Miracle-dz/UniFAR-FTUs huggingface dataset:
A Leadership Development Programme: A Case Study of Transformative Learning in Qatar

Leadership development is an important issue for Qatar as it strives to achieve the ambitious goals set out in its 2030 National Vision (Ministry of Development Planning and Statistics, 2015). Various resources are being invested, but often with minimal results, forcing Qatar to continue to rely on expatriate expertise. Transformative learning experiences that change the deeply held beliefs, worldviews, and frames of reference of what it means to be a 21st century leader in Qatar are needed. This paper presents the case study of an executive leadership development programme to identify key success factors or inhibitors that fostered or hindered transformative learning experiences. It includes in-depth interviews conducted over a 10-month period during 2015. Additional rich data of the participants' experiences were obtained from their blogs, written assignments, and organisational documentation. A thematic analysis identified 11 themes, the inclusion of which fostered transformative learning or, the absence of which, hindered transformative learning: (1) identifying stakeholder expectations, (2) conducting a respected selection process, (3) appropriate English levels, (4) alignment between content and the participant's educational and cognitive skills, (5) time and commitment allocated to a well-structured pre-programme and a (6) post-programme stage, (7) in-depth awareness of the participants' professional and cultural contexts, (8) inclusion of autonomous components, (9) inclusion of personal and cultural interactions, (10) an acceptable balance of travel, stress, uncertainty, and course intensity, and (11) a group dynamic. There were indications the intensity of the programme pushed the participants beyond the required state of disorientation necessary for transformative learning and into one of being overwhelmed and stressed.
Evaluation of the antimicrobial and anticancer properties of the fruits of Synsepalum dulcificum (Sapotaceae)

Purpose: To evaluate the antimicrobial and anticancer activities of the fruit of Synsepalum dulcificum (Sapotaceae) against six bacterial strains and on two different colorectal cancer cell lines, respectively.Methods: The dried fruit of the plant was extracted in a Soxhlet apparatus successively with petroleum ether and ethanol, and concentrated in a rotary evaporator to obtain petroleum ether and ethanol extract, respectively. Phytochemical screening was done on the two extracts. The antimicrobial effects of the extracts against Bacillus subtilis, Staphylococcus aureus, Streptococcus pyogene, Pseudomonas aeruginosa, Klebsiella pneumoniae and Proteus vulgaris were evaluated using agar well diffusiontechnique while the anticancer effect of the ethanol extract was assessed on HCT-116 and primary colon epithelial (PCE) cell lines by MTT assay.Results: The results indicate that the petroleum ether extract of Synsepalum dulcificum fruits exerted stronger antimicrobial activity than the ethanol extract. The ethanol extract also showed significant anticancer activity (p < 0.05). The calculated half-maximal concentration (IC50) of the extract on HCT- 116 cells at 24, 48, and 72 h are 14.99, 8.97, and 8.54 μg/mL, respectively, while the IC50 of the extract on PCE cell lines at 24, 48, and 72 h are 236.25, 206.09, and 196.72 μg/mL, respectively. The extract was more toxic to cancer cells than to normal cells.Conclusion: The results of this study lend some justification for the use of the fruits of Synsepalum dulcificum as an antibacterial and anticancer agent.
Analysis of the influence of MOS device geometry on predicted SEU cross sections

An investigation into the single-event sensitive area geometry of a body-tied-to-source (BTS) SOI nMOS transistor has been performed through a novel simulation technique. Results are presented which demonstrate the influence of spatial variations in charge collection efficiency on the shape of the predicted upset cross section curve. Observations are made on a technique for inferring sensitive area or intra-cell collection efficiencies from cross section data.
Social Networks in Limbo. The Experiences of Older Adults During COVID-19 in Ghana

