Search Results (12,762)

Co-contamination of biphenyl and heavy metals is widespread in industrial environments, but systematic studies on the simultaneous treatment of both pollutants using a single microbial strategy remain limited. In this study, we characterized the biphenyl degradation performance, metabolic pathway, transcriptomic response, and Cr(VI) tolerance of Rhodococcus sp. TG-1, and developed an alkali-modified biochar immobilization system to enhance its degradation efficiency for biphenyl under Cr(VI) stress. Degradation experiments were carried out under optimal conditions (30 °C, pH 7.0), and it was found that strain TG-1 degraded 76.84% of 300 mg/L biphenyl within 3 days. Intermediate metabolites were identified by LC-MS, and five key intermediates were detected, confirming that TG-1 metabolizes biphenyl via the classical 2,3-dihydroxybiphenyl dioxygenase pathway, with subsequent entry into the tricarboxylic acid cycle. Transcriptomic analysis was performed to profile gene expression, revealing 845 differentially expressed genes under biphenyl stress, including 672 upregulated genes significantly enriched in aromatic degradation pathways. Seven complete bph gene clusters responsible for biphenyl catabolism were also identified. Strain TG-1 exhibited high tolerance to Cr(VI), with a minimum inhibitory concentration (MIC) of 500 mg/L. However, its biphenyl degradation efficiency dropped to 51.32% in the presence of 200 mg/L Cr(VI). After immobilization using alkali-modified straw biochar (JBC), heavy metal toxicity was alleviated, and the biphenyl removal rate increased to 99.30% under co-contamination conditions. Scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FTIR) analyses confirmed that TG-1 was stably loaded onto the biochar surface through hydrogen bonding and electrostatic interactions. Altogether, this study provides a promising bacterial strain and a green immobilization strategy for enhancing biphenyl removal in the presence of Cr(VI), offering a practical approach for the treatment of environments co-contaminated with aromatic compounds and heavy metals. Full article

While Zanthoxylum bungeanum Maxim. (Z. bungeanum) pericarps are a globally prized spice, their leaves are frequently discarded as agricultural waste. This study systematically characterizes the aromatic potential of leaf by-products compared with traditional pericarps under diverse extraction strategies, utilizing an integrated flavoromics and sensomics approach. Qualitative GC-MS-O analysis revealed that leaf-derived fractions possess superior aromatic diversity: leaf essential oil and volatile solvent extract yielded 71 and 68 odorants, respectively, significantly surpassing pericarp counterparts (65 and 43 compounds). Concurrently, HS-GC-IMS profiling confirmed that targeted extraction allows leaf-derived flavors to replicate and exceed traditional spice complexity. Specifically, the leaf solvent extract achieved aromatic parity with pericarps by effectively mirroring the core spicy–citrus profile through cuminaldehyde and limonene retention. Conversely, distilled leaf essential oil unlocked a distinctive herbal–woody sensory innovation, driven by eucalyptol and a broader variety of aldehydes and ketones. Sensomics validation, incorporating aroma recombination, omission experiments, and partial least-squares regression modeling, conclusively identified β-myrcene, limonene, caryophyllene, and humulene as core molecular markers dictating these perceptual shifts. Ultimately, this research provides a robust theoretical foundation for upcycling Z. bungeanum leaves into valuable flavoring resources, facilitating circular bio-economy practices by delivering functional equivalence and entirely novel sensory experiences for the global food industry. Full article

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

Whether information consumption guides firms back to their core businesses or instead exacerbates corporate financialization remains empirically underexplored. We use panel data of Chinese A-share listed firms from 2009 to 2024. We take China’s Information Consumption Pilot policy as a quasi-natural experiment and employ a staggered difference-in-differences approach to examine the impact of information consumption on corporate financialization. The findings show that information consumption significantly promotes corporate financialization, with the precautionary motive driving financialization more strongly than the profit-seeking motive. Mechanism tests reveal that information consumption drives corporate financialization by easing financing constraints and improving investment efficiency, while internal corporate governance and external economic policy uncertainty play significant moderating roles. Heterogeneity analysis indicates that the exacerbating effect of information consumption on corporate financialization is more pronounced in non-state-owned enterprises, small-scale firms, non-high-tech industries, and regions with a low level of financial development. Further analysis shows that information consumption not only exacerbates excessive corporate financialization but also triggers peer effects in financialization. Moreover, the financialization induced by information consumption suppresses long-term corporate performance growth. These findings uncover the micro-mechanisms through which information consumption reshapes corporate capital allocation decisions, offering practical implications for refining information consumption policies and channeling financial resources back to the real economy. 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

