Statistical Measurement Research Articles

Анализ влияния пандемии COVID-19 на распространение цифровых технологий в корпоративном секторе Российской Федерации

The purpose of this article is to analyze the impact of the COVID-19 pandemic on the spread of digital technologies in the corporate sector of the Russian Federation. The authors suggested and tested the method of statistical measurement of the spread of digital technologies based on the indicator of the corresponding costs of firms. The costs of firms for the introduction and use of digital technologies are analyzed in the period from 2015 to 2020 in two sections: by types of costs, as well as by types of economic activity, which account for more than 80% of the total value of the indicator for the Russian Federation. In order to make a comprehensive analysis of the impact of the COVID-19 pandemic on the digitalization of the corporate sector in the Russian Federation, in addition to official statistical data, the authors use collections of analytical materials, including a brief overview of the study of the Central Bank of the Russian Federation on changes in business processes after the COVID-19 pandemic. According to the results of the analysis, it was revealed that the COVID-19 pandemic has an ambiguous impact on the digitalization process. On the one hand, the COVID-19 pandemic has certainly become an accelerator of digital development in certain areas, for example, the introduction of cloud solutions that ensure the volume to remote work and distance learning significantly increased. On the other hand, the “coronacrisis” led to a reduction in expenses, including on the IT infrastructure of enterprises whose revenue decreased during this period. The results of the analysis can be used in future studies of modeling and forecasting trends of digital transformation of the economy in the corporate sector in the Russian Federation.

Ultrasound Entropy Imaging for Detection and Monitoring of Thermal Lesion During Microwave Ablation of Liver.

Ultrasonic B-mode imaging offers non-invasive and real-time monitoring of thermal ablation treatment in clinical use, however it faces challenges of moderate lesion-normal contrast and detection accuracy. Quantitative ultrasound imaging techniques have been proposed as promising tools to evaluate the microstructure of ablated tissue. In this study, we introduced Shannon entropy, a non-model based statistical measurement of disorder, to quantitatively detect and monitor microwave-induced ablation in porcine livers. Performance of typical Shannon entropy (TSE), weighted Shannon entropy (WSE), and horizontally normalized Shannon entropy (hNSE) were explored and compared with conventional B-mode imaging. TSE estimated from non-normalized probability distribution histograms was found to have insufficient discernibility of different disorder of data. WSE that improves from TSE by adding signal amplitudes as weights obtained area under receiver operating characteristic (AUROC) curve of 0.895, whereas it underestimated the periphery of lesion region. hNSE provided superior ablated area prediction with the correlation coefficient of 0.90 against ground truth, AUROC of 0.868, and remarkable lesion-normal contrast with contrast-to-noise ratio of 5.86 which was significantly higher than other imaging methods. Data distributions shown in horizontally normalized probability distribution histograms indicated that the disorder of backscattered envelope signal from ablated region increased as treatment went on. These findings suggest that hNSE imaging could be a promising technique to assist ultrasound guided percutaneous thermal ablation.

Classification of lungs infected COVID-19 images based on inception-ResNet

ObjectiveNowadays, COVID-19 is spreading rapidly worldwide, and seriously threatening lives . From the perspective of security and economy, the effective control of COVID-19 has a profound impact on the entire society. An effective strategy is to diagnose earlier to prevent the spread of the disease and prompt treatment of severe cases to improve the chance of survival. MethodsThe method of this paper is as follows: Firstly, the collected data set is processed by chest film image processing, and the bone removal process is carried out in the rib subtraction module. Then, the set preprocessing method performed histogram equalization, sharpening, and other preprocessing operations on the chest film. Finally, shallow and high-level feature mapping through the backbone network extracts the processed chest radiographs. We implement the self-attention mechanism in Inception-Resnet, perform the standard classification, and identify chest radiograph diseases through the classifier to realize the auxiliary COVID-19 diagnosis process at the medical level, all in an effort to further enhance the classification performance of the convolutional neural network. Numerous computer simulations demonstrate that the Inception-Resnet convolutional neural network performs CT image categorization and enhancement with greater efficiency and flexibility than conventional segmentation techniques. ResultsThe experimental COVID-19 CT dataset obtained in this paper is the new data for CT scans and medical imaging of normal, early COVID-19 patients and severe COVID-19 patients from Jinyintan hospital. The experiment plots the relationship between model accuracy, model loss and epoch, using ACC, TPR, SPE, F1 score and G-mean to measure the image maps of patients with and without the disease. Statistical measurement values are obtained by Inception-Resnet are 88.23%, 83.45%, 89.72%, 95.53% and 88.74%. The experimental results show that Inception-Resnet plays a more effective role than other image classification methods in evaluation indicators, and the method has higher robustness, accuracy and intuitiveness. ConclusionWith CT images in the clinical diagnosis of COVID-19 images being widely used and the number of applied samples continuously increasing, the method in this paper is expected to become an additional diagnostic tool that can effectively improve the diagnostic accuracy of clinical COVID-19 images.

