Exploring the relationship between early cognitive ability and age-60 sleep quality: The Newcastle Thousand Families Study birth cohort.

OBJECTIVES: Poor sleep quality has been linked to adverse health outcomes. It is important to understand factors contributing to sleep quality. Previous research has suggested increased cognition and education duration have a protective effect on sleep quality in old age. This study aimed to assess the hypothesis that age-11 intelligence quotient and highest achieved education level are associated with subjective sleep quality at age 60. METHODS: Participants are members of the Newcastle Thousand Families Study birth cohort, all born in 1947. Data included a calculated intelligence quotient score based on participant's 11-plus exam results, highest achieved education level, social class at ages 25 and 50 and global Pittsburgh Sleep Quality Index (PSQI) at age 60. Multivariable regression analysis was used to investigate effect sizes of variables on global PSQI, which formed the basis of a path analysis model. RESULTS: After excluding participants with incomplete data, and those who had been diagnosed with sleep apnea, 251 participants were included in the path analysis model. Education level was associated with global PSQI (R=-0.653; 95% CI -1.161, -0.145; P = .012) but age-11 intelligence quotient was not. While a similar association was seen for women in the stratified analysis, no such associations were seen for men. CONCLUSIONS: The results of this study show an inverse relationship between education level, but not childhood intelligence quotient, and sleep quality in later life, in women only. Future research is needed to examine the mechanism underlying this relationship.

Multi-area collision-free path planning and efficient task scheduling optimization for autonomous agricultural robots.

Collision-free path planning and task scheduling optimization in multi-region operations of autonomous agricultural robots present a complex coupled problem. In addition to considering task access sequences and collision-free path planning, multiple factors such as task priorities, terrain complexity of farmland, and robot energy consumption must be comprehensively addressed. This study aims to explore a hierarchical decoupling approach to tackle the challenges of multi-region path planning. Firstly, we conduct path planning based on the A* algorithm to traverse paths for all tasks and obtain multi-region connected paths. Throughout this process, factors such as path length, turning points, and corner angles are thoroughly considered, and a cost matrix is constructed for subsequent optimization processes. Secondly, we reformulate the multi-region path planning problem into a discrete optimization problem and employ genetic algorithms to optimize the task sequence, thus identifying the optimal task execution order under energy constraints. We finally validate the feasibility of the multi-task planning algorithm proposed by conducting experiments in an open environment, a narrow environment and a large-scale environment. Experimental results demonstrate the method's capability to find feasible collision-free and cost-optimal task access paths in diverse and complex multi-region planning scenarios.

Composition and influencing factors of hospitalization expenses for epilepsy patients based on path analysis.

OBJECTIVE: This study aimed to understand the composition and influencing factors of epilepsy patients' hospitalization expenses, thus providing a reference for reducing the disease burden of epilepsy patients in low- and middle-income developing countries. METHODS: A total of 4206 hospitalized cases of epilepsy from 2018 to 2020 were collected. Descriptive statistics were used to understand the patient cost composition, path analysis was used to understand the direct and indirect factors of hospitalization expenses. RESULTS: From 2018 to 2020, the average hospitalization expenses for epilepsy patients was 4,299.93 RMB yuan, and the average length of stay was 2.47 days. The highest proportion of hospitalization expenses was diagnosis costs (> 50%), followed by comprehensive medical service costs and drug costs. In terms of the total effect coefficient, the major factors affecting the hospitalization expenses were length of stay (0.880), emergency admission(0.463), and the comorbidities and complications(> 0.250). Hospital length of stay, discharge mode(death) and number of hospitalizations(2 times) affect hospitalization expenses through direct effect. Long-term hospitalization (> 30 days), admission routes(emergency), the comorbidities and complications, presence of drug allergy, and age also affect hospitalization expenses through indirect effects. CONCLUSION: Diagnosis costs and length of stay are important factors affecting the medical expenses of epilepsy inpatients. In general, the quality control of the hospital is good, but it still needs to standardize the diagnosis and treatment behavior of medical staff through the clinical path.

Bilateral Blistering Rash Following Gastric Bypass Procedure and Mushroom Soup Diet: Hepatitis C Virus-Seronegative Necrolytic Acral Erythema.

