Metabolic compensation and circadian resilience in prokaryotic cyanobacteria.

For a biological oscillator to function as a circadian pacemaker that confers a fitness advantage, its timing functions must be stable in response to environmental and metabolic fluctuations. One such stability enhancer, temperature compensation, has long been a defining characteristic of these timekeepers. However, an accurate biological timekeeper must also resist changes in metabolism, and this review suggests that temperature compensation is actually a subset of a larger phenomenon, namely metabolic compensation, which maintains the frequency of circadian oscillators in response to a host of factors that impinge on metabolism and would otherwise destabilize these clocks. The circadian system of prokaryotic cyanobacteria is an illustrative model because it is composed of transcriptional and nontranscriptional oscillators that are coupled to promote resilience. Moreover, the cyanobacterial circadian program regulates gene activity and metabolic pathways, and it can be manipulated to improve the expression of bioproducts that have practical value.

Temperature compensation through kinetic regulation in biochemical oscillators.

Nearly all circadian clocks maintain a period that is insensitive to temperature changes, a phenomenon known as temperature compensation (TC). Yet, it is unclear whether there is any common feature among different systems that exhibit TC. From a general timescale invariance, we show that TC relies on the existence of certain period-lengthening reactions wherein the period of the system increases strongly with the rates in these reactions. By studying several generic oscillator models, we show that this counterintuitive dependence is nonetheless a common feature of oscillators in the nonlinear (far-from-onset) regime where the oscillation can be separated into fast and slow phases. The increase of the period with the period-lengthening reaction rates occurs when the amplitude of the slow phase in the oscillation increases with these rates while the progression speed in the slow phase is controlled by other rates of the system. The positive dependence of the period on the period-lengthening rates balances its inverse dependence on other kinetic rates in the system, which gives rise to robust TC in a wide range of parameters. We demonstrate the existence of such period-lengthening reactions and their relevance for TC in all four model systems we considered. Theoretical results for a model of the Kai system are supported by experimental data. A study of the energy dissipation also shows that better TC performance requires higher energy consumption. Our study unveils a general mechanism by which a biochemical oscillator achieves TC by operating in parameter regimes far from the onset where period-lengthening reactions exist.

Temperature-Sensitive Substrate and Product Binding Underlie Temperature-Compensated Phosphorylation in the Clock.

Temperature compensation is a striking feature of the circadian clock. Here we investigate biochemical mechanisms underlying temperature-compensated, CKIδ-dependent multi-site phosphorylation in mammals. We identify two mechanisms for temperature-insensitive phosphorylation at higher temperature: lower substrate affinity to CKIδ-ATP complex and higher product affinity to CKIδ-ADP complex. Inhibitor screening of ADP-dependent phosphatase activity of CKIδ identified aurintricarboxylic acid (ATA) as a temperature-sensitive kinase activator. Docking simulation of ATA and mutagenesis experiment revealed K224D/K224E mutations in CKIδ that impaired product binding and temperature-compensated primed phosphorylation. Importantly, K224D mutation shortens behavioral circadian rhythms and changes the temperature dependency of SCN's circadian period. Interestingly, temperature-compensated phosphorylation was evolutionary conserved in yeast. Molecular dynamics simulation and X-ray crystallography demonstrate that an evolutionally conserved CKI-specific domain around K224 can provide a structural basis for temperature-sensitive substrate and product binding. Surprisingly, this domain can confer temperature compensation on a temperature-sensitive TTBK1. These findings suggest the temperature-sensitive substrate- and product-binding mechanisms underlie temperature compensation.

Characterization of Magnetoresistive Shunts and Its Sensitivity Temperature Compensation.

