Controlling the dynamic behavior of decentralized cluster through centralized approaches

How to control the dynamic behavior of large-scale artificial active matter is a critical concern in experimental research on soft matter, particularly regarding the emergence of collective behaviors and the formation of group patterns. Centralized systems excel in precise control over individual behavior within a group, ensuring high accuracy and controllability in task execution. Nevertheless, their sensitivity to group size may limit their adaptability to diverse tasks. In contrast, decentralized systems empower individuals with autonomous decision-making, enhancing adaptability and system robustness. Yet, this flexibility comes at the cost of reduced accuracy and efficiency in task execution. In this work, we present a unique method for regulating the centralized dynamic behavior of self-organizing clusters based on environmental interactions. Within this environment-coupled robot system, each robot possesses similar dynamic characteristics, and their internal programs are entirely identical. However, their behaviors can be guided by the centralized control of the environment, facilitating the accomplishment of diverse cluster tasks. This approach aims to balance the accuracy and flexibility of centralized control with the robustness and task adaptability of decentralized control. The proactive regulation of dynamic behavioral characteristics in active matter groups, demonstrated in this work through environmental interactions, holds the potential to introduce a novel technological approach and provide experimental references for studying the dynamic behavior control of large-scale artificial active matter systems.

Utilising continuous glucose monitoring for glycemic control in diabetic kidney disease

In this editorial,we comment on the article by Zhang et al.Chronic kidney disease(CKD)presents a significant challenge in managing glycemic control,especially in diabetic patients with diabetic kidney disease undergoing dialysis or kidney transplantation.Conventional markers like glycated haemoglobin(HbA1c)may not accurately reflect glycemic fluctuations in these populations due to factors such as anaemia and kidney dysfunction.This comprehensive review discusses the limitations of HbA1c and explores alternative methods,such as continuous glucose monitoring(CGM)in CKD patients.CGM emerges as a promising technology offering real-time or retrospective glucose concentration measure-ments and overcoming the limitations of HbA1c.Key studies demonstrate the utility of CGM in different CKD settings,including hemodialysis and peritoneal dialysis patients,as well as kidney transplant recipients.Despite challenges like sensor accuracy fluctuation,CGM proves valuable in monitoring glycemic trends and mitigating the risk of hypo-and hyperglycemia,to which CKD patients are prone.The review also addresses the limitations of CGM in CKD patients,emphasizing the need for further research to optimize its utilization in clinical practice.Altogether,this review advocates for integrating CGM into managing glycemia in CKD patients,highlighting its superiority over traditional markers and urging clinicians to consider CGM a valuable tool in their armamentarium.

Accuracy of Mean Value of Central Venous Pressure from Monitor Digital Display: Influence of Amplitude of Central Venous Pressure during Respiration

Background A simple measurement of central venous pressure(CVP)-mean by the digital monitor display has become increasingly popular.However,the agreement between CVP-mean and CVP-end(a standard method of CVP measurement by analyzing the waveform at end-expiration)is not well determined.This study was designed to identify the relationship between CVP-mean and CVP-end in critically ill patients and to introduce a new parameter of CVP amplitude(ΔCVP=CVPmax-CVPmin)during the respiratory period to identify the agreement/disagreement between CVP-mean and CVP-end.Methods In total,291 patients were included in the study.CVP-mean and CVP-end were obtained simultaneously from each patient.CVP measurement difference(|CVP-mean-CVP-end|)was defined as the difference between CVP-mean and CVP-end.TheΔCVP was calculated as the difference between the peak(CVPmax)and the nadir value(CVPmin)during the respiratory cycle,which was automatically recorded on the monitor screen.Subjects with|CVP-mean-CVP-end|≥2 mm Hg were divided into the inconsistent group,while subjects with|CVP-mean-CVP-end|2 mm Hg were divided into the consistent group.ResultsΔCVP was significantly higher in the inconsistent group[7.17(2.77)vs.5.24(2.18),P0.001]than that in the consistent group.There was a significantly positive relationship betweenΔCVP and|CVP-mean-CVP-end|(r=0.283,P 0.0001).Bland-Altman plot showed the bias was-0.61 mm Hg with a wide 95%limit of agreement(-3.34,2.10)of CVP-end and CVP-mean.The area under the receiver operating characteristic curves(AUC)ofΔCVP for predicting|CVP-mean-CVP-end|≥2 mm Hg was 0.709.With a high diagnostic specificity,usingΔCVP3 to detect|CVP-mean-CVP-end|lower than 2mm Hg(consistent measurement)resulted in a sensitivity of 22.37%and a specificity of 93.06%.UsingΔCVP8 to detect|CVP-mean-CVPend|8 mm Hg(inconsistent measurement)resulted in a sensitivity of 31.94%and a specificity of 91.32%.Conclusions CVP-end and CVP-mean have statistical discrepancies in specific clinical scenarios.ΔCVP during the respiratory period is related to the variation of the two CVP methods.A highΔCVP indicates a poor agreement between these two methods,whereas a lowΔCVP indicates a good agreement between these two methods.

