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Synthesis, Characterization, And Bioactivity Evaluation Of (E)-N-(2-Fluoro-6-hydroxybenzylidene)-4-methoxybenzohydrazide And Its Ni(II), Co(II), Cu(II), Pd(II), Mn(II), And Fe(II) Complexes

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Authors: Ranjan Kumar, Vishal Kumar, Niranjan Kumar Mandal

Abstract: This study reports the successful synthesis and comprehensive characterization of a novel hydrazone ligand, (E)-N'-(2-fluoro-6-hydroxybenzylidene)-4-methoxybenzohydrazide, along with its coordination complexes with nickel(II), cobalt(II), copper(II), palladium(II), manganese(II), and iron(II) ions. Structural elucidation was achieved through spectroscopic techniques including FT-IR, 1H and 13C NMR, and mass spectrometry, confirming ligand coordination and complex formation. The antibacterial and antifungal activities of the free ligand and its metal complexes were evaluated against a range of pathogenic strains. The complexes exhibited enhanced antimicrobial efficacy relative to the parent ligand, with significant inhibitory zones observed against Gram-positive and Gram-negative bacteria, as well as fungal species. These findings suggest that metal complexation improves bioactivity, indicative of potential applications in therapeutic agent development. This work contributes to the growing field of bioinorganic chemistry by providing insight into structure-activity relationships of hydrazone-metal complexes as promising antimicrobial candidates.

DOI: https://doi.org/10.5281/zenodo.17121205

 

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Synthesis, Spectroscopic Characterization, And Biological Evaluation Of (2E,3Z)-3-(((E)-2,4-dichlorobenzylidene)hydrazono)butan-2-one Oxime And Its Fe(II), Cu(II), Co(II), Ni(II), And Mn(II) Complexes

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Authors: C. S. Azad, Prateek Mohan Mishra, Niranjan Kumar Mandal

Abstract: A novel Schiff base ligand, (2E,3Z)-3-(((E)-2,3-dichlorobenzylidene)hydrazono)butan-2-one oxime, was synthesized and characterized, along with its coordination complexes with Mn(II), Fe(II), Cu(II), Zn(II), Cd(II), Hg(II), Ni(II), and Pd(II) ions. The ligand and its metal complexes were characterized using various spectroscopic techniques, including FTIR, NMR, UV-Vis, and elemental analysis, to confirm their structural integrity and coordination behavior. The antibacterial and antifungal activities of the ligand and its complexes were evaluated against a range of pathogenic bacterial strains (e.g., Escherichia coli, Staphylococcus aureus) and fungal strains (e.g., Candida albicans, Aspergillus niger). The metal complexes exhibited enhanced antimicrobial activity compared to the free ligand, with the Cu(II) and Ni(II) complexes showing particularly potent inhibitory effects. The structure-activity relationship suggests that the coordination of metal ions enhances the lipophilicity and interaction with microbial cell membranes, thereby improving bioactivity. These findings suggest potential applications of these complexes in the development of new antimicrobial agents.

DOI: https://doi.org/10.5281/zenodo.17120985

 

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Development Of Acylated Pyrazole-Containing Heterocyclic Chalcones: Synthesis, Spectral Studies, And Antibacterial Assessment

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Authors: Ranjan Kumar, Niranjan Kumar Mandala, Poonam Kumaria

Abstract: In this study, a novel series of 1-[3-(4-fluoro-3-methylphenyl)-5-phenyl-4,5-dihydro-1H-pyrazol-1-yl]ethan-1-one derivatives (3a-i) were synthesized, and their chemical structures were studied by 1H NMR, IR, and mass spectroscopy. TLC was used to examine the products that were isolated to determine their level of purity. The results of this study show that these derivatives have interesting properties. The disc diffusion method was used to test the in vitro antimicrobial activity of the synthesized compounds against Escherichia coli (MCC 2412), Staphylococcus aureus (MCC 2408), Bacillus subtilis (MCC 2010), Pseudomonas aeruginosa (MCC 2080), Saccharomyces cerevisiae (MCC 1033), and Candida albicans (MCC 1439).

