Miracle-dz/UniFAR-FTUs huggingface dataset:
Spectrum Management for Proactive Video Caching in Information-Centric Cognitive Radio Networks

To deal with the rapid growth of mobile data traffic and the user interest shift from peer-to-peer communications to content dissemination-based services, such as video streaming, information-centric networking has emerged as a promising architecture and has been increasingly used for wireless and mobile networks. In this paper, we focus on video dissemination in information-centric cognitive radio networks (IC-CRNs) and investigate the use of harvested bands for proactively caching video contents at the locations close to the interested users to improve the performance of video distribution. With consideration of the dynamic and unobservable nature of some parameters, we formulate the allocation of harvested bands as a Markov decision process with hidden and dynamic parameters and transform it into a partially observable Markov decision process and a multi-armed bandit formulation. Based on them, we develop a new spectrum management mechanism, which maximizes the benefit of proactive video caching as well as the efficiency of spectrum utilization in the IC-CRNs. Extensive simulation results demonstrate the significant performance improvement of the proposed scheme for video streaming.
Sparse Recursive Least Mean p-Power Extreme Learning Machine for Regression

Real industrial processes usually are equipped with onboard control or diagnostic systems and limit to store a complicated model. Also, measurement samples from real processes are contaminated with noises of different statistical characteristics and are produced by one-by-one way. In this case, learning algorithms with better learning performance and compact model for systems with noises of various statistics are necessary. This paper proposes a new online extreme learning machine (ELM) algorithm, namely, sparse recursive least mean p-power ELM (SRLMP-ELM). In SRLMP-ELM, a novel cost function, i.e., the sparse least mean p-power (SLMP) error criterion, provides a mechanism to update the output weights sequentially and automatically tune some parameters of the output weights to zeros. The SLMP error criterion aims to minimize the combination of the mean p-power of the errors and a sparsity penalty constraint of the output weights. For real industrial system requirements, the proposed on-line learning algorithm is able to provide more higher accuracy, compact model, and better generalization ability than ELM and online sequential ELM, whereas the non-Gaussian noises impact the processes, especially impulsive noises. Simulations are reported to demonstrate the performance and effectiveness of the proposed methods.
New cascaded multilevel converters based on switched-diode six-level configuration

Multilevel converters have been considered due to several advantages such as high power quality, low switching losses, high efficiency, among others. This paper proposes two new cascaded power converter topologies which are based on five-level sub-module architecture. Both structures are analyzed in symmetrical and asymmetrical operation modes, showing advantages in terms of number of voltage levels, number of semiconductor and driver devices, reducing the area and weight of the converter. The performance of the proposed configurations is verified by considering PSCAD/EMTDC simulation environment.
Remote heart rate variability for emotional state monitoring

Several researches have been conducted to recognize emotions using various modalities such as facial expressions, gestures, speech or physiological signals. Among all these modalities, physiological signals are especially interesting because they are mainly controlled by the autonomic nervous system. It has been shown for example that there is an undeniable relationship between emotional state and Heart Rate Variability (HRV). In this paper, we present a methodology to monitor emotional state from physiological signals acquired remotely. The method is based on a remote photoplethysmography (rPPG) algorithm that estimates remote Heart Rate Variability (rHRV) using a simple camera. We first show that the rHRV signal can be estimated with a high accuracy (more than 96% in frequency domain). Then, frequency-feature of rHRV is calculated and we show that there is a strong correlation between the rHRV feature and different emotional states. This observation has been validated on 12 out of 16 volunteers and video-induced emotions which opens the way to contactless monitoring of emotions from physiological signals.
A Beam Selection Algorithm for Millimeter-Wave Multi-User MIMO Systems