Necrolytic acral erythema (NAE) is an uncommon cutaneous disorder characterized by a symmetric acral distribution of erythematous plaques with underlying epidermal necrosis. While typically presenting in the context of hepatitis C virus (HCV) infection, NAE can also present secondary to nutritional deficiency or systemic disease. We present a case of NAE in a 66-year-old patient with no history of HCV infection status post gastric bypass who had a three-month history of eating only mushroom soup. The patient underwent a punch biopsy and was tested for a variety of nutritional deficiencies. Biopsy demonstrated partial necrosis of the upper epidermis, with subjacent re-epithelialization, squamatization, and vacuolopathy of the basal epidermis. He was treated with zinc replacement therapy after initial trials of tacrolimus and clobetasol were unsuccessful. At follow-up, he had significant improvement of the lesions. This case provides an example of an atypical presentation of NAE in the absence of HCV infection that presented as a complication of gastric bypass-associated nutritional deficiency.

A study on the relationship and path between mental health and burnout of Chinese athletes.

Background: This study aims to explore the relationship and influencing pathways between mental health indicators and athlete burnout among Chinese competitive athletes. Methods: A cross-sectional study was conducted on 501 elite Chinese athletes from several national and provincial sports teams. Generalized Anxiety Disorder Scale (GAD-7) was used to measure anxiety, Patient Health Questionnaire (PHQ- 9) was used to measure depression, Athlete Psychological Strain Questionnaire (APSQ) was used to measure perceived stress, and Athlete Burnout Questionnaire (ABQ) was used to measure burnout. The results were derived using reliability testing, descriptive statistics, correlation analyses, and structural equation modeling. Results: The following results were obtained: (a) the clinical detection rates of anxiety and depression in this sample were within normal levels, but the detection rate of perceived stress (78.64%) was relatively high; (b) all three mental health indicators were significantly correlated with athlete burnout; (c) in our model, there was a direct path with APSQ directly contributing to 69.95% of the variance in athlete burnout, and two indirect paths with APSQ exerting an indirect effect through depression or/and anxiety accounting for 30.05% of the variance. Conclusion: The findings revealed the psychological characteristics of Chinese competitive athletes and the direct and indirect effects of the APSQ on burnout. Future research should actively promote the international development and application of burnout assessment tools, conduct more comprehensive studies on athlete mental health monitoring, and intensify efforts in athlete education, treatment, and support services, as well as strategies for athlete's coping stress.

Factors influencing recommendation intentions for autonomous vehicles: A path analysis in a pilot study.

Over the past decade, the rapid development of artificial intelligence has propelled the transition of autonomous vehicles from laboratories to real-world applications. However, autonomous vehicles are a long way from fully integrating into most people's lives. Previous studies indicate that the word-of-mouth effect is often used by consumers to determine the quality of innovative technologies. Word-of-mouth recommendation can not only increase the income of enterprises by attracting new customers, but also greatly reduce the promotion and publicity expenses of enterprises. Through the word-of-mouth effect, the intention to recommend can contribute to the growth of the autonomous driving market. Therefore, current research explores the mechanisms among the perceived risk of privacy safety, perceived defect, perceived behavioral control, intention to use, and intention to recommend through path analysis. Our findings, based on 433 online questionnaires, indicate that the perceived risk of privacy safety, perceived defects, and perceived behavioral control influence the intention to recommend. Notably, perceived risk of privacy safety and perceived defect directly affects the intention to recommend and also correlates with perceived behavioral control. These findings provide some empirical evidence for the recommendation of autonomous vehicles and the expansion of consumer groups.

A spatial transformation-based CAN model for information integration within grid cell modules.

