Miracle-dz/UniFAR-FTUs huggingface dataset:
Classification of adaptive memetic algorithms: a comparative study

Adaptation of parameters and operators represents one of the recent most important and promising areas of research in evolutionary computations; it is a form of designing self-configuring algorithms that acclimatize to suit the problem in hand. Here, our interests are on a recent breed of hybrid evolutionary algorithms typically known as adaptive memetic algorithms (MAs). One unique feature of adaptive MAs is the choice of local search methods or memes and recent studies have shown that this choice significantly affects the performances of problem searches. In this paper, we present a classification of memes adaptation in adaptive MAs on the basis of the mechanism used and the level of historical knowledge on the memes employed. Then the asymptotic convergence properties of the adaptive MAs considered are analyzed according to the classification. Subsequently, empirical studies on representatives of adaptive MAs for different type-level meme adaptations using continuous benchmark problems indicate that global-level adaptive MAs exhibit better search performances. Finally we conclude with some promising research directions in the area.
A host subtraction database for virus discovery in human cell line sequencing data

The human cell lines HepG2, HuH-7, and Jurkat are commonly used for amplification of the RNA viruses present in environmental samples. To assist with assays by RNAseq, we sequenced these cell lines and developed a subtraction database that contains sequences expected in sequence data from uninfected cells. RNAseq data from cell lines infected with Sendai virus were analyzed to test host subtraction. The process of mapping RNAseq reads to our subtraction database vastly reduced the number non-viral reads in the dataset to allow for efficient secondary analyses.
Thought Bubbles: a conceptual prototype for a Twitter based recommender system for research 2.0

The concept of so called Thought Bubbles deals with the problem of finding appropriate new connections within Social Networks, especially Twitter. As a byproduct of exploring new users, Tweets are classified and rated and are used to generate a kind of news feed, which will extend the personal Twitter feed. Each user has several interests that can be classified by first evaluating their Tweets and then by evaluating user related and already existing contacts. By categorizing a user and related connections, one can be placed in an imaginary category specific subset of users, called Thought Bubbles. Following the trace of people who are also active within the same specific Thought Bubble, should reveal interesting and helpful connections between similar minded users.
Heavy Metal Detection in Scorpaena Porcus Linnaeus, 1758 from Sinop Coast of the Black Sea and Potential Risks to Human Health

The current research was performed to detect the heavy metal amounts (Hg, As, Cd, Pb, Cu, Zn) in one of the most common fish species (Scorpaena porcus Linnaeus, 1758) captured from the south shores of the Black Sea during the fishing season in 2016. Heavy metals were examined by using ICP-MS (Inductively Coupled Plasma - Mass Spectrophotometer). The amounts of the metals (Hg, Pb and Cd) in some specimen were under the detection limits. The maximum levels of all measured metals are as low the recommended limit by the admitted organizations. It was shown that the measured metal amounts in edible tissue of the S. porcus were under the well-being levels for people consumption for the average Estimated Daily Intake (EDI) and Hazarded Index (HI˂1).
Penta-band PIFA with tunable antenna height for wireless multistandard terminals

The proposed penta-band PIFA is designed to operate at the frequencies 860~980 MHZ, 1710~3850 MHz, and 4960~5820 MHz, which cover the main cellular standard bands of GSM-900, DCS, PCS, UTMS, LTE, WiFi-802.11a/b/g/n, and Wimax. A tunable antenna height from 2 to 5 mm is also simulated and measured. This proposed penta-band PIFA with tunable antenna height is low-profile with satisfactory radiation efficiency, and is suitable for use in the smart phones.
From Earthquakes to "#morecowbell": Identifying Sub-topics in Social Network Communications

Social networking websites have become a vital means of communication that can provide information on various topics. The real time nature of the information published on social networking websites coupled with their accessibility as a publishing platform make them a powerful tool for information gathering. Furthermore, many individuals utilize these sorts of platforms to share their knowledge and opinions with others. While this information is useful, there exist a number of challenges to effectively use social networks to gather information about a topic. We propose a methodology that automatically detects sub-topics and groups the social networking messages accordingly.
En Masse Relay Selection Algorithms for Multi-Source, Multi-Relay, Decode-and-Forward Cooperative Systems

Opportunistic relay selection in a multiple source-destination (MSD) cooperative system requires quickly allocating to each source-destination (SD) pair a suitable relay based on channel gains. Since the channel knowledge is available only locally at a relay and not globally, efficient relay selection algorithms are needed. For an MSD system, in which the SD pairs communicate in a time-orthogonal manner with the help of decode-and-forward relays, we propose three novel relay selection algorithms, namely, contention-free en masse assignment (CFEA), contention-based en masse assignment (CBEA), and a hybrid algorithm that combines the best features of CFEA and CBEA. En masse assignment exploits the fact that a relay can often aid not one but multiple SD pairs, and, therefore, can be assigned to multiple SD pairs. This drastically reduces the average time required to allocate an SD pair when compared to allocating the SD pairs one by one. We show that the algorithms are much faster than other selection schemes proposed in the literature and yield significantly higher net system throughputs. Interestingly, CFEA is as effective as CBEA over a wider range of system parameters than in single SD pair systems.
Energy Inputs and Yield Relationship in Greenhouse Okra Production by Bio-priming

