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Abstract Tungsten oxide (WO3)-based memristors show promising applications in neuromorphic computing. However, single-layer WO3 memristors suffer from issues such as weak memory performance and nonlinear conductance variations. In this work, a functional layer based on the hybrids of WO3−x and TiO2 is proposed for constructing flexible memristors featuring outstanding synaptic characteristics. Applying diverse electrical stimulations to the memristor enables a range of synaptic functions, elucidating its conduction mechanism through the conductive filament model. The incorporation of TiO2 not only enhances the memristor’s memory characteristics but makes its conductance more linear, symmetrical and uniform during the long-term changes. Furthermore, in view of the enhanced device performance by TiO2 doping, the potential of this device for simple behavioral simulation and processing of complex computing problems is explored. The “learning-forgetting-relearning” characteristics and device integrability are visually demonstrated. Applying the device to a convolutional neural network, the recognition accuracy of MNIST handwritten digits reaches 98.7%.

Through the innovative combination of CO2 capture in the coal chemical industry and oil reservoir CO2 storage, Yanchang Petroleum has realized multiple benefits such as clean utilization of coal, carbon emission reduction, and crude oil production increase, and explored a new path for low-carbon development of coal chemical industry. The whole process integration technology and field practice of carbon capture, utilization, and storage (CCUS) from Yanchang Petroleum are systematically presented. For CO2 capture, the low-temperature methanol washing and low-cost CO2 capture technology of the coal chemical industry has been formed. The CO2 capture cost is 105 yuan/t, and the capture demonstration scale is 300000 t/a. For CO2 storage, the storage site selection and potential evaluation methods are established. The evaluation results showed that the theoretical CO2 storage capacity of the oilfield is 8.84×108 t, and the suitable storage area is mainly distributed in the west of the oilfield. In addition, the evolution laws of CO2 storage state across the time scale of storage are revealed. Under the water-gas alternation scenario, the proportions of CO2 storage capacity for structure, residual, solubility and mineral trapping corresponding to 10 years and 100 years is 24.38%, 27.19%, 48.38%, and 0.05%; and 15.09%, 38.65%, 45.77% and 0.49%, respectively. Then the sealing evaluation methods of the caprocks are created. It is specified that the Chang 4+5 cap layer in the Huaziping CO2 sequestration test area mainly belongs to type Ⅰ and type Ⅱ. Finally, a “Trinity” CO2 safety monitoring system is established. The results of safety monitoring show that, there is no CO2 geological leakage in the test area and the safety level of CO2 storage is Ⅰ. The safe and effective storage capacity of CO2 is 10.12×104 t in the field application. The technology of CO2 capture in the coal chemical industry and reservoir CO2 storage has achieved good effects of carbon reduction and crude oil production increase in field practice.

Abstract Unmanned Aerial Vehicles (UAVs) have become important in an extensive range of fields such as surveillance, environmental monitoring, agriculture, infrastructure inspection, commercial applications, and many others. Ensuring stable flight and precise control of UAVs, especially in adverse weather conditions or turbulent environments, presents significant challenges. Developing control systems that can adapt to these environmental factors while ensuring safe and reliable operation is a main motivation. Considering the challenges, first, an adaptive model is identified using the input/output data sets. New adaptation laws are obtained for dynamic parameters. Then, a Type-3 (T3) Fuzzy Logic System (FLS) is used to compensate for the error of dynamic identification. T3-FLS is tuned by a sliding mode control (SMC) strategy. The robustness is analyzed considering the adaptation error using the SMC approach. The main idea is that the basic dynamics of UAVs are taken into account, and adaptation laws are designed to enhance the modeling accuracy. On the other hand, an optimized T3-FLS with SMC is introduced to eliminate the adaption errors and ensure robustness. Several simulations show that known parameters converge under uncertainty, and the stability is kept, well. Also, output signals follow the desired trajectories under dynamic perturbations, identification errors, and uncertainties.

Closed-loop deep brain stimulation (cl-DSB) is recognized as one of the most prominent schemes of neurostimulation therapy which aims to reduce the level of hand tremor in Parkinson’s disease (PD). Recently, modern neurostimulation has integrated wireless sensors and programming devices with communication infrastructures to monitor and regulate hand speed remotely. However, deploying wireless communication technologies introduces degradation procedures (i.e., latency, time delay, and so on) which adversely affect the neurostimulation treatment. Thus, in this paper, an adaptive fractional-order model predictive control (FOMPC) is designed to address the challenges of wireless neurostimulation in the cl-DSB system. For this purpose, a deep on-policy learning (DOL) algorithm is adopted to design parameters of the FOMPC in such a way that minimizes the fluctuations of hand speed and eliminates side effects of stimulation. Compared with the integer version of the model predictive controller, further flexibility is achieved by the non-integer orders. Deep neural networks (DNNs) of on-policy learning are trained to regulate tremor speed by dynamically adjusting the parameters of FOMPC in an episodic manner. The main novelty of this work is to consider the challenges of unreliable wireless communications, as it allows us to investigate the effects of latency and time delays in cl-DSB systems. This makes the suggested controller a prominent option for real-time applications where the presence of time delay can threaten the system’s stability. Extensive simulation scenarios under various levels of random communication delays are considered to assess the feasibility of suggested stimulators for wireless cl-DSB systems. Comparative analysis of cl-DBS’s responses reveals the superiority of the suggested FOMPC controller (designed by deep-on-policy learning) to regulate hand tremors over other state-of-the-art strategies.

Ensuring the reliability of wind energy conversion systems (WECSs) is a crucial task for maximizing energy capture from the wind. A detailed model incorporating mechanical and electrical components is essential for accurately diagnosing system errors and assessing their impact on subsystems. Additionally, a fault detection and isolation system is necessary to quickly identify recurring faults and prevent significant economic losses. This study introduces a fault detection and isolation system using dynamic model of WECS based on type-3 (T3) fuzzy logic systems (FLSs). The adaptive random search (ARS) is employed to optimize the T3-FLS parameters and structure for enhanced fault detection accuracy. T3-FLSs handle higher levels of uncertainty and variability compared to traditional FLSs and neural networks. This allows for more accurate fault detection in complex and dynamic systems. One T3-FLS model replicates the system’s normal operation, while another simulates faulty conditions. These T3-FLS models are run in parallel with the actual plant, allowing for comparison of their outputs with the real system’s outputs to pinpoint error timing and location. The ARS is utilized to train the T3-FLSs, eliminating the need for gradient expression calculations. The appropriate number of rules for the T3-FLS is determined using Akaike and final prediction error criteria. Simulation results demonstrate the system’s ability to rapidly detect and isolate errors with minimal false alarms. This research framework can be applied to identify errors in various system components effectively.

