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Silchar Subsection Conference (SILCON), IEEE

作者： A. Balakumar Pravin D Sawant Divya Nimma Shamim Ahmad Khan Ayasha Siddiqua Kalaiarasan C Department of ECE K.Ramakrishnan College of Engineering Trichy India Dept of Commerce Narayan Zantye College of Commerce Bicholim Goa India PhD in computational science University of Southern Mississippi Data Analyst in UMMC USA Glocal School of Science & Technology Glocal University U.P India Dept. of Information Technology and Security Jazan University Jazan KSA Department of Commerce IDE University of Madras Chennai India

ISBN: (数字)9798331540821

ISBN: (纸本)9798331540838

As artificial intelligence (AI) continues to reshape industries, understanding its impact on job displacement and skill development is crucial. This research investigates these dynamics utilization Gradient Boosting Machines (GBM) for predictive analysis and Explainable AI (XAI) techniques to interpret model outcomes. in order to higher recognize those dynamics, this work employs Explainable AI (XAI) tools to examine model findings and Gradient Boosting Machines (GBM) for predictive evaluation. The study intends to expect developments in task displacement and pinpoint new ability calls for via analysing how AI-driven automation impacts employment features throughout several sectors. the prevailing examine offers whole insights into AI's impact on workforce dynamics considering, in comparison to prior research, it combines GBM for precise forecasting with XAI methodologies like SHAP values and characteristic importance rankings for transparent interpretation of models. The research makes use of a dataset that consists of process titles, AI effects, venture distributions, and AI Workload Ratios throughout industrial domains, and it uses Python as the implementation tool. employing XAI approaches, activity displacement chance is anticipated by means of GBM fashions and the motives using these projections are explained. Performance indicators including recall, accuracy, precision, and F1 score attest to the GBM algorithm's effectiveness in forecasting displacement from work. The examination of AI Workload Ratios, that assists in efficient staffing and adaptability, reveals that the effects of automation differ among disciplines. In order to minimise any adverse effects and maximize the potential that artificial intelligence (AI) technologies offer, this research underscores that artificial intelligence is transforming the structure of occupations and emphasizes the necessity for innovative methods in employment growth and policymaking.

关键词： Industries Analytical models Automation Accuracy Explainable AI Employment Predictive models Boosting Forecasting Predictive analytics

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Comparative analysis of various oxygen production techniques using multi-criteria decision-making methods

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International Journal of Thermofluids 2023年 17卷

作者： Aljaghoub, Haya Alasad, Shamma Alashkar, Adnan AlMallahi, Maryam Hasan, Ranem Obaideen, Khaled Alami, Abdul Hai Department of Industrial Engineering and Engineering Management University of Sharjah Sharjah United Arab Emirates Mechanical Engineering Department American University of Sharjah Sharjah United Arab Emirates Materials Science and Engineering PhD Program American University of Sharjah Sharjah United Arab Emirates Department of Mechanical and Aerospace Engineering United Arab Emirates University Al Ain City United Arab Emirates Department of Sustainable and Renewable Energy Engineering University of Sharjah Sharjah United Arab Emirates Sustainable Energy and Power Systems Research Center RISE University of Sharjah Sharjah United Arab Emirates Center for Advanced Materials Research University of Sharjah Sharjah United Arab Emirates

The current pandemic has adversely affected oxygen production and supply chain, where oxygen treatment is essential for the emergency treatment protocol of patients infected by the virus. This work reports on various techniques of oxygen generation methods, as well as the challenges facing its production, storage, and transportation. Moreover, this study offers a comparative analysis of various oxygen production techniques using Multi-Criteria Decision-Making Methods (MCDM). Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), in addition to the weighting methodologies, Multiplicative Exponential Weighting (MEW) and Simple Additive Weighting (SAW) are chosen as tools to carry out the statistical analysis and rank the oxygen production methods. The ranking criteria includes cost, performance, environmental impact and safety. Moreover, this paper utilizes the Entropy Weight Method (EWM), Criteria Importance Through Inter-criteria Correlation (CRITIC) method, and the Stochastic Dominance (SD) method to distribute the criteria weights. The results show thar oxygen production using membrane technologies is the optimal technique based on all the provided tools and criteria. as it attained the maximum Entropy and Sd values of 0.548 through the TOPSIS method, and the highest CRITIC value through SAW and MEW methods which were 0.260 and 0.236, respectively. © 2022 The Author(s)

