Search Results (39,144)

Postharvest fruit softening is a critical determinant of fruit shelf life, significantly influencing mechanical damage susceptibility, pathogen invasion, and consumer preference. Collectively, these factors lead to substantial losses in the fruit industry. The structural modifications of cell wall and cuticle during ripening primarily govern fruit softening. The objective of this review is to synthesize recent advances and provide a comprehensive analysis of the molecular mechanisms underlying this process. In this review, we provide a comprehensive analysis of cell wall composition and softening-associated cell wall remodeling proteins. We examine recent advances in manipulating single or multiple genes encoding cell wall-modifying proteins that influence fruit softening, and identify key transcription factors regulating the expression of these gene networks. This review synthesizes current understanding of the molecular mechanisms governing fruit ripening, providing a foundation for future research in postharvest biology. Full article

It is challenging to manufacture complex and intricate shapes and geometries with desired surface characteristics using a single manufacturing process. Parts often need to undergo post-processing and must be transported from one machine into another between steps. This makes the whole process cumbersome, time-consuming, and inaccurate. These shortcomings play a major role during the manufacturing of micro and nano products. Hybrid manufacturing (HM) has emerged as a favorable solution for these issues. It is a flexible process that combines two or more manufacturing processes, such as additive manufacturing (AM) and subtractive manufacturing (SM), into a single setup. HM works synergistically to produce complex, composite, and customized components. It makes the process more time efficient and accurate and can prevent unnecessary transportation of parts. There are still challenges ahead regarding implementing and integrating sensors that allow the machine to detect defects and repair or customize parts according to needs. Even though modern hybrid machines forecast an exciting future in the manufacturing world, they still lack features such as real-time adaptive manufacturing based on sensors and artificial intelligence (AI). Earlier reviews do not profoundly elaborate on the types of laser HM machines available. Laser technology resolutely handles additive and subtractive manufacturing and is capable of producing groundbreaking parts using a wide scope of materials. This review focuses on HM and presents a compendious overview of the types of hybrid machines and setups used in the scientific community and industry. The study is unique in the sense that it covers different HM setups based on machine axes, materials, and processing parameters. We hope this study proves helpful to process, plan, and impart productivity to HM processes for the betterment of material utilization and efficiency. Full article

The Digital Product Passport (DPP) is an emergent strategic tool poised to significantly enhance traceability, circularity, and sustainability within industrial supply chains, aligning with evolving European Union regulatory frameworks. This paper introduces a conceptual sensor-based DPP architecture specifically designed for the construction industry, exemplified by a real case study for a bio-based manufacturing company. This framework facilitates a transparent and accessible data management approach, crucial for fostering circular practices and guiding stakeholders in decision-making along the value chain. The proposed architecture addresses critical challenges in product-related traceability and information accessibility across the entire product life cycle, spanning from raw material supply to the construction and installation process (A1–A5 stages). Data collected from the low-tech sensor network and digital tools can generate relevant environmental indicators for Life Cycle Assessment (LCA) and DPP creation, thereby offering a comprehensive, detailed, and certified overview of product attributes and their environmental impacts. The study clarifies the benefits and current barriers to implementing a sensor-based DPP architecture in low-tech construction manufacturing, underscoring the potential of lightweight, interoperable sensing solutions to advance compliance, transparency, and digitalization in traditionally under-digitized sectors like construction materials manufacturing. Full article

In aluminum electrolysis workshops, real-time pose perception of shelling heads is crucial to process accuracy and equipment safety. However, due to high temperatures, smoke, dust, and metal obstructions, traditional pose estimation methods struggle to achieve high accuracy and robustness. At the same time, the continuous movement of the shelling head and the similar geometric structures around it make it hard to match point-clouds, which makes it even harder to track the position and orientation. In response to the above challenges, we propose a multi-stage optimization pose estimation algorithm based on point-cloud processing. This method is designed for dynamic perception tasks of three-dimensional components in complex industrial scenarios. First stage improves the accuracy of initial matching by combining a weighted 3D Hough voting and adaptive threshold mechanism with an improved FPFH feature matching strategy. In the second stage, by integrating FPFH and PCA feature information, a stable initial registration is achieved using the RANSAC-IA coarse registration framework. In the third stage, we designed an improved ICP algorithm that effectively improved the convergence of the registration process and the accuracy of the final pose estimation. The experimental results show that the proposed method has good robustness and adaptability in a real electrolysis workshop environment, and can achieve pose estimation of the shelling head in the presence of noise, occlusion, and complex background interference. Full article