Seismic accelerations and interlayer displacements can be reduced by Laminated Rubber Bearings (LRBs) efficiently. Isolators would amplify the displacement of the superstructure by extending the natural period, thereby reducing acceleration and seismic damage. However, as a result, the risk of pounding with adjacent structures would be raised. This study investigated the seismic responses and overturning resistance capacity of base-isolated structures subjected to pounding against an adjacent structure. Parameter studies were conducted to evaluate the effects of gap size, pounding stiffness, and horizontal stiffness of the isolation layer. Results show that poundings are characterized by intense, short forces causing acceleration spikes, amplifying the overturning coefficient and risk. The overturning risk initially decreases then increases with gap size under pulse-like earthquakes, while wider gaps mitigate effects during non-pulse events. Increased pounding stiffness intensifies poundings, heightening vulnerability. The structure’s overturning resistance initially improves with increased horizontal stiffness of the isolation layer but declines excessively with further stiffness increase.

Today, the use of renewable energy is increasing day by day due to its advantages, to solve existing challenges such as the increase in power demand. Microgrids (MGs) which have AC, DC, and DC/AC types, have received much attention due to their many advantages. MGs can be a suitable solution for supplying power to remote and sensitive areas and they can also increase the reliability of the system. Like all systems, MGs need a reliable control system to provide proper operation. There are many control methods such as robust control and adaptive control and control structures can be divided into two types: centralized and decentralized. This paper provides an overview of different decentralized control methods for MGs, based on recently published research. The methods used in each study are fully described, along with their results. Also, several research questions have been suggested for future research that can be used.

Abstract This paper studies the synchronization and control of chaotic systems while proposing a novel chaotic-based path-tracking application for mobile robots (MRs) to ensure their safety and security. In security-based applications that use MRs, such as patrol MRs, the path of the MRs must be complex enough to prevent easy prediction. Multiple chaotic systems with a chaotic switching mechanism are introduced for secure path planning. The main challenges are that the dynamics of MRs are entirely unknown. The modeled dynamics of the MRs are unreliable in practice due to a broad range of uncertainties related to the parameters, operating conditions, environmental impacts, time delays, unmodeled frictions, noisy sensors, and faulty actuators. Also, the chaotic switching of reference signals between chaotic signals imposes a high dynamic perturbation. The main novelties are as follows: (1) a strong secure path is introduced for MRs. (2) A powerful fractional-order predictive controller using type-3 (T3) fuzzy-logic systems (FLSs) is developed. (3) The estimation and prediction errors of T3-FLSs are compensated by a designed parallel compensator. (4) T3-FLSs are tuned online, such that stability is ensured, and prediction accuracy is guaranteed. (5) The suggested scheme is implemented on a real-world MR, and the results demonstrate the feasibility and accuracy of the proposed method. Also, in several simulations, the efficacy of the introduced controller is examined.

In the current era of energy conservation and emission reduction, the development of electric and other new energy vehicles is booming. With their various attributes, lithium batteries have become the ideal power source for new energy vehicles. However, lithium-ion batteries are highly sensitive to temperature changes. Excessive temperatures, either high or low, can lead to abnormal operation of the batteries, posing a threat to the safety of the entire vehicle. Therefore, developing a reliable and efficient Battery Thermal Management System (BTMS) that can monitor battery status and prevent thermal runaway is becoming increasingly important. In recent years, deep learning has gradually become widely applied in various fields as an efficient method, and it has also been applied to some extent in the development of BTMS. In this work, we discuss the basic principles of deep learning and related optimization principles and elaborate on the algorithmic principles, frameworks, and applications of various advanced deep learning methods in BTMS. We also discuss several emerging deep learning algorithms proposed in recent years, their principles, and their feasibility in BTMS applications. Finally, we discuss the obstacles faced by various deep learning algorithms in the development of BTMS and potential directions for development, proposing some ideas for progress. This paper aims to analyze the advanced deep learning technologies commonly used in BTMS and some emerging deep learning technologies and provide new insights into the current combination of deep learning technology in new energy trams to assist the development of BTMS.

Thin wafers and thin wires are beneficial to the photovoltaic industry for reducing costs, increasing efficiency, and reducing the cost of electricity generation. It is a development trend in solar silicon wafer cutting. Thin wire cutting reduces the kerf between silicon wafers to less than 50 μm. Therefore, it is extremely difficult to supply cutting fluid to the cutting area. And this affects cutting performance. This paper proposes the use of the capillary effect produced by ultrasonic waves in fixed diamond wire slicing to improve the cutting fluid supply and reduce wafer adsorption. To explore the rules of ultrasonic capillary action between two plates and guide the industrial applications, the effects of the distance between parallel plates, the distance from the bottom of the parallel plates to the ultrasonic radiation surface, the non-parallelism between the plates, the temperature of the working fluid, the ultrasonic action time, and the type of working fluid on the liquid level rise height were studied. The conclusions can be used to guide the improvement of the supply of working fluid in fixed diamond wire slicing.

This paper innovatively proposes a method of 180 °C high-temperature stirring pretreatment for waste rubber particles and compares this method with untreated, NaOH-treated, and silane coupling agent KH570-treated waste rubber particles. Fourier-transform infrared spectroscopy, X-ray diffraction analysis, water contact angle measurement, scanning electron microscopy, and energy-dispersive X-ray study are used to investigate the effects and mechanisms of different pretreatment methods on waste rubber particles. The results indicate that compared to NaOH-treated and KH570-treated waste rubber particles, the 180 °C high-temperature-stirred pretreated waste rubber particles show significantly improved cleanliness and form a hard oxide film. The study also investigates the effects of different pretreatment methods on the mechanical properties and interface binding performance of rubber concrete made from pretreated waste rubber particles. The results demonstrate that rubber concrete prepared using 180 °C high-temperature-stirred pretreated waste rubber particles substituting 20% fine aggregate exhibits the best mechanical properties and interface bonding performance. The compressive strength recovery rates after 7 and 28 days are 41.6% and 37.3%, respectively; the split tensile strength recovery rates are 47.3% and 60.6%; the axial compressive strength recovery rates are 34.1% and 18.8%; and the static compression moduli of elasticity recovery rates are 46.8% and 26.3%. High-temperature stirring pretreatment of waste rubber particles is simple to operate and suitable for scaled production. Its pretreatment effect is superior to those of the KH570 and NaOH methods, providing a reference value for the scalable application of waste rubber particles as a substitute for fine aggregate in rubber concrete.

The influence mechanism of tensile stress and tensile stress annealing on the strength and residual stress of the C19400 alloy is proved. The results show that after annealing at 100 °C under 35 MPa tensile stress, the tensile strength is 428.2 MPa, and the TD and RD residual stresses are −2 MPa and −8 MPa, respectively, which are 94.4% and 87.9% lower than that of the untreated state. This treatment can significantly reduce the residual stress of the C19400 alloy while maintaining its original tensile strength. The main factor affecting strength is the interaction between the precipitates and dislocations. Interestingly, the tensile stress has not no obvious relationship with the overall texture strength, but is positively related to the volume fraction of Brass texture. In addition, this study find that under the same level of dislocation density, no direct correlation exists between the magnitude of the residual stress and that of dislocation density. The ordered arrangement of dislocations is conducive to reducing the residual stress of the alloy. The results provide a reference for improving the performance of C19400 alloy.

