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Abstract Background There is increasing evidence that exposure to PM2.5 and its constituents is associated with an increased risk of gestational diabetes mellitus (GDM), but studies on the relationship between exposure to PM2.5 constituents and the risk of GDM are still limited. Methods A total of 17,855 pregnant women in Guangzhou were recruited for this retrospective cohort study, and the time-varying average concentration method was used to estimate individual exposure to PM2.5 and its constituents during pregnancy. Logistic regression was used to assess the relationship between exposure to PM2.5 and its constituents and the risk of GDM, and the expected inflection point between exposure to PM2.5 and its constituents and the risk of GDM was estimated using logistic regression combined with restricted cubic spline curves. Stratified analyses and interaction tests were performed. Results After adjustment for confounders, exposure to PM2.5 and its constituents (NO3 −, NH4 +, and OM) was positively associated with the risk of GDM during pregnancy, especially when exposure to NO3 − and NH4 + occurred in the first to second trimester, with each interquartile range increase the risk of GDM by 20.2% (95% CI: 1.118–1.293) and 18.2% (95% CI. 1.107–1.263), respectively. The lowest inflection points between PM2.5, SO4 2−, NO3 −, NH4 +, OM, and BC concentrations and GDM risk throughout the gestation period were 18.96, 5.80, 3.22, 2.67, 4.77 and 0.97 µg/m3, respectively. In the first trimester, an age interaction effect between exposure to SO4 2−, OM, and BC and the risk of GDM was observed. Conclusions This study demonstrates a positive association between exposure to PM2.5 and its constituents and the risk of GDM. Specifically, exposure to NO3 −, NH4 +, and OM was particularly associated with an increased risk of GDM. The present study contributes to a better understanding of the effects of exposure to PM2.5 and its constituents on the risk of GDM.

This paper describes the development of an interactive dashboard in Python with EZproxy log data. Hopefully, this dashboard will help improve the evidence-based decision-making process in electronic resources management and explore the impact of library use.

Abstract Background The study aims were to analyze pregnancy outcomes after the use of emergency cerclage in patients with different BMIs. Methods A total of 76 singleton pregnant patients who underwent emergency cerclage at a tertiary comprehensive hospital in China between Jan 2017 and Dec 2021 were retrospectively divided into an obesity group of 37 patients with BMIs ≥ 28 kg/m2 and a non-obesity group of 39 patients with BMIs 1 cm), which effectively prolonged the gestational week up to ≥ 25 weeks. Obese gravidae had shorter suture-to-delivery intervals and mean pregnancy lengths but more spontaneous preterm births before 37 weeks, and a lower live birth rate (P 28 kg/m2 may ill-serve the gestational outcomes and suture-to-delivery interval after their emergent cerclage. Additionally, BMI, frequency of cerclage and vaginal microecology accounted for higher fetal loss in patients who underwent emergency cerclage.

Objectives This study was to investigate the colonisation rate of Group B Streptococcus (GBS) during pregnancy, and to evaluate the influence of GBS colonisation on pregnancy outcomes.Design A retrospective cohort study.Setting Data of 47 380 pregnant women from 2016 to 2022 were collected from the Maternal and Child Health Hospital of Huadu District, Guangzhou City, China.Participants A total of 15 040 pregnant women were eligible for this study, of which 32 340 were excluded due to non-native pregnant women, in vitro fertilization infants, malformed fetuses, habitual abortion, abortions due to poor reproductive or obstetrical history, artificial insemination, umbilical cord torsion, and other diseases during pregnancy.Primary outcome measures The incidence rates of GBS colonisation and premature delivery, fetal distress, premature rupture of membranes (PROM), low birth weight (LBW), abortion and stillbirth.Results Of the 15 040 pregnant women included in this study, 1445 developed GBS colonisation, with a prevalence of 9.61% (95% CI, 9.15 to 10.09). Advanced maternal age (≥35 years) predisposed women to GBS colonisation, and the occurrence of GBS colonisation varied among different ethnic groups. Our data revealed that fetal distress, PROM and LBW were more common in pregnant women colonised with GBS than in pregnant women not colonised with GBS. The incidence for premature delivery, fetal distress, PROM and LBW in infants of pregnant women colonised with GBS was 41.0% (OR=1.410, 95% CI, 1.134 to 1.753), 282.5% (OR=3.825, 95% CI, 3.185 to 4.593), 14.9% (OR=1.149, 95% CI, 1.005 to 1.313), and 29.7% (OR=1.297, 95% CI, 1.010 to 1.664), respectively.Conclusions GBS colonisation was relatively low in pregnant women in Guangzhou. Women of advanced maternal age were more prone to GBS colonisation, and pregnant women colonised with GBS were more predisposed to fetal distress, PROM and LBW.

Abstract Background The ubiquitin–proteasome and autophagy-lysosomal systems collaborate in regulating the levels of intracellular proteins. Dysregulation of protein homeostasis is a central feature of malignancy. The gene encoding 26S proteasome non-ATPase regulatory subunit 2 (PSMD2) of the ubiquitin–proteasome system is an oncogene in various types of cancer. However, the detailed role of PSMD2 in autophagy and its relationship to tumorigenesis in esophageal squamous cell carcinoma (ESCC) remain unknown. In the present study, we have investigated the tumor-promoting roles of PSMD2 in the context of autophagy in ESCC. Methods Molecular approaches including DAPgreen staining, 5-Ethynyl-2ʹ-deoxyuridine (EdU), cell counting kit 8 (CCK8), colony formation, transwell assays, and cell transfection, xenograft model, immunoblotting and Immunohistochemical analysis were used to investigate the roles of PSMD2 in ESCC cells. Data-independent acquisition (DIA) quantification proteomics analysis and rescue experiments were used to study the roles of PSMD2 in ESCC cells. Results We demonstrate that the overexpression of PSMD2 promotes ESCC cell growth by inhibiting autophagy and is correlated with tumor progression and poor prognosis of ESCC patients. DIA quantification proteomics analysis shows a significant positive correlation between argininosuccinate synthase 1 (ASS1) and PSMD2 levels in ESCC tumors. Further studies indicate that PSMD2 activates the mTOR pathway by upregulating ASS1 to inhibit autophagy. Conclusions PSMD2 plays an important role in repressing autophagy in ESCC, and represents a promising biomarker to predict prognosis and a therapeutic target of ESCC patients.

