Saved Queries

by Yongan Chen, Dazhao Li, Zhijie Yan, Shaobin Bai, Ruofei Xie, Jian Sheng, Jian Zhang, Shuai Li and Jinzhong Zhang

Materials 2024, 17(19), 4835; https://doi.org/10.3390/ma17194835 (registering DOI) - 30 Sep 2024

Abstract

Strong yet ductile alloys have long been desired for industrial applications to enhance structural reliability. This work produced two (CoCrNi)_93.5Al₃Ti₃C_0.5 medium-entropy alloys with exceptional strength–ductility combinations, via short/medium (3 min/30 min) annealing times after hot-rolling. Three types of intergranular precipitates including MC, M₂₃C₆ carbides, and L1₂ phase were detected in both samples. Noticeably, the high-density of intragranular L1₂ precipitates were only found in the medium-time annealed sample. Upon inspection of the deformed substructure, it was revealed that the plane slip is the dominant deformation mechanism of both alloys. This is related to the lower stacking fault energy, higher lattice friction induced by the C solute, and slip-plane softening caused by intragranular dense L1₂ precipitates. Additionally, we noted that the stacking fault and twinning act as the mediated mechanisms in deformation of the short-time annealed alloy, while only the former mechanism was apparent in the medium-time annealed alloy. The inhibited twinning tendency can be attributed to the higher energy stacking faults and the increased critical twinning stress caused by intragranular dense L1₂ precipitates. Our present findings provide not only guidance for optimizing the mechanical properties of high/medium-entropy alloys, but also a fundamental understanding of deformation mechanisms. Full article

(This article belongs to the Special Issue High-Performance Alloys and Steels)

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12 pages, 781 KiB

Open AccessArticle

The Creation of a Domain Structure Using Ultrashort Pulse NIR Laser Irradiation in the Bulk of MgO-Doped Lithium Tantalate

by Boris Lisjikh, Mikhail Kosobokov and Vladimir Shur

Photonics 2024, 11(10), 928; https://doi.org/10.3390/photonics11100928 (registering DOI) - 30 Sep 2024

Abstract

The fabrication of stable, tailored domain patterns in ferroelectric crystals has wide applications in optical and electronic industries. All-optical ferroelectric poling by pulse laser irradiation has been developed recently. In this work, we studied the creation of the domain structures in MgO-doped lithium tantalate by focused irradiation with a femtosecond near-infrared laser. Cherenkov-type second harmonic generation microscopy was used for domain imaging of the bulk. We have revealed the creation of enveloped domains around the induced microtracks under the action of the depolarization field. The domain growth is due to a pyroelectric field caused by a nonuniform temperature change. The domains in the bulk were revealed to have a three-ray star-shaped cross-section. It was shown that an increase in the field excess above the threshold leads to consequential changes in domain shape from a three-ray star to a triangular and a circular shape. The appearance of comb-like domains as a result of linear scanning was demonstrated. All effects were considered in terms of a kinetic approach, taking into account the domain wall motion by step generation and kink motion driven by excess of the local field over the threshold. The obtained knowledge is useful for the all-optical methods of domain engineering in ferroelectrics. Full article

(This article belongs to the Special Issue Ultrashort Laser Pulses)

15 pages, 1605 KiB

Open AccessArticle

A Tomato Recognition and Rapid Sorting System Based on Improved YOLOv10

by Weirui Liu, Su Wang, Xingjun Gao and Hui Yang

Machines 2024, 12(10), 689; https://doi.org/10.3390/machines12100689 (registering DOI) - 30 Sep 2024

Abstract

In order to address the issue of time-consuming, labor-intensive traditional industrial tomato sorting, this paper proposes a high-precision tomato recognition strategy and fast automatic grasping system. Firstly, the Swin Transformer module is integrated into YOLOv10 to reduce the resolution of each layer by half and double the number of channels, improving recognition accuracy. Then, the Simple Attention Module (SimAM) and the Efficient Multi-Scale Attention (EMA) attention mechanisms are added to achieve complete integration of features, and the Bi-level Routing Attention (BiFormer) is introduced for dynamic sparse attention and resource allocation. Finally, a lightweight detection head is added to YOLOv10 to improve the accuracy of tiny target detection. To complement the recognition system, a single-vertex and multi-crease (SVMC) origami soft gripper is employed for rapid adaptive grasping of identified objects through bistable deformation. This innovative system enables quick and accurate tomato grasping post-identification, showcasing significant potential for application in fruit and vegetable sorting operations. Full article

(This article belongs to the Section Machine Design and Theory)

