Generally, drought is influenced by both spatial characteristics and anthropogenic activities within an area. Drought vulnerability assessment is a critical tool that can be effectively used to develop proper drought mitigation strategies to prevent avoidable losses. To develop suitable drought mitigation strategies, the overall drought vulnerability must be assessed, and the interaction among drought-influencing factors in the area should be considered. Consequently, this study aimed to investigate the interactions among critical drought-influencing factors and drought vulnerability in the Lam Ta Kong Watershed via spatial analysis with the analytical hierarchy process (AHP) and geographical information system (GIS) technology. Ten drought-influencing factors were considered in the vulnerability assessment: slope, elevation, soil texture, soil fertility, stream density, precipitation, temperature, precipitation days, evaporation, and land use. The results indicated that the critical drought-influencing factors were precipitation, precipitation days, and land use, resulting in most of the watershed experiencing high drought vulnerability (35.1% of the watershed or 1810.83 km2). Moreover, this research highlighted the interactions among the critical drought-influencing factors. Precipitation interacted with precipitation days to cause drought vulnerability across the watershed, with a p-value <0.05. Similarly, the interactions between precipitation and land use and between precipitation days and land use, with p-values <0.05, showed that they were associated with and influenced by drought in the Lam Ta Kong Watershed. This study further indicated that appropriate drought mitigation strategies for this watershed must consider the interactions among these drought-influencing factors, as well as their specific interactions across the watershed.

Selection of a suitable alternative material from a pool of alternatives with many conflicting criteria becomes a Multi-Criteria Decision Making (MCDM) problem. In the present study, ternary blended mortars were prepared using ceramic tile dust waste (CTD), fly ash (FA), and ground granulated blast furnace slag (GGBFS) as binder components. Crusher dust (CD) was used as a fine aggregate component. Binder to aggregate ratios of 1:3 and 1:1 were prepared considering suitable flow. A total of 16 mortar mixes were cast. These mortars were tested for various conflicting criteria compressive strength, flexural strength, porosity, water absorption, bulk density, thermal conductivity, specific heat, thermal diffusivity, and thermal effusivity whose weightages obtained were 29.09%, 20.08%, 12.77%, 10.60%, 8.74%, 6.74%, 5.54%, 4.47%, and 1.97%, respectively, as per AHP analysis. Later, considering these different criteria and alternate mortars, it was observed that a 1:1 mortar with 20% CTD, 30% FA, and 50% GGBFS (RC20F30G50) is found to be the suitable mortar with the highest relative closeness coefficient of 0.861 and the highest net outranking flow of 0.316 with respect to MCDM techniques: technique for order of preference by similarity to ideal solution (TOPSIS) and preference ranking organization method for enrichment of evaluations (PROMETHEE-II), respectively. The ranking of the mortar in both methods complies with the relative weightages of the criteria and the performance of the mortars with respect to the above criteria.

An evaluation of flood vulnerability is needed to identify flood risk locations and determine mitigation methods. This research introduces an integrated method combining hydro-morphometric modeling and flood susceptibility mapping to assess Padma River Basin\'s flood risk. Flood zoning, flooding classes, and resource flood risk were explicitly analyzed in this river basin study. Flood risk was calculated using GIS-based hydro-morphometric modeling. Using Horton\'s and Strahler\'s methods, drainage density, stream density, and stream order of the Padma River Basin were determined. The Padma River Basin has five sub-basins: A, B, C, D, and E, with stream densities of 0.53 km-2, 0.13 km-2, 0.25 km-2, 0.30 km-2, and 0.28 km-2 and drainage densities of 0.63 km-1, 0.16 km-1, 0.29 km-1, 0.35 km-1, and 0.33 km-1, respectively. Sub-basin A is the most prone to floods due to its high stream and drainage density, whereas B and C are the least susceptible. This study used elevation, TWI, slope, precipitation, NDVI, distance from road, drainage density, distance from river, LU/LC, and soil type to create a flood vulnerability map incorporating GIS and AHP with pair-wise comparison matrix (PCM). The study\'s flood zoning shows that the northeastern part of this basin is more likely to flood than the southwestern part due to its elevation and high-order streams. Moderate River Flooding, the region\'s most hazardous flood class, covers 48.19% of the flooding area, including 1078.30 km2 of agricultural land, 94.86 km2 of bare soil, 486.39 km2 of settlements, 586.42 km2 of vegetation cover, and 39.34 km2 of water bodies. The developed hydro-morphometric model, the flood susceptibility map, and the analysis of this data may be utilized to offer long-term advance alarm insight into areas potentially to be invaded by a flood catastrophe, boosting hazard mitigation and planning.

