Miracle-dz/UniFAR-FTUs huggingface dataset:
Emergency Department Visits Among Pediatric Patients for Sports-related Injury: Basic Epidemiology and Impact of Race/Ethnicity and Insurance Status

Objectives: (1) To characterize the demographics and external causes of pediatric sports injury-related visits (SIRVs) to emergency departments (EDs). (2) To analyze the effect of race/ethnicity and insurance on SIRVs to EDs. Methods: A stratified random-sample cross-sectional survey of EDs in the National Hospital Ambulatory Medical Care Survey was conducted from 1997-2001; for patients younger than 19 years, we used all visits [n = 33,654; injury-related visits (IRVs) = 13,496, SIRVs = 2990]. We examined both the external cause codes and the actual verbatim text of all IRVs. National estimates of pediatric IRVs were obtained using the assigned patient visit weights in the National Hospital Ambulatory Medical Care Survey databases and SUDAAN 9.1 software (SAS Institute, Inc., Cary, NC). Results: Sports injuries resulted in 2.5 million visits annually, or 23% of ED IRVs. Male sex, older age (6-18 years), and white race/ethnicity are associated with higher rates of SIRVs. Cycling, basketball, playground injuries, and football resulted in the largest numbers of ED SIRVs. Leading diagnoses for SIRVs included fractures and dislocations, sprains and strains, open wounds, and contusions. Hispanic race/ethnicity was associated with lower rates of SIRVs across all insurance types. After controlling for demographic factors and insurance, Hispanic children were less likely to have an SIRV than white children (odds ratio, 0.7; 95% confidence interval, 0.6-0.9). Conclusions: Sports and recreation are the leading causes of pediatric ED IRVs. Hispanic children, regardless of insurance status, had lower rates of SIRVs than white children, which helps explain the lower rate of nonfatal IRVs to EDs among Hispanic youth.
k- Nearest Neighbor Algorithm For Improving Accuracy In Clutter Based Location Estimation Of Wireless Nodes

This research is focusing on the precise location estimation of mobile node by using k - nearest neighbor algorithm (kNN). It is based on our previous research findings in which we divided the geographical area into thirteen clutters/terrains based on the behavior of radio waves. We calculated the point-to-point distance from the antennas to mobile node by using receive signal strength and available signal strength information. A C# prototype was developed by using WiFi (IEEE 802.11 b/g standard) to record data points in different clutters at every 2 meter distance. We derived Clutter based Enhanced Error Rate Table (CERT) for precision. Although CERT minimizes errors due to the atmospheric considerations but in highly attenuated clutters the error rate was still high. The k-NN algorithm is used to minimize the error ranging from 1-3 meters to 0.4- 1.2 meter only, depending on the clutter we are dealing with. Current research is divided into three steps. First we calculate the P2P distance in all different clutters by using available signal strength and receive signal strength at five different time intervals (t0 – t4). In step 2 we construct three triangles at random by using the data gathered in step 1 and calculate mean value of predicted locations. Finally based on locations calculated in step 2 we apply the k-NN algorithm to minimize error in the estimated location. Results show that the k-NN can produce from 217% - 289% better results compare to the famous triangulation method. The purpose of this research is to reduce errors in order to achieve estimated position near to accurate.
[Psychoanalytic theories on affect].

It sounds good when knowing the psychoanalytic theories of affect in this website. This is one of the books that many people looking for. In the past, many people ask about this book as their favourite book to read and collect. And now, we present hat you need quickly. It seems to be so happy to offer you this famous book. It will not become a unity of the way for you to get amazing benefits at all. But, it will serve something that will let you get the best time and moment to spend for reading the book.
Rate of Convergence of Lawson's Algorithm

