The present study examines the thermal performance of the cooling system in an electric vehicle controller. This is achieved by injecting bubbles into the water block at various input airflow rates, using quiet-water and circulating-water cooling fluids. The thermal performance of a system is determined by two factors: the maximum temperature at the controller-water block interface and the corresponding thermal resistance. The temperature distribution is analyzed using statistical moments, the Probability Density Function (PDF), and Kolmogorov entropy to determine the irregularity of the temperature signal. The findings indicate that an increase in airflow rate can result in a decrease in maximum temperature and an enhancement in thermal resistance and flatness of the PDF curve. The maximum temperature decreases by 15.5% when the cooling medium is quiet water when compared to bubble injection at an airflow rate of Qa = 1.5 lpm. The bubble-cooling fluid, with an airflow rate of Qa = 1.5 lpm has a relatively uniform entropy across all thermocouple positions. The presence of low Kolmogorov entropy and low PDF flatness indicate a more stable controller response and fewer extreme spikes. In turn, it has an impact on the increasing system reliability and extending the durability of the controller's electronic components.
Understanding in-situ rare oxide formation in tropical lowlands remains challenging due to the extensive peat–clay cover and the limited surface accessibility. This study presents a reproducible integrated workflow combining cloud-based hydrographic analysis in Google Earth Engine (GEE), two-dimensional electrical resistivity tomography (ERT), and X-ray fluorescence (XRF) geochemistry to investigate rare oxide occurrence in South Kalimantan, Indonesia. MERIT Hydro data that had been processed within the GEE framework were utilized for the delineation of buried palaeochannel traces. This was followed by ERT profiling and core drilling to characterize the subsurface lithology. XRF analyses indicate Yb2O3 concentrations of 0.01–0.04 wt% and Re2O7 of 0.00–0.08 wt% within clay layers at approximately 3–4 m depth. The results of spatial correlation analysis demonstrate weak relationships between oxide distribution and palaeochannel proximity (|r| < 0.3) but strong positive relationship between resistivity and oxide concentrations (r > 0.75). The results obtained lend significant support to an in-situ formation model, primarily controlled by lithological and geochemical processes as opposed to fluvial transport. The proposed GEE–ERT–XRF workflow offers a preliminary operational framework for detecting subtle, clay-hosted rare oxide signatures in data-limited tropical lowland environments. The findings demonstrate that efficacy of subsurface resistivity as a proxy for identifying geochemical trapping horizons associated with rare oxide enrichment beneath peat–clay cover. The proposed workflow further provides a cost-effective, scalable, and reproducible approach for early-stage mineral exploration and subsurface resource screening in tropical lowland regions where conventional geological mapping is limited by poor surface exposure.
High-performance volumetric supercapacitors are often limited by the trade-off between porosity and electrode density in biomass-derived carbon. Herein, binder-free monolithic carbon electrodes were produced from Indonesian daikon leaf waste through 0.5 M NaOH pretreatment followed by integrated N2–CO2 thermal processing. NaOH activation increased the surface area from 236 to 570 m2 g−1, increasing carbon purity from 88.34 to 95.45 wt% while retaining 4–5 wt% oxygen functionalities. The activated monolithic carbon electrode (VSAC) delivered a volumetric capacitance of 169 F cm−3 at 1 mV s−1 and 185 F cm−3 at 10 mA cm−2 in a symmetric two-electrode configuration without binders or conductive additives. VSAC also retained 83.2% capacitance at high scan rates and achieved 16.01 Wh L−1 at 838 W L−1. The improved performance arises from interconnected ion pathways, preserved electrode density, and surface oxygen functionalities, demonstrating a sustainable strategy for practical volumetric supercapacitors.
Green-synthesized bimetallic copper oxide–zinc oxide (CuO/ZnO) nanoparticles have attracted considerable interest due to their enhanced physicochemical properties and antibacterial activity. This study proposes a green synthesis approach for CuO/ZnO nanoparticles and evaluates their physicochemical characteristics and antibacterial performance by means of a range of analytical techniques including XRD, XRF, FTIR, SEM–EDX, and UV–Visible spectroscopy. XRD analysis confirmed elevated crystallinity in all samples, while the CuO/ZnO bimetallic variant (Cbest) exhibited the smallest crystallite size (22.93 nm). Antibacterial assays, conducted utilizing the agar well diffusion method demonstrated that pure CuO nanoparticles exhibited the strongest inhibition against Escherichia coli (15.39 mm), whereas the CuO/ZnO bimetallic variant (Cbest) demonstrated the highest activity against Staphylococcus aureus (10.02 mm). The results of the one-way ANOVA indicated significant differences among the treatments (p < 0.05), thus confirming the influence of nanoparticle composition on antibacterial efficacy. These findings highlight the potential of green-synthesized CuO/ZnO nanoparticles as effective antibacterial agents.
The vulnerability of road networks under disaster conditions is typically discussed in terms of hazard exposure. However, the impact of network structure on the consequence of disruption remains an under-explored area. The present paper examines the effect of road network structure on the degradation of connectivity under disruptions caused by floods and landslides. This is achieved through a graph-based analytical framework, with a representative empirical testbed: the primary road network of Central Java, Indonesia. The network is modeled as a graph and the segments at risk of hazard exposure are identified using spatial overlay. These segments are then systematically deleted based on hazard type, hazard level and spatial localization scenarios. The performance of connectivity is measured based on the largest connected component, the average shortest path length and global efficiency. These measurements are taken both with and without the integration of toll roads. The results obtained demonstrated the presence of disruption signatures, each of which is unique to each hazard. Despite the considerable spatial extent of disruptions, the disruptions caused by flooding result in a gradual degradation of the network in view of redundancy. Conversely, landslide-induced disruptions are spatially small, but they have a disproportionate effect on structurally critical links, thereby causing fragmentation. In addition, the integration of toll roads does not necessarily enhance the robustness of the network when dealing with disruption, indicating an alteration in the structural dependency of the network. These findings support a reframing of road network vulnerability as a structure-conditioned response to disruption, highlighting the significance of structural robustness in infrastructure planning beyond conventional connectivity-based assessments, thereby supporting more effective infrastructure planning and risk mitigation strategies.
