Miracle-dz/UniFAR-FTUs huggingface dataset:
Sensing, allocation and trading of spectral resources in cognitive radio networks

The invention of cognitive radio concept is to overcome the spectral scarcity issues of emerging radio systems. In cognitive radio, the secondary users can either opportunistically access the licensed spectrum in the absence of their primary users, or buy/rent the spectrum access rights from the primary users. For the former case, spectrum sensing and resource allocation are highly essential. While spectrum sensing aims to identify the unused spectrum through detecting the presence of primary users’ signal, resource allocation exploits information obtained from spectrum sensing to schedule the unused spectrum among the secondary users. For the latter case, spectrum trading is emphasized. The problems of spectrum sensing, resource allocation and spectrum trading in cognitive radio networks are focused on this thesis. For spectrum sensing, due to its sensing accuracy, the cooperative spectrum sensing rather than stand-alone spectrum sensing is studied. The challenges arising when secondary users have no a priori knowledge of primary users’ bands are discussed. These challenges are to dynamically detect the primary user’s bands and to reduce the overheads required to facilitate fusion center operation. A dynamic band clustering algorithm that uses K-means clustering technique, and a novel entropy-based decision-fusion technique are proposed to address these challenges, respectively. The proposed decision-fusion technique is shown to be comparable to conventional information-fusion techniques while reducing significantly the bandwidth overheads. For resource allocation problem, two non-cooperative games named Interference Minimization game and Capacity Maximization game which reflect the targets of data radios and voice radios are proposed, respectively. From the extensive simulation, it is shown that these proposed games help improve the average SINRs and capacities of all secondary users.
Persepsi Peserta Didik Terhadap Pembelajaran PJOK Secara Daring pada Masa Pandemi Covid-19

Pandemic covid-19 telah membawa perubahan yang disignifikan terhadap proses pembelajaran. Dari yang belajar secara tatap muka menjadi belajar secara daring. Hal ini berpengaruh terhadap keinginan dan hasil belajar peserta didik. Penelitian ini bertujuan untuk mengetahui persepsi peserta didik terhadap pembelajaran PJOK secara daring pada masa pandemi covid-19. Penelitian ini tergolong dalam jenis penelitian deskriptif kualitatif. Rancangan dalam penelitian ini menggunakan teknik pengelola data skala likert dengan 5 kategori penilain. Metode yang digunakan dalam penelitian mengunakan metode survey. Teknik analisis data yang digunakan dalam penelitian ini menggunakan statistik deskriptif. Data penelitian didapatkan dari kuisioner yang disebarkan secara online melalui google form dengan jumlah responden 144. Berdasarkan hasil analisis data dapat disimpulkan bahwa persepsi peserta didik terhadap pembelajaran PJOK secara daring pada masa pandemi covid-19, berada pada kategori cukup/sedang. Disarankan agar peserta didik lebih baik lagi dalam mengikuti pembelajaran daring yang diberikan oleh guru sehingga dapat meningkatkan hasil belajar yang ingin dicapai.
Tracer‐aided modelling reveals quick runoff generation and young streamflow ages in a tropical rainforest catchment

There is still limited understanding of where stream water originates, their flow paths, how water sources mix, and for how long water transits montane tropical catchments. Here, we used a simple gamma convolution integral model (GM), ensemble hydrograph separation (EHS) and a tracer‐aided model (TAM) to assess runoff generation, mixing processes and water ages in the pristine tropical rainforest Quebrada Grande catchment in Costa Rica. Model simulations are based on a four‐year record (2016–2019) of continuous hydrometric and stable isotope observations. Comparative model tests included multi‐objective calibration (2017–2019) and validation (2016) using stream discharge and isotope data as well as an independent model evaluation using groundwater and soil water isotope data. GM and TAM agreed on the dominance of young water in streamflow that was less than 95 days old for 75% of the study period. The EHS suggested a young water fraction threshold of 12 ± 2 days with a transit time distribution that approximates the best‐fit GM. These short water ages are the result of high annual rainfall even during drier years such as 2019 with 4300 mm/a and consistent quick near‐surface runoff generation with limited mixing. A supra‐regional loss (~55%) of likely older groundwater was detected. The TAM‐based hydrograph separation (streamflow KGE > 0.78, δ2H KGE > 0.90) suggested an average near‐surface water contribution of more than 60% to streamflow emphasizing the dominance of quick flow paths. This tropical rainforest represents one of the quickest streamflow responses of mostly young water of pristine catchments globally.
Hair Cortisol, Perceived Stress, and the Effect of Group Dynamics: A Longitudinal Study of Young Men during Compulsory Military Training in Lithuania

