Miracle-dz/UniFAR-FTUs huggingface dataset:
Highly transparent conducting CdO thin films by radiofrequency magnetron sputtering for optoelectronic applications

Abstract. Cadmium oxide (CdO) thin films with low electrical resistivity and higher transparency have been deposited by radiofrequency (RF) magnetron sputtering on glass substrates. Sputtering process was carried out at RF power of 40 W and with varying substrate temperatures. The structural, morphological, electrical, and optical properties of the deposited films are investigated. The structural properties reveals that the as-deposited CdO films shows preferential orientation along (111) plane exhibiting face centered cubic structure. The surface morphology shows that all the films possess well defined grain boundaries with high uniformity. CdO samples deposited at substrate temperature of 150°C with RF power of 40 W exhibits above 95% transparency within the visible wavelength with lower electrical resistivity value in the order of 10−5 Ω·cm. The Raman spectroscopy analysis reveals the possible modes present in CdO films and its dependence on substrate temperature. The comparatively high value of the figure of merit for the optimum sample of CdO deposited at 150°C indicates that these films are suitable for optoelectronic device applications.
Federated and Online Dynamic Spectrum Access for Mobile Secondary Users

Users in dynamic spectrum access (DSA) with federated reinforcement learning (FRL) autonomously access channels, avoiding centralized coordination and protecting users’ privacy. However, existing FRL-based DSA mechanisms are limited to ideal network states, i.e., assuming that channel states and users’ interference relationships are unchanged. Besides, users should upload intermediate results simultaneously for federated aggregation. The above conditions are impractical for mobile users since their network states and locations are unstable. Meanwhile, newly connected users have to train their models through local data with numerous computing resources since global models are unsuitable for them. We propose FRDSA, an FRL-based secure and lightweight channel selection mechanism in DSA for mobile users under dynamic network states. An independent channel selection environment with a virtual group strategy is presented to avoid interference between users under unstable channel states. Furthermore, an asynchronous parameter aggregation method in FRDSA dynamically adjusts the aggregation factors without users simultaneously uploading intermediate results. Simulations based on real trajectory data show that FRDSA significantly reduces approximately 60% interference between mobile users under unstable network states. Newly connected users can directly apply the well-trained global model to access channels autonomously instead of retraining a model, effectively reducing mobile users’ computing resource requirements.
A Study of Japanese University Students' Perceptions of Their Appearance and Their Tendency to Wear Masks During School During the COVID-19

Purpose of the study: This study aims to shed light on the effects of COVID-19 on university students. Specifically, the author clarifies the habit of wearing masks, a characteristic among Japanese, and their self-perceptions regarding their appearance post-COVID. Methodology: The author hypothesizes that students who have not had the opportunity to see the real faces of their classmates since entering university will have great resistance to removing their masks in public. The hypothesis will be tested and analyzed by conducting a questionnaire survey of 357 students in Japan to investigate their perceptions of their faces during FY2023. Main Findings: As of May 2023, 357 students (191 males and 166 females) in Japanese universities underwent Cross tabulation and Chi-Square analysis to examine shifts in their attitudes toward appearance and mask-wearing habits. The study revealed significant differences in attitudes and mask-wearing tendencies between the two genders (p < .001, p = 7.6E-07, = 24.5). Novelty/Originality of this study: It has been discovered through this study that a greater number of female students continue to wear masks even when it is no longer mandated. This difference in behavior between genders is statistically significant, confirming the hypothesis. The survey also uncovered the reasons behind this behavior, including habituation, changes in self-perception, and the influence of the surrounding.
Recent Developments in the Field of Thermal Barrier Coatings Solutions for Structural Repair

The aerospace industry relies heavily on the structural integrity and performance of aircraft to ensure safe and efficient operations. Over time, aircraft structures can experience wear, corrosion or damage due to various factors such as environmental conditions, fatigue or accidents. Structural repairs are necessary to maintain the airworthiness of aircraft and extend their operational life. These brief highlights the importance of coating solutions in structural repair for aerospace. Coatings play an important role in protecting aircraft structures from degradation, preventing further damage and restoring their mechanical properties. In addition, coatings can improve aerodynamic performance, improve fuel efficiency and reduce maintenance costs. The brief discusses the various types of coating solutions used in aeronautical structural repair. These include corrosion-resistant coatings, abrasion-resistant coatings, heat-resistant coatings, and composite bonding systems. Each type of coating serves a specific purpose in mitigating structural damage and restoring the structural integrity of aircraft components. Also, the brief explores the key properties and characteristics of effective coating solutions. These properties include adhesive strength, flexibility, durability, weight considerations, chemical resistance, and thermal stability. Understanding these properties is critical to selecting the appropriate coating solution for specific repair applications. Also summarizes the challenges and considerations associated with coating applications in aerospace structural repair. These challenges include surface preparation, application techniques, curing processes, compatibility with existing coatings and compliance with regulatory requirements. Overcoming these challenges is essential to ensure the successful implementation of coating solutions in structural repair operations. Finally, the abstract discusses emerging trends and developments in aeronautical structural repair coating technologies. These include eco-friendly coatings, self-healing coatings, nano-coatings and smart coatings with sensing capabilities. These developments hold promise for improving the effectiveness and efficiency of structural repair processes in the aerospace industry.
Discretization Method of Continuous-Time Dynamic Linear Model

