Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Student Attitudes and Academic Performance in Introductory Biology Using a Project-Based Learning Community

Students majoring in non-STEM fields often identify introductory biology courses as irrelevant and overly rigorous. Resistance to enroll in a required general education science course, coupled with negative attitudes toward the subject, can adversely affect the academic performance of students; this can especially be present in students from under-represented minority groups. ABSTRACT Students majoring in non-STEM fields often identify introductory biology courses as irrelevant and overly rigorous. Resistance to enroll in a required general education science course, coupled with negative attitudes toward the subject, can adversely affect the academic performance of students; this can especially be present in students from under-represented minority groups. Therefore, instructors have to intentionally design a curriculum that overcomes these factors as they educate non-major students enrolled in introductory biology. BioArt, a learning community, was formed between introductory biology and introduction to graphic design courses to improve the attitudes and academic performance of students in the biology course at a Historically Black College and University (HBCU). The BioArt model incorporated a common theme, project-based learning, and opportunities for experiential learning. To measure outcomes, traditional examinations, non-traditional assessments, failure/withdraw rates, and student attitudes were evaluated. Using this model, introductory biology became less intimidating, more relevant, and improved academic success among freshman minority students. Thus, the BioArt model can be utilized as an intervention at different institutions of higher learning.
High-efficiency generation of far-field spin-polarized wavefronts via designer surface wave metasurfaces

Abstract Achieving a pre-designed scattering pattern from an ultra-compact platform is highly desired for on-chip integration optics, but conventional techniques suffer from the limitations of bulky size, wavelength-scale modulation and low efficiency. Here, we propose a new strategy to efficiently generate arbitrary spin-polarized scattering far-field patterns from surface-wave (SW) excitations on a designer Pancharatnam–Berry (PB) metasurface. We find that a PB meta-atom serves as a subwavelength scatter to decouple impinging SW to a spin-polarized propagating wave (PW) with tailored amplitude and phase, and thus interference among PWs generated by scatterings at different PB meta-atoms can generate a tailored far-field pattern. As a proof of concept, we design and fabricate a series of PB metasurfaces in the microwave regime and experimentally demonstrate that they can generate desired radiation patterns within a broad frequency band, including unidirectional radiation, line/point focusing, vortex beam and hologram. These findings may stimulate important applications in on-chip integrated photonics.
The influence of previous experiences on participant performance during maritime simulation testing

ABSTRACT Motion simulation platforms are often used to study the effects of moving environments on the postural stability of maritime workers. During motion simulation studies, the effects of previous experience are not often considered when recruiting control participants. Previous experience in motion simulation studies may violate assumptions of novice participants, and previous experiences in activities that regularly challenge balance may also influence performance. Thus, the purpose of this study was to investigate the effects of previous participation in motion simulation studies or previous skill training (specifically dance) on postural stability and lower limb neuromuscular activity in a simulated ocean environment. Thirty-seven participants were recruited; 12 participants with previous motion bed experience, 13 dancers, and 12 control participants. Results indicated that both motion bed experience and dance training had significantly better postural stability performance and decreased lower limb muscle activity compared to truly inexperienced control subjects during motion simulation testing.
Insular activation and functional connectivity in firefighters with post-traumatic stress disorder

Background Firefighters are frequently exposed to stressful situations and are at high risk of developing post-traumatic stress disorder (PTSD). Hyperresponsiveness to threatening and emotional stimuli and diminishment of executive control have been suggested as manifestations of PTSD. Aims To examine brain activation in firefighters with PTSD by conducting an executive control-related behavioural task with trauma-related interferences. Method Twelve firefighters with PTSD and 14 healthy firefighters underwent functional magnetic resonance imaging (fMRI) while performing a Stroop match-to-sample task using trauma-related photographic stimuli. Seed-based functional connectivity analysis was conducted using regions identified in fMRI contrast analysis. Results Compared with the controls, the participants with PTSD had longer reaction times when the trauma-related interferences were presented. They showed significantly stronger brain activation to interfering trauma-related stimuli in the left insula, and had weaker insular functional connectivity in the supplementary motor area and the anterior cingulate cortex than the controls. They also showed a significant correlation between left insula–supplementary motor area connectivity strength and the hyperarousal subscale of the Clinician-Administered PTSD Scale. Conclusions Our findings indicate that trauma-related stimuli elicit excessive brain activation in the left insula among firefighters with PTSD. Firefighters with PTSD also appear to have weak left insular functional connectivity with executive control-related brain regions. This aberrant insular activation and functional connectivity could be related to the development and maintenance of PTSD symptoms in firefighters.
The performance of mid-upper arm circumference for identifying children and adolescents with overweight and obesity: a systematic review and meta-analysis

