Engineering data analysis using Discovery [semiconductor manufacture

As artificial intelligence advances, more and more tasks that formerly required human discretion can be automated. The proposed study aims to create an automated supervised, hybrid computing algorithm for the synthesis and analysis of engineering and scientific data and gaining insightful knowledge about the data. A novel iterated function approach has been designed by integrating rational C Bezier function with classical fractal function. Sufficient conditions on the scaling and shape factors have been calculated to obtain various simulating patterns occurring in data. The developed model is trained and tested on a set of data values to predict prevalent shape characteristics of the data. Numerical examples validate the suggested approach.

Modern software projects consist of more than just code: teams follow development processes, the code runs on servers or mobile phones and produces run time logs and users talk about the software in forums like StackOverflow and Twitter and rate it on app stores. Insights stemming from the real-time analysis of combined software engineering data can help software practitioners to conduct faster decision-making. With the development of CodeFeedr, a Real-time Software Analytics Platform, we aim to make software analytics a core feedback loop for software engineering projects. CodeFeedr's vision entails: (1) The ability to unify archival and current software analytics data under a single query language, and (2) The feasibility to apply new techniques and methods for high-level aggregation and summarization of near real-time information on software development. In this paper, we outline three use cases where our platform is expected to have a significant impact on the quality and speed of decision making; dependency management, productivity analytics, and run-time error feedback.

Instead of presenting the standard theoretical treatments that underlie the various numerical methods used by scientists and engineers, Using R for Numerical Analysis in Science and Engineering shows how to use R and its add-on packages to obtain numerical solutions to the complex mathematical problems commonly faced by scientists and engineers. This practical guide to the capabilities of R demonstrates Monte Carlo, stochastic, deterministic, and other numerical methods through an abundance of worked examples and code, covering the solution of systems of linear algebraic equations and nonlinear equations as well as ordinary differential equations and partial differential equations. It not only shows how to use R’s powerful graphic tools to construct the types of plots most useful in scientific and engineering work, but also: Explains how to statistically analyze and fit data to linear and nonlinear models Explores numerical differentiation, integration, and optimization Describes how to find eigenvalues and eigenfunctions Discusses interpolation and curve fitting Considers the analysis of time series Using R for Numerical Analysis in Science and Engineering provides a solid introduction to the most useful numerical methods for scientific and engineering data analysis using R.

After long-term water injection development in Block M of the Gudong Oilfield, the fluid seepage field in the formation has become increasingly complex, and the heterogeneity of the reservoir has intensified. The uneven injection-production and the differences in lithological permeability have further led to a series of problems such as uneven reserve utilization, a low injection-production correspondence rate, and a poor overall development effect. During the development process, severe water flooding at the bottom and rich remaining oil at the top within the layer also emerged. These issues not only affected the economic benefits of the oilfield but also put forward higher requirements for the subsequent development strategies. In order to solve the above problems, based on a detailed analysis of geological characteristics, reservoir engineering and monitoring data, this paper uses the numerical simulation method to systematically simulate the dynamic changes in the residual oil saturation and pressure field in the formation under different development stages. The simulation results show that the pressure and saturation in each layer are both on a downward trend, especially in the layers with large pressure changes, where the oil saturation changes are more significant. Therefore, combined with the results of numerical simulation, a series of profile control and water shutoff schemes were systematically designed. These schemes covered different types of profile control agents and technological parameters. Representative well groups were selected to predict and evaluate the profile control effect. In the technical and economic evaluation of the profile control effect, the input–output ratio method was adopted. Finally, an optimal scheme was selected and applied in the field. The results show that after the implementation of this scheme, the daily oil increment of the well group was remarkable. During the test period, the cumulative oil increment reached 100 t, and the total expected oil increment could reach 190 t. The input–output ratio reached 1:2.1. The profile control measures significantly improved the injection-production correspondence, slowed down the in-layer water flooding, and further enhanced the recovery rate of remaining oil. In conclusion, the methods and achievements of this study can provide important technical references and support for the efficient and long-term development of similar high-water-cut and heterogeneous complex oil reservoirs, and have guiding significance for the subsequent adjustment, potential tapping, stable production and efficiency improvement of oilfields.