Production of chemical fertilizers is associated with consumption of huge energy. In the present study, an attempt was made to assess the energy consumption in bio-priming mediated okra production under greenhouse conditions. Results showed that among all the treatments, seed priming with Trichoderma harzianum (NBRI 1055) along with 90% RDF was not only helpful in reducing fertilizer dose by 10%, but at the same time produced almost similar yield as compared to with 100% RDF. Bio-priming in different combination with chemical fertilizers had a positive impact on the chemical composition of okra. Bioprimed treatments saved 970 to 1670 KJ energy in producing unit produce as compared with 100% RDF. This proved that bio-priming is cost-effective, user, and eco-friendly technique solving numerous problems in agriculture. This technique requires the prerequisite of incorporating organic matter in soil and seen as complementary to inorganic fertilizer source, not as their replacement.
Driver Face Image Enhancement Based on Guided Filter

The paper proposes a method based on guided filter to enhance the driver face image under weak light environment. First, image is converted from RGB color space to HSV color space. Then based on the theory of Retinex algorithm, guided filter is applied to estimate the illumination, obtaining the reflection image which only contains the characteristics of the object. In order to restore the color of image, the coefficient of correlation is used to adaptively adjust the saturation component. Finally, by transforming HSV model to RGB model, the enhanced image with color restoration is obtained. Experimental results show that this algorithm has good enhancement result and high efficiency.
Design of Pre-Rake DS-UWB Downlink with Pre-Equalization

We consider the design of ultra-wideband (UWB) systems that enable high data rate communications for short-range wireless applications. In particular, we consider the downlink of a direct sequence UWB (DS-UWB) system in which the base station is equipped with multiple antennas and employs pre-rake combining, while each user employs a simple single antenna receiver. We propose the use of multiuser filters for the purpose of pre-equalization at the transmitter in order to mitigate the combined effects of intersymbol interference (ISI) and multiuser interference (MUI) that are generated at the receivers as a result of the wideband nature of the users' channels. For this system, we study the joint design of the transmitter's pre-equalization filters and each receiver's scalar gain under two design criteria. The first design minimizes the total transmitted power from the base station subject to achieving physical layer quality of service requirements of different users. For this design, we show that the calculation of the pre-equalization filters and the receiver gains can be formulated as an efficiently solvable convex optimization problem. In the second design, we consider the minimization of a weighted sum of each user's mean-square error. In order to obtain a computationally tractable solution for this design criterion, we exploit the dual DS-UWB uplink that employs rake combining and post-equalization filters at a central receiver. The numerical studies for each design criterion under realistic models of UWB channel propagation demonstrate the effectiveness of the proposed multiuser pre-equalization filter designs in mitigating ISI and MUI, and thus their ability to enable reliable pre-rake DS-UWB downlink transmission.
Background Modeling using Mixture of Gaussians for Foreground Detection - A Survey

Mixture of Gaussians is a widely used approach for background modeling to detect moving objects from static cameras. Numerous improvements of the original method developed by Stauffer and Grimson [1] have been proposed over the recent years and the purpose of this paper is to provide a survey and an original classification of these improvements. We also discuss relevant issues to reduce the computation time. Firstly, the original MOG are reminded and discussed following the challenges met in video sequences. Then, we categorize the different improvements found in the literature. We have classified them in term of strategies used to improve the original MOG and we have discussed them in term of the critical situations they claim to handle. After analyzing the strategies and identifying their limitations, we conclude with several promising directions for future research.
Motion Intention Recognition for Wearable Power Assist System using Multi-Class SVM and Kinematic Model

This paper is aimed at describing a framework to implement Multi-Class Support Vector Machine (MCSVM)-based motion intention recognition. To this end, we primarily constructed a wearable exoskeleton robot of lower body which is employed as an experimentation platform to test the MCSVM-based motion intention recognition. Having disclosed prototype development and MCSVM, experimental results of motion intention recognition of standing up and seating are presented. We examined the accuracy of method of motion intention recognition based on MCSVM. We also examined utility of adding information of kinematic model to training data.
An audit of general practitioner involvement in public community mental health care

