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Thermal and morphological properties of alkali-treated Alstonia macrophylla fibres

Indian Journal of Fibre and Textile Research 2025年第1期50卷 53-61页

作者： Vinu Kumar, S.M. Sakthivelmurugan, E. Singh, Harwinder Department of Mechanical Engineering Sri Krishna College of Technology Kovaipudur Coimbatore641 042 India Department of Mechanical Engineering Bannari Amman Institute of Technology Sathyamangalam Erode638 401 India Department of Textile Engineering Panipat Institute of Engineering & Technology Panipat132 102 India

This study examines the thermal, wettability, and surface properties of the untreated and alkali-treated Alstonia macrophylla (ASM) fibres extracted from the seed pods. Alkali treatment leads to an increase in cellulose content, enhancing the fibre’s thermal stability, crystallinity, and surface roughness. Dynamic contact angle measurements indicate improved wettability of treated ASM fibres, confirming their superior interaction in liquid-phase environments. Field emission scanning electron microscopy and Atomic Force Microscopy analyses reveal a significant increase in surface roughness in treated fibres compared to untreated ones. © 2025, National institute of Science Communication and Policy Research. All rights reserved.

关键词： Field emission microscopes

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Sensors Placement Optimization in Smart Broiler Chicken Farm for Indoor Environment Monitoring

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IEEE Sensors Journal 2025年第9期25卷 15842-15850页

作者： Goyal, Vikas Mukherjee, Rahul Kumar Guha, Prasanta Yadav, Ajay Bennett University Department of Electronics and Communication Engineering Greater Noida201310 India Panipat Institute of Engineering and Technology Department of Electronics and Communication Engineering Panipat132102 India Kharagpur Kharagpur721302 India

In smart broiler chicken farms, the internal environment of the farm depends on the weather seasons and position of various actuators, such as ceiling fans, wet curtains, and heaters. Consequently, numerous sensors are indispensable to effectively monitor the diverse aspects of the farm environment. The selection of optimum locations for sensors from a large pool of sensors involves the spatial estimation representation that closely approximates the performance of all the sensors while minimizing the number of sensors. In the proposed work, 25 Internet of Things data sensing nodes are installed at random locations with the experience of farmers. With the objective of sensors’ location optimization, a novel approach is proposed, integrating Bayesian optimization (BO) in conjunction with the novel ordered common elements crossover genetic optimization algorithm, referred to as Bayesian genetic algorithm (BGA). The primary objective of the BGA is to maximize the fitness function, which measures the gray correlation coefficient (GCC) between the reference trend and the optimal sensor combination. BO is employed to determine the hyperparameters of the genetic optimization algorithm within the specified search space. The proposed solution introduces a novel crossover method for GA known as ordered common elements crossover. The proposed BGA applied to seasonal data to consider the seasonal effects in the sensor location optimization. The effectiveness of this proposed BGA method is compared with the conventional one-point crossover-based BGA. The proposed method has a low value of mean absolute error (MAE) compared to the existing one-point crossover-based BGA, indicating its superior performance. © 2001-2012 IEEE.

关键词： Genetic algorithms

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Comparative analysis of XPM crosstalk in radio over fiber SCM-DWDM network

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Optical and Quantum Electronics 2025年第4期57卷 1-15页

作者： Jain, Vishal Goyal, Shivani Bhatia, Richa ECE Department Panipat Institute of Engineering and Technology Panipat India ECE Department Chandigarh Engineering College- Chandigarh Group of Colleges Jhanjeri Punjab Mohali India ECE Department NSUT East Campus New Delhi India

The investigation of Subcarrier Multiplexing-Dense Wavelength Division Multiplexing (SCM-DWDM) technology-based Radio over Fibre (RoF) systems is driven by the growing need for high data rates and huge channel capacity in broadband optical networks. Cross-phase modulation (XPM) crosstalk is a significant problem in these systems that gets worse with higher frequencies, longer fibre lengths, and smaller channel spacing. Using coupled equations and the nonlinear Schrödinger equation (NLSE), this study compares and analyses the performance of XPM crosstalk in the W band (75–110 GHz) and V band (40–75 GHz), considering the effects of higher-order dispersion up to the eighth order. Examining XPM-induced crosstalk over fibre lengths up to 50 km, input energies up to 20 mW, and different channel spacings are all included in the scope. The findings show that the relationship between XPM crosstalk and frequency, fibre length, and input power is exponential. In terms of reducing XPM crosstalk, the Schrödinger equation-based model performs better than the coupled equation. The optimisation of RoF systems for next-generation, high-capacity optical communication networks can be facilitated by the insights gained from this study. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Group velocity dispersion

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Analyzing the Performance of RoF System at 10 GHZ and 15 GHz with Optical Carrier Suppression 7th

Analyzing the Performance of RoF System at 10 GHZ and 15 G...