The authors' examination of quantitative data analysis tools through Evidence-Based Software Engineering (EBSE) and PRISMA flow, within a 6-year review of published journal articles, is commendable for its potential impact on sustainable engineering practices. Identifying top tools like SPSS, R, Stata, Excel, SAS, SmartPLS, MATLAB, AMOS, LISREL, and MPlus, and emphasizing their usage trends provides valuable insights for researchers and educators. However, the paper could enhance its contribution to sustainability by urging the preference for open-source tools, aligning with principles of collaboration, and minimizing environmental impact. Including an evaluation of tools' ecological footprints, such as energy consumption and waste generation, would further align with sustainable engineering goals. Additionally, considering emerging trends in sustainable data analysis tools could help researchers stay informed about evolving technologies. In summary, while the paper offers crucial insights into quantitative tools, integrating sustainability considerations, promoting open-source options, and evaluating ecological footprints could significantly enhance its relevance to sustainable engineering practices concisely.

For the bogie traction motor bearings of rail vehicles under road transportation conditions, its vibration signal full-time domain data acquisition data volume, storage pressure, subsequent data analysis difficulties, and other problems. This study proposes a data compression acquisition and data reconstruction method for bearing vibration signals based on compressed sensing (CS), which can achieve compressed sampling, transmission, and storage of the original signal with fewer observations, and restore the original signal by combining reconstruction algorithms to achieve low error reconstruction. We study the effects of different sparse transform methods on signal sparsity, as well as the signal sparse performance based on different sparse transforms and the signal reconstruction effect of different reconstruction algorithms, and propose a sparse representation method based on the discrete Fourier transform (DFT) and the generalized orthogonal matching pursuit (gOMP) algorithm for CS. Through simulation and experimental comparison, it is concluded that the method can restore and reconstruct the original high-dimensional signal with small reconstruction error, and solve the problems of massive data, inconvenient transmission, and difficult data analysis in engineering.

This paper presents a Distribution Information Management and Engineering Analysis System (DIMAS), which is an AM/FM/GIS based multi-user software package. A new architecture of DIMAS is introduced and realized, that is to build a basic engineering-analysis object (BEO) between AM/FM and engineering analysis applications. Through BEO, integration of distribution engineering analysis modules in DIMAS becomes more convenient. A new approach called Node-Running-Code (NRC) is developed to solve the network topological problem. Case study indicates that the NRC method can greatly increase the speed of distribution topology processing and has important applying value in destitution topological analysis and other engineering analysis related to topology. On the basis of BEO, several engineering analysis applications are developed or integrated in DIMAS, such as power flow, short circuit, blackout and restoration analysis, reconfiguration and protection coordination.

This study proposes an advanced badminton tactics recommendation system based on Retrieval-Augmented Generation (RAG). By integrating multiple large language models (LLMs), prompt engineering, and match data analysis, the system provides players with tailored tactical suggestions. It efficiently extracts and evaluates key match data—including player positioning, shot selection, and tactical sequences—to identify performance trends. The proposed system aims to optimize training regimens, enhance decision-making in competitive settings, and introduce data-driven strategies into modern badminton coaching.

Protein sequence–function relationships are inherently complex, as amino acids at different positions can interact in highly unpredictable ways. A key question for protein evolution and engineering is how often epistasis extends beyond pairwise interactions to involve three or more positions. Although experimental data has accumulated rapidly in recent years, addressing this question remains challenging, as the number of possible interactions is typically enormous even for proteins of moderate size. Here, we introduce an interpretable machine learning framework for studying higher-order epistasis scalable to full-length proteins. Our model builds on the transformer architecture, with key modifications allowing us to assess the importance of higher-order interactions by fitting a series of models with increasing complexity. Applying our method to 10 large protein sequence-function datasets, we found that while additive effects explain the majority of the variance, within the epistatic component, the contribution of higher-order epistasis ranges from negligible to up to 60%. We also found higher-order epistasis is particularly important for generalizing locally sampled fitness data to distant regions of sequence space and for modeling an additional multi-peak fitness landscape. Our findings suggest that higher-order epistasis can play important roles in protein sequence-function relationships, and thus should be properly considered in protein engineering and evolutionary data analysis.

