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This paper proposes an unmanned combat decision-making algorithm based on PPO and expert systems. The experimental results show that the algorithm has good decision-making ability. A strategy optimization method based on a self-encoding neural network is proposed, which greatly improves the effective decision-making rate of the original algorithm. In view of the opaque problem of the unmanned combat decision-making model obtained by the above deep reinforcement learning algorithm, a local interpretability algorithm GLIME based on Generative Adversarial Network (GAN) and Local interpretable model-agnostic explanations (LIME) is proposed, which improves the stability of the LIME algorithm. Finally, combined with the global interpretability algorithm, Permutation Feature Importance (PFI), the decision-making samples are analyzed from both local and global perspectives, providing comprehensive and stable explanations for the decision-making algorithm, thereby improving the transparency of the decision-making algorithm.

ABSTRACTThe China-Pakistan Transportation Corridor (CPTC) has long faced great challenges in natural disasters and sustainable development. This study develops a localised indicator evaluation system for the CPTC sustainable development. It contains 27 Tier I and 8 Tier II indicators, covering 11 sustainable development goals. Based on this evaluation system, a single-goal, multidimensional, and comprehensive evaluation of the CPTC sustainable development level is conducted for 2015, 2017 and 2020. The results show that, in terms of single goals, Xinjiang has the highest sustainable development level. Khyber Pakhtunkhwa has the poorest level of sustainable development. The Islamabad Capital Territory is better developed for all goals, especially in SDG5, SDG8 and SDG9. Besides these three goals, SDG6, and SDG11 are more prominent in Punjab, whereas SDG2 and SDG3 performed worst. From a multidimensional evaluation, the CPTC sustainable development level has been steadily increasing. However, the social and environmental dimensions of Khyber Pakhtunkhwa have experienced a ‘regression’. The comprehensive evaluation results show that the level of sustainable development in all provinces of the CPTC increase, except for Khyber Pakhtunkhwa. This study identifies CPTC sustainability issues and proposes targeted recommendations for sustainable development.

In the Horqin Sandy Land, the dynamics of the soil-plant-atmosphere-water system and changes in plant water availability significantly impact water resources management and vegetation restoration. This study determined the hydrogen and oxygen isotopic compositions (δD and δ18O) of precipitation, plant water, soil water, and groundwater as well as carbon isotopes of plant leaves during the growing season and different precipitation events (8.9 mm, 18.1 mm, and 28.3 mm) in 2020. Target areas included a semimobile dune with Artemisia halodendron communities (SMAH), a meadow corn field (MCF), and a meadow wetland dominated by Phragmites australis (MPA). The water uptake patterns of the three typical plants were determined using the Iso-Source model. The results showed significant differences in the seasonal variations of δD and δ18O in precipitation, plant water, soil water, and groundwater among SMAH, MCF, and MPA throughout the water cycle, which was attributed to the different origins, distribution, and evaporation mechanisms of water. The influence of precipitation on soil water transport processes varied with the change and transformation of rainfall intensity and ecosystem type. Water conditions and root distribution determined seasonal water use patterns of three plants. The water use efficiency (WUE) of the three typical plants followed the order: corn > Phragmites australis > Artemisia halodendron. And their seasonal WUE were not only consistent with the contribution rate of deep soil water, but also affected by the water use strategies adopted. However, the response of their WUE to rainfall pulses was not significant on the whole. Rainfall intensity, pre-rain mass soil moisture content characteristics and vegetation characteristics are important factors affecting the contribution distribution of different water sources to plants under rainfall events. Artemisia halodendron displays ecological plasticity against seasonal water deficit, facilitating its extensive colonization in dune ecosystem and further promoting the process of dune fixation. The distribution pattern of vegetation water use in the meadow ecosystem is not very ideal at present, and water-carbon processes would gradually become imbalanced in the long run. The research results will provide a reference for sustainable water resource utilization in semi-arid areas through ecohydrological processes.

In this paper, we are interested in identifying the coefficients of the interaction terms in a parabolic system, which describes two subpopulations of the same species competing for the same resources. We use optimal the control theory to study the identification problem. Taking the unknown coefficients as control functions, the identification problem is transformed into an optimal control problem. We prove the existence and local uniqueness of the optimal control, derive the first-order necessary condition, and get the formula of the optimal control.

Forest resources are renewable, and the rational exploitation and utilization of forest resources are not only conducive to sustainable development on a population scale, they can also lead to higher economic benefits. Based on the actual timber harvest problem, this paper establishes the joint harvest model of timber and non-timber with nonlinear harvest items. In the numerical simulation, by comparing the existing proportional harvest model, it is concluded that the optimal harvest strategy of nonlinear harvest items in this paper can obtain larger ecological benefits and be more conducive to the sustainable development of a population. Firstly, using the qualitative theory of ordinary differential equations, the dynamic behavior of the model is studied, and the existence and stability of the equilibrium point of the model are proven. Secondly, the optimal control solution is obtained by using the optimal control theory. Finally, the optimal harvesting strategy of timber and non-timber products is given based on the numerical simulation results, and a comparison of the effects of different parameters on the optimal harvest strategy, which provides a certain theoretical basis for the sustainable development of the ecological economy of forestry, is carried out.