PGT for structural chromosomal rearrangements in 300 couples reveals specific risk factors but an interchromosomal effect is unlikely

Research questionWhat factors affect the proportion of chromosomally balanced embryos in structural rearrangement carriers? Is there any evidence for an interchromosomal effect (ICE)? DesignPreimplantation genetic testing outcomes of 300 couples (198 reciprocal, 60 Robertsonian, 31 inversion and 11 complex structural rearrangement carriers) were assessed retrospectively. Blastocysts were analysed either by array-comparative genomic hybridization or next-generation sequencing techniques. ICE was investigated using a matched control group and sophisticated statistical measurement of effect size (φ). Results300 couples underwent 443 cycles; 1835 embryos were analysed and 23.8% were diagnosed as both normal/balanced and euploid. The overall cumulative clinical pregnancy and live birth rates were 69.5% and 55.8%, respectively. Complex translocations and female age (≥35) were found to be risk factors associated with lower chance of having a transferable embryo (P < 0.001). Based on analysis of 5237 embryos, the cumulative de-novo aneuploidy rate was lower in carriers compared to controls (45.6% versus 53.4%, P < 0.001) but this was a ‘negligible’ association (φ < 0.1). A further assessment of 117,033 chromosomal pairs revealed a higher individual chromosome error rate in embryos of carriers compared to controls (5.3% versus 4.9%), which was also a ‘negligible’ association (φ < 0.1), despite a P-value of 0.007. ConclusionsThese findings suggest that rearrangement type, female age and sex of the carrier have significant impacts on the proportion of transferable embryos. Careful examination of structural rearrangement carriers and controls indicated little or no evidence for an ICE. This study helps to provide a statistical model for investigating ICE and an improved personalized reproductive genetics assessment for structural rearrangement carriers.

A statistical measurement of diseases recorded in The Quarterly Reports of the Ophthalmic Hospital at Canton

The Quarterly Reports of the Ophthalmic Hospital at Canton written by Rev. Peter Parker, an American protestant missionary in China, were serialised in The Chinese Repository from 1836 to 1850. Each report provided the number of patients treated in the corresponding period and described in detail the treatment of diseases which were difficult to deal with. However, due to historical conditions, these reports were inconsistent in terms of the disease classification standards, let alone the statistical deficiencies. This paper aims to regroup the diseases recorded in the 15 reports according to the classification from the eleventh to fifteenth report and recount the patient number of each disease systematically in different periods, with reliable historical data to support such relevant studies as the history of the Ophthalmic Hospital at Canton and the introduction of Western Medicine into China and the development of International Classification of Diseases.

