Search Results (64,258)

In recent years, with continuous technological advances, BIM technology has gradually expanded from the traditional construction industry into the field of hydraulic engineering. Since BIM models, which span the entire project lifecycle, contain substantial amounts of data and the operation and maintenance phase accounts for the majority of this lifecycle, higher computational demands are imposed. Consequently, the lightweighting of BIM models has become imperative. In this study, an improved Quadric Error Metric (QEM) algorithm was applied to simplify the geometric data of the constructed BIM model. The research investigates whether the lightweight model can reduce the computational requirements during its application in the operation and management of hydraulic engineering, thereby enhancing its general applicability. Furthermore, a fuzzy comprehensive evaluation model was established to assess the effectiveness of the lightweighting process. The experimental results indicate that the optimized model occupies significantly less memory space. Additionally, model loading time and rendering CPU usage were substantially improved. The lightweight effect was evaluated as excellent based on the fuzzy comprehensive evaluation. Full article

Background: Carbon-fiber-reinforced polyetheretherketone (CFR-PEEK) vertebral-body replacements (VBRs) aim to mitigate subsidence, minimize imaging artifacts, and facilitate radiation planning while preserving fusion potential. We assessed the safety and efficacy of a novel modular, titanium-coated CFR-PEEK VBR (Kong^®) for anterior column reconstruction (ACR) in the thoracolumbar spine. Primary question: Does the implant safely and effectively achieve and maintain kyphosis correction after ACR for trauma and neoplasms? Methods: A single-center retrospective case series was performed on 28 patients who underwent thoracolumbar ACR with the Kong^® VBR for fractures or tumors (2020–2021). The primary outcome was the bi-segmental kyphotic angle (BKA). Secondary outcomes were screw loosening, cage height loss, fusion rate, subsidence, and tilting. Clinical status was recorded with Odom criteria, Karnofsky Performance Status (KPS), and AOSpine PROST. Results: Twenty-eight patients (mean age, 61 yr; 33% female; mean follow-up, 17.7 mts) were studied. Mean postoperative BKA correction was 16.5° (p = 0.006) and remained 14.5° at final follow-up (p = 0.008); loss of correction was 2.0° (p = 0.568). Subsidence, cage height, and sagittal tilt were unchanged. Fusion (Bridwell grade I/II) was observed in 95% on CT. One deep surgical-site infection occurred. At final follow-up, 91% of patients were graded “excellent” or “good” by Odom. KPS improved by 20 points (p = 0.031), and mean AOSpine PROST was 56.9. Conclusions: Single-center early results indicate that the modular titanium-coated CFR-PEEK VBR is a safe, effective adjunct for thoracolumbar ACR in trauma and neoplasm, providing durable kyphosis correction, mechanical stability and high fusion rates and grants for improved follow-up imaging quality. Full article

Adsorption desulfurization of fuel oil is regarded as one of the most promising technologies for obtaining clean fuel because it can remove refractory sulfur compounds at ambient temperature and pressure. Studies indicate that HKUST-1, as an important type of metal–organic framework (MOF), is a potential candidate for adsorption desulfurization of fuel oil. In this work, we report that defective HKUST-1 can be rapidly synthesized at room temperature with the aid of NH₄F and exhibit superior adsorption desulfurization performance compared to conventional HKUST-1 by the solvothermal method. Moreover, the influence of adsorption parameters on the desulfurization performance of HKUST-1 prepared with the aid of NH₄F was investigated. We used 50 mg of HKUST-1-5 synthesized with 5 wt% added NH₄F to adsorb 5 g of model oil with a sulfur concentration of 1000 ppm at 25 °C for 1 h, and the adsorption capacity of the adsorbent reached 23.8 mgS/g, 46.8 mgS/g and 36.8 mgS/g for benzothiophene (BT), dibenzothiophene (DBT) and 4,6-dimethyldibenzothiophene (4,6-DMDBT), respectively, which are higher values than those of conventional HKUST-1. Such performance can be mainly attributed to its relatively small particle size and the presence of more unsaturated Cu sites. The results of regeneration experiments show that HKUST-1-5 still maintains excellent adsorption performance after four cycles. These findings highlight the great potential of this material as an efficient adsorbent for adsorption desulfurization of fuel oil. Full article

