Browse Articles

A New Therapeutic Broadcast on Hepatitis D, a Satellite Virus

Trion quantum coherence in site-controlled pyramidal InGaAs quantum dots

Correction: DeltaBreed: A BrAPI-centric breeding data information system

Antithrombotic Therapy after Successful Catheter Ablation for Atrial Fibrillation

Broadband slant-polarized series-fed microstrip patch antenna array based on magnetic current feeding technology

Impact of crowdfunding, entrepreneurial finance and varieties of entrepreneurial ecosystems after COVID pandemic for rural women

In this paper, we propose an approach to estimate the role of entrepreneurial finance and social capital distribution in crowdfunding (CF) and varieties of entrepreneurial ecosystems after the COVID pandemic for rural women in Pakistan. Primary data were collected from 1,004 rural women. Using SmartPLS, hypotheses were analyzed through partial least squares structural equation modeling. The mainstream tests used for checking the discriminant validity are the Fornell and Larcker Criteria, Heterotrait–Monotrait ratio, Average Variance Extracted, Variance Inflation Factor (VIF), and the factor loadings of each item for each variable. Similarly, the reliability of the data was analyzed through Cronbach’s alpha and composite reliability. The results showed that crowdfunding, social capital distribution, and the variety of an entrepreneurial ecosystem could play a vital role in the entrepreneurial intentions of rural women in Pakistan. Moreover, the distribution of social capital plays a positive role in the variety of the entrepreneurial ecosystem. The study also provides a benchmark pathway for social change for rural women in Pakistan following the COVID-19 pandemic, revealing how various entrepreneurial ecosystems can assist them in achieving their business goals by providing a clear roadmap for crowdfunding (CF).

Spiraling into a Distant Past

Improved Arabic query expansion using word embedding

Abstract Word embedding enhances pseudo-relevance feedback query expansion (PRFQE), but training word embedding models takes a long time and is applied to large datasets. Moreover, the Arabic language, which has rich morphology, dialectal variations, and a lack of high-quality linguistic resources, training embedding models need special processing. This paper proposes using a representative subset of a dataset to train such models and defines the conditions of representativeness. Using a suitable subset of words to train a word embedding model is effective since it dramatically decreases the training time while preserving the retrieval efficiency. This paper shows that a subset of words is derived from an Arabic dataset, which consumes 10% of the training time of the whole dataset, and preserves retrieval efficiency. The trained models are used to embed words for different scenarios of Arabic query expansion, and the proposed training method shows effectiveness as it outperforms the ordinary PRFQE by at least 7% Mean Average Precision (MAP) and 14.5% precision improvement at the 10th returned document (P10). Moreover, the improvement over not using the query expansion is 21.7% for MAP and 21.32% for the P10. The results show no significant differences between using different word embedding models for Arabic query expansion.

Estimated true prevalence and associated risk factors of bovine paratuberculosis antibodies in dairy herds in Bangladesh

Bovine paratuberculosis (PTB), caused by Mycobacterium avium subsp. paratuberculosis (MAP), is a chronic infection that undermines cattle productivity and farm profitability. In Bangladesh, no previous studies have estimated PTB prevalence or identified associated risk factors. This cross-sectional study (January 2023 to December 2024) was conducted across 14 districts and 138 dairy farms to estimate true MAP antibody prevalence and identify factors associated antibody detection. Bulk milk samples from all herds (n = 138) and 880 individual milk samples from ELISA-positive herds were tested using a commercial indirect ELISA. True herd- and cow-level antibody prevalence was estimated using Bayesian latent class models. Risk factors were identified using multivariable logistic regression (herd-level) and mixed-effects logistic regression (cow-level). The apparent antibody prevalence was 7.2% at the herd-level and 12.2% at the cow-level, with herd-level prevalence ranging from 0.9% to 37%. After adjusting for test sensitivity and specificity, true antibody prevalence estimates decreased to 4.9% (herd level) and 9.4% (cow level). Herds with more than two retained placenta cases had significantly higher odds of MAP antibody detection (odds ratio [OR] = 9.42) than herds with two or fewer such events. At the cow level, older age (>7.9 years) was associated with higher odds of antibody positivity compared with cows ≤4 years. Cows with anestrus (OR = 3.89) or repeat breeding (OR = 2.63) had significantly higher odds of MAP antibody detection compared with cows without these disorders. Milk antibody levels showed a negative association with milk yield, with each unit increase in the sample-to-positive ratio corresponding to a 0.018 L/day reduction. These findings indicate that apparent antibody prevalence substantially overestimates PTB exposure in the study areas. The associations with retained placenta, anestrus, and repeat breeding suggest potential reproductive impacts that warrant further investigation. Given the purposive sampling strategy and reliance on milk ELISA, the results should be interpreted within the context of the study population. Enhancing diagnostic capacity and integrating PTB considerations into reproductive herd management may help reduce productivity losses, while larger, probability-based studies are needed to assess the national burden.