In this paper, the passivation behavior and corrosion resistance of a novel Cr-alloyed steel rebar in carbonized concrete environments were systematically studied, and its passivation mechanism was also revealed in view of the intrinsic relationship between the concrete alkalinity and the microstructure characteristic of the passive film. The electrochemical test results show that the alloyed rebar has excellent passivation performance under all carbonization conditions. Meanwhile, its passivation performance shows a trend of first strengthening and then slightly decreasing with the gradual intensification of carbonization, presenting better passivation performance in the mildly carbonized concrete (pH = 11) than in the non-carbonization (pH = 12.6) situation. X-ray photoelectron spectroscopy and Mott–Schottky tests indicate that the alloyed steel rebar has formed an n-p type semiconductor structure composite passive film, which was composed of Cr and Fe oxides/hydroxides under all conditions. The high passivation performance of the alloyed rebar is dominated by the excellent corrosion resistance of Cr oxide/hydroxide, which does not depend on the concrete alkalinity. On the contrary, the high alkalinity will inhibit the formation of Cr oxide to some extent and weaken the passivation performance of the rebar.

Temporal-based methods effectively improve the utilization rate of remote sensing images but large ratios of missing information still need to be improved in the reconstruction models. In this article, based on the imaging theory with the help of a radiation correction model, a decoupling-reconstruction network (DecRecNet) for image reconstruction is proposed. The network uses a ground content radiation (GCR) correction module and imaging environment radiation (IER) correction module and their corresponding loss functions to decouple the image radiation information into GCR related to the ground objects and IER related to the imaging conditions and carry out targeted processing to achieve the purpose of missing information reconstruction. The GCR consistency loss function is used to preserve the ground content information, and the IER consistency loss function and IER smoothness loss are used to coordinate the imaging environment. The radiation-guiding module further performs targeted radiation adjustment on the foreground and background images to transfer the background imaging environment to the foreground and consistent radiation information of the same ground object. Compared with the classical U-Net, DeepLabV3+, RFR-Net, and spatial-temporal-spectral deep convolutional neural network methods, our model showed remarkable advantages in cloud occlusion and stripes of Landsat-8 (30 m), GaoFen-1 (2 m), and Landsat-7 (30 m) at various missing ratios, data sources, resolutions, and scenes, and achieving the goal of missing information reconstruction in large missing ratios.

Abstract The recovery of low-quality waste heat is a major problem in energy utilization. In order to solve this problem and improve energy utilization, the research group designed a low-quality waste heat power generation device with Roots power machine as the core. However, the device has poor ability to adjust the rotation speed and it is difficult to generate electricity stably. The fundamental reason is that the system has many variables and strong coupling. According to the actual working conditions, the power of the device is 10 kW, and the fluctuation range should be within ± 7%. On the one hand, it can be improved by hardware, on the other hand, the design of software is also very critical. At present, through the investigation of domestic and foreign researches on the control system, it is found that the stability of the system is gradually improved, but the problem of strong coupling between variables has not been effectively solved. Therefore, the research group modeled the variables in the system and obtained a coupled model. Based on the couple model, the research group introduced nonlinear multi-model adaptive closed-loop decoupling control and designed a control system. The simulation results show that the maximum overshoot of the control system is 3.9%, the adjustment time is also reduced, and it is stable in low quality waste heat recovery device. Experimental results show that under the control of the system, the rotational speed of roots motor can keep stable, the maximum deviation is not more than 21.4 r/min, and the fluctuation range is within ± 7%, which meets the requirements of the index. This has laid the foundation for the follow-up research of grid-connected power generation.

The era of big data has arrived, and it has penetrated every aspect of human life.As the geo-spatial information science spawned by the intersection of earth sciences and information sciences, the advent of the era of big data provides it with richer prosperous data protection, but also brings new challenges in data storage, management, analysis, and mining, and even caused a certain degree of “data explosion”.From the perspective of big data, the bottlenecks and challenges in the four core areas of geographic information systems, smart cities, remote sensing big data, and spatial data mining were sorted out.And it was pointed out that geo-spatial information science can provide more accurate and real-time spatial information frameworks and more intelligent and more efficient information processing methods for geoscience research, serving intelligent cities, smart earth construction, and sustainable development in the era of big data.Moreover, in the era of big data, the development of geo-spatial information science is facing the double test of software and hardware levels.

Abstract A loom monitoring system was designed and developed to realize monitoring loom in both industrial field and remote network environments and to meet the on-site workers’ convenient operation and centralized management of enterprises. This article uses the touch screen as the main carrier of the field system and cooperates with configuration technology and expert system to realize fault diagnosis. And this article uses the B/S architecture as the remote system cloud platform network architecture which builds the Redis database and Nginx server operating environment, and the front-end technology such as HTML5 is used to design a web client interface to meet the functional needs. This implementation method has three advantages: First, it breaks the traditional way of mechanical button matching with touch screens in the textile manufacturing industry; second, it can quickly find out the reason for loom failure; and third, it lifts the regional restrictions, thus improving the intelligence and information level of textile machinery and equipment. Article Highlights This monitoring system combines configuration technology with computer technology to realize monitoring in both industrial fields and remote environments. Industrial site workers quickly and directly operate the loom through the on-site touch screen. Enterprise management personnel centrally manage the loom through remote means. Therefore, the system is convenient for different types of users in the enterprise. The industrial field monitoring system has comprehensive functions and friendly interface operation, which realizes the comprehensive monitoring and operating parameter setting of each link of the loom production. It can also meet functional requirements such as fault alarm and debugging and maintenance. The use of the cloud platform and other technologies to achieve remote monitoring of the loom, breaking through the geographical restrictions. It is possible to log in to the remote system anytime and anywhere to monitor the running status and production information of the loom, which also provides convenience for later maintenance and upgrades.

