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Abstract Monitoring the changes in population and anthropogenic CO2 emissions caused by geopolitical conflicts is significant for humanitarian assistance and also for revealing the CO2 emission patterns of human activities. However, the changes in population and anthropogenic CO2 emissions are highly dynamic, and representative survey data are generally unavailable. We monitor the near-real-time and fine-grained changes in war-induced population and anthropogenic CO2 emissions in Ukraine through social media data. One year after the invasion, over 11 million Ukrainians are displaced from the baseline $${0.1}^{\circ}\,*\,{0.1}^{\circ}$$ 0.1 ∘ * 0.1 ∘ gridded regions. There is a significant correlation between the estimated changes and the reference changes for each month in all CO2 emission sectors, with R2 respectively ranging from 0.57 to 0.93, 0.41 to 0.9, 0.74 to 0.99 for residential consumption, ground transport, and industry sectors. Overall, the proposed method provides a new perspective to monitor Ukrainian refugee crisis and measure the spatio-temporal response of anthropogenic CO2 emissions.

The development of mining cities has led to changes in land use and the evolution of landscape patterns. Constructing an ecological security evaluation system can reflect the ecological security status of mining city areas and provide planning references for these cities. This study, based on Heihe City’s land use data from 1980 to 2020, systematically analyzes the spatial and temporal evolution characteristics of land use, landscape patterns, and landscape ecological security levels by constructing a land use transfer matrix and calculating landscape pattern indices and landscape ecological security indices. The results show that: 1) Forest land is the main type of land use in Heihe City, accounting for over 50% of the total area. Land use changes primarily occurred between 2000–2010 and 2010–2020, with the spatial pattern characterized by overall stability and localized dramatic shifts, mainly involving the conversion of forest land to farmland and unused land. 2) From 1980 to 2020, the landscape ecological security pattern in Heihe City improved. Landscape diversity and landscape contagion increased, while landscape fragmentation, the largest patch area, and the average patch area decreased. Land use was optimized overall, but the trend of forest fragmentation became noticeable. 3) Between 1980 and 2020, the landscape ecological security level in Heihe City improved significantly. Driven by ecological restoration policies, the ecological security level in the southern region greatly improved, while the spatial pattern continued to show a trend of lower security in the south and higher security in the north. Specifically, the area proportions of low-security and relatively low-security areas increased by 6.23% and 9.55%, respectively. The spatial clustering of landscape ecological security levels is evident, with high-high value clustering mainly in the north and low-low value clustering mainly in the south. It is necessary to further promote ecological protection in the north to ensure the ecological barrier function, while strengthening ecological restoration in the south to improve ecological security levels. Additionally, continuous macro-policy regulation is needed to maintain long-term ecological security in Heihe City. The ecological security level of mining city landscapes is influenced by economic dependence, policy constraints, and environmental issues. This study can provide guidance for planning improvements in the study area and other mining cities.

Abstract The adverse use of alcohol is a serious global public health problem. Maternal alcohol consumption during pregnancy usually causes prenatal alcohol exposure (PAE) in the developing fetus, leading to a spectrum of disorders known as fetal alcohol spectrum disorders (FASD) and even fetal alcohol syndrome (FAS) throughout the lifelong sufferers. The prevalence of FASD is approximately 7.7 per 1,000 worldwide, and is even higher in developed regions. Generally, Ethanol in alcoholic beverages can impair embryonic neurological development through multiple pathways leading to FASD. Among them, the leading mechanism of FASDs is attributed to ethanol-induced neuroinflammatory damage to the central nervous system (CNS). Although the underlying molecular mechanisms remain unclear, the remaining multiple pathological mechanisms is likely due to the neurotoxic damage of ethanol and the resultant neuronal loss. Regardless of the molecular pathway, the ultimate outcome of the developing CNS exposed to ethanol is almost always the destruction and apoptosis of neurons, which leads to the reduction of neurons and further the development of FASD. In this review, we systematically summarize the current research progress on the pathogenesis of FASD, which hopefully provides new insights into differential early diagnosis, treatment and prevention for patents with FASD.

Summary: Understanding changes in thermodynamic and transport properties during adsorption is crucial for the thermal management of metal-organic frameworks, which also imposes significant challenges for improved performance and energy density of adsorption system. Because of phase transitions in the intermolecular interactions involved in the adsorption phenomena, transport properties including the thermal conductivity exhibit interesting behaviors, yet fully understood. This study employs detailed molecular dynamics simulations to replicate the methane/Cu-BTC adsorption phenomenon for the evaluation of their thermal conductivities across different pressures and temperatures. The molecular simulations show that the thermal conductivities of both the adsorbent (Cu-BTC) and adsorbate (methane, adsorbed phase) vary notably during adsorption processes. Using the concepts of the change in the degree of free movements of the adsorbate molecules and atomic vibration of adsorbent, the reduction of the adsorbate thermal conductivity (∼70–93%) and increase thermal conductivity of the adsorbent (up to 3 times) in Cu-BTC+CH4 pair are explained.

Abstract Background Xerostomia is one of the most common side effects in nasopharyngeal carcinoma (NPC) patients after chemoradiotherapy. To establish a Delta radiomics model for predicting xerostomia secondary to chemoradiotherapy for NPC based on magnetic resonance T1-weighted imaging (T1WI) sequence and evaluate its diagnostic efficacy. Methods Clinical data and Magnetic resonance imaging (MRI) data before treatment and after induction chemotherapy (IC) of 255 NPC patients with stage III-IV were collected retrospectively. Within one week after CCRT, the patients were divided into mild (92 cases) and severe (163 cases) according to the grade of xerostomia. Parotid glands in T1WI sequence images before and after IC were delineated as regions of interest for radiomics feature extraction, and Delta radiomics feature values were calculated. Univariate logistic analysis, correlation, and Gradient Boosting Decision Tree (GBDT) methods were applied to reduce the dimension, select the best radiomics features, and establish pretreatment, post-IC, and Delta radiomics xerostomia grading predictive models. The receiver operating characteristic (ROC) curve and decision curve were drawn to evaluate the predictive efficacy of different models. Results Finally, 15, 10, and 12 optimal features were selected from pretreatment, post-IC, and Delta radiomics features, respectively, and a xerostomia prediction model was constructed with AUC values of 0.738, 0.751, and 0.843 in the training set, respectively. Only age was statistically significant in the clinical data of both groups (P

Modern unmanned aerial vehicle (UAV) reconnaissance is widely used. UAV equipped with FPV camera can realize the reconnaissance of dense small objects with the help of object detection technology. However, it is difficult to detect people or vehicle objects in UAV overhead images because the pixels are small and densely distributed. In this paper, we propose an improved YOLOv7 UAV dense small object detection algorithm (DS-YOLOv7) to improve the UAV detection performance of dense small objects. SFN technology optimizes the fusion network to further improve the semantic information and location information of dense small objects. LDSPP is an optimized module for feature extraction of small objects. It pays more attention to edge information of small objects and reduces the rate of missing detection. Dimensionality reduction techniques focus on reducing model parameters to facilitate the deployment of algorithms on lightweight devices. The experimental verification on the public data set VisDrone2019 shows that mAP50 and mAP50-95 have increased by 4.3% and 3.3% respectively, F1 scores have increased by 3.81%, model volume has decreased by 23.3MB, parameters have decreased by 13 million, and the improved algorithm is more conducive to UAV deployment and dense small object detection.

