Processing Techniques Research Articles

Changes in patient sentiment on Reddit through periods of atopic dermatitis treatment.

To discover patient attitudes toward atopic dermatitis (AD) treatments, 573,788 posts and comments from eczema-associated Reddit communities were analyzed with the latest natural language processing techniques. Our study reveals that patient sentiment toward AD treatments has improved in the periods following the introductions of dupilumab and Janus kinase inhibitors. These insights not only highlight areas for potential improvement in patient care, but also underline the utility of AI in capturing and analyzing nuanced patient experiences in online forums.

Surface Post-Treatments of Different Lattice Structures Manufactured by 3D Printing using Selective Laser Melting

Selective laser melting (SLM) offers great potential for the fabrication of Ti6Al4V lattice structural components, but the resulting surface roughness of the manufactured products severely limits the use of the parts in biomedical applications. To reduce the influence of high surface roughness, the post processing process techniques applicable to all different lattice structures. In this study, two techniques were combined: ultrasonic abrasive flow and electro polishing treatment. To analyze Selective laser melted parts after post processing process, porosity and microstructural analyses were done. Furthermore, checking the influence of Electro polish time on surface of lattice structures. EP treatment conducted, applying a voltage of 20 ,18, and 16(V) And also (8), (6) and (4) minutes sequentially at a temperature of (4-7) C⁰ for HC, FCC, and BCC lattice structure specimens, sequentially. Acid-based electrolyte to perform Electro Polish, its solutions proved to be effective in reducing average surface roughness Ra from 7.325,7.465 and 6.7732.7 μm to 4.661 .3.911 and 4.20 for hexagonal, face center cubic and body center cubic lattice structures. In addition to, Rz from 36.766 ,35.768 and 30.029 to 22.482 ,17.88 and 18.249 μm sequentially. After post processing process, the findings of surface roughness, Young’s modulus, yield strength, and Porosity percentage are compatible with the properties of bone, allowing it to be utilized as a scaffold.

AUTOMATED GESTURE RECOGNITION USING APPLIED LINGUISTICS WITH DATA-DRIVEN DEEP LEARNING FOR ARABIC SPEECH TRANSLATION

Gesture recognition for Arabic speech translation includes developing advanced technologies that correctly translate body and hand movements corresponding to Arabic sign language (ArSL) into spoken Arabic. This leverages machine learning and computer vision techniques in complex systems simulation platforms to scrutinize the gestures utilized in ArSL, detecting mild differences in facial expressions, hand shapes, and movements. Sign Language Recognition (SLR) is paramount in assisting communication for the Deaf and Hard-of-Hearing communities. It includes using vision-based methods and Surface Electromyography (sEMG) signals. The sEMG signal is crucial for recognizing hand gestures and capturing muscular activities in sign language. Researchers have comprehensively shown the capability of EMG signals to approach specific details, mainly in classifying hand gestures. This progression is a stimulating feature in extracting the interpretation and recognition of sign languages and investigating the phonology of signed language. Leveraging machine learning algorithms and signal processing techniques in complex systems simulation platforms, researchers aim to extract relevant traits from the sEMG signals that correspond to different ArSL gestures. This study introduces an Enhanced Dwarf Mongoose Algorithm with a Deep Learning-Driven Arabic Sign Language Detection (EDMODL-ASLD) technique on sEMG data. In the initial phase, the presented EDMODL-ASLD model is subjected to data preprocessing to change the input sEMG data into an attuned format. In the next stage, feature extraction with fractal theories is used to gather relevant and nonredundant data from the EMG window to construct a feature vector. In this study, the absolute envelope (AE), energy (E), root-mean square (RMS), standard deviation (STD), and mean absolute value (MAV) are the five time-domain extracted features for the EMG window observation. Meanwhile, the dilated convolutional long short-term memory (ConvLSTM) technique is used to identify distinct sign languages. To improve the results of the dilated ConvLSTM model, the hyperparameter selection process is executed using the EDMO model. To illustrate the significance of the EDMODL-ASLD technique, a brief experimental validation is made on the Arabic SLR database. The experimental validation of the EDMODL-ASLD technique portrayed a superior accuracy value of 96.47% over recent DL approaches.

