Miracle-dz/UniFAR-FTUs huggingface dataset:
The Implementation of Demonstration Method to Increase Learning Outcome in Natural Science Lessons

This research is motivated by the low understanding of students' concepts in science learning in grade 2 State Elementary Schools. Researchers provide solutions by applying the demonstration method, a learning method by demonstrating items, events, rules and sequences of carrying out an activity, either directly or through the use of teaching media that are relevant to the subject or material being presented. This type of research is classroom action research. This research has two cycles. Each cycle consists of two sessions and four phases, namely planning, implementation, observation and reflection. The subjects of this study were 2nd grade elementary school students, totaling 21 people. The results of the study concluded that the use of the demonstration method could improve student learning outcomes in science subjects in Grade 2 of State Elementary Schools.
Somatostatin Primes Endothelial Cells for Agonist-Induced Hyperpermeability and Angiogenesis In Vitro

Somatostatin is an inhibitory peptide, which regulates the release of several hormones, and affects neurotransmission and cell proliferation via its five Gi protein-coupled receptors (SST1-5). Although its endocrine regulatory and anti-tumour effects have been thoroughly studied, little is known about its effect on the vascular system. The aim of the present study was to analyse the effects and potential mechanisms of somatostatin on endothelial barrier function. Cultured human umbilical vein endothelial cells (HUVECs) express mainly SST1 and SST5 receptors. Somatostatin did not affect the basal HUVEC permeability, but primed HUVEC monolayers for thrombin-induced hyperpermeability. Western blot data demonstrated that somatostatin activated the phosphoinositide 3-kinases (PI3K)/protein kinase B (Akt) and p42/44 mitogen-activated protein kinase (MAPK) pathways by phosphorylation. The HUVEC barrier destabilizing effects were abrogated by pre-treating HUVECs with mitogen-activated protein kinase kinase/extracellular signal regulated kinase (MEK/ERK), but not the Akt inhibitor. Moreover, somatostatin pre-treatment amplified vascular endothelial growth factor (VEGF)-induced angiogenesis (3D spheroid formation) in HUVECs. In conclusion, the data demonstrate that HUVECs under quiescence conditions express SST1 and SST5 receptors. Moreover, somatostatin primes HUVECs for thrombin-induced hyperpermeability mainly via the activation of MEK/ERK signalling and promotes HUVEC proliferation and angiogenesis in vitro.
Integrating quantitative and qualitative data in mixed methods research: An illustration

Employing a mixed methods approach to research is meant to deliver a comprehensive examination of the phenomenon under study. An integral step in mixed methods research is integrating qualitative and quantitative data. However, published reports rarely detail the process of mixing data from both approaches. Presented here is an illustration of integrating qualitative and quantitative data sets using a convergence table. A review of mixed methods research in LIS is presented, and a reflection on the challenges of integration is shared. As the mixed methods approach increases in LIS research, the example offered here aims to make integration more transparent.
Validity and reliability of a social skills scale among Chilean health sciences students: A cross-sectional study

Currently, there is a growing need to assess social skills in young university students since the use and application of these scales can provide benefits in different situations, such as work, education, clinical and psychological. The objective of the study was to verify the validity and reliability of a social skills scale (SS) for students from three professional health sciences programs in Chile. This information will provide understanding and development of SS among health care team members and between them and patients. A cross-sectional study was designed with 185 Chilean university students (141 males and 44 females). Students were recruited voluntarily from a private university in Chile. Ages ranged from 18 to 30 years old. Goldstein’s SS scale (consisting of six dimensions with a total of 50 questions) was used. It was administered virtually. Validity was verified by means of confirmatory factor analysis and reliability through internal consistency. The factor structure of the model confirmed the 50 questions and six dimensions of the scale. In general, the model reflected satisfactory values. Kaiser-Meyer-Olkin’s (KMO) test for the adequacy (suitability) of the sample was 0.834, chi squared X2= 4344.8 (gl= 1425, p< 0.000). The comparative fit index (CFI) was 0.45, goodness of fit index (GFI) was 0.56, the root mean square error of approximation (RMSEA) was 0.091, and the % of variance 54%. The communalities reflected values from 0.31 to 0.76. In addition, Cronbach’s Alpha values varied from 0.72 to 0.75. In its totality, the scale demonstrated a reliability of 0.75. The SS scale is valid and reliable. It can be used with students from nursing, kinesiology, and speech therapy programs. The results suggest the use and application of the scale with professionals working in higher education.
The Drone Scheduling Problem: A Systematic State-of-the-Art Review

