Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effects of Nature Exposure Therapies on Stress, Depression, and Anxiety Levels: A Systematic Review

Background: Mental well-being plays a pivotal role within the broader spectrum of health and illness, encompassing factors such as stress, depression, and anxiety. Nature-based therapeutic interventions have emerged as a promising approach to addressing these mental health challenges. This study seeks to assess the impact of these interventions on stress, depression, and anxiety levels. Methods: We conducted an extensive search for randomized clinical trials that examined stress, anxiety, and depression levels. The selected studies underwent a rigorous risk-of-bias assessment following the guidelines outlined in the Cochrane Handbook for Systematic Reviews. Results: Our review encompassed findings from eight publications. Among them, two studies measuring cortisol levels revealed significant differences between the pre-test and post-test measurements within the intervention groups. In two studies that employed the Stress Response Inventory, a significant decrease in stress levels was observed within the intervention groups in contrast to the control groups. However, no significant differences were noted in studies that utilized the Restorative Outcome Scale. In the assessment of anxiety and depression levels, three studies employed the Positive and Negative Affect Schedule, while four studies utilized The Profile of Mood States scale; none of these studies demonstrated significant differences. Conclusions: The current body of evidence offers limited support for advocating nature-based therapeutic interventions as a primary approach to reducing stress, depression, and anxiety.
Curvilinear version of the most model with application to the coast-wide tsunami forecast

1 Part I: Regional Forecast Version 1 3 1 Ideas behind the Regional Forecast 3 2 Regional Forecast Implementation and Testing 4 Part II: Curvilinear MOST and its First Application: Regional Forecast Version 2 9 1 Curvilinear MOST: The Theory 9 1.1 The Equations . 9 1.2 Splitting . 10 1.3 Particular Case: MOST in Cartesian Coordinates . 11 1.4 Particular Case: MOST on Spherical Earth . 12 2 Curvilinear MOST: The Code 12 2.1 MOST Code Versions . 12 2.2 Metric Factors in the Difference Scheme . 13 2.3 Bathymetry Format for a Curvilinear Grid on the Globe . 16 2.4 Database Input Through the Boundaries . 17 3 Regional Forecast Version 2, West Coast 18 3.1 Shoreline-Oriented Grids for Regional Forecast . 19 3.2 Differences Between the Regional Forecast Versions’ Computation Results . 22 4 Acknowledgments 27
Pulmonary Effects of Traumatic Brain Injury in Mice: A Gene Set Enrichment Analysis

Acute lung injury occurs in 20–25% of cases following traumatic brain injury (TBI). We investigated changes in lung transcriptome expression post-TBI using animal models and bioinformatics. Employing unilateral controlled cortical impact for TBI, we conducted microarray analysis after lung acquisition, followed by gene set enrichment analysis of differentially expressed genes. Our findings indicate significant upregulation of inflammation-related genes and downregulation of nervous system genes. There was enhanced infiltration of adaptive immune cells, evidenced by positive enrichment in Lung-Th1, CD4, and CD8 T cells. Analysis using the Tabula Sapiens database revealed enrichment in lung-adventitial cells, pericytes, myofibroblasts, and fibroblasts, indicating potential effects on lung vasculature and fibrosis. Gene set enrichment analysis linked TBI to lung diseases, notably idiopathic pulmonary hypertension. A Venn diagram overlap analysis identified a common set of 20 genes, with FOSL2 showing the most significant fold change. Additionally, we observed a significant increase in ADRA1A→IL6 production post-TBI using the L1000 library. Our study highlights the impact of brain trauma on lung injury, revealing crucial gene expression changes related to immune cell infiltration, cytokine production, and potential alterations in lung vasculature and fibrosis, along with a specific spectrum of disease influence.
Efficient vehicular networks offloading using Hybrid Localization Algorithm and Deep Reinforcement Learning