Millimeter wave (mmWave) frequencies offer huge transmission bandwidths and allow packing a large number of antennas within a given aperture area, enabling high-dimensional MIMO communications. A major bottleneck in realizing such systems is the requirement of a large number of RF chains. Working in the <italic>beamspace</italic> domain provides an attractive solution through <italic>beam selection</italic>. We propose a novel beam selection algorithm for downlink mmWave multi-user MIMO systems that selects <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> beams for <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> users. The proposed method attempts to maximize the sum-rate and nulls-out the multi-user interference. We show, through simulations, that the proposed method outperforms the existing ones.
Cardiac Autonomic Neuropathy as a Result of Mild Hypercaloric Challenge in Absence of Signs of Diabetes: Modulation by Antidiabetic Drugs

Cardiac autonomic neuropathy (CAN) is an early cardiovascular complication of diabetes occurring before signs of metabolic derangement are evident. The cause of CAN remains elusive and cannot be directly linked to hyperglycemia since pre‐diabetic and metabolic syndrome patients demonstrate signs of cardiac autonomic dysfunction. Recent clinical data report cardioprotective effects of some anti‐diabetic drugs independent of their hypoglycemic action. Significantly, pioglitazone and metformin demonstrated protection in pre‐diabetic patients. Here, we used a rat model receiving limited daily increase in calories from fat (HC diet) to assess whether mild metabolic challenge led to CAN in absence of interfering effects of hyperglycemia, glucose intolerance, or obesity. Compared to controls, rats receiving HC diet for 12 weeks showed reduction in baroreceptor sensitivity and heart rate variability despite lack of change in baseline hemodynamic and cardiovascular structural parameters. Impairment of cardiac autonomic control was accompanied with perivascular adipose inflammation observed as an increased interleukin‐1b (IL‐1b) and tumor necrosis factor‐a (TNF‐a) expression, together with increased cardiac oxidative stress, IL‐1b expression and signaling derangements characteristic of diabetic cardiomyopathy including reduction of AMPK phosphorylation and activation of the TGF‐b/Erk1/2 pathway. At this stage, examination of the brainstem revealed no molecular disorders in support of an early, potentially reversible effect. Two‐week treatment with metformin or pioglitazone rectified the autonomic derangement and corrected the molecular changes in heart and adipose tissue. Switching rats on HC diet to normal chow but not to isocaloric amounts of HC for two weeks reversed CAN. As such, we conclude that adipose inflammation due to increased fat intake might underlie development of CAN and, hence, the beneficial effects of metformin and pioglitazone.
Notice of RetractionResource Acquisition Self-Efficacy, New Venture Creation Activities and Entrepreneurial Success: An Empirical Study

More than half of all attempts to start a business fail. In a quest to help explain the variance in this phenomenon, we develop and test hypotheses related to the role that the resource acquisition self-efficacy, entrepreneurial activities, and start-up success in the new venture creation process. Utilizing data from the three areas of China, we find that resource acquisition self-efficacy is significantly associated with start-up activity. Further, we found that a higher number of start-up activities increased the likelihood of successful new venture creation. Finally, limitation and implications are discussed.
k-NN Graph Construction: a Generic Online Approach

Nearest neighbor search and k-nearest neighbor graph construction are two fundamental issues arise from many disciplines such as information retrieval, data-mining, machine learning and computer vision. Despite continuous efforts have been taken in the last several decades, these two issues remain challenging. They become more and more imminent given the big data emerges in various fields and has been expanded significantly over the years. In this paper, a simple but effective solution both for k-nearest neighbor search and k-nearest neighbor graph construction is presented. Namely, these two issues are addressed jointly. On one hand, the k-nearest neighbor graph construction is treated as a nearest neighbor search task. Each data sample along with its k-nearest neighbors are joined into the k-nearest neighbor graph by sequentially performing the nearest neighbor search on the graph under construction. On the other hand, the built k-nearest neighbor graph is used to support k-nearest neighbor search. Since the graph is built online, dynamic updating of the graph, which is not desirable from most of the existing solutions, is supported. Moreover, this solution is feasible for various distance measures. Its effectiveness both as a k-nearest neighbor construction and k-nearest neighbor search approach is verified across various datasets in different scales, various dimensions and under different metrics.
The Role of Myeloid Lineage Cells on Skin Healing and Skin Regeneration

