Miracle-dz/UniFAR-FTUs huggingface dataset:
Entropy Analysis of Heart Rate Variability in Different Sleep Stages

How the complexity or irregularity of heart rate variability (HRV) changes across different sleep stages and the importance of these features in sleep staging are not fully understood. This study aimed to investigate the complexity or irregularity of the RR interval time series in different sleep stages and explore their values in sleep staging. We performed approximate entropy (ApEn), sample entropy (SampEn), fuzzy entropy (FuzzyEn), distribution entropy (DistEn), conditional entropy (CE), and permutation entropy (PermEn) analyses on RR interval time series extracted from epochs that were constructed based on two methods: (1) 270-s epoch length and (2) 300-s epoch length. To test whether adding the entropy measures can improve the accuracy of sleep staging using linear HRV indices, XGBoost was used to examine the abilities to differentiate among: (i) 5 classes [Wake (W), non-rapid-eye-movement (NREM), which can be divide into 3 sub-stages: stage N1, stage N2, and stage N3, and rapid-eye-movement (REM)]; (ii) 4 classes [W, light sleep (combined N1 and N2), deep sleep (N3), and REM]; and (iii) 3 classes: (W, NREM, and REM). SampEn, FuzzyEn, and CE significantly increased from W to N3 and decreased in REM. DistEn increased from W to N1, decreased in N2, and further decreased in N3; it increased in REM. The average accuracy of the three tasks using linear and entropy features were 42.1%, 59.1%, and 60.8%, respectively, based on 270-s epoch length; all were significantly lower than the performance based on 300-s epoch length (i.e., 54.3%, 63.1%, and 67.5%, respectively). Adding entropy measures to the XGBoost model of linear parameters did not significantly improve the classification performance. However, entropy measures, especially PermEn, DistEn, and FuzzyEn, demonstrated greater importance than most of the linear parameters in the XGBoost model.300-s270-s.
Biologically Inspired Machine Learning-Based Trajectory Analysis in Intelligent Dispatching Energy Storage System

The present work expects to explore the application effect of biologically inspired Plasticity Neural Network in the industrial intelligent dispatching energy storage system, and highlight the intelligence and fault detection performance of the control system. To address the faults in intelligent dispatching energy storage system, the present work implements a fault diagnosis model of intelligent dispatching energy storage system based on Deep Belief Network (DBN), and simulates and analyzes the model. The results show that the transmission probability of the fault diagnosis model of the constructed intelligent energy storage scheduling system is 100% and when the parameters $\lambda $ is between 0.01 and 0.05, the real-time performance of data transmission is the highest. Compared with other classical algorithm models, the success rate and detection accuracy of the proposed algorithm are about 85%, the energy consumption is lower, and the detection effect is more obvious. Therefore, the constructed system obviously has higher real-time performance and more accurate fault detection performance, and significantly better system detection and protection performance. The results provide an experimental basis for the operation and fault detection of intelligent dispatching energy storage system.
Optical PSK Synchronous Heterodyne Experiments at 560 Mbit/s through 4 Gbit/s

A synchronous optical heterodyne receiver was constructed using an optical phase-locked loop based on a 1320nm diode-pumped Nd:YAG laser. Using that receiver and a transmitter based on another Nd: YAG laser, PSK synchronous heterodyne experiments were demonstrated at 560 Mbit/s, 2 Gbit/s and 4 Gbit/s. The receiver sensitivity at the three bit rates was — 48.7 dBm (159 photons/bit), -41.7 dBm (225 photons/bit), and -34.2 dBm (631 photons/bit), resepctively. Performance degradations due to an insufficient local iscillator power, a finite intermediate frequency (IF) and a sub-optimum coupler splitting ratio were investigated. compact, robust and can generate highly coherent 1320 nm radiation with linewidths of the order of 1 Hz [13].
Robust nonsmooth optimality conditions for multiobjective optimization problems with infinitely many uncertain constraints

This paper concentrates on uncertain multiobjective optimization problems with an arbitrary number of uncertain constraints under nonconvex and nonsmooth assumptions. We analyse the properties of robust infinite constraints and arrive at the Clarke subdifferential of the double supremum function. Subsequently, we employ the obtained subdifferential rules to study KKT robust necessary and sufficient optimality conditions for two types of uncertain multiobjective optimization problems. Simultaneously, some illustrative examples are provided to show the validity of the main results. Our results are new and generalize several corresponding results in the literature.
An Investigation of Middle School 8th Grade Students' Metaphoric Perceptions Regarding Historical Empathy

