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Sustainable development goals (SDGs) recognize the importance and interrelation between health and migration. Women migration and health is well researched, yet less attention is paid to their healthcare utilization, especially with regard to overall health and well-being. This paper aims to highlight the gap in the existing literature on health care utilization by women migrants.

A systematic review was carried out following the PRISMA guideline. For the review, the literature was taken from three electronic databases, which were Springer Link, Taylor and Francis and PubMed. From a total of 1,575 studies, seven studies cleared the eligibility screening.

Of seven studies, five were found to focus on the sexual and reproductive health of the women migrants than their general health and well-being, and less attention is paid to health promotion and illness prevention beyond reproductive and sexual health. While, studies on general health have focused on the influence of health status on health care utilization and the influence of health insurance in health care utilization. The review has revealed the disparities faced by migrant women in different countries while seeking health care.

Studies on women migration and health care utilization have largely focused on the reproductive and sexual health needs of women, and this overemphasis often undermines their accessibility and affordability to overall health and well-being. Therefore, the present study has moved away from the concept of sexual and reproductive health tot that of overall health and well-being of women migrants.

Money laundering is the process of concealing unlawfully obtained funds by presenting them as coming from a legitimate source. Criminals use crypto money laundering to hide the illicit origin of funds using a variety of methods. The most simplified form of bitcoin money laundering leans hard on the fact that transactions made in cryptocurrencies are pseudonymous, but open data gives more power to investigators and enables the crowdsourcing of forensic analysis. With the motive to curb these illegal activities, there exist various rules, policies and technologies collectively known as anti-money laundering (AML) tools. When properly implemented, AML restrictions reduce the negative effects of illegal economic activity while also promoting financial market integrity and stability, but these bear high costs for institutions. The purpose of this work is to motivate the opportunity to reconcile the cause of safety with that of financial inclusion, bearing in mind the limitations of the available data. The authors use the Elliptic dataset; to the best of the authors' knowledge, this is the largest labelled transaction dataset publicly available in any cryptocurrency.

AML in bitcoin can be modelled as a node classification task in dynamic networks. In this work, graph convolutional decision forest will be introduced, which combines the potentialities of evolving graph convolutional network and deep neural decision forest (DNDF). This model will be used to classify the unknown transactions in the Elliptic dataset. Additionally, the application of knowledge distillation (KD) over the proposed approach gives finest results compared to all the other experimented techniques.

The importance of utilising a concatenation between dynamic graph learning and ensemble feature learning is demonstrated in this work. The results show the superiority of the proposed model to classify the illicit transactions in the Elliptic dataset. Experiments also show that the results can be further improved when the system is fine-tuned using a KD framework.

Existing works used either ensemble learning or dynamic graph learning to tackle the problem of AML in bitcoin. The proposed model provides a novel view to combine the power of random forest with dynamic graph learning methods. Furthermore, the work also demonstrates the advantage of KD in improving the performance of the whole system.

The purpose of this study is to investigate the influence of mobile applications on investment decisions by retail investors in stocks and mutual funds. This study focuses on how mobile technologies are applied on mobile apps by retail investors for e-trading in emerging financial markets.

The study explored predictive relevance for the adoption behavior of retail investors under the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. Further, goal contagion theory was applied to investigate the adoption behavior of investors towards e-trading. An adapted questionnaire was used to collect the date from April to June 2021 and data analysis was performed on 507 usable responses. The methodology adopted in this study is variance based partial least square structural equational modelling (PLS-SEM). Additionally, the study explains important and performing constructs based on the response of retail investors towards mobile app usage for investment decisions.

The study shows that effort expectancy, performance expectancy followed by perceived return were the primary determinants of behavioral intentions to use mobile applications by retail investors for e-trading. Further, habit of investors determined the adoption behavior of investors towards mobile apps. Additionally, the study revealed that perceived risk is not an important aspect for retail investors in comparison to perceived return.

The study in future can address to the aspect of personality traits of retail investors for technology adoption for investment decisions. Further investigation is required on addressing unobserved heterogeneity of retail investors towards technology adoption process in emerging financial markets.

The study provides theoretical and practical implications for retail investors, financial advisors and technology companies to understand the behavioral pattern and mobile apps adoption behavior of retail investors in emerging financial market. The findings in the study will help broking firms to sensitize their clients for effective use of their respective mobile apps for e-trading purposes. The study will strengthen the knowledge of financial advisors to understand investment behavior of retail investors in emerging financial markets.

