Search Results (752)

During liquid drainage from intermediate vessels in various industrial processes such as continuous steel casting, aircraft fuel supply, and chemical separation, free-surface vortices commonly occur. The formation and evolution of these vortices not only entrain surface slag and gas, but also lead to deterioration of downstream product quality and abnormal equipment operation. The vortex evolution process exhibits notable three-dimensional unsteadiness, multi-scale turbulence, and dynamic gas–liquid interfacial changes, accompanied by strong coupling effects between temperature gradients and flow field structures. Traditional macroscopic numerical models show clear limitations in accurately capturing these complex physical mechanisms. To address these challenges, this study developed a mesoscopic numerical model for gas-liquid two-phase vortex flow based on the lattice Boltzmann method. The model systematically reveals the dynamic behavior during vortex evolution and the multi-field coupling mechanism with the temperature field while providing an in-depth analysis of how initial perturbation velocity regulates vortex intensity and stability. The results indicate that vortex evolution begins near the bottom drain outlet, with the tangential velocity distribution conforming to the theoretical Rankine vortex model. The vortex core velocity during the critical penetration stage is significantly higher than that during the initial depression stage. An increase in the initial perturbation velocity not only enhances vortex intensity and induces low-frequency oscillations of the vortex core but also markedly promotes the global convective heat transfer process. With regard to the temperature field, an increase in fluid temperature reduces the viscosity coefficient, thereby weakening viscous dissipation effects, which accelerates vortex development and prolongs drainage time. Meanwhile, the vortex structure—through the induction of Taylor vortices and a spiral pumping effect—drives shear mixing and radial thermal diffusion between fluid regions at different temperatures, leading to dynamic reconstruction and homogenization of the temperature field. The outcomes of this study not only provide a solid theoretical foundation for understanding the generation, evolution, and heat transfer mechanisms of vortices under industrial thermal conditions, but also offer clear engineering guidance for practical production-enabling optimized operational parameters to suppress vortices and enhance drainage efficiency. Full article

Lignin, the most abundant renewable source of aromatic compounds, plays a pivotal role in advancing sustainable biorefineries and reducing dependence on fossil resources. Recent progress in integrated lignin valorization pathways has unlocked opportunities to convert this complex biopolymer into high-value chemicals, materials, and energy carriers, despite its structural heterogeneity and recalcitrance posing major challenges. This review highlights the significant advancements in depolymerization strategies, including catalytic, oxidative, and biological approaches, which are reinforced by innovations in catalyst design and reaction engineering that enhance selectivity and efficiency. It also discusses emerging technologies, such as hybrid chemo-enzymatic systems, solvent fractionation, and continuous-flow reactors, for their potential to improve scalability and sustainability. Furthermore, this review examines the integration of lignin valorization with upstream pretreatment and downstream recovery, emphasizing process intensification, co-product synergy, and techno-economic optimization to achieve commercial viability. Despite these developments, critical gaps remain in understanding the molecular complexity of lignin, developing universally applicable catalytic systems, and optimizing economic and environmental performance. To guide future research, it poses two key questions: how to design catalysts for selective depolymerization across diverse lignin sources, and how to configure biorefineries for maximum lignin utilization while ensuring sustainability? Addressing these challenges will be essential for lignin’s role in next-generation biorefineries and a circular bioeconomy. Full article

The biological effects of radiofrequency electromagnetic fields (RF-EMFs) remain an unresolved scientific issue with important societal relevance, particularly in the context of the global deployment of fifth-generation (5G) wireless technologies. The skin is continuously exposed to both RF-EMFs and ultraviolet (UV) radiation, a well-established inducer of oxidative stress and DNA damage, making it a relevant model for assessing combined environmental exposures. In this study, we investigated whether post-exposure to 5G RF-EMFs (3.5 and 28 GHz) modulates ultraviolet A (UVA)-induced genotoxic stress in human keratinocytes (HaCaT) and murine melanoma (B16) cells. Post-UV RF-EMF exposure significantly reduced DNA damage markers, including phosphorylated histone H2AX (γH2AX) foci formation (by approximately 30–50%) and comet tail moments (by 60–80%), and suppressed intracellular reactive oxygen species (ROS) accumulation (by 56–93%). These effects were accompanied by selective attenuation of p38 mitogen-activated protein kinase (MAPK) phosphorylation (reduced by 55–85%). The magnitude of molecular protection was comparable to that observed with N-acetylcysteine treatment or pharmacological inhibition of p38 MAPK. In contrast, RF-EMF exposure did not reverse UV-induced reductions in cell viability or alterations in cell cycle distribution, indicating that its protective effects are confined to early molecular stress-response pathways rather than downstream survival outcomes. Together, these findings demonstrate that 5G RF-EMFs can facilitate recovery from UVA-induced molecular damage via redox-sensitive and p38-dependent mechanisms, providing mechanistic insight into the interaction between modern telecommunication frequencies and UV-induced skin stress. Full article