Intercity high-speed rail (HSR) is a type of mass transit built in densely populated metropolitan areas and has become an essential part of sustainable transportation systems in some countries. One of the most challenging problems is how to jointly optimize the train line plan and timetable with simultaneous consideration of the system efficiency, cost, and level of service. In this paper, we focus on optimizing the train line plan and timetable of intercity HSR given actual time-dependent demand while appropriately considering the operational constraints including overtaking. First, we formulate the optimization problem as a mixed integer nonlinear programming (MINLP). Its objective is to minimize the total system cost comprised of the trains’ fixed and variable operation costs, passengers’ travel time cost, and penalties for passengers’ departure time deviation and passengers’ failing to board a train. We then design a double-layer simulated annealing (SA) algorithm, in which the inner layer algorithm optimizes the stop plan, the departure and arrival times at each station, while the outer layer algorithm optimizes the number of trains, their origin–destination stations, and their departure times at origin stations. Next a case study is conducted on Shanghai-Nanjing intercity HSR using actual time-dependent demand. The numerical results show that the proposed model can help significantly reduce the total number of trains used by increasing the average seat occupancy, while creating little impact on the average passenger travel time and departure time. The study provides valuable guidance for optimizing HSR train line plan and timetable based on real-world time-dependent demand.

Understanding the relationship between the plant-soil carbon dynamics and environment is important for the management of arid and semi-arid ecosystems. Under long-term overgrazing and reclamation, the landscape of the Horqin Sandy Land has transformed into a dune-meadow ecotone. However, the effects of landscape evolution on the carbon dynamics in this region remain poorly investigated. This study evaluated changes in plant-soil organic carbon content and stable carbon isotope ratios (δ13C) attributable to vegetation restoration in dune ecosystems and wetland degradation in meadow ecosystems, and evaluate the indicative implications and driving mechanisms of the 13C enrichment in the plant-soil system. The dune ecosystems comprised semimobile dune with 25- and 15-year-old Artemisia Halodendron communities (SMAH1, SMAH2), fixed dune with 30- and 40-year-old Caragana microphylla communities (FCM1, FCM2), semimobile dune with 40-year-old Salix gordejevii community (SMSG), and transitional zone with a 30-year-old artificial Populus forest (TPF) in the gentle lower part. The meadow ecosystems comprised a corn field that transformed from natural meadow over 40 years (MCF) and a natural meadow with Phragmites australis community (MPA). The results show that plant leaves in different landscapes in the cascade ecosystems had various δ13C. And the mechanism of the leaves carbon isotope fractionation with increasing leaf age was significant and consistent, but different vegetation communities had different nutrient utilization strategies during ontogeny. Landscape types and soil depths are the key factors affecting SOC content, its δ13C (δ13Cs) and SOC stock changes. And the overall soil carbon turnover was accelerated with vegetation restoration. Especially in TPF, vegetation restoration is particularly significant for SOC, which is mainly reflected in the upper 20 cm of soil profile. The degradation of MPA to MCF led to loss of SOC content and storage and accelerated the soil carbon turnover rate. The δ13Cs of the dune and meadow ecosystems were positively correlated with organic-rich litter nitrogen content and were significantly negatively correlated with SOC, soil moisture, clay content, and total organic carbon to total nitrogen ratio of organic-rich litter. The revegetation using native and introduced tree and shrub species increased SOC content in dune ecosystems and significantly promoted the degree of its coupling with soil nitrogen and water.

Reinforcement learning (RL) is attempted to be applied to the control of continuum robots. Because of the inefficiency and high cost of collecting samples in the real world, the control strategy is usually learned in simulation. However, due to the gap between the simulation and the real world, the performance of the strategy learned in the simulation will be reduced when it is transferred to the real world. This paper proposes a strategy learning and Simulation-to-Real (Sim-to-Real) transfer framework for the position control of pneumatic continuum manipulator (PCM). The dynamics model of the PCM, which is used as the simulation environment of RL, is represented by long short-term memory (LSTM). The probabilistic inference and learning for control (PILCO) is used to train the control strategy. In order to utilize the information of the strategy learned in simulation, the Sim-to-Real transfer method based on strategy fine-tuning is proposed. By fine-tuning the strategy, the strategy learned in simulation can be applied to the real world. Finally, an experiment is carried out on a PCM to verify the effectiveness of the proposed framework.

Abstract Background The "Law on Doctors of the People's Republic of China," which was officially implemented on March 1, 2022, emphasizes the requirements for rational drug use and the necessity for appropriate management of off-label drug use. The safety and ethical considerations related to off-label drug use are different in children than in adults. There is so far no management guideline for pediatric off-label use of drugs in China, and the applicability of foreign guidelines is limited. Establishing a localized evidence-based management guideline for pediatric off-label use of drugs to support the national legislation and clinical practice is of critical importance. Methods We established a guideline working group, including experts from a broad range of disciplines and developed recommendations following the guidance of the World Health Organization Handbook and the Chinese Medical Association. The following themes were identified by questionnaires and expert interviews to be of great concern in the management of off-label drug use in children: general principles and characteristics of management of pediatric off-label drug use; establishment of expert committees; evidence evaluation; risk–benefit assessment; informed consent; monitoring and assessment of the risk; and monitoring and patient education. Two rounds of Delphi surveys were organized to determine the final recommendations of this guideline. We graded the recommendations based on the body of evidence, referring to the evaluation tool of the Evidence-based management (EBMgt) and the Oxford Center for Evidence-Based Medicine: Level of Evidence (March 2009). Results We developed the first guideline for the management of pediatric off-label use of drugs in China. Conclusions The guideline is to offer guidance for pediatricians, pharmacists, medical managers, policymakers, and primary care physicians on how to manage off-label drug use in pediatrics and to provide recommendations for Chinese healthcare policy in the future.

