Miracle-dz/UniFAR-FTUs huggingface dataset:
Premilinary Results of Surgical Results of Total Hip Joint Replacement at the 7A Military Hospital

Purpose: Initial assessment of total hip replacement surgery at 7A Military Hospital. Subjects and research methods: A cross-sectional descriptive study, conducted from 3/2014 4/2018 on 47 patients with a total of 52 total hip joints (THJ) were replaced at 7A Military Hospital. Result: The average time of surgery was 91.92 ± 15,847 minutes. The average length of treatment: 15.5 ± 4,948 days. Evaluation by HARRIS score: Very good + good: 96.2%, Average + poor 3.8%. Complications: Superficial Infections on incision 1.9%, dislocated joints 1.9%, loose stem 1.9%, no serious complications. Conclusion: Total hip joint replacement to treat osteoarthritis, aseptic necrosis of femoral head, femoral neck fracture is an effective treatment, restoring the patients' joint movement function, and with low complication rate. Keyword: Surgery, total hip replacement, (THJ), osteoarthritis. Copyright @ 2020: This is an open-access article distributed under the terms of the Creative Commons Attribution license which permits unrestricted use, distribution, and reproduction in any medium for non-commercial use (NonCommercial, or CC-BY-NC) provided the original author and source are credited.
Biochemical Hydrogen Potential Tests Using Different Inocula "2279

Four inocula collected from different operating facilities were tested in their hydrogenic performances by means of two biochemical hydrogen potential test set-ups using sucrose and food waste as substrates, with the aim of evaluating the influence of inoculum media in batch fermentative assays. The selected inocula were: activated sludge collected from the aerobic unit of a municipal wastewater treatment plant, digested sludge from an anaerobic reactor treating organic waste and cattle manure, digested sludge from an anaerobic reactor treating agroindustrial residues, and digested sludge from an anaerobic reactor of a municipal wastewater treatment plant. Test results, in terms of specific hydrogen production, hydrogen conversion efficiency, and volatile solids removal efficiency, were significantly dependent on the type of inoculum. Statistical analysis showed different results, indicating that findings were due to the different inocula used in the tests. In particular, assays performed with activated sludge showed the highest performances for both substrates and both experimental set-ups.
Modeling Affect and Cognition: Opportunities and Challenges for Managerial and Organizational Cognition

In this chapter we examine some possibilities of using computer simulation methods to model the interaction of affect and cognition in organizations, with a particular focus on agent-based modeling (ABM) techniques. Our chapter has two main aims. First, we take stock of methodological progress in this area, highlighting important developments in the modeling of affect and cognition in other fields, including psychology and economics. Second, we outline how ABM in particular can help to advance managerial and organizational cognition by building and testing theoretical models predicated on the interaction of affect and cognition. We argue that using ABM for this purpose can improve the level of specificity of cognitive and affective concepts and their interrelationships in organizational theories, yield more behaviorally plausible models of behavior in and of organizations, and deepen understanding of the generative behavioral mechanisms of multi-level organizational phenomena. We highlight possibilities for using ABM to model -affect–cognition interactions in studies of mental models, collective cognition, diversity in work groups and teams, and organizational decision-making.
The utilization of information and communication technology and its effectiveness in learning empirical science among fourth-grade elementary school students

The research aimed to explore the necessity for the utilization of information and communication technology and its effectiveness in learning empirical science among fourth-grade elementary school students and it has also drawn a comparison with the traditional method. The research sample comprised 184 fourth graders under the program of descriptive assessment who were randomly selected and assigned into the experimental and control groups. First, both groups underwent the traditional method and then, the pre-test was administered. The experimental group used new technology at home for reviewing and revising the lessons while the control group reviewed the lessons using the traditional method. The results of post-test and its comparison with the pre-test scores indicated that, the experimental group scored higher on learning as compared to the control group. Moreover, the increasing scores of the experimental group obtained a higher mean as compared to the control group. The statistical analysis indicated that, the value of F was significant for the experimental and control group in the post-test at the level of . 0001.
Clinical experience of laryngeal mask airway in lateral position during anesthesia