IoT-based smart transportation monitors vehicles, cargo, and driver statuses for safe movement. Due to the limited computational capabilities of the sensors, the IoT devices require powerful remote servers to execute their tasks, and this phenomenon is called task offloading. Researchers have developed efficient task offloading and scheduling mechanisms for IoT devices to reduce energy consumption and response time. However, most research has not considered fault-tolerance-based job allocation for IoT logistics trucks, task and data-aware scheduling, priority-based task offloading, or multiple-parameter-based fog node selection. To overcome the limitations, we proposed a Multi-Objective Task-Aware Offloading and Scheduling Framework for IoT Logistics (MT-OSF). The proposed model prioritizes the tasks into delay-sensitive and computation-intensive tasks using a priority-based offloader and forwards the two lists to the Task-Aware Scheduler (TAS) for further processing on fog and cloud nodes. The Task-Aware Scheduler (TAS) uses a multi-criterion decision-making process, i.e., the analytical hierarchy process (AHP), to calculate the fog nodes\' priority for task allocation and scheduling. The AHP decides the fog nodes\' priority based on node energy, bandwidth, RAM, and MIPS power. Similarly, the TAS also calculates the shortest distance between the IoT-enabled vehicle and the fog node to which the IoT tasks are assigned for execution. A task-aware scheduler schedules delay-sensitive tasks on nearby fog nodes while allocating computation-intensive tasks to cloud data centers using the FCFS algorithm. Fault-tolerant manager is used to check task failure; if any task fails, the proposed system re-executes the tasks, and if any fog node fails, the proposed system allocates the tasks to another fog node to reduce the task failure ratio. The proposed model is simulated in iFogSim2 and demonstrates a 7% reduction in response time, 16% reduction in energy consumption, and 22% reduction in task failure ratio in comparison to Ant Colony Optimization and Round Robin.

This research aims to identify the accessibility of the entire population, especially the slum population to existing healthcare facilities (HCF) as well as the slum neighborhoods having low geographic accessibility, and finally, to provide an analytical model for the people living in areas that are outside the coverage range of existing healthcare facilities (HCF) across the study area. Spatial data has been collected and used based on the road network, elevation, location of HCF, municipal boundary, slum point, and satellite images from various sources. Also, non-spatial data such as socioeconomic variables are collected from questionnaires survey within a particular period. The spatial analysis tool like as near, network analysis, and predictive analysis in the ArcGIS platform was used to examine geographic accessibility. The results of the spatial analysis show that the distribution of public healthcare facility centers in the study area has not been uniformly distributed. Across 84% of areas in the study area have sound spatial accessibility with traveling time coverage is about 12 min. However, 16% of areas have a traveling time of 12 to 30 min under low accessibility with existing slum neighborhoods. Therefore, the low spatial accessibility areas are demanding new healthcare facilities in the study area. The Analytical Hierarchy Process (AHP) is employed to find the most optimal and efficient locational suitability for building new healthcare facility centers. The finding of AHP analysis for site suitability of healthcare facilities revealed five major classes as most suitable (2%), suitable (5%), moderate (35%), poor (54%), and very poor (4%) in the study area. Moreover, the realistic framework of this study helps to measure geographic accessibility and suitability in any geographical area.

