nils-herrmann/clean_scirepeval huggingface dataset:
Dynamic changes in arterial waveform derived variables and fluid responsiveness in mechanically ventilated patients: a systematic review of the literature.

: A systematic review of the literature to determine the ability of dynamic changes in arterial waveform-derived variables to predict fluid responsiveness and compare these with static indices of fluid responsiveness. The assessment of a patient's intravascular volume is one of the most difficult tasks in critical care medicine. Conventional static hemodynamic variables have proven unreliable as predictors of volume responsiveness. Dynamic changes in systolic pressure, pulse pressure, and stroke volume in patients undergoing mechanical ventilation have emerged as useful techniques to assess volume responsiveness.



: MEDLINE, EMBASE, Cochrane Register of Controlled Trials and citation review of relevant primary and review articles.



: Clinical studies that evaluated the association between stroke volume variation, pulse pressure variation, and/or stroke volume variation and the change in stroke volume/cardiac index after a fluid or positive end-expiratory pressure challenge.


 Data were abstracted on study design, study size, study setting, patient population, and the correlation coefficient and/or receiver operating characteristic between the baseline systolic pressure variation, stroke volume variation, and/or pulse pressure variation and the change in stroke index/cardiac index after a fluid challenge. When reported, the receiver operating characteristic of the central venous pressure, global end-diastolic volume index, and left ventricular end-diastolic area index were also recorded. Meta-analytic techniques were used to summarize the data. Twenty-nine studies (which enrolled 685 patients) met our inclusion criteria. Overall, 56% of patients responded to a fluid challenge. The pooled correlation coefficients between the baseline pulse pressure variation, stroke volume variation, systolic pressure variation, and the change in stroke/cardiac index were 0.78, 0.72, and 0.72, respectively. The area under the receiver operating characteristic curves were 0.94, 0.84, and 0.86, respectively, compared with 0.55 for the central venous pressure, 0.56 for the global end-diastolic volume index, and 0.64 for the left ventricular end-diastolic area index. The mean threshold values were 12.5 +/- 1.6% for the pulse pressure variation and 11.6 +/- 1.9% for the stroke volume variation. The sensitivity, specificity, and diagnostic odds ratio were 0.89, 0.88, and 59.86 for the pulse pressure variation and 0.82, 0.86, and 27.34 for the stroke volume variation, respectively.



: Dynamic changes of arterial waveform-derived variables during mechanical ventilation are highly accurate in predicting volume responsiveness in critically ill patients with an accuracy greater than that of traditional static indices of volume responsiveness. This technique, however, is limited to patients who receive controlled ventilation and who are not breathing spontaneously.
STEGANOGRAPHY UTILIZING XML , HTML AND XHTML MARKUP LANGUAGES

Nowadays people use web pages and email to share secret information. To ensure the secure message transformation, we used cryptography in combination with Steganography to achieve the desire results. To improve security, the encrypted message is hiding in HTML, XML and XHTML. Our technique is implemented in two levels of randomness i.e. at the file level and content level and encrypted with AES to achieve the maximum security. In addition, the proposed technique is using Unicode languages to take a secret message and has better capacity than the existing methodologies as only two spaces are required to hide the one character. The results show our technique provides high hidden capacity and security than an existing algorithm.
Design of a Solar-Harvesting Circuit for Batteryless Embedded Systems

The limited battery lifetime of modern embedded systems and mobile devices necessitates frequent battery recharging or replacement. Solar energy and small-size photovoltaic (PV) systems are attractive solutions to increase the autonomy of embedded and personal devices attempting to achieve perpetual operation. We present a battery less solar-harvesting circuit that is tailored to the needs of low-power applications. The harvester performs maximum-power-point tracking of solar energy collection under nonstationary light conditions, with high efficiency and low energy cost exploiting miniaturized PV modules. We characterize the performance of the circuit by means of simulation and extensive testing under various charging and discharging conditions. Much attention has been given to identify the power losses of the different circuit components. Results show that our system can achieve low power consumption with increased efficiency and cheap implementation. We discuss how the scavenger improves upon state-of-the-art technology with a measured power consumption of less than 1 mW. We obtain increments of global efficiency up to 80%, diverging from ideality by less than 10%. Moreover, we analyze the behavior of super capacitors. We find that the voltage across the supercapacitor may be an unreliable indicator for the stored energy under some circumstances, and this should be taken into account when energy management policies are used.
Phenomenology and Cognitive Linguistics

