Search Results (811)

In vitro fertilization (IVF) exposes embryos to environmental stressors that can disrupt early development and confer long-term health risks, though the mechanisms remain poorly understood. Here, we tested the hypothesis that reducing incubation temperature during the first zygotic cleavage would promote long-term developmental stability in IVF-conceived offspring. Using a mouse model, we compared the long-term effects of standard (37 °C) versus reduced (35 °C) IVF culture temperature on energy balance, circadian rhythms, sleep architecture, and brain histone modifications. Although offspring from both IVF groups exhibited increased body mass without notable effects on glucose metabolism, significant disruptions in circadian rhythms and sleep–wake patterns were detected. The 37 °C group exhibited altered amplitudes in oxygen consumption rhythms and respiratory exchange ratios, as well as pronounced alterations in sleep–wake patterns, including reduced sleep duration and increased nighttime activity. The 35 °C group displayed intermediate phenotypes, substantiating the importance of optimizing embryo incubation parameters. These metabolic and behavioral changes were paralleled by altered histone modifications in the cerebral cortex of IVF offspring, suggesting an epigenetic basis for circadian misalignment. Our results identify disrupted circadian rhythm and sleep architecture as a novel mechanism contributing to metabolic dysfunction in IVF-conceived offspring. The partial mitigation of these effects through reduced culture temperature underscores the importance of optimizing IVF protocols to minimize long-term epigenetic and metabolic risks. Full article

The uneven distribution of airflow on the blade surface of a yaw wind turbine triggers a complex non-constant flow, resulting in turbine flow field operation disorder, which, in turn, affects the structural field. In view of the different degrees of influence of different blade materials on the structural characteristics of a wind turbine, a numerical simulation of the flow field and structural field of the horizontal-axis wind turbine under different yaw conditions is carried out by using the fluid–solid coupling method to quantitatively analyse the degree of influence of the material on the structural characteristics of the wind turbine. The results show that the average velocity of the wake cross-section shows a trend of decreasing, then increasing, and then stabilising at all yaw angles. The larger the yaw angle, the wider is the vortex structure dispersion. As the wake develops downstream, the turbulence intensity is shown to decrease and then increase, and the yaw perturbation exacerbates the turbulence disorder in the wake flow field. Along the wind turbine blade spreading direction, the blade deformation phenomenon is significant. The yaw angle increases, the wind turbine blade deformation increases, and the maximum blade stress first increases and then decreases. At a 15° yaw angle, the airflow on the blade surface is more easily separated, and vortices are formed in the vicinity, which impede the airflow in the boundary layer and lead to a reduction in the velocity in the boundary layer in this region. The minimum deformation and maximum stress of the three materials under a 15° yaw angle indicate that the blades are more capable of resisting external deformation under this condition, so 15° yaw is the best operating condition for the wind turbine. This paper employs different materials to quantitatively analyse the extent to which structural characteristics influence wind turbine performance. The findings from this research can provide valuable insights for optimising wind turbine designs. Full article

Wake vortex is a critical factor affecting aircraft safety and airport runway capacity. To assess the runway capacity of mixed operations for training and transport airports, this study first simulated the wake vortex dissipation process of the commonly used A321 aircraft at Luoyang Beijiao Airport using a wake vortex prediction model. The SR20 training aircraft was selected as the subject for wake vortex encounters, with the rolling moment coefficient used as an indicator to assess the risk of wake encounters, and the wake vortex safety separation was calculated. Finally, a runway capacity model based on runway average service time for mixed training and transport operations was developed, calculating both runway landing capacity and the total runway capacity in the continuous landing and interleaved takeoff mode. The simulation results indicate that under different atmospheric BV frequencies, the safe wake vortex separations for the A321–SR20 combination are 6375 m, 6188 m, and 5700 m, respectively, representing reductions of 31.5%, 33.2%, and 38.4% shorter than the current CCAR-93TM-R6 regulatory separations, and compared to the RECAT 1.5 and RECAT-EU standards. Under reduced separation conditions, runway capacity demonstrated improvement across various atmospheric conditions and operational modes. Full article

