Search

This article explores the potential of artificial intelligence (AI) for academic advising. Specifically, it examines how AI-powered machine interpretation and data analysis can be used to deliver advising services anytime, anywhere. This system would eliminate the need for students to physically meet with advisors and could answer their academic-related inquiries throughout their studies. Additionally, the system could alleviate the burden on faculty advisors by automating the process of the follow-up with individual students, especially during times of crisis. The research employed a sample of 17 female students enrolled in the pre-master's program at the Faculty of Women, Ain Shams University, during the academic year 2022. The researcher developed several measurement tools, namely, a satisfaction scale to gauge student satisfaction with the intelligent system and advising services, a stress-relief scale to assess student stress levels after using the system, and an experimental treatment material - the design of the intelligent system itself. The results demonstrated the effectiveness of the intelligent system in both increasing student satisfaction and reducing stress levels, particularly during times of crisis. Research limitations/implication: The limited sample size of this study may restrict the generalizability of the findings. Future research could explore the impact of intelligent academic advisors on students from diverse backgrounds to gain a more comprehensive understanding of their broader implications. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Information Technology Management (JITM) is the property of University of Tehran, Faculty of Management and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this research paper, a multivariable prediction control method based on direct vector control is applied to command the active power and reactive power of a doubly-fed induction generator used into a wind turbine system. To obtain high energy performance, the space vector modulation inverter based on fuzzy logic technique (fuzzy space vector modulation) is used to reduce stator currents harmonics and active power and reactive power ripples. Also the direct vector control model of the doubly-fed induction generator is required to ensure a decoupled control. Then its classic proportional integral regulators are replaced by the multivariable prediction controller in order to adjust the active and reactive power. So, in this work, we implement a new method of control for the doubly-fed induction generator energy. This method is carried out for the first time by combining the MPC strategy with artificial intelligence represented by Fuzzy SVM-based converter in order to overcome the drawbacks of other controllers used in renewable energies. The given simulation results using Matlab software show a good performance of the used strategy, particularly with regard to the quality of the energy supplied. [ABSTRACT FROM AUTHOR]

The specific of functioning for artificial intelligence and digital stratum is an important direction of interdisciplinary, including linguistics, researches in the modern world. The subject of the study is the voice assistant Alice of the Yandex company with the inbuilt Yandex GPT2 neural network. The object under the study is the AI functioning on the materials of phraseological units and set phrases of the Chinese and Russian languages with the universal semantic component смерть / death. The conceptual category of смерть / death was chosen due to the existence of the Russian and Chinese cultural tradition represented on the rich background ascendant to folklore images. The novelty of the study consists in understanding the notion of “a digital stratum as a separate component of the linguistic paradigm, according to N.I. Tolstoy, and it is introduced and discussed for the first time. In course of linguistic experiment, we managed to reveal four types of generations on the AI stimulus words: 1) the absence of generation; 2) the generation of the existing phraseological units and set phrases of direct and indirect nominations; 3) the hybrid generation; 4) the digital or arbitrary generation. By means of the continuous sampling method there were identified 50 % of the AI generations both in Russian and in translated into Russian materials. The results of the study allow conclude that there takes place of forming the digital stratum of the linguistic paradigm owing to the evolution of the virtual environment of language functioning. With time, textual generations which at present seem to be antilogical, could enter the live conversational style and become widely-used ones due to the mass use of voice systems in various spheres of daily living activities.

Copyright of RED - Revista de Educación a Distancia is the property of Universidad de Murcia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Non-terrestrial network (NTN) is a trending topic in the field of communication, as it shows promise for scenarios in which terrestrial infrastructure is unavailable. Unmanned autonomous intelligent systems (UAISs), as a physical form of artificial intelligence (AI), have gained significant attention from academia and industry. These systems have various applications in autonomous driving, logistics, area surveillance, and medical services. With the rapid evolution of information and communication technology (ICT), 5G and beyond-5G communication have enabled numerous intelligent applications through the comprehensive utilization of advanced NTN communication technology and artificial intelligence. To meet the demands of complex tasks in remote or communication-challenged areas, there is an urgent need for reliable, ultra-low latency communication networks to enable unmanned autonomous intelligent systems for applications such as localization, navigation, perception, decision-making, and motion planning. However, in remote areas, reliable communication coverage is not available, which poses a significant challenge for intelligent systems applications. The rapid development of non-terrestrial networks (NTNs) communication has shed new light on intelligent applications that require ubiquitous network connections in space, air, ground, and sea. However, challenges arise when using NTN technology in unmanned autonomous intelligent systems. Our research examines the advancements and obstacles in academic research and industry applications of NTN technology concerning UAIS, which is supported by unmanned aerial vehicles (UAV) and other low-altitude platforms. Nevertheless, edge computing and cloud computing are crucial for unmanned autonomous intelligent systems, which also necessitate distributed computation architectures for computationally intensive tasks and massive data offloading. This paper presents a comprehensive analysis of the opportunities and challenges of unmanned autonomous intelligent systems in UAV NTN, along with NTN-based unmanned autonomous intelligent systems and their applications. A field trial case study is presented to demonstrate the application of NTN in UAIS. [ABSTRACT FROM AUTHOR]

