Metres Research Articles

Influence of firmness measurements by dehydration and ripening depends on both measurement device and fruit type

Influence of firmness measurements by dehydration and ripening depends on both measurement device and fruit type

Compact computer controlled biaxial tensile device for low-temperature transport measurements of layered materials.

A biaxial tensile device for the transport study of layered materials is described. The device is mounted on the standard 24 pin zero force connector and can be moved between various setups. The compact design of the device makes it suitable for a wide range of studies. In our case, it is placed inside a 50mm diameter chamber in the cryocooler and is used in the temperature range 9-310K. A sample is glued in the center of a polyimide cruciform substrate, the ends of which are connected to a tension system driven by four computer-controlled stepper motors providing tensile force up to 30 N. Computer simulation results and their experimental verification show that tensile strain along one axis depends on the tensile load along the perpendicular direction, and this dependence turns out to be relatively strong and exceeds 40%. The operation of the device is demonstrated by studying the effect of deformation on the electrical conductivity of the layered compound 2H-NbS2.

Transient temperature measurement of silicon carbide Schottky barrier diode based on thermal reflection.

In this article, we present a transient temperature detection device for silicon carbide (SiC) Schottky barrier diodes (SBDs) based on thermal reflection theory. We constructed a thermal reflection temperature measurement device based on a 530-nm green laser. This device is more suitable for transient temperature measurement of SiC SBDs than previous thermal reflection equipment. The accuracy of temperature measurement by our device was confirmed by comparison with the results of infrared thermal imaging. The high temporal resolution characteristics of the thermal reflection technology allowed the detection of millisecond-level transient temperature changes in SiC SBDs. In addition, we investigated the complementarity of transient temperature change curves during heating and cooling processes, as well as the reasons for the differences between these curves. Finally, we used the structural function method combined with the Bayesian deconvolution algorithm to obtain the thermal resistance along the heat flow path of the device and validated the results using an established thermal resistance testing method.

A new portable penetrometer for measuring the viscosity of active lava.

Viscosity is a fundamental physical property of lava that dictates style and rate of effusive transport. Studies of lava viscosity have predominantly focused on measuring re-melted rocks in the laboratory. While these measurements are well-constrained in temperature, shear rate, and oxygen fugacity, they cannot reproduce the complexities of the natural emplacement environment. Field viscosity measurements of active lava are the only way to fully capture lava's properties, but such measurements are scarce, largely due to a lack of easy-to-use, portable, and accurate measurement devices. Thus, there is a need for developing suitable field instruments to help bolster the understanding of lava. Here, we present a new penetrometer capable of measuring a material's viscosity under the harsh conditions of natural lava emplacement. This device uses a stainless-steel tube with a semi-spherical tip fixed to a load cell that records axial force when pushed into a material, while simultaneously measuring the penetration depth via a free-moving tube that is pushed backward along the penetration tube. The device is portable (1.5m long, 5.5kg in weight) and uses a single-board computer for data acquisition. The penetrometer has an operational range from 2.5 × 102 to 2.1 × 105Pas and was calibrated for viscosities ranging from 5.0 × 102 to 1.6 × 105Pas. It was deployed to the 2023 Litli-Hrútur eruption in Iceland. These field measurements successfully recorded the in situ viscosities of the lava in the range of 1.2 × 104-3.4 × 104Pas, showcasing it as an efficient method of measuring natural lava viscosity.

Exploring the Interdependence of Vertical Extrapolation Uncertainties in Repowering Wind Farms

Assessing a wind farm’s annual energy production (AEP) involves modelling the wind resource and the wind-to-power conversion at the site. The greenfield pre-construction phase generally comprises the installation of wind measurement devices. For repowering projects, the wind data from the pre-construction phase of the existing farm can be used as wind input to assess the energy yield of the repowered wind farm. Indeed, one study demonstrates that when the modelling error correlations are known, the AEP prediction uncertainty of the repowered farm can be reduced by combining the energy production records of the existing farm with the AEP assessment for both farms. Previous studies have successfully identified the correlation structure for certain errors, especially for horizontal flow modelling, but not for vertical flow modelling. However, vertical extrapolation is essential, as the wind measurement heights are generally lower than the hub height on the repowered farm. This paper bridges this research gap and demonstrates that the correlation structure of errors in vertical profile modelling is Gaussian, with parameters dependent on shear values and heights. The distribution is validated against site data from simple to moderately complex sites in France.

