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The utilisation of renewable energy sources for generating electricity and potable water is one of the most sustainable approaches in the current scenario. Therefore, the current research aims to design and develop a novel co-generation system to address the electricity and potable water needs of rural areas.

The cogeneration system mainly consists of a solar parabolic dish concentrator (SPDC) system with a concentrated photo-voltaic module at the receiver for electricity generation. It is further integrated with a low-temperature thermal desalination (LTTD) system for generating potable water. Also, a novel corn cob filtration system is introduced for the pre-treatment to reduce the salt content in seawater before circulating it into the receiver of the SPDC system. The designed novel co-generation system has been numerically and experimentally tested to analyse the performance at Karaikal, U.T. of Puducherry, India.

Because of the pre-treatment with a corn cob, the scale formation in the pipes of the SPDC system is significantly reduced, which enhances the efficiency of the system. It is observed that the conductivity, pH and TDS of seawater are reduced significantly after the pre-treatment by the corncob filtration system. Also, the integrated system is capable of generating 6–8 litres of potable water per day.

The integration of the corncob filtration system reduced the scaling formation compared to the general circulation of water in the hoses. Also, the integrated SPDC and LTTD systems are comparatively economical to generate higher yields of clean water than solar stills.

For some years now, Cameroon has seen a significant increase in its electricity demand, and this need is bound to grow within the next few years owing to the current economic growth and the ambitious projects underway. Therefore, one of the state's priorities is the mastery of electricity demand. In order to get there, it would be helpful to have reliable forecasting tools. This study proposes a novel version of the discrete grey multivariate convolution model (ODGMC(1,N)).

Specifically, a linear corrective term is added to its structure, parameterisation is done in a way that is consistent to the modelling procedure and the cumulated forecasting function of ODGMC(1,N) is obtained through an iterative technique.

Results show that ODGMC(1,N) is more stable and can extract the relationships between the system's input variables. To demonstrate and validate the superiority of ODGMC(1,N), a practical example drawn from the projection of electricity demand in Cameroon till 2030 is used. The findings reveal that the proposed model has a higher prediction precision, with 1.74% mean absolute percentage error and 132.16 root mean square error.

These interesting results are due to (1) the stability of ODGMC(1,N) resulting from a good adequacy between parameters estimation and their implementation, (2) the addition of a term that takes into account the linear impact of time t on the model's performance and (3) the removal of irrelevant information from input data by wavelet transform filtration. Thus, the suggested ODGMC is a robust predictive and monitoring tool for tracking the evolution of electricity needs.

This study aims to investigate whether various types of risks faced by the publicly listed commercial banks of India and Bangladesh are driven by market power and provides comparative insights from both economies.

By using the adjusted Lerner index to gauge bank market power and applying the generalised methods of moments (GMM) regression approach, the research delved into the relationship between bank market power and three distinct facets of risk across a sample of 26 publicly listed commercial banks in India and 22 listed banks in Bangladesh spanning from 2011 to 2022.

The results indicate that for Bangladesh, both “competition fragility” and “competition stability” viewpoints coexist simultaneously across all risk types, supporting a nonlinear relationship between market power and risk. However, in the Indian context, a nonlinear association exists only in the case of credit risk, while the relationship with insolvency risk is linear, substantiating the “competition fragility view”. Apart from market power and bank-specific variables, GDP growth rate has emerged as a prominent driver across all risk categories in both countries.

The filtration of banks is a limitation that might have influenced the outcomes. This study recommends that the Reserve Bank of India encourages further bank consolidation. Along the same line, Bangladesh Bank should closely oversee the growing competitive landscape. Furthermore, the regulators must monitor the elevated levels of non-performing loans to reduce credit risk so as to bolster the stability of their respective banking sectors.

This comparative study is the first attempt to analyse the market power and risk relationship and includes a novel bank-specific variable, i.e. technology, apart from other established variables.

Current evidence indicates that humans and animals are at increased risk of multiple health challenges due to microplastic (MP) profusion. However, mitigation is constrained by inadequate scientific data, further aggravated by the lack of evidence in many African countries. This review therefore synthesized evidence on the current extent of MP pollution in Africa and the analytical techniques for reporting.

A literature search was undertaken in research databases. Medical subject headings (MeSH) terms and keywords were used in the literature search. The authors found 38 studies from 10 countries that met the inclusion criteria.

Marine organisms had MPs prevalence ranging from 19% to 100%, whereas sediments and water samples had between 77 and 100%. The most common and dominant polymers included polypropylene and polyethylene.

This review shows that most studies still use methods that are prone to human errors. Therefore, the concentration of MPs is likely underestimated, even though the authors’ prevalence evaluations show MPs are still largely pervasive across multiple environmental matrices. Also, the study reveals significant spatial disparity in MP research across the African continent, showing the need for further research in other African countries.

Even though some reviews have assessed MPs pollution in Africa, they have not evaluated sample prevalence, which is necessary to understand not only concentration but pervasiveness across the continent. Secondly, this study delves deeper into various methods of sampling, extraction and analysis of MPs, as well as limitations and relevant recommendations.

