Curve Tracking Research Articles

A Design and Safety Analysis of the “Electricity-Hydrogen-Ammonia” Energy Storage System: A Case Study of Haiyang Nuclear Power Plant

With the development of modernization, traditional fossil energy reserves are decreasing, and the power industry, as one of the main energy consumption forces, has begun to pay attention to increasing the proportion of clean energy generation. With the deepening of electrification, the peak-valley difference of residential electricity consumption increases, but photovoltaic and wind power generation have fluctuations and are manifested as reverse peak regulation. Thermal power plants as the main force of peak regulation gradually reduce the market share, making nuclear power plants bear the heavy responsibility of participating in peak regulation. The traditional method of adjusting operating power by inserting and removing control rods has great safety risks and wastes resources. Therefore, this paper proposes a new energy storage system that can keep the nuclear power plant running at full power and produce hydrogen to synthesize ammonia from excess power. A comprehensive evaluation model of energy storage based on z-score data standardization and objective parameter assignment AHP (analytic hierarchy process) analysis method was established to evaluate energy storage systems according to a multi-index system. With an AP1000 daily load tracking curve as the input model, the simulation model built by Aspen Plus V14 was used to calculate the operating conditions of the system. In order to provide a construction basis for practical engineering use, Haiyang Nuclear Power Plant in Shandong Province is taken as an example. The system layout scheme is proposed according to the local environmental conditions. The accident tree analysis method is combined with ALOHA 5.4.1.2 (Areal Locations of Hazardous Atmospheres) hazardous chemical analysis software and MARPLOT 5.1.1 geographic information technology. A qualitative and quantitative assessment of risk factors and the consequences of leakage, fire, and explosion accidents caused by hydrogen and ammonia storage processes is carried out to provide guidance for accident prevention and emergency rescue. The design of an “Electric-Hydrogen-Ammonia” energy storage system proposed in this paper provides a new idea for zero-carbon energy storage for the peak shaving of nuclear power plants and has a certain role in promoting the development of clean energy.

Optimization of chemical engineering control for large-scale alkaline electrolysis systems based on renewable energy sources

In the context of renewable energy, dynamic control is crucial for large-scale alkaline water electrolysis systems. Under traditional Proportional-Integral-Derivative (PID) control, the system's temperature and impurity levels may exceed safe limits during sudden load changes, compromising system safety. Therefore, this study proposes a control strategy based on mathematical models of system temperature and impurities, including Model Predictive Control (MPC) for temperature and Optimal Curve Tracking (OCT) for impurities. The MPC controller offsets the disturbances in temperature caused by current fluctuations through state prediction, while the OCT controller ensures that impurity levels remain within safe limits. Real-time application of these control strategies is achieved through MATLAB-PLC communication, validating controller performance. Experimental results show that the MPC controller exhibits excellent dynamic response and stability in controlling the stack temperature, with a maximum temperature peak deviation of only 2.9 K and a temperature fluctuation range of 5.9 K. This represents a reduction of 1.6 K in peak deviation compared to traditional PID controllers and significantly decreases the temperature fluctuation range. The OCT controller also demonstrates good safety and stability in controlling hydrogen to oxygen (HTO) impurity levels, consistently maintaining the HTO content at the set upper limit of 1.5%, thereby expanding the safe operational range of the system and reducing the need for frequent adjustments to the system setpoints.

Multiwavelength Optical Rotation Detection: An Effective Approach for the Recognition of Analytical and Semi‐Preparative HPLC Enantioseparation of the Chiral Pheromone Olean and Its Stereochemical Characterization