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7th International Conference on Communication, Device and Networking, ICCDN 2024

作者： Singh, Manjit Sarangal, Himali Singh, Butta Kour, Satveer Rani, Kavita Department of Engineering and Technology Guru Nanak Dev University Regional Campus Jalandhar India Department of Computer Engineering and Technology Guru Nanak Dev University Amritsar India Department of CSE Panipat Institute of Engineering and Technology Panipat India

ISBN: (纸本)9789819753369

In the current scenario of wired and wireless communications, Radio over Fiber (RoF) is proving it as backbone for wireless access network due to its high transmission capacity, low cost, low attenuation, significant mobility, flexibility and ease of access. Along with the advantages, there are also some drawbacks of RoF system such as signal impairments, noise and distortion that limit the overall performance of the system which is required to be eliminated. In this paper performance of RoF system having two RF signals (10 GHz and 15 GHz) with optical carrier suppression has been analyzed. The performance of the system has been investigated in terms of bit error rate (BER), eye height and Quality factor by varying the input power with respect to fiber length. It has been observed that by changing the input power as well as distance the output is significantly affected in terms of Q Factor, BER and eye height for both of the RF signals. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Bit error rate

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Enhancing real time object detection for autonomous driving using YOLO-NAS algorithm with CLEO optimizer

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International Journal of Information technology (Singapore) 2025年第3期17卷 1321-1328页

作者： Nandal, Priyanka Pahal, Sudesh Malik, Sunesh Sehrawat, Neetu Mamta Department of Computer Science and Engineering Maharaja Surajmal Institute of Technology New Delhi India Department of Electronics and Communication Engineering Maharaja Surajmal Institute of Technology New Delhi India Department of Information Technology Maharaja Surajmal Institute of Technology New Delhi India Department of Computer Science and Engineering Panipat Institute of Engineering & Technology New Delhi India

For autonomous driving to operate in a safe and effective manner, efficient and precise object detection is essential. The efficacy of the network model is heavily challenged because of the high-speed movement of vehicles and the dynamic nature of the surrounding environment that need the detection of objects of various scales. With notable improvements over earlier iterations, the You Only Look Once-Neural Architecture Search (YOLO-NAS) algorithm is a strong contender for improving real-time object recognition in autonomous vehicles. YOLO-NAS integrated with chimpanzee leader election optimization (CLEO) has the potential to greatly enhance the safety and dependability of autonomous driving systems by utilizing its sophisticated capabilities and skill fully combining it with the vehicle’s perception system. The BDD100K dataset serves as the training and evaluation set for our suggested technique. The YOLO-NAS model with CLEO optimizer achieves mean average precision (mAP) of 83%, which is significantly higher than the mAP of 65.7% attained by the YOLO-NAS model without the optimizer. This improvement highlights the effectiveness of the CLEO optimizer in enhancing detection precision. The proposed approach was compared with existing algorithms and demonstrated remarkable performance in terms of recall, precision, and mAP. It is seen that our proposed model is better by striking equilibrium between detection speed and accuracy. Based on our findings, object detection performance for autonomous driving applications is much enhanced when utilizing our suggested approach. © Bharati Vidyapeeth's institute of Computer Applications and Management 2024.

关键词： Autonomous vehicles Deep learning Object detection YOLO-NAS

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MATHEMATICAL MODEL FOR CALCULATING BREAKING ENERGY OF COMPONENT YARNS IN DUAL-SHEATH SINGLE-CORE HYBRID YARN

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Tekstil ve Muhendis 2025年第137期32卷 1-13页

作者： Islam, Md. Azharul Rathour, Rochak Kumar, Bipin Das, Apurba Kumar, Nandan Department of Textile and Fibre Engineering Indian Institute of Technology Delhi Hauz Khas New Delhi India Department of Textile Engineering Mawlana Bhashani Science and Technology University Santosh Bangladesh High-Performance Textiles Pvt. Ltd. Haryana Panipat India

