Search Results (20,043)

As an emerging energy-saving approach, bio-inspired drag reduction technology has become a key research direction for reducing energy consumption and greenhouse gas emissions. This study introduces the latest research progress on bio-inspired microstructured surfaces in the field of underwater drag reduction, focusing on analyzing the drag reduction mechanism, preparation process, and application effect of the three major technological paths; namely, bio-inspired non-smooth surfaces, bio-inspired superhydrophobic surfaces, and bio-inspired modified coatings. Bio-inspired non-smooth surfaces can significantly reduce the wall shear stress by regulating the flow characteristics of the turbulent boundary layer through microstructure design. Bio-inspired superhydrophobic surfaces form stable gas–liquid interfaces through the construction of micro-nanostructures and reduce frictional resistance by utilizing the slip boundary effect. Bio-inspired modified coatings, on the other hand, realize the synergistic function of drag reduction and antifouling through targeted chemical modification of materials and design of micro-nanostructures. Although these technologies have made significant progress in drag reduction performance, their engineering applications still face bottlenecks such as manufacturing process complexity, gas layer stability, and durability. Future research should focus on the analysis of drag reduction mechanisms and optimization of material properties under multi-physical field coupling conditions, the development of efficient and low-cost manufacturing processes, and the enhancement of surface stability and adaptability through dynamic self-healing coatings and smart response materials. It is hoped that the latest research status of bio-inspired drag reduction technology reviewed in this study provides a theoretical basis and technical reference for the sustainable development and energy-saving design of ships and underwater vehicles. Full article

This paper presents a method to model and optimize the supply chain processes for floating offshore wind turbines using a network model based on Generalized Multi-Commodity Network Flows (GMCNF). The proposed method represents production bases, base ports, installation sites, component transfer areas, and transportation routes as nodes and arcs within the network. The installation process is modeled using three transport concepts: assembling components at the base port, direct assembly and installation at the installation site, and transferring components to the installation vessel at a nearby port. These processes are expressed as a linear network model, with the objective function set to minimize total transportation and assembly costs. The optimal transportation network is derived by solving the network problem while incorporating constraints such as supply, demand, and transportation capacity. Case studies demonstrate the method’s effectiveness in optimizing the supply chain and evaluating potential new production site locations for floating foundations, considering overall supply chain optimization. Full article

This study explores the feasibility of generating a globular microstructure in situ during the thixotropic 3D printing of the EN AW-4043 alloy, starting from a conventional cold-rolled wire. Thermodynamic simulations using Thermo-Calc software were first conducted to identify the semi-solid processing window of the alloy, based on the evolution of liquid and solid fractions as a function of temperature. Guided by these results, thermal treatments were performed on cold-rolled wires to promote the formation of a globular microstructure. A laboratory-scale printing head prototype was then designed and built to test continuous heating and deposition conditions representative of a thixotropic additive manufacturing process. The results showed that a globular microstructure could be achieved in the cold-rolled EN AW-4043 wires by heating them at 590 °C for 5 min in a static muffle furnace. A similar effect was observed when continuously heating the wire while it flowed through the heated printing head. Preliminary deposition tests confirmed the viability of this approach and demonstrated that thixotropic 3D printing of EN AW-4043 alloy is achievable without the need for pre-globular feedstock. Full article

Small and medium-sized enterprises (SMEs) in the manufacturing sector often struggle to make effective use of production data due to fragmented systems and limited digital infrastructure. This paper presents a case study of implementing an AI-enhanced digital twin in a custom sportswear manufacturing SME developed under the AI and Robotics Estonia (AIRE) initiative. The solution integrates real-time production data collection using the Digital Manufacturing Support Application (DIMUSA); data processing and control; clustering-based data analysis; and virtual simulation for evaluating improvement scenarios. The framework was applied in a live production environment to analyze workstation-level performance, identify recurring bottlenecks, and provide interpretable visual insights for decision-makers. K-means clustering and DBSCAN were used to group operational states and detect process anomalies, while simulation was employed to model production flow and assess potential interventions. The results demonstrate how even a lightweight AI-driven system can support human-centered decision-making, improve process transparency, and serve as a scalable foundation for Industry 5.0-aligned digital transformation in SMEs. Full article

