Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Bluetooth Mesh Energy Efﬁciency Using Clustering

—This work proposes a Clustering Algorithm to improve the energy efﬁciency of Bluetooth Mesh networks. To further reduce the burden over the Cluster Heads, a Radio Duty Cycling algorithm that requires only a simple modiﬁcation on the Bluetooth packet transmission logic is proposed. Computer simulations show that the radio duty cycling and clustering methods are effective in improving energy efﬁciency. It is observed that duty cycling provides a 78% improvement on the energy efﬁciency. In addition, simulations show that the proposed clustering technique is effective in controlling the excessive message replication that is inherent in ﬂooding operation, which in turn have a positive impact on packet delivery ratio (PDR) and network scalability. Finally, it can be observed that the proposed clustering algorithm together with the proposed radio duty cycling algorithm can provide an improvement on the energy efﬁciency when compared to the baseline Bluetooth Mesh proﬁle.
Learning English Vocabulary through VOA News Lessons: Being Exposed to Authentic Material or Electronic Colonialism

Teaching and learning languages via the Internet is becoming increasingly common all over the world and therefore, the experts growingly debate around the positive or negative effects of online education. The present study aimed to critically investigate into science and technology lessons of the VOA English Learning Website in a two-year interval, which claims to teach new words and phrases through the authentic VOA world news. Applying van Dijk’s (1998) Ideological Square Model of CDA, the researchers attempted to critically analyze the representation of the key term the United States within the collected VOA news story transcripts in order to observe whether the Website was trying to manipulate the learners’ minds through in-group favoritism and out-group derogation. The findings revealed that the VOA online lessons promoted certain American political, economic, cultural, social, and ideological values through particular discursive structures that tend to describe in-group members in a positive or at least neutral manner. Thus, as McPhail’s (2006) Electronic Colonialism Theory assumes such free online lessons on the VOA Website, which is a core country multimedia giant, struggle to convert and capture the attitudes, desires, beliefs, faiths, lifestyles, and consumer behavior of the other countries. Therefore, it is highly recommended that English teachers and learners try to inspect the content, reflect on the purposes, and evaluate the merits of similar online authentic materials before and while applying them to facilitate the language learning process.
What Do You Expect? Low Self-Control Predicts College Newcomer Expectations

This article investigates the association between low self-control and college newcomer expectations. Examining data from 294 first-year students, the results suggested that low self-control was statistically associated with social expectations (e.g. Greek life, parties) and not associated with formal student affairs related expectations (e.g. campus activities, clubs) or academic expectations (e.g. taking classes, meeting classmates). Existing research literature identifies several channels through which students’ unmet expectations about how college is going to be adversely impacts their academic progress. Institutions may benefit by targeting outreach towards individuals likely to exhibit low self-control, tempering their college newcomer expectations to offer a more realistic preview of their likely lived experience to improve satisfaction and, ultimately, retention.
Medico-legal Concerns of Common Sexual Dysfunction in Partners among Egyptian Sample

Erectile dysfunction (ED) is defined as affection of erection; it ranges from mild to severe degree while impotence is an absolute erectile dysfunction. ED affects physical and psychosocial health and has a significant impact on the quality of life of sufferers and their partners and families. It is a common disorder of men; however vaginismus is a recurrent or persistent involuntary spasm of the musculature of the outer third of the vagina that affects the quality of life and sexual satisfaction level. Objectives:The aim of the current study is to determine the real causes of common sexual dysfunction cases in Egyptian courts which the alleged male impotence and whether it is due to actual organic/vascular causes in male partner or vaginismus of the wife among Egyptian candidates.Subjects & Methods: a comparative study was taken over between the processing of the researched cases on the level of the legal authority and that of the private sector, based on laboratory and radiological investigations to diagnose the causes of apareunia of the couples subject to research in both spouses.Results: (96.7%)with incapability to perform the sexual intercourse were having the problem of vaginismuswhereas a very small percentage of the husbands were suffering from an organic reason with variable degrees of erectile dysfunction (3.3%). Conclusion & Recommendations: hymen is not a reliable sign to indicate male impotence, examination for vaginismus should be done in every case of alleged male impotence and examination for vaginismus.
Head orientation control of projection area for projected virtual hand interface on wheelchair

