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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]

Background: Identification of the source of mosquito blood meals is an important component for disease control and surveillance. Recently, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) profiling has emerged as an effective tool for mosquito blood meal identification, using the abdomens of freshly engorged mosquitoes. In the field, mosquito abdomens are crushed on Whatman filter papers to determine the host feeding patterns by identifying the origin of their blood meals. The aim of this study was to test whether crushing engorged mosquito abdomens on Whatman filter papers was compatible with MALDI-TOF MS for mosquito blood meal identification. Both laboratory reared and field collected mosquitoes were tested. Material and methods: Sixty Anopheles gambiae Giles were experimentally engorged on the blood of six distinct vertebrate hosts (human, sheep, rabbit, dog, chicken and rat). The engorged mosquito abdomens were crushed on Whatman filter papers for MALDI-TOF MS analysis. 150 Whatman filter papers, with mosquitoes engorged on cow and goat blood, were preserved. A total of 77 engorged mosquito abdomens collected in the Comoros Islands and crushed on Whatman filter papers were tested with MALDI-TOF MS. Results: The MS profiles generated from mosquito engorged abdomens crushed on Whatman filter papers exhibited high reproducibility according to the original host blood. The blood meal host was correctly identified from mosquito abdomens crushed on Whatman filter papers by MALDI-TOF MS. The MS spectra obtained after storage were stable regardless of the room temperature and whether or not they were frozen. The MS profiles were reproducible for up to three months. For the Comoros samples, 70/77 quality MS spectra were obtained and matched with human blood spectra. This was confirmed by molecular tools. Conclusion: The results demonstrated that MALDI-TOF MS could identify mosquito blood meals from Whatman filter papers collected in the field during entomological surveys. The application of MALDI-TOF MS has proved to be rapid and successful, making it a new and efficient tool for mosquito-borne disease surveillance. [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]

Background: Research on gender inequality is crucial as it unveils the pervasive disparities that persist across various domains, shedding light on societal imbalances and providing a foundation for informed policy-making. Aim: To investigate gender differences in scientometric indices among faculty members in dental schools across Iran. This included overall data and speciality-specific data. Methods: The publication profiles of academic staff in all dental schools were examined using the Iranian Scientometric Information Database (ISID, http://isid.research.ac.ir). Variables analyzed were working field, academic degree, the total number of papers, papers per year, total number of citations, percentage of self-citation, h-index, g-index, citations per paper, gender, university type, number of years publishing, proportion of international papers, first-author papers, and corresponding-author papers. Mann-Whitney and Kruskal-Wallis nonparametric tests were used to analyze the relationship between background characteristics and scientometric indicators. The extracted data were analyzed using R v4.0.1. Results: The database included 1850 faculty members, of which about 60% (1104 of 1850) were women. Men (n = 746) had a higher number of papers (6583 vs. 6255) and citations (60410 vs. 39559) compared with women; 234 of the 376 faculty members with no papers were women. Almost half of the women (N = 517 of 1104) were in Type 2 universities, and nearly half of the men (N = 361 of the 746) were faculty members at Type 1 universities (Type 1 universities ranking higher than Type 2 and 3 universities). The medians of scientometric indices were higher in men, except for self-citation percentage (0 (IQR = 2) vs. 0 (IQR = 3), P = 0.083), international papers percentage (0 (IQR = 7.5) vs. 0 (IQR = 16.7), P<0.001). The proportion of corresponding-author papers was more than 62% higher in women (25 (IQR = 50) vs. 15.4 (IQR = 40), P<0.001). Men had a two-fold higher median h-index (2 (IQR = 4) vs. 1 (IQR = 3), P<0.001). Restorative dentistry and pediatric dentistry had the highest men-to-women ratios (1.5 for both). Dental materials and oral and maxillofacial surgery showed the lowest men-to-women ratios (0.42 and 0.5, respectively). Conclusions: Women made up the majority of dental faculty members in Iran. Nevertheless, men showed better scientometric results in several significant indices. Having identified scientometric information reflecting differences across faculty members, further research is now needed to better understand the drivers of these differences. [ABSTRACT FROM AUTHOR]

Spatial interaction models with spatial origin-destination (OD) filters are powerful tools to characterize trip flows in space, which is a classic and important problem in regional science. To the authors' knowledge, existing studies adopting OD filters mostly specify the spatial dependence as an autoregressive process, which may not be the full picture of spatial effects. To examine the problem, this paper proposes the hypotheses that 1) spatial OD dependences can take place in both the spatial autoregressive term and the spatial error term in a spatial interaction model. 2) Estimating a spatial autoregressive model with spatial autoregressive disturbances (SARAR) model with OD filters would disentangle where the spatial dependence exists and by how much. 3) The marginal effects obtained from SARAR models would be preferred to analysts when SARAR models outperform spatial autoregressive (SAR) models and spatial error models (SEM) from the statistical point of view. To assess these hypotheses, this paper specifies, estimates, and applies SARAR models with OD filters to investigate trip distributions. By comparing against alternative models, this paper investigates the estimation results in SAR, SEM and SARAR models using an empirical data collected from Hangzhou, China. The contribution of this paper is to be the first in developing an SARAR model with OD filters for trip distribution analyses and examining its performance. [ABSTRACT FROM AUTHOR]

Introduction: Peer-reviewed literature is commonly used to assess academic progress and research excellency. However, representation in authorship of global health publications is biased and unfair. In order to shed light on current gaps towards attaining gender equality in scientific production and shift power asymmetries in global health research, we conducted an assessment of authorship trends from 1972 to 2021 with a focus on gender and geographic representation in scientific articles authored or co-authored by researchers affiliated with UNDP/UNFPA/UNICEF/WHO/World Bank Special Programme of Research, Development and Research Training in Human Reproduction (HRP). Methods: We searched PubMed, Web of Science, and HRP public reports for publications where at least one author was affiliated with HRP. Our main outcome measures were author gender and location of author affiliation, classified by region and country income group. We used descriptive statistics to characterize the publications under analysis as well as the total number of authors from the included papers. We applied a logistic regression model to explore associations between author gender and other characteristics of published articles and a time series analysis to assess how time can influence the inclusion of women as authors in a publication. Python and R were used for all analyses. Results: A total of 1,484 publications with 14,424 listed authors representing 5,950 unique authors were included in our analysis: 42.5% were female, 35.1% male, and 22.4% unknown (p<0.0001). First authorship was more likely female (56.9%) and from a high-income country (74.6%, p<0.0001) while last authorship was mostly male (53.7%) also from a high-income country (82.5%, p<0.0001). Females more frequently published papers using qualitative data (61.4%) and reviews/estimates (59.4%) while men published more case control (70.7%) and randomised controlled studies (53.0%), p<0.0001. The adjusted odds of there being a female author increased 4% for every additional year that passed. Conclusion: While there are more females authoring articles as compared to the past, they are still lagging behind with regards to seniority and prestige. Likewise, female representation is closely tied to what institution they are affiliated with and where that institution is located. Global health research institutions need to actively promote change by ensuring women are included in research and research outputs, giving them opportunities to lead. [ABSTRACT FROM AUTHOR]

Sketch maps are valuable tools used across various disciplines including spatial cognition, environmental psychology, and spatial reasoning. A common approach to evaluate sketch maps in research is to align and compare them with metric maps. However, sketch maps are highly abstract and contain generalized information causing difficulty in their alignment. Current approaches to study sketch maps cannot handle generalized information. They require a one-on-one correspondence between features in the metric map and features in the sketch map. But memory is often generalized. This paper makes two contributions to the research on sketch maps: (i) we present an algorithmic approach to detect generalization in sketch maps (ii) we present an online tool that creates a generalized metric map corresponding to features in sketch maps. Previously, we identified nine types of generalization in sketch maps. In this paper, we develop formal operators to detect these generalizations and implement them as an online tool. We evaluated our algorithm with a set of 11 sketch maps containing 84 instances of generalization. The results indicated that our algorithm consistently detects instances of generalization in sketch maps. [ABSTRACT FROM AUTHOR]

