Processing Applications Research Articles

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.

The application of natural language processing for the extraction of mechanistic information in toxicology

To study the ways in which compounds can induce adverse effects, toxicologists have been constructing Adverse Outcome Pathways (AOPs). An AOP can be considered as a pragmatic tool to capture and visualize mechanisms underlying different types of toxicity inflicted by any kind of stressor, and describes the interactions between key entities that lead to the adverse outcome on multiple biological levels of organization. The construction or optimization of an AOP is a labor intensive process, which currently depends on the manual search, collection, reviewing and synthesis of available scientific literature. This process could however be largely facilitated using Natural Language Processing (NLP) to extract information contained in scientific literature in a systematic, objective, and rapid manner that would lead to greater accuracy and reproducibility. This would support researchers to invest their expertise in the substantive assessment of the AOPs by replacing the time spent on evidence gathering by a critical review of the data extracted by NLP. As case examples, we selected two frequent adversities observed in the liver: namely, cholestasis and steatosis denoting accumulation of bile and lipid, respectively. We used deep learning language models to recognize entities of interest in text and establish causal relationships between them. We demonstrate how an NLP pipeline combining Named Entity Recognition and a simple rules-based relationship extraction model helps screen compounds related to liver adversities in the literature, but also extract mechanistic information for how such adversities develop, from the molecular to the organismal level. Finally, we provide some perspectives opened by the recent progress in Large Language Models and how these could be used in the future. We propose this work brings two main contributions: 1) a proof-of-concept that NLP can support the extraction of information from text for modern toxicology and 2) a template open-source model for recognition of toxicological entities and extraction of their relationships. All resources are openly accessible via GitHub (https://github.com/ontox-project/en-tox).

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.

Optimizing Harvesting Efficiency: Development and Assessment of a Pneumatic Air Jet Excitation Nozzle for Delicate Biostructures in Food Processing

This study presents a new pneumatic air jet excitation nozzle, specifically designed for food processing applications. The device, which uses compressed air equipment and a precision solenoid valve, controls air discharge through a parametric air jet nozzle. Tests showed that the device could achieve shooting frequencies in the 40–45 Hz range, with operational pressures between 5 and 7 bar. A sensor system was used to measure the force generated by the device at different frequencies and pressures. Using the Design of Experiments (DOE) methodology, we identified optimal cavity designs for 5 and 6 bar pressures. These designs outperformed others in generating uniform force and maintaining consistent vibration voltage behavior. This highlights the efficacy of our approach in enhancing device performance under different conditions. The device’s practical application in food processing was demonstrated, particularly in delicate tasks such as the selective harvesting of sensitive crops like coffee fruits. The precise vibrations generated by the device could potentially enhance harvesting efficiency while significantly reducing mechanical damage to plants. The results position the device as a compelling proof of concept, offering an alternative method for exciting biostructures in food processing. This device opens up new possibilities in agricultural and biological fields, providing a non-intrusive and practical approach to manipulating and interacting with delicate, contactless structures, with a specific focus on improving food processing efficiency and quality.

A complexity efficient penta-diagonal quantum smoothing filter for bio-medical signal denoising: a study on ECG

Extracting information-bearing signal from a noisy environment has been a practical challenge in both classical and quantum computing formalism, especially in critical signal processing applications. To filter out the effect of noise, we propose a quantum smoothing filter built upon quantum formalism-based circuits applied for electrocardiogram signal denoising. The proposed quantum filter is a conceptually novel framework with an advantage in computational complexity as compared to the existing classical filters, such as discrete wavelet transform and empirical mode decomposition, whereas it achieves similar performance metrics for the accuracy of the filter. Further, we exploit the penta-diagonal Toeplitz structure of the smoothing filter, which gives approximately 48%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$48\\%$$\\end{document} gate cost reduction for 10 qubit circuit compared to the standard Hamiltonian simulation without structure. The run-time complexity using the quantum matrix inversion technique for the structured matrix is given by O~κ2poly(logN)εP\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ilde{{\\mathscr {O}}}\\left( \\frac{\\kappa ^2 \ ext {poly}(\\log {N})}{\\varepsilon _P}\\right)$$\\end{document} for condition number κ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\kappa$$\\end{document} of the N×N\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N\ imes N$$\\end{document} filter matrix within precision εP\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varepsilon _P$$\\end{document}. Embedding fixed sparsity of the banded matrix, the quantum filter shows potentially better run-time complexity than classical filtering techniques. For the quantifiable research results of our work, we have shown several performance metrics, such as mean-square error and peak signal-to-noise ratio analysis, with a bound of error due to observation noise, simulation error and quantum measurement uncertainty.

