Use Of Intelligent Technologies Research Articles

Intelligent Rapid Asexual Propagation Technology—A Novel Aeroponics Propagation Approach

Various rapid propagation strategies have been discovered, which has facilitated large-scale plant reproduction and cultivar development. These methods, in many plant species, are used to rapidly generate large quantities (900 mini-tubers/m2) of high-quality propagule (free from contamination) at a relatively low cost in a small space. They are also used for plant preservation. This review article aims to provide potential applications for regeneration and clonal propagation. Plant propagation using advanced agrotechnology, such as aeroponics, is becoming increasingly popular among academics and industrialists. The advancement of asexual aeroponic propagation has been achieved through advancements in monitoring and control systems using IoT and smart sensor technology. New sensor technology systems have gained substantial interest in agriculture in recent years. It is used in agriculture to precisely arrange various operations and objectives while harnessing limited resources with minimal human intervention. Modern intelligent technologies and control systems simplify sensor data collection, making it more efficient than manual data collection, which can be slow and prone to errors. Specific ambient variables like temperature, humidity, light intensity, stock solution concentrations (nutrient water), EC (electrical conductivity), pH values, CO2 content, and atomization parameters (frequency and interval) are collected more effectively through these systems. The use of intelligent technologies provides complete control over the system. When combined with IoT, it aids in boosting crop quality and yield while also lowering production costs and providing data directly to tablets and smartphones in aeroponic propagation systems. It can potentially increase the system’s productivity and usefulness compared to the older manual monitoring and operating methods.

Agile Transformation through the Performing Management of It and Cyber Security Projects, at the Government Level

Abstract Modern theories of intelligence have developed from the observation that the person, throughout life, is in constant development. In order to be successful, certain conditions must be met. These conditions for achieving success in life depend on the skills that are formed during formal education, or at least should be pursued as goals, beyond the disciplines. In a current information society, the quality of life as well the perspectives of social change and economic development depend on information and its exploitation, especially in the institutional field of managing computer applications for the absorption of European funds, becoming a matter of national importance, with critical values for national security, characterized by the state of economic equilibrium. Scientific knowledge and information are of particular importance in the global information society by: supporting innovation through technology, promoting economic development through the use of emerging technologies, making decisions efficiently and transparently at government level. The implementation and use of intelligent technologies have a decisive role in the development of the degree of digitization of public services through non-refundable funding from European funds and the National Recovery and Resilience Plan. The reinvention of the government can be transformed into a reality through the digitization and computerization of the public administration, assuming the modernization of the infrastructure through specific sources of funding, such as European funds, ensuring security through advanced cyber protection and defense capabilities against possible vulnerabilities, risks or cyber-attacks. Achieving performance within the public institution requires an increased concern for innovation, creativity and change.

TECHNOLOGICAL AND TACTICAL SUBSYSTEMS OF THE INTELLIGENT MANAGEMENT INFORMATION SYSTEM (ON THE EXAMPLE OF AN OIL AND GAS CORPORATION)

Providing Ukraine with its own oil and natural gas is one of the main tasks of the national economy. The possibility of development of the oil and gas industry is primarily determined by the volume of explored and mined resources. Since the existing explored reserves will not ensure the growth of oil and gas production, it is necessary to increase the amount of investments in the exploration and development of new deposits and hydrocarbon deposits with the aim of increasing their reserves and further growth of production, which requires the adoption of management decisions, which are characterized by increased technological and economic risk, uncertainty.
 Taking into account the main reasons for the decline in oil and gas production in Ukraine, the specifics of the industry and the requirements for information systems in the oil and gas industry, it can be argued that the use of intelligent technologies not only at the strategic, but also at the tactical and technological levels of management - is one of the keys to success in creating/reengineering, implementation and effective use of management decision support systems.
 The scientific and practical results presented in the article regarding the innovative configurations of architecture and design, proposed intellectual methodology for tactical and technological subsystems of the corporation management information systems. The above-proposed solutions - should be taken into account when reengineering corporate information systems not only in the oil and gas industry in Ukraine [1-3]. 
 Moreover, the obtained results are relevant and applicable not only for local companies, but also for international corporations on emerging markets in the context of global and regional macroeconomic and military crisis phenomena.

