Browse Articles

Suppressing spectator-induced dephasing through optimized dynamical decoupling implementation

Out of Africa: The genomic footprints of Vietnamese Robusta coffee

Vietnam is the main producer of Robusta (Coffea canephora) coffee, but faces several future agronomic challenges. These may be addressed through breeding for improved cultivars and more sustainable cropping systems. For such efforts to be successful and efficient, locally available genetic resources must be understood. Indeed, while C. canephora exhibits high genetic diversity in its native tropical African forests, only a part of it contributed to the worldwide diffusion of Robusta. Here we traced the African origins of Robusta accessions cultivated in the Central Highlands of Vietnam. A total of 126 Robusta accessions from the Vietnam coffee germplasm collection were characterized, including historical, elite and local cultivated clones. Their genetic diversity and origins were inferred through comparisons with wild reference samples using a new set of 261 genome-wide SNPs. A core set of 45 accessions that maximize the genetic distance and allelic richness were identified for conservation and breeding priorities. Full genome sequencing of these individuals helped to closely trace the origins of chromosomal segments back to different, geographically-structured wild African genetic groups. All Vietnamese Robusta accessions displayed Congo Basin (ER group) origins, albeit to various extents. However, we also uncovered contribution from several other genetic groups, variously from the Guinean region (D), the central African Atlantic coast (AG), and Eastern CAR/Uganda (OB), in 31 hybrid individuals. These source groups have been widely used in crossbreeding to develop elite clones. In addition, using whole-genome sequencing data, we also identified various admixture patterns at the chromosome level among the hybrids, which might provide valuable information for selecting breeding materials.

Social stability risk analysis caused by land acquisition and migration for water conservancy project construction

Modeling habitat distribution and niche overlap of Asian horseshoe crabs: Implications for conservation

Asian horseshoe crabs are ancient organisms essential for the balance of marine ecosystems. However, detailed information on their ecology and the environmental factors influencing their distribution remains limited. In this study, we analyzed habitat characteristics, potential distribution, and niche overlap for three species: Tachypleus tridentatus, Carcinoscorpius rotundicauda, and Tachypleus gigas. Predictive modeling using MaxEnt and niche analysis revealed that water depth and distance to land are key factors determining species distribution, with species-specific environmental influences: T. tridentatus is affected by maximum summer chlorophyll-a, C. rotundicauda by minimum chlorophyll-a, and T. gigas by wind speed. In terms of niche overlap, the highest degree of overlap was observed between C. rotundicauda and T. gigas, while the overlap between T. tridentatus and T. gigas was the lowest. The results highlight priority conservation areas, providing insights for management and protection strategies amid current environmental threats.

A novel deep learning framework with artificial protozoa optimization-based adaptive environmental response for wind power prediction

Abstract Accurate very short-term wind power forecasting is critical for the reliable integration of renewable energy into modern power systems. However, the inherent variability and non-linearity of wind power data pose significant challenges. To address these, this study proposes a novel hybrid deep learning framework, IAPO-LSTM, which combines Convolutional Neural Networks (CNNs) for spatial feature extraction and Gated Recurrent Units (GRUs) for temporal sequence modeling. The model is optimized using an enhanced Artificial Protozoa Optimizer (IAPO) augmented with an Adaptive Environmental Response Mechanism (AERM), which dynamically adjusts exploration and exploitation strategies based on the problem landscape to improve convergence and hyperparameter tuning efficiency. The proposed IAPO-LSTM model was evaluated on four real-world datasets—NREL WIND, EMD WIND, WWSIS, and ERCOT GRID—and benchmarked against six state-of-the-art forecasting models. Results demonstrate that IAPO-LSTM achieved the lowest forecasting errors across all datasets, with Mean Absolute Error (MAE) as low as 2.78, Root Mean Square Error (RMSE) of 4.50, and Theil’s Inequality Coefficient (TIC) of 0.0292 on the ERCOT dataset. Additionally, the model demonstrated faster inference times and better statistical significance (p < 0.005) compared to baseline methods. These outcomes confirm that IAPO-LSTM is not only highly accurate but also efficient and robust for real-time wind power forecasting applications.

