Search Results (89)

Ion beam figuring (IBF) is an ultra-high-precision surface finishing technology characterized by a distinct trade-off between the spot size of the removal function and its corresponding figuring capabilities. A larger spot size for the removal function leads to higher processing efficiency but lower figuring ability. Conversely, a smaller spot size results in higher figuring ability but lower efficiency. Adjusting the spot size of the removal function using tools with an aperture is a possible approach. However, existing variable-aperture tools have certain limitations in IBF processing. To leverage the advantages of both large and small spot sizes for the removal function during IBF processing, an in situ dynamic beam variable-aperture device has been designed. This device optimizes the parameters of diaphragm sheets and employs FOC for dynamic aperture adjustment. Simulations show that 12 numbers of 0.1 mm-thick sheets minimize removal function distortion, with the thermal strain-induced area variation being <5%. FOC enables rapid (≤0.45 s full range) and precise aperture control. Experiments confirm adjustable spot sizes (FWHM 0.7–17.2 mm) with Gaussian distribution (correlation >96.7%), operational parameter stability (relative change rate ≤5%), and high repeatable positioning precision (relative change rate ≤3.2% in repeated adjustments). The design enhances IBF efficiency, flexibility, and accuracy by enabling in situ spot size optimization, overcoming conventional limitations. Full article

Urban regeneration, as a key strategy for promoting sustainable development of urban areas, requires innovative digital technologies to address increasingly complex urban challenges in its implementation. With the fast advancement of digital technologies such as artificial intelligence (AI), Internet of Things (IoT), and big data, these technologies have extensively penetrated various dimensions of urban regeneration, from planning and design to implementation and post-operation management, providing new possibilities for improving urban regeneration efficiency and quality. However, the existing literature lacks a systematic evaluation of technology application patterns across different project scales and phases, comprehensive analysis of stakeholder–technology interactions, and quantitative assessment of technology distribution throughout the urban regeneration lifecycle. This research gap limits the in-depth understanding of how digital technologies can better support urban regeneration practices. This study aims to identify and quantify digital technology application patterns across urban regeneration stages, scales, and stakeholder configurations through systematic analysis of 56 high-quality articles from the Scopus and Web of Science databases. Using a mixed-methods approach combining a systematic literature review, bibliometric analysis, and meta-analysis, we categorized seven major digital technology types and analyzed their distribution patterns. Key findings reveal distinct temporal patterns: GIS and BIM/CIM technologies dominate in the pre-urban regeneration (Pre-UR) stage (10% and 12% application proportions, respectively). GIS applications increase significantly to 14% in post-urban regeneration (Post-UR) stage, while AI technology remains underutilized across all phases (2% in Pre-UR, decreasing to 1% in Post-UR). Meta-analysis reveals scale-dependent technology adoption patterns, with different technologies showing varying effectiveness at building-level, district-level, and city-level implementations. Research challenges include stakeholder digital divides, scale-dependent adoption barriers, and phase-specific implementation gaps. This study constructs a multi-dimensional analytical framework for digital technology support in urban regeneration, providing quantitative evidence for optimizing technology selection strategies. The framework offers practical guidance for policymakers and practitioners in developing context-appropriate digital technology deployment strategies for urban regeneration projects. Full article

