Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributionally Robust Optimal Reactive Power Dispatch with Wasserstein Distance in Active Distribution Network

The uncertainties from renewable energy sources (RESs) will not only introduce significant influences to active power dispatch, but also bring great challenges to the analysis of optimal reactive power dispatch (ORPD). To address the influence of high penetration of RES integrated into active distribution networks, a distributionally robust chance constraint (DRCC)-based ORPD model considering discrete reactive power compensators is proposed in this paper. The proposed ORPD model combines a second-order cone programming (SOCP)-based model at the nominal operation mode and a linear power flow (LPF) model to reflect the system response under certainties. Then, a Wasserstein-distance-based distributionally robust optimization (WDRO) method is utilized to solve the proposed DRCC-based ORPD model. The WDRO method is data-driven due to the reason that the ambiguity set is constructed by the available historical data without any assumption on the specific probability distribution of the uncertainties. And the more data is available, the smaller the ambiguity would be. Numerical results on IEEE 30-bus and 123-bus systems and comparisons with the other three-benchmark approaches demonstrate the accuracy and effectiveness of the proposed model and method.
Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review-Part I: Evolution and Recent Trends

Deep learning (DL) has great influence on large parts of science and increasingly established itself as an adaptive method for new challenges in the field of Earth observation (EO). Nevertheless, the entry barriers for EO researchers are high due to the dense and rapidly developing field mainly driven by advances in computer vision (CV). To lower the barriers for researchers in EO, this review gives an overview of the evolution of DL with a focus on image segmentation and object detection in convolutional neural networks (CNN). The survey starts in 2012, when a CNN set new standards in image recognition, and lasts until late 2019. Thereby, we highlight the connections between the most important CNN architectures and cornerstones coming from CV in order to alleviate the evaluation of modern DL models. Furthermore, we briefly outline the evolution of the most popular DL frameworks and provide a summary of datasets in EO. By discussing well performing DL architectures on these datasets as well as reflecting on advances made in CV and their impact on future research in EO, we narrow the gap between the reviewed, theoretical concepts from CV and practical application in EO.
Ranking the critical sections of railway networks

Railway systems are now facing an increasing number of threats such as aging infrastructures and climate changes. The identification of critical network sections provides infrastructure managers with the ability to understand the impact of a disruption and creates a suitable preventive strategy to counter such threats. To this end, various vulnerability analysis methods have been proposed for railway networks. Two main types of methods, network topological analysis and network flow-based analysis, have been developed. Both approaches are constructed based on macroscopic models, which take only some railway properties such as network structure, train and passenger flow into account. Thus, the results obtained are high level approximations. This study proposes a new analysis method, which is developed based on the stochastic-microscopic railway network simulation model. The method can be applied to identify the critical sections of a railway network. The effect of impact levels and occurrence times of a disruption on the network section criticality is presented. An application of the proposed model is demonstrated using the Liverpool railway network in the UK.
Relationship matters with citizens returning from prison: qualitative findings from a mentoring program

Abstract Qualitative findings from a reentry mentoring program evaluation demonstrate the importance of establishing and maintaining relationships with specific characteristics during the process of successful transition of returning citizens from prison to their communities. Significant gaps in knowledge of this process include understanding of the experience of the returning citizen as they navigate this extremely challenging transition, and their experiential knowledge of what they need to enhance successful reentry. The men who participated in group and individual mentoring activities during their stay in an adult transition center and after their release to supervision in the community, shared their experiences of the mentoring. Qualitative data collection and analysis, as a component of the evaluation, produced a thematic narrative describing and explaining, from the experiential perspective of the participants, how they perceived and valued the mentoring relationships. The findings suggest the importance and complexity of supportive relationships during the reentry transition, and how such relationships can potentially lead to crystallization of revised identity and an increased capacity to desist from behavior responsive to a negative or crime-saturated identity. Development and maintenance of such supportive relationships are recommended throughout the reentry process to enhance effective use of services and a successful transition.
Magnetic resonance imaging of carotid plaques: current status and clinical perspectives

