Search

The banking sector serves as a nudge between increased financial investments and reduced environmental impacts in the modern era of sustainability thus, integrating the social, environmental, and economic dimensions. This paper aims to explore the practices and ongoing activities on account of sustainable banking which is being practised in the Pakistani Banking Sector. A mixed methods approach using a survey with a sample size of (n = 250) and in-depth interviews of (n = 25) provides significant evidence for the research. SmartPLS4.0 was used for hypotheses testing and to ascertain the path coefficient association within the constructs. This research fills the gap in existing literature by testing and implying the mediating role of Stakeholders' Influence on the relationship between Green Banking Practices and the Bank's Performance. The results of the quantitative analysis show a positive association between variables, highlighting the role of stakeholders and their need to partake efficiently, in the countrywide execution of green banking. The qualitative analysis portrays that; green banking is currently the partial focus of the banking sector in the developing economy of Pakistan whereas, approaches like financing green projects, investments in renewable energy, in-house greening of the banking sector, and provision of easy lending facilities to encourage and support environmental initiatives are some commonly practised accomplishments of the banking sector. [ABSTRACT FROM AUTHOR]

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

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

The government's environmental protection policy can significantly contribute to alleviating resource shortages and curbing environmental pollution, but the impact of various policy instruments implemented by the government on energy efficiency is unclear. Based on the panel data of 30 provinces in China from 2005 to 2021, this paper analyses the impact of environmental regulation and the industrial structure on energy efficiency from the perspective of resource taxes. The U-shaped relationship between environmental regulation and energy efficiency and between the optimization of industrial structure can significantly improve energy efficiency, and the optimization of industrial structure is conducive to weakening the initial inhibitory effect of environmental regulation. In addition, the analysis of regional heterogeneity showed that the impact of environmental regulation was stronger in the central and western regions, while the impact of industrial structure was stronger in the eastern and western regions. The conclusions of this study can help to expand the understanding of the relationship between environmental regulation and industrial structure on energy efficiency, provide policy enlightenment for the realization of green development and high-quality development, and provide Chinese examples and experiences for developing countries to improve energy efficiency. [ABSTRACT FROM AUTHOR]

In the context of integrating sports and medicine domains, the urgent resolution of elderly health supervision requires effective data clustering algorithms. This paper introduces a novel higher-order hybrid clustering algorithm that combines density values and the particle swarm optimization (PSO) algorithm. Initially, the traditional PSO algorithm is enhanced by integrating the Global Evolution Dynamic Model (GEDM) into the Distribution Estimation Algorithm (EDA), constructing a weighted covariance matrix-based GEDM. This adapted PSO algorithm dynamically selects between the Global Evolution Dynamic Model and the standard PSO algorithm to update population information, significantly enhancing convergence speed while mitigating the risk of local optima entrapment. Subsequently, the higher-order hybrid clustering algorithm is formulated based on the density value and the refined PSO algorithm. The PSO clustering algorithm is adopted in the initial clustering phase, culminating in class clusters after a finite number of iterations. These clusters then undergo the application of the density peak search algorithm to identify candidate centroids. The final centroids are determined through a fusion of the initial class clusters and the identified candidate centroids. Results showcase remarkable improvements: achieving 99.13%, 82.22%, and 99.22% for F-measure, recall, and precision on dataset S1, and 75.22%, 64.0%, and 64.4% on dataset CMC. Notably, the proposed algorithm yields a 75.22%, 64.4%, and 64.6% rate on dataset S, significantly surpassing the comparative schemes' performance. Moreover, employing the text vector representation of the LDA topic vector model underscores the efficacy of the higher-order hybrid clustering algorithm in efficiently clustering text information. This innovative approach facilitates swift and accurate clustering of elderly health data from the perspective of sports and medicine integration. It enables the identification of patterns and regularities within the data, facilitating the formulation of personalized health management strategies and addressing latent health concerns among the elderly population. [ABSTRACT FROM AUTHOR]

Major product safety incidents often cause widespread concern among consumers, and these product safety incidents will stimulate consumers' psychology, change their risk perception, and affect the demand for products and services of risk consumers. The change in consumer demand will eventually lead to a change in firm innovation decisions. Using Chinese firm-level data, this paper employs the news reporting of the Bawang event as a quasi-natural experiment to study the impact of risk perception changes on innovation. The empirical results of this study show that increasing consumers' risk perception caused by the negative news coverage of defective products motivates firms to increase their innovation. The effects are heterogeneous, where firms with private ownership and in developed regions are more likely to increase innovation activities. This study suggests that the relationship between consumers' risk perception and firm innovation is primarily driven by market demand. Moreover, the positive effects of risk perception on innovation are more prominent for downstream firms and those having a smaller technological distance. [ABSTRACT FROM AUTHOR]

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

Digital rural construction is a key strategic direction to promote China's rural revitalization and alleviate global climate problems. In order to put forward feasible suggestions for the subsequent development and ensure the smooth development of digital village construction, how to reflect the development level of the digital village through scientific and reasonable comprehensive evaluation has become an urgent problem to be solved. This paper establishes a comprehensive evaluation index system through the Delphi method and principal component analysis method, then assigns weights to the evaluation indicators based on the improved CRITIC-G1 method, and then grades the development level of digital villages according to the extension matter element method. Finally, taking Jiangxi Province in China as an example, the overall development level of digital villages in Jiangxi Province is evaluated from the provincial level according to the proposed method. And put forward the corresponding countermeasures and suggestions. Results: Firstly, the development level of digital villages in Jiangxi Province is good, and there is a trend of excellent development level. Secondly, from different aspects of digital rural development, the digitalization of infrastructure, services, economy, and green production in Jiangxi Province is at a good level, and the digitalization of life has reached an excellent level. Thirdly, from the perspective of development trends, the digitization of infrastructure has a progressive trend towards an excellent level of development, while the digitization of services, economy and green production has signs of development regression. According to the analysis results, the relevant countermeasures and suggestions are put forward from four aspects: talent, capital, governance system and development planning. Other regions can evaluate the development level of the digital village according to the evaluation model proposed in this paper so as to analyze the existing problems and put forward targeted solutions to promote the construction of the digital village. [ABSTRACT FROM AUTHOR]

