Distribution Of Values Research Articles

Tracking full posterior in online Bayesian classification learning: a particle filter approach

The rapid growth of data volume and velocity is challenging traditional methods of classification, making it impossible to store so much data in memory. Developing online classification methods is becoming increasingly important. Online classification approaches have been subject to several assumptions in recent years, such as an independent and consistent distribution of initial values. In this paper, we present a general online Bayesian classification algorithm that can be adapted to handle streaming data based on the Monte Carlo fusion method. Rather than storing the entire raw data, we only update the posterior with historical data and the current batch of data. In addition, the proposed method has the capability of being applied to a wide range of situations with a high degree of accuracy, even in the case of data imbalances and discrepancies between sub-posterior distributions. The proposed algorithm was validated and evaluated through a comprehensive simulation study.

Assessing the impact of climate change on high return levels of peak flows in Bavaria applying the CRCM5 large ensemble

Abstract. ​​​​​​​Severe floods with extreme return periods of 100 years and beyond have been observed in several large rivers in Bavaria in the last 3 decades. Flood protection structures are typically designed based on a 100-year event, relying on statistical extrapolations of relatively short observation time series while ignoring potential temporal non-stationarity. However, future precipitation projections indicate an increase in the frequency and intensity of extreme rainfall events, as well as a shift in seasonality. This study aims to examine the impact of climate change on the 100-year flood (HF100) events of 98 hydrometric gauges within hydrological Bavaria. A hydrological climate change impact (CCI) modeling chain consisting of a regional Single Model Initial-condition Large Ensemble (SMILE) and a single hydrological model was created. The 50 equally probable members of the Canadian Regional Climate Model version 5 large ensemble (CRCM5-LE) were used to drive the hydrological model WaSiM (Water balance Simulation Model) to create a hydro-SMILE. As a result, a database of 1500 model years (50 members × 30 years) per investigated time period was established for extreme value analysis (EVA) to illustrate the benefit of the hydro-SMILE approach for a robust estimation of HF100 based on annual maxima (AM) and to examine the CCI on the frequency and magnitude of HF100 in different discharge regimes under a strong-emission scenario (RCP8.5). The results demonstrate that the hydro-SMILE approach provides a clear advantage for a robust estimation of HF100 using the empirical probability of 1500 AM compared to its estimation using the generalized extreme value (GEV) distribution of 1000 samples of typically available time series sizes of 30, 100, and 200 years. Thereby, by applying the hydro-SMILE framework, the uncertainty from statistical estimation can be reduced. The study highlights the added value of using hydrological SMILEs to project future flood return levels. The CCI of HF100 varies for different flow regimes, with snowmelt-driven catchments experiencing severe increases in frequency and magnitude, leading to unseen extremes that impact the distribution. Pluvial regimes show a lower intensification or even decline. The dynamics of HF100 driving mechanisms depict a decline in snowmelt-driven events in favor of rainfall-driven events, an increase in events driven by convective rainfall, and almost no change in the ratio between single-driver and compound events towards the end of the century.

Social Network Forensics Analysis Model Based on Network Representation Learning

The rapid evolution of computer technology and social networks has led to massive data generation through interpersonal communications, necessitating improved methods for information mining and relational analysis in areas such as criminal activity. This paper introduces a Social Network Forensic Analysis model that employs network representation learning to identify and analyze key figures within criminal networks, including leadership structures. The model incorporates traditional web forensics and community algorithms, utilizing concepts such as centrality and similarity measures and integrating the Deepwalk, Line, and Node2vec algorithms to map criminal networks into vector spaces. This maintains node features and structural information that are crucial for the relational analysis. The model refines node relationships through modified random walk sampling, using BFS and DFS, and employs a Continuous Bag-of-Words with Hierarchical Softmax for node vectorization, optimizing the value distribution via the Huffman tree. Hierarchical clustering and distance measures (cosine and Euclidean) were used to identify the key nodes and establish a hierarchy of influence. The findings demonstrate the effectiveness of the model in accurately vectorizing nodes, enhancing inter-node relationship precision, and optimizing clustering, thereby advancing the tools for combating complex criminal networks.

