Miracle-dz/UniFAR-FTUs huggingface dataset:
Song Playlist Generator System Based on Facial Expression and Song Mood

Because of the hectic pace that people have nowadays, life is incredibly hectic. People are increasingly inclined to listen to music while performing their daily duties which help them relax after a stressful day. As a result, songs become important part of daily lifestyle. Due to the huge demand several music players have entered to the market and try to attempt to deliver the best possible music recommendation for the customer. This paper proposes a Deep Learning based approach for the playlist generation based on human current mood with the help of user’s past history of song selection. In this approach we are trying to generate playlist from the emotion of the user to add touch of current situation of user mood and user personal choices of the songs for providing more personalized experience. After introduction of the Convolutional Neural Network object detection, Image classification, Emotion detection tasks reaches great height. In the proposed method, we use convolution neural network (CNN) for emotion detection task and artificial neural network (ANN) for the song classification task. Experiment result says that our suggested model achieve 84% accuracy on FER-13 dataset which contain around 14k facial images. For song classification task we have used different song-features which is extracted from Spotify music player. We have achieved 82% accuracy in song classification task. Currently this system is only with Spotify music player. Motivation of this approach is to provide better song recommended playlist based on user current mood.
Optical gain and large-signal characteristics of illuminated GaAs MESFET's

The illuminated large-signal characteristics of a uniformly doped epitaxial GaAs MESFET have been measured when optically gated with an He-Ne laser source. The dependence of the measured optical gains on the applied electrical biases and optical intensity, position, and spot size indicate that the dominant dc gain mechanisms are transit time photoconductivity combined with an effective photovoltaic change in the pinchoff voltage. The photovoltaic change in the pinchoff voltage dominates for high optical power levels, while the photoconductive response dominates for low power levels. Optical gains in the range of 5-10 were measured for the photoconductive regime, while gains in the range of 50-70 were measured for the photovoltaic regime.
The Dependence of Ultrasonic Velocity in Ultra-Low Expansion Glass on Temperature

The coefficient of thermal expansion (CTE) is an important property of ultra-low expansion (ULE) glass, and the ultrasonic velocity method has shown excellent performance for the nondestructive measurement of CTE in large ULE glass. In this method, the accurate acquisition of the ultrasonic velocity in ULE glass is necessary. Herein, we present a correlation method to determine the ultrasonic TOF in ULE glass and to further obtain the ultrasonic longitudinal wave velocity (cL) indirectly. The performance of this method was verified by simulations. Considering the dependence of cL on temperature (T), we carried out the derivation of the analytical model between cL and T. Based on reasonable constant assumptions in the physical sense, a cL–T exponential model was produced, and some experimental results support this model. Additional experiments were carried out to validate the accuracy of the cL–T exponential model. The studies we conducted indicate that the cL–T exponential model can reliably predict the ultrasonic velocity in ULE glass at different temperatures, providing a means for the nondestructive CTE measurement of large ULE glass at a specified temperature.
Assimilation of GNSS PWV with NCAR-RTFDDA to Improve Prediction of a Landfall Typhoon

Precipitable water vapor (PWV) retrieved from ground-based global navigation satellite system (GNSS) stations acquisition signal of a navigation satellite system provides high spatial and temporal resolution atmospheric water vapor. In this paper, an observation-nudging-based real-time four-dimensional data assimilation (RTFDDA) approach was used to assimilate the PWV estimated from GNSS observation into the WRF (Weather Research and Forecasting) modeling system. A landfall typhoon, “Mangkhut”, is chosen to evaluate the impact of GNSS PWV data assimilation on its track, intensity, and precipitation prediction. The results show that RTFDDA can assimilate GNSS PWV data into WRF to improve the water vapor distribution associated with the typhoon. Assimilating the GNSS PWV improved the typhoon track and intensity prediction when and after the typhoon made landfall, correcting a 5–10 hPa overestimation (too deep) of the central pressure of the typhoon at landfall. It also improved the occurrence and the intensity of the major typhoon spiral rainbands.
Mycotoxin Interactions along the Gastrointestinal Tract: In Vitro Semi-Dynamic Digestion and Static Colonic Fermentation of a Contaminated Meal

