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Glucose is a major energy substrate for porcine adipocytes and also serves as a regulatory signal for adipogenesis and lipid metabolism. In this study, we combined transcriptome and metabolome analyses to reveal the underlying regulatory mechanisms of high glucose (HG) on adipogenesis by comparing differentially expressed genes (DEGs) and differentially accumulated metabolites (DAMs) identified in porcine adipocytes. Results showed that HG (20 mmol/L) significantly increased fat accumulation in porcine adipocytes compared to low glucose (LG, 5 mmol/L). A total of 843 DEGs and 365 DAMs were identified. Functional enrichment analyses of DEGs found that multiple pathways were related to adipogenesis, lipid metabolism, and immune-inflammatory responses. PPARγ, C/EBPα, ChREBP, and FOS were identified as the key hub genes through module 3 analysis, and PPARγ acted as a central regulator by linking genes involved in lipid metabolism and immune-inflammatory responses. Gene-metabolite networks found that PPARγ-13-HODE was the most important interaction relationship. These results revealed that PPARγ could mediate the cross-talk between adipogenesis and the immune-inflammatory response during adipocyte maturation. This work provides a comprehensive view of the regulatory mechanisms of glucose on adipogenesis in porcine adipocytes.

Multicellular organisms exhibit inherent cellular heterogeneity that cannot be captured by traditional high-throughput sequencing techniques, resulting in the unique cellular characteristics of individual cells being neglected. Single-cell transcriptome sequencing (scRNA-seq) technology can be used to determine the gene expression levels of each individual cell, facilitating the study of intercellular expression heterogeneity. This review provides a comprehensive overview of the development and applications of scRNA-seq technology in plant research. We highlight the significance of integrating single-cell multi-omics approaches to achieve a holistic understanding of plant systems. Additionally, we discuss the current challenges and future research directions for scRNA-seq technology in plant studies, aiming to offer valuable insights for its application across various plant species.

Lunar exploration is of significant importance in the development and utilization of in situ lunar resources, water ice exploration, and astronomical science. In recent years, ground-based radar (GBR) has gained increasing attention in the field of lunar exploration due to its flexibility, low cost, and penetrating capabilities. This paper reviews the scientific research on lunar exploration using GBR, outlining the basic principles of GBR and the progress made in lunar exploration studies. Our paper introduces the fundamental principles of lunar imaging using GBR and systematically reviews studies on lunar surface/subsurface detection, the dielectric properties inversion of the lunar regolith, and polar water ice detection using GBR. In particular, the paper summarizes the current development status of the Chinese GBR and forecasts future development trends in China. This review will enhance the understanding of lunar exploration results using GBR radar, systematically demonstrate the main applications and scientific achievements of GBR in lunar exploration, and provide a reference for GBR radar in future lunar exploration missions.

To address the path planning problem for automated guided vehicles (AGVs) in challenging and complex industrial environments, a hybrid optimization approach is proposed, integrating a Kalman filter with grey wolf optimization (GWO), as well as incorporating partially matched crossover (PMX) mutation operations and roulette wheel selection. Paths are first optimized using GWO, then refined with Kalman filter corrections every ten iterations. Moreover, roulette wheel selection guides robust parent path selection, while an elite strategy and partially matched crossover (PMX) with mutation generate diverse offspring. Extensive simulations and experiments were carried out under a densely packed goods scenario and complex indoor layout scenario, within a fully automated warehouse environment. The results showed that this hybrid method not only enhanced the various optimization metrics but also ensured more predictable and collision-free navigation paths, particularly in environments with complex obstacles. These improvements lead to increased operational efficiency and safety, highlighting the method’s potential in real-world applications.

Background and objectives: The urban underground complex integrates comprehensive architecture and subterranean space, presenting physical and psychological complexities. To mitigate the negative experiences associated with this complexity, we need to explore, in depth, how the built environmental elements of underground complexes affect user perception to design a comfortable, healthy, and biophilic underground space. Methods: We selected Tianfu Square, a typical underground complex in Chengdu, China, as the empirical case. We identified 26 key environmental indicators affecting user perception and designed Kano and satisfaction questionnaires based on these indicators. A total of 476 questionnaires were distributed to collect data on users’ attitudes towards changes in element quality and their satisfaction levels. By integrating the Kano model and IPA method, we defined the perceptual attributes of elements based on their impact on user experiences and assessed the alignment between element attributes and satisfaction. Results: We categorized the elements into five perceptual types: attractive, one-dimensional, indifferent, must-be, and reverse attributes, and assigned corresponding importance levels. We then compared the importance and performance scores of these elements, evaluated their satisfaction alignment, identified elements needing optimization, and proposed improvement strategies. Implications: This research provides valuable guidance for designers on how various built environment elements in underground spaces influence user perceptions, with practical implications for urban underground complex planning and design.

Diabetic peripheral neuropathy (DPN) is the most prevalent chronic complication among diabetic patients and a primary risk factor contributing to the deterioration of diabetic foot conditions. The pathogenesis of DPN remains complex and not fully understood, and there are hardly any effective treatment drugs. Maltol (3-hydroxy-2-methyl-4-pyranone) has demonstrated antioxidant and anti-inflammatory properties. However, the potential role of maltol in the treatment of DPN remains unclear. This study aimed to assess maltol’s effects on DPN rats and high glucose (HG)/palmitic acid (PA)-induced rat Schwann cells (RSC96). The results indicated maltol’s capacity to enhance peripheral nerve function in DPN rats. In RSC96 cells stimulated with high HG and PA, maltol treatment reduced DPN markers and apoptosis-related proteins. Functional enrichment analysis of differentially expressed genes revealed that endoplasmic reticulum (ER) stress pathways were involved in this process. Western blot results demonstrated the activation of ER stress pathway in HG/PA-induced RSC96 cells, with maltol attenuating ER stress-related protein expression. Furthermore, the knockdown of Membrane metallo-endopeptidase (MME) reversed maltol’s effects on apoptosis-related protein expression, suggesting a potential therapeutic role for maltol via MME in treating DPN. These findings indicate that maltol may hold promise as a therapeutic agent for DPN treatment.

Typical seismic sway braces (SSBs) are composed of several bolted steel components. As the connecting bolts become loose, the stiffness and bearing capacity of SSBs decline from which their seismic performance degrades significantly. Based on the vibration response of the brace components, a framework of a vibration-based pre-alarming method for the bolt looseness of SSBs is created and established. Four damage indexes were constructed based on the methods of Fast Fourier Transform (FFT), Wavelet Packet (WP), Intrinsic Mode Function (IMF) and Hilbert-Huang Transform (HHT). Having tested and verified their sensitivity to the bolting torque of bolts, a multi-index hierarchical early warning system (MHES) was established by associating the four damage indexes with the tightening torque of the bolts, by which the threshold of the yellow (slightly damaged) and red (severely damaged) warnings were defined and confirmed. By means of the application in an actual project, the validation of the MHES was finally verified and confirmed according to its stability and accuracy.

