Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Digital Art to Influence Students' Attitudes in High School Science Classrooms.

Whether the activity of creating digital art influences high school students’ attitudes toward science is unknown. The purpose of this study was to determine if the creation of artistic digital chemoscans by high school students influences attitudes toward science. In this study, ninthgrade high school students’ attitudes toward science were examined after participating in the creation of chemoscans in the science classroom. The theory of affective domain helped explain the process that leads to a person’s behavior toward a certain phenomenon in the educational setting. The research questions concerned the use of chemoscan creation in the physical science classroom and if and whether implementation effected a change in students’ attitudes toward science. Archival preand post-test data from the Test of Science Related Attitude was used to measure high school students’ attitudes toward science in 7 categories. Archived student preand post-test data were treated with multiple regression for analysis. Key findings of this study showed that creation of artistic digital chemoscans (a) impacted one of the seven subscales of science attitude from the Test of Science related Attitude (TOSRA) entitled attitude toward the normality of scientists, (b) did not have an impact on the any of the other six subscales from the TOSRA and (c) was influenced by teacher effect. This study may contribute to social change by providing improved training for science teachers who implement digital art activities, which may lead to some students enjoying science more and then possibly going into science careers.
Optimizing the immobilization of lipase enzyme (Aspergillus oryzae) in the silica and silica-cellulose matrix by adsorption method

Silica and silica-cellulose were safe biomaterials used as the matrix for immobilized enzyme or any bigger molecule. Some enzymes play role in some processes to biofuel production. In this study, the enzyme used was the lipase enzyme, to test the ability of the matrix as carrier. This research aimed to know the ability to immobilize lipase enzyme using adsorption method. Spectroscopy methods were involved. The research was divided into 4 steps (1) preparation of silica and silica-cellulose matrices, (2) characterization of silica and silica-cellulose, (3) immobilization of lipase enzyme on the silica and silica-cellulose which included activity test: optimum temperature, optimum pH, and optimum stirring speed, and (4) effectiveness of immobilized lipase enzyme on the silica and silica-cellulose. The result showed silica porous material as a matrix was more effective than silica-cellulose as matrix for lipase enzyme.
Comparative Evaluation of Femoral Nerve Block & Intravenous Fentanly for Positioning During Spinal Anesthesia in Surgery of Femur Fractures

Introduction: Femur fractures configure an important public health issue and are associated with high mortality taxes and lose of functionality. Study aimed to compare the analgesic effects of FNB with intravenous (IV) fentanyl prior to positioning for spinal block in patients with fractured femur. Material and Methods: Sixty patients scheduled for fractured femur surgery under spinal block. They were grouped as FNB group (n=30) and a fentanyl group (n=30). A spinal block was performed. Pain scores 15 minutes after analgesia and during positioning were recorded. A numeric rating pain scale (0 = no pain, 10 = maximal pain) was used. Additional fentanyl requirement during positioning and satisfaction with patient position maintained for spinal block (yes = satisfactory, no = not satisfactory) were also recorded. Results: Base line values for Heart Rate (HR), Mean Arterial Pressure (MAP) and type of surgery were comparable in both the groups. We found an insignificant difference in HR (P>0.001) among the groups and a significantly lower MAP in FENT group (P = 0.0090). SpO2 was significantly lower in FENT group (P < 0.001). Conclusion: Femoral nerve block lowers pain and the need for supplementary analgesia requirement.
H 2 Production by Methanol Steam Reforming over Copper Impregnated Anodized Aluminum Oxide (AAO)

Hydrogen production by methanol steam reforming (MSR) is easy and simple as compared to other reforming methods using fossil fuels such as methane steam reforming. The catalysts for MSR are well developed and available commercially. When constructing a small or micro reformer, the catalyst often needs to be coated on the wall of the metal substrate. In this case, the bonding between the metal surface and the catalyst layer must be strong enough to avoid peeling of the layer. Simple catalyst slurry coating on the metal surface has not been successful due to the inherent weak bonding between the metal and the metal oxide layer. In this study, to develop a robust catalyst layer, we first formed a strongly bonded porous aluminum oxide layer on an aluminum metal surface and then impregnated it with an active metal, Cu. Copper metal is incorporated into the pores of alumina by impregnation using different concentrations of copper nitrate solution (CuAAO). The surface morphology of the catalysts has been monitored by FE-SEM at various stages of synthesis and the amount of Cu metal incorporated has been analyzed by SEM-EDX. This paper discusses the development of Cu-AAO catalyst for methanol steam reforming.
Modification of Mesoporous Silica Surface by Immobilization of Functional Groups for Controlled Drug Release

