Processing Applications Research Articles

Wear Resistance Design of Laser Cladding Ni-Based Self-Fluxing Alloy Coating Using Machine Learning.

To improve the collaborative design of laser cladding Ni-based self-fluxing alloy (SFA) wear-resistant coatings, machine learning methods were applied. A comprehensive database was constructed from the literature, linking alloy composition, processing parameters, testing conditions, and the wear properties of Ni-based SFA coatings. Feature correlation analysis using Pearson's correlation coefficient and feature importance assessment via the random forest (RF) model highlighted the significant impact of C and B elements. The predictive performance of five classical machine learning algorithms was evaluated using metrics such as the squared correlation coefficient (R²) and mean absolute error (MAE). The RF model, which exhibited the best overall performance, was further combined with a genetic algorithm (GA) to optimize both composition and processing parameters collaboratively. This integrated RF-GA optimization system significantly enhanced efficiency and successfully designed multiple composition and process plans. The optimized alloy demonstrated superior wear resistance with an average friction coefficient of only 0.34, attributed to an enhanced solid solution strengthening effect (110 MPa) and increased hard phase content (52%), such as Ni₃Si, CrB, and NbC. These results provide valuable methodological insights and theoretical support for the preparation of laser cladding coatings and enable efficient process optimization for other laser processing applications.

Convergence of Nanotechnology and Machine Learning: The State of the Art, Challenges, and Perspectives.

Nanotechnology and machine learning (ML) are rapidly emerging fields with numerous real-world applications in medicine, materials science, computer engineering, and data processing. ML enhances nanotechnology by facilitating the processing of dataset in nanomaterial synthesis, characterization, and optimization of nanoscale properties. Conversely, nanotechnology improves the speed and efficiency of computing power, which is crucial for ML algorithms. Although the capabilities of nanotechnology and ML are still in their infancy, a review of the research literature provides insights into the exciting frontiers of these fields and suggests that their integration can be transformative. Future research directions include developing tools for manipulating nanomaterials and ensuring ethical and unbiased data collection for ML models. This review emphasizes the importance of the coevolution of these technologies and their mutual reinforcement to advance scientific and societal goals.

Variable gauge length: Processing theory and applications to distributed acoustic sensing

AbstractMost distributed acoustic sensing systems will only process acoustic data with a fixed gauge length for the entire well depth. However, it has been shown that the gauge length is a critical parameter to improve the signal‐to‐noise ratio when used as a function of certain geophysical parameters, such as the apparent velocity. It can also be responsible for significant distortions if tuned incorrectly. In this paper, we first aim to reintroduce the concept of gauge length and derive a robust method to optimize its value based on the geophysical parameters whilst ensuring no distortion to the original signal. We then present a novel method of processing the distributed acoustic sensing data using the concept of a variable gauge length. We finish by showing applications of these techniques on synthetic vertical seismic profiling data and some of the results obtained on actual field datasets.

Alternating Projection Method for Intersection of Convex Sets, Multi-Agent Consensus Algorithms and Averaging Inequalities

The history of the alternating projection method for finding a common point of several convex sets in Euclidean space goes back to the well-known Kaczmarz algorithm for solving systems of linear equations, which was devised in the 1930s and later found wide applications in image processing and computed tomography. An important role in the study of this method was played by I.I. Eremin’s, L.M. Bregman’s, and B.T. Polyak’s works, which appeared nearly simultaneously and contained general results concerning the convergence of alternating projections to a point in the intersection of sets, assuming that this intersection is nonempty. In this paper, we consider a modification of the convex set intersection problem that is related to the theory of multi-agent systems and is called the constrained consensus problem. Each convex set in this problem is associated with a certain agent and, generally speaking, is inaccessible to the other agents. A group of agents is interested in finding a common point of these sets, that is, a point satisfying all the constraints. Distributed analogues of the alternating projection method proposed for solving this problem lead to a rather complicated nonlinear system of equations, the convergence of which is usually proved using special Lyapunov functions. A brief survey of these methods is given, and their relation to the theorem ensuring consensus in a system of averaging inequalities recently proved by the first author is shown (this theorem develops convergence results for the usual method of iterative averaging as applied to the consensus problem).

