Category Archives: Uncategorized

Authors: Vishal Sahu, Sandeep Choudhary

Abstract: Reinforced Cement Concrete (RCC) buildings with floating columns are increasingly adopted in modern urban construction to achieve architectural flexibility, open parking spaces, and large column-free areas. However, the discontinuity introduced by floating columns significantly alters the load transfer mechanism and may adversely affect the structural durability and long-term performance of buildings, particularly under seismic and environmental loading conditions. Recent advances in Artificial Intelligence (AI) provide an opportunity to predict the durability and service life of such complex structural systems more accurately than conventional empirical methods. This review paper presents a comprehensive analysis of AI-based durability prediction techniques for RCC buildings incorporating floating columns, with structural behavior evaluated using STAAD.Pro. The review summarizes the influence of floating column configurations on stress distribution, deflection, drift, and load-carrying capacity, while examining AI approaches such as Artificial Neural Networks (ANN), Machine Learning (ML), Deep Learning (DL), Support Vector Machines (SVM), Random Forest (RF), and ensemble models for predicting durability indicators including crack development, corrosion potential, service life, and structural degradation. The paper also discusses the integration of finite element analysis results obtained from STAAD.Pro with AI algorithms to improve prediction accuracy and facilitate intelligent structural health assessment. Furthermore, existing research gaps, challenges, and future opportunities in AI-assisted durability evaluation are highlighted. The review concludes that the combination of STAAD.Pro-based structural analysis and AI-driven predictive models offers a reliable and efficient framework for enhancing the safety, durability, maintenance planning, and sustainable design of RCC buildings with floating columns. This integrated approach supports data-driven decision-making and contributes to the development of resilient and smart infrastructure.

Published by: vikaspatanker

Authors: Girish Prasad, Professor Vinay Deulkar, Assistant Professor Piyush Mahajan

Abstract: Ultra-High-Performance Concrete (UHPC) is increasingly recognized as an advanced construction material for high-traffic highway pavements because of its outstanding mechanical performance, durability, and extended service life. This review provides a comprehensive assessment of UHPC, covering its development, constituent materials, mix-design principles, mechanical characteristics, durability performance, and recent developments in pavement applications. Particular attention is given to its superior compressive and flexural strength, very low permeability, high abrasion resistance, and enhanced resistance to freeze–thaw cycles and chemical deterioration. These properties make UHPC particularly suitable for pavements subjected to heavy traffic and demanding environmental conditions. Despite these benefits, its wider adoption is constrained by factors such as relatively high initial cost, limited availability of specialized materials, and challenges associated with large-scale construction and implementation. Overall, the reviewed research indicates that UHPC offers considerable potential for improving pavement performance, extending service life, reducing maintenance requirements, and supporting the development of more durable and sustainable highway infrastructure.

Published by: vikaspatanker

Authors: Krishna Kumar Singh, Professor Vinay W. Deulkar, Professor Piyush Mahajan

Abstract: The rapid growth of freight transportation has increased the need for efficient traffic load scheduling in integrated rail-road freight corridors. Conventional scheduling methods often suffer from poor coordination, traffic congestion, delivery delays, and inefficient resource utilization. This research proposes an Artificial Intelligence (AI)-based Coordinated Traffic Load Scheduling Framework using an Artificial Neural Network (ANN) to predict traffic conditions, freight demand, travel time, and scheduling performance. Historical and simulated transportation data are used to train and validate the ANN model, and the predicted outputs are integrated into a traffic scheduling simulation for intelligent freight allocation and route optimization. The performance of the proposed framework is evaluated using key indicators such as transportation cost, travel time, delivery delay, vehicle utilization, rail utilization, fuel consumption, carbon emissions, and scheduling efficiency. The results demonstrate that the ANN-based approach improves prediction accuracy, optimizes multimodal freight scheduling, reduces operational costs and congestion, and enhances the overall efficiency of rail-road freight transportation. The proposed framework provides an effective and intelligent solution for sustainable freight corridor management and future smart logistics systems.

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Authors: Sambhaji Borgude, Vinayak Kanchan, Pranav Ghumare, Sohel Shaikh, Tanmay Gangawane, Bhagyshree Gaikwad

Abstract: The rising worldwide need for safe, cost-effective, and reversible contraception has sparked new interest in using medicinal plants as alternative fertility-controlling solutions. Conventional medical systems have utilized various plants for contraception by impacting ovulation, spermatogenesis, fertilization, implantation, and uterine functions. Recent pharmacological studies have discovered various phytochemicals such as alkaloids, flavonoids, saponins, terpenoids, coumarins, lignans, and phytoestrogens that influence reproductive endocrine pathways and gamete activity. This review thoroughly outlines existing evidence related to phytochemistry, action mechanisms, pharmacological effects, and safety profiles of herbal contraceptives. Key medicinal plants like Azadirachta indica, Carica papaya, Ruta graveolens, Juglans regia, Ricinus communis, Daucus carota, and other commonly utilized species are thoroughly examined with a focus on their antifertility properties, active compounds, and empirical support. Despite considerable in vitro and animal research supplementing contraceptive effectiveness via anti-ovulatory, spermicidal, anti-implantation, endocrine-modulating, and uterotonic processes, strong clinical evidence continues to be scarce. Moreover, issues related to toxicity, reproductive safety, teratogenic effects, dose consistency, and long-term reversibility still impede clinical translation. Future studies must focus on standardized phytochemical profiling, mechanistic molecular investigations, GLP-compliant toxicity assessments, and rigorously structured randomized clinical trials. Herbal contraceptives thus serve as encouraging options for creating new contraceptive methods; however, significant scientific confirmation is necessary before their regular clinical use.