Unveiling Market Sentiments: Finbert-Powered Analysis of Stock News Headlines

This document Sentiment analysis based on news and headlines is a big part of financial markets. Through the utilization of Hugging Face and FinBERT-a specialized model for financial sentiment analysis-and advanced natural language processing techniques, this study makes use of the flexibility of Hugging Face. This study concentrates on pre-processing techniques and the implementation of a model, emphasizing the critical evaluation and correction of inherent biases in sentiment analysis. Results of the experiment show that FinBERT is effective in addressing and reducing biases while extracting diverse sentiments from stock market headlines. This study emphasizes the importance of bias-conscious sentiment analysis for making more informed decisions in financial markets. It highlights the importance of advanced natural language processing models (NLP) like FinBERT and powerful frameworks like Hugging Face. Key Words: optics, Financial Sentiment Analysis, FinBERT, Hugging Face, Natural Language Processing (NLP), Bias Mitigation, Stock Market, Sentiment Analysis, Machine Learning, News Headlines, Investment Decision-making, Data Pre-processing, Neural Networks, Market Sentiments, Text Classification, NLP Frameworks

Characteristics of an antistatic semiconductor bridge based on micro/nano processing techniques

Abstract Regarded as a novel type of igniter, semiconductor bridge (SCB) prove superior in burst time and burst energy (less than 5 mJ). Nevertheless, SCB discharged by strong electrostatic discharge (ESD) inevitably leads to damage. To enhance the electrostatic safety of the semiconductor bridge initiator, an antistatic semiconductor bridge (ASCB) initiator incorporating a transient suppression (TVS) diode was fabricated based on micro/nan processing techniques. Three sizes of polysilicon bridge (10440 µm2, 17020 µm2, and 23760 µm2) and two breakdown voltages of TVS (7.3V and 8.6V) were designed. Then constant discharge test and ESD test were carried out to investigate the effects of bridge area and TVS breakdown voltage on the performance of ASCB. The results showed that, the energy density of polysilicon bridge is a linear function of the ratio of the square of the current to the area of the polysilicon (ISCB2/A). The breakdown voltage will affect the shunt of TVS. During polysilicon heat accumulation process, owing to shunt of TVS, the energy applied to the polysilicon bridge reduced. In addition , the bridge with a larger area were more difficult to reach the explosive limit. Therefore, both the breakdown voltage of the TVS and the area of the polysilicon bridge influenced the explosive characteristics of the ASCB. The antistatic performance of ASCB is obviously stronger than that of SCB, and A2 (ASCB with bridge area of 23760 µm2 and breakdown voltage of 7.3V) is the strongest among the three types of ASCB. Reducing the breakdown voltage of TVS and increasing the bridge area of polysilicon can effectively improve the antistatic performance of ASCB.

Semantic Effects of Long-Distance Wh-Scrambling in Japanese

This paper investigates the semantic effects of long-distance wh-scrambling in Japanese. Through qualitative analysis, it demonstrates that long-distance scrambling is a semantically vacuous movement in declarative sentences where a wh-phrase undergoes radical reconstruction for interpretation. It is demonstrated that it need not flow into semantic interpretations, in contrast to wh-movement. Long-distance wh-scrambling, on the other hand, has an impact on sentential readings in interrogative sentences since the wh-phrase must draw scope from its nearest question marker, which must be understood through the c-command relationship. The difference is discovered to originate from a processing technique, with each alternative interpretation being more prominent in both the jumbled sentence and the canonical form. This study provides a syntactic-semantic perspective and offers new insights into the semantic implications of scrambling, thus contributing to the existing literature.

Quality assessment of various Xiushui Ninghong tea types.

