Welcome to the 2009 International Forum on Information Technology and Applications (IFITA 2009) in Chengdu, China. The many innovations in Information Technology and its applications have greatly propelled Science & Technology and our global society further. With this in mind, IFITA 2009 is expected to promote an academic exchange and cooperation among Chinese and overseas researchers, developers, managers, educators and students of the information technology field and its applications.
This comprehensive literature review analyzes the current state-of-the-art research on 5G applications and technologies in smart grids. Adhering to PRISMA guidelines, the review presents a detailed taxonomy of applications and methodologies being utilized in this field. The review searched various electronic databases from 2012 to 2022, and conducted bibliometric analysis to identify research trends, influential authors, journals, and institutions in this field. The review identifies various 5G applications and technologies used in Smart grids, and the potential benefits of 5G technology in enhancing the efficiency, reliability, and security of smart grid systems. The study provides valuable insights into the research trends, research gaps, and future research directions in this field, and can guide researchers and practitioners in developing and implementing 5G-enabled smart grid systems.
In engineering and electrical engineering higher education, in its practice-oriented laboratory training background, the use of virtual methods is very common, in an environment where the main goal is of course to strengthen and develop practical attitudes.Without neglecting the importance of the computer-based possibilities of simulation and emulation and their practical relevance, there is a danger, one could say with some malice, that we are using virtual tools to train engineers who will be fully capable of solving virtual problems virtually.To avoid all this, we continue to believe that it is important to use real instruments in electrical engineering training, whether they are measuring instruments or electronic circuits to be measured. In this paper, we aim to present an application that supports analog electrical engineering, the operation of analog circuits, where we offer a high degree of flexibility to teachers and students in a real electronic circuit measurement environment.Thus, we will continue to use real instruments, generators, power supplies, oscilloscopes, spectrum analysers, measuring instruments, but we will design the object to be measured using FPAA (Field Programmable Analog Array) devices, by creating different analog circuits to be measured. This allows us to set different circuit parameters, even per measurement place, for the same circuit.
This systematic review aims to explore how the critical role of human participation in cyberspace is reassessed, given the growing importance of Industry 4.0 and 5.0. The focus of the review will be to explore the implications for the labor market and the training of the workforce, in particular the need for the development of soft skills and cyber security awareness. Literature is lacking on the role of cyberspace, cyberawareness and soft skills, their interdependencies and the implications and consequences of developing them. This systematic review provides an overview of the empirical research on soft skills and cybersecurity awareness in the context of industrial revolutions. The development of soft skills and cybersecurity awareness is key to successful adaptation and competitiveness for both individuals and the labor market. In accordance with the screening criteria, 32 articles were identified and were selected for inclusion in the final summary for the period 2017-2022 from a total of 5921 articles. This study examines the human-machine collaboration of Industry 4.0 and 5.0 in cyberspace from a labor market and workforce training perspective. The study highlights the increasing importance of combining technical and soft skills, including digital literacy, emotional intelligence, empathy, and adaptability, for engineers and IT specialists working in this industry. To address the digital and cybersecurity skills gap, the study also highlights the need for continuous skills development, on-the-job learning, and mentoring support.
With the accelerating adoption of artificial intelligence and machine learning in medicine, algorithmic fairness, and the demonstration of the validity, accuracy and clinical relevance of models is becoming increasingly important. EQ-5D-5L is one of the most widely used instrument for measuring health-related quality of life. For the evaluation, selection, and adoption of machine learning models used for the prediction of healt-related quality of life expressed in EQ-5D-5L index scores, we propose a new metric called G as a single measure of model goodness that consolidates measures such as accuracy, bias, and fairness in a value ranging from 0 to 1. Fairness is conceived as the independence of prediction error from EQ-5D-5L, and protected variables, such as sex, age, education, income and work status. The G metric ignores prediction errors that cannot be perceived by patients (e.g., smaller than the minimum clinically important difference (MCID) for EQ-5D-5L). For the computation of G, we estimated the Hungarian MCID for EQ-5D-5L as 0.066617. The proposed metric was tested on simulated prediction errors in a real-world sample of 2000 individuals and synthetic dataset. Consistently with the expectations, in the real-world dataset, highest G values were found with most accurate predictions and independent errors from the protected variables. However, using the same error simulation parameters, the performance of G was inconsistent in the synthetic dataset. Further research is needed to derive a G metric robust to data distribution shifts.