Relationship between high-level color features and temperature mapping of magnesium alloy surface images based on the K-nearest neighbor algorithm

Numerous deformation and processing application scenarios of magnesium alloy sheets require high precision and efficiency in temperature detection. Traditional contact temperature measurement and infrared thermal imaging temperature measurement methods suffer from low accuracy and susceptibility to environmental influences. To this end, a real-time sensing method for determining the average surface temperature of magnesium alloys using visible light images is proposed in this paper. Firstly, real-time surface images of 3 mm thick magnesium alloy sheets were captured as the temperature decreased from 450 °C to 300 °C under varying light intensities. Then, calculate the high-level color features based on the grayscale frequency of the Red Green Blue color space of the image, and select the high-level color features with a strong correlation with temperature by Fisher criterion. Finally, a mapping relationship between the high-level color features of magnesium alloy images and temperature was established using the K-nearest neighbor algorithm. The results show that images in RAW format have a wider grayscale range compared to JPG, which is helpful in determining the mapping relationship between image color features and temperature. After extracting and selecting the high-level color features that have a strong correlation with temperature, the dimensions of input features are reduced. Using the K-nearest neighbor algorithm can fit the complex nonlinear relationship between high-level color features and temperature more accurately.

Application of CNN Classic Model in Modern Image Processing

As a deep learning model, convolutional neural network (CNN) has been widely used in the field of modern image processing and has shown excellent performance. From LeNet to AlexNet, to classic models such as VGGNet and ResNet, CNN has achieved remarkable success in tasks such as image classification, object detection and segmentation through multi-level feature extraction and automatic learning capabilities. This paper first explores the basic structure and working principle of CNN, analyzes the advantages and limitations of classic models, and reviews its specific applications in image processing. By introducing the optimization strategies of various models, it further explores the improvement path of CNN in the field of image processing, including model compression, lightweight design and the introduction of new network structures. In short, the continuous optimization of CNN has enabled it to show powerful performance in multiple complex tasks, promoted the rapid development of image processing technology, and provided strong support for applications in more fields.

Exploring the applications of natural language processing and language models for production, planning, and control activities of SMEs in industry 4.0: a systematic literature review

Exploring the applications of natural language processing and language models for production, planning, and control activities of SMEs in industry 4.0: a systematic literature review

A Survey on Design Space Exploration Approaches for Approximate Computing Systems

Approximate Computing (AxC) has emerged as a promising paradigm to enhance performance and energy efficiency by allowing a controlled trade-off between accuracy and resource consumption. It is extensively adopted across various abstraction levels, from software to architecture and circuit levels, employing diverse methodologies. The primary objective of AxC is to reduce energy consumption for executing error-resilient applications, accepting controlled and inherently acceptable output quality degradation. However, harnessing AxC poses several challenges, including identifying segments within a design amenable to approximation and selecting suitable AxC techniques to fulfill accuracy and performance criteria. This survey provides a comprehensive review of recent methodologies proposed for performing Design Space Exploration (DSE) to find the most suitable AxC techniques, focusing on both hardware and software implementations. DSE is a crucial design process where system designs are modeled, evaluated, and optimized for various extra-functional system behaviors such as performance, power consumption, energy efficiency, and accuracy. A systematic literature review was conducted to identify papers that ascribe their DSE algorithms, excluding those relying on exhaustive search methods. This survey aims to detail the state-of-the-art DSE methodologies that efficiently select AxC techniques, offering insights into their applicability across different hardware platforms and use-case domains. For this purpose, papers were categorized based on the type of search algorithm used, with Machine Learning (ML) and Evolutionary Algorithms (EAs) being the predominant approaches. Further categorization is based on the target hardware, including Field-Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), general-purpose Central Processing Units (CPUs), and Graphics Processing Units (GPUs). A notable observation was that most studies targeted image processing applications due to their tolerance for accuracy loss. By providing an overview of techniques and methods outlined in existing literature pertaining to the DSE of AxC designs, this survey elucidates the current trends and challenges in optimizing approximate designs.

Enhancing Bias Assessment for Complex Term Groups in Language Embedding Models: Quantitative Comparison of Methods.

Artificial intelligence (AI) is rapidly being adopted to build products and aid in the decision-making process across industries. However, AI systems have been shown to exhibit and even amplify biases, causing a growing concern among people worldwide. Thus, investigating methods of measuring and mitigating bias within these AI-powered tools is necessary. In natural language processing applications, the word embedding association test (WEAT) is a popular method of measuring bias in input embeddings, a common area of measure bias in AI. However, certain limitations of the WEAT have been identified (ie, their nonrobust measure of bias and their reliance on predefined and limited groups of words or sentences), which may lead to inadequate measurements and evaluations of bias. Thus, this study takes a new approach at modifying this popular measure of bias, with a focus on making it more robust and applicable in other domains. In this study, we introduce the SD-WEAT, which is a modified version of the WEAT that uses the SD of multiple permutations of the WEATs to calculate bias in input embeddings. With the SD-WEAT, we evaluated the biases and stability of several language embedding models, including Global Vectors for Word Representation (GloVe), Word2Vec, and bidirectional encoder representations from transformers (BERT). This method produces results comparable to those of the WEAT, with strong correlations between the methods' bias scores or effect sizes (r=0.786) and P values (r=0.776), while addressing some of its largest limitations. More specifically, the SD-WEAT is more accessible, as it removes the need to predefine attribute groups, and because the SD-WEAT measures bias over multiple runs rather than one, it reduces the impact of outliers and sample size. Furthermore, the SD-WEAT was found to be more consistent and reliable than its predecessor. Thus, the SD-WEAT shows promise for robustly measuring bias in the input embeddings fed to AI language models.

