Measurement Of Parameters Research Articles

A comprehensive laser image dataset for real-time measurement of wheelset geometric parameters

Railway transportation has experienced significant growth worldwide, offering numerous benefits to society. Most railway accidents are caused by wheelset faults so it’s significant to monitor wheelset conditions. Therefore, we need to collect wheelset images, repaint them, extract laser stripe centerlines, construct 3D contour, and measure their geometric parameters to judge the wheelset’s conditions. Deep learning can fulfill the tasks satisfyingly because it’s adaptable, robust, and generalize compared with traditional methods. To train the deep learning models effectively, we need rich and high-quality wheelset datasets. So far, there are no applicable public train wheelset datasets available, which greatly hinders the research on train wheelsets. Thus we construct a publicly available Wheelset Laser Image Dataset (WLI-Set). WLI-Set consists of four sub-datasets, Original, Inpainting, Segmentation, and Centerline. The dataset contains abundant annotated multiline laser stripe images that can facilitate the research on train wheelsets effectively.

Long-term measurement and characterization of boundary layer optical turbulence

The United States Naval Academy long-term scintillation measurement campaign was a multi-year effort to characterize optical turbulence in the near-maritime atmospheric boundary layer (ABL). At its core, the field experiment consists of in situ measurements of bulk atmospheric and oceanographic parameters, as well as path-averaged measurements of the refractive index structure parameter, C n 2, collected using a large-aperture scintillometer. The field experiment ran from January 1st, 2020, through September 26th, 2023, representing the most comprehensive collection of optical turbulence measurements in the near-maritime ABL to date. Long-term measurements enable researchers to evaluate existing theory and develop new models applicable to this environment. The present study characterizes some of the physical relationships that affect optical turbulence. This characterization focuses on the relationship between local optical turbulence and select atmospheric and oceanographic parameters. The impact of temperature gradients on the extent of optical turbulence was analyzed, along with its interactions with relative humidity and wind speed. The diurnal and seasonal variations in measured C n 2 were examined, with comparisons drawn against other environments. Further information and the full dataset are publicly available through the optical turbulence benchmark repository [Jellen et al., GitHub, 2023].

An Assessment of a Photovoltaic System’s Performance Based on the Measurements of Electric Parameters under Changing External Conditions

This article presents an analysis of the performance of a 14.04 kWp grid-connected photovoltaic (PV) installation consisting of monocrystalline silicon, polycrystalline silicon and bifacial glass–glass monocrystalline silicon modules. The photovoltaic system was mounted in Poland, a location characterized by temperate climate conditions. On the basis of the obtained results, the photovoltaic parameters were determined in accordance with the international standard. The annual energy yield of the entire system was 1033 kWh/kWp, and the performance ratio achieved was 83%. The highest average daily final yield was in the range of 4.0–4.5 kWh/kWp for each photovoltaic technology under investigation. In the cold part of the year, the efficiency of the photovoltaic modules was estimated to be 15%, and it was estimated to be 7% during the warm part of the year. Array capture losses accounted for around 0.75 kWh/kWp of energy loss per day, whereas the inverter efficiency was over 95% during the months that are beneficial for energy production.

Optimal quantum metrology of two-photon absorption

Abstract Two-photon absorption (TPA) is a nonlinear optical process with wide-ranging applications from spectroscopy to super-resolution imaging. Despite this, the precise measurement and characterisation of TPA parameters are challenging due to their inherently weak nature. We study the potential of single-mode quantum light to enhance TPA parameter estimation through the quantum Fisher information (QFI). Discrete variable (DV) quantum states (defined to be a finite superposition of Fock states) are optimised to maximise the QFI for given absorption, revealing a quantum advantage compared to both the coherent state (classical) benchmark and the single-mode squeezed vacuum state. For fixed average energy ̄n ∈ 2N, the Fock state is shown to be optimal for large TPA parameters, while a superposition of vacuum and a particular Fock state is optimal for small absorption for all ̄n. This differs from single-photon absorption where the Fock state is always optimal. Notably, photon counting is demonstrated to offer optimal or nearly optimal performance compared to the QFI bound for all levels of TPA parameters for the optimised quantum probes, and their quantum advantage is shown to be robust to single-photon loss. Our findings provide insight into known limiting behaviours of Gaussian probes and their different Fisher information (FI) scalings under photon counting (∝ ̄n2 for squeezed vacuum states versus ̄n3 for coherent states). The squeezed state outperforms coherent states for small TPA parameters but underperforms in the intermediate regime, becoming comparable in the large absorption limit. This can be explained through fundamental differences between behaviours of even and odd number Fock states: the former’s QFI diverges in both large and small absorption limits, while the latter diverges only in the small absorption limit, dominating at intermediate scales.