This paper presents our study of the deformation and failure characteristics surrounding rock in roadways with super-large sections during the integrated coal pillar excavation, filling and retention process between coalfaces. Based on the theory of complex variable function, the mapping accuracy of conformal transformation is improved, and an analytical solution for surrounding rock stress in super-large sections of roadway is derived. The stress distribution law of the surrounding rock of rectangular roadways is analyzed, and numerical simulation software is used for supplementary analysis and verification. According to the research findings, the compressive stresses on two sides and the tensile stress on the roof of a rectangular roadway with super-large sections decreased with the increase in the side-pressure coefficient; however, when the side-pressure coefficient increased to a certain point, those two sides changed from a pressure-bearing status to a tensile force-bearing status, while the roof changed from a tensile force-bearing status to a pressure-bearing status. In these stress changes, all the stresses upon the surrounding rock of roadways were compressive stresses and the two critical side-pressure coefficient values were λup and λdown. As the aspect ratio of the roadway increased from 1 to 9, its λup increased from 1.823 to 5.865 and its λdown increased from 0.549 to 0.888. When those side-pressure coefficients in the environment where a roadway is located exceed their critical values, tensile stress will take place on the roadway boundary and result in tensile failure, thus leading to instability in the roadway super-large section. The impact of the side-pressure coefficient upon the plastic zone range of roadway surrounding rock is greater than the impact of the roadway width. In order to secure stability in the surrounding rock of roadways with super-large sections during the excavation process, the side-pressure coefficient should remain around 1; in this situation, the plastic zone covers the smallest range and the relevant support work is the easiest. These research findings provide theoretical references for the excavation and support of roadways with super-large sections.

Abstract Tornadoes, as highly destructive weather events, require accurate detection for effective decision‐making. Traditional radar‐based tornado detection algorithms (TDA) face challenges with limited tornado feature extraction capabilities, leading to high false alarm rates and low detection probabilities. This study introduces the Multi‐Task Identification Network (MTI‐Net), leveraging Doppler radar data to enhance tornado recognition. MTI‐Net integrates tornado detection and estimation tasks to acquire comprehensive spatial and locational information. As part of MTI‐Net, we introduce a novel backbone network of Multi‐Head Convolutional Block (MHCB), which incorporates Spatial and Channel Attention Units (SAU and CAU). SAU optimizes local tornado feature extraction, while CAU reduces false alarms by enhancing dependencies among input variables. Experiments demonstrate the superiority of MTI‐Net over TDA, with a decrease in false alarm rates from 0.94 to 0.46 and an increase in hit rates from 0.23 to 0.81, highlighting the effectiveness of MTI‐Net in handling small‐scale tornado events.

Abstract Machine learning‐based approaches demonstrate a significant potential in radar quantitative precipitation estimation (QPE) applications. In contrast to conventional methods that depend on local raindrop size distributions, deep learning (DL) can establish an effective mapping from three‐dimensional radar observations to ground rain rates. However, the lack of transparency in DL models poses challenges toward understanding the underlying physical mechanisms that drive their outcomes. This study aims to develop a DL‐based QPE system and provide a physical explanation of radar precipitation estimation process. This research is designed by employing a deep neural network consisting of two modules. The first module is a quantitative precipitation estimation network that has the capability to learn precipitation patterns and spatial distribution from multidimensional polarimetric radar observations. The second module introduces a quantitative precipitation estimation shapley additive explanations method to quantify the influence of each radar observable on the model estimate across various precipitation intensities.

Among numerous polluting compounds, the organic dyes and antibiotics are two servious factors causing water pollution. Hence, it is urgent to find a fast and safe way to degrade waste water in a sustainable approach. Here, ferroelectric heterostructure based on 2D Bi2MoO6 nanosheets and Bi2S3 nanorods has been prepared by a two-step solvothermal method for the reduction of organic dyes and antibiotics. Due to the energy band balance between Bi2S3 and Bi2MoO6, the formed type-II heterostructure can populate charge transfer at the junction and effectively inhibit electron-hole recombination. Under ultrasonic stimulation, catalytic active sites of a Bi2MoO6/Bi2S3 heterostructure for pollutant degradation can be generated through rapid charge transfer activation, thus enhancing the catalytic degradation effect of organic dyes and antibiotics. The Bi2MoO6/Bi2S3 heterostructure not only has good degradation effect, but also shows better stability. This work opens up new avenues for designing highly-efficient catalysts, which is of importance for wastewater degradation and energy-saving applications.

With the wide application of Underwater Wireless Sensor Networks (UWSNs) in various fields, more and more attention has been paid to deploying and adjusting network nodes. A UWSN is composed of nodes with limited mobility. Drift movement leads to the network structure’s destruction, communication performance decline, and node life-shortening. Therefore, a Node Adjustment Scheme based on Motion Prediction (NAS-MP) is proposed, which integrates the layered model of the ocean current’s uneven depth, the layered ocean current prediction model based on convolutional neural network (CNN)–transformer, the node trajectory prediction model, and the periodic depth adjustment model based on the Seagull Optimization Algorithm (SOA), to improve the network coverage and connectivity. Firstly, the error threshold of the current velocity and direction in the layer was introduced to divide the depth levels, and the regional current data model was constructed according to the measured data. Secondly, the CNN–transformer hybrid network was used to predict stratified ocean currents. Then, the prediction data of layered ocean currents was applied to the nodes’ drift model, and the nodes’ motion trajectory prediction was obtained. Finally, based on the trajectory prediction of nodes, the SOA obtained the optimal depth of nodes to optimize the coverage and connectivity of the UWSN. Experimental simulation results show that the performance of the proposed scheme is superior.

Abstract In recent years, cancer immunotherapy has emerged as an exciting cancer treatment. Immune checkpoint blockade brings new opportunities for more researchers and clinicians. Programmed cell death receptor-1 (PD-1) is a widely studied immune checkpoint, and PD-1 blockade therapy has shown promising results in a variety of tumors, including melanoma, non-small cell lung cancer and renal cell carcinoma, which greatly improves patient overall survival and becomes a promising tool for the eradication of metastatic or inoperable tumors. However, low responsiveness and immune-related adverse effects currently limit its clinical application. Overcoming these difficulties is a major challenge to improve PD-1 blockade therapies. Nanomaterials have unique properties that enable targeted drug delivery, combination therapy through multidrug co-delivery strategies, and controlled drug release through sensitive bonds construction. In recent years, combining nanomaterials with PD-1 blockade therapy to construct novel single-drug-based or combination therapy-based nano-delivery systems has become an effective mean to address the limitations of PD-1 blockade therapy. In this study, the application of nanomaterial carriers in individual delivery of PD-1 inhibitors, combined delivery of PD-1 inhibitors and other immunomodulators, chemotherapeutic drugs, photothermal reagents were reviewed, which provides effective references for designing new PD-1 blockade therapeutic strategies. Graphical Abstract

Abstract In recent years, highway accidents occur frequently, the main reason is that there is always foreign body invasion on the highway, which makes people unable to respond to emergencies in time. In order to reduce the occurrence of highway incidents, an object detection algorithm for highway intrusion was proposed in this paper. Firstly, a new feature extraction module was proposed to better preserve the main information. Secondly, a new feature fusion method was proposed to improve the accuracy of object detection. Finally, a lightweight method was proposed to reduce the computational complexity. We compare the algorithm in this paper with existing algorithms, the experimental results showed that: On the Visdrone dataset (small size targets), (a) the CS-YOLO was 3.6% more accurate than the YOLO v8. (b) The CS-YOLO was 1.2% more accurate than the YOLO v8 on the Tinypersons dataset (minimal size targets). (c) CS-YOLO was 1.4% more accurate than YOLO v8 on VOC2007 data set (normal size).