“Wind theft”, the extraction of upstream wind resources by neighboring wind farms on account of wind farm or cluster wakes, is receiving wider popular attention. Cluster wakes need to be accounted for in wider planning strategies, for which measurements and wake models can be deployed to aid this process. To contribute to such planning measures, a flight campaign for investigating cluster waking and other phenomena in the North Sea was conducted in 2020 and 2021 to contribute extra flight data obtained during the first flight campaign of 2016 and 2017. We report the latest results of the 2020–2021 flight campaign following the work and methodology of Cañadillas et al. (2020), where, using the 2016–2017 flight measurements, wake lengths extending up to approximately 60 km in stable stratification were inferred, consistent with an explicit stability-dependent analytical model. Analysis of the recent 2020–2021 flight data is approximately consistent with the results of Cañadillas et al. (2020) in stable conditions, albeit with greater scatter. This is because Cañadillas et al. (2020) analyzed only flights in which the wind conditions remained nearly constant during the measurement period, whereas the current dataset includes more variable conditions. Comparisons with the analytical-based engineering model show good first-order agreement with the flight data, but higher-order effects, such as flow non-homogeneity, are not accounted for. The application of these results to the stability information for developing offshore wind energy regions such as the East Coast of the USA and Bass Strait, Australia gives an outline of the expected wake lengths there. Simple engineering models, such as that demonstrated here, though primarily designed for commercial applications, need to be further developed into advanced spatial planning frameworks for offshore wind energy areas. Full article

In order to understand how health beliefs map onto health behaviors, a national survey, administered in the wake of the COVID-19 campus closures, was conducted to explore college students’ mind-body health beliefs and their health behaviors (across dimensions of physical exercise, diet/nutrition, and socialization). To this end, the Mind-Body Health Screener (MBHS), a five-item, Likert scale, brief measure, was developed. The present study applied an online survey design administered nationally to U.S. undergraduate students during the initial lockdowns with the pandemic (n = 557). To examine the psychometric properties of the MBHS, exploratory and confirmatory factor analyses were run as well as measures of reliability. Furthermore, linear regressions and effect sizes were computed to understand the connection between mind-body health beliefs and behaviors. While initial data supported the psychometric properties of the Mind-Body Health Screener (MBHS) developed for this purpose, substantive results suggested that mind-body health beliefs did not relate to mind-body health behaviors (either before or after the campus closures), aligning with the Cognitive Dissonance Theory. Post hoc analysis did, however, suggest a significant change in health behaviors from pre-campus closures to during the closures, suggesting students engaged in more physical exercise, eating behaviors, and socializing before campus closed, observed with small to large effects. Taken together, the findings of the present study illustrate how the Cognitive Dissonance Theory is a relevant perspective to consider the relation between health beliefs and behaviors during a period of immense stress, such as the COVID-19 initial campus closures. Full article

As small–medium-sized enterprises (SMEs) increasingly adopt cloud technologies, understanding the factors influencing this shift is crucial as it helps to optimize cloud integration strategies, enabling SMEs to thrive in today’s digital economy. A cross-sectional, quantitative survey was conducted in February 2022 on 626 employees of SMEs in the USA, based on the TAM-2, TAM-3, and UTAUT-2 models. The questionnaire presented satisfactory reliability, as well as factorial and convergent validity. Employees presented positive behavioral intentions to use cloud technologies, particularly during the COVID-19 period. SMEs were satisfied with the use of Software as a Service (SaaS), Infrastructure as a Service (IaaS), and the public cloud development model in the wake of the COVID-19 period. Behavioral intention to use cloud technologies was linked with higher performance and effort expectancy, price, perceived enjoyment, computer self-efficacy, and social influence. A higher behavioral intention was observed in employees (a) with a mid–top-level role; (b) who worked in finance and insurance, information services data, construction, or software and in an SME with 26–500 employees; (c) who had a master’s degree; (d) were 35–44 years old; and (e) had family obligations. Higher experience with the use of cloud technologies enhanced the positive impacts of effort expectancy, computer self-efficacy, and perceived enjoyment on behavioral intention. Full article

