Metrological Data Research Articles

Applications of Iterated Linearization for non-linear errors-in-variable regression to metrological data

Applications of Iterated Linearization for non-linear errors-in-variable regression to metrological data

APPLICATION OF GLOBAL CIRCULATION MODELS (GCMS) TO PREDICT RAINFALL AND TEMPERATURE VARIABILITIES IN KANO STATE, NIGERIA

The study explored present and future climate scenarios of Kano using Global Circulation Models (GCMs). The metrological data from 12 locations were used for calibration of Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). The historical record of 10 years was used for both temperatures and rainfall data which represent a period of 2010 to 2019. The future climate projections were made for 2041 to 2060 centered in the 2050s under RCP2.6 and RCP8.5 emission scenarios. The spatial analysis was conducted using the Inverse Distance Weighted (IDW) tool in an ArcGIS environment. The result shows that the temperature of Kano has increased relative to baseline condition by 1.025oC (2.45%) and 4.6 oC (10.98%) under RCP2.6 and RCP8.5 scenarios respectively. However, the annual rainfall was found to decrease from the historical amount by -37.9 mm (5%), and -68.2 mm (9%) under RCP2.6 and RCP8.5 emission scenarios. Moreover, the spatial analysis indicated that there was climate variability across the width and breadth of the state. The study concludes that the climate change effect will cause the temperature of Kano to rise considerably in the future with the consequences of decreased rainfall amount. The findings recommend that policymakers should deploy adaptation strategies to reduce the adverse effects that might be experienced.

Multi-criteria Decision Making Methods and sustainable Appliations in the Digital Age

Abstract: Designing a sustainable electrification system for remote places in developing countries is very complex while considering various factors such as technical, economic, environmental and social (TEES). Development of an efficient, rational, coherent and a translucent framework is required to address issues related to the requirement of energy needs based on the end user. The main goal of this research is to design a methodological framework based on decision analysis and optimized model with locally available energy sources. The new optimal renewable energy options are required to be used to minimize the fossil fuel dependency of electrical generation. Initially, suitable energy alternatives are selected by considering different criterion with multiple scenarios for decision analysis. The further optimal combination of energy sources evaluated for different possibilities. The new methodology of hybrid renewable optimization is applied to a rural place, Himachal Pradesh to illustrate its effectiveness based on metrological data

Applying machine learning algorithms to estimate PM 2.5 using satellite data and metrological data

Air pollution, particularly fine Particulate Matter (PM 2.5), poses significant health risks and environmental challenges worldwide. Therefore, it is essential to monitor air pollution to act on it. In this study, PM 2.5 was estimated using meteorological data and Sentinel-5P air pollution data using machine learning algorithms. The Sentinel-5P data are and the meteorological data utilized are air temperature, Relative Humidity (RH), and Wind Speed (WS). The three Air Quality Monitoring (AQM) stations in Kathmandu, Nepal, were chosen as a study area for this research. The effectiveness of several machine learning methods, such as K-Nearest Neighbors (KNN), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost), and Random Forest (RF), were evaluated. Both RF and XGBoost consistently performed better than SVM and KNN in terms of PM 2.5 estimation accuracy. RF got the highest R2 value of 0.80 and SVM with the lowest R2 value of 0.62 in the Sentinel-5P dataset only. The addition of meteorological data further improved the model's performance. After including metrological data in Sentinel-5P data the RF demonstrated the maximum R2 score of 0.816 and XGBoost with R2 score of 0.814. Hence, this study demonstrated machine learning algorithms can be used to estimate PM 2.5 by utilizing satellite and meteorological data, providing important information for air quality monitoring and management.

