Miracle-dz/UniFAR-FTUs huggingface dataset:
Nanoscale structure fabrication of multiple AlGaSb∕InGaSb quantum wells by reactive ion etching with chlorine-based gases toward photonic crystals

The authors studied nanoscale structure fabrication of multiple AlGaSb∕InGaSb quantum wells. The fabrication was successfully conducted using simple resist masks for electron beam lithography and reactive ion etching (RIE) with chlorine-based gases. The etching profile after the RIE showed good vertical shape. Etching damage induced by the RIE was revealed by using photoluminescence measurements. The effects of surface recombination at the etching sidewalls were large, but the effects were suppressed by using HCl and (NH4)2Sx treatments. The damage was low enough to create small active optical devices, such as photonic crystals, comparably with current InGaAsP systems.
Alpha-Defensin versus Leukocyte Esterase in Periprosthetic Joint Infection: An Updated Meta-Analysis

Periprosthetic joint infection (PJI) is a devastating complication after arthroplasty. Prompt establishment of an infection diagnosis is critical but can be very challenging at present. In order to evaluate the diagnostic accuracy of alpha-defensin or leukocyte esterase for PJI, we performed systematic research in PubMed, Embase, and Cochrane Library to retrieve relevant studies. Data extraction and quality assessment were performed by two reviewers independently. A total of thirty-one eligible studies were finally included in the quantitative analysis. The pooled sensitivity and specificity of alpha-defensin (21 studies) for the diagnosis of PJI were 0.89 (95% confidence interval (CI), 0.83 to 0.93) and 0.96 (95% CI, 0.95 to 0.97), respectively. The value of the pooled diagnostic odds ratios (DOR) of alpha-defensin for PJI was 209.14 (95% CI, 97.31 to 449.50), and the area under the curve (AUC) was 0.98 (95% CI, 0.96 to 0.99). The pooled sensitivity and specificity of leukocyte esterase (17 studies) for the diagnosis of PJI were 0.90 (95% CI, 0.84 to 0.95) and 0.96 (95% CI, 0.93 to 0.97), respectively. The value of the DOR of leukocyte esterase for PJI was 203.23 (95% CI, 96.14 to 429.61), and the AUC was 0.98 (95% CI, 0.96 to 0.99). Based on the results of our meta-analysis, we can conclude that alpha-defensin and leukocyte esterase are valuable synovial fluid markers for identifying PJI with comparable high diagnostic accuracy.
Black rice with giant embryo ameliorates serum C-reactive protein in adults with metabolic syndrome

Metabolic syndrome is well known to increase the risk of cardiovascular diseases. We have reported that phytochemicals rich black rice with giant embryo reduced fat mass and metabolic disorders in an animal model. However, such effects have not been evaluated in humans. Subjects with metabolic syndrome (n = 49, 38 male, 44.3 ± 6.1 years) were randomly assigned into two groups and ingested roasted black-rice with giant embryo (BR, n = 26, 20 male) or white-rice (WR, n = 23, 18 male) powders mixed with water for breakfast for three months. Subjects were evaluated for various metabolic parameters before and after intervention. All parameters were not significantly different between groups before starting the intervention. After three months of consumption of either BR or WR, changes of body weight in BR vs WR groups (–1.54 kg vs –1.29 kg, p = 0.649) as well as waist circumference (–1.63 cm vs –1.02 cm, p = 0.365) were not significantly different between groups. However, changes in highly-sensitive C reactive proteins in BR vs WR groups (–0.110 mg/dl vs 0.017 mg/dl, p = 0.003) had significant differences. Three months of meal replacement with BR had a significant reduction of highly-sensitive C reactive protein compared to those with WR in adults with metabolic syndrome.
An Enhanced MeanSum Method For Generating Hotel Multi-Review Summarizations

