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 introduces a novel Groundwater Flooding Risk Assessment (GFRA) model to evaluate risks associated with groundwater flooding (GF), a globally significant hazard often overshadowed by surface water flooding. GFRA utilizes a conditional probability function considering critical factors, including topography, ground slope, and land use-recharge to generate a risk assessment map. Additionally, the study evaluates the return period of GF events (GFRP) by fitting annual maxima of groundwater levels to probability distribution functions (PDFs). Approximately 57% of the pilot area falls within high and critical GF risk categories, encompassing residential and recreational areas. Urban sectors in the north and east, containing private buildings, public centers, and industrial structures, exhibit high risk, while developing areas and agricultural lands show low to moderate risk. This serves as an early warning for urban development policies. The Generalized Extreme Value (GEV) distribution effectively captures groundwater level fluctuations. According to the GFRP model, about 21% of the area, predominantly in the city's northeast, has over 50% probability of GF exceedance (1 to 2-year return period). Urban outskirts show higher return values (> 10 years). The model's predictions align with recorded flood events (90% correspondence). This approach offers valuable insights into GF threats for vulnerable locations and aids proactive planning and management to enhance urban resilience and sustainability.

To save patients’ lives, it is important to go for an early diagnosis of intracranial hemorrhage (ICH). For diagnosing ICH, the widely used method is non-contrast computed tomography (NCCT). It has fast acquisition and availability in medical emergency facilities. To predict hematoma progression and mortality, it is important to estimate the volume of intracranial hemorrhage. Radiologists can manually delineate the ICH region to estimate the hematoma volume. This process takes time and undergoes inter-rater variability. In this research paper, we develop and discuss a fine segmentation model and a coarse model for intracranial hemorrhage segmentations. Basically, two different models are discussed for intracranial hemorrhage segmentation. We trained a 2DDensNet in the first model for coarse segmentation and cascaded the coarse segmentation mask output in the fine segmentation model along with input training samples. A nnUNet model is trained in the second fine stage and will use the segmentation labels of the coarse model with true labels for intracranial hemorrhage segmentation. An optimal performance for intracranial hemorrhage segmentation solution is obtained.

This work presents the evaluation of iron oxide nanoparticles obtained from the aqueous extract of Eucalyptus grandis. Twenty-three experiments were carried out where the synthesis of nanoparticles was performed by using the aqueous extract together with salts of iron (II) chloride tetrahydrate and iron (III) chloride hexahydrate. A characterization was carried out by IR, TEM and BET, where bands were presented at 3,440.77, 1,559.26 and 445.31 cm⁻¹, indicating the presence of iron oxide nanoparticles. A relatively high monodispersity was evidenced with particles around 9 nm. By means of BET analysis it was found to present a surface area of 131.897 m²/g. Obtaining nanoparticles by this green method presents yield values of 98%, with application in nanotechnology, biomedicine, environmental treatment, among others, making them highly versatile and their production cost is relatively low.

Implementing green retrofitting can save 50–90% of energy use in buildings built worldwide. Government policies in several developed countries have begun to increase the implementation of green retrofitting buildings in those countries, which must rise by up to 2.5% of the lifespan of buildings by 2030. By 2050, it is hoped that more than 85% of all buildings will have been retrofitted. The high costs of implementing green retrofitting amounting to 20% of the total initial construction costs, as well as the uncertainty of costs due to cost overruns are one of the main problems in achieving the implementation target in 2050. Therefore, increasing the accuracy of the costs of implementing green retrofitting is the best solution to overcome this. This research is limited to analyzing the factors that influence increasing the accuracy of green retrofitting costs based on WBS, BIM, and Information Systems. The results show that there are 10 factors affecting the cost accuracy of retrofitting or customizing high-rise office buildings, namely Energy Use Efficiency, Water Use Efficiency, Use of Environmentally Friendly Materials, Maintenance of Green Building Performance during the Use Period, Initial Survey, Project Information Documents, Cost Estimation Process, Resources, Legal, and Quantity Extraction applied. These factors are shown to increase the accuracy of green retrofitting costs.