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Enhanced MADM Strategy with Heptapartitioned Neutrosophic Distance Metrics

Neutrosophic Sets and Systems 2025年 78卷 74-96页

作者： Eassa, Mohamed Myvizhi, M. Elkholy, Mohamed Abdelhafeez, Ahmed Elbehiery, Hussam Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan Department of Mathematics KPR Institute of Engineering and Technology Tamilnadu Coimbatore India Faculty of Computer Science& Engineering Alamein International University Egypt

In this work, a novel methodological approach to multi-attribute decision-making problems is developed and the notion of Heptapartitioned Neutrosophic Set Distance Measures (HNSDM) is introduced. By averaging the Penta-Partitioned Neutrosophic Distance Measures under particular conditional criteria, the suggested technique ranks the options. Additionally, we explore the fundamental characteristics of HNSDM and provide several illustrative theorems. The Heptapartitioned Neutrosophic Set Distance Measures are formulated based on the Hepta Neutrosophic Set. Lastly, a practical numerical example is provided to illustrate the efficacy of the suggested methodology. © 2025, University of New Mexico. All rights reserved.

关键词： Heptapartitioned Neutrosophic set Heptapartitioned Neutrosophic set and MADM strategy Heptapartitioned set Neutrosophic sine distance measure

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Influence of bismuth doping on the structural, optical characteristics and radiation attenuation properties of zinc sulfide quantum dots

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Optical and Quantum Electronics 2025年第1期57卷 1-19页

作者： Alwany, Abduelwhab. B. Murshed, Mohammad N. Sayed, Mohamed E. El Algradee, Mohammed A. Alnehia, Adnan Physics Department Faculty of Science Ibb University Ibb Yemen Physics Department Faculty of Applied Sciences Taiz University Taiz Yemen Physics Department Faculty of Science and Arts King Khalid University Muhayl Asser Saudi Arabia Faculty of Science and Engineering Aljazeera University Ibb Yemen Physics Department Faculty of Applied Science Thamar University Dhamar87246 Yemen

The co-precipitation technique was utilized to synthesize ZnS quantum dots (QDs) doped with varying concentrations of Bi (0%, 2.5%, 6.5%, and 10.5%). Structural and optical properties were investigated using X-ray diffraction (XRD), transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FTIR), and UV–visible spectroscopy. XRD analysis revealed cubic structures with preferential plane orientation (111) and crystallite sizes ranging from 4.5 nm to 2.5 nm for the QDs. TEM images exhibited agglomerated shapes. FTIR spectroscopy indicated that the intensity of FTIR decreased after Bi-doping. The optical properties of Bi-doped ZnS QDs significantly impacted the optical band gap (Eg), showing a blue shift compared to bulk cubic ZnS due to quantum confinement effects, with Eg values ranging from 3.761 to 3.899 eV, increasing with higher Bi concentrations. The nuclear shielding effectiveness of pristine and Bi-doped ZnS QDs was evaluated, with results indicating superior protection against nuclear radiations for Bi-doped ZnS QDs with the smallest nanoparticle size. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Fourier transform infrared spectroscopy

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Spatial distribution, geochemistry and provenance of heavy minerals in Miri beach sediments, NW Borneo, Malaysia

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Environmental Earth sciences 2025年第6期84卷 1-18页

作者： Naidu, Shyamalan Mohan Viswanathan, Prasanna Department of Applied Sciences Faculty of Engineering and Science Curtin University Malaysia CDT 250 Sarawak Miri98009 Malaysia