The transition from a linear economy (LE) to a circular economy (CE) is not just about mitigating the negative impacts of LE, but also about considering changes in infrastructure, while leveraging the power of technology to reduce resource production and consumption and waste generation, and improve long-term resilience. The existing research suggests that digital technologies (DTs) have great potential to drive the CE. However, despite the exponential growth and increasing interest in studies on DTs and the CE from year 2016 onwards, few systematic studies on the application of DTs to enable the CE have been found. In addition, the current status and development direction of the DT-driven CE is unclear, and the potential of DTs to support CE implementation is under-researched. Therefore, the aim of this paper is to explore the potential of DTs to drive the CE. This paper set out to analyze the current status and development of the DT-driven CE and examine future development trends in the field. Using a systematic literature review approach, this paper is the first attempt to use a mixed method, i.e., to combine macro-quantitative bibliometric methods with a micro-qualitative content analysis method to explore the DT-driven CE. The results, which include the research background, co-occurrence clusters, research hotspots, and development trends of keyword co-occurrence network visualization and keyword burst detection, are presented from a macro perspective using two bibliometric analysis softwares. In addition, the use of 13 specific DTs in the CE is analyzed according to seven disciplinary areas (Environmental Sciences and Ecology, Engineering, Science and Technology and Other Topics, Business Economics, Computer Science, Operations Research and Management Science, and Construction and Building Technology) of greatest interest from a micro-qualitative point of view. Further, future trends and challenges facing DT-driven CE development are explored and feasible directions for solutions are proposed. [ABSTRACT FROM AUTHOR]

ChatGPT (Generative Pretrained Transformer) is currently the most sophisticated chatbot. It can create impressive prose in seconds, unlike other chatbots, and it has generated a lot of hype and doomsday predictions when it comes to student assessment in higher education and a variety of other topics (Rudolph et al., 2023). Nonetheless, despite its impressive capabilities, various reports on ChatGPT have consistently revealed significant remaining challenges (Bang et al., 2023). This study aims to explain the advantages and disadvantages of ChatGPT. A qualitative approach was conducted. In-depth interviews were used with ten key informants, employing purposive sampling. Content analysis and NVivo were utilised to analyse the data. The findings revealed that ChatGPT is a natural language processing (NLP) tool that has the potential to revolutionise the way we communicate. This artificial intelligence (AI) technology can generate text, allowing users to easily create personalised content, and it has gained widespread popularity. However, the reaction has been mixed, with praise for its benefits and potential applications offset by criticism of its limitations and potential drawbacks. Furthermore, ChatGPT is an extremely effective tool. However, it cannot replace human thought and, if not properly fine-tuned, it has the potential to produce biased or insulting content. Thus, it is critical to bear ethical considerations in mind when implementing this technology. [ABSTRACT FROM AUTHOR]

Microwave ablation (MWA) therapy as a thermal ablation procedure is an excellent alternative to open surgery for tumor treatment. The technique is considered advantageous for patients who are not candidates for open surgery due to factors such as age, anatomic limitations, resection, etc. Computed tomography (CT) is a commonly used interventional imaging modality during MWA therapy for localizing the tumor and finalizing the tumor treatment process. However, the CT scan of the body usually includes neighboring organs that are not relevant to lung tumor MWA therapy. Therefore, the segmentation of the lung and lung tumor in CT images provides valuable information about the tumor margin. This information can assist physicians in precisely and completely destroying the tumor during the MWA procedure. To solve the aforementioned problem, deep learning (DL), in particular, achieves a higher level of accuracy in segmentation than machine learning techniques due to its composition of multiple learning layers. The immediate goal is to distinguish among the different tissue structures of the tumor, healthy tissue, and the ablated area in lung CT images using the DL method to segment the organ and cancer area. Researchers have proposed various segmentation models. However, different segmentation tasks require different perception fields. In this study, we propose a new DL model that includes a residual block based on the U-Net model to accurately segment the lung organ and lung tumor tissue. The dataset consists of lung CT images acquired during MWA therapy using a CT scanner at the University Hospital Magdeburg. Manual tumor segmentation has been performed and confirmed by physicians. The results of our proposed method can be compared with those of the U-net model with a SSIM of 90%. Furthermore, accurately determining the margin area of the tumor tissue can decrease insufficient tumor ablation, which often leads to tumor recurrence. We anticipate that our proposed model can be generalized to perform tumor segmentation on CT images of different organs during MWA treatment. Finally, we hope that this method can achieve sufficient accuracy to decrease tumor recurrence and enable dose reduction for patients in interventional CT imaging. Doi: 10.28991/SciMedJ-2023-05-01-01 Full Text: PDF

