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As the digital revolution deepens, the digital economy (DE) is reshaping the global industrial structure (IS). This paper utilizes data from 280 Chinese cities between 2007 and 2020 to conduct an in-depth analysis of how DE propels the upgrading of IS and explores the role of human capital (HC) in this process. The research indicates that DE significantly fosters the optimization of IS. Additionally, it was discovered that the growth of HC plays a pivotal mediating role in this evolution, complementing existing research on the relationship between higher education and industrial upgrading. By adopting spatial econometric methods, this study unveils the spillover effects of DE in geographical space, identifying a positive influence on the industrial upgrading of surrounding regions. The paper also confirms the nonlinear characteristics of DE's impact on IS upgrading, which manifests as a pronounced inverted U-shaped trend, marking a novel discovery. Further findings suggest that in regions with more advanced artificial intelligence technologies, the impact of DE on industrial optimization is more significant, highlighting the role of regional disparities in the digital transformation. In conclusion, the paper proposes policy recommendations based on the research findings to facilitate the development of DE and the elevation of IS levels, thereby promoting high-quality economic growth. [ABSTRACT FROM AUTHOR]

This research addresses the pressing challenge of intrusion detection and prevention in Wireless Sensor Networks (WSNs), offering an innovative and comprehensive approach. The research leverages Support Vector Regression (SVR) models to predict the number of barriers necessary for effective intrusion detection and prevention while optimising their strategic placement. The paper employs the Ant Colony Optimization (ACO) algorithm to enhance the precision of barrier placement and resource allocation. The integrated approach combines SVR predictive modelling with ACO-based optimisation, contributing to advancing adaptive security solutions for WSNs. Feature ranking highlights the critical influence of barrier count attributes, and regularisation techniques are applied to enhance model robustness. Importantly, the results reveal substantial percentage improvements in model accuracy metrics: a 4835.71% reduction in Mean Squared Error (MSE) for ACO-SVR1, an 862.08% improvement in Mean Absolute Error (MAE) for ACO-SVR1, and an 86.29% enhancement in R-squared (R2) for ACO-SVR1. ACO-SVR2 has a 2202.85% reduction in MSE, a 733.98% improvement in MAE, and a 54.03% enhancement in R-squared. These considerable improvements verify the method's effectiveness in enhancing WSNs, ensuring reliability and resilience in critical infrastructure. The paper concludes with a performance comparison and emphasises the remarkable efficacy of regularisation. It also underscores the practicality of precise barrier count estimation and optimised barrier placement, enhancing the security and resilience of WSNs against potential threats. [ABSTRACT FROM AUTHOR]

Sustainable agricultural economic development is the core task for achieving the objective of rural revitalization strategy in China, which cannot be separated from the support and guidance of fiscal policy, and agricultural industry integration is a key path for the fiscal promotion of sustainable agricultural economic development. This paper systematically examines the interaction mechanism between fiscal agricultural expenditures and sustainable agricultural economic development by using 31 provincial panel data in China from 2008 to 2020 and adopting a two-way fixed effect model, a panel quantile model, and a mediating effect model, respectively. The results show that the impact of fiscal agricultural expenditures on sustainable agricultural economic development is significantly positive, and appears a dynamic increasing trend with the agricultural development stage upgrading. Moreover, heterogeneity analysis shows that the effect of fiscal agricultural expenditures is more obvious for the samples in the central region and with a high share of primary industry. Further, a mediating effect test finds that agricultural industry integration plays a mediating mechanism between fiscal agricultural expenditures and sustainable agricultural economic development. Therefore, this paper proposes constructing a long-term investment mechanism for fiscal agricultural expenditures, formulating differentiated fiscal support policies for agriculture, and prioritizing support for agricultural industry integration, which provides theoretical support and policy inspiration for promoting sustainable agricultural economic development. [ABSTRACT FROM AUTHOR]

In recent years, considerable and valuable research progress has been made in indoor positioning technologies based on WLAN Radio Frequency (RF) fingerprinting, identifying it as one of the most promising positioning technologies with substantial potential for wider adoption. However, indoor environmental factors significantly influence the propagation of wireless RF signals, resulting in a considerable decrease in positioning accuracy as the indoor environmental conditions vary. Thus, effectively mitigating the impact of indoor environmental factors on WLAN RF fingerprinting-based positioning systems has become a crucial research problem. Currently, there is a dearth of comprehensive research on the influence of indoor climatic factors, particularly the variations in relative humidity, on the propagation of WLAN RF signals within indoor spaces and its consequential impact on positioning accuracy. To address the aforementioned issues, this paper proposes an Adaptive expansion fingerprint database (AeFd) model based on a regression learning algorithm. The AeFd, through the design of a relationship model describing the interaction between fingerprint databases under varying relative humidity, allows the fingerprint database expanded by AeFd to dynamically adapt to the changes in indoor relative humidity. Our experiments show that using the AeFd model with the KNN algorithm, a 5% performance improvement was observed over 10 days and an 8% improvement over 10 months. According to experimental test results, the fingerprint database expansion model AeFd proposed in this paper can effectively expand the fingerprint database under different relative humidity levels, thereby significantly enhancing the positioning performance of the system and improving its stability. [ABSTRACT FROM AUTHOR]

The motivation of this paper is to solve the problem of tourism majors' lack of theoretical knowledge and professional ability by building a capacity training mechanism model based on grounded theory. The goal of the study is to optimize the ability training strategies of tourism undergraduates and improve their professional quality and competitiveness. The paper adopts the ability training model based on grounded theory, and combines with Back Propagation (BP) neural network for optimization and evaluation. By comparing the performance of different ability training mechanisms, this paper explores the best training strategies to provide support and guidance for the training of tourism undergraduates. Firstly, the employment background of the current market is studied and analyzed. By sorting out the relevant documents of grounded theory and combining with the current training strategies of tourism majors, the students' personality characteristics and the basic principles of establishing models are integrated. The Back Propagation (BP) neural network is combined with the grounded theory. The data input of the student ability model is re-optimized. The undergraduate competency training mechanism model is constructed. The results show that when the number of iterations of the network model is 500.00, the evaluation accuracy of the competency training model based on BP neural network can reach 70.00%. At this time, the evaluation accuracy of competency training model based on content analysis method is only 55.00%. In addition, the results show that with the increase of model iterations, the recognition accuracy of the ability model based on grounded theory and the ability training mechanism of content analysis method is continuously improved. However, the ability evaluation model based on grounded theory has higher accuracy, and the accuracy of ability evaluation even exceeds 78.52% when the number of iterations of the network model is 600. Secondly, through the statistics and comparison of the grading results of students of different majors, it is found that the ability training mechanism based on grounded theory can improve the overall ability level of students more significantly. The research has important reference value for promoting the optimization and perfection of students' training strategies. [ABSTRACT FROM AUTHOR]

Multiple estimates exist of global monetary and multidimensional poverty, but populations at risk of social exclusion still lack a worldwide estimate. This paper fills this gap by providing the first estimates of the share and number of populations at risk of social exclusion worldwide. The paper contributes to the literature in three important respects. First, it develops a conceptual framework of social exclusion that emphasizes the relative, multidimensional, and dynamic features of exclusion. Second, it proposes a macro-counting methodology that allows measuring populations at risk of exclusion based on identity, circumstances, and socioeconomic conditions, while advancing a protocol to avoid double counting of individuals at risk of social exclusion. Third, the empirical strategy provides to the best of our knowledge, the first estimates of populations at risk of social exclusion by dimensions of exclusion on a global and regional scale. Overall, we estimate that between 2.33 and 2.43 billion people—roughly 32 per cent of the global population—are at risk of social exclusion. The South Asia and East Asia and Pacific regions contain 1.3 billion such people, with India and China alone home to 840 million of them. Meanwhile, 52 per cent of sub-Saharan Africa's population is vulnerable to exclusion, the greatest share of any region. Our findings have important policy implications. While antipoverty policies can support household consumption and smooth its volatility among the poor, they are unlikely to address social exclusion stemming from ethnic, racial, or gender discrimination. Therefore, addressing exclusion necessitates a suite of multiple interventions tailored to distinct groups and sustained over time. [ABSTRACT FROM AUTHOR]

