Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of Over-utilisation of Electronic Gadget on Student Health: An Appraisal

: It is known to all electronic gadgets like mobile phones, laptops, desktops and tabs have become an integral part of our daily lives. With the passage of time, population using all these electronic gadgets are rising at a rapid rate. Moreover, the use of these devices has become very popular among the school and college students. Since battle against COVID-19 is still going on, educational institutions are still closed and students have to pursue their academic career by means of online education. As a result, the utilisation of these electronic devices has become inevitable. However, students are using these devices not only for attending their online classes but also for several other purposes to enjoy their leisure hours. So, the degree of exposure to these devices have increased to a marked extent which is having a significant effect on the physical and mental health of the students. The study revealed that most of the students are suffering from problems like eye problems and loss of concentration. Out of all the problems, eye problems and hearing problems turned out to be the dominant physical problems because of over-utilisation of electronic gadgets.
A dataset of lake-catchment characteristics for the Tibetan Plateau

Abstract. The management and conservation of lakes should be conducted in the context of catchments because lakes collect water and materials from their upstream catchments. Thus, the datasets of catchment-level characteristics are essential for limnology studies. Lakes are widely spread on the Tibetan Plateau (TP), with a total lake area exceeding 50 000 km2, accounting for more than half of the total lake area in China. However, there has been no dataset of lake-catchment characteristics in this region to date. This study constructed the first dataset of lake-catchment characteristics for 1525 lakes with areas from 0.2 to 4503 km2 on the TP. Considering that large lakes block the transport of materials from upstream to downstream, lake catchments are delineated in two ways: the full catchment, which refers to the full upstream-contributing area of each lake, and the inter-lake catchments, which are obtained by excluding the contributing areas of upstream lakes larger than 0.2 km2 from the full catchment. There are six categories (i.e., lake body, topography, climate, land cover/use, soil and geology, and anthropogenic activity) and a total of 721 attributes in the dataset. Besides multi-year average attributes, the time series of 16 hydrological and meteorological variables are extracted, which can be used to drive or validate lumped hydrological models and machine learning models for hydrological simulation. The dataset contains fundamental information for analyzing the impact of catchment-level characteristics on lake properties, which on the one hand, can deepen our understanding of the drivers of lake environment change, and on the other hand can be used to predict the water and sediment properties in unsampled lakes based on limited samples. This provides exciting opportunities for lake studies in a spatially explicit context and promotes the development of landscape limnology on the TP. The dataset of lake-catchment characteristics for the Tibetan Plateau (LCC-TP v1.0) is accessible at the National Tibetan Plateau/Third Pole Environment Data Center (https://doi.org/10.11888/Terre.tpdc.272026, Liu, 2022).
The INNOMATTS: A Model of Mathematics Teacher Training Management

10.12973/tused.10259a) ABSTRACT The purpose of this paper is to analyze the impact of the INNOMATTS (Innovative Mathematics Teaching Study) Model on content knowledge and impact on pedagogical content knowledge related to the role of teacher. The research design is an adaptation of research and development research design. Data and information were obtained from Semarang Junior High School (JHS) Mathematics Teachers as trainees. Used data collection tool is questionnaire and test. The data were analyzed descriptively narrative and quantitative calculation. The results of the study and discussion are as follows: (i) In relation to the perception, only 27% of the teachers who think that the government training programs that have been followed so far there is a post-training follow-up program; 60% of the teachers found that they only participated in training if there were programs from the government; (ii) The effectiveness of the overall training model from before to after the model implementation of teacher competence has increased. the magnitude of the normalized average gain effectiveness before and after the model implementation is 0.62 (62%) in the moderate category; while the average effectiveness of the overall model characteristic is 75.35 is included in either category. Taking into account the effectiveness of the empirical model of both process and product as a whole, the overall effectiveness of pretest and posttest, as well as the results of the RPP assessment and its implementation, it can be concluded that the implementation of the model contributes to the improvement of professional competence of JHS Mathematics teachers in Semarang
Design of Smart Switched-beam RFID Reader Based on DSP

The reader system of the UHF RFID is based on AS3992. And its read-distance is about 20m. The hardware’s design and software’s programming are introduced in detail. The design of the hardware is composed of the MCU (TMS320C5509A) circuit, the RF front-end circuit, the power divider and the phase shifter circuit. The programming of the software contains the ISO/IEC18000-6C protocol, the smart switched-beam algorithm, the power control algorithm, and the communication between the microcontroller and the PC through UART. The experimental results show that the reader has many features such as fast recognition speed, long read-distance, large coverage, anti-interference. The system can operate stably, so it has good application prospect.
Evolution of Amazonian biodiversity

