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While the Chinese securities market is booming, the phenomenon of listed companies falling into financial distress is also emerging, which affects the operation and development of enterprises and also jeopardizes the interests of investors. Therefore, it is important to understand how to accurately and reasonably predict the financial distress of enterprises.

In the present study, ensemble feature selection (EFS) and improved stacking were used for financial distress prediction (FDP). Mutual information, analysis of variance (ANOVA), random forest (RF), genetic algorithms, and recursive feature elimination (RFE) were chosen for EFS to select features. Since there may be missing information when feeding the results of the base learner directly into the meta-learner, the features with high importance were fed into the meta-learner together. A screening layer was added to select the meta-learner with better performance. Finally, Optima hyperparameters were used for parameter tuning by the learners.

An empirical study was conducted with a sample of A-share listed companies in China. The F1-score of the model constructed using the features screened by EFS reached 84.55%, representing an improvement of 4.37% compared to the original features. To verify the effectiveness of improved stacking, benchmark model comparison experiments were conducted. Compared to the original stacking model, the accuracy of the improved stacking model was improved by 0.44%, and the F1-score was improved by 0.51%. In addition, the improved stacking model had the highest area under the curve (AUC) value (0.905) among all the compared models.

Compared to previous models, the proposed FDP model has better performance, thus bridging the research gap of feature selection. The present study provides new ideas for stacking improvement research and a reference for subsequent research in this field.

Due to the influence of complex and intersecting factors, self-governed public open spaces (POSs) (managed by local communities) are subject to collective action dilemmas such as tragedy of the commons (overexploitation), free-riding, underinvestment and mismanagement. This review paper adopts a multi-dimensional and multi-tier social-ecological system (SES) framework proposed by McGinnis and Ostrom, drawing on collective action theory to explore the key institutional-social-ecological factors that impact POS self-governance.

In this paper, Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) was utilized to systematically screen and review the relevant literature for the period from 2000 to 2023 in three databases: Web of Science, Scopus and Google Scholar. A total of 57 papers were chosen for in-depth analysis.

The literature review identified and categorized several variables associated with the self-organizing system of POS; consequently, an SES-based POS management framework was developed for the first time, consisting of 114 institutional-social-ecological sub-variables from different dimensions and three levels. Compared to ecological factors, among others, governance organizations, property-rights systems, socioeconomic attributes and actors' knowledge of SES have been commonly and primarily studied.

There is still room for the refinement of the conceptual SES-based POS collective action framework over the time (by adding in new factors), and indefinitely empirical research validating those identified factors is also worth to be undertaken, particularly testing how SES factors and interaction variables affect the POS quality (collective action).

The findings of this study can provide local policy insights and POS management strategies based on the identification of specific SES factors for relevant managers. Moreover, this research makes significant theoretical contributions to the integration of the SES framework and collective action theory with POS governance studies.

This paper aims to provide a complete analysis framework and prediction method for the construction of the patent securitization (PS) basic asset pool.

This paper proposes an integrated classification method based on genetic algorithm and random forest algorithm. First, comprehensively consider the patent value evaluation model and SME credit evaluation model, determine 17 indicators to measure the patent value and SME credit; Secondly, establish the classification label of high-quality basic assets; Then, genetic algorithm and random forest model are used to predict and screen high-quality basic assets; Finally, the performance of the model is evaluated.

The machine learning model proposed in this study is mainly used to solve the screening problem of high-quality patents that constitute the underlying asset pool of PS. The empirical research shows that the integrated classification method based on genetic algorithm and random forest has good performance and prediction accuracy, and is superior to the single method that constitutes it.

The main contributions of the article are twofold: firstly, the machine learning model proposed in this article determines the standards for high-quality basic assets; Secondly, this article addresses the screening issue of basic assets in PS.

Groups of students were enrolled in a course that sought to produce a three-phase theoretical model over three semesters.

A design project to comprehensively address school violence was launched at a university in eastern Pennsylvania.

This article updates the recent and most critical finding of the project by illuminating specific implications of the importance of teacher training and the development toward competence in recognition of children who are emotionally and psychologically injured through proactive measures such as screening for emotional and psychological well-being.

Although the model has not been tested, screening to identify those in need of emotional support and training to support teachers is clear. Screening and training offer important opportunities to help learners build skills toward resilience to soften the effects of trauma.

A view of the “whole child” with regard to academic success could further foster social and emotional development.

Early intervention can prevent the onset of symptoms associated with posttraumatic stress and related disorders. This effort alone may significantly reduce the uncomfortable incidences and perhaps ultimate prevention of the violence that is perpetuated among children.

