Measurement Data Research Articles

Leveraging natural capital accounting to support businesses with nature-related risk assessments and disclosures.

Nature loss threatens businesses, the global economy and financial stability. Understanding and addressing these risks for business will require credible measurement approaches and data. This paper explores how natural capital accounting (NCA) can support business data and information needs related to nature, including disclosures aligned with the Taskforce on Nature-related Financial Disclosures recommendations. As businesses seek to measure, manage and disclose their nature-related risks and opportunities, they will need well-organized, consistent and high-quality information regarding their dependencies and impacts on nature, which few businesses currently collect or track in-house. NCA may be useful for these purposes but has not been widely used or applied by businesses. National NCA guided by the U.N. System of Environmental-Economic Accounting may provide: (i) a useful framework for businesses in conceptualizing, organizing and managing nature-related data and statistics; and (ii) data and information that can directly support business disclosures, corporate NCA and other business applications. This paper explores these opportunities as well as synergies between national and corporate natural capital accounts. In addition, the paper discusses key barriers to advancing the wider use and benefits of NCA for business, including: awareness of NCA, data access, business capabilities related to NCA, spatial and temporal scales of data, audit and assurance considerations, potential risks, and costs and incentives. This article is part of the theme issue 'Bringing nature into decision-making'.

Experimental investigation of a hybrid photovoltaic-thermal energy system for hot air production

Solar energy as a non-fossil alternative energy source has become the best choice to overcome the problem of energy demand in most countries in the world. There are two different techniques to convert solar energy: photovoltaic (PV) panels to produce electricity and thermal collectors to generate heat. The two technologies can be combined to provide electrical and thermal energy either simultaneously or separately. In order to optimize the performance of a hybrid photovoltaic-thermal (PVT) solar air heater, it is necessary to collect experimental data on solar irradiation and temperature. This paper emphasized the development of a PVT energy system for hot air production in a temperature range of 50-55 °C. Additionally, experiments were constructed to monitor the information acquired from the proposed PVT solar air heater and the environment, such as hot air temperature, ambient temperature, and solar irradiation. The real-time monitoring system was set for five sample days. A microcontroller unit was used to control the hot air temperature and save the measurement data into memory. The experimental results showed that the proposed PVT solar air heater is capable of maintaining a certain level of hot air temperature throughout the day and night.

Federated learning enables privacy-preserving and data-efficient dimension prediction and part qualification across additive manufacturing factories

A crucial part of quality control in additive manufacturing (AM) is the decision to accept or reject parts based on their dimensional accuracy. Machine learning (ML) models can learn nuanced relationships between process parameters and resulting geometries; however, obtaining large quantities of supervised learning data to train ML models incurs non-trivial costs. Relevant measurement data may be available at other factories or stations within the same factory but cannot be pooled together for conventional centralized learning (CL) due to privacy constraints. Here, we propose federated learning (FL) that uses private data and information from distributed sources to train ML models that predict part dimensions and inform part qualification, thereby allowing collaborative model building without compromising privacy. We manufacture and measure 405 parts having three different designs and distribute them across three to 45 factories. The performance of FL is evaluated when factories produce similar parts, parts of different designs, and parts of different qualities. When factories produce similar parts or parts of different designs, FL predicts part geometry within 10 µm of the CL benchmark and within 15 µm of the process capability limit, with as few as nine parts at each factory. FL also outperforms individual learning where factories train on their own data by up to 96% in geometry prediction, thereby providing a win-win paradigm for privacy-preserving collaborative learning. When factories produce parts of different qualities, FL outperforms individual factories only when they produce less than 15 parts locally. We show that FL performance is influenced by two factors: the quantity of data in the federation and the statistical homogeneity of data across participating factories. Overall, this research demonstrates the promise of FL for privacy-preserving, data-efficient quality control and offers key insights to design data federations in scalable AM production.