Designing an intelligent territorial safety management system

The conceptual stage of designing an intelligent territorial safety management system is presented. The article shows the problems of information support for management associated with the need to process a large volume of unformalized data and their low reliability. Existing descriptions of objects and processes are not constructive. The approval and decision-making processes are poorly algorithmized and there are no feedback mechanisms reflecting the quality of decisions. As an alternative, the article proposes the use of intelligent technologies that change the mechanisms for collecting, processing and using data. An ontological description of a multi-level management model is presented that formalizes the main tasks of ensuring the natural and technogenic security of territories. Management levels and objects are highlighted, and their information support is shown. The model is based on assessments of territorial risks, representing a combination of comprehensive monitoring indicators. The novelty of the approach lies in the possibility of justifying the types and volumes of activities with the magnitude of risks, as well as linking management results with the necessary resources. The model is the basis of the intelligent system project. Using object-oriented programming methods, the transition from ontological description to structural and functional designing is shown. Representation of ontology elements in the form of sets allows the construction of multidimensional analytical models that reveal ways to solve management problems in specific conditions. This makes it possible to justify the composition of information and the requirements for its quality for each level of the territorial management hierarchy. Problem solving examples of ensuring territorial safety in accordance with the proposed classification are given. The model can be used as a scientific basis for management digitalization programs implemented in the constituent entities of the Russian Federation.

Multi Objective and Multi-Product Perishable Supply Chain with Vendor-Managed Inventory and IoT-Related Technologies

With the emergence of the fourth industrial revolution, the use of intelligent technologies in supply chains is becoming increasingly common. The aim of this research is to propose an optimal design for an intelligent supply chain of multiple perishable products under a vendor-managed inventory management policy aided by IoT-related technologies to address the challenges associated with traditional supply chains. Various levels of the intelligent supply chain employ technologies such as Wireless Sensor Networks (WSNs), Radio Frequency Identification (RFID), and Blockchain. In this paper, we develop a bi-objective nonlinear integer mathematical programming model for designing a four-level supply chain consisting of suppliers, manufacturers, retailers, and customers. The model determines the optimal network nodes, production level, product distribution and sales, and optimal choice of technology for each level. The objective functions are total cost and delivery times. The GAMS 24.2.1 optimization software is employed to solve the mathematical model in small dimensions. Considering the NP-Hard nature of the problem, the Grey Wolf Optimizer (GWO) algorithm is employed, and its performance is compared with the Multi-Objective Whale Optimization Algorithm (MOWOA) and NSGA-III. The results indicate that the adoption of these technologies in the supply chain can reduce delivery times and total supply chain costs.

A Review of Intelligent Techniques Based Speed Control of Brushless DC Motor (BLDC)

This study uses intelligent techniques to regulate brushless direct current speed (BLDC) motors. After these motors solved the problem of using brushes and commutators in traditional DC motors, they succeeded in replacing brushes and commutators with electronic commutators. Due to the use of electronic switching, brushless motor algorithms are more complex than those of conventional motors. In this study, to adjust the PID controller's settings (Kp, Ki, and Kd), a trial-and-error approach was taken, and a completely new method known as the settings of known PID controllers have been modified using the new Gray Wolf algorithm. A BLDC motor's main benefit is that it has easy speed adjustment across a broad range, whereas AC motors often cannot be controlled in this way. Through the use of Matlab/Simulink, the BLDC motor's mathematical model was developed and implemented. The simulation results show that in the first case, a PID controller effectively induces the turbulent dynamic behavior of BLDC under load and no-load conditions, and in the second case, the speed shows the lowest rise time, stability, overshoot, and stability conditions, and performs at its best. The characteristics of the traditional PID controller that regulates the engine speed must be regulated online to achieve the use of intelligent technologies, and the adjustment is done online using the neural network. The results showed that this technology, or feature - online tuning - is the most effective and reliable of all.