Spatially Resolved Raman Spectroscopic Investigation of Uranyl Fluoride: A Case Study in the Importance of Instrument Optimization.

Raman spectroscopy is an emerging technique for rapid and nondestructive analysis of nuclear materials for forensic and nonproliferation applications as it is a powerful tool for distinguishing multiple chemical forms of materials with similar stoichiometries. Recent developments in spectroscopic software have enabled rapid data collection with high-speed Raman spectroscopic mapping capabilities. However, some uranium-rich materials are susceptible to degradation in humid air and/or laser-induced phase transformations. To mitigate environmental or measurement-related sample degradation of potential samples of interest, we have taken a systematic approach to define optimized data collection parameters for high-throughput measurements of uranyl fluoride (UO2F2), which is an important intermediate material in the nuclear fuel cycle. First, we systematically describe the influence of optical magnification (5× to 100×), laser power, and exposure time on obtained signal for identical particles of UO2F2 and find that at low laser power and exposure times, comparable signal is obtained regardless of optical magnification. Second, we ensure sample integrity during data collection, and third, collect spectroscopic maps that employ optimized parameters to reduce the time required to obtain spatially resolved spectroscopic information. Reductions of 90% and 99% in measurement times are discussed as they relate to differences in resolving spectroscopic features of particles in identical mapping areas. During this work, we found that additional data processing options were needed and thus developed a customized Python script for importing, processing, analyzing, and visualizing Raman spectroscopic map data.

Design and Construction of an Operational Prototype of Optical Atmospheric Turbulence Parameters Measurement Device Based on Moiré Deflectometry

Design and Construction of an Operational Prototype of Optical Atmospheric Turbulence Parameters Measurement Device Based on Moiré Deflectometry

DEVELOPMENT OF ENHANCED AGRICULTURAL GREENHOUSE SYSTEMS FOR DEVELOPING COUNTRIES

The Greenhouse system provides a controlled environment for breeding high-value crops, medical plants, and flowers. Literature review shows that crops grown inside the Greenhouse are healthier with better results. The automation of greenhouses is necessaryto solve the problem of over-irrigation of crops due to human error, which results in low crop yield and death of crops. This work examines and investigates farming techniques and proposes how the automation of the greenhouse technology using the Android phone for its operations will be of benefit. This research further developed an IoT agricultural Greenhouse System that will enable farmers to carry out automated irrigation and to remotely view the activities in the greenhouse. This advanced irrigation method uses an IoT technological approach to monitor the temperature of the farm to determine when to turn on the irrigation system, light, and fan for crop growth using the Android system. A user interface, to monitor the temperature and soil humidity, and display the activities in the agricultural Greenhouse System, was also developed. The system evaluation showed higher efficiency in performance using temperature and humidity parameters measurement.

