Search Results (10,833)

High-performance concrete (HPC) is an essential construction material used for modern buildings and infrastructure assets, recognized for its exceptional strength, durability, and performance under harsh situations. Nonetheless, the HPC production process frequently correlates with elevated carbon emissions, principally attributable to the high quantity of cement utilized, which significantly influences its carbon footprint. In this study, data-driven modeling and optimization strategies are employed to minimize the carbon footprint of high-performance concretes while keeping their performance properties. Starting from an experimental dataset, artificial neural networks (ANNs), ensemble techniques (ETs), and Gaussian process regression (GPR) are employed to yield predictive models for compressive strength of HPC mixes. The model’s input variables are the various components of HPC: cement, water, superplasticizer, fly ash, blast furnace slag, and coarse and fine aggregates. Models are trained using a dataset of 356 records. Results proved that the GPR-based model exhibits excellent accuracy with a determination coefficient of 0.90. The prediction model is used in a double objective optimization task formulated to identify mix configurations that allow for high mechanical performance aligned with a reduced carbon emission. The multi-objective optimization task is undertaken using genetic algorithms (GAs). Promising results are obtained when the machine learning prediction model is associated with GA optimization to identify strong yet sustainable mix configurations. Full article

Shield construction of deep tunnels for urban drainage involves many risk factors, and potential safety hazards are difficult to monitor and identify directly. In order to improve the risk management level of shield construction in urban drainage deep tunnel, this study proposes a method for identifying risk factors by combining text mining technology and the entropy weight method. By using this method, 34 safety risk factors were successfully extracted from the safety accident reports of urban drainage deep tunnel shield construction and the related text data. The results of this study show that the text mining method could play an important role in the risk management of urban drainage deep tunnel shield construction; the introduction of the entropy weight method further improved the accuracy of risk factor identification. The results of this study not only enrich the research content of risk management in urban drainage deep tunnel shield construction but also provide theoretical guidance for managers to formulate risk management measures and optimize risk management procedures. Full article

A novel skin test for an in vivo assessment of SARS-CoV-2-specific T-cell immunity was developed using CoronaDermPS, a multiepitope recombinant polypeptide encompassing MHC II–binding CD4+ T-cell epitopes of the SARS-CoV-2 structural proteins (S, E, M) and full length nucleocapsid (N). In silico epitope prediction and modeling guided antigen design, which was expressed in Escherichia coli, was purified (>95% purity) and formulated for intradermal administration. Preclinical evaluation in guinea pigs, mice, and rhesus macaques demonstrated a robust delayed type hypersensitivity (DTH) response at optimal doses (10–75 µg), with no acute or chronic toxicity, mutagenicity, or adverse effects on reproductive organs. An integrated clinical analysis included 374 volunteers stratified by vaccination status (EpiVacCorona, Gam-COVID-Vac, CoviVac) prior to COVID-19 infection (Wuhan/Alpha, Delta, Omicron variants), and SARS-CoV-2–naïve controls. Safety assessments across phase I–II trials recorded 477 adverse events, of which >88% were mild and self-limiting; no severe or anaphylactic reactions occurred. DTH responses were measured at 24 h, 72 h, and 144 h post-injection by papule and hyperemia measurements. Overall, 282/374 participants (75.4%) exhibited a positive skin test. Receiver operating characteristic analysis yielded an overall AUC of 0.825 (95% CI: 0.726–0.924), sensitivity 79.5% (95% CI: 75.1–83.3%), and specificity 85.5% (95% CI: 81.8–88.7%), with comparable diagnostic accuracy across vaccine, and variant subgroups (AUC range 0.782–0.870). CoronaDerm-PS–based skin testing offers a simple, reproducible, and low-cost method for qualitative evaluation of T-cell–mediated immunity to SARS-CoV-2, independent of specialized laboratory equipment (Eurasian Patent No. 047119). Its high safety profile and consistent performance across diverse cohorts support its utility for mass screening and monitoring of cellular immune protection following infection or vaccination. Full article

