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Molecular Communications Loss Budget for tsRNA Detection in the Brain

IEEE Transactions on Molecular, Biological, and Multi-Scale Communications 2025年

作者： Khalil, Aiman Pumares, Kurt J. A. Skogberg, Anne Kallio, Pasi Kilbane, Deirdre Martins, Daniel P. South East Technological University Walton Institute for Information and Communication Systems Science X91 WaterfordP20H Ireland Tampere University BioMediTech Institute Faculty of Medicine and Health Technology Korkeakoulunkatu 3 Tampere33720 Finland South East Technological University FutureNeuro SFI Research Centre Walton Institute for Information and Communication Systems Science X91 WaterfordP20H Ireland University of Essex School of Computer Science and Electronic Engineering Wivenhoe ColchesterCO4 3SQ United Kingdom

Molecular communication (MC) is an emerging framework enabling communication among biological cells and bio-nanomachines at nano and micro scales through biochemical molecules. Recent studies have identified exosomal transfer RNA-derived small RNAs (tsRNAs) as potential biomarkers for epilepsy. Consequently, researchers are exploring innovative methods to predict epileptic seizures through tsRNA measurements, using implantable micro/nanoscale biosensors. This paper presents a propagation model for biomarkers in a heterogeneous fluidic environment, composed of the brain extracellular space (ECS), a polyethersulfone (PES) hollow fiber tube, and a hydrogel (e.g. collagen) containing bioengineered sensing cells for biomarker detection. Our proposed model aims to support the design of biosensing devices for epileptic seizure prediction by characterizing the propagation of biomarkers released from neuronal cells in the brain ECS to the implant. We analyse the communication performance of the proposed system by evaluating propagation loss under varying conditions–brain ECS tortuosity, fiber membrane thickness, permeability, and bioengineered sensing cell density. Furthermore, we develop an MC link budget to assess communication between exosomal tsRNA biomarkers and bioengineered sensing cells, based on received biomarkers. We observed an approximate 8-fold loss in received signal strength, highlighting the impact of MC communication media physicochemical characteristics for accurately designing devices to predict epileptic seizures. © 2015 IEEE.

关键词： Budget control

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EXPERIMENTAL EVALUATION OF THE USEFULNESS OF EQUATIONS DESCRIBING THE APPARENT MAXIMUM REACTION-RATE AND APPARENT MICHAELIS CONSTANT OF AN IMMOBILIZED ENZYME REACTION

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ENZYME AND MICROBIAL TECHNOLOGY 1993年第2期15卷 150-154页

作者： SHIRAISHI, F Department of Biochemical Engineering and Science Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Japan

To determine the usefulness of equations previously proposed for the apparent maximum reaction rate and apparent Michaelis constant of an immobilized enzyme, starch hydrolysis by glucoamylase immobilized on a porous ceramic support was considered as a model system. Initial reaction rates, v0, were measured for a wide range of initial starch concentrations, S(b0), to make a nonlinear plot Of S(b0)/v0 versus S(b0), and the apparent kinetic parameters were determined from the slopes and intercepts of tangents to the nonlinear plot at given values of S(b0). The equations were found to accurately express the diffusional effect on the kinetic parameters.

关键词： MAXIMUM REACTION RATE MICHAELIS CONSTANT APPARENT KINETIC PARAMETERS IMMOBILIZED ENZYME DIFFUSIONAL EFFECT

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Endogenous Security Techniques for Safeguarding Next-Generation Spectrum Usage at the Physical Layer

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IEEE Network 2025年

作者： Li, Guyue Zhou, Yajie Sun, Yingjie Wen, Shiping Wang, Shuai Hu, Aiqun Wen, Guanghui Southeast University School of Cyber Science and Engineering Nanjing210096 China Purple Mountain Laboratories Nanjing210096 China University of Technology Sydney Australian AI Institute Faculty of Engineering and Information Technology NSW2007 Australia Beihang University School of Computer Science and Engineering Beijing100191 China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Southeast University School of Mathematics Department of Systems Science Nanjing211189 China

The rapid increase in the data transmission rates for next generation communication systems has intensified the demand for spectrum, straining limited resources under traditional management. This has spurred a move toward dynamic spectrum sharing and exploration of higher frequency bands. Although spectrum sharing enables other users to access the underutilized whitespace spectrum, it also raises additional security concerns. The possibility of attacks at higher carrier frequencies has not yet been well characterized. To address these challenges, endogenous properties of wireless communication systems can be explored as new opportunities for security design. The purpose of this tutorial is to provide a comprehensive overview of the state-of-the-art wireless endogenous security techniques that guarantee wireless security and privacy for spectrum usage at the physical layer, along with the principles, solutions, and future research trends. © 1986-2012 IEEE.

