Miracle-dz/UniFAR-FTUs huggingface dataset:
An Association Between Job Stress and Poor Social Support Among Healthcare Workers in Northeastern Malaysia

Introduction: Job stress is an important occupational health problem globally. Hence, identification of workers at risk of developing job stress is paramount to the decision-makers. This study aims to estimate the proportion of job stress and its relationship with different categories of healthcare workers (HCWs) in the primary care and public health settings in northeastern Malaysia. Methodology: A cross-sectional study involving 520 HCWs across all categories was conducted in Kelantan State, Malaysia. A proforma and validated Malay version of the Job Content Questionnaires were administered to obtain the data. The participants were then classified into four categories of workers according to Karasek’s job demands-control model classification which were active, passive, high strain, and low strain. Results: We found that a total of 145 (28.5%) HCWs in the study have job stress (high-strain job type). HCWs with a degree or higher qualification had the highest proportion of job stress (41.2%), while the diploma group has the lowest proportion of job stress among the four academic qualification groups (22.9%). Pearson chi-square shows a significant association between Karasek’s job types and the level of social support from their supervisors (p < 0.05) but no association between job strain and the level of supervisor's social support (p > 0.05). Conclusion: Job stress among HCWs is prevalent, and the professional group had the highest percentage of risk job stress as compared to other groups. There is a significant association between the supervisor’s social support and Karasek's job strain categories.
Pulsing and Detection Strategies for Contrast-Enhanced Ultrasound: A Narrative Review

Contrast-enhanced imaging has grown significantly in the past two decades. Technology has evolved from imaging based on linear principles to elaborate pulsing and microbubble-specific detection strategies. This review provides a broad overview of the research published on these topics, emphasizing the progress made, current challenges, and future research considerations. We cover the physical and conceptual underpinnings of imaging based on ultrasound contrast agents, focused on pulsing and detection strategies. The techniques proposed are categorized according to the underlying fundamental physical and signal processing principles. We revisit methods that were previously only of academic interest and may now be clinically feasible with advances in computation and hardware. We discuss unmet challenges and opportunities originating from developments in other sub-fields of ultrasound imaging to enable wider clinical adoption of contrast-enhanced ultrasound.
Copiotrophy in a Marine-Biofilm-Derived Roseobacteraceae Bacterium Can Be Supported by Amino Acid Metabolism and Thiosulfate Oxidation

Copiotrophic bacteria that respond rapidly to nutrient availability, particularly high concentrations of carbon sources, play indispensable roles in marine carbon cycling. However, the molecular and metabolic mechanisms governing their response to carbon concentration gradients are not well understood. Here, we focused on a new member of the family Roseobacteraceae isolated from coastal marine biofilms and explored the growth strategy at different carbon concentrations. When cultured in a carbon-rich medium, the bacterium grew to significantly higher cell densities than Ruegeria pomeroyi DSS-3, although there was no difference when cultured in media with reduced carbon. Genomic analysis showed that the bacterium utilized various pathways involved in biofilm formation, amino acid metabolism, and energy production via the oxidation of inorganic sulfur compounds. Transcriptomic analysis indicated that 28.4% of genes were regulated by carbon concentration, with increased carbon concentration inducing the expression of key enzymes in the EMP, ED, PP, and TCA cycles, genes responsible for the transformation of amino acids into TCA intermediates, as well as the sox genes for thiosulfate oxidation. Metabolomics showed that amino acid metabolism was enhanced and preferred in the presence of a high carbon concentration. Mutation of the sox genes decreased cell proton motive force when grown with amino acids and thiosulfate. In conclusion, we propose that copiotrophy in this Roseobacteraceae bacterium can be supported by amino acid metabolism and thiosulfate oxidation.
MLP-Based Efficient Convolutional Neural Network for Lane Detection

