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This paper explores whether it is feasible to use the RGB color information in images of wheat canopies that were exposed to low temperatures during the growth season to achieve fast, non-destructive, and accurate determination of the severity of any freeze injury it may have incurred. For the study presented in this paper, we compared the accuracy of a number of algorithmic classification models using either meteorological data reported by weather services or the color gradation skewness-distribution from high-definition digital canopy images acquired in situ as inputs against a reference obtained by manually assessing the severity of the freeze injury inflicted upon wheat populations at three experimental stations in Shandong, China. The algorithms we used to construct the models included in our study were based on either K-means clustering, systematic clustering, or naïve Bayesian classification. When analyzing the reliability of our models, we found that, at more than 85%, the accuracy of the Bayesian model, which used the color information as inputs and involved the use of prior data in the form of the reference data we had obtained through manual classification, was significantly higher than that of the models based on systematic or the K-means clustering, which did not involve the use of prior data. It was interesting to note that the determination accuracy of algorithms using meteorological factors as inputs was significantly lower than that of those using color information. We also noted that the determination accuracy of the Bayesian model had some potential for optimization, which prompted us to subject the inputs of the model to a factor analysis in order to identify the key independent leaf color distribution parameters characterizing wheat freeze injury severity. This optimization allowed us to improve the determination accuracy of the model to over 90%, even in environments comprising several different ecological zones, as was the case at one of our experimental sites. In conclusion, our naïve Bayesian classification algorithm, which uses six key color gradation skewness-distribution parameters as inputs and involves the use of prior data in the form of manual assessments, qualifies as a contender for the development of commercial-grade wheat freeze injury severity monitoring systems supporting post-freeze management measures aimed at ensuring food security. [ABSTRACT FROM AUTHOR]

Aiming at the characteristics that the signal noise ratio (SNR) gradually decreases from the near to far range of the swath, an adaptive phase filtering algorithm based on Goldstein filtering and combined with multiple quality-guided graphs was proposed. Firstly, the components used to determine the filtering parameters were obtained through residue density, pseudo-coherence coefficient and pseudo-SNR, the three quality-guided graphs. Then, the filter parameters were calculated by weighting the three components. Finally, the size of filtering window was determined according to the account of residues, and the interferometric phase noise was removed in frequency domain. Simulated data, TSX/TDX data and airborne interferometric imaging radar altimeter data were used to verify the performance of the new algorithm. Compared with the results of Goldstein filtering and its improved algorithms, the results showed that the proposed algorithm can effectively filter out phase noise while maintaining the edge characteristics of interferometric fringe. The section of filtering result can well match with the section of simulated pure interfeometric phase. Moreover, the algorithm proposed in this paper can effectively remove the noise in the interferogram of TSX/TDX sea ice data, and the residues' filtering rate was above 86%, which can effectively remove the phase residues of the sea ice surface while maintaining the characteristics of the sea ice edge. Experimental results showed that the new algorithm provides an effective phase noise filtering method for imaging radar altimeter data processing. [ABSTRACT FROM AUTHOR]

The past COVID-19 outbreak caused a huge impact on China's cruise industry. Now that China's cruise industry is about to recover, how to assess the risks faced by the cruise industry has become an important issue. On this basis, this paper constructs China's cruise tourism supply chain and supply chain risk assessment system based on the research contributions made by previous researchers, evaluates the risk indicators of China's cruise tourism supply chain based on the catastrophe theory, and derives the risk assessment results through the catastrophe progression method, which can be used as a reference for the safe operation of cruise lines in the future. [ABSTRACT FROM AUTHOR]

In numerous developing nations, challenges such as insufficient investment in innovation and limited capabilities for conversion impede the growth of the construction sector, thus affecting the overall economic well-being of these regions. This paper focuses on construction industry innovation (CII) and its correlation with region economic development (RED), providing valuable insights to overcome these challenges and promote sustainable economic advancement. This study references existing literature to devise an evaluation indicator system dedicated for CII and RED. It then proceeds with an empirical analysis of the integration and synergy between CII and the economic development across 31 Chinese provinces from 2012 to 2021. Furthermore, this paper employs ArcGIS and Geoda software to meticulously dissect the spatial distribution characteristics underlying this coordination. The main conclusions are succinctly summarized as follows: CII in China is intricately connected to RED, exhibiting a strong connection that diminishes from south to north. Nonetheless, the coordination level between these factors remains relatively low, with notable regional disparities, particularly from southeast to northwest. The primary obstacles to effective coordination are related to innovation input, output, and economic scale. Additionally, spatial correlation analysis demonstrates pronounced regional clustering, showing stability despite slight fluctuations over the study period. This research underscores the concept of coupling coordination between CII and RED, underpinned by scientific analytical methods. The outcomes provide a definitive guide for advancing the transformation and enhancement of the construction industry while promoting RED. [ABSTRACT FROM AUTHOR]

Innovative products entering the market will cause dynamic changes in market demand, and consumers' purchase regret and their return behavior make the market environment more and more complex, which in turn affects the dynamic decision-making in the supply chain. In this paper, under the situation of discrete decision time, combining with the objective reality, we make discrete modification to the classical Bass diffusion model (Bass model), construct a manufacturer-led, retailer-followed supply chain differential game model, analyze the optimal decision-making of the manufacturer and the retailer by combining with the theory of discrete optimal control, and then verify the conclusions by numerical simulation. The results show that: when retailers purchase directly from the manufacturer and sell in the market, the optimal pricing of the innovative product can make the supply chain as a whole, realizing Pareto optimality; consumer's purchase regret will increase the amount of returns, which will lead to the decrease of product sales and the profits of the manufacturer and the retailer; when the innovative product accounts for a different share of the market, the impact of purchase regret on the wholesale price and the retail price are also different. Therefore, manufacturers need to have an extensive comprehension of the market to minimize the negative effects of consumer regret and returns, and to formulate a reasonable pricing strategy for their products to gain as much profit as possible. [ABSTRACT FROM AUTHOR]

