Window Analysis Research Articles

Study on the synergistic mechanisms of fungal biodiversity and ecosystem multifunctionality across vegetation diversity gradients.

Ecosystem multifunctionality denotes the capacity of an ecosystem to deliver various functions and services concurrently, emphasizing the overall effectiveness of these functions. Although biodiversity is intrinsically linked to ecosystem multifunctionality, research on the determinants of changes in this relationship remains limited. This study focused on 147 research plots across various ecosystems in the Lüliang region. Through high-throughput sequencing and data modeling, it was revealed that there exists a significant positive correlation between soil fungal biodiversity and ecosystem multifunctionality (P<0.05). Notably, this correlation was found to be influenced by specialists and vegetation diversity. The specific results supporting this finding are presented as follows: 1) By means of linear regression and the establishment of various models, it was indicated that specialists exert a more substantial influence on the fungal biodiversity-ecosystem multifunctionality (BEF) relationship compared to generalists. 2) Moving window analysis demonstrated that changes in vegetation diversity affected BEF relationships within fungal communities, leading to synergistic shifts. As vegetation diversity increased, co-occurrence networks generally simplified, and the positive fungal BEF correlation was somewhat decreased. This study enhances the comprehension of fungal BEF relationships in natural ecosystems and provides a foundation for the development of effective management and conservation strategies in response to global changes.

Time Dependent Effects of Cerebellar tDCS on Cerebello-cortical Connectivity Networks in Young Adults.

The cerebellum is involved in non-motor processing, supported by topographically distinct cerebellar activations and closed-loop circuits between the cerebellum and the cortex. Disruptions to cerebellar function may negatively impact prefrontal function and processing. Cerebellar resources may be important for offloading cortical processing, providing crucial scaffolding for normative performance and function. Here, we used transcranial direct current stimulation (tDCS) to temporarily alter cerebellar function and subsequently investigated resting state network connectivity. Critically, what happens to these circuits if the cerebellum is not functioning optimally, or after stimulation, remains relatively unknown. We employed a between-subjects design with 74 participants total (38 female; M = 22.0years, SD = 3.45), applying anodal (n = 25), cathodal (n = 25), or sham (n = 24) stimulation to the cerebellum to examine the effect of stimulation on cerebello-cortical resting state connectivity in young adults. We predicted increased functional connectivity following cathodal stimulation and decreased functional connectivity following anodal stimulation. We found, anodal stimulation resulted in increased connectivity in both ipsilateral and contralateral regions of the cortex, perhaps indicative of a compensatory response to degraded cerebellar output. Additionally, a window analysis also demonstrated a time dependent nature to the impacts of cerebellar tDCS on connectivity, particularly with cognitive regions of the cerebral cortex. This work suggests that when cerebellar outputs are degraded, in this case by tDCS, the cerebellum offloads its processing responsibility which encourages more cortical regions to engage to compensate for the degraded cerebellar output. This results in in differences in cortical activation patterns and performance deficits. These results might inform and update existing compensatory models, which focus primarily on the cortex, to include the cerebellum as a vital structure involved in the scaffolding of cortical processing.

Comparative analysis of the complete chloroplast genome of Pueraria provides insights for species identification, phylogenetic relationships, and taxonomy

