RAJESH KUMAR MISHRA

https://doi.org/10.64823/ijter.2608004

Galactic cosmic rays (GCRs) entering the heliosphere are modulated by the time-varying solar wind and heliospheric magnetic field, producing the well-documented 11-year (Schwabe) and 22-year (Hale) cyclic variation in cosmic ray intensity observed at Earth, together with transient, CME-driven depressions known as Forbush decreases (FDs). This paper (i) reviews the physical basis of cosmic ray modulation, centered on the force-field approximation of Gleeson and Axford and its use in reconstructing the solar modulation potential; (ii) reviews the growing literature applying machine learning and deep learning to cosmic ray intensity and Forbush-decrease forecasting; and (iii) reports two complementary, fully reproducible simulation-based experiments. First, a multi-solar-cycle (62-year) force-field simulator reproduces the characteristic anti-correlation between sunspot number and cosmic ray intensity (correlation coefficient r = -0.904) and the drift-related hysteresis loop between cosmic ray intensity and solar activity for opposite heliospheric magnetic polarity states. Second, an hourly-resolution, 3-year Forbush-decrease simulator, driven by the same class of solar wind turbulence proxies used in the companion geomagnetic-storm study, was used to train and evaluate Random Forest, Gradient Boosting, Multilayer Perceptron, and linear baseline models at 6-, 24-, and 72-hour forecast horizons under a chronologically leakage-aware protocol. The best model (MLP) achieved RMSE = 0.41% and R² = 0.965 at 6 hours, degrading to RMSE = 1.41% and R² = 0.592 at 24 hours, and to marginal skill by 72 hours, closely mirroring the multi-horizon skill-decay pattern reported for geomagnetic Dst forecasting and for real Forbush-decrease nowcasting studies in the literature. Feature-importance and ablation analysis confirm that recent cosmic-ray history and the local interplanetary magnetic field magnitude dominate short-horizon predictability. Because live access to real neutron monitor and OMNI archives was not available in this environment, both experiments are explicitly disclosed as physically-grounded simulations rather than analyses of observational data, and the paper concludes with a discussion of the sim-to-real gap, operational relevance (radiation dose forecasting, single-event upsets, atmospheric ionization), and directions for future work.

https://doi.org/10.64823/ijcee.2601003

River water quality is governed by the interaction of hydrology, temperature-dependent biochemical kinetics, and episodic pollution loading, making both long-term trend characterization and short-term event forecasting central problems in civil and environmental engineering. This paper (i) reviews the classical Streeter-Phelps oxygen-sag framework and the Water Quality Index (WQI) concept that together underpin most operational water-quality assessment; (ii) reviews the rapidly growing literature applying machine learning (ML) to water quality and dissolved-oxygen (DO) prediction; and (iii) reports two complementary, fully reproducible simulation-based experiments. First, a 20-year daily simulation, grounded in the standard Benson-Krause DO-saturation-temperature relationship, quantifies a thermally-driven decline in DO saturation capacity of 0.072 mg/L per decade under a modest (+0.35 °C/decade) warming trend, with temperature and DO saturation correlated at r = -0.997. Second, an hourly-resolution, 3-year Streeter-Phelps pollution-event simulator, driven by episodic biochemical oxygen demand (BOD) loading pulses analogous in mathematical structure to the injection/decay processes used in companion geophysical forecasting studies, was used to train and evaluate Random Forest, Gradient Boosting, Multilayer Perceptron, and linear baseline models predicting WQI at 6-, 24-, and 72-hour horizons under a chronologically leakage-aware protocol. The best model (Gradient Boosting) achieved RMSE = 3.62 WQI points and R² = 0.441 at 6 hours, degrading to R² ≈ 0.10-0.13 by 24-72 hours, a steeper skill decay than reported for single-variable geophysical indices, attributed to the compounding of independent noise sources across the five WQI sub-indices. Feature-importance and ablation analysis show that recent WQI history dominates short-horizon predictability, while hydrological drivers (temperature, flow) alone are competitive at longer horizons. Because live access to real river-monitoring archives was not available in this environment, both experiments are explicitly disclosed as physically-grounded simulations rather than analyses of observational data, and the paper concludes with a discussion of the sim-to-real gap, operational relevance for water treatment and pollution-control decision-making, and directions for future work.