The hippocampal-entorhinal circuit is considered to play an important role in the spatial cognition of animals. However, the mechanism of the information flow within the circuit and its contribution to the function of the grid-cell module are still topics of discussion. Prevailing theories suggest that grid cells are primarily influenced by self-motion inputs from the Medial Entorhinal Cortex, with place cells serving a secondary role by contributing to the visual calibration of grid cells. However, recent evidence suggests that both self-motion inputs and visual cues may collaboratively contribute to the formation of grid-like patterns. In this paper, we introduce a novel Continuous Attractor Network model based on a spatial transformation mechanism. This mechanism enables the integration of self-motion inputs and visual cues within grid-cell modules, synergistically driving the formation of grid-like patterns. From the perspective of individual neurons within the network, our model successfully replicates grid firing patterns. From the view of neural population activity within the network, the network can form and drive the activated bump, which describes the characteristic feature of grid-cell modules, namely, path integration. Through further exploration and experimentation, our model can exhibit significant performance in path integration. This study provides a new insight into understanding the mechanism of how the self-motion and visual inputs contribute to the neural activity within grid-cell modules. Furthermore, it provides theoretical support for achieving accurate path integration, which holds substantial implications for various applications requiring spatial navigation and mapping.

Path analysis reveals combined winter climate and pollution effects on the survival of a marine top predator.

Marine ecosystems are experiencing growing pressure from multiple threats caused by human activities, with far-reaching consequences for marine food webs. Determining the effects of multiple stressors is complex, in part, as they can affect different aspects of biological organisation (behaviour, individual traits and demographic rates). Determining the combined effects of stressors, through different biological pathways, is key to predict the consequences for the viability of populations threatened by global change. Due to their position in the food chain, top predators such as seabirds are considered more sensitive to environmental changes. Climate change is affecting the prey resources available for seabirds, through bottom-up effects, while organic pollutants can bioaccumulate in food chains with the greatest impacts on top predators. However, knowledge of their combined effects on population dynamics is scarce. Using a path analysis, we quantify the effects of climate change and pollution on the survival of adult great black-backed gulls, both directly and through effects of individuals' body mass. Warmer ocean temperatures in gulls' winter foraging areas in the North Sea were correlated with higher survival, potentially explained by shifts in prey availability associated with global climate change. We also found support for indirect negative effects of organochlorines, highly toxic pollutants to seabirds, on survival, which acted, in part, through a negative effect on body mass. The results from this path analysis highlight how, even for such long-lived species where variance in survival tends to be limited, two stressors still have had a marked influence on adult survival and illustrate the potential of path models to improve predictions of population variability under multiple stressors.

Dynamic path analysis for exploring treatment effect mediation processes in clinical trials with time-to-event endpoints.

Why does a beneficial treatment effect on a longitudinal biomarker not translate into overall treatment benefit on survival, when the biomarker is in fact a prognostic factor of survival? In a recent exploratory data analysis in oncology, we were faced with this seemingly paradoxical result. To address this problem, we applied a theoretically principled methodology called dynamic path analysis, which allows us to perform mediation analysis with a longitudinal mediator and survival outcome. The aim of the analysis is to decompose the total treatment effect into a direct treatment effect and an indirect treatment effect mediated through a carefully constructed mediation path. The dynamic nature of the underlying methodology enables us to describe how these effects evolve over time, which can add to the mechanistic understanding of the underlying processes. In this paper, we present a detailed description of the dynamic path analysis framework and illustrate its application to survival mediation analysis using simulated and real data. The use case analysis provides clarity on the specific exploratory question of interest while the methodology generalizes to a wide range of applications in drug development where time-to-event is the primary clinical outcome of interest.

Phenotypic variation in hypocotyl elongation among elite sand rice (Agriophyllum squarrosum) lines.

Sand rice (Agriophyllum squarrosum), widely distributed in Central Arid Asia and prevalent in the sand dunes of northern China, presents a promising potential as a climate-resilient crop. The plasticity of hypocotyl growth is the key trait for sand rice to cope with wind erosion and sand burial, ensure seedling emergence, and determine plant architecture. In this study, we assessed the overall hypocotyl phenotype of six sand rice elite lines, which were collected from different regions of northern China, and selected by our group over past decade through common garden trials. Significant phenotypic variations were observed in thousand-seed weight (TSW), seedling emergence percentage, hypocotyl length and diameter, and seedling fresh weight among the lines. The elite line Aerxiang (AEX) exhibited excellent agronomic performance with superior and synchronous emergence, and high survival percentage, distinguishing itself as a prime candidate for further large-scale cultivation. Contrastingly, the lines from the arid regions showed markedly lower performance. Partial Least Squares Path Modeling (PLSPM) was used to assess the impact of seed provenance climate factors, including annual mean temperature (AMT) and annual mean precipitation (AMP), on trait variability among lines. The findings indicate a significant correlation between climate factors and hypocotyl length, highlighting the intricate adaptation of sand rice to local climate. The comprehensive understanding of the mechanisms behind phenotypic variations offers valuable insights for sand rice de novo domestication and innovative germplasm resources, and lays the foundation for ecological restoration in sandy areas.

