Miracle-dz/UniFAR-FTUs huggingface dataset:
isiXhosa translation of the Patient Health Questionnaire (PHQ-9) shows satisfactory psychometric properties for the measurement of depressive symptoms [Stage 2]

Depression is a debilitating illness, and stigma associated with it often prevents people from seeking support. Easy-to-administer and culturally- inclusive tools can allow for early screening for depressive symptoms in primary care clinics, especially in resource-limited settings. In this pre-registered pilot study (Stage 1 Report available at DOI: 10.3389/fpsyt.2022.840912), we produced an open-access isiXhosa-language version of the nine-item Patient Health Questionnaire (PHQ-9), a well-validated measure of depression incidence and severity, using a transcultural translation framework. We validated this isiXhosa PHQ-9 in a sample of N = 47 adolescents living with and without HIV in Cape Town, South Africa who speak isiXhosa at home. Reliability, convergent validity, and criterion validity were assessed, with T scores on the Achenbach System of Empirically Based Assessment Youth Self Report (YSR) form completed previously as reference standard. Our isiXhosa PHQ-9 exhibited satisfactory reliability, with Cronbach’s α=0.866 , inter-item correlations ranging from 0.229 to 0.730, and mean item-total correlation of 0.69. PHQ-9 score and Withdrawn/Depressed component T scores on the Youth Self Report were moderately associated (Spearman’s ρ=0.40,p=0.011) , indicating acceptable convergent validity. The isiXhosa PHQ-9 showed satisfactory criterion validity (area under the receiver operating characteristic curve, AUC = 0.706), but these analyses were under-powered. Principal component analysis revealed a one-factor solution, with 45.8% of variance explained by the first principal component and all factor loadings above conventional thresholds. Our isiXhosa translation of the PHQ-9 thus exhibited satisfactory psychometric properties in this pilot validation study and performed comparably to other PHQ-9 versions validated in different languages in African and global contexts. This questionnaire may serve as an invaluable culturally-inclusive screening tool for measuring depressive symptoms among isiXhosa speakers. Caution must be exercised as screening tools including the PHQ-9 may over- or under-estimate prevalence of depression. Further validation in larger, independent cohorts may enable wider use of our isiXhosa PHQ-9 as a screening tool in clinics, research studies, and mental health non-profits who serve amaXhosa.
Unveiling the Determinants of Tourist Loyalty in the Homestay Tourism Sector: An Empirical Analysis

Homestay tourism has gained popularity as a growing travel trend, particularly in Malaysia, where tourists have the opportunity to immerse themselves in the local culture by staying with a host family. However, homestay operators are facing challenges due to a decline in repeat visitors. The purpose of this study is to examine the direct and indirect relationship between image, perceived value, safety, safety satisfaction, and tourist loyalty in the Malaysian homestay tourism industry. The research model incorporates three independent variables: image, perceived value, and satisfaction as a mediator, with loyalty as the dependent variable. Primary data was collected through a survey questionnaire, and 239 sets of clean data were analyzed using structural equation modeling. The convergent validity of the measurement model was evaluated for construct reliability and validity, followed by the assessment of discriminant validity using cross-loading and HTMT ratios. The study findings indicate that image does not directly influence loyalty but instead has a positive and significant impact on loyalty through satisfaction as a mediator. Perceived value, on the other hand, directly influences loyalty but does not significantly influence loyalty when satisfaction acts as a mediator. These results suggest that homestay operators should prioritize enhancing their image and delivering high-quality services at reasonable prices to increase satisfaction and foster loyalty. This study offers valuable insights for homestay operators and policymakers in the tourism industry, emphasizing the importance of understanding customer needs and preferences to create a memorable experience.
Ecological Risk Assessments of Priority Substances in Canada: Identification and Resolution of Difficult Issues

Environment Canada has revised its guidance manual for ecological risk assessments of priority substances under the Canadian Environmental Protection Act. This paper briefly summarizes the guidance provided on: problem formulation; data collection and generation; analysis of entry, exposure and effects; and risk characterization. The purpose of this paper is to discuss some of the difficult issues common to all programs conducting ecological risk assessments of substances, and to describe the approaches the authors have taken. Example issues include: (1) how should low toxic effects be determined, (2) how should risks be quantified, and (3) how can the ecological consequences of exposures to substances be determined? Finally, comments are provided on the process followed during the preparation of the revised manual. These comments are provided because the authors believe that the open and transparent process followed was beneficial to the identification and resolution of these and other difficult issues.
“Sweetwoods” Lignin as Promising Raw Material to Obtain Micro-Mesoporous Carbon Materials

