of this study is to quantitatively characterize the temporal dynamics, market concentration, seasonality, and semantic structure of Russian-language search queries regarding GLP-1RAs and GLP-1/GIP dual agonists and to assess their correlation with pharmaceutical demand. Materials and Methods: This was a retrospective study of Yandex.Wordstat data from March 2018 to March 2026 (covering 97 months, 27 INNs and brand names). Time series analysis (trends, structural breaks, Seasonal-Trend decomposition based on Loess (STL decomposition)), calculation of the Herfindahl–Hirschman Index (HHI), and semantic analysis of 4562 unique formulations (bigrams, trigrams, Term Frequency–Inverse Document Frequency (TF-IDF), thematic classification, morphological normalization) were performed. Validation was conducted using DSM Group pharmacy sales data. Results: A total of 46.05 million queries were analyzed. Interest in semaglutide increased 215-fold, with the structural break point identified in January 2021. The HHI decreased from 0.311 (indicating a highly concentrated market) to 0.141 (indicating a competitive market). The share of diabetes-related queries did not exceed 0.46%, while the share of weight-loss-related queries reached 13.91%, and the share of commercial-component queries reached 41.1%. Four semantic signatures were identified: brand-dominant (Ozempic), instruction-targeted (Saxenda), dose-commercial (Tirzetta), and instruction-commercial (Trulicity). No statistically significant seasonality was confirmed after adjustment for multiple comparisons. The correlation between search interest and pharmacy sales was the strongest for Tirzetta (r = 0.976; n = 8; p < 0.001) and remained significant after trend removal (first differences: r = 0.819; p = 0.024). Conclusions: Yandex.Wordstat data provide a valuable supplementary source for digital pharmacoepidemiology. A systematic discrepancy was found between registered indications and actual information demand, a finding that has significant implications for pharmacovigilance and healthcare planning. Full article

Rice husk ash (RHA) has emerged as a promising supplementary cementitious material (SCM) for sustainable construction due to its high silica content and potential to reduce the environmental impact of concrete. However, the evolution of RHA research and its transition toward advanced, data-driven material systems remain insufficiently synthesised. This study presents an integrated bibliometric analysis (BA) and systematic literature review (SLR) in which Scopus-indexed publications from 2007 to 2021 establish the historical evolution and structural dynamics of the field, while studies published between 2022 and 2026 are systematically reviewed to capture emerging developments and ensure contemporary relevance. The bibliometric analysis reveals a steady increase in research activity and identifies a shift from experimental material optimisation toward sustainability and computational modelling. When interpreted alongside the systematic review findings, this trend reflects a structured Past–Present–Future evolution of RHA research, from feasibility studies to microstructural optimisation and, more recently, to predictive and data-driven material design. The systematic review confirms that optimally processed RHA, typically at 10–30% cement replacement, enhances compressive strength, durability, and resistance to aggressive environments through improved pozzolanic reactivity and microstructural densification. Recent studies (2022–2026) further demonstrate the integration of machine learning, life-cycle assessment, and digital modelling approaches for performance prediction, mix optimisation, and sustainability evaluation. By linking bibliometric trends with scientific insights, this study identifies key research gaps, including the lack of standardisation in RHA production, limited large-scale validation, and weak integration of experimental and computational approaches, and proposes an integrated five-tier framework, moving from material standardisation through performance validation and computational intelligence to sustainability integration and industrial-policy adoption, to guide future research and implementation. The findings highlight the transition of RHA from a conventional SCM to a component of intelligent, sustainable, and circular construction systems, providing a foundation for future research and industrial application. Full article

Background/Objectives: Women-specific short-term body mass index and metabolic changes after Roux-en-Y gastric bypass remain incompletely characterized. This retrospective cohort study evaluated 12-month changes in body mass index, glycemic markers, and lipid profile after Roux-en-Y gastric bypass in women with severe obesity treated at a public bariatric reference center in Brazil. Methods: Women who underwent Roux-en-Y gastric bypass between September 2017 and January 2020 were assessed preoperatively and at 30 days, 3 months, 6 months, and 12 months postoperatively. Longitudinal changes were analyzed using linear mixed-effects models. Results: Of 168 women who underwent bariatric surgery, 150 had complete 12-month follow-up data. Mean age was 43.6 ± 9.8 years and mean preoperative body mass index was 45.4 ± 5.4 kg/m². Body mass index decreased progressively from 45.5 kg/m² at baseline to 31.5 kg/m² at 12 months. Fasting plasma glucose and HbA1c declined significantly within the first postoperative month and remained improved. Total cholesterol decreased at 30 days, whereas low-density lipoprotein (LDL) cholesterol and triglycerides decreased by 6 months. Among baseline users, 86% discontinued antihyperglycemic therapy, 87% lipid-lowering therapy, and 72% antihypertensive therapy; these exploratory medication-discontinuation outcomes should not be interpreted as validated remission of diabetes, dyslipidemia, or hypertension. Conclusions: Roux-en-Y gastric bypass was associated with substantial short-term body mass index reduction and improved cardiometabolic markers among women. Full article