Fulfilling corporate social responsibility (CSR) is crucial for society's sustainable development. Executives' academic experience significantly affects their awareness of social responsibility, value orientation, professional ability, and network resources. Thus, it is critical in CSR-related corporate decision-making. This paper explores the impact of executives' academic experience on the fulfillment of CSR. It focuses on non-financial, Special treatment enterprises (ST), and ST* enterprises listed in the A-share market from 2012 to 2021. It utilizes a fixed-effects analysis model to examine the relationship between executives' academic experience and CSR fulfillment. The CSR score and executives' academic experience were positively correlated. This paper also explores the intermediary role of compensation incentives and the moderating effect of marketization level. Both compensation incentives and the level of marketization positively moderated the relationship between executives' academic experience and CSR fulfillment. Meanwhile, the robustness results showed that the experimental findings still held after replacing the explained and explanatory variables. This paper contributes to the advancement of the Upper Echelons Theory and provides empirical evidence for the society's sustainable development. [ABSTRACT FROM AUTHOR]

Advanced Persistent Threat (APT) attacks are causing a lot of damage to critical organizations and institutions. Therefore, early detection and warning of APT attack campaigns are very necessary today. In this paper, we propose a new approach for APT attack detection based on the combination of Feature Intelligent Extraction (FIE) and Representation Learning (RL) techniques. In particular, the proposed FIE technique is a combination of the Bidirectional Long Short-Term Memory (BiLSTM) deep learning network and the Attention network. The FIE combined model has the function of aggregating and extracting unusual behaviors of APT IPs in network traffic. The RL method proposed in this study aims to optimize classifying APT IPs and normal IPs based on two main techniques: rebalancing data and contrastive learning. Specifically, the rebalancing data method supports the training process by rebalancing the experimental dataset. And the contrastive learning method learns APT IP's important features based on finding and pulling similar features together as well as pushing contrasting data points away. The combination of FIE and RL (abbreviated as the FIERL model) is a novel proposal and innovation and has not been proposed and published by any research. The experimental results in the paper have proved that the proposed method in the paper is correct and reasonable when it has shown superior efficiency compared to some other studies and approaches over 5% on all measurements. [ABSTRACT FROM AUTHOR]

Recent research in economics and sociology demonstrates the existence of significant occupational segregation by sexual orientation and gender identity and differences in a range of labor market outcomes, such as hiring chances, earnings, and leadership positions. In this paper, we examine one possible cause of these differences that is associated with the disadvantaged position of sexual and gender minorities in the labor market: LGBTQ* individuals' choices aimed at avoiding possible discrimination. This paper examines LGBTQ* people's relative importance of income, time, promotion prospects, an LGBTQ*-friendly work environment, and diversity management in the decision for or against a job. Based on a discrete choice experiment conducted in a large online sample recruited through social media in Germany (N = 4,507), an LGBTQ*-friendly work climate accounted, on average, for 33.8 percent of respondents' decisions which is comparable with the relative importance of income. Overtime, a diversity management on company level and promotion prospects are less important in the job decision process of LGBTQ* people. While the results show only small differences by sexual orientation, they show group-specific preferences by gender identity. An LGBTQ*-friendly work climate is more important for cisgender women of the LGBTQ* community and gender minorities than for cisgender men of the LGBTQ* community. In contrast, income is less important for gender minorities and cisgender women of the LGBTQ* community than for cisgender men of the LGBTQ* community. [ABSTRACT FROM AUTHOR]

In traditional supply chain finance, the financing of enterprise mainly relies on the credit segmentation of the core enterprise, resulting in a short trust transmission radius and poor financing ability. The development of Internet technology, while expanding financing channels, has also seen an increasing severity in issues such as information fraud and data breaches, which has further aggravated the trust crisis in supply chain finance. This paper integrates blockchain technology into the industrial internet platform and analyzes the applicability of both in empowering supply chain financial trust. Then a supply chain financial trust framework, which emphasizes information sharing, data security, and trust circulation, is proposed. Furthermore, combined with the theories of Funk-SVD and entropy value, this paper designs a global trust evaluation mechanism that facilitates the trust circulation in supply chain finance and proposes a recommendation algorithm for global trust. With the testing conducted using the Epinions dataset, it is found that the algorithm proposed in this paper has a strong data dimensionality reduction and concentration ability, especially for large sample data, it can obtain more accurate evaluation values with less space occupation, thus enhancing the trust circulation ability of supply chain finance. Finally, the paper puts forward specific policy recommendations for the implementation of the supply chain finance information mechanism, aiming to better improve the financing accessibility of enterprises in supply chain, particularly small and medium-sized enterprises. [ABSTRACT FROM AUTHOR]

This paper aims to verify the impact of capital structure on business model innovation and firm performance and the mediating effect of business model innovation. We use the data of the Chinese growth enterprises market (GME) listed high-tech firms from 2016 to 2022 as a dynamic panel data model with the system–generalized method of moments (sys-GMM), adopting return on asset and earning per share as firm performance. Our results show that capital structure has a lag effect on firm performance. The total debt ratio in the last period has a significant non-linear impact on the performance and business model innovation level of nowadays, presenting a U-shaped relationship. The first-order lag short-term debt ratio positively improves firm performance. Business model innovation significantly promotes better firm performance, and business model innovation does exist in the mediating effect between enterprise capital structure and its performance. These results remain robust to different sample sizes or proxy variables. This paper proposes some suggestions for firm operations and government policies based on the findings. [ABSTRACT FROM AUTHOR]

Noise pollution is one of the consequences of urbanization that can cause environmental disturbances in urban areas. Urban ecosystems provide noise reduction services through Urban Green infrastructures (UGIs). Many studies have been conducted to evaluate and model traffic noise, but none have addressed the flow, supply, and demand of noise reduction ecosystem services. The main purpose of this paper is to present a new methodology for estimating flow, supply, and demand for noise reduction in Hamadan city that has not been mentioned in any paper so far. UGIs were classified into six main categories: agricultural lands, gardens, parks, abandoned lands, single trees, and street trees. A total of 57 sampling stations for sound measurement were made in August 2018. The current map of noise pollution (flow) was created using the Kriging method. The amount of supply was measured up to a distance of 50 meters from the main roads based on two approaches (the distance effect and the sound barrier effect). To quantify the demand, the current sound intensity level in the noise-sensitive land uses was compared with standards. Zonal statistics was used for spatial analysis of supply-demand in the urban neighborhood as a working unit. Results showed that at distances of 5m, 10m, 15m, and 20m, the average noise reduction was found to be 1.61, 2.83, 3.92, and 5.33 dB, respectively. Sound barriers at distances of 5m and 10m resulted in an average sound reduction of 1.61 and 2.83 dB, respectively. Individual trees, strip trees, abandoned lands, parks, and gardens led to a decrease in traffic noise by 0.3, 1, 0.1, 3.5, and 4.5 dB, respectively. The clustering analysis revealed a significant spatial clustering of noise pollution in Hamedan. The results and new methodology of this research can be used in similar areas to estimate the supply and demand of noise reduction and also for decision-makers to take management actions to increase supply and meet the demand for noise reduction service. [ABSTRACT FROM AUTHOR]

