Development Of Processing Technology Research Articles

Preface: 3rd International Conference on Software, Data Processing and Information Technology (SDPIT 2024)

The 2024 3rd International Conference on Software, Data Processing and Information Technology (SDPIT 2024) is a leading conference held at Washington DC, USA during February 17-18, 2024. It encourages communication and collaboration, hoping to provide an international platform for its participants to present, share, and learn the latest advance and development of software engineering, data processing, cloud computation, mathematical modeling and information technology. Academics, researchers, and students from across the world are all welcomed to exchange findings and ideas, to draw further inspirations in these research fields. It's a great opportunity to deepen your understanding in the related topics. Everyone interested in these fields were welcomed to join the conference and to give comments and raise questions to the speeches and presentations. We are hoping we could see each other again next year. Each submission to SDPIT 2024 was reviewed by at least two program committee members. We would like to thank the program committee members and external reviewers for their hard work in reviewing and selecting papers. Hopefully, all participants and other interested readers can benefit scientifically from the proceedings and find it rewarding in the process. Chairman of Conference Committee Washington DC, USA

Predicting attitudes toward ambiguity using natural language processing on free descriptions for open-ended question measurements

Individual traits and reactions to ambiguity differ and are conceptualized in terms of an individual’s attitudes toward ambiguity or ambiguity tolerance. The development of natural language processing technology has made it possible to measure mental states and reactions through open-ended questions, rather than predefined numerical rating scales, which have traditionally been the dominant method in psychological research. This study presented three ambiguity-related situations and responses collected online from 591 participants in an open-ended format. After the analysis with bidirectional encoder representations from transformers, correlations were calculated using scores from the numerical evaluation by conventional questionnaire, and a significant moderate positive correlation was found. Therefore, this study found that attitudes toward ambiguity can be measured using an open-ended response method of reporting everyday life states. It is a novel methodology that can be expanded to other scales in psychology and can potentially be used in educational and clinical situations where participants can be asked to respond with minimal burden.

АТОМНО-ДИСЛОКАЦИОННАЯ МОДЕЛЬ УДАЛЕНИЯ ПОВЕРХНОСТНЫХ СЛОЕВ СТРУЙНЫМ ЭЛЕКТРОЛИТНО-ПЛАЗМЕННЫМ ПОЛИРОВАНИЕМ

This work is devoted to the development of a model for the removal of surface layers of anode material due to a jet electrolyte-plasma discharge formed by a hollow cathode. The results of studies of the main types of discharges that occur during the formation of jet electrolyte-plasma processing are presented. The influence of each type of discharge on the morphology of the surface layer of the anode-product was studied. The power, lifetime and energy of single discharges are estimated, and their influence on the surface is assessed. A theory has been proposed for the removal of surface layers by single discharges at the atomic dislocation level. Theoretical calculations are confirmed by the results of experimental studies and multiphysics simulations carried out in the COMSOL Multiphysics program. Practical research results confirm the possibility of reducing the roughness parameter Ra during jet electrolyte plasma by more than 20 times, while obtaining an extremely low parameter Ra of 0.019 μm. The area of application of the results of these studies may be the development of technological processes for dimensional and finishing processing of curved surfaces, such as blisks, turbine blades, and various products for household and medical purposes.

Method for determining the number of defective products in a sample based on one measurement result

The most pressing problems in terms of production are increasing labor productivity and the quality of its results. One of the main ways to increase labor productivity is the development of new progressive technological processes. There are methods for controlling batches of products that can reduce the time of its control and increase labor productivity, which include methods of statistical quality control based on an alternative criterion. However, a sample from a batch of products must be fully controlled. When inspecting with one inspection tool, the sample inspection time is no less than the sum of the inspection times of all sample items. The purpose of this work is to develop a method for determining the number of defective products in a sample with one inspection tool, allowing to reduce the sample inspection time to the minimum possible. A method is proposed for determining the number of defective products of a certain class in a sample with one inspection tool using samples, which allows to signifi cantly reduce the sample inspection time. The samples are connected in parallel to the controlled products and are connected in series. All products in the sample with samples are controlled simultaneously, the total value of the controlled parameter is determined for them, and the number of defective products in the sample is determined by the formula. The effectiveness of the method is shown using the example of monitoring electromagnetic relays. The method makes it possible to reduce the sample inspection time to the minimum possible value, equal to the inspection time of one product.

