Miracle-dz/UniFAR-FTUs huggingface dataset:
A critical reflection on how outdoor learning can be used to facilitate high quality learning and teaching (HQLT)

Conclusion: A review of the data showed two major emerging themes: higher levels of engagement during the lesson leading to higher levels of enjoyment. This led to an increase in self-confidence when working indoors and the children showed signs of increased motivation towards learning during their writing tasks; collaborative learning taking place through effective and purposeful communication between children led to higher cognitive skills being visible to the observers. The study has shown that outdoor learning can have positive impacts on children’s learning and can be used as a means of facilitating HQLT.
Auditing of the phlebotomy system in medical laboratories in Port Sudan City, Sudan

Port Sudan Ahlia College, Port Sudan, Red Sea State, Sudan (Correspondence to: Bahir A. Bashir: bashirbashir17@hotmail.com). Abstract Background: Phlebotomy is one of the most ignored techniques in laboratory medicine and health care. It is a complicated practice that requires wide knowledge and high-level skills. Mistakes in phlebotomy can influence laboratory results (diagnosis) and affect patient care. Aims: To appraise phlebotomists’ practice and assess the extent of compliance with the guidelines and determine the frequency of errors in hospital laboratories in Port Sudan, Sudan. Methods: A cross-sectional observational study was conducted using a structured observation scheme in 8 Sudanese public hospitals between August and September 2017. A structured questionnaire was used to assess the venepuncture procedures. Five diverse blood collections by each phlebotomist were observed at each session. We monitored 120 blood collections by 24 phlebotomists, 16 (66.7%) male, and 8 (33.3%) female, with a mean age of 31.1 years. Results: Three of 8 phlebotomy sites were not covered by standard operating procedures (SOPs). Furthermore, phlebotomists lacked appropriate training plans. At 33.3% of the sessions, phlebotomists did not wear gloves at all, and in 69.2% sessions, they did not use new gloves for each patient. There was a significant correlation between phlebotomists’ expertise and the duration of tourniquet application. Conclusion: This study demonstrates that SOPs were not available in some phlebotomy districts. Phlebotomists did not follow Clinical and Laboratory Standards Institute guidelines. Ongoing assessment and improvement of procedures are fundamental to ensure that the phlebotomy service operates effectively.
COMPARISON OF DIFFERENT SNOW STABILITY TESTS INCLUDING

Several tests have been proposed in the past for evaluating snow stability. However, their performance is presently unclear since few comparative studies have been done. During winter 20072008 we have collected a dataset of 146 snow profiles, consisting of snow stratigraphy, a rutschblock test (RB), one to two extended column tests (ECT) and in most of the cases also one to two compression tests (CT). We studied whether the tests were able to predict stability. Study slopes were classified as rather unstable, when either signs of instability such as whumpfs or recent avalanche activity on nearby slopes were observed, or the profile was classified as poor or very poor. The CT had an almost perfect probability of detection, but as the structural stability index (threshold sum), the CT largely overestimated instability (high proportion of false alarms). Of the small scale tests the ECT was best suited to differentiate between stable and unstable situations. By including the ECT score (number of tabs), the number of false alarms was slightly reduced. The performance was similar to the RB which is, however, not independent of the stability classification we used. With two adjoining ECTs it was possible to classify 87% of our test slopes with an accuracy of about 90% in rather stable or rather unstable. Comparing two adjacent stability test results showed that only in about half of the pairs the same weak layer showed up as the most critical one. The ECT proved more difficult to perform than the RB, but was done faster than the RB. This advantage of the ECT contrasts with the lack of an intermediate stability level.
Deep In-Memory Architectures for Machine Learning–Accuracy Versus Efficiency Trade-Offs

In-memory architectures, in particular, the <italic>deep in-memory architecture</italic> (DIMA) has emerged as an attractive alternative to the traditional von Neumann (digital) architecture for realizing energy and latency-efficient machine learning systems in silicon. Multiple DIMA integrated circuit (IC) prototypes have demonstrated energy-delay product (EDP) gains of up to <inline-formula> <tex-math notation="LaTeX">$100\times $ </tex-math></inline-formula> over a digital architecture. These EDP gains were achieved <italic>minimal</italic> or sometimes <italic>no loss</italic> in decision-making accuracy which is surprising given its intrinsic analog mixed-signal nature. This paper establishes models and methods to understand the fundamental energy-delay and accuracy trade-offs underlying DIMA by: 1) presenting silicon-validated energy, delay, and accuracy models; and 2) employing these to quantify DIMA’s decision-level accuracy and to identify the most effective design parameters to maximize its EDP gains at a given level of accuracy. For example, it is shown that: 1) DIMA has the potential to realize between <inline-formula> <tex-math notation="LaTeX">$21\times $ </tex-math></inline-formula>-to-<inline-formula> <tex-math notation="LaTeX">$1365\times $ </tex-math></inline-formula> gains; 2) its energy-per-decision is approximately <inline-formula> <tex-math notation="LaTeX">$10\times $ </tex-math></inline-formula> lower at the same decision-making accuracy under most conditions; 3) its accuracy can always be improved by increasing the input vector dimension and/or by increasing the bitline swing; and 4) unlike the digital architecture, there are quantifiable conditions under which DIMA’s accuracy is fundamentally limited due to noise.
Studies on Adsorption of Fluorescein Dye from Aqueous Solutions Using Wild Herbs

