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Point cloud deep learning networks have been widely applied in point cloud classification, part segmentation and semantic segmentation. However, current point cloud deep learning networks are insufficient in the local feature extraction of the point cloud, which affects the accuracy of point cloud classification and segmentation. To address this issue, this paper proposes a local domain multi-level feature fusion point cloud deep learning network. First, dynamic graph convolutional operation is utilized to obtain the local neighborhood feature of the point cloud. Then, relation-shape convolution is used to extract a deeper-level edge feature of the point cloud, and max pooling is adopted to aggregate the edge features. Finally, point cloud classification and segmentation are realized based on global features and local features. We use the ModelNet40 and ShapeNet datasets to conduct the comparison experiment, which is a large-scale 3D CAD model dataset and a richly annotated, large-scale dataset of 3D shapes. For ModelNet40, the overall accuracy (OA) of the proposed method is similar to DGCNN, RS-CNN, PointConv and GAPNet, all exceeding 92%. Compared to PointNet, PointNet++, SO-Net and MSHANet, the OA of the proposed method is improved by 5%, 2%, 3% and 2.6%, respectively. For the ShapeNet dataset, the mean Intersection over Union (mIoU) of the part segmentation achieved by the proposed method is 86.3%, which is 2.9%, 1.4%, 1.7%, 1.7%, 1.2%, 0.1% and 1.0% higher than PointNet, RS-Net, SCN, SPLATNet, DGCNN, RS-CNN and LRC-NET, respectively.

This paper reviews the artificial intelligent algorithms in engine management. This study provides a clear image of the current state of affairs for the past 15 years and provides fresh insights and improvements for future directions in the field of engine management. The scope of this paper comprises three main aspects to be discussed, namely, engine performance, engine control, and engine diagnosis. The first is associated with the need to control the basic characteristics that prove that the engine is working properly, namely, emission control and fuel economy. Engine control refers to the ability to identify and fulfill the requirements derived from performance, emissions, and durability. In this part, hybrid electric vehicle (HEV) application and transient operations are discussed. Lastly, engine diagnosis entails assessment techniques that can be used to identify problems in the engine and solve them accordingly. In this part, misfire detection, knock detection, and intake system leakage will be evaluated. In engine performance, neural network algorithms provide efficient results in terms of emission control and fuel economy as the requirements are easily achievable. However, when it comes to engine control and diagnosis, the fuzzy logic rule with its strong robustness and neural networks algorithms are limited in efficiency due to the complex nature of the processes and the presence of big data, for instance, in HEVs in engine control. That has brought forward the usage of reinforcement learning and novel machine learning algorithms in recent years to maximize efficiency in engine control and engine diagnosis, as highlighted in the following part. The PRISMA methodology was used to justify the reference selection in this review.

Thermal efficiency and power density improvement are the main research foci of the literature on diesel engines. The Miller cycle is considered to be one of the most promising methods of diesel engine operation. In this study, a fully variable valve timing and lift mechanism (CD-HFVVS) was studied to determine the possibility of a Miller operation. Firstly, the valve seat impact buffer in the mechanism was tested, which proved that the buffer can effectively eliminate the valve seat impact. Then the influences of different speeds and oil temperatures were studied. The results show that the valve opening duration is prolonged when engine speed increases, and the valve lift and duration are reduced while the oil temperature increased. The valve timing and lift can be fully adjusted by changing the oil discharge position and the initial plunger position, which further proves that CD-HFVVS can achieve the performance optimization of the Miller cycle. By using the mechanism, a single cylinder test was performed. By using variable inlet valve timing, the fuel efficiency can be effectively improved and the peak pressure and in-cylinder average temperature can both be suppressed.

The mechanical properties of resin-bonded sand mixtures at high temperatures significantly affect the quality of casting. However, the existing instruments for high-temperature performances testing mainly focus on inorganic binder-bonded sands no matter the test items or the atmospheric protection, while the instrumentss specially designed for resin-bonded sand are not yet available. A new instrument for testing the high-temperature performance of resin sand was designed including the confirmation of the testing parameters, loading, measurement and control systems, and the design of the frame shape and heating furnace. This instrument can test the compressive strength, heat tolerance time and restraining load of phenol-formaldehyde resin coated sand, self-hardened furan resin sand, and trimethylamine (TEA)-based resin bonded sand at high temperatures. The developed instrument has a high accuracy offering smaller than 0.3% deviation at a full scale in the measurement of the high temperature compressive strength and the restraining load over the range of 0–6.8 MPa and 0–2,000 N, respectively. The high temperature heat tolerance time range is 0–300 s and its measurement accuracy is ±1 s.

The elastic and electronic properties of AlxNiyZrz (AlNiZr, Al2NiZr6, AlNi2Zr, and Al5Ni2Zr) under pressure from 0 to 50 GPa have been investigated by using the density function theory (DFT) within the generalized gradient approximation (GGA). The elastic constants Cij (GPa), Shear modulus G (GPa), Bulk modulus B (GPa), Poisson’s ratio σ, Young’s modulus E (GPa), and the ratio of G/B have been studied under a pressure scale to 50 GPa. The relationship between Young’s modulus of AlxNiyZrz is Al5Ni2Zr > AlNiZr > Al2NiZr6 > AlNi2Zr, which indicates that the relationship between the stiffness of AlxNiyZrz is Al5Ni2Zr > AlNiZr > Al2NiZr6 > AlNi2Zr. The conditions are met at 30 and 50 GPa, respectively. What is more, the G/B ratios for AlNiZr, AlNi2Zr, Al2NiZr6, and Al5Ni2Zr classify these materials as brittle under zero pressure, while with the increasing of the pressure the G/B ratios of AlNiZr, AlNi2Zr, Al2NiZr6, and Al5Ni2Zr all become lower, which indicates that the pressure could enhance the brittle properties of these materials. Poisson’s ratio studies show that AlNiZr, AlNi2Zr, and Al2NiZr6 are all a central force, while Al5Ni2Zr is a non-central force pressure scale to 50 GPa. The energy band structure indicates that they are all metal. The relationship between the electrical conductivity of AlxNiyZrz is Al2NiZr6 > Al5Ni2Zr > AlNi2Zr > AlNiZr. What is more, compared with Al5Ni2Zr, AlNi2Zr has a smaller electron effective mass and larger atom delocalization. By exploring the elastic and electronic properties, they are all used as a superconducting material. However, Al5Ni2Zr is the best of them when used as a superconducting material.

