Search Results (4,334)

Background/Objectives: Digital platforms have increased access to health information. The eHEALS scale evaluates individuals’ capabilities for accessing, assessing, and assimilating health information to help them make well-informed oral health decisions. It would be interesting to examine the association with oral health behavior (OHB), as digital platforms are increasingly seen as a “super social determinant”. Hence, the present study aimed to assess eHealth literacy levels and their association with oral health behaviors among dental outpatients at a dental college in Riyadh, Saudi Arabia. Methods: A cross-sectional survey using the eHEALS questionnaire was conducted after translation into Arabic, with additional questions on oral health behaviors. The internal consistency of the translated questionnaire was good. A total of 213 patients were recruited from the dental college’s outpatient department. Chi-square, followed by multinomial regression, was used in the statistical analysis. Results: The mean total eHEALS score in the sampled population was 26.17 (±7.5) of the 213 participants, with 108 (50.7%) having good oral health behavior practices. The elderly age group (OR 2.67, p = 0.01, CI 1.25–5.68), school-level education (OR 2.82, p = 0.03, CI 1.41–5.66), and low monthly family income (OR 2.53, p = 0.01, CI 1.25–5.11) were significantly associated with inadequate eHealth literacy. Participants with good oral health behavior had significantly lower odds of being categorized into inadequate (OR 0.41, p = 0.01, CI 0.20–0.81) or problematic (OR 0.43, p = 0.01, CI 0.22–0.85) levels of eHealth literacy. Conclusions: There is a significant association between eHealth literacy and individuals’ oral health behavior practices. Age, monthly family income, and education were key predictors of eHealth literacy levels. Full article

Colostrum is crucial for the survival, development, and the future productivity of newborns. In this study, we evaluated the immunological and microbiological quality of colostrum in 28 Rasa Aragonesa ewes and its relationship with offspring growth during the first 48 h postpartum. Colostrum samples were collected by hand milking immediately after parturition. Immunoglobulin concentration was assessed using Brix refractometry and the samples were categorised according to their immunoglobulin content: high (>24 Brix value), medium (19–23 Brix value), and low (< 19 Brix value). Bacterial counts of aerobes and coliforms were determined with the 3M Petrifilm™ system and the weight of the lambs was recorded using a digital suspension scale. The mean aerobic count (AC) was 3.63 ± 0.69 log₁₀ CFU/mL after 24 h of incubation and the mean coliform count (CC) was 1.59 ± 0.82 log₁₀ CFU/mL after 24 h of incubation. Colostrum with a high immunoglobulin concentration had lower aerobic count after 48 h of incubation than that with poor immunological quality. In relation to coliform counts, similar values were found in all groups. No significant differences were observed in terms of lamb weight gain according to colostrum quality. In conclusion, the immunological quality of colostrum affected the AC determined, but it did not affect CC or early postnatal lamb weight. These findings offer preliminary insights into the usefulness of the Petrifilm™ system in microbiological quality determination of colostrum and its relationship with immunological quality determined in vitro. Full article

Background: Truck drivers constitute a high-risk occupational group due to irregular schedules, prolonged sedentary work, fatigue, and limited access to healthcare, contributing to adverse physical and mental health outcomes. Although mobile health (mHealth) tools offer potential to support driver health, sustained engagement remains a persistent challenge. Objectives: This systematic review aimed to identify behavioural, technological, and contextual determinants influencing truck drivers’ compliance, retention, and long-term engagement with digital health interventions. Methods: Following the PRISMA 2020 guidelines, six eligible studies were identified and thematically synthesised across technology acceptance, behaviour change, and persuasive system design perspectives. Results: Across studies, sustained engagement was facilitated by self-monitoring, real-time feedback, goal-setting, coaching support, and simple, flexible system design. In contrast, technological complexity, high interaction demands, limited digital literacy, privacy concerns, misalignment with irregular schedules, and fatigue consistently undermined engagement and retention. Autonomy, trust, and voluntary participation emerged as cross-cutting determinants supporting continued use. Based on the synthesis, an integrative framework was developed to explain how behavioural, technological, and contextual factors interact to shape truck drivers’ compliance, engagement, and retention with mHealth. Despite generally moderate to high study quality, the evidence base remains fragmented and dominated by short-term evaluations. Conclusions: The findings highlight the importance of context-sensitive, user-centred design to support effective digital health interventions in the trucking sector. Full article

