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Abstract Rapid urbanization and industrialization with their negative repercussions draw attention to the need for urban sustainability. This research built on the urban systems understanding linked with resource utilization, environmental effects, people-environment interactions, and adaptive management by integrating quantitative modelling of urban metabolism with a qualitative investigation into human responses. A hybrid methodology was applied in a typical industrial city of Jinchang, China, which exemplified a model of how complex urban dynamics link to and inform sustainability transitions. The study found that Jinchang City had taken on an unsustainable trajectory underpinned by continuous growth of material consumption, whereas a potential for sustainability transitions was also observed in decoupled negative material outputs, improved material efficiency, and environment regeneration collectively. The challenges in industrial cities have been framed in terms of development model lock-in, conceptual and institutional constraints, innovation and human capital deficiencies, and governance failure. The research proposed a government-led transformation system that integrates multi-level and multi-perspective transformations and management employing discipline insights to facilitate urban sustainability transitions. This involves governance, economic and technological innovations, social transformations, and implementations, in which urban governance and public engagement are essential elements for adapting to undesirable difficulties and exploring locally appropriate strategies. This research provided a theoretical and empirical basis along with scientific and policy implications, which contributed to the developing interdisciplinary knowledge on the understanding of complex urban systems and provided insights into real-world sustainability solutions.

Abstract Background Little is known about mobile phone problem use (MPPU) among older adults. This study investigated critical factors affecting MPPU and filled the gap between MPPU and depressive symptoms in older people. Methods A cross-sectional study was conducted in community (n = 376) with questionnaires of Multidimensional Scale of Perceived Social Support (MSPSS), Geriatric Depression Scale (GDS-15), Attitudes to Aging Questionnaire (AAQ) and Mobile Phone Problem Use Scale (MPPUS). Results 80.9% of older people used smartphones and spend less than three hours on mobile phone per day. The average MPPU score of Chinese elderly is greater than the cut off to 41. Female (β = -0.11, P = 0.037), living with spouse (β = -0.17, P = 0.03), and late marriage age (β = -0.16, P = 0.007) are less likely to develop MPPU. The relationship between MPPU and depressive symptoms was partially mediated by social support and attitude to aging. Conclusion Elderly people generally have higher MPPU scores. MPPU was associated with depressive symptoms, through social support and attitude to aging.

Abstract Background Physical frailty (PF) and circadian syndrome (CircS) are proposed as novel risks for cardiovascular disease (CVD), but little attention is paid to their combined impact on CVD. This study aimed to investigate the association of PF, CircS and CVD in middle-aged and older adults. Methods The sample comprised 8512 participants aged at least 45 years from the China Health and Retirement Longitudinal Study (CHARLS) 2011. PF was examined by the physical frailty phenotype scale. CircS was assessed by the components of the International Diabetes Federation (IDF) MetS plus short sleep duration and depression. The cut-off for CircS was set as ≥ 4. CVD was defined as the presence of physician-diagnosed heart disease and/or stroke. A total of 6176 participants without CVD recruited from CHARLS 2011 and were followed up in 2018. Results The prevalence of CVD in total populations, neither CircS or PF, PF alone, CircS alone and both CircS and PF were 13.0%, 7.4%, 15.5%, 17.4%, and 30.2%, respectively. CircS was more likely to be PF [OR (95%CI): 2.070 (1.732 ∼ 2.472)] than those without CircS. Both CircS alone [OR (95% CI): 1.954 (1.663 ∼ 2.296)], and coexisting CircS and PF [3.508 (2.739 ∼ 4.494)] were associated with CVD. Longitudinal analysis showed that individuals with both CircS and PF (HR: 1.716, 95%CI: 1.314 ∼ 2.240) and CircS alone [1.520 (1.331 ∼ 1.737)] were more likely to have new onset CVD than neither CircS or PF peers. Conclusion PF and CircS together are associated with higher CVD risk, which provided new evidence for a strong relation that warrants attention to assessing PF and CircS and in community to promote healthy aging.