Soil erosion across watersheds and river basins is an alarming environmental deterioration process that poses severe risks to hydrological systems, hydrogeochemical processes, agricultural productivity, and the global natural ecosystem. The use of the Analytical Hierarchy Process (AHP) and Geographical Information System (GIS) to assess soil erosivity for the watershed is widely known. This study applied the AHP and GIS to understand the degree of erosivity of the hilly Karnaphuli watershed in Chattogram, Bangladesh. The study used topographical maps, soil maps, and satellite imagery datasets. It implemented the GIS-based AHP and weighted overlay technique to derive eight factors (slope, elevation, Stream Power Index (SPI), Land Use and Land Cover (LULC), curvature, soil, Topographic Wetness Index (TWI), and rainfall. The geological stage of erosion potential was also identified using Digital Elevation Model (DEM) data through GIS-based hypsometric analysis. The findings demonstrated that the eastern and north-western parts are particularly vulnerable to erosion compared to other parts of the study area. The most dominant variables identified to influence the process of soil erosion are slope, LULC, elevation, and SPI. According to the AHP analysis, slope was the most influential factor (26%), followed by LULC (23.8%), elevation (20.3%), and SPI (13.9%) in the soil erosion process, and the geological stage of erosion potential was determined from the hypsometric curve (S-shaped) and hypsometric integral (0.49), which revealed that moderately eroded areas characterized the whole research region. The findings are significant as they provide valuable information for researchers and planners to address soil erosion and develop measures to control it effectively.

Coastal areas are at a higher risk of flooding, and novel changes in the climate are induced to raise the sea level. Flood acceleration and frequency have increased recently because of unplanned infrastructural conveniences and anthropogenic activities. Therefore, the assessment of flood susceptibility mapping is considered the most significant flood management model. In this paper, flood susceptibility identification is performed by applying the innovative Multi-criteria decision-making model (MCDM) called Analytical Hierarchy Process (AHP) by ensembles with Support vector machine (AHP-SVM) and Decision Tree (AHP-DT). This model combines two Representation concentration pathway (RCP) scenarios such as RCP 2.6 & RCP 8.5. The factors influencing the coastal flooding in Bandar Abbas, Iran, identified through Flood susceptibility mapping. Multi-criteria decision-making (MCDM) has been applied to evaluate the Coastal flood conditioning factors, and ensemble machine learning (ML) approaches are employed for Coastal risk factor (CRF) prediction and classification. The statistical variances are measured through Friedman and Wilcoxon signed rank tests and statistical metrics such as Accuracy, sensitivity, and specificity. Among the models, AHP-DT obtained an improved AUC value of ROC as 0.95. After applying the ML models, the northern and western park of Raidak Basin River recognises very low and low flood susceptibility because of their topographic characteristics. The eastern part of the middle section fell very high and high CFSM. Observed from this result analysis, the people living nearer to the coastline are distributed by the low to medium exposure in the region of the west and middle of the considered study area. The results of this study can help decision-makers take necessary risk reduction approaches in the high-risk flooding zones of the coastal system.

The various components of Municipal Solid Waste (MSW) such as collection, sorting, transportation, and disposal have their peculiarities and challenges. However, final disposal sites, generally referred to as landfill sites, present a complex difficulty. The aggregation of the problems of other components has consequences on the microenvironment, public health, and groundwater. Final disposal of MSW, site selection, and management presents an enormous burden for most Metropolitan, Municipal, and District Assembly (s) in Ghana. The case of Bolgatanga Municipality is similar to many others in the Upper East region in particular and Ghana as a whole. The existing landfill site is improperly sited and poses a great danger to adjoining communities. In this work, we used the Analytical Hierarchy Process (AHP) in a G.I.S. suite and Multi-Criteria Evaluation to assess the current location and select a possible new site within the municipality. The criteria used are generally environmental and socio-economic parameters, including; distance from major roads, rivers, settlements, and the selection of an appropriate slope and soil type that suits the guidelines for the siting of a landfill. The outcome of this multi-criteria assessment is the selection of a new site far from the current disposal site indicating the inappropriate location of the current site. A combination of all weighted criteria through a model builder process produced a suitability index map for candidate landfill sites. The selected site at Sherigu is about 16 km by road from the proposed site, which is much bigger than the threshold of 500 sq. meters. The separation distance and size between the current and the proposed site are indications of how economically and environmentally inappropriate the Sherigu site is and the need for a better site that is better situated for socio-economic and environmental considerations.