The main focus of research and studies in the field of historical empathy is to understand and analyze historical events in line with the contextual conditions of the era, without adapting them to today's logic. When the subject is viewed from this point of view, it comes to the fore how students who have historical empathy perceive / liken the concept of historical empathy. In this context, the aim of the study is to determine the perceptions of middle school 8 grade students regarding the concept of historical empathy through metaphors. For this purpose, descriptive model was used in the research. The participants of the study consisted of 50 students at the 8th grade of middle school. The research data were collected through the metaphor form. Content analysis was made on the data. When the mentioned metaphors were examined, it was seen that the students expressed the concept of historical empathy with different metaphors and explanations of these metaphors. Among the 19 metaphors, it was determined that the metaphors most expressed by students regarding the concept of historical empathy were listed as “Hot Pepper, Tornado, History and Video”. When these metaphors are evaluated together; it was concluded that students were able to focus on the details of historical events through historical empathy.
Parliamentary Debates on Gambling Policies as Political Action

The aims of this paper are twofold: first, to demonstrate the importance and relevance of interpretive political analysis to gambling research and second, to analyze from the aforementioned perspective why politicians in Finland talk about gambling harm and gambling revenue the way they do. The speeches of the representatives in the Parliament of Finland during debates on gambling policy are analysed as political action. The analysis has three levels. The first focuses on the themes of the speeches. The results show that there are four distinct thematic dimensions in the speeches: gambling harm, revenue, regulatory system, and regulation. The second level of analysis establishes the contexts where certain themes typically occur. Typically, revenue is discussed in the context of the economic aspect of gambling while gambling harm is discussed in the context of the justification of the regulatory system. The third level of analysis explains why the themes occur in the contexts they do. The representatives´ acceptance of the self-evidence of the regulatory system forecloses any possibility of getting support for major changes to the system. This explains why the official policy aims of reducing and preventing gambling harm have not been realized. It is concluded that the approach introduced can help to understand the political aspects of gambling.
Anomaly Detection using Autoencoder with Gramian Angular Summation Field in Multivariate Time Series Data

Uncertainty is ubiquitous in data and constitutes a challenge in real-life data analysis applications. To deal with this challenge, we propose a novel method for detecting anomalies in time series data based on the Autoencoder method, which encodes a multivariate time series as images by means of the Gramian Angular Summation Field (GASF). Multivariate time series data is represented as 2D image data to enhance the performance of anomaly detection. The proposed method is validated with four time-series data sets. Experimental results show that our proposed method can improve validity and accuracy on all criteria. Therefore, effective anomaly detection in multivariate time series data can be achieved by combining the methods of Autoencoder and Gramian Angular Summation Field.
Perception, Experiences and Challenges of Online and Virtual Learning during Covid-19 Pandemic among Student Midwives of the Garden City University College, Kenyase-Kumasi, Ghana

Purpose: This study aimed to assess the perception, experiences and challenges of online and virtual learning during the COVID-19 pandemic among student midwives of the Garden City University College, Kumasi, Ghana. Approach/Methodology/Design: The study adopted a descriptive quantitative design which was achieved by the administration of questionnaires through google forms to 158 respondents. A systematic sampling technique was employed to select the participants. Data were coded and entered into Statistical Package for Social Sciences (SPSS) version 22.0 and MS. Excel for analysis. The technology acceptance model was used to direct the study. Findings: The study revealed that 51.3% of respondents perceived online and virtual learning to be useful and effective, 41.8% stated they were comfortable with the use of online and virtual learning platforms and tools. 44.3% of the participants were satisfied with the virtual learning, 50.6% stated that their experience with online and virtual learning has improved their ICT skills. However, challenges faced by the participants included the high cost of data (95.6%), poor internet connectivity (91.1%) and 81% expressed distraction when having the online and virtual learning classes. Practical Implications: The study has practical implications for the various educational institutions and concerned bodies to address the issue of high cost, among others. Originality/value: The study concluded that students found e-learning advantageous over conventional face-to-face teaching and learning, as they could study from any location despite some challenges.
A novel hybrid-based approach of snort automatic rule generator and security event correlation (SARG-SEC)