This paper introduces the synthesis of mesoporous silica nanoparticles (MSNs) with three different groups such as amine, thiol, and sulfonic acid, along the internal surface. Trimethyl[3-(trimethoxysilyl)propyl]ammonium chloride was used to modify the external surface of the nanomaterials. Such materials allow control of the drug release from MSN pores. Multifunctional MSNs were loaded with doxycycline (Doxy) to study their capacities and uploading time. The loading profile indicates that sulfonic groups in the internal surface were the most efficient surfaces with a loading capacity of ca. 35% in 90 min in acidic media.
Let’s do the time warp again – embodied learning of the concept of time in an applied school setting

ABSTRACT Embodied Cognition approaches suggest that movements influence the understanding of abstract concepts such as time. It follows that moving the arms as watch hands should boost children’s learning to read the clock. In a school setting, we compared three learning conditions: an embodied (movement) condition, an interactive App condition, and a text condition. Age, self-reported enjoyment, and group size were controlled. In a clock-time-test, the embodied condition resulted in better performances than the mean of the other conditions in small, but not in large groups. This innovative, theory-informed approach may advance learning of abstract concepts in children.
Creating Global Scholars: Experiential Learning and Reflection Transform an International Conference Into Short-Term Study Abroad

ABSTRACT This exploratory qualitative study examines the impact of a short-term study abroad trip for U.S. social work doctoral students built around an international conference in Zagreb, Croatia. The program incorporated reflection and experiential learning. Data were collected using pretrip and posttrip written reflections from study participants (N=4). Two primary themes emerged from thematic analysis that showed a positive impact on participants: an enhanced understanding of themselves as global citizens and an enhanced sense of themselves as global professionals. Three themes also emerged supporting the pedagogic strategy: experiential learning promoted global engagement, reflection catalyzed robust learning, and the impact of the experience was greater than the sum of its parts. Directions for further research on short-term study abroad programs are discussed as well as strategies for enhancing short-term study abroad to promote global engagement in social work at the master’s and doctoral levels.
Damage Initiation and Evolution Analysis of Hot Extruded Recycled Aluminium Alloys (AA6061)

The effects of strain rate on the mechanical behaviour of recycled hot extruded aluminium alloys (AA6061) are investigated using uniaxial tensile tests in this work. The damage characteristics such as micro-cracks and micro-voids are analysed on the microstructure of the material using Scanning Microscope Electron (SEM). It is shown that the mechanical behaviour of the recycled AA6061 is influenced by the loading speed. Besides, the damage parameters such as the micro-cracks and micro-voids in the material are evolved significantly in such materials during finite strain deformation. It can be observed that the evolution speed of the micro-cracks and micro-voids in the materials is increasing with the increase in loading speed. Much work is still required to improve the recycled AA6061 as compared to the primary resources in terms of damage parameters initiation and evolution.
Anaphylaxis following prick‐by‐prick testing with peanut

Anaphylaxis due to prick‐by‐prick testing is a rare but potentially life‐threatening complication. Personnel performing testing should be able to recognize and treat anaphylaxis. Adrenaline must be available. In high‐risk patients with a history of anaphylaxis, specific igE with components and a basophile activation test is desirable before skin prick testing.
Iot and machine learning based-solar automated ambulance

—This paper helps to develop a vehicle that can be self-driven using the latest technologies such as IoT and Machine Learning. The ambulance can be run using battery technology as well as solar energy, this helps in addressing the over usage of fossil fuels. The vehicle is capable of sensing the environment, navigating and fulfilling the human transportation capabilities, the automated vehicle technology here is being used for emergency vehicles in order to reduce delay occurrence in reaching the victim, the same technology can also be used to design and develop cars to help specially-abled people to transport independently.
Considering foreignization and domestication in EU legal translation: a corpus-based study

ABSTRACT This paper investigates foreignizing and domesticating strategies in the translation of EU legislative texts. It starts by briefly introducing the dichotomy between foreignizing and domesticating translations, which has recently been revisited in the field of legal translation. Intrigued by assertions that the translation of a normative text should be primarily foreignizing in order to express the intention of the legislator and criticisms of current EU translation practices for lack of effectiveness due to an approach combining both translation strategies by means of a parallel corpus of EU legislative texts we examine to what extent the two opposing translation strategies are used in the translation of EU law, putting the emphasis on the terminological level. Bearing in mind that the latter field is still relatively unknown this corpus-based study aims to provide more insight into the research of EU translation. While the results of the conducted corpus analysis evidence a mixture of both of these translation strategies, foreignization elements are especially visible in the overuse of nominalizations and in the translation of abbreviations.
Learning words in a second language while cycling and listening to children’s songs: The Noplica Energy Center