Abstract This study investigates aberrant DNA methylations as potential diagnosis and prognosis markers for esophageal squamous-cell carcinoma (ESCC), which if diagnosed at advanced stages has

Glycation by transglutaminase (TGase)-type could effectively improve the structure and functional properties of proteins. However, the influence on intestinal inflammation or the underlying mechanisms has not been investigated. The goal of this research was to compare the bioactivities between glycated casein generated from the TGase-catalyzed reaction and oligochitosan as well as casein using a mouse model of dextran sulfate sodium (DSS)-induced intestinal inflammation to examine the protective effects and the underlying mechanism of glycated casein on intestinal inflammation. Eight groups of C57BL/6 mice were randomly assigned in this study: Control group: standard diet for 35 days; Model group: standard diet for 28 days and then colitis induction; Pretreated groups: different levels (200, 400, 800 mg/kg BW) of casein or glycated casein for 28 days before colitis induction. The mice were drinking water containing a 3% DSS solution for seven days of mice to cause colitis. The results indicated that glycated casein and casein at 200–800 mg/kg BW all relieved DSS-induced weight loss, reduced disease activity index (DAI) score, alleviated colon length shortening, weakened the destruction of colonic mucosal structure, decreased serum LPS, and MPO, IL-1β, IL-6 and TNF-α levels in serum and colon, as well as regulated the expression of proteins involved in the TLR4/NF-κB signaling pathway in a concentration-dependent manner. Glycated caseinate showed a better protective effect against DSS-induced colitis than casein, highlighting that the TGase-type glycation of proteins as a potential functional food ingredient might be a helpful method for gut health.

Aiming at the problems of slow response speed of loom electromagnetic braking system and low accuracy of parking angle after braking, resulting in driving marks and thin and dense roads of fabrics, a braking control system based on fuzzy theory and back propagation (BP) neural network is proposed to improve the response speed and control accuracy of the braking system. Firstly, the transmission torque in the braking process of the loom is dynamically analyzed by establishing the mathematical model of electromagnetic braking and the Ansoft electromagnetic finite element model. The causes of braking angle slip are explored, and the driving mechanism of the electromagnetic clutch control circuit based on PWM pulse is analyzed. Based on the control strategy of excitation current, by introducing the fuzzy theory and BP neural network, a fuzzy electromagnetic braking control system based on a neural network is proposed to improve the adaptive ability of the system. At the same time, the improved bat algorithm (IBA-BP) algorithm is used to train the neural network to avoid the generation of local optimization and improve the control accuracy of the system. The simulation and experimental results show that compared with PID control and fuzzy PID control, the neural network method based on fuzzy theory has a smaller braking slip angle, and the accuracy of parking angle after braking action is less than 10 °, which improves the control accuracy of loom electromagnetic braking system and weaving quality.

In order to improve the effect of intelligent monitoring and condition analysis of textile machinery, some solutions have been proposed to mitigate incomplete monitoring positions, insufficient decision accuracy, uncertainty reasoning and generalization of the current loom monitoring system. Firstly, a model of the weaving machine spindle dynamics was constructed, and the types and sources of monitoring data were specified. Secondly, an improved rough set method is proposed for processing the collected loom attribute data. A genetic multi-objective optimization method combined with a genetic algorithm is proposed to improve the problem of too many reduction results of the rough set method and improve the monitoring system’s reliability. In order to solve the problem that new objects do not have unique matching rules in the constructed rule base, a fusion of Dezert-Smarandache Theory (DSmT) for uncertainty inference is proposed, which increases the distinguishability of decision support probabilities. Experiments show that the improved rough set method based on DSmT and genetic multi-objective optimization has higher classification accuracy and better recognition than the traditional rough set method for weaving machine condition monitoring.

Rapier looms are currently important equipment in the weaving process. The control system of the loom determines the performance of the loom, to a large extent. To effectively reduce the production cost and energy consumption and to improve the start-up performance and production efficiency of rapier looms in industrial production, this paper develops an integrated rapier loom control system based on the direct drive of a switched reluctance motor (SRM) spindle and conducts field tests and applications. The contribution and innovation of this paper is to develop a complete set of low-cost control systems, propose an SRM single neuron fuzzy PID speed control strategy based on voltage chopping control and use it for the control of the main shaft drive technology of rapier looms. The integrated rapier loom control system based on the SRM spindle direct drive proposed in this paper reduces the production costs and energy consumption and improves the start-up performance and production efficiency of the rapier loom. This text carries on the systematic plan design to the control system from the hardware system and the software system. First, according to actual needs, starting from the aspect of reducing control costs and combined with the characteristics of embedded systems, such as tailorability, low cost, and strong scalability, this paper proposes a control system hardware structure based on CAN bus communication and a fully embedded STM32. The control system is divided into multiple control modules, such as the main control module, the spindle drive module, and the power transmission coil module. The system conducts a distributed control to the loom through the CAN bus and is equipped with various communication interfaces, such as Ethernet and RS485. Second, combining the characteristics of the SRM with a simple structure, a large starting torque and the operation mode of the loom, the basic control mode of SRM voltage chopping control is determined. To improve the efficiency and start-up performance of the speed control system, the SRM single neuron PID control algorithm is proposed, and a single neuron is used to improve the PID parameters. On this basis, fuzzy control is introduced to adjust the output gain of a single neuron PID control online to improve the system performance and reduce system energy consumption. Finally, the entire set of rapier loom control systems was verified, tested and debugged on site. The results show that each functional circuit works normally, and that the designed control system can meet the speed response demand of the loom at 850 rpm and reduce the production cost and energy consumption. The comparison experiment between the single neuron fuzzy PID algorithm of the motor and the traditional PID control algorithm in the actual loom production process proves that the proposed control algorithm has a better dynamic response performance. The proposed control algorithm effectively improves the starting performance and production efficiency of rapier looms and meets the actual needs of industrial applications.

