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This paper aims to assess the current and future dynamics of land cover transitions and analyze the vegetation conditions in Abuja city since its establishment as the capital of Nigeria in 1991.

A random forest classifier embedded in the Google Earth Engine platform was used to classify Landsat imagery for the years 1990, 2001, 2014 and 2020. A post-classification comparison was used to detect the dynamics of land cover transitions. A hybrid simulation model that comprised cellular automata and Markovian was used to model the probable scenario of land cover changes for 2050. The trend of Normalized Difference Vegetation Index was examined using Mann–Kendall and Theil Sen’s from 2014 to 2022. Nighttime band data from the National Oceanic and Atmospheric Administration were obtained to analyze the trend of urbanization from 2014 to 2022.

The findings show that built-up areas increased by 40%, while vegetation, bare land and agricultural land decreased by 27%, 7% and 8%, respectively. Vegetation had the highest declining rate at 3.15% per annum. Built-up areas are expected to increase by 17.1% between 2020 and 2050 in contrast with other land cover. The proportion of areas with moderate vegetation improvement is estimated to be 15.10%, while the proportion of areas with no significant change was 38.10%. The overall proportion of degraded areas stands at 46.8% due to urbanization.

The findings provide a comprehensive insight into the dynamics of land cover transitions and vegetation variability induced by rapid urbanization in Abuja city, Nigeria. In addition, the findings provide valuable insights for policymakers and urban planners to develop a sustainable land use policy that promotes inclusivity, safety and resilience.

Despite the enormous need to succeed in the urban model, scientists and policymakers should work consistently to create blueprints to regulate urbanization. The absence of coordination between the crucial requirements and the regional strategies of the local authorities leads to a lack of conformance in urban development. The purpose of this paper is to address this issue.

This study intends to manage future urban growth patterns using integrated methods and then employ the results in the genetic algorithm (GA) model to considerably improve growth behavior. Multi-temporal land-use datasets have been derived from remotely sensed images for the years 1990, 2000, 2010 and 2020. Urban growth patterns and processes were then analyzed with land-use-and-land-cover dynamics. Results were examined for simulation and utilization of the GA.

Model parameters were derived and evaluated, and a preliminary assessment of the effective coefficient in the formation of urbanization is analyzed, showing the city's urbanization pattern has followed along with the transportation infrastructure and outward growth, and the scattering rates are high, with an increase of 5.64% in building area associated with a decrease in agricultural lands and rangelands.

The research achieved a considerable improvement over the growth behavior. The conducted research design was the first of its type in that field to be executed to any specific growth pattern parameters in terms of regulating and policymaking. The method has integrated various artificial intelligence models to monitor, measure and optimize the projected growth by applying this design. Other research on the area was limited to projecting the future of Amman as it is an urbanized distressed city.

The use of improvement tools in the construction sector has shown to be an important determinant of quality. Companies endeavoring to enhance their daily practices require assistance, evidence, standards, frameworks and quantitative models from existing experts to help them set out for the road. This paper is aimed to assist construction managers in the selection of tools to increase customer satisfaction.

This piece of research is based on the results of a previous empirical study on the use, within a sample of Mexican firms, of a set of more than 30 tools. It then proposes a Bayesian network (BN) to select them. By analyzing the variables under study, it is possible to establish their interaction and dependencies. The resultant BN comprises 24 nodes, and it is useful for choosing some tools that help to increase customer satisfaction.

Customers and their needs now have become more complicated and harder to meet than in the past. Then, the use of improvement tools that put quality at the heart of the management strategies is crucial for achieving customer satisfaction. In order to reduce prices, keep product quality and meet delivery times, these tools should be used on a daily basis. Along this line of thought, the overall results from the hypothetical scenarios explored in this were positive, reflecting the relevance of the proposed model. In particular, the use of tools for gathering customer needs, the utilization of technology and the implementation of a quality department are relevant for increasing customer satisfaction in the sector.

The sample size could be further expanded. The customer satisfaction dimensions could be enhanced.

While the sample in which the investigation is based could be expanded along with the number of variables and their states, the BN can help practitioners in the global construction industry to improve their quality practices, to foster loyalty and to grow revenues.

Most of the research reported in the area of continuous improvement in construction focuses on qualitative considerations, and it is still scarce in terms of developing mathematical models for selecting existing tools and, ultimately, satisfying customer’s requirements. This investigation is aimed to bridge this gap in the literature.