Children’s songs are a great source for linguistic learning. Here we explore whether children can acquire novel words in a second language by playing a game featuring children’s songs in a playhouse. The playhouse is designed by the Noplica foundation (www.noplica.nl) to advance language learning through unsupervised play. We present data from three experiments that serve to scientifically prove the functionality of one game of the playhouse: the Energy Center. For this game, children move three hand-bikes mounted on a panel within the playhouse. Once the KEYWORDS
A Double Adversarial Network Model for Multi-Domain and Multi-Task Chinese Named Entity Recognition

SUMMARY Named Entity Recognition (NER) systems are often realized by supervised methods such as CRF and neural network methods, which require large annotated data. In some domains that small annotated training data is available, multi-domain or multi-task learning methods are often used. In this paper, we explore the methods that use news domain and Chinese Word Segmentation (CWS) task to improve the performance of Chinese named entity recognition in weibo domain. We ﬁrst propose two baseline models combining multi-domain and multi-task information. The two baseline models share information between di ﬀ erent domains and tasks through sharing parameters simply. Then, we propose a Double AD-Versarial model (DoubADV model). The model uses two adversarial networks considering the shared and private features in di ﬀ erent domains and tasks. Experimental results show that our DoubADV model outperforms other baseline models and achieves state-of-the-art performance compared with previous works in multi-domain and multi-task situation.
Opportunities and risks of disaster data from social media: a systematic review of incident information

Abstract. Compiling and disseminating information about incidents and disasters are key to disaster management and relief. But due to inherent limitations of the acquisition process, the required information is often incomplete or missing altogether. To fill these gaps, citizen observations spread through social media are widely considered to be a promising source of relevant information, and many studies propose new methods to tap this resource. Yet, the overarching question of whether and under which circumstances social media can supply relevant information (both qualitatively and quantitatively) still remains unanswered. To shed some light on this question, we review 37 disaster and incident databases covering 27 incident types, compile a unified overview of the contained data and their collection processes, and identify the missing or incomplete information. The resulting data collection reveals six major use cases for social media analysis in incident data collection: (1) impact assessment and verification of model predictions, (2) narrative generation, (3) recruiting citizen volunteers, (4) supporting weakly institutionalized areas, (5) narrowing surveillance areas, and (6) reporting triggers for periodical surveillance. Furthermore, we discuss the benefits and shortcomings of using social media data for closing information gaps related to incidents and disasters.
The distributional impact of recurrent immovable property taxation in Greece

During the last decade, Greece faced one of the most severe debt crises among developed countries, leading to Economic Adjustment Programs in order to avoid a disorderly default. Public expenditure was cut, tax rates were increased and new taxes were introduced aiming at restoring public finances. Prominent among the latter were recurrent property taxes that were playing a very minor role before the crisis. These taxes helped boosting public revenues but were hugely unpopular. The paper examines in detail their distributional impact and finds that they led to increases in inequality and (relative) poverty. The result is stronger in the case of inequality indices that are relatively more sensitive to changes close to the bottom of the distribution and poverty indices that are sensitive to the distribution of income among the poor.
Challenging discourses of deficit: Understanding the vibrancy and complexity of multilingualism through language trajectory grids

The ‘multilingual turn’ has opened up a variety of creative, participatory methods for researchers and educators to explore language learners’ linguistic repertoires, language practices and resources, and linguistic experiences. In this article, we draw on data from the English as an additional language (EAL) secondary school classroom context where we use what we call ‘language trajectory grids’ to make visible EAL students’ English language learning experiences. The grid activity involves plotting learners’ emotions, practices, relationships, and life circumstances with their resources, practices and historical events onto a chronological grid. Using a narrative structure to make sense of learners’ grids and the ‘small stories’ (Barkhuizen, 2009) participants shared during the activity, we discuss the affordances of such devices in opening up space for teachers and learners to critically reflect on the complexities and vibrancy of contemporary multilingual language journeys; to recognize situational factors that influence dispositions towards language, language learning and identity; and as a powerful starting point for students and teachers to engage in ‘collaborative relations of power’.
Investigating Wetland and Nonwetland Soil Methane Emissions and Sinks Across the Contiguous United States Using a Land Surface Model