The algorithm of Charles L. Lawson determines uniform approximations of functions as limits of weighted L2 approximations. Lawson noticed from experimental evi- dence that the algorithm seemed to converge linearly and convergence was related to a factor which was the ratio of the largest nonmaximum error of the best uniform approximation to the maximum error. This paper proves the linear convergence and explores the relation of the rate of convergence to this ratio. 1. Introduction. In his Ph.D. dissertation of 1961, Charles L. Lawson discussed an algorithm for solving uniform approximation problems by means of limits of weighted p-norm solutions. Since then, this algorithm has been explored further by several authors. The algorithm is mentioned in Rice (3), and a variation on Lawson's algorithm was shown to produce p-norm approximations (p > 2) as a limit of weighted L2 norm solutions in Rice and Usow (4). In the Ph.D. dissertation of this author (1), Lawson's algorithm (originally defined for approximation on finite sets) was extended to the case of approximation on compact Hausdorff spaces. Presently, attempts are underway extending Lawson's algorithm in a different fashion for solving L, approximation problems. In his dissertation, Lawson gave conditions for convergence of the weighted LP solutions to the uniform solution. In some cases (theoretically possible but com- putationally highly unlikely), the algorithm may have to be restarted a finite number of times before it converges to the proper solution. When it converges to the uniform solution, Lawson noticed experimental results indicating linear convergence with a convergence factor linked to a certain ratio of error at a point to maximum error of the solution. It is the purpose of this paper to show that the Lawson algorithm does have linear convergence and demonstrate the importance of the convergence factor which
Analysis of power system oscillations for developing synchrophasor data applications

Electromechanical modes are predominantly determined by the machine rotor angles and speeds, and as a result, they provide the best visibility of such modes. These electromechanical modes are also observable in the network variables such as voltages and line currents, which are measured by PMUs. In this paper, by analyzing the electromechanical modes in the network variables, we can trace the propagation of electromechanical oscillations in the power network following a disturbance. In large power networks with multiple transfer paths across the interconnection it is not always clear how and where the oscillations propagate. Here, we seek to provide a rationale explaining how (the qualitative behavior) and where (transmission lines) network oscillations propagate. We first analyze the oscillations from real PMU data originating from a system-wide disturbance. Applying eigenvalue and sensitivity analysis we provide an analytical framework to understand the nature of the network oscillations. Closed-form expressions for the network sensitivities are provided. The results here important for building reduced models to assess transient stability and the selection of network variables as input signals to damp inter-area mode oscillations.
Research on text categorization based on machine learning

In recent years, text categorization based on machine learning is a widely used technology in the field of information retrieval and text mining and has gained many advances. This paper presents a survey on the up-to-date development in text categorization based on machine learning, including model, algorithm and evaluation and introduces some machine learning methods applied in text categorization.
Coupling of Self-activating Genes Induces Spontaneous Synchronized Oscillations in Cells

Genetic oscillators are present in a wide range of organisms from bacteria to neurons and coordinate important biological functions. Current models of genetic oscillators are based on auto-repressed genes. In these models a gene produces a repressor protein that binds to the promoter of its own gene repressing the transcription. Different versions of these models have been studied in living organisms and for engineering synthetic clocks. Synchronization of genetic clocks based on this model has also been studied. However, genes with positive feedbacks are also present in natural and synthetic genetic clocks. These self-activating genes provide robustness and frequency tuning to genetic clocks. In this paper we show a novel role of self-activating genes. We demonstrate that the coupling of self-activating genes by small molecules in a cell population produces synchronized oscillations. Our model could be useful for engineering new robust multicellular clocks and better understanding of natural genetic oscillators.
Tree based reconstruction from sparse samples of signals

The study in sparse representation of signals and its applications has changed the field of signal processing into a quickly developing area. These studies have opened tremendous feasible research ideas. Compressed sensing is one among the greatest beneficial field of these studies. Signal sparsity property introduced the idea of reconstruction of signals from very few prototype atoms of an overcomplete dictionary. The novel approach in dictionary learning process is K-SVD method. We propose a method to improve the sparse representation of signal by incorporating A∗ search algorithm in K-SVD. The usage of tree data structure has provided a lot of advantages including the introduction of auxiliary function, tree pruning techniques etc. Experimental results show that the proposed system provides more efficient and improved results than the conventional ones.
Differential Mechanisms of the Effect of Peroxisome Proliferator-Activated Receptor Gamma Agonists on Bleomycin-Induced Lung Fibrosis