The intelligently thermosensitive 2-methacryloyloxyethyl phosphorylcholine (MPC) groups-conjugated methylcellulose (MC) hydrogel, abbreviated as MPC-g-MC, exhibits good potential for prevention of postoperative adhesions. However, its thermosensitive gelation mechanism and why the MPC-g-MC hydrogel shows a lower gelation temperature than that of MC hydrogel are still unclear. Molecular dynamics (MD) simulation was thus used to investigate these mechanisms in this work. After a fully atomistic MPC-g-MC molecular model was constructed, MD simulations during the thermal simulation process and at constant temperatures were performed using GROMACS 2022.3 software. The results indicated that the hydrophobic interactions between the MPC-g-MC molecular chains increased, the interactions between the MPC-g-MC molecular chains and H₂O molecules decreased with the rise in temperature, and the hydrogen bonding structures were changed during the thermal simulation process. As a result, the MPC-g-MC molecular chains began to aggregate at about 33 °C (close to the gelation temperature of 33 °C determined by the rheological measurement), bringing about the formation of the MPC-g-MC hydrogel in the macroscopic state. The mechanism of MPC-g-MC hydrogel formation showed that its lower gelation temperature than that of the MC hydrogel is attributed to the increase in the interactions (including hydrophobic interactions, hydrogen bonding interactions, Van der Waals and Coulomb forces) induced by the side MPC groups of MPC-g-MC molecules. The thermosensitive gelation mechanism revealed in this study provides an important reference for the development of novel thermosensitive MC-derived hydrogels with gelation temperatures close to human body temperature. Full article

The aim of this study was to explore the effects of different lactic acid bacteria additives (Lactiplantibacillus plantarum or Lentilactobacillus buchneri) on the fermentation quality, chemical composition, in vitro digestibility, bacterial community structure, and predictive function of S. perfoliatum silage feed. Fresh S. perfoliatum was wilted overnight, then its moisture content was adjusted between 65 and 70%. The experiment was performed in three groups as follows: (1) the control group (CK group), which lacked a Lactobacillus preparation; (2) the Lactiplantibacillus plantarum (L. plantarum) group (LP group), which was inoculated with L. plantarum at 5 × 10⁶ cfu/g FW; and (3) the Lentilactobacillus buchneri (L. buchneri) group (LB group), which was inoculated with L. buchneri at 5 × 10⁶ cfu/g FW. The results showed that L. plantarum significantly reduced pH and increased lactic acid (LA) content in S. perfoliatum silage compared with the control. L. buchneri, on the other hand, excelled in reducing ammonia nitrogen (NH₃-N) content and significantly increased acetic acid (AA) content. At 60 days of fermentation, the CP content was significantly higher (p < 0.05) in the LP and LB groups than in the CK group (19.29 vs. 15.53 and 15.87). At 60 days of fermentation, the ivCPD was significantly higher (p < 0.05) in the LB group than in the CK and LP groups (57.80 vs. 54.77 and 55.77). The 60-day silage process completely altered the bacterial community of S. perfoliatum silage. In the fresh samples, the dominant genera were Weissella_A and Pantoea_A. Weissella_A and Pantoea_A were gradually replaced by Lentilactobacillus and Lactiplantibacillus after S. perfoliatum ensiling. After 45 days of fermentation, L. buchneri became the dominant strain in CK, LP and LB groups. Inoculation with L. plantarum altered the succession of the bacterial community from 7 to 15 days of fermentation of S. perfoliatum. In contrast, inoculation with L. buchneri affected the succession of the bacterial community from 30 to 60 days of S. perfoliatum fermentation. In S. perfoliatum silage aged 7 to 60 days, the amino acid metabolic pathway in the LB group remained upregulated. The experimental results revealed that inoculation with L. buchneri had a stronger effect on S. perfoliatum silage than inoculation with L. plantarum. Thus, L. buchneri should be selected as an additive for S. perfoliatum silage fermentation in practical production. Full article

Sarcomyxa edulis is a characteristic low-temperature, edible mushroom in Northeast China. It has a delicious taste and rich nutritional and medicinal value. S. edulis can undergo explosive fruiting, neat fruiting, and unified harvesting, making it suitable for factory production. The molecular mechanisms underlying fruiting body development in S. edulis remain poorly understood. This study employed transcriptome analysis to compare the post-ripening mycelium (NPM) and primordial fruiting bodies (PRMs) of the thermostable S. edulis strain PQ650759, which uniquely forms primordia under constant temperature. A total of 4862 differentially expressed genes (DEGs) (|log2(fold change)| ≥ 1) were identified and found to be predominantly enriched in biological processes such as cell wall organization, DNA replication, and carbohydrate metabolism. KEGG pathway analysis revealed significant enrichment in 20 metabolic pathways, including mismatch repair, yeast cell cycle, and starch/sucrose metabolism. Ten candidate genes (e.g., SKP1, MRE11, GPI) linked to cell cycle regulation, DNA repair, and energy metabolism were randomly selected and prioritized for functional analysis. Quantitative PCR validation confirmed the reliability of transcriptome data, with expression trends consistent across both methods. Our findings provide critical insights into the molecular regulation of fruiting body development in S. edulis and establish a foundation for future mechanistic studies and strain optimization in industrial cultivation. Full article