Aflatoxin B1 (AFB1) and ochratoxin A (OTA) naturally co-occur in several foods, but no studies have followed the fate of mycotoxins’ interactions along the gastrointestinal tract using in vitro digestion models. This study used a novel semi-dynamic model that mimics gradual acidification and gastric emptying, coupled with a static colonic fermentation phase, in order to monitor mycotoxins’ bioaccessibility by the oral route. AFB1 and OTA bioaccessibility patterns differed in single or co-exposed scenarios. When co-exposed (MIX meal), AFB1 bioaccessibility at the intestinal level increased by ~16%, while OTA bioaccessibility decreased by ~20%. Additionally, a significant increase was observed in both intestinal cell viability and NO production. With regard to mycotoxin–probiotic interactions, the MIX meal showed a null effect on Lactobacillus and Bifidobacterium strain growth, while isolated AFB1 reduced bacterial growth parameters. These results were confirmed at phylum and family levels using a gut microbiota approach. After colonic fermentation, the fecal supernatant did not trigger the NF-kB activation pathway, indicating reduced toxicity of mycotoxins. In conclusion, if single exposed, AFB1 will have a significant impact on intestinal viability and probiotic growth, while OTA will mostly trigger NO production; in a co-exposure situation, both intestinal viability and inflammation will be affected, but the impact on probiotic growth will be neglected.
The Effect of Excited Species on the Collisional Energy of Argon Inductively Coupled Plasmas: A Global Model Study

Global modeling of inductively coupled plasma (ICP) reactors is a powerful tool to investigate plasma parameters. In this article, the argon ICP global model is revisited to explore the effect of excited species on collisional energy through the study of different approaches to particle and energy balance equations. The collisional energy loss is much more sensitive to modifications in the balance equations than the electron temperature. According to the simulations, the multistep ionization reduces the collisional energy loss in all investigated reaction sets and the inclusion of heavy species reactions has negligible influence. The plasma parameters obtained, such as total energy loss and electron temperature, were compared with experimental results from the literature. The simulated cases that have more excited species and reactions in the energy balance are in better agreement with the experimental measurements.
Classification of Mediterranean Shrub Species from UAV Point Clouds

Modelling fire behaviour in forest fires is based on meteorological, topographical, and vegetation data, including species’ type. To accurately parameterise these models, an inventory of the area of analysis with the maximum spatial and temporal resolution is required. This study investigated the use of UAV-based digital aerial photogrammetry (UAV-DAP) point clouds to classify tree and shrub species in Mediterranean forests, and this information is key for the correct generation of wildfire models. In July 2020, two test sites located in the Natural Park of Sierra Calderona (eastern Spain) were analysed, registering 1036 vegetation individuals as reference data, corresponding to 11 shrub and one tree species. Meanwhile, photogrammetric flights were carried out over the test sites, using a UAV DJI Inspire 2 equipped with a Micasense RedEdge multispectral camera. Geometrical, spectral, and neighbour-based features were obtained from the resulting point cloud generated. Using these features, points belonging to tree and shrub species were classified using several machine learning methods, i.e., Decision Trees, Extra Trees, Gradient Boosting, Random Forest, and MultiLayer Perceptron. The best results were obtained using Gradient Boosting, with a mean cross-validation accuracy of 81.7% and 91.5% for test sites 1 and 2, respectively. Once the best classifier was selected, classified points were clustered based on their geometry and tested with evaluation data, and overall accuracies of 81.9% and 96.4% were obtained for test sites 1 and 2, respectively. Results showed that the use of UAV-DAP allows the classification of Mediterranean tree and shrub species. This technique opens a wide range of possibilities, including the identification of species as a first step for further extraction of structure and fuel variables as input for wildfire behaviour models.
Job Insecurity and Employees’ Taking Charge Behaviors: Testing a Moderated Mediation Model