BACKGROUND The laryngeal mask airway (LMA), inserted blindly into hypopharynx in patients in supine position, has been reported to be successful in the management of the airway and can provide adequate ventilation during anesthesia. In our study, we used LMA to maintain airway during anesthesia in patients in lateral position. METHODS Eighty surgical patients, ASA class I-II, undergoing hemorrhoidectomy, were placed in lateral surgical position before induction. The induction agents were propofol (2 mg/kg), fentanyl (2 micrograms/kg) and atracurium (0.5 mg/kg, i.v.). When mouth opening can be done passively, a LMA was inserted into the mouth and advanced blindly over the tongue into the pharynx until resistance was felt. The rim was immediately inflated with air and several manual positive pressure ventilatory breaths were given to the patient to check for leaks in the LMA system. Chest movement was observed and bilateral lung ausculation was performed to confirm the adequacy of ventilation. Once the LMA was in place, enflurane with oxygen was administered until operation was completed. During the anesthetic course, EKG, blood pressure, pulse oximeter, end-tidal CO2 and inspiratory pressure were monitored. RESULTS Blind insertion of LMA in lateral position was successful in the first attempt in 70% (56/80) of patients. Twenty (18/80) patients required a second or third trial making the total successful rate of LMA insertion as high as 90% (74/80). In four patients, airway control through LMA was unsatisfactory due to large air leakage during manual ventilation. In two patients the LMA could not be inserted in proper position for maintaining airway. In six cases, the LMA was immediately removed and was substituted by face-mask for anesthesia. Two patients complained of postoperative sore throat. No other specific complications such as cough, laryngospasm and aspiration was noted. CONCLUSION We demonstrated that LMA can be easily inserted and can provide effective patency of airway during anesthesia even in lateral position. It can be used as a substitute for face-mask in anesthesia in this position to prevent complications such as pressure injury that a face-mask may cause.
A signature related to lung adenocarcinoma prognosis: A study based on TCGA database

Background: The intrinsic molecular subtypes of lung adenocarcinoma (LUAD) impact clinical treatment decision-making, but the molecular mechanisms are still unclear. Therefore, we aimed to identify sensitive biomarkers to evaluate LUAD patient prognosis. Methods: Differentially expressed RNAs from LUAD patients were obtained from The Cancer Genome Atlas (TCGA) database and they were used to construct a competitive endogenous RNA (ceRNA) network. Based on the examination of clinical data, long noncoding RNAs (lncRNAs) and mRNAs in the network were selected by univariate and multivariate Cox regression analysis. Finally, functional enrichment analysis was used to reveal prognostic signatures based on the classification into high and low-risk groups, survival analysis, and an independence test. Results: The ceRNA network consisted of 21 mRNAs, 53 lncRNAs, and 8 miRNAs that were selected from the differentially expressed RNAs identified. Next, based on univariate and multivariate Cox regression analysis, a prognostic signature, including two mRNAs (HOXA10 and CBX2) and four lncRNAs (LINC00460, LINC00330, DGCR5, and C14orf132) was constructed. Eventually, survival analysis showed that significant differences in survival rates between high and low-risk groups and the area under the curve (AUC) for three‐year survival was 0.714. Compared with clinical risk factors, including age, pathological stage, and TNM stage, our risk score had a higher prognostic value. Conclusion: By screening from a ceRNA network, we constructed a signature, including two mRNAs (HOXA10 and CBX2) and four lncRNAs (LINC00460, LINC00330, DGCR5, and C14orf132), that can be utilized as a prognostic biomarker in LUAD. This signature may provide options for clinical treatment.
Exploring the Effectiveness of Convolutional Neural Network with Ensemble Technique

