Miracle-dz/UniFAR-FTUs huggingface dataset:
A Perceptual Approach to Aging: Latent Aging

This study is a review article. Gerontology literature was reviewed and issues of latent aging were systematically formed. According to the results of the study, latent aging consists of perceptual diagnosis, research, adoption, and reactive actions. Reactive actions include the social, psychological and physical effects of latent aging. The social effects of latent aging are the decline of social relations and social isolation. Depres sion, stress, anxiety, traumas and cognitive decline are among the psychological effects. Chronic musculoskeletal pain, sleep disorders, premature mortality, and suicidal ideation were determined as physical effects. The detection of latent aging is import ant in the prevention of chronic diseases. It was revealed that latent aging has the following four main processes: perceptual diagnosis and coding, research and comparison, adoption and reactive actions. Furthermore, this aging approach has three signific ant effects: social, psychological and physical.
Image Super-Resolution via Residual Blocks and Non-Negative Matrix Decomposition

In order to solve the problems that the existing super-resolution algorithms based on convolutional neural network do not fully utilize the feature information at all levels, which results in low reconstruction accuracy, and the deep network may resulting in the gradient disappearing, an image super-resolution reconstruction algorithm based on residual blocks and non-negative matrix decomposition is proposed. The proposed residual block can gradually learn the characteristics of the input image, the computational burden of the network is shared on each single residual block, thus the network complexity decreases. The input image and output image are fused by non-negative matrix decomposition to obtain more detail information of high-resolution image. Experiments on the benchmark dataset show that the proposed algorithm improves the PSNR, structural similarity and visual effect.
El capital social local y sus determinantes: evidencia de Cataluña

Ante la creciente importancia del estudio del capital social como componente institucional y la escasez de estudios empiricos a menor escala y fuera de los Estados Unidos, este trabajo pretende identificar algunos de determinantes observables del capital social en los municipios y comarcas de Cataluna, asi como su relacion con los niveles de capital social y su distribucion territorial. Esto se consigue mediante el uso de la participacion electoral, la densidad asociativa y las donaciones de sangre como variables de capital social. Usando datos de corte transversal y de panel y mediante regresiones por minimos cuadrados ordinarios y de efectos aleatorios respectivamente se encuentran efectos relevantes sobre el capital social del grado de movilidad residencial, la densidad de poblacion y asociativa, el regimen de propiedad de la vivienda, la renta, el nivel educativo y la edad. En ciertos casos las relaciones se corresponden con las encontradas en la literatura existente. Ademas, extrayendo sus componentes principales se elabora un indice de capital social, con el cual se observa una distribucion geografica desigual del atributo, penalizando el Area Metropolitana de Barcelona y la costa.
Authentic leadership traits, high-performance human resource practices and job performance in Pakistan

This empirical study aims to explore the linkage among authentic leadership traits and job performance via the mediating role of high-performance human resource practices (HPHRPs) in a developing country context. Social exchange theory and trait theory are widely employed in many disciplines but seldom applied to job performance among Pakistan Telecommunication Company Limited (PTCL) employees. ,The sample of the study is PTCL's employees. A cross-sectional design was employed and data was collected from 377 employees via questionnaire. This is an exploratory study; therefore, partial least square–structural equation modeling (PLS-SEM) was employed to answer the research questions. ,The findings of this study revealed that relational transparency, directly and indirectly, influenced job performance with the partial mediating role of selective staffing and extensive training. Furthermore, balance processing, directly and indirectly, influenced job performance with the partial mediating role of incentive reward, while self-awareness, internalized moral perspective, employment security and result-oriented appraisal were insignificant influences on job performance. ,The results of the study delineated practical applications for both the researchers and policymakers. The results of this study would also augment the body of knowledge on human resource practices in both developed and developing countries. ,This study found and reported authentic leadership traits and HPHR as the main sources of job performance in PTCL. This study empirically examined the influence of authentic leadership traits on job performance with the mediating role of a bundle of HPHRPs in a developing context.
Effect of welding pattern during repair and maintenance of Francis runner on sediment erosion: An experimental investigation using RDA

