Search Results (1,741)

Background/Objectives: The increased use of social media in Mexico has given rise to the “fear of missing out” (FoMO) phenomenon, especially among adolescents. This study aimed to measure the extent of FoMO among junior high school students in the metropolitan area of Guadalajara, Mexico, during the COVID-19 pandemic. Additionally, this study explored the association between FoMO levels and demographic characteristics, as well as the type and frequency of social media use. Methods: A cross-sectional survey was conducted from November 2021 to January 2022 in four junior high schools. A total of 1264 students (656 females and 608 males) aged 11–16 years completed the Fear of Missing Out Scale, adapted to the Mexican context. Data on demographics, social media usage, and school shifts were collected. Statistical analyses were performed using t-tests, ANOVA, and correlation coefficients. Results: The mean FoMO score was 1.79 ± 0.64, with higher scores observed in females (p < 0.001) and students attending morning shifts (p = 0.001). Significant associations were found between higher FoMO scores and the use of social media platforms like Instagram, TikTok, and Pinterest (p < 0.001 for each). The most frequently used social media platforms were WhatsApp (1093), TikTok (828), and Instagram (583). Participants who used social media all week exhibited significantly higher FoMO scores than those who used it only on weekends (p < 0.001). Conclusions: FoMO is a significant phenomenon among junior high school students in Guadalajara, Mexico, particularly among females and those who use multiple social media platforms. The findings suggest a need for interventions to manage social media use and mitigate FoMO-related negative health outcomes in this population. Full article

Background: The aryl hydrocarbon receptor (AhR) plays a crucial role in immune and metabolic processes. The large molecular diversity of ligands capable of activating AhR makes it impossible to determine the structural features useful for the design of new potent modulators. Thus, in the field of drug discovery, the intricate nature of AhR activation necessitates the development of novel tools to address related challenges. Methods: In this study, quantitative structure–activity relationship (QSAR) models of classification and regression were developed with the objective of identifying the most effective method for predicting AhR activity. The initial dataset was obtained by combining the ChEMBL and WIPO databases which contained 978 molecules with EC₅₀ values. The predictive models were developed using the automated machine learning platform mljar according to a 10-fold cross validation (10-CV) testing procedure. Results: The classification model demonstrated an accuracy value of 0.760 and F1 value of 0.789 for the test set. The root-mean-squared error (RMSE) was 5444, and the coefficient of determination (R²) was 0.208 for the regression model. The Shapley Additive Explanations (SHAP) method was then employed for a deeper comprehension of the impact of the variables on the model’s predictions. As a practical application for scientific purposes, the best performing classification model was then used to develop an AhR web application. This application is accessible online and has been implemented in Streamlit. Conclusions: The findings may serve as a foundation in prompting further research into the development of a QSAR model, which could enhance comprehension of the influence of ligand structure on the modulation of AhR activity. Full article

Background and objectives: Drunkorexia is a novel alcohol-related disorder prevalent among adolescents and young adults. Extensive research on the causes and their relationship is lacking. Identifying these aspects could improve early detection and management by healthcare professionals. The aim of this review was to identify the influencing factors of drunkorexia in adolescents and young adults, as well as the main opportunities for action by health professionals. Methods: A scoping review was conducted in June and July 2024 using three databases (Pubmed, Scopus, and Web of Science). A search and review protocol were established and registered in PROSPERO. The research questions were formulated in Patient, Concept, Context (PCC) formats for an adequate literature review. Original articles from January 2008 to July 2024 were included. Reviews, meta-analyses, and doctoral theses or academic texts were excluded. In the screening phase, a methodological assessment was conducted using the Joanna Briggs Institute’s (JBI) critical appraisal tools to support study eligibility. Depending on the study design, different checklists were used, and cross-sectional studies that received scores of 4/8 or higher, quasi-experimental designs that obtained 5/9 or higher, and qualitative research that obtained 5/10 or higher were accepted. Results: A total of 1502 studies were initially found. After applying the inclusion/exclusion criteria, 20 studies were selected. Complications of emotion regulation, both positive and negative metacognitive beliefs, inability to effectively manage stress and anxiety, symptoms of post-traumatic stress disorder, self-discipline and self-control, or differences in social expectations are predisposing factors for drunkorexia. The management of malnutrition and dehydration is an opportunity for clinical professionals to address this problem. In addition, mental health issues can provide another opportunity to manage heavy alcohol consumption. Conclusions: Drunkorexia must be recognized as a new disease to be addressed from a multidisciplinary perspective. In this way, increasing research on this trend would support prevention and intervention strategies. The use of digital platforms is essential for raising social awareness of this negative habit. Full article

