Search Results (478)

Background: Insomnia and excessive sleepiness are significant health problems with a complex etiology, increasingly affecting young people, especially students. This study aimed to assess the prevalence of sleep disturbances and patterns of psychoactive drug use among female Polish students. We also explored the potential impact of the COVID-19 pandemic on sleep behaviors. We hypothesized that sleep disorders are common in this group, that medical students are more likely to experience insomnia and excessive sleepiness, and that the pandemic has exacerbated both sleep disturbances and substance use. Methods: This cross-sectional study utilized a custom survey designed using standardized questionnaires—the Athens Insomnia Scale and Epworth Sleepiness Scale—that was distributed online using the Computer-Assisted Web Interviewing method. A total of 11,988 responses were collected from 31 January 2016 to 1 January 2021. Inclusion criteria were being female, having a college student status, and giving informed consent. Results: Among the 11,988 participants, alcohol use declined after the pandemic began (p = 0.001), while sedative use increased (p < 0.001). Insomnia (AIS) was associated with study year, university profile, and field of study (p < 0.001), with the highest rates in first-year and non-medical students. It was more common among users of sedatives, psychostimulants, and multiple substances. No significant change in insomnia was found before and after the pandemic. Excessive sleepiness (ESS) peaked in first-year and medical students. It decreased during the pandemic (p < 0.001) and was linked to the use of alcohol, psychostimulants, cannabinoids, and multiple substances. Conclusions: These findings highlight that female students are particularly vulnerable to sleep disorders. The influence of the COVID-19 pandemic on sleep disturbances remains inconclusive. Given the varied results in the existing literature, further research is needed. Full article

This study aims to critically analyze the theoretical and practical contributions of recent literature on the Critical Chain Project Management (CCPM) method in multi-project environments. To this end, a systematic literature review (SLR) was conducted based on 62 studies indexed in the Scopus and Web of Science databases between 2014 and 2025. The articles were analyzed in terms of application domains, employed methods, obtained results, and proposed integrations with other approaches. Most studies used modeling and simulation, focusing on time reduction, risk mitigation, and cost optimization. A growing trend has been identified toward integrating CCPM with methodologies, such as Scrum, BIM, Lean Construction, Fuzzy FMEA, and predictive algorithms, thereby broadening its applicability in high-complexity scenarios. However, a significant gap remains in empirical studies applied to Engineer-to-Order (ETO) systems and service-based organizations, which are characterized by high customization, variability, and interdependence of resources. The research is justified by the need to consolidate accumulated knowledge on CCPM and to guide future investigations toward underexplored sectors. The findings strengthen the theoretical robustness of the method while indicating concrete opportunities for empirical validation in real-world organizational settings. Full article

Background: People with Parkinson’s disease (PwPD) often experience sleep disturbances and reduced physical activity. Altered sleep behavior and lower daily steps have been linked to disease severity and symptom burden. Although physical activity may influence sleep, few studies have examined the relationship between sleep parameters and daily steps in PD. This scoping review aimed to review current knowledge on sleep parameters and daily steps collected concurrently in PwPD and their potential association. Methods: A systematic search was conducted in five databases, PubMed, Web of Science, Sport Discus, Cochrane Library, and Scopus. Methodological quality was assessed using a customized quality checklist developed by Zanardi and collaborators for observational studies, based on Downs and Black’s work. Results: Out of 1421 records, five studies met the eligibility criteria and were included in the review. Four studies reported wearable-based measurements of both step count and sleep parameters, while one study reported wearable-based measurements of step count and self-reported sleep measures. Two studies examined the association between sleep parameters and step count. One study did not find any correlation between sleep and step count, whereas one study reported a positive correlation between daytime sleepiness and step count. Conclusions: This review highlighted the lack of research investigating the relationship between sleep parameters and step count as an indicator of physical activity in PwPD. Findings are inconsistent with a potential positive correlation emerging between daytime sleepiness and step count. Findings also pointed toward lower step count and reduced sleep duration in PwPD, as measured with wearable devices. Full article

