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Biomass activated carbon air filter for particulate removal

Polimery/Polymers 2025年第4期70卷 266-277页

作者： Amir, Muhammad Ali, Mehmood Textile Engineering Department NED University of Engineering and Technology Karachi75270 Pakistan Department of Environmental Engineering NED University of Engineering and Technology Karachi75270 Pakistan

An air filter prototype was developed, in which a polyester fabric impregnated with activated carbon (10%) obtained from biomass (banana peels, wood sawdust) was used as the filter medium. FT-IR and XRD were used to characterize the activated carbon. The filtration efficiency of the prototype filter was evaluated using the iodine absorption test, air pressure drops, moisture content, quality factor and statistical analysis. The filtration efficiency of the non-impregnated polyester fabric for PM1.0, PM2.5 and PM10.0 particles were 34%, 19% and 8%, respectively. The use of the banana peel activated carbon impregnated filter increased the removal efficiency of PM1.0, PM2.5 and PM10.0 particles to 63%, 43% and 13%, respectively. In the case of the wood sawdust activated carbon filter, the filtration efficiency was 89%, 37% and 10%, respectively. Copyright © 2025 The publisher.

关键词： Microfiltration

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A Hybrid Approach to Measure Students’ Satisfaction on YouTube Educational Videos

International Journal of Emerging Technologies in Learning

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International Journal of Emerging Technologies in Learning 2023年第9期18卷 131-147页

作者： Naz, Samia Hina, Saman Fatima, Ubaida Tabassum, Huma NED University of Engineering & Technology Karachi Pakistan

Every aspect of life has undergone innovation in recent years. Information technology has revolutionized the concept of teaching and learning. Both educators and students are now utilizing contemporary resources for learning and teaching. One of the primary sources of contemporary educational resources is educational videos posted on various channels on YouTube. This study intends to investigate the opinions voiced in YouTube comments for educational videos with the help of Sentiment Analysis. In this research, three techniques namely SentiStrength, TextBlob, and Naive Bayes Classifier were utilized to analyze the sentiments. According to the results, the majority of the remarks made about the videos have positive sentiments that concluded that students are satisfied with YouTube educational videos and consider YouTube as a beneficial learning tool © 2023, International Journal of Emerging Technologies in *** Rights Reserved.

关键词： Students

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Damage quantification of a metallic beam under thermo-mechanical loads using novel empirical correlations and neural network

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Noise and Vibration Worldwide 2024年第1-2期55卷 68-83页

作者： Zai, Behzad Ahmed Mansoor, Asif Siddiqui, Umer Abdullah Javed, Jahanzeb us Saqib, Najam Karachi Pakistan Department of Mechanical Engineering NED University of Engineering and Technology Karachi Pakistan

This research examines the interactions between fracture depth and crack location on a cantilever beam’s dynamic response under thermomechanical loads. The structure’s stiffness is significantly influenced by temperature, and changes in stiffness can alter the response’s damping, frequency, and amplitude. The basis for measuring damage to an Aluminium 2024 specimen under thermomechanical loads is provided by these variations. Cantilever beams are used in experiments that are carried out at elevated temperatures, such as 50°C, 100°C, 150°C, and 200°C, as well as at room temperature (non-heating). This analysis takes into account a cantilever beam with different initial fracture depths and positions. The outcomes of the experimental, analytical, and numerical work are found to be in good accord. This research mainly fill the gaps of recent techniques for structure health monitoring of metallic structures where the coupled loads exist. Dynamic response formulation is presented experimentally on beam for the first time under thermo-mechanical loads. The dynamic parameters vary with material stiffness, thus;this change is also investigated by introducing the temperature as a key variable using a specially designed temperature controller. A novel technique is presented for damage estimation in metallic structures using the experimental data that is currently accessible which is later compared with the neural network approach. This tool helps in finding out and quantifying damagesusing dynamic response, temperature along withfinding of subsurface cracks. The results establisha clearway of diagnosing the crack growth at any particular instant under thermo-mechanical loads within the operational condition. © The Author(s) 2023.

