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Power quality issues highly affect the secure and economic operations of the power system. Although numerous methodologies are reported in the literature, flexible alternating current transmission system (FACTS) devices play a primary role. However, the FACTS devices require optimal location and sizing to perform the power quality enhancement effectively and in a cost efficient manner. This paper aims to attain the maximum power quality improvements in IEEE 30 and IEEE 57 test bus systems.

This paper contributes the adaptive whale optimization algorithm (AWOA) algorithm to solve the power quality issues under deregulated sector, which enhances available transfer capability, maintains voltage stability, minimizes loss and mitigates congestions.

Through the performance analysis, the convergence of the final fitness of AWOA algorithm is 5 per cent better than artificial bee colony (ABC), 3.79 per cent better than genetic algorithm (GA), 2,081 per cent better than particle swarm optimization (PSO) and fire fly (FF) and 2.56 per cent better than whale optimization algorithm (WOA) algorithms at 400 per cent load condition for IEEE 30 test bus system, and the fitness convergence of AWOA algorithm for IEEE 57 test bus system is 4.44, 4.86, 5.49, 7.52 and 9.66 per cent better than FF, ABC, WOA, PSO and GA, respectively.

This paper presents a technique for minimizing the power quality problems using AWOA algorithm. This is the first work to use WOA-based optimization for the power quality improvements.

The pandemic's health and social issues have significantly altered the character and manner of teaching and learning in higher education across the country. The use of technology to replace or integrate face-to-face learning with online learning has become a necessary requirement for promoting and continuing learning processes. Furthermore, integrating technology is a goal of Sustainable Development Goal 4 (SDG 4) to make teaching and learning more innovative and sophisticated. This paper is based on a systematic review grounded in a synthesis of research papers and documents analyzing the current status of teachers' pedagogy through online learning modes in the context of Pakistan.

Through content analyses of academic studies in higher education and reflection on the online teaching experiences, this study discusses how students' learning is associated with teachers' teaching approaches in the modern era of digitalization and innovation.

The review and analysis suggest that online teaching is not viewed as an innovative phenomenon; rather, teachers simply teach their traditionally designed face-to-face courses through the use of technology. The paper suggests that transforming teachers' pedagogical insight to make online learning sustainable is an urgent need for higher education.

The analysis provides a basis for consideration of teacher learning and quality education (SDG #4) to fulfill the nation’s agenda for sustainable development. The analysis helps educators and administrators in higher education institutions reflect on their policies and practices that have short- and long-term effects on students' learning outcomes.

The purpose of this research study is to extract and identify named entities from Hadith literature. Named entity recognition (NER) refers to the identification of the named entities in a computer readable text having an annotation of categorization tags for information extraction. NER is an active research area in information management and information retrieval systems. NER serves as a baseline for machines to understand the context of a given content and helps in knowledge extraction. Although NER is considered as a solved task in major languages such as English, in languages such as Urdu, NER is still a challenging task. Moreover, NER depends on the language and domain of study; thus, it is gaining the attention of researchers in different domains.

This paper proposes a knowledge extraction framework using finite-state transducers (FSTs) – KEFST – to extract the named entities. KEFST consists of five steps: content extraction, tokenization, part of speech tagging, multi-word detection and NER. An extensive empirical analysis using the data corpus of Urdu translation of Sahih Al-Bukhari, a widely known hadith book, reveals that the proposed method effectively recognizes the entities to obtain better results.

The significant performance in terms of f-measure, precision and recall validates that the proposed model outperforms the existing methods for NER in the relevant literature.

This research is novel in this regard that no previous work is proposed in the Urdu language to extract named entities using FSTs and no previous work is proposed for Urdu hadith data NER.

The purpose of the study is to analyse the occurrence and distribution of different tree species in Gilgit-Baltistan, Pakistan, as a baseline for further inventories, and estimate the biomass per species and plot. Furthermore, it aims to measure forest biodiversity using established formulae for tree species diversity index, richness, evenness and accumulative curve.