Smart Water Quality Monitoring with IoT Wireless Sensor Networks

Traditional laboratory-based water quality monitoring and testing approaches are soon to be outdated, mainly because of the need for real-time feedback and immediate responses to emergencies. The more recent wireless sensor network (WSN)-based techniques are evolving to alleviate the problems of monitoring, coverage, and energy management, among others. The inclusion of the Internet of Things (IoT) in WSN techniques can further lead to their improvement in delivering, in real time, effective and efficient water-monitoring systems, reaping from the benefits of IoT wireless systems. However, they still suffer from the inability to deliver accurate real-time data, a lack of reconfigurability, the need to be deployed in ad hoc harsh environments, and their limited acceptability within industry. Electronic sensors are required for them to be effectively incorporated into the IoT WSN water-quality-monitoring system. Very few electronic sensors exist for parameter measurement. This necessitates the incorporation of artificial intelligence (AI) sensory techniques for smart water-quality-monitoring systems for indicators without actual electronic sensors by relating with available sensor data. This approach is in its infancy and is still not yet accepted nor standardized by the industry. This work presents a smart water-quality-monitoring framework featuring an intelligent IoT WSN monitoring system. The system uses AI sensors for indicators without electronic sensors, as the design of electronic sensors is lagging behind monitoring systems. In particular, machine learning algorithms are used to predict E. coli concentrations in water. Six different machine learning models (ridge regression, random forest regressor, stochastic gradient boosting, support vector machine, k-nearest neighbors, and AdaBoost regressor) are used on a sourced dataset. From the results, the best-performing model on average during testing was the AdaBoost regressor (a MAE¯ of 14.37 counts/100 mL), and the worst-performing model was stochastic gradient boosting (a MAE¯ of 42.27 counts/100 mL). The development and application of such a system is not trivial. The best-performing water parameter set (Set A) contained pH, conductivity, chloride, turbidity, nitrates, and chlorophyll.

Population dynamics of the data-poor tiger shark (Galeocerdo cuvier) in the Northwest Pacific Ocean: Measurement of growth parameters and mortality rates

Although tiger sharks (Galeocerdo cuvier) are found globally in major oceans, comprehensive biological and fishery data pertaining to the Northwest Pacific Ocean are scarce. Accordingly, this study was conducted to understand the challenges in measuring tiger sharks’key life history parameters, including von Bertalanffy growth and mortality rates, which are essential for the construction of robust stock assessment models. To evaluate these parameters, we used the TropFishR software package to analyse observer-recorded length-frequency data acquired from the Nanfangao fish market between January 2015 and December 2022. An electronic length-frequency analysis indicated that the asymptotic length and growth coefficient derived for the combined sexes of the tiger shark population were 366.087 FL cm and 0.245 year−1, respectively. Mortality rates were calculated using a length-converted catch curve and empirical models; the results revealed that the annual rates of total (Z), natural (M), and fishing (F) mortalities were 0.346, 0.168, and 0.178 year−1, respectively. Notably, the observed exploitation rate (E) was slightly high at 0.514 year−1. Our findings may facilitate the development of effective strategies for preserving the tiger shark population in the study region by leveraging this initial information for further stock assessment analyses, thereby enabling a more comprehensive evaluation in the future.

Improving the efficacy of phenolic extract from Pimpinella affinis in edible oils through nanoencapsulation: Utilizing chitosan and Salvia macrosiphon gum as coating agents

AbstractIn the present study, a phenolic extract derived from the Pimpinella affinis plant underwent nanoencapsulation. The nanoencapsulation process employed chitosan, Salvia macrosiphon gum (SMG), and a chitosan–SMG complex (1:1) (CCS) as coating agents. The evaluation of nanoemulsions encompassed measurements of particle size, polydispersity index (PDI), ζ‐potential, encapsulation efficiency, and intensity distribution parameters. The overall results of these assessments indicated that the nanoemulsion coated with CCS exhibited the most favorable characteristics when compared to other treatments. Subsequently, this specific nanoencapsulated sample was utilized to enhance the oxidative stability of canola oil at concentrations of 100, 200, and 300 ppm (parts per million). Oxidative stability tests, assessed through the total oxidation value (TOTOX) index, highlighted the superior performance of the nanoencapsulated extract, particularly at a concentration of 300 ppm. This enhancement can be attributed to the increased release of phenolic compounds from the CCS coating into the canola oil. The findings illustrate that the nanoencapsulation process can significantly enhance the efficacy of P. affinis extract in improving the oxidative stability of canola oil.

Artificial intelligence-assisted measurements of coronary computed tomography angiography parameters such as stenosis, flow reserve and fat attenuation for predicting major adverse cardiac events in patients with coronary arterial disease.

