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Aging decreases the precision of visual working memory

Aging, Neuropsychology, and Cognition 2024年第4期31卷 762-776页

作者： Esfahan, Shahrzad M. Nili, Mohammad-Hossein H.K Hatami, Javad Sanayei, Mehdi Rezayat, Ehsan Institute for Cognitive Science Studies (ICSS) Tehran Iran Department of Psychology Psychology and Educational Science Faculty University of Tehran Tehran Iran School of Electrical & Computer Engineering College of Engineering University of Tehran Tehran Iran School of Cognitive Sciences Institute for Research in Fundamental Sciences (IPM) Tehran Iran

Objectives: As individuals age, cognitive abilities such as working memory (WM), decline. In the current study, we investigated the effect of age on WM, and elucidated sources of errors. Method: A total of 102 healthy individuals, aged 18 to 71, participated in this research. We designed and administered a face-based visual WM task, collecting responses via a graded scale in a delayed match-to-sample reproduction task. Results: The error of participants increased significantly as they aged. Our analysis revealed a significant age-related rise in the standard deviation of error distribution. However, there was no significant change in uniform probability with age. Conclusion: We found that WM performance declines through the lifespan. Investigating the sources of error, we found that the precision of WM decreased monotonously with age. The results also indicated that the probability of guessing the response as a measure of random response is not affected by age. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Aging continuous error face working memory working memory task

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Understanding Trends, Patterns, and Dynamics in Global Company Acquisitions: A Network Perspective

arXiv

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arXiv 2024年

作者： Kalhor, Ghazal Bahrak, Behnam School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Tehran Institute for Advanced Studies Tehran Iran

Studying acquisitions offers invaluable insights into startup trends, aiding informed investment decisions for businesses. However, the scarcity of studies in this domain prompts our focus on shedding light in this area. Employing Crunchbase data, our study delves into the global network of company acquisitions using diverse network analysis techniques. Our findings unveil an acquisition network characterized by a primarily sparse structure comprising localized dense connections. We reveal a prevalent tendency among organizations to acquire companies within their own country and industry, as well as those within the same age bracket. Furthermore, we show that the country, region, city, and category of the companies can affect the formation of acquisition relationships between them. Our temporal analysis indicates a growth in the number of weakly connected components of the network over time, accompanied by a trend toward a sparser network. Through centrality metrics computation in the cross-city acquisition network, we identify New York, London, and San Francisco as pivotal and central hubs in the global economic landscape. Finally, we show that the United States, United Kingdom, and Germany are predominant countries in international acquisitions. The insights from our research assist policymakers in crafting better regulations to foster global economic growth, and aid businesses in deciding which startups to acquire and which markets to target for expansion. © 2024, CC BY.

关键词： Investments

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A LESO Based Backstepping Controller Considering Coal Seam Hardness for Rotary Speed in Coal Mine Tunnel Drilling Process

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Journal of Systems Science & Complexity 2024年第5期37卷 1789-1808页

作者： CHEN Luefeng LIU Xiao WU Min LU Chengda PEDRYCZ Witold HIROTA Kaoru School of Automation China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of EducationWuhan 430074China Department of Electrical and Computer Engineering University of AlbertaEdmontonAB T6R2G7Canada The Tokyo Institute of Technology Yokohama 226-8502Japan

In the process of coal mine drilling,controlling the rotary speed is important as it determines the efficiency and safety of *** this paper,a linear extended state observer(LESO)based backstepping controller for rotary speed is proposed,which can overcome the impact of changes in coal seam hardness on rotary ***,the influence of coal seam hardness on the drilling rig’s rotary system is considered for the first time,which is reflected in the numerical variation of load torque,and a dynamic model for the design of rotary speed controller is *** an LESO is designed to observe the load torque,and feedforward compensation is carried out to overcome the influence of coal seam *** on the model of the compensated system,a backstepping method is used to design a controller to achieve tracking control of the rotary ***,the effectiveness of the controller designed in this paper is demonstrated through simulation and field experiments,the steady-state error of the rotary speed in field is 1 r/min,and the overshoot is reduced to 5.8%.This greatly improves the stability and security,which is exactly what the drilling process requires.