An Integration of Statistical and Anthropometric Measurement Approach Towards Improving Ergonomic Design for Production Workbench

Capillary zone electrophoresis (CZE) is a method of haemoglobin (Hb) analysis for the screening of haemoglobinopathies or variant haemoglobins. In our centre, few samples sent for Hb analysis showed Hb peak at zone 10 requiring further evaluation. This pattern was observed in patients with history of diabetes mellitus (DM); hence it was postulated the peak observed is glycated Hb (HbA1c). The objective of this study was to determine whether the expression of Hb peak at zone 10 among blood samples from DM patients correlate with HbA1c values measured using high performance liquid chromatography (HPLC). Methods: This was a cross-sectional study involving blood samples from DM patients. Samples with HbA1c values ≥ 6.5% by HPLC were selected and subsequently analysed by CZE method. Presence of Hb peak in zone 10 were correlated and analysed with the HbA1c levels measured by HPLC method. Results: A total of 131 samples were analysed. Hb peak was detected at zone 10 in 50/131(38.2%). Out of 50 samples, 47 (94%) were from patients with HbA1c level &gt; 10%. Correlation study showed it was 18 times more likely for Hb peak to appear in zone 10 in patients with HbA1c &gt; 10%. Cut off point for HbA1c to appear in CZE is 10.5% with AUC of 0.965. Conclusion: Hb peak detected at zone 10 of CZE was most likely to be HbA1c. However, it is recommended that for every primary method of Hb analysis used be confirmed by secondary method. Therefore, both zone 10 in CZE and P2 peak in HPLC must be correlated together to achieve final diagnosis

Automated development of the contrast-detail curve based on statistical low-contrast detectability in CT images.

PurposeWe have developed a software to automatically find the contrast–detail (C–D) curve based on the statistical low‐contrast detectability (LCD) in images of computed tomography (CT) phantoms at multiple cell sizes and to generate minimum detectable contrast (MDC) characteristics.MethodsA simple graphical user interface was developed to set the initial parameters needed to create multiple grid region of interest of various cell sizes with a 2‐pixel increment. For each cell in the grid, the average CT number was calculated to obtain the standard deviation (SD). Detectability was then calculated by multiplying the SD of the mean CT numbers by 3.29. This process was automatically repeated as many times as the cell size was set at initialization. Based on the obtained LCD, the C–D curve was obtained and the target size at an MDC of 0.6% (i.e., 6‐HU difference) was determined. We subsequently investigated the consistency of the target sizes for a 0.6% MDC at four locations within the homogeneous image. We applied the software to images with six noise levels, images of two modules of the American College of Radiology CT phantom, images of four different phantoms, and images of four different CT scanners. We compared the target sizes at a 0.6% MDC based on the statistical LCD and the results from a human observer.ResultsThe developed system was able to measure C–D curves from different phantoms and scanners. We found that the C–D curves follow a power‐law fit. We found that higher noise levels resulted in a higher MDC for a target of the same size. The low‐contrast module image had a slightly higher MDC than the distance module image. The minimum size of an object detected by visual observation was slightly larger than the size using statistical LCD.ConclusionsThe statistical LCD measurement method can generate a C–D curve automatically, quickly, and objectively.

Smart ensemble machine learner with hyperparameter-free for predicting bond capacity of FRP-to-concrete interface: Multi-national data

Determining the bond capacity of a (fiber-reinforced polymer) FRP-to-concrete interface is important for the design task in repairing or rehabilitating concrete structures in infrastructure. This paper presents a novel framework for creating a hyperparameter-free ensemble model and, using an equilibrium optimizer (EO), demonstrates a radial basis function neural network (RBFNN) and least square support vector regression (LSVR). A new model, the metaheuristic-optimized neuron-vector machine inference model (MNVIM), was used for the accurate estimation of FRP-to-concrete interfacial bond capacity. The MNVIM was constructed using the EO algorithm to blend RBFNN and LSVR adaptively. In the MNVIM framework, the EO is involved in the learning phases of the RBFNN and the LSVR by giving an appropriate set of hyperparameters for the two constituent models, including the number of hidden neurons and the Gaussian impact value of RBFNN, as well as the regularization coefficient and kernel parameter. Simultaneously, EO adaptively adjusts linear combination weight values to blend the prediction values of RBFNN and LSVR. Statistical results from ten runs show that the newly developed model achieved the most desired evaluation criteria, namely, root mean square error (RMSE) (3.989 kN), mean absolute (MAE) (2.632 kN), mean absolute percentage error (MAPE) (15.56%), and R-square (0.839). The t-test statistical measurement further confirms that the prediction accuracy of MNVIM is significantly better than other artificial intelligence (AI) models and sixteen common mathematical approaches. These analytical results indicate that MNVIM is a promising tool for estimating bonding strength; thus, it significantly increases construction safety in concrete element repair/rehabilitation-related engineering design using FRP.