Skin healing a complex and well-orchestrated process that involves the coordination and activity of many cell types. Myeloid lineage cells are inflammatory cells recruited to the wound site that remove injured tissue and invading pathogens. Besides this role, due to their ability to secrete a variety of growth factors and cytokines, myeloid cells influence each stage of wound healing (primarily inflammation and proliferation phases). Abnormalities in myeloid cell function lead to pathologies such as excessive and deficient healing. Therapies based on modulating myeloid cells may hold therapeutic potential. However, further research is needed to fully elucidate the spatial and temporal mechanisms of myeloid cells in skin healing. The objective of this review is to discuss recent findings on the role of myeloid lineage cells in skin healing and regeneration.
FORMULATION AND EVALUATION OF FIXED DOSE COMBINATION PRODUCTS OF ANTITUBERCULAR DRUG

Objective of the present study is to formulate and evaluate fixed dose combination tablets of Rifampicin and Isoniazid. The granules of Rifampicin and Isoniazid were prepared separately by wet granulation, then lubricated blend was compressed using tablet compression machine by setting the machine speed in the range 12- 28 RPM with desired processing parameters such as average weight, uniformity of weight, thickness, hardness & friability. These tablets were evaluated for the various physical parameters like appearance, weight variation, hardness, friability and disintegration. Coating was done in Ganscoater coating pan in two lots. Dissolution studies FDC tablets were performed along with uncoated and film coated tablets separately. The cumulative percentage drug release for coated tablet was found to be around 96%. From in vitro studies it was concluded that the prepared tablet gives more satisfactory results and patient compliance.
Evaluating Mental Health in Cuban Refugees: The Role of the Refugee Health Screener-15

The Refugee Health Screener-15 (RHS-15) is widely used in refugee populations, but the psychometric properties and clinical utility have not been evaluated in Cuban refugees and entrants. The current study explored results from the Spanish version of the RHS-15 in a sample of 53 Cuban entrants and refugees, and of these, 17.6% screened positive for a mental health concern. Analyses suggested that a positive screening was signiﬁcantly related to symptoms of anxiety, depression, and posttraumatic stress disorder; however, it was not associated with demographic variables such as gender, mode of transport, or the number of countries through which a refugee traveled before arriving in the United States. A factor analysis of the measure revealed a 4-factor solution is most appropriate when used with Cuban individuals. Results provide preliminary evidence regarding the measure’s psychometric properties, and demonstrate that the Spanish version of the RHS-15 is an appropriate screening tool for the mental health of newly arrived Cuban entrants and refugees. Future studies should further validate the Spanish version RHS-15 in Cuban entrants and refugees, and explore its efﬁcacy amongst Latino refugees of other nationalities.
Biogenic selenium nanoparticles synthesized by Stenotrophomonas maltophilia SeITE02 loose antibacterial and antibiofilm efficacy as a result of the progressive alteration of their organic coating layer

