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Spatial and seasonal association study between PM2.5 and related contributing factors in India
Global environmental pollution and rapid climate change have become a serious matter of concern. Remarkable spatial and seasonal variations have been observed due to rapid industrialization, urbanization, different festive occasions, etc. Among all the existing pollutants, the fine airborne particles PM2.5 (with aerodynamic equivalent diameter ?2.5?m) and PM10 (with aerodynamic equivalent diameter ?10?m) are associated with chronic diseases. This leads to carry out the study regarding the varying relationship between PM2.5 and other associated factors so that its concentration level might be under control. Existing literature has explored the geographical association between the pollutants and a few other important factors. To address this problem, the present study aims to explore the wide spatio-temporal relationships between the particulate matter (PM2.5) with the other associated factors (e.g., socio-demographic, meteorological factors, and air pollutants). For this analysis, the geographically weighted regression (GWR) model with different kernels (viz. Gaussian and Bisquare kernels) and the ordinary least squares (OLS) model have been carried out to analyze the same from the perspective of the four major seasons (i.e., autumn, winter, summer, and monsoon) in different districts of India. It may be inferred from the results that the local model (i.e., GWR model with Bisquare kernel) captures the spatial heterogeneity in a better way and their performances have been compared in terms of R2 values (>0.99 in all cases) and corrected Akaike information criterion (AICc) (maximum value -618.69 and minimum value -896.88). It has been revealed that there is a strong negative impact between forest coverage and PM pollution in northern India during the major seasons. The same has been found in Delhi, Haryana, and a few districts of Rajasthan during the 1-year cycle (October 2022September 2023). It has also been found that PM concentration levels become high over the specified period with the temperature drop in Delhi, Uttar Pradesh, etc. Moreover, a strong positive association is visible in PM pollution level with the total population. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. -
Spatial variations of landslide severity with respect to meteorological and soil related factors
Landslides, a prevalent natural disaster, wreak havoc on both human lives and vital infrastructure, making them a significant global concern. Their devastating impact is immeasurable, necessitating proactive measures to minimize their occurrence. The ability to accurately forecast the severity of a landslide, including its potential fatality rate and the scale of destruction it may cause, holds tremendous potential for prevention and mitigation to reduce the risk and the damage caused by a landslide to infrastructure and life. In this study, the spatial variability in severity of landslides (in terms of mortality rates) and its dependence on various meteorological, geographical and soil composition has been attempted to be established. To do this, Ordinary Least Squares (global) and various Geographically Weighted (local) models have been employed to observe the varying relation between mortality rates and its various causative factors. Existence of geographical heterogeneity in the relationships is also investigated. The spatial pattern of landslide mortality and its associations with various causative variables in the South Asian Region are investigated and analysed. Through this, insights into targeting of prevention and mitigation measures for landslides based on a given location can be obtained by studying the various forms of heterogeneous spatial associations observed. The outcomes highlight that the local models in the form of Gaussian GWR and Poisson GWR outperform their global counterparts by a huge margin with better R2 and Adj R2 values. In comparison with Poisson GWR and Gaussian GWR, it is seen that Poisson GWR outperforms Gaussian GWR in terms of Mean Absolute Error, Mean Squared Error and Corrected Akaike Information Criterion. Furthermore, several intriguing local relationships patterns are also noted. The Author(s), under exclusive licence to Springer Nature B.V. 2024. -
Spatial variations of landslide severity with respect to meteorological and soil related factors
Landslides, a prevalent natural disaster, wreak havoc on both human lives and vital infrastructure, making them a significant global concern. Their devastating impact is immeasurable, necessitating proactive measures to minimize their occurrence. The ability to accurately forecast the severity of a landslide, including its potential fatality rate and the scale of destruction it may cause, holds tremendous potential for prevention and mitigation to reduce the risk and the damage caused by a landslide to infrastructure and life. In this study, the spatial variability in severity of landslides (in terms of mortality rates) and its dependence on various meteorological, geographical and soil composition has been attempted to be established. To do this, Ordinary Least Squares (global) and various Geographically Weighted (local) models have been employed to observe the varying relation between mortality rates and its various causative factors. Existence of geographical heterogeneity in the relationships is also investigated. The spatial pattern of landslide mortality and its associations with various causative variables in the South Asian Region are investigated and analysed. Through this, insights into targeting of prevention and mitigation measures for landslides based on a given location can be obtained by studying the various forms of heterogeneous spatial associations observed. The outcomes highlight that the local models in the form of Gaussian GWR and Poisson GWR outperform their global counterparts by a huge margin with better R2 and Adj R2 values. In comparison with Poisson GWR and Gaussian GWR, it is seen that Poisson GWR outperforms Gaussian GWR in terms of Mean Absolute Error, Mean Squared Error and Corrected Akaike Information Criterion. Furthermore, several intriguing local relationships patterns are also noted. The Author(s), under exclusive licence to Springer Nature B.V. 2024. -
