Miracle-dz/UniFAR-FTUs huggingface dataset:
Turning the Global Thermostat—Who, When, and How Much?

Engineering the climate via Solar Radiation Management (SRM) is increasingly considered as a component of future climate policies. We study the strategic incentives for countries to choose the level of SRM at different times in the future, accounting for the regionally uneven effect of SRM on climate variables, heterogeneous preferences of countries for the state of the global climate, and climate change adjusted GDP growth rates. We find that even though some countries would have significant gains from realizing their individually preferred level of SRM, the economic incentives for many countries are not sufficient to consider unilateral SRM implementation to be beneficial. In contrast, several countries have strong incentives to join coalitions to prevent that too much SRM is applied. The likely scenario is that a coalition will set a level of SRM close to the global efficient level.
Simulation of adaptive power management circuit for hybrid energy harvester and real-time sensing application

Many wireless sensor network (WSN) applications, nowadays, require real-time communication, which demands cautious design consideration to resolve inherent conflicts between energy efficiency and the need to meet Quality of Services (QoS), such as end-to-end delay communications. Numerous innovative solutions are proposed such as Real-time Power-Aware Routing (RPAR) protocol, which dynamically adapts transmission power to meet specified communication delays at low energy cost. Hence, to enable real-time communication with RPAR protocol, an adaptive Power Management Circuit (PMC) using hybrid energy harvester to support WSN real-time communication is proposed. In this paper, a high-level architecture of the proposed PMC is discussed, which consists of Thermal Energy Generator (TEG), and Piezoelectric Energy Harvester (PEG) as energy providers, with low-power Maximum Power Point Tracking (MPPT) feature enabled. Preliminary simulations which analyze and characterize TEG and PEG system are conducted separately to determine the optimal design parameters to support the conventional WSN QoS requirement. Next, both systems will be integrated into a single PMC implementation prior to fabrication and lab characterization.
Exploiting enzyme speci fi cities in digestions of chondroitin sulfates A and C : Production of well-de fi ned hexasaccharides

Interactions between proteins and glycosaminoglycans (GAGs) of the extracellular matrix are important to the regulation of cellular processes including growth, differentiation and migration. Understanding these processes can benefit greatly from the study of protein–GAG interactions using GAG oligosaccharides of well-defined structure. Materials for such studies have, however, been difficult to obtain because of challenges in synthetic approaches and the extreme structural heterogeneity in GAG polymers. Here, it is demonstrated that diversity in structures of oligosaccharides derived by limited enzymatic digestion of materials from natural sources can be greatly curtailed by a proper selection of combinations of source materials and digestive enzymes, a process aided by an improved understanding of the specificities of certain commercial preparations of hydrolases and lyases. Separation of well-defined oligosaccharides can then be accomplished by size-exclusion chromatography followed by strong anion-exchange chromatography. We focus here on two types of chondroitin sulfate (CS) as starting material (CS-A, and CS-C) and the use of three digestive enzymes with varying specificities (testicular hyaluronidase and bacterial chondroitinases ABC and C). Analysis using nuclear magnetic resonance and mass spectrometry focuses on isolated CS disaccharides and hexasaccharides. In all, 15 CS hexasaccharides have been isolated and characterized. These serve as useful contributions to growing libraries of well-defined GAG oligosaccharides that can be used in further biophysical assays.
Contextual Transformation of Short Text for Improved Classifiability

Text classification is the task of automatically sorting a set of documents into predefined set of categories. This task has several applications including separating positive and negative product reviews by customers, automated indexing of scientific articles, spam filtering and many more. What lies at the core of this problem is to extract features from text data which can be used for classification. One of the common techniques to address this problem is to represent text data as low dimensional continuous vectors such that the semantically unrelated data are well separated from each other. However, sometimes the variability along various dimensions of these vectors is irrelevant as they are dominated by various global factors which are not specific to the classes we are interested in. This irrelevant variability often causes difficulty in classification. In this paper, we propose a technique which takes the initial vectorized representation of the text data through a process of transformation which amplifies relevant variability and suppresses irrelevant variability and then employs a classifier on the transformed data for the classification task. The results show that the same classifier exhibits better accuracy on the transformed data than the initial vectorized representation of text data.
Belief bias and representation in assessing the Bayesian rationality of others

