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The Semantic Link Network is a general semantic model for modeling the structure and the evolution of complex systems. Various semantic links play different roles in rendering the semantics of complex system. One of the basic semantic links represents cause-effect relation, which plays an important role in representation and understanding. This paper verifies the role of the Semantic Link Network in representing the core of text by investigating the contribution of cause-effect link to representing the core of scientific papers. Research carries out with the following steps: (1) Two propositions on the contribution of cause-effect link in rendering the core of paper are proposed and verified through a statistical survey, which shows that the sentences on cause-effect links cover about 65% of key words within each paper on average. (2) An algorithm based on syntactic patterns is designed for automatically extracting cause-effect link from scientific papers, which recalls about 70% of manually annotated cause-effect links on average, indicating that the result adapts to the scale of data sets. (3) The effects of cause-effect link on four schemes of incorporating cause-effect link into the existing instances of the Semantic Link Network for enhancing the summarization of scientific papers are investigated. The experiments show that the quality of the summaries is significantly improved, which verifies the role of semantic links. The significance of this research lies in two aspects: (1) it verifies that the Semantic Link Network connects the important concepts to render the core of text; and, (2) it provides an evidence for realizing content services such as summarization, recommendation and question answering based on the Semantic Link Network, and it can inspire relevant research on content computing. [ABSTRACT FROM AUTHOR]

Poor research reporting is a major contributing factor to low study reproducibility, financial and animal waste. The ARRIVE (Animal Research: Reporting of In Vivo Experiments) guidelines were developed to improve reporting quality and many journals support these guidelines. The influence of this support is unknown. We hypothesized that papers published in journals supporting the ARRIVE guidelines would show improved reporting compared with those in non-supporting journals. In a retrospective, observational cohort study, papers from 5 ARRIVE supporting (SUPP) and 2 non-supporting (nonSUPP) journals, published before (2009) and 5 years after (2015) the ARRIVE guidelines, were selected. Adherence to the ARRIVE checklist of 20 items was independently evaluated by two reviewers and items assessed as fully, partially or not reported. Mean percentages of items reported were compared between journal types and years with an unequal variance t-test. Individual items and sub-items were compared with a chi-square test. From an initial cohort of 956, 236 papers were included: 120 from 2009 (SUPP; n = 52, nonSUPP; n = 68), 116 from 2015 (SUPP; n = 61, nonSUPP; n = 55). The percentage of fully reported items was similar between journal types in 2009 (SUPP: 55.3 ± 11.5% [SD]; nonSUPP: 51.8 ± 9.0%; p = 0.07, 95% CI of mean difference -0.3–7.3%) and 2015 (SUPP: 60.5 ± 11.2%; nonSUPP; 60.2 ± 10.0%; p = 0.89, 95%CI -3.6–4.2%). The small increase in fully reported items between years was similar for both journal types (p = 0.09, 95% CI -0.5–4.3%). No paper fully reported 100% of items on the ARRIVE checklist and measures associated with bias were poorly reported. These results suggest that journal support for the ARRIVE guidelines has not resulted in a meaningful improvement in reporting quality, contributing to ongoing waste in animal research. [ABSTRACT FROM AUTHOR]

In this study, we address the question whether (and to what extent, respectively) altmetrics are related to the scientific quality of papers (as measured by peer assessments). Only a few studies have previously investigated the relationship between altmetrics and assessments by peers. In the first step, we analyse the underlying dimensions of measurement for traditional metrics (citation counts) and altmetrics–by using principal component analysis (PCA) and factor analysis (FA). In the second step, we test the relationship between the dimensions and quality of papers (as measured by the post-publication peer-review system of F1000Prime assessments)–using regression analysis. The results of the PCA and FA show that altmetrics operate along different dimensions, whereas Mendeley counts are related to citation counts, and tweets form a separate dimension. The results of the regression analysis indicate that citation-based metrics and readership counts are significantly more related to quality, than tweets. This result on the one hand questions the use of Twitter counts for research evaluation purposes and on the other hand indicates potential use of Mendeley reader counts. [ABSTRACT FROM AUTHOR]

Before organizing mixed-mode data collection for the self-administered questionnaire of the Belgian Health Interview Survey, measurement effects between the paper-and-pencil and the web-based questionnaire were evaluated. A two-period cross-over study was organized with a sample of 149 employees of two Belgian research institutes (age range 22–62 years, 72% female). Measurement agreement was assessed for a diverse range of health indicators related to general health, mental and psychosocial health, health behaviors and prevention with kappa coefficients and intraclass correlation (ICC). The quality of the data collected by both modes was evaluated by quantifying the missing, ‘don’t know’ and inconsistent values and data entry mistakes. Good to very good agreement was found for all categorical indicators with kappa coefficients superior to 0.60, except for two mental and psychosocial health indicators namely the presence of a sleeping disorder and of a depressive disorder (kappa≥0.50). For the continuous indicators high to acceptable agreement was observed with ICC superior to 0.70. Inconsistent answers and data-entry mistakes were only occurring in the paper-and-pencil mode. There were no less missing values in the web-based mode compared to the paper-and-pencil mode. The study supports the idea that web-based modes provide, in general, equal responses to paper-and-pencil modes. However, health indicators based upon factual and objective items tend to have higher measurement agreement than indicators requiring an assessment of personal subjective feelings. A web-based mode greatly facilitates the data-entry process and guides the completing of a questionnaire. However, item non-response was not positively affected. [ABSTRACT FROM AUTHOR]

The need to complete multiple tasks concurrently is a common occurrence both daily life and in occupational activities, which can often include simultaneous cognitive and physical demands. As one example, there is increasing availability of head-worn display technologies that can be employed when a user is mobile (e.g., while walking). This new method of information presentation may, however, introduce risks of adverse outcomes such as a decrement to gait performance. The goal of this study was thus to quantify the effects of a head-worn display (i.e., smart glasses) on motor variability during gait and to compare these effects with those of other common information displays (i.e., smartphone and paper-based system). Twenty participants completed four walking conditions, as a single task and in three dual-task conditions (three information displays). In the dual-task conditions, the information display was used to present several cognitive tasks. Three different measures were used to quantify variability in gait parameters for each walking condition (using the cycle-to-cycle standard deviation, sample entropy, and the “goal-equivalent manifold” approach). Our results indicated that participants used less adaptable gait strategies in dual-task walking using the paper-based system and smartphone conditions compared with single-task walking. Gait performance, however, was less affected during dual-task walking with the smart glasses. We conclude that the risk of an adverse gait event (e.g., a fall) in head-down walking conditions (i.e., the paper-based system and smartphone conditions) were higher than in single-task walking, and that head-worn displays might help reduce the risk of such events during dual-task gait conditions. [ABSTRACT FROM AUTHOR]

Quantifying the impact of a scholarly paper is of great significance, yet the effect of geographical distance of cited papers has not been explored. In this paper, we examine 30,596 papers published in Physical Review C, and identify the relationship between citations and geographical distances between author affiliations. Subsequently, a relative citation weight is applied to assess the impact of a scholarly paper. A higher-order weighted quantum PageRank algorithm is also developed to address the behavior of multiple step citation flow. Capturing the citation dynamics with higher-order dependencies reveals the actual impact of papers, including necessary self-citations that are sometimes excluded in prior studies. Quantum PageRank is utilized in this paper to help differentiating nodes whose PageRank values are identical. [ABSTRACT FROM AUTHOR]