Reusable versus Single-Use Duodenoscopes

From quantum computing to mRNA therapeutics: seven technologies to watch in 2026

Optimized control, and experimental validation of a novel multi-stage parabolic trough collector for solar water heating systems

Abstract This study presents the development, optimized PID control, and experimental validation of a novel Multi-Stage Parabolic Trough Collector (MPTC) for solar water heating systems, aiming to enhance thermal efficiency and adaptability under varying environmental conditions. The research is structured into three key stages. First, a time-dependent mathematical model is developed to characterize the thermal and dynamic behavior of the system, incorporating essential parameters such as heat absorption, fluid dynamics, and energy losses. Second, a proportional-integral-derivative (PID) controller is designed and optimized using advanced techniques, including Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to ensure robust system stability and dynamic responsiveness. GA and PSO are selected over classical methods (e.g., Ziegler-Nichols) due to their superior ability to handle non-linearities, multi-objective trade-offs, and real-world disturbances typical of solar thermal systems. Lastly, a prototype MPTC featuring a modular, high-efficiency absorber design, is fabricated and experimentally tested under both controlled and real-outdoor conditions. Experimental results demonstrated a steady-state temperature error of 1.111% under dynamic irradiance conditions, confirming the model’s accuracy. These results are compared with simulation outputs to validate the model and fine-tune the control strategies. The present model achieves a thermal efficiency of 16.2%, which exceeds that of comparable models reported in the literature. This integrated approach bridges theoretical modeling with experimental validation, offering a practical and scalable framework for enhancing the performance of adaptive solar thermal systems.

Research on self-adaptive height adjustment control of shearer based on deep deterministic policy gradient

As a core component of the fully mechanized mining face, intelligent control of the shearer is fundamental to achieving unmanned mining and improving equipment reliability. To address the limitations of traditional optimization and deep reinforcement learning algorithms in achieving rapid and accurate self-adaptive control, this study proposes a novel shearer drum height control strategy based on the Deep Deterministic Policy Gradient (DDPG) algorithm. The 4602 workface at Yangcun Coal Mine and the MG2 × 55/250-BWD shearer model were used as engineering cases. A hybrid SVD-CWT and AlexNet transfer learning method was employed to identify coal and rock cutting states, achieving an accuracy of 95.06%. A DDPG-based self-adaptive hydraulic height adjustment model was then developed and validated through Matlab/Simulink and AMESim co-simulation, as well as a similarity-based physical test platform. Results show that the proposed method significantly outperforms conventional and fuzzy PID controls, reducing response time to 0.091 s and steady-state error to 0.00052 mm. Compared with TD3 and SAC algorithms, the system exhibited faster response, higher stability, and stronger anti-interference capability. The mean maximum error between simulation and experimental results was only 3.14%, confirming the feasibility and robustness of the proposed control strategy. This study provides a reliable approach for intelligent, adaptive height control of shearers under complex coal seam conditions.