— The article presents a new discretization method of a continuous-time linear model of sensor dynamics. It can be useful to reduce measuring errors related to the inertia of the sensor. For example it is important in the measurement of rapid processes as temperature changes in combustion chambers, or for shortening the time needed to establish the sensor readings in a transition state. There is assumed that sensor dynamics can be approximated by linear differential equation or transfer function. The searched coefficients of equivalent difference equation or discrete transfer function are obtained from Taylor expansion of a sensor output signal and then on the solution of the linear set of equations. The method does not require decomposition of sensor transfer function for zeros and poles and can be applied to the case of transfer function with zeros equal to zero. The method was used to compensate the dynamics of sensor measuring fast signals. The Bode characteristics of a compensator were compared with others derived using classical methods of discretization of linear models. Additionally, signals in time were presented to show the dynamic error before and after compensation.
Unsupervised Topic and Sentiment Unification Model for Sentiment Analysis

Supervised and semi-supervised sentiment classification methods need label corpora for classifier training.To solve this problem,an unsupervised topic and sentiment unification model(UTSU model) is proposed based on the LDA model.UTSU model imposes a constraint that all words in a sentence are generated from one sentiment and each word is generated from one topic.This constraint conforms to the sentiment expression of language and will not limit the topic relation of words.UTSU model is compeletly unsupervised and it needs neither labeled corpora nor sentiment seed words.The experiments of sentiment classification show that UTSU model comes close to supervised classification methods and outperforms other topic and sentiment unification models.UTSU model improves the F1 value of sentiment classification 2% than ASUM model and 16% than JST model.
Imperfect variables : the combined problem of missing data and mismeasured variables with application to generalized linear models

Observational studies predicated on the secondary use of information from administrative and health databases often encounter the problem of missing and mismeasured data. Although there is much methodological literature pertaining to each problem in isolation, there is a scant body of literature addressing both problems in tandem. I investigate the effect of missing and mismeasured covariates on parameter estimation from a binary logistic regression model and propose a likelihood based method to adjust for the combined data deficiencies. Two simulation studies are used to understand the effect of data imperfection on parameter estimation and to evaluate the utility of a likelihood based adjustment. When missing and mismeasured data occurred for separate covariates, I found that the parameter estimate associated with the mismeasured portion was biased and that the parameter estimate for the missing data aspect may be biased under both missing at random and non-ignorable missing at ran dom assumptions. A Monte Carlo Expectation-Maximization adjustment reduced the magnitude of the bias, but a trade-off was observed. Bias re duction for the mismeasured covariate was achieved by increasing the bias associated with the others. When both problems affected a single covariate, the parameter estimate for the imperfect covariate was biased. Additionally, the parameter estimates for the otehr covariates were also biased. The Monte Carlo Expectation-Maximization adjustment often corrected the bias, but the bias trade-off amongst the covariates was observed. For both simulation studies, I observed a potential dissimilarity across missing data mechanisms. A substantive data set was investigated and by using the second sim ulation study, which was structurally similar, I could provide reasonable conclusions about the nature of the estimates. Also, I could suggest avenues
DOES COVID-19 PANDEMIC HAS IMPACT ON MILLENNIALS' PROPENSITY TO INDEBTEDNESS THROUGH E-MARKETING?