ABSTRACT A projected virtual hand interface, which visually extends the arms of users using projection images, enables wheelchair users to reach unreachable objects; however, its projection area is usually smaller than the reaching area for its users. In our previous research, we developed a wheelchair system with a projected virtual hand system in which users can move the projected area via finger movements on a touch panel. However, it requires some time to move the projected area to the desired position. To address this problem, we propose a wheelchair system enhanced with a projected virtual hand that allows controlling a projection area using a user's head orientation. The proposed system measures the current user's head orientation and distance between a user and a projection surface. Then, it adapts the suitable pan and tilt of the projector by considering its positional relationship with the projection plane. As users can operate the projection area simply by turning their head, this operation can be executed simultaneously with operating a virtual hand using their hands. We propose a control model for projector rotation according to the user's head orientation. We implemented a prototype based on the model and confirmed the system's latency. The usability study revealed that the proposed method enables users to perform pointing tasks in a shorter time compared with the existing method. Moreover, it has acceptable interface usability.
Enhanced Visualization of Customized Manufacturing Data

Recently, customized manufacturing is gaining much momentum. Consumers do not want mass-produced products but are looking for unique and exclusive ones. It is especially evident in the furniture industry. As it is necessary to set an individual price for each individually manufactured product, companies face the need to quickly estimate a preliminary cost and price as soon as an order is received. The task of estimating costs as precise and timely as possible has become critical in customized manufacturing. The cost estimation problem can be solved as a prediction problem using various machine learning (ML) techniques. In order to obtain more accurate price prediction, it is necessary to delve deeper into the data. Data visualization methods are excellent for this purpose. Moreover, it is necessary to consider that the managers who set the price of the product are not ML experts. Thus, data visualization methods should be integrated into the decision support system. On the one hand, these methods should be simple, easily understandable and interpretable. On the other hand, the methods should include more sophisticated approaches that allowed reveal hidden data structure. Here, dimensionality-reduction methods can be employed. In this paper, we propose a data visualization process that can be useful for data analysis in customized furniture manufacturing to get to know the data better, allowing us to develop enhanced price prediction models.
THE EFFECT OF JOB CRAFTING ON WORK PERFORMANCE OF LAN RI MEDIATED BY WORK ENGAGEMENT AND JOB SATISFACTION

This study aims to examine the effect of job crafting on work performance that is respectively mediated by work engagement and job satisfaction on The National Institute of Public Administration of the Republic of Indonesia (LAN RI) employees. The research population was the LAN RI employees, totaling 1.007 employees throughout Indonesia. There were 286 employees chosen as the sample through the Slovin method. Data were analyzed by Structural Equation Modelling (SEM) Analysis of Moment Structures (AMOS). From the result and analysis, we can conclude that job crafting affects work engagement, job crafting affects job satisfaction, job crafting affects work performance, job crafting affects work performance through work engagement, and job crafting affects work performance through job satisfaction. Work engagement and job satisfaction act as partial mediators in the model. These findings, academically illustrate that the model studied has been tested and verified, that for models of improving work performance, the function of increasing job crafting is very necessary to be carried out, so that it can affect work engagement and job satisfaction which will result in increased work performance. This finding has implications for the realm of science management so that further researchers can develop this tested work performance improvement model by adding new variables such as talent management and innovative culture. Keyword: Job Crafting, Work Engagement, Job Satisfaction, Work Performance.
Men's experience of their transition to first-time fatherhood during their partner's pregnancy: an interpretative phenomenological analysis

