Search Results (44,114)

To reveal the controlling mechanism of cooling rate on the continuous cooling transformation, microstructure evolution and mechanical performances of Q580R low-temperature pressure vessel steel, this study took industrial-scale Q580R steel as the research object. The JMatPro thermodynamic software was utilized for simulating and calculating its equilibrium phase diagram, TTT diagram, CCT diagram and mechanical property evolution. Continuous cooling experiments with a wide range of cooling rates between 0.1 and 50 °C/s were executed on a Gleeble-3500 thermal simulator. Combined with optical microscopy, scanning electron microscopy and Vickers hardness tester for microstructure characterization and property testing, the measured CCT diagram was constructed and contrasted with the simulation results for verification. Experimentally, the phase composition of Q580R steel evolves at regular intervals with cooling rate. As the cooling rate rises, the ferrite content constantly decreases, the bainite content first increases and subsequently decreases, and the martensite content constantly increases. When the cooling rate reaches 30 °C/s, the martensite proportion can exceed 90%, and the microstructure is significantly refined. The hardness of the material first increases rapidly and subsequently trends to be steady as the cooling rate rises, reaching 308 HV₁₀ at 50 °C/s. The measured transformation law, microstructure evolution and hardness change exceedingly corresponds to the JMatPro simulation results. This validates the credibility of the simulation prediction. This study clarifies the quantitative relationship among “cooling rate-microstructure-properties” of Q580R steel, which can provide theoretical basis and data support for the precise design of heat treatment process and the optimization of strength and toughness. The established relationship can directly guide the formulation of controlled cooling parameters during hot rolling and off-line quenching and tempering production of Q580R pressure vessel plates, helping manufacturers optimize industrial heat-treatment procedures to satisfy low-temperature toughness requirements for petrochemical and cryogenic pressure vessel service. Full article

Steel surface defect inspection plays an essential role in maintaining product quality and production safety in industrial manufacturing. However, existing detection methods still encounter difficulties in accurately identifying tiny defects, suppressing interference from complex backgrounds, and balancing detection accuracy with computational cost. To address these challenges, this paper proposes CNS-YOLOv8, an improved defect detection model based on YOLOv8n. First, a C2f_SCConv module is introduced to enhance multi-scale feature extraction and spatial representation capability. Second, a Normalization-based Attention Module (NAM) is embedded after the high-level semantic feature layer to improve the model’s sensitivity to critical defect regions. Third, a SlimNeck structure is adopted to strengthen feature fusion while reducing computational overhead. Experimental results on the NEU-DET dataset demonstrate that CNS-YOLOv8 achieves 83.1% mAP@0.5 and 49.6% mAP@0.5:0.95, surpassing YOLOv8n by 3.9 and 1.2 percentage points, respectively. In addition, comparative experiments show that CNS-YOLOv8 outperforms Faster R-CNN and YOLOv7 in terms of mAP@0.5 while requiring substantially fewer GFLOPs. In general, the proposed method balances detection accuracy and computational efficiency effectively, highlighting its potential for real-time industrial surface defect detection. Full article

In many industrial dairy products, yeasts are generally regarded as contaminants. However, in traditional fermented milks, they may contribute to distinctive sensory, technological, and functional properties through associations with bacterial partners, including lactic acid bacteria (LAB). Despite this, a structured synthesis of yeast–bacterium associations across fermented milk typologies is currently lacking. To address this gap, a PRISMA-informed literature search identified 42 studies across 24 traditional fermented milks reporting paired bacterial and fungal communities. A genus-level co-occurrence analysis was used to identify which yeast–bacterium pairs were most frequently co-detected across independently documented products. The main co-occurrence patterns selected for detailed bibliographical discussion were Kluyveromyces with Acetobacter and LAB, including Lactobacillus, Streptococcus, Lentilactobacillus and Lacticaseibacillus; Pichia with LAB; Saccharomyces with LAB, especially Lactobacillus; Kazachstania with Acetobacter; Candida with Leuconostoc and Enterococcus; and Geotrichum with Pseudomonas and Enterococcus. For the selected associations, possible interaction mechanisms and implications for sensory identity, technological potential, and microbiological safety were discussed by integrating evidence from milk co-cultures, controlled model systems, and related fermented foods. Overall, this review provides a structured synthesis of yeast–bacterium associations in traditional fermented milks and identifies candidate consortia for future experimental validation. Full article

