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Although combination antiretroviral therapy (ART) has been a landmark achievement for the treatment of human immunodeficiency virus (HIV), an HIV cure has remained elusive. Elimination of latent HIV reservoirs that persist throughout HIV infection is the most challenging barrier to an HIV cure. The progressive HIV infection is marked by the increasing size and diversity of latent HIV reservoirs until an effective immune response is mobilized, which can control but not eliminate HIV infection. The stalemate between HIV replication and the immune response is manifested by the establishment of a viral set point. ART initiation during the early stage limits HIV reservoir development, preserves immune function, improves the quality of life, and may lead to ART-free viral remission in a few people living with HIV (PLWH). However, for the overwhelming majority of PLWH, early ART initiation alone does not cure HIV, and lifelong ART is needed to sustain viral suppression. A critical area of research is focused on determining whether HIV could be functionally cured if additional treatments are provided alongside early ART. Several HIV interventions including Block and Lock, Shock and Kill, broadly neutralizing antibody (bNAb) therapy, adoptive CD8+ T cell therapy, and gene therapy have demonstrated delayed viral rebound and/or viral remission in animal models and/or some PLWH. Whether or not their application during early infection can improve the success of HIV remission is less studied. Herein, we review the current state of clinical and investigative HIV interventions and discuss their potential to improve the likelihood of post-treatment remission if initiated during early infection.

In order to reveal the mechanism of sleeved stud connectors, 15 push-out specimens were designed, and static loading tests were conducted to evaluate the mechanical performance. The shear performance differences between the novel sleeved studs and conventional welded studs were compared. Referring to the experimental results, an Abaqus nonlinear finite element model was established to study the shear mechanism of sleeved stud connectors. Parametric analysis was conducted to investigate the effects of stud height, sleeve filling material, and sleeve diameter on the mechanical performance of the connectors. The experimental and finite element analysis results indicated that the ultimate shear bearing capacity and shear stiffness of the sleeved stud connectors were higher than those of ordinary welded studs, and the maximum slip was relatively small. Compared to conventional welded studs, the ultimate bearing capacity of sleeved studs increased by 4% to 8%, and the shear stiffness increased by 25% to 35%. Since the shear behavior of sleeved studs mainly occurred at the base of the studs, the influence of stud height on shear performance was relatively small. However, sleeve and stud diameter have a great influence on bearing capacity and stiffness. As the Ultra-High Performance Concrete (UHPC) near the base of the stud effectively enhanced the shear carrying capacity of the sleeved stud connectors, the shear carrying capacity and shear stiffness increased with the increase in the sleeve diameter.

A long-term intake of a high-fat and high-fructose diet (HFFD), even a high-fat, high-fructose but low-protein diet (HFFD + LP), could cause obesity associated with cognitive impairments. In the present study, rats were subjected to a normal diet (ND), an HFFD diet, an HFFD + LP diet, and an HFFD with kidney bean protein (KP) diet for 8 weeks to evaluate the effect of KP on HFFD- or HFFD + LP-induced obesity and cognitive impairment. The results demonstrated that compared with the HFFD diet, KP administration significantly decreased the body weight by 7.7% and the serum Angiotensin-Converting Enzyme 2 (ACE-2) and Insulin-like Growth Factor 1 (IGF-1) levels by 14.4% and 46.8%, respectively (p < 0.05). In addition, KP suppressed HFFD-induced cognitive impairment, which was evidenced by 8.7% less time required to pass the water maze test. The 16s RNA analysis of the colonic contents showed that the relative abundance of Bifidobacterium, Butyricimonas, and Alloprevotella was increased by KP by 5.9, 44.2, and 79.2 times. Additionally, KP supplementation primarily affected the choline metabolic pathway in the liver, and the synthesis and functional pathway of neurotransmitters in the brain, thereby improving obesity and cognitive function in rats.

One-step carbonization was explored to prepare biochar using the residue of a traditional Chinese herbal medicine, Atropa belladonna L. (ABL), as the raw material. The resulting biochar, known as ABLB4, was evaluated for its potential as a sustainable material for norfloxacin (NOR) adsorption in water. Subsequently, a comprehensive analysis of adsorption isotherms, kinetics, and thermodynamics was conducted through batch adsorption experiments. The maximum calculated NOR adsorption capacity was 252.0 mg/g at 298 K, and the spontaneous and exothermic adsorption of NOR on ABLB4 could be better suited to a pseudo-first-order kinetic model and Langmuir model. The adsorption process observed is influenced by pore diffusion, π–π interaction, electrostatic interaction, and hydrogen bonding between ABLB4 and NOR molecules. Moreover, the utilization of response surface modeling (RSM) facilitated the optimization of the removal efficiency of NOR, yielding a maximum removal rate of 97.4% at a temperature of 304.8 K, an initial concentration of 67.1 mg/L, and a pH of 7.4. Furthermore, the biochar demonstrated favorable economic advantages, with a payback of 852.5 USD/t. More importantly, even after undergoing five cycles, ABLB4 exhibited a consistently high NOR removal rate, indicating its significant potential for application in NOR adsorption.

Causality has become a powerful tool for addressing the out-of-distribution (OOD) generalization problem, with the idea of invariant causal features across domains of interest. Most existing methods for learning invariant features are based on optimization, which typically fails to converge to the optimal solution. Therefore, obtaining the variables that cause the target outcome through a causal inference method is a more direct and effective method. This paper presents a new approach for invariant feature learning based on causal inference (IFCI). IFCI detects causal variables unaffected by the environment through the causal inference method. IFCI focuses on partial causal relationships to work efficiently even in the face of high-dimensional data. Our proposed causal inference method can accurately infer causal effects even when the treatment variable has more complex values. Our method can be viewed as a pretreatment of data to filter out variables whose distributions change between different environments, and it can then be combined with any learning method for classification and regression. The result of empirical studies shows that IFCI can detect and filter out environmental variables affected by the environment. After filtering out environmental variables, even a model with a simple structure and common loss function can have strong OOD generalization capability. Furthermore, we provide evidence to show that classifiers utilizing IFCI achieve higher accuracy in classification compared to existing OOD generalization algorithms.

Shield misalignment is a common problem in shield tunnels, which seriously affects the safety and durability of tunnels. However, at present, there is a lack of research on the influence of shield misalignment on the shear capacity of the circumferential joint structure, and the failure mechanism of the circumferential joint structure before and after reinforcement is not clear. Therefore, this paper simulates the influence of misalignment on the performance mechanism of segmented circumferential connection and the effect of channel reinforcement on the ABAQUS platform. The simulation results are compared with the full-scale test results, and the results show that the shear failure process of the circumferential joint can be divided into three stages under the condition of no reinforcement. In the first stage, the vertical load increases, but the misalignment between the shield tunneling sections is very small. In the second stage, the load almost does not increase, but the degree of misalignment increases. In the third stage, the load–displacement relationship is nonlinear, indicating that the bending bolt has been sheared. Under the condition of unreinforced, the bolt will form two plastic hinges when it fails. After reinforcing the channel, the removal of the bolt forms only one plastic hinge. After channel steel reinforcement, the boundary area between the channel steel web and the steel plate first reaches the ultimate tensile strength of the steel plate, and the failure mode becomes channel steel reinforcement failure. Under the same shear load, the misalignment of the circumferential joint reinforced with channel steel is reduced. In this paper, the misalignment relationship of shear load and the yield of the bending bolt obtained through numerical calculation is consistent with the conclusion of the full-scale test. However, the circumferential connection misalignment obtained via numerical calculation is relatively small. The yield position of the bending bolt is also in good agreement with the test results, and the bolt strain obtained through the test is relatively small.