This paper explores the ways in which the digital divide affects labor in the context of sustainable development within the digital economy. It discusses the effects of major indicators such as digital infrastructure construction, digital industry development, and digital-inclusive finance on labor mobility. Although existing research has analyzed the ways in which the digital economy enhances economic vitality, there is insufficient research that investigates how the divide between digital access and usage can be effectively reduced to promote sustainable development. Therefore, through empirical analysis and mechanism research, this study used quantitative measurement and regression analysis methods to conduct an in-depth analysis of the dual effects of digital access and usage divides on the long-term marginal impact for labor. The results show that improving digital infrastructure such as broadband and fiber optic networks not only significantly boosts the economic vitality of underdeveloped areas, but also enhances their ability to participate in sustainable development. This enables more laborers to access new job opportunities and resources provided by the digital economy. While narrowing the digital use divide initially increases labor mobility, uneven dissemination may create barriers to information access, thus limiting mobility. Our research indicates that the development of the digital economy promotes cross-regional labor mobility, which is particularly prominent in the digital platform economy, facilitating more sustainable economic growth. After controlling for variables such as the level of economic development, this positive impact remains robust. This paper suggests that digital infrastructure construction and training in digital skills should be strengthened to narrow the digital divide and promote sustainable, balanced regional development and increased economic vitality. Full article

Radiomics provides a structured approach to support clinical decision-making through key steps; however, users often face difficulties when switching between various software platforms to complete the workflow. To streamline this process, matRadiomics integrates the entire radiomics workflow within a single platform. This study extends matRadiomics to preclinical settings and validates it through a case study focused on early malformation differentiation in a zebrafish model. The proposed plugin incorporates Pyradiomics and streamlines feature extraction, selection, and classification using machine learning models (linear discriminant analysis—LDA; k-nearest neighbors—KNNs; and support vector machines—SVMs) with k-fold cross-validation for model validation. Classifier performances are evaluated using area under the ROC curve (AUC) and accuracy. The case study indicated the criticality of the long time required to extract features from preclinical images, generally of higher resolution than clinical images. To address this, a feature analysis was conducted to optimize settings, reducing extraction time while maintaining similarity to the original features. As a result, SVM exhibited the best performance for early malformation differentiation in zebrafish (AUC = 0.723; accuracy of 0.72). This case study underscores the plugin’s versatility and effectiveness in early biological outcome prediction, emphasizing its applicability across biomedical research fields. Full article