To achieve secure, reliable, and scalable traffic delivery, request streams in mobile Internet of Things (IoT) networks supporting Multi-access Edge Computing (MEC) typically need to pass through a service function chain (SFC) consisting of an ordered series of Virtual Network Functions (VNFs), and then arrive at the target application in the MEC for processing. The high mobility of users and the real-time variability of network traffic in IoT-MEC networks lead to constant changes in the network state, which results in a mismatch between the performance requirements of the currently deployed SFCs and the allocated resources. Meanwhile, there are usually multiple instances of the same VNF in the network, and proactively reconfiguring the deployed SFCs based on the network state changes to ensure high quality of service in the network is a great challenge. In this paper, we study the SFC Reconfiguration Strategy (SFC-RS) based on user mobility and resource demand prediction in IoT MEC networks, aiming to minimize the end-to-end delay and reconfiguration cost of SFCs. First, we model SFC-RS as Integer Linear Programming (ILP). Then, a user trajectory prediction model based on codec movement with attention mechanism and a VNF resource demand prediction model based on the Long Short-Term Memory (LSTM) network are designed to accurately predict user trajectories and node computational and storage resources, respectively. Based on the prediction results, a Prediction-based SFV Active Reconfiguration (PSAR) algorithm is proposed to achieve seamless SFC migration and routing update before the user experience quality degrades, ensuring network consistency and high quality service. Simulation results show that PSAR provides 51.28%, 28.60%, 21.75%, and 16.80% performance improvement over the existing TSRFCM, DDQ, OSA, and DPSM algorithms in terms of end-to-end delay reduction, and 33.32%, 18.94%, 67.42%, and 60.61% performance optimization in terms of reconfiguration cost reduction. [ABSTRACT FROM AUTHOR]

Both filter inductors, electrolytic capacitors, and radiators play a significant role in the inverter of a PV (Photovoltaic) power generation system. These three parts are the largest in an inverter, which affects the performance of the inverter. Aimed to improve the power density of a single-phase PV grid-connected inverter with a decoupling function. This paper derived the control principle that can reduce the volume of the inductor, decoupling capacitor, and the loss of the switching device to begin with the mathematical function of power processing of the filter inductor. And then, the authors deduced a boost-type power decoupled single-phase inverter topology. Based on a novel three-port three-switches flyback series circuit, this paper proposed an efficient power decoupling topology circuit for extracting the maximum power density of a single-phase grid-connected PV inverter. Finally, this article operated the simulation and experiment. Both the simulated and experimental results verified that the proposed method works well. [ABSTRACT FROM AUTHOR]

Using the sample of Chinese A-share listed firms from 2010 to 2020, this study examines the impact of non-controlling large shareholders (NCLSs) on corporate capital structure adjustment. The results show that NCLSs significantly increase the dynamic capital structure adjustment speed and reduce capital structure deviation. NCLSs have an asymmetric influence on capital structure adjustment speed for different deviation directions, i.e. compared to the speed of upward adjustment after a downward deviation of the capital structure, the effect of NCLSs on the speed of downward adjustment of the capital structure after an upward deviation is stronger. Whether in state-owned enterprises (SOEs) or non-state-owned enterprises (NSOEs), NCLSs significantly increase the dynamic capital structure adjustment speed. However, compared with SOEs, NCLSs in NSOEs have a more significant positive impact on the dynamic capital structure adjustment speed. The mechanism analysis suggests that reducing agency costs and mitigating financing constraints serve as the important channels through which NCLSs influence the dynamic adjustment of capital structure. This paper not only enriches and improves the theoretical basis of dynamic capital structure adjustment, but also helps to deepen the understanding of dynamic capital structure adjustment of Chinese listed firms. [ABSTRACT FROM AUTHOR]

Human capital is a nation's primary source of inner strength to achieve sustainable economic growth and development. Meanwhile, income inequality is a critical issue preventing sustainable economic growth and social transformation, especially in developing countries. This paper investigates the effect of human capital on income inequality in both the short and long term using the mean group, pooled mean group, and threshold regressions for the ASEAN-7 (including Indonesia, Laos, Malaysia, the Philippines, Singapore, Thailand, and Vietnam) from 1992 to 2018. The paper develops a theoretical linkage between human capital and income inequality by combining the learning theory and the Kuznets hypothesis. This linkage is then tested using data from the ASEAN countries. Findings from the paper indicate that human capital reduces income inequality in the short run in the ASEAN countries. However, the effect is reverted in the long run, suggesting that human capital may increase the income gap in these countries. Particularly, the inverted U-shaped relationship between human capital and income inequality is established for the ASEAN countries whose GDP per capita is lower than USD 8.2 thousand per year. In contrast, the U-shaped relationship is found for the countries with income per capital of more than USD 8.2 thousand. All these findings suggest that social policies targeting reducing income inequality should be prioritized and stay at the centre of any economic policies to achieve sustainable economic growth and development in the ASEAN countries. [ABSTRACT FROM AUTHOR]

The study of spatio-temporal evolution characteristics and factors affecting the coordinated development of population and green economy (CD_PGE) in Shandong province, China, has significant decision-making implications for promoting high-quality and sustainable regional development. Based on 2001 to 2020 panel data for each city and economic zone in Shandong province, this paper constructs an evaluation model for the CD_PGE systems. Using growth elasticity models, geographic concentration models, kernel density estimation models, spatial autocorrelation, analysis of population and regional green economy development in Shandong Province from the perspective of spatial agglomeration coupling, spatial and temporal coupling coordination patterns, and evolutionary characteristics. In addition, we use the fixed effect models and panel quantile models to empirically test the effects of coordinated demographic and green economy development. The results show that: (1) In terms of demo-graphic and economic development characteristics, Shandong's demographic and green economy development trends are good, but there are still many challenges. (2) According to the time series evolution and spatial distribution characteristics, the degree of CD_PGE in Shandong Province is on the rise, and the level of spatial distribution is distinct. (3) From the spatio-temporal dynamical grid evolution of the degree of CD_PGE, the CD_PGE is characterized by significant spatial clustering, but with large regional differences. (4) From an impact factor perspective, both market mechanisms and government intervention have a significant impact on the degree of CD_PGE, but the direction and extent of the impact vary. [ABSTRACT FROM AUTHOR]

The low-carbon economy represents a global transformation that encompasses production methods, lifestyles, values, national interests, and the destiny of humanity. As a significant contributor to carbon emissions, China has made a momentous strategic decision on carbon peaking and neutralization, infusing momentum into the global effort to address climate change. The rapid growth of the digital economy offers a fresh approach to achieving the "double carbon" objective and advancing the development of low-carbon transformation. Based on the panel data of 30 provinces in China, this paper uses the least square method to investigate the impact of digital economy development on regional low-carbon inclusive development. It is found that there is a significant inverted U shape in the impact of the digital economy on low-carbon inclusive development and the mechanism is resource allocation and ecological inequality. The threshold test found that the role of the digital economy in promoting low-carbon inclusive development shows a marginal decreasing trend. The inverted U-shaped impact of the digital economy on low-carbon inclusive development in the eastern and coastal areas and areas with a low level of factor productivity is more significant. Based on the knowledge factor spillover perspective, we found that the impact of the digital economy on low-carbon inclusive development has a spatial spillover effect, and this effect is more obvious under the role of R&D personnel mobility. [ABSTRACT FROM AUTHOR]

