Engineering Task Research Articles

INCREASE OF THE CORROSION-MECHANICAL WEAR RESISTANCE OF TITANIUM ALLOYS BY NITRIDATION IN THE GLOW DISCHARGE: A REVIEW OF MODERN RESEARCH

In this work, a review of modern research in the field of increasing the corrosion-mechanical wear resistance of titanium alloys using glow discharge nitriding is carried out. Titanium alloys, due to their unique properties, namely high strength, lightness, exceptional corrosion resistance and excellent biocompatibility, are widely used in aviation, medicine, engineering and many other industries. However, operational conditions in some applications require even greater improvement of the wear resistance and corrosion resistance of these alloys, which makes the search for effective methods of their improvement an urgent scientific and engineering task. Among the promising technologies that can be used to improve the corrosion-mechanical properties of titanium alloys, low-temperature anhydrous nitriding in a glow discharge should be considered. The use of low-temperature anhydrous nitriding in a glow discharge eliminates the growth of the base grain and hydrogen embrittlement of the surface. Also, low-temperature nitriding in a glow discharge makes it possible to increase the wear resistance of the surface of titanium alloys, while the saturation of the surface with nitrogen is controlled, which allows predicting the results of processing, does not lead to a change in the shape of parts. The analysis of scientific research shows that increasing the corrosion-mechanical wear resistance of titanium alloys by means of nitriding in a glow discharge is a promising direction that opens up new opportunities for industry and science. Progress in this field depends on further research, innovative development and the integration of the latest technologies, which together will contribute to the creation of more durable, efficient and environmentally friendly materials for a wide range of applications. In general, increasing the corrosion-mechanical resistance of titanium alloys by the method of nitriding in a glow discharge is a promising direction of research in Ukraine, which can lead to the improvement of the properties of titanium alloys and the expansion of their use in various industries.

Time-dependant Bayesian knowledge tracing-Robots that model user skills over time.

Creating an accurate model of a user's skills is an essential task for Intelligent Tutoring Systems (ITS) and robotic tutoring systems. This allows the system to provide personalized help based on the user's knowledge state. Most user skill modeling systems have focused on simpler tasks such as arithmetic or multiple-choice questions, where the user's model is only updated upon task completion. These tasks have a single correct answer and they generate an unambiguous observation of the user's answer. This is not the case for more complex tasks such as programming or engineering tasks, where the user completing the task creates a succession of noisy user observations as they work on different parts of the task. We create an algorithm called Time-Dependant Bayesian Knowledge Tracing (TD-BKT) that tracks users' skills throughout these more complex tasks. We show in simulation that it has a more accurate model of the user's skills and, therefore, can select better teaching actions than previous algorithms. Lastly, we show that a robot can use TD-BKT to model a user and teach electronic circuit tasks to participants during a user study. Our results show that participants significantly improved their skills when modeled using TD-BKT.

A Novel Algorithm for Multi-Criteria Ontology Merging through Iterative Update of RDF Graph

Ontology merging is an important task in ontology engineering to date. However, despite the efforts devoted to ontology merging, the incorporation of relevant features of ontologies such as axioms, individuals and annotations in the output ontologies remains challenging. Consequently, existing ontology-merging solutions produce new ontologies that do not include all the relevant semantic features from the candidate ontologies. To address these limitations, this paper proposes a novel algorithm for multi-criteria ontology merging that automatically builds a new ontology from candidate ontologies by iteratively updating an RDF graph in the memory. The proposed algorithm leverages state-of-the-art Natural Language Processing tools as well as a Machine Learning-based framework to assess the similarities and merge various criteria into the resulting output ontology. The key contribution of the proposed algorithm lies in its ability to merge relevant features from the candidate ontologies to build a more accurate, integrated and cohesive output ontology. The proposed algorithm is tested with five ontologies of different computing domains and evaluated in terms of its asymptotic behavior, quality and computational performance. The experimental results indicate that the proposed algorithm produces output ontologies that meet the integrity, accuracy and cohesion quality criteria better than related studies. This performance demonstrates the effectiveness and superior capabilities of the proposed algorithm. Furthermore, the proposed algorithm enables iterative in-memory update and building of the RDF graph of the resulting output ontology, which enhances the processing speed and improves the computational efficiency, making it an ideal solution for big data applications.