Given the rapid changes in current technologies, business models, and work environments, organizations and managers increasingly rely on their employees’ proactive behaviors, such as taking charge, to gain competitive advantages. Taking charge involves a range of risky and future-oriented behaviors, and it requires employees to work hard to achieve them in the future. For employees with high job-insecurity, their job continuity in the future is threatened. Thus, they may not be willing to take risks to do additional work that is “future-oriented”. To our knowledge, the effect of job insecurity on employees’ taking charge has rarely been studied. As a result, the purpose of our study is to investigate whether, how, and when job insecurity will influence taking charge. Drawing on the conservation of resources theory and proactive motivation model, we develop a theoretical model. Moreover, we employed a multi-wave and multi-source survey to test our predictions. Based on the data from 194 full-time employees paired with their direct supervisors, the results provided consistent support for the proposed hypotheses. Specifically, the results indicate that job insecurity prohibits employees’ taking charge behaviors through deteriorating their work engagement. Furthermore, employees’ perception of interactional justice moderates the negative influence of job insecurity on their work engagement and, consequently, their taking charge behaviors. Theoretical and practical implications are discussed.
Contactless Shaft Torque Detection for Wide Range Performance Measurement of Exhaust Gas Turbocharger Turbines

Turbochargers develop away from an auxiliary component — being “off the shelve” — towards an integrated component of the internal combustion engine. Hence, increased attention is paid to the accuracy of the measured turbine and compressor maps. Especially turbine efficiency measurement under engine-relevant operating conditions (pulsed flow) is recently receiving increased attention in the respective research community. Despite various turbine map extrapolation methods, sufficient accuracy of the input test data is indispensable. Accurate experimental data are necessary to achieve high quality extrapolation results, enabling a wide range and precise prediction of turbine behavior under unsteady flow conditions, determined by intermittent operation of the internal combustion engine. The present work describes the first application of a contactless shaft torque measurement technique — based on magnetostriction — to a small automotive turbocharger. The contactless torque measuring system is presented in detail and sensor principle as well as sensor calibration are illustrated. A sensitivity study regarding sensor position influences onto sensor signal proves the robustness and very good repeatability of the system. In the second part of the paper, steady state experimental results from operation on a conventional hot gas test stand over a wide map range are presented. These results are validated against full turbine stage (adiabatic as well as diabatic) CFD results as well as against “cold” efficiency measurements, based on measured inlet and outlet temperatures. The influence and relevance of bearing friction for such measurements is underlined and the improvements on this matter — achieved by direct torque measurement — are demonstrated.
Validation of an Embedded Motion-Capture and EMG Setup for the Analysis of Musculoskeletal Disorder Risks during Manhole Cover Handling

Musculoskeletal disorders in the workplace are a growing problem in Europe. The measurement of these disorders in a working environment presents multiple limitations concerning equipment and measurement reliability. The aim of this study was to evaluate the use of inertial measurement units against a reference system for their use in the workplace. Ten healthy volunteers conducted three lifting methods (snatching, pushing, and pulling) for manhole cover using a custom-made tool weighting 20 and 30 kg. Participants’ back and dominant arm were equipped with IMU, EMG, and reflective markers for VICON analysis and perception of effort was estimated at each trial using a Visual Analog Scale (VAS). The Bland–Altman method was used and results showed good agreement between IMU and VICON systems for Yaw, Pitch and Roll angles (bias values < 1, −4.4 < LOA < 3.6°). EMG results were compared to VAS results and results showed that both are a valuable means to assess efforts during tasks. This study therefore validates the use of inertial measurement units (IMU) for motion capture and its combination with electromyography (EMG) and a Visual Analogic Scale (VAS) to assess effort for use in real work situations.
Spatiotemporal coupling investigations for Ti:sapphire-based multi-PW lasers