High temperature compressive strength is one of the most important performances of resin sand; its value directly concerns the quality of castings. In order to seek the best testing method of resin sand high temperature compressive strength, a self-developed instrument was used to carry out experiments, and the sample shape and size were designed and studied. The results show that a hollow cylinder sample can reflect the strength difference of different resin sands better than a solid cylinder sample, and its data is stable. The experiments selected フ20/5】30 mm as the size of the hollow cylinder samples. The high temperature compressive strengths of phenol-formaldehyde resin coated sand, furan resin self-setting sand, and TEA resin sand were each tested. For the resin sand used for cast steel and cast iron, 1,000 ìC was selected as the test temperature; for the resin sand used for cast non-ferrous alloy, 800 ìC was selected as the test temperature; and for all the resin sand samples, 1 min was selected as the holding time. This testing method can truthfully reflect the high temperature performance of three kinds of resin sand; it is reproducible, and the variation coefficients of test values are under 10%.

Co2Zr and Co2Ti are both cubic crystals with a Cu2Mg-type structure. The elastic, thermodynamic and electronic properties of the intermetallic compounds Co2Zr and Co2Ti are investigated by using ab initio plane-wave pseudopotential density functional theory (PWPDFT) and generalized gradient approximation (GGA) under high temperature and pressure. The partially calculated results are consistent with the available experimental data. The elastic properties of Co2Zr and Co2Ti under high pressure were first studied by first principles calculations. The results indicate that the elastic constants, elastic modulus and Poisson’s ratio are functions of pressure, indicating that the effect of pressure on the ductility and anisotropy is significant. The thermodynamic properties are also calculated by the quasi-harmonic Debye model. In the range of 0~100 GPa pressure and 0~1500 K temperature, the Debye temperature Θ, the heat capacity CV and the thermal expansion α vary with pressure and temperature. Co2Ti has a higher Debye temperature than Co2Zr under the same pressure. Decreasing temperature and increasing pressure have the same effects on CV and α. The electron density difference and density of states of Co2Zr and Co2Ti are finally investigated. The results show that both Co2Zr and Co2Ti are typically metal crystals but Co2Zr has greater covalence than Co2Ti.

For the detection of deep-sea natural gas hydrates, it is very important to accurately detect the 13CO2/12CO2 isotope ratio of dissolved gas in seawater. In this paper, a 13CO2/12CO2 isotope ratio sensor is investigated, which uses a tunable diode laser absorption spectroscopy (TDLAS) technique at 4.3 μm. The proposed sensor consists of a mid-infrared interband cascade laser (ICL) operating in continuous wave mode, a long optical path multi-pass gas cell (MPGC) of 24 m, and a mid-infrared mercury cadmium telluride (MCT) detector. Aiming at the problem of the strong absorption intensity of the two absorption lines of 13CO2 and 12CO2 being affected by temperature, a high-precision temperature control system for the MPGC was fabricated. Five different concentrations of CO2 gas were configured to calibrate the sensor, and the response linearity could reach 0.9992 for 12CO2 and 0.9996 for 13CO2. The data show that the carbon isotope measurement precision was assessed to be 0.0139‱ when the integration time was 92 s and the optical path length was 24 m. The sensor is combined with a gas−liquid separator to detect the 13CO2/12CO2 isotope ratio of CO2 gas extracted from water. Results validate the reported sensor system’s potential application in deep-sea natural gas hydrate exploration.

Geothermal energy resources, such as a green and clean renewable energy, are utilized all over the world, especially in soft clay areas along coastal cities in China, such as Shanghai. However, the operation of the ground-buried heat exchanger potentially affects the initial temperature field and the thermal balance, which will probably result in some potential engineering or environmental problems. This paper presents results from a series of large-scale laboratory modeling experiments which were conducted to study the thermal balance in saturated soft clay during the operation of the ground source heat pump (GSHP) system. In experiments, thermal phenomena are induced by a heat exchanger which is embedded in the testing soils and investigated by temperature sensor arrays. It summarizes the experimental methodologies and presents the time-dependent temperature distribution within the testing soils both in operation stage and the re-stabilization duration. Characteristics of thermal balance are discussed in two specific parameters of relative time ratio and temperature factor by comparing summer and winter cases. The thermal mattress effect is proposed to provide valuable guidelines for the utilization and operation of the GSHP system in soft clay areas.

Ordinary cement mortar is commonly used in building engineering due to its high strength, affordability, and easy access to raw materials. However, it suffers from high shrinkage and poor impermeability, which result in reduced building service life and significant carbon dioxide emissions during production. Polymer additives have been found to enhance the mechanical properties of cement mortar, leading to increased interest in polymer cement mortar by researchers. This study collected and analyzed 420 papers published between 1995 and 2023 in the field of polymer cement mortar. The analysis included publication trends, author cooperation networks, national cooperation networks, published journals, co-citation of references, and keywords. The findings reveal a rapid publication growth from 2018 to 2023, with China making the most significant contribution in this field. Among the scholars, Ru Wang has published the highest number of articles in the field of polymer cement mortar, while Ohama’s papers have been cited the most. The journal with the most articles is Construction and Building Materials. Research in polymer cement mortar focuses on mechanical properties, performance, hydration process, microstructure, and other related aspects. The reinforcement effect of polymer-modified cement mortar on reinforced concrete and applying superabsorbent polymer-modified cement mortar and polymer fiber in cement mortar have emerged as recent research frontiers. This study can help scholars quickly identify high-quality references and research frontiers in the field of polymer cement mortar while also providing research directions and ideas.

