Miracle-dz/UniFAR-FTUs huggingface dataset:
An Optimal Thévenin Equivalent Estimation Method and its Application to the Voltage Stability Analysis of a Wind Hub

A simplified voltage stability analysis method is to use a static Thévenin equivalent to represent the electrical connection to a load center. Assuming fixed values of the Thévenin voltage and reactance, a power-voltage (<inline-formula><tex-math notation="LaTeX">$PV$</tex-math></inline-formula>) curve analysis can be performed to find the voltage collapse point and stability margin. This paper proposes a method to compute the static Thévenin equivalent voltage and reactance of a power system using measured data. The method is validated with simulated PMU data and with 24-hour SCADA data at a wind hub on a medium voltage transmission system in the US western system. For the wind hub, the Thévenin equivalent parameters are used to compute the maximum power transfer capability of the wind hub as constrained by voltage stability. Then the method is extended to online application by using short data windows and forgetting factors.
PAP ER Spatial Tuples : Augmenting reality with tuples

Correspondence Andrea Omicini, Universita degli Studi di Bologna, DISI, Bologna, Italy. Email: andrea.omicini@unibo.it Abstract We introduce Spatial Tuples, an extension of the basic tuple-based model for distributed multi-agent system coordination where (a) tuples are conceptually placed in regions of the physical world and possibly move anchored to a mobile computational device, (b) the behaviour of standard Linda coordination primitives is extended so as to depend on the spatial properties of the coordinating agents, tuples, and the topology of space, and (c) the tuple space can be conceived as a virtual layer augmenting physical reality. Motivated by the needs of mobile augmented-reality applications, Spatial Tuples explicitly aims at supporting space-aware and space-based coordination in agent-based pervasive computing scenarios. This paper presents the coordination model, its formalization as a process algebra, a library of patterns of coordination it enables, and a discussion of application scenarios, challenges, and open issues for future works.
Multilevel Code-block Group based HARQ-ACK Control Channel Design

Code-block group based HARQ has been agreed in 5G new radio, which requires multiple bits as HARQ-ACK payload to indicate the reception status of code-block group based HARQ-ACK. In order to reduce a large amount of HARQ-ACK payload commonly considering bitmapping fashion, a novel HARQ-ACK feedback scheme associated with multilevel code-block group concept is proposed. This new feedback scheme is able to optimize the performance by balancing the HARQ-ACK payload and the retransmission burden. Based on the numerical analysis, we observe that given a HARQ-ACK payload, the proposed HARQ-ACK feedback system guarantees the minimum retransmission rate and always achieves the outstanding performance as opposed to the benchmark system with bitmapping HARQ-ACK feedback. In addition, the new feedback system does not cost any additional system complexity.
Fuzzy logic for decision extraction from product reviews

Most of manufacturers and merchants sell their products via internet websites where customers buy them and write their reviews regarding the purchased product. These reviews carry a lot of information. Prospective customers are heavily interested in the experience of the predecessor customers where enormous number of reviews are offered by the website per product. In this study, given a dataset of reviews, we give a solution to estimate a total score of sentiment for the product reviewed. For this purpose, fuzzy logic is used to process the subjective comments and to obtain a reliable average estimation for the decision from those individual reviews. The method is executed over different models to search the effects of the frequency of features on the outcome.
Community pharmacist‐led interventions and their impact on patients’ medication adherence and other health outcomes: a systematic review

Medication adherence can be defined as the extent to which one’s medication‐taking behaviour follows that mutually agreed upon by the prescribing physician. Optimal medication adherence is often deemed crucial for the success of a patient's treatment, as suboptimal adherence may lead to treatment failure and unnecessary medical expenditure. Increasing evidence has highlighted the positive contribution community pharmacist‐led interventions can have on improving patients’ adherence and health outcomes.
Impact of type of intracranial EEG sensors on link strengths of evolving functional brain networks

