Quantitative Performance Metrics Research Articles

Automated Ultrasound Object Segmentation Using Combinatorial Active Contour Method

Active Contour (AC) is an algorithm widely used in segmentation for developing Computer-Aided Diagnosis (CAD) systems in ultrasound imaging. Existing AC models still retain an interactive nature. This is due to the large number of parameters and coefficients that require manual tuning to achieve stability. Which can result in human error and various issues caused by the inhomogeneity of ultrasound images, such as leakage, false areas, and local minima. In this study, an automatic object segmentation method was developed to assist radiologists in an efficient diagnosis process. The proposed method is called Automatic Combinatorial Active Contour (ACAC), which combines the simplification of the global region-based CV (Chan-Vese) model and improved-GAC (Geodesic Active Contour) for local segmentation. The results of testing with 50 datasets showed an accuracy value of 98.83%, precision of 95.26%, sensitivity of 86.58%, specificity of 99.63%, similarity of 90.58%, and IoU (Intersection over Union) of 82.87%. These quantitative performance metrics demonstrate that the ACAC method is suitable for implementation in a more efficient and accurate CAD system.

The use of an embryo transfer simulator to compare transfer techniques and pregnancy outcomes among physicians

To evaluate the association between embryo transfer techniques and pregnancy outcomes. This is a prospective observational study with a retrospective cohort. University Clinic. Patients underwent embryo transfers between 2015 and2020. Fourteen physicians performed 25 mock embryo transfers on the embryo transfer simulator and completed a questionnaire assessing preferred embryo transfer techniques. Quantitative performance metrics on the embryo transfer simulator were measured. Individual physician embryo transfer success rates were retrospectively collected from all fresh and cryopreserved embryo transfers between January 1, 2015, and January 1, 2020. Associations between embryo transfer techniques (preferred technique and simulator performance metrics) and each physician's historical patient pregnancy outcomes were assessed. Associations between embryo transfer techniques and live births were assessed. There were significant differences in embryo transfer techniques between physicians, including touches to the fundus, distance to the fundus, duration of embryo transfer, duration of the complete procedure, time spent navigating the cervical canal, velocity of embryo expulsion, time waited after embryo expulsion, and total score on the embryo transfer simulator. After controlling for confounders and multiple transfers per physician, the duration of embryo transfer was significantly associated with live birth, with longer durations associated with decreased live birth rates. Shorter placement distance to the fundus and higher velocity of embryo expulsion were both significantly associated with higher rates of ectopic pregnancy. This study revealed significant differences in transfer techniques among physicians. The use of the embryo transfer simulator for physicians in practice can elucidate differences and create opportunities for data-driven improvement in embryo transfer success rates.

HORIZON: High-Resolution Semantically Controlled Panorama Synthesis

Panorama synthesis endeavors to craft captivating 360-degree visual landscapes, immersing users in the heart of virtual worlds. Nevertheless, contemporary panoramic synthesis techniques grapple with the challenge of semantically guiding the content generation process. Although recent breakthroughs in visual synthesis have unlocked the potential for semantic control in 2D flat images, a direct application of these methods to panorama synthesis yields distorted content. In this study, we unveil an innovative framework for generating high-resolution panoramas, adeptly addressing the issues of spherical distortion and edge discontinuity through sophisticated spherical modeling. Our pioneering approach empowers users with semantic control, harnessing both image and text inputs, while concurrently streamlining the generation of high-resolution panoramas using parallel decoding. We rigorously evaluate our methodology on a diverse array of indoor and outdoor datasets, establishing its superiority over recent related work, in terms of both quantitative and qualitative performance metrics. Our research elevates the controllability, efficiency, and fidelity of panorama synthesis to new levels.

