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Abstract Uncontrollable zinc (Zn) plating and hydrogen evolution greatly undermine Zn anode reversibility. Previous electrolyte designs focus on suppressing H2O reactivity, however, the accumulation of alkaline byproducts during battery calendar aging and cycling still deteriorates the battery performance. Here, we present a direct strategy to tackle such problems using a strong Brønsted acid, bis(trifluoromethanesulfonyl)imide (HTFSI), as the electrolyte additive. This approach reformulates battery interfacial chemistry on both electrodes, suppresses continuous corrosion reactions and promotes uniform Zn deposition. The enrichment of hydrophobic TFSI– anions at the Zn|electrolyte interface creates an H2O-deficient micro-environment, thus inhibiting Zn corrosion reactions and inducing a ZnS-rich interphase. This highly acidic electrolyte demonstrates high Zn plating/stripping Coulombic efficiency up to 99.7% at 1 mA cm–2 ( > 99.8% under higher current density and areal capacity). Additionally, Zn | |ZnV6O9 full cells exhibit a high capacity retention of 76.8% after 2000 cycles.

This article investigates the complex dynamics of ammonia as an alternative fuel within the ambit of China's strategic initiatives aimed at attaining carbon neutrality. Given ammonia's distinctive characteristics as a fuel, this analysis delves into the nascent development status and the multifaceted challenges confronting this sector within China. Through a comprehensive examination of the plethora of new energy policies promulgated by the Chinese government, this article underscores the initial phase of accelerated growth in the ammonia fuel domain. The imposition of carbon emission restrictions, coupled with the momentum generated by technological innovations, positions green ammonia as an efficacious solution for hydrogen storage and utilization, thereby fostering a symbiotic relationship with the hydrogen energy industry chain. The proactive policy framework adopted by China significantly enhances the pivotal role of ammonia fuel technology, making it one of the principal technological pathways for China to realize its carbon neutrality ambitions in the future.

Summary: The electrochemical conversion of carbon dioxide (CO2) to valuable chemicals is gaining significant attention as a pragmatic solution for achieving carbon neutrality and storing renewable energy in a usable form. Recent research increasingly focuses on designing electrocatalysts that specifically convert CO2 into ethanol, a desirable product due to its high-energy density, ease of storage, and portability. However, achieving high-efficiency ethanol production remains a challenge compared to ethylene (a competing product with a similar electron configuration). Existing electrocatalytic systems often suffer from limitations such as low energy efficiency, poor stability, and inadequate selectivity toward ethanol. Inspired by recent progress in the field, this review explores fundamental principles and material advancements in CO2 electroreduction, emphasizing strategies for ethanol production over ethylene. We discuss electrocatalyst design, reaction mechanisms, challenges, and future research directions. These advancements aim to bridge the gap between current research and industrialized applications of this technology.

Abstract Diabetes mellitus (DM) is a common chronic disease affecting humans globally. It is characterized by abnormally elevated blood glucose levels due to the failure of insulin production or reduction of insulin sensitivity and functionality. Insulin and glucagon‐like peptide (GLP)‐1 replenishment or improvement of insulin resistance are the two major strategies to treat diabetes. Recently, optogenetics that uses genetically encoded light‐sensitive proteins to precisely control cell functions has been regarded as a novel therapeutic strategy for diabetes. Here, we summarize the latest development of optogenetics and its integration with synthetic biology approaches to produce light‐responsive cells for insulin/GLP‐1 production, amelioration of insulin resistance and neuromodulation of insulin secretion. In addition, we introduce the development of cell encapsulation and delivery methods and smart bioelectronic devices for the in vivo application of optogenetics‐based cell therapy in diabetes. The remaining challenges for optogenetics‐based cell therapy in the clinical translational study are also discussed.

The complex underground public space brings challenge on pathfinding cognition. This research took Luoxiong Road Station in Wuhan as experimental site, using eye movement and electroencephalogram (EEG) synchronous detection, through pathway analysis and space-time fault method, exploring relationship between pathfinding cognition and complex underground public space. It was found that pathfinding habits and pathfinding decision-making were mainly affected by personal factors and environmental factors, respectively. Pathfinders influenced by personal factors, had behaviors such as left-walking or right-walking tendency, and the ratio of brain wave β/α was low (impression ratio ≤1). Pathfinders influenced by environmental factors, interested in the other side situation, vertical traffic and so on, and the ratio of brain wave β/α was high (impression ratio >1), with the frontal and parietal occipital lobe regions of brain being more active. These conclusions provided theoretical and data supplements for underground rail transit environment optimizing design in future.