Objective. Neural decoding is an important tool in neural engineering and neural data analysis. Of various machine learning algorithms adopted for neural decoding, the recently introduced deep learning is promising to excel. Therefore, we sought to apply deep learning to decode movement trajectories from the activity of motor cortical neurons. Approach. In this paper, we assessed the performance of deep learning methods in three different decoding schemes, concurrent, time-delay, and spatiotemporal. In the concurrent decoding scheme where the input to the network is the neural activity coincidental to the movement, deep learning networks including artificial neural network (ANN) and long-short term memory (LSTM) were applied to decode movement and compared with traditional machine learning algorithms. Both ANN and LSTM were further evaluated in the time-delay decoding scheme in which temporal delays are allowed between neural signals and movements. Lastly, in the spatiotemporal decoding scheme, we trained convolutional neural network (CNN) to extract movement information from images representing the spatial arrangement of neurons, their activity, and connectomes (i.e. the relative strengths of connectivity between neurons) and combined CNN and ANN to develop a hybrid spatiotemporal network. To reveal the input features of the CNN in the hybrid network that deep learning discovered for movement decoding, we performed a sensitivity analysis and identified specific regions in the spatial domain. Main results. Deep learning networks (ANN and LSTM) outperformed traditional machine learning algorithms in the concurrent decoding scheme. The results of ANN and LSTM in the time-delay decoding scheme showed that including neural data from time points preceding movement enabled decoders to perform more robustly when the temporal relationship between the neural activity and movement dynamically changes over time. In the spatiotemporal decoding scheme, the hybrid spatiotemporal network containing the concurrent ANN decoder outperformed single-network concurrent decoders. Significance. Taken together, our study demonstrates that deep learning could become a robust and effective method for the neural decoding of behavior.

In today's tech landscape, business intelligence (BI) is essential, especially with advanced technologies emerging. This study delves into the transformative potential of artificial intelligence and machine learning in predictive analytics, offering crucial insights into data. The chapter provides an extensive overview, focusing on the future of predictive analytics in BI. It highlights real-time applications, featuring practical work and case studies. Key practices for integrating AI and predictive analytics are introduced, covering data preparation, cleaning, preprocessing, feature engineering, and exploratory data analysis. Integration of ML with BI facilitates visualization, report generation, interactive dashboards, and real-time monitoring. However, manual analysis of complex datasets poses challenges, risking valuable insights. This chapter explores how ML automates the identification of patterns, extracting insights. Key challenges and advantages in integrating machine learning into BI frameworks are also discussed.

Protein sequence–function relationships are inherently complex, as amino acids at different positions can interact in highly unpredictable ways. A key question for protein evolution and engineering is how often epistasis extends beyond pairwise interactions to involve three or more positions. Although experimental data has accumulated rapidly in recent years, addressing this question remains challenging, as the number of possible interactions is typically enormous even for proteins of moderate size. Here, we introduce an interpretable machine learning framework for studying higher-order epistasis scalable to full-length proteins. Our model builds on the transformer architecture, with key modifications allowing us to assess the importance of higher-order interactions by fitting a series of models with increasing complexity. Applying our method to 10 large protein sequence-function datasets, we found that while additive effects explain the majority of the variance, within the epistatic component, the contribution of higher-order epistasis ranges from negligible to up to 60%. We also found higher-order epistasis is particularly important for generalizing locally sampled fitness data to distant regions of sequence space and for modeling an additional multi-peak fitness landscape. Our findings suggest that higher-order epistasis can play important roles in protein sequence-function relationships, and thus should be properly considered in protein engineering and evolutionary data analysis.

Since Industrial Revolution, fossil fuel has played an important role in technology, industry, and transportation, where an enormous amount of fossil fuel has been consumed, causing short supply. Transportation with other energy therefore has become the development objective for many countries. With the progress of technology, electric scooters have become popular because of the great supports of the governments. Corresponding to consumers’ demands, the requirements for functions and styles are also enhanced. With present engineering drawing and analysis software, this study aims to discuss the change of stress and fatigue endurance of welded electric scooter frames under loads. A real vehicle is proceeded road tests in order to verify the feasibility on various combinations of roads. The analyses show that, with proper design, the maximum stress of the self-weight of electric scooter frames or when considering carrying a person is lower than the yield stress of the frame itself. Regarding the fatigue endurance analyses, the operational life could satisfy the anticipation of the manufacturers to the functions. The research outcomes are expected to assist domestic manufacturers in designing new-style electric scooters or making improvement.

Identifying emerging failure phenomena in complex systems through engineering data mapping

This research aims to know the result of the ruling of the Constitutional Court the number 71/PUU-XIV/2016 candidature of head region ex-convict against the holding of democratic elections. The research method used is the type of legal normative The nature of the research perspective and applied. The research approach using the approach of legislation, a conceptual approach, and the approach to the case. The data source is primary data, secondary, and tertiary. Engineering data collection using the study of librarianship, engineering data analysis using the method of syllogisms. Based on the results of the research the author did, note that due to the ruling of the Constitutional Court the number 71/PUU-XIV/2016 candidature related head injury can convict the former area of democracy because local communities were not able to shift the candidates who could potentially do harm as well as doing the legal violations in the electoral competition.