Design and Fabrication of Low-Speed Wind Tunnel (LSWT)

The design and fabrication of a low-speed wind tunnel (LSWT), which is a critical component for testing and comprehending aircraft aerodynamics, is presented in this study. Despite the increasing prominence of computational fluid dynamics (CFDs) in manufacturing engineering, wind tunnels remain essential for the intricate development of aircraft and automobile designs with complex flow interactions. Using SolidWorks, we focused on controlling flow turbulence approaching the test section, emphasizing performance and quality parameters. The construction of the wind tunnel used plywood with an axial fan regulating the airspeed, and Arduino facilitated data acquisition. The drag and lift on the Y Clerk Airfoil were quantified by two load cells along the XY-axis, complemented by a Pitot Static Tube and a multitube inclined plane manometer for pressure and velocity calculation. Fusion 360 simulation software was used to analyze pressure and velocity profiles at speeds ranging from 10 to 20 m/s, providing a comprehensive quantitative evaluation of the wind tunnel’s capabilities. By emphasizing both design innovation and quantitative performance metrics, this study underscores the continuing significance of wind tunnels in engineering.

DOMINER: Domain Feature Mining from Unstructured Data for Effective Text Summarization

In the current era of internet, a huge volume of opinionated sentiment reviews is generated on a daily basis. Normally, such reviews are enriched with deep sentiments and express the opinions on various features of a product or service under consideration. Identification of a set of important domain features opens an opportunity to build automatic product and service summarization system. It is observed that the common phenomena is to consider the frequently appearing words as the domainb features. However, not all frequently appearing words can be domain-specific features. In this paper, a novel Domain Feature Miner (DOMINER) approach is proposed for robust extractive summarization. The entire domain feature set mining problem is modelled as a clustering problem. Firstly, the bond energy based clustering technique is employed to cluster the domain features based on their frequency and co-appearance counts. Later, relevant clusters are extracted for the final set of domain feature set retrieval. The Proposed DOMINER scheme is extensively evaluated against quantitative performance metrics such as precision, recall, and F-score for six diversified domains such as Cellphone, Camera, Laptop, Tablet, Television, and Hotel. Experimental results on benchmark data sets reveal that proposed DOMINER scheme mines high quality of domain feature from unstructured reviews evident from precision, recall, and F-score values as 78.10%, 64.21%, and 70.48%, respectively against state of the art existing schemes. The high quality domain features extraction from DOMINER help generate the robust extractive summaries for product and services.

The association between calf birth weight and the postcalving performance of its dairy dam in the absence of dystocia

The objective of the study was to quantify the association between the birth weight of a calf and the subsequent performance of its dairy dam in the absence of any recorded calving assistance. A total of 11,592 lactation records from 4,549 spring-calving dairy cows were used. The association between a series of quantitative cow performance metrics (dependent variable) and calf birth weight (independent variable) was determined using linear mixed models; logistic regression was used where the dependent variable was binary. Nuisance factors in the models were calf sex, heterosis coefficient of both the cow and calf, dry period length immediately before the birth of the calf, cow age at calving relative to the median cow age per parity, breed proportion of the cow, cow live weight between 100 and 200 d of lactation relative to the mean cow weight per parity, and contemporary group. Calf birth weight was included in the model as either a continuous or a categorical variable. Primiparous and multiparous cows were analyzed separately. Mean (SD) calf birth weight was 36.2 (6.8) kg. In primiparous cows, calf birth weight was associated with milk yield in the first 60 d of lactation, calving to first service interval, calving body weight (BW), and both nadir BW and body condition score (BCS). In multiparous cows, calf birth weight was associated with total milk, fat, and protein yield in the first 60 and 305 d of lactation, peak milk yield, total milk solids, both calving and nadir BW, and BCS loss from calving to nadir. Relative to primiparous cows that gave birth to calves weighing 34 to 37 kg (i.e., population mean), their contemporaries who gave birth to calves that weighed 15 to 29 kg produced 9.82 kg more milk in the first 60 d of lactation, had a 2-d shorter interval to first service, and were 8.08 kg and 5.51 kg lighter at calving and nadir BW, respectively; the former was also 0.05 units lower in BCS (5-point scale, 1 = emaciated and 5 = obese) at nadir. Relative to multiparous cows that gave birth to calves that were 34 to 37 kg birth weight, multiparous cows that gave birth to calves that were 15 to 29 kg yielded 59.63 kg, 2.44 kg, and 1.76 kg less milk, fat, and protein, respectively, in the first 60 d of lactation; produced 17.69 kg less milk solids throughout the 305-d lactation; and were also 10.49 kg lighter at nadir and lost 0.01 units more BCS to nadir. In a separate series of analyses, sire breed was added to the model as a fixed effect with and without calf birth weight. When calf birth weight was not adjusted for, 60-d milk yield for multiparous cows who gave birth to calves sired by a traditional beef breed (i.e., Angus, Hereford) produced 59.63 kg more milk than multiparous cows who gave birth to calves sired by a Holstein-Friesian. Hence, calf birth weight is associated with some subsequent dam performance measures; however, where associations do exist, the effect is biologically small.