Background Pueraria is an edible and medicinal raw material, which is of great value to the pharmaceutical and food industries. Nonetheless, due to morphological diversity and complex domestication history, the classification of Pueraria plants is ambiguous. As the varieties on the market are mixed, the species are difficult to distinguish, and their morphological characteristics are similar to the physical and chemical properties. It is difficult to accurately identify them by traditional identification methods. Chloroplast (cp) genomes are widely used in species identification and phylogenetic studies to achieve accurate identification of medicinal plants, and can also provide more reference information for phylogenetic studies. Based on interspecific and intraspecific sampling, the cp genomes of eight species or varieties of Pueraria plants were examined in this study.ResultsThe study unveiled that the cp genome size varied from 151,555 to 153,668 base pairs (bp), with the total GC content ranging from 35.4 to 37.0%. Moreover, it was discerned that the cp genome contained between 128 and 135 genes. Comparative analysis indicated that the highest number of Simple Sequence Repeats (SSRs) was identified in P. montana and P. alopecuroides, with a preponderance of these SSRs being rich in Adenine (A) and Thymine (T) nucleotides. Complete comparison and sliding window analysis of the cp genome established that the non-coding region exhibited greater sequence differences than the coding region, and that the large single copy (LSC) region demonstrated higher nucleotide polymorphism levels. Fourteen highly variable loci such as rpoB,ycf1,rbcL,trnF-GAA-trnL,psbC-psbD, and ycf4-cemA were detected as potential molecular markers for Pueraria species identification. Moreover, the phylogenetic tree demonstrated that other Pueraria species had the most distant relationship with Haymondia wallichii and Toxicopueraria peduncularis, thereby offering fresh perspectives into the species classification of Pueraria. The molecular clock analysis results indicate that the divergence time of Pueraria may occur at ∼6.46 Ma. It is speculated that the cold climate may be the cause of Pueraria species diversity and promote the radiation of the genus.ConclusionThis research provides theoretical backing and serves as a reference point for the identification and taxonomical classification of Pueraria species. The findings will prove beneficial in future studies on the preservation of medicinal resources, phylogenetic relationships, and genetic engineering of Pueraria plants.

Evaluation of Efficiency in Turkish Tax Audit System after Structural Reform via Window Analysis

This study evaluates the efficiency of the tax audit system in Türkiye after the structural reform using window analysis. The efficiency level of the system from 2011 to 2022 is calculated with input variables (number of audit staff and active taxpayers) and output variables (number of taxpayers audited and collection/accrual ratio). The results show that the system was efficient in 2011-2015 but declined in 2016-2022. Despite more audit personnel, the collection/accrual ratio did not improve. The study suggests ways to increase the system’s efficiency and provides insights for future research.

Aberrant Dynamic Network Connectivity Changes in Comorbid Depression and Overweight/Obesity: Insights From the Triple Network Model.

The interaction between major depressive disorder (MDD) and overweight/obesity has received considerable attention owing to its widespread occurrence and the intricate biopsychological implications involved. Despite extensive research, the neural mechanisms underlying these comorbid conditions, particularly in terms of functional network connectivity (FNC), are still not well understood. This study aimed to clarify these mechanisms by utilizing resting-state functional magnetic resonance imaging (rs-fMRI) to examine both static and dynamic FNC. We analyzed data from 57 patients with both MDD and overweight/obesity (MDD-OW), 57 MDD patients of normal weight (MDD-NW), and 44 healthy controls, using techniques such as independent component analysis, sliding window analysis, K-means clustering, and graph theory. In contrast to static FNC, which showed no significant differences, dynamic FNC analysis identified four consistent states across all participants. Both MDD groups demonstrated reduced flexibility in functional coordination among these states and decreased nodal characteristics within the salience network. Notably, the MDD-OW group displayed enhanced dynamic FNC between the default mode network (DMN) and the executive control network (ECN) during certain states, which was inversely associated with the severity of depressive symptoms. These results highlight the importance of altered dynamic connectivity patterns in individuals with MDD and concurrent overweight/obesity, especially between the DMN and ECN, suggesting their potential utility as biomarkers for depressive states. This research contributes to our understanding of how comorbid overweight/obesity affects brain network dynamics in depressive disorders and provides a basis for targeted therapeutic strategies.

St-DenseViT: A Weakly Supervised Spatiotemporal Vision Transformer for Dense Prediction of Dynamic Brain Networks.