The main purpose of the paper is to show how a magnetoresistive (MR) element can work as a current sensor instead of using a Wheatstone bridge composed by four MR elements, defining the concept of a magnetoresistive shunt (MR-shunt). This concept is reached by considering that once the MR element is biased at a constant current, the voltage drop between its terminals offers information, by the MR effect, of the current to be measured, as happens in a conventional shunt resistor. However, an MR-shunt has the advantage of being a non-dissipative shunt since the current of interest does not circulate through the material, preventing its self-heating. Moreover, it provides galvanic isolation. First, we propose an electronic circuitry enabling the utilization of the available MR sensors integrated into a Wheatstone bridge as sensing elements (MR-shunt). This circuitry allows independent characterization of each of the four elements of the bridge. An independently implemented MR element is also analyzed. Secondly, we propose an electronic conditioning circuit for the MR-shunt, which allows both the bridge-integrated element and the single element to function as current sensors in a similar way to the sensing bridge. Third, the thermal variation in the sensitivity of the MR-shunt, and its temperature coefficient, are obtained. An electronic interface is proposed and analyzed for thermal drift compensation of the MR-shunt current sensitivity. With this hardware compensation, temperature coefficients are experimentally reduced from 0.348%/°C without compensation to -0.008%/°C with compensation for an element integrated in a sensor bridge and from 0.474%/°C to -0.0007%/°C for the single element.

A Self-Temperature Compensation Barometer Based on All-Quartz Resonant Pressure Sensor.

This paper reports a self-temperature compensation barometer based on a quartz resonant pressure sensor. A novel sensor chip that contains a double-ended tuning fork (DETF) resonator and a single-ended tuning fork (SETF) resonator is designed and fabricated. The two resonators are designed on the same diaphragm. The DETF resonator works as a pressure sensor. To reduce the influence of the temperature drift, the SETF resonator works as a temperature compensation sensor, which senses the instantaneous temperature of the DETF resonator. The temperature compensation method based on polynomial fitting is studied. The experimental results show that the accuracy is 0.019% F.S. in a pressure range of 200~1200 hPa over a temperature range of -20 °C~+60 °C. The absolute errors of the barometer are within ±23 Pa. To verify its actual performance, a drone flight test was conducted. The test results are consistent with the actual flight trajectory.

The Design and Experimentation of a Corn Moisture Detection Device Based on Double Capacitors.

Detecting the moisture content of grain accurately and rapidly has important significance for harvesting, transport, storage, processing, and precision agriculture. There are some problems with the slow detection speeds, unstable detection, and low detection accuracy of moisture contents in corn harvesters. In that case, an online moisture detection device was designed, which is based on double capacitors. A new method of capacitance complementation and integration was proposed to eliminate the limitation of single data. The device is composed of a sampling mechanism and a double-capacitor sensor consisting of a flatbed capacitor and a cylindrical capacitor. The optimum structure size of the capacitor plates was determined by simulation optimization. In addition to this, the detection system with software and hardware was developed to estimate the moisture content. Indoor dynamic measurement tests were carried out to analyze the influence of temperature and porosity. Based on the influencing factors and capacitance, a model was established to estimate the moisture content. Finally, the support vector machine (SVM) regressions between the capacitance and moisture content were built up so that the R2 values were more than 0.91. In the stability test, the standard deviation of the stability test was 1.09%, and the maximum relative error of the measurement accuracy test was 1.22%. In the dynamic verification test, the maximum error of the measurement was 4.62%, less than 5%. It provides a measurement method for the accurate, rapid, and stable detection of the moisture content of corn and other grains.

Temperature Compensation of Laser Methane Sensor Based on a Large-Scale Dataset and the ISSA-BP Neural Network.

We aimed to improve the detection accuracy of laser methane sensors in expansive temperature application environments. In this paper, a large-scale dataset of the measured concentration of the sensor at different temperatures is established, and a temperature compensation model based on the ISSA-BP neural network is proposed. On the data side, a large-scale dataset of 15,810 sets of laser methane sensors with different temperatures and concentrations was established, and an Improved Isolation Forest algorithm was used to clean the large-scale data and remove the outliers in the dataset. On the modeling framework, a temperature compensation model based on the ISSA-BP neural network is proposed. The quasi-reflective learning, chameleon swarm algorithm, Lévy flight, and artificial rabbits optimization are utilized to improve the initialization of the sparrow population, explorer position, anti-predator position, and position of individual sparrows in each generation, respectively, to improve the global optimization seeking ability of the standard sparrow search algorithm. The ISSA-BP temperature compensation model far outperforms the four models, SVM, RF, BP, and PSO-BP, in model evaluation metrics such as MAE, MAPE, RMSE, and R-square for both the training and test sets. The results show that the algorithm in this paper can significantly improve the detection accuracy of the laser methane sensor under the wide temperature application environment.