To ensure optimal use of images while preserving privacy, it is necessary to partition the shared image into public and private areas, with public areas being openly accessible and private areas being shared in a controlled and privacy-preserving manner. Current works only facilitate image-level sharing and use common cryptographic algorithms. To ensure efficient, controlled, and privacy-preserving image sharing at the area level, this paper proposes an image partition security-sharing mechanism based on blockchain and chaotic encryption, which mainly includes a fine-grained access control method based on Attribute-Based Access Control (ABAC) and an image-specific chaotic encryption scheme. The proposed fine-grained access control method employs smart contracts based on the ABAC model to achieve automatic access control for private areas. It employs a Cuckoo filter-based transaction retrieval technique to enhance the efficiency of smart contracts in retrieving security attributes and policies on the blockchain. The proposed chaotic encryption scheme generates keys based on the private areas' security attributes, largely reducing the number of keys required. It also provides efficient encryption with vector operation acceleration. The security analysis and performance evaluation were conducted comprehensively. The results show that the proposed mechanism has lower time overhead than current works as the number of images increases. [ABSTRACT FROM AUTHOR]

The ice phenomena are an inherent component of rivers in temperate, continental, and polar climate zones. Evident progress in global warming leads to a decrease in snow cover on land and ice phenomena in water bodies, disrupting the stability of the hydrological cycle and aquatic ecosystems. Although common observations indicate the disappearance of ice phenomena in rivers over recent decades, detailed quantitative research is lacking in many regions, especially in the temperate zone. In this paper, ice phenomena were analyzed on the rivers of southern Poland, located in the upland and mountain areas of the country, as no such studies have been conducted so far. The temporal changes in the annual number of days with ice (NDI) phenomena were studied in locations where ice phenomena were observed every year for at least 30 years between 1951 and 2021. Using straightforward but commonly accepted procedures, such as the Mann-Kendall test, statistically significant decreasing trends in the annual NDI were revealed for the majority of gauging stations. The Theil-Sen (TS) slope mean values were -1.66 (ranging from -3.72 to -0.56), -1.41 (from -3.22 to -0.29), and -1.33 (from -2.85 to -0.29) for the datasets representing the periods 1992–2020, 1987–2020, and 1982–2020, respectively. The results for the annual NDI were additionally presented within the context of meteorological characteristics such as annual and winter (Nov-Apr) air temperature, precipitation, and water temperature. Correlation and regression analyses revealed that the main factor triggering the decrease in NDI is the increase in the average winter air temperature. An increase in temperature by 1°C results in a decrease in NDI by up to twenty days. If these negative trends continue, ice phenomena may disappear completely from southern Polish rivers within few decades. [ABSTRACT FROM AUTHOR]

Based upon qualitative interviews with 54 women and men living with HIV across Ontario, Canada, this paper examines the impact of HIV criminalization on the sexual and romantic relationships of people living with HIV. This research highlights the navigation strategies people living with HIV create and employ to both navigate and protect themselves from the law. Through a thematic and intersectional analysis, this study shows how adoption of these strategies is unequal, with access to navigation strategies varying along lines of gender, race, and sexual orientation. As a result, women and racialized people living with HIV face more difficulties navigating the impact of the law. HIV criminalization in Canada fuels and validates HIV stigma and produces vulnerability both within and outside of the relationships of people living with HIV. This paper seeks to understand HIV criminalization from the perspective of those governed by the law, in hopes of producing knowledge which will contribute to legal reform, inform policy, and support the development of efficacious secondary prevention initiatives. [ABSTRACT FROM AUTHOR]

This paper focuses on the methodological and empirical challenges researchers encounter when accessing government open data through the case study of Canada's Open Government Action Plan, with a specific emphasis on datasets hosted by the House of Commons. To address these challenges, we have created an R package designed to streamline the retrieval process of datasets, that are not-so-user-friendly, from the House of Commons website. Furthermore, we have made complete datasets available in both French and English, which are the official languages of Canada, and in multiple formats to improve accessibility. Our package aims to be an invaluable resource for researchers interested in Canadian politics or conducting comparative research. Therefore, a portion of this paper is allocated to showcase the potential utility of our package. Through our research, we highlighted three crucial lessons: firstly, the heterogeneous nature of datasets requires flexibility and adaptability; secondly, open data curators encounter various challenges in addressing user-reported issues; and thirdly, there is a nuanced understanding of "openness" in government datasets. In conclusion, we reflect on the potential scalability of open data initiatives while advocating for a nuanced approach that considers the complex challenges associated with open data accessibility. [ABSTRACT FROM AUTHOR]

Background: As the global community begins recovering from the COVID-19 pandemic, the challenges due to its aftermath remain. This health crisis has highlighted challenges associated with airborne pathogens and their capacity for rapid transmission. While many solutions have emerged to tackle this challenge, very few devices exist that are inexpensive, easy to manufacture, and versatile enough for various settings. Methods: This paper presents a novel suction device designed to counteract the spread of aerosols and droplets and be cost-effective and adaptable to diverse environments. We also conducted an experimental study to evaluate the device's effectiveness using an artificial cough generator, a particle counter, and a mannequin in an isolated system. We measured droplet removal rates with simulated single and repeated cough incidents. Also, measurements were taken at four distinct areas to compare its effectiveness on direct plume versus indirect particle removal. Results: The device reduced airborne disease transmission risk, as evidenced by its capacity to decrease the half-life of aerosol volume from 23.6 minutes to 15.6 minutes, effectively capturing aerosol-sized droplets known for their extended airborne persistence. The suction device lessened the peak total droplet volume from peak counts. At 22 minutes post peak droplet count, the count had dropped 24% without the suction device and 43% with the suction device. Conclusions: The experiment's findings confirm the suction device's capability to effectively remove droplets from the environment, making it a vital tool in enhancing indoor air quality. Given the sustained performance of the suction device irrespective of single or multiple cough events, this demonstrates its potential utility in reducing the risk of airborne disease transmission. 3D printing for fabrication opens the possibility of a rapid iterative design process, flexibility for different configurations, and rapid global deployment for future pandemics. [ABSTRACT FROM AUTHOR]