Real-time lossless image compression based on the JPEG-LS algorithm is in high demand for critical missions such as satellite remote sensing and space exploration due to its excellent balance between complexity and compression rate. However, few researchers have made appropriate modifications to the JPEG-LS algorithm to make it more suitable for high-speed hardware implementation and application to Bayer pattern data. This paper addresses the current limitations by proposing a real-time lossless compression system specifically tailored for Bayer pattern images from spaceborne cameras. The system integrates a hybrid encoding strategy modified from JPEG-LS, combining run-length encoding, predictive encoding, and a non-encoding mode to facilitate high-speed hardware implementation. Images are processed in tiles, with each tile’s color channels processed independently to preserve individual channel characteristics. Moreover, potential error propagation is confined within a single tile. To enhance throughput, the compression algorithm operates within a 20-stage pipeline architecture. Duplication of computation units and the introduction of key-value registers and a bypass mechanism resolve structural and data dependency hazards within the pipeline. A reorder architecture prevents pipeline blocking, further optimizing system throughput. The proposed architecture is implemented on a XILINX XC7Z045-2FFG900C SoC (Xilinx, Inc., San Jose, CA, USA) and achieves a maximum throughput of up to 346.41 MPixel/s, making it the fastest architecture reported in the literature. Full article

Against the backdrop of China’s “dual carbon” goals of achieving carbon peaking by 2030 and carbon neutrality by 2060. Traditional qualitative evaluations struggle with subjectivity; therefore we apply the quantitative PMC Index to systematically assess smart energy policies. This research systematically analyzes 16 representative Chinese smart energy policies using the PMC model, combined with content analysis. An integrated analytical framework was constructed to examine PMC applications across different energy policy fields. Results demonstrate that China’s smart energy policies achieved excellent performance, with an average PMC score of 7.48 out of 10. Furthermore, 68.75% of policies (11 out of 16) reached the ‘excellent’ level (PMC ≥ 8.0), with Policy “P6” achieving the highest score of 8.88 points. Top-performing policies exhibited strong strategic coordination, clear objectives, and comprehensive supporting measures. The findings reveal a well-structured policy cluster with clear objectives and strong coordination. This mature policy package provides a solid institutional foundation for China’s energy system transformation toward smart and green development, offering valuable insights for energy policy optimization and quantitative assessment methodology improvement. Full article

Due to its ecological functions, huge economic benefits, and excellent nutritional and physiological activities, Lentinula edodes is a very popular edible fungus in Asia, especially in China. Changes in the distribution and population of wild L. edodes play an important role in conservation, variety improvements, and breeding. This investigation detected wild L. edodes in 28 provinces and municipalities in China, encompassing approximately 300 regions and natural reserves. MaxEnt analysis of 53 effective distribution locations indicated that host plants, Bio19 (precipitation in the coldest quarter), Bio10 (mean temperature of the warmest quarter), and Bio17 (precipitation in the driest quarter) made the most critical contributions to this model. The areas of suitable and highly suitable habitats were 55.386 × 10⁴ km² and 88.493 × 10⁴ km², respectively. Under four climate change scenarios, the L. edodes distribution was predicted to decrease and the suitable habitat area shifted to the north and west of China. The decrease in highly suitable habitat area ranged from 21.155% in the 2070s under the ssp1-2.6 scenario to 90.522% in the 2050s under the ssp3-7.5 scenario. This sharp reduction in habitat areas suggests that we should take measures to prevent the deterioration of the environment and climate and thus to ensure the survival of L. edodes. Full article

