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Land-use/land cover change (LUCC) is an important problem in developing and under-developing countries with regard to global climatic changes and urban morphological distribution. Since the 1900s, urbanization has become an underlying cause of LUCC, and more than 55% of the world’s population resides in cities. The speedy growth, development and expansion of urban centers, rapid inhabitant’s growth, land insufficiency, the necessity for more manufacture, advancement of technologies remain among the several drivers of LUCC around the globe at present. In this study, the urban expansion or sprawl, together with spatial dynamics of Hyderabad, Pakistan over the last four decades were investigated and reviewed, based on remotely sensed Landsat images from 1979 to 2020. In particular, radiometric and atmospheric corrections were applied to these raw images, then the Gaussian-based Radial Basis Function (RBF) kernel was used for training, within the 10-fold support vector machine (SVM) supervised classification framework. After spatial LUCC maps were retrieved, different metrics like Producer’s Accuracy (PA), User’s Accuracy (UA) and KAPPA coefficient (KC) were adopted for spatial accuracy assessment to ensure the reliability of the proposed satellite-based retrieval mechanism. Landsat-derived results showed that there was an increase in the amount of built-up area and a decrease in vegetation and agricultural lands. Built-up area in 1979 only covered 30.69% of the total area, while it has increased and reached 65.04% after four decades. In contrast, continuous reduction of agricultural land, vegetation, waterbody, and barren land was observed. Overall, throughout the four-decade period, the portions of agricultural land, vegetation, waterbody, and barren land have decreased by 13.74%, 46.41%, 49.64% and 85.27%, respectively. These remotely observed changes highlight and symbolize the spatial characteristics of “rural to urban transition” and socioeconomic development within a modernized city, Hyderabad, which open new windows for detecting potential land-use changes and laying down feasible future urban development and planning strategies.

Urban growth copes with problems in sustainable development. In developing countries, particularly, sustainable development of urban growth copes with severe challenges with respect to sluggish economic and social growth, population boom, environmental deterioration, unemployment, slums and so on. Time series of remote sensing data provide critical support on sustainability assessment. However, the urban spatial extend cannot be accurately extracted from land cover data. Targeting the urban growth and its sustainability in Islamabad, the capital of Pakistan, this study extracts urban area from four periods of Landsat images between 1990 and 2018 using an innovative object-based backdating change detection method and two criteria for extracting urban land from impervious surface. We prove that impervious surface cover and urban area increased 273.10% and 426.21%, respectively, over the last 3 decades. We identify five factors playing important role in urban growth: population, transportation systems, master planning, industrial and real estate development, and neighbor urban effect. In this study, we assess the socio-economic sustainability associated with slum growth and census data, and the environmental sustainability in relation to the variations of normalized difference vegetation index (NDVI) in forest areas. We found that slums increased with the corresponding growth of urban area and population, reflecting sluggish economic increase in Islamabad. We found that the area of woodland increased 9.29%, but its NDVI decreased from 0.668 to 0.551, implying a deteriorative trend of environmental condition.

Rapid urbanization significantly impacts land use and land cover (LULC), leading to various socioeconomic and environmental challenges. Effective monitoring and detection of spatial discrepancies are crucial for urban planners and authorities to manage these changes. This study aims to analyze the spatial dynamics of LULC changes and predict future land use patterns. The specific objectives are to classify historical land use from 1990 to 2020, simulate future land use from 2020 to 2050, and interpret the spatial and temporal results. The study utilized remotely sensed images with the semi-automatic classification plugin (SCP) approach for land use classification from 1990 to 2020. Future land use patterns were simulated using the Modules of Land Use Change Evaluation (MOLUSCE)-based Cellular Automata-Artificial Neural Network (CA-ANN) model. The results were then interpreted to comprehend the dynamics of urban expansion. The conclusions direct a significant increase in built-up and grasslands, with a consistent decline in other land use types. From 1990 to 2020, approximately 423.75 km² and 856.97 km² of land were converted into built-up areas and grasslands, respectively. This was accompanied by a decline in rocky bare and bare soil areas, while the proportions of water bodies and mangroves remained steady. Predictions for 2020 to 2050 suggest an additional increase of 561.93 km² in built-up areas, with a progressive decline in other land use classes. The study emphasizes the critical need for spatial planning policies to address challenges arising from rapid urbanization. By analyzing historical land use changes and predicting future patterns this research offers a comprehensive view of urban growth dynamics. The novel application of these techniques provides valuable insights for urban planners to develop informed strategies for managing expansion and mitigating associated socioeconomic and environmental impacts.