Chemistry researchers are frequently evaluated on the perceived significance of their work with the citation count as the most commonly-used metric for gauging this property. Recent studies have called for a broader evaluation of significance that includes more nuanced bibliometrics as well as altmetrics to more completely evaluate scientific research. To better understand the relationship between metrics and peer judgements of significance in chemistry, we have conducted a survey of chemists to investigate their perceptions of previously published research. Focusing on a specific issue of the Journal of the American Chemical Society published in 2003, respondents were asked to select which articles they thought best matched importance and significance given several contexts: highest number of citations, most significant (subjectively defined), most likely to share among chemists, and most likely to share with a broader audience. The answers to the survey can be summed up in several observations. The ability of respondents to predict the citation counts of established research is markedly lower than the ability of those counts to be predicted by the h-index of the corresponding author of each article. This observation is conserved even when only considering responses from chemists whose expertise falls within the subdiscipline that best describes the work performed in an article. Respondents view both cited papers and significant papers differently than papers that should be shared with chemists. We conclude from our results that peer judgements of importance and significance differ from metrics-based measurements, and that chemists should work with bibliometricians to develop metrics that better capture the nuance of opinions on the importance of a given piece of research. [ABSTRACT FROM AUTHOR]

Pyocyanin is a toxin produced by Pseudomonas aeruginosa. Here we describe a novel paper-based electrochemical sensor for pyocyanin detection, manufactured with a simple and inexpensive approach based on electrode printing on paper. The resulting sensors constitute an effective electrochemical method to quantify pyocyanin in bacterial cultures without the conventional time consuming pretreatment of the samples. The electrochemical properties of the paper-based sensors were evaluated by ferri/ferrocyanide as a redox mediator, and showed reliable sensing performance. The paper-based sensors readily allow for the determination of pyocyanin in bacterial cultures with high reproducibility, achieving a limit of detection of 95 nM and a sensitivity of 4.30 μA/μM in standard culture media. Compared to the similar commercial ceramic based sensors, it is a 2.3-fold enhanced performance. The simple in-house fabrication of sensors for pyocyanin quantification allows researchers to understand in vitro adaptation of P. aeruginosa infections via rapid screenings of bacterial cultures that otherwise are expensive and time-consuming. [ABSTRACT FROM AUTHOR]

Introduction: Data collection using paper-based questionnaires can be time consuming and return errors affect data accuracy, completeness, and information quality in health surveys. We compared smartphone and paper-based data collection systems in the Burden of Obstructive Lung Disease (BOLD) study in rural Sudan. Methods: This exploratory pilot study was designed to run in parallel with the cross-sectional household survey. The Open Data Kit was used to programme questionnaires in Arabic into smartphones. We included 100 study participants (83% women; median age = 41.5 ± 16.4 years) from the BOLD study from 3 rural villages in East-Gezira and Kamleen localities of Gezira state, Sudan. Questionnaire data were collected using smartphone and paper-based technologies simultaneously. We used Kappa statistics and inter-rater class coefficient to test agreement between the two methods. Results: Symptoms reported included cough (24%), phlegm (15%), wheezing (17%), and shortness of breath (18%). One in five were or had been cigarette smokers. The two data collection methods varied between perfect to slight agreement across the 204 variables evaluated (Kappa varied between 1.00 and 0.02 and inter-rater coefficient between 1.00 and -0.12). Errors were most commonly seen with paper questionnaires (83% of errors seen) vs smartphones (17% of errors seen) administered questionnaires with questions with complex skip-patterns being a major source of errors in paper questionnaires. Automated checks and validations in smartphone-administered questionnaires avoided skip-pattern related errors. Incomplete and inconsistent records were more likely seen on paper questionnaires. Conclusion: Compared to paper-based data collection, smartphone technology worked well for data collection in the study, which was conducted in a challenging rural environment in Sudan. This approach provided timely, quality data with fewer errors and inconsistencies compared to paper-based data collection. We recommend this method for future BOLD studies and other population-based studies in similar settings. [ABSTRACT FROM AUTHOR]

Background: There is widespread evidence that statistical methods play an important role in original research articles, especially in medical research. The evaluation of statistical methods and reporting in journals suffers from a lack of standardized methods for assessing the use of statistics. The objective of this study was to develop and evaluate an instrument to assess the statistical intensity in research articles in a standardized way. Methods: A checklist-type measure scale was developed by selecting and refining items from previous reports about the statistical contents of medical journal articles and from published guidelines for statistical reporting. A total of 840 original medical research articles that were published between 2007–2015 in 16 journals were evaluated to test the scoring instrument. The total sum of all items was used to assess the intensity between sub-fields and journals. Inter-rater agreement was examined using a random sample of 40 articles. Four raters read and evaluated the selected articles using the developed instrument. Results: The scale consisted of 66 items. The total summary score adequately discriminated between research articles according to their study design characteristics. The new instrument could also discriminate between journals according to their statistical intensity. The inter-observer agreement measured by the ICC was 0.88 between all four raters. Individual item analysis showed very high agreement between the rater pairs, the percentage agreement ranged from 91.7% to 95.2%. Conclusions: A reliable and applicable instrument for evaluating the statistical intensity in research papers was developed. It is a helpful tool for comparing the statistical intensity between sub-fields and journals. The novel instrument may be applied in manuscript peer review to identify papers in need of additional statistical review. [ABSTRACT FROM AUTHOR]

Research paper recommenders emerged over the last decade to ease finding publications relating to researchers’ area of interest. The challenge was not just to provide researchers with very rich publications at any time, any place and in any form but to also offer the right publication to the right researcher in the right way. Several approaches exist in handling paper recommender systems. However, these approaches assumed the availability of the whole contents of the recommending papers to be freely accessible, which is not always true due to factors such as copyright restrictions. This paper presents a collaborative approach for research paper recommender system. By leveraging the advantages of collaborative filtering approach, we utilize the publicly available contextual metadata to infer the hidden associations that exist between research papers in order to personalize recommendations. The novelty of our proposed approach is that it provides personalized recommendations regardless of the research field and regardless of the user’s expertise. Using a publicly available dataset, our proposed approach has recorded a significant improvement over other baseline methods in measuring both the overall performance and the ability to return relevant and useful publications at the top of the recommendation list. [ABSTRACT FROM AUTHOR]

With the rapid development of information technology, scientific social networks (SSNs) have become the fastest and most convenient way for researchers to communicate with each other. Many published papers are shared via SSNs every day, resulting in the problem of information overload. How to appropriately recommend personalized and highly valuable papers for researchers is becoming more urgent. However, when recommending papers in SSNs, only a small amount of positive instances are available, leaving a vast amount of unlabelled data, in which negative instances and potential unseen positive instances are mixed together, which naturally belongs to One-Class Collaborative Filtering (OCCF) problem. Therefore, considering the extreme data imbalance and data sparsity of this OCCF problem, a hybrid approach of Social and Content aware One-class Recommendation of Papers in SSNs, termed SCORP, is proposed in this study. Unlike previous approaches recommended to address the OCCF problem, social information, which has been proved playing a significant role in performing recommendations in many domains, is applied in both the profiling of content-based filtering and the collaborative filtering to achieve superior recommendations. To verify the effectiveness of the proposed SCORP approach, a real-life dataset from CiteULike was employed. The experimental results demonstrate that the proposed approach is superior to all of the compared approaches, thus providing a more effective method for recommending papers in SSNs. [ABSTRACT FROM AUTHOR]

The digital economy has become a driving force in the rapid development of the global economy and the promotion of export trade. Pivotal in its advent, the digital transformation of enterprises utilizes cloud computing, big data, artificial intelligence, and other digital technologies to provide an impetus for evolution and transformation in various industries and fields. in enhancing quality and efficiency. This has been critical for enhancing both quality and efficiency in enterprises based in the People's Republic of China. Through the available data on its listed enterprises, this paper measures their digital transformation through a textual analysis and examines how this transformation influences their export product quality. We then explore the possible mechanisms at work in this influence from the perspective of enterprise heterogeneity. The results find that: (1) Digital transformation significantly enhances the export product quality in an enterprises, and the empirical findings still hold after a series of robustness tests; (2) Further mechanism analysis reveals that the digital transformation can positively affect export product quality through the two mechanisms of process productivity (φ), the ability to produce output using fewer variable inputs, and product productivity (ξ), the ability to produce quality with fewer fixed outlays; (3) In terms of enterprise heterogeneity, the impact of digital transformation on export product quality is significant for enterprises engaged in general trade or high-tech industries and those with strong corporate governance. In terms of heterogeneity in digital transformation of enterprise and the regional digital infrastructure level, the higher the level of digital transformation and regional digital infrastructure, the greater the impact of digital transformation on export product quality. This paper has practical implications for public policies that offer vital aid to enterprises as they seek digital transformation to remain sync with the digital economy, upgrade their product quality, and drive the sustainable, high-quality, and healthy development of their nation's economy. [ABSTRACT FROM AUTHOR]