Previous research shows a nonlinear dependency between hair cortisol concentrations and perceived stress levels. This may be due to stress being targeted at the individual level despite it also being a social phenomenon which is often affected by group dynamics. Therefore, the objective of this study was to determine the influence of perceived stress on the hair cortisol level, considering the impact of the variables of group dynamics (interpersonal, task, and norm cohesion). Information was collected on 11 groups of, in total, 112 young men in three phases of time during their compulsory military training (covering nine months in total). The classification and regression tree (C&RT) method was used to predict hair cortisol concentrations in groups. The results show that the variability of the hair cortisol level in young men groups can be explained by perceived stress only when the groups were in formation process (47.7% normalised importance in Model 1) and when the groups were working on their final tasks (37.80% normalised importance in Model 3); meanwhile, the importance of perceived stress in explaining hair cortisol concentrations is low when the group is in a routine period of a group life-span (28.9% normalised importance in Model 2). Interpersonal cohesion (normalised importance 100% in Model 1 and 80.0% in Model 3) and task cohesion (normalised importance 78.6% in Model 2) were the most important predictors in the study area. These results point to the importance of the elements of group dynamics when it comes to explaining the nature of hair cortisol as accumulated stress biomarkers in young men.
The implementation of TPS (think pair share) cooperative learning model aided by Google hangouts in literary expression course

This study aims to determine how the application of the TPS (Think-Pair-Share) type of cooperative learning model assisted by Google Hangout can improve the quality of learning in seventh semester students of the Indonesian Language Education Study Program, FKIP UNIB, in the Literary Expression course. The data analysis technique used in this study was a descriptive method that produced an overview of the extent to which the quality of learning has improved by using the TPS (Think-Pair-Share) type of cooperative learning model assisted by Google Hangouts for students who were the research samples. This classroom action research was carried out in two cycles, and each cycle used the main digital learning room, namely the Google Hangout application. Data collection techniques were carried out through non-test techniques at the beginning and end of cycles I and II using questionnaires and observation sheets. In the first cycle, the results obtained had not reached the expected target. The target required was reached in the second cycle. The learning outcomes of the second cycle of students obtained an A score of 13 people (36%), a B value of 18 people (50%), a C value of 5 people (14%), and a D value of 0 people (0%). These results indicate that the target achievement of 30% had been successfully achieved by applying the TPS learning model assisted by the Google Hangouts.
Relative Importance of Landscape and Climate Factors to the Species Diversity of Plant Growth Forms along an East Asian Archipelago

Previous studies on island biogeography theory have limitations in that they are mostly focused on total plant species and the landscape factors of the islands. Our study was conducted to overcome these limitations by dividing the plants into five growth forms and analyzing climate and landscape factors on inhabited islands, uninhabited islands, and overall. This was achieved using plant data from 578 islands of an archipelago in South Korea. To test the relationship between the species richness of each growth form and environmental factors, we performed ordinary least squares regressions and multi-model inference tests. The results showed that the island area had the largest influence on species richness of all growth forms in overall and uninhabited islands. Moreover, climate factors, in addition to island area, significantly affected species richness of all growth forms on inhabited islands. However, the effect and of isolation-related landscape factors (i.e., distance from the mainland and structural connectivity) were different among growth forms and island categories. Our study reveals that there are differences in the effects of environmental factors on the growth forms of plants among island categories. This suggests that biodiversity management and conservation strategies should be applied separately to different growth forms and islands.
Patient–Prosthesis Mismatch in Contemporary Small-Size Mechanical Prostheses Does Not Impact Survival at 10 Years

Background: The effect of PPM in mechanical prostheses on long-term survival is not well-established. Methods: Patients who received a 21 mm or smaller aortic valve between 2000 and 2011 were retrospectively analyzed (n = 416). Propensity matching was used in order to account for baseline differences in patient subgroups (PPM vs. no PPM; severe PPM vs. no severe PPM). Results: Five- and ten-year survival was 78 ± 3.52% and 64.51 ± 4.51% in patients with PPM, versus 83.3 ± 3.12% and 69.37 ± 4.36% in patients without (p = 0.28) when analyzed at 10.39 ± 5.25 years after the primary procedure. Independent risk factors for impaired survival, after matching, were age, serum creatinine, and severe pulmonary hypertension. Five- and ten-year survival in patients with severe PPM was 73.34 ± 6.01% and 61.76 ± 8.17%, respectively, versus 74.72 ± 5.68% and 67.50 ± 7.09% in those without (p = 0.49), at 8.82 ± 5.17 years after SAVR. Age was the only independent variable that influenced long-term survival when severe PPM was added to the model. Conclusions: PPM or severe PPM does not impact long-term survival up to 10 years in mechanical valve recipients when matching for preoperative variables.
The Dynamic Relationship between Investor Attention and Stock Market Volatility: International Evidence