Development of an Infrared Marker based Encoder System as a Non-Contact Positioning System for Ultrasonic Probes

The digitalization of testing procedures is one of the challenges in the development of modern non-destructive testing systems. In order to guarantee a high level of inspection reliability, the assignment of measurement data to the measurement position plays an important role as well as the optimal inspection hardware. In a cooperative research and development project, an optical tracking system has been developed with which it is possible to determine the positions of different ultrasonic probes in space without contact. Thus, the position of the measurement is linked to the measurement results directly. Infrared LED markers attached to the ultrasonic probes are used for this purpose. The position of the markers are tracked in space with a camera. By linking several cameras and trackers, a positional accuracy of a few millimeters can be achieved for a camera specimen distance of 2 m. The more cameras are used the bigger the investigated part can be. For the tracking system, an existing optical camera system is coupled with a conventional of the shelve ultrasonic phased array device by means of a newly developed universal encoder. Different single transducer probes or phased array probes can be used. The costly and time consuming use of mechanical encoders is thus eliminated. This combination enables the further digitalization of hand-guided ultrasonic testing precisely in those areas where robot-based testing is not feasible. Additionally, a much easier way of documentation is possible. Due to the exact position determination a better evaluation of the measurement data can be carried out via a subsequent 3D display of the ultrasound data.

Phase evolution of nanocrystalline Mn-based oxides screened under different calcination temperatures using different precursors for proficient application in near infrared pigmentation

We examined how different calcination temperatures and precursors affect the formation of Mn-based oxides. The formation of these oxides was influenced by both the temperature (400–––1000 °C) and the type of precursors used (MnCl2·4H2O, MnCO3, MnO2, and KMnO4). When MnCl2·4H2O was calcined at 800 and 1000 °C/3h, we obtained pure tetragonal Mn3O4 phase materials. Calcining MnCO3 at 600 and 1000 °C resulted in cubic Mn2O3 and tetragonal Mn3O4 phase materials, respectively. Heating commercial MnO2 powder at 600 and 800 °C transformed it into cubic Mn2O3 phase materials. At 400 °C, the tetragonal MnO2 phase remained, but at 1000 °C, mixed Mn3O4 and Mn2O3 phases formed. Calcining KMnO4 at 1000 °C/3h led to the formation of δ-MnO2 materials. The morphological features of Mn2O3 and Mn3O4 materials exhibited different sizes and shapes, including nanostone-like, nanorod-like, and nanoflakes morphologies. The thermal analysis revealed significant differences in weight loss and thermal events, including exo and endothermic peaks. The Raman spectra and UV–Vis DRS measurements data support the formation of Mn-based oxides. The highest NIR reflectance for the various synthesized materials of Mn3O4 phase indicates a reflectance of 68 % in the solar region and 91 % in the color pigment region.

The Role of Adsorption Phenomena in ac Conductivity Measurements of Dielectric Nanoparticle Suspensions

The authors of numerous measurements of conductivity for suspensions of nanoparticles of various types carried out in the last decade came to the general conclusion that the well-known Maxwell’s theory is not applicable to quantitative explanation of the properties of such systems. In the present work, we demonstrate that the Maxwell’s theory can be still applicable even for such systems, but the specifics of the standard ac measurements have to be correctly taken into account. Namely, the dependence of the capacitance of “metal–electrolyte” cuvette boundaries on nanoparticle adsorption, which in its turn dramatically depends on nanoparticle concentration, has to be taken into account. The latter circumstance strongly (via the RC characteristic of the circuit) affects the results of conductivity measurements. We propose the new algorithm of the impedance measurement data analysis for the particles’ concentration dependence of conductivity σ(ϕ) (where ϕ is the volume fraction of nanoparticles in suspension) for the suspensions of the diamante nanoparticles in alcohol which demonstrates the adequate correspondence of the Maxwell’s theory to the available experimental findings.