Cellular traction forces (CTFs) are generated by adherent cells and involved in regulating migration, morphology, and homeostasis. Accurate measurement of CTFs is crucial for understanding fundamental biological processes such as morphogenesis, angiogenesis, and wound healing. However, directly measuring CTFs, which typically range in the nanonewton scale, is challenging. Cellular traction force microscopy (TFM) has been developed to quantify CTFs, but detailed operational procedures and complex data analysis limit its applicability. In this study, hydrogels embedded with fluo-spheres serve as the substrate for TFM measurement under a detailed TFM protocol. Additionally, we designed a user-friendly program for easy parameter setting. An open-source program called Python Fourier transform traction cytometry (pyFTTC) is introduced for data analysis, utilizing particle image velocimetry (PIV) to calculate the traction force from a batch of images. Cross-correlation based PIV and L2-regularized FTTC are applied to all images for data analysis. This article provides a straightforward protocol for quantifying CTFs in standard laboratories, facilitating both cell biology studies and biomaterials development.

To manage a large amount of flexible distributed energy resources (DERs) in the distribution networks, the virtual power plant (VPP) is introduced into the industry. The VPP can optimally dispatch these resources in a cluster manner and provide flexibility for the power system operation as a whole. Most existing studies formulate the equivalent power flexibility of the aggregating DERs as deterministic optimization models without considering their uncertainties. In this paper, we introduce the stochastic power flexibility range (PFR) and time-coupling flexibility (TCF) to describe the power flexibility of VPP. In this model, both operational constraints and the randomness of the DERs' output are incorporated, and a combined model and data-driven solution is proposed to obtain the stochastic PFR, TCF, and cost function of VPP. The aggregating model can be easily incorporated into the optimization model for the power system operator or market bidding, considering uncertainties. Finally, a numerical test is performed. The results show that the proposed model not only has higher computational efficiency than the scenario-based methods but also achieves more economic benefits.

Purposes To achieve stable and efficient energy storage in the new elastic potential energy storage system, it is first necessary to establish an accurate mathematical model to reveal the operating characteristics of the energy storage box, and complete the calculation and configuration model of its capacity and power; Second, a strong robust control algorithm is needed to achieve the fast torque response while effectively suppressing disturbances. Methods In this paper, an accurate mathematical model of the energy storage system was established. Combined with the backstepping control algorithm, the direct torque adaptive control method of the permanent magnet synchronous motor is designed, and a new robust control strategy for the energy storage process of the elastic potential energy storage system is formed. Conclusions Through experiments, it has been proven that this control strategy has a fast torque response speed and can quickly match the rapidly increasing reverse torque of the energy storage box with minimal pulsation. At the same time, it can effectively restrain the unique moment of inertia parameter disturbance of the energy storage system, and then achieve stable and efficient energy storage.

Local hypoxia in tumors poses significant challenges to the effectiveness of oxygen-dependent photodynamic therapy (PDT). Herein, a versatile mitochondrial targeting and pH-responsive nanozyme platform (noted as Mito CPs-p) is developed for solving local hypoxia in tumors and enhancing the PDT efficiency. Mito CPs-p, which consists of carbon-dot-supported atomically dispersed platinum (CPs) with further surface modifications of triphenylphosphine (PPh3) and pH-responsive porphyrin polymers. The PDT of Mito CPs-p was activated in acidic tumor microenvironment and exposed platinum for catalyzing endogenous H2O2 decomposition to produce O2 at tumor sites, due to the hydrophobic-hydrophilic transition of pH-responsive porphyrin-containing polymers in Mito CPs-p from hydrophobic to hydrophilic, which can markedly enhance PDT efficiency. Besides, the rational integration of mitochondria-targeting will improve the bioavailability of reactive oxygen species and further strengthen the PDT efficiency. In vitro results indicated that Mito CPs-p has exhibited excellent mitochondrial targeting capability and the ROS yield of Mito CPs-p was dramatically amplified. In vivo data further revealed that the Mito CPs-p displayed superb therapeutic efficacy. Hence, the Mito CPs-p provides new insights for efficient PDT in hypoxia solid tumors.

Highlights Comprehensive and systematic discussion of vascularized additive manufacturing scaffolds for bone tissue repair is provided. The development mechanism of blood vessels and the relationship between bone tissue engineering and blood vessels are discussed. Vascularized additively manufactured scaffolds in tissue repair are discussed in terms of issues, opportunities, and challenges. Intelligent vascularized 3D/4D/5D/6D-printed tissue scaffolds are discussed.

Using Antarctic krill (Euphausia superba) as the research object, we optimized the process conditions for Antarctic krill sauce (AkS) by including three factors (salt addition, rock sugar addition, and the oil-to-material ratio) and sensory evaluation as response values. The data from the response surface were fed into the back propagation (BP) neural network training, generating a model mapping the process conditions and sensory scores, which were subsequently combined with the genetic algorithm (GA) for global optimization to determine the optimal process for AkS preparation. The results revealed that the response surface model was well suited to the BP neural network training and prediction sets, with correlation values of 0.98 and 0.95, respectively. The fitting prediction effect was obvious for the sensory scoring results of the product. The parameters obtained from the GA’s global optimization search accord with the analytical results of the response surface. The findings demonstrated that combining a BP neural network with a GA can enhance the AkS preparation technique. Under optimal processing conditions, AkS has a high sensory score and protein and carbohydrate contents, moderate fat content, minimal fat oxidation, and non-detectable pathogens, indicating that the AkS in this study was nutritious and safe to consume.

To optimize energy structure and efficiently utilize renewable energy sources, it is necessary to establish a new electrical power–gas mutual transformation virtual power plant that has low-carbon benefits. To promote the economic and low-carbon operation of a virtual power plant and reduce uncertainty regarding the use of new energy, a multi-timescale (day-ahead to intraday) optimal scheduling model is proposed. First, a basic model of a new interconnected power–gas virtual power plant (power-to-gas demand response virtual power plant, PD-VPP) was established with P2G and comprehensive demand response as the main body. Second, in response to the high volatility of new energy, a day-ahead to intraday multi-timescale collaborative operation optimization model is proposed. In the day-ahead optimization period, the next day’s internal electricity price is formulated, and the price-based demand response load is regulated in advance so as to ensure profit maximization for the virtual power plant. Based on the results of day-ahead modeling, intraday optimization was performed on the output of each distributed unit, considering the cost of the carbon emission reductions to achieve low-carbon economic dispatch with minimal operating costs. Finally, several operation scenarios are established for a simulation case analysis. The validity of the proposed model was verified via comparison.