This paper investigates the role of investor attention in forecasting realized volatility for fourteen international stock markets, by means of Google Trends data, over the sample period January 2004 through November 2021. We devise an augmented Empirical Similarity model that combines three volatility components, defined over different time horizons, using the similarity measure between lagged Google search queries and volatility. Results show that investor attention positively affects future volatility in the short-run. The effect of investor attention is likely to reverse in the long-run, consistently with the price pressure hypothesis. The proposed model demonstrates important gains in terms of volatility forecast accuracy and outperforms highly competitive models.
Beneficial Effects of Young Coconut Juice on Increasing Skin Thickness, Enhancing Skin Whitening, and Reducing Skin Wrinkles in Ovariectomized Rats

It has been previously demonstrated in light microscopic and immunohistochemical studies that ovariectomized rats receiving young coconut juice at 100 mL/kg BW showed much better wound healing and improved skin complexion. Nevertheless, it was found that young coconut juice at 100 mL/kg BW/day caused unfavorable side effects, such as glycogen deposition in the liver. Therefore, in the current study, 3 lower doses (10, 20, and 40 mL/kg BW/day) were optimized, and the ultrastructure was further investigated. Compared to normal rats, all the parameters regarding skin changes, including epidermal and dermal thickness, the number of hair follicles, the diameters of collagen fibrils, perimeters, and nuclei of fibroblast and keratinocyte cells, and ultrastructural changes in keratinocyte and fibroblast cells were significantly reduced in ovariectomized rats. Those parameters in the ovariectomized rats were restored to normal by injecting estradiol benzoate or by feeding young coconut juice to the rats, where the effect was found to be dose-related but not in the case of all the parameters. In most cases, a dose of young coconut juiceof 40 mL/kg BW/day was the optimal dose. The results suggest that young coconut juice may be as effective as estradiol benzoate in reducing skin atrophy/aging, probably as a selective estrogen receptor moderator.
The Deployment and Use of Virtual Training Simulations: What Does it Take to Serve the Needs of Majority Of Its Users?

The potential and the benefits that virtual training simulations have in supplementing the training needs of the military is fairly well recognized, yet we still do not see the evidence of their largescale deployment. The way these systems are currently integrated in training fulfills the needs of two groups of users only - the innovators and early adopters - but it fails to fully serve the largest group of user population - the early and late majority. We elaborate the requirements for most effective large-scale deployments - the factors that can influence the diffusion of this technological innovation and the adoption rate. The results of diffusion research were used to review and comment the practices, trends and common understandings related to the use of simulations, all collected in our extensive user studies in this domain.
Effects of polypropylene fibre on mechanical properties of fat clay stabilised with sludge ash of wood and paper mill

This study aims to evaluate the effect of sludge ash of wood and paper (SAWP) mill and polypropylene fibres on the mechanical properties of fat clay. For this purpose, a total of 122 unconfined compressive strength tests were conducted. X-ray diffraction analyses indicated that the main cementitious component induced by the addition of SAWP was calcite, which is a key factor for the strength enhancement in the stabilised specimens. The results showed that the addition of fibres to the SAWP-stabilised specimens increases their ductility, which can be investigated by energy absorption capacity, brittle index, and mode of failure. The results also illustrated that the fibres significantly improve the durability of the stabilised specimens. The use of fibres, while preventing the leachability of cementitious components and solid particles of the soil, prevents the development and spread of cracks in the specimen. Finally, the leachability of the heavy metals in the SAWP-stabilised specimens was lower than the regulated values. Hence, this study corroborates the effectiveness of fibre-reinforced SAWP-stabilised specimen as a durable and sustainable composite material.
Needs Assessment for Facilitators in Teaching of STEM Subjects in Under-Resourced Rural Schools: Case of Lupane and Hwange Districts in Zimbabwe

Needs Assessment for Facilitators in Teaching Subjects in ABSTRACT The aim of the study was to determine the various needs facilitators and schools lack for effective teaching of science, technology, engineering, and mathematics (STEM) subjects, in both the primary and secondary under-resourced rural schools. This survey was conducted from February 2019 to March 2019, in Lupane and Hwange Districts. A multi-stage sampling method was used for collecting data. Focus group discussions were also used in collecting data and were conducted prior to issuance of questionnaires for completion by the participants. In analyzing data, odds ratio (OR) were calculated to compare the relative odds between outcomes and variables of interest. The survey found out that female facilitators were lagging behind in the uptake and training of teaching of STEM subjects, which could be attributed to family responsibilities. It was established that some needs such as lesson delivery skills, research skills and capacitation were highly needed in primary schools as compared to secondary schools. While other needs identified were equally needed in both primary and secondary schools. These include but are not limited to conducting team teaching with other facilitators, linking topics and lessons across classes and lessons, application skills and STEM activities. It was highly recommended that primary and secondary school facilitators be funded to attend capacitation platforms in teaching of STEM subjects for effective teaching of STEM in rural schools and using locally available resources, as limited resources is the order of the day in rural schools. Schools must be equipped with tools and structures such as laboratories. If schools cannot build laboratories, it was recommended that mobile laboratories be established to improve teaching of STEM subjects in under-resourced rural schools. One mobile laboratory can service many schools in the district and improve the pass rate of STEM subjects.
Theoretical Advances in Service Breakdown Prevention and Recovery: Rich Service Enactment to Improve Server-Client Interactions and Outcomes