While studies exploring COVID-19 and its global influence have begun, social networks and support among older adults in low-and middle-income countries, such as Ghana have been inadequate despite its enormous relevance. Thus, the study presents the voices of older adults in Jamestown, Accra and their social networks during the COVID-19 pandemic in Ghana. Using a phenomenological approach, data were collected from 15 older adults through in-depth interviews on older adults' social network experiences during COVID-19 pandemic situation. Older adults generally struggled to maintain connections with their family members, friends, neighbors, and the community, especially during the lockdown. They ascribed their limited interaction to COVID-19 preventive measures, such as social distancing and the limitation of face-to-face meetings imposed by the government. Loneliness, stress, and depression are also linked to the breakdown of social networks. The findings provide a deeper understanding of the impact of COVID-19 on older adults' quality of life. It emerged that the Ghanaian society could reconsider the professional services of gerontologists, social workers, community outreach workers, and philanthropists in mitigating loneliness, stress, and depression among older adults in current and future pandemics.
Exploration of dietary beliefs and social cognitive factors that influence eating habits among college students attending a rural Midwestern university

Abstract Objective: College students are in a critical period of life where many lifestyle changes occur that influence health. The dietary changes that occur in college can persist throughout life and influence health and weight status. Several factors influence eating behaviors and dietary habits during college, and this study explores the dietary beliefs of college students and the factors that influence students’ diets. Participants: Students (n = 49; 20% male; 76% female; 4% other) attending a rural Midwestern university. Methods: Focus groups (n = 7) were used for exploratory research. Demographic and food security data were also collected. Focus group data were analyzed using open coding methods. Themes and subthemes emerged from analysis. Results: Four major themes emerged across focus groups: 1) food and health beliefs; 2) social influence; 3) university influence; and 4) body image beliefs. Conclusions: Findings support offering nutrition education that incorporates foundational nutrition knowledge and cooking skills to all college students.
Showcasing Mahara: A new open source eportfolio

This poster presents the outcomes of a collaborative project funded by the New Zealand Government to develop an open source electronic portfolio for use in the tertiary sector. The intention is to convey the core principles underpinning the design of the ‘Mahara’ eportfolio and to describe some of the unique features of this new open source system. In particular, the poster will showcase the different ‘views’ of Mahara and the social networking functions, along with illustrating how Massey University is planning to embed eportfolios in an initial teacher education programme.
Next Generation Learner Interactions with Personal Assistants for Learning

Next generation learners engage in self-paced individual and collaborative learning with digital tools, games, intelligent systems, and virtual environments more than ever before. The focus of this paper is on understanding how problem-solving is distributed and negotiated among high school learners and simulated Personal Assistants for Learning (PALs). In our research we envision a PAL as a coach that guides and supports the user’s learning experiences, e.g., an early instantiation could be an intelligent tutor that can function across domains. The OpenSimulator virtual environment (Imaginarium) we use in our study utilizes a factual complex problem scenario that takes place in the Pacific Northwest. Learners virtually explore how culture and the environment together act as a system that impacts sustainability science while interacting with a simulated PAL. Their actions are monitored by the virtual environment, ADL experience API (xAPI), Learning Record Store, and the hybrid chat bot/human PAL. This paper is relevant to the I/ITSEC community because it explores the design and use of an open source virtual environment to 1) understand the future implications of a personal assistant for learning and 2) begin to formulate human-computer interaction (HCI) guidelines for next generation learning systems. This paper describes our study, reports preliminary findings, and addresses the following research questions: How is problem-solving negotiated between high school learners and a simulated PAL? Can rapid prototyping in virtual environments expose assumptions about instructional best practices and improve the design of technology-mediated learning experiences? While human interactions with future PALs cannot be accurately predicted, simulating these phenomena in an immersive environment allows for exploration of the effects of technology on human behavior.
ELECTRICAL LOAD FORECASTS IN LONG -TERM AND IMPACT ON LOAD MANAGEMENT APPLICATION