Carbon emission reduction in the construction industry is pivotal for global carbon emission reduction, yet the lack of coordination mechanisms within the sector limits its effectiveness. This study examines the Yangtze River Economic Belt from 2010 to 2022, capturing the spatial and temporal evolution characteristics and key influencing factors of carbon emission efficiency in the construction industry (CEECI) to achieve coordinated emission reduction. Using the super-efficiency Slack-Based Measure (SBM) model and the Malmquist–Luenberger (ML) index, the study analyzes changes in CEECI, revealing significant regional variations: downstream, midstream, and upstream regions demonstrated average values of 1.10, 1.00, and 0.68, respectively. Resource redundancy is a major issue affecting CEECI, with energy redundancy rates exceeding 20%. The ML index indicates continuous improvement in CEECI, with technological change (TC) contributing the most to this improvement, as shown by index decomposition. Spatial analysis using Moran’s index (Moran’s I) revealed significant positive spatial autocorrelation, with distinct “high-high” (H-H) and “low-low” (L-L) clustering patterns, suggesting that regions with high CEECI positively influence their neighbors. Finally, we built a spatial econometric model to identify key influencing factors, including industrialization level, construction industry production level, energy consumption structure, human resources, and internal innovation levels, which directly or indirectly impact CEECI to varying degrees. These findings highlight the importance of regional coordination and targeted policy interventions to enhance carbon emission efficiency in the construction industry, addressing resource redundancy and leveraging technological advancements to contribute to global carbon reduction goals. Full article

Today, PHB and its copolymers—potential plastic substitutes—are produced by fermenting sugar, which is not scalable to the volumes of plastic consumption. PHB from CH₄ can offer a sustainable process route, with CH₄ potentially produced from a variety of waste biomass streams through anaerobic digestion, gasification, and methanation. The high molar mass (M_w) of PHB is a key determinant of its mechanical properties, and strain, culture conditions and downstream processing influence it. In this work, the strain Methylocystis sp. GB 25 (DSMZ 7674) was grown on natural gas as the sole carbon and energy source and air (1:1) in a loop reactor with 350 L active fermentation volume, at 35 °C and ambient pressure. After two days of continuous growth, the bacteria were limited in P and N for 1, 2, and 2.5 days to determine the optimal conditions for PHB accumulation and the highest Mw as the target. The biomass was then centrifuged and spray-dried. For downstream processing, chloroform solvent extraction and selected enzymatic treatment were deployed, yielding ~40% PHB from the biomass. The PHB obtained by solvent extraction exhibited high average weight molar masses of M_w ~1.1–1.5 × 10⁶ g mol⁻¹. The highest M_w was obtained after one day of limitation, whereas enzyme treatment resulted in partially degraded PHB. Cold chloroform maceration, interesting due to energy savings, did not achieve sufficient extraction efficiency because it was unable to extract high-molar-mass PHB fractions. The extracted PHB has a high molar mass, more than double that of standard commercial PHB, and was characterized by DSC, which showed a high degree of crystallinity of up to 70% with a melting temperature of close to 180 °C. Mechanical tensile properties measurements, as well as dynamic mechanical thermal analysis (DMTA), were performed. Degradation of the PHB by enzymes was also determined. Methanotrophic PHB is a promising bioplastics material. The high M_w can limit and delay polymer degradation in practical processing steps, making the material more versatile and robust. Full article

When a fire occurs in a tunnel during construction, the smoke cannot be discharged in time and continues to settle near the ground, which threatens the safety of personnel. It is essential to understand smoke layer distribution for safe evacuation. To fill the knowledge gap for the spatio-temporal distribution of the smoke layer, a series of fire experiments are carried out in 1/20 reduced-scale tunnel models. Multiple variables are considered, including longitudinal fire location, heat release rate, aspect ratio of the main tunnel, and the inclined shaft length. Two fire scenarios are defined according to the longitudinal fire location in the main tunnel: near the upstream closed end (scenario 1) and near the downstream closed end (scenario 2). The results show that the structural evolution of the smoke layer inside the main tunnel experiences roughly three stages: single-layer smoke flow stage, transition stage, and two-layer smoke flow stage. In different fire scenarios, the reasonable N value is 10, determined by comparing the smoke layer interface height (h_s) predicted by the N-percentage method with the observed results. Moreover, we find that the FDS simulation method has significant deviation in predicting poor stratification situations. Furthermore, the spatio-temporal distributions of h_s in the main tunnel are predicted based on N = 10. The coupled effects of heat release rate and the longitudinal fire location on the h_s values are analyzed. The t_ar value (time of smoke arrival at the respiratory height) is determined, and its spatial variations are predicted. By comparing the t_ar values at position 2^# (near the inclined shaft) in different fire scenarios, we can provide a reference for the evacuation of personnel. Full article

