Miracle-dz/UniFAR-FTUs huggingface dataset:
An Examination of the Decision-Making Process Instructional Designers Use to Complete Projects With the Constraints of Limited Time and Tools

AN EXAMINATION OF THE DECISION-MAKING PROCESS INSTRUCTIONAL DESIGNERS USE TO COMPLETE PROJECTS WITH THE CONSTRAINTS OF LIMTIED TIME AND TOOLS Denesha Kaye Rabel Old Dominion University, 2019 Director: John Baaki Based on a phenomenological theoretical perspective, the purpose of this qualitative study was to explore how instructional designers make decisions related to determining which layers and related instructional design activities to address based on time and tool resource constraints. To explore the topic, this study was guided by five research questions which included: (a) what type of time and tool constraints do instructional design practitioners experience, (b) how do instructional design practitioners make decisions based on time constraints when completing work projects, (c) how do instructional design practitioners make decisions based on tool constraints when completing work projects, (d) how do instructional design practitioners determine which layers or questions to address given project constraints such as time and tool limitations, and (e) what steps do instructional design practitioners omit during work projects that have time and or/tool constraints? The study included 20 instructional designers (n=20) that work in various industries including higher education institutions, consulting, tourism, charity/nonprofit, health care, government, and retail. There were a total 14 female participants and 6 male participants. Upon the completion of 20 interviews and analysis of interview notes, six themes and three patterns emerged. The findings from this study show that in response to the constraint of limited time to design, develop, and implement instructional
Robust Control of an Aerial Manipulator Based on a Variable Inertia Parameters Model

Aerial manipulator, which is composed of an unmanned aerial vehicle (UAV) and a multilink manipulator and can perform aerial manipulation, has shown great potential of applications. However, dynamic coupling between the UAV and the manipulator makes it difficult to control the aerial manipulator with high performance. In this article, system modeling and control problem of the aerial manipulator are studied. First, an UAV dynamic model is proposed with consideration of the dynamic coupling from an attached manipulator, which is treated as disturbance for the UAV. In the dynamic model, the disturbance is affected by the variable inertia parameters of the aerial manipulator system. Then, based on the proposed dynamic model, a disturbance compensation robust $H_{\infty }$ controller is designed to stabilize flight of the UAV while the manipulator is in operation. Finally, experiments are conducted and the experimental results demonstrate the feasibility and validity of the proposed control scheme.
An efficiency analysis of food distribution system through data envelopment analysis

The specific quality and safety requirements, typical of the food supply chain, force to strong action on implementing distribution networks, reducing transport and delivery costs, improving distribution efficiency and performance, increasing carriers control and flexibility. In this context, the selection and evaluation of third-party logistics has become a crucial aspect in order to realise an efficient food products distribution, with both a high level of service and competitive costs. This paper implements data envelopment analysis theory to analyse the case of an Italian food producer, which distributes its products on the national territory, through several third-party logistic carriers. This study made possible to define the most efficient carrier among those responsible for the distribution process, analysing the retail trade and large-scale retail trade. This paper represents a reference guideline to all the food companies involved in the process of evaluation and improvement of the distribution process.
WNIN Mutant Obese Rats Develop Acute Pancreatitis With the Enhanced Inflammatory Milieu

