Miracle-dz/UniFAR-FTUs huggingface dataset:
Equilibration of weakly coupled QCD plasmas

We employ a non-equilibrium Quantum Chromodynamics (QCD) kinetic description to study the kinetic and chemical equilibration of the Quark-Gluon Plasma (QGP) at weak coupling. Based on our numerical framework, which explicitly includes all leading order processes involving light flavor degrees of freedom, we investigate the thermalization process of homogeneous and isotropic plasmas far-from equilibrium and determine the relevant time scales for kinetic and chemical equilibration. We further simulate the longitudinally expanding pre-equilibrium plasma created in ultrarelativistic heavy-ion collisions at zero and non-zero density of the conserved charges and study its microscopic and macroscopic evolution towards equilibrium.
A Square Wave-Based Digital Foreground Calibration Algorithm of a Pipeline ADC Using Approximate Harmonic Sampling

This brief proposes a digital foreground calibration algorithm of a pipeline ADC, using the Square wave signal as the input. The principle of equivalent radix extraction technique using approximate harmonic sampling is used to remove the ADC non-linearities. Linearity errors caused due to finite op amp gain, capacitor mismatches and effect of parasitic capacitance are eliminated by the proposed calibration technique. Two front-end stages of a 12-bit pipeline ADC are incorporated with low amplifier gain of 40 dB, capacitor mismatch of $\pm 5 \%\leq \gamma \leq \pm 10 \%$ . Non-linearities arising due to these factors are calibrated and results are verified using behavioural simulation.
Unsupervised Image Segmentation using Mutual Mean-Teaching

Unsupervised image segmentation aims at assigning the pixels with similar feature into a same cluster without annotation, which is an important task in computer vision. Due to lack of prior knowledge, most of existing model usually need to be trained several times to obtain suitable results. To address this problem, we propose an unsupervised image segmentation model based on the Mutual Mean-Teaching (MMT) framework to produce more stable results. In addition, since the labels of pixels from two model are not matched, a label alignment algorithm based on the Hungarian algorithm is proposed to match the cluster labels. Experimental results demonstrate that the proposed model is able to segment various types of images and achieves better performance than the existing methods.
Monte Carlo Simulation of the Siemens Biograph Vision PET With Extended Axial Field of View Using Sparse Detector Module Rings Configuration

We report on the NEMA-NU2-2012 performance of a hypothetical Monte Carlo (MC) model, Ex-PET, of the Siemens Biograph Vision positron emission tomography (PET)/CT (Bio-Vis) with sparse detector module rings and extended axial field of view (AFOV). MC simulations were performed with the detector module rings interleaved with 32-mm gaps, equivalent to the axial dimension of each detector module, yielding an AFOV of 48.0 cm (Bio-Vis has 25.6-cm AFOV). 3D-PET acquisition combined with a limited continuous-bed-motion (limited-CBM) was used to compensate for the loss in sensitivity within the gaps’ regions. MC simulations of the Bio-Vis were performed for comparison purposes. All MC simulations were performed using GATE MC toolkit. Ex-PET exhibited 0.49, 0.16, and 0.16 mm deterioration in axial resolution at 1, 10, and 20 cm off-center of the transaxial field of view, respectively, compared to Bio-Vis. Only 1% reduction in system sensitivity and 6% reduction in peak NECR was observed with Ex-PET compared to Bio-Vis. 3D-OSEM image reconstruction, combined with CBM, allowed compensating for the lack of counts within the gaps’ regions. NEMA Image Quality test showed < 6% reduction in contrast recovery with Ex-PET versus Bio-Vis, yet the background variability was increased by up to 8%. The feasibility of PET imaging with an easily adoptable sparse detector configuration was demonstrated. This can lay the pathway for future development of cost-effective PET systems with long and conventional AFOV’s.
In Vitro and In Vivo Evaluation of Nanoparticles Prepared by Nano Spray Drying for Stomach Mucoadhesive Drug Delivery