Diet specialization mediates drivers of Cucurbita herbivory in a semi-arid agroecosystem.

Herbivory is a major fitness pressure for plants and a key driver of crop losses in agroecosystems. Dense monocultures are expected to favor specialist herbivorous insects, particularly those who primarily consume crop species; yet, levels and types of herbivory are not uniform within regional cropping systems. It is essential to determine which local and regional ecological factors drive variation in herbivory in order to support functional agroecosystems that rely less on chemical inputs. Crops in the genus Cucurbita host a suite of both generalist and specialist herbivores that inflict significant damage, yet little is known about the relative contribution of these herbivores to variation in herbivory and how local- and landscape-scale Cucurbita resource concentrations, management practices, and natural enemies mediate this relationship. In this study, we tested whether three foundational ecological hypotheses influenced Cucurbita herbivory across 20 pumpkin fields in the semi-arid Southern High Plains Region of Texas. We used generalized linear mixed models and confirmatory path analysis to assess whether the Density-dependent Herbivory Hypothesis, Resource Concentration Hypothesis, or the Natural Enemies Hypothesis, could explain variation in Cucurbita herbivory and insect dynamics in the context of conventional agronomic practices. We found that herbivory increased over time, indicating that herbivores were causing sustained damage throughout the growing season. We also found that fields with higher local Cucurbita resources had lower herbivory, suggesting a resource dilution effect. Natural enemy communities were more abundant and taxonomically rich in sites with greater generalist herbivore abundance, though predator abundance declined over time, indicating that late-season crop fields are most at risk given high herbivory and low natural enemy-based control. Our findings also suggest that while local resource availability may drive the abundance and richness of arthropod communities, additional agronomic and phenological information is needed to anticipate herbivory risk in an agriculturally dominated landscape.

The relationship between attachment, primary emotions and positive/negative spirituality: a path analysis.

Objective: The present study investigates what may influence individuals to experience their religiosity/spirituality as either subjectively positive [religious or spiritual (r/s) wellbeing] or as negative (r/s struggles). Drawing on existing literature attachment insecurity and the seven primary emotions as outlined by Jaak Panksepp in Affective Neuroscience are identified as likely influences. Methods: The final sample consisted of 340 participants (age: M = 36, SD = 14.2; 68.5% = female), among which 65% self-identified as religious/spiritual. A path analysis was conducted to test a proposed mediation model in which the expected effects of primary emotions (B-ANPS) on r/s wellbeing (MI-RSWB) and r/s struggles (RSSS) were mediated through attachment insecurity (ECR-RD8). Results: The data indicated that attachment insecurity fully mediated the relationships between the primary emotions SADNESS and LUST with r/s struggles. Furthermore, the primary emotions FEAR and ANGER displayed small direct effects on both r/s struggles and r/s wellbeing. Overall, the model, which demonstrated excellent model fit, was able to explain 30% of the variance of r/s struggles, 24% of attachment insecurity and 5% of r/s wellbeing. Conclusions: The findings suggest that primary emotions such as SADNESS and LUST substantially explain r/s struggles and that these relationships seem to be mediated through attachment. Moreover, r/s struggles seem to be qualitatively distinct from r/s wellbeing. Finally, a moderate link between LUST and attachment suggests that sexuality plays a significant role in (adult) attachment processes.

Noise barriers as a mitigation measure for highway traffic noise: Empirical evidence from three study cases.