The Coronavirus Disease 2019(COVID-19) has caused widespread and significant harm globally. In order to address the urgent demand for a rapid and reliable diagnostic approach to mitigate transmission, the application of deep learning stands as a viable solution. The impracticality of many existing models is attributed to excessively large parameters, significantly limiting their utility. Additionally, the classification accuracy of the model with few parameters falls short of desirable levels. Motivated by this observation, the present study employs the lightweight network MobileNetV3 as the underlying architecture. This paper incorporates the dense block to capture intricate spatial information in images, as well as the transition layer designed to reduce the size and channel number of the feature map. Furthermore, this paper employs label smoothing loss to address the inter-class similarity effects and uses class weighting to tackle the problem of data imbalance. Additionally, this study applies the pruning technique to eliminate unnecessary structures and further reduce the number of parameters. As a result, this improved model achieves an impressive 98.71% accuracy on an openly accessible database, while utilizing only 5.94 million parameters. Compared to the previous method, this maximum improvement reaches 5.41%. Moreover, this research successfully reduces the parameter count by up to 24 times, showcasing the efficacy of our approach. This demonstrates the significant benefits in regions with limited availability of medical resources. [ABSTRACT FROM AUTHOR]

Blockchain-based applications are becoming more and more widespread in business operations. In view of the shortcomings of existing enterprise blockchain evaluation methods, this paper proposes a multi-source heterogeneous blockchain data quality evaluation model for enterprise business activities, so as to achieve efficient evaluation of business activity information consistency, credibility and value. This paper proposes a multi-source heterogeneous blockchain data quality assessment method for enterprise business activities, aiming at the problems that most of the data in enterprise business activities come from different data sources, information representation is inconsistent, information ambiguity between the same block chain is serious, and it is difficult to evaluate the consistency, credibility and value of information. The method firstly proposes an entity information representation method based on the Representation learning for fusing entity category information (CEKGRL) model, which introduces the triad structure of related entities in blockchain, then associates them with enterprise business activity categories, and carries out similarity calculation through contextual information to achieve blockchain information consistency assessment. After that, a trustworthiness characterization method is proposed based on information sources, information comments, and information contents, to obtain the trustworthiness assessment of the business. Finally, based on the information trustworthiness characterization, a value assessment method is introduced to assess the total value of business activity information in the blockchain, and a blockchain quality assessment model is constructed. The experimental results show that the proposed model has great advantages over existing methods in assessing inter-block consistency, intra-block activity information trustworthiness and the value of blockchain. [ABSTRACT FROM AUTHOR]

The efficiency and responsiveness of supply chains are vitally dependent on inventory replenishment and transportation decisions. In this paper, we study a supply chain consisting of a single retailer ordering seasonal products within the newsvendor framework. The primary objective of the paper is to investigate the retailer's decision-making process, aimed at determining the optimal replenishment quantities and selecting the appropriate mix and size of the truck fleet. Initially, we formulate a mathematical model where the retailer exclusively manages a limited fleet of its own trucks for inbound transportation of a single seasonal product. In this context, we determine a lower breakeven point for the fixed transportation cost than what has been previously proposed in the literature. Subsequently, we examine a commonly encountered transportation scenario where the retailer has the opportunity to expand its fleet size by leasing trucks from the external market. The outcomes of the numerical example indicates that the flexibility resulting from the utilization of different types of trucks can lead to reduced overall costs. We also address the practical transportation problem of efficiently shipping various seasonal products solely with the retailer's own trucks. For this complex problem, we propose an optimal solution procedure based on Lagrangian method. We show that the joint replenishment of multiple products results in cost savings and enhances utilization of the trucks' capacities. [ABSTRACT FROM AUTHOR]

Debris flow risk assessment can provide some reference for debris flow prevention and control projects. In risk assessment, researchers often only focus on the impact of objective or subjective indicators. For this purpose, this paper proposed a weight calculation method based on t-distribution and linear programming optimization algorithm (LPOA). Taking 72 mudslides in Beichuan County as an example, this paper used analytic hierarchy process (AHP), entropy weight method (EWM) and variation coefficient method (VCM) to obtain the initial weights. Based on the initial weights, weight intervals with different confidence levels were obtained by t-distribution. Subsequently, the final weights were obtained by LOPA in the 90% confidence interval. Finally, the final weights were used to calculate the risk score for each debris flow, thus delineating the level of risk for each debris flow. The results showed that this paper's method can avoid overemphasizing the importance of a particular indicator compared to EWM and VCM. In contrast, EWM and VCM ignored the effect of debris flow frequency on debris flow risk. The assessment results showed that the 72 debris flows in Beichuan County were mainly dominated by moderate and light risks. Of these, there were 8 high risk debris flows, 24 medium risk debris flows, and 40 light risk debris flows. The excellent triggering conditions provide favorable conditions for the formation of high-risk debris flows. Slightly and moderate risk debris flows are mainly located on both sides of highways and rivers, still posing a minor threat to Beichuan County. The proposed fusion weighting method effectively avoids the limitations of single weight calculating method. Through comparison and data analysis, the rationality of the proposed method is verified, which can provide some reference for combination weighting method and debris flow risk assessment. [ABSTRACT FROM AUTHOR]

Background: Ankylosing spondylitis(AS) is a chronic inflammatory rheumatic disease that leads to a reduced quality of life. Exercise appears to be one of the promising modes of intervention. The aim of this study was to review the available evidence and compare the effectiveness of different exercises in relieving symptoms of AS. Methods: We searched the Pubmed, WOS, EMbase, CNKI, Cochrane Library, and Scopus databases. The search has spanned from the creation of the database until September 15, 2023. We extracted the first author, year of article publication, sample information, intervention, duration of intervention, and outcome indicators from the literature that met the inclusion criteria. The Cochrane Risk Assessment Tool was used to assess the risk of bias for included studies. I² was used to judge the consistency of the included studies. Egger's test and Begg's test were used to judge whether there was significant publication bias. Forest plots were used to compare intervention effects and SUCRA was provided to rank the effects of the interventions. This study has been registered in PROSPERO(No. CRD42024518522). Results: After screening, 10 papers matched the inclusion criteria for this study, and the total sample size of the 10 papers was 623. Upon analysis, the papers included in this study did not have significant publication bias (Begg's Test P = 0.210) and had good consistency (P>0.05). The direct comparisons showed that Running, Pilates, Stretching, Yoga, and Tai Chi were more effective interventions than traditional therapies. The effect sizes, confidence intervals, and number of studies for each intervention are shown below: Running [MD -1.90 (95% CI -3.14,-0.66) n = 1], Pilates [MD -1.70 (95% CI -2.90,-0.51) n = 1], Stretching [MD -1.54 (95% CI -2.21,-0.88) n = 4], Yoga [MD -1.24 (95% CI -2.18,-0.30) n = 1], Tai Chi [MD -0.78 (95% CI -1.44,-0.12) n = 2], Exergame[MD -0.80 (95% CI -1.99,0.39) n = 1], Swiss balls[MD -1.07 (95% CI -2.58,0.44) n = 1]. The indirect comparisons showed that the range of effect sizes for each sport intervention intersected the null line. Based on cumulative probability, the order of effectiveness of different exercises in relieving AS symptoms is Running, Pilates, Stretching, Yoga, Tai Chi, Exergame, and Swiss ball. Conclusion: Running, Pilates, Stretching, Yoga, and Tai Chi provided significant relief from AS symptoms. Exergame and Swiss ball were not statistically significant in relieving AS symptoms. There were no significant differences in the effectiveness of different exercise interventions in relieving AS symptoms. Running may have the most beneficial effect on alleviating AS symptoms. [ABSTRACT FROM AUTHOR]

