Miracle-dz/UniFAR-FTUs huggingface dataset:
The Use of QR Code-based Digital Flashcard as a Media for Promotion and Socialization to Increase the Competitiveness of the Creative Economy and Small and Medium Enterprises

The China ASEAN Free Trade Area (CAFTA) agreement in 2010 and the ASEAN Economic Community (AEC) in 2015 serve as both an opportunity and a challenge for Small Micro Medium Enterprises’ (SMMEs) products in all cities in Indonesia. In order to further increase sales in traditional markets, especially from SMMEs’ products and creative industries, an information and promotion media is required to increase public interests, both people from Cirebon (indigenous tourism) and outside Cirebon to get to know existing SMMEs’ products and creative industries utilizing the media as an educational technology. This study aims to develop a QR code-based digital flashcard model as a media for promotion and dissemination to increase the competitiveness of the creative economy and Small and Medium Enterprises in Indonesia. From the promotion, it is expected that the public's interest to do shopping in traditional markets will increase so that the sales of products from SMMEs’ can increase. The research model used in this study is a four-D device development model, Model (4D). This four-D development model (4D) consists of four stages, namely the definition phase (Define), the Designing Phase (Design), the Development Stage (Develop), the Deployment Stage (Disseminate).
Synthesis and Characterization of Host Guest Inclusion Complexes of Cyclodextrin Molecules with Theophylline by Diverse Methodologies

Steady host–guest inclusion complexes have been produced with medicinally important guest molecule theophylline within aqueous α-Cyclodextrin and HP-β-Cyclodextrin. α-and HP-β-Cyclodextrins have been established with favorable structural features for inclusion with Theophylline which include diversified applications in modern science such as controlled delivery in the field of pharmaceuticals, food processing, pesticides, foodstuffs etc. Theophylline is one of the most widely accepted drugs for the treatment of asthma and chronic obstructive pulmonary disease (COPD) worldwide, even if it has been used clinically for many years. With both α and HP- β- Cyclodextrins it is found that 1:1 hosts-guest inclusion complexes are formed with the guest molecule theophylline. The construction and quality of the inclusion complexes have been characterized by using conductivity measurement, surface tension study, and Job’s method. The inclusion phenomenon has been confirmed by FTIR spectroscopy, proton NMR study. Association constants and thermodynamic parameters have been evaluated for the created inclusion complexes by ultraviolet spectroscopy.
A RADIOMETRIC NORMALIZATION METHOD OF CONTROLLING NO-CHANGED SET (CNCS) FOR DIVERSE LANDCOVER USING MULTI-SENSOR DATA

Abstract. The accurate acquisition of land surface reflectance (SR) data determines the accuracy of ground objects recognition, classification and land surface parameter inversion using remote sensing data, which is the basis of remote sensing data application. In this study, a Control No-Changed Set (CNCS) radiometric normalization method is proposed to realize spectral information transformation of multi-sensor data, which is based on the Iteratively Reweighted Multivariate Alteration Detection (IR-MAD), and includes automatic selection and step-by-step optimization of no-change pixels. The No-Changed set (NC) is obtained by selecting the original no-change pixels between the target image and the reference image according to the linear relationship. In the obtained original no-change regions, IR-MAD rules with iterative control are used to fix the final no-change pixels, after regression modeling and calculation, the normalized images are obtained. The method is tested on multi-images from multi-sensors in three groups of experiments (GF-1 WFV and Landsat-8 OLI, GF-1 PMS and Sentinel-2 MSI, and Landsat-8 OLI and Sentinel-2 MSI) with different landcover areas. The results of radiometric normalization are evaluated qualitatively and quantitatively. The data of the three groups of experiments have a high correlation (correlation coefficient r values > 0.85), indicating that they can be used together as complementary data. The Root Mean Squared Error (RMSE) values calculate from the NC between the reference and normalized target images are much smaller than those between the reference and original target images. The radiometric colour composition effects, and the typical ground objects spectral reflective curves of the reference and normalized target images are very similar after radiometric normalization. These results indicate that the CNCS method considers the linear relationship of the no-change pixels and is effective, stable, and can be used to improve the consistency of SR of multi-images from multi-sensors.
Estimation of reactive inorganic iodine fluxes in the Indian and Southern Ocean marine boundary layer

