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This paper focuses on the performance of a braced deep excavation in soft soil based on field monitoring and numerical modeling. Laboratory tests were conducted to determine the soil parameters used in the modified Cam–Clay (MCC) model. Intelligent field monitoring means were adopted and a three-dimensional model was established. Spatial and temporal effects induced by the excavation are investigated for the deep-large foundation pit in soft soil. Deformation characteristics of the enclosure structure and the surrounding environment throughout the excavation process are presented. The behaviors of diaphragm walls, columns, the maximum wall deflection rate, ground surface settlement, and utility pipelines were focused on and investigated during the whole excavation process. Besides, the axial forces of the internal supports are analyzed. Based on the measured and simulated data, the following main conclusions were obtained: the numerical simulation results are in good agreement with the measured values, which proves the accuracy of the model parameters; the wall and the ground surface showed the maximum displacement increment at stage 9, which was a coupled product of the “creep effect” of the soft soil in Nanjing, China and the “depth effect” of the excavation; as the excavation progressed, the ground settlement changed from a “rising” to a “spoon-shaped” trend, δvm was measured between δvm = 0.0686%H and δvm = 0.1488%H; the rebound deformation curve of the pit bottom was corrugated, and the depth of disturbance of the pit bottom after the completion of soil unloading was 2–3 times the excavation depth; the closer the pipeline is to the corner of the pit, the less the excavation process will affect the settlement of the pipeline and the less the obvious pit corner effect will occur; the support strength of the buttress and the longest corner brace should be strengthened during the actual construction process to ensure the stability of the foundation deformation.

Both the physical features and social functions of urban green spaces (UGSs) are crucially important to the ecological and social benefits of urban residents. Increasing attention has been focused on exploring how UGS social functions affect the ecological and social benefits of urban residents, but the social functional classification of UGSs has rarely been studied, and related efficient classification methods are urgently needed. Thus, a novel methodological framework for accurately mapping UGS social functional zones was proposed by integrating remote sensing images, crowd-sourced geospatial data (i.e. point of interest data, the OpenStreetMap road network, and Baidu Map boundary), and a deep learning algorithm. A sequence of combination experiments and ablation experiments were designed for performance validation and for quantifying the contributions of individual crowd-sourced geospatial data to UGS social functional classification. The results showed that the proposed methodological framework can precisely and effectively map UGS social functional zones and that all kinds of crowd-sourced geospatial data contributed to improving the accuracy of UGS social functional classification. This study can assist planners and government departments in the rapid monitoring, effective management, and scientific planning of UGS social functional zones by providing accurate data sources and an effective mapping tool.

Abstract The safe operation of oil‐immersed transformers is critical to the safety and stability of the power grid. As the operating time increases, the failure rate of oil‐immersed transformers shows an increasing trend, posing serious challenges to safe operation. It is necessary to investigate the internal state of the oil‐immersed transformer to improve the digital degree and achieve digitalisation and intelligent operation and maintenance. A physics‐informed neural network (PINN) for oil‐immersed transformers was introduced to reconstruct the temperature distribution inside the transformer. According to the approach, the loss function of the network would be optimised by incorporating physical constraint loss terms including heat transfer equations, initial conditions and boundary conditions. The results show that the method proposed can be used to reconstruct and predict the temperature field of transformers in a few seconds with satisfactory accuracy. In conclusion, the PINN proposed outperforms deep neural networks in terms of accuracy, reliability and interpretability, especially in data‐poor cases.

Abstract Palmitoylation is a type of lipid modification that plays an important role in various aspects of neuronal function. Over the past few decades, several studies have shown that the palmitoylation of synaptic proteins is involved in neurotransmission and synaptic functions. Palmitoyl acyltransferases (PATs), which belong to the DHHC family, are major players in the regulation of palmitoylation. Dysregulated palmitoylation of synaptic proteins and mutated/dysregulated DHHC proteins are associated with several neurodegenerative diseases, such as Alzheimer’s disease (AD), Huntington’s disease (HD), and Parkinson’s disease (PD). In this review, we summarize the recent discoveries on the subcellular distribution of DHHC proteins and analyze their expression patterns in different brain cells. In particular, this review discusses how palmitoylation of synaptic proteins regulates synaptic vesicle exocytotic fusion and the localization, clustering, and transport of several postsynaptic receptors, as well as the role of palmitoylation of other proteins in regulating synaptic proteins. Additionally, some of the specific known associations of these factors with neurodegenerative disorders are explored, with a few suggestions for the development of therapeutic strategies. Finally, this review provides possible directions for future research to reveal detailed and specific mechanisms underlying the roles of synaptic protein palmitoylation.

Abstract Background Frequent presenters (FPs) are a group of individuals who visit the hospital emergency department (ED) frequently for urgent care. Many among the group present with the main diagnosis of mental health conditions. This group of individual tend to use ED resources disproportionally and significantly affects overall healthcare outcomes. No previous reviews have examined the profiles of FPs with mental health conditions. Aims This study aims to identify the key socio-demographic and clinical characteristics of patients who frequently present to ED with a mental health primary diagnosis by performing a comprehensive systematic review of the existing literature. Method PRISMA guideline was used. PubMed, PsycINFO, Scopus and Web of Science (WOS) were searched in May 2023. A manual search on the reference list of included articles was conducted at the same time. Covidence was used to perform extraction and screening, which were completed independently by two authors. Inclusion and exclusion criteria were defined. Results The abstracts of 3341 non-duplicate articles were screened, with 40 full texts assessed for eligibility. 20 studies were included from 2004 to 2022 conducted in 6 countries with a total patient number of 25,688 (52% male, 48% female, mean age 40.7 years old). 27% were unemployed, 20% married, 41% homeless, and 17% had tertiary or above education. 44% had a history of substance abuse or alcohol dependence. The top 3 diagnoses are found to be anxiety disorders (44%), depressive disorders (39%) schizophrenia spectrum and other psychotic disorders (33%). Conclusion On average, FPs are middle-aged and equally prevalent in both genders. Current data lacks representation for gender-diverse groups. They are significantly associated with high rates of unemployment, homelessness, lower than average education level, and being single. Anxiety disorder, depressive disorder, and schizophrenia spectrum disorders are the most common clinical diagnoses associated with the group.