Geohazard investigation in volcanic fields is essential for understanding and mitigating risks associated with volcanic activity. Volcanic vents are often concealed by processes such as faulting, subsidence, or uplift, which complicates their detection and hampers hazard assessment. To address this challenge, we developed a predictive framework that integrates high-resolution gravity data with multiple machine learning algorithms. Logistic Regression, Gradient Boosting Machine (GBM), Decision Tree, Support Vector Machine (SVM), and Random Forest models were applied to analyze the gravitational characteristics of known volcanic vents and predict the likelihood of undiscovered vents at other locations. The problem was formulated as a binary classification task, and model performance was assessed using accuracy, precision, recall, F1-score, and the Area Under the Receiver Operating Characteristic Curve (AUC-ROC). The Random Forest algorithm yielded optimal outcomes: 95% classification accuracy, AUC-ROC score of 0.99, 75% geographic correspondence between real and modeled vent sites, and a 95% certainty degree. Spatial density analysis showed that the distribution patterns of predicted and actual vents are highly similar, underscoring the model’s reliability in identifying vent-prone areas. The proposed method offers a valuable tool for geoscientists and disaster management authorities to improve volcanic hazard evaluation and implement effective mitigation strategies. These results represent a significant step forward in our ability to model volcanic dynamics and enhance predictive capabilities for volcanic hazard assessment. Full article

Aiming at the demands for low-carbon transformation in multi-energy-coupled industrial parks, a model is proposed that incorporates a carbon trading system incorporating incentives and penalties. This model includes joint combined heat and power (CHP) units, carbon capture technologies, and power-to-gas (P2G) conversion equipment. Firstly, we develop a modeling framework for the joint operation of cogeneration units to establish a comprehensive energy system within the industrial park that integrates electricity, heat, gas, and cold energy sources. Subsequently, we introduce a reward and punishment carbon trading mechanism into an industrial park to regulate carbon emissions effectively. With an optimization objective focused on minimizing the overall operating costs of the system while considering relevant constraints, we formulate an optimization model. The Gurobi solver is employed through the Yalmip toolkit to address this optimization problem. Finally, four operational scenarios are established to compare and validate the feasibility of our proposed optimization strategy. The results from our computational example demonstrate that integrating combined heat and power along with carbon capture and P2G technologies—coupled with a tiered reward and punishment carbon trading mechanism—can significantly enhance the energy consumption structure of the system. Under this model, the overall expenses are decreased by 12.36%, CO₂ emissions decrease by 33.37%, and renewable energy utilization increases by 36.7%. This approach has effectively improved both wind power consumption capacity and low-carbon economic benefits within the system while ensuring sustainable economic development in alignment with “dual carbon” goals. Full article

Sixth-generation (6G) communication systems, with ultra-wide bands, energy-autonomous end nodes, and dense connectivity, challenge existing network designs. Optimizing time resources with energy harvesting, backscatter communication, and relays is essential to maximize the total bit rate in multi-user symbiotic radio networks (SRNs) with blocked direct paths. The literature lacks a unified optimization treatment that explicitly accounts for imperfect successive interference cancellation (SIC). This study addresses this gap by proposing the first optimization framework to maximize total bit rate for energy-harvesting TDMA/PD–NOMA-based multi-cluster and relay-assisted peer-assisted SR networks. The two-phase architecture defines a tractable constrained optimization problem that jointly adjusts cluster-specific time slots ($\tau$ and $\lambda$). Incorporating QoS, signal power, and reflection coefficient constraints, it provides a compact formulation and numerical solutions for both perfect and imperfect SIC. Detailed simulations performed under typical 6G power levels, bandwidths, and energy-harvesting efficiencies demonstrate graphically that imperfect SIC significantly limits total throughput due to residual interference, while perfect SIC completely eliminates this ceiling under the same conditions, providing a significant capacity advantage. Furthermore, the gap between the two scenarios rapidly closes with increasing relay time margin. The findings demonstrate that network capacity is primarily determined by the triad of base station output power, channel noise, and SIC accuracy, and that the proposed framework achieves strong performance across the explored parameter space. Full article