Study of phase change material on alleviating building overheating risk in cold regions of China

ABSTRACT Rising summer overheating risk in China's cold regions due to climate change has drawn significant attention. Phase change material (PCM) has lowered indoor temperatures and saved energy in some areas, but its suitability and optimal phase change temperature in cold climates remain unconfirmed. This study evaluated overheating risks and assessed PCM's potential impact within these regions. The indoor temperature of an office building was monitored from May to September 2022 to track overheating instances. This measurement data was also used to simulate the PCM's effect in EnergyPlus. According to CIBSE TM52, Yichun experienced 255 h (13.89% of total time) of overheating, Harbin had 744 h (40.54%), Shenyang had 1173 h (63.89%), and Dalian had 1255 h (68.36%). Using PCM could reduce overheating hours by 198 h (10.78%) in Yichun, 346 h (18.85%) in Harbin, 351 h (19.12%) in Shenyang, and 204 h (11.11%) in Dalian. Additionally, it potentially reduced energy consumption by 4.56%-30.57% across these cities. To address climate change challenges, policymakers are suggested to contemplate implementing PCM as an effective measure to protect existing buildings from summer heat in the cold regions of China.

Mismatch Negativity in Children with Deficits in Auditory Abilities

Abstract Introduction Mismatch negativity (MMN) represents a negative component of event-related potentials, which is mentioned by guidelines as an important tool to provide measurable data regarding the functionality of the auditory system in acoustic processing. However, the literature still lacks reliable data that can support the clinical use of this potential in the complementary diagnosis of central auditory processing (CAP) disorder (CAPD). Objectives To analyze whether MMN assessment might be associated with the CAP behavioral test battery, as well as to assess the effects of auditory ability deficits on MMN responses in the pediatric population. Methods In total, 45 age-matched children participated in the study. They were submitted to the CAP behavior assessment and to MMN. The children were tested with a combination of speech contrast consisting of acoustic syllables [da] versus [ta], governed by the oddball paradigm. Results Mismatch negativity did not show a direct association with a single test but with the combination of the four tests used as a behavioral test battery to identify CAPD. The results also indicated that the auditory ability deficits influenced the measurement of MMN latency (p = 0.003*), but not the amplitude (p = 0.857) or the area (p = 0.577). Conclusion Mismatch negativity was shown to be statistically associated with the battery of tests used to identify deficits in auditory abilities in the studied sample rather than with a single behavioral test. The deficits in auditory abilities were observed in the MMN latency. Mismatch negativity can be used to assess children with CAPD.

Analysis of the Accuracy of CAD Modeling in Engineering and Medical Industries Based on Measurement Data Using Reverse Engineering Methods

The reverse engineering (RE) process is often necessary in today’s engineering and medical industries. Expertise in measurement technology, data processing, and CAD modeling is required to ensure accurate reconstruction of an object’s geometry. However, errors are generated at every stage of geometric reconstruction, affecting the dimensional and geometric accuracy of the final 3D-CAD model. In this article, the geometry of reconstructed models was measured using contact and optical methods. The measurement data representing 2D profiles, 3D point clouds, and 2D images acquired in the reconstruction process were saved to a stereolithography (STL) model. The reconstructed models were then subjected to a CAD modeling process, and the accuracy of the parametric modeling was evaluated by comparing the 3D-CAD model to the 3D-STL model. Based on the results, the model used for clamping and positioning parts to perform the machining process and the connecting rod provided the most accurate mapping errors. These models represented deviations within ±0.02 mm and ±0.05 mm. The accuracy of CAD modeling for the turbine blade model and the pelvis part was comparable, presenting deviations within ±0.1 mm. However, the helical gear and the femur models showed the highest deviations of about ±0.2 mm. The procedures presented in the article specify the methods and resolution of the measurement systems and suggest CAD modeling strategies to minimize reconstruction errors. These results can be used as a starting point for further tests to optimize CAD modeling procedures based on the obtained measurement data.