Metabolomics and Proteomics in Prostate Cancer Research: Overview, Analytical Techniques, Data Analysis, and Recent Clinical Applications

Prostate cancer (PCa) is a significant global contributor to mortality, predominantly affecting males aged 65 and above. The field of omics has recently gained traction due to its capacity to provide profound insights into the biochemical mechanisms underlying conditions like prostate cancer. This involves the identification and quantification of low-molecular-weight metabolites and proteins acting as crucial biochemical signals for early detection, therapy assessment, and target identification. A spectrum of analytical methods is employed to discern and measure these molecules, revealing their altered biological pathways within diseased contexts. Metabolomics and proteomics generate refined data subjected to detailed statistical analysis through sophisticated software, yielding substantive insights. This review aims to underscore the major contributions of multi-omics to PCa research, covering its core principles, its role in tumor biology characterization, biomarker discovery, prognostic studies, various analytical technologies such as mass spectrometry and Nuclear Magnetic Resonance, data processing, and recent clinical applications made possible by an integrative “omics” approach. This approach seeks to address the challenges associated with current PCa treatments. Hence, our research endeavors to demonstrate the valuable applications of these potent tools in investigations, offering significant potential for understanding the complex biochemical environment of prostate cancer and advancing tailored therapeutic approaches for further development.

Effects of component ratios on the properties of sweet potato-oat composite dough and the quality of its steamed cake.

To enhance the value proposition of sweet potato and oat while broadening their applicability in further processing, this study systematically investigated the impact of oat flour incorporation ratios (5%-25% of sweet potato dry weight) on the quality attributes of sweet potato-oat composite dough and its resulting steamed cake products. The results showed that the addition of oat flour could promote the rheological, water retention, and thermomechanical properties of the composite dough and improve the internal microstructure, specific volume, texture, and other processing properties of the steamed cake products. The rheology, water retention, and protein stability of the dough were maximized when the proportion of oat flour was 25%. The textural properties of steamed cakes, hardness, elasticity, cohesion, adhesion, chewiness, and recovery significantly increased (p<0.05) and viscosity significantly decreased (p<0.05) with the addition of oat flour. It is noteworthy that thermodynamic properties, internal structure of the dough, and air holding capacity, which are critical for processing, showed the best results at 20% oat flour addition. Therefore, the addition of 20%-25% oats is recommended to produce composite doughs with optimal quality and processing characteristics. PRACTICAL APPLICATION: As living standards improve, traditional cereals may no longer able to meet people's health needs. Therefore, there is an urgent consumer demand for nutritious, tasty alternatives to staple foods. In this study, oat flour and sweet potato mash were mixed to make sweet potato-oat cake, and the effect of ingredient ratio on the performance and quality of composite dough containing sweet potato-oat flour was analyzed, thus proposing an innovative approach to the research, development, and industrial production of sweet potato and oat food products.

Low-Power Approximate Unsigned and Signed Multipliers with Configurable Error Recovery

In the field of Digital Signal Processing and similar applications, Approximate circuits are being explored as a means to enhance performance and energy efficiency by sacrificing a degree of accuracy. Within these circuits, Multipliers play a crucial role and are being investigated for their potential impact on overall system optimization. In this paper, a novel approximate multiplier with a low power consumption and a short critical path is proposed for high-performance DSP applications. This multiplier leverages a newly designed approximate adder that limits its carry propagation to the nearest neighbours for fast partial product accumulation. Different levels of accuracy can be achieved by using either OR gates or the proposed approximate adder in a configurable error recovery. The multipliers using these two error reduction strategies are referred to as Approximate Multiplier 1 (AM1) and Approximate Multiplier 2 (AM2), respectively. Both AM1 and AM2 have a low mean error distance, i.e., most of the errors are not significant in magnitude. Compared with a Unsigned multiplication multiplier, with the signed multiplication. The signed multiplication tends to have lower power consumption is observed. By utilizing an appropriate error recovery, the proposed approximate multipliers achieve similar processing accuracy as traditional exact multipliers, but with significant improvements in power.