The delineation of the open-pit mining boundary, particularly in the context of medium to long-term planning, forms the foundation of mining design. However, due to the non-linear and dynamic nature of the economic and technical parameters influencing boundary delineation, determining the optimal mining boundary can be exceedingly challenging. Currently, most boundary optimization methods assume that block parameters remain fixed, which results in enterprises assuming a certain level of risk when facing changes in internal and external conditions. In this regard, this paper introduces the concept of "achievement degree" to reflect the risk associated with the results of boundary design. Using coal prices as an example, this article applies the predicted coal price curve to boundary optimization adjustments by specifying the "achievement degree" requirements for various time periods, thereby facilitating risk-controlled and economically optimal boundary decisions. Taking the illustrative case of an idealized small-scale inclined coal seam open-pit mine, adjustments to the boundary closely track variations in coal prices, further enhancing returns. The results demonstrate that the method proposed in this paper can increase overall revenue by approximately 51.15% within the forecast period, while effectively managing risks. [ABSTRACT FROM AUTHOR]

With the rapid development of China's economy and society, the innovation of grassroots social governance has become increasingly important. This paper constructs 35 grassroots social governance innovation samples. Using the TOE theoretical framework and a fuzzy set qualitative comparative analysis (fsQCA), this paper analyzes the joint effects and interactive relationships of multiple factors on grassroots social governance innovation performance from three dimensions: technology, organization, and environment. The research reveals that internal environmental openness is a necessary condition for achieving high innovation performance in grassroots social governance, and proposes four grouping models that affect the performance of grassroots social governance innovation. This paper explores the inner logic of grassroots social governance innovation from a histological perspective, and on this basis proposes an adaptive path to enhance the performance of grassroots social governance innovation. [ABSTRACT FROM AUTHOR]

The development of digital economy is a strategic choice to grasp the revolution of new science and technology and the new opportunities of industrial reform. The development of digital economy depends on the good support of policy and theoretical system. Therefore, the quantitative evaluation of policy texts provides the basis of decision-making and the suggestions of path optimization for the formulation and improvement of digital economy policy of China. By selecting the text of digital economy policy issued by China government, the paper constructs a quantitative evaluation model of digital economy policy using the methods of content analysis and text mining. The empirical research results show that the overall design evaluation of the selected policy is relatively reasonable. Six policies were evaluated as excellent and two as acceptable. In view of the problems such as lack of predictive policy in the policy type, lack of specific policy in the policy timeliness, imbalance in the use of policy guarantee, and lack of comprehensive coverage in the policy objectives, the paper puts forward corresponding countermeasures and suggestions. [ABSTRACT FROM AUTHOR]

Previously observed negative correlations between sample size and effect size (n-ES correlation) in psychological research have been interpreted as evidence for publication bias and related undesirable biases. Here, we present two studies aimed at better understanding to what extent negative n-ES correlations reflect such biases or might be explained by unproblematic adjustments of sample size to expected effect sizes. In Study 1, we analysed n-ES correlations in 150 meta-analyses from cognitive, organizational, and social psychology and in 57 multiple replications, which are free from relevant biases. In Study 2, we used a random sample of 160 psychology papers to compare the n-ES correlation for effects that are central to these papers and effects selected at random from these papers. n-ES correlations proved inconspicuous in meta-analyses. In line with previous research, they do not suggest that publication bias and related biases have a strong impact on meta-analyses in psychology. A much higher n-ES correlation emerged for publications' focal effects. To what extent this should be attributed to publication bias and related biases remains unclear. [ABSTRACT FROM AUTHOR]

This paper introduces a method aiming at enhancing the efficacy of speaker identification systems within challenging acoustic environments characterized by noise and reverberation. The methodology encompasses the utilization of diverse feature extraction techniques, including Mel-Frequency Cepstral Coefficients (MFCCs) and discrete transforms, such as Discrete Cosine Transform (DCT), Discrete Sine Transform (DST), and Discrete Wavelet Transform (DWT). Additionally, an Artificial Neural Network (ANN) serves as the classifier for this method. Reverberation is modeled using varying-length comb filters, and its impact on pitch frequency estimation is explored via the Auto Correlation Function (ACF). This paper also contributes to the field of cancelable speaker identification in both open and reverberation environments. The proposed method depends on comb filtering at the feature level, deliberately distorting MFCCs. This distortion, incorporated within a cancelable framework, serves to obscure speaker identities, rendering the system resilient to potential intruders. Three systems are presented in this work; a reverberation-affected speaker identification system, a system depending on cancelable features through comb filtering, and a novel cancelable speaker identification system within reverbration environments. The findings revealed that, in both scenarios with and without reverberation effects, the DWT-based features exhibited superior performance within the speaker identification system. Conversely, within the cancelable speaker identification system, the DCT-based features represent the top-performing choice. [ABSTRACT FROM AUTHOR]

This research paper delves into the behavioral factors that have impact on decision making of Chinese investors in stock markets. As one of the world's most dynamic and rapidly evolving financial landscapes, stock markets of China have witnessed significant growth and transformation in recent years. However, the role of behavioral biases in shaping investment decisions remains a relatively understudied aspect. Drawing upon a detailed review of studies, psychological theories, and empirical studies, this research explores various behavioral factors affecting the decision of investors at Beijing, Shanghai and Shenzhen stock markets. Through a structured questionnaire and by collecting a sample of 521 respondents, this paper investigates that herding, overconfidence, prospect, market, gamble's fallacy, and anchoring-ability bias often lead investors to deviate from rational decision-making and contribute to market inefficiencies. While herding, prospect, and heuristic affect the investment performance in stock markets of China. Moreover, the research underscores the need for investor education programs and regulatory interventions that acknowledge the presence of behavioral biases and encourage more informed decision-making. By shedding light on these dynamics, it provides valuable insights for policymakers, financial institutions, and investors seeking to navigate the intricacies of this rapidly growing financial landscape. [ABSTRACT FROM AUTHOR]

The establishment of green finance reform and innovation (GFRI) pilot zone is an important measure of the Chinese government to urge enterprises to develop green transformation. This paper explores the impact of pilot policies in the GFRI pilot zone on corporate environmental investment. Based on 819 A-share listed enterprises from 2010 to 2020, our staggered difference-in-differences (staggered DID) estimation documents revealed that enterprises in the GFRI pilot zone significantly increased the corporate environmental investment efficiency but reduced the scale of corporate environmental investment.This conclusion remained robust after Propensity Scores Matching difference-in-differences (PSM-DID), replacing dependent variables, and shortening the time window. We contend that the increased research and development (R&D) expenditure and technological innovation are the potential mechanisms at work. Heterogeneity analysis showed that the establishment of GFRI improved the environmental investment efficiency of polluting enterprises but had no effect on green enterprises.Meanwhile, the effect of GFRI exhibited heterogeneity in the type of enterprise ownership. This paper evaluates the implementation effect of GFRI from the perspective of corporate environmental investment, and provides theoretical support and an empirical basis for green finance policy to serve China's green economy. [ABSTRACT FROM AUTHOR]