The intelligent weaving of looms requires networking and intelligent industrial production systems that use industrial equipment with network communication and data computing, creating a “smart factory”. However, limited by the development of industrial technology, existing industrial loom equipment does not have sufficient communication or computing capabilities: communications primarily rely on bus and Ethernet communication, which is limited to wired connections; and computations primarily rely on the host computers and cloud computers that only provide weak real-time computing. Currently, the remote management system used in the loom industry can be constructed using a cloud platform, but machine-machine interconnections between the loom control terminals are ignored. To solve the problem of poor reconfigurability in loom networks that rely on cloud servers, this study analyzes the networking requirements of a loom network and uses mesh network technology to build a wireless self-organizing loom network. A mesh network can be formed with clouds, loom equipment, and other equipment. By constructing an experimental platform with a decentralized self-organizing network to build a remote loom monitoring system, an anti-destruction experiment of the developed self-organizing loom network is performed. After the self-organizing network is constructed, key nodes in the network are disconnected, and the network is automatically reconfigured to resume normal work. The reconfigurability and stability of the network are demonstrated via destructive experiments. Results show that the self-organizing network construction method developed in this study can build a loom self-organizing network, which is anti-destructive and mitigate the poor reconfigurability of existing loom networks. Compared with the wireless communication network of the previous loom remote monitoring system, the loom interconnection self-organizing network is more reliable and anti-interference, which can better meet the network demand of intelligent manufacturing for loom equipment. This study provides an effective example of loom networking technology for the industry and is important when promoting the construction of smart loom factories.

Tension control is very important for the intelligent control system of rapier looms because stable tension guarantees a tight fabric structure, moderate elasticity and good forming. To solve the problems of low-tension measurement accuracy of the existing rapier loom, the complex structure of the tension control strategy and algorithm, and the high research and development cost, this paper proposes new research from the perspective of high-precision and nonlinear processing of tension detection signals. The median filtering and limiting filtering algorithms are integrated to solve the uncertainty problem caused by the disturbance and fluctuation of the tension signal, and an excellent sample dataset is obtained. The attenuation factor and the number of learning times are introduced to design and adjust the learning rate of the back propagation neural network algorithm. In addition, the overfitting problem of the backpropagation neural network model in the current research process is solved. The experimental simulation results show that the tension detection fluctuation range of this method is 0.8 kg, and the tension detection error is within 0.1%. On the basis of the existing tension control algorithm, the tension detection accuracy is improved, which presents a new research perspective for the wide application of high-precision tension control strategies in rapier looms. It is of great importance to improve the production efficiency and fabric quality of rapier looms.

Abstract Radiotherapy remains the mainstay for treatment of various types of human cancer; however, the clinical efficacy is often limited by radioresistance, in which the underlying mechanism is largely unknown. Here, using esophageal squamous cell carcinoma (ESCC) as a model, we demonstrate that guanine nucleotide exchange factor 2 (VAV2), which is overexpressed in most human cancers, plays an important role in primary and secondary radioresistance. We have discovered for the first time that VAV2 is required for the Ku70/Ku80 complex formation and participates in non-homologous end joining repair of DNA damages caused by ionizing radiation. We show that VAV2 overexpression substantially upregulates signal transducer and activator of transcription 1 (STAT1) and the STAT1 inhibitor Fludarabine can significantly promote the sensitivity of radioresistant patient-derived ESCC xenografts in vivo in mice to radiotherapy. These results shed new light on the mechanism of cancer radioresistance, which may be important for improving clinical radiotherapy.

In recent years, reinforcement learning (RL) has been widely used to solve multi-agent navigation tasks, and a high-fidelity level for the simulator is critical to narrow the gap between simulation and real-world tasks. However, high-fidelity simulators have high sampling costs and bottleneck the training model-free RL algorithms. Hence, we propose a Multi-Fidelity Simulator framework to train Multi-Agent Reinforcement Learning (MFS-MARL), reducing the total data cost with samples generated by a low-fidelity simulator. We apply the depth-first search to obtain local feasible policies on the low-fidelity simulator as expert policies to help the original reinforcement learning algorithm explore. We built a multi-vehicle simulator with variable fidelity levels to test the proposed method and compared it with the vanilla Soft Actor-Critic (SAC) and expert actor methods. The results show that our method can effectively obtain local feasible policies and can achieve a 23% cost reduction in multi-agent navigation tasks.

At present, the recovery of low-quality waste heat is a major problem in energy utilization. To solve this problem and improve energy efficiency, this research group designed a low-quality waste heat power generation device with a roots-type power machine as the core component. However, the power generation device produces a large hysteresis in power generation regulation. While the hardware can be improved, the design of the measurement and control system is also critical. In view of the problems existing in low-quality waste heat power generation devices, this research group introduced an internal model controller into the control system and designed an internal model controller and filter through the analysis of each module. In addition, to improve the performance of the controller, this research group applied the deep learning method to optimize the control system and used the prediction function of the deep learning method to further improve the stability of the device. The simulation and experimental results show that the control strategy can make this device for the recovery of low-quality waste heat respond quickly to fluctuations in the gas source and improve the hysteresis problem.