Advancements in artificial intelligence (AI) offer promising tools for improving diagnostic accuracy and patient outcomes in cardiovascular medicine. This study explores the potential of AI-assisted measurements in enhancing the prediction of major adverse cardiac events (MACE) in patients with coronary artery disease (CAD). We conducted a retrospective cohort study involving patients diagnosed with CAD who underwent coronary computed tomography angiography (CCTA). Participants were classified into MACE and non-MACE groups based on their clinical outcomes. Clinical characteristics and AI-assisted measurements of CCTA parameters, including CT-derived fractional flow reserve (CT-FFR) and fat attenuation index (FAI), were collected. Both univariate and multivariable logistic regression analyses were performed to identify independent predictors of MACE, which were used to build predictive models. Statistical analyses revealed three independent predictors of MACE: severe stenosis, CT-FFR ≤ 0.8, and mean FAI (P < 0.05). Seven predictive models incorporating various combinations of these predictors were developed. The model combining all three predictors demonstrated superior performance, as evidenced by the receiver operating characteristic (ROC) curve, with an area under the curve (AUC) of 0.811 (95% CI 0.774 - 0.847), a sensitivity of 0.776, and a specificity of 0.726. Our findings suggest that AI-assisted CCTA analysis, particularly using FFR and FAI, could significantly improve the prediction of MACE in CAD patients, thereby potentially aiding clinical decision-making.

Cyclooxygenase-2 inhibitors alleviated depressive and anxious-like behaviors in mice exposed to lipopolysaccharide: Involvement of oxidative stress and neuroinflammation

Depression and anxiety disorders have their pathophysiologies linked to inflammation and oxidative stress. In this context, celecoxib (CLX) and etoricoxib (ETR) inhibit cyclooxygenase 2 (COX-2), an enzyme expressed by cells involved in the inflammatory process and found in the brain. Studies have been using CLX as a possible drug in the treatment of depression, although its mechanisms at the central nervous system level are not fully elucidated. In this study, the effects of CLX and ETR on behavioral, oxidative, and inflammatory changes induced by systemic exposure to Escherichia coli lipopolysaccharide (LPS) were evaluated in adult male swiss mice. For ten days, the animals received intraperitoneal injections of LPS at 0.5 mg/kg. From the sixth to the tenth day, one hour after LPS exposure, they were treated orally with CLX (15 mg/kg), ETR (10 mg/kg), or fluoxetine (FLU) (20 mg/kg). Twenty-four hours after the last oral administration, the animals underwent evaluation of locomotor activity (open field test), predictive tests for depressive-like behavior (forced swim and tail suspension tests), and anxiolytic-like effect (elevated plus maze and hole board tests). Subsequently, the hippocampus, prefrontal cortex and striatum were dissected for the measurement of oxidative and nitrosative parameters (malondialdehyde, nitrite, and glutathione) and quantification of pro-inflammatory cytokines (IL-1β and IL-6). LPS induced depressive and anxious-like behavior, and treatment with CLX or ETR was able to reverse most of the behavioral changes. It was evidenced that nitrosative stress and the degree of lipid peroxidation induced by LPS were reduced in different brain areas after treatment with the drugs, as well as the endogenous defense system against free radicals was strengthened. CLX and ETR also significantly reduced LPS-induced cytokine levels. These data are expected to expand information on the role of inflammation in depression and anxiety and provide insights into possible mechanisms of COX-2 inhibitors in psychiatric disorders with a neurobiological basis in inflammation and oxidative stress.