The content of unfrozen water in the freezing process of coal body affects the microscopic pore structure and macroscopic mechanical properties of coal body and determines the permeability-enhancement effect of coal seam and the extraction efficiency of coal mine gas. To investigate the evolution mechanism of unfrozen water content in the melting process of lignite, this paper takes the melting process of lignite liquid nitrogen after freezing for 150 min as the research object and quantifies the spatial change process of unfrozen water distribution based on two-dimensional nuclear magnetic resonance technology. Through the accurate interpretation of the superimposed signals of different fluids, the 2D NMR technique can more easily obtain the spatial distribution of different fluids and even the specific content of fluids in different pores in coals. The results show that at −196 °C, the unfrozen water mainly existed in the small coal pore and the small ice pore in the large pore. As the temperature rose, the pores melted, and free water began to be produced. The mathematical model analysis shows that there was intermolecular potential energy between fluid molecules and the coal pore wall, and the pore wall exerted a part of pressure on its internal fluid, and the pressure affected the melting point of pore ice with pore diameter and melting temperature, resulting in the difference of unfrozen water content.

Nowadays, asteroid and comet exploration is one of the most important components of deep space exploration. Through asteroid and comet exploration missions, it is possible to reveal the history of the formation and evolution of the solar system, to understand the origin and evolution of the planets, and to improve scientific models and instruments. As a payload with the advantages of non-destructive, penetrating, and polarizing characteristics, ground-penetrating radar (GPR) has been widely used in lunar and Mars exploration, and will play an important role in planned asteroid and comet exploration missions. In this study, statistics on asteroid and comet exploration missions, scientific results, and space-based ground-penetrating radar (SB-GPR) utilization are presented for the three phases to date. According to the statistics, SB-GPR will play an important role in future Phase 2 and 3 missions. The focus of this study is on analyzing the mission flow, SB-GPR parameters, scientific objectives, and scientific results of the missions that have carried SB-GPR and those that are planned to carry SB-GPR, including the Hera, Rosetta, Castalia, and Tianwen-2 missions. On this basis, the development trends of asteroid and comet exploration missions, as well as the future development trends of SB-GPR design and signal interpretation, are discussed.

In an era where technology increasingly blurs the boundaries between humans and machines, artifacts have become crucial mediums for critically examining the technological, social, and ethical dimensions of Human–Computer Interaction (HCI). This study explores artifacts as a key yet underutilized medium for speculation in the evolving field of HCI from a systemic perspective. While artifacts increasingly enable HCI to move beyond optimizing user experiences towards critically and collaboratively envisioning futures, perspectives comprehensively examining artifacts across the speculative design process and their impacts remain limited. Through a literature review of 53 speculative artifacts within the scope of HCI, this research elucidates the roles of artifacts across intention, making, and impact. Four categories of speculative artifacts emerged—Reflective, Exploratory, Interventional, and Heuristic—demonstrating how artifacts employ material, ambiguous, functional, and provocative forms to shape experiences, behaviors, and social norms. This study highlights the need for HCI to increasingly recognize the capacity of artifacts to support critical, sustained, participatory speculation by providing tangible representations of alternative futures. Speculative artifacts thus serve as powerful mediums to engage in societal discourse around the ethics and values of emerging technologies and to envision and enact responsible innovation. The materialization of alternative futures through artifacts allows researchers to reimagine socio-technological relationships, pushing design into inclusive, controversial spaces where diverse stakeholders can collaboratively shape desired and undesired futures.

In this study, we present a novel dual-loop robust trajectory tracking framework for autonomous underwater vehicles, with the objective of enhancing their performance in underwater searching tasks amidst oceanic disturbances. Initially, a real-world AUV experiment is conducted to validate the efficacy of a cross-rudder AUV configuration in maintaining sailing angle stability during the diving stage, which exhibits a strong capability for straight-line sailing. Building upon the experimental findings, we introduce a state-transform-model predictive guide law to compute the desired velocity for the dynamics loop. This guide law dynamically adjusts the controller across varying depths, thereby reducing model predictive control (MPC) computation while optimizing timing without compromising precision or convergence speed. Subsequently, we incorporate a sliding mode controller with a prescribed disturbance observer into the velocity control loop to concurrently enhance the robustness and convergence rate of the system. This innovative amalgamation of controllers significantly improves tracking precision and convergence rate, while also alleviating the computational burden—a pervasive challenge in AUV MPC control. Finally, various condition simulations are conducted to validate the robustness, effectiveness, and superiority of the proposed method. These simulations underscore the enhanced performance and reliability of our proposed trajectory tracking framework, highlighting its potential utility in real-world AUV applications.

The Schlieren technique is widely adopted for visualizing supersonic jets owing to its non-invasiveness to the flow field. However, extending the classical Schlieren method for quantitative refractive index measurements is cumbersome, especially for three-dimensional supersonic flows. Background-oriented Schlieren has gained increasing popularity owing to its ease of implementation and calibration. This study utilizes multi-view-based tomographic background-oriented Schlieren (TBOS) to reconstruct axisymmetric and weakly non-axisymmetric supersonic jets, highlighting the impact of flow axisymmetry breaking on TBOS reconstructions. Several classical TBOS reconstruction algorithms, including FDK, SART, SIRT, and CGLS, are compared quantitatively regarding reconstruction quality. View spareness is identified to be the main cause of degraded reconstruction quality when the flow experiences axisymmetry breaking. The classic visual hull approach is explored to improve reconstruction quality. Together with the CGLS tomographic algorithm, we successfully reconstruct the weakly non-axisymmetric supersonic jet structures and confirm that increasing the nozzle bevel angle leads to wider jet spreads.

The positioning of civil aviation aircraft relative to a geographic reference point on Earth in a Cartesian frame is significant to detect the deviations from the desired path, especially for high-altitude airports or special airports based on performance-based navigation (PBN). To obtain these critical deviations during aircraft approach and landing, it is fundamental to estimate the continuous flight variables and discrete flight modes simultaneously with enough accuracy. With the coordinate conversion between the North, East, and Down (NED) frame and the geographic coordinate system based on World Geodetic System 1984 (WGS-84) considered, this study proposed a non-linear stochastic hybrid estimation algorithm with adaptive square-root unscented particle filtering (ASR-UPF) to estimate the true path. The probabilities of mode transition, represented by the normal cumulative density function of continuous states, determine whether to proceed with mode transitions. In addition, the adaptive update characterized by tracking variable noise and the importance sampling distributions based on the results of square-root unscented Kalman filtering (SR-UKF), as a comparative study of continuous system filtering, were used. The experiments illustrated the ASR-UPF is able to reduce the state estimation error more effectively, and more promptly track the error caused by incorrect mode estimation with adaptability compared to the SR-UKF. A further test with real flight data indicates that the proposed method gives the refined estimation of position and azimuth in NED frame.