Multinational enterprises frequently divest their foreign assets in the current economic environment. Existing research, based on friction theory, has mainly focused on the impacts of political and economic disparities on foreign divestment while neglecting the nuanced influence of cultural factors. To address this gap, this paper draws on the cultural friction perspective to capture the diverse cultural resistance faced by each enterprise and explore the relationship between cultural friction and foreign divestment. Data from Chinese publicly listed enterprises engaged in foreign investment are leveraged, and a dual-level analysis is conducted using Logit panel regression and Cox survival analysis to examine the relationship between cultural friction and foreign divestment from both the viewpoints of the parent company and the overseas subsidiary. Additionally, the paper examines the marginal factors that affect the relationship between them from an institutional perspective. The findings reveal that cultural friction has a positive influence on the propensity of multinational enterprises to divest from foreign markets. Interestingly, a "formal institutional distance paradox" is demonstrated in our study, and politically connected enterprises are found to be more vulnerable to foreign divestment due to the "curse of political affiliations". [ABSTRACT FROM AUTHOR]

The purpose of this paper is to address the chasm between academic research on social media as an expanding academic discipline and at the same time a growing marketing function. A bibliometric analysis indicated the evolution of academic research on social media. The results of a survey of 280 social media practitioners shed the light on the gap between academic social media research and the practice of professionals. A qualitative study also offered novel insights and recommendations for future developments in academic research on social media. The findings of this paper showed that academic research on social media is growing in terms of the number of publications but is struggling in three areas: visibility, relevance, and influence on practitioners. This study contributes to the body of knowledge on social media. The implications of our study are derived from the importance of our findings on the directions to publish more relevant and timely academic research on social media. While extensive studies exist on social media, their influence on practitioners is still limited. [ABSTRACT FROM AUTHOR]

With the advancement of touch screen technology, the application of touch screens in civil aircraft cockpits has become increasingly popular. However, further analysis and research are required to fully promote its applications. The paper researched the usability of touch screens in aircraft cockpit considering the operation performance and subjective NASA-TLX workload evaluation, conducted experimental research on three touch gestures: click, drag, and zoom. Additionally, a comparative analysis was conducted on the touch performance under different layouts, positions, touch sizes, dragging direction angles, and zoom multiples. The touch performance indicators include operation time, error rate, operation speed, and workload. The experimental results show that the 21 mm size has the minimum operation time and workload, and 18 mm size has the lowest error rate in the clicking tasks. Additionally, the performance and workload of the captain's layout are better than those of the co-pilot's layout, and the performance of the center console position is best. The operation speed of the dragging tasks is faster when performed at position R3 compared to other positions. The dragging moving angles with better operation speed are 80°-190° and 250°-290°. The operation performance and workload of the zooming tasks vary depending on the zoom multiples. As the multiple increases, the operation time and workload also increase. There is no difference in operation performance or workload between zooming in and zooming out. The paper provides experimental support and suggestions based on human operation and subjective NASA-TLX workload evaluation for the application of touch screens in civil aircraft cockpits. [ABSTRACT FROM AUTHOR]

Purpose: Due to the existence of information opacity, there is a common problem of adverse selection in the process of screening alternative technology start-ups (TSs) and determining investment targets by venture capital institutions, which does not reveal the true value of enterprises and makes the market inefficient. The aim of this paper is to design an evaluation and screening system help venture capital institutions to select the qualified TSs as their investment objective. Design: A research framework of four dimensions that include conception, technical innovation, business model and team structure, was built based on previous studies. Based on the research framework, 15 second-level indicators and 33 third-level indicators were extracted with literature research method. This paper proposes an evaluation model with back propagation neural network (BPNN) optimized by genetic algorithm (GA) to improve the rate of selecting and investing in qualified start-ups. Findings: The results show that the evaluation accuracy of the evaluation model for qualified and unqualified enterprises can reach 80.33% and 93.67% respectively, which has verified the effectiveness of the model and algorithm. Originality/Value: This paper established an effective evaluation system based on PCA and GA-BPNN to help venture capital institutions preliminarily screen potential technology start-ups, which provides the possibility for venture capital institutions to greatly reduce the screening time and cost, improve the screening efficiency of TSs, and scientifically assess the risk of investee projects or investee enterprises to obtain sustainable and stable excess profits. [ABSTRACT FROM AUTHOR]

To further enhance the residual current detection capability of low-voltage distribution networks, an improved adaptive residual current detection method that combines variational modal decomposition (VMD) and BP neural network (BPNN) is proposed. Firstly, the method employs the envelope entropy as the adaptability function, optimizes the [k, ɑ] combination value of the VMD decomposition using the bacterial foraging-particle swarm algorithm (BFO-PSO), and utilizes the interrelation number R as the classification index with the Least Mean Square Algorithm (LMS) to classify, filter, and extract the effective signal from the decomposed signal. Then, the extracted signals are detected by BPNN, and the training data are utilized to predict the residual current signals. Simulation and experimental data demonstrate that the proposed algorithm exhibits strong robustness and high detection accuracy. With an ambient noise of 10dB, the signal-to-noise ratio is 16.3108dB, the RMSE is 0.4359, and the goodness-of-fit is 0.9627 after processing by the algorithm presented in this paper, which are superior to the Variational Modal Decomposition-Long Short-Term Memory (VMD-LSTM) and Normalized-Least Mean Square (N-LMS) detection methods. The results were also statistically analyzed in conjunction with the Kolmogorov-Smirnov test, which demonstrated significance at the experimental data level, indicating the high accuracy of the algorithms presented in this paper and providing a certain reference for new residual current protection devices for biological body electrocution. [ABSTRACT FROM AUTHOR]

With the increasing uncertainty of urban security, urban resilience construction with risk awareness and bottom-line thinking has become essential for promoting sustainable urban development. This paper measures China's urban resilience development index (CRDI) based on 284 cities in China (except Tibet) using the entropy method from four dimensions: economic, social, environmental, and infrastructure, and analyzes it by combining coupling coordination degree and barrier factor analysis. We find that: (1) At the national level, CRDI and its sub-dimensions show an increasing trend in time, a decreasing spatial layout from coastal to inland, and a "high-high-low-low" clustering feature in space. (2) At the regional level, the CRDI is from high to low in the east, middle, and west order. The sub-dimensions are from high to low in the order of east, middle, and west for economic, social, and infrastructure resilience and from high to low in the order of east, west, and middle for environmental resilience. (3) To coupling coordination degree, the CRDI index coupling coordination is increasing in time trend but is still on the verge of dissonance. (4) Social resilience is the main obstacle factor. In the indicator layer, human resources, innovation, education, security, living, and environmental protection are the areas where CRDI coordinated development is the key to improvement. Based on the above empirical evidence, this paper proposes countermeasures to optimize urban resilience construction from four perspectives: economic, social, environmental, and infrastructure. [ABSTRACT FROM AUTHOR]

The micro-occurrence characterization of shale oil is a key geological issue that restricts the effective development of continental shale oil in China. In order to make up for the lack of research in this area, this paper carries out a series of experiments on the shale oil of the Lucaogou Formation using a multi-step extraction method, with the aim of exploring the micro-occurrence types and mechanisms of shale oil in the Lucaogou Formation, as well as exploring its direct connection with production and development. In this paper, shale oil in the reservoir is divided into two categories: free oil and residual oil. The polar substances and OSN compounds are the key factors determining the occurrence state of shale oil. Abundant polar substances and OSN compounds can preferentially react with mineral surfaces (including coordination, complexation, ionic exchange, and so on) to form a stable adsorption layer, making it difficult to extract residual oil in actual exploitation. Free oil is mainly composed of aliphatic hydrocarbons, and its adsorption capacity is related to the length of the carbon chain, i.e. long carbon chain, strong adsorption capacity, and poor movability. Free oil is widely stored in pores and cracks, and that with high mobility can be the most easily extracted, making it the main target at present exploitation. In the current state of drilling and fracturing technology, research should prioritize understanding the adsorption and desorption mechanisms of crude oil, particularly residual oil. This will help optimize exploitation programs, such as carbon dioxide fracturing and displacement, to enhance shale oil production. [ABSTRACT FROM AUTHOR]