Methodological features of the development of intelligent information technology for the transportation process in railway transport

The article presents methodological features of the development of intelligent information technology (hereinafter referred to as IIT) for implementation in the transportation process in railway transport. The purpose of this study is to integrate intelligent information technology into the railway transportation process using the IDEF0 functional modeling methodology. The implementation of IIT will ensure monitoring of the condition of wagons and cargo from the moment of loading to the moment of unloading along the entire route. Methods: Analysis of the current state of the subject area, synthesis of solutions, mathematical modeling, provisions of the theory of automatic control are applied. Results: World experience in the use of intelligent technologies in railway transport was analyzed, a diagram of a control system for the process of accepting a car for transportation and a mathematical model of the problem that IIT solves was developed. The role of IIT in the transportation process is determined using the IDEF0 functional modeling methodology. The importance of integrating IIT into the process of freight transportation by rail in increasing their efficiency through the use of new methods is shown.

Superfrontiers of Crop Production: Artificial Intelligence in Formation the Grain Production Ecosystem

The paper presents a study dedicated to analyzing the role of crop production superfrontiers in forming the agricultural production ecosystem, with a focus on the grain sector. Special attention is given to the superfrontier - data intelligence analytics as an effective tool for optimizing grain production. The authors consider specific examples of successful integration of intelligent analytical methods into practice, identifying their positive impact on improving production efficiency, including cost reduction and business process management enhancement. Among the main barriers to the implementation and use of intelligent technologies and systems, the lack of a unified methodology for collecting and preparing data for training and configuring intelligent grain production systems as a whole is noted. It is shown that artificial intelligence forms the basis of modern monitoring systems, permeating the ecosystem at all levels (supply, production, sales). Considering these features, the article presents a fundamental scheme for organizing the grain production ecosystem.

A way to combine music education and intelligent algorithms to cultivate innovative musical talents

Abstract The arrival of the intelligent era makes intelligent music education a reality, which is a product of the combination of intelligent technology and music education. The article starts with the intelligent transformation of music education, analyzes the talent cultivation mode of music education, and establishes the intelligent classroom teaching mode of music education through the use of intelligent technology. Combined with the data of students’ online music learning behavior, a personalized music learning path model is established, and a nonlinear factor is introduced to optimize the SBPSO intelligent algorithm in order to realize the accurate recommendation of a personalized learning path. A DFN model for the automatic generation of music accompaniment was established based on the Flow model, and simulation analysis and teaching experiments were carried out to analyze the effectiveness of the above methods in music-intelligent classroom teaching. The convergence value of the NFSBPSO algorithm was in the range of 0.026~0.043, and the discrepancy between the mean value of the note length of the accompaniment generated by the DFN model and that of real data was only 0.35% and the total average score of the generated accompaniment was 7.0%. Accompaniment had an overall mean score of 7.23. The observation group scored 83.64 on their final music course grade, which was 11.46 points more than the control group’s average grade. Combining music education with intelligent algorithms can achieve intelligent transformations in music education and improve the quality of talent cultivation.

Digitalising oil and gas production: issues, challenges and threats

The article analyzes the technical and regulatory restrictions that complicate the production of hydrocarbons at the final stage of operation, as well as the directions of resource and innovative development of the fuel and energy complex in the context of sanctions and restrictions in the development of national priorities. The features of regulatory regulation of legislation and indicators of digital transformation for previously developed fields and the preservation of hydrocarbon markets, the development of national economies in the long term, taking into account the widespread use of intelligent technologies and digital platforms, are considered. Taking into account the advantages of technology, it is strongly recommended to implement a project of digitalization of oil and gas wells using fiber-optic technologies. This will allow the creation of intelligent wells and deposits, which, with limited financial resources, will ensure an increase in recoverable reserves of gas and oil production by at least 10% during operation, will reduce the downtime of wells by about 50% from the initial level and will reduce operating costs by 10-25%. Keywords: automation; mining; modeling; regulation; transformation; innovation; intelligent technology; digital economy; digital platform.