Due to factors such as low porosity and permeability, thin sand body thickness, and strong interlayer heterogeneity, the fluid flow in the tight reservoir (beach-bar sandstone reservoir) exhibits obvious nonlinear seepage characteristics. Considering the time-varying physical parameters of different types of sand bodies, a nonlinear seepage coefficient is derived based on permeability and capillary force, and a low-velocity nonlinear seepage model for beach bar sand reservoirs is established. Based on core displacement experiments of different types of sand bodies, the low-velocity nonlinear seepage coefficient was fitted and numerical simulation of low-velocity nonlinear seepage in beach-bar sandstone reservoirs was carried out. The research results show that the displacement pressure and flow rate of low-permeability tight reservoirs exhibit a significant nonlinear relationship. The lower the permeability and the smaller the displacement pressure, the more significant the nonlinear seepage characteristics. Compared to the bar sand reservoir, the water injection pressure in the tight reservoir of the beach sand is higher. In the nonlinear seepage model, the bottom hole pressure of the water injection well increases by 10.56% compared to the linear model, indicating that water injection is more difficult in the beach sand reservoir. Compared to matrix type beach sand reservoirs, natural fractures can effectively reduce the impact of fluid nonlinear seepage characteristics on the injection and production process of beach sand reservoirs. Based on the nonlinear seepage characteristics, the beach-bar sandstone reservoir can be divided into four flow zones during the injection production process, including linear seepage zone, nonlinear seepage zone, non-flow zone affected by pressure, and non-flow zone not affected by pressure. The research results can effectively guide the development of beach-bar sandstone reservoirs, reduce the impact of low-speed nonlinear seepage, and enhance oil recovery. Full article

The aim of this study is to assess endothelial progenitor cells (EPCs) and circulating endothelial cells (CECs) at the time of type 1 diabetes (T1D) recognition concerning patients’ clinical state, remaining insulin secretion, and further partial remission (PR) occurrence. We recruited 45 children that were admitted to hospital due to newly diagnosed T1D (median age 10.8 yrs), and 20 healthy peers as a control group. EPC and CEC levels were measured at disease onset in PBMC isolated from whole peripheral blood with the use of flow cytometry. Clinical data regarding patients’ condition, C-peptide secretion, and further PR prevalence were analyzed. T1D-diagnosed patients presented higher EPC levels than the control group (p = 0.026), while no statistical differences in CEC levels and EPC/CEC ratio were observed. Considering only T1D patients, those with better clinical conditions presented lower EPCs (p = 0.021) and lower EPC/CEC ratios (p = 0.0002). Patients with C-peptide secretion within a normal range at disease onset presented lower EPC/CEC ratios (p = 0.027). Higher levels of EPCs were observed more frequently in patients with higher glucose, decreased fasting C-peptide, and lower stimulated C-peptide (all p < 0.05). The presence of DKA was related to higher EPC/CEC ratios (p = 0.034). Significantly higher levels of CECs were observed in patients who presented partial remission of the disease at 6 months after diagnosis (p = 0.03) only. In the study group, positive correlations of CECs with age, BMI at onset, and BMI in following years were observed. EPC/CEC ratios correlated positively with glucose levels at hospital admission and negatively with age, BMI, pH, and stimulated C-peptide level. We reveal a new potential for the application of EPCs and CECs as biomarkers, reflecting both endothelial injury and reconstruction processes in children with T1D. There is a need for further research in order to reduce cardiovascular risk in children with T1D. Full article

In this study, MIL-101 (Cr) was synthesized and characterized in terms of its physical properties. The adsorption breakthrough curves for toluene were measured and compared to those of conventional adsorbents (i.e., silica gel and activated carbon) at typical indoor concentrations of toluene. The results show that MIL-101 (Cr) exhibits a 5–8 times higher adsorption capacity for toluene compared to silica gel at low concentrations. The adsorption isotherm of MIL-101 (Cr) for toluene conforms to the Langmuir model. Increasing temperature reduces the adsorption breakthrough time and saturation time, but it leads to a significant decrease in the adsorption capacity. During the breakthrough experiment, flow rate had little effect on adsorption capacity, but higher flow rates notably decreased the breakthrough and saturation times. The negative values of ΔG, ΔH, and ΔS indicate that the adsorption of toluene on MIL-101 (Cr) is a spontaneous and exothermic process. Compared to traditional adsorbents, MIL-101 (Cr) exhibits desirable performance in toluene adsorption in indoor environments. It shows significant potential for indoor air purification applications. Full article