Abstract This study presents an innovative path-following scheme using a new intelligent type-3 fuzzy system for mobile robots. By designing a non-singleton FS and incorporating error measurement signals, this system is able to handle natural disturbances and dynamics uncertainties. To further enhance accuracy, a Boltzmann machine (BM) models tracking errors and predicts compensators. A parallel supervisor is also included in the central controller to ensure robustness. The BM model is trained using contrastive divergence, while adaptive rules extracted from a stability theorem train the NT3FS. Simulation results using chaotic reference signals show that the proposed scheme is accurate and robust, even in the face of unknown dynamics and disturbances. Moreover, a practical implementation on a real-world robot proves the feasibility of the designed controller. To watch a short video of the scheme in action, visit shorturl.at/imoCH.

The crack detection method based on image processing has been a new achievement in the field of civil engineering inspection in recent years. Column piers are generally used in bridge structures. When a digital camera collects cracks on the pier surface, the loss of crack dimension information leads to errors in crack detection results. In this paper, an image stitching method based on Speed-Up Robust Features (SURFs) is adopted to stitch the surface crack images captured from different angles into a complete crack image to improve the accuracy of the crack detection method based on image processing in curved structures. Based on the proposed method, simulated crack tests of vertical, inclined, and transverse cracks on five different structural surfaces were conducted. The results showed that the influence of structural curvature on the measurement results of vertical cracks is very small and can be ignored. Nevertheless, the loss of depth information at both ends of curved cracks will lead to errors in crack measurement outcomes, and the factors that affect the precision of crack detection include the curvature of the surface and the length of the crack. Compared with inclined cracks, the structural curvature significantly influences the measurement results of transverse cracks, especially the length measurement results of transverse cracks. The image stitching method can effectively reduce the errors caused by the structural curved surface, and the stitching effect of three images is better than that of two images.

Rubberized concrete (RC) has received widespread attention due to its energy absorption and crack resistance properties. However, due to its low compressive strength, it is not recommended for structural applications. The rubber size and content affect RC’s mechanical properties. This study investigated and formulated the behavior of RC with different particle sizes and contents under dynamic and static loading. Quasi-static compressive and dynamic tests were conducted on RC with varying content of rubber (0–30%) and rubber sizes (0.1–20 mm). It was found that the rubber particle size was 0.5mm and the rubber content was 2%. An equation was derived from the experimental data to forecast the impact of rubber size and content on compressive strength. Additionally, by combining the literature and this research’s data, a model was established based on neural networks to predict the strength of RC. SHPB tests were carried out to study the stress–strain curves under dynamic load. The peak stress, fragment analysis, and energy absorption of RC with varying content of rubber and rubber sizes at three different strain rates (100 s−1, 160 s−1, and 290 s−1) were investigated. Equations describing the relationship between dynamic increase factor (DIF), rubber material content, and strain rate on different particle sizes were obtained by fitting. The DIF increased as the content of the rubber increased. By analyzing energy absorption data, it was found that the optimal ratio for energy absorption was RC-0.5-30, RC-0.1-30, and RC-10-30 at strain rates of 100 s−1, 160 s−1, and 290 s−1. This study could be a good guideline for other researchers to easily select the content and size of the rubber in RC for their applications. It also has a positive significance in promoting the development of green building materials.

Strength serves as a vital performance metric for assessing long-term durability of cement-based materials. Nevertheless, there is a scarcity of models available for predicting residual strength of in-situ structures made of cement-based materials exposed to sulphate conditions. To address this challenge, this study presents a novel approach using deep learning to predict the degradation of compressive strength of cement-based materials under marine environments. Specifically, a deep convolutional neural network (DCNN) is established, consisting of two convolutional layers, one pooling layer, and two fully connected layers. In this innovative model, contents of cement, water-to-cement ratio, sand, sulphate concentration and exposure temperature are selected as inputs, while the output is strength of cement-based materials subjected to sulphate deterioration. To improve the forecast capability, particle swarm optimization is adopted for optimizing hyperparameters of DCNN, which can be implemented by reducing the discrepancy between model prediction and measured strength. Finally, experimental data are used to establish and evaluate proposed method. The results show that the proposed deep learning-based predictive model has the best performance of strength degradation prediction of cement-based materials suffering from sulphate attack via a comparison with other commonly used models. The outcome of this research offers a potential solution for predicting remaining strength of cement-based materials that undergo practical sulphate attack.

Chronic obstructive pulmonary disease (COPD) is characterized by progressive and irreversible airflow obstruction with abnormal lung function. Because its pathogenesis involves multiple aspects of oxidative stress, immunity and inflammation, apoptosis, airway and lung repair and destruction, the clinical approach to COPD treatment is not further updated. Therefore, it is crucial to discover a new means of COPD diagnosis and treatment. COPD etiology is associated with complex interactions between environmental and genetic determinants. Numerous genes are involved in the pathogenic process of this illness in research samples exposed to hazardous environmental conditions. Among them, Long non-coding RNAs (lncRNAs) have been reported to be involved in the molecular mechanisms of COPD development induced by different environmental exposures and genetic susceptibility encounters, and some potential lncRNA biomarkers have been identified as early diagnostic, disease course determination, and therapeutic targets for COPD. In this review, we summarize the expression profiles of the reported lncRNAs that have been reported in COPD studies related to environmental risk factors such as smoking and air pollution exposure and provided an overview of the roles of those lncRNAs in the pathogenesis of the disease.

A limit cycle is a cyclic path of oscillation on which the states of a nonlinear system settle. A considerable number of practical systems such as robots, converters, and heartbeat require generating the sustainable oscillatory behaviours. The objective is to design a controller to generate limit cycle with specific behaviours. In this paper, a novel fuzzy control (FC) strategy is introduced to create the limit-cycle for nonlinear complex dynamics with unknown uncertainties. The suggested controller benefits from new interval type-3 fuzzy logic, allowing the control synthesis to improve the quality of closed-loop response and robust performance. The adaptively learned backstepping controller based on FC is employed to analyze the convergence and robustness. Various simulations are proposed to ensure the efficiency of the fuzzy-based control law and adaption rules.

The PID controller is a popular controller that is widely used in various industrial applications. On the other hand, the control problems in microgrids (MGs) are so challenging, because of natural disturbances such as wind speed changes, load variation, and changes in other sources. This paper proposes an input-output scaling factor tuning of interval type-2 fuzzy (IT2F) PID controller using a multi-objective optimization technique. The suggested controller is applied to an MG frequency regulation problem. In the introduced controller the effect of variations of renewable energies (REs) and other disturbances are taken into account, and the robustness is investigated. In the multi-objective scheme, some factors such as least overshoot, and minimum settling/rising time are considered. The simulations show that by considering the suitable adjustment the desired regulation accuracy is achieved, such that the frequency trajectory shows the desired overshoot, and settling/rising time.