Objective: To end the AIDS epidemic by 2030, despite the increasing poverty and inequalities, policies should be designed to deal with population heterogeneity and environmental changes. Bottom-up designs, such as the Agent-Based Model (ABM), can model these features, dealing with such complexity. HIV/AIDS has a complex dynamic of structural factors, risk behaviors, biomedical characteristics and interventions. All embedded in unequal, stigmatized and heterogeneous social structure. To understand how ABMs can model this complexity, we performed a scoping review of HIV applications, highlighting their potentialities. Methods: We searched on PubMed, Web of Science, and Scopus repositories following the PRISMA extension for scoping reviews. Our inclusion criteria were HIV/AIDS studies with an ABM application. We identified the main articles using a local co-citation analysis and categorized the overall literature aims, (sub)populations, regions, and if the papers declared the use of ODD protocol and limitations. Results: We found 154 articles. We identified eleven main papers, and discussed them using the overall category results. Most studies model Transmission Dynamics (37/154), about Men who have sex with Men (MSM) (41/154), or individuals living in the US or South Africa (84/154). Recent studies applied ABM to model PrEP interventions (17/154) and Racial Disparities (12/154). Only six papers declared the use of ODD Protocol (6/154), and 34/154 didn't mention the study limitations. Conclusions: While ABM is among the most sophisticated techniques available to model HIV/AIDS complexity. Their applications are still restricted to some realities. However, researchers are challenged to think about social structure due model characteristics, the inclusion of these features is still restricted to case-specific. Data and computational power availability can enhance this feature providing insightful results. [ABSTRACT FROM AUTHOR]

Frequent occlusion of tracking targets leads to poor performance of tracking algorithms. A common practice in multi-target tracking algorithms is to re-identify the occluded tracking targets, which increases the number of identity switching occurrences. This paper focuses on online multi-object tracking and designs an anti-occlusion, robust association strategy, and feature extraction model. Specifically, the least squares algorithm and the Kalman filter are used to predict the trajectory of the tracking target, while the two-way self-attention mechanism is employed to extract the features of the tracking target, as well as positive and negative samples. After the tracking target is occluded, the association strategy is used to assign the identity information from before the occlusion. The experimental results demonstrate that the algorithm proposed in this paper has achieved excellent tracking performance on the MOT dataset. [ABSTRACT FROM AUTHOR]

Fuzzy graphs are very important when we are trying to understand and study complex systems with uncertain and not exact information. Among different types of fuzzy graphs, cubic fuzzy graphs are special due to their ability to represent the membership degree of both vertices and edges using intervals and fuzzy numbers, respectively. To figure out how things are connected in cubic fuzzy graphs, we need to know about cubic α−strong, cubic β−strong and cubic δ−weak edges. These concepts better help in making decisions, solving problems and analyzing things like transportation, social networks and communication systems. The applicability of connectivity and comprehension of cubic fuzzy graphs have urged us to discuss connectivity in the domain of cubic fuzzy graphs. In this paper, the terms partial cubic α−strong and partial cubic δ−weak edges are introduced for cubic fuzzy graphs. The bounds and exact expression of connectivity index for several cubic fuzzy graphs are estimated. The average connectivity index for cubic fuzzy graphs is also defined and some results pertaining to these concepts are proved in this paper. The results demonstrate that removing some vertices or edges may cause a change in the value of connectivity index or average connectivity index, but the change will not necessarily be related to both values. This paper also defines the concepts of partial cubic connectivity enhancing node and partial cubic connectivity reducing node and some related results are proved. Furthermore, the concepts of cubic α−strong, cubic β− strong, cubic δ−weak edge, partial cubic α−strong and partial cubic δ−weak edges are utilized to identify areas most affected by a tsunami resulting from an earthquake. Finally, the research findings are compared with the existing methods to demonstrate their suitability and creativity. [ABSTRACT FROM AUTHOR]

Selecting an appropriate intensity of epidemic prevention and control measures is of vital significance to promoting the two-way dynamic coordination of epidemic prevention and control and economic development. In order to balance epidemic control and economic development and suggest scientific and reasonable traffic control measures, this paper proposes a SEIQR model considering population migration and the propagation characteristics of the exposed and the asymptomatic, based on the data of COVID-19 cases, Baidu Migration, and the tourist economy. Further, the factor traffic control intensity is included in the model. After determining the functional relationship between the control intensity and the number of tourists and the cumulative number of confirmed cases, the NSGA-II algorithm is employed to perform multi-objective optimization with consideration of the requirements for epidemic prevention and control and for economic development to get an appropriate traffic control intensity and suggest scientific traffic control measures. With Xi'an City as an example. The results show that the Pearson correlation coefficient between the predicted data of this improved model and the actual data is 0.996, the R-square in the regression analysis is 0.993, with a significance level of below 0.001, suggesting that the predicted data of the model are more accurate. With the continuous rise of traffic control intensity in different simulation scenarios, the cumulative number of cases decreases by a significant amplitude. While balancing the requirements for epidemic prevention and control and for tourist economy development, the model works out the control intensity to be 0.68, under which some traffic control measures are suggested. The model presented in this paper can be used to analyze the impacts of different traffic control intensities on epidemic transmission. The research results in this paper reveal the traffic control measures balancing the requirements for epidemic prevention and control and for economic development. [ABSTRACT FROM AUTHOR]

Sustainable intensification of agriculture requires understanding of the effect of soil characteristics and nutrient supply on crop growth. As farms are increasing in size by acquiring small fields from various farmers, the soil characteristics and nutrient supply might be very different from field to field, while at the same time specific soil properties might limit the nutrient uptake. As a result, there might be a large number of heterogeneous reasons why crop growth varies significantly. New data analysis techniques can help to explain variability in crop growth among fields. This paper introduces Exceptional Growth Mining (EGM) as a first contribution. EGM instantiates the data mining framework Exceptional Model Mining (EMM) such that subgroups of fields can be found that grow exceptionally in terms of three growth parameters (high/low maximum growth, steep/flat linear growth and early/late midpoint of maximum growth). As second contribution, we apply EGM to a case study by analyzing the dataset of a potato farm in the south of the Netherlands. EGM consists of (i) estimating growth curves by applying nonlinear mixed models, (ii) investigating the correlation between the estimated growth parameters, and (iii) applying EMM on these growth curve parameters using a growth curve-specific quality measure. By applying EGM on the data of the potato farm, we obtain the following results: 1) the estimated growth curves represent the variability in potato tuber growth very well (R2 of 0.92), 2) the steepness of the growth curve has a strong correlation with the maximum growth and the midpoint of maximum growth, and the correlation between the midpoint of maximum growth and maximum growth is weak, 3) the subgroup analyses indicate that: high values of K correspond to high maxima; low values of K correspond to low maxima, steep growth curves', and a late midpoint of halfway growth; Mg influences the midpoint of the growth curve; values of B are higher on dry soils with high tuber growth, while low values of B are found on wet soils with high tuber growth; high values of Zn, Mn, and Fe are found in subgroups with low tuber weight, probably related to the soil's low pH. In summary, this paper introduces EGM to obtain understanding in crop response to soil properties and nutrient supply. In addition, EGM provides a way to analyze only small parts of a large dataset, such that the impact of soil factors on growth can be analyzed on a more detailed level than existing methods. [ABSTRACT FROM AUTHOR]

In order to improve the decorative properties of Magnesium-Glass-Board (MGB), the surface morphology and decoration performance of MGB, are studied in detail by using profilometer, microscope and SEM, and the influence of its characterization, such as surface roughness, surface porosity and wettability, on decorative properties of MGB is analyzed by comparing with medium density fiberboard (MDF) and medium density particleboard (MDP). The results first showed that the surface of MGB has a porous structure, but MDF and MDP are not, resulting in a poor decorative performance of MGB. Second, it is found that the surface wettability of MGB is better than others. Third, the hot-pressing parameters including pressure, temperature and time have different influence on decorative performance of MGB during hot-pressing experiment. Finally, the surface bonding strength is positively correlated with pressure, but not with temperature and time. In general, a higher surface bonding strength led to a better decorative performance of MGB. The furthermore research can concentrate on the modification method of the MGB's surface according to this paper's conclusion to improve the lamination performance of melamine paper. [ABSTRACT FROM AUTHOR]