关键词： Backstepping extended state observer hydraulic tunnel drilling machine rotary speed

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Real-Time Tunable Gas Sensing Platform Based on SnO_(2) Nanoparticles Activated by Blue Micro-Light-Emitting Diodes

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Nano-Micro Letters 2024年第12期16卷 103-119页

作者： Gi Baek Nam Jung-El Ryu Tae Hoon Eom Seung Ju Kim Jun Min Suh Seungmin Lee Sungkyun Choi Cheon Woo Moon Seon Ju Park Soo Min Lee Byungsoo Kim Sung Hyuk Park Jin Wook Yang Sangjin Min Sohyeon Park Sung Hwan Cho Hyuk Jin Kim Sang Eon Jun Tae Hyung Lee Yeong Jae Kim Jae Young Kim Young Joon Hong Jong-In Shim Hyung-Gi Byun Yongjo Park Inkyu Park Sang-Wan Ryu Ho Won Jang Department of Materials Science and Engineering Seoul National UniversitySeoul 08826Republic of Korea Research Laboratory of Electronics Department of Mechanical EngineeringMassachusetts Institute of TechnologyCambridgeMA 02139USA Ming Hsieh Department of Electrical and Computer Engineering University of Southern CaliforniaLos AngelesCA 90089USA Department of Display Materials Engineering Soonchunhyang UniversityAsan 31538Republic of Korea Department of Photonics and Nanoelectronics BK21 FOUR ERICA-ACE CenterHanyang University ERICAAnsan 15588Republic of Korea Department of Nanotechnology and Advanced Materials Engineering Sejong UniversitySeoul 05006Republic of Korea Department of Electronics Information and Communication EngineeringKangwon National UniversitySamcheok 25913Republic of Korea Advance Institute of Convergence Technology Seoul National UniversitySuwon 16229Republic of Korea Department of Mechanical Engineering Korea Advanced Institute of Science and TechnologyDaejeon 34141Republic of Korea Department of Physics Chonnam National UniversityGwangju 500-757Republic of Korea

Micro-light-emitting diodes(μLEDs)have gained significant interest as an activation source for gas sensors owing to their advantages,including room temperature operation and low power ***,despite these benefits,challenges still exist such as a limited range of detectable gases and slow *** this study,we present a blueμLED-integrated light-activated gas sensor array based on SnO_(2)nanoparticles(NPs)that exhibit excellent sensitivity,tunable selectivity,and rapid detection with micro-watt level power *** optimal power forμLED is observed at the highest gas response,supported by finite-difference time-domain ***,we first report the visible light-activated selective detection of reducing gases using noble metal-decorated SnO_(2)*** noble metals induce catalytic interaction with reducing gases,clearly distinguishing NH3,H2,and ***-time gas monitoring based on a fully hardwareimplemented light-activated sensing array was demonstrated,opening up new avenues for advancements in light-activated electronic nose technologies.

关键词： Micro-LED Gas sensor array Low power consumption Metal decoration Real-time detection

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An Experimental Evaluation of an Effective QR Code Based Duplicate Product Detection Using Blockchain Technology

An Experimental Evaluation of an Effective QR Code Based Dup...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Geetha, R. Jayakumar, D. Prabakaran, P. Nivedha, S. Sindhu, M. Thivyarathi, R.C. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Electrical and Electronics Engineering Tamil Nadu Chennai India R.M.D. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India St.Joseph's Institute of Technology Department of Management Studies Tamil Nadu Chennai India Jeppiaar Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Rmk College of Engineering and Technology Department of Science and Humanities Tamil Nadu Chennai India

ISBN: (纸本)9798350375237

Over the span of the last few years, innovations in blockchain technology have received a great deal of attention. Currency exchange, which has a variety of applications in addition to digital money, is the subject that receives the most attention. Because of this, it may have an effect on multiple industries. Large-scale financial transactions have become more transparent and straightforward as a result of the implementation of blockchain technology. Through the utilization of blockchain technology, we are able to recognize counterfeit merchandise. When it comes to making a purchase of any kind in this day and age, the question of authenticity frequently makes an appearance. In addition, studies have shown that these aspects are essential for the expansion of the economy. In light of this, it is of the utmost importance to educate consumers about the transparency of the items in order to decrease the number of counterfeit goods. With blockchain technology, we have taken a step closer to eliminating the ever-increasing presence of false and hazardous items from the global market. This is a cause for concern. Each and every person ought to be made aware of the fact that the utilization of technology will result in a reduction in the production of merchandise that is counterfeit. Every item must be given a one-of-a-kind digital code in order to guarantee that the production and packaging processes are carried out correctly. In this research, a novel method for detecting counterfeit goods is presented. This method is referred to as the Blockchain and QR based Product Analyzer (BQRPA). It undergoes cross-validation with the traditional Classical Fake Product Identifier (CFPI) in order to determine whether or not it is effective. Within the context of the software installation process, this programme performs a scan of the product code in order to ascertain whether or not the product in question is a counterfeit. © 2024 IEEE.

关键词： Blockchain

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Defense of Military Installations from UAV-Borne Attacks using Deep Learning

Defense of Military Installations from UAV-Borne Attacks usi...