Modeling dynamic neuronal activity within brain networks enables the precise tracking of rapid temporal fluctuations across different brain regions. However, current approaches in computational neuroscience fall short of capturing and representing the spatiotemporal dynamics within each brain network. We developed a novel weakly supervised spatiotemporal dense prediction model capable of generating personalized 4D dynamic brain networks from fMRI data, providing a more granular representation of brain activity over time. We developed a model that leverages the vision transformer (ViT) as its backbone, jointly encoding spatial and temporal information from fMRI inputs using two different configurations: space-time and sequential encoders. The model generates 4D brain network maps that evolve over time, capturing dynamic changes in both spatial and temporal dimensions. In the absence of ground-truth data, we used spatially constrained windowed independent component analysis (ICA) components derived from fMRI data as weak supervision to guide the training process. The model was evaluated using large-scale resting-state fMRI datasets, and statistical analyses were conducted to assess the effectiveness of the generated dynamic maps using various metrics. Our model effectively produced 4D brain maps that captured both inter-subject and temporal variations, offering a dynamic representation of evolving brain networks. Notably, the model demonstrated the ability to produce smooth maps from noisy priors, effectively denoising the resulting brain dynamics. Additionally, statistically significant differences were observed in the temporally averaged brain maps, as well as in the summation of absolute temporal gradient maps, between patients with schizophrenia and healthy controls. For example, within the Default Mode Network (DMN), significant differences emerged in the temporally averaged space-time configurations, particularly in the thalamus, where healthy controls exhibited higher activity levels compared to subjects with schizophrenia. These findings highlight the model's potential for differentiating between clinical populations. The proposed spatiotemporal dense prediction model offers an effective approach for generating dynamic brain maps by capturing significant spatiotemporal variations in brain activity. Leveraging weak supervision through ICA components enables the model to learn dynamic patterns without direct ground-truth data, making it a robust and efficient tool for brain mapping. This work presents an important new approach for dynamic brain mapping, potentially opening up new opportunities for studying brain dynamics within specific networks. By framing the problem as a spatiotemporal dense prediction task in computer vision, we leverage the spatiotemporal ViT architecture combined with weakly supervised learning techniques to efficiently and effectively estimate these maps.

Assessment of the Dietary Pattern of Mothers on the Nutritional Status of their Under Five Children in Ilorin Metropolis

Malnutrition is a significant contributor to child mortality and morbidity in Nigeria, with poor dietary practices of mothers of under five children being a critical indicator of their nutritional outcomes and health status. This paper reviews studies on the influence of dietary practices on the nutritional status of children in Ilorin Metropolis, Nigeria. The studies were sourced from various electronic databases, including PubMed, Google Scholar, Sci-space, and Consensus. The study was conducted in the Ilorin metropolis, Kwara State, between February 15th and March 31st, 2024. The research aimed to assess the dietary patterns of mothers of their under five children in the city. The study used a descriptive design and a multistage sampling technique to select 225 children of under-five age. The sample size was determined using the Leslie Kish multistage formula. Data collection tools included self-structured questionnaires, weighing scales for children, and meter rules. The study used SPSS 25.0 windows for data analysis, which was presented using frequency distribution tables, percentages, chi-square, and univariate regression statistical methods. The findings showed that dietary practices by mothers significantly influence the body max index of children (at p&lt;0.05), with undernutrition being more prevalent. Overweight and normal children were the least associated with dietary practices (at p&lt;0.05). The study also found that the dietary practice of children is affected by mothers aged at first birth (at p&lt;0.05), suggesting that mothers aged at first birth can contribute to formulating policies on right dietary practices to enhance nutrition security, especially for young mothers between 18 and 25 years old. The findings provided insights into strategic interventions to enhance dietary practices and eradicate all forms of malnutrition associated with undernutrition in children under five years old.

Late Bone Marrow Mononuclear Cell Transplantation in Rats with Sciatic Nerve Crush: Analysis of a Potential Therapeutic Time Window.

After peripheral nerve injury, axon and myelin regeneration are key events for optimal clinical improvements. We have previously shown that early bone marrow mononuclear cell (BMMC) transplantation exerts beneficial effects on myelin regeneration. In the present study, we analyze whether there is a temporal window in which BMMCs migrate more efficiently to damaged nerves while still retaining their positive effects. Adult Wistar rats of both sexes, with sciatic nerve crush, were systemically transplanted with BMMC at different days post injury. Vehicle-treated, naïve, and sham rats were also included. Morphological, functional, and behavioral analyses were performed in nerves from each experimental group at different survival times. BMMC transplantation between 0 and 7 days after injury resulted in the largest number of nested cells within the injured sciatic nerve, which supports the therapeutic value of BMMC administration within the first week after injury. Most importantly, later BMMC administration 7 days after sciatic nerve crush was associated with neuropathic pain reversion, improved morphological appearance of the damaged nerves, and a tendency toward faster recovery in the sciatic functional index and electrophysiological parameters. Our results thus support the notion that even delayed BMMC treatment may represent a promising therapeutic strategy for peripheral nerve injuries.