Using the classical model for structured expert judgment to estimate extremes: a case study of discharges in the Meuse River

Abstract. Accurate estimation of extreme discharges in rivers, such as the Meuse, is crucial for effective flood risk assessment. However, hydrological models that estimate such discharges often lack transparency regarding the uncertainty in their predictions. This was evidenced by the devastating flood that occurred in July 2021, which was not captured by the existing model for estimating design discharges. This article proposes an approach to obtain uncertainty estimates for extremes with structured expert judgment using the classical model (CM). A simple statistical model was developed for the river basin, consisting of correlated generalized extreme value (GEV) distributions for discharges from upstream tributaries. The model was fitted to seven experts' estimates and historical measurements using Bayesian inference. Results were fitted only to the measurements were solely informative for more frequent events, while fitting only to the expert estimates reduced uncertainty solely for extremes. Combining both historical observations and estimates of extremes provided the most plausible results. The classical model reduced the uncertainty by appointing the most weight to the two most accurate experts, based on their estimates of less extreme discharges. The study demonstrates that with the presented Bayesian approach that combines historical data and expert-informed priors, a group of hydrological experts can provide plausible estimates for discharges and potentially also other (hydrological) extremes with relatively manageable effort.

Опросник академической компетентности студентов

<p>The results of the development and standardization of an academic competence questionnaire are presented. The method is based on the understanding of academic competence as a complex of independently developed personal qualities, abilities and skills that ensure the success of educational activities in conditions of its variability during higher education. The theoretical model includes 6 structural and content components (academic motivation, self-organization of educational activities, emotional self-regulation, skills and abilities to work with information, proficiency in oral and written language, communication skills), presented at 3 levels of generality and freedom of transformation (reproductive, reflective, functional). Based on the results of Study 1 (N=199), the preliminary version of the questionnaire was reduced from 144 to 69 items. Comparing the results of successful and unsuccessful students confirmed predictive validity. External criterion validity using the contrast group method is based on comparison of groups of undergraduate and graduate students, correlations with self-organization and academic motivation questionnaires (AMS). Study 2 (N=355) demonstrated a relatively acceptable fit of the data to the theoretical model based on confirmatory factor analysis, consistency with educational experience questionnaire scales. The internal consistency of the scales was confirmed by Cronbach's alpha indicators based on data from studies 1 and 2. Descriptive statistics for Study 2 are provided. Standardization is proposed based on percentile values, because the distributions of values for most scales differ significantly from normal.</p>

Image Feature Extraction Using Symbolic Data of Cumulative Distribution Functions

Symbolic data analysis is an emerging field in statistics with great potential to become a standard inferential technique. This research introduces a new approach to image feature extraction using the empirical cumulative distribution function (ECDF) and distribution function of distribution values (DFDV) as symbolic data. The main objective is to reduce the dimension of huge pixel data by organizing them into more coherent pixel-intensity distributions. We propose a partitioning method with different breakpoints to capture pixel intensity variations effectively. This results in an ECDF representing the proportion of pixel intensities and a DFDV representing the probability distribution at specific points. The novelty of this approach lies in using ECDF and DFDV as symbolic features, thus summarizing the data and providing a more informative representation of the pixel value distribution, facilitating image classification analysis based on intensity distribution. The experimental results underscore the potential of this method in distinguishing image characteristics among existing image classes. Image features extracted using this approach promise image classification analysis with more informative image representations. In addition, theoretical insights into the properties of DFDV distribution functions are gained.

Evaluation of the Condition of the Bottom of the Tanks for Petroleum Products-Forecast of the Remaining Operating Life

Corrosion rate measurements in the tank enable us to develop a new model for predicting the remaining operating period of the tank. The empirical model for the study of bottom plate thicknesses and corrosion pits is more conservative than the standard linear model, as it considers the autocatalytic nature of the corrosion process. We used the double exponential distribution of maximum values (Gumbel's) to evaluate the maximum depth of pits, and the double exponential distribution of minimum values to evaluate the minimum values of the plate thickness. A comparison of the values of the parameters obtained using linear extrapolation and exponential models indicates the unreliability of linear extrapolation, since disregarding dynamic processes underestimates the actual rate of corrosion.