Measurement of body size parameters and body weight prediction in beef cattle based on image analysis

The body size parameter of cattle is an important index reflecting the growth and development and health condition of cattle. The traditional manual contact measurement is not only a large workload and difficult to measure, but also prone to problems such as affecting the normal life habits of cattle. In this paper, we address this problem by proposing a contactless body size measurement method for cattle based on machine vision. Firstly, the cattle is confined to a fixed space using a position-limiting device, and images of the body of the cattle are taken from three directions: top, left, and right, using multiple cameras. Secondly, the image is segmented using a fuzzy clustering algorithm based on neighborhood adaptive local spatial information improvement, and the image is processed to extract the contour images of the top view and side view. The key points of body measurements were extracted using interval division and curvature calculation for the side view images, and the key point information was extracted using skeleton extraction and pruning for the top view images, which realized the measurements of body height(BH), rump height(RH), body slanting length(BSL), and abdominal circumference(AC) parameters of the cattle. The correlation between body size and weight data obtained by contactless methods was investigated and the modeled using one-factor linear regression, one-factor nonlinear regression, multivariate stepwise regression, RBF network fitting, BP neural network fitting, support vector machine, and particle swarm optimization-based support vector machine methods, respectively. Information on body size parameters was collected from 137 cattles, and the results showed that the maximum errors between the measured and actual values of BH, RH, BSL and AC were 5.0%, 4.4%, 3.6%, and 5.5%, respectively. Correlation of BH, RH, BSL and AC with weight obtained by non-contact methods was &gt;  0.75. The BH parameter can be selected in the single-factor growth monitoring. The multi-body scale can reflect the growth status of cattle more comprehensively, in which RH, BSL and AC are important detection parameter; the multi-factor nonlinear model can reflect the growth characteristics of cattle more comprehensively. The contactless measurement method proposed in the paper can effectively improve the work efficiency and reduce the stress reaction of cattle, which is a long-term and effective monitoring method, and is of great significance in promoting accurate and welfare cattle rearing.

Two-dimensional investigation of characteristic parameters and their gradients for the self-generated electric and magnetic fields of laser-induced zirconium plasma

Two-dimensional diagnosis of laser-induced zirconium (Zr) plasma has been experimentally performed using the time-of-flight method by employing Faraday cups in addition to electric and magnetic probes. The characteristic parameters of laser-induced Zr plasma have been evaluated as a function of different laser irradiances ranging from 4.5 to 11.7 GW cm−2 at different axial positions of 1–4 cm with a fixed radial distance of 2 cm. A well-supporting correlation between the plume parameters and the laser-plasma-produced spontaneous electric and magnetic (E and B) fields was established. The measurements of the characteristic parameters and spontaneously induced fields were observed to have an increasing trend with the increasing laser irradiance. However, when increasing the spatial distance in both the axial and radial directions, the plasma parameters (electron/ion number density, temperature and kinetic energy) did not show either continuously increasing or decreasing trends due to various kinetic and dynamic processes during the spatial evolution of the plume. However, the E and B fields were observed to be always diffusing away from the target. The radial component of electron number densities remained higher than the axial number density component, whereas the axial ion number density at all laser irradiances and axial distances remained higher than the radial ion number density. The higher axial self-generated electric field (SGEF) values than radial SGEF values are correlated with the effective charge-separation mechanism of electrons and ions. The generation of a self-generated magnetic field is observed dominantly in the radial direction at increasing laser irradiance as compared to the axial one due to the deflection of fast-moving electrons and the persistence of two-electron temperature on the radial axis.

Aeolian desert dust as a primary estuarine sediment source: Fine sediment transport and dynamics in the northern Arabian/Persian gulf

Fluvial discharge and freshwater runoff are recognized as the main sediment sources in many estuaries worldwide. Precipitation is, however, limited in arid and semi-arid areas, allowing for high aeolian dust activity, and inducing limited river runoff. As such, aeolian dust could contribute to the estuarine sediment sources in such areas. Uniquely, the Northern Arabian/Persian Gulf (NG) is continuously loaded with vast amounts of aeolian desert dust annually. This study focuses on the role of desert dust in the distribution of suspended sediment in the Northern Arabian/Persian Gulf (NG), employing in-situ measurements of physical parameters (collected in 2015–2016), satellite imagery, and a flexible mesh hydrodynamic model (Delft3D-FM) coupled with a sediment transport model (Delft-WAQ). The model showed reasonable performance in representing the complex system of NG. The study revealed that more than 90% of the sediment along the NG is aeolian dust, with the remaining being fluvial. Owing to salinity-induced density gradients, estuarine circulation keeps the fluvial sediment confined to the estuarine zone of the Shatt Al-Arab even during the events of increased freshwater discharges. In the NG most of the suspended sediments have an aeolian origin. They were found in shallow zones within the 5 m isobath, including the estuary of Shatt Al-Arab where they are mixed with the fluvial sediment fractions. Tides were found to play a key role in sediment mixing and horizontal dispersion in the shallow area of the NG most of the time, while the Shamal increased the transport of fine sediment to offshore areas (seaward). Based on current knowledge, this study represents a rare instance where estuarine sediment dynamics are primarily governed by aeolian deposits from a nearby desert.

