The intricacies of communication within intimate relationships, including the choice of address terms, are influenced by various factors. Although address terms are commonplace in various interactive encounters, they remain under-researched in spousal interactions. This study, therefore, investigated address terms used among educated Ghanaian couples across different social contexts in the Cape Coast Metropolis (Ghana), utilising Brown and Gilman’s (1960) Theory of Power and Solidarity Postulates. The data for this study were collected through a written Discourse Completion Test (DCT) administered to some willing and available married close friends. Three key findings emerged from the analysis. First, educated Ghanaian couples in Cape Coast Metropolis commonly employed six major address terms in their face-to-face interactions, which include endearment terms, first names, clipped first names, descriptive phrases, titles/honorifics, and zero address terms. Second, the choice of address terms by educated couples is highly context-dependent, as they primarily consider the interaction setting before choosing a particular address form. Lastly, couples exhibit creativity and innovation in their choice of address terms to reinforce mutual communication and maintain intimacy and affection. These findings contribute to the sociolinguistic scholarship on familial address practices in multilingual Ghanaian contexts and offer implications for cultural maintenance, marriage communication, and future research on non-educated couples in Ghana.

In recent years, the growing trend and adaptability of green technology, particularly in agribusiness, have garnered significant attention. Consequently, this research emphasises the adoption of green technology in agri-business and underscores its role in enhancing income levels. Additionally, this study aims to identify key obstacles to the sustainable adoption of green technology, including rising inflation, financial crises, and environmental emissions. To achieve this objective, a secondary dataset has been compiled from 30 different rural areas in China. For empirical analysis, time-series data spanning from 2011 to 2025 have been collected. Subsequently, the variables examined in this research include green agribusiness, gross regional product per capita, digital inclusive finance, inflation, research and development expenses, and CO2 emissions. The concluding remarks of this research suggest that green technology significantly contributes to green agribusiness and regional income per capita. Moreover, it underscores the importance of considering the role of CO2 emissions, inflation, and the adoption of modern financial mechanisms in this context.

This study examined the relationship between taxation and economic growth in Pakistan using time-series data from 1990 to 2020. Employing a quantitative research design, the study utilised multiple regression analysis, the Augmented Dickey-Fuller (ADF) test, the Johansen co integration test, and the Granger causality test to investigate the impact of direct taxes, indirect taxes, and total tax revenue on GDP growth rate. The simulated data revealed that taxation demonstrated a significant negative relationship with economic growth in Pakistan (β = -0.428, p < 0.01). Direct taxes showed a stronger negative impact (β = -0.512, p < 0.001) compared to indirect taxes (β = -0.287, p < 0.05) on GDP growth. The Johansen co-integration test confirmed a long-run equilibrium relationship between taxation variables and economic growth. Granger causality analysis indicated unidirectional causality running from taxation to economic growth, suggesting that tax policy changes preceded growth fluctuations. The findings suggested that while taxation generated necessary government revenue, excessive tax burdens could constrain private sector investment and consumption, thereby limiting economic expansion. The study recommended determining the optimal tax rate, enhancing tax administration efficiency, and adopting a balanced tax structure to promote sustainable economic growth in Pakistan.

Artificial intelligence (AI) is revolutionizing the agriculture sector by tackling key challenges such as climate change, resource scarcity, and the necessity for sustainable food production. The incorporation of AI technologies, like machine learning, IoT, and AIoT, offers transformative opportunities to enhance agricultural productivity, efficiency, and sustainability. Precision farming, predictive analytics, and agricultural robotics are examples of AI applications in agriculture that optimize resource utilization, enhance decision-making, and reduce reliance on labor. However, implementing artificial intelligence in agriculture poses several challenges. Issues such as data quality, connectivity, cost, privacy, and user acceptance need to be addressed before AI can fully unlock its potential. Additionally, ethical considerations and trust-building among farmers are crucial for the successful integration of AI technologies. Future research should focus on building advanced AI algorithms, increasing interoperability, and exploring AI's role in climate resilience and resource management. By tackling these challenges, AI can significantly contribute to the resilience and food security of agriculture.

This study examines how financial development (FD) and technological innovation (TI) influence environmental performance in developing economies, using a balanced panel of 51 countries from 1995–2024. To account for cross-sectional dependence, unobserved heterogeneity, and heterogeneous long-run dynamics, the analysis applies Driscoll–Kraay pooled OLS, Driscoll–Kraay fixed effects, and the Augmented Mean Group (AMG) estimator, with additional estimates for lower-middle-income (26) and upper-middle-income (25) subsamples. The results indicate that the effects of FD and TI are not uniform across estimators or income groups. Financial development is generally weak in pooled and fixed-effects models but becomes positive in the full-sample AMG specification, suggesting that finance contributes to environmental performance only when long-run heterogeneity and common shocks are properly modelled. Technological innovation is positive in pooled and fixed-effects estimates for upper-middle-income countries and the full sample, yet loses robustness under AMG, implying that measured patenting does not consistently translate into greener outcomes across heterogeneous developing contexts. GDP is more consistently positive, especially in upper middle-income economies, while the AMG common dynamic process is large and significant, highlighting the role of shared global trends such as climate policies and energy-price cycles. The findings underscore that greener development requires directing finance and innovation toward clean investment and eco-innovation, supported by strong institutions and targeted policy instruments.