Distributed l2−l∞ filtering over wireless sensors based on adaptive event triggering

This paper investigates the design problem of a set of distributed filters. These adaptive event-triggered (AET) filters have the [Formula: see text] property and are suitable for sensor networks characterized by random measurement data loss and dynamic topology switching. In a distributed filtering network, each local filter estimates the system’s state not only relying on the node’s individual information but also using information from neighboring nodes in the network topology. The adaptive event triggering condition is determined by the combination of the latest release data of the filter itself, the estimated value of the current moment, and the latest release data of the neighboring nodes. The event triggering mechanism adopts a threshold adaptive adjustment scheme, which dynamically changes the threshold parameter according to the filtering error under the premise of guaranteeing the filter performance, thus maximally saving the network communication resources. Initially, we used a binary sequence to describe the random loss of sensor measurements, and a Markov chain is used to describe the switching of the filtering network topology. Secondly, a Lyapunov function is created to examine the [Formula: see text] performance index of the filtering error system and investigate its mean square exponential stability. Subsequently, the distributed filter is formulated, and its parameters are determined using the linear matrix inequality approach. Ultimately, the validity of the proposed method is substantiated through numerical illustrations.

Tyre and brake thermal management in NASCAR

The need to manage race car tyres is routine faire and several academic papers have already appeared on this topic. We contribute to this literature with refined tyre grip and wear-rate models. Less well known is a methodology for managing the thermal transients in the car's braking system. This knowledge gap is addressed with a physically-based disc-and-calliper brake-heating model. The parameters in each of the models are optimised against measurement data captured on an instrumented Generation 7 (Gen 7) NASCAR driven on the Darlington speedway. These models are replicated on each corner of the vehicle. The remainder of the paper is directed to studying the minimum lap time performance of a Gen 7 NASCAR that is subject to tyre wear and temperature variations; brake heating influences are emphasised. Due to their novelty, the results focus on the performance impact of temperature variations and constraints on the front brake disc temperatures. Constraints on the brake temperatures are introduced to protect the brakes from fade and thermally-induced damage.

Risk of Tree Fall on High-Traffic Roads: A Case Study of the S6 in Poland

Modern technologies, such as airborne laser scanning (ALS) and advanced data analysis algorithms, allow for the efficient and safe use of resources to protect infrastructure from potential threats. This publication presents a study to identify trees that may fall on highways. The study used free measurement data from airborne laser scanning and wind speed and direction data from the Institute of Meteorology and Water Management in Poland. Two methods were used to determine the crown tops of trees: PyCrown and OPALS. The effect of wind direction on potential hazards was then analyzed. The OPALS method showed the best performance in terms of detecting trees, with an accuracy of 74%. The analysis showed that the most common winds clustered between 260° and 290°. Potential threats, i.e., trees that could fall on the road, were selected. As a result of the analysis, OPALS detected between 140 and 577 trees, depending on the chosen strategy. The presented research shows that combining ALS technology with advanced algorithms and wind data can be an effective tool for identifying potential hazards associated with falling trees on highways.

Technical, Economical, and Marketing Analyses of Corotating Dual‐Rotor Wind Turbines

In this study, a comprehensive analysis is done on a dual rotor wind turbine (DRWT) in co‐rotating arrangement. First, a co‐rotating dual rotor is designed and its performance is analyzed by blade element momentum theory simulation. Later, the designed co‐rotating DRWT is manufactured and then tested in the field by mounting the wind turbine on a truck. Running the truck steadily at different speeds while logging the performance parameters of the wind turbine, the power curve of the DRWT is obtained. Then, using the power curve of the co‐rotating DRWT and the actual wind measurement data from Çatalca Istanbul, the annual electricity generation of the designed turbine is calculated. The present techno‐economic analysis states that the co‐rotating DRWTs are more efficient compared to single rotor wind turbines (SRWTs), in accordance with the literature. Analyses show that with DRWT, around 22% higher power generation can be achieved. Additionally, the payback period for DRWT was found to be less than 1 year. The marketing strategy for introducing the product emphasizes identifying and targeting early adopters and influencers within the market. The present techno‐economic analysis states that the co‐rotating DRWTs are more efficient compared to SRWTs, in accordance with the literature.