Recently, powerful instruments for biomedical engineering research studies, including disease modeling, drug designing and nano-drug delivering, have been extremely investigated by researchers. Particularly, investigation in various microfluidics techniques and novel biomedical approaches for microfluidic-based substrate have progressed in recent years, and therefore, various cell culture platforms have been manufactured for these types of approaches. These microinstruments, known as tissue chip platforms, mimic in vivo living tissue and exhibit more physiologically similar vitro models of human tissues. Using lab-on-a-chip technologies in vitro cell culturing quickly caused in optimized systems of tissues compared to static culture. These chipsets prepare cell culture media to mimic physiological reactions and behaviors.

The authors used the application of lab chip instruments as a versatile tool for point of health-care (PHC) applications, and the authors applied a current progress in various platforms toward biochip DNA sensors as an alternative to the general bio electrochemical sensors. Basically, optical sensing is related to the intercalation between glass surfaces containing biomolecules with fluorescence and, subsequently, its reflected light that arises from the characteristics of the chemical agents. Recently, various techniques using optical fiber have progressed significantly, and researchers apply highlighted remarks and future perspectives of these kinds of platforms for PHC applications.

The authors assembled several microfluidic chips through cell culture and immune-fluorescent, as well as using microscopy measurement and image analysis for RNA sequencing. By this work, several chip assemblies were fabricated, and the application of the fluidic routing mechanism enables us to provide chip-to-chip communication with a variety of tissue-on-a-chip. By lab-on-a-chip techniques, the authors exhibited that coating the cell membrane via poly-dopamine and collagen was the best cell membrane coating due to the monolayer growth and differentiation of the cell types during the differentiation period. The authors found the artificial membrane, through coating with Collagen-A, has improved the growth of mouse podocytes cells-5 compared with the fibronectin-coated membrane.

The authors could distinguish the differences across the patient cohort when they used a collagen-coated microfluidic chip. For instance, von Willebrand factor, a blood glycoprotein that promotes hemostasis, can be identified and measured through these type-coated microfluidic chips.

The purpose of this paper is to investigate the efficacy of bacterial exopolysaccharides (Eps) in reactive black 5 (RB5) textile dye wastewater bioremediation.

The Eps were produced by bacteria isolated from cotton gin trash soils collected from different cotton-growing regions in Kenya for comparison purposes. A broth medium reconstituted using molasses was assessed for its capacity to produce the Eps. RB5 textile dye wastewater was optimized for dye removal under different temperatures, times and molasses concentrations. Dye removal was studied by Lovibond-Day Light Comparator, UV–Vis spectrophotometer and FTIR.

It was found that cotton gin trash soils contained Eps-producing bacteria. Three of the Eps studied were found to have the capacity to remove at least 80% of the dye from the wastewater.

This research did not assess the efficacy of the RB5 dye removal from the wastewater by mixtures of the Eps.

Bioremediation of textile dye wastewater with Eps produced by bacteria cultured from cotton gin trash soil is significant because it will offer an effective and cleaner alternative to the chemical coagulants.

Alternative treatment of textile wastewater with the Eps would result in safer water being released into the water bodies as opposed to the chemically treated wastewater that contains remnant chemicals.

Research on the use of Eps produced by bacteria isolated from cotton gin trash soils for removal of RB5 dye from textile wastewater has not been done before.

Foresight J's journey started in 1999, and in 2022, it marked the conclusion of its 24 years of publication. This paper aims to provide an overall overview of important research trends published in Foresight J between 1999 and 2022 by conducting a quantitative analysis of the journal’s literature. The overarching goal is to provide valuable insights into the dynamics of scholarly communication, aiding researchers, institutions and policymakers in assessing the significance and influence of academic work, guiding future research directions and academic evaluation.

The two bibliometrics methodologies that make up the methodology of this article are scientific mapping and performance analysis. Authors have explained the development and composition of the Foresight J using these methods. The SCOPUS database is being used in current research to analyse several dimensions, such as the evolution of publications by year, the most cited papers, core authors and researchers, leading countries and prolific institutions. Moreover, the conceptual structure, scope, burst detection and co-occurrence analysis of the journal are mapped using network visualization software such as VOSviewer, CiteSpace and RStudio.

With a strong track record of output over the years, Foresight J has continued to develop in terms of publications. It is determined that “Saritas” is the author with the greatest overall impact. However, according to SCOPUS bibliometric data, “Blackman” and “Richardson” are the authors with the greatest relevance in terms of the quantity of articles. In addition, it becomes apparent that the USA, Australia and the UK are very productive nations in terms of publications. The most popular fields of the journal have always been forecasting, foresight, scenario planning, strategic planning, decision-making, technology and sustainable development. These are also the author keywords that appear the most frequently. In contrast, new study themes in the Foresight J include digital technologies, innovation, sustainability, blockchain, artificial intelligence and sustainability.

