Search Results (396)

Effective patent recommendation plays a crucial role in bridging the gap between enterprise technological demands and patent supply. However, semantic mismatches and incomplete demand expressions often hinder accurate supply–demand matching. This research proposes a demand-driven patent recommendation method. First, content analysis and topic clustering were used to construct an enterprise demand element system, dividing the demand content into five elements: materials, methods, efficacy, products, and applications. Based on the completeness of these elements, enterprise demands were further classified into explicit and implicit types. Second, an enterprise technical problem space and a patent solution space were established, identifying ten types of enterprise technical problems and fifteen types of patent solution categories. These were connected through supply–demand elements to build corresponding correlation systems for explicit and implicit demands. Finally, according to different types of supply–demand correlations and demand characteristics, differentiated patent recommendation methods were designed. Taking various demands in the lithium battery industry as empirical cases, the results show that the proposed method based on demand classification and supply–demand element association effectively achieves accurate patent matching and addresses the challenges caused by incomplete demand information. The study provides an intelligent, content-based recommendation pathway for enterprise technology acquisition and patent transformation, offering theoretical and practical significance for enhancing patent commercialization and improving the efficiency of technological achievement transformation. Full article

Background: Qu was the core starter of traditional Chinese fermentation and had long relied on artisan experience, which led to limited batch stability and standardization. This review organized the preparation processes, microbial diversity, and application patterns of qu in foods from experience to science perspective. Methods: This work summarized typical process parameters for daqu, xiaoqu, hongqu, wheat bran or jiangqu, douchi qu, and qu for mold curd blocks used for furu. Parameters covered raw material moisture, bed thickness, aeration or turning, drying, final moisture, and classification by peak temperature. Multi-omics evidence was used to analyze the coupling of temperature regime, community assembly, and functional differentiation. Indicators for pigment or enzyme production efficiency and safety control such as citrinin in hongqu were included. Results: Daqu showed low, medium, and high temperature regimes. Thermal history governed differences in communities and enzyme profiles and determined downstream fermentation fitness. Xiaoqu rapidly established a three-stage symbiotic network of Rhizopus, Saccharomyces, and lactic acid bacteria, which supported integrated saccharification and alcohol fermentation. Hongqu centered on Monascus and achieved coordinated pigment and aroma formation with toxin risk control through programmed control of temperature, humidity, and final moisture. Wheat bran or jiangqu served as an enzyme production engine for salt-tolerant fermentation, and the combined effects of heat and humidity during the qu period, aeration, and bed loading determined hydrolysis efficiency in salt. Douchi and furu mold curd blocks used thin-layer cultivation and near-saturated humidity to achieve stable mold growth and reproducible interfacial moisture. Conclusions: Parameterizing and online monitoring of key variables in qu making built a process fingerprint with peak temperature, heating rate, and moisture rebound curve at its core. Standardization and functional customization guided by temperature regime, community, and function were the key path for the transition of qu from workshop practice to industry and from experience to science. This approach provided replicable solutions for flavor consistency and safety in alcoholic beverages, sauces, vinegars, and soybean products. Full article

The genus Auricularia, and specifically the species A. auricula, is a globally significant edible fungus with a long history of cultivation and notable nutritional and medicinal properties. This review systematically examines the taxonomic classification, morphological and physiological characteristics, and bioactive components (such as polysaccharides, melanin, proteins and polyphenols) of A. auricula, as well as their pharmacological effects and industrial applications. Recent molecular biological advances have clarified taxonomic uncertainties, including the reclassification of ‘heimuer’ as A. cornea, and emphasized the species’ genetic diversity. A. auricula thrives in temperate and subtropical regions, with cultivation techniques evolving from traditional wood log inoculation to modern substrate-based methods. However, sustainability challenges persist, including reliance on virgin wood substrates and the need for improved spent substrate management. The fungus exhibits remarkable nutritional properties, with polysaccharides (up to 66.1% of dry weight) demonstrating hypoglycemic, antitumor, immunomodulatory, and antioxidant activities. Melanin and proteins further contribute to hepatoprotection, antimicrobial effects, and metabolic regulation. Industrial applications of Auricularia species extend beyond food into pharmaceuticals and functional materials. Polysaccharides are explored as drug carriers, while melanin shows promise in antioxidant and antibacterial formulations. Despite these advances, gaps remain in understanding the mechanistic basis of bioactive compound functions and optimizing cultivation for sustainable production. Future research should integrate multi-omics approaches to elucidate genetic regulation, enhance substrate formulations, and develop value-added products. This review underscores the potential of Auricularia species as a functional food and biotechnological resource, advocating for interdisciplinary efforts to address current challenges and unlock its full industrial potential. Full article