Background and Objectives: Bone regeneration (BR) is a cornerstone technique in reconstructive dental surgery, traditionally using either barrier membranes, titanium meshes, or perforated non-resorbable membranes to facilitate bone regeneration. Recent advancements in 3D technology, including CAD/CAM and additive manufacturing, have enabled the development of customized scaffolds tailored to patient needs, potentially overcoming the limitations of conventional methods. Materials and Methods: A scoping review was conducted according to the PRISMA guidelines. Electronic searches were performed in MEDLINE (PubMed), the Cochrane Library, Scopus, and Web of Science up to January 2025 to identify studies on digital technologies applied to bone augmentation. Eligible studies encompassed randomized controlled trials, cohort studies, case series, and case reports, all published in English. Data regarding digital workflows, software, materials, printing techniques, and sterilization methods were extracted from 23 studies published between 2015 and 2024. Results: The review highlights a diverse range of digital workflows, beginning with CBCT-based DICOM to STL conversion using software such as Mimics and Btk-3D^®. Customized titanium meshes and other meshes like Poly Ether-Ether Ketone (PEEK) meshes were produced via techniques including direct metal laser sintering (DMLS), selective laser melting (SLM), and five-axis milling. Although titanium remained the predominant material, studies reported variations in mesh design, thickness, and sterilization protocols. The findings underscore that digital customization enhances surgical precision and efficiency in BR, with several studies demonstrating improved bone gain and reduced operative time compared to conventional approaches. Conclusions: This scoping review confirms that 3D techniques represent a promising advancement in BR. Customized digital workflows provide superior accuracy and support for BR procedures, yet variability in protocols and limited high-quality trials underscore the need for further clinical research to standardize techniques and validate long-term outcomes. Full article

The aim of this study is to investigate how consumers’ cognitive and affective responses to web-based augmented reality affect their intention to continue to use augmented reality. The novelty of this study is the integration of the Stimulus–Organism–Response model with Technology Continuance Theory, allowing for an investigation of the relationships among the following critical variables: augmented reality (AR), utilitarian value, perceived risk, user satisfaction, attitude toward AR, and continuance intention. The study sample consisted of 452 participants. Data were analyzed using the Partial Least Squares–Structural Equation Modeling (PLS-SEM) approach. The results indicate significant direct relationships between all variables. Furthermore, this study demonstrated an indirect relationship between AR and continuance intention, mediated sequentially by cognitive responses, namely, utilitarian value and perceived risk, and affective responses, including user satisfaction and attitude toward AR. Consequently, it was revealed that all indirect relationships were significant, except for the pathways from AR to continuance intention involving perceived risk. This study presents key insights for online retailers, demonstrating how the integration of AR technology into conventional online shopping platforms can optimize user experiences by enhancing the cognitive and affective responses of customers. This, in turn, strengthens their intention to continue using AR technology, fostering sustained engagement and the long-term adoption of AR technology. Full article

The rapid growth of scientific publications, coupled with rising retraction rates, has intensified the challenge of identifying trustworthy academic articles. To address this issue, we propose a three-layer ranking system that integrates natural language processing and machine learning techniques for relevance and trust assessment. First, we apply BERT-based embeddings to semantically match user queries with article content. Second, a Random Forest classifier is used to eliminate potentially problematic articles, leveraging features such as citation count, Altmetric score, and journal impact factor. Third, a custom ranking function combines relevance and trust indicators to score and sort the remaining articles. Evaluation using 16,052 articles from Retraction Watch and Web of Science datasets shows that our classifier achieves 90% accuracy and 97% recall for retracted articles. Citations emerged as the most influential trust signal (53.26%), followed by Altmetric and impact factors. This multi-layered approach offers a transparent and efficient alternative to conventional ranking algorithms, which can help researchers discover not only relevant but also reliable literature. Our system is adaptable to various domains and represents a promising tool for improving literature search and evaluation in the open science environment. Full article