In Nepal, sediment erosion is a major cause of hydro-turbine failure. Every year turbine parts expose to water got eroded and failed ultimately resulting in loss of efficiency as well as increase in the repair cost. For repair of these eroded surfaces welding is quit famous in Nepal due to low cost to build up the eroded surface however specific welding pattern on the eroded surfaces has not been used which results low strength in repaired equipment. In this paper, the study of the relation of the welding pattern on the erosion rate had carried out taking the Francis turbine blade of Dhamile Khola (14 kW) as reference. Experimental analysis was adopted for studying the effect of welding pattern on erosion rate through laboratory tests. Four different welding patterns on four different specimens were developed and accelerated tests were carried out in the Rotating Disc Apparatus (RDA) at Turbine Testing Lab (TTL), Kathmandu University. Furthermore, erosion pattern on forged blades were compared with the CFD analysis carried out on same blade which is similar. After an operation time of 1050 minutes, the extent of wear was found significantly less in the test specimen with right inclined welding pattern taking material loss as a basis for comparison. The aim of this paper is to provide a specific welding pattern with relatively less erosion rate during the repair and maintenance of the sediment eroded surface of the runners.
Isomass and Probability Maps of Ash Fallout Due to Vulcanian Eruptions at Tungurahua Volcano (Ecuador) Deduced from Historical Forecasting

Since April of 2015, the ash dispersion and ash fallout due to Vulcanian eruptions at Tungurahua, one of the most active volcanoes in Ecuador, have been forecasted daily. For this purpose, our forecasting system uses the meteorological Weather Research and Forecasting (WRF) and the FALL3D models. Previously, and based on field data, laboratory, and numerical studies, corresponding eruption source parameters (ESP) have been defined. We analyzed the historically forecasted results of the ash fallout quantities over four years (April 2015 to March 2019), in order to obtain the average isomass and probability maps for three-month periods: February–March–April (FMA), May–June–July (MJJ), August–September–October (ASO), and November–December–January (NDJ). Our results indicate similar ash fallout shapes during MJJ and ASO, with a clear and defined tendency toward the west of the volcano; this tendency is less defined during NDJ and FMA. The proximal region west of the volcano (about 100 km to the west) has the highest probability (>70%) of being affected by ash fallout. The distant region to the west (more than 100 km west) presented low to medium probabilities (10%–70%) of ash fallout. The cities of Guaranda (W, 60% to 90%), Riobamba (SW, 70%), and Ambato (NW, 50% to 60%) have the highest probabilities of being affected by ash fallout. Among the large Ecuadorian cities, Guayaquil (SW, 10% to 30%) has low probability, and Quito (N, ≤5%) and Cuenca (SSE, <5%) have very low probabilities of being affected by ash fallout. High ash clouds can move in different directions, compared to wind transport near the surface. Therefore, it is possible to detect ash clouds by remote sensing which, in Ecuador, is limited to the layers over the meteorological clouds, which move in a different direction than low wind; the latter produces ash fallout over regions in different directions compared to the detected ash clouds. In addition to the isomass/probability maps and detected ash clouds, forecasting is permanently required in Ecuador.
BONUS FOR QUALITY OF PRODUCTS AND SERVICES IN THE TQM CONCEPT. THE PROBLEMS OF REALITY AND JUSTICE

The article discusses the importance of remuneration systems in organizations using Total Quality Management (TQM) and presents the current state of knowledge regarding the role of remuneration systems in quality improvement. The main objective of this paper is to determine the role played by (rarely researched) bonus systems in the process of motivating employees to improve product and service quality. Firstly, the authors indicate to what extent quality criteria are included in the designing of remuneration systems. Secondly, they provide employee feedback on how motivating their pay systems are. Thirdly, they determine to what extent different pay solutions are assessed as fair. Findings: decisions on bonus granting are based on not only objective product/service quality criteria but also opinions such as assessments made by superiors and criteria such as zero work-related accidents, zero sickness absences, or customer satisfaction levels. Thus, it is difficult to assume that bonus granting criteria are completely fair. The conducted analysis shows that a considerable group of the respondents consider bonus awarding as a source of perceived injustice. The most important factors influencing employee motivation include commendations from superiors (in service enterprises) and the possibility of self-control in production enterprises. Contributions: the conducted research extends the knowledge of the role of bonus systems in processes aimed at improving the quality of services and products.
Smart vibration control of structures with unknown structural parameters using integrated VSM/LQR approach

Correspondence Pedram Ghaderi, School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran. Email: p_ghaderi@iust.ac. ir Abstract Although vibration control and health monitoring of structures have been treated separately in recent years, it will be beneficial to integrate these two systems and develop a smart structure with its own sensors, processors, and actuators. In this article, an integrated virtual synchronization method/linear-quadratic regulator (VSM/LQR) approach for system identification and vibration control of structures with unknown physical parameters is proposed. First, based on the measured state of the structure, the unknown structural parameters are estimated using the VSM in a relatively short duration. The estimated parameters are then employed to determine optimal control forces for the purpose of the vibration control. The feasibility and effectiveness of the proposed approach are numerically examined through the time-history analysis of two shear-type frames with unknown stiffness coefficients. The results from the numerical investigation of the example structures demonstrate that the proposed method is effective in the vibration attenuation of structures with unknown parameters. Moreover, it was shown that the proposed VSM/LQR approach is more promising for addressing unknown structural parameters than the recursive least square method and conventional LQR method.
Analysis of an Image Recognition Method on Group Activities