This study investigates the impact of training need assessment, training design, and trainer competence on cGMP compliance behavior, both directly and through the mediation of employee perceived training utility, in the Pakistani pharmaceutical industry. Primary data were collected from a sample of 412 employees using Likert scale-based measures adapted from cGMP training parameters. The hypothesized relationships were tested using SPSS. The results show that training needs assessment, training design, and trainer competency have significant positive direct effects on perceived utility of training (PUOT) and cGMP compliance behavior. It was also found that PUOT partially mediates the relationships between all three training variables and cGMP compliance behavior, suggesting that the training program components play a vital role in fostering cGMP compliance behavior directly, and through perceived training usefulness. The research expands empirical work on training and compliance, asserting the need to ensure systematic training need assessment, well-structured and valid training design, and trainers’ competencies to foster cGMP compliance behavior among pharma employees, thereby improving training utility perception and regulatory compliance outcomes.

Educational practices have been positively impacted by Generative Artificial Intelligence (AI) and Large Language Models (LLMs) such as ChatGPT. Unlike traditional systems and learning platforms, ChatGPT engages users in conversations, provides tailored feedback, and creates new knowledge in virtually any area. This paper provides a detailed model for educational practice using ChatGPT and other generative AI systems. The model attempts to address, in an equitable manner, the three most challenging aspects of educational practice: pedagogical concerns, technological availability, and ethical peripherals. The model enhances students’ engagement, promotes the development of their higher-order thinking skills, and is designed to improve learning outcomes through personalization. This paper discusses a conceptual model for adaptive generative tutoring and provides technical illustrations, tables, and formulae to describe the process. The use of generative AI in STEM problem solving and language learning is primary, but the challenges of academic honesty, bias, and data privacy are also addressed. The current paper provides a potential framework for the thoughtful and purposeful use of ChatGPT in educational settings and makes a valuable contribution to the discourse on the intersection of educational technology and the ethics of AI in teaching.

This study aims to investigate the relationship between academic anxiety and academic procrastination, with perceived stress as a mediator, among university students in Pakistan. A quantitative correlational design was employed, and data were collected from 292 students aged 18 to 35 years, enrolled in public and private universities. Standardized measures, including the academic anxiety scale, perceived stress scale, and academic procrastination scale, were administered. Results indicate that academic anxiety is positively and significantly associated with both perceived stress and academic procrastination, while perceived stress is also positively and significantly related to academic procrastination. Mediation analysis using Hayes Process Macro 4.2 (Model 4) further demonstrates that perceived stress partially mediates the relationship between academic anxiety and academic procrastination, as the direct effect remains significant. The study emphasizes the importance of stress management and psychological support programs in reducing procrastination and enhancing academic performance among university students.

The global food system faces unprecedented challenges, including population growth, climate change, resource scarcity, and supply chain inefficiencies. In response, a technological paradigm shift towards "Smart Food Systems" is underway, driven by the integration of the Internet of Things (IoT) and Deep Learning (DL). This paper provides a comprehensive review of the synergies between IoT and DP and their transformative impact across the food value chain. We begin with an overview of the core concepts of IoT and DP. We then trace technological advancements that enable their application, from historical milestones to the roles of enabling technologies such as sensors, cloud/edge computing, 5G, blockchain, and robotics. The paper examines the application of this integrated framework to transform the food system digitally—from smart farming and processing to intelligent retail and supply chain management—with a focus on concepts such as digital twins. Methodologically, this review synthesises recent peer-reviewed articles, industry reports, and case studies published between 2023 and 2025, organising insights across technological evolution, industrial applications, and adoption barriers, and provides a detailed analysis of specific applications within the meat, dairy, baking, and beverage industries, highlighting practical use cases and their benefits. Finally, we identify and discuss the significant technical, economic, and regulatory challenges that impede widespread adoption and propose future research directions, including the potential of Explainable AI (XAI) and federated learning, to realise a more efficient, sustainable, and resilient global food system by 2025 and beyond.

Womanhood is depicted in literature in such a way that mirrors and emphasizes the norms of a particular society, primarily through the intricate use of mythologies of love and sacrifice. The present study analyses the semiotic relationship between womanhood and oppression in Khalid Husseini's novel "A Thousand Splendid Suns" by focusing on the myths of love and sacrifice. This study also explores how Husseini constructs and deconstructs the myths of love and sacrifice to reveal the imposition of patriarchal society on an oppressed woman. Drawing on the concepts of Ronald Barthes theory of semiotics, the study's findings revealed how neutral signs carry the hidden ideologies of a society. The researcher explored the various signs and symbols that represent resilience, endurance, sacrifice, and loyalty associated with womanhood. The textual analysis provides insight into significant motifs, such as motherhood, the institution of marriage, and the sacrifice of women, which showcase their ideological roles. The findings further indicate that love and sacrifice are portrayed as noble virtues; however, they advocate for patriarchal oppression. The author's deconstruction of these myths perpetuates the idea that the way socially constructed gender is represented in literature can be challenged by empowering women.