Graphene and derivatives have been frequently used to form advanced nanocomposites. A very significant utilization of polymer/graphene nanocomposite was found in the membrane sector. The up-to-date overview essentially highlights the design, features, and advanced functions of graphene nanocomposite membranes towards gas separations. In this concern, pristine thin layer graphene as well as graphene nanocomposites with poly(dimethyl siloxane), polysulfone, poly(methyl methacrylate), polyimide, and other matrices have been perceived as gas separation membranes. In these membranes, the graphene dispersion and interaction with polymers through applying the appropriate processing techniques have led to optimum porosity, pore sizes, and pore distribution, i.e., suitable for selective separation of gaseous molecules. Consequently, the graphene-derived nanocomposites brought about numerous revolutions in high-performance gas separation membranes. The structural diversity of polymer/graphene nanocomposites has facilitated the membrane selective separation, permeation, and barrier processes, especially in the separation of desired gaseous molecules, ions, and contaminants. Future research on the innovative nanoporous graphene-based membrane can overcome design/performance-related challenging factors for technical utilizations.

Eucalyptus is an important source of cellulose and a widely cultivated plant. Biotechnology tools can save time spent in breeding and transcriptomic approaches generate a gene profile that allows the identification of candidates involved in processes of interest. RNA-seq is a commonly used technology for transcript analysis and it provides an overview of regulatory pathways. Here, we selected two contrasting Eucalyptus species for cold acclimatization and focused in responsive genes under cold condition aiming woody properties – lignin and cellulose. The number of differentially expressed genes identified in stem sections were 3.300 in Eucalyptus globulus and 1370 in Eucalyptus urograndis. We listed genes with expression higher than 10 times including NAC, MYB and DUF family members. The GO analysis indicates increased oxidative process for E. urograndis. This data can provide information for more detailed analyses for breeding, especially in perennial plants.

This study explores the critical role of the retail sector in the global economy and the importance of working capital management within retail businesses. Recognizing retail’s influence beyond just income generation, the research examines its impact on economic stability, job creation, and national GDP, and how it links industries such as manufacturing and logistics. Employing a blended-methods approach, the study integrates quantitative analysis using AMOS software with qualitative insights from interviews with financial managers and retail experts. Key focus areas include cash flow management, market demand, and supplier relationship management in the context of working capital management. Findings highlight the necessity of effective working capital management in maintaining financial stability, optimizing shareholder wealth, and ensuring long-term business viability in the retail sector. Strategies for enhancing profitability, such as improving supplier relationships and adapting to market demands, are identified. This research contributes to understanding the economic impact of the retail sector and the intricacies of working capital management. It offers insights for policymakers, retail managers, and academics, emphasizing the need for supportive retail industry measures and effective financial management practices. The study fills a gap in literature and sets a foundation for future research in this critical area of economic studies and retail management.

Photovoltaic systems have shown significant attention in energy systems due to the recent machine learning approach to addressing photovoltaic technical failures and energy crises. A precise power production analysis is utilized for failure identification and detection. Therefore, detecting faults in photovoltaic systems produces a considerable challenge, as it needs to determine the fault type and location rapidly and economically while ensuring continuous system operation. Thus, applying an effective fault detection system becomes necessary to moderate damages caused by faulty photovoltaic devices and protect the system against possible losses. The contribution of this study is in two folds: firstly, the paper presents several categories of photovoltaic systems faults in literature, including line-to-line, degradation, partial shading effect, open/close circuits and bypass diode faults and explores fault discovery approaches with specific importance on detecting intricate faults earlier unexplored to address this issue; secondly, VOSviewer software is presented to assess and review the utilization of machine learning within the solar photovoltaic system sector. To achieve the aims, 2258 articles retrieved from Scopus, Google Scholar, and ScienceDirect were examined across different machine learning and energy-related keywords from 1990 to the most recent research papers on 14 January 2025. The results emphasise the efficiency of the established methods in attaining fault detection with a high accuracy of over 98%. It is also observed that considering their effortlessness and performance accuracy, artificial neural networks are the most promising technique in finding a central photovoltaic system fault detection. In this regard, an extensive application of machine learning to solar photovoltaic systems could thus clinch a quicker route through sustainable energy production.

Enhancing the emphasis on incorporating sustainable practices reinforces a linear transition towards a circular economy by organizations. Nevertheless, although studies on circular economy demonstrate an increasing trend, the drivers that support circular economy practices towards sustainable business performance in the Small and Medium-Sized Enterprise (SME) sector, especially in developing nations, demand exploration. Accordingly, the study examines circular economy drivers, i.e., green human resource management, in establishing sustainability performance and environmental dynamism as moderating variables. The study engaged 207 SMEs and 621 respondents who were analyzed utilizing structural equation modeling. The analysis indicated that sustainable business performance was affected by green human resource management and a circular economy. Subsequently, the circular economy mediated the linkage between green human resources management and sustainable business performance. The environmental dynamism moderated the linkage between green human resources management and the circular economy.

For this, the primary aim of this study was to analyze of the impact of cultural accessibility and ICT (information and communication technology) infrastructure on economic growth in Kazakhstan, employing regression models to asses a single country data from 2008 to 2022. The research focuses on two sets of variables: cultural development variables (e.g., number of theaters, museums, and others) and ICT infrastructure variables (e.g., number of fixed Internet subscribers, total costs of ICT, and others). Principal component analysis (PCA) as employed to reduce the dimensionality of the data and identify the most significant predictors for the regression models. The findings indicate that in the cultural development model (Model 1), the number of recreational parks and students are significant positive predictors of GDP per capita. In the ICT infrastructure model (Model 2), ICT costs are found to have a significant positive impact on GDP per capita. Conversely, traditional connectivity indicators, such as the number of fixed telephone lines, show a low dependence on economic growth, suggesting diminishing returns on investment in these outdated forms of ICT. These results suggest that investments in cultural and ICT infrastructure are crucial for economic development. The study provides valuable insights for policymakers, emphasizing the need for quality improvements in education and strategic modernization of communication technologies.