Miracle-dz/UniFAR-FTUs huggingface dataset:
Linear models for the total coverage problem in wireless sensor networks

In this paper, we consider the total grid coverage problem in wireless sensor networks. Our proposal aims to determine the optimal number of sensors and their positions in a sensing area represented by a grid. The deployed sensors should achieve the total grid point coverage. The problem is proved NP-complete in [15]. We propose two mathematical linear models to solve optimally two problem cases. In the first case, the connectivity between deployed sensors is not required. However, in the second case, the sensors should communicate with each other. Computational experiments are generated on different grid sizes and multiple sensor ranges. The results show that the proposed linear models can produce appropriate solutions for the two problem cases.
A probabilistic method for the computation of testability of RTL constructs

Validation of RTL descriptions remains one of the principal bottlenecks in the circuit design process. Random simulation based methods for functional validation suffer from fundamental limitations and may be inappropriate or too expensive. In fact, for some circuits, a large number of vector is required in order to make the circuit reach hard to test constructs and obtains accurate values for their testability. In this work, we present a static, non-simulation based, method for the determination of the controllability of RTL constructs that is efficient and gives accurate feedback to the designers in what regards the presence of hard to control constructs in their RTL code. The method takes as input a Verilog RTL description, solves the Chapman-Kolmogorov equations that describe the steady-state of the circuit and outputs the computed values for the controllability of the RTL constructs. To avoid the exponential blow-up that results from writing one equation for each circuit state and solving the resulting system of equations, an approximation method is used. We present results showing that the approximation is effective and describe how the method can be used to bias a random test generator in order to achieve higher coverage using a smaller number of vectors.
Reflected Skills of the Science Student Teachers in Teaching Practices.

The aim of this study is to determine the level of opportunities given to science student teachers by practice schools in order to reflect their skills they acquired through curricula in their faculty. This study has been carried out by using case study approach. As the first step, the curricula of the teaching professional courses offered in numerous teacher education faculties are tabulated, using document analysis. Then, the expected behavioral skills for student teachers are listed in accordance with interviews with four professors. With the help of these data, to determine both the degree of opportunity and the level of success, the professional activities of six sciencestudent-teachers in these schools are observed using an observation chart. As a final step, semistructured interviews are conducted to get ideas, expectations and comments of six student teachers and mentors.
Creative language explorations through a high-expressivity N-grams query language

In computation linguistics a combination of syntagmatic and paradigmatic features is often exploited. While the first aspects are typically managed by information present in large n-gram databases, domain and ontological aspects are more properly modeled by lexical ontologies such as WordNet and semantic similarity spaces. This interconnection is even stricter when we are dealing with creative language phenomena, such as metaphors, prototypical properties, puns generation, hyperbolae and other rhetorical phenomena. This paper describes a way to focus on and accomplish some of these tasks by exploiting NgramQuery, a generalized query language on Google N-gram database. The expressiveness of this query language is boosted by plugging semantic similarity acquired both from corpora (e.g. LSA) and from WordNet, also integrating operators for phonetics and sentiment analysis. The paper reports a number of examples of usage in some creative language tasks.
A novel method for early software quality prediction based on support vector machine

The software development process imposes major impacts on the quality of software at every development stage; therefore, a common goal of each software development phase concerns how to improve software quality. Software quality prediction thus aims to evaluate software quality level periodically and to indicate software quality problems early. In this paper, we propose a novel technique to predict software quality by adopting support vector machine (SVM) in the classification of software modules based on complexity metrics. Because only limited information of software complexity metrics is available in early software life cycle, ordinary software quality models cannot make good predictions generally. It is well known that SVM generalizes well even in high dimensional spaces under small training sample conditions. We consequently propose a SVM-based software classification model, whose characteristic is appropriate for early software quality predictions when only a small number of sample data are available. Experimental results with a medical imaging system software metrics data show that our SVM prediction model achieves better software quality prediction than some commonly used software quality prediction models
Global reprogramming of transcription and metabolism in Medicago truncatula during progressive drought and after rewatering

