Data Acquisition Techniques Research Articles

The Influence of Transformational Leadership Style on The Work Motivation of Parepare City Health Service Employees With The Work Environment As a Mediation Variable

The Influence of Transformational Leadership Style On Employee Work Motivation With Work Environment As A Mediation Variable". This study aims to know 1) The influence of transformational leadership style on work motivation, 2) The influence of leadership style on the work environment 3) The influence of the work environment on work motivation, 4) The influence of leadership style on work motivation with the work environment as a mediator. The data collection techniques used are observation and distribution of questionnaires. The population in this study was all employees of the ASN (State Civil Apparatus) of the Parepare City Health Service which amounted to 348 with a proportionate stratified sampling technique randem sampling 50% peer sample so that a peer sample of 177 respondents was obtained.the data acquisition technique used was the SEM-PLS (Structural Equation Modeling-Partial Least Square) analysis technique. by using the WarpPLS 7.0 application. The results of this study are 1) Transformational leadership style has a significant effect on employee work motivation, 2) Leadership style has a significant effect on the work environment, 3) The work environment has a significant effect on employee work motivation, and 4) Leadership style has a significant effect on employee work motivation and is mediated by the work environment

Legal Text Mining Using Legal Judgements

Abstract: Rapid advancements in digital data acquisition techniques have led to massive amounts of data. Above 80 percent of today’s data is composed of unstructured or semi-structured data. The discovery of appropriate patterns and trends to analyse the text documents from massive volume of data is a big issue. Thus, text mining is believed to have high commercial value. Text mining is the process of extracting useful patterns from text data which plays an effective role in decision making task. Here, we will use the legal judgements as the input document then we will apply cosine similarity method to see which judgements are similar and finally we will develop a web application in which the user can search for a particular judgement.

Design and Validation of an Accurate Low-Cost Data Acquisition System for Structural Health Monitoring of a Pedestrian Bridge

Structural health monitoring (SHM) is an effective operating technique devoted to enhance the robustness of an infrastructure, and to validate its safety requirements. The aim of SHM is to determine a structure’s reaction when subjected to any type of excitation, by means of identifying modifications in basic vibration measurements and modal parameters such as natural frequencies, damping and mode shapes. Consequently, sensors are mounted on a structure intending to record data on equal time intervals basis prior to, during and after an induced stimulation. Therefore, the necessity to adopt a computer-based data acquisition (DAQ) technique is required in this analytical approach in order to evaluate vibrational signals collected by sensors placed on a structure. In this work an accurate microcontroller-based DAQ system is proposed to monitor a pedestrian bridge located in Athens Greece for the purpose of characterizing the system state and evaluate the modal properties of the investigated structure. Four low-cost yet accurate triaxial accelerometers were systematically placed along the bridge intending to report the system response toward different generated perturbations. The proposed monitoring and computational system was tested in laboratory conditions prior to the bridge assessment. Three triaxial accelerometer were installed on a steel cantilever beam. A comparative analysis between the results of the suggested DAQ system and that of the standard laboratory DAQ system National Instrument DAQ was performed to test the accuracy of the suggested framework.

Compressive Sensing Based Space Flight Instrument Constellation for Measuring Gravitational Microlensing Parallax

In this work, we provide a compressive sensing architecture for implementing on a space based observatory for detecting transient photometric parallax caused by gravitational microlensing events. Compressive sensing (CS) is a simultaneous data acquisition and compression technique, which can greatly reduce on-board resources required for space flight data storage and ground transmission. We simulate microlensing parallax observations using a space observatory constellation, based on CS detectors. Our results show that average CS error is less than 0.5% using 25% Nyquist rate samples. The error at peak magnification time is significantly lower than the error for distinguishing any two microlensing parallax curves at their peak magnification. Thus, CS is an enabling technology for detecting microlensing parallax, without causing any loss in detection accuracy.

