Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of phonetic encoding for analyzing.pdf

Due to illiteracy and lack of standardized healthcare systems, patients in Bangladesh usually, provide misspelled names while making their entry via these systems. Different records with slightly misspelled names are thus generated which makes data mining and others tasks quite challenging and inefficient. In this paper, we have looked into the underlying problem of misspelled names of patients in healthcare systems and proposed a modified version of NameSignificance algorithm. Our proposed algorithm has performed significantly better than the existing solutions like NameSignificance, Modified Soundex, Double Metaphone encoding for Bangla as we founded our algorithm on the phonetic nature of Bengali names written in English transliterated form. Our algorithm achieved a staggering 77% matching of names whereas relevant algorithm could not pass 70% correct matches. This proposed method could pave the way for better record linkage and data analysis of the medical patient dataset. Our syllable based approach also helped us identify the reason behind the wrong matches appeared through our algorithm which will surely pave the way to purify our algorithm in the future. Keywords—Record Linkage; phonetic encoding; Name; Bangladesh; Health Data;
The Feasibility of Nonlinear Dimensionality Reduction for the Rapid Analysis of Persistent Surveillance Data, including the Detection of IED Placement Activity

Abstract : Our ability to analyze large, complex data sets, such as persistent surveillance data, has often far outstripped our ability to rapidly analyze that data. We have identified a class of intelligent data reduction algorithms, known collectively as Nonlinear Dimensionality Reduction (NLDR), and we believe the utilization of NLDR approaches will allow a significant performance improvement for automated data analysis systems. In this report, we review the basic elements of NLDR techniques, we discuss the advantages of these techniques over more traditional approaches such as Principal-Component Analysis (PCA), and we outline an approach for utilizing NLDR to detect activities leading to the placement of IEDs based on airborne persistent surveillance video data.
Bayes linear Bayes graphical models in the design of optimal test strategies

Test and analysis plays a vital role in reducing uncertainty about the true performance of an engineering system. However tests can be expensive and designing an optimal test strategy can be challenging. We propose a Bayesian modelling process, which takes the form of a Bayesian Network, to determine anticipated test efficacy. Such a model supports engineering managers in assessing trade-offs between test resources and uncertainty reduction. Inference based on a full Bayesian model can be computationally demanding to the extent that it can limit practical application. To overcome this constraint, we develop a Bayes linear approximation for inference. This approach is known as a Bayes linear Bayes graphical model. After explaining the key principals of the method, we provide an application to a real industrial test to establish the condition of an ageing engineering system.
Exploring Direct and Indirect Effects of Information and Communications Technology (ICT) Investment : Experience of the Indian Manufacturing Sector (特集 高橋公明教授 退職記念号)

The relationship between the introduction of information and communications technology (ICT) and improvements in the aggregate economic growth and industrial performance has been discussed in the previous literature. ICT is considered as a general purpose technology and an important element of infrastructure that has great implications not only for the advanced economies but also for developing countries in their industrial development and beyond. Due to the importance of externalities emerging from ICT investment and infrastructure, it is critical to understand whether such externalities affect the manufacturing sector in addition to its conventional role as a factor of production. Hence, this paper seeks to explore both the effects (direct and indirect) of ICT under its role as a factor of production and as a critical infrastructure on the performance of the Indian (registered) manufacturing sector using a panel of 22 industries over a period of 1999―2000 to 2007―2008. Using an econometric analysis of the production function, this paper examines the effect of ICT capital on the output and labor productivity of the manufacturing sector and in the next stage, assesses the link between ICT infrastructure and the manufacturing sector’s estimated total factor productivity (TFP). The study has found a significant effect of ICT capital, and the results also suggest the importance of ICT infrastructure in relation to the industrial performance of India. The residual-based co-integration tests also establish a linear relationship between the ICT infrastructure of India and the estimates of manufacturing sector’s TFP.
Experimental Investigation on the Effect of Reinforcement Particles on the Forgeability and the Mechanical Properties of Aluminum Metal Matrix Composites

The wide choice of materials, today’s engineers are posed with a big challenge for the right selection of a material and as well as the right selection of a manufacturing process for an application. Aluminium Metal Matrix Composites is a relatively new material among all the engineering materials. It has proved its position in automobile, aerospace, and many other engineering applications due its wear resistance properties and due to its substantial hardness. One of the most important criteria is forgeability by which the workability of the material can be determined. The nature of distribution of reinforcing phase in the matrix greatly influenced the properties of Aluminum Metal Matrix Composites. The forgeability of Aluminum Metal Matrix Composites, which are produced by powder metallurgy method, are greatly depends on the size and percentage of reinforcement materials, compacting load, sintering temperature and soaking time etc. In this present work, the forgeability of Aluminum Metal Matrix Composites reinforced with silicon carbide (400 meshes) has investigated. A comparison have been made with different types of Aluminum Silicon Carbide Metal Matrix Composite materials contains 0%5%,10%,15%&20% by weight of silicon carbide. The mechanical properties like hardness of the different composites have also investigated. It is observed that the forgeabilty of the composites decreases with increasing the wt% of SiC but the mechanical properties like hardness enhanced on increasing the wt% of SiC.
Optimal Designs for Nonlinear Models with Random Block Effects