Xiushui Ninghong tea (XSNH) has a long history and is renowned both in China and internationally. Based on different processing techniques, XSNH can be classified into Ninghong Congou, Ninghong Tea Jinhao, Ninghong Tea Longxucha and other types. To investigate the differences in nutrient compounds and mineral element contents among various types of XSNH, 34 samples from seven types were collected, primarily from tea-producing areas. Statistical analysis indicated no significant differences in the contents of crude polysaccharides, K, Mg and Fe, whereas significant differences were observed in the levels of moisture, free amino acids, caffeine, tea polyphenols, thearubigin, theaflavins, Zn, P, Mn, Cu and Se. The data were analyzed using various statistical methods such as hierarchical cluster analysis, principal component analysis and partial least squares discriminant analysis. Characteristic compounds and elements such as theaflavin, Se, free amino acids, P and tea polyphenols were identified as key differential components for distinguishing different sample types. Our research has highlighted the differences in chemical indicators among various types of XSNH, providing a crucial theoretical basis for the future classification and grading of XSNH quality. © 2024 Society of Chemical Industry.

From CNN to GAN: The evolution of deep learning techniques in image processing

In recent years, the technique for image processing has developed to improve the visual effects of images. This paper introduces ways to express images and basic operations. For the rest part of this paper, we elaborate on the current techniques for image processing including CNNs, RNNs, and GANs. each of them has different benefits and drawbacks. This paper also have further introduction about the theory and structure of neural networks above, and the reason for creating GANs, which is two adversarial network to improve the visual effects.

Do help options facilitate computer‐based L2 listening comprehension? Evidence from an activity theory perspective

AbstractDespite the proliferation of help options for computer‐based second language (L2) listening comprehension, little attention has been given to a comprehensive understanding of the pedagogical effects. To gain a thorough understanding of the overall and moderator effects, drawing on the activity theory (AT), this study conducted a meta‐analysis of 33 valid independent studies on help options for computer‐based L2 listening comprehension with 3697 participants involved. Results demonstrated that (a) compared with those traditional methods, help options are found to be more facilitative for L2 listening comprehension with a small‐to‐moderate effect of g = 0.449, 95% CI [0.303, 0.596]; and (b) the effects of help options for computer‐based L2 listening comprehension are moderated by option types, feedback types, language distance types, intervention durations and learning activity types. Motivated by the results obtained, this study also discussed pedagogical implications for future studies. Practitioner notesWhat is already known about this topic The improved pedagogical effects of help options for computer‐based second language (L2) listening comprehension have been well‐documented. A large number of studies on the use of help options for computer‐based L2 listening comprehension have been published. There has been no research aggregating the overall effects of help options for computer‐based L2 listening comprehension. Some potential factors that may moderate the effects of help options for computer‐based L2 listening comprehension remain underexplored. What this paper adds This paper aims to aggregate the overall and moderator effects from the existing publications of help options for computer‐based L2 listening comprehension. Compared with those traditional methods, help options are found to be more facilitative for L2 listening comprehension with a small‐to‐moderate effect of g = 0.449. The effects of help options for computer‐based L2 listening are moderated by option types, feedback types, language distance types, intervention durations and learning activity types. Implications for practice and/or policy Help options have been confirmed to facilitate computer‐based L2 listening comprehension, especially among learners of primary and secondary levels. Call for more empirical studies investigating how different types of options, feedback, language distance and intervention durations along with learning activity moderate the effectiveness. Further attempts should investigate help options for computer‐based L2 listening comprehension with the integration of generative AI, large language model (LLM) and natural language processing (NLP) techniques.

Detecting and evaluating surface defects in additive manufacturing composite materials via image processing technique

Purpose This study aims to offer an image-based robust edge detection system that can estimate, identify, locate and label surface flaws during manufacturing for real-time surface issue diagnostics. Of great concern, this methodology extrapolates surface defect information from scanning electron microscopy (SEM) images of composite fracture surfaces. This study predicts changes in topological intensity of composite fracture surfaces and display them as real-time surface intensity values for the first time. Design/methodology/approach This work, however, introduces a novel robust edge detection method – based image processing – as it is shown to be effective in locating defects, as measured by SEM images of composite fracture surfaces created using additive manufacturing (AM). SEM images, obtained in this study, are related to previous study considering the fracture surfaces of reinforced thermoset composites created via the AM method. These SEM images are of two types: fracture surface of AM of carbon fiber reinforced thermoset composites and fracture surface of AM of syntactic foam reinforced thermoset composites. Initially, MATLAB environment is used for analyzing the SEM images; the technique used, as well as the validity are explained more in the methodology section. Findings The robust surface defect inspection approach used herein is found to be capable of predicting, identifying, localizing and labeling surface defects during production, allowing for real-time surface issue diagnosis. Further, this work makes it possible to use image processing and analysis of these surfaces to anticipate fluctuations in the topological intensity of the fracture surfaces of composites and represent them as values of surface intensity in real time. Originality/value Rising worldwide company rivalry requires a fast, accurate component failure diagnostic method. To create an efficient feature set, a surface defect inspection system must identify product flaws in real time. Thus, this study proposed an image-based robust edge detection system – based on MATLAB environment – that is capable of estimating, identifying, locating and labeling surface faults during production. This paves the way for an extensive set of high-quality tools for dealing with a wide range of problems associated with digital image processing in composites. As a result, the ability to define methodologies and rapidly prototype prospective solutions typically minimizes the cost and time required to implement a successful system during the design phase of an image processing system.

