Miracle-dz/UniFAR-FTUs huggingface dataset:
7th International Building Physics Conference IBPC 2018 Proceedings: Healthy, Intelligent and Resilient Buildings and Urban Environments

Current modelling approaches for energy simulations in green roofs use a range of values for parameters such as stomatal resistance (rs) of the vegetation. rs reflects the capability of a plant to transpire, thus it has a direct relation to the cooling potential of green roofs in buildings. Therefore, rs values need to be revised based on differences among species and contrasting environmental conditions, considering anatomical and physiological characteristics among species and their changes throughout the day. In order to provide real data on species commonly used for green roofs in semiarid climates, this paper aims to evaluate the stomatal resistance of nine species of groundcovers and to compare this data with current models. rs was measured for each species at 8:00 h, 12:00 h, 16:00 h and 20:00 h during day and nighttime in winter in a leaf located at the middle of the stem. The results of this study showed that rs varies significantly among species, throughout the day and between the side of the leaf (adaxial or abaxial). The lowest rs values for species was at noon ranging from 264 to 807 s m1 and the highest rs was at night ranging from 568 to 973 s m-1. Sedum spurium red, Sedum hybrid, and white and pink Verbena sp. had the largest rs variation in the day-night cycle. The results of rs are higher than those values recommended for some energy simulation models.
Fast multiquadratic S-unit computation and
application to the calculation of class groups

Let L = Q( √ d1, . . . , √ dn) be a real multiquadratic field and S be a set of prime ideals of L that does not contain any divisors of 2. In this paper, we present a heuristic algorithm for the computation of the S-class group and the S-unit group that runs in time Poly(log(∆), Size(S))eÕ( √ ln d) where d = maxi≤n di and ∆ is the discriminant of L. We use this method to compute the ideal class group of the maximal order OL of L in time Poly(log(∆))eÕ( √ log d). When log(d) ≤ log(log(∆))c for some constant c < 2, these methods run in polynomial time. We implemented our algorithm using Sage 7.5. 1.
Assessing Post-Fire Douglas-Fir Mortality and Douglas-Fir Beetle Attacks in the Northern Rocky Mountains

Douglas-fir has life history traits that greatly enhance resistance to injury from fire, thereby increasing post-fire survival rates. Tools for predicting the probability of tree mortality following fire are important components of both pre-fire planning and post-fire management efforts. Using data from mixed-severity wildfire in Montana and Wyoming, Hood and Bentz (2007) developed models for predicting the probability of Douglas-fir mortality and Douglas-fir bark beetle attack based on fire injury and stand characteristics. This guide is based on information in Hood and Bentz (2007) and is intended for use in development of post-fire management and prescribed burn plans. Included are descriptions of both models and variables that significantly influence post-fire Douglas-fir mortality and bark beetle attack. A supplemental field guide provides photographs of a range of levels for each fire-related injury and descriptions for measuring each characteristic in the field. Also provided are discussions on how to interpret Douglas-fir mortality and bark beetle attack models for use in management decision-making regarding wild and prescribed fires in the Northern Rocky Mountains.
Estimation of site-specific and spatially-distributed ground motion in the Christchurch earthquakes: Application to liquefaction evaluation and ground motion selection for post-event investigation

This paper presents a methodology by which both site-specific and spatially distributed ground motion intensity can be obtained immediately following an earthquake event. The methodology makes use of both prediction models for ground motion intensity and its correlation over spatial distances. A key benefit of the methodology is that the ground motion intensity at a given location is not a single value but a distribution of values. The distribution is comprised of both a mean and also standard deviation, with the standard deviation being a function of the distance to nearby strong motion stations. The methodology is illustrated for two applications. Firstly, maps of conditional peak ground acceleration (PGA) have been developed for the major events in the Canterbury earthquake sequence. It is illustrated how these conditional maps can be used for post-event evaluation of liquefaction triggering criteria which have been adopted by the Department of Building and Housing (DBH). Secondly, the conditional distribution of response spectral ordinates is obtained at a specific location for the purposes of determining appropriate ground motion records for use in seismic response analyses of important structures at locations where direct recordings are absent.
Dealing with Consumer Default : Bankruptcy vs Garnishment