Introduction: Research indicates that sexual and gender minority youth [SGMY] may engage more with information communication technologies [ICTs] more than their non-SGMY counterparts Craig SL et al. 2020. While scholarship generally explores youth's use of ICTs, there are gaps in scholarship that connect SGMY, their ICT engagement and influences to mental health. This scoping review will synthesize the literature that connects these core concepts in order to better understand the influence ITCs have on the mental health of SGMY and to develop a more fulsome understanding of this emerging area of literature. Methods and analysis: Following the scoping review framework of Arksey and O'Malley, the search will be conducted in the PsycINFO [Ovid interface, 1980-], MEDLINE [Ovid interface, 1948-], CINAHL [EBSCO interface, 1937-], Sociological Abstracts [ProQuest interface, 1952-], Social Services Abstracts [ProQuest interface, 1979-], and Scopus. Descriptive summaries and thematic analysis will summarize the articles that meet the inclusion criteria using an extraction table. Ethics and dissemination: The review outlined in this paper provides an overview of information that exists on the technology use of SGMY, ICTs and the interconnection with mental health. Results will be disseminated through peer reviewed journals and national and international conferences. As information collected for this paper as is retrieved from publicly available sources, ethics approval is not required. [ABSTRACT FROM AUTHOR]

Manifold visualisation techniques are commonly used to visualise high-dimensional datasets in physical sciences. In this paper, we apply a recently introduced manifold visualisation method, slisemap, on datasets from physics and chemistry. slisemap combines manifold visualisation with explainable artificial intelligence. Explainable artificial intelligence investigates the decision processes of black box machine learning models and complex simulators. With slisemap, we find an embedding such that data items with similar local explanations are grouped together. Hence, slisemap gives us an overview of the different behaviours of a black box model, where the patterns in the embedding reflect a target property. In this paper, we show how slisemap can be used and evaluated on physical data and that it is helpful in finding meaningful information on classification and regression models trained on these datasets. [ABSTRACT FROM AUTHOR]

The utilization efficiency of land resources is an essential embodiment of economic development, social development, and ecological development and is a critical core to measure how to maximize the efficiency of land resources under limited conditions. The land is an important content and essential carrier of the research of tourism development level. This paper selects Panel Data from 2010 to 2019 to research the Guangxi regional tourism development. The entropy weight method and stochastic frontier production function (SFA) model were used to evaluate the development level of urban-rural tourism and the utilization efficiency of land resources in Guangxi. This paper uses the Panel Vector Autoregression (PVAR) model to analyze the internal relationship between urban-rural tourism development. The results show that: (1) Guangxi has a good level of tourism development and a high land use efficiency. (2) There is a reciprocal causation relationship between the regional tourism development level and land use efficiency in Guangxi, with significant levels of 0.005 and 0.034 respectively, indicating high credibility. This indicates that there is a mutual promotion and interaction between the two, which rely on and drive each other, promoting the joint sustainability of tourism development and land use efficiency. (3). The tourism development level is greatly influenced by itself, with impact values all above 0.99. At the same time, land use also has a significant self-impact, with impact values all above 0.87. Their internal optimization system is solid and endogenous impetus is robust, which can drive their development. Establishing an effective strategy for developing and protecting land use is beneficial to promote the long-term effectiveness of sustainable tourism development, enhancing high-quality development of the tourism economy and improving people's living standards and quality. [ABSTRACT FROM AUTHOR]

Background: Latent class analysis (LCA) identifies distinct groups within a heterogeneous population, but its application to accelerometry-assessed physical activity and sedentary behavior has not been systematically explored. We conducted a systematic scoping review to describe the application of LCA to accelerometry. Methods: Comprehensive searches in PubMed, Web of Science, CINHAL, SPORTDiscus, and Embase identified studies published through December 31, 2021. Using Covidence, two researchers independently evaluated inclusion criteria and discrepancies were resolved by consensus. Studies with LCA applied to accelerometry or combined accelerometry/self-reported measures were selected. Data extracted included study characteristics and both accelerometry and LCA methods. Results: Of 2555 papers found, 66 full-text papers were screened, and 12 papers (11 cross-sectional, 1 cohort) from 8 unique studies were included. Study sample sizes ranged from 217–7931 (mean 2249, standard deviation 2780). Across 8 unique studies, latent class variables included measures of physical activity (100%) and sedentary behavior (75%). About two-thirds (63%) of the studies used accelerometry only and 38% combined accelerometry and self-report to derive latent classes. The accelerometer-based variables in the LCA model included measures by day of the week (38%), weekday vs. weekend (13%), weekly average (13%), dichotomized minutes/day (13%), sex specific z-scores (13%), and hour-by-hour (13%). The criteria to guide the selection of the final number of classes and model fit varied across studies, including Bayesian Information Criterion (63%), substantive knowledge (63%), entropy (50%), Akaike information criterion (50%), sample size (50%), Bootstrap likelihood ratio test (38%), and visual inspection (38%). The studies explored up to 5 (25%), 6 (38%), or 7+ (38%) classes, ending with 3 (50%), 4 (13%), or 5 (38%) final classes. Conclusions: This review explored the application of LCA to physical activity and sedentary behavior and identified areas of improvement for future studies leveraging LCA. LCA was used to identify unique groupings as a data reduction tool, to combine self-report and accelerometry, and to combine different physical activity intensities and sedentary behavior in one LCA model or separate models. [ABSTRACT FROM AUTHOR]

With the development of the Internet of Things (IoT), the use of UAV-based data collection systems has become a very popular research topic. This paper focuses on the energy consumption problem of this system. Genetic algorithms and swarm algorithms are effective approaches for solving this problem. However, optimizing UAV energy consumption remains a challenging task due to the inherent characteristics of these algorithms, which make it difficult to achieve the optimum solution. In this paper, a novel particle swarm optimization (PSO) algorithm called Double Self-Limiting PSO (DSLPSO) is proposed to minimize the energy consumption of the unmanned aerial vehicle (UAV). DSLPSO refers to the operational principle of PSO and incorporates two new mechanisms. The first mechanism is to restrict the particle movement, improving the local search capability of the algorithm. The second mechanism dynamically adjusts the search range, which improves the algorithm's global search capability. DSLPSO employs a variable population strategy that treats the entire population as a single mission plan for the UAV and dynamically adjusts the number of stopping points. In addition, the proposed algorithm was also simulated using public and random datasets. The effectiveness of the proposed DSLPSO and the two new mechanisms has been verified through experiments. The DSLPSO algorithm can effectively improve the lifetime of the UAV, and the two newly proposed mechanisms have potential for optimization work. [ABSTRACT FROM AUTHOR]

