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ß-thalassemia is a hereditary disorder due to mutation in the ß-globin gene on chromosome 11. Out of 200 known ß-globin gene chain mutations recognized, it is better to identify the most common mutation in specific regions and ethnicity for cost-effective molecular diagnosis of this disorder. Therefore, this study aims to practice multiplex-amplification refractory mutation system (ARMS) PCR on patients with thalassemia in Khyber Pakhtunkhwa (KP) to investigate the most common mutations in the ß-globin chain gene.

Twenty-two individuals (patients, their parents and non-affected siblings) with signed consent were studied from six consanguineous families of ß-thalassemia. Blood samples were collected for DNA isolation. For the detection of mutations in the ß-globin gene, ARMS-PCR was used. The amplicon was visualized through 2% Agarose Gel.

The most common mutations among different ethnic groups in the study area residents were Fr 8-9 (+G) and IVS 1-5 (G> C). The prominent enhancing factors for ß-thalassemia are inter-family marriages and lack of awareness.

Multiplex ARMS_PCR is the most valuable technique for assessing multiple mutations in a single reaction tube.

Due to extensively found ethnic and regional variations and a high rate of consanguinity, the Pashtun population has a great risk of mutations in their genome. Therefore, ARMS-PCR is a cost-effective mutational diagnostic strategy that can help to control disease burden.

Limited studies using ARMS-PCR for mutational analysis in the ß-globin gene are conducted. This study is unique as it targeted consanguineous families of KP Pakistan.

The aim of this paper is to analyse the spillover effects of microcredit on the economy of Ecuador, with a particular focus on its potential as a poverty alleviation mechanism.

To address our research questions, we take into account the distance between cantons (Ecuador’s own administrative distribution) by adopting a spatial autoregressive (SAR) model. To this end, a database will be constructed with macroeconomic information about the country, broken down by canton (administrative division of Ecuador), and in a 2019 cross section, with a total of 1,331 microcredit operations in all 221 of Ecuador’s cantons.

We find a positive effect of microcredit on these neighbouring regions in terms of wealth generation.

We acknowledge that this study is limited to Ecuadorian cantons. Nonetheless, it is crucial to emphasize that focussing on an under-represented developing country like Ecuador adds significant value to the research.

Facilitating access to microcredit is one of the main solutions to address the goals proposed in the sustainable development goals (SDGs).

Microcredit activity contributes to the creation of value and wealth in Ecuador, exerting a spillover effect in neighbouring areas that can generate positive multiplier effects and alleviate poverty. For all of the above reasons, our proposal for the country is to support and promote microcredit as one of the main solutions to address the goals proposed in the SDGs.

The novelty of this study lies in the use of spatial econometrics to observe the indirect effects of microcredit on the regions bordering the canton in which it was issued, thus examining the spatial effects of microcredit on wealth distribution.

This study aims to give a glimpse of the existing blockchain applications across industries and add to a complete knowledge of the blockchain’s properties.

Systematic literature review is used as the research strategy for this investigation and other aspects of the preferred reporting items for systematic reviews and meta-analyses framework have been incorporated to create a scholarly publications evaluation of the blockchain-based application in the financial arena and its future. The research looks at 86 studies published between 2018 and 2022.

There has been a steady but noticeable increase in the study of blockchain’s potential in many application domains over the past few of years. This rising tendency illustrates the newness and potential of blockchain technology, as well as the increasing attention from academics. According to the findings, blockchain is an appropriate solution for processing transactions using cryptocurrencies; nevertheless, it still has significant technical issues and limits that require to be exploring and solving before it can be considered a viable option. It is therefore, necessary to have a high level of reliability for payments and confidentiality, in addition to maintaining the anonymity of nodes, to stop assaults and efforts to disrupt transactions in the blockchain.

This study has several important theoretical and practical implications. First, it adds to the body of knowledge on blockchain and Fintech, focusing on the transaction side. While much blockchain research has focused on how the technology may affect strategic choices, this study has shed light on its potential from the perspective of financial reporting. Second, by highlighting the importance of the demand for the prompt identification of losses, this work adds to the body of knowledge on the factors that influence transaction frauds involving paper money. Additionally, by establishing the link between transparency and virtual transactions, the author backs up the asymmetric responses of investors to different investment possibilities. It looks at the evolution of financial technology (Fintech) and shows how it can be used to take the advantage of unique opportunities.

The study is different and novel from the previously published literature on this topic mainly because of its comprehensiveness, as it revolves around all industrial and commercial areas. The three main lines of research have been outlined, namely, classifying the many blockchain-based innovations that will alter the financial landscape in many industries; identifying whether these industries are a good fit for blockchain’s wealth creation potential; and directing researchers by outlining prospective study pathways.

In recent times, the field of corporate intelligence has gained substantial prominence, employing advanced data analysis techniques to yield pivotal insights for instantaneous strategic and tactical decision-making. Expanding beyond rudimentary post observation and analysis, social media analytics unfolds a comprehensive exploration of diverse data streams encompassing social media platforms and blogs, thereby facilitating an all-encompassing understanding of the dynamic social customer landscape. During an extensive evaluation of social media presence, various indicators such as popularity, impressions, user engagement, content flow, and brand references undergo meticulous scrutiny. Invaluable intelligence lies within user-generated data stemming from social media platforms, encompassing valuable customer perspectives, feedback, and recommendations that have the potential to revolutionize numerous operational facets, including supply chain management. Despite its intrinsic worth, the actual business value of social media data is frequently overshadowed due to the pervasive abundance of content saturating the digital realm. In response to this concern, the present study introduces a cutting-edge system known as the Enterprise Just-in-time Decision Support System (EJDSS).

Leveraging deep learning techniques and advanced analytics of social media data, the EJDSS aims to propel business operations forward. Specifically tailored to the domain of marketing, the framework delineates a practical methodology for extracting invaluable insights from the vast expanse of social data. This scholarly work offers a comprehensive overview of fundamental principles, pertinent challenges, functional aspects, and significant advancements in the realm of extensive social data analysis. Moreover, it presents compelling real-world scenarios that vividly illustrate the tangible advantages companies stand to gain by incorporating social data analytics into their decision-making processes and capitalizing on emerging investment prospects.

To substantiate the efficacy of the EJDSS, a detailed case study centered around reverse logistics resource recycling is presented, accompanied by experimental findings that underscore the system’s exceptional performance. The study showcases remarkable precision, robustness, F1 score, and variance statistics, attaining impressive figures of 83.62%, 78.44%, 83.67%, and 3.79%, respectively.

This scholarly work offers a comprehensive overview of fundamental principles, pertinent challenges, functional aspects, and significant advancements in the realm of extensive social data analysis. Moreover, it presents compelling real-world scenarios that vividly illustrate the tangible advantages companies stand to gain by incorporating social data analytics into their decision-making processes and capitalizing on emerging investment prospects.