Innovative novel regularized memory graph attention capsule network for financial fraud detection

Financial fraud detection (FFD) is crucial for ensuring the safety and efficiency of financial transactions. This article presents the Regularised Memory Graph Attention Capsule Network (RMGACNet), an original architecture aiming at improving fraud detection using Bidirectional Long Short-Term Memory (BiLSTM) networks combined with advanced feature extraction and classification algorithms. The model is tested on two reliable datasets: the European Cardholder (ECH) transactions dataset, which contains 284,807 transactions and 492 fraud instances, and the IEEE-CIS dataset, which has more than 1 million transactions. Our approach enhances comparison to existing methods of feature selection and classification accuracy. On the ECH dataset, RMGACNet achieves an accuracy of 0.9772, a precision of 0.9768, and an F1 score of 0.9770 measures; on the IEEE-CIS dataset, it achieves an accuracy of 0.9882, a precision of 0.9876 and an F1 score of 0.9879. The findings indicate that RMGACNet routinely surpasses existing models’ efficiency and accuracy while ensuring strong execution time performance, especially when handling large-scale datasets. The suggested model demonstrates scalability and stability, making it suitable for real-time financial systems.

Network pharmacology, prognostic analysis and experimental validation elucidate the therapeutic mechanism of Dingxiang Guanshitong in esophageal cancer

Telemedicine via data glasses in CBRN protection suit—Evaluation of medical qualification and technical feasibility

Objective Telemedicine in the context of chemical, biological, radiological and nuclear threats (CBRN) must adapt to the special features of the CBRN protection suit (hazmat suit). In a simulation, telemedicine with data glasses (smart glasses) was examined for its technical feasibility and the minimum required medical qualification. Methodology The study was designed as an intervention study. A medical scenario was developed in which paramedics with four different medical qualifications were to provide initial care to a contaminated patient. Using data glasses worn in the CBRN protection suit, a telemedicine physician directed the simulation via video streaming. The times and attempts for each measure were examined, as well as the users’ evaluation of two different data glasses. Results A total of 40 participants were enrolled in the study. There were no significant differences could be found between participants of the next higher qualifications in terms of the duration of the guided measures and the success. Significant differences only occurred when comparing all evaluated qualifications to each other. Both data glasses have difficulties in the CBRN protection suit. The participants were not satisfied with the wearing comfort and the technical limitations of the glasses. Summary In conclusion, telemedicine is feasible for emergency responders regardless of qualification in CBRN operations, although the data glasses currently appear unsuitable. Alternative hardware should be used and evaluated.

Enhancing Security in CPS Industry 5.0 using Lightweight MobileNetV3 with Adaptive Optimization Technique

Overseas background executives and enterprise ESG performance -The moderating effect of the executive pay gap

Enhancing corporate ESG (Environmental, Social, and Governance) performance is a critical issue garnering widespread attention across various sectors. This research examines the impact of executives with overseas backgrounds on corporate ESG performance, utilizing a two-way fixed-effects model with data from Chinese A-share listed companies in Shanghai and Shenzhen spanning from 2008 to 2022. The findings indicate that: (ⅰ) executives with overseas backgrounds positively influence corporate ESG performance, with this effect amplifying as the proportion of such executives within the executive team increases; (ⅱ)both internal and external pay gaps significantly positively moderate the relationship between overseas background executives and corporate ESG performance; (ⅲ) the influence of overseas executives on ESG performance is more pronounced in state-owned enterprises, large-scale firms, firms in the eastern region, and those in high-pollution industries. This study expands the understanding of factors affecting corporate ESG performance, offers empirical insights for listed companies aiming to enhance their ESG performance, and provides valuable implications for executive team construction, executive compensation strategies, government talent policies, and the pursuit of high-quality economic development.

Machine learning based differential diagnosis of SAPHO syndrome and secondary bone tumors using whole body bone scintigraphy

Is there a competitive advantage to using multivariate statistical or machine learning methods over the Bross formula in the hdPS framework for bias and variance estimation?