Metro stations have become a crucial aspect of urban rail transportation, integrating facilities, equipment and pedestrians. Impractical physical layout designs and pedestrian psychology impact the effectiveness of an evacuation during a metro fire. Prior research on emergency evacuation has overlooked the complexity of metro stations and failed to adequately consider the physical heterogeneity of stations and pedestrian psychology. Therefore, this study aims to develop a comprehensive evacuation optimization strategy for metro stations by applying the concept of design for safety (DFS) to an emergency evacuation. This approach offers novel insights into the management of complex systems in metro stations during emergencies.

Physical and social factors affecting evacuations are identified. Moreover, the social force model (SFM) is modified by combining the fire dynamics model (FDM) and considering pedestrians' impatience and panic psychology. Based on the Nanjing South Metro Station, a multiagent-based simulation (MABS) model is developed. Finally, based on DFS, optimization strategies for metro stations are suggested.

The most effective evacuation occurs when the width of the stairs is 3 meters and the transfer corridor is 14 meters. Additionally, a luggage disposal area should be set up. The exit strategy of the fewest evacuees is better than the nearest-exit strategy, and the staff in the metro station should guide pedestrians correctly.

Previous studies rarely consider metro stations as sociotechnical systems or apply DFS to proactively reduce evacuation risks. This study provides a new perspective on the evacuation framework of metro stations, which can guide the designers and managers of metro stations.

The presence of green spaces plays a vital role in promoting urban sustainability. Urban green parks (UGPs) help create sustainable cities while providing fundamental ecological functions. However, rapid urbanization has destroyed crucial green areas in Ranchi City, endangering inhabitants’ health. This study aims to locate current UGPs and predict future UGP sites in Ranchi City, Jharkhand.

It uses geographic information system (GIS) and analytical hierarchical process (AHP) to evaluate potential UGP sites. It involves the active participation of urban communities to ensure that the UGPs are designed to meet dweller’s needs. The site suitability assessment is based on several parameters, including the normalized difference vegetation index (NDVI), land use and land cover (LULC), population distribution, PM 2.5 levels and the Urban Heat Island (UHI) effect. The integration of these factors enables an evaluation of potential UGP’s sites.

The findings of this research reveal that 54.39% of the evaluated areas are unsuitable, 15.55% are less suitable, 12.76% are moderately suitable, 11.52% are highly suitable and 5.78% are very highly suitable for UGPs site selection. These results emphasize that the middle and outer regions of Ranchi City are the most favorable locations for establishing UGPs. The NDVI is the most important element in UGP site appropriateness, followed by LULC, population distribution, PM 2.5 levels and the UHI effect.

This study improves the process of integrating AHP and GIS, and UGPs site selection maps help urban planners and decision-makers make better choices for Ranchi City’s sustainability and greenness.

This study looked at the potential applications of geographic information systems (GIS) and remote sensing (RS) for inclusive community development and participation, sustainable tourism, and rural community-based natural resource management (CBNRM) in sub-Saharan Africa and other rural areas worldwide.

To evaluate resource management systems for rural tourism and the environment in Africa and abroad. The study makes use of reviews of relevant literature and documents, and while linking applications for sustainable tourism and local community empowerment with CBNRM and GIS, vital content was manually analyzed.

The study shows a potential affinity between agricultural and tourism businesses that GIS in line with the CBNRM conception can strengthen. In many rural and underdeveloped regions of the continent, this highlights the need for a credible and varied tourism strategy to develop and empower the relevant communities.

Most agricultural communities in Africa are located in low-income regions. Such areas are rich in natural wildlife and have popular tourist destinations. A mix of regional community development initiatives can be built using GIS, sustainable tourism, CBNRM, and community-based tourism (CBT).

This paper expects to analyze the connection between occupational stigma and job meaningfulness among Chinese takeaway riders, the mediating role of occupational identity and relative deprivation, and the moderating effect of job stress based on resource conservation theory.

The sample was derived from 371 takeaway riders across China. PLS-SEM was mainly utilized for the data analysis.

The findings of the study indicated a significant negative correlation between occupational stigma and job meaningfulness. Furthermore, it is worth noting that relative deprivation and occupational identity served as mediators and masks, respectively, in the relationship between occupational stigma and job meaningfulness. Furthermore, job stress amplifies the association between occupational stigma and occupational identity. Additionally, job stress diminishes the connection between occupational stigma and relative deprivation.

