Empirical Characteristics Research Articles

Characteristics of musculoskeletal disorders and satisfaction with in-house physical therapy clinics in office workers.

Musculoskeletal disorders are not properly managed in office workers because of their busy work life. In-house physical therapy is a good way to manage the musculoskeletal disorders in office workers. Despite the numerous advantages of in-house physical therapy, the establishment and research of in-house physical therapy were insufficient. The purpose this study was to determine the characteristics of musculoskeletal disorders in office workers and to investigate their satisfaction with in-house physical therapy clinics and the associated factors. In this study, 664 office workers who used in-house physical therapy clinics were surveyed for characteristics of musculoskeletal disorders and satisfaction with in-house physical therapy clinics. Of these office workers, the most common causes of damage were nontraumatic (36.8%) and the most common lesion sites were the neck (30.3%) and lower back (25.6%). In the empirical characteristics of in-house physical therapy clinics, basic thermoelectric treatments were the most common (46.8%). The satisfaction with in-house physical therapy clinic was generally high. In addition, the cause of damage and treatment contents affected treatment-related and functional return-related satisfaction. In-house physical therapy, including therapeutic exercises and self-management education, is a good system to manage musculoskeletal disorders in office workers.

Evaluating the electricity saving potential of electrochromic glazing for cooling and lighting at the scale of the Swiss non-residential national building stock using a Monte Carlo model

Novel electrochromic glazing technology has been identified as an emerging option for reducing cooling and lighting electricity demand. As this technology is particularly promising for office building we assess the related technical energy saving potential in case of nation-wide implementation in Swiss office buildings. A Monte Carlo model of Swiss office building stock using distributions of empirical building characteristics was coupled with a statistical model of energy savings of electrochromic glazing.The building stock model for Swiss office buildings was shown to produce cooling and lighting electricity demand estimates in agreement with the existing case study literature. Total yearly electricity demand for lighting and cooling was calculated to be 1152 ± 32 GWh. Electrochromic glazed saved 125 ± 6 GWh or on average 11% of lighting and cooling electricity demand. Electrochromic glazing was found to be particularly effective in highly cooled office buildings, where cooling accounted for 20 kWh/m2 and total electricity saving potential was estimated at 5.5 kWh/m2 or 5.2% of today’s typical total electricity demand of an office building across all uses. Areas where electrochromic glazing would have particularly high potential are highlighted.

Vibration-based fault diagnosis of a face milling tool using empirical mode decomposition features and artificial neural network

Vibration-based fault diagnosis of a face milling tool using empirical mode decomposition features and artificial neural network

Controversies of Historical Narratives of Conflicts: The Israeli Case Regaridng the Palestinian Refugee Problem

Collective memory of intractable conflicts significantly influences these conflicts. It is composed of the historical narratives of the conflicts, narratives which are often addressed in controversies regarding their accuracy and use. These controversies significantly affect the extent to which these narratives are adopted in the given society/country. To date, however, many theoretical aspects of such controversies have not been addressed in the literature and this also holds true for empirical features of historical controversies in Israel with regard to the Israeli-Arab/Palestinian (“conflict”). This article addresses these shortcomings. Methodologically, the article uses content analysis of publications and interviews with key people in the institutions that produced these publications. Empirically, the article for the first time assembles all the main historical controversies taking place in Israel from 1949 to 2004 with regard to the major historical event in the conflict: the Nakba/1948 Palestinian exodus (“exodus”). Additionally, empirically, and theoretically, the article offers these contributions: a typology of controversies (inter- vs. intra-group, societal vs. state, public vs. private, and public – concrete vs. general), a description of their four aspects (historical, instrumental, interpretational and methodological), a discussion of controversy consequences, and a description of the factors that determine their importance in the memory realm.

Neural recovery machine for Chinese dropped pronoun

Dropped pronouns (DPs) are ubiquitous in prodrop languages like Chinese, Japanese etc. Previous work mainly focused on painstakingly exploring the empirical features for DPs recovery. In this paper, we propose a neural recovery machine (NRM) to model and recover DPs in Chinese to avoid the non-trivial feature engineering process. The experimental results show that the proposed NRM significantly outperforms the state-of-the-art approaches on two heterogeneous datasets. Further experimental results of Chinese zero pronoun (ZP) resolution show that the performance of ZP resolution can also be improved by recovering the ZPs to DPs.