Objective and Approach: Investigating properties of evolving functional brain networks has become a valuable tool to characterize the complex dynamics of the epileptic brain. Such networks are usually derived from electroencephalograms (EEG) recorded with sensors implanted chronically into deeper structures of the brain and/or placed onto the cortex. It is still unclear, however, whether the use of different sensors for an identification of network nodes affects properties of functional brain networks. We address this question by investigating properties of links of such networks that we characterize by assessing interactions in multi-sensor, multi-day EEG data recorded from 49 epilepsy patients during presurgical evaluation. These data allow us to study the impact of different types of sensors together with the impact of various physiologic and pathophysiologic activities on the properties of links. Main results: We observe that different types of sensors differently impact on spatial means and temporal fluctuations of link strengths. Moreover, the impact depends on the relative anatomical location of sensors with respect to location and extent of sources of the prevailing activities. Significance: Type and location of sensors should be considered when constructing networks.
Leaf recognition using multi-layer perceptron

In this work, we employ multi-layer feed forward network algorithm for leaf recognition using its shape, color and vein features. The leaf images are pre-processed and segmented. The shape features like area, convex area, diameter, length, width, perimeter, eccentricity, solidity, major axis length and Minor axis length are extracted by taking the features from the segmented leaf image. The mean, standard deviation, skewness and kurtosis for red, green and blue color features are extracted. Wiener filtering and canny edge detection are used to identify the vein features v1, v2, v3, v4 at different thresholds by calculating gray level value from the gray level histogram. The features are then exposed to Principal Component Analysis (PCA) for feature reduction and the resultant reduced feature set contains five shape features, six color features and three vein features. The features are then taken as input vectors for multi-layer neural network. Total 150 leaves from Columbia leaf image database are taken as samples. 150 leaves represent 10 different leaf species. The system was trained with 104 leaf images and was tested and validated using 23 leaf images each. The success rate of reduced feature set of leaf shapes and color was 84.7% and by combining leaf shape, color and vein features outputted a success rate of 95.3%.
Ultra-Rapid serial visual presentation reveals dynamics of feedforward and feedback processes in the ventral visual pathway

Human visual recognition activates a dense network of overlapping feedforward and recurrent neuronal processes, making it hard to disentangle processing in the feedforward from the feedback direction. Here, we used ultra-rapid serial visual presentation to suppress sustained activity that blurs the boundaries of processing steps, enabling us to resolve two distinct stages of processing with MEG multivariate pattern classification. The first processing stage was the rapid activation cascade of the bottom-up sweep, which terminated early as visual stimuli were presented at progressively faster rates. The second stage was the emergence of categorical information with peak latency that shifted later in time with progressively faster stimulus presentations, indexing time-consuming recurrent processing. Using MEG-fMRI fusion with representational similarity, we localized recurrent signals in early visual cortex. Together, our findings segregated an initial bottom-up sweep from subsequent feedback processing, and revealed the neural signature of increased recurrent processing demands for challenging viewing conditions.
Role of MxB in Alpha Interferon-Mediated Inhibition of HIV-1 Infection