To enhance the effect of seismic mitigation in medium-sized buildings, this study introduced a novel friction damper within a braced frame, forming a friction energy-dissipating braced frame (FDBF). The seismic reduction mechanism of the FDBF was examined, and its performance was evaluated through shaking-table tests and finite element simulations. The hysteresis performance of the novel damper was assessed through low-cycle repeated loading tests, which yielded predominantly rectangular and full hysteresis curves, exemplifying robust energy dissipation capacity. The shaking-table tests of the FDBF indicated significant modifications in the dynamic characteristics of the original frame structure, which notably reduced the natural vibration period and enhanced the damping. Additionally, the FDBF remarkably reduced both acceleration and displacement responses during seismic excitation. Optimizing the orientation of the energy dissipation brace significantly improved seismic reduction efficiency. A dynamic time history analysis, employing finite element software, was conducted on the FDBF equipped with a friction energy dissipation brace at each level. Comparative analysis with both the moment-resistant frame and ordinary braced frame revealed that the FDBF substantially lowered the peak acceleration at the apex of the structure, achieving a reduction rate of 40–50%. Under both design and rare earthquake conditions, the FDBF demonstrated superior seismic mitigation capabilities, especially under rare earthquakes. Future studies should investigate various structural types with energy dissipation braces at different levels to identify the most efficient layout for the novel friction energy dissipation brace, thereby guiding relevant engineering practices.

It has been proven that structural damage can be successfully identified using trendlines of structural acceleration responses. In previous numerical and experimental studies, the Savitzky–Golay filter and moving average filter were adjusted to determine suitable trendlines and locate structural damage in a simply supported bridge. In this study, the quadratic regression technique was studied and employed to calculate the trendlines of the bridge acceleration responses. The normalized energies of the resulting trendlines were then used as a damage index to identify the location and severity of the structural bridge damage. An ABAQUS model of a 25 m simply supported bridge under a truckload with different velocities was used to verify the accuracy of the proposed method. The structural damage was numerically modeled as cracks at the bottom of the bridge, so the stiffness at the damage positions was decreased accordingly. Four different velocities from 1 m/s to 8 m/s were used. The proposed method can identify structural damage in noisy environments without monitoring the dynamic modal parameters. Moreover, the accuracy of the newly proposed trendline-based method was increased compared to the previous method. For velocities up to 4 m/s, the damage in all single- and multiple-damage scenarios was successfully identified. For the velocity of 8 m/s, the damage in some scenarios was not located accurately. Additionally, it should be noted that the proposed method can be categorized as an online, quick, and baseline-free structural damage-detection method.

Abstract With loading of different shapes of nanoparticles, the solidification speed can be changed which was scrutinized in current work. Although the nanoparticles dispersion can decline the heat capacity, the conduction mode can be improved with such technique and changing the styles of nano-powders can alter the strength of conduction. The velocity terms were neglected in freezing, thus, the main equations include two equations with unsteady form for scalars of solid fraction and temperature. Grid adaption with position of ice front has been considered in simulations utilizing FEM. The upper sinusoidal and inner rectangular walls maintain cold temperature and freezing starts from these regions. Adding nanomaterial can expedite the process around 15.75% (for m = 4.8) and 29.8% (for m = 8.6). Also, utilizing particles with shapes of blade form can augment the freezing rate around 16.69%. The efficacy of m on freezing process rises around 4% with elevate of concentration of nanoparticles.

Since we are able to use 3D printers, producing porous metal scaffolds become very easy. Contrary to usual methods, 3D printing of porous scaffolds is determined by a controllable and precise manufacturing process. That property allows us to form customized prefabricated implants for individual patients and make a regular pore distribution at the micro-scale as same as the structure of a bone, design of a structure like bone is very complicated because the pores of that structure must have enough space for cell attachment and proliferation. The reaction of cells and bone ingrowth can influence the effect of 3D printed porous metal scaffolds on bone ingrowth. This review introduces 3D printing techniques brief and focuses on the factors that potentially influence bone ingrowth into 3D printed porous metal scaffolds like materials, pore size, porosity, pore structure, surface modification, and mechanical properties. In each section, we described the mechanisms underlying cell-scaffold interactions in detail also there is a short introduction of clinical application of 3D printing. After all, there is a list that shows the most appropriate parameters for a flawless porous metal scaffold, and it is lead to finding a combination of these parameters that foretaste good bone ingrowth.

Using the trendlines of an acceleration response as a tool to decompose a structural response is a new topic that was proposed by authors in 2020. This paper provides a numerical/experimental investigation of using a Savitzky–Golay filter (SGF) in a method to calculate the trendline and decompose building acceleration responses when subjected to a seismic load. Hence, this paper proposes an output-only, energy-based, damage detection method in which the trend lines of a building’s structural acceleration responses are used to locate the damage. For this purpose, an adjusted SGF is utilized to calculate an especial trend line for each floor’s acceleration response of the building structural model. The energy of these trend lines is then calculated and normalized. Two damage indices are used, of which, the second one is being proposed for the first time in this paper. The accuracy of the proposed method is numerically and experimentally investigated using a five-floor building structural model subjected to white noise excitation through a shake table. The results prove that the proposed method is capable of accurately locating and quantifying structural damages with a severity of more than 10% in a noisy environment. In view that the proposed method locates the damage with no need of determining the structural modal properties or parameters, it can be categorized as an online and quick structural damage detection method.

People use trains as a means of transportation to travel to various nearby and distant destinations, resulting in an increasing amount of time spent inside such vehicles. Consequently, railway transportation sectors are focusing significantly more on enhancing passengers’ demand and level of satisfaction, including but not limited to high-speed travel, safety, and riding comfort. This paper provides a state-of-the-art review of available solutions for reducing undesired vibrations that have a substantial impact on improving the critical velocity limits of trains, maintaining the quality of riding comfort, and resolving safety concerns. In this regard, the solutions proposed for train vibration control are divided into two main categories: direct and indirect solutions. The direct solutions are those methods that are applied directly to trains’ bodies or bogies to attenuate the undesired vibrations, for example, improving the suspension/damper system, implementing active/semi-active control strategies, or applying various carriage optimization design modifications. Indirect solutions, on the other hand, are those that could indirectly affect trains’ vibrations, for example, controlling vibrations in railway bridge structures during train passages. Since the identification of vibration characteristics is assumed to be the first step in choosing proper solutions, particularly for active or semi-active control implementations, this review has examined the literature pertinent to modal identification of trains and railway infrastructures. Additionally, despite the installation of dampers between the bogie and suspension, the train’s equation of motion, and consequently, the vibration control of the bogie, has relied on the sky-hook model for decades. The fundamental problem for systems whose control strategies are based on the sky-hook concept is that the ‘sky’ does not actually exist. A solution to solve the sky-hook logic issue is using tuned rotatory inertia dampers and active rotatory inertia drivers which is addressed in this paper as well. Finally, it is concluded that these three solutions – employing a suspension/damper system in the bogie, an elastic connection for the train’s under-chassis-suspended equipment, and a ballastless railway – can practically mitigate vibration and noise in a train.

To simplify the circuit design and control complexity, a wireless charging system with a switchable hybrid topology based on an integrated coil structure is proposed in this paper. Constant current (CC) and constant voltage (CV) outputs under zero phase angle (ZPA) conditions can be achieved at a fixed frequency by simply throwing the compensation element at the receiver. Firstly, the system is modeled using mutual coupling theory, and the parameter configuration conditions for implementing CC or CV outputs under ZPA conditions are analyzed. Then, according to the obtained parameter configuration conditions, the parameters of the switchable hybrid topology are designed. Finally, an experimental prototype with 72 V charging voltage and 4 A charging current is implemented to validate the proposal. The experimental results agree well with the theoretical analysis.