The purpose of this chapter is to describe some similarities, as well as differences, between theoretical proposals emanating from the tradition of phenomenology and the currently popular approach to language and cognition known as cognitive linguistics (hence CL). This is a rather demanding and potentially controversial topic. For one thing, neither CL nor phenomenology constitute monolithic theories, and are actually rife with internal controversies. This forces me to make certain “schematizations”, since it is impossible to deal with the complexity of these debates in the space here allotted.
Overview of Missile Flight Control Systems

the guidance system. The types of steering commands vary depending on the phase of flight and the type of interceptor. For example, in the boost phase the flight control system may be designed to force the missile to track a desired flight-path angle or attitude. In the midcourse and terminal phases the system may be designed to track acceleration commands to effect an intercept of the target. This article explores several aspects of the missile flight control system, including its role in the overall missile system, its subsystems, types of flight control systems, design objectives, and design challenges. Also discussed are some of APL’s contributions to the field, which have come primarily through our role as Technical Direction Agent on a variety of Navy missile programs. he flight control system is a key element that allows the missile to meet its system performance requirements. The objective of the flight control system is to force the missile to achieve the steering commands developed by
ChaD: Chat-Oriented Dialog Systems

Historically, conversational systems have focused on goal-directed interaction and this focus defined much of the work in the field of spoken dialog systems. More recently researchers have started to focus on nongoal-oriented dialog systems often referred to as ”chat” systems. We can refer to these as Chat-oriented Dialog (CHAD)systems. CHAD systems are not task-oriented and focus on what can be described as social conversation where the goal is to interact while maintaining an appropriate level of engagement with a human interlocutor. Work to date has identified a number of techniques that can be used to implement working CHADs but it has also highlighted important limitations. This note describes CHAD characteristics and proposes a research agenda.
Organizational issues in the implementation and adoption of health information technology innovations: An interpretative review

Implementations of health information technologies are notoriously difficult, which is due to a range of inter-related technical, social and organizational factors that need to be considered. In the light of an apparent lack of empirically based integrated accounts surrounding these issues, this interpretative review aims to provide an overview and extract potentially generalizable findings across settings.



We conducted a systematic search and critique of the empirical literature published between 1997 and 2010. In doing so, we searched a range of medical databases to identify review papers that related to the implementation and adoption of eHealth applications in organizational settings. We qualitatively synthesized this literature extracting data relating to technologies, contexts, stakeholders, and their inter-relationships.



From a total body of 121 systematic reviews, we identified 13 systematic reviews encompassing organizational issues surrounding health information technology implementations. By and large, the evidence indicates that there are a range of technical, social and organizational considerations that need to be deliberated when attempting to ensure that technological innovations are useful for both individuals and organizational processes. However, these dimensions are inter-related, requiring a careful balancing act of strategic implementation decisions in order to ensure that unintended consequences resulting from technology introduction do not pose a threat to patients.



Organizational issues surrounding technology implementations in healthcare settings are crucially important, but have as yet not received adequate research attention. This may in part be due to the subjective nature of factors, but also due to a lack of coordinated efforts toward more theoretically-informed work. Our findings may be used as the basis for the development of best practice guidelines in this area.
Data-driven modeling for boiling heat transfer: using deep neural networks and high-fidelity simulation results

Boiling heat transfer occurs in many situations and can be used for thermal management in various engineered systems with high energy density, from power electronics to heat exchangers in power plants and nuclear reactors. Essentially, boiling is a complex physical process that involves interactions between heating surface, liquid, and vapor. For engineering applications, the boiling heat transfer is usually predicted by empirical correlations or semi-empirical models, which has relatively large uncertainty. In this paper, a data-driven approach based on deep feedforward neural networks is studied. The proposed networks use near wall local features to predict the boiling heat transfer. The inputs of networks include the local momentum and energy convective transport, pressure gradients, turbulent viscosity, and surface information. The outputs of the networks are the quantities of interest of a typical boiling system, including heat transfer components, wall superheat, and near wall void fraction. The networks are trained by the high-fidelity data processed from first principle simulation of pool boiling under varying input heat fluxes. State-of-the-art algorithms are applied to prevent the overfitting issue when training the deep networks. The trained networks are tested in interpolation cases and extrapolation cases which both demonstrate good agreement with the original high-fidelity simulation results.
Planar Segments Based Three-dimensional Robotic Mapping in Outdoor Environments