The introduction of electronic marketing (e-marketing) has a huge impact on consumer behavior as products are brought closer to consumers and barriers are lifted by breaking the time and physical constraints in marketing activities. A pertinent question to ask if the COVID-19 pandemic have further increased the tendency of consumers’ involvement in e-marketing? This study aims to explore the impact of e-marketing on the propensity to indebtedness among the Millennials in Malaysia during the COVID-19 outbreak. According to the literature, the purchasing intention and behavior of consumers are influenced by advertising in its various forms. Constant exposure to advertisements encourages people to make unplanned purchases and increases the likelihood of tolerating compulsive shopping. As a result, financial complications and over-spending are the most obvious problems associated with compulsive shopping, as people more often spend beyond their means. With the temptations presented by marketers and easy access to a wide range of payment methods, the financial well-being of customers, especially the technology-oriented generation, is threatened. Thus, it is expected that being constantly exposed to e-marketing could lead to risky financial situations, with greater tendency to incur debt. In testing this hypothesis, the study adopts a quantitative research to determine the impact of e-marketing on respondents' tendency towards indebtedness. A survey was conducted for data collection, with relevant questionnaires being designed to address this issue in the context of the undergraduate and postgraduate students at several universities in Malaysia. In analyzing the data, descriptive statistics and multivariate analysis techniques were applied using the SPSS and AMOS software.
Perception of the Residential Environmental Quality of Yenagoa Metropolis, Nigeria

Residential environmental quality (REQ) affects human health and quality of life (QoL). Therefore, this study as sessed residents’ perception of the REQ of the Yenagoa metropolis. Data for the study were sourced from the 400 administered questionnaires, which required respondents to rate their REQ based on seven selected indicators (air quality, drinking water quality, housing location, sanitation, waste management, housing accessibility and noise pollution). The respondents were sampled using the multistage sampling technique. The data were analyzed using frequency, percent age, t-test, ANOVA and REQ model. The findings show that the overall calculated REQ of Yenagoa was classified as “good quality". The best-rated indicator was drinking water quality, while the least-rated was noise pollution. Ratings based on respondents' sex, income and educational status recorded similar results. Also, the respondents' perception of the REQ across the four zones was similar as the calculated ratings of all the zones fell under the "good quality" classification. Furthermore, the hypotheses tested revealed that there were no significant differences in the perception of the REQ by sex and income status, while significant variation exists by education status. Despite the general "good quality" rating, there is still room for improvement, especially in the areas of noise pollution, sanitation and housing location, which received relatively low ratings.
Impact of Land Value Tax on the Equity of Planning Outcomes

The question of land ownership is fundamental to any modern society and has been blamed as the root of numerous social inequalities. This paper explores one potential solution to the land question, Land Value Tax (LVT), which was first popularised in the 1880s by Henry George. Despite its long history, LVT has not been widely implemented, but recently proposals to replace existing property taxes with this form of taxation have re-emerged on the political scene. While the Welsh Government has recently explored the idea of introducing an LVT, there are limited studies on this form of taxation in the context of a discretionary planning system, such as the Welsh planning system. This paper conducts exploratory qualitative research using Wales as a case study. Data was collected via semi-structured interviews with experts across various related fields, primarily within taxation and planning, to establish the potential effects of LVT in practice, with a particular focus on how it might affect the equity of planning outcomes. Overall, the research showed that despite the strong theoretical potential, fundamental practical issues would diminish the ability of LVT to effectively influence planning. LVT is argued to be largely incompatible with a discretionary planning system (e.g., the Welsh planning system), and therefore, the research concluded that many of the potential benefits of LVT could be better achieved through reforms to existing taxes and planning processes, particularly land value capture mechanisms.
A METHOD FOR DETECTING THE CHANGE OF AURORAL ACTIVITIES BASED ON THE ALL‐SKY IMAGE SEQUENCE

Faced with the annually increasing amount of auroral data, we need to quickly browse the auroral activities and exactly find similar auroral events as more as possible. Traditional method of browsing auroral activities is Keogram. The Keogram loses two-dimensional information about appearance and motion, which result in missing several auroral events, such as aurora images including curl structure or luminance structure not appearing on the magnetic meridian. In this paper, considering the fact that the solar winds have magnetic fluid nature, we model the auroral motion using the continuous equation. And thereby relative motion in neighborhood is represented by the local vector difference (LVD) based on motion field. Spatiotemporally statistics of the LVDs is used as the representation of auroral sequences. By computing the difference between two successive auroral sequences using metric-square histogram distance, the change of auroral activities can be quantitatively measured. The local peaks of the curve indicate the changes of auroral activities. Because motion field provides the information about two-dimensional appearance and motion, the proposed method can detect auroral change and thereby the change curve makes it feasible to fast browse auroral activities and to study auroral activity period.
Numerical simulations of particulate suspensions via a discretized Boltzmann equation. Part 2. Numerical results