Biorefineries with the significant amounts of lignin as a by-product have a potential to increase business revenues by using this residue to produce high value-added materials. The carbon materials from biomass waste increases the profitability of the production of porous carbon used for sorbents and energy production. The purpose of this research is to study the chemical properties of lignin from “Sweetwoods” biorefinery as well as to characterize lignin carbonizates and activated carbons synthesized from them. This paper describes the effect of carbonization conditions (thermal or hydrothermal) on the properties of activated carbon material. It can be concluded that, depending on the carbonization method, the three-dimensional hierarchical porous structure of activated carbon materials based on “Sweetwoods” lignin, has micro- and mesopores of various sizes and can be used for number of purposes: both for high-quality sorbents, catalysts for electrochemical reduction reactions, providing sufficient space for ion mass transfer in electrodes for energy storage and transfer.
Efficient Deep-Learning 4D Automotive Radar Odometry Method

Odometry is a crucial technology for the autonomous positioning of intelligent vehicles. While estimating the odometry from LiDAR and cameras has progressed recently, it remains to be seen how to estimate the odometry from a 4D radar, an emerging sensor with unique advantages over cameras and LiDAR. In this study, a deep-learning-based 4D radar odometry method, named 4DRO-Net, is proposed. The method employs a coarse-to-fine hierarchical optimization technique based on a sliding window to estimate and refine an autonomous vehicle's pose in an iterative manner. A feature-extraction network for 4D radar point clouds is proposed to achieve efficient learning of sparse point clouds. An initial pose-generation module is constructed to obtain the initial pose, which is used to warp the first point cloud and bridge the distance to the second point cloud. A velocity-aware attention cost volume module is then developed to correlate the warped first point cloud with the second point cloud to obtain point-motion information. The velocity information of the radar points is used to learn the attention weights to increase the robustness of the motion information estimation. Motion information is used to regress the corrected pose, which is then used to refine the initial pose to obtain a more accurate final pose. The superior performance and effectiveness of our 4D radar odometry method are demonstrated on both the View-of-Delft and an in-house dataset.
Optimization of the equilibrium magnetic sensor set for the SPARC tokamak

Accurate reconstruction of the plasma equilibrium is imperative for successful operation of the SPARC tokamak. In order to assess the expected reconstruction accuracy throughout the duration of design-point discharges, the EFIT equilibrium reconstruction code was deployed for SPARC. Reconstructions from SPARC baseline scenarios were compared with free-boundary equilibria generated by FreeGS, Toksys, and the Tokamak Simulation Code. The key geometric areas of interest, where design constraints are imposed, included: the inner and outer midplane gaps, the X-point locations, as well as the strike point locations. Successful reconstructions of various reference discharges, using deviations in these key geometric quantities as metrics, were calculated from synthetic signals considering an optimized equilibrium magnetic sensor set. The optimization process for this sensor set combined a scan of randomized sensor placement with a linear perturbation analysis to determine critical sensor locations, while simultaneously conforming to design constraints on the sensor placement. This optimized set was also successful in performing equilibrium reconstructions with the addition of error to synthetic measurements of magnetic flux and magnetic field, as well as contributions from eddy currents in conducting structures. These methods represent a workflow of optimization and validation that balances the engineering constraints of sensor placement with achieving sufficient reconstruction fidelity for science and operations missions for SPARC.
Computer-aided forensic facial comparison

Countless facial images are generated everyday through digital and cell phone cameras, surveillance video systems, webcams, and traditional film and broadcast video. As a result, law enforcement and intelligence agencies have numerous opportunities to acquire and analyze images that depict persons of interest. Computer-Aided Forensic Facial Comparison is a comprehensive exploration of the scientific, technical, and statistical challenges facing researchers investigating courtroom identification from facial images.
The Practice of Teaching and Scientific Research on Cadaveric Material Remains Crucial for Medical Education

The practice of teaching and scientific research on cadaveric material remains crucial for medical education, especially in surgical disciplines. However, in Italy, this practice has been neglected due to legislative insufficiency and financial constraints. Although innovative methods and tools like simulators and e-learning have been adopted, direct hands-on experience with human cadavers remains irreplaceable for medical and surgical education. The absence of clear legislation governing cadaveric dissection has limited availability for teaching and research, resulting in economic burdens for universities and individuals seeking proper surgical training. To address this issue, Law No. 10/2020 and the recent implementing decree were introduced in Italy, providing detailed legislation on the donation of bodies for educational and research purposes. The law emphasizes the importance of respecting the donor’s specific choices and aligns with constitutional principles promoting culture, research, and health protection. However, some critical issues related to consent procedures, duration of body availability, and preservation of anatomical parts remain. Additionally, the law’s dissemination among the population needs improvement. Future optimization could include allowing donors to choose the timing of body donation and considering different timeframes for body availability. Furthermore, the implementation of consent procedures could be simplified to increase donations. The law should also address the need for appropriate reception centers and allocate resources for effective dissemination. Despite these challenges, Law No. 10/2020 represents a significant step forward in enhancing medical-surgical training, scientific research, and the overall quality of patient care in Italy.
LPPDA: A Light-Weight Privacy-Preserving Data Aggregation Protocol for Smart Grids

