Processing Applications Research Articles

Effects of plasma-activated slightly acidic electrolyzed water on salmon myofibrillar protein: Insights from structure and molecular docking

The present study investigated the impact of plasma-activated water (PAW), slightly acidic electrolytic water (SAEW) and plasma-activated slightly acidic electrolytic water (PASW) treatment on myofibrillar protein (MP) in salmon fillets. Additionally, the interaction mechanism between myosin and reactive oxygen species was explored by molecular docking. Compared with the control group (719.26 nm), PASW treatment group exhibited the smallest particle size (408.97 nm). The PASW treatment exhibited efficacy in reducing MP aggregation and inhibiting protein oxidation. In comparison with other treatments, PASW treatment demonstrated a greater ability to mitigate damage to the secondary and tertiary structures of MP. O3 and H2O2 interact with myosin through hydrogen bonding. Specifically, O3 interacts with Lys676, Gly677, and Met678 of myosin while H2O2 binds to Thr681, Asp626, Arg680, and Met678. This study offers novel insights into the impact of PASW on MP, and provides a theoretical foundation for its application in aquatic product processing.

A quantitative study of microstructure of Indian Gondwana shale: a fractal and algebraic topology approach

This paper covers a novel micro-level application of image processing in understanding the topological and petrophysical properties of Indian Gondwana Shale using an X-ray computed micro tomography images. The complexity and randomness in the pore system are explained through the concept of fractal dimension (FD). In this article, a quantitative analysis of 2D and 3D fractal dimension of pores, grains, and interface has been done for the Indian Gondwana Shale, using the Box counting method. A pore network is formed by the connection of many sub-pore clusters, each with a different volume. Hence, an image segmentation algorithm has been applied to label different sub-clusters, and subsequently, analysis of FD is done on such sub-clusters of pores and grains. We implemented a novel application of Betti numbers (B0, B1 and B2) and Euler characteristics on our sample and calculated the possible flow channels of the sample. The FD of grains was found to be greater than the FD of pore-grains interface, while the FD of pores was found to be the least. Consequently, we also observed how the FD of both pores and grains is majorly controlled by the largest sub-cluster, and during fluid intrusion, we see a significant decrease in FD of pores. Lastly, the pore network, with a larger B0 and larger difference of B1 can be proved best for the storage of hydrocarbon content and its fluid movement understanding due to more flow channels.

Multi-step Transfer Learning in Natural Language Processing for the Health Domain

The restricted access to data in healthcare facilities due to patient privacy and confidentiality policies has led to the application of general natural language processing (NLP) techniques advancing relatively slowly in the health domain. Additionally, because clinical data is unique to various institutions and laboratories, there are not enough standards and conventions for data annotation. In places without robust death registration systems, the cause of death (COD) is determined through a verbal autopsy (VA) report. A non-clinician field agent completes a VA report using a set of standardized questions as guide to identify the symptoms of a COD. The narrative text of the VA report is used as a case study to examine the difficulties of applying NLP techniques to the healthcare domain. This paper presents a framework that leverages knowledge across multiple domains via two domain adaptation techniques: feature extraction and fine-tuning. These techniques aim to improve VA text representations for COD classification tasks in the health domain. The framework is motivated by multi-step learning, where a final learning task is realized via a sequence of intermediate learning tasks. The framework builds upon the strengths of the Bidirectional Encoder Representations from Transformers (BERT) and Embeddings from Language Models (ELMo) models pretrained on the general English and biomedical domains. These models are employed to extract features from the VA narratives. Our results demonstrate improved performance when initializing the learning of BERT embeddings with ELMo embeddings. The benefit of incorporating character-level information for learning word embeddings in the English domain, coupled with word-level information for learning word embeddings in the biomedical domain, is also evident.