Spatio - Temporal Analysis of Temperature in Indian States
Data, the oil of the century, is available in multiple formats for various applications. It is collected, stored, and distributed across different use cases in various forms. Researchers study, analyse and use data for numerous analyses and predictions. There is an increase in demand and consideration of spatiotemporal data analysis. Analysing and obtaining insights from the spatiotemporal data are carried out by various researchers. Many investigations have started investigating the strategies for spatial-transient examination and applying spatial-transient information investigation procedures to different areas. Analysing spatiotemporal data has been an advanced task; with the help of various Python libraries, Spatio Temporal dataset about the temperature of states of India is analysed to support the harsh climate near the region of tropic of cancer. Across the decade, there has been a cyclic trend in the temperature, which keeps toggling yet increases over time. It remains a question of worry and genuine concern to predict climatic conditions. Spatio-temporal analysis of temperature in Indian states involves analysing the spatial and temporal variations in temperature across different states in India. The study can use various statistical and geographic information systems (GIS) tools. Spatio-temporal analysis of temperature in Indian states can provide valuable insights into the changing climate patterns in different regions of the country, which can be helpful for policymakers, researchers, and other stakeholders to make informed decisions related to climate change mitigation and adaptation. 2023 American Institute of Physics Inc.. All rights reserved. -
Spatio-temporal Crime Analysis and Forecasting on Twitter Data Using Machine LearningAlgorithms
The concept of social media began to gain popularity in the late 1990s and has played a significant role in connecting people across the globe. The constant addition of features to old social media platforms and the creation of new ones have helped amass and retain an extensive user base. Users could now share their views and provide detailed accounts of events from worldwide to reach like-minded people. This led to the popularization of blogging and brought into focus the posts of the commoner. These posts began to be verified and included in mainstream news articles bringing about a revolution in journalism. This research aims to use a social media platform, Twitter, to classify, visualize, and forecast Indian crime tweet data and provide a spatio-temporal view of crime in the country using statistical and machine learning models. The Tweepy Python module's search function and '#crime' query have been used to scrape relevant tweets under geographical constraints, followed by substring-keyword classification using 318 unique crime keywords. The Bokeh and gmaps Python modules create analytical and geospatial visualizations, respectively. Time series forecasting of crime tweet count is performed by comparing the accuracy of Long Short-Term Memory (LSTM), Auto-Regressive Integrated Moving Average (ARIMA), and Seasonal Auto-Regressivee Integrated Moving Average (SARIMA) models to determine the best model. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. -
Spatio-temporal crime analysis using KDE and ARIMA models in the Indian context
In developing countries like India, crime plays a detrimental role in economic growth and prosperity. With the increase in delinquencies, law enforcement needs to deploy limited resources optimally to protect citizens. Data mining and predictive analytics provide the best options for the same. This paper examines the news feed data collected from various sources regarding crime in India and Bangalore city. The crimes are then classified on the geographic density and the crime patterns such as time of day to identify and visualize the distribution of national and regional crime such as theft, murder, alcoholism, assault, etc. In total, 68 types of crime-related dictionary keywords are classified into six classes based on the news feed data collected for one year. Kernel density estimation method is used to identify the hotspots of crime. With the help of the ARIMA model, time series prediction is performed on the data. The diversity of crime patterns is visualized in a customizable way with the help of a data mining platform. Copyright 2020, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. -
Spatiotemporal analysis and intensity prediction of forest fires using cuckoo search hybrid models