Classification is a crucial task for reintroducing end-of-life fabrics as raw materials in a circular process, thus reducing reliance on dyeing processes. In this context, this review explores the evolution of automated and semi-automated colour classification methods, emphasizing the transition from deterministic techniques to advanced methods, with a focus on machine learning, deep learning, and particularly Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs). These technologies show potential for improving accuracy and efficiency. The results highlight the need for enriched datasets, deeper AI integration into industrial processes, and alignment with circular economy objectives to enhance sustainability without compromising industrial performance. Tested against a case study, the different architectures confirmed the state-of-the-art statements demonstrating that they are effective in classification, with better performance reached by CNN-based methods, which outperforms other methods in most colour families, with an average accuracy of 86.1%, indicating its adaptability for this task. The adoption of the proposed AI-based colour-classification roadmap could be effective in reducing dyeing operations, lower costs, and improve sorting efficiency for textile SMEs. Full article

Human Activity Recognition (HAR) is widely used for healthcare, but few works focus on Manual Material Handling (MMH) activities, despite their diffusion and impact on the workers’ health. We propose four Deep Learning algorithms for HAR in MMH: Bidirectional Long Short-Term Memory (BiLSTM), Sparse Denoising Autoencoder (Sp-DAE), Recurrent Sp-DAE, and Recurrent Convolutional Neural Network (RCNN). We explored different hyperparameter combinations to maximize the classification performance (F1-score,) using wearable sensors’ data gathered from 14 subjects. We investigated the best three-parameter combinations for each network using the full dataset to select the two best-performing networks, which were then compared using 14 datasets with increasing subject numerosity, 70–30% split, and Leave-One-Subject-Out (LOSO) validation, to evaluate whether they may perform better with a larger dataset. The benchmarking network DeepConvLSTM was tested on the full dataset. BiLSTM performs best in classification and complexity (95.7% 70–30% split; 90.3% LOSO). RCNN performed similarly (95.9%; 89.2%) with a positive trend with subject numerosity. DeepConvLSTM achieves similar classification performance (95.2%; 90.3%) but requires $\times 57.1$ and $\times 31.3$ more Multiply and ACcumulate (MAC) and $\times 100.8$ and $\times 28.3$ more Multiplication and Addition (MA) operations, which measure the complexity of the network’s inference process, than BiLSTM and RCNN, respectively. The BILSTM and RCNN perform close to DeepConvLSTM while being computationally lighter, fostering their use in embedded systems. Such lighter algorithms can be readily used in the automatic ergonomic and biomechanical risk assessment systems, enabling personalization of risk assessment and easing the adoption of safety measures in industrial practices involving MMH. Full article

This study focuses on first-generation crystalline silicon photovoltaic (PV) cells, which remain the core of the PV industry. It outlines the structure and operation of single-junction cells, distinguishing between monocrystalline and polycrystalline technologies. A literature review was conducted using databases such as Web of Science and Scopus to identify research trends and inform future research directions. PV cell classification by generation is also presented based on production methods and materials. The experimental section includes both electrical and structural characterisation of crystalline silicon solar cells, with particular emphasis on the influence of the number and geometry of front-side busbars on metal-semiconductor contact resistance and electrical properties. Additionally, the paper highlights the use of dedicated laboratory equipment—such as a solar simulator (for determining photovoltaic cell parameters from current-voltage characteristics) and Corescan equipment (for determining layer parameters using the single-tip probe method)—in evaluating PV cell properties. This equipment is part of the Photovoltaics and Electrical Properties Laboratory at the Silesian University of Technology. The findings demonstrate clear structural correlations that can contribute to optimising the performance and longevity of silicon-based PV cells. Full article