Abstract Objective: This study aimed to synthesise the existing evidence on the performance of mid-upper arm circumference (MUAC) to identify children and adolescents with overweight and obesity. Design: Systematic review and meta-analysis. Setting: We searched PubMed, EMBASE, SCOPUS, Cochrane Library, Web of Science, CINAHL and Google scholar databases from their inception to December 10, 2021, for relevant studies. There were no restrictions regarding the language of publication. Studies reporting measures for the diagnostic performance of MUAC compared with a reference standard for diagnosing overweight and obesity in children and adolescents aged 2–19 years were included. Participants: A total of 54 381 children and adolescents from twenty-one studies were reviewed; ten studies contributed to meta-analyses. Results: In boys, MUAC showed a pooled AUC of 0·92 (95 % CI 0·89, 0·94), sensitivity of 84·4 (95 % CI 84·6, 90·8) and a specificity of 86·0 (95 % CI 79·2, 90·8), when compared against BMI z-score, defined overweight and obesity. As for girls, MUAC showed a pooled AUC of 0·93 (95 % CI 0·90, 0·95), sensitivity of 86·4 (95 % CI 79·8, 91·0), specificity of 86·6 (95 % CI 82·2, 90·1) when compared against overweight and obesity defined using BMI z-scores. Conclusion: In comparison with BMI, MUAC has an excellent performance to identify overweight and obesity in children and adolescents. However, no sufficient evidence on the performance of MUAC compared with gold standard measures of adiposity. Future research should compare performance of MUAC to the ‘golden standard’ measure of excess adiposity.
Study of a rough-wall turbulent boundary layer under pressure gradient

Abstract The behaviour of a fully rough-wall turbulent boundary layer subjected to different pressure gradients is investigated for different Reynolds numbers using hot-wire measurements. Mean velocity and velocity root-mean-square measurements indicate that the boundary layer remains in a self-preserving state regardless of the pressure gradient. However, different pressure gradients lead to different self-preservation states, as suggested by the lack of collapse of the velocity profile between the pressure gradient cases. The results also indicate that the roughness effect is more important than the pressure gradient; particularly, the closer the wall, the more dominant the roughness effect over the pressure gradient effect on the boundary layer. Finally, both spectral and proper orthogonal decomposition analyses applied to the hot-wire measurements indicate that the pressure gradient impacts predominantly the large-scale motion.
Reactive scalars in incompressible turbulence with strongly out of equilibrium chemistry

Abstract We study the statistical properties of scalar fields undergoing reversible chemical reactions in a turbulent environment by means of numerical simulations. To produce strong chemical fluctuations in a wide region of the domain, an original flow configuration has been proposed, where the species are supplied from buffer boundaries with adjustable thickness, while the flow is developed homogeneous and isotropic turbulence in a periodic domain. With the presence of the mean scalar gradient in the bulk region, the strength of turbulent advection is comparable with the chemical source, which is quantified by the Damköhler number. Our analysis focuses on the global and spatial properties of the reactive scalars in their statistically steady regime. We show how for the case of a second-order reaction such features can be connected to the properties of a non-reactive scalar field advected in the same system. Analytical predictions of the scalar moments in the fast reaction regime agree satisfactorily with the direct numerical simulation results. In comparison with the existing results of the isotropic turbulence case, we conclude that the scalar correlation is jointly determined by both the chemical source and the flow configuration. Moreover, the chemical reaction also plays an important role in determining the scalar energy spectra.
Toward global digital literate citizens: A case of Thailand's aging generation

Digital technology offers solutions for enhancing older adults' wellbeing. Older adults need digital knowledge and skills to ensure the quality of life, increase cognitive engagement in daily routines, and better connection with loved ones. Thailand is moving toward a knowledge‐based economy, and the Thai government has declared interest in digital ecosystem development. Therefore, emphasis must be placed on the older adults and other underprivileged living in remote areas. However, to date, there are few comprehensive studies on the levels of digital competency and on how older adults accept and use digital technologies. For this reason, this paper constructed a model demonstrating the digital literacy competency among Thai older adults in rural areas. The exploratory research examined the older adults' digital competencies through 97 interviews. Moreover, the results revealed the existing competency gap. The top three desired digital competencies were safety, communication and collaboration, and information and data literacy. Examining these findings nurtures the formation of digital literacy competency from the needs of older adults. Thus, the results contribute to scholarship on workable digital literacy curriculum development designed especially for older adults and help Thailand achieve its vision of “Digital Thailand” that all Thais will become digitally literate.
E3 ubiquitin ligase Nedd4‐2 exerts neuroprotective effects during endoplasmic reticulum stress