The target of this paper is impacting Demand Side Management (DSM) when it is applied at different sectors; residential, commercial, public, government, agriculture, industrial and the total load of these sectors on national network of power system in Egypt for year (2009-2010) and estimating load forecast for these sectors by Adaptive Network Fuzzy Inference System (ANFIS) techniques during the period from year (2010-2011) to year (2020-2021). The daily load curve for maximum load of August month for year (2009-2010) as base year on Egyptian power system is expressed. Daily load forecast by using three techniques; Adaptive Network Fuzzy Inference System (ANFIS) technique, Artificial Neural Network (ANN) technique and Regression technique are estimated during period from year (2010-2011) to year (2020-2021), he is found the ANFIS technique is more suitable than other techniques. The daily load curve for maximum load of day from year (2009-2010) as base year on Egyptian power system is presented. Estimated daily load forecast by (ANFIS) technique during periodfrom year (2010-2011) to year (2020-2021)is shown. (4 pages)
A review of maximum power-point tracking techniques for photovoltaic systems

This paper provides a detailed review of the maximum power-point tracking (MPPT) techniques used in photovoltaic (PV) systems. The MPPT technique proves to be an essential part of the PV system, and hence the most appropriate method should be chosen in order to optimise the efficiency of the system, keeping in consideration the economic point of view. A small description of various MPPT techniques has been provided and comparison based on features, such as convergence speed, implementation complexity, accuracy, the relative cost of implementing the set-up and their commercial availability has been done. This paper aims at choosing the most appropriate technique for any particular PV application taking into account all the above-mentioned factors, especially the cost, complexity and accuracy, so as to maximise the effectiveness of the system by optimising all the parameters. It aims to serve as a useful guide for both users and manufacturers.
Organophosphonate Functionalization of Al2O3-Coated Nanopores

We present a novel approach for the functionalization of aluminum oxide (Al2O3) coated solid-state nanopores. Silicon nitride nanopores fabricated by e-beam lithography and reactive ion etching were coated with Al2O3 using atomic layer deposition. Formation of self-assembled monolayers (SAMs) of organophosphonic acids (organophosphonates) at the pore walls was studied for three different precursor molecules, namely 1,10-decyldiphosphonic acid, 4-aminobutylphosphonic acid and 12-mercapto-dodecylphosphonic acid. We show by ionic conductance measurements that the nanopores were successfully functionalized with SAMs, leading to the anticipated reduction in pore diameter, hence pore conductance. Functionalized nanopores allow for specifically tailoring the interaction of pore-translocating biomolecules, e.g., for sensing applications.
Edge Artificial Intelligence: A Multi-Camera Video Surveillance Application

Nowadays, video surveillance has a crucial role. Analyzing surveillance videos is, however, a time consuming and tiresome procedure. In the last years, artificial intelligence paved the way for automatic and accurate surveillance-video analysis. In parallel to the development of artificial-intelligence methodologies, edge computing is becoming an active field of research with the final goal to provide cost-effective and real time deployment of the developed methodologies. In this work, we present an edge artificial intelligence application to video surveillance. Our approach relies on a set of four IP cameras, which acquire video frames that are processed on the edge using the NVIDIA® Jetson Nano. A state-of-the-art deep-learning model, called Single Shot multibox Detector (SSD) MobileNetV2 network, is used to perform object and people detection in real-time. The proposed infrastructure obtained an inference speed of ∼10.0 Frames per Second (FPS) for each parallel video stream. These results prompt the possibility of translating our work into a real word scenario. The integration of the presented application into a wider monitoring system with a central unit could bring benefits to the overall infrastructure. Indeed our application could send only video-related high-level information to the central unit, allowing it to combine information with data coming from other sensing devices without unuseful data overload. This would ensure a fast response in case of emergency or detected anomalies. We hope this work will contribute to stimulate the research in the field of edge artificial intelligence for video surveillance.
Study of the Reliability of the Ship Transportation by Applying the Historical Hindcast Weather Data