Resource-based regions support national economic development and are essential sources of basic energy and raw materials. In the post-pandemic era, however, there are practical situations to deal with, such as a fractured industrial chain, a weaker industrial structure, and a sharp reduction in economic benefits. Based on data collected from 68 cities in China, from 2010 to 2021, with 816 observations, this paper explores the industrial development process of resource-based regions in China and the change in the toughness of the industrial structure under the impact of COVID-19. The paper studies and analyzes industrial development trends, industrial structure toughness, and spatial spillover effects. The methods used are the Markov chain model and the Industrial Structure Advancement Index. By building the spatial Dubin model, the paper analyzes the spatial spillover effect of regional industrial development. It decomposes the spillover effect using the partial differential model based on regression. The results show that, during the study period, the comprehensive development level of industries in resource-based regions in China was slowly improving and tended to stabilize after entering the post-pandemic era. The evolution of an advanced industrial structure is significantly heterogeneous among regions, and each region has different toughness. The impact of COVID-19 has reduced the toughness of China's resource-based regions' industrial structure. The spatial spillover effect of regional industrial development is significant. Labor force, technology input, and industrial-structure optimization have different impacts on the industrial development of neighboring regions. In the post-pandemic era, China has used new management methods for more innovation. In order to achieve low-carbon, environmental protection, and sustainable development of resources, realize the rapid recovery of the toughness of industrial structure in China's resource-based cities, and reduce the impact of the COVID-19 pandemic, China proposes to expand the supply of resources, improve the allocation of resources, optimize the direction, promote the rational flow and efficient aggregation of various factors, and enhance the impetus for innovation and development. [ABSTRACT FROM AUTHOR]

The recognition of food images is of great significance for nutrition monitoring, food retrieval and food recommendation. However, the accuracy of recognition had not been high enough due to the complex background of food images and the characteristics of small inter-class differences and large intra-class differences. To solve these problems, this paper proposed a food image recognition method based on transfer learning and ensemble learning. Firstly, generic image features were extracted by using the convolutional neural network models (VGG19, ResNet50, MobileNet V2, AlexNet) pre-trained on the ImageNet dataset. Secondly, the 4 pre-trained models were transferred to the food image dataset for model fine-tuning. Finally, different basic learner combination strategies were adopted to establish the ensemble model and classify feature information. In this paper, several kinds of experiments were performed to compare the results of food image recognition between single models and ensemble models on food-11 dataset. The experimental results demonstrated that the accuracy of the ensemble model was the highest, reaching 96.88%, which was superior to any base learner. Therefore, the convolutional neural network model based on transfer learning and ensemble learning has strong learning ability and generalization ability, and it is feasible and practical to apply the method to food image recognition. [ABSTRACT FROM AUTHOR]

While measures to detect psychophysical olfactory ability are a crucial part of clinicians' assessment of potential olfactory loss, it gives no indication of how olfaction is experienced by the patient and these different aspects often deviate substantially. To ensure quality and reproducibility of subjectively reported olfactory experience and significance, the Importance of Olfaction Questionnaire (IO-Q) was introduced around a decade ago, and while initial validations have produced promising results, important aspects remain nearly unexamined. For example, the test-retest reliability has rarely been examined and the difference of online versus pen-and-paper administration remains unexplored. Here, we translated IO-Q to Danish and examined its validity, test-retest reliability and mode of administration. A cohort of 179 younger, Danish participants with a high level of English proficiency took the test twice with varying time in-between. The first test was taken digitally and in English, while the second was taken using pen-and-paper and in Danish. The distribution of scores and the relationship between the IO-Q and subscale scores were nearly identical between tests, indicating little to no influence of language/test modality in the sampled population. The internal consistency was comparable to previously published results. Likewise, an acceptable test-retest reliability was observed for the full IO-Q and slightly lower for subscales. No significant effect of time was found across several weeks. In conclusion, the IO-Q performed satisfactorily in all examinations and could therefore serve as a valuable clinical measure of subjective olfactory experience, and its Danish translation shows highly similar characteristics to the original, English version. [ABSTRACT FROM AUTHOR]

Economic policy uncertainty has had an important impact on trade and sustainable economic development. Especially in some specific industries, uncertainty has increased dramatically. The extant related literature mainly analyzes the nexus between uncertainty and trade across different industries and focuses less on a specific industry. Using Chinese customs data on HS 8-digit products over the period of 2000–2013, this paper first investigates the impact of both foreign economic policy uncertainty (EPU) and domestic intra-industry trade on China's mechanical and electrical product exports to 23 trading partners and applies pooled OLS regressions to conduct an empirical study. This paper finds that EPU has a significant inhibition effect on mechanical and electrical product exports; conversely, intra-industry trade can both significantly promote exports and alleviate the inhibition effect of EPU. In addition, the export impact of EPU varied with different trade patterns. It can significantly inhibit processing exports, while it has no effect on ordinary exports. The results of this paper indicate that in the context of increasing uncertainty, our findings could have far-reaching policy implications for China to build a new development pattern of domestic and international dual circulation. [ABSTRACT FROM AUTHOR]

A brain tumor magnetic resonance image processing algorithm can help doctors to diagnose and treat the patient's condition, which has important application significance in clinical medicine. This paper proposes a network model based on the combination of U-net and DenseNet to solve the problems of class imbalance in multi-modal brain tumor image segmentation and the loss of effective information features caused by the integration of features in the traditional U-net network. The standard convolution blocks of the coding path and decoding path on the original network are improved to dense blocks, which enhances the transmission of features. The mixed loss function composed of the Binary Cross Entropy Loss function and the Tversky coefficient is used to replace the original single cross-entropy loss, which restrains the influence of irrelevant features on segmentation accuracy. Compared with U-Net, U-Net++, and PA-Net the algorithm in this paper has significantly improved the segmentation accuracy, reaching 0.846, 0.861, and 0.782 respectively in the Dice coefficient index of WT, TC, and ET. The PPV coefficient index has reached 0.849, 0.883, and 0.786 respectively. Compared with the traditional U-net network, the Dice coefficient index of the proposed algorithm exceeds 0.8%, 4.0%, and 1.4%, respectively, and the PPV coefficient index in the tumor core area and tumor enhancement area increases by 3% and 1.2% respectively. The proposed algorithm has the best performance in tumor core area segmentation, and its Sensitivity index has reached 0.924, which has good research significance and application value. [ABSTRACT FROM AUTHOR]

The question of whether China can become a creative nation has been a topic of much debate in academic circles. The Chinese government has expressed its belief that China can develop a unique form of creativity to move the country from the periphery to the center of the global creative ecosystem. This perspective has led to a series of state-led trials and experiments, including the adoption of cultural and creative industries, creative clusters and cities, and the recent maker movement. This paper utilizes the center-periphery theory to analyze the emergence, development, and evolution of China's maker movement, aiming to revisit the creativity issues in contemporary China. Based on three years of ethnographic research, the paper unpacks the maker movement at three interrelated levels: individual, organizational, and urban. Empirical data indicates that the transformation of China's maker movement is characterized by commodification, formalization, and infrastructuralization processes. The tension between growth and development, and stability and control has turned the once grassroots maker movement into a contested creative hybrid. This paper challenges the conventional view that China is resistant to change and incapable of creativity due to institutional and ideological influences. It demonstrates how an alternative mode of creativity can emerge outside global creative centers and proposes a new perspective on China's potential to become a creative nation. [ABSTRACT FROM AUTHOR]

This paper introduces N-bipolar hypersoft (N-BHS) sets, a versatile extension of bipolar hypersoft (BHS) sets designed to effectively manage evaluations encompassing both binary and non-binary data, thereby exhibiting heightened versatility. The major contributions of this framework are twofold: Firstly, the N-BHS set introduces a parameterized representation of the universe, providing a nuanced and finite granularity in perceiving attributes, thereby distinguishing itself from conventional binary BHS sets and continuous fuzzy BHS sets. Secondly, this model signifies a new area of research aimed at overcoming limitations inherent in the N-bipolar soft set when handling multi-argument approximate functions. Through the strategic partitioning of attributes into distinct subattribute values using disjoint sets, the N-BHS set emerges as a powerful tool for effectively addressing uncertainty-related problems. In pursuit of these objectives, the paper outlines various algebraic definitions, including incomplete N-BHS sets, efficient N-BHS sets, normalized N-BHS sets, equivalence under normalization, N-BHS complements, and BHS sets derived from a threshold, exemplified through illustrative examples. Additionally, the article explores set-theoretic operations within the N-BHS sets framework, such as relative null/whole N-BHS sets, N-BHS subsets, and two distinct approaches to N-BHS extended/restricted union and intersection. Finally, it proposes and compares decision-making methodologies regarding N-BHS sets, including a comprehensive comparison with relevant existing models. [ABSTRACT FROM AUTHOR]

