Search Results (24,113)

Ternary hybrid nanofluid have been revealed to possess a wide range of application disciplines reaching from biomedical engineering, detection of cancer, over or photovoltaic panels and cells, nuclear power plant engineering, to the automobile industry, smart cells and and eventually to heat exchange systems. Inspired by the recent developments in nanotechnology and in particular the high potential ability of use of such nanofluids in practical problems, this paper deals with the flow of a three phase nanofluid of MWCNT-Au/Ag nanoparticles dispersed in blood in the presence of a bidirectional stretching sheet. The model derived in this study yields a set of linked nonlinear PDEs, which are first transformed into dimensionless ODEs. From these ODEs we get a dataset with the help of MATHEMATICA environment, then solved using AI-based technique utilizing Levenberg Marquardt Feedforward Algorithm. In this work, flow characteristics under varying physical parameters have been studied and analyzed and the boundary layer phenomena has been investigated. In detail horizontal, vertical velocity profiles as well as temperature distribution are analyzed. The findings reveal that as the stretching ratio of the surface coincide with an increase the vertical velocity as the surface has thinned in this direction minimizing resistance to the fluid flow. Full article

Reducing pesticide residues in crops is essential to ensure food safety, protect human health, and promote environmental sustainability. In this study, activated carbon (AC) was applied as a soil amendment to investigate its effect on reducing residues of the pesticides cyantraniliprole and fluopyram in greenhouse-grown lettuce. The effectiveness of AC in reducing pesticide residues varies significantly based on pesticides and crops. Pesticide dissipation patterns in the soil and a set of pesticide residues of lettuce leaf and root tissues, as well as the soil surrounding the roots for each of the tested pesticides, were analyzed using liquid chromatography with tandem mass spectrometry (LC-MS/MS) during the test periods. The results showed different pesticide dissipation patterns for cyantraniliprole, fitting the first-order kinetics, and fluopyram. Nevertheless, both AC treatments exhibited a similar decreasing tendency in which cyantraniliprole residues ranged from 0.050 to 0.064 mg/kg in leaf and 0.019 to 0.034 mg/kg in root samples, while fluopyram residues ranged from 0.168 to 0.509 mg/kg in leaf and 0.315 to 0.787 mg/kg in root samples. The highest percentage reductions were 71.3% and 77.3% for cyantraniliprole in leaf and root samples, respectively, and 79.7% and 87.5% for fluopyram in leaf and root samples. In addition, the soil samples showed a more dynamic pattern of pesticide residues compared to those of the lettuce samples. The overall findings suggest that applying AC as a soil amendment in pesticide-treated soils has a positive effect on reducing residues of cyantraniliprole and fluopyram in lettuce. Therefore, this AC-treated soil amendment could be considered a safer agricultural practice with great potential for producing safer food resources from pesticide-contaminated soils. Thus, it is expected that proper utilization of AC plays an important role in the production of safe agri-food products to manage and generate a sustainable agricultural environment. Full article

Bovine Viral Diarrhea Virus (BVDV) is a globally important cattle pathogen causing substantial economic losses. In Kazakhstan, BVDV’s epidemiological status remains poorly characterized due to the absence of systematic surveillance. We carried out a cross-sectional study of cattle herds across Kazakhstan, using ELISA to detect anti-BVDV antibodies and RT-PCR to identify active infections. Positive samples underwent sequencing for phylogenetic analysis of circulating strains. Additionally, a standard reference serum panel was developed to measure virus neutralization titers (ND₅₀) and to evaluate cross-neutralization with Border Disease virus (BDV). Antibodies against BVDV were prevalent, with seropositivity ranging from 28.89% to 96.13% across surveyed regions. Active BVDV infection was confirmed by RT-PCR in 17 animals. Phylogenetic analysis with 2 samples from Mangystau region classified the virus as BVDV2 genotype. The reference serum panel exhibited high neutralizing titers ND₅₀ up to 1:286 against the local BVDV-1 isolate. Notably, these sera also neutralized BDV, albeit at lower titers ND₅₀ 1:45. These findings provide crucial baseline epidemiological data and enhanced diagnostic tools for BVDV in Kazakhstan. They highlight the need for improved surveillance and will inform strategic control measures against this economically significant cattle disease. Full article