https://doi.org/10.64823/ijcml.2601003

The news media industry has undergone one of the most sustained business model disruptions of any sector in the digital economy, as advertising revenue has migrated to platform intermediaries, audiences have fragmented across social and video platforms, and generative artificial intelligence has begun to reshape both news production and news discovery. This paper presents a systematic, thematically organized review of academic and high-quality industry literature on the digital transformation of news media business models, published or released primarily between 2014 and 2026. Following a PRISMA-inspired identification, screening, and eligibility process, 48 sources were retained for thematic synthesis, spanning peer-reviewed communication and media-management scholarship, and authoritative industry and statistical sources including the Reuters Institute Digital News Report, the Pew Research Center, Statistics Canada, and Press Gazette's journalism job-cuts tracker. The review identifies five interlocking themes: the structural collapse of advertising-based revenue models; the rise, and subsequent plateauing, of reader-revenue and subscription models; growing platform dependency and algorithmic distribution risk; sustained newsroom restructuring and employment contraction; and the accelerating, ambivalent integration of artificial intelligence into news production and distribution. Drawing on this synthesis, the paper proposes a five-stage conceptual model from print-centric models through digitization, platformization, the reader-revenue pivot, to the AI-augmented newsroom that situates these themes within a single explanatory framework. The paper closes with theoretical and practical implications for media managers, journalism educators, and policymakers, and identifies priority directions for future research, including the long-term sustainability of reader-revenue models and the net effect of generative AI on public trust in journalism.

https://doi.org/10.64823/ijpcs.2601003

Cosmic ray modulation is one of the most fundamental processes in heliophysics, describing the temporal, spatial, and energy-dependent variation of galactic cosmic ray (GCR) intensities as they propagate through the turbulent heliosphere before reaching Earth. The heliosphere, formed by the continuous expansion of the solar wind and permeated by the heliospheric magnetic field (HMF), acts as a dynamic magnetic shield that modifies the transport of energetic charged particles through diffusion, convection, gradient and curvature drifts, and adiabatic energy changes. These transport mechanisms are strongly influenced by the approximately 11-year solar activity cycle and the 22-year Hale magnetic polarity cycle, resulting in long-term modulation of cosmic ray fluxes as well as short-term transient phenomena associated with coronal mass ejections (CMEs), interplanetary shocks, magnetic clouds, and high-speed solar wind streams. Understanding cosmic ray modulation is essential not only for advancing heliospheric physics but also for improving space weather forecasting, assessing radiation hazards to astronauts and spacecraft, protecting satellite electronics, ensuring aviation safety on polar routes, and investigating atmospheric ionization and cosmogenic isotope production. This review presents a comprehensive synthesis of the current understanding of galactic cosmic ray modulation by integrating classical transport theory, heliospheric plasma physics, long-term observational datasets, numerical simulations, and emerging artificial intelligence (AI) techniques. The review begins by discussing the origin, acceleration mechanisms, energy spectrum, and chemical composition of cosmic rays, followed by an examination of heliospheric structure, including the solar wind, Parker spiral magnetic field, heliospheric current sheet, termination shock, heliosheath, heliopause, and the local interstellar medium. Particular emphasis is placed on the Parker Transport Equation, which forms the theoretical foundation for describing cosmic ray propagation through the combined effects of anisotropic spatial diffusion, solar wind convection, gradient and curvature drifts, current-sheet drift, and adiabatic energy losses. The review further examines the modulation of cosmic rays during Solar Cycles 20–25, highlighting the influence of solar magnetic polarity reversals, heliospheric magnetic field evolution, magnetic turbulence, recurrent high-speed streams, and transient solar eruptions on cosmic ray intensity. A critical assessment is presented of observational evidence obtained from ground-based neutron monitor networks and major space missions, including Voyager 1 and 2, Ulysses, ACE, SOHO, STEREO, PAMELA, AMS-02, Parker Solar Probe, Solar Orbiter, and Aditya-L1, which together provide continuous measurements of energetic particles, solar wind plasma, and heliospheric magnetic fields across multiple spatial scales. Modern numerical approaches—including finite-difference methods, stochastic differential equation (SDE) models, Monte Carlo simulations, and three-dimensional magnetohydrodynamic (MHD) heliospheric models such as HelMod, WSA–ENLIL, and EUHFORIA—are critically compared with respect to their capabilities and limitations in reproducing observed modulation patterns. The review also highlights the rapidly expanding role of artificial intelligence and machine learning in cosmic ray research. Recent developments involving recurrent neural networks (RNNs), long short-term memory (LSTM) networks, convolutional neural networks (CNNs), transformer architectures, random forests, gradient boosting algorithms, physics-informed neural networks (PINNs), explainable artificial intelligence (XAI), and digital-twin frameworks are examined for their ability to improve prediction of neutron monitor counts, Forbush decreases, solar energetic particle events, and heliospheric transport parameters. These data-driven methods, when integrated with first-principles transport theory, offer significant potential for real-time forecasting and operational space weather services. Finally, the review identifies major scientific challenges related to turbulence modeling, time-dependent heliospheric structure, multi-scale particle transport, uncertainty quantification, and physics–AI integration. Future research directions are proposed that emphasize high-performance computing, data assimilation, digital heliosphere modeling, multi-spacecraft observations, and next-generation AI-assisted forecasting systems. By combining theoretical developments, observational advances, computational methodologies, and intelligent data-driven techniques, this review provides a comprehensive and up-to-date perspective on cosmic ray modulation and its broad implications for heliophysics, astrophysics, and space weather science.