Abstract Exposure to PM2.5, a harmful type of air pollution, has been associated with compromised male reproductive health; however, it remains unclear whether such exposure can elicit transgenerational effects on male fertility. Here, we aim to examine the effect of paternal exposure to real-world PM2.5 on the reproductive health of male offspring. We have observed that paternal exposure to real-world PM2.5 can lead to transgenerational primary hypogonadism in a sex-selective manner, and we have also confirmed this phenotype by using an external model. Mechanically, we have identified small RNAs (sRNAs) that play a critical role in mediating these transgenerational effects. Specifically, miR6240 and piR016061, which are present in F0 PM sperm, regulate intergenerational transmission by targeting Lhcgr and Nsd1, respectively. We have also uncovered that piR033435 and piR006695 indirectly regulate F1 PM sperm methylation by binding to the 3′-untranslated region of Tet1 mRNA. The reduced expression of Tet1 resulted in hypermethylation of several testosterone synthesis genes, including Lhcgr and Gnas, impaired Leydig cell function and ultimately led to transgenerational primary hypogonadism. Our findings provide insights into the mechanisms underlying the transgenerational effects of paternal PM2.5 exposure on reproductive health, highlighting the crucial role played by sRNAs in mediating these effects. The findings underscore the significance of paternal pre-conception interventions in alleviating the adverse effects of environmental pollutants on reproductive health.

Traditional coordinated motion planning schemes for mobile robot manipulators are primarily designed at the velocity level, which limits their applicability to acceleration-oriented systems. To address this limitation, this paper proposes, for the first time, a kinematic model and a trajectory tracking scheme for a two-wheeled mobile robot manipulator (TWMRM) at the acceleration level. This innovation enables the TWMRM system to achieve improved precision and stability under complex dynamic conditions. Compared to velocity-level approaches, the proposed acceleration-level scheme is applicable to both acceleration-oriented and velocity-oriented mobile robot manipulators, offering a broader range of applications. To verify the proposed approach, trajectory-tracking simulations show that the TWMRM’s end-effector closely follows the desired trajectory, with position errors on the order of ${10}^{-7}$ and no divergence issue. Additionally, practical experiments confirm the feasibility of the acceleration-level kinematic model and trajectory tracking scheme for real-world applications.

The International Maritime Organization (IMO)’s annual operational carbon intensity index (CII) rating requires that from 1 January 2023, all applicable ships meet both technical and operational energy efficiency requirements. In this paper, we conduct a comparative study of different alternative fuel options based on a CII model from the perspective of shipowners. The advantages and disadvantages of alternative fuel options, such as liquefied natural gas (LNG), methanol, ammonia, and hydrogen, are presented. A numerical example using data from three China Ocean Shipping (Group) shipping lines is analyzed. It was found that the overall attained CII of different ship types showed a decreasing trend with the increase of the ship’s deadweight tonnage. A larger ship size choice can obtain better carbon emission reduction for the carbon emission reduction investment program using alternative fuels. The recommended options of using LNG fuel and zero-carbon fuel (ammonia and hydrogen) on Route 1 and Route 3 during the study period were analyzed for the shipowners. Carbon reduction scenarios using low-carbon fuels (LNG and methanol) and zero-carbon fuels (ammonia and hydrogen) on Route 2 are in line with IMO requirements for CII.

Amyloid-beta (Aβ) is a key factor in the onset and progression of Alzheimer's disease (AD). Selenium (Se) compounds show promise in AD treatment. Here, we revealed that selenoprotein K (SELENOK), a selenoprotein involved in immune regulation and potentially related to AD pathology, plays a critical role in microglial immune response, migration, and phagocytosis. In vivo and in vitro studies corroborated that SELENOK deficiency inhibits microglial Aβ phagocytosis, exacerbating cognitive deficits in 5xFAD mice, which are reversed by SELENOK overexpression. Mechanistically, SELENOK is involved in CD36 palmitoylation through DHHC6, regulating CD36 localization to microglial plasma membranes and thus impacting Aβ phagocytosis. CD36 palmitoylation was reduced in the brains of patients and mice with AD. Se supplementation promoted SELENOK expression and CD36 palmitoylation, enhancing microglial Aβ phagocytosis and mitigating AD progression. We have identified the regulatory mechanisms from Se-dependent selenoproteins to Aβ pathology, providing novel insights into potential therapeutic strategies involving Se and selenoproteins.