A novel deep learning based underwater image de-noising and detecting suspicious object

Exploration of underwater resource play a vital role for nation development. Underwater surveillance systems play a crucial role in security applications, requiring accurate detection of suspicious objects in underwater images. However, the presence of noise, poor visibility, and uneven lighting conditions in underwater environments pose significant challenges for reliable object detection. This work proposes an integrated approach for underwater image de-noising, pre-processing, enhancement, and subsequent suspicious object detection by combining the DnCNN (Deep Convolutional Neural Network), CLAHE (Contrast Limited Adaptive Histogram Equalization), and additional image enhancement techniques. In addition to de-noising and pre-processing, it incorporate various image enhancement techniques to further improve object detection performance. These techniques include color correction, contrast adjustment, and edge enhancement, aiming to enhance the visual characteristics and saliency of suspicious objects in underwater images. To evaluate the effectiveness of proposed approach, this work conducted extensive experiments on an underwater image dataset containing diverse scenes and suspicious objects. The work compares proposed method with existing de-noising, preprocessing, and object detection techniques, analyzing the results using quantitative performance metrics, including precision, recall, and F1 score. The experimental results demonstrate that proposed integrated approach outperforms individual methods and achieves superior detection performance by enhancing the quality of underwater images and improving the visibility of suspicious objects.

THE INFLUENCE OF TECHNOLOGICAL CAPACITY AND FINANCIAL CAPACITY ON PROMOTING FIRM COMPETITIVENESS AND FIRM PERFORMANCE

In today's dynamic economic landscape, businesses continually strive to enhance their competitive edge and overall performance. This study investigates the crucial roles played by technological and financial stability in a company's success and competitiveness. By analyzing a diverse range of businesses across various industries, we aim to illuminate the intricate relationship between these dimensions and their combined impact on a firm's operational effectiveness. Our comprehensive approach employs quantitative financial analysis, technology assessments, and performance metrics, supported by advanced statistical methods. Additionally, qualitative insights from expert interviews enrich our understanding. The results highlight the interdependence between technological and financial strength, underscoring their significance in boosting corporate competitiveness. A firm's capacity to capitalize on growth opportunities and navigate economic challenges is notably influenced by financial factors such as capital accessibility, liquidity, and investment strategies. Similarly, technological capacity, encompassing digital infrastructure and innovation adoption, drives operational efficiency and market distinctiveness. This research unveils the synergistic effects of harnessing both technology and financial resources, enabling strategic resource allocation that empowers businesses to sustain competitiveness, foster innovation, and adapt to evolving market dynamics. These insights hold profound implications for both theoretical research and practical management, emphasizing the need to balance investments in technical and financial capabilities. They provide valuable guidance for researchers, policymakers, and business leaders grappling with an increasingly competitive landscape. Ultimately, this study advances our comprehension of the factors shaping firm success and equips businesses with a roadmap for surmounting challenges and seizing opportunities in today's dynamic market.