Mechanisms of seismic attenuation beneath Bhutan Himalaya

We have analyzed 614 high quality local earthquake (1.5 ≤Ml (local magnitude) ≤ 5.5) data recorded by temporary GANSSER network of 44 broadband stations to investigate the attenuation mechanism of Bhutan Himalaya. Initially, the single isotropic scattering model is applied to study the coda wave attenuation (Qc−1). Subsequently, we have used the Multiple Lapse Time Window Analysis (MLTWA) to estimate the relative contribution of scattering (Qsc−1) and intrinsic (Qi−1) attenuation to the total attenuation (Qt−1) under the assumption of multiple isotropic scattering with uniform half space medium. The analysis has been carried out for five different central frequencies within the range of 1.5 to 18 Hz. All the estimated values of Q exhibit high frequency dependent nature. Interestingly, scattering attenuation is found to be the dominant factor attenuating the seismic waves in the crust of Bhutan Himalaya which is different from the rest of the Himalayas except Garwhal–Kumaun Himalaya and the adjacent Sikkim Himalaya. This strongly suggests that the relative role of both scattering and intrinsic attenuation varies across the Himalaya and is likely to be associated with the structural variabilities among different segments. The role of Main Himalayan Thrust (MHT) in changing the differential stress regime across the region could be the major cause of the intra-crustal deformation which resulted in the predominance of scattering attenuation in the crust of Bhutan Himalaya.

Challenges in the measurement and interpretation of dynamic functional connectivity

Abstract In functional MRI (fMRI), dynamic functional connectivity (dFC) typically refers to fluctuations in measured functional connectivity on a time scale of seconds. This perspective piece focuses on challenges in the measurement and interpretation of functional connectivity dynamics. Sampling error, physiological artifacts, arousal level, and task state all contribute to variability in observed functional connectivity. In our view, the central challenge in the interpretation of functional connectivity dynamics is distinguishing between these sources of variability. We believe that applications of functional connectivity dynamics to track spontaneous cognition or as a biomarker of neuropsychiatric conditions must contend with these statistical issues as well as interpretative complications. In this perspective, we include a systematic survey of the recent literature, in which sliding window analysis remains the dominant methodology (79%). We identify limitations with this approach and discuss strategies for improving the analysis and interpretation of sliding window dFC by considering the time scale of measurement and appropriate experimental controls. We also highlight avenues of investigation that could help the field to move forward.

Performance analysis of a novel photocatalytic double-layer ventilation window

The application of renewable energy in the building sector has received increasing attention. In this work, a zero-energy photocatalytic double-layer ventilation window was proposed to reduce building energy consumption and improve indoor air quality. The effects of environmental and operational parameters on the system’s performance were investigated through experimental testing and simulation analysis. The results show that the thermal efficiency of the system increases with the rise in solar irradiation, while the degradation rate initially increases and then decreases. The performance of the system is significantly affected by the inlet air speed and temperature. When the photocatalyst is coated on both sides of the window compared to one side under solar irradiation of 600 W/m2, the thermal efficiency and degradation rate of the system increase by 29 % and 74 %, respectively. This study demonstrates the potential of photocatalytic double-layer ventilation windows in energy-efficient buildings and provides an important reference for sustainable building design.