Raindrops contain abundant renewable energy including both kinetic energy and electrostatic energy, and how to effectively harvest it becomes a hot research topic. Recently, a triboelectric nanogenerator (TENG) using liquid–solid contact electrification has been demonstrated for achieving an ultra-high instantaneous power output. However, when harvesting the energy from the dense raindrops instead of a single droplet, a more rational structure to eliminate the mutual influence of individual generation units is needed for maximize the output. In this work, a “solar panel-like” bridge array generators (BAGs) is proposed. By adopting array lower electrodes (ALE) and bridge reflux structure (BRS), BAGs could minimize the sharp drop in the peak power output for large-scale energy harvesting devices. When the area of the raindrop energy harvesting device is 15 × 15 cm2, the peak power output of BAGs reached 200 W/m2, which is remarkable for paving a potential industrial approach for effective harvesting raindrop energy at a large scale.

Acoustic signal analysis is an important component of transformer online monitoring. Currently, traditional methods have problems such as low spectral resolution, imbalanced sample distribution, and unsatisfactory classification performance. This article first introduces the matrix pencil algorithm for time-frequency spectrum analysis of acoustic signals, and then uses the SMOTE algorithm to expand the imbalanced samples. Then, an ACmix hybrid deep neural network model is constructed to classify 11 types of transformer operation and environmental acoustic signals. Finally, detailed experiments were conducted on the method proposed in this paper, and the experimental results showed that the matrix pencil algorithm has high time-frequency resolution and good noise resistance performance. The SMOTE sample expansion method can significantly improve the recognition accuracy by more than 2%. Overall accuracy of the proposed method in acoustic signal classification tasks reaches 91.81%.

Traditional short-term traffic volume forecasting approaches make it difficult to predict the highly spatiotemporally coupled short-time traffic. To tackle the problem, this paper first proposes a variational modal algorithm (GWO-VMD) based on the optimization of the gray wolf search algorithm. It aims to decompose and reduce the noise of short-time traffic flows. Meanwhile, it reduces the intricacy of data sequences and enhances the regularity pattern. To address the insufficient utilization of spatiotemporal features, this paper presents an innovative deep-learning traffic prediction framework based on the stacking of multiple temporal trend-aware graph attention (TGA) layers and gated temporal convolution (GTC) layers, which are called trend-aware temporal graph neural network (TTGAN). TGA dynamically models the space-time relationships of traffic data, and GTC models the temporal characteristics of traffic data. The experimental findings demonstrate that the MAPE model, as presented, achieves a reduction of 9% and 2% compared to the AGCRN and GWNET models, respectively, in the domain of deep spatiotemporal graph modeling. Data decomposition and noise reduction are necessary to achieve accurate results. This model has superior performance in terms of mean absolute error (MAE), coefficient of determination (R2), and explained variance score (EVAR).

Abstract Private information retrieval (PIR) means a user retrieves a file while revealing no information on which file is being retrieved. In classic PIR, the user needs to wait for the responses of all the storage nodes. However, in general, there may be some nodes that are unresponsive, in which case it is formulated as v‐robust PIR in the network coding (NC) structured distributed storage system (DSS), where v denotes the number of unresponsive nodes. We consider PIR with unresponsive nodes under the framework of NC structured DSS. In particular, the storage nodes adopt combination property with zigzag decodable (CP‐ZD) storage code since it has the advantage of extremely low decoding complexity, which is promised by zigzag decoding (ZD) within a binary field. We design a v‐robust PIR scheme with its download communication cost slightly larger than existing studies, but its decoding complexity is significantly reduced when compared with existing studies.

National park agglomerations are a collection of multiple types of regional protected areas, and these agglomerations are of great importance for giving play to the functions of various ecosystem services in protected areas, establishing ecological corridors, and improving the level of regional ecological protection. The Greater Shangri-La region is located at the junction of Sichuan, Yunnan, and Tibet Provinces in China and is in the transition zone of two major biogeographical regions, the Palearctic and Indo-Malay regions, with numerous and rich types of protected areas. This study summarizes the practice of combined national park protection globally, analyzes the spatial pattern of 87 protected areas in the Greater Shangri-La region, proposes a framework for the spatial identification and screening of national park agglomerations based on ecoregions, and establishes the ecosystem, landscape type, cultural heritage, spatial distance, and protection management used as indicators for the screening framework of national park agglomerations. Five national park agglomerations, namely, Three Parallel Rivers, Shaluli Mountain-Daxue Mountain, Southeast Tibetan Plateau, Liangshan Mountain, and Panxi Rift, are present in this area, covering 52 protected areas, with characteristics of spatial agglomeration, diverse types, and natural and cultural heritage-rich features. These results help improve the environmental protection system in the Greater Shangri-La region and better promote the construction of national park agglomerations in other places of the world.

IntroductionThe advent of bigdata era fundamentally transformed the nature of medical information seeking and the traditional binary medical relationship. Weaving stress coping theory and information processing theory, we developed an integrative perspective on information seeking behavior and explored the antecedent and consequence of such behavior.MethodsData were collected from 573 women suffering from infertility who was seeking assisted reproductive technology treatment in China. We used AMOS 22.0 and the PROCESS macro in SPSS 25.0 software to test our model.ResultsOur findings demonstrated that patients’ satisfaction with information received from the physicians negatively predicted their behavior involvement in information seeking, such behavior positively related to their perceived information overload, and the latter negatively related to patient-physician relationship quality. Further findings showed that medical information seeking behavior and perceived information overload would serially mediate the impacts of satisfaction with information received from physicians on patient-physician relationship quality.DiscussionThis study extends knowledge of information seeking behavior by proposing an integrative model and expands the application of stress coping theory and information processing theory. Additionally, it provides valuable implications for patients, physicians and public health information service providers.