Sparsity in function and derivative approximation via the empirical feature space

Several practical applications require estimation of the values of a function and its derivative at specific sample locations.This is a challenging task particularly when the explicit forms of the function and its derivative are not known. There have been a few methods proposed in the literature to learn an approximant that simultaneously uses values of a function as well as values of its derivatives or partial derivatives. These methods typically use Support Vector Regression (SVR) and solve a Quadratic Programming Problem (QPP) for the task, which results in a learning model that can estimate the function and derivative values. In this paper, we propose an alternative novel approach that focuses on introducing sparsity in such a learning model, that is based on minimizing the model complexity in the Empirical Feature Space (EFS). Sparsity in such a model is useful when it needs to be evaluated a large number of times as it entails lower computational cost compared to a dense model. The proposed approach, called the EFSRD (EFS Regression for Function and Derivative approximation), involves solving a Linear Programming Problem (LPP). On a number of benchmark examples, EFSRD learns models that offer comparable or better performance, while learning models that are nearly a fourth the size of those obtained by existing approaches.

Grammatical taboos

Abstract This explorative study focuses on grammatical taboos in German, morphosyntactic constructions which are subject to stigmatisation, as they regularly occur in standard languages. They are subjected to systematic experimental testing in a questionnaire study with gradient rating scales on two salient and two non-salient grammatical taboo phenomena of German. The study is divided into three subexperiments with different judgement types, an aesthetic judgement, a norm-oriented judgement and the sort of possibility judgement that comes closest to linguists’ understanding of grammar. Included in the investigated material are also examples of ordinary gradient grammaticality: unmarked, marked and ungrammatical sentences. The empirical characteristics of grammatical taboos are compared to those ordinary cases with the finding that they are rated at the level of markedness, but differ from ordinary markedness in that they produce a different pattern of between-subject variance. In addition, we find that grammatical taboos have a particular disadvantage under the aesthetic judgement type. The paper also introduces the concept of empirical grammaticality as a necessary theoretical cornerstone for empirical linguistics. Methodically, the study applies a mix of parametric and non-parametric methods of statistical analysis.

An intelligent system for gastrointestinal polyp detection in endoscopic video using fusion of bidimensional empirical mode decomposition and convolutional neural network features

AbstractThis paper presents an intelligent system for gastrointestinal polyp detection in endoscopic video. Video endoscopy is a popular diagnostic modality in assessing the gastrointestinal polyps. But the accuracy of diagnosis mostly depends on doctors' experience that is crucial to detect polyps in many cases. Computer‐aided polyp detection is promising to reduce the miss detection rate of polyp and thus improve the accuracy of diagnosis results. The proposed method illustrates an automatic system based on a new color feature extraction scheme as a support for gastrointestinal polyp detection. The scheme is the combination of color empirical mode decomposition features and convolutional neural network features extracted from video frames. The features are fed into a linear support vector machine to train the classifier. Experiments on standard public databases show that the proposed scheme outperforms the previous conventional methods, gaining accuracy of 99.53%, sensitivity of 99.91%, and specificity of 99.15%.

Study on Icing Prediction of Power Transmission Lines Based on Ensemble Empirical Mode Decomposition and Feature Selection Optimized Extreme Learning Machine

The ice coating on the transmission line is extremely destructive to the safe operation of the power grid. Under natural conditions, the thickness of ice coating on the transmission line shows a nonlinear growth trend and many influencing factors increase the difficulty of forecasting. Therefore, a hybrid model was proposed in this paper, which mixed Ensemble Empirical Mode Decomposition (EEMD), Random Forest (RF) and Chaotic Grey Wolf Optimization-Extreme Learning Machine (CGWO-ELM) algorithms to predict short-term ice thickness. Firstly, the Ensemble Profit Mode Decomposition model was introduced to decompose the original ice thickness data into components representing different wave characteristics and to eliminate irregular components. In order to verify the accuracy of the model, two transmission lines in ‘hunan’ province were selected for case study. Then the reserved components were modeled one by one, building the random forest feature selection algorithm and Partial Autocorrelation Function (PACF) to extract the feature input of the model. At last, a component prediction model of ice thickness based on feature selection and CGWO-ELM was established for prediction. Simulation results show that the model proposed in this paper not only has good prediction performance, but also can greatly improve the accuracy of ice thickness prediction by selecting input terminal according to RF characteristics.