This paper uses data from the American Voices Project, an interview study based on a random population sample, to explore the relationship between assault experiences and how people narrate their lives. Using quantitative sentiment analysis, we find that survivors of assault express significantly greater negative sentiment when asked to tell their life stories. These negative sentiments are observable throughout the entire interview, including before questions of assault are asked. Survivors of assault narrate their experiences with more anger, disgust, fear, and sadness, and less anticipation, joy, and trust than those who do not report assault experiences. We provide evidence that the negative sentiment associated with sexual violence is greater than having lost a parent and less than having experienced a significant financial hardship within the last month. We contextualize these findings with a qualitative evaluation of the interview transcripts, further substantiating our finding. Overall, this paper suggests that sexual violence is part of what we have called, drawing inspiration from the work of Beth Richie, a "matrix of harm" that structures people's lives. While our finding is consistent with what we might expect given the negative life experiences and sequalae associated with sexual assault, it has important implications. Sentimental differences in narrating life experience are an important yet relatively understudied phenomenon, and experiences of assault are rarely asked about yet may be consequential to both quantitative and qualitative accounts of social processes. [ABSTRACT FROM AUTHOR]

CNN has demonstrated remarkable performance in EEG signal detection, yet it still faces limitations in terms of global perception. Additionally, due to individual differences in EEG signals, the generalization ability of epilepsy detection models is week. To address this issue, this paper presents a cross-patient epilepsy detection method utilizing a multi-head self-attention mechanism. This method first utilizes Short-Time Fourier Transform (STFT) to transform the original EEG signals into time-frequency features, then models local information using Convolutional Neural Network (CNN), subsequently captures global dependency relationships between features using the multi-head self-attention mechanism of Transformer, and finally performs epilepsy detection using these features. Meanwhile, this model employs a light multi-head attention mechanism module with an alternating structure, which can comprehensively extract multi-scale features while significantly reducing computational costs. Experimental results on the CHB-MIT dataset show that the proposed model achieves accuracy, sensitivity, specificity, F1 score, and AUC of 92.89%, 96.17%, 92.99%, 94.41%, and 96.77%, respectively. Compared to the existing methods, the method proposed in this paper obtains better performance along with better generalization. [ABSTRACT FROM AUTHOR]

Using a multilevel modelling approach to analyse a novel dataset of academic publications at all business schools in 11 European countries, this paper finds that the influence of organisational- and country-level contextual factors on researchers varies considerably based on the type of institution and the development level of the country they are located in. At the organisational-level, we find that greater spatial connectivity–operationalised through proximity to nearby business schools, rail stations, and airports–is positively related to scientific research volume and public dissemination (news mentions). While this result is significant only for high-income countries (above EU-average 2018 GDP per capita), this is likely because the low-income countries (below EU-average 2018 GDP per capita) examined here lack a 'critical mass' of well-connected universities to generate observable agglomeration effects. At the country-level, the results indicate that in high-income countries, less prestigious schools benefit from higher rates of recent international immigration from any foreign country, providing a direct policy pathway for increasing research output for universities that aren't already well-known enough to attract the most talented researchers. In low-income countries, recent immigration rates are even stronger predictors of research performance across all levels of institutional prestige; more open immigration policies would likely benefit research performance in these countries to an even greater extent. Finally, the paper's results show that, in low-income countries, a composite measure of a country's quality of life (including self-rated life satisfaction, health, working hours, and housing overcrowding) is positively related to research outcomes through its interaction with school prestige. This suggests that the lower a country's quality of life, the more researchers are incentivised to produce higher levels of research output. While this may in part reflect the greater disparities inherent in these countries' economic systems, it is noteworthy–and perhaps concerning–that we have observed a negative correlation between country-level quality of life and research performance in low-income countries, which is particularly felt by researchers at less prestigious institutions. [ABSTRACT FROM AUTHOR]

This paper aims to explore the impact of China's value-added tax (VAT) credit refunds policy on the enterprises' labor demand through a paradigm combining ex-ante analysis and ex-post test. By introducing the VAT credit refunds into the production-decision model of the enterprise, calibrating the parameters and conducting the dynamic effects tests using the data of Chinese A-share listed enterprises, this paper finds that the labor employment of the pilot enterprises exhibits a V-shaped fluctuation trend. In the initial implementation of the policy, due to the existence of layoff costs, iso-cost line of the enterprise bends, which results in that the enterprise with a capital-labor substitution elasticity greater than 1 will not reduce labor hiring, as it has already deployed labor force before the implementation of the policy. When the enterprise enter the next production cycle where the labor force can be freely allocated, the labor employment of the enterprise with a capital-labor elasticity of substitution greater than 1 will decline compared to that without the policy. In the long run, as output increases, the labor demand will recover. The results of ex-post test are consistent with that of the ex-ante analysis. Additionally, heterogeneity test reveals that the greater the elasticity of capital-labor substitution of the sub-industry is, the more severe the degree of the V-shaped fluctuation is. Following the implementation of the policy, the continuous increase in enterprise output and capital stock verifies the relevant transmission mechanism. This study provides a more detailed perspective for comprehensively understanding the impact of VAT credit refunds policy on employment. [ABSTRACT FROM AUTHOR]

Accurately modeling artificial boundary conditions and wave inputs is paramount for numerical simulations of wave scattering in semi-infinite domains within seismic engineering. Traditionally, analysts focused on one- or two-dimensional free-field problems to determine wave inputs, primarily for vertically incident plane waves or obliquely incident waves parallel to two axes. However, these methods were inadequate for handling arbitrary incident directions in three-dimensional scenarios. This paper proposes a method for modeling seismic wave incidents in arbitrary directions. The basic theory of viscoelastic boundaries is leveraged, and a plane containing an arbitrary incident direction and the vertical coordinate axis is selected to establish a two-dimensional plane coordinate system. The two-dimensional free-field problem in this coordinate system is derived using the transfer matrix method. Subsequently, displacement, velocity, and stress are converted into the coordinate system where the three-dimensional calculation model is located, providing input for the three-dimensional scattering problem. Furthermore, the implementation of transmitting boundary conditions and viscoelastic boundary wave inputs is presented to enable incident wave scattering problems at any angle of the plane. The effect of oblique-incidence soil-structure dynamic interaction is also discussed, focusing on the parallel technology method adopted in this paper. With the relatively mature technology route and method, together with nuclear power systems and large-span deep-water bridge models, through examples of comparative analysis, qualitative and quantitative analyses are made on the impact on the soil mass, foundation, and structure when the seismic wave is an oblique incident. [ABSTRACT FROM AUTHOR]

The demographic structure is an important factor influencing the development of the services industry. As the country with the world's most serious aging problem, China's service industry structure is likely to undergo profound changes in response to the rapid demographic transition. Therefore, this paper examines the effect of population aging on the development of the service industry in the context of China's accelerating population aging. The study found that: (1) Population aging has a significant "inverted U" effect on the development of the services industry. (2) The impact of population aging on the development of the service industry has obvious regional and industry heterogeneity. The study of regional heterogeneity found that population aging in economically developed regions has a more obvious effect on the development of the service industry than in economically less developed regions. Industry heterogeneity studies found that population aging has an obvious promotional effect on the development of medical and other rigid demand industries, while the effect on other non-rigid demand industries is not significant. (3) The threshold effect test found that when the degree of population aging exceeds the threshold, the stimulating effect of population aging on the development of the services industry is no longer significant. The research in this paper provides useful insights into the likely response to changes in the industrial structure of the services industry, and offers some implications for countries with similar demographic profiles to China. [ABSTRACT FROM AUTHOR]

Currently, digital transformation is having various impacts on enterprises around the world, including the green innovation. However, the current literature on the relationship between digitalization and green innovation in enterprises is scarce. What is the relationship between them, and whether heterogeneous environmental regulation has mediating effects, are questions that are worth exploring. Using a sample of listed manufacturing enterprises in China, this paper empirically tests the impact of digital transformation on enterprise green innovation. The results show that: (1) Digital transformation has a significant positive impact on green innovation, including green innovation output and green innovation capability. (2) Diverse environmental regulation may have mediating effects of digital transformation's influence on green innovation. (3) After a number of robustness tests, the conclusions are still valid. This paper can provide a reference for developing green development strategies for manufacturing enterprises. [ABSTRACT FROM AUTHOR]