It is an accepted paradigm in the already matured industry of Quantum Key Distribution (QKD) implementations that when the quantum channel is attacked or unresponsive, the system reverts to classical security. Thus, in times of crises, when the quantum system is severely damaged, the saving resort is considered to be the classical solution. This paper explores the opposite approach. In the case of disaster, when parts of the classical part of the key distribution system are broken, are there any possible crisis management options to give some limited functionality? The result of this research shows that if the classical channel fails, the quantum channel can still produce and distribute keys. The experimental results of the contingency QKD show that, using positive operator-valued measurements (POVMs), keys can still be produced and shared. The scheme described in this paper uses the quantum channel only to distribute imperfect keys. Any one distributed key has a theoretical overlap of approximately $75\%$ between Alice’s key and Bob’s key, respectively. The experimental POVM circuit is implemented with two different Naimark dilation approximations: one using $R_z$ gates and the other using $R_y$ gates. The practical implementation results are close to the theoretical analysis. As the keys have a partial overlap, the encryption/ decryption algorithm also needs to adjust to this reality. The encryption/decryption algorithm used in the experiments is a repetition algorithm that is simple but shows the resilience of the scheme. Ultimately, the classical channel is not used during the contingency QKD at all, while the quantum channel is assumed trusted under a restricted adversary model in which Eve is limited to individual attacks. Under this model, partial secrecy is retained for all non-zero channel error rates below a pre-agreed threshold. 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 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

Reliable fault diagnosis of rotating machinery is critical for averting serious failures in modern industrial systems. While data-driven deep learning has advanced condition monitoring, its success is fundamentally predicated on the availability of independent and identically distributed (I.I.D.) datasets. In realistic operational environments, machinery frequently experiences massive domain shifts induced by varying rotational speeds. Concurrently, acquiring high-fidelity fault instances is limited compared to abundant healthy baseline data, often resulting in a long-tailed distribution. Under such data-starved conditions, conventional few-shot domain adaptation (FSDA) methodologies often may be affected by distributional erasure; global alignment objectives are mainly driven by the healthy majority, causing sparse fault signatures to be erroneously absorbed as noise and leading to severe diagnostic performance degradation. To address this setting, this study develops a physics-guided dual-stream fusion framework for extreme few-shot cross-domain fault diagnosis. The method does not treat the Laplace wavelet, STFT, CNNs, or AdaBN as newly introduced techniques. Instead, it integrates these components into a unified diagnostic pipeline designed for long-tailed target support sets under large speed shifts. A learnable Laplace wavelet convolution is used in the temporal branch to emphasize transient impact responses, while STFT spectrograms provide a complementary time-frequency representation for the two-dimensional branch. The two feature streams are then fused for target fault classification. For domain adaptation, a Strict AdaBN strategy is applied using only the target support set, rather than the target test data or a large unlabeled target pool. Under the evaluated 50 healthy + 12 fault support condition, the healthy samples provide target-domain operating-background statistics for BN recalibration, while the limited fault samples are used for supervised classifier adjustment. Experiments on the HUSTbearing and Torino DIRG datasets show that the proposed integrated framework achieves stable performance under the evaluated few-shot cross-speed settings. These results suggest that combining physics-guided Laplace convolution, time-frequency representations, and support-set-restricted BN recalibration can be useful for bearing fault diagnosis when target fault samples are limited. Full article

Steel dust is a waste generated during steelmaking in an electric arc furnace (EAF), which contains a high proportion of iron-bearing compounds, leading to the inclusion of this waste as a resource in the circular economy for steelmaking. In addition to the limitation related to granulation (the waste must be processed to obtain larger particle sizes), a limiting factor is the increasingly high Zn content due to the low-quality ferrous charge. For the recycling of steelmaking dust, preliminary processing is necessary to reduce zinc. The paper presents, in addition to qualitative characterization of steel dust, laboratory experiments on the compositional changes associated with zinc redistribution applying the hydrometallurgical leaching process in an alkaline environment, using sodium hydroxide (NaOH). The changes in the chemical composition were identified and evaluated using X-ray fluorescence (XRF) and energy-dispersive X-ray spectroscopy (EDX). The experiments consisted of treating steel dust samples with 5 M NaOH at 25, 70, 80 and 90 °C for 60 min, using solid-to-liquid ratios of 10, 15, and 25 g/L. The results indicate a reduction in ZnO content ranging from 4.52% to 16.82%, as determined from Na₂O-free normalization data. Room-temperature samples show only marginal changes in ZnO content. The XRF and EDX analyses indicate a moderate and condition-dependent redistribution of zinc in the solid phase after alkaline treatment, as evaluated using Na₂O-free normalized data. These values are derived exclusively from solid-phase measurements (XRF/EDX) and do not include zinc in the leachate; therefore, true zinc extraction efficiency cannot be determined. The research results attest to the viability and efficiency (as a solid-phase compositional transformation process) using NaOH as a leaching agent for the studied steel dust, thus providing a potential pathway for improved waste recycling in the steel industry. 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