https://doi.org/10.64823/ijpcs.2601002

Geomagnetic storms, driven primarily by coronal mass ejections (CMEs) and high-speed solar wind streams interacting with the Earth's magnetosphere, pose significant risks to satellite operations, power grid infrastructure, aviation, and communication systems. Traditional empirical and physics-based models of geomagnetic indices such as Dst and Kp, while physically interpretable, often struggle to capture the highly nonlinear and non-stationary dynamics of solar wind-magnetosphere coupling, particularly during the storm recovery phase. Artificial Intelligence (AI) techniques, including artificial neural networks, ensemble tree-based methods, and multilayer perceptron (MLP) models, have demonstrated considerable promise for improving the accuracy and lead time of geomagnetic storm forecasts. This paper (i) reviews the evolution of AI-based approaches to Dst/Kp prediction; (ii) proposes a consolidated methodological framework for building and validating AI-driven space-weather forecasting systems; and (iii) reports a complete, reproducible experimental case study in which a physically-grounded Burton-McPherron-Russell (BMR) ring-current simulator was used to generate a multi-year hourly solar-wind-driven Dst dataset, on which Random Forest, Gradient Boosting, Multilayer Perceptron, and linear baseline models were trained and evaluated under a chronologically leakage-aware train/validation/test protocol at 1-, 3-, and 6-hour forecast horizons. The best-performing model (MLP) achieved RMSE = 3.82 nT and R² = 0.956 at the 1-hour horizon, degrading to RMSE = 10.27 nT and R² = 0.684 at 6 hours, consistent with the general skill-decay pattern reported in the literature for physically driven Dst forecasting. Feature importance analysis confirmed that the most recent lagged Dst value and the 6-hour rolling mean of the southward IMF component (Bz) dominate predictive skill. Key challenges relating to data leakage, class imbalance for extreme events, multimodal data fusion, and model interpretability are discussed, along with directions for future research including physics-informed machine learning and probabilistic forecasting.

https://doi.org/10.64823/ijpcs.2601001

Artificial intelligence (AI) and machine learning (ML) are increasingly used as general-purpose research instruments across the physical sciences, accelerating tasks that were traditionally limited by trial-and-error experimentation, computational cost, or the sheer dimensionality of the underlying physics. Three areas illustrate this shift with particular clarity: computational materials discovery, nanostructured electrode design for energy storage, and space weather / heliophysics forecasting. Despite substantial progress in each area individually, limited work has examined them together, quantitatively, as expressions of a single underlying trend — the convergence of AI methodology with core physical and chemical science. Methods: This study used a narrative and scoping review methodology, incorporating quantitative benchmarks drawn directly from primary and independent critical sources. Peer-reviewed literature, preprints, direct observational monitoring data, and ResearchGate-hosted scholarly works published primarily between 2023 and 2026 were identified through structured searches combining terms from materials informatics, nanostructured energy-storage materials, and AI-based space weather forecasting. Sources were screened for topical relevance and synthesized thematically, with reported quantitative claims cross-checked against independent critical appraisal where available. Results: The synthesis identifies convergent innovation across three domains, with all three now quantitatively documented: (1) AI-driven inverse design (exemplified by a 2.2-million-structure materials search yielding roughly 380,000 candidate stable materials) is accelerating materials discovery, though independent re-analysis found only a small fraction of these structures met joint criteria of novelty, credibility, and utility; (2) nanostructured graphene–metal oxide composite electrodes span a wide reported performance envelope (specific capacitances from roughly 100 F/g to over 1000 F/g; energy densities up to roughly 100+ Wh/kg), with statistically designed synthesis optimization improving reproducibility; and (3) AI-assisted forecasting achieved approximately one-minute precision in reconstructing a major 2024 geomagnetic superstorm, in contrast to a roughly 40% amplitude error and nine-month timing error in the leading pre-cycle statistical/physical forecast of Solar Cycle 25's overall intensity. Discussion: A common methodological pattern recurs across all three domains: AI is used not to replace physical theory but to navigate high-dimensional parameter spaces that are analytically or computationally intractable by classical means alone, and the strongest, most defensible results are those subjected to independent, domain-expert critical appraisal rather than accepted at face value. Conclusion: Continued progress will depend on higher-quality shared datasets, physics-informed model architectures, standardized benchmarking protocols, and routine independent critical appraisal as a formal part of the AI-for-science publication cycle.