Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of Importance - Performance Analysis Model in Evaluation Service Quality of Diving Tour Operator in Nha Trang (Viet Nam)

The purpose of this study is to improve service quality for dive tour operators in Nha Trang (Viet Nam), so the objectiveswere set to understand the divers’ opinion on dimensions of service quality of diving experiences in Nha Trang, and their evaluation of the diving tour operators’ performance. Analysis employed the Importance – Performance analysis (IPA) paradigm based on five dimensions and twenty five attributes of service quality (Parasuraman, 1988). The results are significant to dive tour operators because it showed clearly the managerial implications in order to provide a quality service during the dive tourism experience. In details, it indicated that Tangibles, Responsiveness and Reliability were in the quadrant “Low Priority”, which means low importance and low performance. Moreover, Assurance and Empathy dimensions had high importance and high performance, so they were in the quadrant of “Keep Up The Good Work”.
THE INFLUENCE OF STRENGTH TRAINING ON TAEKWONDO ATHLETES’ REACTION SPEED

ABSTRACT Introduction: Taekwondo is a skill-oriented confrontational event. Athletes’ physical fitness and flexible changes in skills and tactics have become the cornerstone of athletes’ victory. Objective: The objective was to explore the influence of strength training on the reaction speed of boxing athletes. Method: 8 master-class Taekwondo athletes, 16 first-class Taekwondo athletes, and 30 second-class Taekwondo athletes from a particular province were selected as Group A, B, and C. The control groups were Group A1, B1, and C1. Results: Group B athletes showed significant changes in 15s front kick and 15s high leg lift. Group C athletes showed highly significant differences in rapid strength. After the traditional fast strength training in the control group, all the Group C1 had significant changes in the test. However, Group B1 athletes only had substantial changes in 15s down split and 15s front kick. There was no significant change in all indexes of Group A1 athletes before and after the experiment. Conclusion: The analysis of experimental data shows that core strength training significantly improves the reaction speed of Taekwondo athletes. Level of evidence II; Therapeutic studies - investigation of treatment results.
Review and Update on Some Connections between a Spring-Block SOC Model and Actual Seismicity in the Case of Subduction Zones

The self-organized critical (SOC) spring-block models are accessible and powerful computational tools for the study of seismic subduction. This work aims to highlight some important findings through an integrative approach of several actual seismic properties, reproduced by using the Olami, Feder, and Christensen (OFC) SOC model and some variations of it. A few interesting updates are also included. These results encompass some properties of the power laws present in the model, such as the Gutenberg-Richter (GR) law, the correlation between the parameters a and b of the linear frequency-magnitude relationship, the stepped plots for cumulative seismicity, and the distribution of the recurrence times of large earthquakes. The spring-block model has been related to other relevant properties of seismic phenomena, such as the fractal distribution of fault sizes, and can be combined with the work of Aki, who established an interesting relationship between the fractal dimension and the b-value of the Gutenberg-Richter relationship. Also included is the work incorporating the idea of asperities, which allowed us to incorporate several inhomogeneous models in the spring-block automaton. Finally, the incorporation of a Ruff-Kanamori-type diagram for synthetic seismicity, which is in reasonable accordance with the original Ruff and Kanamori diagram for real seismicity, is discussed.
A Parallel Architecture for High-Data-Rate Digital Receivers in Scaled

Reduction in cost terminal electronics is essential in order for fiber optic technology to penetrate LAN and telephony subscriber loop applications extensively. Currently, high-data-rate fiber transceivers electronics are implemented principally with the multichip approach, with bipolar and gallium arsenide technology. The objective of this research is to develop a new parallel architecture to use the lower cost and higher integration of scaled CMOS technology to address the cost problem in fiber transceivers. The problem is first attacked by integrating the parallel-to-serial conversion, automatic gain control (AGC), decision, and clock recovery functions in CMOS at rates about 500Mb/s. The inherently slower speed of CMOS compared to bipolar and GaAs is compensated through the use of a high degree of parallel signal processing in the signal path. To verify the main idea about the new parallel architecture proposed in this thesis, an experimental prototype with 8 parallel channels was designed and fabricated in a 1.2um CMOS technology. A bit rate of 480 MB/s is achieved with a minimum peak-to-peak input voltage of 18mV. The area of the chip is 160 milX160 mil(4mm X 4 mm) and it consumes 900mW of power. This thesis arrives at three main conclusions. First a minimum device f(T) to data rate ratio of 4:1 can be achieved with the parallel architecture enabling CMOS technology to be used for Gb/s digital optical fiber receivers. Second, clock recovery can be done, through a decision directed scheme with two times over-sampling, by inserting one or more timing channels in between the parallel data channels. Finally, the area and power consumption of the parallel architecture are comparable to implementations in bipolar and GaAs.
Dense Residual LSTM-Attention Network for Boiler Steam Temperature Prediction with Uncertainty Analysis