Globally, around 1.3 billion tonnes of waste are generated annually, and solid waste management has thus become a major concern worldwide. There are projections of a 70% increase in waste generation from 2016 to 2050 owing to urbanization and the rapid growth of the global population. Estimates indicate that around 38,200 tonnes of waste are generated per day in Malaysia, and this volume of waste is significantly shortening the planned life spans of operating sanitary landfills in the country. Batu Pahat is a district in the state of Johor, Malaysia, with a relatively large population of 495,000 and with no record of an operational sanitary landfill. This study was conducted to identify and classify the most suitable sites for sanitary landfill developments in southern Peninsular Malaysia by means of the Analytical Hierarchy Process (AHP), which is recognized as a competent technique for multicriteria decision-making. The resulting landfill site suitability index map established 33.88 km2 of area coverage as very highly suitable for landfill development, while 353.86 km2 of area coverage was classified as unsuitable. Sites 1-6 were identified as the most suitable for landfill activities. Sites 1-5 are situated in agricultural land areas, while site 6 is in a forested land area; this implies public participation and the adoption of compensatory measures in the event of landfill development in these areas, given their socioeconomic importance. The six suitable sites are all at least 2000 m from rivers: 2000-3000 m for sites 1, 3, and 5 and > 3000 m for sites 2, 4, and 6. The six sites are all > 3000 m from fault zones and > 1000 m from flood-prone areas, meaning that occurrences such as fault movements and flooding will have minimal impact on the operational activities of landfills at these sites. The selection of sites 1-6 as very suitable for landfill development was associated with an overall accuracy rating of 93.33% and kappa coefficient score of 0.92 based on accuracy assessment analysis of all sites. This study will guide the actions of policymakers, city planners, and local authorities toward sustainable and environment-friendly landfill development and operation in Batu Pahat and other districts in the state of Johor.

Healthcare workers\' relationship with industry is not merely an agent mediating between consumer and vendor, but they are also inventors of the interventions they exist to deliver. Driven by the background of the digital health era, scientific research and technological (Sci-tech) innovation in the medical field are becoming more and more closely integrated. However, scholars shed little light on Sci-tech relevance to evaluate the innovation performance of healthcare organizations, a distinctive feature of healthcare organizations\' innovation in the digital health era.
Academic publications and patents are the manifestations of scientific research outputs and technological innovation outcomes, respectively. The study extracted data from publications and patents of 159 hospitals in China to evaluate their innovation performance. A total of 18 indicators were constructed, four of which were based on text similarity match and represented the Sci-tech relevance. We then applied factor analyses, analytical hierarchy process, and logistic regression to construct an evaluation model. We also examined the relationship between hospitals\' innovation performance and their geographical locations. Finally, we implemented a mediation analysis to show the influence of digital health on hospital innovation performance.
A total of 16 indicators were involved, four of which represented the Sci-tech including the number of articles matched per patent (NAMP), the number of patents matched per article (NPMA), the proportion of highly matched patents (HMP), and the proportion of highly matched articles (HMA). Indicators of HMP (r = 0.52, P = 2.40 × 10-12), NAMP (r = 0.52, P = 2.54 × 10-12), and NPMA (r = 0.51, P = 5.53 × 10-12) showed a strong positive correlation with hospital innovation performance score. The evaluation model in this study was different from other Chinese existing hospital ranking systems. The regional innovation performance index (RIP) of healthcare organizations is highly correlated with per capita disposable income (r = 0.58) and regional GDP (r = 0.60). There was a positive correlation between digital health innovation performance scores and overall hospital innovation performance scores (r = 0.20). In addition, the hospitals\' digital health innovation performance affected the hospital\'s overall innovation score with the mediation of Sci-tech relevance indicators (NPMA and HMA). The hospitals\' digital health innovation performance score showed a significant correlation with the number of healthcare workers (r = 0.44).
This study constructed an assessment model with four invented indicators focusing on Sci-tech relevance to provide a novel tool for researchers to evaluate the innovation performance of healthcare organizations in the digital health era. The regions with high RIP were concentrated on the eastern coastal areas with a higher level of economic development. Therefore, the promotion of scientific and technological innovation policies could be carried out in advance in areas with better economic development. The innovations in the digital health field by healthcare workers enhance the Sci-tech relevance in hospitals and boost their innovation performance. The development of digital health in hospitals depends on the input of medical personnel.