关键词： Stochastic systems

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Macrophage-mediated controlled release of cysteine protease inhibitor from PLGA-PEG/hydroxyapatite microspheres for targeting cathepsin S in Alzheimer's disease

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European Polymer Journal 2024年 214卷

作者： Yang, I-Hsuan Kuan, Che-Yung Zhang, Sheng-Long Chen, Zhi-Yu Li, Chi-Han Liang, Ya-Jyun Kuo, Wei-Ting Chang, Chia-Ting Lin, Jason Hsieh, Hsing-Pang Chang, Jang-Yang Lin, Feng-Huei Institute of Biomedical Engineering and Nanomedicine National Health Research Institutes Miaoli County Taiwan Department of Materials Science and Engineering National United University Miaoli County Taiwan Department of Biomedical Engineering College of Medicine and College of Engineering National Taiwan University Taipei Taiwan PhD Program in Tissue Engineering and Regenerative Medicine National Chung Hsing University Taichung Taiwan Institute of Biotechnology and Pharmaceutical Research National Health Research Institutes Miaoli County Taiwan Taipei Cancer Center Taipei Medical University Hospital Taipei Taiwan TMU Research Center of Cancer Translational Medicine Taipei Medical University Taipei Taiwan

The cause of Alzheimer's disease (AD) remains unknown;however, studies have indicated the increased expression of cathepsin S (CTSS) in AD brains. Cathepsin S acts as a β-secretase, prompting the release of amyloid-β peptides and stimulating microglial cell activation, causing microglial migration, and contributing to neuroinflammation and disease progression. In this study, CT001, an inhibitor of the cysteine protease CTSS, was encapsulated in poly(lactic-co-glycolic acid)-polyethylene glycol microspheres combined with hydroxyapatite microspheres (CPPMs/HAMs) to achieve a constant and long-term drug release for AD. The results showed that CPPMs/HAMs had a uniform diameter of approximately 1 μm, enabling macrophage endocytosis and facilitating sustained CT001 release into the bloodstream, ultimately reaching the brain for AD treatment. The developed CPPMs/HAMs demonstrated excellent anti-inflammatory properties and mitigated CTSS expression in an in vitro inflammation microglial model. Intramuscular administration of CPPMs/HAMs improved learning and memory in AD mice during behavioral assessments. Furthermore, CPPMs/HAMs reduced CTSS expression, amyloid-beta accumulation, and deposition in vivo. This study demonstrated the potential of CT001 delivered via CPPMs/HAMs as a promising approach for managing AD in patients. © 2024

关键词： Peptides

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Thin Film Spectral Measurement System Developed Based on Micro Spectrometer

Thin Film Spectral Measurement System Developed Based on Mic...

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International Conference on Advanced Robotics and Intelligent Systems (ARIS)

作者： Ciao-Ming Tsai Ting-Wen Weng Wei-Yi Kong Chang-Ting Yang Cheng-Hao Ko Department of Power Mechanical Engineering National Tsing Hua University PhD Student Hsinchu Taiwan Department of Graduate Institute of Automation and Control National Taiwan University of Science and Technology MS student Taipei Taiwan Department of Material and Chemical Research Laboratories Industrial Technology Research Institute Research Fellow Hsinchu Taiwan

Thin film technology has wide applications in several fields such as semiconductor manufacturing, biochemical sensors, solar cells and more. However, the instruments used to detect the properties of thin films remain bulky and expensive. In this study, we use a micro spectrometer fabricated using MEMS (Micro-Electro-Mechanical Systems) technology to design a system for thin film spectral measurements. The performance of this measurement system is validated using a quantum dot film. We also measured the interference spectrum of the quantum dot film in a sealed gas chamber environment. A reaction between the film and water vapor was observed, where the adsorption of water vapor by the film resulted in a shift of the interference spectral peak towards a longer wavelength. Regression analysis revealed a strong correlation (R 2 >0.95) between the wavelength shift of the peak and the relative humidity within the gas chamber.

