Original Database Research Articles

Safety and Risk of Operating Facilities: Rapid Assessment Methodology

Two methods for methodology uses “risk indices” (dam condition index I), as well as “fuzzy logic methods” to combine the original quantitative and qualitative (expert) information on the operated dam condition. The approach applied is in accordance with the recommendations of IEC 31010: 2019. Risk Management — Risk Assessment Techniques. Deterministic assessments of the condition in the form of “risk indices” were also used as input data in assessing the probability of the failure and in developing a probabilistic risk assessment methodology. The original database, as well as the damage assessment scale, modified in the course of the research, summarize the experience of surveys and examination of safety declarations of more than 180 hydraulic works in Russia. A description is given of the methods of initial assessment and digitization (quantification) of the condition index I, as well as combining the initial quantitative and qualitative (expert) information about various damages. The practical feasibility and the possibility of categorizing (with fuzzy boundaries) conditions and levels of damage to hydraulic structures are shown. With regard to various conditions and levels of damage, proposals are made for practical actions to ensure the safety of dams in the process of monitoring, inspection, development of a reconstruction project and its expertise. As a result of the research, the dependence of the probability of the failure pfailure on the average value of the Iср index has been established and the graph “pfailure – Iср” is presented, which is well described by the exponential and is convenient for practical application. The value of I ср is determined according to the data of visual and instrumental control of the dam condition of the as well as according to expert estimates. The methodology for creation the indicated graph is presented. The creation of this graph became possible on the basis of: statistical processing, proof of the “normality” of the distribution of the I indices and the estimation (according to the distribution functions constructed for each level of damage) the probability of the failure pfailure, as well as during the survey and examination of the dam project. Proposals are formulated for the practical application of the proposed methods for assessing the risk of operating dams, and the near-term prospect of research in the field of risk assessment and ensuring dam safety is formulated.

Analysis of bankruptcy factors of the Russian developers

In 2019, there was a transition to project financing of housing construction in Russia. As a result, by the end of 2021, more than 73% of the area of apartments under construction are sold using escrow accounts. However, about 21% of the apartments being sold are being built by developers who are in the process of bankruptcy. Regional primary housing markets are, firstly, segmented, and secondly, in some regions – mainly in the regions of the Far North, Eastern Siberia and the Far East – there is a high concentration of developers, which can lead to additional negative consequences in terms of the development of housing markets of these regions in case of bankruptcy of regional companies. In this situation, it is important to form additional tools for assessing the risks of bankruptcy of companies in order to be able to prevent negative consequences in a timely manner in the future. Despite the fact that a large number of scientific publications of both foreign and domestic researchers have been devoted to the issues of modeling the risks of bankruptcy of firms, only a small number of authors have addressed the issues of assessing the financial stability of Russian developers. The novelty of this study consists in constructing a model for predicting the probability of bankruptcy of developers in Russia (that is, a risk management system) based on an original database, which, in addition to financial coefficients from companies' reports, also includes industry-specific indicators. The aim of the work is to build a quantitative model of bankruptcy risks of developers (in the short and medium term) based on easily observable characteristics of their activities. The results obtained show that the probability of bankruptcy is high for companies with low values of return on assets, asset turnover ratio and current liquidity ratio. Additional factors that reduce the likelihood of bankruptcy are the fact of the developer's group membership, experience in housing construction. Also, the financial stability of companies in the industry as a whole is affected by macroeconomic shocks.