Field data were collected, including stratification of forest sample plots. Statistical analysis of the data was carried out, and locally appropriate allometric equations were applied for biomass estimation.

Representative circular 556 forest sample plots of 1,000 m² contained 13,135 trees belonging to nine tree species with a total aboveground biomass of 12,887 tonnes. Sixty-eight per cent of the trees were found between 2,600 and 3,400 masl; approximately 63 per cent had a diameter at breast height equal to 30 cm, and 45 per cent were less than 12 m in height. The Shannon diversity index was 1.82, and Simpson’s index of diversity was 0.813.

Rough terrain, long distances, harsh weather conditions and location of forest in steep narrow valleys presented challenges for the field crews, and meant that fieldwork took longer than planned.

Estimating biomass in Gilgit-Baltistan’s forests using locally developed allometric equations will provide transparency in estimates of forest reference levels, National Forest Monitoring System in Pakistan and devising Reducing Emissions from Deforestation and Forest Degradation national strategies and for effective implementation.

This paper presents the first detailed forest inventory carried out for the dry temperate and semi-arid cold region of Gilgit-Baltistan, Pakistan.

This study aims to assesses the factors that affected the development of cloud-based accounting education (DCBAE) and students' academic performance at Oman Universities.

In this paper a questionnaire was used to collect data for a sample of students. Partial least squares (PLS) were used to assess the hypotheses and model.

Results indicate that human factors and cultural and social factors have a direct positive effect on the DCBAE and students' academic performance.

This study is carried out after five semesters since the beginning of the COVID-19 pandemic. Thus, it can help universities (accounting education in particular) in the Sultanate of Oman know the factors that affect the development of accounting education and adopt policies and strategies that depend on cloud computing in education operations, even after the COVID-19 pandemic.

The main contribution of this study is to evaluate the factors that affected the DCBAE and the academic performance of students in the Sultanate of Oman from March 15, 2020 to June 2022, a period that witnessed the application of cloud-based education, either in whole or in part using a questionnaire about the opinions of students.

This study aims to empirically investigate the relationship between different global software development (GSD) challenges (management, process, social, technical and environmental challenges) and software project success. Further, the study examines the moderating role of total quality management (TQM) between the relationship of GSD challenges and success of software projects.

Using two field studies, the authors collected data form software developers working in globally distributed teams. In study 1 (n = 194), relationship of different dimensions of GSD challenges (management, process, social, technical and environmental challenges) and project success was examined. In study 2 (n = 138), moderating role of TQM was examined on the relationship of GSD challenges and project success.

The results of study 1 indicate that there is a negative relationship between all dimensions of GSD challenges and project success. Findings of study 2 confirmed that TQM practices moderate the negative relationship between GSD challenges and project success.

The findings of the study provide guidelines to the project managers of software industry to mitigate GSD challenges using TQM practices.

Study adds in the literature of TQM, GSD challenges and project success by (1) empirically investigating the relationship between different GSD challenges and software project success and (2) by examining the moderating role of TQM practices on relationship of GSD challenges and project success in global software development industry.

This study aims to develop a valid and reliable scale for measuring an integrated total quality and socially responsible management (TQSR-M) model, which affects business excellence.

Data were collected through a questionnaire in two phases (first for scale development and second for scale validation purpose) from managers employed in Pakistan Stock Exchange (PSX) listed companies. The structural equation modeling technique (SEM) was used to develop and validate the measurement and structural model.

This study confirmed the empirical validation of the eight distinct dimensions (top management, strategic planning management, human resource management, supplier management, customer management, social/community management and environmental management) of TQSR-M. Moreover, the validation of the TQSR-M model is further confirmed by testing the relationship between TQSR-M and the hypothetically associated business performance variables through SEM.

