Search results for: organization learning

Authors: T. Benazzouz, K. Auhmani

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This paper presents a Hospital 4.0 Maturity Assessment Model based on the Industry 4.0 concepts. The self-assessment model defines current and target states of digital transformation by considering multiple aspects of a hospital and a healthcare supply chain. The developed model was validated and evaluated on real-life cases. The resulting model consisted of 5 domains: Technology, Strategy 4.0, Human resources 4.0 & Culture 4.0, Supply chain 4.0 management, and Patient journeys management. Each domain is further divided into several sub-domains, totally 34 sub-domains are identified, that reflect different facets of a hospital 4.0 mature organization.

Keywords: hospital 4.0, Industry 4.0, maturity assessment model, supply chain 4.0, patient

Procedia PDF Downloads 83

Authors: Linda Blümel, Bettina Bert, Jan Brosda, Heidrun Fink, Melanie Hamann

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Age-related cognitive decline can eventually lead to dementia, the most common mental illness in elderly people and an immense challenge for patients, their families and caregivers. Cholinesterase inhibitors constitute the most commonly used antidementia prescription medication. The standardized Ginkgo biloba leaf extract EGb 761® is approved for treating age-associated cognitive impairment and has been shown to improve the quality of life in patients suffering from mild dementia. A clinical trial with 96 Alzheimer´s disease patients indicated that the combined treatment with donepezil and EGb 761® had fewer side effects than donepezil alone. In an animal model of cognitive aging, we compared the effect of combined treatment with EGb 761® or donepezil monotherapy and vehicle. We compared the effect of chronic treatment (15 days of pretreatment) with donepezil (1.5 mg/kg p. o.), EGb 761® (100 mg/kg p. o.), or the combination of the two drugs, or vehicle in 18 – 20 month old male OFA rats. Learning and memory performance were assessed by Morris water maze testing, motor behavior in an open field paradigm. In addition to chronic treatment, the substances were administered orally 30 minutes before testing. Compared to the first day and to the control group, only the combination group showed a significant reduction in latency to reach the hidden platform on the second day of testing. Moreover, from the second day of testing onwards, the donepezil, the EGb 761® and the combination group required less time to reach the hidden platform compared to the first day. The control group did not reach the same latency reduction until day three. There were no effects on motor behavior. These results suggest a superiority of the combined treatment of donepezil with EGb 761® compared to monotherapy.

Keywords: age-related cognitive decline, dementia, ginkgo biloba leaf extract EGb 761®, learning and memory, old rats

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Authors: Regis Pochon, Nicolas Stefaniak, Veronique Baltazart, Pamela Gobin

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Anxiety is known to have an impact on working memory. In reasoning or memory tasks, individuals with anxiety tend to show longer response times and poorer performance. Furthermore, there is a memory bias for negative information in anxiety. Given the crucial role of working memory in lexical learning, anxious students may encounter more difficulties in learning to read and spell. Anxiety could even affect an earlier learning, that is the activation of phonological representations, which are decisive for the learning of reading and writing. The aim of this study is to compare the access to phonological representations of beginning readers and writers according to their level of anxiety, using an auditory lexical decision task. Eighty students of 6- to 9-years-old completed the French version of the Revised Children's Manifest Anxiety Scale and were then divided into four anxiety groups according to their total score (Low, Median-Low, Median-High and High). Two set of eighty-one stimuli (words and non-words) have been auditory presented to these students by means of a laptop computer. Stimuli words were selected according to their emotional valence (positive, negative, neutral). Students had to decide as quickly and accurately as possible whether the presented stimulus was a real word or not (lexical decision). Response times and accuracy were recorded automatically on each trial. It was anticipated a) longer response times for the Median-High and High anxiety groups in comparison with the two others groups, b) faster response times for negative-valence words in comparison with positive and neutral-valence words only for the Median-High and High anxiety groups, c) lower response accuracy for Median-High and High anxiety groups in comparison with the two others groups, d) better response accuracy for negative-valence words in comparison with positive and neutral-valence words only for the Median-High and High anxiety groups. Concerning the response times, our results showed no difference between the four groups. Furthermore, inside each group, the average response times was very close regardless the emotional valence. Otherwise, group differences appear when considering the error rates. Median-High and High anxiety groups made significantly more errors in lexical decision than Median-Low and Low groups. Better response accuracy, however, is not found for negative-valence words in comparison with positive and neutral-valence words in the Median-High and High anxiety groups. Thus, these results showed a lower response accuracy for above-median anxiety groups than below-median groups but without specificity for the negative-valence words. This study suggests that anxiety can negatively impact the lexical processing in young students. Although the lexical processing speed seems preserved, the accuracy of this processing may be altered in students with moderate or high level of anxiety. This finding has important implication for the prevention of reading and spelling difficulties. Indeed, during these learnings, if anxiety affects the access to phonological representations, anxious students could be disturbed when they have to match phonological representations with new orthographic representations, because of less efficient lexical representations. This study should be continued in order to precise the impact of anxiety on basic school learning.