Abstract. Iodine chemistry has noteworthy impacts on the oxidising capacity of the marine boundary layer (MBL) through the depletion of ozone (O3) and changes to HOx (OH∕HO2) and NOx (NO∕NO2) ratios. Hitherto, studies have shown that the reaction of atmospheric O3 with surface seawater iodide (I−) contributes to the flux of iodine species into the MBL mainly as hypoiodous acid (HOI) and molecular iodine (I2). Here, we present the first concomitant observations of iodine oxide (IO), O3 in the gas phase, and sea surface iodide concentrations. The results from three field campaigns in the Indian Ocean and the Southern Ocean during 2015–2017 are used to compute reactive iodine fluxes in the MBL. Observations of atmospheric IO by multi-axis differential optical absorption spectroscopy (MAX-DOAS) show active iodine chemistry in this environment, with IO values up to 1 pptv (parts per trillion by volume) below latitudes of 40∘ S. In order to compute the sea-to-air iodine flux supporting this chemistry, we compare previously established global sea surface iodide parameterisations with new region-specific parameterisations based on the new iodide observations. This study shows that regional changes in salinity and sea surface temperature play a role in surface seawater iodide estimation. Sea–air fluxes of HOI and I2, calculated from the atmospheric ozone and seawater iodide concentrations (observed and predicted), failed to adequately explain the detected IO in this region. This discrepancy highlights the need to measure direct fluxes of inorganic and organic iodine species in the marine environment. Amongst other potential drivers of reactive iodine chemistry investigated, chlorophyll a showed a significant correlation with atmospheric IO (R=0.7 above the 99 % significance level) to the north of the polar front. This correlation might be indicative of a biogenic control on iodine sources in this region.
Speech denoising with re-synthesis

When performing speech denoising, the goal of and algorithm is to remove the unwanted frequency, known as noise, while preserving the feature vectors of the signal. In this text, we present an algorithm for speech denoising that removes the noise by converting the speech signal to text and then regenerating the speech signal from the text. We also aim to perform voice adaptation and retention of the emotional components (prosodic parameters) of the original speech in order to preserve the speech feature vectors. In order to do this, we studied the techniques and performance of some automatic speech recognition (ASR) and text-to-speech (TTS) systems and chose the architecture that is best suited for our purpose. Based on the results, we decided to use Google Cloud Speech API for ASR and hybrid speech synthesis for this project.
Bulletin of the Geological Society of Greece

The sustainable and environmentally friendly energy production has been a major issue of the world energy sector in recent years. Coal is a major fossil fuel that provides approximately 25% of the total energy demand worldwide; coal reserves still remain significant, although in several cases its exploitation trends to be economically marginal. Underground Coal Gasification (UCG) has been identified as a technology which can bridge the gap between energy production and environmental and financial sustainability. Several UCG trials have taken place, although, there are still questions relative to their safety, performance and applicability. To that direction, modelling can prove to be a very effective and practical tool for the prediction of the project performance and the reduction of the risk involved. UCG is a complex process which incorporates mechanical and chemical processes thus modelling is complex since it demands coupling the aforementioned processes. The current study aims at investigating the applicability of the UCG process in a Bulgarian coal site through 2D modelling. The proposed approach uses FLAC software as a modelling tool and attempts to combine thermal and mechanical effects during the gasification process. Several simulation runs have taken place in an attempt to quantify the effect of the different mechanical and thermal properties of the surrounding rocks to the UCG process, the environmental effects and the stability of the geological formations.
Proposed Algorithm For Using GLCM Properties To Distinguishing Geometric Shapes

In this research, an algorithm was used to look at the characteristics of a set of images for geometric shapes and then to classify them into totals based on four characteristics obtained from the co-occurrence matrix (energy, contrast, correlation and homogeneity). Studying the above four characteristics in detail and then presenting a complete presentation on the extent of their effect on the distinctive characteristics of the geometrical shapes. The adopted algorithm shows that the above four qualities can be new features of geometric shapes in digital images
Sailing Speed Optimization Model for Slow Steaming Considering Loss Aversion Mechanism

This paper analyses loss aversion mechanism (LAM) of the shipping company’s decision-makers about the risk-based decision (RBD) for slow steaming and generalizes a novel optimization model for the sailing speed through the trade-off between fuel consumption, SOx emissions and delivery delay. The value functions against the benchmark speed were constructed based on physiological expected utility (PEU) to reveal the features of loss aversion, and the objective function was derived from these value functions with the aim to optimize the sailing speed. After that, a Genetic Algorithm (GA) solution with fitness function and special operators was built to solve the proposed model. Finally, the model was applied to pinpoint the PEU for the optimal sailing speed against the benchmark speed, and the sensitivity of the model was discussed with different benchmark speeds, value function weights and input parameters. The analysis shows that the proposed model can assist the slow steaming RBD based on the inner feelings of the shipping company’s decision-makers, offering a novel tool for sailing speed optimization.
vcemway: A one-stop solution for robust inference with multi-way clustering

