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Abstract With the increasingly severe energy supply and environmental pressures, high-end equipment is gradually adopted to reduce the carbon emissions of manufacturing industry which makes its low-carbon structural design a critical research hotspot. The best structural scheme can be got by multi-attribute decision-making (MADM) with design requirements. However, the decision-making attributes in the structural design of high-end equipment are too many at first and low-carbon attributes are seldom fully considered. Moreover, there are a large amount of related data with linguistic vagueness, interval uncertainty, and information incompleteness, which fail to be handled simultaneously. There, this paper proposes an integrated MADM method of low-carbon structural design for high-end equipment based on attribute reduction considering incomplete interval uncertainties. First, distribution reduction of low-carbon structural design is carried out to obtain the minimum attribute set and encompass low-carbon attributes comprehensively. Second, a collaborative filtering algorithm is utilized to complete the missing data in the subsequent design process. Third, interval rough numbers (IRNs) are integrated into DEMATEL-ANP (DANP) and multi-attribute border approximation area comparison (MABAC) to quickly rank the alternative schemes for high-end equipment and determine which is the best. The rationality and robustness of the proposed method are verified through the case study and comparative analysis of a hydraulic forming machine.

Hulun Lake, the largest inland steppe lake in China, is encountering severe water quality degradation. Estuaries play important roles in material and energetic exchange between rivers and lakes. The water quality at the estuaries of Hulun Lake directly reflects the impact of both human activities and natural factors on the lake’s overall water quality, especially during rainfall events. From July 28, 2021, to August 4, 2021, water samples from 62 sites were collected in the three estuaries of Hulun Lake before and after a moderate rainfall event. 13 water parameters, including dissolved oxygen (DO), Turbidity (Tur), Total Nitrogen (TN), Total Phosphorus (TP), Total Organic Nitrogen (TON), and Total Organic Phosphorus (TOP) were measured. The spatio-temporal distribution of water quality in the estuaries was assessed based on water quality index (WQI). Besides, an improved approach integrating stepwise linear regression (SLR) and principal component analysis (PCA) was utilized to construct a WQImin model for an effective assessment of water quality in these estuaries. Furthermore, the absolute principal component scores-multiple linear regression (APCS-MLR) model was employed to identify and quantify the environmental drivers underlying the water quality in the estuaries. The results of WQI indicated that the water quality of the sites in the estuaries of Hulun Lake was “medium” or “poor”, both before and after the rainfall, with a general deterioration in water quality in response to the rainfall. The simplified WQImin model consisted of 5 crucial parameters (i.e., TN, TP, ammonium (NH4+-N), Tur, and permanganate index (CODMn)), and it performed well without parameter weights. Spatial differences in some water parameters among the estuaries were detected, which were attributed to the natural factors and human activities upstream. The principal environmental factors affecting the water quality in the estuaries consisted of hydrodynamic processes, internal phosphorus release, external phosphorus input, external nitrogen input, nitrification in the estuaries, and external organic input and internal organic release. Therefore, we propose basin management strategies such as limiting grazing pressure, adopting enclosed pasture, wetland restoration, optimizing water renewal cycle in Hulun Lake, and transboundary water quality management to tackle water contamination in Hulun Lake.

Abstract Background We aim to compare and assess the surgical parameters and follow-up information of one-hole split endoscopic discectomy (OSE) and microendoscopic discectomy (MED) in the treatment of LDH. Methods This study included 154 patients with degenerative lumbar disk disease. Sixty-eight patients underwent OSE and 86 patients MED. The VAS score for lower back and lower limb radiation pain, ODI score, modified MacNab score, estimated blood loss (EBL), length of the incision, amount of C-reactive protein, and recurrence and complication rates were examined as indicators for clinical outcomes and adverse events. Results After surgery, the VAS and ODI scores in the two groups significantly decreased. On the third day after surgery, the VAS and ODI scores of the OSE group were significantly better than those of the MED group. The VAS and ODI scores preoperatively and at 1 month, 3 months, 6 months, and 12 months following the procedure did not substantially vary between the two groups. There was less EBL and a shorter incision with OSE than with MED. There was no significant difference in the rate of complications between the two groups. Conclusion Compared with MED, OSE is a new alternative option for LDH that can achieve similar and satisfactory clinical outcomes. Furthermore, OSE has many advantages, including less EBL and a smaller incision. Further clinical studies are needed to confirm the effectiveness of OSE.

