Miracle-dz/UniFAR-FTUs huggingface dataset:
Measuring the human psychophysiological conditions without contact

Heart Rate Variability, HRV, studies the variations of cardiac rhythm caused by the autonomic regulation. HRV analysis can be applied to the study of the effects of mental or physical stressors on the psychophysiological conditions. The present work is a pilot study performed on a 23-year-old healthy subject. The measurement of HRV was performed by means of two sensors, that is an electrocardiograph and a Laser Doppler Vibrometer, which is a non-contact device able to detect the skin vibrations related to the cardiac activity. The present study aims to evaluate the effects of a physical task on HRV parameters (in both time and frequency domain), and consequently on the autonomic regulation, and the capability of Laser Doppler Vibrometry in correctly detecting the effects of stress on the Heart Variability. The results show a significant reduction of HRV parameters caused by the execution of the physical task (i.e. variations of 25-40% for parameters in time domain, also higher in frequency domain); this is consistent with the fact that stress causes a reduced capability of the organism in varying the Heart Rate (and, consequently, a limited HRV). LDV was able to correctly detect this phenomenon in the time domain, while the parameters in the frequency domain show significant deviations with respect to the gold standard technique (i.e. ECG). This may be due to the movement artefacts that have consistently modified the shape of the vibration signal measured by means of LDV, after having performed the physical task. In the future, in order to avoid this drawback, the LDV technique could be used to evaluate the effects of a mental task on HRV signals (i.e. the evaluation of mental stress).
Balancing reality with hope in epilepsy therapy

When a person receives an initial diagnosis of epilepsy, the first question asked of the physician is “Why? ” But the next question is “Can the seizures be controlled? ” Our ability to answer that question is inadequate, even though the question of prognosis is implicit in every subsequent clinical interaction. At every visit, we as clinicians project the hope that our next maneuver will be the magic bullet. In this issue of Neurology®, Hughes et al.,1 analyzing the outcomes of 1,752 participants in the Standard and New Antiepileptic Drug (SANAD) study of new-onset epilepsy in the United Kingdom,2 report a new algorithm for the prediction of seizure control. Recognizing that prognosis is a moving target, they have developed a method for assessing it at each visit. They report that as time goes by in each person's course, it is possible to be more and more accurate in forecasting the patient’s condition. This sounds like a truism for any disease, but the course of epilepsy varies so much between individuals that quantitative data are especially welcome.
Transgenerational Epigenetic Inheritance Is Negatively Regulated by the HERI-1 Chromodomain Protein

Transgenerational epigenetic inheritance (TEI) is the inheritance of epigenetic information for two or more generations. In most cases, TEI is limited to a small number of generations (two to three). The short-term nature of TEI could be set by innate biochemical limitations to TEI or by genetically encoded systems that actively limit TEI. In Caenorhabditis elegans, double-stranded RNA (dsRNA)-mediated gene silencing [RNAi (RNA interference)] can be inherited (termed RNAi inheritance or RNA-directed TEI). To identify systems that might actively limit RNA-directed TEI, we conducted a forward genetic screen for factors whose mutation enhanced RNAi inheritance. This screen identified the gene heritable enhancer of RNAi (heri-1), whose mutation causes RNAi inheritance to last longer (> 20 generations) than normal. heri-1 encodes a protein with a chromodomain, and a kinase homology domain that is expressed in germ cells and localizes to nuclei. In C. elegans, a nuclear branch of the RNAi pathway [termed the nuclear RNAi or NRDE (nuclear RNA defective) pathway] promotes RNAi inheritance. We find that heri-1(−) animals have defects in spermatogenesis that are suppressible by mutations in the nuclear RNAi Argonaute (Ago) HRDE-1, suggesting that HERI-1 might normally act in sperm progenitor cells to limit nuclear RNAi and/or RNAi inheritance. Consistent with this idea, we find that the NRDE nuclear RNAi pathway is hyperresponsive to experimental RNAi treatments in heri-1 mutant animals. Interestingly, HERI-1 binds to genes targeted by RNAi, suggesting that HERI-1 may have a direct role in limiting nuclear RNAi and, therefore, RNAi inheritance. Finally, the recruitment of HERI-1 to chromatin depends upon the same factors that drive cotranscriptional gene silencing, suggesting that the generational perdurance of RNAi inheritance in C. elegans may be set by competing pro- and antisilencing outputs of the nuclear RNAi machinery.
Incidental discovery of Amyand’s hernia