Metal surface defect detection based on Latitude-Longitude light sources

Surface defect detection is a common engineering task, and traditional human visual inspection suffers from low efficiency, poor resolution, and a lack of traceable data. We propose a surface-topography detection system based on multicolor latitudinal and longitudinal light sources. By activating multicolor latitudinal and multicolor longitudinal light sources at the same position but at different times, decoupling and highlighting of the surface gradient magnitude and direction in the hue domain of the target surface can be achieved. We used a hot-rolled aluminum sheet as a target and calibrated the vision system, achieving recovery of the gradient magnitude and direction, which provided the necessary conditions for defect data analysis. We also used a hot-rolled aluminum sheet as the target for collecting co-located latitudinal and longitudinal images. In these images, various surface defects were highlighted relative to the background, with good differentiation between the different defects.

Soft Computing-Based System for Performance Modeling of Object-Oriented Programming (OOP) Software

Performance is a vital attribute for most software, making performance investigation an essential software engineering task. The problem is that modern applications are challenging to analyse for performance. The advancement of Software engineering has seen the application of scientific approach at various stages of Software Development Life Cycle (SDLC). New technique is available for implementation known as soft computing. This approach is a synergistic combination of artificial intelligence methodologies to model and solve real world problems that are either impossible or challenging to be modeled mathematically. The complexity, the commercial constraints and the expectation for high quality software demand, measuring the quality performance of an Object-Oriented Software (OOS). This work presents a set of testing metric (criteria) which hold the potential for ensuring remarkable improvements in quality performance of Object-Oriented software (OOS). A Prediction Model focused on identifying and evaluating the quality of the performance model based on the data set of software design components. The software design properties are comprehensive elucidated and integrating concepts of fuzzy logic and software engineering. The system enables the performance of modeling of Object-Oriented software using Fuzzy Inference System (FIS) to accurately predict the quality Object Oriented Software based on quality performance criteria such as reliability, efficiency, functionality and maintainability. Empirical validation of the results using checking data set is made to prove the usefulness of the design metrics and design quality attributes on the software prediction models. The purpose of this work is to provide development tool to solves practical problems, and focus on identifying and evaluating the quality performance of software design components. Again, the results demonstrate that, despite the research including approaches explicitly aimed at object-oriented software, there are substantial challenges in providing realistic feedback on the performance of large-scale object-oriented applications accordingly.

Heavy fermions vs doped Mott physics in heterogeneous Ta-dichalcogenide bilayers

Controlling and understanding electron correlations in quantum matter is one of the most challenging tasks in materials engineering. In the past years a plethora of new puzzling correlated states have been found by carefully stacking and twisting two-dimensional van der Waals materials of different kind. Unique to these stacked structures is the emergence of correlated phases not foreseeable from the single layers alone. In Ta-dichalcogenide heterostructures made of a good metallic “1H”- and a Mott insulating “1T”-layer, recent reports have evidenced a cross-breed itinerant and localized nature of the electronic excitations, similar to what is typically found in heavy fermion systems. Here, we put forward a new interpretation based on first-principles calculations which indicates a sizeable charge transfer of electrons (0.4-0.6 e) from 1T to 1H layers at an elevated interlayer distance. We accurately quantify the strength of the interlayer hybridization which allows us to unambiguously determine that the system is much closer to a doped Mott insulator than to a heavy fermion scenario. Ta-based heterolayers provide therefore a new ground for quantum-materials engineering in the regime of heavily doped Mott insulators hybridized with metallic states at a van der Waals distance.