Long-term nighttime light data can effectively reflect the spatiotemporal evolution pattern of urban areas. Based on nighttime light data obtained from 1992 to 2021, the spatiotemporal expansion modes of urban areas on the Loess Plateau were investigated by remote sensing image band fusion processing, geospatial analysis, statistics and classification methods. Results show that the characteristics of the spatiotemporal expansion of urban areas were retained in the extraction boundaries. From the perspective of time scales, the urban areas have grown tripled to 11,481 km2. Four significant instances of urban areas increase occurred, with one large increase occurring almost every ten years. From the perspective of spatial scales, urban area expansion has two important features. First, the expansion speed of urban areas, which is based on three indicators, has large spatial dissimilarities. Five modes can be divided based on the expansion velocity of 44 cities/counties. Second, urban development increases uniquely, and can be described based on three expansion modes: Ring Extension, which mainly occurs in wide and flat basins of the main rivers; Linear Extension primarily arises on both banks of rivers in hilly and gully areas; and Point Extension mainly occurs in areas away from the main rivers. Our results highlight that the spatiotemporal expansion modes of urban areas on the Loess Plateau retain the significant influence of the geological environment, which can provide scientific guidance for the sustainable development of human-land relationships.

Exoskeletal robots are of critical importance in the domain of mechanical boosting. The pneumatic artificial muscle (PAM) is commonly used as a flexible actuator in exoskeletal robots designed for upper limbs due to its high power-to-weight ratio, conformability, and safety. This study establishes a new model based on the existing model to improve its control precision by implementing elastic and frictional forces and empirical coefficients, battling against the time-variant hysteresis that PAM’s output force exhibits. In the meantime, a BP neural network is employed in reverse modeling, followed by the adoption of the least-square-based particle swarm optimization algorithm in order to determine the optimized parameter values. PAM provides the Upper Limb Exoskeletal Robot with appropriate auxiliary power, which can be adjusted to accommodate variations in posture change during the lifting process. PAM is also capable of handling variable loads based on the principle of torque balance, constructing a control system according to the inverse dynamics of exoskeletal robots accompanied by an inverse model of PAM’s output force, and finally, rendering tracking control of the elbow angle during the auxiliary process possible. Finally, the tracking error results are calculated and shown; the maximum angular error in the tracking process is 0.0175 rad, MAE value is 0.0038 rad, RMSE value is 0.0048 rad, and IEAT value is 4.6426 rad. This control method is able to improve the precision of tracking control of the elbow angle of the upper limb–exoskeleton coupled system during the process of lifting goods.

BackgroundTourniquet pain is the most prominent problem in ankle surgery, and there is no proper method to predict it. It was reported that pain sensitivity questionnaires could evaluate the pain sensitivity of subjects. Its potential to predict tourniquet pain in ankle surgery is constructive and meaningful.MethodsOne hundred and twenty patients undergoing ankle surgery were included in this study. The pain sensitivity questionnaire (PSQ) and self-rating anxiety scale (SAS) were completed before the operation. The methods included an ultrasound-guided popliteal sciatic, a femoral nerve block, and a proximal thigh tourniquet. The pressure of the tourniquet was set according to the systolic blood pressure (SBP + 100 mmHg). A visual analogue scale (VAS) was used to assess the tourniquet pain. Also, the onset time of tourniquet pain ≥4 VAS units was recorded.ResultsThe PSQ-total and PSQ-minor scores were significantly correlated with the onset time when the tourniquet pain ≥4 VAS units (r = −0.763, r = −0.731, P

Aiming at the impact of bearing bushes on the lubrication and friction and wear of diesel engine connecting rod big-end bearings, a certain type of diesel engine connecting rod big-end bearing bush was taken as the research object and a multi-body dynamics numerical calculation model of the connecting rod group was built. The connecting rod big-end bearing bushes with four profiles: exponential, hyperelliptic, barrel and trapezoid were used to study the effect on bearing lubrication. The study found that the hyperelliptic bush has the best lubrication performance for the connecting rod big-end bearing. On the basis of the hyperelliptic bush, the bearing clearance, bearing width, journal oil hole diameter and oil supply pressure are used as design variables, using the Box–Behnken experimental design and radial basis function (RBF) neural network method to construct an approximate multi-objective model, which the minimum oil film thickness (abbreviated as MOFT) and average rough contact effective pressure are the objectives. A non-dominated sorting genetic algorithm (NSGA-II) is used for multi-objective optimization. The optimization results show that the bearing clearance remains basically unchanged, the bearing width, journal oil hole diameter and oil supply pressure increase, so that MOFT of the connecting rod big-end bearing rises from 1.56 μm to 1.97 μm, and the average rough contact effective pressure increases from 3.97 MPa decreases to 0.25 MPa. The research results can provide a reference for the analysis and optimization of the lubrication performance of the connecting rod big-end bearing.

Coastal wetland is a crucial part of the blue carbon ecosystem, having unique structural characteristics and serving both land and water functions. These areas also offer strong carbon sequestration and storage capacity. However, human activities and climate change have led to an ongoing decline in wetland areas. As an important wetland reserve in China, the coastal wetlands of Yancheng, Jiangsu Province, bring together a great number of water resources, land resources, biological resources, landscape resources, mineral resources, energy resources, and many other resources. However, in recent years, the coastal wetlands of Yancheng have also faced a continuous reduction in area. In this study, an integrated model of the Extreme Gradient Boosting (XGBoost) algorithm and multi-criteria evaluation-cellular automata-Markov (MCE-CA-Markov) model was designed to determine the importance of the driving factors of landscape pattern change and predict the landscape pattern of coastal wetlands in 2025, based on the analysis of landscape pattern changes in 2005, 2010, 2015, and 2020 in Yancheng coastal wetlands, Jiangsu Province. The results showed that the landscape configuration of coastal wetlands in Yancheng changed significantly during 2005–2020. The total natural wetland landscape area declined steadily over that 15-year span, showing a total decrease of 2.971×104 ha; on the contrary, the artificial wetlands increased. The analysis of the driving factors of wetland damage showed that gross domestic product (GDP), distance to roads, and average precipitation were the key factors. Based on this model, the natural wetland landscape of Yancheng’s coastal wetlands is predicted to decline sharply in 2025, and the wetland salt marsh vegetation will be extensively converted to aquaculture and agricultural land. Constructed land is predicted to increase significantly, and the trend of urban expansion is obvious. The study concludes with practical suggestions for the sustainable development and ecological conservation of the Yancheng coastal wetland ecosystem based on the predicted scenario.