The rapid advanced technological development alongside the Internet with its cutting-edge applications has positively impacted human society in many aspects. Nevertheless, it equally comes with the escalating privacy and critical cybersecurity concerns that can lead to catastrophic consequences, such as overwhelming the current network security frameworks. Consequently, both the industry and academia have been tirelessly harnessing various approaches to design, implement and deploy intrusion detection systems (IDSs) with event correlation frameworks to help mitigate some of these contemporary challenges. There are two common types of IDS: signature and anomaly-based IDS. Signature-based IDS, specifically, Snort works on the concepts of rules. However, the conventional way of creating Snort rules can be very costly and error-prone. Also, the massively generated alerts from heterogeneous anomaly-based IDSs is a significant research challenge yet to be addressed. Therefore, this paper proposed a novel Snort Automatic Rule Generator (SARG) that exploits the network packet contents to automatically generate efficient and reliable Snort rules with less human intervention. Furthermore, we evaluated the effectiveness and reliability of the generated Snort rules, which produced promising results. In addition, this paper proposed a novel Security Event Correlator (SEC) that effectively accepts raw events (alerts) without prior knowledge and produces a much more manageable set of alerts for easy analysis and interpretation. As a result, alleviating the massive false alarm rate (FAR) challenges of existing IDSs. Lastly, we have performed a series of experiments to test the proposed systems. It is evident from the experimental results that SARG-SEC has demonstrated impressive performance and could significantly mitigate the existing challenges of dealing with the vast generated alerts and the labor-intensive creation of Snort rules.
BIM for construction safety improvement in Gaza strip: awareness, applications and barriers

The objectives of this paper are to elicit the perception of contracting parties regarding building information modelling (BIM), the most important safety-related applications of BIM and the barriers to adopting BIM in the local construction industry. Data for this study were collected through a questionnaire survey. A total of 75 questionnaires were randomly distributed to contractors, owners and consultants, and 37 were returned, representing a 49% response rate. The findings indicated that 33% of the respondents had no knowledge of the concept of BIM, 28% of the respondents’ organizations are not using BIM and if used it was implemented to a limited extent. The results indicated that ‘hazard identification and minimization’ and ‘safety training and education’ are the most important safety-related applications provided by BIM tools to improve safety performance in construction. Lack of universal use in the construction sector and insufficient training availability are the highest ranked barriers to adopting BIM in construction to improve safety. The research results may assist BIM developers to introduce the best applicable tools to improve safety performance in the construction industry. It is recommended that training courses be conducted regarding BIM concept and the benefits of its application in the construction industry.
Intelligent Micron Optical Character Recognition of DFB Chip Using Deep Convolutional Neural Network

The microcharacter recognition on the distributed feedback (DFB) laser chip is critically essential but a challenging task for the quality control in the incoming chip inspection and optical device manufacturing lines. Hence, this article proposes, designs, and implements a micro-optical character recognition system based on the microscopic imaging system and deep convolutional neural network to facilitate the microcharacter recognition of DFB chips. The proposed micron optical character recognition (MOCR) contains four components, namely, a microscopic imaging system-based visual-based measurement (VBM), character detection, orientation correction, and character recognition. In particular, data augmentation methods considering limited datasets and some chip character features are studied so as to accommodate complex industrial environments and tiny DFB chips. Experiments from the DFB image dataset of a real optical device manufacturing line showed that it reached the accuracy of 98.8%, which took a high speed of over 12.8 fps coping with DFB chip images, and saved 72% of the labor in the inspection position compared to traditional methods. To the best of our knowledge, there are no existing reports focused on MOCR on DFB chips. So, the first realization and successful application of MOCR played a key role in the intelligent manufacturing and quality traceability of DFB chips and devices in the field of optical communications, which is also of great significance for further accelerating the development of the industrial Internet.
Physical Activity Determinants in Older German Adults at Increased Dementia Risk with Multimorbidity: Baseline Results of the AgeWell.de Study

Background: Multimorbidity is a common issue in aging societies and is usually associated with dementia in older people. Physical activity (PA) may be a beneficial nonpharmacological strategy for patients with complex health needs. However, insufficient PA is predominant in this population. Thus, there is an evident need to expand the knowledge on potential determinants influencing PA engagement among elderly persons at risk of dementia and multimorbidity. Methods: We used baseline data from the multicenter, cluster-randomized controlled AgeWell.de study. The main aim was to describe PA engagement and identify potential PA determinants in a sample of community-dwelling Germans aged 60–77 years old with an increased risk of dementia and multimorbidity. Results: Of the 1030 included participants, approximately half (51.8%) engaged in PA ≥2 times/week for at least 30 min at baseline. We identified self-efficacy (beta = 0.202, (p < 0.001) and BMI (beta = −0.055, (p < 0.001) as potential PA determinants. Conclusions: The identified determinants, self-efficacy, and BMI are consistent with those reported in the literature. Specific knowledge on PA determinants and stages of change in persons with risk of dementia and multimorbidity might guide the development of effective future prevention measures and health services tailored to this population. Trial registration: German Clinical Trials Register (reference number: DRKS00013555).
Walking Behavior

