nils-herrmann/clean_scirepeval huggingface dataset:
Design and analysis of a hybrid energy harvester for self-powered sensor

This paper presents a slightly different concept of energy harvesting method as compared to the conventional energy harvesters. The new energy harvester of interest is known as Hybrid Energy Harvester (HEH). It comprises of the Piezoelectric and Magnetostrictive/Electromagnetic transducers integrated into a system. Theoretically, the hybrid energy harvester device developed is targeted to expand the vibration bandwidth as compared to the standalone energy harvesters, hence higher output power will be generated. To develop the hybrid energy harvester, mathematical models of each respective transducers are formulated, then simulations are carried out using the MATLAB/Simulink analysis tool. Next, a macro-scale prototype device is designed, fabricated and validated. From the experimental characterizations, it is unveiled that the maximum open circuit output voltage produced by the hybrid energy harvester is 6.23 V at a resonant frequency of 59.6 Hz.
Examining students' online interaction in a live video streaming environment using data mining and text mining

0747-5632/$ see front matter 2012  A http://dx.doi.org/10.1016/j.chb.2012.07.020 ⇑ Tel. 5008;  E-mail address: whe@odu.edu This study analyses the online questions and chat messages automatically recorded by a live video streaming (LVS) system using data mining and text mining techniques. We apply data mining and text mining techniques to analyze two different datasets and then conducted an in-depth correlation analysis for two educational courses with the most online questions and chat messages respectively. The study found the discrepancies as well as similarities in the students’ patterns and themes of participation between online questions (student–instructor interaction) and online chat messages (student–students interaction or peer interaction). The results also identify disciplinary differences in students’ online participation. A correlation is found between the number of online questions students asked and students’ final grades. The data suggests that a combination of using data mining and text mining techniques for a large amount of online learning data can yield considerable insights and reveal valuable patterns in students’ learning behaviors. Limitations with data and text mining were also revealed and discussed in the paper.
The neural substrates of procrastination: A voxel-based morphometry study

Procrastination is a pervasive phenomenon across different cultures and brings about lots of serious consequences, including performance, subjective well-being, and even public policy. However, little is known about the neural substrates of procrastination. In order to shed light upon this question, we investigated the neuroanatomical substrates of procrastination across two independent samples using voxel-based morphometry (VBM) method. The whole-brain analysis showed procrastination was positively correlated with the graymatter (GM) volume of clusters in the parahippocampal gyrus (PHG) and the orbital frontal cortex (OFC), while negatively correlated with the GM volume of clusters in the inferior frontal gyrus (IFG) and the middle frontal gyrus (MFG) in sample one (151 participants). We further conducted a verification procedure on another sample (108 participants) using region-of-interest analysis to examine the reliability of these results. Results showed procrastination can be predicted by the GM volume of the OFC and the MFG. The present findings suggest that the MFG and OFC, which are the key regions of self-control and emotion regulation, may play an important role in procrastination.
Dielectric properties of human normal, malignant and cirrhotic liver tissue: in vivo and ex vivo measurements from 0.5 to 20 GHz using a precision open-ended coaxial probe.

Hepatic malignancies have historically been treated with surgical resection. Due to the shortcomings of this technique, there is interest in other, less invasive, treatment modalities, such as microwave hepatic ablation. Crucial to the development of this technique is the accurate knowledge of the dielectric properties of human liver tissue at microwave frequencies. To this end, we characterized the dielectric properties of in vivo and ex vivo normal, malignant and cirrhotic human liver tissues from 0.5 to 20 GHz. Analysis of our data at 915 MHz and 2.45 GHz indicates that the dielectric properties of ex vivo malignant liver tissue are 19 to 30% higher than normal tissue. The differences in the dielectric properties of in vivo malignant and normal liver tissue are not statistically significant (with the exception of effective conductivity at 915 MHz, where malignant tissue properties are 16% higher than normal). Also, the dielectric properties of in vivo normal liver tissue at 915 MHz and 2.45 GHz are 16 to 43% higher than ex vivo. No statistically significant differences were found between the dielectric properties of in vivo and ex vivo malignant tissue (with the exception of effective conductivity at 915 MHz, where malignant tissue properties are 28% higher than normal). We report the one-pole Cole-Cole parameters for ex vivo normal, malignant and cirrhotic liver tissue in this frequency range. We observe that wideband dielectric properties of in vivo liver tissue are different from the wideband dielectric properties of ex vivo liver tissue, and that the in vivo data cannot be represented in terms of a Cole-Cole model. Further work is needed to uncover the mechanisms responsible for the observed wideband trends in the in vivo liver data.
Interobserver variation in the assessment of pelvic organ prolapse