In small-scale livestock production systems, low-quality diets constrain animal performance and increase enteric emissions, but both these impacts can be remediated using optimized feeding strategies. An experiment was conducted with lambs fed at two levels—maintenance (MN) and growth (GR)—using multi-nutrient blocks formulated with different concentrations of polyherbal nutraceuticals to compare the lambs’ reactions in terms of their productive performance and estimated enteric methane emissions. Thirty-two lambs were fed at two feeding levels—(a) maintenance (MN) at 9% CP and 1.85 Mcal ME/kg DM and (b) growth (GR) at 13.24% CP and 2.15 Mcal ME/kg DM)—and did or did not have access to MBs with different polyherbal percentages (BioCholine^®, OptiLysine^®, and OptiMethione^® (0:0:0, 3:0:0, 3:0.75:0.25)). No interactions between the feeding level and supplementation were detected. Lambs fed at the MN level showed lower productive indicators (p < 0.001) than those fed at the GR level, with a lower dry matter intake (DMI, 512 vs. 1009 g/d), MB consumption (61 vs. 84 g/d), and daily weight gain (26 vs. 187 g/d), resulting in lower enteric methane emissions (8.74 vs. 18.18 g CH₄ /d) and a lower emission intensity (15.25 vs. 16.55 CH₄ g/kg DM). Supplementation with MBs improved the average daily weight gain (ADG) (p < 0.001) at the GR level, but no differences were detected at the MN level. However, lambs in the control group lost weight (−20 g/d) and those supplemented gained weight (g/d), with increases of 49 (0:0:0), 25 (3:0:0), and 52 (3:0.75:0.25). The highest ADG for lambs in the GR group was observed with MBs containing all three polyherbals (215a, 3:0.75:0.25), an intermediate ADG was seen with MBs without herbals or with Biocholine (200.75ab, 0:0:0; 198ab, 3:0:0), and the lowest ADG was observed with no MBs (134c g/d). The use of MBs reduces the time to reach market weight by 265 days, resulting in a 50% reduction in the enteric methane emissions per product (animal by animal), making multi-nutrient blocks a viable option to improve production indicators and reduce enteric methane emissions. Full article

This study investigates collaborative disaster response strategies involving the government and social organizations from a dynamic perspective, incorporating stochastic disturbances that influence emergency resource supply. To examine the strategic interactions among the participants, three stochastic differential game models are formulated under distinct scenarios: centralized decision making for collusive emergency response, decentralized emergency response without a cost-sharing contract, and decentralized emergency response with a cost-sharing contract. Under an infinite-horizon planning framework, the closed-form solutions for the optimal response efforts and the corresponding value functions are derived for all three scenarios and comparatively analyzed. The results indicate that compared with the purely decentralized scenario, introducing a cost-sharing mechanism achieves a Pareto improvement by optimizing both overall system efficiency and emergency supply availability. Although the centralized collusive model results in the highest expected level of emergency resource supply, it is also associated with the greatest uncertainty. Furthermore, a numerical simulation based on emergency resource allocation during the Wenchuan earthquake is conducted. The results show significant differences in resource availability and response performance under different response mechanisms. Centralized collaboration, together with a well-designed cost-sharing mechanism, can significantly enhance the robustness and efficiency of the overall system, offering important insights for optimizing real-world disaster response strategies. Full article

Aluminum nitride (AlN) ceramics exhibit exceptional properties that render them highly valuable for diverse industrial applications. However, conventional manufacturing techniques encounter significant challenges in fabricating complex AlN components with precise geometries. To address these limitations, digital light processing (DLP) has emerged as a promising additive manufacturing approach for AlN ceramics. This study presents a systematic investigation of the monomer composition in the photopolymer resin system through a comprehensive experimental evaluation. The results demonstrate that an optimized mixture of monomers ACMO (56.7 wt%), DEGDA (2.7 wt%), and TMPTA (40.6 wt%) yields photopolymer resin with superior comprehensive performance. Utilizing this optimized formulation, a 50 vol% solid loading AlN ceramic slurry was successfully prepared, and subsequently, dense AlN ceramic components were fabricated through DLP. This provides an important basis for optimizing the slurry preparation of AlN ceramic fabrication based on DLP 3D printing. Full article