Drones are receiving popularity with time due to their advanced mobility. Although they were initially deployed for military purposes, they now have a wide array of applications in various public and private sectors. Further deployment of drones can promote the global economic recovery from the COVID-19 pandemic. Even though drones offer a number of advantages, they have limited flying time and weight carrying capacity. Effective drone schedules may assist with overcoming such limitations. Drone scheduling is associated with optimization of drone flight paths and may include other features, such as determination of arrival time at each node, utilization of drones, battery capacity considerations, and battery recharging considerations. A number of studies on drone scheduling have been published over the past years. However, there is a lack of a systematic literature survey that provides a holistic overview of the drone scheduling problem, existing tendencies, main research limitations, and future research needs. Therefore, this study conducts an extensive survey of the scientific literature that assessed drone scheduling. The collected studies are grouped into different categories, including general drone scheduling, drone scheduling for delivery of goods, drone scheduling for monitoring, and drone scheduling with recharge considerations. A detailed review of the collected studies is presented for each of the categories. Representative mathematical models are provided for each category of studies, accompanied by a summary of findings, existing gaps in the state-of-the-art, and future research needs. The outcomes of this research are expected to assist the relevant stakeholders with an effective drone schedule design.
Automated image localization to support rapid building reconnaissance in a large‐scale area

Collecting massive amounts of image data is a common way to record the postevent condition of buildings, to be used by engineers and researchers to learn from that event. Key information needed to interpret the image data collected during these reconnaissance missions is the location within the building where each image was taken. However, image localization is difficult in an indoor environment, as GPS is not generally available because of weak or broken signals. To support rapid, seamless data collection during a reconnaissance mission, we develop and validate a fully automated technique to provide robust indoor localization while requiring no prior information about the condition or spatial layout of an indoor environment. The technique is meant for large‐scale data collection across multiple floors within multiple buildings. A systematic method is designed to separate the reconnaissance data into individual buildings and individual floors. Then, for data within each floor, an optimization problem is formulated to automatically overlay the path onto the structural drawings providing robust results, and subsequently, yielding the image locations. The end‐to‐end technique only requires the data collector to wear an additional inexpensive motion camera, thus, it does not add time or effort to the current rapid reconnaissance protocol. As no prior information about the condition or spatial layout of the indoor environment is needed, this technique can be adapted to a large variety of building environments and does not require any type of preparation in the postevent settings. This technique is validated using data collected from several real buildings.
Measurement of Gloss Profile

Gloss distribution of coated paper was measured using a newly developed apparatus which had a 75 degree light source, a 75 degree detector and a scanning X-Y stage. The gloss profile of commercial coated papers was measured with the apparatus. A good correlation between the standard deviation of sheet gloss and the visual rating of the sheet appearance was obtained. The coated paper samples, whose standard deviation of sheet gloss were close to 2%, were evaluated as poor in appearance by most people. The coating profile, the surface latex concentration profile using ultraviolet method, and the sheet gloss profile were compared with coated paper samples in laboratory scale. The high coat weight parts of a coated paper sample showed high surface latex concentration and high gloss, and vice versa. The results obtained suggested the importance of gloss profile as well as surface binder profile for the study of mottling mechanisms.
Down Syndrome-Associated Arthritis (DA): Diagnostic and Management Challenges

Abstract Down syndrome (DS) is one of the most common birth defects in the United States, the most common genomic disorder of intellectual disability, and results from trisomy 21. This chromosome disorder causes an extensive, heterogenous phenotype that results in a broad presentation of symptoms that includes atlantoaxial instability, congenital heart defects, muscle hypotonia, hypothyroidism, hematologic disorders, recurrent infections, and autoimmune diseases. The autoimmune diseases are caused by immune system dysregulation that results in increased pro-inflammatory cytokines, along with other innate and adaptive immune system dysregulation. This is the likely cause of the increased risk of inflammatory arthritis or Down syndrome-associated arthritis (DA) seen in individuals with DS. Most individuals with DA present with polyarticular (five or more joints with arthritis at presentation of disease), rheumatoid factor and anti-nuclear antibody negative disease that is aggressive with bone and joint damage at presentation. There is notable delay in diagnosis of DA as there are no formal guidelines on screening or monitoring for inflammatory arthritis in individuals with DS. Once diagnosed, and despite aggressive therapy with disease modifying antirheumatic drugs, disease burden is high for those with DA. Therapy can also be challenging for those with DA as many require second and third-line disease modifying therapies. Many also struggle with medication toxicity and ineffectiveness that further causes challenges with management and outcomes. The purpose of this current review is to provide an up-to-date summary of the literature related to DA in children and adolescents with focus on presentation, diagnosis, and management considerations, along with current barriers that inhibit optimal care.
Mask-Guided Deformation Adaptive Network for Human Parsing