The popular press (and the academic press to some extent) look at the newly released GPT-4 language model and ask "What’s it good forƒ" Teachers and lecturers, however, look at GPT-4 and panic saying "Pupils and students will cheat by having GPT-4 do their homework for them!". Even tech employers look at their tech-test gate keepers which screen applicants for tech positions and wonder if potential employees are having GPT-4 generate the answers to their tech-tests. These are all valid questions to ask. This paper takes another approach. This paper asks the question "Can GPT-4 be configured to tutor people without just providing complete answers to questionsƒ" Specifically, this paper looks at tutoring pupils and students in the Java programming language. The research question is "Can GPT-4 be configured to help people learn Java programmingƒ" Can GPT-4 be configured to assist pupils and students to learn how to program in Java without just generating the answer algorithms for the pupils and studentsƒ
Model predictive control is slowly but surly finding its way into industrial applications. While there are a lot of model predictive control algorithms for stable processes, the model predictive control algorithms for unstable processes are not so common. In this paper a design of predictive functional controller is presented that copes with stabilizing an unstable process. The design procedure is demonstrated on currently popular problem of magnet control. In the presented example the controller is designed to control a small scale magnetic suspension device where the magnet is used for positioning the metal rod to the desired distance.
Nowadays, facing the challenge of global climate change, recognizing and promoting biodiversity is very important. Local and worldwide researchers would have it easier to access and process the numerous biodiversity data using digital technologies. The case study is the order ODONATA (class Insecta), which plays a major ecological role as an important connecting link in the food chain in freshwater and terrestrial habitats in Albania. ODONATA has been considered an indicator group to understand the status of biodiversity and as a model organism to assess the global climate change effects. The aim of this paper is to build a software application that will facilitate access to biodiversity data for ODONATA species and their habitats in Albania. This will increase the quality of scientific research and teaching-learning through biodiversity recognition and its role in the national heritage through a dedicated online digital platform.
This article presents a comprehensive review of the machine learning methods used to model the service life of various products, which is a critical aspect of product development and production. With the recent advances in machine learning, it has become increasingly feasible to utilize these methods for modeling service life accurately. This review provides a detailed examination of the existing literature on machine learning applications for modeling service life, including a bibliometric analysis of the most frequently cited works. Furthermore, this review presents a taxonomy of the various machine learning methods employed in service life modeling, highlighting the fundamental methods such as Artificial neural networks, Support vector machine, and decision trees. The results of this review demonstrate the potential of machine learning methods for accurately modeling service life, while also emphasizing the need for further research in this field. Overall, this article provides a valuable resource for researchers and practitioners looking to apply machine learning methods for modeling service life.
Significant advancements in communication and processing technology over the past two decades have led to the emergence of new intelligent objects known as the "Internet of Things" (IoT). These "things" range from wearable technology, such as smartwatches, to infrastructures for transportation, energy, information, health care, and financial services. They are linked through an Internet-connected control system in order to provide services. The majority of modern industrial critical infrastructures (CIs) rely on Supervisory Regulate and Data Acquisition (SCADA) systems to observe, monitor and control the whole business cycle of operations and data. This article aims to overview and study ICT security in the 5G environment, with many outlines connecting security issues and critical infrastructure. Starting with Protecting 5G infrastructure from cyber risks, to SCADA-based IoT critical infrastructure, third the Security and Privacy Issues of Fog Computing Supported Internet of Things, an intrusion detection system (IDS), Best practices to avoid cyber threats, security recruitment, 5G network vulnerabilities. Finally, concluding remarks and future work are discussed.
Detection of unknown inputs affecting dynamical systems appears in various applications.One problem formulation is called fault detection (FD) and isolation (FDI) where the changes interpreted as faults in some cases, enter the system as an additional disturbance (fault) signal. The goal is then either to decouple the effect of these signals from certain sensor measurements (outputs) of the system or to detect (reconstruct) their occurrence based on the available information.
Trauma is the most frequent cause of death among young individuals: mathematical modeling may help mitigate the consequences of penetrating and blunt trauma in humans, particularly in the area of civil defense and military operations. This is made possible when model-based decision support systems are employed in the field, in order to predict the physiological state of multiple victims. Clearly, an in-depth, quantitative, mechanistic understanding of the compensation mechanisms at play would be generally desirable, but can currently be meaningfully proposed only for circulatory compensation. In the present work we describe a practical asynchronous Bayesian approach for updating the probability distribution of the Expected Time to Death, based upon the collection from the field of observations relevant to 10 physiological dimensions. Further, recent mathematical models of the dynamical response to hemorrhage are compared and their applicability to real-life situations is examined. New types of representation are explored and conclusions are drawn as to the most promising feasible approaches to a formalization of this problem.
In this paper, a complete machine learning solution is designed, implemented optimized as well as tested to cover automatic pathology examination of the DNA sequencing results produced by Next Generation Sequencer (NGS). The primary goal is to predict oncogenicity for single cells with unknown DNA mutation based on genomic metadata. Obtaining the goal could lead the physicians to make diagnose at an earlier stage, the patients to get the results faster and undergo less burdensome treatment, moreover, the hospitals and clinics to carry out a medical examination in a less expensive way.
