Miracle-dz/UniFAR-FTUs huggingface dataset:
Support in Clinical Settings as Perceived by Nursing Students in Iran: A Qualitative Study

Background Although support is one of the most substantial needs of nursing students during clinical education, it is not clearly defined in the literature. Objectives The current study aimed to explore the concept of support in clinical settings as perceived by nursing students. Materials and Methods A qualitative content analysis was used to explore the meaning of student support in clinical settings. A purposive sampling with maximum variation was used to select the participants among bachelor nursing students in the nursing school of Babol University of Medical Sciences in the north of Iran. Semi-structured interviews were conducted to gather the perceptions and experiences of seventeen nursing students. Conventional content analysis was applied to analyze the data. Results In the current study, the main theme, nurturance, was emerged with seven subthemes of humanistic behavior with the student, respectful communication with students, accepting the student in the clinical setting, sustaining confidence, need based supervision, accepting the profession in the society and empowerment. Conclusions Nursing students support in the clinical education requires a nurturing care; a care that leads to the sense of worthiness and respectability in students and contributes to the improvement of their clinical abilities.
Adaptive and Robust Control for Nonlinear HVAC System

In this paper, we design adaptive and robust controller for the nonlinear MIMO heating, ventilating, and air conditioning (HVAC) system, respectively. The designed adaptive controller maintains good tracking performance for temperature and humidity ratio regardless of the slowly time-varying load changes, which are regarded as parametric uncertainties in thermal space. The robust controller considering non-parametric uncertainties is designed so as to remedy the drawback of feedback linearization technique. The obtained control system shows robustness to the non-constant thermal loads and mismatched uncertainties, respectively. Some simulation results are provided to illustrate the satisfactory control performance of the adaptive controller.
A Functional Dissection of PTEN N-Terminus: Implications in PTEN Subcellular Targeting and Tumor Suppressor Activity

Spatial regulation of the tumor suppressor PTEN is exerted through alternative plasma membrane, cytoplasmic, and nuclear subcellular locations. The N-terminal region of PTEN is important for the control of PTEN subcellular localization and function. It contains both an active nuclear localization signal (NLS) and an overlapping PIP2-binding motif (PBM) involved in plasma membrane targeting. We report a comprehensive mutational and functional analysis of the PTEN N-terminus, including a panel of tumor-related mutations at this region. Nuclear/cytoplasmic partitioning in mammalian cells and PIP3 phosphatase assays in reconstituted S. cerevisiae defined categories of PTEN N-terminal mutations with distinct PIP3 phosphatase and nuclear accumulation properties. Noticeably, most tumor-related mutations that lost PIP3 phosphatase activity also displayed impaired nuclear localization. Cell proliferation and soft-agar colony formation analysis in mammalian cells of mutations with distinctive nuclear accumulation and catalytic activity patterns suggested a contribution of both properties to PTEN tumor suppressor activity. Our functional dissection of the PTEN N-terminus provides the basis for a systematic analysis of tumor-related and experimentally engineered PTEN mutations.
Complexity and algorithms for reasoning about time: a graph-theoretic approach

Temporal events are regarded here as intervals on a time line. This paper deals with problems in reasoning about such intervals when the precise topological relationship between them is unknown or only partially specified. This work unifies notions of interval algebras in artificial intelligence with those of interval orders and interval graphs in combinatorics. The satisfiability, minimal labeling, all solutions, and all realizations problems are considered for temporal (internal) data. Several versions are investigated by restricting the possible interval relationships yielding different complexity results. We show that even when the temporal data comprises of subsets of relations based on intersection and precedence only, the satisfiability question is NP-complete
Using a Fuzzy-Petri-Net-Based Approach for the Offshore Weather Prediction

For the huge amounts of data related to the atmospheric elements, the difficulty of weather forecasting is very large, which require an intelligent environment. To solve the problem, a fuzzy Petri net (FPN) model to represent knowledge and the behavior of an intelligent object-oriented database environment is proposed. In order to foresee and solve problematic behavior patterns, a static rule analysis on the FPN structure is employed. In addition, with the proposed fuzzy inference algorithm, the confluent rule executions can be guaranteed. The techniques and solutions provided in this paper can also be used in various complex systems, such as environmental information systems and defense application.
Portfolio greedy search and simulation for large-scale combat in starcraft

