Miracle-dz/UniFAR-FTUs huggingface dataset:
Watersheds for Solutions of Parabolic Systems

In this paper we describe a technique that we have used in a number of publications to find the “watershed” under which the initial condition of a positive solution of a nonlinear reaction-diffusion equation must lie, so that this solution does not develop into a traveling wave, but decays into a trivial solution. The watershed consists of the positive solution of the steady-state problem together with positive pieces of nodal solutions ( with identical boundary conditions). We prove in this paper that our method for finding watersheds works in R k , k ≥ 1, for increasing functions f (z)/z. In addition, we weaken the condition that f (z)/z be increasing, and show that the method also works in R 1 when f (z)/z is bounded. The decay rate is exponential.
FPGA implementation and verification of LDPC encoder with weight (3, 6) approximate lower triangular matrix

Compared with general linear block code encoding, LDPC encoding with lower triangular check matrix and approximate lower triangular check matrix carry out encoding directly by parity check matrix H. This study used the weight (3, 6) approximate lower triangular regular parity check matrix to implement the LDPC encoding on the 5641R FPGA of the Software Define Radio system developed by National Instruments (NI) [1]. This study provided a detailed introduction to the encoding mechanism of the approximate lower triangular LDPC, and completed the implementation and hardware verification of FPGA SDR system.
Correlation of platelets count with endoscopic findings in a cohort of Egyptian patients with liver cirrhosis

AbstractScreening endoscopy is recommended for early detection of esophageal varices (EVs) in cirrhotic patients with portal hypertension. However, this approach is limited by its invasiveness and cost. The aim of the study was to determine if platelet count can predict the presence of EVs, especially large (grade III, IV) EVs in need of prophylactic therapy, in a cohort of Egyptian patients with liver cirrhosis. In all, 110 patients with cirrhosis were prospectively analyzed. The presence of medium or large EVs was correlated with patients’ platelet count and FIB-4. Esophageal varices were present in 87 (79.09%) patients. Among those with thrombocytopenia (platelet level below 150,000), 25.97% (20 patients) and 27.27% (21 patients) had EV grade II and EV grade III or IV, respectively. Whereas in patients in whom the platelet count was above 150,000, only 21.21% (7 patients) and 9.09% (3 patients) of patients had grade II EV and EV grade III or IV, respectively. A platelet count cut-off value of 149,000 was found to have specificity of 82% and sensitivity 39% for detection of presence of varices. A FIB-4 cut-off value of 3.175 was found to have an 83.3% sensitivity and 39.5% specificity in detecting large (grade III, IV) EVs. Platelet count is a noninvasive parameter with high accuracy for prediction of EVs. Cirrhotic patients with normal platelet counts (above 150,000), especially in financially deprived developing countries, can avoid screening endoscopy as they are at a low risk for variceal bleeding, and presence of large EVs in these patients is much less common than in those with thrombocytopenia. A 3.175 cut-off value of FIB-4 could be useful as a noninvasive predictor of large varices requiring prophylactic banding in cirrhotic patients.
A P ] 2 1 Ju l 1 99 9 Bulk Burning Rate in Passive-Reactive Diffusion

We consider a passive scalar that is advected by a prescribed mean zero divergence-free velocity field, diffuses, and reacts according to a KPP-type nonlinear reaction. We introduce a quantity, the bulk burning rate, that makes both mathematical and physical sense in general situations and extends the often ill-defined notion of front speed. We establish rigorous lower bounds for the bulk burning rate that are linear in the amplitude of the advecting velocity for a large class of flows. These " percolating " flows are characterized by the presence of tubes of streamlines connecting distant regions of burned and unburned material and generalize shear flows. The bound contains geometric information on the velocity streamlines and degenerates when these oscillate on scales that are finer than the width of the laminar burning region. We give also examples of very different kind of flows, cellular flows with closed streamlines, and rigorously prove that these can produce only sub-linear enhancement of the bulk burning rate.
Reputational Incentives for Restaurant Hygiene

How can consumers be assured that firms will endeavor to provide good quality when quality is unobservable prior to purchase? We test the hypothesis that reputational incentives are effective at causing restaurants to maintain good hygiene quality. We find that chain affiliation provides reputational incentives and franchised units tend to free-ride on chain reputation. We also show that regional variation in the degree of repeat customers affects the strength of reputational incentives for good hygiene at both chain and nonchain restaurants. Despite these incentives, a policy intervention in the form of posted hygiene grade cards causes significant improvements in restaurant hygiene. (JEL I18, I19, L14, L83).
Unsupervised image segmentation using wavelet-domain hidden Markov models