DOI: https://doi.org/10.5281/zenodo.17120673

 

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Water Pollution In Two Canals Across The Ajay River Due To Coal Mining: A Seasonal Analysis

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Authors: Dr. Sanjay Kumar Singh, Mr. Sujeet Kumar, Dr. Niranjan Kumar Mandal

Abstract: Coal mining activities significantly contribute to the degradation of water quality, especially in areas close to mining operations. This study examines the water quality in two different canals across the Ajay River, assessing seasonal variations in physicochemical parameters and heavy metal concentrations during the rainy, winter, and summer seasons. Parameters such as pH, turbidity, conductance, hardness, alkalinity, total solids, and concentrations of heavy metals including arsenic, iron, zinc, and others were evaluated. Results indicate that water pollution fluctuates seasonally, with the highest contamination observed in the rainy season. These findings underscore the need for continuous monitoring and effective water management strategies to mitigate the adverse effects of coal mining on water quality.

DOI: https://doi.org/10.5281/zenodo.17120411

 

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Experimental Study On The Properties Of Concrete Using Marble Powder And Steel Fibres As Partial Cement Replacement

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Authors: Deepak Kumar Mishra

Abstract: Concrete, a fundamental material in construction, is increasingly being modified to incorporate sustainable alternatives that enhance performance while minimizing environmental impact. This study investigates the effects of partially replacing cement with marble powder and adding steel fibres in varying proportions (0%, 0.5%, 1%, 1.5%, and 2.0%) on the mechanical properties of M25 grade concrete. Results show that a mix containing 15% marble powder and 1% steel fibre achieves optimal compressive, split tensile, and flexural strength at 28 days. The marble powder improves workability due to its smooth texture and spherical shape, while the addition of steel fibre, though reducing workability, enhances bonding and overall strength. The findings suggest that the combination of marble powder and steel fibres can be effectively used in structural applications such as multistoried buildings and bridges. A recommended optimal mix of 15% marble powder and 1% steel fibre offers the best performance, though further long-term studies are advised to assess durability and field performance.

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Socio-Economic-Factors Affecting Fresh Tomato Marketing In Kitgum Main Market, Uganda.

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Authors: Denish Ocira, Edward Ssemakula

Abstract: The continuous rise of urbanization has led to an overwhelming increase in waste generation with serious consequences for the environment and humans. Most waste disposal methods are inefficient, with little accountability or participation from the community, hence we propose a Smart Waste Management System (SWMS) built on AI technologies that employs computer vision and cloud computing to track on a real-time basis, facilitating improved waste sorting and the complaint making towards upcycling. The system allows the community to upload pictures of items to be reused and are identified as categories using an artificial intelligence model through which there is a trgging of the item for appropriate action. The platform also enables conversations on tracking complaints and donations of reusable items, thereby enabling data emergence for urban waste management authorities in making decisions. This paper explains the system design and implementation and is sustainability implications.

DOI: https://doi.org/10.5281/zenodo.17119882

 

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Adaptive Credit Card Fraud Detection Using Machine Learning And Deep Reinforcement Learning_699

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Authors: Sai Rithwik Nooguri

Abstract: Credit card fraud detection is a challenge in the financial sector, where the rarity of fraudulent transactions makes accurate classification particularly difficult. This study presents a comprehensive approach that integrates data preprocessing, resampling techniques, traditional machine learning models, anomaly detection methods, and deep reinforcement learning for effective fraud detection. Initially, extensive exploratory data analysis (EDA) was conducted, followed by handling missing values and applying Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance. A variety of supervised models, including Logistic Regression, Random Forest, XGBoost, and Multi-Layer Perceptron (MLP), as well as unsupervised anomaly detection methods like Isolation Forest and Local Outlier Factor, were evaluated. Subsequently, a Deep Q-Learning Network (DQN) was implemented to model fraud detection as a sequential decision-making problem, allowing the system to dynamically learn fraud patterns. The experimental results demonstrate that DQN achieved high precision, recall, and F1- score, outperforming several traditional classifiers. This study highlights the importance of combining classical and modern learning paradigms to enhance information assurance in credit card transaction systems. The code supports reproducibility and future research.