Active and passive dosimetry for beta-emitting radiopharmaceutical therapy agents in a custom SPECT/CT compatible phantom

Objective. This work introduces a novel approach to performing active and passive dosimetry for beta-emitting radionuclides in solution using common dosimeters. The measurements are compared to absorbed dose to water (D w) estimates from Monte Carlo (MC) simulations. We present a method for obtaining absorbed dose to water, measured with dosimeters, from beta-emitting radiopharmaceutical agents using a custom SPECT/CT compatible phantom for validation of Monte Carlo based absorbed dose to water estimates. Approach. A cylindrical, acrylic SPECT/CT compatible phantom capable of housing an IBA EFD diode, Exradin A20-375 parallel plate ion chamber, unlaminated EBT3 film, and thin TLD100 microcubes was constructed for the purpose of measuring absorbed dose to water from solutions of common beta-emitting radiopharmaceutical therapy agents. The phantom is equipped with removable detector inserts that allow for multiple configurations and is designed to be used for validation of image-based absorbed dose estimates with detector measurements. Two experiments with 131I and one experiment with 177Lu were conducted over extended measurement intervals with starting activities of approximately 150–350 MBq. Measurement data was compared to Monte Carlo simulations using the egs_chamber user code in EGSnrc 2019. Main results. Agreement within k = 1 uncertainty between measured and MC predicted D w was observed for all dosimeters, except the A20-375 ion chamber during the second 131I experiment. Despite the agreement, the measured values were generally lower than predicted values by 5%–15%. The uncertainties at k = 1 remain large (5%–30% depending on the dosimeter) relative to other forms of radiation therapy. Significance. Despite high uncertainties, the overall agreement between measured and simulated absorbed doses is promising for the use of dosimeter-based RPT measurements in the validation of MC predicted D w.

Flash Flood Risk Assessment in the Asir Region, Southwestern Saudi Arabia, Using a Physically-Based Distributed Hydrological Model and GPM IMERG Satellite Rainfall Data

Floods in southwestern Saudi Arabia, especially in the Asir region, are among the major natural disasters caused by natural and human factors. In this region, flash floods that occur in the Wadi Hail Basin greatly affect human life and activities, damaging property, the built environment, infrastructure, landscapes, and facilities. A previous study carried out for the same basin has effectively revealed zones of flood risk using such an approach. However, the utilization of the HEC–HMS (Hydrologic Engineering Center–Hydrologic Modeling System) model and IMERG data for delineating areas prone to flash floods remain unexplored. In response to this advantage, this work primarily focused on flood generation assessment in the Wadi Hail Basin, one of the major basins in the region that is frequently prone to severe flash flood damage, from a single extreme rainfall event. We employed a fully physical-based, distributed hydrological model run with HEC–HMS software version 4.11 and Integrated Multi-satellite Retrievals of Global Precipitation Measurement (IMERG V.06) data, as well as other geo-environmental variables, to simulate the water flow within the Wadi Basin, and predict flash flood hazard. Discharge from the wadi and its sub-basins was predicted using 1 mm rainfall over an 8-h occurrence time. Significant peak discharge (3.6 m3/s) was found in eastern and southern upstream sub-basins and crossing points, rather than those downstream, due to their high-density drainage network (0.12) and CNs (88.4). Generally, four flood hazard levels were identified in the study basin: ‘low risk’, ‘moderate risk’, ‘high risk’, and ‘very high risk’. It was found that 43.8% of the total area of the Wadi Hail Basin is highly prone to flooding. Furthermore, medium- and low-hazard areas make up 4.5–11.2% of the total area, respectively. We found that the peak discharge value of sub-basin 11 (1.8 m3/s) covers 13.2% of the total Wadi Hail area; so, it poses more flood risk than other Wadi Hail sub-basins. The obtained results demonstrated the usefulness of the methods used to develop useful hydrological information in a region lacking ungagged data. This study will play a useful role in identifying the impact of extreme rainfall events on locations that may be susceptible to flash flooding, which will help authorities to develop flood management strategies, particularly in response to extreme events. The study results have potential and valuable policy implications for planners and decision-makers regarding infrastructural development and ensuring environmental stability. The study recommends further research to understand how flash flood hazards correlate with changes at different land use/cover (LULC) classes. This could refine flash flood hazards results and maximize its effectiveness.