Background: WNIN/Gr-Ob rats demonstrate features of metabolic syndrome that include obesity, insulin resistance, impaired glucose tolerance, and hyperinsulinemia. The impetus obtained from earlier studies on these rats demonstrates an inflammatory milieu peaking between 6 and 9 months of age in adipose, pancreas, and bone marrow/mesenchymal stem cells. Methods: We evaluated the outcome of L-arginine-induced acute pancreatitis, in WNIN/Gr-Ob rats as compared to control rats. We assessed key parameters associated with severity of acute pancreatitis such as pancreatic inflammation and extra-pancretic damage. Result: We demonstrated increased pancreatic inflammation (inflammatory cytokines, malondialdehyde, and reactive oxygen species) in WNIN/Gr-Ob rats as compared to the control rats administered with L-arginine. Increased systemic inflammation and islet dysfunction were also observed in WNIN/Gr-Ob rats as compared to control when L-arginine was administered. Molecular, cellular and biochemical data from our study indicate increased severity of pancreatitis in mutant rats as compared to control rats. Conclusion: We advocate WNIN/Gr-Ob rats as a novel model system to study the pathophysiology of severe acute pancreatitis associated with obesity. Cell Mol Med Res. 2017;1(1):20-31 doi: https://doi.org/10.14740/cmmr11e
The Memory Advantage of Long Short-Term Memory Networks for Bond Yield Forecasting

The importance of bond markets in the financial industry stems from its dimension, its direct relevance for other asset classes and for the overall economy. In this paper, we conduct the first study of bond yield forecasting using deep learning long short-term memory (LSTM) networks, validating the potential of LSTMs networks for that purpose, and identifying the LSTM's memory advantage over standard feedforward neural networks, in particular, the multilayer perceptron (MLP). Specifically, we model the 10-year Euro government bond yield using univariate LSTMs with different input sequences (6, 21 and 61 time steps), considering five forecasting horizons, from next day to 20 days ahead. We compare those LSTM models with MLPs, both univariate as well as using the most relevant features for each forecasting horizon. The results show that the univariate LSTM model with additional memory is capable of achieving similar results as the multivariate MLP using information from markets and the economy. Moreover, the direct comparison of models in identical conditions, i.e. small input sequence of 5 time steps, leads to results with LSTMs that are similar or better with lower standard deviations. Furthermore, with the LSTMs, shorter forecasting horizons require smaller input sequences and vice-versa. In summary, the results are encouraging for the use of LSTMs in decision support systems for the asset management industry, incorporating macroeconomic / market information and adjusting the input sequence length to the forecasting horizon considered.
Indigenous instructors’ perspectives on pre-service teacher education: poetic responses to difficult learning and teaching

ABSTRACT Instructors teaching an Indigenous education course face the challenges of shifting students’ understanding and inviting them into the work of decolonizing education. Indigenous instructors take on the embodied and emotional work of highlighting diverse representations of Indigenous peoples, histories, and perspectives in scholarship in order to make this learning meaningful to students. Bringing such views to education students, who are mostly non-Indigenous, is no easy task. In this study, we examine instructor experiences of difficult teaching within a mandatory Indigenous education course in Canada. We adopt a ‘poetics of anti-racism’ to represent and explore the moments of difficult teaching that are indicated by what is said, and unsaid, by the Indigenous instructors we interviewed. We argue that poetic approaches are powerful in articulating the complexity of Indigenous instructors’ experiences, as well as inspiring moments of transformation in education.
Longevity records show that Upland Sandpipers are long-lived birds

The life-history traits of migratory waders are characterized by low reproductive rates, delayed maturity, and high survivorship (Myers et al. 1987, Sandercock 2003), which leads to low rates of maximum population growth. Due to their suite of life-history traits, many populations of migratory waders have been negatively impacted by environmental change, habitat loss, harvest, and other perturbations. Conservation planning has been assisted by improved estimates of survival for wader populations (Méndez et al. 2018), but basic data are still lacking for many poorly studied species (Vickery et al. 2010, PearceHiggins et al. 2017). One indication that sandpipers and other scolopacid waders might have high annual survival is that records of maximum longevity are often quite high. Scolopacid waders with estimates of maximum longevity over 10 years in the Western Hemisphere include: 12–13+ years for Sanderling Calidris alba (Boates & McNeil 1984) and Wandering Tattler Tringa incana (Gill et al. 2010), 14+ years for Dunlin C. alpina (Warnock & Gill 1996) and Short-billed Dowitcher Limnodromus griseus (BBL 2019), 16+ years for Whimbrel Numenius phaeopus (Klima et al. 2013), Ruddy Turnstone Arenaria interpres (BBL 2019), Least Sandpiper C. minutilla (Miller et al. 1988), and Semipalmated Sandpiper C. pusilla (Gratto-Trevor & Vacek 2001), 19 years for Red Knot C. canutus (rufa subspecies; Baker et al. 2013), 23+ years for Bristle-thighed Curlew N. tahitiensis (Marks 1992), and 25–29+ years for Marbled Godwit Limosa fedoa (Colwell et al. 1995, Gratto-Trevor 2000).
Comparison of different quantile regression methods to estimate predictive hydrological uncertainty in the Upper Chao Phraya River Basin, Thailand