Stable and wavelength-tunable multiwavelength laser for high-rate measurement device independent quantum key distribution

Measurement device independent quantum key distribution (MDI QKD) has attracted growing attention for its immunity to attacks at the measurement unit, but its unique structure limits the secret key rate. Utilizing the wavelength division multiplexing (WDM) technique and reducing error rates are effective strategies for enhancing the secret key rate. Reducing error rates often requires active feedback control of wavelengths using precise external references. However, for a multiwavelength laser, employing multiple references to stabilize each wavelength output places stringent demands on these references and significantly increases system complexity. Here, we demonstrate a stable, wavelength-tunable multiwavelength laser with an output wavelength ranging from 1270 to 1610 nm. Through precise temperature control and stable drive current, we passively lock the laser wavelength, achieving remarkable wavelength stability. This significantly reduce the error rate, leading to an almost doubling of the secret key rate compared to previous experiments. Furthermore, the exceptional wavelength stability offered by our multiwavelength laser, combined with the WDM technique, has further boosted the secret key rate of MDI QKD. With a wide wavelength tuning range of 5.1 nm, our multiwavelength laser facilitates flexible operation across multiple dense wavelength division multiplexing channels. Coupled with high wavelength stability and multiple wavelength outputs simultaneously, this laser offers a promising solution for a high-rate MDI QKD system.

Compact and cost-effective laser-powered speckle contrast optical spectroscopy fiber-free device for measuring cerebral blood flow.

In the realm of cerebrovascular monitoring, primary metrics typically include blood pressure, which influences cerebral blood flow (CBF) and is contingent upon vessel radius. Measuring CBF noninvasively poses a persistent challenge, primarily attributed to the difficulty of accessing and obtaining signal from the brain. Our study aims to introduce a compact speckle contrast optical spectroscopy device for noninvasive CBF measurements at long source-to-detector distances, offering cost-effectiveness, and scalability while tracking blood flow (BF) with remarkable sensitivity and temporal resolution. The wearable sensor module consists solely of a laser diode and a board camera. It can be easily placed on a subject's head to measure BF at a sampling rate of 80Hz. Compared to the single-fiber-based version, the proposed device achieved a signal gain of about 70 times, showed superior stability, reproducibility, and signal-to-noise ratio for measuring BF at long source-to-detector distances. The device can be distributed in multiple configurations around the head. Given its cost-effectiveness, scalability, and simplicity, this laser-centric tool offers significant potential in advancing noninvasive cerebral monitoring technologies.

Design and Testing of a Pressure Measurement and Adjustment Device for Fracture Ends

To design and test a device which is capable of accurately measuring and dynamically adjusting the axial pressure at the fracture end in real-time. Upon completion of the design, the pressure measurement and adjustment device was implemented in a canine tibial fracture external fixation model. A pressure sensor was mounted at the fracture end, and the displayed values of the pressure sensor were used as the standard for comparison. The relationship between the displayed values of the measurement and adjustment device and the pressure sensor under identical conditions was examined. The device was utilized in external fixation models of tibial fractures in five beagles. A linear correlation was observed between the displayed values of the device and the pressure sensor at the fracture end. The measurement values from the device could be transformed into fracture end pressure through the application of coefficients, thereby facilitating accurate measurement and dynamic adjustment of the fracture end pressure. The pressure measurement and adjustment device at the fracture end is easy to operate, enabling precise measurement and dynamic regulation of the pressure at the fracture end. It is well-suited for animal experiments aimed at investigating the impact of axial compression on fracture healing, demonstrating promising potential for experimental applications.