Disordered immunity, aging, human immunodeficiency virus type one (HIV-1) infection, and responses to antiretroviral therapy are linked. However, how each factor is linked with the other(s) remains incompletely understood. It has been reported that accelerated aging, advanced HIV-1 infection, inflammation, and host genetic factors are associated with host cellular, mitochondrial, and metabolic alterations. However, the underlying mechanism remains elusive. With these questions in mind, we used chronically HIV-1-infected CD34-NSG humanized mice (hu-mice) to model older people living with HIV and uncover associations between HIV-1 infection and aging. Adult humanized mice were infected with HIV-1 at the age of 20 weeks and maintained for another 40 weeks before sacrifice. Animal brains were collected and subjected to transcriptomics, qPCR, and immunofluorescence assays to uncover immune disease-based biomarkers. CD4+ T cell decline was associated with viral level and age. Upregulated C1QA, CD163, and CXCL16 and downregulated LMNA and CLU were identified as age-associated genes tied to HIV-1 infection. Ingenuity pathway analysis affirmed links to innate immune activation, pyroptosis signaling, neuroinflammation, mitochondrial dysfunction, cellular senescence, and neuronal dysfunction. In summary, CD34-NSG humanized mice are identified as a valuable model for studying HIV-1-associated aging. Biomarkers of immune senescence and neuronal signaling are both age- and virus-associated. By exploring the underlying biological mechanisms that are linked to these biomarkers, interventions for next generation HIV-1-infected patients can be realized.

Methamphetamine (METH) abuse inflicts both physical and psychological harm. While our previous research has established the regulatory role of miR-29c-3p in behavior sensitization, the underlying mechanisms and target genes remain incompletely understood. In this study, we employed the isobaric tags for relative and absolute quantitation (iTRAQ) technique in conjunction with Ingenuity pathway analysis (IPA) to probe the putative molecular mechanisms of METH sensitization through miR-29c-3p inhibition. Through a microinjection of AAV-anti-miR-29c-3p into the nucleus accumbens (NAc) of mice, we observed the attenuation of METH-induced locomotor effects. Subsequent iTRAQ analysis identified 70 differentially expressed proteins (DEPs), with 22 up-regulated potential target proteins identified through miR-29c-3p target gene prediction and IPA analysis. Our focus extended to the number of neuronal branches, the excitatory synapse count, and locomotion-related pathways. Notably, GPR37, NPC1, and IREB2 emerged as potential target molecules for miR-29c-3p regulation, suggesting their involvement in the modulation of METH sensitization. Quantitative PCR confirmed the METH-induced aberrant expression of Gpr37, Npc1, and Ireb2 in the NAc of mice. Specifically, the over-expression of miR-29c-3p led to a significant reduction in the mRNA level of Gpr37, while the inhibition of miR-29c-3p resulted in a significant increase in the mRNA level of Gpr37, consistent with the regulatory principle of miRNAs modulating target gene expression. This suggests that miR-29c-3p potentially influences METH sensitization through its regulation of neuroplasticity. Our research indicates that miR-29c-3p plays a crucial role in regulating METH-induced sensitization, and it identified the potential molecular of miR-29c-3p in regulating METH-induced sensitization.

Intestinal flora plays an important role in maintaining the internal stability and health of the intestine. Currently, intestinal microbes are considered an important “organ” but are mostly ignored by people. This study evaluated the flora structure of each intestinal segment of blue foxes pre-weaning and explored the differences between the fecal flora and intestinal flora structure of each segment after weaning. Samples of intestinal contents from three blue foxes at 45 days of age (before weaning) and intestinal contents and feces samples from at 80 days (after weaning) were collected for 16s rRNA flora analysis. The species and distribution characteristics of microorganisms in different intestinal segments of blue foxes before and after weaning were different. Except for the rectum, the dominant flora of each intestinal segment of blue fox changed significantly after experiencing weaning, and the fecal flora structure of young fox at the weaning stage did not represent the whole intestinal flora structure but was highly similar to that of the colon and rectum. To sum up, the intestinal flora of blue foxes changed systematically before and after weaning. When performing non-invasive experiments, the microflora structure of the colon and rectum of blue foxes can be predicted by collecting fecal samples.

As human–robot interaction and teleoperation technologies advance, anthropomorphic control of humanoid arms has garnered increasing attention. However, accurately translating sensor-detected arm motions to the multi-degree freedom of a humanoid robotic arm is challenging, primarily due to occlusion issues with single-sensor setups, which reduce recognition accuracy. To overcome this problem, we propose a human-like arm control strategy based on multi-sensor fusion. We defined the finger bending angle to represent finger posture and employed a depth camera to capture arm movement. Consequently, we developed an arm movement tracking system and achieved anthropomorphic control of the imitation human arm. Finally, we verified our proposed method’s effectiveness through a series of experiments, evaluating the system’s robustness and real-time performance. The experimental results show that this control strategy can control the motion of the humanoid arm stably, and maintain a high recognition accuracy in the face of complex situations such as occlusion.

In practical applications, learning models that can perform well even when the data distribution is different from the training set are essential and meaningful. Such problems are often referred to as out-of-distribution (OOD) generalization problems. In this paper, we propose a method for OOD generalization based on causal inference. Unlike the prevalent OOD generalization methods, our approach does not require the environment labels associated with the data in the training set. We analyze the causes of distributional shifts in data from a causal modeling perspective and then propose a backdoor adjustment method based on variational inference. Finally, we constructed a unique network structure to simulate the variational inference process. The proposed variational backdoor adjustment (VBA) framework can be combined with any mainstream backbone network. In addition to theoretical derivation, we conduct experiments on different datasets to demonstrate that our method performs well in prediction accuracy and generalization gaps. Furthermore, by comparing the VBA framework with other mainstream OOD methods, we show that VBA performs better than mainstream methods.

Most failures in steel materials are due to fatigue damage, so it is of great significance to analyze the key features of fatigue strength (FS) in order to improve fatigue performance. This study collected data on the fatigue strength of steel materials and established a predictive model for FS based on machine learning (ML). Three feature-construction strategies were proposed based on the dataset, and compared on four typical ML algorithms. The combination of Strategy Ⅲ (composition, heat-treatment, and atomic features) and the GBT algorithm showed the best performance. Subsequently, input features were selected step by step using methods such as the analysis of variance (ANOVA), embedded method, recursive method, and exhaustive method. The key features affecting FS were found to be TT, mE, APID, and Mo. Based on these key features and Bayesian optimization, an ML model was established, which showed a good performance. Finally, Shapley additive explanations (SHAP) and symbolic regression (SR) are introduced to improve the interpretability of the prediction model. It had been discovered through SHAP analysis that TT and Mo had the most significant impact on FS. Specifically, it was observed that 160 < TT < 500 and Mo > 0.15 was beneficial for increasing the value of FS. SR was used to establish a significant mathematical relationship between these key features and FS.