Swarm-founding wasps are endemic and common representatives of neotropical fauna and compose an interesting social tribe of vespids, presenting both complex social characteristics and uncommon traits for a eusocial group, such as the absence of castes with distinct morphology. The paper wasp Protonectarina sylveirae (Saussure) presents a broad distribution from Brazil, Argentina and Paraguay, occurring widespread in the Atlantic rainforest and arboreal Caatinga, being absent in the Amazon region. Given the peculiar distribution among swarm-founding wasps, an integrative approach to reconstruct the evolutionary history of P. sylveirae in a spatial-temporal framework was performed to investigate: the presence of genetic structure and its relationship with the geography, the evolution of distinct morphologic lineages and the possible historical event(s) in Neotropical region, which could explain the observed phylogeographic pattern. Individuals of P. sylveirae were obtained from populations of 16 areas throughout its distribution for DNA extraction and amplification of mitochondrial genes 12S, 16S and COI. Analysis of genetic diversity, construction of haplotype net, analysis of population structure and dating analysis of divergence time were performed. A morphometric analysis was also performed using 8 measures of the body of the adult (workers) to test if there are morphological distinction among populations. Thirty-five haplotypes were identified, most of them exclusively of a group and a high population structure was found. The possibility of genetic divergence because of isolation by distance was rejected. Morphological analysis pointed to a great uniformity in phenotypes, with only a small degree of differentiation between populations of south and the remaining. Divergence time analysis showed a Middle/Late Miocene origin, a period where an extensive marine ingression occurred in South America. Divergence of haplogroups began from the Plio/Pleistocene boundary and the last glacial maximum most likely modeled the current distribution of species, even though it was not the cause of genetic breaks. [ABSTRACT FROM AUTHOR]

The action required to stem the environmental and social implications of climate change depends crucially on how humankind shapes technology, economy, lifestyle and policy. With transport CO2 emissions accounting for about a quarter of the total, we examine the contribution of CO2 output by scientific travel. Thankfully for the reputation of the scientific community, CO2 emissions associated with the trips required to present a paper at a scientific conference account for just 0.003% of the yearly total. However, with CO2 emissions for a single conference trip amounting to 7% of an average individual’s total CO2 emissions, scientists should lead by example by demonstrating leadership in addressing the issue. [ABSTRACT FROM AUTHOR]

Understanding intraspecific geographic variation in animal signals poses a challenging evolutionary problem. Studies addressing geographic variation typically focus on signals used in mate-choice, however, geographic variation in intrasexual signals involved in competition is also known to occur. In Polistes dominulus paper wasps, females have black facial spots that signal dominance: individuals wasps with more complex 'broken' facial patterns are better fighters and are avoided by rivals. Recent work suggests there is dramatic geographic variation in these visual signals of quality, though this variation has not been explicitly described or quantified. Here, we analyze variation in P. dominulus signals across six populations and explore how environmental conditions may account for this variation. Overall, we found substantial variation in facial pattern brokenness across populations and castes. Workers have less broken facial patterns than gynes and queens, which have similar facial patterns. Strepsipteran parasitism, body size and temperature are all correlated with the facial pattern variation, suggesting that developmental plasticity likely plays a key role in this variation. First, the extent of parasitism varies across populations and parasitized individuals have lower facial pattern brokenness than unparasitized individuals. Second, there is substantial variation in body size across populations and a weak but significant relationship between facial pattern brokenness and body size. Wasps from populations with smaller body size (e.g. Italy) tend to have less broken facial patterns than wasps from populations with larger body size (e.g. New York, USA). Third, there is an apparent association between facial patterns and climate, with wasp from cooler locations tending to have higher facial pattern brokenness than wasps from warmer locations. Additional experimental work testing the causes and consequences of facial pattern variation will be important, as geographic variation in signals has important consequences for the evolution of communication systems and social behavior. [ABSTRACT FROM AUTHOR]

Type 1 diabetes (T1D) is increasing in incidence and predictable with measurement of serum islet autoantibodies (iAb) years prior to clinical disease onset. Identifying iAb positive individuals reduces diabetic ketoacidosis and identifies individuals for T1D prevention trials. However, large scale screening for iAb remains challenging as assays have varying sensitivities and specificities, insulin autoantibodies remain difficult to measure and venipuncture is generally required to obtain serum. We developed an approach to reliably measure all four major iAb, including insulin autoantibodies, from dried blood spots (DBS) on filter-paper. By spiking iAb positive serum into iAb negative whole blood in a dose titration, we optimized the conditions for autoantibody elution from filter paper as measured by fluid phase radioimmunoassays. After assessing stability of measuring iAb from DBS over time, we then screened iAb from DBS and the corresponding serum in new-onset T1D (n = 52), and controls (n = 72) which included first-degree relatives of T1D patients. iAb measured from eluted DBS in new-onset T1D strongly correlated with serum measurements (R2 = 0.96 for mIAA, GADA = 0.94, IA-2A = 0.85, ZnT8A = 0.82, p<0.01 for each autoantibody). There were no false positives in control subjects, and 5/6 with previously unknown iAb positivity in sera were detected using DBS. With further validation, measuring iAb from DBS can be a reliable method to screen for T1D risk. [ABSTRACT FROM AUTHOR]

This paper investigates the long-run and short-run relationship between money supply and inflation in Pakistan, utilizing annual data spanning from 1981 to 2021. The key objective is to assess the impact of monetary policy, specifically money supply, on inflation dynamics in the country. To achieve this, the Autoregressive Distributed Lag (ARDL) bounds testing approach is employed, which is suitable for analyzing cointegration among variables with mixed integration orders. The results reveal both short and long-run cointegration between inflation, money supply, unemployment, and interest rates. Notably, unemployment demonstrates a negative correlation with inflation, while money supply and interest rates exhibit a positive relationship. These findings underscore the importance of dedicated policy measures to manage inflation effectively. The paper concludes by recommending the establishment of a policy implementation body and collaboration between the government and the central bank to ensure financial stability and control inflation through well-calibrated monetary and fiscal policies. [ABSTRACT FROM AUTHOR]

This paper introduces network science to museum studies. The spatial structure of the museum and the exhibit display largely determine what visitors see and in which order, thereby shaping their visit experience. Despite the importance of spatial properties in museum studies, few scientific tools have been developed to analyze and compare the results across museums. This paper introduces the six habitually used network science indices and assesses their applicability to museum studies. Network science is an empirical research field that focuses on analyzing the relationships between components in an attempt to understand how individual behaviors can be converted into collective behaviors. By taking the museum and the visitors as the network, this methodology could reveal unknown aspects of museum functions and visitor behavior, which could enhance exhibition knowledge and lead to better methods for creating museum narratives along the routes. [ABSTRACT FROM AUTHOR]

Neighborhood effects are a common strategy for rural households to deal with irrational situations such as deficient information and ability. Based on the 2019 CHFS survey data, we designed a Probit model to verify whether neighborhood effects exist in the online financial investment of rural households. Our paper constructs a multiple mediation model to explore its mechanism. Otherwise, we execute the heterogeneity analysis by dividing the total sample into groups. Our paper proved that (1) Rural households have significant neighborhood effects on online financial investment. (2) Heterogeneity analysis shows that neighborhood effects are stronger among women, the younger, low-education, and low-income rural households. (3) Through the multiple mediation model, we proved that the neighborhood effects on online financial investment of the peasant household work by the financial knowledge spillover and risk-taking enhancement. Our study conduces to a better understanding of the financial decision-making of rural households, which may provide a practical implication for the popularization of new financial products and the optimal design of policy interventions. [ABSTRACT FROM AUTHOR]