Objectives Incomplete combustion of solid fuel and exposure to secondhand smoke (SHS) are the primary causes of indoor air pollution (IAP), potentially leading to detrimental effects on individual mental health. However, current evidence regarding the association between IAP and depression remains inconclusive. This study aims to systematically investigate the evidence regarding the association between IAP and the risk of depression.Design Systematic review and meta-analysis of cohort studies.Data sources Two independent reviewers searched PubMed, the Cochrane Library, Web of Science and EMBASE for available studies published up to 13 January 2024.Eligibility criteria We included all cohort studies published in English that aimed to explore the relationship between IAP from solid fuel use and SHS exposure and the risk of depression.Data extraction and synthesis Two independent reviewers extracted data and assessed the risk of bias. The association between IAP and depression was calculated using pooled relative risk (RR) with 95% CIs. Heterogeneity was assessed using the I2 value, and the effect estimates were pooled using fixed-effects or random-effects models depending on the results of homogeneity analysis.Results We included 12 articles with data from 61 217 participants. The overall findings demonstrated a significant association between IAP exposure and depression (RR=1.22, 95% CI: 1.13 to 1.31), although with substantial heterogeneity (I2=75%). Subgroup analyses based on pollutant type revealed that IAP from solid fuel use was associated with a higher risk of depression (RR=1.20, 95% CI: 1.13 to 1.26; I2=62%; 5 studies, 36 768 participants) than that from SHS exposure (RR=1.11, 95% CI: 0.87 to 1.41; I2=80%; 7 studies, 24 449 participants). In terms of fuel use, the use of solid fuel for cooking (RR: 1.23, 95% CI: 1.16 to 1.31; I2=58%; 4 studies, 34 044 participants) and heating (RR 1.15, 95% CI: 1.04 to 1.27; I2=65%; 3 studies, 24 874 participants) was associated with increased depression risk.Conclusions The findings from this systematic review and meta-analysis of cohort studies indicated an association between exposure to IAP and depression.PROSPERO registration number CRD42022383285.

Luteolin, a naturally occurring pharmaceutical compound with significant antitumor properties, faces challenges in clinical applications due to its low solubility in water and limited bioavailability. To address these issues, a one-step synthesis method was employed to encapsulate luteolin within ZIF-8. The successful preparation of luteolin@ ZIF-8 nanoparticles was confirmed through various analytical techniques, including fourier-transform infrared spectroscopy (FTIR), transmission electron microscopy (TEM), laser size distribution analysis, X-ray diffraction (XRD), and release curve assessment. Results indicate that the formulated luteolin@ ZIF-8 nanoparticles exhibited high drug loading (1360 mg/g) and demonstrated selective drug release in acidic microenvironments. Furthermore, the encapsulation of luteolin increased the size of ZIF-8 from 168.4 ± 0.2 nm to 384.7 ± 1.4 nm, but did not change its crystalline structure significantly. Notably, the results of in vitro anti-cervical and prostate cancers experiments revealed that luteolin@ ZIF-8 had better efficacy in inhibiting the proliferation and migration of HeLa and PC3 cells than free luteolin. The antitumor activity of luteolin@ ZIF-8 was sustained for 72 h, with a particularly pronounced inhibitory effect on HeLa cells as compared to PC3 cells. This study underscores the effective enhancement of luteolin's antitumor activity through encapsulation in ZIF-8, offering substantial implications for improving its clinical applications.

ObjectiveThis study aims to develop and validate a prediction model for evaluating the social participation in the community middle-aged and older adult stroke survivors.MethodsThe predictive model is based on data from the China Health and Retirement Longitudinal Study (CHARLS), which focused on individuals aged 45 years or older. The study utilized subjects from the CHARLS 2015 and 2018 wave, eighteen factors including socio-demographic variables, behavioral and health status, mental health parameters, were analyzed in this study. To ensure the reliability of the model, the study cohort was randomly split into a training set (70%) and a validation set (30%). The Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis was used to identify the most effective predictors of the model through a 10-fold cross-validation. The logistic regression model was employed to investigate the factors associated with social participation in stroke patients. A nomogram was constructed to develop a prediction model. Calibration curves were used to assess the accuracy of the nomogram model. The model’s performance was evaluated using the area under the curve (AUC) and decision curve analysis (DCA).ResultA total of 1,239 subjects with stroke from the CHARLS database collected in 2013 and 2015 wave were eligible in the final analysis. Out of these, 539 (43.5%) subjects had social participation. The model considered nineteen factors, the LASSO regression selected eleven factors, including age, gender, residence type, education level, pension, insurance, financial dependence, physical function (PF), self-reported healthy,cognition and satisfaction in the prediction model. These factors were used to construct the nomogram model, which showed a certain extent good concordance and accuracy. The AUC values of training and internal validation sets were 0.669 (95%CI 0.631–0.707) and 0.635 (95% CI 0.573–0.698), respectively. Hosmer–Lemeshow test values were p = 0.588 and p = 0.563. Calibration curves showed agreement between the nomogram model and actual observations. ROC and DCA indicated that the nomogram had predictive performance.ConclusionThe nomogram constructed in this study can be used to evaluate the probability of social participation in middle-aged individuals and identify those who may have low social participation after experiencing a stroke.