Artificial intelligence (AI) is reshaping mobile food-ordering apps, yet its impact on customer experience (CX) has not been fully mapped. Following systematic review guidelines (PRISMA 2020), a search of SCOPUS, Web of Science, ScienceDirect, and Google Scholar in March 2025 identified 55 studies published between 2022 and 2025. Since 2022, research has expanded from intention-based studies to include real-time app interactions and live app experiments. This shift has helped to identify five key CX dimensions: (1) instrumental usability: how quickly and smoothly users can order; (2) personalization value: AI-generated menus and meal suggestions; (3) affective engagement: emotional appeal of the app interface; (4) data trust and procedural fairness: users’ confidence in fair pricing and responsible data handling; (5) social co-experience: sharing orders and interacting through live reviews. Studies have shown that personalized recommendations and chatbots enhance relevance and enjoyment, while unclear surge pricing, repetitive menus, and algorithmic anxiety reduce trust and satisfaction. Given the limitations of this study, including its reliance on English-only sources, a cross-sectional design, and limited cultural representation, future research should investigate long-term usage patterns across diverse markets. This approach would help uncover nutritional biases, cultural variations, and sustained effects on customer experience. Full article

Parkinson’s disease (PD) is a progressive neurodegenerative disorder leading to movement impairment, cognitive decline, and psychiatric symptoms. Key manifestations of PD include bradykinesia (the slowness of movement), changes in voice or speech, and gait disturbances. The quantification of neurological disorders through voice analysis has emerged as a rapidly expanding research domain, offering the potential for non-invasive and large-scale monitoring. This review explores existing research on the application of machine learning (ML) in speech, voice, and language processing for the diagnosis of PD. It comprehensively analyzes current methodologies, highlights key findings and their associated limitations, and proposes strategies to address existing challenges. A systematic review was conducted following PRISMA guidelines. We searched four databases: PubMed, Web of Science, Scopus, and IEEE Xplore. The primary focus was on the diagnosis, detection, or identification of PD through voice, speech, and language characteristics. We included 34 studies that used ML techniques to detect or classify PD based on vocal features. The most used approaches involved free speech and reading-speech tasks. In addition to widely used feature extraction toolkits, several studies implemented custom-built feature sets. Although nearly all studies reported high classification performance, significant limitations were identified, including challenges in comparability and incomplete integration with clinical applications. Emerging trends in this field include the collection of real-world, everyday speech data to facilitate longitudinal tracking and capture participants’ natural behaviors. Another promising direction involves the incorporation of additional modalities alongside voice analysis, which may enhance both analytical performance and clinical applicability. Further research is required to determine optimal methodologies for leveraging speech and voice changes as early biomarkers of PD, thereby enhancing early detection and informing clinical intervention strategies. Full article

To fully leverage Google Analytics and derive actionable insights, web analytics practitioners must go beyond standard implementation and customize the setup for specific functional requirements, which involves additional web development efforts. Previous studies have not provided solutions for estimating web analytics development efforts, and practitioners must rely on ad hoc practices for time and budget estimation. This study presents a COSMIC-based measurement framework to measure the functional size of Google Analytics implementations, including two examples. Next, a set of 50 web analytics projects were sized in COSMIC Function Points and used as inputs to various machine learning (ML) effort estimation models. A comparison of predicted effort values with actual values indicated that Linear Regression, Extra Trees, and Random Forest ML models performed well in terms of low Root Mean Square Error (RMSE), high Testing Accuracy, and strong Standard Accuracy (SA) scores. These results demonstrate the feasibility of applying functional size for web analytics and its usefulness in predicting web analytics project efforts. This study contributes to enhancing rigor in web analytics project management, thereby enabling more effective resource planning and allocation. Full article