The filamentous fungus Trichoderma reesei is one of the most important workhorses for industrial enzyme production, but the cellular mechanisms that balance protein folding stress with secretion, such as the unfolded protein response (UPR) and repression under secretion stress (RESS), are still not fully understood. In this study, we set out to clarify how these pathways contribute to secretion in both laboratory settings and industrial-scale fermentations. Exposure to the reductive agent dithiothreitol for 5 h increased transcript levels of UPR-related genes at least 6-fold, and, simultaneously, transcript levels of target genes cbh1 and egl2 were reduced at least 5- or 6-fold, respectively. Interestingly, RESS was detected even when UPR was suppressed by the prevention of protein de novo synthesis, pointing to a non-hierarchical relation of the two mechanisms. With the aim to understand on which levels RESS is acting, in particular, whether it is transcription initiation or transcript stability, an experiment involving blocking the transcription was performed. Further, a recombinant strain with an exchanged promoter had an at least 45-fold-increased cbh1 transcript level, while a terminator exchange did not increase chb1 transcript levels, indicating that RESS operates mainly at the level of transcription initiation. Importantly, whole transcriptome data from industrial cellulase production did not reveal the signatures of UPR or RESS despite the heavy secretory load. Instead, expression profiles highlighted the induction of diverse hydrolytic enzymes and pathway adjustments that support efficient production. Full article

The Chishui River Basin, a core production area for Chinese sauce-aroma Baijiu (exemplified by Moutai), supports sorghum cultivation critical to the liquor’s distinctive quality. The soil environment quality within this region, therefore, directly impacts the safety and quality of both raw material and the final distilled spirit. To underpin the safe production and sustainable development of this iconic beverage, it is essential to assess soil heavy metal contamination in the soils and quantify the contributions from various sources. In this study, 172 surface soil samples were collected from typical sorghum planting bases in the Renhuai area. Concentrations of eight heavy metals (loids) (As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn) were determined. The contamination status was evaluated using the geostatistical inverse distance weighting interpolation, the Nemerow pollution index (P_N), and the potential ecological risk index (RI). Source identification and quantification were performed using the positive matrix factorization receptor model (PMF). Results revealed significant enrichment of Cd and Hg in the soil, with mean concentrations 2.07 times and 2.54 times the soil background values for Guizhou Province, respectively. Pollution index results (P_i, P_N) indicated that soil Cd contamination is relatively severe, whereas contamination from other elements is minimal. Overall, approximately 86.5% of the study area was classified as clean or only slightly polluted. Cd poses a moderate ecological risk and was the primary contributor to the total ecological hazard. Other elements exhibited lower risk, resulting in a slight overall potential ecological risk. The soil environmental quality in certified organic sorghum bases was generally favorable. PMF analysis identified three principal sources: historic industrial emissions and traffic-related sources (contributing 46%), weathering of carbonate rocks combined with agricultural activities (37%), and natural background coupled with organic fertilizer application (17%). In conclusion, while the overall soil heavy metal pollution level in the sorghum planting areas is low, the notable enrichment and higher ecological risk of Cd necessitate enhanced dynamic monitoring and targeted risk control measures to ensure long-term soil health and product safety. Full article