Preliminary research supports a continued conversation regarding effective tools to find children emotionally and psychologically at-risk, which allows teachers an opportunity for timely emotional and psychological interventions.

Identifying the health-related needs in transgender (TG) people can help to formulate strategies for providing appropriate and accessible health services and promoting health and social justice, as well as human rights in these populations. This systematic review aims to determine health-related needs, problems and barriers, as well as ways to solve them in TG people from the viewpoint of TG individuals and health policymakers.

All international electronic databases such as PubMed (Medline), Embase, CINAHL, Scopus, Web of Sciences, Cochrane, PsycInfo and Google Scholar (Gray Literature) were searched from December 1990 to December 2019. After the search, the articles were screened based on their title, abstract and full text. The quality of articles was assessed using the Strengthening the reporting of observational studies in epidemiology (STROBE), Consolidated Standards of Reporting Trials (CONSORT) and Standards for Reporting Qualitative Research (SRQR) checklists. The search strategy, data extraction and quality evaluation of articles were independently performed by two researchers.

The general health-related needs identified in TG individuals from the viewpoint of themselves included access to legal hormone therapy, psychological and psychiatric counseling, privacy, health and hygiene needs, equality and freedom of expression. General health-related needs in TG individuals from the viewpoint of health policymakers included screening tests to detect sexually transmitted diseases, especially HIV, cancers and other diseases, as well as training service providers (physicians, nurses, health workers, etc.).

One of the limitations of this study was nonreporting of health-related needs in initial articles by different TG groups because these groups have had different needs and different barriers to accessing health-care services. In this study, health-related needs and barriers to satisfy them were categorized from the viewpoint of TG populations and health policymakers around the world, which may influence future decisions to provide services to TG populations. The results of this systematic review can help to develop different strategies by considering all TGs from individual, family and social aspects to better provide services for this group. However, given the dynamics and changes in the existing communities and the limited studies on gender minorities in developing countries, further research is required to comprehensively address the subject.

The findings can be used as an incentive to improve existing conditions and to address problems and shortcomings. The results of this systematic review formulate strategies for providing appropriate and accessible health services and better lives for TGs, planning for more effective participation of these individuals in local communities, improving their physical problems and mental health through counseling, as well as promoting health and social justice, and human rights for these populations.

The purpose of this study is to look at how Shariah-compliant status and Shariah regulation affect the demand for initial public offerings (IPOs) in Pakistan. The Shariah-compliant status, which is seen as a method that offers a credible signal to investors, may explain the anomaly in IPO demand.

This research used multivariate and quantile regression models to assess data from 85 IPOs issued on the Pakistan Stock Exchange between 2000 and 2019.

Shariah-compliant status has a considerable negative association with IPO demand. Nevertheless, there is a considerable positive association among Shariah regulation and IPO demand. Furthermore, the interaction among regulatory quality and Shariah-compliant status has a considerable strong influence on IPO demand. As a consequence, the findings show that Shariah-compliant firms might possibly attract the attention of investors. Investors were found to concur on the amicability of rigorous rules and permissible Shariah-compliance aspects.

Future studies could analyse the financial ratio benchmark (cash and debt) to determine the Shariah-compliant status and Shariah regulation to better understand the problem of IPO demand in the context of Pakistan.

The outcomes of this research are useful for issuers and underwriters in comprehending the characteristics that influence high and early IPO success. Such knowledge may assist issuers and underwriters in responsibly planning and managing the IPO process.

The results may be useful to investors looking for critical information in prospectuses to make the best choice when subscribing to IPOs in Pakistan.

This is one of the first studies to provide empirical data on the links among Shariah-compliant status, Shariah regulation and IPO demand in Pakistan. Furthermore, this research demonstrates the interaction impact of regulatory quality and Shariah-compliant status on IPO demand.

Given that existing retail brand research tends to treat each level of a retail brand as a separate concept, this paper aims to unveil the holistic nature of a multi-level retail brand, considering the distinctiveness of each level and the interrelationships between the images of different levels.

This study uses a scoping review approach that includes 478 retail brand articles. Subsequently, a thematic analysis method is applied.

The brand attributes that shape the distinct image of each retail brand level encompass diverse intrinsic and extrinsic attributes. Moreover, the holistic nature of a multi-level retail brand is formed by the interrelationships between the images of different levels, which are reflected in the presence of common extrinsic attributes and their interplay at attribute, benefit and attitude levels.

Theoretically, this review provides conceptual clarity by unveiling the multi-level yet holistic nature of a retail brand, helping researchers refine and extend existing theories in retail branding, while also providing new research opportunities in this field. Practically, the findings could guide retailers in implementing differentiated branding strategies at each level while achieving synergy across all levels.