Keywords: anxiety, emotional valence, childhood, lexical access

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Authors: Morolake Josephine Adeagbo

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South Africa is one of those countries that openly rejected racism, and this is entrenched in its Bill of Rights. Despite the acceptance and incorporation of racial integration into the South Africa Constitution, the implementation within some sectors, most especially the educational sector, seems difficult. Recent occurrences of racism in some higher institutions of learning in South Africa are indications that racial integration / racial transformation is still farfetched in the country’s higher educational sector. It is against this background that this study was conducted to understand how gender and residential background influence racial integration in a South African university which was predominantly a white Afrikaner institution. Using a quantitative method to test the attitude of different categories of undergraduate students at the university, this study found that the factors- residential background and gender- used in measuring student’s attitude do not necessarily have a significant relationship towards racial integration. However, this study concludes with a call for more research with a range of other factors in order to better understand how racial integration can be promoted in South African institutions of higher learning.

Keywords: racial integration, gender, residential background, transformation

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Authors: Mohini Mohamed, Nurul Huda Mas’od

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This study was aimed at designing and developing an assistive mathematical teaching aid (courseware) in helping dyslexic students in learning multiplication. Computers and multimedia interactive courseware has benefits students in terms of increase learner’s motivation and engage them to stay on task in classroom. Most disability student has short attention span thus with the advantage offered by multimedia interactive courseware allows them to retain the learning process for longer period as compared to traditional chalk and talk method. This study was conducted in a public school at a primary level with the help of three special education teachers and six dyslexic students as participants. Qualitative methodology using interview with special education teachers and observations in classes were conducted. The development of the multimedia interactive courseware in this study was divided to three processes which were analysis and design, development and evaluation. The courseware was evaluated by using User Acceptance Survey Form and interview. Feedbacks from teachers were used to alter, correct and develop the application for a better multimedia interactive courseware.

Keywords: disability students, dyslexia, mathematics teaching aid, multimedia interactive courseware

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Authors: Pradeep Singh, Shrish Verma

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Software fault prediction models are created by using the source code, processed metrics from the same or previous version of code and related fault data. Some company do not store and keep track of all artifacts which are required for software fault prediction. To construct fault prediction model for such company, the training data from the other projects can be one potential solution. The earlier we predict the fault the less cost it requires to correct. The training data consists of metrics data and related fault data at function/module level. This paper investigates fault predictions at early stage using the cross-project data focusing on the design metrics. In this study, empirical analysis is carried out to validate design metrics for cross project fault prediction. The machine learning techniques used for evaluation is Naïve Bayes. The design phase metrics of other projects can be used as initial guideline for the projects where no previous fault data is available. We analyze seven data sets from NASA Metrics Data Program which offer design as well as code metrics. Overall, the results of cross project is comparable to the within company data learning.

Keywords: software metrics, fault prediction, cross project, within project.

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Authors: Djelloul Chafia, Demagh Rafik, Kareche Toufik

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Human activities, made without precautions, accelerate the degradation of the soil structure and reduces its resistance. Operations, such as tunnel construction may exercise an influence more or less permanent on the grounds which surrounded them, these structures alter soil it is necessary to predict their impacts by suitable measures. This research is a numerical analysis that deals the risks and effects due to the weakening of the soil after digging two parallel vertical circular tunnels in urban areas, and suggests forecasting techniques based essentially on the organization of underground space. The simulations are performed using the finite-difference code FLAC in a two-dimensional case and with an elasto-plastic behavior of the soil.

Keywords: sol, weakening, degradation, prevention, tunnel

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Authors: Muhammad Waqas

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In this competitive environment, organizations in the service sector and industrial sector are trying their best to win the loyalty of their customers by providing superior quality services and innovative products to remain competitive in the market. The objective of this study is to focus on the concept that public dealing and tripping of electricity have a significant impact on customer satisfaction. This study is focused on the banking sector. It is concluded that quality in service sectors strongly depends on employees' commitment to the organization for providing superior services to the customers to enhance customers' satisfaction.

Keywords: customer complaints, banking sector, customer satisfaction, Islamic banking

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Authors: Elnaz Lashgari, Emel Demircan

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Electromyography (EMG) is one of the most important interfaces between humans and robots for rehabilitation. Decoding this signal helps to recognize muscle activation and converts it into smooth motion for the robots. Detecting each muscle’s pattern during walking and running is vital for improving the quality of a patient’s life. In this study, EMG data from 10 muscles in 10 subjects at 4 different speeds were analyzed. EMG signals are nonlinear with high dimensionality. To deal with this challenge, we extracted some features in time-frequency domain and used manifold learning and Laplacian Eigenmaps algorithm to find the intrinsic features that represent data in low-dimensional space. We then used the Bayesian classifier to identify various patterns of EMG signals for different muscles across a range of running speeds. The best result for vastus medialis muscle corresponds to 97.87±0.69 for sensitivity and 88.37±0.79 for specificity with 97.07±0.29 accuracy using Bayesian classifier. The results of this study provide important insight into human movement and its application for robotics research.

Keywords: electromyography, manifold learning, ISOMAP, Laplacian Eigenmaps, locally linear embedding

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Authors: Kunto Nurcahyoko

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The fundamental perception used in this study is that teaching and learning activities in Indonesian classroom have potentially generated individual’s sensitivity on cross-cultural understanding. This study aims at investigating Indonesian university students’ perception on cross-cultural understanding after doing Cultural Snapshot Project. The data was critically analyzed through multicultural ideology and diversity theories. The subjects were 30 EFL college students in one of colleges in Indonesia. Each student was assigned to capture a photo which depicted the existence of any cultural manifestation in their surrounding such as discrimination, prejudice and stereotype. Students were then requested asked to reflect on the picture by writing a short description on the picture and make an exhibition using their pictures. In the end of the project, students were instructed to fill in questionnaires to show their perception before and after the project. The result reveals that Cultural Snapshot Project has given the opportunity for the students to better realize cross-cultural understanding in their environment. In conclusion, the study shows that Cultural Snapshot Project has specifically enhanced students’ perception of multiculturalism in three major areas: cultural sensitivity and empathy, social tolerance, and understanding of diversity.