BackgroundAtrial fibrillation detected after stroke (AFDAS) is associated with an increased risk of ischemic stroke (IS) recurrence and death. Early diagnosis can help identify strategies for secondary prevention and improve prognosis. However, there are no validated predictive tools to assess the population at risk for AFDAS. Therefore, this study aimed to develop and validate a predictive model for assessing the incidence of AFDAS after acute ischemic stroke (AIS).MethodsThis study was a multicenter retrospective study. We collected clinical data from 5332 patients with AIS at two hospitals between 2014.01 and 2021.12 and divided the development and validation of clinical prediction models into a training cohort (n = 3173) and a validation cohort (n = 2159). Characteristic variables were selected from the training cohort using the least absolute shrinkage and selection operator (LASSO) algorithm and multivariable logistic regression analysis. A nomogram model was developed, and its performance was evaluated regarding calibration, discrimination, and clinical utility.ResultsWe found the best subset of risk factors based on clinical characteristics and laboratory variables, including age, congestive heart failure (CHF), previous AIS/transient ischemia attack (TIA), national institutes of health stroke scale (NIHSS) score, C-reactive protein (CRP), and B-type natriuretic peptide (BNP). A predictive model was developed. The model showed good calibration and discrimination, with calibration values of Hosmer-Lemeshow χ2 = 4.813, P = 0.732 and Hosmer-Lemeshow χ2 = 4.248, P = 0.834 in the training and validation cohorts, respectively. The area under the ROC curve (AUC) was 0.815, 95% CI (0.777–0.853) and 0.808, 95% CI (0.770–0.847). The inclusion of neuroimaging variables significantly improved the performance of the integrated model in both the training cohort (AUC. 0.846 (0.811–0.882) vs. 0.815 (0.777–0.853), P = 0.001) and the validation cohort (AUC: 0.841 (0.804–0.877) vs. 0.808 (0.770–0.847), P = 0.001). The decision curves showed that the integrated model added more net benefit in predicting the incidence of AFDAS.ConclusionPredictive models based on clinical characteristics, laboratory variables, and neuroimaging variables showed good calibration and high net clinical benefit, informing clinical decision-making in diagnosing and treating patients with AFDAS.

Background: This study aims to investigate the needs of child caregivers for popular science about safe medication for children, to deeply explore the characteristics of child caregivers’ demand for safe medication and the shortcomings of current popular science work, and then to seek better coping strategies to ensure children’s safe medication.Methods: A questionnaire was designed based on Lasswell’s “5W” communication model to investigate the needs of child caregivers in terms of content, channels, and forms of popular healthcare science on the safe usage of children’s medication.Results: The primary ways caregivers receive popular healthcare science education concerning safe medication usage knowledge are through medical institutions, notification by medical staff, and personal media. The caregivers of children have a high demand for the presentation of text, pictures, and videos in three forms of popular healthcare science content. Caregivers placed significant importance on the popularization of safe medication usage for children. The survey results showed that the top 3 ways for caregivers to think that the quality of popular healthcare science content was “very good” came from medical institutions, medical staff notifications, and personal media, effectively increasing popular healthcare information accuracy. The intelligibility and pertinence of content expression are urgently needed within the caregiver population.Conclusion: Caregivers are very concerned about the popular science of safe medication for children, and are willing to learn about relevant content. Guided by the demand, we should actively disseminate accurate and easy-to-understand popular science about safe medication for children to caregivers through online or offline channels so as to promote safe medication for children.

In the inland closed area of Horqin Sandy Land, China, lakes are the most important source of water, and they maintain the regional hydrological balance and ecosystem health. Clarifying long-term continuous changes of inland lake surface area and water storage in the sandy land is thus of great significance to the management of water resources in arid and semi-arid areas. This study estimated changes in the surface area and water storage of small lakes in the sandy land during 1984–2021 using a multiple index threshold method and an empirical equation based on Shuttle Radar Topography Mission (SRTM) DEM (digital elevation model) data and Landsat 5/7/8 images. Hydro-meteorological variables were also incorporated to explore their potential relationship with changes in the lake elements. The lakes in the sandy land resemble stars or dots, with distinct inhomogeneity. Permanent lakes account for the majority of the total lake area, mostly distributed in the center and east of the study area, whereas most seasonal lakes are small water bodies, with surface areas of 0.1–0.5 km2 and irregularly distributed. Lake surface area and water storage underwent frequent changes during the 38 years, and could be divided into three characteristic fluctuation phases. From 1984 to 1999, the lake surface area fluctuated greatly, and the water storage increased by approximately 0.3732 Gt, with an average expansion rate of 0.0572 Gt/y. Specifically, lakes with a surface area >2 km2 primarily accounted for the expansion. From 1999 to 2004, the lake area shrank sharply to the lowest point, and the water storage capacity decreased by approximately 0.4003 Gt. From 2004 to 2021, the lake surface area and water storage tended to be stable. Annual fluctuations of lake surface area and water storage were mostly affected by precipitation and evapotranspiration, followed by vapor pressure deficit, wet day frequency, and temperature, which have significant periodicity and hysteresis.