Lane detection is an important and fundamental task in autonomous driving. Modern convolutional neural network (CNN) methods have achieved high performance in lane detection; however, the intrinsic locality of convolution operations makes these methods limited in effectively modeling the long-range dependencies that are vital to capture global information of lanes. Additionally, numerous convolution operations result in considerable computational cost for high complexity. To overcome these difficulties, we propose an efficient lane detection method by combining CNN with a multilayer perceptron (MLP). First, an improved bottleneck-1D layer is used to replace the standard convolutional layer in overall network to reduce the computational cost and parameters while applying hybrid dilated convolution (HDC) to better capture multiscale lane information. Second, we construct a hybrid MLP block in the latent space to capture the long-range dependencies of lanes. The hybrid MLP projects tokenized convolutional features from spatial locations and channels, and then, they are fused together to obtain global representation, in which each output pixel is related to each input pixel. The introduction of MLP further decreases computational complexity and makes the proposed architecture more efficient for lane detection. Experimental results on two challenging datasets (CULane, Tusimple) demonstrate that our method can achieve a higher computational efficiency while maintaining a decent detection performance compared with other state-of-the-art methods. Furthermore, this study indicates that integrating the global representation capacity of an MLP with local prior information of convolution is an effective and potential perspective in lane detection.
Optimal transmission strategy for spatially correlated MIMO systems with channel statistical information

In real multiple-input multiple-output (MIMO) systems, the perfect channel state information (CSI) may be costly or impossible to acquire. But the channel statistical information can be considered relatively stationary during long-term transmission. The statistical information can be obtained at the receiver and fed back to the transmitter and do not require frequent update. By exploiting channel mean and covariance information at the transmitter simultaneously, this paper investigates the optimal transmission strategy for spatially correlated MIMO channels. An upper bound of ergodic capacity is derived and taken as the performance criterion. Simulation results are also given to show the performance improvement of the optimal transmission strategy.
Signal revivals in pulsed Rydberg four-wave mixing in thermal ensembles

We present resonant four-wave mixing (FWM) signals involving a Rydberg state in a thermal Rb vapor. The dynamic behavior of the FWM signals exhibits revival peaks shortly after the incident pulse, which are due to coherent collective emission among all Doppler classes. The FWM signals have dephasing times up to 7 ns, and strongly depend on the excitation bandwidth to the Rydberg state. Our numerical simulations based on a four-level model including the whole Doppler broadened ensemble can describe the data quite well.
Systematic Analysis of Genes Related to Selenium Bioaccumulation in Microalgae: A Review

Simple Summary Selenium (Se) is an essential non-metallic nutrient in animals and humans. Its deficiency causes heart disease, infertility, neuronal/neuromuscular diseases, and increased susceptibility to cancer, infections, and heavy metal toxicity. Therefore, a sustainable strategy for bio-based products enriched with Se is microalgae. These are characterized by the ability to bioaccumulate inorganic Se and metabolize it into organic Se for product formulations of industrial interest. This review article attempts to elucidate the bioaccumulation of Selenium (Se) in microalgae from the analysis of genes or groups of genes that trigger biological responses associated with Se-metabolization in microalgae. Abstract Se is one of the essential nutrients for human health and animal growth; it participates in various physiological functions, such as antioxidant and immune response and metabolism. Se deficiency is related in the animal industry to poor production performance and the appearance of health problems in humans. Therefore, interest has arisen in producing fortified foods, nutritional supplements, and animal feed products enriched with Se. A sustainable strategy for bio-based products enriched with Se is microalgae. These are characterized by the ability to bioaccumulate inorganic Se and metabolize it into organic Se for product formulations of industrial interest. Although there are some reports on Se bioaccumulation, further exploration is needed to understand the effects of Se bioaccumulation in microalgae. Therefore, this article presents a systematic review of the genes or groups of genes that trigger biological responses associated with the metabolization of Se in microalgae. A total of 54,541 genes related to Se metabolization distributed in 160 different classes were found. Similarly, trends were identified through bibliometric networks on strains of greatest interest, bioproducts, and scientific production.
Polyphenols and Cardiometabolic Health: Knowledge and Concern among Romanian People

The cardiometabolic health of the population is a crucial indicator of public health, considering the significant impact of cardiovascular disease (CVD) and diabetes on global mortality. Determining the population’s knowledge and the predictors of these pathologies is essential in developing effective educational and clinical strategies for the prevention and management of cardiometabolic risk (CMR). Polyphenols are natural compounds with a multitude of beneficial effects on cardiometabolic health. This study explored the current knowledge, understanding, and awareness of CMR, the benefits of polyphenols among Romanians, and how sociodemographic and clinical characteristics influence this aspect. Five hundred forty-six subjects responded anonymously to an online questionnaire designed to assess their knowledge. The data were collected and analyzed based on gender, age, education level, and BMI status. Most respondents expressed concern to a great or very great extent about their health (78%) and food (60%), with significant differences (p < 0.05) depending on age, educational level, and BMI status. Of the respondents, 64.8% declared that they were familiar with the CMR term. Still, the results showed a weak correlation between the stated risk factors and the self-assessment of increased risk (r = 0.027) for CVD or diabetes. Only 35% of the respondents reported a good or very good knowledge of the term “polyphenols”, 86% recognized the antioxidant effect, and significantly fewer (26%) recognized the prebiotic effect. Developing and implementing targeted educational strategies to enhance learning and individual behaviors related to CMR factors and the benefits of polyphenols is necessary.
Research and Implementation of AC Distortion Factor Measurement