Determinants of Farmers' Level Awareness on Pesticide Use in a Selected Region in Bangladesh

The study aimed to investigate farmers' awareness level of pesticide use in the study area and to pursue the relationship between the selected characteristics of the respondents and their awareness level of pesticide use. A mixed-method approach was used in which qualitative and quantitative analyses were blended rationally to have in-depth Knowledge and understanding of research problems. The area of Dumki Upazila under Patuakhali district in Bangladesh was selected for conducting this study successfully. The simple random sampling technique was used to select 110 farmers engaged in various types of crop production using multiple pesticides. Data was collected through face-to-face interviews using a pretested interview schedule from April 15 to June 30, 2020. Descriptive statistical measurement and the software SPSS were used to analyze collected data. The results of the Pearson's Product Moment correlation coefficient identified that, among the independent variables, only six variables such as training received, percentages of land ownership, communication exposure, yearly household income, risk orientation, and Knowledge of pesticide use had a positive and significant relationship on farmers' level of awareness of pesticide use. At the same time, the stepwise multiple regression analysis demonstrated that four variables, such as Knowledge of pesticide use, communication exposure, risk orientation, and training received, had a positive and significant contribution to the farmers' awareness of pesticide use. These four variables also explained 54.55% of the farmers' total variation in awareness level.

The Manifestation of EFL Teachers’ Self-Efficacy and TPACK with their Teaching Performance

Teachers play very important roles on students learning, especially during the pandemic situation where emergency online learning has replaced the traditional classroom learning. Some factors can influence teachers’ performance such as teachers’ self-efficacy and TPACK (technological pedagogical content knowledge) framework. Here, the teacher participants have perception of their capabilities to accomplish the task by integrating content, pedagogy, and technology. This paper addresses the issue by employing a qualitative case study to investigate the manifestation of teacher participants’ self-efficacy and TPACK, as well as the relation between the two with teaching performance. Three EFL teachers at SMA 2 Demak are purposefully selected as the subjects of the research. The findings reveal that (1) the dominant source of self-efficacy in teacher A is psychological and emotional arousal but in teacher B and teacher C is mastery experience. (2) based on the statistical measurement, the score of teacher A’s self-efficacy was 65.0% and 63.9% in the TPACK framework. The score of self-efficacy in teacher B was 85.0% and 68.5% in TPACK framework. In teacher C, the score of both terms was 83.3%. (3) some factors of teacher participants’ self-efficacy affected the successful level of teaching performance. In a nutshell, self-efficacy fosters teachers’ motivation, develops teachers’ TPACK, and influences the successful level of teaching performance to implement TPACK framework. The present research provided a valuable knowledge about the importance of self-efficacy and TPACK framework and suggestion to construct some developmental programs to encourage student teachers in preparing better teaching learning process in the future.

Attention allocation, factor stock adjustment, and high-quality product development

The manufacturing industry is the foundation for building and strengthening a country. Micro-manufacturing enterprises are the basis for the development of manufacturing, and products are the core and life of enterprises. Based on micro data from industrial enterprises, this study conducts a statistical measurement on the indicators of high-quality product development. Taking the Baidu Index as the proxy variable for attention allocation, the influence of attention allocation on high-quality development of products is systematically examined both theoretically and empirically; Additionally, it seeks to ascertain whether factor stock adjustment has a mediating effect. The findings are as follows: 1. Within the full sample, 63.63% of the attention allocation process to promote high-quality development of products is achieved by optimizing the factor stock adjustment channels. 2. Capital stock and labor stock adjustment have a partial mediating effect on the high-quality development of products in capital-intensive industries, while labor stock adjustment has a partial mediating effect on labor-intensive industries. 3. The regional results show that the adjustment of capital and labor stock has a partial mediating effect in the eastern region. This conclusion provides a theoretical basis for realizing the high-quality development of China's manufacturing industry.

