Search Results (4,793)

Carrot juice processing generates large amounts of pomace, a fibre-rich by-product with significant valorisation potential. This study explored the feasibility of fermenting carrot by-product with Levilactobacillus brevis AM7 and Leuconostoc pseudomesenteroides DSM20193 to produce exopolysaccharide (EPS)-enriched functional beverages. Beverages were fermented with or without sucrose addition (EPS⁺ and EPS⁻, respectively) and characterized for microbiological, biochemical, rheological, and sensory attributes. Both strains showed robust growth (>8 log cfu/mL) and acidification (final pH below 4.8), comparable to plant-based yoghurt alternatives, with EPS synthesis markedly enhanced in sucrose-supplemented beverages. Leuc. pseudomesenteroides DSM20193 synthesized the highest EPS concentration (16.8 g/100 g dry weight), resulting in a 6-fold viscosity increase compared to EPS⁻ samples, thus improving the adherence to the spoon and preventing syneresis of the beverages. Sensory evaluation revealed that EPS⁺ carrot-based beverages had improved sweetness due to a slight sucrose residue, aroma, and mouthfeel, while maintaining low off-flavours and high colour uniformity. The results highlight carrot by-product as a promising substrate for developing clean-label beverages that are rich in dietary fibres and polyphenols and show antioxidant and potential prebiotic properties through sustainable fermentation processes. Full article

The cutterhead torque of a full-face tunnel boring machine (TBM) is a pivotal parameter that characterises the rock-machine interaction. Its dynamic prediction is of considerable significance to achieve intelligent regulation of the boring parameters and enhance the construction efficiency and safety. In order to achieve high-precision time series prediction of cutterhead torque under complex geological conditions, this study proposes an intelligent prediction method (VBGAP) that integrates signal decomposition mechanism and physical constraints. At the data preprocessing level, a multi-step data cleaning process is designed. This process comprises the following steps: the processing of invalid values, the detection of outliers, and normalisation. The non-smooth torque time-series signal is decomposed by variational mode decomposition (VMD) into narrow-band sub-signals that serve as a data-driven, frequency-specific input for subsequent modelling, and a hybrid deep learning model based on Bi-GRU and self-attention mechanism is built for each sub-signal. Finally, the prediction results of each component are linearly superimposed to achieve signal reconstruction. Concurrently, a novel modal energy conservation loss function is proposed, with the objective of effectively constraining the information entropy decay in the decomposition-reconstruction process. The validity of the proposed method is supported by empirical evidence from a real tunnel project dataset in Northeast China, which demonstrates an average accuracy of over 90% in a multi-step prediction task with a time step of 30 s. This suggests that the proposed method exhibits superior adaptability and prediction accuracy in comparison to existing mainstream deep learning models. The findings of the research provide novel concepts and methodologies for the intelligent regulation of TBM boring parameters. Full article

The proliferation of biometric authentication systems in critical security applications has highlighted the urgent need for robust defense mechanisms against sophisticated adversarial attacks. This paper presents ShieldNet, an adversarially resilient Convolutional Neural Network (CNN) framework specifically designed for secure iris biometric authentication. Unlike existing approaches that apply adversarial training or gradient regularization independently, ShieldNet introduces a synergistic dual-layer defense framework featuring three key components: (1) an attack-aware adaptive weighting mechanism that dynamically balances defense priorities across multiple attack types, (2) a smoothness-regularized gradient penalty formulation that maintains differentiable gradients while encouraging locally smooth loss landscapes, and (3) a consistency loss component that enforces prediction stability between clean and adversarial inputs. Through extensive experimental validation across three diverse iris datasets, MMU1, CASIA-Iris-Africa, and UBIRIS.v2, and rigorous evaluation against strong adaptive attacks including AutoAttack, PGD-100 with random restarts, and transfer-based black-box attacks, ShieldNet demonstrated robust performance, achieving 87.3% adversarial accuracy under AutoAttack on MMU1, 85.1% on CASIA-Iris-Africa, and 82.4% on UBIRIS.v2, while maintaining competitive clean data accuracies of 94.7%, 93.9%, and 92.8%, respectively. The proposed framework outperforms existing state-of-the-art defense methods including TRADES, MART, and AWP, achieving an equal error rate (EER) as low as 2.8% and demonstrating consistent robustness across both gradient-based and gradient-free attack scenarios. Comprehensive ablation studies validate the complementary contributions of each defense component, while latent space analysis confirms that ShieldNet learns genuinely robust feature representations rather than relying on gradient obfuscation. These results establish ShieldNet as a practical and reliable solution for deployment in high-security biometric authentication environments. Full article