This was a qualitative study employing Interpretative Phenomenological Analysis (IPA) to capture the lived experiences and sense-making of seven male participants (aged 29–41), whose partner was in their second or third trimester of pregnancy. Data was collected through semi-structured interviews and four superordinate themes emerged during the analysis: (1) Pushed and Pulled in Different Directions, (2) A Rollercoaster of Emotions, (3) A Long and Lonely Road and (4) Anticipating Fatherhood. The prenatal period seemed to constitute a turbulent time in the participants' lives and involved them experiencing intense, conflicting emotions and psychological states amidst a shifting sense of self, with few support resources available to them. Implications for counselling psychologists are discussed, with suggestions for future research made.
Relation of Observable Stellar Parameters to Mass-loss Rate of AGB Stars in the LMC

Using the data set of Riebel et al. for 6889 pulsating AGB stars in the LMC, we have derived formulae for mass-loss rate as a function of luminosity and pulsation period, or luminosity and mass, in three ways, for each of five subsets of data: fundamental-mode oxygen-rich stars, first-overtone-mode oxygen-rich stars, fundamental-mode carbon stars, first-overtone-mode carbon stars, and extreme carbon stars. Using the distribution of the stars in period versus luminosity and mass versus luminosity, we are able to derive a power-law fit to the dependence of mass-loss rate on those quantities. This results in formulae that reproduce observed mass-loss rates and are in general agreement with the expectation from mass-loss models that the mass-loss rate is highly sensitive to luminosity, mass, and pulsation period. In the process of carrying out this analysis we have found radius–mass–luminosity relations and examined pulsation–mass–radius relations using published evolutionary and pulsation models. These allow us to derive mass and radius from the observed quantities luminosity and pulsation period. We also derived new mass-loss rate versus color relations.
Consumer Engagement with Brands’ COVID-19 Messaging on Social Media: The Role of Perceived Brand–Social Issue Fit and Brand Opportunism

Abstract While the trade press has started to report on consumer responses to brand messaging about the ongoing novel coronavirus (COVID-19) pandemic, empirical results have yet to emerge. In this study, we investigated how consumers in the United States responded to Instagram ads containing COVID-19 claims based on the perceived brand–social issue fit, which is determined by whether the brand’s product was considered essential or nonessential. Results showed perceived brand–social issue fit to be of dual nature, where stronger perceptions of fit (i.e., essential products advertised with COVID-19 claims) resulted in more positive ad evaluations, brand attitudes, and consumer engagement intentions. Lower perceptions of fit (i.e., nonessential products with COVID-19 claims) led to perceptions of brand opportunism, which we conceptualized as a negative evaluative outcome that negatively impacted ad/brand attitude and consumer engagement intentions. The findings provide insights for brands interested in placing ads addressing the changing environmental conditions resulting from COVID-19 and advances the literature on pandemic messaging.
A Representation Theory Perspective on the Repurposing of Personal Technologies for Work-Related Tasks

Individuals often blur the line between technologies used for personal means and those used to complete work-related tasks. The increasing level of capabilities offered by personal technologies presents opportunities to repurpose them for work. With guidance from representation theory, we describe how cross-context representational fidelity predicts repurposing intentions across domains of use (e.g., personal to work-related). An empirical study of 311 full-time employees demonstrates that congruence between prior technology use and potential work use increases an individual’s belief that a technology can be useful for work purposes. Furthermore, we show that, in repurposing situations, usefulness is also influenced by an individual’s confidence in using the technology on a work device(s). These findings, among others, shed new light on our understanding of the influence of experience on repurposing technologies for use in the professional domain.
Denoising Medical Images Using Deep Learning in IoT Environment