Smart campuses employ advanced digital technologies and intelligent communication systems to enhance educational, operational, and living environments. This study investigates stakeholder perceptions of smart campus priorities in Saudi Arabia through a structured questionnaire administered to students and faculty. The study considered King Fahd University of Petroleum and Minerals (KFUPM) in Dhahran as a case study in this regard. The survey examined 22 smart campus aspects grouped into six domains: smart education, smart mobility, smart energy and waste management, smart buildings and work environment, smart safety and security, and smart open spaces. The results indicated strong consensus regarding the importance of all domains, with an overall mean rating of 4.3 out of 5.0 and Relative Importance Index (RII) values ranging from 0.77 to 0.91. The highest-ranked aspects included IoT-enabled cooling energy optimization, smart public transportation, smart lighting systems, smart workflow management, e-libraries, and fire prevention and detection systems, reflecting a pronounced emphasis on infrastructure quality, energy efficiency, and operational effectiveness. The findings suggest that smart campus development in Saudi Arabia should prioritize high-impact, user-valued initiatives that align with Vision 2030 objectives including digital transformation. Strategic early investments in smart buildings, energy management, and mobility systems can deliver measurable benefits in this regard. Further research is recommended to consider additional case studies in the Saudi context to ensure that smart campuses remain contextualized and responsive to user needs. Full article

This paper proposes the development of an IEC 61850-compliant platform that is readily programmable and deployable for future digital twin applications. Given the compatibility between IEC-61850 and digital twin concepts, a focused case study was conducted involving the robust development of a Raspberry Pi platform with protection relay functionality using the open-source libIEC61850 library. Leveraging IEC-61850’s object-oriented data modelling, the relay can be represented by fully consistent virtual and physical models, providing an essential foundation for accurate digital twin instantiation. The relay implementation supports high-speed Sampled Value (SV) subscription, real-time RMS calculations, IEC Standard Inverse overcurrent trip behaviour according to IEC-60255, and Generic Object-Oriented Substation Event (GOOSE) publishing. Further integration includes setting group functionality for dynamic parameter switching, report control blocks for MMS client–server monitoring, and GOOSE subscription to simulate backup relay protection behaviour with peer trip messages. A staged development methodology was used to iteratively develop features from simple to complex. At the end of each stage, the functionality of the added features was verified before proceeding to the next stage. The integration of the Raspberry Pi into Curtin’s IEC = 61,850 digital substation was undertaken to verify interoperability between IEDs, a key outcome relevant to large-scale digital twin systems. The experimental results confirm GOOSE transmission times below 4 ms, tight adherence to trip-time curves, and performance under higher network traffic. Such measured RMS and trip-time errors fall well within industry and IEC limits, confirming the reliability of the relay logic. The takeaways from this case study establish a high-performing, standardised foundation for a digital twin system that requires fast, bidirectional communication between a virtual and a physical system. Full article