The spatial overlay effect of urban waterlogging risk and land use value

Urban waterlogging poses a severe threat to lives and property globally, making it crucial to identify the distribution of urban value and waterlogging risk. Previous research has overlooked the heterogeneity of value and risk in spatial distribution. To identify the overlay effect of urban land value and risk, this study employs the Entropy Weighting Method (EM) to assess urban value, Principal Component Analysis (PCA) to determine waterlogging risk and key areas (RK), local Moran's I (SC) to identify key areas (HK), and finally Bivariate local Moran's I (DC) to comprehensively evaluate urban value and waterlogging risk to delineate key areas (BH). The results indicate that waterlogging risk is primarily influenced by proximity to water systems (PCA coefficient: 0.567), population density (0.550), and rainfall (0.445). There is a positive correlation between urban value and waterlogging risk, with a global Moran's I of 0.536, indicating that areas with higher urban value also face greater waterlogging risk. The DC method improved identification precision, reducing the BH area by 6.42 and 3.51 km2 compared to RK and HK, accounting for 25.50 % and 15.76 % of the RK and HK identified areas, respectively. At present, rescue resources can access less than one-third of the area within 5 min, but with the DC method, during the centennial rainfall scenario, the accessibility rate within 5 min for the BH area reaches 63 %, and all BH key areas can be covered within 15 min. This study provides a new methodology for identifying key areas of waterlogging disasters and can be used to enhance urban rescue efficiency and the precision management of flood disasters.

Sex as a predictor of clinical phenotype and determinant of immune response in IgG4-related disease: a retrospective study of patients fulfilling the American College of Rheumatology-European League Against Rheumatism classification criteria.

IgG4-related disease is a multiorgan fibroinflammatory disease considered to have an autoimmune origin. Case series describing individual organ involvement have suggested differences in phenotypic expression between males and females. We aimed to characterise differences in IgG4-related disease manifestations between male and female patients in a large single-centre cohort. In this retrospective, single-centre cohort study, patients were recruited from the Massachusetts General Hospital Rheumatology Clinic (Boston, MA, USA) and classified according to the American College of Rheumatology-European Alliance of Associations for Rheumatology (ACR-EULAR) classification criteria. Only patients satisfying the ACR-EULAR classification criteria were included in the study. Data on age at diagnosis, organ involvement at baseline, treatment status, and pre-treatment laboratory values were collected. Circulating plasmablasts and B-cell subsets were quantitated by flow cytometry. Active disease was defined by an IgG4-related disease Responder Index score of more than 0. Laboratory values were analysed for patients who were untreated at baseline and had active IgG4-related disease. The main outcomes were assessed in all participants with available data. Of the 564 participants enrolled in the Massachusetts General Hospital Rheumatology Clinic IgG4-related disease Registry, 328 fulfilled ACR-EULAR classification criteria and were included between January, 2008, and May, 2023. There was a strong male predominance (male:female ratio 2·2:1) with 226 (69%) males and 102 (31%) females, which contrasted markedly with our general rheumatology clinic population (0·4:1; p<0·001). The male predominance increased with each decade of life starting at age 40 years. On average, male patients were 5·5 years older at diagnosis than female patients (63·7 years vs 58·2 years; p=0·0031). We observed male patients to have higher ACR-EULAR classification criteria scores at baseline with a median score of 35·0 (IQR 28·0-46·0), compared with 29·5 (25·0-39·0) for females (p=0·0010). The proportion of male patients with pancreatic and renal involvement was almost double the proportion observed in female patients (50% of the male patients had pancreatic involvement, compared with about 26% of the female patients; p<0·0001). Male patients were more likely to have serological abnormalities at baseline. The distribution of IgG4 values differed significantly between male an female sexes, favouring higher values in males. We found that male patients with IgG4-related disease were more likely to have active B-cell responses in the blood as defined by plasmablast expansions. IgG4-related disease is unusual among autoimmune diseases in that it is more likely to affect males than females and to present with a striking sex-dependent organ distribution and degree of B-cell response. These findings highlight important variation between IgG4-related disease and other conditions generally believed to have an autoimmune basis. Most autoimmune diseases, by contrast to IgG4-related disease, demonstrate pronounced predilections for affecting females more frequently than males. Hypotheses surrounding the cause and pathophysiology of this condition need to consider this unusual sex distribution among patients with IgG4-related disease. National Institutes of Health, National Institute of Allergy and Infectious Diseases, Rheumatology Research Foundation, and the National Institute of Arthritis and Musculoskeletal and Skin Diseases.