Russian industry intellectualization development: Strategic approach

The current stage of industrial production digital transformation consists in developing and integrating artificial intelligence technologies. Strategic importance of Russian industry intellectualization requires synchronous involvement of all industries in this process in order to ensure effective and proportional development of the country’s economy. In this regard, the purpose of the study is to develop proposals for coordinated and balanced strategizing in the field of intellectual development of the country’s industry. The subject of the study is the relationship of digitalization processes and the use of intelligent technologies with strategies implementation for developing all industries and the Digital Economy National Program. The hypothesis of the study is based on the problem of low efficiency of implementing existing strategies of sectoral development and production digitalization processes, the causal result of which is inconsistency and imbalance of their development in time due to the lack of common views, common standards, and common space of information and communication interaction. Research objectives are to study statistical materials and practical developments in the field of industry digitalization; to analyze the current strategies for developing industrial sectors up to 2035; to analyze the processes of industry digitalization on the basis of reports on implementing the Industry 4.0 concept, various digital platforms for production dispatching, predictive analytics, etc.; to develop the concept of intelligent industrial development and the mechanism of integrating all participants in the processes of digital transformation, including the state. The authors propose the CBS concept for the development of the strategy of industry intellectualization and the mechanism of integrating subjects of the strategic process in a single space of information and communication interaction for the purposes of systematic linkage of industry development strategies on the basis of end-to-end technologies and artificial intelligence.

Response of the elderly to cognitive intervention through brainHQ training software; LatAm‐FINGERS Mexico

AbstractBackgroundComputerized training has been used to improve cognitive functioning and has been shown to be effective in a wide range of populations. However, its use in elders can be complicated due to many factors including schooling, motivation, perception of difficulty and unfamiliarity with the technology. For the multidomain dementia prevention program LatAm‐FINGERS, the brainHQ software is being used to perform the cognitive training. The aim of this work is to show the perception of participants about this program.MethodTwenty‐five urban‐Mexican elders cognitively healthy but with risk factors for dementia, are receiving the cognitive training through brainHQ. They indicated in a 5‐point Likert scale (1 = low, 5 = high) how enthusiastic and fearful they felt to begin the training and how difficult it has been. They also reported if they used a smartphone, a computer or a tablet before and how comfortable they felt using it.ResultsParticipants were between 60‐77 years old, with an average age of 70.9 ± 4.7 years old and an average schooling of 9.33 ± 3.5 years, 56% were females. Most of the sample (92%) uses smart technology, mainly smartphones, but only 8% feels very comfortable using it, 55% reports regular comfort and 37% feels not comfortable at all. When asked about brainHQ training, over half of the sample (56%) reported high enthusiasm, 68% reported little fear and more than half reported some degree of difficulty (see figure 1). No significant differences between men and women were found. Using Spearman`s test, a significant negative correlation between schooling and perceived difficulty was found (rho = ‐.68, p = .03), but not between schooling and enthusiasm or fear.ConclusionIn general, the participants have a good attitude towards the use of brainHQ, although they reported not feeling very comfortable with the use of intelligent technologies, they were enthusiastic about starting the training, and most of them were not afraid. However, the perceived difficulty could hinder training, especially in those with less schooling, therefore, it is important to develop strategies that encourage them to continue with the program to obtain the benefits of computerized training.

A Multi-Ship Collision Avoidance Algorithm Using Data-Driven Multi-Agent Deep Reinforcement Learning

Maritime Autonomous Surface Ships (MASS) are becoming of interest to the maritime sector and are also on the agenda of the International Maritime Organization (IMO). With the boom in global maritime traffic, the number of ships is increasing rapidly. The use of intelligent technology to achieve autonomous collision avoidance is a hot issue widely discussed in the industry. In the endeavor to solve this problem, multi-ship coordinated collision avoidance has become a crucial challenge. This paper proposes a multi-ship autonomous collision avoidance decision-making algorithm by a data-driven method and adopts the Multi-agent Deep Reinforcement Learning (MADRL) framework for its design. Firstly, the overall framework of this paper and its components follow the principle of “reality as primary and simulation as supplementary”, so a real data-driven AIS (Automatic Identification System) dominates the model construction. Secondly, the agent’s observation state is determined by quantifying the hazardous area. Then, based on a full understanding of the International Regulations for Preventing Collisions at Sea (COLREGs) and the preliminary data collection, this paper combines the statistical results of the real water traffic data to guide and design the algorithm framework and selects the representative influencing factors to be designed in the collision avoidance decision-making algorithm’s reward function. Next, we train the algorithmic model using both real data and simulation data. Meanwhile, Prioritized Experience Replay (PER) is adopted to accelerate the model’s learning efficiency. Finally, 40 encounter scenarios are designed and extended to verify the algorithm performance based on the idea of the Imazu problem. The experimental results show that this algorithm can efficiently make a ship collision avoidance decision in compliance with COLREGs. Multi-agent learning through shared network policies can ensure that the agents pass beyond the safe distance in unknown environments. We can apply the trained model to the system with different numbers of agents to provide a reference for the research of autonomous collision avoidance in ships.