Soft manipulators can perform continuous operations due to their inherent compliance and dexterity, thus enabling safe interactions and smooth movements in confined environments. However, high compliance usually means low load capacity. It is important for a soft manipulator to possess proper flexibility while maintaining an acceptable stiffness to widen its applications. This paper has hence devoted efforts to a kind of variable stiffness mechanism for a soft manipulator actuated by pneumatic artificial muscles (PAMs). Due to the combination of contractile and extensor PAMs, the manipulator is able to vary its stiffness independently from the configuration. The stiffness characteristics of the soft manipulator are quantitatively analyzed by bending shape prediction under different loading and inflation conditions, and the prediction is built upon a nonlinear statics model coupled with PAM nonlinearity and the Cosserat theory. In addition, experimental measurements are conducted to further validate the expected performance of the manipulator design. The experimental and verified theoretical analysis results indicate that the manipulator shape and stiffness are greatly affected by the pressure variation of PAMs, realizing a large bending space with a high output force. The variable stiffness design obviously increases the manipulator's ability to resist additional interference at the same position.

Compared with the traditional rigid mechanism, the flexible mechanism has more advantages, which play an important role in critical situations such as microsurgery, IC (integrated circuit) fabrication/detection, and some precision operating environment. Especially, there is an increasing need for 3-DOF (degrees-of-freedom) compliant translational micro-platform (CTMP) providing good performance characteristics with large motion range, low cross coupling, and high spatial density. Decoupled topology design of the CTMP can easily realize these merits without increasing the difficulty of controlling. This paper proposes a new three DOF compliant hybrid micromanipulator which have large range of motion up to 100 μm × 100 μm × 100 μm in the direction in the dimension of 90 mm × 90 mm × 50 mm, smaller cross-axis coupling (the max coupling only 2.5%) than the initial XY compliant platform in XY axial.

For the nonlinear, time-variable and unpredictable environment (such as the manipulator workspace that has many random and moveable obstacles), a Hammerstein adaptive (HA) impedance controller based on a Hammerstein impedance model is proposed in this paper. The model consists of a static nonlinear element and a linear dynamic element, which can describe the nonlinearity of the environment. Then, an adaptive law is designed by a Lyapunov function, which can adjust the parameters of the static nonlinear element, so that the model has good adaptability and robustness for the environment. By introducing a parameters self-adjust model-free adaptive controller as the inner position controller, the HA impedance control system is built, which does not require the knowledge of the robot dynamics. In order to verify the force-control performance of the proposed control scheme, some simulations and experiments are done on a bionic wrist joint actuated by pneumatic muscles. Compared with the conventional impedance controller with constant parameters, experimental results demonstrate that HA impedance controller has better force tracking characteristic and can restrict the contact force effectively, no matter how the external environment changes, which increases the safety between robots and humans or operation objects.

N, S-doped ultramicroporous carbons (NSUC-x) with a high nitrogen/sulfur content and a narrow pore-size distribution of around 0.55 nm were firstly prepared using L-cysteine as a nitrogen and sulfur source. The phase, graphitization degree, morphology, specific surface area, pore structure and surface condition of NSUC-x are investigated to analyze the key role in electrochemical performance. Such an ultramicroporous structure and N, S doping not merely provide a high-specific surface area and a suitable pore size, but also induce a good wettability for the fast transport and adsorption of electrolyte ions. Due to the above strategies, the typical NSUC-0.4 exhibits a high gravimetric capacitance of 339 F g−1 at 0.5 A g−1 as well as a capacity retention of 91.6% after 10,000 cycles in a three-electrode system using a 6 M KOH electrolyte. More attractively, a NSUC-0.4-assembled symmetrical supercapacitor delivers an energy output of 7.4 Wh kg−1 at 100 W kg−1 in 6 M KOH as well as a capacity retention of 92.4% after 10,000 cycles, indicating its practical application prospect. Our findings open up new prospects for the design and electrochemical application of N, S-doped ultramicroporous carbons.

Manual lifting tasks involve repetitive raising, holding and stacking movements with heavy objects. These arm movements are notable risk factors for muscle pain, fatigue, and musculoskeletal disorders in workers. An upper-limb wearable robot, as a 6-DOF dual-arm exoskeleton, which was designed to augment workers’ strength and minimize muscular activation in the arm during repetitive lifting tasks. To adjust the robot joint trajectory, the user needs to apply an interactive torque to operate the robot during lifting tasks when a standard virtual mechanical impedance control structure is used. To reduce overshooting of the interactive torque on the user’s joint, a three-tier hierarchical control structure was developed for the robot in this study. At the highest level, a human arm movement detection module is used to detect the user’s arm motion according to the surface electromyography signals. Then, a Hammerstein adaptive virtual mechanical impedance controller is used at the middle level to reduce overshooting and yield an acceptable value of torque for the user’s elbow joint in actual lifting tasks. At the lowest level, the actuator controller on each joint of the robot controls the robot to complete lifting tasks. Several experiments were conducted, and the results showed that the interactive torque on the user’s elbow was limited and the muscular activations of erector spinae and biceps brachii muscles were effectively decreased. The proposed scheme prevents potential harm to the user due to excessive interactive torque on the human elbow joint, such as related muscle fatigue and joint injuries.

Rolling bearings are the most frequently failed components in rotating machinery. Once a failure occurs, the entire system will be shut down or even cause catastrophic consequences. Therefore, a fault detection of rolling bearings is of great significance. Due to the complexity of the mechanical system, the randomness of the vibration signal appears on different scales. Based on the multi-scale fuzzy entropy (FE) analysis of the vibration signal, a rolling bearing fault diagnosis method based on smoothness priors approach (SPA) -FE-IFSVM is proposed. The SPA method was used to adaptively decompose the vibration signal and obtain the trend item and de-trend item of the vibration signal. Then the fuzzy entropy of the trend item and de-trend item was calculated respectively. Meanwhile, aiming at the problem that the support vector machine (SVM) cannot process the data set containing fuzzy messages and was sensitive to noise, the fuzzy support vector machine (FSVM) was introduced and improved, and then the FE as the feature vector was input into the improved fuzzy support vector machine (IFSVM) to identify the failure. The method was applied to the rolling bearing experimental data. The analysis results show that: this method can achieve 100% fault diagnosis accuracy when only two component features are extracted, which can effectively realize the fault diagnosis of rolling bearings.