Multi-document summaritazion is the process of taking multiple texts as input and producing a short summary text based on the content of input texts. Up until recently, multi-document summarizers are mostly supervised extractive. However, supervised methods require datasets of large, paired document-summary examples which are rare and expensive to produce. In 2018, an unsupervised multi-document abstractive summarization method(Meansum) was proposed by Chu and Liu, and demonstrated competitive performances comparing to extractive methods. Despite good evaluation results on automatic metrics, Meansum has multiple limitations, notably the inability of dealing with multiple aspects. The aim of this work was to use Multi-Aspect Masker(MAM) as content selector to address the issue with multi-aspect. Moreover, we propose a regularizer to control the length of the generated summaries. Through a series of experiments on the hotel dataset from Trip Advisor, we validate our assumption and show that our improved model achieves higher ROUGE, Sentiment Accuracy than the original Meansum method and also beats/ comprarable/close to the supervised baseline.
On anisotropy and inhomogeneity parameter estimation using traveltimes

We consider an anisotropic inhomogeneous model to simulate measured vertical-seismic-profile traveltimes. In this model, we assume that velocity increases linearly with depth and anisotropy is the result of elliptical velocity dependence. Using a series of sources in a line to a single receiver, we minimize the least-squares residual between measured and modelled traveltimes to estimate the anisotropy and inhomogeneity parameters of the subsurface. To verify the approach, we construct synthetic traveltime data for a one- and two-layer model and perform the traveltime inversion. We justify the convergence-ensuring specifications and model-parameter restrictions that are intrinsic to the parameter estimation. To determine the approach's practicality, we assess the reliability of results under the influence of noise. From this assessment, we discern the noise threshold for both models and determine that the noise restriction is severely restrictive for increasing model complexity. We estimate parameters, using the same methodology, for a real-data case and conclude with a discussion of results.
The effects of solvent composition on the affinity of a peptide towards hair keratin: experimental and molecular dynamics data

The study of the interaction between hair filaments and formulations or peptides is of utmost importance in fields like cosmetic research. Keratin intermediate filament structure is not fully described, limiting the molecular dynamics (MD) studies in this field despite its high potential to improve the area. We developed a computational model of a truncated protofibril, simulated its behavior in alcoholic based formulations and with one peptide. The simulations showed a strong interaction between the benzyl alcohol molecules of the formulations and the model, leading to the disorganization of the keratin chains, which regress with the removal of the alcohol molecules. This behavior can explain the increase of peptide uptake in hair shafts evidenced in fluorescence microscopy pictures. The model developed is valid to computationally reproduce the interaction between hair and alcoholic formulations and provide a robust base for new MD studies about hair properties. It is shown that the MD simulations can improve hair cosmetic research, improving the uptake of a compound of interest.
Rapid biexciton-state preparation in a quantum dot using on-off pulse sequences

We consider the problem of pulsed biexciton preparation in a quantum dot and show that a pulse-sequence with a simple on-off-on modulation can achieve complete preparation of the target state faster than the commonly used constant and hyperbolic secant pulses. The durations of the pulses composing the sequence are obtained from the solution of a transcendental equation. Furthermore, using numerical optimal control, we demonstrate that for a wide range of values of the maximum pulse amplitude, the proposed pulse-sequence prepares the biexciton state in the numerically obtained minimum time, for the specific system under consideration. We finally show with numerical simulations that, even in the presence of dissipation and dephasing, high levels of biexciton state fidelity can be generated in short times.
PMP-Net: Point Cloud Completion by Learning Multi-step Point Moving Paths

The task of point cloud completion aims to predict the missing part for an incomplete 3D shape. A widely used strategy is to generate a complete point cloud from the incomplete one. However, the unordered nature of point clouds will degrade the generation of high-quality 3D shapes, as the detailed topology and structure of discrete points are hard to be captured by the generative process only using a latent code. In this paper, we address the above problem by reconsidering the completion task from a new perspective, where we formulate the prediction as a point cloud deformation process. Specifically, we design a novel neural network, named PMP-Net, to mimic the behavior of an earth mover. It moves move each point of the incomplete input to complete the point cloud, where the total distance of point moving paths (PMP) should be shortest. Therefore, PMP-Net predicts a unique point moving path for each point according to the constraint of total point moving distances. As a result, the network learns a strict and unique correspondence on point-level, and thus improves the quality of the predicted complete shape. We conduct comprehensive experiments on Completion3D and PCN datasets, which demonstrate our advantages over the state-of-the-art point cloud completion methods. Code will be available at https://github.com/diviswen/PMP-Net.
Dilute solution properties of imogolite

