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Articles 2641 - 2670 of 3699
Full-Text Articles in Computer Sciences
Random Forests For Detecting Weak Signals And Extracting Physical Information: A Case Study Of Magnetic Navigation, Mohammadamin Moradi, Zheng-Meng Zhai, Aaron Nielsen, Ying-Cheng Lai, Aaron P. Nielsen
Random Forests For Detecting Weak Signals And Extracting Physical Information: A Case Study Of Magnetic Navigation, Mohammadamin Moradi, Zheng-Meng Zhai, Aaron Nielsen, Ying-Cheng Lai, Aaron P. Nielsen
Faculty Publications
It has been recently demonstrated that two machine-learning architectures, reservoir computing and time-delayed feed-forward neural networks, can be exploited for detecting the Earth’s anomaly magnetic field immersed in overwhelming complex signals for magnetic navigation in a GPS-denied environment. The accuracy of the detected anomaly field corresponds to a positioning accuracy in the range of 10–40 m. To increase the accuracy and reduce the uncertainty of weak signal detection as well as to directly obtain the position information, we exploit the machine-learning model of random forests that combines the output of multiple decision trees to give optimal values of the physical …
Relocating Lubra Village And Visualizing Himalayan Flood Damages With Remote Sensing, Ronan Wallace, Yungdrung Tsewang Gurung, Ryan Kastner
Relocating Lubra Village And Visualizing Himalayan Flood Damages With Remote Sensing, Ronan Wallace, Yungdrung Tsewang Gurung, Ryan Kastner
Journal of Critical Global Issues
As weather patterns change worldwide, isolated communities impacted by climate change go unnoticed and we need community-driven solutions. In Himalayan Mustang, Nepal, indigenous Lubra Village faces threats of increasing flash flooding. After every flood, residual muddy sediment hardens across the riverbed like concrete, causing the riverbed elevation to rise. As elevation increases, sediment encroaches on Lubra’s agricultural fields and homes, magnifying flood vulnerability. In the last monsoon season alone, the Lubra community witnessed floods swallowing several agricultural fields and damaging two homes. One solution considers relocating the village to a new location entirely. However, relocation poses a challenging task, as …
Book Review: Tracers In The Dark: The Global Hunt For The Crime Lords Of Cryptocurrency, Marion Jones
Book Review: Tracers In The Dark: The Global Hunt For The Crime Lords Of Cryptocurrency, Marion Jones
International Journal of Cybersecurity Intelligence & Cybercrime
Doubleday released Andy Greenberg’s Tracers in the Dark: The Global Hunt for the Crime Lords of Cryptocurrency in November 2022. Through vivid case studies of global criminal investigations, the book dispels myths about the anonymizing power of cryptocurrency. The book details how the ability to identify cryptocurrency users and payment methods successfully brought down several large criminal empires, while also highlighting the continuous cat-and-mouse game between law enforcement officials and criminal actors using cryptocurrency. The book is an excellent resource for law enforcement officials, academics, and general cybersecurity practitioners interested in cryptocurrency-related criminal activities and law enforcement techniques.
Identification Of Faults In Highways Using Approximation Methods And Algorithms, Khudayberdiyev Khakkulmirzayevich Mirzaakbar, Anvar Asatilloyevich Ravshanov
Identification Of Faults In Highways Using Approximation Methods And Algorithms, Khudayberdiyev Khakkulmirzayevich Mirzaakbar, Anvar Asatilloyevich Ravshanov
Chemical Technology, Control and Management
Many fast Fourier transforms are used to identify defective parts of uneven surfaces on roads and send information to relevant organizations on the road, using the " RAVON YO‘LLAR" application installed on a mobile device during car movement. We determine the uneven parts of the road. Smooth and well-maintained roads reduce the risk of vehicle collisions, skidding and other road-related incidents. Timely measures contribute to overall safety, comfort and economic efficiency.
Navigating The Digital Frontier: The Intersection Of Cybersecurity Challenges And Young Adult Life, Hannarae Lee
Navigating The Digital Frontier: The Intersection Of Cybersecurity Challenges And Young Adult Life, Hannarae Lee
International Journal of Cybersecurity Intelligence & Cybercrime
Papers from this issue advocate for empowering young adults with knowledge and tools to navigate cyberspace safely, emphasizing the necessity of heightened cybersecurity measures and proactive education. As we advance into the digital abyss, this call becomes imperative, ensuring that the young adults' experience remains a journey of growth and enlightenment, unaffected by the shadows of unseen online threats.