Optimisation of a grass cutting blade using design of experiments with qualitative and quantitative performance metrics

Optimisation of a grass cutting blade using design of experiments with qualitative and quantitative performance metrics

SOC Analyst Performance Metrics: Towards an optimal performance model

Security Operation Center represents nowadays an indispensable component of the socio-technical system by supporting businesses to protect their security and ensure the confidentiality, integrity, and availability against cyberthreats and security attacks.The Security Operation Center provides various service levels and capabilities that need to be continuously assessed and tracked to ensure improvement of the main success factor of the SOC, which include technologies, processes, and SOC analysts. SOC analysts’ performance evaluation remains problematic due to the choice of the performance metrics and their inadequacy with the SOC socio-technical system. While we have some quantitative and qualitative measures to assess the performance of a SOC analyst, SOC capabilities, and SOC maturity levels, this evaluation is based on root cause analysis and independent evaluation of SOC elements, which is unrealistic, given the complex and evolving nature of SOC systems. However, the baselines of the performance metrics are the SOC challenges announced by the SOC analysts and their ability to face, reduce, and overcome them to provide and maintain a high detection rate of malicious and abnormal behaviors. We provide a comprehensive overview of the challenges faced by SOC analysts based on our previous study, and we provide a deep analysis of the challenges and the interconnexion of those challenges. Furthermore, we present the quantitative performance metrics and their weaknesses to assess the performance of the SOC analysts due to the SOC socio-technical system nature. Our study will enable SOC managers, analysts, and decision-makers to have clear visibility and details on the quantitative performance metrics and will provide a baseline for a new performance metrics model.

Hardware Suitability of Complex Natural Resonances Extraction Algorithms in Backscattered Radar Signals

Complex natural resonances (CNRs) extraction methods such as matrix pencil method (MPM), Cauchy, vector-fitting Cauchy method (VCM), or Prony’s method decompose a signal in terms of frequency components and damping factors based on Baum’s singularity expansion method (SEM) either in the time or frequency domain. The validation of these CNRs is accomplished through a reconstruction of the signal based on these complex poles and residues and a comparison with the input signal. Here, we perform quantitative performance metrics in order to have an evaluation of each method’s hardware suitability factor before selecting a hardware platform using benchmark signals, simulations of backscattering scenarios, and experiments.

Multi-criteria evaluation of satellite-based precipitation estimates over agro-climatic zones of India

Satellite-based precipitation estimates (SPEs) available at a high spatio-temporal resolution with near-global coverage are substitutes for ground-based datasets. In this study, the quality of popular SPEs was evaluated at daily time scale over agro-climatic (ACL) zones of India. The SPEs were analyzed in two different groups, i.e., (i) satellite-only precipitation estimates (SOPEs) including 5 products namely, CMORPH, PERSIANN, SM2RAIN-ASCAT, SM2RAIN-CCI, and SM2RAIN-GPM, and (ii) gauge-corrected satellite precipitation estimates (GCSPEs) including 4 products namely, CHIRPS, GSMaP, IMERG and MSWEP. The analysis was carried out by quantifying the qualitative and quantitative performance metrics as well as by decomposing the errors into random and systematic components. In general, SPEs underestimated the high rainfall events and overestimated the low rainfall events. The performance of SPEs was observed to be poor over the western Himalayan region. The efficiency and agreement of Global Precipitation Measurement (GPM) based products (including SM2RAIN-GPM, GSMaP and IMERG) with observed precipitation were better. The superiority of SM2RAIN-GPM, GSMap and IMERG products has also been supported by the lower magnitude of errors and biases. The error decomposition reveals high random errors in most of the SPEs; however, SM2RAIN-based products have low random error. It was also observed that the total bias is highly underestimated due to opposite-natured bias components. Further, the association of vegetation and topographic complexity with the performance of precipitation products was investigated, and a negative/positive association of performance metrics of SPEs with topographic/vegetation characteristics was observed. A multi-criteria decision-making (MCDM) approach was then employed to rank the datasets based on multiple performance indicators. MCDM approach reveals the superiority of SM2RAIN-GPM product amongst SOPEs and GSMaP (followed by IMERG) amongst GCSPEs over a majority of the regions of India.