Rupture of a vulnerable carotid plaque is one of the leading causes of stroke. Carotid magnetic resonance imaging (MRI) is able to visualize all the main hallmarks of plaque vulnerability. Various MRI sequences have been developed in the last two decades to quantify carotid plaque burden and composition. Often, a combination of multiple sequences is used. These MRI techniques have been extensively validated with histological analysis of carotid endarterectomy specimens. High agreement between the MRI and histological measures of plaque burden, intraplaque hemorrhage (IPH), lipid-rich necrotic core (LRNC), fibrous cap (FC) status, inflammation and neovascularization has been demonstrated. Novel MRI sequences allow to generate three-dimensional isotropic images with a large longitudinal coverage. Other new sequences can acquire multiple contrasts using a single sequence leading to a tremendous reduction in scan time. IPH can be easily identified as a hyperintense signal in the bulk of the plaque on strongly T1-weighted images, such as magnetization-prepared rapid acquisition gradient echo images, acquired within a few minutes with a standard neurovascular coil. Carotid MRI can also be used to evaluate treatment effects. Several meta-analyses have demonstrated a strong predictive value of IPH, LRNC, thinning or rupture of the FC for ischemic cerebrovascular events. Recently, in a large meta-analysis based on individual patient data of asymptomatic and symptomatic individuals with carotid artery stenosis, it was shown that IPH on MRI is an independent risk predictor for stroke, stronger than any known clinical risk parameter. Expert recommendations on carotid plaque MRI protocols have recently been described in a white paper. The present review provides an overview of the current status and applications of carotid plaque MR imaging and its future potential in daily clinical practice.
Physico-chemical changes during growth and development of papaya fruit. II: Chemical changes

Physical changes during growth and development of female and hermaphrodite fruits of 'Ekostika I', 'Ekostika II' and 'Baladi' papaya fruits were studied. The fruits of both types of the three cultivars followed typical simple sigmoid curves. Fruit fresh weight and volume progressively increased up to physiological maturity (120 days after anthesis, DAA) and remained constant afterwards. The female fruits of the three cultivars were shorter and wider and with higher fresh weight and volume at maturity, compared to hermaphrodite fruits. Respiration curves of both fruit types of the three papaya cultivars exhibited a typical climacteric pattern. Respiration rate progressively declined to a minimum value at physiological maturity (120 DAA) and then increased in a climacteric pattern with peak of respiration at 140 DAA. Fruit flesh firmness slightly decreased up the physiological maturity and then sharply declined in a similar manner. Most of that decline occurred during the ripening phase (125-145 DAA). Papaya fruits should be harvested shortly (5-10 days) after physiological maturity, where the fruit attains maximum size and weight, it is still firm and climacteric rise phase has just started.
Effect of solutes and pH on the structure and firmness of cooked carrot

Summary. Xylary segments of carrot root were cooked in solutions of different hydrogenion concentration (pH 3-8) and different solute composition. ‘Firmness’was measured as resistance of the cooked segments to compression, and histological sections were prepared. the segments were softer, the higher the pH of the solution. This softening was attended by increasing cellular separation and eventual cellular collapse at higher values of pH. Firmness increased (over that in water) in sucrose solutions and solutions with divalent cations, and it decreased in solutions with monovalent cations. Explanations are considered for these effects and for the relatively minor increase in firmness in solutions of A1Cl3.
Cotyledon cell development in Arabidopsis thaliana during reserve deposition

Cotyledon cell development in Arabidopsis thaliana L. during reserve deposition has been analyzed qualitatively and quantitatively. Development has been related to the previously defined time scale for Arabidopsis, hours after flowering. Between 144 and 216 h after flowering the major cell changes in the cotyledon are an increase in the cell volume, a decrease in the volume fraction of cytoplasm and plastids, and an increase in lipid and vacuole volume fractions. The endoplasmic reticulum and dictyosome volume fractions are high during early reserve formation (144 – 168 h after flowering) but decrease significantly thereafter. Evidence as to the origin of the storage lipid is inconclusive, although a dual involvement of plastids and rough endoplasmic reticulum is a likely theory. The 3-nm lipid body membrane, which allows the bodies to retain their individuality during accumulation, is probably a half-unit biological membrane, derived from closely associated rough endoplasmic reticulum cisternae. Much of .
The Future of Medical Documentation - Review of Selected Literature

This work addresses the problem of the application of artificial intelligence to the creation and maintenance of medical documentation and the use of big data in medicine to support efficient patient diagnosis and treatment. This study covers the latest advances in AI and big data, based on literature reviews and interviews with leading experts in these fields. The following conclusions were obtained: (1) Based on the needs of patients and providers of medical services, and given the latest technological advances, all medical documentation should be digital and the processes of its creation, access, sharing, and consistency checking should by supported by suitably designed AI systems. (2) The knowledge contained in medical documentation constitutes a resource of strategic importance for humanity, with almost unlimited potential. (3) All medical documentation should be anonymised and should be made widely available, just like data and research results in the field of experimental physics. (4) This will accelerate development of new treatments, best practice and help to identify new medical emergencies, such as Covid-19. In practice today, unfortunately, the design of medical record systems is fragmented between institutions and countries, often focusing discussions on narrow technical details, and forcing clinicians to waste time on filling up multiple pages of illness history. This leads to many inefficiencies and lost opportunities and necessitates a fundamentally new approach.
Dietary segregation of krill-eating South Georgia seabirds