Regularity of ship transportation depends strongly on environmental conditions, on the ship’s route and on the seakeeping characteristics of a vessel. The ship’s route property selected and the ship characteristics designed appropriately to a region of the ship operation can assure a higher regularity of the transportation.In the presented paper an exemplifying analysis of the ship transportation on a selected typical ship’s route has been performed. The principal purpose of such an analysis was to determine the magnitude and distribution of speed losses in a realistic operational environment to serve as a basis for estimation of reliability of the sea transportation.The aim of the analysis precludes the use of long term statistics as this would not give a correct representation of the extreme value tail of the probability distribution of sailing time. Instead of this, the ship performances on the considered sea routes were simulated in roundabout trips by applying time series of historical (hindcast) weather data (waves, swell, wind, and ocean current). The applied data cover three complete years starting from February 2003 and they are specified with the 6 hour time interval at about 3000 grid points covering almost the whole world ocean area.Two vessels which represent typical classes of ships (a tanker and a container ship) were used in this analysis. The ships voyages were simulated with different departure time and with the time interval of 6 hours providing high sailing frequency and the considerable large amount of results. Results of the calculations were analyzed with respect to the seasonal variations, alternative routes, and different power settings. The purpose of these numerical exercises was to illustrate how to perform the analysis and to present a scope of the obtained results.Copyright © 2012 by ASME
SUBSURFACE DRIP IRRIGATION LATERAL SPACING AND MANAGEMENT FOR COTTON IN THE SOUTHEASTERN COASTAL PLAIN

The cost of drip irrigation can be reduced by using both wider lateral spacings and the same laterals for multiple years, as with subsurface placement. Multiple, low-rate fertilizer and water applications may reduce N fertilizer needs by improving efficiency and limiting the potential for leaching. The combination of these technologies may make drip irrigation of cotton profitable. Four years of continuous cotton and two years of cotton rotated with peanut were evaluated. Two subsurface drip irrigation lateral spacings (every row, 1 m, and alternate furrow, 2 m) and three sidedress-nitrogen methods (STD, single application of 112 kg/ha; INC, 112 kg/ha in five equal applications; and GOS, applications determined by GOSSYM/COMAX) were evaluated for cotton during 1991-1994. Two of the sidedressnitrogen methods (STD and GOS) were evaluated for a rainfall-only treatment. Lint yields did not differ between the lateral spacings in any year. Yields for irrigated treatments were 16 and 65% greater than rainfall-only yields in 1992 and 1993, respectively. The GOSSYM/COMAX-managed nitrogen treatment received 30% less nitrogen fertilizer than other treatments, but had similar lint yield. Several fiber physical properties were affected by irrigation and nitrogen, but these effects were small and inconsistent. For continuous cotton, or cotton rotated with peanut, the wider lateral spacing is preferred to the every-row spacing because of its lower initial cost (about 30%). The combination of lower system cost, longer system life, and lower N-fertilizer requirements could make subsurface drip irrigation of cotton profitable for southeastern Coastal Plain soils, and reduce the potential for ground water contamination.
Emotion Recognition with Short-Period Physiological Signals Using Bimodal Sparse Autoencoders

With the advancement of human-computer interaction and artificial intelligence, emotion recognition has received significant research attention. The most commonly used technique for emotion recognition is EEG, which is directly associated with the central nervous system and contains strong emotional features. However, there are some disadvantages to using EEG signals. They require high dimensionality, diverse and complex processing procedures which make real-time computation difficult. In addition, there are problems in data acquisition and interpretation due to body movement or reduced concentration of the experimenter. In this paper, we used photoplethysmography (PPG) and electromyography (EMG) to record signals. Firstly, we segmented the emotion data into 10-pulses during preprocessing to identify emotions with short period signals. These segmented data were input to the proposed bimodal stacked sparse auto-encoder model. To enhance recognition performance, we adopted a bimodal structure to extract shared PPG and EMG representations. This approach provided more detailed arousal-valence mapping compared with the current high/low binary classification. We created a dataset of PPG and EMG signals, called the emotion dataset dividing into four classes to help understand emotion levels. We achieved high performance of 80.18% and 75.86% for arousal and valence, respectively, despite more class classification. Experimental results validated that the proposed method significantly enhanced emotion recognition performance.
Data-mining-based dynamic dispatching rule selection mechanism for shop floor control systems using a support vector machine approach