According to a World Health Organization report published in 2010, 346 million people worldwide have diabetes; in Saudi Arabia, 16.8% of the population has been diagnosed with type 2 diabetes. Many patients with type 2 diabetes are unable to maintain adequate glycemic control even after receiving recent antidiabetic drugs. A combination of vildagliptin and metformin hydrochloride tablets is available; however, both conventional drugs have a short half-life. The formulation of mucoadhesive nanoparticles for these classes of drugs can be most beneficial, because they release the drug slowly into the gastrointestinal tract and maintain the therapeutic drug concentration. Vildagliptin and metformin hydrochloride were prepared with Carbopol (VMCN) using a Buchi Nano Spray Dryer B-90. The surface morphology was found to be shriveled (due to surface folding), and the average particle size was 437 nm. The percentage drug yield for vildagliptin and metformin hydrochloride in the nanoparticles was 71 ± 0.5% and 74 ± 0.3%, respectively, and the drug content was 84 ± 0.4% and 89 ± 0.4%, respectively. Ex vivo studies of mucoadhesion were 6.9 ± 0.3 h. The percentage of swelling was recorded as 162 ± 4%. In vivo studies of nanoparticles containing vildagliptin and metformin hydrochloride showed that these values were 3.9 and 4.1% at 12 h, respectively.
Prediksi Harga Sembako di DKI Jakarta Menggunakan Artificial Neural Network

Indonesia merupakan salah satu negara berkembang yang pertumbuhan ekonominya stabil. Hal ini dapat dilihat dari data statistik yang dikeluarkan oleh Badan Pusat statistik nasional. Pertumbuhan ekonomi yang stabil tidak diimbangi dengan stabilnya harga komoditas sembako di Indonesia khususnya di provinsi DKI Jakarta. Kondisi tersebut terlihat dari harga komoditas sembako (Sembilan Bahan Pokok) yang bergerak fluktuatif. Kenaikan harga BBM, cuaca serta adanya hari-hari besar menjadi faktor umum yang mempengaruhi harga komoditas sembako. Akibatnya harga komoditas sembako tidak dapat ditentukan karena kondisi-kondisi yang mempengaruhinya. Oleh karena itu, diperlukan model komputasi yang dapat memprediksi harga komoditas sembako di DKI Jakarta menggunakan metode yang memiliki akurasi yang baik. Pada penelitian sebelumnya pernah dilakukan penelitian prediksi harga dengan komoditas yang berbeda menggunakan metode Artificial Neural Network dengan akurasi 78% hingga 86%. Penelitian ini menghasilkan Sistem Prediksi Harga Sembako di DKI Jakarta menggunakan metode Artificial Neural Network. Dengan hasil terbaik didapatkan dengan parameter learning rate 0.01 dan toleransi error 0.01 dengan maksimum iterasi 500 yaitu akurasi sebesar 82% komoditas beras, 80% untuk komoditas Cabe dan 78% untuk komoditas bawang merah yang dihasilkan dari pengujian terhdap data uji.
SPATIAL STAND STRUCTURE ANALYSIS OF ULUDAĞ FIR FORESTS IN THE NORTHWEST OF TURKEY

Forest structural characteristics, which are one of the key determinants of species and habitat diversity, have also been used as a constitutive factor in sustainable forest management. This study aimed to analyse the spatial structure (Clark-Evans, segregation and species mingling) of pure fir (7 plots, 10001600 m a.s.l.), fir dominated (5 plots, 1000-1400 m a.s.l.) and fir-beech co-dominated (7 plots, 10001300 m a.s.l.) stands at the center and edge distribution of Abies nordmanniana ssp. bornmuelleriana in Sub-euxine zone of Euro-Siberian region of Turkey. The Shannon diversity of tree species was found significantly (P < 0.01) lower in the edge distribution (Bolu-Aladağ) (0.418 ± 0.081) compared to Yenice (0.824 ± 066). Fagus orientalis showed a clumped distribution in fir dominated stands where it is not competitive compared to Abies nordmanniana ssp. bornmuelleriana but it had a spatial association with other species in co-dominated stands. Light demanding deciduous trees such as Quercus hartwissiana, Carpinus betulus and Populus tremula were observed in pure groups, whereas Pinus sylvestris and Pinus nigra did not show a tendency towards clumping and had a spatial association with other species since they were located in upper stand layer. As shown in this study, spatial data in stand profiles provide new opportunities for understanding ecosystem structure and silvicultural treatments.
Performance of Two-Hop Links With an Energy Buffer-Aided IoT Source and a Data Buffer-Aided Relay