In this paper, we study unsupervised image segmentation using wavelet-domain hidden Markov models (HMMs), where three clustering methods are used to obtain the initial segmentation results. We first review recent supervised Bayesian image segmentation algorithms using wavelet-domain HMMs. Then, a new unsupervised segmentation approach is developed by capturing the likelihood disparity of different texture features with respect to wavelet-domain HMMs. Three clustering methods, i.e., K-mean, soft clustering and multiscale clustering, are studied to convert the unsupervised segmentation problem into the self-supervised process by identifying the reliable training samples. The simulation results on synthetic mosaics and real images show that the proposed unsupervised segmentation algorithms can achieve high classification accuracy.
Applications of Artificial Neural Networks to Facial Image Processing

During the past 20 years, artificial neural networks was successfully applied for solving signal processing problems. Researchers proposed many different models of artificial neural networks. A challenge is to identify the most appropriate neural network model which can work reliably for solving realistic problem. This chapter provides some basic neural network model and efficiently applying these models in facial image processing problem. In detail, three techniques : a hybrid model of combining AdaBoost and Artificial Neural Network (AANN) to detect human faces, a local texture model based on Multi Layer Perceptron (MLP) for face alignment and a model which combines many Neural Networks applied for facial expression classification are present. This case study demonstrates how to solve face recognition in the neural network paradigm. Each of these techniques is introducted as follows: Technique 1 an approach to combine adaBoost and artificial neural network for detecting human faces: The human face image recognition is one of the prominent problems at present. Recognizing human faces correctly will aid some fields such as national defense and person verification. One of the most vital processing of recognizing face images is to detect human faces in the images. Some approaches have been used to detect human faces. However, they still have some limitations. In the research, some popular methods, AdaBoost, Artificial Neural Network (ANN) were considered for detecting human faces. Then, a hybrid model of combining AdaBoost and Artificial Neural Network was applied to solve the process efficiently. The system which was build from the hybrid model has been conducted on database CalTech. The recognition correctness is more than 96%. It shows the feasibility of the proposed model. Technique 2 local texture classifiers based on multi layer perceptron for face alignment: Local texture models for face alignment have been proposed by many different authors. One of popular models is Principle Component Analysis (PCA) local texture model in Active Shape Model (ASM). The method uses local 1-D profile texture model to search for a new position for every label point. However, it is not sufficient to distinguish feature points from their neighbours; i.e., the ASM algorithm often faces local minima problem. In the research, a new local texture model based on Multi Layer Perceptron (MLP) was proposed. The model is trained from large databases. The classifier of the model significantly improves accuracy and robustness of local searching on faces with expression variation and ambiguous contours. Achieved experimental results on CalTech database show its practicality.
Facial emotion recognising memristive threshold logic system

The edge features in facial images change with changes in images, and are useful for automatically recognising the facial expressions. We present an edge feature extraction method using neuronal threshold logic models to automatically recognise the facial expressions. The system is simulated at digital system level consisting of reading an image followed up with edge extraction system that can be implemented with hybrid CMOS-memristive digital circuits. The results indicate robust edges of the facial features and its useful in development of real-time emotion recognizing digital chip.
Intrusion Detection Scheme based on IPSO-RBF

In order to overcome the separately selection advantages of traditional feature and RBF neural network parameter, increase accuracy rate of network’s intrusion detection, there came up with a research on neural network intrusion detection of improved particle swarm optimization. According to optimize the feature selection of network and RBF neural network parameter, established a neural network intrusion detection model of IPSO-RBF, made convergence and disturbance variation analysis on improving optimized particle swarm optimization, finally made a experimental simulation to this detection model, the result showed: compared with traditional models, the neural network intrusion detection of improved particle swarm optimization was faster and has a higher accuracy rate as well as efficiency.
A Lattice Approach to Bayesian Networks

Bayesian networks and conditional independence is studied via functional dependences. Armstrongs axioms known from the theory of relational databases is used to reformulate the concept of Bayesian networks into the theory of join-semidistributive lattices. Lattice theory provides us with a richer language to discuss causation than graph theory does.
Anti-Inflammatory Activity of the Solvent-Partitioned Fractions from Spergularia marina in LPS-Stimulated RAW 264.7 Cells