The main goal of this research is to develop a highly advanced anthropomorphic control system utilizing multiple sensor technologies to achieve precise control of a robotic arm. Combining Kinect and IMU sensors, together with a data glove, we aim to create a multimodal sensor system for capturing rich information of human upper body movements. Specifically, the four angles of upper limb joints are collected using the Kinect sensor and IMU sensor. In order to improve the accuracy and stability of motion tracking, we use the Kalman filter method to fuse the Kinect and IMU data. In addition, we introduce data glove technology to collect the angle information of the wrist and fingers in seven different directions. The integration and fusion of multiple sensors provides us with full control over the robotic arm, giving it flexibility with 11 degrees of freedom. We successfully achieved a variety of anthropomorphic movements, including shoulder flexion, abduction, rotation, elbow flexion, and fine movements of the wrist and fingers. Most importantly, our experimental results demonstrate that the anthropomorphic control system we developed is highly accurate, real-time, and operable. In summary, the contribution of this study lies in the creation of a multimodal sensor system capable of capturing and precisely controlling human upper limb movements, which provides a solid foundation for the future development of anthropomorphic control technologies. This technology has a wide range of application prospects and can be used for rehabilitation in the medical field, robot collaboration in industrial automation, and immersive experience in virtual reality environments.

As the fourth paradigm of materials research and development, the materials genome paradigm can significantly improve the efficiency of research and development for austenitic stainless steel. In this study, by collecting experimental data of austenitic stainless steel, the chemical composition of austenitic stainless steel is optimized by machine learning and a genetic algorithm, so that the production cost is reduced, and the research and development of new steel grades is accelerated without reducing the mechanical properties. Specifically, four machine learning prediction models were established for different mechanical properties, with the gradient boosting regression (gbr) algorithm demonstrating superior prediction accuracy compared to other commonly used machine learning algorithms. Bayesian optimization was then employed to optimize the hyperparameters in the gbr algorithm, resulting in the identification of the optimal combination of hyperparameters. The mechanical properties prediction model established at this stage had good prediction accuracy on the test set (yield strength: R2 = 0.88, MAE = 4.89 MPa; ultimate tensile strength: R2 = 0.99, MAE = 2.65 MPa; elongation: R2 = 0.84, MAE = 1.42%; reduction in area: R2 = 0.88, MAE = 1.39%). Moreover, feature importance and Shapley Additive Explanation (SHAP) values were utilized to analyze the interpretability of the performance prediction models and to assess how the features influence the overall performance. Finally, the NSGA-III algorithm was used to simultaneously maximize the mechanical property prediction models within the search space, thereby obtaining the corresponding non-dominated solution set of chemical composition and achieving the optimization of austenitic stainless-steel compositions.

Deep-learning-based SAR ship classification has become a research hotspot in the military and civilian fields and achieved remarkable performance. However, the volume of available SAR ship classification data is relatively small, meaning that previous deep-learning-based methods have usually struggled with overfitting problems. Moreover, due to the limitation of the SAR imaging mechanism, the large intraclass diversity and small interclass similarity further degrade the classification performance. To address these issues, we propose a label smoothing auxiliary classifier generative adversarial network with triplet loss (LST-ACGAN) for SAR ship classification. In our method, an ACGAN is introduced to generate SAR ship samples with category labels. To address the model collapse problem in the ACGAN, the smooth category labels are assigned to generated samples. Moreover, triplet loss is integrated into the ACGAN for discriminative feature learning to enhance the margin of different classes. Extensive experiments on the OpenSARShip dataset demonstrate the superior performance of our method compared to the previous methods.

Ultrasonic non-destructive evaluation, which has been used widely, can detect and size critical flaws in structures. Advances in sound field calculations can further improve its effectiveness. Two calculation methods were used to characterize the relevant sound fields of an ultrasonic transducer and the results were applied to construct and evaluate Distance-Gain-Size (DGS) diagrams, which are useful in flaw sizing. Two published DGS diagrams were found to be deficient because the backward diffraction path was overly simplified and the third one included an arbitrary procedure. Newly constructed DGS diagrams exhibited transducer size dependence, revealing another deficiency in the existing DGS diagrams. However, the extent of the present calculations must be expanded to provide a catalog of DGS diagrams to cover a wide range of practical needs. Details of the new construction method are presented, incorporating two-way diffraction procedures.

The performance of urea-formaldehyde (UF) resin and its formaldehyde emission is a natural contradiction. High molar ratio UF resin performance is very good, but its formaldehyde release is high; low molar ratio UF resin formaldehyde release is reduced, but the resin itself performance becomes very bad. In order to solve this traditional problem, an excellent strategy of UF resin modified by hyperbranched polyurea is proposed. In this work, hyperbranched polyurea (UPA6N) is first synthesized by a simple method without any solvent. UPA6N is then added into industrial UF resin in different proportions as additives to manufacture particleboard and test its related properties. UF resin with a low molar ratio has a crystalline lamellar structure, and UF-UPA6N resin has an amorphous structure and rough surface. The results show that internal bonding strength increased by 58.5%, modulus of rupture increased by 24.4%, 24 h thickness swelling rate (%) decreased by 54.4%, and formaldehyde emission decreased by 34.6% compared with the unmodified UF particleboard. This may be ascribed to the polycondensation between UF and UPA6N, while UF-UPA6N resin forms more dense three-dimensional network structures. Finally, the application of UF-UPA6N resin adhesives to bond particleboard significantly improves the adhesive strength and water resistance and reduces formaldehyde emission, suggesting that the adhesive can be used as a green and eco-friendly adhesive resource for the wood industry.

In the process of material production, the mismatch between raw material parameters and manufacturing processing parameters may lead to fluctuations in product properties and ultimately to unstable or unqualified product quality. In this paper, we propose the concept of the Quality Filter model for process optimization. The Quality Filter model uses the property prediction model as a surrogate model and integrates expert experience and process window constraints to construct a loss function. When raw material parameters are supplied, the suitable processing parameters can be automatically matched, and the processing fluctuation can be used to hedge the fluctuations in raw material, thus stabilizing the product quality and improving overall product properties. A trial production data set of 128 samples of wind power steel from a steel plant was used to test the model. We selected the ellipsoid discriminant analysis model with a classification accuracy rate of 82.81% as the surrogate model, which gives a highly interpretable visualization result. Finally, the results show that the properties of the samples that underwent the optimized process are improved.