The valorization of phosphogypsum (PG), a byproduct of phosphoric acid production, along with waste glass (WG) and silica fume (SF) into value-added materials has attracted growing attention in recent years. The present study aims to synthesize three types of porous geopolymers (GD, GDP, and GDG) using Tunisian clay and locally available mineral wastes, and to investigate their potential as low-cost adsorbents for the removal of methylene blue (MB) dye from aqueous solutions. The physicochemical characteristics of the raw precursors and the resulting porous geopolymers were analyzed using various techniques, including FTIR, XRD, BET, and SEM. Variations in Si/Al, Na/Al, and Ca/Al ratios play a critical role in the geopolymer structure. The high Ca/Al ratio in GDP (porous geopolymer from calcined clay and phosphogypsum) promotes the formation of C-A-S-H, leading to increased macroporosity, which favors adsorption capacity despite the presence of a more heterogeneous morphology. The results indicated that the maximum adsorption capacity (Qmax) for MB dye was obtained for the GDP sample, reaching 68 mg/g. Adsorption experiments revealed the successful removal of MB dye by geopolymers, with the Langmuir isotherm and pseudo-second-order kinetic models adequately describing the adsorption process. The MB uptake by geopolymers was facilitated by weak physicochemical interactions, including electrostatic attraction, hydrogen bonding, and π–π interactions. This study proposes a simple and effective alkali activation strategy that combines different industrial wastes within a single geopolymer system, resulting in improved porosity and adsorption efficiency. Overall, the findings highlight the potential of these waste-derived geopolymers as promising and sustainable adsorbents for wastewater treatment applications. Full article

Industrial robotic arms are fundamental components of modern automated production lines, executing critical tasks such as welding, painting, and assembly. Such high-precision operations often require careful manual tool positioning during the initial setup. To automate and refine this process, a highly accurate kinematic model of the robot is essential. In this paper, the authors propose a novel algorithm for kinematic parameter calibration using photogrammetry to track multiple robot links simultaneously. The proposed multi-link calibration approach provides a more precise parameter estimation and introduces the practical possibility of continuous parameter refinement while the robot executes its primary operational tasks. The superior accuracy and robustness of the proposed methodology are confirmed through comprehensive simulation experiments, and the feasibility of the approach is successfully demonstrated on a real robotic arm. Full article

Spatially explicit socioeconomic data are critical for regional analysis, yet they remain scarce at subnational scales in many African contexts. This study presents a transparent and reproducible open-data framework to generate high-resolution gridded Gross Domestic Product (GDP) and derived socioeconomic and energy indicators. The approach combines gridded population and Night-Time Light (NTL) through the LitPop method to downscale provincial GDP to 1 km resolution for the Inkomati-Usuthu Water Management Area (IUWMA) in South Africa. The resulting GDP dataset is subsequently used as a spatial proxy to disaggregate compensation of employees, gross capital formation, fixed capital stock, net exports, gross operational surplus and sectoral Total Final Energy Consumption (TFEC). Results show strong consistency with official provincial GDP totals, with deviations ±0.4% after 2017. In 2024, LitPop allocated 4.26 billion constant 2015 USD to the IUWMA, equivalent to 16% of Mpumalanga’s GDP, compared with 47.3% under area-based allocation and 51.3% under population-based allocation. These differences reveal the strong influence of spatially concentrated industrial and energy-intensive activity. The workflow provides a scalable and replicable solution to generate coherent gridded socioeconomic datasets for WEF Nexus modelling, although estimates remain proxy-based and sensitive to NTL-related biases, particularly the overrepresentation of highly illuminated industrial assets and the underrepresentation of less luminous activities. Full article