We estimated the distribution of CH4 emissions and sinks from wetlands (including freshwater and coastal wetlands) and nonwetland (including wet and dry soils) with a newly developed vertically resolved soil CH4 model, integrated into a global land surface model (ISAM). We calibrated and tested this integrated model with CH4 observations at test sites in the Contiguous United States (CONUS). ISAM is applied across the CONUS to estimate CH4 emissions and sinks given both recent past observed climate and wetland extent, and future climate and wetland extent driven by two scenarios, RCP4.5 and RCP8.5. Estimated net CH4 emissions for the 2000s are 13.8 TgCH4 yr−1, mostly from wetland soils. Estimated net emissions under RCP4.5 and RCP8.5 are 30% and 64% higher, respectively, in the 2090s than in the 2000s due to (1) higher temperature and seasonal wetland extent (driven by higher precipitation in the climate scenarios), which increase modeled methanogenic activity more than methanotrophic activity in soils and (2) altered transport in the soil column and exchange with the atmosphere by modeled transport processes (diffusion, ebullition, and aerenchyma transport). Nonwetland soils emit CH4 (1.4 TgCH4 yr−1) in some areas and take up CH4 (−2.9 TgCH4 yr−1) in other areas, resulting in a net estimated sink for the 2000s; the net nonwetland soil sink increases by 15% and 46% by the 2090s under RCP4.5 and RCP8.5, respectively, mainly due to drier soil conditions, which enhances methanotrophic activity and oxidation of CH4 diffused into soil from a future atmosphere with higher CH4 concentration.
A novel algorithm for infrared image contrast enhancement based on neutrosophic sets

ABSTRACT Due to the narrow thermal window of infrared (IR) image sensor array, the represented image becomes dim and its details become blurred. Different contrast and detail enhancement technologies were deployed to improve image quality. An enhancement algorithm with bilateral filter is a state of art method, which first transforms an infrared image into a base part and a detail part, and then expands the detail part and suppresses the base part to enhancement the contrast of the IR image. However, this method cannot efficiently distinguish the background and objectives of the detail part. As a result, the noise information gets amplified when the detail part is expanded, leading to increase noise and to obstruct the sharpening of the detail part. To solve these problems, a novel enhancement algorithm based on the neutrosophic sets is proposed, which transforms the detail part into the neutrosophic domain. Our method utilises three membership functions of pixels in the neutrosophic domain: T (True), F (False) and I (Indeterminacy), which correspond to objective, background and transitional regions of image, respectively. The proposed algorithm is verified and compared with other existing algorithms. The experiment results show that the proposed algorithm can effectively enhance the contrast and preserve the details of an infrared image.
DenseNet-201-Based Deep Neural Network with Composite Learning Factor and Precomputation for Multiple Sclerosis Classification

(Aim) Multiple sclerosis is a neurological condition that may cause neurologic disability. Convolutional neural network can achieve good results, but tuning hyperparameters of CNN needs expert knowledge and are difficult and time-consuming. To identify multiple sclerosis more accurately, this article proposed a new transfer-learning-based approach. (Method) DenseNet-121, DenseNet-169, and DenseNet-201 neural networks were compared. In addition, we proposed the use of a composite learning factor (CLF) that assigns different learning factor to three types of layers: early frozen layers, middle layers, and late replaced layers. How to allocate layers into those three layers remains a problem. Hence, four transfer learning settings (viz., Settings A, B, C, and D) were tested and compared. A precomputation method was utilized to reduce the storage burden and accelerate the program. (Results) We observed that DenseNet-201-D (the layers from CP to T3 are frozen, the layers of D4 are updated with learning factor of 1, and the final new layers of FCL are randomly initialized with learning factor of 10) can achieve the best performance. The sensitivity, specificity, and accuracy of DenseNet-201-D was 98.27± 0.58, 98.35± 0.69, and 98.31± 0.53, respectively. (Conclusion) Our method gives better performances than state-of-the-art approaches. Furthermore, this composite learning rate gives superior results to traditional simple learning factor (SLF) strategy.
An interesting oddity in the theory of large amplitude internal solitary waves

In the theory of internal waves in the coastal ocean, linear stratification plays an exceptional role. This is because the nonlinearity coefficient in KdV theory vanishes, and in the case of large amplitude waves, the DJL theory linearizes and fails to give solitary wave solutions. We consider small, physically consistent perturbations of a linearly stratified fluid that would result from a localized mixing near a particular depth. We demonstrate that the DJL equation does yield exact internal solitary waves in this case. These waves are long due to the weak nonlinearity, and we explore how this weak nonlinearity manifests during shoaling.