The sine wave distortion factor can be calculated by the curve- fitting method theoretically,but the sine wave curve- fitting is a four- parameter optimization problem,which makes the traditional algorithm easily fall into local extremum in calculating the globally optimal solution since it is sensitive to the initial value. Thus this will limit the application of curve- fitting in distortion factor calculation. Aiming at such a problem,a three- parameter curve- fitfting method has been introduced. Then an ARM based AC distortion factor measurement has been developed and the key technology in hardware and software design has been illustrated. Experimental results show that the method can complete the AC distortion factor measurement and meet the application requirements.
Bioclimatic Characterisation of Specific Native Californian Pinales and Their Future Suitability under Climate Change

Rising temperatures and changes in precipitation patterns under climate change scenarios are accelerating the depletion of soil moisture and increasing the risk of drought, disrupting the conditions that many plant species need to survive. This study aims to establish the bioclimatic characterisation, both qualitative and quantitative, of ten native Californian Pinales for the period 1980–2019, and to determine their habitat suitability by 2050. To achieve this, an exhaustive search of the Gbif database for records of ten conifer taxa was carried out. To conduct the bioclimatic characterisation of the studied taxa, we worked with the monthly values of average temperature and precipitation for the period 1980–2019 from 177 meteorological stations. Linear regressions was performed in order to compile the future evolution of California’s climate. Suitable areas and optimal areas were defined at the present time (1980–2019) and its future projection (2050). We applied Boolean logic and, in this investigation, the Conditional Logic Operator (CON) was used to determine the possible species presence (one) or absence (zero) for each of the 15 variables analysed. In general, most of the conifers studied here will experience a reduction in their habitat range in California by the year 2050 due to climate change, as well as the displacement of species towards optimal areas. Furthermore, the results have highlighted the applicability of bioclimatology to future conditions under climate change. This will aid conservation managers in implementing strategic measures to ameliorate the detrimental impacts of climate change, thereby ensuring the ecological integrity and sustainability of the affected conifer species.
Seems fishy: environmental DNA impacts on sketa22 quality control in salmonidae dominated waterbodies using qPCR and ddPCR

Globally, water resources used for recreation and drinking water are threatened by fecal pollution. These pollutants can cause gastrointestinal illness and environmental degradation. Additionally, most sources of fecal pollution are non-point sources stemming from multiple species. Identifying these sources is vital to categorizing the exposure risk from contact and improving remediation efforts. A common technique to provide species-specific information for fecal source identification is microbial source tracking (MST). MST quantifies DNA of host or host-associated microorganisms through polymerase chain reaction (PCR) technologies such as quantitative PCR (qPCR) or droplet digital PCR (ddPCR). MST techniques have been implemented globally and are used for routine monitoring. In the United States (US), the US Environmental Protection Agency has provided several approved standard PCR methods for MST and other recreational water quality applications. These methods have specified quality controls including sample processing controls (SPC) and assessments for sample inhibition. A standard SPC used in EPA methods involves spiking samples with salmon testes DNA (nominally originating from Chum Salmon, Oncorhynchus keta and quantifying them using Sketa22, a genus specific TaqManTM assay). This quality control (QC) behaves similarly to the microbial species being monitored. MST testing in Fall 2022 indicated elevated Sketa22 recoveries and re-analysis of samples indicated the detection of external Salmonidae DNA on both qPCR and ddPCR platforms. Our research was designed to identify the cause of this interference. Results indicate that the primer probe set may react with wild Salmonidae DNA. Analyzing the Sketa22 sequence using BLAST indicated matches with many species of Salmonidae present in the sampled stream system. Consequently, further research is required to identify the effectiveness of Sketa22 as a QC when native and migratory Salmonidae are present. General recommendations are provided to account for excess ambient Salmonidae DNA.
Molecular characterization of Astyanax species (Characiformes: Characidae) from the upper Paraguaçu River basin, a hydrographic system with high endemism