Abstract With the increasing attention to the state and role of people in intelligent manufacturing, there is a strong demand for human-cyber-physical systems (HCPS) that focus on human-robot interaction. The existing intelligent manufacturing system cannot satisfy efficient human-robot collaborative work. However, unlike machines equipped with sensors, human characteristic information is difficult to be perceived and digitized instantly. In view of the high complexity and uncertainty of the human body, this paper proposes a framework for building a human digital twin (HDT) model based on multimodal data and expounds on the key technologies. Data acquisition system is built to dynamically acquire and update the body state data and physiological data of the human body and realize the digital expression of multi-source heterogeneous human body information. A bidirectional long short-term memory and convolutional neural network (BiLSTM-CNN) based network is devised to fuse multimodal human data and extract the spatiotemporal features, and the human locomotion mode identification is taken as an application case. A series of optimization experiments are carried out to improve the performance of the proposed BiLSTM-CNN-based network model. The proposed model is compared with traditional locomotion mode identification models. The experimental results proved the superiority of the HDT framework for human locomotion mode identification.

Abstract Network function virtualization (NFV) is an appealing solution that transforms complex network functions from dedicated hardware to software instances running in a virtualized environment. However, some new challenges will arise when deploying virtual network functions to meet the needs of NFV and edge‐computing (EC) enabled 5G networks. In this paper, we focus on the service function chain (SFC) orchestration problem for EC‐enabled networks to maximize the profit of network service providers. First, the mathematical model of SFC orchestration in NFV and EC‐enabled networks is defined. Then, a two‐stage heuristic algorithm is proposed to optimize the total revenue. Finally, the performance of the method is evaluated by simulation experiments and the results show its effectiveness.

Objective. To investigate the safety and efficacy of piezosurgery in anterior cervical discectomy and fusion (ACDF) for cervical spondylotic myelopathy (CSM). Methods. 47 patients with complex CSM (cCSM) underwent ACDF surgery from 2014 to 2017. Among these patients, 26 underwent ACDF using piezosurgery (group A) and 21 underwent ACDF by using traditional tools such as high-speed air drill, bone curette, and Kerrison bone punch (group B). Average surgical time, intraoperative blood loss, surgical complications, preoperative and postoperative Japanese Orthopaedic Association (JOA) scores, and improvement rate were measured. Results. Average surgical time and intraoperative blood loss were significantly lower in group A than those in group B (P0.05). Conclusion. For treating cCSM, both the piezosurgery and traditional tools led to significant neurological improvement. However, the piezosurgery was superior to the traditional tools in terms of surgical time, blood loss, and complication rate. Hence, piezosurgery was a safe and effective adjunct for ACDF treating cCSM.