In this study, the spatial distribution of heavy minerals (HMs) in Miri beach sediments was assessed to quantify the abundance and determine the provenance. Surface sediment samples were collected in Miri beaches during low tide with respect to longitudinal, lateral and depth samplings. The collected samples were processed for grain size analysis by using sieving method. Then the HMs separation at different grain size fractions was carried out by using gravity and magnetic separation methods. Polarising Microscope was used to identify various HMs present in the sediment samples. In addition, selected sediment samples were analysed by using X-ray Fluorescence (XRF) to determine the elemental composition and major oxides for the geochemistry and provenance. From the results, HMs found in Miri beach sediments were zircon, magnetite, tourmaline, and rutile. Magnetite was abundant in 600–250 μm size fraction, zircon in 125–63 μm size fraction, tourmaline in 125–63 μm size fraction, and rutile in 250–125 μm size fraction. Longitudinally, magnetite was found to have higher abundance towards the southern part of the coastline. However, the abundance of rutile and tourmaline was found in the northern part of the coastline. Zircon was more abundant in the middle and southern parts of the coastline. Laterally, the concentration of HMs was higher towards the landward side. In terms of depth, the distribution of HMs varies and abundant in the surface sample. Geochemically, the beach sediments were found to be highly weathered in the north beaches (CIA = 78.9), moderately weathered in the middle (CIA = 63.8 to 66.5) and poorly weathered in the southern beaches (CIA = 40.4–49.6). The high content of quartz in the sediments are derived from the quartzose sedimentary origin, which undergone recycling process. Environmental indices such as contamination factor (CF), geoaccumulation index (Igeo), and enrichment factor (EF) indicate that the Miri beach sediments were highly concentrate

关键词： Magnetite

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Discrete Symmetry Analysis of Free Convection in a Cylindrical Porous Annular Microchannel at Low-Pressure Level

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ASME Journal of Heat and Mass Transfer 2025年第6期147卷 062701页

作者： Avramenko, Andriy A. Shevchuk, Igor V. Kobzar, Andrii S. Institute of Engineering Thermophysics National Academy of Sciences Kiev03057 Ukraine Faculty of Computer Science and Engineering Science TH Köln - University of Applied Sciences Gummersbach51643 Germany

This article studies the natural convection in an annular porous microchannel in case of one wall being heated and another being cooled. For the first time, such a problem was solved using discrete symmetries of the Navier-Stokes equations. Using discrete symmetries, self-similar forms of differential equations were obtained. Solutions of self-similar equations made it possible to obtain velocity and temperature profiles incorporating slip and temperature jump at the channel walls as boundary conditions. The effects of Grashof, Knudsen, Darcy, and Prandtl numbers on the velocity and temperature profiles in the microchannel and Nusselt numbers are demonstrated. At high Grashof numbers, an ascending flow forms near the hot cylinder, while a descending flow develops near the cold cylinder. As the Knudsen number increases, rise of velocity and temperature jump at the walls as well as heat transfer coefficients decrease is observed. An increase in the Darcy number results in higher velocities for both flows. The temperature jump at the heated cylinder increases, remaining unchanged at the cooled cylinder, and the heat transfer coefficient at the heated cylinder drops. © 2025 by ASME.

关键词： Navier Stokes equations

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Human-in-the-loop transfer learning in collision avoidance of autonomous robots

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Biomimetic Intelligence & Robotics 2025年第1期5卷 124-137页

作者： Minako Oriyama Pitoyo Hartono Hideyuki Sawada Department of Pure and Applied Physics Graduate School of Advanced Science and EngineeringWaseda UniversityTokyo 169-8555Japan School of Engineering Chukyo UniversityAichi 466-8666Japan Department of Applied Physics Faculty of Science and EngineeringWaseda UniversityTokyo 169-8555Japan

Neural networks have demonstrated exceptional performance across a range of ***,their training often demands substantial time and data resources,presenting a challenge for autonomous robots operating in real-world environments where real-time learning is *** mitigate this constraint,we propose a novel human-in-the-loop framework that harnesses human expertise to mitigate the learning challenges of autonomous *** approach centers on directly incorporating human knowledge and insights into the robot’s learning *** proposed framework incorporates a mechanism for autonomous learning from the environment via reinforcement learning,utilizing a pre-trained model that encapsulates human knowledge as its *** integrating human-provided knowledge and evaluation,we aim to bridge the division between human intuition and machine learning *** a series of collision avoidance experiments,we validated that incorporating human knowledge significantly improves both learning efficiency and generalization *** collaborative learning paradigm enables robots to utilize human common sense and domain-specific expertise,resulting in faster convergence and better performance in complex *** research contributes to the development of more efficient and adaptable autonomous robots and seeks to analyze how humans can effectively participate in robot learning and the effects of such participation,illuminating the intricate interplay between human cognition and artificial intelligence.