Abstract Background and aim Eating Disorders (ED) result in impaired well-being, but there exist an insufficient number of studies that have focused on the influence of sex and sexual orientation disparities within ED behaviors. Thus, we aimed to investigate ED behaviors among male and female adolescents with different sexual orientations in a school sample to understand prevalence and correlates of different ED behaviors. Method Data was analysed from 11,440 Chinese school adolescents with a mean age of 14.74 years (SD = 1.46). Reported data was gathered on sociodemographic information including sexual orientation, ED behaviors, health factors (reported health, cognitive function), mental health factors (depression, anxiety, suicidal ideation, non-suicidal self-injurious behavior), and social functioning (school bully victimization, and school bully perpetration). Logistic regression models were used to estimate the associations with ED behaviors, using the heterosexual orientation as the reference group as they are the majority. Results Compared to female adolescents, male adolescents reported lower anxiety symptoms (t = − 12.39, p

Objective Although Caspar cervical retractor system (CCRS) is commonly utilized in anterior cervical decompression and fixation (ACDF), most urban hospitals still use both traditional S retractor and CCRS in conventional anterior cervical surgeries. Related data to evaluate the effect are required to be reported. The aim of this study is to compare the efficacy between using the traditional cervical S retractor and CCRS in anterior cervical decompression and fixation ACDF. Methods The retrospective study that total 360 patients received ACDF using different retractors (traditional S retractor or CCRS) were enrolled in this study from January 2010 to January 2020. Width change rate of cervical prevertebral soft tissue, throat symptoms, and subjective experiences of the operating surgeons were evaluated by t‐test or analysis of variance (ANOVA) respectively. Results The width change rate of prevertebral soft tissue was significantly higher in the S retractor group than that of the CCRS group both in single segment group (40.9% vs 20.8%, P 0.05). The incidence rates of dysphagia, dyspnea, and throat discomfort in the traditional S retractor group were significantly higher compared to the CCRS group (P 0.05), as well as the width change rate of anterior soft tissue and the DNRS score (P > 0.05). Conclusion CCRS was superior compared to the traditional S retractor in reducing the postoperative complications and the postoperative fatigue of surgeon. Meanwhile, the width change rate of prevertebral soft tissue was not related to operative time and DNRS score.

Compared with the interior environment, there exists a lot of noise and dust in an underground tunnel, and the light is unstable, which is difficult to track by the direct method. Moreover, errors in line segment projection and line feature drift under the influence of light can lead to significant deviations in the odometry. Consequently, to improve the accuracy and robustness of visual odometry, a point-line feature stereo visual odometry system is proposed in this paper. The system combines ORB features and LSD line features; using the angle relationship of line projection, we propose a new method for calculating the reprojection error of line features, reconstruct the reprojection model based on line features, and construct a new reprojection error model based on point-line features, which adds an angle constraint to the reprojection of line features and solves the instability caused by line projection error. It is shown in our experiments on the KITTI, New College dataset, that the translation error of our system is reduced by about 40% on average compared to PLVO, with a reduction in relative positioning error. Experiments in the hallway and underground tunnel environments have shown that the maximum positioning error of our system has been reduced by 75% in hallways and by 56.7% in an underground tunnel. Therefore, our algorithm effectively improves the localization accuracy and is more advantageous in low-texture environments.

To address the problem of system instability during vehicle low-speed driving, we propose improving the visual odometer using ORB (Oriented FAST and Rotated BRIEF) features. The homogeneity of ORB features leads to poor corner point properties of some feature points. When the environmental texture lacks richness, it leads to poor matching performance and low matching accuracy of the feature points. We solve the problem of the corner point properties of feature points using weight calculation for regions with different textures. When the vehicle speed is too low, the continuous frames captured by the camera will overlap significantly, causing large fluctuations in the system error. We use motion model estimation to solve this problem. Meanwhile, experimental validation using the KITTI dataset achieves good results.