Although an increasing number of studies have examined issues relating to the preservation and development of intangible cultural heritage (ICH), there has been limited research on how tourists perceive ICH. Moreover, UNESCO asserts that the concept of "authenticity" is not applicable to ICH, and so far, no valid instrument for measuring tourists' subjective perceptions of ICH has been developed, even though their perceptions play a very important role in the preservation and development of ICH. Therefore, this paper aims to develop a measurement scale for ICH image, using both qualitative and quantitative research methods. Participant observation, semi-structured in-depth interviews, secondary data collection, and a literature review were conducted to generate the initial scale items, and then the main surveys were conducted to collect data for the model tests. Four dimensions were extracted by exploratory factor analysis: transmission, localization, vitality, and association. The reliability and validity of the measurement model were demonstrated through confirmatory factor analysis. We further determined that the transmission, vitality, and association of ICH image have a positive impact on tourists' revisit intention. The paper highlights the crucial role of ICH image in sustainable tourism development. The theoretical and managerial implications of the study are discussed, followed by suggestions for future research. [ABSTRACT FROM AUTHOR]

With the accelerated development of the technological power of society, aerial images of drones gradually penetrated various industries. Due to the variable speed of drones, the captured images are shadowed, blurred, and obscured. Second, drones fly at varying altitudes, leading to changing target scales and making it difficult to detect and identify small targets. In order to solve the above problems, an improved ASG-YOLOv5 model is proposed in this paper. Firstly, this research proposes a dynamic contextual attention module, which uses feature scores to dynamically assign feature weights and output feature information through channel dimensions to improve the model's attention to small target feature information and increase the network's ability to extract contextual information; secondly, this research designs a spatial gating filtering multi-directional weighted fusion module, which uses spatial filtering and weighted bidirectional fusion in the multi-scale fusion stage to improve the characterization of weak targets, reduce the interference of redundant information, and better adapt to the detection of weak targets in images under unmanned aerial vehicle remote sensing aerial photography; meanwhile, using Normalized Wasserstein Distance and CIoU regression loss function, the similarity metric value of the regression frame is obtained by modeling the Gaussian distribution of the regression frame, which increases the smoothing of the positional difference of the small targets and solves the problem that the positional deviation of the small targets is very sensitive, so that the model's detection accuracy of the small targets is effectively improved. This paper trains and tests the model on the VisDrone2021 and AI-TOD datasets. This study used the NWPU-RESISC dataset for visual detection validation. The experimental results show that ASG-YOLOv5 has a better detection effect in unmanned aerial vehicle remote sensing aerial images, and the frames per second (FPS) reaches 86, which meets the requirement of real-time small target detection, and it can be better adapted to the detection of the weak and small targets in the aerial image dataset, and ASG-YOLOv5 outperforms many existing target detection methods, and its detection accuracy reaches 21.1% mAP value. The mAP values are improved by 2.9% and 1.4%, respectively, compared with the YOLOv5 model. The project is available at https://github.com/woaini-shw/asg-yolov5.git. [ABSTRACT FROM AUTHOR]

During the machine vision inspection of the inner section of bottle caps within pharmaceutical packaging, the unique conca bottom and convex side walls often create obstructions to the illumination. Consequently, this results in challenges such as irregular background and diminished feature contrast in the image, ultimately leading to the misidentification of defects. As a solution, a vision system characterized by a Low-Angle and Large Divergence Angle (LALDA) is presented in this paper. Using the large divergence angle of LED, combined with low-angle illumination, a uniform image of the side wall region with bright-field characteristics and a uniform image of inner circle region at the bottom with dark-field characteristics are obtained, thus solving the problems of light being obscured and brightness overexposure of the background. Based on the imaging characteristics of LALDA, a multi-channel segmentation (MCS) algorithm is designed. The HSV color space has been transformed, and the image is automatically segmented into multiple sub-regions by mutual calculation of different channels. Further, image homogenization and enhancement are used to eliminate fluctuations in the background and to enhance the contrast of defects. In addition, a variety of defect extraction methods are designed based on the imaging characteristics of different sub-regions, which can avoid the problem of over-segmentation in detection. In this paper, the LALDA is applied to the defect detection inside the cap of capsule medicine bottle, the detection speed is better than 400 pcs/min and the detection accuracy is better than 95%, which can meet the actual production line capacity and detection requirements. [ABSTRACT FROM AUTHOR]

Skin cancer has a significant impact on the lives of many individuals annually and is recognized as the most prevalent type of cancer. In the United States, an estimated annual incidence of approximately 3.5 million people receiving a diagnosis of skin cancer underscores its widespread prevalence. Furthermore, the prognosis for individuals afflicted with advancing stages of skin cancer experiences a substantial decline in survival rates. This paper is dedicated to aiding healthcare experts in distinguishing between benign and malignant skin cancer cases by employing a range of machine learning and deep learning techniques and different feature extractors and feature selectors to enhance the evaluation metrics. In this paper, different transfer learning models are employed as feature extractors, and to enhance the evaluation metrics, a feature selection layer is designed, which includes diverse techniques such as Univariate, Mutual Information, ANOVA, PCA, XGB, Lasso, Random Forest, and Variance. Among transfer models, DenseNet-201 was selected as the primary feature extractor to identify features from data. Subsequently, the Lasso method was applied for feature selection, utilizing diverse machine learning approaches such as MLP, XGB, RF, and NB. To optimize accuracy and precision, ensemble methods were employed to identify and enhance the best-performing models. The study provides accuracy and sensitivity rates of 87.72% and 92.15%, respectively. [ABSTRACT FROM AUTHOR]

Computer-aided design (CAD) models can now be directly converted into products and structures. One technique to realize such approach is through Additive Manufacturing (AM). AM is relatively new manufacturing technology in which products are manufactured by layering various materials like rubber, metal, ceramic, composites, and polymers. However, the use of this technology requires consideration of its associated cost to ensure its competitiveness. In this paper, a simplified mathematical cost model is suggested. The model considers the main components of costs. The model formula utilizes expenses related to the pre-processing, main processing, and the post-processing operations. To validate the model, it is tested to estimate the cost of medical implants manufacturing using AM technique. In many cases, medical implants require unique or dedicated design for each patient. Hence cost estimation will help to assess and estimate the required financial resources for such operations. A case study is provided in this paper to estimate the manufacturing cost of a finger's phalanges bone, with metal implant using AM technique. The developed model may be described as Activity Based Costing (ABC). The model is introduced to estimate the cost of parts produced using AM technique. Although the model is developed to suit custom implant manufacturing using AM technique, its use may also be adapted to suit the manufacturing of many other parts and products. The developed model is aiming to achieve several tasks namely assigning cost drivers to each activity, estimating the cost of individual actions, allocating overhead expenses, calculating the overall production cost, and establishing an acceptable selling price. It assists companies in computing the cost of custom implants for customers, enhancing the accuracy of production cost estimates, and ultimately boosting profitability. [ABSTRACT FROM AUTHOR]

