This article uses a qualitative descriptive approach, through field visits with observations and in-depth interviews. The research location chosen was a representative village in accordance with the Tourism Village classification of the Gunung Kidul Regency Tourism Office. A tourist village is a form of integration between attractions, accommodation and supporting facilities presented in a structure of community life that is integrated with applicable procedures and traditions. In line with this, the existence of tourist villages can be an alternative strategy for increasing village original income (PADes) to support poverty alleviation. Measuring the impact of tourism village innovation on increasing Village Original Income (PADes) in supporting poverty reduction can provide a complete picture of how the implementation of tourism village innovation has a significant impact on village development through increasing PADes. Gunung Kidul Regency is one of the areas that has succeeded in developing tourist villages, this can be seen from the reduction in poverty rates in the last 10 years.

This study updates Pereira and Pereira by revisiting the macroeconomic and budgetary effects of infrastructure investment in Portugal using a dataset from the Portuguese Ministry of the Economy covering 1980–2019, thereby capturing a period of austerity and decreased investment in the 2010s. A vector-autoregressive approach re-estimates the elasticity and marginal product of twelve infrastructure types on private investment, employment, and output. The most significant long-term accumulated effects on output accrue from investments in airports, ports, health, highways, water, and railroads. In contrast, those in municipal roads, electricity and gas, and refineries are statistically insignificant. All statistically significant infrastructure investments pay for themselves over time through additional tax revenues. Compared to the previous study, highways, water, and ports have more than doubled their estimated marginal products due to a significant increase in relative scarcity over the last decade. In addition, our analysis reveals an important shift in the impacts of infrastructure investment, now producing more substantial immediate effects but weaker long-term impacts. This change offers policymakers a powerful tool for short-term economic stimulus and is particularly useful in addressing immediate economic challenges.

It is proposed to use angular descriptors (in polar and Euler coordinates or quaternions), as well as radiation patterns of many variables, in HF radiofrequency and microwave thermal analysis of anisotropic systems.

With the wide application of the Internet and smart systems, data centers (DCs) have become a hot spot of global concern. The energy saving for data centers is at the core of the related works. The thermal performance of a data center directly affects its total energy consumption, as cooling consumption accounts for nearly 50% of total energy consumption. Superior power distribution is a reliable method to improve the thermal performance of DCs. Therefore, analyzing the effects of different power distribution on thermal performance is a challenge for DCs. This paper analyzes the thermal performance numerically and experimentally in DCs with different power distribution. First, it uses Fluent simulate the temperature distribution and flow field distribution in the room, taking the cloud computing room as the research object. Then, it summarizes a formula based on the computing power distribution in a certain range by the numerical and experimental analysis. Finally, it calculates an optimal cooling power by analyzing the cooling power distribution. The results shows that it reduces the maximum temperature difference between the highest temperature of the cabinet from 5-7k to within 1.2k. In addition, the cooling energy consumption is reduced by more than 5%.

Biomass energy is abundant, clean, and carbon dioxide neutral, making it a viable alternative to fossil fuels in the near future. The release of syngas from biomass thermochemical treatments is particularly appealing since it may be used in a variety of heat and power generation systems. When a syngas with low tar and contaminants is required, downdraft gasifiers are usually one of the first gasification devices deployed. It is time-consuming and impractical to evaluate a gasification system's performance under multiple parameters, using every type of biomass currently available, which makes rapid simulation techniques with well-developed mathematical models necessary for the efficient and economical use of energy resources. This work attempts to examine, through model and experimentation, how well a throated downdraft gasification system performs when using pretreatment biomass feedstock that has been characterized. For the analyses, peanut shell (PS), a biomass waste easily obtained locally, was used. The producer gas generated with 9 mm PS pellets had a composition of 17.93% H2, 24.43 % CO, 12.47 % CO2, and 1.22% CH4 on a wet basis at the value of 0.3 equivalency ratio and 800 °C gasification temperature. The calorific value was found to be 4.96 MJ/Nm3. The biomass feedstock PS is found to be suitable for biomass gasification in order to produce syngas.

Vehicle detection stands out as a rapidly developing technology today and is further strengthened by deep learning algorithms. This technology is critical in traffic management, automated driving systems, security, urban planning, environmental impacts, transportation, and emergency response applications. Vehicle detection, which is used in many application areas such as monitoring traffic flow, assessing density, increasing security, and vehicle detection in automatic driving systems, makes an effective contribution to a wide range of areas, from urban planning to security measures. Moreover, the integration of this technology represents an important step for the development of smart cities and sustainable urban life. Deep learning models, especially algorithms such as You Only Look Once version 5 (YOLOv5) and You Only Look Once version 8 (YOLOv8), show effective vehicle detection results with satellite image data. According to the comparisons, the precision and recall values of the YOLOv5 model are 1.63% and 2.49% higher, respectively, than the YOLOv8 model. The reason for this difference is that the YOLOv8 model makes more sensitive vehicle detection than the YOLOv5. In the comparison based on the F1 score, the F1 score of YOLOv5 was measured as 0.958, while the F1 score of YOLOv8 was measured as 0.938. Ignoring sensitivity amounts, the increase in F1 score of YOLOv8 compared to YOLOv5 was found to be 0.06%.