The ever-growing trend of electricity demand and environmental concerns have mandated the operation of electrical energy grids in a more economical and environmentally friendly manner. In the past few years, the integration of combined heat and power units has offered a promising solution to these concerns, however, at the same time a new challenging problem has revealed itself that is finding a simultaneous optimal solution between two competing criteria of power and heat. Furthermore, this problem will be more complex when the reduction of emission gasses is taken into consideration. Thus, to solve optimal scheduling of combined heat and power units, this study proposes an intelligent sequential algorithm based on the hybridization of teaching and learning-based optimization algorithm and an improved version of particle swarm optimization. The proposed algorithm is uniquely capable of the concurrent minimization of total generation costs and multi-pollutant gasses while several physical, operational, and environmental constraints are considered. Also, to ensure the safe maintenance of systems’ constraints, this study employs an adaptive violation constraint handling technique in conjunction with the proposed hybridized optimization algorithm. Finally, the performance of the proposed algorithm is compared to the recently developed methods, in which the proposed algorithm of the study outperforms all the other algorithms and achieves up to 2.2% lower overall costs of operation in most of the studied cases.

The energy absorption capacity of materials with negative Poisson’s ratio (NPR) is attracting interest from both industry and academia due to the excellent impact resistance of the local shrinkage of materials. However, understanding the compressive behavior of 3D auxetic structures at different strain rates and developing design methods are challenging tasks due to the limited literature and insufficient data. This paper presents a study on the behavior of Poisson’s ratio of an advanced 3D chiral structure, which is formed of two orthogonally positioned 2D hexagonal nodes-based chiral structures. Firstly, both theoretical analysis and numerical simulations are conducted to identify the Poisson’s ratio of 2D chiral structures. The same theoretical value of −1 is obtained for 2D chiral structures with a bending-dominated ligaments assumption. Thereafter, the Poisson’s ratio of 3D chiral structures is determined numerically using a low-speed loaded model composed of 5 × 5 × 8 3D unit cells for eliminating the boundary effects. The results show that impact velocity can strongly affect the energy absorption and deformation behavior of the proposed 3D chiral structure. Increasing the beam radius results in reduced energy absorption capability. However, the energy absorption capability of the 3D chiral structure is not sensitive to the yield strength of nodes. Impact direction affects the energy absorption performance of the 3D chiral structure, depending on the crushing strain. The research results could be used to optimize the design of the proposed novel 3D chiral honeycombs for various applications, such as impact energy absorbers and vibration-resistant dampers.

Studying the mechanisms embedded in the immiscible displacement of the two-phase fluids has many engineering and natural implications, like the CO2 geological storage, the secondary or ternary oil and gas recovery and the NAPL remediation in underground formations. The pore-scale phenomena such as Haines jump and capillary filling are recognized in the literatures to have a strong impact on the displacement phenomena. Haines jump is characterized by an abrupt jump of some certain two-phase interfaces while rearranging the configuration of the meniscus of the surrounding pores. In contrast, the capillary filling induces a flat interface movement, where non-wetting phase fluid favors the larger pores due to capillary forces, and vice versa. In this article, we have simulated the drainage process in randomly distributed pore structures with varied pore throat diameters. The Haines jump and capillary filling phenomena are recognized to play a significant role in the saturation and frontal position curves. As a result, Haines jump tends to induce a hop of frontal position given the same saturation, whereas the capillary filling evades pore spaces with a fixed frontal position. In addition, the influence of capillary number and viscosity ratio are thoroughly analyzed. The result shows that higher capillary number and higher viscosity ratio are deemed favorable for the entrapment of non-wetting phase fluid (like CO2). The energy equilibrium study shows around 48% of the external input work is dissipated through these instantaneous irreversible events. Ways to reduce the occurrence of these events will undoubtedly increase the displacement energy efficiency. This work has a direct implication for CO2 geological storage.

Abstract Background Virus-induced gene silencing (VIGS) is a useful tool for functional characterizations of plant genes. However, the penetrance of VIGS varies depending on the genes to be silenced, and has to be evaluated by examining the transcript levels of target genes. Results In this report, we report the development of a novel VIGS vector that permits a preliminary assessment of the silencing penetrance. This new vector is based on an attenuated variant of Turnip crinkle virus (TCV) known as CPB that can be readily used in Arabidopsis thaliana to interrogate genes of this model plant. A CPB derivative, designated CPB1B, was produced by inserting a 46 nucleotide section of the Arabidopsis PHYTOENE DESATURASE (PDS) gene into CPB, in antisense orientation. CPB1B induced robust PDS silencing, causing easily visible photobleaching in systemically infected Arabidopsis leaves. More importantly, CPB1B can accommodate additional inserts, derived from other Arabidopsis genes, causing the silencing of two or more genes simultaneously. With photobleaching as a visual marker, we adopted the CPB1B vector to validate the involvement of DICER-LIKE 4 (DCL4) in antiviral defense against TCV. We further revealed the involvement of ARGONAUTE 2 (AGO2) in PDS silencing and antiviral defense against TCV in dcl2drb4 double mutant plants. These results demonstrated that DOUBLE-STRANDED RNA-BINDING PROTEIN 4 (DRB4), whose protein product (DRB4) commonly partners with DCL4 in the antiviral silencing pathway, was dispensable for PDS silencing induced by CPB1B derivative in dcl2drb4 double mutant plants. Conclusions The CPB1B-based vector developed in this work is a valuable tool with visualizable indicator of the silencing penetrance for interrogating Arabidopsis genes, especially those involved in the RNA silencing pathways.

Collision risk early warning is critical to sailing safety in vessel encounter situations because it provides ship officers with sufficient time to react to emergencies and take evasive actions in advance. In this study, we take spatiotemporal motion behaviors of encountering vessels into account since vessel motion behaviors have great influences on the occurrence of a dangerous situation. For this purpose, a data-driven approach is proposed to associate the motion behaviors with the future risk and early prediction of risk is achieved through classifying the behaviors into corresponding risk level. Specifically, we first derive a sequence of relative motion features between encountering vessels to characterize the spatial interactions that vary over time. Then a novel deep learning architecture, which combines bidirectional long short-term memory (BiLSTM) and attention mechanism, is developed to capture the spatial-temporal dependences of behaviors as well as their impacts on future risk. In particular, the BiLSTM is able to discover correlations among behaviors and the attention mechanism can emphasize the key information relevant to the risk prediction task. Exploiting the advantages of these two mechanisms makes the risk prediction more reasonable and reliable. Extensive experiments using ship trace data from the Yangtze River Estuary demonstrate that the proposed Attention-BiLSTM approach outperforms conventional LSTM in terms of accuracy and stability. Moreover, the real-time capability of the approach gives it a significant potential for use in predicting collisions at the early stages.

