Category Archives: Uncategorized

Authors: Adebiyi Adeniyi Mayowa

Abstract: Effective leadership behaviour is critical for the successful delivery of building construction projects, particularly in complex and stakeholder-intensive environments such as the Federal Capital Territory, Abuja. This study evaluates the leadership behaviours demonstrated by project managers on building construction projects in Abuja, aiming to determine the prevalence of negative leadership traits in project practice. A quantitative research design was employed, utilizing structured questionnaires distributed to built environment professionals. Descriptive statistics, including frequency, percentage, mean score, and standard deviation, were used for data analysis. The study achieved a response rate of 84%, with 185 valid responses analyzed. The results indicate that the most prominent negative leadership behaviour was insensitivity to team members’ behaviour (Mean = 3.2083), followed by poor communication behaviour (Mean = 2.8375) and assuming little or no responsibility (Mean = 2.5875), all rated at a neutral level. Other behaviours, such as inadequate planning and organizing (Mean = 2.4042), highly autocratic behaviour (Mean = 2.4000), overdependence (Mean = 2.3750), inability to select competent personnel (Mean = 2.3208), high reliance on subordinates (Mean = 2.1583), and nonchalant attitude (Mean = 2.1292), were rated lower and considered less prevalent. The average total mean score of 2.4912 suggests general disagreement that these negative leadership behaviours are commonly exhibited by project managers, indicating that project managers in the study area generally display acceptable and positive leadership practices. The study recommends ongoing leadership development through training in communication, emotional intelligence, interpersonal relations, and team management, as well as the promotion of participative leadership styles, enhanced accountability systems, and mentorship for emerging project managers. Strengthening leadership capacity among project managers is identified as essential for improving team coordination, stakeholder satisfaction, and project delivery performance in Nigeria’s construction industry.

DOI: https://doi.org/10.5281/zenodo.20135601

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Authors: Bobelya A

Abstract: Drug trafficking and drug addiction represent major global challenges affecting public health, social stability, and national security. Drug trafficking refers to the illegal production, transportation, and distribution of narcotic drugs and psychotropic substances across national and international borders. This illicit trade contributes significantly to the spread of drug addiction, organized crime, corruption, and violence. Drug addiction, on the other hand, is a chronic disorder characterized by compulsive drug use despite harmful consequences, leading to severe physical, psychological, and social problems. At the international level, several conventions and organizations have been established to control drug trafficking and abuse. Key legal frameworks include the Single Convention on Narcotic Drugs, 1961, the Convention on Psychotropic Substances, 1971, and the United Nations Convention Against Illicit Traffic in Narcotic Drugs and Psychotropic Substances, 1988. These conventions aim to regulate the production, distribution, and use of narcotic substances and encourage international cooperation in combating drug-related crimes. Organizations such as the United Nations Office on Drugs and Crime play a crucial role in monitoring global drug trends and assisting countries in implementing drug control policies. At the national level, many countries have enacted strict legislation to prevent drug trafficking and substance abuse. In India, the primary law governing drug control is the Narcotic Drugs and Psychotropic Substances Act, 1985, which criminalizes the manufacture, possession, sale, purchase, and transportation of narcotic drugs without authorization. The Act also provides stringent penalties for offenders and empowers enforcement agencies to investigate and prosecute drug-related crimes. This study highlights the relationship between drug trafficking and addiction while examining the legal frameworks designed to control these issues at both international and national levels. It emphasizes the need for stronger cooperation among governments, effective law enforcement, public awareness, and rehabilitation programs to address the growing problem of drug abuse and trafficking worldwide.

DOI: http://doi.org/10.5281/zenodo.20134480

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Authors: Timothy O. Oni, Iboyi Nathaniel Onuche, Dokubo Chinweike Unoma, Odo Vincentmary C, Kene-Okonkwo Adachukwu, Iboko Ifeoma Juliet