Natural Language Processing (NLP) in Recommendation Systems

This paper delves into the application of Natural Language Processing (NLP) techniques in recommendation systems, specifically focusing on novel approaches to enhance recommendation accuracy and user satisfaction. The utilization of NLP algorithms has revolutionized how content is recommended to users, leveraging linguistic analysis and machine learning to understand user preferences and provide tailored suggestions. Our research explores various NLP methodologies, including sentiment analysis, topic modelling, and semantic analysis, to extract meaningful insights from textual data. Furthermore, we investigate the integration of deep learning models, such as neural networks and transformer architectures, to capture complex patterns and improve recommendation precision. A key highlight of our study is the introduction of a novel recommendation method termed "Knowledge Graph Embedding for Contextual Recommendation." This innovative approach combines knowledge graph representation with contextual understanding, allowing for more nuanced and personalized recommendations based on user interactions, historical data, and contextual relevance. We delve into the intricacies of this technique, detailing its implementation, training process, and evaluation metrics.

Applications of natural language processing tools in the surgical journey

BackgroundNatural language processing tools are becoming increasingly adopted in multiple industries worldwide. They have shown promising results however their use in the field of surgery is under-recognised. Many trials have assessed these benefits in small settings with promising results before large scale adoption can be considered in surgery. This study aims to review the current research and insights into the potential for implementation of natural language processing tools into surgery.MethodsA narrative review was conducted following a computer-assisted literature search on Medline, EMBASE and Google Scholar databases. Papers related to natural language processing tools and consideration into their use for surgery were considered.ResultsCurrent applications of natural language processing tools within surgery are limited. From the literature, there is evidence of potential improvement in surgical capability and service delivery, such as through the use of these technologies to streamline processes including surgical triaging, data collection and auditing, surgical communication and documentation. Additionally, there is potential to extend these capabilities to surgical academia to improve processes in surgical research and allow innovation in the development of educational resources. Despite these outcomes, the evidence to support these findings are challenged by small sample sizes with limited applicability to broader settings.ConclusionWith the increasing adoption of natural language processing technology, such as in popular forms like ChatGPT, there has been increasing research in the use of these tools within surgery to improve surgical workflow and efficiency. This review highlights multifaceted applications of natural language processing within surgery, albeit with clear limitations due to the infancy of the infrastructure available to leverage these technologies. There remains room for more rigorous research into broader capability of natural language processing technology within the field of surgery and the need for cross-sectoral collaboration to understand the ways in which these algorithms can best be integrated.

Image fusion-based low-dose CBCT enhancement method for visualizing miniscrew insertion in the infrazygomatic crest

Digital dental technology covers oral cone-beam computed tomography (CBCT) image processing and low-dose CBCT dental applications. A low-dose CBCT image enhancement method based on image fusion is proposed to address the need for subzygomatic small screw insertion. Specifically, firstly, a sharpening correction module is proposed, where the CBCT image is sharpened to compensate for the loss of details in the underexposed/over-exposed region. Secondly, a visibility restoration module based on type II fuzzy sets is designed, and a contrast enhancement module using curve transformation is designed. In addition to this, we propose a perceptual fusion module that fuses visibility and contrast of oral CBCT images. As a result, the problems of overexposure/underexposure, low visibility, and low contrast that occur in oral CBCT images can be effectively addressed with consistent interpretability. The proposed algorithm was analyzed in comparison experiments with a variety of algorithms, as well as ablation experiments. After analysis, compared with advanced enhancement algorithms, this algorithm achieved excellent results in low-dose CBCT enhancement and effective observation of subzygomatic small screw implantation. Compared with the best performing method, the evaluation metric is 0.07–2 higher on both datasets. The project can be found at: https://github.com/sunpeipei2024/low-dose-CBCT.

Breaking Boundaries with Ceramic Matrix Composites: A Comprehensive Overview of Materials, Manufacturing Techniques, Transformative Applications, Recent Advancements, and Future Prospects

Ceramic matrix composites (CMCs) are a category of advanced materials which have gained significant interest recently due to their remarkable mechanical and thermal characteristics. These composites are composed of ceramic fibers, particles, or other types of ceramics incorporated in a ceramic matrix and have shown the capability to be implemented in several sectors, including aerospace, energy, and biomedical engineering. This review paper will provide a synopsis of the current scenario and recent progress in CMCs, including materials and processing techniques, characterization methods, and applications. The paper discusses the advantages and limitations of CMCs, recent advancements, and future trends in research. The microstructural and mechanical properties of CMCs are also reviewed, highlighting their potential for various applications. The paper’s conclusion delivers a summary of the essential findings and a discussion of future directions for CMC research.