Forest fire forecasting is a critical aspect of environmental conservation and ecological risk management, particularly in biodiversitysensitive areas like Uttara Kannada, India. In this research, this article suggests a new hybrid modeling ap-proach that combines Cuckoo Search Optimization (CSO) with ensemble machine learning techniques, namely Random Forest (RF) and XGBoost (XGB), for forecasting fire intensity levels. Known as CSORF and CS-XGB, the hybrid models were trained and validated against a spatiotemporally dense dataset from 2009 to 2024, with primary environmental, topographic, and anthropogenic predictors. Aside from classification modeling, spatiotemporal analyses such as Kernel Density Estimation (KDE), seasonal fire patterns, and influence studies on features were performed to determine high-risk seasons and areas. CSO was used to automate the hyperparameter tuning process for both classifiers, yielding a significant boost in performance. The CS-XGB model registered the top accuracy of 99.49%, better than CSORF's 98.99%. Feature importance testing confirmed ecological significance, and humidity, temperature, and rainfall were the top-ranked variables. The work adds a scalable and precise prediction model that can assist in early warning systems and forest manage-ment practices. Kamal Upreti et al. -
Spatiotemporal Forecasting and Environmental Driver Modeling of Marine Microplastic Pollution: an Interpretable Deep Learning Approach for Sustainable Ocean Policy
Marine microplastic contamination presents a significant risk to ocean health, necessitating precise spatiotemporal predictions for effective marine policy development. This study introduces a transparent deep learning model to examine and forecast microplastic levels in global oceans by leveraging historical sampling data, seasonal variations, and climatic factors. A comprehensive global dataset is curated and analyzed, integrating environmental indices such as ENSO, PDO, NAO, and MEI to model the influence of large-scale ocean-atmosphere interactions. Temporal decomposition, Mann-Kendall trend testing, Theil-Sen regression, and seasonal analysis reveal statistically significant monthly and interannual variations in microplastic concentration. Correlations with climate drivers underscore the dynamic environmental control on pollutant distribution. By incorporating interpretable environmental modeling, the proposed framework supports data-driven marine pollution mitigation and policy strategies aligned with UN Sustainable Development Goal 14 (Life Below Water). This work establishes a foundation for future extensions involving LSTM- and Transformer-based time series forecasting combined with SHAP-based explainability for enhanced decision-making. Furthermore, anomaly detection employing Prophet residuals and Isolation Forest reveals sudden increases in pollutants, providing early warning systems for disturbances to marine ecosystems. High-risk areas that need focused regulatory actions are further identified using clustering analysis. All things considered, the model makes it possible to forecast marine plastic pollution in a comprehensive, comprehensible, and scalable manner-a crucial component of sustainable ocean governance. 2025 IEEE. -
Special Military Application Antenna for Robotics Process Automation
A special military application antenna for robotics process automation is presented in the following chapter. An antenna is a device that uses wireless communication. Wireless communications main advantage is protecting our soldiers from undefined enemies. To keep this thing in mind, we have designed a special military application antenna. The presented antenna is useful for defense and satellite communication, including wi-fi and Wimax, which is useful for the robotics automation process. Most of the military robotics automation is based on wireless communication. Our proposed antenna is very useful and capable of receiving or transmitting high signals in terms of GHz. The presented geometry can radiate the large frequency band from 2.9 to 11.6 GHz, which covers the 5G-(I) Sub- 6GHz band and X-Band Communication, with high efficiency. The impedance bandwidth of the radiator is 120%, with an electrical size of .14?x.14?x0.014? in lambda. The antenna is simulated with an FR4 substrate using a CST Simulator. Simulations also investigate the 08-stages evolution process and corresponding S-parameter results are presented. The proposed structure also demonstrates stable radiation patterns across the operating bandwidth. The proposed radiator has a high gain of 6.78 dBi and an efficiency of 89%. Therefore, it is useful for 5G-(I) Sub-6GHz band and X-band military applications, including satellite mobile, Radar, and Satellite microwave communication. 2023 Scrivener Publishing LLC. -
Specialized CNN Architectures for Enhanced Image Classification Performance
Image classification is one of the important tasks in computer vision, with a greater number of applications from facial recognition, medical imaging, object recognition and many more. Convolutional Neural Networks (CNNs) have developed as the foundation for image all classification tasks, showcasing the capacity to learn the hierarchical features automatically. In this study proposed three custom CNN models and its comprehensive analysis for the image classification tasks. The models are evaluated using CIFAR-10 dataset to assess the performance and efficiency. The experimental results shows that the proposed custom CNN Model-3 performance is better than the other two models. Our findings demonstrate that Model 3, featuring with the global average pooling, achieves the highest overall accuracy of 94 % with competitive computational efficiency. This suggests that global average pooling is the valuable technique for balanced and accurate image classification. 2024 IEEE. -