Population study of pupillary unrest in ambient light

IntroductionPupillary unrest in ambient light (PUAL) describes the fluctuation of pupil diameter observed in normal, awake subjects under typical levels of indoor light. PUAL becomes low to absent in young healthy subjects during opioid intoxication. We sought to determine the age-related distribution of PUAL values in a random sample of ambulatory participants. MethodsSubjects ≥18 years of age were recruited. All were identified by age range (18–29, 30–49, 50–69, and ≥70), and surveyed for diabetes, beta-blocker use, and prior 24-hour opioid use. Relationship between mean PUAL, age group, comorbidity and opioid use were examined by Kruskal Wallis test, and PUAL and was modeled using stepwise multilevel linear regression, including diabetes, beta blocker use, prior 24-hour opioid use, autonomic dysfunction, and pupil diameter as fixed effects and subject as random effect. ResultsAmong 150 subjects, 17 reported diabetes, 12 reported beta-blocker use, 14 reported prior 24-hour opioid use, and 120 reported no comorbid conditions. PUAL declined in higher age categories (by 0.0307, P < 0.001), with diabetes (by 0.0481, P = 0.025), and with beta-blocker use (by 0.0616, P = 0.005). Opioid related PUAL decline was observed, but statistical significance varied by model. Among healthy subjects, no PUAL value fell within range indicating high likelihood of opioid toxicity based on previous data from healthy subjects undergoing opioid infusion. ConclusionPUAL declined in higher age groups, diabetes and beta-blocker use, conditions associated with impaired autonomic function, and with opioid use but significance varied depending on the chosen model.

Charged particle reflection in a magnetic mirror

We study the reflection of electrons in a magnetic field mirror. It is a field with a gradient from a lowest value B0 to a largest one Bm. With this purpose, Montecarlo simulations are made. We use a number of 5,000 particles with random pitch angles. The frequency distribution of these random values follows a Gaussian distribution centered at \theta = 0 and with standard deviation \sigma. Various values of \sigma and also different values of the B0 /Bm ratio are used in the simulations. The simulations show that \sigma has an important influence on the confinement of charged particles, for the different B0 /Bm ratios here studied. However, the percent of reflected particles differs from a B0 /Bm ratio to another in the whole range of \sigma, although for \sigma = 10 the differences between different ratios are small (&lt;5%). The number of reflected particles increases very rapidly with \sigma, in the range of 10° to 40°. For the \sigma range from 20° to 40°, the percent of reflected particles is \leq 20% larger for B0 /Bm = 0.10 than for the 0.55 ratio.

An extreme value analysis of water levels at the Akosombo dam, Ghana

The Akosombo Dam is the largest dam in Ghana and is linked to the world's largest man-made lake by surface area. The top of flood control pool of the dam has been breached a number of times, so it is of interest to know the corresponding probability. The paper fits the generalized extreme value distribution to the extreme water levels – with all three of its parameters (including the shape parameter) accounting for various linear trends, seasonality and cyclic trends with respect to time, the first time such a model has been fitted. The fitted model contains in total 50 parameters. It provided an adequate fit, as evaluated by probability plots, quantile plots, and the Kolmogorov-Smirnov test. It is used to provide return level estimates as well as probabilities of the top of flood control pool of the dam being breached.