Precision measurement of the application-dependent current consumption of a wireless transceiver chip in the time and frequency domain

Abstract. Modern production concepts generate a demand for reliable, energy-efficient, fast, and secure wireless communication solutions. Therefore, the current consumption should not increase substantially due to additional cryptographic operations. This paper shows a principle current measurement method that is exemplary of a transceiver for the IO-Link Wireless protocol. Low-pass filtering and single-sided amplitude spectrum analysis are used to evaluate the main information of the current measurement. An uncertainty estimation is realized using statistical measurement data and considering the measurement setup in order to approximate the combined standard uncertainty. The results show that the current consumption only increases slightly when using additional cryptographic operations. This can be measured with acceptable uncertainty.

Lensless light intensity model for quasi-spherical cell size measurement.

Quasi-spherical cell size measurement plays an important role in medical test. Traditional methods such as a microscope and a flow cytometer are either it depends on professionals and cannot be automated, or it is expensive and bulky, which are not suitable for point-of-care test. Lab-on-a-chip technology using the lensless imaging system gives a good solution for obtaining the quasi-spherical cell size. The diffraction effects and the low resolution are the two main problems faced by the lensless imaging system. In this paper, a lensless light intensity model for the quasi-spherical cell size measurement is given. First, the diffraction characteristics of a quasi-spherical cell edge are given. Then, a diffraction model at an arc edge is constructed based on the Fresnel diffraction at a straight edge. Using the diffraction model at an arc edge, we explained the mechanism of the formation of the quasi-spherical cell diffraction fringes. Finally, the light intensity of the first bright ring of the quasi-spherical cell diffraction pattern is used to achieve quasi-spherical cell size measurement. The required equipment and the measurement methods are extremely simple, very suitable for point-of-care test. The experimental results show that the proposed model can realize the statistical measurement of the quasi-spherical cells and the classification of the quasi-spherical cells with a difference of 1 [Formula: see text].

Sklar’s Omega: A Gaussian copula-based framework for assessing agreement

The statistical measurement of agreement—the most commonly used form of which is inter-coder agreement (also called inter-rater reliability), i.e., consistency of scoring among two or more coders for the same units of analysis—is important in a number of fields, e.g., content analysis, education, computational linguistics, sports. We propose Sklar’s Omega, a Gaussian copula-based framework for measuring not only inter-coder agreement but also intra-coder agreement, inter-method agreement, and agreement relative to a gold standard. We demonstrate the efficacy and advantages of our approach by applying both Sklar’s Omega and Krippendorff’s Alpha (a well-established nonparametric agreement coefficient) to simulated data, to nominal data previously analyzed by Krippendorff, and to continuous data from an imaging study of hip cartilage in femoroacetabular impingement. Application of our proposed methodology is supported by our open-source R package, sklarsomega, which is available for download from the Comprehensive R Archive Network. The package permits users to apply the Omega methodology to nominal scores, ordinal scores, percentages, counts, amounts (i.e., non-negative real numbers), and balances (i.e., any real number); and can accommodate any number of units, any number of coders, and missingness. Classical inference is available for all levels of measurement while Bayesian inference is available for continuous outcomes only.

The Research of Key Indicators of Performance to Predict to Advance the Knock Out Stage in the International Football Tournament

The aim of this research was to analyse football tournaments, UEFA Euro 2021 and Copa America 2021, to provide statistical measurement to predict which teams were likely to be qualified to the next round in the knockout stage. The research method used to collect six key variables statistics including total attempts, on target, possession, passes attempted, passing accuracy, and corners taken from 79 matches in UEFA Euro 2021 and Copa America 2021. The data were analysed by the grey relational analysis (GRA). The results were as follows: first, this study presented variables related to goals scored and offense correlated on goals scored. Second, this study proposed a GRA model that provides an efficient way to predict match outcomes. Third, the GRA model will perform better than the FIFA world ranking when more teams participated and the world ranking were tight.