An accurate estimation of wind energy productivity is crucial for the success of energy transition strategies in developing countries such as Pakistan, for which the deployment of renewables is essential. This study investigates the use of machine learning and deep learning techniques to improve wind farm producibility assessments, tailored to the Pakistani context. SCADA data from a wind turbine in Türkiye were used to train and validate five predictive models. Among these, Random Forest proved most reliable, attaining a coefficient of determination of 0.97 on the testing dataset. The trained model was then employed to simulate the annual production of a 5 × 5 wind farm at two representative sites in Pakistan—one onshore and one offshore—that had been selected using ERA5 reanalysis data. In comparison with conventional estimates based on the theoretical power curve, the machine learning-based approach resulted in net energy predictions up to 20% lower. This is attributable to real-world effects such as wake and grid losses. The onshore site yielded an LCOE of 0.059 USD/kWh, closely aligning with the IRENA’s 2024 national average of approximately 0.06 USD/kWh, thereby confirming the reliability of the estimates. In contrast, the offshore site exhibited an LCOE of 0.120 USD/kWh, thus underscoring the need for incentives to support offshore development in Pakistan’s renewable energy strategy. Full article

Objective: To investigate whether a detailed historical questionnaire can predict the affected semicircular canal and lateralization in patients with benign paroxysmal positional vertigo (BPPV). Methods: In this retrospective study, 459 patients with positional vertigo were evaluated, of whom 236 patients diagnosed with BPPV completed a symptom-based questionnaire. Based on the questionnaire data, logistic regression models were developed to predict lateralization and the affected semicircular canal. The clinical diagnosis of BPPV served as the reference standard. A nomogram was constructed based on the final logistic regression model, and model performance was evaluated using area under the receiver operating characteristic curve (AUC) in both training and validation cohorts. Results: Among 220 BPPV patients included, 152 (69.09%) were diagnosed with posterior semicircular canal BPPV (PSC-BPPV), 49 (22.27%) with horizontal semicircular canal canalolithiasis (HSC-BPPV-can), and 19 (8.64%) with horizontal semicircular canal cupulolithiasis (HSC-BPPV-cup). Waking up, lying down and rotating the head toward the left/right in the supine position, triggering vertigo, were significant predictors of the affected semicircular canal. Rotating the head toward the left/right in the supine position and vertigo duration were significantly predictors for lateralization. The AUCs were 0.787 and 0.714 for lateralization, and 0.814 and 0.842 for canal prediction in training and validation cohorts, respectively. Conclusions: The nomogram demonstrated good consistency with the reference standard diagnoses and may facilitate the identification of the affected side and canal in BPPV. Full article

Background: Postpartum depression is a common mental health condition that can occur up to one year after childbirth. Recent studies have increasingly used machine learning techniques to predict its occurrence; however, few have comprehensively explored the use of electronic health record data, particularly in tertiary care settings where such data can be fragmented. Methods: We analyzed electronic health record data from 12,284 women who delivered at The Birth Center at Atrium Health Wake Forest Baptist Medical Center, excluding those with missing data or no prenatal or postpartum visits. To define the target variable, we examined different combinations of depression screening tools (Edinburgh Postnatal Depression Scale and Patient Health Questionnaire-9), along with diagnosis codes specific to postpartum depression. We then trained a random forest classification model to predict postpartum depression. Results: The model achieved an area under the receiver operating characteristic curve of 0.733 ± 0.008, which is comparable to previous studies. Adding socioeconomic features from census tract data did not improve predictive performance, underscoring the importance of individual-level data. Incorporating national survey data, such as the Pregnancy Risk Assessment Monitoring System, also did not improve performance due to limited overlap in data features. Interestingly, model performance was slightly lower among Hispanic patients (area under the curve = 0.713 ± 0.040), although this difference was not statistically significant (p = 0.17), likely due to the small sample size. A similar, but statistically significant trend was observed in the larger national survey dataset (area under the curve = 0.699 ± 0.019 for Hispanic patients versus 0.735 ± 0.010 for White patients, p < 0.01). Conclusions: While our model demonstrates moderate predictive capability, further validation and prospective testing are needed before clinical implementation. This work also identified an optimal approach for digital phenotyping postpartum depression in electronic health record data and highlighted key gaps in data quality and completeness. These findings emphasize the importance of robust data when developing predictive models for real-world clinical use. Full article