ABSTRACTOlean is the chiral spiroacetal sex pheromone of the female olive fruit fly Bactrocera oleae. Laboratory tests have demonstrated that the (R)‐(−)‐olean enantiomer is active on males, whereas females respond to (S)‐(+)‐olean. Here we present the first HPLC enantioseparation of olean using polysaccharide derivatives as chiral stationary phases and a polarimetric detector equipped with a micro‐flow cell capable of detecting optical rotation at six different wavelengths. Unlike blind UV detection, the polarimetric detector allows the chiral spiroacetal, which lacks a chromophore, to be detected as bisegnate peaks, indicating the opposite sign of the optical rotation. The HPLC enantioseparation was optimized on the coated type amylose‐based Chiralpak AS‐H CSP using a mobile phase consisting of n‐hexane‐2‐propanol 99.99:0.01 (v/v). These conditions were scaled up to a semi‐preparative level and allowed resolution of 2.5 mg of racemic sample in 5 min. Multiwavelength optical rotation detection during HPLC enantioseparation of racemic samples provides a direct readout of the stereochemistry of olean and allows tracking of virtual optical rotation dispersion curves without the need for preliminary collection of enantiomeric samples by semi‐preparative HPLC.

A novel Linetype Recognition Algorithm Based on Track Geometry Detection Data

Abstract The detection and recognition of track geometry are crucial for ensuring railway transportation safety and maintenance efficiency. Traditional linetype recognition algorithms struggle to effectively handle the complexities of variable track geometry and noise interference. To address these challenges, a novel linetype recognition algorithm (NLRA) is proposed, which thoroughly analyzes the characteristics of track curvature across straight lines, transition curves, and circular segments. By employing partial differential equations for data preprocessing, NLRA effectively filters out noise. Utilizing curve design parameters and integrating line anti-noise recognition with multi-segment line fitting techniques, the algorithm accurately identifies each line segment and incorporates a single-point recognition method for data gaps. Experimental results demonstrate that NLRA outperforms traditional algorithms in terms of real-time performance, recognition accuracy, anti-interference capability, and noise handling. Notably, NLRA enhances accuracy by at least 35.1% and recall by at least 17.4% on selected typical routes. This work underscores the algorithm's potential to provide efficient and accurate tools for track maintenance, facilitate advancements in track detection software, and significantly contribute to railway transportation safety.

A numerical study on locomotive wheel wear on curved track subjected to adhesion control

The wheel wear of heavy-duty locomotives is more complicated than that of other vehicles when it affects tractive force and adhesion control. The purpose of this article is to calculate wheel wear on a curved track using a co-simulation dynamic model of a heavy-duty locomotive with adhesion control. Additionally, the effect of creep threshold, decline slope, curve radius, and passing speed on wheel wear is further investigated. The findings demonstrate that wheel wear can be successfully decreased by adjusting the creep threshold and decline slope of adhesion control parameters. The wheel-rail stick-slip vibration behavior on the curve track can be eliminated by increasing the curve’s radius and passing speed, but the vertical wear at the left wheel’s flange will rise sharply with the increase of passing speed on right hand curve.

Research on design and control methods of a lightweight upper limb joint isokinetic rehabilitation training equipment.

Isokinetic exercise can improve joint muscle strength and stability, making it suitable for early rehabilitation of stroke patients. However, traditional isokinetic equipment is bulky and costly, and cannot effectively avoid external environmental interference. This paper designed a lightweight upper limb joint isokinetic rehabilitation training equipment, with a control system that includes a speed planning strategy and speed control with disturbance rejection. Based on the established human-machine kinematic closed-loop model between the equipment and the user, a dynamic evaluation method of torque at the joint level was proposed. To validate the effectiveness of the equipment, experiments were conducted by manually applying random disturbances to the equipment operated at an isokinetic speed. The results showed that the root mean square error between the observed torque curve of the second-order linear extended state observer used in this paper and the actual disturbance curve was 0.52, and the maximum speed tracking error of the speed control algorithm was 1.27%. In fast and slow sinusoidal speed curve tracking experiments, the root mean square errors of the speed tracking results for this algorithm were 9.65 and 5.27, respectively, while the tracking errors for the PID speed control algorithm under the same environment were 19.94 and 12.11. The research results indicate that compared with traditional PID control method, the proposed control strategy demonstrates superior performance in achieving isokinetic control and suppressing external disturbances, thereby exhibiting significant potential in promoting upper limb rehabilitation among patients.

