Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards a framework for psychological resilience in children and adolescents with Borderline Intellectual Functioning

Psychological well-being is one of the greatest concerns in children and adolescents with Borderline Intellectual Functioning (BIF). Those youths are frequently exposed to stress and social inequality, and they are particularly prone to developing mental health issues which persist through adolescence and into adult life. The purpose of this article is to introduce a framework for promoting psychological resilience in children and adolescents with BIF. Three interrelated and complementary factors require professional attention and efforts to improve resilience in children with borderline intelligence: a) protecting a child’s self-worth, b) generating sources of social support, c) training of adaptive coping skills. The significance of early diagnosis and continuous monitoring of a child’s development is also discussed. Children with BIF should be provided with internal (self-worth, coping skills) and external (social support) resources to enhance their resilience and ability to confront adversities, and to reduce the risk of mental health issues.
Visual Network Exploration for Data Journalists

Networks are classic but under-acknowledged figures of journalistic storytelling. Who is connected to whom and by which means? Which organizations receive support from which others? What resources or information circulate through which channels and which intermediaries enable and regulate their flows? These are all customary stories and lines of inquiry in journalism and they all have to do with networks. Additionally, the recent spread of digital media has increasingly confronted journalists with information coming not only in the traditional form of statistic tables, but also of relational databases. Yet, journalists have so far made little use of the analytical resources offered by networks. To address this problem in this chapter we examine how " visual network exploration " may be brought to bear in the context of data journalism in order to explore, narrate and make sense of large and complex relational datasets. We borrow the more familiar vocabulary of geographical maps to show how key graphical variables such as position, size and hue can be used to interpret and characterise graph structures and properties. We illustrate this technique by taking as a starting point a recent example from journalism, namely a catalogue of French information sources compiled by Le Monde's The Decodex. We establish that good visual exploration of networks is an iterative process where practices to demarcate categories and territories are entangled and mutually constitutive. To enrich investigation we suggest ways in which the insights of the visual exploration of networks can be supplemented with simple calculations and statistics of distributions of nodes and links across the network. We conclude with reflection on the knowledge-making capacities of this technique and how these compare to the insights and instruments that journalists have used in the Decodex project – suggesting that visual network exploration is a fertile area for further exploration and collaborations between data journalists and digital researchers.
Generalized Semi Exponential Type Estimator under Systematic Sampling

In sample surveys, collection of auxiliary information together with the main variable of interest is very important to increase the efficiency of the estimators of population parameters of interest. Regression and ratio estimation are very popular and are widely used methods that benefit from the use of auxiliary information for the estimation of population parameters like mean, total, variance, proportion etc. A generalized semi-exponential type estimator is proposed in this paper using two auxiliary variables under the framework of systematic sampling. The expressions of approximate bias and mean square error of the proposed estimator have been derived. Algebraic conditions have been obtained under which the proposed estimator is more efficient than the competing estimators considered here. An empirical study has been carried out to show the improvement in efficiency of the proposed estimator as compared to the existing estimators.
Bioinformatics Component in Personalized Medicine

Past few decades have seen rapid growth in sequencing technology and software tools to aid their processing and analysis. The cost of genome sequencing of whole human data has dropped to couple thousand dollars from what it used to be about a million dollars. In parallel there has been significant growth in supercomputing power as per the Moore’s law with multi-and-many-core computers being a common commodity, needless to mention the GPUs which promise to bring supercomputing power at desktop space. Parallel advancement has been in the domain of proteomics and transcriptomics. The ‘gaps’ today are integrating these hardware, software and human resources for a better bioinformatics solution to aid a personalized medicine age to practice.
A Survey on Big Data Mining Algorithms

In recent years with the explosive development of internet the size of data has grown a large and reached petabytes size. Bigdataisan immense collection of both structured and unstructured data. Due to its large size discovering knowledge or obtaining pattern from big data within an elapsed time is a complicated task. A number of algorithmic techniques have been designed for big data mining in an effective manner. The various mining algorithms like Two-Phase Top -Down Specialization approach (TPTDS), Tree- Based Association rules (TARs), FuzzyC - Means (FCM)algorithm and Associate Rule Mining (ARM) algorithm are surveyed in this paper and the results obtained are compared and evaluated by the parameters such as execution time, information loss and extraction time.
Acute effects of different warm-up protocols on highly skilled golfers’ drive performance