TeReKG: A temporal collaborative knowledge graph framework for software team recommendation

Successful software development requires a cohesive team with the right mix of technical skills and the ability to collaborate effectively. However, forming a software team that can execute tasks with precision and efficiency requires a deep understanding of each member’s competence, experience, and cooperation history. Previously, automated software team selection has evaluated technical skills, cohesion, and cooperation history. However, the previous method had some limitations. Particularly, local features directly calculated from team members were subjective to the researchers’ views, and the method ignored the temporal aspect of open-source software development. To overcome these limitations, this paper proposes a knowledge-graph software team recommendation framework called TeReKG. This framework encapsulates temporal collaboration patterns and uses a temporal knowledge graph to encode software collaboration history, technical abilities, task dependencies, and project structure. TeReKG was against state-of-the-art team recommendation algorithms using three popular open-source software projects: Moodle, Apache, and Atlassian. The evaluation results show that TeReKG outperforms the state-of-the-art baselines in both single-role and team recommendation tasks. These findings demonstrate that knowledge graph embedding can be effectively utilized in automated recommendation tasks in software engineering. Additionally, this highlights the potential for knowledge graphs to capture global information that can benefit various software development applications, including impact prediction of software repositories, code clone detection, and source code retrieval.

Updating Turbomachinery Aerodynamics Teaching on an Undergraduate Course Using Three-Dimensional Design Tools

Abstract This paper outlines the update of a turbomachinery course to cover three-dimensional (3D) aerodynamics using a Reynolds-Averaged Navier Stokes solver. Prior to the activities outlined in this paper, the course was taught in a conventional way with a series of lectures and a timed, written, open book examination in a formal exam setting. Students were equipped with a calculator and set of notes including correlations from Howell and Soderberg. The students had around 1 h to answer the turbomachinery question, which was revealed to them only when they opened the paper at the start of the exam. This limited the depth of any aerodynamic problem they could get through and so they were restricted to simple design or analysis exercises. The genesis of the course update was the release in 2017 of “MULTALL OPEN” (Denton, J. D., 2017, “Multall: An Open Source, Computational Fluid Dynamics Based, Turbomachinery Design System,” ASME J. Turbomach., 139(12), p. 121001), a freely available turbomachinery design system, and this was adopted as the course software—although other software choices were considered. Students now produce two turbomachinery designs during the course. These are both based on the J85 turbojet—largely to keep cycle calculations to a minimum but it was also important to ensure that students recognize that they were completing a real engineering task. The first task was a 3D aerodynamic design of multistage turbine, which includes compressible flow, tip clearance, and stacking techniques such as lean or sweep. The second task was a midspan (two-dimensional) compressor design. The exact cycle conditions were varied each year to explore design scenarios such as an increase in firing temperature. Assessment was by means of a short report where the best students report on their design choices, provide critical analysis of the design using appropriate postprocessing techniques, and compare their designs to the state of the art in the wider literature. As well as describing the successes and challenges of the update to 3D design methods the paper provides some guidance for educators thinking about adopting a similar approach.

Study on engineering and aesthetic design in shape determination of wind turbine

This study discusses the importance of wind turbine design in achieving social acceptance. It examines the factors that determine the shape of wind turbines from two perspectives: engineering and aesthetics. Using the development of New Energy and Industrial Technology Development Organization wind turbines for remote islands in Okinawa as an example, in collaboration with an aircraft manufacturer. This study explores the differences and issues between two aspects, namely engineering design and aesthetic design, and proposes solutions. The following conclusions were drawn regarding achieving an integrated and aesthetically pleasing shape: (1) Engineering and aesthetic designs employ different methods and criteria for shaping a product. (2) Respecting the methodologies and processes of both disciplines and engaging in early-stage discussions on design concepts and approaches can facilitate smooth development. (3) It is recommended to integrate the design processes of engineering and visual tasks and proceed concurrently under the same operational system.