Design and Implementation of a Cost‐Effective, Portable Impedance Analyzer Device with AD5941

This study proposes a cost‐effective, small‐size, and portable impedance analyzer using the AD5941 Analog Front End (AFE) that makes frequency sweep measurement for various applications. The design utilizes a microcontroller and an AD5941 circuit for impedance measurement. The AD541 is a high precision impedance converter system that consists of a 16‐bit, 1.6 MSPS, analog to digital converter (ADC), an integrated waveform generator, and a digital signal processing (DSP) block. The AD5941 has many advantages over the AD5933 which was used in numerous studies in the past decade. Also, the device implements the four‐wire impedance method which is an impedance measuring technique that reduces the effect of electrodes to achieve higher accuracy. A supporting Graphic User Interface (GUI) software is employed to control the Impedance Analyzer device and to visualize the measurement. This impedance analyzer achieves a frequency resolution of 0.015 Hz and can generate sinusoid up to 200 kHz. In frequency range from 10 kHz to 150 kHz, the device can measure impedance in the range of 10 Ω–100 kΩ with less than 4.3% of error in comparison with benchtop impedance analyzer, Microtest 6632. Moreover, our impedance measurement device has undergone testing involving human calf measurements and electrochemical quantification, particularly for estimating Sodium Chlorite. The experimental outcomes demonstrate that our design not only fulfills the criteria of an impedance analyzer device but also performs effectively across diverse applications. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

The adsorption characteristics of helium under low temperature

To study the separation of helium isotopes by cryogenic adsorption, figuring out the adsorption characteristics of helium isotopes at low temperature is necessary. In this paper, a cryogenic adsorption measurement device has been established. It consists of a cryostat with a GM Cryocooler as cold source, a Setaram gas sorption instrument and a temperature controller. The helium-4 adsorption isotherms were studied under different temperatures and pressures (15–300 K, 0–1.5 MPa) by volumetric method. The results showed that a little helium was adsorbed by activated carbon at 300 K and 77 K. The amount of adsorbed helium increased with the temperature decreased from 20 K to 15 K. At pressures lower than 0.2 bar, the adsorption capacity increased rapidly as the pressure increased. Besides, the isosteric heats of adsorption were obtained, which was around 515 J/mol and changed little. Moreover, the cryogenic adsorption results were analyzed by Langmuir model and Toth model.

Design and development of an occlusion-free surface profile measurement device

The prevalent surface profile measurement techniques in the sub-millimetre measurement scale, based on active triangulation (such as laser scanners), stereo vision and photometric stereo, may miss certain parts of the surface of the object due to occlusion. Occlusion occurs in these techniques as they rely on a top-view configuration, where the illumination rays and the surface profile lie on the same plane. In this study, a surface profile measurement device is designed and developed, which can generate occlusion-free measurements by adopting a side-view configuration. This proposed measurement technique is tested on a few surfaces, and the results are presented.

Enhancing tissue wet pressing performance and dry end material efficiency for cost savings

The steadily growing global tissue paper demand lays a foundation for new technologies targeting tissue production optimization, as well as improved material and time efficiency. Tissue making is an especially energy-intensive field of paper industry, creating unique demands for performance in wet pressing and drying processes to cut energy usage. Intelligent roll solutions offer new tools for tissue making to achieve these goals. These tools aim at improving press nip, moisture, and tension profiles; reeling nip and parent roll hardness profiles; rewinder runnability; and end product web handling characteristics in converting. Intelligent rolls can be utilized in all the main processes and positions on tissue production lines. With these tools, production cost reductions and energy savings can be obtained by optimizing the press nip-to-Yankee cylinder contact, avoiding moisture profile errors requiring overdrying with the Yankee hood and reducing reeling/winding broke under low nip load conditions typical to tissue windups. The intelligent roll system consists of a helically mounted force or temperature sensors, roll covers, measurement electronics, digital radio transmission, and a receiver system connected to a user interface or the mill automation system. What distinguishes these implementations is that no layout changes or added external measurement devices are required, helping to fit into compact tissue machine environments, regardless of the equipment type – traditional, hybrid, or through-air drying (TAD) concept. In tissue processing equipment, the optimal positions for these rolls are press nip rollers, reeling cylinders, rewinder or converting line paper lead rolls, or rewinder winding drums. In addition to these, temperature profile measurements are utilized, with the main application being the sheet temperature profile detection after Yankee drying for moisture profile and drying process optimization.