Optimal designs for nonlinear model with random block effects are systematically studied. For a large class of nonlinear models, we prove that any optimal design can be based on some simple structures. We further derive the corresponding general equivalence theorem. This result allows us to propose an efficient algorithm of deriving specific optimal designs. The application of the algorithm is demonstrated through deriving a variety of locally optimal designs and accessing their robustness under different nonlinear models.
A Conflict-free Multi-Symbol Arithmetic Encoder for H.264/AVC

This paper proposed a conflict-free multi-symbol arithmetic encoder (AE) for H.264/AVC. The throughput of a multi-symbol AE is usually limited by concurrent access of context memory which degrades the performance of the whole H.264/AVC encoder. With proposed Hybrid Context Memory architectures (HCM), about 1/10 contexts under critical conflict are implemented by Critical Register Array to solve the insufficient ports bottleneck. The number of encoding symbol per cycle is improved by 15% and 55.5% in 2-symbol and 4symbol version, respectively. The implementation results also show that the hardware design can achieve a high encoding rate and throughput comparing to the previous works.
The importance of spinner dolphin (Stenella longirostris) resting habitat: implications for management

Summary 1. Linking key ecological characteristics with animal behaviour is essential for identifying and protecting important habitats that support life functions. Spinner dolphins display a predictable diurnal behavioural pattern where they forage offshore at night and return to sheltered bays during daytime to rest. These bays, which are also subject to considerable use by humans, have long been recognized as key habitats for this species although the extent to which dolphins rely on specific characteristics of these habitats for rest has not been quantified. 2. An integration of boat-based and land-based group focal follow sampling regimes and three gradient boosting generalized additive models were developed to identify habitat features that contribute to the occurrence of resting spinner dolphins in coastal waters off Hawai'i Island. Two ‘in-bay’ models used data collected within bays, and a third ‘coastal’ model (near-shore, outside of bays) used data collected both inside and outside of bays. 3. The coastal model identified that spinner dolphins were unlikely to rest outside sheltered bays. In-bay models showed that dolphins rested throughout daylight hours within bays with a peak resting period between 10.00 h to 14.00 h. The models also identified bottom-substrate-type as an important predictor of rest. Pseudo R2 values of 0·61 and 0·70 for the in-bay models and 0·66 for the coastal model showed that these models provided a good fit to the behavioural data for the occurrence of resting spinner dolphins. 4. Synthesis and application. To date, studies evaluating spinner dolphin resting habitat have focussed on areas inside bays only. Here, we combined data collected inside and outside bays, and illustrate that should resting spinner dolphins be displaced from resting bays, they are unlikely to engage in resting behaviour elsewhere. Results provide further information on the importance of bays as important habitat for resting spinner dolphins. To mitigate the disturbance from human interactions during important rest periods, we recommend that management keep the spinner dolphin resting areas free from human activities. Our quantitative approach where models explicitly link behaviour with habitat characteristics is applicable to identify important habitats for protection of other taxa.
Asymptotic Stabilization by State Feedback for a Class of Stochastic Nonlinear Systems with Time-Varying Coefficients

This paper investigates the problem of state-feedback stabilization for a class of upper-triangular stochastic nonlinear systems with time-varying control coefficients. By introducing effective coordinates, the original system is transformed into an equivalent one with tunable gain. After that, by using the low gain homogeneous domination technique and choosing the low gain parameter skillfully, the closed-loop system can be proved to be globally asymptotically stable in probability. The efficiency of the state-feedback controller is demonstrated by a simulation example.
CVaR-Based Optimal Partial Hedging

In this paper, we consider the problem of optimal partial hedging for a contingent claim subject to a preset hedging budget constraint. Under some technical assumptions on the hedged loss function and the market pricing functional, the optimal partial hedging strategy, which minimizes the conditional Value-at-Risk (CVaR) of the hedger’s total risk exposure, is derived explicitly. Some in-depth analysis is conducted for a utility based indifference pricing functional. Ample numerical examples are presented to highlight the comparative advantages of the proposed CVaR-based hedging strategy relative to other hedging strategies including the expected shortfall hedging, the VaR-based hedging strategies and the CVaR hedging strategy of Melnikov and Smirnov (2012). Among these hedging strategies, the numerical examples demonstrate that our proposed CVaR-based hedging is more robust and more effective in terms of managing the tail risk of the hedger’s risk exposure.
Diversity Management Strategies and Quality Decisions in the Banking Sector