The estimation of predictive uncertainty and its application as a post‐processor of hydrological model output, such as water level, can provide additional information useful for short‐term hydrological forecasting. In this study, We applied quantile regression models for estimating predictive hydrological uncertainty and used it to derive probabilistic hydrological forecasts. Forecast water levels and associated forecast errors were used as predictor and predictand, respectively, to develop three regression models: (a) linear quantile regression (LQR), (b) weighted LQR and (c) LQR in Gaussian space using Normal Quantile Transformation. These different models for hydrological forecasting were developed for, and applied to, the operational flood forecasting system in the Upper Chao Phraya River, Thailand. The quality of these forecasts in terms of reliability, sharpness and overall skill were assessed using various graphical and numerical verification metrics. Results show that the improvement of forecast in terms of either reliability or sharpness depends upon the configurations used. With comparable overall performance, weighted LQR provided a relatively simple configuration, which can be used for estimating uncertainty in hydrological forecasting.
The Effect of Curriculum Peculiarities on Psychological Well-Being Possessed by Students and Alumni of SMART Ekselensia Indonesia School

Adolescents are actually the next generation and future human resources who play an important role in the times. Psychological Well-Being is a condition of an individual who has a positive attitude towards himself in managing his behavior, can create an environment that is compatible with his needs, has a purpose in life and makes an adolescent's life more meaningful, and tries to explore and develop himself. Psychological Well-Being tests were conducted to describe the psychological health of individuals, in this study the adolescents who were still status as SMART Ekselensia Indonesia students and adolescents who were already alumni, based on fulfilling the positive psychology function criteria. Psychological Well-Being has several variables, including the variable selfacceptance, positive relations with others, personal growth, purpose in life, environmental mastery, and autonomy. SMART Ekselensia Indonesia as one of the Dompet Dhuafa programs in the field of education, dares to answer the challenge of forming adolescents who are independent and ready to face their roles as the next generation as well as resources that will play an important role in civilization through its specific programs and curriculum. Obtained results of the level of Psychological Well-Being held by alumni tend to be higher than students. This shows that the programs and curriculum characteristics that are owned by SMART Ekselensia Indonesia are able to be well received by students during their schooling and are able to increase the level of Psychological Well-Being until students become alumni.
Nesting biology of Centris (Paracentris) burgdorfi (Apidae: Centridini)

We describe the nesting biology of Centris (Paracentris) burgdorfi, a solitary bee that nests in sandstone in northeastern Brazil. The nest consists of a shallow tunnel with access to the brood cells. Females of C. burgdorfi made 1–7 brood cells per nest with each cell requiring 2.58 ± 0.40 ( ± SD) days to construct. The average cell-building construction time was longer when compared to other Centris species. Females were larger than males, and this difference was reflected in the size of their respective emergence cells. The temperature within C. burgdorfi nests was lower when compared to ambient temperature. Our study is the first to report the nesting biology of C. burgdorfi. The detailed behavior of the female inside the nest was also described, which is unusual in the study of solitary bee nesting biology.
Hierarchical Intrusion Detection Using Machine Learning and Knowledge Model