The goal of this paper is to study, in a quantitative-theoretic fashion, the positive and normative implications of two different approaches to handling consumer default: bankruptcy and garnishment. We find that which of these two legal systems is in place makes a large difference to prices, allocations and welfare. Garnishment increases the level of commitment displayed by debtors toward meeting their obligations and with competition and free entry into intermediation, the added commitment translates into lower interest rates on debt. The result is a very large expansion in unsecured debt, a drop in capital per worker as the additional borrowing crowds out business fixed investment, a significant decrease in wages and a significant increase in interest rates. Nevertheless, despite these apparently adverse general equilibrium effects of credit expansion, every single individual in our baseline economy is better off without the bankruptcy option. The improved ability to borrow against future earnings and the improved risk-sharing afforded by a well-functioning consumer credit market underpin these welfare gains.
Small area estimation under unit-level temporal linear mixed models

ABSTRACT Data from past time periods and temporal correlation are rich sources of information for estimating small area parameters at the current period. This paper investigates the use of unit-level temporal linear mixed models for estimating linear parameters. Two models are considered, with domain and domain-time random effects. The first model assumes time independency and the second one AR(1)-type time correlation. They are fitted by a Fisher-scoring algorithm that calculates the residual maximum likelihood estimators of the model parameters. Based on the introduced models, empirical best linear unbiased predictors of small area linear parameters are studied, and analytic estimators for evaluating the performance of their mean squared errors are proposed. Three simulation experiments are carried out to study the behaviour of the fitting algorithm, the small area predictors and the estimators of the mean squared error. By using data of the Spanish surveys of income and living conditions of 2004–2008, an application to the estimation of 2008 average normalized net annual incomes in Spanish provinces by sex is given.
A critical survey of GEOBIA methods for forest image detection and classification

Abstract Modern earth observation sensors have revolutionized the remote sensing community by improving remote sensing image quality. However, Pixel-based image analysis methods have challenges in handling very high-resolution (VHR) imagery. Geographic Based Image Analysis (GEOBIA) yielded promising results, but it is not inflexible in capturing domain experts’ expressions, therefore geographic information system professionals shifted to ontologies for remote sensing science. This paper advocates for the adoption of knowledge representation using ontologies in remote sensing. To this end, a survey of GEOBIA studies for image analysis and classification is presented, and the limitations of existing methods in reaching the remote sensing expert-level expectation are clarified. New GEOBIA development techniques as well as opportunities for improving GEOBIA models have been looked into. Recent studies that adopted ontologies in forest image classification are analyzed and recommendations for the remote sensing science community are provided, to highlight the advantages of ontologies in interpreting satellite images.
" Liquidity and Liquidity Risks ": What Has Changed, and Why ? " the Interbank Market after August 2007: What Has Changed, and Why?

The outbreak of the financial crisis coincided with a sharp increase of worldwide interbank interest rates. We analyze the micro-and macroeconomic determinants of this phenomenon, finding that before August 2007 interbank rates were insensitive to borrower characteristics, whereas afterwards they became reactive to borrowers' creditworthiness. At the same time, conditions for large borrowers became relatively more favorable, both before and after the failure of Lehman Brothers. This suggests that banks have become more discerning in their lending, a welcome change, but that moral hazard considerations related to the " too big to fail " argument should remain a main concern for central banks.
Climatic predictability, hydrology and water resources over Nordeste Brazil

Seasonal climate forecasting can be produced with relatively high accuracy for the Northeast region of Brazil. During the past decade, relevant achievements were obtained in the management of water in the region, particularly for coping with drought, using climate forecasting information. This paper presents the state of the art in climate forecasting for this region, as well as results of the application of a high resolution atmospheric nested model into hydrological models for daily runoff prediction and their use for simulating future water storage in reservoirs. The results show that the information produced by this forecasting process can be useful for decision making in reservoir operation, despite the associated uncertainties.
Automatic Multiview Alignment of RGB-D Range Maps of Upper Limb Anatomy

Digital representations of anatomical parts are crucial for various biomedical applications. This paper presents an automatic alignment procedure for creating accurate 3D models of upper limb anatomy using a low-cost handheld 3D scanner. The goal is to overcome the challenges associated with forearm 3D scanning, such as needing multiple views, stability requirements, and optical undercuts. While bulky and expensive multi-camera systems have been used in previous research, this study explores the feasibility of using multiple consumer RGB-D sensors for scanning human anatomies. The proposed scanner comprises three Intel® RealSenseTM D415 depth cameras assembled on a lightweight circular jig, enabling simultaneous acquisition from three viewpoints. To achieve automatic alignment, the paper introduces a procedure that extracts common key points between acquisitions deriving from different scanner poses. Relevant hand key points are detected using a neural network, which works on the RGB images captured by the depth cameras. A set of forearm key points is meanwhile identified by processing the acquired data through a specifically developed algorithm that seeks the forearm’s skeleton line. The alignment process involves automatic, rough 3D alignment and fine registration using an iterative-closest-point (ICP) algorithm expressly developed for this application. The proposed method was tested on forearm scans and compared the results obtained by a manual coarse alignment followed by an ICP algorithm for fine registration using commercial software. Deviations below 5 mm, with a mean value of 1.5 mm, were found. The obtained results are critically discussed and compared with the available implementations of published methods. The results demonstrate significant improvements to the state of the art and the potential of the proposed approach to accelerate the acquisition process and automatically register point clouds from different scanner poses without the intervention of skilled operators. This study contributes to developing effective upper limb rehabilitation frameworks and personalized biomedical applications by addressing these critical challenges.
Role of Nigella sativa in Preventing Oxidative Stress Damage Using Planaria as Model Organism