This eight-week feeding trial was conducted to investigate whether adding yeast cell wall extract mixed with bacteria producing mycotoxin-degrading enzymes (YEMDE) to an aflatoxin B1 (AFB1)-contaminated diet affects the growth performance, liver health, antioxidant status, and intestinal microbiota status of juvenile turbot. A total of 480 fish were divided into four groups (three replicate tanks each group): basal diet (Control); Control diet supplemented with 100 μg/kg AFB1 (AFB1); and AFB1 supplemented with 500 mg/kg or 1000 mg/kg YEMDE (AFY500 and AFY1000, respectively). The results showed that a minor variation in growth performance was observed among all groups (P > 0.05). Compared to the Control diet, the AFB1 diet significantly deceased the activities of triglyceride and catalase in the serum (P

The water-soluble polysaccharides from plants have attracted ever-increasing attention in the field of food and drug due to their various activities and low toxicity. CBP50-1, as a purified fraction of polysaccharide from the rhizome of Cibotium barometz (CBP), mainly consisted of glucose (55.45%) and xylose (25.27%). CBP50-1 showed excellent antioxidant activity for scavenging 2,2‑Diphenyl‑1‑picryl‑hydrazy (DPPH) radical and hydroxyl radical, further inhibiting lipid peroxidation. CBP50-1 significantly improved the survival rate of Caenorhabditis elegans under thermal and oxidative stress. Furthermore, CBP50-1 reduced the paralysis and oxidative damage induced by amyloid-beta (Aβ) and increased the antioxidant enzyme activity in Alzheimer’s disease (AD) model C. elegans CL4176 through c-Jun N-terminal kinase (JNK) mitogen-activated protein kinase (MAPK) signal pathway. Thus, CBP50-1 had a potential application in health industries.

Taxis have always been an important part of the urban transportation system in metropolises since they can meet many demands and greatly improve travel efficiency. This article explores the distributions of hot spot destinations of taxi, land surface temperature data, and visible infrared imaging radiometer (VIIRS) nighttime light data in Beijing, Shanghai, and Wuhan. This article further selects point of interests (POIs) that are very close to taxi ridership patterns. This article finally uses a geographically weighted regression model to examine the relationship between population flow by taxi and urban vitality, which is represented by land surface temperature, nighttime light data, and influential POIs. The results suggest that urban structure has an impact on the distribution of drop-off points and nighttime light. The implementation of the relevant policies is revealed to have a certain influence on the alleviation of the urban thermal environment. Traffic stations and financial facilities contribute to an increase in travel, and their influences vary spatially. Similarly, the relationships between the drop-off points and LST and NTL vary spatially. This article aids in understanding population flow and urban vitality development. In addition, this article provides valuable results for policy-makers to promote urban vitality development.

A 12-week feeding trial was conducted to investigate the effect of the same fishmeal protein level replaced by black soldier fly larvae (Hermetia illucens) meal (BSFL) with different lipid contents on the growth performance and intestinal health of juvenile turbot (Scophthalmus maximus L.) (initial body weight 12.64 g). Three isonitrogenous and isolipidic diets were formulated: fish meal-based diet (FM), diets DF and FF, in which 14% fish meal protein of the FM diet was replaced by defatted and full-fat BSFL, respectively. There were no significant differences in growth performance, intestinal morphology, and mucosal barrier function between the DF and the FM group. However, diet FF markedly reduced the growth performance, intestinal perimeter ratio, and the gene expression of anti-inflammatory cytokine TGF-β (P

Purpose – Quantitative and spatial-explicit flood risk information is of great importance for strengthening climate change adaptation and flood resilience. Shanghai is a coastal megacity at large estuary delta with rising flood risks. This study aims to quantify the overall economic-societal risks of storm flooding and their spatial patterns in Shanghai. Design/methodology/approach – Based on multiple storm flood scenarios at different return periods, as well as fine-scale data sets including gridded GDP, gridded population and vector land-use, a probabilistic risk model incorporating geographic information system is used to assess the economic-societal risks of flooding and their spatial distributions. Findings – Our results show that, from 1/200 to 1/5,000-year floods, the exposed assets will increase from USD 85.4bn to USD 657.6bn, and the direct economic losses will increase from USD 3.06bn to USD 52bn. The expected annual damage (EAD) of assets is around USD 84.36m. Hotpots of EAD are mainly distributed in the city center, the depressions along the upper Huangpu River in the southwest, the north coast of Hangzhou Bay, and the confluence of the Huangpu River and Yangtze River in the northeast. From 1/200 to 1/5,000-year floods, the exposed population will rise from 280 thousand to 2,420 thousand, and the estimated casualties will rise from 299 to 1,045. The expected annual casualties (EAC) are around 2.28. Hotspots of casualties are generally consistent with those of EAD. Originality/value – In contrast to previous studies that focus on a single flood scenario or a particular type of flood exposure/risk in Shanghai, the findings contribute to an understanding of overall flood risks and their spatial patterns, which have significant implications for cost-benefit analysis of flood resilience strategies.