The neural precursor cell expressed developmentally down‐regulated protein 4‐like (Nedd4‐2) is an E3 ubiquitin ligase critical for neurodevelopment and homeostasis of neural circuit excitability. While dysregulation of Nedd4‐2 has been linked to elevated seizure susceptibility through impaired ubiquitination of multiple direct substrates, it remains largely unclear whether Nedd4‐2 interconnects other cellular pathways that affect neuronal activity and seizure susceptibility. Here, we first showed that Nedd4‐2 associates with the endoplasmic reticulum (ER) and regulates the expression of multiple ER‐resident proteins. Furthermore, utilizing Nedd4‐2 conditional knockout mice, we showed that Nedd4‐2 is required for the maintenance of spontaneous neural activity and excitatory synapses following the induction of ER stress. When analyzing activation of the canonical pathways of ER stress response, we found that Nedd4‐2 is required for phosphorylation of eIF2α. While phosphorylation of eIF2α has been shown to reduce seizure susceptibility, attempts to facilitate phosphorylation of eIF2α in Nedd4‐2 conditional knockout mice failed to produce such a beneficial function, suggesting a role for Nedd4‐2 in integrating the ER stress response to modulate seizure susceptibility. Altogether, our study demonstrates neuroprotective functions of Nedd4‐2 during ER stress in neurons and could provide insight into neurological diseases in which the expression or activity of Nedd4‐2 is impaired.
Relationship between Online Incentive Teaching and Engagement among Chinese University Students against the COVID-19 Pandemic Background

This study reports the results of a questionnaire survey on online incentive teaching and engagement among Chinese university students during the COVID-19. It aims to understand the students’ perceived online incentive teaching of their teachers in terms of Learning Resources, Interaction and Praise, Feedback and Evaluation, and Emotional Caring, and their online learning engagement in terms of participation, persistence and interaction, as well as the relationship between the two constructs. Data were collected by means of a questionnaire upon 150 undergraduate students at a university in China. Analysis of the collected data reveals that the participants perceived their teachers’ incentive online teaching to be effective, particularly in emotional caring. The participants displayed a high level of engagement, interaction and persistence in particular, though moderately satisfactory in participation. The results also show that the perceived incentive teaching was positively correlated with learner engagement in the overall and dimensional levels. The results are implicative for providing effective support to students in online learning during the difficult time in China and beyond.
Stratix 10 NX Architecture

The advent of AI has driven the exploration of high-density low-precision arithmetic on FPGAs. This has resulted in new methods in mapping both arithmetic functions as well as dataflows onto the fabric, as well as some changes to the embedded DSP Blocks. Technologies outside of the FPGA realm have also evolved, such as the addition of tensor structures for GPUs, as well as the introduction of numerous AI ASSPs, all of which have a higher claimed performance and efficiency than current FPGAs. In this article, we will introduce the Stratix 10 NX device, which is a variant of FPGA specifically optimized for the AI application space. In addition to the computational capabilities of the standard programmable soft-logic fabric, a new type of DSP Block provides the dense arrays of low-precision multipliers typically used in AI implementations. The architecture of the block is tuned for the common matrix-matrix or vector-matrix multiplications in AI, with capabilities designed to work efficiently for both small and large matrix sizes. The base precisions are INT8 and INT4, along with shared exponent support to support block FP16 and block FP12 numerics. All additions/accumulations can be done in INT32 or IEEE-754 single precision floating point (FP32), and multiple blocks can be cascaded together to support larger matrices. We will also describe methods by which the smaller precision multipliers can be aggregated to create larger multipliers that are more applicable to standard signal processing requirements. In the AI market, the FPGA must compete directly with other types of devices, rather than occupy a unique niche. Deterministic system performance is as important as the performance of individual FPGA elements, such as logic, memory, and DSP. We will show that the feed forward datapath structures that are needed to support the typical AI matrix-vector and matrix-matrix multiplication operations can consistently close timing at over 500 MHz on a mid-speed grade device, even if all of the Tensor Blocks on the device are used. We will also show a full-chip NPU processor implementation that out performs GPUs at the same process node for a variety of AI inferencing workloads, even though it has a lower operating frequency of 365 MHz. In terms of overall compute throughput, Stratix 10 NX is specified at 143 INT8/FP16 TOPs/FLOPs or 286 INT4/FP12 TOPS/FLOPs. Depending on the configuration, power efficiency is in the range of 1–4 TOPs or TFLOPs/W.
Study on Self-adaptive Step Tangent Optimization Algorithm and Application for Optimizing Listed Companies' Corporate Performance