With the growing interest in nonmotorized modes of transportation in general and walking in particular, there is an increasing need to understand the underlying motives and determinants of walking behavior. Active modes such as walking and biking can enhance public health and safety. As many cities seek to shift drivers out of their vehicles toward more active transportation modes by building the appropriate walking and biking infrastructure, research should capture the individual and community attributes that influence these travel mode choices. While this question has been addressed in the literature, through analysis of the role of neighborhood characteristics, walkability, self-selection, attitudes, and inertia, among others, the aim of this paper was to explore the role of childhood experience on these factors. To do so, this study gathered retrospective information through an attitudinal survey divided into sections pertaining to sociodemographics, neighborhood characteristics, residential location choice factors, and precollege travel experience. The survey results proved the existence of a relationship between travel behavior during childhood and the determinants of walking behavior during adulthood. The results also suggested that the determinants of walking differed on the basis of the individual's region of residence. These results might suggest that transportation policy at the childhood level could result in benefits not only during childhood but also throughout the life cycle of the individual.
Adaptive Control of Rotary System with Nonlinear Frictions.

This paper is concerned with the design of a Model Reference Adaptive Control System (MRACS) for motion with unknown nonlinear frictions. From the control system viewpoint, the static friction and Coulomb friction are characterized by a nonlinear deadzone element and constant disturbance for the control input signal, respectively. Then, the MRACS for the nonlinear frictions is considered for a nonlinear system which is a cascade combination of an unknown deadzone block or disturbance and a linear dynamic block. In order to verify the validity of the theoretical results, the design method described above is applied to a servo system influenced by friction. Experimental results show the high performance of the constructed MRACS and the effectiveness of the proposed method.
An Effort to Increase the Role of Halal Tourism: West Nusa Tenggara Islamic Center

Recently, religious tourism has attracted the attention of various parties because of the high interest of Muslim tourists. West Nusa Tenggara (NTB) is one of the provinces that carry religious or halal tourism. It was proven by attaining the halal tourism destination award by the West Nusa Tenggara Islamic Center in 2015. However, the West Nusa Tenggara Islamic Center still lacks development and literacy, especially tourism. Therefore, the motivation of this research is to find alternative strategies that might be used for the development process and increase literacy. By interviewing four respondents, the authors uncovered alternative strategies using SWOT analysis. The five strategies might be beneficial: collaborating with stakeholders, creating new attractions, marketing destinations, improving service quality, and strengthening the destination concept.
Effectiveness Check of the Integration of Synthetic Aperture Technique and Field Decomposition in Enhancement of MCSEM Detectability

This study presents an application of the synthetic aperture source(SAS) and field decomposition to marine controlled source electromagnetic(MCSEM) data to improve the detectability of deeply buried under sea floor target. We show that the detectability of MCSEM responses to the undersea floor target could be enhanced significantly by appropriate amplitude compensation and phase coefficients of the corresponding synthetic aperture, furthermore, the airwave and direct wave can be suppressed by separation of the MCSEM fields into up and down going waves. The effectiveness of the synthetic aperture and field decomposition method has been validated by numerical modeling EM dataset. And we also compare the effects of different synthetic weights and decomposition parameters on the results of the SAS and decomposition field components. It is suggested that the integration of the synthetic aperture and field decomposition for marine controlled source electromagnetic (MCSEM) data can be used to alleviate the distorted airwave and direct wave effects, and enhance the reservoir EM signal at the same time.
Prototype Filter for QAM-FBMC Systems Based on Discrete Prolate Spheroidal Sequences (DPSS)

In conventional wireless communications, cyclic-prefix orthogonal frequency division multiplexing (CP-OFDM) has been adopted as the baseline multicarrier scheme. Despite their corroborated merits, they are explored only in the asynchronous and strictly orthogonal scenario. To overcome the limitations observed in CP-OFDM, to support the constraints imposed by different 5G scenarios, and also to improve robustness against channel impairments, several waveforms have been investigated. Quadrature amplitude modulation associated with filter-bank multicarrier (QAM-FBMC) has been an auspicious technology for 5G communication systems and beyond. The main feature of QAM-FBMC is its capacity for high spectral confinement, which is possible thanks to the per-subcarrier filtering. Aiming to improve the QAM-FBMC performance, in this paper, we propose a prototype filter design based on the discrete prolate spheroidal sequences (DPSS), also known as Slepian sequences. The presented filters were obtained by optimizing the weights that are used to compose the desired prototype filter to such an extent that they minimize the intrinsic interference of the system. At the same time, these weights keep spectral confinement of the filter for a previously determined bandwidth limitation. Simulation results show that the optimized filters achieve higher intrinsic interference attenuation than the competitors. Indeed, we can confirm the improvement in the system performance brought by the usage of the optimized filters through the bit error rate (BER) evaluation. Furthermore, the proposed method is flexible thanks to the suitability of its parameters.