How Can Women Take Advantage of the High-Tech Era to be Perceived as Effective Leaders? Being Tech-Savvy Helps

Given the increasing use of intelligent technologies and the resulting requirement for leaders to be tech-savvy, an important question that emerges is whether perceptions of leadership effectiveness are changing too? Drawing from gender role theory and expectation violation theory, we propose and test a model of whether and how females can benefit from being tech-savvy. To explore whether women may stand to gain from the changing nature of the workforce or not, we ran a series of studies. Our data sources included a cross-sectional time-lagged field study and experimental studies with students and online adults. We found that a definitive skill of the current technological era, tech-savviness, changes perceptions and evaluations of female leaders in a much more positive way than it does for male leaders, as such allowing female leaders to be more likely to be perceived as effective leaders. The reason for the effect that tech-savvy women were more likely to be perceived as effective leaders was driven by the fact that tech-savvy women are seen as more competent and hence, attributed more social status. Our research goes beyond merely focusing on the negative effects of gender stereotypes by providing an actionable solution—that comes from promoting tech knowledge and savviness—for women to counter gender stereotypes that undermine them from being viewed as effective leaders.

Decision Support System for autonomous vehicle maintenance management

Autonomous vehicles are complex objects equipped with the tools for monitoring the technical condition and transmitting data for diagnostics and prediction. The design of new models of autonomous and robotic vehicles is inextricably linked with the development of a maintenance system. The efficient functioning of the maintenance system is ensured by the use of intelligent technologies and digital twins. The article proposes an approach to designing a decision support system for the maintenance and repair of a fleet of autonomous vehicles. The decision support system is focused on robotic agricultural vehicles. The proposed structure of the system is of a general nature and can be used in transport and logistics enterprises in various sectors of the economy. The general architecture of the maintenance system is presented, including the neuro-digital twin of the vehicle, the analytics unit, simulation models of operation processes, and service centers. The components of the neuro-digital twin are digital twins of car units, a knowledge base, domain ontology, artificial neural networks, and a team of experts. The proposed approach is based on a combination of intelligent technologies and simulation modeling in collaboration with a team of experts. A set of original simulation models for the operation of a fleet of autonomous vehicles has been developed. New models based on stochastic timed colored Petri nets are proposed to analyze the processes during the autonomous vehicle operation. The developed decision support system can be used both at the stage of virtual commissioning and in the real operation of autonomous vehicles.

Factors Influencing the Success Rate of Pre-Litigation Mediation in China: An Empirical Study Based on Mediators and Case Parties

This study uses both qualitative and quantitative data to demonstrate that case types are not the only factor affecting the success rate of pre-litigation mediation in China. Instead, the type of cases is only a "symptom," and other factors are also at play. These factors include the expense of the lawsuit, its duration, the protection of the confidential information of the case, the friendliness of the parties and so on. In order to determine the variables that affect pre-litigation mediation's success rate and analyze their relative weights, the author used qualitative and quantitative methodologies in this research. By encouraging the use of intelligent technology in conflict resolution or by popularizing platforms, Chinese courts have promoted pre-litigation mediation. The empirical studies conducted in this article show that the general public in China is satisfied with the current mediation resources available, so Chinese courts should focus on resolving other issues that could improve the success rate of pre-litigation mediation.

A novel approach to intelligent monitoring of gas composition and light mode of greenhouse crop growing zone on the basis of fuzzy modelling and human-in-the-loop techniques