Landslide displacement prediction is crucial for the early warning of slope failure but remains a challenging task due to its spatiotemporal complexity. Although temporal dependency has been well studied and discussed, spatial dependence is relatively less explored due to its significant variations of the spatial structure of landslides. In this study, a novel graph convolutional incorporating GRU network (GC-GRU-N) is proposed and applied to landslide displacement forecasts. The model conducts attribute-augmented graph convolution (GC) operations on GNSS displacement data with weighted adjacency matrices and an attribute-augmented unit to combine features, including the displacements, the distance, and other external influence factors to capture spatial dependence. The output of multi-weight graph convolution is then applied to the gated recurrent unit (GRU) network to learn temporal dependencies. The related optimal hyper-parameters are determined by comparison experiments. When applied to two typical landslide sites in the Three Gorge Reservoir (TGR), China, GC-GRU-N outperformed the comparative models in both cases. The ablation experiment results also show that the attribute augmentation, which considers external factors of landslide displacement, can further improve the model’s prediction performance. We conclude that the GC-GRU-N model can provide robust landslide displacement forecasting with high efficiency.

Soft robotic grippers with compliance have great superiority in grabbing objects with irregular shape or fragility compared with traditional rigid grippers. The main limitations of such systems are small grasping force resulted from properties of soft actuators and lacking variable stiffness of soft robotic grippers, which prevent them from a larger wide range of applications. This article proposes a shape-memory alloy (SMA)-based soft gripper with variable stiffness composed of three robotic fingers for grasping compliantly at low stiffness and holding robustly at high stiffness. Each robotic finger mainly consisted of stiff parts and two variable stiffness joints is installed on the base with a specific angle. The paraffin as a variable stiffness material in the joint can be heated or cooled to change the stiffness of the robotic fingers. Results of experiments have shown that a single robotic finger can approximately achieve 18-fold stiffness enhancement. Each finger with two joints can actively achieve multiple postures by both changing the corresponding stiffness of joints and actuating the SMA wire. Based on these principles, the gripper can be applied to grasp objects with different shapes and a large range of weights, and the maximum grasping force of the gripper is increased to about 10 times using the variable stiffness joints. The final experiment is conducted to validate variable stiffness of the proposed soft grippers grasping an object.

The running of high-speed electrically driven feed pump has a direct impact on the safety of personnel equipment and economic benefits of power plant, as the result, intelligent condition monitoring and fault diagnosis of electrically driven feed pump becomes an urgent need. In the practical process of electrically driven feed pump fault diagnosis, the running of the equipment is in normal state for a long time, occasionally, with faults, which makes the fault data very rare in a large number of monitoring data, and makes it difficult to extract the internal fault features behind the original time series data, When the deep learning theory is used in practice, the imbalance between the fault data and the normal data occurs in the operation data set. In order to solve the problem of data imbalance, this paper proposes a fault diagnosis method of GAN-SAE. This method first makes compensation for the imbalance of sample data based on the Generative Adversarial Network (GAN), and then uses the Stacked Auto Encoder (SAE) method to extract the signal features. By designing the fault diagnosis program, compared with only using SAE, back propagation neural networks (BP) and multi-hidden layer neural networks(MNN) method, the GAN-SAE method can offer better capability of extracting features, and the accuracy of fault diagnosis of electrically driven feed pump could be improved to 98.89%.

Several studies have demonstrated the significant antiviral, antimicrobial, antiplasmodial, antioxidative, antifungal, antimutagenic, and antitumor properties of harmine hydrochloride (HMH). The main objective of the present study was to investigate the antifungal effects and underlying mechanisms of HMH when combined with azoles to determine whether such combinations act in a synergistic manner. As a result, we found that HMH exhibits synergistic antifungal effects in combination with azoles against resistant Candida albicans (C. albicans) planktonic cells, as well as resistant C. albicans biofilm in the early stage. Antifungal potential of HMH with fluconazole was also explored in vivo using an invertebrate model. Our results suggest that HMH combined with azoles is synergistic against resistant C. albicans in vitro and in vivo. No synergy is seen with azole sensitive C. albicans strains nor with other Candida species. Such synergistic mechanisms on resistance C. albicans are involved in increasing intracellular azoles, inhibiting hyphal growth, disturbing cytosolic calcium concentration, and inducing apoptosis of resistant C. albicans cells.

This paper presents an assistance equipment of human elbow joint, which aims to help patients who have elbow injury issues in smart campus environment. A new type of soft pneumatic elbow pad (SPEP) which adopted soft material manufacturing technology is presented. Compared with the rigid assistant equipment, the SPEP shows more comfort, smaller equipment size, and convenience of wearing. Meanwhile, three different types of sensors (the air pressure sensor, bending sensor, and force sensor) are used in the SPEP to discover the two important characteristics which are applied. In order to verify the effectiveness of the designed SPEP, two analysis methods are presented. The surface electromyogram signal and the root mean square analysis are used in the first method, which prove that the SPEP could reduce the activity of the muscle and assist power during the elbow movement. Compared with the first method, the second method which is the model of calculation after wearing SPEP can obtain specific value of assisting power in the biceps and olecranon. Thus, the aforementioned methods illustrate the feasibility of the SPEP. Finally, in order to make the SPEP used in daily life, a portable backpack with the control system and the energy system is applied to the field of human assistance equipment.