In the process of national economic and social development, the new energy industry has played an important role in optimizing and upgrading the China's economic system. It is particularly important to correctly predict the corporate performance of new energy enterprises. In this study, the new energy enterprises in the listed companies are the research objects. With the rapid development of artificial intelligence technology recently, scholars have proposed different intelligent approaches to improve the model accuracy. In this study, self-adaptive step tangent optimization (SASTO) algorithm is proposed and applied to optimize new energy enterprises’ corporate performance. The result shows SASTO can effectively improve the prediction effectiveness and efficiency of the model.
Deep Learning for Fine-Grained Image Recognition: A Comprehensive Study

In computer vision, image recognition is a noteworthy and hot research area which develops rapidly. The principal task of this technique is to automatically predict which pre-defined categories an image might belong to. Traditional image recognition targets to classify images into diversified highly distinguished categories. However, Fine-Grained Image Recognition (FGIR) aims to recognize the variances among images categorized in subordinate classes, e.g., species of birds, types of cars or species of flowers, which are equivalent to “species” in Taxonomy in certain aspects. As a result, models of FGIR are required to pick out features from finer granularity. Conventional methods apply special feature encoding to explore discernible attributes, while recent methods of FGIR makes great advancement with assistance of deep learning which has obtained the remarkable development nowadays. In this paper, we provide a new integration of the current leading FGIR models according to how they improve the development of FGIR. We classified them into five main categories and then compared their performance on three popular datasets and analyzed the results. To advance the further development of this topic, we point out some open problems worth further exploring.
Screen Defect Detection Based on Machine Vision

The application of deep learning based screen defect detection technology in industrial assembly lines has received a lot of attention. In this paper, the traditional screen defect detection methods based on image processing and the screen defect detection methods based on deep learning in recent years are sorted out, the algorithms involved are analyzed and summarized, and the advantages and disadvantages of each method are compared. The process of the two methods is introduced and the future trend of screen defect detection based on deep learning is presented.
Prediction of Internet Users' Emotion Tendency in Emergency Events Based on Data Mining

Through the emotional tendency analysis of subjective texts in emergency events, it can effectively predict the trend of online public opinion, and provide theoretical support for the government and relevant departments to guide online public opinion in the first time. This article uses web crawler technology to crawl and cancel the comment information and personal information of netizens in typical cases of anti-rescue, and uses the emotion analysis method based on emotion dictionary to analyze the emotion of the crawled data. By drawing charts of the proportions of various emotional tendencies of different groups of people, studying the characteristics of emotional tendencies of different groups of people, and predicting the emotional tendencies in emergency events based on the laws drawn from typical cases. And for the predicted groups of people who are prone to negative emotions, the principles and methods for firefighting teams to deal with emergencies are put forward to provide a reference for firefighting teams to deal with online public opinion on emergency events.
Bhutanese Textile Recognition Using Artificial Deep Neural Network

Bhutanese textiles reflect the country's cultural aesthetics and traditions, and it is passed down through the generations. Bhutanese textiles reveal citizens’ abilities and innovations, as well as distinctive characteristics of traditional attire that preserve the country's unity and sovereignty. Many Bhutanese people, particularly younger generations, and tourists, have difficulty recognizing and differentiating between these textiles. This study presents the classification of Bhutanese textiles into seven categories using a convolutional neural network (CNN) which implements the VGG16 architecture. For this study, a dataset was created consisting of 7 classes represented in 700 unique images. To enhance the dataset size, a variety of augmentation approaches were used. The 16 layered VGGNet outperformed the other models when compared to SVM, KNN, and AlexNet in recognizing and classifying the Bhutanese textile types. The VGG16 model obtained an accuracy of 98.33%.
Prediction of virtual currency prices based on improved SVR algorithm

With the continuous development of blockchain technology, the virtual currency market has greatly attracted the attention of investors. At present, the market is in urgent need of an efficient investment theory. In this dissertation, we propose an improved SVR algorithm, which integrates SVR and several other models with the method of ensemble learning. The finial result of prediction is obtained by the weighted summation among multiple models. The improved algorithm is simulated and compared with SVR and K-nearest neighbor algorithms, and the determination coefficient and root mean square error are used as evaluation indexes. The experimental results show that the improved SVR algorithm is significantly better than the traditional SVR algorithm.
Research on Application of Financial Abnormal Data Monitoring and Analysis Algorithm

In order to monitor financial abnormal data by technical means and analyze abnormal phenomena through algorithms, this paper will carry out relevant research, mainly showing the basic structure of the monitoring and analysis system, and then discussing the system implementation method, introducing data analysis algorithms synchronously, and finally verifying. Through the verification of the effective application of the system, it can monitor the abnormal financial data, and analyze it through the algorithm, and output accurate results.