#2285 Potentially modifiable factors influencing longitudinal health-related quality of life for those with chronic kidney disease in the NURTuRE-CKD cohort

Abstract Background and Aims Factors affecting poor health-related quality of life (HRQoL) for people with chronic kidney disease (CKD) have been previously described in literature, and worse HRQoL is associated with CKD progression and death. Little emphasis has been placed on which factors may be potentially modifiable, and longitudinal HRQoL outcome analyses in non-dialysis dependent CKD cohorts are few in number. Baseline analysis of the NURTuRE-CKD cohort identified several associations between these factors and HRQoL and these associations can now be examined with longitudinal data. Method The NURTuRE-CKD cohort study recruited 2996 participants with non-dialysis dependent CKD between 2017-2019, collecting biochemical, anthropometric, sociodemographic, medical history and patient reported outcome measure (PROM) data. Face-to-face follow-up repeated these measures. Longitudinal worsening overall HRQoL was the main outcome of interest, represented by mapped EQ-5D-3L index value and visual analogue score (VAS). Multivariable linear mixed effects models were fitted in R using the lmer and lmerTest packages. Models for index value were fit separately for each independent variable of interest and collated, as well as fitting models with all variables of interest. Models were adjusted for available confounders, repeated measures and follow-up time. P-values were adjusted for multiple testing using false discovery rate correction. Results 2054/2996 (68.6%) had complete HRQoL data at follow-up, of whom most had CKD stage G3a-G4 (n = 1597, 77.8%). 1817 (88.5%) were white and 1196 (58.2%) were male. Mean age was 63.7 (SD ±14.5) years and the mean number of comorbidities was 3.7 (SD ±2.2). Median (IQR) time between baseline and follow-up was 518 (410) days. At follow-up, mean mapped EQ-5D-3L index value was 0.72 (SD ±0.3) and mean VAS score was 70.4/100 (SD ±20.6). 1595 (77.7%) reported problems in at least one EQ-5D-5L dimension, with 255 (12.4%) developing issues since baseline. 1203 (58.6%) reported worse HRQoL index values at follow up and 910 (44.3%) reported worse VAS. The dimension with the highest number of newly reported issues at follow-up was ‘usual activities’ with 301 (14.7%). Multivariable linear mixed methods models showed independent associations with worse HRQoL (Figs 1 and 2) for those on ten or more medications (β −0.118, CI −0.137 to −0.100, p < 0.001), current smokers (β −0.036, CI −0.067 to −0.006, p = 0.0.012), obesity (β-0.059, CI −0.080 to-0.039, p < 0.001), haemoglobin of <100g/L (β-0.044, CI −0.073 to-0.016, p = 0.014), hospital anxiety and depression scale scores of 8 or above for anxiety (β-0.146, CI −0.161 to-0.130, p < 0.001) and depression (β-0.202, CI-0.219 to −0.186, p < 0.001), worse health literacy (β −0.099, CI −0.136 to −0.063, p < 0.001), and pain (β −0.148, CI −0.161 to −0.135, p ≤ 0.001), weakness (β −0.106, CI −0.120 to −0.092, p < 0.001) and shortness of breath symptoms (β −0.074, CI −0.087 to −0.060, p < 0.001). Conclusion Potentially modifiable factors influencing longitudinal worsening of HRQoL for people with CKD include polypharmacy, smoking, obesity, anaemia, depression and anxiety, limited health literacy, pain, weakness and shortness of breath symptoms. This highlights the need for high quality randomised controlled trials to test interventions addressing these factors with reported HRQoL outcomes. Further work in this cohort is required, examining a third timepoint for HRQoL and linked clinical outcome data.