Piezoelectric-actuated precision positioning stages are widely used in high-precision instruments and high-end equipment due to their advantages of high resolution, fast response, and compact size. However, due to the strong nonlinearity of hysteresis, the presence of hysteresis in piezoelectric actuators seriously affects the positioning accuracy of the system. In addition, it is challenging to identify the model parameters for hysteresis. In this paper, an adaptive backstepping time delay control method is proposed for piezoelectric devices system with unknown hysteresis. Firstly, the Bouc–Wen model is used to describe the hysteresis characteristics, and the model is interpreted as a linear term and a bounded uncertain hysteresis term. Then, the time delay estimation technique is used to estimate the hysteresis term of the Bouc–Wen model online, and the unknown parameters of the system and hysteresis model are obtained through adaptive updating laws. Furthermore, the stability of the control scheme is proved based on Lyapunov stability theory. Finally, the effectiveness and superiority of the proposed control scheme are demonstrated by comparing it with two typical hysteresis compensation control algorithms through three different sets of input signals.

The rapid development of artificial intelligence (AI) and deep learning has revolutionized the field of data analysis in recent years, including signal data acquired by remote sensors. Light Detection and Ranging (LiDAR) technology is widely used in atmospheric research for measuring various atmospheric parameters. Wind measurement using LiDAR data has traditionally relied on the spectral centroid (SC) algorithm. However, this approach has limitations in handling LiDAR data, particularly in low signal-to-noise ratio (SNR) regions. To overcome these limitations, this study leverages the capabilities of customized deep-learning techniques to achieve accurate wind profile reconstruction. The study uses datasets obtained from the European Centre for Medium Weather Forecasting (ECMWF) Reanalysis v5 (ERA5) and the mobile Incoherent Doppler LiDAR (ICDL) system constructed by the University of Science and Technology of China. We present a simulation-based approach for generating wind profiles from the statistical data and the associated theoretical calculations. Whereafter, our team constructed a convolutional neural network (CNN) model based on the U-Net architecture to replace the SC algorithm for LiDAR data post-processing. The CNN-generated results are evaluated and compared with the SC results and the ERA5 data. This study highlights the potential of deep learning-based techniques in atmospheric research and their ability to provide more accurate and reliable results.

This article presents lithium niobate (LiNbO3) based on shear horizontal (SH0) resonators, utilizing a suspended structure, for radio frequency (RF) applications. It demonstrates the design, analysis, and fabrication of SH0 resonators based on a 36Y-cut LiNbO3 thin film. The spurious-free SH0 resonator achieves an electromechanical coupling coefficient (kt2) of 42.67% and a quality factor (Qr) of 254 at the wave-propagating orientation of 0° in the 36Y-cut plane.

In this study, a refined model of the Shanghai Damaogang Bridge’s (hybrid beam type) box deck joints is established. The correctness of the model is verified by construction monitoring. For the front and back bearing plates, the force performance of the joint members under the most unfavorable loads is investigated, and the force transmission mechanism is analyzed. The influence of the bearing plate thickness and the joints’ stiffness on the stress distribution of the joint members, the internal force of the joints, and the force-transfer efficiency is investigated by the method of controlling variables, and the optimal structural parameters of the nodes are also studied. The results show that, within the proximity of the back bearing plate, the thickness of the back bearing plate affects stress distribution in the joint. The increased stiffness of the welding studs makes the range of shear force along the bridge direction of the top and bottom welding studs larger, and the longitudinal distribution of welding stud shear force is more uneven. The concrete structure bears a higher proportion of the internal force in the joint compared to the steel structure.

Credit risk prediction heavily relies on historical data provided by financial institutions. The goal is to identify commonalities among defaulting users based on existing information. However, data on defaulters is often limited, leading to a concentration of credit data where positive samples (defaults) are significantly fewer than negative samples (nondefaults). It poses a serious challenge known as the class imbalance problem, which can substantially impact data quality and predictive model effectiveness. To address the problem, various resampling techniques have been proposed and studied extensively. However, despite ongoing research, there is no consensus on the most effective technique. The choice of resampling technique is closely related to the dataset size and imbalance ratio, and its effectiveness varies across different classifiers. Moreover, there is a notable gap in research concerning suitable techniques for extremely imbalanced datasets. Therefore, this study aims to compare popular resampling techniques across different datasets and classifiers while also proposing a novel hybrid sampling method tailored for extremely imbalanced datasets. Our experimental results demonstrate that this new technique significantly enhances classifier predictive performance, shedding light on effective strategies for managing the class imbalance problem in credit risk prediction.

The connotation and measurement standard of “fairness” in parkland planning is closely related to the level of urban development and residents’ needs, with solid realism and dynamics, and it is still a complex problem to control accurately in planning. Therefore, to conduct a more comprehensive study, taking the main urban area of Harbin as an example, this paper combined urban development background and practical problems at different stages and, based on POI and AOI data, divided the connotation of “equity” in park green space planning into two levels: “spatial equity” and “social equity”. Based on these two levels of fairness, the research framework of urban park green space layout is constructed. Kernel density estimation and GIS-based network analysis were used to study the spatial layout and accessibility of urban parkland. The ability to match supply and demand for parkland in the study area as a whole was analyzed using a gridded two-step moving search method, as well as the degree of equity in the enjoyment of urban parkland by vulnerable groups including the elderly and children using the Lorenz curve with the Gini coefficient, the share index, and the entropy of the locality. The results show that: (1) the parkland is concentrated in the seven main urban areas of the two–two junctions, and the cohesion distribution trend is outside the distribution. (2) Motorized vehicles have the best 15-min accessibility for travel, but it still does not cover all the parkland. (3) Supply and demand within the region are unsatisfactory, with the area with the strongest supply and demand capacity for parkland accounting for only 3.19% of the total area. (4) The Gini coefficient of parkland enjoyed by the residents of Harbin city center is 0.407, with a massive gap in the overall distribution. In addition, the share index of vulnerable groups of the elderly and children all have share indices below 1, and the results of the analysis of locational entropy indicate that the level of street park green space resources enjoyed by these two groups belongs to the low socio-spatial equity pattern, which is not balanced. This study investigates the spatial layout of urban parkland from two equity levels, which can provide new ideas for the equity study of urban green space planning.