In this paper, the main concepts of interval type-2 (IT2), generalized type-2 (GT2), and interval type-3 (IT3) fuzzy logic systems (FLSs) are mathematically and graphically studied. In representation approaches of fuzzy sets (FSs), the main differences between IT2, GT2, and IT3 fuzzy sets were investigated. For the first time, the simple Matlab Simulink and M-files by illustrative examples and symmetrical FSs are presented for the practical use of IT3-FLSs. The computations were simplified for the practical use of IT3-FLSs. By the use of various examples, such as online identification, offline time series modeling, and a robotic control system, the design of IT3-FLSs is elaborated. The required derivative equations are also presented to design the adaptation laws for the rule parameters easily in other learning schemes. Some simulation examples show that the designed M-files and Simulink work well and result in a good performance.

In this paper, the control of robotic manipulators (RMs) is studied. The RMs are widely used in industry. The RMs are multi-input-multi-output systems, and their dynamics are highly nonlinear. To improve the accuracy in practice, it is impossible to ignore the influence of nonlinear dynamics and the interaction of inputs–outputs. Non-structural uncertainties such as friction, disturbance, and unmodeled dynamics are other challenges of these systems. Recently, type-3 (T3) fuzzy logic systems (FLSs) have been suggested that result in better accuracy in a noisy environment. In this paper, a new control idea on the basis of T3-FLSs is suggested. T3-FLSs are used to estimate the dynamics of RMs and the symmetrical perturbations. The T3-FLSs are learned using online laws to enhance the stability. To eliminate the effect of the interconnection of inputs and estimation errors, a compensator is developed. By several simulations, the superiority of the suggested controller is demonstrated.

A study on the first natural frequency of rotating nanocomposite beams is addressed in this research. The beam has been reinforced with carbon nanotube. With the purpose of incorporating the shearability of the beam, the Reddy's third order shear deformation theory has been assigned. The motion equations are discretized in space employing the well-know Ritz-technique. For the sake of study on the first natural frequency, the nonlinear in time discretized equations of motion are linearized around the static deformation induced by the centrifugal force. The linearized discretized governing equations around the post-buckling state are exploited to find the first natural frequency after the buckling incidence. The outcomes reveal that although a stationary free-clamped(simply-clamped) beam and a stationary clamped-free(clamped-simply) beam treat similarly in the analyses, the associated rotating beams demonstrate a qualitative discrepancy. Moreover, the outcomes based upon the Reddy's third order shear deformation theory deviate from the results in regard of the Timoshenko beam theory approximately below the length to the height of the beam equal to 12 for CS FGX CNTRC beams. Nevertheless, for the FGO CNTRC beams the results in reference to the both mentioned theories approximately are consistent to each other.

Dynamic behavior of a new class of nanocomposites consisted of metal foam as matrix and graphene oxide powders as reinforcement is presented in this study in the framework of forced vibration. Graphene oxide powders are dispersed through the thickness of a plate made from metal foam material according to four various functionally graded patterns on the basis of the Halpin-Tsai micromechanical homogenization method. Also, three kinds of porosity distributions including two symmetric and one uniform patterns are considered for the metal foam matrix. As external effects, the plate is rested on the Winkler-Pasternak substrate and under uniform thermal and transverse dynamic loadings. By an incorporation of the refined higher order plate theory and Hamilton's principle, the governing equations of the dynamically loaded graphene oxide powder reinforced metal foam nanocomposite plate are derived and then solved with Galerkin exact solution method to achieve the resonance frequencies and dynamic deflections of the structure. Moreover, the influence of different boundary conditions is taken into account. The results indicate that the forced vibrational response of the graphene oxide powder strengthened metal foam nanocomposite plate is dramatically dependent on various parameters such as graphene oxide powders' weight fraction, different boundary conditions, various porosity distributions, foundation parameters and temperature change of uniform thermal loading.

In the area of the heating industry, a heat pump is an efficient alternative technology to achieve energy saving and carbon emission reduction. The conventional heat pump has gradually been applied to replace the traditional direct electrical heating method while the required temperature is below 100 °C. A heat pump with temperatures between 100–140 °C is in the stage of rapid development. However, a heat pump with temperatures above 150 °C has received relatively little attention. In this paper, two systems combining a heat pump and water vapor compression (CHPVC and HPTVC) have been studied for waste heat recovery from 45 °C to a water vapor supply with a temperature above 150 °C. A thermodynamic model has been proposed to analyze the performance of the two systems, and a twin-screw compressor model has been developed to calculate the isentropic efficiency of the compressor applied in the heat pump. Four different parameters have been used to analyze the energy efficiency. The simulation results show that while the inlet water temperature is 45 °C and the required vapor temperature is 150 °C, the optimal COPs of CHPVC and HPTVC are 2.432 and 2.436, respectively. Moreover, CHPVC is more suitable for the large saturation temperature lift, and HPTVC is more suitable for a relatively small temperature difference between the inlet water and the required vapor. Compared with the direct electrical heating method or the conventional two-stage heat pump, these two systems are remarkably efficient and show good energy-saving potential.

Chaos dynamics is an interesting nonlinear effect that can be observed in many chemical, electrical, and mechanical systems. The chaos phenomenon has many applications in various branches of engineering. On the other hand, the control of mobile robots to track unpredictable chaotic trajectories has a valuable application in many security problems and military missions. The main objective in this problem is to design a controller such that the robot tracks a desired chaotic path. In this paper, the concept of synchronization of chaotic systems is studied, and a new type-3 fuzzy system (T3FLS)-based controller is designed. The T3FLS is learned by some new adaptive rules. The new learning scheme of T3FLS helps to better stabilize and synchronize. The suggested controller has a better ability to cope with high-level uncertainties. Because, in addition to the fact that the T3FLSs have better ability in an uncertain environment, the designed compensator also improves the accuracy and robustness. Several simulations show better synchronization and control accuracy of the designed controller.

A compact temperature-refractive index (RI) flat photonic crystal fiber (PCF) sensor based on surface plasmon resonance (SPR) is presented in this paper. Sensing of temperature and RI takes place in the x- and y- polarization, respectively, to avoid the sensing crossover, eliminating the need for matrix calculation. Simultaneous detection of dual parameters can be implemented by monitoring the loss spectrum of core modes in two polarizations. Compared with the reported multi-function sensors, the designed PCF sensor provides higher sensitivities for both RI and temperature detection. A maximum wavelength sensitivity of −5 nm/°C is achieved in the temperature range of −30–40 °C. An excellent optimal wavelength sensitivity of 17,000 nm/RIU is accomplished in the RI range of 1.32–1.41. The best amplitude sensitivity of RI is up to 354.39 RIU−1. The resolution of RI and temperature sensing is 5.88 × 10−6 RIU and 0.02 °C, respectively. The highest value of the figure of merit (FOM) is 216.74 RIU−1. In addition, the flat polishing area of the gold layer reduces the manufacturing difficulty. The proposed sensor has the characteristics of high sensitivity, simple structure, good fabrication repeatability, and flexible operation. It has potential in medical diagnosis, chemical inspection, and many other fields.

