Miracle-dz/UniFAR-FTUs huggingface dataset:
Time evolution of entanglement entropy in holographic FLRW cosmologies

To understand the time-dependent quantum correlation in expanding universes, we study the time-dependent entanglement entropy in the braneworld model. If we take into account a generalized string cloud geometry caused by uniformly distributed open strings, cosmologies on the braneworld result in the standard Friedmann-Lemaitre-Robertson-Walker cosmologies with various matter contents. On the dual field theory side, open strings are reinterpreted as a fundamental matter, while the black hole mass corresponds to the excitation energy of massless gauge bosons. In this work, we show how the string cloud geometry is matched to various braneworld cosmologie,s for example, eternal inflation, radiation-, and matter-dominated universes. Then, we investigate how the entanglement entropy evolves in those expanding universes.
Thermodynamic equilibrium of massless fermions with vorticity, chirality and magnetic field

The present thesis aimed to examine the effects of vorticity on the thermodynamics of relativistic quantum systems. We extend the Zubarev's non-equilibrium statistical operator method to address quantum effects induced by vorticity in the presence of chiral matter and external electromagnetic field and keeping full covariant and quantum properties of the system. To investigate the effects of vorticity, this work has been focused on systems consisting of massless chiral fermions. We recovered the significant quantum phenomena known in the literature, namely the chiral magnetic effect, the chiral vortical effect, the axial vortical effect and the chiral separation effect and we also revealed the presence of additional effects at second-order on thermal vorticity. This study has also identified and presented the exact solutions of thermal states for a system at global thermal equilibrium consisting of chiral massless fermions under the action of an external constant homogeneous magnetic field. Taking advantage of these exact solutions and conservation equations, the study also proved that the thermal coefficients related to first-order effects on thermal vorticity do not receive corrections from the external electromagnetic field. The same argument revealed existing relations between those thermal coefficients, even connecting coefficients related to vorticity to other related to electromagnetic field. For instance, this analysis has found that the chiral vortical effect and the chiral magnetic effect conductivities are connected one to the other by a differential equation. Therefore, this research provides the first steps into deriving the relations between the effects and the interplay of electromagnetic fields and vorticity.
Gravitational waves from the fragmentation of axion-like particle dark matter

The misalignment mechanism allows for the efficient, and usually very cold, production of light scalar bosons, such as axion-like particles (ALPs), making them an appealing dark matter candidate. However, in certain cases, such as in the presence of a monodromy, the self-interactions of ALPs can be sufficiently strong such that the homogeneous field fragments soon after the onset of oscillations. The resulting large inhomogeneities can lead to the production of gravitational waves (GWs). We investigate the nonlinear dynamics of fragmentation, as well as of the subsequent turbulent regime, and calculate the stochastic GW background that is produced from this process. The GW background can be enhanced if the time evolution features an extended intermediate phase of ultrarelativistic dynamics due to a small mass at the bottom of the potential. Yet, this enhancement is limited by the requirement that the dark matter remains sufficiently cold. In some cases the resulting GWs may be within reach of future GW detectors, allowing a complementary probe of this type of dark matter.
Clareamento dentário durante tratamento ortodôntico

Objetivo: foi revisar a literatura atualmente disponívelsobre clareamento dentário durante o tratamento ortodôntico. Revisão de literatura: uma pesquisa bibliográficaexploratória foi realizada na base de dados eletrônicaPublic Medline (PubMed) empregando os termos de busca“dental bleaching” ou “tooth whitening” e “orthodontics”. Os critérios de inclusão foram: artigos de pesquisasoriginais e ensaios clínicos, publicados em português,espanhol ou inglês. Não foram aplicados limites à data depublicação. A busca resultou em um total de três estudos,publicados em 2011, 2014 e 2016. Há evidência de estudosclínicos que ressaltam a eficácia do clareamento dentárioem pacientes que usam aparelho ortodôntico. Outro estudomostra diferenças significativas entre a cor geral do dente ea da superfície abaixo dos braquetes ortodônticos. Consideraçõesfinais: há necessidade de estudos com metodologiasacuradas, a fim de que se possam fornecer dados maisprecisos para alicerçar recomendações clínicas, técnicas emateriais, quando se considera essa abordagem de tratamento. Palavras-chave: Clareamento dental. Estética dentária. Ortodontia.
Effects of preservation duration at 4 °C on the quality of RNA in rabbit blood specimens