ABSTRACT Previous research has highlighted the positive effect that different warm-up protocols have on golf performance (e.g. Sorbie et al., 2016; Tilley & Macfarlane, 2012) with the design of warm-ups and programmes targeting and improving golf performance through the activation and development of specific muscle groups. This study aimed to examine the acute effects of two warm-up protocols on golf drive performance in comparison to a control condition. Using a randomised counterbalanced design over three testing sessions, twenty-three highly skilled golfers completed the control, dynamic and resistance-band warm-up conditions. Following each condition, a GC2 launch monitor was used to record ball velocity and other launch parameters of ten shots hit with the participants’ own driver. A repeated-measures ANOVA found significant increases in ball velocity (ηp2 = .217) between the control and both the dynamic and resistance-band warm-up conditions but no difference between these latter two, and a reduction in launch angle between control and dynamic conditions. The use of either a dynamic stretching or resistance-band warm-up can have acute benefits on ball velocity but golfers should liaise with a PGA Professional golf coach to effectively integrate this into their golf driving performance.
How the Level of Reward Awareness Changes the Computational and Electrophysiological Signatures of Reinforcement Learning

The extent to which subjective awareness influences reward processing, and thereby affects future decisions, is currently largely unknown. In the present report, we investigated this question in a reinforcement learning framework, combining perceptual masking, computational modeling, and electroencephalographic recordings (human male and female participants). Our results indicate that degrading the visibility of the reward decreased, without completely obliterating, the ability of participants to learn from outcomes, but concurrently increased their tendency to repeat previous choices. We dissociated electrophysiological signatures evoked by the reward-based learning processes from those elicited by the reward-independent repetition of previous choices and showed that these neural activities were significantly modulated by reward visibility. Overall, this report sheds new light on the neural computations underlying reward-based learning and decision-making and highlights that awareness is beneficial for the trial-by-trial adjustment of decision-making strategies. SIGNIFICANCE STATEMENT The notion of reward is strongly associated with subjective evaluation, related to conscious processes such as “pleasure,” “liking,” and “wanting. ” Here we show that degrading reward visibility in a reinforcement learning task decreases, without completely obliterating, the ability of participants to learn from outcomes, but concurrently increases subjects' tendency to repeat previous choices. Electrophysiological recordings, in combination with computational modeling, show that neural activities were significantly modulated by reward visibility. Overall, we dissociate different neural computations underlying reward-based learning and decision-making, which highlights a beneficial role of reward awareness in adjusting decision-making strategies.
Modeling and Control for a Multi-Rope Parallel Suspension Lifting System under Spatial Distributed Tensions and Multiple Constraints

The modeling and control of the multi-rope parallel suspension lifting system (MPSLS) are investigated in the presence of different and spatial distributed tensions; unknown boundary disturbances; and multiple constraints, including time varying geometric constraint, input saturation, and output constraint. To describe the system dynamics more accurately, the MPSLS is modelled by a set of partial differential equations and ordinary differential equations (PDEs-ODEs) with multiple constraints, which is a nonhomogeneous and coupled PDEs-ODEs, and makes its control more difficult. Adaptive boundary control is a recommended method for position regulation and vibration degradation of the MPSLS, where adaptation laws and a boundary disturbance observer are formulated to handle system uncertainties. The system stability is rigorously proved by using Lyapunov’s direct method, and the position and vibration eventually diminish to a bounded neighborhood of origin. The original PDEs-ODEs are solved by finite difference method, and the multiple constraints problem is processed simultaneously. Finally, the performance of the proposed control is demonstrated by both the results of ADAMS simulation and numerical calculation.
Design Pattern Analysis with Software Evolution Data

The influences of design patterns on software quality have attracted increasing attention in the area of software engineering, as design patterns encapsulate valuable knowledge to resolve design problems, and more importantly to improve the design quality. Since most design patterns are designed to enhance the maintainability, a system with such design patterns is expected to have lower maintenance load in its further evolution. However, sometimes design patterns are over applied or mis-used in many systems, which will cause another maintenance problem or impair the system performance. Therefore, lots of researchers proposed their approaches to evaluate the quality of design patterns or their deployment. However, there is no approach taking the software evolution into concern, even it is the major issue a design pattern addresses. In this paper, we propose a new approach to formulate the evaluation of a design pattern's utilization using the evolution data. We also conduct our approach to 11 design patterns over 15 projects which provide software evolution data. The analysis results show that the utilization of deployed design patterns does not have significant difference among the design patterns in the evolution of software design. JOURNAL OF INFORMATION SCIENCE AND ENGINEERING XX, XXXX-XXXX (2018) Design Pattern Analysis with Software Evolution Data NIEN-LIN HSUEH Department of Information Engineering and Computer Science Feng Chia University No. 100, Road Wen-Hwa, Taichung City, Taiwan E-mail: {nlhsueh}@fcu.edu. tw The influences of design patterns on software quality have attracted increasing attention in the area of software engineering, as design patterns encapsulate valuable knowledge to resolve design problems, and more importantly to improve the design quality. Since most design patterns are designed to enhance the maintainability, a system with such design patterns is expected to have lower maintenance load in its further evolution. However, sometimes design patterns are over applied or mis-used in many systems, which will cause another maintenance problem or impair the system performance. Therefore, lots of researchers proposed their approaches to evaluate the quality of design patterns or their deployment. However, there is no approach taking the software evolution into concern, even it is the major issue a design pattern addresses. In this paper, we propose a new approach to formulate the evaluation of a design pattern’s utilization using the evolution data. We also conduct our approach to 11 design patterns over 15 projects which provide software evolution data. The analysis results show that the utilization of deployed design patterns does not have significant difference among the design patterns in the evolution of software design.
The reaction of investors to analyst recommendations of stocks listed on the WIG20 index

