Search results for: machine learning in healthcare

Authors: Hui Fang Huang Su, Jia Borror

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This study compared two interventions for teaching math to preschool-aged students with autism spectrum disorder (ASD). The first is considered the business as usual (BAU) intervention, which uses the Strategies for Teaching Based on Autism Research (STAR) curriculum and discrete trial teaching as the instructional methodology. The second is the Math is Not Difficult (Project MIND) activity-embedded, naturalistic intervention. These interventions were randomly assigned to four preschool students with ASD classrooms and implemented over three months for Project Mind. We used measurement gained during the same three months for the STAR intervention. In addition, we used A quasi-experimental, pre-test/post-test design to compare the effectiveness of these two interventions in building mathematical knowledge and skills. The pre-post measures include three standardized instruments: the Test of Early Math Ability-3, the Problem Solving and Calculation subtests of the Woodcock-Johnson Test of Achievement IV, and the Bracken Test of Basic Concepts-3 Receptive. The STAR curriculum-based assessment is administered to all Baudhuin students three times per year, and we used the results in this study. We anticipated that implementing these two approaches would improve the mathematical knowledge and skills of children with ASD. Still, it is crucial to see whether a behavioral or naturalistic teaching approach leads to more significant results.

Keywords: early learning, autism, math for pre-schoolers, special education, teaching strategies

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Authors: Anita Sadat Sadati Rostami, Reza Almasi Ghaleh

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Surface electromyography (sEMG) offers important insights into muscle activation and has applications in fields including rehabilitation and human-computer interaction. The purpose of this work is to predict the degree of activation of two joints in the index finger using an LSTM-Transformer architecture trained on sEMG data from the Ninapro DB8 dataset. We apply advanced preprocessing techniques, such as multi-band filtering and customizable rectification methods, to enhance the encoding of sEMG data into features that are beneficial for regression tasks. The processed data is converted into spike patterns and simulated using Leaky Integrate-and-Fire (LIF) neuron models, allowing for neuromorphic-inspired processing. Our findings demonstrate that adjusting filtering parameters and neuron dynamics and employing the LSTM-Transformer model improves joint angle prediction performance. This study contributes to the ongoing development of deep learning frameworks for sEMG analysis, which could lead to improvements in motor control systems.

Keywords: surface electromyography, LSTM-transformer, spiking neural networks, hand kinematics, leaky integrate-and-fire neuron, band-pass filtering, muscle activity decoding

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Authors: Narendra Akhadkar, Silvestre Cano, Christophe Gourru

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Injection-molded parts are widely used in power system protection products. One of the biggest challenges in an injection molding process is shrinkage and warpage of the molded parts. All these geometrical variations may have an adverse effect on the quality of the product, functionality, cost, and time-to-market. The situation becomes more challenging in the case of intricate shapes and in mass production using multi-cavity tools. To control the effects of shrinkage and warpage, it is very important to correctly find out the input parameters that could affect the product performance. With the advances in the computer-aided engineering (CAE), different tools are available to simulate the injection molding process. For our case study, we used the MoldFlow insight tool. Our aim is to predict the spread of the functional dimensions and geometrical variations on the part due to variations in the input parameters such as material viscosity, packing pressure, mold temperature, melt temperature, and injection speed. The input parameters may vary during batch production or due to variations in the machine process settings. To perform the accurate product assembly variation simulation, the first step is to perform an individual part variation simulation to render realistic tolerance ranges. In this article, we present a method to simulate part variations coming from the input parameters variation during batch production. The method is based on computer simulations and experimental validation using the full factorial design of experiments (DoE). The robustness of the simulation model is verified through input parameter wise sensitivity analysis study performed using simulations and experiments; all the results show a very good correlation in the material flow direction. There exists a non-linear interaction between material and the input process variables. It is observed that the parameters such as packing pressure, material, and mold temperature play an important role in spread on functional dimensions and geometrical variations. This method will allow us in the future to develop accurate/realistic virtual prototypes based on trusted simulated process variation and, therefore, increase the product quality and potentially decrease the time to market.