At present, the weft selection and selvedge control systems available on the loom do not support precise control of the weft selection and selvedge devices, which will reduce the dynamic performance and production efficiency of the loom. The low-frequency oscillation and back Electromotive force (EMF) of the stepping motor are the main factors that affect the stability of the control system. This paper proposes an optimized mixed current attenuation algorithm that is dedicated to improving the performance of the system. The contribution and innovation of this paper is that the mixed current attenuation algorithm is proposed to improve the low frequency oscillation and back-EMF of the stepping motor and applied to the actual loom production. In this paper, the current subdivision drive is used to improve the low-frequency oscillation of the stepper motor of the weft selection and twisting device, and the back-EMF is reduced by the mixed current attenuation algorithm. First, to verify the effectiveness of the mixed current attenuation algorithm, this paper conducts a motor winding current waveform test. Then, by comparing the acceleration and deceleration control effects in the current self-decay mode, it is verified that the mixed current attenuation algorithm can effectively reduce the low-frequency oscillation and back-EMF of the stepper motor, and improve the dynamic performance and production efficiency of the stepper motor. Finally, the algorithm was debugged and verified on the high-speed rapier loom test platform. By recording and comparing the number of stops of the loom in a certain period of time, it can be concluded that the number of stops caused by the process error of weft selection and selvedge is greatly reduced. Experiments show that the weft selection and selvedge control system using the mixed current attenuation algorithm can achieve precise control of the weft selection and selvedge device, which can effectively improve the productivity and dynamic performance of the loom while reducing the low-frequency oscillation and back-EMF of the stepping motor. Experiments show that the mixed current attenuation algorithm meets the technological requirements of weft selection and selvedge and has a good application prospect.

The reuse of industrial waste heat is an important way to promote energy conservation and reduce emissions. However, low-quality waste heat has a wide distribution range. It is difficult to recover, and the recycling technology needs to be a breakthrough. Therefore, in this paper, we propose a Roots waste heat power generation device which is used to recover low-quality waste heat. However, the relative fluctuation of the air source is greater with the low-quality waste heat than that with the medium- and high-quality waste heat, which makes it difficult for the actual working condition of the device to maintain stability and even causes the generator to cut off automatically. Therefore, it is necessary to perform a study on the robustness of the measurement and control system of the Roots waste heat power generation process and to improve the stability of the device operation. To solve these problems, we propose a control method that combines a robust controller and an internal model controller to improve the robustness of the device. The experimental results showed that the robust internal model controller has a strong anti-interference ability and that the tracking performance is obviously improved.

Abstract Background Diabetes is known to increase morbidity and 30-day mortality in adults undergoing non-cardiac surgery, but longer term outcomes are less studied. This study was done to explore how undiagnosed and known diabetes affect 30-day and one-year morbidity and mortality outcomes. The secondary aim was to study the prevalence of undiagnosed diabetics in our perioperative Asian surgical population. Methods A retrospective cohort study of 2106 patients aged > 45 years undergoing non-cardiac surgery in a single tertiary hospital was performed. Undiagnosed diabetics were identified (HbA1c ≥6.5% or fasting blood glucose ≥126 mg/dL) and relevant demographic, clinical and surgical data were analyzed to elicit the relationship to adverse outcomes. Univariate analysis was first performed to identify significant variables with p-values ≤0.1, which were then analyzed using multiple logistic regression to calculate the adjusted odds ratio. Results The prevalence of undiagnosed diabetes was 7.4%. The mean and median HbA1c of known diabetics were 7.9 and 7.5%, while the mean and median HbA1c for undiagnosed diabetics were 7.2 and 6.8% respectively. 36.4% of known diabetics and 20.5% of undiagnosed diabetics respectively had a random blood glucose > 200 mg/dL. Undiagnosed diabetics had a three-fold increase in 1-year mortality compared to non-diabetics (adjusted OR 3.46(1.80–6.49) p

Abstract Background Chronic osteoarthritic pain is not well understood in terms of its pathophysiological mechanism. Activated glial cells are thought to play a role in the maintenance of chronic pain. T98G glioblastoma cell line was previously observed to release higher amounts of interleukin-6 (IL-6) when treated with cerebrospinal fluid (CSF) from patients with another chronic pain condition, post-herpetic neuralgia. In this study, we investigated the ability of CSF from patients diagnosed with knee osteoarthritis suffering from chronic pain, to trigger the release of pro-inflammatory cytokines, IL-6, IL-1beta and tumour necrosis factor alpha (TNF-α) from T98G. Characterization of upstream signalling was also explored. Methods Fifteen osteoarthritis patients undergoing total knee replacement due to chronic knee pain and 15 patients without pain undergoing other surgeries with spinal anaesthesia were prospectively recruited. CSF was collected during anaesthesia. CSF were added to cultured T98G cells in the presence of lipopolysaccharide. IL-6, IL-1β and TNF-α release from T98G cells were measured using enzyme immunoassay. Antibody array and western blotting were performed using CSF-triggered T98G cell lysates to identify possible signalling targets. Age, gender and pain scores were recorded. Mann-Whitney U test was used to compare IL-6 release and protein expression between groups. Association between IL-6 and pain score was analysed using linear regression. Results Significant higher levels of IL-6 were released by T98G cells when induced by osteoarthritis patients’ CSF in the presence of LPS. The IL-6 levels showed positive association with pain score (adjusted B estimate = 10.1 (95% Confidence Interval 4.3–15.9); p = 0.001). Antibody array conducted with 6 pooled T98G cell lysate induced with osteoarthritis pain patient CSF identified greater than 2-fold proteins including STE20-related kinase adaptor protein and spleen tyrosine kinase. Further validation done using western blotting of individual CSF-triggered T98G cell lysate showed non-significant increase. Conclusion Higher IL-6 release from T98G when triggered by OA-CSF, in the presence of LPS, suggest the presence of “unknown molecule” in CSF that may be crucial in the maintenance phase of chronic pain in our osteoarthritis population. Further studies on the signalling pathways involved in pain and relevance of IL-6 release from T98G cells in other pain models are needed.