Smart meters are continuously being deployed in several countries as a step in the direction of modernizing the power grid. Smart meters allow for automatic electricity consumption reporting to energy providers to facilitate billing and demand-based power generation. However, research has shown that such high resolution reporting to suppliers can potentially be used to invade consumers’ privacy; by identifying and predicting their behavior based on their consumption readings. This work presents a new protocol to preserve users’ privacy while maintaining the benefits of smart grids. The proposed method utilizes different techniques like randomization, masking, and differential privacy to build the scheme. The proposed method is shown to be more efficient compared to previous work in terms of performance and communication overhead. The implementation, simulation, and analysis are performed on datasets of real smart meters readings of households and electric vehicle chargers.
Mechanism of Action and Efficiency of Ag3PO4-Based Photocatalysts for the Control of Hazardous Gram-Positive Pathogens

Silver phosphate and its composites have been attracting extensive interest as photocatalysts potentially effective against pathogenic microorganisms. The purpose of the present study was to investigate the mechanism of bactericidal action on cells of opportunistic pathogens. The Ag3PO4/P25 (AGP/P25) and Ag3PO4/HA (HA/AGP) powders were prepared via a co-precipitation method. Thereafter, their antimicrobial properties against Enterococcus faecalis, Staphylococcus epidermidis, and Staphylococcus aureus (clinical and reference strains) were analyzed in the dark and after exposure to visible light (VIS). The mechanism leading to cell death was investigated by the leakage of metabolites and potassium ions, oxidative stress, and ROS production. Morphological changes of the bacterial cells were visualized by transmission electron microscopy (TEM) and scanning transmission electron microscopy with energy-dispersive X-ray spectroscopy (SEM EDS) analysis. It has been shown that Ag3PO4-based composites are highly effective agents that can eradicate 100% of bacterial populations during the 60 min photocatalytic inactivation. Their action is mainly due to the production of hydroxyl radicals and photogenerated holes which lead to oxidative stress in cells. The strong affinity to the bacterial cell wall, as well as the well-known biocidal properties of silver itself, increase undoubtedly the antimicrobial potential of the Ag3PO4-based composites.
B-type natriuretic peptide and N-terminal Pro-B-type natriuretic peptide in severe aortic stenosis: a comprehensive literature review

B-type natriuretic peptide (BNP) and N-terminal pro-BNP (NT-pro BNP) are cardiac biomarkers that are released in response to increased ventricular and atrial wall stress. Aortic stenosis (AS) leads to hemodynamic changes and left ventricular hypertrophy and may be associated with natriuretic peptide levels. Several studies have shown that increased natriuretic peptide levels are correlated with AS severity and can predict the need for intervention. It can be useful in risk stratification, monitoring follow-up, and predicting cardiovascular outcomes of patients with severe AS. This paper aims to summarize the evidence of the role of BNP and NT-pro BNP in AS, before and after intervention.
MOTION OF SOLID SPHERES UNDER SOLITARY WAVE ATTACK: PHYSICAL AND NUMERICAL MODELING

In this study, physical and numerical test are carried out focusing on the motion of two solid spheres under solitary wave attack. The numerical model CADMAS-SURF/3D-2F-DEM coupling a multiphase flow solver solving Reynolds Averaged Navier-Stokes Equations based on a porous body model and a discrete element method solver for Newtonâ€™s equations of motion is validated against the data of physical model experiments carried out in the wave flume of METU Ocean Engineering Research Center. Comparisons of the numerical simulations and physical model experiments show that the numerical model is capable of simulating the motion of solid spheres under solitary wave attack in a reasonably well accuracy.
PreLoc: predicting pedestrian's location and learning unmapped pathways by long short-term memory model

With increasing popularity of smartphone apps such as fitness assistants, location-based services, more and more pedestrians now travel in short distances with purposes of leisure, running exercise or social activities. As users move from place to place, the mobile apps capture the data showing the history of change in location in a definite time interval. Given the set of global positioning system (GPS) location points a user has travelled along, this study presents, PreLoc, a system that accurately predicts the user's future location. By preprocessing the raw data, the authors eliminate existing noises and extract valid trajectories from pedestrians or runners. Then the long short-term memory model is applied to predict the next GPS point that the user will pass through. Meanwhile, PreLoc is able to learn an unmapped pathway based on the trajectory data of mass users that have travelled over this pathway. Experimental results over real-world dataset show the distance error could achieve about 12 m in residential area, and achieve acceptable distance errors (about 30 m) for other larger and complicated sites. Moreover, they provide real scenarios for unmapped pathway learning.
Deep learning-based super-resolution for harmful algal bloom monitoring of inland water