Specialized Metabolites of Mangroves and Their Biological Activities
Mangroves are woody plants that are found in intertidal zones, where land meets the sea, especially in the tropical and subtropical regions of the world. They synthesize and accumulate diverse specialized metabolites that fall into major categories such as phenolics, terpenes, and alkaloids. Mangrove-derived chemical compounds have also been shown to exhibit a variety of biological properties including anticancer, antidiabetic, anti-inflammatory, antibacterial, antioxidant, and neuroprotective activities. In this chapter, we present the chemistry and biological activities of the mangrove-specialized metabolites. Springer Nature Switzerland AG 2025. -
Specialized Metabolites of Mangroves and Their Biological Activities
Mangroves are woody plants that are found in intertidal zones, where land meets the sea, especially in the tropical and subtropical regions of the world. They synthesize and accumulate diverse specialized metabolites that fall into major categories such as phenolics, terpenes, and alkaloids. Mangrove-derived chemical compounds have also been shown to exhibit a variety of biological properties including anticancer, antidiabetic, anti-inflammatory, antibacterial, antioxidant, and neuroprotective activities. In this chapter, we present the chemistry and biological activities of the mangrove-specialized metabolites. Springer Nature Switzerland AG 2026. -
Specific learning disability and psychological impact among school going adolescents
Specific Learning disability (SLD) is a mental health concern among school going children in India. Considering the need for early identification and intervention, this study has been contextualized to explore the impact. Methodology: Samples have been selectedfrom five schools which are situated in South Bengaluru, India, 100 children have been identified with SLD and further they have been screened for mental health Problems. Results: High prevalence ofmild to moderate anxiety, depression and stress is major finding of the study. High rate of anxiety (37%), depression (47%) and stress (33%) among adolescents with SLD indicate the gravity of the problem. Conclusion: Findings underline the need of the structured interventions by school psychologists in school settings. 2019 Institute for Leadership and Organization Effectiveness. All rights reserved. -
Spectral and power efficiency investigation in single- and multi-line-rate optical wavelength division multiplexed (WDM) networks
In order to tackle the increasing heterogeneous global Internet traffic, mixed-line-rate (MLR) optical wavelength division multiplexed (WDM) networks have emerged as the cost- and power-efficient solution. In MLR WDM networks, channels are structured as sub-bands, each of which consists of wavelengths operating at a similar data rate. By reducing the (1) spacing within a sub-band, or (2) spacing between sub-bands operating at different data rates, spectral efficiency can be improved. However, owing to high physical layer impairment levels, decrease in sub-band spacing adversely affects transmission reach of the channels, which results in higher power consumption due to requirement of increased signal regeneration. In this work, we compare power efficiency of various MLR and single-line-rate (SLR) solutions, and also investigate the trade-off that exists between spectral and power efficiency in a WDM network. Simulation results indicate that (1) for high transmission capacities, a combination of 100Gbps transponders and 40Gbps regenerators will obtain the highest power efficiency; (2) for long connection distances, a point ofmerging occurs for various SLR and MLR designs, where power consumption is independent of the frequency band distribution; and (3) for MLR systems, both spectral and power efficiency can be improved by using either shorter links with higher bandwidth assignment to 100Gbps wavelengths, or longer links with higher bandwidth assignment to 40Gbps wavelengths. Finally, the results indicate that focusing on spectral efficiency alone results in extra power consumption, since high quality of transmission and spectral efficiency leads to increased regeneration. 2016, Springer Science+Business Media New York. -
Spectral and power efficiency investigation in single- and multi-line-rate optical wavelength division multiplexed (WDM) networks /
Photonic Network Communications, Vol.33, Issue 1, pp.39–51, ISSN: 1572-8188 (Online) 1387-974X (Print). -
Spectral and temporal features of GX 13+1 as revealed by AstroSat
GX 13+1, a neutron star low-mass X-ray binary that exhibits the properties of both atoll and Z sources, is studied using data from Soft X-ray Telescope and Large Area X-ray Proportional Counter (LAXPC) onboard AstroSat. The source traces a ? shaped track in its hardness-intensity diagram (HID). Spectral modelling of the data in the 0.7-30.0 keV energy range, with the model-+, yields orbital inclination angle (?) of 77. Flux resolved spectral analysis reveals the ? shaped pattern in the plots of spectral parameters kTe, kTbb, and ? versus Fbol, closely resembling the pattern traced in LAXPC HID. This indicates changes in the spectral properties of the corona and the boundary layer/accretion disc. Assuming that the accretion disc truncates at the AlfvCrossed D sign n radius, the upper limit of the magnetic field strength (B) at the poles of neutron star in GX 13+1 is calculated to be 5.10 108 G (for kA = 1 and ? = 0.1), which is close to that of atoll sources. Furthermore, thickness of the boundary layer is estimated to be 5.70 km, which results in the neutron star radius value of 14.50 km. Quasi-periodic oscillations (QPOs) at 56 4 and 54 4 Hz are detected in Regions D and E of HID, respectively. The frequencies of these QPOs are similar to the characteristic frequency of horizontal branch oscillation and these do not exhibit a positive correlation with mass accretion rate. -