The measurement scale helps companies assess the TQSR-M model related to business excellence. It assists managers in identifying the factors for implementing total quality management (TQM) and corporate social responsibility (CSR) practices in their organization for developing TQM and CSR plans, policies and strategies.

This study confirmed that combined TQM-CSR approaches are potential sources of achieving business excellence, sustainable competitive advantages and improving the quality of life of the workforce, local communities and the environment in particular and even of society in general.

Despite such studies exploring the association between TQM and CSR, it remains unclear how the integration of both the concepts develops. Academics and managers lack a tool to measure TQM-CSR together. Therefore, this study integrates both distinct areas of TQM and CSR into a single comprehensive model by developing a measurement scale.

Finance continuously evolves as the technological innovation progresses in the society. Numerous prior studies have discussed emerging financial services due to this innovation. However, limited scholarly work has evaluated the trends and state of the art of financial innovation. Therefore, this study aims to review recent literature on financial innovation by using a bibliometric and content-analysis approach.

Documents for this study are sampled from financial innovation, a journal focusing on recent innovations in finance. A total of 354 peer-reviewed articles published in eight years (2015–2022) are first examined and mapped using the bibliometrix package in RStudio software. Furthermore, content analysis was performed to investigate the adopted research methods and types, and produce directions for future studies.

The trend of financial innovation research kept increasing, with China as the leader in publication quantity, affiliation productivity and paper citation acquisition. Topics related to “FinTech,” “Bitcoin” and “Covid-19” have been the most discussed topics by financial innovation researchers. FinTech and Bitcoin studies are expected to grow in emerging countries like China, India and Pakistan. The study also indicates that most financial innovation studies use quantitative research methods and are categorized as empirical papers.

This study contributes to the finance literature by comprehensively evaluating current research on financial innovation using one specific journal in the field. Also, this study examines financial innovation literature using different approaches from previous bibliometric financial innovation studies.

This paper aims to examine the mediating role of environmental concern in the relationship of green purchase awareness and purchasing behavior of fast food consumers keeping in view the theory of planned behavior.

A quantitative, cross-sectional design is used by collecting primary responses through a validated questionnaire. In all, 1,008 male and female buyers of fast food were sampled. Structural equation modeling is applied.

The results revealed that green purchase awareness has a positive relationship with green purchase behavior, and environmental concern has no mediation in the relationship. Upon having awareness, the respondents adopted green or pro-environmental behavior, but at the same time, they were found having least concern for the protection of environment.

This is a cross-sectional study with questionnaire. Multiple sources of data collection results in weakening self-reporting bias.

Implications count toward individuals, enterprises and society at general.

The study highlights the issue of not having concern for the protection of the environment even after having green purchase awareness. This is the first time the environmental concern is examined as a mediator in the selected relationship. The contradictory results of having no environmental concern differentiate this study from others.

This study aims to evaluate papers that discuss the stock market during the COVID-19 pandemic to discover lessons that may be beneficial for coping with similar situations in the future.

This study used the review procedures following Hoque (2014) with modifications, including co-words analysis to map themes. The articles to be reviewed were identified by entering the search keywords “capital market” AND “Covid” and “stock market” AND “Covid” in the Scopus database. After applying a set of criteria, 89 articles were used in the subsequent analysis. The country setting and study findings are recognized, and the lessons learned are further determined.

As COVID-19 has been designated a global pandemic by the WHO, and its impact is seen in many countries, the setting adopted by many researchers includes two or more countries (i.e., “International”). Six clusters of themes are identified, namely, market responses, spillover/contagion, investor sentiment, investor herding, policy and asset intensity. In this way, the lessons gained cover several stock market elements, including the market, industry, investors, government and companies.

Given the importance of understanding the COVID-19 pandemic and the relevance of the stock market in indicating its severity, to the best of the authors’ knowledge, there has been no literature review research on the stock market during COVID-19. Furthermore, this study also defines what lessons can be drawn.