Revolutionizing Cytology and Cytopathology with Natural Language Processing and Chatbot Technologies: A Narrative Review on Current Trends and Future Directions.

The application of chatbots and Natural Language Processing (NLP) in cytology and cytopathology is an emerging field, which is currently characterized by a limited but growing body of research. Here, a narrative review has been proposed utilizing a standardized checklist and quality control procedure for including scientific papers. This narrative review explores the early developments and potential future impact of these technologies in medical diagnostics. The current literature, comprising 11 studies (after excluding comments, letters, and editorials) suggests that chatbots and NLP offer significant opportunities to enhance diagnostic accuracy, streamline clinical workflows, and improve patient engagement. By automating the extraction and classification of medical information, these technologies can reduce human error and increase precision. They also promise to make patient information more accessible and facilitate complex decision-making processes, thereby fostering greater patient involvement in healthcare. Despite these promising prospects, several challenges need to be addressed for the full potential of these technologies to be realized. These include the need for data standardization, mitigation of biases in Artificial Intelligence (AI) systems, and comprehensive clinical validation. Furthermore, ethical, privacy, and legal considerations must be navigated carefully to ensure responsible AI deployment. Compared to the more established fields of histology, histopathology, and especially radiology, the integration of digital tools in cytology and cytopathology is still in its infancy. Building on the advancements in related fields, especially radiology's experience with digital integration, where these technologies already offer promising solutions in mentoring, second opinions, and education, we can leverage this knowledge to further develop chatbots and natural language processing in cytology and cytopathology. Overall, this review underscores the transformative potential of these technologies while outlining the critical areas for future research and development.

Ozonation as an emerging technique for inactivating microorganisms and mitigating pesticides in apple juice

ABSTRACT Microorganisms and pesticide residues are among the major health concerns associated with food products. Although ozone (O3) is currently being employed in various applications in apple processing, there are few studies investigating the effects of O3 treatments on microorganisms and pesticides in apple juice. The purpose of this study was to investigate the effects of O3 treatment on microorganisms, pesticide residues, and physicochemical properties of apple juice and to compare with that of classical pasteurization. O3 was as effective as pasteurization against E. coli and coliform bacteria; the counts of these microorganisms were below the detectable level after a treatment for 15 min. O3 was also effective in reducing the counts of total aerobic mesophilic bacteria and yeast-mold, but longer treatments were required for total inactivation. All the pesticides investigated were degraded by O3, but at different rates. Reduction rates were quite high (75–100%) for methoxyfenozide, dimethoate, and pyrimethanil, however, acetamiprid and imidacloprid were degraded by only 7.1% and 14.5%, respectively. O3 treatment caused drastic decreases in color and turbidity values, total phenolic content, and antioxidant activity of apple juice. Results revealed that the use of ozone in juice processing has potential as a technological solution to product contamination problem.

High-Extinction Photonic Filters by Cascaded Mach–Zehnder Interferometer-Coupled Resonators

In this study, we demonstrate high-extinction stop-band photonic filters based on Mach–Zehnder interferometer (MZI)-coupled silicon nitride (Si3N4) resonators fabricated using I-line lithography technology. Leveraging the low-loss silicon nitride waveguide, our approach enables the creation of stable, high-performance filters suitable for applications in quantum and nonlinear photonics. With destructive interference at the feedback loop, photonic filters with an extinction ratio of 35 dB are demonstrated with four cascaded MZI-coupled resonators. This cascading design not only enhances the filter’s extinction but also improves its spectral sharpness, providing a more selective stop-band profile. Experimental results agree well with the theoretical results, showing linear scaling of extinction ratios with the number of cascaded MZI-coupled resonators. The scalability of this architecture opens the possibility for further integration and optimization in complex photonic circuits, where high extinction ratios and precise wavelength selectivity are critical for advanced signal processing and quantum information applications.