The purpose of this paper is to develop a data-mining-based dynamic dispatching rule selection mechanism for a shop floor control system to make real-time scheduling decisions. In data mining processes, data transformations (including data normalisation and feature selection) and data mining algorithms greatly influence the predictive accuracy of data mining tasks. Here, the z-scores data normalisation mechanism and genetic-algorithm-based feature selection mechanism are used for data transformation tasks, then support vector machines (SVMs) is applied for the dynamic dispatching rule selection classifier. The simulation experiments demonstrate that the proposed data-mining-based approach is more generalisable than approaches that do not employ a data-mining-based approach, in terms of accurately assigning the best dispatching strategy for the next scheduling period. Moreover, the proposed SVM classifier using the data-mining-based approach yields a better system performance than obtained with a classical SVM-based dynamic dispatching rule selection mechanism and heuristic individual dispatching rules under various performance criteria over a long period.
CASE STUDY OF TECHNOLOGICAL CHANGE PROCESS IN SOFTWARE UPGRADES MANAGEMENT Migrating from Windows-based to Web-based Application

Hassanbasry Andrea (Writer) (Last Name) (First) (Initial) Case Study of Technological Change Process in Software Upgrades Management: (Title) Migrating from Windows-based to Web-based Application Management Technology Dr. Sheryl Johnson May 2001 40 (Graduate Major) (Research Advisor) (Month/Year) (No. of Pages) APA (Name of Style Manual Used in this Study) Software upgrades is one type of technological change that happens very common in a workplace. Organizations often overlook the importance of effectively managing it, which could lead to employees’ resistance to change thus decrease in productivity. The purpose of this study was to discover how an organization and its software’s users cope with a technological change in software upgrades. Four research questions were used as guidelines to build this study. These questions helped the researcher to better understand the migration process and its problems from the users’ and the management’s point of view.
Parametric analysis of axial flux wind generators focused on total harmonic distortion evaluation

A parametric analysis of axial flux disc machines (AFDMs) is performed in order to evaluate the total harmonic distortion (THD) of output voltage depending on the shape and size of permanent magnets. In renewable energy applications of AFDM (wind generators), THD is an important issue and the proposed simulation model can be used to predict the THD of the output voltage of a machine on the basis of key parameters. A simplified but reasonably accurate mathematical model has been developed to determine the equivalent circuit parameters of AFDMs of whatever configuration. The analytical model, validated by means of FEM and experimental data, allows extremely fast (almost instantaneous) calculations with an accuracy comparable with that of much slower and complex methods. Parametric analyses have been performed on an AFDM-12 – a machine with a stator and two rotors, and one coil per pole and per phase – with different shapes and sizes of permanent magnets. The harmonic content and THD of each configuration (with trapezoidal, triangular, rectangular and circular magnets) have been calculated and compared.
Supercritical Flow Measurement Using a Small Parshall Flume

An experimental program was conducted to determine if a Parshall flume, developed to accurately measure open-channel subcritical flow, could also be used to measure discharge in a supercritical flow regime. Fifteen experimental configurations were tested using two small Parshall flumes [6-in. (15.2-cm) and 9-in. (22.9-cm) crest width] with varying approach channel slopes, approach channel roughness, and flume convergence. It was determined that a single Parshall flume can be used to measure flow (within ±5%) for both supercritical and subcritical flow regimes for a specified range of flows. The original Parshall flume equation was then modified to incorporate crest width, channel slope, channel roughness, and convergence in the prediction algorithm. Unique expressions were developed for both supercritical and subcritical flow regimes to estimate the discharge. A single expression does not appear feasible for accurate discharge measurement for both flow regimes in a Parshall flume at this time.