关键词： next-generation spectrum usage physical layer security spectrum sharing wireless endogenous security

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An efficient binary salp swarm algorithm for user selection in multiuser MIMO antenna systems

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Scientific Reports 2025年第1期15卷 1-17页

作者： Sasikumar, A. Ravi, Logesh Devarajan, Malathi Almazyad, Abdulaziz S. De, Shuvodeep Xiong, Guojiang Mousavirad, Seyed Jalaleddin Mohamed, Ali Wagdy Department of Data Science and Business Systems Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur 603203 India Centre for Advanced Data Science Tamil Nadu Chennai 600127 India School of Electronics Engineering Vellore Institute of Technology Tamil Nadu Chennai 600127 India School of Computer Science and Engineering Vellore Institute of Technology Chennai 600127 India Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh 11543 Saudi Arabia Virginia Tech Blacksburg United States Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical Engineering Guizhou University Guiyang 550025 China Department of Computer and Electrical Engineering Mid Sweden University Sundsvall Sweden Operations Research Department Faculty of Graduate Studies for Statistical Research Cairo University Giza 12613 Egypt Applied Science Research Center Applied Science Private University Amman 11931 Jordan Centre for Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India

The past ten years have seen notable research activity and significant advancements in multiuser multiple-input multiple-output (MU-MIMO) antennas. An MU-MIMO antenna system must accommodate many subscribers without additional bandwidth or energy. User scheduling becomes a critical strategy to take advantage of multiuser heterogeneity and acquire maximum gain in systems where the total number of recipients exceeds the number of transmitting antennas. Due to their high computational cost, many user selection methods currently in use, such as greedy algorithms and exhaustive search are unsuitable for MU-MIMO systems. A suitable scheduling mechanism is essential for the various users in an MU-MIMO system to utilise bandwidth and enhance the system’s total rate effectively. In this article, we proposed a user and antenna scheduling with a population-based meta-heuristic approach, namely the binary salp swarm algorithm (binary SSA), to increase the system sum rate with low computing complexity. We specifically used a population-based meta-heuristics optimisation technique to simulate the user scheduling problem in MU-MIMO systems, characterising complicated issues with binary decisions. Additionally, binary SSA significantly outperforms existing population-based models, such as the binary bat algorithm (binary BA), PSO, SSA, FPA and binary flower pollination algorithm (binary FPA), regarding system throughput/sum rate. The proposed binary SSA technique also effectively achieves a system sum rate compared to a random search scheme and other existing suboptimal scheduling methods. Compared to binary BA and binary FPA approaches, the binary SSA has a higher convergence rate and superior searching capabilities. The simulation outcomes show the proposed binary SSA-based scheduling scheme delivers noticeable performance benefits. © The Author(s) 2025.

关键词： Antenna Binary salp swarm algorithm Metaheuristics optimization Multiuser MIMO User scheduling

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LEARNING OF MODULAR STRUCTURED NETWORKS

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ARTIFICIAL INTELLIGENCE 1995年第1期75卷 51-62页

作者： ISHIKAWA, M Department of Control Engineering and Science Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Fukuoka 820 Japan

Learning of large-scale neural networks suffers from computational cost and the local minima problem. One solution to these difficulties is the use of modular structured networks. Proposed here is the learning of modular networks using structural learning with forgetting. It enables the formation of modules. It also enables automatic utilization of appropriate modules from among the previously learned ones. This not only achieves efficient learning, but also makes the resulting network understandable due to its modular character. In the learning of a Boolean function, the present module acquires information from its subtask module without any supervision. In the parity problem, a previously learned lower-order parity problem is automatically used. The geometrical transformation of figures can be realized by a sequence of elementary transformations. This sequence can also be discovered by the learning of multi-layer modular networks. These examples well demonstrate the effectiveness of modular structured networks constructed by structural learning with forgetting.