Background: Hepatitis C virus (HCV) infection presents a significant public health challenge globally, particularly in high-burden countries like Nigeria, where an estimated 2.4 million individuals are living with HCV. HCV self-testing (HCVST) can potentially bridge the significant diagnosis gap and help individuals to determine their HCV status in the privacy of their homes. It offers a solution to overcome barriers related to stigma and limited access to healthcare. In Nigeria, Self-testing for hepatitis C has only been implemented in a pilot research context. This study aimed to assess stakeholder and community awareness and perceptions of HCVST in Nigeria. The findings will provide insights that could inform effective policies and future scale-up programs for HCV control. Methods: A cross-sectional descriptive study was conducted using an online social media survey administered through SurveyMonkey. The survey was disseminated across social media platforms and groups between October–November 2023. Participants included Nigerians (both health professionals and non-health professionals) aged 18 years or older residing in any of the 36 states and the Federal Capital Territory (FCT). Data collected include sociodemographic characteristics, awareness and perceptions of HCVST, and perceived benefits and barriers. Results: Of 321 respondents, 94% perceived HCVST as highly important. While 77% of respondents knew about HIVST, only 58% had prior knowledge of HCVST. The analysis also showed that healthcare workers had greater awareness of HIV self-testing (82.3%) compared to non-healthcare workers (50.0%). Most respondents (88%) were highly likely to recommend HCVST and perceived it as a cost-effective alternative to traditional testing. Key perceived benefits included increased disease detection and control (67%), improved access to testing (21%), and reduced stigma (11%). In the unadjusted model, geographical zone (Southern Nigeria: cOR = 0.49, 95% CI: 0.30–0.77, p = 0.002), work experience (more than 20 years: cOR = 2.79, 95% CI: 1.11–8.07, p = 0.039), and prior awareness of HIV self-testing (cOR = 5.24, 95% CI: 3.00–9.43, p < 0.001) were significant predictors of HCVST awareness. However, in the adjusted model, only prior awareness of HIV self-testing remained significant (aOR = 4.77, 95% CI: 2.62–8.94, p < 0.001). Conclusions: The strong support for HCVST among stakeholders in Nigeria highlights its potential to enhance HCV control, especially within the broader context of infectious diseases like STIs. The greater awareness of HIV self-testing among healthcare workers compared to non-healthcare workers indicates the need for targeted awareness campaigns for non-healthcare populations. Addressing these awareness gaps, leveraging lessons from HIVST, and using existing infrastructure will be crucial. Prioritizing public education, outreach, and effective linkage to care will drive the impact of HCVST in achieving HCV elimination goals and position it as a model for expanding similar STI interventions in Nigeria. Full article

The active form of vitamin D, 1,25-dihydroxyvitamin D (1,25D), binds to the vitamin D receptor (VDR) with high affinity. The VDR then heterodimerizes with the retinoid X receptor (RXR) and associates with vitamin D response elements (VDREs) to regulate the transcription of target genes. Bexarotene (Bex) is an RXR ligand (rexinoid) developed to treat cutaneous T-cell lymphoma and is a putative therapeutic for other diseases. We postulate that VDR ligands (1,25D) and RXR ligands (Bex/analogs) can “synergize” to “super-activate” the VDR-RXR heterodimer. This “cross-talk” could allow disorders treated with high-dose Bex therapy (leading to significant adverse side effects) to instead be treated using both low-dose Bex and vitamin D. Thus, we designed experiments to examine the effect of both VDR and RXR ligands, alone and in combination, to activate VDR-RXR-mediated transcription. The goal was to determine if selected RXR-specific ligands can synergize with vitamin D to amplify RXR-VDR activity. The results demonstrate a synergistic effect with both Bex and 1,25D which could be further modulated by (1) the protein levels (or polymorphic version) of VDR present in the cell, (2) the concentration of the ligands, (3) the cellular “background” (e.g., brain cells versus kidney cells), (4) the nature of the VDRE platform, or (5) the type of rexinoid (Bex analogs). Our findings suggest that diseases that respond to treatment with either vitamin D, or with rexinoids, may be amenable to enhanced therapeutic potential by employing multi-ligand dosing via combinatorial therapy. Full article

High-wind structures were identified in the Cross-Calibrated Multi-Platform (CCMP) ocean wind vector reanalysis for comparison with winds measured by satellite radiometers, scatterometers, and synthetic aperture radar (SAR) instruments from February to October 2023. The comparison aims to evaluate bias, uncertainty, and spatial correlations with the goal of enhancing the accuracy of ocean wind datasets during tropical cyclones (TCs). In 10° longitude × 10° latitude blocks, each containing a TC, Soil Moisture Active Passive (SMAP) and Advanced Microwave Scanning Radiometer 2 (AMSR2) winds are 6.5 and 4.8% higher than CCMP, while Advanced Scatterometer (ASCATB) is 0.8% lower. For extratropical cyclones, AMSR2 and SMAP also show stronger winds with a 5% difference, and ASCATB is about 0.3% weaker compared to CCMP. The comparison between SAR and CCMP for TC winds, sampled at the locations and time frames of SAR tiles, indicates that SAR winds around TCs are about 9% higher than CCMP winds. Using empirically defined TC structural indices, we find that the TCs observed by CCMP are shifted in locations and lack a compact core region. A Random Forest (RF) regressor was applied to TCs in CCMP with corresponding SAR observations, nearly correcting the full magnitude of low bias in CCMP statistically, with a 15 m/s correction in the core region. The hierarchy of importance among the predictors is as follows: CCMP wind speed (62%), distance of SAR pixels to the eye region (21%) and eye center (7%), and distance of CCMP pixels to the eye region (5%) and eye center (5%). Full article