This research focused on the development and characterization of gelatin-based films incorporated with cellulose microfibrils (CMFs) extracted from cowpea pod skin (Vigna unguiculata, CPMC) and corn straw (Zea mays, CSMC). The use of CPMC to produce gelatin films has not been previously reported in the literature. Eleven formulations were prepared based on a 2² factorial design with four axial and three central points, in addition to a control film (FC) composed of 1.00% gelatin and 1.00% glycerol without CMFs. The physical, chemical, structural, and mechanical properties of the films were evaluated. The optimized formulation (FO), containing 1.00% CPMC and 1.00% CSMC, exhibited a four-fold increase in tensile strength (2.71 MPa) compared to the control. Water vapor permeability was significantly reduced (from 6.33 × 10⁻⁴ to 2.82 × 10⁻⁴ gH₂O·mm/m²·h·mmHg), and solubility decreased to 75.82%. Biodegradability was modulated, with FO exhibiting 73.06% degradation over three days versus complete degradation of FC within one day. The incorporation of CMFs, particularly from agro-industrial residues, significantly improved the structural integrity and barrier properties of the films, highlighting their potential for use in biodegradable packaging systems. Full article

Vehicular edge computing (VEC) represents a concrete application of mobile edge computing (MEC) in the field of intelligent transportation, with task offloading serving as one of its core components. The design of efficient task offloading strategies poses significant challenges due to the dynamic network topology, stringent low-latency requirements, and massive data processing demands. This paper proposes a digital twin (DT)-assisted intelligent task offloading approach, which establishes a dynamic interaction and mapping between the virtual and physical worlds to enable real-time monitoring of VEC network states, thereby optimizing offloading decisions. First, to meet diverse user service requirements, an optimization model is formulated with the objective of minimizing task processing latency and energy consumption. Next, a gravity model-based vehicle clustering algorithm is integrated with digital twin technology to find the optimal offloading space and ensure link stability among vehicles within aggregated clusters. Furthermore, to minimize overall system costs, the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm is utilized to train the offloading policy, enabling automatic optimization of both latency and energy consumption. consumption. Finally, a feedback mechanism is introduced to dynamically adjust parameters and enhance the robustness of the clustering process. Simulation results demonstrate that the proposed approach significantly outperforms baseline methods in terms of task completion cost, energy consumption, delay, and success rate, thereby validating its potential and superior performance in dynamic vehicular network environments. Full article

Optical remote sensing images are often partially obscured by clouds due to the inability of visible light to penetrate cloud cover, which significantly limits their subsequent applications. Most existing cloud removal methods formulate the problem using low-rank and sparse priors within a discrete representation framework. However, these approaches typically rely on manually designed regularization terms, which fail to accurately capture the complex geostructural patterns in remote sensing imagery. In response to this issue, we develop a continuous blind cloud removal model. Specifically, the cloud-free component is represented using a continuous tensor function that integrates implicit neural representations with low-rank tensor decomposition. This representation enables the model to capture both global correlations and local smoothness. Furthermore, a band-wise sparsity constraint is employed to represent the cloud component. To preserve the information in regions not covered by clouds during reconstruction, a box constraint is incorporated. In this constraint, cloud detection is performed using an adaptive thresholding strategy, and a morphological erosion function is employed to ensure accurate detection of cloud boundaries. To efficiently handle the developed model, we formulate an alternating minimization algorithm that decouples the optimization into three interpretable subproblems: cloud-free reconstruction, cloud component estimation, and cloud detection. Our extensive evaluations on both synthetic and real-world data reveal that the proposed method performs competitively against state-of-the-art cloud removal methods. Full article

This review aims to provide a comprehensive overview of the botany, phytochemical composition, and dermatological effects of Cydonia oblonga (CO), with a particular focus on its therapeutic mechanisms across various skin conditions. Among the different parts of the plant, the fruit and peel are especially rich in bioactive compounds, primarily polyphenols such as phenolic acids, anthocyanins, and flavonoids, which are known for their potent antioxidant activity. These constituents contribute significantly to the fruit and peel’s health-promoting properties. To date, multiple extracts derived from various CO parts have been studied in both in vitro and in vivo models. Reported dermatological effects include antioxidant, antimicrobial, anti-inflammatory, anti-allergic, UV-protective, moisturizing, and anti-aging effects, as well as beneficial outcomes in conditions such as wound healing, erythema, and hyperpigmentation. As a result, formulations containing CO-derived compounds have been developed for use in both diseased and healthy skin care. However, only a limited number of these effects have been validated in human clinical studies. Given the promising results from preclinical research, future directions should prioritize in vivo investigations in human subjects to determine optimal concentrations and delivery systems for targeting specific skin disorders. Full article