In the present study, sixteen novel RNA mycoviruses co-infecting a single strain of Rhizoctonia zeae (strain D40) were identified and molecularly characterized using metatranscriptome sequencing combined with a method for rapid amplification of cDNA ends. The fungal strain was isolated from diseased seedlings of sugar beet with damping-off symptoms. Based on genome analysis and phylogenetic analysis of amino acid sequences of RNA-dependent RNA polymerase, the sixteen mycoviruses associated with strain D40 contained three genome types with nine distinct lineages, including positive single-stranded RNA (Hypoviridae, Yadokariviridae, Botourmiaviridae, and Gammaflexiviridae), double-stranded RNA (Phlegiviridae, Megabirnaviridae, Megatotiviridae, and Yadonushiviridae), and negative single-stranded RNA (Tulasviridae), suggesting a complex composition of a mycoviral community in this single strain of R. zeae (strain D40). Full genome sequences of six novel mycoviruses and the nearly full-length sequences of the remaining ten novel mycoviruses were obtained. Furthermore, seven of these sixteen mycoviruses were confirmed to assemble virus particles present in the R. zeae strain D40. To the best of our knowledge, this is the first detailed study of mycoviruses infecting R. zeae.

Pedestrian avoidance behavior often occurs in underground public spaces that connect urban rail transit and commercial complexes. This study proposes a co-monitoring method based on eye movement and electroencephalogram (EEG) to study pedestrian avoidance behavior in a real environment, taking the underground public space of the commercial complex of the Luoxiong Road railway station in Wuhan City as an experimental site. It is found that pedestrian avoidance behavior is influenced by both personal and environmental factors. The pedestrian avoidance behavior is a comprehensive response to the evaded person and the current environment. The personal factors mainly affect the pedestrian avoidance mode, while the environmental factors mainly affect the frequency of avoidance behavior. Avoidance patterns are related to the tendency of Chinese pedestrians to walk right, and the frequency of avoidance behavior is related to the complexity of the intersection of pedestrian walking routes within the environment, so avoidance behavior can be reduced by using spaces with good spatial connectivity in the design of underground public spaces. These findings provide theoretical support and data supplement for future environmental design optimization of underground public spaces.

Cyprinid herpesvirus 2 (CyHV-2) infects gibel carp (Carassius auratus gibelio) and causes severe losses. Microbiota in animal guts involves nutrition intake, development, immunity, and disease resistance. However, the relationship between gibel carp gut microbiota and CyHV-2 infection is not well known. Herein, we analyzed the gut microbiota composition and metabolite profiles in CyHV-2-infected and -uninfected fish using high-throughput sequencing and gas chromatography/mass spectrometry. Results showed that CyHV-2 infection significantly changed gut microbiota and metabolite profiles (p < 0.05). High-throughput sequencing demonstrated that the relative abundance of Aeromonas in the midgut increased dramatically while Cetobacterium decreased. Time-course analysis showed that the number of Aeromonas in the midgut of infected fish increased more than 1,000 times within 5 days post infection. Metabolome analysis illustrated that CyHV-2 infection significantly altered 24 metabolites in the midgut of gibel carp, annotating to the anomaly of digestion and metabolisms of amino acids, carbohydrates, and lipids, such as tryptophan (Trp) metabolism. The Mantel test demonstrated that gut microbiota and metabolite profiles were well related (r = 0.89). Furthermore, Trp metabolism responded to CyHV-2 infection closely was taken as one example to prove the correlation among CyHV-2 infection, metabolites and microbiota in the midgut, and host immunity. Results showed that modulating Trp metabolism could affect the relative abundance of Aeromonas in the midgut of fish, transcription of antiviral cytokines, and CyHV-2 infection. Therefore, we can conclude that CyHV-2 infection significantly perturbed the gut microbiome, disrupted its’ metabolic functions, and caused the proliferation of the opportunistic pathogen Aeromonas. This study also suggests that modulation of the gut microbiome will open a therapeutic opportunity to control CyHV-2 infection in gibel carp.

