Miracle-dz/UniFAR-FTUs huggingface dataset:
A Proposed Taxonomy of Teaching Models in STEM Education: Robotics as an Example

Many countries and regions have reached a consensus to promote science, technology, engineering, and mathematics (STEM) education in the past decade. A body of studies have demonstrated that the design and organization of interdisciplinary teaching activities are important for the effective implementation of STEM education. So far, however, little attention has been paid to the taxonomy of teaching models in STEM education. This paper aims to propose a taxonomy based on the dimensions of learning outcomes (i.e., product-oriented and knowledge-oriented) and teaching process (i.e., forward teaching model and reverse teaching model) in STEM education. Through the intersection of the above two dimensions, four teaching models in STEM education have emerged, including project-based learning (PBL), reverse engineering (RE), scientific inquiry (SI), and troubleshooting/debugging (T/D). In addition, four cases are introduced to explain how these four teaching models operate in STEM education. The implications of this work for future research are also discussed. It is promising that the study will be valuable to enrich the current research, and shed light on the theory and practice of STEM education.
Models of Mechanics and Growth in Developmental Biology: A Computational Morphodinamics approach

Recent evidence has revealed the role of mechanical cues in the development of shapes in organisms. This thesis is an effort to test some of the fundamental hypotheses about the relation between mechanics and patterning in plants. To do this, we develop mechanical models designed to include specific features of plant cell walls. These are heterogeneous stiffness and material anisotropy as well as rates and directions of growth, which we then relate to different domains of the plant tissue.In plant cell walls, anisotropic fiber deposition is the main controller of longitudinal growth. In our model, this is achieved spontaneously, by applying feedback from the maximal stress direction to the fiber orientation. We show that a stress feedback model is in fact an energy minimization process. This can be considered as an evolutionary motivation for the emergence of a stress feedback mechanism. Then we add continuous growth and cell division to the model and employ the strain signal directing large growth deformations. We show the advantages of strain-based growth model for emergence of plant-like organ shapes as well as for reproducing microtubular dynamics in hypocotyls and roots. We also investigate possibilities for describing microtubular patterns, at root hair outgrowth sites according to stress patterns. Altogether, the work described in this thesis, provides a new improved growth model for plant tissue, where mechanical properties are handled with appropriate care in the event of growth driven by either molecular or mechanical signals. The model unifies the patterning process for several different plant tissues, from shoot to single root hair cells, where it correctly predict microtubular dynamics and growth patterns. In a long-term perspective, this understanding can propagate to novel technologies for improvement of yield in agriculture and the forest industry. (Less)
Alteration of Gene and miRNA Expression in Cervical Intraepithelial Neoplasia and Cervical Cancer

Background: Cervical cancer is one of the most common malignancies in women in terms of prevalence and mortality. Cervical cancer has some particularities that distinguish it from any other oncologic pathology: first, it is completely preventable by prompt detection of its precursor, cervical intraepithelial neoplasia (CIN); second, the Human Papillomavirus (HPV) infection is a known etiological agent; third, the mean age at diagnosis is much lower than in other oncologic conditions, as a consequence of the sexually-transmitted HPV. Methods: We evaluated the expression level of several long noncoding RNAs and a microRNA in samples from 30 patients with CIN, 9 with cervical cancer and 38 normal samples using qRT-PCR technology. Results: We observed higher expression levels for MEG3, DAPK1, MLH1 and MALAT1 in CIN samples than in normal samples, whereas TIMP3 and SOX1 had lower expression levels. For cancer samples, DAPK1, MLH1 and MALAT1 had higher expression, and MEG3, TIMP3 and SOX1 had lower expression when compared to normal samples. In the case of CIN versus cancer samples, only MEG3 gene showed a statistically significant difference. The expression of miR-205-5p was lower in both CIN and cancer samples compared to normal samples. Conclusion: Decreased MEG3 expression could be considered an alarm signal in the transition from a premalignant cervical lesion to invasive cancer, while altered expression levels of TIMP3, SOX1, MLH1, MALAT1 and miR-205-5p could serve as early biomarkers in the diagnosis of premalignant cervical lesions. Future studies, including a larger number of patients with CIN, will be of particular importance in validating these observations.
Smart Garbage Monitoring System

