Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-Contact Heart Rate Monitoring Based on Millimeter Wave Radar

Millimeter Wave(mmWave) radar has been widely used in vital sign monitoring with non-contact and privacy protection. To reduce the effect of random body motion, respiration, and its harmonics on heart rate estimation, the heart rate monitoring based on frequency-modulated continuous-wave (FMCW) radar is researched in this paper. First, a vital sign signal extraction algorithm is designed for the Range Bin variation caused by random body motion and heavy breathing. Second, a heartbeat signal extraction algorithm is designed based on Adaptive Notch Filter (ANF) and Empirical Wavelet Transform (EWT). The harmonic of respiration will be suppressed, and the heartbeat signal will be separated. Finally, the weighted estimation is performed according to the relationship of harmonics of the heartbeat signal to obtain the heart rate. Twenty subjects are invited to the experiment. The experimental results show that the proposed method can improve the signal-to-noise ratio (SNR), reduce the harmonic interference, and estimate the heart rate with a mean absolute error less than 4 BPM.
60 GHz Slot antenna integrated on SOI

In this paper, we propose a 60 GHz Slot antenna integrated on 0.13(m Silicon on Insulator (SOI) substrate. The antenna is composed of a CPW transmission line and the radiating slots. The design was based on electromagnetic simulations made using the CST Microwave Studio software package. The antenna exhibits a return loss of –12dB at 60GHz with a bandwidth of 10%. Backside substrate metallization has been used to improve radiation properties. The simulated radiation efficiency is 60 % with a gain around 3 dBi.
Involution-based network with contrastive learning for efficient image super-resolution

Abstract. Single-image super-resolution (SISR) studies have achieved superior improvement with the development of convolution neural networks. However, most methods sink into the high computation cost. To tackle this issue, we propose an involution-based lightweight method with contrastive learning for efficient SISR. Unlike the original involution, we set the group number of involution operations to the input feature channels. This setting guarantees the spatial- and channel-specific peculiarity. Moreover, our implemented involution not only learns the weight but also the bias for convolution. Simultaneously, we rethink the kernel generation functions of involution. Instead, we utilize Sigmoid with reparameterized convolution. We additionally apply residual path to involution operation. Furthermore, contrastive learning is adopted during training to learn universal features. Compared with state-of-the-art efficient SISR methods, our proposed methods achieve the best performance with similar or fewer parameters.
A novel trust recommendation model in online social networks using soft computing methods

Social network (OSN) is an emerging platform through which people can connect with their friends, relatives, and other like‐minded people. On the other hand, users' personal information might be misused because of other users' biased and malicious behavior. Establishing a trusted environment in social networks is one of the current research problems. Some of the research papers proposed to trust computational methods, but still, there is a lack of methods to handle biased recommendations and loss of trust accuracy towards the target user. In this article, to address these open issues, “a novel trust recommendation model in online social networks using soft computing methods (TRMSC)” is proposed for the Twitter social networks. Here direct and indirect trust is computed for known and unknown users, respectively. The direct trust of a user is computed using clustering methods based on his social activities (posts, retweets received, mentions received, listed count, and follower count) with other users. In the computation of indirect trust, the impact of biased recommendations is suppressed using the Dempster Shafer theory(DST) method, and loss of trust is minimized using trust transitive matrices. The performance of the proposed method is analyzed theoretically and experimentally. Time and space complexities are measured using asymptotic notations. In experimental results, TRMSC is evaluated for different network sizes and for target users at different distances (2 to 4‐hops), where it could perform better than existing methods.
Nontechnological Online Challenges Faced by Health Professions Students during COVID-19: A Questionnaire Study