Analyst recommendations are one of the types of information whose appearance on the market can have an influence on security prices. In this paper, I study the impact of analyst recommendations on stocks listed on the WIG20 Index, using event-study methodology and linearregression models. The dataset contains 576 absolute recommendations published from the1st January 2012 to the 1st of September 2015 by various analyst houses. The prefatory study researches price reaction to positive, neutral, and negative recommendations separately. Subsequently, to check if investor reaction depends on a change in the level of recommendation, corresponding research is repeated for events clustered in nine groups defined in terms of possible level changes. Linear regression models with categorical variables are used in searchof additional factors affecting investor reactions. Changes in the level of recommendation, size of the company, and reputation of brokerage house represent explanatory variables. Preliminary results point out that the direction of investor reaction is generally consistent with the information contained in the recommendation, and that the reaction of the market seems to be stronger in the case of positive events than in the case of negative ones. The analysis of recommendation changes reflects more-detailed dependents. In particular, the interpretation of a neutral recommendation depends strongly on the level of the previous recommendation. If it represents growth from SELL or REDUCE, the reaction is positive, while in the case of drop from ACCUMULATE or BUY, it leads to negative abnormal returns. This relationship is additionally confirmed by results from the linear regression models. The models show the size of the firm as a significant factor that has an influence on the reaction to a recommendation: the smaller the firm, the stronger the reaction.
Performance and outcome of zirconia dental implants in clinical studies: A meta‐analysis

OBJECTIVES To evaluate implant survival, peri-implant marginal bone loss, technical, and biological complications as well as aesthetic outcomes of zirconia implants in clinical studies. MATERIAL AND METHODS Electronic (Medline, Embase) and hand searches were performed to identify clinical studies published between January 2004 and March 2017 investigating zirconia dental implants with a mean follow-up of at least 12 months. Primary outcomes were implant survival and peri-implant marginal bone loss. Secondary outcomes included technical and biological complications as well as aesthetic outcomes. Meta-analyses were performed to estimate implant survival and marginal bone loss. RESULTS From 943 titles, 264 abstracts were selected. Subsequently, 80 full-text articles were screened, and 18 studies were included for data extraction. One- (14 studies) and 2-piece zirconia implants (4 studies) were investigated. Commercially available (CA) (510 implants, 398 patients) and not commercially available (NCA) zirconia implants (618 implants, 343 patients) were identified. For CA implants (follow-up: 12-61.20 months), technical complications (1.6%), implant fractures (0.2%) and biological complications (4.2%) were reported. Meta-analyses estimated 1- and 2-year survival rates of 98.3% (95% CI: 97.0%-99.6%) and 97.2% (95% CI: 94.7%-99.7%), respectively, and a mean 1-year marginal bone loss of 0.7 mm (95% CI: 0.4-1.0 mm). CONCLUSIONS Since 2004, the survival rates of CA implants significantly improved compared with NCA implants. CA 1-piece zirconia implants showed similar 1- and 2-year mean survival rates and marginal bone loss after 1 year compared with published data for titanium implants. However, more clinical long-term data are needed to confirm the presently evaluated promising short-term outcomes.
Growth and characterization of bis ( thiourea ) potassium chloride crystals for NLO applications

Growth of Bis(Thiourea) KCl doped with NaCl single crystals from aqueous solution by slow evaporation technique was reported. The solubility of the grown samples was found at various temperatures. Structural characterization of the grown crystals was determined by the single crystal X-ray diffraction (XRD). EDAX studies indicate the presence of thiourea in the doped L-alanine crystals. The functional groups were identified by FTIR spectroscopy. The UVVisNIR spectral studies were carried out to analyze the optical absorption of the grown crystals and found that the absorption is very low in the wavelength region 290 to 1000 nm for both the samples. Improvement in the SHG efficiency was studied by the Kurtz and Perry method. Thermal analysis was carried out on the grown crystal.
Iraq ’ s calutrons Electromagnetic isotope separation , beam technology , and nuclear weapon proliferation