The aim of this study was to determine the interobserver agreement of two grading systems for pelvic organ prolapse: the vaginal profile and the International Continence Society (ICS) draft proposal. Forty-nine consecutive women referred for evaluation of urinary incontinence and/or pelvic organ prolapse were studied. Patients were first examined by a physician and a nurse clinician using the vaginal profile, followed by an examination according to the technique described in the ICS draft proposal for standardization of terminology (1994). κ statistic and Pearson's correlation coefficient were used to determine interobserver variability for the ICS system by overall stage, by stage-specific comparison, and by specific anatomic location. The vaginal profile was evaluated by obtaining a κ for overall degree of prolapse, stage-specific comparison and by anatomic area. The κ for the ICS stage was 0.79 (P<0.001), and the κ for the vaginal profile by area of greatest prolapse was 0.68 (P<0.001), indicating substantial interobserver agreement for both systems. The ICS system was noted to have substantial interobserver agreement by a stage-specific comparison. All anatomic locations of the ICS staging system were found to correlate significantly, and a high degree of interobserver precision was found. The vaginal profile also showed significant interobserver agreement by overall degree of prolapse, by specific degree of prolapse, and by anatomic area. It was concluded that both the proposed ICS staging system and the traditional vaginal profile show significant interobserver agreement both by overall stage, stage-specific analysis and specific location. The registered nurse examination correlated well with the physican examination, indicating that the most important factor in obtaining reproducible results may be definition and close attention to examination technique.
Indoor scene segmentation using a structured light sensor

In this paper we explore how a structured light depth sensor, in the form of the Microsoft Kinect, can assist with indoor scene segmentation. We use a CRF-based model to evaluate a range of different representations for depth information and propose a novel prior on 3D location. We introduce a new and challenging indoor scene dataset, complete with accurate depth maps and dense label coverage. Evaluating our model on this dataset reveals that the combination of depth and intensity images gives dramatic performance gains over intensity images alone. Our results clearly demonstrate the utility of structured light sensors for scene understanding.
Factors Influencing Citizen Adoption of E-Government in Developing Countries: The Case of Jordan

The main purposes of this study are to investigate citizen adoption of e-government services in Jordan and to explore factors affecting the level of adoption of e-government services. Importantly, this study aims to develop a conceptual framework that is based on previous literature of Technology Acceptance Model (TAM) in order to examine the relationships between certain factors (government trustworthiness, service quality and citizen satisfaction) and citizen adoption of e-government services. A self-administered questionnaire was used to capture data from 356 Jordanian citizens across the country randomly. The findings indicate that perceived usefulness, perceived ease of use, citizen satisfaction and trustworthiness are significant predictors of the Jordanian intention to use an e-government service. The results also showed that the service quality dimensions: responsiveness, reliability, and empathy have significant impacts on the citizen satisfaction. The study has made significant contributions to the body of knowledge at academic and practical levels as an important exploratory study that was conducted in the context of Jordan, a developing country with genuine need for more research works on e-government issues. In addition, this study provides some valuable insights into the performance and adoption of e-government in Jordan that could help government agencies to improve the effectiveness of their services. Omar Al Hujran Princess Sumaya University for Technology, Jordan Anas Aloudat University of Jordan, Jordan Ikhlas Altarawneh Al-Faisal University, Saudi Arabia
Wind Energy : Forecasting Challenges for Its Operational Management

Renewable energy sources, especially wind energy, are to play a larger role in providing electricity to industrial and domestic consumers. This is already the case today for a number of European countries, closely followed by the US and high growth countries, for example, Brazil, India and China. There exist a number of technological, environmental and political challenges linked to supplementing existing electricity generation capacities with wind energy. Here, mathematicians and statisticians could make a substantial contribution at the interface of meteorology and decision-making, in connection with the generation of forecasts tailored to the various operational decision problems involved. Indeed, while wind energy may be seen as an environmentally friendly source of energy, full benefits from its usage can only be obtained if one is able to accommodate its variability and limited predictability. Based on a short presentation of its physical basics, the importance of considering wind power generation as a stochastic process is motivated. After describing representative operational decision-making problems for both market participants and system operators, it is underlined that forecasts should be issued in a probabilistic framework. Even though, eventually, the forecaster may only communicate single-valued predictions. The existing approaches to wind power forecasting are subsequently described, with focus on single-valued predictions, predictive marginal densities and space–time trajectories. Upcoming challenges related to generating improved and new types of forecasts, as well as their verification and value to forecast users, are finally discussed.
Framing the Salmonidae Family Phylogenetic Portrait: A More Complete Picture from Increased Taxon Sampling