Accelerated construction of stress relief music datasets using CNN and the Mel-scaled spectrogram.

Listening to music is a crucial tool for relieving stress and promoting relaxation. However, the limited options available for stress-relief music do not cater to individual preferences, compromising its effectiveness. Traditional methods of curating stress-relief music rely heavily on measuring biological responses, which is time-consuming, expensive, and requires specialized measurement devices. In this paper, a deep learning approach to solve this problem is introduced that explicitly uses convolutional neural networks and provides a more efficient and economical method for generating large datasets of stress-relief music. These datasets are composed of Mel-scaled spectrograms that include essential sound elements (such as frequency, amplitude, and waveform) that can be directly extracted from the music. The trained model demonstrated a test accuracy of 98.7%, and a clinical study indicated that the model-selected music was as effective as researcher-verified music in terms of stress-relieving capacity. This paper underlines the transformative potential of deep learning in addressing the challenge of limited music options for stress relief. More importantly, the proposed method has profound implications for music therapy because it enables a more personalized approach to stress-relief music selection, offering the potential for enhanced emotional well-being.

Changes in Countermovement Jump Height in Elite Volleyball Players in Two Competitive Seasons: Consideration on the Technique of Execution of the Jump

Monitoring athletes’ well-being during the competitive season is a common practice in team sports, and countermovement jump (CMJ) is often introduced to monitor neuromuscular fatigue of the players. In this pilot observational study, we aimed to evaluate changes in jump performance in professional volleyball players during their two competitive seasons after introducing different executions of the jump test. Different execution of the flight phase of the jumps was introduced as a differentiating factor. Nine professional volleyball players (age: 23.3 ± 1.9 years; volleyball training experience: 10.4 ± 1.9 years; body height: 192.3 ± 8.3) performed CMJ measurements 1–2 times a week on a contact mat during two competitive seasons, and a total number of 1008 jumps (540 in season 1 and 468 in season 2) was collected. Players’ CMJ height significantly decreased (p < 0.05) in season 2 by approximately 4%, and standard deviation of the data reduced by approximately 30%. Our study confirms that the testing procedures need to be rigorous as different execution of the jump may be associated with changes in CMJ height. Execution of the jump should not be interchangeable using the same measurement device, and the technique introduced in season 2 seems to be more appropriate for this measurement device.

Research progress of small size seed metering devices

Due to the characteristics of small size seeds, such as light weight, easy to jam, difficult to control the seeding, resulting in its low degree of mechanical automation, so the in-depth development of small size seed metering device is urgent. Firstly, we analyses the research status, research hotspots and research progress. Secondly,we specifically presents seed metering device classification, monitoring system and test method. The results indicate that (1) Before 2013, the number of Chinese research was relatively similar to that of international numbers, while after 2013, the growth rate of Chinese research results accelerated, and the international showed an overall fluctuating increase. (2) Keyword co-occurrence network reveals that the research broadly focuses on equipment performance evaluation and analysis, equipment experimentation and optimization, application and evaluation of new technologies & methods, and specific effects of external conditions on seeding equipment, etc. (3) Each of the six kinds seed metering devices prevailing on the market has its own application range. (4) Monitoring system and test method are important testing tool for the development of seed metering device refinement. This research can provide guidance and reference for designing new types of seed metering device and help budding researchers to quickly understand the research history.

#2714 An AI based tool for hydration management in dialysis patients.