Twist-Angle-Controlled Nonlinear Circular Dichroism on Gold-Crystal Hybrid Metasurfaces.

Optical chirality, which plays important roles in liquid crystal display and biological and chemical detection, has been attracting scientists' attention due to its potential applications in optical information processing. Usually, the chiral optical response of natural molecules is very weak. However, the emergence of metasurfaces offers a promising solution to solve this issue. By judiciously designing the geometry of meta-atoms, we have realized strong optical circular dichroism (CD) in both linear and nonlinear optical regimes. However, tuning of the CD with a metasurface remains challenging. Here, we propose the twist-angle-controlled nonlinear CD effect by using the second-harmonic generation process on a gold-crystal hybrid metasurface. The CD effect of the second-harmonic waves can be tuned well by controlling the twist angle between the two constituent materials. The proposed hybrid metasurface may open new avenues for developing ultracompact and multifunctional nonlinear optical devices.

Dual perfect vectorial vortex beam generation with a single spin-multiplexed metasurface

Perfect optical vortex beams (POVBs) carrying orbital angular momentum (OAM) possess annular intensity profiles that are independent of the topological charge. Unlike POVBs, perfect vectorial vortex beams (PVVBs) not only carry orbital angular momentum but also exhibit spin angular momentum (SAM). By incorporating a Dammann vortex grating (DVG) on an all-dielectric metasurface, we demonstrate an approach to create a pair of PVVBs on a hybrid-order Poincaré sphere. Benefiting flexible phase modulation, by engineering the DVG and changing the input-beam state we are able to freely tailor the topological OAM and polarization eigenstates of the output PVVBs. This work demonstrates a versatile flat-optics platform for high-quality PVVB generation and may pave the way for applications in optical communication and quantum information processing.

Case study using APDMS and RPEMS for SAWL pipes and benefits for offshore pipelay

This paper reviews an Automatic Pipe Dimension Measurement System (APDMS) and a Robotic Pipe End Measurement System (RPEMS) which can measure 19 and 9 parameters with the help of 72 and 6 measurement sensors respectively. APDMS and RPEMS are integrated into one optical measurement system, using laser triangulation, coupled with Systems, Applications and Products in Data Processing (SAP) integration and its own calibration modulus which provides an accurate solution to all pipe measurements. Two longitudinal submerged arc welded offshore projects were executed for an offshore pipe lay with close dimensional tolerances measured using the APDMS for one project and RPEMS for the other project. The radii data, assessed from the newly calculated common centre of all cross-sectional data, was used to line up the two adjacent pipes in a faster and more accurate way. By knowing the shape of the pipe ends based on this radii data, the best relative rotation of these two pipes can be determined and any misalignment will be verified and will stay within the requirements. The extensive database available for each pipe helped to match the line pipes with similar or close out-of-roundness at pipe ends resulting in good fitment of the pipes and ultimately quality welds, together with more pipe joints per day, resulting in a faster completion of the project. This paper shows the significance of using both systems.

Condition Monitoring of a Cartesian Robot with a Mechanically Damaged Gear to Create a Fuzzy Logic Control and Diagnosis Algorithm

The detection of faults during an operational process constitutes a crucial objective within the framework of developing a control system to monitor the structure of industrial mechanisms. Even minor faults can give rise to significant consequences that require swift resolution. This research investigates the impact of overtension in the tooth belt transmission and heating of the screw transmission worm on the vibration signals in a robotic system. Utilizing FFT techniques, distinct frequency characteristics associated with different faults were identified. Overtension in the tooth belt transmission caused localized oscillations, addressed by adjusting the acceleration and deceleration speeds. Heating of the screw transmission worm led to widespread disturbances affecting servo stress and positioning accuracy. A fuzzy logic algorithm based on spectral analysis was proposed for adaptive control, considering the vibration’s frequency and amplitude. The simulation results demonstrated effective damage mitigation, reducing wear on the mechanical parts. The diagnostic approach, relying on limited data, emphasized the feasibility of identifying transmission damage, thereby minimizing maintenance costs. This research contributes a comprehensive and adaptive solution for robotic system diagnostics and control, with the proposed fuzzy logic algorithm showing promise for efficient signal processing and machine learning applications.