The global digital wave has flourished in recent years, and the digital technology revolution has emerged. Digitalization plays an undeniable role in promoting modern economic and social development in multiple aspects such as economy, society, innovation, public services and sustainable development, China's digitalization application is also developing rapidly. In order to better measure the current situation of China's digitalization development level, this paper constructs a comprehensive evaluation index system of digitalization development from four dimensions of talents in the digital field, digital infrastructure construction, digitalization innovation ability and international competitiveness, and tests the index system. The entropy method is used to measure the level of digitalization development between China and the United States, the United Kingdom, France and other major developed countries in the world, and the coefficient of variation method, kernel density estimation and Dagum Gini coefficient method are used to analyze the temporal and spatial characteristics and regional differences of digitalization development level of seven countries. This paper makes a comparative analysis between China and major developed countries from the historical perspective of the evolution of the techno-economic paradigm. With a view to summarizing and exploring from it and drawing on the advanced experiences accumulated by the developed countries over a long period of time, so as to provide China with useful insights and bases for further enhancing its digitalization development level. [ABSTRACT FROM AUTHOR]

Biodiversity is in rapid decline globally with agriculture being one of the leading causes. Within agricultural landscapes, some features provide a benefit to biodiversity that is disproportionate to their spatial area. An interesting example is artificial ponds–or farm dams–which can support a large variety of taxa. Here, we present a global review of farm dam research related to biodiversity conservation objectives to provide an overview of the topics, key research insights, and the characteristics of current research. We used a three-stage process to screen literature and identified 104 relevant papers across 27 countries encompassing studies of 13 different taxa. Most of the studies were short-term (less than 5 years) with small sample sizes (less than 20 sites). Of the 104 papers, 88 were focussed primarily on ecological outcomes, such as species richness or abundance, and 15 on primary production outcomes, such as crop and livestock yield, despite addressing or measuring ecological metrics. Only one study measured both ecological and primary production outcomes. Studies frequently examined how the features of dams (79 studies) and attributes of the surrounding landscape (47 studies) impact particular species and communities. Terrestrial mammals (1 study) were under-represented in the literature with macrophytes (28 studies), macroinvertebrates (26 studies), and amphibians (19 studies) receiving the most attention. Our results reveal a growing trend towards recognizing farm dams as habitats for various taxa, including amphibians, beetles, dragonflies, and other macroinvertebrates within agricultural environments. Significant knowledge gaps exist in understanding how dam age, invasive species, and effective management practices impact the biodiversity conservation values of farm dams. Future research should emphasize enhancing biodiversity by collaborating with landholders to increase habitat through strategic vegetation planning, minimizing runoff and nutrient inflow, and restricting stock access. [ABSTRACT FROM AUTHOR]

In Zimbabwe, the ZAZIC consortium employs two-way, text-based (2wT) follow-up to strengthen post-operative care for voluntary medical male circumcision (VMMC). 2wT scaled nationally with evidence of client support and strengthened follow-up. However, 2wT uptake among healthcare providers remains suboptimal. Understanding the gap between mobile health (mHealth) potential for innovation expansion and scale-up realization is critical for 2wT and other mHealth innovations. Therefore, we conducted an exploratory qualitative study with the objective of identifying 2wT program strengths, challenges, and suggestions for scale up as part of routine VMMC services. A total of 16 in-depth interviews (IDIs) with diverse 2wT stakeholders were conducted, including nurses, monitoring & evaluation teams, and technology partners–a combination of perspectives that provide new insights. We used both inductive and deductive coding for thematic analysis. Among 2wT drivers of expansion success, interviewees noted: 2wT care benefits for clients; effective hands-on 2wT training; ease of app use for providers; 2wT saved time and money; and 2wT strengthened client/provider interaction. For 2wT scale-up challenges, staff shortages; network infrastructure constraints; client costs; duplication of paper and electronic reporting; and complexity of digital tools integration. To improve 2wT robustness, respondents suggested: more staff training to offset turnover; making 2wT free for clients; using 2wT to replace paper VMMC reporting; integrating with routine VMMC reporting systems; and expanding 2wT to other health areas. High stakeholder participation in app design, implementation strengthening, and evaluation were appreciated. Several 2wT improvements stemmed from this study, including enrollment of multiple people on one number to account for phone sharing; 2wT inclusion of minors ages 15+; clients provided with $1 to offset SMS costs; and reduced SMS messages to clients. Continued 2wT mentoring for staff, harmonization of 2wT with Ministry e-health data systems, and increased awareness of 2wT's client and provider benefits will help ensure successful 2wT scale-up. [ABSTRACT FROM AUTHOR]

Objective: To assess the reporting quality of published RCT abstracts regarding patients with endometriosis pelvic pain and investigate the prevalence and characteristics of spin in these abstracts Methods: PubMed and Scopus were searched for RCT abstracts addressing endometriosis pelvic pain published from January 1st, 2010 to December 1st, 2023.The reporting quality of RCT abstracts was assessed using the CONSORT statement for abstracts. Additionally, spin was evaluated in the results and conclusions section of the abstracts, defined as the misleading reporting of study findings to emphasize the perceived benefits of an intervention or to confound readers from statistically non-significant results. Assessing factors affecting the reporting quality and spin existence, linear and logistic regression was used, respectively. Results: A total of 47 RCT abstracts were included. Out of 16 checklist items, only three items including objective, intervention and conclusions were sufficiently reported in the most abstracts (more than 95%), and none of the abstracts presented precise data as required by the CONSORT-A guidelines. In the reporting quality of material and method section, trial design, type of randomization, the generation of random allocation sequences, the allocation concealment and blinding were most items identified that were suboptimal. The total score for the quality varied between 5 and 15 (mean: 9.59, SD: 3.03, median: 9, IQR: 5). Word count (beta = 0.015, p-value = 0.005) and publishing in open-accessed journals (beta = 2.023, p-value = 0.023) were the significant factors that affecting the reporting quality. Evaluating spin within each included paper, we found that 18 (51.43%) papers had statistically non-significant results. From these studies, 12 (66.66%) had spin in both results and conclusion sections. Furthermore, the spin intensity increased during 2010–2023 and 38.29% of abstracts had spin in both results and conclusion sections. Conclusion: Overall poor adherence to CONSORT-A was observed, with spin detected in several RCTs featuring non-significant primary endpoints in obstetrics and gynecology literature. [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]

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]