A prolonged preservation duration of blood specimens at 4 °C may occur due to the distance from collection points to storage facilities in many biobanks, especially for multicenter studies. This could lead to RNA degradation, affecting downstream analyses. However, effects of preservation durations at 4 °C on RNA quality in blood specimens need to be studied. We collected rabbit blood using EDTA tubes and stored them at 4 °C for different preservation durations. Then, we examined the quality of RNA from whole blood and leukocytes isolated from rabbit blood. Our results show that the purity of whole blood RNA and leukocyte RNA does not indicate significant change after rabbit blood is stored at 4 °C for different preservation durations (from 1 h to 7 days). The integrity of leukocyte RNA indicates the same result as above, but the integrity of whole blood RNA is significantly decreased after rabbit blood is stored at 4 °C for over 3 days. Moreover, expression of SMAD7, MKI67, FOS, TGFβ1 and HIF1α of whole blood RNA and leukocyte RNA remains basically stable, but PCNA expression of whole blood RNA or leukocyte RNA is significantly decreased after rabbit blood is stored at 4 °C for over 24 h or 7 days. Therefore, these results suggest that high-quality RNA is obtained from the fresher blood specimens and if blood specimens are stored for over 3 days at 4 °C, the quality of leukocyte RNA is more stable and of better quality than that of whole blood RNA.
Cloning and preliminary verification of telomere-associated sequences in upland cotton

Abstract Telomeres are structures enriched in repetitive sequences at the end of chromosomes. In this study, using the telomere primer AA(CCCTAAA)3CCC for the single primer PCR, two DNA sequences were obtained from Gossypium hirsutum (Linnaeus, 1753) accession (acc.) TM-1. Sequence analysis showed that the two obtained sequences were all rich in A/T base, which was consistent with the characteristic of the telomere-associated sequence (TAS). They were designated as GhTAS1 and GhTAS2 respectively. GhTAS1 is 489 bp long, with 57.6% of A/T, and GhTAS2 is 539 bp long, with 63.9% of A/T. Fluorescence in situ hybridization results showed that both of the cloned TASs were located at the ends of the partial chromosomes of G. hirsutum, with the strong signals, which further confirmed that GhTAS1 and GhTAS2 were telomere-associated sequences including highly tandemly repetitive sequences. Results of blast against the assembled genome of G. hirsutum showed that GhTAS sequences may be missed on some assembled chromosomes. The results provide important evidence for the evaluation of the integrity of assembled chromosome end sequences, and will also contribute to the further perfection of the draft genomes of cotton.
Conversion regular patterns of acetic acid, propionic acid and butyric acid in UASB reactor

On the basis of continuous tests and batch tests, conversion regular patterns of acetate, propionate and butyrate in activated sludge at different heights of the UASB reactor were conducted. Results indicated that the conversion capacity of the microbial is decided by the substrate characteristic when sole VFA is used as the only substrate. But when mixed substrates are used,the conversion regulations would have changed accordingly. Relationships of different substrates vary according to their locations. In the whole reactor, propionate's conversion is restrained by acetate and butyrate of high concentration. On the top and at the bottom of the reactor, conversion of acetate, but butyrate, is restrained by propionate. And in the midst, acetate's conversion is accelerated by propionate while that of butyrate is restrained. It is proved, based on the analysis of specific conversion rate, that the space distribution of the microbe is the main factor that affects substrates' conversion. The ethanol-type fermentation of the acidogenic-phase is the optimal acid-type fermentation for the two-phase anaerobic process.
Sensorless Control of Nonsinusoidal Back-EMF PMSM Based on State Observer

This paper presents a sensorless vector control method based on current and back-EMF state observers for a surface-mounted permanent magnet synchronous machine (PMSM) with nonsinusoidal back electromtive force (EMF). The mathematical modeling of the PMSM is presented in the stationary reference frame. The estimated rotor position is presented in terms of flux linkage in the stationary reference frame. Then, current and back-EMF observers are used, in order to obtain both actual and observed αβ currents and αβ EMF. The performance of the proposed approach for speed and current control is demonstrated by simulation results.
Numerical and Experimental Investigations of Cavitation Phenomena Inside the Pilot Stage of the Deflector Jet Servo-Valve

Two-stage electrohydraulic servo-valves play an important role in the most modern hydraulic servo control systems for fight wing controls, manufacturing robots, and many engineering applications. The existence of cavitation inside the pilot stage is one of the root causes of self-excited noise, cavitation erosion, system jam, and frequent failures of electrohydraulic servo-valves. Thus, experimental and numerical investigations of the flow field and cavitation phenomena inside the pilot stage of deflector jet servo-valve under different supply pressures conditions are conducted in this paper. An assembly for experimental verification of the flow visualization process that represents the deflector jet pilot stage is produced and discussed. To test and verify the numerical simulations for cavitation phenomena, the flow field characteristics in the deflector jet pilot stage are investigated by using experimental flow visualization. The cavitation inception inside the pilot stage of the deflector jet servo-valve is experimentally confirmed through flow visualization. More importantly, the attached cloud-like cavitation or bubble shedding is observed along with the jet flow and the significant locations of cavitation inception are also identified for varying supply pressures. The profile of turbulent intensity confirms to conclude that turbulent pressure fluctuations contribute to cavitation. The result also shows that the increment of supply pressure intensifies cavitation and output pressure plays a significant role in reducing the intensity of cavitation inside the pilot stage of deflector jet servo-valve. Finally, the numerical results show good agreement with experimental results.
Building Activity Profiling: Explainable and Predictive Modeling for Building Automation