The carbon emissions of the power industry account for over 50% of China’s total carbon emissions, so achieving carbon peak and carbon neutrality in the power sector is crucial. This study aims to simulate the impacts of three energy policies—carbon constraints, the development of a high proportion of renewable energy, and carbon trading—on China’s energy transition, economic development, and the power sector’s energy mix. Through the construction of a dynamic computable general equilibrium (CGE) model for China and its integration with the SWITCH-China electricity model, the impact of diverse energy policies on China’s energy transition, economic progress, and the power mix within the electricity industry has been simulated. The integration of the SWITCH-China model can address the limitations of the CGE model in providing a detailed understanding of the specific intricacies of the electricity sector. The results indicate that increasing the stringency of carbon restrictions compels a reduction in fossil energy use, controlling the output of coal-fired power units, and thereby reducing carbon emissions. The development of a high proportion of renewable energy enhances the cleanliness of the power sector’s generation structure, further promoting the national energy transition. Implementing a carbon trading policy, where the entire industry shares the burden of carbon reduction costs, can effectively mitigate the economic losses of the power sector. Finally, the policies to further enhance the implementation of carbon trading policies, strengthen effective governmental regulation, and escalate the deployment of renewable energy sources are recommended.

With the continuous integration of renewable energy and the complexity of power system, dynamic voltage regulation of low-voltage distribution network has become an urgent problem to be solved. Through in-depth analysis and theoretical discussion, the mathematical model of low-voltage distribution network is established, and the control strategy based on renewable energy is designed for dynamic voltage regulation. Through simulation experiment and field verification, the superiority of the proposed method compared with the existing method is demonstrated. In addition, the sensitivity of the parameters and the stability of the control strategy are also comprehensively analyzed. Despite some limitations and challenges, the results of this study provide strong support for the sustainable, efficient and safe operation of low-voltage distribution networks, and further optimization and expansion in many aspects are needed in the future.

Introduction: The stress-strain relationship of clays characterized by strain hardening exhibits varying curves under different confining pressures and dry densities.Methods: Considering the viscoelastic properties of clays, a normalized viscoelastic model of strain-hardening clay was established based on fractional derivatives, and normalization factors were proposed.Results: The experimental results showed that the stress-strain relationship of the clay was strain hardening. It shows that Chengdu clay has better normalization conditions. Furthermore, the normalized analysis of this clay through the viscoelastic normalization model revealed that the straight line of normalized data displayed a goodness-of-fit of over 0.98. The obtained values were consistent with experimental results, suggesting the reasonability of the normalized strain-hardening parameters and elastic moduli.Discussion: In addition, the superiority of the developed model was verified by testing the strain-hardening clays in Wuhan, China and Bangkok, Thailand. After analyzing the strain-hardening parameters and normalization factors of our model, it was found that the slope of the normalized line can accurately reflect the strain-hardening ability of the clay. These findings demonstrated that the proposed normalization factor is preferred for a normalized viscoelastic model. It shows that the model proposed in this paper has clearer physical meaning and advancement.

With the gradual emphasis on health by people, the research on the pathogenesis of endemic diseases has become increasingly in-depth. Through analyzing the environmental selenium characteristics and conducting a comparative study in typical areas of Chinese loess and black soil in this paper, it is concluded that the environmental selenium in the two regions has different characteristics. The soil in the loess area has the characteristics of high alkalinity, low selenium, and relatively high selenium availability, and the crops are selenium-deficient, while the soil in the black soil area has the characteristics of high organic matter, low selenium availability, and relatively high selenium in crops. The research concluded that the environmental occurrence mechanism of Keshan disease in the loess area and the black soil area is different. Keshan disease can be induced in both low-selenium and sufficient-selenium environments, and environmental selenium should be one of the inducing factors of Keshan disease. This research provides a reference for predicting the areas where Keshan disease occurs and for disease prevention, and it can also serve in the prevention and control of endemic diseases.

This study proposes four kinds of hybrid source–grid–storage systems consisting of photovoltaic and wind energy, and a power grid including different batteries and hydrogen storage systems for Sanjiao town. HOMER-PRO was applied for the optimal design and techno-economic analysis of each case, aiming to explore reproducible energy supply solutions for China’s industrial clusters. The results show that the proposed system is a fully feasible and reliable solution for industry-based towns, like Sanjiao, in their pursuit of carbon neutrality. In addition, the source-side price sensitivity analysis found that the hydrogen storage solution was cost-competitive only when the capital costs on the storage and source sides were reduced by about 70%. However, the hydrogen storage system had the lowest carbon emissions, about 14% lower than the battery ones. It was also found that power generation cost reduction had a more prominent effect on the whole system’s NPC and LCOE reduction. This suggests that policy support needs to continue to push for generation-side innovation and scaling up, while research on different energy storage types should be encouraged to serve the needs of different source–grid–load–storage systems.

In adapting to the double-high development trend of high-voltage direct current (HVDC) receiving-end power systems and solving optimization problems in emergency frequency control (EFC) supporting virtual power plants (VPPs) in large-scale power systems, a parameter estimation method for a VPP frequency response model based on a successive linear programming (SLP) method is proposed. First, a “centralized/decentralized” hierarchical control architecture for VPP participation in EFC is designed. Second, the frequency response characteristics of multiple flexible resources are scientifically analyzed, and the system frequency response (SFR) model and equivalent model of VPP are constructed. Subsequently, parameter estimation of the VPP frequency response model is carried out based on the SLP method, aiming to balance the accuracy and computational efficiency of the model. Finally, the effectiveness of the proposed methodology is verified by using PSD-BPA to simulate and test the three-zone HVDC recipient area grid.

Chronic Obstructive Pulmonary Disease (COPD), as the third leading cause of death worldwide, is a major global health issue. The early detection and grading of COPD are pivotal for effective treatment. Traditional spirometry tests, requiring considerable physical effort and strict adherence to quality standards, pose challenges in COPD diagnosis. Volumetric capnography (VCap), which can be performed during natural breathing without requiring additional compliance, presents a promising alternative tool. In this study, the dataset comprised 279 subjects with normal pulmonary function and 148 patients diagnosed with COPD. We introduced a novel quantitative analysis method for VCap. Volumetric capnograms were converted into two-dimensional grayscale images through the application of Gramian Angular Field (GAF) transformation. Subsequently, a multi-scale convolutional neural network, CapnoNet, was conducted to extract features and facilitate classification. To improve CapnoNet’s performance, two data augmentation techniques were implemented. The proposed model exhibited a detection accuracy for COPD of 95.83%, with precision, recall, and F1 measures of 95.21%, 95.70%, and 95.45%, respectively. In the task of grading the severity of COPD, the model attained an accuracy of 96.36%, complemented by precision, recall, and F1 scores of 88.49%, 89.99%, and 89.15%, respectively. This work provides a new perspective for the quantitative analysis of volumetric capnography and demonstrates the strong performance of the proposed CapnoNet in the diagnosis and grading of COPD. It offers direction and an effective solution for the clinical application of capnography.