Background: Neurocognitive dysfunction is a common complication of coronary artery bypass grafting (CABG) with incidence of 19–38%. The miniaturized cardiopulmonary bypass (MCPB) system was developed to reduce hemodilution and inflammation and provides better cerebral protection than conventional cardiopulmonary bypass (CCPB). In a meta-analysis, MCPB was associated with a 10-fold reduction in the incidence of strokes. However, its effect on postoperative cognitive decline (POCD) is unknown. We assessed if MCPB decreases POCD after CABG and compared the risk factors. Methods: A total of 71 Asian patients presenting for elective CABG at a tertiary center were enrolled. They were randomly assigned to MCPB (n = 36) or CCPB group (n = 35) and followed up in a single-blinded, prospective, randomized controlled trial. The primary outcome was POCD as measured by the repeatable battery of neuropsychological status (RBANS). Inflammatory markers (tumor necrosis factor-alpha and interleukin-6), hematocrit levels, and neutron-specific enolase (NSE) levels were studied. Results: Overall, the incidence of POCD at 3 months was 50%, and this was not significantly different between both groups (51.4 vs 50.0%, P = 0.90). Having

Breast cancer gene 1 (BRCA1) and BRCA2 are tumor suppressors involved in DNA damage response and repair. Carriers of germline pathogenic or likely pathogenic variants in BRCA1 or BRCA2 have significantly increased lifetime risks of breast cancer, ovarian cancer, and other cancer types; this phenomenon is known as hereditary breast and ovarian cancer (HBOC) syndrome. Accurate interpretation of BRCA1 and BRCA2 variants is important not only for disease management in patients, but also for determining preventative measures for their families. BRCA1:c.132C>T (p.Cys44=) is a synonymous variant recorded in the ClinVar database with “conflicting interpretations of its pathogenicity”. Here, we report our clinical tests in which we identified this variant in two unrelated patients, both of whom developed breast cancer at an early age with ovarian presentation a few years later and had a family history of relevant cancers. Minigene assay showed that this change caused a four-nucleotide loss at the end of exon 3, resulting in a truncated p.Cys44Tyrfs*5 protein. Reverse transcription-polymerase chain reaction identified two fragments (123 and 119 bp) using RNA isolated from patient blood samples, in consistency with the results of the minigene assay. Collectively, we classified BRCA1:c.132C>T (p.Cys44=) as a pathogenic variant, as evidenced by functional studies, RNA analysis, and the patients’ family histories. By analyzing variants recorded in the BRCA Exchange database, we found synonymous changes at the ends of exons could potentially influence splicing; meanwhile, current in silico tools could not predict splicing changes efficiently if the variants were in the middle of an exon, or in the deep intron region. Future studies should attempt to identify variants that influence gene expression and post-transcription modifications to improve our understanding of BRCA1 and BRCA2, as well as their related cancers.

At present, the global demand for lithium batteries is still in a high growth state, and the traditional lithium battery pole mill control system is still dominated by ARM (Artificial Intelligence Enhanced Computing), DSP (Digital Signal Processing), and other single-chip control methods. There are problems such as poor anti-interference ability and insufficient real-time online analysis of production data. This paper adopts the dual-chip control system architecture based on "ARM+DSP", starting from the mechanical characteristics and operating signal features of the pole mill. The hardware system adopts a three-unit joint control hardware structure, which separates the control unit from the data processing unit and improves the operation of the system. The software system adopts fuzzy PID algorithm to realize deflection control and tension control, and verifies that the Fuzzy PID (Proportion Integration Differentiation) control algorithm can effectively improve the anti-interference ability of the deflection system and tension system. The results show that the data loss rate is low with the SPI communication between DSP and ARM. The tension error of the "ARM+DSP" control system does not exceed 5%, and the deviation of the correction band is within ±4mm. The dedicated dual-chip hardware architecture effectively improves the robustness and operation efficiency of the pole mill, solves the problem of low tension control accuracy, and provides a theoretical basis for the application of the dual-roll mill.

At present, the rapier loom has gradually become the mainstream equipment in the manufacturing industry. In order to make the rapier loom realize automated production and further improve the production efficiency of the rapier loom, improve the programmability of the system, and reduce the cost of system maintenance. The thesis developed a rapier loom control system based on embedded soft PLC, and carried out experiments and applications in the field. The contribution and innovation of this paper is to develop a complete low-cost control system, and through a genetic algorithm optimized PID algorithm to complete the more effective control of the loom tension system. The embedded soft PLC system proposed in this paper reduces the overall maintenance cost of the system and improves the programmability of the system. This text carries on the systematic scheme design to the embedded soft PLC from the hardware system and the software system respectively. First, according to the actual requirements, this article designs the overall scheme of the embedded software PLC hardware system with STM32F407ZGT6 as the core. Then this article is based on the embedded soft PLC hardware platform, according to the international standard of industrial control programming, writes the embedded soft PLC low-level driver software. Secondly, this article analyzes the factors that affect the warp tension during the operation of the rapier loom, and proposes the use of genetic algorithm to optimize the warp tension control method of the traditional PID algorithm. Finally, we conducted verification tests and on-site application debugging for the entire set of rapier loom embedded soft PLC control system. We controlled the warp tension as the main experimental object. The results show that this control system effectively improves the control accuracy of the warp tension of the rapier loom and meets the actual needs of industrial applications. The whole system has a good application prospect in the warp tension control of rapier looms.