A data-driven approach to quantify track buckling strength through the development and application of a Track Strength Index (TSI)

Railroads are increasingly adopting advanced technologies to enhance safety and state of good repair of their track infrastructure, some of which employ artificial intelligence-based inspection methods. The objectivity of these inspection techniques, along with their detailed measuring capabilities, has created opportunities for railroads to improve both inspection and operational efficiency. This is achieved through more frequent and precise track data collections and technical data aggregations. This paper explores the potential of these track inspection methods to address one of the few drawbacks of continuous welded rail (CWR): track buckling. While track buckling mechanics and prevention have been the subject of numerous studies, the need for a practical and objective method to assess resistance to buckling remains. Numerous track health indices for geometry parameters and some for railway components have been developed and utilized in various applications. Yet, the opportunity exists to develop a metric that combines geometric parameters with the condition levels of railway components, particularly designed to quantify the resistance of track to buckling. Such a holistic view of the track and network-wide time series analyses of the proposed metric demonstrate whether the buckling resistance improves, remains in a steady state, or declines. In this study, a sensitivity analysis conducted using CWR-Risk software identified misalignment amplitude, track curvature, lateral resistance, torsional resistance, and longitudinal resistance as the main factors contributing to buckling resistance. Based on the sensitivity study and with a focus on the capacity side of the track buckling equation, a methodology was proposed for converting inspection data into 10-point scales that were combined through weighted averaging into a single Track Strength Index (TSI) to quantitatively assess the resistance to buckling at a track-system level. The influence of ballast condition on TSI output was evaluated using 15 ballast configuration scenarios, ensuring that the index accurately reflects ballast deficiencies in a proportionate manner. Lastly, the proposed metric was tested leveraging revenue service data collected from a Class I railroad mainline in the United States.

Advancing urban electric vehicle charging stations: AI-driven day-ahead optimization of pricing and Nudge strategies utilizing multi-agent deep reinforcement learning

Public charging stations (CSs) serve for electric vehicles (EVs) to charge during urban travel. Optimizing the charging time, location distribution, and power of EVs can increase the revenue of charging system operators (CSOs) and provide flexible regulation resources for the power grid. However, the optimization scheduling of CSs involves the charging choices of various users, which are influenced by their autonomy and bounded rationality. To guide users and encourage their participation in the charging schedule, we introduce the Nudge method from behavioral economics. To achieve collaborative optimization of non-economic Nudges and economic incentive strategies applying to multiple charging stations in a complex nonlinear environment involving users, CSO, and the transportation network, we leverage multi-agent deep reinforcement learning (MADRL). We construct a simulation environment using historical and survey data tailored to real users. This environment facilitates the training of agent groups to enhance decision-making processes. Case studies in a metropolis demonstrate that the agent group aimed at revenue improvement yields significant improvements in the CSO's revenue compared to fixed service fees and pricing strategies without Nudges. Moreover, the agent group aimed at power curve tracking achieves a lower average relative error in aligning the total charging power with the desired curve of the power system. This paper integrates sociological methods into the optimization of physical systems by MADRL, providing a new approach for the scheduling of EV charging considering user behavior.

Diastolic dysfunction in adults with uncomplicated obesity evaluated with left atrial and left ventricular tissue tracking and ventricular volume-time curve: a prospective cardiac magnetic resonance study.

