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Stereoselective recognition of morphine enantiomers by μ-opioid receptor

National Science Review 2024年第3期11卷 183-191页

作者： Yibo Wang Van A.Ngo Xiaohui Wang Laboratory of Chemical Biology Changchun Institute of Applied ChemistryChinese Academy of Sciences Advanced Computing for Life Sciences and Engineering Group Science Engagement Section National Center for Computational Sciences Oak Ridge National Lab School of Applied Chemistry and Engineering University of Science and Technology of China Beijing National Laboratory for Molecular Sciences

Stereospecific recognition of chiral molecules plays a crucial role in biological systems. The μ-opioid receptor（MOR） exhibits binding affinity towards（-）-morphine, a well-established gold standard in pain management, while it shows minimal binding affinity for the（+）-morphine enantiomer, resulting in a lack of analgesic activity. Understanding how MOR stereoselectively recognizes morphine enantiomers has remained a puzzle in neuroscience and pharmacology for over half-a-century due to the lack of direct observation techniques. To unravel this mystery, we constructed the binding and unbinding processes of morphine enantiomers with MOR via molecular dynamics simulations to investigate the thermodynamics and kinetics governing MOR's stereoselective recognition of morphine enantiomers. Our findings reveal that the binding of（-）-morphine stabilizes MOR in its activated state, exhibiting a deep energy well and a prolonged residence time. In contrast,（+）-morphine fails to sustain the activation state of ***, the results suggest that specific residues, namely D1142.50and D1473.32, are deprotonated in the active state of MOR bound to（-）-morphine. This work highlights that the selectivity in molecular recognition goes beyond binding affinities, extending into the realm of residence time.

关键词： stereoselective recognition thermodynamics and kinetics residence time morphine enantiomers μ-opioid receptor

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LLM Generative AI and Students' Exam Code Evaluation: Qualitative and Quantitative Analysis 47

LLM Generative AI and Students' Exam Code Evaluation: Qualit...

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47th ICT and Electronics Convention, MIPRO 2024

作者： Smolić, E. Pavelić, M. Boras, B. Mekterović, I. Jagušt, T. University Of Zagreb Faculty Of Electrical Engineering And Computing Department Of Applied Computing Croatia Šibenik University Of Applied Sciences Croatia

ISBN: (纸本)9798350382495

Since the introduction of generative artificial intelligence (GAI) technology in the context of large language models (LLMs), it has been widely used for information extraction and/or extrapolation from different sources. In computer science education, a potential application of such technology is for automatic code review, i. e. shifting the burden of debugging non-compilable code, detecting overlooked optimization concerns such as poor memory management in code that otherwise passes automated tests, and other advanced tasks from a human grader to LLMs. However, LLMs are currently not capable of evaluating code or mathematical expressions with 100% reliability, i. e. beyond token pattern recognition and subsequent probabilistic answer generation. With that in mind, in this paper, we explore the risk of incorrect LLM code evaluation, both descriptive and numerical, as well as begin research on its mitigation and propose further work directions. © 2024 IEEE.

关键词： Grading

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Artificial Intelligence and Machine Learning for Accessibility: Smart Reader as an Assistive Tool for the Visually Impaired 2

Artificial Intelligence and Machine Learning for Accessibili...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Palarimath, Suresh Radhakrishnan, Venkateswaran Veerasamy, Devarajan Valsalan, Prajoona Viswan, Vimbi Thumu, Muni Balaji College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Oman College of Engineering Dhofar Univesity Electrical and Computer Engineering Department Salalah Oman College of Computing and Information Sciences University of Technology and Applied Sciences Sohar Oman

ISBN: (纸本)9798331505745

The amalgamation of artificial intelligence (AI) and machine learning (ML) has greatly improved accessibility for visually impaired individuals, fostering increased autonomy through pioneering assistive technology. This study introduces the Smart Reader, a device that integrates optical character recognition (OCR) and text-to-speech (TTS) technologies to enhance accessibility to printed documents. The Smart Reader utilizes a camera to take text images, translates them into digital representation through OCR, and converts the text into audible speech via TTS. The system has an intuitive user interface, an automatic page-turning mechanism, and a dictionary lookup function, guaranteeing a seamless reading experience. The findings indicate that the Smart Reader significantly improves access to written information, hence enhancing the quality of life for visually impaired individuals, especially in educational environments such as schools and libraries. The device is engineered for user-friendliness, enhancing accessibility and autonomy. The Smart Reader utilizes AI and ML, marking a significant progression in assistive technology, promoting an inclusive society where visually impaired individuals can engage with printed material with assurance. In addition to functionality, it seeks to empower people by connecting capability with opportunity, guaranteeing equitable access to information for everyone. © 2025 IEEE.

关键词： Contrastive Learning

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Salp Swarm Algorithm Application in Simultaneous Parameter Selection of Generators' Excitation Controllers for Power System Rotor Angle Stability Enhancement - WSCC Case Study 47

Salp Swarm Algorithm Application in Simultaneous Parameter S...