Keywords: cultural snapshot, cross-cultural understanding, students’ perception, multiculturalism

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Authors: Yangzhi Li

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Network transfer of information and performance customization is now a viable method of digital industrial production in the era of Industry 4.0. Robot platforms and network platforms have grown more important in digital design and construction. The pressing need for novel building techniques is driven by the growing labor scarcity problem and increased awareness of construction safety. Robotic approaches in construction research are regarded as an extension of operational and production tools. Several technological theories related to robot autonomous recognition, which include high-performance computing, physical system modeling, extensive sensor coordination, and dataset deep learning, have not been explored using intelligent construction. Relevant transdisciplinary theory and practice research still has specific gaps. Optimizing high-performance computing and autonomous recognition visual guidance technologies improves the robot's grasp of the scene and capacity for autonomous operation. Intelligent vision guidance technology for industrial robots has a serious issue with camera calibration, and the use of intelligent visual guiding and identification technologies for industrial robots in industrial production has strict accuracy requirements. It can be considered that visual recognition systems have challenges with precision issues. In such a situation, it will directly impact the effectiveness and standard of industrial production, necessitating a strengthening of the visual guiding study on positioning precision in recognition technology. To best facilitate the handling of complicated components, an approach for the visual recognition of parts utilizing machine learning algorithms is proposed. This study will identify the position of target components by detecting the information at the boundary and corner of a dense point cloud and determining the aspect ratio in accordance with the guidelines for the modularization of building components. To collect and use components, operational processing systems assign them to the same coordinate system based on their locations and postures. The RGB image's inclination detection and the depth image's verification will be used to determine the component's present posture. Finally, a virtual environment model for the robot's obstacle-avoidance route will be constructed using the point cloud information.

Keywords: robotic construction, robotic assembly, visual guidance, machine learning

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Authors: Muhammad Arif Khan, Haroon Idrees, Imran Aziz, Sidra Mushtaq

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The purpose of this study is to investigate the current level of knowledge mapping skills of the public sector employees in Pakistan. National Institute of Management is one of the premiere public sector training organization for mid-career public sector employees in Pakistan. This study is conducted on participants of fourteen weeks long training course called Mid-Career Management Course (MCMC) which is mandatory for public sector employees in order to ascertain how to enhance their knowledge mapping skills. Methodology: Researcher used both qualitative and quantitative approach to conduct this study. Primary data about current level of participants’ understanding of knowledge mapping was collected through structured questionnaire. Later on, Participant Observation method was used where researchers acted as part of the group to gathered data from the trainees during their performance in training activities and tasks. Findings: Respondents of the study were examined for skills and abilities to organizing ideas, helping groups to develop conceptual framework, identifying critical knowledge areas of an organization, study large networks and identifying the knowledge flow using nodes and vertices, visualizing information, represent organizational structure etc. Overall, the responses varied in different skills depending on the performance and presentations. However, generally all participants have demonstrated average level of using both the IT and Non-IT K-mapping tools and techniques during simulation exercises, analysis paper de-briefing, case study reports, post visit presentation, course review, current issue presentation, syndicate meetings, and daily synopsis. Research Limitations: This study is conducted on a small-scale population of 67 public sector employees nominated by federal government to undergo 14 weeks extensive training program called MCMC (Mid-Career Management Course) at National Institute of Management, Peshawar, Pakistan. Results, however, reflects only a specific class of public sector employees i.e. working in grade 18 and having more than 5 years of work. Practical Implications: Research findings are useful for trainers, training agencies, government functionaries, and organizations working for capacity building of public sector employees.

Keywords: knowledge management, km in public sector, knowledge management and professional development, knowledge management in training, knowledge mapping

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Authors: Kemal Polat

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In this paper, three feature weighting methods have been used to improve the classification performance of diabetic retinopathy (DR). To classify the diabetic retinopathy, features extracted from the output of several retinal image processing algorithms, such as image-level, lesion-specific and anatomical components, have been used and fed them into the classifier algorithms. The dataset used in this study has been taken from University of California, Irvine (UCI) machine learning repository. Feature weighting methods including the fuzzy c-means clustering based feature weighting, subtractive clustering based feature weighting, and Gaussian mixture clustering based feature weighting, have been used and compered with each other in the classification of DR. After feature weighting, five different classifier algorithms comprising multi-layer perceptron (MLP), k- nearest neighbor (k-NN), decision tree, support vector machine (SVM), and Naïve Bayes have been used. The hybrid method based on combination of subtractive clustering based feature weighting and decision tree classifier has been obtained the classification accuracy of 100% in the screening of DR. These results have demonstrated that the proposed hybrid scheme is very promising in the medical data set classification.