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2023 Congress in computer Science, computer engineering, and Applied Computing, CSCE 2023

作者： Schaefer, Nicholas Alghazo, Jafaar M. Abdelhamid, Sherif E. Latif, Ghazanfar Virginia Military Institute Department of International Studies and Political Science LexingtonVA United States Virginia Military Institute Department of Electrical and Computer Engineering LexingtonVA United States Virginia Military Institue Department of Computer and Information Sciences LexingtonVA United States Prince Mohammad Bin Fahd University Department of Computer Science Alkhobar Saudi Arabia

ISBN: (纸本)9798350327595

Consumer-grade Unmanned Aerial Vehicles (UAVs) are becoming more common capabilities on the modern battlefield, finding use by both formal standing armies and non-state sponsored organizations with small budgets. The threats posed by these UAVs are varied, ranging from intelligence gathered from reconnaissance, spotting for indirect fires, or attacking with a payload on the UAV itself. These dangers create new challenges that militaries must adapt to ensure soldiers are protected and mission completion is possible despite the threat of UAV interdiction. In this paper, we propose an AI object detection model that is capable of identifying UAVs in the visible spectrums and distinguishing them from images that contain no UAVs. This model can be used as a targeting system for anti-UAV countermeasures. The dataset used to train the model consists of 2,000 images. 1000 images are of UAVs and 1000 contain no UAVs. The models we tested were ResNet18, ResNet50, and GoogleNet. GoogleNet achieved the best results, yielding a precision of 0.995, recall of 0.995, F1-score of 0.995, and test accuracy of 98.44%. These results and the initial dataset present a good base for researchers to explore and design a practical solution for defense against small drone attacks. © 2023 IEEE.

关键词： AlexNet Convolutional Neural Network Drones GoogleNet military ResNet Transfer Learning UAV

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IoT-Enabled Smart Irrigation System with ABiLSTM for Optimized Water Management and Crop Yields 3

IoT-Enabled Smart Irrigation System with ABiLSTM for Optimiz...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Chaturvedi, Abhay Rao, Praveena Venkata Maruti Prasad, S. Malini, T. Prabagar, S. Bharathi, K. Gla University Department of Electronics and Communication Engineering Mathura India Institute of Aeronautical Engineering Department of Civil Engineering Hyderabad India Aditya Institute of Technology & Management Department of Bs & H Tekkali India Sri Krishna College of Engineering and Technology Department of Electrical and Electronics Engineering Coimbatore India School of Advanced Computing Alliance University Department of Computer Science and Engineering Bangalore India Erode Sengunthar Engineering College Department of Agricultural Engineering Perundurai India

ISBN: (纸本)9798331508456

An efficient management of water resources in agriculture is now crucial in light of the growing global population and increased demand for water. Innovative solutions are needed to improve water efficiency since conventional irrigation methods are not sustainable anymore. Through the use of precision agriculture and the Internet of Things (IoT), smart irrigation has revolutionised water management, leading to increased crop yields. In order to improve the efficiency of decision-making, this research presents an Internet of Things (IoT) smart irrigation system that uses SA-BiLSTM (Self-Attention Bidirectional Long Short-Term Memory). Using Min-Max and Z-score normalisation approaches for feature scaling, the methodology includes data preparation, feature selection, and model training. Methods based on ranking, such as the Chi-Square Test, Pearson Correlation Coefficient (PCC), and Mutual Information (MI), were used to prioritise feature selection. Classification and prediction testing showed that the proposed SA-BiLSTM model outperformed state-of-the-art models like HGAT and Linear SVM, with an astounding 95.63% accuracy. Improved resource allocation and irrigation effectiveness, leading to increased agricultural output, are highlighted by the results, which highlight the effectiveness of advanced machine learning approaches in precision agriculture. © 2025 IEEE.

关键词： Resource allocation

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Marine Predators Algorithm with Deep Learning-Based Leukemia Cancer Classification on Medical Images

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computer Modeling in engineering & Sciences 2024年第10期141卷 893-916页

作者： Sonali Das Saroja Kumar Rout Sujit Kumar Panda Pradyumna Kumar Mohapatra Abdulaziz S.Almazyad Muhammed Basheer Jasser Guojiang Xiong Ali Wagdy Mohamed Department of Computer Science&Engineering Gandhi Institute for TechnologyBPUTBhubaneswar752054India Department of Information Technology Vardhaman College of Engineering(Autonomous)Hyderabad501218TelanganaIndia Department of Electronic&Communication Engineering Vedang Institute of TechnologyBhubaneswar752010India Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computing and Information Systems School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Research Centre for Human-Machine Collaboration(HUMAC) School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11931Jordan