Flexible operation of large-scale boilers for electricity generation is essential in modern power systems. An accurate prediction of boiler steam temperature is of great importance to the operational efficiency of boiler units to prevent the occurrence of overtemperature. In this study, a dense, residual long short-term memory network (LSTM)-attention model is proposed for steam temperature prediction. In particular, the residual elements in the proposed model have a great advantage in improving the accuracy by adding short skip connections between layers. To provide overall information for the steam temperature prediction, uncertainty analysis based on the proposed model is performed to quantify the uncertainties in steam temperature variations. Our results demonstrate that the proposed method exhibits great performance in steam temperature prediction with a mean absolute error (MAE) of less than 0.6 °C. Compared to algorithms such as support-vector regression (SVR), ridge regression (RIDGE), the recurrent neural network (RNN), the gated recurrent unit (GRU), and LSTM, the prediction accuracy of the proposed model outperforms by 32, 16, 12, 10, and 11% in terms of MAE, respectively. According to our analysis, the dense residual LSTM-attention model is shown to provide an accurate early warning of overtemperature, enabling the development of real-time steam temperature control.
Deep Neural Network Training with Distributed K-FAC

Scaling deep neural network training to more processors and larger batch sizes is key to reducing end-to-end training time; yet, maintaining comparable convergence and hardware utilization at larger scales is a challenge. Increases in training scales has enabled natural gradient optimization methods as a reasonable alternative to SGD and variants thereof. Kronecker-factored Approximate Curvature (K-FAC), a natural gradient method, preconditions gradients with an efficient approximation of the Fisher Information Matrix to improve per-iteration progress when optimizing an objective function. In this work, we propose a scalable K-FAC algorithm and investigate K-FACs applicability in large-scale deep neural network training. Specifically, we explore layer-wise distribution strategies, inverse-free second-order gradient evaluation, and dynamic K-FAC update decoupling with the goal of preserving convergence while minimizing training time. A study on the convergence and scaling properties of our K-FAC gradient preconditioner is presented using applications in the image classification, object detection, and language modeling domains. In all applications, our implementation converges to baseline performance targets in 925% less than the standard first-order optimizers on GPU clusters across a variety scales
Implementation Design In the Creation of Companies In the 4.0 Technology Era

Industry 4.0 has been referred to as a new modern stage in which various developing technologies come together to deliver continuous innovation. After all, there is no understanding of how the organization does this innovation. After all, no one knows how the company performs this type of innovation. In this way, we expect to gain a better understanding of how Industry 4.0 ideas are accepted in assembly plants. We present a theoretical framework for this advancement, which we divide into two categories: front-end and base innovation. Front-end innovation considers four factors: smart manufacturing, smart products, a smart inventory network, and smart work, whereas basic advancement considers four factors: the internet of things, cloud administration, big data, and investigation. The sampling method, variable definition, sample and variance method, and data analysis are all used in this study. To focus on adopting these advancements, we reviewed 92 assembly organizations. Our findings imply that Industry 4.0 is linked to a fundamental embrace of front-end innovations, with Smart Manufacturing taking center stage. Our findings also suggest that implementing basic innovations is putting the organization to the test, given the sample in question still has very little knowledge and testing. We propose the creation of an Industry 4.0 innovation layer and demonstrate the extent of adoption of these advancements as well as their company-building ideas.
Time-Dependent Performance Modeling for Platooning Communications at Intersection

With the development of Internet of Vehicles, the platooning strategy has been widely studied as the potential approach to ensure the safety of autonomous driving. Vehicles in the form of platoon adopt 802.11p to exchange messages through vehicle-to-vehicle (V2V) communications. When multiple platoons arrive at an intersection, the leader vehicle of each platoon adjusts its movement characteristics to ensure that it can cross the intersection, and thus the following vehicles have to adjust their movement characteristics accordingly. In this case, the time-varying connectivity among vehicles leads to the significant nonstationary performance change in platooning communications, which may incur safety issues. In this article, we construct the time-dependent model to evaluate the platooning communication performance at the intersection based on the initial movement characteristics. We first consider the movement behaviors of vehicles at the intersection, including turning, accelerating, decelerating, and stopping as well as the periodic change of traffic lights to construct a movement model and then establish a hearing network to reflect the time-varying connectivity among vehicles. Afterward, we adopt the pointwise stationary fluid flow approximation (PSFFA) to model the nonstationary behavior of the transmission queue. Then, we consider four access categories (ACs) and continuous backoff freezing of 802.11p to construct the models to describe the time-dependent access process of 802.11p. Finally, based on the time-dependent model, the packet transmission delay (PTD) and packet delivery ratio (PDR) are derived. The accuracy of our proposed model is verified by comparing the simulation results with analytical results.
Polarimetric Analysis of ALOS PALSAR data (POL-SAR) over Test Areas in North-West Bulgaria – Polarimetric Descriptors, Decompositions and Classifications