Ultra-deep tight sandy conglomerate reservoirs in the Junggar Basin are characterized by vertically alternating lithologies that include mudstone, sandy conglomerate, and sandstone. High in situ stresses and formation temperatures contribute to a brittle–ductile transition process in the reservoir rocks. However, the brittle behavior and ductile hydraulic fracture propagation mechanisms under in situ conditions remain inadequately understood. In this study, ultra-deep core samples were subjected to triaxial compression tests under varying confining pressures and temperatures to simulate different burial depths and evaluate their brittleness. A three-dimensional hydraulic fracture propagation model was developed in ABAQUS 2023 finite element software, incorporating a cohesive zone ductile constitutive model. Numerical simulations were conducted, considering interlayer horizontal stress differences, injection rate, and fracturing fluid viscosity, to systematically analyze the influence of geological and engineering factors on ductile fracture propagation. A fracture length–height competition diagram was constructed to illustrate the propagation mechanisms. The results reveal that high temperatures significantly accelerate the brittle–ductile transition, which occurs at confining pressures between 55 and 65 MPa. Following this transition, failure modes shift from single-shear failure to a multi-localized fracture with bulging deformation. Interlayer horizontal stress differences were found to strongly influence fracture penetration, with larger stress differences hindering vertical growth. Increasing injection rates promoted the uniform distribution of lateral fractures and fracture tip development, while medium- to high-viscosity fracturing fluids enhanced fracture width and vertical stimulation uniformity. These findings provide important insights for optimizing fracturing strategies and expanding the effective stimulation volume in the ultra-deep tight sandy conglomerate reservoirs of the Junggar Basin. Full article

Background/Objectives: This study aims to develop and evaluate neutrophil-membrane-coated nanoparticles (Siv@NMs) encapsulating sivelestat for the treatment of sepsis-induced endothelial injury. Leveraging the intrinsic chemotactic properties of neutrophil membranes, Siv@NMs are engineered to achieve site-specific delivery of sivelestat to damaged endothelia, thereby overcoming the limitations of conventional therapies in mitigating endothelial dysfunction and multiorgan failure associated with sepsis. Methods: Siv@NMs were synthesized through a combination of ultrasonication and extrusion techniques to encapsulate sivelestat within neutrophil-membrane-derived vesicles. Comprehensive physicochemical characterization included analysis of particle size distribution, zeta potential, and encapsulation efficiency. Stability profiles and controlled release kinetics were systematically evaluated under simulated conditions. In vitro investigations encompassed (1) endothelial cell biocompatibility assessment via cytotoxicity assays, (2) investigation of the targeting efficiency in suppressing endothelial neutrophil extracellular trap generation during inflammation, and (3) ROS-scavenging capacity quantification using flow cytometry with DCFH-DA fluorescent probes. In vivo therapeutic efficacy was validated using a cecal ligation and puncture (CLP) sepsis mouse model, with multiparametric monitoring of endothelial function, inflammatory markers, ROS levels, and survival outcomes. Results: The optimized Siv@NMs exhibited an average particle size of approximately 150 nm, and a zeta potential of −10 mV was achieved. Cellular studies revealed that (1) Siv@NMs selectively bound to inflammatory endothelial cells with minimal cytotoxicity, and (2) Siv@NMs significantly reduced ROS accumulation in endothelial cells subjected to septic stimuli. In vitro experiments demonstrated that Siv@NMs treatment markedly attenuated endothelial injury biomarkers’ expression (ICAM-1 and iNOS), suppressed formation of neutrophil extracellular traps, and improved survival rates compared to treatment with free sivelestat. Conclusions: The neutrophil-membrane-coated nanoparticles loaded with sivelestat present a breakthrough strategy for precision therapy of sepsis-associated endothelial injury. This bioengineered system synergistically combines targeted drug delivery with multimodal therapeutic effects, including ROS mitigation, anti-inflammatory action, and endothelial protection. These findings substantiate the clinical translation potential of Siv@NMs as a next-generation nanotherapeutic for sepsis management. Full article