Sensor fusion is a technique that combines information from multiple sensors in order to improve the accuracy and reliability of the data being collected. In the context of teleoperation control of an anthropomorphic robotic arm, sensor fusion technology can be used to enhance the precise control of anthropomorphic robotic arms by combining data from multiple sensors, such as cameras, data gloves, force sensors, etc. By fusing and processing this sensing information, it can enable real-time control of anthropomorphic robotic arms and dexterous hands, replicating the motion of human manipulators. In this paper, we present a sensor fusion-based teleoperation control system for the anthropomorphic robotic arm and dexterous hand, which utilizes a filter to fuse data from multiple sensors in real-time. As such, the real-time perceived human arms motion posture information is analyzed and processed, and wireless communication is used to intelligently and flexibly control the anthropomorphic robotic arm and dexterous hand. Finally, the user is able to manage the anthropomorphic operation function in a stable and reliable manner. We also discussed the implementation and experimental evaluation of the system, showing that it is able to achieve improved performance and stability compared to traditional teleoperation control methods.

A hybrid girder bridge adopts a steel segment at the mid-span of the main span of a continuous concrete girder bridge. The critical point of the hybrid solution is the transition zone, connecting the steel and concrete segments of the beam. Although many girder tests revealing the structural behavior of hybrid girders have been conducted by previous studies, few specimens took the full section of a steel–concrete joint due to the large size of prototype hybrid bridges. In this study, a static load test on a composite segment to bridge the joint between the concrete and steel parts of a hybrid bridge with full section was conducted. A finite element model replicating the tested specimen results was established through Abaqus, while parametric studies were also conducted. The test and numerical results revealed that the concrete filling in the composite solution prevented the steel flange from extensive buckling, which significantly improved the load-carrying capacity of the steel–concrete joint. Meanwhile, strengthening the interaction between the steel and concrete helps to prevent the interlayer slip and simultaneously contributes to a higher flexural stiffness. These results are an important basis for establishing a rational design scheme for the steel–concrete joint of hybrid girder bridges.

Instead of acquiring the previous aberrations of an optical wavefront with a sensor, wavefront sensor-less (WFSless) adaptive optics (AO) systems compensate for wavefront distortion by optimizing the performance metric directly. The stochastic parallel gradient descent (SPGD) algorithm is pervasively adopted to achieve performance metric optimization. In this work, we incorporate CoolMomentum, a method for stochastic optimization by Langevin dynamics with simulated annealing, into SPGD. Numerical simulations reveal that, compared with the state-of-the-art SPGD variant, the proposed CoolMomentum-SPGD algorithm achieves better convergence speed under various atmospheric turbulence conditions while requiring only two tunable parameters.

In this paper, the effect of deep cryogenic treatment on the corrosion resistance of 42CrMo low alloy steel is investigated and compared with conventional heat-treated counterparts. The low-temperature treatments of the cryogenic process are −120 °C, −160 °C, and −190 °C, respectively. Electrochemical corrosion tests show that the self-corrosion current density of −120 °C, −160 °C and −190 deep-cooled specimens is reduced by 38%, 20% and 30% respectively compared to the usual heat-treated specimens. Scanning electron microscope analysis shows that the precipitation of fine carbides on the surface of the samples treated at −120 °C has improved their corrosion resistance. Electrochemical impedance spectroscopy also shows that the samples treated with −120 °C cryogenic treatment have the smallest corrosion tendency. At a −160 °C deep-cooling process, the precipitated carbide aggregation limits the corrosion resistance of the material. The corrosion resistance of the samples in the −190 °C process group is between the two. The simulation results also express a similar trend to the electrochemical corrosion results.

In the development of digital agriculture, agricultural robots play a unique role and confer numerous advantages in farming production. From the invention of the first industrial robots in the 1950s, robots have begun to capture the attention of both research and industry. Thanks to the recent advancements in computer science, sensing, and control approaches, agricultural robots have experienced a rapid evolution, relying on various cutting-edge technologies for different application scenarios. Indeed, significant refinements have been achieved by integrating perception, decision-making, control, and execution techniques. However, most agricultural robots continue to require intelligence solutions, limiting them to small-scale applications without quantity production because of their lack of integration with artificial intelligence. Therefore, to help researchers and engineers grasp the prevalent research status of agricultural robots, in this review we refer to more than 100 pieces of literature according to the category of agricultural robots under discussion. In this context, we bring together diverse agricultural robot research statuses and applications and discuss the benefits and challenges involved in further applications. Finally, directional indications are put forward with respect to the research trends relating to agricultural robots.

In this work, first-principles methods were performed to simulate interactions between hydrogen and common alloying elements of high strength low alloy (HSLA) steel. The world has been convinced that hydrogen could be one of the future clean energy sources. HSLA steel with a balance of strength, toughness, and hydrogen embrittlement susceptibility is expected for application in large-scale hydrogen storage and transportation. To evaluate the property deterioration under a hydrogen atmosphere, hydrogen embrittlement (HE) of HSLA steel attracts attention. However, due to the small size of hydrogen atoms, the mechanism of HE is challenging to observe directly by current experimental methods. To understand the HE mechanism at an atomic level, DFT methods were applied to simulate the effects of alloying elements doping in bcc-Fe bulk structure and grain boundary structure. Furthermore, the potential application of DFT to provide theoretical advice for HSLA steel design is discussed.

The augmentation of the Global Navigation Satellite System (GNSS) by Low Earth Orbit (LEO) satellites is proposed as an effective method to improve the precision and shorten the convergence time of Precise Point Positioning (PPP). Serving as navigation satellites in the future, LEO satellites need to be provided with their high-accuracy orbits in real-time. This would potentially enable the high-accuracy real-time LEO satellite clock determination, and eventually facilitate the high-accuracy ground-based positioning. Studies have been performed to achieve such real-time orbits using a Kalman filter in both the kinematic and reduced-dynamic modes. Batch Least-Squares (BLS) adjustment delivers more stable orbits in near-real-time, as it performs better phase screening. However, it suffers from longer delays compared to the Kalman filter. With the LEO satellite orbit prediction strategies improved over time, this latency can be bridged by short-term orbit prediction. In this study, using real-time GNSS satellite products, the real-time LEO satellite orbits are obtained based on the batch least-squares adjustment and short-term prediction. LEO ephemeris parameters are generated within specific prediction time windows. Using real data from the 500 km GRACE C satellite and 810 km Sentinel-3B satellite, the near-real-time BLS Precise Orbit Determination (POD) results exhibit good accuracy with an Orbital User Range Error (OURE) of 2–4 cm using different real-time GNSS products. A range of delays of the BLS POD processes are assumed, based on tests performed on different processing machines, leading to various prediction windows, from 3–8 min to 12–17 min that correspond to the real-time usage. The orbital prediction errors are shown to be highly correlated with the orbital height and the prediction time. The computational efficiency thus becomes essential to reduce the prediction errors for a certain LEO satellite. For advanced processing units leading to a prediction window shorter or equal to 6–11 min, one can expect a total real-time orbital error budget of 3–5 cm, provided that an appropriate prediction strategy is applied and high-quality GNSS products are used. For a given fitting interval, the ephemeris fitting errors are generally related to the number of ephemeris parameters and the orbital height. Compared with the prediction errors, the ephemeris fitting errors do not play a significant role in the total error budget when using 22 ephemeris parameters.