COVID-19 forced universities to shift to online learning (emergency remote teaching (ERT)). This study aimed at identifying the nontechnological challenges that faced Sultan Qaboos University medical and biomedical sciences students during the pandemic. This was a survey-based, cross-sectional study aimed at identifying nontechnological challenges using Likert scale, multiple-choice, and open-ended questions. Students participated voluntarily and gave their consent; anonymity was maintained and all data were encrypted. The response rate was 17.95% (n = 131) with no statistically significant difference based on gender or majors (p-value > 0.05). Of the sample, 102 (77.9%) were stressed by exam location uncertainty, 96 (73.3%) felt easily distracted, 98 (74.8%) suffered physical health issues, and 89 (67.9%) struggled with time management. The main barriers were lack of motivation (92 (70.2%)), instruction/information overload (78 (59.5%)), and poor communication with teachers (74 (56.5%)). Furthermore, 57 (43.5%) said their prayer time was affected, and 65 (49.6%) had difficulties studying during Ramadan. The most important qualitative findings were poor communication and lack of motivation, which were reflected in student comments. While ERT had positive aspects, it precipitated many nontechnological challenges that highlight the inapplicability of ERT as a method of online learning for long-term e-learning initiatives. Challenges must be considered by the faculty to provide the best learning experience for students in the future.
Business strategy and stock price crash risk: international evidence

ABSTRACT Employing a large international sample spanning from 1985 to 2017 (i.e. 65,774 firm-year observations), this paper examines the relationship between business strategy and stock price crash risk. Empirical results suggest that prospective business strategy is significantly and positively related to stock price crash risk, in line with the ‘bad news hoarding hypothesis’. Further research shows that in companies with higher information asymmetry or more overconfident CEOs, prospective business strategy has a larger positive impact on stock price crash risk. In addition, the above positive effects are more significant in countries with higher individualism, lower power distance, and weaker uncertainty avoidance. Our results remain after a battery of robustness checks.
Characterization of Endothelial Progenitor Cell: Past, Present, and Future

Endothelial progenitor cells (EPCs) are currently being studied as candidate cell sources for revascularization strategies. Despite these promising results, widespread clinical acceptance of EPCs for clinical therapies remains hampered by several challenges. The challenges and issues surrounding the use of EPCs and the current paradigm being developed to improve the harvest efficiency and functionality of EPCs for application in regenerative medicine are discussed. It has been observed that controversies have emerged regarding the isolation techniques and classification and origin of EPCs. This manuscript attempts to highlight the concept of EPCs in a sequential manner, from the initial discovery to the present (origin, sources of EPCs, isolation, and identification techniques). Human and murine EPC marker diversity is also discussed. Additionally, this manuscript is aimed at summarizing our current and future prospects regarding the crosstalk of EPCs with the biology of hematopoietic cells and culture techniques in the context of regeneration-associated cells (RACs).
The Research of “Products Rapidly Attracting Users” Based on the Fully Integrated Link Prediction Algorithm

One of the main problems encountered by social networks is the cold start problem. The term “cold start problem” refers to the difficulty in predicting new users’ friendships due to the limited number of links those users have with existing nodes. To fill the gap, this paper proposes a Fully Integrated Link Prediction Algorithm (FILPA) that describes the social distance of nodes by using “betweenness centrality,” and develops a Social Distance Index (SDI) based on micro- and macro-network structure according to social distance. With the aim of constructing adaptive SDIs that are suitable for the characteristics of a network, a naive Bayes (NB) method is firstly adopted to select appropriate SDIs according to the density and social distance characteristics of common neighbors in the local network. To avoid the risk of algorithm accuracy reduction caused by blind combination of SDIs, the AdaBoost meta-learning strategy is applied to develop a Fully Integrated Social Distance Index (FISDI) composed of the best SDIs screened by NB. The possible friendships among nodes will then be comprehensively presented using high performance FISDI. Finally, in order to realize the “products rapidly attracting users” in new user marketing, FILPA is used to predict the possible friendship between new users in an online brand community and others in different product circles.
An Energy Efficient, Mismatch Tolerant Offset Compensating Hybrid MTJ/CMOS Magnetic Full Adder