The objective of this research is to apply Quality 4.0 (Q4.0) concept in Agriculture 4.0 (A4.0) to digitize the traditional quality management (QM) system and demonstrate the effectiveness of zero-defect manufacturing (ZDM) in the agricultural part manufacturing industry. An autonomous quality management system was developed based on the ZDM system using the Industrial Internet of Things (IIoT). Both traditional and autonomous quality management systems were evaluated using six-sigma quality indicators and machining and inspection cost analysis. The ZDM resulted in a significant improvement in the quality of CARD148 manufacturing, increasing the manufacturing process from a low level of sigma to a high level of sigma (0.75 to 5.10 sigma). The component rejection rate was reduced by a high percentage, leading to significant economic benefits and a significant reduction in machining cost. The process yield was also increased to a high percentage. The developed ZDM was found to be consistent in improving the quality of the turning process, with notable increases in tool life and reduction in inspection cost. The total component cost was reduced significantly, while the PPM value increased notably. While this study focuses on agriculture-related manufacturing organizations, the developed ZDM has potential for other machining industries to improve sigma levels, particularly in industries such as automotive and medical.

Synthetic biology provides a new paradigm for life science research ("build to learn") and opens the future journey of biotechnology ("build to use"). Here, we discuss advances of various principles and technologies in the mainstream of the enabling technology of synthetic biology, including synthesis and assembly of a genome, DNA storage, gene editing, molecular evolution and de novo design of function proteins, cell and gene circuit engineering, cell-free synthetic biology, artificial intelligence (AI)-aided synthetic biology, as well as biofoundries. We also introduce the concept of quantitative synthetic biology, which is guiding synthetic biology towards increased accuracy and predictability or the real rational design. We conclude that synthetic biology will establish its disciplinary system with the iterative development of enabling technologies and the maturity of the core theory. [ABSTRACT FROM AUTHOR]

The present study evaluates the optimal design of a renewable system based on solar and geothermal energy for power generation and cooling based on a solar cycle with thermal energy storage and an electrolyzer to produce hydrogen fuel for the combustion chamber. The subsystems include solar collectors, gas turbines, an electrolyzer, an absorption chiller, and compressed air energy storage. The solar collector surface area, geothermal source temperature, steam turbine input pressure, and evaporator input temperature were found to be major determinants. The economic analysis of the system showed that the solar subsystem, steam Rankine cycle, and compressed air energy storage accounted for the largest portions of the cost rate. The exergy analysis of the system demonstrated that the solar subsystem and SRC had the highest contributions to total exergy destruction. A comparative case study was conducted on Isfahan, Bandar Abbas, Mashhad, Semnan, and Zanjan in Iran to evaluate the performance of the proposed system at different ambient temperatures and irradiance levels during the year. To optimize the system and find the optimal objective functions, the NSGA-II algorithm was employed. The contradictory objective functions of the system included exergy efficiency maximization and cost rate minimization. The optimal Exergy round trip efficiency and cost rate were found to be 29.25% and 714.25 ($/h), respectively. • Optimal design of a renewable system based on solar and geothermal energy for power generation and cooling. • The system was thermodynamically modeled in EES. • To optimize the system and find the optimal objective functions, the NSGA-II algorithm was employed. • The optimal Exergy round trip efficiency and cost rate were found to be 29.25% and 714.25 ($/h). [ABSTRACT FROM AUTHOR]