Gas composition and light mode of industrial greenhouses are some of the most determining factors in the process of growing vegetable crops in greenhouse conditions. The intelligentisation of information technologies for monitoring and control based on artificial intelligence methods can increase the efficiency of agrotechnical procedures for greenhouse cultivation. One of the efficient approaches in today's world practice is the development and implementation of trustworthy hybrid decision-support systems based on the techniques of Fuzzy logic and Human-in-the-Loop. The research object is non-stationary processes of complex intelligent transformation of measurement data on the concentration of carbon dioxide and effective energy illumination in the growing zone of industrial greenhouses. The scientific novelty and practical value of the obtained research results consist in creating a computer model for aggregation and intelligent processing of agricultural monitoring data for greenhouses. The developed computer model is fully transformed into peripheral-level software of Internet of Things systems for agricultural purposes. This makes it possible to implement the for-computing architecture of monitoring systems in greenhouses. The obtained research results make it possible to optimise the resources used in growing crops in greenhouse conditions through the implementation of hardware and software intelligent monitoring tools that are adaptive to the types and periods of crop vegetation. The scientific and applied effect of the research is creating the novel approach of development and practical use of intelligent technologies for agrotechnical monitoring by substantiating the methodological provisions for synthesis of structural and algorithmic organisation of the corresponding software and hardware solutions.

Effective detection algorithm of electronic information and signal processing based on multi-sensor data fusion

Appropriate vibration diagnostics when cutting a variety of products is critical for cutting quality and scrap and consumables reduction. Collecting and combining data from various sensors allows for real-time data collection during processing for system monitoring. The purpose of this study is to develop a method for effectively diagnosing milling machine vibrations using multi-sensor data fusion. It is proposed that sound and frequency signals be preprocessed to isolate specific vibration features, and then classified and aggregated for a complete vibration diagnostics system. After preliminary wavelet signal conversion, a deep belief network has been developed for each sensor to classify vibration features. When combining classification results, the fuzzy Choquet integral algorithm is used. Thus, a comprehensive judgment is obtained. It has been demonstrated that for neural networks, 100 epochs are sufficient for learning to take place. It has been discovered that the accuracy of classification and inference of the correct solution increases from 75% to 98% as the number of vibration feature combinations increases. When compared to the Dempster-Schafer evidence theory, the fuzzy integral fusion algorithm provides a high level of vibration detection accuracy of up to 98.7%. The resulting diagnostic procedure is simple and effective. It can be used to control the cutting process on machine tools in industrial settings. Improvements to the scheme are suggested, including the use of intelligent technologies to automate the process.

On educating ethics in the AI era: why business schools need to move beyond digital upskilling, towards ethical upskilling

Uncritical assumptions about the perfect rationality of AI—coupled with the single-minded focus on profit maximization and rational efficiency in organizations—have resulted in business leaders believing that they can delegate critical morally sensitive decision-making to AI, potentially resulting in harmful outcomes. As we will argue, business education thus far has been largely complicit in enabling such harmful aspects of AI-driven decision-making in organizations. This needs urgent remediation. Our opinion article aims to clarify what exactly we—as management scholars and practitioners—have been getting wrong thus far, and how we might ensure that the business leaders of tomorrow are well-equipped to step up and guard against the irresponsible and harmful use of intelligent technologies.

Research on the Intelligent Construction and Optimized Development Path of College Stadiums

The research on the optimal development path of intelligent construction of college stadiums can improve the level of intelligent service of college stadiums at home and abroad, enhance the utilization rate of college stadiums, effectively crack the problem of unbalanced and insufficient allocation of college stadium resources, meet the growing and diversified exercise needs of students and teachers to the maximum extent, and promote the development of higher quality of national fitness, which has very important theoretical guidance significance and it is of great theoretical guidance and practical application value. As an important place in colleges and universities, college stadiums must keep up with the trend of the times and make full use of intelligent technology to provide better service and intelligent experience. The construction of college intelligent stadiums is imperative. General Secretary Xi Jinping emphasized that "the concept of health-first education should be established, and physical education classes should be offered in full, so as to help students have fun, enhance their physical fitness, improve their personality and refine their will in physical exercise. In this paper, by investigating the wisdom status of sports venues in the most representative 15 colleges and universities in Ningbo, a random sample of research was conducted, using literature, mathematical statistics, fieldwork, Delphi method and questionnaire survey. In the process of wisdom construction, the special characteristics of different venues need to be considered, and it is important not to copy the model of popular venues, but must adopt the path suitable for universities. On the other hand, using the Internet of Things, big data, VR technology, cloud computing, virtual reality and other emerging information technology, we can solve the problems of "low utilization rate, poor service quality, slow service level, old facilities and equipment" and other problems in the construction of university sports venues, and provide important feasible ideas for further development.