Most Stata commands allow cluster(varname) as an option, popularizing the use of standard errors that are robust to one-way clustering. But when it comes to adjusting standard errors for multi-way clustering, there is no solution that is as widely applicable. While several user-written packages support multi-way clustering, each package is compatible with only a subset of models that Stata’s ever-expanding library of commands allows the researcher to estimate. We introduce a command vcemway that provides a one-stop solution for multi-way clustering. vcemway works with any estimation command that allows cluster(varname) as an option, and adjusts standard errors, individual significance statistics and confidence intervals in output tables for multi-way clustering in specified dimensions. The covariance matrix used in making this adjustment is stored in e(V), meaning that any subsequent call to postestimation commands that use e(V) as input (e.g. test and margins) will also produce results that are robust to multi-way clustering.
RDCC: A New Metric for Processor Workload Characteristics Evaluation

—Understanding the characteristics of workloads is extremely important in the hardware and software design of computer systems. Better metrics and methods for evaluating workloads are continually needed. Here we propose a quantitative metric, namely RDCC (Ratio of Dynamic Computation to Control operations), for evaluating workloads. The RDCC values of a great many benchmarks are measured in various conditions. The evaluation results indicate that the intrinsic properties of the application can be marked with the RDCC value. The applications with lower RDCC values contain more control operations and less computational operations. In contrast, the applications with higher RDCC values consist of less control operations and more computational operations. In addition, the RDCC value of one application is relatively stable with different compiler optimizations and inputs. Furthermore, this paper presents a quantitative approach for identifying computation-intensive and control-intensive applications by RDCC values, and provides ﬁve levels of classiﬁcation based on the K-means clustering of RDCC values. The feasibility is veriﬁed by many evaluation results. Therefore, RDCC can be used as a metric to identify and evaluate the characteristics of applications and is much helpful for the design of high-efﬁciency processor architecture and software optimization.
On-Street vs. Off-Street Parking: An Urban Economic Analysis

On-street parking is suggested to be associated with congestion due to cruising for parking. Against this background, we examine a full switch from on-street parking to off-street by means of a spatial urban CGE model. Hereby, we model cruising for parking, on-street and off-street parking together with location decisions, land and labor markets. We consider different funding schemes for infrastructure: lump-sum taxation, congestion tolls and parking fees. We find that prohibiting on-street parking generates a positive social net benefit if it is financed by the urban society as whole (lump-sum), and not by car users alone. Further, urban welfare declines in any case due to the effects on the land markets. This may help to explain why local policymakers are reluctant to foster off-street parking. In all cases, distributional effects are substantial with landowners being the main beneficiary of the measure.
THE PENNSYLVANIA STATE UNIVERSITY SCHREYER HONORS COLLEGE DEPARTMENT OF PSYCHOLOGY ASSESSING THE SELF-CONCEPT CLARITY IN REPORTED PRECIPITANTS OF NSSI

The purpose of this study was to examine precipitants of Non-Suicidal Self-Injury (NSSI) in undergraduate students. Recent research has found that as rates of NSSI increase, reports of clarity in one’s sense of self and identity have decreased. Defined as how clearly and stably one’s beliefs about oneself are defined, research suggests that self-concept clarity is an important predictor for suicidal urges and NSSI. The present study aimed to assess the relationship between self-concept clarity, the ability for participants to identify a precipitant for self-harm, and the associated markers of severity of self-injury including methods used, physical state following self-harm, and necessity of medical intervention. It was predicted that individuals with poor sense of self will report more diffuse reasons for self-harm rather than a concrete precipitating event and will be more likely to engage in riskier self-harm with more severe outcomes. While neither of these hypotheses were supported by the study findings, results showed that individuals with poorer self-concept were more likely to engage in NSSI on the different day as the reported precipitating event and were more likely to have sought medical consultation following the self-injury. Furthermore, the concreteness of the precipitant was found to be associated with two markers of NSSI severity, namely dissociation occurring after the initiation of NSSI, and the number of cuts within one NSSI episode when the participant indicated cutting as their primary method of NSSI.
Influence of Synthetic Label Image Object Properties on GAN Supported Segmentation Pipelines