People often assess the reasonableness of another person’s judgments. When doing so, the evaluator should set aside knowledge that would not have been available to the evaluatee to assess whether the evaluatee made a reasonable decision, given the available information. But under what circumstances does the evaluator set aside information? On the one hand, if the evaluator fails to set aside prior information, not available to the evaluatee, they exhibit belief bias. But on the other hand, when Bayesian inference is called for, the evaluator should generally incorporate prior knowledge about relevant probabilities in decision making. The present research integrated these two perspectives in two experiments. Participants were asked to take the perspective of a fictitious evaluatee and to evaluate the reasonableness of the evaluatee’s decision. The participant was privy to information that the fictitious evaluatee did not have. Specifically, the participant knew whether the evaluatee’s decision judgment was factually correct. Participants’ judgments were biased (Experiments 1 and 2) by the factuality of the conclusion as they assessed the evaluatee’s reasonableness. We also found that the format of information presentation (Experiment 2) influenced the degree to which participants’ reasonableness ratings were responsive to the evaluatee’s Bayesian rationality. Specifically, responsivity was greater when the information was presented in an icon-based, graphical, natural-frequency format than when presented in either a numerical natural-frequency format or a probability format. We interpreted the effects of format to suggest that graphical presentation can help organize information into nested sets, which in turn enhances Bayesian rationality.
Chinese Version of Groningen Frailty Index: Validation in a Chinese Elderly Population

Objective Early recognition and assessment of frailty status in elderly is essential for clinical treatment. The standard frailty assessment tools normally include a comprehensive but timeconsuming geriatric assessment requiring special training. We translated the Groningen Frailty Indicator (GFI), a 15-item frailty screening tool, into Chinese language and validated the translated version. Methods The English version GFI was translated into Chinese following the Brislin’s forward and backward translation model, and validated in a group of community-dwelling elderly. Content validity index (CVI), confirmatory factor analysis (CFA) and exploratory factor analysis (EFA) were used to evaluate the content validity and construct validity, respectively. A total of 331 participants were recruited for construct validity and 54 of them were interviewed twice for the use of test-retest reliability. All statistical analyses were calculated using SPSS 18.0 for Windows. Results The Chinese GFI demonstrated excellent test-retest reliability. The I-CVI values ranged from 0.83 to 1.0, and the S-CVI was 0.98. The EFA yielded a 4-factor structure and subsequent CFA results were satisfactory. Individual linear regressions showed that gender, age and marital status were associated with frailty. Conclusion The Chinese GFI is a valid and specific screening tool for frailty in Chinese population. A priority of preventive care in very old subjects is recommended with distinguished strategies in different genders according to our findings. Further investigation of possible reasons of lower frailty prevalence in Chinese population and necessity of additional items to increase sensitivity of the tool are warranted.
Chloroplast genome and phylogenetic analyses of Morus alba (Moraceae)

Abstract Morus alba is an important medicinal plant that is used to treat human diseases. The complete chloroplast (cp) genome of M. alba was assembled based on the Illumina sequencing reads. The cp genome of M. alba was 159,290 bp and contained two short inverted repeat regions (25,690 bp) which were separated by a small single copy region (19,845 bp) and a large single copy region (88,065 bp). The cp genome encodes 111 unique genes, including 77 protein-coding genes, 30 transfer RNA genes and four ribosomal RNA genes. The topology of the phylogenetic tree showed that M. alba is closely clustered with species M. cathayana and M. mongolica.
ITERATIVE METHOD FOR CONTROLLING WITH A COMMAND PROFILE THE SWAY OF A PAYLOAD FOR GANTRY AND OVERHEAD TRAVELING CRANES

In this work, a novel anti-sway control system, obtained with a command profile, for gantry or overhead crane is proposed. The proposed method is based on an iterative calculation of the sway angle and of the corresponding applied velocity profile as input to the crane motors. A comprehensive mathematical model is developed. Simulation results show that the proposed anti-sway method is able to damp the oscillations of the suspended load during the contemporary movement along the three perpendicular Cartesian axes (both horizontal axis and also vertical axis). The system is schematized as a double-pendulum. Besides, as a consequence of being an iterative method, it is possible to obtain the total damping of the sway changing many times the velocity set, also if the previous damping control of the oscillations is again in progress. That makes the method extremely useful in the manual control of the crane by an operator as well as with automatic control.
Simulation of Quasi-static Crack Propagation in Functionally Graded Materials

In this chapter we present some ideas how quasi-static crack propagation in anisotropic functionally graded materials can be predicted using the Griffith' energy principle for plane problems. From a physical point of view the energy principle, already formulated by Gri th in 1921, can be applied in anisotropic and inhomogeneous materials to compute quasi-static crack propagation: A crack is growing in such a way that the total energy always is minimal. The total energy is composed from the surface energy and the potential energy, the latter is the di erence of the elastic energy and the work performed by external forces. In linear-elastic plane structures the change of energy produced by a small elongation of the crack can be calculated asymptotically precise, if the material is homogeneous. Using methods of asymptotic analysis, the change of potential energy can be expressed in terms of the stress intensity factors and certain integral characteristics, depending on the geometry of the (small) crack elongation and the material properties. The main focus of this work is to show if and how these methods can be transferred to functionally graded materials. After presenting theoretical results, we setup a complete quasi-static framework in order to make these ideas applicable for the simulation of practical crack problems and discuss methods to calculate all required quantities numerically. Based on a dual-weighted-residual approach, integral characteristics can be calculated precisely by the nite element method. Finally, we present numerical results.
Original contribution Depression in mothers of burned children