Composite materials are popular because of their high performance capabilities, but new material development is time-consuming. To accelerate this process, researchers studying material informatics, an academic discipline combining computational science and material science, have developed less time-consuming approaches for predicting possible material combinations. However, these processes remain problematic because some materials are not suited for them. The limitations of specific candidates for new composites may cause potential new material pairs to be overlooked. To solve this problem, we developed a new method to predict possible composite material pairs by considering more materials than previous techniques. We predicted possible material pairs by conducting link predictions of material word co-occurrence networks while assuming that co-occurring material word pairs in scientific papers on composites were reported as composite materials. As a result, we succeeded in predicting the co-occurrence of material words with high specificity. Nodes tended to link to many other words, generating new links in the created co-occurrence material word network; notably, the number of material words co-occurring with graphene increased rapidly. This phenomenon confirmed that graphene is an attractive composite component. We expect our method to contribute to the accelerated development of new composite materials. [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 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]

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]

Background and objectives: Vertical femoral neck fractures (VFNFs) in young patients lead to significant biomechanical instability. Multitudinous internal fixation devices have been developed and utilized in clinical interventions. However, there has yet to be a consensus expert opinion regarding the optimal internal fixation configurations. This study aims to conduct a network meta-analysis to evaluate the safety and efficacy of all currently recognized internal fixation procedures for the treatment of VFNFs in young individuals. Methods: Comprehensive literature searches will be performed in China National Knowledge Infrastructure, the Cochrane Library, PubMed, Web of Science, Embase, the Wanfang Database, and the Chinese Biomedical Literature Database, covering the entire database history up until May 21, 2024. Individual papers will be evaluated for possible bias using RoB 2.0, the most recent version of the randomized trial Cochrane risk-of-bias approach. Pairwise meta-analysis and network meta-analysis (NMA) will be conducted for data analysis using STATA 15.0 and R 4.1.3. Inconsistency tests, subgroup analyses, sensitivity analyses, and assessments of publication bias will also be performed. Conclusion: The study will provide evidence-based recommendations for the optimal internal fixation methods in treating young patients with VFNFs. Trial registration: INPLASY202460017. [ABSTRACT FROM AUTHOR]

In light of the rapid development of digital technology, it is imperative to study the impact of digital technology on the labour force's entrepreneurial choices with the utmost urgency. This paper first constructs a theoretical mechanism for how digital technology affects individual entrepreneurship. It then empirically examines data from the China General Social Survey (CGSS) to test the theory. The results show that digital technology significantly increases individual entrepreneurial choices. Furthermore, the conclusions of the study are robust even when the estimation method and variable measurement are changed. Finally, the study finds that digital technology has the greatest impact on entrepreneurship among individuals with low education, the second-largest impact on those with medium education, and the third-largest impact on those with high education. Individuals with higher education levels have the second largest impact on the entrepreneurship of individuals with higher education levels, while the smallest impact is observed in this group. Digital technology development has a stronger role in promoting entrepreneurship of individuals with urban household registration than those with rural household registration. In terms of sub-region, digital technology has a larger role in individual entrepreneurship in the eastern and central regions, and has a less significant role in the western region. The findings of this study suggest that there is a need to implement measures to accelerate the pace of digital technology development, enhance the training of entrepreneurial skills and attitudes among highly educated individuals, and direct efforts towards enhancing digital technology development in rural and western China. [ABSTRACT FROM AUTHOR]

Based on small scale sample of accident data from specific scenarios, fully exploring the potential influencing factors of the severity of traffic accidents has become a key and effective research method. In order to analyze the factors mentioned above in the scenario of urban ring roads, this paper collected data records of 1250 traffic accidents involving different severity on urban ring road of a central city in northwest China in the past 3 years. Firstly, the Support Vector Machine (SVM) model of non-parametric method is utilized to analyze the data above, and three kernel functions of linear, inhomogeneous polynomial and Gaussian radial basis are constructed respectively. Considering comprehensively 16 potential influencing factors covering the driver-vehicle-road-environment integrated system, the SVM models of above three kernel functions are verified, accuracy reaches 0.771 and F1 reaches 0.841. Then, Bayesian Optimization (BO), Grids Search (GS) and Rough Set (RS) are utilized as optimizer to adjust the parameters of Gaussian radial basis function SVM model, the performance of BO-SVM is further improved and reaches the optimum, with an average accuracy of 0.875 on the test set and a F1 of 0.886, completely outperforming the benchmark models of GS-SVM, RS-SVM, Bilayer-LSTM and BP. Finally, the sensitivity analysis method is utilized to quantify the sensitivity of the potential influencing factors to the severity of road accidents, and the backward selection method is utilized to screen the core influencing factors that influence the severity of accident, concluded that core influencing factors are age, driving mileage and vehicle type. This paper will provide reference for the analysis of the significant influencing factors for road accidents severity, and to provide theoretical support for the precise formulation of accident improvement strategies. [ABSTRACT FROM AUTHOR]

Landscape research in archaeology has greatly benefited from the increasing application of computational methods over the last decades. Spatial statistical methods such as point pattern analysis have been particularly revolutionary. Archaeologists have used point pattern analysis to explore spatial arrangements and relations between 'points' (e.g., locations of artefacts or archaeological sites). However, the results obtained from these techniques can be greatly affected by the uncertainty coming from the fragmentary nature of archaeological data, their irregular distribution in the landscape, and the working methods used to study them. Furthermore, the quantification of uncertainty in spatial data coming from non-systematic surveys has never been fully addressed. To overcome this challenge, archaeologists have increasingly relied on applying advanced methods from statistics, data science, and geography. While the application of advanced methods from formal sciences will provide robustness to models based on uncertain datasets, as with uncertainty, robustness must be assessed in relation to the case study, the regional context, and the methods used. These issues are of great importance when the models from advanced methods are directly used to create narratives about past landscapes. In this paper, we gather previous research on uncertainty quantification in archaeology and formalize its best practices into a framework to assess robustness and uncertainty in spatial statistical models, particularly focusing on one commonly used in the discipline, i.e., the Pair Correlation Function. This framework allows us to understand better how incomplete data affect a model, quantify the model uncertainties, and assess the robustness of the results achieved with spatial point processes. [ABSTRACT FROM AUTHOR]