Background: University students are increasingly inclined to use the Internet for health-related purposes, and their sleep problems are becoming increasingly prominent. Currently, the relationship between sleep quality and online health-related searches is poorly understood. The aim of this study was to exam the associations of sleep quality, Internet use, eHealth literacy, online health information seeking and cyberchondria in the sample of Chinese university students. Methods: A total of 2744 students completed self-reported questionnaires online containing the Pittsburgh Sleep Quality Index (PSQI), eHealth Literacy Scale, Online Health Information Seeking, Cyberchondria Severity Scale (CSS) and questions regarding sleep duration, Internet use, health status, and demographic information. Results: The prevalence of poor sleep quality (PSQI >7) among the university students was 19.9% and 15.6% students slept less than 7 h per day. As time spent on online daily and playing phone before bed increased, the prevalence of sleep disturbance gained. Sleep disturbance was significantly associated with cyberchondria (OR = 1.545, p = 0.001), health status [good (OR = 0.625, p = 0.039), poor (OR = 3.128, p = 0.010), and fair (OR = 1.932, p = 0.001)]. Sleep quality, online health information seeking and eHealth literacy positively influenced with cyberchondria. Compared to 7–8 h sleep duration, online health information seeking (OR = 0.750, p = 0.012) was significantly associated with ≥8 h sleep duration. Conclusion: Our findings highlighted poor health status, too much time spent on online daily and high cyberchondria level might decrease sleep quality in the sample of Chinese university students, further suggesting the need for developing interventions based on online health-related searches for improving sleep quality among university students.

In response to the challenge of small and imbalanced Datasets, where the total Sample size is limited and healthy Samples significantly outweigh faulty ones, we propose a diagnostic framework designed to tackle Class imbalance, denoted as the Dual-Stream Adaptive Deep Residual Shrinkage Vision Transformer with Interclass–Intraclass Rebalancing Loss (DSADRSViT-IIRL). Firstly, to address the issue of limited Sample quantity, we incorporated the Dual-Stream Adaptive Deep Residual Shrinkage Block (DSA-DRSB) into the Vision Transformer (ViT) architecture, creating a DSA-DRSB that adaptively removes redundant signal information based on the input data characteristics. This enhancement enables the model to focus on the Global receptive field while capturing crucial local fault discrimination features from the extremely limited Samples. Furthermore, to tackle the problem of a significant Class imbalance in long-tailed Datasets, we designed an Interclass–Intraclass Rebalancing Loss (IIRL), which decouples the contributions of the Intraclass and Interclass Samples during training, thus promoting the stable convergence of the model. Finally, we conducted experiments on the Laboratory and CWRU bearing Datasets, validating the superiority of the DSADRSViT-IIRL algorithm in handling Class imbalance within mixed-load Datasets.

Abstract Background Neuroinflammation and apoptosis are involved in the pathogenesis of ischaemic stroke. Alisol A 24-acetate (24A) exerts a strong inhibitory effect on inflammation and cell apoptosis. The neuroprotective effect of 24A on global cerebral ischaemia/reperfusion (GCI/R) injury remains unclear. Methods GCI/R mice were used to investigate the neuroprotective effect of 24A. Modified neurological deficit scores, Morris water maze and object recognition tests were used to evaluate behaviours. Metabolism in brain regions was detected using magnetic resonance spectroscopy (MRS), and changes in microglia, astrocytes and neurons were detected. Inflammation and apoptosis were measured. Results The results showed that 24A suppressed neurological deficits scores and improved GCI/R induced cognitive dysfunction. It was also observed that 24A could alleviate neuroinflammation, which manifested as 24A inhibited microglia and astrocytes proliferation, downregulated the expression of interleukin (IL)-1β, tumor necrosis factor (TNF)-α, and inducible nitric oxide synthase (iNOS) in the GCI/R mice brain. The apoptosis of neurons reduced, and dendritic spines of hippocampal neurons increased in the presence of 24A. In addition, 24A could up-regulate the expression of phosphorylated phosphoinositide 3-kinases (p-PI3K) and phosphorylated protein kinase B (p-AKT) in GCI/R mice brain, and all the morphological, neurological, and biochemical changes of 24A treatment were abolished by the application of PI3K/AKT pathway inhibitor LY294002. Conclusions Taken together, our study indicated that 24A alleviated GCI/R injury by inhibiting neuroinflammation and apoptosis through the regulation of the PI3K/AKT pathway.