Information availability through the web has been both a challenge and an asset for healthcare support, as evidence-based information coexists with unsupported claims. With the emergence of artificial intelligence (AI), this situation may be enhanced or improved. The aim of the present study was to compare the quality assessment of online dietary weight loss information conducted by an AI assistant (ChatGPT 4.5) to that of health professionals. Thus, 177 webpages publishing dietary advice on weight loss were retrieved from the web and assessed by ChatGPT-4.5 and by dietitians through (1) a validated instrument (DISCERN) and (2) a self-made scale based on official guidelines for weight management. Also, webpages were assessed by a ChatGPT custom scoring system. Analysis revealed no significant differences in quantitative quality scores between human raters, ChatGPT-4.5, and the AI-derived system (p = 0.528). On the contrary, statistically significant differences were found between the three content accuracy scores (p < 0.001), with scores assigned by ChatGPT-4.5 being higher than those assigned by humans (all p < 0.001). Our findings suggest that ChatGPT-4.5 could complement human experts in evaluating online weight loss information, when using a validated instrument like DISCERN. However, more relevant research is needed before forming any suggestions. Full article

Background and Objectives: Complete dentures remain a primary solution for oral rehabilitation in aging and medically compromised populations. The integration of digital workflows, regenerative materials, and smart technologies is propelling prosthodontics towards a new era, transcending the limitations of traditional static prostheses. Materials and Methods: This narrative review synthesizes historical developments, current practices, and future innovations in complete denture therapy. A comprehensive review of literature from PubMed, Scopus, and Web of Science (2000–2025) was conducted, with a focus on materials science, digital design, patient-centered care, artificial intelligence (AI), and sustainable fabrication. Results: Innovations in the field include high-performance polymers, CAD–CAM systems, digital impressions, smart sensors, and bioactive liners. Recent trends in the field include the development of self-monitoring prostheses, artificial intelligence (AI)-driven design platforms, and bioprinted regenerative bases. These advances have been shown to enhance customization, durability, hygiene, and patient satisfaction. However, challenges persist in terms of accessibility, clinician training, regulatory validation, and ethical integration of digital data. Conclusions: The field of complete denture therapy is undergoing a transition toward a new paradigm of prosthetics that are personalized, intelligent, and sustainable. To ensure the integration of these technologies into standard care, ongoing interdisciplinary research, clinical validation, and equitable implementation are imperative. Full article

In today’s hyperconnected world, disaster announcements—regardless of actual impact—can significantly shape consumer behaviour and brand perception in the hospitality sector. This study investigates how customers respond online to disaster-related signals, focusing on digital marketing activities by luxury hotels in Santorini, Greece. Drawing on a case study of the Santorini Earthquake in February 2025—during which the Greek government declared a state of emergency—we use big data analytics, including web traffic metrics, social media interaction and fuzzy cognitive mapping, to analyse behavioural shifts across platforms. The findings indicate that disaster signals trigger increased engagement, altered sentiment and changes in advertising efficiency. This study provides actionable recommendations for tourism destinations and hospitality brands on how to adapt digital strategies during crisis periods. Full article

Background. Needle-related procedures (NRPs) in cancer care are often associated with significant pain and anxiety, contributing to psychological and physiological distress. This study aimed to assess the effectiveness of virtual reality (VR)-based interventions in reducing anxiety, pain, depression, fear, and physiological parameters (pulse rate and respiratory rate) in patients with cancer undergoing NRPs. Methods. A systematic search of 11 databases (CINAHL, Cochrane Library, Embase, IEEE Xplore, Medline, ProQuest, PsycINFO, PubMed, Scopus, Web of Science, and CNKI) was conducted from inception to 15 May 2025. Two independent reviewers selected and extracted studies based on predefined inclusion and exclusion criteria. Meta-analyses were performed using Cochrane RevMan 2024 software. Heterogeneity was assessed using Higgins’ I² statistics and Cochran’s Q test. The GRADE framework was applied to evaluate the quality of evidence. Results. Fourteen randomized controlled trials (RCTs) with 1089 participants were included. VR interventions showed significant benefits compared to controls in reducing anxiety (standard mean difference [SMD] = −1.74, 95% confidence interval [CI]: −2.47 to −1.01, p < 0.001), pain (SMD = −1.30, 95% CI: −1.93 to −0.67, p < 0.001), depression (SMD = −0.73, 95% CI: −0.96 to −0.50, p < 0.001), fear (mean difference [MD] = −1.31, 95% CI: −1.56 to −1.06, p < 0.001), and respiratory rate (MD = −3.85, 95% CI: −6.18 to −1.52, p = 0.001). However, no significant difference was found in pulse rate (MD = 0.25, 95% CI: −14.32 to 14.81, p = 0.97). Conclusions. VR-based interventions are effective in alleviating psychological symptoms (anxiety, depression, fear) and physiological distress (pain, respiratory rate) in patients with cancer undergoing NRPs. However, they do not significantly impact pulse rate. Interpretation of findings should consider limitations such as the small number of studies, limited sample sizes, and high heterogeneity. Further high-quality RCTs with follow-up assessments are warranted. Customizing VR interventions to address demographic and procedural needs may further enhance their effectiveness. Full article