Sabit resimler uzerinde grup aktivitesi tanima oldukca zorlayici bir problemdir. Resimler uzerinde on ve arka plan ayrimini yapmak, uzamsal ve zamansal bilginin olmamasi nedeniyle, bu problemi video uzerinde grup aktivite tanima problemine gore daha zor kilmaktadir. Bu calismada grup aktivite tanima icin olusturulmus olan Volleyball video veri kumesi uzerinde, sabit resim tanima tabanli bir yontemi incelemekteyiz. Kullanilan veri kumesindeki her bir video icin bulunan hedef video karesine ek olarak, hedef karenin onceki ve sonraki karelerinden elde edilen ortalama goruntulerin resimler uzerinde zamansal bilgiye yaptigi katkinin analizi incelenmektedir. Video grup aktivite tanima problemlerinde sikca kullanilan zamansal bilgi, ortalama resim kareleri uzerinden elde edilmekte ve resim tanima yonteminin egitim asamasinda kullanilmaktadir. Deney sonuclarindan anlasildigi uzere, onerilen yontemimiz son teknoloji zamansal bilgiyi kullanan video tabanli calismalar ile karsilastirilabilecek sonuclar alabilmektedir.
An Empirical Study on Utilizing Neural Network for Event Information Retrieval

Event information retrieval (EIR) is the task of retrieving news articles in response to the event-oriented query, which tailors for dealing with a large scale of news articles on breaking news (e.g., natural disaster). However, the challenges caused by the dynamic and complex natures of the event make EIR less studied than traditional information retrieval (IR). The existing studies usually employed either traditional IR models or machine learning approaches over hand-crafted features to retrieve events, which fails to capture more-complex structures of events with event evolution well. To address this issue, we exploit neural models incorporating the characteristics of events to EIR task. Experimental results demonstrate the effectiveness of our method over two datasets, which can regard as the simple but strong baseline for further research in EIR.
Attention-BLSTM-CRF Based Method for Named Entity Recognition in Judicial Domain

Although the texts in the judicial field are relatively standardized, the entity categories are rich and the structure is different, and the entity expression is special in some legal documents, electronic files and guiding cases. In order to improve the effect of named entity recognition in the field of justice, this paper entity type can be divided into four categories, and presents a model of named entity recognition based on attention mechanism, structure improvement of input vector fusion CNN embed mode, BLSTM neural network at the same time we constantly study the characteristics of the context, finally by CRF decoding output sequence. The experimental results show that the method is helpful to the application research in the judicial field, and the experiment on the self-built corpus test set has achieved a good entity identification effect.
Additive Manufacturing (AM) of Metallic Alloys

The introduction of metal additive manufacturing (AM) processes in industrial sectors, such as the aerospace, automotive, defense, jewelry, medical and tool-making fields, has led to a significant reduction in waste material and in the lead times of the components, innovative designs with higher strength, lower weight and fewer potential failure points from joining features [. ]
Collaborative Filtering Recommendation Algorithm Based on User Characteristics and User Interests

Among many e-commerce platforms, collaborative filtering recommendation algorithm is currently the most widely used recommendation technology. In order to alleviate the deficiencies of the traditional user-based collaborative filtering algorithm in cold start and recommendation accuracy, this paper proposes a collaborative filtering recommendation algorithm based on user characteristics and user interests. The similarity of the algorithm in this paper is composed of user score similarity, user attribute feature similarity and user interest similarity, in which user registration information is used to extract attribute features to calculate user feature similarity; use the number of user evaluations of project attributes to measure users’ interest in different project attributes, use the similarity calculation formula to calculate the interest similarity value between users. The user attribute feature similarity and user interest similarity are combined with the user rating similarity to obtain the final similarity for recommendation. Finally, a simulation experiment is performed on the MovieLens movie data set. Through the experimental results, it can be seen that the improved collaborative filtering algorithm based on user characteristics and user interests not only solves the cold start problem, but also improves the recommendation accuracy.
Understanding Multi-Vehicle Collision Patterns on Freeways—A Machine Learning Approach