Objective: Existing guidelines suggest that collaboration between general practitioners and mental health services may improve the physical health of people with serious mental illness. This study investigated the extent of general practitioner involvement in a community mental health centre and examined whether the presence of a documented general practitioner in the patient’s medical records was associated with markers of better health outcomes. Methods: The medical records of current patients were audited, including those receiving medical care and case management and those only receiving medical care. The demographic and diagnostic information, evidence of metabolic screening, blood test results, and medications of patients with a recorded general practitioner were compared with those of patients with no recorded general practitioner. Results: Ninety-eight of 191 (51%) of patients had details of a general practitioner documented in their medical records. There were no significant differences in rates of metabolic screening between the two groups. Those with a general practitioner had more medical diagnoses and were taking a greater number of psychiatric medications. Conclusion: Although the medical comorbidities of serious mental illness are becoming increasingly recognised, our findings suggest the need for ongoing and coordinated efforts by policymakers, general practitioners, mental health services and patients to ensure the health and longevity of people with serious mental illness.
Endogenous Interleukin 4 Is Required for Development of Protective CD4+ T Helper Type 1 Cell Responses to Candida albicans

Interleukin (IL)-4–deficient mice were used to assess susceptibility to systemic or gastrointestinal Candida albicans infections, as well as parameters of innate and elicited T helper immunity. In the early stage of systemic infection with virulent C. albicans, an unopposed interferon (IFN)-γ response renders IL-4–deficient mice more resistant than wild-type mice to infection. Yet, IL-4–deficient mice failed to efficiently control infection in the late stage and succumbed to it. Defective IFN-γ and IL-12 production, but not IL-12 responsiveness, was observed in IL-4–deficient mice that failed to mount protective T helper type 1 cell (Th1)-mediated acquired immunity in response to a live vaccine strain of the yeast or upon mucosal immunization in vivo. In vitro, IL-4 primed neutrophils for cytokine release, including IL-12. However, late treatment with exogenous IL-4, while improving the outcome of infection, potentiated CD4+ Th1 responses even in the absence of neutrophils. These findings indicate that endogenous IL-4 is required for the induction of CD4+ Th1 protective antifungal responses, possibly through the combined activity on cells of the innate and adaptive immune systems.
PelA and PelB proteins form a modification and secretion complex essential for Pel polysaccharide-dependent biofilm formation in Pseudomonas aeruginosa

The pellicle (PEL) polysaccharide is synthesized by the opportunistic pathogen Pseudomonas aeruginosa and is an important biofilm constituent critical for bacterial virulence and persistence. PEL is a cationic polymer that promotes cell–cell interactions within the biofilm matrix through electrostatic interactions with extracellular DNA. Translocation of PEL across the outer membrane is proposed to occur via PelB, a membrane-embedded porin with a large periplasmic domain predicted to contain 19 tetratricopeptide repeats (TPRs). TPR-containing domains are typically involved in protein–protein interactions, and we therefore sought to determine whether PelB serves as a periplasmic scaffold that recruits other components of the PEL secretion apparatus. In this study, we show that the TPR domain of PelB interacts with PelA, an enzyme with PEL deacetylase and hydrolase activities. Structure determination of PelB TPRs 8–11 enabled us to design systematic deletions of individual TPRs and revealed that repeats 9–14, which are required for the cellular localization of PelA with PelB are also essential for PEL-dependent biofilm formation. Copurification experiments indicated that the interaction between PelA and PelB is direct and that the deacetylase activity of PelA increases and its hydrolase activity decreases when these proteins interact. Combined, our results indicate that the TPR-containing domain of PelB localizes PelA to the PEL secretion apparatus within the periplasm and that this may allow for efficient deacetylation of PEL before its export from the cell.
Effects of Peer Tutoring, Attitude and Personality on Academic Performance of First Year Introductory Programming Students

Programming is the core of any computer science degree. Failure to grasp programming concepts such as logical reasoning and problem solving skills will have a negative impact on student's academic performance. A preliminary research was carried out at the University of Technology, Jamaica amongst first year computing students to ascertain factors that affect poor performance in introductory programming course. The study sought to examine the effect of peer tutoring, and attitude on academic performance. A control and experimental group was established, and an instrument was administered to measure students' attitude to programming along with the use of pre and post test scores. The study suggested that peer tutoring had a positive effect on students' attitude, namely, personal confidence, and academic performance. Further investigations were carried with a similar methodology and personality was explored as an additional factor affecting academic performance. Both studies revealed that personal confidence in learning programming contributed more significantly than the usefulness of programming and the perception of teacher motivation. The statistical analysis revealed no significant influence of personality types on academic performance, which suggests that it does not play an important role
Automatic Acquisition of Named Entity Tagged Corpus from World Wide Web

In this paper, we present a method that automatically constructs a Named Entity (NE) tagged corpus from the web to be used for learning of Named Entity Recognition systems. We use an NE list and an web search engine to collect web documents which contain the NE instances. The documents are refined through sentence separation and text refinement procedures and NE instances are finally tagged with the appropriate NE categories. Our experiments demonstrates that the suggested method can acquire enough NE tagged corpus equally useful to the manually tagged one without any human intervention.
Vector Decoupling Controlled PWM Rcetifier for Wind Power Grid-Connected Inverter

