Abstract This study examines the socioeconomic impacts of the March 2024 earthquake on household resilience in Bawean Island, East Java, Indonesia. Data for this study were collected through a survey with a snowball sampling technique, with a total of 221 valid respondents from affected regions. Using a household survey across 12 affected villages and partial least squares-structural equation modeling (PLS-SEM), the study identifies critical factors contributing to household resilience, namely social, economic, human, and physical capital assets. The findings can be explained in two ways. First, the result confirms social, economic, human, and physical capital as critical resilience determinants. Second, higher household resilience could reduce the impact of natural disasters. This study can be an input for policymakers to strengthen household resilience in disaster-prone areas to support sustainable development goals (SDGs).
Where should Indonesia prioritize decentralized food-waste recovery to reduce landfill methane? This study develops a district-level screening framework that combines public waste records with decentralized infrastructure data to identify where food-waste methane-mitigation potential is high and where local system conditions make near-term intervention more plausible. Using 2024 SIPSN waste-generation, composition, and management records together with district-level Info3R infrastructure records, we estimate food waste exposed to low-management conditions, translate that burden into scenario-based methane-mitigation potential, and compare it with a decentralized system-presence proxy. To address the limits of facility-count data, we also rebuild the Info3R panel and test an organics-oriented sensitivity proxy based on TPS3R, composting, and energy-recovery records. The results show that mitigation opportunity is spatially concentrated and that broad decentralized infrastructure is not equivalent to organics-treatment readiness. The framework is intended as a transparent public-data screen for prioritizing follow-up validation, not as a direct measure of facility capacity or realized emissions.
Sustainable tourism villages face complex governance challenges because they must simultaneously support economic development, protect local culture, preserve ecological resources, and respond to changing social conditions. Existing studies have examined sustainable tourism governance mainly from administrative, community-based, or institutional perspectives, but less attention has been given to how distributed authority interacts with nonlinear adaptive dynamics in village-level governance systems. To address this gap, this study develops an integrated framework that combines polycentric governance theory and chaos theory. Using a quantitative explanatory design, survey data were collected from 590 stakeholders involved in tourism village governance across four regencies and one city in Yogyakarta Province, Indonesia. The data were analyzed using partial least squares structural equation modeling (PLS-SEM). The findings indicate that institutional diversity has the strongest association with sustainable tourism village governance, followed by decentralization and local autonomy, bifurcation and cosmology, community participation, flexibility and adaptability, edge of chaos and the butterfly effect, and self-organization and strange attractors. The model yields a very high coefficient of determination, which suggests a close within sample fit between the proposed constructs and the observed perception data. This result is interpreted cautiously because the study is cross-sectional and several constructs are conceptually proximate. The study contributes to governance theory by showing that sustainable tourism village governance can be understood as both polycentric and complex-adaptive. Practically, the findings suggest that policymakers and tourism village managers should strengthen multi-actor coordination while maintaining flexibility to respond to uncertainty and local change.
High-speed data acquisition is essential for pushbroom imaging sensors on Low Earth Orbit (LEO) satellites, where timing accuracy, word reconstruction, and frame-boundary identification affect image fidelity and real-time operation. Previous FPGA-based LVDS, SERDES, and SoC studies have mainly emphasized throughput or individual interface functions, while hardware-validated integration of independent dual-bank timing, serialized boundary detection, word reconstruction, and mission-representative line timing remains limited. This work presents and experimentally evaluates a dual-bank FPGA acquisition architecture for the NEO-1 Medium Resolution Imager. The design combines independent PLL-derived 4× retiming clocks, serialized synchronization-pattern detection, and Serial-In-Parallel-Out reconstruction of 14-bit pixel words. Ground-based tests used the physical sensor with clock, trigger, pixel, and line-period parameters representative of the nominal 500 km imaging scenario. At a nominal 66 MHz sensor clock, the implemented acquisition period is 149,857 cycles, or approximately 2.271 ms, and the calculated aggregate payload rate is 178.282 Mbps. The non-instrumented design uses 543 logic elements, 413 registers, and two PLLs, with an estimated FPGA thermal power dissipation of 234.18 mW and positive setup and hold slack for all constrained intra-domain paths. Across five Signal Tap sessions, 138 pixel intervals yielded a mean period of 151.520 ns, with no variation resolved beyond the 3.788 ns sampling interval. One mismatch among 29,904 captured bit observations gave a capture-based bit-mismatch fraction of 3.344×10−5. These results demonstrate timing-consistent and resource-efficient dual-bank acquisition under the evaluated ground conditions. Formal physical-link BER, complete-frame FER, long-duration stability, and environmental qualification remain future work.
Background. Ginger is a horticultural commodity and a biopharmaceutical crop with many benefits and high economic value. Ginger is widely used as a raw material in the functional food, cosmetics, and herbal pharmaceutical industries. Water content is a critical parameter affecting the quality, storage stability, and activity of bioactive compounds in ginger rhizomes. Purpose. This study aims to develop a calibration model to predict ginger rhizome moisture content using near-infrared spectroscopy and chemometric modeling for fresh, dried, and fresh-dried ginger. Materials and methods. This study used three varieties of ginger, i.e., merah ginger, emprit ginger, and gajah ginger, obtained from the Beringharjo market in Yogyakarta, Indonesia. Two moisture-content conditions were applied: fresh ginger obtained directly from the market and dried ginger after two weeks of storage at room temperature (28°C) with a relative humidity of 76.1%. The spectroscopic instrument used in this study was a NIR spectrometer (Flame-NIR-Ocean Optics, USA) operating over the wavelength range of 950–1700 nm. The reflectance spectra of three varieties of ginger rhizomes, i.e., emprit, gajah, and merah, were measured at wavelengths of 950–1700 nm, with reference water content values measured using the thermogravimetric method. The partial least squares regression (PLSR) model was used to predict the moisture content of dried and fresh ginger, as well as a combination of fresh and dried ginger. Results. The dried ginger model showed the highest prediction accuracy (R²P = 0.75; RPD = 2.00). The key wavelength contributing to water content prediction was identified in the 1400–1500 nm region, corresponding to the first overtone of O–H stretching vibrations. These results indicate that near-infrared spectroscopy can be used for fast and non-destructive prediction of water content in ginger rhizomes.