The Need For A Cybersecurity Education Program For Internet Users With Limited English Proficiency: Results From A Pilot Study, Fawn T. Ngo, Rustu Deryol, Brian Turnbull, Jack Drobisz
The Need For A Cybersecurity Education Program For Internet Users With Limited English Proficiency: Results From A Pilot Study, Fawn T. Ngo, Rustu Deryol, Brian Turnbull, Jack Drobisz
International Journal of Cybersecurity Intelligence & Cybercrime
According to security experts, cybersecurity education and awareness at the user level are key in combating cybercrime. Hence, in the U.S., cybersecurity and Internet safety workshops, classes, and resources targeting children, adolescents, adults, and senior citizens abound. However, most cybercrime prevention programs are only available in English, thus, ignoring a substantial proportion of Internet users and potential cybercrime victims—Internet users with limited English proficiency (LEP). Yet, successfully combating cybercrime requires that all computer and Internet users, regardless of their language abilities and skills, have access to pertinent cybersecurity information and resources to protect themselves online. This paper presents the results …
The Impact Of Artificial Intelligence And Machine Learning On Organizations Cybersecurity, Mustafa Abdulhussein
The Impact Of Artificial Intelligence And Machine Learning On Organizations Cybersecurity, Mustafa Abdulhussein
Doctoral Dissertations and Projects
As internet technology proliferate in volume and complexity, the ever-evolving landscape of malicious cyberattacks presents unprecedented security risks in cyberspace. Cybersecurity challenges have been further exacerbated by the continuous growth in the prevalence and sophistication of cyber-attacks. These threats have the capacity to disrupt business operations, erase critical data, and inflict reputational damage, constituting an existential threat to businesses, critical services, and infrastructure. The escalating threat is further compounded by the malicious use of artificial intelligence (AI) and machine learning (ML), which have increasingly become tools in the cybercriminal arsenal. In this dynamic landscape, the emergence of offensive AI introduces …
Securing Synchrophasors Using Data Provenance In The Quantum Era, Kashif Javed, Mansoor Ali Khan, Mukhtar Ullah, Muhammad Naveed Aman, Biplab Sikdar
Securing Synchrophasors Using Data Provenance In The Quantum Era, Kashif Javed, Mansoor Ali Khan, Mukhtar Ullah, Muhammad Naveed Aman, Biplab Sikdar
School of Computing: Faculty Publications
Trust in the fidelity of synchrophasor measurements is crucial for the correct operation of modern power grids. While most of the existing research on data provenance focuses on the Internet of Things, there is a significant need for effective malicious data detection in power systems. Current methods either fail to detect malicious data modifications or require certain Phasor Measurement Units (PMUs) to be physically secured. To solve these issues, this paper presents a new protocol to establish data provenance in synchrophasor networks. The proposed protocol is based on Physically Unclonable Functions (PUFs) and harnesses the principles of quantum unreality and …
Path-Bigbird: An Ai-Driven Transformer Approach To Classification Of Cancer Pathology Reports, Mayanka Chandrashekar, Isaac Lyngaas, Heidi A. Hanson, Shang Gao, Xiao Cheng Wu, John Gounley
Path-Bigbird: An Ai-Driven Transformer Approach To Classification Of Cancer Pathology Reports, Mayanka Chandrashekar, Isaac Lyngaas, Heidi A. Hanson, Shang Gao, Xiao Cheng Wu, John Gounley
School of Public Health Faculty Publications
PURPOSE: Surgical pathology reports are critical for cancer diagnosis and management. To accurately extract information about tumor characteristics from pathology reports in near real time, we explore the impact of using domain-specific transformer models that understand cancer pathology reports. METHODS: We built a pathology transformer model, Path-BigBird, by using 2.7 million pathology reports from six SEER cancer registries. We then compare different variations of Path-BigBird with two less computationally intensive methods: Hierarchical Self-Attention Network (HiSAN) classification model and an off-the-shelf clinical transformer model (Clinical BigBird). We use five pathology information extraction tasks for evaluation: site, subsite, laterality, histology, and behavior. …
University Of Johannesburg Institutional Repository Cybersecurity Output: 2015-2021 Interdisciplinary Study, Mancha J. Sekgololo
University Of Johannesburg Institutional Repository Cybersecurity Output: 2015-2021 Interdisciplinary Study, Mancha J. Sekgololo
Journal of Cybersecurity Education, Research and Practice
This study examines cybersecurity awareness in universities by analyzing related research output across different disciplines at the University of Johannesburg. The diffusion of innovation theory is used in this study as a theoretical framework to explain how cybersecurity awareness diffuses across disciplines. The University of Johannesburg Institutional Repository database was the data source for this study. Variations in cybersecurity keyword searches and topic modeling techniques were used to identify the frequency and distribution of research output across different disciplines. The study reveals that cybersecurity awareness has diffused across various disciplines, including non-computer science disciplines such as business, accounting, and social …