The temporal graph can represent a temporal relationship widely used in compound synthesis analysis, biological gene analysis, etc. However, the temporal graph would embody vertex updates frequently, high time resolution, and not enumerated rules. The construction and update of some temporal graph models are too dependent on the graph operation sequence, which leads to a lack of an effective model. Simultaneously, the temporal subgraph clustering of the temporal graph with frequent updating for the lack of an effective model leads to low accuracy. Therefore, we propose an efficient and frequently updated temporal graph model as vertex driven and corresponding temporal subgraph clustering method. First, we propose a temporal graph construction algorithm and set two thresholds to divide the temporal graph on a timeline to obtain temporal subgraphs. Next, an enhancement strategy based on the sliding window is proposed to accelerate the construction process. Third, we offer a double-standard temporal subgraph clustering method based on community comparison and temporal distance. The temporal subgraph can be effectively distinguished in temporal and structure dimensions. Lastly, experimental results on both real and synthetic datasets show that the temporal graph model proposed in this work can reduce the time overhead of construction compared to other existing models. The cluster method improves the clustering accuracy of temporal subgraphs. The clustering results show through the hierarchical clustering at the same time.

BackgroundSocial media use has been linked to poor sleep outcomes among university students in the cyber age, but the association between the negative consequences of social media use and sleep problems is not yet well understood. The present study investigated the relationships among social media usage, social media fatigue (SMF), fear of missing out (FoMO), social media addiction (SMA) and sleep quality in university students.MethodAn online survey was administered to 2744 respondents that included the Pittsburgh Sleep Quality Index (PSQI); questionnaires evaluating FoMO, SMF, and SMA; and questions regarding sleep duration, social media use, health status, and demographic information.ResultA total of 19.9% of respondents suffered from sleep disturbance. A total of 15.6% of participants had sleep durations less than 5 h, and 21.6% of subjects had sleep durations longer than 9 h. Sleep quality was positively associated with SMF (OR = 1.387, 95% CI: 1.103~1.743), and SMA (OR = 1.415, 95% CI: 1.118~1.791). The relationship between FoMO and sleep disturbance was not significant. Compared to a sleep duration > 9 h, SMF increased the risk of shorter sleep durations [5-6 h sleep (OR = 2.226, 95% CI: 1.132~4.375), 6-7 h sleep (OR = 1.458, 95% CI: 1.061~2.002), and 7-8 h sleep (OR = 1.296, 95% CI: 1.007~1.670)]. FoMO and SMA did not significantly affect sleep duration. In addition, SMA (OR = 3.775, 95% CI: 3.141~4.537), FoMO (OR = 3.301, 95% CI: 2.753~3.958), and sleep disorders (OR = 1.284, 95% CI: 1.006~1.638) increased SMF.ConclusionUpon experiencing negative consequences of social media use, such as SMF and SMA, university students were likely to experience sleep problems. Further research exploring the interventions that improve sleep and alleviate negative consequences of social media use should be conducted.