Three-dimensional multi-object tracking (3D MOT) plays a vital role in enabling reliable perception for LiDAR-based autonomous systems. However, LiDAR measurements often exhibit sparsity, occlusion, and sensor noise that lead to uncertainty and instability in downstream tracking. To address these challenges, we propose FGO-PMB, a unified probabilistic framework that integrates the Poisson Multi-Bernoulli (PMB) filter from Random Finite Set (RFS) theory with Factor Graph Optimization (FGO) for robust LiDAR-based object tracking. In the proposed framework, object states, existence probabilities, and association weights are jointly formulated as optimizable variables within a factor graph. Four factors, including state transition, observation, existence, and association consistency, are formulated to uniformly encode the spatio-temporal constraints among these variables. By unifying the uncertainty modeling capability of RFS with the global optimization strength of FGO, the proposed framework achieves temporally consistent and uncertainty-aware estimation across continuous LiDAR scans. Experiments on KITTI and nuScenes indicate that the proposed method achieves competitive 3D MOT accuracy while maintaining real-time performance. Full article

Supervised fine-tuning is key for model alignment, but its mechanisms are debated, with conflicting evidence supporting either a superficial alignment hypothesis or significant task improvements. This paper examines supervised fine-tuning’s impact from the perspective of verbatim memorization. Using the open-source OLMo-2 model series and test datasets (instruction format, safety-sensitive, and factual knowledge) constructed from its pre-training corpus, we analyzed changes across memorization, linguistic styles, and task performance. We found that supervised fine-tuning significantly weakens the model’s verbatim memorization of pre-training data. Simultaneously, it improves generated text in terms of alignment objectives, such as polite expression and structured organization. However, this process also leads to performance degradation on knowledge-intensive downstream tasks. Further representation analysis reveals that these changes are mainly concentrated in the later layers of the model. We conclude that supervised fine-tuning acts as a continuation of the learning process on new data. By adjusting model representations, supervised fine-tuning induces a learning tilt toward the styles and content of the instruction-tuning dataset. This inclination successfully instills alignment objectives while consequently reducing the effective accessibility of previously learned knowledge, which indicates the observed degradation in both pre-training data memorization and factual task performance. The source code is publicly available. Full article

To manage sewer networks, reliable data is needed, which is often challenging. This study proposes a novel methodology to reconstruct the sewer network topology using graph theory. Two core procedures—flow adjustment and edge addition—re-establish hydraulically consistent flow paths and restore connectivity in disconnected portions of the network by reversing and adding links. The proposed approach operates at the pipe level, repairing directional reachability. It leverages only the existing network topology to reconstruct connectivity, guided by the principle that every node must have a downstream path to an outlet. The methodology is first applied to reconstruct the sewer network of Montpellier Metropolis in the South of France. Then it is validated by deliberately removing and reversing edges and applying the algorithms to test the methodology’s capability in recovering the correct topology. Both methods performed well individually, especially at lower percentages of reversal (1%) and removal (1%), with a correctness of 0.99 for flow adjustment and 0.8 for edge addition. Although the results were poorer when combining the methods and increasing data degradation—particularly at 10% reversal and 10% removal (correctness of 0.64)—the methodology continued to produce a functionally consistent and logically coherent network, highlighting its robustness given the absence of supporting attribute data. Full article