We propose a technique to model the activities in a building, which we call Building Activity Profiling (BAP). BAP is modeled using a hybrid system composed of continuous time-series data and discrete series of states. The time-series data are provided by various sensors of a Building Automation System, and the states are derived from the characteristics of the time-series data. Unlike a simple list of data, BAP enables us to provide a better understanding of the activities in a building and a precise prediction of what will happen next. The experimental results with real office buildings show that BAP successfully perceives seasonal periodicity and state transitions of activities such as on weekdays and weekends, and predicts electrical loads more accurately than a naive method of the time-series analysis.
Explainability of a Machine Learning Granting Scoring Model in Peer-to-Peer Lending

Peer-to-peer (P2P) lending demands effective and explainable credit risk models. Typical machine learning algorithms offer high prediction performance, but most of them lack explanatory power. However, this deficiency can be solved with the help of the explainability tools proposed in the last few years, such as the SHAP values. In this work, we assess the well-known logistic regression model and several machine learning algorithms for granting scoring in P2P lending. The comparison reveals that the machine learning alternative is superior in terms of not only classification performance but also explainability. More precisely, the SHAP values reveal that machine learning algorithms can reflect dispersion, nonlinearity and structural breaks in the relationships between each feature and the target variable. Our results demonstrate that is possible to have machine learning credit scoring models be both accurate and transparent. Such models provide the trust that the industry, regulators and end-users demand in P2P lending and may lead to a wider adoption of machine learning in this and other risk assessment applications where explainability is required.
Evaluating Low-Cost in Internal Crowdsourcing for Software Engineering: The Case of Feature Location in an Industrial Environment

Internal crowdsourcing in software engineering is a mechanism for recruiting engineers to carry out more efficiently software engineering tasks. However, engineers are busy resources and time is a valuable asset in industry, which hinders internal crowdsourcing in software engineering from becoming a widespread practice. In this work, we propose a low-cost variant of internal crowdsourcing for locating features in models, which limits the time that engineers can spend for providing knowledge. Our approach uses the knowledge provided by the internal crowd to automatically reformulate an initial feature description. The result is taken as input to automatically locate the relevant model fragment using Latent Semantic Indexing. We evaluate our approach using four query reformulation techniques in a real-world case study from our industrial partner. We compare the results of our approach in terms of recall, precision and F-measure with a baseline by means of statistical methods to show that the impact of the results of our approach is significant. Despite the limitation of time, the results show that low-cost in internal crowdsourcing improves significantly the results in an industrial context where engineers’ availability is scarce.
Automatic Keyword and Sentence-Based Text Summarization for Software Bug Reports

Text Summarization is a process which efficiently retrieves the relevant information from documents. The objective of the proposed, unsupervised approach is to summarize bug reports (software artefacts) with complete content and diversified information. The proposed approach utilizes Rapid Automatic Keyword Extraction and term frequency-inverse document frequency method to extract meaningful keywords and key-phrases with a relevant score. For sentence extraction, fuzzy C-means clustering is used to extracts sentences having high degree of membership from each cluster above a set threshold value. A rule-engine is used for sentence selection. The rules are generated with the domain knowledge and based on the extracted information by the keywords and sentences selected by the clustering method. Cohesive and coherent summary is generated by the proposed method on apache bug reports. For redundancy removal and to re-rank generated summary, hierarchical clustering is presented to enrich the extracted summary. The proposed approach is evaluated on newly constructed Apache project Bug Report Corpus (APBRC) and existing Bug Report Corpus (BRC). The results are compared on the basis of performance metrics such as precision, recall, pyramid precision and F-score. The experimental results depict that our proposed approach attains significant improvement over other baseline approaches such as BRC and LRCA. It also attains significant improvement over existing state-of-art unsupervised approaches such as Hurried, centroid and others. It extracts significant keyword phrases and sentences from each cluster to achieve full coverage and coherent summary. The results evaluated on APBRC corpus attains an average value of 78.22%, 82.18%, 80.10% and 81.66% for precision, recall, f-score and pyramid precision respectively.
DRAM-Less: Hardware Acceleration of Data Processing with New Memory