Purpose: We aim to evaluate various proxy selection methods within the context of high-dimensional propensity score (hdPS) analysis. This study aimed to systematically evaluate and compare the performance of traditional statistical methods and machine learning approaches within the hdPS framework, focusing on key metrics such as bias, standard error (SE), and coverage, under various exposure and outcome prevalence scenarios. Methods: We conducted a plasmode simulation study using data from the National Health and Nutrition Examination Survey (NHANES) cycles from 2013 to 2018. We compared methods including the kitchen sink model, Bross-based hdPS, Hybrid hdPS, LASSO, Elastic Net, Random Forest, XGBoost, and Genetic Algorithm (GA). The performance of each inverse probability weighted method was assessed based on bias, MSE, coverage probability, and SE estimation across three epidemiological scenarios: frequent exposure and outcome, rare exposure and frequent outcome, and frequent exposure and rare outcome. Results: XGBoost consistently demonstrated strong performance in terms of MSE and coverage, making it effective for scenarios prioritizing precision. However, it exhibited higher bias, particularly in rare exposure scenarios, suggesting it is less suited when minimizing bias is critical. In contrast, GA showed significant limitations, with consistently high bias and MSE, making it the least reliable method. Bross-based hdPS, and Hybrid hdPS methods provided a balanced approach, with low bias and moderate MSE, though coverage varied depending on the scenario. Rare outcome scenarios generally resulted in lower MSE and better precision, while rare exposure scenarios were associated with higher bias and MSE. Notably, traditional statistical approaches such as forward selection and backward elimination performed comparably to more sophisticated machine learning methods in terms of bias and coverage, suggesting that these simpler approaches may be viable alternatives due to their computational efficiency. Conclusion: The results highlight the importance of selecting hdPS methods based on the specific characteristics of the data, such as exposure and outcome prevalence. While advanced machine learning methods such as XGBoost can enhance precision, simpler methods such as forward selection or backward elimination may offer similar performance in terms of bias and coverage with fewer computational demands. Tailoring the choice of method to the epidemiological scenario is essential for optimizing the balance between bias reduction and precision.

Causal relationship between glycemic traits and inflammatory eye diseases and their complications, and myopia: a Mendelian randomization analysis

A novel true triaxial apparatus for high-stress low-frequency disturbance in hard rocks: Development, validation, and application

A novel true triaxial apparatus (TTA) has been designed and fabricated to investigate the mechanical behavior of deep underground engineering under high-stress conditions and low-frequency disturbance loads. This apparatus features a two-rigid, one-flexible loading system, with rigid loading applied along the directions of the maximum and intermediate principal stresses, offering maximum load capacities of 2000 kN and 4000 kN, respectively. The direction of the maximum principal stress is also equipped with dynamic loading capabilities, enabling low-frequency disturbance loads with frequencies up to 20 Hz and amplitudes of 0.5 mm. The minimum principal stress direction utilizes flexible loading, with pressure capabilities of up to 120 MPa. Moreover, the integration of a high-rigidity loading frame and high-precision servo control systems has significantly enhanced the apparatus’s performance and data accuracy, particularly in small-scale deformation tests. Additionally, a dual-actuator, dual-loop servo control mode is employed to effectively suppress eccentric loading effects in true triaxial tests. To validate the reliability of the TTA and to preliminarily explore the effects of stress paths and disturbances on deep rock mechanical properties, true triaxial tests were conducted using granite. The results demonstrate that both the intermediate principal stress and disturbance frequency significantly influence the strength and failure modes of the rock. Static and disturbance tests exhibited excellent high repeatability and consistency, further confirming the accuracy and reliability of the apparatus. Overall, the TTA provides a novel methodology for investigating the mechanical properties of deep rock masses under high-stress and low-frequency disturbance conditions, making it an effective tool for addressing related scientific and engineering challenges.

Comparison of thrombectomy alone versus bridging thrombolysis in a US population using regression discontinuity analysis

FURIOUS: Fully unified risk-assessment with interactive operational user system for vessels

Ship collision risk assessment has advanced over recent years, enhancing maritime safety. However, existing studies often describe ship domains and collision risk assessments in a static manner, lacking interactivity. Interactive visualization of collision risk, especially in multi-ship scenarios has not been sufficiently developed. This gap prompted the development of “FURIOUS: Fully Unified Risk-assessment with Interactive Operational User System for vessels.” This tool aids in visualizing and analyzing collision risk of multi-ship encounter situation through real-time visualization. Our system processes data from Automatic Identification System (AIS). The system performs ship domain calculations and collision risk assessments supported by geographical computations, and includes features like real-time vessel display and collision type detection. Interactive and user-selectable elements, along with dynamic maps enhance real-time decision-making to ensure navigation safety. Additionally, the system aids both experienced and novice users in understanding complicated maritime dynamic environments. Users can adjust parameters like ship type, ship IDs, time window and map type for tailored analyses and proactive collision avoidance. We conducted a user study to validate these features, confirming that they effectively improve situational awareness and enhance decision-making capabilities in real-world scenarios. This paper details the design, implementation, and evaluation of this tool, highlighting its potential to transform maritime decision-making by improving situational awareness and enhancing operational efficiency.