In this article, we analyze the performance of a two-hop communication link between an energy-buffer-equipped IoT-type self-sustaining source and a destination, aided by a data-buffer-equipped relay. The source harvests radio-frequency energy from the ambience and stores it in an energy buffer. Using a discrete-time continuous-state Markov chain to model the energy buffer and a discrete-state Markov chain to model the data buffer, we analyze outage and throughput performance of the link. In typical applications, feedback of channel state information and buffer state information is not feasible, so simple link selection schemes are evolved. In the first only buffer-status (OBS) scheme, link selection is based on the energy-buffer status alone. A modified OBS scheme is then suggested, and its performance is analyzed. The third scheme uses both energy-buffer status and first-hop channel knowledge for link selection. Expressions are derived for throughput and limiting distributions of stored energy with all the three schemes. We show how the continuous-state energy buffer and the discrete-state data buffer can both be balanced while maximizing the throughput by suitable choice of the source transmit power, relay transmit power, and target information rates at the source and the relay. We also analyze statistical properties of the energy-buffer size with these schemes. The derived analytical expressions are validated by the Monte Carlo simulations.
Psychological Impacts Related to Stress and Fear during the COVID-19 Pandemic: Cardiovascular Diseases, Diabetes and Psychological Disorders as Risk Factors

Background and Aims: Patients and the general public are under insurmountable psychological pressure which may lead to various psychological problems, such as anxiety, fear, depression, and insomnia, causing, consequently, the impaired quality of life. Psychological crisis intervention plays a pivotal role in the overall deployment of health-related quality of life and disease control. A novel Severe Acute Respiratory Syndrome Coronavirus-2 (SARS- CoV-2), a pathogen of the new coronavirus disease (COVID-19), has affected several sector activities, including people’s health. To enhance infection control methods, appropriate interventions, and public health policies, the present study aims to assess the fear and peri-traumatic stress during the COVID-19 inBrazil. Method: A cross-sectional survey has been conducted from April 12th to 18th from 2020, using the Peri-Traumatic Distress Scale (CPDI) and the Fear Scale (FCV-19S) aiming to measure the peri-traumatic stress and fear as psychological reactions during the COVID-19 pandemic. For that purpose, an online spreadsheet was used to send the questionnaire and scales to a sample of 1844 participants as a collecting information tool. After the data analysis, the individuals were separated into 4 groups: Group 1 (1232) population without chronic health conditions; group 2 (298) patients with previous psychological suffering, group 3 (229) patients with cardiovascular diseases, group 4 (71) patients with diabetes. For analysis, G1 were considered control for comparison with groups 2, 3 and 4. Results: All the groups showed the CPDI and FCV-19S increased in comparison with the G1 group. Concerning CPDI, the G 3 was increased when compared to G1, G2 and G4. The G3 had the FCV-19S higher in comparison with G1, G2 and G4. The Wilcoxon-Mann-Whitney test showed a statistical difference between the control group in comparison with 2 and 3 groups (Mann-Whitney p Conclusion: The COVID-19 pandemic had a significant impact on the Brazilian population, with patients with heart disease and hypertension presenting the highest numbers of stress and fear, with numbers comparable and even higher than those who reported previous psychological distress.
Accelerating black holes and spinning spindles

We study solutions in the Pleba\'nski--Demia\'nski family which describe an accelerating, rotating and dyonically charged black hole in $AdS_4$. These are solutions of $D=4$ Einstein-Maxwell theory with a negative cosmological constant and hence minimal $D=4$ gauged supergravity. It is well known that when the acceleration is non-vanishing the $D=4$ black hole metrics have conical singularities. By uplifting the solutions to $D=11$ supergravity using a regular Sasaki-Einstein $7$-manifold, $SE_7$, we show how the free parameters can be chosen to eliminate the conical singularities. Topologically, the $D=11$ solutions incorporate an $SE_7$ fibration over a two-dimensional weighted projective space, $\mathbb{WCP}^1_{[n_-,n_+]}$, also known as a spindle, which is labelled by two integers that determine the conical singularities of the $D=4$ metrics. We also discuss the supersymmetric and extremal limit and show that the near horizon limit gives rise to a new family of regular supersymmetric $AdS_2\times Y_9$ solutions of $D=11$ supergravity, which generalise a known family by the addition of a rotation parameter. We calculate the entropy of these black holes and argue that it should be possible to derive this from certain ${\cal N}=2$, $d=3$ quiver gauge theories compactified on a spinning spindle with appropriate magnetic flux.
AIST: An Interpretable Attention-Based Deep Learning Model for Crime Prediction