Skyline query is very useful in decision-making systems, WSN and so on. As a variation of skyline query, skyline-join query can return the results from multiple datasets. However, incomplete datasets are a frequent phenomenon due to the widespread use of automated information extraction and aggregation. Existing methods for dealing with incomplete data, such as probability, data padding can solve the problem, but cannot effectively reflect the real situation and are lack of integrality. Therefore, in this paper, in order to reflect the situation more accuracy and more user-centric, we research the problem of skyline-join query over incomplete datasets with crowdsourcing, named CrowdSJ. The crowdsourcing-based skyline-join query processing problem over incomplete datasets is divided into two situations. One is the skyline-join query only involves the unknown crowdsourcing attribute and the join attribute, named Partial Skyline-Join with Crowdsourcing (PSJCrowd). The other one is the skyline-join query involves all the attributes, named All Skyline-Join with Crowdsourcing (ASJCrowd). For PSJCrowd, first, we filter the known dataset. Then, we present the level-preference-tree-index, and propose the partial skyline-join with crowdsourcing algorithm. For ASJCrowd, first, we filter the known dataset too. Second, we build a level-preference-tree-index based on the known attributes of the incomplete dataset. Third, we propose the skyline-join with crowdsourcing on single dataset algorithm, CrowdSJ-single, to filter the dataset containing unknown attributes. Then, we build a global level-preference-tree-index based on the known attributes of the incomplete dataset and the complete dataset. We propose the skyline-join with crowdsourcing on multiple datasets algorithm, CrowdSJ-multiple. We filter the linked tuples based on the global level-preference-tree-index and the results of each round of crowdsourcing. Numerous experiments on synthetic and real datasets demonstrate that our algorithms are efficient and effective.

BackgroundExcessive self-disclosure online may risk the reputations, mental health problems, and professional lives of nursing students. This study investigated nursing students' usage of social media, their attitudes towards social media, mental health problems and self-disclosures, and the relationships of these variables.MethodsA cross-sectional study was conducted online (n = 1054) with questionnaires of Fear of Missing Out (FoMO), Social Media Fatigue (SMF), Students' Uses and Views of Social Media (SUVSM) and self-disclosure in social media which included self-information shown on social media and information viewed by others.ResultsAlthough most of them held positive attitudes towards social media, 17.4% of the participants acknowledged that they had posted inappropriate contents online and 37.6% witnessed improper posts from schoolmates or teachers online. SMF was affected by familiar with relevant regulations on the social media usage (β = -.10, p < .001), FoMO (β = .41, p < .001), and SUVSM (β = .17, p < .001). Additionally, nearly 1/3 participants reported their net-friends could view following information: gender, age, occupation, education level and location. Self- disclosure in social media was positively influenced by education (β = .10, p < .001), sharing moments or Weibo, etc. (β = .009, P = 0.009), time spent on social media daily (β = .11, p < .001), accepting stranger's "friend request" (β = .06, P = 0.047), FoMO (β = .14, p < .001) and SMF (β = .19, p < .001). Furthermore, effect of SUVSM on self-disclosure in social media was mediated by FoMO and SMF.ConclusionInappropriate contents are posted and witnessed by appreciable proportions of nursing students. Positive attitude towards social media may strengthen FoMO and SMF, which may increase self-disclosure in social media in turn.

Ammonia gas is a well-known environmental pollution gas, threatening human health. Ammonia gas is also one of the most harmful gases to livestock and poultry for many years. Many studies have demonstrated toxic effect of ammonia gas on animal health, such as eyes, respiratory system, and digestive system. However, the effect of ammonia gas toxicity on chicken livers and underlying molecular mechanism remains unclear. In this study, we selected chicken liver as research object and duplicated successfully ammonia gas poisoning model of chickens. 1-day-old Ross-308 broilers were randomly divided into the control group (the low ammonia gas group), and two treatment groups (the middle ammonia gas group and the high ammonia gas group) (3 replicates per group and 12 chickens per replicate). Ammonia gas concentration in the low ammonia gas group was ≤5 mg/m3 during day 1–42. Ammonia gas concentration in the middle group was set as 10 ± 0.5 mg/m3 during day 1–21, and 15 ± 0.5 mg/m3 during day 22–42). Ammonia gas concentration in the high ammonia gas group was set as 20 ± 0.5 mg/m3 during day 1–21, and 45 ± 0.5 mg/m3 during day 22–42. The ultrastructure of chicken livers was observed. The activities of four ATPases (Na+K+-ATPase, Mg++-ATPase, Ca++-ATPase, and Ca++Mg++-ATPase), the expression of twelve energy metabolism-related genes (HK1, HK2, PK, PFK, PDHX, CS, LDHA, LDHB, SDHA, SDHB, avUCP, and AMPK), as well as the expression of ten autophagy-related genes (PI3K, LC3I, LC3II, Beclin1, SQSTM1, mTOR, ULK1, ATG5, ATG12, and ATG13) were measured to explore the effect of ammonia gas on energy metabolism and autophagy in chicken livers. Our results showed that excess ammonia gas induced mitochondrial and autophagic damage in chicken liver tissue cells. Meanwhile, ATPases activities were inhibited and the expression of energy metabolism-related genes changed during ammonia gas treatment, meaning that excess ammonia gas caused energy metabolism disorder. Furthermore, ammonia gas exposure altered the expression of autophagy-related genes, suggesting that ammonia gas treatment caused autophagy in chicken livers. Moreover, ammonia gas-induced AMPK compensatory up-regulation activated autophagy process through inhibiting mTOR and promoting ULK1. In addition. there were dose-dependent and time-dependent effects on all detected indexes in ammonia gas-caused chicken liver cell damage. Taken together, AMPK/mTOR/ULK1-Beclin1 pathway participated in energy metabolism disorder-mediated autophagic injury caused by ammonia gas exposure in chicken livers.

