Assessing the suitability of the SLEUTH cellular automata model for capturing heterogeneous urban growth in developing cities: A case study in Northern Nigeria.

Cellular automata (CA) models like SLEUTH (an acronym for slope, land use, excluded area, urban extent, transport-network and hill shade) have predominantly been developed and applied in developed countries. Modeling can serve as a tool to guide policy measures in facing urbanization challenges. However, developing cities have peculiar differences (heterogeneity, poor planning, and low infrastructure) thus the existing modeling approaches may not be able to apprehend heterogeneous urban growth. This research will use selected cities with similar spatial extents as controls but disparate urban extents, and growth indices to analyze the performance of SLEUTH simulations. Presumably, a comparison of the model simulations of the cities would display some significant differences, due to these variations and the scale of observation that has to be used for the model simulations. The results for the successfully calibrated cities (Kano/Funtua couple: 0.48/0.02. Katsina/Kaduna: 0.48/0.83 respectively) showed that in each city couple, the more expansive city with the most compact urban settlement pattern had a higher prediction accuracy, also predicted images of the cities showed underestimation of the urban areas over the years with the exception of Katsina city. The study further showed the model's effectiveness in modeling cities in developing countries, such as Nigeria. It is recommended that the type of urban growth experienced by cities be taken into consideration when implementing SLEUTH. Limitations of the study are centered on the inherent limitations of the model, the possibility of the occurrence of errors in data preparation, the scale and urban settlement type, which play an important role in the success of the calibration. Future research could be focused on adding other relevant inputs to the model and creating a metric that ascertains the best satellite image resolutions for a particular study area's growth coefficient values.

Exploring soil radon (Rn) concentrations and their connection to geological and meteorological factors.

The relationship between soil radon and meteorological parameters in a region can provide insight into natural processes occurring between the lithosphere and the atmosphere. Understanding this relationship can help models establish more realistic results, rather than depending on theoretical consequences. Radon variation can be complicated to model due to the various physical variables which can affect it, posing a limitation in atmospheric studies. To predict Rn variation from meteorological parameters, a hybrid mod el called multiANN, which is a combination of multi-regression and artificial neural network (ANN) models, is established. The model was trained with 70% of the data and tested on the remaining 30%, and its robustness was tested using the Monte-Carlo method. The regions with low performance are identified and possibly related to seismic events. This model can be a good candidate for predicting Rn concentrations from meteorological parameters and establishing the lower boundary conditions in seismo-ionospheric coupling models.

Analysis of land use and land cover change using machine learning algorithm in Yola North Local Government Area of Adamawa State, Nigeria.

The dynamic use of land that results from urbanization has an impact on the urban ecosystem. Yola North Local Government Area (Yola North LGA) of Adamawa state, Nigeria, has experienced tremendous changes in its land use and land cover (LULC) over the past two decades due to the influx of people from rural areas seeking for the benefits of its economic activities. The goal of this research is to develop an efficient and accurate framework for continuous monitoring of land use and land cover (LULC) change and quantify the transformation in land use and land cover pattern over a specific period (between 2002 and 2022). Land sat images of 2002, 2012, and 2022 were obtained, and the Support Vector Machine classification method was utilized to stratify the images. Land Change Modeler (LCM) tool in Idrissi Selva software was then used to analyze the LULC change. SVM produced a good classification result for all three years, with 2022 having the highest overall accuracy of 95.5%, followed by 2002 with 90% and 2012 with 87.7% which indicates the validity of the algorithm for future predictions. The results showed that severe land changes have occurred over the course of two decades in built-up (37.32%), vegetation (forest, scrubland, and grassland) (-3.27%), bare surface (-33.47%), and water bodies (-0.59%). Such changes in LULC could lead to agricultural land lost and reduced food supply. This research develops a robust framework for continuous land use monitoring, utilizing machine learning and geo-spatial data for urban planning, natural resource management, and environmental conservation. In conclusion, this study underscores the efficacy of support vector machine algorithm in analyzing complex land use and land cover changes.

Remote sensing approach in evaluating anthropogenic impacts on the spatiotemporal changes in net primary productivity of the Niger river basin, from 2000 to 2020.

Deterioration of the environment can be examined by utilizing a statistical evaluation of the effects of anthropogenic activities (beneficial or detrimental) on net primary productivity. The Niger River Basin's net primary productivity is significant both theoretically and practically for the management of the natural environment. It is important for her member countries to understand vegetation dynamics, maintain carbon balance, and ensure food security in the region. The research applied remote sensing to determine the relative impact of human activities on the net primary productivity of the Niger River Basin from 2000 to 2020. The study simulated the actual and potential net primary productivity using the Carnegie Ames Stanford Approach and Thornthwaite's Memorial Model respectively, while the result of the simulations was used to calculate human-influenced net primary productivity. The slope of the three simulations was calculated and merged in several scenarios using ArcGIS 10.8 to determine the impact of human activities on net primary productivity of the study area. The negative impacts of human activities were recorded in 89.88 % of the investigated area, while 10.12 % of the NRB had signs of positive impacts. Amongst the biomes, urban areas and bare land experienced the largest negative impacts (97.2 % and 99.8 %, respectively). The study advised the effectiveness of ecological restoration programs, through sound scientific and technical methods, such as those used in rural development, nomadic herding, environmental protection, and natural resource management policies.