Development mode adjustment is an important measure to further enhance oil recovery after primary waterflooding. Investigating the oil-water flow behaviors in the pores is significant to deepening the understanding of the macrobehavior of waterflooding and the designation of the reservoir development plan. Previous studies mainly focused on the change in recovery factor and macroflow characteristics, while less attention was paid to the causes of the change in recovery factor and the difference in macrocharacteristics. In this paper, the numerical simulation technology by coupling the Navier-Stokes equation with the method of volume of fluid is employed to investigate the dynamic formation mechanism of the remaining oil at pore-scale in the primary waterflooding, and then the pore-scale oil-water two-phase behaviors under three waterflooding development adjustment schemes: changing flooding direction, turning extraction well to injection well, and increasing injection rate are studied. The research shows that when the viscous resistance of the water-bearing channel is less than the sum of the capillary barrier and drainage capillary resistance (the resistance in the displacement of wetting phase displacement using the nonwetting phase), the remaining oil is formed. Water➔oil➔water➔oil displacement mode is formed in the process of changing flow direction, which makes the force of the phase interface tend to balance, reduces the capillary effect in waterflooding, and improves oil recovery. In the process of developing the scheme of turning extraction into an injection well, through multipoint injection, it advances from the central water-bearing area to the oil-bearing area on both sides in multiple paths, forming a larger spatial spread range than that in the change of flooding direction. Under the influence of the capillary barrier effect and drainage capillary resistance, when the injection rate is increased, the remaining oil can restart to move only when the flowing rate exceeds a certain value. The small viscous resistance in the water-bearing channel and the lateral resistance from the capillary barrier limiting the lateral sweep of water are the primary reasons for the insignificant improvement of oil recovery under the condition of a low liquid injection rate. The findings of this study can help for better understanding of pore-scale flow mechanism behaviors and their influences on the macroscopic development features in the waterflooding process.

The study provides a comprehensive bibliometric analysis of imaging and non-imaging spectroscopy for wheat scab (INISWS) using CiteSpace. Therefore, we underpinned the developments of global INISWS detection at kernel, spike, and canopy scales, considering sensors, sensitive wavelengths, and algorithmic approaches. The study retrieved original articles from the Web of Science core collection (WOSCC) using a combination of advanced keyword searches related to INISWS. Afterward, visualization networks of author co-authorship, institution co-authorship, and country co-authorship were created to categorize the productive authors, countries, and institutions. Furthermore, the most significant authors and the core journals were identified by visualizing the journal co-citation, top research articles, document co-citation, and author co-citation networks. The investigation examined the major contributions of INISWS research at the micro, meso, and macro levels and highlighted the degree of collaboration between them and INISWS knowledge sources. Furthermore, it identifies the main research areas of INISWS and the current state of knowledge and provides future research directions. Moreover, an examination of grants and cooperating countries shows that the policy support from the People’s Republic of China, the United States of America, Germany, and Italy significantly benefits the progress of INISWS research. The co-occurrence analysis of keywords was carried out to highlight the new research frontiers and current hotspots. Lastly, the findings of kernel, spike, and canopy scales are presented regarding the best algorithmic, sensitive feature, and instrument techniques.

Abstract Background Ten years after the introduction of the Chinese Ministry of Health (MoH) version of Surgical Safety Checklist (SSC) we wished to assess the ongoing influence of the World Health Organisation (WHO) SSC by observing all three checklist components during elective surgical procedures in China, as well as survey operating room staff and surgeons more widely about the WHO SSC. Methods A questionnaire was designed to gain authentic views on the WHO SSC. We also conducted a prospective cross-sectional study at five level 3 hospitals. Local data collectors were trained to document specific item performance. Adverse events which delayed the operation were recorded as well as the individuals leading or participating in the three SSC components. Results A total of 846 operating room staff and surgeons from 138 hospitals representing every mainland province responded to the survey. There was widespread acceptance of the checklist and its value in improving patient safety. 860 operations were observed for SSC compliance. Overall compliance was 79.8%. Compliance in surgeon-dependent items of the ‘time-out’ component reduced when it was nurse-led (p

Assessment of the state of a pulse power supply requires effective and accurate methods to measure and reconstruct the tracking error. This paper proposes a tracking error measurement method for a digital pulse power supply. A de-noising algorithm based on Empirical Mode Decomposition (EMD) is used to analyse the energy of each Intrinsic Mode Function (IMF) component, identify the turning point of energy, and reconstruct the signal to obtain the accurate tracking error. The effectiveness of this EMD method is demonstrated by simulation and actual measurement. Simulation was used to compare the performance of time domain filtering, wavelet threshold de-noising, and the EMD de-noising algorithm. In practical use, the feedback of current on the prototype of the power supply is sampled and analysed as experimental data.