: Garbage bins are found at all the places in a particular collage, school, hospital, bank, shopping malls etc. Every time it is not possible to check whether the bin is full or empty, so in this paper we come up with a solution to monitor the status of every bin inside the campus area of any school, collage or any other place. Here we actually use arduino board connected to an ultrasonic sensor and a Wi-Fi module and this entire system is connected to every single bin inside the campus area. The ultrasonic sensor is directed towards the face of the bin and whenever the bin is less than 5 cm empty the status of the bin will be shown as full, otherwise it will be shown as empty. We actually aim to implement the system inside the campus of the VIT University. There will be a webpage displaying the status of the bin. There will be a centralized server which will access the status of the bins at regular intervals and inform the sweepers accordingly. The WiFi module will actually send the data to the nearest router and it is expected to route through the routers and send the correct data to the server every time. This is an IOT-Based Garbage system , and as a part of future work we hope to implement the payment module in this system, where the users using the bin have to pay online maybe weekly or monthly.
Progesterone Signaling in Endometrial Epithelial Organoids

For pregnancy to be established, uterine cells respond to the ovarian hormones, estrogen, and progesterone, via their nuclear receptors, the estrogen receptor (ESR1) and progesterone receptor (PGR). ESR1 and PGR regulate genes by binding chromatin at genes and at distal enhancer regions, which interact via dynamic 3-dimensional chromatin structures. Endometrial epithelial cells are the initial site of embryo attachment and invasion, and thus understanding the processes that yield their receptive state is important. Here, we cultured and treated organoids derived from human epithelial cells, isolated from endometrial biopsies, with estrogen and progesterone and evaluated their transcriptional profiles, their PGR cistrome, and their chromatin conformation. Progesterone attenuated estrogen-dependent gene responses but otherwise minimally impacted the organoid transcriptome. PGR ChIPseq peaks were co-localized with previously described organoid ESR1 peaks, and most PGR and ESR1 peaks were in B (inactive) compartment regions of chromatin. Significantly more ESR1 peaks were assigned to estrogen-regulated genes by considering chromatin loops identified using HiC than were identified using ESR1 peak location relative to closest genes. Overall, the organoids model allowed a definition of the chromatin regulatory components governing hormone responsiveness.
Adaptive Integration Algorithm of Sports Event Network Marketing Data Based on Big Data

To address the issues of low-data integration accuracy and efficiency, as well as a lack of data integration impact, an adaptive data integration algorithm for sports event network marketing data based on big data is presented. The fundamental theory of tensor is researched by examining the notion and features of big data and by using the associated technologies of the big data framework. Collect network-marketing data from a variety of sporting events and feed it to a big data platform. Combined with MapReduce parallelization mode, tensor represents the online marketing data of sports events according to the structured, semistructured, and unstructured characteristics of different big data. Integrate each tensor model based on semitensor product, build a unified data adaptive integration tensor model, and realize the adaptive integration of sports event network marketing data. The experimental results show that the proposed algorithm has a good effect on the adaptive integration of sports event network marketing data and can effectively improve the accuracy and efficiency of data adaptive integration.
Control of parallel inverters in distributed AC power systems with consideration of line impedance effect

The authors have developed a new control technique which allows paralleled inverters to share linear or nonlinear load in a distributed AC power supply system. This technique does not require control interconnections and automatically compensates for inverter parameter variations and line impedance imbalances. Experimental results are provided in the paper to prove the concept.
Semi-supervised Multiple Instance Learning using Variational Auto-Encoders

We consider the multiple-instance learning (MIL) paradigm, which is a special case 1 of supervised learning where training instances are grouped into bags. In MIL, the 2 hidden instance labels do not have to be the same as the label of the comprising bag. 3 On the other hand, the hybrid modelling approach is known to possess advantages 4 basically due to the smooth consolidation of both discriminative and generative 5 components. In this paper, we investigate whether we can get the best of both 6 worlds (MIL and hybrid modelling), especially in a semi-supervised learning (SSL) 7 setting. We ﬁrst integrate a variational autoencoder (VAE), which is a powerful 8 deep generative model, with an attention-based MIL classiﬁer, then evaluate the 9 performance of the resulting model in SSL. We assess the proposed approach on 10 an established benchmark as well as a real-world medical dataset. 11
Molecular Cloning of a Novel Mannose-binding Lectin Gene from Bulbs of Amaryllis vittata (Amaryllidaceae)

A new mannose-binding agglutinin gene was cloned from bulbs of Amaryllis vittata Ait. The fulllength cDNA of A. vittata agglutinin (AVA) was 686 bp. The start codon of ava cDNA was at 41-43 bp and the stop codon was at 515-517 bp. Analysis in the BLAST of GenBank showed that ava gene encodes a protein precursor composed of a signal peptide, mature protein and C-terminal amino acid cleavage sequence. The mature protein of AVA includes 109 amino acid residues and the molecular weight is 11.9 kD. The homologous analysis showed that the identity between AVA and Galanthus nivalis agglutinin, Narcissus hybrid cultivar agglutinin, Lycoris radiata agglutinin, Clivia miniata agglutinin are 73.4%, 85.3%, 80.7%, 83.5%, respectively. Molecular modeling of AVA indicated that its three-dimensional structure strongly resembles that of the snowdrop agglutinin. Blocks’ analysis revealed that the deduced amino acid sequence of AVA has three functional domains specific for agglutination and three carbohydrate-binding boxes (QDNY).
Few-shot image classification by generating natural language rules