The adsorption of fluorescein dye (FD) on wild herb microparticles (Juniperus (JH) and Solenostemma argel (Del) Hayne (SH)) was studied to elucidate the changes in adsorption behavior with various parameters, such as initial concentration, adsorbent dosage, pH, contact time, and temperature. It was determined that the adsorption percentage of JH for FD was as high as 85.5%, which was higher than that of SH (71.9%). The morphologies of JH and SH were analyzed using Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and scanning electron microscopy (SEM) analyses. The JH and SH adsorbents contained different functional groups, which were involved in the binding of the FD molecules during adsorption. The XRD patterns of JH and SH confirmed the presence of a combination of amorphous and crystalline phases in their structures. The SEM images of the surface of JH revealed the presence of deep pores ranging in size from 1.9 to 3.5 μm, while SH contained smaller pores ranging in size from 130 to 350 μm, which could help absorb large quantities of FD. The Freundlich model fitted the adsorption isotherms better than the Langmuir model. The values of the Freundlich equilibrium coefficient and separation factor ranged from 1 to 2 and from 0 to 1, respectively. The maximum adsorption capacities of JH and SH were determined to be 2.91 and 2.565 mg/g, respectively. Four kinetic models were used to analyze the experimental data, and it was determined that the pseudo-second-order kinetic model best described the adsorption process, which involved chemical adsorption and the internal diffusion. Thermodynamic parameters, including the enthalpy, entropy, and Gibbs free energy, were calculated. These parameters indicated that the adsorption of FD on JH was spontaneous and endothermic and the adsorption of FD on SH was unspontaneous and exothermic.
Discriminative Probabilistic Pattern Mining using Graph for Electronic Health Records

Electronic Health Records (EHR) contains plenty of useful information about patient’s medical history. However, EHR is highly unstructured data and amount of it is growing continuously, that is why there is a need in a reliable data mining technique to group and categorize clinical notes. Although, many existing data mining techniques for group classification use frequent patterns generated based on frequencies of keywords, these patterns do not possess strong enough distinguishing characteristics to show the difference between datasets to classify complex data such as clinical notes in EHR. Also, these techniques encounter scalability and computational cost problems when used on large EHR dataset. To address these issues, we introduce discriminative probabilistic pattern mining algorithm that uses a graph (DPPMG) to generate the subgraphs of frequent patterns for classification in electronic health records. We use co-occurrence, a combination of binary features, which is more discriminative than individual keywords to construct discriminative probabilistic frequent patterns graph for clinical notes classification. Each co-occurrence has a weight of log-odds score that is associated with its discriminative power. The graph, which reflects the essence of clinical notes is searched to find discriminative probabilistic frequent subgraphs. To discover the discriminative frequent subgraphs, we start from a hub node in the graph and use dynamic programming to find a path. The discriminative probabilistic frequent subgraphs discovered by this approach are later used to classify clinical notes of electronic health records.
A new method for monitoring machinery movement using an Unmanned Aerial Vehicle (UAV) system

Hot Mixed Asphalt (HMA) is one of the most used materials in road construction. To receive a longer lifespan of asphalt and lower maintenance cost, quality control during asphalt paving is crucial. The temperature of the asphalt mixture is considered a key factor that impacts the final performance of the pavement. The temperature of the asphalt mix is changing continuously during the compaction. If the asphalt is not compacted within a suitable temperature window, it will reduce cracking toughness and raise the chances of crack propagation. Researchers are currently using thermal sensors to monitor the temperature both in paving and compaction process. Also, Global Positioning Systems (GPS) is used to monitor the movement of road construction equipment (i.e., pavers and rollers). In these systems, the geo-referenced temperature data are displayed to operators, so that they can develop their strategies during the paving process. However, the current monitoring system has several limitations: (1) the system requires a high initial investment, (2) instruments need to be mounted on every machine, setup and adjustment of these instruments are time-consuming, and (3) high density of buildings and tall trees disturb or even block GPS signals in some construction environments. On the other hand, Unmanned Aerial Vehicles (UAVs) offer a potential solution to the three limitations. UAVs are becoming increasingly more mature and available and they started to be used in the civil engineering industry as a data acquisition platform and an instrument for surveying purposes. This research aims to investigate the applicability of UAV-based monitoring systems for paving operations. To this end, a UAV-based method for monitoring the movement of the paving machinery during road construction is developed. In this method, markers are placed on (1) static known locations on the site, and (2) moving equipment. Computer vision and photogrammetry methods are used to localize moving equipment in each frame of the video based on location of known markers. The performance of the proposed method is tested in several case studies. The UAV based solution is found to be a promising method for tracking road construction machinery. The solution can reduce initial investment and at the same time ensure adequate accuracy of tracking targets. In addition, UAVs allow adding or replacing several components based on demand. For example, onboard computer for real-time data processing and thermal camera for temperature monitoring. Thus, the UAV based solution can be extended to both monitor paving operations and temperature during road construction in the future.
Principal Component Analysis of Wide Area Phasor Measurements for Islanding Detection – A Geometric View