With the rapid expansion of Industrial Internet of Things, cloud computing and artificial intelligence, many intelligent information services have been developed in smart factories. One of the most important applications is helping factory managers predict the quality of assembled products. Traditional prediction methods of assembly quality mainly focus on building classification or regression models with high accuracy. However, less attention is paid to high-dimensional and imbalanced data, which is a special but common scenario at real-life assembly quality prediction. In this paper, we first use random forest to reduce dimension and analyze critical-to-quality characteristics. Then, a SMOTE-Adaboost method with jointly optimized hyperparameters is proposed for imbalanced data classification in assembly quality prediction. In addition, edge computing is introduced to improve the efficiency and flexibility of quality prediction. Finally, the practicality and effectiveness of the proposed method are verified by a case study of wheel bearing assembly line, and the experimental results show that the proposed method is superior to other classification methods in assembly quality prediction.

Recently, the permanent magnet linear synchronous motor (PMLSM) servo control technology has become a research hotspot. However, the control performance under the traditional PID control is not ideal, which usually has the problems of slow response speed, large overshoot and poor anti-interference performance. In addition, adding nonlinear links with certain characteristics purposefully can greatly improve the performance of the system and achieve the expected effect that the traditional linear controllers cannot achieve. Based on this, this paper introduces the nonlinear PID control strategy into the field of servo control, and proposes a nonlinear control strategy of the three-loop of the servo system to improve the above problems. Firstly, the proposed structure is explained and the rule of nonlinear parameters selection is discussed. Secondly, the conditions for the structural stability of the proposed nonlinear control strategy are analyzed. Finally, experiments on the servo system platform are carried out and the results verify the fast dynamic response, precise positioning and good anti-disturbance performance.

Active power filter (APF) has become an effective approach to suppress harmonic and compensating the reactive power of a power grid. In this paper, the topological structure of the single-phase APF is T-type three-level which can suppress harmonic. For the traditional dual-loop PI control of APF, the voltage loop has poor disturbance rejection performance and long response time under nonlinear load and load mutation. To eliminate the impact on the reduction of power quality of the power grid, this paper uses a PI control of the current loop and a linear active disturbance rejection control (LADRC) of voltage loop. The first-order LADRC controller, which uses a linear extended state observer (ESO) and a linear state error feedback (SEF), does not depend on an explicit mathematical model of the controlled object. In this paper, a 2kVA experimental prototype with a processor of TMS320F28335 is designed to testify the performance and effectiveness of the presented method. The results of simulation and experiments show that the LADRC has better disturbance rejection performance and faster dynamic response than PI control under nonlinear load and load mutation.

Water flooding technology is widely used to improve oil recovery efficiency in oilfields. The capillary barrier effect induced by the complex pore structures in the reservoir rocks is a crucial reason for the trapping of a great deal of residual oil in oil reservoirs after water flooding. However, the formation condition along with the effect on the recovery rate of the capillary barrier under different wettability conditions should be investigated further. To bridge the gap between the microscopic mechanism of the capillary barrier effect and the macroscopic mechanism of oil displacement efficiency, a simple conceptual capillary model is constructed to obtain the formation conditions of the capillary barrier using the analysis method, and its influence on macroscopic oil displacement efficiency in the porous media model with an opening angle of 45° is systematically investigated in this study using direct numerical simulations (DNS) coupled with the volume of fluid method. The results showed that the capillary barrier effect plays a significant role in the formation of the residual oil in the reservoir rock and the contact angle and the opening angle are the primary factors for the formation of the capillary barrier. The capillary force is the driving force when the oil–water interface advances in the throat channel under water-wet conditions, while the capillary force hinders the movement of oil–water movement when the liquid flows out of the throat channel and when θ + β > 90o. Furthermore, the highest oil displacement efficiency is achieved at the intermediate capillary number and in the case that the minimum conditions of occurrence of the capillary barrier phenomenon are satisfied. This is of great significance for controlling the optimized contact angle to further enhance the oil recovery rate of current oil reservoirs using waterflooding technology.