The increasing utilization of multicomponent hybrid yarns in present world highlights their critical role in advancing protective textile technologies. In this study, hybrid yarns with dual sheath and a single core were produced in varying linear densities and twist directions where polyester and ultra-high molecular weight polyethylene (HPPE) were considered as the sheath components and stainless steel (SS)/glass yarn was taken as core component. The failure pattern and energy required for breakage of hybrid yarn were analyzed, showing that glass-core yarns exhibited multiple cracking tendency, while (SS)-core yarns rarely did. This was attributed to the lower breaking extension % of glass fibers (3.61%-3.81%) compared to SS (18.52%-28.05%), HPPE (5.20%-6.21%), and polyester (17.05%). Glass-core yarns reached their breaking extension earlier, leading to premature breakage. A mathematical model developed from load-extension curves demonstrated that HPPE contributed the most to breaking energy (68.67%), followed by polyester (18.49%) and glass (10.92%). The average absolute error of the model was calculated as 4.87% that led to average ~95% accuracy. The reason for this error was the assumptions about HPPE breakage that were considered during modeling. These findings support researchers in identifying high-performance yarns suitable for cut, stab, and slash-resistant fabrics, ensuring compliance with energy failure standards and allied industrial practices. © (2024), (Chamber of Textile Engineers). All Rights Reserved.

关键词： Glass fibers

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GEODESICS OF CHARGED PARTICLE IN ELECTROMAGNETIC FIELD

arXiv

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arXiv 2025年

作者： Yadav, Nitish Jangir, Seema School of Basic and Applied Science K. R. Mangalam University Haryana Gurugram122103 India Panipat Institute of Engineering and Technology Haryana Panipat132102 India

We report calculations about the motion of a charged particle in an external electric and magnetic field. The metric for the particle moving on a slope with non-zero traction and coefficient of friction is also evaluated for weak fields. We have geometric solutions in terms of Finsler geometry. We show that our solution metrics belong to the (α, β)-metric class for cases with and without motion on a slope. Further, the external magnetic field is manifested in an additional parameter in the metric. The geodesic spray coefficients under the influence of magnetic field have also been calculated. Finally, we have estimated the indicatrices and geodesics for slippery plane and cone. © 2025, CC BY-NC-SA.

关键词： Magnetic fields

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Machine Learning Based Skin Cancer Detection and Recognitions Techniques in IoT Environment 1st

Machine Learning Based Skin Cancer Detection and Recognition...

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1st International Conference on Artificial Intelligence and its Applications, ICAIA 2023

作者： Kumar, Arhath Talukdar, Veera Talukdar, Suryansh Bhaskar Gupta, Neeraj Gupta, Ankur Mittal, Manisha Anand, Rohit Karnataka Udupi India Department of Computer Science D Y Patil International University Maharashtra Akurdi Pune India School of Computer Science and Engineering VIT Bhopal Madhya Pradesh India Department of Information Technology Panipat Institute of Engineering and Technology Panipat Haryana Samalkha India Department of CSE Vaish College of Engineering Haryana Rohtak India Department of ECE Guru Tegh Bahadur Institute of Technology New Delhi India New Delhi110020 India

ISBN: (纸本)9783031843969

In healthcare, IoT-based applications are growing day by day to perform predictions regarding chronic diseases using machine learning. There is a widespread consensus that melanoma, or cancer of the skin, is one of the worst illnesses in the world. A precise classification of skin lesions in their early stages might likely assist in the process of therapeutic decision-making, therefore improving the probability of a cure before cancer develops. The likelihood of developing cancer of the skin is highest in those parts of the body that are often exposed to the damaging effects of direct sunlight. In men, these body parts include the head, face, lips, and ears;in females, the chest, arms, and hands;and in both sexes, the legs. On the other hand, it is also possible for it to develop on sections of your body that are seldom exposed to air and light, such as your hands, feet, and other spots on your body. Deep learning has been considered a subset of machine learning that is frequently used to develop detection and classification mechanisms. In the current study, the deep learning methodology is being considered as a potential method for detecting skin cancer in a shorter amount of time and with more precision. Compression operations have been performed to increase performance, and hybrid deep learning models have been used to improve accuracy measures such as recall, precision, and f1-score. In the IoT environment, individuals can benefit from continuous and real-time skin health monitoring. As ML algorithms continue to evolve and gain access to extensive datasets, their role in skin cancer detection and recognition within IoT holds immense promise for enhancing healthcare and prevention strategies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Diseases

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Prediction of Air Quality Index (AQI) using machine learning-based tree models—a case study of Gurugram City, India