Inspired by the biological nervous system, deep neural networks (DNNs) are able to achieve remarkable performance in various tasks. However, they struggle to handle label noise, which can poison the memorization effects of DNNs. Co-teaching-based methods are popular in learning with noisy labels. These methods cross-train two DNNs based on the small-loss criterion and employ a strategy using either “disagreement” or “consistency” to obtain the divergence of the two networks. However, these methods are sample-inefficient for generalization in noisy scenarios. In this paper, we propose CoDC, a novel Co-teaching-basedmethod for accurate learning with label noise via both Disagreement and Consistency strategies. Specifically, CoDC maintains disagreement at the feature level and consistency at the prediction level using a balanced loss function. Additionally, a weighted cross-entropy loss is proposed based on information derived from the historical training process. Moreover, the valuable knowledge involved in “large-loss” samples is further developed and utilized by assigning pseudo-labels. Comprehensive experiments were conducted on both synthetic and real-world noise and under various noise types. CoDC achieved 72.81% accuracy on the Clothing1M dataset and 76.96% (Top1) accuracy on the WebVision1.0 dataset. These superior results demonstrate the effectiveness and robustness of learning with noisy labels.

Underwater bionic-legged robots encounter significant challenges in attitude, velocity, and positional control due to lift and drag in water current environments, making it difficult to balance operational efficiency with motion stability. This study delves into the hydrodynamic properties of a bionic crab robot’s shell, drawing inspiration from the sea crab’s motion postures. It further refines the robot’s underwater locomotion strategy based on these insights. Initially, the research involved collecting attitude data from crabs during underwater movement through biological observation. Subsequently, hydrodynamic simulations and experimental validations of the bionic shell were conducted, examining the impact of attitude parameters on hydrodynamic performance. The findings reveal that the transverse angle predominantly influences lift and drag. Experiments in a test pool with a crab-like robot, altering transverse angles, demonstrated that increased transverse angles enhance the robot’s underwater walking efficiency, stability, and overall performance.

In recent decades, extensive research has led to the understanding that Mars once hosted substantial liquid-water reserves. While the current Martian landscape boasts significant water-ice deposits at its North and South poles, the elusive presence of liquid-water bodies has remained undetected. A breakthrough occurred with the identification of radar-echo reflections at the base of the Martian South Pole, using MARSIS (Mars Advanced Radar for Subsurface and Ionospheric Sounding) in 2018. These radar echoes strongly suggest the presence of a highly concentrated liquid-water body. However, a counter-narrative has emerged, contending that the subterranean conditions beneath the ice cap, encompassing factors like temperature and pressure, may be inhospitable to liquid water. Consequently, alternative hypotheses posit that the observed bright echoes could be attributed to conductive minerals or water-absorbing clay-like materials. The ongoing discourse regarding the presence of liquid water beneath the southern polar ice cap is a hot topic in the realm of Martian exploration. The primary focus of this paper is to provide a comprehensive overview of Martian radar detection, the recent controversies regarding liquid water’s existence in the Martian South Pole, and the implications regarding the potential existence of Martian life forms in the water on Mars. The revelation of liquid water on Mars fundamentally suggests an environment conducive to the viability of Martian life, consequently furnishing invaluable insights for future exploratory endeavors in the pursuit of Martian biospheres. In addition, this paper anticipates the forthcoming research dedicated to Martian liquid water and potential life forms, while also underscoring the profound significance of identifying liquid water on Mars in propelling the field of astrobiology forward.

Attack investigation is an important research field in forensics analysis. Many existing supervised attack investigation methods rely on well-labeled data for effective training. While the unsupervised approach based on BERT can mitigate the issues, the high degree of similarity between certain real-world attacks and normal behaviors makes it challenging to accurately identify disguised attacks. This paper proposes ConLBS, an attack investigation approach that combines the contrastive learning framework and multi-layer transformer network to realize the classification of behavior sequences. Specifically, ConLBS constructs behavior sequences describing behavior patterns from audit logs, and a novel lemmatization strategy is proposed to map the semantics to the attack pattern layer. Four different augmentation strategies are explored to enhance the differentiation between attack and normal behavior sequences. Moreover, ConLBS can perform unsupervised representation learning on unlabeled sequences, and can be trained either supervised or unsupervised depending on the availability of labeled data. The performance of ConLBS is evaluated in two public datasets. The results show that ConLBS can effectively identify attack behavior sequences in the cases of unlabeled data or less labeled data to realize attack investigation, and can achieve superior effectiveness compared to existing methods and models.

With the development of IoT technology, many dimensions of data are generated in the environment where we live. The study of these data is critical to our understanding of the relationships between people and between people and cities. The core components of IoT technology are sensors and control circuits. However, merging various sensor data and real-time data processing is often a difficult problem, usually related to factors such as coverage, lighting conditions, and accuracy of object detection. Therefore, we firstly propose a wireless transmission hardware architecture for data acquisition mainly based on vision sensors, and at the same time, incorporate some sensors for data calibration to improve the accuracy of data detection. The collected data are fed back to the edge computing platform for fast processing. The edge platform is designed with a lightweight target detection model and data analysis model. Through this multidimensional data collection and analysis, a generalised functional model for public space utilization can be fitted, which enables the calculation of utilization rates for any parameter in public space. The technology improves a technical reference for multi-dimensional data collection and analysis.

Bionic-legged robots draw inspiration from animal locomotion methods and structures, demonstrating the potential to traverse irregular and unstructured environments. The ability of Portunus trituberculatus (Portunus) to run flexibly and quickly in amphibious environments inspires the design of systems and locomotion methods for amphibious robots. This research describes an amphibious crab-like robot based on Portunus and designs a parallel leg mechanism for the robot based on biological observations. The research creates the group and sequential gait commonly used in multiped robots combined with the form of the robot’s leg mechanism arrangement. This research designed the parallel leg mechanism and modeled its dynamics. Utilizing the outcomes of the dynamics modeling, we calculate the force and torque exerted on each joint of the leg mechanism during group gait and sequential gait when the robot is moving with a load. This analysis aims to assess the performance of the robot’s motion. Finally, a series of performance evaluation experiments are conducted on land and underwater, which show that the amphibious crab-like robot has good walking performance. The crab-like robot can perform forward, backward, left, and right walking well using group and sequential gaits. Simultaneously, the crab-like robot showcases faster movement in group gaits and a more substantial load capacity in sequential gaits.

