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This study reports an alkali-activated binder including blast furnace slag (BFS) together with marble waste (MW). Cement is an industrial product that emits a significant amount of CO2 during its production and incurs high energy costs. MW is generated during the extraction, cutting, and processing of marble in production facilities, where dust mixes with water to form a settling sludge. This sludge is an environmentally harmful waste that must be disposed of in accordance with legal regulations. In this study, a substantial amount of MW, a by-product with considerable environmental and economic impacts worldwide, was utilized in the production of a binder through the alkaline activation of BFS. In doing this, different experimental parameters were tested to obtain the best binder samples according to workability and mechanical properties. Then, some experiments such as drying shrinkage determination, strength testing, and microstructure analyses were fulfilled through samples with the best values. The findings supported the improvement of the rapid-setting property of BFS by means of the addition of MW. MW reduced the time-dependent drying shrinkage values of BFS by 55%, especially in slag alkaline activation systems with a low or moderate alkali activator content. The substitution of MW (≤50%) in BFS increased flexural and compressive strengths (4.5 and 61.7 MPa), while a reference sample contained BFS only. Although the use of MW did not create a new phase, it contributed to a C-S-H bonding structure during the alkali activation of BFS in a microstructure analysis. [ABSTRACT FROM AUTHOR]

In order to solve the problems of a high mismatching rate and being easily affected by noise and gray transformation, an improved product defect detection method combining centroid distance and textural information is proposed in this paper. Based on image preprocessing, the improved fuzzy C-means clustering method is used to extract the closed contour features. Then, the contour center distance description operator is used for bidirectional matching, and a robust coarse matching contour pair is obtained. After the coarse matching contour pair is screened, the refined matching result is obtained by using the improved local binary pattern operator. Finally, by comparing whether the number of fine matching pairs is consistent with the number of template outlines, the detection of good and bad industrial products is realized, and the closed contour extraction experiment, the anti-rotation matching experiment, the anti-gray difference matching experiment, and the defect detection experiment of three different products are designed. The experimental results show that the improved product defect detection method has good performance in relation to anti-rotation transformation and anti-gray difference, the detection accuracy can reach more than 90%, and the detection time is up to 362.6 ms, which can meet the requirements of industrial real-time detection. [ABSTRACT FROM AUTHOR]

As an intellectual property product that is highly farmer-friendly, geographical indication (GI) products have always garnered significant attention. In recent years, research on how GI products promote agricultural development has been increasing, yet the academic community remains divided on this issue. On one hand, some studies point out that GI products can drive agricultural development; on the other hand, other studies suggest that the impact of GI products is not significant or varies. Meta-analysis is a method that leverages statistical techniques to integrate the findings of multiple studies with a common research objective, addressing controversial issues and arriving at generalizable conclusions. Therefore, to more precisely uncover the intrinsic relationship between GI products and agricultural development and to delve deeper into the root causes of the aforementioned discrepancies, this study employed a meta-analytic approach. We extracted 478 correlation coefficients (r) as effect sizes from 82 empirical articles worldwide. Using these coefficients, we calculated the overall effect size and moderating effects of GI products on promoting agricultural development. Research indicates that GI products exert a positive influence on agricultural development. There is a low positive correlation between the two (r = 0.197). Further analysis reveals that various factors at the sample, data, literature, and methodology levels all impact the outcomes of GI products' promotion of agricultural development. Research has shown that, in pursuit of sustainable agricultural development goals, it is further recommended that governments should accord high priority to the cultivation and development of GI products. This is aimed at providing practical insights to facilitate the sustainable advancement of GI products and bolster agricultural competitiveness. [ABSTRACT FROM AUTHOR]

Strip steel serves as a crucial raw material in numerous industries, including aircraft and automobile manufacturing. Surface defects in strip steel can degrade the performance, quality, and appearance of industrial steel products. Detecting surface defects in steel strip products is challenging due to the low contrast between defects and background, small defect targets, as well as significant variations in defect sizes. To address these challenges, a two-stage attention-based feature-enhancement network (TAFENet) is proposed, wherein the first-stage feature-enhancement procedure utilizes an attentional convolutional fusion module with convolution to combine all four-level features and then strengthens the features of different levels via a residual spatial-channel attention connection module (RSC). The second-stage feature-enhancement procedure combines three-level features using an attentional self-attention fusion module and then strengthens the features using a RSC attention module. Experiments on the NEU-DET and GC10-DET datasets demonstrated that the proposed method significantly improved detection accuracy, thereby confirming the effectiveness and generalization capability of the proposed method. [ABSTRACT FROM AUTHOR]

Form deviation generated during the milling profile process challenges the precision and functionality of industrial fixtures and product manufacturing across various sectors. Inspecting contour profile quality relies on commonly employed contact methods for measuring form deviation. However, the methods employed frequently face limitations that can impact the reliability and overall accuracy of the inspection process. This paper introduces a novel approach, the novel intelligent inspection method (NIIM), developed to accurately inspect and categorize contour profiles in machined parts manufactured through the milling process by computer numerical control (CNC) machines. The NIIM integrates a calibration piece, a vision system ( R A M - S t a r l i t e T M ), and machine learning techniques to analyze the line profile and classify the quality of contour profile deformation generated during CNC milling. The calibration piece is specifically designed to identify form deviations in the contour profile during the milling process. The R A M - S t a r l i t e T M vision system captures contour profile images corresponding to curves, lines, and slopes. An algorithm generates a profile signature, extracting Fourier descriptor features from the contour profile to analyze form deviations compared to an image reference. A feed-forward neural network is employed to classify contour profiles based on quality properties. Experimental evaluations involving 60 machined calibration pieces, resulting in 356 images for training and testing, demonstrate the accuracy and computational efficiency of the proposed NIIM for profile line tolerance inspection. The results demonstrate that the NIIM offers 96.99% accuracy, low computational requirements, 100% inspection capability, and valuable information to improve machining parameters, as well as quality classification. [ABSTRACT FROM AUTHOR]

There is a growing need for resilient and renewable materials to aid society in global sustainability. It is incumbent upon the agricultural and manufacturing industries to work together to achieve this vision. In particular, the hemp plant has been identified as an emerging industrial crop that will be pivotal in achieving the United Nations Sustainable Development Goals. However, this nascent industry has received an influx of research and development activity, resulting in various methods and practices globally, challenging the repeatability of results, research advancement, standards development, and sustainability assessment. A systematic literature review is conducted to identify and document (1) the various practices for harvesting and converting industrial hemp into materials and products and (2) existing hemp-derived products and those under development. Using the PRISMA methodology, 5295 articles were identified, and 109 articles were included for review. Unlike prior reviews focusing on specific hemp plant components, materials, or products, this study systematically evaluates the utilization pathways of the whole plant (stalk, flower, leaf, and seed) to traditional, industrial, and emerging products. Further, myriad opportunities for hemp material and product applications, sustainability performance assessment, and future research are discussed. This review will benefit future hemp research, advancing process technologies, developing novel products, establishing policies and standards, and assessing sustainability performance. [ABSTRACT FROM AUTHOR]