Generating meaningful inferences from crash data is vital to improving highway safety. Classic statistical methods are fundamental to crash data analysis and often regarded for their interpretability. However, given the complexity of crash mechanisms and associated heterogeneity, classic statistical methods, which lack versatility, might not be sufficient for granular crash analysis because of the high dimensional features involved in crash-related data. In contrast, machine learning approaches, which are more flexible in structure and capable of harnessing richer data sources available today, emerges as a suitable alternative. With the aid of new methods for model interpretation, the complex machine learning models, previously considered enigmatic, can be properly interpreted. In this study, two modern machine learning techniques, Linear Discriminate Analysis and eXtreme Gradient Boosting, were explored to classify three major types of multi-vehicle crashes (i.e., rear-end, same-direction sideswipe, and angle) occurred on Interstate 285 in Georgia. The study demonstrated the utility and versatility of modern machine learning methods in the context of crash analysis, particularly in understanding the potential features underlying different crash patterns on freeways.
High Performance Liquid Chromatography and its role in identification of Mycobacteriae: An Overview

HPLC has over the past decade become the method of choice for the analysis of a wide variety of compounds. Its main advantage over Gas Chromatography (GC) is that the analytes do not have to be volatile, so macromolecules are suitable for HPLC analysis. HPLC is accomplished by the injection of a small amount of liquid sample into a moving stream of liquid – the mobile phase, which passes through a column packed with particles of the stationary phase .
Development and Verification of a Three‐Dimensional Variably Saturated Flow Model for Assessment of Future Global Water Resources

Global water resource assessment has been conducted primarily for surface water and shallow groundwater (unconfined aquifers). Groundwater is a major water resource worldwide. Nevertheless, future water resource assessment integrating surface water and groundwater has not been carried out at the global scale. Large grid sizes are used in global‐scale models due to computer resource constraints. Underground, the unsaturated zone that connects surface water and groundwater is expressed using a vertically one‐dimensional grid of fixed size. However, the thickness of the unsaturated zone varies greatly between dry and humid areas, and future unsaturated zone conditions may differ from those at present due to climate change. In addition, for mountainous areas with steep slopes, representing the unsaturated zone in one vertical dimension is a major limitation. We believe that such a representation causes difficulty in adequately expressing underground water flow and thus introduces uncertainties into future prediction results. In this study, we developed a code to represent variably saturated flow with groundwater storativity in three dimensions based on physical equations, which can be used as references for parameterization in the future. To confirm the accuracy of this code, we verified it using vertical one‐ and two‐dimensional infiltration problems, a three‐dimensional groundwater pumping problem, hillslope problem, and water balance. The code achieved reliable results for each problem.
Tibetan-Chinese Neural Machine Translation Combining Attention Mechanism

Neural Machine Translation (NMT) has developed rapidly in recent years, and translation effects in most languages have surpassed statistical machine translation methods. The Seq2Seq framework has brought great advantages to machine translation, but the model still has great limitations in the ability to capture long-distance information. Recurrent neural networks (RNN) and LSTM networks are proposed to improve this problem, but the effect is not obvious. However, the proposal and application of the attention mechanism effectively compensated for this defect. This paper uses the attention mechanism as the basis to construct the encoder-decoder framework. This method analyzes the mechanism and principle of the attention model to implement the Tibetan-Chinese neural machine translation system. Compared with the previous neural network translation models, the results show that the translation model that incorporates the attention mechanism can obtain better translation results.
Water Intrusion Characterization in Naturally Fractured Gas Reservoir Based on Spatial DFN Connectivity Analysis

In this study, the non-intrusive embedded discrete fracture model (EDFM) in combination with the Oda method are employed to characterize natural fracture networks fast and accurately, by identifying the dominant water flow paths through spatial connectivity analysis. The purpose of this study is to present a successful field case application in which a novel workflow integrates field data, discrete fracture network (DFN), and production analysis with spatial fracture connectivity analysis to characterize dominant flow paths for water intrusion in a field-scale numerical simulation. Initially, the water intrusion of single-well sector models was history matched. Then, resulting parameters of the single-well models were incorporated into the full field model, and the pressure and water breakthrough of all the producing wells were matched. Finally, forecast results were evaluated. Consequently, one of the findings is that wellbore connectivity to the fracture network has a considerable effect on characterizing the water intrusion in fractured gas reservoirs. Additionally, dominant water flow paths within the fracture network, easily modeled by EDFM as effective fracture zones, aid in understanding and predicting the water intrusion phenomena. Therefore, fracture clustering as shortest paths from the water contacts to the wellbore endorses the results of the numerical simulation. Finally, matching the breakthrough time depends on merging responses from multiple dominant water flow paths within the distributions of the fracture network. The conclusions of this investigation are crucial to field modeling and the decision-making process of well operation by anticipating water intrusion behavior through probable flow paths within the fracture networks.