With the rapid development of the air transportation industry, the air traffic situation is becoming more and more complicated. Determining the situation of air traffic is of great significance to ensure the safety and smoothness of air traffic. The strong subjectivity of assessment criteria, inaccurate assessment results and weak systemic assessment method are the main problems in air traffic situation assessment research. The aim of our research is to present an objective and accurate situation assessment method for air traffic systems. The paper presents a model to assess air traffic situation based on the complex network theory and ensemble learning. The air traffic weighted network model was introduced to systematically describe the real state of an air traffic system. Assessment criteria based on the complex network analysis method can systematically reflect the operational state of an air traffic weighted network system. We transformed the air traffic situation assessment into a binary classification, which makes situation assessment objective and accurate. Ensemble learning was introduced to improve the classification accuracy, which further improves the accuracy of the situation assessment model. The model was trained and tested on the dataset of the East China air traffic weighted network in 2019. Its average classification accuracy is 0.98. The recall and precision rates both exceed 0.95. Experiments have confirmed that the situation assessment model can accurately output air traffic situation value and situation level. Furthermore, the assessment results are consistent with the real operational situation of the air traffic in East China.

The plasma analyzer (PMA) on the Fengyun-3E (FY-3E) meteorological satellite series is a critical sensor of the space environment monitoring package that is capable of the comprehensive in situ detection of the thermal plasma environment and surface discharge effects. In this paper, we conducted a thorough evaluation of the PMA’s performance and reliability through a combination of ground-based laboratory calibration and in-orbit testing. During the ground-based calibration, the PMA underwent assessments for the energy range, field of view (FOV), and measurement accuracy, and obtained the detection accuracy and the geometric factors. During the in-orbit testing, the PMA successfully obtained the typical distribution characteristics of low-energy ions and electrons in orbital space regions, as well as the precipitating particles in the middle and high latitudes of both hemispheres. Notably, the PMA observed an expansion of the particle distribution in the high-latitude regions during a moderate geomagnetic storm. The results from both the ground-based calibration and in-orbit testing demonstrated that the PMA met the requirements for thermal plasma detection, with reliable and scientifically valid in-orbit detection data. These results provide a crucial foundation for studying spatial weather variations, improving the accuracy of space environment forecasts and enhancing disaster detection and monitoring capabilities.

To effectively utilize the effectively the solid waste-soda residue (SR) and fly ash (FA), a mixture of 70% SR, 20% FA, and 10% clay was formed to produce soda residue soil (SRS). By adding cement and/or lime, four different SRS proportions were prepared. The compaction properties, mechanical properties, and dry–wet stability. Furthermore, the mechanical mechanisms of SRS were explored, and the improvement effect and efficiency of cement and/or lime on the mechanical performance of SRS were analyzed. The results show that the SRS is lightweight and easy to compact and shape, which is conducive to construction. Compared to cement, lime has a greater impat on the compaction properties of SRS. The mechanical properties of SRS are enhanced after adding cement and/or lime; the unconfined compressive strength (UCS), California Bearing Ratio (CBR), and resilient modulus all increase significantly. The UCS, CBR, and resilient modulus of SRS with different proportions all increase with the increase of compaction degree; the CBR and resilient modulus can meet the requirements of the subgrade, and the UCS can meet the requirements of the base and sub-base. The contribution rate of cement per unit amount is better than that of lime to the mechanical performance of SRS. As dry–wet cycles increase, the dry–wet stability of SRS with different proportions first increases and then decreases, which is affected by hydration and dry–wet deterioration together, with a critical cycle number for the strength transition. The excellent mechanical performance of SRS benefits from the gradation and chemical action. The particles of SR, FA, and clay can complement each other and perform an interlocking action. Therefore, SRS has a good gradation and forms a dense and stable structure. Also, the chemical reactions between materials are very important. The cement and/or lime have a hydration and gelling effect, FA and clay participate in pozzolanic reactions, and SR and FA have certain alkali-activated effects.

Studying the distribution characteristics of bioaerosols and their interaction with the environment is crucial for dairy farms. The distribution of aerosols differs in dairy farming from farming of other livestock, and their sensitivity to environmental factors varies across sites. Field experiments were conducted in an intensive commercial dairy farm in Northern China to investigate the horizontal and vertical distribution of culturable bacterial bioaerosols. Concentration levels and particle size ranges were analyzed, and the impact of multiple environmental factors on culturable bacterial bioaerosols was assessed. Significant variations in culturable bacterial bioaerosol concentrations were observed across eight functional zones, ranging from 1.14 × 103 to 7.35 × 103 CFU/m3. Culturable bacterial bioaerosols exhibited consistent carrier distribution patterns across six different size ranges. Vertical analysis revealed significantly higher culturable bacterial bioaerosol concentrations at a 1 m height compared to 4 m (p < 0.05), while similar size distributions were observed at different heights of the same sampling location. The top three environmental factors influencing culturable bacterial bioaerosol concentrations were PM100 concentration, wind direction, and air temperature. This study provides insights into the distribution characteristics of culturable bacterial bioaerosols on dairy farms and their response to environmental factors. The findings serve as a reference for evaluating bioaerosol emissions and establishing daily disinfection management measures on dairy farms.

This study is a driving analysis of the transfer data of container terminals based on simulation interactive modeling technology. In the context of a container yard, a model was established to analyze and predict the arrival time and influencing factors of container transportation through the data from the control center of the yard. The economic benefit index in the index system was determined through expert consultation, the automatic terminal can be obtained by acquiring the actual operating parameters of the terminal, and the terminal to be built can be acquired mainly through simulation modeling. Therefore, when determining the design scheme before constructing the automated container terminal, a terminal simulation model needs to be established that meets the requirements of loading and unloading operations and terminal production operations. In addition, an automated container terminal simulation model needs to be implemented to verify the feasibility of the evaluation model. The results reveal that the accuracy of the current prediction model is still limited—the highest accuracy is only 72%, whether there are continuous or discrete variables, traffic or weather variables. Moreover, the study denotes that the relationship between weather and specific time factors and the arrival time of containers is weak, even negligible. This study provides guidance and decision-making support for the construction of automated terminals.

To address the search-and-docking problem in multi-stage prescribed performance switching (MPPS) scenarios, this paper presents a novel compound control method for three-dimensional (3D) underwater trajectory tracking control of unmanned underwater vehicles (UUVs) subjected to unknown disturbances. The proposed control framework can be divided into two parts: kinematics control and dynamics control. In the kinematics control loop, a novel parallel model predictive control (PMPC) law is proposed, which is composed of a soft-constrained model predictive controller (SMPC) and hard-constrained model predictive controller (HMPC), and utilizes a weight allocator to enable switching between soft and hard constraints based on task goals, thus achieving global optimal control in MPPS scenarios. In the dynamics control loop, a finite-time terminal sliding mode control (FTTSMC) method combining a finite-time radial basis function neural network adaptive disturbance observer (RBFNN-FTTSMC) is proposed to achieve disturbance estimation and fast convergence of velocity tracking errors. The simulation results demonstrate that the proposed PMPC-FTTSMC approach achieved an average improvement of 33% and 80% in the number of iterations compared with MPC with sliding mode control (MPC-SMC) and traditional MPC methods, respectively. Furthermore, the approach improved the speed of response by 35% and 44%, respectively, while accurately achieving disturbance observation and enhancing the system robustness.