Fe-25Cr-15Co (wt.%) permanent magnets were fabricated via selective laser melting (SLM) and in situ alloying from a blend of Fe, Cr and Co elemental powders. Under the optimal laser scanning process, the as-built Fe-25Cr-15Co alloy has a homogeneous composition distribution without defects such as holes or un-melted particles, and presents a single α phase with the bcc crystal structure. The density of as-built samples was 7.705 g/cm3 (the relative density is 99.32%). The preferred magnetic properties of the sample in the isotropic state were obtained as Hc = 22.84 kA/m, Br = 0.86 T and (BH)max = 7.98 kJ/m3. The hardness and yield strength of Fe-25Cr-15Co permanent magnets are above 331.5 HV and 800 MPa, respectively. The results of this study verified the feasibility of fabricating Fe-Cr-Co permanent magnets by SLM in situ alloying and can be extended to a wide range of applications that require complex shapes with variable magnetic circuit characteristics or gradient structures.

As applied sciences grow by leaps and bounds, semiparametric regression analyses have broad applications in various fields, such as engineering, finance, medicine, and public health. Single-index varying-coefficient model is a common class of semiparametric models due to its flexibility and ease of interpretation. The standard single-index varying-coefficient regression models consist mainly of parametric regression and semiparametric regression, which assume that all covariates can be observed. The assumptions are relaxed by taking the models with missing covariates into consideration. To eliminate the possibility of bias due to missing data, we propose a probability weighted objective function. In this paper, we investigate the robust variable selection for a single-index varying-coefficient model with missing covariates. Using parametric and nonparametric estimates of the likelihood of observations with fully observed covariates, we examine the estimators for estimating the likelihood of observations. For variable selection, we use a weighted objective function penalized by a non-convex SCAD. Theoretical challenges include the treatment of missing data and a single-index varying-coefficient model that uses both the non-smooth loss function and the non-convex penalty function. We provide Monte Carlo simulations to evaluate the performance of our approach.

Dietary protein is a key nutritional parameter and warrants special attention in animal husbandry. This study aimed to evaluate the effect of replacing fish meal (F) with fly maggot protein (M) on the growth performance, antioxidant levels, immune indexes, and fecal microflora in weaned blue foxes (Alopex lagopus). Twenty weaned blue foxes were randomly assigned to the control (F diet; 6% of F) or experimental (M diet; F substituted by M) group (10 blue foxes per group). The duration of the trial was 28 days. The results showed that there was no significant difference in average daily gain between group M and group F during the experiment (p = 0.473). Moreover, the diarrhea index was similar between group M and group F during the entire experimental period (p = 0.112). At the end of the experiment, the levels of IL-6 and IgG in group M at 28 d were significantly higher than that in group F (p = 0.004, p = 0.025, respectively), but not IL-1β, IL-2, SIgA, IgM, and TNF-α. The levels of SOD in group M at 28 d were significantly higher than those in group F (p = 0.001), and no difference of MDA and T-AOC was found between group F and M (p = 0.073, p = 0.196, respectively). In both groups, the diversity of fecal microbes first increased and then decreased with the progress of the experimental period. Initially, there were differences in the composition of microbial communities between the two groups. However, this difference was attenuated at later stages of the experimental period. In conclusion, fly maggot protein can replace fish meal as a source of animal protein in feed material for blue foxes during the weaning period.

After the revival of deep learning in computer vision in 2012, SAR ship detection comes into the deep learning era too. The deep learning-based computer vision algorithms can work in an end-to-end pipeline, without the need of designing features manually, and they have amazing performance. As a result, it is also used to detect ships in SAR images. The beginning of this direction is the paper we published in 2017BIGSARDATA, in which the first dataset SSDD was used and shared with peers. Since then, lots of researchers focus their attention on this field. In this paper, we analyze the past, present, and future of the deep learning-based ship detection algorithms in SAR images. In the past section, we analyze the difference between traditional CFAR (constant false alarm rate) based and deep learning-based detectors through theory and experiment. The traditional method is unsupervised while the deep learning is strongly supervised, and their performance varies several times. In the present part, we analyze the 177 published papers about SAR ship detection. We highlight the dataset, algorithm, performance, deep learning framework, country, timeline, etc. After that, we introduce the use of single-stage, two-stage, anchor-free, train from scratch, oriented bounding box, multi-scale, and real-time detectors in detail in the 177 papers. The advantages and disadvantages of speed and accuracy are also analyzed. In the future part, we list the problem and direction of this field. We can find that, in the past five years, the AP50 has boosted from 78.8% in 2017 to 97.8 % in 2022 on SSDD. Additionally, we think that researchers should design algorithms according to the specific characteristics of SAR images. What we should do next is to bridge the gap between SAR ship detection and computer vision by merging the small datasets into a large one and formulating corresponding standards and benchmarks. We expect that this survey of 177 papers can make people better understand these algorithms and stimulate more research in this field.

Bionic hands have been employed in a wide range of applications, including prosthetics, robotic grasping, and human–robot interaction. However, considering the underactuated and nonlinear characteristics, as well as the mechanical structure’s backlash, achieving natural and intuitive teleoperation control of an underactuated bionic hand remains a critical issue. In this paper, the teleoperation control of an underactuated bionic hand using wearable and vision-tracking system-based methods is investigated. Firstly, the nonlinear behaviour of the bionic hand is observed and the kinematics model is formulated. Then, the wearable-glove-based and the vision-tracking-based teleoperation control frameworks are implemented, respectively. Furthermore, experiments are conducted to demonstrate the feasibility and performance of these two methods in terms of accuracy in both static and dynamic scenarios. Finally, a user study and demonstration experiments are conducted to verify the performance of these two approaches in grasp tasks. Both developed systems proved to be exploitable in both powered and precise grasp tasks using the underactuated bionic hand, with a success rate of 98.6% and 96.5%, respectively. The glove-based method turned out to be more accurate and better performing than the vision-based one, but also less comfortable, requiring greater effort by the user. By further incorporating a robot manipulator, the system can be utilised to perform grasp, delivery, or handover tasks in daily, risky, and infectious scenarios.

Fluorescence nanoparticles have gained much attention due to their unique properties in the sensing and imaging fields. Among the very successful candidates are fluorescent polydopamine (FPDA) nanoparticles, attributed to their simplicity in tracing and excellent biocompatibility. This article aims to highlight the recent achievements in FPDA materials, especially on the part of luminescence mechanisms. We focus on the intrinsic fluorescence of PDA and will not discuss fluorescent reaction with a fluorometric reagent or coupling reaction with a fluorophore, which may cause more in vivo interferences. We believe that intrinsic FPDA presents great potential in bioapplications.