A 58-year-old woman was referred to Hospital Tuanku Ja’afar (Seremban, Malaysia) for contrast-enhanced CT abdomen and pelvis following discovery of a solid mass in the right adnexa on ultrasonography. CT findings revealed a well-defined, fat-containing mass with calcifications in the right adnexa measuring 4.5×7.8×4 cm, consistent with ovarian teratoma (figure 1). Incidentally, the vermiform appendix appeared to be elongated and extended into the right inguinal canal. It was not enlarged, and there was no surrounding fat streakiness (figures 1 and 2). A diagnosis of Amyand’s hernia was made. She was advised for conservative (non-surgical) treatment as she was asymptomatic for both ovarian mass and Amyand’s hernia. She was then given appointment for ultrasound follow-up. Figure 1 Contrast-enhanced CT abdomen and pelvis in axial sections showing the appendix (arrows) extending into the right inguinal canal. Right ovarian teratoma is also …
PVA/Chitosan/Silver Nanoparticles Electrospun Nanocomposites: Molecular Relaxations Investigated by Modern Broadband Dielectric Spectroscopy

In this study, we used broadband dielectric spectroscopy to analyze polymer nanofibers of poly(vinyl alcohol)/chitosan/silver nanoparticles. We also studied the effect of incorporating silver nanoparticles in the polymeric mat, on the chain motion dynamics and their interactions with chitosan nanofibers, and we calculated the activation energies of the sub-Tg relaxation processes. Results revealed the existence of two sub-Tg relaxations, the first gets activated at very low temperature (−90 °C) and accounts for motions of the side groups within the repeating unit such as –NH2, –OH, and –CH2OH in chitosan and poly(vinyl alcohol). The second process gets activated around −10 °C and it is thought to be related to the local main chain segments’ motions that are facilitated by fluctuations within the glycosidic bonds of chitosan. The activation energy for the chitosan/PVA/AgNPs nanocomposite nanofibers is much higher than that of the chitosan control film due to the presence of strong interactions between the amine groups and the silver nanoparticles. Kramers–Krönig integral transformation of the ε′′ vs. f spectra in the region of the chitosan Tg helped resolve this relaxation and displayed the progress of its maxima with increasing temperature in the regular manner.
Distribuição de sedimentos costeiros em ambiente de baixa energia Enseadas de Arraial do Cabo, Rio de Janeiro, Brasil

O presente trabalho discute temas relacionados a dinâmica de praias de enseada e de ambientes de baixa energia no que diz respeito o estoque e a distribuicao das caracteristicas de sedimentos de fundo. O objetivo central e caracterizar o deposito costeiro por meio de analises em laboratorio e elaboracao de mapas de distribuicao. Foram selecionadas tres enseadas no municipio de Arraial do Cabo (RJ). As etapas metodologicas partiram da definicao de uma malha amostral de 96 amostras coletadas desde a praia emersa ate profundidades de 12 metros, seguido por analises em laboratorio atraves do metodo de peneiramento a seco e metodo otico. Os resultados indicam a ocorrencia de depositos majoritariamente de areias finas e medias moderadamente selecionadas. Cabe ainda a discussao inicial sobre a origem dos depositos siltosos encontrados em maiores profundidades.
Fusion Research of Visible and Infrared Images Based on IHS Transform and Regional Variance Wavelet Transform