Strain Virtual Sensing Applied to Industrial Presses for Fatigue Monitoring

The techniques that allow one to estimate measurements at the unsensed points of a system are known as virtual sensing. These techniques are useful for the implementation of condition monitoring systems in industrial equipment subjected to high cyclic loads that can cause fatigue damage, such as industrial presses. In this article, three different virtual sensing algorithms for strain estimation are tested using real measurement data obtained from a scaled bed press prototype: two deterministic algorithms (Direct Strain Observer and Least-Squares Strain Estimation) and one stochastic algorithm (Static Strain Kalman Filter). The prototype is subjected to cyclic loads using a hydraulic fatigue testing machine and is sensorized with strain gauges. Results show that sufficiently accurate strain estimations can be obtained using virtual sensing algorithms and a reduced number of strain gauges as input sensors when the monitored structure is subjected to static and quasi-static loads. Results also show that is possible to estimate the initiation of fatigue cracks at critical points of a structural component using virtual strain sensors.

A corrosion model for CZ cladding based on in-pile irradiation data

Corrosion resistance is the critical in-pile property for developing new Zircaloy cladding. As a new Zircaloy developed by China General Nuclear Power Corporation, CZ will be used as fuel rod cladding. This study is devoted to investigating the corrosion behavior of CZ. A two-stage Arrhenius equation is proposed to model the corrosion rate. A total of 1,510 measurement data points taken from 151 fuel rods irradiated for 1–4 cycles were used to develop the corrosion model. The burnup of these fuel rods ranges from 13.7 MWd/kgU to 55.9 MWd/kgU. The mean value and standard deviation of P−M (predicted value minus measured value) are 0.54 and 3.65 microns, respectively, and the mean value and standard deviation of P/M (predicted value divided by measured value) are 1.09 and 0.34 microns, respectively. P−M and P/M increase with the local burnup. When the burnup is greater than 40 MWd/kgU, the mean values of P−M and P/M are 6.2 and 1.20 microns, respectively; on the other hand, the prediction of the proposed model is more conservative in high burnup. In the typical case in which the average burnup reaches 57 MWd/kgU, the oxide thickness of CZ cladding is much less than 30 microns, whereas the oxide thickness of Zr-4 cladding is approximately 80 microns, and this manifests that CZ cladding possesses excellent performance in corrosion resistance.

Mitigating Measurement Inaccuracies in Digital Twins of Construction Machinery through Multi-Objective Optimization

The advent of digital twins facilitates the generation of high-fidelity replicas of actual systems or assets, thereby enhancing the design’s performance and feasibility. When developing digital twins, precise measurement data is essential to ensure alignment between the actual and digital models. However, inherent uncertainties in sensors and models lead to disparities between observed and predicted (simulated) behaviors. To mitigate these uncertainties, this study originally proposes a multi-objective optimization strategy utilizing a Gaussian process regression surrogate model, which integrates various uncertain parameters, such as load angle, bucket cylinder stroke, arm cylinder stroke, and boom cylinder stroke. This optimization employs a genetic algorithm to indicate the Pareto frontiers regarding the pressure exerted on the boom, arm, and bucket cylinders. Subsequently, TOPSIS is applied to ascertain the optimal candidate among the identified Pareto optima. The findings reveal a substantial congruence between the experimental and numerical outcomes of the devised virtual model, in conjunction with the TOPSIS-derived optimal parameter configuration.

From Planning Stage Towards FAIR Data: A Practical Metadatasheet For Biomedical Scientists

Datasets consist of measurement data and metadata. Metadata provides context, essential for understanding and (re-)using data. Various metadata standards exist for different methods, systems and contexts. However, relevant information resides at differing stages across the data-lifecycle. Often, this information is defined and standardized only at publication stage, which can lead to data loss and workload increase. In this study, we developed Metadatasheet, a metadata standard based on interviews with members of two biomedical consortia and systematic screening of data repositories. It aligns with the data-lifecycle allowing synchronous metadata recording within Microsoft Excel, a widespread data recording software. Additionally, we provide an implementation, the Metadata Workbook, that offers user-friendly features like automation, dynamic adaption, metadata integrity checks, and export options for various metadata standards. By design and due to its extensive documentation, the proposed metadata standard simplifies recording and structuring of metadata for biomedical scientists, promoting practicality and convenience in data management. This framework can accelerate scientific progress by enhancing collaboration and knowledge transfer throughout the intermediate steps of data creation.