Abstract: Alcohol (ethanol) is a hepatotoxic agent that induces oxidative stress and disrupts liver function. This study evaluated the effects of three commercial ethanol brands (Seaman, Chelsea, and Lords) at high (3 ml/kg/day) and low (1.5 ml/kg/day) doses on liver function and antioxidant status in Wistar rats. Thirty-five rats (150–160 g) were divided into seven groups (n = 5) and treated for two weeks. Liver enzymes (ALT, AST), protein profile, bilirubin, and oxidative stress markers (SOD, CAT, LDH, MDA) were analyzed using standard methods. Control values were ALT = 24.26 U/L and AST = 138.23 U/L. High-dose Seaman produced the greatest hepatotoxicity (ALT = 39.33 U/L; AST = 203.53 U/L), followed by Lords (ALT = 32.23 U/L; AST = 156.33 U/L), while Chelsea reduced AST (101.66 U/L). Albumin decreased markedly in Seaman high-dose (1.23 g/dL vs. 2.22 g/dL control). Oxidative markers showed SOD = 27.01 ×10⁻⁶ U/mL and CAT = 88.02 U/mL in controls. Lords low-dose caused severe depletion (SOD = 6.63 ×10⁻⁶ U/mL), while Chelsea high-dose increased antioxidant activity (SOD = 43.11 ×10⁻⁶ U/mL; CAT = 122.73 U/mL) but elevated LDH (489.75 μg/mL). Ethanol induced dose- and brand-dependent hepatotoxicity. Seaman was most hepatotoxic, Lords caused greatest oxidative depletion, while Chelsea elicited adaptive antioxidant responses with cellular damage.

DOI: http://doi.org/10.5281/zenodo.20134138

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Authors: Jeevanantham D, Nithya M

Abstract: In the modern construction field, many advanced software tools are available for structural design, but they are often complex and not easily accessible for all users. Microsoft Excel plays a major role in engineering calculations because it is simple, flexible, and capable of mapping data efficiently without errors. In this study, a spreadsheet-based tool is developed for the design of reinforced cement concrete (RCC) elements such as slab, beam, column, and footing. The design follows a load transfer path from slab to footing. The spreadsheet is created based on IS 456:2000 provisions, where only selected input cells are editable and all other cells are pre-formulated. The tool helps to determine whether the structure is safe or unsafe and provides reinforcement details. This method is useful for basic design understanding and preliminary structural verification.

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Authors: Saurabh Singh

Abstract: The present study investigates the temporal variation of river water quality during Maha Kumbh 2025 in the Prayagraj Sangam region using Central Pollution Control Board (CPCB) monitoring observations. The assessment was carried out using major physicochemical and biological parameters including turbidity, dissolved oxygen (DO), biological oxygen demand (BOD), chemical oxygen demand (COD), pH, and fecal coliform. Temporal trend analysis and statistical interpretation were performed to evaluate the impact of large-scale pilgrimage activities on river water quality. The results indicated noticeable fluctuations in turbidity, COD, BOD, and fecal coliform concentrations during major bathing events, suggesting enhanced anthropogenic influence and sediment disturbance in the river system. Dissolved oxygen remained relatively stable throughout the monitoring period, while pH values stayed within acceptable environmental limits. Correlation analysis revealed positive relationships among turbidity, COD, and fecal coliform, indicating combined effects of organic and microbial contamination during intensive bathing periods. The study highlights the significance of continuous water quality monitoring during mass religious gatherings for sustainable river management and environmental protection.

DOI: http://doi.org/10.5281/zenodo.20133734

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Authors: Miss Gaikwad Janhvi Anurath, Miss. Kadam Vaishnavi Raju, Mr. Chinmay Shinde, Mr. Narayanpure Sujal, Prof. Dr.Ashish Kumar

Abstract: Friction Stir Welding (FSW) is an advanced solid-state joining technique used for welding similar and dissimilar metals without melting the base materials. In this project, an experimental investigation has been carried out to study the mechanical properties of dissimilar joints between Aluminium Alloy AA6061 and Copper (ETP Copper) using the Friction Stir Welding process. The purpose of this study is to evaluate the effect of welding parameters on the strength and quality of the welded joints. The welding experiments were performed using a carbide conical ball nose tool under different process conditions such as rotational speed, welding speed, and plunge depth. Proper fixture arrangements and clamping systems were used to obtain defect-free joints. AA6061 aluminium and copper were selected due to their wide applications in aerospace, automobile, marine, electrical, and heat transfer industries where light weight materials with high thermal and electrical conductivity are required. After the welding process, the joints were examined through visual inspection and tested for various mechanical properties including tensile strength, hardness, and microstructural characteristics. The experimental results showed that welding parameters greatly affect heat generation, material flow, and intermetallic compound formation at the weld interface. Optimized welding conditions produced sound joints with better tensile strength and uniform hardness distribution. The investigation concludes that Friction Stir Welding is an efficient and economical process for joining dissimilar aluminium-copper materials with fewer defects and Improved mechanical properties compared to conventional fusion welding methods. The results of this project can be useful for industrial applications requiring strong, lightweight, and conductive dissimilar metal joints.