This paper presents a new technique for the detection of islanding conditions in electrical power systems. This problem is especially prevalent in systems with signifi cant penetrations of distributed renewable generation. The proposed technique is based on the application of principal componen t analysis (PCA) to data sets of wide-area frequency measurem ents, recorded by phasor measurement units. The PCA approach was able to detect islanding accurately and quickly when compared with conventional RoCoF techniques, as well as with th e frequency difference and change of angle difference method s recently proposed in the literature. The reliability and accuracy of the proposed PCA approach is demonstrated using a number of test cases, which consider both islanding and non-island ing events. The test cases are based on real data, recorded from several phasor measurement units located in the UK power system.
R&D and Foreign Sales: Evidence from Swedish MuItinationals

The simultaneous relationship between industrial R&D and internationalization is investigated, using unique data on Swedish multinationals in manufacturing 1986-90. The statistical analysis supports the view that MNCs partIy base their international competitiveness, measured as foreign sales, on technological capabilities created through R&D. On the other hand, the analysis also indicates that more sales in foreign markets induce larger R&D expenditures, e.g. by increasing the rate of return on each R&D dollar spent.
COMPUTATION OF THE FIELDS AND POTENTIALS FOR PARTICLE TRACING UNDER THE EFFECT OF ELECTROMAGNETIC FORCES

In this work we describe a model for the computation of the scalar and vector potentials associated with known electric and magnetic fields, as well as for the inverse problem. The formulation is general, but the applications motivating our study are related to the requirements for advanced modeling of charged particle dynamics in plasma-driven electromagnetic environments. The dependence of the electromagnetic field and its potentials in space and time is assumed to be separable, where the spatial part is connected to established solutions of the static problem, and the temporal part is derived from a phenomenological description based on time-series of measurements. We benchmark our model in the simple problem of a finite current-carrying conductor, for which an analytical solution is feasible, and then present numerical results from simulations of a magnetospheric disturbance in geospace.
Food classification system based on food processing and its relationship with nutritional status of adults in Terengganu, Malaysia

F U L L P A P E R Food classification system based on food processing and its relationship with nutritional status of adults in Terengganu, Malaysia *Asma’, A. , Gan, H.J., Hayati, M.Y. , Khairil-Shazmin, K. and Zainudin, A.A. School of Food Science and Technology, Universiti Malaysia Terengganu, 21030, Kuala Nerus, Terengganu, Malaysia. Centre for Fundamental and Liberal Education, Universiti Malaysia Terengganu, 21030, Kuala Nerus, Terengganu, Malaysia. Institute for Public Health, 40170 Shah Alam, Selangor
Evaluating the Combination of Word Embeddings with Mixture of Experts and Cascading gcForest In Identifying Sentiment Polarity

Neural word embeddings have been able to deliver impressive re-sults in many Natural Language Processing tasks. The quality of the word embedding determines the performance of a supervised model. However, choosing the right set of word embeddings for a given dataset is a major challenging task for enhancing the results. In this paper, we have evaluated neural word embeddings with (i) a mixture of classification experts (MoCE) model for sentiment classification task, (ii) to compare and improve the classification accuracy by different combination of word embedding as first level of features and pass it to cascade model inspired by GcForest for extracting diverse features. We argue that each expert learns a certain positive and negative examples corresponding to its category of features and resulting features on a given task (polarity identification) can achieve competitive performance with state of the art methods in terms of accuracy, precision, and recall using gcForest.
Tight Approximation Algorithms for Maximum Separable Assignment Problems