A New Calculation Model for Equivalent Circulating Density Considering Interface Effect between Various Fluids during Cementing Process

Summary Equivalent circulating density (ECD) is an essential parameter in the construction of oil and gas wells, which can be used to control the downhole pressure and prevent serious incidents such as well leakage or blowouts. Different engineering tasks will lead to changes in the ECD calculation model; the classical pipe flow theory can be used to calculate the ECD of circulating drilling fluid, but it is not suitable for the cementing displacement because it does not consider the interface effect between various fluids. In this paper, a new ECD calculation model has been developed that accounts for the changes in fluid interface morphology between displacing fluid and displaced fluid during the cementing process. Moreover, a simulation of the effects of borehole radius, casing eccentricity, displaced fluid density, and flow rate on the ECD were analyzed and quantified by a numerical approach to solve the fluid dynamics equilibrium equations that describe the flow in the eccentric annuli based on the semi-implicit method for pressure-linked equations (SIMPLE) algorithm. The results show that the calculated ECD predicted by the proposed model is larger than the traditional model with the reduction in the borehole radius, and the maximum deviation at the inlet position can reach 0.03 g·cm−3. Variation in the length of the interface transition zone is the main reason for the deviation in ECD prediction accuracy. In the case of constant casing outer diameter, fluid rheology, and displaced fluid density, the ECD decreases with increasing borehole radius. Meanwhile, it increases with higher displaced fluid density, eccentricity, and annular velocity. It is also shown that the length of the mixing zone can reach up to 40% of the pipe length under the conditions of eccentricity and displacement described in the paper. The proposed model predicts the ECD in the annulus by considering the influence of different engineering parameters on the movement of displacement interface, which can effectively ensure the safety of the cementing operation.

Codon language embeddings provide strong signals for use in protein engineering

Protein representations from deep language models have yielded state-of-the-art performance across many tasks in computational protein engineering. In recent years, progress has primarily focused on parameter count, with recent models’ capacities surpassing the size of the very datasets they were trained on. Here we propose an alternative direction. We show that large language models trained on codons, instead of amino acid sequences, provide high-quality representations that outperform comparable state-of-the-art models across a variety of tasks. In some tasks, such as species recognition, prediction of protein and transcript abundance or melting point estimation, we show that a language model trained on codons outperforms every other published protein language model, including some that contain over 50 times more parameters. These results indicate that, in addition to commonly studied scale and model complexity, the information content of biological data provides an orthogonal direction to improve the power of machine learning in biology.

Improving the kinematic accuracy of a collaborative continuum robot by using flexure-hinges

Within various unstructured industrial environments, there is often the requirement to conduct remote engineering tasks, such as sampling the structure for analysis prior to decommissioning. Most existing tools are simply not dexterous enough to fulfil this task, and thus new technology is required. We describe here a simple, lightweight, and water-resistant collaborative dual-arm continuum robot system which can aid in this task. To improve the kinematic accuracy of the system, a class of flexible hinges have been combined with a conventional continuum robot configuration. The thickness and width of said flexible hinges can be adjusted to adapt to various tasks. Kinematic and stiffness models have further been developed, incorporating the influence of these flexible hinges. A set of experiments have been conducted to validate the proposed model and demonstrate the advantages of the platform. It was found that the kinematic accuracy of the continuum robot can be improved by a factor of around 10 with the aid of said hinges.

Linguistic Features of Copywriting and Rewriting in the Field of Text Content for Corporate Websites: Semantic Aspect.