Crop Monitoring System Using IOT

Currently, the low agricultural output is causing farmers to suffer. Low crop growth is primarily caused by knowledge of soil fertility and crop selection, even though proper crop selection is the primary booster key for enhancing agricultural yield by doing soil analysis and taking metrological elements into consideration. Farmers with limited experience in conventional farming are finding it difficult to make wise crop selection choices in the current environment. The low soil fertility is caused by the same crop being chosen for each seasonal cycle. The goal of this research is to use IoT devices to create an accurate and efficient system. Compared to the outdated manual laboratory testing procedures, which are subject to human errors, such a system is predictable. A crop's proper selection is mostly important in the agricultural field. As a contribution, we offer Smart Crop Selection (SCS), an IOT-based approach that is based on soil and metrological factor data. These variables include soil moisture content, temperature, and precipitation. Because these issues are not given as much care as they should, existing IoT-based systems are less efficient than our proposed model. Real-time sensory data is transmitted to Firebase cloud for analysis in the suggested model

In-vitro Efficacy of Different Chemical Fungicides and Plant Extracts Against Alternaria alternata Causing Alternaria Leaf Spot Disease in Brinjal

Five different chemical fungicides and three plant extracts were evaluated against Alternaria alternate causing Alternaria leaf spot disease in brinjal. The experiment was carried out in the laboratory of plant pathology at Chaudhary Charan Singh University Meerut, during kharif season 2021-22. During survey the maximum disease incidence was recorded (37.8%) Rajpura in Meerut and minimum (10.9%) incidence was recorded in Lakhaoti, survey and metrological data presented in the (Tables 2 & 3). The incidence of the disease varied from 10.9 to 37.8 percent due to difference in environmental condition. The concentration of all fungicides and Plant-extracts was kept at 500, 1000 and 2000 ppm. Mycelial growth was measured to continue zero to ten days. Mainly four chemical fungicides (Thaiophinate Methyl 70%WP, Propineb 70% WP, Carbendazim 50%WP and Chlorothalonil 75% WP) and Neem extract significantly inhibited the mycelial growth of the pathogen (Alternaria alternata). Among all those, four fungicides and Neem extract giving the better results. Maximum 100% mycelial growth inhibition was recorded in Thaiophinate Methyl 70%WP, 94.45% mycelial growth inhibition was recorded in Propineb 70% WP, 86.09% mycelial growth inhibition was recorded in Carbendazim 50%WP and 84.80 growth inhibition was recorded in Chlorothalonil 75% WP and in plant extract 18.53% inhibition was observed in Neem and 14.33% inhibition was observed in garlic. However, Azoxystrobin 23% SC and Onion also had some effect, which is mentioned in the result of the paper.

Immersive visualisation for a large metrological data set

A purpose of scientific visualisation is to facilitate the extraction of information within underlying data. Visualisation of a large data set requires an appropriate visualisation method where, instead of focusing on expected features, all acquired data are treated on an equal footing. Here, we identify critical differences between raw and processed data acquired from displacement measurements at the nanoscale. We illustrate the strength of immersive data visualisation in rapidly and intuitively identifying non-obvious data trends, supported by correlation with the associated measurement uncertainties. These insights can be directly leveraged for optimisation of instrumentation and measurement protocols, application of appropriate measurement corrections, and iterative experimental design changes.

A Logistic Regression Model to Predict Malaria Severity in Children

One of the main causes of death around the globe is malaria. Researchers have sought to develop predicting models for malaria outbreaks based on metrological data, climate data and the breeding cycle of plasmodium, the causative agent of malaria. This study predicts the severity of malaria based on environmental and biological factors. A logistic regression model was developed in this study to predict the severity of malaria based on such factors as sickle cell disease, stagnant water, garbage dumps, wet lawns, and the use of treated mosquito nets with an 83.3% accuracy rate. The study was carried out in the Bosomtwe District of Ghana with 417 respondents. It was deduced that although children in the district are highly prone to malaria infection, the severity is very low. The study recommends that not just having a good sample size alone is important during machine learning model development but also having a good sample representation of the various class labels is equally important.