DAMPAK PEMBANGUNAN SARANA DAN PRASARANA TERHADAP PENINGKATAN KUALITAS PROSES BELAJAR SISWA DI SMA KRISTEN KETAPANG 1 JAKARTA

Presentation of data on the impact of building facilities and infrastructure on the quality of student learning at SMA Kristen Ketapang1 Jakarta. To obtain data using quantitative methods as many as 110 people, with a population of six classes divided into MIPA and IPS majors. A sample of 10% is taken from the specified population. By using the purposive sampling method, 11 students attended the Ketapang 1 Jakarta Christian School from KB-TK to SMA. Data acquisition techniques are carried out through observation, surveys, and documentation. The product-moment correlation equation was used to analyze the data. From the research output it is accepted that the hypothesis (Ha) uses the interpretation that the impact of infrastructure development and infrastructure on the quality of the student learning process at SMA Kristen Ketapang1 Jakarta is classified as high using a coefficient interval of 0.629 more than the "r" table of 0.602. Based on the output of the Hypothesis Test (Ha), it can be concluded that there is a significant influence between the impacts of infrastructure development & infrastructure on the quality of the student learning process at the Christian High School Ketapang 1 Jakarta.

An interpretative view of open-vent volcanoes

Open-vent volcanoes are special systems where the dynamics of sustained magmatic processes can be thoroughly investigated and where new monitoring tools can be tested and applied. However, various aspects remain puzzling at open-vent volcanoes for which forecasting their behaviour can be an important challenge. Recent papers highlight the very rapid improvements in spaceborne instruments, data acquisition techniques, data treatment and modelling over the last decade and illustrate the fundamental contribution of long time-series data, either discontinuous or continuous, and the development of multiparameter studies. Here we provide an interpretative overview of the main characteristics of open-vent volcanoes on the basis of selected examples chosen to be representative of the diversity of their magma composition, their eruptive activity and their geodynamic context. We choose typical open-vent volcanoes (Stromboli, Yasur and Erebus), some of them hosting a lava lake (Erta ‘Ale, Nyiragongo, Villarrica, Ambrym and Masaya), to those with vigourous activity, which are associated to a long-lasting eruption (Arenal, Fuego, Popocatépetl, Santiaguito). We briefly review their surface activity and report the values of SO2 flux and the derived magma supply rate with emphasis on the key results found on their behaviour. We show the key role of the magma viscosity and its implication on the degassing. We present the current models used to explain how an open-vent volcano could be maintained, such as by the simultaneous rise and fall of a degassing and degassed magma (bi-directional flow models) and the few thermal models at lava lakes and in the conduit. Finally, we discuss the sulphur evolution for three nearby volcanoes at the triple junction in Central America (Pacaya, Fuego, Santiaguito).

Tracking Nanoparticle Degradation across Fuel Cell Electrodes by Automated Analytical Electron Microscopy.

Nanoparticles are an important class of materials that exhibit special properties arising from their high surface area-to-volume ratio. Scanning transmission electron microscopy (STEM) has played an important role in nanoparticle characterization, owing to its high spatial resolution, which allows direct visualization of composition and morphology with atomic precision. This typically comes at the cost of sample size, potentially limiting the accuracy and relevance of STEM results, as well as the ability to meaningfully track changes in properties that vary spatially. In this work, automated STEM data acquisition and analysis techniques are employed that enable physical and compositional properties of nanoparticles to be obtained at high resolution over length scales on the order of microns. This is demonstrated by studying the localized effects of potential cycling on electrocatalyst degradation across proton exchange membrane fuel cell cathodes. In contrast to conventional, manual STEM measurements, which produce particle size distributions representing hundreds of particles, these high-throughput automated methods capture tens of thousands of particles and enable nanoparticle size, number density, and composition to be measured as a function of position within the cathode. Comparing the properties of pristine and degraded fuel cells provides statistically robust evidence for the inhomogeneous nature of catalyst degradation across electrodes. These results demonstrate how high-throughput automated STEM techniques can be utilized to investigate local phenomena occurring in nanoparticle systems employed in practical devices.

Impact of Online Advertisement on Customer Satisfaction With the Mediating Effect of Brand Knowledge.