14 pages, 3572 KiB

Open AccessArticle

Laser Metal Deposition of Rene 80—Microstructure and Solidification Behavior Modelling

by Krishnanand Srinivasan, Andrey Gumenyuk and Michael Rethmeier

Micromachines 2024, 15(10), 1234; https://doi.org/10.3390/mi15101234 - 30 Sep 2024

Abstract

New developments in nickel-based superalloys and production methods, such as the use of additive manufacturing (AM), can result in innovative designs for turbines. It is crucial to understand how the material behaves during the AM process to advance the industrial use of these techniques. An analytical model based on reaction–diffusion formalism is developed to better explain the solidification behavior of the material during laser metal deposition (LMD). The well-known Scheil–Gulliver theory has some drawbacks, such as the assumption of equilibrium at the solid–liquid interface, which is addressed by this method. The solidified fractions under the Scheil model and the pure equilibrium model are calculated using CALPHAD simulations. A differential scanning calorimeter is used to measure the heat flow during the solid–liquid phase transformation, the result of which is further converted to solidified fractions. The analytical model is compared with all the other models for validation. Full article

(This article belongs to the Special Issue Ultrafast Laser Micro- and Nanoprocessing, 2nd Edition)

15 pages, 3980 KiB

Open AccessArticle

Analysis of the Distribution and Influencing Factors of Antibiotic Partition Coefficients in the Fenhe River Basin

by Jing Zhao, Hailong Yin and Linfang Wang

Water 2024, 16(19), 2793; https://doi.org/10.3390/w16192793 - 30 Sep 2024

Abstract

Affected by point and non-point source pollution, the Fenhe River Basin faces significant environmental challenges. This study aimed to analyze the distribution characteristics and influencing factors of antibiotics in the water and sediments of the Fenhe River Basin. Samples were collected from 23 sites within the basin, and 26 antibiotics from five different classes were detected and analyzed using high-performance liquid chromatography–tandem mass spectrometry (HPLC-MS/MS). The water–sediment partition coefficient (Kp) was calculated, and spatial analysis was conducted using geographic information system (GIS) technology. The results showed that 25 antibiotics were detected in the water, with concentrations ranging from 130 to 1615 ng/L, and 17 antibiotics were detected in the sediments, with concentrations ranging from 121 to 426 μg/kg. For quinolones (QNs), except for ofloxacin, all others could be calculated with overall high values of Kp ranging from 692 to 16,106 L/kg. The Kp values for QNs were generally higher in the midstream, with considerable point source pollution from industries and non-point source pollution from developed agriculture. The distribution of Kp is closely associated with risk. This study found that the Kp values of the antibiotics were influenced by various factors such as temperature, water flow, and the physicochemical properties of sediments. Correlation analysis revealed significant relationships between Kp and parameters such as river width, water depth, water quality (total nitrogen, total phosphorus, and chemical oxygen demand), and sediment pH and clay content. Full article

(This article belongs to the Special Issue Basin Non-point Source Pollution)

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24 pages, 1761 KiB

Open AccessArticle

Towards a QBLM-Based Qualification-Management Methodology Supporting Human-Resource Management and Development

by Adrian Vogler, Binh Vu, Matthias Then and Matthias Hemmje

Information 2024, 15(10), 600; https://doi.org/10.3390/info15100600 - 30 Sep 2024

Abstract

Abstract: This position paper presents a novel perspective on addressing the challenges of digital transformation in higher education through the development of a qualification-based learning model (QBLM) qualification management methodology. It argues that the rapid pace of technological advancement and the resulting need for continuous upskilling and reskilling necessitate a more dynamic and adaptive approach to human-resource management and development. The paper posits that by extending QBLM through the integration of artificial intelligence (AI) and machine learning (ML), a more effective system for analyzing competence requirements and designing personalized learning pathways can be created. The paper proposes a three-fold approach: (1) developing the FPHR ontology to support semantic annotation of HR qualifications in higher-education institutions (HEIs), (2) integrating this ontology into QBLM to ensure the machine-readability of qualifications, and (3) modeling a knowledge-based production process for HRs in skills-based learning. This paper outlines the current state of the art, presents conceptual models, and describes planned proof-of-concept implementations and evaluations. It contends that this approach will significantly enhance the effectiveness of human-resource development in the rapidly evolving digital knowledge society. By presenting this position, the paper aims to stimulate discussion and collaboration within the academic community on innovative approaches to qualification management in higher education. The work addresses critical issues arising from technological development and offers a forward-thinking solution to bridge the gap between current and future skill requirements in industry and academia. Full article

(This article belongs to the Special Issue Accessibility and Inclusion in Education: Enabling Digital Technologies)