The electrical control system of rapier weaving machines is susceptible to various disturbances during operation and is prone to failures. This will seriously affect the production and a fault diagnosis system is needed to reduce this effect. However, the existing popular fault diagnosis systems and methods need to be improved due to the limitations of rapier weaving machine process and electrical characteristics. Based on this, this paper presents an in-depth study of rapier loom fault diagnosis system and proposes a rapier loom fault diagnosis method combining edge expert system and cloud-based rough set and Bayesian network. By analyzing the process and fault characteristics of rapier loom, the electrical faults of rapier loom are classified into common faults and other faults according to the frequency of occurrence. An expert system is built in the field for edge computing based on knowledge fault diagnosis experience to diagnose common loom faults and reduce the computing pressure in the cloud. Collect loom fault data in the cloud, train loom fault diagnosis algorithms to diagnose other faults, and handle other faults diagnosed by the expert system. The effectiveness of loom fault diagnosis is verified by on-site operation and remote monitoring of the loom human-machine interaction system. Technical examples are provided for the research of loom fault diagnosis system.

Rechargeable aqueous zinc ion batteries (ZIBs) have attracted increasingly solicitude in the application of large-scale electrochemical energy storage system (EES) as a result of their low-price, high security and environment-friendly. The synthesis of mass-produced electrode materials and the exploration of their potential electrochemical properties are essential steps to achieve superior large-scale EES. In this work, the large-scale preparation of vanadium oxyphosphate hydrate (VOPO4⋅2H2O) cathode material with impressively zinc storage ability is successfully demonstrated. Specially, it exhibits a high specific capacity of 165 mAh g–1 at 0.05 A g–1, and prominent rate property (90 and 75 mAh g–1 at 2 and 5 A g–1, respectively), as well as stable cyclability of 76% after 1000 cycles under a high current density of 5 A g–1 within the voltage window of 0.4–1.6 V (versus Zn2+/Zn). Moreover, the VOPO4⋅2H2O not only spreads superiority in electrochemical performance, but also shows the advantages of scalable production based on simple controllable adjustment in synthesis, which is expected to exhibit great development potential in the field of large-scale EES application.

Na-ion batteries (SIBs) are anticipated to capture a broad development space in the field of large-scale energy storage due to the abundant sodium resources. High-performance cathode materials are very critical. VOPO4⋅2H2O with a two-dimensional (2D) layered structure is a very promising candidate for SIBs because of its high working voltage and theoretical specific capacity. Herein, a simple one-step reflux method is designed to fabricate a cathode of VOPO4⋅2H2O nanosheets. It exhibits a high average operating potential of ∼3.5 V, remarkable specific capacity (e.g., 135 mAh g–1 at 0.05 C), favorable high current charge-discharge ability (e.g., 58 mAh g–1 even at 20 C) as well as extralong cyclability (e.g., 0.026% capacity fading rate for per cycle at 20 C during 1000 cycles). The kinetic analysis implies that the superior sodium storage performance is mainly benefiting from the advantages of unique nanosheet structure, accelerating the rapid Na-ion diffusion.

With a goal to examine the role of Chinese American librarians in Library and Information Science (LIS) diversity in the 21st century, the authors conducted a study funded by the ALA (American Library Association) Diversity Research Program between 2013 and 2014. This study included a national survey of CALA (Chinese American Librarians Association) members and qualitative semi-structured interviews of ten selected survey participants (both senior and emerging leaders of CALA members). Despite some limitations of the study, the research findings document best practices and lessons learned among Chinese American librarians to inform and improve the diversity initiative, namely by increasing CALA’s leadership training and mentorship opportunities, improving communication and cultural understanding, and developing connections with other people or organizations with diversity agendas. The findings have provided fruitful ideas to CALA in many ways especially its 2020 strategic planning, collaboration with other organizations and Leadership Training initiatives occurring between 2015 and 2017. The findings also suggest strategies for the recruitment and retention of Chinese American librarians to the LIS field. The findings and outcomes also provide much needed evidence to increase the role of Chinese American librarians in the diversity efforts that support 2017 ALA’s Strategic Directions: Equity, Diversity and Inclusion.

Detecting changes in vegetation, distinguishing the persistence of changes, and seeking their causes during multiple periods are important to gaining a deeper understanding of vegetation dynamics. Using the Global Inventory Modeling and Mapping Studies Normalized Difference Vegetation Index (NDVI) version NDVI3g dataset in the Tibetan Plateau, the trends in the seasonal components of NDVI and their linkage with climatic factors were analyzed over 14 asymptotic periods of 18–31 years since 1982. Dynamic trends in vegetation experienced an obvious increase at regional scale, but the increases of vegetation activity mostly tended to stall or slow down as the studied time period was extended. At pixel scale, areas with significant browning significantly expanded over 14 periods for all seasons, but for significant greening significantly increased only in autumn. The changes of vegetation activity in spring were the most drastic among three seasons. Increased increments of NDVI in summer, spring, and autumn took turns being the main reason for the enhanced vegetation activity in the growing season in the nested 14 periods. Vegetation activity was mainly regulated by a thermal factor, and the dominant climatic drivers of vegetation growth varied across different seasons and regions. We speculate that the increase of NDVI will continue but the increments will decline in all seasons except autumn.