Hydrogeochemical Characteristics and Enrichment Regularities of Groundwater Uranium in the Erlian Basin, China

Groundwater is a key medium for the migration and enrichment of elements into ore bodies in sandstone-type uranium deposits, with anomalies of uranium elements and their associated components in water serving as indicators for exploring such deposits. The Erlian Basin, located in central-northern Inner Mongolia, is a crucial production area for sandstone-type uranium deposits in China. The groundwater flow system in the basin plays a significant role in the formation of uranium deposits. To study the hydrogeochemical characteristics and enrichment regularities of groundwater uranium in the Erlian Basin, 269 groundwater samples were collected. Hydrogeochemical analysis methods, such as Piper diagrams, Gibbs diagrams, and contour maps, were employed to determine the distribution characteristics and occurrence forms of groundwater uranium in the study area. The primary water chemistry types in the study area were HCO3-Ca•Na, HCO3-Na, HCO3•SO4-Na, and SO4•HCO3-Na•Ca. The distribution range of uranium content in groundwater in the study area was 0.1–453 μg/L (average of 53.08 μg/L). A comprehensive analysis, based on the direction of groundwater flow and changes in the redox environment, suggested that the highest uranium values in groundwater were distributed in the runoff and discharge areas, with uranium anomaly points existing in areas with alternating oxidation and reduction zones. These uranium anomaly points were in good agreement with the known large uranium deposits of Nuheting, Qiharigetu, Daoersu, and others, thereby predicting six other uranium hydrochemical anomaly points as prospective areas for uranium mineralization. The pH value distribution range in the study area’s groundwater was 6.8–9.1, and the redox potential (Eh) value range was -126–52 mV, indicating that uranium exists in groundwater in the form of UO2(CO3)34- and UO2(CO3)22-. An increase in bicarbonate (HCO3-) is conducive to uranium dissolution. The enrichment and occurrence forms of uranium in groundwater are influenced by the concentrations of Fe and (Ca2++Mg2+), with uranium precipitation and enrichment occurring as the groundwater evolves and changes in the redox environment. The uranium content in deep wells is higher than in nearby shallow wells, indicating that deep water circulation is beneficial for mineralization. The pH, Eh, HCO3-, Fe, and other hydrogeochemical indicators are indicative of uranium enrichment; thus, they can be considered as reference bases when exploring for potential uranium resources.

MEASURING THICKNESS OF FLAX FIBRES IN A CLUSTER USING COMPUTER VISION

Authors analyse a method of indirect measurement of flax fibre linear density using its thickness, based on computer vision approach and “distance transform” algorithm in particular. A number of flaws of this method are identified. The main reason for the discrepancy is the effect of crossed fibres within the analysed sample. The secondary reason is the specific shape of fibre’s topological skeletonnear its tips. Based on these observations, a software model of the problem has been created. The software produces a simplified image of crossed fibres with specified properties, and measures this image using the above method. The effect of various factors on the discrepancy in the distribution of thickness values appears to match well with theoretical analysis. Additionally, a number of ways to improve the software model are outlined, related to how typicallyfibres are oriented within the analysed sample.

Strategies for multivariate characterization and classification of pulps and papers by near-infrared spectroscopy

BackgroundMultivariate calibration by Partial Least Squares (PLS) on near-infrared data has been applied successfully in several industrial sectors, including pulp and paper. The creation of multivariate calibration models relies on a set of well-characterised samples that cover the range of the intended application. However, sample sets that originate from an industrial process often show an uneven distribution of reference values. This can be addressed by curation of the reference data and the methodology for multivariate calibration. It needs to be better understood, how these approaches affect the quality and scope of the final model. ResultsWe describe the effect of log10 transformation of the reference values, regular PLS, robust PLS, the newly introduced bin PLS, and their combinations to select more evenly distributed reference values for the quantification of five pulp characteristics (kappa number, R18, R10, cuen viscosity, and brightness; 200 samples) by near-infrared spectroscopy. The quality of the models was assessed by root mean squared error of prediction, calibration range, and coverage of sample types. The best models yielded uncertainty levels equivalent to that of the reference measurement. The optimal approach depended on the investigated reference value. SignificanceRobust PLS commonly gives the model with the lowest error, but this usually comes at the cost of a notably reduced calibration range. The other approaches rarely impacted the calibration range. None of them stood out as superior; their performance depended on the calibrated parameter. It is therefore worthwhile to investigate various calibration options to obtain a model that matches the requirements of the application without compromising calibration range and sample coverage.