Abstract Molecular tools have been employed to improve the knowledge about freshwater Neotropical fishes. Such approaches supporting studies of groups including species complexes such as Astyanax, one of the most diversified and taxonomically complex genus of the family Characidae. Here, we employed species delimitation analyses in four Astyanax species described for the upper Paraguaçu River basin, a drainage within Northeastern Mata Atlântica freshwater ecoregion with high endemism. We implemented single and multilocus approaches based on two mitochondrial and one nuclear markers. Cytochrome c Oxidase I sequences previously available for Astyanax species were also added to our dataset. The single locus analyses showed A. epiagos, A. rupestris, and A. aff. rupestris as different Molecular Operational Taxonomic Units (MOTUs), while A. brucutu and A. lorien were grouped. However, the multilocus approach distinguished these two species and showed congruence for the remaining single locus results. Astyanax aff. rupestris was separated into two MOTUs using both approaches, highlighting the need for an integrative taxonomic revision including A. aff. rupestris. These findings contribute to a better understanding of the diversity of this fish group in the upper Paraguaçu, identifying hidden diversity and reinforcing the relevance of this hydrographic system as a notable hotspot for ichthyofauna biodiversity endemism.
Energy-Efficient Maximization for RIS-Aided MISO Symbiotic Radio Systems

Symbiotic radio (SR) and reconfigurable intelligent surface (RIS) can serve as a complementary technique for each other regarding spectrum efficiency and energy efficiency (EE) improvements. In this paper, we investigate a RIS-aided multiple-input single-output SR system with multiple backscatter devices (BDs). In particular, an EE-based maximization resource allocation problem is formulated by jointly optimizing the passive beamforming vector of the RIS, the active beamforming vector of the primary transmitter (PT), and the reflection coefficient of each BD under multiple constraints, including the minimum rate of each user, the maximum transmit power of the PT, and the minimum harvested energy of each BD. To solve the non-convex problem, a Dinkelbach-based block coordinate descent algorithm is developed to obtain the sub-optimal solution. Numerical results verify that the proposed algorithm achieves two times more than the traditional algorithms in terms of system EE.
A New Soft-Switched High Step-Up Trans-Inverse DC/DC Converter Based on Built-In Transformer

This article proposes a new Zero-Voltage Switching (ZVS) high step-up DC/DC converter based on a built-in transformer for renewable energy applications. The proposed topology utilizes a Three-Winding Built-In Transformer (TWBT) to increase the voltage gain, but unlike most coupled-inductor-based DC-DC converters, high output voltages can be obtained under a lower magnetic turns ratio. In this circuit, with the help of a regenerative active clamp circuit, the energy of the leakage inductor from the TWBT is absorbed and transferred to the output, therefore, the ZVS conditions at turn-on time are achieved for switches. The voltage stresses across the switches of the proposed topology are limited, and the diodes reverse-recovery issue are eliminated. Due to the low input current ripple, the suggested topology can be used for renewable energy sources. Furthermore, because of the low number of components along with the soft-switching operation, the proposed circuit can offer enough high efficiency. The operational principle, steady-state analysis, and characteristics of the proposed converter are provided. Finally, a 200 W prototype with 25 V input and 400 V output voltage is built to validate the analytical results.
A Novel Deep Learning-Based Robust Dual-Rate Dynamic Data Modeling for Quality Prediction

Traditional data-driven quality prediction methods are mainly built from static models using clean data with a slow sampling rate, leaving the process dynamics unused. To make full use of dynamic process data collected at a fast sampling rate, this article proposes a novel deep learning-based robust dual-rate dynamic data modeling method for quality prediction of dynamic nonlinear processes. A new dynamic data denoising generative adversarial imputation network is first proposed for the missing value imputation among the dynamic process data. Then, a new hint convolutional neural network (HCNN) is established for dual-rate data based quality prediction. The proposed HCNN incorporates the information hint mechanism of channel expansion into the convolutional neural network to extract the dynamic features with definitive time and variable information. Finally, the proposed method is verified using the Dow distillation process dataset and Beijing multisite air quality dataset.
Design problems for the pure birth process

Let Y(t) be a pure birth process. If a maximum likelihood estimator of the birth intensity is desired and the number n of observational points and the last observation T are given in advance, it is shown that equidistant sampling is not an optimal procedure. Properties of ‘optimal' designs and the corresponding maximum likelihood estimators are investigated and compared with equidistant and continuous sampling.