The optimal designing of the hybrid energy system (HES) is a challenging task due to the multiple objectives and various uncertainties. Especially for HES, primarily powered by solar energy, the reference solar radiation data directly impact the result of the optimization design. To incorporate the stochastic characteristics of solar radiation into the sizing process, a data-driven stochastic modeling method for solar radiation is proposed. The method involves two layers of stochastic processes that capture the intraday variation and daily evolution of solar radiation. First, the clearness index (CI) is introduced to describe the radiation intensity at different times. Then, the daily clearness state (DCS) is proposed, based on the statistical indicators of the intraday CI. The Markov model is used to describe the stochastic evolutionary characteristics between different DCSs. The probabilistic distribution of the CI under different DCS is obtained based on the diffusion kernel density estimation (DKDE), which is used for the stochastic generation of the CI at various times of the day. Finally, the radiation profile required for the optimal design is obtained by the stochastic generation of the DCS sequences and the intraday clearness index under corresponding states. A case study of an off-grid solar-powered HES is provided to illustrate this methodology.

Cementitious composites are ubiquitous in construction, and more and more research is focused on improving mechanical properties and environmental effects. However, the jury is still out on which material can achieve low-carbon and high-performance cementitious composites. This article compares the mechanical and environmental performance of zero-dimensional fullerenes, one-dimensional carbon nanotubes (CNTs), two-dimensional graphene oxide (GO), and three-dimensional nano-graphite platelets (NGPs) on cementitious composites. The literature review shows that two-dimensional (2D) GO has the best mechanical and environmental performance, followed by 3D NGPs, 1D CNTs, and 0D fullerenes. Specifically, GO stands out for its lower energy consumption (120–140 MJ/kg) and CO2 emissions (0.17 kg/kg). When the optimal dosage (0.01–0.05 wt%) of GO is selected, due to its high specific surface area and strong adhesion to the matrix, the compressive strength of the cementitious composites is improved by nearly 50%. This study will help engineers and researchers better utilize carbon-based nanomaterials and provide guidance and direction for future research in related fields.

This research is dedicated to optimizing the design of microfluidic cells to minimize mass transfer effects and ensure a uniform flow field distribution, which is essential for accurate SPR array detection. Employing finite element simulations, this study methodically explored the internal flow dynamics within various microfluidic cell designs to assess the impact of different contact angles on flow uniformity. The cells, constructed from Polydimethylsiloxane (PDMS), were subjected to micro-particle image velocimetry to measure flow velocities in targeted sections. The results demonstrate that a contact angle of 135° achieves the most uniform flow distribution, significantly enhancing the capability for high-throughput array detection. While the experimental results generally corroborated the simulations, minor deviations were observed, likely due to fabrication inaccuracies. The microfluidic cells, evaluated using a custom-built SPR system, showed consistent repeatability.

Cleaning products are often formulated as mixtures of surfactants because the properties of surfactant mixtures are easier to adjust than those of a single surfactant. Therefore, it is of great significance to study the phase diagram of surfactant mixtures. In this paper, the phase behavior of the alkyl ethoxysulfate (AES)/cocamidopropyl betaine (CAPB)/H2O ternary system was investigated at room temperature using polarizing optical microscopy (POM) and small angle X-ray scattering (SAXS), and the identified phases of the samples with various compositions were used to construct the ternary phase diagram of the AES/CAPB/H2O system which contains normal micellar phase (L1), normal hexagonal phase (H1), lamella phase (Lα), and one transition region (L1 → H1). The viscosity distribution of the AES/CAPB/H2O system was determined by a Brookfield DV2T touch screen viscometer. In addition, the effects of the weight percentage of CAPB and salts on the viscosity and rheological properties of the AES/CAPB/H2O system were also investigated. This work not only enriches the phase diagram of surfactant systems, but also has important guiding significance for the design and development of cleaning products.

A reasonable allocation of production schedules and savings in overall electricity costs are crucial for large manufacturing conglomerates. In this study, we develop an optimization model of off-site industrial production scheduling to address the problems of high electricity costs due to the irrational allocation of production schedules on the demand side of China’s power supply, and the difficulty in promoting industrial and commercial distributed photovoltaic (PV) projects in China. The model makes full use of the conditions of different PV resources and variations in electricity prices in different places to optimize the scheduling of industrial production in various locations. The model is embedded with two sub-models, i.e., an electricity price prediction model and a distributed photovoltaic power cost model to complete the model parameters, in which the electricity price prediction model utilizes a Long Short-Term Memory (LSTM) neural network. Then, the particle swarm optimization algorithm is used to solve the optimization model. Finally, the production data of two off-site pharmaceutical factories belonging to the same large group of enterprises are substituted into the model for example analysis, and it is concluded that the optimization model can significantly reduce the electricity consumption costs of the enterprises by about 7.9%. This verifies the effectiveness of the optimization model established in this paper in reducing the cost of electricity consumption on the demand side.