Camellia drupifera is mainly used in forestry for its high-value industrial products; however, limited information is available on its transcriptome. This study aimed to construct a full-length transcriptome sequence based on the PacBio sequencing platform for various plant parts of C. drupifera, including flower buds, leaves, leaf buds, branches, the pericarp, and seed kernels. The transcriptomes were annotated with 23,207 genes, with 58 subgroups in the GO classification. The KEGG database revealed 10,407 genes involved in the metabolic pathway analysis, with 68,192 coding sequences, 3352 TF families, 48,541 SSRs, 1421 IncRNAs, and 2625 variable shears predicted. The transcriptomes of different parts were analyzed and compared. The majority of differentially expressed genes (DEGs) were found between the pericarp and seed kernels, followed by leaves and the pericarp with 5662 DEGs, and flower buds and leaf buds with 1616 DEGs. GO and KEGG enrichment analyses showed that KEGG differential genes were significant in microbial metabolism, carbon metabolism, and other functions. The data annotation and analysis of the full-length transcriptome and the comparative analysis between different plant parts provided a theoretical basis for studying gene function, metabolic pathway regulation, and gene expression analysis in KEGG. [ABSTRACT FROM AUTHOR]

The current object detection algorithm based on CNN makes it difficult to effectively capture the characteristics of subtle defects in online industrial product packaging bags. These defects are often visually similar to the texture or background of normal product packaging bags, and the model cannot effectively distinguish them. In order to deal with these challenges, this paper optimizes and improves the network structure based on YOLOv8 to achieve accurate identification of defects. First, in order to solve the long-tail distribution problem of data, a fuzzy search data enhancement algorithm is introduced to effectively increase the number of samples. Secondly, a joint network of FasterNet and SPD-Conv is proposed to replace the original backbone network of YOLOv8, which effectively reduces the computing load and improves the accuracy of defect identification. In addition, in order to further improve the performance of multiscale feature fusion, a weighted bidirectional feature pyramid network (BiFPN) is introduced, which effectively enhances the model's ability to detect defects at different scales through the fusion of deep information and shallow information. Finally, in order to reduce the sensitivity of the defect position deviation, the NWD loss function is used to optimize the positioning performance of the model better and reduce detection errors caused by position errors. Experimental results show that the FSNB_YOLOv8 model proposed in this paper can reach 98.8% mAP50 accuracy. This success not only verifies the effectiveness of the optimization and improvement of this article's model but also provides an efficient and accurate solution for surface defect detection of industrial product packaging bags on artificial assembly systems. [ABSTRACT FROM AUTHOR]

Tractors are the most important agricultural power machinery. With the development of tractors toward large-scale and electrification, the design of modern tractor transmission systems increasingly relies on computer simulation technology. However, tractor transmission systems, especially power-shift transmissions and CVTs, are highly complex industrial products that involve specialized knowledge from multiple disciplines. Engineers and researchers find it difficult to choose the correct mathematical model and grasp the trend of technological development when applying simulation technology. To address this issue, we conducted a systematic review of the field and carried out the following work: First, the types and basic principles of tractor transmission systems were introduced; Second, the modeling methods and applications of computer simulation technology in the structural optimization, power-shift control, and energy saving of tractor transmission systems were reviewed; Finally, the method of obtaining simulation parameters through system identification was introduced. Although computer simulation technology has been applied in the development of all tractor transmission systems, there are still some issues that need attention, such as the lack of established shift quality evaluation indicators and driving cycles suitable for tractors, which are crucial for the reliability of simulation results but are rarely studied. These issues need to be addressed in future work. [ABSTRACT FROM AUTHOR]

The Finite Element Method (FEM) has emerged as a powerful tool for predicting the behavior of industrial products, including those with complex geometries or uncommon materials. Finite Element Analysis (FEA) is widely used to study structural vibration-related aspects such as stress, displacement, and velocity. Modal analysis, a standard technique for characterizing the vibrational behavior of structures, is essential for identifying resonance frequencies, optimizing component design, and assessing structural integrity. Finite Elements (FE) modal analysis enables engineers to evaluate numerically the modal parameters, whereas model order reduction (MOR) schemes are exploited to achieve a balance between computational efficiency and accuracy, enabling a more efficient solution for computing transient dynamic analysis. Assessing the accuracy and reliability of FE solutions is a crucial aspect of the design cycle, and model-updating procedures are commonly employed to maximize the correlation between measured and predicted dynamic behavior. This study investigated the accuracy and computational efficiency of linear, quadratic, and cubic hexahedral FE formulations for modal analysis and transient dynamic solutions. More specifically, the documented results demonstrate the profitable use of the eigenenergy norm obtained in eigen solutions as a valid predictor of the accuracy reported using either the time response assurance criterion (TRAC) or the frequency response assurance criterion (FRAC), measured in transient dynamic cases. Moreover, our results also highlight the superior computational efficiency of higher-order formulations for both the eigen and transient dynamic solutions. [ABSTRACT FROM AUTHOR]

Per- and polyfluoroalkyl substances (PFAS) are found in many consumer and industrial products. While some PFAS, notably perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS), are developmentally toxic in mammals, the vast majority of PFAS have not been evaluated for developmental toxicity potential. A concentration–response study of 182 unique PFAS chemicals using the zebrafish medium-throughput, developmental vertebrate toxicity assay was conducted to investigate chemical structural identifiers for toxicity. Embryos were exposed to each PFAS compound (≤100 μM) beginning on the day of fertilization. At 6 days post-fertilization (dpf), two independent observers graded developmental landmarks for each larva (e.g., mortality, hatching, swim bladder inflation, edema, abnormal spine/tail, or craniofacial structure). Thirty percent of the PFAS were developmentally toxic, but there was no enrichment of any OECD structural category. PFOS was developmentally toxic (benchmark concentration [BMC] = 7.48 μM); however, other chemicals were more potent: perfluorooctanesulfonamide (PFOSA), N-methylperfluorooctane sulfonamide (N-MeFOSA), ((perfluorooctyl)ethyl)phosphonic acid, perfluoro-3,6,9-trioxatridecanoic acid, and perfluorohexane sulfonamide. The developmental toxicity profile for these more potent PFAS is largely unexplored in mammals and other species. Based on these zebrafish developmental toxicity results, additional screening may be warranted to understand the toxicity profile of these chemicals in other species. [ABSTRACT FROM AUTHOR]

Bamboo breeding strategies are essential in realizing "Bamboo as a Substitute for Plastic (BASP)". This review article aims to explore the crucial role of breeding strategies in achieving the substitution of plastic products with bamboo as outlined under the BASP Initiative. Firstly, we address the issue of plastic pollution, along with the background of reducing the market share and demand for plastic products. It categorizes the types of bamboo products that can fully or partially replace plastic products in various categories, such as daily necessities, building materials, and industrial products. Then, we investigate which bamboo species can replace which plastic products and propose the need for bamboo improvement. Furthermore, it presents data from positioning observation research stations for bamboo forest ecosystems in China and outlines the essential traits necessary for bamboo substitution, including characteristics like long internode length, extended fiber length, thick culm wall, and optimal cellulose-to-lignin content ratio, among others. Finally, we discuss breeding methods and genetic improvement as key strategies to achieve bamboo substitution and suggest the potential of enhancing bamboo traits to serve as a viable replacement source for plastics. This comprehensive approach aims to enhance bamboo's growth features and physical properties to meet the criteria for substituting bamboo for plastics effectively. [ABSTRACT FROM AUTHOR]