IntroductionThis paper explores the scalability of PCSWMM's Low Impact Development (LID) modeling tools within the urban stormwater computer model.MethodsThe scalability is assessed for a variety of spatial and temporal scales and for event (50-year return storm) and continuous inputs (daily rainfall for an 11 month period), and with a focus on bioretention cells. The model is calibrated for a moderate to large scale, semi-urban watershed on Vancouver Island, British Columbia, Canada. Sensitivity analysis and specialized metrics are used to verify internal model processes at a variety of scales.ResultsWith regard to spatial scaling, changes in flow path length and slope derived from Digital Elevation Models were the most impactful spatial information when modeling flood event and the model's surface layer was the dominant contributor to peak flowrate and volume mitigation by the bioretention cell. However, when modeling the continuous rainfall inputs, storage layer related parameters dominated model outputs. Aside from the soil layer's depth, soil layer parameters such as hydraulic conductivity, showed negligible influence on response to time series rainfall. Parameters that are kept static by the model such as vegetation cover, hydraulic conductivity and storage void ratio (but are naturally dynamic), were tested for their impact on response if allowed to change seasonally or with excessive loading. Runoff coefficients were greatly impacted by storage layer parameter dynamics with very little impact from vegetation. For event simulations, the berm height in the surface layer was the dominant player in reducing peak flow as well as total volume. An analysis to help illustrate sensitivity across spatial scales is proposed.DiscussionThe Spatial Dynamic Sensitivity Analysis shows that parameter sensitivity changes dynamically as LID implementation percentage changes. In particular, the clogging factor, which is a parameter associated with the storage layer, was highly influential for time series rainfall analysis. The LID model concepts in PCSWM seem appropriate for events because the surface layer dominates the response for very large storms. For smaller storms, continuous time series, and larger spatial scales, the model could be revised to better represent soil layer dynamics and vegetation cover, which were both currently inconsequential to the model's output.

The effects of ambient fine particles on male reproductive health have raised widespread concern. The particular underlying mechanisms of the damage remain largely unclear and demand more research in new directions. Previous research has revealed that DNA methylation plays an important role in male reproductive development and is also vulnerable to environmental influences. However, there hasn't been enough investigation into the involvement of DNA methylation in PM2.5-induced male reproductive toxicity. Here, we establish a real-time PM2.5 exposure model and revealed that PM2.5 exposure could lead to testicular dysfunction including spermatogenesis impairment and steroid hormone dysfunction. In particular, the decrease in the testicular global level of 5-methylcytosine (5mC) indicated a possible association of DNA methylation with testicular injury induced by PM2.5 exposure. Further genome-wide methylation analysis revealed genomic hypomethylation of testicular DNA and identified more than 1000 differentially methylated regions in both CAP and UA versus FA, indicating that PM2.5 exposure, even low-dose, could modulate the testicular methylome. Furthermore, integrated analysis of methylome and transcriptome identified some key methylated genes and networks, which may be involved in spermatogenesis and synthesis of steroid hormone. The testicular methylation levels of key genes especially Cyp11a1 and Pax8 raised, and their consequent reduced expression may impair the testosterone and sperm production process. Our research provides fundamental knowledge as well as novel insights into the possible involvement of DNA methylation in PM2.5-induced male reproductive harm.

Sertoli cells play a pivotal role in the complex spermatogenesis process. This study aimed to investigate the effects of PM2.5 on Sertoli cells using the TM4 cell line and a real time whole-body PM2.5 exposure mouse model, and further explore the underlying mechanisms through the application of metabolomics and transcriptomics. The results in vivo and in vitro showed that PM2.5 reduced Sertoli cells number in seminiferous tubules and inhibited cell proliferation. PM2.5 exposure also induced Sertoli cell dysfunction by increasing androgen binding protein (ABP) concentration, reducing the blood-testis barrier (BTB)-related protein expression, and decreasing glycolysis capacity and lactate production. The results of transcriptomics, metabolomics, and integrative analysis of multi-omics in the TM4 Sertoli cells revealed the activation of xenobiotic metabolism, and the disturbance of glutathione and purine metabolism after PM2.5 exposure. Further tests verified the reduced GSH/GSSG ratio and the elevation of xanthine oxidase (XO) activity in the PM2.5-exposed TM4 cells, indicating that excessive reactive oxygen species (ROS) was generated via metabolic disorder caused by PM2.5. Moreover, the redox imbalance was proved by the increase in the mitochondrial ROS level, superoxide dismutase (SOD) and catalase (CAT) activity, as well as the activation of the Nrf2 antioxidative pathway. Further study found that the redox imbalance caused by PM2.5 induced DNA damage response and cell cycle arrest. Additionally, PM2.5 induced ferroptosis through iron overload and lipid peroxidation. Taken all together, our study provided new insights for understanding proliferation inhibition and dysfunction of TM4 Sertoli cells exposed to PM2.5 via metabolic disorder and redox imbalance-mediated DNA damage response and ferroptosis.

Urban agglomeration is one maturely spatial organized form in the urbanization development process, and often presents in the area with prominent ecological and environmental problems. As a widely used scientific indicator, ecosystem health aims at measuring the ecosystem variation along with the development of urban agglomeration. Assessing the ecosystem health of urban agglomeration in the past stages is the foundation of sustainable development of urban agglomeration. Based on a hidden Markov Model and unscented Kalman filtering method, this research monitor and predict the ecosystem health of Shanghai-Hangzhou Bay Urban Agglomeration (SHBUA). More specifically, we propose hidden Markov model (HMM) decoding scheme and HMM-based Kalman filter framework to simulate and predict the ecosystem health status in SHBUA. The feasibility and simulation accuracy of two prediction cases is verified in ecosystem health assessment of urban agglomerations. Unscented Kalman filter (UKF) is used to refine results of the prediction procedure. We construct the HMM-UKF scheme and HMM decoding scheme using Future Land Use Simulation (FLUS)-based land use data. Analysis of results of two prediction cases indicates the spatial heterogeneity of the UKF correction effect. We construct sequences extension strategies to meet the requirements of the UKF algorithm. The result shows that sequences extension strategies optimize the prediction mechanism of the HMM-UKF scheme. Theoretically, UKF can derive interpretable ecological health prediction results based on both measurement and decoding values of EHI. The application of UKF improves the prediction accuracy of HMM from 74.60% to 81.27%. This research may provide an available framework for quantitative prediction of ecosystem health based on short-term time series, using the sequence expansion strategy as an option to improve the performance.