Intrusion detection systems (IDS) present a critical component of network infrastructures. Machine learning models are widely used in the IDS to learn the patterns in the network data and to detect the possible attacks in the network traffic. Ensemble models combining a variety of different machine learning models proved to be efficient in this domain. On the other hand, knowledge models have been explicitly designed for the description of the attacks and used in ontology-based IDS. In this paper, we propose a hierarchical IDS based on the original symmetrical combination of machine learning approach with knowledge-based approach to support detection of existing types and severity of new types of network attacks. Multi-stage hierarchical prediction consists of the predictive models able to distinguish the normal connections from the attacks and then to predict the attack classes and concrete attack types. The knowledge model enables to navigate through the attack taxonomy and to select the appropriate model to perform a prediction on the selected level. Designed IDS was evaluated on a widely used KDD 99 dataset and compared to similar approaches.
Fast Digital Clock Calibration of a Differential 6.4 Gb/s/pin Bidirectional Asymmetric Memory Interface

A differential 6.4 Gb/s/pin bidirectional asymmetric memory interface with a fast all-digital clock calibration method is presented. The proposed clock calibration reduces the link training time of the interface compared to the conventional data eye detection by eliminating the need to sweep the receiver clock phase. Instead a scalable synthesized phase-to-digital converter (PDC) is used to measure the offset from the ideal 90° shift while a clock pattern is transmitted. Phase interpolator nonlinearity is compensated in an additional adjustment step in order to minimize the clock centering error. A prototype ASIC which can be configured to act as either a controller or a memory device was fabricated in a 65nm CMOS process and tested. The measured read and write training durations are 3.1<inline-formula> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula> and 3.3<inline-formula> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula>, respectively. The additional area required for the PDC in each transceiver is 0.004mm<sup>2</sup>.
The Linkages Between Investments in Innovation and Business Performance in Serbia

Motivation: Driven by an increasingly competitive marketplace, the savviest businesses invest heavily in corporate innovation as a kind of competitive intelligence that gives a business what it needs to operate with poise and precision. The strategy of investments in innovation by firms can potentially explain the heterogeneity of their income increase based on market success. Such a stimulus for growth has been a motivation for the research question being examined in this paper, namely the link between corporate financing and investment decisions of Serbian firms based on the bank loans as sources of innovation and hence improved enterprise performance. The paper is based on the research of Hottenrott et al. (2014), together with Ferrando & Preuss (2017) and Aerts & Schmidt (2008) concerning the relationship between external finance and business innovation activities. This paper provides information about a bank loan as a financing source that firms use to fund their innovative activities, with the research question whether that has the subsequent impact on the company's performance. The idea of the paper is that investments of enterprises in innovation significantly affect their revenue. Data: Empirical research has been provided by a survey in Serbia in 2017. The sample comprised 152 enterprises, mostly privately owned, of all sizes. Descriptive statistics: Cronbach's Alpha coefficient, regression analysis is used as the research tool and method. The link between business finance and innovation, and furthermore the link between innovation and income of Serbian firms have been investigated. Three groups of factors show that the degree of income and market success of the enterprise increases with the level of investment in innovative activities. These are: sources of financing and financing conditions as independent variables, and company's income revenues, as the dependent variable. Findings: Firms that use bank loans as financial instruments for innovation activities and investments are more likely to develop new products, methods and processes, and successfully increase their revenues and income based on developing this kind of added value. The findings indicate that tangible asset investment of SME’s is positively related to the use of bank finance, to new product development and to enterprise performance improvement. Contribution: Results of the research show that financial investments in business innovation directly contribute to the business sector performance improvement. It also demonstrates the theory of innovative enterprise, the significance of financing, and the impact of banking on the overall development of the economy. The results point to the need for further research in the area of​​ access to finance, as well as the parallel development of non-banking sources of financing.
Data Sharing Technique Modeling for Naive Bayes Classifier for Eligibility Classification of Recipient Students in the Smart Indonesia Program