Intermittent-Aware Design Exploration Of Systolic Array Using Various Non-Volatile Memory: A Comparative Study, Nedasadat Taheri, Sepehr Tabrizchi, Arman Roohi
Intermittent-Aware Design Exploration Of Systolic Array Using Various Non-Volatile Memory: A Comparative Study, Nedasadat Taheri, Sepehr Tabrizchi, Arman Roohi
School of Computing: Faculty Publications
This paper conducts a comprehensive study on intermittent computing within IoT environments, emphasizing the interplay between different dataflows—row, weight, and output—and a variety of non-volatile memory technologies. We then delve into the architectural optimization of these systems using a spatial architecture, namely IDEA, with their processing elements efficiently arranged in a rhythmic pattern, providing enhanced performance in the presence of power failures. This exploration aims to highlight the diverse advantages and potential applications of each combination, offering a comparative perspective. In our findings, using IDEA for the row stationary dataflow with AlexNet on the CIFAR10 dataset, we observe a power …
Blockchain Applications In Higher Education Based On The Nist Cybersecurity Framework, Brady Lund Ph.D.
Blockchain Applications In Higher Education Based On The Nist Cybersecurity Framework, Brady Lund Ph.D.
Journal of Cybersecurity Education, Research and Practice
This paper investigates the integration of blockchain technology into core systems within institutions of higher education, utilizing the National Institute of Standards and Technology’s (NIST) Cybersecurity Framework as a guiding framework. It supplies definitions of key terminology including blockchain, consensus mechanisms, decentralized identity, and smart contracts, and examines the application of secure blockchain across various educational functions such as enrollment management, degree auditing, and award processing. Each facet of the NIST Framework is utilized to explore the integration of blockchain technology and address persistent security concerns. The paper contributes to the literature by defining blockchain technology applications and opportunities within …
Improving Belonging And Connectedness In The Cybersecurity Workforce: From College To The Profession, Mary Beth Klinger
Improving Belonging And Connectedness In The Cybersecurity Workforce: From College To The Profession, Mary Beth Klinger
Journal of Cybersecurity Education, Research and Practice
This article explores the results of a project aimed at supporting community college students in their academic pursuit of an Associate of Applied Science (AAS) degree in Cybersecurity through mentorship, collaboration, skill preparation, and other activities and touch points to increase students’ sense of belonging and connectedness in the cybersecurity profession. The goal of the project was focused on developing diverse, educated, and skilled cybersecurity personnel for employment within local industry and government to help curtail the current regional cybersecurity workforce gap that is emblematic of the lack of qualified cybersecurity personnel that presently exists nationwide. Emphasis throughout the project …
Neutrosophic Insights Into Military Interventions: Assessing Legitimacy And Consequences In International Law, Salame Ortiz Mónica Alexandra, Jiménez Martínez Roberto Carlos, Piñas Piñas Luis Fernando
Neutrosophic Insights Into Military Interventions: Assessing Legitimacy And Consequences In International Law, Salame Ortiz Mónica Alexandra, Jiménez Martínez Roberto Carlos, Piñas Piñas Luis Fernando
Neutrosophic Systems with Applications
This scientific paper analyzes the legitimacy of military interventions within the framework of international law and their potential consequences. It highlights the need to support these interventions with robust legal and moral reasoning due to their complexity and controversy in the international community. The interpretation of legal and ethical principles can be subjective and lead to disagreements among states and international actors. The consequences of military interventions are explored, ranging from loss of life and infrastructure destruction to population displacement, political instability, and humanitarian crises. Legality and proportionality in interventions are essential to ensuring their legitimacy, and the potential consequences …
Neutrosophic Insights Into Military Interventions: Assessing Legitimacy And Consequences In International Law, Salame Ortiz Mónica Alexandra, Jiménez Martínez Roberto Carlos, Piñas Piñas Luis Fernando
Neutrosophic Insights Into Military Interventions: Assessing Legitimacy And Consequences In International Law, Salame Ortiz Mónica Alexandra, Jiménez Martínez Roberto Carlos, Piñas Piñas Luis Fernando
Neutrosophic Systems with Applications