The miniaturization of CMOS have lead to high leakage and process variation at sub-micron technology. Hybrid circuits including CMOS and magnetic tunnel junction (MTJ), a spintronic device have been put forward to conquer aforesaid limitations. MTJs have appeared to be most assuring candidates owing to it’s compatibility with CMOS and almost zero leakage power. In this brief an offset compensating magnetic full adder (OCMFA) (a hybrid circuit of CMOS and MTJ) has been proposed, which compensates the offset voltage occurring due to process variation and mismatch. The proposed OCMFA compensates the offset voltage in the precharge mode by pre-charging the outputs associated with high resistance branch at a higher voltage and that with lower resistance branch at a lower voltage. The asymmetrical pre-charging of output nodes is assisted by the write current, to provide input to MTJs and load connected diodes. Thus, it is robust against process variation and upgrades the performance of MFA. Power, delay, variability, and corner analysis are performed utilizing UMC 40nm CMOS technology kit at the cadence virtuoso platform and the compact model of perpendicular magnetic anisotropy (PMA) MTJ. This brief contributes in the reliability analysis of proposed MFA with the earlier reported MFA. The proposed OCMFA provides 65% lower PDP and shows 84.6% higher reliability as compared to conventional precharge sense amplifier based magnetic full adder (PCSA-MFA).
Tree-based Filtering in Pulse-Line Intersection Method Outputs for An Outlier-tolerant Data Processing

Pulse palpation is one of the non-invasive patient observations that identify patient conditions based on the shape of the human pulse. The observations have been practiced by Traditional Chinese Medicine (TCM) practitioners since thousands of years ago. The practitioners measure the patient’s arterial pulses in three points of both patient wrists called chun, guan, and chy, then diagnose based on their knowledge and experience. Pulse-Line Intersection (PLI) method extract features of each pulse from the observed pulse wave sequence. PLI is performed by summing the number of intersections between the artificial line and the pulse wave. The method is proven in differentiating between hesitant with moderate pulse waves. As the method implemented in Clinical Decision Support System (CDSS) related to pulse palpation, some outlier data might emerge and affect the measurement result. Thus, outlier filtering is needed to prevent unnecessary prediction processes by machine learning (ML) models inside CDSS. This study proposed an outlier filtering model using a decision tree algorithm. This concept is designed by analyzing pulse features values and the chance of odd values combination. Then inappropriate values are excepted using several rules. Every pulse feature list that did not pass the filtering rule is categorized as outliers and were not included for further process. The proposed model works more efficiently than ML models dealing with outliers since this procedure is unsupervised learning with a small number of parameters. Overall, the proposed filtering method can be used in pulse measurement applications by eliminating outlier data that might decrease the performance of ML models.
Cost efficient Multi-Sensor Interpretation

This paper presents a multi-sensor color image interpolation algorithm. Color filter array consists of three primary colors: red, green, and blue. Green component has twice more information than that of red or blue components. The missing green components are estimated based on the color channel difference along various edge directions. Then, red and blue components are calculated using pre-interpolated green components. It can be found from the simulation results section that iterative process and inter-channel scheme provided satisfactory results. We used three objective color metrics which proved our method’s feasibility. The visual performance comparison was conducted for subjective performance assessment. From all the comparison shown above, it can be concluded that the proposed method required less computations while providing better performance.
Considering Leaf Extract of Miracle Tree (Moringa Oleifera L.) and Potassium Nutrition for Contending Cotton Leaf Curl Virus (CLCuV) Disease of Cotton (Gossypium Hirsutum L.)

Cotton leaf curl virus disease (CLCuV) is a devastating biotic factor that imposed crop productivity a huge loss. To battle this issue this study was assessed in 2019 for the evaluation of the response of leaf extract of miracle tree (Moringa oleifera L.) and potassium (K) nutrition to mitigate the plague of CLCuV disease in cotton variety CRIS-129. Treatments have consisted of the various K dosses Viz., 0, 100, and 150 kg K2SO4 ha-1, and 3% leaf extract of moringa was sprayed at 30, 60, and 90 days after emergence. Results regarding yield components i-e boll weight (g) and seed index (g) were increased by increasing K application and foliar spray of moringa leaf extract but statistically non-significant (p<0.05). However, seed cotton yield and K contents in cotton leaves were significantly increased with all the treatments. The maximum seed cotton yield (2620 kg ha-1) was achieved with 150 kg K2O ha-1 application and it was increased by about 52% over the control treatment. In the case of incidence % of CLCuV disease, both K nutrition as well as foliar spray played a significant role and CLCuV intensity reduced with increasing rate of K doses and foliar sprays.
In Vitro and Ex Vivo Evaluation of Bi-Layered Effervescent Microenvironmental PH Modifying Buccal Films with Saquinavir