In vitro co-culture techniques that allow the growth of plants and pathogens under controlled environmental conditions are being used to re-create host plant infection. These approaches reduce infection times, promote reproducibility, and enable a rapid evaluation of plant-pathogen interactions. As a result, these systems have become essential in breeding programs aimed at developing plant resistance to diseases. In this study, we developed and validated an in vitro co-culture system to investigate the Armillaria root rot (ARR) affecting Prunus spp. This disease, caused by fungi Armillaria spp. and Desarmillaria caespitosa, poses a severe threat to the stone and nut fruit industry due to the susceptibility of most commercial rootstocks to infection and the lack of effective management options for its control. The system consists of a fiber-supported liquid approach in sterile plastic vessels that allows a fast and reproducible fungal infection under controlled environmental conditions. The floor of the vessels was covered with a polyester-fiber matte and a germination paper that served as an interface between the mycelia and the plant roots. The vessels were subjected to inoculation with Armillaria mellea and D. caespitosa, and three Prunus genotypes ('Guardian®', 'MP-29', and Prunus cerasifera '14–4') were co-cultured with both fungi. Disease progression and plant and fungal biomass were monitored during co-culture. The presented in vitro co-culture approach facilitates the concurrent growth of Armillaria/Desarmillaria spp. and Prunus spp., excluding most of the limitations associated with greenhouses and field experiments. This system provides consistent and reproducible conditions for investigating a prominent plant disease affecting Prunus spp. [ABSTRACT FROM AUTHOR]

Effective congestion control on the internet has been a problem since its inception. Transmission Control Protocol (TCP), being the most widely used transport layer protocol tries to mitigate it using a variety of congestion control algorithms. Cubic, Reno, and Bottleneck Bandwidth and Round-trip propagation time (BBR) are the most deployed congestion controls. BBR v2 is leading the congestion control race with its superior performance in terms of better throughput and lower latency. Furthermore, Active Queue Management (AQM) algorithms try to mitigate the congestion control at the network layer through active buffer control to avoid bufferbloat. The most efficient congestion control occurs when TCP and AQM work together. Indeed, it is the TCP-AQM algorithm "Golden pair" that can result in the most efficient performance. This paper proposes such a novel pair based on our previously tested and published BBR-n (BBR new) with the most effective of the modern AQMs, that completely gels together to provide lower latency in wireless networks based on Wireless N/AC. Real-time experiments were performed using Flent on our physical testbed with BBR-n and modern AQMs such as Fair Queuing (FQ), Constrained Delay (CoDel), Proportional Integral controller Enhanced (PIE), Common Applications Kept Enhanced (Cake) and Flow Queuing Controlled Delay (FQ_CoDel). Various tests done on our physical testbed helped us identify CAKE as the most optimum AQM that fits with our proposed BBR-n while providing optimum throughput and lower latency in 802.11N/AC-based wireless networks. [ABSTRACT FROM AUTHOR]

We discuss the invasion of the Omicron BA.1 variant into England as a paradigm for real-time model fitting and projection. Here we use a mixture of simple SIR-type models, analysis of the early data and a more complex age-structure model fit to the outbreak to understand the dynamics. In particular, we highlight that early data shows that the invading Omicron variant had a substantial growth advantage over the resident Delta variant. However, early data does not allow us to reliably infer other key epidemiological parameters—such as generation time and severity—which influence the expected peak hospital numbers. With more complete epidemic data from January 2022 are we able to capture the true scale of the epidemic in terms of both infections and hospital admissions, driven by different infection characteristics of Omicron compared to Delta and a substantial shift in estimated precautionary behaviour during December. This work highlights the challenges of real time forecasting, in a rapidly changing environment with limited information on the variant's epidemiological characteristics. Author summary: One of the key challenges for modellers at the start of any outbreak is to predict its likely scale and the associated burden on the health systems. This was the scenario in December of 2021, when the UK was faced with the invasion of the Omicron (BA.1) variant. For the Omicron BA.1 wave the peak in hospital admission was initially overestimated due to the simplifying assumption that Omicron would behave like a faster spreading version of the Delta variant, and that there would be little or no change to population behaviour during the wave. In this paper, we use a range of models and analyses to understand the challenges of correctly predicting the peak. Compared with forecasts made from the earliest data, the peak was reduced by the shorter infectious period of Omicron, its lower severity but mostly by changes in behaviour and testing over the Christmas holiday season. Historic projections made in December of 2021 that assumed a set of imposed mitigation measures were therefore more accurate than those that assumed no controls, although in practice the controls were largely self-imposed. This paper therefore highlights the challenges of forecasting the likely scale of outbreaks and the importance of understanding the population response to public health messaging. [ABSTRACT FROM AUTHOR]

A promising approach for scalable Gaussian processes (GPs) is the Karhunen-Loève (KL) decomposition, in which the GP kernel is represented by a set of basis functions which are the eigenfunctions of the kernel operator. Such decomposed kernels have the potential to be very fast, and do not depend on the selection of a reduced set of inducing points. However KL decompositions lead to high dimensionality, and variable selection thus becomes paramount. This paper reports a new method of forward variable selection, enabled by the ordered nature of the basis functions in the KL expansion of the Bayesian Smoothing Spline ANOVA kernel (BSS-ANOVA), coupled with fast Gibbs sampling in a fully Bayesian approach. It quickly and effectively limits the number of terms, yielding a method with competitive accuracies, training and inference times for tabular datasets of low feature set dimensionality. Theoretical computational complexities are O(NP2) in training and O(P) per point in inference, where N is the number of instances and P the number of expansion terms. The inference speed and accuracy makes the method especially useful for dynamic systems identification, by modeling the dynamics in the tangent space as a static problem, then integrating the learned dynamics using a high-order scheme. The methods are demonstrated on two dynamic datasets: a 'Susceptible, Infected, Recovered' (SIR) toy problem, along with the experimental 'Cascaded Tanks' benchmark dataset. Comparisons on the static prediction of time derivatives are made with a random forest (RF), a residual neural network (ResNet), and the Orthogonal Additive Kernel (OAK) inducing points scalable GP, while for the timeseries prediction comparisons are made with LSTM and GRU recurrent neural networks (RNNs) along with the SINDy package. [ABSTRACT FROM AUTHOR]