This paper explores a brand-new interdisciplinary approach applied to an enduring problem: the communication of severe diagnoses. The moment when physicians explain the diagnosis to patients and their relatives is sensitive, particularly for a disease that is rarely diagnosed early. The first part of the article is dedicated to the context of this delicate doctor-patient interaction. With this framework in mind, the paper delves into the innovative interdisciplinary methodology developed in the pilot study Communi.CARE, conducted in a hospital in Northern Italy, which focuses on the diagnosis of pancreatic ductal adenocarcinoma (PDAC). SARS-CoV-2 impact on the study development is highlighted. The study aims to explore the topic by combining different areas of expertise, including medicine, philosophy, sociology, and psychology. The contribution of philosophy is here presented as essential: it has a leading role in the conception of the study, its development, and the elaboration of results. It is shown throughout the study, from methodology to the analysis of results. Strengths and weaknesses of the methodology are discussed. In conclusion, further philosophical considerations on effective and ethical communication in this delicate context are recommended. [ABSTRACT FROM AUTHOR]

Research on political participation almost unanimously assumes that social pressure by neighbors induces collective behavior. Yet most experimental studies focus on individually based forms of political and civic behavior, such as voting and recycling, in Western industrialized societies. The paper tests the effect of neighborly social pressure on collective action in Tunisia. In a field experiment, I manipulate whether neighbors or community outsiders invite citizens to contribute to a public good (i.e., trash collection). I run the experiment in three neighborhoods of varying socioeconomic composition in Tunis (n = 1199). I do not find evidence to suggest that neighborly social pressure encourages participation in neighborhood cleanups, with low participation rates both for the neighbor and outsider contact conditions. While the effect of social pressure does not significantly vary across communities, overall participation rates do. Residents of the poor neighborhood are most likely to respond in a socially desirable way when asked about their intentions but least likely to participate. The paper also discusses some limitations of the study and outlines avenues for future research. [ABSTRACT FROM AUTHOR]

The handaxe is an iconic stone tool form used to define and symbolise both the Acheulean and the wider Palaeolithic. There has long been debate around the extent of its morphological variability between sites, and the role that extrinsic factors (especially raw material, blank type, and the extent of resharpening) have played in driving this variability, but there has been a lack of high-resolution examinations of these factors in the same study. In this paper, we present a 2D geometric morphometric analysis of 1097 handaxes from across Africa, the Levant, and western Europe to examine the patterning of this variability and what it can tell us about hominin behaviour. We replicate the findings of previous studies, that handaxe shape varies significantly between sites and entire continental regions, but we find no evidence for raw material, blank type, or resharpening in determining this pattern. What we do find, however, is that markers of reduction trajectory vary substantially between sites, suggesting that handaxes were deployed differently according to hominin need at a given site. We argue this is reflective of a continuum of reduction strategies, from those focused on the maintenance of a sharp cutting edge (i.e. direct use in cutting activities), to those focused on maintaining tip shapes, and perhaps a corresponding production of flakes. Implications for hominin behavioural flexibility are discussed. [ABSTRACT FROM AUTHOR]

To explore whether digital finance can reduce agricultural carbon emissions, promote regional convergence, and foster inclusivity in rural revitalization and shared prosperity, this paper uses the provincial-level index of digital financial inclusion to analyze the impact of digital financial inclusion on the intensity of agricultural carbon emissions and the Degum Gini coefficient (D-Gini coefficient) of regional carbon emission intensity in 30 sample provinces from 2010 to 2020. It examines the mechanism of the impact of digital financial inclusion on both variables to understand the underlying factors better. The main conclusions are as follows: (1) Digital financial inclusion significantly reduces the intensity of agricultural carbon emissions and narrows the gap in carbon emission intensity between regions. (2) The unconditional quantile regression coefficients show that the negative coefficients of the digital financial inclusion index and the three-dimensional indices decrease with increasing quantiles. However, the significant effects vary significantly at different quantiles. (3) Technological progress and the government's ability to allocate financial resources play a significant mediating role, and the income gap between urban and rural areas can be further narrowed, as well as the carbon emission intensity gap between provinces. The empirical results are robust and proven by replacing the econometric analysis method, changing the core variables, and other methods. [ABSTRACT FROM AUTHOR]

Speech enhancement is crucial both for human and machine listening applications. Over the last decade, the use of deep learning for speech enhancement has resulted in tremendous improvement over the classical signal processing and machine learning methods. However, training a deep neural network is not only time-consuming; it also requires extensive computational resources and a large training dataset. Transfer learning, i.e. using a pretrained network for a new task, comes to the rescue by reducing the amount of training time, computational resources, and the required dataset, but the network still needs to be fine-tuned for the new task. This paper presents a novel method of speech denoising and dereverberation (SD&D) on an end-to-end frozen binaural anechoic speech separation network. The frozen network requires neither any architectural change nor any fine-tuning for the new task, as is usually required for transfer learning. The interaural cues of a source placed inside noisy and echoic surroundings are given as input to this pretrained network to extract the target speech from noise and reverberation. Although the pretrained model used in this paper has never seen noisy reverberant conditions during its training, it performs satisfactorily for zero-shot testing (ZST) under these conditions. It is because the pretrained model used here has been trained on the direct-path interaural cues of an active source and so it can recognize them even in the presence of echoes and noise. ZST on the same dataset on which the pretrained network was trained (homo-corpus) for the unseen class of interference, has shown considerable improvement over the weighted prediction error (WPE) algorithm in terms of four objective speech quality and intelligibility metrics. Also, the proposed model offers similar performance provided by a deep learning SD&D algorithm for this dataset under varying conditions of noise and reverberations. Similarly, ZST on a different dataset has provided an improvement in intelligibility and almost equivalent quality as provided by the WPE algorithm. [ABSTRACT FROM AUTHOR]