Sentiment Analysis has recently been considered as the most active research field in the natural language processing (NLP) domain. Deep Learning is a subset of the large family of Machine Learning and becoming a growing trend due to its automatic learning capability with impressive consequences across different NLP tasks. Hence, a fusion-based Machine Learning framework has been attempted by merging the Traditional Machine Learning method with Deep Learning techniques to tackle the challenge of sentiment prediction for a massive amount of unstructured review dataset. The proposed architecture aims to utilize the Convolutional Neural Network (CNN) with a backpropagation algorithm to extract embedded feature vectors from the top hidden layer. Thereafter, these vectors augmented to an optimized feature set generated from binary particle swarm optimization (BPSO) method. Finally, a traditional SVM classifier is trained with these extended features set to determine the optimal hyper-plane for separating two classes of review datasets. The evaluation of this research work has been carried out on two benchmark movie review datasets IMDB, SST2. Experimental results with comparative studies based on performance accuracy and F-score value are reported to highlight the benefits of the developed frameworks.
Scaling K2. I. Revised Parameters for 222,088 K2 Stars and a K2 Planet Radius Valley at 1.9 R⊕

Previous measurements of stellar properties for K2 stars in the Ecliptic Plane Input Catalog largely relied on photometry and proper motion measurements, with some added information from available spectra and parallaxes. Combining Gaia DR2 distances with spectroscopic measurements of effective temperatures, surface gravities, and metallicities from the Large Sky Area Multi-Object Fibre Spectroscopic Telescope (LAMOST) DR5, we computed updated stellar radii and masses for 26,838 K2 stars. For 195,250 targets without a LAMOST spectrum, we derived stellar parameters using random forest regression on photometric colors trained on the LAMOST sample. In total, we measured spectral types, effective temperatures, surface gravities, metallicities, radii, and masses for 222,088 A, F, G, K, and M-type K2 stars. With these new stellar radii, we performed a simple reanalysis of 299 confirmed and 517 candidate K2 planet radii from Campaigns 1–13, elucidating a distinct planet radius valley around 1.9 R⊕, a feature thus far only conclusively identified with Kepler planets, and tentatively identified with K2 planets. These updated stellar parameters are a crucial step in the process toward computing K2 planet occurrence rates.
Geometry based Stochastic Channel Modeling using Ambit Processes

The simulation of vehicular wireless channels using geometry-based radio channel models is computationally intensive when the number of scatterers is significantly high. In this paper, we propose a new geometry-based stochastic channel model to simulate and analyze the aforementioned channels based on a framework developed from the theory of ambit processes. Under reasonable assumptions, the underlying mathematical structure of the proposed channel model enables the characterization of high mobility channels in terms of fading statistics, spatiotemporal channel correlation, and Doppler spectrum, besides ensuring tractable analysis. The developed algorithm facilitates fast simulation of high mobility channels and accounts for key features of vehicular channels including appearance and disappearance of multi-path components, spatial consistency, and captures the correlation between time-evolving delay and Doppler associated with multi-path components. Finally, we carry out simulations to obtain crucial insights about the characteristics of typical vehicle-to-infrastructure channels based on the proposed channel model.
Annihilator varieties of distinguished modules of reductive Lie algebras

Abstract We provide a microlocal necessary condition for distinction of admissible representations of real reductive groups in the context of spherical pairs. Let ${\mathbf {G}}$ be a complex algebraic reductive group and ${\mathbf {H}}\subset {\mathbf {G}}$ be a spherical algebraic subgroup. Let ${\mathfrak {g}},{\mathfrak {h}}$ denote the Lie algebras of ${\mathbf {G}}$ and ${\mathbf {H}}$ , and let ${\mathfrak {h}}^{\bot }$ denote the orthogonal complement to ${\mathfrak {h}}$ in ${\mathfrak {g}}^*$ . A ${\mathfrak {g}}$ -module is called ${\mathfrak {h}}$ -distinguished if it admits a nonzero ${\mathfrak {h}}$ -invariant functional. We show that the maximal ${\mathbf {G}}$ -orbit in the annihilator variety of any irreducible ${\mathfrak {h}}$ -distinguished ${\mathfrak {g}}$ -module intersects ${\mathfrak {h}}^{\bot }$ . This generalises a result of Vogan [Vog91]. We apply this to Casselman–Wallach representations of real reductive groups to obtain information on branching problems, translation functors and Jacquet modules. Further, we prove in many cases that – as suggested by [Pra19, Question 1] – when H is a symmetric subgroup of a real reductive group G, the existence of a tempered H-distinguished representation of G implies the existence of a generic H-distinguished representation of G. Many of the models studied in the theory of automorphic forms involve an additive character on the unipotent radical of the subgroup $\bf H$ , and we have devised a twisted version of our theorem that yields necessary conditions for the existence of those mixed models. Our method of proof here is inspired by the theory of modules over W-algebras. As an application of our theorem we derive necessary conditions for the existence of Rankin–Selberg, Bessel, Klyachko and Shalika models. Our results are compatible with the recent Gan–Gross–Prasad conjectures for nongeneric representations [GGP20]. Finally, we provide more general results that ease the sphericity assumption on the subgroups, and apply them to local theta correspondence in type II and to degenerate Whittaker models.
A Simple Local Minimal Intensity Prior and an Improved Algorithm for Blind Image Deblurring