The ability to generate rules and hypotheses plays a key role in multiple aspects of human cognition including concept learning and explanation. Previous research has framed this ability as a form of inference via probabilistic program induction. However, this approach requires careful construction of the right grammar and hypothesis space for a particular task. In this work, we propose an alternative computational account of rule generation and concept learning that sidesteps some of these issues. By leveraging advances in multimodal learning and large language models, we extend the latent language framework from Andreas et al. (2017) to work in a zero-shot manner. Taking naturalistic images as input, our computational model is capable of generating candidate rules that are specified in natural language, and verifying them against the observed data. We show that our model can generate, in a zero-shot manner, plausible rules for visual concepts in two domains.
Phonological Evidence for a Distinction Between Russian Prepositions and Prefixes

A less discussed aspect of these data is that prepositions (PREP) and prefixes (PFX) cannot be unified as one class morphosyntactically. While it might be tempting to view this fact as indicative of a mismatch between phonology and morphosyntax, the central claim of this paper is that there is no such mismatch, only the superficial appearance of one: PREP and PFX can and should be analyzed as both phonologically and morphosyntactically distinct. In particular, careful examination reveals that the morphosyntactic distinction between PREP and PFX is reflected in their phonology, via differing jer realization patterns PFXvs. PREP-finally. In what follows, I present evidence to substantiate the above claims about the morphosyntactic and phonological disunity of PREP and PFX (Section 2), concentrating especially on the phonological evidence. I then discuss a Stratal OT approach to the evaluation of PREP and PFX, with details about how we might accommodate jer vocalization PREP-finally (Section 3). Finally, I argue against an alternative approach of Rubach’s (2000), in which PREP and PFX are treated as identical (Section 4).
bmemLavaan: An R Package for Estimating and Testing Mediation Effect in Mediation Models

An R package bmemLavaan for estimating and testing mediation effect in mediation models is developed. The package uses the popular R package lavaan to specify mediation models. The package is especially useful to handle complex real data such as missing data and non-normal data, with four methods to deal with different missing data mechanisms and a robust method to deal with non-normal data. Three methods are included to conduct significance testing for mediation effects, two of which can efficiently deal with non-normal data. The package can also be used for sample size planning. This paper explains the aims and principles of the package and provides six examples of different complexities on how to use the package to conduct different types of mediation analysis
8 Legitimating confrontational discourses by local environmental groups: The case of air quality monitoring in a Spanish industrial area

business and society discourse interacts with social movements discourse and governance discourse in the following ways: it attempts to (1) co- opt social movements through partnership and collaborative articulations, (2) suppress those accounts of social movements theory that are still focused on adversarial relationships, and (3) place business at the center of governance discourse notably through articulations that draw on “deliberative democracy” and the “post- political” perspective.
Botulinum Toxin Type A Injection May Restore Ankle Strategy Use in Stroke Patients: A Preliminary Report

Introduction: Stroke causes upper motor neuron syndrome, and spasticity of foot ankle complex further leads to equine gait and posture stability disturbance. Botulinum type A (BTX-A) injection has been widely used in treating spasticity, yet few studies report its effect on posture control. This study aimed at applying BTX-A injection in treating ankle-foot complex spasticity and investigating its effect on ankle strategy use with sensory organization test. Material and method: 9 stroke patients and 5 healthy subjects were recruited. BTX-A injection was applied over calf flexor muscle group under electrical stimulation guide. Strategy score (ST) and Composite score were assessed using standard sensory organization test(SOT) with SMART Balance Master. The higher ST and Composite score indicated that the subjects used more ankle strategy. The individual test conditions were as following: EO: eyes open; EC: eyes closed; SV: swayed vision and fixed support; EOSS: eyes open and swayed support; ECSS: eyes closed and swayed support; SVSS: swayed vision and swayed support). For the patient group, SOT was performed before BTX-A injection, and at 4 weeks, 8 weeks and 12 weeks follow up. For control group, the assessments were performed once. Generalized estimating equation was implemented for data analysis, and set p<0.05 as statistical significance. Result: Before BTX-A injection, Composite score of patient group was significant lower than control group. After BTX-A injection, there were no significant differences of Composite score comparing control group to patient group at 4 weeks, 8 weeks and 12 weeks follow up. Comparison within patient group revealed that Composite scores were significant higher after BTX-A injection at 4 weeks and 12 weeks follow up. For ST of individual test conditions, improvement trend of ST after BTX-A injection was noted in EO, EC, SV and EOSS condition. And the improvement in EOSS condition was most consistent. Conclusion: BTX-A injection might restore the use of ankle strategy in stroke patients and might serve as the mechanism of the improvement in posture control.
‘Why Not This MAPF Plan Instead?’ Contrastive Map-based Explanations for Optimal MAPF