Combined ns pulsed-RF excitation and impedance matching considerations for the production of moderate E/n atmospheric pressure discharges for gas conversion

Abstract Sub-breakdown radiofrequency (RF) discharges enabled by a nanosecond (ns) pulse ignition source are studied at atmospheric pressure in a range of gas mixtures from completely inert (in Ar) to completely reactive (in CO2). An electrical characterisation of the continuous wave (CW) RF discharge (13.56MHz) is performed to determine plasma impedance and plasma power dissipation. Two different measurement methods to electrically characterize the system are described and compared. One method uses in-situ measurements of discharge parameters (voltage, current and the phase angle), and the other method performs ex-situ measurements of the load circuit using a vector network analyser. It was found that RF plasma power deposition depended on the applied RF power as well as the gas mixture composition. Using the in-situ voltage, current and phase angle measurements, plasma power deposition was calculated to be as much as 85% and 76% of the applied RF power for the pure Ar and pure CO2 cases, respectively. A preliminary qualitative assessment of the plasma composition was performed by optical emission spectroscopy, and CO2 conversion by mass spectrometry. CO2 to CO conversions of 11.2% and 5.5% in a 20:80 (CO2:Ar) mixture and in 100% CO2, respectively, were observed. This study demonstrates a RF plasma source for gas conversion applications at atmospheric pressure in a completely reactive gas.

Cheese positively affects serum osteocalcin levels, bone turnover markers and bone mineral density in cross-country skiers: a dose-response study

Background: Vitamin K2-rich Jarlsberg Cheese is demonstrated to increase Osteocalcin and bone turnover markers in fertile women. The impact on endurance-trained young cross-country skiers (XCS) with elevated bone turnover remains unclear. Purpose of study was to estimate the optimal daily efficacy dose of Jarlsberg cheese to increase serum Osteocalcin level in young female and male XCS and estimate the Jarlsberg effect on bone turnover markers (BTMs) and bone mineral density (BMD). Methods: In a parallel group study consisting of three design levels, 10 female and 10 male XCS were included, using Response Surface Pathway design. Blood samples were taken at each level for measurements of Osteocalcin, vitamin K2, BTMs and other biochemical parameters. Resting metabolic rate (RMR), BMD, VO2max and muscle strength were measured at start and at the end of the study. Results: The Osteocalcin development with increasing dose of Jarlsberg cheese was almost parallel in both sexes. These variables were reduced significantly from baseline during the first two design levels but increased above baseline by the end of the study. BTMs decreased significantly during the first and second level but increased during the third level. Total and L1-L4 BMD, s-phosphate, s-urea, RMR, muscle strength and Peak VO2 increased significantly while s-calcium and s-magnesium decreased. Conclusions: Estimated OED of Jarlsberg cheese was 73 and 84 g/day for females and male athletes, respectively. The development in OC, BTMs and BMD suggest an antiresorptive and perhaps anabolic effect of Jarlsberg Cheese on bone tissue. VO2 max, RMR and muscular strength development indicated an anabolic situation.