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BRAIN-TO-TEXT BENCHMARK’24: LESSONS LEARNED

arXiv

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arXiv 2024年

作者： Willett, Francis R. Li, Jingyuan Le, Trung Fan, Chaofei Chen, Mingfei Shlizerman, Eli Chen, Yue Zheng, Xin Okubo, Tatsuo S. Benster, Tyler Lee, Hyun Dong Kounga, Maxwell Buchanan, E. Kelly Zoltowski, David Linderman, Scott W. Henderson, Jaimie M. Department of Neurosurgery Stanford University United States Department of Electrical & Computer Engineering University of Washington United States Department of Computer Science Stanford University United States Department of Applied Mathematics University of Washington United States China Neurosciences Phd Program Stanford University United States Wu Tsai Neurosciences Institute Department of Statistics Stanford University United States

Speech brain-computer interfaces aim to decipher what a person is trying to say from neural activity alone, restoring communication to people with paralysis who have lost the ability to speak intelligibly. The Brain-to-Text Benchmark’24 and associated competition was created to foster the advancement of decoding algorithms that convert neural activity to text. Here, we summarize the lessons learned from the competition ending on June 1, 2024 (the top 4 entrants also presented their experiences in a recorded webinar). The largest improvements in accuracy were achieved using an ensembling approach, where the output of multiple independent decoders was merged using a fine-tuned large language model (an approach used by all 3 top entrants). Performance gains were also found by improving how the baseline recurrent neural network (RNN) model was trained, including by optimizing learning rate scheduling and by using a diphone training objective. Improving upon the model architecture itself proved more difficult, however, with attempts to use deep state space models or transformers not yet appearing to offer a benefit over the RNN baseline. The benchmark will remain open indefinitely to support further work towards increasing the accuracy of brain-to-text algorithms. © 2024, CC BY.

关键词： Recurrent neural networks

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To Sharpen or not to Sharpen? On the Effect of Sharpening Filters Applied to Magnified Images 14

To Sharpen or not to Sharpen? On the Effect of Sharpening Fi...

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14th International Conference on Digital Image Processing, ICDIP 2022

作者： Emmendorfer, Leonardo R. de Olviveira, Vinicius Menezes Schein, Tatiana T. de Jesus, Junior C. Rodriguez, Bárbara D.A. Electrical Energy Processing Department Federal University of Santa Maria 97105-340 Brazil Center for Computational Science Federal University of Rio Grande 96201-900 Brazil Computational Modeling PhD Program Federal University of Rio Grande 96201-900 Brazil Computer Engineering Graduate Program Federal University of Rio Grande 96201-900 Brazil Institute of Mathematics Statistics and Physics Federal University of Rio Grande 96201-900 Brazil

ISBN: (纸本)9781510657564

Super-resolution algorithms aim to produce magnified high-resolution versions from low-resolution images. Some methods, however, are prone to generate blur during the process. Simple sharpening filters are adopted to alleviate this type of artifact. However, the actual effectiveness of this approach is not clear-cut in the literature. This work evaluates the effect of three simple sharpening filters on the quality of images obtained from super-resolution methods. Two metrics were considered in the evaluation: the Peak signal-to-noise ratio (PSNR) metric, and the Learned Perceptual Image Patch Similarity (LPIPS). One of the filters could consistently improve the LPIPS metric of magnified images from diverse benchmark sets on top of seven super-resolution methods. The increments obtained for the perceptual metric seem to occur due to the sharpening effect. Improvements on PSNR values were not as consistent. © 2022 SPIE.

关键词： Quality control

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Cybersecurity Enhancement in IoT-Enabled Public Health Information Systems Using Deep Learning Techniques

Cybersecurity Enhancement in IoT-Enabled Public Health Infor...