Advancing Slim-Hole Drilling Accuracy: A C-I-WOA-CNN Approach for Temperature-Compensated Pressure Measurements.

This paper presents a novel method to improve drill pressure measurement accuracy in slim-hole drilling within the petroleum industry, a sector often plagued by extreme conditions that compromise data integrity. We introduce a temperature compensation model based on a Chaotic-Initiated Adaptive Whale Optimization Algorithm (C-I-WOA) for optimizing Convolutional Neural Networks (CNNs), dubbed the C-I-WOA-CNN model. This approach enhances the Whale Optimization Algorithm (WOA) initialization through chaotic mapping, boosts the population diversity, and features an adaptive weight recalibration mechanism for an improved global search and local optimization. Our results reveal that the C-I-WOA-CNN model significantly outperforms traditional CNNs in its convergence speed, global searching, and local exploitation capabilities, reducing the average absolute percentage error in pressure parameter predictions from 1.9089% to 0.86504%, thereby providing a dependable solution for correcting temperature-induced measurement errors in downhole settings.

Temperature Compensation Method Based on Bilinear Interpolation for Downhole High-Temperature Pressure Sensors.

Due to their high accuracy, excellent stability, minor size, and low cost, silicon piezoresistive pressure sensors are used to monitor downhole pressure under high-temperature, high-pressure conditions. However, due to silicon's temperature sensitivity, high and very varied downhole temperatures cause a significant bias in pressure measurement by the pressure sensor. The temperature coefficients differ from manufacturer to manufacturer and even vary from batch to batch within the same manufacturer. To ensure high accuracy and long-term stability for downhole pressure monitoring at high temperatures, this study proposes a temperature compensation method based on bilinear interpolation for piezoresistive pressure sensors under downhole high-temperature and high-pressure environments. A number of calibrations were performed with high-temperature co-calibration equipment to obtain the individual temperature characteristics of each sensor. Through the calibration, it was found that the output of the tested pressure measurement system is positively linear with pressure at the same temperatures and nearly negatively linear with temperature at the same pressures, which serves as the bias correction for the subsequent bilinear interpolation temperature compensation method. Based on this result, after least squares fitting and interpolating, a bilinear interpolation approach was introduced to compensate for temperature-induced pressure bias, which is easier to implement in a microcontroller (MCU). The test results show that the proposed method significantly improves the overall measurement accuracy of the tested sensor from 21.2% F.S. to 0.1% F.S. In addition, it reduces the MCU computational complexity of the compensation model, meeting the high accuracy demand for downhole pressure monitoring at high temperatures and pressures.

Accurate Ultrasonic Thickness Measurement for Arbitrary Time-Variant Thermal Profile.

Ultrasonic thickness measurement of mechanical structures is one of the most popular and commonly used nondestructive methods for various kinds of process control and corrosion monitoring. With ultrasonic propagation speed being temperature-dependent, the thickness measurement can be performed reliably only when the thermal profile is completely known. Most conventional techniques assume the temperature of the test structure is uniform and at room temperature across its thickness. Such assumptions may lead to large errors in the thickness measurement, especially when there are significant temperature variations across the thickness. State-of-the-art techniques use external temperature measurements or implement iterative methods to compensate for the unknown thermal profiles. However, such techniques produce unsatisfactory results when the heat distribution is complex or varies rapidly with time. In this work, we propose a two-sensors technique, using both compressive and shear excitations, with a non-iterative rapid data processing method for accurate thickness measurement under arbitrary time-variant thermal profile. The independent behavior of shear and compressive waves is used to formulate a real-time thickness estimation technique. The developed technique is experimentally validated on a steel plate with fixed acoustic sensors. Test results show that the error in thickness estimation can be reduced by up to 98% compared to conventional thickness gauging methods.

Temperature Compensation Method for Piezoresistive Pressure Sensors Based on Gated Recurrent Unit.