Alsophila spinulosa is a tree-like fern, and many evidences suggested that plant polyphenols had the potential therapeutic for Alzheimer s disease (AD). Herein, polyphenols (ASP) was isolated from A. spinulosa leaves and its major constituent were isoorientin and vitexin. ASP displayed excellent antioxidant activity and obvious anti-lipid peroxidation capacity in vitro. ASP improved the survival rate of C. elegans under high temperature by enhancing the antioxidant enzymes activities and decreasing the lipid peroxidation level. Moreover, ASP alleviated β-amyloid (Aβ) induced paralysis and reduced Aβ deposition, decreased reactive oxygen species (ROS) accumulation and improved the level of skn-1 mRNA. In addition, ASP decreased the levels of pdk-1 and akt-1 mRNA in P13K/AKT signaling pathway. In conclusion, ASP may be a potential ingredient for the alleviation of AD.

Fine particulate matter (PM2.5)-induced male reproductive toxicity arouses global public health concerns. However, the mechanisms of toxicity remain unclear. This study aimed to further investigate toxicity pathways by exposure to PM2.5in vitro and in vivo through the application of metabolomics and transcriptomics. In vitro, spermatocyte-derived GC-2spd cells were treated with 0, 25, 50, 100 μg/mL PM2.5 for 48 h. In vivo, the real-world exposure of PM2.5 for mouse was established. Forty-five male C57BL/6 mice were exposed to filtered air, unfiltered air, and concentrated ambient PM2.5 in Tangshan of China for 8 weeks, respectively. The results in vitro and in vivo showed that PM2.5 exposure inhibited GC-2spd cell proliferation and reduced sperm motility. Mitochondrial damage was observed after PM2.5 treatment. Increased Humanin and MOTS-c levels and decreased mitochondrial respiratory indicated that mitochondrial function was disturbed. Furthermore, nontargeted metabolomics analysis revealed that PM2.5 exposure could disturb the citrate cycle (TCA cycle) and reduce amino acids and nucleotide synthesis. Mechanically, the aryl hydrocarbon receptor (AhR) pathway was activated after exposure to PM2.5, with a significant increase in CYP1A1 expression. Further studies showed that PM2.5 exposure significantly increased both intracellular and mitochondrial reactive oxygen species (ROS) and activated NRF2 antioxidative pathway. With the RNA-sequencing technique, the differentially expressed genes induced by PM2.5 exposure were mainly enriched in the metabolism of xenobiotics by the cytochrome P450 pathway, of which Cyp1a1 was the most significantly changed gene. Our findings demonstrated that PM2.5 exposure could induce spermatocyte damage and energy metabolism disorder. The activation of the aryl hydrocarbon receptor might be involved in the mechanism of male reproductive toxicity.

In this paper, we propose a novel target-aware token design for transformer-based object detection. To tackle the target attribute diffusion challenge of transformer-based object detection, we propose two key components in the new target-aware token design mechanism. Firstly, we propose a target-aware sampling module, which forces the sampling patterns to converge inside the target region and obtain its representative encoded features. More specifically, a set of four sampling patterns are designed, including small and large patterns, which focus on the detailed and overall characteristics of a target, respectively, as well as the vertical and horizontal patterns, which handle the object’s directional structures. Secondly, we propose a target-aware key-value matrix. This is a unified, learnable, feature-embedding matrix which is directly weighted on the feature map to reduce the interference of non-target regions. With such a new design, we propose a new variant of the transformer-based object-detection model, called Focal DETR, which achieves superior performance over the state-of-the-art transformer-based object-detection models on the COCO object-detection benchmark dataset. Experimental results demonstrate that our Focal DETR achieves a 44.7 AP in the coco2017 test set, which is 2.7 AP and 0.9 AP higher than the DETR and deformable DETR using the same training strategy and the same feature-extraction network.

In this paper, we demonstrate a domain inversion crystal structure to study the cascaded three-wave mixing process in a two peak Stark-chirped rapid adiabatic passage. We have achieved efficient wavelength conversion, which can be performed in intuitive order and counterintuitive order. The requirement of crystal is reduced and the flexibility of structure design is improved. When the conversion wavelength is fixed, increasing the coupling coefficient between the two peaks can reduce the intensity of the intermediate wavelength while maintaining high conversion efficiency. Compared with the cascaded wavelength conversion process based on stimulated Raman rapid adiabatic passage, the two-peak modulated Stark-chirped rapid adiabatic passage has a larger convertible wavelength bandwidth. This scheme provides a theoretical basis for obtaining mid-infrared laser source via flexible crystal structure.