Distributed generation can actively participate in the day-ahead markets, real-time power balance, and wholesale gas markets to achieve various goals, such as supplying gas to various electric power generation plants. A multi-objective network with two types of loads is considered in this paper. The reason for the simultaneous optimization of these two networks is that these two energy carriers are dependent on each other and gas is needed to produce electricity, so this issue can be addressed with a multi-objective function. The simulation carried out in this article is coded in GAMS software as a mixed integer linear programming (MILP). The efficiency of gas turbines and fuel cells in this article is dependent on their working point, and considering the exact model of these resources and the relationships related to the calculation of their fuel consumption is non-linear. On the other hand, a binary variable has been used to show the charging and discharging state of the storage and the on-and-off state of the gas turbines. Therefore, the problem considered in this article is a MILP problem. The results of this article are the proper planning of charging and discharging of the energy storage system with the proper planning of the power generation of different energy sources considering the network loads in two optimized and non-optimized scenarios. [ABSTRACT FROM AUTHOR]

The unauthorized replication and distribution of digital images pose significant challenges to copyright protection. While existing solutions incorporate blockchain-based techniques such as perceptual hashing and digital watermarking, they lack large-scale experimental validation and a dedicated blockchain consensus protocol for image copyright management. This paper introduces DRPChain, a novel digital image copyright management system that addresses these issues. DRPChain employs an efficient cropping-resistant robust image hashing algorithm to defend against 14 common image attacks, demonstrating an 85% success rate in watermark extraction, 10% higher than the original scheme. Moreover, the paper designs the K-Raft consensus algorithm tailored for image copyright protection. Comparative experiments with Raft and benchmarking against PoW and PBFT algorithms show that K-Raft reduces block error rates by 2%, improves efficiency by 300ms compared to Raft, and exhibits superior efficiency,decentralization, and throughput compared to PoW and PBFT. These advantages make K-Raft more suitable for digital image copyright protection. This research contributes valuable insights into using blockchain technology for digital copyright protection, providing a solid foundation for future exploration. [ABSTRACT FROM AUTHOR]

This paper systematically analyzes the spatiotemporal evolution trends and macroeconomic driving factors of farmland transfer at the provincial level in China since 2005, aiming to offer a new perspective for understanding the dynamic mechanisms of China's farmland transfer. Through the integrated use of kernel density estimation, the Markov model, and panel quantile regression methods, this study finds the following: (1) Farmland transfer rates across Chinese provinces show an overall upward trend, but regional differences exhibit a "U-shaped" evolution characterized by initially narrowing and then widening; (2) although provinces have relatively stable farmland transfer levels, there is potential for dynamic transitions; (3) factors such as per capita arable land, farmers' disposable income, the social security level, the urban‒rural income gap, the urbanization rate, government intervention, and the marketization level significantly promote farmland transfer, while inclusive finance inhibits transfer, and agricultural mechanization level and population aging have heterogeneous impacts. Therefore, to achieve convergence of low farmland transfer regions to medium levels while promoting medium-level regions to higher levels, it is recommended that the government increase support for agricultural mechanization, increase farmers' income and social security levels, and optimize marketization processes and government intervention strategies. The main contributions of this paper are (1) systematically revealing the spatiotemporal evolution patterns of China's farmland transfer and (2) employing panel quantile regression methods to explore the heterogeneous impacts of driving factors, providing more precise and detailed empirical support for the government's formulation of farmland transfer policies. [ABSTRACT FROM AUTHOR]

Background: In sub-Saharan Africa, the number of maternal deaths due to unsafe abortions has been gradually rising. In Ethiopia, unplanned pregnancies contribute to 25% of births, accounting for 6%–9% of the maternal deaths resulting from unsafe abortions. Despite several disjointed cross-sectional studies that have been carried out in the past, there is no comprehensive data on the attitudes of healthcare practitioners and other related aspects regarding safe abortion in Ethiopia. This study attempted to measure pooled health care providers' attitudes and determinants of safe abortion in Ethiopia. Methods: African Journals Online, Medline/PubMed, EMBASE, Science Direct, Hinari, and Google Scholar were the databases that were accessed. The studies were evaluated critically by using the Joanna Briggs Critical Appraisal methods. The study followed the recommendations set forth by Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA). Data were extracted in an Excel spreadsheet and imported to STATA versions 17 software for meta-analysis. The random- effects model was used to pooled the health care providers' attitudes toward safe abortion. Heterogeneity between studies was evaluated using the Cochrane Q-test and I2 statistics (I squared statistics). To evaluate publication bias, egger's tests and funnel plots were employed. Forest plot was used to present the odds ratio (OR) with a 95% confidence interval. Results: In this review and meta-analysis, a total of eight papers with a 2,826 sample size were considered. Overall, 65.49% of Ethiopian health care professionals had a positive attitude towards safe abortion (95%CI: 49.64, 81.34; I2 = 99.20%, P = 0.000). Knowledge of the abortion law (OR = 2.25, 95% CI: 1.06, 3.43), being a male provider (OR = 1.89, 95% CI: 1.23, 2.54), receiving training on abortion (OR = 2.91, 95% CI: 1.17, 4.65), working as a midwife (OR = 3.029, 95% CI: 1.605, 4.453) and practicing abortion procedures (OR = 2.55, 95% CI: 1.32, 3.78) were positively associated with the attitudes of the providers regarding safe abortion in Ethiopia. Conclusion: In Ethiopia, there was a low pooled prevalence of positive attitude towards safe abortion. Safe abortion services in Ethiopia are more likely to be viewed favorably by health care professionals who have received abortion service training and are familiar with abortion laws. As a result, it is imperative that all healthcare facilities and other relevant parties ensure that health professionals receive training on safe abortion services and are aware of Ethiopia's abortion laws. [ABSTRACT FROM AUTHOR]

Graph theory provides a systematic method for modeling and analysing complicated biological data as an effective bioinformatics tool. Based on current trends, the number of DNA sequences in the DNA database is growing quickly. To determine the origin of a species and identify homologous sequences, it is crucial to detect similarities in DNA sequences. Alignment-free techniques are required for accurate measures of sequence similarity, which has been one of the main issues facing computational biologists. The current study provides a mathematical technique for comparing DNA sequences that are constructed in graph theory. The sequences of each DNA were divided into pairs of nucleotides, from which weighted loop digraphs and corresponding weighted vectors were computed. To check the sequence similarity, distance measures like Cosine, Correlation, and Jaccard were employed. To verify the method, DNA segments from the genomes of ten species of cotton were tested. Furthermore, to evaluate the efficacy of the proposed methodology, a K-means clustering method was performed. This study proposes a proof-of-model that utilises a distance matrix approach that promises impressive outcomes with future optimisations to be made to the suggested solution to get the hundred percent accurate result. In the realm of bioinformatics, this paper highlights the use of graph theory as an effective tool for biological data study and sequence comparison. It's expected that further optimization in the proposed solution can bring remarkable results, as this paper presents a proof-of-concept implementation for a given set of data using the proposed distance matrix technique. [ABSTRACT FROM AUTHOR]