Real-time strategy video games have proven to be a very challenging area for applications of artificial intelligence research. With their vast state and action spaces and real-time constraints, existing AI solutions have been shown to be too slow, or only able to be applied to small problem sets, while human players still dominate RTS AI systems. This paper makes three contributions to advancing the state of AI for popular commercial RTS game combat, which can consist of battles of dozens of units. First, we present an efficient system for modelling abstract RTS combat called SparCraft, which can perform millions of unit actions per second and visualize them. We then present a modification of the UCT algorithm capable of performing search in games with simultaneous and durative actions. Finally, a novel greedy search algorithm called Portfolio Greedy Search is presented which uses hill climbing and accurate playout-based evaluations to efficiently search even the largest combat scenarios. We demonstrate that Portfolio Greedy Search outperforms state of the art Alpha-Beta and UCT search methods for large StarCraft combat scenarios of up to 50 vs. 50 units under real-time search constraints of 40 ms per search episode.
Data Structures and Algorithms for Domain Decomposition and Distributed Sparse Matrices

Although Domain Decomposition (DD) techniques constitute an important class of methods few actual general purpose codes based on these techniques have been developed so far. It is important to develop algorithms as well as software libraries and tools to facilitate parallel implementations of such techniques. In this paper we explore these ideas and propose a data structure for implementing`distributed sparse matrices', which is a broader concept than that of domain decomposition. We also give an overview of how the data structure can be exploited when implementing a parallel sparse matrix iterative solver.
Selecting a Comprehensive Set of Reviews

Consumer reviews describing a personal evaluation of a product play a central role in the decision-making process of potential future consumers. Over the years, popular Internet-based retailers and service providers have accumulated vast databases sometimes containing hundreds of user-generated reviews for a single product. In order to be of any use for users interested in a specific product, mechanisms to retrieve small, relevant and comprehensive subsets of this abundance of reviews are required. Popular approaches displaying reviews based on rankings like average user-ratings fall short to account for redundancy in top-rated reviews or that the top reviews all may have the same view-point on the product. This report describes the approach developed in [1], which formulates the review retrieval problem as a maximum coverage problem and tries to select a small number of high-quality reviews having different view-points and covering a maximum number of different aspects reviewed product.
Simulation and Modelling of a Turbocharged Compression Ignition Engine

The increase in fuel price is constraining car manufacturers to produce highly efficient engines with more regulations in terms of pollutant emissions. The increasing complexity of modern engines has rendered the prototyping phase long and expensive. This is where engine modeling becomes in the recent years extremely useful and can be used as an indispensable tool when developing new engine concepts. This study deals with the numerical simulation and performance prediction of a turbocharged diesel engine with direct injection. To predict the engine performances, we developed a computer program for simulating the operation of a turbocharged diesel engine, and used the commercial GT-Power software to validate the simulation results. In this work we carried out a comparative study of indicated mean effective pressure, mean effective pressure, power, torque and brake specific fuel consumption obtained by the analytical model for thermodynamic cycle simulation of a turbocharged diesel engine with the computer program developed in the language FORTRAN and those with the GT-Power software. The language FORTRAN program developed is currently used in the course of modeling and simulation of engine performance.
Iconic Text Entry Using a Numeric Keypad

An iconic text entry system using the widely entrenched numeric keypad is presented. In an iconic system, text entry is designed to remind the user of existing notions of letter shape. For example, the letter “L” is entered by pressing buttons down the center of the keypad, and then to the right, calling to mind the shape of a capital L. The letter “O” is entered by pressing the digit 0, which resembles “O”. An iconic system has a number of advantages over systems designed solely for rapid input by expert users. First, an iconic system lends itself well to deployment on existing equipment, such as TV remote controls, without specialized button layouts or labeling. Second, a user need not become an expert in order to be comfortable entering simple text. Third, it is more easily used when visual feedback is difficult.
A Grateful Mind: The Impact of Felt Fluency on Subjective Well-Being

