Search Results (51,203)

Drivers with physical and/or mental impairments face many driving challenges. However, not many studies have been carried out to understand the factors that contribute to crashes involving these drivers and how these factors influence their crash outcomes. This study aims to address this gap in the road safety literature. The study uses historical crash data from the State of Alabama for at-fault physically impaired drivers and utilizes a random parameter with heterogeneity in a mean modeling approach to account for unobserved heterogeneity. The model estimation results reveal that in rural areas, driving over the speed limit, the time of crash being between 6.00 p.m. and 11.59 p.m., younger drivers, employed and distracted drivers were associated with severe injuries. Minor injury crashes are found to be associated with female drivers, state roads and residential areas. Finally, property-damage-only crashes are more associated with weekdays, driving under the influence of alcohol or drugs, a road with left curvature, driving too fast for the road conditions and intersections. The results obtained provide a foundation for the adoption of targeted countermeasures to improve highway safety for physically impaired drivers and all road users in general. Full article

Salmonella enterica subsp. enterica serovar Infantis poses a growing threat to public health, due to its increasing prevalence worldwide and its association with high levels of antimicrobial resistance. Among livestock, S. Infantis is especially isolated from broilers. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a systematic review was conducted by searching in three databases (Web of Science, Scopus, and PubMed) for English-language studies (1957–2023) that reported the prevalence of S. Infantis in broiler farms. Eligible studies included epidemiological investigations conducted in broiler chickens by sampling the house environment (flock-level prevalence) or the birds (individual-level prevalence). A random-effect model was applied to calculate S. Infantis pooled prevalence estimates with 95% confidence intervals (CIs). Furthermore, to assess between-study heterogeneity, the inconsistency index statistic (I²) was calculated. Among 537 studies retrieved, a total of 9 studies reporting flock-level prevalence of S. Infantis and 4 reporting individual-level prevalence were retained for analysis. The flock-level pooled prevalence was estimated to be 9% (95% CI: 1–26%) and a high between-study heterogeneity was found (I² = 99%, p < 0.01). Concerning individual-level prevalence, a meta-analysis was not performed due to the scarcity of eligible studies. The data presented underscore the significant occurrence of S. Infantis in broilers at the farm level. By summarizing the existing literature, this work provides useful insights for conducting future surveys of Salmonella spp. in live broiler chickens as a preliminary step for developing more efficient control strategies. Full article

Background: Everolimus is approved for treating breast, renal, and pancreatic neuroendocrine cancers but carries the risk of hepatitis B virus (HBV) reactivation (HBVr) and hepatitis. However, data on HBVr in everolimus-treated patients are limited. This study evaluates the risk of hepatitis and HBVr in cancer patients with current or past HBV infection. Methods: This retrospective study analyzed patients prescribed everolimus between 1 January 2011 and 31 May 2022, using a private healthcare system database in Taiwan. Patients with HBsAg positivity or HBsAg negativity and anti-HBs or anti-HBc results were included. The cumulative incidence function and risk of hepatitis from a competing risk model, which estimates Fine-Gray subdistribution hazard (SDH), were analyzed across different HBV serological subgroups. The risk of hepatitis B reactivation was also calculated. Results: Of 377 patients, 45% (36/80) of HBsAg-positive and 0.67% (2/297) of HBsAg-negative patients received nucleos(t)ide analogues (NUCs) prophylaxis. Hepatitis occurred in 28.75% of HBsAg-positive and 17.85% of HBsAg-negative patients. Baseline HBsAg positivity and exemestane use increased hepatitis risk. HBVr occurred in 11.36% (5/44) of HBsAg-positive patients without NUCs and 5.56% (2/36) with prophylaxis. Two HBsAg-negative, anti-HBc-positive patients developed severe HBVr-related hepatitis. Conclusion: Hepatitis occurred in 28.75% of HBsAg-positive and 17.85% of HBsAg-negative patients on everolimus. HBVr was common in HBsAg-positive patients but rare in HBsAg-negative individuals. HBV screening and liver function monitoring are critical for patients with past or current HBV infection receiving everolimus, especially in endemic areas. Full article