关键词： Human-in-the-loop Transfer learning Autonomous robots Neural networks Reinforcement learning

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Planar structure of organic photodetector for low dark current

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Chinese Physics B 2025年第2期34卷 290-295页

作者： Mohammad Nofil Amirul Ashraf Md Sabri Fadlan Arif Natashah Tahani M Bawazeer Mohammad S Alsoufi Nur Adilah Roslan Azzuliani Supangat Low Dimensional Materials Research Centre Department of PhysicsFaculty of ScienceUniversiti MalayaKuala Lumpur50603Malaysia Department of Chemistry Faculty of Applied ScienceUmm Al-Qura UniversityMakkahSaudi Arabia Mechanical Engineering Department College of Engineering and Islamic ArchitectureUmm Al-Qura UniversityMakkahSaudi Arabia Department of Physics Faculty of ScienceUniversiti Putra MalaysiaSerdang43400Malaysia

The focus of this study is on investigating the vanadyl 2,9,16,23-tetraphenoxy-29H, 31H-phthalocyanine(VOPc Ph O)and its blend with o-xylenyl C60 bis-adduct(OXCBA), for use as a lateral ultraviolet organic photodetector. The research focuses on improving dark current reduction, which is a challenge in lateral organic photodetector. By integrating the OXCBA, low dark current values of 4.83 nA·cm^(-2)(D_(shot)^(*)= 1.414 × 10^(11)Jones) have been achieved as compared to the stand-alone VoPcPhO device of 14.06 nA·cm^(-2). The major contributing factors to dark current reduction are due to the efficient charge transfer at the photoactive-electrode interface, the deep highest occupied molecular orbital(HOMO)level of OXCBA, which leads to favorable energy level alignments hindering hole injection, and the occurrence of bulk heterojunction vertical phase segregation between VOPcPhO and OXCBA. These findings shed light on the relationship between the organic photoconductor's material composition, morphology, and performance metrics and open new avenues for metal phthalocyanine-based lateral ultraviolet organic photodetectors with low dark current and enhanced performance.

关键词： small molecule dark current ultra-violet(UV)sensor device physics

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Generative AI for Energy Harvesting Internet of Things Network: Fundamental, Applications, and Opportunities

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IEEE Internet of Things Magazine 2025年第3期8卷 72-80页

作者： Xie, Wenwen Sun, Geng Li, Jiahui Wang, Jiacheng Du, Hongyang Niyato, Dusit Dobre, Octavia A. Jilin University China The College of Computing and Data Science Nanyang Technological University Singapore The University of Hong Kong China Memorial University The Faculty of Engineering and Applied Science Canada

Internet of Things (IoT) devices are typically powered by small-sized batteries with limited energy storage capacity, requiring regular replacement or recharging. To reduce costs and maintain connectivity in IoT networks, energy harvesting technologies are regarded as a promising solution. Notably, due to its robust analytical and generative capabilities, generative artificial intelligence (GenAl) has demonstrated significant potential in optimizing energy harvesting networks. Therefore, we discuss key applications of GenAl in improving energy harvesting IoT networks in this article. Specifically, we first review the key technologies of GenAI and the architecture of energy harvesting IoT networks. Then, we show how GenAI can address different problems to improve the performance of the energy harvesting IoT networks. Subsequently, we present a case study of unmanned aerial vehicle (UAV)-enabled data col-lection and energy transfer. The case study shows distinctively the necessity of energy harvesting technology and verify the effectiveness of GenAI-based methods. Finally, we discuss some import-ant open directions. © 2018 IEEE.

关键词： Costs Generative AI Reviews Energy exchange Data collection Autonomous aerial vehicles Batteries Energy harvesting Internet of Things Battery management systems Energy storage

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Skin-Parallel Type Transcutaneous Energy Transmission Transformer Considering the Biological Effects of Electromagnetic Fields －When Receiving Coils are Arranged in Series and Parallel－

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, Information and Systems 2025年第3期145卷 399-407页

作者： Fujitsuka, Takuya Shiba, Kenji Department of Applied Electronics Faculty of Advanced Engineering Tokyo University of Science 6-3-1 Niijuku Katsushika-ku Tokyo125-8585 Japan

In this study, we created a skin-parallel type transcutaneous transmission system for a ventricular assist device using plate-shaped solenoid as the transmitting coil. We compared the biological effects and radiated magnetic fields of six models in which two receiving coils were placed in series and in parallel. The results showed that the power transmission efficiency was around 90% for all models, and there was no major difference between the two layouts. It was also found that by placing two receiving coils in parallel and separating them by 10 mm (Type D), was possible to suppress the electric field strength within the body Ein by more than 10 V/m. However, the limit value was exceeded by placing two receiving coils in series and separating them by 0.5 mm. In addition, the local SAR for Type D was 1.2595 W/kg, which was below the limit value. © 2025 The Institute of Electrical Engineers of Japan.