Optimized design and comparative analysis of double-glazed photovoltaic windows for enhanced light harvesting and energy efficiency in cold regions of China

This study investigates the daylighting performance and energy efficiency optimization strategies of double-glazed photovoltaic windows (DS-STPV) in cold regions of China. By conducting a comprehensive comparative analysis with traditional and energy-efficient window systems, this research aims to identify high-efficiency building solutions tailored to extreme climatic conditions. Amidst the escalating global energy demand and the pressing need for energy conservation and emission reduction, Building-Integrated Photovoltaic (BIPV) technology is increasingly recognized for its potential and value as a critical method for harnessing green energy. However, the widespread adoption of BIPV technology faces several challenges, including cost-effectiveness, conversion efficiency, system stability, and architectural aesthetic integration. Utilizing the T&A House from the 3rd International Solar Decathlon as an empirical case study, this research employs Ecotect and DesignBuilder simulation software to systematically evaluate the daylighting effect and energy performance of DS-STPV. The analysis considers various key design parameters, including photovoltaic cell coverage, window orientation, and window-to-wall ratio. Through refined modeling and multi-dimensional analysis, this study aims to identify the optimal design configurations of DS-STPV windows in cold regions, with the goal of simultaneously achieving superior natural lighting quality and significant building energy efficiency. The findings indicate that a south-facing DS-STPV window design with approximately 30% photovoltaic cell coverage and a window-to-wall ratio of 30% effectively balances daylighting requirements and energy efficiency in cold regions of China. This design strategy not only ensures an abundance of natural light in the room, but also significantly reduces the building’s energy consumption, proving the superior performance of DS-STPV windows in cold climates. In addition, the unique optical properties of DS-STPV windows reduce glare, further improving the overall quality of the indoor environment. In summary, this study provides a robust scientific foundation for the application of DS-STPV windows in cold regions, offering practical guidance and reference for optimizing energy efficiency and facilitating green transformation in future building design.

Rainfall is associated with divorce in the socially monogamous Seychelles warbler.

Divorce-terminating a pair bond whilst both members are alive-is a mating strategy observed in many socially monogamous species often linked to poor reproductive success. As environmental factors directly affect individual condition and reproductive performance, they can indirectly influence divorce. Given current climate change, understanding how environmental fluctuations affect partnership stability has important implications, including for conservation. Yet, the relationship between the environment and divorce remains largely unstudied. We examined the influence of temporal environmental variability on the prevalence of within- and between-season divorce and the possible underlying mechanisms in a socially monogamous passerine. Analysing 16 years of data from a longitudinal dataset, we investigated the relationship between rainfall and divorce in the Seychelles warbler (Acrocephalus sechellensis). First, we performed climate window analyses to identify the temporal windows of rainfall that best predict reproductive success and divorce. Then, we tested the effects of these temporal windows of rainfall on reproductive success and divorce and the influence of reproductive success on divorce whilst controlling for covariates. Annual divorce rates varied from 1% to 16%. The probability of divorce was significantly associated with the quadratic effect of 7 months of total rainfall before and during the breeding season, with divorce increasing in years with low and high rainfall. This quadratic relationship was driven by a heavy rainfall event in 1997, as excluding 1997 from our analyses left a significant negative linear relationship between rainfall and divorce. Although the same temporal window of rainfall predicting divorce significantly influenced reproductive success, we found no significant correlation between reproductive success and divorce. Our findings suggest that rainfall impacts divorce. Given that this effect is likely not directly mediated by reproductive success, we discuss other possible drivers. Although the 1997 super El Niño event shows how heavy rainfall may affect socially monogamous partnerships, more data are required to estimate the robustness of this effect. By adding to the growing body of literature showing that environmental conditions influence the stability of socially monogamous partnerships, we provide novel insights that may also be important for conservation efforts in times of climate change.

Analysis of Optical Window for Constant Cooling Heat Flux‐Based Spectral Splitting in Concentrator Photovoltaic System

The solar cells cooled to constant temperature at different concentration ratios (CR) and spectral bands (SB) require the same cooling heat flux (CHF), but the output power varies significantly. Thus, it is necessary to clarify the relationship of CHF‐CR‐SB to provide a reference for photovoltaic systems to select the output power generation of the spectral band under constant cooling heat flux. In this article, a selecting spectra model of a centralized photovoltaic (CPV) system is established and the selection of the spectrum based on CHF and the CR is analyzed. Theory shows that the wider the spectral division of the same CR, the larger the cooling heat flux consumed. The narrower the spectrum division, the higher the CR that the cell receiver can withstand. The experiments show that the higher the photoelectric conversion efficiency (PCE), the lower the cooling heat flux to be consumed. The cooling heat flux of 650 filter consumes 22.74% more than the UV700 filter, which means the demand for CHF is more severe when the heat spectrum distribution is wide. The spectral division should be carried out according to the requirements of high‐energy flux but a small thermal energy proportion to achieve efficient PCE.