After wars, some unexploded bombs remained underground, and these faulty bombs seriously threaten the safety of people. The ability to accurately identify targets is crucial for subsequent mining work. A deep learning algorithm is used to recognize targets, which significantly improves recognition accuracy compared with the traditional recognition algorithm for measuring the magnetic moment of the target and the included geomagnetism angle. In this paper, a ResNet-18-based recognition system is presented for classifying metallic object types. First, a fluxgate magnetometer cube arrangement structure (FMCAS) magnetic field feature collector is constructed, utilizing an eight-fluxgate magnetometer sensor array structure that provides a 400 mm separation between each sensitive unit. Magnetic field data are acquired, along an east–west survey line on the northern side of the measured target using the FMCAS. Next, the location and type of targets are modified to create a database of magnetic target models, increasing the diversity of the training dataset. The experimental dataset is constructed by constructing the magnetic flux density tensor matrix. Finally, the enhanced ResNet-18 is used to train the data for the classification recognition recognizer. According to the test findings of 107 validation set groups, this method’s recognition accuracy is 84.1 percent. With a recognition accuracy rate of 96.3 percent, a recall rate of 96.4 percent, and a precision rate of 96.4 percent, the target with the largest magnetic moment has the best recognition impact. Experimental findings demonstrate that our enhanced RestNet-18 network can efficiently classify metallic items. This provides a new idea for underground metal target identification and classification.

Image interpolation is an essential process for image processing and computer graphics in wide applications to medical imaging. For image interpolation used in medical diagnosis, the two-dimensional (2D) to three-dimensional (3D) transformation can significantly reduce human error, leading to better decisions. This research proposes the type-2 fuzzy neural networks method which is a hybrid of the fuzzy logic and neural networks as well as recurrent type-2 fuzzy neural networks (RT2FNNs) for advancing a novel 2D to 3D strategy. The ability of the proposed methods in the approximation of the function for image interpolation is investigated. The results report that both proposed methods are reliable for medical diagnosis. However, the RT2FNN model outperforms the type-2 fuzzy neural networks model. The average squares error for the recurrent network and the typical network reported 0.016 and 0.025, respectively. On the other hand, the number of fuzzy rules for the recurrent network and the typical network reported 16 and 22, respectively.

The current study is aimed to investigate the thermo-mechanical buckling responses of the sandwich microplate with Graphene nanoplatelets (GNPs)-reinforced Epoxy core fully bonded to single-walled carbon nanotubes (SWCNTs)-reinforced piezoelectric patches. The face sheets were exposed to the electric field and the microplate was assumed to locate on the Pasternak elastic substrate. The GNPs and SWCNTs were dispersed throughout the core and face thickness according to some specific functions. To consider the shear deformation effect, tangential shear deformation theory (TSDT), as a trigonometric higher-order theory, was used. The size effects were also included through applying and the modified strain gradient theory (MSGT). The virtual displacement principle was utilized to derive the governing equations and then by employing an analytical method, they were solved. The validity of the results was confirmed by comparing the results for simpler state with previously published ones. A parametric study is provided to observe behavior of the microstructure in different conditions. It was observed that GNPs and CNTs dispersion patterns play an important role in the microplate buckling behavior, as well as temperature variations. The outcomes of this work may help to manufacture more efficient engineering smart structures, such as micro/nanoelectromechanical systems.

We present a cross-disciplinary approach applied to the strength prediction and correlation of cement composites in this paper. The empirical models for predicting flexural and compressive strengths of a kind of fiber reinforced cement composites, known as engineered cementitious composites (ECCs), were first developed. Nonlinear regressions were applied to the measurement data of a destructive compression test and four-point bending test. Each of these tests was conducted on 36 specimens at growing ages (3, 7, 14, and 28 days), wherein three mixes of ECC (PE1, PE2, and PE3) were prepared for each test. Each mix had different water-to-binder (w/b) ratios of 0.20, 0.255, and 0.30 whereas the total volume of each mix remained the same. The correlations among flexural strength, compressive strength, and electrical conductance of ECCs were then developed based on the early-age measurement using the microwave non-destructive test (mDNT). The primary objective of this research is to study the relations between flexural strength and compressive strength of ECC specimens under the influence of small deviations in w/b ratio. The secondary objective is to predict the strength growth of ECCs with the microwave effective conductance. The correlation results show that the flexural strength behaves as a quasi-linear function with increasing compressive strength, which has been verified by using direct measurement data. This cross-disciplinary approach highlights the effectiveness of the mDNT in predicting the mature strengths of ECCs at early ages.

The system performance of the supercritical CO2 Brayton cycle for the Sodium Fast Reactor with a partial-cooling layout was studied, and an economic analysis was carried out. The energetic, exergetic, and exergoeconomic analyses are presented, and the optimized results were compared with the recompression cycle. The sensitivity analyses were conducted by considering the variations in the pressure ratios and inlet temperatures of the main compressor and the turbine. The exergy efficiency of the partial-cooling cycle reached 63.65% with a net power output of 34.39 MW via optimization. The partial-cooling cycle obtained a minimum total cost rate of 2230.36 USD/h and exergy efficiency of 63.65% when the pressure ratio was equal to 3.50. The inlet temperature of the main compressor was equal to 35 °C, and the inlet temperature of the turbine was equal to 480 °C. The total cost of recuperators decreased with the increase in the pressure ratio and the inlet temperatures of the main compressor. In addition, the total cost of recuperator could be reduced by increasing the outlet temperature of the turbine. The change in cost from exergy loss and destruction with the pressure ratio was substantially larger than with the inlet temperature of the turbine or the main compressor. Manipulating the pressure ratio is an essential method to guarantee good economy of the system. Moreover, capital investment, operation, and maintenance costs normally accounted for large proportions of the total cost rate, being almost double the cost from the exergy loss and destruction occurring in each condition.

Encounter risk prediction is critical for safe ship navigation, especially in congested waters, where ships sail very near to each other during various encounter situations. Prior studies on the risk of ship collisions were unable to address the uncertainty of the encounter process when ignoring the complex motions constituting the dynamic ship encounter behavior, which may seriously affect the risk prediction performance. To fill this gap, a novel AIS data-driven approach is proposed for ship encounter risk prediction by modeling intership behavior patterns. In particular, multidimensional features of intership behaviors are extracted from the AIS trace data to capture spatial dependencies between encountering ships. Then, the challenging task of risk prediction is to discover the complex and uncertain relationship between intership behaviors and future collision risk. To address this issue, we propose a deep learning framework. To represent the temporal dynamics of the encounter process, we use the sliding window technique to generate the sequences of behavioral features. The collision risk level at a future time is taken as the class label of the sequence. Then, the long short-term memory network, which has a strong ability to model temporal dependency and complex patterns, is extended to establish the relationship. The benefit of our approach is that it transforms the complex problem for risk prediction into a time series classification task, which makes collision risk prediction reliable and easier to implement. Experiments were conducted on a set of naturalistic data from various encounter scenarios in the South Channel of the Yangtze River Estuary. The results show that the proposed data-driven approach can predict future collision risk with high accuracy and efficiency. The approach is expected to be applied for the early prediction of encountering ships and as decision support to improve navigation safety.