Background and Objectives: Peroxisome proliferator-activated receptor-g (PPAR-g) is a nuclear receptor whose activation regulates inflammation and fibrosis in various organs. We aimed to investigate the effect of two PPAR-g ligands, telmisartan and rosiglitazone, on lung injury and fibrosis induced by intratracheal bleomycin (BLM). Methods: Lung injury and fibrosis was induced in female C57Bl/6 mice by intratracheal instillation of 1.0 mg/kg of BLM. Some of the animals received rosiglitazone intraperitoneally or telmisartan in drinking water. Bronchoalveolar lavage (BAL) was performed 2, 7, 14 or 21 days after BLM instillation for cell counting and measurement of mediators in the lung. In a separate series, the lungs were sampled for collagen assay and histopathological evaluation. Results: Treatment with rosiglitazone or telmisartan significantly attenuated the BLM-induced increases in lung collagen content, pathological score, and inflammatory cells in BAL fluid. Rosiglitazone significantly suppressed BLM-induced elevation of TGF-b1, MCP-1, and IL-6 levels in the lung. In contrast, telmisartan made no changes in these cytokines, whereas it mitigated the BLM-induced increase in prostaglandin F2a in the lung. Higher concentrations of rosiglitazone and telmisartan attenuated proliferation of lung fibroblasts in vitro. Conclusions: Two PPAR-g ligands, rosiglitazone and telmisartan, exert protective effects on BLM-induced lung fibrosis through the suppression of different profibrotic mediators.
Receptor Usage and Cell Entry of Porcine Epidemic Diarrhea Coronavirus

ABSTRACT Porcine epidemic diarrhea coronavirus (PEDV) has significantly damaged America's pork industry. Here we investigate the receptor usage and cell entry of PEDV. PEDV recognizes protein receptor aminopeptidase N from pig and human and sugar coreceptor N-acetylneuraminic acid. Moreover, PEDV infects cells from pig, human, monkey, and bat. These results support the idea of bats as an evolutionary origin for PEDV, implicate PEDV as a potential threat to other species, and suggest antiviral strategies to control its spread.
Time Delay in the Kuramoto Model of Coupled Oscillators

We generalize the Kuramoto model of coupled oscillators to allow time-delayed interactions. New phenomena include bistability between synchronized and incoherent states, and unsteady solutions with time-dependent order parameters. We derive exact formulas for the stability boundaries of the incoherent and synchronized states, as a function of the delay, in the special case where the oscillators are identical. The experimental implications of the model are discussed for populations of chirping crickets, where the finite speed of sound causes communication delays, and for physical systems such as coupled phase-locked loops or lasers. [S0031-9007(98)08184-8]
Effects of platelet‐derived growth factor on bone formation in vitro

Platelet‐derived growth factor (PDGF) is a polypeptide found in a variety of tissues, including bone, where it could act as an autologous regulator of skeletal remodeling. Therefore, a recombinant B chain homodimer of human PDGF was studied for its effects on bone formation in cultured rat calvariae. PDGF at 10‐100 ng/ml stimulated [3H]thymidine incorporation into DNA by up to sixfold and increased the DNA content and the number of colcemid‐induced metaphase arrested cells. This effect was observed in the fibroblast and precursor cell‐rich periosteum. As a result of its mitogenic actions, PDGF enhanced [3H]proline incorporation into collagen, an effect that was observed primarily in the osteoblast‐rich central bone. The effect of PDGF was not specific for collagen since it also increased noncollagen protein synthesis. In addition, PDGF increased bone collagen degradation. PDGF and insulin‐like growth factor (IGF) I had additive effects on calvarial DNA synthesis, but PDGF opposed the stimulatory effect of IGF I on collagen synthesis and IGF I prevented the PDGF effect on collagen degradation.
Design and implementation of a random access file system for NVRAM

