Miracle-dz/UniFAR-FTUs huggingface dataset:
MODELLING PRESSURE-LEAKAGE RESPONSE IN WATER DISTRIBUTION SYSTEMS CONSIDERING LEAK AREA VARIATION .

Research has shown that area of leak openings varies linearly with pressure under both elastic and viscoelastic conditions. However, hydraulic modelling of water distribution systems currently uses a power function that does not explicitly take into account leak area variation with pressure. This function is commonly known as the N1 power equation. This paper investigates the possibility of explicitly incorporating leak area variation into hydraulic modelling of water distribution systems. The FAVAD (Fixed And Variable Area Discharges) equation, which takes into account leak area variation with pressure, was incorporated into the widely used Global Gradient Algorithm (GGA) in EPANET. A stochastic model was developed to generate and distribute leaks in a standard water distribution system. It was based on existing theories about leakage in water distribution systems, taking into account physical properties of the system. Thereafter, hydraulic simulations were run using both FAVAD and N1 equations. The results show that the N1 power equation estimates the total system leakage with an error of up to 20%. On a specific junction, the leakage estimation error was found to be as high as 60%.
AN ANALYSIS OF GROWTH OF KNOWLEDGE BASED ON CONCEPTS AND PREDICATES-A PRELIMINARY STUDY

Using the methodology of Refined Concept Mapping, we rerepresent the domain of cell biology of secondary and higher secondary levels of textbooks. In this study, we demonstrate that although the number of concept terms increase progressively, the predicate terms achieve constancy depicting a finite set. This indicates that for acquiring expertise in a domain, a finite set of predicate terms is sufficient. In this paper, we illustrate the methodology and discuss the pedagogical implications of the study.
Self-interpreter for Prolog

The semantics of Prolog programs is usually given in terms of model theoretic semantics. However, this does not adequately characterize the computational behavior of Prolog programs. Prolog implementations typically use a depth-first, left-to-right evaluation strategy based on the textual order of clauses and literals in a program. In this paper we introduce a self-interpreter for Prolog, which is a formalization of the syntax and semantics of Prolog using Prolog. This interpreter is a running program that mimics the depth-first, left-to-right evaluation strategy of Prolog interpreters. This means that, the computational behavior of Prolog is captured by obtaining an operational semantics of Prolog based on the logic + control perspective of Prolog. In addition, this paper explains the important difference between the self-interpretation approach used in this paper and the meta-circular interpretation approach. And how self-interpretation considered a true semantic definition of the object language as it sheds light onto all features of the object language, and does not hide features in the features of the defining language.
EVOLVING SELF-TAUGHT NEURAL NETWORKS: THE BALDWIN EFFECT AND THE EMERGENCE OF INTELLIGENCE

The so-called Baldwin Effect generally says how learning, as a form of ontogenetic adaptation, can influence the process of phylogenetic adaptation, or evolution. This idea has also been taken into computation in which evolution and learning are used as computational metaphors, including evolving neural networks. This paper presents a technique called evolving self-taught neural networks – neural networks that can teach themselves without external supervision or reward. The self-taught neural network is intrinsically motivated. Moreover, the self-taught neural network is the product of the interplay between evolution and learning. We simulate a multi-agent system in which neural networks are used to control autonomous agents. These agents have to forage for resources and compete for their own survival. Experimental results show that the interaction between evolution and the ability to teach oneself in self-taught neural networks outperform evolution and self-teaching alone. More specifically, the emergence of an intelligent foraging strategy is also demonstrated through that interaction. Indications for future work on evolving neural networks are also presented.
Overexpression of CD55 correlates with tumor progression and poor prognosis in gastric stromal tumors