The diets of six of the main seabird species (two petrels, two albatrosses, two penguins) breeding at Bird Island, South Georgia were studied simultaneously during the chick-rearing period in 1986. For five species, Antarctic krill Euphausia superba was the main food (39–98% by mass); grey-headed albatrosses took mainly the ommastrephid squid Martialia hyadesi (71%) and only 16% krill. The size of the krill taken was similar between seabird species, although there were small but significant differences between penguins and the other species. Sex and reproductive status of krill, however, was different between all seabird species, reflecting some combination of differences in foraging ranges, selectivity by predators, or differences in escape responses of krill. For the krill-eating species, the rest of the diet varied substantially between species, comprising Martialia and nototheniid fish (blackbrowed albatross and, along with lanternfish, white-chinned petrel), lanternfish and amphipods (Antarctic prion and macaroni penguin), and icefish (gentoo penguin). Long-term data on breeding success and information on diet in 5–10 other years suggest that in 1986 seabird diet and reproductive performance was indicative of a year of good availability of krill around South Georgia. In such circumstances, ecological segregation between krill-eating species appears to be maintained chiefly by differences in foraging range and feeding methods, which are reviewed. This situation is rather different from the few studies of seabird communities elsewhere, where prey type and size are believed to be the main mechanisms of dietary segregation.
CORRELATION BETWEEN TYPE D PERSONALITY TRAITS AND PROBLEM SOLVING IN STUDENTS

Type D personality is characterized by the synergic effects of two persistent personality traits: negative affectivity combined with simultaneous suppression of these emotions in social situations to avoid disapproval or rejection. Type D personality traits are more prominent in certain professions and specialties, including assistance and care, which affect both behavior and problem solving in many ways, resulting in emotionally focused problem solving and avoidance style. The aim of the study determine the relationship between Type D personality traits and problem-solving skills in students. Research question: Is there a correlation between Type D personality traits and problem-solving skills? 160 students from two faculties of Rīga Stradiņš University participated in the study Faculty of Public Health and Social Welfare and Faculty of Rehabilitation. The permission from the Ethics Committee of Rīga Stradiņš University was obtained to do the research. Data were obtained electronically in May 2019. Three surveys were used in the research: Three questionnaires are used in the study: demographic questionnaire, DS14: Standard Assessment of Negative Affectivity, Social Inhibition, and Type D Personality and Problem Solving Inventory. The study found a statistically significant positive correlation between the level of social inhibition and the level of negative affectivity, there is a statistically significant positive correlation between the level of problem solving and the level of avoidance style, there is a statistically significant negative correlation between the age of the respondents and the level of negative affectivity and there is a statistically significant positive correlation between the level of negative affectivity and the level of problem solving.
Macroeconomic dynamics at the Cowles Commission from the 1930s to the 1950s

Abstract This paper explores the development of dynamic modelling of macroeconomic fluctuations at the Cowles Commission from Roos, Dynamic Economics (Cowles Monograph No. 1, 1934) and Davis, Analysis of Economic Time Series (Cowles Monograph No. 6, 1941) to Koopmans, ed., Statistical Inference in Dynamic Economic Models (Cowles Monograph No. 10, 1950) and Klein’s Economic Fluctuations in the United States, 1921–1941 (Cowles Monograph No. 11, 1950), emphasising the emergence of a distinctive Cowles Commission approach to structural modelling of macroeconomic fluctuations influenced by Cowles Commission work on structural estimation of simulation equations models, as advanced by Haavelmo (“A Probability Approach to Econometrics,” Cowles Commission Paper No. 4, 1944) and in Cowles Monographs Nos. 10 and 14.
National Culture and Advertising Sensitivity to Business Cycles: A Reexamination

