Miracle-dz/UniFAR-FTUs huggingface dataset:
A Thorny Tale of Parasites: Screening for Enteric Protozoan Parasites in Hedgehogs from Portugal

Simple Summary This study represents the first investigation of enteric protozoan parasites in hedgehogs in mainland Portugal, focusing on prevalent parasites, including Blastocystis sp. , Balantioides coli, Cryptosporidium spp. , and Giardia duodenalis. Blastocystis and Cryptosporidium were detected with an occurrence of 0.91% (1/110, 95% CI: 0.02–4.96). These findings highlight the need for continued research and surveillance and mark the first report of Blastocystis and Cryptosporidium occurrences in hedgehogs of Portugal, providing valuable data to address the occurrence and distribution of these parasites. The detection of parasites with zoonotic potential raises concerns about potential transmission to humans, emphasizing the necessity for continuous monitoring. This study suggests the importance of additional research with larger sample sizes and a wider geographical representation. This can contribute to the development of prevention and control strategies, aiming to better understand and manage potential risks in hedgehog populations and their interaction with other animals and humans. Abstract Enteric protozoan parasites, such as Blastocystis sp. , Balantioides coli, Cryptosporidium spp. , and Giardia duodenalis, may have implications for both animal and human health.Transmitted through the fecal–oral route, these parasites cause symptoms such as diarrhea, abdominal pain, and weight loss. This study investigated the presence of these enteric protozoan parasites and genetically characterized them in hedgehogs from Portugal. A total of 110 hedgehog stool samples were collected. Molecular detection methods showed an overall occurrence of protozoa in 1.82% (2/110 95% CI: 0.22–6.41) of hedgehogs, with Blastocystis being found in one hedgehog and Cryptosporidium being found in another. No evidence for the presence of B. coli or G. duodenalis was found. This study suggests that there is a need to stay aware of hedgehogs as potential hosts of enteric protozoa. Ongoing research and surveillance efforts are recommended to explore practical prevention and control strategies. The results contribute to the limited knowledge of these parasites in Portuguese hedgehog populations and underscore their potential relevance to both veterinary and public health.
Personzentrierte Therapie bei Borderline-Persönlichkeitsstörungen

Die Arbeit zeichnet den Prozess einer Therapiestunde in der personzentrierten Behandlung einer Klientin mit einer Borderline-Persönlichkeitsstörung nach. Anhand der Fallskizze werden das störungsbezogene Vorgehen sowie das konkrete Beziehungsangebot in der therapeutischen Begegnung veranschaulicht. Die konkreten Interventionen der Autorin werden im Hinblick auf die Verwirklichung eines optimalen Beziehungsangebotes sowie den Therapieerfolg diskutiert.
The Molecular Mechanism Underlying the Therapeutic Effect of Dihydromyricetin on Type 2 Diabetes Mellitus Based on Network Pharmacology, Molecular Docking, and Transcriptomics