关键词： Dynamic response

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Characterization of Denim Effluent Treatment Plant Sludge and its Influence on Concrete Hardened Properties

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Iranian Journal of Science and technology - Transactions of Civil engineering 2024年第1期49卷 533-545页

作者： Batool, Farnaz Naqvi, Shenela Faisal, Saira Sangi, Abdul Jabbar Rabbani, Mishkat Azhar, Masooma Ayoob, Kainat Jamal, Wareesha Department of Civil Engineering NED University of Engineering and Technology Karachi Pakistan Department of Textile Engineering NED University of Engineering and Technology Karachi Pakistan

Effluent Treatment Plant (ETP) in denim industries produces tonnes of sludge daily. Removal of this sludge stands among the most difficult natural issues in wastewater treatment methodologies. Inappropriate sludge disposal poses a potent threat to flora, fauna, and human health so there is a dire need to explore the uses of ETP sludge without compromising on sustainability and the environment. The usage of ETP sludge as a cement replacement material is proposed to effectively manage a waste product to support sustainability and circularity for urban renewal. ETP sludge acquired from a denim processing plant was ground to powder form after sun-drying, cleaning and oven-drying. Insight into the chemical and microstructural characterization of processed sludge was carried out by using X-ray Fluorescence, X-ray diffraction, Fourier transform infrared and Scanning Electron Microscope techniques. The presence of heavy metals along with silica was found by using XRF and was verified through XRD patterns. Detailed characterization supported sludge addition as a cement in the concrete samples in a proportion of 10%, 20% and 30%;with and without recyclable, environmentally friendly and reinforced PolyPropylene fibers (1%). It was found that workability for sludge concrete mixes with and without fibers increases with higher substitution. Also, the sludge concrete with 10% substitution was found optimal for achieving higher compressive, tensile, and flexural strength whereas the influence of fibers on the sludge substitution ratio was found to be insignificant in the proposed proportion. ETP Sludge without the additional process of firing or inceneration, can be utilized in light to medium loads bearing construction applications such as paver, paver blocks and bricks. © The Author(s), under exclusive licence to Shiraz university 2024.

关键词： Chemical characterization Concrete Effluent treatment plant sludge Microstructural characterization PolyPropylene fibers

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Real-Time Safety Helmet Detection Using Yolov5 at Construction Sites

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Intelligent Automation & Soft Computing 2023年第4期36卷 911-927页

作者： Kisaezehra Muhammad Umer Farooq Muhammad Aslam Bhutto Abdul Karim Kazi Department of Computer Science and Information Technology NED University of Engineering&TechnologyKarachi75270Pakistan Department of Civil Engineering NED University of Engineering&TechnologyKarachi75270Pakistan

The construction industry has always remained the economic and social backbone of any country in the world where occupational health and safety(OHS)is of prime *** in other developing countries,this industry pays very little,rather negligible attention to OHS practices in Pakistan,resulting in the occurrence of a wide variety of accidents,mishaps,and near-misses every *** of the major causes of such mishaps is the non-wearing of safety helmets(hard hats)at construction sites where falling objects from a height are *** most cases,this leads to serious brain injuries in people present at the site in general and the workers in *** is one of the leading causes of human fatalities at construction *** the United States,the Occupational Safety and Health Administration(OSHA)requires construction companies through safety laws to ensure the use of well-deﬁned personal protective equipment(PPE).It has long been a problem to ensure the use of PPE because round-the-clock human monitoring is not ***,such monitoring through technological aids or automated tools is very much *** present study describes a systema-tic strategy based on deep learning(DL)models built on the You-Only-Look-Once(YOLOV5)architecture that could be used for monitoring workers’hard hats in *** can indicate whether a worker is wearing a hat or *** proposed system usesﬁve different models of the YOLOV5,namely YOLOV5n,YOLOv5s,YOLOv5 m,YOLOv5l,and YOLOv5x for object detection with the support of PyTorch,involving 7063 *** results of the study show that among the DL models,the YOLOV5x has a high performance of 95.8%in terms of the mAP,while the YOLOV5n has the fastest detection speed of 70.4 frames per second(FPS).The proposed model can be successfully used in practice to recognize the hard hat worn by a worker.

关键词： Object detection computer-vision personal protective equipment(PPE) deep learning industry revolution(IR)4.0 safety helmet detection

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Development of a novel OMNET++ framework for analysis of an NB-IoT uplink network

Development of a novel OMNET++ framework for analysis of an ...

