Heat removal has become an increasingly crucial issue for microelectronic chips due to increasingly high speed and high performance. One solution is to increase the thermal conductivity of the corresponding dielectrics. However, traditional approach to adding solid heat conductive nanoparticles to polymer dielectrics led to a significant weight increase. Here we propose a dielectric polymer filled with heat conductive hollow nanoparticles to mitigate the weight gain. Our mesoscale simulation of heat conduction through this dielectric polymer composite microstructure using the phase-field spectral iterative perturbation method demonstrates the simultaneous achievement of enhanced effective thermal conductivity and the low density. It is shown that additional heat conductivity enhancement can be achieved by wrapping the hollow nanoparticles with graphene layers. The underlying mesoscale mechanism of such a microstructure design and the quantitative effect of interfacial thermal resistance will be discussed. This work is expected to stimulate future efforts to develop light-weight thermal conductive polymer nanocomposites.

An appraisal of the groundwater potential of Alex Ekwueme Federal University Ndufu Alike was carried out by integrating datasets from geology, geographic information system and electrical resistivity survey of the area. The study area is underlain by the Asu River group of Albian age. The Asu River Group in the Southern Benue Trough comprises of Shales, Limestones and Sandstone lenses of the Abakaliki Formation in Abakaliki and Ikwo areas. The shales are generally weathered, fissile, thinly laminated and highly fractured and varies between greyish brown to pinkish red in colour. Twenty (20) Vertical Electrical Sounding data were acquired using SAS 1000 ABEM Terrameter and processed to obtain layer parameters for the study area. A maximum current electrode spacing (AB) of 300 meters was used for data acquisition. Computer aided iterative modelling using IPI2 Win was used to determine layer parameters. In-situ Hydraulic Conductivity measurements at seven parametric locations within the study area were conducted and integrated with Electrical Resistivity measurements to determine aquifer parameters (e.g., Hydraulic conductivity and Transmissivity) in real time. This technique reduces the attendant huge costs associated with pumping tests and timelines required to carry out the technique. Accurate delineation of aquifer parameters and geometries will aid water resource planners and developers on favourable areas to site boreholes in the area. Several correlative cross-sections were generated from the interpreted results and used to assess the groundwater potential of the study area. Results show that the resistivity of the the aquifer ranges from 7.3 Wm–530 Wm while depth to water ranges from 11.4 m to 55.3 m. Aquifer thicknesses range from 8.7 m at VES 5 to 36.3 m at VES 6 locations. Hydraulic conductivity ranges from 1.55 m/day at VES 15.18, and 19 locations to 9.8 m/day at VES 3 and 4 locations respectively. Transmissivity varies from 17.48 m²/day at VES 19 to 98 m²/day at VES 3 locations respectively. Areas with relatively high transmissivities coupled with good aquifer thicknesses should be the target of water resource planners and developers when proposing sites for drilling productive boreholes within Alex Ekwueme federal University Ndufu Alike.

In Indonesia, the village government organization is part of local democracy. This includes the local democracy in indigenous villages. Indigenous villages have their own customary rules for implementing village elections. They have their own conflict resolution systems in implementing the village government. The implementation of the indigenous village governance leaves conflicts. So, there is a need for a suitable model for resolving problems in the implementation of village elections. The method used in this research is the qualitative research method with the juridical empirical approach. The locus of this research is in the Baduy, Tengger, and Samin indigenous village communities. The conflict resolution model in the administration of the Baduy, Tengger, and Samin customary villages differs in the right mechanism, but in substance, the resolution model is the same, as they use a deliberation model for consensus. In resolving conflicts, indigenous peoples fully submit to traditional leaders. The provincial and the regency/city governments are expected to give greater attention to the conditions of villages with customary government characteristics.

This research examines three data mining approaches employing cost management datasets from 391 Thai contractor companies to investigate the predictive modeling of construction project failure with nine parameters. Artificial neural networks, naive bayes, and decision trees with attribute selection are some of the algorithms that were explored. In comparison to artificial neural network’s (91.33%) and naive bays’ (70.01%) accuracy rates, the decision trees with attribute selection demonstrated greater classification efficiency, registering an accuracy of 98.14%. Finally, the nine parameters include: 1) planning according to the current situation; 2) the company’s cost management strategy; 3) control and coordination from employees at different levels of the organization to survive on the basis of various uncertainties; 4) the importance of labor management factors; 5) the general status of the company, which has a significant effect on the project success; 6) the cost of procurement of the field office location; 7) the operational constraints and long-term safe work procedures; 8) the implementation of the construction system system piece by piece, using prefabricated parts; 9) dealing with the COVID-19 crisis, which is crucial for preventing project failure. The results show how advanced data mining approaches can improve cost estimation and prevent project failure, as well as how computational methods can enhance sustainability in the building industry. Although the results are encouraging, they also highlight issues including data asymmetry and the potential for overfitting in the decision tree model, necessitating careful consideration.