Type 2 diabetes mellitus (T2DM) is a chronic and complex disease, and traditional drugs have many side effects. The active compound dihydromyricetin (DHM), derived from natural plants, has been shown in our previous study to possess the potential for reducing blood glucose levels; however, its precise molecular mechanism remains unclear. In the present study, network pharmacology and transcriptomics were performed to screen the molecular targets and signaling pathways of DHM disturbed associated with T2DM, and the results were partially verified by molecular docking, RT-PCR, and Western blotting at in vivo levels. Firstly, the effect of DHM on blood glucose, lipid profile, and liver oxidative stress in db/db mice was explored and the results showed that DHM could reduce blood glucose and improve oxidative stress in the liver. Secondly, GO analysis based on network pharmacology and transcriptomics results showed that DHM mainly played a significant role in anti-inflammatory, antioxidant, and fatty acid metabolism in biological processes, on lipoprotein and respiratory chain on cell components, and on redox-related enzyme activity, iron ion binding, and glutathione transferase on molecular functional processes. KEGG system analysis results showed that the PI3K-Akt signaling pathway, IL17 signaling pathway, HIF signaling pathway, MAPK signaling pathway, AGE-RAGE signaling pathway in diabetic complications, and TNF signaling pathway were typical signaling pathways disturbed by DHM in T2DM. Thirdly, molecular docking results showed that VEGFA, SRC, HIF1A, ESR1, KDR, MMP9, PPARG, and MAPK14 are key target genes, five genes of which were verified by RT-PCR in a dose-dependent manner. Finally, Western blotting results revealed that DHM effectively upregulated the expression of AKT protein and downregulated the expression of MEK protein in the liver of db/db mice. Therefore, our study found that DHM played a therapeutic effect partially by activation of the PI3K/AKT/MAPK signaling pathway. This study establishes the foundation for DHM as a novel therapeutic agent for T2DM. Additionally, it presents a fresh approach to utilizing natural plant extracts for chemoprevention and treatment of T2DM.
On Designs of Decentralized Reputation Management for Permissioned Blockchain Networks

In permissioned blockchain networks, the Proof of Authority (PoA) consensus, which uses the election of authorized nodes to validate transactions and blocks, has been widely advocated thanks to its high transaction throughput and fault tolerance. However, PoA suffers from the drawback of centralization dominated by a limited number of authorized nodes and the lack of anonymity due to the round-robin block proposal mechanism. As a result, traditional PoA is vulnerable to a single point of failure that compromises the security of the blockchain network. To address these issues, we propose a novel decentralized reputation management mechanism for permissioned blockchain networks to enhance security, promote liveness, and mitigate centralization while retaining the same throughput as traditional PoA. This paper aims to design an off-chain reputation evaluation and an on-chain reputation-aided consensus. First, we evaluate the nodes’ reputation in the context of the blockchain networks and make the reputation globally verifiable through smart contracts. Second, building upon traditional PoA, we propose a reputation-aided PoA (rPoA) consensus to enhance security without sacrificing throughput. In particular, rPoA can incentivize nodes to autonomously form committees based on reputation authority, which prevents block generation from being tracked through the randomness of reputation variation. Moreover, we develop a reputation-aided fork-choice rule for rPoA to promote the network’s liveness. Finally, experimental results show that the proposed rPoA achieves higher security performance while retaining transaction throughput compared to traditional PoA.
Computational analysis of source receptor air pollution problems

This study introduces a method of graph computing for Environmental Economics. Different visualization modules are used to reproduce source-receptor air pollution schemes and identify their structure. Data resources are emissions-depositions tables, available online from the European Monitoring and Evaluation Program (EMEP) of the Long-Range Transmission of Air Pollutants in Europe. In network models of pollutants exchange, we quantify the responsibility of polluters by exploring graph measures and metrics. In a second step, we depict the size of the responsibility of EU countries. We create pollution schemes for ranking the blame for the change in pollutants in the extended EMEP area. Our approach considers both the activity and the amount of pollution for each polluter. To go a step further in qualitative analysis of pollution features, we cluster countries in communities, bonded with strong polluting-based relationships. The network framework and pollution pattern visualization in tabular representations is integrated in Mathematica computer software.
Cyberloafing: A preliminary study from Indonesian workers

The primary objective of this study was to create a comprehensive measurement scale for cyberloafing from the perspective of Generation Z in Indonesia. The research was conducted in two stages. The first stage involved 145 participants for initial validity, and the second stage included 214 respondents for the final validation. Two distinct factors were identified through exploratory factor analysis (EFA) and confirmatory factor analysis (CFA): personal interest and social cyberloafing. These factors comprised 11 items, providing a more in-depth understanding of cyberloafing and its prevalence in Gen Z populations. The article also discussed the study's limitations and provided recommendations for future research.
Mapping halophytes using decision tree approach