The Impact of Pulsed Electric Field Technology on Enzymes, Microbial and Nutritional Quality in Milk Processing - A Comprehensive Review

A pulsed electric field is a non-thermal technology used in the processing of foods, especially liquid foods such as milk, fruit juices, etc. The objective of this review is to provide an understanding of pulsed electric field technology and its application in milk processing, particularly in whole milk, skim milk, and UHT milk. This technology can be used as an alternative to traditional heat treatments such as pasteurizing, hot filling, blanching, and sterilization. In this technology, short high-intensity electric field is used at moderate temperatures. A pulsed electric field is an effective technology to inactivate microorganisms and enzymes with minimum changes in nutritional, functional, and organoleptic qualities compared to heat treatments. Pathogenic and spoilage bacteria can be reduced effectively when using pulsed electric field technology. However, there are variations between studies. As a recent advance, the pulsed electric field is used with mild heating. This shows a significant reduction in microbial count with less detrimental effects on the physico-chemical and organoleptic properties of food. It also has minimal impact on nutrients present in the milk such as proteins, milk fat globules, and vitamins. In this review, we describe in detail the introduction, the principle of pulsed electric field, the impact of pulsed electric field on microorganisms, enzymes, and nutrients, and the advantages and disadvantages of using PEF technology. Further studies can be conducted to investigate the combined approach of the pulsed electric field with mild heating treatment methods including pasteurization and sterilization.

Application of Natural Language Processing in Electronic Health Record Data Extraction for Navigating Prostate Cancer Care: A Narrative Review.

Introduction: Natural language processing (NLP)-based data extraction from electronic health records (EHRs) holds significant potential to simplify clinical management and aid research. This review aims to evaluate the current landscape of NLP-based data extraction in prostate cancer (PCa) management. Materials and Methods: We conducted a literature search of PubMed and Google Scholar databases using the keywords: "Natural Language Processing," "Prostate Cancer," "data extraction," and "EHR" with variations of each. No language or time limits were imposed. All results were collected in a standardized manner, including country of origin, sample size, algorithm, objective of outcome, and model performance. The precision, recall, and the F1 score of studies were collected as a metric of model performance. Results: Of the 14 studies included in the review, 2 articles focused on documenting digital rectal examinations, 1 on identifying and quantifying pain secondary to PCa, 8 on extracting staging/grading information from clinical reports, with an emphasis on TNM-classification, risk stratification, and identifying metastasis, 2 articles focused on patient-centered post-treatment outcomes such as incontinence, erectile, and bowel dysfunction, and 1 on loneliness/social isolation following PCa diagnosis. All models showed moderate to high data annotation/extraction accuracy compared with the gold standard method of manual data extraction by chart review. Despite their potential, NLPs face challenges in handling ambiguous, institution-specific language and context nuances, leading to occasional inaccuracies in clinical data interpretation. Conclusion: NLP-based data extraction has effectively extracted various outcomes from PCa patients' EHRs. It holds the potential for automating outcome monitoring and data collection, resulting in time and labor savings.

Acoustic Resonance Tuning by High-Order Lorentzian Mixing.