A new and very general technique for simulating solid–fluid suspensions has been described in a previous paper (Part 1); the most important feature of the new method is that the computational cost scales linearly with the number of particles. In this paper (Part 2), extensive numerical tests of the method are described; results are presented for creeping flows, both with and without Brownian motion, and at finite Reynolds numbers. Hydrodynamic interactions, transport coefficients, and the short-time dynamics of random dispersions of up to 1024 colloidal particles have been simulated.
Heat Budget Analysis for the Extended Development of the 2014–2015 Warming Event

In order to figure out the associated underlying dynamical processes of the 2014–2015 warming event, we used the ECCO (Estimating the Circulation and Climate of the Ocean) reanalysis from 1993 to 2016 and two combined scatterometers, QuikSCAT and ASCAT, to analysis hydrodynamic condition and ocean heat budget balance process in the equatorial tropical pacific. The spatiotemporal characteristics of that warming event were revealed by comparing the results with a composite El Niño. The results showed that the significant differences between the 2014 and 2015 warming periods were the magnitudes and positions of the equatorial easterly wind anomalies during the summer months. The abruptly easterly wind anomalies of 2014 that spread across the entire equatorial Pacific triggered the upwelling of the equatorial Kelvin waves and pushed the eastern edge of the warm pool back westward. These combined effects caused abrupt decreases in the sea surface temperatures (SST) and upper ocean heat content (OHC) and damped the 2014 warming process into an El Niño. In addition, the ocean budget of the upper 300 m of the El Niño 3.4 region showed that different dynamical processes were responsible for different warming phases. For example, at the beginning of 2014 and 2015, the U advection and subsurface processes played dominant roles in the positive ocean heat content tendency. During the easterly wind anomalies period of 2014, the U advection process mainly caused a negative tendency and halted the development of the warming phase. In regard to the easterly wind anomalies of 2015, the U advection and subsurface processes were weaker negatively when compared with that in 2014. However, the V advection processes were consistently positive, taking a leading role in the positive trends observed in the middle of 2015.
Automatic Recognition of Beam Attachment for Massive MIMO System in Densely Distributed Renewable Energy Resources

Several large-scale and distributed systems such as renewable energy systems (RESs) require ubiquitous and reliable communication. RESs are designed to provide efficient power management and improve both energy production and consumption. Decision making in RESs heavily depends on real-time communication. Fifth and sixth-generation (5G, 6G) wireless networks promise to deliver significant communication advantages to RESs including ultra-low latency, high throughput and improved coverage. However, the communication behavior in RESs is categorized as unpredictable due to aspects such as system flexibility and equipment heterogeneity. This may affect the stability of the entire RES, which results in further issues such as signal reliability and degraded coverage. Therefore, precise identification of user equipment’s (UE) location greatly improves the sustainability of 5G and 6G wireless services. In this work, we propose a novel low-complexity technique to automatically recognize UE locations in an area of interest. The approach aims at providing precise identification of UE with minimum memory and feature requirements. We use the lazy learning approach to build a prediction model to construct beam-attachment maps. We then train the model to provide distributed intelligent models to automatically recognize beam-attachment indexes. We compare the proposed approach with instance-based techniques to measure its ability at predicting beam-attachment maps. The results show that the proposed model has the ability to provide an accurate prediction with respect to the beam-attachment index (around 100%) with minimal complexity.
Pseudo Features-Guided Self-Training for Domain Adaptive Semantic Segmentation of Satellite Images

Semantic segmentation is a fundamental and crucial task that is of great importance to real-world satellite image-based applications. Yet a widely acknowledged issue that occurs when applying the semantic segmentation models to unseen scenery is that the model will perform much poorer than when it was applied to scenery similar to the training data. This phenomenon is usually termed as the domain shift problem. To tackle it, this article presents a self-training-based unsupervised domain adaptation (UDA) method. Different from the previous self-training approaches which focus on rectifying and improving the quality of the pseudo labels, we instead seek to exploit feature-level relation among neighboring pixels to structure and regularize the prediction of the adapted model. Based on the assumption that spatial topological relation is maintained despite the impact of the domain shift, we propose a novel self-training mechanism to perform DA by exploiting local relation in the feature space spanned by the teacher model, from which the pseudo labels are generated. Quantitative experiments on four different public benchmarks demonstrate that the proposed method can outperform the other UDA methods. Besides, analytical experiments also intuitively verify the proposed assumption. Codes will be publicly available at https://github.com/zhu-xlab/PFST.