The objective of this research is to apply Quality 4.0 (Q4.0) concept in Agriculture 4.0 (A4.0) to digitize the traditional quality management (QM) system and demonstrate the effectiveness of zero-defect manufacturing (ZDM) in the agricultural part manufacturing industry. An autonomous quality management system was developed based on the ZDM system using the Industrial Internet of Things (IIoT). Both traditional and autonomous quality management systems were evaluated using six-sigma quality indicators and machining and inspection cost analysis. The ZDM resulted in a significant improvement in the quality of CARD148 manufacturing, increasing the manufacturing process from a low level of sigma to a high level of sigma (0.75 to 5.10 sigma). The component rejection rate was reduced by a high percentage, leading to significant economic benefits and a significant reduction in machining cost. The process yield was also increased to a high percentage. The developed ZDM was found to be consistent in improving the quality of the turning process, with notable increases in tool life and reduction in inspection cost. The total component cost was reduced significantly, while the PPM value increased notably. While this study focuses on agriculture-related manufacturing organizations, the developed ZDM has potential for other machining industries to improve sigma levels, particularly in industries such as automotive and medical. [ABSTRACT FROM AUTHOR]

The internet-of-Vehicle (IoV) can facilitate seamless connectivity between connected vehicles (CV), autonomous vehicles (AV), and other IoV entities. Intrusion Detection Systems (IDSs) for IoV networks can rely on machine learning (ML) to protect the in-vehicle network from cyber-attacks. Blockchain-based Federated Forests (BFFs) could be used to train ML models based on data from IoV entities while protecting the confidentiality of the data and reducing the risks of tampering with the data. However, ML models are still vulnerable to evasion, poisoning and exploratory attacks by adversarial examples. The BFF-IDS offers partial defence against poisoning but has no measure for evasion attacks, the most common attack/threat faced by ML models. Besides, the impact of adversarial examples transferability in CAN IDS has largely remained untested. This paper investigates the impact of various possible adversarial examples on the BFF-IDS. We also investigated the statistical adversarial detector’s effectiveness and resilience in detecting the attacks and subsequent countermeasures by augmenting the model with detected samples. Our investigation results established that BFF-IDS is very vulnerable to adversarial examples attacks. The statistical adversarial detector and the subsequent BFF-IDS augmentation (BFF-IDS(AUG)) provide an effective mechanism against the adversarial examples. Consequently, integrating the statistical adversarial detector and the subsequent BFF-IDS augmentation with the detected adversarial samples provides a sustainable security framework against adversarial examples and other unknown attacks.

Microwave filters are indispensable passive devices for modern wireless communication systems. Nowadays, electromagnetic (EM) simulation-based design process is a norm for filter designs. Many EM-based design methodologies for microwave filter design have emerged in recent years to achieve efficiency, automation, and customizability. The majority of EM-based design methods exploit low-cost models (i.e., surrogates) in various forms, and artificial intelligence techniques assist the surrogate modeling and optimization processes. Focusing on surrogate-assisted microwave filter designs, this article first analyzes the characteristic of filter design based on different design objective functions. Then, the state-of-the-art filter design methodologies are reviewed, including surrogate modeling (machine learning) methods and advanced optimization algorithms. Three essential techniques in filter designs are included: 1) smart data sampling techniques; 2) advanced surrogate modeling techniques; and 3) advanced optimization methods and frameworks. To achieve success and stability, they have to be tailored or combined together to achieve the specific characteristics of the microwave filters. Finally, new emerging design applications and future trends in the filter design are discussed. [ABSTRACT FROM AUTHOR]

The purpose of this study is to find and analyze who can be categorized as the subject of rights owners in Artificial Intelligent (AI) and whether Law No. 28 of 2014 concerning Copyright has been able to accommodate the rights of creators in Artificial Intelligent (AI) as Copyright works. This research is normative juridical research. The results show that in Indonesia, Artificial Intelligence (AI) cannot be a legal subject based on considerations of legal consequences. Law No. 28 of 2014 requires changes to adapt to technological developments. [ABSTRACT FROM AUTHOR]

Purpose Purpose: This study attempts to identify and rank the factors that influence Malaysian educators' adoption of artificial intelligence (AI)-based pedagogical solutions. Design / methodology / approach: This study conducted pair-wise comparisons using a statistically significant sample of 218 Malaysian university professors. Findings: The findings demonstrate that schools must equip teachers with the resources, support, and recognition they need to adopt AI-based pedagogies. Furthermore, higher education institutions (HEIs) must offer their academic members adequate resources, including money and technological equipment. Research limitations/implications: Practically, this research highlighted that AI-based classroom solutions may be substantial in teaching. Practitioners can use the results of this study to enhance the teaching methodologies. While making the strategies to improve teaching among educational institutions, the results of this study are quite helpful for management. As this study reported the significant role of AI-based classroom among the educational institutions. Originality/value: This research is the original work that is based on the novel idea to contribute significantly to the literature. The relationship developed by this research are new and enhanced the knowledge of teaching with articfical intelligence. [ABSTRACT FROM AUTHOR]