Medicago truncatula is a model legume forage crop native to the arid and semi-arid environments of the Mediterranean. Given its drought-adapted nature, it is an ideal candidate to study the molecular and biochemical mechanisms conferring drought resistance in plants. Medicago plants were subjected to a progressive drought stress over 14 d of water withholding followed by rewatering under controlled environmental conditions. Based on physiological measurements of plant water status and changes in morphology, plants experienced mild, moderate and severe water stress before rehydration. Transcriptome analysis of roots and shoots from control, mildly, moderately and severely stressed, and rewatered plants, identified many thousands of genes that were altered in expression in response to drought. Many genes with expression tightly coupled to the plant water potential (i.e. drought intensity) were identified suggesting an involvement in Medicago drought adaptation responses. Metabolite profiling of drought-stressed plants revealed the presence of 135 polar and 165 non-polar compounds in roots and shoots. Combining Medicago metabolomic data with transcriptomic data yielded insight into the regulation of metabolic pathways operating under drought stress. Among the metabolites detected in drought-stressed Medicago plants, myo-inositol and proline had striking regulatory profiles indicating involvement in Medicago drought tolerance. Global transcriptional and metabolic responses to drought and rewatering were investigated in Medicago truncatula, a naturally drought-adapted model legume species. Integration of metabolomic and transcriptomic data yielded insights into the regulation of metabolic pathways underlying drought-stress adaptation. Many genes and metabolites with expression tightly coupled to drought intensity were identified, suggesting active involvement in Medicago drought resistance. These could prove useful targets for future translational approaches to improve closely related crop plants such as common bean, soybean and pea.
Effectiveness of oral moxifloxacin in standard first-line therapy in community-acquired pneumonia

Based on recent guidelines for the management of community-acquired pneumonia, this study was designed to evaluate the effectiveness of a new fluoroquinolone compared with standard antimicrobial regimens, in conditions relating as closely as possible to the real world setting. In this study, 564 patients were randomised to either oral moxifloxacin (400 mg o.d.) or to standard oral therapy (amoxicillin 1 g t.i.d. or clarithromycin 500 mg b.i.d. alone or in combination) for up to 14 days using a double-blind procedure. The choice between the three standard regimens was made by the clinician prior to randomisation. Clinical response, quality of life, symptoms, healthcare resources and safety were assessed. In the per-protocol population, clinical success was reported for 201 of 215 (93.5%) and 217 of 231 (93.9%) in the moxifloxacin and standard groups, respectively, at 7–10 days post-therapy. At 28–35 days follow-up, continued clinical cure was observed in 183 of 192 (95.3%) moxifloxacin and 207 of 221 (93.7%) standard groups. Drug-related adverse events were reported in 55 of 274 (20%) moxifloxacin and 86 of 279 (31%) standard patients with diarrhoea >5%. Oral moxifloxacin monotherapy was as effective as, and better tolerated than, optimal antibiotic strategy represented either by mono- or combination therapy (amoxicillin and/or clarithromycin) in community-acquired pneumonia management.
Prior expectations about where other people are likely to direct their attention systematically influence gaze perception

Different locations in the visual environment vary greatly in terms of how likely they are to draw a person's attention. When inferring the most likely target of another person's gaze, it would therefore be a reasonable strategy to incorporate expectations about the relative visual salience of these various locations, weighing this prior knowledge against incoming social signals (e.g., eye cues). This Bayesian approach to modeling gaze perception has informed computer vision techniques, but whether this model accounts well for human performance remains an untested hypothesis. We present subjects with a “gazer” fixating his eyes on various locations on a two-dimensional surface, and project arbitrary photographic images onto that surface. Subjects judge where the gazer is looking in each image. A full Bayesian model, which takes image salience information into account, fits subjects' gaze judgments better than a reduced model that only considers the perceived direction of the gazer's eyes. Varying the amount of time the subject is allowed to view the gazer reveals that center bias tends to dominate gaze judgments early, whereas salient features specific to the projected image influence judgments at longer viewing durations.
A Study on Significance of System Dynamics Approach in Understanding Adoption of Information Technology in Building Construction Projects

Today several activities of a construction project can be supported with information technology tools. However, there have been many cases of technology adoption failure. Lack of understanding about the process of technology adoption is often pointed out as the reason. This paper is based on a study to explore BIM technology adoption process and attempt to model and evaluate the process using system dynamics. The paper has its base on literature survey and five month field studies of three construction projects where BIM was being introduced. Insights from literature survey and case studies were used to develop a core system dynamics model of technology adoption. To this core, project and technology specific data was added to develop a preliminary system dynamics model. The research indicates that system dynamics is a promising approach in improving our understanding of technology adoption in construction projects.
Feasible Interior Point Method for Absolute Value Equation

A feasible interior point method is proposed for solving the NP-hard absolute value equation (AVE) when the singular values of A exceed one. We formulate the NP-hard AVE as linear complementary problem, and prove that the solution to AVE is existent and unique under suitable assumptions. Then we present a feasible interior point algorithm for AVE based on the Newton direction and centering direction. We show that this algorithm has the polynomial complexity. Preliminary numerical results show that this method is promising.
CNP Signal Peptide in Patients with Cardiovascular Disease