Nanoscale mechanical resonators have attracted a great deal of attention for signal processing, sensors, and quantum applications. Recent progress in ultrahigh-Q acoustic cavities in nanostructures allows strong interactions with various physical systems and advanced functional devices. Those acoustic cavities are highly sensitive to external perturbations, and it is hard to control those resonance properties since those responses are determined by the geometry and material. In this paper, we demonstrate a novel acoustic resonance tuning method by mixing high-order Lorentzian responses in an optomechanical system. Using weakly coupled phononic-crystal acoustic cavities, we achieve coherent mixing of second- and third-order Lorentzian responses, which is capable of the fine-tunability of the bandwidth and peak frequency of the resonance with a tuning range comparable to the acoustic dissipation rate of the device. This novel resonance tuning method can be widely applied to Lorentzian-response systems and optomechanics, especially active compensation for environmental fluctuation and fabrication errors.

Measurement insights and error analysis of electronic parameters for ultrasonic transducers

Abstract Piezoelectric ultrasonic transducers with the function of energy conversation, as well as their numerous advantages, such as high-power density, quick response, flexible design, and service reliability, are involved in wide applications of industrial processing, precision driving, smart sensing, and medical services. The electromechanical equivalent circuit and Kirchhoff’s law indicate that mechatronics parameters are essential for performance evaluation, reliability analysis, and fault diagnosis of ultrasonic transducers. Importantly, the ultrasonic transducer is a time variant system, data of one single parameter collected from a certain test can’t match with the data of another single parameter acquired from a different test. So, a synchronous and precise online measurement of electronic parameters is encouraged for performance evaluation and optimization design of ultrasonic transducers. With the combination of virtual instrument technology, an asynchronous measurement system of electrical excitation parameters for the ultrasonic transducers of linear driving motors was established in this study. Furthermore, the systematic measurement methods and error theory, including the calculation methods and measuring circuits of electric signals, were illustrated. Experimental results proved that the proposed system and methods for measuring the input electronic power of ultrasonic transducer (e.g., effective value method for voltage and current, energy moment method for frequency, and spectrum analysis method for phase difference) are highly precise, quick responsive, robust, and reliable for ultrasonic transducers. The findings of this study provide valuable references and suggestions for efficient, accurate, and online performance evaluation of ultrasonic transducers, particularly for piezoelectric transducers utilizing ultrasonic high-voltage exciting signals.

Salient object detection: a mini review

This paper presents a mini-review of recent works in Salient Object Detection (SOD). First, We introduce SOD and its application in image processing tasks and applications. Following this, we discuss the conventional methods for SOD and present several recent works in this category. With the start of deep learning AI algorithms, SOD has also benefited from deep learning. Here, we present and discuss Deep learning-based SOD according to its training mechanism, i.e., fully supervised and weakly supervised. For the benefit of the readers, we have also included some standard data sets assembled for SOD research.

Part-of-speech Tagging for Low-resource Languages: Activation Function for Deep Learning Network to Work with Minimal Training Data

Numerous natural language processing (NLP) applications exist today, especially for the most commonly spoken languages such as English, Chinese, and Spanish. Popular traditional methods such as Rule based methods, Naive Bayes classifiers, Hidden Markov models, Conditional Random field-based classifiers, and other stochastic methods have contributed to this improvement in the past. Recently, deep learning has led to exciting breakthroughs in several areas of artificial intelligence, including image processing and natural language processing. It is important to label words as parts of speech to begin developing most of the NLP applications. A deep study in this area reveals that many popular approaches used for this purpose require massive training data. Therefore, these approaches have not been helpful for languages not rich in digital resources. Applying these methods with very little training data prompts the need for innovative problem-solving. This article describes our research, which examines the strengths and weaknesses of well-known approaches, such as conditional random fields and state-of-the-art deep learning models, when applied for part-of-speech tagging using minimal training data for Assamese and English. We also examine the factors affecting them. We discuss our deep learning architecture and the proposed activation function, which shows promise with little training data. The activation function categorizes words belonging to different classes with more confidence by using the outcomes of statistical methods with SMTaylor SoftMax in our deep learning model. With minimal training, our deep learning architecture using the proposed modification of SM-Taylor SoftMax improves accuracy upto 4%, for our small dataset. This technique is a combination of SMTaylor SoftMax and statistical probability distribution of words over tags.