In the context of growing urbanization and increased vehicular traffic, the demand for efficient computation and location-based services is paramount. This paper proposes a pioneering solution to address the challenges of precise location services in Vehicular Networks within urban settings. The system combines a Hybrid Localization Algorithm (HLA) that integrates multiple methods for improved location accuracy with Deep Reinforcement Learning (DRL) for intelligent and adaptive offloading decisions based on real-time traffic conditions. Extensive simulations demonstrate the effectiveness of our approach in reducing response times, optimizing offloading strategies, and alleviating the burden of urban peak vehicle navigation pressure. This research paves the way for enhanced location-based services and intelligent transportation systems in urban areas.
The influence mechanism underlying meaning in life on career adaptability among college students: a chain intermediary model

Introduction The coronavirus disease 2019 (COVID-19) has posed a huge challenge to the career situation of college students. This study aimed to understand the mechanism underlying meaning in life on career adaptability among college students during the COVID-19 pandemic. Methods A quantitative method was adopted. In total, 1,182 college students were surveyed using the Meaning in Life Questionnaire, the Simplified Coping Style Questionnaire, the Adult General Hope Scale, and the Career Adapt-Abilities Scale. Results There was a significant positive correlation between meaning in life, positive coping styles, hope, and career adaptability. Positive coping styles and hope play a separate mediating role and a chain mediating role. Discussion The findings of this study emphasize the importance of meaning in life among college students to improve their career adaptability. Furthermore, positive coping styles and increased levels of hope contribute to the development of career adaptability among college students.
A One-Pot Convenient RPA-CRISPR-Based Assay for Salmonella enterica Serovar Indiana Detection

Salmonella enterica serovar Indiana (S. Indiana) is among the most prevalent serovars of Salmonella and is closely associated with foodborne diseases worldwide. In this study, we combined a recombinase polymerase amplification (RPA) technique with clustered regularly interspaced short palindromic repeat (CRISPR) and CRISPR-associated (Cas) protein Cas12b (CRISPR/Cas12b)-based biosensing in a one-pot platform to develop a novel one-step identification method for S. Indiana infection diagnosis. The entire RPA-CRISPR/Cas12b reaction can be completed at 41 °C within 1 h without the need for specific instruments. The optimal concentrations of Cas12b and single-guide RNA (sgRNA) for the reaction were the same at 250 nM. The single-stranded DNA (ssDNA) reporter 8C-FQ (5′-/6-FAM/CCCCCCCC/BHQ1/-3′) presented the best performance in the reaction compared with the other reporters. The limit of detection (LoD) of the RPA-CRISPR/Cas12b assay was 14.4 copies per reaction. As for specificity, we successfully identified four S. Indiana strains among twenty-two Salmonella strains without any false-positive results, presenting 100% accuracy for S. Indiana, and no cross-reactions were observed in eight other pathogens. Moreover, a total of 109 chicken carcasses were classified by the S. Indiana RPA-CRISPR assay and PCR methods from three processing points, including 43 post-shedding, 35 post-evisceration, and 31 post-chilling. There were 17 S. Indiana-positive samples identified during the whole processing step, consisting of nine post-shedding, five post-evisceration, and three post-chilling. The corresponding S. Indiana-positive rates of post-shedding, post-evisceration, and post-chilling were 20.93% (9/43), 14.29% (5/35), and 9.68% (3/31), respectively. Results from the S. Indiana one-step RPA-CRISPR/Cas12b assay were totally in agreement with those obtained using a traditional culture method, demonstrating 100% agreement with no false-positive or false-negative results observed. Altogether, the RPA-CRISPR/Cas12b assay developed in this study represents a promising, accurate, and simple diagnostic tool for S. Indiana detection.
Microstructural Imaging of Shock-Recovered Berea Sandstone and Quartz Sand Using Scanning Electron Microscopy