Ghannouch in the south-east of Tunisia is colonized by halophytes. Mapping and monitoring with remote sensing approach are previsioned as the ways to trace the spatial and temporal confines of their distribution in order to reveal the extent of salinization and its dynamic. The distinguishing of halophyte vegetation can be done by examining optical remote detecting information as a tree approach applied to European Space Agency Sentinel-2 symbolism. As a result, for an area of interest of 50 × 50 km², at least 68% was classified as halophyte land cover. This mapping exercise represents an important step toward improved halophyte mapping in Tunisia and could be used to monitor the status of other salinity-prone regions in the world.
Ultrawideband, Dual-Linearly Polarized Tightly Coupled Dipole Array With Composite Wide-Angle Scanning Superstrate

We present an ultra-wideband (UWB) dual-linearly polarized tightly coupled dipole array (TCDA) with composite impedance matching superstrate for wide-angle scanning. The proposed design provides a superior performance of 10.6:1 bandwidth (1.8–19.1 GHz) with active voltage standing wave ratio (VSWR) less than 3 when scanning up to ±45° in both E and H planes, which is mainly due to use of double-layer composite frequency selective surface (FSS) impedance matching superstrate that contains two types of unit cells. Besides, the array achieves average efficiency of 80% by removing the resistive FSS layer between dipole and ground plane in conventional designs. The overall profile is 0. 68λmiddle, which makes the antenna suitable for the application of satellite communications.
Performance of the Alice PDx Device With the Somnolyzer Automated Scoring Algorithm for the Diagnosis of Obstructive Sleep Apnea

Objective Automated scoring of respiratory events could allow a swifter obstructive sleep apnea (OSA) identification. We assessed the accuracy of the Alice PDx device with the Somnolyzer automated scoring algorithm, compared to the manually reviewed scoring by a trained sleep technician, for the diagnosis of OSA. Methods A prospective study was conducted between March 2021 and March 2022 in Centro Hospitalar do Baixo Vouga, a level 2 hospital in Aveiro, Portugal. Patients with high pre-test probability for OSA performed a type III home sleep apnea testing with the Alice PDx device. Data were scored automatedly by the Sleepware G3 with the Somnolyzer digital system and manually by a trained sleep technician. Correlation and dependent t-tests were used. Sensitivity, specificity, positive predictive values (PPVs), negative predictive values (NPVs), and area under the receiver operating characteristic curve (AUROC) of automated scoring were calculated. Data were analyzed using the Stata Statistical Software (Release 17, StataCorp., 2023, College Station, TX: StataCorp LLC). Results In 150 participants (mean age 57.8 ± 13.9 years), the mean apnea-hypopnea index (AHI) was 21.9 ± 21.8 events/hour by manual scoring and 25.4 ± 21.6 events/hour by automated scoring. The mean difference was 3.4 ± 4.4 events/hour, and a strong, positive, linear correlation was found between the two scores (r = 0.98). At the altered AHI (AHI ≥ 5 events/hour), mild, moderate, and severe OSA, the automated scoring sensitivity/specificity values were 91.2%/100.0%, 80.0%/68.6%, 91.6%/41.9%, and 98.1%/80.9%, respectively. The PPVs/NPVs for the same categories were 100.0%/69.4%, 89.3%/51.1%, 79.7%/66.7%, and 91.8%/95.0%, respectively. Finally, the AUROC was 0.85, 0.70, 0.73, and 0.93, respectively. Conclusion The automated scoring obtained from the Alice PDx portable device, using Sleepware G3 with the Somnolyzer digital system, seems accurate enough to diagnose OSA and validate the initiation of PAP therapy in the correct clinical setting. Nevertheless, it does not replace manual reviewing by a trained sleep technician in the case of mild and moderate OSA, to obtain a correct severity classification. With this valuable time-saving tool, we expect to hasten OSA diagnosis and treatment and thus tackle the underdiagnosis problem.
Impacts of Spaceborne GNSS-R System Observations on Storm Surge Prediction: A Hurricane Harvey Case Study Using CYGNSS Full DDM Downlinks