Study on Rapid Non-Destructive Detection Method of Corn Freshness Based on Hyperspectral Imaging Technology

(1) Background: To achieve the rapid, non-destructive detection of corn freshness and staleness for better use in the storage, processing and utilization of corn. (2) Methods: In this study, three varieties of corn were subjected to accelerated aging treatment to study the trend in fatty acid values of corn. The study focused on the use of hyperspectral imaging technology to collect information from corn samples with different aging levels. Spectral data were preprocessed by a convolutional smoothing derivative method (SG, SG1, SG2), derivative method (D1, D2), multiple scattering correction (MSC), and standard normal transform (SNV); the characteristic wavelengths were extracted by the competitive adaptive reweighting method (CARS) and successive projection algorithm (SPA); a neural network (BP) and random forest (RF) were utilized to establish a prediction model for the quantification of fatty acid values of corn. And, the distribution of fatty acid values was visualized based on fatty acid values under the corresponding optimal prediction model. (3) Results: With the prolongation of the aging time, all three varieties of corn showed an overall increasing trend. The fatty acid value of corn can be used as the most important index for characterizing the degree of aging of corn. SG2-SPA-RF was the quantitative prediction model for optimal fatty acid values of corn. The model extracted 31 wavelengths, only 12.11% of the total number of wavelengths, where the coefficient of determination RP2 of the test set was 0.9655 and the root mean square error (RMSE) was 3.6255. (4) Conclusions: This study can provide a reliable and effective new method for the rapid non-destructive testing of corn freshness.

Research on the influence of pits on the propagation law of explosion shock waves

To study the influence of pits on shock wave propagation and the propagation of shock waves within pits, numerical simulations were used to calculate the distribution of overpressure peak values at the bottom and rear of the pits at 5 depths and 5 explosion center distances. The results indicate that diffraction occurs when the explosion shock wave passes through the edge of the crater; The peak overpressure of the shock wave at the bottom of the pit exhibits a "spoon shaped" distribution, and the peak overpressure on the right side is significantly higher than that on the left side; There are two distinct boundary regions for the overpressure of the shock wave behind the crater due to the influence of the crater; The distance between the explosion centers has little effect on the distribution trend of the overpressure peak of the shock wave at the bottom and rear of the pit, mainly affecting the magnitude of the overpressure peak. The research results provide theoretical support for the analysis of the propagation law of explosion shock waves and guidance for the design of protective engineering structures, with significant engineering application value.

ESMAC Best Paper Award 2023: Increased knee flexion in participants with cerebral palsy results in altered stresses at the distal femoral growth plate compared to a typically developing cohort

IntroductionFemoral deformities are highly prevalent in children with cerebral palsy (CP) and can have a severe impact on patients’ gait abilities. While the mechanical stress regime within the distal femoral growth plate remains underexplored, understanding it is crucial given bone's adaptive response to mechanical stimuli. We quantified stresses at the distal femoral growth plate to deepen our understanding of the relationship between healthy and pathological gait patterns, internal loading, and femoral growth patterns. MethodsThis study included three-dimensional motion capture data and magnetic resonance images of 13 typically developing children and twelve participants with cerebral palsy. Employing a multi-scale mechanobiological approach, integrating musculoskeletal simulations and subject-specific finite element analysis, we investigated the orientation of the distal femoral growth plate and the stresses within it. Limbs of participants with CP were grouped depending on their knee flexion kinematics during stance phase as this potentially changes the stresses induced by knee and patellofemoral joint contact forces. ResultsDespite similar growth plate orientation across groups, significant differences were observed in the shape and distribution of growth values. Higher growth rates were noted in the anterior compartment in CP limbs with high knee flexion while CP limbs with normal knee flexion showed high similarity to the group of healthy participants. DiscussionResults indicate that the knee flexion angle during the stance phase is of high relevance for typical bone growth at the distal femur. The evaluated growth rates reveal plausible results, as long-term promoted growth in the anterior compartment leads to anterior bending of the femur which was confirmed for the group with high knee flexion through analyses of the femoral geometry. The framework for these multi-scale simulations has been made accessible on GitHub, empowering peers to conduct similar mechanobiological studies. Advancing our understanding of femoral bone development could ultimately support clinical decision-making.