The "Three Rural Issues", encompass challenges related to agriculture, farmer, and rural area, which hold significant importance in driving comprehensive rural revitalization efforts in China. Farmer entrepreneurship, as a crucial means to enhance productivity, create job opportunities, and increase residents' income, has gradually become a key driving force in promoting rural revitalization in the new stage of development in China. With the rapid development of rural e-commerce, farmer entrepreneurship has encountered new opportunities. This study utilizes the 2020 China Family Panel Studies (CFPS) data and employs a structural equation model (SEM) to analyze the direct impact of rural e-commerce participation on farmer entrepreneurial behavior, considering factors such as human capital, social capital, and network infrastructure. This study further explores the indirect effects and mechanisms of e-commerce participation as a mediating variable and analyzes the impact and mechanisms on agricultural entrepreneurship behavior. The findings are as follows: (1) E-commerce participation significantly promotes farmer entrepreneurial behavior; (2) E-commerce participation as a mediating variable has a positive indirect effect on the relationship between social trust, network infrastructure, human capital, and farmer entrepreneurial behavior; (3) E-commerce participation has a significant positive influence on farmer entrepreneurship in the agricultural sector, and farmers with higher levels of network infrastructure and human capital have a higher probability of choosing agricultural entrepreneurship under the influence of e-commerce participation. Finally, this study provides policy recommendations in terms of infrastructure construction, entrepreneurial policy environment, and education level, aiming to optimize the situation of farmer entrepreneurship and contribute to the comprehensive promotion of rural revitalization.Overall, the research in this paper effectively combines theory and empirical evidence to outline the direct and indirect impact mechanisms of rural e-commerce participation on farmers' entrepreneurial behavior and agriculture-related entrepreneurial behavior and to test the effects of their impacts. First, most of the existing literature deals with farmers in individual sample areas, while the sample selected in this paper is farmers in the whole country, which is relatively more generalizable; second, most of the previous studies explore the level of e-commerce in the inter-provincial or county areas, while this paper expands the empirical study of rural e-commerce on the entrepreneurial behavior of farmers and the micro-period of agricultural entrepreneurial behavior, and focuses on the impacts of the e-commerce activities of farmers on their entrepreneurial behavior.

Abstract Urban areas are known to modify the spatial pattern of precipitation climatology. Existing observational evidence suggests that precipitation can be enhanced downwind of a city. Among the proposed mechanisms, the thermodynamic and aerodynamic processes in the urban lower atmosphere interact with the meteorological conditions and can play a key role in determining the resulting precipitation patterns. In addition, these processes are influenced by urban form, such as the impervious surface extent. This study aims to unravel how different urban forms impact the spatial patterns of precipitation climatology under different meteorological conditions. We use the Multi‐Radar Multi‐Sensor quantitative precipitation estimation data products and analyze the hourly precipitation maps for 27 selected cities across the continental United States from the years 2015–2021 summer months. Results show that about 80% of the studied cities exhibit a statistically significant downwind enhancement of precipitation. Additionally, we find that the precipitation pattern tends to be more spatially clustered in intensity under higher wind speed; the location of radial precipitation maxima is located closer to the city center under low background winds but shifts downwind under high wind conditions. The magnitude of downwind precipitation enhancement is highly dependent on wind directions and is positively correlated with the city size for the south, southwest, and west directions. This study presents observational evidence through a cross‐city analysis that the urban precipitation pattern can be influenced by the urban modification of atmospheric processes, providing insight into the mechanistic link between future urban land‐use change and hydroclimates.

Abstract As the road traffic situation becomes complex, the task of traffic management takes on an increasingly heavy load. The air-to-ground traffic administration network of drones has become an important tool to promote the high quality of traffic police work in many places. Drones can be used instead of a large number of human beings to perform daily tasks, as: traffic offense detection, daily crowd detection, etc. Drones are aerial operations and shoot small targets. So the detection accuracy of drones is less. To address the problem of low accuracy of Unmanned Aerial Vehicles (UAVs) in detecting small targets, we designed a more suitable algorithm for UAV detection and called GBS-YOLOv5. It was an improvement on the original YOLOv5 model. Firstly, in the default model, there was a problem of serious loss of small target information and insufficient utilization of shallow feature information as the depth of the feature extraction network deepened. We designed the efficient spatio-temporal interaction module to replace the residual network structure in the original network. The role of this module was to increase the network depth for feature extraction. Then, we added the spatial pyramid convolution module on top of YOLOv5. Its function was to mine small target information and act as a detection head for small size targets. Finally, to better preserve the detailed information of small targets in the shallow features, we proposed the shallow bottleneck. And the introduction of recursive gated convolution in the feature fusion section enabled better interaction of higher-order spatial semantic information. The GBS-YOLOv5 algorithm conducted experiments showing that the value of mAP@0.5 was 35.3 $$\%$$ % and the mAP@0.5:0.95 was 20.0 $$\%$$ % . Compared to the default YOLOv5 algorithm was boosted by 4.0 $$\%$$ % and 3.5 $$\%$$ % , respectively.

Abstract YOLOv5 is one of the most popular object detection algorithms, which is divided into multiple series according to the control of network depth and width. To realize the deployment of mobile devices or embedded devices, the paper proposes a lightweight aerial image object detection algorithm (LAI-YOLOv5s) based on the improvement of YOLOv5s with a relatively small amount of calculation and parameter and relatively fast reasoning speed. Firstly, to better detect small objects, the paper replaces the minimum detection head with the maximum detection head and proposes a new feature fusion method, DFM-CPFN(Deep Feature Map Cross Path Fusion Network), to enrich the semantic information of deep features. Secondly, the paper designs a new module based on VoVNet to improve the feature extraction ability of the backbone network. Finally, based on the idea of ShuffleNetV2, the paper makes the network more lightweight without affecting detection accuracy. Based on the VisDrone2019 dataset, the detection accuracy of LAI-YOLOv5s on the mAP@0.5 index is 8.3% higher than that of the original algorithm. Compared with other series of YOLOv5 and YOLOv3 algorithms, LAI-YOLOv5s has the advantages of low computational cost and high detection accuracy.

Abstract Background The fall armyworm, Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae), is one of the most destructive invasive pests causing serious damage to maize crops in China. At present, the use of chemical pesticides is the main method to prevent and control S. frugiperda. However, the excessive use of chemical pesticides not only causes toxicity in humans and animals but also pollutes the environment and causes public harm. In this context, the present study aimed to isolate, identify and evaluate the pathogenicity of the native strain of Metarhizium guizhouense against eggs, larvae and pupae of S. frugiperda under laboratory conditions. Results The fungal isolate was identified as M. guizhouense (Xct1) based on its morphological and molecular characteristics by sequencing ITS, β-tubulin and EF1-α gene. Meanwhile, the fungal isolate was highly pathogenic to eggs of the S. frugiperda, at the highest concentration of 1 × 108 spores/ml, the cumulative egg hatching rate was only 8.33% at 10 days post exposure. After 10 days of exposure to the highest concentration of 1 × 108 spores/ml, the LC50 values for the first–fifth larval instars of S. frugiperda were 8.12 × 104, 1.11 × 105, 5.55 × 105, 3.87 × 106 and 1.43 × 107 spores/ml, respectively. Conclusions The pathogenicity of M. guizhouense Xct1 to S. frugiperda larvae gradually decreased with increasing the developmental stages. Metarhizium guizhouense Xct1 was more pathogenic to egg and younger larvae of S. frugiperda. This provides a basis for determining the optimal period of control.