Determination of Monitoring Control Value for Concrete Gravity Dam Spatial Deformation Based on POT Model

Deformation can directly reflect the working behavior of the dam,so determining the deformation monitoring control value can effectively monitor the safety of dam operation.The traditional dam deformation monitoring control value only considers the single measuring point.In order to overcome the limitation,this paper presents a new method to determine the monitoring control value for concrete gravity dam based on the deformations of multi-measuring points.A dam’s comprehensive deformation displacement is determined by the measured values at different measuring points on the positive inverted vertical line and the corresponding weight of eachmeasuring point.The projection pursuit method(PPM)combined with the grey wolf optimization(GWO)algorithm is used to determine the weight of each measuring point according to the spatial correlation distribution characteristics of dam deformation.The peaks over threshold(POT)model based on the extreme value theory is adopted to determine the monitoring control value with the obtained dam comprehensive deformation displacement.In addition,the POTmodel is improved with the automatic threshold determinationmethod based on the 3σcriterion in probability theory and the GWO algorithm,which can avoid subjectivity and randomness in determining the threshold.The results of the engineering application show the feasibility and applicability of the proposed method.

Analysis of the Effect of Quality Control Circle on the Central Sterile Supply Department

Objective:To analyze the effect of quality control circle on the central sterile supply department(CSSD).Methods:The control group and the observation group each consisted of 180 instruments received by the sterilization supply center from January to March 2023 and 11 CSSD staff.The control group underwent routine management while quality control circle was implemented in the observation group.The quality of work,disinfection and sterilization qualification rates,disinfection and sterilization of various instruments,cleaning indicators,and management satisfaction of both groups were compared.Results:The observation group scored higher in terms of work quality,the qualification rate of disinfection and sterilization in each link,the disinfection and sterilization of instruments,and cleaning indicators compared to the control group.Besides,the management satisfaction of the observation group was higher than that of the control group(P<0.05).Conclusion:A quality control circle ensures the quality of work,improves the cleaning,disinfection,and sterilization of instruments of the CSSD,and improves the management satisfaction of the CSSD staff.

A hierarchical architecture of centralized monitoring and controlling system and its high-performance and interoperability protocol

This paper describes a hierarchical architecture and a high-performance and interoperability protocol for centralized monitoring and controlling systems (CMCS) . The protocol we proposed can interoperate different monitoring and controlling systems constructed by different companies, each with different functions and communication protocols. The protocol reduces the amount of traffic and has real-time and high-perfor-mance advantages. The protocol was implemented in CMCS for telecommunication power supply and air-condi-tioner used by the Telecommunication Bureau of Zhejiang Province. This paper deals with the hierarchical architecture and function of CMCS and packet format, command ID, and SDL description of its protocol. We also discuss the properties of the interoperability and performance of the protocol in this paper.

Can the Glucose Central Control System Dysfunctions Induce Diabetes Mellitus?

We study afresh how the glucose control system anomalies impact the organicity of the glucose homeostasis and build up events of persistent hyperglycemia and diabetes mellitus. We have used critically the state of art literature related to the subject, in order to cross, to compare, and to organize the relevant contents to create a logical and consistent support to the finds. We show that it is consistent to assume that persistent hyperglycemia and diabetes mellitus can have precursors not only in pancreas, but also in brain, mainly induced by noxious dysfunctions of hypothalamus sensor neurons circuits and external noxious elements, causing pancreas overload, and the consequent exhaustion—overburden.

Mechanism,prevention,and control of mining-induced dynamic disasters in underground metal mines in China:Challenges and solutions

Metal mineral resources play an indispensable role in the development of the national economy.Dynamic disasters in underground metal mines seriously threaten mining safety,which are major scientific and technological problems to be solved urgently.In this article,the occurrence status and grand challenges of some typical dynamic disasters involving roof falling,spalling,collapse,large deformation,rockburst,surface subsidence,and water inrush in metal mines in China are systematically presented,the characteristics of mining-induced dynamic disasters are analyzed,the examples of dynamic disasters occurring in some metal mines in China are summarized,the occurrence mechanism,monitoring and early warning methods,and prevention and control techniques of these disasters are highlighted,and some new opinions,suggestions,and solutions are proposed simultaneously.Moreover,some shortcomings in current disaster research are pointed out,and the direction of efforts to improve the prevention and control level of dynamic disasters in China’s metal mines in the future is prospected.The integration of forward-looking key innovative theories and technologies in the abovementioned aspects will greatly enhance the cognitive level of disaster prevention and mitigation in China’s metal mining industry and achieve a significant shift from passive disaster relief to active disaster prevention.