To study the mechanical properties of steel–concrete joints during construction, the Mao Port Bridge in Shanghai is used as a case study. The mechanical properties of the bridge and the joint under the construction conditions were studied based on the site construction monitoring results, the finite element calculation of the entire bridge and the refined model of the joint. The results show that the finite element analysis of the bridge and the stress analysis of the joint during the construction phase agreed with the measured values, the end of block 0# of the main span remained in compression during construction and the compressive stresses varied in a zigzag pattern with the progress of construction. The lifting of the mid-span steel beam is a critical construction condition where the side spans of the girders are stretched upwards by 20.9 mm and the main spans are stretched downwards by 32.3 mm. When the steel beam was lifted, the joint was compressed as a whole. At the joint, the longitudinal stresses in the steel structure gradually decreased from the front bearing plate to the joint face, while the longitudinal stresses in the concrete structure gradually increased.

In 6G-oriented vehicular Internet of things (IoT) services, the integration of a low altitude platform (LAP) and intelligent reflecting surfaces (IRS) provides a promising solution to achieve seamless coverage and massive connections at low cost. However, due to the open nature of wireless channels, how to protect the transmission of privacy information in LAP-based IRS symbiotic vehicular networks remains a challenge. Motivated by the above, this paper investigates the LAP and IRS enhanced secure transmission problem in the presence of an eavesdropper. Specifically, we first deploy a fixed LAP equipped with IRS to overcome the blockages and introduce artificial noise against the eavesdropper. Next, we formulate a total secure channel capacity maximization problem by optimizing the phase shift, power distribution coefficient, and channel allocation. To effectively solve the formulated problem, we design an iterative algorithm with polynomial complexity, where the optimization variables are solved in turn. In addition, the complexity and convergence of the proposed iterative algorithm are analyzed theoretically. Finally, numerical results show that our proposed secure transmission scheme outperforms the comparison schemes in terms of the total secure channel capacity.

Maize is the most important cereal crop globally. However, in recent years, maize production faced numerous challenges from environmental factors due to the changing climate. Salt stress is among the major environmental factors that negatively impact crop productivity worldwide. To cope with salt stress, plants developed various strategies, such as producing osmolytes, increasing antioxidant enzyme activity, maintaining reactive oxygen species homeostasis, and regulating ion transport. This review provides an overview of the intricate relationships between salt stress and several plant defense mechanisms, including osmolytes, antioxidant enzymes, reactive oxygen species, plant hormones, and ions (Na+, K+, Cl−), which are critical for salt tolerance in maize. It addresses the regulatory strategies and key factors involved in salt tolerance, aiming to foster a comprehensive understanding of the salt tolerance regulatory networks in maize. These new insights will also pave the way for further investigations into the significance of these regulations in elucidating how maize coordinates its defense system to resist salt stress.

The preoperative differentiation of breast phyllodes tumors (PTs) from fibroadenomas (FAs) plays a critical role in identifying an appropriate surgical treatment. Although several imaging modalities are available, reliable differentiation between PT and FA remains a great challenge for radiologists in clinical work. Artificial intelligence (AI)-assisted diagnosis has shown promise in distinguishing PT from FA. However, a very small sample size was adopted in previous studies. In this work, we retrospectively enrolled 656 breast tumors (372 FAs and 284 PTs) with 1945 ultrasound images in total. Two experienced ultrasound physicians independently evaluated the ultrasound images. Meanwhile, three deep-learning models (i.e., ResNet, VGG, and GoogLeNet) were applied to classify FAs and PTs. The robustness of the models was evaluated by fivefold cross validation. The performance of each model was assessed by using the receiver operating characteristic (ROC) curve. The area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were also calculated. Among the three models, the ResNet model yielded the highest AUC value, of 0.91, with an accuracy value of 95.3%, a sensitivity value of 96.2%, and a specificity value of 94.7% in the testing data set. In contrast, the two physicians yielded an average AUC value of 0.69, an accuracy value of 70.7%, a sensitivity value of 54.4%, and a specificity value of 53.2%. Our findings indicate that the diagnostic performance of deep learning is better than that of physicians in the distinction of PTs from FAs. This further suggests that AI is a valuable tool for aiding clinical diagnosis, thereby advancing precision therapy.

This paper investigates the secure rate-splitting multiple access (RSMA) cooperation for the maritime cognitive unmanned aerial vehicle (UAV) network. Specifically, we first take into account the primary privacy information and the secondary maritime UAV’s quality of service. Then, we formulate an optimization problem to maximize UAV’s transmission rate according to the RSMA decoding principle and primary information security requirements. To solve this non-convex problem, we design a CPFS algorithm to allocate the transmit power and adjust the UAV’s location. In addition, the worst case is analyzed, which is the lower-bound secondary transmission rate. Finally, simulation results indicate that the proposed scheme improves the UAV’s transmission rate compared with the traditional schemes.

Breast cancer is now the most common cancer worldwide, and it is also the main cause of cancer-related death in women. Survival rates for female breast cancer have significantly improved due to early diagnosis and better treatment. Nevertheless, for patients with advanced or metastatic breast cancer, the survival rate is still low, reflecting a need for the development of new therapies. Mechanistic insights into metastatic breast cancer have provided excellent opportunities for developing novel therapeutic strategies. Although high-throughput approaches have identified several therapeutic targets in metastatic disease, some subtypes such as triple-negative breast cancer do not yet have an apparent tumor-specific receptor or pathway to target. Therefore, exploring new druggable targets in metastatic disease is a high clinical priority. In this review, we summarize the emerging intrinsic therapeutic targets for metastatic breast cancer, including cyclin D-dependent kinases CDK4 and CDK6, the PI3K/AKT/mTOR pathway, the insulin/IGF1R pathway, the EGFR/HER family, the JAK/STAT pathway, poly(ADP-ribose) polymerases (PARP), TROP-2, Src kinases, histone modification enzymes, activated growth factor receptors, androgen receptors, breast cancer stem cells, matrix metalloproteinases, and immune checkpoint proteins. We also review the latest development in breast cancer immunotherapy. Drugs that target these molecules/pathways are either already FDA-approved or currently being tested in clinical trials.