Empirical random phase transitions between free flow and synchronized flow at highway bottlenecks

Based on a study of probe vehicle data measured on different highways during different days we have revealed some common empirical microscopic characteristic features of sequences of phase transitions from free flow to synchronized flow and back from synchronized flow to free flow occurring before traffic breakdown at a highway bottleneck predicted theoretically by Kerner. Following common empirical features of sequences of these phase transitions have been found: (i) Sequences of empirical phase transitions from free flow to synchronized flow and back from synchronized flow to free flow are observed on different highways at different bottlenecks. (ii) Empirical sequences of the phase transitions exhibit a random character, in particular, at different days very different sequences of phase transitions from free flow to synchronized flow and back from synchronized flow to free flow have been observed at the same bottleneck. (iii) Time durations between the transition from free flow to synchronized flow and the subsequent transition from synchronized flow to free flow within a sequence of the phase transitions are random values. Time intervals between subsequent sequences of the phase transitions are also random values. (iv) Regions of dissolving synchronized flow that result from sequences of phase transitions from free flow to synchronized flow and back from synchronized flow to free flow exhibit random characteristics. (v) Several following each other sequences of phase transitions from free flow to synchronized flow and back from synchronized flow to free flow can lead to waves of synchronized flow between free flow.

Credit Supply and the Housing Boom

An increase in credit supply driven by looser lending constraints in the mortgage market is the key force behind four empirical features of the housing boom before the Great Recession: the unprecedented rise in home prices, the surge in household debt, the stability of debt relative to house values, and the fall in mortgage rates. These facts are more difficult to reconcile with the popular view that attributes the housing boom only to looser borrowing constraints associated with lower collateral requirements, because they shift the demand for credit.

Quantitative Methods for the Comparative Analysis of Cities in History

Comparative studies of cities throughout history are one of the greatest sources of insight into the nature of change in human societies. This paper discusses strategies to anchor these comparisons on well-defined, quantitative and empirical characteristics of cities, derived from theory and observable in the archeological and historical records. We show how quantitative comparisons based on a few simple variables across settlements allow us to analyze how different places and peoples dealt with general problems of any society. These include demographic change, the organization of built spaces, the intensity and size of socioeconomic networks and the processes underlying technological change and economic growth. Because the historical record contains a much more varied and more independent set of experiences than contemporary urbanization, it has a unique power of illuminating present puzzles of human development and testing emergent urban theory.

Intelligent fault diagnosis of rotating machinery based on deep learning with feature selection

In the absence of a priori knowledge, manual feature selection is too blind to find the sensitive features which can effectively classify the different fault features. And it is difficult to obtain a large number of typical fault samples in practice to train the intelligent classifier. A novel intelligent fault diagnosis method based on feature selection and deep learning is proposed for rotating machine mechanical in the paper. In this method, the deep neural network is not only used for feature extraction but also for fault diagnosis. First, the deep neural network 1 is used to extract feature from the spectral signal of the original signal. In addition, the original vibration signal is decomposed to a series of intrinsic mode function components by empirical mode decomposition, and the statistical features of each intrinsic mode function component are extracted by the deep neural network 2 in time domain and frequency domain. Second, the extraction features of the original signal spectrum and the extraction features of each intrinsic mode function component are evaluated, respectively. After features evaluation, the selected sensitive features are combined together to construct a joint feature. Finally, the joint feature is put into the deep neural network 3 to realize the automatic recognition of different fault states of rotating machinery. The experimental results show that the method proposed in this paper which integrated time-domain, frequency-domain statistical characteristics, empirical mode decomposition, feature selection, and deep learning methods can obtain the fault information in detail and can select sensitive features from a large number of fault features. The method can reduce the network size, improve the mechanical fault diagnosis classification accuracy, and has strong robustness.