This study unfolds a novel framework of research to understand the technology adoption pattern of retail investors for e-trading by mobile applications in emerging financial markets. The present study provides significant understanding in the domain of technology adoption by retail investors under behavioral finance environment.

“What’s happening?” around you can be spread through the very pronounced social media to everybody. It provides a powerful platform that brings to light the latest news, trends and happenings around the world in “near instant” time. Microblog is a popular Web service that enables users to post small pieces of digital content, such as text, picture, video and link to external resource. The raw data from microblog prove indispensable in extracting information from it, offering a way to single out the physical events and popular topics prevalent in social media. This study aims to present and review the varied methods carried out for event detection from microblogs. An event is an activity or action with a clear finite duration in which the target entity plays a key role. Event detection helps in the timely understanding of people’s opinion and actual condition of the detected events.

This paper presents a study of various approaches adopted for event detection from microblogs. The approaches are reviewed according to the techniques used, applications and the element detected (event or topic).

Various ideas explored, important observations inferred, corresponding outcomes and assessment of results from those approaches are discussed.

The approaches and techniques for event detection are studied in two categories: first, based on the kind of event being detected (physical occurrence or emerging/popular topic) and second, within each category, the approaches further categorized into supervised- and unsupervised-based techniques.

The study explored various dimensions of overconfidence bias (OB) among retail investors in Indian financial markets. Further, these dimensions were validated through formative assessments for OB.

The study applied exploratory factor analysis (EFA) to 764 respondents to explore dimensions of OB. These were validated with formative assessments on 489 respondents by the partial least square path modeling (PLS-PM) approach in SmartPLS 4.0 software.

The major findings of EFA explored four dimensions for OB, i.e. accuracy, perceived control, positive illusions and past investment success. The formative assessments revealed that positive illusions followed by past investment success among retail investors played an instrumental role in orchestrating the OBs that affect investment decisions in financial markets.

The formative index of OB has several practical implications for registered financial and investment advisors, bank advisors, business media companies and portfolio managers, besides individual investors in the domain of behavioral finance.

This research provides a novel approach to provide a formative index of OB with four dimensions. This formative index can acts as an overview for upcoming researchers to investigate the OB of retail individual investors.

1. Overconfidence bias is an important predictor of retail investors' behavior

2. Formative dimensions of the overconfidence bias index.

3. Accuracy, perceived control, positive illusions and past investment success are important dimensions of overconfidence bias.

4. Modern portfolio theory and illusion of control theory support this study.

Overconfidence bias is an important predictor of retail investors' behavior

Formative dimensions of the overconfidence bias index.

Accuracy, perceived control, positive illusions and past investment success are important dimensions of overconfidence bias.

Modern portfolio theory and illusion of control theory support this study.

The purpose of this paper is to develop a novel intuitionistic fuzzy seasonality regression (IFSR) with particle swarm optimization (PSO) algorithms to accurately forecast air pollutions, which are typical seasonal time series data. Seasonal time series prediction is a critical topic, and some time series data contain uncertain or unpredictable factors. To handle such seasonal factors and uncertain forecasting seasonal time series data, the proposed IFSR with the PSO method effectively extends the intuitionistic fuzzy linear regression (IFLR).

The prediction model sets up IFLR with spreads unrestricted so as to correctly approach the trend of seasonal time series data when the decomposition method is used. PSO algorithms were simultaneously employed to select the parameters of the IFSR model. In this study, IFSR with the PSO method was first compared with fuzzy seasonality regression, providing evidence that the concept of the intuitionistic fuzzy set can improve performance in forecasting the daily concentration of carbon monoxide (CO). Furthermore, the risk management system also implemented is based on the forecasting results for decision-maker.

Seasonal autoregressive integrated moving average and deep belief network were then employed as comparative models for forecasting the daily concentration of CO. The empirical results of the proposed IFSR with PSO model revealed improved performance regarding forecasting accuracy, compared with the other methods.

This study presents IFSR with PSO to accurately forecast air pollutions. The proposed IFSR with PSO model can efficiently provide credible values of prediction for seasonal time series data in uncertain environments.

The mortality rate due to skin cancers has been increasing over the past decades. Early detection and treatment of skin cancers can save lives. However, due to visual resemblance of normal skin and lesion and blurred lesion borders, skin cancer diagnosis has become a challenging task even for skilled dermatologists. Hence, the purpose of this study is to present an image-based automatic approach for multiclass skin lesion classification and compare the performance of various models.

In this paper, the authors have presented a multiclass skin lesion classification approach based on transfer learning of deep convolutional neural network. The following pre-trained models have been used: VGG16, VGG19, ResNet50, ResNet101, ResNet152, Xception, MobileNet and compared their performances on skin cancer classification.