Scrutinizing the Relationship between Subjective Anomalous Experiences and Psychotic Symptoms

Abstract&#x0D; This research was exploratory and its main objective was to analyze whether anomalous experiences related to parapsychology had similar statistical behavior to psychotic-like experiences (e.g., hallucinations). If psi phenomena have a different ontology from psychotic-like experiences, then they should have a different statistical representation and measurement. In this hypothetical scenario, there would be empirical-statistical grounds for discriminating between psychotic perceptual distortions and anomalous experiences without clinical origin. Different clinical variables common in psychotic disorders were measured in 562 participants. Psychotic-like experiences (as hallucinations) and anomalous experiences (as experiences outside the framework of psychosis) were also quantified. Several forward stepwise multiple regression models and techniques based on Exploratory Factor Analysis were used. The EFA extracted 2 factors; the first grouped the variables that measured anomalous phenomena from the continuum of psychosis model and the second gathered the variables that measured them as anomalous perceptions without scientific explanation. Both EFAs explained more than 70% of the variance. Only 3 clinical variables were necessary to predict 58.8% of psychotic –like experiences assessed from the psychopathological model. Up to 6 indicators were necessary to predict 54.5% of the unexplained anomalous experiences. There are empirical-statistical indicators in the used sample that enable the differentiation of the anomalous phenomena in the two prominent models. The variables that characterize the psychotic phenotype predict more successfully psychotic-like experiences than anomalous experiences. It is discussed whether the factors extracted in the EFA represent psychological constructs with different etiologies or if both dimensions come from the same underlying construct.&#x0D; Keywords: Anomalous Experiences; Schizotypy; Psychotic-like experiences; Paranormal Beliefs; Psi Phenomena.

Vegetation health conditions assessment and mapping using AVIRIS-NG hyperspectral and field spectroscopy data for -environmental impact assessment in coal mining sites

This paper focuses on vegetation health conditions (VHC) assessment and mapping using high resolution airborne hyperspectral AVIRIS-NG imagery and validated with field spectroscopy-based vegetation spectral data. It also quantified the effect of mining on vegetation health for geo-environmental impact assessment at a fine level scale. In this study, we have developed and modified vegetation indices (VIs) based model for VHC assessment and mapping in coal mining sites. We have used thirty narrow banded VIs based on the statistical measurement for suitable VIs identification. The highest Pearson's r, R2, lowest RMSE, and P values indices have been used for VIs combined pixels analysis. The highest different (Healthy vs. unhealthy) vegetation combination index (VCI) has been selected for VHC assessment and mapping. We have also compared VIs model-based VHC results to ENVI (software) forest health tool and Spectral-based SAM classification results. The 1st VCI result showed the highest difference (72.07%) from other VCI. The AUC values of the ROC curve have shown a better fit for the VIs model (0.79) than Spectral classification (0.74), and ENVI FHT (0.68) based on VHC results. The VHC results showed that unhealthy vegetation classes are located at low distances from mine sites, and healthy vegetation classes are situated at high distances. It is also seen that there is a highly significant positive relationship (R2 =0.70) between VHC classes and distance from mines. These results will provide a guideline for geo-environmental impact assessment in coal mining sites.

Exploring the Co-creation Value of Residents to Tourists From the Perspective of Place Attachment and Economic Benefits.