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AI-Driven Smart Healthcare for Society 5.0

作者： Mahesh Kumar Mishra Lalit Mohan Trivedi Mansoor Farooq Divya Nimma Malik Bader Alazzam B Kiran Bala Sr SAP Consultant Delta System and Software Inc Texas USA Department of ASH Moradabad Institute of Technology Moradabad UP India Department of Cybersecurity American University of Bahrain Bahrain PhD in Computational Science University of Southern Mississippi Data Analyst in UMMC USA Faculty of Information Technology Jadara University Irbid Jordan Department of AI & DS K.Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798331536336

ISBN: (纸本)9798331536343

The rising use of IoT devices in Public Health Information Systems (PHIS) has revolutionized the way patient surveillance and record keeping is being done in real-time, but this has exposed systems to serious cybersecurity threats. Generated by using traditional approaches, like rule-based detection and basic artificial neural network-based models, is not capable of dealing with the dynamic and diverse nature of threats in IoT networks. These approaches are often inadequate in providing a detailed analysis of the attack especially because they do not have the ability to learn from sequences of features. This work presents an improved predictive analytics model using CNN with BiLSTM to improve cybersecurity in IoT connected PHIS. The CNN component performs feature extraction on spatial elements in the data received while the BiLSTM uncovers temporal dependencies and sequence of attacks, which enhance threats identification accuracy. The process of the proposed methodology includes dataset acquisition and cleansing of IoT healthcare datasets and finally, the training and testing of the model with the help of relevant parameter such as accuracy, precision, and recall values. Qualitative comparison with the baseline models also shows that the proposed hybrid CNN-BiLSTM model performs better, and it has fewer false alarms to detect a range of cyber threats. Accordingly, the results point to a feasible solution for enhancing security and reliability of IoT-based healthcare applications to protect patients’ record from cyber threats.

关键词： Training Accuracy Surveillance Bidirectional long short term memory Threat assessment Internet of Things Computer security Public healthcare Information systems Testing

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Patterning Networks of Grade IV Glioblastoma on Silicon Chip*

Patterning Networks of Grade IV Glioblastoma on Silicon Chip...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Nicholas G. Mellor Sylvia A. Cheung Pierrette Michaux Joe Firth E. Scott Graham Bryan W. Day Charles P. Unsworth PhD Student With the Department of Engineering Science The University of Auckland Auckland New Zealand Postdoctoral Research Fellow With The Lowy Cancer Centre University of New South Wales Australia Process Engineers at the Australian National Fabrication Facility and School of Electrical Engineering & Telecommunications University of New South Wales Australia Associate Professor With the Department of Molecular Medicine and Pathology & the Centre for Brain Research The University of Auckland New Zealand Professor With the QIMR Berghofer Medical Research Institute Brisbane Australia Associate Professor (and Main Supervisor of The Work) With the Department of Engineering Science The University of Auckland Auckland New Zealand

Glioblastoma (GBM) is the most aggressive high-grade brain cancer with a median survival time of <15 months. Due to GBMs fast and infiltrative growth patient prognosis is poor with recurrence after treatment common. Investigating GBMs ability to communicate, specifically via Ca 2+ signaling, within its functional tumour networks may unlock new therapeutics to reduce the rapid infiltration and growth which currently makes treatment ineffective. This work aims to produce patterned networks of GBM cells such that the Ca 2+ communication at a network level can be repeatedly and reliably investigated.