Imogolite, a natural product in the clay fraction of Japanese soil, was characterized through its dilute solution properties. Various methods were employed for this characterization, including viscosity, sedimentation, static/dynamic light scattering, and small angle X-ray scattering. All these measurements have revealed consistently that imogolite is represented by a rigid thin rod within the accuracy of available theories, where its repeat unit is composed of twelve gibbsite units. Since the evaluation of the chain length from the observed quantities depends on the molecular weight distribution, its effect was also considered where Mw/Mn ≈ 1,2 was estimated from the sedimentation profile.
Feasibility and Safety of Prone Position Transport for Severe Hypoxemic Respiratory Failure Due to Coronavirus Disease 2019

Supplemental Digital Content is available in the text. Objectives: To assess the safety and feasibility of a new protocol for interhospital critical care transport of mechanically ventilated patients in the prone position during the coronavirus disease 2019 pandemic by nurse and paramedic critical care transport teams. Design: Retrospective observational study. Setting: Single critical care transport agency serving multiple centers in the greater Boston area. Patients: All transports of intubated patients in the prone position with severe hypoxemic respiratory failure secondary to coronavirus disease 2019. Interventions: Records were reviewed for patients transported in the prone position. Major adverse events in transport, defined as severe hypoxemia (oxygen saturation < 80% or an absolute decrease in oxygen saturation > 10%), hypotension (mean arterial pressure < 65 mm Hg) not responsive to vasopressors or inotropes, endotracheal tube or vascular catheter dislodgement, and cardiac arrest, were recorded. Measurements and Main Results: A total of 25 patients were transported in prone position. The mean Pao2:Fio2 ratio in the group was 101.3 mm Hg, and 76% (n = 19) were on vasopressors. Fourteen patients (56%) had hypotension with at least one episode of mean arterial pressure less than 65 mm Hg en route, and seven (28%) had an episode of oxygen desaturation less than 88%. Only one major adverse event of severe hypoxemia (oxygen saturation < 80%) was noted. Conclusions: Critical care transport of severe hypoxemic respiratory failure patients with coronavirus disease 2019 in the prone position is safe when performed by a dedicated team of critical care nurse and paramedics with an established protocol.
Consensus Control of Linear Multi-Agent Systems with Non-uniform Time-varying Communication Delays

This paper is concerned with the consensus problem for multi-agent systems subject to communication delays between the neighboring agents. We consider a scenario where each agent is characterized by a general high-order linear system and the communication delays between the agents are non-uniform and time-varying. We design a distributed control protocol for the agents and provide an equivalent stability problem to be solved that guarantees the state consensus in the group of agents. Moreover, a delay-dependent stability criterion is provided by combining the Lyapunov-Krasovskii method with the linear matrix inequality approach.
Semi-supervised Soil Moisture Prediction through Graph Neural Networks

Recent improvement and availability of remote satellite and IoT data offers interesting and diverse applications of artificial intelligence in precision agriculture. Soil moisture is an important component of multiple agricultural and food supply chain practices. It measures the amount of water stored in various depth of soil. Existing data driven approaches for soil moisture prediction use conventional models which fail to capture the dynamic dependency of soil moisture values in near-by locations over time. In this work, we propose to convert the problem of soil moisture prediction as a semi-supervised learning on temporal graphs. We propose a dynamic graph neural network which can use the dependency of related locations over a region to predict soil moisture. However, unlike social or information networks, graph structure is not explicitly given for soil moisture prediction. Hence, we incorporate the problem of graph structure learning in the framework of dynamic GNN. Our algorithm, referred as DGLR, provides an end-to-end learning which can predict soil moisture over multiple locations in a region over time and also update the graph structure in between. Our solution achieves state-of-the-art results on real-world soil moisture datasets compared to existing machine learning approaches.
Air Particulate Emissions in Developing Countries: A Case Study in South Africa