This study investigates the application of deep learning (DL) techniques for predicting temperature fields in the SM1 swirl-stabilized turbulent non-premixed flame. Two distinct DL approaches were developed using a comprehensive CFD database generated via the steady laminar flamelet model coupled with the SST k-ω turbulence model. The first approach employs a fully connected dense neural network to directly map scalar input parameters—fuel velocity, swirl ratio, and equivalence ratio—to high-resolution temperature contour images. In addition, a comparison was made with different deep learning networks, namely Res-Net, EfficientNetB0, and Inception Net V3, to better understand the performance of the model. In the first approach, the results of the Inception V3 model and the developed Dense Model were found to be better than Res-Net and Efficient Net. At the same time, file sizes and usability were examined. The second framework employs a U-Net-based convolutional neural network enhanced by an RGB Fusion preprocessing technique, which integrates multiple scalar fields from non-reacting (cold flow) conditions into composite images, significantly improving spatial feature extraction. The training and validation processes for both models were conducted using 80% of the CFD data for training and 20% for testing, which helped assess their ability to generalize new input conditions. In the secondary approach, similar to the first approach, studies were conducted with different deep learning models, namely Res-Net, Efficient Net, and Inception Net, to evaluate model performance. The U-Net model, which is well developed, stands out with its low error and small file size. The dense network is appropriate for direct parametric analyses, while the image-based U-Net model provides a rapid and scalable option to utilize the cold flow CFD images. This framework can be further refined in future research to estimate more flow factors and tested against experimental measurements for enhanced applicability. Full article

This paper introduces sustainable engineering systems built using digital twin technology and circular economy principles. This research presents a framework for monitoring, modeling, and making decisions in real timusing virtual replicas of physical products, processes, and systems in product lifecycles. A digital twin was used to show that through a digital twin, waste was reduced by 27%, energy consumption was reduced by 32%, and the resource recovery rate increased to 45%. The proposed approach under the framework employs various machine learning algorithms, IoT sensor networks, and advanced data analytics to support closed-loop flows of materials. The results show how digital twins can enhance progress toward the goals the circular economy sets to identify inefficiencies, predict maintenance needs, and optimize the use of resources. This integration is a promising industry approach that will introduce more sustainable operations and maintain economic viability. Full article

The performance of the turbine in a variable geometry turbocharger (VGT) may be affected by changes in the vane operating angle and heat transfer loss during operation. However, existing studies have been conducted under the assumption of an adiabatic process. In this study, we investigated the effect of heat transfer between all working fluids and a VGT structure when using computational fluid dynamics to evaluate turbine performance. Through this study, we confirmed that when heat transfer was considered, the turbine efficiency decreased by approximately 2–6%, depending on the vane position angle change, compared to when heat transfer was not considered. In addition, the total entropy production ratio, which represented the flow loss in the turbine during operation, increased by approximately 0.2–0.5% when heat transfer was considered. In conclusion, the findings confirmed that the heat transfer phenomenon directly affected the efficiency and flow loss during the turbine performance evaluation process. Full article

Accurate forecasting of river water flow helps to warn of floods and droughts in advance, provides a basis for the rational allocation of water resources, and at the same time, offers important support for the safe operation of hydropower stations and water conservancy projects. Water flow is characterized by time series, but the existing models focus on the positive series when LSTM is applied, without considering the different contributions of the water flow series to the model at different moments. In order to solve this problem, this study proposes a river water flow prediction model, named AT-BiLSTM, which mainly consists of a bidirectional layer and an attention layer. The bidirectional layer is able to better capture the long-distance dependencies in the sequential data by combining the forward and backward information processing capabilities. In addition, the attention layer focuses on key parts and ignores irrelevant information when processing water flow data series. The effectiveness of the proposed method was validated against an actual dataset from the Shizuishan monitoring station on the Yellow River in China. The results confirmed that compared with the RNN model, the proposed model significantly reduced the MAE, MSE, and RMSE on the dataset by 27.16%, 42.01%, and 23.85%, respectively, providing the best predictive performance among the six compared models. Moreover, this attention mechanism enables the model to show good performance in 72 h (3 days) forecast, keeping the average prediction error below 6%. This implies that the proposed hybrid model could provide a decision base for river flow flood control and resource allocation. Full article

Highly infectious and pathogenic viruses seriously threaten global public health, underscoring the need for rapid and accurate diagnostic methods to effectively manage and control outbreaks. In this study, we developed a comprehensive Surface-Enhanced Raman Scattering–Lateral Flow Immunoassay (SERS-LFIA) detection system that integrates SERS scanning imaging with artificial intelligence (AI)-based result discrimination. This system was based on an ultra-sensitive SERS-LFIA strip with SiO₂-Au NSs as the immunoprobe (with a theoretical limit of detection (LOD) of 1.8 pg/mL). On this basis, a negative–positive discrimination method combining SERS scanning imaging with a deep learning model (ResNet-18) was developed to analyze probe distribution patterns near the T line. The proposed machine learning method significantly reduced the interference of abnormal signals and achieved reliable detection at concentrations as low as 2.5 pg/mL, which was close to the theoretical Raman LOD. The accuracy of the proposed ResNet-18 image recognition model was 100% for the training set and 94.52% for the testing set, respectively. In summary, the proposed SERS-LFIA detection system that integrates detection, scanning, imaging, and AI automated result determination can achieve the simplification of detection process, elimination of the need for specialized personnel, reduction in test time, and improvement of diagnostic reliability, which exhibits great clinical potential and offers a robust technical foundation for detecting other highly pathogenic viruses, providing a versatile and highly sensitive detection method adaptable for future pandemic prevention. Full article