Backgrounds: Accumulating evidences have demonstrated that CD55 can protect cells from complement-mediated attack, and is involved in tumor dedifferentiation, migration, invasiveness, and metastasis. However, the role of CD55 in gastrointestinal stromal tumors (GISTs) has not been investigated. Aims: Our study aimed to analyze the expression of CD55 in gastric GISTs and its correlations with clinicopathologic characteristics and prognosis. Materials and methods: A total of 118 gastric GIST patients were included in our study. CD55 expression in GIST tissue samples was evaluated using immunohistochemistry. Cumulative survival was conducted using the Kaplan–Meier method. Cox regression analyses were performed to identify factors associated with progression-free survival (PFS) for patients with gastric GISTs. Results: Of 118 gastric GISTs patients included in our study, 44 (37.3%) were positive for CD55 expression. Positive CD55 expression in gastric GISTs was closely associated with tumor size (13.52±7.35 vs 5.07±1.90 cm, respectively; P<0.001), Ki 67 labeling index (P=0.001), mitotic counts (P=0.005), NIH risk classification (P<0.001), PLR (P<0.001), and metastasis at initial diagnosis (P=0.002). Kaplan–Meier analyses revealed that tumor size (P<0.001), mitotic counts (P<0.001), Ki 67 labeling index (P<0.001), PLR (P<0.001), metastasis at initial diagnosis (P=0.031), and CD55 expression (P<0.001) were statistically significant risk factors affecting PFS of patients with gastric GISTs. Cox multivariate survival analysis showed that mitotic counts, Ki 67 labeling index, and CD55 expression were independent predictors of PFS for gastric GISTs. Conclusion: CD55 may be a potential prognostic marker in gastric GISTs patients.
Design of Medical Insurance Supervision System Based on Active Data Warehouse and SOA

Supervision and audit of Medical insurance expenditure is very important to Medical insurance expenditure control. In this paper, characteristics of medical service market and strategy of control medical expenditure has been discussed. The paper puts forward a new design method of medical insurance supervision system which is base on active data warehouse and SOA. The system improves real-time performance of data analysis and queries, and improves efficiency of medical expenditure supervision.
Effect of stimulus distribution on the buildup of contextual plasticity in sound localization

Contextual plasticity (CP) is a short-term adaptation effect in sound localization (Kopco et al., 2007). CP is exhibited by biases in localization of click stimuli in target-alone trials when, on interleaved contextual trials, a distractor-target click pair is presented. In previous CP studies, a gradual drift in localization responses towards the front was observed. The drift was not attributable to CP but can potentially confound its analysis. Here, we analyze several CP data sets with various distributions of contextual and target-alone stimuli in order to test whether the drift is related to asymmetry in the distribution of the stimuli relative to the subject's midline. The results show that the drift tends to have a steeper slope when the stimulus distribution mean is more lateral, confirming that it is likely related to the distribution asymmetry.
Implementation of three DoFs small satellite ground simulation system

Along with progress in space technologies, new space missions, for example, autonomous rendezvous and docking, electrodynamic tether and space debris removal are bringing new challenges to spacecraft system design. To verify the effectiveness of theories and algorithms applied for those new missions, it is necessary to conduct systematic physical experiments on the ground. Thus, a 3 Degree of Freedoms(DOFs) small satellite ground simulation system is designed for this purpose. In this paper, major characteristics and performances of the simulator are summarized. Subsystems’ description is given while design efforts to ensure system’s upgradability and expandability would be highlighted. Then a major focus is set on the infrared navigation system’s implementation. Image processing and recognition procedures are discussed in detail. Eventually, control strategy is illustrated and the system is tested by a series of maneuvers including slewing, position maneuver and trajectory tracking.
Duration Calculus: A Formal Approach to Real-Time Systems

The Duration Calculus (DC) represents a logical approach to formal design of real-time systems. DC is based on interval logic, and uses real numbers to model time, and Boolean-valued (i.e. 0, 1-valued) functions over time to model states of real-time systems. The duration of a state in a time interval is the accumulated presence time of the state in the interval. DC extends interval logic with a calculus to specify and reason about properties of state durations. The first paper of DC was published in 1991, and dozens of papers of DC have been published since then, which cover developments of logical calculi, their applications and mechanical support tools. This paper will give a brief introduction to DC and also an overview of the research of DC.
Serial and Parallel Bayesian Spam Filtering using Aho- Corasick and PFAC