Abstract Background The existing studies show that circRNAs can be used as a biomarker of diseases and play a prominent role in the treatment and diagnosis of diseases. However, the relationships between the vast majority of circRNAs and diseases are still unclear, and more experiments are needed to study the mechanism of circRNAs. Nowadays, some scholars use the attributes between circRNAs and diseases to study and predict their associations. Nonetheless, most of the existing experimental methods use less information about the attributes of circRNAs, which has a certain impact on the accuracy of the final prediction results. On the other hand, some scholars also apply experimental methods to predict the associations between circRNAs and diseases. But such methods are usually expensive and time-consuming. Based on the above shortcomings, follow-up research is needed to propose a more efficient calculation-based method to predict the associations between circRNAs and diseases. Results In this study, a novel algorithm (method) is proposed, which is based on the Graph Convolutional Network (GCN) constructed with Random Walk with Restart (RWR) and Principal Component Analysis (PCA) to predict the associations between circRNAs and diseases (CRPGCN). In the construction of CRPGCN, the RWR algorithm is used to improve the similarity associations of the computed nodes with their neighbours. After that, the PCA method is used to dimensionality reduction and extract features, it makes the connection between circRNAs with higher similarity and diseases closer. Finally, The GCN algorithm is used to learn the features between circRNAs and diseases and calculate the final similarity scores, and the learning datas are constructed from the adjacency matrix, similarity matrix and feature matrix as a heterogeneous adjacency matrix and a heterogeneous feature matrix. Conclusions After 2-fold cross-validation, 5-fold cross-validation and 10-fold cross-validation, the area under the ROC curve of the CRPGCN is 0.9490, 0.9720 and 0.9722, respectively. The CRPGCN method has a valuable effect in predict the associations between circRNAs and diseases.

Political participation has become an important topic in contemporary political thinking. Western scholars’ views that reflect experiences in Western societies have become predominant theoretical articulations on political participation. This study offers a critical review of the conceptual frameworks of political participation in democratic and non-democratic societies. Drawing on previous research, it has developed a new definition and typology of political participation in China. It eschews voluntarism in the Western definition of political activities and participation to creatively propose a well-developed definition of political participation in China for which it constructs a ladder model. This study summarises Chinese political participation according to four categories: non-participation, tokenism, active participation, and radical participation. This study proposes a typology that motivates us to think of many issues, including online interactions. This study makes a much-needed contribution to the literature on the theoretical development of political participation in China.

China’s Guizhou is a typical karst landscape province with high production of chili pepper, but it is mostly planted in mountainous areas, while manual harvesting of chili pepper has the deficiencies of high labor intensity, low efficiency, and high labor cost; in addition, there is no harvesting machinery applicable to the dense planting pattern of the chili pepper in mountainous areas in China. The fully hydraulic mountain track-based self-propelled pepper harvester 4JZ-1.0A is designed to solve the above problems. The pepper harvester spiral comb picking head is an important part of the whole machine design, the design of the hydraulic system of the working circuit of the picking head is the key to realizing the hydraulic control part of the whole system. In this paper, the working principle diagram of the improved load-sensitive hydraulic system is designed and analyzed for the study of whether the working circuit of the pepper picking head of the pepper machine can meet the requirements of mountain operation, taking the working circuit of the mountain pepper harvester as the research object. In addition, the load-sensitive pump model and the simulation model of the whole working circuit are established by the AMESim platform 2019.2 (Siemens simcenter amesim). The operating performance of the system under variable flow conditions, variable load conditions, and an improved sensitive system is analyzed. The simulation results show that the improved load-sensitive system can effectively reduce the oscillation and cavitation during cylinder operation and improve the system efficiency and the performance and service life of the components. The performance of the hydraulic system of the working circuit of the mountain pepper harvester was verified in the test, meeting the requirements of working use. This provides a theoretical basis for the improvement and optimal design of a mountain pepper harvester hydraulic system.

Abstract Background This is the first study to investigate the effect of parental psychological abuse on potential psychopathological symptoms in gender minority youth subgroups, including transgender women, transgender men, and gender queer individuals. Methods Data was analysed from the Chinese National Transgender Survey in 2017; the survey was distributed through community-based organizations to transgender adolescents and adults residing in China, with representation from all 32 provinces and autonomous regions. A total of 1293 youth that self-identified as transgender or gender queer completed the study. Measures covered psychopathological symptoms including depression, anxiety, risk of suicideand self-harm. Parental psychological abuse was assessed in terms of neglect and avoidance, force to change, and verbal insults. Both the edges and centralities were computed via network analysis, and the network properties were then compared among the three gender minority subgroups. In addition, linear regression was adopted to test the predictive ability of node centrality for low self-esteem. Results Descriptive analysis revealed that among the three subgroups, transgender women had more severe psychopathological symptoms and reported the most psychological abuse. Network analysis revealed that the risk of suicide and self-harm was directly connected with one type of parental psychological abuse (“neglect and avoidance”). Node centrality was significantly associated with the predicting value of the nodes on low self-esteem (r 2 = 0.25, 0.17, 0.31) among all three gender minority subgroups. Conclusions The distinctive core psychopathological symptoms, within the networks of the gender minority subgroups, revealed specific symptoms across each group. The significant association between node centrality and low self-esteem indicated the extent of parental psychological abuse. Parental psychological abuse directed towards gender minority youth should be recognized as a form of family cold violence. It is recommended that schools and local communities should support early intervention to improve psychological well-being.