关键词： Learning systems

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Harnessing optimization with deep learning approach on intelligent transportation system for anomaly detection in pedestrian walkways

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Scientific Reports 2025年第1期15卷 1-28页

作者： Alotaibi, Sultan Refa Alrayes, Fatma S. Maashi, Mashael Maray, Mohammed Alliheedi, Mohammed A. Badawood, Donia Alotaibi, Moneerah Department of Computer Science College of Science and Humanities Dawadmi Shaqra University Shaqra Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University PO Box 84428 Riyadh 11671 Saudi Arabia Department of Software Engineering College of Computer and Information Sciences King Saud University PO Box 103786 Riyadh 11543 Saudi Arabia Department of Information Systems College of Computer Science King Khalid University Abha Saudi Arabia Department of Computing Faculty of Computing and Information Al-Baha University Al Baha Saudi Arabia Department of Data Science School of Computers Umm Al-Qura University Makkah Saudi Arabia

Anomaly Detection (AD) in pedestrian walkways is significant in urban safety and security methods. It is generally employed for perceiving unusual or abnormal situations, behaviours, or actions in regions devoted to pedestrian traffic, like pedestrian bridges, sidewalks, or crosswalks. The main aim is to enhance security, efficiency, and safety in the urban environment by classifying changes and observing the activities of pedestrians from recognized models. This type of AD usually has sensors, surveillance cameras, and advanced software methods. By applying innovative computer vision (CV) and machine learning (ML) methods, this approach always examines the pedestrian area to detect potential anomalies and attacks. Deep learning (DL) aided AD in pedestrian walkways, which displays a new and very effective method to progress safety and security in urban environments. Therefore, this study designs a novel Harnessing Optimization with a DL Approach on an Intelligent Transportation System with Anomaly Detection in Pedestrian Walkways (HODLAITS-ADPW) method. The presented HODLAITS-ADPW method utilizes the CV and DL process to detect pedestrians accurately. To attain this, the HODLAITS-ADPW method performs image pre-processing using Median Filtering to enrich the quality of input frames. Next, the HODLAITS-ADPW method utilizes an enhanced YOLOv7 object detector for the object’s detection in the frame. Besides, the Chameleon Swarm Algorithm is employed for the optimum hyperparameter tuning method. The HODLAITS-ADPW method uses an Attention Pyramid Convolutional Neural Network model for anomaly recognition. The experimental values of the HODLAITS-ADPW technique were certified on the pedestrian dataset, and the outcomes can be examined in numerous aspects. The empirical findings reported better outcomes for the HODLAITS-ADPW technique when compared to other models. © The Author(s) 2025.

关键词： Chameleon swarm algorithm Intelligent transportation system Median filtering Pedestrian walkway YOLOv7

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Efficient spin-orbit torque driven magnetization switching of GdFe using phosphorus-implanted platinum layers

arXiv

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arXiv 2025年

作者： Shintaku, Kazuki Mathew, Arun Jacob Iwamoto, Akihisa Mohammadi, Mojtaba Awano, Hiroyuki Asada, Hironori Fukuma, Yasuhiro Department of Physics and Information Technology Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka820-8502 Japan Memory Engineering Laboratory Toyota Technological Institute Nagoya468-8511 Japan Graduate School of Sciences and Technology for Innovation Yamaguchi University Ube755-8611 Japan Research Center for Neuromorphic AI hardware Kyushu Institute of Technology Kitakyushu808-0196 Japan

The capability of the spin-orbit torque (SOT) generated via phenomena such as the spin Hall effect in heavy metals, in switching the magnetization of an adjacent magnetic material, has been studied extensively over the last decade. The efficiency of SOT generation is commonly quantified in terms of the spin Hall angle θSH. In this work, we demonstrate experimentally that implanting platinum (Pt) with phosphorus (P), resulting in Pt (P) d, where d denotes the implantation dose, increases θSH by a factor of 7, from 0.06 (d = 0) to 0.43 (d = 10 × 1016 ions/cm2). The enhanced θSH along with factors such as perpendicular magnetic anisotropy and resistivity, lead to reduction of the critical current density for switching the perpendicular magnetization of ferrimagnetic rare earth-transition metal alloy Gd26Fe74, by a factor of nearly 27, from 4.0 × 1011 A/m2 (d = 0) to 1.5 × 1010 A/m2 (d = 10 × 1016 ions/cm2). Further, the switching current density at zero thermal fluctuations and thermal stability factor were evaluated and found to be 2.0 × 1010 A/m2 and 61.4 (d = 10 × 1016 ions/cm2), with the latter being sufficiently above the required threshold for commercial memory applications. Our results suggest that Pt (P) could be a strong candidate in realizing efficient SOT driven magnetization switching leading to the development of improved memory and logic devices in the future. Copyright © 2025, The Authors. All rights reserved.