INTELLIGENT DATA ANALYSIS ON AN ANALYTICAL PLATFORM

The article discusses methods for processing unstructured data using an analytical platform. The authors analyze existing methods and technologies used to implement data processing and propose new approaches to solving this problem. The possibilities of using analytical platforms to solve the problem of processing source data are considered. The purpose of the article is to explore the possibilities of data import, partial preprocessing, missing data recovery, anomaly removal, spectral processing and noise removal. The authors explored how analytics platforms can function without a data warehouse, obtaining information from any other sources, but the most optimal way is to use them together, and how big data and unstructured data can be processed using an analytics platform. The authors solved a specific problem related to processing problems and proposed ways to solve them using an analytical platform. Particular attention is paid to a complete set of mechanisms that allows you to obtain information from any data source, carry out the entire processing cycle and display the results. Overall, the paper represents an important contribution to the development of raw data processing technologies. The authors plan to continue research in the field of processing big unstructured data.

Разработка гибридного ветро-солнечного энергокомплекса

The relevance of the problem of energy and environmental security is increasing every year. Therefore, in the energy sector, the improvement of renewable energy sources is extremely important. The comprehensive development of optimal technological processes that are cost-effective and allow increasing the efficiency of wind-solar installations is an important problem today. The article discusses issues related to the use of renewable energy sources. An energy system has been created that combines wind and solar energy. Its design includes a horizontal axis wind turbine and solar panels. The blades of a wind turbine are made of polyvinyl chloride. The project presents modeling of two new energy supply systems using renewable energy sources: solar and wind. The energy produced by both sources was combined using a charge controller and then transferred to the inverter. The resulting energy was used to provide electricity to residential premises. The prototype demonstrates an evaluation of the effectiveness of a combined solar and wind system to provide essential home comfort needs such as lighting, fans, etc. In addition, the wind turbine is able to operate at lower wind speeds, which increases the overall efficiency of the system. This prototype was tested in combination with photovoltaic panels to verify the power output and overall efficiency of the integrated system. The comparison between the proposed and current systems confirmed many advantages of the proposed configuration. The efficiency of the wind turbine has been improved during its manufacturing process, especially when used in conjunction with photovoltaic panels, significantly increasing overall productivity

Effect Analysis of Fourier Transform and Neural Network on Image Noise Reduction

During the acquisition, transmission, and processing of digital images, noise often appears in the pictures, seriously affecting the quality of the images and the accuracy of subsequent analysis. To solve this problem, many scholars have conducted in-depth research on image denoising, aiming to find denoising methods suitable for different scenarios. This study aims to compare the effects of Fourier transform and neural network methods in eliminating stripes in pictures. The discrete Fourier transform denoising method performs best in sharpness and has a fast processing time, but its image restoration is poor. Although the artificial neural network method has a long processing time and depends on the training set, it has good noise reduction effect. The Photoshop (beta) method is relatively simple and easy to use, with good overall effect. It is hoped that through further research, a better understanding of the role of Fourier transform and neural network methods in image noise elimination can be achieved, providing more references for the development of image processing technology. This article introduces in detail the Fourier transform method and neural network method, analyzes their advantages and disadvantages and scope of application. These results shed light on guiding further exploration of noise reduction.

STUDYING THE APPROACHES OF EUROPEAN COUNTRIES IN FORESIGHT STUDIES OF THE DEVELOPMENT OF SCIENCE AND GREEN TECHNOLOGIES FOR ADOPTING THEIR EXPERIENCE IN THE CONTEXT OF KAZAKHSTAN