This scientific paper analyzes the legitimacy of military interventions within the framework of international law and their potential consequences. It highlights the need to support these interventions with robust legal and moral reasoning due to their complexity and controversy in the international community. The interpretation of legal and ethical principles can be subjective and lead to disagreements among states and international actors. The consequences of military interventions are explored, ranging from loss of life and infrastructure destruction to population displacement, political instability, and humanitarian crises. Legality and proportionality in interventions are essential to ensuring their legitimacy, and the potential consequences …
Brain-Inspired Continual Learning: Robust Feature Distillation And Re-Consolidation For Class Incremental Learning, Hikmat Khan, Nidhal Carla Bouaynaya, Ghulam Rasool
Brain-Inspired Continual Learning: Robust Feature Distillation And Re-Consolidation For Class Incremental Learning, Hikmat Khan, Nidhal Carla Bouaynaya, Ghulam Rasool
Henry M. Rowan College of Engineering Departmental Research
Artificial intelligence and neuroscience have a long and intertwined history. Advancements in neuroscience research have significantly influenced the development of artificial intelligence systems that have the potential to retain knowledge akin to humans. Building upon foundational insights from neuroscience and existing research in adversarial and continual learning fields, we introduce a novel framework that comprises two key concepts: feature distillation and re-consolidation. The framework distills continual learning (CL) robust features and rehearses them while learning the next task, aiming to replicate the mammalian brain's process of consolidating memories through rehearsing the distilled version of the waking experiences. Furthermore, the proposed …
Comprehensive Survey On Applications Of Internet Of Things, Machine Learning And Artificial Intelligence In Precision Agriculture, Paul Stone Stone Brown Macheso S.B.
Comprehensive Survey On Applications Of Internet Of Things, Machine Learning And Artificial Intelligence In Precision Agriculture, Paul Stone Stone Brown Macheso S.B.
Tanzania Journal of Engineering and Technology (TJET)
A comprehensive, multidisciplinary analysis of the latest developments in digital agriculture is conducted with the use of artificial intelligence (AI), machine learning (ML), and the Internet of Things. By automation and the use of modern, scalable technology solutions that reduce risks, support sustainability, and give farmers predictive advice, traditional agricultural processes are being updated and improved to maximize production. In this paper, the applications of AI, IoT, and ML in agricultural production systems are discussed in detail. The applications that have been explored can be broadly categorized into three areas: soil management, livestock management, and crop management. Weed detection, disease …
An Overview Of Maternity Healthcare Monitoring In Developing Nations, Godphrey Kyambille
An Overview Of Maternity Healthcare Monitoring In Developing Nations, Godphrey Kyambille
Tanzania Journal of Engineering and Technology (TJET)
A healthcare monitoring arrangement is essential for frequently monitoring a patient's health status. Specifically, maternal healthcare systems for tracking are utilized to evaluate the clinical status and monitor any abnormal condition changes during all three trimesters. This paper's objective is to conduct an extensive literature review and acknowledge earlier initiatives and studies conducted previously in maternal health care monitoring. This review focuses on accumulating information from earlier work and presents a general overview of previous studies concerning maternal health care monitoring (MHCM). The paper focuses on the maternal healthcare systems in developing countries accessed by pregnant women during the antenatal …
Investigations Of The Eutectic Formation And Skin Rejuvenation By Hyaluronan - Kojic Acid Dipalmitate System, Syed Waqar Hussain Shah, Sumbal Imran, Iram Bibi, Kashif Ali, Nadia Bashir
Investigations Of The Eutectic Formation And Skin Rejuvenation By Hyaluronan - Kojic Acid Dipalmitate System, Syed Waqar Hussain Shah, Sumbal Imran, Iram Bibi, Kashif Ali, Nadia Bashir
Karbala International Journal of Modern Science
Eutectic phenomenon has been investigated in binary system based on biopolymer hyaluronan (HN) and kojic acid dipalmitate (KAD). Solid-liquid phase diagram showed a significant dependence of melting points on weight fraction of KAD up to KAD < 0.5. A negligible regain to melting temperature of pure KAD occurred later. Simulations of molecular mechanics using a four-unit segment of HN and KAD revealed the interaction between carbonyl of KAD with 4-OH on N-acetylglucosamine unit of oligomer. Infrared vibrational spectroscopy also endorsed the existence of a weakly interacting system. Such behavior was expected due to steric hinderance and rigidity of biopolymer. The thermal decomposition temperature of HN (i.e., 215 °C) was increased to 322 °C in HK50 having HN and KAD in 1:50 w/w. Bioelectric impedance analysis revealed that these green materials could promote skin health in humans.