As a part of ongoing research to elucidate and characterize antioxidant and anti-inflammatory nutraceuticals, solvent-partitioned fractions from Spergularia marina were tested for their ability to scavenge radicals and suppress inflammation. The results of the 2′,7′-dichlorofluorescein diacetate assay indicate that solvent-partitioned fractions from S. marina scavenged intracellular radicals in H2O2-stimulated mouse macrophages. The tested fractions decreased the generation of nitric oxide (NO) and the expression of inflammation mediators, namely, inducible nitric oxide synthase (iNOS) and interleukin (IL)-6, by lipopolysaccharide (LPS)-induced mouse macrophages, indicating that S. marina decreases inflammation. Among all tested fractions [i.e., H2O, n-buthanol (n-BuOH), 85% aqueous methanol (aq. MeOH), and n-hexane], the 85% aq. MeOH fraction showed the strongest antioxidant and anti-inflammatory response. The 85% aq. MeOH fraction scavenged 80% of the free radicals produced by H2O2-induced control cells. In addition, NO production was 98% lower in 85% aq. MeOH fraction-treated cells compared to LPS-induced control cells. The mRNA expression of iNOS and IL-6 was also suppressed in 85% aq. MeOH fraction-treated cells. The results of the current study suggest that the phenolic compound components of S. marina are responsible for its antioxidant and anti-inflammatory effects.
Towards an Understanding of the Low Bioavailability of Quercetin: A Study of Its Interaction with Intestinal Lipids

We have studied the uptake of quercetin aglycone into CaCo-2/TC7 cells in the presence and absence of mixed micelles that are present in the human small intestine. The micelles inhibited the transport of quercetin into the cells. To gain an understanding of why this is the case we examined the solubilisation of quercetin in micelles of differing composition and into pure lipid phases. We did this by using the environmental sensitivity of quercetin’s UV-visible absorption spectra and measurement of free quercetin by filtration of the micellar solutions. The nature of the micelles was also studied by pyrene fluorescence. We found that the partitioning of quercetin into simple bile salt micelles was low and for mixed micelles was inhibited by increasing the bile salt concentration. The affinity of quercetin decreased in the order egg phosphatidylcholine (PC) = lysoPC > mixed micelles > bile salts. These results, together with the innate properties of quercetin, contribute to an understanding of the low bioavailability of quercetin.
A multi-radio architecture for neighbor-to-neighbor communication in modular robots

Decentralized control of modular robots requires reliable inter-module communication. Communication links must tolerate module misalignment and implement the neighbor-to-neighbor communication model. We propose a wireless system based on multiple radios per module that addresses these challenges. Our system scales to large robots because available bandwidth is independent of the number of modules. In this paper, we present our multi-radio single-channel architecture and validate its performance through hardware experiments. Results show that radios can provide reliable neighbor-to-neighbor communication suitable for modular robots.
Low-complexity turbo decoding scheme for two-way relay network

A low-complexity two-user turbo decoding scheme for two-way relay network is proposed. In the relay, the superimposed packets have to be decoded to the arithmetic summation of two source packets based on the sum trellis. In the sum trellis, there are some symmetrical states, especially when the sources use identical component codes. These symmetrical states provide same information to the turbo decoder. This observation motives a reconstruction of the sum trellis by merging the symmetrical sates in the trellis. By this mergence, a simplified sum trellis with the less state number is given. Complexity analysis shows that the computational complexity of two-user turbo decoding with simplified sum trellis is reduced to half, compared with one with sum trellis.
A multiagent approach for logistics performance prediction using historical and context information

This paper presents a multiagent architecture and methods for intelligent decision support in logistics processes. It extends current advanced prediction systems by providing the ability to combine history and situated reasoning. The contribution of the paper is threefold: first, a multi-agent architecture and learning algorithms are developed that enables us to combine background models learned from history data with context-related knowledge about the current situation; second, using a large real data set we show that adding situated knowledge actually improves the performance of a supply chain decision support system; and third, for our settings we evaluate the degree to which agent-assisted decision support is actually usable/sufficient to improve human decision-making and to support automated decision-making in dynamic supply network management scenarios.
Complete Genome Sequence of Mumps Virus Isolated from Karnataka State, India

ABSTRACT We report the first whole-genome sequence of mumps virus isolated from a two-year-old girl with bilateral parotitis from a Chikkahallivana village in the Davangere district of Karnataka State, India. The genome of the Davangere mumps isolate was 15,384 bp in length and identical to previously published mumps virus (MuV) genomes from India. BLAST results show 99.1% identity with previously sequenced genotype C viruses isolated from the states of Maharashtra, Tamil Nadu, and Uttar Pradesh.
Practical Vector Dirty Paper Coding for MIMO Gaussian Broadcast Channels

Recently, the vector dirty paper coding (DPC) achievable rate region has been shown to be the capacity region of a multiple-input multiple-output Gaussian broadcast channel (MIMO GBC). With DPC, the multiuser interference noncausally known at the transmitter can be completely removed. In this paper, we present a vector DPC structure for MIMO GBC. It is a generalization of the single antenna superposition dirty paper coding for the scalar Gaussian dirty paper problem proposed by Bennatan et al. In a theoretical random code setting, this construction is shown to be able to achieve the promised rate performance of the MIMO GBC. We also implement it with existing vector quantizer and capacity-achieving channel coding. Combined with iterative decoding, a design example validates the effectiveness of our methods.
Maternal Butalbital Use and Selected Defects in the National Birth Defects Prevention Study