Increasing emergence of drug‐resistant microorganisms poses a great concern to clinicians; thus, new active products are urgently required to treat a number of infectious disease cases. Different metallic and metalloid nanoparticles have so far been reported as possessing antimicrobial properties and proposed as a possible alternative therapy against resistant pathogenic microorganisms. In this study, selenium nanoparticles (SeNPs) synthesized by the environmental bacterial isolate Stenotrophomonas maltophilia SeITE02 were shown to exert a clear antimicrobial and antibiofilm activity against different pathogenic bacteria, either reference strains or clinical isolates. Antimicrobial and antibiofilm capacity seems to be strictly linked to the organic cap surrounding biogenic nanoparticles, although the actual role played by this coating layer in the biocidal action remains still undefined. Nevertheless, evidence has been gained that the progressive loss in protein and carbohydrate content of the organic cap determines a decrease in nanoparticle stability. This leads to an alteration of size and electrical properties of SeNPs along with a gradual attenuation of their antibacterial efficacy. Denaturation of the coating layer was proved even to have a negative effect on the antibiofilm activity of these nanoparticles. The pronounced antimicrobial efficacy of biogenic SeNPs compared to the denatured ones can – in first instance – be associated with their smaller dimensions. This study showed that the native organic coating layer of biogenic SeNPs functions in avoiding aggregation and maintaining electrostatic stability of the nanoparticles, thus allowing them to maintain efficient antimicrobial and antibiofilm capabilities.
A Comparison of Planning Based Models for Component Reconfiguration ; CU-CS-995-05

Dynamic Reconfiguration is the process of changing a component-based system with minimum disruption and maximum automation. This process consists of three phases i.e. sensing the present state, planning for a new state and acting on the system to move it to the new state. Although, there are techniques and models for the sensing and acting phases, planning is a relatively neglected phase in this process. We have been using planning for the dynamic reconfiguration of component-based systems. However, planning systems provide different modeling languages. These languages have their own characteristics. Not all of these characteristics are important for the dynamic reconfiguration of software systems. In this paper we compare and evaluate the strengths and weaknesses of two planning languages: PDDL (Planning Domain and Definition Language) and AML (Aspen Modeling Language). Although the original target of both of these languages is not dynamic reconfiguration, we will discuss how their features can be used to model planning the dynamic reconfiguration of component based systems.
Evaluation of predictive data mining algorithms in soil data classification for optimized crop recommendation

Agricultural research has strengthened the optimized economical profit, internationally and is very vast and important field to gain more benefits. However, it can be enhanced by the use of different technological resources, tool, and procedures. Today, the term data mining [1][2] is an interdisciplinary process of analyzing, processing and evaluating the real-world datasets and prediction on the basis of the findings. Our case-based analysis provides empirical evidence that we can use different data mining classification algorithms to classify the dataset of agricultural regions on the basis of soil properties. Additionally, we have investigated the most performing algorithm having powerful prediction accuracy to recommend the best crop for better yield.
Microblogging User Tag Prediction Based on Bayesian

In the social network, to users, the tag is an important basis to mark and classify the resource. The tag of microblogging users can be used for advertising and network marketing. This paper presents a method based on naive Bayesian to predict the user tag. We use the user's basic attributes and some popular public tags as the features in Bayesian to predict whether a public tag belongs to a user. The experimental results show that the proposed method can achieve 87% accuracy.
Radiolological predictors of recurrence of chronic subdural hematoma

Objective: Chronic subdural hematoma is one of the most common clinical entities encountered in daily neurosurgical practice. Considerable recurrence rates have been reported for chronic subdural hematoma following surgical evacuation. Many studies have suggested various radiological factors that may be associated with the recurrence of CSDH. However, the results are inconsistent. This study focuses on determining the radiological factors predictive of chronic subdural hematoma recurrence. Methods: A retrospective analysis of 113 patients diagnosed with chronic subdural hematoma who were surgically treated between August 2013 and December 2014 was performed. The radiological features were analyzed to clarify the correlation between these radiological factors and postoperative recurrence of chronic subdural hematoma. Results: Twenty patients (17.7%) experienced recurrence. Chronic subdural hematoma recurrence was found to be significantly associated (p<0.05) with preoperative hematoma thickness ≥ 20 mm. Midline shift, hematoma density and bilaterality were not significantly associated with recurrence. Post operative drainage also significantly (p<0.05) reduced chronic subdural hematoma recurrence. Conclusion: Preoperative hematoma thickness ≥ 20 mm is an independent predictor of recurrence of chronic subdural hematoma. Postoperative drainage also significantly reduces chronic subdural hematoma recurrence.
Chapter 2 A Framework for Teachable Collaborative Problem Solving Skills