With the rapid growth of Internet, E-mail, with its convenient and efficient characteristics, has become an important means of communication in people‟s life. It reduces the cost of communication. It comes with Spam. Spam emails, also known as „junk e-mails‟, are unsolicited one‟s sent in bulk with hidden or forged identity of the sender, address, and header information. It is vital to pursue more effective spam filtering approaches to maintain normal operations of e-mail systems and to protect the interests of email users. In this paper we developed a Spam filter based on Bayesian filtering method using Aho-corasick and PFAC string matching algorithm. This filter developed an improved version of spam filter based on traditional Bayesian spam filtering to improve spam filtering efficiency, and to reduce chances of misjudgement of malignant spam. For further improvement of Spam filtering process we are transform the filter in to parallel spam filter on GPGPU's by using PFAC Algorithm.
Morphometry of eggs and immatures of Culex ( Culex ) saltanensis Dyar ( Diptera , Culicidae ) obtained in the laboratory and on the field

The objectives of this morphometric study of Culex (Culex) saltanensis Dyar, 1928 larvae were to observe the continuous growth of siphon and cephalic capsule length and width in each instar, and to propose a linear equation to differentiate instars according to the size of both structures. Larvae obtained in laboratory at 27 ± 1oC, photoperiod 14L:12D were studied. Larvae of the forth instar were collected on the field and compared with same instar larvae reared in the laboratory. Egg size and pigmentation pattern, first instar continuous growth, and differentiation of larval stages measurements were studied. Egg pigmentation patterns and the shapes of raft eggs differentiated specimens of C. saltanensis from Culex (Culex) quinquefasciatus Say, 1823 found in polluted pond water.
A maximum neural network for the max cut problem

The max cut problem, one of the NP-complete problems, was chosen to test the capability of an artificial neural network. The algorithm based on the maximum neural network was tested by 1000 randomly generated examples, including up to 300 vertex problems. The simulation result shows that the proposed parallel algorithm using the maximum neural network generates better solutions than Hsu's algorithm (1983) within one hundred iteration steps, regardless of the problem size. <<ETX>>
Analysis of the Anatomical and F basis of Autism Spectrum C Magnetic Resonance Imaging and Electroencephalography

xi RESUMO xiii PUBLICATIONS xvii ACKNOWLEDGEMENTS xix 1. GENERAL INTRODUCTION 1 1.1. AUTISM SPECTRUM CONDITIONS 1 1.1.1. MECHANISM AND CAUSES 2 1.2. MAGNETIC RESONANCE IMAGING 4 1.2. 1. PHYSICAL PRINCIPLES 4 1.2. 2. ECHOES 9 1.2. 3. SPATIAL ENCODING 11 1.2. 4. FUNCTIONAL MRI 15 1.3. ELECTROENCEPHALOGRAPHY 18 1.3. 1. BRAIN ANATOMY AND PHYSIOLOGY 18 1.3. 2. EEG RECORDING 21 LIMITATIONS 22 TYPICAL ACTIVITY 23 1.3. 3. SIGNAL ANALYSIS 24 FREQUENCY AND POWER ANALYSIS 24 EVENT RELATED POTENTIALS 25 NON-LINEAR, COMPLEXITY AND CONNECTIVITY ANALYSIS OF THE EEG SIGNAL 26
A speed-based adaptive dynamic parallel downloading technique

In this paper, we describe adPD, an improved parallel downloading approach for Peer-to-Peer environment. adPD assigns each server equal portion of file as large as possible, and ensures client downloading from each server without interrupt. And after faster servers finish their own work, adPD reallocates part of unfinished work of some slower servers to them. This reallocation is proportional to the ratio of server speed. By this means, adPD dynamically adjusts the proportion of a file retrieved from each server during the downloading process to match the bandwidth of each connection, and also reduces the number of block requests as many as possible. Our experiment results show that adPD is an effective parallel download scheme in Peer-to-Peer environment.
Recognition on Online Social Network by user's writing style