The author reexamines the cyclical sensitivity of national advertising expenditure with a longitudinal data set of 59 countries over 35 years. In contrast to prior studies, the author examines the effects of the entire set of Hofstede culture dimensions to study cross-country variation in the advertising sensitivity and investigates how the emergence and growth of online advertising has transformed the cyclical sensitivity of advertising spending. National culture substantially affects advertising’s cyclical sensitivity, but in different ways than hypothesized previously. Consistent with the literature, advertising sensitivity is lower in long-term-oriented and high-uncertainty-avoidant countries and is unrelated to individualism. However, power distance is unassociated with cyclical sensitivity, and masculinity and indulgence—the two dimensions ignored in previous research—reduce cyclical sensitivity. Moreover, there is evidence that culture operates differently for the cyclical sensitivity of online advertising. Advertising expenditure is cyclically much more sensitive than documented previously and has grown more so over time since the advent of online advertising. This study provides initial evidence that online spending is more elastic than traditional advertising and that traditional spending has become more procyclical. The author advances timely and refined empirical generalizations on the cyclical sensitivity of advertising expenditure.
Pattern Recognition and Its Application in Image Processing

With the continuous improvement of China’s national economy and the unprecedented innovation and development of modern science and technology, artificial intelligence technology has been widely used in China’s industrial production, power transportation, aerospace and other fields. As an extension of modern artificial intelligence technology, pattern recognition technology plays an important role in modern computer image processing by virtue of its own intelligence and automation. It fundamentally guarantees the accuracy of image processing and greatly improves computer image recognition and information processing. Overall work efficiency. This paper mainly analyses the main characteristics of pattern recognition technology, and further studies the practical application of pattern recognition technology in image processing, hoping to provide corresponding reference for the future application and development of pattern recognition technology in image processing.
Predicting the Sensing Radius of a Coaxial Probe Based on the Probe Dimensions

The coaxial probe technique is used to acquire the dielectric properties of biological tissues in the microwave frequency range. To dielectrically characterize heterogeneous samples, the sensing radius of the probe must be known. Thus, in this article, for the first time, both experimental and numerical investigations were conducted to analyze and model the sensing radius dependence on the probe dimensions. The results suggest that 1) the sensing radius increases linearly with the inner radius of the outer conductor and is not affected by the width of the outer conductor; 2) the inner conductor has higher impact than the insulator on the sensing radius; and 3) although the sensing radius depends on the dielectric properties of the investigated samples, the trend of the sensing radius relative to the probe dimensions is the same across different samples. Furthermore, a method for predicting the sensing radius, through use of neural networks, is proposed.
Review of image processing approaches for detecting plant diseases

There is intense pressure on agricultural productivity due to the ever-growing population. Several diseases affect crop yield and thus, effective control of these can significantly improve the production of food for all. In this regard, detection of diseases at an early stage and quantification of the severity, in general, has acquired urgent attention of the researchers. In this study, a summary of prevalent techniques and methodologies used for the detection, quantification and classification of diseases is presented to understand the scope of improvement. The study pays attention to critical gaps that exist in available approaches and enhance them for the early prediction of diseases. Diseases affect almost all parts of plants, e.g. root, stem, flower, leaf; a manifestation in different ways for different parts of the plant of the same disease presents a challenge for researchers. This study extends the review work published by JGA Barbedo in 2013, as there have been significant advances and numerous new techniques introduced since then. A novel approach of classifying and categorisation of the existing techniques based on pathogen types is a significant contribution by the authors in this study.
Reconstruction of Electron and Ion Distribution Functions in a Magnetotail Reconnection Diffusion Region

In the diffusion region of magnetotail reconnection, particle distributions are highly structured, exhibiting triangular shapes and multiple striations that deviate dramatically from the Maxwellian distribution. Fully kinetic simulations have been demonstrated to be capable of producing the essential structures of the observed distribution functions, yet are computationally not feasible for 3D global simulations. The fluid models used for large‐scale simulations, on the other hand, do not have the kinetic physics necessary for describing reconnection accurately. Our study aims to bridge fully kinetic and fluid simulations by quantifying the information required to capture the non‐Maxwellian features in the distributions underlying the closures used in the fluid code. We compare the results of fully kinetic simulations with observed electron velocity distributions in a magnetotail reconnection diffusion region and use the maximum entropy model to reconstruct electron and ion distributions using various numbers of moments obtained from the simulation. Our results indicate that using only local moments, the maximum entropy model can reproduce many of the features of the distributions: (1) the electron outflow distribution with a tilted triangular structure is reproduced with 21 or more moments in agreement with Ng et al. (2018, https://doi.org/10.1063/1.5041758) and (2) counterstreaming distributions can be captured with the 35‐moment model when the separation in velocity space between the populations is large.