The practice of gratitude has become increasingly prevalent in popular culture as a selfhelp intervention aimed at improving individuals’ happiness and overall well-being. Although a great deal of empirical work has promoted the benefits of practicing gratitude (see Davis et al., 2016 for a review), less work has been conducted examining the conditions under which the traditional gratitude interventions are less effective, or even entirely ineffective. One potential boundary condition that may be associated with the benefit individuals report after engaging in the practice of gratitude is fluency – an individual’s subjective experience of ease or difficulty. When tasks are experienced as relatively easy, people are more likely to indicate they possess the qualities inherent within the task (e.g., Schwarz et al., 1991). That is, if a gratitude exercise is experienced with relative ease, this ought to be reflected in their subsequent self-relevant judgments (e.g., greater reported gratitude and well-being). In Studies 1-3 we attempted to manipulate fluency (i.e., subjective experience of ease) in a common gratitude intervention in order to assess its role as a potential moderator between the practice of gratitude and well-being. Although the manipulation was unsuccessful, we found individual difference evidence that engaging in a gratitude task was associated with greater well-being when the task was experienced as subjectively easy; but was no different from a control when the task was experienced as subjectively difficult. Next, we tried to mitigate the negative impact of low fluency by providing an alternative explanation for its meaning – namely that it was not important diagnostically (Study 4) or that it indicated more meaningful responding (Study 5). Although Study 5 showed some dampening of the effect of fluency, it failed to reach significance. Finally, overall effects were examined in a series of meta-analyses. Overall, the results provided evidence that gratitude tasks were associated with greater well-being when they Running head: A GRATEFUL MIND 3 were experienced as easy, but were no better than a control when they were experienced as difficult. Further, as subjective difficulty increased, gratitude tasks became less effective (as evidenced by an overall negative slope) as a means of boosting well-being; control tasks however were unaffected by subjective difficulty (as evidenced by a flat slope). These studies represent some of the first steps toward understanding the role of individuals’ phenomenological experience in response to a gratitude intervention. Future directions and real-world implications are discussed.
A strategy for dealing with missing values by using selective activation neurons in a multi-topology framework

Neural Networks (NN) have proven to be able to successfully solve problems in many areas. However, for large scale real problems, data is often incomplete. This is a serious problem, because NN cannot handle directly missing values. The usual approach to solve this problem consists of removing attributes and/or samples containing unknown values. This strategy is very attractive since it is simple to implement and reduces the dimensionality of data, therefore potentially reducing the complexity of the problem. However removing features or instances containing vital information, which can not be compensated by the remaining data, may result in the unattainability of accurate NN models. Another strategy consists of estimating missing values. However, wrong estimations of crucial data can lead to unpredicted results. Moreover these techniques do not account for real situations (e.g. where sensors may fail) and the output of such NN models may cause instabilities in the whole process. In this paper we propose a technique for handling missing values that accounts for the creation of different transparent NN models with respect to the missing features instead of relying on tedious data pre-processing techniques. The resulting models are bounded to share information among them. Contrary to the imputation of data (estimate) our models take into account the uncertainty caused by unknown values. Moreover the presented technique is prepared to deal with faulty sensors. The preliminary results obtained in several datasets show the efficacy of the proposed approach.
The Supply-Chain Management Effect

Over the last decade, supply-chain-management concepts have sparked a boom in internal cross-business coordination. But although definitions have broadened and shifted, many executives believe that the field is mainly about installing IT systems for streamlined processes. Wrong, say two researchers from Dartmouth's Tuck School of Business. Their data on companies that lead in supply-chain management illuminate six ways it spurs more-creative thinking on growing a business. The researchers predict that current trends toward restructured supply networks and improved coordination will continue, with more supplier integration and a proliferation of product customization, business complexity and uniquely defined customer relationships. But supply-chain management also will affect industry structure in new ways. Companies in the middle of the supply chain ? contract manufacturers, logistics-service providers and distributors ? will redefine themselves. Also, rebranding and repositioning will occur. Companies across the chain will vie for control of the customer relationship and will find that when value propositions derive from supply-chain capabilities, new cobranding and copositioning strategies are critical. When executives look back after another decade, they'll understand that supply-chain management, having shifted business focus in its first 10 years, created an opportunity for the second 10 years to redefine the competitive landscape.
Building better corpora for summarisation

Annotated corpora have proved essential in many areas of NLP, and over the years have been successfully exploited for a wide range of tasks in Computational Linguistics, including part-of-speech tagging, parsing and information extraction. One field in which they have been particularly useful is automatic summarisation. Within this field, annotated corpora are mainly used for machine learning, to learn patterns for the extraction of important (and other) information from texts, as well as for the more complex task of evaluation of summarisation methods (Edmundson, 1969; Kupiec, Pederson and Chen, 1995; Zechner, 1996; Marcu, 1997). When annotating corpora, one (accurate) method is to employ humans to indicate those parts of text to be annotated with whatever information necessary. These human-selected units of text can then be used as a gold standard by which to measure the performance of a system, as well as for discerning which types of units are chosen or discarded by humans during the summarisation process. There are semi-automatic (Orasan, 2002) and automatic (Jing and McKeown, 1999; Marcu, 1999) ways to annotate corpora, but given that we are investigating new types of information to be marked, manual annotation is most appropriate here. Despite the fact that they are vital to the field, corpora annotated for summarisation are relatively sparse, and those resources which do exist do not contain as much information as they could.
Forbidden zones for the expectation. New mathematical results for behavioral and social sciences