Additive manufacturing offers rapid and customizable production, yet conventional plastic-based methods remain energy-intensive and environmentally harmful, often resulting in higher impacts per part than traditional manufacturing. The goal of this study was to evaluate whether upcycled biomaterials, specifically oyster shells, pistachio shells, and clay, could be used as lower-impact alternatives to PLA in 3D printing. The scope included detailed measurement of print parameters for each material and a full life cycle assessment (LCA) of the printed elements, covering printer manufacturing, raw material extraction, transport, operation, and end of life. The results show that ambient-temperature extrusion of these upcycled biomaterials can reduce energy consumption by up to 89% and overall environmental impact by up to 94% (as measured by ReCiPe Endpoint H points) compared to PLA printing. These reductions were observed for the Netherlands and EU contexts, where electricity mixes are relatively clean and recycling rates are high; even greater improvements were observed for the US. Although the printed biomaterial objects exhibit lower mechanical strength, limited waterproofness, and reduced print resolution, they are already suitable for low-load applications such as prototypes and architectural models. Overall, the findings demonstrate that upcycled biomaterial extrusion has strong sustainability potential, outperforming both conventional plastics and bioplastics such as PLA in terms of material impacts and energy use. Continued development of material formulations as well as pre- and post-processing techniques could further expand functionality and support the broader adoption of low-impact 3D printing across a wide range of applications. Full article

Dual-LiDAR systems are widely deployed in autonomous driving, yet extrinsic calibration remains challenging in non-overlapping field-of-view (FoV) configurations where correspondence-based methods are unreliable. We propose an engineering-oriented 2.5D calibration framework that estimates horizontal extrinsics $(x,y,\mathrm{yaw})$ via motion-guided planar alignment and then refines them using Gaussian Process Implicit Surfaces (GPIS), which provide continuous and probabilistic surface constraints from spatially disjoint scans. This design avoids calibration targets and reduces dependence on strong scene assumptions, improving robustness under noise and weak structure. Extensive high-fidelity simulation experiments demonstrate centimeter-level lateral accuracy and sub-degree yaw error, consistently outperforming representative motion-based and BEV-based baselines under both clean and noisy settings. To further assess real-world applicability, we conduct a preliminary nuScenes case study by splitting LiDAR scans into front and rear subsets to emulate a non-overlapping dual-LiDAR setup, achieving improved yaw accuracy and competitive lateral precision. Overall, the proposed method serves as a practical refinement stage for non-overlapping dual-LiDAR calibration, with a favorable balance of accuracy, robustness, and engineering feasibility. Full article

Studies on two-dimensional materials (such as topological insulators or transition metal dichalcogenides) have shown that they exhibit unique properties, including high charge carrier mobility and tunable bandgaps, making them attractive for next-generation electronics. Some of these materials (e.g., HfSe${}_2$) also offer thickness-dependent bandgap engineering. However, the standard device fabrication techniques often introduce processing contamination, which reduces device efficiency. In this paper, we present a modified mechanical exfoliation technique, the Reversed Structuring Procedure, which enables the fabrication of hybrid systems based on 2D microflakes with improved interface cleanness and contact quality. Hall effect measurements on Bi${}_2$Se${}_3$ and HfSe${}_2$ devices confirm enhanced electrical performance, including the decrease in the measured total resistance. We also introduce a novel Star-Shaped Electrode Structure, which allows for accurate Hall measurements and the exploration of geometric magnetoresistance effects within the same device. This dual-purpose geometry enhances the flexibility and demonstrates broader functionality of the proposed fabrication method. The presented results validate the Reversed Structuring Procedure method as a robust and versatile approach for laboratory test-platforms for electronic applications of new types of layered materials whose fabrication technology is not yet compatible with CMOS. Full article