Buccal delivery of saquinavir has the advantage to bypass the hepatic first-pass metabolism associated with oral administration. Local microenvironmental pH (pH M ) modification at the buccal mucosa might increase absorption of saquinavir by balancing the solubility and partition of saquinavir into the buccal mucosa. The present study aimed to evaluate a novel saquinavir pH M modifying buccal film using effervescence, as well as to elucidate the relationship between pH M and permeation of saquinavir released from the buccal films. Hydroxypropyl methylcellulose-based films were prepared: 1) a bilayered effervescent film composed of an alkaline layer and a layer containing saquinavir and malic acid, 2) a monolayered film composed of saquinavir and malic acid (pH M modifying film), and 3) a monolayered film composed of saquinvir (control). The release of saquinavir from these films and permeation of saquinavir across porcine mucosae were evaluated. The monolayered pH M modifying film led to a decrease in pH M from pH 6.8 to 3.0 after 5.5 min, while the effervescent film had an initial decrease in pH M (from pH 6.8 to 4.0) caused by the co-release of malic acid and a subsequent pH M increase (from pH 4.0 to 5.9) caused by the release of carbonate from the alkaline layer within 15 min. Saquinavir released faster from the pH M modifying film than from the effervescent film. However, a higher permeation of saquinavir and mucosal accumulation was observed for the effervescent film. This could be attributed to the higher concentration of ionized specie and a faster tissue partitioning of unionized saquinavir, respectively. These results suggest that effervescent pH M modifying film is a potential formulation strategy for buccal delivery of saquinavir.
Evaluation Model to Assess the Effectiveness of Coordination Processes in Global Software Development Projects: A Roadmap

Research shows that software organizations are facing many challenges related to coordinate issues by adopting Global Software Development (GSD) approach. Coordination is a primary mechanism used in between collocated and distributed software development teams in GSD environment. A lack of coordination in GSD can decrease the productivity, complicate the process and delay the completion of tasks. Effective coordination is a crucial aspect in successful software projects. In order to coordinate the processes effectively, it need to be assessed. Research shows that there were less studies on assessing the effectiveness of the coordination processes. Hence, this study intends to identify the coordination processes, coordination strategies, indicators related to the identified coordination processes and coordination strategies used in GSDbase software development organization. This paper presents the roadmap to formulate the evaluation model for GSD coordination processes; made up of indicators for every coordination processes components. In general, project managers can utilise this model as it will serve as a guideline to assess the coordination processes effectively between collocated and distributed team in GSD environment.
MLP-NARX Bitcoin Price Prediction Model Integrating System Identification Modelling Principles

Bitcoin is a decentralized digital currency that enables people to exchange value without requiring a third-party intermediary. Due to its many advantages, it has received much interest from institutional and individual investors. Despite its meteoric increase, the price of Bitcoin extremely volatile asset class as it purely relies on supply and demand. This presents an interesting opportunity to create a forecasting model. However, many research papers in this area does not analyse the residuals as part of the forecasting resulting in potentially biased models. In this paper, we demonstrate System Identification (SI) residual analysis techniques to the analysis of our forecasting model. The Multi-Layer Perceptron (MLP) Nonlinear Autoregressive with Exogeneous Inputs (NARX) uses historical price data and several technical indicators to predict the future price movements of Bitcoin. The Particle Swarm Optimization (PSO) algorithm was used to find optimal parameters for the model. The model was able to predict one day ahead price in the prediction test. The model has successfully captured the dynamics of the data through the tests performed on residuals. It is also proving the randomness of residuals, albeit some minor violations.
Investigation of the Atomic Layer Deposition of the Titanium Dioxide (TiO2) Film as pH Sensor Using a Switched Capacitor Amplifier