THE APPLICATION OF DATA MINING AND OLAP TECHNOLOGIES IN HEALTHCARE

The article examines the application of Data Mining and real-time Online Analytical Processing (OLAP) in healthcare. The calculation of analytical indicators and solving planning problems in healthcare are complex and multi-stage processes. The extraction of knowledge from large volumes of data accumulated in databases is referred to as Data Mining. Today, databases are measured in terabytes, containing strategically significant information. One of the most critical issues is determining methods for discovering important information within such vast amounts of data. Data Mining technology, by utilizing a set of analytical tools, helps to uncover relationships within large datasets and make predictions about future trends. Simply defined, Data Mining automatically identifies relevant patterns within databases. For many years, analysts manually recorded statistically significant relationships within databases. Today, Data Mining automates this process. The goal of Data Mining is to create decision-making models that predict future behavior based on the analysis of past actions. With the advent of computers and the rise of the digital information age, there have been changes in both the scale and complexity of existing problems in terms of analytical analysis. Advances in the storage, management, and examination of data have led to the emergence of a new field that promotes the development of bioinformatics and introduces analytical analysis and computational problems in the fields of biology and medicine. Keywords: Data Mining, OLAP, database, factor analysis, healthcare network, multidimensional models.

Research on image recognition and processing application technology of unmanned vehicle based on deep learning

Abstract. Image recognition technology is critically important in various fields, including the rapidly advancing sector of autonomous vehicles. As one of the core components enabling driverless cars to perceive their environment and make informed decisions, image recognition has seen significant advancements due to deep learning. This paper focuses on the application of deep learning in image recognition for self-driving cars and explores its implications for the future of autonomous driving technology. To begin with, this paper examines the empirical evaluation of deep learning models in highway driving scenarios. By employing Convolutional Neural Networks (CNN), these models achieve high detection rates and superior accuracy in recognizing vehicles and lanes. The robustness of these models is tested under varying weather conditions and times, demonstrating their effectiveness compared to classical computer vision techniques. Next, the paper discusses radar-camera fusion technology, highlighting different fusion strategies such as data-level, feature-level, object-level, and hybrid-level. The findings suggest that while feature-level fusion excels in detecting small objects in complex scenes, hybrid-level fusion is optimal for diverse driving situations. This section provides valuable insights into the integration of multimodal data for improved object recognition and semantic segmentation. After discussing fusion technologies, the paper finally reviews the security challenges posed by adversarial attacks on deep learning-based unmanned systems.

Application of Artificial Intelligence and Image Processing for the Cultivation of Chlorella sp. Using Tubular Photobioreactors.

By integrating innovative technologies to enhance the efficiency and sustainability of production, this study specifies the establishment of a cutting-edge growing system for Chlorella sp. microalgae. Improvement of a system for the real-time, noninvasive observation and management of algae growth employing a closed tubular photobioreactor (PBR) engineered with computational fluid dynamics (CFD), combined with the Internet of things (IoT), artificial intelligence (AI), and image processing technologies was the major goal of this research. The fitting of seven types of sensors to identify key characteristics such as temperature, pH, light intensity, electrical conductivity (EC), flow rate, oxygen content, and light exposure duration was included in the research method. To manage the gaining of sensor data and system operations, an ESP8266 microcontroller was used as the main control unit, while 33 × 33 pixel images were taken with an ESP32 camera at 30 min intervals to assess growth by evaluating color intensity, enabling real-time evaluation of algal density without sampling or disturbing growth. Forecasting and enhancing farming situations was the goal of producing these machine learning (ML) models. Uniformly dispersed between 12 and 24 h light cycles, the data set comprised 602 samples. Considerable improvements were observed in the results for biomass productivity, with constant 24 h lighting yielding a 7.19% increase, counter to a 2.09% increase seen in the 12 h cycle. Temperature and light intensity are the most significant parameters for growth, as revealed by analysis of Feature Importance. The eXtreme Gradient Boosting (XGBoost) model showed remarkable effectiveness in terms of projecting growth, attaining an R 2 value of 0.9997 for the training data set. With important benefits for the development of renewable energy, food supply, and environmental modification in the future, this research highlights the competence of intelligent technology to strengthen microalgae production.