11 pages, 904 KiB

Open AccessArticle

Leaching Platinum Group Metals from Simulated Spent Auto-Catalyst Material Using Ozone and Hydrochloric Acid

by Marcus Knight, Petrie van Wyk, Guven Akdogan and Steven Bradshaw

Minerals 2024, 14(10), 998; https://doi.org/10.3390/min14100998 - 30 Sep 2024

Abstract

This paper reports the development of a process for leaching Pt, Pd, and Rh from simulated spent auto-catalyst material using ozone and hydrochloric acid in order to produce a pregnant leach solution that could be fed to an industrial precious metal refinery. The effects of O₃ mass flow, initial acid concentration, and temperature were investigated using a Box–Behnken experimental design with three centre-point runs and a total leach time of 6 hours. Set points of 3.34, 5.01, and 6.68 g/h; 1.0 M, 3.0 M, and 5.0 M; and 30, 60, and 90^oC were used for O₃ mass flow, hydrochloric acid concentration, and temperature, respectively. The optimal extractions for Pt, Pd, and Rh were 80%, 85%, and 42%, respectively, at 5.01 g/h O₃, 5.0 M HCl, and 90^oC. Statistical analyses indicated high dependencies of Pd and Rh on hydrochloric acid concentration and temperature, with only Pt displaying a significant dependence on O₃ mass flow. Full article

(This article belongs to the Section Mineral Processing and Extractive Metallurgy)

19 pages, 1456 KiB

Open AccessArticle

Experimental Design, Statistical Analysis, and Modeling of the Reduction in Methane Emissions from Dam Lake Treatment Using Agro-Industrial Biochar: A New Methane Capture Index

by Pelin Soyertaş Yapıcıoğlu and Mehmet İrfan Yeşilnacar

Water 2024, 16(19), 2792; https://doi.org/10.3390/w16192792 - 30 Sep 2024

Abstract

This study aimed to reduce the methane (CH₄) emissions originating from dam lake treatment using malt dust-derived biochar, which is an agro-industrial byproduct of the brewery industry. Optimum operating and water quality parameters for CH₄reduction were determined using statistical analyses based on the Box–Behnken design method. Also, a Monte Carlo simulation was performed to determine the correlation between CH₄emissions and operating parameters. According to the simulation, dissolved oxygen (DO) and the oxidation–reduction potential (ORP) had the highest correlation with CH₄ emissions, with values of 92.03% and 94.57%, respectively. According to the Box–Behnken design methodology, the optimum operating parameters were 4 mg/L of dissolved oxygen, −359 mV of ORP, and 7.5 pH for the minimum CH₄ emissions. There was a reported reduction of up to 19.4% in CH₄ emissions for the dam lake treatment using malt dust-derived biochar. Finally, a new methane capture index, based on the biochar application (MCI), was developed and validated. The largest methane capture capacity was related to the malt dust-derived biochar produced at the lowest temperature (M1). Full article

(This article belongs to the Section Water and Climate Change)

20 pages, 3147 KiB

Open AccessArticle

Valorisation of Blackcurrant Pomace by Extraction of Pectin-Rich Fractions: Structural Characterization and Evaluation as Multifunctional Cosmetic Ingredient

by Marija Ćorović, Anja Petrov Ivanković, Ana Milivojević, Milica Veljković, Milica Simović, Paula López-Revenga, Antonia Montilla, Francisco Javier Moreno and Dejan Bezbradica

Polymers 2024, 16(19), 2779; https://doi.org/10.3390/polym16192779 - 30 Sep 2024

Abstract

Blackcurrant pomace is a widely available waste stream derived from the industrial production of juice rich in pectin and unextracted polyphenols. Since pectin, an emerging class of gastrointestinal prebiotics, is also a common cosmetic ingredient, the aim of this work was to evaluate blackcurrant pomace as a source of pectin-rich fractions suitable for application in prebiotic cosmetics. Hereby, this raw material was valorised by sequential extraction of acid-soluble (by citric acid, CAP) and Ca-bound (by ammonium oxalate, AOPP) pectic polysaccharides. Both fractions had favourable physicochemical features and a similar degree of methyl-esterification between low- and high-methoxyl pectin (approx. 50%), but CAP had significantly higher galacturonic acid content (72.3%), branching, and purity. Regardless of that, both had very high oil (18.96 mL/g for CAP and 19.32 mL/g for AOPP) and water (9.97 mL/g for CAP and 7.32 mL/g for AOPP)-holding capacities and excellent emulsifying properties, making them promising cosmetic ingredients. The polyphenol content was 10 times higher in CAP, while corresponding antioxidant activity was 3-fold higher. Finally, the influence of varying CAP and AOPP concentrations on common skin pathogen, Staphylococcus aureus, and beneficial skin bacteria, Staphylococcus epidermidis, was examined. The results show significant prebiotic potential of two pectic fractions since they were capable of selectively stimulating S. epidermidis, while S. aureus growth was inhibited, whereas CAP demonstrated a particularly high capacity of up to 2.2, even with methicillin-resistant S. aureus. Full article