Background: Mental health disorders, including depression, constitute a major global public health concern that can lead to significant morbidity as well as societal costs. Internally displaced people are at risk for depression. This study aimed to assess the prevalence of depressive symptoms among adults in Atbara, northern Sudan, and identify the key factors associated with depressive symptoms, including internal displacement. Methods: Using a stratified sampling technique, data were collected by trained research assistants through face-to-face interviews using a structured questionnaire. The Patient Health Questionnaire-9 (PHQ-9) was used to assess depressive symptoms. Multivariate binary analysis was performed. Results: Three hundred and seventy-three adults were enrolled in this study; 104 (27.9%) were males. Their median age was 42.0 years. Of the 373 adults, 81 (21.7%, 95.0% confidence interval = 17.5–25.9%) had depressive symptoms (PHQ-9 scores ≥ 8). The prevalence of depressive symptoms was significantly higher among displaced adults: 39 (28.1%) of the 139 displaced adults had depression, and 42 (17.9%) of the non-displaced adults had depression, p = 0.027. In multivariate binary analysis, being internally displaced was the only factor associated with depressive symptoms (adjusted odds ratio = 1.82, 95% confidence interval = 1.07–3.08); age, body mass index, sex, education, and marital status were not associated with depressive symptoms. Conclusions: One out of five adults in this region of Sudan had depressive symptoms, which was associated with internal displacement. More effort and further research are needed to explore possible interventions to improve mental health and livelihoods, especially among internally displaced people. Full article

Background: Post-stroke recovery varies widely, and pharmacological options to enhance rehabilitation outcomes remain limited. MLC601/MLC901 (NeuroAiD), a natural neurorestorative product, has been evaluated as an adjunct to standard care to improve functional and motor recovery after ischaemic stroke. This systematic review and meta-analysis assessed its efficacy using validated outcome measures. Methods: A systematic PubMed search identified randomised controlled trials comparing MLC601/MLC901 with placebo or active comparators in adults with ischaemic stroke. Functional outcomes included modified Rankin Scale (mRS), Barthel Index (BI), and Diagnostic Therapeutic Effects of Apoplexy (DTER) item 8 scores. Motor outcomes included Fugl–Meyer Assessment (FMA), DTER motor items, and National Institutes of Health Stroke Scale (NIHSS) motor scores. Data were pooled using fixed- and random-effects models. Odds ratios (ORs) and standardised mean differences (SMDs) were calculated. Risk of bias was assessed using the Cochrane RoB 1.0 tool. Results: Six publications reporting seven randomised clinical studies were included in the meta-analysis. Of the 7 studies, 5 were assessed as having a low risk of bias, while 2 were assessed as having an unclear risk. Altogether, 1535 participants for functional outcomes and 1774 for motor outcomes were analysed. Functional recovery significantly favoured MLC601/MLC901 at 1 month (OR 2.61; p = 0.004), 6 months (OR 1.38; p = 0.002), 12 months (OR 1.33; p = 0.03), and end-of-study (OR 1.40; p = 0.007), with benefits persisting up to 24 months. Motor recovery was assessed at months 1, 2 and 3 and at the end of the study. It also improved consistently over time, with the greatest effects during the first two months. Benefits were most evident in patients with moderately severe stroke (NIHSS 8–14). Clinical studies consistently indicate that NeuroAiD is safe and well-tolerated as an adjunct to standard ischaemic stroke care. Conclusions: MLC601/MLC901 is associated with improved functional independence and motor recovery after ischaemic stroke. Benefits appear within 1 month and may persist for up to 2 years, supporting early use alongside rehabilitation to optimise recovery. Full article

Ports are critical nodes in global supply chains and increasingly depend on intelligent, energy-efficient, and autonomous technologies to improve operational performance and sustainability. Within this context, unmanned aerial vehicles (UAVs) are evolving from isolated aerial platforms into network-enabled systems capable of supporting sensing, communication, computation, and decision-support functions. This study examines the evolution of energy-efficient UAV-enabled systems for sustainable port logistics, with particular emphasis on autonomous system architectures, mobile edge computing, artificial intelligence, optimisation, and Internet of Things integration. Following PRISMA 2020 reporting guidelines, a bibliometric and systematic review was conducted based on a curated dataset of 49 peer-reviewed publications indexed in Scopus between 2001 and 2026. The analysis combines performance indicators with science mapping techniques, including bibliographic coupling and keyword co-occurrence, to identify research trends, influential contributions, and thematic structures. The results reveal a transition from fragmented exploratory studies to a rapidly expanding research field shaped by UAV-assisted edge computing, resource allocation, intelligent optimisation, and energy-aware system design. Four main thematic clusters are identified: UAV-assisted mobile edge computing and network optimisation; advanced optimisation and intelligent UAV systems; energy efficiency and resource allocation; and application-oriented developments and hardware innovations. The findings indicate that energy efficiency is the central design principle connecting UAV autonomy, system integration, and sustainability-oriented port logistics. Full article