Several noteworthy research implications are provided by the bibliometric study of Foresight J. “Saritas” is the author with the most overall impact, indicating that the precise contributions and influence of this researcher in the fields of forecasting, foresight and related fields. Given that “Blackman” and “Richardson” are well-known writers, it is also critical to examine the scope and complexity of their contributions to potentially identify recurring themes or patterns in their writing. The geographic productivity results, which show that the USA, Australia and the UK are the top three countries for Foresight J publications, may encourage more research into regional differences, patterns of collaboration and the worldwide distribution of research endeavours in the context of forecasting and foresight. Popular fields including scenario planning, forecasting, foresight and sustainable development are consistent, indicating persistent research interests. Examining the causes of these subjects’ ongoing relevance can reveal information about the consistency and development of scholarly interests over time.

Foresight J’s bibliometric analysis has real-world applications for many stakeholders. It helps editors and publishers make strategic decisions about outreach and content by providing insights regarding the journal’s influence. Assessing organizational and author productivity helps institutions allocate resources more effectively. Policymakers acquire an instrument to evaluate research patterns and distribute funds efficiently. In general, bibliometric study of a journal helps decisionmakers in academic publishing make well-informed choices that maximize the potential of options for authors, editors, institutions and policymakers.

The societal ramifications of bibliometrically analysing Foresight J from 1999 and 2022 are substantial. This analysis highlights, over the past 24 years, research trends, technological developments and societal priorities have changed by methodically looking through the journal’s articles. Gaining knowledge about the academic environment covered by the journal can help raise public awareness of important topics and promote critical thinking. In addition, the analysis can support evidence-based decision-making by alerting decision makers to the influential research that was published in Foresight J. This could have an impact on the course of policies pertaining to innovation, technology and societal development.

This study presents a first comprehensive article that provides a general overview of the main trends and patterns of the research over the Foresight J’s history since its inception. Also, the paper will help the scientific community to know the value and impact of Foresight J.

We present an approach for pricing American put options with a regime-switching volatility. Our method reveals that the option price can be expressed as the sum of two components: the price of a European put option and the premium associated with the early exercise privilege. Our analysis demonstrates that, under these conditions, the perpetual put option consistently commands a higher price during periods of high volatility compared to those of low volatility. Moreover, we establish that the optimal exercise boundary is lower in high-volatility regimes than in low-volatility regimes. Additionally, we develop an analytical framework to describe American puts with an Erlang-distributed random-time horizon, which allows us to propose a numerical technique for approximating the value of American puts with finite expiry. We also show that a combined approach involving randomization and Richardson extrapolation can be a robust numerical algorithm for estimating American put prices with finite expiry.

This study aims to ensure reliable analysis of dynamic responses in asphalt pavement structures. It investigates noise reduction and data mining techniques for pavement dynamic response signals.

The paper conducts time-frequency analysis on signals of pavement dynamic response initially. It also uses two common noise reduction methods, namely, low-pass filtering and wavelet decomposition reconstruction, to evaluate their effectiveness in reducing noise in these signals. Furthermore, as these signals are generated in response to vehicle loading, they contain a substantial amount of data and are prone to environmental interference, potentially resulting in outliers. Hence, it becomes crucial to extract dynamic strain response features (e.g. peaks and peak intervals) in real-time and efficiently.

The study introduces an improved density-based spatial clustering of applications with Noise (DBSCAN) algorithm for identifying outliers in denoised data. The results demonstrate that low-pass filtering is highly effective in reducing noise in pavement dynamic response signals within specified frequency ranges. The improved DBSCAN algorithm effectively identifies outliers in these signals through testing. Furthermore, the peak detection process, using the enhanced findpeaks function, consistently achieves excellent performance in identifying peak values, even when complex multi-axle heavy-duty truck strain signals are present.

The authors identified a suitable frequency domain range for low-pass filtering in asphalt road dynamic response signals, revealing minimal amplitude loss and effective strain information reflection between road layers. Furthermore, the authors introduced the DBSCAN-based anomaly data detection method and enhancements to the Matlab findpeaks function, enabling the detection of anomalies in road sensor data and automated peak identification.

This study aims to develop a comprehensive framework for Islamic social entrepreneurship (ISE) by synthesizing Islamic principles and social entrepreneurship concepts, bridging the gap between theory and practice.

Using a systematic literature review, this study focuses on scholarly works published from 1992 to 2023, uses thematic analysis and engages with subject experts to craft a framework for ISE.

The study identified 39 sub-dimensions grouped into 13 core dimensions. These findings highlight the multifaceted impact on ISE, emphasizing its commitment to ethical, socially responsible practices and achieving lasting social impact through collaborative, innovative approaches guided by Islamic principles.

Limitations include regional focus, lack of longitudinal data and absence of quantitative testing for the framework. Future research should expand scope, use quantitative analysis and explore gender dynamics, policy implications and standardized impact metrics to enhance the framework’s robustness.

The study’s comprehensive framework aids ISE practitioners in aligning their ventures with Islamic ethics and social impact. As interest in ISE grows, particularly in Muslim-majority contexts, this research facilitates the integration of Islamic values into social entrepreneurship, addressing pressing societal challenges.

This study contributes to the field of ISE by proposing a meticulously crafted framework that synthesizes Islamic principles and social entrepreneurship concepts. It stands out as a unique endeavor that bridges the gap between theory and practice in ISE, offering practical guidance while enriching the scholarly discourse on the subject.