Pyrolysis has emerged as a commercially viable material recovery process that supports circularity in the tyre industry. Here, it is demonstrated that a high degree of control can be imparted over the UK tyre waste stream and that statistically different feedstocks can be used to produce different grades of rCB based on their ash contents. The lower ash content rCB produced from truck tyres had superior in-rubber properties, closely matching those of the N550 reference. Silica, when not paired with a coupling agent, is known to be less reinforcing than CB, lowering the reinforcing behaviour of the high ash content rCB variant produced from car tyres. This justifiably places ash content within the classification and specification development discussion. However, a proximate analysis of UK waste tyres suggests that the typical rCB ash specifications of <20 wt% are unrealistic. Such limits would force producers to consider modifying process conditions to allow the deposition of carbonaceous residues to artificially dilute the ash content. This study investigates this process philosophy but conclusively demonstrates that carbonaceous residue is more detrimental to rCB performance than ash content. As such, carbonaceous residue content demands far more attention from the industry than it is currently afforded. Full article

Keratin is a fibrous structural protein found in various natural materials such as hair, feathers, and nails. Its high stability and cross-linked structure make it resistant to degradation by common proteases, leading to the accumulation of keratinous waste in various industries. In this study, we developed and validated an effective bioinformatics-driven strategy for mining novel keratinase genes from the Esmatlas (ESM Metagenomic Atlas) macrogenomic database. Two candidate genes, ker820 and ker907, were identified through sequence alignment, structural modeling, and phylogenetic analysis, and were subsequently heterologously expressed in Escherichia coli Rosetta (DE3) with the assistance of a solubility-enhancing chaperone system. Both enzymes belong to the Peptidase S8 family. Enzymatic characterization revealed that GST-tagged ker820 and ker907 exhibited strong keratinolytic activity, with optimal conditions at pH 9.0 and temperatures of 60 °C and 50 °C, respectively. Both enzymes showed significant degradation of feather and cat-hair keratin. Kinetic analysis showed favorable catalytic parameters, including $K_m$ values of 9.81 mg/mL (ker820) and 5.25 mg/mL (ker907), and $V_{max}$ values of 120.99 U/mg (ker820) and 89.52 U/mg (ker907). Stability tests indicated that GST-ker820 retained 70% activity at 60 °C for 120 min, while both enzymes remained stable at 4 °C for up to 10 days. These results demonstrate the high catalytic capacity, thermal stability, and substrate specificity of the enzymes, supporting their classification as active keratinases. This study introduces a promising strategy for efficiently discovering novel functional enzymes using an integrated computational and experimental approach. Beyond keratinases, this methodology could be extended to screen for enzymes with potential applications in environmental remediation. Full article

Several reviews have been published addressing the challenges of waste collection and recycling across various sectors, including municipal, industrial, construction, and agricultural domains. These studies often emphasize the role of existing technologies in addressing recycling-related issues. Among the diverse range of waste materials, glass remains a significant component, frequently grouped with other multi-class waste types (such as plastic, cardboard, and metal) for segregation and classification processes. The primary aim of this review is to examine the technologies specifically involved in the collection and separation stages of waste in which glass represents a major or exclusive fraction. The second objective is to present the main technologies and computer vision sensors currently used in managing glass waste. This study not only references laboratory developments or experiments on standard datasets, but also includes projects, patents, and real-world implementations that are already delivering measurable results. The review discusses the technological possibilities, gaps, and challenges faced in this specialized field of research. Full article