: Storage Class Memory (SCM) emerges as a potential technology because of its byte-addressability and non-volatility. However, existing ﬁ le systems are not ef ﬁ cient for SCM because they are optimized for hard disks devices. In this paper, we propose a new ﬁ le system called random access ﬁ le system (RAFS), which is implemented on Linux as a prototype. In RAFS, we simplify system architecture and adopt memory mapping and XIP mechanisms to improve performance. We have analyzed and evaluated its performance with multiple workloads. Results show that RAFS outperforms Ext4 under the same condition, especially for random access scenarios.
Prominence of cooperative communication in 5G cognitive radio systems

Cognitive Radio is a promising way to overcome the spectrum scarcity for wireless communication and improving the spectral efficiency by using the vacant licensed spectrum band. Cooperative communication is a new communication technique which utilizes the help of neighboring nodes to reduce the bit error rate (BER) in a harmful fading environment. The challenging factor is to combine the cooperative communication in cognitive radio to improve the spectral efficiency and to reduce the BER factor of unlicensed user's communication. In this paper, a comparative study of different communication techniques are done by considering Rayleigh fading channel environment and the advantages of cooperative communication is analyzed. Also, the paper deals with the challenges of integrating cooperative communication in cognitive radio network are discussed.
Temperature Dependence of the Threshold Voltage Shift Induced by Carrier Injection in Integrated STI-Based LDMOS Transistors

Large threshold voltage shifts (ΔVt) are experimentally observed in n-channel lateral DMOS transistors under high current-voltage regime. The effect is enhanced by the gate voltage as well as by the ambient temperature (TA) . By approximating the curves with the usually adopted power-law dependence (ΔVt = Atn), two different contributions are observed, and a clear increase of the exponent n is found. A numerical investigation is carried out, revealing that the electric field normal to the oxide interface (En) as well as the internal temperature (T) close to the source side of the MOS channel is mainly responsible for such enhanced degradation.
EVALUATION OF THE MICROHARDNESS OF ROOT CANAL DENTIN AFTER DIFFERENT IRRIGATION PROTOCOLS

INTRODUCTION: Different irrigations solutions may cause alteration in the physicochemical properties of dentin structure thereby affectingthe microhardness of root canal dentin.OBJECTIVES: to evaluate the effect of different irrigation protocols on microhardness of human root canal dentin.MATERIALS AND METHODS: Forty extracted single rooted lower premolars were used. All teeth were instrumented using manualstainless steel files and irrigated by 2ml distilled water between each file, then were sectioned by longitudinal splitting of each tooth. The roothalves were randomly assigned into 4 parallel groups (n=10) and immersed for 5 minutes with one of the following irrigants: Group I: 10 mlof 2.5% Sodium Hypochlorite (NaOCL), Group II: 10 ml of 17% ethylene diamine tetra-acetic acid (EDTA) followed by 10 ml of 2.5%NaOCL, Group III: 10 ml of 2.5% NaOCL followed by 10 ml of 2% chlorhexidine digluconate (CHX), Group IV: 10 ml of 2.5% NaOCLfollowed by 10 ml distilled water then were followed by 10ml of 2% CHX. Ten root halves from each group were prepared to measure dentinmicrohardness at baseline measurement and after treatment to determine the change in microhardness, using Vickers tester.RESULTS: Data were analysed using t-test, ANOVA test and Post Hoc test.Group II showed the highest percentage decrease in microhardnessvalues, followed by group III, then group IV and the lowest was group I. All groups showed a significant difference between each other (P <0.05), except group III and IV. The coronal third showed the highest percentage decrease with significant difference between apical and middlethirds (P < 0.05), in which there was no significant difference between them.CONCLUSIONS: CHX is the best final irrigant if there is excellent intermediate flush for prevention of its precipitation with NaOCL.Thecoronal third needs conservative approach as it is the most affected third.
A Kernel Compression Scheme for Fractional Differential Equations