DOI: https://doi.org/10.5281/zenodo.20133147

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Mrs. Malati Vaibhav Tribhuwan
Affiliation	Assistant Professor and HOA, Blockchain Technology, Department of Technology, SPPU
Email-Id:	tmalativ@gmail.com
Publication:  Books: Python Programming – T.Y.B.Sc.(Computer Science), Sem-IV Vision Publication 2022. Publications: An Analysis of Internet of Behaviour (IOB): A Crucial Footprint on Healthcare Sector ,London Metropolitan University, London, UK (Venue Partner)2025. Intelligent Agriscience: Epoch of AI in Indian Agriculture IJREAM, Volume-6, Issue-11, ISSN: 2454-9150 February 2021. 3D Password: A Secured Authentication System IJRAR, Volume-6 Issue-2, ISSN No: 2349-5138 April 2019. AIoT with PUF: A Concrete Security IJITEE, Volume-9 Issue-7, ISSN: 2278 ,May 2020. Dr. D. Y. Patil ACS College, Pimpri, Pune ,A study of Artificial Intelligence and its Applications in different areas February 2018

Published by: vikaspatanker

Mrs. Malati Vaibhav Tribhuwan
Affiliation	Assistant Professor and HOA, Blockchain Technology, Department of Technology, SPPU
Email-Id:	tmalativ@gmail.com
Publication:  Books: Python Programming – T.Y.B.Sc.(Computer Science), Sem-IV Vision Publication 2022. Publications: An Analysis of Internet of Behaviour (IOB): A Crucial Footprint on Healthcare Sector ,London Metropolitan University, London, UK (Venue Partner)2025. Intelligent Agriscience: Epoch of AI in Indian Agriculture IJREAM, Volume-6, Issue-11, ISSN: 2454-9150 February 2021. 3D Password: A Secured Authentication System IJRAR, Volume-6 Issue-2, ISSN No: 2349-5138 April 2019. AIoT with PUF: A Concrete Security IJITEE, Volume-9 Issue-7, ISSN: 2278 ,May 2020. Dr. D. Y. Patil ACS College, Pimpri, Pune ,A study of Artificial Intelligence and its Applications in different areas February 2018

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Authors: R.Renuka, Dr.S.Mohana

Abstract: Typically, sleep disorders like insomnia, sleep apnea, and narcolepsy may not receive appropriate diagnosis until serious physical and mental health issues develop. Traditional techniques, though effective, involve polysomnography, which is not only labor-intensive and time-consuming but also demands special clinical conditions. Hence, this study aims to develop a framework that relies on AI techniques to utilize a hybrid model of Deep Learning techniques, including Convolutional Neural Networks (CNN) and Long Short- Term Memory (LSTM), to process EEG signals to identify sleep disorders. The CNN model can automatically identify spatial features in the raw signals, and the LSTM model can identify temporal dependencies in the signals to correctly classify Awake, REM, and NREM stages. Preprocessing techniques have been employed to clean and normalize the signals. The system, trained and validated using standardized data sets like PhysioNet, exhibits robustness and generalization in dealing with different patterns of sleep. It can also be used to analyze new EEG signals in real-time, detect abnormal sleep patterns, and predict the occurrence of sleep disorders. This intelligent system can greatly improve the efficiency of diagnosis and reduce the need to rely on manual diagnosis. It can also prove to be a cost-effective solution.

DOI: https://doi.org/10.5281/zenodo.20132129

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Authors: Prof. G. R. Padule, Shweta Anil Bhosale, Dnyaneshwari Ravikant Patil, Vrushali Vishal Zambare

Abstract: Induction motors are vital components in industrial and commercial systems, where unexpected failures can lead to costly downtime and reduced productivity. Traditional maintenance strategies such as corrective and preventive maintenance are often inefficient, either reacting too late or performing unnecessary servicing. Predictive maintenance, powered by machine learning (ML) techniques, offers a smarter approach by forecasting motor health conditions based on real-time data analysis. This review paper presents an overview of recent advancements in predictive maintenance for induction motors using ML algorithms. Various techniques such as support vector machines (SVM), artificial neural networks (ANN), random forests, and deep learning models are discussed for fault detection, diagnosis, and remaining useful life (RUL) estimation. The paper also highlights the importance of feature extraction from vibration, current, and temperature signals, as well as the integration of Internet of Things (IoT) and cloud computing for real-time monitoring. Comparative analysis of different ML approaches is provided to identify their strengths, limitations, and potential for industrial application. Finally, the review outlines current challenges and future research directions for developing efficient, scalable, and interpretable predictive maintenance frameworks for induction motors.

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