We have previously reported that signal peptide fragments of C-type natriuretic peptide (CNP) are present in the human circulation. Here, we provide the first preliminary assessment of the potential utility of CNP signal peptide (CNPsp) measurement in acute cardiovascular disease. Utilizing our specific and sensitive immunoassay, we assessed the potential of CNPsp measurement to assist in the identification of acute coronary syndromes in 494 patients presenting consecutively with chest pain. The diagnostic and prognostic potential of CNPsp were assessed in conjunction with a contemporary clinical troponin I assay, an investigational highly sensitive troponin T assay and NT-proBNP measurement. Utility was assessed via receiver operator curve characteristic analysis. CNPsp did not identify patients with myocardial infarction (MI) or those with unstable angina, nor did it assist the diagnostic ability of clinical or investigational troponin measurement. CNPsp levels were significantly elevated in patients presenting with atrial fibrillation (P < 0.05) and were significantly lower in those with a history of previous MI (P < 0.05). CNPsp could identify those at risk of mortality within 1 year (P < 0.05) and also could identify those at risk of death or re-infarction within 1 year (P < 0.01). This is the first exploratory report describing the potential of CNPsp measurement in acute cardiovascular disease. While CNPsp does not have utility in acute diagnosis, it may have potential in assisting risk prognosis with respect to mortality and re-infarction.
Characterizing psychological dimensions in non-pathological subjects through autonomic nervous system dynamics

The objective assessment of psychological traits of healthy subjects and psychiatric patients has been growing interest in clinical and bioengineering research fields during the last decade. Several experimental evidences strongly suggest that a link between Autonomic Nervous System (ANS) dynamics and specific dimensions such as anxiety, social phobia, stress, and emotional regulation might exist. Nevertheless, an extensive investigation on a wide range of psycho-cognitive scales and ANS non-invasive markers gathered from standard and non-linear analysis still needs to be addressed. In this study, we analyzed the discerning and correlation capabilities of a comprehensive set of ANS features and psycho-cognitive scales in 29 non-pathological subjects monitored during resting conditions. In particular, the state of the art of standard and non-linear analysis was performed on Heart Rate Variability, InterBreath Interval series, and InterBeat Respiration series, which were considered as monovariate and multivariate measurements. Experimental results show that each ANS feature is linked to specific psychological traits. Moreover, non-linear analysis outperforms the psychological assessment with respect to standard analysis. Considering that the current clinical practice relies only on subjective scores from interviews and questionnaires, this study provides objective tools for the assessment of psychological dimensions.
Choosing the best φ-divergence goodness-of-fit statistic in multinomial sampling with linear constraints

In this paper we present a simulation study to analyze the behavior of the $\phi $-divergence test statistics in the problem of goodness-of-fit for loglinear models with linear constraints and multinomial sampling. We pay special attention to the Renyi’s and $I_{r}$-divergence measures.
Social inequalities in mental health and mortality among refugees and other immigrants to Sweden – epidemiological studies of register data

The aim of this PhD project was to increase knowledge, using population-based registers, of how pre- and post-migration factors and social determinants of health are associated with inequalities in poor mental health and mortality among refugees and other immigrants to Sweden. Study I and II had cross-sectional designs and used logistic regression analysis to study differences in poor mental health (measured with prescribed psychotropic drugs purchased) between refugee and non-refugee immigrants. In Study I, there was a significant difference in poor mental health between female refugees and non-refugees (OR=1.27; CI=1.15–1.40) when adjusted for socio-economic factors. In Study II, refugees of most origins had a higher likelihood of poor mental health than non-refugees of the same origin. Study III and IV had cohort designs and used Cox regression analysis. Study III analysed mortality rates among non-labour immigrants. Male refugees had higher relative risks of mortality from cardiovascular disease (HR=1.53; CI=1.04–2.24) and external causes (HR=1.59; CI=1.01–2.50) than male non-refugees did, adjusted for socio-economic factors. Study IV included the population with a strong connection to the labour market in 1999 to analyse the relative risk of hospitalisation due to depressive disorder following unemployment. The lowest relative risk was found among employed Swedish-born men and the highest among foreign-born females who lost employment during follow-up (HR=3.47; CI=3.02–3.98). Immigrants, and particularly refugees, have poorer mental health than native Swedes. Refugee men have a higher relative mortality risk for cardiovascular disease and external causes of death than do non-refugees. The relative risk of hospitalisation due to depressive disorder following unemployment was highest among immigrant women. To promote mental health and reduce mortality among immigrants, it is important to consider pre- and post-migration factors and the general social determinants of health.
Crystalline phases of II‐VI compound semiconductors grown by pulsed laser deposition