Objective: We evaluate the performance and relevance of MRI to detect csPC in men with normal PSA. Methods: Out of our database of patients referred for prostate MRI, we selected men with PSA < 4 ng/mL for whom histopathology or at least 2 years of clinical follow-up data were available as standard of reference. Subgroup analyses were performed for the patients with PSA < 3 ng/mL, <2 ng/mL, and 2–3.9 ng/mL. The reasons for prostate MRI referral despite their normal PSA level were retrieved by exploring the patients’ files. The prostate MRIs were reported according to the Prostate Imaging and Reporting Data System (PI-RADS), and the overall assessment score was registered. For evaluation of the performance, PI-RADS ≥ 3 was set as a threshold for a positive exam. The patients without PC or only International Society of Urological Pathology (ISUP) grade group 1 PC (Gleason 3+3) were considered as one category having no csPC. The performance of prostate MRI was separately evaluated for detection of ISUP ≥ 2 and for ISUP ≥ 3 csPC. Results: A total of 148 men were included, with PSA ranging from 0.42 to 3.99 ng/mL (median 2.95, IQR 1.68–3.50) and age ranging from 36 to 84 years (median 58, IQR 52–66). A total of 74 men (50.0%) had a PSA level < 3 ng/mL, 42 (28.4%) had a PSA level < 2 ng/mL, and 106 (71.6%) had a PSA level of 2–3.9 ng/mL. They were referred for prostate MRI for a wide variety, and usually a combination of, reasons, such as younger age (<60 years in 55.4%, N = 82; <50 years in 17.6%, N = 26), abnormal digital rectal examination in 31.8% of cases (N = 47), suspicious PSA dynamics in 29.7% (N = 44), positive familial history in 27.0% (N = 40), clinical signs of prostatitis in 18.2% (N = 27), suspicious findings on Transrectal Ultrasound (TRUS) in 16.9% (N = 25), hematospermia in 7.4% (N = 11), hematuria in 4.1% (N = 6), incidental hot spot in the prostate on Fluoro-Deoxy-Glucose (FDG) Positron Emission Tomography (PET)–Computed Tomography (CT) in 4.1% (N = 6), lymphadenopathies on CT in 2.7% (N = 4), or severe patient anxiety in 3.4% (N = 5). Overall, ISUP ≥ 2 PC was present in 18.9% (N = 28) of cases, and MRI detected this with a sensitivity of 92.9%, a specificity of 66.7%, and a positive predictive value of 39.4%. ISUP ≥ 3 PC was present in 9.5% (N = 14) of cases, and prostate MRI detected this with a sensitivity of 100%, a specificity of 61.2%, and a positive predictive value of 21.2%. In patients with PSA < 2 ng/mL (N = 42), no csPC was found, but MRI generated false positives in 33.3%. Conclusions: Performing prostate MRI in men with normal PSA (<4 ng/mL) seems useful if there are other reasons that increase the clinical suspicion of csPC. In about one-fifth of these patients, csPC is present and MRI has high sensitivity for its detection. Prostate MRI has, however, low positive predictive value in this patient group, and clinicians should be aware of the risk of false-positive MRI. Below a PSA level of 2 ng/mL, no csPC was found and prostate MRI generated only false positives, suggesting limited value in this subgroup. Full article

Overlay restorations offer a conservative solution for teeth with substantial loss of tooth structure, but their success depends largely on the preparation design, material type, and fabrication technique. This study aimed to assess the effects of two different preparation designs and fabrication techniques on the fracture resistance and marginal adaptation of overlays fabricated from zirconia-reinforced lithium silicate (ZLS) and 3D-printed resin. Forty extracted human molars were randomly divided into two preparation design groups: occlusal reduction (O) and occlusal reduction with a round shoulder (OS). Each group was subdivided based on the material type: ZLS or 3D-printed resin (n = 10 per subgroup). Restorations were designed using CAD and manufactured using milling (ZLS) or additive manufacturing (3D-Printed). After cementation and thermomechanical aging (5500 cycles, 5–50 °C), marginal gaps were measured at 20 predefined points using scanning electron microscopy (SEM). The fracture resistance was tested using a universal testing machine. Data were analyzed using two-way ANOVA and post hoc tests (α = 0.05). The preparation design had a significant effect on both fracture resistance and marginal adaptation (p < 0.05). Group O showed significantly smaller marginal gaps than Group OS for both materials. The ZLS overlays exhibited a significantly higher fracture resistance than the 3D-printed resin overlays. All groups demonstrated marginal gaps within the clinically acceptable range (<120 μm). The fracture resistance and marginal adaptation of overlay restorations are significantly influenced by the preparation design and material type. A simpler occlusal reduction design results in better marginal adaptation, whereas round shoulder preparations provide a higher fracture resistance. Although the 3D-printed resin showed lower fracture resistance, its marginal adaptation was comparable to that of milled restorations, suggesting its potential as a conservative and cost-effective polymer composite alternative for digitally fabricated overlay restorations. Full article