Genetic data visualization using literature text-based neural networks: Examples associated with myocardial infarction

Data visualization is critical to unraveling hidden information from complex and high-dimensional data. Interpretable visualization methods are critical, especially in the biology and medical fields, however, there are limited effective visualization methods for large genetic data. Current visualization methods are limited to lower-dimensional data and their performance suffers if there is missing data. In this study, we propose a literature-based visualization method to reduce high-dimensional data without compromising the dynamics of the single nucleotide polymorphisms (SNP) and textual interpretability. Our method is innovative because it is shown to (1) preserves both global and local structures of SNP while reducing the dimension of the data using literature text representations, and (2) enables interpretable visualizations using textual information. For performance evaluations, we examined the proposed approach to classify various classification categories including race, myocardial infarction event age groups, and sex using several machine learning models on the literature-derived SNP data. We used visualization approaches to examine clustering of data as well as quantitative performance metrics for the classification of the risk factors examined above. Our method outperformed all popular dimensionality reduction and visualization methods for both classification and visualization, and it is robust against missing and higher-dimensional data. Moreover, we found it feasible to incorporate both genetic and other risk information obtained from literature with our method.

NSF Fellows’ perceptions about incentives, research misconduct, and scientific integrity in STEM academia

There is increased concern about perverse incentives, quantitative performance metrics, and hyper-competition for funding and faculty positions in US academia. Recipients of the prestigious National Science Foundation Graduate Research Fellowships (n = 244) from Civil and Environmental Engineering (45.5%) and Computer Science and Engineering (54.5%) were anonymously surveyed to create a baseline snapshot of their perceptions, behaviors and experiences. NSF Fellows ranked scientific advancement as the top metric for evaluating academics followed by publishing in high-impact journals, social impact of research, and publication/citation counts. The self-reported rate of academic cheating was 16.7% and of research misconduct was 3.7%. Thirty-one percent of fellows reported direct knowledge of graduate peers cheating, and 11.9% had knowledge of research misconduct by colleagues. Only 30.7% said they would report suspected misconduct. A majority of fellows (55.3%) felt that mandatory ethics trainings left them unprepared for dealing with ethical issues. Fellows stated academic freedom, flexible schedules and opportunity to mentor students were the most positive aspects of academia, whereas pressures for funding, publication, and tenure were cited as the most negative aspects. These data may be useful in considering how to better prepare STEM graduate trainees for academic careers.

Generation of fused visible and thermal-infrared images for uncooperative spacecraft proximity navigation

State-of-the-art techniques for vision-based relative navigation rely on images acquired in the visible spectral band. Consequently, the accuracy and robustness of the navigation is strongly influenced by the illumination conditions. The exploitation of thermal-infrared images for navigation purposes is studied in the present paper, as a possible solution to improve navigation in close proximity with a target spacecraft. Thermal-infrared images depend on the thermal radiation emitted by the target, hence, they are independent from light conditions; however, they suffer from a poorer texture and a lower contrast with respect to visible ones. This paper proposes pixel-level image fusion to overcome the limitations of the two types of images. The two source images are merged into a more informative one, retaining the strengths of the distinguished sensing modalities. The contribution of this work is twofold: firstly, a realistic thermal infrared images rendering tool for artificial targets is implemented; secondly, different pixel-level visible-thermal infrared images fusion techniques are assessed through qualitative and quantitative performance metrics to ease and improve the subsequent image processing step. The work presents a comprehensive evaluation of the best fusion techniques for on-board implementation, paving the way to the development of a multispectral end-to-end navigation chain.