Insomnia is a common and complex disorder, rooted in the dysregulation of circadian rhythms, impaired neurotransmitter function, and disturbances in sleep–wake homeostasis. While conventional hypnotics such as benzodiazepines and Z-drugs are effective in the short term, their use is limited by a high potential for dependence, cognitive side effects, and withdrawal symptoms. In contrast, melatonergic receptor agonists—melatonin, ramelteon, tasimelteon, and agomelatine—represent a pharmacologically targeted alternative that modulates MT1 and MT2 receptors, which are pivotal to the regulation of circadian timing and sleep initiation. Clinical evidence supports the efficacy of these agents in reducing sleep onset latency, extending total sleep duration, and re-aligning disrupted circadian rhythms, particularly among older individuals and patients with non-24 h sleep–wake disorders. Notably, agomelatine offers additional antidepressant properties through selective antagonism of the 5-HT_2C receptor in micromolar concentrations. In contrast, its agonistic activity at melatonergic receptors is observed in the low sub-nanomolar range, which illustrates the complexity of this drug’s interactions with the human body. All compounds reviewed demonstrate a generally favorable safety and tolerability profile. Accumulating evidence highlights that selected medicinal plants, such as chamomilla, lemon balm, black cumin, valeriana, passionflower and lavender, may exert relevant hypnotic or anxiolytic effects, thus complementing melatonergic strategies in the management of insomnia. This structured narrative review presents a comprehensive analysis of the molecular pharmacology, receptor affinity, signaling pathways, and clinical outcomes associated with melatonergic agents. It also examines their functional interplay with serotonergic, GABAergic, dopaminergic, and orexinergic systems involved in arousal and sleep regulation. Through comparative synthesis of pharmacokinetics and neurochemical mechanisms, this work aims to inform the development of evidence-based strategies for the treatment of insomnia and circadian rhythm sleep–wake disorders. Full article

The wire tightener is a critical tool in the construction and maintenance of power lines. Failure to detect tension overload in a timely manner may lead to plastic deformation or even breakage of the tool, potentially causing serious safety accidents. To address this issue, a force monitoring sensor was developed to track the real-time load on wire tighteners. In terms of hardware design, a foil strain gauge was integrated with an ultra-low-power mixed-signal microcontroller based on the mechanical characteristics of the wire tightener, enabling accurate acquisition and processing of load data. Low-power LoRa technology was employed for wireless data transmission, and an adaptive sleep–wake strategy was implemented to optimize power efficiency during data collection. The sensor’s material, geometry, and structure were tailored to the tool’s composition and working environment. Experimental results showed that the average relative error between the sensor readings and the reference values was less than 0.5%. The sensor has been successfully deployed in practical engineering applications, consuming approximately 4500 mWh over an 8 h continuous monitoring period. Full article

Background/Objectives: Time spent with parents and educators encompasses a large portion of a child’s waking hours, with the home and early childhood education and care serving as two of the first settings in which children develop social and cognitive abilities. While previous studies have used social and cognitive tests to examine antecedents of child behavior, we extend such studies to take into account the congruence and incongruence of parents’ and teachers’ views on those antecedents. We examine the importance of parent-teacher alignment on the perceptions of a child’s personality and abilities in early development. Methods: Parents and teachers of 2968 German Kindergarten-aged (4–5 years old) children were surveyed using the National Educational Panel Study (NEPS). Parents and teachers independently rated 10 child behavioral traits, with higher scores indicating more prosocial behavior. Educators also rated children on five developmental abilities (social abilities, ability to concentrate, language abilities, general knowledgeability, and mathematical reasoning) compared to the student’s peers. While previous work has often examined how parental investments in children or teachers’ views of children might be related to development, we provide a new take by examining parents and teachers in conjunction with each other. Research that has looked at both parents and teachers has tended to examine alignment, or lack thereof, on child behaviors and personality traits. We analyzed child developmental abilities using OLS regression models, measures of parent–teacher divergences in ratings of child behavior, and demographic controls. Results: Greater differences in parent and teacher perceptions of desire for knowledge were negatively associated with all five developmental abilities. Differences in parent and teacher perceptions on talkativeness, confidence, good-naturedness, and understanding were negatively associated with at least one developmental outcome. By contrast, differences in perceptions of children’s neatness were positively associated with all five developmental abilities. Conclusions: Using both parent and teacher perceptions of child behaviors and abilities is a unique approach to understanding the relevance of parent and educator perceptions to a child’s development. Our findings indicate the need for collaboration across young children’s home and school or care settings. Establishing congruence in perceptions and the kinds of relationships that can lead to such congruence can help children with behavioral issues receive support in both home and educational settings and encourage mutual respect and partnership between parents and educators. Full article