A number of shock-recovery experiments have been performed on Berea sandstone for different conditions: dry, water-saturated, hydrostatically water-pressurized and Helium gas-pressurized. The authors also conducted experiments with purified quartz sand in dry and water-saturated conditions with a grain size between 212 to 250 {micro}m and 250 to 300 {micro}m to compare with damaged Berea sandstone. The shock stresses in the range between 1.2 to 9.8 GPa were achieved by impacting projectiles accelerated by a single-stage light-gas gun. Different flyer plate thicknesses were used to produce different shock pulse durations. The water-pressurized sandstone targets were hydrostatically pressurized between 7.58-7.79 MPa, whereas the gas-pressure samples were pressurized to 27.5 MPa using helium gas. The microstructural damage of all specimens is being investigated by using scanning electron microscopy (SEM) in order to determine differences for these conditions. In this report they will present the results of the systematic SEM investigations for each experiment. The scientific results and discussions including X-ray computed micro tomography and statistical analysis are presented elsewhere. Overall, they collected around 1600 SEM pictures, which are available in electronic form on Compact Disks (CDs). They also provide the results of the laser particle analysis on the CDs.
Aquifer Barreiras: Evaluation of ground water reserves in northern Espírito Santo, southeastern Brazil

The aquifer Barreiras is porous, free and semi-confined, with high porosity and permeability. It is composed of alluvial and fluvial sediments of the Barreiras Formation, in a complex association of permeable/impermeable strata. The mean hydrodynamic parameters of the 107 registered and evaluated tubular wells are a depth of 100.81 m, static level of 14.18 m, dynamic level of 51.48 m, drawdown of 37.30 m, a flow rate of 43.52 m, specific flow of 2,3681 m and transmissivity of 175.54 m/day. The natural outflow of Barreiras Aquifer is 296.10 m/year, the Infiltration Rate is 0.82%, the Regulatory Reserve or Recharge Volume 321.10 m and the Permanent Reserve volume 1,27.10 m. The hydrodynamic characteristics of the Aquifer Barreiras indicate a promising underground source and high heterogeneity throughout the formation. Keywords— Aquifer Barreiras; Groundwater; Rio Doce
Automated Detection of Primary Liver Cancer Using Different Deep Learning Approaches

Hepatocellular carcinoma (HCC) is the world's sixth-most common malignancy and the fourth-leading cause of cancer death. This paper introduces an early diagnose to HCC using fully autonomous system by artificial intelligence (AI) techniques. This system consists of three stages; preprocessing, liver segmentation, and classifier stage.Preprocessing improves the quality of the abdominal Computed Tomography(CT) scan to be better analyzed. The liver is extracted and segmented from improving CT image using various deep learning techniques, and semantic segmentation is used to separate lesions from a tumor-filled liver in cases of HCC by classifier. U-Net and Resnet50 deep learning algorithms are used to segment the liver and tumour automatically, but the Resnet50 achived the better results with a DICE score of 0.926, and loss of 0.0028. The classifier's robustness is justified using 5-fold cross-validation (CV). The Resnet 50 classifier, which is a classification approach based on transfer learning (TL), achieved 96% accuracy for the binary classifier. In comparison with the linked methods, the proposed system confirms that it is the promised fully autonomous system for liver segmentation and tumour segmentation in cases diagnosed with HCC.
Identification of Similarities and Clusters of Bread Baking Recipes Based on Data of Ingredients

We define the similarity of bakery recipes and identify groups of similar recipes using different clustering algorithms. Our analyses are based on the relative amounts of ingredients included in the recipes. We use different clustering algorithms to find the optimal clusters for all recipes, namely k-means, k-medoid, and hierarchical clustering. In addition to standard similarity measures we define a similarity measure using the logarithm of the original data to reduce the impact of raw materials that are used in large quantities. Clustering recipes based on their ingredients can improve the search for similar recipes and therefore help with the time-consuming process of developing new recipes. Using the k-medoid method, we can separate 1271 recipes into six different clusters. We visualize our results via dendrograms that represent the hierarchical separation of the recipes into individual groups and sub-groups.
Elevating Student Performance Prediction using Extra-Trees Classifier and Meta-Heuristic Optimization Algorithms