Media information plays an essential role in the stock market. Recent financial research has verified that media information could shock stock price by influencing investors' expectation. Now, a new type of interactive media, called Digital Interactive Media (DIM), is popular in Chinese stock market and becomes the main channel for investors to understand listed companies. Unlike general news media or investor forums, DIM enables direct interaction between listed companies and investors. In the modern society where digital economy is booming, media information would largely affect investors' decisions. Therefore, it is urgent to use natural language processing (NLP) technology to deconstruct the massive questions and answers (Q&A) interactive information in DIM and extract valuable factors that affect stock prices and stock performances to explore the influence mechanism of digital interactive information on stock performances. This paper firstly uses web crawling technology to obtain approximately 110000 Q&A text information from the digital interactive platform ('Panoramic Network') from 2015 to 2021. Then we use big data text analysis technology and emotional quantification technology to extract valuable influencing factors from the massive text. A Multiple Linear Regression (MLR) model was created to explore specific influence mechanism of digital interactive information on stock price performance. The empirical results show that the emotions implicit in investors' questions do not significantly impact stock performance. However, the emotions and attitudes of the answers by listed companies can significantly affect corresponding stock prices, which indirectly confirms the Proximate Cause Effect of behavioral finance. This effect is particularly evident in the stock prices on the current trading day and the next trading day. In the Robustness Test, this paper replaces dependent variable and adds relevant control variables, and the conclusion remains valid. In the Endogeneity Test, this paper selects sample data before the launch of Panorama Network in 2014 as a comparison, and uses a Difference-in-Difference (DID) model to prove the significant impact of the launch of Panorama Network on Chinese stock market. In the Heterogeneity Test, the paper classifies the market value, region, and industry of listed companies and regressed the sub samples, once again confirming the reliability of the empirical conclusions. The results of Robustness Test, Endogeneity Test, and Heterogeneity Test conducted in this paper all support empirical conclusions. [ABSTRACT FROM AUTHOR]

In this paper, we focus on a dynamic Cournot game in the market with a nonlinear (isoelastic) demand function. In our model, there are N competing firms featured by nonlinear cost functions, which enhances our model's resemblance to real-world scenarios. Firstly, we focus on the homogeneous case where firms' marginal costs change at equal rates. We establish analytical expressions of the market supply at equilibrium and perform comparative static analysis. In addition, we investigate the local stability under different economies of scale and show that there could be multiple stable equilibria if firms face economies of scale. The heterogeneous case where firms' marginal costs change at distinct rates is much more complex, thus we investigate the duopoly game with only two firms involved. Methods of symbolic computations such as triangular decomposition and partial cylindrical algebraic decomposition are employed in the analytical investigations of the equilibrium, which is nearly impossible by using the pencil-and-paper approach since the closed-form equilibrium is quite complicated. According to the computational results, we derive that two stable positive equilibria may coexist if both firms face economies of scale. Additionally, we conduct preliminary numerical simulations and find two different types of complex dynamics of the model considered in this paper: complex trajectories such as periodic and chaotic orbits may appear; the topological structure of the basins of attraction may be complex. [ABSTRACT FROM AUTHOR]

Listening to music is a crucial tool for relieving stress and promoting relaxation. However, the limited options available for stress-relief music do not cater to individual preferences, compromising its effectiveness. Traditional methods of curating stress-relief music rely heavily on measuring biological responses, which is time-consuming, expensive, and requires specialized measurement devices. In this paper, a deep learning approach to solve this problem is introduced that explicitly uses convolutional neural networks and provides a more efficient and economical method for generating large datasets of stress-relief music. These datasets are composed of Mel-scaled spectrograms that include essential sound elements (such as frequency, amplitude, and waveform) that can be directly extracted from the music. The trained model demonstrated a test accuracy of 98.7%, and a clinical study indicated that the model-selected music was as effective as researcher-verified music in terms of stress-relieving capacity. This paper underlines the transformative potential of deep learning in addressing the challenge of limited music options for stress relief. More importantly, the proposed method has profound implications for music therapy because it enables a more personalized approach to stress-relief music selection, offering the potential for enhanced emotional well-being. [ABSTRACT FROM AUTHOR]

In recent years, blockchain technology has developed rapidly and has been widely used in medical, financial, energy and other fields. However, in the process of practical application, each blockchain is a small independent ecosystem, with all transactions and operations limited to the chain, resulting in a large number of mutually heterogeneous to independent blockchains. It presents challenges for cross-chain interactions, cross-organization data sharing, and cross-blockchain expansion, and hinders the wider application of blockchain technology. In addition, the traditional digital signature method based on elliptic curve cipher faces the threat of being cracked by quantum computing attacks. To solve the aforementioned problems, this paper proposed a blockchain smart contract technique based on quantum computing attack resistance(BSCTQCAT). The technique first introduces the digital signature of the lattice cipher into the blockchain to resist the quantum search algorithm attack. Then, based on the smart contract authentication scheme, the nodes on multiple heterogeneous chains are organized into an identity agent layer P2P network, through which transactions on the chain will establish a credible identity management and message authentication mechanism between different chains, solving the current problem that each chain is difficult to communicate with each other. In this paper, the performance of the algorithm is evaluated by simulating the Bitcoin transaction scenario and analyzing the experimental data. [ABSTRACT FROM AUTHOR]

Background: Iron deficiency anemia (IDA) is a prevalent hematological complication associated with gastrointestinal (GI) cancers due to an increased loss of iron and decreased iron absorption. The purpose of this systematic review is to evaluate the use of parenteral iron to treat IDA in patients with GI cancer. Methods: PubMed, Cochrane, EMBASE, CINHAL and Scopus were searched from January 1, 2010 to September 29, 2023 with no language restrictions. We excluded editorials, case reports, abstracts, conference papers, and poster presentations. Studies were included if they discussed IDA, GI neoplasms, use of iron supplementation (with or without erythropoietin-stimulating agents [ESAs]), defined anemia and had an adult patient population. We assessed the efficacy of parenteral iron in comparison to other iron supplementation methods when treating IDA in patients with GI cancer. The Cochrane Risk of Bias Tool 2 (RoB 2) and the Risk Of Bias In Non-randomized Studies of Interventions (ROBINS-I) assessment tools were used to assess the quality of the included studies. Moreover, the Cochrane Effective Practice and Organization data collection form was used to collect pertinent study information. Results: Our search yielded 3,969 studies across all databases. Twenty-one studies were included (6 randomized control trials; 15 non-randomized studies). Of the 15 studies evaluating hemoglobin (Hb) response, seven studies found an increase in Hb levels when patients were treated with IV iron. The 14 studies evaluating red blood cell (RBC) transfusion rates found conflicting differences in RBC transfusion needs when treated with IV iron. Studies analyzing health related outcomes typically found an increase in quality of life and decreased post-operative complications. Discussion: This review demonstrates improved outcomes of IDA in patients with GI cancer treated with IV iron instead of other iron supplementation methods. Timely diagnosis and appropriate IDA management can greatly improve quality of life in this patient population, especially if myelosuppressive chemotherapy is required. [ABSTRACT FROM AUTHOR]

China has experienced rapid development in the digital economy. Using data from 30 provinces in China between 2011 and 2017, this paper constructs a two-way fixed effects model to study the effects and mechanisms of the digital economy development on social insurance funds revenue. An increase of one unit in digital economy development led to a 0.56% increase in basic endowment insurance funds revenue and a 0.33% increase in basic health insurance funds revenue. The digital economy increased the social insurance funds revenue by promoting employment and increasing income. Furthermore, the effects of digital economic development on social insurance funds revenue were heterogeneous for different levels of economic development and urbanization. The conclusions stood after robustness tests by changing the method of weighting the digital economy indicators and using instrumental variables. This paper confirmed the positive role of the development of the digital economy in increasing the revenue of social insurance funds from the perspective of quantitative research and explored the mechanisms in depth. In order to increase social insurance funds revenue, it is essential to accelerate the development of the digital economy, especially in regions with lower economic development and urbanization, and to address the needs of the technically unemployed and those engaged in flexible employment. [ABSTRACT FROM AUTHOR]