The objective of Smart Indonesia Program (Program Indonesia Pintar: PIP) is to help school-aged people from poor / vulnerable / priority families to continue to receive education services to graduate from secondary education, both through formal and non-formal education channels. In its implementation, there are still many fraudulent in the proces of nominating proposal PIP funds and there are still many prospective students who should not receive PIP because they do not meet the technical guidelines provided by the Ministry of Education and Culture to determine the eligibility of prospective recipients of PIP funds can be done by schools and stakeholders, one of them by using classification techniques. One algorithm that is widely used in classification is the Naive Bayes Classifier (NBC) algorithm. In this study three data sharing techniques were used, namely Hold Out 70% training data and 30% testing data, K-Means Clustering, and also 10 Fold Cross Validation. Determination of the best data sharing technique will be determined by looking at the value of Accuracy, Precision, and Recall and also the value of Area Under Curve (AUC) which is illustrated by the Receiver Operating Characteristic (ROC) curve so that the NBC algorithm is generated with 10 Fold Cross Validation has a very good classification level with the values of accuracy, precision, and recall respectively at 97.40%; 100%; and 76.14%.
Pre-harvest management of the oriental fruit fly.

The oriental fruit fly, Bactrocera dorsalis (Hendel) (Diptera: Tephritidae), is an important pest of commercial fruit and vegetables in various parts of Asia and Africa, in some South Pacific islands and in Hawaii. Commercial fruit and vegetables can be protected from this pest prior to harvest using multiple control tactics. This paper reviews measures being used and developed for pre-harvest management of B. dorsalis. An integrated use of monitoring and combinations of control tactics on an area-wide basis for the pest is discussed.
Millet-based supplement restored microbiota diversity of acute undernourished pigs

The strong connection between undernutrition and gut microbiota (GM) has enabled microbiota-targeting to become an evolving strategy, which witnessed urgent need for fortified formula of supplementary food in undernutrition therapy. Using undernourished pigs as models, we investigated how corn- and millet-based nutritional supplement acted differently in modulating microbiota. Undernourished pigs at age of 9 weeks were fed with pure maize diet (Maize), corn-based (CSB+) and millet-based (MSB+) supplementary food for 3 weeks. Compared with Maize group, both CSB+ and MSB+ improved serum total protein and globulin level, but no physiological improvement was observed by short-term food intervention. MSB+ shown superior influence on GM immaturity which were more normally-grown like at both structural and functional level. Higher level of Bacteriodetes, Firmicutes and lower level of Preteobacteria were detected in MSB+ fed piglets in contrast with CSB+. Lachnospira.spp was significantly raised after nutritional intervention, indicating high correlation with the undernutrition-associated phenotype. Thus, especially from the GM aspect, millet could be one promising source to help undernourished children reconstruct balanced microbiota in short therapeutic term.
Stencil‐based 3D facial relief creation from RGBD images for 3D printing

Three‐dimensional (3D) selfie services, one of the major 3D printing services, print 3D models of an individual's face via scanning. However, most of these services require expensive full‐color supporting 3D printers. The high cost of such printers poses a challenge in launching a variety of 3D printing application services. This paper presents a stencil‐based 3D facial relief creation method employing a low‐cost RGBD sensor and a 3D printer. Stencil‐based 3D facial relief is an artwork in which some parts are holes, similar to that in a stencil, and other parts stand out, as in a relief. The proposed method creates a new type of relief by combining the existing stencil techniques and relief techniques. As a result, the 3D printed product resembles a two‐colored object rather than a one‐colored object even when a monochrome 3D printer is used. Unlike existing personalization‐based 3D printing services, the proposed method enables the printing and delivery of products to customers in a short period of time. Experimental results reveal that, compared to existing 3D selfie products printed by monochrome 3D printers, our products have a higher degree of similarity and are more profitable.