Divyansh 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.