Information hiding in images has gained popularity. As image steganography gains relevance, techniques for detecting hidden messages have emerged. Statistical steganalysis mechanisms detect the presence of hidden secret messages in images, rendering images a prime target for cyber-attacks. Also, studies examining image steganography techniques are limited. This paper aims to fill the existing gap in extant literature on image steganography schemes capable of resisting statistical steganalysis attacks, by providing a comprehensive systematic literature review. This will ensure image steganography researchers and data protection practitioners are updated on current trends in information security assurance mechanisms. The study sampled 125 articles from ACM Digital Library, IEEE Explore, Science Direct, and Wiley. Using PRISMA, articles were synthesized and analyzed using quantitative and qualitative methods. A comprehensive discussion on image steganography techniques in terms of their robustness against well-known universal statistical steganalysis attacks including Regular-Singular (RS) and Chi-Square (X2) are provided. Trends in publication, techniques and methods, performance evaluation metrics, and security impacts were discussed. Extensive comparisons were drawn among existing techniques to evaluate their merits and limitations. It was observed that Generative Adversarial Networks dominate image steganography techniques and have become the preferred method by scholars within the domain. Artificial intelligence-powered algorithms including Machine Learning, Deep Learning, Convolutional Neural Networks, and Genetic Algorithms are recently dominating image steganography research as they enhance security. The implication is that previously preferred traditional techniques such as LSB algorithms are receiving less attention. Future Research may consider emerging technologies like blockchain technology, artificial neural networks, and biometric and facial recognition technologies to improve the robustness and security capabilities of image steganography applications. [ABSTRACT FROM AUTHOR]

The studies presented in this paper constitute the first scientific attempt to interpret the manner whereby notched implements made of scapulae were made and used. These implements have been found at numerous European and non-European sites, usually dated to the Neolithic–Early Iron Age (predominantly the Early Bronze Age). Research has examined these products since the beginning of the 20th century, resulting in the development of several hypotheses regarding their functions. This paper presents the analysis results of 41 such artefacts from the early Bronze Age defensive settlement in Bruszczewo (central Poland). This is the largest collection of these products from a single site worldwide. The artefacts were subjected to multi-aspect traceological tests (both technological and functional) using optical, metallographic, and scanning electron microscopes. The residues identified on their surfaces were analysed using optical microscopy and scanning electron microscopy and energy dispersive X-rays spectroscopy. Moreover, the studies reported herein involved extensive experimental research. Consequently, the chain of operations followed in producing these tools was reconstructed and the use-wear traces present on their surfaces were classified; it was determined that these tools were most likely used for threshing cereals but might also have been used for threshing legumes. This is the first proof of threshing in central Europe in the Early Bronze Age and the first scientifically substantiated hypothesis regarding the function of these important artefacts. [ABSTRACT FROM AUTHOR]

Monkeypox (MPXV) is one of the infectious viruses which caused morbidity and mortality problems in these years. Despite its danger to public health, there is no approved drug to stand and handle MPXV. On the other hand, drug repurposing is a promising screening method for the low-cost introduction of approved drugs for emerging diseases and viruses which utilizes computational methods. Therefore, drug repurposing is a promising approach to suggesting approved drugs for the MPXV. This paper proposes a computational framework for MPXV antiviral prediction. To do this, we have generated a new virus-antiviral dataset. Moreover, we applied several machine learning and one deep learning method for virus-antiviral prediction. The suggested drugs by the learning methods have been investigated using docking studies. The target protein structure is modeled using homology modeling and, then, refined and validated. To the best of our knowledge, this work is the first work to study deep learning methods for the prediction of MPXV antivirals. The screening results confirm that Tilorone, Valacyclovir, Ribavirin, Favipiravir, and Baloxavir marboxil are effective drugs for MPXV treatment. [ABSTRACT FROM AUTHOR]

Introduction: The North-eastern region of India has a relatively higher prevalence of substance use, which together with poor dietary practices and a lack of physical activity is one of the key risk factors for NCDs among older adults in the region. Understanding the prevalence of NCDs and their relationship to substance use can help develop preventive strategies and sensitization in North-eastern India. Objective: To assess the prevalence of NCDs and the strength of the association of substance abuse among the geriatric population of North-eastern states in India, for the development of preventive strategies. Methods: Data from the Longitudinal Ageing Study in India (LASI Wave-I, 2017–18) were drawn to develop this paper. The bi-variate and binary logistic regression analyses were carried out to predict the association between non-communicable diseases and substance use adjusting select socio-demographic characteristics. Results: The paper revealed the prevalence of NCDs among urban people (61.45%) is higher than among rural people (42.45%). Hypertension (37.29%) can be seen as the most prevalent disease among the following given NCDs followed by Diabetes (8.94%). The chances of having Cancer are nineteen times higher (OR = 19.8; C.I. = 18.82–20.83) if an individual has past smoking behaviour after controlling for socio-demographic and physical activity variables. Conclusion: Since, the high prevalence of hypertension correlated with the high level of substance abuse, require immediate attention to develop appropriate intervention strategies for its control (substance abuse) and prevention of hypertension. In a lower middle-income country like India, preventive measures, rather than curative measures will be cost-effective and helpful. [ABSTRACT FROM AUTHOR]

Conditional image retrieval (CIR), which involves retrieving images by a query image along with user-specified conditions, is essential in computer vision research for efficient image search and automated image analysis. The existing approaches, such as composed image retrieval (CoIR) methods, have been actively studied. However, these methods face challenges as they require either a triplet dataset or richly annotated image-text pairs, which are expensive to obtain. In this work, we demonstrate that CIR at the image-level concept can be achieved using an inverse mapping approach that explores the model's inductive knowledge. Our proposed CIR method, called Backward Search, updates the query embedding to conform to the condition. Specifically, the embedding of the query image is updated by predicting the probability of the label and minimizing the difference from the condition label. This enables CIR with image-level concepts while preserving the context of the query. In this paper, we introduce the Backward Search method that enables single and multi-conditional image retrieval. Moreover, we efficiently reduce the computation time by distilling the knowledge. We conduct experiments using the WikiArt, aPY, and CUB benchmark datasets. The proposed method achieves an average mAP@10 of 0.541 on the datasets, demonstrating a marked improvement compared to the CoIR methods in our comparative experiments. Furthermore, by employing knowledge distillation with the Backward Search model as the teacher, the student model achieves a significant reduction in computation time, up to 160 times faster with only a slight decrease in performance. The implementation of our method is available at the following URL: https://github.com/dhlee-work/BackwardSearch. [ABSTRACT FROM AUTHOR]