This article describes the implemented technological solution of utilizing waste heat by upgrading the potato protein drying line and using energy recuperation in the drying plant. In this article, the technological sequence of the potato starch and potato protein production plant was analyzed and the identification of possible solutions that lead to a reduction in energy demand was described. The method of analyzing the processing data is based on existing models describing the flow of mass and energy fluxes. The authors did not seek new mathematical descriptions of the physicochemical phenomena occurring during the drying processes, and only modification of the technological line based on the current state of knowledge in process engineering has been proposed. The full heat recovery of the production line was applied, and the exhaust air after drying and the heat from the decanter leachate after centrifugation of the coagulated potato protein, from two energy-coupled starch dryers, were used as the source of recovered heat energy. Temperature measurements were taken at key process nodes, and the energy effects were estimated after the process line upgrade. The solution proposed in the article fits with circular economy, bringing notable economic and environmental benefits consisting of utilizing waste heat from technological processes in the food industry. Full article

Arsenic-containing wastewater and soil systems are a serious hazard to public health and the environment, particularly in areas where agriculture and drinking water depend on groundwater. Therefore, the removal of arsenic contamination from soil, water, and the environment is of great importance for human welfare. Most of the conventional methods are inefficient and have very high operational costs, especially for metals at low concentrations or in large solution volumes. This review delivers a comprehensive approach to arsenic remediation, including microbiological processes, phytoremediation, biochar technologies, bio-based adsorbents, and nanomaterial-assisted techniques. All of these methods are thoroughly examined in terms of removal competence, their mechanisms, environmental impact, cost-effectiveness, and scalability. Phytoremediation and microbial remediation techniques are self-regenerating and eco-friendly, whereas fruit-waste-derived materials and biochar provide abundant adsorbents, and are therefore low-cost. On the other hand, nanotechnology-based approaches show remarkable effectiveness but raise concerns regarding economic feasibility and environmental safety. Additionally, this review represents a comparative analysis and discusses synergistic and hybrid systems that combine multiple technologies for enhancing the remediation performance. Future research directions are emphasized along with challenges such as material stability, regeneration, and policy integration. This review aims to guide decision-makers, research scholars, and industry stakeholders toward affordable, sustainable, and high-performance arsenic remediation techniques for practical use. Full article

This paper innovatively constructs a comprehensive material cycle network framework for the circular economy system of the coal industry and evaluates the circular economy efficiency of China’s provincial coal industry from 2011 to 2021 using a comprehensive evaluation model that integrates emergy analysis and dynamic network data envelopment analysis (DEA). The research delves into the evolutionary characteristics of the coal industry’s circular economy and identifies the underlying causes of inefficiency. The results reveal that the circular economy in China’s coal industry has gone through three stages: the transformation period, the reinforcement period, and the growth period, with the inefficiency of waste reutilization being the key factor restricting the overall improvement in efficiency. The circular economy model in the production phase is gradually shifting from an extensive linear model to a clean, closed-loop model, while a significant gap remains between the high-emission linear model and the low-pollution closed-loop model in the utilization phase. Furthermore, regional heterogeneity mainly arises from imbalances in the operational efficiency of the circular economy system. This study not only reveals the deep-seated reasons for the low efficiency of circular economy in China’s coal industry but also provides strategies and directions for achieving a more efficient circular economy and carbon mitigation goals. Full article

The partnership between government and market plays a crucial role in allocating green resources and fostering collaboration across organizations and departments. It integrates diverse knowledge types into the green innovation process and offers multifaceted insights into enterprises’ responses to green governance decisions. However, existing research predominantly examines the interplay among government green governance instruments, with insufficient exploration of the synergistic impacts of government and market in green governance. This study constructs a capacity coupling coefficient model to measure the synergy degree of “government–market” green governance (GMGG). Exploiting a balanced dynamic panel of 28,451 firm-year observations for 3807 Chinese listed companies from 2010 to 2020, we estimate the causal effect of GMGG synergy on corporate green innovation (CGI) and further dissect the underlying transmission mechanisms as well as the moderating channels through which the effect operates. Empirical results reveal that the effect of GMGG synergy on CGI is subject to diminishing marginal returns, with the effect being significantly more pronounced for substantive green innovation. Heterogeneity analysis indicates that non-state-owned firms, eastern-region firms, and those in non-heavy-polluting industries respond with markedly greater sensitivity. Mechanism analysis further demonstrates that the extent of marketization serves as a mediating channel, whereas an elevated level of digital-economy development mitigates the impact of GMGG synergy on CGI. This study delineates the effective boundary of GMCC synergy in stimulating CGI, providing empirical benchmarks for the synergistic implementation of effective government and efficient market actions in green governance. It further corroborates the positive roles of marketization and the digital economy as novel governance instruments, thereby offering critical policy insights for the coordinated advancement of the “dual-carbon” goals and high-quality economic development. Full article