Keywords: machine learning, data weighting, classification, data mining

Procedia PDF Downloads 323

Authors: Dawood Al Chanti, Alice Caplier

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Sparse representations of signals have received a great deal of attention in recent years. In this paper, the interest of using sparse representation as a mean for performing sparse discriminative analysis between spontaneous facial expressions is demonstrated. An automatic facial expressions recognition system is presented. It uses a KSVD-SVM approach which is made of three main stages: A pre-processing and feature extraction stage, which solves the problem of shared subspace distribution based on the random projection theory, to obtain low dimensional discriminative and reconstructive features; A dictionary learning and sparse coding stage, which uses the KSVD model to learn discriminative under or over dictionaries for sparse coding; Finally a classification stage, which uses a SVM classifier for facial expressions recognition. Our main concern is to be able to recognize non-basic affective states and non-acted expressions. Extensive experiments on the JAFFE static acted facial expressions database but also on the DynEmo dynamic spontaneous facial expressions database exhibit very good recognition rates.

Keywords: dictionary learning, random projection, pose and spontaneous facial expression, sparse representation

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Authors: Pei Yi Lin

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Objective: The objective of this study is to use machine learning methods to build an early prediction classifier model for acute delirium to improve the quality of medical care for intensive care patients. Background: Delirium is a common acute and sudden disturbance of consciousness in critically ill patients. After the occurrence, it is easy to prolong the length of hospital stay and increase medical costs and mortality. In 2021, the incidence of delirium in the intensive care unit of internal medicine was as high as 59.78%, which indirectly prolonged the average length of hospital stay by 8.28 days, and the mortality rate is about 2.22% in the past three years. Therefore, it is expected to build a delirium prediction classifier through big data analysis and machine learning methods to detect delirium early. Method: This study is a retrospective study, using the artificial intelligence big data database to extract the characteristic factors related to delirium in intensive care unit patients and let the machine learn. The study included patients aged over 20 years old who were admitted to the intensive care unit between May 1, 2022, and December 31, 2022, excluding GCS assessment <4 points, admission to ICU for less than 24 hours, and CAM-ICU evaluation. The CAMICU delirium assessment results every 8 hours within 30 days of hospitalization are regarded as an event, and the cumulative data from ICU admission to the prediction time point are extracted to predict the possibility of delirium occurring in the next 8 hours, and collect a total of 63,754 research case data, extract 12 feature selections to train the model, including age, sex, average ICU stay hours, visual and auditory abnormalities, RASS assessment score, APACHE-II Score score, number of invasive catheters indwelling, restraint and sedative and hypnotic drugs. Through feature data cleaning, processing and KNN interpolation method supplementation, a total of 54595 research case events were extracted to provide machine learning model analysis, using the research events from May 01 to November 30, 2022, as the model training data, 80% of which is the training set for model training, and 20% for the internal verification of the verification set, and then from December 01 to December 2022 The CU research event on the 31st is an external verification set data, and finally the model inference and performance evaluation are performed, and then the model has trained again by adjusting the model parameters. Results: In this study, XG Boost, Random Forest, Logistic Regression, and Decision Tree were used to analyze and compare four machine learning models. The average accuracy rate of internal verification was highest in Random Forest (AUC=0.86), and the average accuracy rate of external verification was in Random Forest and XG Boost was the highest, AUC was 0.86, and the average accuracy of cross-validation was the highest in Random Forest (ACC=0.77). Conclusion: Clinically, medical staff usually conduct CAM-ICU assessments at the bedside of critically ill patients in clinical practice, but there is a lack of machine learning classification methods to assist ICU patients in real-time assessment, resulting in the inability to provide more objective and continuous monitoring data to assist Clinical staff can more accurately identify and predict the occurrence of delirium in patients. It is hoped that the development and construction of predictive models through machine learning can predict delirium early and immediately, make clinical decisions at the best time, and cooperate with PADIS delirium care measures to provide individualized non-drug interventional care measures to maintain patient safety, and then Improve the quality of care.

Keywords: critically ill patients, machine learning methods, delirium prediction, classifier model

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Authors: Chandu Rathnayake, Isuri Anuradha

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Early detection and accurate diagnosis of lung diseases play a crucial role in improving patient prognosis. However, conventional diagnostic methods heavily rely on subjective symptom assessments and medical imaging, often causing delays in diagnosis and treatment. To overcome this challenge, we propose a novel lung disease prediction system that integrates patient symptoms and X-ray images to provide a comprehensive and reliable diagnosis.In this project, develop a mobile application specifically designed for detecting lung diseases. Our application leverages both patient symptoms and X-ray images to facilitate diagnosis. By combining these two sources of information, our application delivers a more accurate and comprehensive assessment of the patient's condition, minimizing the risk of misdiagnosis. Our primary aim is to create a user-friendly and accessible tool, particularly important given the current circumstances where many patients face limitations in visiting healthcare facilities. To achieve this, we employ several state-of-the-art algorithms. Firstly, the Decision Tree algorithm is utilized for efficient symptom-based classification. It analyzes patient symptoms and creates a tree-like model to predict the presence of specific lung diseases. Secondly, we employ the Random Forest algorithm, which enhances predictive power by aggregating multiple decision trees. This ensemble technique improves the accuracy and robustness of the diagnosis. Furthermore, we incorporate a deep learning model using Convolutional Neural Network (CNN) with the RestNet50 pre-trained model. CNNs are well-suited for image analysis and feature extraction. By training CNN on a large dataset of X-ray images, it learns to identify patterns and features indicative of lung diseases. The RestNet50 architecture, known for its excellent performance in image recognition tasks, enhances the efficiency and accuracy of our deep learning model. By combining the outputs of the decision tree-based algorithms and the deep learning model, our mobile application generates a comprehensive lung disease prediction. The application provides users with an intuitive interface to input their symptoms and upload X-ray images for analysis. The prediction generated by the system offers valuable insights into the likelihood of various lung diseases, enabling individuals to take appropriate actions and seek timely medical attention. Our proposed mobile application has significant potential to address the rising prevalence of lung diseases, particularly among young individuals with smoking addictions. By providing a quick and user-friendly approach to assessing lung health, our application empowers individuals to monitor their well-being conveniently. This solution also offers immense value in the context of limited access to healthcare facilities, enabling timely detection and intervention. In conclusion, our research presents a comprehensive lung disease prediction system that combines patient symptoms and X-ray images using advanced algorithms. By developing a mobile application, we provide an accessible tool for individuals to assess their lung health conveniently. This solution has the potential to make a significant impact on the early detection and management of lung diseases, benefiting both patients and healthcare providers.