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Expansion cooling prospects for large scale applications

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International Journal of Thermofluids 2023年 20卷

作者： Alami, Abdul Hai Alrashid, Rashid Mdallal, Ayman Yasin, Ahmad Ayoub, Mohamad Alasad, Shamma Aljaghoub, Haya Alashkar, Adnan Abdelkareem, Mohammad Ali Olabi, Abdul Ghani Orhan, Mehmet Department of Sustainable and Renewable Energy Engineering University of Sharjah P.O. Box 27272 Sharjah United Arab Emirates Sustainable Energy and Power Systems Research Centre RISE University of Sharjah P.O. Box 27272 Sharjah United Arab Emirates Department of Mechanical Engineering American University of Sharjah P.O. Box 26666 Sharjah United Arab Emirates Department of Mechatronics Engineering American University of Sharjah P.O. Box 26666 Sharjah United Arab Emirates Department of Industrial Engineering and Engineering Management University of Sharjah P.O. Box 27272 Sharjah United Arab Emirates Materials Science and Engineering PhD Program American University of Sharjah P.O. Box 26666 Sharjah United Arab Emirates Chemical Engineering Department Minia University Elminia Egypt Mechanical Engineering and Design School of Engineering and Applied Science Aston University Aston Triangle BirminghamB4 7ET United Kingdom

Air conditioning in general and cooling in particular are the biggest energy sinks among all electricity consumers. Mitigating energy needs of this sector by active or passive means is a necessity. This paper offers a comprehensive review of various cooling technologies for large scale applications and compares them in terms of economy, environmental footprint and most importantly, energy requirements. There is a main focus on the potential of using expansion cooling, which is defined in conjunction with cooling effects generated during the expansion process of a gas. The interest in such cooling technology stems from the double benefit of utilizing a large-scale compressed air energy storage (CAES) system that can also provide cooling via the expansion of air. This consequently manifests in reducing the electrical demand required for cooling applications. Such a concept is beneficial for hot climates, where most of the electrical demand is reserved for offsetting the cooling load. © 2023 The Author(s)

关键词： Compressed air energy storage

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Bulk passivation of perovskite films utilizing halide anion ionic liquids

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International Journal of Thermofluids 2023年 20卷

作者： Alashkar, Adnan Ayoub, Mohamad Ibrahim, Taleb Khamis, Mustafa Nancarrow, Paul Alami, Abdul Hai Tabet, Nouar PhD Program in Materials Science and Engineering American University of Sharjah Sharjah 26666 United Arab Emirates Sustainable and Renewable Energy Engineering Department University of Sharjah Sharjah 27272 United Arab Emirates Sustainable Energy & Power Systems Research Centre RISE University of Sharjah Sharjah 27272 United Arab Emirates Department of Chemical Engineering American University of Sharjah Sharjah 26666 United Arab Emirates Department of Biology Chemistry and Environmental Sciences College of Arts and Sciences American University of Sharjah Sharjah 26666 United Arab Emirates Center for Advanced Materials Research Research Institute of Science and Engineering University of Sharjah Sharjah 27272 United Arab Emirates Department of Applied Physics and Astronomy University of Sharjah Sharjah 27272 United Arab Emirates

Chemical additives to perovskite precursors constitute a predominant route to passivate defects in perovskite films and improve carrier charge dynamics. In this work, ionic liquids (ILs) with chloride and iodide anions are used as additives for bulk passivation of perovskite films. The ionic liquids in the form of 1‑butyl‑4-methylpyradinium iodide (IL-I) and 1-ethylimidazolium chloride (IL-Cl) are mixed individually with a precursor of Cesium Formamidinium Lead Triiodide and Methylammonium Chloride (CsFAPbI3−MACl). The decay of charge carriers within the perovskite film before and after passivation with ILs was measured by Time Resolved Photoluminescence Spectroscopy (TRPL). The effect of added ILs on the recombination rate of the photocarriers was investigated by analyzing the decay of the TRPL signal in all samples. The average lifetimes obtained within one day of preparations were 79 ns, 73 ns and 275 ns for pristine perovskite, IL-I passivated perovskite and IL-Cl passivated perovskite films, respectively. These values show that the presence of the IL with chloride anion greatly improves the average lifetime of the charge carrier by around 250%, while IL with iodide anion decreased the lifetime by 32% as compared with the pristine perovskite sample. This finding renders the IL-Cl passivated perovskite films as a valuable resource in applications ranging from photoelectric to hydrogen generation devices. © 2023

关键词： Ionic liquids

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