Keywords: correlation, molding process, tolerance, sensitivity analysis, variation simulation

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Authors: Ayush Aditya

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In today's digitised world, the extent of an individual’s social involvement is largely determined by their interaction over the internet. The Internet has emerged as a primary source of information consumption and a reliable medium for receiving updates on everyday activities. Owing to this change in the information consumption pattern, the internet has also emerged as a hotbed of misinformation. Experts are of the view that media literacy has emerged as one of the most effective strategies for addressing the issue of misinformation. This paper aims to study the evolution of the Kerala government's media literacy policy, its implementation strategy, challenges and opportunities. The objective of this paper is to create a conceptual framework containing details of the implementation strategy based on the Kerala model. Extensive secondary research of literature, newspaper articles, and other online sources was carried out to locate the timeline of this policy. This was followed by semi-structured interview discussions with government officials from Kerala to trace the origin and evolution of this policy. Preliminary findings based on the collected data suggest that this policy is a case of policy by chance, as the officer who headed this policy during the state level implementation was the one who has already piloted a media literacy program in a district called Kannur as the district collector. Through this paper, an attempt is made to trace the history of the media literacy policy starting from the Kannur intervention in 2018, which was started to address the issue of vaccine hesitancy around measles rubella(MR) vaccination. If not for the vaccine hesitancy, this program would not have been rolled out in Kannur. Interviews with government officials suggest that when authorities decided to take up this initiative in 2020, a huge amount of misinformation emerging during the COVID-19 pandemic was the trigger. There was misinformation regarding government orders, healthcare facilities, vaccination, and lockdown regulations, which affected everyone, unlike the case of Kannur, where it was only a certain age group of kids. As a solution to this problem, the state government decided to create a media literacy curriculum to be taught in all government schools of the state starting from standard 8 till graduation. This was a tricky task, as a new course had to be immediately introduced in the school curriculum amid all the disruptions in the education system caused by the pandemic. It was revealed during the interview that in the case of the state-wide implementation, every step involved multiple checks and balances, unlike the earlier program where stakeholders were roped-in as and when the need emerged. On the pedagogy, while the training during the pilot could be managed through PowerPoint presentation, designing a state-wide curriculum involved multiple iterations and expert approvals. The reason for this is COVID-19 related misinformation has lost its significance. In the next phase of the research, an attempt will be made to compare other aspects of the pilot implementation with the state-wide implementation.

Keywords: media literacy, digital media literacy, curriculum based media literacy intervention, misinformation

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Authors: Z. Yousefniayejahr, S. Bolognini, A. Bonini, C. Campobasso, N. Poma, F. Vivaldi, M. Di Luca, A. Tavanti, F. Di Francesco

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Pathogenic bacteria represent a threat to healthcare systems and the food industry because their rapid detection remains challenging. Electrochemical biosensors are gaining prominence as a novel technology for the detection of pathogens due to intrinsic features such as low cost, rapid response time, and portability, which make them a valuable alternative to traditional methodologies. These sensors use biorecognition elements that are crucial for the identification of specific bacteria. In this context, bacteriophages are promising tools for their inherent high selectivity towards bacterial hosts, which is of fundamental importance when detecting bacterial pathogens in complex biological samples. In this study, we present the development of a low-cost and portable sensor based on the Zeno phage for the rapid detection of Staphylococcus aureus. Screen-printed gold electrodes functionalized with the Zeno phage were used, and electrochemical impedance spectroscopy was applied to evaluate the change of the charge transfer resistance (Rct) as a result of the interaction with S. aureus MRSA ATCC 43300. The phage-based biosensor showed a linear range from 101 to 104 CFU/mL with a 20-minute response time and a limit of detection (LOD) of 1.2 CFU/mL under physiological conditions. The biosensor’s ability to recognize various strains of staphylococci was also successfully demonstrated in the presence of clinical isolates collected from different geographic areas. Assays using S. epidermidis were also carried out to verify the species-specificity of the phage sensor. We only observed a remarkable change of the Rct in the presence of the target S. aureus bacteria, while no substantial binding to S. epidermidis occurred. This confirmed that the Zeno phage sensor only targets S. aureus species within the genus Staphylococcus. In addition, the biosensor's specificity with respect to other bacterial species, including gram-positive bacteria like Enterococcus faecium and the gram-negative bacterium Pseudomonas aeruginosa, was evaluated, and a non-significant impedimetric signal was observed. Notably, the biosensor successfully identified S. aureus bacterial cells in a complex matrix such as a nasal swab, opening the possibility of its use in a real-case scenario. We diluted different concentrations of S. aureus from 108 to 100 CFU/mL with a ratio of 1:10 in the nasal swap matrices collected from healthy donors. Three different sensors were applied to measure various concentrations of bacteria. Our sensor indicated high selectivity to detect S. aureus in biological matrices compared to time-consuming traditional methods, such as enzyme-linked immunosorbent assay (ELISA), polymerase chain reaction (PCR), and radioimmunoassay (RIA), etc. With the aim to study the possibility to use this biosensor to address the challenge associated to pathogen detection, ongoing research is focused on the assessment of the biosensor’s analytical performances in different biological samples and the discovery of new phage bioreceptors.