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2024 International Conference on Emerging Trends in Smart Technologies, ICETST 2024

作者： Ali, Sundus Aslam, Muhammad Imran Ahmed, Irfan Jafri, Syed Talib Abbas Aslam, Tahniyat Ned University Of Engineering And Technology Department Of Telecommunications Engineering Karachi Pakistan Ned University Of Engineering And Technology Department Of Physics Karachi Pakistan Ned University Of Engineering And Technology Department Of Electronic Engineering Karachi Pakistan

ISBN: (纸本)9798331515850

In this paper, we have presented the development of a novel narrowband-IoT (NB-IoT) simulation framework using event driven simulator, OMNET++. NB-IoT as a cellular technology finds promising potential in massive machine-type-communication (mMTC) use cases but unfortunately there is a lack of evaluation resources for such network deployments. Our implemented simulation framework can be used to effectively evaluate uplink network performance of NB-IoT networks. Moreover, we have considered mobility model in our framework to assess the effect of mobility of NB-IoT nodes on the network performance. We have presented the feature details of our developed framework and showcased some of the results obtained using this framework. Our framework is extendable to include advance features of NB-IoT as standardized in later 3GPP releases as well as incorporate different network and environment conditions to gauge its uplink performance. © 2024 IEEE.

关键词： Mobile telecommunication systems

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Shale Swelling Inhibitor Utilizing Taro Root, Alginate, and Activated Carbon from Waste Material

Shale Swelling Inhibitor Utilizing Taro Root, Alginate, and ...

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2024 Abu Dhabi International Petroleum Exhibition and Conference, ADIPEC 2024

作者： ur Rahman, Saad Siddiqui, Rida Mustafa, Muhammad Lalji, Shaine MohammadAli Ali, Syed Imran Abbasi, Faiq Azhar NED University of Engineering and Technology Pakistan

ISBN: (纸本)9781959025498

This research explores eco-friendly methods to prevent shale swelling while drilling. It evaluates the effectiveness of using alginate, taro root, and activated carbon from waste materials as additives to drilling fluids in Pakistan's shale formations. This study optimizes the inhibition of a chosen drilling fluid through various experiments, including immersion tests, determination of contact angles, rheological properties, HPHT (high pressure and high temperature) filtration loss. Equipment such as a mortar grinder, sieve shaker, pH meter, heating oven, hydraulic compactor, and ImageJ software were used for analysis. Taro root, activated carbon, and alginate showed promise as additives for enhancing water-based drilling fluids. Optimal particle size and mass fraction solutions of taro root reduce shale collapse and filtration-induced mud loss. Activated carbon purifies water, lowering impurity levels and stabilizing drilling by mitigating clay swelling. Alginate improves mud rheological properties. Both alginate and taro root reduce fluid loss and friction, providing a thin filter cake and lubricity. These additives offer a green, environmentally friendly alternative for inhibiting clay swelling. This innovative approach utilizes waste material for swelling inhibitor in drilling fluids. It contributes to the existing literature by demonstrating its inhibitory properties, improving drilling mud characteristics, and offering an environmentally friendly alternative for clay swelling inhibition. These insights support the petroleum industry's pursuit of sustainable and eco-friendly drilling practices. Copyright 2024, Society of Petroleum Engineers.

关键词： Drilling fluids

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Predictive Maintenance Strategies for Oil and Gas Facilities Using AI-Based Vibration Monitoring 13th

Predictive Maintenance Strategies for Oil and Gas Facilities...

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13th International Field Exploration and Development Conference, IFEDC 2023

作者： Irfan, Muhammad Bin Ahmed, Umair Siddiqui, Taha Department of Petroleum Engineering NED University of Engineering and Technology Karachi Pakistan Department of Chemical Engineering NED University of Engineering and Technology Karachi Pakistan