Aiming at the problems of the poor robustness and universality of traditional contour matching algorithms in engineering applications, a method for improving the surface defect detection of industrial products based on contour matching algorithms is detailed in this paper. Based on the image pyramid optimization method, a three-level matching method is designed, which can quickly obtain the candidate pose of the target contour at the top of the image pyramid, combining the integral graph and the integration graph acceleration strategy based on weak classification. It can quickly obtain the rough positioning and rough angle of the target contour, which greatly improves the performance of the algorithm. In addition, to solve the problem that a large number of duplicate candidate points will be generated when the target candidate points are expanded, a method to obtain the optimal candidate points in the neighborhood of the target candidate points is designed, which can guarantee the matching accuracy and greatly reduce the calculation amount. In order to verify the effectiveness of the algorithm, functional test experiments were designed for template building function and contour matching function, including uniform illumination condition, nonlinear condition and contour matching detection under different conditions. The results show that: (1) Under uniform illumination conditions, the detection accuracy can be maintained at about 93%. (2) Under nonlinear illumination conditions, the detection accuracy can be maintained at about 91.84%. (3) When there is an external interference source, there will be a false detection or no detection, and the overall defect detection rate remains above 94%. It is verified that the proposed method can meet the application requirements of common defect detection, and has good robustness and meets the expected functional requirements of the algorithm, providing a strong technical guarantee and data support for the design of embedded image sensors in the later stage. [ABSTRACT FROM AUTHOR]

The precise mapping of magnetic fields emitted by various objects holds critical importance in the fabrication of industrial products. To meet this requirement, this study introduces an advanced magnetic detection device boasting high spatial resolution. The device's sensor, an array comprising 256 unpackaged gallium arsenide (GaAs) Hall elements arranged in a 16 × 16 matrix, spans an effective area of 19.2 mm × 19.2 mm. The design maintains a 1.2 mm separation between adjacent elements. For enhanced resolution, the probe scans the sample via a motorized rail system capable of executing specialized movement patterns. A support structure incorporated into the probe minimizes the measurement distance to below 0.5 mm, thereby amplifying the magnetic signal and mitigating errors from nonparallel probe–sample alignment. The accompanying interactive software utilizes cubic spline interpolation to transform magnetic readings into detailed two- and three-dimensional magnetic field distribution maps, signifying field strength and polarity through variations in color intensity and amplitude sign. The device's efficacy in accurately mapping surface magnetic field distributions of magnetic and magnetized materials was corroborated through tests on three distinct samples: a neodymium–iron–boron magnet, the circular magnetic array from a smartphone, and a magnetized 430 steel plate. These tests, focused on imaging quality and magnetic field characterization, underscore the device's proficiency in nondestructive magnetic field analysis. [ABSTRACT FROM AUTHOR]

The character of atmospheric pollution and its impact on surface waters may vary substantially in space, and hence, we add a potentially important location for the studies of atmospheric air pollution to the map of the High Arctic. We have investigated the anthropogenic particle characteristics and selected persistent organic pollutant concentrations, in a priorly unmonitored location in the Arctic (Svalbard), exposed to a climatic gradient. Single-particle analysis of PM indicates that besides the prevailing natural aerosol particles, anthropogenic ones were present. The likely anthropogenic origin of some particles was established for spherical Fe-rich or aluminosilicate particles formed in high-temperature processes or metal-rich particles of the chemical composition corresponding to industrial products and atypical for natural minerals; soot, tar balls, and secondary sulfate were also likely of anthropogenic origin. Some of the observed anthropogenic particles could only come from remote industrial sources. POP concentrations indicated a background of LRAT, consistent with the ΣPCB concentrations and volatility profile. However, the ΣDDX composition indicating aged sources and an order of magnitude higher concentrations of both ΣDDXs and ΣHCHs than at other High Arctic monitoring stations indicate their potential source in two types of re-emission from secondary sources, i.e., from seawater and snowpack, respectively. [ABSTRACT FROM AUTHOR]

Thanks to the line-scanning camera, the measurement method based on line-scanning stereo vision has high optical accuracy, data transmission efficiency, and a wide field of vision. It is more suitable for continuous operation and high-speed transmission of industrial product detection sites. However, the one-dimensional imaging characteristics of the line-scanning camera cause motion distortion during image data acquisition, which directly affects the accuracy of detection. Effectively reducing the influence of motion distortion is the primary problem to ensure detection accuracy. To obtain the two-dimensional color image and three-dimensional contour data of the heavy rail surface at the same time, a binocular color line-scanning stereo vision system is designed to collect the heavy rail surface data combined with the bright field illumination of the symmetrical linear light source. Aiming at the image motion distortion caused by system installation error and collaborative acquisition frame rate mismatch, this paper uses the checkerboard target and two-step cubature Kalman filter algorithm to solve the nonlinear parameters in the motion distortion model, estimate the real motion, and correct the image information. The experiments show that the accuracy of the data contained in the image is improved by 57.3% after correction. [ABSTRACT FROM AUTHOR]

The application of blockchain technology in industrial product quality traceability is analyzed to construct a new model of product quality traceability that is mainly based on blockchain technology and supplemented by an identity system. The blockchain-enabled overall technical architecture of an industrial product quality traceability system is explored, and a blockchain-based industrial product full life cycle information traceability system is constructed. First, the weights of the information indicators of different links of the industrial equipment information traceability system were calculated using the EAHP hierarchical analysis method. The manufacturing link had the largest weight, with a value of 18.8%. Second, the system's functional module design is based on the weights. We designed and developed the industrial product information traceability platform based on the hybrid blockchain chain structure of private chain + alliance chain. Finally, a manufacturing enterprise in the Xinjiang region is taken as the research object, query validation is carried out for the products produced by the enterprise, and the average query time of the system is measured to be 65.376 ms. It can meet the traceability needs of consumers and enterprise users. The research can provide theoretical support and reference for the whole life cycle information traceability of industrial products. [ABSTRACT FROM AUTHOR]

Starch, as a primary component of wheat, plays a crucial role in determining the quality of noodles and pasta. A deep understanding of the impact of starch on the quality of noodles and pasta is fundamentally important for the industrial progression of these products. The starch structure exerts an influence on the quality of noodles and pasta by affecting its functional attributes and the interaction of starch–gluten proteins. The effects of starch structure (amylopectin structure, amylose content, granules size, damaged starch content) on the quality of noodles and pasta is discussed. The relationship between the functional properties of starch, particularly its swelling power and pasting properties, and the texture of noodles and pasta is discussed. It is important to note that the functional properties of starch can be modified during the processing of noodles and pasta, potentially impacting the quality of the end product, However, this aspect is often overlooked. Additionally, the interaction between starch and gluten is addressed in relation to its impact on the quality of noodles and pasta. Finally, the application of exogenous starch in improving the quality of noodles and pasta is highlighted. [ABSTRACT FROM AUTHOR]