One-step fermentation, inoculated with Lactobacillus fermentum (L. fermentum) in shrimp by-products, was carried out to obtain chitin and flavor protein hydrolysates at the same time. The fermentation conditions were optimized using response surface methodology, resulting in chitin with a demineralization rate of 89.48%, a deproteinization rate of 85.11%, and a chitin yield of 16.3%. The surface of chitin after fermentation was shown to be not dense, and there were a lot of pores. According to Fourier transform infrared spectroscopy and X-ray diffraction patterns, the fermented chitin belonged to α-chitin. More than 60 volatiles were identified from the fermentation broth after chitin extraction using gas chromatography–ion transfer spectrometry analysis. L. fermentum fermentation decreased the intensities of volatile compounds related to unsaturated fatty acid oxidation or amino acid deamination. By contrast, much more pleasant flavors related to fruity and roasted aroma were all enhanced in the fermentation broth. Our results suggest an efficient one-step fermentation technique to recover chitin and to increase aroma and flavor constituents from shrimp by-products.

The construction of transformer substations in transmission lines is a systematic, technical, and complex project with the need for numerous materials and resources. Under the development of the green economy, the requirements for energy conservation and carbon reduction have improved; hence, an assessment of carbon emissions in transformer substations is urgently needed. A calculation method was proposed in the present study to analyze the carbon emissions of transformer substations with different kinds of data sources, which were collected from several practical projects in the west-to-east power transmission project. In this study, a detailed comparison and discussion regarding the differences in carbon emissions of 750 kV transformer substations caused by hydrology, geology, engineering quantity, and other factors were conducted. The mean value, standard deviation, and 90% confidence interval of carbon emissions were obtained by Monte Carlo simulation through MATLAB. Results show that the total carbon emissions of the selected 750 kV transformer substations are between [56,000, 68,000] t CO2 eq. Construction engineering accounts for more than 50% of carbon emissions, followed by installation engineering and additional services. In terms of input items, electricity distribution buildings contribute more than 39% of total carbon emissions, followed by cable/earthing systems, which account for 14% of total carbon emissions. Gas insulated switchgear (GIS) and air insulated switchgear (AIS) could adopt different types of equipment foundations, and GIS equipment foundations would generate fewer carbon emissions due to the smaller land area and input materials. This study can provide experience and reference for similar projects and further guide the substation carbon emission reduction work.

With the remarkable development of deep learning in the field of science, deep neural networks provide a new way to solve the Stefan problem. In this paper, deep neural networks combined with small sample learning and a general deep learning framework are proposed to solve the two-dimensional Stefan problem. In the case of adding less sample data, the model can be modified and the prediction accuracy can be improved. In addition, by solving the forward and inverse problems of the two-dimensional single-phase Stefan problem, it is verified that the improved method can accurately predict the solutions of the partial differential equations of the moving boundary and the dynamic interface.

Autonomous decision-making for ships to avoid collision is core to the autonomous navigation of intelligent ships. In recent years, related research has shown explosive growth. However, owing to the complex constraints of navigation environments, the Convention of the International Regulations for Preventing Collisions at Sea, 1972 (COLREGs), and the underactuated characteristics of ships, it is extremely challenging to design a decision-making algorithm for autonomous collision avoidance (CA) that is practically useful. Based on the investigation of many studies, current decision-making algorithms can be attributed to three strategies: alteration of course alone, alteration of speed alone, and alteration of both course and speed. This study discusses the implementation methods of each strategy in detail and compares the specific ways, applicable scenes, and limiting conditions of these methods to achieve alteration of course and/or speed to avoid collision, especially their advantages and disadvantages. Additionally, this study quantitatively analyzes the coupling mechanisms of alterations of course and speed for autonomous CA decision-making under different encounter situations, supplementing and optimizing the decision-making theory for ship autonomous CA. Finally, several feasible algorithms and improvement schemes for autonomous CA decision-making, combined with course and speed alterations, are discussed.

The radiation damage effect is relatively serious during spacecraft operation in orbit. To know the real cumulative dose in orbit accurately, a radiation dose detector is carried on the FY-4B satellite. It comprises one electric control unit and three dose detectors, with a total of four payloads. Each dose detector includes five dose-monitoring points corresponding to different shielding thicknesses, which are mainly used for the measurement of the dose depth. Three dose detectors with the same function are installed in the X, Y and Z positions and can detect the total dose in different directions during the operation of the satellite. Each detector has the characteristics of small size, high integration and low power consumption. At present, the FY-4B satellite is in orbit. The detectors have been working normally since they were started up, and good preliminary detection results have been obtained. From the power-up of the detectors to April 2022, the maximum dose has reached 2.7 × 103 rad(Si). We conducted a preliminary analysis of the relationship between total dose and shielding thickness. In addition, the relationship between daily average dose and shielding thickness was further analyzed. These studies have application value for radiation protection design.

This article introduces the instrument design of the medium energy proton detector (energy range: 30 keV–5 MeV) mounted on the FY-3E satellite. Through the design and optimization of the sensor signal processing circuit, the anti-electromagnetic interference ability of the medium energy particle detector is greatly enhanced. The designed aluminum plating on sensors can effectively exclude the light pollution to the medium energy protons. The designed permanent annular magnet has a deflection efficiency of more than 95% for medium energy electrons below 1.0 MeV. Additionally, by designing the logical working mode of the sensor, the contamination by other high energy particles (high energy electrons > 1.5 MeV, high energy protons > 5 MeV, and heavy ions) is excluded. Combining the above methods, the detector achieves the detection lower limit of 30 keV for medium energy protons. Its energy resolution is better than 15%@100 keV and the mixing ratio of electrons is less than 2%.