Sensitivity of Marine Controllable Source Electromagnetic Soundings for Identifying Plume Migration in Offshore CO_(2) Storage

Offshore carbon dioxide(CO_(2)) storage is an effective method for reducing greenhouse gas emissions. However, when using traditional seismic wave methods to monitor the migration of sequestration CO_(2) plumes, the characteristics of wave velocity changes tend to become insignificant beyond a certain limit. In contrast, the controllable source electromagnetic method(CSEM) remains highly sensitive to resistivity changes. By simulating different CO_(2) plume migration conditions, we established the relevant models and calculated the corresponding electric field response characteristic curves, allowing us to analyze the CSEM's ability to monitor CO_(2) plumes. We considered potential scenarios for the migration and diffusion of offshore CO_(2) storage, including various burial depths, vertical extension diffusion, lateral extension diffusion,multiple combinations of lateral intervals, and electric field components. We also obtained differences in resistivity inversion imaging obtained by CSEM to evaluate its feasibility in monitoring and to analyze all the electric field(Ex, Ey, and Ez) response characteristics. CSEM has great potential in monitoring CO_(2) plume migration in offshore saltwater reservoirs due to its high sensitivity and accuracy. Furthermore, changes in electromagnetic field response reflect the transport status of CO_(2) plumes, providing an important basis for monitoring and evaluating CO_(2)transport behavior during storage processes.

Tool Condition Monitoring Based on Nonlinear Output Frequency Response Functions and Multivariate Control Chart

Tool condition monitoring(TCM)is a key technology for intelligent manufacturing.The objective is to monitor the tool operation status and detect tool breakage so that the tool can be changed in time to avoid significant damage to workpieces and reduce manufacturing costs.Recently,an innovative TCM approach based on sensor data modelling and model frequency analysis has been proposed.Different from traditional signal feature-based monitoring,the data from sensors are utilized to build a dynamic process model.Then,the nonlinear output frequency response functions,a concept which extends the linear system frequency response function to the nonlinear case,over the frequency range of the tooth passing frequency of the machining process are extracted to reveal tool health conditions.In order to extend the novel sensor data modelling and model frequency analysis to unsupervised condition monitoring of cutting tools,in the present study,a multivariate control chart is proposed for TCM based on the frequency domain properties of machining processes derived from the innovative sensor data modelling and model frequency analysis.The feature dimension is reduced by principal component analysis first.Then the moving average strategy is exploited to generate monitoring variables and overcome the effects of noises.The milling experiments of titanium alloys are conducted to verify the effectiveness of the proposed approach in detecting excessive flank wear of solid carbide end mills.The results demonstrate the advantages of the new approach over conventional TCM techniques and its potential in industrial applications.

Continuous glucose monitoring metrics in pregnancy with type 1 diabetes mellitus

Managing diabetes during pregnancy is challenging,given the significant risk it poses for both maternal and foetal health outcomes.While traditional methods involve capillary self-monitoring of blood glucose level monitoring and periodic HbA1c tests,the advent of continuous glucose monitoring(CGM)systems has revolutionized the approach.These devices offer a safe and reliable means of tracking glucose levels in real-time,benefiting both women with diabetes during pregnancy and the healthcare providers.Moreover,CGM systems have shown a low rate of side effects and high feasibility when used in pregnancies complicated by diabetes,especially when paired with continuous subcutaneous insulin infusion pump as hybrid closed loop device.Such a combined approach has been demonstrated to improve overall blood sugar control,lessen the occurrence of preeclampsia and neonatal hypoglycaemia,and minimize the duration of neonatal intensive care unit stays.This paper aims to offer a comprehensive evaluation of CGM metrics specifically tailored for pregnancies impacted by type 1 diabetes mellitus.