Abstract Background and Aims Hemodialysis patients often experience over-hydration (OH), a critical factor derived from excess extracellular water and associated with cardiovascular complications and overall morbidity. Accurate estimation of pre-dialysis OH is essential for optimizing fluid management and improving patient outcomes. Traditional methods for assessing OH, such as the Body Composition Monitor (BCM) are, in general, time-consuming for clinical staff and costly for healthcare providers. In this study, we present a machine learning model combining data automatically recorded by dialysis machine sensors and medical information captured in electronic chart records to estimate pre-dialysis OH. The aim of this study is to evaluate the possibility of a tool that predicts OH from patient's data without, or less frequently using, a bioimpedance device for OH measurements. Method We collected a comprehensive dataset from a cohort of CKD patients undergoing hemodialysis, including demographic information, clinical parameters, and hydration status parameters. The value of OH was assessed using BCM device. A feature selection approach, guided by a balance between prediction accuracy and model complexity, was used to identify the optimal set of input variables. All variables utilized in the model are either regularly gathered in clinical practice or automatically recorded from the dialysis machine. We tried different combinations of features using an XGBoost regressor algorithm. We focused the study on the comparison of the prediction of OH with the input variable of the average BCM OH value in the past year (Model 1) and without this variable (Model 2). Mean absolute error (MAE), Root mean square error (RMSE), R2, calibration and residuals plots were used to assess the models’ accuracy. The measurement unit is in litres. The models were validated with nested 5-fold cross-validation, dividing the data repeatedly into disjunct train, validation and test folds and 5 outer loops, totalling 25 folds. Results The dataset contains the observations from 37,011 patients for a total of 934,386 BCM assessments collected in the EuCliD database by Fresenius Medical Care clinics in Cech Republic, Italy, Portugal, Slovakia, Spain, and the United Kingdom between January 2016 to September 2023. The average value of OH is 2.06 (std 1.88). Model 1 generalizes on unseen data samples with an MAE of 0.74, RMSE of 1.02 and R2 of 0.57 after evaluating the performance on the test folds. Model 2 MAE is 0.93, RMSE is 1.25 and R2 is 0.36. The degradation of the accuracy between the two models indicates the importance of patients’ past BCM information for the prediction of the next OH. This degradation is also clear in the calibration and residuals plots (Fig. 1). Nevertheless, model 2 is able to predict OH with an acceptable level of accuracy. Conclusion Fluid management is a critical task for physicians and nurses. We present an AI-based tool that can be utilized to support clinicians in their daily decisions about fluid management, with and without the use of a device to measure OH. The results, as expected, show an important degradation of the accuracy between model 1 and 2, however, model 2 has sufficient overall accuracy to consider the approach valuable, meriting further exploration. In general, this approach can be used to reduce the number of direct OH assessments.

From Time-Series to Hybrid Models: Advancements in Short-Term Load Forecasting Embracing Smart Grid Paradigm

This review paper is a foundational resource for power distribution and management decisions, thoroughly examining short-term load forecasting (STLF) models within power systems. The study categorizes these models into three groups: statistical approaches, intelligent-computing-based methods, and hybrid models. Performance indicators are compared, revealing the superiority of heuristic search and population-based optimization learning algorithms integrated with artificial neural networks (ANNs) for STLF. However, challenges persist in ANN models, particularly in weight initialization and susceptibility to local minima. The investigation underscores the necessity for sophisticated predictive models to enhance forecasting accuracy, advocating for the efficacy of hybrid models incorporating multiple predictive approaches. Acknowledging the changing landscape, the focus shifts to STLF in smart grids, exploring the transformative potential of advanced power networks. Smart measurement devices and storage systems are pivotal in boosting STLF accuracy, enabling more efficient energy management and resource allocation in evolving smart grid technologies. In summary, this review provides a comprehensive analysis of contemporary predictive models and suggests that ANNs and hybrid models could be the most suitable methods to attain reliable and accurate STLF. However, further research is required, including considerations of network complexity, improved training techniques, convergence rates, and highly correlated inputs to enhance STLF model performance in modern power systems.