Automated Traffic Incident Detection: Coping With Imbalanced and Small Datasets

Automatic incident detection (AID) has always been one of the focus issues in the field of transportation. However, due to the contingency and randomness of traffic incident, traffic incident samples are scarce and far less than non-incident samples. Therefore, unlike other scenarios using large-scale deep networks, traffic incident detection tackle at small and imbalanced sample size. Imbalanced, small sample data sets, inappropriate and incomplete initial variable sets make the AID model insensitive to incident samples, resulting in unsatisfactory model performance (low detection rate or high false alarm rate). Therefore, a hybrid AID method (SASYNO-RF-RSKNN) is proposed using self-adaptive synthetic over-sampling, random forest and random subspace k nearest neighbor. First, the spatial-temporal and real-time characteristics of traffic stream are used for the selection of appropriate initial variables to construct a relatively complete set of initial variables. Second, the SASYNO oversampling method is used to expand the original imbalanced sample database, so that the number of minority class samples is consistent with the number of most class samples. Then, feature variables are selected from the initial variables using the RF algorithm. Finally, the RSKNN ensemble algorithm with feature variables as input is employed to detect traffic incident. In addition, six indexes are used to evaluate model performance, including accuracy (ACC), false alarm rate (FAR), detection rate (DR), precision, Matthews correlation coefficient (MCC) and F1-score. Simultaneously, we also designed horizontal and vertical contrast experiments, and the experimental results show that SASYNO-RF-RSKNN model has superior performance. It is worth mentioning that experiments are implemented on two real-world datasets. Most indexes of the proposed model are the best compared with other five excellent machine learning algorithms. On the whole, the proposed model has a dependable and high-performance for traffic incident detection.

Нейромережевий метод виявлення текстурних аномалій у цифровому зображенні

Modern computer vision systems often use neural networks to process images. But to use neural networks, you need to create databases to train these neural networks. In some cases, creating a training database takes the vast majority of the project's financial and human resources. Therefore, the actual task of finding methods to improve the quality of learning neural networks on small data is considered in this article. The ability to process data, which nature was not present in the original training database is relevant, also. To solve the problem of improving the quality of image segmentation by textural anomalies, this research is proposed to use as input to the neural network not only the image but also its local statistic data. It can increase the information content of the input information for the neural network. Therefore, neural networks do not need to learn to choose statistical features but simply use them. This investigation classifies the requirements for image segmentation systems to indicate atypical texture anomalies. The literature analysis revealed various methods and algorithms for solving such problems. As a result, in this science work, the process of finding features in the photo is summarized in stages. The division into stages of search for features allowed to choose arguments for methods and algorithms that can perform the task. At each stage, requirements were formed for methods, that allowed separate the transformation of image fragments into a vector of features by using an artificial neural network (trained on a separate image of the autoencoder). Statistical features supplement by the vector of features of the image fragment. Numerous experiments have shown that the generated feature vectors improve the classification result for an artificial Kohonen neural network, which is able to detect atypical image fragments.

Design of a prefixspan algorithm based on prefix position form

PrefixSpan algorithm based on sequence pattern is an association algorithm without generating candidate sequences and only needs to scan the original sequence database twice in the whole operation process. Compared with other sequence algorithms, the PrefixSpan algorithm is more efficient and fast. In this paper, PrefixSpan Base on Prefix Position Form, PPFPrefixSpan is proposed by studying the characteristics of the PrefixSpan algorithm and optimizing it based on it. , which solves the problem that the PrefixSpan algorithm produces too many duplicate projection databases in the process of operation, and optimates the PrefixSpan algorithm in space/time.

Deep Learning-Based Algorithm for Multi-Type Defects Detection in Solar Cells with Aerial EL Images for Photovoltaic Plants

Defects detection with Electroluminescence (EL) image for photovoltaic (PV) module has become a standard test procedure during the process of production, installation, and operation of solar modules. There are some typical defects types, such as crack, finger interruption, that can be recognized with high accuracy. However, due to the complexity of EL images and the limitation of the dataset, it is hard to label all types of defects during the inspection process. The unknown or unlabeled create significant difficulties in the practical application of the automatic defects detection technique. To address the problem, we proposed an evolutionary algorithm combined with traditional image processing technology, deep learning, transfer learning, and deep clustering, which can recognize the unknown or unlabeled in the original dataset defects automatically along with the increasing of the dataset size. Specifically, we first propose a deep learning-based features extractor and defects classifier. Then, the unlabeled defects can be classified by the deep clustering algorithm and stored separately to update the original database without human intervention. When the number of unknown images reaches the preset values, transfer learning is introduced to train the classifier with the updated database. The fine-tuned model can detect new defects with high accuracy. Finally, numerical results confirm that the proposed solution can carry out efficient and accurate defect detection automatically using electroluminescence images.