DOI: https://doi.org/10.5281/zenodo.17119185

 

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Public-Private Partnerships: Catalyzing Sustainable Infrastructure and Service Innovation

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Authors: Podapala Siva Reddy, Ch. V. Radhika, Gadiraju Parvathi

Abstract: Public-private partnerships (PPPs) have changed over centuries, notably since the Roman Empire, to modern forms of infrastructure production and public service delivery. There has been a resurgence of interest beginning in the late twentieth century in seeing PPPs as a way to engage in infrastructure development via viable financing alternatives and efficient risk sharing. This study examined whether PPPs are effective in both mobilizing private-sector capital for infrastructure development, whether they do so through-efficient risk allocation, and also whether this risk allocation model for service delivery yields improved and responsive public service delivery within the context of infrastructure production. The study considered international examples of implementation, the most salient contractual and other governance characteristics of PPPs, and critically examined factors that impact sustainability of PPPs. The study was mixed-methods and underscored how PPPs can enhance infrastructure production/quality and that future research should focus on sectoral frameworks, the socio-economic implications of PPPs for communities, and advancements of the governance framework for PPPs.

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Comparative Review On Self-Healing Concrete Using Bacteria And Crystalline Admixtures

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Authors: Mrs. Vandana Rahul Shah

Abstract: Concrete, though the most widely used construction material, is prone to cracking, which compromises durability, service life, and sustainability of structures. Conventional repair methods are temporary, costly, and labor-intensive. In recent years, self-healing concrete has emerged as a promising alternative, capable of autonomously repairing cracks and enhancing long-term performance. This review focuses on two major self-healing approaches: bacterial concrete, where microorganisms such as Bacillus subtilis precipitate calcium carbonate within cracks, and crystalline admixture-based concrete, where chemical additives react with unhydrated cement particles and moisture to form insoluble crystals. A comparative analysis of past studies indicates that bacterial concrete can effectively heal cracks up to 0.8 mm, providing superior strength and durability improvements, though at higher cost. Crystalline admixtures, on the other hand, are economical, commercially available, and suitable for healing micro-cracks up to 0.5 mm, particularly in water-retaining structures. The paper highlights the mechanisms, advantages, limitations, and applications of both approaches, and identifies future research directions including hybrid systems, large-scale field trials, and cost optimization. Findings suggest that self-healing concrete technologies have significant potential to reduce maintenance needs and promote sustainable infrastructure development.

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White Wine Pricing A Mathematical Model for Determining Optimal Retail Value Based on Chemical Properties

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Authors: Safaan Shawl

Abstract: In an era increasingly dominated by algorithmic precision and data-driven decision-making, the question of whether an artisanal product such as white wine can be priced through a deterministic model seems both audacious and tantalising. This paper embarks on precisely that odyssey—an independent attempt to formulate an original pricing algorithm for white wines by reverse-engineering the latent relationships between their physicochemical properties and their market value. Drawing from publicly available datasets and deploying statistical intuition rather than merely machine learning brute force, this research proposes a novel, human-designed formula that accurately estimates the price of white wines. The formula integrates variables such as acidity, sulphates, residual sugar, and volatile acidity—each weighted with philosophical and economic significance—into a predictive framework that is both interpretable and intuitive. Unlike conventional black-box regressions, the methodology underscores transparency, causal inference, and domain-sensitive calibration. This work is not only a tribute to the enduring relevance of analytical thinking in a machine age but also a call for more interdisciplinary bridges between oenology and economics, chemistry and computation, palate and price. It aims to empower connoisseurs, traders, and vineyards alike to understand, forecast, and perhaps demystify the economics swirling within every bottle. The findings reveal a striking congruence between predicted and actual price tiers, suggesting that white wine pricing, far from being capricious or arbitrary, often adheres to a hidden logic that this paper attempts to uncover and articulate.

DOI: https://doi.org/10.5281/zenodo.17104035

 

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