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Theoretical and Applied Climatology 2025年第6期156卷 1-18页

作者： Kumar, Akshay Sihag, Parveen Singh, Karan Esmaeilbeiki, Fatemeh Singh, Balraj Om, Hari Lovely School of Architecture and Design Lovely Professional University Phagwara India Department of Civil Engineering Chandigarh University Mohali India Department of Civil Engineering Graphic Era (deemed to be) University Dehradun India Eastern Shore Agricultural Research and Extension Center Virginia Tech Painter United States Department of Civil Engineering Panipat Institute of Engineering and Technology Samalkha India Department of Geography Government College Safidon Haryana Jind India

This study aims to assess the effectiveness of various Machine learning based models in predicting the Air Quality Index (AQI) for Gurugram City, India. The Air Quality Index (AQI) pertains to many Sustainable Development Goals (SDGs), mainly those associated with health, sustainable urban development, and climate action. This analysis utilize tree-based techniques, including M5P, Random Forest, Random Tree, and Reduced Error Pruned Tree. The data set was collected from the Central Pollution Control Board (CPCB), containing daily AQI observations from January 2020 to December 2023. The first 3 years of daily data were used for model development, and the rest of 1 year was used for model validation. 15 different models were developed using various input combinations by the lag of the days from 1 to 15. The detailed analysis demonstrates that the M5P-based model exhibits superior predictive accuracy compared to other models in testing stage, achieving a maximum coefficient of correlation (CC) value of 0.8287. The mean absolute error (MAE) is also recorded at 33.6908, while the root mean square error (RMSE) is 45.1823. The scattering index (SI) during the testing phase is 0.2375. Results also show that M5P models outperform with only 5 input variables AQIT-1,AQIT-2,AQIT-3,AQIT-4,AQIT-5. Results of the Taylor diagram show that M5P-T5 outperforms the other models, followed by RF, REP Tree, and RT. The M5P T-5 model performs better than all other models using different input combinations. In contrast, using this dataset, the RT model demonstrates the least efficacy among those utilized for predicting AQI in Gurugram City, India. © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2025.

关键词： Central pollution control board M5P Random forest Random tree Reduced error pruned tree SDG

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Optimizing and predicting energy at break of dual-sheath single-core hybrid yarn using Box Behnken Design

Progress in Engineering Science

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Progress in engineering Science 2025年第1期2卷

作者： Islam, Md. Azharul Rathour, Rochak Kumar, Bipin Das, Apurba Kumar, Nandan Department of Textile and Fibre Engineering Indian Institute of Technology Delhi Hauz Khas New Delhi 110016 India High Performance Textiles Pvt. Ltd Haryana Panipat India Department of Textile Engineering Mawlana Bhashani Science and Technology University Santosh Tangail 1902 Bangladesh

Hybrid yarns are critical for high-performance applications, as their energy absorption at failure determines their mechanical reliability. Despite this importance, the prediction and optimization of breaking energy of hybrid yarn remain underexplored. This study focuses on optimizing and predicting the energy required to break a dual-sheath single-core hybrid yarn. To achieve this, the Box-Behnken Design (BBD), a variant of the Response Surface Methodology (RSM), was utilized to assess the effects of three key parameters—HPPE denier, stainless steel micron size, and twist per meter—on the yarn breaking energy. The hybrid yarn under investigation consisted of a stainless-steel core, a polyester inner layer, and an ultra-high molecular weight polyethylene (HPPE) outer layer with twists in opposing directions. The ASTM D2256 method was followed to determine the energy required to break the yarn and results revealed the optimal configuration, featuring 200 twists per meter, 400 Den HPPE, and 50-micron stainless steel with a maximum breaking energy of approximately 0.428 J. The analysis suggested both linear and two-factor interaction (2FI) regression models, with the 2FI model being chosen for its superior fit, as evidenced by R² values close to 1.00 (R² = 0.9621, adjusted R² = 0.9337, predicted R² = 0.7996) and greater adequacy precision value of 19.6816. The model significance was further confirmed by ANOVA results (F-value = 33.84, P-value < 0.0001), and its predictive accuracy was demonstrated with an average relative error of 3.46 %. These findings highlight the utility of the BBD approach in optimizing the energy required to break hybrid yarns and provide valuable insights for enhancing their performance in various cut protective applications. © 2024 Elsevier B.V.

关键词： Box Behnken Design Composite yarn Energy at break Response Surface Methodology Stainless steel yarn

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