(This article belongs to the Section Biobased and Biodegradable Polymers)

36 pages, 726 KiB

Open AccessArticle

Adoption Intention of Blockchain Technologies for Sustainable Supply Chain Management in Indian MSMEs

by Vineet Paliwal, Shalini Chandra and Suneel Sharma

Sustainability 2024, 16(19), 8527; https://doi.org/10.3390/su16198527 - 30 Sep 2024

Abstract

This study explores the determinants of the intention to adopt blockchain technology for sustainable supply chain management in Indian micro, small, and medium enterprises. Different from existing studies that advocate the use of socio-technical theory for blockchain technologies, we develop a new theoretical framework, called “SOS,” based on a review of the existing literature. This is an adaptation of the technology–organization–environment framework that examines the measures and scales from socio-technical, organizational, and sustainability contexts. We use ADANCO 2.3.2 for variance-based structural equation modeling. The results show that two of the nine hypotheses are negatively significant, while the rest are positive. In our context, social sustainability and computer self-efficacy are strongly negatively significant for the adoption intention of blockchain technology in our context. Software quality and environmental sustainability are strongly positively significant. Meanwhile, collaboration, economic sustainability, and relative advantage mediated by experience are positively significant. Our study contributes to the literature by offering a new theoretical framework, fresh insights from the Indian industry, and several recommendations to practitioners. Full article

30 pages, 13385 KiB

Open AccessArticle

Enhancing 3D Models with Spectral Imaging for Surface Reflectivity

by Adam Stech, Patrik Kamencay and Robert Hudec

Sensors 2024, 24(19), 6352; https://doi.org/10.3390/s24196352 - 30 Sep 2024

Abstract

The increasing demand for accurate and detailed 3D modeling in fields such as cultural heritage preservation, industrial inspection, and scientific research necessitates advanced techniques to enhance model quality. This paper addresses this necessity by incorporating spectral imaging data to improve the surface detail and reflectivity of 3D models. The methodology integrates spectral imaging with traditional 3D modeling processes, offering a novel approach to capturing fine textures and subtle surface variations. The experimental results of this paper underscore the advantages of incorporating spectral imaging data in the creation of 3D models, particularly in terms of enhancing surface detail and reflectivity. The achieved experimental results demonstrate that 3D models generated with spectral imaging data exhibit significant improvements in surface detail and accuracy, particularly for objects with intricate surface patterns. These findings highlight the potential of spectral imaging in enhancing 3D model quality. This approach offers significant advancements in 3D modeling, contributing to more precise and reliable representations of complex surfaces. Full article

(This article belongs to the Special Issue 3D Reconstruction with RGB-D Cameras and Multi-sensors)

17 pages, 3193 KiB

Open AccessArticle

Mathematical Modeling of the Global Engineering Process for Optimizing Product Quality in the Aerospace Industry

by Aurel Mihail Titu, Gheorghe Ioan Pop and Alina Bianca Pop

Aerospace 2024, 11(10), 804; https://doi.org/10.3390/aerospace11100804 - 30 Sep 2024

Abstract

The aerospace industry faces the challenge of maintaining product excellence amidst intricate processes and demands for cost and time efficiency. Mathematical modeling emerges as a valuable tool for optimizing the engineering process and enhancing quality, with potential applications extending beyond aerospace to other sectors with high quality and safety standards. This study develops and validates a mathematical model specific to the aerospace industry, aiming to assess the impact of human resource expertise on product quality. Through a case study within an aerospace organization, an IDEF0-methodology-based mathematical model, coupled with weighted averages, was constructed to depict the comprehensive engineering process and quantify knowledge’s impact on deliverable quality. Simulation data, gathered through human resource knowledge assessments and non-conformity analyses, revealed a direct correlation between technical knowledge levels and deliverable quality, consequently impacting final product quality. The proposed model serves as a tool for estimating potential deliverable error rates and pinpointing critical areas within the process that necessitate refinement. The research underscores the significance of knowledge investment and effective knowledge management strategies in upholding quality and competitiveness across industries with stringent quality requirements. Full article