This study investigates service breakdowns and describes interventions, including simulations of learner-created service interactions. Constructing and enacting these interactions help in enabling agile, effective server responses. The research investigated the effectiveness of training using live role-playing in dealing with negative turns and solving ad hoc dilemmas in real client-server encounters, thus advancing service excellence and service recovery theory and practice. Seven tertiary institutions cross five nations engaged in training students in client-service performance in simulated contexts. Findings support the positive impact of the proposed iterative competency development plan on impromptu responses, higher-order thinking and situational memory in trainees/servers. The development of Rich Service Enactment Theory (RiSET) extends three perspectives. First, most service training focuses restrictively on what-to-do, excluding necessary training on what-not-to-do. Second, practicing in stimulating contexts with peer feedback helps to prevent repeated mistakes and disastrous service failures. Third, the RiSET model provides a new framework for educators/trainers to develop models that prepare trainees for dealing with unknown, possibly high-risk encounters. The study focuses on surfacing server knowledge and implementing server training to prevent or reduce dramatic turns during client-server encounters, rather than empirically testing a well-formed theory. The study offers empirical researchers’ configurations of conditions for contextual experimentation.
Effects of Climate Change on Vegetation Growth in the Yellow River Basin from 2000 to 2019

A changing climate has been posing significant impacts on vegetation growth, especially in the Yellow River Basin (YRB) where agriculture and ecosystems are extremely vulnerable. In this study, the data for normalized difference vegetation index (NDVI) obtained from moderate-resolution imaging spectroradiometer (MODIS) sensors and climate data (precipitation and temperature) derived from the national meteorological stations were employed to examine the spatiotemporal differences in vegetation growth and its reaction to climate changes in the YRB from 2000‒2019, using several sophisticated statistical methods. The results showed that both NDVI and climatic variables exhibited overall increasing trends during this period, and positive correlations at different significant levels were found between temperature/precipitation and NDVI. Furthermore, NDVI in spring had the strongest response to temperature/precipitation, and the correlation coefficient of NDVI with temperature and precipitation was 0.485 and 0.726, respectively. However, an opposite situation was detected in autumn (September to November) since NDVIs exhibited the weakest responses to temperatures/precipitation, and the NDVI’s correlation with both temperature and precipitation was 0.13. This indicated that, compared to other seasons, increasing the temperature and precipitation has the most significant effect on NDVI in spring (March to May). Except for a few places in the northern, southern, and southwestern regions of the YRB, NDVI was positively correlated with precipitation in most areas. There was an inverse relationship between NDVI and temperature in most parts of the central YRB, especially in summer (June to August) and growing season (May to September); however, there was a positive correlation in most areas of the YRB in spring. Finally, continuous attention must be given to the influence of other factors in the YRB.
An Efficient Deep Unsupervised Domain Adaptation for Unknown Malware Detection

As an innovative way of communicating information, the Internet has become an indispensable part of our lives. However, it also facilitates a more widespread attack of malware. With the assistance of modern cryptanalysis, emerging malware having symmetric properties, such as encryption and decryption, pack and unpack, presents new challenges to effective malware detection. Currently, numerous malware detection approaches are based on supervised learning. The biggest challenge is that the existing systems rely on a large amount of labeled data, which is usually difficult to gain. Moreover, since the newly emerging malware has a different data distribution from the original training samples, the detection performance of these systems will degrade along with the emergence of new malware. To solve these problems, we propose an Unsupervised Domain Adaptation (UDA)-based malware detection method by jointly aligning the distribution of known and unknown malware. Firstly, the distribution divergence between the source and target domain is minimized with the help of symmetric adversarial learning to learn shared feature representations. Secondly, to further obtain semantic information of unlabeled target domain data, this paper reduces the class-level distribution divergence by aligning the class center of labeled source and pseudo-labeled target domain data. Finally, we mainly use a residual network with a self-attention mechanism to extract more accurate feature information. A series of experiments are performed on two public datasets. Experimental results illustrate that the proposed approach outperforms the existing detection methods with an accuracy of 95.63% and 95.04% in detecting unknown malware on two datasets, respectively.