Keywords: CNN, random forest, decision tree, machine learning, deep learning

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Authors: Paramvir Singh

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The locomotion of aquatic organisms has long fascinated biologists and engineers alike, with fish fins serving as a prime example of nature's remarkable adaptations for efficient underwater propulsion. This paper presents a comprehensive study focused on the hydrodynamic analysis of fish fin kinematics, employing an innovative approach that combines machine learning and image processing techniques. Through high-speed videography and advanced computational tools, we gain insights into the complex and dynamic motion of the fins of a Tilapia (Oreochromis Niloticus) fish. This study was initially done by experimentally capturing videos of the various motions of a Tilapia in a custom-made setup. Using deep learning and image processing on the videos, the motion of the Caudal and Pectoral fin was extracted. This motion included the fin configuration (i.e., the angle of deviation from the mean position) with respect to time. Numerical investigations for the flapping fins are then performed using a Computational Fluid Dynamics (CFD) solver. 3D models of the fins were created, mimicking the real-life geometry of the fins. Thrust Characteristics of separate fins (i.e., Caudal and Pectoral separately) and when the fins are together were studied. The relationship and the phase between caudal and pectoral fin motion were also discussed. The key objectives include mathematical modeling of the motion of a flapping fin at different naturally occurring frequencies and amplitudes. The interactions between both fins (caudal and pectoral) were also an area of keen interest. This work aims to improve on research that has been done in the past on similar topics. Also, these results can help in the better and more efficient design of the propulsion systems for biomimetic underwater vehicles that are used to study aquatic ecosystems, explore uncharted or challenging underwater regions, do ocean bed modeling, etc.

Keywords: biomimetics, fish fin kinematics, image processing, fish tracking, underwater vehicles

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Authors: Meenalochana Inguva

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The context: The Center for Preparatory study is one of the strongest and most vibrant academic teaching units of the Sultan Qaboos University (SQU). The Foundation Programme English Language (FPEL) is part of a larger foundation programme which was implemented at SQU in fall 2010. The programme has been designed to prepare the students who have been accepted to study in the university in order to achieve the required educational goals (the learning outcomes) that have been designed according to Oman Academic Standards and published by the Omani Authority for Academic Accreditation (OAAA) for the English language component. The curriculum: At the CPS, the English language curriculum is based on the learning outcomes drafted for each level. These learning outcomes guide the students in meeting what is expected of them by the end of each level. These six levels are progressive in nature and are seen as a continuum. The study: A periodic evaluation of language programmes is necessary to improve the quality of the programmes and to meet the set goals of the programmes. An evaluation may be carried out internally or externally depending on the purpose and context. A self-study programme was initiated at the beginning of spring semester 2015 with a team comprising a total of 11 members who worked with-in the assigned course areas (level and programme specific). Only areas specific to FPEL have been included in the study. The study was divided into smaller tasks and members focused on their assigned courses. The self-study primarily focused on analyzing the programme LOs, curriculum planning, materials used and their relevance against the GFP exit standards. The review team also reflected on the assessment methods and procedures followed to reflect on student learning. The team has paid attention to having standard criteria for assessment and transparency in procedures. A special attention was paid to the staging of LOs across levels to determine students’ language and study skills ability to cope with higher level courses. Findings: The findings showed that most of the LOs are met through the materials used for teaching. Students score low on objective tests and high on subjective tests. Motivated students take advantage of academic support activities others do not utilize the student support activities to their advantage. Reading should get more hours. In listening, the format of the listening materials in CT 2 does not match the test format. Some of the course materials need revision. For e.g. APA citation, referencing etc. No specific time is allotted for teaching grammar Conclusion: The findings resulted in taking actions in bridging gaps. It will also help the center to be better prepared for the external review of its FPEL curriculum. It will also provide a useful base to prepare for the self-study portfolio for GFP standards assessment and future audit.