A power dispatch allocation strategy to produce green hydrogen in a grid-integrated offshore hybrid energy system

A dedicated grid-tied offshore hybrid energy system for hydrogen production is a promising solution to unlock the full benefit of offshore wind and solar energy and realize decarbonization and sustainable energy security targets in electricity and other sectors. Current knowledge of these offshore hybrid systems is limited, particularly in the integration, component control, and allocation aspects. Therefore, a grid-integrated analytical model with a power dispatch allocation strategy between the grid and electrolyzer for the co-production of hydrogen from the offshore hybrid energy system is developed in this paper. While producing hydrogen, the proposed offshore hybrid energy system supplies a percentage of its capacity to the onshore grid facility, and the amount of the electricity is quantified based on the electricity market price and available total offshore generation. The detailed controls of each component are discussed. A case study considers a hypothetical hybrid offshore energy system of 10 MW situated in a potential offshore off the NSW of Australia based on realistic metrological data. A grid-scale proton-exchange membrane electrolyzer stack is used and a model predictive power controller is implemented on the distributed hydrogen generation scheme. The model is helpful for the assessment or optimization of both the economics and feasibility of the dedicated offshore hybrid energy farm for hydrogen production systems.

Modeling climate change adaptation for sustainable coastal zones using GIS and AHP

The world is currently confronting one of its biggest environmental challenges: combating climate change. Coastal zones are one of the areas thought to be most sensitive to current and future climate change threats. The paper integrates Remote Sensing (RS), Geographic Information System (GIS) techniques, and Multi-Criteria Decision Analysis (MCDA) to detect vulnerable areas from climate change impacts in coastal zones in order to recommend adaptation systems in new coastal zones that can withstand various climatic changes. The proposed decision-making framework was developed in three phases: 1) climate data collection and processing; 2) Coastal Climate Impact Assessment (CCIA) model development; and 3) implementation and adaptation system selection. The climate data collection and processing phase involves determining the most significant climate change parameters and their indicators that affect coastal zone stability, extracting climatic data indicators from different climate database sources, and prioritizing the selected indicators. The indicators’ weights were estimated using the Analytical Hierarchy Process (AHP) through a questionnaire survey shared with experts in climate change impacts. A CCIA model development phase involves the formulation of the proposed model using GIS technique to discover the vulnerable areas according to the most dominant impact. The implementation and adaptation system selection phase involves the application of the framework to Al-Alamein New City in Egypt. A sensitivity analysis was conducted to measure the behavior of several climate change parameters to identify the most critical parameter for climate change in Al-Alamein New City. The results showed that the geology of the region is the most crucial component influenced by climate change. It is capable of producing a very sensitive area in the coastal zone while also taking other factors into account. When creating new urban neighborhoods, the erosion of the shoreline is the least important factor to consider. This is because coastal deterioration is caused by both the influence of metrological data on the region and the impact of human activity. Shoreline deterioration will be reduced if climate conditions are maintained while limiting the impact of human activities. To adapt to the long-term effects of climate change on coastal zones, a combination of soft and hard protection systems should be considered.

Single- and combined-source typical metrological year solar energy data modelling

Single- and combined-source typical metrological year solar energy data modelling

A hybrid meteorological data simulation framework based on time-series generative adversarial network for global daily solar radiation estimation

Solar energy has received considerable attention worldwide because of its clean, safe and easily accessible characteristics. Accurate global solar radiation estimation is crucial for local energy potential assessments and deployment of photovoltaic power generation systems. However, the distribution of metrological data varies considerably between regions, and its quality considerably affects the accuracy of the estimation model. Motivated by these concerns, a metrological data estimation framework based on the time-series generative adversarial network (TimeGAN) and deep belief network (DBN) was proposed. First, the metrological data collected from 30 urban meteorological stations in China were analyzed through time-series aggregation. Next, a novel metrological data simulation model based on TimeGAN was proposed to enrich the quality of training samples. Furthermore, a generalized estimation model of daily solar radiation based on DBN was constructed. In the case study in China, TimeGAN outperforms other conventional generative adversarial networks in terms of data generation performance. The simulation results reveal that the TimeGAN-based simulation model can capture the inherent temporal characteristics of the original data, generate high-quality simulation data to reasonably improve the distribution of training data in various cities. In addition, the estimation results indicate that the proposed hybrid framework improve the accuracy of daily solar radiation estimation.