This work assesses the impact of the Cyclone Global Navigation Satellite System (CYGNSS) (full delay-Doppler map) informed wind fields on storm surge simulations using the constellation's storm observations. In order to assess the impact of the CYGNSS-enhanced wind fields, storm surge simulations are performed with the ADvanced CIRCulation (ADCIRC) model and validation studies are performed with high water mark data provided by the U.S. Geological Survey. To provide context for the CYGNSS-based results, comparisons to storm surge predictions using Modern-Era Retrospective analysis for Research and Applications, Version 2,wind fields are also performed using the example of hurricane Harvey. In this initial assessment, it is observed that augmenting existing wind estimates with information provided by Global Navigation Satellite Systems Reflectometry systems, such as CYGNSS, has the potential of improving surge predictions relative to existing sources of wind information.
Comparison of Two Methods for Measuring the Temperature Dependence of H2 Permeation Parameters in Nitrile Butadiene Rubber Polymer Composites Blended with Fillers: The Volumetric Analysis Method and the Differential Pressure Method

Hydrogen uptake/diffusivity in nitrile butadiene rubber (NBR) blended with carbon black (CB) and silica fillers was measured with a volumetric analysis method in the 258–323 K temperature range. The temperature-dependent H2 diffusivity was obtained by assuming constant solubility with temperature variations. The logarithmic diffusivity decreased linearly with increasing reciprocal temperature. The diffusion activation energies were calculated with the Arrhenius equation. The activation energies for NBR blended with high-abrasion furnace CB and silica fillers increased linearly with increasing filler content. For NBR blended with medium thermal CB filler, the activation energy decreased with increasing filler content. The activation energy filler dependency is similar to the glass transition temperature filler dependency, as determined with dynamic mechanical analysis. Additionally, the activation energy was compared with that obtained by the differential pressure method through permeability temperature dependence. The same activation energy between diffusion and permeation in the range of 33–39 kJ/mol was obtained, supporting the temperature-independent H2 solubility and H2 physisorption in polymer composites.
Two-dimensional morphological simulation in transcritical flow

A two-dimensional Finite Element hydrodynamic model with capabilities for transcritical flow has been coupled with a sediment transport model to simulate bed elevation changes in alluvial beds. The model has been tested for a knickpoint migration experiment that involved a short and very steep reach of 10% bed slope. The initial Froude was Fr = 3.5 downstream from the knickpoint with a hydraulic jump formed close to the toe of the oversteepened reach. The results computed by the model agreed with the experimental data. The new upstream location of the knickpoint was correctly predicted and the computed bed profile seemed to follow the average trend of the measured profile. The model seems as a promising morphodynamic tool for alluvial streams subject to transcritical flow.
Comparative Analysis of Rhizosphere and Endosphere Fungal Communities in Healthy and Diseased Faba Bean Plants

This study used the ITS approach based on Illumina MiSeq sequencing to assess the endosphere and rhizosphere fungal communities in healthy and diseased faba bean plants. The findings indicate that the most predominant phyla in all samples were Ascomycota (49.89–99.56%) and Basidiomycota (0.33–25.78%). In healthy endosphere samples, Glomeromycota (0.08–1.17%) was the only predominant phylum. In diseased endosphere samples, Olpidiomycota (0.04–1.75%) was the only predominant phylum. At the genus level, Penicillium (0.47–35.21%) was more abundant in rhizosphere soil, while Paraphoma (3.48–91.16%) was predominant in the endosphere roots of faba bean plants. Significant differences were observed in the alpha diversity of rhizosphere samples from different germplasm resources (p < 0.05). The fungal community structures were clearly distinguished between rhizosphere and endosphere samples and between healthy and diseased endosphere samples (p < 0.05). Saccharomyces was significantly enriched in diseased endosphere samples, whereas Apiotrichum was enriched in healthy endosphere samples. Vishniacozyma and Phialophora were enriched in diseased rhizosphere samples, while Pseudogymnoascus was enriched in healthy rhizosphere samples. Diseased samples displayed more strongly correlated genera than healthy samples. Saprotrophs accounted for a larger proportion of the fungal microbes in rhizosphere soil than in endosphere roots. This study provides a better understanding of the composition and diversity of fungal communities in the rhizosphere and endosphere of faba bean plants as well as a theoretical guidance for future research on the prevention or control of faba bean root rot disease.
Performance Comparison of Linear and Nonlinear Precoding for Massive MIMO