Aiming at the problem of low contrast between target and background in fusion of infrared and visible images, a fusion method of infrared and visible images based on IHS transform and wavelet region transformation is proposed. The first of the visible light IHS transform the luminance component of infrared image gray processing, then the luminance component of visible light and infrared image of three level wavelet transform to obtain high frequency component and low frequency component respectively, the low frequency sub-band coefficients fusion rules comparing the area difference, and then calculate the weighted wavelet coefficients; in the high frequency sub-band coefficients using regional variance adaptive fusion rules. The experimental results show that compared with other algorithms, the fusion image obtained by this method enhances the contrast between the thermal target and the background, and keeps the details of the background, which is more consistent with human vision perception.
Automated Test Case Generation Based on Differential Evolution With Relationship Matrix for iFogSim Toolkit

Fog computing plays an important role in industrial and information process. The programs in fog computing, such as iFogSim toolkit, usually contain some infeasible paths (paths that cannot be covered), which makes it impossible to compare algorithm in models that require covering all paths. In this paper, we proposed a mathematical model to build automated test case generation based on path coverage (ATCG-PC) in fog computing programs as a single-objective problem. Single objective helps to reduce the cost of evaluation functions, which is proportional to the number of test cases. When infeasible paths are contained in tested programs, algorithms can also be compared in this model. In this paper, classical differential evolution (DE) is used to solve the ATCG-PC. However, it is difficult for DE to use generated test cases covering remaining paths in the ATCG-PC of fog computing. Therefore, we proposed a test-case-path relationship matrix to empower DE (RP-DE). Experiment results show that RP-DE uses significantly less test cases and achieves higher path coverage rate than compared state-of-the-art algorithms.
Analyzing Employee Voice Using Real-Time Feedback

People nowadays tend to use social media as a platform to share their reviews, emotions, and opinions, including about their jobs. Thus, a lot of data is available on the web. Therefore, a rapid response is needed to analyze and interpret the data. Unfortunately, many organizations still use annual surveys to assess satisfaction, engagement, and culture in the workplace. Compared to other conventional datasets such as company survey and questionnaire, decision-makers could make decision effectively and efficiently by using the interpreted data. This may be done with the help of sentiment analysis method. In this research, we classify the feedback based on its category and sentiment. Several classification algorithms are used in opinion mining; two of them are Naive Bayes Classifier (NBC) and Support Vector Machine (SVM). This paper aims to classify feedback based on sentiments using NBC and SVM.
Malware Propagations in Wireless Ad Hoc Networks

Accurate malware propagation modeling in wireless ad hoc networks (WANETs) represents a fundamental and open research issue which shows distinguished challenges due to complicated access competition, severe channel interference, and dynamic connectivity. As an effort towards the issue, in this paper, we investigate the malware propagation under two spread schemes including Unicast and Broadcast, in Spread Mode and Communication Mode, respectively. We highlight our contributions in three-fold in the light of previous literature works. First, a bound of malware infection rate for each scheme is provided by applying the wireless network capacity theories. Second, the impact of mobility on malware propagations has been studied. Third, discussion of the relationship between different schemes and practical applications is provided. Numerical simulations and detailed performance analysis show that the Broadcast Scheme with Spread Mode is most dangerous in the sense of malware propagation speed in WANETs, and mobility will greatly increase the risk further. The results achieved in this paper not only provide insights on the malware propagation characteristics in WANETs, but also serve as fundamental guidelines on designing defense schemes.
A Hybrid Model for Music Genre Classification Using LSTM and SVM

With today's cutting edge technology and intractable access to voluminous data files via the internet, it is important to meet the computational needs of every user. Machine learning is one such growing branch of artificial intelligence that has made such demands of the users viable. Machine learning models are paving the way for classification techniques such as in music genre classification, and have shown to be efficient in predicting classes to a great extent. To exploit the time dependent nature of the dataset Long Short-Term Memory (LSTM) Neural Network is used for music genre classification and combined with Support Vector Machine (SVM) classifier to enhance its performance. The hybrid model of these two classifiers resulted into an increase in the accuracy of prediction of the individual models. This hybrid model is imposed on GTZAN music dataset and is compared with the results of standalone models of LSTM and SVM. The proposed model exceeded the independent accuracies of the LSTM and SVM classifiers with an accuracy of 89%, reaffirming the efficient utilization of each classifier.
FP-AK-QIEA-ℝ seed to help the convergence of PECO