This dissertation focuses on the problem of three-dimensional (3D) outdoor robotic mapping. Laser scanners are chosen as the primary sensors and a novel approach for scan registration based on planar segments is developed. Unlike most existing approaches, it does not require an a-priori pose estimation from other sensors such as odometers and inertial measurement units (IMUs). Instead, the transformation is determined globally by searching corresponding planar segments between overlapping scans. There are three steps in the approach: The first step is to segment each point cloud into planar segments. Depending on the clutter level of the environment and whether the point cloud is organized, four complementary strategies have been proposed, namely a point based, a subwindow based, a hybrid and a cached octree region growing algorithm. Among them, the former three are limited to organized point clouds; in addition, the second one is restricted to structured environments. Based on observations from field experiments, the hybrid/cached octree region growing algorithms are recommended for organized/unorganized point clouds. The second step is to calculate the area of each segment resulting from the first step. Again, segments from organized and unorganized point clouds are distinguished, where an alpha-shape based algorithm is proposed for unorganized point sets and a range-image based method is proposed for organized point sets, respectively. The third step is to find segment correspondences and compute the transformation based on matched segments. The correspondences are searched globally in order to maximize a spherical-correlation-like metric, wherein the search space is pruned by both self-similarity and interrelations (geometric constraints). The novelty of the search algorithm is that only the area and plane parameters of each segment are required. Four datasets acquired by scanners with different field of views have been used to evaluate the proposed approach: three are publicly available and one stems from our custom-built platform. Based on these datasets, the following evaluations have been done: segmentation speed-benchmarking, segment area calculation accuracyand speed-benchmarking, and registration accuracy comparison with ground-truth. Also, the robustness of the approach with respect to occlusions and partial observations has been proven. The approach has been compared to the Iterative Closest Point (ICP)
The role of smartphone usage types , emotional intelligence , social stress , self-regulation , age , and gender

The present study investigates the role of process and social oriented smartphone usage, emotional intelligence, social stress, self-regulation, gender, and age in relation to habitual and addictive smartphone behavior. We conducted an online survey among 386 respondents. The results revealed that habitual smartphone use is an important contributor to addictive smartphone behavior. Process related smartphone use is a strong determinant for both developing habitual and addictive smartphone behavior. People who extensively use their smartphones for social purposes develop smartphone habits faster, which in turn might lead to addictive smartphone behavior. We did not find an influence of emotional intelligence on habitual or addictive smartphone behavior, while social stress positively influences addictive smartphone behavior, and a failure of self-regulation seems to cause a higher risk of addictive smartphone behavior. Finally, men experience less social stress than women, and use their smartphones less for social purposes. The result is that women have a higher chance in developing habitual or addictive smartphone behavior. Age negatively affects process and social usage, and social stress. There is a positive effect on self-regulation. Older people are therefore less likely to develop habitual or addictive smartphone behaviors.
Financial Time Series Prediction Using Elman Recurrent Random Neural Networks

In recent years, financial market dynamics forecasting has been a focus of economic research. To predict the price indices of stock markets, we developed an architecture which combined Elman recurrent neural networks with stochastic time effective function. By analyzing the proposed model with the linear regression, complexity invariant distance (CID), and multiscale CID (MCID) analysis methods and taking the model compared with different models such as the backpropagation neural network (BPNN), the stochastic time effective neural network (STNN), and the Elman recurrent neural network (ERNN), the empirical results show that the proposed neural network displays the best performance among these neural networks in financial time series forecasting. Further, the empirical research is performed in testing the predictive effects of SSE, TWSE, KOSPI, and Nikkei225 with the established model, and the corresponding statistical comparisons of the above market indices are also exhibited. The experimental results show that this approach gives good performance in predicting the values from the stock market indices.
Where to Focus: Deep Attention-based Spatially Recurrent Bilinear Networks for Fine-Grained Visual Recognition