Urban ventilation corridors serve as channels of fresh air flow between the city and suburbs, helping to improve the wind and thermal environments and thermal comfort. However, owing to the limited number of weather stations, it is impossible to quantitatively reveal the effective effect range of urban ventilation corridors on urban thermal comfort at the scale of 100 × 100 m, which is optimal for urban ventilation corridors. In this study, we integrated building data, the European Centre for Medium-Range weather forecast data (ECMWF), MOD13Q1, and other multisource data to analyse the effect of urban ventilation corridors on urban thermal comfort at a unified scale of 100 × 100 m. The results showed that ECMWF and Landsat8 data could be used as substitute factors to improve the universal thermal climate index (UTCI) urban spatial resolution. The effective range of urban ventilation corridor effects on the urban surface temperature and urban comfort was ≤1000 m, with building density and vegetation coverage as the main factors limiting this range. Therefore, attention should be paid to the effective range of urban ventilation corridors, the surrounding building density, vegetation coverage, and the rational use of urban ventilation corridors to reduce the energy consumption of air conditioning in summer.

It has been a decade since Tim Berners-Lee coined Linked Data in 2006. More and more Linked Data datasets have been made available for information retrieval on the Web. It is essential for librarians, especially academic librarians, to keep up with the state of Linked Data. There is so much information about Linked Data that one may wonder where to begin when they want to join the Linked Data community. With this in mind, the author compiled this annotated bibliography as a starter kit. Due to the many resources available, this list focuses on literature in English only and of specific projects, case studies, research studies, and tools that may be helpful to academic librarians, in addition to the overview of Linked Data concept and the current state of Linked Data evolution and adoption.

It has been a decade since Tim Berners-Lee coined Linked Data in 2006. More and more Linked Data datasets have been made available for information retrieval on the Web. It is essential for librarians, especially academic librarians, to keep up with the state of Linked Data. There is so much information about Linked Data that one may wonder where to begin when they want to join the Linked Data community. With this in mind, the author compiled this annotated bibliography as a starter kit. Due to the many resources available, this list focuses on literature in English only and of specific projects, case studies, research studies, and tools that may be helpful to academic librarians, in addition to the overview of Linked Data concept and the current state of Linked Data evolution and adoption.

Connection looseness phenomena of steel grid structures might induce issues of lowering integrity, large deformation, even total collapse of the structures. The goal of this article is to propose an evaluation method for bolt-sphere joint looseness of steel grid structures using piezoceramic guided wave–based method through experiments and numerical simulations. A single bolt-sphere joint looseness experimental model is established and tested, considering grid member connection angles of 0°, 45°, 90°, and 180°, respectively. Then, multiple bolt-sphere joint looseness detection tests by selecting six kinds of cases for a steel grid structure model are performed. Piezoceramic patch arrays bonded on the surface of grid members are used as transducers to generate and receive detection guided waves, and external torques are applied to indirectly simulate the bolt-sphere joint looseness effect. The experimental results show that the bolt-sphere joint looseness impact on the ultrasonic wave energy attenuation has a nonlinear regularity. Based on the regularity, an evaluation method and key techniques for the bolt-sphere joint looseness detection based on guided wave energy are proposed and experimentally validated. To further clarify the bolt-sphere joint looseness detection mechanism, the ABAQUS software is used for a finite element analysis of the single bolt-sphere joint looseness evaluation. The numerical and experimental results match well, verifying the feasibility of the proposed method.

The spatial evolution of land use in Jinzhou area was simulated using fuzzy cellular automata to determine all factors influencing urban land use change. For this purpose, land use data of multiple sources and remote sensing images from 2003 to 2015 were analyzed. The following results were obtained: (1) real land use data and simulation data for 2015 were tested using four indices. Under the 5 m × 5 m scale, the model shows good performance for simulation of areas with various land use types. (2) Among simulations under four scenarios, the effect of traffic guidance on the development of construction land was more distinct under the economic development mode, which clearly showed the phenomenon of “agglomeration” along major traffic lines. (3) Jinshitan Street is adjacent to the sea, and land use changes are significant under the 4th scenario, and thus related departments should pay more attention. (4) Shortcomings of conventional cellular automata model in processing complex systems could be mitigated through the integration of fuzzy sets.

INTRODUCTION:Postoperative atrial fibrillation (POAF) is a common complication following cardiac surgery associated with increased morbidity and mortality. Although sustained hyperglycemia is a known risk factor of AF and poor ICU outcomes, emerging in-vitro studies reveal acute glycemic fluctuations to be an additional independent predictor of AF. The effect of acute glycemic fluctuations on the incidence of POAF in the clinical setting remains unclear. We aim to investigate the effect of the magnitude of acute perioperative glycemic fluctuations on the incidence of POAF in a multi-ethnic Southeast-Asian population. METHODS:We obtained data from1743 patients who underwent elective CABG in a tertiary heart centre from 2009-2011. Patients were kept to a tight baseline glycemic control in accordance with hospital protocol. The magnitude of the difference between the highest and lowest perioperative glucose levels up till the first 48 postoperative hours was employed as a measure of glycemic fluctuation. Patients were divided into 4 groups for analysis based on the magnitude of glycemic fluctuation:A)0-2mmol/L(N = 147); B)>2-4mmol/L(N = 426); C)>4-6mmol/L(N = 513); D)>6mmol/L(N = 657).Our primary outcome was the incidence of POAF. Secondary outcomes included ICU and 30-day mortality and length of stay. RESULTS:The overall incidence of POAF was 14.7%. This increased as the magnitude of glycemic fluctuation increased, and was statistically highest in Group D(16.4%) as compared with the other 3 sub-groups. Multivariate logistic regression revealed the magnitude of perioperative glycemic fluctuation to be an independent risk factor of POAF(O.R.1.06, 95% C.I.1.01-1.11, p = 0.014).ICU length of stay was statistically highest in Group D(63.1 hours, p = < .001). However, ICU and 30 day mortality rates were similar among the 4 groups. CONCLUSION:Increased magnitudes of acute perioperative glycemic fluctuations are associated with a significantly increased risk of POAF and length of ICU stay; and should therefore be minimised but balanced against the risks of hypoglycemia so as to avoid POAF and optimise patient outcomes.