Atmospheric aerosols were collected during the winter in Bethlehem, South Africa. The particulate mass concentrations, ambient carbon mass concentrations, and chemical composition of various particulate fractions showed that the area is highly polluted. The fine particle mass concentrations peaked at 1000 µg/m3 for PM2.5. Ambient carbon mass concentrations ranged from 20 to 40 µg/m3. Single particle analysis confirmed that the fine particle fraction was dominated by organic particles. The topographical conditions, causing a low inversion, together with the high amounts of emissions from biomass burning, result in unacceptable levels of air pollution and pose a considerable health threat to the population.
Self-organized pattern formation of an atmospheric pressure plasma jet in a dielectric barrier discharge configuration

This letter reports the observation of self-organized patterns formed in a 29mm wide atmospheric pressure plasma jet. By altering the gas flow rate and/or the applied voltage, the plasma jet is seen to have at least three different modes, namely, a diffuse-looking discharge, a self-organized discharge, and an unstable discharge with randomly occurring plasma channels. The self-organized discharge mode is characterized by several bright plasma channels embedded in a diffuse and dim plasma background. These plasma channels are regularly spaced from each other and their self-organized patterns are shown to evolve abruptly.
Low-Rank Tensor Recovery with Euclidean-Norm-Induced Schatten-p Quasi-Norm Regularization

The nuclear norm and Schatten-$p$ quasi-norm of a matrix are popular rank proxies in low-rank matrix recovery. Unfortunately, computing the nuclear norm or Schatten-$p$ quasi-norm of a tensor is NP-hard, which is a pity for low-rank tensor completion (LRTC) and tensor robust principal component analysis (TRPCA). In this paper, we propose a new class of rank regularizers based on the Euclidean norms of the CP component vectors of a tensor and show that these regularizers are monotonic transformations of tensor Schatten-$p$ quasi-norm. This connection enables us to minimize the Schatten-$p$ quasi-norm in LRTC and TRPCA implicitly. The methods do not use the singular value decomposition and hence scale to big tensors. Moreover, the methods are not sensitive to the choice of initial rank and provide an arbitrarily sharper rank proxy for low-rank tensor recovery compared to nuclear norm. We provide theoretical guarantees in terms of recovery error for LRTC and TRPCA, which show relatively smaller $p$ of Schatten-$p$ quasi-norm leads to tighter error bounds. Experiments using LRTC and TRPCA on synthetic data and natural images verify the effectiveness and superiority of our methods compared to baseline methods.
Clinical Factors Related to Improved Scapular Control After a Scapular Conscious Control Program in Symptomatic Overhead Athletes: Secondary Analysis of a Randomized Controlled Trial

Background: Predictive variables associated with the effects of a scapular conscious control program should be identified and used to guide rehabilitation programs. Purpose: To determine whether potential factors are associated with the success of scapular muscle balance with an early control program in patients with subacromial pain and scapular dyskinesis. Study Design: Case-control study; Level of evidence, 3. Methods: A total of 38 amateur overhead athletes with subacromial pain and medial border prominence were recruited. They performed progressive conscious control of scapular orientation during 45° and 90° of arm elevation. Stepwise logistic regression and receiver operating characteristic curve were used to determine the optimal cutoff point of related factors for success or failure of the program. Potential factors including pain level during activity, pain duration, anterior/posterior shoulder flexibility, forward shoulder posture, posterior displacement of root of spine and inferior angle, scapular kinematics, and muscle activation before conscious control program were recorded as independent variables. Successful control defined as decreases of the upper trapezius/serratus anterior ratio in 2 consecutive trials of the 90° program or failure in the program was used as a dependent variable. Results: Having a posterior displacement of the inferior angle of the scapula of ≤16.4 mm and scapular posterior tipping during arm elevation of ≤3.3° (collected before the control program) were associated with the success of the program (R 2 = 0.286; P < .05). Additionally, participants with each or both variables present at baseline had probabilities of success of 78% and 95%, respectively. Conclusion: The value of scapular posterior displacement and posterior tilt should be considered before early scapular control program. Other factors related to the success of the program should be found due to the limited variance explained in the regression model.
Multivalente Rezeptoren auf Phosphonatbasis - Molekulare Erkennung und Selbstorganisation in Wasser