Abstract Fault detection can increase the reliability and efficiency of power electronic converters employed in the power systems. Among the converters in the power system, voltage source inverters are used to drive electric motors. Due to high pressure and complex work in this environment, these inverters are prone to breakdowns and faults. That's why providing a way to recognize faults in the inverters is very important. This article studies the open-circuit fault of IGBT in an electric vehicle in dynamic conditions. The three-phase current and wavelet transform is used to identify the state of the system and extracting the current waveform. MLP Neural network algorithms, SVM, SOM, and K-means is used to detect and classify the faults. A comparison of various algorithms used in this paper was done. Electric vehicles have been studied and analyzed in 5 dynamics and 5 static modes in a total of 220 fault cases. The results show the detection and recognition of all forms of faults. [ABSTRACT FROM AUTHOR]

In this study, information pertaining to the development of artificial intelligence (AI) technology for improving the performance of heating, ventilation, and air conditioning (HVAC) systems was collected. Among the 18 AI tools developed for HVAC control during the past 20 years, only three functions, including weather forecasting, optimization, and predictive controls, have become mainstream. Based on the presented data, the energy savings of HVAC systems that have AI functionality is less than those equipped with traditional energy management system (EMS) controlling techniques. This is because the existing sensors cannot meet the required demand for AI functionality. The errors of most of the existing sensors are less than 5%. However, most of the prediction errors of AI tools are larger than 7%, except for the weather forecast. The normalized Harris index (NHI) is able to evaluate the energy saving percentages and the maximum saving rations of different kinds of HVAC controls. Based on the NHI, the estimated average energy savings percentage and the maximum saving rations of AI-assisted HVAC control are 14.4% and 44.04%, respectively. Data regarding the hypothesis of AI forecasting or prediction tools having less accuracy forms Part 1 of this series of research.

This paper deals with a new design of a sensor-less vector control algorithm of an induction motor using Artificial Intelligent (AI) techniques. The study details the control of the speed of a three phase induction sensor-less motor using Neural Network method, to produce accurate determination of the motor parameters (electromagnetic and stator flux) and to enable them to be set directly. The analysis is done with PI, Fuzzy & ANN controllers. An optimal SIMULINK/MATLAB model has been designed to achieve the speed control of the control method.. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes a method for incipient fault diagnosis in oil-immersed transformers using grey clustering analysis (GCA). Incipient faults can produce hydrocarbon molecules and carbon oxides due to the thermal decomposition of oil, cellulose, and other solid insulation. The power transformers can be detected and monitor abnormal conditions with dissolved gas analysis (DGA). Various artificial intelligent (AI) techniques have been proposed for transformer fault diagnosis; however they have some limitations such as accuracy of diagnosis, requirement of inference rules, and determination of the detection architecture. IEC/Cigre standard and GCA are applied to diagnose internal faults including thermal faults, electrical faults, and faults with cellulosic insulation degrading. Compared with other diagnostic techniques, numerical tests with practical gas records were conducted to show the effectiveness of the proposed model, and are easy to implement with the portable device and hardware device. [Copyright &y& Elsevier]

A set of systematic structure model of IDSS-SPD (Intelligent Decision Support System in Ship Preliminary Design) which is an aid to decision making was presented. The frame and theory of the IDSS were provided in the process of decision making of ship's form. The main task for this IDSS is to aid the ship designers when the information about the ship's particular dimensions is limited. It proves feasible and dependable when applied to an instance of selecting the best ship's form among others. At the same time, the prospect and forecast of IDSS-SPD was also given for ship designers. [ABSTRACT FROM AUTHOR]

In this study, information pertaining to the development of artificial intelligence (AI) technology for improving the performance of heating, ventilation, and air conditioning (HVAC) systems was collected. Among the 18 AI tools developed for HVAC control during the past 20 years, only three functions, including weather forecasting, optimization, and predictive controls, have become mainstream. Based on the presented data, the energy savings of HVAC systems that have AI functionality is less than those equipped with traditional energy management system (EMS) controlling techniques. This is because the existing sensors cannot meet the required demand for AI functionality. The errors of most of the existing sensors are less than 5%. However, most of the prediction errors of AI tools are larger than 7%, except for the weather forecast. The normalized Harris index (NHI) is able to evaluate the energy saving percentages and the maximum saving rations of different kinds of HVAC controls. Based on the NHI, the estimated average energy savings percentage and the maximum saving rations of AI-assisted HVAC control are 14.4% and 44.04%, respectively. Data regarding the hypothesis of AI forecasting or prediction tools having less accuracy forms Part 1 of this series of research. [ABSTRACT FROM AUTHOR]