Dynamics of Land Use and Coverage and its Impacts on the Upper Moquegua River Hydrographic Basin

Objective: Evaluate; identify and qualify the impacts of the dynamics of land use and coverage in the years 1973 and 2016 in the Hydrographic Basin and in the marginal strip of the Upper Moquegua River in Moquegua Peru. Theoretical framework: In the area studied, human activities and global climate change have disturbed the balance of natural systems, causing major impacts and complex consequences on land use and cover. Method: Digital image processing techniques and supervised classification using the maximum likelihood method were used, followed by matrix editing with refinement of the base and thematic cartography, reaching 94.4% accuracy in interpretation for the year 2016. Result and discussion: The main changes identified regarding the dynamics of land use and coverage were similar in the basin area and in the marginal strip. An increase was noted in the urban area of the entire basin of 568.53ha (1,097.96%) and 134.86ha (1,263.33%) in the marginal strip, which may have been largely influenced by mining activity, which was not observed in 1973, but present in 2016, occupying an area of 1,092.77ha throughout the basin, with 171.99ha in the marginal strip. The areas that had the greatest reduction were those of the glacier, with 2,275.39ha (-93.33%) in the entire basin and 83.77ha (-100.00%) in the marginal strip. Conclusion: With the results of this research, we hope to contribute to the literature related to the topic and to actions and public policies that encourage the appropriate use of land in the Upper Moquegua River Basin, especially in environmental protection areas.

Impact Assessment of Natural Stone Processing Techniques on the Grain Composition of Sludge Admixtures for Press Powder

Impact Assessment of Natural Stone Processing Techniques on the Grain Composition of Sludge Admixtures for Press Powder

Detecting Suicidal Ideations in Online Forums with Textual and Psycholinguistic Features

Suicide is a global public health problem that takes hundreds of thousands of lives each year. The key to effective suicide prevention is early detection of suicidal ideations and timely intervention. However, several factors hinder traditional suicide risk screening methods. Primarily, the social stigma associated with suicide presents a challenge to suicidal ideation detection, as existing methods require patients to explicitly communicate their suicidal propensities. In contrast, progressively more at-risk people choose online platforms—such as Reddit—as their preferred avenues for sharing their suicidal experiences and seeking emotional support. As a result, these online platforms have become an unobtrusive source of user-generated textual data that can be used to detect suicidality with supervised machine learning and natural language processing techniques. In this paper, we proposed a suicidal ideation detection approach that combines textual and psycholinguistic features extracted from the Reddit forum. Subsequently, we selected the most informative features using the Boruta algorithm and employed four classifiers: logistic regression, naïve Bayes, support vector machines, and random forest. The naïve Bayes models trained with the combination of term frequency-inverse document frequency (TF-IDF) and National Research Council (NRC) features demonstrated the highest performance, obtaining a F1 score of 70.99%. Our experimental results illustrate that a combination of textual and psycholinguistic features yields better classification performance compared to using those features separately.