The Top Management Team diversity discussed in the study is the extent to which the executive team is heterogeneous with respect to gender, age, academic qualifications, tenure, professional qualifications and functional backgrounds and how this aids quality decisions in the banking sector. Furthermore the study suggests that top management characteristics, specifically their demographic configuration could impair decision making which is the main objective and focus. Other researchers have shown that workforce diversity can provide positive organizational outcomes such as increased morale, higher satisfaction, greater commitment and improved performance. The questionnaire was the main tool of data collection. Cross sectional survey was adapted as the methodology of research where the target respondents were Heads of Human Resources in 43 banks. The data was analyzed using descriptive statistics and regression analysis. The TMT diversity attributes under study were age, gender, academic qualifications, tenure in the bank and at the top, professional and functional background diversity. These variables were used to determine the existing gaps in knowledge, whether these attributes were crucial in making quality decisions. The study also used secondary data obtained from the Banking sector to measure performance in terms of increase in customer deposits, increase in average assets and profit before tax. The study established that tenure in the banking sector has a major implication in the quality of decisions made within the Top Management Teams of organizations. Normal 0 false false false EN-US X-NONE AR-SA
Implantable Loop Recorder – A Good Opportunity to Diagnose Unexplained Syncope

Summary Implantable loop recorder (ILR) is a method in cardiology, which is used for the diagnosis of unexplained syncope in patients who were not treated successfully using standard methods. Implantable loop recorder is a diagnostic device that is surgically implanted under the skin of the chest area. This device does not have the endovenous implantation of electrodes; instead, electrodes are attached to the machine housing. The device records the heart rhythm continuously, up to 14 months, and stores data outside the activator whenever symptoms appear, or by the automatic activation of the predefined program for bradycardia, asystole, and tachycardia. The aim of this paper was to describe the method for the detection of cardiac syncope with the use of implantable loop recorder.
Mn2+‐induced room‐temperature ferromagnetism and spin‐glass behavior in hydrothermally grown Mn‐doped ZnO nanorods

The magnetic properties of Mn‐doped ZnO (ZnO:Mn) nanorods grown by hydrothermal process at a temperature of 200 °C and a growth time of 3 h have been studied. The samples were characterized by using powder X‐ray diffraction with Rietveld refinement, scanning electron microscopy, energy‐dispersive X‐ray analysis and SQUID magnetometry. Mn (3 wt%) and (5 wt%)‐doped ZnO samples exhibit paramagnetic and ferromagnetic behavior, respectively, at room temperature. The spin‐glass behavior is observed from the samples with respect to the decrease of temperature. At 10 K, both samples exhibit a hysteresis loop with relatively low coercivity. The room‐temperature ferromagnetism in 5 wt% Mn‐doped ZnO nanorods is attributed to the increase in the specific area of grain boundaries, interaction between dopant Mn2+ ions substituted at Zn2+ site and the interaction between Mn2+ ions and Zn2+ ions from the ZnO host lattice.
Computational Modeling of Cardiac Biomechanics

OF DISSERTATION COMPUTATIONAL MODELING OF CARDIAC BIOMECHANICS The goal of this dissertation was to develop a realistic and patient-specific computational model of the heart that ultimately would help medical scientists to better diagnose and treat heart diseases. In order to achieve this goal, a three dimensional finite element model of the heart was created using magnetic resonance images of the beating pig heart. This model was loaded by the pressure of blood inside the left ventricle which was measured by synchronous catheterization. A recently developed structurally based constitutive model of the myocardium was incorporated in the finite element solver to model passive left ventricular myocardium. Additionally, an unloading algorithm originally designed for arteries was adapted to estimate the stress-free geometry of the heart from its partiallyloaded geometry obtained from magnetic resonance imaging. Finally, a regionally varying growth module was added to the computational model to predict eccentric hypertrophy of the heart under various pathological conditions that result in volume overload of the heart. The computational model was validated using experimental data obtained from porcine heart such as in vivo strains measured from magnetic resonance imaging.
A Context Aware Attack Detection System Across Multiple Gateways