Massive Multiple-Input Multiple-Output (MIMO) answers the exponentially increasing demand for comprehensive fixed broadband and broadcast wireless communication services. Massive MIMO is a pivotal technology in the 5G and beyond (5GaB) wireless communication systems. This paper compares linear precoding algorithms such as Minimum Mean Square Error (MMSE), Neumann Series Approximation (NSA) with nonlinear precoders such as Tomlinson-Harashima Precoder (THP), and Lattice Reduction (LR) algorithm, the Lenstra-Lenstra-Lov'asz (LLL) precoder. The comparison was conducted using Bit-Error Rate (BER) and signal-to-noise ratio (SNR) performance measures. Simulated results prove that nonlinear precoders outperform linear precoding in high SNR regions.
Exploring the impact of workload, organizational support, and work engagement on teachers’ psychological wellbeing: a structural equation modeling approach

Introduction This study delves into the intricate relationships among workload, perceived organizational support, work engagement, and psychological wellbeing within the context of 572 secondary school teachers in China. Methods Utilizing structural equation modeling (SEM), this research rigorously examined construct validity and the intricate interrelationships among latent variables. The data were collected and analyzed to determine the associations between workload, perceived organizational support, work engagement, and psychological wellbeing. Results The findings unveiled compelling negative associations between workload and perceived organizational support, workload and work engagement, and workload and psychological wellbeing among the secondary school teachers. Conversely, positive correlations emerged between perceived organizational support, work engagement, and psychological wellbeing. The structural equation modeling analysis demonstrated strong fit indices, affirming robust alignment with the anticipated models. Discussion Mediation analyses further elucidated the significance of work engagement as a mediator in the relationships between workload and psychological wellbeing, as well as between perceived organizational support and psychological wellbeing. These results underscore the pivotal role of work engagement in shaping the impact of workload and organizational support on the psychological wellbeing of secondary school teachers in Chinese educational settings.
Detecting and Classifying Changes in Traffic Rules using Induction Loop Data

Up-to-date and accurate road maps with traffic rules are crucial for traffic safety and efficiency. However, detecting changes in traffic rules, particularly in road signs in an efficient manner still remains an open challenge. This paper proposes a method to detect and classify seven types of changes, relying on observable traffic flow changes like average vehicle speed. Our method employs a deep learning-based binary relevance approach, treating each type of change as a separate binary classification task. Input data comprise information from citywide induction loops, detailing congestion levels and average speed for each road, which vary with time and location. Our model outputs change type probabilities for 2.5 km2 city regions for every 10 minutes. Unlike GPS traces of buses and taxis, which provide merely a partial view of the traffic, induction loop data offers a comprehensive, cost-effective view of traffic. However, there are three key challenges of utilizing induction loop data for change detection and classification, including sparse deployment of induction loops, high dimensionality of input data, and severe class imbalance. To address the first two challenges, we introduce novel strategies comprising dimensionality reduction, multi-dimensional sliding windows, neural network architectural choices such as residual connections and stateful recurrency, and data augmentation. Regarding the class imbalance, we apply sample weighing in the loss function. These novel design choices result in effective solutions with F1-scores exceeding 80% in experiments with simulated and real-world traffic data.