This paper explores predicting early signals of business failure using modern models for bankruptcy prediction. It reviews how continuous operations enhance market value, strengthening competitiveness and reputation among stakeholders. The study involves medium and large companies in the Montenegrin market from 2015 to 2020, comprising 30 bankrupt and 70 financially stable firms. Logistic regression is also employed to create a logit model for early detection of bankruptcy signals in companies. This research establishes the empirical validity of modern models in predicting business failure in the Montenegrin market, particularly through logistic regression. Significant indicators, such as the Degree of Indebtedness (DI) and turnover ratio of business assets (TR), exhibit strong predictive power with a p-value less than 0.001 according to Likelihood ratio tests. The paper underscores the potential benefits of bankruptcy prediction for both internal and external stakeholders, especially investors, in enhancing the competitiveness of Montenegro's large and medium-sized companies. Notably, the research contributes by bridging the gap between theory and practice in Montenegro, as bankruptcy prediction models have not been extensively applied in the market. The authors suggest the possible applicability of the created logit model to neighboring countries with similar economic development levels. In that sense, the concept of predicting bankruptcy is positioned as integral to corporate strategy, impacting the overall reduction of bankruptcies. The paper concludes by highlighting its role as a foundation for future research, addressing the literature gap in the application of bankruptcy prediction models in Montenegro. The created logit model, tailored to the specific needs of Montenegrin companies, is presented as an original contribution, emphasizing its potential to strengthen the competitiveness of companies in the market. [ABSTRACT FROM AUTHOR]

The following paper describes a steady-state model of concurrent choice, termed the active time model (ATM). ATM is derived from maximization principles and is characterized by a semi-Markov process. The model proposes that the controlling stimulus in concurrent variable-interval (VI) VI schedules of reinforcement is the time interval since the most recent response, termed here "the active interresponse time" or simply "active time." In the model after a response is generated, it is categorized by a function that relates active times to switch/stay probabilities. In the paper the output of ATM is compared with predictions made by three other models of operant conditioning: melioration, a version of scalar expectancy theory (SET), and momentary maximization. Data sets considered include preferences in multiple-concurrent VI VI schedules, molecular choice patterns, correlations between switching and perseveration, and molar choice proportions. It is shown that ATM can account for all of these data sets, while the other models produce more limited fits. However, rather than argue that ATM is the singular model for concurrent VI VI choice, a consideration of its concept space leads to the conclusion that operant choice is multiply-determined, and that an adaptive viewpoint–one that considers experimental procedures both as selecting mechanisms for animal choice as well as tests of the controlling variables of that choice–is warranted. [ABSTRACT FROM AUTHOR]

With the increasing importance of the stock market, it is of great practical significance to accurately describe the systemic risk of the stock market and conduct more accurate early warning research on it. However, the existing research on the systemic risk of the stock market lacks multi-dimensional factors, and there is still room for improvement in the forecasting model. Therefore, to further measure the systemic risk profile of the Chinese stock market, establish a risk early warning system suitable for the Chinese stock market, and improve the risk management awareness of investors and regulators. This paper proposes a combination model of EEMD-LSTM, which can describe the complex nonlinear interaction. Firstly, 35 stock market systemic risk indicators are selected from the perspectives of macroeconomic operation, market cross-contagion and the stock market itself to build a comprehensive indicator system that conforms to the reality of China. Furthermore, based on TEI@I complex system methodology, an EEMD-LSTM model is proposed. The EEMD method is adopted to decompose the composite index sequence into intrinsic mode function components (IMF) of different scales and one trend term. Then the LSTM algorithm is used to predicted and model the decomposed sub-sequences. Finally, the forecast result of the composite index is obtained through integration. The empirical results show that the stock market systemic risk index constructed in this paper can effectively identify important risk events within the sample period. In addition, compared with the benchmark model, the EEMD-LSTM model constructed in this paper shows a stronger early warning ability for systemic financial risks in the stock market. [ABSTRACT FROM AUTHOR]

Background: Fertility intentions, as a direct driver of fertility behavior, play an important role in the implementation of national fertility policy and population development. This study explored the influencing factors of college teachers' second child fertility intentions in Jinan, China on the basis of TPB. Methodology/Principal findings: Based on the theory of planned behavior, this paper employs basic characteristics analysis, difference analysis, and factor analysis related to the fertility intentions of the participants. Analysis found participants between 31 and 40 years old had the highest second child fertility intentions, and participants with a college-age first child had the lowest second child fertility intentions. Attitude and subjective norms had a positive impact on second child fertility intentions, and policy awareness had a positive impact on attitude, which indirectly affected second child fertility intentions. Subjective norms had the greatest influence on second child fertility intentions, followed by attitude, and policy awareness had the least influence on second child fertility intentions. Significance/Future research: The findings of this paper can enrich the theoretical research on fertility intentions, and also provide more optimal practical references for the formulation and propagation of China's fertility policy as well as the improvement of the division of family roles in China. Future research can further explore the impact of fertility policy on the fertility intentions of other groups. [ABSTRACT FROM AUTHOR]

Public tolerance for corruption within a society significantly influences the prevalence of corrupt practices, but less is known about how this tolerance evolves with social norms. This paper presents experimental evidences demonstrating that the descriptive social norm indicating widespread corruption can lead to increased tolerance for corruptive acts. We introduce an asymmetric information ultimatum game to simulate the interactions between embezzlers and citizens. Game theoretical analysis reveals that victims anticipating corruption will exhibit greater compliance with embezzlement when the offers are evaluated based on descriptive norms. To test the hypothesis, we employ a framing effect to induce variations in descriptive norms within a behavioral experiment. Although the treatment effect is significant only in the subgroup of student cadres, this subgroup demonstrated increased beliefs about embezzlement, greater tolerance for corruption, and a heightened propensity to embezzle when exposed to framings with hierarchical implications. This paper contributes to the corruption literature by examining the effects of descriptive norms on victims' responses to embezzlement. It offers a more comprehensive perspective on how social standards shape public opinions and corrupt actions, enhancing our understanding of the self-reinforcing nature of corruption. [ABSTRACT FROM AUTHOR]

With the increasing applications of traffic scene image classification in intelligent transportation systems, there is a growing demand for improved accuracy and robustness in this classification task. However, due to weather conditions, time, lighting variations, and annotation costs, traditional deep learning methods still have limitations in extracting complex traffic scene features and achieving higher recognition accuracy. The previous classification methods for traffic scene images had gaps in multi-scale feature extraction and the combination of frequency domain, spatial, and channel attention. To address these issues, this paper proposes a multi-scale and multi-attention model based on Res2Net. Our proposed framework introduces an Adaptive Feature Refinement Pyramid Module (AFRPM) to enhance multi-scale feature extraction, thus improving the accuracy of traffic scene image classification. Additionally, we integrate frequency domain and spatial-channel attention mechanisms to develop recognition capabilities for complex backgrounds, objects of different scales, and local details in traffic scene images. The paper conducts the task of classifying traffic scene images using the Traffic-Net dataset. The experimental results demonstrate that our model achieves an accuracy of 96.88% on this dataset, which is an improvement of approximately 2% compared to the baseline Res2Net network. Furthermore, we validate the effectiveness of the proposed modules through ablation experiments. [ABSTRACT FROM AUTHOR]

This paper employs a unique data set to analyze the trading behavior of wealthy individual investors across Mainland China and their impact on Chinese stock markets' tail risk. Results show that the wealthy individual investors' trading behavior can explain Chinese stock markets' tail risk, and the daily investment portfolios based on the network density of wealthy individual investors have significant excess returns. This paper also investigates the determinants of wealthy individual investors' trading behavior with the social network method and the spatial econometric model, and reveals that wealthy individuals benefit from the spillover effect of their trading behavior through the investor networks. The results of this paper not only reveal micro evidence for the formation mechanism of asset prices, but also provide insight into the behavior of wealthy individual investors. [ABSTRACT FROM AUTHOR]