Considerable research efforts have focused on elucidating the systematic relationships among salmonid fishes; an understanding of these patterns of relatedness will inform conservation- and fisheries-related issues, as well as provide a framework for investigating evolutionary mechanisms in the group. However, uncertainties persist in current Salmonidae phylogenies due to biological and methodological factors, and a comprehensive phylogeny including most representatives of the family could provide insight into the causes of these difficulties. Here we increase taxon sampling by including nearly all described salmonid species (n = 63) to present a time-calibrated and more complete portrait of Salmonidae using a combination of molecular markers and analytical techniques. This strategy improved resolution by increasing the signal-to-noise ratio and helped discriminate methodological and systematic errors from sources of difficulty associated with biological processes. Our results highlight novel aspects of salmonid evolution. First, we call into question the widely-accepted evolutionary relationships among sub-families and suggest that Thymallinae, rather than Coregoninae, is the sister group to the remainder of Salmonidae. Second, we find that some groups in Salmonidae are older than previously thought and that the mitochondrial rate of molecular divergence varies markedly among genes and clades. We estimate the age of the family to be 59.1 MY (CI: 63.2-58.1 MY) old, which likely corresponds to the timing of whole genome duplication in salmonids. The average, albeit highly variable, mitochondrial rate of molecular divergence was estimated as ~0.31%/MY (CI: 0.27-0.36%/MY). Finally, we suggest that some species require taxonomic revision, including two monotypic genera, Stenodus and Salvethymus. In addition, we resolve some relationships that have been notoriously difficult to discern and present a clearer picture of the evolution of the group. Our findings represent an important contribution to the systematics of Salmonidae, and provide a useful tool for addressing questions related to fundamental and applied evolutionary issues.
Oxytocin is associated with human trustworthiness

Human beings exhibit substantial interpersonal trust-even with strangers. The neuroactive hormone oxytocin facilitates social recognition in animals, and we examine if oxytocin is related to trustworthiness between humans. This paper reports the results of an experiment to test this hypothesis, where trust and trustworthiness are measured using the sequential anonymous "trust game" with monetary payoffs. We find that oxytocin levels are higher in subjects who receive a monetary transfer that reflects an intention of trust relative to an unintentional monetary transfer of the same amount. In addition, higher oxytocin levels are associated with trustworthy behavior (the reciprocation of trust). Absent intentionality, both the oxytocin and behavioral responses are extinguished. We conclude that perceptions of intentions of trust affect levels of circulating oxytocin.
Social Stigma and Self-Esteem : The Self-Protective Properties of Stigma

Although several psychological theories predict that members of stigmatized groups should have low global self-esteem, empirical research typically does not support this prediction. It is proposed here that this discrepancy may be explained by considering the ways in which membership in a stigmatized group may protect the self-concept It is proposed that members of stigmatized groups may (a) attribute negative feedback to prejudice against their group, (b) compare their outcomes with those of the ingroup, rather than with the relatively advantaged outgroup, and (c) selectively devalue those dimensions on which their group fares poorly and value those dimensions on which their group excels. Evidence for each of these processes and their consequences for self-esteem and motivation is reviewed. Factors that moderate the use of these strategies and implications of this analysis for treatment of stigmas are also discussed.
Security in the Internet of Things: A Survey on Application Layer Protocols

The rapid development of technology nowadays led people to a new and revolutionary concept, named the Internet of Things. This model imposes that all "objects", such as personal objects (smartphones, notebooks, smart watches, tablets etc), electronic equipment embed with sensors and other environmental elements are always connected to a common network. Therefore, one can access any resource at any time, by using a device recognized in the network. While the IoT may be economically and socially beneficial, the implementation of such a system poses many difficulties, risks and security issues that must be taken into consideration. Nowadays, the architecture of the Internet must be updated and rethought in order to interconnect trillions of devices and to ensure interoperability between them. Nevertheless, the most important problem is the security requirements of the IoT, which is probably one of the main reasons of the relatively slow development of this field. This paper presents the most important application layer protocols that are currently used in the IoT context: CoAP, MQTT, XMPP. We discuss them both separately and by comparison, with focus on the security provided by these protocols. Finally, we provide some future research opportunities and conclusions.
Comparing word, character, and phoneme n-grams for subjective utterance recognition