SecEDMO: Enabling Efficient Data Mining with Strong Privacy Protection in Cloud Computing

Frequent itemsets mining and association rules mining are among the top used algorithms in the area of data mining. Secure outsourcing of data mining tasks to the third-party cloud is an effective option for data owners. However, due to the untrust cloud and the distrust between data owners, the traditional algorithms which only work over plaintext should be re-considered to take security and privacy concerns into account. For example, each data owner may not be willing to disclose their own private data to others during the cooperative data mining process. The previous solutions are either not sufficiently secure or not efficient. Therefore, we propose a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Sec</u> ure and <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</u> fficient <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</u> ata <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</u> ining <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</u> utsourcing (SecEDMO) scheme for secure outsourcing of frequent itemsets mining and association rules mining over the joint database (i.e., database aggregated from multiple data owners) in the paradigm of cloud computing. Based on our customized lightweight symmetric homomorphic encryption algorithm and a secure comparison algorithm, SecEDMO can ensure strong privacy protection and low data mining latency simultaneously. Moreover, the well-designed virtual transaction insertion algorithm can hide the information of the original database while still preserving the cloud’s ability to perform data mining over the obfuscated data. By evaluation of a numerical experiment and theoretical comparisons, the correctness, security, and efficiency of SecEDMO are confirmed.

The Supreme Court, the President, and Congress

AbstractPresidents may react to Supreme Court decisions by supporting or opposing them in their public rhetoric and by calling on Congress to take action to alter or implement the Court’s decisions. We investigate this unique form of lawmaking using an original database of presidential calls to Congress and congressional reactions. We find that presidents call for congressional action to pursue their policy goals and enhance the power of the presidency; we also find that Congress reacts when it is asked to do so by both the Court and the president, as well as when presidents support the implementation of the Court’s decisions.

RANDOM FOREST MODEL TO PREDICT THE HEIGHT OF EUCALYPTUS

Eucalyptus (Eucalyptus urograndis) production has significantly advanced over the past few years in Brazil, especially with regard to acreage and productivity. Machine learning has made significant advances in most varied fields of agrarian sciences. In this context, this study aimed to use physicochemical variables of the soil as well as climatic and dendrometric variables of eucalyptus to predict its height using the random forest algorithm. The study was conducted in the municipality of Três Lagoas, in Mato Grosso do Sul, Brazil. The original database consisted of 49 soil physicochemical variables collected at 0–0.20 m and 0.20–0.40 m, two dendrometric and four climatic variables, and one response variable related to the height of eucalyptus. A correlation matrix was applied to select variables. Furthermore, modeling was performed using the random forest algorithm, which performed well (r = 0.98, R2 = 0.96) in predicting the height of eucalyptus. Overall, the most important variables to predict the eucalyptus plant height included diameter at breast height (DBH), phosphorus content (P1), gravimetric moisture (GM1) at a soil depth between 0.00 m and 0.20 m, and exchangeable aluminum content (Al2) between 0.20 m to 0.40 m of soil.

Policy networks in refugee education

This paper presents a social network analysis of partnerships in refugee education and shows how relationships between organisations change in response to multiple crises, including political and economic crises, disaster, and COVID-19. We draw insights from an original database of 440 state and nonstate entities engaged in Syria refugee education in Lebanon (2018–2021). We find that partnerships in refugee education comprise a complex web of global, national, and local actors. The policy network arising from these partnerships reflects power inequities wherein international organisations hold most influence. Moreover, crises impact the network, which expands to include new private actors. While the COVID-19 pandemic is thought to accelerate localisation, our analysis suggests the durability of global power hierarchies in refugee education response.