BackgroundThis study aims to introduce a reconstruction method of applying allografts and absorbable screws to repair large bone defects caused by the resection of giant osteochondroma.MethodsA retrospective study of a series of patients who underwent the resection of giant osteochondroma reconstructed by allografts and absorbable screws was conducted from February 2020 to September 2021. Their demographic data, location site, area of bone defect, and pertinent operative details were recorded. The reconstruction modality of allografts was elaborated on. In the follow-up, radiographic images were utilized to determine bone union, and the Musculoskeletal Tumor Society score was used to evaluate postoperative limb function.ResultsA total of seven patients were included, including three males and four females with an average age of 16.6 ± 6.5 years. Among them, three cases of tumors occurred in the humerus and four cases occurred in the femur. The average follow-up time was 11.3 ± 3.0 months. The average area of bone defect was 25.9 ± 8.3 cm2. No complications such as infection, nonunion, and allograft bone fracture were found during the follow-up period. Six months after the operation, the average Musculoskeletal Tumor Society score was 26.4 ± 1.6, with acceptable postoperative function.ConclusionsThe cooperative application of absorbable screw fixation and allografts including mixed cortical bone and cancellous bone, which yielded satisfactory functional outcomes and acceptable postoperative complications, is an effective reconstruction method for a massive bone defect after the resection of giant osteochondroma.

Anastomosis groups (AGs) or subgroups of 244 Rhizoctonia isolates recovered from sugar beet roots with symptoms of root and crown rot were characterized to be AG-A, AG-K, AG-2-2IIIB, AG-2-2IV, AG-3 PT, AG-4HGI, AG-4HGII, and AG-4HGIII, with AG-4HGI (108 isolates, 44.26%) and AG-2-2IIIB (107 isolates, 43.85%) being predominate. Four unclassified mycoviruses and one hundred and one putative mycoviruses belonging to six families, namely Mitoviridae (60.00%), Narnaviridae (18.10%), Partitiviridae (7.62%), Benyviridae (4.76%), Hypoviridae (3.81%), and Botourmiaviridae (1.90%), were found to be present in these 244 Rhizoctonia isolates, most of which (88.57%) contained positive single-stranded RNA genome. The 244 Rhizoctonia isolates were all sensitive to flutolanil and thifluzamide, with average median effective concentration (EC50) value of 0.3199 ± 0.0149 μg·mL−1 and 0.1081 ± 0.0044 μg·mL−1, respectively. Among the 244 isolates, except for 20 Rhizoctonia isolates (seven isolates of AG-A and AG-K, one isolate of AG-4HGI, and 12 isolates of AG-4HGII), 117 isolates of AG-2-2IIIB, AG-2-2IV, AG-3 PT, and AG-4HGIII, 107 isolates of AG-4HGI, and six isolates of AG-4HGII were sensitive to pencycuron, with average EC50 value of 0.0339 ± 0.0012 μg·mL−1. Correlation index (ρ) of cross-resistance level between flutolanil and thifluzamide, flutolanil and pencycuron, and thifluzamide and pencycuron was 0.398, 0.315, and 0.125, respectively. This is the first detailed study on AG identification, mycovirome analysis, and sensitivity to flutolanil, thifluzamide, and pencycuron of Rhizoctonia isolates associated with sugar beet root and crown rot.

This paper focuses on moving-target detection and tracking in a three-dimensional (3D) space, and proposes a visual target tracking system only using a two-dimensional (2D) camera. To quickly detect moving targets, an improved optical flow method with detailed modifications in the pyramid, warping, and cost volume network (PWC-Net) is applied. Meanwhile, a clustering algorithm is used to accurately extract the moving target from a noisy background. Then, the target position is estimated using a proposed geometrical pinhole imaging algorithm and cubature Kalman filter (CKF). Specifically, the camera’s installation position and inner parameters are applied to calculate the azimuth, elevation angles, and depth of the target while only using 2D measurements. The proposed geometrical solution has a simple structure and fast computational speed. Different simulations and experiments verify the effectiveness of the proposed method.