When large-scale electric vehicles are connected to the grid for unordered charging, it will seriously affect the stability and security of the power system. To solve this problem, this paper proposes a regional power network optimization scheduling method considering vehicle network interaction. Initially, based on the user behavior characteristics and charging and discharging characteristics of electric vehicles, a charging and discharging behavior model of electric vehicles was established. Based on the Monte Carlo sampling algorithm, the scheduling upper and lower limits of each scheduling cycle of electric vehicles were described, and the scheduling potential of each scheduling cycle of electric vehicles was obtained. Then, the electricity price is then used as an incentive parameter to guide EV users to charge during periods of low electricity prices and participate in discharge during periods of peak electricity prices. Aiming at the highest economic efficiency, the best consumption effect of new energy and the smoothest demand-side power curve of regional power grid, a three-objective optimal dispatching model was established. In the later stage, uncertainty factors are taken into consideration by introducing the concept of interval numbers, and an interval multi-objective optimization dispatching model is established. The two dispatching models are solved by NSGA-II algorithm and improved NSGA-II algorithm, and the Pareto solution set is obtained. Finally, based on the analytic Hierarchy Process (AHP), the optimal scheduling scheme is determined. The Monte Carlo sampling method is used to simulate the user side charging demand, and the effectiveness of this method is verified. In addition, the results of the interval multi-objective optimization model and the deterministic multi-objective optimization model are compared, and it is proved that the solution results of the interval multi-objective model are more adaptive, practical and robust to the uncertain factors. [ABSTRACT FROM AUTHOR]

Introduction: The Soweto Derby is one of Africa's largest football derbies. The two rival teams, Kaizer Chiefs and Orlando Pirates, both originate in Soweto, a sprawling township 20km outside Johannesburg. Soweto is infamous for the high levels of violent crime and trauma, but also for Chris Hani Baragwanath Academic Hospital (CHBAH), with one of the world's largest trauma emergency departments (ED). Research globally, describing the impact of sports events on public health care systems is conflicting, with evidence showing both increases and decreases in spectator related trauma. This paper seeks to describe the trauma burden during the Soweto Derby and add to the research concerning trauma relating to sporting derbies in low to middle income countries. Objectives: To analyze the impact of the Soweto Derby on the trauma ED at CHBAH over a 24-hour period. Methods: A retrospective comparative study at the CHBAH Trauma ED of 13 Soweto Derbies played over a 5 year period between 2015–2019, compared to the corresponding non-Soweto Derby days of the preceding year. Patients were triaged according to the South African Triage Scale and Advanced Trauma Life Support (ATLS) principles. Data was organized into 3 time frames where the triage score and mechanism of injuries were compared: 1) 4 hours pre-match, 2) 2 hours during the match, and 3) 18 hours post-match. Results: Thirteen Soweto Derbies and 2552 patients were included. The median age was 29 with males accounting for 73.4% of all trauma cases. Significantly more P1 patients presented during the Soweto Derby. Pre-match there were 3x less P1 patients presenting to the ED (4.7% vs 12%, p = 0.044). During the match, there was a 40% drop in males presenting to ED (5.95% vs 9.45%, p = 0.015). Post-match there was a significant increase in P1 patients treated (17.4% vs 13.5%, p = 0.021)), with the majority being young males. There was no increase in either female or paediatric visits to the ED. Conclusion: The Soweto Derby has a direct effect on the trauma burden at CHBAH, with more P1 patients presenting post-match. Young African males are disproportionally affected by severe trauma requiring increased health care resources in an already overburdened hospital. [ABSTRACT FROM AUTHOR]

The traditional method for power load forecasting is susceptible to various factors, including holidays, seasonal variations, weather conditions, and more. These factors make it challenging to ensure the accuracy of forecasting results. Additionally, there is a limitation in extracting meaningful physical signs from power data, which ultimately reduces prediction accuracy. This paper aims to address these issues by introducing a novel approach called VCAG (Variable Mode Decomposition—Convolutional Neural Network—Attention Mechanism—Gated Recurrent Unit) for combined power load forecasting. In this approach, we integrate Variable Mode Decomposition (VMD) with Convolutional Neural Network (CNN). VMD is employed to decompose power load data, extracting valuable time-frequency features from each component. These features then serve as input for the CNN. Subsequently, an attention mechanism is applied to give importance to specific features generated by the CNN, enhancing the weight of crucial information. Finally, the weighted features are fed into a Gated Recurrent Unit (GRU) network for time series modeling, ultimately yielding accurate load forecasting results.To validate the effectiveness of our proposed model, we conducted experiments using two publicly available datasets. The results of these experiments demonstrate that our VCAG method achieves high accuracy and stability in power load forecasting, effectively overcoming the limitations associated with traditional forecasting techniques. As a result, this approach holds significant promise for broad applications in the field of power load forecasting. [ABSTRACT FROM AUTHOR]

Background: The Sustainable Development Goal 15 (SDG15) deals with protecting, restoring, and promoting the sustainable use of terrestrial ecosystems, sustainably managing forests, halting and reversing land degradation, combating desertification and halting biodiversity loss. The purpose of this scoping review was to map the current evidence on the association between SDG 15 and Early Childhood Caries (ECC). Methods: This scoping review was reported in accordance with the preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews (PRISMA-ScR) guidelines. Formal literature searches were conducted in PubMed, Web of Science, and Scopus in March 2023 using key search terms. Studies with the criteria (in English, with full text available, addressing component of life on land, focusing on dental caries in humans, with results that can be extrapolated to control ECC in children less than 6 years of age) were included. Retrieved papers were summarised and a conceptual framework developed regarding the postulated link between SDG15 and ECC. Results: Two publications met the inclusion criteria. Both publications were ecological studies relating environmental findings to aggregated health data at the area level. One study concluded that the eco-hydrogeological environment was associated with human health, including caries. The other reported that excessive calcium was associated with the presence of compounds increasing groundwater acidity that had an impact on human health, including caries. The two ecological studies were linked to SDG 15.1. It is also plausible that SDG 15.2 and SDG 15.3 may reduce the risk for food insecurity, unemployment, gender inequality, zoonotic infections, conflict and migration; while SDG 15.4 may improve access to medicinal plants such as anticariogenic chewing sticks and reduction in the consumption of cariogenic diets. Conclusions: There are currently no studies to support an association between ECC and SDG15 although there are multiple plausible pathways for such an association that can be explored. There is also the possibility of synergistic actions between the elements of soil, water and air in ways that differentially affect the risk of ECC. Studies on the direct link between the SDG15 and ECC are needed. These studies will require the use of innovative research approaches. [ABSTRACT FROM AUTHOR]