Background: Atherogenic dyslipidemia is defined by the coexistence of high triglyceride concentrations, low levels of high-density lipoprotein cholesterol (HDL-C), and an excess of small, dense particles of low-density lipoprotein cholesterol (LDL-C). This lipid profile is strongly associated with an increased burden of cardiovascular disease and represents a leading cause of global morbidity and mortality. To better capture this risk, composite lipid ratios—including total cholesterol to HDL-C (TC/HDL-C), LDL-C to HDL-C (LDL-C/HDL-C), triglycerides to HDL-C (TG/HDL-C), and the atherogenic dyslipidemia index (AD)—have emerged as robust markers of cardiometabolic health, frequently demonstrating superior predictive capacity compared with isolated lipid measures. Despite extensive evidence linking these ratios to cardiovascular disease, few large-scale studies have examined their association with sociodemographic characteristics, lifestyle behaviors, and social isolation in working populations. Methods: We conducted a cross-sectional analysis of a large occupational cohort of Spanish workers evaluated between January 2021 and December 2024. Anthropometric, biochemical, and sociodemographic data were collected through standardized clinical protocols. Indices of atherogenic risk—namely the ratios TC/HDL-C, LDL-C/HDL-C, TG/HDL-C, and the atherogenic dyslipidemia index (AD)—were derived from fasting lipid measurements. The assessment of lifestyle factors included tobacco use, physical activity evaluated through the International Physical Activity Questionnaire (IPAQ), adherence to the Mediterranean dietary pattern using the MEDAS questionnaire, and perceived social isolation measured by the Lubben Social Network Scale. Socioeconomic classification was established following the criteria proposed by the Spanish Society of Epidemiology. Logistic regression models were fitted to identify factors independently associated with moderate-to-high risk for each lipid indicator, adjusting for potential confounders. Results: A total of 117,298 workers (71,384 men and 45,914 women) were included. Men showed significantly higher odds of elevated TG/HDL-C (OR 4.22, 95% CI 3.70–4.75) and AD (OR 2.95, 95% CI 2.70–3.21) compared with women, whereas LDL-C/HDL-C ratios were lower (OR 0.86, 95% CI 0.83–0.89). Advancing age was positively associated with all lipid ratios, with the highest risk observed in participants aged 60–69 years. Lower social class, smoking, physical inactivity, poor adherence to the Mediterranean diet, and low social isolation scores were consistently linked to higher atherogenic risk. Physical inactivity showed the strongest associations across all indicators, with ORs ranging from 3.54 for TC/HDL-C to 7.12 for AD. Conclusions: Atherogenic dyslipidemia and elevated lipid ratios are strongly associated with male sex, older age, lower socioeconomic status, unhealthy lifestyle behaviors, and reduced social integration among Spanish workers. These findings highlight the importance of workplace-based cardiovascular risk screening and targeted prevention strategies, particularly in high-risk subgroups. Interventions to promote physical activity, healthy dietary patterns, and social connectedness may contribute to lowering atherogenic risk in occupational settings. Full article

This study presents the deployment of Machine Learning (ML) models on low-cost edge devices (ESP32) for real-time water quality prediction in tilapia aquaculture. A compact monitoring and control system was developed with low-cost sensors to capture key environmental parameters under field conditions in Northern Thailand. Three ML models—Multiple Linear Regression (MLR), Decision Tree Regression (DTR), and Random Forest Regression (RFR)—were evaluated. RFR achieved the highest accuracy (R² > 0.80), while MLR, with moderate performance (R² ≈ 0.65–0.72), was identified as the most practical choice for ESP32 deployment due to its computational efficiency and offline operability. The system integrates sensing, prediction, and actuation, enabling autonomous regulation of dissolved oxygen and pH without constant cloud connectivity. Field validation demonstrated the system’s ability to maintain DO within biologically safe ranges and stabilize pH within an hour, supporting fish health and reducing production risks. These findings underline the potential of Edge AIoT as a scalable solution for small-scale aquaculture in resource-limited contexts. Future work will expand seasonal data coverage, explore federated learning approaches, and include economic assessments to ensure long-term robustness and sustainability. Full article