The real-time sensing data in sensor networks are mostly data stream, and due to the inevitable factors such as external natural environment and man-made interference of sensors deployment, the phenomenon of data drift like slowing down or aggravation often appears when the perceived data stream propagates in the time domain, making it impossible to match the real-time data with standardized event templates in real time. Thus, it is difficult to identify the results through the existing data stream monitoring approaches before the ends of disaster events, and the accuracy is extremely low. Therefore, aiming at the deficiencies of the existing real-time identification approaches, this paper proposes a multi-stage real-time identification approach (MRIA) for data stream events with data drift feature based on dynamic time warping. First, the initial identification domain of data stream events is determined, and an anti-aliasing model based on dynamic time warping is constructed for the drift feature of the data stream, realizing the real-time similarity matching between the real-time data and event template. Second, a variable sliding window mechanism is introduced to determine the starting position of events, and an optimized matching approach for the incremental sequence is proposed to reduce the computational cost of re-matching in the process of the matching, and it obtains the identification benchmark of the similarity matching between the real-time data and the event template dynamically by dynamic threshold setting, which can improve the accuracy of matching. On this basis, an event multi-stage real-time identification approach based on identification proportion allocation is proposed, which can obtain the possibilities of events occurrence and the information of final disaster events quickly through the initial real-time identification and the final real-time identification process. The data compression optimization strategy based on the piecewise aggregate approximation approach is proposed to reduce the data scale, which further improves the identification efficiency. Furthermore, it provides an effective way for real-time identification of data stream events. The experimental results show that the approach proposed in this paper has great advantages in the efficiency and accuracy of data stream events identification.

Deep learning has obtained remarkable achievements in computer vision, especially image and video processing. However, in synthetic aperture radar (SAR) image recognition, the application of DNNs is usually restricted due to data insufficiency. To augment datasets, generative adversarial networks (GANs) are usually used to generate numerous photo-realistic SAR images. Although there are many pixel-level metrics to measure GAN’s performance from the quality of generated SAR images, there are few measurements to evaluate whether the generated SAR images include the most representative features of the target. In this case, the classifier probably categorizes a SAR image into the corresponding class based on “wrong” criterion, i.e., “Clever Hans”. In this paper, local interpretable model-agnostic explanation (LIME) is innovatively utilized to evaluate whether a generated SAR image possessed the most representative features of a specific kind of target. Firstly, LIME is used to visualize positive contributions of the input SAR image to the correct prediction of the classifier. Subsequently, these representative SAR images can be selected handily by evaluating how much the positive contribution region matches the target. Experimental results demonstrate that the proposed method can ally “Clever Hans” phenomenon greatly caused by the spurious relationship between generated SAR images and the corresponding classes.