Safflower (Carthamus tinctorius L.) is an ancient oilseed crop of interest due to its diversity of end-use industrial and food products. Proteomic and metabolomic profiling of its organs during seed development, which can provide further insights on seed quality attributes to assist in variety and product development, has not yet been undertaken. In this study, an integrated proteome and metabolic analysis have shown a high complexity of lipophilic proteins and metabolites differentially expressed across organs and tissues during seed development and petal wilting. We demonstrated that these approaches successfully discriminated safflower reproductive organs and developmental stages with the identification of 2179 unique compounds and 3043 peptides matching 724 unique proteins. A comparison between cotyledon and husk tissues revealed the complementarity of using both technologies, with husks mostly featuring metabolites (99%), while cotyledons predominantly yielded peptides (90%). This provided a more complete picture of mechanisms discriminating the seed envelope from what it protected. Furthermore, we showed distinct molecular signatures of petal wilting and colour transition, seed growth, and maturation. We revealed the molecular makeup shift occurring during petal colour transition and wilting, as well as the importance of benzenoids, phenylpropanoids, flavonoids, and pigments. Finally, our study emphasizes that the biochemical mechanisms implicated in the growing and maturing of safflower seeds are complex and far-reaching, as evidenced by AraCyc, PaintOmics, and MetaboAnalyst mapping capabilities. This study provides a new resource for functional knowledge of safflower seed and potentially further enables the precision development of novel products and safflower varieties with biotechnology and molecular farming applications. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "The Evolution of Fermented Milks, from Artisanal to Industrial Products: A Critical Review." The correction includes changes to the email address of one of the authors and revisions to the text of the article. The revised text provides updated information on the microbial communities found in kefir grains and kefir, highlighting the changes in dominant species over time. It also corrects mis-cited references related to the therapeutic role of probiotics, the effect of fermented milks on blood cholesterol levels, and the protective effect of Lb. rhamnosus GG. The correction notice also includes a reference correction and identifies incorrect information in several references. This article provides an overview of the challenges and opportunities associated with including probiotics in fermented buffalo milk. It also references various studies on traditional fermented milk products from different regions, including Bangladesh, Ethiopia, Italy, Mongolia, Bulgaria, Kenya, Sudan, Ghana, Namibia, Egypt, Russia, Lebanon, and Iceland. The studies explore the microbiological characteristics, composition, and health benefits of these products. The article aims to contribute to the understanding of fermented milk products and their potential as probiotic sources. This document is a list of references related to the production and properties of fermented milk products. It includes studies on specific products such as Torba yoghurt, kishk, tarhana, zabady, kashk-e zard, and viili, as well as general topics like the microbi [Extracted from the article]

Cured meat products constitute one of the meat categories commonly consumed in Ireland and has been part of the Irish cuisine and diet for many years. Ham, gammon, and bacon are some of the products that involve curing as part of the traditional processing methods. Common among these products are high levels of salt and the addition of nitrites. These products undergo processing treatments to create variety, preserve shelf-life, and develop their unique quality and safety characteristics. However, consumers are becoming more conscious of the level of processing involved in these products, and the effects of some components and ingredients might be perceived as unhealthy. Meat product developers have been exploring ways to reduce the amount of ingredients such as salt, saturated fat, and chemical preservatives (e.g., nitrites), which are linked to health concerns. This is a challenging task as these ingredients play an important techno-functional role in the products' quality, safety, and identity. While innovative processing techniques are being introduced and progress has been made in reformulation and packaging technologies, much is still unknown, especially regarding the applicability of many of the proposed interventions to a wide range of meat products and their sustainability at the industrial scale. [ABSTRACT FROM AUTHOR]

A Shape-From-Focus (SFF) is a three-dimensional imaging method based on focus information. It is not yet widely used for in-line industrial inspection or measurement tasks. The main reasons are the time it takes to capture a 3D image of the inspected product and the presence of interference affecting image quality. This paper compares operators for estimating focus in source images in the scope of their use in constructing an industrial 3D scanner. Interferences were introduced by using additional illuminators and changing the acquisition parameters. The use of industrial-grade cameras, industrial-grade illuminators, and electrically controlled optics are discussed. A novel approach in the research is using an electrically tunable lens to move the position of the image plane during 3D image acquisition. The research was conducted on various surfaces found on typical industrial products. The research showed which focus estimation operators can be applied to SFF imaging within the range of interference considered. It was also confirmed that using the centre of gravity method for scene reconstruction allows for an increase in resolution compared to the maximum method. [ABSTRACT FROM AUTHOR]

Polyurea has gained significant attention in recent years as a functional polymer material, specifically regarding blast and impact protection. The molecular structure of polyurea is characterized by the rapid reaction between isocyanate and the terminal amine component, and forms an elastomeric copolymer that enhances substrate protection against blast impact and fragmentation penetration. At the nanoscale, a phase-separated microstructure emerges, with dispersed hard segment microregions within a continuous matrix of soft segments. This unique microstructure contributes to the remarkable mechanical properties of polyurea. To maximize these properties, it is crucial to analyze the molecular structure and explore methods like formulation optimization and the incorporation of reinforcing materials or fibers. Current research efforts in polyurea applications for protective purposes primarily concentrate on construction, infrastructure, military, transportation and industrial products and facilities. Future research directions should encompass deliberate formulation design and modification, systematic exploration of factors influencing protective performance across various applications and the integration of numerical simulations and experiments to reveal the protective mechanisms of polyurea. This paper provides an extensive literature review that specifically examines the utilization of polyurea for blast and impact protection. It encompasses discussions on material optimization, protective mechanisms and its applications in blast and impact protection. [ABSTRACT FROM AUTHOR]

The Industrial Product Service System (IPS2) is considered a sustainable and efficient business model, which has been gradually popularized in manufacturing fields since it can reduce costs and satisfy customization. However, a comprehensive design method for IPS2 is absent, particularly in terms of requirement perception, resource allocation, and service activity arrangement of specific industrial fields. Meanwhile, the planning and scheduling of multiple parallel service activities throughout the delivery of IPS2 are also in urgent need of resolution. This paper proposes a method containing service order design, service resource configuration, and service flow modeling to establish an IPS2 for robot-driven sanding processing lines. In addition, we adopt the modified Deep Q-network (DQN) to realize a scheduling scheme aimed at minimizing the total tardiness of multiple parallel service flows. Finally, our industrial case study validates the effectiveness of our methods for IPS2 design, demonstrating that the modified deep reinforcement learning algorithm reliably generates robust scheduling schemes. [ABSTRACT FROM AUTHOR]

In this work, we investigated the mechanical behavior of a low-cost Zr51.9Cu23.3Ni10.5Al14.3 (at. %) metallic glass ribbon prepared with industrial-grade material through the melt-spinning method. The ribbons have good appearances and almost no defects. The mechanical behavior associated with the corresponding microstructure of the ribbon was tested at different strain rates. Striation and veining patterns were observed in the crack propagation zone and the fast fracture zone. The results show that the tensile strength of the ribbons exceeds 1 GPa. Therefore, they are considered to have good potential for industrial applications. This study could contribute to the preparation of low-cost bulk metallic glass. [ABSTRACT FROM AUTHOR]