Butalbital is a barbiturate contained in combination products with caffeine and an analgesic prescribed for the treatment of migraine and tension‐type headaches. Controversy exists as to whether butalbital should continue to be prescribed in the United States because of the potential for abuse, overuse headache, and withdrawal syndromes. Butalbital crosses the placenta but there is limited information about potential teratogenicity.
EPAC: Examination Paper Automatic Construction Based on K-Means Algorithm

Constructing examination paper is one of the important parts of teaching process. A good examination paper should meet a lot of specific requirements, like knowledge points, quantities, types and difficulties. So it is a multi-targets optimization problem for constructing examination papers, which is hard to realize automatically. This paper provides the examination paper automatic construction system (EPAC), exploiting K-Means algorithm as its core for multi-targets optimization. And EPAC is realized through the example of Computer Networks, whose performance is confirmed comparing to randomly chosen process.
Direct Targeting as an NGO Political Strategy: Examining Private Authority Regimes in the Forestry Sector

In recent years, International Political Economy literature on "politics beyond state" has emphasized the role of non-governmental organizations (NGOs) in broader policy processes, both national and international. In addition to their impact on states, NGOs influence the policies of non-state actors such as firms via public and private politics. Dissatisfied with the progress firms have made in response to public regulation, NGOs have sponsored private authority regimes in several issue areas and pushed firms to participate in them. Across the world, the contest between NGOs and firms has provoked substantial behavioral and programmatic changeincluding widespread participation in these private authority regimesamong firms seeking to escape NGO pressures. Using firm-level data, this paper examines why direct targeting has not led firms in the U.S. forest products sector to participate in an NGO-sponsored private authority regime, the Forest Stewardship Council. This global regime has been adopted widely in Europe, but U.S.-based forestry firms have tended to favor a domestic industry-sponsored regime, the Sustainable Forestry Initiative. Our analysis suggests that the desire of firms to maintain control over their institutional environment in light of hostile relations with NGOs has led US-based firms to favor the Sustainable Forestry Initiative.
Reorganization of microtubules during mitosis in Dictyostelium: Dissociation from MTOC and selective assembly/disassembly in situ

We applied the “agar-overaly” immunofluorescence techinque (Yumura, S., H. Mori, and Y. Fukui, J. Cell Biol. 99:894–899, 1984) to a semisynchronous culture of Dictyostelium discoideum for studying the organization changes in the microtubule system during mitosis. Using a flurescent DNA dye DAPI (4′,6′ -diamidino-2-phenylindole), chromatin fibers and individual chromosomes were visible in cells prepared by this method, whereby the mitotic phase could be critically evaluated. We found that a rapid shortening of the cytoplasmic microtubules was preceded by a structural dislocation from their organizing centers (MTOCs) in the midprophase, resulting in the transient occurrence of free microtubules in the cytoplasm. Statistic analyses showed that microtubule disassembly in prophase was diphasic. Initially long, wavy microtubules shortened from their distal ends. Following dissociation of their proximal ends from the MTOC, all microtubules initiated rapid disassembly, probably from both ends. During this process, microtubule assembly from the now duplicated spindle pole body (SPB) resumed. This study also revealed novel information on the dynamics of the Dictyostelium mitotic spindle: 1) Half spindles interdigitate in the spindle center, and the extent of interdigition increases coincidentally with the spindle elongation, and 2) during the anaphase to telophase, a subpopulation of spindle microtubules elongates while the rest of the microtubules disasemble very rapidly. Overall this study indicates the presence of elaborate mechanisms responsible for the selective assembly/disassembly of particular microtubule subpopulations in situ.
Multilingual pronunciation modeling for improving multilingual speech recognition

Multilinguality aspects are becoming increasingly important in the Automatic Speech Recognition (ASR) systems. It is apparent that coping with large variability of the speech signal is an even bigger challenge in multilingual ASR systems than it has been in conventional monolingual systems. In this paper, we address the importance of combining multilingual pronunciation modeling and acoustic model adaptation. To compensate the pronunciation variability across various speakers, multilingual pronunciation modeling method is proposed. Due to the limited processing power and memory resources available in many systems, we also propose a pruning scheme that removes pronunciation variants from the vocabulary based on the statistical scores obtained during the deployment of the system. To further compensate the mismatches between the multilingual acoustic models and the speaker's pronunciation, on-line MAP acoustic model adaptation is applied. Experimental results with 25 languages indicate the usefulness and efficiency of the joint use of these techniques both in clean and noisy conditions.