The purpose of this article is to explain how online advertising affects customer satisfaction through the mediation of brand knowledge. The sample size of this survey is based on 100 participants in the Multan region. This study collects data by conducting various unstructured interviews. In this study, we used a qualitative data acquisition technique. The results show that online advertising does not have a significant impact on customer satisfaction. However, when brand knowledge is included as a parameter, the correlation between online advertising and customer satisfaction increases. Online advertising is a new advertising tool used by most organizations. This manuscript helps practitioners choose better tools for online promotion and uses a variety of recognition techniques to improve their brand knowledge. It has been known through this study, that building customer confidence in product quality is a very effective approach in front of business owners, as brand reputation enhances customer satisfaction. This study is unique in that previous studies considered elements of brand knowledge as parameters and ignored to find a direct relationship between online advertising and customer satisfaction. This study highlights key points that will help emerging researchers critically analyze such aspects in future studies.

An Adaptive Data Acquisition Technique to Enhance the Speed of Near-Field Antenna Measurement

In this article, a new approach is proposed to improve the speed of near-field measurement of antennas. The near-field measurements are mainly performed using a planar near-field scanner, named RFX2. In RFX2, which is an electronically switched probe array, the probes in the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$x$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$y$ </tex-math></inline-formula> directions cannot be fabricated at the same place. At each point, only one tangential component of the magnetic field is measured, and the other component is estimated numerically using an interpolation technique. Here, an adaptive data acquisition technique is proposed that sequentially collects samples from the field in the areas with highly dynamic behavior and skips the regions that have smooth near-field variations. Since the newly introduced points at each iteration of the algorithm are not necessarily laid on the probe locations of the RFX2, the values of the data at these points are calculated using an interpolation method. The proposed adaptive algorithm in this work requires remarkably fewer samples to reach the same accuracy as uniform sampling. This method is also utilized in spherical NF measurement over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\theta $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\phi $ </tex-math></inline-formula> plane. The validity of the approach is verified using various numerical and measurement results.

A Reel-to-Reel Scanning Hall Probe Microscope for Characterizing Long REBCO Conductor in Magnetic Fields Up to 5 Tesla

We have developed and routinely used a reel-to-reel (R2R) scanning Hall-probe microscopy (SHPM) system to characterize REBCO conductors developed at the University of Houston. By mapping the screening-current-induced field of a magnetized REBCO conductor, the SHPM system enables 2-dimensional imaging of critical current density (<i>J</i>_c) distribution in a contact-free, non-destructive approach. Most existing SHPM systems investigate the magnetized REBCO conductors at remnant state without external background field. However, because of the inhomogeneity in the distribution of nanoscale artificial pinning centers in advanced REBCO conductors, there is a demand for reel-to-reel in-field examination over long conductor lengths. We have been developing a new R2R SHPM, capable of measuring the <i>J</i>_c profile of long REBCO conductors at the temperature of 63.5&#x2013;77 K and in an applied field up to 5 T. With optimal data acquisition and processing techniques, we overcame the challenge of measuring weak screening-current-field signals in a strong background field. R2R <i>J</i>_c mapping has been performed on long REBCO conductors with a spatial resolution as low as 0.15 mm² and a longitudinal scanning speed comparable with our existing system. Our previous study revealed a good correlation between the REBCO conductors&#x2019; 65-K in-field <i>J</i>_c and their <i>J</i>_c over a wide span of temperatures and fields. Using this correlation, the in-field R2R SHPM will serve not only as a means of quality control, but also provide insight into high-field performance of advanced REBCO conductors at low temperatures.

Multiple Sclerosis Lesion Analysis in Brain Magnetic Resonance Images: Techniques and Clinical Applications.