The study's relevance lies in the distinction between the cognitive processes of human readers and search robots, particularly the contextual understanding and relevance determination capabilities of modern search engines. This study aims to address contemporary challenges related to online textual content, specifically focusing on texts found on corporate websites and corporate blogs. The study employs a comprehensive approach to analyze the creation of texts, considering both copywriting and rewriting techniques. The analysis includes an examination of textual activity on corporate websites and blogs. The research methodology involves investigating linguistic and psycholinguistic aspects, as well as the principles underlying search engines and their role in organic website promotion. The study also delves into the nature of multimedia articles as a distinct format of online content. The major finding of this research lies in the proposed method for assessing the cognitive effectiveness of copywriting and rewriting. The study sheds light on the intricate interplay between multimedia articles and their textual components, showcasing their potential for impactful audience engagement. The analysis of internet copywriting and rewriting in the realm of marketing discourse reveals their significance for search engine tasks and user engagement. In conclusion, this research underscores the practical importance of understanding the cognitive effectiveness of texts generated through copywriting and rewriting. The findings emphasize the role of multimedia articles and their textual elements in influencing audience engagement online. The study highlights the crucial connection between internet copywriting, rewriting, and successful marketing discourse, optimizing content for search engines and enhancing user experience.

Feasibility of using geopolymer solution in the conservation of architectural monuments

the ordinary cement mortar. The authors present the argument for the expediency of using a Preservation and maintenance of architectural monuments have always been the number one task for civil engineers. In this context, the engineer encounters a “wagon load” of problems. On the one hand, there are strict requirements for the composition of materials used for restoration. On the other hand, it is the almost insurmountable challenge of selecting the right materials with the essential properties for the proper restoration of an architectural monument. Fixing of cracks and cavities is crucial for the durability of reinforced concrete and stone structures. These need to be treated scientifically to ensure usability.
 In this setting, the use of Epoxy resins has become common. While Epoxy resins perform well under certain circumstances, they have many disadvantages such as heat instability, high costs, high resource demand, and serious health and environmental hazards. Furthermore, Epoxy resins are proscribed in monument preservation. To overcome the potential disadvantages of Epoxy resins, Geopolymers offer a promising alternative. Geopolymers are an environmentally friendly and durable substitute with low viscosity, supported by high-temperature stability. Using inorganic additives, geopolymer can expand and create a preload during solidification; this is a salient favorable feature since any decrease in volume will lead to the creation of a new cavity or crack of a smaller size. According to previous studies [1], the implementation of this low-CO2 geopolymer cement in construction and civil engineering would reduce CO2 emissions caused by the cement and concrete industry by 80%
 This article presents the main advantages and disadvantages of the proposed geopolymer solution, in comparison to geopolymer solution in the reconstruction of architectural monuments.

Structural system modelling from base excitation measurements using swarm intelligence

Structural system identification is a fundamental task in mechanical engineering since it enables the characterization of the dynamic behaviour of structures from experimental data. The identification of the mass, stiffness, and damping matrices of a structure is essential for the design, analysis, and control of mechanical systems. This paper proposes a novel method for identifying the spatial properties of a structure using base excitation measurements and swarm intelligence. Maintaining relevant physical properties is ensured by placing algebraic constraints on factors of the mass and stiffness matrices. This formulation circumvents the complexities of additional constraints in metaheuristic techniques. Consistency of the eigenvalues with respect to the test data is assured through an integrated refinement process during the stiffness matrix estimation. A healing process enables the rescaling of the mass matrix, guaranteeing an accurate representation of the structure’s total mass. The search space definition only requires prior knowledge of the total mass of the structure and an estimate of anticipated maximum natural frequencies within the targeted frequency range. The efficacy of this method is tested on a numerical example.

