Indonesia, as a maritime country, has many coastal areas with fishing villages that have significant potential, especially in sociological, economic, and environmental aspects, to be developed as models for sustainable development. Indonesia, with its long-standing fishing traditions, showcases the abundant potential and traditional that could help address global challenges such as climate change, rapid urbanization, and environmental and economic issues. This study aims to develop a conceptual model for sustainable cities and communities based on local potential and Wisdom towards the establishment of a Blue Village in the fishing village of Mundu Pesisir, Cirebon, Indonesia. The urgency of this study lies in the importance of developing sustainable strategies to address these challenges in coastal towns. This study involves an interdisciplinary team, including experts in sociology, social welfare, architecture, law, economics, and information technology. Through the identification of local natural and sociocultural resources, as well as the formulation of sustainable development strategies, this study develops a conceptual Blue Village model that can be applied to other coastal villages. The method employed in this study is qualitative descriptive, involving the steps of conducting a literature review, analyzing local potential, organizing focus group discussions, conducting interviews, and finalizing the conceptual model. The study employed, a purposive sampling technique, involving 110 participants. The results of the study include the modeling of a sustainable city and community development based on local potential and Wisdom aimed at creating Blue Villages in Indonesia, and It is expected to make a significant contribution to the creation of competitive and sustainable coastal areas capable of addressing the challenges of climate change and socioeconomic dynamics in the future.

Farm households in developing countries are often involved in a variety of livelihood income-generating activities to achieve basic needs and enhance food security. However, little attention has been given to investigating the effect of livelihood diversification strategies on the adoption of agricultural land management practices. This study explored the nexus between livelihood diversification and Agricultural Land Management (ALM) practices in the Southern Ethiopian Highlands. Data for this study were gathered through a structured questionnaire, interviews, and focus group discussions. A total of 423 sample respondents were selected by using multistage random sampling techniques. The data were analyzed using the Inverse Herfindahl Hirschman Diversity Index (IHHDI), the multinomial logit model (MNL), and the probit regression model. The findings of the study revealed that on-farm income activities are the most dominant livelihood income strategies (69.1%), followed by non-farm (21%) and off-farm (9.64%). The multinomial logit model analysis demonstrated that variables such as sex, education, family size, distance to market, land size, extension contact, membership in cooperatives, and household income were the major drivers of farmers income diversification activities (p<0.05). The results of the probit analysis indicated that income from crop production, daily labor work, rents from farmland, and farm assets have a positive and significant effect on households' decisions to implement ALM practices. In contrast, incomes from remittance and migrant sources have a negative but statistically significant impact on the adoption of ALM measures. The farm household sources of income-generating strategies substantially affected the adoption intensity of ALM measures. Income generated from the on-farm sector alone cannot be considered a core income-generating activity for households or a means of achieving food security. Therefore, land management policies and program implementations should consider farmers’ livelihood diversification and income-generating strategies. In addition, such interventions need to promote sustainable farming practices, enhance innovation, and related measures for the adoption of ALM measures to ensure land sustainability.

We report on the measurement of the response of Rhodamine 6G (R6G) dye to enhanced local surface plasmon resonance (LSPR) using a plasmonic-active nanostructured thin gold film (PANTF) sensor. This sensor features an active area of approximately ≈ 2.5 × 10¹³ nm² and is immobilized with gold nanourchins (GNU) on a thin gold film substrate (TGFS). The hexane-functionalized TGFS was immobilized with a 90 nm diameter GNU via the strong sulfhydryl group (SH) thiol bond and excited by a 637 nm Raman probe. To collect both Raman and SERS spectra, 10 μL of R6G was used at concentrations of 1 μM (6 × 10¹² molecules) and 10 mM (600 × 10¹⁴ molecules), respectively. FT-NIR showed a higher reflectivity of PANTF than TGFS. SERS was performed three times at three different laser powers for TGFS and PANTF with R6G. Two PANTF substrates were prepared at different GNU incubation times of 10 and 60 min for the purpose of comparison. The code for processing the data was written in Python. The data was filtered using the filtfilt filter from scipy.signals, and baseline corrected using the Improved Asymmetric Least Squares (ISALS) function from the pybaselines.Whittaker library. The results were then normalized using the minmax_scale function from sklearn.preprocessing. Atomic force microscopy (AFM) was used to capture the topography of the substrates. Signals exhibited a stochastic fluctuation in intensity and shape. An average corresponding enhancement factor (EF) of 0.3 × 10⁵ and 0.14 × 10⁵ was determinedforPANTFincubated at 10 and 60 min, respectively.

Boron and tungsten carbides, B₄C and WC, are hard materials widely used in modern technologies. Further improvement of their performance characteristics involves the development of new B₄C and WC-based and/or related composites in a nanodispersed state. This article provides a review of available literature research on B-C-W systems, which would be useful in future studies in this direction.

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.

Air pollution in Jakarta has become a severe concern in the last four months. IQAir, in August 2023, revealed that the level of air pollution had reached 161 points on the Air Pollution Standard Index (APSI). The negative impact on society has placed air pollution as a concern for environmental safety and survival in danger. This condition will encourage the development of a national policy agenda to integrate environmental welfare through various energy efficiency channels. This research analyzes the relationship between air pollutant elements that can reduce air quality. The analysis includes pollutant intensity measured by APSI per unit of pollutant as a measure of efficiency. The aim is to observe energy use, which causes an increase in pollutant levels. This research utilizes dynamic system modeling to produce relationships between parameters to produce factors that cause pollution. The parameters used are motorized vehicles, waste burning in landfills, industry, and power plants. The results of historical behavioral tests and statistical suitability tests show that the behavior is suitable for the short and long term. The simulation results show that the pollution level will worsen by the end of 2027, a hazardous condition for society. The optimistic scenario simulation model proposes immediate counter-measures to reduce pollution to 45.01, the ideal condition. To accelerate improvements in air quality, the Government can plan policies to reduce the use of coal by power plants and industry, as well as the use of electric motorized vehicles, resulting in an ideal reduction in pollution by 2024. In conclusion, pollution can be reduced effectively if the Government firmly implements policies to maintain that air quality remains stable below 50 points.