Interoperability between different virtual platforms requires the ability to search and transfer digital assets across platforms. Digital assets in virtual platforms are represented in different forms or modalities, such as images, meshes, and point clouds. The cross-modal retrieval of three-dimensional (3D) object representations is challenging due to data representation diversity, making common feature space discovery difficult. Recent studies have been focused on obtaining feature consistency within the same classes and modalities using cross-modal center loss. However, center features are sensitive to hyperparameter variations, making cross-modal center loss susceptible to performance degradation. This paper proposes a new 3D cross-modal retrieval method that uses cross-modal supervised contrastive learning (CSupCon) and the fixed projection head (FPH) strategy. Contrastive learning mitigates the influence of hyperparameters by maximizing feature distinctiveness. The FPH strategy prevents gradient updates in the projection network, enabling the focused training of the backbone networks. The proposed method shows a mean average precision (mAP) increase of 1.17 and 0.14 in 3D cross-modal object retrieval experiments using ModelNet10 and ModelNet40 datasets compared to state-of-the-art (SOTA) methods. Full article

Due to the high cost of robots, the algorithm testing cost for physical robots is high, and the construction of motion control programs is complex, with low operation fault tolerance. To address this issue, this paper proposes a low-cost, cross-platform SCARA robot digital-twin simulation system based on the concept of digital twins. This method establishes a 5D architecture based on the characteristics of different platforms, classifies data and integrates functions, and designs a data-processing layer for motion trajectory calculation and data storage for a virtual-reality robot. To address the complexity of data interaction under different cross-platform communication forms, an editable, modular, cross-platform communication system is constructed, and various control commands are encapsulated into simple programming statements for easy invocation. Experimental results showed that, based on modular communication control, users can accurately control data communication and synchronous motion between virtual and physical models using simple command statements, reducing the development cost of control algorithms. Meanwhile, the virtual-robot simulation system, as a data mapping of the real experimental platform, accurately simulated the physical robot’s operating state and spatial environment. The robot algorithms tested using the virtual simulation system can be successfully applied to real robot platforms, accurately reproducing the operating results of the virtual system. Full article

Wind estimation is crucial for studying the atmospheric boundary layer. Traditional methods such as weather balloons offer limited in situ capabilities; besides an Air Data System (ADS) combined with inertial measurements and satellite positioning is required to estimate the wind on fixed-wing drones. As pressure probes are an important constituent of an ADS, they are susceptible to malfunctioning or failure due to blockages, thus affecting the capability of wind sensing and possibly the safety of the drone. This paper presents a novel approach, using low-fidelity aerodynamic models of drones to estimate wind synthetically. In our work, the aerodynamic model parameters are derived from post-processed flight data, in contrast to existing approaches that use expensive wind tunnel calibration for identifying the same. In sum, our method integrates aerodynamic force and moment models into a Vehicle Dynamic Model (VDM)-based navigation filter to yield a synthetic wind estimate without relying on an airspeed sensor. We validate our approach using two geometrically distinct drones, each characterized by a unique aerodynamic model and different quality of inertial sensors, altogether tested across several flights. Experimental results demonstrate that the proposed cross-platform method provides a synthetic wind velocity estimate, thus offering a practical backup to traditional techniques. Full article