Digital technologies that continuously quantify animal behavior, physiology, and production offer significant potential for the early identification of health and welfare disorders of dairy cows. In this study, a multimodal artificial intelligence (AI) framework is proposed for real-time health monitoring of dairy cows through the integration of physiological, behavioral, production, and thermal imaging data, targeting veterinarian-confirmed udder, leg, and hoof infections. Predictions are generated at the cow-day level by aggregating multimodal measurements collected during daily milking events. The dataset comprised 88 lactating cows, including veterinarian-confirmed udder, leg, and hoof infections grouped under a single ‘sick’ label. To prevent information leakage, model evaluation was performed using a cow-level data split, ensuring that data from the same animal did not appear in both training and testing sets. The system is designed to detect early deviations from normal health trajectories prior to the appearance of overt clinical symptoms. All measurements, with the exception of the intra-ruminal bolus sensor, were obtained non-invasively within a commercial dairy farm equipped with automated milking and monitoring infrastructure. A key novelty of this work is the simultaneous integration of data from three independent sources: an automated milking system, a thermal imaging camera, and an intra-ruminal bolus sensor. A hybrid deep learning architecture is introduced that combines the core components of established models, including U-Net, O-Net, and ResNet, to exploit their complementary strengths for the analysis of dairy cow health states. The proposed multimodal approach achieved an overall accuracy of 91.62% and an AUC of 0.94 and improved classification performance by up to 3% compared with single-modality models, demonstrating enhanced robustness and sensitivity to early-stage disease. Full article

Food waste is a persistent global concern, requiring behavioral and systemic responses from consumers. The current study investigated the effect of sociodemographic factors and digital activism on food waste prevention behavior. Data from 390 respondents living in Italy (65% females, from 18 to 75 years old, grouped into four generations) were collected through an online survey covering these sections: sociodemographic variables, digital activism, knowledge, attitudes, and food waste behaviors. A Food Waste Prevention Index (FWPI) was computed to assess self-reported adherence to waste-reducing practices, and differences across three groups identified through tertiles were tested. Women displayed higher levels of digital activism; Gen Z was the most engaged generation in seeking information about food, while interest in food issues declined with age. Gender, geographical area, and dietary orientation significantly influenced food waste prevention, with women, rural residents, and individuals adopting flexitarian or vegetarian diets tending towards more virtuous behavior (higher FWPI). According to digital activism, less virtuous waste behavior (lower FWPI) was associated with a lower social media and apps usage frequency. Furthermore, higher FWPI individuals self-reported stronger sensitivity to sustainability-related topics such as circular economy, short food chains, and ethical or environmental motivations for vegetarianism. Overall, awareness and digital activism may synergistically foster more responsible food consumption, and targeted communication and digital tools can effectively support household food waste reduction strategies. Full article

Dimensional accuracy of 3D-printed implant models is essential for precise implant-supported restorations. The objective of this study was to evaluate the influence of printing orientation and model base design on the accuracy of implant position transfer. A standardized maxillary model with four implants was scanned using an intraoral scanner. Solid and hollow models were designed and printed using digital light processing (DLP) technology at orientations of 0°, 45°, and 90° (n = 10 per group). All models were digitized with a high-precision industrial scanner, and implant position deviations were determined by comparing corresponding reference points with the master model. Data were analyzed using two-way analysis of variance and post hoc tests (α = 0.05). Printing orientation significantly affected accuracy (p < 0.001). Models printed at 45° showed the highest deviations, whereas those printed at 0° and 90° exhibited comparable and superior accuracy. Model design (solid vs. hollow) had no significant influence at 0° and 90°, but hollow models were more accurate at 45° (p < 0.001). Mean deviations ranged from 131 μm to 382 μm. Printing at 0° or 90° is recommended, while 45° orientations should be avoided. Model design showed minimal effect on accuracy. Full article