Compromising legitimate accounts is the most popular way of disseminating fraudulent content in Online Social Networks (OSN). To address this issue, we propose an approach for recognition of compromised Twitter accounts based on Authorship Verification. Our solution can detect accounts that became compromised by analysing their user writing styles. This way, when an account content does not match its user writing style, we affirm that the account has been compromised, similar to Authorship Verification. Our approach follows the profile-based paradigm and uses N-grams as its kernel. Then, a threshold is found to represent the boundary of an account writing style. Experiments were performed using two subsampled datasets from Twitter. Experimental results showed the developed model is very suitable for compromised recognition of Online Social Networks accounts due to the capacity of recognizing user styles over 95% accuracy for both datasets.
Debris flow in uniform conditions

The present paper aims to present the issues and the scope of the IMPACT research project in the field of dam-break induced geomorphic flows, to give an overview of the experimental work carried out in the frame of the research program, to summarize the new developments in modeling, to outline the validation process and to give some practical conclusions for the future of dam-break wave modeling.
eview echnology-assisted interventions for parents of young children : merging practices , current research , and future directions

Technology can potentially expand the reach and cut the costs of providing effective, evidence-based interventions. This paper reviews existing publications that describe the application and evaluation of technology-assisted interventions for parents of young children. A broad review of the early childhood literature revealed 48 studies describing technology-assisted parent education and interventions. Across these studies, multiple forms of technology were used, including web-based platforms, discussion forums,
A Efficient Method for Intrusion Detection using GNP based on Fuzzy Class Association Rule Mining

ue to increasing internet services, many kinds and a large number of security threats are increasing. Therefore, intrusion detection systems are effectively used for detecting intrusion accesses. This paper describes a novel fuzzy class association rule mining method based on genetic network programming (GNP) for detecting network intrusions.GNP is an evolutionary optimization technique, which uses directed graph structures instead of strings in genetic algorithm or trees in genetic programming, which enhance the representation ability with compact programs derived from the reusability of nodes in a graph structure. By combining fuzzy set theory with GNP, the proposed method can deal with the mixed database that contains both discrete and continuous attributes and also extract many important class association rules that contribute to enhancing detection ability. Therefore, the proposed method can be flexibly applied to both misuse and anomaly detection in network-intrusion-detection problems. There are number of features of proposed methods like sub attribute utilization, accuracy in mining rules and high detection rates. Experimental results with DARPA98 and DARPA99 databases from MIT Lincoln Laboratory shows that the proposed method provides competitively high detection rates(DR) compared with other machine-learning techniques and GNP with crisp data mining.
Bare-Bones Dependency Parsing - A Case for Occam's Razor?

If all we want from a syntactic parser is a dependency tree, what do we gain by first computing a different representation such as a phrase structure tree? The principle of parsimony suggests that a simpler model should be preferred over a more complex model, all other things being equal, and the simplest model is arguably one that maps a sentence directly to a dependency tree ‐ a bare-bones dependency parser. In this paper, I characterize the parsing problem faced by such a system, survey the major parsing techniques currently in use, and begin to examine whether the simpler model can in fact rival the performance of more complex systems. Although the empirical evidence is still limited, I conclude that bare-bones dependency parsers fare well in terms of parsing accuracy and often excel in terms of efficiency.
Phenological changes of shoot carbohydrates and plant growth characteristics in Vitis labrusca L. grape.

