Pandit Ravishankar Shukla University

Traditional solid fuels are extensively used for domestic heating and cooking in developing countries, especially in rural and semi-urban regions. Combustion of these fuels is a major source of nitrogen oxides (NOx), sulfur dioxide (SO2), and volatile organic compounds (TVOCs), which significantly contribute to air pollution, respiratory disorders, secondary aerosol formation, and atmospheric photochemical reactions. The generation and release of these pollutants are strongly influenced by fuel moisture content, elemental composition, and inorganic constituents. This study presents a comprehensive investigation of the chemical characteristics of commonly used solid fuels and evaluates the potential of advanced functional materials for mitigating NOx, SO2, and TVOC emissions from domestic combustion sources.Representative fuel samples, including fuel wood (FW), coal balls (CB), dung cake (DC), and crop residues (CR), were obtained from the Raipur–Durg–Bhilai region of Chhattisgarh, India, selected based on their prevalence and area-specific usage patterns. The samples were air-dried, pulverized, and homogenized prior to analysis. Moisture content was determined gravimetrically by oven drying at 105 °C. Ultimate analysis of carbon (C), hydrogen (H), nitrogen (N), sulfur (S), and oxygen (O) was performed using a CHNS/O elemental analyzer. Ionic species, including nitrate, sulfate, chloride, and major alkali and alkaline earth metals, were quantified using ion chromatography to assess their role in pollutant formation and combustion behavior. These chemical parameters were used to infer emission potential for NOx, SO2, and TVOCs.NO emissions were generally higher for AR and DC, while FW showed the lowest NO EF. SO2 emissions followed a similar trend, with DC producing the highest levels and FW the lowest. TVOC emissions were elevated for fuels with higher moisture and inorganic content, such as AR and DC, whereas FW exhibited the lowest TVOC emission potential. CB displayed intermediate to high emissions, with particularly high TVOC formation due to its variable composition. Emission factors developed in simulated experimental chambers were validated against real-world measurements, indicating that domestic household emissions closely correspond to chamber-based estimates.To address post-combustion emission control, advanced materials including graphene-based materials, biochar, graphitic carbon nitride (g-C3N4), metal oxides (MnO2/TiO2), zeolites, metal–organic frameworks (MOFs), covalent organic frameworks (COFs), and silica-based adsorbents were considered for NOx, SO2, and TVOC mitigation. Materials were characterized using BET surface area analysis, X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM), confirming high surface activity and strong gas affinity. Their physicochemical properties, high specific surface area, tunable pore size distribution, and surface functional groups, enable efficient adsorption and catalytic transformation of pollutants. Graphene-based materials and biochar adsorb acidic gases through π–π interactions and surface oxygen functional groups, while g-C3N4 facilitates photocatalytic oxidation of NOx under visible light. Metal oxides such as MnO2/TiO2 catalyze the oxidation of SO2 to sulfate and TVOCs to less harmful products via surface redox cycles. Zeolites and MOFs provide selective adsorption of NOx and TVOCs through microporous confinement and acid–base interactions.

The print media is ever getting pressurized to produce output of high quality, customized output, shorter turn-around time, low operation cost and enhanced sustainability. The customary procedures of print media management, which are manualized in coordinating, fixed scheduling as well as fragmented data disclosures are progressively insufficient to the requirements. The given paper explores the implementation of intelligent workflow automation in the management of print media using the combination of artificial intelligence, machine learning, Internet of Things (IoT), and digital twin technologies. A perception-cognition-action-based paradigm is suggested to be the structured AI-driven workflow architecture to allow the adaptive data-driven orchestration between pre-press, printing, post-press, and distribution stages. Predictive quality control, dynamic scheduling, anomaly detection, and resource optimization are performed with the help of machine learning and decision-support models, whereas real-time physical-virtual synchronization and scenario-based planning is performed with the help of digital twins. The quantitative assessment indicates that there are great improvements in turnaround time, schedule compliance, machine use, quality rework, downtime, material waste, and energy usage. The findings reveal that intelligent workflow automation has multidimensional benefits of efficient operations, cost-saving, and sustainability, and maintains human controls by providing supervisory decision-support interfaces. The research paper finds that intelligent workflow automation is a strategic asset in the production workflow of contemporary print media organizations that aim to have resilient, efficient, and sustainable production systems.

The disappearance of modernist artworks through the historical conflict, material degradation and partiality of archival regimes poses a continuous problem in terms of art history and cultural heritage conservation. It is a foregone conclusion that such works are difficult to reconstruct, since modernist aesthetics is focused on abstraction, fragmentation, and conceptual purpose over representational devotion. In this paper, the author suggests a reconstruction model based on AI that views artistic reconstruction as a probabilistic inference problem, as opposed to a deterministic recovery problem. The system is already based on the framework of representation learning, conditional generative modeling, uncertainty-aware sampling, and human-in-the-loop validation to produce several possible discovery hypotheses based on the existing evidence and style samples. The hierarchical style modeling strategy gives reconstruction possibilities at artist and movements levels, whereas explicit uncertainty signs circumvent overconfidence in interpretation. The framework is tested by using representative case studies which are partially documented, partially fragmented and entirely lost modernist artworks. The quantitative metrics of consistency, the analysis of perceptual styles, and uncertainty analysis and the qualitative evaluation process performed by experts prove that the reconstruction fidelity is proportional to the strength of evidence and that the uncertainty is explicitly magnified in the case of sparse constraints. The findings indicate the significance of AI as a supportive analysis tool that can aid the judgment of curators and art-historians but does not sacrifice the interpretative responsibility.

High-throughput screening (HTS) is a crucial technique that identifies hits using chemical libraries, which can serve as leads for optimizing phytomedicines. Screening of natural extracts using HTS involves automated techniques and robotics. The primary techniques employed are microscopic biological tests, automated liquid handling, transfer processes, and automated quantitative assay readouts. Furthermore, integrating HTS into numerous cell-based assays facilitates the discovery procedure of phytomedicines. The advancement of multiple libraries, notably the fraction and natural product extract libraries, as well as the identification of bioactive compounds, has been greatly supported by HTS, as discussed thoroughly in this chapter. Meanwhile, this chapter pertains to the advantages and challenges of HTS in phytomedicine discovery.

Phytocompounds have shown potential in combating human pathogens, including bacteria, fungi, and viruses, by disrupting their key biological processes and inhibiting their proliferation. This chapter explores the in-depth analysis of the biomechanics of natural products and phytocompounds as an antipathogenic agent. The chapter also provides an overview of molecular and cellular mechanisms, including biofilm inhibition, efflux pump modulation, cell wall and cell membrane disruption, antioxidant activity, and immunomodulatory actions. These mechanisms target vital pathogens’ systems, help treat infection, and provide solutions to resistance development. The innovation in antipathogenic strategies, particularly concentrating on associating biomechanical studies with other technologies, such as nanotechnology, and bioinformatics to improve phytocompound-based therapies, is also expounded in detail.