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18 pages, 63250 KiB

Open AccessArticle

Mechanism-Based Fault Diagnosis Deep Learning Method for Permanent Magnet Synchronous Motor

by Li Li, Shenghui Liao, Beiji Zou and Jiantao Liu

Sensors 2024, 24(19), 6349; https://doi.org/10.3390/s24196349 - 30 Sep 2024

Abstract

As an important driving device, the permanent magnet synchronous motor (PMSM) plays a critical role in modern industrial fields. Given the harsh working environment, research into accurate PMSM fault diagnosis methods is of practical significance. Time–frequency analysis captures the rich features of PMSM operating conditions, and convolutional neural networks (CNNs) offer excellent feature extraction capabilities. This study proposes an intelligent fault diagnosis method based on continuous wavelet transform (CWT) and CNNs. Initially, a mechanism analysis is conducted on the inter-turn short-circuit and demagnetization faults of PMSMs, identifying and displaying the key feature frequency range in a time–frequency format. Subsequently, a CNN model is developed to extract and classify these time–frequency images. The feature extraction and diagnosis results are visualized with t-distributed stochastic neighbor embedding (t-SNE). The results demonstrate that our method achieves an accuracy rate of over 98.6% for inter-turn short-circuit and demagnetization faults in PMSMs of various severities. Full article

(This article belongs to the Section Fault Diagnosis & Sensors)

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17 pages, 1851 KiB

Open AccessArticle

Implementing a Vision-Based ROS Package for Reliable Part Localization and Displacement from Conveyor Belts

by Eber L. Gouveia, John G. Lyons and Declan M. Devine

J. Manuf. Mater. Process. 2024, 8(5), 218; https://doi.org/10.3390/jmmp8050218 - 30 Sep 2024

Abstract

The use of computer vision in the industry has become fundamental, playing an essential role in areas such as quality control and inspection, object recognition/tracking, and automation. Despite this constant growth, robotic cell systems employing computer vision encounter significant challenges, such as a lack of flexibility to adapt to different tasks or types of objects, necessitating extensive adjustments each time a change is required. This highlights the importance of developing a system that can be easily reused and reconfigured to address these challenges. This paper introduces a versatile and adaptable framework that exploits Computer Vision and the Robot Operating System (ROS) to facilitate pick-and-place operations within robotic cells, offering a comprehensive solution for handling and sorting random-flow objects on conveyor belts. Designed to be easily configured and reconfigured, it accommodates ROS-compatible robotic arms and 3D vision systems, ensuring adaptability to different technological requirements and reducing deployment costs. Experimental results demonstrate the framework’s high precision and accuracy in manipulating and sorting tested objects. Thus, this framework enhances the efficiency and flexibility of industrial robotic systems, making object manipulation more adaptable for unpredictable manufacturing environments. Full article

(This article belongs to the Special Issue Robotics in Manufacturing Processes)

56 pages, 1879 KiB

Open AccessReview

Unlocking the Potential of Hydrosols: Transforming Essential Oil Byproducts into Valuable Resources

by Heloísa H. S. Almeida, Isabel P. Fernandes, Joana S. Amaral, Alírio E. Rodrigues and Maria-Filomena Barreiro

Molecules 2024, 29(19), 4660; https://doi.org/10.3390/molecules29194660 - 30 Sep 2024

Abstract

The global demand for sustainable and non-toxic alternatives across various industries is driving the exploration of naturally derived solutions. Hydrosols, also known as hydrolates, represent a promising yet underutilised byproduct of the extraction process of essential oils (EOs). These aqueous solutions contain a complex mixture of EO traces and water-soluble compounds and exhibit significant biological activity. To fully use these new solutions, it is necessary to understand how factors, such as distillation time and plant-to-water ratio, affect their chemical composition and biological activity. Such insights are crucial for the standardisation and quality control of hydrosols. Hydrosols have demonstrated noteworthy properties as natural antimicrobials, capable of preventing biofilm formation, and as antioxidants, mitigating oxidative stress. These characteristics position hydrosols as versatile ingredients for various applications, including biopesticides, preservatives, food additives, anti-browning agents, pharmaceutical antibiotics, cosmetic bioactives, and even anti-tumour agents in medical treatments. Understanding the underlying mechanisms of these activities is also essential for advancing their use. In this context, this review compiles and analyses the current literature on hydrosols’ chemical and biological properties, highlighting their potential applications and envisioning future research directions. These developments are consistent with a circular bio-based economy, where an industrial byproduct derived from biological sources is repurposed for new applications. Full article

(This article belongs to the Special Issue Featured Reviews in Applied Chemistry 2.0)

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