The Use of Sustainable Financial Instruments in Relation to the Social Impact Investment: ESG Policies, Capital Markets’ Approach and Investors’ Protection: An Innovative Perspective for a Global Surveillance Authority

Social Impact Investment (SII) refers to the provision of finance to organizations addressing social needs. It is intended to achieve both a social impact as well as garner financial returns. Thus, SII may be understood as a business case for what was previously considered mostly philanthropic activities. In the case of SRI (Sustainable and Responsible Investment), investors decide in which company to invest on the basis of Environmental, Social and Governance (ESG) criteria. They consider a company's performance with respect to its effects on nature, its relationships with employees, clients and society, and the transparency of its governance. Where possible, companies should provide quantitative, comparable and forward-looking performance metrics to facilitate ESG integration. It is recommended that companies should use credible indicators which are included in internationally recognised reporting frameworks like GRI, SASB, IIRC, TCFD, CDP or UNGC. Companies should disclose their methodology and provide explanations to support quantitative indicators (ESG metrics), establishing the link between ESG and financial performance. The investor perspective is in important issue, which should be taken under consideration by European and local supervising authorities, under the provisions of the Market Abuse Directive and the MiFID, especially after the introduction and use of ESG and sustainable financial instruments, in both, Banking and Capital Markets. Therefore, SII can be a very useful tool for achieving EU Social Policy objectives. EU efforts (DG EMPL/FISMA) can be targeted at four specific aims that concern impact measurement: 1) promotion of the standardisation of impact measurement through the further development of the taxonomy regulations to include social objectives or the creation of standardised guidelines or the further development of ESG metrics; 2) capacity building in the area of impact measurement; 3) increasing the availability of the data needed; and 4) the promotion of additional and more specialized sustainable financial instruments linked to the Banking Sector and Capital Markets, which in turn will leverage more the total investment amount derived from the InvestEU tool and offer to all stakeholders more flexibility. Therefore, the global liquidity will be increased, and a strong economic turmoil could be avoided under the surveillance of a theoretically established Global Surveillance Authority. The use of sustainable financial instruments by a P.E.S. (social enterprise) under a public-private partnership (PPPs) is also feasible and socially rewarding.

Prognosis prediction for glioblastoma multiforme patients using machine learning approaches: Development of the clinically applicable model

Background and purposeWe aimed to develop a clinically applicable prognosis prediction model predicting overall survival (OS) and progression-free survival (PFS) for glioblastoma multiforme (GBM) patients. Materials and methodsAll 467 patients treated with concurrent chemoradiotherapy at Yonsei Cancer Center from 2016 to 2020 were included in this study. We developed a conventional linear regression, Cox proportional hazards (COX), and non-linear machine learning algorithms, random survival forest (RSF) and survival support vector machine (SVM) based on 16 clinical variables. After backward feature selection and hyperparameter tuning using grid search, we repeated 100 times of cross-validations to combat overfitting and enhance the model performance. Harrell's concordance index (C-index) and integrated brier score (IBS) were employed as quantitative performance metrics. ResultsIn both predictions, RSF performed much better than COX and SVM. (For OS prediction: RSF C-index = 0.72 90%CI [0.71–0.72] and IBS = 0.12 90%CI [0.10–0.13]; For PFS prediction: RSF C-index = 0.70 90%CI [0.70–0.71] and IBS = 0.12 90%CI [0.10–0.14]). Permutation feature importance confirmed that MGMT promoter methylation, extent of resection, age, cone down planning target volume, and subventricular zone involvement are significant prognostic factors for OS. The importance of the extent of resection and MGMT promoter methylation was much higher than other selected input factors in PFS. Our final models accurately stratified two risk groups with root mean square errors less than 0.07. The sensitivity analysis revealed that our final models are highly applicable to newly diagnosed GBM patients. ConclusionOur final models can provide a reliable outcome prediction for individual GBM. The final OS and PFS predicting models we developed accurately stratify high-risk groups up to 5-years, and the sensitivity analysis confirmed that both final models are clinically applicable.