Acquiring Gamma-Ray Energy Spectrum Using a Plastic Scintillation Optical Fiber Detector

The plastic scintillation optical fiber (PSOF) detector, characterized by its large contact area with measurement targets, effectively detects and quantifies radiation in diverse radiation-contaminated areas and liquid environments. While it is extensively utilized for measuring alpha, beta, gamma, and neutron radiations, comprehensive documentation on the spectrum measurement and energy calibration methods for gamma nuclides has not been reported. Accurate energy calibration is crucial for the precise quantification of radiation doses from various sources. The pulse-height spectrum produced by the PSOF detector does not display a Compton maximum because of the significant Gaussian energy broadening. Additionally, this spectrum compresses as the distance increases between the radiation source and the light measurement device. In this study, the energy spectrum of a PSOF for gamma nuclides was characterized by energy calibrations using Compton edge (CE). The CE channel in the measurement spectrum of the PSOF detector for three gamma nuclides was identified using the first-order differentiation method. This technique was successfully applied to spectra measured at various radiation source positions to determine the attenuation coefficients. The proposed energy calibration method allows for the conversion of pulse-height spectra obtained from alpha, beta, and neutron radiation measured with PSOF detectors into energy spectra.

Experimental study of simple flumes with converging triangular walls

For water management, the accurate measurement of discharge in irrigation channels is essential. Static flow measuring devices such as weirs and flumes play a significant role in discharge measurement in open channels. Flumes are one of the most commonly used devices for flow measurement. Many researchers have focused on application of simple flumes in irrigation networks. This investigation aims to study the flow discharge through a simple flume with converging triangular walls under free-flow conditions. The flume is constructed by placing two right-angled triangular plates on either side of a rectangular open channel. The channel cross section is rectangular while height of the converging walls reaches to zero at the end of the flume opening. The proposed flume is inexpensive, its operation is simple, its installation is easy and does not require high maintenance (maintenance free). The present study is designed to determine the effect of different variables on flow discharge of this kind of flumes. For this, an experimental program was conducted under upstream subcritical flow regime and under free outflow conditions to formulate the flow discharge of the flume. Flume discharge relationship was deduced using dimensional analysis method and then calibrated using the experimental data collected in this study (318 runs). The proposed discharge equation has an average absolute relative error of 1.66 %, and for 96.5 % of the measured discharge values, the relative errors are within ±5 %. To reliably estimate the flume discharge, free-flow and submerged-flow conditions should be distinguished. For this, suitable equation with an average absolute relative error less than 3.44 % was presented to estimate the submergence threshold. This flume is suitable for the rectangular channel for accurate measurement of free flow at any location on field. This particular device has considerable advantages over the other devices, as it can be quickly installed and removed for use in irrigation channels. The findings and results of this study will be of interest for practical applications.

Whale Optimization for Cloud-Edge-Offloading Decision-Making for Smart Grid Services.

As IoT metering devices become increasingly prevalent, the smart energy grid encounters challenges associated with the transmission of large volumes of data affecting the latency of control services and the secure delivery of energy. Offloading computational work towards the edge is a viable option; however, effectively coordinating service execution on edge nodes presents significant challenges due to the vast search space making it difficult to identify optimal decisions within a limited timeframe. In this research paper, we utilize the whale optimization algorithm to decide and select the optimal edge nodes for executing services' computational tasks. We employ a directed acyclic graph to model dependencies among computational nodes, data network links, smart grid energy assets, and energy network organization, thereby facilitating more efficient navigation within the decision space to identify the optimal solution. The offloading decision variables are represented as a binary vector, which is evaluated using a fitness function considering round-trip time and the correlation between edge-task computational resources. To effectively explore offloading strategies and prevent convergence to suboptimal solutions, we adapt the feedback mechanisms, an inertia weight coefficient, and a nonlinear convergence factor. The evaluation results are promising, demonstrating that the proposed solution can effectively consider both energy and data network constraints while enduring faster decision-making for optimization, with notable improvements in response time and a low average execution time of approximately 0.03 s per iteration. Additionally, on complex computational infrastructures modeled, our solution shows strong features in terms of diversity, fitness evolution, and execution time.