Fine-grained visual recognition typically depends on modeling subtle difference from object parts. However, these parts often exhibit dramatic visual variations such as occlusions, viewpoints, and spatial transformations, making it hard to detect. In this paper, we present a novel attention-based model to automatically, selectively and accurately focus on critical object regions with higher importance against appearance variations. Given an image, two different Convolutional Neural Networks (CNNs) are constructed, where the outputs of two CNNs are correlated through bilinear pooling to simultaneously focus on discriminative regions and extract relevant features. To capture spatial distributions among the local regions with visual attention, soft attention based spatial LongShort Term Memory units (LSTMs) are incorporated to realize spatially recurrent yet visually selective over local input patterns. All the above intuitions equip our network with the following novel model: two-stream CNN layers, bilinear pooling layer, spatial recurrent layer with location attention are jointly trained via an end-to-end fashion to serve as the part detector and feature extractor, whereby relevant features are localized and extracted attentively. We show the significance of our network against two well-known visual recognition tasks: fine-grained image classification and person re-identification.
Lifetime Probability Of Default Modeling For Hungarian Corporate Debt Instruments

The paper attempts to provide forecast methodological framework and concrete models to estimate long run probability of default term structure for Hungarian corporate debt instruments, in line with IFRS 9 requirements. Long run probability of default and expected loss can be estimated by various methods and has fifty-five years of history in literature. After studying literature and empirical models, the Markov chain approach was selected to accomplish lifetime probability of default modeling for Hungarian corporate debt instruments. Empirical results reveal that both discrete and continuous homogeneous Markov chain models systematically overestimate the long term corporate probability of default. However, the continuous nonhomogeneous Markov chain gives both intuitively and empirically appropriate probability of default trajectories. The estimated term structure mathematically and professionally properly expresses the probability of default element of expected loss that can realistically occur in the long-run in Hungarian corporate lending. The elaborated models can be easily implemented at Hungarian corporate financial institutions.
Downlink NOMA with SIC using unified user grouping for non-orthogonal user multiplexing and decoding order

In this paper, we propose a practical method for user grouping and decoding-order setting in a successive interference canceller (SIC) for downlink non-orthogonal multiple access (NOMA). While the optimal user grouping and decoding order, which depend on the instantaneous channel conditions among users within a cell, are assumed in previous work, the proposed method uses user grouping and a decoding order that are unified among all frequency blocks. The proposed decoding order in the SIC enables the application of NOMA with a SIC to a system where all the elements within a codeword for a user are distributed among multiple frequency blocks (resource blocks). The unified user grouping eases the complexity in the SIC process at the user terminal. The unified user grouping also reduces the complexity of the efficient downlink control signaling in NOMA with a SIC. The unified user grouping and decoding order among frequency blocks in principle reduce the achievable throughput compared to the optimal one. However, based on numerical results, we show that the proposed method does not significantly degrade the system-level throughput in downlink cellular networks.
Cloud Computing : Empirical Studies in Higher Education

The advent of cloud computing (CC) in recent years has attracted substantial interest from various institutions, especially higher education institutions, which wish to consider the advantages of its features. Many universities have migrated from traditional forms of teaching to electronic learning services, and they rely upon information and communication technology services. The usage of CC in educational environments provides many benefits, such as low-cost services for academics and students. The expanded use of CC comes with significant adoption challenges. Understanding the position of higher education institutions with respect to CC adoption is an essential research area. This paper investigated the current state of CC adoption in the higher education sector in order to enrich the research in this area of interest. Existing limitations and knowledge gaps in current empirical studies are identified. Moreover, suggested areas for further researches will be highlighted for the benefit of other researchers who are interesting in this topic. These researches encourage institutions of education especially in higher education to adopted cloud computing technology. Cloud computing; education system; e-learning; information and communication technology (ICT)
Adoption of internet banking: An empirical study in Hong Kong

This study investigates how customers perceive and adopt Internet Banking (IB) in Hong Kong. We developed a theoretical model based on the Technology Acceptance Model (TAM) with an added construct Perceived Web Security, and empirically tested its ability in predicting customers’ behavioral intention of adopting IB. We designed a questionnaire and used it to survey a randomly selected sample of customers of IB from the Yellow Pages, and obtained 203 usable responses. We analyzed the data using Structured Equation Modeling (SEM) to evaluate the strength of the hypothesized relationships, if any, among the constructs, which include Perceived Ease of Use and Perceived Web Security as independent variables, Perceived Usefulness and Attitude as intervening variables, and Intention to Use as the dependent variable. The results provide support of the extended TAM model and confirm its robustness in predicting customers’ intention of adoption of IB. This study contributes to the literature by formulating and validating TAM to predict IB adoption, and its findings provide useful information for bank management in formulating IB marketing strategies.