DAT: Data Architecture Modeling Tool for Data-Driven Applications

Data is the key to success for any Data-Driven Organization, and managing it is considered the most challenging task. Data Architecture (DA) focuses on describing, collecting, storing, processing, and analyzing the data to meet business needs. In this tool demo paper, we present the DAT, a model-driven engineering tool enabling data architects, data engineers, and other stakeholders to describe how data flows through the system and provides a blueprint for managing data that saves time and effort dedicated to Data Architectures for IoT applications. We evaluated this work by modeling five case studies, receiving expressiveness and ease of use feedback from two companies, more than six researchers, and eighteen undergraduate students from the software architecture cours

Localization Process for WSNs with Various Grid-Based Topology Using Artificial Neural Network

Wireless Sensor Network (WSN) is a technology that can aid human life by providing ubiquitous communication, sensing, and computing capabilities. It allows people to be more able to interact with the environment. The environment contains many nodes to monitor and collect data. Localizing nodes distributed in different locations covering different regions is a challenge in WSN. Localization of accurate and low-cost sensors is an urgent need to deploy WSN in various applications. In this paper, we propose an artificial automatic neural network method for sensor node localization. The proposed method in WSN is implemented with network-based topology in different regions. To demonstrate the accuracy of the proposed method, we compared the estimated locations of the proposed feedforward neural network (FFNN) with the estimated locations of the deep feedforward neural network (DFF) and the weighted centroid localization (WCL) algorithm based on the strength of the received signal index. The proposed FFNN model outperformed alternative methods in terms of its lower average localization error which is 0.056m. Furthermore, it demonstrated its capability to predict sensor locations in wireless sensor networks (WSNs) across various grid-based topologies

Localization Process for WSNs with Various Grid-Based Topology Using Artificial Neural Network

Wireless Sensor Network (WSN) is a technology that can aid human life by providing ubiquitous communication, sensing, and computing capabilities. It allows people to be more able to interact with the environment. The environment contains many nodes to monitor and collect data. Localizing nodes distributed in different locations covering different regions is a challenge in WSN. Localization of accurate and low-cost sensors is an urgent need to deploy WSN in various applications. In this paper, we propose an artificial automatic neural network method for sensor node localization. The proposed method in WSN is implemented with network-based topology in different regions. To demonstrate the accuracy of the proposed method, we compared the estimated locations of the proposed feedforward neural network (FFNN) with the estimated locations of the deep feedforward neural network (DFF) and the weighted centroid localization (WCL) algorithm based on the strength of the received signal index. The proposed FFNN model outperformed alternative methods in terms of its lower average localization error which is 0.056m. Furthermore, it demonstrated its capability to predict sensor locations in wireless sensor networks (WSNs) across various grid-based topologies

Exploring the relationship between robot employees' perceptions and robot-induced unemployment under COVID-19 in the Jordanian hospitality sector

The topic of robots is gaining traction in both academic discourse and popular media due to their growing prevalence in the hospitality sector. The hospitality industry is likely to encounter practical challenges in adopting the increased use of robots. The primary objective of the present research was to conduct an empirical investigation into a theoretical framework that explores how employees view robot-caused unemployment, with particular attention given to their perceptions of robot adoption. The study utilised structural equation modelling to analyse data obtained from 401 service employees in Jordan through online questionnaires. The findings indicate that the perception of robot-induced unemployment among employees is significantly influenced by their social skills, perceived risk, awareness, and trust in using service robots. The present study established a theoretical framework for investigating user perceptions of robot-induced unemployment within the particular setting of hospitality robots. The findings offer valuable insights for guiding future development and research efforts in the hotel service robot industry as well as informing marketing strategies for hotel managers. Ultimately, these efforts may contribute to the sustainable growth of service robots and associated sectors, including the hotel service sector

Embedded two level direct adaptive fuzzy controller for DC motor speed control

AbstractThis paper presents a proposed approach based on an adaptive fuzzy logic controller for precise control of the DC motor speed. In this concern, the proposed Direct Adaptive Fuzzy Logic Controller (DAFLC) is estimated from two levels, where the lower level uses a Mamdani fuzzy controller and the upper level is an inverse model based on a Takagi–Sugeno (T–S) method in which its output is used to adapt the parameters of the fuzzy controller in the lower level. The proposed controller is implemented using an Arduino DUE kit. From the practical results, it is proved that the proposed adaptive controller improves, successfully both the performance response and the disturbance due to the load in the speed control of the DC motor