This paper presents a custom made small rover based surveying, mapping and building information modeling solution. Majority of the commercially available mobile surveying systems are larger in size which restricts their maneuverability in the targeted indoor vicinities. Furthermore their functional cost is unaffordable for low budget projects belonging to developing markets. Keeping in view these challenges, an economical indigenous rover based scanning and mapping system has developed using orthogonal integration of two low cost RPLidar A1 laser scanners. All the instrumentation of the rover has been interfaced with Robot Operating System (ROS) for online processing and recording of all sensorial data. The ROS based pose and map estimations of the rover have performed using Simultaneous Localization and Mapping (SLAM) technique. The perceived class 1 laser scans data belonging to distinct vicinities with variable reflective properties have been successfully tested and validated for required structural modeling. Systematically the recorded scans have been used in offline mode to generate the 3D point cloud map of the surveyed environment. Later the structural planes extraction from the point cloud data has been done using Random Sampling and Consensus (RANSAC) technique. Finally the 2D floor plan and 3D building model have been developed using point cloud processing in appropriate software. Multiple interiors of existing buildings and under construction indoor sites have been scanned, mapped and modelled as presented in this paper. In addition, the validation of the as-built models have been performed by comparing with the actual architecture design of the surveyed buildings. In comparison to available surveying solutions present in the local market, the developed system has been found faster, accurate and user friendly to produce more enhanced structural results with minute details. [ABSTRACT FROM AUTHOR]

This paper presents a novel sound event detection (SED) system for rare events occurring in an open environment. Wavelet multiresolution analysis (MRA) is used to decompose the input audio clip of 30 seconds into five levels. Wavelet denoising is then applied on the third and fifth levels of MRA to filter out the background. Significant transitions, which may represent the onset of a rare event, are then estimated in these two levels by combining the peak-finding algorithm with the K-medoids clustering algorithm. The small portions of one-second duration, called 'chunks' are cropped from the input audio signal corresponding to the estimated locations of the significant transitions. Features from these chunks are extracted by the wavelet scattering network (WSN) and are given as input to a support vector machine (SVM) classifier, which classifies them. The proposed SED framework produces an error rate comparable to the SED systems based on convolutional neural network (CNN) architecture. Also, the proposed algorithm is computationally efficient and lightweight as compared to deep learning models, as it has no learnable parameter. It requires only a single epoch of training, which is 5, 10, 200, and 600 times lesser than the models based on CNNs and deep neural networks (DNNs), CNN with long short-term memory (LSTM) network, convolutional recurrent neural network (CRNN), and CNN respectively. The proposed model neither requires concatenation with previous frames for anomaly detection nor any additional training data creation needed for other comparative deep learning models. It needs to check almost 360 times fewer chunks for the presence of rare events than the other baseline systems used for comparison in this paper. All these characteristics make the proposed system suitable for real-time applications on resource-limited devices. [ABSTRACT FROM AUTHOR]

Android malware is becoming more common, and its invasion of smart devices has brought immeasurable losses to people's lives. Most existing Android malware detection methods extract Android features from the original application files without considering the high-order hidden information behind them, but these hidden information can reflect malicious behaviors. To solve this problem, this paper proposes Z2F, a detection framework based on multidimensional Android feature extraction and graph neural networks for Android applications. Z2F first extracts seven types of Android features from the original Android application and then embeds them into a heterogeneous graph. On this basis, we design 12 kinds of meta-structures to analyze different semantic spaces of heterogeneous graphs, mine high-order hidden semantic information, and adopt a multi-layer graph attention mechanism to iteratively embed and update information. In this paper, a total of 14429 Android applications were detected and 1039726 Android features were extracted, with a detection accuracy of 99.7%. [ABSTRACT FROM AUTHOR]

Taking Chinese non-financial A-share companies listed on the Shenzhen Stock Exchange (SZSE) between 2003 and 2018 as a sample, this paper empirically examines whether and how institutional investors' site visits (SVs) affect corporate investment-cash flow sensitivity (ICFS). The results show that institutional investors' SVs can reduce ICFS, and this effect is more obvious for companies with fewer investment opportunities, larger sizes, higher internal cash flows, and higher agency costs, indicating that institutional investors' SVs primarily inhibit ICFS caused by agency conflicts rather than financing constraints. In addition, the inhibitory effect of institutional investors' SVs on ICFS exists mainly in companies with poor internal supervision governance and weak executive compensation incentive mechanisms, indicating that institutional investors' SVs and other forms of corporate governance mechanisms operate as substitutes in reducing ICFS. This paper reveals the important role of institutional investors' SVs in reducing ICFS, with important theoretical and practical implications for regulators to progressively regulate and promote this form of investor activity. [ABSTRACT FROM AUTHOR]

Although the Chinese government has implemented a variety of measures, the gender wage gap in 21st century China has not decreased. A significant body of literature has studied this phenomenon using sector segmentation theory, but these studies have overlooked the importance of the collective economy beyond the public and private sectors. Moreover, they have lacked assessment of the gender wage gap across different wage groups, hindering an accurate estimation of the gender wage gap in China, and the formulation of appropriate recommendations. Utilizing micro-level data from 2004, 2008, and 2013, this paper examines trends in the gender wage gap within the public sector, private sector, and collective economy. Employing a selection bias correction based on the multinomial logit model, this study finds that the gender wage gap is smallest and most stable within the public sector. Furthermore, the private sector surpasses the collective economy in this period, becoming the sector with the largest gender wage gap. Meanwhile, a recentered influence function regression reveals a substantial gender wage gap among the low-wage population in all three sectors, as well as among the high-wage population in the private sector. Additionally, employing Brown wage decomposition, this study concludes that inter-sector, rather than intra-sector, differences account for the largest share of the gender wage gap, with gender discrimination in certain sectors identified as the primary cause. Finally, this paper provides policy recommendations aimed at addressing the gender wage gap among low-wage groups and within the private sector. [ABSTRACT FROM AUTHOR]

The Zarafshan River runs from the mountains of Tajikistan and terminates in the sands of the Kyzyl-Kum Desert in Uzbekistan; it served as a communication route and homeland for the Sogdians. The Sogdians are historically depicted as merchants existing from the end of the first millennium BC through the first millennium AD. While recent research has provided the first glimpse into cultivation, commerce, communication, and consumption in the Lower Zarafshan, the agricultural heartland of the Middle Zarafshan Basin has remained unstudied. This paper presents the results of archaeobotanical investigations conducted at five ancient urban sites/areas spanning the fifth to the twelfth centuries AD: Kainar (Penjikent citadel), Penjikent (shahristan), Sanjar-Shah, Kuk-Tosh (pre-Mongol Penjikent), and Afrasiab. Collectively, these data show that cereals, legumes, oil/fiber crops, fruits, and nuts were cultivated on the fertile Zarafshan floodplains. In this paper, we discuss evidence for the diversification of the agricultural assemblage over time, including the introduction of new staple crops and fruits into an already complex cultivation system. In addition, we contrast our data with previously published results from sites along the course of the Zarafshan to determine whether there is a dietary difference between pre-and post-Islamic conquest periods at settlements located along the river. [ABSTRACT FROM AUTHOR]