General purpose hardware accelerators have become major data processing resources in many computing domains. However, the processing capability of hardware accelerations is often limited by costly software interventions and memory copies to support compulsory data movement between different processors and solid-state drives (SSDs). This in turn also wastes a significant amount of energy in modern accelerated systems. In this work, we propose, DRAM-less, a hardware automation approach that precisely integrates many state-of-the-art phase change memory (PRAM) modules into its data processing network to dramatically reduce unnecessary data copies with a minimum of software modifications. We implement a new memory controller that plugs a real 3x nm multi-partition PRAM to 28nm technology FPGA logic cells and interoperate its design into a real PCIe accelerator emulation platform. The evaluation results reveal that our DRAM-less achieves, on average, 47% better performance than advanced acceleration approaches that use a peer-to-peer DMA.
Boost Power Factor Correction Converter fed Domestic Induction Heating System

Unique advantages of induction heating (IH) technology in terms of efficiency and performance has increased its endorsement in industrial along with domestic applications. Owing to this, the usage of IH technology for domestic cooking is being increased and the increasing application of this technology is leading to power quality issue in terms of THD, low power factor problem and electromagnetic interference (EMI) effect. In order to mitigate these issues, in this paper a domestic IH system has been designed using boost power factor correction (BPFC) converter which acts as a front end converter. The BPFC converter corrects the input power factor (PF) as well as regulates the DC link voltage. This DC link voltage is fed to the full bridge series resonant inverter (FB-SRI) which generates high frequency AC which is the need of domestic IH system. A 2.2 kW domestic IH system has been designed and validated through the Power simulation (PSIM) software.
Pedestrian Detection in Severe Weather Conditions

Pedestrian detection has never been an easy task for computer vision and the automotive industry. Systems like the advanced driver-assistance system (ADAS) highly rely on far-infrared (FIR) data captured to detect pedestrians at nighttime. The recent development of deep learning-based detectors has proven the excellent results of pedestrian detection in perfect weather conditions. However, it is still unknown what the performance in adverse weather conditions is. In this paper, we introduce a 16-bit thermal data dataset called ZUT (Zachodniopomorski Uniwersytet Technologiczny) as having the widest variety of fine-grained annotated images captured in the four biggest European Union countries captured during severe weather conditions. We also provide a synchronized Controller Area Network (CAN bus) data, including driving speed, brake pedal status, and outside temperature for future ADAS system development. Furthermore, we have tested and provided 16-bit depth modifications for the YOLOv3 deep neural network (DNN) based detector, reaching a mean Average Precision (mAP) up to 89.1%. The ZUT dataset is published and publicly available at IEEE Dataport and Github.
Delay and Bypass: Ready and Criticality Aware Instruction Scheduling in Out-of-Order Processors

Flexible instruction scheduling is essential for performance in out-of-order processors. This is typically achieved by using CAM-based Instruction Queues (IQs) that provide complete flexibility in choosing ready instructions for execution, but at the cost of significant scheduling energy. In this work we seek to reduce the instruction scheduling energy by reducing the depth and width of the IQ. We do so by classifying instructions based on their readiness and criticality, and using this information to bypass the IQ for instructions that will not benefit from its expensive scheduling structures and delay instructions that will not harm performance. Combined, these approaches allow us to offload a significant portion of the instructions from the IQ to much cheaper FIFO-based scheduling structures without hurting performance. As a result we can reduce the IQ depth and width by half, thereby saving energy. Our design, Delay and Bypass (DNB), is the first design to explicitly address both readiness and criticality to reduce scheduling energy. By handling both classes we are able to achieve 95% of the baseline out-of-order performance while only using 33% of the scheduling energy. This represents a significant improvement over previous designs which addressed only criticality or readiness (91%/89% performance at 74%/53% energy).
The Short-Term Repeatability of Subdermal Electrical Stimulation for Sensory Feedback

Modern hand prostheses are used to restore the motor functions lost due to an amputation. However, the lack of sensory feedback remains a major challenge. Subdermal stimulation is a promising technique to restore tactile sensations when using prostheses, since it may overcome the disadvantages of surface electrodes without resorting to surgery that is required for a direct nerve interface. The present study evaluated the short-term repeatability of the perceptual properties of subdermal electrical stimulation over eight hours in healthy subjects and compared them to those of surface stimulation. Specifically, the detection threshold, pain threshold, dynamic range, just noticeable difference, resolution and quality of evoked sensations were tested and used for short-term repeatability evaluation. The results demonstrated that the detection threshold was more stable under subdermal stimulation, whereas the short-term repeatability of the pain threshold and just noticeable difference was better under surface stimulation. On the other hand, several psychometric parameters (dynamic range, resolution, sensation quality, intensity, and comfort) were equally stable and did not change significantly across sessions in either surface or subdermal stimulation. The subdermal stimulation was better localized and elicited fewer unwanted sensation modalities (p < 0.05), whereas surface stimulation was characterized by a higher resolution (p < 0.05). The results suggest that subdermal stimulation could be a viable alternative for the implementation of electro-tactile feedback as it generates sensations that are equally stable as in surface stimulation, and yet it has some important advantages for the practical applications (e.g., compact interface, permanent placement).