Die Natur hat die Molekulare Erkennung von geladenen Gastmolekulen perfektioniert und bedient sich hierbei haufig polyvalenter Wechselwirkungen. Gastmolekule werden glechzeitig von mehreren Rezeptorstellen eines Wirtmolekuls gebunden, wodurch es zu einer erheblichen Bindungsverstarkung kommt. Dies geschieht haufig aufgrund elektrostatischer Wechselwirkungen, Pi-Pi-Wechselwirkungen aber auch Wasserstoffbrucken. In dieser Arbeit wurde das Konzept der Multivalenz auf die molekulare Erkennung von Aminosauren durch ein macrocyclisches, tetraphosphonathaltiges Wirtmolekul ubertragen, welches Lysin und Histidin in Methanol und Wasser besonders gut bindet. Im zweiten Teil wurde das Konzept der Multivalenz auf die molekulare Erkennung der Bisamidine Pentamidin und DAPI ubertragen. Diese wurden durch ein Tetraphosphonathaltiges tetraanionisches Rezeptormolekul in Methanol und Wasser bevorzugt in 2:1-Komplexen gebunden. Im letzten Teil wurde das Konzept der Multivalenz auf die Selbstorganisation von zunachst dreizahnigen, spater vierzahnigen komplementaren, unterschiedlich geladenen Kapselhalbschalen ubertragen, wodurch sich in Methanol und Wasser definierte 1:1-Komplexe ausbildeten, deren Komplexierungseigenschaften untersucht wurden.
Motor Imagery Classification Emphasizing Corresponding Frequency Domain Method based on Deep Learning Framework

The electroencephalogram, a type of non-invasive-based brain signal that has a user intention-related feature using in brain-computer interface (BCI) provides an efficient bidirectional pathway between user and computer. In this work, we proposed a deep learning framework based on the corresponding frequency emphasize method to decode the motor imagery (MI) data from the 2020 International BCI competition dataset. The MI dataset consists of 3-class, namely ‘Cylindrical’, ‘Spherical’, and ‘Lumbrical’. We utilized power spectral density as an emphasize method and a convolutional neural network to classify the modified MI data. The results showed that the MI-related frequency range was activated during the MI task, and provide neurophysiological evidence to design the proposed method. When using the proposed method, the average classification performance in the intra-session condition was 69.68% and the average classification performance in the inter-session condition was 52.76%. Our results provided the possibility of developing a BCI-based device control system for practical applications.
Interaction of Plastics in Mixed-Plastics Pyrolysis

The pyrolysis of mixed-plastic waste has been proposed as a means of recycling to produce petrochemical feedstock. The interaction of the main plastic types in plastic mixtures is significant in predicting the likely yield and composition of products from different plastic mixtures. The six main plastics in municipal solid waste are high-density polyethylene (HDPE), low-density polyethylene (LDPE), polypropylene (PP), polystyrene (PS), poly(vinyl chloride) (PVC), and poly(ethylene terephthalate) (PET). Each of the plastics was pyrolyzed individually in a fixed-bed reactor heated at 25 °C min-1 to a final temperature of 700 °C. Polystyrene was then mixed with each of the other five plastics in a ratio of 1:1 and pyrolyzed in the fixed-bed reactor under the same pyrolysis conditions. The yield and composition of the derived oil/wax and gases was determined. The main gases produced from the individual plastics were hydrogen, methane, ethane, ethene, propane, propene, butane, and butene and for the PET plasti.