A proposed neural network model for obtaining precipitable water vapor

Abstract The atmospheric Precipitable water vapor (PWV) is a variable key for weather forecasting and climate change. It is a considerable component of the atmosphere, influencing numerous atmospheric processes, and having physical characteristics. It can be measured directly using radiosonde stations (RS), which are not always accessible and difficult to measure with acceptable spatial and time precision. This study uses the artificial neural network (ANN) application to propose a simple model based on RS data to estimate PWV from surface metrological data. Ten RS stations were used to develop the new model for eight and a half years. In addition, two and a half years of data were used to validate the developed model. The study period is based on the data accessible between 2010 and 2020. The new model needs to collect (vapor pressure, temperature, latitude, longitude, height, day of year, and relative humidity) as input parameters in ANN to predict the PWV. The ANN model validations were based on the root mean square (RMS), correlation coefficient (CC), and T-test. According to the results, the proposed ANN can accurately predict the PWV over Egypt. The results of the new ANN model and eight other empirical models (Saastamoinen, Askne and Nordius, Okulov et al., Maghrabi et al., Phokate., Falaiye et al. (A&B), Qian et al. and ERA 5) are compared in addition, the new PWV model can achieve the best performance with RMS of 0.21 mm. The new model can serve as a will be of practical utility with a high degree of precision in PWV estimation.

PERFORMANCE EVALUATION OF THE IMPACTS OF METROLOGICAL PARAMETERS ON CRYSTALLINE AND AMORPHOUS MODULES AT MINNA, NIGERIA

Photovoltaic (PV) module performance is rated under standard test conditions (STC) i.e. irradiance of 1000 W/m², solar spectrum of Air Mass 1.5 and module temperature at 25°C. Manufacturers of photovoltaic modules typically provide the ratings at only one operating condition i.e. STC. However, PV module operates over a large range of environmental conditions at the field. So the manufacturer’s information is not sufficient to determine the actual performance of the module at field. Optimization of solar energy is affected by so many factors ranging from conversion efficiency of PV module to local metrological conditions. The research work therefore, evaluates the performance of three PV technologies using performance ratio. Metrological parameters such as solar radiation intensity, wind speed, relative humidity, and air temperature were measured simultaneously with the output electrical parameters from the three modules exposed to field test using metrological sensors and a CR1000 software-based data logging system with computer interface attached to the modules. Four years consecutives metrological and modules output data’s were collected from the modules and analyzed. The findings indicates that metrological parameters fluctuate non-linear with the modules output, under this conditions the trends as measured by the output power revealed that polycrystalline module has a better performance than amorphous module followed by mono-crystalline module in this experiment. The paper recommends the need to mitigate substandard modules entering our market through appropriate monitoring agencies and the setting of solar module laboratory for locally production of solar modules that would captures our local metrological parameters towards greater efficiency.

Water Balance Components Estimation using WetSpass Model: A Case study of Mekelle Area, Tigray, Ethiopia

This research was aimed at estimating the spatially distributed Water balance components of the catchments in Mekelle area by using WetSpass hydrological model and verifying the model outputs with ground truth. Long term mean metrological data and physical characteristics of the catchments were used as an input to the model. The input data were manipulated using Arc- GIS tools. Results of this model depicted that about 73.13% and 19.96% of the precipitation in the study area was lost through evapotranspiration and surface runoff respectively. However, 7% of it replenished the groundwater. The annual runoff and groundwater recharge estimated in WetSpass model accumulated using Arc-GIS were verified using the annual runoff gauged records and base flow measurements. Accordingly the accumulated runoff and base flow derived from the WetSpass model using Arc-GIS software results were quite close to the observed runoff and base flow values measured in the gauging points. Therefore, WetSpass model is appropriate model in estimating water balance components in the study area.