The rural B&B industry is a key component of rural tourism, local economic development, and the wider rural revitalization strategy. Despite the abundance of tourism resources in Yunnan, the B&B sector faces significant challenges. It is therefore imperative to accurately identify the most pressing issues within the current B&B industry and formulate appropriate solutions to advance Yunnan's rural revitalization efforts. This study uses recent reviews of rural B&Bs on Ctrip.com and employs machine learning techniques, including Bert, CNN, LSTM, and GRU, to identify the key management challenges currently facing Yunnan's rural B&B industry. An analysis is then conducted to identify the key stakeholders involved in the process of improving the management of Yunnan's B&Bs. To assess the willingness of each stakeholder to support the improvement of the rural B&B industry, this paper establishes a three-party evolutionary game model and examines the dynamic evolutionary process of management improvement within Yunnan's rural B&B industry. Two scenarios of evolutionarily stable strategies are analyzed, and parameters impacting stakeholders' strategy choices are simulated and evaluated. The results show that: i) Improving the "human factor" is the top priority for the current management improvement because tourists are most concerned about the emotional experience. Operators need to focus on improving service attitude and emotional experience; ii) The main stakeholders in the current management optimization process of Yunnan B&Bs are the local government, B&B operators, and tourists. Under appropriate conditions, the evolutionarily stable strategy of (1, 1, 1) is reachable. iii) variables such as additional costs, tourists' choice preferences, and government penalties significantly affect the strategy choices of stakeholders, especially B&B operators. This paper offers effective strategies for improving B&B management that can benefit the government, B&B operators, and tourists, and ultimately contribute to the promotion of quality rural revitalization. The paper not only identifies focal areas for improving B&B management in rural Yunnan, but also provides an in-depth understanding of stakeholder dynamics. As a result, it provides valuable insights to further the cause of quality rural revitalization. [ABSTRACT FROM AUTHOR]

The text describes improvements made to the random forest model to enhance its distinctiveness in addressing tax risks within the real estate industry, thereby tackling issues related to tax losses. Firstly, the paper introduces the potential application of the random forest model in identifying tax risks. Subsequently, the experimental analysis focuses on the selection of indicators for tax risk. Finally, the paper develops and utilizes actual taxpayer data to test a risk identification model, confirming its effectiveness. The experimental results indicate that the model's output report includes basic taxpayer information, a summary of tax compliance risks, value-added tax refund situations, directions of suspicious items, and detailed information on common indicators. This paper comprehensively presents detailed taxpayer data, providing an intuitive understanding of tax-related risks. Additionally, the paper reveals the level of enterprise risk registration assessment, risk probability, risk value, and risk assessment ranking. Further analysis shows that enterprise risk points primarily exist in operating income, selling expenses, financial expenses, and total profit. Additionally, the results indicate significant differences between the model's judgment values and declared values, especially in the high-risk probability of total operating income and profit. This implies a significant underreporting issue concerning corporate income tax for real estate enterprises. Therefore, this paper contributes to enhancing the identification of tax risks for real estate enterprises. Using the optimized random forest model makes it possible to accurately assess enterprises' tax compliance risks and identify specific risk points. [ABSTRACT FROM AUTHOR]

Since the concept of toughness was introduced to transportation systems, transportation system toughness has received extensive attention from researchers in the field of transportation worldwide. In this paper, a methodology for quantifying and assessing the toughness of urban transportation systems is proposed in the context of the New Crown epidemic. Firstly, the definition of urban transportation system toughness in this context is clarified, and the entropy evaluation method is applied to construct the performance curve of urban transportation systems over time. Then, it is proposed to quantify the system's resistance, recovery, and adaptive ability in terms of the change in the cumulative amount of system performance. Finally, the three characteristic abilities of system toughness are organically combined to obtain a comprehensive assessment of system toughness. Example calculations and analyses are carried out in four Chinese cities with different levels of development, and the results show that the performance of urban transportation systems is positively correlated with their levels of development, and all of them fluctuate greatly under the influence of the epidemic, but Wuhan has the strongest resistance and recovery ability of the transportation system, and shows the highest toughness, followed by Lanzhou, Changchun, and Shanghai. The system toughness quantification and assessment methods proposed in this paper provide a reference for research on improving the ability of urban transportation systems to deal with multiple uncertainty disturbances. [ABSTRACT FROM AUTHOR]

This paper takes the risk preference of modern listed companies as the research object, uses the financial data of Chinese listed companies combined with the literary inquisition file in Qing Dynasty to conduct an empirical study, and examines the influence of literary inquisition on the risk preference of modern corporate CEOs in Qing Dynasty. The study found that the literary inquisition incident in Qing Dynasty significantly affected and reduced the risk preference of modern enterprises. The competitive hypothesis of the influence of Confucian culture and China City Commercial Credit Environment Index (CEI) on CEOs' risk preference is excluded. In addition, through the study of heterogeneity, this paper also verifies that the influence of literary inquisition is more significant in areas with a higher degree of marketization, indicating that the influence of informal institutions depends on the establishment of formal institutions. Finally, in the mechanism study, this paper points out that the rulers' suppression of ideas will change long-term social capital and lead to the decrease of general trust in society, which will make the enterprise managers born in the region tend to be conservative in their risk preference. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has become one of the most popular technologies in recent years. Advances in computing capabilities, hardware accessibility, and wireless connectivity make possible communication between people, processes, and devices for all kinds of applications and industries. However, the deployment of this technology is confined almost entirely to tech companies, leaving end users with only access to specific functionalities. This paper presents a framework that allows users with no technical knowledge to build their own IoT applications according to their needs. To this end, a framework consisting of two building blocks is presented. A friendly interface block lets users tell the system what to do using simple operating rules such as "if the temperature is cold, turn on the heater." On the other hand, a fuzzy logic reasoner block built by experts translates the ambiguity of human language to specific actions to the actuators, such as "call the police." The proposed system can also detect and inform the user if the inserted rules have inconsistencies in real time. Moreover, a formal model is introduced, based on fuzzy description logic, for the consistency of IoT systems. Finally, this paper presents various experiments using a fuzzy logic reasoner to show the viability of the proposed framework using a smart-home IoT security system as an example. [ABSTRACT FROM AUTHOR]

This paper utilizes an improved undesirable output DEA model to measure the eco-efficiency of cities in five major urban agglomerations in China during 2006–2020. It employs the Theil Index and Geodetector to investigate the spatial-temporal distribution differentiation characteristics and driving factors of urban eco-efficiency. The main findings are as follows. Firstly, the eco-efficiency of all urban agglomerations showed a fluctuating upward trend, but the eco-efficiency performance of different urban agglomerations in China shows a stratification characteristic. Specifically, the Pearl River Delta urban agglomeration consistently ranks first in China, while the mean values of the Yangtze River Delta urban agglomeration, Beijing-Tianjin-Hebei urban agglomeration, and Chengdu-Chongqing urban agglomeration are lower than the national average. Secondly, the overall differences in the urban eco-efficiency of all sample cities show a consistently fluctuating downward trend. The factor that affects the level differences of eco-efficiency in different cities is the intra-regional differences. Last but not least, the top three factors affecting the spatial distribution difference of urban eco-efficiency in the whole sample are environmental pollution control investments, innovation level, and environmental infrastructure investments. In the end, this paper proposes that reducing the intra-regional differences is the primary task to achieve the coordinated improvement of urban eco-efficiency in urban agglomerations, and then puts forward some policy suggestions to improve eco-efficiency for the five major urban agglomerations. [ABSTRACT FROM AUTHOR]