Background/Objectives: Social media’s impact on adult well-being varies by engagement patterns, highlighting the need for evidence to inform digital well-being strategies. This study examines the association between social media use and self-esteem, a key psychological indicator linked to adult well-being, with the aim of identifying modifiable behavioral targets relevant to clinical, workplace, and public health contexts. Methods: A cross-sectional survey of 81 Greek adults assessed daily social media use, engagement patterns, and self-esteem using the Rosenberg Self-Esteem Scale. Analyses included linear and exploratory quadratic regression models, multiple regression with demographic covariates (age, gender), and descriptive group comparisons. Results: A small but statistically significant negative association was observed between daily social media use and self-esteem (R² = 0.078), indicating limited explanatory power. Exploratory analyses did not provide strong evidence of non-linear effects. Demographic factors and usage categories were not significant predictors, likely reflecting limited statistical power. Participant self-reports highlighted potentially disruptive patterns such as intensive use at specific times/conditions, perceived sleep impact, and cognitive preoccupation with social media, as well as motivation to reduce or stop use. Conclusions: Time spent online is a weak predictor of self-esteem, underscoring the importance of engagement quality over frequency. From a management perspective, the findings support shifting attention from generic screen-time reduction to targeting specific potentially high-risk patterns of engagement in future policy and practice. This exploratory pilot study provides initial, hypothesis-generating evidence within a Greek adult sample and highlights the need for larger, population-based studies to confirm and extend these findings. Full article

Acne vulgaris is a prevalent dermatological disorder characterized by excessive sebum production, impaired skin hydration, enlarged pores, and persistent lesions. Chemical peeling is a well-established procedure in cosmetic dermatology, while microneedling has emerged as a promising minimally invasive procedure; however, evidence on their combined use remains limited. This pilot study aimed to compare the efficacy of chemical peeling, combined chemical peeling with microneedling, and a classic cosmetic protocol in patients with mild to moderate acne. Fifteen participants aged 18–45 years were divided into three groups according to the treatment protocol. Groups 1 (chemical peeling) and 3 (classic cosmetic care) each received four sessions at two-week intervals, whereas Group 2 (combined peeling with microneedling) completed seven sessions. Sebum levels, hydration, pore counts, and acne lesions were assessed using digital skin analysis and evaluated statistically by one-way ANOVA followed by Tukey’s HSD test (p < 0.05). Chemical peeling reduced sebum secretion (−17–18%) and acne lesions (−14%) and increased hydration (+22%), although pore counts increased (+8–18%). The combined protocol achieved the most pronounced seboregulation (−23–25%) and lesion reduction (−22%) with pore reduction (−7%), but hydration decreased (−14–19%). The classic treatment produced only modest effects, mainly a slight decrease in sebum (−10%) and lesions (−8%), accompanied by dehydration (−23–26%) and increased pore counts (+14–16%). These findings indicate the efficacy of chemical peeling and its enhancement through combination with microneedling, emphasizing the need for individualized cosmetic strategies and further validation in larger controlled trials. Full article