In this paper, On the basis of analyzing the model of three-phase PWM rectifier for wind power grid-connected inverter, the mathematical model of the vector decoupling controlled PWM rectifier at three-phase synchronous rotating coordinate system is deduced. This paper proposes a novel vector decoupling control strategy, which consists of two control loop, i.e. outer voltage loop and inner current loop. It can realize decoupling control of active current and reactive current, thus the active power and reactive power can be regulated independently. PI regulators are used in voltage and current loop to achieve no static error control. Experimental results prove that the proposed rectifier has the satisfactory control performance, and it can achieve approximate unity power factor correction. The experiments also show that the efficiency of proposed rectifier is high, the reliability is well and its application is wide.
An integrated approach to inferring gene–disease associations in humans

One of the most important tasks of modern bioinformatics is the development of computational tools that can be used to understand and treat human disease. To date, a variety of methods have been explored and algorithms for candidate gene prioritization are gaining in their usefulness. Here, we propose an algorithm for detecting gene–disease associations based on the human protein–protein interaction network, known gene–disease associations, protein sequence, and protein functional information at the molecular level. Our method, PhenoPred, is supervised: first, we mapped each gene/protein onto the spaces of disease and functional terms based on distance to all annotated proteins in the protein interaction network. We also encoded sequence, function, physicochemical, and predicted structural properties, such as secondary structure and flexibility. We then trained support vector machines to detect gene–disease associations for a number of terms in Disease Ontology and provided evidence that, despite the noise/incompleteness of experimental data and unfinished ontology of diseases, identification of candidate genes can be successful even when a large number of candidate disease terms are predicted on simultaneously. Availability: www.phenopred. org. Proteins 2008. © 2008 Wiley‐Liss, Inc.
LBNL-54228 M-theory and E 10 : Billiards , Branes , and Imaginary Roots

Eleven dimensional supergravity compactified on T 10 admits classical solutions describing what is known as billiard cosmology – a dynamics expressible as an abstract (billiard) ball moving in the 10-dimensional root space of the infinite dimensional Lie algebra E10, occasionally bouncing off walls in that space. Unlike finite dimensional Lie algebras, E10 has negative and zero norm roots, in addition to the positive norm roots. The walls above are related to physical fluxes that, in turn, are related to positive norm roots (called real roots) of E10. We propose that zero and negative norm roots, called imaginary roots, are related to physical branes. Adding “matter” to the billiard cosmology corresponds to adding potential terms associated to imaginary roots. The, as yet, mysterious relation between E10 and M-theory on T 10 can now be expanded as follows: real roots correspond to fluxes or instantons, and imaginary roots correspond to particles and branes (in the cases we checked). Interactions between fluxes and branes and between branes and branes are classified according to the inner product of the corresponding roots (again in the cases we checked). We conclude with a discussion of an effective Hamiltonian description that captures some features of M-theory on T .
Probabilistic and ensemble simulation approaches for input uncertainty quantification of artificial neural network hydrological models

ABSTRACT Artificial neural network (ANN) has been demonstrated to be a promising modelling tool for the improved prediction/forecasting of hydrological variables. However, the quantification of uncertainty in ANN is a major issue, as high uncertainty would hinder the reliable application of these models. While several sources have been ascribed, the quantification of input uncertainty in ANN has received little attention. The reason is that each measured input quantity is likely to vary uniquely, which prevents quantification of a reliable prediction uncertainty. In this paper, an optimization method, which integrates probabilistic and ensemble simulation approaches, is proposed for the quantification of input uncertainty of ANN models. The proposed approach is demonstrated through rainfall-runoff modelling for the Leaf River watershed, USA. The results suggest that ignoring explicit quantification of input uncertainty leads to under/over estimation of model prediction uncertainty. It also facilitates identification of appropriate model parameters for better characterizing the hydrological processes.
An Unusual Recurrent Intrathoracic Goiter: Case Report

Most intrathoracic goiters are located in the anterior mediastinum, frontal to the recurrent laryngeal nerve and anterolateral to the trachea. Posterior mediastinal goiters account for only 10 to 15% of all intrathoracic goiters and arise from the posterolateral portion of the thyroid gland. We present a case involving a 59-year-old man with history of gradual-onset dyspnea who was referred to us for evaluation of a large mediastinal mass. He had undergone bilateral thyroid lobectomy for a cervical goiter 10 years previously, with no subsequent complications. Contrast-enhanced computed tomography demonstrated a large, well-circumscribed mass extending paratracheally from the thoracic inlet to the posterior mediastinum. The mass was removed via a transcervical and transthoracic approach.