Abstract The aim of this study was to explore a novel method to determine the orientation of acetabular prosthesis in total hip arthroplasty (THA) by refering to the anatomical landmarker of acetabular notches. Forty-one normal developmental hips were included in the present study. The acetabulums were reamed according to standard surgical procedures of THA on life-size 3D printing pelvis models. The inferior edge of acetabular cup were placed (1–5) mm proximal and distal to the proximal line of the anterior and posterior acetabular notches (PLAPAN) respectively to determine cup inclination. The inferior edge of acetabular cup were placed (1–5) mm pronating and supinating around the proximal point of acetabular posterior notch (PPAPN) respectively to determine cup anteversion. The pelvis plain radiographs were took and the inclination and anteversion of the acetabular cup at 22 positions were calculated. In the normal developmental hip, the mean inclination of acetabular prothesis were (35.10 ± 3.22)° and (45.90 ± 2.68)° when the inferior edge of the acetabular cup was 3 mm proximal and 1 mm distal to the PLAPAN. The optimal cup inclination could be obtained when the inferior edge of the acetabular cup was 1 mm proximal to the PLAPAN (the mean inclination was (40.71 ± 2.80)°). The mean anteversion of acetabular prothesis were (10.67 ± 4.55)° and (20.86 ± 4.44)° when the inferior edge of the acetabular cup was 1 mm pronating and 1 mm supinating around the PPAPN. The optimal cup anteversion could be obtained when the inferior edge of the acetabular cup was parallel to the PLAPAN (the mean anteversion was (18.00 ± 1.64)°). The inclination and anteversion of acetabular prosthesis could be determined by refering the anatomical landmarks of acetabular notches, which could help orthopedists to install the acetabular prosthesis quickly and safely in THA.

In-cloud vertical air motion is a key parameter to describe cloud dynamics and lifecycles. Short-wavelength ($Ka$- and $W$-band) radars are often used to observe clouds and extract the vertical air motion from the radar-measured Doppler velocity. However, the vertical air motion retrieval techniques developed using ground-based radar Doppler spectrum may be problematic for airborne cloud radars due to nonideal radar system performance (e.g., receiver saturation) and the Doppler velocity measurement uncertainties caused by aircraft motion, aircraft speed, and the large aircraft motion induced horizontal wind. This article presents a new and simple approach for estimating the in-cloud vertical air motion using airborne $W$-band radar measurements, which is applicable to cloud measurements without precipitation or with weak precipitation. In particular, a power-law relation between cloud and precipitation particle fall speed and attenuation corrected radar reflectivity is established first. Then, the particle fall speeds estimated from radar reflectivity using the established power-law relation are compared with the radar-measured Doppler velocities to derive the vertical air motions. This technique is demonstrated with $W$-band airborne radar measurements from the National Oceanic and Atmospheric Administration Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign from January to February 2020, which was designed to investigate atmospheric shallow convection and air-sea interaction in the tropical North Atlantic east of Barbados. The retrieved in-cloud air motion is compared with results reported in the literature for a nearby domain, suggesting that this simple retrieval technique performs reasonably well. Since this approach is applicable for airborne radar measurements with high-frequency noises, it can be used as an effective tool for investigating the mean profile of vertical air motion.

Abstract Background Microsatellite instability‐high (MSI‐H) subgroup of gastric cancer (GC) is characterized by a high tumor mutational burden, increased lymphocytic infiltration, and enhanced inflammatory cytokines. GC patients with MSI‐H status have a good response to immune checkpoint blockade management. However, heterogeneity within the subtype and the underlying mechanisms of shaping tumor microenvironments remain poorly understood. Methods RNA expression levels and clinical parameters of GC were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The data were analyzed using single‐sample Gene Set Enrichment Analysis (ssGSEA), univariate Cox regression, multivariate Cox regression, and Least Absolute Shrinkage Selection Operator (LASSO) regression. In addition, multiplex immunohistochemistry (mIHC) was used in our clinical cohort for the tumor microenvironment study. Results By ssGSEA and survival analysis, the EMT signaling pathway was identified as a representative pathway, which can stratify the patients with MSI‐H GC with significant survival predictive power. Then, a novel representative EMT‐related five‐gene signature (namely CALU, PCOLCE2, PLOD2, SGCD, and THBS2) was established from EMT signaling gene set, which sensitivity and specificity were further validated in the independent GEO database (GSE62254) cohort for disease outcome prediction. Based on public single‐cell data and in situ immunohistochemistry, we found that most of these five genes were abundantly expressed in cancer‐associated fibroblasts. Furthermore, patients with high or low risk divided by this five‐gene signature exhibited a strong correlation of the distinct patterns of tumor immune microenvironment. By mIHC staining of sections from 30 patients with MSI‐H status, we showed that the patients with better prognoses had the increased infiltration of CD8+ cells in the primary tumoral tissue. Conclusion Our study developed a simple five‐gene signature for stratifying MSI‐H GC patients with survival predictive power.

ImportancePrimary insomnia (PI) has a high global incidence, and effective treatments with fewer side effects are needed. Acupuncture, a treatment used in traditional Chinese medicine, has become increasingly established as a treatment method for PI and is recognized by many physicians and patients. Some evidence has suggested that acupuncture was associated with improvements in objective sleep parameters and might induce changes in some brain regions. Individual studies with limited sample size and low detection thresholds may lead to false positives, and no systematic review of the effects of acupuncture has been conducted in PI.ObjectiveThe aim of this systematic review and coordinate-based meta-analysis was to summarize the literature on fMRI evaluation of patients with PI treated with acupuncture.DesignWe performed a methodical and comprehensive search of multiple publication databases (from inception to December 2022): Web of Science, PubMed, ScienceDirect, Embase, Wan Fang, China National Knowledge Infrastructure, and Chinese Scientific Journal Database. Bias and quality of studies were evaluated by three researchers. Furthermore, a seed-based D-mapping meta-analysis with permutation of subject images (SDM-PSI) was applied to investigate the central mechanisms behind acupuncture treatment at PI. The International Prospective Registry of Systematic Reviews received the protocol for this study. (PROSPERO: CRD42023400086).ResultsThe analysis included 305 patients with PI and 116 healthy controls from 11 studies. SDM-PSI analysis showed that patients with PI exhibited increased amplitudes of regional homogeneity and low-frequency fluctuations in the left superior frontal gyrus (1352 voxels, p = 0.0028), right angular gyrus (14 voxels, p = 0.0457), and cerebellum (12 voxels, p = 0.0446). Acupuncture improved the function of right superior frontal gyrus (1, 404 voxels, p = 0.0123), left inferior frontal gyrus (1068 voxels, p = 0.0088), left inferior temporal gyrus (903 voxels, p = 0.0074), left supramarginal gyrus (888 voxels, p = 0.0113), left precuneus (457 voxels, p = 0.0247), right precuneus (302 voxels, p = 0.0191), left supplementary motor area (82 voxels, p = 0.0354), and right parahippocampal gyrus (28 voxels, p = 0.0379). The brain regions affected by non-acupoint acupuncture were all located in the frontal lobe. The Cochrane risk-of bias tool and MINORS5 were used for quality assessment and the included articles had high performance bias and attrition bias.ConclusionThis coordinate-based meta-analysis found that acupuncture in patients with PI had significant effects on the default mode network, particularly on the frontal lobe and precuneus, and that non-acupoint acupuncture may provide some benefit to frontal brain region function.Systematic review registrationPROSPERO: CRD42023400086.

Bridges are a critical component of transportation infrastructure, playing a vital role in connectivity. The safe operation of bridges demands significant resource and capital investment, particularly as the operation phase is the most extended period in a bridge’s life cycle. Therefore, the efficient allocation of resources and funds is crucial for the maintenance and repair of bridges. This study addresses the need to predict changes in bridge condition over time. The commonly used state-based Markov chain method for bridge condition rating prediction is straightforward but limited by its assumptions of homogeneity and memorylessness. To improve upon this, we propose a novel method that integrates an Elman neural network with a Markov chain to predict the bridge condition rating. Initially, the ReliefF algorithm conducts a sensitivity analysis on bridge features to obtain the importance ranking of these features that affect the bridge condition. Next, six significant features are selected for data classification: bridge age, average daily truck traffic volume, material type, skew angle between bridges and roads, bridge deck structure type, and bridge type. The Elman neural network is then trained to train a prediction model for bridge condition ratings using the classified data, which can predict the condition levels of bridges. The Markov chain’s transition probability matrix is derived using a genetic algorithm to match the deterioration curve predicted by the Elman neural network. This proposed method, when applied to actual bridge data, demonstrates its effectiveness as evidenced by the condition rating of an actual bridge.