This study investigates the dynamic response mechanisms of discharge capacity in the Han River Estuary to hydrological process changes at the Yangtze–Han River confluence. By constructing a one-dimensional hydrodynamic model for the 265 km Xinglong–Hankou reach, we quantitatively decouple the synergistic effects of riverbed scouring (mean annual incision rate: 0.12 m) and Three Gorges Dam (TGD) operation through four orthogonal scenarios. Key findings reveal: (1) Riverbed incision dominates discharge variation (annual mean contribution >84%), enhancing flood conveyance efficiency with a peak flow increase of 21.3 m³/s during July–September; (2) TGD regulation exhibits spatiotemporal intermittency, contributing 25–36% during impoundment periods (September–October) by reducing Yangtze backwater effects; (3) Nonlinear interactions between drivers reconfigure flow paths—antagonism occurs at low confluence ratios (R < 0.15, e.g., Cd increases to 45 under TGD but decreases to 8 under incision), while synergy at high ratios (R > 0.25) reduces Hanchuan Station flow by 13.84 m³/s; (4) The 180–265 km confluence-proximal zone is identified as a sensitive area, where coupled drivers amplify water surface gradients to −1.41 × 10⁻³ m/km (2.3× upstream) and velocity increments to 0.0027 m/s. The proposed “Natural/Anthropogenic Dual-Stressor Framework” elucidates estuary discharge mechanisms under intensive human interference, providing critical insights for flood control and trans-basin water resource management in tide-free estuaries globally. Full article

The prediction of production decline rate in the development of offshore high water-cut reservoirs predominantly relies on the traditional Arps decline curves. However, the solution process is complex, and the interpretation efficiency is low, making it difficult to meet the demand for rapid prediction of production decline rates. To address this, this paper first identifies the key influencing factors of production decline rate through comprehensive feature engineering. Subsequently, it proposes a novel prediction method for the production decline rate in offshore high water-cut reservoirs by integrating Moth–Flame Optimization with Extreme Gradient Boosting Tree (MFO-XGBoost). This method utilizes seven dynamic and static influencing factors, namely vertical thickness, perforated thickness, shale content, permeability, crude oil viscosity, formation flow coefficient, and well deviation angle, to predict the production decline rate. The forecasting outcomes of the MFO-XGBoost method are then compared with those of standard RF, standard DT, the standalone XGBoost model, and the calculated results from the exponential decline model. Additionally, the forecasting capability of the MFO-XGBoost method is benchmarked against Particle Swarm Optimization–XGBoost (PSO-XGBoost) and Bayesian Optimization–XGBoost methods for predicting the production decline rate in offshore high water-cut reservoirs. The findings from the experiments show that the MFO-XGBoost method can achieve accurate prediction of the production decline rate in offshore high water-cut reservoirs, with a coefficient of determination (R²) reaching 0.9128, thereby providing a basis for strategies to mitigate the production decline rate. Full article

In this investigation, a hierarchical FeCo-layered double hydroxide (FeCo-LDH) electrochemical membrane material was prepared by a simple in situ hydrothermal method. The prepared material formed a 3D honeycomb-structured FeCo-LDH-modified carbon felt (FeCo-LDH/CF) catalytic layer with uniform open pores on a CF substrate with excellent catalytic activity and was served as the cathode in a flow-through electro-Fenton (FTEF) reactor. The electrocatalyst demonstrated excellent treatment performance (99%) in phenol simulated wastewater (30 mg L⁻¹) under the optimized operating conditions (applied voltage = 3.5 V, pH = 6, influent flow rate = 15 mL min⁻¹) of the FTEF system. The high removal rate could be attributed to (i) the excellent electrocatalytic oxidation performance and low interfacial charge transfer resistance of the FeCo-LDH/CF electrode as the cathode, (ii) the ability of the synthesized FeCo-LDH to effectively promote the conversion of H₂O₂ to •OH under certain conditions, and (iii) the flow-through system improving the mass transfer efficiency. In addition, the degradation process of pollutants within the FTEF system was additionally illustrated by the •OH dominant ROS pathway based on free radical burst experiments and electron paramagnetic resonance tests. This study may provide new insights to explore reaction mechanisms in FTEF systems. Full article