As a fundamental metric for assessing carbon sequestration, Net Primary Productivity (NPP) and the mechanisms driving its spatiotemporal dynamics constitute a critical research domain within global change science. This research centered on the Huang–Huai–Hai Plain (HHHP), combining 2001–2023 MODIS-NPP data with natural (landform, temperature, precipitation, soil) and socio-economic (population density, GDP density, land use) drivers. Trend analysis, coefficient of variation, and Hurst index were applied to clarify the spatiotemporal evolution of NPP and its future trends, while geographic detectors and structural equation models were used to quantify the contribution of drivers. Key findings: (1) Across the HHHP, the multi-year average NPP ranged between 30.05 and 1019.76 gC·m⁻²·a⁻¹, with higher values found in Shandong and Henan provinces, and lower values concentrated in the northwestern dam-top plateau and central plain regions; 44.11% of the entire region showed a statistically highly significant increasing trend. (2) The overall fluctuation of NPP was low-amplitude, with a stable center of gravity and the standard deviation ellipse retaining a southwest-to-northeast direction. (3) Future changes in NPP exhibited persistence and anti-persistence, with 44.98% of the region being confronted with vegetation degradation risk. (4) NPP variations originated from the synergistic impacts of multiple elements: among individual elements, precipitation, soil type, and elevation had the highest explanatory capacity, while synergistic interactions between two elements notably enhanced the explanatory capacity. (5) Climate variation exerted the strongest influence on NPP (direct coefficient of 0.743), followed by the basic natural environment (0.734), whereas human-related activities had the weakest direct impact (−0.098). This research offers scientific backing for regional carbon sink evaluation, ecological security early warning, and sustainable development policies. Full article

We observed vibrational eigenmodes for a variety of millimeter-scale objects, including glass and sapphire lenses, by placing them on a piezoelectric ‘shaker’ driven by a broadband noise or frequency sweep signal, and using an optomechanical microphone to pick up their vibrational signatures emitted into the surrounding air. High-quality vibrational modes were detected over the ~0–8 MHz range for a typical object–microphone spacing of 1–10 mm. The observed eigenfrequencies are shown to be in excellent agreement with numerical predictions. Non-contact detection of resonant vibrational eigenmodes in the MHz ultrasound range could find application in the quality control of numerous industrial parts, such as ball bearings and lenses. Full article

This study aimed to examine the proximal effects of blood flow restriction (BFR) training on shoulder muscle function and subjective discomfort during low-intensity external rotation exercise. Twenty-four healthy adults were randomly assigned to a BFR group or a control group and performed shoulder stabilization exercises with or without BFR. Outcome measures included shoulder external rotation range of motion, maximal isometric strength, muscle endurance, electromyographic activity of the rotator cuff muscles, and perceived discomfort. Both groups demonstrated significant within-group improvements in all outcomes except posterior deltoid and supraspinatus activity (p < 0.05). Between-group comparisons showed significantly greater gains in maximal strength and infraspinatus and teres minor activation in the BFR group than in the control group (p < 0.05), while discomfort and fatigue scores were also higher in the BFR group (p < 0.05). These findings suggest that BFR applied at the proximal upper arm can enhance the strength and activation of key rotator cuff muscles even when cuff placement near the shoulder is limited by anatomy. Proximal BFR may serve as an effective intervention for improving shoulder function when high-intensity exercise is contraindicated, although strategies to minimize discomfort are needed to improve clinical feasibility. Full article

Comprehending the effects of climate change on the range of endangered species is essential for formulating successful conservation strategies. This research examines two nationally protected dung beetle species (Heliocopris dominus and Heliocopris bucephalus) in China to forecast their probable habitat range under present and future climate scenarios. Employing MaxEnt modeling with validated occurrence records and environmental variables, we discerned critical factors affecting their distribution and anticipated changes in habitat suitability. Results reveal that isothermality, temperature seasonality, maximum temperature of the warmest month, and annual precipitation are the principal environmental drivers. Presently, appropriate habitats are primarily located in southern Yunnan and Hainan, with future forecasts indicating a northward extension into additional areas. These findings offer critical insights for choosing conservation zones for these vulnerable species amid shifting climate conditions. Full article