Obesity is commonly linked with heart failure (HF) with preserved ejection fraction, with diastolic dysfunction playing an important role in this type of HF. However, diastolic function has not been well clarified in obese patients free of overt comorbidities. We aimed to comprehensively assess diastolic function in adults with uncomplicated obesity by combining left atrial (LA) and left ventricular (LV) strain and ventricular volume-time curve based on cardiac magnetic resonance (CMR), and to evaluate its association with body fat distribution. A cross-sectional study was conducted with 49 uncomplicated obese participants and 43 healthy controls who were continuously recruited in West China Hospital, Sichuan University from September 2019 to June 2022. LA strain indices [total, passive, and active strains (εs, εe, and εa) and peak positive, early negative, and late negative strain rates (SRs, SRe, and SRa)], LV strain rates [peak diastolic strain rate (PDSR) and peak systolic strain rate (PSSR)], and LV volume-time curve parameters [peak filling rate index (PFRI) and peak ejection rate index (PERI)] were measured. Body fat distribution was assessed by dual-energy X-ray absorptiometry. Correlation between body fat distribution and LA and LV function was evaluated by multiple linear regression. The obese participants had impaired diastolic function, manifested as lower LV circumferential and longitudinal PDSR (1.3±0.2 vs. 1.5±0.3 s-1, P=0.014; 0.8±0.2 vs. 1.1±0.2 s-1, P<0.001), LV PFRI (3.5±0.6 vs. 3.9±0.7 s-1, P=0.012), and declined LA reservoir function [εs and SRs (46.4%±8.4% vs. 51%±12%, P=0.045; 1.9±0.5 vs. 2.3±0.5 s-1, P<0.001)] and conduit function [εe and SRe (30.8%±8.0% vs. 35.5%±9.8%, P=0.019; -3.1±0.8 vs. -3.5±1.0 s-1, P=0.030)] compared with controls. The LA pumping function (εa and SRa) and LV systolic function [LV ejection fraction (LVEF), PSSR and PERI] were not different between obese and control participants. Multivariable analysis indicated that trunk fat had independent relationships with LA εe (β=-0.520, P<0.001) and LV circumferential PDSR (β=-0.418, P=0.003); visceral fat and peripheral fat were associated with LV longitudinal PDSR (β=-0.342, P=0.038; β=0.376, P=0.024); gynoid fat was associated with LA εs (β=0.384, P=0.014) and PFRI (β=0.286, P=0.047) in obesity. The obese participants (uncomplicated obese adults with preserved LVEF) had impaired subclinical diastolic function. Central adipose tissue deposits (trunk fat and visceral fat) may exhibit inverse relationships with LV and LA function in obesity. However, peripheral adipose tissue deposits (peripheral fat and gynoid fat) may show positive relationships with LV and LA function.

Model and Parameter Adaptive MPC Path Tracking Control Study of Rear-Wheel-Steering Agricultural Machinery

To further enhance the precision and the adaptability of path tracking control, and considering that most of the research is focused on front-wheel steering, an adaptive parametric model predictive control (MPC) was proposed for rear-wheel-steering agricultural machinery. Firstly, the kinematic and dynamic models of rear-wheel-steering agricultural machinery were established. Secondly, the influence laws of curvature and velocity on the prediction horizon Np, control horizon Nc, and preview value Npre were obtained by simulating and analyzing the factors influencing the MPC tracking effect. The results revealed that raising Npre can improve curve tracking performance. Np was correlated negatively with the curvature change, whereas Nc and Npre were positively connected. Np, Nc, and Npre were correlated positively with the velocity change. Then, the parameters for self-adaptation of Np, Nc, and Npre were accomplished via fuzzy control (FC), and particle swarm optimization (PSO) was utilized to optimize the three parameters to determine the optimal parameter combination. Finally, simulation and comparative analysis were conducted to assess the tracking effects of the manual tuning MPC, the FC_MPC, and the PSO_MPC under U-shaped and complex curve paths. The results indicated that there was no significant difference and all three methods achieved better tracking effects under no disturbance, with the mean absolute value of lateral error ≤0.18 cm, standard deviation ≤0.37 cm, maximum deviation of U-shaped path &lt;2.38 cm, and maximum deviation of complex curve path &lt;3.15 cm. The mean absolute value of heading error was ≤0.0096 rad, the standard deviation was ≤0.0091 rad, and the maximum deviation was &lt;0.0325 rad, indicating that manual tuning can find optimal parameters, but with high uncertainty and low efficiency. However, FC_MPC and PSO_MPC have better adaptability and tracking performance compared to the manual tuning MPC with fixed horizons under variable-speed disturbance and are more able to meet the actual needs of agricultural machinery operations.