BackgroundThe novel coronavirus disease pandemic is still proliferating and is not expected to end any time soon. Several lockdowns and social distancing measures might be implemented in the future. A growing body of research has explored the effect of personality on individuals' psychological wellbeing during the pandemic. However, most prior studies have not discussed the dynamic and reciprocal transactions between personality and psychological distress in various situations. Therefore, this study aims to explore the internal mechanisms of the ways in which certain personality traits triggered specific symptoms during and after college lockdown, by using network analysis.MethodsBased on survey data from 525 university students in China, the study detected the connection between individual personality and psychological distress through network analysis. Of the participants, 70.1% were female, and 20.9% were male. The mean age of the participants was 19.701 (SD = 1.319) years. We estimated networks via two steps: First, two networks that only contain the Big Five personality traits and the six symptoms of psychological distress during and after the lockdown measure were estimated. Second, we add control variables and re-estimated the networks to check whether the linkages among the Big Five personality traits and the six symptoms of psychological distress observed in the first step were stable. Moreover, we employed strength centrality as the key indicator to present the potential significance of diverse variables within a network.ResultsThe findings demonstrate that, first, “depress” was the central symptom in the network during the college lockdown, while “efforts” was the central symptom after the lockdown. Second, the symptoms of “restless” and “worthless” significantly declined after the lockdown. Third, we found that there is an internal mechanism through which personality affected certain psychological symptoms during and after lockdowns. Specifically, neuroticism triggered certain symptoms during and after the lockdown, while extraversion and conscientiousness suppressed certain symptoms. Substantial evidence on internal linkages is imperative to develop effective interventions.ConclusionThis study explores the internal mechanisms of the ways in which certain personality traits trigger specific symptoms. Overall, our results provide empirical evidence that personality traits play a key role in how individuals with certain traits respond to college lockdown during a pandemic. The study makes a significant contribution to the literature because it is among the first few studies which explores the effects of personality traits on individual psychological distress using network analysis during the pandemic.

BackgroundTo date, the relationship between diverse time use behaviors and depression status among emerging adults have not been disentangled in the literature. Therefore, if and how the time displacement mechanism activates depressive symptoms among emerging adults remains unclear.MethodsTo fill this gap in the literature, we employed a network analysis to make estimations. The emerging adult sample (N = 1,811) was collected by the Guizhou Population Health Cohort Study. Time use behaviors were measured by an adaption of the self-administered International Physical Activity Questionnaire, and depressive symptoms were assessed using the 9-item Patient Health Questionnaire (PHQ-9).ResultsThe results revealed that the time displacement mechanism of emerging adults differed from that of adolescents. Sleep duration was not crowded out by other activities, while the time spent on computer use was found to be negatively related to time spent on heavy work activities. Moreover, computer use behavior triggered three depressive symptoms (“Anhedonia,” “Guilt,” and “Motor”), but inhibited “Suicide.” The results of the directed acyclic graph revealed that females and heavy drinkers were at risk of depression.LimitationsThe study sample was confined to only one province, which may limit its generalizability. The cross-sectional design impeded the ability to draw causal inferences.ConclusionOur results enhance the current understanding of the internal mechanism of how time use behaviors influence depressive symptoms among emerging adults.

In recent years, the long noncoding RNA (lncRNA) has been shown to be involved in many disease processes. The prediction of the lncRNA–disease association is helpful to clarify the mechanism of disease occurrence and bring some new methods of disease prevention and treatment. The current methods for predicting the potential lncRNA–disease association seldom consider the heterogeneous networks with complex node paths, and these methods have the problem of unbalanced positive and negative samples. To solve this problem, a method based on the Gradient Boosting Decision Tree (GBDT) and logistic regression (LR) to predict the lncRNA–disease association (GBDTLRL2D) is proposed in this paper. MetaGraph2Vec is used for feature learning, and negative sample sets are selected by using K-means clustering. The innovation of the GBDTLRL2D is that the clustering algorithm is used to select a representative negative sample set, and the use of MetaGraph2Vec can better retain the semantic and structural features in heterogeneous networks. The average area under the receiver operating characteristic curve (AUC) values of GBDTLRL2D obtained on the three datasets are 0.98, 0.98, and 0.96 in 10-fold cross-validation.