Cervical cancer is a significant health challenge, yet it can be effectively prevented through early detection. Cytology-based screening is critical for identifying cancerous and precancerous lesions; however, the process is labor-intensive and reliant on trained experts to scan through hundreds of thousands of mostly normal cells. To address these challenges, we propose a novel distribution-augmented approach using contrastive self-supervised learning for detecting abnormal squamous cervical cells from cytological images. Our method utilizes color augmentations to enhance the model’s ability to differentiate between normal and high-grade precancerous cells; specifically, high-grade squamous intraepithelial lesions (HSILs) and atypical squamous cells–cannot exclude HSIL (ASC-H). Our model was trained exclusively on normal cervical cell images and achieved high diagnostic accuracy, demonstrating robustness against color distribution shifts. We employed kernel density estimation (KDE) to assess cell type distributions, further facilitating the identification of abnormalities. Our results indicate that our approach improves screening accuracy and reduces the workload for cytopathologists, contributing to more efficient cervical cancer screening programs. Full article

Soil erosion has become an increasingly serious issue, drawing global attention. As one of the countries facing severe soil erosion in the world, China confronts significant ecological challenges. Against this backdrop, the country places great emphasis on soil conservation efforts, considering them a crucial component of ecological civilization construction. This study focuses on the carbon sink benefits of comprehensive soil conservation management in the loess hilly region and sandy slopes, using the Xiaonanshan Mountain small watershed in Youyu County, Shanxi Province, as a typical case for in-depth analysis. In terms of research methodology, an integrated monitoring approach combining fundamental data, measured data, and remote sensing data was developed. A comprehensive survey of the Xiaonanshan Mountain small watershed was conducted to categorize plant carbon pools and soil carbon pools, establish baseline scenarios, and utilize methods such as inverse distance spatial interpolation, sample calculation, and feature extraction to estimate forest carbon storage across different years and determine changes in soil and vegetation carbon storage. Simultaneously, data collection and preprocessing were carried out, including the gathering of fundamental data, field data collection, and internal data preprocessing. On this basis, a vegetation carbon storage model was constructed, and an assessment of soil carbon pool storage was conducted. The research results indicate that from 2002 to 2024, the continuous implementation of various soil conservation measures over 22 years has led to a significant increase in carbon storage within the Xiaonanshan Mountain small watershed. The vegetation carbon density of the entire small watershed increased from 14.66 t C/ha to 27.02 t C/ha, and the soil carbon density rose from 28.92 t C/ha to 32.48 t C/ha. The net carbon sink amount was 18,422.20 t C (corresponding to 67,548.08 t CO₂e in terms of carbon dioxide equivalent). Populus simonii and Pinus sylvestris var. mongholica significantly contribute to the carbon sink; however, due to partial degradation of Populus simonii, its net carbon sink amount is less than that of Pinus sylvestris var. mongholica. Additionally, the carbon sink capacity of the small watershed exhibits spatial differences influenced by conservation measures, with high carbon density areas primarily concentrated within the range of Populus simonii, while low carbon density areas are mainly found in shrub zones. The increase in carbon storage within the small watershed is primarily attributed to the contributions of vegetation and soil carbon storage, indicating that comprehensive soil erosion management has a significant carbon accumulation effect; moreover, the annual growth rate of vegetation carbon storage exceeds that of soil carbon storage, with the proportion of soil carbon storage increasing year by year. Furthermore, the vegetation carbon sink, soil carbon sink, and total carbon sink of the small watershed were separately calculated. In terms of benefit analysis, the Xiaonanshan Mountain small watershed offers ecological benefits such as increased forest coverage, carbon fixation and oxygen release, and biodiversity conservation; from an economic perspective, the value of carbon trading is substantial, promoting soil conservation and rural revitalization, with the total value of timber reaching 7.6 million yuan, of which the value of standing timber constitutes the largest proportion; social benefits include the improvement of environmental landscapes, stimulation of ecological tourism, and attraction of investment, with the Xiaonanshan Mountain Ecological Park receiving numerous visitors and generating significant tourism revenue. This research provides a theoretical basis and data foundation for comprehensive soil conservation management in project areas or small watersheds within the loess hilly and sandy slope regions, offering technical and methodological support for other soil conservation carbon sink projects in the area. Full article

