Mapping land use and land cover (LULC) is essential for comprehending changes in the environment and promoting sustainable planning. To achieve accurate and effective LULC mapping, this work investigates the integration of Geographic Information Systems (GIS) with Machine Learning (ML) methodology. Different types of land covers in the Lucknow district were classified using the Random Forest (RF) algorithm and Landsat satellite images. Since the research area consists of a variety of landforms, there are issues with classification accuracy. These challenges are met by combining supplementary data into the GIS framework and adjusting algorithm parameters like selection of cloud free images and homogeneous training samples. The result demonstrates a net increase of 484.59 km² in built-up areas. A net decrement of 75.44 km² was observed in forest areas. A drastic net decrease of 674.52 km² was observed for wetlands. Most of the wastelands have been converted into urban areas and agricultural land based on their suitability with settlements or crops. The classifications achieved an overall accuracy near 90%. This strategy provides a reliable way to track changes in land cover, supporting resource management, urban planning, and environmental preservation. The results highlight how sophisticated computational methods can enhance the accuracy of LULC evaluations.

Nickel Oxide (NiO) nanoparticles (NPs), doped with manganese (Mn) and cobalt (Co) at concentrations up to 8%, were synthesized using the composite hydroxide method (CHM). X-ray diffraction (XRD) analysis confirmed the formation of a cubic NiO structure, with no additional peaks detected, indicating successful doping. The average crystallite size was determined to range from 15 to 17.8 nm, depending on the dopant concentration. Scanning electron microscopy (SEM) images revealed mostly spherical, agglomerated particles, likely due to magnetic interactions. Fourier Transform Infrared Spectroscopy (FTIR) confirmed the incorporation of Mn and Co into the NiO lattice, consistent with the XRD results. The dielectric properties exhibited a high dielectric constant at low frequencies, which can be attributed to ion jump orientation and space charge effects. The imaginary part of the dielectric constant decreased with increasing frequency, as it became harder for electrons to align with the alternating field at higher frequencies. Both the real and imaginary dielectric constants showed behavior consistent with Koop’s theory, increasing at low frequencies and decreasing at higher frequencies. Dielectric loss was primarily attributed to dipole flipping and charge migration. AC conductivity increased with frequency, and exhibited higher conductivity at high frequencies due to small polaron hopping. These co-doped NPs show potential for applications in solid oxide fuel cells.

The policy to accelerate the design of the Detailed Spatial Plan regulation document (RDTR) is a strategic step to enhance ease of doing business and promote sustainable development in Indonesia. Targeting 2036 RDTR sites nationwide, the initiative relies on various policy interventions and technical approaches. However, as of 8 January 2024, only 399 RDTRs (19.59%) were enacted after four years of implementation. This underperformance suggests the need to examine factors influencing the process, including issues at each stage of the RDTR design business process. While often overlooked due to its perceived irrelevance to the core substance of planning, analyzing the process is crucial to addressing operational and procedural challenges. This research identifies critical issues arising from the preparation to the enactment stage of RDTR regulations and proposes necessary policy changes. Using an explanatory approach, the study employs methods such as Analytic Hierarchy Process (AHP), post-review analysis, stakeholder analysis, business process evaluation, and scenario planning. Results show several impediments, including challenges related to commitment, technical and substantive issues, managerial coordination, policy frameworks, ICT support, and data availability. These findings serve as inputs for the development of business process improvement scenarios and reengineering schemes based on Business Process Management principles.

With its inherent characteristics of decentralization, immutability, and transparency, blockchain technology presents a promising opportunity to revolutionize the South African food supply chains. Blockchain technology, with its decentralized, immutable, and secure nature, offers solutions to these challenges by improving traceability and accountability across the supply chain. This study investigates the role of blockchain technology in enhancing transparency in the food supply chain among small and medium enterprises in South Africa. SMEs form a critical part of the country’s agri-food sector but face challenges such as food fraud, inefficient inventory management, and lack of transparency, which impact food safety and trust. The research adopts a mixed-method approach, utilizing the Technology-Organization-Environment framework and Institutional Theory to explain blockchain adoption among SMEs. The results demonstrate that blockchain-enabled practices, such as smart contracts, records traceability, production tracking, and distribution monitoring, significantly enhance supply chain transparency. The findings highlight blockchain’s potential to increase operational efficiency, regulatory compliance, and stakeholder trust. This research provides valuable insights for policymakers and practitioners, emphasizing the need for regulatory support and strategic investment in blockchain solutions to promote sustainability and competitiveness in the agri-food sector.

Understanding the factors that influence early science achievement is crucial for developing effective educational policies and ensuring equity within the education system. Despite its importance, research on the patterns of young children achieving science learning milestones and the factors that can reduce disparities between students with and without disabilities remains limited. This study analyzes data from the Early Childhood Longitudinal Study of Kindergarten Cohort 2011 (ECLS-K: 2011), which includes 18,174 children from 1328 schools across the United States, selected through a complex sampling process and spanning kindergarten to 5th grade. Utilizing survival analysis, the study finds that children with disabilities achieve science milestones later than their peers without disabilities, with these disparities persisting from early grades. The research highlights the effectiveness of center-based programs in enhancing science learning, particularly in narrowing the achievement gap between children with and without disabilities. These findings contribute to the broader discourse on equity in the education system and policy by introducing novel methodologies for assessing the frequency and duration of science learning milestones, and by providing insights into effective strategies that support equitable science education.