The Science Panel for the Amazon is an unprecedented initiative convened under the auspices of the United Nations Sustainable Development Solutions Network (SDSN). The SPA is composed of over 200 preeminent scientists and researchers from the eight Amazonian countries, French Guiana, and global partners. These experts came together to debate, analyze, and assemble the accumulated knowledge of the scientific community, Indigenous peoples, and other stakeholders that live and work in the Amazon. The Panel is inspired by the Leticia Pact for the Amazon. This is a first-of-its-kind Report which provides a comprehensive, objective, open, transparent, systematic, and rigorous scie ntific assessment of the state of the Amazon’s ecosystems, current trends, and their implications for the long-term well-being of the region, as well as opportunities and policy relevant options for conservation and sustainable development. Abstract The Amazon constitutes the greatest concentration of biodiversity on Earth, with >10% of the world’s described species compressed into only about 0.5% the Earth’s total surface area. This immense diversity of strategic habitat conservation plans that will allow continuity of the evolutionary and ecological processes taking place across the Amazon basin, now and into the future.
CLASSIFICATION OF TOMATO DISEASES USING ENSEMBLE LEARNING

A Plant disease is any dysfunction of a plant, caused by living organisms, which affects the quality and quantity of yield. These symptoms are visually shown on the plant leaves. This paper discusses classification of Tomato diseases such as Late Blight, Septoria Leaf Spot and Yellow leaf curl virus while distinguishing the healthy leaf at the same time. An experimental sample size of 1817 was considered in conducting this study. This work differentiates diseased tomato leaf images with healthy leaf images. The classifiers Random Forest, Multilayer Perceptron Neural Network and Support Vector Machines were trained and got a prediction accuracy of 88.74%, 89.84%, and 92.86% respectively in classifying diseases. Then, the prediction results of Random Forest, Multilayer Perceptron and Support Vector Machines were combined using Soft Voting classifier and obtained a highest accuracy of 93.13% in classifying tomato diseases.
Determinants of Financial Performance of SME; In Case of Gamo Zone, Arba Minch Town, Southern Ethiopia;

studies ABSTRACT Financial reporting system of an entity is essential to ensure that the economic resources of the SME are used in efficiently and effectively in pursuit its goals. Globally, the most successful companies use financial records as a basis for performance of SMEs. The study aims to identify to identify the determinants of financial performances of SMEs in Gamo Zone; Arba Minch Town. To achieve the “general and specific objectives” this study were used descriptive research design and quantitative research approach. The primary sources of data were collected from SME who were engaged on service, manufacturing and merchandising business. The sampling techniques were used for this study is stratified sampling technique. However, the sample will be selected from 92 SMEs members who were engaged service, manufacturing and merchandising business. For data analysis, multiple liner regression model and descriptive statistics including mean, std. deviation, frequency and percentages were used. Finally, these studies conclude that financial reporting practice, financial management practice, recordkeeping and inventory management systems are positively or significant relationship with financial performances of SMEs. Hence, to increase the financial performances of SMEs, it is suggests the SMEs supervisor, SMEs owners and Government bodiesto improve the knowledge and awareness of SMEs related with financial management, recordkeeping, inventory control and preparation of financial statement. The concerned bodies to solve financial factors by provide chances to get access to finance, technology, skilled man power, marketing areas and necessary infrastructure.
The Role of Nanotechnology in the Fight against COVID-19

COVID-19 has lately emerged as one of the most difficult pandemics of the twentieth century, with lethal consequences and a high rate of replication. It emphasizes the critical importance of developing effective vaccines to prevent virus infection, early and rapid diagnosis using high sensitivity and selectivity diagnostic kits, and effective antiviral and protective therapeutics to reduce and eliminate viral load and tissue damage-related side effects. As a result, non-toxic antiviral nanoparticles (NPs) are being developed for therapeutic use in the prevention and treatment of COVID-19. Nanoparticles have shown considerable promise in the development of nano vaccines to combat viral diseases. In this paper, we look at the potential of nanoparticles as a medicine or as a platform for drug and vaccine repurposing and development. Meanwhile, sophisticated virus detection methodologies based on NPs will be detailed, with the goal of inspiring scientists to develop cost-effective Nano platforms for prevention, diagnosis, and therapy.
The Non-equivalence of Language-Value between Chinese and American Cultures Based on the Sapir-Whorf Hypothesis

The Sapir-Whorf Hypothesis advocates the ontology of language, arguing that language affects and even determines human thinking to some extent. In view of the fact that human thinking is abstract and intangible, and that values are the soul of the way of thinking, this paper compares the three sets of non-equivalent phenomena between Chinese and American languages and values. As a result, it can be exemplified that language cannot determine or influence values to a certain extent, that is, language cannot determine or influence people's thinking to a certain extent, thus revealing the irrationality of the Sapir-Whorf Hypothesis. Through in-depth analysis of the differences between Chinese and American values as well as language characteristics, the cultural concepts behind language can be more deeply understood, which is conducive to improving the awareness of cross-cultural communication and enhancing cross-cultural communication ability; on the other hand, the accuracy and authenticity of language selection can also be improved so as to ensure the reliability in the translation practice.
Quantities with Prices: Price-Responsive Allowance Supply in Environmental Markets