The treatment of burned children needs to address also their psychic state. Since the child’s emotional state depends heavily on the emotional state of its parents, especially the mother’s, interest should also be focused on diagnosing and treating secondary psychiatric disorders in mothers of burned children. The aim of this work was to study the prevalence of depression in mothers of burned children and its predicting factors. Methods: The enrolment of mothers of burned children was done in the Burns Department, Ibn Rushd University Hospital in Casablanca, Morocco. Twenty-eight mothers of seriously burned children agreed to participate in this study. A psychiatric interview was conducted after informed consent was obtained. The mothers completed a questionnaire; the diagnosis was made according to DSM IV criteria using the Mini International Neuro-psychiatric Interview (MINI). The Hamilton Depression Rating Scale and the Hamilton Anxiety Rating Scale were used to evaluate respectively the intensity of depression and the level of anxiety. Social functioning was assessed by the Global Assessment of Functioning scale (GAF). The minimum time interval between the child’s being burned and the interview with the mother was one month. Results: The prevalence of depression was 35.7%, which is much higher in the mothers of burned children as compared to the general population. Several risk factors were found such as: several burned children or burn of the only child, severity and complications of burn, and the socio-economic level. Conclusion: Depression is a common disorder in mothers of burned children. Thus early screening for depression in mothers and psychological and psychiatric support must be provided in conjunction with the treatment of the burned child.
Kobe University Repository : Kernel タイトル Tit le Subspace based least squares support vector machines for pattern

In this paper, we discuss subspace based least squares support vector machines (SSLS-SVMs), in which an input vector is classified into the class with the maximum similarity. Namely, we define the similarity measure for each class by the weighted sum of vectors called dictionaries and optimize the weights so that the margin between classes is optimized. Because the similarity measure is defined for each class, the similarity measure associated with a data sample needs to be the largest among all the similarity measures. Introducing slack variables we define these constraints by equality constraints. Then the proposed SSLS-SVMs is similar to LS-SVMs by all-at-once formulation. Because all-at-once formulation is inefficient, we also propose SSLS-SVMs by oneagainst-all formulation. We demonstrate the effectiveness of the proposed methods with the conventional method for two-class problems.
A Review Of Recent Detection Methods For HTTP Ddos Attacks

: There has been a tremendous increase in dependency on web technologies alongside which its disruption attempts have also increased. Web servers that run on Hypertext Transfer Protocol (HTTP) are exposed to Denial of Service (DoS) attacks. Distributed denial of service attack is among the most dangerous internet attacks with the ability to overwhelm a web server. The content provides review of recent detection methods in recognizing detection attacks at the application layer. Trojan, Botnets, Advanced Persistent threat, Distributed Denial of Service attacks are increasing the security risks and are posing a great threat to critical information. Also, applications of Machine learning and Deep learning helps in the exclusion of false positives.
Symplectic Structures on the Integration of Exact Courant Algebroids

We construct an infinite-dimensional symplectic 2-groupoid as the integration of an exact Courant algebroid. We show that every integrable Dirac structure integrates to a “Lagrangian” sub-2-groupoid of this symplectic 2-groupoid. As a corollary, we recover a result of Bursztyn-CrainicWeinstein-Zhu that every integrable Dirac structure integrates to a presymplectic groupoid.
Data and text mining BioWeka — extending the Weka framework for bioinformatics

Summary: Given the growing amount of biological data, data mining methods have become an integral part of bioinformatics research. Unfortunately, standard data mining tools are often not sufficiently equipped for handling raw data such as e.g. amino acid sequences. One popular and freely available framework that contains many well-known data mining algorithms is the Waikato Environment for Knowledge Analysis (Weka). In the BioWeka project, we introduce various input formats for bioinformatics data and bioinformatics methods like alignments to Weka. This allows users to easily combine them with Weka’s classification, clustering, validation and visualization facilities on a single platform and therefore reduces the overhead of converting data between different data formats as well as the need to write custom evaluation procedures that can deal with many different programs. We encourage users to participate in this project by adding their own components and data formats to
Adaptive Probabilistic Modelling Method of Guided Wave Features under Time Varying Conditions