Advancements in bioinks and three-dimensional (3D) printing and bioprinting have significantly advanced cardiovascular tissue engineering by enabling the fabrication of biomimetic cardiac and vascular constructs. Traditional 3D printing has contributed to the development of acellular scaffolds, vascular grafts, and patient-specific cardiovascular models that support surgical planning and biomedical applications. In contrast, 3D bioprinting has emerged as a transformative biofabrication technology that allows for the spatially controlled deposition of living cells and biomaterials to construct functional tissues in vitro. Bioinks—derived from natural biomaterials such as collagen and decellularized matrix, synthetic polymers such as polyethylene glycol (PEG) and polycaprolactone (PCL), or hybrid combinations—have been engineered to replicate extracellular environments while offering tunable mechanical properties. These formulations ensure biocompatibility, appropriate mechanical strength, and high printing fidelity, thereby maintaining cell viability, structural integrity, and precise architectural resolution in the printed constructs. Advanced bioprinting modalities, including extrusion-based bioprinting (such as the FRESH technique), droplet/inkjet bioprinting, digital light processing (DLP), two-photon polymerization (TPP), and melt electrowriting (MEW), enable the fabrication of complex cardiovascular structures such as vascular patches, ventricle-like heart pumps, and perfusable vascular networks, demonstrating the feasibility of constructing functional cardiac tissues in vitro. This review highlights the respective strengths of these technologies—for example, extrusion’s ability to print high-cell-density bioinks and MEW’s ultrafine fiber resolution—as well as their limitations, including shear-induced cell stress in extrusion and limited throughput in TPP. The integration of optimized bioink formulations with appropriate printing and bioprinting platforms has significantly enhanced the replication of native cardiac and vascular architectures, thereby advancing the functional maturation of engineered cardiovascular constructs. Full article

To maximize the benefits of power generation and water supply of the reservoir under the premise of ensuring ecological flow as much as possible, it is necessary to formulate a highly operational release scheme in the actual production scheduling process. To mitigate the ecological impacts of reservoir operations, enhanced environmental flow releases are required; however, this results in diminished reservoir economic outputs. Therefore, in order to determine the government subsidy standards for ecological regulation of reservoirs and improve the enthusiasm of water conservancy departments for ecological regulation, it is necessary to conduct comprehensive analysis and research on the benefits of ecological regulation. According to the ecological releases of the reservoir, the reservoir operation scheme is formulated, and the comprehensive benefits of the reservoir operation are analyzed and studied to determine the optimal operation scheme. Based on the monthly inflow runoff of the Baishi Reservoir to the Daling River from 1956 to 2011, constrained by the ecological base flow specified by the government, and combined with the water supply and power generation functions of the reservoir, an optimal operation model of the Baishi Reservoir based on ecological release is constructed. The water supply, power generation, and ecological benefits of the reservoir discharge are comprehensively analyzed and calculated to analyze and study the loss of economic benefits caused by the reservoir discharge and the ecological benefits that can be obtained from the ecological discharge. Based on the comprehensive evaluation of multiple indicators, the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) fuzzy comprehensive evaluation method is used to select the optimal scheduling scheme. The optimal scheduling plan for a reservoir is closely related to its characteristic water level. In order to improve the efficiency of reservoir scheduling, monthly control of reservoir discharge can be implemented. The guarantee rate of urban domestic water supply and ecological water use can be increased as much as possible, while the guarantee rate of agricultural water use can be appropriately reduced to obtain the optimal comprehensive benefits. The outflow considering ecological release is 6.5–7 m³/s from June to April and 1 m³/s in May. The outflow without considering ecological release is 4 m³/s from June to April and 1 m³/s in May. This study has certain guiding significance and value for the formulation of an ecological operation scheme for reservoirs and the analysis of benefits. Full article