: Medical Resonance Imaging (MRI) is a noninvasive, nonradioactive, and meticulous diagnostic modality capability in the field of medical imaging. However, the efficiency of MR image reconstruction is affected by its bulky image sets and slow process implementation. Therefore, to obtain a high-quality reconstructed image we presented a sparse aware noise removal technique that uses convolution neural network (SANR_CNN) for eliminat-ing noise and improving the MR image reconstruction quality. The proposed noise removal or denoising technique adopts a fast CNN architecture that aids in training larger datasets with improved quality, and SARN algorithm is used for building a dictionary learning technique for denoising large image datasets. The proposed SANR_CNN model also preserves the details and edges in the image during reconstruction. An experiment was conducted to analyze the performance of SANR_CNN in a few existing models in regard with peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mean squared error (MSE). The proposed SANR_CNN model achieved higher PSNR, SSIM, and MSE efficiency than the other noise removal techniques. The proposed architecture also provides transmission of these denoised medical images through secured IoT architecture. better architecture is needed for effective performance. Hence, the study presented an efficient SARN and reconstruction technique based on the CNN architecture to obtain a denoised image. 3.2 GHz with 12 GB RAM using a Windows oper-ating system. The proposed SANR_CNN model was compared with the different noise removal TNRD, BM3D, DnCNN and MP-DCNN. The proposed model has depth same as DnCNN approach. The initial metrics are (i) alpha1, alpha2, epsilon, and learning rate that are 0.9, 0.999, 1 * 10 − 8 , and 1 * 10 − 3 respectively. (ii) The number of epochs is 180 and number of batches set to 226. The learning rate of epochs are 1 * 10 − 3 to 1 * 10 − 8 . The entire experiments conducted using
On the Caputo-Fabrizio fractal fractional representation for the Lorenz chaotic system

The widespread application of chaotic dynamical systems in different fields of science and engineering has attracted the attention of many researchers. Hence, understanding and capturing the complexities and the dynamical behavior of these chaotic systems is essential. The newly proposed fractal-fractional derivative and integral operators have been used in literature to predict the chaotic behavior of some of the attractors. It is argued that putting together the concept of fractional and fractal derivatives can help us understand the existing complexities better since fractional derivatives capture a limited number of problems and on the other side fractal derivatives also capture different kinds of complexities. In this study, we use the newly proposed Caputo-Fabrizio fractal-fractional derivatives and integral operators to capture and predict the behavior of the Lorenz chaotic system for different values of the fractional dimension $ q $ and the fractal dimension $ k $. We will look at the well-posedness of the solution. For the effect of the Caputo-Fabrizio fractal-fractional derivatives operator on the behavior, we present the numerical scheme to study the graphical numerical solution for different values of $ q $ and $ k $.
An AMC-Based Circularly Polarized Antenna for 5G sub-6 GHz Communications

: This paper presents an AMC (artificial magnetic conductor)-based wideband circularly polarized printed monopole antenna for unidirectional radiation. The antenna includes an AMC reflector, a coplanar waveguide (CPW) feed structure to excite the antenna, a ground plane with a rectangular slot on the left side of feedline, and an asymmetrical ground plane on its right side. The induced surface currents on CWP feedline, rectangularly slotted, and asymmetrical ground planes cause circularly polarized radiations. The AMC reflector consisting periodic metallic square patches is used instead of the conventional PEC (perfect electric conductor) reflector, the distance between the antenna and reflector is reduced from 0.25 λ 0 to 0.18 λ 0 with performance improvement. By incorporating AMC layer with the monopole antenna, the gain of antenna is increased from 3.3 dBic to 8.7 dBic while the axial ratio bandwidth (ARBW) of antenna is increased from 27.27% to 51.67%. The simulated and measured results show that the proposed antenna has an over-lapping 10-dB |S 11 | and 3-dB ARBW of 51.67% (3.0–5.09 GHz). The overall dimensions of monopole antenna backed by AMC reflector is 1.20 λ 0 × 1.20 λ 0 × 0.21 λ 0 and covers 5G sub-6 GHz new radio bands (n77/n78/n79) for wireless communication systems.
Coordinated and Priority‐Based Surgical Care: An Integrated Distributionally Robust Stochastic Optimization Approach