Photovoltaic-thermal (PVT) solar collector technologies are considered a highly efficient solution for sustainable energy generation, capable of producing electricity and heat simultaneously. This paper reviews and discusses different aspects of PVT collectors, including fundamental principles, materials, diverse classifications, such as air-type and water-type, and different cooling mechanisms to boost their performance, such as nano-fluids, Phase Change Materials (PCMs), and Thermoelectric Generators (TEGs). At the system level, this paper analyses PVT technologies’ integration in buildings and industrial applications and gives a comprehensive market overview. The methodology focused on evaluating advancements in design, thermal management, and overall system efficiency based on existing literature published from 2010 to 2025. From the findings of various studies, water-based PVT systems provide electrical efficiencies ranging from 8% to 22% and thermal efficiencies between 30% and 70%, which are almost always higher than air-based alternatives. Innovations, including nanofluids, phase change materials, and hybrid topologies, have improved energy conversion and storage. Market data indicates growing adoption in Europe and Asia, stressing significant investments led by Sunmaxx, Abora Solar, Naked Energy, and DualSun. Nonetheless, obstacles to PVT arise regarding aspects such as cost, design complexity, lack of awareness, and economic incentives. According to the findings of this study, additional research is required to reduce the operational expenses of such systems, improve system integration, and build supportive policy frameworks. This paper offers guidance on PVT technologies and how they can be integrated into different setups based on current normativity and regulatory frameworks. Full article

A surge in environmental pollution compels society to utilize food processing wastes to produce valuable compounds. Enzymatic technology, specifically cellulase-mediated hydrolysis, provides an eco-friendly and effective approach for treating food processing leftovers. The main objective of this review is to explore the significant contributions of cellulase, both in industrial settings and from an environmental perspective. Therefore, this review covers all the aspects of cellulase structural identification, classification, and evolution to its profound applications. The review initially explores cellulases’ structural and functional characteristics based on the catalytic and cellulose-binding domains and discusses cellulases’ evolutionary origin. A thorough understanding of cellulase properties is essential for overcoming the challenges associated with its commercial production for various applications. In this regard, the optimization for cellulase production through several approaches, including rational design, direct evolution, genetic engineering, and fermentation technology, is also reviewed. In addition, it also underscores the significance of agro-industrial biorefineries, which provide scalable and sustainable solutions to meet future demands for food, chemicals, materials, and fuels. Finally, the last sections of the review solely highlight the potential applications of microbial cellulases in bioremediation. In summary, this review outlines the role of cellulase in efficient valorization aimed at producing multiple bioproducts and the enhancement of environmental remediation efforts. Full article

The adoption of artificial intelligence (AI) in industrial manufacturing lags behind research progress, partly due to smaller, imbalanced datasets derived from real processes. In non-destructive aerospace testing, this challenge is amplified by the low defect rates of high-quality manufacturing. This study evaluates the use of synthetic data, generated via multiresolution stochastic texture synthesis, to mitigate class imbalance in material defect classification for the superalloy Inconel 718. Multiple datasets with increasing imbalance were sampled, and an image classification model was tested under three conditions: native data, data augmentation, and synthetic data inclusion. Additionally, round robin tests with experts assessed the realism and quality of synthetic samples. Results show that synthetic data significantly improved model performance on highly imbalanced datasets. Expert evaluations provided insights into identifiable artificial properties and class-specific accuracy. Finally, a quality assessment model was implemented to filter low-quality synthetic samples, further boosting classification performance to near the balanced reference level. These findings demonstrate that synthetic data generation, combined with quality control, is an effective strategy for addressing class imbalance in industrial AI applications. Full article