Abstract Passive Aquatic Listeners (PALs) have been increasingly deployed to collect minute‐scale surface oceanic rainfall and wind information, with a sampling area similar to the spaceborne sensor footprints. This provides an unprecedented opportunity to validate satellite precipitation products over oceans. This study evaluates the Global Precipitation Climatology Project (GPCP) daily products, including the widely used GPCP v1.3 and the newly released GPCP v3.2, over oceans using 58 PALs as references. The study shows that the GPCP performance depends on time scale, region, and rainfall intensity. The two versions of GPCP perform similarly at multi‐year and monthly scales, while GPCP v3.2 shows substantial improvements in representing rain occurrence and rain intensity at daily scale. The results also highlight the challenge of precipitation measurement over certain regions such as the tropical Southeastern Pacific and extratropical North Pacific, where both versions of the GPCP products perform similarly but exhibit noticeable differences compared to PAL observations.

Accurate nowcasting (short-term prediction, 0–6 h) of high-impact weather, such as landfalling hurricanes and extreme convective precipitation, plays a critical role in natural disaster monitoring and mitigation. A number of nowcasting approaches have been developed in the past few decades, such as optical flow and the tracking radar echoes by correlation system. Most of these mainstream operational techniques are based on radar echo map extrapolation, which determines the velocity and direction of precipitation systems using historical and current radar observations. However, the skill of the traditional extrapolation method decreases rapidly within the first hour. In order to improve nowcasting skill, recent studies have proposed using deep learning methods, such as convolutional recurrent neural network and trajectory gate recurrent unit. But none of these methods focuses on high-impact weather events, and the deep learning models trained based on general precipitation events cannot meet the demand of accurate warnings and decision-making at the scales required for high-impact weather events, such as hurricanes. Using multiradar observations, this article introduces the idea of self-attention and develops a self-attention-based gate recurrent unit (SaGRU) to enhance its generalization capability and scalability in predicting high-impact weather events. In particular, two types of high-impact weather systems, namely, landfalling hurricanes and extreme convective precipitation events, are investigated. Three models are trained based on hurricane events, heavy rainfall (i.e., nonhurricane) events, and all events combined in the southeast United States during 2015 and 2020. The impacts of different data sources on the nowcasting performance are quantified. The evaluation results of nowcasting products show that our SaGRU performs very well in predicting hurricane-induced rainfall. In the new methodology, the data from nonhurricane events are shown to provide useful information in enhancing the nowcasting performance during hurricane events as the model trained by combining all the hurricane and nonhurricane events has the best performance. In addition, this article quantifies the impact of the sequence length of input radar observations on the nowcasting performance, which shows that five consecutive observations are sufficient to obtain a stable model, and even two consecutive observations can produce reasonable results.

In this article, two multitask deep learning models, multilayer perceptron (MLP) and convolutional neural networks (CNNs) are constructed to detect precipitation flags and retrieve precipitation rates simultaneously over the Northwest Pacific area. The retrieval results are verified by integrated multisatellite retrievals for GPM. Compared with using a single payload, the results show the retrieval advantages of incorporating two microwave spaceborne payloads (microwave humidity and temperature sounder and microwave radiation imager). In addition, each separated task in multitasking deep learning models can outperform the single-task models. The MAE values of MLP and CNN are decreased by 0.05 and 0.04 mm/h, respectively. Nevertheless, it is still a challenge to extend the application of these models, which demands extracting the features learned from a certain area to other areas characterized by different precipitation features. A flexible network framework is developed with two transfer learning (TL) methods (freeze and fine-tuning) to tackle this problem. The pretrained models (MLP and CNN) are trained from the Northwest Pacific area, whereas the transferred models are applied to the Northeast Pacific area. The encouraging results show that the performance of the TL model using the fine-tune method has exceeded that of the comparison model without TL, which is trained by four times more data. The main advantage of using TL is the time reduction and efficiency improvements.

This paper presents a method to calculate vertical overlap probability based on the position error caused by pilot control. Firstly, a fuzzy correlation model is established and combined with Quick Access Recorder (QAR) data. The correlation rules between QAR parameters and vertical position error are mined, and the relevance is calculated according to the rules. The results show that the correlation between the control column position and the vertical position error is the strongest. According to the flight dynamics principle, the Vertical Attitude Adjustment Model (VAAM) of an aircraft during the approach process is then established. The Pilot Operating Model (POM), which describes different actions taken by pilots when they encounter different position errors, is also created by employing the concepts of the Markov decision process. The VAAM can restore the process of changing the attitude of the aircraft by the pilot operating column and can simulate the position error of the aircraft in the approach process when combined with the POM. Based on the previous simulated result and considering the overlap condition of the Interval Management (IM) aircraft overlap, the vertical overlap process in the paired approach can then be simulated. We employ MATLAB to carry out numerous simulations. Changing the accuracy of pilot operation in the simulations show that the lower the accuracy, the greater the overlap probability and frequency. However, when the accuracy is between 61% and 73% (61%–73% is the range of operational accuracy based on QAR data analysis), the vertical overlap probability and frequency are maintained at about 5% and 20 flights per hour, respectively.

In this study, we sequenced and characterized the complete mitochondrial genome (mitogenome) of Quasilineus sinicus Gibson, 1990 (Heteronemertea, Nemertea) using Illumina sequencing technology. The circular mitogenome was 16,358 bp in length and comprised 22 transfer RNA genes, 13 protein-coding genes, and two ribosomal RNA genes. Its overall base composition included 20.82% A, 41.06% T, 26.68% G, and 11.44% C; in fact, the mitogenome had a high A + T content of 61.88%. Furthermore, our phylogenetic analysis demonstrated that Paleonemertea, Pilidiophora, and Hoplonemertea were monophyletic groups, and Q. sinicus was most closely related to Iwatanemertes piperata.

Weather radars commonly suffer from the data-missing problem that limits their data quality and applications. Traditional methods for the completion of weather radar missing data, which are based on radar physics and statistics, have shown defects in various aspects. Several deep learning (DL) models have been designed and applied to weather radar completion tasks but have been limited by low accuracy. This study proposes a dilated and self-attentional UNet (DSA-UNet) model to improve the completion of weather radar missing data. The model is trained and evaluated on a radar dataset built with random sector masking from the Yizhuang radar observations during the warm seasons from 2017 to 2019, which is further analyzed with two cases from the dataset. The performance of the DSA-UNet model is compared to two traditional statistical methods and a DL model. The evaluation methods consist of three quantitative metrics and three diagrams. The results show that the DL models can produce less biased and more accurate radar reflectivity values for data-missing areas than traditional statistical methods. Compared to the other DL model, the DSA-UNet model can not only produce a completion closer to the observation, especially for extreme values, but also improve the detection and reconstruction of local-scale radar echo patterns. Our study provides an effective solution for improving the completion of weather radar missing data, which is indispensable in radar quantitative applications.