Consequences of Thermal Diffusion and Chemical Reaction on Mixed Convection MHD Casson Fluid through Porous Media with Inclined Plates

In this present article, we analyzed the effects of Thermal diffusion and chemical reaction on nonlinear mixed convection MHD flow of viscous, incompressible and electrically conducting fluid past an inclined porous channel under the influence of thermal radiation and chemical reaction. The transformed conservation equations are solved analytically subject to physically appropriate boundary conditions by using two term perturbation technique. The numerical values of fluid velocity, fluid temperature and species concentration are displayed graphically whereas those of skin friction coefficient, rate of heat transfer and rate of mass transfer at the plate are presented in tabular form for various values of pertinent flow parameters. It is observed that the velocity is decreased with increasing magnetic field parameter. The resultant velocity and concentration has enhances with increasing thermal diffusion parameters. The study is relevant to chemical materials processing applications.

Opportunities and Challenges of Soy Proteins with Different Processing Applications

Opportunities and Challenges of Soy Proteins with Different Processing Applications

Polarization and frequency multiplexed multi-channel focusing terahertz vortex with a birefringent dielectric metasurface

We propose an approach to generate polarization and frequency multiplexed focusing vortex beams using a birefringent dielectric metasurface in terahertz (THz) range. The designed metadevice consists of Si nanopillars with different sizes. The transmission efficiency of each unit cell under orthogonal polarized illumination can exceed 70% at both operating frequencies 1 THz and 1.2 THz. Combining the functions of beam deflection, focusing, and vortex beam generation in the interference holography design strategy, polarization and frequency multiplexed multi-channel focusing THz vortex beams with different topological charges and generation positions can be achieved, which can greatly improve the transmission capacity of THz communications. Combining its compact and efficient features with multiplexing methods, our designed metadevice has enormous potential for application in THz vortex generation and information processing.

Optimizing microwave energy utilization & heating efficiency: Comparative analysis of susceptor materials for enhanced microwave absorption performance

Susceptors are vital components in microwave heating systems, ensuring efficient, selective, and uniform heating while improving both energy utilization and time effectiveness in various microwave processing applications. The present investigation underlines the microwave absorption performance of various silicon carbide (SiC) susceptors during microwave hybrid heating. The current study also explores methods, including theoretical analysis, simulations, and experiments, to identify most suitable SiC susceptor materials for efficient microwave heating (high microwave absorbing and energy utilization efficiency). Among all susceptor materials, porous nitride-bonded SiC exhibited combination of high properties including permittivity (9.64), loss tangent (0.268), reflection loss (−13.32 dB) and attenuation constant (21.12 Np/m) when microwave heating carried at 1.2 kW, 900 s. Porous nitride-bonded SiC susceptor emerged as optimal choice for most appropriate susceptor material on the basis of theoretical analysis (reflection loss, impedance, microwave attenuation constant, dielectric properties) as compared to other susceptors. The microwave absorption mechanism of various SiC susceptor materials have been established and summarized. The microwave heating behaviour of various SiC susceptors with different compositions were analysed using COMSOL simulations. A higher microwave utilization efficiency was observed by COMSOL simulation for the porous nitride-bonded SiC (95 %) susceptor than dense nitride-bonded SiC (78 %), dense SiC (69 %) and dense oxide-bonded SiC (58 %). The excellent microwave absorption capability of porous nitride-bonded SiC susceptor can be ascribed to synergistic efforts of various polarization mechanisms: dipole polarization, interfacial polarization, multiple reflections and scatterings, defect-induced dipole polarization and conductive loss.

Brillouin light storage for 100 pulse widths

Signal processing based on stimulated Brillouin scattering (SBS) is limited by the narrow linewidth of the optoacoustic response, which confines many Brillouin applications to continuous wave signals or optical pulses longer than several nanoseconds. In this work, we experimentally demonstrate Brillouin interactions at the 150 ps time scale and a delay for a record 15 ns which corresponds to a delay of 100 pulse widths. This breakthrough experimental result was enabled by the high local gain of the chalcogenide waveguides as the optoacoustic interaction length reduces with pulse width. We successfully transfer 150 ps-long pulses to traveling acoustic waves within a Brillouin-based memory setup. The information encoded in the optical pulses is stored for 15 ns in the acoustic field. We show the retrieval of eight amplitude levels, multiple consecutive pulses, and low distortion in pulse shape. The extension of Brillouin-based storage to the ultra-short pulse regime is an important step for the realization of practical Brillouin-based delay lines and other optical processing applications.

