Small-scale businesses have long been recognized as an important part of economic development and integrating them with industrial parks is both recommended and necessary for long-term success. In line of this, the objective of this study was to investigate the role of IPs entrepreneurial ecosystem in boosting the capabilities of small businesses. Data were collected from 245 small manufacturing business owners via simple random sampling and analysed using multivariate regression analysis. Thus, the ability of small enterprises is positively impacted by the presence of a more robust and appropriate entrepreneurial ecosystem. Similarly, a firm’s resource capabilities are more impacted by the entrepreneurial ecosystem when there is a better link between academia and industry. Furthermore, entrepreneurial skills are found to play a mediating role between the entrepreneurial ecosystem and firms’ technological capabilities. Another finding revealed that managerial expertise significantly mediates entrepreneurial ecosystems and firms’ resource capabilities. This finding suggested that the policymakers, better to formulate policies that encourages small businesses to engage in the industrial parks which results in an inclusive firm’s performance.

This study explores the integration of data mining, customer relationship management (CRM), and strategic management to enhance the understanding of customer behavior and drive revenue growth. The main goal is the use of application of data mining techniques in customer analytics, focusing on the Extended RFM (Recency, Frequency, Monetary Value and count day) model within the context of online retailing. The Extended RFM model enhances traditional RFM analysis by incorporating customer demographics and psychographics to segment customers more effectively based on their purchasing patterns. The study further investigates the integration of the BCG (Boston Consulting Group) matrix with the Extended RFM model to provide a strategic view of customer purchase behavior in product portfolio management. By analyzing online retail customer data, this research identifies distinct customer segments and their preferences, which can inform targeted marketing strategies and personalized customer experiences. The integration of the BCG matrix allows for a nuanced understanding of which segments are inclined to purchase from different categories such as “stars” or “cash cows,” enabling businesses to align marketing efforts with customer tendencies. The findings suggest that leveraging the Extended RFM model in conjunction with the BCG matrix can lead to increased customer satisfaction, loyalty, and informed decision-making for product development and resource allocation, thereby driving growth in the competitive online retail sector. The findings are expected to contribute to the field of Infrastructure Finance by providing actionable insights for firms to refine their strategic policies in CRM.

The incorporation of artificial intelligence (AI) into language education has created new opportunities for improving the instruction and acquisition of Chinese characters. Nevertheless, the cognitive difficulties linked to the acquisition of Chinese characters, such as their intricate visual features and lack of clear meaning, necessitate thoughtful deliberation when developing AI-supported learning interventions. The objective of this project is to explore the capacity of a collaborative method between humans and machines in teaching Chinese characters, utilising the advantages of both human expertise and AI technology. We specifically investigate the utilisation of ChatGPT, a substantial language model, for the creation of instructional materials and evaluation methods aimed at teaching Chinese characters to individuals who are not native speakers. The study utilises a mixed-methods approach, which involves both qualitative examination of lesson plans created by ChatGPT and quantitative evaluation of student learning outcomes. The results indicate that the suggested framework for human-machine collaboration can successfully tackle the cognitive difficulties associated with learning Chinese characters, resulting in enhanced learner involvement and performance. Nevertheless, the research also emphasises the constraints of AI-generated material and the significance of human involvement in guaranteeing the accuracy and dependability of educational interventions. This research adds to the expanding collection of literature on AI-assisted language learning and offers practical insights for educators and instructional designers who aim to use AI tools into Chinese language curriculum. The results emphasise the necessity of employing a multi-disciplinary strategy in AI-supported language learning, incorporating knowledge from cognitive psychology, educational technology, and second language acquisition.

We develop a relatively cheap technology of processing a scrap in the form of already used tungsten-containing products (spirals, plates, wires, rods, etc.), as well not conditional tungsten powders. The main stages of the proposed W-scrap recycling method are its dispersing and subsequent dissolution under controlled conditions in hydrogen peroxide aqueous solution resulting in the PTA (PeroxpolyTungstic Acid) formation. The filtered solution, as well as the solid acid obtained by its evaporation, are used to synthesize various tungsten compounds and composites. Good solubility of PTA in water and some other solvents allows preparing homogeneous liquid charges, heat treatment of which yield WC and WC–Co in form of ultradispersed powders. GO (Graphene Oxide) and PTA composite is obtained and its phase transition in vacuum and reducing atmosphere (H₂) is studied. By vacuum-thermal exfoliation of GO–PTA composite at 170–500℃ the rGO (reduced GO) and WO_2.9 tungsten oxide are obtained, and at 700℃—rGO–WO₂ composite. WC, W₂C and WC–Co are obtained from PTA at high temperature (900–1000℃). By reducing PTA in a hydrogen atmosphere, metallic tungsten powder is obtained, which was used to obtain sandwich composites with boron carbide B₄C, W/B₄C, and W/(B₄C–W), as neutron shield materials. Composites of sandwich morphology are formed by SPS (Spark-Plasma Sintering) method.

In the modern economy, non-financial reporting has become an essential tool for evaluating the social performance of companies. This article explores the importance of non-financial reporting as a central element in assessing sustainable performance, focusing on analyzing sustainability reports published by 20 companies listed on the Bucharest Stock Exchange (BVB). The study examines how these companies approach environmental, social, and governance (ESG) aspects in their reports and what is the relationship between these aspects and financial reporting indicators. Through the statistical analysis of the non-financial reports published by companies participating in the study with the help of the Pearson coefficient and the regression equations, the correlation between the financial and non-financial indicators is determined in order to validate the research hypotheses. The results indicate increased attention to transparency and social responsibility, highlighting the correlation between sound reporting practices and cooperative performance by combining social and environmental aspects with financial information. The research also highlights the challenges encountered in the reporting process and the level of compliance with international sustainability standards.

Endosulfan (6,7,8,9,10,10-Hexachloro-1,5,5a,6,9,9a-hexahydro-6,9-methano-2,4,3-benzodioxathiepine-3-oxide) is an off-patent insecticide used in agricultural farms. Its usage as a pesticide has become highly controversial over the last few decades. This is due to its reported hazardous nature to health and side effects like growth retardation, hydrocephalus, and undesired changes in the male and female hormones leading to complications in sexual maturity. Endosulfan is the main culprit among all pesticide poisoning incidents around the world. Though the usage of this dreaded pesticide is banned by most countries, the high stability of this molecule to withstand degradation for a long period poses a threat to mankind even today. So, it has become highly essential to detect the presence of this poisonous pesticide in the drinking water and milk around these places. It is also advisable to check the presence of this toxic material in the blood of the population living in and around these places so that an early and appropriate management strategy can be adopted. With this aim, we have developed a sensor for endosulfan that displayed high selectivity and sensitivity among all other common analytes in water and biological samples, with a wide linear concentration range (2 fM to 2 mM), a low detection limit (2 fM), and rapid response. A citrate-functionalized cadmium-selenium quantum dot was used for this purpose, which showed a concentration-dependent fluorescence enhancement, enabling easy and sensitive sensing. This sensor was utilized to detect endosulfan in different sources of water, human blood serum, and milk samples with good recoveries. It is also noted that the quantum dot forms a stable complex with endosulfan and is easy to separate from the contaminated source, paving the way for purifying the contaminated water. More detailed tests and validation of the sensor are needed to confirm these observations.