ISBN: (纸本)9789819702671

In industries such as oil and gas, the effective maintenance of facilities is of paramount importance to ensure uninterrupted operations, minimize downtime, and optimize resource utilization. Traditional maintenance approaches often rely on scheduled interventions, which can lead to inefficiencies and costly downtime. To address these challenges, this research paper presents a comprehensive study on the application of AI-based vibration monitoring techniques for predictive maintenance strategies in oil and gas facilities. The study focuses on utilizing accelerometer sensor data to predict equipment failures and recommend timely maintenance actions. Two prominent machine learning models, Linear Regression and Random Forest, are employed to analyze the sensor data and forecast potential equipment anomalies. The performance of these models is evaluated using key metrics, including the coefficient of determination (R-squared) and accuracy. The findings reveal contrasting outcomes for the applied models. Linear Regression yielded an R-squared value of -0.01048, indicating limited predictive capabilities in this context. Conversely, the Random Forest model exhibited a more promising performance with an R-squared value of 0.3685, suggesting a degree of predictive power in detecting equipment malfunctions. The implications of these results are substantial. This paper contributes to the evolving field of predictive maintenance by empirically evaluating the performance of AI-based models on real-world vibration sensor data from oil and gas facilities. As technological advancements continue to shape industrial practices, embracing such predictive strategies can drive efficiency, reliability, and sustainability across the sector. Further research avenues include exploring advanced AI techniques, integrating multiple sensor data sources, and refining models to achieve even higher predictive accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Digital storage

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Unveiling the Power of Deep Learning: A Comparative Study of LSTM, BERT, and GRU for Disaster Tweet Classification

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IEIE Transactions on Smart Processing and Computing 2023年第6期12卷 526-534页

作者： Ullah, Ihsan Jamil, Anum Ul Hassan, Imtiaz Kim, Byung-Seo Department of Software and Communication Engineering Hongik University Korea Republic of Department of Physics NED University of Engineering & Technology Karachi Pakistan Department of Computer Science and Information Technology NED University of Engineering & Technology Karachi Pakistan

Disasters have serious effects on people's lives and buildings. Therefore, social media platforms, such as Twitter, have become more critical. They are crucial tools for responding to and managing disasters effectively. This study examined the effectiveness of various deep learning models, such as bidirectional encoder representations from transformers (BERT), gated recurrent units (GRU), and long short-term memory (LSTM) for classifying disaster-related tweets. Twitter data related to different disasters were collected using hashtags. The data were then cleaned, preprocessed, and manually annotated by a team. The annotated data were divided into training, validation, and testing sets. The data were used to train three models based on BERT, GRU, and LSTM for the categorical classification of disaster tweets. Finally, the three models were evaluated and compared using the test data. BERT achieved an accuracy of 96.2%, making it the most effective model. In contrast, the LSTM and GRU models achieved an accuracy of 93.2% and 88.4%, respectively. These findings underscore the potential effectiveness of deep learning models in classifying disaster-related tweets, offering insights that could enhance disaster management strategies, refine social media monitoring processes, bolster public safety, and provide directions for future research. © 2023 Institute of Electronics and Information Engineers. All rights reserved.

关键词： Disasters

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Early Stage Breast Cancer Prediction Using Image Processing 4

Early Stage Breast Cancer Prediction Using Image Processing

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4th International Conference on Innovations in Computer Science, ICONICS 2024

作者： Zardari, Shehnila Shar, Nisar Ahmed Munawwar, Maria Usman, Ume-Hani Shabbir, Zeba Khan, Mahrukh NED University of Engineering and Technology Department of Software Engineering Karachi Pakistan NED University of Engineering and Technology Department of Biomedical Engineering Karachi Pakistan

ISBN: (纸本)9798331504342

Breast cancer is the most frequent cause of death in women, being the second leading cause of cancer deaths worldwide. Early detection is a good remedy hence we have devised a Computer Aided Detection (CAD) method to detect not only cancer but also the type and stage of cancer for early and immediate treatments and classify them into four classes namely benign mass, benign calcification, malignant mass and malignant calcification. Our CAD system is based on Convolutional Neural Network (CNN) for image classification with ResN etSO Architecture. The data is obtained from an open-source database TCIA (The Cancer Imaging Archive) and the dataset was DDSM (CBIS-DDSM). Training on a large dataset gives high accuracy but due to limited image data we used data augmentation techniques to create around 8,000 images from 2,400 images. The image preprocessing steps include DICOM to PNG conversion, resizing, and pectoral muscle removal. The accuracy is 81 % on the CBIS-DDSM dataset. © 2024 IEEE.

关键词： Lung cancer

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