DOI: https://doi.org/10.5281/zenodo.22875171

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Authors: Dhananjay Kumar Singh, Assistant Professor Dr. Amit Kumar Ahirwar

Abstract: The stiff aged binder contained in reclaimed asphalt pavement (RAP) and recycled asphalt shingles (RAS) can reduce the demand for virgin binder, but it may also limit workability and cracking tolerance. This study develops a dense-graded hot mix asphalt containing a combined 31.53% RAP and tear-off RAS, 0.50% rejuvenator, 1.94% hydrated lime, and VG-30 virgin bitumen. The aggregate blend was checked against the applicable Ministry of Road Transport and Highways grading envelope, and Marshall specimens were evaluated at total binder contents of 5.4, 5.6, 5.8, 6.0, and 6.2%. The measured responses included bulk and maximum specific gravity, air voids, voids in mineral aggregate, voids filled with bitumen, stability, flow, and Marshall quotient. All combined gradation values remained within the specified limits. Marshall stability increased from 902.12 kg at 5.4% binder to a maximum of 1232.70 kg at 5.8%, then decreased to 905.33 kg at 6.2%. At 5.8% binder, the mixture achieved a bulk specific gravity of 2.443, maximum specific gravity of 2.545, 3.99% air voids, 14.19% VMA, 71.89% VFB, 3.27 mm flow, and a Marshall quotient of 3.77 kN/mm-equivalent as reported from the adopted calculation basis. The convergence of maximum stability, maximum bulk density, and approximately 4% air voids established 5.8% as the optimum binder content. The results show that a properly graded RAP-RAS blend with a modest rejuvenator dosage can satisfy Marshall volumetric and strength requirements. Direct wheel-tracking, fatigue, moisture-susceptibility, and field validation are recommended before project-scale implementation.

DOI: https://doi.org/10.5281/zenodo.22873662

Published by: vikaspatanker

Authors: Dhananjay Kumar Singh, Assistant Professor Dr. Amit Kumar Ahirwar

Abstract: The use of reclaimed asphalt pavement (RAP) and recycled asphalt shingles (RAS) can support material circularity in highway construction by returning aged aggregate and binder to productive use. This study evaluates the resource-substitution potential and engineering feasibility of a dense-graded hot mix asphalt containing a combined 31.53% RAP and tear-off RAS. The mixture incorporated 19.89% virgin 13.2 mm aggregate, 43.66% virgin 6 mm aggregate, 1.94% hydrated lime, 2.48% VG-30 virgin bitumen, and 0.50% rejuvenator. A material-flow assessment was combined with gradation compliance and Marshall design results. Recycled binder supplied 3.33 percentage points of the 5.80% total binder content, corresponding to 57.4% of the binder system; RAS alone supplied 1.88 percentage points. All sieve results remained within the adopted Ministry of Road Transport and Highways limits. At the selected 5.8% binder content, the mixture achieved 1232.70 kg Marshall stability, 3.27 mm flow, bulk specific gravity of 2.443, 3.99% air voids, 14.19% VMA, and 71.89% VFB. These results demonstrate that substantial recycled-material and recycled-binder contributions can be achieved without losing basic Marshall and volumetric compliance. The design offers a technically credible pathway for reducing dependence on virgin pavement materials. However, project-specific cost, energy, emissions, leaching, durability, and field-performance data are required before claiming quantified environmental or economic benefits.

DOI: https://doi.org/10.5281/zenodo.22872539

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Authors: Pawandeep Kaur, Jasleen Kaur, Priya Bhat, Upasana Bhumbla