The rising levels of Co(II) in aquatic environments present considerable risks, thereby necessitating the development of effective remediation strategies. This study introduces an innovative pre-hydration method for synthesizing carbonated calcined clay dolomite (CCCD) to efficiently remove Co(II) from contaminated water. This pre-hydration treatment successfully reduced the complete carbonation temperature of the material from 500 °C to 400 °C, significantly enhancing energy efficiency. The Co(II) removal performance was systematically investigated by varying key parameters such as contact time, initial Co(II) concentration, pH, and solid/liquid ratio. Optimal removal was achieved at 318 K with pH of 4 and a solid/liquid ratio of 0.5 g·L⁻¹. Continuous flow column experiments confirmed the excellent long-term stability of CCCD, maintaining a consistent Co(II) removal efficiency of 99.0% and a stable effluent pH of 8.5 over one month. Isotherm and kinetic models were used to empirically describe concentration-dependent and time-dependent uptake behavior. The equilibrium data were best described by the Langmuir model, while kinetics followed a pseudo-second-order model. An apparent maximum removal capacity of 621.1 mg g⁻¹ was obtained from Langmuir fitting of equilibrium uptake data. Mechanistic insights from Visual MINTEQ calculations and solid phase characterizations (XRD, XPS, and TEM) indicate that Co(II) removal is dominated by mineral water interface precipitation. The gradual hydration of periclase (MgO) forms Mg(OH)₂, which creates localized alkaline microenvironments at particle surfaces and drives Co(OH)₂ formation. Carbonate availability further favors CoCO₃ formation and retention on CCCD. Importantly, this localized precipitation pathway maintains a stable, mildly alkaline effluent pH (around 8.5), reducing downstream pH adjustment demand and improving operational compatibility. Overall, CCCD combines high Co(II) immobilization efficiency, strong long-term stability, and an energy-efficient preparation route, supporting its potential for scalable remediation of Co(II) contaminated water. Full article

Cavitation in aircraft hydraulic systems continues to pose a serious problem for the aviation industry. This paper presents a new study on cavitation in aviation hydraulic fluid AMG-10 at its inception condition, corresponding to a relative pressure drop of Δp = 0.58, within a small-scale rectangular throttle channel of specified dimensions. Numerical simulations were performed in a quasi-steady-state framework using the realizable k–ε turbulence model combined with the Enhanced Wall Treatment approach, and the results were validated against time-integrated experimental data obtained via the shadowgraphy method. Cavitation was modeled using the Zwart–Gerber–Belamri model. The validated numerical model, which showed a pressure deviation of less than 10% from experimental data on the upper and lower walls, also demonstrated good agreement in the dimensions of the cavitation regions, confirming that the upper region is consistently larger than the lower one. Quantitative analysis demonstrated that regions with high vapor concentration are highly localized, representing only 0.048% of the channel volume at a 0.8 vapor fraction threshold. The analysis reveals that the cavitation regions spatially coincide with local pressure drops to values as low as 214 and 236 Pa near the upper and lower walls. These regions are also associated with wall jets, accelerated by the flow constriction to velocities up to 41.98 m/s. Furthermore, the cavitation region corresponds to a distinct peak in the mean turbulent kinetic energy field, reaching 164.5 m²/s², which decays downstream. Full article

The reliable and continuous acquisition of seismic data from multiple open sources is essential for real-time monitoring, hazard assessment, and early-warning systems. However, the heterogeneity among existing data providers such as the United States Geological Survey, the European-Mediterranean Seismological Centre, and the Spanish National Geographic Institute creates significant challenges due to differences in formats, update frequencies, and access methods. To overcome these limitations, this paper presents a modular and automated framework for the scheduled near-real-time ingestion of global seismic data using open APIs and semi-structured web data. The system, implemented using a Docker-based architecture, automatically retrieves, harmonizes, and stores seismic information from heterogeneous sources at regular intervals using a cron-based scheduler. Data are standardized into a unified schema, validated to remove duplicates, and persisted in a relational database for downstream analytics and visualization. The proposed framework adheres to the FAIR data principles by ensuring that all seismic events are uniquely identifiable, source-traceable, and stored in interoperable formats. Its lightweight and containerized design enables deployment as a microservice within emerging data spaces and open environmental data infrastructures. Experimental validation was conducted using a two-phase evaluation. This evaluation consisted of a high-frequency 24 h stress test and a subsequent seven-day continuous deployment under steady-state conditions. The system maintained stable operation with 100% availability across all sources, successfully integrating 4533 newly published seismic events during the seven-day period and identifying 595 duplicated detections across providers. These results demonstrate that the framework provides a robust foundation for the automated integration of multi-source seismic catalogs. This integration supports the construction of more comprehensive and globally accessible earthquake datasets for research and near-real-time applications. By enabling automated and interoperable integration of seismic information from diverse providers, this approach supports the construction of more comprehensive and globally accessible earthquake catalogs, strengthening data-driven research and situational awareness across regions and institutions worldwide. Full article