Synthesis And Characterization Of Renewable Heterogeneous Catalyst Zno Supported Biogenic Silica From Pineapple Leaves Ash For Sustainable Biodiesel Conversion, Nadila Pratiwi, Suriati Eka Putri, Yulia Shinta, Arya Ibnu Batara, Diana Eka Pratiwi, Abd Rahman, Nur Ahmad, Heryanto Heryanto
Synthesis And Characterization Of Renewable Heterogeneous Catalyst Zno Supported Biogenic Silica From Pineapple Leaves Ash For Sustainable Biodiesel Conversion, Nadila Pratiwi, Suriati Eka Putri, Yulia Shinta, Arya Ibnu Batara, Diana Eka Pratiwi, Abd Rahman, Nur Ahmad, Heryanto Heryanto
Karbala International Journal of Modern Science
This study reports on the first case of the low-cost and environmentally friendly ZnO/SiO2 heterogeneous catalyst from pineapple leaves ash (PLA). Catalyst shows excellent performance in catalyzing the transesterification of waste cooking oil (WCO) with methanol for biodiesel conversion. This study focuses on assessing the influence of Zn content on physicochemical characteristics, using XRD, FTIR, SEM, and N2 adsorption-desorption methods. In addition, three different Zn content levels (20, 25, and 30 %wt) were applied. The results showed that all ZnO/SiO2 samples exhibited characteristics suitable for use as catalyst with an average crystallite size of 31.83-34.15 nm, and a surface area …
Evaluating Future Water Availability In Texas Through The Lens Of A Data-Driven Approach Leveraged With Cmip6 General Circulation Models, Wenzhao Li, Dongfeng Li, Hesham El-Askary, Joshua B. Fisher, Zheng N. Fang
Evaluating Future Water Availability In Texas Through The Lens Of A Data-Driven Approach Leveraged With Cmip6 General Circulation Models, Wenzhao Li, Dongfeng Li, Hesham El-Askary, Joshua B. Fisher, Zheng N. Fang
Mathematics, Physics, and Computer Science Faculty Articles and Research
Climate change is escalating the frequency and intensity of extreme precipitation events, significantly influencing the spatial and temporal distributions of water resources. This is particularly evident in Texas, a rapidly growing state with a pronounced west-east gradient in water supply. This study utilizes Coupled Model Intercomparison Project Phase 6 (CMIP6) data and data-driven methodology to improve projections of Texas's future water resources, focusing on actual evapotranspiration (AET) and water availability through enhanced Multi-Model Ensembles. The results reveal that the data-driven model significantly outperforms the CMIP5 and CMIP6 models across all skill metrics, underscoring the potential of data-driven methodologies in advancing …
Ai-Based Investigation And Mitigation Of Rain Effect On Channel Performance With Aid Of A Novel 3d Slot Array Antenna Design For High Throughput Satellite System, Ali M. Al-Saegh, Fatma Taher, Taha A. Elwi, Mohammad Alibakhshikenari, Bal S. Virdee, Osama Abdullah, Salahuddin Khan, Patrizia Livreri, Abdulmajeed Al-Jumaily, Mohamed Fathy Abo Sree, Arkan Mousa Majeed, Lida Kouhalvandi, Zaid A. Abdul Hassain, Giovanni Pau
Ai-Based Investigation And Mitigation Of Rain Effect On Channel Performance With Aid Of A Novel 3d Slot Array Antenna Design For High Throughput Satellite System, Ali M. Al-Saegh, Fatma Taher, Taha A. Elwi, Mohammad Alibakhshikenari, Bal S. Virdee, Osama Abdullah, Salahuddin Khan, Patrizia Livreri, Abdulmajeed Al-Jumaily, Mohamed Fathy Abo Sree, Arkan Mousa Majeed, Lida Kouhalvandi, Zaid A. Abdul Hassain, Giovanni Pau
All Works
Rain attenuation poses a significant challenge for high-throughput communication systems. In response, this paper introduces an artificial intelligence (AI) model designed for predicting and mitigating rain-induced impairments in high-throughput satellite (HTS) to land channels. The model is based on three AI algorithms developed using 3D antenna design to characterize, analyze, and mitigate rain-induced attenuation, optimizing channel quality specifically in the United Arab Emirates (UAE). The study evaluates various parameters, including rain-specific attenuation, effective slant path through rain, rain-induced attenuation, signal carrier-to-noise ratio, and symbol error rate, for five conventional modulation schemes: Quadrature Phase-Shift Keying (QPSK), 8-Phase Shift Keying (8-PSK), 16-Quadrature …
Music Genre Classification Capabilities Of Enhanced Neural Network Architectures, Joshua Engelkes
Music Genre Classification Capabilities Of Enhanced Neural Network Architectures, Joshua Engelkes
Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal
With the increase of digital music audio uploads, applications that deal with music information have been widely requested by streaming platforms. Automatic music genre classification is an important function of music recommendation and music search applications. Since the music genre categorization criteria continually shift, data-driven methods such as neural networks have been proven especially useful to music information retrieval. An enhanced CNN architecture, the Bottom-up Broadcast Neural Network, uses mel-spectrograms to push music data through a network where important low-level information is preserved. An enhanced RNN architecture, the Independent Recurrent Neural Network for Music Genre Classification, takes advantage of the …
Generative Mechanisms Of Ai Implementation: A Critical Realist Perspective On Predictive Maintenance, Alexander Stohr, Philip Ollig, Robert Keller, Alexander Rieger
Generative Mechanisms Of Ai Implementation: A Critical Realist Perspective On Predictive Maintenance, Alexander Stohr, Philip Ollig, Robert Keller, Alexander Rieger
Information Systems Faculty Publications and Presentations
Artificial intelligence (AI) promises various new opportunities to create and appropriate business value. However, many organizations – especially those in more traditional industries – struggle to seize these opportunities. To unpack the underlying reasons, we investigate how more traditional industries implement predictive maintenance, a promising application of AI in manufacturing organizations. For our analysis, we employ a multiple-case design and adopt a critical realist perspective to identify generative mechanisms of AI implementation. Overall, we find five interdependent mechanisms: experimentation; knowledge building and integration; data; anxiety; and inspiration. Using causal loop diagramming, we flesh out the socio-technical dynamics of these mechanisms …
Anomaly Detection On Small Wind Turbine Blades Using Deep Learning Algorithms, Bridger Altice, Edwin Nazario, Mason Davis, Mohammad Shekaramiz, Todd K. Moon, Mohammad A. S. Masoum
Anomaly Detection On Small Wind Turbine Blades Using Deep Learning Algorithms, Bridger Altice, Edwin Nazario, Mason Davis, Mohammad Shekaramiz, Todd K. Moon, Mohammad A. S. Masoum
Electrical and Computer Engineering Faculty Publications
Wind turbine blade maintenance is expensive, dangerous, time-consuming, and prone to misdiagnosis. A potential solution to aid preventative maintenance is using deep learning and drones for inspection and early fault detection. In this research, five base deep learning architectures are investigated for anomaly detection on wind turbine blades, including Xception, Resnet-50, AlexNet, and VGG-19, along with a custom convolutional neural network. For further analysis, transfer learning approaches were also proposed and developed, utilizing these architectures as the feature extraction layers. In order to investigate model performance, a new dataset containing 6000 RGB images was created, making use of indoor and …
Thoughts On Transformation Of Scientific And Technological Achievements In Field Of Information Technology, Ninghui Sun, Xiaojuan Li
Thoughts On Transformation Of Scientific And Technological Achievements In Field Of Information Technology, Ninghui Sun, Xiaojuan Li
Bulletin of Chinese Academy of Sciences (Chinese Version)
To promote the transformation of scientific and technological achievements is one of the key points of China’s national science and technology innovation policy. Nevertheless, due to the particularity, complexity, and professionalism of technological achievements, being difficult to transform scientific and technological achievements is a worldwide common problem. There are many issues worth discussing and exploring in China’s transformation of scientific and technological achievements, especially when it comes to whether research institutes can transform their achievements by establishing enterprises, the answers remain controversial. The authors intend to take the field of information technology as an example, by analyzing the advantages, disadvantages, …
Analysis And Recommendations For Energy Conservation And Carbon Emission Reduction In Industry Boosted By Digital Energy Management Systems, Duanyang Geng, Tong Xu, Qinghua Zhu, Steve Evans