Practical hardware demonstration of a multi-sensor goal-oriented semantic signal processing and communications network

Recent advancements in machine learning, particularly real-time extraction of rich semantic information, reshape signal processing techniques and related hardware architectures. To address the highly challenging requirements of next-generation signal processing applications in networked platforms, we investigate low-power hardware implementation alternatives for a multi-sensor, goal-oriented semantic communications network. Specifically, we focus on cost-effective Raspberry Pis in a multi-sensor semantic video communication application, showcasing adaptability from traditional CPU/GPU configurations. Additionally, we provide a preliminary investigation on implementing semantic extraction tasks through in-memory computation using memristor arrays to further emphasize the potential future of low-power low-cost semantic signal processing. Hardware demonstrations using Raspberry Pi 4Bs and simulations with in-memory computation architectures offer promising hardware architectures with cost-effective and low-power sensor alternatives to the next-generation semantic signal processing applications and semantic communication systems.

Resonance scattering generated by rotating bodies

Abstract The scattering of rotating bodies to a polarized plane wave, including the dielectric cylinder and sphere, is studied. The resonance caused by rotation is emphasized. Numerical results prove that the resonance scattering caused by rotation can be realized in the optical range. It is sensitive to the rotation dimensionless parameter γ. The internal Mie mode corresponding to the electromagnetic field intensity changes with γ, and the resonant mode appears when the particle rotates at a specific speed. Moreover, the resonant mode changes with γ. It causes resonance scattering to appear in the same particle at different speeds. Inside particles, resonant rings are composed of a series of array points and are determined by γ. Under resonance conditions, the energy near the rotating cylinder is consistent with its rotation direction. In contrast, the direction of energy flow in the rotating sphere model is opposite to the direction of particle rotation. This work provides a novel idea for the design of ultra-sensitive sensors and resonators. It has promising applications in optical communication, optical microscopy, and optical signal processing.

Pathway-directed recyclable chirality inversion of coordinated supramolecular polymers

It remains challenging to elucidate the fundamental mechanisms behind the dynamic chirality inversion of supramolecular assemblies with pathway complexity. Herein, metal coordination driven assembly systems based on pyridyl-conjugated cholesterol (PVPCC) and metal ions (Ag+ or Al3+) are established to demonstrate pathway-directed, recyclable chirality inversion and assembly polymorphism. In the Ag(I)/PVPCC system, a competitive pathway leads Ag-Complex to form either kinetically controlled supramolecular polymer (Ag-SP I) or thermodynamically favored Ag-SP II, accompanied by reversible chiroptical inversion. Conversely, the Al(III)/PVPCC system displays a solvent-assisted consecutive pathway: the Al-Complex initially forms ethanol-containing Al-SP II, and subsequently converts into ethanol-free Al-SP I with opposite chiroptical performance upon thermal treatment. Moreover, stable chirality inversion in the solid state enables potential dynamic circularly polarized luminescence encryption when Ag(I)/PVPCC is co-assembled with thioflavin T. These findings provide the guidance for the dynamic modulation of chirality functionality in supramolecular materials for applications in information processing, data encryption, and chiral spintronics.

Silicon–based integrated orbital angular momentum parity sorter

Abstract A silicon–based integrated orbital angular momentum (OAM) parity sorter using two-dimensional multimode interference (2D MMI) waveguides is designed and numerically analyzed. An OAM parity sorter, which sorts OAM modes according to their parity (even or odd) is an elegant device in a broad range of OAM states processing applications including quantum gates, quantum key distribution, generation of high dimensional quantum gates, quantum information, and teleportation. The presented three–part integrated parity sorter is realized based on the self–imaging and field–splitting properties of MMI structures. Its total length is 11 444 µm. The proposed device works for OAM modes with |l| ⩽ 5. The performance of OAM sorting is investigated with the results confirming the high purity (up to 98.33% and 94.45% at even and odd ports, respectively) for the telecommunication wavelength λ 0 = 1550 nm.

Laser rapid scanning strategy for bitmaps via image segmentation utilizing grey wolf optimizer

A novel laser scanning processing methodology is introduced for bitmap aimed at reducing time losses associated with multiple galvanometer scanning cycles. The Multi-threshold Otsu method, resolved via the Grey Wolf optimizer (GWO), effectively segments the target image into background and target regions. To handle the resulting threshold distribution, four mapping functions with distinct growth trends are to adjust pixel gray values within specified regions, thereby modulating scanning input powers accordingly. An experimental investigation was conducted utilizing a galvanometer scanner and a set of sample images. A comparative analysis of the image segmentation outcomes demonstrates that the GWO consistently delivers robust segmentation results and superior solution stability compared to other methodologies. Furthermore, when combined with the adaptive power mapping functions, this new approach shows remarkable adaptability to a range of graphical representations. Ultimately, the scanning process duration is reduced by as much as 89 % when contrasted with traditional point carving and layer-by-layer scanning. This discovery signifies that this approach holds significant promise for enhancing productivity in laser processing applications.