The results of this study reconcile the controversial reports regarding the anti-HIV-1 function of alpha interferon-induced MxB protein. In addition to the different cell types that may have contributed to the different observations, our data also suggest that VSV G protein-pseudotyped HIV-1 is much less inhibited by alpha interferon-induced MxB than HIV-1 itself is. Our results clearly demonstrate an important contribution of MxB to alpha interferon-mediated inhibition of HIV-1 in CD4+ T cells, which calls for using HIV-1 target cells and wild-type virus to test the relevance of the anti-HIV-1 activity of endogenous MxB and other restriction factors. ABSTRACT Type I interferon inhibits viruses through inducing the expression of antiviral proteins, including the myxovirus resistance (Mx) proteins. Compared to the human MxA protein, which inhibits a wide range of viruses, the MxB protein has been reported to specifically inhibit primate lentiviruses, including HIV-1, and herpesviruses. Further, the role of endogenous MxB in alpha interferon-mediated inhibition of HIV-1 infection was questioned by a recent study showing that MxB knockout did not increase the level of infection by HIV-1 which carried the G protein of vesicular stomatitis virus (VSV), allowing infection of CD4-negative HT1080 cells. In order to further examine the anti-HIV-1 activity of endogenous MxB, we have used CRISPR/Cas9 to deplete MxB in different cell lines and observed a substantial restoration of HIV-1 infection in the presence of alpha interferon treatment. However, this rescue effect of MxB knockout became much less pronounced when infection was performed with HIV-1 carrying the VSV G protein. Interestingly, a CRISPR/Cas9 knockout screen of alpha interferon-stimulated genes in U87-MG cells revealed that the genes for interferon-induced transmembrane protein 2 (IFITM2) and IFITM3 inhibited VSV G-pseudotyped HIV-1 much more strongly than the rest of the genes tested, including the gene for MxB. Therefore, our results demonstrate the importance of MxB in alpha interferon-mediated inhibition of HIV-1 infection, which, however, can be underestimated if infection is performed with VSV G protein-pseudotyped HIV-1, due to the high sensitivity of VSV G-mediated infection to inhibition by IFITM proteins. IMPORTANCE The results of this study reconcile the controversial reports regarding the anti-HIV-1 function of alpha interferon-induced MxB protein. In addition to the different cell types that may have contributed to the different observations, our data also suggest that VSV G protein-pseudotyped HIV-1 is much less inhibited by alpha interferon-induced MxB than HIV-1 itself is. Our results clearly demonstrate an important contribution of MxB to alpha interferon-mediated inhibition of HIV-1 in CD4+ T cells, which calls for using HIV-1 target cells and wild-type virus to test the relevance of the anti-HIV-1 activity of endogenous MxB and other restriction factors.
Food restriction promotes damage reduction in rat models of type 2 diabetes mellitus

There are several animal models of type 2 diabetes mellitus induction but the comparison between models is scarce. Food restriction generates benefits, such as reducing oxidative stress, but there are few studies on its effects on diabetes. The objective of this study is to evaluate the differences in physiological and biochemical parameters between diabetes models and their responses to food restriction. For this, 30 male Wistar rats were distributed in 3 groups (n = 10/group): control (C); diabetes with streptozotocin and cafeteria-style diet (DE); and diabetes with streptozotocin and nicotinamide (DN), all treated for two months (pre-food restriction period). Then, the 3 groups were subdivided into 6, generating the groups CC (control), CCR (control+food restriction), DEC (diabetic+standard diet), DER (diabetic+food restriction), DNC (diabetic+standard diet) and DNR (diabetic+food restriction), treated for an additional two months (food restriction period). The food restriction (FR) used was 50% of the average daily dietary intake of group C. Throughout the treatment, physiological and biochemical parameters were evaluated. At the end of the treatment, serum biochemical parameters, oxidative stress and insulin were evaluated. Both diabetic models produced hyperglycemia, polyphagia, polydipsia, insulin resistance, high fructosamine, hepatic damage and reduced insulin, although only DE presented human diabetes-like alterations, such as dyslipidemia and neuropathy symptoms. Both DEC and DNC diabetic groups presented higher levels of protein carbonyl groups associated to lower antioxidant capacity in the plasma. FR promoted improvement of glycemia in DNR, lipid profile in DER, and insulin resistance and hepatic damage in both diabetes models. FR also reduced the protein carbonyl groups of both DER and DNR diabetic groups, but the antioxidant capacity was improved only in the plasma of DER group. It is concluded that FR is beneficial for diabetes but should be used in conjunction with other therapies.
GrCAN: Gradient Boost Convolutional Autoencoder with Neural Decision Forest