Global demand for sustainability drives interest in bioenergy from sustainable feedstock. Agro-industrial waste such as brewer’s spent grains (BSG) is an important by-product of brewing. This study provides a comprehensive review of the current technologies of BSG for energy recovery and BSG-based materials for energy storage applications. The latest scientific progress, not only from conventional processes on anaerobic digestion, combustion, gasification, pyrolysis, torrefaction, and hydrothermal liquefaction but also from several integrated technologies, pretreatment methods, and additives/catalysts regarding the improvement of energy efficiency and process sustainability, was reviewed. In addition, the co-feedstock practices (co-combustion, anaerobic co-digestion, hydrothermal co-liquefaction, anaerobic co-fermentation) and co-production were examined. AD of BSG yields about 302 NL CH₄/kg COD, generating roughly 0.39 kWh of electricity/kg BSG and 1.71 MJ of thermal energy/kg BSG. Ultrasonic pretreatment enhances methane production up to four times (107 L CH₄/kg TVS) and reduces CO₂ emissions by 0.083 t CO₂eq/t BSG. Anaerobic co-digestion of BSG with other brewery waste increased the yield up to 88 mL CH₄/g TVS, generated approx. 0.348 kWh/kg TVS electricity, and reduced emissions by 0.114 kg CO₂eq/kg TVS. Bioethanol yields can reach 72%, while biohydrogen generation was up to 5154 mL H₂/g glucose. BSG pyrolysis provides up to 71.8% bio-oil, and its calorific value is 18–25 MJ/kg. BSG-derived activated biocarbon has a notable surface area (1792 m²/g) for lithium–sulfur batteries. The assessment showed that BSG’s transformation into bioenergy and energy storage materials aligns with waste reduction and sustainable development goals. However, future research on combined alternative wastes, integrated technologies, green nanotechnology, and artificial intelligence technology could lead to optimal performance and facilitate their industrial application. Full article

This paper proposes a genetic algorithm (GA) for the Dual-Resource Flexible Job Shop Scheduling Problem with Partial Resource Allocation (DRFJSSP-PRA), a particular variant of a dual-resource constrained scheduling problem that has not been fully explored due to its intricate nature. The DRFJSSP-PRA poses a challenging scheduling problem, having several applications in many industries, including food, chemistry and pharmaceutics. The proposed algorithm is applied to real-world scheduling instances in pharmaceutical quality control. The objective function considered is the total completion time. The GA is compared with three state-of-the-art algorithms. For small- and medium-size instances, the proposed algorithm achieves optimal or near optimal results for the majority of the instances tested. For large-sized instances, the proposed GA outperforms all the other algorithms, in all of the tested instances. Thus, the experimental results show that the proposed GA achieves competitive results for any type of instance. The proposed algorithm also has the ability to optimize production processes through scheduling, leading to potential cost savings, increased efficiency, and improved competitiveness. Full article

The significant challenges of machining hard-to-cut materials pose an important problem for the manufacturing industries, as it can lead to increased tool wear, higher machining costs, and reduced productivity. Apart from experimental investigations, which are rather expensive and cannot always provide a comprehensive view of the process outcome due to limitations in measurement techniques, it is possible to use validated models to predict the temperature and stress state of the workpieces or test the effect of different process conditions. Although many Finite Element (FE) models have been developed for the turning process, usually accurate representation of the machining setup with a realistic 3D geometry for both cutting tool and workpiece is not taken into account. Thus, in this work, two different representations of the machining setup, including curved workpiece geometry, which is more rarely studied, are compared for the case of Ti-6Al-4V ELI turning under various conditions, and their effect on the accuracy of the prediction of the cutting force and chip morphology is investigated. It was found that the model with the straight workpiece overpredicts the cutting force to a higher extent compared to the model with the curved workpiece and also predicts a much higher workpiece temperature, whereas chip morphology was mainly affected by feed rate. No noticeable differences were observed between the two models. These results indicate that in most cases, the use of geometry with curved workpiece is more suitable for better prediction of the cutting forces. Full article

In tropical countries, broiler chickens are exposed to elevated ambient temperatures and humidity, which are sometimes exacerbated by high stocking densities and poor litter quality, thereby predisposing birds to severe stress, weakening immune function, and promoting BCO lameness progression. BCO lameness causes tremendous economic losses to the poultry industry and increases the risk of foodborne disease. BCO is frequently underdiagnosed in live populations, resulting in an iceberg phenomenon in which subclinical lesions are more prevalent than clinically apparent lameness. Therefore, a total of 500 Cobb500 broiler chickens from five commercial broiler flocks in Central Java, Indonesia, were randomly selected, weighed, slaughtered, and necropsied to evaluate the prevalence of BCO lameness lesions in the proximal femoral and tibial heads across distinct market ages ranging from 33 to 43 days. The ambient housing temperature in the region can reach 28–29 °C during the day. The results showed that more than 80% of the samples had normal femora at 33 days of age with an average body weight of 1.9 kg. A significant increase in the frequency and severity of femoral and tibial lesions was recorded at 35 to 36 days of age, when the average body weight reached approximately 2.5 kg. The high frequency of worsening BCO lesions observed during the 5th week suggests an age-related pattern in BCO occurrence during the late stages of grow-out. These findings suggest that improvements in nutrition, environment, and production management strategies before 36 days of age are necessary to mitigate the impact of BCO lameness in the poultry industry. Full article