This study is a comprehensive assessment of the waste management system in Besisahar municipality. Information and some data have been collected from the municipality of Besisahar, followed by interviews with municipal officials responsible for waste management, stakeholders, waste workers, and residents. A total of 230 households, 20 schools, 10 government and private offices, 10 financial institutions, 60 commercial hotels, restaurants, and shops, and 20 medical shops and healthcare institutions, were selected in this study by random sampling. An extensive field study was conducted within all municipal wards and at dump sites. The results indicated that 42.14% of solid waste was collected through door-to-door collection services, 5.87% was mismanaged in open public places, 11.21% was used as compost manure, and the rest was discarded on riverbanks, dug up, and burned. A large component of the characterization of household waste consisted of organic waste (68.03%), followed by paper/paper products (8.13%), agricultural waste (5.5%), plastic (5.21%), construction (3.81%), textile (2.72%), metals (0.54%), glass (1.01%), rubber (0.10%), electronic (0.05%), pharmaceutical (0.1%) and others (4.78%) in the Besishahar municipality. Solid waste generation was found to be at 197.604 g/capita/day, as revealed by cluster sampling in 230 households. Around 4.285 tons-solid waste/day were generated in urban areas, while 16.13 tons-solid waste/day was estimated for the whole municipality. An important correlation between the parameters of solid waste was found by statistical analysis. Currently, solid waste is dumped on riverbanks, open fields, and springs, creating environmental and health hazards. The findings of this study will be useful to Besisahar municipality and its stakeholders in forming policies that facilitate waste management practices in this region and promote sustainable waste management systems. [ABSTRACT FROM AUTHOR]

Multiple studies across a variety of scientific disciplines have shown that the number of times that a paper is shared on Twitter (now called X) is correlated with the number of citations that paper receives. However, these studies were not designed to answer whether tweeting about scientific papers causes an increase in citations, or whether they were simply highlighting that some papers have higher relevance, importance or quality and are therefore both tweeted about more and cited more. The authors of this study are leading science communicators on Twitter from several life science disciplines, with substantially higher follower counts than the average scientist, making us uniquely placed to address this question. We conducted a three-year-long controlled experiment, randomly selecting five articles published in the same month and journal, and randomly tweeting one while retaining the others as controls. This process was repeated for 10 articles from each of 11 journals, recording Altmetric scores, number of tweets, and citation counts before and after tweeting. Randomization tests revealed that tweeted articles were downloaded 2.6–3.9 times more often than controls immediately after tweeting, and retained significantly higher Altmetric scores (+81%) and number of tweets (+105%) three years after tweeting. However, while some tweeted papers were cited more than their respective control papers published in the same journal and month, the overall increase in citation counts after three years (+7% for Web of Science and +12% for Google Scholar) was not statistically significant (p > 0.15). Therefore while discussing science on social media has many professional and societal benefits (and has been a lot of fun), increasing the citation rate of a scientist's papers is likely not among them. [ABSTRACT FROM AUTHOR]

Channel coding technology plays an important role in wireless communication systems, and it serves as a crucial mechanism to reduce interference during the transmission process. As the fifth-generation (5G) and sixth-generation (6G) wireless communication systems rapidly advance, requirements of the users on the quality and security of wireless service are increasing. To solve these problems, it calls for us to explore the new channel coding technologies. In this paper, a linear feedback coding scheme for fading multiple-access channels with degraded message sets (FMAC-DMS) is proposed. In this scheme, the transmitting beamforming and channel splitting are used to transform the channel with complex signals into scalar equivalent sub-channels. Then, the extended Schalkwijk-Kailath coding scheme (SK) is further applied to each sub-channel. The channel capacity, finite blocklength (FBL) sum-rate and FBL secrecy achievable sum-rate of the FMAC-DMS in single-input single-output (SISO) and multi-input single-output (MISO) cases are derived. Finally, we show that the proposed scheme not only provides a FBL coding solution but also guarantees physical layer security(PLS). The numerical and simulation results show the effectiveness of the proposed scheme as a channel coding solution. The study of this paper provides a new method to construct a practical FBL scheme for the FMAC-DMS. [ABSTRACT FROM AUTHOR]

The paper aims to conduct an analysis of pricing strategies in a dual channel supply chain under external uncertainty, utilizing Interval numbers theory and Game theory as the theoretical basis. The focus is on maximizing the expected profits of manufacturers and retailers. Four models are considered: centralized decision-making, manufacturer's Stackelberg, retailer's Stackelberg strategy, and vertical Nash model, with the decision variable being the product price. By solving the game model, the paper compares the optimal decisions under the four models and conducts sensitivity analysis to reflect the influence of key parameters and analyze their relationships. The ultimate goal is to optimize profits under various circumstances by adjusting market potential and price parameters to determine the best price level. The findings suggest that decision-maker's risk indicators have a greater impact on decision results when market demand is less sensitive to price, and that the size of the market has a negative correlation with the impact of decision-maker's risk indicators on decision results. [ABSTRACT FROM AUTHOR]

Marine fishery carbon emissions play a significant role in agricultural carbon emissions, making resource allocation a crucial topic for the overall marine ecological protection. This paper evaluates the dynamic iteration method as a research approach with the factors of resource allocation consisting of value assessment, optimization objective, difference between value assessment and objective, and optimization calculation. The paper selects the shadow price from the Super-SBM model as the judgment function for the goal value, aiming for the fairness criterion. From an equity standpoint, the allocation of carbon resources in marine capture fisheries proves to be unreasonable. The fishery model exhibits an excessive supply of carbon resources, resulting in wastage, while the green fishery model faces a relatively limited supply, with a focus on energy conservation and environmental protection. To address this issue, this paper proposes a new method and discusses the corrective results. This result shows that the stabilization point achieved is a short-term equilibrium rather than a long-term one. By rectifying the social contradiction of profit-oriented approaches, this research provides a fresh perspective for economic studies and applications, particularly in industrial layout and resource utilization optimization. [ABSTRACT FROM AUTHOR]

Background: The global outbreak of COVID-19 has brought to light the profound impact that large-scale disease outbreaks can have on healthcare systems and the dedicated professionals who serve within them. It becomes increasingly important to explore strategies for retaining nurses and physicians within hospital settings during such challenging times. This paper aims to investigate the determinants of retention among nurses and physicians during the COVID-19 pandemic. Method: A systematic review of other potential determinants impacting retention rates during the pandemic was carried out. Secondly, a meta-analysis on the prevalence of intention to leave for nurses and physicians during the COVID-19 pandemic. Findings: A comprehensive search was performed within four electronic databases on March 17 2023. Fifty-five papers were included in the systematic review, whereas thirty-three papers fulfilled the eligibility criteria for the meta-analysis. The systematic review resulted in six themes of determinants impacting intention to leave: personal characteristics, job demands, employment services, working conditions, work relationships, and organisational culture. The main determinants impacting the intention to leave are the fear of COVID-19, age, experience, burnout symptoms and support. Meta-analysis showed a prevalence of intent to leave the current job of 38% for nurses (95% CI: 26%-51%) and 29% for physicians (95% CI: 21%-39%), whereas intention to leave the profession for nurses 28% (95% CI: 21%-34%) and 24% for physicians (95% CI: 23%-25%). Conclusion: The findings of this paper showed the critical need for hospital managers to address the concerning increase in nurses' and physicians' intentions to leave during the COVID-19 pandemic. This intention to leave is affected by a complex conjunction of multiple determinants, including the fear of COVID-19 and the confidence in and availability of personal protective equipment. Moreover, individual factors like age, experience, burnout symptoms, and support are maintained in this review. Understanding the influence of determinants on retention during the COVID-19 pandemic offers an opportunity to formulate prospective strategies for retaining nurses and physicians within hospital settings. [ABSTRACT FROM AUTHOR]

High-density waste drilling fluid contains an abundance of recyclable weighting reagents, direct disposal can pollute the environment. In this paper, the primary mineral composition of a high-density waste drilling fluid from a well in the southwest oil and gas field was analyzed. This paper proposes β-cyclodextrin (β-CD) as a depressant for the recovery of barite from waste drilling fluid. The recovery process was investigated through inverse flotation experiments, and the mechanism was analyzed using zeta potential, contact angle analysis, and FTIR. The flotation experiments showed that under the SDS flotation system, when the pH was 9.0 and the amount of depressant β-CD was 900 g/t, the barite recovery and density reached the highest values, which were 87.41% and 4.042 g/cm3, respectively. Zeta potential experiments, contact angle analysis, and FTIR analysis indicate that β-CD adsorbed onto barite through enhancing the hydrophilicity of barite, electrostatic force adsorption, and strong adsorption, which could not be displayed by SDS through competitive adsorption. Furthermore, β-CD exhibited a selective inhibitory effect on barite and enabled reverse flotation. The mechanism model of the flotation separation process was established. [ABSTRACT FROM AUTHOR]