Piezoresistive pressure sensors have broad applications but often face accuracy challenges due to temperature-induced drift. Traditional compensation methods based on discrete data, such as polynomial interpolation, support vector machine (SVM), and artificial neural network (ANN), overlook the thermal hysteresis, resulting in lower accuracy. Considering the sequence-dependent nature of temperature drift, we propose the RF-IWOA-GRU temperature compensation model. Random forest (RF) is used to interpolate missing values in continuous data. A combination of gated recurrent unit (GRU) networks and an improved whale optimization algorithm (IWOA) is employed for temperature compensation. This model leverages the memory capability of GRU and the optimization efficiency of the IWOA to enhance the accuracy and stability of the pressure sensors. To validate the compensation method, experiments were designed under continuous variations in temperature and actual pressure. The experimental results show that the compensation capability of the proposed RF-IWOA-GRU model significantly outperforms that of traditional methods. After compensation, the standard deviation of pressure decreased from 10.18 kPa to 1.14 kPa, and the mean absolute error and root mean squared error were reduced by 75.10% and 76.15%, respectively.

Trend Decomposition for Temperature Compensation in a Radar-Based Structural Health Monitoring System of Wind Turbine Blades.

The compensation of temperature is critical in every structural health monitoring (SHM) system for achieving maximum damage detection performance. This paper analyses a novel approach based on seasonal trend decomposition to eliminate the temperature effect in a radar-based SHM system for wind turbine blades that operates in the frequency band from 58 to 63.5 GHz. While the original seasonal trend decomposition searches for the trend of a periodic signal in its entirety, the new method uses a moving average to determine trends for each point of a periodic signal. The points of the seasonal signal no longer need to have the same trend. Based on the determined trends, the measurement signal can be corrected by temperature effects, providing accurate damage detection results under changing temperature conditions. The performance of the trend decomposition is demonstrated with experimental data obtained during a full-scale fatigue test of a 31 m long wind turbine blade subjected to ambient temperature variations. For comparison, the well-known optimal baseline selection (OBS) approach is used, which is based on multiple baseline measurements at different temperature conditions. The use of metrics, such as the contrast in damage indicators, enables the performance assessment of both methods.

Sensor-Integrated Chairs for Lower Body Strength and Endurance Assessment.

This paper describes an automated method and device to conduct the Chair Stand Tests of the Fullerton Functional Test Battery. The Fullerton Functional Test is a suite of physical tests designed to assess the physical fitness of older adults. The Chair Stand Tests, which include the Five Times Sit-to-Stand Test (5xSST) and the 30 Second Sit-to-Stand Test (30CST), are the standard for measuring lower-body strength in older adults. However, these tests are performed manually, which can be labor-intensive and prone to error. We developed a sensor-integrated chair that automatically captures the dynamic weight and distribution on the chair. The collected time series weight-sensor data is automatically uploaded for immediate determination of the sit-to-stand timing and counts, as well as providing a record for future comparison of lower-body strength progression. The automatic test administration can provide significant labor savings for medical personnel and deliver much more accurate data. Data from 10 patients showed good agreement between the manually collected and sensor-collected 30CST data (M = 0.5, SD = 1.58, 95% CI = 1.13). Additional data processing will be able to yield measurements of fatigue and balance and evaluate the mechanisms of failed standing attempts.

Mutation of a PER2 phosphodegron perturbs the circadian phosphoswitch.

Casein kinase 1 (CK1) plays a central role in regulating the period of the circadian clock. In mammals, PER2 protein abundance is regulated by CK1-mediated phosphorylation and proteasomal degradation. On the other hand, recent studies have questioned whether the degradation of the core circadian machinery is a critical step in clock regulation. Prior cell-based studies found that CK1 phosphorylation of PER2 at Ser478 recruits the ubiquitin E3 ligase ß-TrCP, leading to PER2 degradation. Creation of this phosphodegron is regulated by a phosphoswitch that is also implicated in temperature compensation. However, in vivo evidence that this phosphodegron influences circadian period is lacking. Here, we generated and analyzed PER2-Ser478Ala knock-in mice. The mice showed longer circadian period in behavioral analysis. Molecularly, mutant PER2 protein accumulated in both the nucleus and cytoplasm of the mouse liver, while Per2 messenger RNA (mRNA) levels were minimally affected. Nuclear PER1, CRY1, and CRY2 proteins also increased, probably due to stabilization of PER2-containing complexes. In mouse embryonic fibroblasts derived from PER2-Ser478Ala::LUC mice, three-phase decay and temperature compensation of the circadian period was perturbed. These data provide direct in vivo evidence for the importance of phosphorylation-regulated PER2 stability in the circadian clock and validate the phosphoswitch in a mouse model.