Multi-Agent Path Finding (MAPF) plans can be very complex to analyze and understand. Recent user studies have shown that explanations would be a welcome tool for MAPF practitioners and developers to better understand plans, as well as to tune map layouts and cost functions. In this paper we formulate two variants of an explanation problem in MAPF that we call contrastive “map-based explanation”. The problem consists of answering the question “why don’t agents A follow paths P’ instead? ”—by ﬁnding regions of the map that would have to be an obstacle in order for the expected plan to be optimal. We propose three different methods to compute these explanations, and evaluate them quantitatively on a set of benchmark problems that we make publicly available. Motivations for generating this type of explanation are discussed in the paper and include both user understanding of MAPF problems, and designer-aids to guide the improvement of map layouts.
Numerical model for time-dependent fracturing of concrete structures and its applications

A finite-element formulation for the analysis of time-dependent failure of concrete is presented. The proposed formulation incorporates: (1) the viscoelastic behavior of uncracked concrete through a Maxwell chain model; and (2) the inelastic behavior of damaged concrete, characterized by a modified version of the microplane Model M4 which includes the rate dependence of fracturing. The proposed formulation is applied to the simulation of quasi-static concrete failure in the time domain. The different effects of creep and rate dependence of crack growth and their role in the lifetime of concrete structures are studied. The influence of different loading rates on the size effect is also analyzed with reference to single notched specimens, revealing the link between the size of the fracture process zone and the loading rate. The capability of the proposed numerical formulation is also verified for the case of sustained uniaxial compressive loads.
Radiative impact of improved global parameterisations of oceanic dry deposition of ozone and lightning-generated NOx

Abstract. We investigated the radiative impact of recent process-based improvements to oceanic ozone (O3) dry deposition parameterisation and empirical improvements to lightning-generated oxides of nitrogen (LNOx) parameterisation by conducting a 5-year simulation of the Australian Community Climate and Earth System Simulator – United Kingdom Chemistry and Aerosol (ACCESS-UKCA) global chemistry–climate model, with radiative effects of O3, methane (CH4) and aerosol included. Compared to the base parameterisations, the global consequences of the two improved parameterisations on atmospheric composition are dominated by the LNOx change (which increases the LNOx production from 4.8 to 6.9 Tg N yr−1) and include (a) an increase in the O3 column of 3.75 DU, and this O3 change is centred on the tropical upper troposphere where O3 is most effective as a radiative forcer; (b) a decrease of 0.64 years in the atmospheric lifetime of CH4 due to an increase in hydroxyl radical, which corresponds to a decrease of 0.31 years in the CH4 lifetime per Tg N yr−1 change in LNOx; (c) an increase of 6.7 % in the column integrated condensation nuclei concentration; and (d) a slight increase in high-level cloud cover. The two combined parameterisation changes cause an increase of 86.3 mW m−2 in the globally-averaged all-sky net downward top-of-atmosphere (TOA) radiative flux (which is akin to instantaneous radiative forcing), and only 5 % of which is due to the dry deposition parameterisation change. Other global radiative changes from the use of the two parameterisations together include an increase in the downward longwave radiation and a decrease in the downward shortwave radiation at the earth's surface. The indirect effect of LNOx on aerosol and cloud cover can at least partly explain the differences in the downward shortwave flux at the surface. It is demonstrated that although the total global LNOx production may be the same, how LNOx is distributed spatially makes a difference to radiative transfer. We estimate that for a reported uncertainty range of 5±3 Tg N yr−1 in global estimates of LNOx, the uncertainty in the net downward TOA radiation is ±119 mW m−2. The corresponding uncertainly in the atmospheric methane lifetime is ±0.92 years. Thus, the value of LNOx used within a model will influence the effective radiative forcing (ERF) and global warming potential (GWP) of anthropogenic CH4, and influence the results of climate scenario modelling.
Eventpad: Rapid Event Mining with Visual Analytics

In this paper we demonstrate how we can significantly speed up the discovery of anomalies and patterns in event sequences using our visual analytics system Eventpad: the notepad editor for event data. We illustrate the effectiveness of multivariate regular expressions, pattern aggregations, and selections to quickly discover patterns within and between sequences. In particular we show how we used Eventpad to win the IEEE Visual Analytics Science and Technology 2017 Challenge 1 by reducing months of data analysis to just a few hours.