Water flooding technology is an important measure to enhance oil recovery in oilfields. Understanding the pore-scale flow mechanism in the water flooding process is of great significance for the optimization of water flooding development schemes. Viscous action and capillarity are crucial factors in the determination of the oil recovery rate of water flooding. In this paper, a direct numerical simulation (DNS) method based on a Navier–Stokes equation and a volume of fluid (VOF) method is employed to investigate the dynamic behavior of the oil–water flow in the pore structure of a low-permeability sandstone reservoir in depth, and the influencing mechanism of viscous action and capillarity on the oil–water flow is explored. The results show that the inhomogeneity variation of viscous action resulted from the viscosity difference of oil and water, and the complex pore-scale oil–water two-phase flow dynamic behaviors exhibited by capillarity play a decisive role in determining the spatial sweep region and the final oil recovery rate. The larger the viscosity ratio is, the stronger the dynamic inhomogeneity will be as the displacement process proceeds, and the greater the difference in distribution of the volumetric flow rate in different channels, which will lead to the formation of a growing viscous fingering phenomenon, thus lowering the oil recovery rate. Under the same viscosity ratio, the absolute viscosity of the oil and water will also have an essential impact on the oil recovery rate by adjusting the relative importance between viscous action and capillarity. Capillarity is the direct cause of the rapid change of the flow velocity, the flow path diversion, and the formation of residual oil in the pore space. Furthermore, influenced by the wettability of the channel and the pore structure’s characteristics, the pore-scale behaviors of capillary force—including the capillary barrier induced by the abrupt change of pore channel positions, the inhibiting effect of capillary imbibition on the flow of parallel channels, and the blockage effect induced by the newly formed oil–water interface—play a vital role in determining the pore-scale oil–water flow dynamics, and influence the final oil recovery rate of the water flooding.

Synthetic aperture radar (SAR) monopulse angle measurement is an adequate method of target angle measurement in low signal-to-clutter and noise ratio. However, there is a contradiction between the instantaneous angle measurement and the SAR coherent accumulation. In this study, the authors use the total variation method to solve the above problem, and propose the monotone iterative method to enhance the efficiency of the total variation method, which is verified in the simulation.

With environmental pollution and the shortage of resources becoming increasingly serious, the disassembly of certain component in mechanical products for reuse and recycling has received more attention. However, how to model a complex mechanical product accurately and simply, and minimize the number of components involved in the disassembly process remain unsolved problems. The identification of subassembly can reduce energy consumption, but the process is recursive and may change the number of components to be disassembled. In this paper, a method aiming at reducing the energy consumption based on the constraints relation graph (CRG) and the improved ant colony optimization algorithm (IACO) is proposed to find the optimal disassembly sequence. Using the CRG, the subassembly is identified and the number of components that need to be disassembled is minimized. Subsequently, the optimal disassembly sequence can be planned using IACO where a new pheromone factor is proposed to improve the convergence performance of the ant colony algorithm. Furthermore, a case study is presented to illustrate the effectiveness of the proposed method.

The vigorous development of the human cyber-physical system (HCPS) and the next generation of artificial intelligence provide new ideas for smart manufacturing, where manufacturing quality prediction is an important issue in the manufacturing system. However, the small-scale data from humans in emerging HCPS-enabled manufacturing restrict the development of traditional quality prediction methods. To address this question, a data augmentation-based manufacturing quality prediction approach in human cyber-physical systems is proposed in this paper. Specifically, a Data Augmentation-Gradient Boosting Decision Tree (DA-GBDT) model is developed for quality prediction under the HCPS context. In addition, an adaptive selection algorithm of data augmentation rate is designed to balance the trade-off between the training time of the prediction model and the prediction accuracy. Finally, the experimental results of automobile covering products demonstrate that the proposed method can improve the average prediction error of the model compared with the prevailing quality prediction methods. Moreover, the predicted quality information can provide guidance for product optimization decisions in smart manufacturing systems. [ABSTRACT FROM AUTHOR]