The nonlocal nature of the fractional integral makes the numerical treatment of fractional dierential equations expensive in terms of computational eort and memory requirements. In this paper we propose a method to reduce these costs while controlling the accuracy of the scheme. This is achieved by splitting the fractional integral of a function f into a local term and a history term. Observing that the history term is a convolution of the history of f and a regular kernel, we derive a multipole approximation to the Laplace transform of the kernel. This enables the history term to be replaced by a linear combination of auxiliary variables dened as solutions to standard ordinary dierential equations. We derive a priori error estimates, uniform in f, and obtain estimates on the number of auxiliary variables required to satisfy an error tolerance. The resulting formulation is discretized to produce a time stepping method. The method is applied to some test cases to illustrate the performance of the scheme.
Nested Named Entity Recognition with Span-level Graphs

Span-based methods with the neural networks backbone have great potential for the nested named entity recognition (NER) problem. However, they face problems such as degenerating when positive instances and negative instances largely overlap. Besides, the generalization ability matters a lot in nested NER, as a large proportion of entities in the test set hardly appear in the training set. In this work, we try to improve the span representation by utilizing retrieval-based span-level graphs, connecting spans and entities in the training data based on n-gram features. Specifically, we build the entity-entity graph and span-entity graph globally based on n-gram similarity to integrate the information of similar neighbor entities into the span representation. To evaluate our method, we conduct experiments on three common nested NER datasets, ACE2004, ACE2005, and GENIA datasets. Experimental results show that our method achieves general improvements on all three benchmarks (+0.30 \sim 0.85 micro-F1), and obtains special superiority on low frequency entities (+0.56 \sim 2.08 recall).
CUET-NLP@DravidianLangTech-ACL2022: Exploiting Textual Features to Classify Sentiment of Multimodal Movie Reviews

With the proliferation of internet usage, a massive growth of consumer-generated content on social media has been witnessed in recent years that provide people’s opinions on diverse issues. Through social media, users can convey their emotions and thoughts in distinctive forms such as text, image, audio, video, and emoji, which leads to the advancement of the multimodality of the content users on social networking sites. This paper presents a technique for classifying multimodal sentiment using the text modality into five categories: highly positive, positive, neutral, negative, and highly negative categories. A shared task was organized to develop models that can identify the sentiments expressed by the videos of movie reviewers in both Malayalam and Tamil languages. This work applied several machine learning techniques (LR, DT, MNB, SVM) and deep learning (BiLSTM, CNN+BiLSTM) to accomplish the task. Results demonstrate that the proposed model with the decision tree (DT) outperformed the other methods and won the competition by acquiring the highest macro f_1-score of 0.24.
Urinary bladder cancer risk factors: a Lebanese case- control study.

BACKGROUND Bladder cancer is the second most incident malignancy among Lebanese men. The purpose of this study was to investigate potential risk factors associated with this observed high incidence. METHODS A case-control study (54 cases and 105 hospital-based controls) was conducted in two major hospitals in Beirut. Cases were randomly selected from patients diagnosed in the period of 2002-2008. Controls were conveniently selected from the same settings. Data were collected using interview questionnaire and blood analysis. Exposure data were collected using a structured face-to-face interview questionnaire. Blood samples were collected to determine N-acetyltransferase1 (NAT1) genotype by PCR-RFLP. Analyses revolved around univariate, bivariate and multivariate logistic regression, along with checks for effect modification. RESULTS The odds of having bladder cancer among smokers was 1.02 times significantly higher in cases vs. controls. The odds of exposure to occupational diesel or fuel combustion fumes were 4.1 times significantly higher in cases vs controls. The odds of prostate-related morbidity were 5.6 times significantly higher in cases vs controls. Cases and controls showed different clustering patterns of NAT1 alleles. No significant differences between cases and controls were found for consumption of alcohol, coffee, tea, or artificial sweeteners. CONCLUSIONS This is the first case-control study investigating bladder cancer risk factors in the Lebanese context. Results confirmed established risk factors in the literature, particularly smoking and occupational exposure to diesel. The herein observed associations should be used to develop appropriate prevention policies and intervention strategies, in order to control this alarming disease in Lebanon.