Privacy Preserving Data Mining Framework for Negative Association Rules: An Application to Healthcare Informatics

Protecting the privacy of healthcare information is an important part of encouraging data custodians to give accurate records so that mining may proceed with confidence. The application of association rule mining in healthcare data has been widespread to this point in time. Most applications focus on positive association rules, ignoring the negative consequences of particular diagnostic techniques. When it comes to bridging divergent diseases and drugs, negative association rules may give more helpful information than positive ones. This is especially true when it comes to physicians and social organizations (e.g., a certain symptom will not arise when certain symptoms exist). Data mining in healthcare must be done in a way that protects the identity of patients, especially when dealing with sensitive information. However, revealing this information puts it at risk of attack. Healthcare data privacy protection has lately been addressed by technologies that disrupt data (data sanitization) and reconstruct aggregate distributions in the interest of doing research in data mining. In this study, metaheuristic-based data sanitization for healthcare data mining is investigated in order to keep patient privacy protected. It is hoped that by using the Tabu-genetic algorithm as an optimization tool, the suggested technique chooses item sets to be sanitized (modified) from transactions that satisfy sensitive negative criteria with the goal of minimizing changes to the original database. Experiments with benchmark healthcare datasets show that the suggested privacy preserving data mining (PPDM) method outperforms existing algorithms in terms of Hiding Failure (HF), Artificial Rule Generation (AR), and Lost Rules (LR).

Exploring the universal healthy human gut microbiota around the World

The human gut holds a special place in the study of different microbial environments due to growing evidence that the gut microbiota is related to host health. However, despite extensive research, there is still a lack of knowledge about the core taxa forming the gut microbiota and, moreover, available information is biased towards western microbiomes in both genome databases and most core taxa studies. To tackle these limitations, we tested a database enrichment strategy and analyzed public datasets of whole-genome shotgun data, generated from 545 fecal samples, comprising three gradients of westernization. The NT database was selected as a baseline of biological diversity, subsequently being combined with various studies of interest related to the human microbiota. This enrichment strategy made it possible to improve classification capacity, compared to the original unenriched database, regarding the various lifestyles and populations studied. The effects of incomplete-taxonomy metagenome-assembled genomes on genome database enrichment were also examined, revealing that, while they are helpful, they should be used with caution depending on the taxonomic level of interest. Moreover, in terms of high prevalence, the core analysis revealed a conserved set of bacterial taxa in the healthy human gut microbiota worldwide, despite apparent lifestyle differences. Such taxa show a set of traits, metabolic roles, and ancestral status, making them suitable candidates for a hypothetical phylogenetic core of mutualistic microorganisms co-evolving with the human species.

Early Diagnosis of Multiple Sclerosis Using Swept-Source Optical Coherence Tomography and Convolutional Neural Networks Trained with Data Augmentation.

Background: The aim of this paper is to implement a system to facilitate the diagnosis of multiple sclerosis (MS) in its initial stages. It does so using a convolutional neural network (CNN) to classify images captured with swept-source optical coherence tomography (SS-OCT). Methods: SS-OCT images from 48 control subjects and 48 recently diagnosed MS patients have been used. These images show the thicknesses (45 × 60 points) of the following structures: complete retina, retinal nerve fiber layer, two ganglion cell layers (GCL+, GCL++) and choroid. The Cohen distance is used to identify the structures and the regions within them with greatest discriminant capacity. The original database of OCT images is augmented by a deep convolutional generative adversarial network to expand the CNN’s training set. Results: The retinal structures with greatest discriminant capacity are the GCL++ (44.99% of image points), complete retina (26.71%) and GCL+ (22.93%). Thresholding these images and using them as inputs to a CNN comprising two convolution modules and one classification module obtains sensitivity = specificity = 1.0. Conclusions: Feature pre-selection and the use of a convolutional neural network may be a promising, nonharmful, low-cost, easy-to-perform and effective means of assisting the early diagnosis of MS based on SS-OCT thickness data.