Background: The heightened risk of dementia resulting from multiple comorbid conditions calls for innovative strategies. Engaging in physical and cognitive activities emerges as a protective measure against cognitive decline. This protocol aims to discuss a multidomain intervention targeting individuals with dementias secondary to cerebrovascular or other medical diseases, emphasizing an often underrepresented demographic. Methods: This study primary objectives are: a) to identify patients affected by Neurocognitive disorder due to vascular disease or multiple etiologies (screening and diagnostic phase) and b) to evaluate the effectiveness of distinct rehabilitation protocols (intervention phase): motor training alone, paper-based cognitive rehabilitation combined with motor training, digital-based cognitive rehabilitation coupled with motor training. Discussion: Identifying cognitive impairment beyond rigid neurological contexts can facilitate timely and targeted interventions. This protocol strives to address the complex interplay of cognitive decline and comorbidities through a multidimensional approach, providing insights that can shape future interventions and enhancing overall well-being in this vulnerable population. Trial registration: The study has been registered on July 13, 2023 with the ClinicalTrials.gov NCT05954741 registration number (https://classic.clinicaltrials.gov/ct2/show/NCT05954741). [ABSTRACT FROM AUTHOR]

The meta-learning method proposed in this paper addresses the issue of small-sample regression in the application of engineering data analysis, which is a highly promising direction for research. By integrating traditional regression models with optimization-based data augmentation from meta-learning, the proposed deep neural network demonstrates excellent performance in optimizing glass fiber reinforced plastic (GFRP) for wrapping concrete short columns. When compared with traditional regression models, such as Support Vector Regression (SVR), Gaussian Process Regression (GPR), and Radial Basis Function Neural Networks (RBFNN), the meta-learning method proposed here performs better in modeling small data samples. The success of this approach illustrates the potential of deep learning in dealing with limited amounts of data, offering new opportunities in the field of material data analysis. [ABSTRACT FROM AUTHOR]

Shared manufacturing is a new business form that focuses on all aspects of production and manufacturing, mainly relying on the shared manufacturing platform to achieve the optimal allocation of idle resources. For enterprises, in the process of deciding to lease idle resources, the pricing and advertising investment efficiency of the shared manufacturing platform is a valuable research issue. The shared manufacturing model in this paper consists of one manufacturer and one shared manufacturing platform, which will invest in cooperative advertising while the shared manufacturing process is completed. The cooperative advertising involves four models: the traditional cooperation model, the cost-sharing contract model, the revenue-sharing contract model, and the bilateral cost-sharing contract model. We investigate the impact of some key parameters on the prices and profits of the manufacturer and the shared manufacturing platform based on the differential game. The numerical examples demonstrate the viability of the model. Finally, we provide suggestions based on the decision-making of the manufacturer and the shared manufacturing platform under different cooperative advertising models. [ABSTRACT FROM AUTHOR]

The burgeoning field of fog computing introduces a transformative computing paradigm with extensive applications across diverse sectors. At the heart of this paradigm lies the pivotal role of edge servers, which are entrusted with critical computing and storage functions. The optimization of these servers' storage capacities emerges as a crucial factor in augmenting the efficacy of fog computing infrastructures. This paper presents a novel storage optimization algorithm, dubbed LIRU (Low Interference Recently Used), which synthesizes the strengths of the LIRS (Low Interference Recency Set) and LRU (Least Recently Used) replacement algorithms. Set against the backdrop of constrained storage resources, this research endeavours to formulate an algorithm that optimizes storage space utilization, elevates data access efficiency, and diminishes access latencies. The investigation initiates a comprehensive analysis of the storage resources available on edge servers, pinpointing the essential considerations for optimization algorithms: storage resource utilization and data access frequency. The study then constructs an optimization model that harmonizes data frequency with cache capacity, employing optimization theory to discern the optimal solution for storage maximization. Subsequent experimental validations of the LIRU algorithm underscore its superiority over conventional replacement algorithms, showcasing significant improvements in storage utilization, data access efficiency, and reduced access delays. Notably, the LIRU algorithm registers a 5% increment in one-hop hit ratio relative to the LFU algorithm, a 66% enhancement over the LRU algorithm, and a 14% elevation in system hit ratio against the LRU algorithm. Moreover, it curtails the average system response time by 2.4% and 16.5% compared to the LRU and LFU algorithms, respectively, particularly in scenarios involving large cache sizes. This research not only sheds light on the intricacies of edge server storage optimization but also significantly propels the performance and efficiency of the broader fog computing ecosystem. Through these insights, the study contributes a valuable framework for enhancing data management strategies within fog computing architectures, marking a noteworthy advancement in the field. [ABSTRACT FROM AUTHOR]

Antennas play a crucial role in designing an efficient communication system. However, reducing the maximum sidelobe level (SLL) of the beam pattern is a crucial challenge in antenna arrays. Pattern synthesis in smart antennas is a major area of research because of its widespread application across various radar and communication systems. This paper presents an effective technique to minimize the SLL and thus improve the radiation pattern of the linear antenna array (LAA) using the chaotic inertia-weighted Wild Horse optimization (IERWHO) algorithm. The wild horse optimizer (WHO) is a new metaheuristic algorithm based on the social behavior of wild horses. The IERWHO algorithm is an improved Wild Horse optimization (WHO) algorithm that combines the concepts of chaotic sequence factor, nonlinear factor, and inertia weights factor. In this paper, the method is applied for the first time in antenna array synthesis by optimizing parameters such as inter-element spacing and excitation to minimize the SLL while keeping other constraints within the boundary limits, while ensuring that the performance is not affected. For performance evaluation, the simulation tests include 12 benchmark test functions and 12 test functions to verify the effectiveness of the improvement strategies. According to the encouraging research results in this paper, the IERWHO algorithm proposed has a place in the field of optimization. [ABSTRACT FROM AUTHOR]

The organizational forms of infrastructure in China are divided into two categories, the traditional Public Procurement Model (PUB) model and Public–Private Partnership(PPP) model. The main difference is the separation or binding of the construction and operation phases. A systematic understanding is needed of how Chinese local governments choose between these two models. In this paper, we take public capital congestion and local government objectives as the entry point to study the effects of both on PPP choice. Firstly, by constructing an endogenous economic growth model under the PPP model, and comparing it with the model under the PUB model, this paper initially explains how the rise in public capital congestion affects the choice of the PPP by growth-oriented local governments. Then the data from prefecture-level cities from 2009–2018 are utilized to conduct empirical tests. We find that urban economic growth pressures have a positive effect on the choice of PPP when the congestion of public capital increases. Furthermore, the implementation of PPP is indeed conducive to economic performance, and its core mechanism is to provide more infrastructure (like roads) rather than tax competition. The PPP model is more sustainable. We are the first to employ both modeling approach and the empirical research to address the implementation of Public–Private Partnership in China. And we have systematically analyzed the conditions and results of PPP selection by local governments. It formulates the Chinese PPP theory. [ABSTRACT FROM AUTHOR]