With the development of streaming data processing technology, real-time event monitoring and querying has become a hot issue in this field. In this article, an investigation based on coal mine disaster events is carried out, and a new anti-aliasing model for abnormal events is proposed, as well as a multistage identification method. Coal mine micro-seismic signal is of great importance in the investigation of vibration characteristic, attenuation law, and disaster assessment of coal mine disasters. However, as affected by factors like geological structure and energy losses, the micro-seismic signals of the same kind of disasters may produce data drift in the time domain transmission, such as weak or enhanced signals, which affects the accuracy of the identification of abnormal events (“the coal mine disaster events”). The current mine disaster event monitoring method is a lagged identification, which is based on monitoring a series of sensors with a 10-s-long data waveform as the monitoring unit. The identification method proposed in this article first takes advantages of the dynamic time warping algorithm, which is widely applied in the field of audio recognition, to build an anti-aliasing model and identifies whether the perceived data are disaster signal based on the similarity fitting between them and the template waveform of historical disaster data, and second, since the real-time monitoring data are continuous streaming data, it is necessary to identify the start point of the disaster waveform before the identification of the disaster signal. Therefore, this article proposes a strategy based on a variable sliding window to align two waveforms, locating the start point of perceptual disaster wave and template wave by gradually sliding the perceptual window, which can guarantee the accuracy of the matching. Finally, this article proposes a multistage identification mechanism based on the sliding window matching strategy and the characteristics of the waveforms of coal mine disasters, adjusting the early warning level according to the identification extent of the disaster signal, which increases the early warning level gradually with the successful result of the matching of 1/ N size of the template, and the piecewise aggregate approximation method is used to optimize the calculation process. Experimental results show that the method proposed in this article is more accurate and be used in real time.

Skyline query is widely used in multi-standard decision making process. As the extension to skyline query, p-dominant skyline query controls the base of the result set by adjusting data proportion, which is suitable for the application on fast deciding data stream. However, skyline query on data stream cannot satisfy the need for skyline query of corresponding value ratio, and the traditional p-dominant skyline query cannot provide skyline query with frequent data update. As a result, a data stream-based algorithm that can achieve the goal of p-dominant skyline query is proposed. First, the definitions of full-dominant relationship and p-dominance are given, after which the definitions of p-dominant skyline and p-dominant skyline based on data stream are proposed. Second, by analyzing the characters of data stream-based p-dominant skyline, a new data filtering method is established. Based on the method, p-Dominant Skyline Query on data stream over Sliding Window (DSSW) is proposed in order to improve the efficiency of p-dominant skyline query. After that, we expend the p-dominant skyline query of data stream into n-of-N p-dominant skyline query and the (n1, n2)-of-Np-dominant skyline query in data stream. Finally, we prove that DSSW has obvious advantages in responding time and storage skyline space by comparing with the traditional p-dominant skyline query algorithms through abundant experiments.

Frequent structures have emerged as resolving the structural pattern mining issues, such as chemistry, Web applications, and other related problems. Top-k subgraph query as an important technology of graph search is widely used in emerging fields. Different from the traditional subgraph query, the query requirement studied in this paper has two unique properties: 1) data graphs change dynamically over time, including vertices/edges insert, delete, and frequent labels update and 2) query with limits of labels and k. Existing graph index and query techniques rarely considered these. Therefore, this paper proposes a method called frequent subgraph dynamic Top-k query with label value constraint to address both the challenges. We propose a two-level index with the frequent structure mapping and label value aggregation, which locates the query structure quickly, then prunes, and filters according to the constraint to narrow search range and improve query efficiency. The method uses a two-level index to filter the initial result and combines the dynamic changes of graphs to modify query results. In addition, it also proposed an incremental dynamic maintenance strategy over the proposed index, which only updated the partial contents to avoid the high cost caused by global update. The experimental results demonstrate that the proposed Top-k query method outperforms the baseline approaches up to 37%.

Target recognition is one of the most challenging tasks in synthetic aperture radar (SAR) image processing since it is highly affected by a series of pre-processing techniques which usually require sophisticated manipulation for different data and consume huge calculation resources. To alleviate this limitation, numerous deep-learning based target recognition methods are proposed, particularly combined with convolutional neural network (CNN) due to its strong capability of data abstraction and end-to-end structure. In this case, although complex pre-processing can be avoided, the inner mechanism of CNN is still unclear. Such a “black box” only tells a result but not what CNN learned from the input data, thus it is difficult for researchers to further analyze the causes of errors. Layer-wise relevance propagation (LRP) is a prevalent pixel-level rearrangement algorithm to visualize neural networks’ inner mechanism. LRP is usually applied in sparse auto-encoder with only fully-connected layers rather than CNN, but such network structure usually obtains much lower recognition accuracy than CNN. In this paper, we propose a novel LRP algorithm particularly designed for understanding CNN’s performance on SAR image target recognition. We provide a concise form of the correlation between output of a layer and weights of the next layer in CNNs. The proposed method can provide positive and negative contributions in input SAR images for CNN’s classification, viewed as a clear visual understanding of CNN’s recognition mechanism. Numerous experimental results demonstrate the proposed method outperforms common LRP.