The electric power sector is the primary contributor to carbon emissions in China. Considering the context of dual carbon goals, this paper examines carbon emissions within China's electricity sector. The research utilizes the LMDI approach for methodological rigor. The results show that the cumulative contribution of economies scale, power consumption factors and energy structure are 114.91%, 85.17% and 0.94%, which contribute to the increase of carbon emissions, the cumulative contribution of power generation efficiency and ratio of power dissipation to generation factor are -19.15% and -0.01%, which promotes the carbon reduction. The decomposition analysis highlights the significant influence of economic scale on carbon emissions in the electricity industry, among the seven factors investigated. Meanwhile, STIRPAT model, Logistic model and GM(1,1) model are used to predict carbon emissions, the average relative error between actual carbon emissions and the predicted values are 0.23%, 8.72% and 7.05%, which indicates that STIRPAT model is more suitable for medium- to long-term predictions. Based on these findings, the paper proposes practical suggestions to reduce carbon emissions and achieve the dual carbon goals of the power industry. [ABSTRACT FROM AUTHOR]

Online research methods have grown in popularity due in part to the globalised and far-reaching nature of the internet but also linked to the Covid-19 pandemic whereby restrictions to travel and face to face contact necessitated a shift in methods of research recruitment and data collection. Ethical guidance exists to support researchers in conducting online research, however this is lacking within health fields. This scoping review aims to synthesise formal ethical guidance for applying online methods within health research as well as provide examples of where guidance has been used. A systematic search of literature was conducted, restricted to English language records between 2013 and 2022. Eligibility focused on whether the records were providing ethical guidance or recommendations, were situated or relevant to health disciplines, and involved the use or discussion of online research methods. Following exclusion of ineligible records and duplicate removal, three organisational ethical guidance and 24 research papers were charted and thematically analysed. Four key themes were identified within the guidance documents, 1) consent, 2) confidentiality and privacy, 3) protecting participants from harm and 4) protecting researchers from harm with the research papers describing additional context and understanding around these issues. The review identified that there are currently no specific guidelines aimed at health researchers, with the most cited guidance coming from broader methodological perspectives and disciplines or auxiliary fields. All guidance discussed each of the four key themes within the wider context of sensitive topics and vulnerable populations, areas and issues which are often prominent within health research thus highlighting the need for unifying guidance specific for health researchers. Further research should aim to understand better how online health studies apply ethical principles, to support in informing gaps across both research and guidance. [ABSTRACT FROM AUTHOR]

To analyze and study the behaviour of the shallow water waves, the perturbed Boussinesq equation has acquired fundamental importance. The principal objective of this paper is to manifest the exact traveling wave solution of the perturbed Boussinesq equation by two well known techniques named as, two variables (G′G,1G) expansion method and generalized projective Riccati equations method. A diverse array of soliton solutions, encompassing periodic, bright solitons, singular solitons and bright singular solitons are obtained by the applications of proposed techniques. The constraint conditions for newly constructed solutions are also specified. To enhance comprehension, the numerical illustrations of constructed solutions have been represented using surface plots, 2D plots and density plots. The results delineated in this paper transcend existing analysis, offering a novel, well-structured, and modern perspective. The solutions obtained not only enrich understanding of shallow water wave models but also exhibit efficacy in providing detailed descriptions of their dynamics. [ABSTRACT FROM AUTHOR]

China eliminated rural poverty under current poverty standards in 2020. However, compared with rural poverty, urban poverty in China has been somewhat neglected. This paper aims to discover the changes and determinants of multidimensional urban poverty in Shandong Province, a representative province in Eastern China. Using a nationally representative panel dataset, the China Family Panel Studies, and the Dual Cutoff method, this study creates a multidimensional poverty index with four dimensions and 11 indicators to measure urban poverty in Shandong Province. This paper discovers that while the incidence of multidimensional urban poverty in Shandong Province decreased from 47.62% in 2010 to 36.45% in 2018, the intensity of multidimensional poverty only decreased from 41.27% to 37.25%, which indicates the inadequacy of urban anti-poverty efforts in Shandong Province. This paper also uses logistic regression to identify the determinants of multidimensional urban poverty. The findings suggest that income, health, drinking water, and durable goods are the main determinants of multidimensional urban poverty in Shandong Province. Based on these findings, this study provides targeted recommendations for future urban anti-poverty policies in Shandong Province. [ABSTRACT FROM AUTHOR]

Background: A maternal mortality ratio is a sensitive indicator when comparing the overall maternal health between countries and its very high figure indicates the failure of maternal healthcare efforts. Cambodia, Laos, Myanmar, and Vietnam-CLMV countries are the low-income countries of the South-East Asia region where their maternal mortality ratios are disproportionately high. This systematic review aimed to summarize all possible factors influencing maternal mortality in CLMV countries. Methods: This systematic review applied "The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Checklist (2020)", Three key phrases: "Maternal Mortality and Health Outcome", "Maternal Healthcare Interventions" and "CLMV Countries" were used for the literature search. 75 full-text papers were systematically selected from three databases (PubMed, Google Scholar and Hinari). Two stages of data analysis were descriptive analysis of the general information of the included papers and qualitative analysis of key findings. Results: Poor family income, illiteracy, low education levels, living in poor households, and agricultural and unskilled manual job types of mothers contributed to insufficient antenatal care. Maternal factors like non-marital status and sex-associated work were highly associated with induced abortions while being rural women, ethnic minorities, poor maternal knowledge and attitudes, certain social and cultural beliefs and husbands' influences directly contributed to the limitations of maternal healthcare services. Maternal factors that made more contributions to poor maternal healthcare outcomes included lower quintiles of wealth index, maternal smoking and drinking behaviours, early and elderly age at marriage, over 35 years pregnancies, unfavourable birth history, gender-based violence experiences, multigravida and higher parity. Higher unmet needs and lower demands for maternal healthcare services occurred among women living far from healthcare facilities. Regarding the maternal healthcare workforce, the quality and number of healthcare providers, the development of healthcare infrastructures and human resource management policy appeared to be arguable. Concerning maternal healthcare service use, the provisions of mobile and outreach maternal healthcare services were inconvenient and limited. Conclusion: Low utilization rates were due to several supply-side constraints. The results will advance knowledge about maternal healthcare and mortality and provide a valuable summary to policymakers for developing policies and strategies promoting high-quality maternal healthcare. [ABSTRACT FROM AUTHOR]

The capitalisation on and transfer of technological, engineering and scientific knowledge associated with empirical know-how is an important issue for the sustainability and development of manufacturing. Indeed, certain sectors of industry are facing the increasing ageing of the labour force, recruitment difficulties and high staff turnover, leading to a loss of knowledge and know-how. In a context of numerical and digital transition and the migration of processes to industry 4.0, one of major challenges manufacturers face today is their capacity to build intelligent platforms for acquiring, storing and transferring their know-how and knowledge. It is crucial to create new media and tools for staff training and development capable of capturing knowledge and reusing it to create a project history through expertise and data collection. This paper presents the methodology and guidelines for implementing electronic knowledge books (eK-Books), along with their uses. The eK-Book is a semantic web-based hypertext medium (channel) allowing stakeholders to capitalise on, structure and transfer knowledge by using concept maps, process maps, influence graphs, downloadable documents, web pages and hypermedia knowledge sheets. They are intended for engineers, expert or novice technicians, manufacturers, sector coordinators and plant managers, as well as trainers and learners. They are usable and manageable in all types of environments and with different levels of accessibility. This paper highlights (1) the transfer knowledge capacity of eK-Books and (2) their usability in two agri-food sectors namely (1) the cheese sector with protected designation of origin (PDO) and protected geographical indication (PGI), and (2) the butchery and cold meat sectors. [ABSTRACT FROM AUTHOR]