Temperature Compensation for Reusable Piezo Configuration for Condition Monitoring of Metallic Structures: EMI Approach.

This paper presents a novel algorithm for compensating the changes in conductance signatures of a piezo sensor due to the temperature variation employed in condition monitoring using the electro-mechanical impedance (EMI) approach. It is crucial to consider the changes in an EMI signature due to temperature before using it for comparison with the baseline signature. The shifts in the signature due to temperature can be misinterpreted as damages to the structure, which might also result in a false alarm. In the present study, the compensation values are calculated based on experiments on piezo sensors both in a free boundary condition and in a bonded condition on a metallic host structure. The values were further validated experimentally for damage detection on a large 2D steel plate structure. The variation in first natural frequency values for the unbonded piezo sensor at different temperatures has been used to develop the compensation algorithms. Whereas, in the case of the bonded sensor, the shift in structural peaks has been used. The developed compensation relations showed promising results in damage detection. Lastly, a finite element-based study has also been performed, supporting the experimental findings. The outcome of this study will aid in the compensation of the signatures in the structure due to temperature variation in the conductance signature.

A Novel Catheter Shape-Sensing Method Based on Deep Learning with a Multi-Core Optical Fiber.

In this paper, we propose a novel shape-sensing method based on deep learning with a multi-core optical fiber for the accurate shape-sensing of catheters and guidewires. Firstly, we designed a catheter with embedded multi-core fiber containing three sensing outer cores and one temperature compensation middle core. Then, we analyzed the relationship between the central wavelength shift, the curvature of the multi-core Fiber Bragg Grating (FBG), and temperature compensation methods to establish a Particle Swarm Optimization (PSO) BP neural network-based catheter shape sensing method. Finally, experiments were conducted in both constant and variable temperature environments to validate the method. The average and maximum distance errors of the PSO-BP neural network were 0.57 and 1.33 mm, respectively, under constant temperature conditions, and 0.36 and 0.96 mm, respectively, under variable temperature conditions. This well-sensed catheter shape demonstrates the effectiveness of the shape-sensing method proposed in this paper and its potential applications in real surgical catheters and guidewire.

Compact Back-End Electronics with Temperature Compensation and Efficient Data Management for In Situ SiPM-Based Radiation Detection.

A compact back-end interface for silicon photomultipliers (SiPMs) implementing Zener diode-based temperature compensation has been developed for the remote detection of beta and gamma radiation. Remote detection is facilitated by the development of an efficient data management system utilising MySQL database storage for recording periodic spectra data for wireless access over a private Wi-Fi network. A trapezoidal peak shaping algorithm has been implemented on an FPGA for the continuous conversation of pulses from the SiPM, signifying the detection of a radiological particle, into spectra. This system has been designed to fit within a 46 mm cylindrical diameter for in situ characterization, and can be attached to one or more SiPMs used in conjunction with a range of scintillators. LED blink tests have been used to optimise the trapezoidal shaper coefficients to maximise the resolution of the recorded spectra. Experiments with an array of SiPMs integrated with a NaI(Tl) scintillator exposed to sealed sources of Co-60, Cs-137, Na-22 and Am-241 have shown that the detector achieves a peak efficiency of 27.09 ± 0.13% for a gamma peak at 59.54 keV produced by Am-241, and a minimum energy resolution (Delta E/E) of 4.27 ± 1.16% for the 1332.5 keV gamma peak from Co-60.

Thermal Calibration of Triaxial Accelerometer for Tilt Measurement.