A HybridManufacturing ProcessMonitoringMethod Using Stacked Gated Recurrent Unit and Random Forest

This study proposed a new real-time manufacturing process monitoring method to monitor and detect process shifts in manufacturing operations.Since real-time production process monitoring is critical in today’s smart manufacturing.The more robust the monitoring model,the more reliable a process is to be under control.In the past,many researchers have developed real-time monitoring methods to detect process shifts early.However,thesemethods have limitations in detecting process shifts as quickly as possible and handling various data volumes and varieties.In this paper,a robust monitoring model combining Gated Recurrent Unit(GRU)and Random Forest(RF)with Real-Time Contrast(RTC)called GRU-RF-RTC was proposed to detect process shifts rapidly.The effectiveness of the proposed GRU-RF-RTC model is first evaluated using multivariate normal and nonnormal distribution datasets.Then,to prove the applicability of the proposed model in a realmanufacturing setting,the model was evaluated using real-world normal and non-normal problems.The results demonstrate that the proposed GRU-RF-RTC outperforms other methods in detecting process shifts quickly with the lowest average out-of-control run length(ARL1)in all synthesis and real-world problems under normal and non-normal cases.The experiment results on real-world problems highlight the significance of the proposed GRU-RF-RTC model in modern manufacturing process monitoring applications.The result reveals that the proposed method improves the shift detection capability by 42.14%in normal and 43.64%in gamma distribution problems.

A wireless solution for greenhouse monitoring and control system based on ZigBee technology

With the rapid development of wireless technologies, it is possible for Chinese greenhouses to be equipped with wireless sensor networks due to their low-cost, simplicity and mobility. In the current study, we compared the advantages of ZigBee with other two similar wireless networking protocols, Wi-Fi and Bluetooth, and proposed a wireless solution for green- house monitoring and control system based on ZigBee technology. As an explorative application of ZigBee technology in Chinese greenhouse, it may promote Chinese protected agriculture.

Underground ground control monitoring and interpretation,and numerical modeling, and shield capacity design

Mine or longwall panel layout is a 3D structure with highly non-uniform stress distribution. Recognition of such fact will facilitate underground problem identification/investigation and solving by numerical modeling through proper model construction. Due to its versatility, numerical modeling is the most popular method for ground control design and problem solving. However numerical modeling results require highly experienced professionals to interpret its validity/applicability to actual mining operations due to complicated mining and geological conditions. Underground ground control monitoring is routinely performed to predict roof behavior such as weighting and weighting interval without matching observation of face mining condition while the mining pressures are being monitored, resulting in unrealistic interpretation of the obtained data on mining pressure. The importance of ground control pressure monitoring and simultaneous observation of mining and geological conditions is illustrated by an example of shield leg pressure monitoring and interpretation in an U.S. longwall coal mine: it was found that the roof strata act like a plate, not an individual block of the size of a shield dimension, as commonly assumed by all researchers and shield capacity is not a fixed property for a longwall panel or a mine or a coal seam. A new mechanism on the interaction between shield's hydraulic leg pressure and roof strata for shield loading is proposed.

Nonparametric Control Scheme for Monitoring Phase Ⅱ Nonlinear Profiles with Varied Argument Values

Profile monitoring is used to check the stability of the quality of a product over time when the product quality is best represented by a function at each time point.However,most previous monitoring approaches have not considered that the argument values may vary from profile to profile,which is common in practice.A novel nonparametric control scheme based on profile error is proposed for monitoring nonlinear profiles with varied argument values.The proposed scheme uses the metrics of profile error as the statistics to construct the control charts.More details about the design of this nonparametric scheme are also discussed.The monitoring performance of the combined control scheme is compared with that of alternative nonparametric methods via simulation.Simulation studies show that the combined scheme is effective in detecting parameter error and is sensitive to small shifts in the process.In addition,due to the properties of the charting statistics,the out-of-control signal can provide diagnostic information for the users.Finally,the implementation steps of the proposed monitoring scheme are given and applied for monitoring the blade manufacturing process.With the application in blade manufacturing of aircraft engines,the proposed nonparametric control scheme is effective,interpretable,and easy to apply.

Performance Monitoring and Diagnosis of Multivariable Model Predictive Control Using Statistical Analysis

A statistic-based benchmark was proposed for performance assessment and monitoring of model predic- tive control; the benchmark was straightforward and achievable by recording a set of output data only when the control performance was good according to the user’s selection. Principal component model was built and an auto- regressive moving average filter was identified to monitor the performance; an improved T2 statistic was selected as the performance monitor index. When performance changes were detected, diagnosis was done by model validation using recursive analysis and generalized likelihood ratio (GLR) method. This distinguished the fact that the per- formance change was due to plant model mismatch or due to disturbance term. Simulation was done about a heavy oil fractionator system and good results were obtained. The diagnosis result was helpful for the operator to improve the system performance.