This study is focused on utilizing full-polarimetric L-band radar data from ALOS PALSAR (JAXA) by means of Polarimetry (POL-SAR), over mountainous test sites in Bulgaria. General aim is to show feasibility of the Polarimetry to describe natural targets, which exhibits various scattering mechanisms in respect to their bio-physical and geometrical properties. Firstly, the importance of Covariance and Coherent matrices is shown, which is followed by calculation of the polarimetric coherences with their particular significance. The mathematical and physical model based decompositions are applied to describe backscattering media from scattering mechanisms. Radar indices resulted from H/A/α-decomposition showed radnomization of scattering mechanisms over forest areas, whilst two major scattering mechanisms are observed mainly in crop lands. Α comparison is made between polarimetric descriptors from acquisitions in different seasonality over mountainous forest and agricultural lands. Polarimetric segmentations and classifications are applied, with 8 (H/A) and 16 (H/A/α) components. Finally, a forest mask is proposed based on relevant polarimetric descriptors. Study showed good utilization and importance of the full-polarimetric L-band SAR data, derived from ALOS PALSAR, in natural targets and forest areas. This report resulted from a course GEO414 -“Polarimetrie”, held at the University of Jena, Lehrstuhl für Fernerkundung, in the framework of ERASMUS+, with the kind support of – Dr. T. Jagdhuber (DLR) and Prof. C. Schmullius.
Hospital Service Quality and Behavioral Compliance: The Role of Patient Satisfaction as Mediator

The notion of behavioral compliance in the healthcare sector has been getting attention from academics and practitioners. However, the determinants of behavioral compliance are still debated. This study was conducted to investigate the impact of patient satisfaction as a mediator on the relationship between hospital service quality and behavioral compliance. The results revealed that hospital service quality had a significant effect on patient satisfaction and behavioral compliance. Furthermore, behavioral compliance was positively explained by patient satisfaction. Interestingly, this study demonstrated that patient satisfaction mediated the hospital service quality and behavioral compliance relationship. Keywords: hospital service quality, patient satisfaction, behavioral compliance
Extreme Winds Alter Influence of Fuels and Topography on Megafire Burn Severity in Seasonal Temperate Rainforests under Record Fuel Aridity

Nearly 0.8 million hectares of land were burned in the North American Pacific Northwest (PNW) over two weeks under record-breaking fuel aridity and winds during the extraordinary 2020 fire season, representing a rare example of megafires in forests west of the Cascade Mountains. We quantified the relative influence of weather, vegetation, and topography on patterns of high burn severity (>75% tree mortality) among five synchronous megafires in the western Cascade Mountains. Despite the conventional wisdom in climate-limited fire regimes that regional drivers (e.g., extreme aridity, and synoptic winds) overwhelm local controls on vegetation mortality patterns (e.g., vegetation structure and topography), we hypothesized that local controls remain important influences on burn severity patterns in these rugged forested landscapes. To study these influences, we developed remotely sensed fire extent and burn severity maps for two distinct weather periods, thereby isolating the effect of extreme east winds on drivers of burn severity. Our results confirm that wind was the major driver of the 2020 megafires, but also that both vegetation structure and topography significantly affect burn severity patterns even under extreme fuel aridity and winds. Early-seral forests primarily concentrated on private lands, burned more severely than their older and taller counterparts, over the entire megafire event regardless of topography. Meanwhile, mature stands burned severely only under extreme winds and especially on steeper slopes. Although climate change and land-use legacies may prime temperate rainforests to burn more frequently and at higher severities than has been historically observed, our work suggests that future high-severity megafires are only likely to occur during coinciding periods of heat, fuel aridity, and extreme winds.
Impact of Low‐Latitude Ionospheric Effects on Precise Position Determination