Abnormal intrinsic brain functional network dynamics in patients with retinal detachment based on graph theory and machine learning

Backgroundand purpose: The investigation of functional plasticity and remodeling of the brain in patients with retinal detachment (RD) has gained increasing attention and validation. However, the precise alterations in the topological configuration of dynamic functional networks are still not fully understood. This study aimed to investigate the topological structure of dynamic brain functional networks in RD patients. MethodsWe recruited 32 patients with RD and 33 healthy controls (HCs) to participate in resting-state fMRI. Employing the sliding time window analysis and K-means clustering method, we sought to identify dynamic functional connectivity (dFC) variability patterns in both groups. The investigation into the topological structure of whole-brain functional networks utilized a graph theoretical approach. Furthermore, we employed machine learning analysis, selecting altered topological properties as classification features to distinguish RD patients from HCs. ResultsAll participants exhibited four distinct states of dynamic functional connectivity. Compared to the healthy control (HC) group, patients with RD experienced a significant reduction in the number of transitions among these four states. Additionally, the dynamic topological properties of RD patients demonstrated notable changes in both global and node-specific characteristics, with these changes correlating with clinical parameters. The support vector machine (SVM) model used for classification achieved an accuracy of 0.938, an area under the curve (AUC) of 0.988, and both sensitivity and specificity of 0.937. ConclusionThe alterations in the topological properties of the brain in RD patients may indicate the integration function and information exchange efficiency of the whole brain network were reduced. In addition, the topological properties hold considerable promise for distinguishing between RD and HCs.

The experimental and numerical analysis of semitransparent double-skin ventilated window for heating, shading and air purification based on a flexible thermal catalysis film

A novel semitransparent thermal-catalytic glass (TC-glass) for heating, daylighting and air purification was proposed, which was applied to double-skin window (TC-glass window). Firstly, a novel flexible TC film was designed, the purification performance and kinetic model were investigated. Secondly, the experimental system of TC-glass window was built and the thermal performance and purification performance were investigated by all-day experiment. Thirdly, the heat and mass transfer model of TC-glass window was established and verified. Main results were: (1) The adding of polyvinyl alcohol in TC materials increased 10.07 kJ/mol activation energy. (2) The daylighting and formaldehyde removal analysis on TC-glass showed that the catalyst coverage ratio of 60 % was recommended as the optimal ratio. (3) The experimental average thermal, formaldehyde once-through conversion and daily total clean air volume of TC-glass window glass were 40.8 %, 31.1 % and 238.18 m³, respectively. (4) Throughout the year, with 40 %, 60 % and 80 % catalyst coverage ratios, the heat gain from channel air over heating season were 213.47, 261.62, and 284.22 MJ/m2, respectively, the total clean air were 11952, 16672, and 19851 m3/m2, respectively, the heat carried away by the channel air over summer were 274.53, 287.2 and 297.44 MJ/m2, respectively.

DEA와 SFA 모형을 이용한 한국 프로야구의 비용효율성 분석

The purpose of this study is to analyze the cost efficiency of Korean professional baseball(KBO) teams through Data Envelopment Analysis(DEA) and Stochastic Frontier Analysis(SFA) and to examine its determinants.. In this study, we used 20 years of data from 2003 to 2022 for eight teams to examine the cost efficiency and its influencing factors of each team through DEA window analysis and SFA reflecting the time series. The empirical results showed that the cost efficiency of KBO teams is not high, but it is generally improving. Among them, the size of the stadium and the number of wins have a positive effect on cost efficiency, while the number of new stadiums and free agents has a negative effect on cost efficiency. In particular, the difference between the efficiency values derived from the two methods is quite large, which means that it is highly sensitive to external shocks due to the probable error. The fact that many teams react greatly to external shocks suggests that the support of parent companies and local governments greatly affects the sustainability of the teams due to the lack of self-reliance of the teams. In order to increase the self-reliance of KBO teams facing a new competitive environment after COVID-19, continuous efforts are needed to transform into solid companies, including positive external support and cooperation with local governments, as well as creating higher-quality cultural content and discovering diverse sources of revenue.