Probucol mitigates high-fat diet-induced cognitive and social impairments by regulating brain redox and insulin resistance

Probucol has been utilized as a cholesterol-lowering drug with antioxidative properties. However, the impact and fundamental mechanisms of probucol in obesity-related cognitive decline are unclear. In this study, male C57BL/6J mice were allocated to a normal chow diet (NCD) group or a high-fat diet (HFD) group, followed by administration of probucol to half of the mice on the HFD regimen. Subsequently, the mice were subjected to a series of behavioral assessments, alongside the measurement of metabolic and redox parameters. Notably, probucol treatment effectively alleviates cognitive and social impairments induced by HFD in mice, while exhibiting no discernible influence on mood-related behaviors. Notably, the beneficial effects of probucol arise independently of rectifying obesity or restoring systemic glucose and lipid homeostasis, as evidenced by the lack of changes in body weight, serum cholesterol levels, blood glucose, hyperinsulinemia, systemic insulin resistance, and oxidative stress. Instead, probucol could regulate the levels of nitric oxide and superoxide-generating proteins, and it could specifically alleviate HFD-induced hippocampal insulin resistance. These findings shed light on the potential role of probucol in modulating obesity-related cognitive decline and urge reevaluation of the underlying mechanisms by which probucol exerts its beneficial effects.

Two-dimensional speckle-tracking echocardiography in left ventricular systolic function in patients with systemic lupus erythematosus.

To investigate whether two-dimensional speckle-tracking echocardiography (2DSTE) can be considered a criterion for early left ventricular (LV) systolic impairment in patients with systemic lupus erythematosus (SLE) and to further explore the association with each other. We included 38 patients with SLE and assessed the degree of disease activity according to the Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) 2000 scoring criteria, together with 38 healthy controls who were matched by sex and age. Routine LV systolic function evaluation parameters were obtained by echocardiography as well as 2DSTE measurement of LV strain parameters to obtain global longitudinal strain (GLS) values, respectively. (I) On routine LV function parameters such as ejection fractions (EF) and left ventricular end-diastolic internal diameter (LVIDd), the SLE group and the control group did not reflect differences. In contrast, on the LV strain parameter obtained from 2DSTE measurements, the GLS values in all cardiac planes were lower in the SLE group than in the control group and showed statistically significant differences. (II) Correlation analysis showed that there was a correlation between SLEDAI and GLS, especially a meaningful correlation with GLS Avg and GLS A4C, with correlation coefficients of 0.35 and 0.47, respectively. The use of 2DSTE can detect early impaired LV systolic function in SLE patients, and GLS is progressively gaining attention as an indicator of subclinical myocardial injury and LV function in SLE patients. The correlation that exists between GLS and SLEDAI might contribute to a better assessment of cardiac involvement in SLE patients. Key Points • Cardiac involvement has become one of the major factors in the poor prognosis of SLE patients, which directly affects the mortality of SLE patients. Traditional echocardiography is difficult to detect early left ventricular function impairment, thus affecting clinicians' judgment and diagnosis. • 2DSTE can recognize subclinical myocardial injury in SLE patients at an early stage, and its derived strain parameters may be used as an indicator to evaluate myocardial involvement and reflect disease activity in SLE patients.

A review of imaging methods for detection of photoluminescence in field-installed photovoltaic modules

Abstract It is predicted that the photovoltaic energy conversion will be the largest installed power capacity by 2027. The least costly option for new electricity generation in many of the world’s countries will be the utility-scale solar photovoltaic electricity generation. Accurate monitoring of solar plants for localizing and detecting faults is expected to be one of the critical tasks facing the energy industry. Imaging of photovoltaic modules for the purpose of fault detection can be more efficient and accurate compared to measurements of electrical parameters. Different spectral regions provide different types of information about a faulty module. Detection of photoluminescence, that is, radiation emitted upon band-to-band recombination after charge carrier excitation with an illumination source, has shown a great potential in the laboratory setting. In the recent years, the first approaches in the outdoor setting have been conducted on silicon modules with the Sun and a LED module as excitation source. The present study sums up the different methods for outdoor photoluminescence imaging and emphasizes their differences regarding filtering of the reflected light from the photoluminescence signal. The different types of photoluminescence images obtained from each method and the image processing algorithms are described. Finally, the interpretation of the different types of photoluminescence images is addressed.