Short-term load forecasting for microgrid energy management system using hybrid SPM-LSTM

Load forecasting in power microgrids and load management systems is still a challenge and needs an accurate method. Although in recent years, short-term load forecasting is done by statistical or learning algorithms. There are still two unsolvable challenges in the conventional data-driven-based prediction methods. The first challenge is that extracting the correlation between metrological and load data still cannot be taken full advantage of, and the other is that extracting patterns independently of the fixed pattern length is not supported. To address these challenges, a fixed SPM-LSTM approach is proposed. SPM for discrete data and LSTM for continuous data. The proposed model uses past consumption, temperature, humidity, wind speed, and weather data. A sequential pattern mining algorithm is used to extract sequential patterns that are independent of the fixed pattern length of correlated data between load and metrological data and can predict only the range of future load. Then, an LSTM network is used for exact load forecasting. It was tested using real data from the consumption and generation data retrieved from the ENTSOE project and its performance was compared with other forecasting methods. Results have shown that the proposed approach with the correlation squared (R2) of 0.951 in the best situation outperformed other methods like LSTM, LSTM-ANN, and CNN-GA. Also, the proposed method reduced training time by one-fifth against others.

The Development of Krakow Type Staters / Ewolucja staterów typu krakowskiego

The paper discusses the coins derived from shell staters and referred to collectively as the Krakow type. A die study with an emphasis on the analysis of iconography, conducted on a corpus consisting of twenty-nine specimens, allows three typologically distinct stages to be distinguished in the development of the series. The identification of prototypes employed at each stage enables the origins of all discussed types to be reconstructed and provides a new terminus post quem for the series. A brief summary of the available metrological and metallurgical data is also given, as well as known findspots, which, in the case of several previously unpublished specimens, shed new light on the distribution of the last issues.

The Efficiency of SCS-CN, HEC-1, HEC-HMS, TR55, RATIONAL, and SNYDER UNIT Hydrograph Models for Determining Peak Flood Discharge in the Upper Part of Lesser Zab Basin, Kurdistan Region, Iraq

The Unit Hydrograph (UH) and Soil Conservation Service Curve Number (SCS-CN) are prevalent methods for estimating peak flow and peak time within the hydrological river basins. Different types of data, such as gauging data, morphometric analysis, and Land Use-Land Cover (LULC), are used to derive UH for the Kanarwe River Basin (KRB), which is an off-the-Lesser Zab River Basin (LZRB). Different hydrograph models, including HEC-1 (Hydrological Engineering Centre), TR55 (Technical release 55), HEC-HMS (Hydrological Engineering Centre-Hydrological Modeling System), Rational method, and Snyder unit hydrograph, have been applied and correlated with field data. Metrological data, geological setting, and land cover were integrated into the Geographic Information Systems (GIS) and Watershed Modelling System (WMS 11.1). The peak time (Tp) and peak flow (Qp) were estimated based on the five applied models. The results for models are (Qp = 739.93 m3/sec, Tp = 20 hr), (Qp 181.4 m3/sec, Tp = 14 hr), (Qp = 800 m3/sec, Tp = 12 hr, (Qp = 341.13 m3/sec, Tp = 11.65 hr), (Qp = 443 m3/sec, Tp = 19.9 hr), (Qp = 243 m3/sec) for HEC-1, TR55, HEC-HMS, Rational method, Synder unit hydrograph, and observed data respectively. The observed model (field data) peak time and peak flow value best agreed with the peak time and peak flow value of TR55, Snyder, and Rational models. Our finding confirmed that the geomorphoclimatic unit hydrograph, such as (Snyder) is highly efficient and more realistic for estimating peak time and peak flow for large basins than other models because it relates to basin characteristics.