This paper reviews the application of foresight methods in the field of science, especially in the development of green technologies in various European countries, with the aim of identifying the most suitable approaches for future use in Kazakhstan. The analysis shows that green technology forecasting promises to be an effective tool for managing sustainable development. Methods for forecasting green technologies are focused on a systematic approach and integrated solutions, taking into account the environment and social aspects. It is also important to consider both global and local environmental issues to gain a full understanding of future development trends. The article provides a comparison of the Dutch and Danish approaches. Green technology foresight research can play an important role at the national level, especially in integrating sustainability considerations into technology development and innovation processes. They also contribute to the development of more effective and sustainable development strategies for companies and government in Kazakhstan. These studies contribute to increasing environmental awareness and awareness in society regarding the importance of sustainable development. Technology forecasting values the use of Delphi surveys to provide a better understanding of possible future trends. The knowledge gained is the basis for developing strategic plans in the field of green technologies and ensuring sustainable development of the energy sector.

Climate services for agriculture: Steering towards inclusive innovation in Australian climate services design and delivery

CONTEXTClimate services are integral to climate change adaptation in agriculture. Inclusive innovation prioritises inclusion of marginalised actors, grassroots knowledge and accessibility of use. Yet many innovation processes are multi-stakeholder, complex and may not prioritise inclusion from the outset. In these circumstances, researchers and designers may seek to influence existing projects towards greater inclusivity. OBJECTIVESThis paper explores efforts to increase the inclusivity of existing innovation processes, focusing on the design of My Climate View, an online Australian climate services platform for agriculture. Findings relate to contemporary issues in inclusive innovation literature, including: (1) Who should be included in innovation processes (within given resourcing)?; (2) How requirements for inclusion or exclusion can emerge through the design and use of a technology; and (3) The influence of program level structures upon inclusivity and design. METHODSOur approach is informed by Responsible Innovation and Inclusive Innovation theory and enacted through user-centred design. Findings are synthesised from analysis of 60 semi-structured interviews with farmers, advisors and extension officers. We additionally draw on observations as researchers in the multi-actor My Climate View development team, where extensive engagement separate to research activities has included agricultural and Indigenous stakeholders. RESULTS AND CONCLUSIONEngaging with a broad sample of potential target users enabled diverse and sometimes unexpected use cases. Customisation of interface parameters allowed the incorporation of tacit knowledge and positively affected the inclusivity of My Climate View, extending the value of the platform to growers of niche commodities outside of the original scope. Inclusivity in innovation can be impacted by diverse factors, including design team decisions, structural factors, budget and external factors. Requirements for inclusion and exclusion can emerge and evolve throughout a design process, supporting Responsible Innovation's framing of inclusivity alongside principles of reflexivity and responsiveness. SIGNIFICANCEThe findings enrich the evidence base for climate services and broader agricultural technology development processes by providing insights for those seeking greater impact by practicing inclusivity in innovation. We provide four implications for increasing inclusivity in the design of climate services: (1) Maximising customisation and avoiding prescriptive interpretation enables greater flexibility of use; (2) Sampling broadly within target users to uncover unanticipated values and opportunities for inclusion and exclusion; (3) It is important to embed efforts towards inclusion alongside processes of reflexivity; and (4) Responsible and inclusive approaches to climate services design are valuable even if brought in once development has commenced.

Effects of processing methods on the aroma of Poria cocos and its changing regulations during processing

Poria cocos is a natural source of fungal food raw materials. Processing method is a key effecting the aroma of Poria cocos. In this study, the aroma compounds of Poria cocos products processed using sweating-low-temperature drying (SW-LD), sweating-high-temperature drying (SW-HD), steaming-low-temperature drying (ST-LD), and steaming-high-temperature drying (ST-HD) were compared by headspace solid-phase microextraction (HS-SPME) combined with gas chromatography–mass spectrometry (GC–MS), and the changes in aroma compounds of Poria cocos products during processing were analyzed. GC–MS analysis showed SW-HD product had highest content of aroma compounds. Aroma activity value (OAV) analysis indicated that 9 aroma compounds contributed to the overall aroma of Poria cocos. Among 9 compounds of Poria cocos, 1-octen-3-ol, hexanal, nonanal, octanal, trans-2-octenal, and heptanal contributed to mushroom, refreshing, sweet and fatty characters. In addition, the aroma compound changes during the processing were analyzed, revealing that steaming and sweating were the key processes affecting the aroma of Poria cocos products. The findings of this study provide valuable theoretical guidance for the development of Poria cocos processing technology.