Keywords: curriculum planning, learning outcomes, reflections, self-evaluation

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Authors: Kuhorfah Emmanuel Mawuli

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Generally, for firms to enhance their operational efficiency of logistics, it is imperative to assess the logistics function. The cost of logistics conventionally represents a key consideration in the pricing decisions of firms, which suggests that cost efficiency in logistics can go a long way to improve margins. Warehousing, which is a key part of logistics operations, has the prospect of influencing operational efficiency in logistics management as well as customer value, but this potential has often not been recognized. It has been found that there is a paucity of research that evaluates the efficiency of warehouses. Indeed, limited research has been conducted to examine potential barriers to effective warehousing management. Due to this paucity of research, there is limited knowledge on how to address the obstacles associated with warehousing management. In order for warehousing management to become profitable, there is the need to integrate, balance, and manage the economic inputs and outputs of the entire warehouse operations, something that many firms tend to ignore. Management of warehousing is not solely related to storage functions. Instead, effective warehousing management requires such practices as maximum possible mechanization and automation of operations, optimal use of space and capacity of storage facilities, organization through "continuous flow" of goods, a planned system of storage operations, and safety of goods. For example, there is an important need for space utilization of the warehouse surface as it is a good way to evaluate the storing operation and pick items per hour. In the setting of Mantrac Ghana, not much knowledge regarding the management of the warehouses exists. The researcher has personally observed many gaps in the management of the warehouse facilities in the case organization Mantrac Ghana. It is important, therefore, to assess the warehouse facility management of the case company with the objective of identifying weaknesses for improvement. The study employs an in-depth qualitative research approach using interviews as a mode of data collection. Respondents in the study mainly comprised warehouse facility managers in the studied company. A total of 10 participants were selected for the study using a purposive sampling strategy. Results emanating from the study demonstrate limited warehousing effectiveness in the case company. Findings further reveal that the major barriers to effective warehousing facility management comprise poor layout, poor picking optimization, labour costs, and inaccurate orders; policy implications of the study findings are finally outlined.

Keywords: assessing, warehousing, facility, management

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Authors: Yongjiang Zhang, Kallol Barai, Umesh R. Hodeghatta, Trang Tran, Vikas Dhiman

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Detecting water stress on crops early and accurately is crucial to minimize its impact. This study aims to measure water stress in wild blueberry crops non-destructively by analyzing proximal hyperspectral data. The data collection took place in the summer growing season of 2022. A drought experiment was conducted on wild blueberries in the randomized block design in the greenhouse, incorporating various genotypes and irrigation treatments. Hyperspectral data ( spectral range: 400-1000 nm) using a handheld spectroradiometer and leaf water potential data using a pressure chamber were collected from wild blueberry plants. Machine learning techniques, including multiple regression analysis and random forest models, were employed to predict leaf water potential (MPa). We explored the optimal wavelength bands for simple differences (RY1-R Y2), simple ratios (RY1/RY2), and normalized differences (|RY1-R Y2|/ (RY1-R Y2)). NDWI ((R857 - R1241)/(R857 + R1241)), SD (R2188 – R2245), and SR (R1752 / R1756) emerged as top predictors for predicting leaf water potential, significantly contributing to the highest model performance. The base learner models achieved an R-squared value of approximately 0.81, indicating their capacity to explain 81% of the variance. Research is underway to develop a neural vegetation index (NVI) that automates the process of index development by searching for specific wavelengths in the space ratio of linear functions of reflectance. The NVI framework could work across species and predict different physiological parameters.

Keywords: hyperspectral reflectance, water potential, spectral indices, machine learning, wild blueberries, optimal bands

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Authors: M. S. Riyazulla

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Since the dawn of the 21st century, there has been a perceptible decline in Islamic supremacy in world affairs, apart from the gradual waning of the amiable relations and relevance of Islamic countries in the International political arena. For a long, Islamic countries have been marginalised by the superpowers in the global conflicting issues. This was evident in the context of their recent invasions and interference in Afghanistan, Syria, Iraq, and Libya. The leading International Islamic organizations like the Arab League, Organization of Islamic Cooperation, Gulf Cooperation Council, and Muslim World League did not play any prominent role there in resolving the crisis that ensued due to the exogenous and endogenous causes. Hence, there is a need for Islamic countries to create a credible International Islamic organization that could dictate its terms and shape a new Islamic world order. The prominent Islamic countries are divided on ideological and religious fault lines. Their concord is indispensable to enhance their image and placate the relations with other countries and communities. The massive boon of oil and gas could be synergistically utilised to exhibit their omnipotence and eminence through constructive ways. The prevailing menace of Islamophobia could be abated through syncretic messages, discussions, and deliberations by the sagacious Islamic scholars with the other community leaders. Presently, as Muslims are at a crossroads, a dynamic leadership could navigate the agitated Muslim community on the constructive path and herald political stability around the world. The present political disorder, chaos, and economic challenges necessities a paradigm shift in approach to worldly affairs. This could also be accomplished through the advancement in science and technology, particularly space exploration, for peaceful purposes. The Islamic world, in order to regain its lost preeminence, should rise to the occasion in promoting peace and tranquility in the world and should evolve a rational and human-centric solution to global disputes and concerns. As a splendid contribution to humanity and for amicable international relations, they should devote all their resources and scientific intellect towards space exploration and should safely transport man from the Earth to the nearest and most accessible cosmic body, the Moon, within one hundred years as the mankind is facing the existential threat on the planet.