In blood or bone marrow,leukemia is a form of cancer.A person with leukemia has an expansion of white blood cells(WBCs).It primarily affects children and rarely affects *** depends on the type of leukemia and the extent to which cancer has established throughout the *** leukemia in the initial stage is vital to providing timely patient *** image-analysis-related approaches grant safer,quicker,and less costly solutions while ignoring the difficulties of these invasive *** can be simple to generalize computer vision(CV)-based and image-processing techniques and eradicate human *** researchers have implemented computer-aided diagnosticmethods andmachine learning(ML)for laboratory image analysis,hopefully overcoming the limitations of late leukemia detection and determining its *** study establishes a Marine Predators Algorithm with Deep Learning Leukemia Cancer Classification(MPADL-LCC)algorithm onMedical *** projectedMPADL-LCC system uses a bilateral filtering(BF)technique to pre-process medical *** MPADL-LCC system uses Faster SqueezeNet withMarine Predators Algorithm(MPA)as a hyperparameter optimizer for feature ***,the denoising autoencoder(DAE)methodology can be executed to accurately detect and classify leukemia *** hyperparameter tuning process using MPA helps enhance leukemia cancer classification *** results are compared with other recent approaches concerning various measurements and the MPADL-LCC algorithm exhibits the best results over other recent approaches.

关键词： Leukemia cancer medical imaging image classification deep learning marine predators algorithm

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Studying the Fundamentals of Internet of Things (IoT) in the Healthcare Applications 1

Studying the Fundamentals of Internet of Things (IoT) in the...

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1st International Conference on Healthcare Innovations, Software and engineering Technologies, HISET 2024

作者： Abu Eid, Aws Ismail Kadam Rangrao, Shrikant Patil Abhijit, Seema Sharma, Lakshay Rani, D Shobha Ukirade, Chaitali Arab Open University Faculty of Computing Studies Amman Jordan Jaywant College of Engineering and Polytechnic Department of Computer Science Engineering Karad India Jaywant College of Management Karad India Indraprastha Institute of Information Technology Delhi India Institute of Aeronautical Engineering Department of Electrical and Electronics Engineering Hyderabad India Department of Obstetrics and Gynecology Karad India

ISBN: (纸本)9798350360707

The Internet of Things (IoT) has brought smart healthcare systems to the medical industry. These systems are typically made up of a network, a remote server, and sensors with smart capabilities. The primary goals of this system are to offer multidimensional feature monitoring and fundamental therapy recommendations. The primary goals of Internet of Things (IoT)-based healthcare services are to enhance quality of life and offer a great user experience at an affordable price. The primary purpose of IoT is to give elderly patients with chronic illnesses access to trustworthy, efficient, and intelligent healthcare services as well as connectivity to the medical resources that are readily available. Thus, many studies are developed on the different applications which worked on the IoT for the healthcare which includes wearable as well as non-wearable devices or machines. Thus, it makes the users to obtain the data from anywhere and wherever required for the study or analysis of the patients. In the meantime, the Internet of Things is expanding to include even the tiniest sensors and the fundamentals of automation and machine-to-machine communication. © 2024 IEEE.

关键词： healthcare internet of things (IoT) network service

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Millimeter-sized, ultra-sensitive planar Hall effect gradiometer

Millimeter-sized, ultra-sensitive planar Hall effect gradiom...

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2023 IEEE International Magnetic Conference - Short Papers, INTERMAG Short Papers 2023

作者： Nhalil, Hariharan Schultz, Moty Amrusi, Shai Grosz, Asaf Klein, Lior Institute of Nanotechnology and Advanced Materials Bar-Ilan University Department of Physics Ramat-Gan52900 Israel Ben-Gurion University of the Negev Department of Electrical and Computer Engineering Beer-Sheva84105 Israel

ISBN: (纸本)9798350338362

We present a miniature, ultra-sensitive magnetic field gradiometer in the form of a single elliptical planar Hall effect sensor that allows measuring magnetic field at 9 different locations on a 4 mm length scale [1]. The gradiometer detects magnetic field gradients with equivalent gradient magnetic noise levels of ∼ 958, ∼ 192, ∼ 51, and ∼ 26 \nT\m\surd Hz at 0.1, 1, 10, and 50 Hz, respectively, and tested under ambient conditions by measuring the field gradient produced by an electric current flowing through a straight wire. The compact size, low noise level, versatility, simple design, and low cost of this gradiometer makes it a suitable choice for detecting magnetic field gradients in small, confined spaces such as current probes or wearable electronic medical devices. © 2023 IEEE.

关键词： Hall effect

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