The present study describes the ten-year (2014–2024) validation of a Class 100,000ISO 8 qualified air system used in the manufacture of non-sterile pharmaceutical dosage forms in a GMP-certified facility. The lifecycle evaluation included design, installation, qualification, continuous operation, environmental monitoring, cleaning and disinfection verification, and annual third-party validation. The system was assessed for critical parameters, including air renewal rates, airflow directionality, the integrity of high-efficiency particulate air (HEPA) filters and ultra-low penetration air (ULPA) filters, environmental recovery times, and non-viable particle counts. Particle monitoring focused on 0.5 $\mathsf{\mu }$m and 1.0 $\mathsf{\mu }$m channels within the 0.5–5 $\mathsf{\mu }$m range specified by ISO 14644-1 for ISO 8 areas. The 0.5–1.0 $\mathsf{\mu }$m range was prioritized because it provides higher statistical representativeness for evaluating filter performance and controlling fine particulate dispersion, which is particularly relevant in non-sterile pharmaceutical production, while larger particles (>5 $\mathsf{\mu }$m) are more critical in aseptic processes. The influence of personnel and air exchange rates on cleanliness was also assessed during the final years of the study. Results demonstrate that continuous, systematic validation ensures the controlled environmental conditions required for pharmaceutical production and supports the sustained quality and safety of the finished products. This study provides a technical reference for engineers, pharmacists, and quality professionals involved in cleanroom design, qualification, and regulatory compliance. Full article

Fluorinated colloidal nanosystems have attracted significant attention for their advantageous properties and potential applications in the biomedical field, especially in ¹⁹F magnetic resonance imaging. These nanosystems are known for their high specificity, excellent biocompatibility, and ease of functional modification. Furthermore, they offer unique advantages for functional surface coating due to their surface performance and chemical resistance. This paper discusses recent developments in fluorinated colloidal nanosystems, including applications in biological detection (such as enzymes, proteins, pH levels, ions, reducing environments, and reactive oxygen species) and surface coating (such as self-cleaning, self-healing, antibacterial properties, anti-fogging, antifouling, and oil–water separation). This article also highlights current challenges and provides suggestions for future research directions in the field of fluorinated colloidal nanosystems. Full article

The application of bioactive nanoemulsions in the meat industry has attracted great interest due to their ability to improve the stability, bioavailability, and functionality of bioactive compounds, contributing to the extension of the shelf-life of highly perishable products, such as sausages and meat patties. Thus, this review provides a critical analysis of the application of nanoemulsions in sausages and meat patties, with emphasis on their mechanisms of action, formulation strategies, and performance in improving oxidative stability and microbial safety. Nanoemulsions, typically characterized by droplet sizes below 200 nm, increase interfacial area and penetration into meat matrices, resulting in reductions of 30–60% in lipid oxidation markers and decreases of 1–2 log CFU/g in spoilage and pathogenic microorganisms. Preparation and stabilization approaches, including high-energy and low-energy methods, are summarized, and the influence of nanoemulsion characteristics on texture, color, pH, and sensory perception is discussed. Particular attention is given to technological barriers, such as scale-up feasibility, stability during processing and storage, interactions with meat proteins, as well as regulatory and labeling considerations related to nano-enabled foods. Overall, the current evidence indicates that NEs represent a viable strategy to replace synthetic preservatives while supporting clean-label product development; however, further research on safety assessment, optimal dosing, and consumer acceptance is still required for broader industrial implementation. Full article