Psychological capital (PsyCap) is a multidimensional concept entailing hope, self-efficacy, optimism, and resilience. This paper argues that it can be considered a form of "capital" explaining social inequality. We test whether PsyCap can be integrated into the Bourdieusian capital framework by assessing its relationship with social, economic, and cultural capital. We also identify different types of social positions based on the volume and composition of psychological, economic, cultural, and social capital. We use cross-sectional data from the European Social Survey of 2012 (N = 35,313 respondents; 29 countries). To test the associations with the Bourdieusian capital types, we calculated multilevel spearman rank correlations and performed confirmatory factor analyses (CFA). Latent Class Analysis identified different types of social positions. We found positive weak correlations between PsyCap and the indicators of cultural capital (r ≤.14) and positive moderate correlations with the indicators of economic and social capital (r ≤.24). The results of the CFA showed that the fit of the 4-capital model was superior to that of the 3-capital model. We identified six types of social positions: two deprived types (with overall low capital levels); two well-off types (with overall high capital levels) and two types with high psychological and social capital in combination with varying levels of cultural and economic capital. Including PsyCap in the Bourdieusian capital framework acknowledges the power of positive psychological states regarding processes of social mobility and social inequality on the one hand and calls for understanding PsyCap as a social and group-level phenomenon on the other hand. As such, integrating PsyCap into the Bourdieusian framework can help to address the longstanding issue of understanding the relationship between social and individual differences in the study of social inequalities. [ABSTRACT FROM AUTHOR]

Optimal resource allocation is crucial for 5G and beyond networks, especially when connecting numerous IoT devices. In this paper, user clustering and power allocation challenges in the downlink of a multi-carrier NOMA system are investigated, with sum rate as the optimization objective. The paper presents an iterative optimization process, starting with user clustering followed by power allocation of the users. Although the simultaneous transmission for multiple users achieves high system throughput in NOMA, it leads to more energy consumption, which is limited by the battery capacity of IoT devices. Enhancing energy efficiency by considering the QoS requirement is a primary challenge in NOMA-enabled IoT devices. Currently, fixed user clustering techniques are proposed without considering the diversity and heterogeneity of channels, leading to poor throughput performance. The proposed user clustering technique is based on the partial brute force search (P-BFS) method, which reduces complexity compared to the traditional exhaustive search method. After the user clustering, we performed optimal power allocation using the Lagrangian multiplier method with Karush-Kuhn-Tucker (KKT) optimal conditions for each user assigned to a subchannel in each cluster. Lastly, a deep neural network (DNN) based proposed P-BFS scheme is used to reduce resource allocation's complexity further. The simulation results show a significant improvement in the sum rate of the network. [ABSTRACT FROM AUTHOR]

In location-based service (LBS), private information retrieval (PIR) is an efficient strategy used for preserving personal privacy. However, schemes with traditional strategy that constructed by information indexing are usually denounced by its processing time and ineffective in preserving the attribute privacy of the user. Thus, in order to cope with above two weaknesses, in this paper, based on the conception of ciphertext policy attribute-based encryption (CP-ABE), a PIR scheme based on CP-ABE is proposed for preserving the personal privacy in LBS (location privacy preservation scheme with CP-ABE based PIR, short for LPPCAP). In this scheme, query and feedback are encrypted with security two-parties calculation by the user and the LBS server, so as not to violate any personal privacy and decrease the processing time in encrypting the retrieved information. In addition, this scheme can also preserve the attribute privacy of users such as the query frequency as well as the moving manner. At last, we analyzed the availability and the privacy of the proposed scheme, and then several groups of comparison experiment are given, so that the effectiveness and the usability of proposed scheme can be verified theoretically, practically, and the quality of service is also preserved. [ABSTRACT FROM AUTHOR]

Steganography, the use of algorithms to embed secret information in a carrier image, is widely used in the field of information transmission, but steganalysis tools built using traditional steganographic algorithms can easily identify them. Steganography without embedding (SWE) can effectively resist detection by steganography analysis tools by mapping noise onto secret information and generating secret images from secret noise. However, most SWE still have problems with the small capacity of steganographic data and the difficulty of extracting the data. Based on the above problems, this paper proposes image steganography without embedding carrier secret information. The objective of this approach is to enhance the capacity of secret information and the accuracy of secret information extraction for the purpose of improving the performance of security network communication. The proposed technique exploits the carrier characteristics to generate the carrier secret tensor, which improves the accuracy of information extraction while ensuring the accuracy of secret information extraction. Furthermore, the Wasserstein distance is employed as a constraint for the discriminator, and weight clipping is introduced to enhance the secret information capacity and extraction accuracy. Experimental results show that the proposed method can improve the data extraction accuracy by 10.03% at the capacity of 2304 bits, which verifies the effectiveness and universality of the method. The research presented here introduces a new intelligent information steganography secure communication model for secure communication in networks, which can improve the information capacity and extraction accuracy of image steganography without embedding. [ABSTRACT FROM AUTHOR]

Based on panel data collected from 2003 to 2020 across 30 provinces in China, the paper employs the spatial vector angle method and spatial Durbin model to investigate industrial agglomeration's nonlinear and spatial spillover effects on the energy consumption structure's low-carbon transition process (Lct). The results indicate the following: First, the influence of industrial agglomeration on Lct exhibits an inverted U-shaped pattern. As the degree of industrial agglomeration expands, its effect on Lct shifts from positive to negative. Second, industrial agglomeration demonstrates spatial spillover effects. It promotes the improvement of Lct in neighboring provinces through agglomeration effects. However, the continuous expansion of industrial agglomeration inhibits the improvement of Lct in neighboring provinces through congestion effects. Third, the heterogeneity test finds that industrial agglomeration has a significant role in promoting Lct in the samples of eastern region, but this effect is not significant in the samples of western and middle regions. [ABSTRACT FROM AUTHOR]