Environmental policy with uncertainty is often posed as a choice between price and quantity instruments. Paradoxically, this economic advice employs architecture derived from a first-best global framework that applies imperfectly to the partial-equilibrium, multifaceted regulatory policy setting where it is applied. This paper evaluates instrument design and the evolution of environmental pricing in this sequential policy environment. Quantity “cap” instruments are more often used in practice. Recently, however, automatically adjusting emissions allowance supply schedules have been emerging in existing trading programs. We propose a conceptual framework for a price-responsive supply schedule and use simulation modeling and laboratory experiments to explore its performance and design, including its application in a specific regional market. A price-responsive supply schedule offers efficiency advantanges over either a price or a quantity instrument and preserves the roles for technology and energy policies that are expected to lower costs over time.
Highly supercooled riming and unusual triple-frequency radar signatures over Antarctica

Riming of ice crystals by supercooled water droplets is an efﬁcient ice growth process, but its basic properties are still poorly known. While it has been shown to contribute signiﬁcantly to surface precipitation at mid-latitudes, little is known about its occurrence at high latitudes. In Antarctica, two competing effects can inﬂuence the occurrence of riming: the scarcity of supercooled liquid water clouds due to the extremely low tropospheric temperatures and the low aerosol concentration, which may lead to the formation of fewer and larger supercooled drops potentially resulting in an enhanced riming efﬁciency. 5 In this work, by exploiting the deployment of an unprecedented number of multi-wavelength active and passive remote sensing systems (including triple-frequency radar measurements) in West Antarctica, during the Atmospheric Radiation Measurements West Antarctic Radiation Experiment (AWARE) ﬁeld campaign, we evaluate the importance of riming incidence in Antarctica and ﬁnd that riming occurs at much lower temperatures compared to the mid-latitudes. We then focus on a case study featuring a persistent layer of unexpectedly pronounced triple-frequency radar signatures 10 but only a relatively modest amount of supercooled liquid water. In-depth analysis of the radar observations suggests that such signatures can only be explained by the combined effects of moderately rimed aggregates or similarly shaped ﬂorid polycrystals and a narrow particle size distribution (PSD). Simulations of this case study performed with a 1D bin model indicate that similar triple frequency radar observations can be reproduced when narrow PSDs are simulated. Such narrow PSDs can in turn be explained by two key factors: (i) the presence of a shallow homogeneous droplet or humidiﬁed aerosol 15 freezing layer aloft seeding an underlying supercooled liquid layer, and (ii) the absence of turbulent mixing throughout a stable polar atmosphere that sustains narrow PSDs, as hydrometeors grow from the nucleation region aloft to several millimeter ice particles, by vapor deposition and then riming. several state-of-the-art microphysics and scattering models (EM-MIC models) are used. The combination of the triple-frequency radar reﬂectivities, the differential spectral width and a proxy of the ice particles fall velocities derived from the Doppler spectra allows to constrain the ice particles microphysical properties. Results suggest that the fall velocity associated recent rimed aggregates EM-MIC models is too large, and a novel potentially more realistic EM-MIC model is therefore proposed. Even if a non-negligible uncertainty remains on the size of the retrieved ice particles, results indicate that the observed DWR signatures can only be explained by the combined effects of moderately rimed aggregates or similarly shaped polycrystals and a narrow particle size distribution (PSD). More studies are needed to validate the retrieval algorithm the algorithm results and/or in-situ
Experimental method for determining removal efficiency of house dust by mechanical ventilation

Biological contamination has recently become an important issue in the residential indoor environment. In fact, one of the leading causes of allergic diseases is the presence of mold and mites in house dust that accumulates on the floors, near the breathing zone for infants and toddlers. In this research, experimental studies were carried out in order to examine particle removal efficiency in a room with two ventilation systems: a ceiling exhaust system and a slit exhaust system. The results indicated that there was no clear relationship between removal efficiency and two different outlet locations. It was found that the use of fluorescent particles to simulate house dust was effective for determining the removal efficiency of ventilation systems.
AGCN: Adversarial Graph Convolutional Network for 3D Point Cloud Segmentation