Application of industry 4.0 enablers in supply chain management: Scientometric analysis and critical review.

The supply chain management (SCM) environment is rapidly evolving as a result of the critical role industry 4.0 enablers are playing. Consequently, and to leverage the power of industry 4.0 enablers (I4Es) including; artificial intelligence (AI), machine learning (ML), internet of things (IoT) and big data (BD), researchers and industry practitioners have employed these I4Es to resolve several pain points in supply chain management at all levels, improve operational efficiency, manage demand volatility, tackle cost fluctuations, and make data-driven decisions. Thus, I4Es are working as an evolutionary catalyst for supply chain management in myriads of ways. As such, the application of I4Es in supply chain management (I4Es-in-SCM) research has witnessed tremendous growth over the past years. This study conducted a scientometric analysis and critical review of the I4Es-in-SCM research to monitor trends, visualize the structure of knowledge, identify gaps, and highlight future research avenues. The paper recruited and analysed bibliographic data of 786 papers from Scopus on the application of I4Es-in-SCM research. Analysis showed that the last two decades witnessed a phenomenal growth in research on the application of I4Es-in-SCM, with at least 42 % of all countries making contributions. The analysis showed wider collaboration between countries and noticed a rather significant collaboration among researchers within a given continent. The study also identified the most influential researchers, journals, and countries as well as trending themes and topics in the application of I4Es-in-SCM research. After delineating boundaries of scientific knowledge, the study proffered areas that require further research. The novelty of this study lies in providing a more holistic statistical and visualized analysis of the structure of knowledge, productivity, and scientific collaborations of researchers, journals and countries in the application of I4Es-in-SCM management research. Accordingly, the study outcomes may serve as a useful reference to supply chain academics, early-stage researchers, practitioners, policymakers, and organizations in understanding the structure of knowledge on the application of I4Es-in-SCM research and may constitute a basis for future research.

Physical modeling of ribosomes along messenger RNA: Estimating kinetic parameters from ribosome profiling experiments using a ballistic model.

Gene expression is the synthesis of proteins from the information encoded on DNA. One of the two main steps of gene expression is the translation of messenger RNA (mRNA) into polypeptide sequences of amino acids. Here, by taking into account mRNA degradation, we model the motion of ribosomes along mRNA with a ballistic model where particles advance along a filament without excluded volume interactions. Unidirectional models of transport have previously been used to fit the average density of ribosomes obtained by the experimental ribo-sequencing (Ribo-seq) technique in order to obtain the kinetic rates. The degradation rate is not, however, accounted for and experimental data from different experiments are needed to have enough parameters for the fit. Here, we propose an entirely novel experimental setup and theoretical framework consisting in splitting the mRNAs into categories depending on the number of ribosomes from one to four. We solve analytically the ballistic model for a fixed number of ribosomes per mRNA, study the different regimes of degradation, and propose a criterion for the quality of the inverse fit. The proposed method provides a high sensitivity to the mRNA degradation rate. The additional equations coming from using the monosome (single ribosome) and polysome (arbitrary number) ribo-seq profiles enable us to determine all the kinetic rates in terms of the experimentally accessible mRNA degradation rate.

A Hybrid Framework for Direct CO2 Emissions Quantification in China's Construction Sector.

Carbon emission quantifications in China are not consistent, with many standards and methods having been used over the years. This study identified the non-consideration of China-specific technology and databases as a factor limiting comprehensive quantification. The study aimed to comprehensively quantify regional direct CO2 emission in the industry using a hybrid of economic and environmental data. We retrieved nineteen (19) sets of fossil fuel and electricity data from provincial energy yearbooks between 1997 and 2015 for the study. To generate regression models for each of the six regional construction industries in China, the study further integrated the results with three sets of econometric data: total annual construction output, cement, and steel product yearly consumption data. The study identified the North China region as the main source of direct CO2 emission with over 30%, while Southeast China contributed the least. While there is a gradual shift to other energy sources, the study identified coal and crude oil to remain as the main energy sources in the industry. Cement and steel data exhibited a significant predictive relationship with CO2 emissions in five regional construction industries. The study identified the need to have policies tailored to technological improvements to enhance renewable energy generation and usage in the industry. The models developed in this study could be used to generate initial quantifications of carbon emissions in construction industries with similar carbon-emitting characteristics for carbon tracking, and energy policies for decision making. However, the three economic indicators used in the study could be extended to generate more robust models in future research.