In solving the whole process of interaction between soft rock and yielding support in high-stress environments in tunnels using mechanical analysis methods, it is challenging to simultaneously satisfy both displacement coordination and static equilibrium at the contact surface between the rock and the support structure. This paper, based on the mechanical analysis of rock and rigid support, considers the impact of the circumferential installation of yielding elements on radial displacement, and proposes displacement approximation and support force approximation methods using displacement coordination and static equilibrium as approximation conditions. The study fits curves of numerical simulation results and laboratory test results of yielding elements, and attempts to directly use the laboratory test data set of yielding elements as computational data. By calculating two circular tunnel examples and comparing the effects of the trisection method, bisection method, and substitution method on the convergence of the displacement approximation method, the effectiveness of the methods proposed in this paper is verified. The research results show that the two approximation algorithms proposed in this paper have good accuracy and reliability in calculating the relative displacement of rock and yielding support structure contact surfaces, and the support force of yielding support. The bisection method outperforms the trisection and substitution methods in terms of stability and convergence. However, there are certain limitations in this study, such as the effectiveness of the algorithm may be influenced by geological conditions; the complexity of actual geological conditions may exceed the assumptions of the current rock-support mechanical analysis model. [ABSTRACT FROM AUTHOR]

The regional networking strategy is widely implemented in China as a normative policy aimed at fostering cohesion and enhancing competitiveness. However, the empirical basis for this strategy remains relatively weak due to limitations in measurement methods and data availability. This paper establishes the urban networks by the enterprise investment data, and then accurately measures the network's external effects of each city by the method of MGWR model. The results show that: (1) Regional networking plays a significant role in urban development, although it is not the dominant factor. (2) The benefits of network connections may vary depending on the location and level of cities. (3) The major cities assume a pivotal role in the urban network. Based upon the aforementioned research conclusions, this paper presents strategic measures to enhance the network's external impacts, aiming to offer insights for other regions in formulating regional development strategies and establishing regional urban networks. [ABSTRACT FROM AUTHOR]

When Chinese coal mines are mining Carboniferous Permian coal seams, the mechanism of water inrush from the roof of the working face usually conforms to the "Upper Three Zones" or "Upper Four Zones" theory. The water inrush passageway is water-conducting fracture zone, and the water inrush position is located in the goaf. However, when mining Jurassic coal seams in Chinese coal mines, the location of water inrush often appears at the head-on working face, above the coal mining machine. Due to the support of the fully mechanized mining support, the roof rock layer cannot collapse and therefore cannot form water-conducting fracture zone. Therefore, the water inrush mechanism cannot be explained by the above two theories. This paper is guided by the Practical Mine Pressure Control Theory, and based on the explanation of the motion forms of bending (pulling) failure movement and shearing (cutting) failure movement, and combined with on-site examples, it is revealed that the passageways leading to from the head-on working face roof in a Jurassic coal seam in the Ordos Basin are splitting zones type, fracture line type and structural fracture type, respectively. Taking the changes in water inflow during the mining process of the 3301 and 3302 working faces in Zhujiamao Coal Mine as examples, this paper reveals the mechanism of water inrush from the head-on working face roof caused by splitting zones type, and proves the existence of this passageway through on-site 3D high-density electrical detection and tracing experiments. Taking two catastrophic water inrush accidents that occurred head-on in the 1309 working face of Guojiahe Coal Industry Co., Ltd. as examples, the water inrush mechanism of the fracture line type and the water inrush mechanism of the structural fracture type were respectively revealed. Based on mechanism of water inrush from head-on roof of working face and the analysis of the on-site water inrush process, a method for distinguishing the type of water inrush passageway from the front roof of the working face is proposed. The results indicate that the Jurassic coal seam mining in the Ordos Basin is prone to shearing (cutting) failure movement, resulting in the frequent formation of the three types of water inrush passageways mentioned above. [ABSTRACT FROM AUTHOR]

With the rapid development of the world city network, the traditional location theory has gradually been disproven, and the advantages of the flow space over the traditional vertical organizational structure are gradually being revealed. Therefore, from corporate branch networks and corporate investment networks, 21 cities in urban agglomerations of Guangdong are taken as case studies for this paper. Furthermore, in this paper, 5 representative types of corporate contact data (catering service, financial service, life service, sports and leisure and accommodation service) are selected, the social network analysis (SNA) method is used to quantitatively analyze the network structure characteristics of urban agglomerations, and a spatial interaction model is constructed to explore the factors influencing. The results indicate that secondary networks have developed in Guangdong. The financial service network is the most complex, followed by the life services, sports and leisure and catering networks. The accommodation service network structure is the simplest. Among all kinds of networks, Guangzhou and Shenzhen have the highest status. The catering and accommodation corporations in Yangjiang in the west have a relatively major external development. Shantou in the east has many branches of various types, while most of the capital exchange in the region is concentrated in Heyuan and Qingyuan in the north. The coefficients of geographical proximity and the urban development level play a significant role in promoting the development of networks. However, administrative capacity limits the attractiveness of origin cities to a certain extent. [ABSTRACT FROM AUTHOR]

Structural planes decrease the strength and stability of rock masses, severely affecting their mechanical properties and deformation and failure characteristics. Therefore, investigation and analysis of structural planes are crucial tasks in mining rock mechanics. The drilling camera obtains image information of deep structural planes of rock masses through high-definition camera methods, providing important data sources for the analysis of deep structural planes of rock masses. This paper addresses the problems of high workload, low efficiency, high subjectivity, and poor accuracy brought about by manual processing based on current borehole image analysis and conducts an intelligent segmentation study of borehole image structural planes based on the U2-Net network. By collecting data from 20 different borehole images in different lithological regions, a dataset consisting of 1,013 borehole images with structural plane type, lithology, and color was established. Data augmentation methods such as image flipping, color jittering, blurring, and mixup were applied to expand the dataset to 12,421 images, meeting the requirements for deep network training data. Based on the PyTorch deep learning framework, the initial U2-Net network weights were set, the learning rate was set to 0.001, the training batch was 4, and the Adam optimizer adaptively adjusted the learning rate during the training process. A dedicated network model for segmenting structural planes was obtained, and the model achieved a maximum F-measure value of 0.749 when the confidence threshold was set to 0.7, with an accuracy rate of up to 0.85 within the range of recall rate greater than 0.5. Overall, the model has high accuracy for segmenting structural planes and very low mean absolute error, indicating good segmentation accuracy and certain generalization of the network. The research method in this paper can serve as a reference for the study of intelligent identification of structural planes in borehole images. [ABSTRACT FROM AUTHOR]

The uneven settlement of the surrounding ground surface caused by subway construction is not only complicated but also liable to cause casualties and property damage, so a timely understanding of the ground settlement deformation in the subway excavation and its prediction in real time is of practical significance. Due to the complex nonlinear relationship between subway settlement deformation and numerous influencing factors, as well as the existence of a time lag effect and the influence of various factors in the process, the prediction performance and accuracy of traditional prediction methods can no longer meet industry demands. Therefore, this paper proposes a surface settlement deformation prediction model by combining noise reduction and attention mechanism (AM) with the long short-term memory (LSTM). The complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and independent component analysis (ICA) methods are used to denoise the input original data and then combined with AM and LSTM for prediction to obtain the CEEMDAN-ICA-AM-LSTM (CIAL) prediction model. Taking the settlement monitoring data of the construction site of Urumqi Rail Transit Line 1 as an example for analysis reveals that the model in this paper has better effectiveness and applicability in the prediction of surface settlement deformation than multiple prediction models. The RMSE, MAE, and MAPE values of the CIAL model are 0.041, 0.033 and 0.384%; R2 is the largest; the prediction effect is the best; the prediction accuracy is the highest; and its reliability is good. The new method is effective for monitoring the safety of surface settlement deformation. [ABSTRACT FROM AUTHOR]