As a subtask of sentiment analysis, aspect-based sentiment analysis (ABSA) refers to identifying the sentiment polarity of the given aspect. The state-of-the-art ABSA models are developed by using the graph neural networks to deal with the semantics and the syntax of the sentence. These methods are challenged by two issues. For one thing, the semantic-based graph convolution networks fail to capture the relation between aspect and its opinion word. For another, minor attention is assigned to the aspect word within graph convolution, resulting in the introduction of contextual noise. In this work, we propose a knowledge-enhanced dual-channel graph convolutional network. On the task of ABSA, a semantic-based graph convolutional netwok (GCN) and a syntactic-based GCN are established. With respect to semantic learning, the sentence semantics are enhanced by using commonsense knowledge. The multi-head attention mechanism is taken to construct the semantic graph and filter the noise, which facilitates the information aggregation of the aspect and the opinion words. For syntactic information processing, the syntax dependency tree is pruned to remove the irrelevant words, based on which more attention weights are given to the aspect words. Experiments are carried out on four benchmark datasets to evaluate the working performance of the proposed model. Our model significantly outperforms the baseline models and verifies its effectiveness in ABSA tasks.

The use of quartz crystal microbalance in trace mass detection is restricted by unsatisfactory sensitivity, especially in damping media, due to the worsening of the quality factor of the damping resonator. The enhancement of the sensor performance could be realized by increasing the innate resonant frequency of quartz oscillators. Herein, increased working temperature of QCM systems was proved to bring an enhancement of the original resonant frequency. In addition, the measurement of ion osmotic pressure, single layer formation and single nucleotide polymorphism (SNP) at different temperatures demonstrated that an increased working temperature could enhance the sensitivity and accuracy, suggesting a potential application in a series of trace detections.

Interactions between genetic factors and environmental factors (EFs) play an important role in many diseases. Many diseases result from the interaction between genetics and EFs. The long non-coding RNA (lncRNA) is an important non-coding RNA that regulates life processes. The ability to predict the associations between lncRNAs and EFs is of important practical significance. However, the recent methods for predicting lncRNA-EF associations rarely use the topological information of heterogenous biological networks or simply treat all objects as the same type without considering the different and subtle semantic meanings of various paths in the heterogeneous network. In order to address this issue, a method based on the Gradient Boosting Decision Tree (GBDT) to predict the association between lncRNAs and EFs (GBDTL2E) is proposed in this paper. The innovation of the GBDTL2E integrates the structural information and heterogenous networks, combines the Hetesim features and the diffusion features based on multi-feature fusion, and uses the machine learning algorithm GBDT to predict the association between lncRNAs and EFs based on heterogeneous networks. The experimental results demonstrate that the proposed algorithm achieves a high performance.

Introduction: Very few studies have explored the effect of exposure to disaster memorial reports in the media on long-term sleep quality during the recovery period and post-disaster era. This study investigated the relationship between exposure to memorial reports and adult survivors' long-term sleep quality 10 years after the 5.12 Wenchuan earthquake in 2008, and determined whether exposure to memorial reports are associated with poor sleep quality. Method: Using a cross-sectional methodology, we surveyed participants (N = 1000) recruited from six disaster-affected counties. We measured sleep quality using the Pittsburgh Sleep Quality Index questionnaire (PSQI). Results: After controlling for the effects of demographic factors, socioeconomic status, and earthquake exposure factors, exposure to memorial reports and the PSQI global score had a strong inverted U-shaped relationship. Relationships among three sub-scores of PSQI (subjective sleep quality, sleep latency, daytime dysfunction) were also found to have similar relationships with memorial report exposure. That is, survivors who were exposed to low or high degrees of memorial reports had better sleep quality than those who were exposed to intermediate memorial reports. Discussion: The curvilinear relationship between media exposure and sleep quality in the post-disaster era reveals that media exposure could be used to identify victims at risk of poor sleep quality. Clinicians and practitioners may consider adopting psychological intervention programs to enhance victims' psychological controllability to overcome mental disorders caused by media exposure. Keywords: Sleep quality, PSQI, Media exposure, Memorial reports, Sichuan/Wenchuan

We show the properties and characterization of coherence witnesses. We show methods for constructing coherence witnesses for an arbitrary coherent state. We investigate the problem of finding common coherence witnesses for certain class of states. We show that finitely many different witnesses W1,W2,⋯,Wn can detect some common coherent states if and only if ∑i=1ntiWi is still a witnesses for any nonnegative numbers ti(i=1,2,⋯,n). We show coherent states play the role of high-level witnesses. Thus, the common state problem is changed into the question of when different high-level witnesses (coherent states) can detect the same coherence witnesses. Moreover, we show a coherent state and its robust state have no common coherence witness and give a general way to construct optimal coherence witnesses for any comparable states.