The growth environment of corps requires necessary improvements by Chinese solar greenhouses with Pad–Fan Cooling (PFC) systems for reducing their high temperatures in summer. Although computational fluid dynamics (CFD) could dynamically display the changes in humidity, temperature, and wind speed in solar greenhouses, its computational efficiency and accuracy are relatively low. In addition, the use of PFC systems can cool down solar greenhouses in summer, but they will also cause excessive humidity inside the greenhouses, thereby reducing the production efficiency of crops. Most existing studies only verify the effectiveness of a single machine learning (such as ARMA or ARIMA) or deep learning model (such as LSTM or TCN), lacking systematic comparison of different models. In the current study, two machine learning algorithms and three deep learning algorithms were used for their ability to predict a PFC system’s cooling effect, including on humidity, temperature, and wind speed, which were examined using Auto Regression Moving Average (ARMA), Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Time Convolutional Network (TCN), and Glavnoe Razvedivatelnoe Upravlenie (GRU), respectively. These results show that deep learning algorithms are significantly more effective than traditional machine learning algorithms in capturing the complex nonlinear relationships and spatiotemporal changes inside solar greenhouses. The LSTM model achieves R² values of 0.918 for temperature, 0.896 for humidity, and 0.849 for wind speed on the test set. TCN showed strong performance in identifying high-frequency fluctuations and extreme nonlinear features, particularly in wind speed prediction (test set R² = 0.861). However, it exhibited limitations in modeling certain temperature dynamics (e.g., T6 test set R² = 0.242) and humidity evaporation processes (e.g., T7 training set R² = −0.856). GRU delivered excellent performance, achieving a favorable balance between accuracy and efficiency. It attained the highest prediction accuracy for temperature (test set R² = 0.925) and humidity (test set R² = 0.901), and performed only slightly worse than TCN in wind speed prediction. In summary, deep learning models, particularly GRU, offer more reliable methodological support for greenhouse microclimate prediction, thereby facilitating the precise regulation of cooling systems and scientifically informed crop management. Full article

Heike 88, a new soybean variety developed through strategic hybridization of Heijiao 08-1611 × Heihe 43 followed by pedigree selection, was evaluated across seven locations in Heilongjiang Province from 2019 to 2022. The variety demonstrated stable performance with a 10.3% average yield advantage over regional check varieties and mean yields of 3188 kg ha⁻¹. Principal component analysis revealed that genetic variation accounted for 43.4% and 32.6% of performance variance in the first two components, indicating successful transgressive segregation where the pure line exceeded both parental lines through complementary gene action. Performance relative to parental averages ranged from −20% to +40% across the temperature gradient, demonstrating strong genotype-environment interaction effects. Machine learning analysis identified year effect (13% importance), accumulated temperature (7.6% importance), and oil content (4% importance) as primary yield drivers. Complete resistance to soybean mosaic virous (SMV) and cyst nematode attack was observed across all locations, with excellent gray leaf spot resistance (grades 0–1) maintained under natural pathogen pressure. Seed quality parameters remained stable across environments, with protein content ranging from 41.69% to 42.25% and oil content from 19.74% to 20.13%, indicating minimal environmental effects on compositional traits. Yield stability improved progressively over the evaluation period, with the coefficient of variation decreasing from 18.7% in 2019 to 6.7% in 2022, while absolute yields increased from 2550 to 3200 kg ha⁻¹. These results demonstrate successful exploitation of transgressive segregation for regional adaptation through strategic parent selection and pedigree breeding, supporting commercial deployment in northern China’s challenging production environments while providing methodological guidance for future breeding programs targeting environmental specificity. Full article

High-entropy alloys (HEAs) are highly concentrated, multicomponent alloys that have received significant attention due to their superior properties compared to conventional alloys. The mechanical properties and hardness are interrelated, and it is widely known that the hardness of HEAs depends on the principal alloying elements and their composition. Therefore, the desired hardness prediction to develop new HEAs is more interesting. However, the relationship of these compositions with the HEA hardness is very complex and nonlinear. In this study, we develop an artificial neural network (ANN) model using experimental data sets (535). The compositional elements—Al, Co, Cr, Cu, Mn, Ni, Fe, W, Mo, and Ti—are considered input parameters, and hardness is considered as an output parameter. The developed model shows excellent correlation coefficients (Adj R2) of 99.84% and 99.3% for training and testing data sets, respectively. We developed a user-friendly graphical interface for the model. The developed model was used to understand the effect of alloying elements on hardness. It was identified that the Al, Cr, and Mn were found to significantly enhance hardness by promoting the formation and stabilization of BCC and B2 phases, which are inherently harder due to limited active slip systems. In contrast, elements such as Co, Cu, Fe, and Ni led to a reduction in hardness, primarily due to their role in stabilizing the ductile FCC phase. The addition of W markedly increased the hardness by inducing severe lattice distortion and promoting the formation of hard intermetallic compounds. Full article