Spectral and temporal studies of Swift J1658.24242 using AstroSat observations with the JeTCAF model
We present the X-ray spectral and temporal analysis of the black hole X-ray transient Swift J1658.2-4242 observed by AstroSat. Three epochs of data have been analysed using the JeTCAF model to estimate the mass accretion rates and to understand the geometry of the flow. The best-fitting disc mass accretion rate (? d) varies between 0.90+-000102 and 1.09+-000304 M?Edd in these observations, while the halo mass accretion rate changes from 0.15+-000101 to 0.25+-000102 M?Edd. We estimate the size of the dynamic corona that varies substantially from 64.9+-3319 to 34.5+-1250 rg and a moderately high jet/outflow collimation factor stipulates isotropic outflow. The inferred high disc mass accretion rate and bigger corona size indicate that the source might be in the intermediate to soft spectral state of black hole X-ray binaries. The mass of the black hole estimated from different model combinations is ?14 M?. In addition, we compute the quasi-periodic oscillation (QPO) frequencies from the model-fitted parameters, which match the observed QPOs. We further calculate the binary parameters of the system from the decay profile of the light curve and the spectral parameters. The estimated orbital period of the system is 4.0 0.4 h by assuming the companion as a mid or late K-type star. Our analysis using the JeTCAF model sheds light on the physical origin of the spectrotemporal behaviour of the source, and the observed properties are mainly due to the change in both the mass accretion rates and absorbing column density. 2023 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society. -
Spectral and type I X-ray burst studies of 4U 1702?429 using AstroSat observations
4U 1702?429, an atoll-type neutron star low-mass X-ray binary, was observed twice by the AstroSat/Soft X-ray Telescope (SXT) and Large Area X-ray Proportional Counters (LAXPC-20) on 2018 April 27 and 2019 August 8. Persistent emission spectra of the source were well fitted with the model combination - constant tbabs (thcomp diskbb+powerlaw). The parameters obtained from the spectral analysis revealed the source to be in a hard spectral state during the observations. Time-resolved spectral analyses were performed on the three type I X-ray bursts detected from the source. Burst analysis showed that the source underwent a photospheric radius expansion. Consequently, the radius of the neutron star and distance to the source (with isotropic and anisotropic burst emission) were obtained as 12.65+?008690 km and 6.92+?000916 and 8.43+?001020 kpc, respectively. 2024 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society. -
Spectral and type I X-ray burst studies of M15 X-2 using NICER observations
In this work, we present spectral and burst analyses of three thermonuclear type I X-ray bursts (B1, B2, and B3) detected from the ultracompact neutron star low-mass X-ray binary M15 X-2, using data from the Neutron Star Interior Composition Explorer (NICER). Time-averaged spectral fitting with the model tbabsthcompiskbb) suggests that the source was in a soft or soft-intermediate spectral state, characterized by a photon power law index of ??2 and an average mass accretion rate of ?0.09 m?Edd. The type I X-ray bursts exhibited rapid rise times of 1.25?2.75 s, followed by longer decay phases lasting 14.50?23.25 s, with characteristic burst timescales (?) of ?11 s, which are consistent with pure helium burning. Notably, burst B3 displayed a double-peaked profile indicative of a photospheric radius expansion event, from which we inferred the neutron star radius to be 10.8+2.4?2.2 km. Based on the peak flux of the burst, we estimated the source distance to be 10.54+1.43?1.26 kpc under the assumption of isotropic emission, and 14.06+1.90?1.68 kpc for anisotropic emission geometry. A strong ?420 Hz burst oscillation candidate was detected in the cooling tail of burst B1. 2025 Elsevier B.V. -
Spectral characteristics of the black hole binary 4U 1957+115: a multi mission perspective
We report spectral analysis of the persistent black hole X-ray binary, 4U 1957+115, using AstroSat, Swift, and NuSTAR observations carried out between 2016 and 2019. Modelling with a disc emission, thermal Comptonization, and blurred reflection components revealed that the source was in the high-soft state with the disc flux ?87 per cent of the total and high-energy photon index ?2.6. There is an evidence that either the inner disc radius varied by ?25 per cent or the colour hardening factor changed by ?12 per cent. The values of the inner disc radius imply that for a non-spinning black hole, the black hole mass is < 7 M ? and the source is located > 30 kpc away. On the other hand, a rapidly spinning black hole would be consistent with the more plausible black hole mass of < 10 M ? and a source distance of ?10 kpc. Fixing the distance to 10 kpc and using a relativistic accretion disc model, constrained the black hole mass to 6 M? and inclination angle to 72. A positive correlation is detected between the accretion rate and inner radii or equivalently between the accretion rate and colour factor. 2022 The Author(s).