The experiments have been performed on HAM10000 dataset, which contains 10,015 dermoscopic images of seven skin lesion classes. The categorical accuracy of 83.69%, Top2 accuracy of 91.48% and Top3 accuracy of 96.19% has been obtained.

Early detection and treatment of skin cancer can save millions of lives. This work demonstrates that the transfer learning can be an effective way to classify skin cancer images, providing adequate performance with less computational complexity.

This study aims to develop and validate a multidimensional scale to measure the motivating factors that lead to opportunity recognition in social entrepreneurship among higher education institute (HEI) students.

The scale was developed through two phases; in phase 1, semi-structured interviews with social entrepreneurs and aspiring students were conducted to explore themes for item generation. Phase 2 included developing and validating the scale using exploratory (EFA) and confirmatory factor analysis (CFA). The sample included HEI students (n = 300 for EFA, n = 300 for CFA) with either academic background or volunteering experiences in social entrepreneurship.

A 24-item scale is developed in the study, with six factors measuring the motivating factors influencing opportunity recognition in social entrepreneurship: life experiences, social awareness, social inclination, community development, institutional voids and natural option for a meaningful career.

The scale facilitates the development of theories and models in social entrepreneurship. The scale also enables policymakers and social entrepreneurship educators to understand the motivating factors that lead to opportunity recognition among students. It would help them to provide target-specific support to students.

To the best of the authors’ knowledge, this study is the first attempt to develop a scale that measures opportunity recognition in social entrepreneurship based on specific motivating factors. The study used the model by Yitshaki and Kropp (2016) as the conceptual framework. This study is the first attempt to triangulate the model’s findings using a quantitative methodology and through the development of a measurement scale. Besides, the scale adds value to social entrepreneurship research, which lacks empirical research on HEI students.

The purpose of this paper is to check the Solar Photovoltaic (PV) inverter working condition with modified unipolar switching pulse. The gate pulse for the inverter switches is generated in MATLAB simulation and interfaced with hardware protype. Simulation results can be compared with hardware results.

A considerable amount of research has been done on different Pulse Width Modulation (PWM) techniques. Based on the findings, a modified Unipolar Sinusoidal PWM technique was created with one reference signal and two carrier signals+ (one for the positive half cycle and the other for the negative half cycle) and simulated in the MATLAB/Simulink platform. The prototype inverter module receives the simulated switching pulses via dSPACE DS1104 hardware software interfacing board. The hardware implementation has been done, and the hardware results compared with simulation results for various input voltage levels using resistive load.

This modified switching pulse has dead band and additional hardware setup is not required. 3-phase multi-level inverter output waveform has been achieved with six switches in this method and with low filter values, pure sine wave output can be obtained in simulation. By this method of switching pulse generation and testing, for every modification in switching pulse hardware gate driver is not required. Resulting time consumption and money investment are lower.

Modified Unipolar SPWM pulse generation technique is novel method for solar PV inverter. The switching pulse has been designed and tested in both MATLAB/Simulation and hardware prototype inverter. Hardware and software results are identical. This method of pulse generation and hardware implementation has not been done anywhere before.

Financial capability is considered to be an important concept that has drawn the attention of many world nations. While the literature suggests various studies on financial capability and financial wellbeing, focus on their combined significance has been limited. The purpose of this paper is to examine how financial capability affects the financial wellbeing of women in community-based organizations and how decision-making ability mediated this relationship.

In total, 1,000 women who are associated with the community-based organization – Kudumbashree in the state of Kerala, India participated in the survey-based study.

The structural equation modelling results show that there exists a significant relationship between financial capability and the financial wellbeing of women in CBOs. Further, decision-making ability was identified as a significant mediator in this relationship thus establishing a partial mediation effect.

The financial social workers can focus their activities on promoting financial capability and decision making aspects of women from middle/low income families to facilitate their financial wellbeing. The scope for financial socialisation and proper orientation is more for the women associated with the community based organisations. This opportunity can be made use by the government authorities and other practitioners to change their financial outlook and contribute towards the empowerment of these women from the grass root level.

The studies related to financial literacy and financial inclusion are available in the Indian context, but the conceptualization of financial capability is still an under-researched area in India. Hence, this study is an attempt to explain the capability-wellbeing relationship from a financial point of view in the Indian context, and further establishes its connection with the individual's decision-making ability. To strengthen the research base, the study was conducted among the women in the community-based organization who belong to middle and low-income families.