The synthesis of silver nanoparticles (AgNPs) using sustainable and non-toxic methods has become an important research focus due to the limitations of conventional chemical approaches, which often involve hazardous reagents and produce unstable products. In particular, the effects of reaction conditions on the quality and stability of AgNPs obtained via green synthesis remain insufficiently understood. This study addresses this gap by examining the influence of pH and temperature on the synthesis of AgNPs using Rosmarinus officinalis extract as both reducing and stabilizing agents. UV-vis spectroscopy and TEM analysis revealed that optimal conditions for producing uniform, stable, and spherical AgNPs were achieved at pH 8, with a narrow size distribution (~17.5 nm). At extreme pH values (≤3 or ≥13), nanoparticle formation was hindered by aggregation or precipitation, while elevated temperatures mainly accelerated reaction without altering particle morphology. HRTEM and SAED confirmed the crystalline face-centered cubic structure, and colloids synthesized at pH 8 showed excellent stability over 30 days. Overall, the results demonstrate that precise pH control is critical for obtaining high-quality AgNPs via a simple, scalable, and environmentally friendly approach. Their stability and homogeneous size highlight potential applications in biomedicine, food packaging, and sensing, where reproducibility and long-term functionality are essential. Full article

This review examines the vast opportunities and key challenges in renewable waste management across the Gulf region, with a particular emphasis on Saudi Arabia. As global demand for sustainable energy intensifies, driven by technological advancements and environmental concerns, the Gulf Cooperation Council nations, notably Saudi Arabia, are beginning to acknowledge the urgency of transitioning from fossil fuel reliance to renewable waste management. This review identifies the abundant renewable resources in the region and highlights progress in policy development while emphasizing the need for comprehensive frameworks and financial incentives to drive further investment and innovation. Waste-to-energy (WTE) technologies offer a promising avenue for reducing environmental degradation and bolstering energy security. With Saudi Arabia targeting the development of 3 Gigawatts of WTE capacity by 2030 as part of national sustainability initiatives, barriers such as regulatory complexities, financial constraints, and public misconceptions persist. Ultimately, this review concludes that advancing renewable waste management in the Gulf, particularly through stronger policies, stakeholders’ collaboration, investment in WTE and an enhancement in public awareness and education, is critical for achieving sustainability goals. By harnessing these opportunities, the region can take decisive steps toward achieving sustainability, positioning Saudi Arabia as a leader in the global fight against climate change and resource depletion. Full article

Contemporary artificial intelligence excels at pattern recognition but lacks genuine understanding, temporal awareness, and ethical reasoning. Critics argue that AI systems manipulate statistical correlations without grasping concepts, time, or moral implications. This article presents Phase 2, a component of the emerging infrastructure called Sophimatics, a computational framework that translates philosophical categories into working algorithms through the integration of complex time. Our approach operationalizes Aristotelian substance theory, Augustinian temporal consciousness, Husserlian intentionality, and Hegelian dialectics within a unified temporal–semantic architecture. The system represents time as both chronological and experiential, allowing navigation between memory and imagination while maintaining conceptual coherence. Validation through a Data Protection Policy use case demonstrates significant improvements: confidence in decisions increased from 6.50 to 9.40 on a decimal scale, temporal awareness from 2.00 to 9.50, and regulatory compliance from 6.00 to 9.00 compared to traditional approaches. The framework successfully links philosophical authenticity with computational practicality, offering greater ethical consistency and contextual adaptability for AI systems that require temporal reasoning and ethical foundations. Full article