ABSTRACT Inland water frequently occurs during harmful algal blooms (HABs), rendering it challenging to comprehend the spatiotemporal features of algal dynamics. Recently, remote sensing has been applied to effectively detect the algal spatiotemporal behaviors in expensive water bodies. However, image sensor resolution limitation can render the understanding of spatiotemporal features of relatively small water bodies challenging. In addition, few studies have improved the resolution of remote sensing images to investigate inland water quality, owing to the image sensor resolution limitations. Therefore, this study applied deep learning-based Super-resolution for transforming satellite imagery of 20 m to airborne imagery of 5 m. After performing atmospheric correction for the acquired images, we adopted super-resolution (SR) methodologies using a super-resolution convolutional neural network (SRCNN) and super-resolution generative adversarial networks (SRGAN) to estimate the Chlorophyll-a (Chl-a) concentration in the Geum River of South Korea. Both methods generated SR images with water reflectance at 665, 705, and 740 nm. Then, two band-ratio algorithms at 665 and 740 nm wavelengths were applied to the reflectance images to estimate the Chl-a concentration maps. The SRCNN model outperformed SRGAN and bicubic interpolation with peak signal-to-noise ratios (PSNR), mean square errors (MSE), and structural similarity index measures (SSIM) for the validation dataset of 24.47 (dB), 0.0074, and 0.74, respectively. SR maps from the SRCNN provided more detailed spatial information on Chl-a in the Geum River compared to the information obtained from satellite images. Therefore, these findings showed the potential of deep learning-based SR algorithms by providing further information according to the algal dynamics for inland water management with remote sensing images.
Performance analysis of cache-enabled wireless networks considering stochastic geometry approach

Caching of the popular content in the storages of local nodes near to the users has been proposed as a promising approach to handle the network data traffic and the backhaul constraint of access points in the cellular networks. These local storages, also called caches or helpers nodes, are the nodes with the limited communication range but with considerable storage capacities which are generally established condensed to realise the above goals. This dense distribution makes possible the collaboration among the helper nodes for exchanging their embedded files and delivery of the requested files to the users. In this study, to manage the traffic in smaller areas and also to establish more users' accesses to their requested files, clustered dense helper scenario is considered. More precisely, the helper nodes are grouped in disjoint clusters, through the clustering strategy induced by Matern hard core point process. Then the closed-form expressions based on stochastic geometry are derived for some performance metrics of the network. The analytical derivations are validated with Monte Carlo simulations which also compare two cases of clustered dense helper nodes and fewer helper nodes without clustering scenarios. The results show the advantage of the former from the outage probability perspective.
A fitted approximate method for a Volterra delay-integro-differential equation with initial layer

This study is concerned with the finite-difference solution of singularly perturbed initial value problem for a linear first order Volterra integro-differential equation with delay. The method is based on the method of integral identities with the use of exponential basis functions and interpolating quadrature rules with the weight and remainder terms in integral form. The emphasis is on the convergence of numerical method. It is shown that the method displays uniform convergence in respect to the perturbation parameter. Numerical results are also given.
Effects of Job Crafting on Job Engagement of College Teachers in Hebei Province, China

Considering job resources of college teachers as a mediating variable, this study analyzed the relationship between job crafting and job engagement and established a model of the mechanism underlying the effects of job crafting on job engagement. This theoretical hypothesis model was empirically validated using 300 valid questionnaires completed by college teachers in Hebei Province, China, who were selected through convenience sampling. Job crafting directly affected job resources, and job resources exerted a direct effect on job engagement. However, job resources played a full mediating role in the relationship between job crafting and job engagement after it was added as a mediating variable.
Holographic Recording of Unslanted Volume Transmission Gratings in Acrylamide/Propargyl Acrylate Hydrogel Layers: Towards Nucleic Acids Biosensing

The role of volume hydrogel holographic gratings as optical transducers in sensor devices for point-of-care applications is increasing due to their ability to be functionalized for achieving enhanced selectivity. The first step in the development of these transducers is the optimization of the holographic recording process. The optimization aims at achieving gratings with reproducible diffraction efficiency, which remains stable after reiterative washings, typically required when working with analytes of a biological nature or several step tests. The recording process of volume phase transmission gratings within Acrylamide/Propargyl Acrylate hydrogel layers reported in this work was successfully performed, and the obtained diffraction gratings were optically characterized. Unslanted volume transmission gratings were recorded in the hydrogel layers diffraction efficiencies; up to 80% were achieved. Additionally, the recorded gratings demonstrated stability in water after multiple washing steps. The hydrogels, after functionalization with oligonucleotide probes, yields a specific hybridization response, recognizing the complementary strand as demonstrated by fluorescence. Analyte-sensitive hydrogel layers with holographic structures are a promising candidate for the next generation of in vitro diagnostic tests.