Autologous Pericardial Band for Tricuspid Valve Annuloplasty: Midterm Results

Background: Even though tricuspid regurgitation (TR) is a frequent cardiac valve disorder, and tricuspid valve annuloplasty (TVA) has been evolved to manage TR for more than 50 years, there is still a substantial controversy regarding the best durable method for TVA. We reported our midterm (3 years) outcomes of TVA using autologous pericardial (AP) band comparing it with DeVega annuloplasty for the management of functional TR. Methods: Between January 2017 and November 2018, about 175 cases with moderate or more TR underwent TVA as a part of primary left-sided valve replacement surgery. Autologous pericardial (AP) TVA was performed in 100 patients, and DeVega TVA in 75 patients. Results: Both groups are comparable as regards preoperative characteristics. Immediate postoperatively, regarding NYHA class, degree of TR, ejection fraction, and pulmonary artery systolic pressure, there was a marked improvement within the 2 groups compared to the preoperative values, without a significant difference between both groups. 94% of patients completed the follow-up period. In hospital death was 2% in the AP group, and 1% in the DeVega group. The AP group showed a marked improvement in the mean degree of TR at the same follow-up period compared to the DeVega group, 12% patients of the AP group and 21% patients of the De Vega group had 3+ or 4+ TR at 3 years postoperative follow up. There was a marked improvement in the Diastolic tricuspid annuloplasty diameter in the AP group compared to the DeVega group. There were 6.3% late deaths in our study. Conclusion:  TVA with an AP was more durable than the DeVega in avoiding postoperative TR progression on the midterm results

Catalytic effectiveness of azobisisobutyronitrile/[SiMes)Ru(PPH3)(Ind)Cl2 initiating system in the polymerization of methyl methacrylate and other vinylic monomers

The catalytic system of azo-bis-isobutyronitrile (AIBN) combined with (SiMes)Ru(PPH3)(Ind)Cl-2 [M-20] was investigated for the controlled radical polymerization of methyl methacrylate (MMA) in solution. Various factors that may influence the catalytic polymerization process, such as the aging time of the initiating system, AIBN/M-20 ratio, concentration of monomer, polymerization time, temperature, and the nature of solvent were examined. The results showed that the yield, molecular weight, and molecular distribution are practically unaffected by these parameters; however, the syndiotactic stereo-structure tendency that characterizes the produced poly(methyl methacrylate) (PMMA) varied with temperature. The optimum conditions for PMMA synthesis were determined to produce an essentially syndiotactic material with uniformly high molecular weights. It was also revealed that the kinetics of MMA polymerization is of first order with respect to the concentration of monomer. A comparison was also made for some vinylic polymers synthesized either with the AIBN alone or with the AIBN/M-20 initiating system under the same conditions

Accurate Reader Identification for the Arabic Holy Quran Recitations Based on an Enhanced VQ Algorithm

The Speaker identification process is not a new trend; however, for the Arabic Holy Quran recitation, there are still quite improvements that can make this process more accurate and reliable. This paper collected the input data from 14 native Arabic reciters, consisting of “Surah Al-Kawthar” speech signals from the Holy Quran. Moreover, this paper discusses the accuracy rates for 8 and 16 features. Indeed, a modified Vector Quantization (VQ) technique will be presented, in addition to realistically matching the centroids of the various codebooks and measuring systems’ effectiveness. Note that the VQ technique will be utilized to generate the codebooks by clustering these features into a finite number of centroids. The proposed system’s software was built and executed using MATLAB®. The proposed system’s total accuracy rate was 97.92% and 98.51% for 8 and 16 centroids codebooks, respectively. However, this study discussed two validation tactics to ensure that the outcomes are reliable and can be reproduced. Hence, the K-mean clustering algorithm has been used to validate the obtained results and discuss the outcomes of this study. Finally, it has been found that the improved VQ method gives a better result than the K-means method

Clinical Study Implications of Foot Ulceration in Hemodialysis Patients: A 5-Year Observational Study

Foot ulceration (FU) remains a serious concern for patients worldwide. We analyzed the incidence, risk factors, and outcome of FU in hemodialysis (HD) patients. A retrospective cohort study was conducted for 252 HD patients who were followed up for 5 years. Patients were categorized according to whether they developed FU or not. The FU group (17%) was older and had significantly higher incidence of nephropathy, retinopathy, peripheral (PAD), coronary artery disease (CAD), and diabetes mellitus (DM) as compared to no-FU group. FU group had higher frequency of major amputation ( = 0.001) and HD vascular access ( = 0.01). Patients with combined DM and PAD had a 10-fold increased risk of FU in comparison to those who had DM alone. Presence of PAD was the main independent predictor for development of FU in HD with an adjusted odd ratio (aOR) of 16.0 (95% CI: 4.41-62.18; = 0.001). After adjusting for age, sex, and CAD, predictors for mortality were PAD (aOR 4.3), FU (aOR 3.6), and DM (aOR 2.6). FU is common in HD patients regardless of DM. However, the presence of PAD is significantly associated with more FU and mortality in HD. HD patients need intensive foot care and warrant progressive modification of vascular risk factors