Precise Point Positioning (PPP) techniques serve as a very important tool, to provide high precision position information, using data from a grid of networked receivers. However, satellite signals are frequently perturbed by inhomogeneities present in the medium of propagation, introducing errors in the measured position, the dominant contribution of which could be attributed to the ionosphere. Low‐latitude stations, like Calcutta (22.58°N, 88.38°E geographic; magnetic dip 34.54°), being situated near the northern crest of Equatorial Ionization Anomaly (EIA), experience some of the worst cases of ionospheric effects on position determination. However, such effects are usually minimal during early morning hours (typically 03–05 local time [LT]). In this study, PPP of the station Calcutta has been obtained under different solar activity conditions to assess and validate the applicability of sub‐centimeter level position accuracy. The PPP values have also been compared with early morning stand‐alone dual‐frequency GPS receiver‐derived position to assess and validate the diurnal ionospheric quiet time. Efforts have also been made to observe the effects of EIA on PPP solution. Data are recorded during a high (March 2014) and low (March 2019) solar activity period. Results of this study reveal agreement with the centimeter‐level accuracy of PPP; however, effect of solar activity is prominent when inspected in millimeter order. Additionally, the diurnal ionospheric quiet time is observed to lie within 04–06 and 02–04 LT for a high and a low solar activity period, respectively. Irrespective of the solar activity conditions, receiver noise contribution in position deviation is measured to be within 150 cm.
Syntegra™ : Vollständige Integration von Antriebs-, Drehgestell- und Bremstechnologie

Syntegra™ terms the intelligent integration of traction, bogie and braking technology, developed by siemens TS und Siemens A&Dfor the use in urban-sijtburban commuter railway systems. The innovative Syntegra™ motor bogie technology breaks with today's technology inthreernaih areas. Bogie, traction and braking technology are merged to an entire integrated system. In addition to this Integration, several leaps in technology have been made. The new generation of motor bogies provides high efficiency, reduced mass aid reduced emissions,. The paper reports on the electrical and mechanical design of the traction colour the braking concept and the new bogie development, and on the first results of operation.
Design of Automatic Tool for Diagnosis of Pneumonia Using Boosting Techniques

Abstract Covid-19 is today's pandemic disease and can cause the hospital crowded. Additionally, It affects the lungs and may cause pneumonia. The most popular technique for diagnosis of pneumonia is the evaluation of X-ray. However, a sufficient number of radiologists are needed to interpret the X-ray images. High rates of child deaths due to pneumonia have been encountered. Using this type of system, a diagnosis can be made quickly, and then the treatment process can be started rapidly. This study aims to diagnose pneumonia using boosting techniques by the automatic tool. With this tool, the workload of the doctors/radiologists is reduced. The boosting techniques are a family of machine learning techniques. Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost) are used for the study. These techniques are chosen because of their simulation duration for modeling and convenience for real-time applications. L2 normalization and feature selection are applied to the data before applying the techniques. Random Forest Classifier is used for feature selection estimator. After the modeling, Categorical Boosting algorithm is observed as faster than the other techniques. Simulation duration is obtained as 0.7 seconds. By using this automatic tool, the user can be able to upload the desired X-ray image to the system and get the result easily from the screen without any radiologist/doctor.
A Differential Privacy Strategy Based on Local Features of Non-Gaussian Noise in Federated Learning

As an emerging artificial intelligence technology, federated learning plays a significant role in privacy preservation in machine learning, although its main objective is to prevent peers from peeping data. However, attackers from the outside can steal metadata in transit and through data reconstruction or other techniques to obtain the original data, which poses a great threat to the security of the federated learning system. In this paper, we propose a differential privacy strategy including encryption and decryption methods based on local features of non-Gaussian noise, which aggregates the noisy metadata through a sequential Kalman filter in federated learning scenarios to increase the reliability of the federated learning method. We name the local features of non-Gaussian noise as the non-Gaussian noise fragments. Compared with the traditional methods, the proposed method shows stronger security performance for two reasons. Firstly, non-Gaussian noise fragments contain more complex statistics, making them more difficult for attackers to identify. Secondly, in order to obtain accurate statistical features, attackers must aggregate all of the noise fragments, which is very difficult due to the increasing number of clients. We conduct experiments that demonstrate that the proposed method can greatly enhanced the system’s security.
Effectiveness body weight strength training and plyometric in the speed and agility taekwondo athletes

From the observation of 24 athletes Taekwondo Jayabaya produced as much as 79.1% - 95.8% of athletes have the physical ability, especially the speed and agility under average value. This study aims to determine the effectiveness of exercises body weight strength training and plyometric to increase physical ability. The study used an experimental research method with a quasi-experimental type using a 2x2 factorial design. The sampling technique used purposive sampling based on inclusion criteria. The total number of samples used is 24 athletes who will be divided into 2 exercise groups. The study was conducted on the Jayabaya Taekwondo team for 6 weeks. Data collection techniques used 35 meters speed test and Illinois agility run test. Variable variance analysis using MANOVA test. In the MANOVA test, the value is obtained sig. multivariate test (0.00) and between subjects effects test of exercise on speed and agility (0.00). The results of descriptive statistics on exercises body weight strength training and plyometric on speed time (5.12)>(4.57), and agility time (19.53)> (17.64). The conclusion of this study is that body weight strength training and plyometric exercises significantly affect the physical ability of taekwondo athletes.training was Plyometric more effective in increasing the speed and agility of taekwondo athletes during the 6-week training program.