A separable assignment problem (SAP) is defined by a set of bins and a set of items to pack in each bin; a value, fij , for assigning item j to bin i; and a separate packing constraint for each bin – i.e. for bin i, a family Ii of subsets of items that fit in bin i. The goal is to pack items into bins to maximize the aggregate value. This class of problems includes the maximum generalized assignment problem (GAP)1) and a distributed caching problem (DCP) described in this paper. Given a β-approximation algorithm for finding the highest value packing of a single bin, we give 1. A polynomial-time LP-rounding based ((1 − 1 e )β)approximation algorithm. 2. A simple polynomial-time local search ( β β+1 − )approximation algorithm, for any > 0. Therefore, for all examples of SAP that admit an approximation scheme for the single-bin problem, we obtain an LPbased algorithm with (1− 1 e − )-approximation and a local search algorithm with ( 12 − )-approximation guarantee. Furthermore, for cases in which the subproblem admits a fully polynomial approximation scheme (such as for GAP), the LP-based algorithm analysis can be strengthened to give a guarantee of 1 − 1 e . The best previously known approximation algorithm for GAP is a 12 -approximation by Shmoys and Tardos; and Chekuri and Khanna. Our LP algorithm is based on rounding a new linear programming relaxation, with a provably better integrality gap. To complement these results, we show that SAP and DCP cannot be approximated within a factor better than 1− 1 e unless NP⊆ DTIME(n log ), even if there exists a polynomial-time exact algorithm for the single-bin problem. IBM T. J. Watson Research Center. Email: {lkf,sviri}@watson.ibm. com MIT Department of Mathematics. Email: goemans@math.mit.edu, mirrokni@theory.csail. mit.edu. Supported in part by NSF grants CCR0098018 and ITR-0121495, and ONR grant N00014-05-1-0148. 1GAP is as follows: given a set of bins and a set of items that have a different size and value for each bin, pack a maximum-valued subset of items into the bins. We extend the (1 − 1 e )-approximation algorithm to a nonseparable assignment problem with applications in maximizing revenue for budget-constrained combinatorial auctions and the AdWords assignment problem. We generalize the local search algorithm to yield a 12 − approximation algorithm for the k-median problem with hard capacities. Finally, we study naturally defined game-theoretic versions of these problems, and show that they have price of anarchy of 2. We also prove the existence of cycles of best response moves, and exponentially long best-response paths to (pure or sink) equilibria.
Testing the conditional correlations and volatility spillovers between US and ASEAN Islamic stock markets: A Multivariate GARCH Analysis

This study examines the conditional correlations and volatility spillovers between the US and ASEAN Islamic stock markets. The empirical design uses MSCI (Morgan Stanley Capital International) Islamic indexes as it adopted stringent restriction to include companies in sharia list. By using a three multivariate GARCH models (BEKK, diagonal VECH, and CCC model), we find evidence of returns and volatility spillovers from the US to the ASEAN Islamic stock markets. However, as the estimated time-varying conditional correlations and volatilities indicate there is still a room for diversification benefits, particularly in the single markets. The Islamic MSCI of Thailand, Indonesia, and Singapore are less correlate to the US MSCI Islamic index. The implication is that foreign investors may benefit from the reduction of risk by adding the Islamic stocks in those countries.
How Much Information Do Monetary Policy Committees Disclose? Evidence from the FOMC's Minutes and Transcripts

The purpose of central bank minutes is to give an account of monetary policy meeting discussions to outside observers, thereby enabling them to draw informed conclusions about future policy. However, minutes are by necessity a shortened and edited representation of a broader discussion. Consequently, they may omit information that is predictive of future policy decisions. To investigate this, we compare the information content of the FOMC's minutes and transcripts, focusing on three dimensions which are likely to be excluded from the minutes: 1) the committee's degree of hawkishness; 2) the chairperson's degree of hawkishness; and 3) the level of agreement between committee members. We measure committee and chairperson hawkishness with a novel dictionary that is constructed using the FOMC's minutes and transcripts. We measure agreement by performing deep transfer learning, a technique that involves training a deep learning model on one set of documents - U.S. congressional debates - and then making predictions on another: FOMC transcripts. Our findings suggest that transcripts are more informative than minutes and heightened committee agreement typically precedes policy rate increases.
A Blockchain-Based Hybrid Incentive Model for Crowdsensing

Crowdsensing is an emerging paradigm of data aggregation, which has a pivotal role in data-driven applications. By leveraging the recruitment, a crowdsensing system collects a large amount of data from mobile devices at a low cost. The critical issues in the development of crowdsensing are platform security, privacy protection, and incentive. However, the existing centralized, platform-based approaches suffer from the single point of failure which may result in data leakage. Besides, few previous studies have addressed the considerations of both the economic incentive and data quality. In this paper, we propose a decentralized crowdsensing architecture based on blockchain technology which will help improve the attack resistance. Furthermore, we present a hybrid incentive mechanism, which integrates the data quality, reputation, and monetary factors to encourage participants to contribute their sensing data while discouraging malicious behaviors. The effectiveness our of proposed incentive model is verified through a combination of the theory of mechanism design. The performance analysis and simulation results illustrate that the proposed hybrid incentive model is a reliable and efficient mean to promote data security and incentivizing positive conduct on the crowdsensing application.