ABSTRACT Antioxidative effects of N. sativa were explored using planaria as a model organism. Planaria were cultured in water, N. sativa seed extract, hydrogen peroxide, and a combination of hydrogen peroxide and N. sativa. Planaria were decapitated and observed through stages of regeneration. N. sativa treatment increased Planarian growth, locomotion, and protein. Among hydrogen peroxide stress-induced groups, growth was 92% greater, locomotion was 26% higher, and protein was 86% greater when treated with N. sativa. N. sativa accelerated the regeneration of planaria and minimized the effects of stress. N. sativa can be a promising option for preventing neurodegenerative diseases.
Usability and ease of use of long-term remote monitoring of physical activity for individuals with acquired brain injury in community: a qualitative analysis

Introduction Objective and continuous monitoring of physical activity over the long-term in the community is perhaps the most important step in the paradigm shift toward evidence-based practice and personalized therapy for successful community integration. With the advancement in technology, physical activity monitors have become the go-to tools for objective and continuous monitoring of everyday physical activity in the community. While these devices are widely used in many patient populations, their use in individuals with acquired brain injury is slowly gaining traction. The first step before using activity monitors in this population is to understand the patient perspective on usability and ease of use of physical activity monitors at different wear locations. However, there are no studies that have looked at the feasibility and patient perspectives on long-term utilization of activity monitors in individuals with acquired brain injury. Methods This pilot study aims to fill this gap and understand patient-reported aspects of the feasibility of using physical activity monitors for long-term use in community-dwelling individuals with acquired brain injury. Results This pilot study found that patients with acquired brain injury faced challenges specific to their functional limitations and that the activity monitors worn on the waist or wrist may be better suited in this population. Discussion The unique wear location-specific challenges faced by individuals with ABI need to be taken into account when selecting wearable activity monitors for long term use in this population.
Improved H∞ sampled‐data control for semilinear parabolic PDE systems

In this paper, an H∞ sampled‐data control problem is addressed for semilinear parabolic partial differential equation (PDE) systems. By using a time‐dependent Lyapunov functional and vector Poincare's inequality, a sampled‐data controller under spatially averaged measurements is developed to stabilize exponentially the PDE system with an H∞ control performance. The stabilization condition is presented in terms of a set of linear matrix inequalities. Finally, simulation results on the control of the diffusion equation and the FitzHugh‐Nagumo equation are given to illustrate the effectiveness of the proposed design method.
Lifelong Learning for Scene Recognition in Remote Sensing Images

The development of visual sensing technologies has made it possible to obtain some high resolution and to gather many high-resolution satellite images. To make the best use of these images, it is essential to be able to recognize and retrieve their intrinsic scene information. The problem of scene recognition in remote sensing images has recently aroused considerable interest, mainly due to the great success achieved by deep learning methods in generic image classification. Nevertheless, such methods usually require large amounts of labeled data. By contrast, remote sensing images are relatively scarce and expensive to obtain. Moreover, data sets from different aerospace research institutions exhibit large disparities. In order to address these problems, we propose a model based on a meta-learning method with the ability of learning a classifier from just few-shot samples. With the proposed model, the knowledge learned from one data set can be easily adapted to a new data set, which, in turn, would serve in the lifelong few-shot learning. Scene-level image recognition experiments, on public high-resolution remote sensing image data sets, validate our proposed lifelong few-shot learning model.
Pre-organized landscape of T cell surface