Background/Objectives: The neuroprotective role of dietary choline during adulthood has not yet been conclusively proven. This study aims to investigate the influence of long-term choline and its constituent intakes on cognitive decline in the Chinese population. Methods: A total of 4502 subjects (≥55 years) with at least two waves of completed data and without cognitive decline at baseline were selected from the China Health and Nutrition Survey 1997–2018. Three consecutive 24 h dietary recalls were performed to collect dietary intake information for choline, phosphatidylcholine (PC), and glycerophosphocholine (GPC) measures. Several items from the Telephone Interview for Cognitive Status (Modified) were employed to perform a cognitive assessment. Cox frailty models were used to estimate hazard ratios (HRs) and 95% CIs. Results: A total of 783 participants developed cognitive decline during 26,080 person-years of follow-up. Cumulative average intakes of choline, PC, and GPC were 188.0, 126.7, and 17.1 mg/d, respectively. In the total population, after full adjustment, subjects in the lower (Q2), medium (Q3), higher (Q4), and highest (Q5) quintiles of dietary choline showed 27.8% (95% CI: 0.584, 0.894), 33.9% (95% CI: 0.522, 0.836), 23.0% (95% CI: 0.599, 0.990), and 29.3% (95% CI: 0.526, 0.949) decreases in the risk of cognitive decline compared to the lowest (Q1), respectively. Similar results were observed in PC but not GPC measures. Both higher choline and PC intakes induced a lower risk of cognitive decline for subjects ≥ 65 years at baseline (Q3 and Q4) and females (Q2–Q5). A marginally significant association of GPC was found for subjects ≥ 65 years (Q5) and males (Q4). Conclusions: These findings identify age and gender disparities relating to the protective associations of dietary choline, PC, and GPC with incident cognitive decline in middle-aged and older Chinese populations. Full article

In this paper, we propose a sparse partial envelope model that performs response variable selection efficiently under the partial envelope model. We discuss its theoretical properties including consistency, an oracle property and the asymptotic distribution of the sparse partial envelope estimator. A large-sample situation and high-dimensional situation are both considered. Numerical experiments demonstrate that the sparse partial envelope estimator has excellent response variable selection performance both in the large-sample situation and the high-dimensional situation. Moreover, simulation studies and real data analysis suggest that the sparse partial envelope estimator has a much more competitive performance than the standard estimator, the oracle partial envelope estimator, the active partial envelope estimator and the sparse envelope estimator, whether it is in the large-sample situation or the high-dimensional situation. Full article

Knee joint function deterioration significantly impacts quality of life. This study developed estimation models for ten knee indicators using data from in-shoe motion sensors to assess knee movement during everyday activities. Sixty-six healthy young participants were involved, and multivariate linear regression was employed to construct the models. The results showed that eight out of ten models achieved a “fair” to “good” agreement based on intra-class correlation coefficients (ICCs), with three knee joint angle indicators reaching the “fair” agreement. One temporal indicator model displayed a “good” agreement, while another had a “fair” agreement. For the angular jerk cost indicators, three out of four attained a “fair” or “good” agreement. The model accuracy was generally acceptable, with the mean absolute error ranging from 0.54 to 0.75 times the standard deviation of the true values and errors less than 1% from the true mean values. The significant predictors included the sole-to-ground angles, particularly the foot posture angles in the sagittal and frontal planes. These findings support the feasibility of estimating knee function solely from foot motion data, offering potential for daily life monitoring and rehabilitation applications. However, discrepancies in the two models were influenced by the variance in the baseline knee flexion and sensor placement. Future work will test these models on older and osteoarthritis-affected individuals to evaluate their broader applicability, with prospects for user-tailored rehabilitation applications. This study is a step towards simplified, accessible knee health monitoring through wearable technology. Full article