Noise barriers are path interventions between noise sources and human receivers used mainly along road corridors to improve the acoustic environment for affected residents. Despite their widespread use, the impact of these interventions on community perception is still insufficiently investigated. This paper presents findings from a longitudinal study evaluating the efficacy of noise barriers in three residential areas alongside highways, compared to a reference case in a relatively quiet area. Noise exposure was objectively quantified via acoustic measurements and noise modelling, while socio-acoustic surveys measured the residents' subjective response in terms of noise annoyance as well as other aspects of quality of life. While noise exposure (Lday) decreased on average by 4.4-11.7 dBA at near-barrier points, direct reductions in pre to post-intervention noise annoyance were observed only in one case. Additionally, only in this particular case were the appraisals of the acoustic environment restored to a condition similar to low-level noise emissions (reference case). Contextual factors potentially downgrading the interventions' effectiveness are discussed, such as the history of complaints and coping, mistrust towards road authorities, high expectations, and the impacts of the COVID-19 pandemic. While noise exposure reductions did not directly lead to noise annoyance decreases, an ordinal regression pooling all cases revealed that larger reductions in noise exposure were associated with a higher likelihood of residents reporting decreased traffic noise annoyance in the post-survey. No evidence was found regarding noise barriers' impact on the subjective assessment of other aspects of quality of life, such as health complaints, concentration disturbance, and sleep quality.

Unraveling Residual Stress Distribution Characteristics of 6061-T6 Aluminum Alloy Induced by Laser Shock Peening.

Laser shock peening (LSP) is a powerful technique for improving the fatigue performance of metallic components by customizing compressive residual stresses in the desired near-surface regions. In this study, the residual stress distribution characteristics of 6061-T6 aluminum alloy induced by LSP were identified by the X-ray diffraction method, and their dependent factors (i.e., LSP coverage, LSP energy, and scanning path) were evaluated quantitatively by numerical simulations, exploring the formation mechanism of LSP residual stresses and the key role factor of the distribution characteristics. The results show that LSP is capable of creating anisotropic compressive residual stresses on the specimen surface without visible deformation. Compressive residual stresses are positively correlated with LSP coverage. The greater the coverage, the higher the residual stress, but the longer the scanning time required. Raising LSP energy contributes to compressive residual stresses, but excessive energy may lead to a reduction in the surface compressive residual stress. More importantly, the anisotropy of residual stresses was thoroughly explored, identifying the scanning path as the key to causing the anisotropy. The present work provides scientific guidance for efficiently tailoring LSP-induced compressive residual stresses to improve component fatigue life.

Optimization Design of Laser Arrays Based on Absorption Spectroscopy Imaging for Detecting Temperature and Concentration Fields.

Detecting temperature and concentration fields within engine combustors holds paramount significance in enhancing combustion efficiency and ensuring operational safety. Within the realm of engine combustors, the laminar absorption spectroscopy technique has garnered considerable attention. Particularly crucial is the optimization of the optical path configuration to enhance the efficacy of reconstruction. This study presents a flame parameter field reconstruction model founded on laminar absorption spectroscopy. Furthermore, an optimization approach for refining the optical path configuration is delineated. In addressing non-axisymmetric flames, the simulated annealing algorithm (SA) and Harris's Hawk algorithm (HHO) are employed to optimize the optical path layout across varying beam quantities. The findings underscore a marked reduction in imaging errors with the optimized optical path configuration compared to conventional setups, thereby elevating detection precision. Notably, the HHO algorithm demonstrates superior performance over the SA algorithm in terms of optimization outcomes and computational efficiency. Compared with the parallel optical path, the optimized optical path of the HHO algorithm reduces the temperature field error by 25.5% and the concentration field error by 26.5%.

Increasing Optical Path Lengths in Micro-Fluidic Devices Using a Multi-Pass Cell.

This study presents a novel absorption cell with a circular geometry that can be integrated into microfluidic devices for optical spectroscopy applications. The absorption cell is made of PDMS/SU8 and offers an optical path length that is 8.5 times its diameter, resulting in a significant increase in the sensitivity of the measurements. Overall, this design provides a reliable and efficient solution for optical spectroscopy in microfluidic systems, enabling the precise detection and analysis of small quantities of analytes.

Model Predictive Control with Variational Autoencoders for Signal Temporal Logic Specifications.