The electrical and chemical properties of the titanium dioxide (TiO2) coated spirals grown by the atomic layer deposition (ALD) technique in two different temperatures of 150 °C and 300 °C are studied. The thickness of the TiO2 layers studied are 20, 40, and 80 nm. A switched capacitor amplifier is used to investigate the pH response and the capacitance of the samples. It is found that the performance of the TiO2 samples depends on either the thickness or the deposition temperature due to the differences in the physical properties of the oxide layer such as surface roughness and film density. The high temperature samples are more crystalline, whereas the low temperature samples are more amorphous. Since there is a low pass filter effect in the electrolyte–sample interface, the TiO2 coated samples show the better response to the pH change for the high temperature samples as the sensor surface area for binding the hydrogen ions is larger and the charge transfer resistance is smaller. Furthermore, more roughness on the surface can be obtained by increasing the thickness, which reduces the charge transfer resistance. In this study, the 80 nm sample deposited at 300 °C gives the best pH response of 40 mV/pH.
Energy Consumption and Financial Development in NAFTA Countries, 1971–2015

To satisfy human needs and desires, it is necessary to produce goods and services that require the use of some production factors, such as labor, capital, and energy, among others. Nowadays, energy is a key production factor for economic activity in all countries. The main objective of this paper is to analyze the relationship between energy, economic growth, urbanization, and financial development in the country-members of the North American Free Trade Agreement (NAFTA) during the period of 1971–2015. Panel data Econometric methods are applied in this research, namely cross-section dependence (Pesaran test), unit root (Cross-sectional Augmented Dickey Fuller and Cross-sectional Im, Pesaran, and Shin tests), cointegration (Kao and Fisher–Johansen tests), and heterogeneous causality (Hurlin and Dumitrescu test). The results achieved in this research demonstrate that the variables of this model are characterized by a cross-section dependence, and they are integrated in order one. An equilibrium or long-term relationship between them exists. By means of the Fully Modified OLS and Dynamic OLS estimators it this demonstrated that there is a positive relationship between GDP and EC, while there is a negative relationship between FD, CPI, URB, and TO and EC. The economic policy recommendations drawn from this investigation are that financial development promotion, urbanization, and trade openness may contribute to reducing energy consumption in these countries.
Screening of Genes Related to Sex Determination and Differentiation in Mandarin Fish (Siniperca chuatsi)

Mandarin fish has an XX/XY sex-determination system. The female mandarin fish is typically larger than the male. Sex identification and the discovery of genes related to sex determination in mandarin fish have important theoretical significance in the elucidation of the regulation and evolutionary mechanism of animal reproductive development. In this study, the chromosome-level genome of a female mandarin fish was assembled, and we found that LG24 of the genome was an X chromosome. A total of 61 genes on the X chromosome showed sex-biased expression. Only six gonadal genes (LG24G00426, LG24G003280, LG24G003300, LG24G003730, LG24G004200, and LG24G004770) were expressed in the testes, and the expression of the other gene LG24G003870 isoform 1 in the ovaries was significantly higher than that in the testes (p < 0.01). Five (except LG24G003280 and LG24G003300) of the seven aforementioned genes were expressed at the embryonic development stage, suggesting their involvement in early sex determination. The expression of LG24G004770 (encoding HS6ST 3-B-like) was also significantly higher in female muscles than in male muscles (p < 0.01), indicating other functions related to female growth. ZP3 encoded by LG24G003870 isoform 1 increased the C-terminal transmembrane domain, compared with that encoded by other fish zp3 isoforms, indicating their different functions in sex determination or differentiation. This study provides a foundation for the identification of sex-determining genes in mandarin fish.