The Karakoram-Himalayan (KH) region comprises high mountains across seven countries (i.e., China, Afghanistan, Pakistan, India, Nepal, Bhutan, and Myanmar) with a variety of management regimes and priorities and capacities for conservation. Currently, there is no comprehensive framework for assessing cooperation on protected areas (PAs) in the KH region. Such a framework is essential to guide managers and policymakers in the formulation of consistent management plans and strategies for regional sustainable development. In this study, an indicator framework for assessing cooperation on PAs was developed based on four factors: the natural environment, the human environment, transport accessibility, and the diplomatic environment. The assessment involved 49 cross-border national parks and nature reserves in this region. Furthermore, the indicators were analyzed by using the analytic hierarchy process, resulting in eight cross-border groups. The analysis reveals that the region has a good foundation for cooperative cross-border conservation in general. Among the eight groups, the Karakoram-Pamir, Kangchenjunga, and Everest groups exhibit greater potential for closer cooperation, whereas the Kashmir group has a lower basis for cooperation. The results suggest that the establishment of transboundary conservation areas (TBCAs) and academic and scientific development can provide opportunities to enhance our understanding of the KH region and to collaborate across national boundaries. This case study explains that the indicator framework can be used as a reference for cooperative cross-border conservation in other regions worldwide.

Objective: Parkinson's disease (PD) is a serious neurodegenerative disorder. It is reported that most of PD patients have voice impairments. But these voice impairments are not perceptible to common listeners. Therefore, different machine learning methods have been developed for automated PD detection. However, these methods either lack generalization and clinically significant classification performance or face the problem of subject overlap. Methods: To overcome the problems discussed above, we attempt to develop a hybrid intelligent system that can automatically perform acoustic analysis of voice signals in order to detect PD. The proposed intelligent system uses linear discriminant analysis (LDA) for dimensionality reduction and genetic algorithm (GA) for hyperparameters optimization of neural network (NN) which is used as a predictive model. Moreover, to avoid subject overlap, we use leave one subject out (LOSO) validation. Results: The proposed method namely LDA-NN-GA is evaluated in numerical experiments on multiple types of sustained phonations data in terms of accuracy, sensitivity, specificity, and Matthew correlation coefficient. It achieves classification accuracy of 95% on training database and 100% on testing database using all the extracted features. However, as the dataset is imbalanced in terms of gender, thus, to obtain unbiased results, we eliminated the gender dependent features and obtained accuracy of 80% for training database and 82.14% for testing database, which seems to be more unbiased results. Conclusion: Compared with the previous machine learning methods, the proposed LDA-NN-GA method shows better performance and lower complexity. Clinical Impact: The experimental results suggest that the proposed automated diagnostic system has the potential to classify PD patients from healthy subjects. Additionally, in future the proposed method can also be exploited for prodromal and differential diagnosis, which are considered challenging tasks.

To develop a more efficient and environmentally friendly exploitation and production method of gas hydrate, a better understanding of the formation and disassociation process mechanism of gas hydrate in porous media is indispensable. The permittivity spectra of gas hydrate differs from water, oil, ice and gas, and thus measurement of its permittivity properties is a potential approach to detect hydrate existence and content. In this study, THF (tetrahydrofuran) was used as the hydrate former. The permittivity dispersion of hydrate in quartz sand was measured with open-ended coaxial method in the processes of formation and disassociation at a wide radio frequency band from 1MHz to 3GHz. From the dielectric measurements, the hydrate concentration in the porous media was identified with dispersion properties, and the characteristic of the permittivity response upon the hydrate formation and dissociation were also provided. Moreover, by applying dielectric mixing models, the fractions of the solution and hydrate could be estimated. These results implied that the permittivity dispersion could be an effective monitor for the formation and disassociation of the hydrate, and hydrate content could be evaluated in real time with complex permittivity spectra.

Coal 3 in group A is employed as a protective layer to release long-distance coal 4 in group B in Paner colliery (approximately 80 m vertical interval) as the mining depth extends downward, which is the first engineering test in the Huainan coal mining area. To evaluate the validity of the scheme, the permeability distribution, and evolution law, gas pressure distribution characteristics, swelling deformation, pressure relief range, and gas drainage volume of the protected coal seam are analyzed using a FLAC3D numerical simulation and field measurements. Therefore, different stress-permeability models are adopted for caved, fractured, and continuous deformation zones, and a double-yield model is applied in the goaf based on compaction theory to improve the accuracy of the numerical simulation. The results indicate that the extraction of coal 3 has a positive effect on permeability enhancement and pressure relief gas drainage. However, the dip angle of coal measurements causes asymmetric strata movement, which leads to the pressure relief and permeability enhancement area shifting to the downhill side, where the permeability enhancement effect of the downhill side is better than that of the uphill side. The permeability enhancement zone is an inverted trapezoid, but the effective pressure relief range is a positive trapezoid. The permeability of the protected coal seam in the pressure relief zone is significantly higher than that in the compressive failure zone. The permeability in the pressure relief zone will decrease again due to the recompaction of the coal seam with an advancement of the longwall face. Thus, pressure relief gas drainage is suggested during long-distance protective coal seam mining to eliminate gas hazards.