Keywords: carboniferous period, Earth, extinction, fossil fuels, global leaders, Islamic glory, international order, life, marginalization, Moon, natural catastrophes

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Authors: Matthew Armitt

Abstract:

The revolutionary school of architectural teaching – VKhUTEAMAS (1923-1926) was a new approach for a new society bringing architectural education to the masses and masses to the growing industrial production. The school's pedagogical contribution of the 1920s made it an important school of the modernist movement, engaging pedagogy as a mode of experimentation. The teachers and students saw design education not just as a process of knowledge transfer but as a vehicle for design innovation developing an approach without precedent. This process of teaching and learning served as a vehicle for venturing into the unknown through a discipline of architectural teaching called “Space” developed by the Soviet architect Nikolai Ladovskii (1881-1941). The creation of “Space” was paramount not only for its innovative pedagogy but also as an experimental laboratory for developing new architectural language. This paper discusses whether the historical teaching of “Space” can function in the construction of the modern teaching identity today to promote value, richness, quality, and diversity inherent in architectural design education. The history of “Space” teaching remains unknown within academic circles and separate from the current architectural teaching debate. Using VKhUTEMAS and the teaching of “Space” as a pedagogical lens and drawing upon research carried out in the Russian Federation, America, Canada, Germany, and the UK, this paper discusses how historically different models of teaching and learning can intersect through examining historical based educational research by exploring different design studio initiatives; pedagogical methodologies; teaching and learning theories and problem-based projects. There are strong arguments and desire for pedagogical change and this paper will promote new historical and educational research to widen the current academic debate by exposing new approaches to architectural teaching today.

Keywords: VKhUTEMAS, discipline space, modernist pedagogy, teaching identity

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Authors: Amin Tarzi

Abstract:

The government of the Islamic Republic of Afghanistan was expected to keep the Taliban insurgents at bay after the departure of North Atlantic Treaty Organization (NATO)-led forces in 2021, especially given the two decades of effort to establish security forces to safeguard Western-backed governing institutions. This articles reviews the reasons for the failure of the much larger and better-equipped Afghan National Security Forces (ANSF) to stop the Taliban from taking over the Afghan capital of Kabul in a few days and analyzes the often-forgotten dimension of strategic calculations in this dialogue—namely the societal dimension. In this article, the author argues that this is one of the primary reasons that the ANSF and the Afghan government collapsed.

Keywords: societal warfare, Afghanistan, NATO, Taliban, military strategy

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Authors: Hongmei Chi

Abstract:

The Blockchain has become a necessity for many different societal industries and ordinary lives including cryptocurrency technology, supply chain, health care, public safety, education, etc. Therefore, training our future blockchain developers to know blockchain programming vulnerability and I.T. students' cyber security is in high demand. In this work, we propose a framework including learning modules and hands-on labs to guide future I.T. professionals towards developing secure blockchain programming habits and mitigating source code vulnerabilities at the early stages of the software development lifecycle following the concept of Secure Software Development Life Cycle (SSDLC). In this research, our goal is to make blockchain programmers and I.T. students aware of the vulnerabilities of blockchains. In summary, we develop a framework that will (1) improve students' skills and awareness of blockchain source code vulnerabilities, detection tools, and mitigation techniques (2) integrate concepts of blockchain vulnerabilities for IT students, (3) improve future IT workers’ ability to master the concepts of blockchain attacks.

Keywords: software vulnerability detection, hands-on lab, static analysis tools, vulnerabilities, blockchain, active learning

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Authors: Bhekisisa Nyoni, Nomakhosi Ndiweni, Annatoria Chinyama

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The aim of this paper was to explore ways to enhance the quality of higher education with a bias towards engineering education in Zimbabwe universities. A search through relevant literature was conducted looking at both international and local scholars. It also involved reviewing the Dakar Framework for Action and Incheon Declaration and Framework for Action plans for education for sustainable development. Goals were set for 2030 as a standard for quality to be adopted by all countries in improving access as well as the quality of education from early childhood and through to adult learning. Despite the definition of quality being difficult to express due to diverse expectations from different stakeholders, the view of quality adopted is based on the World Education Forum’s propositions on quality education going beyond the classroom experience. It considers factors such as learning environment, governance and management, and teacher caliber. The study concludes by illustrating that the quality of engineering education in Zimbabwe has come a long way. It has made strides in increasing access and variety to education though at the expense of quality in its totality. To improve the quality of engineering education, programs have been introduced to promote the professionalism of lecturers, such as industrial secondment and professional development courses.

Keywords: engineering education, quality of education, professional development, industrial secondment

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Authors: Ahmed Mohammed Omer Alghamdi

Abstract:

Since the COVID-19 pandemic started, a rapid, unexpected transition from face-to-face to virtual classroom (VC) teaching has involved several challenges and obstacles. However, there are also opportunities and thoughts that need to be examined and discussed. In addition, the entire world is witnessing that the teaching system and, more particularly, higher education institutes have been interrupted. To maintain the learning and teaching practices as usual, countries were forced to transition from traditional to virtual classes using various technology-based devices. In this regard, the Kingdom of Saudi Arabia (KSA) is no exception. Focusing on how the current situation has forced many higher education institutes to change to virtual classes may possibly provide a clear insight into adopted practices and implications. The main purpose of this study, therefore, was to investigate how both Saudi English as a foreign language (EFL) teachers and students perceived the implementation of virtual classes as a key factor for useful language teaching and learning process during the COVID-19 pandemic period at a Saudi university. The impetus for the research was, therefore, the need to find ways of identifying the deficiencies in this application and to suggest possible solutions that might rectify those deficiencies. This study seeks to answer the following overarching research question: “How do Saudi EFL instructors and students perceive the use of virtual classes during the COVID-19 pandemic period in their language teaching and learning context?” The following sub-questions are also used to guide the design of the study to answer the main research question: (1) To what extent are virtual classes important intra-pandemic from Saudi EFL instructors’ and students’ perspectives? (2) How effective are virtual classes for fostering English language students’ achievement? (3) What are the challenges and obstacles that instructors and students may face during the implementation of virtual teaching? A mixed method approach was employed in this study; the questionnaire data collection represented the quantitative method approach for this study, whereas the transcripts of recorded interviews represented the qualitative method approach. The participants included EFL teachers (N = 4) and male and female EFL students (N = 36). Based on the findings of this study, various aspects from teachers' and students’ perspectives were examined to determine the use of the virtual classroom applications in terms of fulfilling the students’ English language learning needs. The major findings of the study revealed that the virtual classroom applications during the current pandemic situation encountered three major challenges, among which the existence of the following essential aspects, namely lack of technology and an internet connection, having a large number of students in a virtual classroom and lack of students’ and teachers’ interactions during the virtual classroom applications. Finally, the findings indicated that although Saudi EFL students and teachers view the virtual classrooms in a positive light during the pandemic period, they reported that for long and post-pandemic period, they preferred the traditional face-to-face teaching procedure.