Due to the challenges of densely compacted body parts, nonrigid clothing items, and severe overlap in crowd scenes, human parsing needs to focus more on multilevel feature representations compared to general scene parsing tasks. Based on this observation, we propose to introduce the auxiliary task of human mask and edge detection to facilitate human parsing. Different from human parsing, which exploits the discriminative features of each category, human mask and edge detection emphasizes the boundaries of semantic parsing regions and the difference between foreground humans and background clutter, which benefits the parsing predictions of crowd scenes and small human parts. Specifically, we extract human mask and edge labels from the human parsing annotations and train a shared encoder with three independent decoders for the three mutually beneficial tasks. Furthermore, the decoder feature maps of the human mask prediction branch are further exploited as attention maps, indicating human regions to facilitate the decoding process of human parsing and human edge detection. In addition to these auxiliary tasks, we further alleviate the problem of deformed clothing items under various human poses by tracking the deformation patterns with the deformable convolution. Extensive experiments show that the proposed method can achieve superior performance against state-of-the-art methods on both single and multiple human parsing datasets. Codes and trained models are available https://github.com/ViktorLiang/MGDAN.
Science literacy promotes energy conservation behaviors in Filipino youth via climate change knowledge efficacy: Evidence from PISA 2018

Abstract Today’s youth will inherit the brunt of climate change. Science literacy plays a critical role in raising future adults who commit to climate change mitigation by reducing daily household energy use. The objective of this study was to examine the mediating role of climate change knowledge efficacy on the positive influence of science literacy on engagement in energy conservation at home among Filipino adolescents. Data from the Program for International Student Assessment 2018 which included 7233 15-year-old high school students from 187 schools across 17 regions in the Philippines was used to address the study’s objective. Results showed that climate change knowledge efficacy fully mediated the positive association between science literacy and household energy conservation among Filipino adolescents. The study discussed the importance of emphasizing environmentalism in science education, parenting, and community programs as a viable and long-term climate change mitigation response.
Convolutional Self-Attention Neural Network for Multi-Step Forecasting of Environmental Image Series

Convolutional long short-term memory (ConvL-STM) is an enhancement of the state-of-the-art long short-term memory (LSTM) model, which has been widely used in forecasting spatiotemporal data from natural environments. ConvLSTM captures local correlations within a small receptive field through a convolutional operator, which is limited to extracting global interactions among latent variables. To further improve the prediction performance, the present paper proposes a novel attention-based ConvLSTM model for multi-step forecasting of environmental image series. Particularly, a convolutional self-attention (CSA) mechanism is developed to highlight the dependencies within hidden features during the regression and prediction processes. To validate the performance of the proposed method, experiments using a benchmark dataset and a real-world environmental dataset are conducted. The experimental results demonstrate the improved accuracy and reliability of the proposed method for multi-step forecasting of environmental image series.
A combined denoising algorithm for roadside LiDAR point clouds under snowy condition

Roadside Light Detection and Ranging (LiDAR) can provide over the horizon perception information for connected vehicles (CV). However, its performance may be affected by the weather, especially in rainy and snowfall weather. To improve all-weather working ability, a combined denoising algorithm is proposed in this paper after analyzing the shortcomings of the existing filters. The filter is composed of crop box filter, ray ground filter, voxel filter, and statistical outlier filter. By combining multiple point clouds filters, the snowfall points are removed and the effectiveness of general filters in complex scenes is verified. The experiment shows that it not only can retain the traffic objects’ features, but also realize denoising on real point clouds data of snowfall weather.
Mapping terrestrial groundwater‐dependent ecosystems in arid Australia using Landsat‐8 time‐series data and singular value decomposition

The spatial extent of terrestrial vegetation types reliant on groundwater in arid Australia is poorly known, largely because they are located in remote areas that are expensive to survey. In previous attempts, the use of traditional remote sensing approaches failed to discriminate vegetation using groundwater from surrounding vegetation. Difficulties in discerning vegetation groundwater use by remote sensing may be exacerbated by the unpredictable rainfall patterns and lack of annual wet and dry seasons common in arid Australia. This study presents a novel approach to mapping terrestrial groundwater‐dependent ecosystems (GDEs) by applying singular value decomposition (SVD) to time‐series of vegetation indices derived from Landsat‐8 data, to isolate the temporal and spatial sources of variation associated with groundwater use. In‐situ data from 442 sites were used to supervise and validate logistic regression models and neural networks, to determine whether sites could be correctly classified as GDEs using components obtained from the SVD. These results were used to produce a probability map of GDE occurrence across a 557 000 ha study area. Overall accuracy of the final classification map was 79%, with 72% of sites correctly identified as GDEs (true positives) and 16% incorrectly classified as GDEs (false positives). The approach is broadly applicable in arid regions globally, and is easily validated if general background knowledge of regional vegetation exists. Globally, and going forward, increased water extraction is expected to severely limit water available for GDEs. Successfully mapping GDEs in arid environments is a critical step towards their sustainable management, and the human and natural systems reliant upon them.
EU-Net: A Novel Semantic Segmentation Architecture for Surface Defect Detection of Mobile Phone Screens