High power-to-weight ratio soft artificial muscles are of overarching importance to enable inherently safer solutions to human-robot interaction. Traditional air-driven soft McKibben artificial muscles are linear actuators, and it is impossible for them to realize bending motions through use of a single muscle. More than two McKibben muscles are normally used to achieve bending or rotational motions, leading to heavier and larger systems. In addition, air-driven McKibben muscles are highly nonlinear in nature, making them difficult to be controlled precisely. An shape memory alloy (SMA)-fishing-line-McKibben (SFLM) bending actuator has been developed. This novel artificial actuator, made of an SMA-fishing-line muscle and a McKibben muscle, was able to produce the maximum output force of 3.0 N and the maximum bending angle (the rotation of the end face) of 61°. This may promote the application of individual McKibben muscles or SMA-fishing-line muscles alone. An output force control method for the SFLM is proposed, and based on MATLAB/Simulink software, an experiment platform is set up and the effectiveness of control system is verified through output force experiments. A three-fingered SFLM gripper driven by three SFLMs has been designed for a case study and for which the maximum carrying capacity is 650.4 ± 0.2 g.

In this study, a model-free adaptive sliding mode control method was developed in combination with the prescribed performance method. On this basis, this study attempted to fulfill the joint position tracking trajectory task for the one-degree of freedom (DOF) upper-limb exoskeleton in passive robot-assisted rehabilitation. The proposed method is capable of addressing the defect of the initial error in the controller design and the application by adopting a tuning function, as compared with other prescribed performance methods. Moreover, the method developed here was not determined by the dynamic model parameters, which merely exploit the input and output data. Theoretically, the stability exhibited by the proposed controller and the tracking performance can be demonstrated. From the experimental results, the root mean square of the tracking error is equal to 1.06 degrees, and the steady-state tracking error converges to 1.91 degrees. These results can verify the expected performance of the developed control method.

The shape memory alloy (SMA)-based robotic hand has been a new emerging technology with potential applications ranging from life service to surgical treatment, because of the characteristics of SMA, such as high power-to-weight ratio, small volume and low driving voltage. However, due to the complex dynamic model and nonlinear aspects of SMA, it is complicated to control an SMA-based robotic hand. This paper presents a novel model free adaptive control for the SMA-based robotic hand system. By applying the Taylor series expansion method and the differential mean value theorem, the SMA based robotic hand system can be transformed into an equivalent linearization model, which merely depends on measurement data without any information on the system. Combined with prescribed performance control, the novel control method can constrain the tracking error in a preassigned domain. Experiments are conducted on the SMA-based robotic hand system to verify the performance of the presented control method.

Autonomous, flexible, and human–robot collaboration are the key features of the next-generation robot. Such unstructured and dynamic environments bring great challenges in online adaptive path planning. The robots have to avoid dynamic obstacles and follow the original task path as much as possible. A robust and efficient online path planning method is required accordingly. A method based on the Gaussian Mixture Model (GMM), Gaussian Mixture Regression (GMR), and the Probabilistic Roadmap (PRM) is proposed to overcome the above difficulties. During the offline stage, the GMM was used to model teaching data, and it can represent the offline-demonstrated motion and constraints. The optimal solution was encoded in the mean value, while the environmental constraints were encoded in the variance value. The GMR generated a smooth path with variance as the resample space according to the GMM of the teaching data. This representation isolated the old environment model with the novel obstacle. During the online stage, a Modified Probabilistic Roadmap (MPRM) was used to plan the motion locally. Because the GMM provides the distribution of all the feasible motion, the sampling space of the MPRM was generated by the variable density resampling method, and then, the roadmap was constructed according to the Euclidean and Probability Distance (EPD). The Dijkstra algorithm was used to search for the feasible path between the starting point and the target point. Finally, shortcut pruning and B-spline interpolation were used to generate a smooth path. During the simulation experiment, two obstacles were added to the recurrent scene to indicate the difference from the teaching scene, and the GMM/GMR-MPRM algorithm was used for path planning. The result showed that it can still plan a feasible path when the recurrent scene is not the same as the teaching scene. Finally, the effectiveness of the algorithm was verified on the IRB1200 robot experiment platform.

A mega project, Mountain Excavation and City Construction (MECC), was launched in the hilly and gully region of the Chinese Loess Plateau in 2012, in order to address the shortage of available land and create new flat land for urban construction. However, large-scale land creation and urban expansion significantly alters the local geological environment, leading to severe ground deformation. This study investigated the topographic changes, ground deformation, and their interactions due to the MECC project in the Yan’an New District (YND). First, new surface elevations were generated using ZiYuan-3 (ZY-3) stereo images acquired after the construction in order to map the local topographic changes and the fill thickness associated with the MECC project. Then, the interferometric synthetic aperture radar (InSAR) time series and 32 Sentinel-1A images were used to assess the spatial patterns of the ground deformation in the YND during the postconstruction period (2017–2018). By combining the InSAR-derived results and topographic change features, the relationship between the ground deformation and large-scale land creation was further analyzed. The results indicated that the MECC project in the YND has created over 22 km2 of flat land, including 10.8 km2 of filled area, with a maximum fill thickness of ~110 m. Significant uneven ground deformation was detected in the land-creation area, with a maximum subsidence rate of approximately 121 mm/year, which was consistent with the field survey. The strong correlation between the observed subsidence patterns and the land creation project suggested that this recorded uneven subsidence was primarily related to the spatial distribution of the filling works, along with the changes in the thickness and geotechnical properties of the filled loess; moreover, rapid urbanization, such as road construction, can accelerate the subsidence process. These findings can guide improvements in urban planning and the mitigation of geohazards in regions experiencing large-scale land construction.