Sub-Saharan Africa has the potential to achieve sustainable development through facilitating green transition projects, leveraging the revenue generated from its abundant natural resources. However, the resource curse hypothesis suggests that developing nations often face problems with corruption that hinder economic development in these countries. The present study aims to investigate how environmental sustainability can be advanced in Sub-Saharan Africa using revenue from natural resources in the presence of green financial technology and clean energy. Therefore, data for Sub-Saharan Africa from 2000 to 2023 are employed in the analysis. The analysis of these data is undertaken with the ‘Method of Moments Quantile Regression’ technique, and the ‘Panel Correlated Standard Errors’ is used for robustness checks. The key findings presented in this research depict the importance of natural resource rents in supporting sustainable environments in Sub-Saharan Africa. Therefore, the revenue from natural resources can be used to support green transition projects in developing nations with high natural resource endowments. Moreover, renewable energy and green finance foster a reduction in ecological footprint, hence supporting environmental sustainability. Consequently, technological innovation and financial development do not promote the achievement of environmental sustainability, raising questions about the environmental policies and regulations in Sub-Saharan Africa. To this end, there is a need for policy reforms and corruption control in order to prevent the misallocation and misuse of resources designed to support green transition projects. Full article

Coalbed methane, an abundant clean energy resource in China, is gaining significant attention. Electric submersible progressive cavity pumps, ideal for downhole extraction with high solids content, are vital in coalbed methane operations. Current fault diagnosis research for these pumps mainly relies on machine learning algorithms to identify fault features, but complex working conditions and imbalanced sample distributions challenge these models’ ability to perceive multi-scale and multi-dimensional features. To enhance the model’s perception of deep abnormal data in complex multi-case industrial datasets, this study proposes a deep learning model based on a multi-scale extraction and residual module convolutional neural network. Innovatively, a cross-attention module using global autocorrelation and local cross-correlation is introduced to constrain the multi-scale feature extraction process, making the model better suited to specific and differentiated data environments. Post feature extraction, the model employs Borderline-SMOTE to augment minority class samples and uses Tomek Links for noise removal. These enhancements improve the comprehensive perception of fault types with significant differences in period, amplitude, and dimension, as well as the learning capability for rare faults. Based on field-collected fault data and using enhanced and cleaned features for classifier training, tests on a real industrial dataset show the proposed model achieves an F1 Measure of 90.7%—an improvement of 13.38% over the unimproved model and 9.15–31.64% over other common fault diagnosis models. Experimental results confirm the method’s effectiveness in adapting to extremely imbalanced sample distributions and complex, variable field data characteristics. Full article

The global food industry is undergoing a major shift driven by increasing consumer demand for clean-label and naturally preserved foods. Fresh pasta is highly vulnerable to fungal damage because of its high water activity (a_w > 0.85), typically ranging between 0.92 and 0.97, moderate to near-neutral pH (around 5.0–7.0), and nutrient-rich composition, all of which create favorable conditions for fungal growth during refrigeration, mainly by genera such as Penicillium and Aspergillus. Fungal contamination results in significant economic losses due to reduced product quality and poses potential health risks associated with mycotoxin production. Although conventional chemical preservatives are relatively effective in preventing spoilage, their use conflicts with clean-label trends and faces growing regulatory and consumer scrutiny. In this context, antifungal lactic acid bacteria (LAB) have emerged as a promising natural alternative for biopreservation. Several LAB strains, particularly those isolated from cereal-based environments (e.g., Lactobacillus plantarum and L. amylovorus), produce a broad spectrum of antifungal metabolites, including organic acids, phenylalanine-derived acids, cyclic dipeptides, and volatile compounds. These metabolites act synergistically to inhibit fungal growth through multiple mechanisms, such as cytoplasmic acidification, energy depletion, and membrane disruption. However, the application of LAB in fresh pasta production requires overcoming several challenges, including the scale-up from laboratory to industrial processes, the maintenance of metabolic activity within the complex pasta matrix, and the preservation of desirable sensory attributes. Furthermore, regulatory approval (GRAS/QPS status), economic feasibility, and effective consumer communication are crucial for successful commercial implementation. This review analyzes studies published over the past decade on fresh pasta spoilage and the antifungal activity of lactic acid bacteria (LAB), highlighting the progressive refinement of LAB-based biopreservation strategies. The literature demonstrates a transition from early descriptive studies to recent research focused on strain-specific mechanisms and technological integration. Overall, LAB-mediated biopreservation emerges as a sustainable, clean-label approach for extending the shelf life and safety of fresh pasta, with future developments relying on targeted strain selection and synergistic preservation strategies. Full article