Abstract Emerging multi-PW-class lasers and their envisioned laser–plasma interaction applications in unprecedented intensity regimes set a very demanding frame for the precise understanding of the finest properties of these systems. In this work we present a synthesis of simulation studies on a series of less known or even completely disregarded spatiotemporal effects that could potentially impact greatly the performances of high-intensity lasers.
Omega-6 and Omega-3 Fatty Acid-Derived Oxylipins from the Lipoxygenase Pathway in Maternal and Umbilical Cord Plasma at Delivery and Their Relationship with Infant Growth

Omega-3 and omega-6 fatty acids are important for neonatal development and health. One mechanism by which omega-3 and omega-6 fatty acids exert their effects is through their metabolism into oxylipins and specialized pro-resolving mediators. However, the influence of oxylipins on fetal growth is not well understood. Therefore, the objective of this study was to identify oxylipins present in maternal and umbilical cord plasma and investigate their relationship with infant growth. Liquid chromatography–tandem mass spectrometry was used to quantify oxylipin levels in plasma collected at the time of delivery. Spearman’s correlations highlighted significant correlations between metabolite levels and infant growth. They were then adjusted for maternal obesity (normal body mass index (BMI: ≤30 kg/m2) vs. obese BMI (>30 kg/m2) and smoking status (never vs. current/former smoker) using linear regression modeling. A p-value < 0.05 was considered statistically significant. Our study demonstrated a diverse panel of oxylipins from the lipoxygenase pathway present at the time of delivery. In addition, both omega-3 and omega-6 oxylipins demonstrated potential influences on the birth length and weight percentiles. The oxylipins present during pregnancy may influence fetal growth and development, suggesting potential metabolites to be used as biomarkers for infant outcomes.
Nonmyopic Informative Path Planning Based on Global Kriging Variance Minimization

Remote, hazardous, and extreme exploration missions require robots to be equipped with onboard sensors for rich and heterogeneous information during deployment. In such tasks, path planning can directly affect the quality and quantity of the observations obtained under temporal and energetic constraints. While most informative path planners can only plan for a short horizon ahead of time (referred as to myopic planners), we propose a novel planner that is capable of planning global paths that have the suboptimal exploration efficiency guarantee over a nonmyopic planning horizon. To achieve this, a novel sampling algorithm named MPE is proposed to adaptively sample landmarks that are associated with high information capacity, in order to minimize the global Kriging variance. The traverse path for the landmarks is then obtained by the IPP-MPE algorithm for minimizing the overall traveling cost. The algorithm is flexible enough to be applied to various information acquisition tasks. The tractable computational cost allows the horizon to be long enough for scene coverage. The algorithm was deployed on a real robot for accomplishing tactile based object searching tasks, which shows superior efficiency compared to the myopic planner baseline. Last, the complexity and other theoretical analysis of the algorithm is provided.
Drowsiness Classification for Internal Driving Situation Awareness on Mobile Platform

the sleeping driver is potentially more likely to cause an accident than the person who speeds up since the driver is the victim of sleepiness. Automobile industry researchers, including manufacturers, seek to solve this issue with various technical solutions that can avoid such a situation. This paper proposes an implementation of a lightweight method to detect driver's sleepiness using facial landmarks and head pose estimation based on neural network methodologies on a mobile device. We try to improve the accurateness by using face images that the camera detects and passes to CNN to identify sleepiness. Firstly, applied a behavioral landmark's sleepiness detection process. Then, an integrated Head Pose Estimation technique will strengthen the system's reliability. The preliminary findings of the tests demonstrate that with real-time capability, more than 86% identification accuracy can be reached in several real-world scenarios for all classes, including with glasses, without glasses, and light-dark background. This work aims to classify drowsiness, warn, and inform drivers, helping them to stop falling asleep at the wheel. The integrated CNN-based method is used to create a high accuracy and simple-to-use real-time driver drowsiness monitoring framework for embedded devices and Android phones
Design of a High-voltage Successive Approximation Register Analog-to-Digital Converter