Green credit is changing industrial structure and corporate behavior, but little attention has been paid to the relationship between green credit and corporate cash management behavior. Based on the typical fact that the allocation of traditional bank credit funds is biased towards heavily polluting industries and the exogenous impact event of green credit policy, this paper takes A-share listed companies in China's capital market from 2008 to 2015 as samples, and uses the DID model to investigate the impact of green credit policy on excess cash holdings of heavily polluting enterprises. The findings indicate that the green credit policy has reduced the excessive cash holdings of heavily polluting enterprises, suggesting that it can correct the issue and align their cash holdings with the requirements of normal production and operations. The mechanism test demonstrates that the green credit policy can alleviate agency conflicts and influence enterprise cash holdings. Moreover, a cross-sectional investigation reveals that the inhibitory effect of the green credit policy on cash holdings is more pronounced in large-scale and state-owned enterprises compared to small-scale and non-state-owned enterprises. Finally, an analysis of the economic consequences reveals that the green credit policy indirectly enhances corporate value by reducing excessive cash holdings. Based on this, banks and financial institutions continue to treat the credit granting of heavily polluting enterprises cautiously, optimize the structure of green financial products, fully consider the different types and nature of customers, and develop differentiated lending conditions and diversified evaluation mechanisms. This paper has enriched the research on the economic consequences of green credit and the influencing factors of corporate cash holdings, and provided policy enlightenment for regulators and listed companies to correctly understand and make full use of green credit policies to keep corporate cash stable through the crisis. [ABSTRACT FROM AUTHOR]

The rapid growth of on-demand ride service platforms has made it increasingly important for these platforms to efficiently match services by understanding driver characteristics and consumer preferences. This paper aims to investigate the pricing strategy by considering the impact of consumer preference heterogeneity and the different service types offered by drivers. The findings of this study reveal the need for the platform to strike a balance between service cost and the benefits of high-quality drivers, which can be referred to as the "cost-performance ratio". If the "cost-performance ratio" that attracts high-quality drivers is high, the platform will attract high-quality drivers or drivers of all types to participate while offering differentiated services. Otherwise, the platform will only provide services through low-quality drivers. Furthermore, the platform will also consider when to offer differentiated services based on network externalities and service quality. When the network externalities of the two types of services are similar, the platform will differentiate them based on service quality differences. Overall, considering consumer preference heterogeneity, drivers of service types, and network externalities, this paper provides guidance for platforms to make optimal decisions that enhance their service offerings and improve overall customer satisfaction. [ABSTRACT FROM AUTHOR]

In order to enhance market share and competitiveness, large banks are increasingly focusing on promoting marketing strategies. However, the traditional bank marketing strategy often leads to the homogenization of customer demand, making it challenging to distinguish among various products. To address this issue, this paper presents a customer demand learning model based on financial datasets and optimizes the distribution model of bank big data channels through induction to rectify the imbalance in bank customer transaction data. By comparing the prediction models of random forest model and support vector machine (SVM), this paper analyzes the ability of the prediction model based on ensemble learning to significantly enhance the market segmentation of e-commerce banks. The empirical results reveal that the accuracy of random forest model reaches 92%, while the accuracy of SVM model reaches 87%. This indicates that the ensemble learning model has higher accuracy and forecasting ability than the single model. It enables the bank marketing system to implement targeted marketing, effectively maintain the relationship between customers and banks, and significantly improve the success probability of product marketing. Meanwhile, the marketing model based on ensemble learning has achieved a sales growth rate of 20% and improved customer satisfaction by 30%. This demonstrates that the implementation of the ensemble learning model has also significantly elevated the overall marketing level of bank e-commerce services. Therefore, this paper offers valuable academic guidance for bank marketing decision-making and holds important academic and practical significance in predicting bank customer demand and optimizing product marketing strategy. [ABSTRACT FROM AUTHOR]

Purpose: Metastatic breast cancer [MBC] is the leading cause of cancer death in women globally with no cure. Women diagnosed with MBC endure a catastrophic upheaval to multiple aspects of their life and a radically transformed future landscape. Evidence suggests that the provision of care for women living with metastatic breast cancer is inadequate, socially isolating and stigmatising. To date, this topic has received little research attention. To increase understanding of the experiences of women living with MBC, a synthesis of current evidence is required. This paper presents a review of qualitative evidence on women's experiences of MBC. Methods: A qualitative evidence synthesis [QES] was conducted to synthesise primary qualitative research on the experiences of women living with MBC. Searches were performed of electronic databases Medline, Medline Ovid, PsycINFO, Psych articles, PubMED, CINAHL Complete, Scopus and grey literature databases. The methodological quality of the included studies was appraised using a modified version of the Critical Appraisal Skills Programme [CASP]. Title, abstract, and full-text screening were undertaken. A 'best fit' framework approach using the ARC [Adversity, Restoration, Compatibility] framework was used to guide data extraction and synthesis. Confidence in the findings was assessed using the Grading of Recommendations Assessment, Development and Evaluation, Confidence in the Evidence from Reviews of Qualitative research [GRADE-CERQual]. Results: 28 papers from 21 research studies containing 478 women's experiences of living with MBC were deemed suitable for inclusion in this qualitative evidence synthesis. Findings are presented in a new conceptual framework RAAW [adapted from ARC] for women living with MBC under themes: Reality, Adversity, Adjustment and Wellbeing. Findings revealed that a diagnosis of MBC impacted every aspect of women's lives; this is different to a diagnosis of early breast cancer. An overarching theme of lack of support extended across various facets of their lives. A lack of psychological, emotional, and psychosocial support was evident, with a critical finding that models of care were not fit for purpose. Deficits included a lack of information, knowledge, inclusion in shared decision-making and MDT support, specifically the need for palliative care/oncology support access. Some women living with MBC wanted to be identified as having a chronic illness not a life-limiting illness. Culture and socioeconomic standing influenced the availability of various types of support. The impact of treatment and symptoms had an adverse effect on women's quality of life and affected their ability to adjust. Conclusion: This review synthesised the qualitative literature on the experiences of women living with MBC. The ARC framework used in the synthesis was adapted to develop a revised conceptual framework titled RAAW to represent the evidence from this review on experiences for women living with MBC; Reality & Adversity: A diagnosis of MBC; Adjustment: Living with MBC; Wellbeing: Awareness, meaning, engagement [RAAW; MBC]. [ABSTRACT FROM AUTHOR]

The rapid increase in the number of older people under the background of population aging has gradually changed the disease spectrum of society, making aging diseases more prevalent, and increasing the demand for health care services, medical and health services, and health insurance among older people, ultimately leading to increasing household and social spending on old age. This study is conducted to assess the impact of those spending burden on the sustainable development of economy and find out some practical and effective solutions. This paper constructs a theoretical model to illustrate the relationship between the old-age dependency ratio and the marginal product of capital (MPK), and then establishes a two-way fixed effect model based on transnational panel data of 81 countries from 1981 to 2017 to verify this relationship empirically. This paper finds that, after controlling a series of variables, an increased burden of old-age dependency leads to a decline in the MPK, a key macroeconomic variable and also a sustainable development criteria, but in which health care, health security systems, and technological innovation play a key and moderating role. The conclusion is also valid after tackling the problem of endogeneity with different methods, like two-stage least squares (TSLS) and the generalized methods of moments (GMM). Overall, before population aging, countries that are old-but-not-rich should encourage more supply-side investments in public health system or technological innovation, and adjust retirement system, or gradually encourage childbearing to strive for time and space for later sustainable development of public health system and economy. [ABSTRACT FROM AUTHOR]