Random forest and deep neural network are two schools of effective classification methods in machine learning. While the random forest is robust irrespective of the data domain, the deep neural network has advantages in handling high dimensional data. In view that a differentiable neural decision forest can be added to the neural network to fully exploit the benefits of both models, in our work, we further combine convolutional autoencoder with neural decision forest, where autoencoder has its advantages in finding the hidden representations of the input data. We develop a gradient boost module and embed it into the proposed convolutional autoencoder with neural decision forest to improve the performance. The idea of gradient boost is to learn and use the residual in the prediction. In addition, we design a structure to learn the parameters of the neural decision forest and gradient boost module at contiguous steps. The extensive experiments on several public datasets demonstrate that our proposed model achieves good efficiency and prediction performance compared with a series of baseline methods.
A methodology for creating reusable ontologies

Applications within the Internet of Things (IoT) increasingly build upon Semantic Web technologies to ensure interoperability of devices and applications. Hereby, a central aspect is knowledge representation, and in particular how devices can store and exchange semantic data. This paper defines and discusses a methodology for creating application-specific ontologies for the IoT. The two main objectives are to reuse existing knowledge on one hand and to derive reusable ontologies on the other hand. Thereby, a multi-agent system for switching optimization in the smart energy domain serves as a motivating example.
Maximum clearance rapid motion planning algorithm

SUMMARY This paper proposes a new path-planning algorithm which is close to the family of bug algorithms. Path planning is one of the challenging problems in the area of service robotics. In practical applications, traditional methods have some limitations with respect to cost, efficiency, security, flexibility, portability, etc. Our proposed algorithm offers a computationally inexpensive goal-oriented strategy by following a smooth and short trajectory. The paper also presents comparisons with other algorithms. In addition, the paper also presents a test bed which is created to test the algorithm. We have used a two-wheeled differential drive robot for the navigation and only a single camera is used as a feedback sensor. Using an extended Kalman filter, we localize the robot efficiently in the map. Furthermore, we compare the actual path, predicted path and planned path to check the effectiveness of the control system.
RELIABILITY, VALIDITY AND FACTOR STRUCTURE OF THE COMPULSIVE ACQUISITION SCALE (CAS)

Objective: Much research and theorizing suggest that compulsive acquisition is a central component of hoarding disease. The Compulsive Acquisition Scale (CAS; Frost et al. 2002) is a self-rating scale measuring the extent to which individuals acquire and feel compelled to acquire possessions. The present study aimed to evaluate the psychometric properties of the CAS, in terms of reliability and construct validity. Method: The scale was administered to a sample of 491 non-clinical adults along with a battery of selected self-report measures. Participants were randomly divided into two subsamples. Both exploratory and confirmatory factor analyses were performed. Support for construct validity was provided by correlations between CAS and the other administered measures. Results: Unlike the originally suggested 2-factor structure, both exploratory and confirmatory factor analyses revealed an underlying 3-factor structure (Emotions, Time and Money, and Free Stuff). Our outcomes showed good subscales homogeneity; support for concurrent validity was provided by significantly positively correlations between CAS scores and the other measures of hoarding severity; convergent validity was gathered examining correlations between CAS and measures of depression, anxiety, and obsessive compulsive disorder. Conclusions: Overall, empirical results indicated promising psychometric properties of the CAS Italian version in a nonclinical sample. Limitations of the study and the need for further research are discussed.
Fault tolerance performance of self-stabilizing independent set algorithms on a covering-based problem: The case of link monitoring in WSNs

Vertex cover (VC) is one of the most fundamental graph-theoretical problems and has been widely used in wireless sensor networks (WSNs), particularly for the link monitoring problem. It is well known that a solution to the independent set problem (IS), which is another fundamental graph-theoretical problem, is complement of a VC. Self- stabilization is an important concept for designing fault tolerance systems. There have been many self-stabilizing VC and IS algorithms in the field. Even though a self-stabilizing IS algorithm can provide VC solutions, it does not give a theoretical guarantee on approximation ratio. In this work, we focus on practical fault tolerance performance of self- stabilizing IS algorithms in case of a vertex cover problem, particularly link monitoring in WSNs. We implement all existing self-stabilizing VC and IS algorithms and make simulations assuming a WSN in which nodes run synchronously. Results show that self-stabilizing IS algorithms in general are able to find better covers than VC algorithms, as they provide roughly 15% smaller solution sets. Furthermore, IS algorithms that run under distributed scheduler converges to a desired configuration in considerably less number of rounds than VC algorithms.
Video QoE Prediction Based on User Profile

