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Autonomous laboratories can accelerate discoveries in chemical synthesis, but this requires automated measurements coupled with reliable decision-making 1 , 2 . Most autonomous laboratories involve bespoke automated equipment 3 – 6 , and reaction outcomes are often assessed using a single, hard-wired characterization technique 7 . Any decision-making algorithms 8 must then operate using this narrow range of characterization data 9 , 10 . By contrast, manual experiments tend to draw on a wider range of instruments to characterize reaction products, and decisions are rarely taken based on one measurement alone. Here we show that a synthesis laboratory can be integrated into an autonomous laboratory by using mobile robots 11 – 13 that operate equipment and make decisions in a human-like way. Our modular workflow combines mobile robots, an automated synthesis platform, a liquid chromatography–mass spectrometer and a benchtop nuclear magnetic resonance spectrometer. This allows robots to share existing laboratory equipment with human researchers without monopolizing it or requiring extensive redesign. A heuristic decision-maker processes the orthogonal measurement data, selecting successful reactions to take forward and automatically checking the reproducibility of any screening hits. We exemplify this approach in the three areas of structural diversification chemistry, supramolecular host–guest chemistry and photochemical synthesis. This strategy is particularly suited to exploratory chemistry that can yield multiple potential products, as for supramolecular assemblies, where we also extend the method to an autonomous function assay by evaluating host–guest binding properties. A modular autonomous platform for general exploratory synthetic chemistry uses mobile robots to integrate an automated synthesis platform and two analysis platforms.

The Internet of Things (IoT) extends the electronic connectivity into millions of IoT nodes around the world. In order to achieve the autonomy to make sound decisions based on the collected and analysed information, artificial intelligence algorithms are for networks to leverage to turn into autonomous IoT (AIoT). The core value of these AIoT applications is from analysing the information provided by these smart entities and performing autonomous decision-making. However, general issues in networks, such as the outlier raging, communication overhead, memory shortage, etc., render the information fusion process ineffective, which leads to the unreliable AIoT applications. Thus, the advanced information fusion methods must be proposed to address such issues for the sake of fully utilizing these smart applications and services.To solve such real-life problems, various information fusion approaches are developed for the AIoT systems. For the outlier raging problems in AIoT applications, the distance-based method inspired by the KNN rule has been used. In addition, artificial intelligence algorithms, such as the deep learning and reinforcement learning, are integrated with consensus theories to handle the distributed learning problems in AIoT applications for the sake of solving the communication overhead and memory shortage in networks. Also, the comprehensive evaluation works show the proposed approaches emerge the powerful capability, and they provide important operational advantages over traditional centralized methods and typical distributed approaches.The main contributions of this thesis are proposing two outlier detection methods with compelling features, such as low computational complexity and memory usage, for the outlier raging in networks, and one novel distributed learning framework that can save the average training time and memory storage compared with the typical methods.

High-performance refractory alloys with ultrahigh strength and ductility are in demand for a wide range of critical applications, such as plasma-facing components. However, it remains challenging to increase the strength of these alloys without seriously compromising their tensile ductility. Here, we put forward a strategy to “defeat” this trade-off in tungsten refractory high-entropy alloys by stepwise controllable coherent nanoprecipitations (SCCPs). The coherent interfaces of SCCPs facilitate the dislocation transmission and relieve the stress concentrations that can lead to premature crack initiation. As a consequence, our alloy displays an ultrahigh strength of 2.15 GPa with a tensile ductility of 15% at ambient temperature, with a high yield strength of 1.05 GPa at 800 °C. The SCCPs design concept may afford a means to develop a wide range of ultrahigh-strength metallic materials by providing a pathway for alloy design. Tungsten-based alloys with ultrahigh strength and ductility are in high demand for a wide range of applications, potentially for fusion reactors. Here the authors develop a tungsten refractory high-entropy alloy with high strength (~2.15 GPa) and sufficient ductility (~15%).