Stored carbohydrates are used to supply energy in the next season for shoot growing, flowering and ripening in grapevines. Carbohydrates are transfers from storage organs to growth areas following the bud burst in spring. The present study was carried out to determine the effect of different rootstocks on growth characteristics and phenological changes of shoot carbohydrates in 10 years-old Vitis labrusca L. grapes. The grapevines are grafted on 140Ru, 5BB and 5C rootstocks. In the experiment; changes of sugar, starch and total carbohydrate contents in annual shoots were investigated in different phenological stages. While the highest sugar and total carbohydrate content of shoots were determined in veraison period on 5BB grafted grapevines, starch content was high in the harvest on 140Ru. While the highest mean and total leaf areas were calculated in grapevines grafted on 140Ru rootstock, shoot length and diameter was the highest on 5BB rootstock. Leaf chlorophyll content was the highest in blooming period on 5C grafted grapevines.The heavier fruit clusters, berries and total yield per vine were obtained on 5BB grafted vines. In this study, 5BB rootstock was found to be favorable in terms of shoot carbohydrate content and growth characteristics for Vitis labrusca L. grape.
Soft Computing Techniques Applied to Corporate and Personal Security

Inside a "Bring Your Own Device" environment, the employees can freely use their devices. This allows them mix their personal and work life, but at the same time, if the users are not aware of a risky situation, or that situation is not covered by a company security policy or rule, this environment can become very insecure. The aim of this paper is defining a novel system architecture able to self-adapt itself, in the sense that it will learn from past, non secure situations, and therefore will be able to determine whether a new situation is risky or not. This Paper proposes the use of a variety of techniques, from Data Mining of big amounts of recorded data to Evolutionary Algorithms for refining a set of existing policies, maybe creating new ones. A preliminary method that automatically extracts rules to avoid or deny URL connections helps to demonstrate that, by performing a good preprocessing of the data, useful conclusions can be extracted from new - unknown - situations. Therefore, it is possible to successfully extend a set of rules, usually laid out by the company, for covering new, and potentially dangerous, situations.
OXIDATIVE STRESS AND GESTATIONAL DIABETES MELLITUS . THE EFFECTS OF SUPPLIMENTS ON OXIDATIVE STRESS

The prevalence of diabetes in pregnancy has been increasing. GDM is characterized by increased risk of macrosomia and birth complications and an increased risk of maternal type 2 diabetes after pregnancy. Any imbalance between the reactive species (RS) and antioxidants leads to a condition known as “oxidative stress”. Antioxidants obtained from nature helps in neutralization of reactive oxygen species and significantly reduce the probability of progression of diabetic complications [2, 3]. The basic aim of this review was to summarize the basics of oxidative stress in geastational diabetes mellitus and to examine the association between different supplements, biomarkers of oxidative stress and GDM and the posibility to improve the pregnacy outcomes.
Airway Management in Trauma 2

Ultimately, the goal of airway management in trauma is to establish and/or maintain adequate oxygenation, ventilation, and airway protection. It is the first priority in the acute phase of care of the trauma patient and consists of evaluation and, when indicated, intervention using various techniques and devices. It involves the recognition of any trauma to the airway or surrounding tissues, anticipation of their respiratory consequences, and planning and application of management, keeping in mind the potential for exacerbation of existing airway or other injuries by the contemplated strategies. It also involves prediction and prevention of progression of airway or surrounding tissue injury with increasing airway compromise. Although with certain modifications, the American Society of Anesthesiologists (ASA) difficult airway algorithm can be applied to various trauma-induced airway issues [1], it may not be applicable in some clinical scenarios. For example, cancellation of airway management when difficulty arises may not be an option in the acute trauma setting. Likewise, awake rather than asleep intubation or a surgical airway from the outset may be the preferred choice in some situations. Modifications of the ASA difficult airway algorithm are available for various trauma-induced clinical situations [2].
Genetic and backtracking search optimisation algorithms applied to localisation problems

The localisation problem arises from the need of the elements of a swarm of robots, or of a wireless sensor network WSN, to determine its position without the use of external references, such as the global positioning system GPS, for example. In this problem, the location is based on calculations that use distance measurements to anchor nodes that have known positions. In the search for efficient algorithms to calculate the location, some algorithms inspired by nature, such as genetic algorithm GA and particle swarm optimisation PSO algorithm, have been used. Accordingly, in order to obtain better solutions to the localisation problem, this paper presents the results obtained with the backtracking search optimisation algorithm BSA and compares them with those obtained with the GA.