This study investigates iron acetylacetonate (Fe(acac)₃) as a catalyst for enhancing high-temperature oxidation (HTO) during in situ combustion (ISC) of heavy oil. Thermal analysis revealed that Fe(acac)₃ decomposes at 360 °C to form crystalline magnetite (Fe₃O₄). This transformation precedes the HTO regime. Differential scanning calorimetry demonstrated significantly intensified HTO reactions in catalytic systems, as peak temperatures were lower than those in non-catalytic reactions. Kinetic analysis showed that the catalyst reduces HTO activation energy by 15.6%, substantially increasing reaction rates across the HTO temperature range. X-ray powder diffraction confirmed that the mixed-valence Fe²⁺/Fe³⁺ configuration in the magnetite structure facilitates electron transfer during oxidation, enabling more complete combustion at lower temperatures. These findings represent a novel approach to catalyst design, from general activity to temperature-specific activation for a more stable and efficient in situ combustion process. Full article

This study investigates the influence of driver-related factors on on-street parking choice by integrating stated preference (SP) and revealed preference (RP) survey methods. A hybrid SP–RP survey was designed to simulate realistic parking scenarios, and 423 valid questionnaires were collected online and offline. Key factors affecting parking choice were identified through descriptive analysis, including user acceptance of differentiated pricing and satisfaction with existing policies. The Kaiser–Meyer–Olkin (KMO = 0.904) and Bartlett’s test (p < 0.001) confirmed data suitability for factor analysis. A binary logistic regression model was developed to quantify variable effects under different travel purposes. Key findings include the following: monthly parking fee had the strongest effect (OR = 6.691, p = 0.010) on parking choice for shopping/entertainment trips; model prediction accuracy ranged from 80.87% to 83.56% across travel purposes; and goodness-of-fit metrics were strong (McFadden R² = 0.630, Nagelkerke R² = 0.772). The results provide empirical evidence on parking choice determinants and support the design of demand-responsive parking policies through dynamic and differentiated pricing strategies. Full article

Background: Traumatic brain injury (TBI) is a major public health concern. Creatinine (Cr) has been well studied as a marker of renal function, specifically the development of acute kidney injury (AKI) in TBI patients. We aimed to evaluate the effect of Cr on various clinical outcomes in patients with severe TBI. Methods: We investigated the relationship between Cr levels at various time points and a range of clinical variables, using parametric and non-parametric statistical testing. Results: 1000 patients were included in our study. We found a significant association between sex and Cr level at intensive care unit (ICU) admission and ICU discharge. Cr was positively correlated with ISS at hospital admission, ICU admission, ICU discharge, and at death. Conversely, Cr was negatively correlated with GCS at hospital admission, ICU admission, ICU discharge, and at death. Larger decreases in Cr from Hospital to ICU admission were significantly correlated with increased vent days. Larger decreases in Cr from ICU admission to ICU discharge were significantly correlated with increased hospital length of stay (LOS), ICU LOS, and vent days, likely reflecting the degree of initial hypercreatinemia. For all patients, there were significant positive correlations between Cr at admission and ICU LOS, Cr at ICU admission and ICU LOS, and Cr at ICU admission and vent days. Conclusions: Our findings support existing literature that demonstrates a positive relationship between Cr levels, ICU LOS, and vent days amongst patients with severe TBI. These data suggest renal injury is predictive of TBI outcomes. Future research should investigate the role of renal therapeutic interventions in TBI recovery. Full article