In this paper, we introduce a reconfigurable artistic patch antenna with unidirectional circular-polarization (CP) radiation. The novel geometry composed of a Chinese character Meng shaped patch and dual L-shaped feeding probes that are connected to a PIN-diode-based switching circuit. The simulated and experimental results show that the proposed antenna achieves the operating bandwidth from 2.4 to 2.5 GHz that covers dynamically the 14 channels of IEEE 802.11/b/g/b/ax. By switching the L-probes, either left-handed CP or right-handed CP can be generated to cover different seven-channel blocks in order to enhance the channel efficiency. A broadside realized gain varying between 5-6 dBic and an efficiency of -0.5dB were recorded. The proposed antenna holds an overall dimension of 0.72λ0 × 0.72λ0 × 0.12λ0 with an aperture efficiency of 59% at 2.45 GHz.

This paper discusses a global buckling analysis approach for sandwich plates with stiffening ribs. The approach is based on theoretical study and is implemented by the finite element method (FEM). The equilibrium equation corresponding to critical global buckling of the sandwich plate with stiffening ribs under simple supported boundary condition is established by the energy method. The critical buckling solutions for a typical rectangular sandwich plate system (SPS) with a single stiffening rib in the longitudinal direction are then investigated while varying the potential influencing factors. The shear rigidity within the inner core exerts little effect on global buckling and can be neglected. An FEM study on elastic buckling was then conducted via ANSYS software. The advantages of the SPS were highlighted via its elastic eigenvalue buckling numerical analysis with multiple stiffeners. The ultimate buckling loads were computed similarly for different influential factors. Finally, an SPS specimen was tested in a compression test. The results showed that when the rib spacing is large, the local buckling of the plates in the grillage is controllable and the SPS is more resistant to both local and global buckling. The results based on our theoretical method agreed well with those of the FEM and experimental results.

Damage characteristics and dynamic stress response of aging masonry structures for blast-induced ground motion were performed using high-order local modal analysis method. A complete investigation of damage types and locations of aging masonry buildings due to tunnel blasting vibration were performed by on-site survey. A typical 2-storey aging masonry building located above a tunnel was selected for dynamic response analysis. The experimental dynamic characteristics of the structure were determined by using the operational modal analysis (OMA) method. Finite element models for the masonry structures were updated by modifying material parameters based on OMA results. The first five natural frequencies of the updated finite element models ranged from 8.80–24.99 Hz, and the first five modes were global modes. The sixth to twentieth natural frequencies ranged from 26.10–36.34 Hz, and the sixth to twentieth modes were local modes whose deformation was greater than the global deformation. Since the principal frequencies of the tunnel blast vibration were mostly higher than the natural global modes’ frequencies and were much closer to the natural frequencies of local members, local members experienced more intensive vibrations compared to the main body structure. The principal compressive stress (PCS) and principal tensile stress (PTS) of local members were several times greater than that of the main body structure. Therefore, local members of the masonry building suffered most from the tunnel blasting vibration. Corners due to stress concentration, the contact area between brick and concrete, local members, and precast floor seams are prone to damage during tunnel blasting. With the vibration velocity increasing, the PCS and PTS of local members gradually increase. But, the PTS ratio of local members decreases with the increase of peak particle velocities. The dynamic response analysis result and the damage locations using high-order local modal analysis method are in accordance with the damage found at the site.

CO2 enhanced oil recovery (CO2-EOR) technology is a competitive strategy to improve oil field economic returns and reduce greenhouse gas emissions. However, the arbitrary emissions or combustion of the associated gas, which mainly consists of CO2 and CH4, will cause the aggravation of the greenhouse effect and a huge waste of resources. In this paper, the high-performance facilitated transport multilayer composite membrane for CO2/CH4 separation was prepared by individually adjusting the membrane structure of each layer. The effect of test conditions on the CO2/CH4 separation performance was systematically investigated. The membrane exhibits high CO2 permeance of 3.451 × 10−7 mol·m−2·s−1·Pa−1 and CO2/CH4 selectivity of 62 at 298 K and 0.15 MPa feed gas pressure. The cost analysis was investigated by simulating the two-stage system. When the recovery rate and purity of CH4 are 98%, the minimum specific cost of separating CO2/CH4 (45/55 vol%) can be reduced to 0.046 $·Nm−3 CH4. The excellent short-to-mid-term stability indicates the great potential of large industrial application in the CH4 recovery and CO2 reinjection from oilfield associated gas.

Background/Aims: Mesenchymal stem cell (MSC) transplantation has emerged as an option for the treatment of chronic hepatic cirrhosis, while its therapeutic efficacy could be improved. The bcl-2 gene is anti-apoptotic and can help cell survival and proliferation. Therefore, we explored whether transplanted MSCs with enhanced bcl-2 expression may be beneficial in the treatment of experimental cirrhosis in rats. Methods: MSCs were isolated from rat bone marrow, expanded in vitro and transfected with adeno-associated virus (AAV) engineered the bcl-2 gene (AAV-bcl-2). Rats with cirrhosis induced by carbon tetrachloride (CCl4) were treated with AAV-bcl-2 infected BMSCs-AAV-bcl-2, with the cells traced in vivo post transplantation. Liver pathology and function were evaluated 7, 14, 21, and 28 days post transplantation, respectively. Results: On day 7 post transplantation, the infused AAV-bcl-2 had integrated into the hepatocyte-like cells (HLCs) that expressed albumin (ALB), Cytokeratin 18 (CK18), and hepatocytes nuclear factor 4a (HNF4a). On day 28 post transplantation, rats in the cirrhosis + BMSCs-AAV-bcl-2 group showed the most dense HLCs, highest mRNA and protein levels of ALB, CK18, and HNF4a, compared to the other groups. Their liver function recovered most rapidly in 4 week observation, while histological sign of cirrhosis remained at the end of this period. Conclusion: BMSCs over expressing bcl-2 gene showed better survival, and enhanced the differentiation into hepatocytes-like cells, and appeared to promote the recovery of liver function in rats with experimental cirrhosis.

The preparation of rhenium powder by a hydrogen reduction of ammonium perrhenate is the only industrial production method. However, due to the uneven particle size distribution and large particle size of rhenium powder, it is difficult to prepare high-density rhenium ingot. Moreover, the existing process requires a secondary high-temperature reduction and the deoxidization process is complex and requires a high-temperature resistance of the equipment. Attempting to tackle the difficulties, this paper described a novel process to improve the particle size distribution uniformity and reduce the particle size of rhenium powder, aiming to produce a high-density rhenium ingot, and ammonium perrhenate is completely reduced by hydrogen at a low temperature. When the particle size of the rhenium powder was 19.74 µm, the density of the pressed rhenium ingot was 20.106 g/cm3, which was close to the theoretical density of rhenium. In addition, the hydrogen reduction mechanism of ammonium perrhenate was investigated in this paper. The results showed that the disproportionation of ReO3 decreased the rate of the reduction reaction, and the XRD and XPS patterns showed that the increase in the reduction temperature was conducive to increasing the reduction reaction rate and reducing the influence of disproportionation on the reduction process. At the same reduction temperature, reducing the particle sizes of ammonium perrhenate was conducive to increasing the hydrogen reduction rate and reducing the influence of the disproportionation.