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47th ICT and Electronics Convention, MIPRO 2024

作者： Špoljarić, Tomislav Šešok, Anica Pavić, Ivica Komljen, Branimir Vuletić University Of Applied Sciences Zagreb Croatia Faculty Of Electrical Engineering And Computing Zagreb Croatia

ISBN: (纸本)9798350382495

In this paper a rotor angle stability of a power system, represented by WSCC model (Western standard coordinated council) is investigated after testing such a system in several disturbance conditions. In these conditions generators' excitation controllers AVR and PSS are being simultaneously tuned with the purpose of achieving satisfactory oscillations in angular speeds, voltages, and transmission line power transfer. Four different single-objective metaheuristic algorithms are used for controllers' parameter change, algorithm comparison and performance analysis. Every algorithm has the same starting population, two different single objective functions (time-domain error-oriented function and transitional process quality-oriented function) and three power system disturbance scenarios. Proposed SSA algorithm is also compared with three other algorithms: PSO (particle swarm optimization), SA (simulated annealing) and ALO (ant lion optimizer). Algorithms' performance is compared by quality indicators: best result, mean value, standard deviation, and convergence capability. © 2024 IEEE.

关键词： Simulated annealing

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Avian Hazard Mitigation: A Classification Approach to Bird Strike Analysis 5

Avian Hazard Mitigation: A Classification Approach to Bird S...

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5th IEEE International Conference on Advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Al-Zadjali, Najiba Said Hamed Savarimuthu, Charles Sudaravadivazhagan, B. Rances, Emanuel O. College of Computing and Information Sciences University of Technology and Applied Sciences-Muscat Muscat Oman University of Technology and Applied Sciences-Al Musanah College of Computing and Information Sciences Al-Musannah Oman College of Engineering and Technology University of Technology and Applied Sciences-Al Musanah Al-Musannah Oman

ISBN: (纸本)9798350356236

Bird strikes during landing or takeoff can result in fatalities and catastrophic accidents, raising concerns about loss of life for the aviation sector. This study explored the potential of employing machine-learning algorithms to mitigate bird strikes in aviation. Bird strike data were collected, and the Boruta algorithm feature-selection technique was applied to identify relevant features for an experiment. Irrelevant features were omitted, and relevant features related to bird strikes were identified for analysis. The multinomial Naive Bayes algorithm is then used to segregate objects based on their features. The experimental results illustrate the power of using machine learning techniques to segregate and analyze bird strike data, thereby aiding in determining the causes and effects of damage. © 2025 IEEE.

关键词： Aviation

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AI-Integrated Sensor Data Analytics for Real-Time Decision-Making in Wireless Sensor Networks 2

AI-Integrated Sensor Data Analytics for Real-Time Decision-M...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Wasim Haidar, S.K. Wilfred Blessing, N.R. Chaubey, Sudhakar Kumar Mehmood, Zahid Kavitha, S.J. Sutherlin Subitha, G. College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Oman College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman College of Computing and Information Sciences University of Technology and Applied Sciences Shinas Oman Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Tamil Nadu Chennai India Stella Mary's College of Engineering Department of Computer Science and Engineering Aruthenganvilai India

ISBN: (纸本)9798331505745

The study compares the performance of Wireless Sensor Networks (WSNs) with 20, 30, 40, and 50 nodes in real-time AI-enhanced sensor analytics using the AODV routing protocol. All configurations showed a consistent Packet Delivery Ratio (PDR) of 100%. However, the end-to-end delay increased slightly from 0.2 sec (20 nodes) to 0.3 sec (30, 40, 50 nodes), indicating that larger networks result in longer paths. Throughput decreased from 2560 bps at 20 nodes to 1706.67 bps in larger configurations, probable due to network congestion. Energy efficiency also decreased as the number of nodes increased, suggesting higher energy consumption per transmission. © 2025 IEEE.

关键词： Sensor nodes

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An Epsilon constraint-based evolutionary algorithm and multi-objective quality metrics for combined economic emission dispatch problem

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Neural computing and Applications 2025年 1-30页

作者： Chan, Kit Yan Yiu, Ka Fai Cedric Kim, Dowon School Electrical Engineering Computing and Mathematical Sciences Curtin University Perth Australia Department of Applied Mathematics The Hong Kong Polytechnic University Hung Hom Hong Kong

Solving combined economic emission dispatch (CEED) problem optimizes power generation by balancing cost minimization with emission reduction, addressing economic and environmental goals simultaneously. This trade-off results in a Pareto front, where each non-dominated solution represents an optimal balance between costs and emissions. Decision-makers can select solutions based on their preferences. This paper proposes an Epsilon-based multi-objective genetic algorithm (MOGA) to solve the CEED problem. By integrating evolutionary techniques and Epsilon constraint methods, the proposed method actively explores diverse solutions, avoiding local optima and enhancing the Pareto front. The two-objective CEED problem is reformulated into two single-objective problems, alternately minimizing cost or emissions. The Epsilon constraint algorithm accelerates the search for optimal solutions. Two quality indicators are proposed to evaluate the Pareto fronts. The first indicator measures solution spread;it assesses the diversity of generator settings and the dominated volume. The second indicator evaluates the uniformity of solution distribution, where smaller distances indicate better uniformity. High spread and uniform distribution signify superior Pareto fronts. If diversity is insufficient, the proposed Epsilon-based MOGA continues to refine the front. The performance of the proposed method was tested on IEEE 30-bus and 118-bus systems, showing improved results compared to RNSGA-II, the Epsilon constraint algorithm, and NSGA-II. The proposed method produced more diverse and uniformly distributed non-dominated solutions;it offers grid operators a broader range of options to balance costs and emissions. Additionally, it achieved lower costs and emissions. The computational time for the larger IEEE 118 system is manageable and does not increase exponentially compared to the smaller IEEE 30 system. © The Author(s) 2025.