This paper presents a control strategy synthesis method for dynamical systems with differential constraints, emphasizing the prioritization of specific rules. Special attention is given to scenarios where not all rules can be simultaneously satisfied to complete a given task, necessitating decisions on the extent to which each rule is satisfied, including which rules must be upheld or disregarded. We propose a learning-based Model Predictive Control (MPC) method designed to address these challenges. Our approach integrates a learning method with a traditional control scheme, enabling the controller to emulate human expert behavior. Rules are represented as Signal Temporal Logic (STL) formulas. A robustness margin, quantifying the degree of rule satisfaction, is learned from expert demonstrations using a Conditional Variational Autoencoder (CVAE). This learned margin is then applied in the MPC process to guide the prioritization or exclusion of rules. In a track driving simulation, our method demonstrates the ability to generate behavior resembling that of human experts and effectively manage rule-based dilemmas.

Single Image Super-Resolution via Wide-Activation Feature Distillation Network.

Feature extraction plays a pivotal role in the context of single image super-resolution. Nonetheless, relying on a single feature extraction method often undermines the full potential of feature representation, hampering the model's overall performance. To tackle this issue, this study introduces the wide-activation feature distillation network (WFDN), which realizes single image super-resolution through dual-path learning. Initially, a dual-path parallel network structure is employed, utilizing a residual network as the backbone and incorporating global residual connections to enhance feature exploitation and expedite network convergence. Subsequently, a feature distillation block is adopted, characterized by fast training speed and a low parameter count. Simultaneously, a wide-activation mechanism is integrated to further enhance the representational capacity of high-frequency features. Lastly, a gated fusion mechanism is introduced to weight the fusion of feature information extracted from the dual branches. This mechanism enhances reconstruction performance while mitigating information redundancy. Extensive experiments demonstrate that the proposed algorithm achieves stable and superior results compared to the state-of-the-art methods, as evidenced by quantitative evaluation metrics tests conducted on four benchmark datasets. Furthermore, our WFDN excels in reconstructing images with richer detailed textures, more realistic lines, and clearer structures, affirming its exceptional superiority and robustness.

An allocentric human odometer for perceiving distances on the ground plane.

We reliably judge locations of static objects when we walk despite the retinal images of these objects moving with every step we take. Here, we showed our brains solve this optical illusion by adopting an allocentric spatial reference frame. We measured perceived target location after the observer walked a short distance from the home base. Supporting the allocentric coding scheme, we found the intrinsic bias , which acts as a spatial reference frame for perceiving location of a dimly lit target in the dark, remained grounded at the home base rather than traveled along with the observer. The path-integration mechanism responsible for this can utilize both active and passive (vestibular) translational motion signals, but only along the horizontal direction. This asymmetric path-integration finding in human visual space perception is reminiscent of the asymmetric spatial memory finding in desert ants, pointing to nature's wondrous and logically simple design for terrestrial creatures.

Research on detection method of photovoltaic cell surface dirt based on image processing technology.

In view of the reduced power generation efficiency caused by ash or dirt on the surface of photovoltaic panels, and the problems of heavy workload and low efficiency faced by manual detection, this study proposes a method to detect dust or dust on the surface of photovoltaic cells with the help of image processing technology to timely eliminate hidden dangers and improve power generation efficiency.This paper introduces image processing methods based on mathematical morphology, such as image enhancement, image sharpening, image filtering and image closing operation, which makes the image better highlight the target to be recognized. At the same time, it also solves the problem of uneven image binarization caused by uneven illumination in the process of image acquisition. By using the image histogram equalization, the gray level concentration area of the original image is opened or the gray level is evenly distributed, so that the dynamic range of the pixel gray level is increased, so that the image contrast or contrast is increased, the image details are clear, to achieve the purpose of enhancement. When identifying the target area, the method of calculating the proportion of the dirt area to the whole image area is adopted, and the ratio exceeding a certain threshold is judged as a fault. In addition, the improved A* path planning algorithm is adopted in this study, which greatly improves the efficiency of the unmanned aerial vehicle detection of photovoltaic cell dirt, saves time and resources, reduces operation and maintenance costs, and improves the operation and maintenance level of photovoltaic units.