We present a reliable and all-fiberized single-polarization, high-order mode fiber laser. The experimental setup employed polarization-maintaining ytterbium-doped fibers and a combination of different fiber Bragg gratings to achieve high mode purity and stable output. The system achieved a maximum output power of 3.8 W, a polarization extinction ratio (PER) of 96.7%, and a mode purity of 95.32% for the LP₁₁ mode. Furthermore, the laser demonstrated notable stability; during a 120 min stability test, the standard deviation of the output power was measured at 0.15%, while the standard deviation of the polarization extinction ratio (PER) was 0.07%. This work offers a reliable solution for the generation of stable, high-purity LP₁₁ mode lasers. Full article

In space gravitational wave detection, the phase information of interfering signals is read out by a phasemeter, typically output sampling at a MHz frequency. To transmit the phase information between space and ground, it must be downsampled; however, spectral aliasing during downsampling will affect the performance of the phasemeter.This paper presents a four-module cascaded downsampling filter (FCDF) with detailed module parameter design. On-board experiments conducted in a phasemeter environment demonstrate that the FCDF achieves a passband attenuation of less than $8.68\times {10}^{-6}$ dB and a stopband attenuation exceeding 160 dB, enabling downsampling from 80 MHz to 3.4 Hz. Additionally, the FCDF offers improved low-frequency noise suppression, which can enhance phasemeter performance. Full article

Photosynthesis, which is the foundation of crop growth and development, is accompanied by complex transcriptional regulatory mechanisms. Research has established that brassinosteroids (BRs) play a role in regulating plant photosynthesis, with the majority of research focusing on the physiological level and regulation of rate-limiting enzymes in the dark reactions of photosynthesis. However, studies on their effects on maize photosynthesis, specifically on light-harvesting antenna proteins, have yet to be conducted. The peripheral light-harvesting antenna protein Lhcb5 is crucial for capturing and dissipating light energy. Herein, by analyzing the transcriptomic data of maize seedling leaves treated with 24-epibrassinolide (EBR) and verifying them using qPCR experiments, we found that the MYBR17 transcription factor may regulate the expression of the photosynthetic light-harvesting antenna protein gene. Further experiments using protoplast transient expression and yeast one-hybrid tests showed that the maize transcription factor MYBR17 responds to EBR signals and binds to the promoter of the light-harvesting antenna protein Lhcb5, thereby upregulating its expression. These results were validated using an Arabidopsis mybr17 mutant. Our results offer a theoretical foundation for the application of BRs to enhance the photosynthetic efficiency of maize. Full article

Mesoscale eddies play a critical role in sea navigation and route planning, yet traditional prediction methods have often overlooked their spatial relationships, relying on indirect approaches to capture their distribution across extensive maps. To address this limitation, we present BiST-SA-LSTM, an end-to-end prediction framework that combines Bidirectional Spatial Temporal LSTM and Self-Attention mechanisms. Utilizing data sourced from the South China Sea and its surrounding regions, which are renowned for their intricate maritime dynamics, our methodology outperforms similar models across a range of evaluation metrics and visual assessments. This is particularly evident in our ability to provide accurate long-term forecasts that extend for up to 10 days. Furthermore, integrating sea surface variables enhances forecasting accuracy, contributing to advancements in oceanic physics. Full article