Wire and cable are important industrial products involving the national economy and people's livelihood, which are hailed as the "blood vessel" and "nerve" of the national economy, providing the basic guarantee for the normal operation of modern economy and society. The data traceability of their production and circulation process is a key factor in ensuring their quality and safety management. We aim to solve the problems of unsafe data transmission, weak quality control, and information islands in the process of wire and cable quality traceability in order to improve the production management efficiency of wire and cable manufacturing enterprises and to reduce the cost of consumer quality traceability of wire and cable products. We analyzed the technical characteristics and advantages of Industrial Internet of Things (IIoT) identity resolution and the blockchain. Key technologies are introduced, a traceability method that integrates the two is proposed, and a quality traceability framework based on the IIoT identity resolution system and blockchain technology is constructed. By analyzing the quality information composition of the wire and cable supply chain, a new quality traceability model based on the wire and cable supply chain is established. Finally, through the verification of the developed quality traceability system, the quality traceability function and quality information of each production link of wire and cable are successfully realized. This paper fills a gap in the field of cable product quality traceability using the combination of IIoT and blockchain technology. According to this model, it also has some potential for the traceability of other industrial products. [ABSTRACT FROM AUTHOR]

Unripe tomatoes represent an agri-food waste resulting from industrial by-processing products of tomatoes, yielding products with a high content of bioactive compounds with potential nutraceutical properties. The food-matrix biological properties are attributed to the high steroidal glycoalkaloid (SGA) content. Among them, α-tomatine is the main SGA reported in unripe green tomatoes. This review provides an overview of the main chemical and pharmacological features of α-tomatine and green tomato extracts. The extraction processes and methods employed in SGA identification and the quantification are discussed. Special attention was given to the methods used in α-tomatine qualitative and quantitative analyses, including the extraction procedures and the clean-up methods applied in the analysis of Solanum lycopersicum L. extracts. Finally, the health-beneficial properties and the pharmacokinetics and toxicological aspects of SGAs and α-tomatine-containing extracts are considered in depth. In particular, the relevant results of the main in vivo and in vitro studies reporting the therapeutic properties and the mechanisms of action were described in detail. [ABSTRACT FROM AUTHOR]

In the field of historical buildings, the wide use of lime as a binder in various contexts of application emerges from a series of artistic and archaeological evidence in the Apulia (Italy) from the 4th century BCE to the 15th century CE. The large availability of carbonate rocks in the geological substratum from Daunian Subappennines to Salento areas strongly influenced the material culture of the region. In this paper, significant study cases were presented to bring to light the technological complexity, almost completely cancelled by the widespread presence of industrial products, in the use of lime over the centuries. Through examples of use from antiquity to the modern age in Apulia (Egnatia, Lamapopoli, Tertiveri, Siponto, Lucera and Monopoli sites), technological solutions indicating an ecological dimension of production were discussed, bearing witness to technologies on a human scale and sustainability. The comparison of petrographical (POM, SEM-EDS) and mineralogical (XRPD) results indicated the technological trend and custom for lime production in the Apulian region that starts from the choice of the stone to be calcined and the aggregates and passes through the modalities of lime hydration and preparation of the mixture up to the laying. [ABSTRACT FROM AUTHOR]

The increased utilization of titanium dioxide (TiO2) nanoparticles (TNPs) in various industrial and consumer products has raised concerns regarding its harmful effect due to its accumulation within the different systems of the human body. Here, we focused on the influence of TNPs on the growth and aggregation of two crucial crystalline substances, calcium phosphate (CaP) and monosodium urate (MSU), particularly its implications in gout disease. In this study, we adopted microscopic techniques and generated kinetic models to examine the interactions between TNPs, CaP and MSU, and crystallization, under controlled laboratory conditions. Our findings reveal that TNPs not only facilitate the growth of these crystals but also promote their co-aggregations. Crystal dissolution kinetics also exhibit that an increase in TNPs concentration corresponds to a reduction in the dissolution rate of CaP and MSU crystals in presence of the dissoluting agent hydroxycitrate (Hcit). These observations suggest that TNPs can stabilize CaP+MSU mixed crystals, which underscores the significance of TNPs' exposure in the pathogenesis of gout disease. [ABSTRACT FROM AUTHOR]

Simple Summary: Limnospira maxima, a photosynthetic bacterium with valuable proteins and pigments, faces cultivation challenges due to a lack of management knowledge and expensive nitrogen sources. This study aimed to find an efficient method for large-scale cultivation through testing different light colors and nitrogen sources. The study's results showed that using white lights and potassium nitrate was the most effective approach, promoting growth and increasing the production of phycocyanin, a valuable blue pigment with applications in food, health, and the dye and pigment industry. In summary, this research offers a cost-effective and efficient solution for cultivating Limnospira maxima on a larger scale. This has the potential to positively impact food production and various industrial goods, providing a more sustainable and accessible source of phycocyanin and other valuable components. Cultivating Limnospira maxima, renowned for its abundant proteins and valuable pigments, faces substantial challenges rooted in the limited understanding of its optimal growth parameters, associated high costs, and constraints in the procurement of traditional nitrogen sources, particularly NaNO3. To overcome these challenges, we conducted a comprehensive 4 × 3 factorial design study. Factors considered included white, red, blue, and yellow light spectra, along with nitrogen sources NaNO3 and KNO3, as well as a nitrogen-free control, for large-scale implementation. Optimal growth, measured by Optical Density, occurred with white and yellow light combined with KNO3 as the nitrogen source. These conditions also increased dry weight and Chl-a content. Cultures with nitrogen deprivation exhibited high values for these variables, attributed to carbon accumulation in response to nitrogen scarcity. Phycocyanin, a crucial pigment for nutrition and industry, reached its highest levels in cultures exposed to white light and supplemented with KNO3, with an impressive content of 384.11 g kg−1 of dry weight. These results highlight the efficacy and cost-efficiency of using a combination of white light and KNO3 for large-scale L. maxima cultivation. This strategy offers promising opportunities to address global food security challenges and enhance the production of industrially relevant pigments. [ABSTRACT FROM AUTHOR]

The development of human society has led to the growing consumption of industrial products, which generates significant amounts of carbon emissions. However, relatively few in-depth studies have been conducted on the influence of different demand factors (e.g., household consumption, government consumption, export, and capital formation) on carbon emissions, which hinders the development of targeted industrial policies. To address this issue, an analytical framework based on input–output theory, the hypothesis extraction method, and complex network analysis was established to estimate the intrinsic influence of different demand factors on the embodied carbon transfer between provinces in China. The key findings can be summed up as follows: (1) The macro direction of China's embodied carbon transfer runs from resource-rich northern provinces to industrially developed southern provinces. (2) From the perspective of different demand factors, capital formation is the most significant contributor to China's embodied carbon transfer, with the construction industry being the most important driver. In contrast, government consumption causes the least embodied carbon transfer, but it has the highest average carbon emission intensity. (3) According to complex network theory, the carbon transfer networks via provinces and industries caused by exports are the most concentrated, with the manufacture of electrical machinery and electronic equipment serving as the main source of demand. In contrast, the carbon transfer network resulting from household consumption exhibits a high level of decentralization, with dominant sectors including electric power, gas and water production, and supply and other services. Based on these findings, this study is expected to contribute targeted suggestions with which provinces and industries can formulate demand-side carbon reduction policies for different demand factors, which will contribute to the achievement of "carbon peaking and carbon neutrality". [ABSTRACT FROM AUTHOR]