The global clean energy transition requires power conversion technologies that combine high efficiency, operational flexibility, and reduced environmental impact over their entire service life. Solid-state transformers (SSTs) have emerged as a promising alternative to conventional line-frequency transformers, offering bidirectional power flow, high-frequency isolation, and advanced control capabilities that support renewable integration and electrified infrastructures. This paper presents a comparative life cycle assessment (LCA) of conventional transformers and SSTs across representative power-system applications, including residential and industrial distribution networks, electric vehicle fast-charging infrastructure, and transmission–distribution interface substations. The analysis follows a cradle-to-grave approach and is based on literature-derived LCA data, manufacturer specifications, and harmonized engineering assumptions applied consistently across all case studies. The results show that, under identical assumptions, SST-based solutions are associated with indicative lifecycle CO₂ emission reductions of approximately 10–30% compared to conventional transformers, depending on power rating and operating profile (≈90–1000 t CO₂ over 25 years across the four cases). These reductions are primarily driven by lower operational losses and reduced material intensity, while additional system-level benefits arise from enhanced controllability and compatibility with renewable-rich and hybrid AC/DC grids. The study also identifies key challenges that influence the sustainability performance of SSTs, including higher capital cost, thermal management requirements, and the long-term reliability of power-electronic components. Overall, the results indicate that SSTs represent a relevant enabling technology for future low-carbon power systems, while highlighting the importance of transparent assumptions and lifecycle-oriented evaluation when comparing emerging grid technologies. Full article

The inner spacer module, which profoundly affects the final performance of a device, is a critical component in GAA NSFET (Gate-all-around Nanosheet Field Effect Transistor) manufacturing and necessitates systematic optimization and fundamental innovation. This work aims to develop an advanced SiGe etching process with high selectivity, uniformity and low damage to achieve an ideal inner spacer structure for logic GAA NSFETs. For three distinct dry etching technologies, ICP (Inductively Coupled Plasma Technology), RPS (Remote Plasma Source) and Gas Etching, we evaluated their potential and comparative advantages for inner spacer cavity etching under the same experimental conditions. The experimental results demonstrated that Gas Etching technology possesses the uniquely high selectivity of the SiGe sacrificial layer, making it the most suitable approach for inner spacer cavity etching to reduce Si nanosheet damage. Based on the results, in the stacked structures, the SiGe/Si selectivity ratio exhibited in Gas Etching is ~9 times higher than ICP and ~2 times higher than RPS. Through systematic optimization of pre-clean conditions, temperature and chamber pressure control, we successfully achieved a remarkable performance target of cavity etching: the average SiGe/Si etching selectivity is ~56, the inner spacer shape index is 0.92 and the local etching distance variation is only 0.65 nm across different layers. These findings provide valuable guidance for equipment selection in highly selective SiGe etching and offer critical insights into key process module development for GAA NSFETs. Full article

Armillaria mellea (A. mellea) serves as a crucial nutritional source for Gastrodia elata (GE) growth, and its origin directly influences the GE quality and yield. This study analyzed GE symbiotic with A. mellea from different sources using metabolomics and transcriptomics. Results demonstrated that Group A exhibited significant differences in metabolites and gene expression compared to other groups. Group A showed significantly higher accumulation of active components like gastrodin and p-hydroxybenzyl alcohol than others, but its yield was lower than Group B. Metabolomic analysis identified 2418 metabolites, while transcriptomic sequencing produced 964,110,904 clean reads, with 14,637 annotated transcripts. KEGG analysis revealed that Group A’s DEGs and DEMs were co-enriched in three key pathways, including flavonoid biosynthesis, phenylpropanoid biosynthesis, and plant hormone signal transduction, such as the positive regulatory roles of key genes (CHS, 4CL, MYC2) on metabolites such as hesperetin, ferulate, and jasmonic acid, respectively. The coordinated upregulation of gene–metabolite interactions in Group A GE may be closely related to the accumulation of major active components, indirectly suggesting the influence of the A. mellea source on metabolic and transcriptional response differences in GE. This study, centered on the host GE, indirectly deduces the association between A. mellea and GE, providing a theoretical basis for screening high-quality “fungus-GE” combinations. Further in-depth research and validation experiments will be conducted in conjunction with fungal omics. Full article