Bacterial cellulose (BC) possesses the desirable properties of biocompatibility, high porosity, high surface area and noticeable mechanical strength as a scaffold in bone tissue engineering. However, the lack of osteogenic activity restricts its application. In this study, gold nanoparticles (GNPs) with excellent osteogenic differentiation ability were incorporated into the network of BC hydrogel (Au/BC hydrogels) by the in-situ fermentation. The effects of GNPs on physicochemical properties of BC hydrogel and subsequently in vitro osteogenic differentiation and in vivo bone regeneration of Au/BC hydrogels were comprehensively investigated. The results showed that the increased feeding amounts of GNPs could remarkablly enhance the Au/BC hydrogels with better mechanical properties, higher porosity, larger surface area, and biocompatibility. The sustainable release of GNPs endowed the hydrogels with an outstanding biological activity in facilitating osteogenic differentiation of human bone marrow-derived mesenchymal stem cells (hBMSCs). Mechanism research showed that autophagy might be a potential pathway for Au/BC hydrogels-induced osteogenic differentiation of hBMSCs. In addition, Au/BC hydrogel exhibited an excellent in vivo bone repair performance in a rabbit model of femoral defect, which was evidenced by the significant newly bone formation. Overall, the multifunctional Au/BC hydrogels fabricated by in-situ fermentation could serve as a good scaffold for promoting bone tissue regeneration in clinic.

Abstract Photothermal therapy (PTT) combined with second near-infrared (NIR-II) fluorescence imaging (FI) has received increasing attention owing to its capacity for precise diagnosis and real-time monitoring of the therapeutic effects. It is of great clinical value to study organic small molecular fluorophores with both PTT and NIR-II FI functions. In this work, we report a skillfully fluorescent lipid nanosystem, the RR9 (RGDRRRRRRRRRC) peptide-coated anionic liposome loaded with organic NIR-II fluorophore IR-1061 and chemotherapeutic drug carboplatin, which is named RRIALP-C4. According to the structural interaction between IR-1061 and phospholipid bilayer demonstrated by molecular dynamics simulations, IR-1061 is rationally designed to possess the H-aggregated state versus the free state, thus rendering RRIALP-C4 with the activated dual-channel integrated function of intravital NIR-II FI and NIR-I PTT. Functionalization of RRIALP-C4 with RR9 peptide endows the specifically targeting capacity for αvβ3-overexpressed tumor cells and, more importantly, allows IR-1061 to transfer the H-aggregated state from liposomes to the tumor cell membrane through enhanced membrane fusion, thereby maintaining its PTT effect in tumor tissues. In vivo experiments demonstrate that RRIALP-C4 can effectively visualize tumor tissues and systemic blood vessels with a high sign-to-background ratio (SBR) to realize the synergistic treatment of thermochemotherapy by PTT synergistically with temperature-sensitive drug release. Therefore, the strategy of enhanced PTT through H-aggregation of NIR-II fluorophore in the tumor cell membrane has great potential for developing lipid nanosystems with integrated diagnosis and treatment function.

Due to the exceptional learning capabilities of deep neural networks (DNNs), they continue to struggle to handle label noise. To address this challenge, the pseudo-label approach has emerged as a preferred solution. Recent works have achieved significant improvements by exploring the information involved in DNN predictions and designing a straightforward method to incorporate model predictions into the training process by using a convex combination of original labels and predictions as the training targets. However, these methods overlook the feature-level information contained in the sample, which significantly impacts the accuracy of the pseudo label. This study introduces a straightforward yet potent technique named FPL (feature pseudo-label), which leverages information from model predictions as well as feature similarity. Additionally, we utilize an exponential moving average scheme to bolster the stability of corrected labels while upholding the stability of pseudo-labels. Extensive experiments were carried out on synthetic and real datasets across different noise types. The CIFAR10 dataset yielded the highest accuracy of 94.13% (Top1), while Clothing1M achieved 73.54%. The impressive outcomes showcased the efficacy and robustness of learning amid label noise.

Distortion deformation usually imposes a potential threat to bridge safety. In order to comprehensively understand the distortion effect on thin-walled ultra-high performance concrete (UHPC) box girders, an innovative approach encompassing the governing distortion differential equation is introduced in this study based on the general definition of distortion angle within the cross-section plane. The analytical results obtained from the proposed method are in accordance with those obtained from the energy method, and exhibit favorable agreement with experimental findings documented in the existing literature. Furthermore, a finite element model is developed on the ANSYS 2021 R1 software platform with the employment of a Shell 63 element. Numerical outcomes are also in good agreement with the experimental data, affirming the validity and reliability of the findings. In addition, parameter analysis results indicate that the distortion angle remains approximately constant at a location approximately 1/10 of the span from the mid-span cross-section of the box girder, regardless of changes in the span-to-depth ratio. Increasing the web thickness yields a notable reduction in the distortion effects, and decreasing the wall thickness can effectively mitigate the distortion-induced transverse bending moment. Compared with normal-strength concrete box girders, UHPC box girders can reduce the distortion angle within the span range, which is beneficial for maintaining the overall stability of the box girders. The outcomes obtained from this study yield engineers an enhanced understanding of distortion effect on the UHPC girder performance.

This paper presents a numerical investigation of the flexural behavior of timber beams externally strengthened with carbon-fiber-reinforced polymer (CFRP) sheets. At first, the accuracy of linear elastic and elastic-plastic models in predicting the behavior of bare timber beams was compared. Then, two modeling approaches (i.e., the perfect bond method and progressive damage technique using the cohesive zone model (CZM)) were considered to simulate the interfacial behavior between FRP and timber. The models were validated against published experimental data, and the most accurate numerical procedure was identified and subsequently used for a parametric study. The length of FRP sheets varied from 50% to 100% of the total length of the beam, while different FRP layers were considered. Moreover, the effects of two strengthening configurations (i.e., FRP attached in the tensile zone only and in both the tensile and compressive zones) on load-deflection response, flexural strength, and flexural rigidity were considered. The results showed that elastic-plastic models are more accurate than linear elastic models in predicting the flexural strength and failure patterns of bare timber beams. In addition, with increasing FRP length, the increase in flexural strength ranged from 10.3% to 52.9%, while no further increase in flexural strength could be achieved beyond an effective length of 80% of the total length of the beam. Attaching the FRP to both the tensile and compressive zone was more effective in enhancing the flexural properties of the timber beam than attaching the FRP to the tensile zone only.