An automated financial indices-processing scheme for classifying market liquidity regimes

A multivariate hidden Markov model (HMM)-based approach is developed to capture simultaneously the regime-switching dynamics of four financial market indicators: Treasury-Euro Dollar rate spread, US dollar index, volatility index and S&P 500 bid-ask spread. These indicators exhibit stochasticity, mean reversion, spikes and state memory, and they are deemed to drive the main characteristics of liquidity risk and regarded to mirror financial markets' liquidity levels. In this paper, an online system is proposed in which observed indicators are processed and the results are then interfaced with an advanced alert mechanism that gives out appropriate measures. In particular, two stochastic models, with HMM-modulated parameters switching between liquidity regimes, are integrated to capture the evolutions of the four time series or their transformations. Parameter estimation is accomplished by deriving adaptive multivariate filters. Indicators' joint empirical characteristics are captured well and useful early warnings are obtained for occurrence prediction of illiquidity episodes.

Minimize pressure drop and maximize heat transfer coefficient by the new proposed multi-objective optimization/statistical model composed of “ANN + Genetic Algorithm” based on empirical data of CuO/paraffin nanofluid in a pipe

A new multi-objective optimization model composed of the artificial neural network (ANN) and the genetic algorithm (GA) methods based on the empirical thermo-physical characteristics of CuO/liquid paraffin nanofluid flow in a pipe is presented for the first time. It means a new optimization /statistical approach is achieved based on ANN together with GA; so that at first ANN is employed to predict the nanofluid thermo-physical properties and then the heat transfer coefficient and the pressure drop ratios of the nanofluid to the basefluid, are optimized as well as to minimize the pressure drop ratio and maximize the heat transfer coefficient ratio by using the multi-objective optimization approach of GA. The results of the multi-objective optimization via the GA show that the Pareto optimal front quantifies the trade-offs in satisfying the two fitness function of heat transfer coefficient and the pressure drop ratios.

Tourism, amenities, and welfare in an urban setting

AbstractUsing data on Italian cities, we document that over the period of 2001–2011, the number of establishments and employment in some key service industries are positively related to the inflow of tourists. We then build a general equilibrium model of small open cities encompassing these empirical features to study the impact of tourism on endogenous consumption amenities, factors’ allocation across sectors, prices, and welfare. We also study the interplay of exogenous historical amenities, tourism, and residents welfare in a system of two cities. When residents are immobile, they are unambiguously better off when they live in a city with richer historical amenities, and thus more tourists, than the other city. When residents are mobile and their welfare is equalized between cities, the strength of consumption amenities becomes crucial to determine whether they are better off living in an urban system where cities are heterogeneous or similar in terms of historical amenities.

Jewels mechanics: structural research applied in conception and development

The creation and production of jewelry, as adornment objects, started at the beginnings of humanity. Throughout the history, the construction techniques were improved, but production methods of the jewelry industry still have an empirical feature. With this premise, this research analyzes the simulation and evaluation of jewelry, in stages before the production. Searching how the structural analysis through finite elements can support designers in first steps of jewelry development. First the test body, a virtual model, is created through the software Rhinoceros 3D. This model is exported to software Abaqus CAE, where material properties, loads and support conditions are inputted, simulating the situation of daily use. For the material, it is opted for the silver because it is a precious metal widely used in the jewelry industry. The results of simulations in Abaqus CAE allow the evaluation of minimum and maximum von Mises stresses as the displacements generated in the 3D model. The process helps to identify fragile points in the structure or points in which it is possible to be a reduction of the used material. The results show the importance of this kind of analysis applied before the production, taking jewelry as structures subject to external loads of destructive character and searching for the suitable way for optimizing the use of materials.

Value-Added Application of Waste Rubber and Waste Plastic in Asphalt Binder as a Multifunctional Additive.