An Improved Multi-point Chord Reference Measurement Device and Error Correction Method for Corrugation Measuring in Sharp Curves

Background: The traditional chord reference method and its device have faced challenges matching the sharp curve track. Thus, it is of utmost importance to research and develop a measurement device and method that can obtain accurate and comprehensive information regarding rail running band corrugation. Aims: It provides a solution for the measurement of the multi-point chord reference method on sharply curved rails and, at the same time, meets the demand for data richness and accuracy in theoretical studies on the causes of rail corrugation and wheel-rail contact relationships. Objective: This paper aims to research and design a measurement device based on the multi-point chord reference method for the rail running band corrugation. Meanwhile, the error in measuring the sharp curve rail is corrected and verified by simulation. Methods: The lateral information of rail running band is obtained by using a line laser profile sensor; the rail corrugation measurement model and error correction model of the rail running band area are established based on multi-point reference system; the simulation of the system model is carried out using Matlab; and the mechanical structure is built for static testing. Results: The measurement model based on the profile sensor and multi-point chord reference system can accurately obtain the rail corrugation waveform of any longitudinal measurement line in the running band area. The measurement results can be accurately corrected for a radius of 200m- 400m, the error parameters can be effectively improved, and the mechanical mechanism of the measurement system runs stably. Conclusion: The results show that the measurement model can accurately measure the rail corrugation of the sharp curve section, and the multi-waveform in the rail running band area has good measurement performance, but the mechanical structure can be optimized and improved in the light weight.

Research on Maximum Power Control of Direct-Drive Wave Power Generation Device Based on BP Neural Network PID Method

Ocean wave energy is a new type of clean energy. To improve the power generation and wave energy conversion efficiency of the direct-drive wave power generation system, by addressing the issue of large output errors and poor system stability commonly associated with the currently used PID (proportional, integral, and derivative) control methods, this paper proposes a maximum power control method based on BP (back propagation) neural network PID control. Combined with Kalman filtering, this method not only achieves maximum power tracking but also reduces output ripple and tracking error, thereby enhancing the system’s control quality. This study uses a permanent magnet linear generator as the power generation device, establishes a system dynamics model, and predicts the main frequency of irregular waves through the Fast Fourier Transform method. It designs a desired current tracking curve that meets the maximum power strategy. On this basis, a comparative analysis of the control accuracy and stability of three control methods is conducted. The simulation results show that the BP neural network PID control method improves power generation and exhibits better accuracy and stability.

Research on Cooperative Control of Multiple Intelligent Networked Vehicles Based on the Improved Leader–Follower Method

In order to study the group cooperative control method of multiple intelligent networked vehicles, the multiple intelligent networked vehicles can move in the form of a fleet. Based on the leader–follower method, the paper optimizes the control effect of the leader–follower method by solving the error transmission phenomenon in the leader–follower method. In this paper, the modeling of the multiple intelligent connected vehicle adopts the vehicle dynamics model and the Magic Formula/Swift Magic tire model, and adopts the model predictive control (MPC) dynamics trajectory tracking controller for control. Through the CarSim–Simulink multi-vehicle dynamics co-simulation platform established in this paper, the group cooperative control experiments of multiple intelligent networked vehicles under different working conditions were carried out for simulation verification. The analysis results show that the maximum average error of the proposed method decreases from 8.802 to 0.094 in the case of straight line and 0.669 to 0.379 in the case of curve tracking, which proves that the method can effectively reduce the transmission of errors.

Characterization modeling of wake vortex circulation decay under reasonable worst case conditions with cuckoo search algorithm

Wake vortex (WV) produced by a large aircraft has the potential to cause serious damage to smaller aircraft following it. In this context, characterization of WV circulation decay under the reasonable worst case (RWC) conditions allows the separation minima to be found safely. In this study, modeling of dimensionless decay curves, which were developed using three experimental LIDAR (Light Detection and Ranging) datasets in the RECAT-EU project and is a useful tool to characterize the wake vortex circulation decay under RWC conditions, was carried out using cuckoo search algorithm (CSA). The decay curves used in the modeling are the median (P50), 10th (P10), and 90th (P90) percentile decay curves of the RWC tracks, which constitute the top 2% longest lasting wakes. The fact that the correlation coefficient (R) values are very close to 1 for all datasets as a result of the error analysis shows that the prediction success of the CSA model is quite high.