Multiple sclerosis (MS) is a chronic inflammatory and degenerative disease of the central nervous system, characterized by the appearance of focal lesions in the white and gray matter that topographically correlate with an individual patient's neurological symptoms and signs. Magnetic resonance imaging (MRI) provides detailed in-vivo structural information, permitting the quantification and categorization of MS lesions that critically inform disease management. Traditionally, MS lesions have been manually annotated on 2D MRI slices, a process that is inefficient and prone to inter-/intra-observer errors. Recently, automated statistical imaging analysis techniques have been proposed to detect and segment MS lesions based on MRI voxel intensity. However, their effectiveness is limited by the heterogeneity of both MRI data acquisition techniques and the appearance of MS lesions. By learning complex lesion representations directly from images, deep learning techniques have achieved remarkable breakthroughs in the MS lesion segmentation task. Here, we provide a comprehensive review of state-of-the-art automatic statistical and deep-learning MS segmentation methods and discuss current and future clinical applications. Further, we review technical strategies, such as domain adaptation, to enhance MS lesion segmentation in real-world clinical settings.

Detailed Three-Dimensional Building Façade Reconstruction: A Review on Applications, Data and Technologies

Urban environments are regions of complex and diverse architecture. Their reconstruction and representation as three-dimensional city models have attracted the attention of many researchers and industry specialists, as they increasingly recognise the potential for new applications requiring detailed building models. Nevertheless, despite being investigated for a few decades, the comprehensive reconstruction of buildings remains a challenging task. While there is a considerable body of literature on this topic, including several systematic reviews summarising ways of acquiring and reconstructing coarse building structures, there is a paucity of in-depth research on the detection and reconstruction of façade openings (i.e., windows and doors). In this review, we provide an overview of emerging applications, data acquisition and processing techniques for building façade reconstruction, emphasising building opening detection. The use of traditional technologies from terrestrial and aerial platforms, along with emerging approaches, such as mobile phones and volunteered geography information, is discussed. The current status of approaches for opening detection is then examined in detail, separated into methods for three-dimensional and two-dimensional data. Based on the review, it is clear that a key limitation associated with façade reconstruction is process automation and the need for user intervention. Another limitation is the incompleteness of the data due to occlusion, which can be reduced by data fusion. In addition, the lack of available diverse benchmark datasets and further investigation into deep-learning methods for façade openings extraction present crucial opportunities for future research.

Accurate Models of Substrate Preferences of Post-Translational Modification Enzymes from a Combination of mRNA Display and Deep Learning.

Promiscuous post-translational modification (PTM) enzymes often display nonobvious substrate preferences by acting on diverse yet well-defined sets of peptides and/or proteins. Understanding of substrate fitness landscapes for PTM enzymes is important in many areas of contemporary science, including natural product biosynthesis, molecular biology, and biotechnology. Here, we report an integrated platform for accurate profiling of substrate preferences for PTM enzymes. The platform features (i) a combination of mRNA display with next-generation sequencing as an ultrahigh throughput technique for data acquisition and (ii) deep learning for data analysis. The high accuracy (>0.99 in each of two studies) of the resulting deep learning models enables comprehensive analysis of enzymatic substrate preferences. The models can quantify fitness across sequence space, map modification sites, and identify important amino acids in the substrate. To benchmark the platform, we performed profiling of a Ser dehydratase (LazBF) and a Cys/Ser cyclodehydratase (LazDEF), two enzymes from the lactazole biosynthesis pathway. In both studies, our results point to complex enzymatic preferences, which, particularly for LazBF, cannot be reduced to a set of simple rules. The ability of the constructed models to dissect such complexity suggests that the developed platform can facilitate a wider study of PTM enzymes.

Tarih bilinci üzerine Türkçe yazıların Alman tarih eğitimcisi Jörn Rüsen’in tarih bilinci modeline göre değerlendirilmesi

