Named Entity Recognition (NER), a core task in Information Extraction (IE) alongside Relation Extraction (RE), identifies and extracts entities like place and person names in various domains. NER has improved business processes in both public and private sectors but remains underutilized in government institutions, especially in developing countries like Indonesia. This study examines which government fields have utilized NER over the past five years, evaluates system performance, identifies common methods, highlights countries with significant adoption, and outlines current challenges. Over 64 international studies from 15 countries were selected using PRISMA 2020 guidelines. The findings are synthesized into a preliminary ontology design for Government NER.

In this paper, we modeled and simulated two tandem solar cell structures (a) and (b), in a two-terminal configuration based on inorganic and lead-free absorber materials. The structures are composed of sub-cells already studied in our previous work, where we simulated the impact of defect density and recombination rate at the interfaces, as well as that of the thicknesses of the charge transport and absorber layers, on the photovoltaic performance. We also studied the performance resulting from the use of different materials for the electron and hole transport layers. The two structures studied include a bottom cell based on the perovskite material CsSnI₃ with a band gap energy of 1.3 eV and a thickness of 1.5 µm. The first structure has an upper sub-cell based on the CsSnGeI₃ material with an energy of 1.5 eV, while the second has an upper sub-cell made of Cs₂TiBr₆ with a band gap energy of 1.6 eV. The theoretical model used to evaluate the photocurrent density, current-voltage characteristic, and photovoltaic parameters of the constituent sub-cells and the tandem device was described. Current matching analysis was performed to find the ideal combination of absorber thicknesses that allows the same current density to be shared. An efficiency of 29.8% was obtained with a short circuit current density J_sc = 19.92 mA/cm², an open circuit potential V_oc = 1.46 V and a form factor FF = 91.5% with the first structure (a), for a top absorber thickness of CsSnGeI₃ of 190 nm, while an efficiency of 26.8% with J_sc = 16.74, V_oc = 1.50 V and FF = 91.4% was obtained with the second structure (b), for a top absorber thickness of Cs₂TiBr₆ of 300 nm. The objective of this study is to develop efficient, low-cost, stable and non-toxic tandem devices based on lead-free and inorganic perovskite.

This study examines how circular economy (CE) practices contribute to energy resilience by mitigating the impacts of energy shocks and supporting sustainable development. Through a systematic literature review (SLR) of recent studies, we analyze the ways in which CE strategies—such as resource recovery, renewable energy integration, and closed-loop supply chains—enhance energy security and reduce vulnerability to energy disruptions. Our research draws on academic databases, focusing on publications from 2018 to 2024, to identify key themes and practices that illustrate the transformative potential of the circular economy. Findings reveal that CE practices at macro, mezzo, and micro levels support resilience by fostering efficient resource use, reducing dependency on non-renewable energy sources, and promoting sustainable economic growth. Additionally, we highlight the roles of foreign direct investment (FDI), research and development (R&D), and supportive policies in accelerating the adoption of circular systems. The study concludes with recommendations for future research to address identified gaps, suggesting a roadmap for advancing circular economy practices as a means to enhance energy resilience and sustainability aims to reveal how wide array of factors affect transition towards more sustainable or circular economy.

This study explores the intricate relationship between emotional cues present in food delivery app reviews, normative ratings, and reader engagement. Utilizing lexicon-based unsupervised machine learning, our aim is to identify eight distinct emotional states within user reviews sourced from the Google Play Store. Our primary goal is to understand how reviewer star ratings impact reader engagement, particularly through thumbs-up reactions. By analyzing the influence of emotional expressions in user-generated content on review scores and subsequent reader engagement, we seek to provide insights into their complex interplay. Our methodology employs advanced machine learning techniques to uncover subtle emotional nuances within user-generated content, offering novel insights into their relationship. The findings reveal an inverse correlation between review length and positive sentiment, emphasizing the importance of concise feedback. Additionally, the study highlights the differential impact of emotional tones on review scores and reader engagement metrics. Surprisingly, user-assigned ratings negatively affect reader engagement, suggesting potential disparities between perceived quality and reader preferences. In summary, this study pioneers the use of advanced machine learning techniques to unravel the complex relationship between emotional cues in customer evaluations, normative ratings, and subsequent reader engagement within the food delivery app context.

Green cosmetics made from organic ingredients are becoming increasingly popular due to their environmentally friendly nature. However, research on consumer behavior towards green cosmetics is rare, especially in developing countries like Pakistan. Previous studies have primarily focused on female consumers, and little is known about the behavior of male consumers. Therefore, this research aims to investigate the behavior of both male and female consumers towards green cosmetic products and analyze the factors that affect their purchase behavior. This study employs a quantitative approach with deductive reasoning and collects data through a questionnaire from major cities in Pakistan. The study finds that eco-awareness, social influence, price-quality instructions, health consciousness, and the need for uniqueness significantly influence consumer purchase behavior when buying green cosmetics. Interestingly, price sensitivity does not significantly affect consumer purchase behavior as consumers are willing to pay for high-quality green cosmetics. Based on the findings, the study recommends promoting eco-awareness and health consciousness among consumers through educational campaigns and workshops launched by the government and the private sector. Future research can explore factors such as age, gender, and specific generations like millennials and Generation Z, as well as packaging, branding, and product design to promote environmentally friendly and health-conscious products. Additionally, comparative studies between countries can identify universal and region-specific factors, and examining the overall impact of green cosmetic products on the environment can highlight areas for improvement in sustainability.