The impact of industrial agglomeration on new quality productive forces enhancement in China’s pig farming industry

Co-infections and risk factors of Toxoplasma gondii infection among pregnant women in Ghana: A facility-based cross-sectional study

This study assessed the prevalence of co-infections (human immunodeficiency virus, hepatitis B, and syphilis) and associated risk factors for Toxoplasma gondii infection among pregnant women in Mampong Municipality, Ghana. A cross-sectional design was used to recruit 201 pregnant women from six health facilities conveniently. Participants’ socio-demographics, clinical and environmental data were collected using a structured questionnaire. Using 2 ml of blood, T. gondii seroprevalence was determined by the TOXO IgG/IgM Rapid Test Cassette. Data was analyzed using descriptive and logistic regression analysis with SPSS version 27 to determine the prevalence and associations of T. gondii infection with other variables, respectively. The seroprevalence of T. gondii was 49.75%, of which 40.30%, 2.49%, and 6.97% tested positive for IgG, IgM, and IgG/IgM, respectively. Co-infection of toxoplasmosis with viral hepatitis B, human immunodeficiency virus (HIV), and syphilis rates were 15%, 1%, and 4%, respectively and were not risk factors for T. gondii transmission. Educational level and residential status were associated with toxoplasmosis [p < 0.05]. Participants with higher education had a reduced risk of T. gondii infections compared to a lower level of education [AOR = 0.39 (0.13, 0.99) p = 0.049]. Similarly, the risk of T. gondii infection was significantly lower among individuals residing in peri-urban [AOR = 0.13 (0.02–0.70), p = 0.02] and urban areas [AOR = 0.10 (0.02–0.78), p = 0.03] compared to those in rural areas. Backyard animals with extensive and semi-intensive systems, without veterinary care, and contact with animal droppings and water sources were significant risk factors for T. gondii infection [p < 0.05]. Miscarriage was associated with T. gondii infection [p < 0.05]. The burden of T. gondii infection was high among the study population, posing a risk of mother-to-child transmission. Key risk factors included low education, rural residence, backyard animal exposure, poor hygiene, and unsafe water sources. Toxoplasmosis was associated with miscarriage; thus, integrating it into routine antenatal screening could improve pregnancy outcomes. Health promotion interventions such as education on zoonotic risks, improved sanitation, safe water practices, and veterinary care for domestic animals are recommended to reduce infection risk among pregnant women.

A meta-analysis of genome-wide association studies revealed significant QTL and candidate genes for loin muscle area in three breeding pigs

Catastrophic famine in Gaza: Unprecedented levels of hunger post-October 7th. A real population-based study from the Gaza Strip

Background The Gaza Strip, spanning approximately 365 square kilometers, has been a focal point of geopolitical tensions and humanitarian crises. The military escalation on October 7th exacerbated existing vulnerabilities, notably food security and hunger, with an estimated 85750 deaths due to Israeli attacks, representing about 8% of the 2.34 million population. This research aims to provide policymakers and humanitarian organizations with actionable insights, such as identifying the most vulnerable populations, quantifying the impact of specific restrictions, and informing the development and implementation of targeted interventions that improve long-term food security and alleviate human suffering in Gaza. Methods A cross-sectional study was conducted from May to July 2024, assessing food insecurity and hunger among Palestinian households across the five governorates of Gaza. The study applied a quantitative research approach, utilized the Household Food Security Survey Module (HFSSM), Household Food Insecurity Access Scale (HFIAS), and Household Hunger Scale (HHS) to measure food insecurity, famine, and hunger. Self-reported anthropometric data and socioeconomic status were also collected. Data were analyzed using SPSS version 29, employing correlation tests, chi-square analysis, and logistic regression. Results A survey of 1209 households across the Gaza Strip revealed a catastrophic humanitarian crisis. More than 54% of households experienced complete house destruction. Food insecurity reached unprecedented levels, with about 98% of households experiencing severe food insecurity, according to the HFIAS, while 100% experienced different levels of food insecurity as per the HFSSM. A staggering 95% of households experienced other sorts of hunger. The war was associated with significant (p < 0.001) weight loss among individuals, with the average weight dropping from 74.8 ± 15.9 kg before the war to 64.8 ± 15.2 kg, concomitant with significant (p < 0.001) reduction in BMI from 26.4 ± 5.4 to 22.8 ± 5.2 kg/m2. Factors such as displacement, age, socioeconomic status, and educational level significantly exacerbated hunger severity. Conclusion The study reveals a severe food insecurity and hunger crisis in the Gaza Strip, exacerbated by the ongoing damaging attacks by Israeli forces. These findings underscore the urgent need for immediate and sustained humanitarian assistance to address the critical food security and nutritional needs of the Gazan population.