The consciousness of history, which is one of the most discussed topics in the philosophy of history of the Age of Enlightenment and modernity, has been among the main goals and behaviors that students want to gain through history education, especially since the last quarter of the twentieth century. According to the German historical philosopher and educator, Jörn Rüsen, historical consciousness establishes a connection between the past, present, and future; shapes the future behavior of individuals. Rüsen, historical consciousness; He stated that he has four dimensions: traditionalist, idealizing, critical and dynamic conceptualization and examined them in terms of time experience, historically significant models, external life orientation, internal life orientation, relationship with moral values, and relationship with moral reasoning. The aim of this study is to determine which historical consciousness type Turkish articles on historical consciousness fall into according to Jörn Rüsen's "History Consciousness Types Performance Indicators" (HCTPI). Within the scope of the study, Hilmi Ziya Ülken (1948), Nihal Atsız (1951), Osman Turan (1968), Nevzat Kösoğlu (1970?), İbrahim Kafesoğlu (1986), Şahin Uçar (1987), İlhan Tekeli (1998), Oktay Ekinci (1999), İskender Öksüz (2007) and M. Şükrü Hanioğlu's (2012) articles on historical consciousness were evaluated within the framework of Jörn Rüsen's “History Consciousness Types Performance Indicators” (HCTPI). The method of the research was basic qualitative research, the technique of data acquisition was document analysis, the technique of data analysis was content analysis, and the sampling method was "criterion sampling". According to the results of the research, out of 10 Turkish articles, 1 article showed the features of Traditionalist Conceptualization Performance Indicator (TCPI), 2 articles of Idealizing Conceptualization Performance Indicator (ICPI), 4 articles of Critical Conceptualization Performance Indicator (CPCI), and 3 articles of Dynamic Conceptualization Performance Indicator (DCPI). As a result of the study, it has been determined that the writings of the study group on historical consciousness, which show dynamic and critical conceptualization features, are more than those that show traditionalist and idealizing conceptualization features.

A Review on the Possibilities and Challenges of Today’s Soil and Soil Surface Assessment Techniques in the Context of Process-Based Soil Erosion Models

To investigate relevant processes as well as to predict the possible impact of soil erosion, many soil erosion modelling tools have been developed. The most productive development of process-based models took place at the end of the 20th century. Since then, the methods available to observe and measure soil erosion features as well as methods to inter- and extrapolate such data have undergone rapid development, e.g., photogrammetry, light detection and ranging (LiDAR) and sediment tracing are now readily available methods, which can be applied by a broader community with lower effort. This review takes 13 process-based soil erosion models and different assessment techniques into account. It shows where and how such methods were already implemented in soil erosion modelling approaches. Several areas were found in which the models miss the capability to fully implement the information, which can be drawn from the now-available observation and data preparation methods. So far, most process-based models are not capable of implementing cross-scale erosional processes and can only in parts profit from the available resolution on a temporal and spatial scale. We conclude that the models’ process description, adaptability to scale, parameterization, and calibration need further development. The main challenge is to enhance the models, so they are able to simulate soil erosion processes as complex as they need to be. Thanks to the progress made in data acquisition techniques, achieving this aim is closer than ever, if models are able to reap the benefit.

The Implementation of the Mind Mapping Model in Developing Children's Creativity in Thematic Lessons

Children's creativity is a potential that must be developed through the educational process. This study aims to describe the implementation of the mind mapping model in developing children's creativity in thematic lessons. This study uses a qualitative approach with a descriptive study method. The background of this research is in MIN Medan. Observation techniques, interviews, and document studies are three research data acquisition techniques. Then, the data were analyzed using an interactive analysis model, through the steps of data collection, data sorting, data presentation, and drawing conclusions. The results of this study indicate that the application of the mind mapping model makes it easier for educators during the thematic learning process, because it stimulates students to develop their creativity based on the spirit of group learning. Likewise, there are obstacles in implementing the learning model, namely the low focus of students in group activities, difficulties for teachers to control student learning behavior, and some students like to disturb their friends.

Spatial pattern‐based performance evaluation and uncertainty analysis of a distributed hydrological model