Fundamental research, exploration, extraction, and metallurgical studies of rare earth elements (REEs) require the use of analytical techniques. Recently, emerging developments of analytical instrumentation for REEs have taken place, with some of them having shrunk in size, becoming handheld devices. The Flame and Graphite Furnace AAS, ICP-OES, and MP-AES are standard laboratory techniques used for the analysis of REEs. ICP-MS, ICP-MS/MS, ICP-TOF-MS, HR-ICP-MS, MH-ICP-MS, and MC-ICP-MS are popular techniques for REE analysis thanks to their ultrahigh sensitivity, minimal interference effects, and broad applicability. The INAA, XRF, LIBS, and LA-based ICP-MS techniques are widely employed for the direct analysis of solid samples. The TIMS, SIMS, and SHRIMP are common techniques used for dating isotopic REE deposits. The portable XRF, LIBS, and Raman spectrometer devices can perform on-the-spot in situ analysis, which may help make speedy decisions in the exploration study of REEs. Currently, hyperspectral remote sensing platforms, such as handheld, drone, and satellite-based devices, are preferred for the exploration of REEs due to their cost-effectiveness, which enables the coverage of large areas in a limited amount of time. The use of microanalytical sensors installed on remotely operated vehicles has been successfully applied in analyzing rich REE-bearing deposits in the deep sea. In general, this review provides in-depth information on all essential aspects, from analytical instruments to cutting-edge developments in the analysis of REE-bearing resources. Full article

Spent coffee grounds (SCGs), a globally abundant by-product of the coffee industry, represent a significant source of lignocellulosic biomass with considerable valorization potential. Rich in organic compounds, lipids, and antioxidants, SCGs are increasingly recognized as a sustainable feedstock for energy, materials, and environmental applications within a circular bioeconomy framework. This review critically examines recent advances in SCG valorization via thermochemical, biochemical, and material-based pathways. The review focuses on the conversion of SCGs into biofuels (biodiesel, bioethanol, biogas, and bio-oil), activated carbon for water and air purification, biodegradable polymers, and soil-enhancing amendments. Comparative analyses of process conditions, product yields, and techno-economic feasibility are provided through summarized tables. Although laboratory-scale studies demonstrate promising outcomes, challenges persist in terms of process scalability, environmental impacts, feedstock variability, and lack of regulatory standardization. Furthermore, comprehensive life cycle assessments and policy integration remain underdeveloped. By merging all findings, this review identifies key knowledge gaps and outlines strategic directions for future research, including the development of integrated valorization platforms, hybrid conversion systems, and industrial-scale implementation. The findings support the role of SCG valorization in advancing sustainable resource management and contribute directly to the achievement of multiple Sustainable Development Goals. Full article

Maintaining the freshness of shrimp is a critical issue in quality and safety control within the food processing industry. Traditional methods often rely on destructive techniques, which are difficult to apply in online real-time monitoring. To address this challenge, this study aims to propose a non-destructive approach for shrimp freshness assessment based on imaging and deep learning, enabling efficient and reliable freshness classification. The core innovation of the method lies in constructing an improved GoogLeNet architecture. By incorporating the ELU activation function, L2 regularization, and the RMSProp optimizer, combined with a transfer learning strategy, the model effectively enhances generalization capability and stability under limited sample conditions. Evaluated on a shrimp image dataset rigorously annotated based on TVB-N reference values, the proposed model achieved an accuracy of 93% with a test loss of only 0.2. Ablation studies further confirmed the contribution of architectural and training strategy modifications to performance improvement. The results demonstrate that the method enables rapid, non-contact freshness discrimination, making it suitable for real-time sorting and quality monitoring in shrimp processing lines, and providing a feasible pathway for deployment on edge computing devices. This study offers a practical solution for intelligent non-destructive detection in aquatic products, with strong potential for engineering applications. Full article

Microbial growth and inhibition are complex biological processes influenced by diverse environmental and chemical factors, posing challenges for accurate modelling and prediction. Traditional mechanistic models often struggle to capture the nonlinear and multidimensional interactions inherent in real-world food systems, especially when multiple environmental variables and inhibitors are involved. This study presents the development of a novel, dynamic software platform that integrates classical predictive microbiology models—including both one-step and two-step frameworks—with advanced machine learning (ML) methods such as Support Vector Regression, Random Forest Regression, and Gaussian Process Regression. Uniquely, this platform enables direct comparisons between two-step and one-step modelling approaches across four widely used growth models (modified Gompertz, Logistic, Baranyi, and Huang) and three inhibition models (Log-Linear, Log-Linear + Tail, and Weibull), offering unprecedented flexibility for model evaluation and selection. Furthermore, the platform incorporates ML-based modelling for both microbial growth and inhibition, expanding predictive capabilities beyond traditional parametric frameworks. Validation against experimental and literature datasets demonstrated the platform’s high predictive accuracy and robustness, with machine learning models, particularly Gaussian Process Regression and Random Forest Regression, outperforming classical models. This versatile platform provides a powerful, data-driven decision-support tool for researchers, industry professionals, and regulatory bodies in areas such as food safety management, shelf-life estimation, antimicrobial testing, and environmental monitoring. Future work will focus on further optimization, integration with large public microbial databases, and expanding applications in emerging fields of predictive microbiology. Full article