Artificial neural networks (ANNs) are a state-of-the-art method for image segmentation [1, 2, 3]. To train a neural network for a specific segmentation task, a dataset containing images and corresponding label images is required [4]. Usually, label images are annotated manually or semi-automatically. This is time consuming, expensive and prone to errors. A recent approach substitutes manual labeling by a Generative Adversarial Network (GAN) [5, 6, 7, 8, 9, 10, 11, 12, 13]. GANs are able to transform data from one domain into another. For example from the domain of horse images to the domain of zebra images, while preserving the general properties of the image [14]. This approach can be used to create a synthetic training dataset based on real-world images and synthetic label images (which have to be created manually or semi-automatically). The main advantage of this approach is, that during the training of the GAN no direct one-to-one relation between image and label is required. The GAN learns the mapping from the label domain to the image domain and vice versa. During inference, the trained model can be used to create a paired synthetic dataset from the synthetic label images [12, 9, 11]. For example, this can be achieved by the CycleGAN architecture [14].
Educational Data Mining and Learning Analytics: Past, Present and Future

Educational data mining and learning analytics have recently emerged as two important fields aimed at rendering e-learning environments more effective. Aim of this study seeks first to reveal the differences between these two fields and then to discuss the future of these concepts by evaluating how they changed throughout history. Educational data mining refers to uncovering the patterns hidden in the big data whilst learning analytics is the use of these patterns to optimize e-learning environments. One of the purposes of the study is to add to the literature on the future trends regarding these concepts. In the very near future, it seems that studies will be performed on EDM and the Industry 4.0 and one of its application areas, “(Internet of Things-IoT)” and EDM has the potential to substantially help researchers in discovering the patterns in the interaction data in the Learning Management Systems and in designing more effective learning environments. The studies on the future of learning analytics are categorized in five main headings: personalization of learning processes, learning design, learning experience design, dashboard design and the Industry 4.0 applications.
A High School Level Health and Disease-Focused Biology Curriculum Promotes Higher Level Skills in Nutrition Literacy.

Objective To determine the efficacy of a high school biology curriculum focused on promoting nutrition literacy skills. Design High school students participated in a six-week biology curriculum focused on the three subdomains of nutrition literacy: functional use of factual knowledge (FNL); interactive skills in seeking out information (INL); critical interpretation and analysis (CNL). We used a mixed-methods, change-over-time model that leverages longitudinal aspects of instructor practice and students' development. Pre- and posttest measures of FNL, INL and CNL were administered. Students were also given a retrospective pre-post online survey to measure interactive nutrition literacy and self-efficacy towards learning about nutrition topics. Participants A total of 111 high school 11th and 12th grade students from four sections of a Biology II course participated. Results Students' overall NL scores improved (P<0.0001) and they also showed gains in each subdomain (FNL, INL and CNL, P<0.0001). Self-efficacy toward learning about nutrition also increased (P<0.0001). Students reported increased communication about the topics with family and peers who were neither classmates or friends (P<0.0001). Conclusions Participation improved nutrition literacy in each of the subdomains, as well as self-efficacy. Self-efficacy was strongly related to increased communication.
List of Tables Table 1 : Table outlining the frequencies of participants Table 2 : Descriptive statistics on the Big Five factors of personality amongst millennials

Purpose This research study focuses on the millennial generation. It explores retention, motivation and burnout amongst millennials, and whether they differ amongst permanent employees and contingent employees Design & Methodology This study utilises an objective and quantitative approach, which involves the use of existing self-administered questionnaires. Data was obtained from permanent and contingent millennial employees working within a global tech company (which will remain anonymous throughout). Data was collected from 95 millennials, of which there were 35 permanent employees and 60 contingent employees. The questionnaires administered aim to determine Millennial Characteristics by use of The Big Five Questionnaire (John & Srivastava, 1999), Employee Retention by use of Kyndt et al. (2009) retention questionnaire, Motivation by use of Deci & Ryan (2000) WEIMS (Work Extrinsic and Intrinsic Motivation Scale), and Burnout through Maslach’s Burnout Inventory (MBI) (1996). Findings – Key findings show that permanent employees experience higher motivation levels and lower levels of burnout in comparison to contingent employees. Contingent employees show a greater intention to leave the company than permanent employees, who show a high level of retention. In addition, results of regression analyses were supportive of motivation and burnout’s ability to predict retention. Recommendations – The main recommendations are for HR to implement practices focusing on the needs of contingent employees in addition to understanding the millennial motivations as a whole, in order to curtail high turnover. Limitations – This study is limited by the single industry data and sample size, amongst other limitations. That said, this study provides the foundation for further research into factors affecting permanent and contingent millennial employees. Practical Implications This study may contribute to factors affecting high turnover. It has the potential to enhance HR practices within an organisation that employ contingent employees.