Autonomous underwater vehicle (AUV) motion planning in complex three-dimensional ocean environments remains challenging due to the simultaneous requirements of obstacle avoidance, dynamic feasibility, and energy efficiency. Current approaches often decouple these factors or exhibit high computational overhead, limiting applicability in real-time or large-scale missions. This work proposes a hierarchical trajectory planning framework designed to address these coupled constraints in an integrated manner. The framework consists of two stages: (i) a current-biased sampling-based planner (CB-RRT*) is introduced to incorporate ocean current information into the path generation process. By leveraging flow field distributions, the planner improves path geometric continuity and reduces steering variations compared with benchmark algorithms; (ii) spatial–temporal alternating optimization is performed within underwater safe corridors, where Bézier curve parameterization is utilized to jointly optimize spatial shapes and temporal profiles, producing dynamically feasible and energy-efficient trajectories. Simulation results in dense obstacle fields, heterogeneous flow environments, and large-scale maps demonstrate that the proposed method reduces the maximum steering angle by up to 63% in downstream scenarios, achieving a mean maximum turning angle of 0.06 rad after optimization. The framework consistently attains the lowest energy consumption across all tests while maintaining an average computation time of 0.68 s in typical environments. These results confirm the framework’s suitability for practical AUV applications, providing a computationally efficient solution for generating safe, kinematically feasible, and energy-efficient trajectories in real-world ocean settings. Full article

Coronary artery disease (CAD) remains a leading cause of mortality worldwide, and accurate coronary artery segmentation in X-ray coronary angiography (XCA) is challenged by low contrast, structural ambiguity, and anisotropic vessel trajectories, which hinder quantitative coronary angiography. We propose HR-UMamba++, a U-Mamba-based framework centered on a rotation-aligned multi-directional state-space scan for modeling long-range vessel continuity across multiple orientations. To preserve thin distal branches, the framework is equipped with (i) a persistent high-resolution bypass that injects undownsampled structural details and (ii) a UNet++-style dense decoder topology for cross-scale topological fusion. On an in-house dataset of 739 XCA images from 374 patients, HR-UMamba++ is evaluated using eight segmentation metrics, fractal-geometry descriptors, and multi-view expert scoring. Compared with U-Net, Attention U-Net, HRNet, U-Mamba, DeepLabv3+, and YOLO11-seg, HR-UMamba++ achieves the best performance (Dice 0.8706, IoU 0.7794, HD95 16.99), yielding a relative Dice improvement of 6.0% over U-Mamba and reducing the deviation in fractal dimension by up to 57% relative to U-Net. Expert evaluation across eight angiographic views yields a mean score of 4.24 ± 0.49/5 with high inter-rater agreement. These results indicate that HR-UMamba++ produces anatomically faithful coronary trees and clinically useful segmentations that can serve as robust structural priors for downstream quantitative coronary analysis. Full article

The worldwide population is anticipated to reach 10.12 billion by the year 2100, thereby amplifying the necessity for sustainable agricultural methodologies to secure food availability while reducing ecological consequences. Conventional synthetic pesticides, while capable of increasing crop yields by as much as 50%, present considerable hazards such as toxicity, the emergence of resistance, and environmental pollution. This review examines biopesticides, originating from microbial (e.g., Bacillus thuringiensis, Trichoderma spp.), plant, or animal sources, as environmentally sustainable alternatives which address pest control through mechanisms including antibiosis, hyperparasitism, and competition. Biopesticides provide advantages such as biodegradability, minimal toxicity to non-target organisms, and a lower likelihood of resistance development. The global market for biopesticides is projected to be valued between USD 8 and 10 billion by 2025, accounting for 3–4% of the overall pesticide sector, and is expected to grow at a compound annual growth rate (CAGR) of 12–16%. To mitigate production costs, agro-industrial byproducts such as rice husk and starch wastewater can be utilized as economical substrates in both solid-state and submerged fermentation processes, which may lead to a reduction in expenses ranging from 35% to 59%. Strategies for process intensification, such as the implementation of intensified bioreactors, continuous cultivation methods, and artificial intelligence (AI)-driven monitoring systems, significantly improve the upstream stages (including strain development and fermentation), downstream processes (such as purification and drying), and formulation phases. These advancements result in enhanced productivity, reduced energy consumption, and greater product stability. Patent activity, exemplified by 2371 documents from 1982 to 2021, highlights advancements in formulations and microbial strains. The integration of circular economy principles in biopesticide production through process intensification enhances the safety, quality, and sustainability of food systems. Projections suggest that by the 2040s to 2050s, biopesticides may achieve market parity with synthetic alternatives. Obstacles encompass the alignment of regulations and the ability to scale in order to completely achieve these benefits. Full article