In the field of robotics, soft robots have been showing great potential in the areas of medical care, education, service, rescue, exploration, detection, and wearable devices due to their inherently high flexibility, good compliance, excellent adaptability, and natural and safe interactivity. Pneumatic soft robots occupy an essential position among soft robots because of their features such as lightweight, high efficiency, non-pollution, and environmental adaptability. Thanks to its mentioned benefits, increasing research interests have been attracted to the development of novel types of pneumatic soft robots in the last decades. This article aims to investigate the solutions to develop and research the pneumatic soft robot. This paper reviews the status and the main progress of the recent research on pneumatic soft robots. Furthermore, a discussion about the challenges and benefits of the recent advancement of the pneumatic soft robot is provided.

A high sensitivity optical fiber gas pressure sensor based on paralleled Fabry–Pérot interferometers (FPIs) was demonstrated. One micro-cavity FPI is used as a reference FPI (FPI-1) to generate a Vernier effect and the other FPI (FPI-2) is used as a sensing tip. Both FPIs are connected by a 3-dB coupler to form a paralleled structure. The FPI-1 was fabricated by fusion splicing a piece of hollow core fiber (HCF) between two sections of single-mode fibers (SMF), whereas FPI-2 was formed by fusion splicing a section of HCF between SMF and a piece of HCF with a slightly smaller inner diameter for sensing pressure. The gas pressure sensitivity was amplified from 4 nm/MPa of single FPI to 45.76 nm/MPa of paralleled FPIs with an amplification factor of 11.44 and a linearity of 99.9%. Compared with the traditional fiber gas pressure sensors, the proposed sensor showed great advantages in sensitivity, mechanical strength, cost, and temperature influence resistant, which has potential in adverse-circumstance gas pressure sensing.

Citrus canker caused by Xanthomonas citri subsp. citri (Xcc) is an economically important disease in most citrus production regions worldwide. Xcc secretes a transcriptional activator like effector (TALE) PthA4 to bind to the effector binding elements (EBEs) in the promoter region of canker susceptibility gene LOB1 to activate its expression, which in turn causes canker symptoms. Editing the EBE region with Cas9/gRNA has been used to generate canker resistant citrus plants. However, most of the EBE-edited lines generated contain indels of 1–2 bp, which has higher possibility to be overcome by PthA4 adaptation. The adaptation capacity of TALEs inversely correlates with the number of mismatches with the EBE. LbCas12a/crRNA is known to generate longer deletion than Cas9. In this study, we used a temperature-tolerant and more efficient LbCas12a variant (ttLbCas12a), harboring the single substitution D156R, to modify the EBE region of LOB1. We first constructed GFP-p1380N-ttLbCas12a:LOBP, which was shown to be functional via Xcc-facilitated agroinfiltration in Pummelo (Citrus maxima) leaves. Subsequently, we stably expressed ttLbCas12a:LOBP in Pummelo. Eight transgenic lines were generated, with seven lines showing 100% mutations of the EBE, among which one line is homozygous. The EBE-edited lines had the ttLbCas12a-mediated deletions of up to 10 bp. Importantly, the seven lines were canker resistant and no off-targets were detected. In summary, ttLbCas12a can be used to efficiently generate biallelic/homozygous citrus mutant lines with short deletions, thus providing a useful tool for the functional study and breeding of citrus.

Water vapor is one of the most important greenhouse gases in the world. There are many techniques that can measure water vapor directly or remotely. In this work, we first study the Global Positioning System (GPS)- and the Global Navigation Satellite System (GLONASS)-derived Zenith Wet Delay (ZWD) time series based on 11 years of the second reprocessing campaign of International Global Navigation Satellite Systems (GNSS) Service (IGS) using 320 globally distributed stations. The amount of measurement, the local environment, and the antenna radome are shown to be the main factors that affect the GNSS ZWDs and the corresponding a posteriori formal errors. Furthermore, antenna radome is able to effectively reduce the systematic bias of ZWDs and a posteriori formal errors between the GPS- and GLONASS-based solutions. With the development of the GLONASS, the ZWD differences between the GPS- and the GLONASS-based solutions have gradually decreased to sub-mm-level after GLONASS was fully operated. As the GPS-based Precipitable Water Vapor (PWV) is usually used as the reference to evaluate the other PWV products, the PWV consistency among several common techniques is evaluated, including GNSSs, spaceborne sensors, and numerical products from the European Center for Medium-Range Weather Forecasts (ECMWF). As an example of the results from a detailed comparison analysis, the long-term global analysis shows that the PWV obtained from the GNSS and the ECMWF have great intra-agreements. Based on the global distribution of the magnitude of the PWV and the PWV drift, most of the techniques showed superior agreement and proved their ability to do climate research. With a detailed study performed for the ZWDs and PWV on a long-term global scale, this contribution provides a useful supplement for future research on the GNSS ZWD and PWV.

The generalized sidelobe canceller (GSC) method is a common algorithm to enhance audio signals using a microphone array. Distortion of the enhanced audio signal consists of two parts: the residual acoustic noise and the distortion of the desired audio signal, which means that the desired audio signal is damaged. This paper proposes a modified GSC method to reduce both kinds of distortion when the desired audio signal is a non-stationary speech signal. First, the cross-correlation coefficient between the canceling signal and the error signal of the least mean square (LMS) algorithm was added to the adaptive process of the GSC method to reduce the distortion of the enhanced signal while the energy of the desired signal frame was increased suddenly. The sidelobe pattern of beamforming was then presented to estimate the noise signal in the beamforming output signal of the GSC method. The noise component of the beamforming output signal was decreased by subtracting the estimated noise signal to improve the denoising performance of the GSC method. Finally, the GSC-SN-MCC method was proposed by merging the above two methods. The experiment was performed in an anechoic chamber to validate the proposed method in various SNR conditions. Furthermore, the simulated calculation with inaccurate noise directions was conducted based on the experiment data to inspect the robustness of the proposed method to the error of the estimated noise direction. The experiment data and calculation results indicated that the proposed method could reduce the distortion effectively under various SNR conditions and would not cause more distortion if the estimated noise direction is far from the actual noise direction.

A new advanced microwave humidity sounder FY-3D MWHS2 radiance has been assimilated under the clear-sky conditions by implementing its data assimilation interface. The case of the tropical storm Ampil in 2018 is selected to address the effectiveness of the new-built module in the initialization and forecast of typhoons. Apart from the experiment assimilating both the Global Telecommunications System (GTS) data and the FY-3D MWHS2 radiance data, an experiment with only GTS data is also conducted for comparison. The results show that the bias correction of this humidity sounder is effective, and the analysis field after assimilating its radiance data matches well with the observation. The increment of specific humidity below the middle layers is evident after the assimilation of the radiance data. Besides, the geopotential height increment and the specific humidity increment at 500 hPa and 850 hPa, respectively, are favorable, resulting in more accurate rain belt distribution and a higher fraction skill score (FSS). In the deterministic forecast, the track error of the FY-3D MWHS2 experiment is consistently less than 90 km.