In this paper, a new semantic segmentation algorithm, EU-Net (Efficient U-Net), is proposed to realize surface defect detection of mobile phone screens. Compared with U-Net, the encoder and decoder of EU-Net are modified with EfficientNet-B0 and MBconv Block to enhance the detection efficiency and accuracy. Due to the loss of feature information in the cropping operation, it is removed in our EU-Net to improve the detection accuracy. In addition, conventional image processing techniques are used to enhance the dataset. The experiments are conducted on a dataset collected from a production site of the mobile phone screens to verify the superiority of the proposed algorithm.
Some Short-Length Girth-8 QC-LDPC Codes From Primes of the Form t2 + 1

We propose a simple algebraic construction for girth-8 regular QC-LDPC codes of short lengths, a few hundreds, based on the square matrix from some prime integers of the form <inline-formula> <tex-math notation="LaTeX">$t^{2}+1=P$ </tex-math></inline-formula> and a multiplication table method. We generalize the conventional multiplication table method in a way that the size <inline-formula> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula> of the circular permutation matrix (CPM) can be different from the modulus <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> in the calculation of the exponent matrix. We classify and suggest the parameters <inline-formula> <tex-math notation="LaTeX">$P\leq M\leq T$ </tex-math></inline-formula> with <inline-formula> <tex-math notation="LaTeX">$M=kP$ </tex-math></inline-formula> so that the resulting codes have girth 8. In particular, we prove the existence of a threshold <inline-formula> <tex-math notation="LaTeX">$T_{0}$ </tex-math></inline-formula> so that the resulting code will always have girth 8 if <inline-formula> <tex-math notation="LaTeX">$T > T_{0}$ </tex-math></inline-formula> is used, given that <inline-formula> <tex-math notation="LaTeX">$M=kP$ </tex-math></inline-formula>. Finally, we present various simulation results and theoretical analysis, one of which shows that the proposed codes of length around 250 have an additional coding gain of about 0.4 dB over the 5G NR LDPC codes.
Product form design method driven by design intent

To reduce the cognition difference of product form between users and designers,the design intent in product form design process was divided into design need intent,design encoding intent and design decoding intent,and the design intent model was constructed.A solution algorithm was proposed aiming to meet users' need intent and demonstrate designers' design encoding intent,and reduce decoding and encoding divergence between users and designers.The process of product form design driven by design intent was discussed.Interactive Genetic Algorithms(IGA)was applied to seek an intelligent solution of design intent.The family car face design was taken as an example to verify the effectiveness of the proposed method by researching design need intent acquisition and mapping,design encoding intent expression and design evolution and decoding based on IGA were researched respectively.
Joint Trust Management and Sharing Provisioning in IoV-Based Urban Road Network

Internet of Vehicles (IoV) is a novel technology to enhance the safety, intelligence, and efficiency of traffic systems, where vehicles can exchange critical information with other vehicles, roadside units, pedestrians, and cloud platforms. However, the dynamic network topology, high speed, and exposed communication links inevitably pose security threats to IoV. It is pivotal to establish a trust management and trust-sharing mechanism between vehicles to guarantee the safety of IoV. This paper proposes a distributed trust management scheme to discriminate malicious vehicles utilizing the machine learning technology Random Forest (RF). With the help of the sliding time window technology, the trust degree of vehicles can be comprehensively evaluated through the CART trees according to the current and historical records. To further improve the security of communication processes, we also introduce a lightweight cryptography mechanism. In addition, a trust-sharing mechanism based on path prediction algorithm is proposed to guarantee the consistency of trust information in the network. Finally, extensive simulations are conducted to demonstrate the feasibility and efficiency of the proposed scheme.
A user study with aspect‐based sentiment analysis for similarity of items in content‐based recommendations

Most studies on recommender systems focus on collaborative algorithm approaches over content‐based recommendation due to their better accuracy results. However, the advantage of the latter is that it is more effective and more transparent with user applications. This article proposes WordRecommender, an explainable content‐based algorithm that calculates similarity by semantic proximity. Its preprocessing step involves analyses of movie reviews to obtain aspects, defined as relevant words of high sentimental value. Recommendations are generated by a neighbourhood algorithm that calculates the similarity of films based on the semantic proximity of the aspects ordered by their emotional score. It can also consider a semantic comparison of metadata using the most related aspects from the recommended movie and one enjoyed by the user for the production of textual explanations. The accuracy of the algorithm was competitive with those of other baseline neighbourhood methods, and the semantic data of items can be the source of both representative information and reasoning in recommender systems.