Tourism digitalization is widely viewed as a tool for sustainable local development, yet whether policy-led digital transformation generates measurable improvements in tourism-economy quality remains insufficiently tested. Treating the staggered establishment of China’s National Big Data Comprehensive Pilot Zones as a quasi-natural experiment, a sustainability-oriented index of high-quality tourism-economy development was constructed using 2011–2019 provincial panel data, and the policy effect was estimated with difference-in-differences and propensity-score-matched difference-in-differences models. The results show that the pilot zones significantly improve the sustainability-oriented quality of the tourism economy, a finding supported by parallel-trends tests, placebo tests, and multiple robustness checks. Heterogeneity analyses indicate positive effects across regional contexts and relatively larger estimated responses in the innovation, green, and shared development dimensions. For pilot-zone type, a more precisely estimated positive effect is shown for regional pilot zones within the current sample. Mechanism-oriented analyses show empirical patterns consistent with improvements in digital infrastructure, digital industry development, and regional innovation capacity as plausible explanatory channels. Quasi-natural experimental evidence is thus provided on how digital policy supports sustainable tourism and local development, with implications for destination governance, tourism service quality, and responsible digital transformation. Full article

Phallus indusiatus is a prestigious macro-fungus with both nutritional and medicinal significance. However, its industrial development is limited by low yields and inconsistent quality, largely due to an incomplete understanding of the underlying soil microecological mechanisms. In this study, field experiments were conducted to measure soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), total potassium (TK), and pH across different growth stages. High-throughput sequencing was further employed to characterize the dynamic successions of bacterial and fungal communities. The results revealed a continuous depletion of SOC throughout the growth cycle, with a marked decrease in TN during the ovoid stage, whereas TP, TK, and pH showed increasing trends. Bacterial abundance followed a fluctuating “increase–decrease–increase” pattern, reaching its lowest level during the ovoid stage; similarly, fungal abundance initially decreased and subsequently increased, also attaining its minimum at the ovoid stage. Based on these stage-specific soil dynamics, targeted management strategies are proposed, including the application of basal carbon fertilizers supplemented with low-concentration phosphorus and potassium, the integration of slow-release nitrogen fertilizers, and the inoculation of functional microbes such as Massilia, Acidobacteriaceae, and Terriglobales. Dynamic regulation of soil pH is also recommended. This study provides a theoretical framework and technical guidance for the sustainable and high-efficiency cultivation of P. indusiatus and contributes to the broader development of the edible fungus industry. Full article

Proper handling during harvesting and subsequent postharvest management is essential to reduce losses in fruits and vegetables, particularly because these products remain metabolically active after harvest. Physiological processes such as respiration, transpiration, ethylene production, softening, physiological disorders, and postharvest diseases determine quality deterioration, shelf life, and marketability. However, these processes do not affect all commodities in the same way; for example, climacteric fruits are strongly influenced by ethylene during ripening, whereas non-climacteric fruits generally show lower ethylene production and different postharvest behavior. In Mexico, postharvest management is especially relevant because fruit and vegetable producers differ widely in terms of production scale, infrastructure, access to technology, financing capacity, and market destination. Producers with limited access to technology require practical and low-cost alternatives, while more technologically advanced producers may use specialized systems but still experience postharvest losses due to physiological deterioration, handling conditions, logistics, and market constraints. Therefore, this review summarizes the main postharvest physiological processes affecting fruits and vegetables and discusses their implications for knowledge transfer, technology adoption, and sustainability among local producers in Mexico. The review highlights that reducing postharvest losses requires commodity-specific management, continuous technical support, low-cost and locally adaptable technologies, and coordinated participation among researchers, extension personnel, producers, government institutions, industry, and market actors. Strengthening postharvest knowledge transfer to small and local producers is essential to reduce losses, improve marketability, and promote more sustainable fruit and vegetable systems in Mexico. Full article

Instance segmentation of surface defects is one of the research hotspots in the field of image segmentation. Due to limitations such as restricted receptive fields or the loss of fine-grained details, traditional neural network models still struggle to achieve sufficiently high-segmentation accuracy for surface defects. To meet the demand for high precision segmentation of surface defects on copper strips and plates in industrial quality inspection, this paper proposes a feature fusion segmentation network, termed DSFFNet. First, a dual-branch structure is designed in DSFFNet to fuse spatial-domain features with discrete cosine transform (DCT)-domain features, thereby obtaining richer feature information. Second, a 2D-DCT frequency feature extraction module is developed to more effectively capture the edge information of targets. Third, a triplet attention mechanism is introduced into the backbone network to form an attention-centric network. Finally, a bidirectional fusion module and a multi-scale fusion network are designed to capture finer-grained feature information. Comparative experiments conducted on the KUST-SEG-Dataset demonstrate that DSFFNet achieves 94.66% ± 1.07% (mask)mAP${}_{50}$ and 95.38% ± 0.06% (box)mAP${}_{50}$, outperforming several classic image segmentation methods. Furthermore, generalization experiments on the public NEU-Seg dataset yield a (mask)mAP${}_{50}$ of 86.27% ± 0.01%. The generalization results indicate that DSFFNet is robust to datasets with similar defect types. Full article