China's agricultural economy has been hindered by insufficient accumulation of agricultural capital and credit constraints. It is worth investigating whether China's county financial marketization reform policy can alleviate these constraints and promote high-quality development of the agricultural economy (HQDAE). This paper presents an empirical analysis of the impact of county financial marketization reform on the HQDAE, based on county panel data. The focus is on the mechanism of county urbanization in the above relationship. The results show that county financial marketization has a significant non-linear impact on the HQDAE. Specifically, it has a 'U-shaped' impact on the overall growth of the agricultural economy and an inverted 'N-shaped' impact on the quality improvement of the agricultural economy. Secondly, the relationship between county financial marketization and the HQDAE is influenced by a threshold effect based on the level of county urbanization. As the level of county urbanization increases, the promoting effect of county financial marketization on HQDAE also increases significantly. Additionally, county financial marketization helps to promote county urbanization and accelerate urban-rural integration, which in turn leads to HQDAE. The research in this paper suggests that county-level local governments should promote a differentiated county financial system. In the early stages of financial market-oriented reform, the government should enhance the capacity of financial services in rural areas through tax breaks, policy incentives, and other measures to prevent financial leakage from agriculture. In the later stages of financial marketization reform, the government should strengthen financial supervision to prevent financial resources from being diverted from industry to capital. Moreover, to achieve the HQDAE, it is necessary to promote county financial market-oriented reform and accelerate the construction of county urbanization. This will help break the dual economic structure of urban and rural areas and promote the flow of financial capital, technology, and human capital from county cities to rural areas. [ABSTRACT FROM AUTHOR]

Gene expression is highly impacted by the environment and can be reflective of past events that affected developmental processes. It is therefore expected that gene expression can serve as a signal of a current or future phenotypic traits. In this paper we identify sets of genes, which we call Prognostic Transcriptomic Biomarkers (PTBs), that can predict firmness in Malus domestica (apple) fruits. In apples, all individuals of a cultivar are clones, and differences in fruit quality are due to the environment. The apples transcriptome responds to these differences in environment, which makes PTBs an attractive predictor of future fruit quality. PTBs have the potential to enhance supply chain efficiency, reduce crop loss, and provide higher and more consistent quality for consumers. However, several questions must be addressed. In this paper we answer the question of which of two common modeling approaches, Random Forest or ElasticNet, outperforms the other. We answer if PTBs with few genes are efficient at predicting traits. This is important because we need few genes to perform qPCR, and we answer the question if qPCR is a cost-effective assay as input for PTBs modeled using high-throughput RNA-seq. To do this, we conducted a pilot study using fruit texture in the 'Gala' variety of apples across several postharvest storage regiments. Fruit texture in 'Gala' apples is highly controllable by post-harvest treatments and is therefore a good candidate to explore the use of PTBs. We find that the RandomForest model is more consistent than an ElasticNet model and is predictive of firmness (r2 = 0.78) with as few as 15 genes. We also show that qPCR is reasonably consistent with RNA-seq in a follow up experiment. Results are promising for PTBs, yet more work is needed to ensure that PTBs are robust across various environmental conditions and storage treatments. [ABSTRACT FROM AUTHOR]

Background: Commercial electroencephalography (EEG) devices have become increasingly available over the last decade. These devices have been used in a wide variety of fields ranging from engineering to cognitive neuroscience. Purpose: The aim of this study was to chart peer-review articles that used consumer-grade EEG devices to collect neural data. We provide an overview of the research conducted with these relatively more affordable and user-friendly devices. We also inform future research by exploring the current and potential scope of consumer-grade EEG. Methods: We followed a five-stage methodological framework for a scoping review that included a systematic search using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. We searched the following online databases: PsycINFO, MEDLINE, Embase, Web of Science, and IEEE Xplore. We charted study data according to application (BCI, experimental research, validation, signal processing, and clinical) and location of use as indexed by the first author's country. Results: We identified 916 studies that used data recorded with consumer-grade EEG: 531 were reported in journal articles and 385 in conference papers. Emotiv devices were used most, followed by the NeuroSky MindWave, OpenBCI, interaXon Muse, and MyndPlay Mindband. The most common usage was for brain-computer interfaces, followed by experimental research, signal processing, validation, and clinical purposes. Conclusions: Consumer-grade EEG is a useful tool for neuroscientific research and will likely continue to be used well into the future. Our study provides a comprehensive review of their application, as well as future directions for researchers who plan to use these devices. [ABSTRACT FROM AUTHOR]

Sunken green space is one of the urban rainwater collection facilities, which belongs to Low Impact Development (LID) techniques. It plays a key role in the construction of sponge city, and the amount of runoff collection is usually affected by the area of the sunken green space, the infiltration rate of the soil, and the annual runoff collection rate. To determine the minimum soil infiltration rate of sunken green space considering the annual runoff collection ratio of sponge cities, this paper selects a residential district in Hefei city, China, as the case study. Based on 45 years of precipitation data, the designed rainfall corresponding to annual runoff collection ratios of 75%, 80% and 85% is 21.3 mm, 23.4 mm and 27.5 mm, respectively. The characteristics of rainfall infiltration in sunken green space are analyzed by using the water balance model and runoff yield and conflux model. The results reveal that the soil infiltration rate is 1.16×10−4 cm/s~3.88×10−3 cm/s when the sunken depth is 0.1 m~0.3 m and that the ratio of green space area is 5%~25%. The runoff collection of the reconstructed sunken green space is 2.87 times and 1.98 times that of the nonsunken green space and the nonreconstructed sunken green space, respectively. That is to say, under the comprehensive performance of the sunken depth, sunken green space area, the steady soil infiltration rater of the reconstructed sunken green space cannot be less than the value obtained in this paper. Otherwise, the requirements of annual total runoff reduction ratio of the sponge city cannot be met. Therefore, this study provides references for realizing the ratio of annual runoff collection and sponge city construction in similar urban areas. It can also be applied to optimal selection of sunken green space in some sponge city projects. [ABSTRACT FROM AUTHOR]

Understanding air quality requires a comprehensive understanding of its various factors. Most of the association rule techniques focuses on high frequency terms, ignoring the potential importance of low- frequency terms and causing unnecessary storage space waste. Therefore, a dynamic genetic association rule mining algorithm is proposed in this paper, which combines the improved dynamic genetic algorithm with the association rule mining algorithm to realize the importance mining of low- frequency terms. Firstly, in the chromosome coding phase of genetic algorithm, an innovative multi-information coding strategy is proposed, which selectively stores similar values of different levels in one storage unit. It avoids storing all the values at once and facilitates efficient mining of valid rules later. Secondly, by weighting the evaluation indicators such as support, confidence and promotion in association rule mining, a new evaluation index is formed, avoiding the need to set a minimum threshold for high-interest rules. Finally, in order to improve the mining performance of the rules, the dynamic crossover rate and mutation rate are set to improve the search efficiency of the algorithm. In the experimental stage, this paper adopts the 2016 annual air quality data set of Beijing to verify the effectiveness of the unit point multi-information coding strategy in reducing the rule storage air, the effectiveness of mining the rules formed by the low frequency item set, and the effectiveness of combining the rule mining algorithm with the swarm intelligence optimization algorithm in terms of search time and convergence. In the experimental stage, this paper adopts the 2016 annual air quality data set of Beijing to verify the effectiveness of the above three aspects. The unit point multi-information coding strategy reduced the rule space storage consumption by 50%, the new evaluation index can mine more interesting rules whose interest level can be up to 90%, while mining the rules formed by the lower frequency terms, and in terms of search time, we reduced it about 20% compared with some meta-heuristic algorithms, while improving convergence. [ABSTRACT FROM AUTHOR]