The past and present status of high-current electromagnetic isotope separation (EMIS) technology for uranium and plutonium enrichment (i.e. calutrons) is reviewed in the five nuclear weapons states and in four critical states: Japan, India, Israel and Iraq. The circumstances and significance of the 1979 discovery at CERN, the European center for nuclear research in Geneva, of Iraq’s definite interest in calutron technology, is discussed in detail, together with the problem of publishing independent opinions on the nuclear proliferation implications of particle accelerator and fusion technologies. The conclusion stresses the potential of "old" beam technologies such as calutrons, e.g., for the transformation of reactor-grade into weapons-grade plutonium, and of particle accelerators for the efficient production of plutonium or tritium. UN Security Council Resolutions 687 and 707, obliging Iraq to all proliferating nuclear activities, are shown to provide a legal precedent for the unambiguous definition of strictly peaceful nuclear activities. The "failure" of Western intelligence in detecting Iraq’s gigantic calutron program is questioned, and the relation of this "failure" to the justification of past and possible future coercive counter-proliferation actions is investigated.
Integrated transcriptome, small RNA and degradome sequencing approaches provide insights into Ascochyta blight resistance in chickpea

Summary Ascochyta blight (AB) is one of the major biotic stresses known to limit the chickpea production worldwide. To dissect the complex mechanisms of AB resistance in chickpea, three approaches, namely, transcriptome, small RNA and degradome sequencing were used. The transcriptome sequencing of 20 samples including two resistant genotypes, two susceptible genotypes and one introgression line under control and stress conditions at two time points (3rd and 7th day post inoculation) identified a total of 6767 differentially expressed genes (DEGs). These DEGs were mainly related to pathogenesis‐related proteins, disease resistance genes like NBS‐LRR, cell wall biosynthesis and various secondary metabolite synthesis genes. The small RNA sequencing of the samples resulted in the identification of 651 miRNAs which included 478 known and 173 novel miRNAs. A total of 297 miRNAs were differentially expressed between different genotypes, conditions and time points. Using degradome sequencing and in silico approaches, 2131 targets were predicted for 629 miRNAs. The combined analysis of both small RNA and transcriptome datasets identified 12 miRNA‐mRNA interaction pairs that exhibited contrasting expression in resistant and susceptible genotypes and also, a subset of genes that might be post‐transcriptionally silenced during AB infection. The comprehensive integrated analysis in the study provides better insights into the transcriptome dynamics and regulatory network components associated with AB stress in chickpea and, also offers candidate genes for chickpea improvement.
Filling method for soil moisture based on BP neural network

Abstract. Soil moisture data obtained by inversion of Fengyun 3B remote sensing data, are widely used in drought monitoring and global climate change research, however, some regional data are missing in this data set, which reduces the application effect. Based on backpropagation neural network (BPNN), we established a filling method and filled the missing area with moderate resolution imaging spectroradiometer (MODIS) inversion products, including land surface temperature, normalized difference vegetation index, and albedo. We named it the multilayer BPNN filling algorithm. The algorithm consists of two neural network layers. The first network layer is used for the spatial scaling of MODIS inversion products, and the second network layer uses the scaling products to further generate soil moisture values. We compared the proposed method to a discrete cosine transform and partial least square (DCT-PLS) and a kriging using the same data set. The experiments demonstrate that our method could obtain good filling results in both homogeneous areas and areas with high data variations, whereas DCT-PLS and kriging could only get good filling results in homogeneous areas.
Italian municipalities and the politics of financial derivatives: Rethinking the Foucauldian perspective

This study examines the use of interest rate swaps by Italian municipalities during the 2000–2014 period. Through a constructive critique of Foucauldian political economy, it argues that local administrators did not enter the swaps market to perform and depoliticize the disciplinary power of financialized discourses. The evidence suggests many more layers of historical complexity underlie the municipalities’ actions than a Foucauldian approach can grasp. Local governments used swaps to circumvent the budget constraints imposed by the European Stability and Growth Pact. In this sense, municipalities acted more for political-strategic than for performative and depoliticized purposes. Rethinking the dimensions of performativity and disciplinary power – through an approach that is more sensitive than Foucauldian analysis to historical agency and asymmetrical power relations – is crucial to address the differential and politically shaped contours of financialization across the globe.