Conventional mechanical machining of a composite material comprising an aluminum matrix reinforced with a high volume fraction of SiC particles (hereinafter referred to as an SiCp/Al composite) faces problems such as rapid tool wear, high specific cutting force, and poor surface integrity. Instead, a promising method for solving these problems is laser-induced oxidation-assisted milling (LOAM): under laser irradiation, the local workpiece material reacts with oxygen, thus forming loose and porous oxides that are easily removed. In the present work, the oxidation mechanism of SiCp/Al irradiated by a nanosecond pulsed laser is studied to better understand the laser-induced oxidation behavior and control the characteristics of the oxides, with laser irradiation experiments performed on a 65% SiCp/Al composite with various laser parameters and auxiliary gases (oxygen, nitrogen, and argon). With increasing laser pulse energy density, both the ablated groove depth and the width of the heat-affected zone increase. When oxygen is used as the auxiliary gas, an oxide layer composed of SiO2 and Al2O3 forms, and CO2 is produced and escapes from the material, thereby forming pores in the oxides. However, when nitrogen or argon is used as the auxiliary gas, a recast layer is produced that is relatively difficult to remove. Under laser irradiation, the sputtered material reacts with oxygen to form oxides on both sides of the ablated groove, and as the laser scanning path advances, the produced oxides accumulate to form an oxide layer. LOAM and conventional milling are compared using the same milling parameters, and LOAM is found to be better for reduced milling force and tool wear and improved machined surface quality.

Urban spatial morphology reflects the processes of external urban expansion and the internal spatial structure of the human economy, and exploring urban spatial morphology is important for regional management of PM2.5 pollutants in the air environment. Whether urban spatial morphology can influence air pollution is a question that has been explored and evaluated. Therefore, we constructed a spatial-temporal driving mechanism framework and explored the interactions among urban expansion and PM2.5 pollutants in the “2 + 26” cities of Beijing-Tianjin-Hebei (BTH). Based on 2010, 2015, and 2020 urban expansion factors and PM2.5 pollutants data from the “2 + 26” cities of BTH, the results of the Geo-Detector medal imply that: (1) The spatial distribution characteristics of areas with high PM2.5 pollutants in the “2 + 26” cities of BTH were affected by the PM2.5 concentration value, and the high-value phenomenon appeared to decrease with the improvement of PM2.5 pollutants. (2) The level of urbanization in the “2 + 26” cities of BTH had been increasing annually, with the most significant growth in construction land area located in the northeastern coastal cities. (3) The compactness index, was the most critical influencing urban expansion factor, explaining 0.63, 0.64, and 0.66 of PM2.5 pollutants in 2010, 2015, and 2020. (4) In factorial interactions, the compactness index and the centrality ratio explained spatial and temporal analysis characteristics of PM2.5 pollutants, and the interaction factor enhanced the explanation for the spatial-temporal characteristics. This study reveals the coupled spatial-temporal driving mechanism of air pollution in the “2 + 26” cities of BTH and also shows that reasonable urban planning has a positive impact on the regional ecological environment in the future.

The surge in malware threats propelled by the rapid evolution of the internet and smart device technology necessitates effective automatic malware classification for robust system security. While existing research has primarily relied on some feature extraction techniques, issues such as information loss and computational overhead persist, especially in instruction-level tracking. To address these issues, this paper focuses on the nuanced analysis of API (Application Programming Interface) call sequences between the malware and system and introduces TTDAT (Two-step Training Dual Attention Transformer) for malware classification. TTDAT utilizes Transformer architecture with original multi-head attention and an integrated local attention module, streamlining the encoding of API sequences and extracting both global and local patterns. To expedite detection, we introduce a two-step training strategy: ensemble Transformer models to generate class representation vectors, thereby bolstering efficiency and adaptability. Our extensive experiments demonstrate TTDAT’s effectiveness, showcasing state-of-the-art results with an average F1 score of 0.90 and an accuracy of 0.96.

There is a lack of research in using electromyography (EMG) signals to control a continuous passive motion (CPM) machine. This study aimed to develop an interface instrument for digitalising EMG signals and controlling a CPM machine. Methods: The proposed device was designed with the following: (1) a signal processing unit which converted the EMGs from analogue to digital for the controller; (2) a personal computer which stored and displayed the EMG signals; (3) an LCD device to display the running angle of the CPM; and (4) a microcontroller unit to control the input/output signals and process the algorithm, driving the CPM. To validate the reliability of the proposed system, a total of 600 EMG trials were collected from 10 healthy subjects by using the proposed device via the Delsys® TringoTM EMG system and simultaneously using the Vicon® motion capture system. Result: This proposed device was able to digitalise and process EMG signals from eight channels of muscles, and the signals were able to drive a CPM. The validated results showed that the digitalised EMG signals by the proposed device were statistically similar to and correlated with the signals by the Vicon system with a median correlation coefficient of 0.81, with the 25% and 75% range being 0.56–0.92 with all pairs (300 pairs of EMG trials) (p < 0.001). Conclusions: This study confirmed that the developed device can digitalise EMG signals and drive a CPM as an applicable prototype that can work as an interface between EMG and CPM devices with high reliability.

Enhanced oil recovery (EOR) is a complex process which has high investment cost and involves multiple disciplines including reservoir engineering, chemical engineering, geological engineering, etc. Finding the most suitable EOR technique for the candidate reservoir is time consuming and critical for reservoir engineers. The objective of this research is to propose a new methodology to assist engineers to make fast and scientific decisions on the EOR selection process by implementing machine learning algorithms to worldwide EOR projects. First, worldwide EOR project information were collected from oil companies, the extensive literature, and reports. Then, exploratory data analysis methods were employed to reveal the distribution and relationships among different reservoir/fluid parameters. Random forest, artificial neural networks, naïve Bayes, support vector machines, and decision trees were applied to the dataset to establish classification models, and five-fold cross-validation was performed to fully apply the dataset and ensure the performance of the model. Utilizing random search, we optimized the model’s hyper parameters to achieve optimal classification results. The results show that the random forest classification model has the highest accuracy and the accuracy of the test set increased from 88.54% to 91.15% without or with the optimization process, achieving an accuracy improvement of 2.61%. The prediction accuracy in the three categories of thermal flooding, gas injection, and chemical flooding were 100%, 96.51%, and 88.46%, respectively. The results also show that the established RF classification model has good capability to make recommendations of the EOR technique for a new candidate oil reservoir.