Selective laser melting (SLM) in situ alloying is an effective way to design and fabricate novel materials in which the elemental powder is adopted as the raw material and micro-areas of elemental powder blend are alloyed synchronously in the forming process of selective laser melting (SLM). The pre-alloying process of preparation of raw material powder can be left out, and a batch of bulk samples can be prepared via the technology combined with quantitative powder mixing and feeding. The technique can be applied to high-throughput sample preparation to efficiently obtain a microstructure and performance data for material design. In the present work, bulk equiatomic FeCoCrNi high-entropy alloys with different processing parameters were fabricated via laser in situ alloying. Finite element simulation and CALPHAD calculation were used to determine the appropriate SLM and post-heating parameters. SEM (scanning electron microscope), EDS (energy dispersive spectroscopy), XRD (X-ray diffraction), and mechanical testing were used to characterize the composition, microstructure, and mechanical properties of as-printed and post-heat-treated samples. The experimental results show that the composition deviation of laser in situ alloying samples could be controlled within 20 wt %. The crystal structure of as-printed samples is a single-phase face-centered cubic (FCC), which is the same as those prepared by the traditional method. The mechanical properties of the samples prepared by laser in situ alloying with elemental powder blend are comparable to those prepared by pre-alloying powder and much higher than those prepared by the traditional method (arc melting). As-printed samples can get a homogeneous microstructure under the optimal laser in situ alloying process combined with post-heat treatment at 1200 °C for 20 h.

The main means of secondary oil recovery is water flooding, which has been widely used in various oilfields. Different flow rates have a great impact on the recovery ratio and the occurrence of remaining oil. Scholars have carried out extensive research on it, but mostly on the macro scale, and research on the three-dimensional micro scale is also limited by accuracy and a lack of accurate understanding. In this paper, micro-CT and core displacement experiments are used to intuitively show the occurrence state of remaining oil under different flow rates. Through a series of quantitative image processing methods and remaining oil classification methods, the occurrence characteristics of remaining oil under different flow rates are systematically evaluated and studied. The results show that: (1) As the displacement rate increases, the remaining oil saturation decreases (61%; 35%; 23%), but the remaining oil is more evenly distributed along the slice; (2) Two lower displacement speeds (0.003 mL/min; 0.03 mL/min) can reduce the volume of huge oil clusters under oil-saturated conditions, and the highest displacement speed (0.3 mL/min) can completely break up large oil clusters into small oil droplets. At the same time, the shape factor of the oil clusters also gradually increases; (3) The proportion of continuous remaining oil volume decreases, and the proportion of discontinuous remaining oil increases. Discontinuous remaining oil is the main production target of EOR; (4) After water flooding, the microscopic remaining oil is more inclined to the middle and corner parts of the larger pores.

In this paper, a thin metasurface perfect absorber based on refractory titanium nitride (TiN) is proposed. The size parameter of the metasurface is investigated based on the finite difference time domain method and transfer matrix method. With only a 15-nm-thick TiN layer inside the silica/TiN/silica stacks standing on the TiN substrate, the near-perfect absorption throughout the visible regime is realized. The cross-talk between the upper and lower dielectric layers enables the broadening of the absorption peak. After patterning the thin film into a nanodisk array, the resonances from the nanodisk array emerge to broaden the high absorption bandwidth. As a result, the proposed metasurface achieves perfect absorption in the waveband from 400 to 2000 nm with an average absorption of 95% and polarization-insensitivity under the normal incidence. The proposed metasurface maintains average absorbance of 90% up to 50-degree oblique incidence for unpolarized light. Our work shows promising potential in the application of solar energy harvesting and other applications requiring refractory metasurfaces.

Straightness error compensation is a critical process for high-accuracy topography measurement. In this paper, a straightness measurement system was presented based on the principle of fringe interferometry. This system consisted of a moving optical flat and a stationary prism placed close to each other. With a properly aligned incident light beam, the air wedge between the optical flat and the prism would generate the interferogram, which was captured by a digital camera. When the optical flat was moving with the motion stage, the variation in air wedge thickness due to the imperfect straightness of the guideway would lead to a phase shift of the interferogram. The phase shift could be calculated, and the air wedge thickness could be measured accordingly using the image processing algorithm developed in-house. This air wedge thickness was directly correlated with the straightness of the motion stage. A commercial confocal sensor was employed as the reference system. Experimental results showed that the repeatability of the proposed film interferometer represented by σ was within 25 nm. The measurement deviation between the film interferometer and the reference confocal sensor was within ±0.1 µm. Compared with other interferometric straightness measurement technologies, the presented methodology was featured by a simplified design and good environment robustness. The presented system could potentially be able to measure straightness in both linear and angular values, and the main focus was to analyze its linear value measurement capability.

The oleaginous microalgae species Chlorococcum sphacosum GD is a promising feedstock for biodiesel production from soil. However, its metabolic mechanism of lipid production remains unclear. In this study, the lipid accumulation and metabolism mechanisms of Chlorococcum sphacosum GD were analyzed under salt stress based on transcriptome sequencing. The biomass and lipid content of the alga strain were determined under different NaCl concentrations, and total RNA from fresh cells were isolated and sequenced by HiSeq 2000 high throughput sequencing technology. As the salt concentration increased in culture medium, the algal lipid content increased but the biomass decreased. Following transcriptome sequencing by assembly and splicing, 24,128 unigenes were annotated, with read lengths mostly distributed in the 200–300 bp interval. Statistically significant differentially expressed unigenes were observed in different experimental groups, with 2051 up-regulated genes and 1835 down-regulated genes. The lipid metabolism pathway analysis showed that, under salt stress, gene-related fatty acid biosynthesis (ACCase, KASII, KAR, HAD, FATA) was significantly up-regulated, but some gene-related fatty acid degradation was significantly down-regulated. The comprehensive results showed that salt concentration can affect the lipid accumulation and metabolism of C. sphacosum GD, and the lipid accumulation is closely related to the fatty acid synthesis pathway.

Satellite timing is an effective and convenient method that has been widely accepted in the time community. The key to satellite timing is obtaining a clean receiver clock offset. In this paper, instead of regarding the receiver clock offset as white noise, a two-state stochastic clock model involving three kinds of noise was conceived and used in PPP filter estimation. The influence of clock type and sampling time on satellite timing performance was first analysed. In addition, the kinematic scheme and static scheme were both investigated for meeting the demands of multi-occasional users. The values show that the model works well for both the kinematic scheme and static scheme; in contrast to that of the white noise model, the timing stability is enhanced at all the sampling times. For the six stations, especially when the averaging time is less than 1000 s, the average stability improvement values of the kinematic scheme are 75.53, 43.24, 75.00, 69.05, 40.57, and 25.45%, and the average improvement values of the static scheme are 65.49, 77.94, 56.71, 60.78, 64.41, and 39.41%. Furthermore, the enhancement magnitude is related to clock type. For a high-stability clock, the improvement of the kinematic scheme is greater than that of the static scheme, whereas for a low-stability clock, the improvement of the kinematic scheme is less than that of the static scheme.