The current mainstream image restoration methods have difficulty fully learning the structure and color information of murals in mural image restoration tasks due to the limited size of the available datasets, resulting in problems such as structural loss and texture errors. This study proposes a two-stage mural restoration network based on an edge-constrained attention mechanism. This paper introduces additional sketches as inputs during the coarse restoration phase and incorporates a local edge loss function to enable the network to generate corresponding structural information based on the sketches. In the fine restoration phase, the calculation for the similarity between missing areas and known areas is optimized to enhance the consistency of the restoration results with the texture of the known areas. Furthermore, a structure-guided attention propagation block is introduced after adopting the attention mechanism. This block selectively integrates surrounding contextual information to update the attention score map, thereby enhancing the coherence and plausibility of the generated textures. The experimental results show that the proposed method outperforms the current mainstream restoration methods according to various assessment indices. The proposed method generates high-quality structural information according to user guidance information, and the repaired texture is highly visually consistent with that of the original mural, with few noticeable deviations. This study provides a new approach for mural restoration, which may positively impact cultural heritage protection and artistic restoration applications. [ABSTRACT FROM AUTHOR]

As an important part of terrestrial carbon cycle research, net primary productivity is an important parameter to evaluate the quality of terrestrial ecosystem and plays an important role in the analysis of global climate change and carbon balance. Anhui Province is in the Yangtze River Delta region in eastern China. Based on the theoretical basis of CASA model, this paper uses MODIS NDVI, vegetation type data, meteorological data, and LUCC to estimate the NPP of Anhui Province during 2001–2020 and analyzes its spatial-temporal pattern. The results showed that the average NPP in Anhui province was 508.95 gC· (m2 ·a) -1, and the spatial heterogeneity of NPP was strong, and the high value areas were mainly distributed in the Jiangnan Mountains and Dabie Mountains. NPP increased in most areas of Anhui Province, but decreased significantly in 17.60% of the area, mainly in the central area affected by urban and rural expansion and the transformation of the Yangtze River. The dynamic change of NPP in Anhui province is the result of climate change and land use change. Meteorological data are positively correlated with NPP. Among them, the correlation between temperature and solar radiation is higher, and the correlation between NPP and precipitation is the lowest among the three. The NPP of all land cover types was more affected by temperature than precipitation, especially forest land and grassland. The decrease of cultivated land and the increase of Artificial Surfaces (AS) may have contributed to the decrease of NPP in Anhui Province. Human activities have weakened the increase in NPP caused by climate change. In conclusion, this study refined the drivers of spatial heterogeneity of NPP changes in Anhui province, which is conducive to rational planning of terrestrial ecosystems and carbon balance measures. [ABSTRACT FROM AUTHOR]

Stable isotope data have made pivotal contributions to nearly every discipline of the physical and natural sciences. As the generation and application of stable isotope data continues to grow exponentially, so does the need for a unifying data repository to improve accessibility and promote collaborative engagement. This paper provides an overview of the design, development, and implementation of IsoBank (www.isobank.org), a community-driven initiative to create an open-access repository for stable isotope data implemented online in 2021. A central goal of IsoBank is to provide a web-accessible database supporting interdisciplinary stable isotope research and educational opportunities. To achieve this goal, we convened a multi-disciplinary group of over 40 analytical experts, stable isotope researchers, database managers, and web developers to collaboratively design the database. This paper outlines the main features of IsoBank and provides a focused description of the core metadata structure. We present plans for future database and tool development and engagement across the scientific community. These efforts will help facilitate interdisciplinary collaboration among the many users of stable isotopic data while also offering useful data resources and standardization of metadata reporting across eco-geoinformatics landscapes. [ABSTRACT FROM AUTHOR]

This study utilizes data from A-share listed companies between 2011 and 2020 to empirically investigate the impact and mechanism of public welfare donations on the internal income gap of enterprises. The research findings indicate that public welfare donations significantly increase the per capita salary of management, while their impact on the per capita salary of ordinary employees is not significant, thus leading to an expansion of the internal income gap within enterprises. The results from mechanism testing reveal that the income tax benefits resulting from charitable donations and the rise in corporate operating income have contributed to an increase in excess rent shared by enterprises and employees. Due to a stronger bargaining power, management shares more excess rents, thereby widening the income gap within the enterprise. Heterogeneity analysis demonstrates that public welfare donations have a greater impact on the internal income gap of non-state-owned enterprises; however, limiting executive compensation and enhancing employees' bargaining power can mitigate this widening effect caused by public welfare donations on enterprise's internal income gap. The research value of this study is threefold. Firstly, there is a scarcity of studies on the impact of public welfare donations on the income gap within enterprises, and this study contributes to enriching the research in this area. Secondly, this paper examines the effect of tax incentives for public welfare donations on the internal income gap of enterprises, thereby deepening the research on the impact of tax reduction and fee reduction, as well as expanding our understanding of corporate income tax preferential policies. Thirdly, it offers insights into improving enterprise compensation systems and enhancing corporate governance. Senior executives can potentially allocate more excess rent through their strong bargaining power. If their compensation remains unrestricted, it may lead to a widening internal income gap and negatively affect company operational efficiency. [ABSTRACT FROM AUTHOR]

This paper analyzes the potential shortsightedness of enterprise managers through annual reports. Additionally, we use corporate financial statement data to measure enterprises over-financialization in terms of resource allocation. After testing with a causal inference model, we find that firms with managerial myopia significantly contribute to over-financialization. It remains robust even after the instrumental variable of whether the manager has experienced a famine is used. Furthermore, financial distress and financing constraints amplify the inclination of short-term-focused managers to amass greater financial assets. [ABSTRACT FROM AUTHOR]

This paper explores the impact of the CEO's supervisory role as an independent director on firm performance, utilizing measures such as Return on Equity (ROE) and Tobin's Q. The supervisory effect is assessed through the remuneration of directors and supervisors. Empirical findings indicate a significant supervisory role when the CEO also serves as an independent director. Whether viewed from the perspective of shareholders' equity or company growth, the CEO's involvement as an independent director positively influences firm performance. Furthermore, the concurrent roles of CEO and independent director contribute to enhanced firm performance, irrespective of their relationship. [ABSTRACT FROM AUTHOR]