Multivariate Statistical Process Monitoring and Control: Recent Developments and Applications to Chemical Industry

Multivariate statistical process monitoring and control (MSPM&C) methods for chemical process monitoring with statistical projection techniques such as principal component analysis (PCA) and partial least squares (PLS) are surveyed in this paper. The four-step procedure of performing MSPM&C for chemical process, modeling of processes, detecting abnormal events or faults, identifying the variable(s) responsible for the faults and diagnosing the source cause for the abnormal behavior, is analyzed. Several main research directions of MSPM&C reported in the literature are discussed, such as multi-way principal component analysis (MPCA) for batch process, statistical monitoring and control for nonlinear process, dynamic PCA and dynamic PLS, and on-line quality control by inferential models. Industrial applications of MSPM&C to several typical chemical processes, such as chemical reactor, distillation column, polymerization process, petroleum refinery units, are summarized. Finally, some concluding remarks and future considerations are made.

Analysis of Ornidazole Injection in Clinical Use at Post-marketing Stage by Centralized Hospital Monitoring System

This study aims to analyze the clinical use of ornidazole injection at the post-marketing stage by centralized hospital monitoring system method,and investigate its widespread use in patients,in order to regulate and guide the rational drug use,improve the drug specificity and provide a basis for drug therapy.The study adopts a prospective,multi-center,large sample size,centralized hospital monitoring system.We selected five leading hospitals in Hubei province,and observed the inpatients who received the ornidazole injection from July 1,2015 to October 31,2015.The basic information of patients was recorded,as well as the drug use and adverse events.The statistical analysis was performed based on these data.A total of 4396 individuals were enrolled in this study,most of them were middle-aged female patients and the ornidazole injection was mainly used as prophylactic prior to surgery to prevent the infections,and surgical treatment of anaerobic infections,abdominal infections and pelvic infections.The irrational drug use existed mainly in the prescribing and administration process,including unreasonable dosing frequency,rapid intravenous drip speed and extended duration of drug use.Eleven cases of adverse reactions were collected during the monitoring,incidence rate of adverse reactions was 2.5‰;adverse drug reactions occurred within 30 min.The study results fully reflected the usage of ornidazole injection in the real world.Based on the study,we calculated the adverse reaction incidence of ornidazole and identified the risk factors which may affect the safety of ornidazole injection.Study results strongly recommend that the manufacturers should publish standards for inpatient use and doctors should prescribe with caution accordingly.

Improved Smith prediction monitoring AGC system based on feedback-assisted iterative learning control

The performance of Smith prediction monitoring automatic gauge control(AGC) system is influenced by model mismatching greatly in strip rolling process. Aiming at this problem, a feedback-assisted iterative learning control strategy, which learned unknown modeling error by using previous control information repeatedly, was introduced into Smith prediction monitoring AGC system. Firstly, conventional Smith predictor and improved Smith predictor with PI-P controller were analyzed. Secondly, on the basis of establishing of feedback-assisted iterative learning control strategy for improved Smith predictor, process control signal update law and control error were deduced, then convergence condition of this strategy was put forward and proved. Finally, after modeling the automatic position control system, the PI-P Smith prediction monitoring AGC system with feedback-assisted iterative learning control was researched through simulation. Simulation results indicate that this system remains stable during model mismatching. The robustness and response of monitoring AGC is improved by development of feedback-assisted iterative learning control strategy for PI-P Smith predictor.

Monitoring and Assessment of Water Quality of Centralized Drinking Water Sources in Kaixian County during the " Twelfth Five-year Plan " Period

Based on the monitoring data of water quality of more than 40 centralized drinking water sources in 40 towns （townships or streets） of Kaixian County in the first and second half of each year during the ＂Twelfth Five-year Plan＂ period, the changing rules of the water quality were studied to provide scientific references for the improvement of drinking water safety of urban and rural residents and drinking water quality. The re- sults show that the water quality of centralized drinking water sources in Kaixian County improved year by year during the ＂Twelfth Five-year Plan＂ period, and most monitoring sites with water quality exceeding the standard are distributed in reservoirs. Total phosphorus, total nitrogen, chemical oxygen demand, and permanganate index exceeded the standard obviously. Main pollution sources are domestic pollution and non-point source pol- lution caused by excessive discharge of nitrogen, phosphorus and organic pollutants. To improve drinking water quality, it is suggested that some towns can get drinking water from other reservoirs, surface water or underground water with better quality instead of previous reservoirs with water quality exceeding the standard, and the control of non-point source pollution should be enhanced.