Perception and Attitude of Consumer Patronage of Ghana-Grown Rice: Evidence from Kumasi, Ghana

Increasing human population, urbanization, and changing consumer trends have contributed to the emergence of rice as the leading staple food in Ghana. Despite its importance in the Ghanaian diet, patronage and consumption of locally grown rice are not yet widespread. This study investigates consumer patronage of locally grown rice in the Kumasi Metropolis, Ghana, and fills a critical gap in understanding the specific factors that influence consumer preferences in urban settings. Data were collected through a cross-sectional survey of 120 respondents across different income categories using a multistage sampling technique and analyzed using descriptive statistics, a Probit model, and Kendall's coefficient of concordance. The study found that cooking quality, palatability, colour, and fragrance were the primary attributes that influenced consumer preferences. Probit analysis revealed that income and the frequency of rice consumption significantly influenced the likelihood of purchasing locally grown rice. Specifically, a higher income decreases, while frequent rice consumption increases the likelihood of choosing local rice. Key constraints identified include perceived high cost, limited availability, and concerns about the quality of rice. This study recommends that local rice producers, agricultural organizations, and government bodies should make efforts to improve the sensory and culinary qualities of locally grown rice through improved processing techniques, increased promotion, and campaigns to ensure the commodity is readily available to consumers. These strategies aim to boost local rice consumption and support agricultural sustainability and food security in Ghana.

Enhancing Income for Farmer Producer Organizations (FPOs) through Ginger Value Addition: Opportunities, Strategies and Market Insights

Ginger (Zingiber officinale) is one of the most valuable spices globally, known for its culinary, medicinal, and industrial uses. As a primary cash crop in many regions, it offers substantial income opportunities for smallholder farmers. However, the traditional farming and marketing methods often limit the potential earnings of smallholder farmers. This review explores the concept of value addition in ginger, focussing on post-harvest processing, product diversification, packaging innovations, and market expansion strategies. It further highlights how Farmer Producer Organizations (FPOs) can leverage these techniques to improve profitability and sustainability. The paper also discusses the challenges faced by FPOs, such as financial constraints and market access barriers, while highlighting opportunities supported by government policies and technological advancements. Advanced processing techniques, improved package, branding, and exploring new markets would achieve profitability and sustainability of FPOs. The reduction of value chain and directly paying to the farmers give opportunity to the consumers to get the ginger products at better prices.

Deep learning-assisted morphological segmentation for effective particle area estimation and prediction of interfacial properties in polymer composites.

The link between the macroscopic properties of polymer nanocomposites and the underlying microstructural features necessitates an understanding of nanoparticle dispersion. The dispersion of nanoparticles introduces variability, potentially leading to clustering and localized accumulation of nanoparticles. This non-uniform dispersion impacts the accuracy of predictive models. In response to this challenge, this study developed an automated and precise technique for particle recognition and detailed mapping of particle positions in scanning electron microscopy (SEM) micrographs. This was achieved by integrating deep convolutional neural networks with advanced image processing techniques. Following particle detection, two dispersion factors were introduced, namely size uniformity and supercritical clustering, to quantify the impact of particle dispersion on properties. These factors, estimated using the computer vision technique, were subsequently used to calculate the effective load-bearing area influenced by the particles. An adapted micromechanical model was then employed to quantify the interfacial strength and thickness of the nanocomposites. This approach enabled the establishment of a correlation between dispersion characteristics and interfacial properties by integrating experimental data, relevant micromechanical models, and quantified dispersion factors. The proposed systematic procedure demonstrates considerable promise in utilizing deep learning to capture and quantify particle dispersion characteristics for structure-property analyses in polymer nanocomposites.

The Impact of Food Processing Flows and the Additive on The Nutritional Content and Values of Food

Food processing plays a pivotal role in influencing the nutritional content and value of food. Recent advancements in food processing techniques and the use of additives have led to substantial changes in nutrient levels and bioavailability, especially increasing the safety of food. This paper examines the dual impact of various food processing methods and additives on nutrient levels and bioavailability. Key processing methods such as thermal processing, cold processing, fermentation, drying are classified to primary and secondary processing and being explored for their effects on food nutrients. Furthermore, the role of additives, including natural substances like ascorbic acid, is also discussed in extending shelf life and improving flavor and appearance. Through a comprehensive review of existing literature, this study finds that while food processing can enhance certain aspects like safety and convenience, it can also lead to the loss of essential nutrients and the nutritional value. Besides, the study found that the loss of nutrients may focused on the preparation stage, which also called primary food processing. However, the increase of nutritional value and content may increase in the secondary food processing through the effort and rich experiment of human, like fermentation. The findings highlight the importance to distinguish the function and impact of various food processing methods to the food nutrition. Furthermore, it also emphasizes the need for balanced food processing approaches to optimize nutritional value while mitigating adverse effects on health.