Personal Data in Artificial Intelligence Systems: Natural Language Processing Technology

Objective: to conceptualize, from the viewpoint of personal data protection legislation, the development of natural language processing technology, identifying possible legal barriers to such development and directions for further research of the issue.Methods: the research is based on general scientific methods of cognition, along with which formal-legal and comparative-legal methods were applied, as well as the method of theoretical modeling.Results: it was found that the observance of personal data regime natural language processing in the development of natural language processing technology leads technology, to a conflict between private-legal and public-legal interests, which, personal data in turn, creates obstacles for further development of this technology. The shortcomings of the existing legal order are shown, namely, the insufficient correspondence to the technical features of technology development. This may lead to the risks of excessive regulation, or, on the contrary, to the risks of neglecting critical areas that require protection. Problems in qualifying the data involved in the technology development are outlined. An attempt is made to define the limits of ensuring the lawfulness of personal data processing within the natural language processing technology. The material, temporal and territorial effect of the legal regulation in this field is identified as the limits of ensuring the legality. The author touches upon the possibility of using personal data as a consideration, which is important for the development of natural language processing technology and for the improvement of the information and communication technology industry.Scientific novelty: the paper supplements the scientific discussion on the legal regulation of personal data processing by artificial intelligence systems with an analysis of natural language processing technology. The latter is insufficiently studied, making it relevant to research information law, namely, the legal relations arising around artificial intelligence systems, and to assess the impact of a personal data regime on the development of natural language processing technology.Practical relevance: the applied aspects of the problems researched and the results obtained can be used to improve the legal regulation of public relations in the field of creation and development of artificial intelligence, as well as to identify and assess the legal risks arising in the personal data processing by developers of digital products based on natural language processing technology.

Natural language command parsing for agricultural measurement and control based on AMR and entity recognition

There is scope to enhance agricultural measurement and control systems user interactivity, which typically necessitates training for users to perform specific operations successfully. With the continuous development of natural language semantic processing technology, it has become essential to augment the user-friendliness of multifaceted control and query operations in the agricultural measurement and control sector, ultimately leading to reduced operation costs for users. The study aims to focus on command parsing. The proposed AMR-OPO semantic parsing framework is based on the natural language understanding method of Abstract Meaning Representation of Rooted Markup Graphs (AMR). It transforms the user’s natural language inputs into structured ternary (OPO) statements (operation-place-object) and converts the corresponding parameters of the user’s input commands. The framework subsequently sends the transformed commands to the relevant devices via the IoT gateway. To tackle the intricate task of parsing instructions, we developed a BERT-BiLSTM-ATT-CRF-OPO entity recognition model. This model can detect and extract entities from agricultural instructions, and precisely populate them into OPO statements. Our model shows exceptional accuracy in instruction parsing, with precision, recall, and F-value all measuring at 92.13%, 93.12%, and 92.76%, correspondingly. The findings from our experiment reveal outstanding and precise performance of our approach. It is anticipated that our algorithm will enhance the user experience offered by agricultural measurement and control systems, while also making them more user-friendly.

DESIGN AND BUILD A COCONUT HUSK CHOPPER MACHINE USING AN 8 HP GASOLINE ENGINE

The coconut frond cutter or coconut frond shredder is a device used to transform coconut fronds into small pieces, even turning them into a finely textured material. This study examines the design and development of a coconut-chopping machine that uses an 8 HP combustion motor. Structural analysis was performed on the machine frame by considering the maximum and minimum Von Mises Stresses. The analysis results show that the maximum Von Mises Stress in the frame is 1.670e+06 N/mm², while the minimum stress is 2.338e+04 N/mm². The displacement of the frame was observed at 2.642e-02 mm to 1.000e-30 mm. The safety factor of the frame was found to be 1.366e+02, indicating an adequate level of safety. Next, this study analyzed the shredding capacity of the machine. The analysis showed that the largest shredding capacity was 3619.30 kg/hour, which occurred at a high rotation of young fronds, producing fine shreds. Meanwhile, the smallest shredding capacity of 612.66 kg/hour occurs at low rotation of old fronds and produces coarse shreds. These results provide essential insights into using coconut shredding machines based on the rotation level and the processed material. This research contributes to the development of efficient and effective coconut processing technology.