This study investigated the automatic segmentation and classification of mitral regurgitation (MR) and tricuspid regurgitation (TR) using a deep learning-based method, aiming to improve the efficiency and accuracy of diagnosis of valvular regurgitations. A VABC-UNet model was proposed consisting of VGG16 encoder, U-Net decoder, batch normalization, attention block and deepened convolution layer based on the U-Net backbone. Then, a VABC-UNet-based assessment framework was established for automatic segmentation, classification, and evaluation of valvular regurgitations. A total of 315 color Doppler echocardiography images of MR and/or TR in an apical four-chamber view were collected, including 35 images in the test dataset and 280 images in the training dataset. In comparison with the classic U-Net and VGG16-UNet models, the segmentation performance of the VABC-UNet model was evaluated via four metrics: Dice, Jaccard, Precision, and Recall. According to the features of regurgitation jet and atrium, the regurgitation could automatically be classified into MR or TR, and evaluated to mild, moderate, moderate–severe, or severe grade by the framework. The results show that the VABC-UNet model has a superior performance in the segmentation of valvular regurgitation jets and atria to the other two models and consequently a higher accuracy of classification and evaluation. There were fewer pseudo- and over-segmentations by the VABC-UNet model and the values of the metrics significantly improved (p < 0.05). The proposed VABC-UNet-based framework achieves automatic segmentation, classification, and evaluation of MR and TR, having potential to assist radiologists in clinical decision making of the regurgitations in valvular heart diseases.

In the backdrop of the ongoing reforms within the electricity market and the escalating integration of renewable energy sources, power service providers encounter substantial trading risks stemming from the inherent uncertainties surrounding market prices and load demands. This paper endeavors to address these challenges by proposing a credibility theory-based information gap decision theory (CTbIGDT) to improve robustness of electricity trading under uncertainties. To begin, we establish credibility theory as a foundational risk assessment methodology for uncertain price and load, incorporating both necessity and randomness measures. Subsequently, we advance the concept by developing the CTbIGDT optimization model, grounded in the consideration of expected costs, with the primary aim of fortifying the robustness of electricity trading practices. The ensuing model is then transformed into an equivalent form and solved using established standard optimization techniques. To validate the efficacy and robustness of our proposed methodology, a case study is conducted utilizing a modified IEEE 33-node distribution network system. The results of this study serve to underscore the viability and potency of the CTbIGDT model in enhancing the effectiveness of electricity trading strategies in an uncertain environment.

With their rich surface chemistry, high electrical conductivity, variable bandgap, and thermal stability, 2D materials have been developed for effective electrochemical energy conversion systems over the past decade. Due to the diversity brought about by the use of transition metals and C/N pairings, the 2D material MXene has found excellent applications in many fields. Among the various applications, many breakthroughs have been made in electrocatalytic applications. Nevertheless, related studies on topics such as the factors affecting the material properties and safer and greener preparation methods have not been reported in detail. Therefore, in this paper, we review the relevant preparation methods of MXene and the safer, more environmentally friendly preparation techniques in detail, and summarize the progress of research on MXene-based materials as highly efficient electrocatalysts in the electrocatalytic field of hydrogen precipitation reaction, nitrogen reduction reaction, oxygen precipitation reaction, oxygen reduction reaction, and carbon dioxide reduction reaction. We also discuss the technology related to MXene materials for hydrogen storage. The main challenges and opportunities for MXene-based materials, which constitute a platform for next-generation electrocatalysis in basic research and practical applications, are highlighted. This paper aims to promote the further development of MXenes and related materials for electrocatalytic applications.

As a key component in fields such as high-precision machine tools, aerospace and weaponry, the thermal deformations in a differential planetary roller screw (DPRS) caused by the changes in ambient temperatures can lead to uneven load distribution and increased wear, which can seriously affect its transmission accuracy, efficiency, and service life. Therefore, the temperature rise of a DPRS at different ambient temperatures was studied using the block thermal network method, and the thermal deformations were calculated. A load distribution model of DPRS considering thermal deformation was developed, and a wear prediction method combined with surface roughness was proposed. The results show that the increase in ambient temperature has an obvious negative effect on the load distribution and wear depth of DPRS. Moreover, the DPRS temperature rise, load distribution and wear depth dramatically change with the variation of parameters such as load, speed and thread pitch. A DPRS durability test device that considers changes in ambient temperature was developed. Experiments with temperature rise and DPRS wear under different ambient temperatures were conducted to validate the theoretical analysis model.

This study quantitatively analysed the influence of cooling water parameters on the performance of a modular high-temperature gas-cooled reactor (MHTGR) nuclear power plant (NPP). The secondary circuit system and cold-end system were modelled using EBSILON software, version 16.0. The influence of cooling water inlet temperature and mass flow rate on the thermal performance of the secondary circuit system was analysed over the full power range with the goal of optimising net power. Under 100% rated condition, for each 1 °C increase in cooling water inlet temperature between 10 and 33 °C, the net power and cycle efficiency decreased by 0.67 MW and 0.14%, respectively, whereas the heat consumption rate increased by 28.72 kJ/(kW·h). The optimal cooling water mass flow rates corresponding to cooling water inlet temperatures of 16 °C and 33 °C were obtained. The optimal cooling water mass flow rate decreased nonlinearly with decreasing power levels. At a cooling water inlet temperature of 33 °C, an increase in cooling water mass flow rate from the designed value (7697.61 kg/s) to the optimal value (10,922.14 kg/s) resulted in a 1.03 MW increase in net power. These findings provide guidelines for MHTGR NPP operation optimisation and economic improvement, especially under high-temperature weather conditions.

This article proposes and designs a novel variable pitch adjustment device for small wind turbines. The generator spindle is designed to be hollow so that the drive rod passes through it and connects the pitch drive mechanism to the pitch actuator. The article introduces the basic structure and working principle of the pitch mechanism and verifies the feasibility of the pitch device by using 3D printing technology to produce a small-scale model. The stress analysis of the wind turbine was carried out using the unidirectional fluid–structure coupling method. The results show that the maximum equivalent stress of the pitch mechanism is 27.42 MPa, the maximum tooth surface contact stress of the gear is 38.40 MPa, and the maximum tooth root bending stress is 18.13 MPa. The rack synchronous disk, blade handle, and gear rack mechanism were designed with light weight using various optimization schemes. The results of the optimization showed that the overall mass of the pitch mechanism was reduced by 33.2%, improving the applicability of the new pitch mechanism.