Reconstructing original cusp dimensions in worn molars represents a fundamental challenge across dentistry, anthropology, and paleontology, as dental wear obscures critical morphological information. In this proof-of-concept study, we present a standardized machine learning pipeline for predicting original cusp height, specifically the horn tips of the enamel–dentine junction (EDJ), in worn lower molars using three-dimensional morphometric data from micro-computed tomography (micro-CT). We analyzed 40 permanent lower first (M1) and second (M2) molars from four hominin groups, systematically evaluated across three wear stages: original, moderately worn (worn1), and severely worn (worn2). Morphometric variables including height, area, and volume were quantified for each cusp, with Random Forest and multiple linear regression models developed individually and combined through ensemble methods. To mimic realistic reconstruction scenarios while preserving a known ground truth, models were trained on unworn specimens (original EDJ morphology) and tested on other teeth after digitally simulated wear (worn1 and worn2). Predictive performance was evaluated using root mean square error (RMSE) and coefficient of determination (R²). Our results demonstrate that under moderate wear (worn1), the ensemble models achieved normalized RMSE values between 11% and 17%. Absolute errors typically below 0.25 mm for most cusps, with R² values up to ~0.69. Performance deteriorated under severe wear (worn2), particularly for morphologically variable cusps such as the hypoconid and entoconid, but generally remained within sub-millimetric error ranges for several structures. Random Forests and linear models showed complementary strengths, and the ensemble generally offered the most stable performance across cusps and wear states. To enhance transparency and accessibility, we provide a comprehensive, user-friendly software pipeline including pre-trained models, automated prediction scripts, standardized data templates, and detailed documentation. This implementation allows researchers without advanced machine learning expertise to explore EDJ-based reconstruction from standard morphometric measurements in new datasets, while explicitly acknowledging the limitations imposed by our modest and taxonomically unbalanced sample. More broadly, the framework represents an initial step toward predicting complete crown morphology, including enamel thickness, in worn or damaged teeth. As such, it offers a validated methodological foundation for future developments in cusp and crown reconstruction in both clinical and evolutionary dental research. Full article

The rapid expansion of the digital economy and advances in artificial intelligence have elevated digital governance to a pivotal role in promoting environmental sustainability. Using data from the China Family Panel Studies, this study constructs a household-level indicator of the digital divide and systematically investigates its effects on household carbon emissions through three key mechanisms: consumption hypersensitivity, green technology adoption, and environmental awareness. The empirical findings demonstrate that the digital divide significantly increases household carbon emissions. Specifically, a one-unit increase in the digital divide is associated with an average rise of approximately 38.6% in household carbon emissions. Importantly, this result remains robust across a range of robustness checks and endogeneity controls. Further mechanism analysis reveals that the digital divide amplifies households’ sensitivity to consumption, diminishes their likelihood of adopting green technologies, and weakens their environmental awareness, thereby leading to an increase in household carbon emissions. Heterogeneity analysis indicates that these negative effects are particularly pronounced in regions with underdeveloped digital inclusive finance, among households headed by middle-aged and older individuals, and within populations with lower educational attainment. Based on these findings, policy initiatives should focus on improving the accessibility and inclusiveness of digital infrastructure, developing tiered frameworks to support green behavioral transformation and capacity building, and strengthening green finance initiatives alongside offline support mechanisms for digitally disadvantaged groups. Together, these measures can help bridge the digital divide and foster a more equitable, inclusive, and sustainable transition toward a low-carbon society. Full article

The non-destructive and chemical-free determination of anthocyanin content in single maize kernels is of great importance for plant-breeding programs. Previous studies have mainly relied on Near-Infrared Reflectance (NIR) spectroscopy and color-based approaches, often using conventional or randomly selected modeling techniques. In this study, an Automated Machine Learning (AutoML) framework was employed to predict anthocyanin content using spectral and digital image data obtained from individual maize kernels measured in two orientations (embryo-up and embryo-down). Forty colored maize genotypes representing diverse phenotypic characteristics were analyzed. Digital images were acquired in RGB, HSV, and LAB color spaces, together with NIR spectral data, from a total of 200 kernels. Reference anthocyanin content was determined using a colorimetric method. Ten datasets were constructed by combining different color space and spectral features and were grouped according to kernel orientation. AutoML was used to evaluate nine machine learning algorithms, while Partial Least Squares Regression (PLSR) served as a classical benchmark method, resulting in the development of 1918 predictive models. Kernel orientation had a notable effect on model performance and outlier detection. The best predictions were obtained from the RGB dataset for embryo-up kernels and from the combined RGB+HSV+LAB+NIR dataset for embryo-down kernels. Overall, AutoML outperformed conventional modeling by automatically identifying optimal algorithms for specific data structures, demonstrating its potential as an efficient screening tool for anthocyanin content at the single-kernel level. Full article