The effect of pressure on degradation kinetics of polyphenols: Impact of polyphenol structure at hyperbaric storage conditions

Understanding reaction kinetics at elevated pressures is essential for developing efficient pressure-based technologies. It is especially vital for hyperbaric storage (HS), where pressure is applied for long durations. In single and multi-component models, the influence of pressure on the degradation kinetics of different catechins and anthocyanins was studied at accelerated conditions using neutral pH at pressures up to 200 MPa (HS levels) for several hours. In the neutral baroresistant buffer, pressure enhanced the degradation of all tested flavonoids with a negative activation volume. Despite the significant structural variety and major differences in the degradation rates within each tested subgroup, no significant structure-dependent effect of pressure on the activation volume was observed, which is suggestive of a similar mechanism by which pressure accelerates the degradation. The antioxidant capacity of catechins was not affected by pressure compared to control samples when the concentration of the original catechin was similar. Industrial relevanceA better understanding of pressure effects on the degradation reaction kinetics of food components can contribute to the general understanding of mechanisms related to food component degradation under high-pressure treatments. Plant-based foods are rich in polyphenolic compounds and responsible for sensory characteristics and their related health-promoting properties. In conditions similar to HS, that are known to inhibit and even, in some cases, inactivate microorganisms, when food is exposed to high pressures for a long time, it is imperative to investigate the effect of pressure on polyphenol degradation kinetics, which can lead to differences in the polyphenol compositions during the pressurized storage. However, the impact of pressure on polyphenol degradation kinetics is often overlooked. This study provides fundamental information regarding the polyphenol structure-dependent degradation kinetics of catechins and anthocyanins under pressure while taking into account the often overlooked pressure-induced pH shifts. The presented work can promote the development of novel storage and processing technologies based on high pressure that will better preserve oxidation-liable compounds with lower energetic costs.

Mechanism of initiation of ethylene cationic polymerization in the presence of an aluminum chloride aquacomplex in heptane with a stoichiometric composition of 1:1:1:2 at the quantum level

In this paper, the mechanism of ethylene initiation in the presence of an aluminum chloride – water catalyst in a heptane solvent of stoichiometric composition 1:1:1:2 was studied using the ab initio RHF theoretical quantum chemical method. The activation energy and thermal effect of this reaction were obtained. These data can be useful for further research in the field of cationic polymerization, and can also be used in the development of new technological processes of polyethylene with specified physicochemical properties characteristic of fiber optics.

Исследование влияния комбинированного модификатора из отходов кремниевого производства на свойства серых чугунов

Introduction. During the metallurgical production of silicon, waste is generated that accumulates in dumps, causing harm to the environment. Disposal and recycling of solid waste from silicon production is especially important because They contain important chemical compounds (silicon dioxide, silicon carbide, carbon nanotubes) that can be used in other industries, which will bring greater economic value. Considering the possibilities for extracting these useful components from silicon production waste, it is necessary to bring processing technologies to the stage of widespread practical application. Therefore, the development of a special waste processing technology to obtain a useful product in the form of a composition of silicon dioxide and silicon carbide remains an urgent problem. Purpose of the work: to study the formation of the morphological form of graphite with the introduction of nano-modifiers from silicon production waste. The work examined samples of gray cast iron after modification with a combined modifier obtained from silicon production waste. The research methods are mechanical tests for statistical tension, analysis of the chemical composition and metallographic studies. Results and discussion. It was revealed that the mechanical properties of gray cast iron increased by 30-50% after modification with a combined modifier, compared with witness samples. The morphology of graphite is an important parameter affecting the properties of cast iron. It has been established that during the modification process the morphology of graphite changes from lamellar to vermicular. Specimens of gray cast iron with vermicular form of graphite have high strength values compared to specimens of gray cast iron with lamellar form of graphite. The presented results confirm the promise of the developed approach aimed at obtaining new classes of modifiers and products made of gray cast iron with a high range of mechanical properties.