The 15-minute community life circle (15min-CLC) strategy is one of Shanghai's important methods for building a global city and facing a society with a more diverse population structure in the future. In the existing research, the balance between the construction of the life circle and the needs of the people in the life circle still needs to be further fulfilled. This paper is based on the city's multi-source large data set including 2018 AutoNavi POI (Point of Interests), OSM (OpenStreetMap) road network data and LandScan population data set, and evaluates the current status of Shanghai's 15min-CLC through the fusion of kernel density estimation, service area analysis and other statistical models and proposes relevant optimization suggestions. The results show that there are the following shortcomings: (1) From the perspective of different types of infrastructure service facilities, the spatial construction of Shanghai's overall life service facilities and shopping service facilities needs to be optimized. (2) From the perspective of comprehensive evaluation, the comprehensive service convenience of infrastructure service facilities in the downtown area is relatively high, while the comprehensive service convenience of urban infrastructure service facilities in the suburbs and outer suburbs is relatively low; The diversity of basic service facilities in the 15min-CLC in the downtown area is more consistent with the population distribution; However, in the peripheral areas of the urban area, too many infrastructure service facilities have been constructed. Based on the above shortcomings and the perspective of supply and demand matching, relevant optimization strategies are proposed in different regions and different types of infrastructure service facilities: (1) focus on the construction of basic service facilities in the urban fringe and urban-rural areas, improve the full coverage of the basic service facilities, and appropriately reduce the number of basic service facilities in the downtown area. (2) The development of community business models can be used to promote the development of new life service facilities and shopping service facilities. (3) Improve community medical institutions through facility function conversion, merger and reconstruction, etc. (4) Optimize the hierarchical basic service facility system and improve the population supporting facilities of basic service facilities in the 15min-CLC. This paper incorporates people's needs and concerns on the living environment into the 15min-CLC evaluation model, and uses Shanghai as an example to conduct research, summarizes the existing shortcomings, and proposes corresponding optimization strategies based on the matching of supply and demand. This article attempts to explore a replicable 15min-CLC planning model, so that it can be extended to the Yangtze River Delta urban agglomeration, to provide reference for further research on the 15min-CLC, and to promote urban construction under the concept of sustainable development.

Ecosystem health assessment is an important method for obtaining information on ecosystem conditions, and it plays a vital role in preserving and enhancing ecosystem health status. In addition, it provides useful information and knowledge for urban agglomeration development decision makers. However, ecological phenomena often vary considerably from one observation to the next, which makes it difficult to distinguish different status of the ecosystem health. In this study, hidden Markov model (HMM) was employed to simulate the internal-external correlations of ecosystem status through establishing the relationships between internal ecological health level and combination state of external observation. Based on the statistics and land use data in 2001, 2007 and 2013, the Vigor-Organization-Resilience (VOR) framework was employed to identify the ecosystem health in Shanghai-Hangzhou Bay Metropolitan (SHBM), in which the ecosystem health state was considered as a hidden state that could be estimated according to the conditions of vigor, organization and resilience. In addition, two parameter learning cases including mathematical statistics and extensible sequence method were employed to solve the iterative convergence problem of parameters in short-time series of ecosystem health simulation. Results show that HMM not only provides a comparable descriptive ability to that of the VOR model, but also can monitor ecosystem health at the optimal grid scale in SHBM. The combination of HMM and VOR greatly expands the spatiotemporal characteristics and provides a new research approach for the study of ecosystem health assessment of urban agglomerations. Keywords: Ecosystem health assessment, Vigor-Organization-Resilience (VOR) model, Hidden Markov model (HMM), Shanghai-Hangzhou Bay Metropolitan (SHBM)

Using box-counting and spatial regression, this paper analyzes the morphological characteristics of coastal settlement patterns and their spatial determinants, with a case of the Wen-Tai region on the Chinese eastern coast. Coastal settlement patterns, which reflect the interactions between people and the surrounding environment, can indicate the anthropogenic pressure sustained in the coastal zones. Characterization of settlement patterns in coastal zones is definitely needed for coastal management. Results indicate that coastal settlement patterns in the Wen-Tai region present significant fractal characteristics, and exhibit obvious spatial variations. The morphological characteristics of settlement patterns are significantly correlated with the standard deviation value of elevation and slope, as well as percentage of loam soils. In particular, cities with greater relief amplitude, higher slope variability, and higher percentage of loam soils would present more complexity in form. Proximity to roads and rivers are insignificant determinants. Our study contributes to the understanding of the spatial determinants of the morphological characteristics of settlement patterns in coastal zones. We argue that fractal dimension provides a useful tool to facilitate the identification of vulnerability hotspots for coastal studies.

Al-air batteries are regarded as potential power source for flexible and wearable devices. However, the traditional cathodes of Al-air batteries are easy to be broken after continuous bending. This is why few Al-air batteries have been tested under the state of dynamic bending so far. Herein, carbon nanofibers incorporated with Mn3O4 catalyst have been prepared as bending-resistant cathodes through direct electrospinning. The cathode assembled in Al-air battery showed excellent electrochemical and mechanical stability. A high specific capacity of 1021 mAh/cm2 was achieved after bending 1000 times, which is 81.7% of that in platform state. This work will facilitate the progress of using Al-air battery in flexible electronics.

An improved current sensor aimed at measuring currents of different parts in composite insulator samples was proposed. Conventional current sensors used in water diffusion tests aim to examine the performance of composite insulators, however, it is difficult for the conventional current sensors to locate the defects. Thus, we designed a new electrode structure to measure the currents of different components in short samples of composite insulators. Based on a finite analysis method, the influence of relative permittivity and conductivity on the current was analyzed. New samples with different interfaces and samples after operation were tested using the new and conventional current sensors. The performance of a certain part in short samples can be diagnosed by analyzing the current and phase information extracted from the test results. By comparing the test results of new and traditional current sensors, it was proved that the new electrode structure is more effective in locating the defects of insulators.