Background The number of chronic diseases has been associated with changes in depressive symptoms over time among middle-aged and older adults. This study aimed to explore the association between the number of chronic diseases and trajectories of depressive symptoms and the role of age in this association. Methods A total of 12,974 middle-aged and older Chinese adults (≥ 45 years) participated in the China Health and Retirement Longitudinal Study (CHARLS) in waves 2011, 2013, 2015, 2018, and 2020. The number of chronic diseases was determined by self-reported hospital diagnosis of hypertension, dyslipidemia, diabetes, cancer, chronic lung diseases, liver disease, heart diseases, stroke, kidney diseases, digestive diseases, emotional, nervous, or psychiatric problems, memory-related disease, arthritis or rheumatism, asthma, and then obtaining the total number of chronic diseases. Depressive symptoms were measured by the 10-item Center for Epidemiologic Studies Depression Scale (CESD-10). Group-based trajectory modeling (GBTM) was adopted to capture the trajectories of depressive symptoms over time. Multinomial logistic regressions were conducted to examine the association between the number of chronic diseases and trajectories of depressive symptoms and the role of age in this association. Results Four distinct trajectories of depressive symptoms were observed in 34.68% individuals in mild, 40.76% in moderate, 19.41% in increasing, and 5.15% in severe group. Compared to participants without chronic diseases, those with one, two, three or more chronic diseases had a 1.81, 3, and 7.49-fold higher risk of developing severe depressive symptom trajectory, respectively. Moreover, the association between the number of chronic diseases and severe depressive symptoms trajectory differed by age (45–59 and ≥ 60 years) ( P for interaction < 0.05). Conclusion Participants with middle age may play a promoting role in the association between the number of chronic disease and severe depressive symptoms. The severe depressive symptoms intervention may be more beneficial for middle-aged adults.

Water from hydraulic technology affects the desorption of gas from coal seams. Gas desorption behavior is critical information for gas control in coal mines. In this study, a designed coal seam water injection simulation experimental device was utilized to conduct dynamic water injection experiments on coal samples at different adsorption equilibrium pressures, analyzing the gas desorption law under dynamic water injection, as well as the role of water replacement, water imbibition and water blockage in gas desorption. The results showed that water altered the gas desorption rate in coal, causing fluctuating attenuation of the desorption rate of a water-injected coal sample (WCS). Under the same adsorption equilibrium pressure, the relationship between the desorption rate of the WCS and the non-water-injected coal samples (NCS) underwent a transition in desorption time. In contrast to the NCS desorption curves, the WCS desorption curves lacked a rapid growth phase and exhibited only a slow growth phase and a stopping phase. Water imbibition and water replacement promoted the desorption of gas in the non-wet area during the water injection process, while it inhibited the desorption of gas in the wet area. Under the effects of water imbibition, water blockage, and water replacement, the discharge rate of WCS is greater than the desorption rate of NCS, indicating that water injection increases the total amount of gas desorption. The study results have significant implications for gas extraction and the prevention and control of coal and gas outbursts.

Indoor testing are performed to explore the charge induction law during the uniaxial compression fracture process of coal samples, and the charge time and frequency domain signals of coal samples with different primary fissures are analyzed in the paper. On-site monitoring of charge in different fissures distribution areas of underground coal tunnels, and the charge signals of different drillingdepths in coal seams are analyzed. The results show that the uniaxial compressive strength and elastic modulus of multi-fissured coal samples are less than those of less fissured coal samples, and the Poisson’s ratio is greater than those of less fissured coal samples. The charge induction signal intensity during the fracture process of multi-fissured coal samples is relatively low, but it is concentrated at the low frequency of 0–50 Hz in the compacting elasticity stage. The charge signal intensity during the fracture process of coal samples with less fissure is relatively high, and the charge frequency during the reinforcement damage stage is concentrated at a low frequency of 0–50 Hz. Therefore, the sudden appearance of low-frequency charge signals is more suitable as effective precursor information for the instability and failure of less fissured coal bodies. The average charge intensity is small in the multi-fissured area with a drilling depth of 1–4 m in the coal seam, and the average charge intensity of the coal body with less fissures is larger in the 5–12 m region. The on-site charge monitoring results have good consistency with the indoor test results. This study has guiding significance in setting up a charge monitoring warning index of instability failure in different coal body fissures regions.

Rock burst disasters severely restrict the safe and efficient mining of coal. The fundamental cause of their occurrence is the concentration of stress within the coal mass. Stress monitoring in coal seam drilling is widely used as an effective method for rock burst monitoring. However, how to scientifically and reasonably set the critical values of early warning indicators that match the conditions of each mine has always been a key issue restricting the accurate prediction of rock burst by the drilling stress method. This paper adopts a method combining theoretical analysis and field practice to conduct research on the critical values of drilling stress early warning indicators. Based on perturbation response instability theory, a mechanical model for the occurrence of impact ground pressure has been established. Based on the instability theory of disturbance response, a mechanical model for the occurrence of impact ground pressure has been established, leading to the derivation of the expression for the near-field critical stress of impact ground pressure events. The theoretical formula for the critical value of drilling stress early warning indicators was obtained based on the difference between the critical stress of rock burst occurrence and the actual stress in the roadway. This formula includes the mechanical parameters of the coal mass and its propensity for rock burst, roadway support stress, mining depth, stress concentration coefficient, and the initial installation pressure of the stress gauge. They can be determined by the geological and mining technical conditions of each mine. This theoretical formula breaks the uniformity of the critical values for stress warning indicators in various mine drill holes, allowing each mine to scientifically determine its critical value based on its own conditions. This theoretical method has been applied to a high-stress mine in Shanxi, China, and the critical values of drilling stress early warning indicators were obtained. When the monitored stress exceeded the critical value, dynamic phenomena of anchor rod and cable fractures occurred in the roadway roof. The distribution of microseismic events also shifted towards the warning area, and the microseismic monitoring indicators reached the warning values. This confirmed the engineering feasibility of the critical values for drilling stress early warning indicators determined by the theoretical method.