We study a coordinated clinic and surgery appointment scheduling problem for in‐advance scheduling of surgical patients. Our models seek to provide timely access to care by coordinating clinic and surgery appointments to ensure that patients can see a surgeon in the clinic and (if needed) schedule their surgery within a maximum wait time target based on patient classes. There are different types of uncertainty including the number of appointment requests, whether a patient requires surgery, and surgery durations. We develop an integrated multi‐stage stochastic and distributionally robust optimization (IMSDRO) approach to determine the optimal clinic and surgery dates for patients such that the access target constraints are satisfied, and the clinical and surgical overtimes are minimized. The IMSDRO approach synergizes multi‐stage stochastic optimization with distributionally robust optimization to simultaneously incorporate multiple types of uncertainties by including stochastic scenarios for appointment request arrivals and ambiguity sets for surgery durations. Several new transformations are introduced to turn the nonlinear model derived from the IMSDRO approach to a tractable one, and a constraint generation algorithm is developed to solve it efficiently. We propose a data‐driven rolling horizon procedure to facilitate implementation. We use case data to assess the performance of our policies. The results suggest that our policy can significantly improve surgical access delay times compared to the current practice. Our methodology is not limited to a particular setting and can be applied to other service industries where access delay matters.
DFF-SC4N: A Deep Federated Defence Framework for Protecting Supply Chain 4.0 Networks

The management of contemporary communication networks of supply chain (SC) 4.0 is becoming more complex due to the heterogeneity requirements of new devices concerning the integration of the Internet of Things in the legacy industry networks. Hence, it becomes a challenging task to secure networks of SC 4.0 from cyber-attacks and provide a robust and efficient defence framework that can resist sophisticated attacks. Machine learning-based intelligent detection algorithms are often trained at either a centralized or single server, which makes it difficult to train an effective model and also it violates privacy concerns if gathering data from other servers at the edge. Classical machine learning approaches function on the legacy group of data placed on a central or single server, which brands it the least favored choice for supply chain networks, with data privacy issues. To address these problems, this article proposes a federated learning-based efficient detection model named, DFF-SC4N, to proactively identify intrusions from SC 4.0 networks using distributed local data training. DFF-SC4N uses communication rounds in a federated learning manner having gated recurrent units by only sharing the learned parameters and keeps the data intact on local servers. The accuracy of the global model is optimized by an aggregating model, which updates from multiple servers and multiple SC 4.0 networks. Extensive experiments on real industrial network data demonstrate that the DFF-SC4N outperforms both centralized training models and state-of-the-art peer methods in protecting SC 4.0 networks.
Design, Fabrication, and Characterization of a Micro Coriolis Mass Flow Sensor Driven by PZT Thin Film Actuators

A micro Coriolis flow sensor accurately measures small mass flows without being affected by properties of the fluid. This paper presents a micro Coriolis mass flow sensor driven by PZT thin film piezoelectric actuators, which allows a combination of large actuation force, low power dissipation and virtually no self-heating. The sensor was fabricated using surface channel technology, resulting in a tube diameter of 40<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> and a thin silicon-rich silicon nitride tube wall of 1.2<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> thick, with a few additional steps to form the integrated piezoelectric actuators. The integrated actuators can actuate the sensor into both swing mode and twist mode. Both actuation modes were evaluated for different fluids and the results are presented in this paper. In the case of swing mode actuation, an optical readout was used to detect the resulting Coriolis movement, resulting in a relatively low sensitivity. In the case of twist mode actuation, a capacitive readout was used and, in comparison to previously published devices, a high mass flow sensitivity was achieved thanks to an optimized readout design. The measurement results show the read-out noise has a standard deviation of 0.15% of the full scale of 1.8 gh<sup>−1</sup> when actuated in the twist mode. [2020-0392]