—In the highly competitive landscape of academia, the study addresses the multifaceted challenge of analyzing voluminous and diverse educational datasets through the application of machine learning, specifically emphasizing dimensionality reduction techniques. This sophisticated approach facilitates educators in making data-informed decisions, providing timely guidance for targeted academic improvement, and enhancing the overall educational experience by stratifying individuals based on their innate aptitudes and mitigating failure rates. To fortify predictive capabilities, the study employs the robust Extra-Trees Classifier (ETC) model for classification tasks. This model is enhanced by integrating the Gorilla Troops Optimizer (GTO) and Reptile Search Algorithm (RSA), cutting-edge optimization algorithms designed to refine decision-making processes and improve predictive precision. This strategic amalgamation underscores the research's commitment to leveraging advanced machine learning and bio-inspired algorithms to achieve more accurate and resilient student performance predictions in the mathematics course, ultimately aiming to elevate educational outcomes. Analyses of G1 and G3 showcase the efficacy of the ETRS model, demonstrating 97.5% Accuracy, F1-Score, and Recall in predicting the G1 values. Similarly, the ETRS model emerges as the premier predictor for G3, attaining 95.3% Accuracy, Recall, and F1-Score, respectively. These outcomes underscore the significant contributions of the proposed models in advancing precision and discernment in student performance prediction, aligning with the overarching goal of refining educational outcomes.
Ensemble flood forecasting based on ensemble NWP and the GMKHM distributed hydrological model

The incorporation of numerical weather predictions (NWP) into a flood forecasting system can increase forecast lead times from a few hours to a few days. A single NWP forecast from a single forecast centre, however, is insufficient as it involves considerable non-predictable uncertainties and lead to a high number of false alarms. The availability of global ensemble numerical weather prediction systems through the THORPEX Interactive Grand Global Ensemble’ (TIGGE) offers a new opportunity for flood forecast. The GMKHM distributed hydrological model, which is based on a mixed runoff generation model and overland flow routing model based on kinematic wave theory, and the topographical information of each grid cell extracted from the Digital Elevation Model (DEM), is coupled with ensemble weather predictions based on the TIGGE database (CMC, CMA,ECWMF,UKMO,NCEP) for flood forecast. This paper presents a case study using the coupled flood forecasting model on the Xixian catchment (a drainage area of 8826 km2) located in Henan province, China. A probabilistic discharge is provided as the end product of flood forecast. Results show that the association of the GMKHM model and the TIGGE database gives a promising tool for the anticipation of flood events several days ahead,, comparable with that driven by raingauge observation.
Contexts Matter but How? Course-Level Correlates of Performance and Fairness Shift in Predictive Model Transfer

Learning analytics research has highlighted that contexts matter for predictive models, but little research has explicated how contexts matter for models’ utility. Such insights are critical for real-world applications where predictive models are frequently deployed across instructional and institutional contexts. Building upon administrative records and behavioral traces from 37,089 students across 1,493 courses, we provide a comprehensive evaluation of performance and fairness shifts of predictive models when transferred across different course contexts. We specifically quantify how differences in various contextual factors moderate model portability. Our findings indicate an average decline in model performance and inconsistent directions in fairness shifts, without a direct trade-off, when models are transferred across different courses within the same institution. Among the course-to-course contextual differences we examined, differences in admin features account for the largest portion of both performance and fairness loss. Differences in student composition can simultaneously amplify drops in performance and fairness while differences in learning design have a greater impact on performance degradation. Given these complexities, our results highlight the importance of considering multiple dimensions of course contexts and evaluating fairness shifts in addition to performance loss when conducting transfer learning of predictive models in education.
A synopsis of Ruellia (Acanthaceae) in Angola, including two species new to science

: A synoptic revision of the genus Ruellia L. (Acanthaceae: Acanthoideae: Ruellieae: Ruelliinae) in Angola is provided. Eight species are documented, including two species new to science, R. angolensis I.Darbysh. & E.Tripp and R. quipungoensis I.Darbysh. & E.Tripp, both of which are known from specimens collected between the 1930s–1970s. The affinity of the two new species is discussed; both are hypothesised to belong to sect. Discifolia E.Tripp & I.Darbysh.
Haploinsufficiency of Adenomatous Polyposis Coli Coupled with Kirsten Rat Sarcoma Viral Oncogene Homologue Activation and P53 Loss Provokes High-Grade Glioblastoma Formation in Mice