AbstractAdvancement in data acquisition techniques (e.g., satellites) has provided enormous opportunities to improve the predictions of distributed hydrological models by incorporating new types of information. Moreover, such advancements have paved the way for the spatial scale evaluation rather than the conventional temporal evaluation of models for more accurate water management by considering the spatial variability. The present study focuses on the spatial pattern‐oriented model evaluation by intercomparing the satellite remote sensing data‐driven patterns of actual evapotranspiration (AET) and soil moisture with simulated patterns from a hydrological model for the 19 276 km2 Subarnarekha basin in Eastern India. Pattern comparison is carried out by incorporating three spatial performance metrics as diagnostic tools, that is, joint empirical orthogonal functions (EOF), fractional skill score (FSS), and spatial efficiency (SPAEF). Moreover, the present study made a novel contribution to spatial pattern evaluation by incorporating the uncertainty associated with the patterns through the “Quantile Regression’ technique”. The EOF analysis indicated a significant similarity between simulated and observed patterns. High scores for both the FSS and SPAEF metrics also revealed similarities between simulated and observed spatial patterns. Overall, the results show an acceptable spatial pattern performance of the hydrological model mimicking the observed spatial patterns across time. The uncertainty analysis represents reasonable predictive ranges, with a 95 per cent prediction uncertainty (PPU) band enclosing 80%–95% of AET and 79%–92% of the soil moisture observations. The present study demonstrated a framework for improving the basin's overall water balance analysis by incorporating a spatial pattern‐based evaluation of hydrological models.

Construction of an Smart Progress Production Unit Based on Paddle Boring

With the continuous improvement of hardware product performance and data processing capability and the rapid development of network communication technology, the intelligent technology of computer systems is becoming more and more mature, and the intelligence of manufacturing has become the focus of people's attention. The intelligent performance of manufacturing is to use the knowledge obtained from data analysis to make inferences and decisions to solve problems, and the Cyber-Physical System (CPS) supports the deep integration of informationization and industrialization by integrating advanced information technology and automatic control technology, such as perception, computation, communication, control, etc. Although there has been great progress in the research of CPS, it has been widely used in the development of computer systems. Despite significant progress in CPS research, some key issues have yet to be fully addressed at the shop floor level, including dynamic reorganization and local intelligence. These issues have hindered the research on the intelligence of complex processes. For progress units of complex processes, this study proposes a CPS-based smart progress unit (SPU) construction method. In order to improve machining efficiency and quality consistency, this study proposes a multi-source data acquisition technique and a multi-objective optimization algorithm to process the data within the machining unit. Finally, this study takes the paddle boring process as an example, and designs hardware and software systems to automate the paddle boring process and meet the needs of automation and intelligent transformation of the paddle boring process.

3D Modelling of Roller Assembly of Base Cutting Machine

Abstract: 3D modelling is an important technique for many studies and applications. Model data acquisition and modelling 3D modelling techniques have their own specialties in various research and application areas. This article systematically introduces the equipment of 3D modelling method. This document also introduces a 3D modelling application, including all model views created in Fusion 360. Fusion 360's direct modelling tools allow you to change modal geometry without having to edit the mid-levels of your model. You can seamlessly make design changes without causing modelling errors. The authors consider the construction of a roller assembly of Base cutting machine is demonstrated. The constructions are considered as applied to the Autodesk fusion 360 software. The paper is aimed at improving the graphic methods of machine design and improving the educational process when training the Bachelor’s degree students majoring in machine design. Finally, some major issues and some shortcomings are identified, and further challenges are predicted from various aspects such as modelling recovery, digitization methods, and dynamic modelling. Keywords: Base Cutting Machine, Roller Assembly, 3D Modelling, Fusion 360, CAD.

An impact of three dimensional techniques in virtual reality

Three dimensional (3D) imaging play a prominent role in the diagnosis, treatment planning, and post-therapeutic monitoring of patients with Rheumatic Heart Disease (RHD) or mitral valve disease. More interactive and realistic medical experiences take an advantage of advanced visualization techniques like augmented, mixed, and virtual reality to analyze the 3D models. Further, 3D printed mitral valve model is being used in medical field. All these technologies improve the understanding of the complex morphologies of mitral valve disease. Real-time 3D Echocardiography has attracted much more attention in medical researches because it provides interactive feedback to acquire high-quality images as well as timely spatial information of the scanned area and hence is necessary for intraoperative ultrasound examinations. In this article, three dimensional techniques and its impacts in mitral valve disease are reviewed. Specifically, the data acquisition techniques, reconstruction algorithms with clinical applications are presented. Moreover, the advantages and disadvantages of state-of-the-art approaches are discussed in detail.