High-performance sports cars rely on aerodynamics for stability and speed, but developing aero packages is challenging when wind tunnel testing is limited. In this study, we employed a simulation-guided design loop to maximize downforce and minimize drag on a sports car using Computational Fluid Dynamics (CFD). Thirteen aerodynamic modifications—including splitters, ducts, diffusers, and a Drag Reduction System (DRS)—were iteratively tested using CFD. To ensure numerical reliability, a mesh independence study and convergence analysis were performed, confirming stable aerodynamic predictions. The final configuration achieved an ~11× increase in downforce at 120 km/h (from about 320 N to 3588 N), meeting the design goal of roughly 2000 kg of downforce at 177 mph when scaled. This extreme downforce came with higher drag ($C_D$ ≈ 0.83), so a dual-mode approach was developed: a DRS configuration provides moderate downforce with 50% less drag ($C_D$ ≈ 0.41) for high-speed efficiency. A 1:12-scale wind tunnel test qualitatively supported the CFD predictions by visualizing wake narrowing and improved flow attachment. While quantitative force validation was not possible due to Reynolds mismatch and facility constraints, the qualitative results increased confidence in the CFD-based findings. Overall, the study demonstrates that substantial aerodynamic gains can be achieved under resource constraints, offering a practical framework for motorsport engineers and manufacturers to optimize aero kits when conventional full-scale testing is not accessible. Full article

The development of viral vaccines faces persistent scientific and logistical challenges, particularly in the wake of the COVID-19 pandemic. This review critically examines emerging strategies to overcome key barriers in viral vaccine design and deployment. We focus on four major areas: (1) structure-guided antigen engineering to stabilize conformations; (2) the mRNA platform and its delivery system; (3) advanced adjuvant systems that enhance cellular and humoral immunity; and (4) approaches to mitigate immune imprinting and antigenic variability, such as chimeric antigens and glycan shielding. We also explore anti-idiotypic vaccination strategies and the limitations of current animal models in predicting human immune responses. In addition, to address vaccine hesitancy and inequitable access, we advocate for global collaboration in manufacturing, distribution, and public education to ensure inclusive immunization strategies. By integrating molecular insights with platform technologies, we aim to inform the rational design of future vaccines with improved efficacy and public acceptance. Full article

The duct, a crucial component of tidal energy power generation devices, is designed to enhance the environmental benefits of tidal energy by optimizing water flow paths and improving energy conversion efficiency. Traditional duct design methods are often considered overly complex, lacking precision, and exhibiting poor optimization efficiency and accuracy. In this study, computational fluid dynamics (CFD) and multi-layer perceptron (MLP) models are employed to investigate the impact of various duct designs on turbine power output and thrust. The MLP model is trained using numerical simulation results, which are then validated by comparing them with experimental data from the literature. Under optimized conditions—specifically, an attack angle of 20°, a blade tip distance of 8 mm, and a cubic curve X_m = 0.796—the power coefficient is found to increase by approximately 11.14% compared to the conventional duct 1, while thrust is reduced by about 52.11% compared to the conventional duct 2. Furthermore, energy loss in the wake vortex is minimized. Flow field analysis is conducted to further confirm the effectiveness of the optimized design, with the high-speed zone area being expanded and pressure extremes reduced by approximately 31.71%. These results demonstrate that machine learning methods can effectively be used to extract nonlinear relationships between complex parameters, offering more design options for duct development and facilitating the engineering application of tidal energy generation technology. Full article