A high-voltage successive approximation register (SAR) analog-to-digital converter (ADC) with the switched-capacitor step-down technique is presented. Considering the input voltage range over supply voltage, charge redistribution architecture and capacitor switching technique are adopted to shrink the high voltage input signals to lower voltage domain of SAR ADC. Meanwhile, the partial thermometer code is adopted in the DAC capacitor array to improve the linearity. The SAR ADC with a maximum input range of 20 V is realized in 180 nm high-voltage mixed-signal CMOS technology. The post-layout simulation results show that the prototype ADC achieves SNDR, SNR, and SFDR of 73.30 dB (11.88 bit), 77.18 dB and 75.67 dB, respectively, at the sampling rate of 625 KS/s. The chip occupies 1.68*0.8 mm2 and consumes 16.3 mW.
Molecular Investigation of miRNA Biomarkers as Chemoresistance Regulators in Melanoma: A Protocol for Systematic Review and Meta-Analysis

Introduction: Melanoma is a global disease that is predominant in Western countries. However, reliable data resources and comprehensive studies on the theragnostic efficiency of miRNAs in melanoma are scarce. Hence, a decisive study or comprehensive review is required to collate the evidence for profiling miRNAs as a theragnostic marker. This protocol details a comprehensive systematic review and meta-analysis on the impact of miRNAs on chemoresistance and their association with theragnosis in melanoma. Methods and analysis: The articles will be retrieved from online bibliographic databases, including Cochrane Review, EMBASE, MEDLINE, PubMed, Scopus, Science Direct, and Web of Science, with different permutations of ‘keywords’. To obtain full-text papers of relevant research, a stated search method will be used, along with selection criteria. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis for Protocols 2015 (PRISMA-P) standards were used to create this study protocol. The hazard ratio (HR) with a 95% confidence interval will be analyzed using Comprehensive Meta-Analysis (CMA) software 3.0. (CI). The pooled effect size will be calculated using a random or fixed-effects meta-analysis model. Cochran’s Q test and the I2 statistic will be used to determine heterogeneity. Egger’s bias indicator test, Orwin’s and the classic fail-safe N tests, the Begg and Mazumdar rank collection test, and Duval and Tweedie’s trim and fill calculation will all be used to determine publication bias. The overall standard deviation will be evaluated using Z-statistics. Subgroup analyses will be performed according to the melanoma participants’ clinicopathological and biological characteristics and methodological factors if sufficient studies and retrieved data are identified and available. The source of heterogeneity will be assessed using a meta-regression analysis. A pairwise matrix could be developed using either a pairwise correlation or expression associations of miRNA with patients’ survival for the same studies.
Regulation of Clostridium tetani Neurotoxin Expression by Culture Conditions

Background: Ensuring consistency of tetanus neurotoxin (TeNT) production by Clostridium tetani could help to ensure consistent product quality in tetanus vaccine manufacturing, ultimately contributing to reduced animal testing. The aim of this study was to identify RNA signatures related to consistent TeNT production using standard and non-standard culture conditions. Methods: We applied RNA sequencing (RNA-Seq) to study C. tetani gene expression in small-scale batches under several culture conditions. Results: We identified 1381 time-dependent differentially expressed genes (DEGs) reflecting, among others, changes in growth rate and metabolism. Comparing non-standard versus standard culture conditions identified 82 condition-dependent DEGs, most of which were specific for one condition. The tetanus neurotoxin gene (tetX) was highly expressed but showed expression changes over time and between culture conditions. The tetX gene showed significant down-regulation at higher pH levels (pH 7.8), which was confirmed by the quantification data obtained with the recently validated targeted LC-MS/MS approach. Conclusions: Non-standard culture conditions lead to different gene expression responses. The tetX gene appears to be the best transcriptional biomarker for monitoring TeNT production as part of batch-to-batch consistency testing during tetanus vaccine manufacturing.