3D point cloud segmentation provides a high-level semantic understanding of object structure that is valuable in applications such as medicine, robotics and self-driving. In this paper, we propose an Adversarial Graph Convolutional Network for 3D point cloud segmentation. Many current networks encounter problems such as low segmentation accuracy and high complexities due to their crude network architectures and local feature aggregation methods. To overcome these problems, we propose a) a graph convolutional network (GCN) in an adversarial learning scheme where a discriminator network provides a segmentation network with informative information to improve segmentation accuracy and b) a graph convolution, GeoEdgeConv, as a means of local feature aggregation to improve segmentation accuracy and space and time complexities. By using an embedding L 2 loss as an adversarial loss, the proposed network is learned to reduce noisy labels by enforcing the consistency between neighbouring labels. Preserving geometric structures over convolution layers by using both point and relative position features, GeoEdgeConv helps learn ﬁne details of complex structures, and thus improves segmentation accuracy in boundaries and reduces label noise inside a class without increased computational complexity. Experiments on ShapeNet Part demonstrate that our model outperforms the state-of-the-art (SOTA) with lower complexity and it has strong prospects in applications requiring low power but high segmentation performance.
Augmented education within a physical space Augmented education within a physical space

This research aims to explore how to enhance student engagement in higher education institutions (HEIs) using a novel conversational system (chatbots). The study applies a design science research (DSR) methodology and is executed in three iterations: persona elicitation, survey and student engagement factor models (SEFMs), and chatbots interactions analysis. In the first iteration, two k-means clustering analyses are applied to student data, including engagement on campus and student interaction with a virtual learning environment (VLE). The first analysis produces four different types of students based on their engagement and performance data, while the second analysis produces two clusters based on the students’ interactions with a VLE (in this case, Blackboard). The second iteration will produce SEFMs, which will include the factors that affect student engagement, confirmed using structural equation modelling (SEM). Finally, the third iteration will produce effective and usable chatbots that enhance student engagement. The pragmatic findings from this study will make three contributions to the current literature. Firstly, machine learning is used to build data-driven personas using k-means clustering. Secondly, a persona template is designed for university students, which supports the construction of data-driven personas. Thirdly, SEFMs will be built. Future iterations will build tailored interaction models for these personas and evaluate them using chatbots technology.
Restoration priorities and benefits within landscapes and catchments and across the Amazon basin in the Amazon

The Science Panel for the Amazon is an unprecedented initiative convened under the auspices of the United Nations Sustainable Development Solutions Network (SDSN). The SPA is composed of over 200 preeminent scientists and researchers from the eight Amazonian countries, French Guiana, and global partners. These experts came together to debate, analyze, and assemble the accumulated knowledge of the scientific community, Indigenous peoples, and other stakeholders that live and work in the Amazon. The Panel is inspired by the Leticia Pact for the Amazon. This is a first-of-its-kind Report which provides a comprehensive, objective, open, transparent, systematic, and rigorous scienti fic assessment of the state of the Amazon’s ecosystems, current trends, and their implications for the long-term well-being of the region, as well as opportunities and policy relevant options for conservation and sustainable development. Restoration can be applied in many different Amazonian contexts but will be most effective at leveraging environmental and social benefits when it is prioritized across the Amazon Basin and within landscapes and catchments. Here we outline the considerations that are most relevant for planning and scaling restoration.
Impact of North Brazil Current rings on air-sea CO 2 flux variability in winter 2020

The North Brazil Current (NBC) flows northward across the Equator, passes the mouth of the Amazon River, and forms large oceanic eddies near 8°N. We investigate the processes driving the variability of air-sea CO 2 fluxes at different scales in early 2020 in the region [50°W-59°W – 5°N-16°N]. This region is a pathway between the equatorial and North 15 Atlantic Ocean and was surveyed during the EUREC 4 A-OA/ATOMIC campaign. In-situ surface fugacity of CO 2 (fCO 2 ), salinity and temperature combined with maps of satellite salinity, chlorophyll-a and temperature highlight contrasting properties in the region. In February 2020, the area is a CO 2 sink (-1.7 TgC.month -1 ), previously underestimated by a factor 10. The NBC rings transport saline and high fCO 2 water indicative of their equatorial origins and are a small source of CO 2 at regional scale. Their main impact on the variability of biogeochemical parameters is through the filaments they entrain into 20 the open ocean. During the campaign, a nutrient rich freshwater plume from the Amazon River is entrained from the shelf up to 12°N and caused a phytoplankton bloom leading to a significant carbon drawdown (~20 % of the total sink). On the other hand, saltier filaments of shelf water rich in detrital material act as strong local sources of CO 2 . Spatial distribution of fCO 2 is therefore strongly influenced by ocean dynamics south of 12°N. The less variable North Atlantic subtropical water extends from Barbados northward. They represent ~60 % of the total sink due to their lower temperature associated with winter cooling 25 and strong winds.
Responsibility for Authoritative DNSSEC Mistakes

DNS Security Extensions (DNSSEC) is now entering widespread deployment. However, domain signing tools and processes are not yet as mature and reliable as is the case for non-DNSSEC-related domain administration tools and processes. Authoritative DNS operators should focus on improving these processes and establishing a high level of quality in their work.