Solid oxide fuel cells (SOFCs) are vital for alternative energy, powering motors effi-ciently. They offer fuel versatility and waste heat recovery, making them ideal for various applications. Optimizing interconnector structures is crucial for SOFC advancement. This paper introduces a novel 2D simulation model for interconnector SOFCs, aiming to enhance their performance. We initially construct a single half-cell model for a conventional interconnector SOFC, ensuring model accuracy. Subsequently, we propose an innovative interconnector SOFC model, which outperforms the conventional counterpart in various aspects. [ABSTRACT FROM AUTHOR]

Background: Despite ongoing efforts, perinatal morbidity and mortality persist across all settings, imposing a dual burden of clinical and economic strain. Besides, the fragmented nature of economic evidence on perinatal health interventions hinders the formulation of effective health policies. Our review aims to comprehensively and critically assess the economic evidence for such interventions in high-income countries, where the balance of health outcomes and fiscal prudence is paramount. Methods and analysis: We will conduct a comprehensive search for studies using databases including EconLit (EBSCO), Cost Effectiveness Analysis (CEA) Registry, Medline (Ovid), Embase (Ovid), CINAHL Ultimate (EBSCO), Global Health (Ovid), and PubMed. Furthermore, we will broaden our search to include Google Scholar and conduct snowballing from the final articles included. The search terms will encompass economic evaluation, perinatal health interventions, morbidity and mortality, and high-income countries. We will include full economic evaluations focusing on cost-effectiveness, cost-benefit, cost-utility, and cost-minimisation analyses. We will exclude partial economic evaluations, reports, qualitative studies, conference papers, editorials, and systematic reviews. Date restrictions will limit the review to studies published after 2010 and those in English during the study selection process. We will use the modified Drummond checklist to evaluate the quality of each included study. Our findings will adhere to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) 2020 statement. A summary will include estimated costs, effectiveness, benefits, and the incremental cost-effectiveness ratio (ICER). We also plan to conduct a subgroup analysis. To aid comparability, we will standardise all costs to the United States Dollar, adjusting them to their 2022 value using country-specific consumer price index and purchasing power parity. Ethics and dissemination: This systematic review will not involve human participants and requires no ethical approval. We will publish the results in a peer-reviewed journal. Trial registration: We registered our record on PROSPERO (registration #: CRD42023432232). [ABSTRACT FROM AUTHOR]

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

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

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

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

Multi-stage attacks are one of the most critical security threats in the current cyberspace. To accurately identify multi-stage attacks, this paper proposes an anomaly-based multi-stage attack detection method. It constructs a Multi-Stage Profile (MSP) by modeling the stable system's normal state to detect attack behaviors. Initially, the method employs Doc2Vec to vectorize alert messages generated by the intrusion detection systems (IDS), extracting profound inter-message correlations. Subsequently, Hidden Markov Models (HMM) are employed to model the normal system state, constructing an MSP, with relevant HMM parameters dynamically acquired via clustering algorithms. Finally, the detection of attacks is achieved by determining the anomaly threshold through the generation probability (GP). To evaluate the performance of the proposed method, experiments were conducted using three public datasets and compared with three advanced multi-stage attack detection methods. The experimental results demonstrate that our method achieves an accuracy of over 99% and precision of 100% in multi-stage attack detection. This confirms the effectiveness of our method in adapting to different attack scenarios and ultimately completing attack detection. [ABSTRACT FROM AUTHOR]

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

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

Judicial improvement and trade structure optimization are important elements in achieving a high level of openness. This paper develops a theoretical framework on the impact of local protectionism on market integration and export technical complexity, and then empirically analyses the effect of judicial improvement on the technical complexity of urban product exports and its mechanism of action based on a quasi-natural experiment with the establishment of circuit courts. The study finds that the establishment of circuit courts significantly increases the technical complexity of product exports, and the findings remain valid after a series of robustness tests. Further analysis shows that the technology enhancement effect of the establishment of circuit courts is more pronounced in the group of cities with higher administrative levels, better location advantages, and less advanced rule of law. Mechanism Analysis shows that a superior institutional environment can have a two-sided effect on different types of firms. Overall, however, the productivity-enhancing effect of low-productivity firms exiting the market is lower than the productivity-reducing effect of higher-productivity firms' increased compliance costs, which in turn weakens the positive promotional effect of circuit courts. The above theoretical and empirical findings provide policy implications for maximizing the technology enhancement effect of the establishment of circuit courts on product exports, thereby promoting a high level of openness in China. [ABSTRACT FROM AUTHOR]

Climate change-induced pan-financial market and the contagion of systemic financial risks are becoming important issues in the financial sector. The paper measures the temperature difference in terms of the degree and direction of deviation of the actual temperature relative to the average temperature of the same historical period. Based on the high-dimensional time-series variable LASSO-VAR-DY framework, we construct a pan-financial market volatility correlation network consisting of 112 Chinese listed companies in banking, insurance, securities, real estate, traditional energy, and new energy, use eigenvector centrality to measure the systematic risk of each firm, and then empirically test the effect of temperature difference on systematic risk under pan-financial market scenario. The results of the study show that (ⅰ) There is a significant difference among the systemic risk of financial sectors such as banking, insurance, and securities in the financial market pan-financial market scenario and the systemic risk when the financial market pan-financial market is not taken into account;(ⅱ) Higher temperature significantly exacerbates systemic financial risk, while colder temperature significantly mitigates systemic risk, but both have an asymmetric effect on systemic risk, and there is sectoral heterogeneity.(ⅲ) From the dynamic evolutionary characteristics, there are significant differences in the response of systemic financial risk to positive and negative temperature shocks;(iv) The results of the systemic risk variance decomposition indicate that the temperature change contributes more to the variance of systemic risk in the banking and securities sectors in pan-financial market;(ⅴ) The contagion source of financial systemic risk shows an obvious path of leaping and changing characteristics, and the contagion source of systemic risk (source of impact) shows the evolution law of "bank → real estate → new energy → temperature difference," which means that the temperature difference has become the contagion source of systemic financial risk. This study provides a reference for preventing and resolving systemic risks under pan-financial market scenario and provides a basis for improving the current macroprudential regulatory framework. [ABSTRACT FROM AUTHOR]