According to the characteristics of the LEO space particles radiation environment of China’s Fengyun No. 3 (FY-3) polar-orbiting meteorological satellites, in order to monitor the characteristics, and space–time distribution of charged particle radiation in the orbit space, it is proposed to install a three-directional high-energy particle detector (HEPD) in the three vertical orthogonal directions of FY-3E, so as to carry out the energy spectrum and flux observation of high-energy protons and electrons in the three directions of the satellite, namely, −X, +Y, and −Z. The on-orbit detection data acquired by these payloads can be used for space environment modeling and solar-terrestrial physics research, and provide data sources for operational space environment weather warning and forecasting. Through the ground accelerator calibration experiment and simulation analysis of the three-directional HEPDs developed in the flight model phase, the experimental results show that all the HEPDs’ measured values meet the requirements for technical indexes, such as the detection energy range (high-energy protons: 3–300 MeV; high-energy electrons: 0.15–5.7 MeV), energy span accuracy (

With the quick advancement of new generation information technologies like the Internet, big data, cloud computing, artificial intelligence, and blockchain, digitalization is emerging as a crucial tool for restructuring factor resources, reshaping the economic landscape, and altering competitiveness. However, there is no literature on the impact and mechanism of digitalization on environmental pollution management. Thus, this study measured the level of digital development in each province using principal component analysis based on panel data of 30 Chinese provinces during 2006–2019. On this basis, the impact of digitalization on haze and its mechanisms were explored using regression models and mediating effect models, respectively. The findings demonstrate that (1) haze pollution may be reduced through digital progresses, and there is an inverse U-shaped non-linear link between them, that is, as digital technology is refined, its impact on haze pollution shifts from facilitation to suppression; (2) digital development can reduce haze pollution by promoting technological innovation and improving the efficiency of environmental management; and (3) there is regional heterogeneity in the influence of digitalization on air pollution. In heavily polluted areas, the suppression effect of digital technology on air pollution is more than three times that of other areas. The goal of this study is to investigate how digitalization affects haze pollution and its mechanisms, as well as to offer some scientific guidance for China’s efforts to build a “Digital China” under the banner of digitalization.

Spectrum resources are becoming increasingly crowded, and the isolation interval between different systems is getting smaller and smaller. This puts forward higher requirements for the duplexer. The duplexer is an important part of the radio frequency front end, and the isolation requirement is becoming higher. This paper presents a phase canceling circuit to improve the performance of the duplexer to meet the requirement of the communication system for isolation. A phase canceling circuit is an effective method to enhance the isolation through use of a surface acoustic wave (SAW) on-chip circuit. It contains a duplexer and a branch. The branch is designed for diminishing the leakage signal of the duplexer. Compared with the leakage signal, the branch consists of two attenuators and a phase shifter to generate a signal which has equal extent and reverse phase. As a result, this method is capable of increasing the isolation of band 5 by 12 dB in the downlink frequency. Meanwhile, it neither affects other factors, such as insertion loss or return loss, nor increases the size of the chip. The phase canceling circuit is expected to promote the quality of duplexer to satisfy the strict requirements in 4G and 5G systems.

The images acquired by a single visible light sensor are very susceptible to light conditions, weather changes, and other factors, while the images acquired by a single infrared light sensor generally have poor resolution, low contrast, low signal-to-noise ratio, and blurred visual effects. The fusion of visible and infrared light can avoid the disadvantages of two single sensors and, in fusing the advantages of both sensors, significantly improve the quality of the images. The fusion of infrared and visible images is widely used in agriculture, industry, medicine, and other fields. In this study, firstly, the architecture of mainstream infrared and visible image fusion technology and application was reviewed; secondly, the application status in robot vision, medical imaging, agricultural remote sensing, and industrial defect detection fields was discussed; thirdly, the evaluation indicators of the main image fusion methods were combined into the subjective evaluation and the objective evaluation, the properties of current mainstream technologies were then specifically analyzed and compared, and the outlook for image fusion was assessed; finally, infrared and visible image fusion was summarized. The results show that the definition and efficiency of the fused infrared and visible image had been improved significantly. However, there were still some problems, such as the poor accuracy of the fused image, and irretrievably lost pixels. There is a need to improve the adaptive design of the traditional algorithm parameters, to combine the innovation of the fusion algorithm and the optimization of the neural network, so as to further improve the image fusion accuracy, reduce noise interference, and improve the real-time performance of the algorithm.

Bowel sounds contain some important human physiological parameters which can reflect information about intestinal function. In this work, in order to realize real-time monitoring of bowel sounds, a portable and wearable bowel sound electronic monitor based on piezoelectric micromachined ultrasonic transducers (PMUTs) is proposed. This prototype consists of a sensing module to collect bowel sounds and a GUI (graphical user interface) based on LabVIEW to display real-time bowel sound signals. The sensing module is composed of four PMUTs connected in parallel and a signal conditioning circuit. The sensitivity, noise resolution, and non-linearity of the bowel sound monitor are measured in this work. The result indicates that the designed prototype has high sensitivity (−142.69 dB), high noise resolution (50 dB at 100 Hz), and small non-linearity. To demonstrate the characteristic of the designed electronic monitor, continuous bowel sound monitoring is performed using the electronic monitor and a stethoscope on a healthy human before and after a meal. Through comparing the experimental results and analyzing the signals in the time domain and frequency domain, this bowel sound monitor is demonstrated to record bowel sounds from the human intestine. This work displays the potential of the sensor for the daily monitoring of bowel sounds.

Bionic amphibious robots are the intersection of biology and robotics; they have the advantages of environmental adaptability and maneuverability. An amphibious robot that combines walking and swimming move modes inspired by a crab (Portunus) is presented in this article. The outstanding characteristic of the robot is that its environmental adaptability relies on the bionic multi-modal movement, which is based on two modular bionic swimming legs and six modular walking legs. We designed the biomimetic crab robot based on the biological observation results. The design, analysis, and simulation of its structure and motion parameters are introduced in this paper. The swimming propulsion capability and the walking performance are verified through indoor, pool, and seaside experiments. In conclusion, the designed bionic crab robot provides a platform with practical application capabilities in amphibious environment detection, concealed reconnaissance, and aquaculture.

Osteoarthritis (OA) is a low-level inflammatory disease in which synovial macrophage M1 polarization exacerbates the progression of synovitis and OA. Notedly, the ROS (reactive oxygen species) level in macrophages is intimately implicated in macrophage M1 polarization. TRPV4 (transient receptor potential channel subfamily V member 4), as an ion channel, plays a pivotal role in oxidative stress and inflammation. In this study, we investigated the role of TRPV4 in OA progression and M1 macrophage polarization. Male adult Sprague–Dawley (SD) rats underwent a medial meniscus radial transection operation to create an OA model in vivo and RAW 264.7 cells were intervened with 100 ng/mL LPS (lipopolysaccharide) to induce M1-polarized macrophages in vitro. We demonstrated that the infiltration of M1 synovial macrophages and the expression of TRPV4 were increased significantly in OA synovium. In addition, intra-articular injection of HC067074 (a specific inhibitor of TRPV4) alleviated the progression of rat OA and significantly decreased synovial macrophage M1 polarization. Further mechanisms suggested that ROS production by M1 macrophages was decreased after TRPV4 inhibition. In addition, NLRP3 (pyrin domain containing protein 3) as a downstream effector of ROS in M1-polarized macrophage, was significantly suppressed following TRPV4 inhibition. In conclusion, this study discovered that inhibition of TRPV4 delays OA progression by inhibiting M1 synovial macrophage polarization through the ROS/NLRP3 pathway.