Defining the Success Status of Construction Projects Based on Quantitative Performance Metrics Thresholds

Despite the high spending and high employment, the construction industry struggles to agree how to go about project performance assessment and what constitutes a successful project. This does not allow stakeholders to address the issues in and drawbacks of their respective projects. Hence, this research outlines performance metrics upper limits, which can be used in defining a successful project. Data were collected from Construction Industry Institute (CII) members and multiple construction firms through University of Wisconsin–Madison active research projects. Two classification and regression tree (CART) models were developed using 4-fold randomized cross-validation to provide the metrics thresholds of success with a classification accuracy of 81% and 85%, respectively. CART models revealed that a successful project can be defined as having construction schedule growth less than 10.4%, construction cost growth less than 9.8%, requests for information (RFIs) per $million less than or equal to 8.6, and RFI processing time less than or equal to 7 days. Additional thresholds showed that a high-performing successful project would have a change orders per $million of less than 0.39 and rework of less than 1.5%. These thresholds can serve as a step toward a quantitative definition of success by translating qualitative success status into quantitative success status. Additionally, the established thresholds can be used as benchmarks for acceptable construction performance, which can then be adopted as risk thresholds or tolerances during project planning.

A stepwise framework for interpolating land surface temperature under cloudy conditions based on the solar-cloud-satellite geometry

Thermal infrared land surface temperature (LST) data from satellites often contain extensive missing values due to high cloudiness degree, which severely hinders their use in applications. Despite the many methods developed, common methods, such as fusion with microwave or reanalysis data and the surface energy budget approach, still remain subject to important limitations and uncertainties, such as dependency on coarse resolution data and difficulty in interpolation for large-scale missing LST data. In this study, we proposed a stepwise framework for estimating missing cloudy-sky LST values of Moderate Resolution Imaging Spectroradiometer (MODIS) from informative samples owing to the solar-cloud-satellite geometry (SCSG) effect, by which satellite imagery records the cloudy-sky LST values of a portion of pixels. We first estimated the clear-sky LST equivalents for all cloud-affected pixels via a similarity-based approach and then determined unknown LSTs for cloudy pixels by training a machine-learning model on cloudy-sky LST values observed owing to the SCSG effect. We demonstrated the utility of this approach by using MODIS/Aqua daytime LST data over Qinghai-Tibet Plateau (QTP) and validated the interpolation results against representative in-situ LST observations and two recently published all-weather LST datasets. When compared to the corresponding in-situ measurements, the interpolated cloudy-sky LST values showed satisfactory accuracy with a mean absolute error (MAE) value of 3.99 °C and a coefficient of determination (R2) value of 0.74, while the MODIS/Aqua clear-sky LST values led to an MAE value of 2.66 °C and an R2 value of 0.86. Compared to the two all-weather LST datasets, results of this study showed the highest accuracy over the data-gap-filled regions in terms of all quantitative performance metrics, more natural transition textures, and better representation of seasonal characteristics. The proposed framework has the advantage of relying on the MODIS family data and handling extensive missing data as well as triggers opportunities to leverage the SCSG effect to produce high-quality all-weather LST data.

Analyzing the Effect of COVID-19 on Education by Processing Users’ Sentiments

COVID-19 infection has been a major topic of discussion on social media platforms since its pandemic outbreak in the year 2020. From daily activities to direct health consequences, COVID-19 has undeniably affected lives significantly. In this paper, we especially analyze the effect of COVID-19 on education by examining social media statements made via Twitter. We first propose a lexicon related to education. Then, based on the proposed dictionary, we automatically extract the education-related tweets and also the educational parameters of learning and assessment. Afterwards, by analyzing the content of the tweets, we determine the location of each tweet. Then the sentiments of the tweets are analyzed and examined to extract the frequency trends of positive and negative tweets for the whole world, and especially for countries with a significant share of COVID-19 cases. According to the analysis of the trends, individuals were globally concerned about education after the COVID-19 outbreak. By comparing between the years 2020 and 2021, we discovered that due to the sudden shift from traditional to electronic education, people were significantly more concerned about education within the first year of the pandemic. However, these concerns decreased in 2021. The proposed methodology was evaluated using quantitative performance metrics, such as the F1-score, precision, and recall.