FP-AK-QIEA-ℝ has been used in Benchmark Functions, Protein Folding Problem and multi-objective problems. PECO was a proposed algorithm with good performance in Protein Folding Problem. The objective of this paper is perform experiments to improve the performance of PECO using FP-AK-QIEA-ℝ to initialize PECO. After plotting the best of ever, a meaningful change on the convergence is observed showing the modification of the convergence and after plotting the number of habitats is noticed a reduction of the number of habitats. In conclusion, adding FP-AK-QIEA-R solution improves the performance of PECO.
Early Miocene CO2 estimates from a Neotropical fossil leaf assemblage exceed 400 ppm.

PREMISE OF THE STUDY The global climate during the early Miocene was warmer than the present and preceded the even warmer middle Miocene climatic optimum. The paleo-CO2 records for this interval suggest paradoxically low concentrations (<450 ppm) that are difficult to reconcile with a warmer-than-present global climate. METHODS In this study, we use a leaf gas-exchange model to estimate CO2 concentrations using stomatal characteristics of fossil leaves from a late early Miocene Neotropical assemblage from Panama that we date to 18.01 ± 0.17 Ma via 238 U/206 Pb zircon geochronology. We first validated the model for Neotropical environments by estimating CO2 from canopy leaves of 21 extant species in a natural Panamanian forest and from leaves of seven Neotropical species in greenhouse experiments at 400 and 700 ppm. KEY RESULTS The results showed that the most probable combined CO2 estimate from the natural forests and 400 ppm experiments is 475 ppm, and for the 700 ppm experiments is 665 ppm. CO2 estimates from the five fossil species exhibit bimodality, with two species most consistent with a low mode (528 ppm) and three with a high mode (912 ppm). CONCLUSIONS Despite uncertainties, it is very likely (at >95% confidence) that CO2 during the late early Miocene exceeded 400 ppm. These results revise upwards the likely CO2 concentration at this time, more in keeping with a CO2 -forced greenhouse climate.
Automated Testing for Cyber-physical Systems: From Scenarios to Executable Tests

This work is part of an M&SBSE (Modeling and Simulation-Based Systems Engineering) initiative for cyber-physical systems. The design and the validation of such systems, in which the operations of physical entities are controlled by computing cores, are particularly challenging since they require the cooperation of many disciplines. To address the verification challenge, we have previously proposed a requirement driven testing approach. In that context, we present here the specification and the implementation of a method that automatically transforms models of test scenarios originating from requirements into automated tests, thus driving the verification process from early design phases to simulation, and ultimately to the validation of the final product.
Visualizing the Indicators of Diabetic Retinopathy Learnt by Convolutional Neural Networks

This study proposes a novel application of visualizing features learnt by convolutional neural networks with the aim to further the understanding of Diabetic Retinopathy. A convolutional neural network is first trained to recognize and classify fundus images of diabetic and non-diabetic patients. The network is then visualized, using a technique of pixel optimization, to discover the features that the trained network looks for to classify the image. Through this novel application of network visualization, we show that critical features for diabetic retinopathy can be re-discovered, leaving great scope for its application in scarcely explored diseases using minimal resources.
Review of Millimeter-Wave CMOS Power Amplifiers

This paper reviews the current status of millimeter-wave CMOS power amplifiers. The basic power amplifier architectures will be presented, together with the power combining techniques for higher output power amplifiers. State-of-the-art performances of CMOS power amplifiers, including power gain, output power, and power added efficiency, will be summarized along with various frequencies in millimeter-wave regime. Due to the requirement of modern wireless communication applications for high data rate transmission, the linearity becomes crucial for power amplifier in digital communication systems. Moreover, the efficiency at output power back-off operation is also important, especially for the mobile digital communication devices. The reported linearization techniques in millimeter-wave CMOS power amplifiers will also be presented, with the illustration of design examples.
Performance Analysis of Waveguide BPF Loaded with Dielectric Frequency Selective Structure