Effects of Five Different Withering Methods on the Composition and Quality of Congou Black Tea

To explore the effects of different withering methods on the quality of Congou black tea, this study focused on five different withering methods: natural withering, warm-air withering, sun–natural combined withering, sun withering, and shaking withering. Gas chromatography‒mass spectrometry (GC‒MS), high-performance liquid chromatography (HPLC), and ion-exchange chromatography techniques were used to analyze the nonvolatile and volatile components and composition of the tea. The results revealed significant differences (p < 0.05) in the contents of nonvolatile constituents including caffeine, polyphenols, soluble sugars, free amino acids and their components, theaflavins, thearubigins, and catechins among the five different withering methods, with varying degrees of correlation between these components. A total of 227 aroma compounds were detected, and significant differences in the contents of alcohols, aldehydes, and ketones were observed. A relative odor activity value (ROVA) analysis of the aroma compounds revealed that 19 compounds had an ROVA > 1. Among them, benzylaldehyde, trans-2-decenal, decanal, benzaldehyde, nonanal, hexanal, trans-linalool, and geraniol from the shaking withering method had significantly higher ROVA values than those from the other withering methods, which may be the reason for the prominent floral and fruity aroma of shaking withering. This study revealed the impact of different withering methods on the quality of Congou black tea, providing a scientific basis for the development of Congou black tea with different flavors and the improvement of Congou black tea processing techniques.

Application of LIBS and LA-ICP-MS analysis to Hellenistic tableware

Greek and Hellenistic painted vases have attracted scholars of classical archaeology for centuries, either because of the artistic value of the painted decorations or because of the depicted scenes of ancient everyday life. Due to the application of thin slips on the surface, different methods are used for the chemical characterisation of the slips and the body of the same vase. The slips are commonly analysed by X-ray microanalysis, offering point-by-point analysis of mostly major elements, while different tools, such as X-ray fluorescence (XRF), inductively coupled mass spectrometry (ICP-MS), and instrumental neutron activation analysis (INAA), are used for bulk body analysis. This can cause difficulties in comparing the bulk composition and thus in determining the similarities and differences in the preparation process of the clay paste for the slips and bodies of an object. Given the artistic value of these objects, museum curators tend to be reluctant to provide samples for invasive characterisation. Micro-destructive laser ablation methods offer a robust solution to addressing both the relationships between ceramic body and surface treatment chemistry and destructive sampling. To provide a proof-of concept, we analysed slips and bodies of a small sample of Hellenistic fine wares from the Greek colony of Issa in modern Croatia with Laser-Induced Breakdown Spectroscopy (LIBS) and Laser ablation-ICP-MS. Results show remarkable diversity in the use of clay types and processing techniques.

Review of Recent Developments in AI-based Data Processing and Prediction for Braced Excavation Design

Abstract This paper presents a review of the recent advances in the application of Artificial intelligence (AI) techniques for data processing and prediction in the design and construction of braced excavation systems. It introduces various AI algorithms employed in addressing complex data processing and deformation prediction challenges in braced excavation. A pivotal development has been in the area of soil parameter and in-situ monitoring data processing, which facilitates more reliable site characterization for efficient design. This paper delves into wall system and deformation predictions, showcasing AI’s ability to integrate multi-source data for real-time prediction. It also addresses spatiotemporal prediction, enhancing prediction accuracy by accounting for uncertainties. The significance of groundwater is highlighted by introducing predictive models that consider groundwater drawdown. Additionally, it discusses stability prediction based on the factor of safety of braced excavation, enabling proactive safety management for the retaining wall system and the surrounding environment. Furthermore, the paper discusses the challenges and opportunities associated with the use of generative AI and large language models, providing an overview of their significance, applications, and future directions in braced excavation.