In this paper, we introduce the first validation framework of an indoor navigation system for blind and visually impaired (BVI) users, which is a significant step toward the development of cost effective indoor way-finding solutions for BVI users. The BVI users require detailed landmark-based navigation instructions that will help them arrive at the chosen destination independently. These users will interact with the navigation instructions on a smartphone using an accessible user interface. The validation framework includes the following three main components: 1) virtual reality-based simulation that simulates a BVI user traversing and interacting with the physical environment, developed using Unity game engine; 2) generation of action codes that emulate the avatar movement in the virtual environment, developed using a natural language processing parser of the navigation instructions; and 3) accessible user interface, which enables the user to access the navigation instructions, developed using Sikuli script library. We introduce a case study that illustrates the use of the validation tool using PERCEPT system. It is our strong belief that the validation framework we provide in this paper will encourage other developers to invent indoor navigation systems for BVI users. In addition, we would like to mention that this tool is the first step of validating an indoor navigation system and should be followed by trials with human subjects.

For reducing protein aggregation in foam fractionation, the role of pH-induced structural change in the interface-induced protein aggregation was analyzed using bovine serum albumin (BSA) as a model protein. The results show that the decrease in pH from 7.0 to 3.0 gradually unfolded the BSA structure to increase the molecular size and the relative content of β-sheet and thus reduced the stability of BSA in the aqueous solution. At the isoelectric point (pH 4.7), BSA suffered the lowest level in protein aggregation induced by the gas–liquid interface. In the pH range from 7.0 to 4.7, most BSA aggregates were formed in the defoaming process while in the pH range from 4.7 to 3.0, the BSA aggregates were formed at the gas–liquid interface due to the unfolded BSA structure and they further aggregated to form insoluble ones in the desorption process.

Eph receptor A4 (EphA4), a member of the erythropoietin-producing hepatocellular (Eph) family, has been reported to upregulate in several tumors. However, the role of EphA4 in multiple myeloma has not been clarified yet. In this study, we found that EphA4 promoted proliferation of multiple myeloma cells via the regulation of cell cycle. Besides, EphA4 was closely related to cell adhesion of multiple myeloma cells and promoted cell adhesion–mediated drug resistance by enhancing the phosphorylation levels of Akt (p-AKT) expression in multiple myeloma. More interestingly, we discovered that EphA4 can interact with cyclin-dependent kinase 5 (CDK5) and regulate its expression in multiple myeloma. CDK5 has been reported to be overexpressed in multiple myeloma which mediated bortezomib resistance and also participated in AKT pathway. And we have also proved the fact. So, we supposed that EphA4 interacted with CDK5 and promoted its expression which in turn enhanced p-AKT expression and promoted cell adhesion–mediated drug resistance in multiple myeloma. Therefore, this study clarifies the molecular mechanism of cell adhesion–mediated drug resistance and may be useful in identifying potential target for treatment of multiple myeloma.

A labeled graph is a special structure with node identification capability, which is often used in information networks, biological networks, and other fields. The subgraph query is widely used as an important means of graph data analysis. As the size of the labeled graph increases and changes dynamically, users tend to focus on the high-match results that are of interest to them, and they want to take advantage of the relationship and number of results to get the results of the query quickly. For this reason, we consider the individual needs of users and propose a dynamic Top-K interesting subgraph query. This method establishes a novel graph topology feature index (GTSF index) including a node topology feature index (NTF index) and an edge feature index (EF index), which can effectively prune and filter the invalid nodes and edges that do not meet the restricted condition. The multi-factor candidate set filtering strategy is proposed based on the GTSF index, which can be further pruned to obtain fewer candidate sets. Then, we propose a dynamic Top-K interesting subgraph query method based on the idea of the sliding window to realize the dynamic modification of the matching results of the subgraph in the dynamic evolution of the label graph, to ensure real-time and accurate results of the query. In addition, considering the factors, such as frequent Input/Output (I/O) and network communication overheads, the optimization mechanism of the graph changes and an incremental maintenance strategy for the index are proposed to reduce the huge cost of redundant operation and global updates. The experimental results show that the proposed method can effectively deal with a dynamic Top-K interesting subgraph query on a large-scale labeled graph, at the same time the optimization mechanism of graph changes and the incremental maintenance strategy of the index can effectively reduce the maintenance overheads.