The University of Allahabad is a collegiate central university located in Allahbad, Uttar Pradesh, India. It was established on 23 September 1887 by an Act of Parliament and is recognised as an Institute of National Importance (INI). It is one of the oldest modern universities in India. Its origins lie in the Muir Central College, named after Lt. Governor of North-Western Provinces, Sir William Muir in 1876, who suggested the idea of a Central University at Allahabad, which later evolved to the present university. it was known as the "Oxford of the East". Its Central University status was re-established through the University of Allahabad Act 2005 by the Parliament of India.
This study presents a comprehensive machine learning framework for the optimisation of metallurgical parameters for the grinding, leaching and solid/liquid separation in a Congolese Copperbelt hydrometallurgical plant. Three deep learning architectures namely Deep Neural Network (DNN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) were designed, trained, and comparatively evaluated. Models were trained to predict Cu and Co leaching recoveries from ten critical physicochemical process variables: feed rate, particle size distribution (PSD), percentage solids at key settling or decantation stages (Leach feed thickener (LFT), Counter current decantation (CCD4), Leach discharge thickener (LDT)), metal losses in overflow streams, and flocculant addition rates. The DNN achieved superior predictive performance with MAE = 1.328 % and R2 = 0.721 for Cu recovery, substantially outperforming CNN (MAE = 2.381 %, R2 = 0.017) and LSTM (MAE = 2.421 %, R2 = − 0.024). Permutation-based feature importance identifies PSD (granulometry) as the dominant predictor (ΔMAE = +0.368), followed by metal losses and thickener underflow densities. A 3-month DNN forecast projects Cu recovery improving from the 2024–2025 mean of 96.0 % to 97.2–97.8 % as gradual PSD and LDT improvements are implemented, with Co recovery reaching 79.8–80.0 %. The critical operational recommendation is to improve milling fineness from the current mean PSD of 61 % to the 75–80 % at 75 µm target range through SAG mill circuit optimisation.
Organised cybercrime has emerged as a major threat to global security, economies, and society in the digital era. Unlike traditional organised crime, which relies on geographically based groups, cybercrime operates through borderless, anonymous, and highly scalable digital networks. Advances in communication technologies have enabled cybercriminals to conduct ransomware attacks, financial fraud, cyber espionage, cryptocurrency laundering, and illicit online trading with greater sophistication. This chapter examines the evolution, theoretical foundations, organisational structures, technological enablers, operational strategies, and network architectures of organised cybercrime, highlighting its relationship with traditional organised crime. It also explores emerging trends and future challenges, providing insights that support the development of effective detection, prevention, and law enforcement strategies in an increasingly interconnected digital world.
Stable carbon isotope (δ13C) fractionation is an integral process in soil organic matter (SOM) dynamics, typically resulting in 13C-depleted respired CO2 and 13C-enriched residual pools. However, the long-term isotopic trajectory of specific molecular fractions during ecosystem degradation remains poorly understood. We utilized glomalin-related soil protein (GRSP) as a robust SOM proxy to trace these isotopic shifts across a land-use chronosequence, comprising forest (initial), 5-year fallow (intermediate), and 47-year agriculture (late stage), along with their respective one-year laboratory-incubated counterparts. Our results reveal a progressive, stage-dependent ‘isotopic convergence’ as degradation progresses. In undisturbed forests, the soil micro-architecture maintained high isotopic heterogeneity across sequential GRSP fractions (−27.1‰ to −23.7‰), reflecting the sequestration of distinct carbon inputs. This isotopic diversity narrows moderately in the transitional fallow soils before converging completely toward a uniform, 13C-enriched signature (−22.8‰) in the long-term agricultural soils. This isotopic terminality reflects a state of functional exhaustion, where the lack of fresh organic inputs and physical breakdown of the soil matrix is consistent with a potential metabolic shift toward the utilization of historical carbon reserves. Our findings also demonstrate that the “labile-stable” continuum of GRSP is not an intrinsic chemical property, but a state-dependent feature of a healthy soil matrix. Ultimately, this study establishes δ13C variance across GRSP fractions as a high-resolution fingerprint for tracking bidirectional state transitions between functional carbon sinks and exhausted soils.
Electric vehicles’ (EVs’) accelerated expansion is changing transportation systems and making managing the charging infrastructure and power grid much more difficult. In order to handle issues like demand variations, charging congestion, grid instability, intermittent renewable energy, and cybersecurity risks, intelligent coordination techniques are needed. Through data-driven forecasting, predictive control, and automated decision-making, artificial intelligence (AI) has become a crucial enabling technology for optimizing EV charging networks. An organized and thorough analysis of AI methods used in intelligent infrastructure for EV charging is presented in this research. System architecture, operational difficulties, optimization techniques, deployment obstacles, and new research avenues are all methodically examined in this paper. To guarantee openness and repeatability, a PRISMA-based approach for choosing literature is used. The scalability and practical viability of major AI technologies are compared, including deep learning for predictive maintenance, optimization-based scheduling, machine learning for demand forecasting, and reinforcement learning in order for real-time charging control. Additionally, this assessment highlights important implementation gaps in the areas of economic modeling, cybersecurity integration, distributed intelligence, and standards. Future research avenues for secure, self-optimizing, and autonomous charging ecosystems are explored. Academics, system operators, and politicians working toward the widespread implementation of AI-enabled EV infrastructure can use the findings as technical assistance.
We develop a thermodynamically consistent nonperturbative framework for equilibrium criticality in QCD matter by unifying Dyson-Schwinger quark propagation, functional renormalization-group (FRG) evolution of the effective action, and Polyakov- Nambu-Jona-Lasinio (PNJL) thermodynamics for the coupled chiral and deconfinement order parameters. A holographic Maxwell-Chern-Simons sector supplies the topological response, and its topological susceptibility is fed into the FRG flow of the determinantal ('t Hooft) interaction to encode the evolution of the axial anomaly across the phase diagram. At mu(B) = 0, the construction is anchored to continuum-extrapolated lattice thermodynamics and conserved-charge susceptibilities through a lattice-calibrated Polyakov sector, while exact thermodynamic identities are enforced by evaluating all derivatives at the stationary solution of the grand potential at each RG scale. Solving the coupled DSE, FRG, and holographic system yields, within this framework and at the present level of approximation, an equilibrium critical end point (CEP) at T-CEP similar or equal to 130 to 135 MeV and mu(B,CEP) similar or equal to 600 MeV together with an internally quantified sensitivity to regulator, Polyakov-sector, and holographicnormalization variations. The critical region is organized by a nonperturbative mapping to universal 3D Ising scaling variables with anomalous-dimension effects absorbed into nonuniversal metric factors, leading to equilibrium predictionsfor the hierarchy, nonmonotonicity, and sign structure of higher-order net-baryon cumulant ratios along the smooth freeze-out trajectories, as well as equilibrium softening of the speed of sound. Comparisons to RHIC beam energy scan fluctuation measurements are presented as qualitative consistency checks on correlated equilibrium trends and sign patterns, because finite size and lifetime, critical slowing down, baryon number conservation, acceptance and efficiency corrections, the net-proton to net-baryon conversion, and baryon-transport dynamics can round or reshape cumulants in the experimental system. The results, therefore, provide a unified equilibrium baseline and a set of controlled inputs for finite-size scaling and dynamical embeddings of heavy-ion data.