Anticoagulation after Ablation for Atrial Fibrillation — Navigating the OCEAN of Possibilities

Towards sustainable pharmaceutical QC: micellar-UPLC strategy for concomitant analysis of a cardiovascular polypill with aniline and salicylic acid impurities

Abstract Medication compliance is a key factor in achieving positive health outcomes, and this is difficult for patients, especially the elderly, who need polytherapy. Therefore, pharmacy experts have devised a single-pill combination that combines more than one active pharmaceutical ingredient to treat multiple diseases and improve patient compliance with medication. Here, an affordable UPLC approach was designed for the estimation of a fixed-dose combination that cures hypertension, hyperlipidemia, and blood coagulation and subsequently prevents heart attacks. This combination includes four active ingredients: aspirin (ASR), atorvastatin calcium (ATC), metoprolol succinate (MES), and ramipril (RAP). The applied approach was devised to determine the mentioned drugs in their commercial dosage form and also in the presence of ASR’s impurity (salicylic acid) and ATC’s impurity (aniline). The target drugs were separated on a Kinetex ® XB-C18 column using an isocratic micellar eluent system composed of 0.02 M Brij-35 at pH 3.0 (using orthophosphoric acid) and 10% n-propanol, flowed at 0.20 mL/min. The mentioned drugs were UV-scanned at 230.0 nm. ICH requirements were followed for all validation items. Whiteness and greenness appraisals were performed, affirming the applied approach’s friendliness to the environment. The priority of this UPLC approach over the published LC ones is due to using a micellar moving phase, making it more eco-friendly to the environment, and the determination of the aforementioned drugs even in the presence of aniline (toxic impurity) and salicylic acid.

Bureaucracy, happiness, and satisfaction at work

Despite increasing material prosperity, industrialized nations face declining self-reported happiness and increasing workplace dissatisfaction. This study investigates bureaucratic burden as a driver of diminishing job satisfaction, analyzing 7.9 million Glassdoor reviews of more than 8,000 companies from 2008-2023 using natural language processing and instrumental variables methods. We identify reviews mentioning bureaucracy and quantify their association with 1-5 star employer ratings. Mentioning bureaucracy corresponds to around 0.7-point lower ratings (a 22% decline from the mean), comparable to the impacts of mentioning low pay (–0.8) or workplace conflict (–0.9). Two-stage least squares analysis, instrumenting with future bureaucratic mentions at the same company, implies a causal relationship. These findings support theories about the harm of “illegitimate tasks” at work and suggest revisiting conventional efficiency rationales for workplace bureaucratization. Organizational practices emphasizing employee autonomy and meaningful tasks could partly mitigate declines in satisfaction.

<i>Pneumocystis jirovecii</i> Pneumonia

Water hyacinth detection for autonomous navigation mapping using image segmentation cascaded classifier

Phytoplankton fatty acid proportions in the Canadian Arctic are strongly affected by temperature, salinity, and phosphate in late summer

Phytoplankton form the base of the food web and act as the main source of both energy, and ω3 essential fatty acids in marine ecosystems. In the Arctic, the effects of climate change are exacerbated compared to lower latitudes. Expected shifts in phytoplankton growth due to climate change in the Arctic are dependent on water physicochemistry, especially temperature, salinity, and nutrient concentrations. The goal of this investigation was to, 1) assess which Canadian Arctic (CA) regions were significantly different based on biomarkers of phytoplankton condition and community assemblage, and 2) identify which hydrographic properties and dissolved inorganic nutrient concentrations correlated with those biomarkers. Phytoplankton samples were collected from the CA in 2019 (July-September) and 2021 (August-October). CA regions were clustered based on similar hydrographic properties, and then lipid and FA profiles were compared. In 2019, there was variation in monounsaturated fatty acid (MUFA) proportions: cluster groups in very deep water, with higher temperature and lower oxygen saturation (Davis Strait stations) were significantly lower compared to cluster groups defined by lower salinity and light transmission (East Barrow Strait and Lancaster Sound stations) in both surface (p = 0.001) and sub-surface chlorophyll maximum waters (SCM)(p = 0.008). In 2021, a year with more expansive coverage of the CA compared to 2019, Σω3% and polyunsaturated fatty acids (PUFA) were significantly higher in cluster groups containing Baffin Bay stations compared to the clusters defined by lower salinity (Beaufort Sea and the Canadian Arctic Archipelago stations) in both surface (p &lt; 0.001) and SCM waters (p &lt; 0.001). Across both years and both SCM and surface waters, DHA/EPA (two ω3 essential fatty acids) negatively correlated with phosphate. Overall, FA proportions in the CA are strongly affected by physiochemical conditions in late summer with MUFA responding mainly to physical changes, while PUFA responded heavily to phosphate changes.

Promoting Fairness in Screening Programs for Late-Career Practitioners