关键词： Iron alloys

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Enhancing lung cancer detection through integrated deep learning and transformer models

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Scientific Reports 2025年第1期15卷 1-28页

作者： Durgam, Revathi Panduri, Bharathi Balaji, V. Khadidos, Adil O. Khadidos, Alaa O. Selvarajan, Shitharth Department of Data Science AVN Institute of Engineering and Technology Hyderabad India Department of Information Technology Gokaraju Rangaraju Institute of Engineering & Technology Hyderabad India Department of CSE-AIML Vardhaman College of Engineering Hyderabad India Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah 21589 Saudi Arabia Department of Computer Science and Engineering Kebri Dehar University Kebri Dehar Ethiopia Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India Department of Computer Science and Engineering Chennai Institute of Technology Chennai India Center of Research Excellence in Artificial Intelligence and Data Science King Abdulaziz University Jeddah Saudi Arabia

Lung cancer has been stated as one of the prevalent killers of cancer up to this present time and this clearly underlines the rationale for early diagnosis to enhance life expectancy of patients afflicted with the condition. The reasons behind the usage of the transformer and deep learning classifiers for the detection of lung cancer include accuracy, robustness along with the capability to handle and evaluate large data sets and much more. Such models can be more complex and can help to utilize multiple modalities of data to give extensive information that will be critical in ascertaining the right diagnosis at the right time. However, the existing works encounter several limitations including reliance on large annotated data, overfitting, high computation complexity, and interpretability. Third, the issue of the stability of these models’ performance when applied to actual clinical datasets is still an open question;this is an even bigger issue that will greatly reduce the actual utilization of these models in clinical practice. To tackle these, we develop a novel Cancer Nexus Synergy (CanNS), which applies of A. Swin-Transformer UNet (SwiNet) Model for segmentation, Xception-LSTM GAN (XLG) CancerNet for classification, and Devilish Levy Optimization (DevLO) for fine-tuning parameters. This paper breaks new ground in that the presented elements are incorporated in a manner that co-operatively elevates the diagnostic capabilities while at the same time being computationally light and resilient. These are SwiNet for segmented analysis, XLG CancerNet for precise classification of the cases, and DevLO that optimizes the parameters of the lung cancer detection system, making the system more sensible and efficient. The performance outcomes indicate that the CanNS framework enhances the detection’s accuracy, sensitivity, and specificity compared to the previous approaches. © The Author(s) 2025.

关键词： And classification Deep learning Disease detection Lung Cancer Optimization Segmentation Transformer models

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PreMatch: A Prediction-based Scheduler for Dataflow Asynchronous Processing on the Computing Continuum 25

PreMatch: A Prediction-based Scheduler for Dataflow Asynchro...

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Proceedings of the 40th ACM/SIGAPP Symposium on Applied Computing

作者： Narges Mehran Zahra Najafabadi Samani Samira Afzal Radu Prodan Frank Pallas Peter Dorfinger Intelligent Connectivity Group Salzburg Research Forschungsgesellschaft mbH Salzburg Salzburg Austria Faculty of Digital and Analytical Sciences Paris Lodron University of Salzburg Salzburg Salzburg Austria Distributed and Parallel Systems Group University of Innsbruck Innsbruck Tyrol Austria Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede Overijssel Netherlands Institute of Information Technology Alpen-Adria-University of Klagenfurt Klagenfurt Carinthia Austria

ISBN: (纸本)9798400706295

The popularity of asynchronous data exchange patterns has recently increased, as evidenced by 23% of the communication between microservices in an Alibaba trace analysis. Such workloads necessitate methods for reducing dataflow processing and completion time by forecasting the future requirements of their microservices and (re-)scheduling them. Therefore, we investigate a prediction-based scheduling method of asynchronous dataflow processing applications by considering the stochastic changes due to dynamic user requirements. We present a microservice scaling and scheduling method named PreMatch combining a machine learning (ML) prediction strategy based on gradient boosting with ranking and game theory matching scheduling principles. Firstly, PreMatch predicts the number of microservice replicas, and then, the ranking method orders the microservice replicas and devices based on microservice and transmission times. Thereafter, the PreMatch schedules microservice replicas requiring dataflow processing on computing devices. Experimental analysis of the PreMatch method shows lower completion times on average 13% compared to a related prediction-based scheduling method.

关键词： microservice

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On the generalized derivations of bimodules

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Frontiers of Mathematics in China 2017年第1期12卷 135-142页

作者： Hiroaki KOMATSU Faculty of Computer Science and Systems Engineering Okayama Prefectural University Soja 719-1197 Japan

Recently, we introduced the notion of a generalized derivation from a bimodule to a bimodule. In this paper, we give a more general notion based on commutators which covers generalized derivations as a special case. U... 详细信息

关键词： Generalized derivation separable algebra extension

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