The increasing popularity of online video content and adaptive video streaming services, especially ones based on HTTP Adaptive Streaming, highlights the need for streaming optimization solutions. Predicting end users Quality of Experience (QoE) by using machine learning algorithms, may allow content servers to allocate bandwidth smartly and more efficiently. In this work, we present a new user quality of experience prediction algorithm which extracts features based on user traffic pattern parameters such as bit-rate, resolution, frame rate, etc. In order to optimize the features set and the corresponding machine learning algorithms, we have used three different feature selection algorithms and six different classifiers. We show that the Decision Tree algorithm achieved 86% accuracy in predicting the user quality of experience.
Automatic calibration of multiple cameras on a nonplanar ground for visual surveillance systems

Multiple camera based surveillance systems provide us with a more robust tracking of objects. To take advantage of additional cameras, it is necessary to establish geometrical relationship between the cameras and relationship between an object and a camera. In recent years several techniques have been proposed, which estimate only the relation of a dominant ground plane between different views instead of fully geometrical relation of camera-object-camera. They are however neither fully automatic nor suitable for a non-planar ground. We propose a fully automatic calibration algorithm which can cope with complex environment, including non-planar ground. The proposed algorithm automatically tracks and matches objects between different views, determines the overlapped region, and aligns each piece-wisely segmented plane between two views. The proposed calibration algorithm minimizes the effect of occlusion and improves the accuracy of 3D measurements by using multiple views. The algorithm can also provide us large field of view by concatenating a series of cameras.
Heel-strike and toe-off motions optimization for humanoid robots equipped with active toe joints

SUMMARY In this paper, a walking pattern optimization procedure is implemented to yield the optimal heel-strike and toe-off motions for different goal functions. To this end, first, a full dynamic model of a humanoid robot equipped with active toe joints is developed. This model consists of two parts: multi-body dynamics of the robot which is obtained by Lagrange and Kane methods and power transmission dynamic model which is developed using system identification approach. Then, a gait planning routine is presented and consistent parameters are specified. Several simulations and experimental tests are carried out on SURENA III humanoid robot which is designed and fabricated at the Center of Advanced Systems and Technologies located in the University of Tehran. Afterward, a genetic algorithm optimization is adopted to compute the optimal walking patterns for five different goal functions including energy consumption, stability margin, joint velocity, joint torque and required friction coefficient. Also, several parametric analyses are performed to characterize the effects of heel-strike and toe-off angle and toe link mass and length on these five goal functions. Finally, it is concluded that walking pattern without heel-strike and toe-off motions requires less friction coefficient than the pattern with heel-strike and toe-off motions. Also, heavier toe link lowers tip-over instability and slippage occurrence possibility, but requires more energy consumption and joint torque.
Performance Engineering in Distributed Event-sourced Systems

Distributed event-sourced systems adopt a fairly new architectural style for data-intensive applications that maintains the full history of the application state. However, the performance implications of such systems are not yet well explored, let alone how the performance of these systems can be improved. A central issue is the lack of systematic performance engineering approaches that take into account the specific characteristics of these systems. To address this problem, we suggest a methodology for performance engineering and performance analysis of distributed event-sourced systems based on specific measurements and subsequent, targeted optimizations. The methodology blends in well into existing software engineering processes and helps developers to identify bottlenecks and to resolve performance issues. Using our structured approach, we improved an existing event-sourced system prototype and increased its performance considerably.