Increasing volatility in manufacturing and rising sustainability requirements demand more efficient processes in production, especially in employee qualification and engineering during development and on-site adjustments before and after the start of production. One possible solution is using digital twins for virtual commissioning, which can speed up engineering processes, qualify employees, and save valuable resources. To solve these challenges, it is necessary to identify promising approaches for using the digital twin and virtual commissioning. Furthermore, creating an environment where these approaches can be optimally explored is essential. This paper presents promising research approaches and demonstrates the development of an assembly process and a production system with a digital twin designed to explore these aspects. The presented system is an interlinked production system for assembling an actual industrial product. It includes different levels of human–robot interaction and automation, which can be implemented virtually in the digital twin. [ABSTRACT FROM AUTHOR]

The research on Paulownia spp. has increased in the last twenty years thanks to the growing interest in the application modalities of this plant in various sectors such as wood, phytoremediation, environmental protection, paper, biofuel, chemistry and medicine. For the first time, this study analyzed the papers present in the Web of Science Core Collection on "Paulownia" to obtain a set of characteristics in the work carried out from 1971 to 2021. This analysis selected and took into account 820 articles and provided evidence of the scientific production of authors, institutions, and countries. This work showed that the most studied species was Paulownia tomentosa, followed by P. fortunei and P. elongate. The JCR category and research area with the most publications was plant science, with 20.4% of the total. The papers were published in 460 journals and in a book series. The journals with the most publications were Bioresources, Advanced Material Research, Agroforestry Systems, Journal of Wood Science and Industrial Crops and Products. The institutions with the most prolific affiliation with the field of Paulownia spp. research were Henan University, the US Department of Agriculture, Belgrade University, the Chinese Academy, and Georgia University. Finally, the 3842 keywords were divided into nine different clusters and the trends of interest in the last fifteen years were highlighted. [ABSTRACT FROM AUTHOR]

Soybean (Glycine max L.) is gaining in importance due to its many uses, including as a food crop and a source of industrial products, among others. Increasing efforts are made to accelerate soybean research and develop new soybean varieties to meet global demands. Soybean research, breeding, identification, and variety protection all rely on precise genomic information. While DNA markers are invaluable tools for these purposes, the older generations, especially those developed before the advent of genome sequencing, lack precision and specificity. Thankfully, advancements in genome sequencing technologies have generated vast amounts of sequence data over the past decade, allowing precise and high-resolution analyses. However, making sense of the genomic information requires a certain level of professional training and computational power, which are not universally available to researchers. To address this, we generated a set of PCR-based DNA markers out of the existing genomic data from 228 popular soybean varieties that offer precise, unambiguous genomic information and can be easily adapted in various applications. A standard operating procedure (SOP) was also designed for these markers and validated on diverse soybean varieties to ensure their reproducibility. This user-friendly universal panel of DNA markers, along with the SOP, will facilitate soybean research and breeding programs through simple applications. [ABSTRACT FROM AUTHOR]

Surface defect detection in industrial environments is crucial for quality management and has significant research value. General detection networks, such as the YOLO series, have proven effective in various dataset detections. However, due to the complex and varied surface defects of industrial products, many defects occupy a small proportion of the surface and fall into the category of typical small target detection problems. Moreover, the complexity of general detection network architectures relies on high-tech hardware, making it difficult to deploy on devices without GPUs or on edge computing and mobile devices. To meet the practical needs of industrial product defect inspection applications, this paper proposes a lightweight network specifically designed for defect detection in industrial fields. This network is composed of four parts: a backbone network, a multiscale feature aggregation network, a residual enhancement network, and an attention enhancement network. The network includes a backbone network that integrates attention layers for feature extraction, a multiscale feature aggregation network for semantic information, a residual enhancement network for spatial focus, and an attention enhancement network for global–local feature interaction. These components enhance detection performance for diverse defects while maintaining low hardware requirements. Experimental results show that this network outperforms the latest and most popular YOLOv5n and YOLOv8n models in the five indicators P, R, F1, mAP@.5, and GFLOPS when used on four public datasets. It even approaches or surpasses the YOLOv8s and YOLOv5s models with several times the GFLOPS computation. It balances the requirements of lightweight real-time and accuracy in the scenario of industrial product surface defect detection. [ABSTRACT FROM AUTHOR]

During industrial production activities, industrial products serve as critical resources whose performance is subject to various external factors and usage conditions. To ensure uninterrupted production processes and to guarantee the safety of the production personnel, a real-time analysis of the industrial product quality and subsequent decision making are essential. Conventional detection methods have inherent limitations in meeting the real-time demands of processing large volumes of data and achieving high response speeds. For instance, the regular inspection and maintenance of cars can be time-consuming and labor-intensive if performed manually. Furthermore, monitoring the damage situation of bearings in real time through a manual inspection may lead to delays and may hinder production efficiency. Therefore, this paper presents online machine-learning-based methods to address these two practical problems and simulates them on various datasets to meet the requirements of efficiency and speed. Prior to being fed into the network for training, the data undergo identity parsing to transform them into easily identifiable streaming data. The training process demonstrates that online machine learning ensures timely model updates as small batches of data are sent to the network. The test results indicate that the online learning method exhibits highly stable and effective performance, optimizing the training process. [ABSTRACT FROM AUTHOR]

Deep learning networks have shown excellent performance in surface defect recognition and classification of certain industrial products. However, most industrial product defect samples are scarce and have a wide variety of defect types, making methods that require a large number of defect samples for training unsuitable. In this paper, a lightweight surface defect detection network (LRN-L) based on texture complexity analysis is proposed. Only a large number of defect-free samples, which can be easily obtained, are needed to detect defects. LRN-L includes two stages: texture reconstruction stage and defect localization stage. In the texture reconstruction phase, a lightweight reconstruction network (LRN) based on convolutional autoencoder is designed, which can reconstruct defect-free texture images; a loss function combining structural loss and L1 loss is proposed to improve the detection effect; we built a calculation model for image complexity, calculated the texture complexity for texture samples, and divided textures into three levels based on complexity. In the defect localization stage, the residual between the reconstructed image and the original image is taken as the possible region of the defect, and the defect localization is realized via a segmentation algorithm. In this paper, the network structure, loss function, texture complexity and other factors of LRN-L are analyzed in detail and compared with other similar algorithms on multiple texture datasets. The results show that LRN-L has strong robustness, accuracy and generalization ability, and is more suitable for industrial online detection. [ABSTRACT FROM AUTHOR]

With environmental issues arising from the excessive use of fossil fuels, clean energy has gained widespread attention, particularly the application of lithium-ion batteries. Lithium-ion batteries are integrated into various industrial products, which necessitates higher safety requirements. Narrowband Internet of Things (NB-IoT) is an LPWA (Low Power Wide Area Network) technology that provides IoT devices with low-power, low-cost, long-endurance, and wide-coverage wireless connectivity. This study addresses the shortcomings of existing lithium-ion battery pack detection systems and proposes a lithium-ion battery monitoring system based on NB-IoT-ZigBee technology. The system operates in a master-slave mode, with the subordinate module collecting and fusing multi-source sensor data, while the master control module uploads the data to local monitoring centers and cloud platforms via TCP and NB-IoT. Experimental validation demonstrates that the design functions effectively, accomplishing the monitoring and protection of lithium-ion battery packs in energy storage power stations. [ABSTRACT FROM AUTHOR]