Active Probe Atomic Force Microscopy with Quattro-Parallel Cantilever Arrays for High-Throughput Large-Scale Sample Inspection.

An Atomic Force Microscope(AFM) isa powerful and versatile tool for nanoscale surface studies to capture 3D topography images of samples. However, due to their limited imaging throughput, AFMs have not been widely adopted for large-scale inspection purposes. Researchers have developed high-speed AFM systems to record dynamic process videos in chemical and biological reactions at tens of frames per second, at the cost of a small imaging area of up to several square micrometers. In contrast, inspecting large-scale nanofabricated structures, such as semiconductor wafers, requires nanoscale spatial resolution imaging of a static sample over hundreds of square centimeters with high productivity. Conventional AFMs use a single passive cantilever probe with an optical beam deflection system, which can only collect one pixel at a time during AFM imaging, resulting in low imaging throughput. This work utilizes an array of active cantilevers with embedded piezoresistive sensors and thermomechanical actuators, which allows simultaneous multi-cantilever operation in parallel operation for increased imaging throughput. When combined with large-range nano-positioners and proper control algorithms, each cantilever can be individually controlled to capture multiple AFM images. With data-driven post-processing algorithms, the images can be stitched together, and defect detection can be performed by comparing them to the desired geometry. This paper introduces principles of the custom AFM using the active cantilever arrays, followed by a discussion on practical experiment considerations for inspection applications. Selected example images of silicon calibration grating, highly-oriented pyrolytic graphite, and extreme ultraviolet lithography masks are captured using an array of four active cantilevers ("Quattro") with a 125 µm tip separation distance. With more engineering integration, this high-throughput, large-scale imaging tool can provide 3D metrological data for extreme ultraviolet (EUV) masks, chemical mechanical planarization (CMP) inspection, failure analysis, displays, thin-film step measurements, roughness measurement dies, and laser-engraved dry gas seal grooves.

The (mis)identification of high-latitude dust events using remote sensing methods in the Yukon, Canada: a sub-daily variability analysis

Abstract. The observation and quantification of mineral dust fluxes from high-latitude sources remains difficult due to a known paucity of year-round in situ observations and known limitations of satellite remote sensing data (e.g. cloud cover and dust detection). Here we explore the chronology of dust emissions at a known and instrumented high-latitude dust source: Lhù'ààn Mân (Kluane Lake) in Yukon, Canada. At this location we use oblique time-lapse (RC) cameras as a baseline for analysis of aerosol retrievals from in situ metrological data, AERONET, and co-incident MODIS MAIAC to (i) investigate the daily to annual chronology of dust emissions recorded by these instrumental and remote sensing methods (at timescales ranging from minutes to years) and (ii) use data intercomparisons to comment on the principal factors that control the detection of dust in each case. Lhù'ààn Mân is a prolific mineral dust source; on 24 May 2018 the RC captured dust in motion throughout the entire day, with the longest dust-free period lasting only 30 min. When compared with time series of RC data, optimized AERONET data only manage an overall 26 % detection rate for events (sub-day) but 100 % detection rate for dust event days (DEDs) when dust was within the field of view. In this instance, RC and remote sensing data were able to suggest that the low event detection rate was attributed to fundamental variations in dust advection trajectory, dust plume height, and inherent restrictions in sun angle at high latitudes. Working with a time series of optimized aerosol optical depth (AOD) data (covering 2018/2019), we were able to investigate the gross impacts of data quality (DQ) choice on DED detection at the month or year scale. Relative to ground observations, AERONET's DQ2.0 cloud-screening algorithm may remove as much as 97 % of known dust events (3 % detection). Finally, when undertaking an AOD comparison for DED and non-DED retrievals, we find that cloud screening of MODIS/AERONET lead to a combined low sample of co-incident dust events and weak correlations between retrievals. Our results quantify and explain the extent of under-representation of dust in both ground and space remote sensing methods; this is a factor that impacts on the effective calibration and validation of global climate and dust models.