Based on the data of China Family Panel Studies (CFPS), this paper decomposed Chinese residents' income into labor income and capital income by income source, and measured the income inequality and income composition inequality of Chinese residents during 2010–2018. We take the Gini coefficient as a measure of inequality and, by decomposing it by income source, analyze the absolute and relative marginal effects of capital income and labor income on the overall income inequality. On this basis, this paper discusses the redistributive effect of financial instruments such as personal income tax and transfer payment on income inequality and income composition inequality. The results show that capital income is not only the main driving factor for the increase of overall income inequality, but also its influence on inequality is gradually increasing. The results of the redistribution effect of fiscal instruments show that although individual income tax and transfer payment both help to reduce the overall income inequality, only individual income tax can reduce the inequality of income composition, while transfer payment will exacerbate it. In the background of the rising share of capital income, it may widen the income distribution gap in the long run. Hence, future fiscal redistribution efforts should consider the income composition inequality. This includes further promotion of individual income tax reforms, optimization of the tax rate structure, enhancement of relevant tax laws governing capital income like property income, and continuous improvement in the redistributive impact of fiscal instruments. [ABSTRACT FROM AUTHOR]

This paper aims to study the relationship between regional logistics efficiency and economic development in 31 provinces of China and analyze their coupling coordination. To comprehensively evaluate the coordination between logistics and the economy, we introduced external indicators, such as carbon emissions, based on traditional evaluation indicators. We constructed an evaluation index system to coordinate regional logistics efficiency and economic development. The research approach used in this paper is the cross-DEA method, and data from 2010 to 2019 were selected for empirical calculation. The research findings indicate that Eastern and Northern regions of China show higher logistics efficiency, while Northwestern and Southwestern regions exhibit lower logistics efficiency. Coastal areas have relatively higher economic development levels compared to inland areas. Regarding the coupling coordination between logistics efficiency and economic development, different regions show temporal fluctuations and spatial disparities. Some regions demonstrate higher coordination between logistics efficiency and economic development, while others show lower coordination. Additionally, as the economy experiences rapid growth, logistics efficiency also improves, but the level of coordination varies among different provinces. [ABSTRACT FROM AUTHOR]

Bisphenol A (BPA) is an endocrine disrupting chemical used in a wide range of consumer products including photoactive dyes used in thermal paper. Recent studies have shown that dermal absorption of BPA can occur when handling these papers. Yet, regulatory agencies have largely dismissed thermal paper as a major source of BPA exposure. Exposure estimates provided by agencies such as the European Food Safety Authority (EFSA) are based on assumptions about how humans interact with this material, stating that ‘typical’ exposures for adults involve only one handling per day for short periods of time (<1 minute), with limited exposure surfaces (three fingertips). The objective of this study was to determine how individuals handle thermal paper in one common setting: a cafeteria providing short-order meals. We observed thermal paper handling in a college-aged population (n = 698 subjects) at the University of Massachusetts’ dining facility. We find that in this setting, individuals handle receipts for an average of 11.5 min, that >30% of individuals hold thermal paper with more than three fingertips, and >60% allow the paper to touch their palm. Only 11% of the participants we observed were consistent with the EFSA model for time of contact and dermal surface area. Mathematical modeling based on handling times we measured and previously published transfer coefficients, concentrations of BPA in paper, and absorption factors indicate the most conservative estimated intake from handling thermal paper in this population is 51.1 ng/kg/day, similar to EFSA’s estimates of 59 ng/kg/day from dermal exposures. Less conservative estimates, using published data on concentrations in thermal paper and transfer rates to skin, indicate that exposures are likely significantly higher. Based on our observational data, we propose that the current models for estimating dermal BPA exposures are not consistent with normal human behavior and should be reevaluated. [ABSTRACT FROM AUTHOR]

Moderate-to-severe psoriasis is associated with significant comorbidity, an impaired quality of life, and increased medical costs, including those associated with treatments. Systematic reviews (SRs) and meta-analyses (MAs) of randomized clinical trials are considered two of the best approaches to the summarization of high-quality evidence. However, methodological bias can reduce the validity of conclusions from these types of studies and subsequently impair the quality of decision making. As co-authorship is among the most well-documented forms of research collaboration, the present study aimed to explore whether authors’ collaboration methods might influence the methodological quality of SRs and MAs of psoriasis. Methodological quality was assessed by two raters who extracted information from full articles. After calculating total and per-item Assessment of Multiple Systematic Reviews (AMSTAR) scores, reviews were classified as low (0-4), medium (5-8), or high (9-11) quality. Article metadata and journal-related bibliometric indices were also obtained. A total of 741 authors from 520 different institutions and 32 countries published 220 reviews that were classified as high (17.2%), moderate (55%), or low (27.7%) methodological quality. The high methodological quality subnetwork was larger but had a lower connection density than the low and moderate methodological quality subnetworks; specifically, the former contained relatively fewer nodes (authors and reviews), reviews by authors, and collaborators per author. Furthermore, the high methodological quality subnetwork was highly compartmentalized, with several modules representing few poorly interconnected communities. In conclusion, structural differences in author-paper affiliation network may influence the methodological quality of SRs and MAs on psoriasis. As the author-paper affiliation network structure affects study quality in this research field, authors who maintain an appropriate balance between scientific quality and productivity are more likely to develop higher quality reviews. [ABSTRACT FROM AUTHOR]

University scientific research ability is an important indicator to express the strength of universities. In this paper, the evaluation of university scientific research ability is investigated based on the output of sci-tech papers. Four university alliances from North America, UK, Australia, and China, are selected as the case study of the university scientific research evaluation. Data coming from Thomson Reuters InCites are collected to support the evaluation. The work has contributed new framework to the issue of university scientific research ability evaluation. At first, we have established a hierarchical structure to show the factors that impact the evaluation of university scientific research ability. Then, a new MCDM method called D-AHP model is used to implement the evaluation and ranking of different university alliances, in which a data-driven approach is proposed to automatically generate the D numbers preference relations. Next, a sensitivity analysis has been given to show the impact of weights of factors and sub-factors on the evaluation result. At last, the results obtained by using different methods are compared and discussed to verify the effectiveness and reasonability of this study, and some suggestions are given to promote China’s scientific research ability. [ABSTRACT FROM AUTHOR]

Do highly productive researchers have significantly higher probability to produce top cited papers? Or do high productive researchers mainly produce a sea of irrelevant papers—in other words do we find a diminishing marginal result from productivity? The answer on these questions is important, as it may help to answer the question of whether the increased competition and increased use of indicators for research evaluation and accountability focus has perverse effects or not. We use a Swedish author disambiguated dataset consisting of 48.000 researchers and their WoS-publications during the period of 2008–2011 with citations until 2014 to investigate the relation between productivity and production of highly cited papers. As the analysis shows, quantity does make a difference. [ABSTRACT FROM AUTHOR]

Authorship of peer-reviewed journal articles and abstracts has become the primary currency and reward unit in academia. Such a reward is crucial for students and postdocs who are often under-compensated and thus highly value authorship as an incentive. While numerous scientific and publishing organizations have written guidelines for determining author qualifications and author order, there remains much ambiguity when it comes to how these criteria are weighed by research faculty. Here, we sought to provide some initial insight on how faculty view the relative importance of 11 criteria for scientific authorship. We distributed an online survey to 564 biomedical engineering, biology, and bioengineering faculty members at 10 research institutions across the United States. The response rate was approximately 18%, resulting in a final sample of 102 respondents. Results revealed an agreement on some criteria, such as time spent conducting experiments, but there was a lack of agreement regarding the role of funding procurement. This study provides quantitative assessments of how faculty members in the biosciences evaluate authorship criteria. We discuss the implications of these findings for researchers, especially new graduate students, to help navigate the discrepancy between official policies for authorship and the contributions that faculty truly value. [ABSTRACT FROM AUTHOR]