Simple Summary Glioblastoma multiforme (GBM) ranks as the most frequent form of primary malignant brain tumor. The prognosis for individuals diagnosed with this disease typically leads to a median survival of under two years. Here, we developed mouse models to better understand the genetic basis of GBM, a highly aggressive brain tumor. We found that certain genetic alterations, such as Kirsten rat sarcoma viral oncogene homologue (KRAS) activation and p53 deficiency, cooperate to initiate glioma tumorigenesis. Combining these alterations with adenomatous polyposis coli (APC) haploinsufficiency led to the rapid progression of GBM in the mice, resembling the human disease. These models are valuable for identifying early disease biomarkers and may offer insights for improving the diagnosis and treatment of this challenging brain tumor. Abstract Glioblastoma multiforme (GBM) is the most common and deadly type of brain tumor originating from glial cells. Despite decades of clinical trials and research, there has been limited success in improving survival rates. However, molecular pathology studies have provided a detailed understanding of the genetic alterations associated with the formation and progression of glioblastoma—such as Kirsten rat sarcoma viral oncogene homolog (KRAS) signaling activation (5%), P53 mutations (25%), and adenomatous polyposis coli (APC) alterations (2%)—laying the groundwork for further investigation into the biological and biochemical basis of this malignancy. These analyses have been crucial in revealing the sequential appearance of specific genetic lesions at distinct histopathological stages during the development of GBM. To further explore the pathogenesis and progression of glioblastoma, here, we developed the glial-fibrillary-acidic-protein (GFAP)-Cre-driven mouse model and demonstrated that activated KRAS and p53 deficiencies play distinct and cooperative roles in initiating glioma tumorigenesis. Additionally, the combination of APC haploinsufficiency with mutant Kras activation and p53 deletion resulted in the rapid progression of GBM, characterized by perivascular inflammation, large necrotic areas, and multinucleated giant cells. Consequently, our GBM models have proven to be invaluable resources for identifying early disease biomarkers in glioblastoma, as they closely mimic the human disease. The insights gained from these models may pave the way for potential advancements in the diagnosis and treatment of this challenging brain tumor.
Talent Management and Generation Z: A Systematic Literature Review through the Lens of Employer Branding

Generation Z is arriving on the labour scene. In the near future, this generation will dominate organisations’ recruitment and selection processes. Theories regarding the attraction and retention of talent were initially developed based on the characteristics of previous generations in the late 20th century. This study aimed to conduct the first comprehensive literature review focused on identifying the attributes of attracting and retaining Generation Z talent. The findings will allow for the identification of the most significant attributes within the examined context. However, certain gaps were uncovered, such as the limited availability of publications explaining the applicability of the conventional theory of attracting and retaining talent to Generation Z natives. Significant lines of research are also suggested for the future.
Prehistoric landscape mapping along the Scheldt by camera- and conductivity CPT-E

Over the past decade, paleolandscape reconstruction was introduced as part of a preventive archaeological evaluation strategy along the Scheldt river, due to the unexpected discovery of well-preserved prehistoric landscapes and sites during construction works in the Antwerp harbor area. Hereby, CPT is an important tool in combination with coring and/or near surface geophysical survey. Applications of CPT range from desktop studies, which determine evaluation strategies, to actual paleolandscape mapping by sedimentological data interpretation. CPT-Es (with added camera and/or electrical conductivity sensors) calibrate and validate geophysical subsurface modelling and soil behavior types are interpreted. Particularly, (electrical conductivity) CPT-C disentangles sedimentological and hydrological variations in electrical conductivity values. On the other hand, camera CPT improves differentiation of organic rich sediments and detection of thin organic soil horizons within homogenous (cover)sands. The usability of CPT is illustrated through recent prehistoric landscape evaluation studies along the Scheldt river.