Local development enhances the economic capacity and quality of life of the residents and, in particular, attracts tourism to the area. The co-creative value of the residents and the tourists can improve the consensus of the residents on the sustainable development of the place. This study focuses on the factors influencing the co-creation of value between residents and visitors in the Tamsui area near Taipei. The research hypothesis is based on the components of local attachment, economic benefits brought by tourists, environmental costs, social and cultural welfare of the place, life satisfaction of the residents, and the value of co-creation between residents and tourists. A total of 430 questionnaires were collected through a questionnaire survey and statistical data were analyzed using a structural equation model, including descriptive statistical analysis, measurement reliability and validity verification, model fit, and structural model analysis to validate the research hypotheses. The study found that place attachment positively and significantly affects the economic benefits, environmental costs, and socio-cultural welfare of residents about tourists. Resident satisfaction is positively and significantly affected by the environmental costs from visitors and by socio-cultural welfare, but there is no significant impact from economic benefits. Finally, based on the findings of the study, practical recommendations were made for enhancing co-creation value between Tamsui residents and visitors, including enhancing residents’ feelings of place attachment and construction of local social culture and welfare. For the residents of Tamsui, unlike the local government and enterprises, need to be able to create value with tourists in order to have a friendly relationship with them and develop regional tourism in a sustainable manner.

The electricity scene from above: Exploring power grid inconsistencies using satellite data in Accra, Ghana

Complicated systems are complicated to monitor. The electric grid is one of the most complicated systems, and subsequently goes under-monitored in many regions around the world that cannot easily afford expensive meters. However, the electric grid is also critical for sustaining a high quality of life, and requires better monitoring than is often available to ensure consistent service is provided. Past work has shown that images taken by satellites during the night, capturing nighttime illumination (“nightlights”), could provide a proxy measurement of grid performance for minimal cost. We build upon earlier work by identifying the pixel z-score – a statistical measurement of a pixel’s illumination relative to its history – as a key method for detecting electricity outages from the often-noisy nightlights dataset. We then train and validate our approach against observations from a network of on-the-ground power outage sensors in our observation area of Accra, Ghana, a dataset representing the largest collection of utility-independent electricity reliability measurements on the African continent. Using multiple machine learning techniques for estimating potential outages from nightlight images, we obtain high performance for predicting outages in Accra at scales as small as a single pixel (0.2 km2) and with training datasets as small as three months of illumination/sensor data. We further validate our methodology beyond the spatio-temporal coverage of the on-the-ground sensor deployment against a human-labeled dataset of outages by neighborhood throughout Accra. Delving deeper into the applications and limitations of available datasets and our work, we conclude by highlighting questions about the generality of our method vital to understanding its potential for low-cost worldwide measurements of grid reliability.

Investigation and analysis of actual atmospheric scattered radiance and slant visibility by two-wavelength Raman-Mie lidar

Considering the necessity of lidar for atmospheric scattered radiance measurement, and the application scenario in civil aviation airports, an eye-safe two-wavelength Raman-Mie lidar is proposed for slant visibility measurements, which differs from the existing Mie-scattering slant visibility lidar. Lidar system is to provide atmospheric aerosol parameters for SBDART model, so as to solve the calculation of actual atmospheric scattered radiance. The obtained information including the total irradiance, the scattered radiance, and the atmospheric transparency are combined to calculate the contrast ratio between the object and the background.Lidar experiments were carried out over Xi'an University of Technology, China (34.233°N, 108.911°E), and two cases conducted under overcast and haze conditions are investigated. Using the 33-layer atmospheric aerosol list by vertical lidar detection, the 33-layer actual atmospheric scattered radiance and the total solar irradiance are obtained by SBDART model. The atmospheric transparency is further obtained by slant scanning detection. Taking the playground runway and the grassland as the object and the background, the contrast ratio curves can be calculated with consideration of actual atmospheric scattered radiance and atmospheric transparency, and the slant visibility distances can be thus obtained at any slant directions under the contrast ratio of 0.05. The results under overcast condition showed that, the obtained slant visibility is at the distance of 9 km and 11.2 km at a zenith angle of 50° and 70°, with the statistical measurement error of 12.5% and 6.54%. The results under haze weather condition also differs at slant directions, the slant visibility distances varied ranging from 4.4 km to 6.51 km, and the statistical measurement errors are approximately of 10.21% and 8.89%. The results demonstrated the developed two-wavelength Raman-Mie lidar provides a new solution to actual atmospheric scattered radiance measurements, and also validated its feasibility and reliability for accurate slant visibility measurements.