Blind image deblurring is a long standing challenging problem in image processing and low-level vision. Recently, sophisticated priors such as dark channel prior, extreme channel prior, and local maximum gradient prior, have shown promising effectiveness. However, these methods are computationally expensive. Meanwhile, since these priors involved subproblems cannot be solved explicitly, approximate solution is commonly used, which limits the best exploitation of their capability. To address these problems, this work firstly proposes a simplified sparsity prior of local minimal pixels, namely patch-wise minimal pixels (PMP). The PMP of clear images is much more sparse than that of blurred ones, and hence is very effective in discriminating between clear and blurred images. Then, a novel algorithm is designed to efficiently exploit the sparsity of PMP in deblurring. The new algorithm flexibly imposes sparsity inducing on the PMP under the maximum a posterior (MAP) framework rather than directly uses the half quadratic splitting algorithm. By this, it avoids non-rigorous approximation solution in existing algorithms, while being much more computationally efficient. Extensive experiments demonstrate that the proposed algorithm can achieve better practical stability compared with state-of-the-arts. In terms of deblurring quality, robustness and computational efficiency, the new algorithm is superior to state-of-the-arts. Code for reproducing the results of the new method is available at https://github.com/FWen/deblur-pmp.git.
The Competitive Effects of Variance-based Pricing

In many markets, like electricity or cloud computing markets, providers incur large costs for keeping sufficient capacity in reserve to accommodate demand fluctuations of a mostly fixed user base. These costs are significantly affected by the unpredictability of the users' demand. Nevertheless, standard mechanisms charge fixed per-unit prices that do not depend on the variability of the users' demand. In this paper, we study a variance-based pricing rule in a two-provider market setting and perform a game-theoretic analysis of the resulting competitive effects. We show that an innovative provider who employs variance-based pricing can choose a pricing strategy that guarantees himself a higher profit than using fixed per-unit prices for any individually rational response of a provider playing a fixed pricing strategy. We then characterize all equilibria for the setting where both providers use variance-based pricing strategies. We show that, in equilibrium, the providers' profits may increase or decrease, depending on their cost functions. However, social welfare always weakly increases.
Accurate Ground State Electronic and Related Properties of Hexagonal Boron Nitride (h-BN)

We present an ab initio, self consistent density functional theory (DFT) description of ground state electronic and related properties of hexagonal boron nitride (hex-BN). We used a local density approximation (LDA) potential and the linear combination of atomic orbitals (LCAO) formalism. We rigorously implemented the Bagayoko, Zhao, and Williams (BZW) method, as enhanced by Ekuma and Franklin (BZW-EF). The method ensures a generalized minimization of the energy that is far beyond what can be obtained with self-consistency iterations using a single basis set. The method leads to the ground state of the material, in a verifiable manner, without employing over-complete basis sets. Consequently, our results possess the full, physical content of DFT, as per the second DFT theorem. We report the ground state band structure, band gap, total and partial densities of states, and electron and hole effective masses. Our calculated, indirect band gap of 4.37 eV,obtained with room temperature experimental lattice constant, is in agreement with the measured value of 4.3 eV. The valence band maximum is slightly to the left of the K point, while the conduction band minimum is at the M point. Our calculated total width of the valence and total and partial densities of states are in agreement with corresponding, experimental findings.
Convolutional Neural Networks as Summary Statistics for Approximate Bayesian Computation