To solve the problem of anomaly detection in annular metal turning surfaces, this paper develops an anomaly detection algorithm based on a priori information and a multi-scale self-referencing template by combining the imaging characteristics of annular workpieces. First, the annular metal turning surface is unfolded into a rectangular expanded image using bilinear interpolation to facilitate subsequent algorithm development. Second, the grayscale information from the positive samples is used to obtain the a priori information, and a multi-scale self-referencing template method is used to obtain its own multi-scale information. Then, the phase error and large-size anomaly interference problems of the self-referencing method are overcome by combining the a priori information with its own information, and an accurate response to anomalous regions of various sizes is realized. Finally, the segmentation completeness of the anomalous region is improved by utilizing the region growing method. The experimental results show that the proposed method achieves a mean pixel AUROC of 0.977, and the mean M_IOU of segmentation reaches 0.788. In terms of efficiency, this method is also much more efficient than the commonly used anomaly detection algorithms. The proposed method can achieve rapid and accurate detection of defects in annular metal turning surfaces and has good industrial application value. [ABSTRACT FROM AUTHOR]

The increasing demand for industrial products has expanded production quantities, leading to negative effects on product quality, worker productivity, and safety during working hours. Therefore, monitoring the conditions in manufacturing environments, particularly human workers, is crucial. Accordingly, this study presents a model that detects workers' anomalous behavior in manufacturing environments. The objective is to determine worker movements, postures, and interactions with surrounding objects based on human–object interactions using a Mask R-CNN, MediaPipe Holistic, a long short-term memory (LSTM), and worker behavior description algorithm. The process begins by recognizing the objects within video frames using a Mask R-CNN. Afterward, worker poses are recognized and classified based on object positions using a deep learning-based approach. Next, we identified the patterns or characteristics that signified normal or anomalous behavior. In this case, anomalous behavior consists of anomalies correlated with human pose recognition (emergencies: worker falls, slips, or becomes ill) and human pose recognition with object positions (tool breakage and machine failure). The findings suggest that the model successfully distinguished anomalous behavior and attained the highest pose recognition accuracy (approximately 96%) for standing, touching, and holding, and the lowest accuracy (approximately 88%) for sitting. In addition, the model achieved an object detection accuracy of approximately 97%. [ABSTRACT FROM AUTHOR]

Commercial plantations of Eucalyptus species have been established in Malaysia, especially during the past 10 years, with the aim of sustaining the supply of wood and timber products for industrial use in Malaysia. As part of an assessment of fungal diseases affecting Eucalyptus species in four regions in Malaysia, including Kelantan, Pahang, Sabah, and Selangor, stem canker disease was discovered to be a widespread disease infecting Eucalyptus species in Malaysia. This study aimed to identify the fungus-causing stem canker disease, test its pathogenicity in Eucalyptus, and determine the mating type of isolates from the infected trees. The fungi were identified based on morphology and through comparisons of DNA sequence data from the ITS, β-tubulin 2 gene, and TEF-1α gene regions. Phylogenetic analyses showed that the causal agent of the stem canker was Chrysoporthe cubensis infecting Eucalyptus plantations in Pahang and Chrysoporthe deuterocubensis infecting Eucalyptus plantations in Kelantan, Sabah, and Selangor. We believe this is the first report of Chrysoporthe cubensis-infected Eucalyptus in Malaysia and Southeast Asia, while Chrysoporthe deuterocubensis is the first-reported species infecting Eucalyptus pellita in Malaysia. Moreover, the fact that the mating-type MAT1-1 and MAT1-2 genes and the pheromone genes ppg1, ppg2, pre1, and pre2 were identified in all isolates indicates that Chrysoporthe cubensis and Chrysoporthe deuterocubensis are homothallic mating systems. Pathogenicity was tested on a 3-year-old standing tree, 1-year-old seedling, and detached healthy leaves, which were re-isolated for fulfilling Koch's postulates. In pathogenicity trials, both Chrysoporthe cubensis and Chrysoporthe deuterocubensis gave rise to lesions on wounded Eucalyptus. Both Chrysoporthe spp. were equally pathogenic to Eucalyptus urograndis and Eucalyptus pellita and should be regarded as a biosecurity concern in Malaysia's forest plantation industry. [ABSTRACT FROM AUTHOR]

Steel surface defects have a significant impact on the quality and performance of many industrial products and cause huge economic losses. Therefore, it is meaningful to detect steel surface defects in real time. To improve the detection performance of steel surface defects with variable scales and complex backgrounds, in this paper, a novel method for detecting steel surface defects through a multiscale local and global feature fusion mechanism is proposed. The proposed method uses a convolution operation with a downsampling mechanism in the convolutional neural network model to obtain rough multiscale feature maps. Then, a context-extraction block (CEB) is proposed to adopt self-attention learning on the feature maps extracted by the convolution operation at each scale to obtain multiscale global context information to make up for the shortcomings of convolutional neural networks (CNNs), thus forming a novel multiscale self-attention mechanism. Afterwards, using the feature pyramid structure, multiscale feature maps are fused to improve multiscale object detection. Finally, the channel and spatial attention module and the WIOU (Wise Intersection over Union) loss function are introduced. The model achieved 78.2% and 71.9% mAP respectively on the NEU-DET and GC10-DET dataset. Compared to algorithms such as Faster RCNN and EDDN, this method is effective in improving the detection performance of steel surface defects. [ABSTRACT FROM AUTHOR]

Automatic measurements via image processing can accelerate measurements and provide comprehensive evaluations of mechanical parts. This paper presents a comprehensive approach to automating evaluations of planar dimensions in mechanical parts, providing significant advancements in terms of cost-effectiveness, accuracy, and repeatability. The methodology employed in this study utilizes a configuration comprising commonly available products in the industrial computer vision market, therefore enabling precise determinations of external contour specifications for mechanical components. Furthermore, it presents a functional prototype for making planar measurements by incorporating an improved subpixel edge-detection method, thus ensuring precise image-based measurements. The article highlights key concepts, describes the measurement procedures, and provides comparisons and traceability tests as a proof of concept for the system. The results show that this vision system did achieve suitable precision, with a mean error of 0.008 mm and a standard deviation of 0.0063 mm, when measuring gauge blocks of varying lengths at different heights. Moreover, when evaluating a circular sample, the system resulted in a maximum deviation of 0.013 mm, compared to an alternative calibrated measurement machine. In conclusion, the prototype validates the methods for planar dimension evaluations, highlighting the potential for enhancing manual measurements, while also maintaining accessibility. The presented system expands the possibilities of machine vision in manufacturing, especially in cases where the cost or agility of current systems is limited. [ABSTRACT FROM AUTHOR]

The surface defect detection of industrial products has become a crucial link in industrial manufacturing. It has a series of chain effects on the control of product quality, the safety of the subsequent use of products, the reputation of products, and production efficiency. However, in actual production, it is often difficult to collect defect image samples. Without a sufficient number of defect image samples, training defect detection models is difficult to achieve. In this paper, a defect image generation method DG-GAN is proposed for defect detection. Based on the idea of the progressive generative adversarial, D 2 adversarial loss function, cyclic consistency loss function, a data augmentation module, and a self-attention mechanism are introduced to improve the training stability and generative ability of the network. The DG-GAN method can generate high-quality and high-diversity surface defect images. The surface defect image generated by the model can be used to train the defect detection model and improve the convergence stability and detection accuracy of the defect detection model. Validation was performed on two data sets. Compared to the previous methods, the FID score of the generated defect images was significantly reduced (mean reductions of 16.17 and 20.06, respectively). The YOLOX detection accuracy was significantly improved with the increase in generated defect images (the highest increases were 6.1% and 20.4%, respectively). Experimental results showed that the DG-GAN model is effective in surface defect detection tasks. [ABSTRACT FROM AUTHOR]