The growing demand for sustainable energy carriers has intensified interest in hydrogen production from renewable resources and waste-derived substrates. In this context, microbial electrolysis cells (MECs) have emerged as a promising technology for the simultaneous treatment of organic waste and biohydrogen generation. This review provides an overview of recent advances in MEC systems, focusing on reactor configurations, performance indicators such as hydrogen production rate, coulombic efficiency, and chemical oxygen demand removal. Attention is given to the valorization of real waste streams, including municipal and agro-industrial effluents, highlighting the differences between laboratory- and pilot-scale applications. While numerous studies have demonstrated the technical feasibility of MECs, several bottlenecks still limit their large-scale implementation, including challenges associated with the use of complex substrates. In particular, untreated wastewater often leads to reduced process efficiency due to its variable composition and the occurrence of competing microbial pathways. To overcome these limitations, integrated approaches are also discussed, with emphasis on the coupling of dark fermentation, capable of enhancing substrate biodegradability through the production of volatile fatty acids, with MEC systems. Overall, MEC technology represents a promising pathway for sustainable hydrogen production within circular waste management frameworks, although further advancements are required to enable its practical application. Full article

The mining industry faces increasing challenges related to the growing volume of tailings generated during mineral processing. This study presents a case study of the Complex Mine Waste Reduction (CMWR) concept implemented at the Polkowice Concentrator operated by KGHM Polska Miedź S.A. The approach integrates coarse ore sorting with tailings reprocessing for construction material production. Sorting improves flotation feed quality by rejecting low-grade gangue, while reprocessing converts fine tailings into value-added products. The combined implementation reduces tailing deposition by up to 22% and improves the operational copper recovery in flotation while maintaining overall process recovery at an essentially unchanged level. The results demonstrate the potential of integrated solutions for sustainable and circular mining. Full article

Manual classification of rice grain defects remains a persistent challenge in the Peruvian rice industry, as it relies heavily on human inspection, leading to variability, inconsistency, and reduced efficiency when processing large volumes of product. This study evaluates the effectiveness of transfer learning and convolutional neural networks (CNNs) for the automatic classification of four rice grain categories relevant to quality assessment: Whole, Stained, Broken, and Chalky. A dataset comprising 6599 RGB images was employed. To ensure a reliable evaluation protocol, the dataset was first partitioned into training (70%), validation (15%), and test (15%) subsets, after which data augmentation was independently applied within each partition to balance class distributions. Five pretrained CNN architectures were evaluated: MobileNetV2, EfficientNetB0, ResNet50, DenseNet121, and InceptionV3, all of which share a common classification head. Models were trained using transfer learning and early stopping based on validation loss. Performance was assessed using accuracy, precision, recall, F1-score, confusion matrices, 95% confidence intervals, and pairwise McNemar statistical tests. The results showed that ResNet50 achieved the highest classification accuracy (84.71%), followed by EfficientNetB0 (83.60%) and DenseNet121 (83.20%). Statistical analysis indicated that performance differences among the top-performing architectures were relatively small, with significant differences observed only for selected model pairs. Across all evaluated models, the discrimination between Whole and Chalky grains remained the most challenging classification task due to their high visual similarity. Overall, the findings demonstrate that transfer learning-based CNNs provide an effective and scalable approach for automated rice grain defect identification and quality assessment in agricultural environments. Full article