The performance analysis of waveguide bandpass filter (BPF) loaded with dielectric frequency selective surface is presented. The proposed filter is constructed by use of a rectangular waveguide encapsulates 2 layers of FR4 epoxy dielectric substrate which are flanking rectangular strip lines yielding a dielectric frequency selective structure. A WR284 type rectangular waveguide designed to work on the TE10 mode is employed for the filter implementation. The performance of filter focused on its bandwidth response and $S$-parameters, i.e. $S_{11}$ and $S_{21}$, is analyzed through simulation by investigating the parameters of rectangular strip lines such as the length of strip line, the separation between strip lines, and the number of strip lines. As an experimental characterization, a waveguide BPF is realized with a dielectric frequency selective structure comprising 3 rectangular strip lines resulting in the filter performance comparable to the design result.
Demand Side Management in Microgrids

India is a developing nation with growing energy demand. With the limitations in monetary resources, increasing the generation capacity should not be the only way to achieve this target. Demand Side Management (DSM) is one such viable solution to this problem. Management of power from the demand side is DSM. The recent trend to use distributed generation like Solar Photovoltaics and Wind turbines has led to increasing usage of Microgrids nowadays. DSM can be applied to such Microgrids for their better and optimal performance and thus manage power to reduce investment. The applications of different objectives of DSM to microgrid are discussed in this paper. One such microgrid is taken as a case study. Application of DSM to this case study is discussed. The techno-economic analysis of the proposed case study is verified with the load flow analysis employing Newton Trust Region Method and DSM indices.
Hybrid, Multi-Source, and Integrated Energy Harvesters

Energy harvesting is one of the most rapidly growing of the emerging technologies. This field has arrived at the hybrid and multi-source era, where hybrid structures and novel materials are able to boost the energy conversion efficiency and/or make the harvesters capable of benefitting from multiple energy sources simultaneously. Such hybrid and multi-source energy harvesters have not frequently been reviewed in the past, potentially because of the small number of publications compared to that of their single-source and individual counterparts. However, as their number is becoming larger, it is now necessary to give sufficient and frequent reviews of developments in the field. Furthermore, an increasing number of developed energy harvesters are moving out of the laboratory into industrial markets. In practice, energy harvesters need to be integrated with energy storage and/or end users such as sensors and wireless sensor networks. Therefore, the harvester-storage and harvester-sensor integration systems also need to be reviewed frequently. This mini-review includes works reported in the first half of 2018 and provides a timely update to the published review. It focuses on the above-mentioned hybrid and multi-source energy harvesters as well as on integrated harvesters, energy storage systems and end users (e.g. sensors), including CMOS (complementary metal-oxide-semiconductor) technology-based harvesters and systems.
Woven Antennas Fabrication: Conductive Linen and Conductive Cloth Technologies Comparisons

The development of woven antennas will enable novel applications in communications, sensing and safety, due to its inherent features such flexibility and textile-integration. For these reasons, this technology has attracted the attention of academic and industrial communities. However, there are still some factors hindering its extensive use. The aim of this paper is to compare woven antennas using two different technologies exploring the reasons that hinder its application. The technologies tested are: conductive linen and conductive cloth. The woven antennas are designed using a microstrip-design approach and simulations as starting base-line. In both linen and cloth technologies, the manufactured antennas observe variability in its return loss caused by bending (flexiblility) of the antenna body that also contributes to an imperfect connection with feeding cables. The manufactured conductive cloth antennas observe similar results as its corresponding microstrip pairs. On the other hand, conductive linen antennas yield larger bandwidth than expected and more resonances of |S11|. Thus, they can be potential candidates for applications with multi-band specifications.