Carbon Fiber-Reinforced Polymer (CFRP) composites, widely used across industries, exhibit various damage mechanisms depending on the loading conditions applied. This study employs a structural health monitoring (SHM) approach to investigate the three primary failure modes, fiber breakage, matrix cracking, and delamination, in thermoset quasi-isotropic CFRPs subjected to quasi-static tensile loading until failure. Acoustic emission (AE) signals acquired from an experiment were leveraged to analyze and classify these real-time signals into the failure modes using machine learning (ML) techniques. Due to the extensive number of AE signals recorded during testing, manually classifying these failure mechanisms through waveform inspection was impractical. ML, alongside ensemble learning, algorithms were implemented to streamline the classification, making it more efficient, accurate, and reliable. Conventional AE parameters from the data acquisition system and feature extraction techniques applied to the recorded waveforms were implemented exclusively as classification features to investigate their reliability and accuracy in classifying failure modes in CFRPs. The classification models exhibited up to 99% accuracy, as depicted by evaluation metrics. Further studies, using cross-correlation techniques, ascertained the presence of fiber break events occurring in the bundles as the thermoset CFRP composite approached failure. These findings highlight the significance of integrating machine learning into SHM for the early detection of real-time damage and effective monitoring of residual life in composite materials. Full article

Recently, deep learning models such as convolutional neural networks (CNNs), convolutional autoencoders (CAEs), CNN-based support vector machines (SVMs), YOLO, fully convolutional networks (FCNs), fully convolutional data descriptions (FCDDs) and so on have been applied to defect detections and anomaly detections of various kinds of industrial products, materials and systems. In those models, downsampled images, including target features, are used for training and testing. On the other hand, although various types of anomaly detection systems based on time series data such as sounds and vibrations are also applied to manufacturing processes, complicated conversions to the frequency domain are basically needed in conventional approaches. This paper addresses an important industrial problem for detecting anomalies in machine tools at low cost using audio data. Intelligent anomaly diagnosis systems for computer numerical control (CNC) machine tools are considered and proposed, in which raw time-series data without the need of conversion to the frequency domain can be directly used for training and testing. As for the NN models for comparison, conventional shallow NN, RNN and 1D CNN are designed and trained using the nine kinds of mechanical sounds. Classification results of test sound block (SB) data by the three models are shown. Then, an autoencoder (AE) is designed and considered for the identifier by training it using only normal SB data of a machine tool. One of the technical needs in dealing with time-series data such as SB data by NNs is how to clearly visualize and understand anomalous regions in concurrence with identification. Finally, we propose the SB data-based FCDD model to meet this need. Basic performance of the SB data-based FCDD model is evaluated in terms of anomaly detection and concurrent visualization of understanding. Full article

Australia consumes approximately 29 million m³ of concrete each year with an estimated embodied carbon (EC) of 12 Mt CO_2e. High consumption of concrete makes it critical for successful decarbonization to support the achievement of ‘Net Zero 2050’ objectives of the Australian construction industry. Portland cement (PC) constitutes only 12–15% of the concrete mix but is responsible for approximately 90% of concrete’s EC. This necessitates reducing the PC in concrete with supplementary cementitious materials (SCMs) or using alternative binders such as geopolymer concrete. Concrete mixes including a combination of PC and SCMs as a binder have lower embodied carbon (EC) than those with only PC and are termed as low-carbon concrete (LCC). SCM addition to a concrete mix not only reduces EC but also enhances its mechanical and durability properties. Fly ash (FA) and granulated ground blast furnace slag (GGBFS) are the most used SCMs in Australia. It is noted that other SCMs such as limestone, metakaolin or calcinated clay, Delithiated Beta Spodumene (DBS) or lithium slag, etc., are being trialed. This technical paper presents a methodology that enables selecting LCCs with various degrees of SCMs for various elements of bridge structure without compromising their functional performance. The proposed methodology includes controls that need to be applied during the design/selection process of LCC, from material quality control to concrete mix design to EC evaluation for every element of a bridge, to minimize the overall carbon footprint of a bridge. Typical properties of LCC with FA and GGBFS as binary and ternary blends are also included for preliminary design of a fit-for-purpose LCC. An example for a bridge located in the B2 exposure classification zone (exposed to both carbonation on chloride ingress deterioration mechanisms) has also been included to test the methodology, which demonstrates that EC of the bridge may be reduced by up to 53% by use of the proposed methodology. Full article