The objective of this study was to develop linear and nonlinear statistical models for predicting enteric methane emissions from beef and dairy cattle (EME, MJ/day). Ration nutrient composition (g/kg), nutrient (kg/day), energy (MJ/day), and energy and organic matter (OM) digestibility (g/kg) were used as predictors of CH₄ production. Three databases of beef cattle, dairy cattle, and their combinations were developed using 34 published experiments to model EME predictions. Linear and nonlinear regression models were developed using a mixed-model approach to predict CH₄ production (MJ/day) of individual animals based on feed composition. For the beef cattle database, Equation methane (MJ/d) = 1.6063 (±0.757) + 0.4256 (±0.0745) × DMI + 1.2213 (±0.1715) × NDFI + −0.475 (±0.446) × ADFI had the smallest RMSPE (21.99%), with 83.51% of this coming from random error and a regression bias was 16.49%. For the dairy cattle database, the RMSPE was minimized (15.99%) for methane (MJ/d) = 0.3989 (±1.1073) + 0.8685 (±0.1585) × DMI + 0.6675 (±0.4264) × NDFI, of which 85.11% was from random error and the regression deviation was 14.89%. When the beef and dairy cattle databases were combined, the RMSPE was minimized (24.4%) for methane(MJ/d) = −0.3496 (±0.723) + 0.5941 (±0.0851) × DMI + 1.388 (±0.2203) × NDFI + −0.027 (±0.4223) × ADFI, of which 85.62% was from the random error and the regression bias was 14.38%. Among the nonlinear equations for the three databases, the DMI-based exponential model outperformed the other nonlinear models, but the predictability and goodness of fit of the equations did not improve compared to the linear model. The existing equations overestimate CH₄ production with low accuracy and precision. Therefore, the equations developed in this study improve the preparation of methane inventories and thus improve the estimation of methane production in beef and dairy cattle. Full article

Sugarcane is the primary crop in the global sugar industry, yet it remains highly susceptible to a wide range of diseases that significantly impact its yield and quality. An effective solution is required to address the issues caused by the manual identification of plant diseases, which is time-consuming and has low detection accuracy. This paper proposes the development of a robust Deep Ensemble Convolutional Neural Network (DECNN) model for the accurate detection of sugarcane leaf diseases. Initially, several transfer learning (TL) models, including EfficientNetB0, MobileNetV2, DenseNet121, NASNetMobile, and EfficientNetV2B0, were enhanced through the addition of specific layers. A comparative analysis was then conducted on the enlarged dataset of sugarcane leaf diseases, which was divided into six categories and 4800 images. The application of data augmentation, along with the addition of dense layers, batch normalization layers, and dropout layers, led to improved detection accuracy, precision, recall, and F1 scores for each model. Among the five enhanced transfer learning models, the modified EfficientNetB0 model demonstrated the highest detection accuracy, ranging from 97.08% to 98.54%. In conclusion, the DECNN model was developed by integrating the modified EfficientNetB0, MobileNetV2, and DenseNet121 models using a distinctive performance-based custom-weighted ensemble method, with weight optimization carried out using the Tree-structured Parzen Estimator (TPE) technique. This resulted in a model that achieved a detection accuracy of 99.17%, which outperformed the individual performance of the modified EfficientNetB0, MobileNetV2, and DenseNet121 models in detecting sugarcane leaf diseases. Full article

The presence of vegetation in submerged conditions and bedforms are a reality in coarse-bed streams. However, this reality has not been well investigated in the literature, despite being a major challenge for natural stream restoration. In order to control many unknown factors affecting prototype scale, this experimental study has been conducted in a laboratory flume, considering 3D bedforms. The results of this study show that 3D bedforms with submerged vegetation elements may change all estimations from 3D to 2D forms near the bed due to the change in roughness. This will change the classic determinations of resistance to flow and sediment transport via Reynolds stress and turbulent flow and may lead to more-affordable complex hydraulic process modeling. Full article

Foodborne disease presents a substantial challenge to researchers, as foliar water intake greatly influences pathogen internalization via stomata. Comprehending plant–pathogen interactions, especially under fluctuating humidity and temperature circumstances, is crucial for formulating ways to prevent pathogen ingress and diminish foodborne hazards. This study introduces a computational model utilizing neural networks to anticipate pathogen internalization via stomata, contrasting with previous research that emphasized biocontrol techniques. Computational modeling assesses the likelihood and duration of internalization for bacterial pathogens such as Salmonella enterica (S. enterica), considering various environmental factors including humidity and temperature. The estimated likelihood ranges from 0.6200 to 0.8820, while the internalization time varies from 4000 s to 5080 s, assessed at 50% and 100% humidity levels. The difference in internalization time, roughly 1042.73 s shorter at 100% humidity, correlates with a 26.2% increase in the likelihood of internalization, rising from 0.6200 to 0.8820. A neural network model has been developed to quantitatively predict these values, thereby enhancing the understanding of plant–microbe interactions. These methods will aid researchers in understanding plant–pathogen interactions, especially in environments characterized by varying humidity and temperature and are essential for formulating strategies to prevent pathogen ingress and tackle foodborne illnesses within a technologically advanced context. Full article