It is well known that intrusion detection systems can make smarter decisions if the context of the traffic being observed is known. This paper examines whether an attack detection system, looking at traffic as it arrives at gateways or firewalls, can make smarter decisions if the context of attack patterns across a class of IP addresses is known. A system that detects and forestalls the continuation of both fast attacks and slow attacks across several IP addresses is described and the development of heuristics both to ban activity from hostile IP addresses and then lift these bans is illustrated. The system not only facilitates detection of methodical multiple gateway attacks, but also acts to defeat the attack before penetration can occur.
Comparison of external load in high speed actions between friendly matches and training sessions

espanolEl objetivo esta investigacion fue encontrar las diferencias que existen entre la carga externa en acciones de alta velocidad (SP+) en paridos amistosos de pretemporada y las sesiones de entrenamientos en un equipo de futbol profesional. Tomamos como indicadores SP + aquellas acciones que realizan los jugadores de futbol por encima de 23 km·h-1 y pudiendo ser mantenidas durante al menos 1 segundo. Se monitorizaron 25 jugadores profesionales pertenecientes a la primera plantilla de un equipo de la primera division de futbol espanol, y se utilizaron 10 dispositivos GPS (SPI, ELITE model, GPSport, Canberra, Australia). Estos transmisores GPSORT, tienen una frecuencia de muestreo de 1 Hz. Los resultados de nuestro estudio manifestaron como durante el partido, los jugadores realizaron de manera sustancial mas en numero de SP+ de 1 s, 2s, 3s y 4 s (por minuto de actividad) que durante los entrenamientos. EnglishThe aim of the current research was to analyze the differences in high-velocity actions external load (SP+) between competitions and training sessions in professional soccer team. We considered any action soccer players performed over 23 km·h-1 and could be held for at least 1 second as SP+ indicators. A sample of 25 professional soccer players belonging to the first team of a first Spanish soccer division team were monitored using 10 GPS devices (SPI, ELITE model, GPSport, Canberra, Australia). These GSPORT transmitters have a sampling frequency of 1 Hz. The results of our study showed how players performed a substantially higher number of SP+ of 1 s, 2s, 3s y 4 s (per minute of activity) during matches than during training.
Performance Comparison of Classification Methods with the Combinations of the Imputation and Gene Selection Methods

Gene expression data is obtained through many stages of an experiment and errors produced during the process may cause missing values. Due to the distinctness of the data so called ‘small n large p’, genes have to be selected for statistical analysis, like classification analysis. For this reason, imputation and gene selection are important in a microarray data analysis. In the literature, imputation, gene selection and classification analysis have been studied respectively. However, imputation, gene selection and classification analysis are sequential processing. For this aspect, we compare the performance of classification methods after imputation and gene selection methods are applied to microarray data. Numerical simulations are carried out to evaluate the classification methods that use various combinations of the imputation and gene selection methods.
Multi-Finger 3D Landmark Detection using Bi-Directional Hierarchical Regression

Over the past years, many application technologies related to Human Computer Interaction (HCI) have been developed for intelligent devices and wearable sensors. Moreover, they also have been received attention from both the research commu-nity and the industry. Almost of them make use of hand infor-mation such as hand gesture and posture. Consequently, the technical performance is highly depends on the information of humans’ hand and also, the number of products equipped with these technologies rapidly increases. Therefore, the accurate estimation of hand pose becomes a crucial issue to improve the performance of HCI applications. To detect landmarks, many algorithms have been develop-ing in this research field. Most of them are based on the pixel-based classification (1) and deformable part models (DPM) (2). However, they does not fit well in finger pose estimation since there are large pose variations due to camera viewpoints and various finger gestures. It is challenging to precisely detect the principal landmarks of fingers, as they flexibly deform with a large degree of freedom.In this paper, the regression based approach is used for esti-mating 3D multi-finger landmarks. Cascaded regression ap-proaches (3, 4) already used for hand pose estimation. Our ap-proach, however, concentrates on hierarchical structure of the detection strategy and initialization for better accuracy and stability. In our method, the bi-directional search structure is used for estimate landmarks with random forest regression. To
Development and Validation of HPLC Method for the Determination ofAlcaftadine in Bulk Drug and its Ophthalmic Solution

A simple, precise and accurate stability indicating RP-HPLC method was developed for the estimation of Alcaftadine in bulk drug and its ophthalmic dosage form. Chromatographic separation of target analyte and degradation products was achieved using Enable HPLC ODS C18 G (250 × 4.6 mm, 5 μm) column and mobile phase comprising of methanol: water, 50:50% v/v at a flow rate of 1.2 ml/min with UV detection at 282 nm. The correlation coefficient (r2) was found to be 0.999 in the concentration range of 1-16 μg/ml. The retention time was found to be 3.15 min. The limit of detection and limit of quantitation was found to be 0.25 μg/ml and 0.75 μg/ml respectively. Stress degradation of Alcaftadine was carried out under stress conditions like acid and base hydrolysis, oxidation, thermal and photolytic stress. The degradation products generated as a result of stress did not show any interference to the detection of Alcaftadine. The developed method was validated, and selectivity of the method was demonstrated by its ability to quantify the target analyte in presence of its degradation products.