Online Dispute Resolution systems enable parties to resolve conflicts at a distance by harnessing the connectivity of the internet. Online Dispute Resolution systems are a subset of alternate dispute resolution systems which include such things as negotiation, mediation and arbitration. In these situations two parties (known traditionally as the first and second party) present their case to a trusted third party (such as an impartial mediator or impartial arbitrator). In the case of an Online Dispute Resolution system, a technology platform enables the case to proceed. Katsch and Rifkin names this technology platform the Fourth Party and itemised a number of features which this fourth party should have. This paper will summarize some of Katsch and Rifkin’s finding and then specifically expand on how the data collected by the fourth party can be used to assist in the dispute resolution process.
This paper examines the challenges of isokinetic sampling for gas and particle measurements, with a particular focus on testing homogeneity with the applied standards and interruptions in sampling. The article discusses the importance of homogeneity testing to ensure accurate and representative measurements, as well as the various factors that can interrupt and impact sampling, such as filter clogging, equipment failure, and power outages. Additionally, the article explores the possible solutions to overcome these challenges and ensure reliable and consistent sampling. Overall, this article highlights the crucial role of isokinetic sampling in gas and particle measurements and provides insights into the best practices for achieving accurate and representative data.
Optimizing computer-generated therapy may be one of the most promising tools for future medical treatments. In silico experiments are crucial for therapy planning and testing, this requires a reliable virtual patient model. We present a noise model that can be used to model real measurement noise from mice experiments and add this noise to the virtual patient model. In the experiments, the measurements are performed with a digital caliper, and the tumor is measured through the skin, which is the reason why significant nonlinear measurement noise can be expected. We use data from preclinical experiments to estimate the measurement noise and use a nonlinear transformation to whiten the noise. Finally, we find the distributions that most accurately describe the transformed noises based on the Anderson-Darling test. We show that a virtually generated measurement with noise generated by our noise model is similar to actual measurements.
The work is devoted to the analysis of technologies and the design of a responsive social network application with elements of decentralization. Despite all the positives that social networks bring, we can’t forget the potential problems associated with their use. Some of the problems can be minimized by using decentralization, which has the effect of limiting third-party control.The centralized part is created using the Vue.js and Laravel frameworks, which provide basic functions. In the context of decentralization, is very important the creation of a smart contract that is stored in the Ethereum blockchain. By using the Web3.js library, it is possible with a smart contract and a MetaMask wallet to carry out the transactions necessary for storing data on the blockchain.To increase efficiency, the IPFS distributed file system is used, which offers storage of larger files or texts and generates their content, while it is possible to access this data. By decentralizing these functions, data immutability is achieved, censorship is excluded, and data security is also provided.
Increasing the efficiency of milling processes demands the automated evaluation of time series data collected from the machine. Solutions based on Artificial Intelligence are becoming popular for this purpose, which require tailored datasets for model training. The programmed strategies for milling particular features divide the process into subsections reflected by repeating patterns in multiple signals. In this paper, a clustering model is developed, which differentiates similar milling strategies based on these patterns. The model is combined with a classification model, so that recurring milling strategies of future processes are continuously assigned with corresponding cluster labels. Storing these labels in combination with the raw data enables finding customized data in growing data lakes.
Fluorescent live cell imaging is a widely used method in studying in vitro cell cultures, thus the behavior of in vitro tumor spheroids can be examined cheaply and simply over several days. However, during several days lasting experiments with many measurement points, the strong laser light that excites the fluorescent sample can have a destructive effect on living cells. This phenomenon is phototoxicity, the consideration of which is of fundamental importance during such long-term cytotoxicity measurements, in order to filter out those cell-damaging effects that did not occur as a result of the investigated chemotherapy agent. Modeling phototoxicity was the focus of this paper. Here we present two models of tumor dynamics tailored for in vitro tumor spheroids, containing tumor proliferation, tumor inhibition by chemotherapeutic agents, and inhibition by phototoxicity. The two models differ in the phototoxic term. Thus one of them describes phototoxicity as a continuous event during the measurement, while the other one approach this phenomenon as an impulsive effect. The model fitting and parameter estimation were carried out using the same cytotoxicity measurement data of tumor spheroids in both cases. We compared the fitting results of the two models by determining their worst case SSE values.
In recent years, the digital twin concept has gained traction in both academia and industry. But what is a digital twin? It is quite common to see many kinds of publications from scientific research to news articles on digital twins mentioning that there is no exact definition for the term. In this paper, we will go through the digital database of IEEE Xplore in an attempt to find out how the publications on digital twins use the term, and how the twins could be categorized and defined more clearly. Our focus is on literature that studies the digital twins within the context of Internet-of-Things (IoT) and industry. Our studies will show that there is indeed a need for a more standardized definition for the term, and that digital twin is often used as a blanket term to cover many systems, prototypes and implementations that may or may not be actual digital twins.