The detection of multi-class small objects poses a significant challenge in the field of computer vision. While the original YOLOv5 algorithm is more suited for detecting full-scale objects, it may not perform optimally for this specific task. To address this issue, we proposed MC-YOLOv5, an algorithm specifically designed for multi-class small object detection. Our approach incorporates three key innovations: (1) the application of an improved CB module during feature extraction to capture edge information that may be less apparent in small objects, thereby enhancing detection precision; (2) the introduction of a new shallow network optimization strategy (SNO) to expand the receptive field of convolutional layers and reduce missed detections in dense small object scenarios; and (3) the utilization of an anchor frame-based decoupled head to expedite training and improve overall efficiency. Extensive evaluations on VisDrone2019, Tinyperson, and RSOD datasets demonstrate the feasibility of MC-YOLOv5 in detecting multi-class small objects. Taking VisDrone2019 dataset as an example, our algorithm outperforms the original YOLOv5L with improvements observed across various metrics: mAP50 increased by 8.2%, mAP50-95 improved by 5.3%, F1 score increased by 7%, inference time accelerated by 1.8 ms, and computational requirements reduced by 35.3%. Similar performance gains were also achieved on other datasets. Overall, our findings validate MC-YOLOv5 as a viable solution for accurate multi-class small object detection.

Weather radar plays an important role in accurate weather monitoring and modern weather forecasting, as it can provide timely and refined weather forecasts for the public and for decision makers. Deep learning has been applied in radar nowcasting tasks and has exhibited a better performance than traditional radar echo extrapolation methods. However, current deep learning-based radar nowcasting models are found to suffer from a spatial “blurry” effect that can be attributed to a deficiency in spatial variability representation. This study proposes a Spatial Variability Representation Enhancement (SVRE) loss function and an effective nowcasting model, named the Attentional Generative Adversarial Network (AGAN), to alleviate this blurry effect by enhancing the spatial variability representation of radar nowcasting. An ablation experiment and a comparison experiment were implemented to assess the effect of the generative adversarial (GA) training strategy and the SVRE loss, as well as to compare the performance of the AGAN and SVRE loss function with the current advanced radar nowcasting models. The performances of the models were validated on the whole test set and inspected in two storm cases. The results showed that both the GA strategy and SVRE loss function could alleviate the blurry effect by enhancing the spatial variability representation, which helps the AGAN to achieve better nowcasting performance than the other competitor models. Our study provides a feasible solution for high-precision radar nowcasting applications.

Intercropping orchards with cover crops is an important practice for achieving sustainable soil management. However, little research has addressed the development of a soil quality index (SQI) to evaluate cover crop effects on orchard soil quality. The aim of this study was to ascertain whether cover cropping improves soil quality and fruit yield of Goji (Lycium barbarum L.) while reducing or replacing organic fertilizer application. The main treatments were the traditional management of L. barbarum as a monocrop (M) and intercropping Goji with radish (Raphanus sativus L.) as an annual cover crop (I). Within the main treatments, different levels of organic fertilizer were applied at 0 kg·plant−1 (M0), 2 kg·plant−1 (M1), and 4 kg·plant−1 (M2). After six years of planting, we analyzed the changes in soil quality caused by cover cropping with different organic fertilizer levels based on the SQI method. Goji yields were used for validation of the SQI derived from a minimum data set of soil quality indicators. In contrast with traditional monocropping, cover cropping increased soil total nitrogen, available nitrogen, and available phosphorus contents (by 78.60%, 30.30%, and 138.08%, respectively). There were also increased microbial biomass carbon and nitrogen contents (by 79.01% and 184.01%, respectively), enhanced urease and sucrase activities (by 41.02% and 56.81%, respectively), and reduced bulk density (by 1.92%) in the soil as a result of cover cropping. Compared with IM0 treatment, soil microbial biomass carbon and nitrogen contents considerably increased under IM1 treatment, whereas soil available nitrogen and potassium contents as well as electrical conductivity increased under IM2 treatment. The SQI, which varied among treatments in the order IM1 > IM2 > MM2 > MM1 > IM0 > MM0, was positively correlated with Goji yield. From the soil quality and Goji yield perspective, cover cropping with a medium level of organic fertilizer is the optimal soil management practice for the L. barbarum planting system in arid areas of Ningxia, Northwest China.

One approach used in GIScience to investigate movement data is adopting time-geography theory and its important principles/methods: space-time paths and space-time cube. Thus, the movement of a moving object can be represented as a 3D polyline in a space-time cube. However, with the advent of larger movement datasets, this type of display can easily become cluttered and incomprehensible. In this article, we propose a new space-time aggregation algorithm, i.e., directional-layered space-time densities (DLSTDs), to solve the problem of trajectory clutter in a space-time cube. The approach is an extension of the pixel (2D raster cell) density around a point in two-dimensional space to the voxel density around a trajectory line segment in three-dimensional space-time (x-y+t). In the algorithm, the horizontal or vertical voxel layers are first calculated around the trajectory based on the direction of each trajectory line segment. Subsequently, the voxel density is calculated by substituting the distance of the voxel from the trajectory segment into a Gaussian-based attenuation function. Finally, the resulting layered densities are presented using volume rendering techniques. We demonstrate the DLSTDs on 30-day movement data of transport trucks in a mine in China and compare the algorithm with two other algorithms. At the end of the article, we summarize the research and consider further developments of the proposed approach.

In this article, a spectral polarimetric filter named moving spectral depolarization ratio (MsDR) filter is proposed to mitigate the effects of clutter and noise for dual-polarization weather radar without cross-polar measurements. The filter is based on a spectral polarimetric variable termed as spectral depolarization ratio (sDR). sDR can be estimated by any polarimetric weather radar, making the MsDR filter widely applicable. Taking advantage of the spectral polarimetric features and the range-Doppler continuity of precipitation, the MsDR filter is implemented in the range-Doppler spectrogram. The performance of the MsDR filter is assessed using data collected by 1) an X-band full-polarimetric radar interfered by narrow-band clutter (both stationary and moving) and sidelobe wind turbine clutter; 2) a C-band operational dual-polarization radar affected by radio frequency interference. In addition, the implementation of the MsDR filter is straightforward, and the computational complexity is relatively low. Hence, the MsDR filter has great potential to be applied in real-time for operational radar systems at different frequencies.

An EF1 tornado associated with tropical cyclone (TC) Ewiniar hit Dali Town, Foshan, Guangdong Province at 06:03 UTC on June 8, 2018, and the first special tornado warning was issued to five towns at 05:05 UTC. This article utilizes minute-scale observations from an X-band dual-polarization radar and measurements from an S-band Doppler radar to resolve the polarization characteristics associated with this tornado. In addition, second-scale atmospheric pressure data obtained from micropressure gauge and NCEP FNL (Final) Operational Global Analysis data are used to investigate the synoptic conditions and features of gravity waves (GWs). The conspicuous features of the descending reflectivity core, Doppler velocity couplet, Z$_{\text{DR}}$ arc, K$_{\text{DP}}$ foot, and the separation of the Z$_{\text{DR}}$ arc and K$_{\text{DP}}$ foot are detailed to quantify the tornadic evolution. The amplitude fluctuation of the GWs suddenly increased to 77.3 Pa, 2 h before the tornado occurred. Two focus regions with K$_{\text{DP}}$ values greater than 6$^\circ$/km are discussed by combining the Doppler velocity couplet and Z$_{\text{DR}}$ arc. The separation distance of the Z$_{\text{DR}}$ arc and K$_{\text{DP}}$ foot was approximately 2.1 km. The appearance of these features may be indicative of fundamental processes intrinsic to tornado storms.