The main aim of our research was to develop a methodology of chlorophyll content in the leaves of apple trees non-invasive assessment in apple orchards and its adaptation to Early Gold and Golden Reinders based on spectral characteristics of chlorophyll content in the canopy. In each measurement period, 30 samples were collected from each of the two apple cultivars studied. For spectral data collection of leaf samples, an AvaSpec 2048 spectrometer was used in the wavelength range 400–1000 nm in three replicates. Principal component analysis (PCA) with varimax rotation was used to identify the wavelength with the highest factor weight to identify the chlorophyll-sensitive wavelength. The models were calibrated with 2/3 of the values in the database and validated with the remaining 1/3. The simple linear regression method generated the model for estimating chlorophyll. The coefficient of determination (R²) was used to compare the strength of the regression models, and the Root Mean Square Error (RMSE), Normalized Root Mean Square Error (NRMSE), Nash–Sutcliffe efficiency (NSE), Mean Absolute Error (MAE) and Mean Bias Error (MBE) functions were used to measure the accuracy of the estimator models. These metrics help to quickly assess how reliable and accurate a model’s predictions are. Nine indices were obtained based on the precision values, and CHL_apple1 performed best (R² = 0.633, RMSE = 298.28 µg/g, NRMSE = 9.61%, NSE = 0.60 MBE = 84.59, and MAE = 243.39). Full article

The concept of the water footprint (WF) has not adequately explored the combined effects of climate change and salinity. For this aim, the effects of future climate conditions on tomato WF irrigated with moderately saline water (EC = 2.9 dS m⁻¹) were examined, considering an expected increase in salinity reaching 5.9 dS m⁻¹ by 2050. Reference evapotranspiration (ETo), effective rainfall (ER), tomato crop evapotranspiration (ETc), leaching requirement (LR), net irrigation requirement (NIR), and tomato yield were estimated using CropWat and AquaCrop models. The blue (WFBlue), green (WFGreen), gray (WFGray), and total WF (TWF) were calculated. Results showed that ETo, ETc, and ER are expected to increase, while tomato yields will show a slight decrease. NIR is expected to increase depending on climate change scenarios and the increasing salinity of water irrigation. Calculated WF components showed significant increases, which consequently led to an increase in WFT exceeding the Tunisian national and regional levels by 15% and 18% between 2023 and 2050 under two scenarios, RCP4.5 and RCP8.5. The results highlighted the importance of WF for developing adaptation strategies to manage limited water resources, while advanced research on a large scale based on smart assessment tools is required to find best practices for water use reduction. Full article

The spatial distribution characteristics and slip rate in the Xianshuihe Fault Zone (XSHFZ) are still subject to controversy, and the segments where creeping movement occurs within the fault remain unclear. In this paper, the three-dimensional deformation field of the XSHFZ and its neighboring areas is obtained by integrating InSAR and GNSS data. Subsequently, based on the three-dimensional deformation field, an elastic dislocation model is employed to analyze the slip rate, locking state, and creeping movement within the XSHFZ. The results show that the XSHFZ is a typical sinistral strike–slip fault with compressional characteristics. The slip rate of the XSHFZ ranges from 9.3 to 14.3 mm/yr. The average strike–slip rate of the Qianning and Kangding segments surpasses that of the eastern and western segments, while the Moxi segment exhibits the lowest slip rate. The locking depth of the XSHFZ is estimated to be between 13 and 26 km, with shallow creep movement predominantly concentrated in three segments: Daofu, Qianning, and Kangding, where the shallow creep rate ranges from 1.5 to 4.9 mm/yr. The XSHFZ is known for its short recurrence period of strong earthquakes and frequent seismic activities. A quantitative study of fault slip rates, locking depth, and creeping movement provides essential support for analyzing its seismic hazards. The seismic hazard of each segment of the Xianshuihe Fault Zone (XSHFZ) was analyzed based on the principle of seismic moment balance. The areas with high seismic hazards in the Xianshuihe Fault Zone correspond to the locations of seismic gaps along the fault. Specifically, the Qianning segment and the Yalahe and Selaha faults within the Kangding segment are associated with seismic gaps and are at risk of experiencing earthquakes with magnitudes of 6.9, 6.9, and 6.6, respectively. The results highlight the importance of continuous monitoring and preparedness measures to mitigate the seismic risks present in the XSHFZ. Full article