Global population growth and dietary transition have intensified demand for livestock and aquaculture products, thereby escalating demand for high-quality animal feed. Conventional protein sources, including soybean meal and fishmeal, face severe supply constraints driven by intense competition for arable land, worsening water scarcity, overexploitation of fishery resources, and rising production costs. These challenges are especially pronounced within agricultural systems. Evidence demonstrates that converting agriculturally derived organic wastes and agri-industrial by-products into feed protein can simultaneously alleviate these pressures, address agricultural waste disposal challenges, and reduce the carbon footprint associated with agricultural production. This review synthesizes fermentation processes for generating feed protein from agricultural organic wastes by employing functionally adapted microorganisms or microbial consortia. This distinguishes it from prior studies, which focused solely on single waste streams or individual microbial strains. It aims to advance feed protein production through an integrated approach that unites agricultural organic wastes, microorganisms, and fermentation processes, thereby promoting resource-oriented utilization of agricultural organic wastes and providing actionable solutions to alleviate feed protein scarcity. Full article

Achieving synergistic effects in pollution reduction and carbon mitigation is of great significance for promoting the comprehensive green transformation of economic and social development. This study focuses on the textile industry in a specific city, aiming to (1) analyze the energy consumption and pollutant emission characteristics of the textile industry in a district of Suzhou from 2017 to 2021; (2) conduct carbon accounting for 18 typical textile enterprises using the emission factor method with extended accounting boundaries; and (3) explore targeted low-carbon collaborative control pathways for pollution and carbon reduction. The results show that from 2017 to 2021, the proportion of raw coal in the comprehensive energy consumption of the textile industry in the city decreased annually to 35.68%, while the proportion of natural gas increased to 13.96%. The adoption of natural gas significantly reduced carbon emissions. The industry’s total output value rose markedly, while energy consumption intensity declined noticeably. The production and emission of volatile organic compounds (VOCs) generally decreased, with the proportion of final combustion emissions of VOCs in carbon accounting being relatively low (0–19.79%). Based on the findings, this study provides strategic foundations for collaborative governance, including optimizing energy structures, substituting VOC-containing raw materials, and improving production processes. Full article

Accurate stockpile volume estimation is essential for industries that manage bulk materials across various stages of production. Conventional ground-based methods such as walking wheels, total stations, Global Navigation Satellite Systems (GNSSs), and Terrestrial Laser Scanners (TLSs) have been widely used, but often involve significant safety risks, particularly when accessing hard-to-reach or hazardous areas. Unmanned Aerial Systems (UASs) provide a safer and more efficient alternative for surveying irregularly shaped stockpiles. This study evaluates UAS-based methods for estimating the volume of coal stockpiles at a storage facility near Cadiz, Ohio. Two sensor platforms were deployed: a Freefly Alta X quadcopter equipped with a Real-Time Kinematic (RTK) Light Detection and Ranging (LiDAR, active sensor) and a WingtraOne UAS with Post-Processed Kinematic (PPK) multispectral imaging (optical, passive sensor). Three approaches were compared: (1) LiDAR; (2) Structure-from-Motion (SfM) photogrammetry with a Digital Surface Model (DSM) and Digital Terrain Model (DTM) (SfM–DTM); and (3) an SfM-derived DSM combined with a kriging-interpolated DTM (SfM–intDTM). An automated boundary detection workflow was developed, integrating slope thresholding, Near-Infrared (NIR) spectral filtering, and Canny edge detection. Volume estimates from SfM–DTM and SfM–intDTM closely matched LiDAR-based reference estimates, with Root Mean Square Error (RMSE) values of 147.51 m³ and 146.18 m³, respectively. The SfM–intDTM approach achieved a Mean Absolute Percentage Error (MAPE) of ~2%, indicating strong agreement with LiDAR and improved accuracy compared to prior studies. A sensitivity analysis further highlighted the role of spatial resolution in volume estimation. While RMSE values remained consistent (141–162 m³) and the MAPE below 2.5% for resolutions between 0.06 m and 5 m, accuracy declined at coarser resolutions, with the MAPE rising to 11.76% at 10 m. This emphasizes the need to balance the resolution with the study objectives, geographic extent, and computational costs when selecting elevation data for volume estimation. Overall, UAS-based SfM photogrammetry combined with interpolated DTMs and automated boundary extraction offers a scalable, cost-effective, and accurate approach for stockpile volume estimation. The methodology is well-suited for both the high-precision monitoring of individual stockpiles and broader regional-scale assessments and can be readily adapted to other domains such as quarrying, agricultural storage, and forestry operations. Full article