ObjectiveWe aimed to establish an MRI radiomics model and a Delta radiomics model to predict tumor retraction after induction chemotherapy (IC) combined with concurrent chemoradiotherapy (CCRT) for primary nasopharyngeal carcinoma (NPC) in non-endemic areas and to validate its efficacy.MethodsA total of 272 patients (155 in the training set, 66 in the internal validation set, and 51 in the external validation set) with biopsy pathologically confirmed primary NPC who were screened for pretreatment MRI were retrospectively collected. The NPC tumor was delineated as a region of interest in the two sequenced images of MRI before treatment and after IC, followed by radiomics feature extraction. With the use of maximum relevance minimum redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) algorithms, logistic regression was performed to establish pretreatment MRI radiomics and pre- and post-IC Delta radiomics models. The optimal Youden’s index was taken; the receiver operating characteristic (ROC) curve, calibration curve, and decision curve were drawn to evaluate the predictive efficacy of different models.ResultsSeven optimal feature subsets were selected from the pretreatment MRI radiomics model, and twelve optimal subsets were selected from the Delta radiomics model. The area under the ROC curve, accuracy, sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV) of the MRI radiomics model were 0.865, 0.827, 0.837, 0.813, 0.776, and 0.865, respectively; the corresponding indicators of the Delta radiomics model were 0.941, 0.883, 0.793, 0.968, 0.833, and 0.958, respectively.ConclusionThe pretreatment MRI radiomics model and pre- and post-IC Delta radiomics models could predict the IC-CCRT response of NPC in non-epidemic areas.

Abstract Background Since the first case of a coronavirus disease 2019 (COVID-19) infection pneumonia was detected in Wuhan, China, a series of confirmed cases of the COVID-19 were found in Southwest China. The aim of this study was to describe the imaging manifestations of hospitalized patients with confirmed COVID-19 infection in southwest China. Methods In this retrospective study, data were collected from 131 patients with confirmed coronavirus disease 2019 (COVID-19) from 3 Chinese hospitals. Their common clinical manifestations, as well as characteristics and evolvement features of chest CT images, were analyzed. Results A total of 100 (76%) patients had a history of close contact with people living in Wuhan, Hubei. The clinical manifestations of COVID-19 included cough, fever. Most of the lesions identified in chest CT images were multiple lesions of bilateral lungs, lesions were more localized in the peripheral lung, 109 (83%) patients had more than two lobes involved, 20 (15%) patients presented with patchy ground glass opacities, patchy ground glass opacities and consolidation of lesions co-existing in 61 (47%) cases. Complications such as pleural thickening, hydrothorax, pericardial effusion, and enlarged mediastinal lymph nodes were detected but only in rare cases. For the follow-up chest CT examinations (91 cases), We found 66 (73%) cases changed very quickly, with an average of 3.5 days, 25 cases (27%) presented absorbed lesions, progression was observed in 41 cases (46%), 25 (27%) cases showed no significant changes. Conclusion Chest CT plays an important role in diagnosing COVID-19. The imaging pattern of multifocal peripheral ground glass or mixed consolidation is highly suspicious of COVID-19, that can quickly change over a short period of time.

Accurate and continuous rainfall monitoring is essential for effective water resources management, especially in arid and semi-arid regions such as the United Arab Emirates (UAE). Significant spatio-temporal precipitation variation in the UAE necessitates the use of the latest techniques to measure rainfall intensity accurately. This study investigates the consistency and applicability of four satellite precipitation products, namely PERSIANN, PERSIANN-CCS, PERSIANN-CDR, and PDIR-Now, over the UAE. Daily time series data from 2011 to 2020 were analyzed using various statistical measures and climate indices to develop the belief in the products and for their inter-comparison. The analysis revealed that the average probability of detection (POD) for PDIR and CDR was the highest, with values ranging from 0.7–0.9 and 0.6–0.9, respectively. Similarly, CDR has a better Heidke Skill Score (HSS) with an average value of 0.26. CDR outperformed its counterparts with an average correlation coefficient value of 0.70 vs. 0.65, 0.40, and 0.34 for PDIR, CCS, and PERSIANN, respectively. Precipitation indices analysis revealed that all the products overestimated the number of consecutive wet days by 15–20%, while underestimating consecutive dry days by 5–10%. The quantitative estimations indicate that all the products were matching with the gauge values during the wet months (January–April), while they showed significant overestimation during the dry months. CDR and PDIR were in close agreement with the gauge data in terms of maximum daily rainfall with an error of less than 10% for both products. As compared to others, PERSIANN-CDR provided better estimates, particularly in terms of capturing extreme rainfall events and spatial distribution of rainfall. This study provides the first comprehensive evaluation of four PERSIANN family products based on recent daily rainfall data of UAE. The findings can provide future insights into the applicability and improvement of PERSIANN products in arid and semi-arid regions.

We generalize an approach to studying the quantum state tomography (QST) of open systems in terms of the dynamical map in Kraus representation within the framework of dynamic generation of informationally complete positive operator-valued measures. As applications, we use the generalized approach to theoretically study the QST of qubit systems in the presence of nonequilibrium environments which exhibit nonstationary and non-Markovian random telegraph noise statistical properties. We derive the time-dependent measurement operators for the quantum state reconstruction of the single qubit and two-qubit systems in terms of the polarization operator basis. It is shown that the behavior of the time-dependent measurement operators is closely associated with the dynamical map of the qubit systems.

A uniformity index for the axis ratios (Uar) derived from dual polarization weather radar data is proposed for raindrop area identification and analysis. The derivation of this new parameter is based on radar scattering simulations and assumptions. Uar is between 0 and 1 and can be calculated from the differential reflectivity (ZDR) and the copolar correlation coefficient (ρhv), which reflects the uniformity of the axis ratio (r) of the particle group. For raindrops, Uar is close to 1 under ideal conditions, but is clearly different from that of ice particles whose value is close to 0. Studies conducted during two convective weather events observed by X-band and S-band radar are presented to show the Uar features. In convective areas, high Uar presents a U-shaped vertical structure. One branch corresponds to the ZDR column, while the other branch is located at the rear of the convective cloud zone and is lower in altitude, representing the process of ice particles melting into raindrops and then being transported upward by a strong updraft. In stratiform cloud areas, a more than 95% overall identification ratio is obtained when the threshold of Uar is set to 0.2~0.3 for discriminating rain layers.

Recent advancements in remote sensing have led to the development of several useful technologies that would significantly improve our understanding of atmospheric sciences. The ability to identify atmospheric conditions and determine the possibility and intensity of rainfall over a specific location represents one of the most important advantages. However, the use of remote sensing to measure precipitation in arid regions has revealed significant disparities due to a mixture of climatic and terrestrial factors. The objective of this study is to assess the precipitation consistency, variability, and concentration over the UAE using four multi-satellite remote sensing products, namely CHIRPS, CMORPH, GPM-IMERG, and the PERSIANN-CDR, considering daily rainfall data from 50 rain gauges for the period from 2004 through 2020. The study area is divided into various geomorphological regions to assess the accuracy of the products in different regions. Results reveal that the products with a finer spatial resolution such as CHIRPS and CMORPH are better in terms of annual and daily average values. CHIRPS and GPM-IMERG demonstrated better POD values of 0.80 and 0.78, respectively, while CMORPH and the PERSIANN-CDR showed POD values of 0.72 and 0.44, respectively. The correlation and error estimate analysis showed that the performance of different products varies in each region. The PERSIANN-CDR registered the highest correlation of 0.8 for the East Coast, while for other regions it could not correlate well. IMERG and CHIRPS were able to exhibit a good correlation value (up to 0.8) with the gauge observations. Precipitation concentration and variability analysis revealed that GPM-IMERG represents a better alternative to gauge data. It is concluded that multiple hydro-climatological measures should be utilized to assess the effectiveness of satellite products and select the best product for specific studies.

In consideration of secure and convenient, face gains increasing attention in variety of fields during the past decades. Since human face is most accessible from our daily life and preserves the richest information, face based biometric systems are widely used in person authentication applications. However, face recognition systems are always challenged by face spoofing attacks. Although, researchers have proposed many face spoofing detection methods, which have achieved great performances, we aim to develop a method to counter face spoofing, which combines the face detection stage and face spoofing detection stage together. In this paper, we design face anti-spoofing region-based convolutional neural network (FARCNN), based on improved Faster region-based convolutional neural network (R-CNN) framework. Motivated by face detection, we regard the face spoofing detection as a three-way classification to distinguish real face, fake face and background. We extend the typical Faster R-CNN scheme by optimizing several important strategies, including roi-pooling feature fusion and adding Crystal Loss function to the original multi-task loss function. In addition, an improved Retinex based LBP is presented to handle the different illumination conditions in face spoofing detection. Finally, these two detectors are further cascaded and achieve promising performances on the benchmark databases: CASIA-FASD, REPLAY-ATTACK and OULU-NPU. Besides, for the purpose of verifying the generalization capacity of the proposed cascade detector, we perform experiments on cross-databases and the results testify the effectiveness of our proposed method.