关键词： Magnetic field effects

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Early Parkinson's Disease Detection and Classification using Machine Learning and Neutrosophic Sets

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Neutrosophic Sets and Systems 2025年 82卷 873-886页

作者： Oudah, Hasan H. Metwaly, Ahmed A. Eassa, Mohamed Abdelhafeez, Ahmed Nagm, Ahmad M. Salama, Ahmed S. College of Arts Department of Journalism Ahl Al Bayt University Karbala Iraq Department of Computer Science Faculty of Computers and Informatics Zagazig University Zagazig44519 Egypt Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Engineering and Electronics Cairo Higher Institute for Engineering Computer Science and Management New Cairo Egypt

Optimizing therapy and rehabilitation for Parkinson's disease (PD) requires early identification and precise evaluation of the illness's course. However, there is disagreement about the best way to use gait analysis to categorize the severity of motor symptoms and identify early-stage Parkinson's disease. The precision of machine learning (ML) models in identifying early and intermediate stages of Parkinson's disease was assessed in this study. Six ML models are used in this study for the prediction of PD. Different metrics are used to evaluate ML models such as accuracy, precision, recall, f1-score, and AUC score. Then we propose a multi-criteria decision-making methodology (MCDM) to evaluate the ML models and select the best one. Two MCDM methods are used such as CRITIC method to compute the criteria weights and the TOPSIS method to rank the alternatives. These methods are used under the bipolar neutrosophic sets (BNSs) to deal with uncertainty information. The results show the support vector machine is the best ML model for the prediction of PD. © 2025, University of New Mexico. All rights reserved.

关键词： Neurodegenerative diseases

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An IoT Intrusion Detection Approach Based on Salp Swarm and Artificial Neural Network

An IoT Intrusion Detection Approach Based on Salp Swarm and ...

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作者： Alzubi, Omar A. Alzubi, Jafar A. Qiqieh, Issa Al-Zoubi, Ala' M. Faculty of Artificial Intelligence Al-Balqa Applied University Salt Jordan Faculty of Engineering Al-Balqa Applied University Salt Jordan Faculty of Information Technology Applied Science Private University Amman Jordan

The Internet of Things has emerged as a significant and influential technology in modern times. IoT presents solutions to reduce the need for human intervention and emphasizes task automation. According to a Cisco report, there were over 14.7 billion IoT devices in 2023. However, as the number of devices and users utilizing this technology grows, so does the potential for security breaches and intrusions. For instance, insecure IoT devices, such as smart home appliances or industrial sensors, can be vulnerable to hacking attempts. Hackers might exploit these vulnerabilities to gain unauthorized access to sensitive data or even control the devices remotely. To address and prevent this issue, this work proposes integrating intrusion detection systems (IDSs) with an artificial neural network (ANN) and a salp swarm algorithm (SSA) to enhance intrusion detection in an IoT environment. The SSA functions as an optimization algorithm that selects optimal networks for the multilayer perceptron (MLP). The proposed approach has been evaluated using three novel benchmarks: Edge-IIoTset, WUSTL-IIOT-2021, and IoTID20. Additionally, various experiments have been conducted to assess the effectiveness of the proposed approach. Additionally, a comparison is made between the proposed approach and several approaches from the literature, particularly SVM combined with various metaheuristic algorithms. Then, identify the most crucial features for each dataset to improve detection performance. The SSA-MLP outperforms the other algorithms with 88.241%, 93.610%, and 97.698% for Edge-IIoTset, IoTID20, and WUSTL, respectively. © 2024 John Wiley & Sons Ltd.

关键词： Network intrusion

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