Safety design is a critical concern for energetic materials, with sensitivities and performance parameters becoming increasingly important as energy density rises. However, obtaining experimental data for these properties is costly and risky. Although linear methods and neural networks have been applied to predict these properties, the limited sample size of experimental data has led to models with limitations, including inadequate accuracy, lack of effective predictive models, and inevitable reliance on experimental properties. To address these challenges, this study utilizes kernel methods and heuristic algorithms, including Genetic Algorithm and Particle Swarm Optimization, to develop effective models for predicting the impact sensitivity, electric spark sensitivity, and detonation velocity of energetic materials. After optimizing the modeling process with Particle Swarm Optimization, the models achieved R² values of 0.871, 0.898, and 0.942 on the test sets, respectively, surpassing those of neural network models, with R² values of 0.827, 0.826, and 0.909, and support vector regression models, with R² values of 0.822, 0.862, and 0.894. The proposed models significantly improve the accuracy of impact sensitivity predictions and, for the first time, offer an effective model for predicting electric spark sensitivity. By being entirely based on computational descriptors, these models expand the application range compared to previous empirical formulas. These results demonstrate the high effectiveness and accuracy of this methodology in predicting the hazardous properties of chemicals with limited experimental data. Full article

Sarcopenia is characterized by a degeneration of muscle mass and strength that incurs impaired mobility, posing grievous impacts on the quality of life and well-being of older adults worldwide. In 2018, a new international consensus was formulated to incorporate ultrasound imaging of the rectus femoris (RF) muscle for early sarcopenia assessment. Nonetheless, current clinical RF muscle identification and delineation procedures are manual, subjective, inaccurate, and challenging. Thus, developing an effective AI-empowered RF segmentation model to streamline downstream sarcopenia assessment is highly desirable. Yet, this area of research readily goes unnoticed compared to other disciplines, and relevant research is desperately wanted, especially in comparison among traditional, classic, and cutting-edge segmentation networks. This study evaluated an emerging Automatic Segment Anything Model (AutoSAM) compared to the U-Net and nnU-Net models for RF segmentation on ultrasound images. We prospectively analyzed ultrasound images of 257 older adults (aged > 65) in a community setting from Hong Kong’s District Elderly Community Centers. Three models were developed on a training set (n = 219) and independently evaluated on a testing set (n = 38) in aspects of DICE, Intersection-over-Union, Hausdorff Distance (HD), accuracy, precision, recall, as well as stability. The results indicated that the AutoSAM achieved the best segmentation agreement in all the evaluating metrics, consistently outperforming the U-Net and nnU-Net models. The results offered an effective state-of-the-art RF muscle segmentation tool for sarcopenia assessment in the future. Full article

Antibiotic therapy has been a common method for treating bacterial infections over the past century, but with the rise in bacterial resistance caused by antibiotic abuse, better control and more rational use of antibiotics have been increasingly demanded. At the same time, a journey to explore alternatives to antibiotic therapies has also been undertaken. Chitosan and its derivatives, materials with good biocompatibility, biodegradability, and excellent antibacterial properties, have garnered significant attention, and more and more studies on chitosan and its derivatives have been conducted in recent years. In this work, we aim to elucidate the biological properties of chitosan and its derivatives and to track their clinical applications, as well as to propose issues that need to be addressed and possible solutions to further their future development and application. Full article

This study explores reinforcement learning algorithms combined with graph embedding methods to optimize the assembly sequence of complex single-layer reticulate shells. To minimize the number of temporary support brackets during installation, the structural assembly process is modeled using the inverse dismantling process. The remaining members of the structure at each iteration step are scored, and the one with the highest score for removal is selected. Next, this study trains an effective intelligent agent to assemble the structure. The proposed method can be used to design several types of latticed shells. The trained intelligent model can complete the assembly sequence design of the mesh shell without requiring any other data except for previous structural information. To verify the feasibility of the novel method, it is compared with the empirical approach used in the traditional assembly sequence design process. The feasibility of the new method is demonstrated. It is indicated that the novel method can obtain the optimal solution accurately and efficiently. In addition, it has more innovative choices for installation sequences than the conventional technique. It has enormous potential and application in the civil engineering field. Full article