Remote monitoring and maintenance are important for improving the performance of production systems. However, existing studies on this topic usually focus on the monitoring and maintenance of the working conditions of the equipment and pay relatively less attention to the processing craft and processing quality. In addition, as far as we know, there are relatively few industrial case studies on the real applications of remote monitoring and maintenance systems that include both conventional and advanced maintenance techniques under the context of Industry 4.0. Addressing these issues, an industrial case study on the monitoring and maintenance service system for a robot-driven carbon block polishing service system is presented, including its application background and engineering problems, software/hardware architecture and running logic, the monitoring and maintenance-related enabling techniques, and the configuration and operation workflows of the system in the form of screenshots of the functional WebAPPs of the software system. The case study can provide real examples and references for the industrial application of remote monitoring and maintenance service systems on industrial product service systems under the context of Industry 4.0. Advanced techniques such as the Industrial Internet of Things, digital twins, deep learning, and edge/cloud/fog computing have been applied to the system. [ABSTRACT FROM AUTHOR]

Reliable functionality in anomaly detection in thermal image datasets is crucial for defect detection of industrial products. Nevertheless, achieving reliable functionality is challenging, especially when datasets are image sequences captured during equipment runtime with a smooth transition from healthy to defective images. This causes contamination of healthy training data with defective samples. Anomaly detection methods based on autoencoders are susceptible to a slight violation of a clean training dataset and lead to challenging threshold determination for sample classification. This paper indicates that combining anomaly scores leads to better threshold determination that effectively separates healthy and defective data. Our research results show that our approach helps to overcome these challenges. The autoencoder models in our research are trained with healthy images optimizing two loss functions: mean squared error (MSE) and structural similarity index measure (SSIM). Anomaly score outputs are used for classification. Three anomaly scores are applied: MSE, SSIM, and kernel density estimation (KDE). The proposed method is trained and tested on the 32 × 32-sized thermal images, including one contaminated dataset. The model achieved the following average accuracies across the datasets: MSE, 95.33%; SSIM, 88.37%; and KDE, 92.81%. Using a combination of anomaly scores could assist in solving a low classification accuracy. The use of KDE improves performance when healthy training data are contaminated. The MSE+ and SSIM+ methods, as well as two parameters to control quantitative anomaly localization using SSIM, are introduced. [ABSTRACT FROM AUTHOR]

Prolonged development of ore deposits, ore beneficiation and metallurgical smelting of concentrates result in the accumulation of wastes, forming large-scale dumps and sludge ponds negatively affecting the environment and human health. The creation and introduction of industrial waste treatment technologies will make it possible to dispose of them with the production of valuable commercial products and improve the environmental situation. In a research article on chemical beneficiation with the subsequent gravitational beneficiation of chrome-containing slurry tailings of Donskoy Ore-Mining and Beneficiation Plant (DOMBP), which processes chrome ore of the Kempirsay deposit in the Republic of Kazakhstan, Aktobe region, having considerable stocks of such waste is presented. The chromium-containing slurry tailings were heat-treated at 1100 °C, sintered with ammonium sulfate, water and sulfuric acid in a defined ratio at 300 °C, and then the resulting sinter was leached with water at 90 °C. The cake was washed to separate the liquid part from the solid part. The solid precipitate is the chromium-containing cake, which is upgraded by gravity concentration to a grade concentrate for ferroalloy production. When the solution is evaporated, magnesium sulfate with ammonium sulfate forms the Tutton's salt ammoshenite. High-throughput chromium oxide extraction of 93.9% was achieved as a result of the research, and three products were obtained: (1) standard chromium concentrate with 49.48% Cr2O3 content, (2) ammoshenite used as a nitrogen–magnesium fertilizer and (3) forsterite, which is concentrated in gravity concentration tailings and can be used in the production of refractory minerals. [ABSTRACT FROM AUTHOR]

Under the development of interdisciplinary fusion, user research has been greatly influenced by technology-driven neuroscience and sensory science, in terms of thinking and methodology. The use of technical methods, such as EEG and eye-tracking, has gradually become a research trend and hotspot in this field, in order to explore the deep cognitive states behind users' objective behaviors. This review outlines the applications of EEG and eye-tracking technology in the field of user research, with the aim of promoting future research and proposing reliable reference indicators and a research scope. It provides important reference information for other researchers in the field. The article summarizes the key reference indicators and research paradigms of EEG and eye-tracking in current user research, focusing on the user research situation in industrial products, digital interfaces and spatial environments. The limitations and research trends in current technological applications are also discussed. The feasibility of experimental equipment in outdoor environments, the long preparation time of EEG experimental equipment, and the accuracy error of physiological signal acquisition are currently existing problems. In the future, research on multi-sensory and behavioral interactions and universal studies of multiple technology fusions will be the next stage of research topics. The measurement of different user differentiation needs can be explored by integrating various physiological measurements such as EEG signals and eye-tracking signals, skin electrical signals, respiration, and heart rate. [ABSTRACT FROM AUTHOR]

Ensuring the expected quality of materials is still a challenge, mainly in order to precisely plan improvement actions that allow for stabilization of the production process. Therefore, the purpose of this research was to develop a novel procedure to identify critical causes of material incompatibility—the causes that have the largest negative impact on material deterioration, and the natural environment. The main originality of this procedure is developing a way to coherent analyse the mutual influence of the many causes of incompatibility of any material, after which the critical causes are identified and a ranking of improvement actions to eliminate these causes is created. A novelty is also developed in the algorithm supporting this procedure, which can be realized in three different ways to solve this problem, i.e.; by considering the impact of material incompatibility on: (i) the deterioration of the material quality; (ii) the deterioration of the natural environment; and (iii) simultaneously the deterioration of the quality of the material and the natural environment. The effectiveness of this procedure was confirmed after tests on 410 alloy, from which a mechanical seal was made. However, this procedure can be useful for any material or industrial product. [ABSTRACT FROM AUTHOR]

Buckwheat is a pseudo-cereal widely grown and consumed throughout the world. Buckwheat is recognized as a good source of nutrients and, in combination with other health-promoting components, is receiving increasing attention as a potential functional food. Despite the high nutritional value of buckwheat, a variety of anti-nutritional features makes it difficult to exploit its full potential. In this framework, sprouting (or germination) may represent a process capable of improving the macromolecular profile, including reducing anti-nutritional factors and/or synthesizing or releasing bioactives. This study addressed changes in the biomolecular profile and composition of buckwheat that was sprouted for 48 and 72 h. Sprouting increased the content of peptides and free-phenolic compounds and the antioxidant activity, caused a marked decline in the concentration of several anti-nutritional components, and affected the metabolomic profile with an overall improvement in the nutritional characteristics. These results further confirm sprouting as a process suitable for improving the compositional traits of cereals and pseudo-cereals, and are further steps towards the exploitation of sprouted buckwheat as a high-quality ingredient in innovative products of industrial interest. [ABSTRACT FROM AUTHOR]