A forbidden zones theorem, mathematical approach and model are proposed in the present article. In particular, the approach supposes that people decide as if there were some biases of the expectations of measurement data. The article is motivated by the need of a theoretical support for the practical analysis performed for the purposes of utility and prospect theories, behavioral economics, psychology, decision and social sciences. Possible general consequences and applications of the theorem and approach for a noise and biases of measurement data are preliminary considered as well.
Validity of the Montreal Battery of Evaluation of Amusia: An Analysis Using Structural Equation Modeling

The Montreal Battery of Evaluation of Amusia (MBEA) is the gold standard for diagnosing amusia. We aimed to evaluate its factorial and convergent validity. Data were collected for the MBEA and a self-report Amusic Dysfunction Inventory on a non-random sample (n = 249), and the following Structural Equation Modeling (SEM) procedures were conducted: confirmatory factor analysis of the theoretical model; exploratory SEM for alternative non-restricted factor solutions; and structural models with each of these solutions as predictors of the inventory’s items. The theoretical model did not prove acceptable goodness of fit, and twoand threefactor non-restricted models were better-fitted solutions for Scale, Contour and Interval tests, and Meter and Memory tests, respectively, than the theoretical one-factor model. This may reflect distinct perceptual processes rela ted to neurocognitive demand. The non-restricted models of Scale, Meter and Memory showed to be acceptable predictors of self-reported capacity for melodic perception, vocal production, rhythmic coordination, and memory.
Durable Sustained Virologic Response After Oral Directly Acting Antiviral Therapy Despite Immunosuppressive Treatment

Treatment for hepatitis C has evolved from interferon-based therapy to all oral, directly acting antiviral (DAA) therapy. The influence of immunosuppression on maintaining sustained virologic response (SVR) in patients who have been treated with these directly acting agents is unknown. In this study, we report sustained hepatitis C virus (HCV) suppression in 3 patients undergoing various immunosuppressive treatments after achieving SVR with DAA therapy. Three patients, who were enrolled in 1 of 2 single-center National Institutes of Health clinical trials, achieved SVR12. Each patient had undergone between 6 and 24 weeks of DAA therapy with or without ribavirin. Immunosuppression was varied among the 3 patients. Therapy included adalimumab, carboplatin/irinotecan, or capecitabine. In all 3 cases, patients maintained HCV RNA levels below detection after immunosuppression. All patients had undetectable viral load and normalized liver-related enzymes during immunosuppressive therapy. This report suggests that SVR as a result of novel DAA therapy is durable and likely not affected by immunosuppressive therapy. Larger studies are required to confirm these results, but findings are promising for the treatment of large numbers of HCV-infected patients who may require subsequent immunosuppressive or immunomodulating therapies.
Charge reversal of colloidal particles

A theory is presented for the effective charge of colloidal particles in suspensions containing multivalent counterions. It is shown that if colloids are sufficiently strongly charged, the number of condensed multivalent counterion can exceed the bare colloidal charge leading to charge reversal. Charge renormalization in suspensions with multivalent counterions depends on a subtle interplay between the solvation energies of the multivalent counterions in the bulk and near the colloidal surface. We find that the effective charge is not a monotonically decreasing function of the multivalent salt concentration. Furthermore, contrary to the previous theories, it is found that except at very low concentrations, monovalent salt hinders the charge reversal. This conclusion is in agreement with the recent experiments and simulations.
PROPOSAL OF FLUID SELF-EXCITED OSCILLATION PECULIAR TO A FLAT RING TUBE AND ITS APPLICATION

A novel fluid power actuator called Flat Ring Tube (FRT) is introduced in this paper. The mechanism of FRT is so simple that it only needs a urethane flat tube and water pressure power source. No valves or switches are required. Applying constant water pressure results in periodic oscillation of the tube. The frequency is proportional to the flow rate of water and inversely proportional to the tube length. By contacting the tube with a passively supported shaft, it rotates due to the periodic tube oscillation. This principle can also generate the linear driving force, when FRT is mounted so as to kick the ground. Such kind of function can be also expected to stimulate the blood flow rate, when FRT touches on the human body appropriately. The experimental results showed us the developed wearable massage device could effectively improve both the circulation of the blood flow and the density of oxygen in blood, which resulted in let the people feel comfortable.
Technology review - Biometrics-Technology, Application, Challenge, and Computational Intelligence Solutions

Biometrics is the science of the measurement of unique human characteristics, both physical and behavioral. Various biometric technologies are available for identifying or verifying an individual by measuring fingerprint, hand, face, signature, voice, or a combination of these traits. This paper aims to assist readers as they consider biometric solutions by examining common biometric technologies, introducing different biometric applications, and reviewing recent CI solutions presented at the 2006 IEEE WCCI