Candida albicans is a common opportunistic fungal pathogen that may cause nosocomial fungal infections. The resistance of Candida albicans to traditional antifungal drugs has been increasing rapidly in recent years, and it brings a great challenge in clinical treatment. N-butylphthalide is originally extracted from the seed of Apium graveolens and is currently used for the treatment of ischemic stroke in the clinic. This study demonstrated that n-butylphthalide exhibited antifungal activity against Candida albicans with minimum inhibitory concentrations of 128 μg/ml; moreover, n-butylphthalide combined with fluconazole showed synergistic antifungal effects against resistant Candida albicans, resulting in a decrease in the minimum inhibitory concentrations of fluconazole from >512 to 0.25–1 μg/ml. Time-killing curves verified the antifungal activity in dynamic. Besides, n-butylphthalide exhibited anti-biofilm activity against Candida albicans, biofilms preformed 1,024 to 0.5–8 μg/ml. Furthermore, in vitro antifungal effects of n-butylphthalide were confirmed in vivo. N-butylphthalide prolonged survival rate of larvae infected by Candida albicans, reduced the fungal burden in larvae and caused less damage to larval tissues. Notably, n-butylphthalide inhibited hyphal growth and induced intracellular reactive oxygen species accumulation and a loss in mitochondrial membrane potential, which was a potential antifungal mechanism. Besides, the synergistic effects between n-butylphthalide and fluconazole potentially relied on the mechanism that n-butylphthalide significantly promoted drug uptake, and suppressed drug efflux via down-regulating the drug transporter encoding genes CDR1 and CDR2. These findings demonstrated the antifungal effects and mechanisms of n-butylphthalide against Candida albicans for the first time, which might provide broad prospects for the identification of new potential antifungal targets.

The incidence of resistant Candida isolates has increased continuously in recent decades, especially Candida albicans. To overcome this resistance, research on antifungal sensitizers has attracted considerable attention. Linezolid was found to inhibit the growth of Pythium insidiosum and synergize with amphotericin B against Cryptococcus neoformans. The objective of this study was to determine the interactions of linezolid and azoles against C. albicans in vitro and in vivo. In vitro, linezolid combined with azoles induced synergistic effects not only against some susceptible C. albicans isolates, but also against all tested resistant C. albicans isolates. For all resistant isolates, exposure to the combination of linezolid with azoles induced a significant decrease in the minimum inhibitory concentrations (MIC) of azoles, from >512 to 0.5–1 μg/mL for fluconazole, from >16 to 0.25–1 μg/mL for itraconazole, and from >16 to 0.03–0.25 μg/mL for voriconazole. Additionally, linezolid synergized with fluconazole against biofilms that were preformed for ≤ 12 h from both susceptible and resistant C. albicans, and the sessile MIC of fluconazole decreased from >1024 to 1–4 μg/mL. In vivo, linezolid plus azoles prolonged the survival rate of infected Galleria mellonella larvae twofold compared with the azole monotherapy group, significantly decreased the fungal burden of the infected larvae, and reduced the damage of resistant C. albicans to the larval tissue. These findings will contribute to antifungal agent discovery and new approaches for the treatment of candidiasis caused by C. albicans.

With the rapid development of big data, numerous industries have turned their focus from information research and construction to big data technologies. Earth science and geographic information systems industries are highly information-intensive, and thus there is an urgent need to study and integrate big data technologies to improve their level of information. However, there is a large gap between existing big data and traditional geographic information technologies. Owing to certain characteristics, it is difficult to quickly and easily apply big data to geographic information technologies. Through the research, development, and application practices achieved in recent years, we have gradually developed a common geospatial big data solution. Based on the formation of a set of geospatial big data frameworks, a complete geospatial big data platform system called BiGeo was developed. Through the management and analysis of massive amounts of spatial data from Sichuan Province, China, the basic framework of this platform can be better utilized to meet our needs. This paper summarizes the design, implementation, and experimental experience of BiGeo, which provides a new type of solution to the research and construction of geospatial big data.

The aim of this research is to develop a human/murine chimeric Fab antibody which neutralizes the anthrax toxin, protective antigen (PA). The chimeric Fab was constructed using variable regions of murine anti-PA monoclonal antibody in combination with constant regions of human IgG. The chimeric PA6-Fab was expressed in E. coli. BL21 and evaluated by ELISA and co-immunoprecipitation- mass spectra. The potency of PA6-Fab to neutralize LeTx was examined in J774A.1 cell viability in vitro and in Fisher 344 rats in vivo. The PA6-Fab did not have domain similarity corresponding to the current anti PA mAbs, but specifically bound to anthrax PA at an affinity of 1.76 nM, and was able to neutralize LeTx in vitro and protected 56.9% cells at 20 μg/mL against anthrax LeTx. One hundred μg PA6-Fab could neutralize 300 μg LeTx in vivo. The PA6-Fab has potential as a therapeutic mAb for treatment of anthrax.

An integrated optimization model was developed for the spatial distribution of agricultural crops in order to efficiently utilize agricultural water and land resources simultaneously. The model is based on the spatial distribution of crop suitability, spatial distribution of population density, and agricultural land use data. Multi-source remote sensing data are combined with constraints of optimal crop area, which are obtained from agricultural cropping pattern optimization model. Using the middle reaches of the Heihe River basin as an example, the spatial distribution of maize and wheat were optimized by minimizing cross-entropy between crop distribution probabilities and desired but unknown distribution probabilities. Results showed that the area of maize should increase and the area of wheat should decrease in the study area compared with the situation in 2013. The comprehensive suitable area distribution of maize is approximately in accordance with the distribution in the present situation; however, the comprehensive suitable area distribution of wheat is not consistent with the distribution in the present situation. Through optimization, the high proportion of maize and wheat area was more concentrated than before. The maize area with more than 80% allocation concentrates on the south of the study area, and the wheat area with more than 30% allocation concentrates on the central part of the study area. The outcome of this study provides a scientific basis for farmers to select crops that are suitable in a particular area.