This study proposes a positive correlation between occupational stigma and occupational identity in the Chinese context. It also enriches the empirical research based on resource conservation theory. Furthermore, it holds practical implications for takeaway riders in China, offering insights to bolster their job meaningfulness.

This study aims to assess support vector machine (SVM) models' predictive ability to estimate half-cell potential (HCP) values from input parameters by using Bayesian optimization, grid search and random search.

A data set with 1,134 rows and 6 columns is used for principal component analysis (PCA) to minimize dimensionality and preserve 95% of explained variance. HCP is output from temperature, age, relative humidity, X and Y lengths. Root mean square error (RMSE), R-squared, mean squared error (MSE), mean absolute error, prediction speed and training time are used to measure model effectiveness. SHAPLEY analysis is also executed.

The study reveals variations in predictive performance across different optimization methods, with RMSE values ranging from 18.365 to 30.205 and R-squared values spanning from 0.88 to 0.96. Additionally, differences in training times, prediction speeds and model complexities are observed, highlighting the trade-offs between model accuracy and computational efficiency.

This study contributes to the understanding of SVM model efficacy in HCP prediction, emphasizing the importance of optimization techniques, model complexity and dimensionality reduction methods such as PCA.

Transient response of continuous composite material (CCM) fin made of high thermally conductive composite material is presented. The continuously varying effective properties of composite material such as thermal conductivity, heat capacity and density have been modelled using the Mori-Tanaka homogenization theory and rule of mixture. Additionally, temperature dependency of thermal conductivity, heat generation (composite materials) and convection coefficient (fluid properties) have also been incorporated. Different base boundary conditions are addressed such as oscillating heat flow, oscillating temperature, step-changing heat flow and step-changing temperature. At the other boundary, the fin is assumed to have a convective tip.

Lattice Boltzmann method is implemented using an in-house source code for obtaining the numerical solution of typical non-linear heat balance equation of the aforementioned problem under various transient base boundary conditions.

The effects of various thermal parameters such as material diffusivity ratio and conductivity ratio, area ratio and Biot number on transient response of fin and temperature distribution of fins are studied and interpreted. The heat transfer rate and time for attainment of steady state temperature of metal matrix composite (MMC) fin are found to be proportionally dependent on their diffusivity ratio. Additionally for higher values of area ratio and biot number, MMC fins are reported to dissipate the heat more efficiently in comparision to homogeneous fins in terms of time required to attain the steady state and surface temperature.

Response of transient fin associated with advanced class of material can facilitates the practicing engineers for designing high-performance and/or miniaturized thermal management devices as used in electronic packaging industries.

Studies of composite fin consisting of laminating second layer of material over the first layer have been reported previously, however transient response of CCM fin fabricated by continuously varying the volume fraction of two materials along the fin length has not been reported till date. Such material finds its application in thermal management and electronic packaging industries. Results are plotted in form of a graph for different application-wise material combinations that have not been reported earlier, and it can be treated as design data.

Security is the major issue that motivates multiple scholars to discover security solutions apart from the advantages of wireless sensor networks (WSN) such as strong compatibility, flexible communication and low cost. However, there exist a few challenges, such as the complexity of choosing the expected cluster, communication overhead, routing selection and the energy level that affects the entire communication. The ultimate aim of the research is to secure data communication in WSN using prairie indica optimization.

Initially, the network simulator sets up clusters of sensor nodes. The simulator then selects the Cluster Head and optimizes routing using an advanced Prairie Indica Optimization algorithm to find the most efficient communication paths. Sensor nodes collect data, which is securely transmitted to the base station. By applying prairie indica optimization to WSNs, optimize key aspects of data communication, including secure routing and encryption, to protect sensitive information from potential threats.

The Prairie Indica Optimization, as proposed, achieves impressive results for networks comprising 50 nodes, with delay, energy and throughput values of 77.39 ms, 21.68 J and 22.59 bps. In the case of 100-node networks, the achieved values are 80.95 ms, 27.74 J and 22.03 bps, significantly surpassing the performance of current techniques. These outcomes underscore the substantial improvements brought about by the Prairie Indica Optimization in enhancing WSN data communication.

In this research, the Prairie Indica Optimization is designed to enhance the security of data communication within WSN.