Components of Context: Respecifying the Role of Context in Migration Research

For a century, scholars have studied immigrant integration in a range of destinations. Yet, the precise role of context in shaping integration outcomes remains poorly understood. Drawing from an analysis of an original database of articles and books in migration studies, I argue that this knowledge gap may be due to two closely related tendencies in the scholarship. First, case selection has relied on criteria such as the immigrant population's size and growth rate that are not clearly connected to integration outcomes. Second, most scholars have studied either heavily urban contexts (with large immigrant populations) or very rural contexts (where the immigrant population is growing rapidly), while much less attention has been given to destinations in-between. To improve the understandings of the role of context in immigrant integration, migration scholars should endeavor to move past population criteria when selecting study sites and to study the full range of contexts where immigrants are settling. To contribute to these efforts, I propose a framework that does not rely on population or newness as criteria for case selection and that focuses, instead, on the components of context that existing research has shown matters for intergenerational mobility. I also introduce a typology of contexts based on possible combinations of four of these components and offer some initial hypotheses of how these context types might affect immigrant integration. The arguments presented here recenter the role of local context in migration studies and contribute to debates about where and how scholars should study context moving forward.

Theoretical investigation on atmospheric reaction mechanism, kinetics and SAR estimations of four-carbon ketones and alcohols

The oxidation mechanism, thermochemistry and kinetics of 3-hydroxy-2-butanone (3H2B) and 2-methyl-1-propanol (2M1P) initiated by NO3 radicals were theoretically investigated at the CCSD(T)//BH&HLYP/6–311++G(d,p) level. The thermodynamic calculation results show that the channels of α-CH and α-CH2 sites are dominant for 3H2B and 2M1P. The strategy of experiment-SAR-DFT combined was proposed for the first time to discuss reaction kinetic information. The estimated rate constants of hydroxylketones and saturated alcohols with the total carbon number of four to six were provided by using the SAR method. The revised and supplemented group substituent factors F(–CH<) and F(–CH2OH) were provided to the original NO3 database, and the values obtained are 1.67 and 18.3. The rate constants for H-atom abstraction reactions of 3H2B and 2M1P with NO3 were evaluated over the temperature range of 250–350 K to verify the experiment-SAR-DFT strategy. The calculated values of 3H2B and 2M1P are 6.93 × 10−16 and 2.67 × 10−15 cm3 molecule−1 s−1 at 298 K, which are in close agreement with the reported experimental results and updated estimated values. Our theoretical research outcomes further attest that the new group substituent factor F(–CH2OH) is reliable. The nighttime atmospheric lifetime of 3H2B and 2M1P were estimated to be around 33 and 9 days, indicating that 3H2B has an effect on the atmospheric environment at night. The results can improve the reliability and accuracy of NO3 radicals kinetic data required to predict the contributions of 3H2B and 2M1P to the atmospheric environment.

What can city leaders do for climate change? Insights from the C40 Cities Climate Leadership Group network

ABSTRACT This paper sheds light on what city leaders can do about climate change in the context of the complex extra-territorial challenges they face. We created an original database of those actions implemented by members of the C40 Cities Climate Leadership Group and investigated which boundary organizations, objects and experiences (‘boundary cooperative arrangements’) can be triggered to undertake climate change actions by city leaders. Through a secondary qualitative analysis, we identified six main actions. The research advances knowledge on both place leadership and collaborative governance, and the findings represent a tool for better tackling the ‘wicked problem’ of climate change.

Prelarge-Based Utility-Oriented Data Analytics for Transaction Modifications in Internet of Things

The Internet of Things (IoT) environment includes things that exchange information using data generated through sensors. The IoT technology can be executed efficiently by providing the necessary information to things instead of transmitting all the data. There is a need for an algorithm that can extract meaningful information for the things from the data. High utility pattern mining, which can handle the characteristics of real-world databases better than traditional pattern mining methods, has been actively researched. The traditional high utility pattern mining techniques find meaningful patterns from static databases. Therefore, these techniques are not suitable for the dynamically changing databases in the real world. In order to solve this problem, a variety of methods that consider the modifications, deletions, and insertions of transactions are proposed. The pre-large concept, which can be efficiently operated by reducing the rescanning of the original database using two thresholds, was also proposed in order to handle the limitations of the methods that are used for static databases. In the pre-large concept, the large patterns and the pre-large patterns that are discovered are maintained and used for the next transaction change. In this paper, we proposed a new method (PIHUP-MOD) implemented as a tree structure in order to handle the transaction modifications. The method has an efficient structure and techniques to mine high utility patterns in the transaction modifications with the pre-large concept. We conducted the performance evaluation, and the experimental results on the real datasets and the synthetic datasets showed that the proposed approach has a better performance than the state-of-the-art approaches in terms of the runtime, the memory usage, and the scalability.