Abstract: Objective: To characterize the aerobic bacterial and fungal isolates recovered from patients with chronic suppurative otitis media (CSOM) and to evaluate their antimicrobial susceptibility profiles along with selected phenotypic resistance mechanisms. Methods: A prospective cross-sectional study was conducted over six months at Adesh Institute of Medical Sciences & Research, Bathinda. A total of 178 ear discharge specimens obtained from patients clinically suspected of having CSOM were processed for isolation and identification of aerobic bacteria and fungi. Organisms were identified by conventional microbiological methods and, where applicable, confirmed using the VITEK® 2 Compact system. Antimicrobial susceptibility was assessed by the Kirby–Bauer disk diffusion method in accordance with CLSI M100 (2024). Phenotypic testing included detection of extended-spectrum β-lactamase (ESBL) production among selected Enterobacterales, metallo-β-lactamase (MBL) production among Gram-negative isolates, and methicillin resistance in Staphylococcus aureus. Colistin susceptibility among multidrug-resistant Gram-negative isolates was determined by broth microdilution following EUCAST recommendations. Results: Of the 178 specimens examined, 132 (74.16%) yielded microbial growth, whereas 46 (25.84%) showed no growth. All culture-positive specimens demonstrated monomicrobial growth. Culture-positive cases were predominantly male (88/132, 66.67%), and the 41–50-year age group accounted for the largest proportion (50/132, 37.88%). Tubotympanic disease constituted 66.67% of cases, while atticoantral disease accounted for 33.33%. Pseudomonas aeruginosa was the predominant bacterial isolate (60/132, 45.45%), followed by Staphylococcus aureus (28/132, 21.21%). Among fungal isolates, Candida albicans was more frequently recovered (5/132, 3.79%) than Aspergillus niger (3/132, 2.27%). S. aureus exhibited complete susceptibility to vancomycin, linezolid, and teicoplanin. Conclusion: The study demonstrated a predominance of Gram-negative organisms, particularly P. aeruginosa, in CSOM, along with notable antimicrobial resistance among several bacterial isolates and the presence of phenotypic resistance mechanisms. These findings emphasize the value of periodic local antimicrobial resistance surveillance and microbiologically guided treatment for CSOM. Such surveillance may assist in optimizing antimicrobial selection and strengthening antimicrobial stewardship.

DOI: http://doi.org/10.5281/zenodo.22845104

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Authors: Sagar Shivaji Thakare

Abstract: Artificial intelligence (AI) is becoming an important part of digital marketing. Marketers now use AI for personalized recommendations, customer service chatbots, targeted advertising, content creation, customer analysis, and other activities across the digital customer journey. These applications can make marketing communication faster and more relevant, but they also raise questions about privacy, transparency, trust, authenticity, and the appropriate role of human interaction. This conceptual paper reviews and connects research on AI and marketing to explain how AI-enabled marketing activities may influence consumer engagement and purchase intention. The paper uses a structured literature-based approach and does not use primary survey data. The review suggests that AI capability alone does not determine consumer response. Instead, consumer perceptions of relevance, usefulness, convenience, transparency, privacy, and trust play an important role in shaping outcomes. Based on the literature, the paper develops a conceptual framework and six propositions linking AI-enabled digital marketing with consumer engagement, trust, and purchase intention. The paper also discusses practical implications for marketers and identifies directions for future empirical research.

Published by: vikaspatanker

Authors: Rajesh Chauhan, Akshay Bhardwaj, Shubham Rana

Abstract: Phishing websites are still one of the most common infection vectors for stealing credentials, financial information and other sensitive data from users of digital platforms, and there is a growing demand for accurate and deployable automated phishing-detection systems. However, models trained on a set of phishing URLs may experience a large performance drop when transferred to another feature scheme or a different collection window outside the scope of their training. In this work, we compare four classical machine-learning models, and five deep-learning and hybrid architectures for within-domain and cross-domain phishing-website detection. We evaluate our approach over five publicly available benchmark datasets, which contain more than 445k labeled instances in total, including UCI Phishing Websites Dataset, PhiUSIIL Phishing URL Dataset, Web-Page Phishing Detection Dataset, Mendeley Web-Page Phishing Dataset and Phishing Dataset based on Machine Learning. The models are evaluated in terms of classification accuracy, F1-score, transfer of cross domain performance, computational cost, data needs and interpretability. The Hybrid CNN-BiLSTM with attention model achieved the highest accuracy of 98.1% on PhiUSIIL and 85.6% on the transfer task from the PhiUSIIL to the Mendeley Web-Page dataset, outperforming the Linear SVM model by 13.1 percentage points. However, classical models still work well in resource-limited and interpretability-sensitive settings, and a simple BiLSTM with additive attention offers a balanced compromise. The results suggest that model selection for phishing detection should not only focus on predictive performance, but also consider operational deployment constraints.

Published by: vikaspatanker

Authors: Amit Jalindar Sawant

Abstract: Learning English language’ is essential for career growth. There are different ways of learning English language. Using different ways of learning English language is really helpful to be comfortable and fluent. If we don’t practice enough to be habitual then definitely it does not work. We often learn different things knowingly-unknowingly because of being practiced or habitual after revising many more times. Similar thing works for learning English language. So many tactics are there to learn, generally we notice, how people are there, who they use differente words, even sentences but they don’t know, which word they should use in their mother tongue, and that is the practice and habitual process. Surrounding really well important to become a part of learning English language. It says, what people are there in our surrounding, so we become one of them, likewise, if there are English language speakers, we tend to speak English language without any hesitation. Actually, there are different expectation, demands, requirements of English language users, average, proficient, expert etc. And definitely no one is born perfectionist, we become educated through process, different ways, ‘Learning English language’ is a part of ways of learning.

Published by: vikaspatanker