Improved Sliding Mode Variable Structure Control of Robot Manipulators with Flexible Joints based on Singular Perturbation

This paper discusses the kinematics analysis method of six link mechanism based on virtual prototype technology and Adams. Firstly, the kinematics mathematical model of the six-bar linkage is deduced. Then, based on the design and development process of virtual prototype technology, the virtual prototype model of the six-bar press is established in ADAMS software. Finally, the kinematics simulation analysis is carried out for the model. The proposed method provides a reference for the parametric and optimal design analysis of the multi bar linkage of the mechanical press.

IT Skills of Young and Older People: A Qualitative Study

Both younger and older people have different ways of using and interacting with technical devices (smartphones, laptops and tablets). This study compares both user groups and explores how the technical aids are used in everyday life, what barriers exist and how technical problems are handled. In July and August 2021, 20 semi-structured interviews with young and old people were conducted and analysed using qualitative content analysis. The results show that young people continue to use smartphones more frequently than the older participants and that smartphone functions such as making phone calls are more important for the older participants. Older people prefer a personal support by family members when technical problems occur, whereas the younger study participants favour the more impersonal option via hotlines etc. Finally, it seems to be useful to involve older people in the development process of new technologies, so that the applications can be designed to be as user-friendly as possible.

Public–Private Partnering in Nuclear Reactor Development—Historical Review and Implications for Today

Abstract The dominant nuclear reactor technologies that comprise the current global operating fleet were developed and deployed over a relatively short, midtwentieth century, period spanning the 1950s and 60 s. Four of these technologies were deployed at fleet scales and commercially exported. The historical record indicates a remarkably consistent process of phased technology development that enabled the commercialization of designs that would define the global nuclear marketplace, beginning with research and development (R&D) and advancing through test reactors, small and large demonstration reactors, and first commercial-scale units. Following proof-of-principle R&D, historical commercialization lead times (from decision to construction of a demonstration reactor to first commercial launch) ranged from 12 to16 years for these four commercial technologies. Key factors contributing to successful commercialization included durable government support for early R&D and varying degrees of public–private partnering through commercial launch. This partnering included arrangements for technical support, siting, facility ownership, nuclear material provision, and cost sharing. The policy environment was characterized by unambiguous government support; stabile, effective and informed government program management and oversight; and flexibility in the public–private partnership arrangements to promote technology development and demonstration. Government advocacy was structured to support progressively increasing industry independence and self-sufficiency. This experience is documented and analyzed in this paper to provide salient lessons and example program elements for contemporary efforts to stimulate development and commercialization of a new generation of advanced nuclear technologies through collaboration and public–private partnerships.

Knowledge-enhanced model with dual-graph interaction for confusing legal charge prediction

The rapid development of natural language processing (NLP) technologies has enabled the emergence of legal intelligence assistance systems, with legal charge prediction (LCP) being a critical technology. The automatic LCP aims to determine the final charges based on fact descriptions of criminal cases. LCP assists human judges in managing workloads and improving efficiency, provides accessible legal guidance for individuals, and supports enterprises in litigation financing and compliance monitoring. However, distinguishing between confusing charges in real-world judicial practice remains a significant challenge. Most exist works cannot effectively capture complex relationships and discern subtle differences in fact descriptions while ignoring the legal schematic knowledge. In order to improve confusing LCP performance, we propose a novel knowledge-aware model for legal charge prediction that leverages Graph Neural Networks (GNNs) to capture complex relationships within criminal case descriptions. Specifically, the model constructs structural and semantic graphs from fact descriptions and integrates information from both through a dual-graph interaction process. A legal knowledge transformer generates key knowledge representations at schema and charge levels, while a knowledge matching network incorporates hierarchical charge knowledge into facts. Besides, we also propose two real-world datasets namely Criminal-All and Criminal-Confusing, containing 203 different charges and 86 confusing charges, respectively. To the best of our knowledge, these datasets are the first well-organized datasets for confusing LCP task. Experimental results demonstrate that the proposed model outperforms baselines and significantly improves the distinction of confusing charges, providing valuable support for intelligent legal judgment systems.