Assessing the International Influence of Private Philanthropy: The Case of Open Society Foundations

Abstract This article evaluates the international influence of Open Society Foundations (OSF) based on its goals and allocation of resources and takes into consideration the concerns raised by its critics. The study analyzes the objectives of this private philanthropic organization as well as its origins and the goals of its founder, George Soros. By assembling and using an original database, it also provides an overall analysis of all OSF grants assigned worldwide in the years 1999–2018 and quantitatively assesses their macro-level impact on democratic governance, freedom of expression, government accountability, and societies that promote justice and equity. Based on the available data, we find no evidence that OSF grants produce a positive impact at the macro-level on these goals nor that they effectively contribute to destabilize countries through protests and mass migrations as its critics claim. These results may have to do with lack of effectiveness or with motivations not matching the outcomes measured here. However, it is early to draw conclusions due to limited data.

Developmental features of the canid proximocaudal femur.

We continued direct morphological studies of the canid coxofemoral joint, considering early-life spatial relationships around the locus of the proximocaudal joint capsule insertion. Our primary goal was to elucidate the postnatal developmental gross anatomy of the proximocaudal femur, among juveniles across Canidae. From an original database of 267 independent (museum) specimens from 11 canid taxa and 1 hybrid taxon, we identified 29 ancient or modern candidate juvenile specimens (nine taxa and one hybrid taxon). Based on optimal ability to recognize landmarks, the best photographic data were categorized into five groups of four each (n= 20). The data groups approximated early juvenile, early-mid juvenile, mid-juvenile, mid-late juvenile; and young adult stages. In this descriptive photographic essay, we demonstrate the developmental spatial proximity among (a) the dorsal meeting of the respective lateral and medial extensions from the growth centers of the femoral head and greater trochanter; (b) the caudodorsal aspect of the coxofemoral joint capsule attachment; (c) a segment of the proximocaudal femoral shaft physis; and (d) an eventual associated mineralized prominence. The latter occurs frequently but not universally, suggesting natural population variability across taxa. Across taxa and juvenile age categories, the morphology thus supports developmental conservation among ancient and modern Canidae. The biomechanical and biological cause-effect implications are not yet clear. For zoological purposes, we apply the term postdevelopmental mineralized prominence to the residual caudolateral surface feature. We extend the original anatomical work of Morgan in zoological and phylogenic arenas, using direct observation of cleared skeletal specimens.

Constructing a regional human rights legal order: The Inter-American Court, national courts, and judicial dialogue, 1988–2014

Abstract Why do courts rely on specific bodies of jurisprudence to justify decisions? We analyze judicial dialogue in the Inter-American System, where the Inter-American Court of Human Rights (IACtHR) has defined its mission as the construction of a regional legal order. This order needs courts at all levels to engage with each other. Original databases of citations by the IACtHR to the judgments of national courts, and in the opposite direction, allow us to establish whether such practices are emerging. Furthermore, the article asks why the IACtHR cites some courts but not others, and to what end. Statistical models reveal that the IACtHR is more likely to cite case law from countries that exhibit characteristics that are more conducive to the creation of a regional human rights legal order, and jurisprudence from countries with which it has had more extensive experience and interaction. Qualitative content analysis suggests that the IACtHR uses citations as a source of persuasive authority, but also to showcase domestic acceptance of its doctrines and decisions. This leads us to characterize citations as an effort to educate courts in the use of Inter-American jurisprudence and thus foster greater integration. At the national level, we find considerable temporal/cross-country variation in openness to the dialogue. We rely on original quantitative indicators and case studies to show this is as a function of the IACtHR’s growing visibility and networking efforts, as well as country-level changes in legal cultures and judicial personnel that push courts away from formalism.