In this paper, we compare the performance of classifiers trained using word n-grams, character n-grams, and phoneme n-grams for recognizing subjective utterances in multiparty conversation. We show that there is value in using very shallow linguistic representations, such as character n-grams, for recognizing subjective utterances, in particular, gains in the recall of subjective utterances.
The role of autophagy in cancer development and response to therapy

Autophagy is a process in which subcellular membranes undergo dynamic morphological changes that lead to the degradation of cellular proteins and cytoplasmic organelles. This process is an important cellular response to stress or starvation. Many studies have shed light on the importance of autophagy in cancer, but it is still unclear whether autophagy suppresses tumorigenesis or provides cancer cells with a rescue mechanism under unfavourable conditions. What is the present state of our knowledge about the role of autophagy in cancer development, and in response to therapy? And how can the autophagic process be manipulated to improve anticancer therapeutics?
Spatio-Temporal Recurrent Convolutional Networks for Citywide Short-term Crowd Flows Prediction

With the rapid development of urban traffic, forecasting the flows of crowd plays an increasingly important role in traffic management and public safety. However, it is very challenging as it is affected by many complex factors, including spatio-temporal dependencies of regions and other external factors such as weather and holiday. In this paper, we proposed a deep-learning-based approach, named STRCNs, to forecast both inflow and outflow of crowds in every region of a city. STRCNs combines Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network structures to capture spatio-temporal dependencies, simultaneously. More particularly, our model can be decomposed into four components: Closeness captures the changes of instantaneous flows; Daily influence detects the changes of daily influence flows regularly; Weekly influence reacts weekly patterns of influence flows and External influence gets the influence of external factors. For the first three properties (Closeness, Daily influence and Weekly influence), we give a branch of recurrent convolutional network units to learn both spatial and temporal dependencies in crowd flows. External factors are fed into a two-layers fully connected neural network. STRCNs assigns different weights to different branches, and then merges the outputs of the four parts together. Experimental results on two data sets (MobileBJ and TaxiBJ) demonstrate that STRCNs outperforms classical time series and other deep-learning-based prediction methods.
The Human BNST: Functional Role in Anxiety and Addiction

The consequences of chronic stress on brain structure and function are far reaching. Whereas stress can produce short-term adaptive changes in the brain, chronic stress leads to long-term maladaptive changes that increase vulnerability to psychiatric disorders, such as anxiety and addiction. These two disorders are the most prevalent psychiatric disorders in the United States, and are typically chronic, disabling, and highly comorbid. Emerging evidence implicates a tiny brain region—the bed nucleus of the stria terminalis (BNST)—in the body’s stress response and in anxiety and addiction. Rodent studies provide compelling evidence that the BNST plays a central role in sustained threat monitoring, a form of adaptive anxiety, and in the withdrawal and relapse stages of addiction; however, little is known about the role of BNST in humans. Here, we review current evidence for BNST function in humans, including evidence for a role in the production of both adaptive and maladaptive anxiety. We also review preliminary evidence of the role of BNST in addiction in humans. Together, these studies provide a foundation of knowledge about the role of BNST in adaptive anxiety and stress-related disorders. Although the field is in its infancy, future investigations of human BNST function have tremendous potential to illuminate mechanisms underlying stress-related disorders and identify novel neural targets for treatment.
Multiple UAV cooperative search under collision avoidance and limited range communication constraints

This paper focuses on the problem of cooperatively searching, using a team of unmanned air vehicles (UAVs), an area of interest that contains regions of opportunity and regions of potential hazard. The objective of the UAV team is to visit as many opportunities as possible, while avoiding as many hazards as possible. To enable cooperation, the UAVs are constrained to stay within communication range of one another. Collision avoidance is also required. Algorithms for teamoptimal and individually-optimal/team-suboptimal solutions are developed and their computational complexity compared. Simulation results demonstrating the feasibility of the cooperative search algorithms are presented.
Optimization of Control Parameters for Genetic Algorithms

The task of optimizing a complex system presents at least two levels of problems for the system designer. First, a class of optimization algorithms must be chosen that is suitable for application to the system. Second, various parameters of the optimization algorithm need to be tuned for efficiency. A class of adaptive search procedures called genetic algorithms (GA) has been used to optimize a wide variety of complex systems. GA's are applied to the second level task of identifying efficient GA's for a set of numerical optimization problems. The results are validated on an image registration problem. GA's are shown to be effective for both levels of the systems optimization problem.