Praziquantel (PZQ) is currently the only agent for treating schistosomiasis, but it is plagued by suboptimal efficacy to juvenile parasites, looming drug resistance, and inability to prevent reinfection. Thioredoxin glutathione reductase (TGR) is regarded as a promising therapeutic target due to its essential role in maintaining schistosome redox homeostasis. Herein, the crystal structures of Schistosoma japonicum TGR (SjTGR) in multiple redox states and in complex with NADPH, GSH, and the anti-helminthic agent Auranofin were elucidated. Structural analyses identified the hook-shaped conformation at the C-terminal redox center, which DTNB assays further confirmed enhances electron transfer efficiency. Structural and ITC data indicated that R317 was critical for NADPH binding via hydrogen-bond interactions. The analysis also indicated that the structure basis of Auranofin’s potency was its tripartite interaction at the redox-active sites. In addition, we investigated the substrate specificity of SjTrx1i and SjTRP14, downstream proteins regulated by SjTGR, and elucidated the structural basis for this specificity by determining their oxidized/reduced structures. Furthermore, in vivo RNAi indicated knockdown of SjTGR or SjTRP14 blocked the survival and oviposition of schistosomes, thus ameliorating egg-induced granulomatous pathology in mice. This work provided a framework for knowledge-based design of novel anti-schistosomals targeting parasite-specific redox vulnerabilities.

Risk evaluation of roadway rockburst refers to quantitatively dividing the rockburst-prone area into multiple sections with different risk levels at the mining design stage, according to the geological and mining technical factors influencing the rockburst occurrence, for partitioned and graded management of rockburst. Based on this, a targeted rockburst monitoring and early warning system can be formulated for mining working faces, and it is the premise of rockburst management and control. To determine the theoretical criteria of rockburst risk evaluation and improve the accuracy of the evaluation results, a mechanical analysis model of roadway rockburst with the structure of “elastic zone–plastic softening zone–fracture zone” was established. Theoretical calculation formulas for critical ground stress, critical mining stress, critical depth of the plastic softening zone, and fracture zone of rockburst occurrence were obtained. These were related to the mechanical parameters of the coal and rock mass, geometric parameters of the roadway, ground stress, and support strength. Accordingly, a critical mining stress index was defined (the ratio of actual mining stress to critical mining stress), a quantitative evaluation method for rockburst risk based on the critical mining stress index was proposed, and the process and steps of a critical mining stress index method for rockburst risk evaluation were explained. A rockburst risk evaluation was performed for the 302 (upper) working face in a coal mine in Shandong, China. The critical mining stress index method for rockburst risk evaluation was validated by comparing results with the monitoring data obtained using the drill cutting method, microseismic monitoring method, and comprehensive index evaluation method.HighlightsCritical indexes and criterion for rockburst risk evaluation were explored.The definition and calculation method of mining stress index were proposed.A quantitative evaluation method of rockburst risk was proposed.Process and steps of the new evaluation method for rockburst were elaborated.

As a significant component of non-terrestrial networks, satellite communication has gradually evolved into a heterogeneous topology where the network nodes are classified as low Earth orbit (LEO) and geostationary Earth orbit (GEO) nodes. Considering the advantage of low transmission delay of LEO satellites in a heterogeneous network, ground users are arranged to access LEO satellites in the first priority. By fully understanding the equivalent channel gain difference in the beam-edge and beam-center users, we develop non-orthogonal multiple access (NOMA) in an LEO network where different users are allocated in the same resource block. As a complementary strategy, the beam-edge user has the ability to connect the GEO satellite if the LEO links are not available. That is, the two users are served cooperatively by LEO and GEO satellites in an orthogonal manner. In this paper, we provide deep insights for the uplink transmission performance of NOMA and cooperative orthogonal multiple access methods. As a general metric, the analytical expressions of outage probability are derived and the diversity orders are also provided. The simulation results show that NOMA is capable of providing remarkable performance in low signal-to-noise ratio regions and is more promising when the channels are assumed to be ordered.