A deep learning-based super-resolution method for building height estimation at 2.5 m spatial resolution in the Northern Hemisphere

Building height is an important indicator for assessing the level of urban development along the vertical dimension. Existing large-scale building height estimation studies focus on coarse spatial resolution (e.g., 10, 500, and 1000 m), which cannot reveal height variations across buildings in urban areas. High-resolution images (e.g., < 5 m resolution) can support building-scale height estimation, but they usually have small spatial coverage and are not openly accessible. In this context, we proposed a deep learning-based super-resolution method to generate building height maps at a spatial resolution of 2.5 m using Sentinel-1/2 images. The proposed method consisted of two parts: 1) a super-resolution module (SR) for learning high-resolution features; and 2) a height stratification estimation module (HS) for guiding the network to learn different height levels to mitigate the imbalanced distribution of height values. We created an open building height dataset with 45,000 samples covering multiple urban areas in the Northern Hemisphere, including China, the conterminous United States (CONUS), and Europe. Experimental results showed that for height estimation at the pixel level, the proposed method obtained a root mean square error of 10.318 m in China, 5.654 m in CONUS, and 4.113 m in Europe, respectively. Predicted results provided rich spatial details, due to the inclusion of the super-resolution module, which was heavily missed by existing large-scale studies. Moreover, we calculated the mean and standard deviation of building height in 301 urban centers, each having at least a population of 500,000, and found that the buildings in China were the highest (11.353 m ± 9.543 m), followed by CONUS (8.487 m ± 6.202 m) and Europe (8.136 m ± 5.020 m). Ablation studies indicated that the joint use of Sentinel-1/2 images and the proposed modules (SR and HS) can effectively improve the performance of building height estimation. The building dataset we generated provides great potential in high-resolution database updating, urban planning, and natural disaster assessment, and indeed, a new perspective of how we can utilize cutting-edge satellite imaging technology in urban observation, measurement, monitoring, and management. The dataset and code of this study will be available at: https://github.com/lauraset/Super-resolution-building-height-estimation.

Threat and Vulnerability of Thermal Discomfort in Yogyakarta City

The world’s attention to climate issues is increasing as the issue of global warming and climate change emerges. Several studies with the scope of cities and urban areas concluded that urban surface temperatures have an increasing trend. This phenomenon will have an impact on the thermal comfort of urban population. Yogyakarta City as one of the National Activity Centers (PKN), is experiencing rapid urban development and leads to become a metropolitan city so it needs to be studied regarding its thermal comfort. This research is intended to identify the potential and vulnerability of thermal discomfort in Yogyakarta City. The identification of potential discomfort is carried out using the Discomfort Index (DI) approach and the Land Surface Temperature (LST) value. Meanwhile, vulnerability is identified in the social aspects of population. Furthermore, the potential and vulnerability scores are assessed to get an overview of thermal discomfort mitigation priorities. Based on DI analysis, thermal discomfort is felt by more than 50% of the Yogyakarta’s residents and it is estimated that in the future will get worse. By looking at the distribution of LST values, the high potential for discomfort is in the city center and has expanded to the north and northeast. Meanwhile, areas with high vulnerability are located in the center and south side of Yogyakarta City. From the combined assessment of threat and vulnerability, the areas that are the top priority for mitigation are in the center of Yogyakarta City, which include 7 districts, namely Danurejan, Gedongtengen, Gondomanan, Jetis, Kraton, Ngampilan, and Pakualaman. The medium priority districts are Gondokusuman and Wirobrajan.