T cell activation is initiated by the recognition of specific antigenic peptides and subsequently accomplished by complex signaling cascades. These aspects have been extensively studied for decades as pivotal factors in the establishment of adaptive immunity. However, how receptors or signaling molecules are organized in the resting state prior to encountering antigens has received less attention. Recent advancements in super-resolution microscopy techniques have revealed topographically controlled pre-formed organization of key molecules involved in antigen recognition and signal transduction on microvillar projections of T cells before activation and substantial effort has been dedicated to characterizing the topological structure of resting T cells over the past decade. This review will summarize our current understanding of how key surface receptors are pre-organized on the T-cell plasma membrane and discuss the potential role of these receptors, which are preassembled prior to ligand binding in the early activation events of T cells.
The Second Order Confirmatory Factor Analyses (CFA) Model Fit Assessments of Brand Awareness with Brand Loyalty: In Case of Private and Public Banks of Ethiopia, Bule Hora Town

The study confirmed literature in Brand Awareness indicators Perceived Awareness, Relevance Awareness, Knowledge Awareness and Esteem Awareness, and brand Loyalty attitude and behavioral indicators to investigate second-ordered or Multiple model of Confirmatory data analyses with respect to brand loyalty in particular. This study empirically attempts to discuss the particular issue by examining the relationship between two or more variables assessed in second Order or Multiple models of brand awareness and brand loyalty. The data were collected from Private and public banks of Ethiopia Bule Hora town from customers and Employees of banks. For quantitative research, design used SPSS v.25 and for the Confirmation data to use AMOS V.23, and CFA Model of Second Orderd or multiple data investigation Loading factors and Model fit indexed techniques were applied to a sample of 384 respondents of customers, and Employees of private and public Banks of Ethiopia, Bule Hora town. The results revealed that brand awareness and brand loyalty have a significant and positive relationship with Perceived Awareness, Relevance Awareness, Knowledge Awareness Esteem Awareness, and brand Loyalty attitude and behavioral indicators. The value of factor loading investigation over all data purification index value is fully verified between Brand awareness and Brand Loyalty within his indicators, and most second Order or multiple model results have high loading factor output indicated above 0.05% and this paper has discussed the results, and an implication compared with prior discussed conclusion and recommendations to be verified in this investigation.
SOMATIC METAPHORS OF NIKITA KHRUSHCHEV’S SECRET SPEECH IN TRANSLATION

It is generally recognized that a linguocultural analysis makes it possible to identify equivalents in the phraseology of different languages, to indicate what is common and unique for the languages analyzed and to reveal various kinds of translation difficulties, in particular, the rendition of phraseo-logical units with somatic components. As the previous research shows, this issue has not been well-examined in relation to the political discourse yet. Thereby, the present study makes a contribution to the field of phraseology as well as to the area of translation. The article is devoted to the comparative analysis of the Russian phraseologisms with the somatic elements which were employed by N. S. Khrushchev in his report “On the Cult of Personality and Its Consequences” at the closed session of the 20 th Communist Congress on February 25, 1956, and the ways of their translation into English. The paper describes the connotations that arise, while interpreting the paremia images in the space of historical and cultural knowledge, identifies cultural layers in the semantics of the idioms, allowing to highlight the commonalities or differences of the meanings that are characteristic for the languages in question. The contrastive analysis of Russian and English somatic metaphors reveals similarities in the processes and ways of phrase formation of a number of idioms which enable translators to use appropriate equivalents. Of particular interest are the cases when there is a lexical substitution of the Russian somatism for a different English phrase-lexeme which, in turn, creates certain challenges when translating such idioms.
A Graph-Based Reinforcement Learning Method with Converged State Exploration and Exploitation

: In any classical value-based reinforcement learning method, an agent, despite of its continuous interactions with the environment, is yet unable to quickly generate a complete and independent description of the entire environment, leaving the learning method to struggle with a difficult dilemma of choosing between the two tasks, namely exploration and exploitation. This problem becomes more pronounced when the agent has to deal with a dynamic environment, of which the configuration and/or parameters are constantly changing. In this paper, this problem is approached by first mapping a reinforcement learning scheme to a directed graph, and the set that contains all the states already explored shall continue to be exploited in the context of such a graph. We have proved that the two tasks of exploration and exploitation eventually converge in the decision-making process, and thus, there is no need to face the exploration vs. exploitation tradeoff as all the existing reinforcement learning methods do. Rather this observation indicates that a reinforcement learning scheme is essentially the same as searching for the shortest path in a dynamic environment, which is readily tackled by a modified Floyd-Warshall algorithm as proposed in the paper. The experimental results have confirmed that the proposed graph-based reinforcement learning algorithm has significantly higher performance than both standard Q-learning algorithm and improved Q-learning algorithm in solving mazes, rendering it an algorithm of choice in applications involving dynamic environments.