The application of MEMS accelerometers used to measure inclination is constrained by their temperature dependence, and each accelerometer needs to be calibrated individually to increase stability and accuracy. This paper presents a calibration and thermal compensation method for triaxial accelerometers that aims to minimize cost and processing time while maintaining high accuracy. First, the number of positions to perform the calibration procedure is optimized based on the Levenberg-Marquardt algorithm, and then, based on this optimized calibration number, thermal compensation is performed based on the least squares method, which is necessary for environments with large temperature variations, since calibration parameters change at different temperatures. The calibration procedures and algorithms were experimentally validated on marketed accelerometers. Based on the optimized calibration method, the calibrated results achieved nearly 100 times improvement. Thermal drift calibration experiments on the triaxial accelerometer show that the thermal compensation scheme in this paper can effectively reduce drift in the temperature range of -40 °C to 60 °C. The temperature drifts of x- and y-axes are reduced from -13.2 and 11.8 mg to -0.9 and -1.1 mg, respectively. The z-axis temperature drift is reduced from -17.9 to 1.8 mg. We have conducted various experiments on the proposed calibration method and demonstrated its capacity to calibrate the sensor frame error model (SFEM) parameters. This research proposes a new low-cost and efficient strategy for increasing the practical applicability of triaxial accelerometers.

Development and Temperature Correction of Piezoelectric Ceramic Sensor for Traffic Weighing-In-Motion.

Weighing-In-Motion (WIM) technology is one of the main tools for pavement management. It can accurately describe the traffic situation on the road and minimize overload problems. WIM sensors are the core elements of the WIM system. The excellent basic performance of WIMs sensor and its ability to maintain a stable output under different temperature environments are critical to the entire process of WIM. In this study, a WIM sensor was developed, which adopted a PZT-5H piezoelectric ceramic and integrated a temperature probe into the sensor. The designed WIM sensor has the advantages of having a small size, simple structure, high sensitivity, and low cost. A sine loading test was designed to test the basic performance of the piezoelectric sensor by using amplitude scanning and frequency scanning. The test results indicated that the piezoelectric sensor exhibits a clear linear relationship between input load and output voltage under constant environmental temperature. The linear correlation coefficient R2 of the fitting line is up to 0.999, and the sensitivity is 4.04858 mV/N at a loading frequency of 2 Hz at room temperature. The sensor has good frequency-independent characteristics. However, the temperature has a significant impact on it. Therefore, the output performance of the piezoelectric ceramic sensor is stabilized under different temperature conditions by using a multivariate nonlinear fitting algorithm for temperature compensation. The fitting result R2 is 0.9686, the root mean square error (RMSE) is 0.2497, and temperature correction was achieved. This study has significant implications for the application of piezoelectric ceramic sensors in road WIM systems.

Accurate Cutting-Force Measurement with Smart Tool Holder in Lathe.

Cutting force in lathe work is closely related to tool wear and affects the turning quality. Direct measurement of the cutting force by measuring the strain of the tool holder is challenging because the tool holder design aims to be highly rigid in order to undertake large cutting forces. Accordingly, the most popular dynamometer designs modify the standard tool holder by decreasing the structural rigidity of the holder, which reduces the machining precision and is not widely accepted. In order to solve the issue of the low stiffness of the dynamometer reducing the machining precision, in this paper, the ultra-low strain on the tool holder was successfully detected by the highly sensitive semiconductor strain gauges (SCSG) adjacent to the blade cutting insert. However, the cutting process would generate much heat, which increases the force measuring area temperature of the tool holder by about 30 °C. As a result, the readout drifted significantly with the temperature changes due to the high temperature coefficient of SCSG. To solve this problem, the temperature on the tool holder was monitored and a BP neural network was proposed to compensate for temperature drift errors. Our methods improved the sensitivity (1.14 × 10-2 mV/N) and the average relative error of the BP neural network prediction (≤1.48%) while maintaining the original stiffness of the tool holder. The smart tool holder developed possesses high natural frequency (≥6 kHz), it is very suitable for dynamic cutting-force measurement. The cutting experiment data in the lathe work show comparable performance with the traditional dynamometers and the resolution of the smart tool holder is 2 N (0.25% of total range).