Accuracy and interpretability are two essential properties for a crime prediction model. Accurate prediction of future crime occurrences along with the reason behind a prediction would allow us to plan the crime prevention steps accordingly. The key challenge in developing the model is to capture the non-linear and dynamic spatial dependency and temporal patterns of a specific crime category, while keeping the underlying structure of the model interpretable. In this article, we develop AIST, an Attention-based Interpretable Spatio Temporal Network for crime prediction. AIST models the dynamic spatio-temporal correlations for a crime category based on past crime occurrences, external features (e.g., traffic flow and point of interest information) and recurring trends of crime. Extensive experiments show that AIST outperforms the state-of-the-art techniques in terms of accuracy (e.g., AIST shows a decrease of 4.1% on mean average error and 7.45% on mean square error for the Chicago 2019 crime dataset) and interpretability.1
mmEye: Super-Resolution Millimeter Wave Imaging

RF imaging is a dream that has been pursued for years yet not achieved in the evolving wireless sensing. The existing solutions on WiFi bands, however, either require specialized hardware with large antenna arrays or suffer from poor resolution due to fundamental limits in bandwidth, the number of antennas, and the carrier frequency of 2.4 GHz/5 GHz WiFi. In this article, we observe a new opportunity in the increasingly popular 60-GHz WiFi, which overcomes such limits. We present mmEye, a super-resolution imaging system toward a millimeter-wave camera by reusing a single commodity 60-GHz WiFi radios. The key challenge arises from the extremely small aperture (antenna size), e.g., < 2 cm, which physically limits the spatial resolution. mmEye’s core contribution is a super-resolution imaging algorithm that breaks the resolution limits by leveraging all available information at both the transmitter and receiver sides. Based on the MUSIC algorithm, we devise a novel technique of joint transmitter smoothing, which jointly uses the transmit and receive arrays to boost the spatial resolution while not sacrificing the aperture of the antenna array. Built upon this core, we design and implement a functional system on commodity 60-GHz WiFi chipsets. We evaluate mmEye on different persons and objects under various settings. Results show that it achieves a median silhouette (shape) difference of 27.2% and a median boundary keypoint precision of 7.6 cm, and it can image a person even through a thin drywall. The visual results show that the imaging quality is close to that of commercial products like Kinect, making for the first-time super-resolution imaging available on the commodity 60-GHz WiFi devices.
EffMoP: Efficient Motion Planning Based on Heuristic-Guided Motion Primitives Pruning and Path Optimization With Sparse-Banded Structure

To solve the autonomous navigation problem in complex environments, an efficient motion planning approach called EffMoP is presented in this paper. Considering the challenges from large-scale, partially unknown complex environments, a three-layer motion planning framework is elaborately designed, including global path planning, local path optimization, and time-optimal velocity planning. Compared with existing approaches, the novelty of this work is twofold: 1) a novel heuristic-guided pruning strategy of motion primitives is proposed and fully integrated into the state lattice-based global path planner to further improve the computational efficiency of graph search, and 2) a new soft-constrained local path optimization approach is proposed, wherein the sparse-banded system structure of the underlying optimization problem is fully exploited to efficiently solve the problem. We validate the safety, smoothness, flexibility, and efficiency of EffMoP in various complex simulation scenarios and challenging real-world tasks.
Correction: Self-assembled blue-light emitting materials for their liquid crystalline and OLED applications: from a simple molecular design to supramolecular materials

Correction for ‘Self-assembled blue-light emitting materials for their liquid crystalline and OLED applications: from a simple molecular design to supramolecular materials’ by Vinay S. Sharma et al., Mol. Syst. Des. Eng., 2020, 5, 1691–1705, DOI: 10.1039/D0ME00117A.
Selection of sunflower genotypes for salt stress and mechanisms of salt tolerance in contrasting genotypes