As a frontier research topic in the field of scientific visualization, trajectory data visualization extracts valuable patterns and knowledge from trajectory data for decision support via spatiotemporal trajectory visualization techniques. We propose the concept of multivariate trajectory data and interpret two categories of attributes that are based on geographical space and abstract space. Properly analyzing multivariate trajectory data depends on many factors such as visualization task and data sparsity. Therefore, we generalize rich interactions to explore the evolution of trajectory events and transform the issue into a more intelligibly perceptual task, which derives our discussion regarding advantages and limitations of the analytical methods. This review endeavors to provide a quick and thorough cognition and comprehension with regard to fundamental features and numerous outcomes in visual analytics for trajectory data, seeks to promote comparisons and criticisms about the descriptive framework for multivariate spatiotemporal trajectory data visualization, and aims to encourage the exploration of emerging methods and techniques.

We theoretically study the non-Markovian disentanglement dynamics of a two-qubit system coupled to nonequilibrium environments with nonstationary and non-Markovian random telegraph noise statistical properties. The reduced density matrix of the two-qubit system can be expressed as the Kraus representation in terms of the tensor products of the single qubit Kraus operators. We derive the relation between the entanglement and nonlocality of the two-qubit system which are both closely associated with the decoherence function. We identify the threshold values of the decoherence function to ensure the existences of the concurrence and nonlocal quantum correlations for an arbitrary evolution time when the two-qubit system is initially prepared in the composite Bell states and the Werner states, respectively. It is shown that the environmental nonequilibrium feature can suppress the disentanglement dynamics and reduce the entanglement revivals in non-Markovian dynamics regime. In addition, the environmental nonequilibrium feature can enhance the nonlocality of the two-qubit system. Moreover, the entanglement sudden death and rebirth phenomena and the transition between quantum and classical nonlocalities closely depend on the parameters of the initial states and the environmental parameters in nonequilibrium environments.

Globally consistent long-term radar measurements are imperative for understanding the global climatology and potential trends of convection. This study investigates the consistency of vertical profiles of reflectivity (VPR) and 20-dBZ echo-top height (Topht20) between the two precipitation radars onboard the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement (GPM) satellites. Results show that VPR coincidently observed by the TRMM’s and GPM’s Ku-band radar agree well for both convective and stratiform precipitation, although certain discrepancies exist in the VPR of weak convection. Topht20s of the TRMM and GPM are consistent either for coincident events, or latitudinal mean during the 7-month common period, all with biases within the radar range resolution (0.1–0.2 km). The largest difference in the Topht20 between the TRMM’s and GPM’s Ku-band radar occurs in shallow precipitation. Possible reasons for this discrepancy are discussed, including sidelobe clutter, beam-mismatch, non-uniform beam filling, and insufficient sampling. Finally, a 23-year (1998–2020) climatology of Topht20 has been constructed from the two spaceborne radars, and the global mean Topht20 time series shows no significant trend in convective depth during the last two decades.

The Coronavirus Disease 2019 (COVID-19) has proved a globally prevalent outbreak since December 2019. As a focused country to alleviate the epidemic impact, China implemented a range of public health interventions to prevent the disease from further transmission, including the pandemic lockdown in Wuhan and other cities. This paper establishes China's mobility network by a flight dataset and proposes a model without epidemiological parameters to indicate the spread risks through the network, which is termed as epidemic strength. By simply adjusting an intervention parameter, traffic volumes under different travel-restriction levels can be simulated to analyze how the containment strategy can mitigate the virus dissemination through traffic. This approach is successfully applied to a network of Chinese provinces and the epidemic strength is smoothly interpreted by flow maps. Through this node-to-node interpretation of transmission risks, both overall and detailed epidemic hazards are properly analyzed, which can provide valuable intervention advice during public health emergencies.

As an important component of the Earth system, precipitation plays a vital role in regional and global water cycles. Based on Microwave Humidity and Temperature Sounder (MWHTS) onboard FY-3D satellite, four machine learning models, random forest regression (RFR), support vector machine (SVM), multilayer perceptron (MLP), and gradient boosting regression tree (GBRT), are implemented to retrieve precipitation rate, and verified with Integrated Multi-satellite Retrievals for GPM (IMERG). This paper determines the optimal hyperparameters of the machine models and proposes three linear combinations of MWHTS channels (183.31 ± 1.0–183.31 ± 3.0 GHz, 183.31 ± 1.0–183.31 ± 7.0 GHz, and 183.31 ± 3.0–183.31 ± 7.0 GHz), which can better characterize precipitation of different intensities. With the inclusion of three linear combinations, the performances of all four machine learning models are significantly improved. It is concluded that the RFR and GBRT have the best retrieval accuracy. Over ocean, the MSE, MAE, and R2 values of precipitation estimates using RFR are 1.75 mm/h, 0.44 mm/h, and 0.80, respectively, and are 1.80 mm/h, 0.45 mm/h, and 0.78 for GBRT. Simultaneously, this paper analyzes the retrieval results from the perspective of the different rain rates and temporal matching difference between MWHTS and IMERG data. The RFR and GBRT also maintain the best retrieval accuracy under the condition of Gaussian noise, indicating the relatively strong robustness and antinoise performance of ensemble learning models for precipitation retrieval.

Water is an important source for humankind. However, the amount of available clean water has rapidly reduced worldwide. To combat this issue, the solar-energy-driven evaporation technique is newly proposed to produce clean water. Here, biochar derived from sorghum stalk with a multi-level pore bundle structure is utilized to fabricate a solar-driven evaporator for the first time. The biochar displays rapid water transfer and low thermal conductivity (ca. 0.0405 W m−1 K−1), which is vitally important for such an application purpose. The evaporation rate and energy conversion efficiency of the solar evaporator based on carbonized sorghum stalk can achieve up to 3.173 kg m−2 h−1 and 100%, respectively, which are better than most of the previously reported biomass materials. Furthermore, the carbonized sorghum stalk also displays good resistance to salt crystallization, anti-acidic/basic, and organic pollutants by producing drinking water using seawater, acidic/basic waste water, and organic polluted water, respectively. The direct application of processed water in food production was also investigated. The present solar steam evaporator based on the carbonized sorghum stalk has the potential to create practical drinking water production by using various water sources.

A competitive fluorescence immunoassay for the quantitative detection of 2-amino-3-methylimidazo [4,5-f] quinoline (IQ) in pan-fried meat patties was developed, using magnetic nanoparticles coupled with coating antigen as the capture probe and anti-IQ antibody coupled with NaYF4: Yb, Er upconversion nanoparticles as the signal probe. Under optimal conditionals, the wide detection range for IQ in phosphate buffer saline is from 0.01 to 100 μg·L−1 (R2 = 0.991) with a detection limit of 0.007 μg·L−1. This proposed method has been applied to detect IQ in two different types of pan-fried meat patties at varying frying times, and the IQ content in chicken patties and fish patties are 2.11–3.47 μg·kg−1 and 1.35–2.85 μg·kg−1, respectively. These results are consistent with that of the ultraperformance liquid chromatography-tandem mass spectrometry. In summary, this method can serve as a sensitive and specific test tool for the determination of IQ in processed meat.