Based on the unbalanced panel data of Chinese professional health insurance companies from 2011 to 2021, the paper constructs "PW+PCSE" model to empirically investigate the main factors affecting the commercial health insurance surrender in China from the company level. The results show that asset-liability ratio has a significant positive effect on health insurance surrender rate. The value preservation and appreciation rate of capital and R&D expenditure rate both have significant negative effects on health insurance surrender rate. These studies bring important enlightenment for domestic health insurance companies to avoid surrender risk. [ABSTRACT FROM AUTHOR]

Background and aims: As a part of the framework of the EU-funded Energy efficiency through Behavior CHANge Transition (ENCHANT) project, the present paper intends to provide a "Research Protocol" of a web-based trial to: (i) assess the effectiveness of behavioral intervention strategies––either single or in combination––on electricity saving, and (ii) unravel the psychological factors contributing to intervention effectiveness in households across Europe. Methods and materials: Six distinct interventions (i.e., information provision, collective vs. individual message framing, social norms, consumption feedback, competitive elements, and commitment strategies) targeting electricity saving in households from six European countries (i.e., Austria, Germany, Italy, Norway, Romania, and Türkiye) are evaluated, with an initial expected samples of about 1500 households per country randomly assigned to 12 intervention groups and two control groups, and data is collected through an ad-hoc online platform. The primary outcome is the weekly electricity consumption normalized to the last seven days before measurement per person per household. Secondary outcomes are the peak consumption during the last day before measurement and the self-reported implementation of electricity saving behaviors (e.g., deicing the refrigerator). The underlying psychological factors expected to mediate and/or moderate the intervention effects on these outcomes are intentions to save electricity, perceived difficulty of saving energy, attitudes to electricity saving, electricity saving habit strength, social norms to save electricity, personal norms, collective efficacy, emotional reaction to electricity consumption, and national identity. The intervention effectiveness will be evaluated by comparing psychological factors and consumption variables before and after the intervention, leading to a 14 (groups including 2 control groups) × 6 (time) mixed factorial design, with one factor between (group) and one factor within subjects (time)–6 measurements of the psychological factors and 6 readings of the electricity meters, which gives then 5 weeks of electricity consumption. Results: Data collection for the present RCT started in January 2023, and by October 2023 data collection will conclude. Discussion: Upon establishing feasibility and effectiveness, the outcomes of this study will assist policymakers, municipalities, NGOs, and other communal entities in identifying impactful interventions tailored to their unique circumstances and available resources. Researchers will benefit from a flexible, structured tool that allows the design, implementation and monitoring of complex interventions protocols. Crucially, the intervention participants will benefit from electricity saving strategies, fostering immediate effectiveness of the interventions in real-life contexts. Trial registration: This trial was preregistered in the Open Science Framework: https://osf.io/9vtn4. [ABSTRACT FROM AUTHOR]

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

KRAS is a pathogenic gene frequently implicated in non-small cell lung cancer (NSCLC). However, biopsy as a diagnostic method has practical limitations. Therefore, it is important to accurately determine the mutation status of the KRAS gene non-invasively by combining NSCLC CT images and genetic data for early diagnosis and subsequent targeted therapy of patients. This paper proposes a Semi-supervised Multimodal Multiscale Attention Model (S2MMAM). S2MMAM comprises a Supervised Multilevel Fusion Segmentation Network (SMF-SN) and a Semi-supervised Multimodal Fusion Classification Network (S2MF-CN). S2MMAM facilitates the execution of the classification task by transferring the useful information captured in SMF-SN to the S2MF-CN to improve the model prediction accuracy. In SMF-SN, we propose a Triple Attention-guided Feature Aggregation module for obtaining segmentation features that incorporate high-level semantic abstract features and low-level semantic detail features. Segmentation features provide pre-guidance and key information expansion for S2MF-CN. S2MF-CN shares the encoder and decoder parameters of SMF-SN, which enables S2MF-CN to obtain rich classification features. S2MF-CN uses the proposed Intra and Inter Mutual Guidance Attention Fusion (I2MGAF) module to first guide segmentation and classification feature fusion to extract hidden multi-scale contextual information. I2MGAF then guides the multidimensional fusion of genetic data and CT image data to compensate for the lack of information in single modality data. S2MMAM achieved 83.27% AUC and 81.67% accuracy in predicting KRAS gene mutation status in NSCLC. This method uses medical image CT and genetic data to effectively improve the accuracy of predicting KRAS gene mutation status in NSCLC. [ABSTRACT FROM AUTHOR]

With the intensification of global economic competition, innovation has become one of the core elements of vigorous development in various regions. Improving the innovation ability of cross-regional central cities is the main factor influencing whether a region can achieve economic and social development. In this paper, an innovation primacy index system is designed according to the links of the innovation value chain, and the innovation advantages and empirical effects are comprehensively analyzed by using the point-to-point method, entropy weight method, gravity model and two-way fixed effect model. Based on the data of 8 cities in the core area of the Huaihai Economic Zone from 2010 to 2020 in China, Xuzhou's innovation primacy ranks first in the core area of the Huaihai Economic Zone, which accords with its status as the central city of the region. Its index has been rising, and its development trend is also good. However, the overall innovation ability of the core area of the Huaihai Economic Zone is unbalanced; the overall economic foundation is not solid enough. In terms of knowledge innovation, R&D innovation and industrial innovation, the industrialization level, around the activation of Xuzhou city vitality, enhances the Xuzhou innovation radiation drive, leading to a decrease in the Huaihai Economic Zone. Finally, some corresponding suggestions on innovation primacy have been proposed for the city of the Huaihai Economic Zone. [ABSTRACT FROM AUTHOR]

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

Life tables are one of the most common tools to describe the biology of insect species and their response to environmental conditions. Although the benefits of life tables are beyond question, we raise some doubts about the completeness of the information reported in life tables. To substantiate these doubts, we consider a case study (Corcyra cephalonica) for which the raw dataset is available. The data suggest that the Gaussian approximation of the development times which is implied by the average and standard error usually reported in life tables does not describe reliably the actual distribution of the data which can be misleading and hide interesting biological aspects. Furthermore, it can be risky when life table data are used to build models to predict the demographic changes of the population. The present study highlights this aspect by comparing the impulse response generated by the raw data and by its Gaussian approximation based on the mean and the standard error. The conclusions of this paper highlight: i) the importance of adding more information to life tables and, ii) the role of raw data to ensure the completeness of this kind of studies. Given the importance of raw data, we also point out the need for further developments of a standard in the community for sharing and analysing data of life tables experiments. [ABSTRACT FROM AUTHOR]

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