Comparison of EEG signal statistical parameters between healthy and SARS-CoV-2 affected individuals

This paper proposes a methodology for selecting and analyzing electroencephalographic (EEG) signals to compare patients with neurological changes due to SARS-CoV-2 infection with healthy individuals. The processing approach involves multiple steps, including windowing, filtering, and frequency analysis, all applied to the EEG data. These methods ensure a clear distinction between healthy and affected brain activity. After processing, key statistical parameters are extracted, averaged, and visualized to highlight the differences between the two groups. Specifically, skewness, kurtosis, and dominant frequency show notable variations. Skewness measures the asymmetry of the signal, kurtosis reflects the sharpness of the peaks, and dominant frequency captures the most prominent oscillations in the brain's activity. The analysis reveals that these parameters significantly differ between healthy individuals and those with neurological changes due to the virus. These differences can provide insights into the neurological impacts of SARS-CoV-2, offering a potential basis for further diagnosis and monitoring. Overall, the proposed methodology presents a systematic way to understand and compare the brain function of affected individuals against healthy controls.

Wildfire Seasons, Prenatal PM2.5 Exposure, and Respiratory Infections by Age 1 Year: A Population-Based Case-Control Analysis of Critical Developmental Windows.

The 2017 and 2018 wildfire seasons in British Columbia (BC), Canada were unprecedented. Among all the pollutants in wildfire smoke, fine particulate matter (PM2.5) poses the most significant risk to human health. There is limited research on prenatal wildfire smoke exposure and its impacts on infant health. We used a population-based nested case-control design to assess the association between daily PM2.5 exposures during specific developmental windows and the occurrence of otitis media or lower respiratory infections by age 1 year, including infections associated with dispensations of the antibiotic amoxicillin. We observed the strongest association between per 10 μg/m3 increase in PM2.5 exposure and otitis media during the fourth window of eustachian tube development (weeks 19-28) with an OR [95% confidence interval] of 1.31 [1.22, 1.41]. Similarly, the canalicular stage of lower respiratory tract development (weeks 18-27) was associated with the highest odds of lower respiratory infections, with an OR of 1.21 [1.15, 1.28]. Measures to reduce wildfire smoke exposure during pregnancy are warranted.

Decision-making optimization for post-disaster restoration of multimodal transport networks in terms of resilience

IntroductionPublic health emergencies can have a ripple effect on the resilience of multimodal transport network, which will lead to problems such as route disruptions or blockages, route selection change and information transmission delay spreading to the whole network, further hindering the transportation planning and operational efficiency of the network. MethodsThis study constructs a multimodal transport route optimization model under uncertainty with the objective of the sum of transportation cost, transshipment cost, penalty cost and carbon emission cost. To enhance the computational efficiency of the model, a novel invasive weed optimization with memory and encoding value clustering capabilities is proposed. In addition, by fusing the Q-learning algorithm in reinforcement learning with the novel invasive weed algorithm, the action-value function table obtained from the training facilitates the selection of optimal routes. Based on empirical data, explore the sensitivity analysis of node disruptions, time windows, and fuzzy demand on route decision-making under public health emergencies. ResultsThe transport network is affected by public health emergencies, which makes the optimal route deviate from the expected goal, resulting in an increase in the total cost. The proportion of total cost is determined by the position of nodes in the network, with critical nodes suffering more losses than ordinary nodes. Reasonable setting of time windows and fuzzy demand intervals is an effective way to improve the resilience and transportation efficiency of multimodal transport network. ConclusionsThis study provides more applicable decision-making references for enterprises to prevent the risk of supply chain disruptions caused by public health emergencies.