Motivated by empirical features that characterize cryptocurrency volatility data, the authors develop a forecasting strategy that can account for both model uncertainty and heteroskedasticity of unknown form. The theoretical investigation establishes the asymptotic optimality of the proposed heteroskedastic model averaging heterogeneous autoregressive (H-MAHAR) estimator under mild conditions. The authors additionally examine the convergence rate of the estimated weights of the proposed H-MAHAR estimator. This analysis sheds new light on the asymptotic properties of the least squares model averaging estimator under alternative complicated data generating processes (DGPs). To examine the performance of the H-MAHAR estimator, the authors conduct an out-of-sample forecasting application involving 22 different cryptocurrency assets. The results emphasize the importance of accounting for both model uncertainty and heteroskedasticity in practice.

This study has mainly aimed to compare and contrast two completely different image processing algorithms that are very adaptive for detecting prostate cancer using wearable Internet of Things (IoT) devices. Cancer in these modern times is still considered as one of the most dreaded disease, which is continuously pestering the mankind over a past few decades. According to Indian Council of Medical Research, India alone registers about 11.5 lakh cancer related cases every year and closely up to 8 lakh people die with cancer related issues each year. Earlier the incidence of prostate cancer was commonly seen in men aged above 60 years, but a recent study has revealed that this type of cancer has been on rise even in men between the age groups of 35 and 60 years as well. These findings make it even more necessary to prioritize the research on diagnosing the prostate cancer at an early stage, so that the patients can be cured and can lead a normal life.

The research focuses on two types of feature extraction algorithms, namely, scale invariant feature transform (SIFT) and gray level co-occurrence matrix (GLCM) that are commonly used in medical image processing, in an attempt to discover and improve the gap present in the potential detection of prostate cancer in medical IoT. Later the results obtained by these two strategies are classified separately using a machine learning based classification model called multi-class support vector machine (SVM). Owing to the advantage of better tissue discrimination and contrast resolution, magnetic resonance imaging images have been considered for this study. The classification results obtained for both the SIFT as well as GLCM methods are then compared to check, which feature extraction strategy provides the most accurate results for diagnosing the prostate cancer.

The potential of both the models has been evaluated in terms of three aspects, namely, accuracy, sensitivity and specificity. Each model’s result was checked against diversified ranges of training and test data set. It was found that the SIFT-multiclass SVM model achieved a highest performance rate of 99.9451% accuracy, 100% sensitivity and 99% specificity at 40:60 ratio of the training and testing data set.

The SIFT-multi SVM versus GLCM-multi SVM based comparison has been introduced for the first time to perceive the best model to be used for the accurate diagnosis of prostate cancer. The performance of the classification for each of the feature extraction strategies is enumerated in terms of accuracy, sensitivity and specificity.

This review provides an overview of recent advances in electrochemical sensors for analyte detection in saliva, highlighting their potential applications in diagnostics and health care. The purpose of this paper is to summarize the current state of the field, identify challenges and limitations and discuss future prospects for the development of saliva-based electrochemical sensors.

The paper reviews relevant literature and research articles to examine the latest developments in electrochemical sensing technologies for saliva analysis. It explores the use of various electrode materials, including carbon nanomaterial, metal nanoparticles and conducting polymers, as well as the integration of microfluidics, lab-on-a-chip (LOC) devices and wearable/implantable technologies. The design and fabrication methodologies used in these sensors are discussed, along with sample preparation techniques and biorecognition elements for enhancing sensor performance.

Electrochemical sensors for salivary analyte detection have demonstrated excellent potential for noninvasive, rapid and cost-effective diagnostics. Recent advancements have resulted in improved sensor selectivity, stability, sensitivity and compatibility with complex saliva samples. Integration with microfluidics and LOC technologies has shown promise in enhancing sensor efficiency and accuracy. In addition, wearable and implantable sensors enable continuous, real-time monitoring of salivary analytes, opening new avenues for personalized health care and disease management.

This review presents an up-to-date overview of electrochemical sensors for analyte detection in saliva, offering insights into their design, fabrication and performance. It highlights the originality and value of integrating electrochemical sensing with microfluidics, wearable/implantable technologies and point-of-care testing platforms. The review also identifies challenges and limitations, such as interference from other saliva components and the need for improved stability and reproducibility. Future prospects include the development of novel microfluidic devices, advanced materials and user-friendly diagnostic devices to unlock the full potential of saliva-based electrochemical sensing in clinical practice.