ABSTRACT Salinity is one of the main limiting factors for crop growth. The metabolic responses to salt stress are variable and depend on species characteristics. This study aimed to select sunflower genotypes tolerant to salt stress and evaluate some mechanisms of salt tolerance in two contrasting (salt-tolerant and salt-sensitive) genotypes. In the first assay, the biomass production and the accumulation of Na+ and K+ in 26 sunflower genotypes were evaluated. Genotypes AG963, AG967, AG972, BRS321, BRS324, H251, H360 and H863 showed lower biomass production and were characterized as salt-sensitive and the genotypes BRS323, Catisol, EXP11-26, EXP44-49, EXP60050, EXP887, HLA860HO and Olisun 5 showed higher biomass production and were considered salt-tolerant. The high K+ content and the low Na+ content in the leaves were the ion traits related to salt tolerance and can be used in sunflower breeding programs for this purpose. In the second assay, the plants of salt-tolerant BRS323 had lower Na+ and Cl- contents and higher levels of K+ than plants of salt-sensitive AG967. A better homeostasis in the mechanisms of transport, distribution and accumulation of inorganic solutes in conjunction with a more efficient osmoregulation mechanism through the synthesis of organic solutes may, at least in part, explain the greater salt-tolerance of BRS323 genotype in comparison to AG967.
Information retrieval system for silte language using BM25 weighting

The main aim of an information retrieval system is to extract appropriate information from an enormous collection of data based on users need. The basic concept of the information retrieval system is that when a user sends out a query, the system would try to generate a list of related documents ranked in order, according to their degree of relevance. Digital unstructured Silte text documents increase from time to time. The growth of digital text information makes the utilization and access of the right information difficult. Thus, developing an information retrieval system for Silte language allows searching and retrieving relevant documents that satisfy information need of users. In this research, we design probabilistic information retrieval system for Silte language. The system has both indexing and searching part was created. In these modules, different text operations such as tokenization, stemming, stop word removal and synonym is included.
1.2–2.8-GHz 32.4-dBm Digital Power Amplifier With Balance-Compensated Matching Network

In this letter, a wideband watt-level digital power amplifier (DPA) with a balance-compensated matching network is proposed for polar transmitters. The balance response of the differential to the single-ended transformer is enhanced by a series-loaded compensation capacitor, which leads to the improvement of the DPA efficiency. To verify the mechanisms, a prototype DPA is fabricated in conventional 40-nm CMOS technology. The proposed DPA operates over 1.2–2.8 GHz and exhibits a peak output power of 32.4 dBm at 2 GHz and a peak drain efficiency of 53.8% at 1.8 GHz. It supports 50-MHz 64-quadratic-amplitude modulation (QAM) with average output power (<inline-formula> <tex-math notation="LaTeX">$P_{\text {avg}}$ </tex-math></inline-formula>) of 25.37 dBm, error vector magnitude (EVM) of −26.97 dB, adjacent channel power ratio (ACPR) of −29.61 dBc, and 10-MHz 1024-QAM with <inline-formula> <tex-math notation="LaTeX">$P_{\text {avg}}$ </tex-math></inline-formula> of 22.14 dBm, EVM of −35.75 dB, and ACPR of −35.37 dBc, respectively.
Triple-Mode Isolated Resonant Buck–Boost Converter Over Wide Input Voltage Range for Residential Applications

This article proposes a triple-mode isolated resonant buck–boost converter over a wide input voltage range for residential applications of photovoltaic arrays and fuel cells. First, in case that the input voltage is smaller than the nominal voltage, it operates in a resonant-boost mode that boosts the input voltage by using a bridgeless structure on the secondary-side. Next, if the input voltage is within the range of the nominal voltage, it operates in a fully series-resonant mode, which results in high efficiency by the soft-switching on the primary-side switches and reduced conduction loss. Last, if the input voltage is more than the nominal voltage, it operates in a pulsewidth modulation series-resonant buck mode, which can achieve the step-down conversion by reducing the duty cycle. Unlike the conventional resonant converters, the proposed converter achieves high efficiency and less number of power components over a wide input voltage range. Finally, the performance of the proposed triple-mode isolated resonant converter has been fully verified on a prototype 400 W test-bed in the wide input voltage of 35–65 V.