Investigating the stick-slip vibration behavior of a locomotive with adhesion control in a curve

In order to explain the wheel-rail stick-slip vibration phenomenon of alocomotive in a curve, a co-simulation dynamic model taking into account adhesion control was established to reproduce the locomotive wheel-rail curve stick-slip vibration behavior, and the effect of parameters such as creep threshold, descent slope, rail surface condition and track curve radius on the stick-slip vibration behavior was measured by the traction force and the overall dispersion of adhesion coefficient. The results illustrate that the wheel-rail curve stick-slip vibration is caused by the dynamic traction force fluctuation under the adhesion control, and the increase of creep threshold will lead to the decrease of tractive force fluctuation amplitude and the decrease of stick-slip vibration intensity, which will increase the adhesion utilization rate, However, the increase of descent slope, the decrease of track curve radius and wheel-rail friction coefficient have the opposite effect on stick-slip vibration behavior. This phenomenon can be eliminated by improving the rail surface condition, expanding the track curve radius and lowering the locomotive traction force.

The parameter identification of metro rail corrugation based on effective signal extraction and inertial reference method

Rail corrugation is a severe track damage and failure problem in metro lines. So, the accurate assessment of rail corrugation is significant for the safety and comfort of vehicles. It is an efficient means to detect rail corrugation through the acceleration signal. However, the acceleration signal contains the mixed signal caused by vehicle component noise and wheel polygonal wear, resulting in poor rail corrugation detection accuracy. Therefore, a new method is presented to extract the effective signal components and accurately identify the rail corrugation. Firstly, the Cubic mapping, modified sine algorithm, and Lévy flight are introduced into the dung beetle optimizer algorithm. A new improved dung beetle optimizer (IDBO) algorithm is presented to optimize the hyperparameters of the VMD algorithm, which can increase the signal decomposition performance of the VMD. Secondly, the fast independent component analysis method separates the independent components (ICs) from the VMD modes and original acceleration signal. Based on the selection criteria of ICs, the dominant signal components related to rail corrugation are determined. The rail corrugation amplitude can be obtained through the inertial reference method. The experimental results demonstrate that the presented method has better rail corrugation recognition accuracy, the root mean square error is smaller than the comparison method, and the corrugation wavelength of the straight track is larger than the curve track.

Robust Cubature Kalman Filter for Moving-Target Tracking with Missing Measurements.

Handling the challenge of missing measurements in nonlinear systems is a difficult problem in various scientific and engineering fields. Missing measurements, which can arise from technical faults during observation, diffusion channel shrinking, or the loss of specific metrics, can bring many challenges when estimating the state of nonlinear systems. To tackle this issue, this paper proposes a technique that utilizes a robust cubature Kalman filter (RCKF) by integrating Huber's M-estimation theory with the standard conventional cubature Kalman filter (CKF). Although a CKF is often used for solving nonlinear filtering problems, its effectiveness might be limited due to a lack of knowledge regarding the nonlinear model of the state and noise-related statistical information. In contrast, the RCKF demonstrates an ability to mitigate performance degradation and discretization issues related to track curves by leveraging covariance matrix predictions for state estimation and output control amidst dynamic disruption errors-even when noise statistics deviate from prior assumptions. The performance of extended Kalman filters (EKFs), unscented Kalman filters (UKFs), CKFs, and RCKFs was compared and evaluated using two numerical examples involving the Univariate Non-stationary Growth Model (UNGM) and bearing-only tracking (BOT). The numerical experiments demonstrated that the RCKF outperformed the EKF, EnKF, and CKF in effectively handling anomaly errors. Specifically, in the UNGM example, the RCKF achieved a significantly lower ARMSE (4.83) and ANCI (3.27)-similar outcomes were observed in the BOT example.