Effect Of Compensation, Work Motivation, Training On Employee Performance Through Job Satisfaction As An Intervening Variable

The study aims to examine the effect of compensation, work motivation, training on employee performance through job satisfaction as an intervening variable. The sample of this research is 81 employees at PT Cheil Jedang Indonesia Pasuruan. The main data source in this research is questionnaires. The data processing application uses Smart PLS. The results showed that compensation (X1) has a significant positive effect on job satisfaction (Z) of PT Cheil Jedang Indonesia Pasuruan employees. Work motivation (X1) has a significant positive influence on job satisfaction (Z) of PT Cheil Jedang Indonesia Pasuruan employees. Job Training (X1) has a significant positive influence on job satisfaction (Z) of PT Cheil Jedang Indonesia Pasuruan employees. Compensation (X1) has a significant positive influence on the performance (Y) of employees of PT Cheil Jedang Indonesia Pasuruan. Work Motivation (X1) has no influence on the performance (Y) of employees of PT Cheil Jedang Indonesia Pasuruan. Training (X1) has a significant positive influence on the performance (Y) of employees of PT Cheil Jedang Indonesia Pasuruan Job Satisfaction (X1) has a significant positive influence on the performance (Y) of employees of PT Cheil Jedang Indonesia Pasuruan. Job satisfaction (X1) can mediate the effect of compensation, work motivation and training on performance (Y).

Feature selection algorithm based on optimized genetic algorithm and the application in high-dimensional data processing

High-dimensional data is widely used in many fields, but selecting key features from it is challenging. Feature selection can reduce data dimensionality and weaken noise interference, thereby improving model efficiency and enhancing model interpretability. In order to improve the efficiency and accuracy of high-dimensional data processing, a feature selection method based on optimized genetic algorithm is proposed in this study. The algorithm simulates the process of natural selection, searches for possible subsets of feature, and finds the subsets of feature that optimizes the performance of the model. The results show that when the value of K is less than 4 or more than 8, the recognition rate is very low. After adaptive bias filtering, 724 features are filtered to 372, and the accuracy is improved from 0.9352 to 0.9815. From 714 features to 406 Gaussian codes, the accuracy is improved from 0.9625 to 0.9754. Among all tests, the colon has the highest average accuracy, followed by small round blue cell tumor(SRBCT), lymphoma, central nervous system(CNS) and ovaries. The green curve is the best, with stable performance and a time range of 0–300. While maintaining the efficiency, it can reach 4.48 as soon as possible. The feature selection method has practical significance for high-dimensional data processing, improves the efficiency and accuracy of data processing, and provides an effective new method for high-dimensional data processing.

Features of determining the subspecies status of honey bees (Apis mellifera) based on morphometric wing indicators of drones

As of today, in Ukraine, there are no opportunities to conduct genetic research on a sufficient scale to identify the composition of the genomes of honey bees. Wing morphometrics of drones is a readily available alternative for obtaining information about the approximate composition of DNA. The goal of the work was to formulate the principles of the main stages of research and illustrate the procedure for obtaining morphometric data, methods of their processing, and possibilities for interpreting the obtained results. In the package of statistical data processing application programs Statistica, the classification of 2,147 drone wings obtained from 6 apiaries of Zhytomyr, Mykolaiv, and Kyiv regions was carried out according to eight indices: Ci, Dbi, Disc.sh, Pci, Ri, Ci.3, Ci.2.1, and Ci.2.2. We propose a method for obtaining morphometric wing standards of bees based on studying a significant number of bee colonies from various apiaries in specific regions of Ukraine. Using a two-stage methodology, three datasets were formed and recommended for use as standards: one for the local population of Ukrainian bees and two for populations of the A.m.mellifera subspecies distributed in the Polissia region. We analyzed the reliability of results in determining the taxonomic affiliation of bees was analyzed. Using the example of a single bee colony, a detailed procedure is demonstrated to identify taxonomic affiliation through utilization of the obtained standards. It has been demonstrated that in case of reliable identification of standards belonging to specific subspecies, it is possible to predict the likely composition of chromosomal sets in the genomes of drones, queens, and worker bees partially corresponding to the wing phenotype.