Active prosthetic knees (APKs) are widely used in the past decades. However, it is still challenging to make them more natural and controllable because: (1) most existing APKs that use rigid actuators have difficulty obtaining more natural walking; and (2) traditional finite-state impedance control has difficulty adjusting parameters for different motions and users. In this paper, a flexible APK with a compact variable stiffness actuator (VSA) is designed for obtaining more flexible bionic characteristics. The VSA joint is implemented by two motors of different sizes, which connect the knee angle and the joint stiffness. Considering the complexity of prothetic lower limb control due to unknown APK dynamics, as well as strong coupling between biological joints and prosthetic joints, an adaptive robust force/position control method is designed for generating a desired gait trajectory of the prosthesis. It can operate without the explicit model of the system dynamics and multiple tuning parameters of different gaits. The proposed model-free scheme utilizes the time-delay estimation technique, sliding mode control, and fuzzy neural network to realize finite-time convergence and gait trajectory tracking. The virtual prototype of APK was established in ADAMS as a testing platform and compared with two traditional time-delay control schemes. Some demonstrations are illustrated, which show that the proposed method has superior tracking characteristics and stronger robustness under uncertain disturbances within the trajectory error in ± 0 . 5 degrees. The VSA joint can reduce energy consumption by adjusting stiffness appropriately. Furthermore, the feasibility of this method was verified in a human–machine hybrid control model.

Diseases caused by viruses threaten the production industry and food safety of aquaculture which is a great animal protein source. Grass carp reovirus (GCRV) has caused tremendous loss, and the molecular function of viral proteins during infection needs further research, as for most aquatic viruses. In this study, interaction between GCRV major outer capsid protein VP4 and RIG-I, a critical viral RNA sensor, was screened out by GST pull-down, endogenous immunoprecipitation and subsequent LC-MS/MS, and then verified by co-IP and an advanced far-red fluorescence complementation system. VP4 was proved to bind to the CARD and RD domains of RIG-I and promoted K48-linked ubiquitination of RIG-I to degrade RIG-I. VP4 reduced mRNA and promoter activities of key genes of RLR pathway and sequential IFN production. As a consequence, antiviral effectors were suppressed and GCRV replication increased, resulting in intensified cytopathic effect. Furthermore, results of transcriptome sequencing of VP4 stably expressed CIK (C. idella kidney) cells indicated that VP4 activated the MyD88-dependent TLR pathway. Knockdown of VP4 obtained opposite effects. These results collectively revealed that VP4 interacts with RIG-I to restrain interferon response and assist GCRV invasion. This study lays the foundation for anti-dsRNA virus molecular function research in teleost and provides a novel insight into the strategy of immune evasion for aquatic virus.

In this paper, a novel design of a surface topography measurement system is proposed, to address the challenge of accurate measurement in a relatively large area. This system was able to achieve nanometer-scale accuracy in a measurement range of 100 mm × 100 mm. The high accuracy in a relatively large area was achieved by implementing two concepts: (1) A static coordinate system was configured to minimize the Abbe errors. (2) A differential measurement configuration was developed by setting up a confocal sensor and a film interferometry module to separate the motion error. In order to accommodate the differential measurement probes from both sides of the central stage and ensure the system rigidity with balanced supports, separate linear guides were introduced in this system. Therefore, the motion Degree of Freedom (DoF) was analyzed in order to address the challenge of an over-constrained mechanism due to multiple kinematic pairs. An optimal configuration and a quick assembly process were proposed accordingly. The experimental results presented in this paper showed that the proposed modular measurement system was able to achieve 10 nm accuracy in measuring the surface roughness and 100 nm accuracy in measuring the step height in the range of 100 mm × 100 mm. In summary, the novel concept of this study is the build of a high-accuracy system with conventional mechanical components.

Condensation products of 5-substituted phenyl-2-furoyl hydrazide with different monosaccharides d-glucose, d-galactose, d-mannose, d-fucose and d-arabinose were prepared. The anomerization and cyclic-acyclic isomers were investigated by 1H NMR spectroscopy. The results showed that, except for the d-glucose derivatives, which were in the presence of β-anomeric forms, all derivatives were in an acyclic Schiff base form. Their antifungal and antitumor activities were studied. The bioassay results indicated that some title compounds showed superior effects over the commercial positive controls.

In robot control with physical interaction, like robot-assisted surgery and bilateral teleoperation, the availability of reliable interaction force information has proved to be capable of increasing the control precision and of dealing with the surrounding complex environments. Usually, force sensors are mounted between the end effector of the robot manipulator and the tool for measuring the interaction forces on the tooltip. In this case, the force acquired from the force sensor includes not only the interaction force but also the gravity force of the tool. Hence the tool dynamic identification is required for accurate dynamic simulation and model-based control. Although model-based techniques have already been widely used in traditional robotic arms control, their accuracy is limited due to the lack of specific dynamic models. This work proposes a model-free technique for dynamic identification using multi-layer neural networks (MNN). It utilizes two types of MNN architectures based on both feed-forward networks (FF-MNN) and cascade-forward networks (CF-MNN) to model the tool dynamics. Compared with the model-based technique, i.e., curve fitting (CF), the accuracy of the tool identification is improved. After the identification and calibration, a further demonstration of bilateral teleoperation is presented using a serial robot (LWR4+, KUKA, Germany) and a haptic manipulator (SIGMA 7, Force Dimension, Switzerland). Results demonstrate the promising performance of the model-free tool identification technique using MNN, improving the results provided by model-based methods.

As a significant role in healthcare and sports applications, human activity recognition (HAR) techniques are capable of monitoring humans’ daily behavior. It has spurred the demand for intelligent sensors and has been giving rise to the explosive growth of wearable and mobile devices. They provide the most availability of human activity data (big data). Powerful algorithms are required to analyze these heterogeneous and high-dimension streaming data efficiently. This paper proposes a novel fast and robust deep convolutional neural network structure (FR-DCNN) for human activity recognition (HAR) using a smartphone. It enhances the effectiveness and extends the information of the collected raw data from the inertial measurement unit (IMU) sensors by integrating a series of signal processing algorithms and a signal selection module. It enables a fast computational method for building the DCNN classifier by adding a data compression module. Experimental results on the sampled 12 complex activities dataset show that the proposed FR-DCNN model is the best method for fast computation and high accuracy recognition. The FR-DCNN model only needs 0.0029 s to predict activity in an online way with 95.27% accuracy. Meanwhile, it only takes 88 s (average) to establish the DCNN classifier on the compressed dataset with less precision loss 94.18%.

The mobile robot kinematic model is a nonlinear affine system, which is constrained by velocity and acceleration limits. Therefore, the traditional control methods may not solve the tracking problem because of the physical constraint. In this paper, we present the nonlinear model predictive control (NMPC) algorithm to track the desired trajectory based on neural-dynamic optimization. In the proposed algorithm, the NMPC scheme utilizes a new neural network named the varying-parameter convergent differential neural network (VPCDNN) which is a Hopfifield-neural network structure with respect to the differential equation theory to solve the quadratic programming (QP) problem. The new network structure converges to the global optimal solution and it is more efficient than traditional numerical methods. In the simulation, we verify that the proposed method is able to successfully track reference trajectories with a two-wheel mobile robot. The experimental validation has been conducted in simulation and the results show that the proposed method is able to precisely track the trajectory maintaining a high robustness based on the VPCDNN solver.