Rapid urbanization significantly impacts land use and land cover (LULC), leading to various socioeconomic and environmental challenges. Effective monitoring and detection of spatial discrepancies are crucial for urban planners and authorities to manage these changes. This study aims to analyze the spatial dynamics of LULC changes and predict future land use patterns. The specific objectives are to classify historical land use from 1990 to 2020, simulate future land use from 2020 to 2050, and interpret the spatial and temporal results. The study utilized remotely sensed images with the semi-automatic classification plugin (SCP) approach for land use classification from 1990 to 2020. Future land use patterns were simulated using the Modules of Land Use Change Evaluation (MOLUSCE)-based Cellular Automata-Artificial Neural Network (CA-ANN) model. The results were then interpreted to comprehend the dynamics of urban expansion. The conclusions direct a significant increase in built-up and grasslands, with a consistent decline in other land use types. From 1990 to 2020, approximately 423.75 km² and 856.97 km² of land were converted into built-up areas and grasslands, respectively. This was accompanied by a decline in rocky bare and bare soil areas, while the proportions of water bodies and mangroves remained steady. Predictions for 2020 to 2050 suggest an additional increase of 561.93 km² in built-up areas, with a progressive decline in other land use classes. The study emphasizes the critical need for spatial planning policies to address challenges arising from rapid urbanization. By analyzing historical land use changes and predicting future patterns this research offers a comprehensive view of urban growth dynamics. The novel application of these techniques provides valuable insights for urban planners to develop informed strategies for managing expansion and mitigating associated socioeconomic and environmental impacts.

Rapid urbanization significantly impacts land use and land cover (LULC), leading to various socioeconomic and environmental challenges. Effective monitoring and detection of spatial discrepancies are crucial for urban planners and authorities to manage these changes. This study aims to analyze the spatial dynamics of LULC changes and predict future land use patterns. The specific objectives are to classify historical land use from 1990 to 2020, simulate future land use from 2020 to 2050, and interpret the spatial and temporal results. The study utilized remotely sensed images with the semi-automatic classification plugin (SCP) approach for land use classification from 1990 to 2020. Future land use patterns were simulated using the Modules of Land Use Change Evaluation (MOLUSCE)-based Cellular Automata-Artificial Neural Network (CA-ANN) model. The results were then interpreted to comprehend the dynamics of urban expansion. The conclusions direct a significant increase in built-up and grasslands, with a consistent decline in other land use types. From 1990 to 2020, approximately 423.75 km² and 856.97 km² of land were converted into built-up areas and grasslands, respectively. This was accompanied by a decline in rocky bare and bare soil areas, while the proportions of water bodies and mangroves remained steady. Predictions for 2020 to 2050 suggest an additional increase of 561.93 km² in built-up areas, with a progressive decline in other land use classes. The study emphasizes the critical need for spatial planning policies to address challenges arising from rapid urbanization. By analyzing historical land use changes and predicting future patterns this research offers a comprehensive view of urban growth dynamics. The novel application of these techniques provides valuable insights for urban planners to develop informed strategies for managing expansion and mitigating associated socioeconomic and environmental impacts.

Antibiotic resistance (AR) is the result of microbes’ natural evolution to withstand the action of antibiotics used against them. AR is rising to a high level across the globe, and novel resistant strains are emerging and spreading very fast. Acinetobacter baumannii is a multidrug resistant Gram-negative bacteria, responsible for causing severe nosocomial infections that are treated with several broad spectrum antibiotics: carbapenems, β-lactam, aminoglycosides, tetracycline, gentamicin, impanel, piperacillin, and amikacin. The A. baumannii genome is superplastic to acquire new resistant mechanisms and, as there is no vaccine in the development process for this pathogen, the situation is more worrisome. This study was conducted to identify protective antigens from the core genome of the pathogen. Genomic data of fully sequenced strains of A. baumannii were retrieved from the national center for biotechnological information (NCBI) database and subjected to various genomics, immunoinformatics, proteomics, and biophysical analyses to identify potential vaccine antigens against A. baumannii. By doing so, four outer membrane proteins were prioritized: TonB-dependent siderphore receptor, OmpA family protein, type IV pilus biogenesis stability protein, and OprD family outer membrane porin. Immuoinformatics predicted B-cell and T-cell epitopes from all four proteins. The antigenic epitopes were linked to design a multi-epitopes vaccine construct using GPGPG linkers and adjuvant cholera toxin B subunit to boost the immune responses. A 3D model of the vaccine construct was built, loop refined, and considered for extensive error examination. Disulfide engineering was performed for the stability of the vaccine construct. Blind docking of the vaccine was conducted with host MHC-I, MHC-II, and toll-like receptors 4 (TLR-4) molecules. Molecular dynamic simulation was carried out to understand the vaccine-receptors dynamics and binding stability, as well as to evaluate the presentation of epitopes to the host immune system. Binding energies estimation was achieved to understand intermolecular interaction energies and validate docking and simulation studies. The results suggested that the designed vaccine construct has high potential to induce protective host immune responses and can be a good vaccine candidate for experimental in vivo and in vitro studies.