For the practical aerospace application of structural health monitoring (SHM), one of the key challenges is the reliability of damage evaluation under time varying conditions. Among the existing SHM methods, guided wave based SHM method is popular but the time-varying problem still needs to be addressed. Recently, probabilistic modelling methods have been studied to deal with this problem. However, the probability model in most of these methods could not change with the time varying conditions. In this paper, an adaptive probabilistic modelling method is proposed for the influence of time varying conditions on guided wave features. In this method, an adaptive clustering based on searching probability density peaks is proposed to initialize the probability model. Then a stable probability model is established by the Expectation Maximization algorithm efficiently. The proposed probabilistic modelling method is simple, stable and adaptive. The applicability of the method in SHM is validated in a hole-edge crack monitoring of an aluminium tension sample under varying loading condition.
Sieved orthogonal polynomials and discrete measures with jumps dense in an interval

We investigate particular classes of sieved Jacobi polynomials for which the weight function vanishes at the zeros of a Chebyshev polynomial of the first kind. These polynomials are then used to give a proof, using only orthogonal polynomials on (-1,1), that the discrete orthogonal polynomials introduced by Lubinsky have converging recurrence coefficients. We construct similar discrete measures with jumps dense in (—1,1) and use sieved ultra- spherical polynomials to show that their recurrence coefficients converge.
Liberalizing Art. Evidence on the Impressionists at the End of the Paris Salon

We analyze the Paris art market between the government-controlled Salon as a centralized organization of art exhibition and the system liberalized by the Republican government based on competition between independent exhibitions. The jury of the old Salon decided on submissions with a bias toward conservative art of the academic insiders, reducing demand for the outsiders, as confirmed by the impact on prices of the acceptance of Impressionists. With a difference-in difference estimation we provide evidence that the end of the government-controlled Salon in 1880 started the increase of the prices of the Impressionists relative to the insiders.
Combining Intra Block Copy and Neighboring Samples Using Convolutional Neural Network for Image Coding

Intra prediction is an essential step to remove spatial redundancy in image coding. How to best predict current block given surrounding pixels is the key efficiency challenge. Inspired by the recent success in applying deep learning to image/video coding systems, we propose an intra prediction method by combining intra block copy and neighboring samples using convolutional neural networks. A novel CNN is developed to further exploit the spatial correlation. Instead of only considering local information, the proposed method can infer the current block via fusing the non-local recurrent features, which is captured by intra block copy, with the local samples located at the left and above boundaries of current block. We also investigate how the performance is affected by the way of fusing IBC and reference boundary pixels. In additional, training data pre-processing is studied to enable the CNN with a better learning capability. Simulation results yield promising coding gain and indicate great potential ability that CNN can be used for next generation video coding framework.
Resource Integration, Reconfiguration, and Sustainable Competitive Advantages: The Differences between Traditional and Emerging Industries

Emerging industries bear great difference from traditional industries. It is valuable to explore the effectiveness of different resource management methods in the two industries. Based on this, the purposes of this paper are first to define and distinguish two core resource management methods (i.e., resource integration and resource reconfiguration), and second to research the different impact paths of resource integration and resource reconfiguration on the sustainable competitive advantages in different industries. Primarily, in order to achieve these purposes, this paper explores the generation path of resource integration and resource reconfiguration from the perspective of organizational learning; secondly, the empirical analysis method is applied to examine the different influences between resource integration and resource reconfiguration on sustainable competitive advantages. Based on 208 samples in traditional industries and 220 samples in emerging industries, the results show that resource integration and resource reconfiguration are the consequence of organizational learning. In traditional industries, resource integration and resource reconfiguration have a positive impact on sustainable competitive advantages, respectively, resulting in a “concerto effect” on sustainable competitive advantages. While, in emerging industries, though resource integration has a positive impact on sustainable competitive advantages, however, there is an inverted U-shaped relationship between resource reconfiguration and sustainable competitive advantages. In such a situation, the “concerto effect” disappeared. This paper not only reveals the uniqueness of different resource management methods in different industries but also enriches the applications of resource management theories in different situations.
The Effect of Abortion Legalization on Fertility, Marriage and Long-Term Outcomes for Women

We evaluate the short- and long-term effects for women of access to subsidized, legal abortion by exploiting the Spanish legalization of abortion in 1985. Using birth records and survey data, we find robust evidence that the legalization led to an immediate decrease in the number of births to women aged 21 and younger. This effect was driven by provinces with a higher supply of abortion services. In those regions, young women affected by the reform were also less likely to marry. Using data from the Labor Force Survey and exploiting the rollout of abortion clinics across provinces and over time, we find evidence that the affected cohorts of women, who were able to postpone fertility as a result of the legalization of abortion, achieved higher educational attainment and had higher life satisfaction 20 years after the reform. We do not find evidence of increases in the probability of being employed.