II‐VI compound semiconductors, ZnS, ZnSe, CdS, CdSe, and CdTe, were grown epitaxially on (111) and (100) InP and GaAs substrates by excimer laser ablation. All of these films have good crystalline quality (fully in‐plane aligned) and mirror‐like surface morphology. It was found that, on (111)‐oriented substrates, CdS and CdSe films were in the hexagonal phase with the c axis perpendicular to the surface, while ZnS and ZnSe films were in the cubic phase. The films grown on (100)‐oriented substrates were all cubic. These high quality films should be useful in optoelectronics applications.
Long-term photometric monitoring with the Mercator telescope. Frequencies and multicolour amplitudes

Context. γ Dor stars are excellent targets for asteroseismology since the gravity modes present in these stars probe the deep stellar interiors. Mode identification will improve the knowledge of these stars considerably. Aims. A selected group of γ Dor stars and some candidates were observed with the Mercator telescope to find and/or confirm the periodicities in the light variations and to derive reliable amplitude ratios in different pass bands. Methods. A frequency analysis was performed on all new data obtained in the Geneva photometric system. In order to have more reliable and accurate frequencies, the new data were combined with similar data from the literature and with Hipparcos observations. A set of frequencies that minimized the residuals in a harmonic fit was searched for while allowing means and amplitudes to vary from one observation set to another. Results. Frequencies and amplitudes in the photometric passbands of the Geneva system are given for 21 γ Dor stars. We report the discovery of HD 74504 as a newly found γ Dor star. Conclusions. Our study provides the first extensive multicolour database for the understanding of gravity modes in F-type stars.
Decentralized Dynamic Workflow Scheduling for Grid Computing using Reinforcement Learning

The workflow enactment engine is used to execute grid workflow on heterogeneous and distributed resources. However, in the literature, the efficient workflow scheduling algorithm designating resources to tasks in a dynamic environment has not been carefully investigated. In the paper, a decentralized dynamic workflow scheduling algorithm using reinforcement learning (DDWS-RL) is proposed. The on-line model-free RL algorithm is embedded into the decentralized just in-time scheduling system together with a RL agent. At the time of tasks execution, the decentralized task schedulers query information from the RL agent, designate resources to tasks and update the RL agent. To evaluate the efficiency of the DDWS-RL algorithm, a real grid network is built. The Globus Toolkit 2.4 is installed as the middleware for the testbed and the workflow enactment engine and the DDWS-RL algorithm are realized in Java programming. The experiment results show that the proposed DDWS-RL algorithm converges to the theoretical shortest execution time of the workflow in the homogeneous environment. In the heterogeneous environment, the algorithm reaches the sub-optimal execution time due to the self-interest of the independent learner applied in task scheduler
Study of Effect of DCT Domain Steganography Techniques in Spatial Domain for JPEG Images Steganalysis

Steganography is a technique of hiding secret data into digital images in different domain like frequency, spatial or wavelet. Data hiding in image change its statistical properties which leaves vulnerability for Steganalysis. In this paper a effective study is carried out for frequency domain Steganography and It’s effects in spatial domain. Study shows that secret data embedding in frequency domain reflects significant changes in spatial domain w.r. t embedding algorithm. A set of feature is identified for the analysis of covert communication through the image.
Patient Satisfaction and Service Quality with Access to 1Malaysia Clinic

Patient satisfaction is a valid indicator for measurement of service quality. Patients' judgment is important because dissatisfaction hint the opportunities for improvement. We evaluated the relationship between satisfaction of patients and service quality with primary care in two 1Malaysia Clin ics in Selangor, Malaysia. A correlational study was carried out using the SERVQUA L questionnaire. Reliability and validity of the SERVQUA L instrument were established. The respondent for this study was 366 patients who visited 1Malaysia Clinics in Go mbak and Rawang, Selangor, Malaysia. The findings indicated that there was a negative, weak, and significant relationship between the two variables (r=-.304, n=366, p<.05). Results of the correlation ind icate that the higher the service quality is associated with lower patient satisfaction.
Multi-Output Artificial Neural Network for Storm Surge Prediction in North Carolina

During hurricane seasons, emergency managers and other decision makers need accurate and `on-time' information on potential storm surge impacts. Fully dynamical computer models, such as the ADCIRC tide, storm surge, and wind-wave model take several hours to complete a forecast when configured at high spatial resolution. Additionally, statically meaningful ensembles of high-resolution models (needed for uncertainty estimation) cannot easily be computed in near real-time. This paper discusses an artificial neural network model for storm surge prediction in North Carolina. The network model provides fast, real-time storm surge estimates at coastal locations in North Carolina. The paper studies the performance of the neural network model vs. other models on synthetic and real hurricane data.