Analysis And Recommendations For Energy Conservation And Carbon Emission Reduction In Industry Boosted By Digital Energy Management Systems, Duanyang Geng, Tong Xu, Qinghua Zhu, Steve Evans
Bulletin of Chinese Academy of Sciences (Chinese Version)
Energy consumption during production processes in the industry is a main source of carbon dioxide emissions. Therefore, for China’s dual-carbon goals, industrial enterprises need to focus on reducing energy waste to achieve energy-efficient production, thereby effectively reducing carbon emissions in industrial production. In recent years, with the continuous development and popularization of digital technology, digital energy management systems have played a crucial role in energy saving by visualizing invisible energy in the industry. In this context, this study first analyses the current status of digital energy management system applications in the UK, the US, Germany, and Sweden, summarizes their characteristics …
Key Elements, Mechanism Analysis And Evaluation Indicators Of Digital And Intelligent Integration Transformation And Development Of Manufacturing Industry, Xiaoqiang Sun, Xiuyun Gao, Yumei Wang
Key Elements, Mechanism Analysis And Evaluation Indicators Of Digital And Intelligent Integration Transformation And Development Of Manufacturing Industry, Xiaoqiang Sun, Xiuyun Gao, Yumei Wang
Bulletin of Chinese Academy of Sciences (Chinese Version)
The digital and intelligent integration transformation of manufacturing industry has become an important driving force for the high-quality development of traditional manufacturing enterprises. This study clarifies the main research context and key issues of scholars on the digital and intelligent integration transformation of manufacturing industry, refines the goals, main elements, and influencing factors of digital and intelligent integration transformation of manufacturing industry, builds a power network model for the transformation and development of digital and intelligent integration of manufacturing industry according to the system feedback principle of system dynamics, analyzes the mechanism of action between various elements of the system, …
A New Scientific Research Paradigm Driven By Ai And Its Applications In Academic Disciplines, Jiang Yu, Yue Zhang, Yi Zhou
A New Scientific Research Paradigm Driven By Ai And Its Applications In Academic Disciplines, Jiang Yu, Yue Zhang, Yi Zhou
Bulletin of Chinese Academy of Sciences (Chinese Version)
The artificial intelligence (AI) -driven paradigm of scientific research, deeply embedded through the integration of data, computing power, and algorithms, has triggered profound changes in the research process, thinking logic, and organizational patterns. This study systematically summarizes the main characteristics and forms of the AI-driven new scientific research paradigm. It proposes that the evolution of the AI-driven research paradigm is shifting from “automated research” to “model-based research” and “intelligent research.” The depth and scope of AI applications in scientific research continue to expand, and this will drive significant changes in research organization and governance models. In addition, this study discusses …
Path And Strategy Of Pollution And Carbon Reduction By Digitization In Electric Power Enterprises, Xiaohong Chen, Runcheng Tang, Dongbin Hu, Xuesong Xu, Xiangbo Tang, Guodong Yi, Weiwei Zhang
Path And Strategy Of Pollution And Carbon Reduction By Digitization In Electric Power Enterprises, Xiaohong Chen, Runcheng Tang, Dongbin Hu, Xuesong Xu, Xiangbo Tang, Guodong Yi, Weiwei Zhang
Bulletin of Chinese Academy of Sciences (Chinese Version)
With the extensive application and innovation of digital technology in the energy sector, digital technology has become increasingly crucial for the power industry to achieve the goal of reducing pollution and carbon emissions. How digital technology enables electric power enterprises to achieve this goal has attracted much attention. Firstly, the study analyzes the progress of digital technology applications in pollution reduction and carbon reduction in electric power enterprises. Then, it identifies the existing problems in the current application of digital technology in the power industry for reducing pollution and carbon emissions. Finally, it explores the potential ways and approaches of …