Synergistic effect of combining umami substances enhances perceived saltiness

Umami substances have the potential to enhance the perception of saltiness and thus reduce sodium intake. Two sensory evaluation experiments were conducted, involving participants tasting salt solutions, and solutions with added umami substances at equal sodium concentrations. Umami substances included sodium glutamate (MSG), disodium inosinate (IMP), and the combination of them which has synergistic effect and closer to commonly-consumed foods. In Experiment 1, using the two-alternative forced-choice (2-AFC) method by 330 consumers, paired comparisons were conducted at three different sodium concentrations. The combination of MSG and IMP enhanced the perception of saltiness (p < .001 in the difference test), whereas presenting either umami substance in isolation failed to do so (p > .05 in the similarity test). Significant order effects occurred in paired comparisons. In Experiment 2, a two-sip time-intensity (TI) analysis with trained panellists verified these results and found that tasting MSG and IMP either simultaneously or successively enhanced saltiness perception at equal sodium concentrations. These findings indicate that the synergistic effect of umami substances may be the cause of saltiness enhancement, and represents a potential strategy for sodium reduction while satisfying the consumer demand for saltiness perception. Considering the application in food processing and in food pairing, umami substances can potentially be used to help to reduce salt intake in food consumption.

Microstructure, mechanical and corrosion study of friction stir processed Inconel 625 additive layers deposited via wire arc direct energy deposition

This study explores the application of friction stir processing (FSP) as an effective post-deposition surface treatment technique for improving the mechanical and corrosion properties of wire arc direct energy deposited (WA-DED) Inconel 625 additive layers. The coarse and cube-oriented microstructure observed in as-built Inconel 625 components results in anisotropic mechanical properties. The non-equilibrium solidification in WA-DED Inconel 625 leads to severe micro-segregation of alloying elements, forming large Nb and Mo-rich Laves and carbide phases in the inter-dendritic areas. This causes a heterogeneous distribution of vital alloying elements within the γ-Ni matrix of the material, adversely affecting its mechanical properties and corrosion resistance, thereby restricting the utility of this manufacturing technique in critical applications. The incorporation of FSP has been observed to successfully eradicate the presence of typical coarse and highly oriented microstructure through grain refinement, achieving a more uniform microstructure. Through severe plastic deformation (SPD), coarse eutectic phases are broken down and redistributed within the γ-Ni matrix, enhancing the uniformity of material properties. The research thoroughly investigates the changes in microstructure, mechanical performance, and corrosion resistance of both WA-DED and FSP-enhanced WA-DED Inconel 625 samples. A Thermo-Calc simulation using the classical Scheil model was conducted to understand the solidification pathway and the formation mechanism of the Laves phase in the WA-DED sample. The grain size was significantly reduced by 98.23%, from 138µm in the WA-DED sample to 2.44µm in the stir zone (SZ) of the FSP-treated area, due to dynamic recrystallization (DRX), particle-stimulated nucleation (PSN), and the Zener pinning effect. This grain refinement led to remarkable enhancements in the yield strength (YS) by approximately 113%, ultimate tensile strength (UTS) by about 37% and micro-hardness by around 60% compared to the WA-DED sample. In a 3.5wt% NaCl solution, the FSP-treated sample demonstrated superior corrosion resistance by forming a stable, thicker passive film than the as-built sample. The results of this study will offer valuable insights into utilizing the FSP technique as post-weld surface treatment of WA-DED deposited IN625 superalloy.

A-type proanthocyanidins: Sources, structure, bioactivity, processing, nutrition, and potential applications.

A-type proanthocyanidins (PAs) are a subgroup of PAs that differ from B-type PAs by the presence of an ether bond between two consecutive constitutive units. This additional C-O-C bond gives them a more stable and hydrophobic character. They are of increasing interest due to their potential multiple nutritional effects with low toxicity in food processing and supplement development. They have been identified in several plants. However, the role of A-type PAs, especially their complex polymeric form (degree of polymerization and linkage), has not been specifically discussed and explored. Therefore, recent advances in the physicochemical and structural changes of A-type PAs and their functional properties during extraction, processing, and storing are evaluated. In addition, discussions on the sources, structures, bioactivities, potential applications in the food industry, and future research trends of their derivatives are highlighted. Litchis, cranberries, avocados, and persimmons are all favorable plant sources. Α-type PAs contribute directly or indirectly to human nutrition via the regulation of different degrees of polymerization and bonding types. Thermal processing could have a negative impact on the amount and structure of A-type PAs in the food matrix. More attention should be focused on nonthermal technologies that could better preserve their architecture and structure. The diversity and complexity of these compounds, as well as the difficulty in isolating and purifying natural A-type PAs, remain obstacles to their further applications. A-type PAs have received widespread acceptance and attention in the food industry but have not yet achieved their maximum potential for the future of food. Further research and development are therefore needed.