关键词： Genetic algorithms

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Predicting Lung Cancer Risk Using Artificial Intelligence 2

Predicting Lung Cancer Risk Using Artificial Intelligence

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2nd International Conference on computing and Data Analytics, ICCDA 2024

作者： Shah, Syed Mehr Ali Jamil, Danish Khan, Muhammad Numan Ali Al-Jarwani, Fatma Mahfoodh Mohammed College of Computing & Information Sciences University of Technology and Applied Sciences Musandam Oman University of Santiago de Compostela Spain Sir Syed University of Engineering & Technology Department of Software Engineering India College of Computing & Information Sciences University of Technology and Applied Sciences shinas Oman

ISBN: (纸本)9798331542573

This research proposes a method to accurately forecast the risk of lung cancer in its initial stages by the combination of medical diagnosis and artificial intelligence. Two distinct models are employed: a symptoms-based model utilizing linear regression, and VGG19, a component of CNNs is employed for the analysis of chest x-ray images. The objective of this dual-model approach is to improve comprehensiveness and precision in the early prediction of lung cancer risk. The JSRT and Chest Xray-14 datasets were utilized to train and validate the convolutional neural network (CNN). The Regression model was trained using the Survey Lung cancer Dataset. This initiative prioritizes user-friendliness, interpretability, and ethical considerations of healthcare AI. Initial positive results show that this combined approach has a lot of potential for early detection © 2024 IEEE.

关键词： Lung cancer

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Sakanat: A Competent Mobile Application to Automate Real-Estate 14

Sakanat: A Competent Mobile Application to Automate Real-Est...

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14th IEEE International Conference on Control System, computing and engineering, ICCSCE 2024

作者： Qassabi, Saeed Saud Sulaiman Al Wilfred Blessing, N.R. Tahir, Nooritawati Md University of Technology and Applied Sciences-Muscat College of Computing and Information Sciences Muscat Oman University of Technology and Applied Sciences-Ibri College of Computing and Information Sciences Ibri Oman Malaysia Universiti Teknologi MARA College of Engineering Malaysia Applied College Riyadh Saudi Arabia

ISBN: (纸本)9798350364507

The traditional methods of searching for flats are often inefficient and lack necessary features, making it difficult for both landlords and tenants to find what they are looking for. The 'Sakanat' mobile application is designed to bridge this gap by offering a user-friendly platform for property listing, searching, and management. Landlords can easily upload details, photos, and set rental terms, while tenants can efficiently find suitable flats based on various criteria using advanced search filters. Rent payments can be made directly through the app using a secure payment gateway, and tenants can also access a variety of additional services such as maintenance companies and flat transfer services. Sakanat leverages Flutter for cross-platform app development and Firebase for backend services and data storage. Development will prioritize user-friendliness, performance, and security. By facilitating connections between landlords and tenants, simplifying rent collection, and offering valuable additional services, Sakanat has the potential to revolutionize the flat rental experience. © 2024 IEEE.

关键词： Gateways (computer networks)

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An Interpretable Machine Learning Method to Inform Urban Adaptation to Heat Waves

An Interpretable Machine Learning Method to Inform Urban Ada...

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ASCE International Conference on computing in Civil engineering 2023: Resilience, Safety, and Sustainability, i3CE 2023

作者： Liu, Tong Fan, Chao School of Civil and Environmental Engineering and Earth Sciences College of Engineering Computing and Applied Sciences Clemson Univ. ClemsonSC United States

ISBN: (数字)9780784485248

ISBN: (纸本)9780784485248

As the deadliest climate event over the past 30 years in the US, heat waves are projected to be more intense and dangerous under global warming. Cities, composed of unevenly distributed populations and diverse landscape, suffer from significant adverse impacts of heat waves with varying land surface temperatures across different areas. In addition to the climate, effective adaptation to heat waves also requires an understanding and evidence regarding how urban natural, physical, and social processes influence urban thermal variations. Prior studies that mostly rely on traditional regression methods fall short in interpreting the complex role of these factors in influencing fine-scale thermal variations within and even beyond the physical boundaries of these factors. To address this research gap, we propose an interpretable machine learning method based on graph neural network to disentangle the significant factors that influence local thermal conditions. Our model can explain 35.3% extra variations compared to the baseline ridge regression model. The results of the model can inform urban adaptation strategies, including nature-based solutions, such as evaluating the cooling efficiency of spatially variant greening interventions at fine-scale levels. © ASCE *** rights reserved.

关键词： Global warming

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