StructCoder: Structure-Aware Transformer for Code Generation

There has been a recent surge of interest in automating software engineering tasks using deep learning. This article addresses the problem of code generation, in which the goal is to generate target code given source code in a different language or a natural language description. Most state-of-the-art deep learning models for code generation use training strategies primarily designed for natural language. However, understanding and generating code requires a more rigorous comprehension of the code syntax and semantics. With this motivation, we develop an encoder-decoder Transformer model in which both the encoder and decoder are explicitly trained to recognize the syntax and dataflow in the source and target codes, respectively. We not only make the encoder structure aware by leveraging the source code’s syntax tree and dataflow graph, but we also support the decoder in preserving the syntax and dataflow of the target code by introducing two novel auxiliary tasks: Abstract Syntax Tree (AST) path prediction and dataflow prediction. To the best of our knowledge, this is the first work to introduce a structure-aware Transformer decoder that models both syntax and dataflow to enhance the quality of generated code. The proposed StructCoder model achieves state-of-the-art performance on code translation and text-to-code generation tasks in the CodeXGLUE benchmark and improves over baselines of similar size on the APPS code generation benchmark. Our code is publicly available at https://github.com/reddy-lab-code-research/StructCoder/ .

Empirical validation of ProteinMPNN's efficiency in enhancing protein fitness.

Introduction: Protein engineering, which aims to improve the properties and functions of proteins, holds great research significance and application value. However, current models that predict the effects of amino acid substitutions often perform poorly when evaluated for precision. Recent research has shown that ProteinMPNN, a large-scale pre-training sequence design model based on protein structure, performs exceptionally well. It is capable of designing mutants with structures similar to the original protein. When applied to the field of protein engineering, the diverse designs for mutation positions generated by this model can be viewed as a more precise mutation range. Methods: We collected three biological experimental datasets and compared the design results of ProteinMPNN for wild-type proteins with the experimental datasets to verify the ability of ProteinMPNN in improving protein fitness. Results: The validation on biological experimental datasets shows that ProteinMPNN has the ability to design mutation types with higher fitness in single and multi-point mutations. We have verified the high accuracy of ProteinMPNN in protein engineering tasks from both positive and negative perspectives. Discussion: Our research indicates that using large-scale pre trained models to design protein mutants provides a new approach for protein engineering, providing strong support for guiding biological experiments and applications in biotechnology.

Formation of Information Technology in Uzbekistan

Taking into account that the development of information technology engineering is relevant today, the development and improvement of society has accelerated, the engineering task of exchanging information over the network is inevitable.

A novel data-driven model-free synchronization protocol for discrete-time multi-agent systems via TD3 based algorithm

System modeling is a complex and time-consuming task in engineering, and traditional model-based or observer-based methods are unable to deal situations when the system models are unknown or affected by elements such as temperature. As a result, it is critical to build model-free methods capable of iterative learning and real-time updating. In this paper, a data-driven, model-free TD3-based algorithm is proposed. The neural network is used to address the issue of dimensional explosion in the state and action space. Besides, each agent can set a variable number of neighbors via virtual neighbor technology, giving the connection topology more flexibility. What is more, the proposed controller is only constructed based on the consensus error, which can quickly realize synchronization while consuming the least amount of energy. Finally, proofs and some simulation examples are given to verify the efficiency of our algorithm.

User Experience Assessment on Virtual-Reality Robot-Assisted Smart Hybrid Prototype of a Vehicle Trunk Lid

Engineering tasks in product development are becoming increasingly complex and must consider the individualization of products, the adaptability to customer requirements, shortened development times and high product quality standards. CAD and CAE models enable computer-based modeling and testing of virtual prototypes, replacing physical prototypes, leading to reduced manufacturing cost and development times. However, possibilities to perform user studies for quality testing with solely virtual prototypes are limited. Smart Hybrid Prototyping (SHP) combines functional virtual prototyping with extended reality technologies and haptic interaction features, enabling early multimodal design testing. The aim is to design future products faster and more cost-effectively by integrating user tests on SHP prototypes at an early stage, reducing the number of development steps and avoiding changes at later phases in product development. To achieve this goal a hybrid prototype was developed by the authors as part of the public research project “RobVRAR”. The hybrid prototype simulates the opening and closing behavior of an automotive trunk lid, offers haptic interaction features through a robot arm and displays 3D-models of the vehicle in real-time in virtual reality (VR). Subsequently a user study comparing a real trunk lid with our prototype was conducted to investigate the quality of user experience.