The feasibility and effectivity of recycling waste rubber and waste plastic (WRP) into asphalt binder as a waste treatment approach has been documented. However, directly blending WRP with asphalt binder brings secondary environmental pollution. Recent research has shown that the addition of WRP into asphalt binder may potentially improve the workability of asphalt binder without significantly compromising its mechanical properties. This study evaluates the feasibility of using the additives derived from WRP as a multifunctional additive which improves both the workability and mechanical properties of asphalt binder. For this purpose, WRP-derived additives were prepared in laboratory. Then, three empirical characteristics—viscosity, rutting factor, fatigue life were analyzed. Fourier transform infrared spectroscopy (FTIR) and differential scanning calorimetry (DSC) were employed to evaluate the effect of WRP-derived additive on the workability and chemical and mechanical properties of base binder. The dispersity of WRP-derived additive inside asphalt binder was also characterized using fluorescence microscope (FM). Results from this study showed that adding WRP-derived additive increases the workability of base binder. The WRP-derived additive appears positive on the high- and low- temperature performance as well as the fatigue life of base binder. The distribution of the WRP-derived additive inside base binder was uniform. In addition, the modification mechanism of WRP-derived additive was also proposed in this paper.

A CNN-Based Method for Intent Recognition Using Inertial Measurement Units and Intelligent Lower Limb Prosthesis.

Powered intelligent lower limb prosthesis can actuate the knee and ankle joints, allowing transfemoral amputees to perform seamless transitions between locomotion states with the help of an intent recognition system. However, prior intent recognition studies often installed multiple sensors on the prosthesis, and they employed machine learning techniques to analyze time-series data with empirical features. We alternatively propose a novel method for training an intent recognition system that provides natural transitions between level walk, stair ascent / descent, and ramp ascent / descent. Since the transition between two neighboring states is driven by motion intent, we aim to explore the mapping between the motion state of a healthy leg and an amputee's motion intent before the upcoming transition of the prosthesis. We use inertial measurement units (IMUs) and put them on the healthy leg of lower limb amputees for monitoring its locomotion state. We analyze IMU data within the early swing phase of the healthy leg, and feed data into a convolutional neural network (CNN) to learn the feature mapping without expert participation. The proposed method can predict the motion intent of both unilateral amputees and the able-bodied, and help to adaptively calibrate the control strategy for actuating powered intelligent prosthesis in advance. The experimental results show that the recognition accuracy can reach a high level (94.15% for the able-bodied, 89.23% for amputees) on 13 classes of motion intent, containing five steady states on different terrains as well as eight transitional states among the steady states.

Relationship of Mixolab characteristics with protein, pasting, dynamic and empirical rheological characteristics of flours from Indian wheat varieties with diverse grain hardness.

Mixolab properties of different Indian extraordinarily soft (Ex-SW), hard (HW) and medium hard (MHW) wheat varieties were evaluated and related to damaged starch content, particle size distribution, pasting, Farinographic and Mixographic properties. Water absorption (WA) of HW varieties was higher as compared to other varieties. Higher damaged starch led to more WA in HW varieties while lower in Ex-SW varieties. Unextratable polymeric protein, damaged starch and arabinoxylans were related to dough consistency. Mixolab measurement C3 (peak viscosity) and C5 (starch retrogradation) decreased with increase in grain hardness index, damaged starch content, and sodium solvent retention capacity. Dough stability (DS) and dough development time (DDT) measured by Mixolab and farinograph were significantly correlated. Mixolab parameters (C3, C4 and C5) related positively to DDT and DS while negatively to WA. HW varieties showed higher shear thinning as compared to MHW and Ex-SW varieties. C4 (hot paste stability) was lower for HW but higher for Ex-SW varieties. SuSRC was negatively related to C4 indicating that HW flours had lower starch retrogradation due to higher arabinoxylans. C3, C4 and C5 related positively to small size particles while negatively to large size particles. Slope beta (β) measured by mixolab indicated that the speed of starch gelatinization was lower for Ex-SW varieties than MHW and HW varieties.