This article describes a robust Gaussian Prior process state space modeling (GPSSM) approach to assess the impact of an intervention in a time series. Numerous applications can benefit from this approach. Examples include: (1) time series could be the stock price of a company and the intervention could be the acquisition of another company; (2) the time series under concern could be the noise coming out of an engine, and the intervention could be a corrective step taken to reduce the noise; (3) the time series could be the number of visits to a web service, and the intervention is changes done to the user interface; and so on. The approach we describe in this article applies to any times series and intervention combination. It is well known that Gaussian process (GP) prior models provide flexibility by placing a non-parametric prior on the functional form of the model. While GPSSMs enable us to model a time series in a state space framework by placing a Gaussian Process (GP) prior over the state transition function, probabilistic recurrent state space models (PRSSM) employ variational approximations for handling complicated posterior distributions in GPSSMs. The robust PRSSMs (R-PRSSMs) discussed in this article assume a scale mixture of normal distributions instead of the usually proposed normal distribution. This assumption will accommodate heavy-tailed behavior or anomalous observations in the time series. On any exogenous intervention, we use R-PRSSM for Bayesian fitting and forecasting of the IoT time series. By comparing forecasts with the future internal temperature observations, we can assess with a high level of confidence the impact of an intervention. The techniques presented in this paper are very generic and apply to any time series and intervention combination. To illustrate our techniques clearly, we employ a concrete example. The time series of interest will be an Internet of Things (IoT) stream of internal temperatures measured by an insurance firm to address the risk of pipe-freeze hazard in a building. We treat the pipe-freeze hazard alert as an exogenous intervention. A comparison of forecasts and the future observed temperatures will be utilized to assess whether an alerted customer took preventive action to prevent pipe-freeze loss. Full article

The management of medical waste (MW) is a critical global challenge, contributing to toxic effects on humans, environmental degradation, and economic burdens. Despite advancements, gaps remain in adopting sustainable waste disposal practices, with limited bibliometric analysis in this field. The rising volume of MW, exacerbated by global health crises, strains existing systems. This study uses bibliometric analysis of 3025 publications from 1975 to 2024, employing Web of Science data with specific Boolean operators and keywords for efficient searching algorithms. Data visualization and analysis were carried out with software such as VOSviewer version 1.6.20 and Bibliometrix 5.0.0, along with custom Python 3.12.3 thesaurus files to standardize terminology. The results reveal a significant rise in publications post-2000, particularly during the COVID-19 pandemic, with China, India, and the US as major contributors. South Korea stands out for high citation rates. Network analysis identified collaboration patterns, while trend mapping highlighted a shift toward sustainable waste management practices. The evaluation insights revealed a clear transition from incineration-based methods toward sustainable and innovative solutions such as autoclaving, plasma pyrolysis, and advanced oxidation processes, driven by environmental concerns and regulatory frameworks. This study underscores the implications of MW and the importance of analyzing publication trends over time to understand the ongoing need for development, grounded in a legislative policy framework, which is essential for advancing sustainable practices in MW management. Full article