This study explores the use of mid-infrared (MIR) free-electron laser (FEL) irradiation as a tool for tailoring the surface properties of electrochemically synthesized TiO₂—graphene quantum dots (QDs). The QDs, prepared in colloidal form via a cost-effective electrochemical method in a KCl—citric acid medium, were exposed to MIR wavelengths (5.76, 8.02, and 9.10 µm) at the Kyoto University FEL facility. Post-irradiation measurements revealed a pronounced inversion of zeta potential by 40–50 mV and approximately 10% reduction in hydrodynamic size, indicating double-layer contraction and ionic redistribution at the QD—solvent interface. Photoluminescence spectra showed enhanced emission for GQDs and TiO₂/GQD composites, while Tauc analysis revealed modest bandgap blue shifts (0.04–0.08 eV), both consistent with trap-state passivation and sharper band edges. TEM confirmed intact crystalline structures, verifying that FEL-induced modifications were confined to surface chemistry rather than bulk lattice damage. Taken together, these results demonstrate that MIR FEL irradiation provides a resonance-driven, non-contact method to reorganize ions, suppress defect states, and improve the optoelectronic quality of QDs. This approach offers a scalable post-synthetic pathway for enhancing electron transport layers in perovskite solar cells and highlights the broader potential of photonic infrastructure for advanced nanomaterial processing and interface engineering in optoelectronic and energy applications. Full article

Neoadjuvant therapy has become the standard of care in early-stage triple-negative breast cancer (TNBC), providing both prognostic information and a platform for treatment individualization. The achievement of a pathological complete response (pCR) is strongly associated with excellent long-term outcomes, whereas the presence of residual disease (RD) indicates a markedly increased risk of recurrence. This dual prognostic value has established post-neoadjuvant treatment as a critical arena for risk-adapted strategies. In patients achieving pCR, de-escalation of adjuvant therapy is under active investigation, with several randomized trials assessing whether surveillance may safely replace prolonged immunotherapy. Conversely, the management of patients with RD has become increasingly complex, as clinicians must navigate between established options such as capecitabine, olaparib, and pembrolizumab, while antibody-drug conjugates are likely to emerge as future therapeutic options in this high-risk setting. In parallel, locoregional approaches are evolving, with trials evaluating axillary de-escalation and even the omission of surgery in highly selected cases. Looking forward, the integration of biomarkers such as circulating tumor DNA and tumor-infiltrating lymphocytes may help refine these strategies, paving the way toward truly personalized post-neoadjuvant care in TNBC. Full article

This paper presents an innovative protocol for fabric functionalization using Tormentillae rhizoma extract, the chemical composition of which was proved via LC/MS analysis. The extract demonstrated antioxidant activity > 99%, and antibacterial efficacy against E. coli and S. aureus > 99%. Cotton, wool, polyamide, and cellulose acetate were functionalized with the prepared extract, all showing > 90% antioxidant activity. Functionalized cotton, wool, and polyamide exhibited > 99% antibacterial activity against both bacteria. Based on these findings and the fabrics’ ability to release bioactive compounds, functionalized cotton and polyamide fabrics having excellent bioactivity but a lower ability to release bioactive compounds can serve as protective fabrics for people with sensitive skin prone to wounds, and various products for hospitals. Functionalized wool was identified as the most suitable wound dressing for in vivo preclinical investigation on Wistar albino rats. The obtained results showcased a wound-healing rate of 95.54%, and hydroxyproline content of 8.08 µg/mg dry tissue for rats treated with functionalized wool. Compared to negative, positive, and a group of rats treated with non-functionalized wool, those treated with functionalized wool demonstrated elevated values of tissue redox state parameters, superoxide dismutase (SOD) and catalase (CAT), and a notable reduction in thiobarbituric acid reactive substances (TBARS) value. Analysis of the blood samples of rats treated with functionalized wool indicated increased levels of antioxidant defense system parameters (SOD and CAT) and decreased pro-oxidative markers superoxide (O₂⁻) and TBARS. Further clinical trials are needed to validate these findings. Full article