Spinal cord injury (SCI) often results in long-term locomotor impairments, and strategies to enhance functional recovery remain limited. While epidural electrical stimulation (EES) has shown clinical promise, our understanding of the mechanisms by which it improves function remains incomplete. Here, we use genetic tools in an animal model to perform neuromodulation and treadmill rehabilitation in a manner similar to EES, but with the benefit of the genetic tools and animal model allowing for targeted manipulation, precise quantification of the cells and circuits that were manipulated, and the gathering of extensive kinematic data. We used a viral construct that selectively transduces large diameter afferent fibers (LDAFs) with a designer receptor exclusively activated by a designer drug (hM3Dq DREADD; a chemogenetic construct) to increase the excitability of large fibers specifically, in the rat contusion SCI model. As changes in locomotion with afferent stimulation can be subtle, we carried out a detailed characterization of the kinematics of locomotor recovery over time. Adult Long-Evans rats received contusion injuries and direct intraganglionic injections containing AAV2-hSyn-hM3Dq-mCherry, a viral vector that has been shown to preferentially transduce LDAFs, or a control with tracer only (AAV2-hSyn-mCherry). These neurons then had their activity increased by application of the designer drug Clozapine-N-oxide (CNO), inducing tonic excitation during treadmill training in the recovery phase. Kinematic data were collected during treadmill locomotion across a range of speeds over nine weeks post-injury. Data were analyzed using a mixed effects model chosen from amongst several models using information criteria. That model included fixed effects for treatment (DREADDs vs. control injection), time (weeks post injury), and speed, with random intercepts for rat and time point nested within rat. Significant effects of treatment and treatment interactions were found in many parameters, with a sometimes complicated dependence on speed. Generally, DREADDs activation resulted in shorter stance duration, but less reduction in swing duration with speed, yielding lower duty factors. Interestingly, our finding of shorter stance durations with DREADDs activation mimics a past study in the hemi-section injury model, but other changes, including the variability of anterior superior iliac spine (ASIS) height, showed an opposite trend. These may reflect differences in injury severity and laterality (i.e., in the hemi-section injury the contralateral limb is expected to be largely functional). Furthermore, as with that study, withdrawal of DREADDs activation in week seven did not cause significant changes in kinematics, suggesting that activation may have dwindling effects at this later stage. This study highlights the utility of high-resolution kinematics for detecting subtle changes during recovery, and will enable the refinement of neuromechanical models that predict how locomotion changes with afferent neuromodulation, injury, and recovery, suggesting new directions for treatment of SCI. Full article

This study examines how cognitive biases may shape ethical decision-making in AI-mediated environments, particularly within education and research. As AI tools increasingly influence human judgment, biases such as normalization, complacency, rationalization, and authority bias can lead to ethical lapses, including academic misconduct, uncritical reliance on AI-generated content, and acceptance of misinformation. To explore these dynamics, we developed an LLM-generated synthetic behavior estimation framework that modeled six decision-making scenarios with probabilistic representations of key cognitive biases. The scenarios addressed issues ranging from loss of human agency to biased evaluations and homogenization of thought. Statistical summaries of the synthetic dataset indicated that 71% of agents engaged in unethical behavior influenced by biases like normalization and complacency, 78% relied on AI outputs without scrutiny due to automation and authority biases, and misinformation was accepted in 65% of cases, largely driven by projection and authority biases. These statistics are descriptive of this synthetic dataset only and are not intended as inferential claims about real-world populations. The findings nevertheless suggest the potential value of targeted interventions—such as AI literacy programs, systematic bias audits, and equitable access to AI tools—to promote responsible AI use. As a proof-of-concept, the framework offers controlled exploratory insights, but all reported outcomes reflect text-based pattern generation by an LLM rather than observed human behavior. Future research should validate and extend these findings with longitudinal and field data. Full article

Accurate prediction of earthquake—induced building damage is essential for timely disaster response and effective risk mitigation. This study explores a machine learning (ML)-based classification approach using data from the 2015 Gorkha, Nepal earthquake, with a specific focus on reinforced concrete (RC) structures. The original dataset from the 2015 Nepal earthquake contained 762,094 building entries across 127 variables describing structural, functional, and contextual characteristics. Three ensemble ML modelsGradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM) were trained and tested on both the full dataset and a filtered RC-only subset. Two target variables were considered: a three-class variable (damage_class) and the original five-level damage grade (damage_grade). To address class imbalance, oversampling and undersampling techniques were applied, and model performance was evaluated using accuracy and F1 scores. The results showed that LightGBM consistently outperformed the other models, especially when oversampling was applied. For the RC dataset, LightGBM achieved up to 98% accuracy for damage_class and 93% accuracy for damage_grade, along with high F1 scores ranging between 0.84 and 1.00 across all classes. Feature importance analysis revealed that structural characteristics such as building area, age, and height were the most influential predictors of damage. These findings highlight the value of building-type-specific modeling combined with class balancing techniques to improve the reliability and generalizability of ML-based earthquake damage prediction. Full article