Synthesizing plans for a deformable object to transit from initial observations to goal observations, both of which are represented by high-dimensional data (namely “raw” data), is challenging due to the difficulty of learning abstract state representations of raw data and transition models of continuous states and continuous actions. Even though there have been some approaches making remarkable progress regarding the planning problem, they often neglect actions between observations and are unable to generate action sequences from initial observations to goal observations. In this paper, we propose a novel algorithm framework, namely AGN. We first learn a state-abstractor model to abstract states from raw observations, a state-generator model to generate raw observations from states, a heuristic model to predict actions to be executed in current states, and a transition model to transform current states to next states after executing specific actions. After that, we directly generate plans for a deformable object by performing the four models. We evaluate our approach in continuous domains and show that our approach is effective with comparison to state-of-the-art algorithms.

With the rapid development of the internet of things (IoT), sustainable self-powered wireless sensory systems and diverse wearable and implantable electronic devices have surged recently. Under such an opportunity, nanogenerators, which can convert continuous mechanical energy into usable electricity, have been regarded as one of the critical technologies for self-powered systems, based on the high sensitivity, flexibility, and biocompatibility of piezoelectric nanogenerators (PENGs) and triboelectric nanogenerators (TENGs). In this review, we have thoroughly analyzed the materials and structures of wearable and implantable PENGs and TENGs, aiming to make clear how to tailor a self-power system into specific applications. The advantages in TENG and PENG are taken to effectuate wearable and implantable human-oriented applications, such as self-charging power packages, physiological and kinematic monitoring, in vivo and in vitro healing, and electrical stimulation. This review comprehensively elucidates the recent advances and future outlook regarding the human body’s self-powered systems.

We present an optimized flutter-driven triboelectric nanogenerator (TENG) for wind energy harvesting. The vibration and power generation characteristics of this TENG are investigated in detail, and a low cut-in wind speed of 3.4 m/s is achieved. It is found that the air speed, the thickness and length of the membrane, and the distance between the electrode plates mainly determine the PTFE membrane’s vibration behavior and the performance of TENG. With the optimized value of the thickness and length of the membrane and the distance of the electrode plates, the peak open-circuit voltage and output power of TENG reach 297 V and 0.46 mW at a wind speed of 10 m/s. The energy generated by TENG can directly light up dozens of LEDs and keep a digital watch running continuously by charging a capacitor of 100 μF at a wind speed of 8 m/s.

As an independent building entity on the sea, the ship has a large number of internal electrical equipment and a compact space structure, which is prone to fire. This paper proposes a key technology of virtual dynamic escape of ships based on the fire spread prediction model for research. Taking the 63,500 DWT(Dead Weight Tonnage) tanker cabin as a research entity, the mathematical and physical models of ship fire simulation are established. Through the graphical analysis of the experimental data of the fire spread simulation, the temperature, CO concentration, and smoke concentration change rules under different working conditions at the fixed detection point position are obtained. Then, based on temperature, CO concentration and smoke concentration three impact factors, set up a comprehensive fire real-time situational risk evaluation index system. Using the MATLAB software, based on the principle of the fuzzy neural network fire ship’s integrated real-time situational risk evaluation model structure design and simulation test, obtained the corresponding training to comprehensive risk evaluation model of the network. Generate navigation grid according to the law of fire sprawl, and plan escape path. The traditional A* algorithm is improved, and an example is used to prove that the path-finding result after the improved algorithm is shorter than the path found by the traditional algorithm, which meets the path-finding requirements in a three-dimensional environment.

In this paper, we present a measure of quantum correlation for a multipartite system, defined as the sum of the correlations for all possible partitions. Our measure can be defined for quantum discord (QD), geometric quantum discord or even for entanglement of formation (EOF). For tripartite pure states, we show that the multipartite measures for the QD and the EOF are equivalent, which allows direct comparison of the distribution and the robustness of these correlations in open quantum systems. We study dissipative dynamics for two distinct families of entanglement: a W state and a GHZ state. We show that, for the W state, the QD is more robust than the entanglement, while for the GHZ state, this is not true. It turns out that the initial genuine multipartite entanglement present in the GHZ state makes the EOF more robust than the QD.