Keywords: virtual classes, English as a foreign language, COVID-19, Internet, pandemic

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Authors: Rui Yin, Haojie Wang, Xun Li

Abstract:

Recently, mainstream dense retrieval methods have obtained state-of-the-art results on some datasets and tasks. However, they require large amounts of training data, which is not available in most domains. The severe performance degradation of dense retrievers on new data domains has limited the use of dense retrieval methods to only a few domains with large training datasets. In this paper, we propose an unsupervised domain-adaptive approach based on query generation. First, a generative model is used to generate relevant queries for each passage in the target corpus, and then, the generated queries are used for mining negative passages. Finally, the query-passage pairs are labeled with a cross-encoder and used to train a domain-adapted dense retriever. We also explore contrastive learning as a method for training domain-adapted dense retrievers and show that it leads to strong performance in various retrieval settings. Experiments show that our approach is more robust than previous methods in target domains that require less unlabeled data.

Keywords: dense retrieval, query generation, contrastive learning, unsupervised training

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Authors: Güzi̇n Ti̇rkeş, Özge Teki̇n, Kerem Kurtuluş, Y. Yekta Yurtseven, Murat Baran

Abstract:

A scanning electron microscope (SEM) is a type of electron microscope mainly used in nanoscience and nanotechnology areas. Automatic image recognition and classification are among the general areas of application concerning SEM. In line with these usages, the present paper proposes a deep learning algorithm that classifies SEM images into nine categories by means of an online application to simplify the process. The NFFA-EUROPE - 100% SEM data set, containing approximately 21,000 images, was used to train and test the algorithm at 80% and 20%, respectively. Validation was carried out using a separate data set obtained from the Middle East Technical University (METU) in Turkey. To increase the accuracy in the results, the Inception ResNet-V2 model was used in view of the Fine-Tuning approach. By using a confusion matrix, it was observed that the coated-surface category has a negative effect on the accuracy of the results since it contains other categories in the data set, thereby confusing the model when detecting category-specific patterns. For this reason, the coated-surface category was removed from the train data set, hence increasing accuracy by up to 96.5%.

Keywords: convolutional neural networks, deep learning, image classification, scanning electron microscope

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Authors: Maria Poulou

Abstract:

The study investigated how teachers’ self-rated Emotional Intelligence (EI), competence in implementing Social and Emotional Learning (SEL) skills and teaching efficacy relate to teacher-student relationships and students’ emotional and behavioral difficulties. Participants were 98 elementary teachers from public schools in central Greece. They completed the Self-Rated Emotional Intelligence Scale (SREIS), the Teacher SEL Beliefs Scale, the Teachers’ Sense of Efficacy Scale (TSES), the Student-Teacher Relationships Scale-Short Form (STRS-SF) and the Strengths and Difficulties Questionnaire (SDQ) for 617 of their students, aged 6-11 years old. Structural equation modeling was used to examine an exploratory model of the variables. It was demonstrated that teachers’ emotional intelligence, SEL beliefs and teaching efficacy were significantly related to teacher-student relationships, but they were not related to students’ emotional and behavioral difficulties. Rather, teachers’ perceptions of teacher-students relationships were significantly related to these difficulties. These findings and their implications for research and practice are discussed.

Keywords: emotional intelligence, social and emotional learning, teacher-student relationships, teaching efficacy

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Authors: Ruwan Gunawardane

Abstract:

Second language teaching and learning is a complex process in which various factors are identified as having a negative impact on the competency in English among undergraduates of Sri Lanka. One such issue is the lack of intrinsic motivation among them to learn English despite the fact that they all know the importance of English. This study attempted to ascertain how the intrinsic motivation of undergraduates to learn English can be improved through reading out of curiosity. Humans are curious by nature, and cognitive psychology says that curiosity facilitates learning, memory, and motivation. The researcher carried out this study during the closure of universities due to the outbreak of the coronavirus through ‘Online Reading Café’, an online reading programme introduced by himself. He invited 1166 students of the Faculty of Science, University of Ruhuna, to read 50 articles taken from CNN and the BBC and posted at least two to three articles on the LMS of the faculty almost every day over a period of 23 days. The themes of the articles were based on the universe, exploration of planets, scientific experiments, evolution, etc., and the students were encouraged to collect as many words, phrases, and sentence structures as possible while reading and to form meaningful sentences using them. The data obtained through the students’ feedback was qualitatively analyzed. It was found that these undergraduates were interested in reading something out of curiosity, due to which intrinsic motivation is enhanced, and it facilitates competence in L2.

Keywords: English, competence, reading, curiosity

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