With the rapid development of digital technology, the role of digitalisation in urban governance continues to emerge. Building a theoretical analysis framework and evaluation system of digitally driven urban governance has important theoretical and practical significance for stimulating the efficiency of digital technology tools and improving the energy level of urban digital governance. This paper aims to explore the mechanism of urban governance enabled by digital technology, innovatively change the previous thinking mode that only attaches importance to facility construction and e-government platforms, adopt ecological thinking, and comprehensively consider the role of “soft elements” such as strategic support, industrial support, the security environment, talent support, and the market environment. Then, the extreme value variance method and the coefficient of variation method are used to calculate the overall capacity and secondary index scores of each city, and the standard deviation of secondary index scores is used to represent the sub-environmental balance of the cross-sectional data of China’s provinces. In order to further explore which indicators restrict the improvement of China’s urban digital governance capacity, this study also constructs an obstacle degree model. The results show the following: (1) The overall capability of China’s digitally driven urban governance is low, with a total score of 27.25, indicating that China’s digitally driven urban governance is in its infancy. (2) There is a significant development imbalance among Chinese provinces, with Beijing ranking first with a score of 81.16, and Tibet, Qinghai, Xinjiang, Heilongjiang, and Ningxia scoring less than 13.30 points, ranking as the bottom 5 among the 31 provinces. (3) The shortcomings of talent support, industrial support, and the security environment restrict the improvement of the entire digital ecological governance ability. Full article

Microarray and Single-Molecule Molecular Inversion Probe (smMIP)-based targeted RNA sequencing are two RNA profiling platforms for identifying disease-associated biomarkers. The microarray uses a GeneChip array with oligonucleotide probes to measure expression levels across thousands of genes, while smMIPs capture and quantify RNA transcripts and transcript variants via next-generation sequencing. To evaluate the strengths and weaknesses of both platforms, a comparative gene expression profiling study was conducted using RNA samples from 52 prostate tissues (normal, benign prostatic hyperplasia (BPH) and various prostate cancer (PCa) grades). Of all genes covered by both platforms, only 35% of the expression levels aligned, with 45% showing discrepancies. Both platforms identified the same 17 genes as potential PCa biomarkers. Microarray analysis identified an additional 253 genes that were not covered or not identified by smMIP technology, while smMIP technology identified eight markers not covered or not identified in the microarray core gene analysis, including fusion genes and splice variants. For high-grade prostate cancer (HG-PCa), the smMIP-method identified 8 markers, and the microarray identified 17 markers, with FOLH1, FAP and CLDN3 being common across both platforms. The choice of RNA expression analysis technology depends on research objectives; microarray technology is useful for the evaluation of a wide range of genes but has low throughput. In contrast, smMIP-based RNA sequencing enables sensitive analysis with minimal RNA in a medium- to high-throughput setting. Full article

This paper presents the Hierarchical Spectral–Spatial Transformer (HSST) network, a novel approach applicable to both drone-based and broader remote sensing platforms for integrating hyperspectral (HSI) and multispectral (MSI) imagery. The HSST network improves upon conventional multi-head self-attention transformers by integrating cross attention, effectively capturing spectral and spatial features across different modalities and scales. The network’s hierarchical design facilitates the extraction of multi-scale information and employs a progressive fusion strategy to incrementally refine spatial details through upsampling. Evaluations on three prominent hyperspectral datasets confirm the HSST’s superior efficacy over existing methods. The findings underscore the HSST’s utility for applications, including drone operations, where the high-fidelity fusion of HSI and MSI data is crucial. Full article

The current study evaluates the performance of three machine learning models—Decision Trees, Random Forest, and Linear Regression—applied to aquaculture data to mitigate risks in aquaculture management. The performances of these models are analyzed and properly demonstrated using metrics including the Mean Squared Error (MSE), R-squared (R²), Root Mean Squared Error (RMSE), and Concordance Index (C-index). The Random Forest model achieved the highest prediction accuracy among all machine learning models, followed by Linear Regression and the Decision Trees. The scatter plot for Linear Regression demonstrates good predictive accuracy for mid-range values. However, it shows significant deviations at the extremes, indicating that the model struggles to capture the full range of variability in the data. The bar chart of coefficients pinpoints the variables with the greatest impact on the predictions, providing suggestions for potential areas that can be improved and providing model interpretability. Future work could incorporate more predictive statistics models focusing on improving the models for extreme values by assessing non-linear models, feature engineering methods, and expanding research into less influential variables. The results greatly impact several sections, including aquaculture management, policy-making, and operational strategies, providing valuable insights for stakeholders and decision-makers. Apache Spark was used for data processing and machine learning model implementation; Apache Cassandra was also used for data storage, ensuring efficient large dataset management and SQL tools for structured data handling; Oracle VM VirtualBox for cross-platform virtualization; and Spark Connector was also used. Full article