In his book “Cognition in the Wild”, Hutchins ( 1995 ) invites his readers to scan their immediate environment for objects that were not produced through collaborative efforts of several people, and remarks that the only object in his personal environment that passed this test was a small pebble on his desk. In fact, it is remarkable how our daily lives are shaped by collaboration. Whether it is in schools, at the workplace, or in our free time, we are constantly embedded in environments that require us to make use of social skills in order to coordinate with other people. Given the pervasiveness of collaboration in everyday life, it is somewhat surprising that the development of social and collaborative skills is largely regarded as something that will occur naturally and does not require any further facilitation. In fact, groups often fail to make use of their potential (Schulz-Hardt, Brodbeck, Group performance and leadership. In: Hewstone M, Stroebe W, Jonas K (eds) Introduction to social psychology: a European perspective, 4th edn, pp 264–289. Blackwell, Oxford, 2008 ) and people differ in the extent to which they are capable of collaborating effi ciently with others. Therefore, there is a growing awareness that collaborative skills require dedicated teaching efforts (Schoenfeld, Looking toward the 21st century: challenges of educational theory and practice. Edu Res 28:4–14, 1999 ). Collaborative problem solving has been identifi ed as a particularly promising task that draws upon various social and cognitive skills, and that can be analysed in classroom environments where skills are both measurable and teachable. This chapter provides a conceptual framework of collaborative problem solving that is informed by fi ndings from fi elds of research as diverse as cognitive science, education, social psychology and psycholinguistics. F. Hesse (*) • J. Buder • K. Sassenberg Knowledge Media Research Center , Tübingen , Germany e-mail: fh@iwm-kmrc.de E. Care • P. Griffi n Assessment Research Centre, Melbourne Graduate School of Education , University of Melbourne , Parkville , VIC , Australia e-mail: e.care@unimelb.edu. au; p.griffi n@unimelb.edu. au
Revenue Maximization in Time-Varying Multi-Hop Wireless Networks: A Dynamic Pricing Approach

In this paper, we study a wireless multi-hop network where multiple flows co-exist and share the network resource collectively. Each flow is associated with a user which has specific requirements on its tradeoff between cost and quality of service. To support heterogeneous transmissions efficiently, we propose a quality-aware dynamic pricing algorithm, namely, QADP, which provably maximizes the overall network revenue while maintaining the stability of the network. Our proposed scheme enjoys the merit of self-adaptability due to its online nature.
Power and sample-size analysis for the Royston–Parmar combined test in clinical trials with a time-to-event outcome

Randomized controlled trials with a time-to-event outcome are usually designed and analyzed assuming proportional hazards (PH) of the treatment effect. The sample-size calculation is based on a log-rank test or the nearly identical Cox test, henceforth called the Cox/log-rank test. Nonproportional hazards (non-PH) has become more common in trials and is recognized as a potential threat to interpreting the trial treatment effect and the power of the log-rank test—hence to the success of the trial. To address the issue, in 2016, Royston and Parmar (BMC Medical Research Methodology 16: 16) proposed a “combined test” of the global null hypothesis of identical survival curves in each trial arm. The Cox/log-rank test is combined with a new test derived from the maximal standardized difference in restricted mean survival time (RMST) between the trial arms. The test statistic is based on evaluations of the between-arm difference in RMST over several preselected time points. The combined test involves the minimum p-value across the Cox/log-rank and RMST-based tests, appropriately standardized to have the correct distribution under the global null hypothesis. In this article, I introduce a new command, power_ct, that uses simulation to implement power and sample-size calculations for the combined test. power_ct supports designs with PH or non-PH of the treatment effect. I provide examples in which the power of the combined test is compared with that of the Cox/log-rank test under PH and non-PH scenarios. I conclude by offering guidance for sample-size calculations in time-to-event trials to allow for possible non-PH.