Unusual Tropical Cyclone Tracks under the Influence of Upper-Tropospheric Cold Low

Abstract Tropical cyclones (TCs) accompanied by an upper-tropospheric cold low (CL) can experience unusual tracks. Idealized simulations resembling observed scenarios are designed in this study to investigate the impacts of a CL on TC tracks. The sensitivity of the TC motion to its location relative to the CL is examined. The results show that a TC follows a counterclockwise semicircle track if initially located east of a CL, while a TC experiences a small southward-looping track, followed by a sudden northward turn if initially located west of a CL. A TC on the west side experiences opposing CL and β steering, while they act in the same direction when a TC is on the east side of CL. The steering flow analyses show that the steering vector is dominated by upper-level flow induced by the CL at an early stage. The influence of CL extends downward and contributes to the lower-tropospheric asymmetric flow pattern of TC. As these two systems approach, the TC divergent outflow erodes the CL. The CL circulation is deformed and eventually merged with the TC when they are close. Since the erosion of CL, the TC motion is primarily related to β gyres at a later stage. The sensitivity of TC motion to the CL depth is also examined. TCs located west of a CL experience a westward track if the CL is shallow. In contrast, TCs initially located east of a CL all take a smooth track irrespective of the CL depth, and the CL depth mainly influences the track curvature and the TC translation speed. Significance Statement The purpose of this study is to better understand how an upper-tropospheric cold low affects the motion of a nearby tropical cyclone. Our findings highlight distinct track patterns based on the relative positions of the tropical cyclone and the cold low. When the tropical cyclone is located on the east side of a cold low, a mutual rotation occurs, leading to a counterclockwise semicircle track of tropical cyclone. Conversely, if the tropical cyclone is located to the west side of a cold low, the cold low approaches and captures it, resulting in an abrupt northward turn when the cold low is eroded by the tropical cyclone. These insights improve the predictability of tropical cyclones in the vicinity of cold lows.

Geopolymer Made from Kaolin, Diatomite, and Rice Husk Ash for Ceiling Thermal Insulation

In this study, geopolymers made of metakaolin (MK), diatomite (D), and rice husk ash (RHA) were developed for ceiling thermal insulation in houses to provide protection against cold temperatures. The influence of the constituent mixing ratio and the temperature of curing on the heat conductivity and compressive strength of the geopolymer was investigated. Specimens were formed according to a 10-level mix design with three replicates and subjected to curing at 40 °C and 80 °C. Heat conductivity and compressive strength were determined in accordance with established standards. The simplex lattice method was used to obtain the response surfaces, contour plots, and tracking curves. The geopolymers under study displayed a reduction in heat conductivity and an increase in compressive strength when the curing temperature was raised. The optimal mixing ratio to achieve a balance between the compressive strength and thermal conductivity of the geopolymers investigated was 0.50 MK and 0.50 RHA. Diatomite’s thermal insulation contribution is neutralized when crystals from the geopolymer gel fill the pore volume. The mixture’s optimal results were achieved when cured at 80 °C, demonstrating a thermal conductivity of 0.10 W/m·K and a compressive strength of 5.37 MPa.

Самоконтроль гликемии: от теории к практике (на примере пациента с сопутствующей онкологической патологией)

Self-monitoring of blood glucose (BG) levels occupies an important place in the treatment of diabetes mellitus. With an increase in the frequency of self-monitoring of BG, in patients with diabetes, carbohydrate metabolism indicators improve. Thus, self-monitoring of BG is an important factor in the prevention of acute and chronic complications of diabetes. Although new methods for measuring BG levels, such as continuous glycemic curve tracking and flash monitoring, are now widely used, they cannot completely replace BG monitoring with a glucometer. Data are presented on the dynamics of glycemia in a patient with type 1 diabetes, with a disease duration of 58 years, before and after the introduction of self-monitoring of BG.