Approximate Bayesian Computation is widely used in systems biology for inferring parameters in stochastic gene regulatory network models. Its performance hinges critically on the ability to summarize high-dimensional system responses such as time series into a few informative, low-dimensional summary statistics. The quality of those statistics acutely impacts the accuracy of the inference task. Existing methods to select the best subset out of a pool of candidate statistics do not scale well with large pools of several tens to hundreds of candidate statistics. Since high quality statistics are imperative for good performance, this becomes a serious bottleneck when performing inference on complex and high-dimensional problems. This paper proposes a convolutional neural network architecture for automatically learning informative summary statistics of temporal responses. We show that the proposed network can effectively circumvent the statistics selection problem of the preprocessing step for ABC inference. The proposed approach is demonstrated on two benchmark problem and one challenging inference problem learning parameters in a high-dimensional stochastic genetic oscillator. We also study the impact of experimental design on network performance by comparing different data richness and data acquisition strategies.
The ambiguous transient ASASSN-17hx

Aims. Some transients, although classified as novae based on their maximum and early decline optical spectra, cast doubts on their true nature, and raise the question of whether nova impostors might exist. Methods. We monitored a candidate nova that displayed a distinctly unusual light curve at maximum and early decline through optical spectroscopy (3000–10 000 Å, 500 < R < 100 000) complemented with Swift UV and AAVSO optical photometry. We use the spectral line series to characterize the ejecta dynamics, structure, and mass. Results. We find that the ejecta are in free ballistic expansion and have a typical classical nova structure. However, their derived mass is at least an order of magnitude higher than the typical ejecta masses obtained for classical novae. Specifically, we find Mej ≃9 × 10−3 M⊙ independent of the distance for a filling factor ε = 1. By constraining the distance we derived ε in the range 0.08–0.10, giving a mass 7 × 10−4 ≲ Mej ≲ 9 × 10−4 M⊙. The nebular spectrum, characterized by unusually strong coronal emission lines, confines the ionizing source energy to the range 20–250 eV, possibly peaking in the range 75–100 or 75–150 eV. Conclusions. We link this source to other slow novae that show similar behavior, and we suggest that they might form a distinct physical subgroup. The sources may result from a classical nova explosion occurring on a very low-mass white dwarf or they may be impostors for an entirely different type of transient.
Contractibility of space of stability conditions on the projective plane via global dimension function

We compute the global dimension function $\mathrm{gldim}$ on the principal component $\mathrm{Stab}^†(\mathbb{P}^2)$ of the space of Bridgeland stability conditions on $\mathbb{P}^2$. We describe the preimage $\mathrm{gldim}^{-1}(2)$ of the minimal value $2$ of $\mathrm{gldim}$, which is contained in the closure $\overline{\mathrm{Stab}^{\mathrm{Geo}}(\mathbb{P}^2)}$ of the subspace consisting of geometric stability conditions. We show that $\mathrm{gldim}^{-1}[2,x)$ contracts to $\mathrm{gldim}^{-1}(2)$ for any real number $x\geq 2$ and that $\mathrm{gldim}^{-1}(2)$ is contractible.
Low-Complexity Distributed XL-MIMO for Multiuser Detection

In this paper, the zero-forcing and regularized zero-forcing schemes operating in crowded extra-large MIMO (XL-MIMO) scenarios with a fixed number of subarrays have been emulated using the randomized Kaczmarz algorithm (rKA). For that, non-stationary properties have been deployed through the concept of visibility regions when considering two different power normalization methods of non-stationary channels. We address the randomness design of rKA based on the exploitation of spatial non-stationary properties. Numerical results show that, in general, the proposed rKA-based combiner applicable to XL-MIMO systems can considerably decrease computational complexity of the signal detector by paying with small performance losses.