Keywords: electrochemical impedance spectroscopy, bacteriophage, biosensor, Staphylococcus aureus

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Authors: Osatuyi Kehinde Micheal

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There is much research that has contributed to an improved understanding of the future of food security, especially during the COVID-19 pandemic. A pathway was developed by using a local community kitchen in Muizenberg in western cape province, cape town, south Africa, a case study to map out the future of food security in times of crisis. This kitchen aims to provide nutritious, affordable, plant-based meals to our community. It is also a place of diverse learning, sharing, empowering the volunteers, and growth to support the local economy and future resilience by sustaining our community kitchen for the community. This document contains an overview of the story of the community kitchen on how we create self-sustainability as a new pathway development to sustain the community and reduce Zero hunger in the regional food system. This paper describes the key elements of how we respond to covid-19 pandemic by sharing food parcels and creating 13 soup kitchens across the community to tackle the immediate response to covid-19 pandemic and agricultural systems by growing home food gardening in different homes, also having a consciousness Dry goods store to reduce Zero waste and a local currency as an innovation to reduce food crisis. Insights gained from our article and outreach and their value in how we create adaptation, transformation, and sustainability as a new development pathway to solve any future problem crisis in the food security system in our society.

Keywords: sustainability, food security, community development, adapatation, transformation

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Authors: Penn P. Wu, Jeannie Jo

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Blockchain technology has rapidly gained popularity in industry. This paper attempts to assist academia to answer four questions. First, should community colleges begin offering education to nurture blockchain-literate students for the job market? Second, what are the appropriate topical areas to cover? Third, should it be an individual course? And forth, should it be a technical or management course? This paper starts with identifying the knowledge domains of blockchain technology and the topical areas each domain has, and continues with placing them in appropriate academic territories (Computer Sciences vs. Business) and subjects (programming, management, marketing, and laws), and then develops an evaluation model to determine the appropriate topical area for community colleges to teach. The evaluation is based on seven factors: maturity of technology, impacts on management, real-world applications, subject classification, knowledge prerequisites, textbook readiness, and recommended pedagogies. The evaluation results point to an interesting direction that offering an introductory course is an ideal option to guide students through the learning journey of what blockchain is and how it applies to business. Such an introductory course does not need to engage students in the discussions of mathematics and sciences that make blockchain technologies possible. While it is inevitable to brief technical topics to help students build a solid knowledge foundation of blockchain technologies, community colleges should avoid offering students a course centered on the discussion of developing blockchain applications.

Keywords: blockchain, pedagogies, blockchain technologies, blockchain course, blockchain pedagogies

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Authors: Ganesh B. Shinde, Vijaya B. Musande

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Cotton crop identification based on the timely information has significant advantage to the different implications of food, economic and environment. Due to the significant advantages, the accurate detection of cotton crop regions using supervised learning procedure is challenging problem in remote sensing. Here, classifiers on the direct image are played a major role but the results are not much satisfactorily. In order to further improve the effectiveness, variety of vegetation indices are proposed in the literature. But, recently, the major challenge is to find the better vegetation indices for the cotton crop identification through the proposed methodology. Accordingly, fuzzy c-means clustering is combined with neural network algorithm, trained by Levenberg-Marquardt for cotton crop classification. To experiment the proposed method, five LISS-III satellite images was taken and the experimentation was done with six vegetation indices such as Simple Ratio, Normalized Difference Vegetation Index, Enhanced Vegetation Index, Green Atmospherically Resistant Vegetation Index, Wide-Dynamic Range Vegetation Index, Green Chlorophyll Index. Along with these indices, Green Leaf Area Index is also considered for investigation. From the research outcome, Green Atmospherically Resistant Vegetation Index outperformed with all other indices by reaching the average accuracy value of 95.21%.

Keywords: Fuzzy C-Means clustering (FCM), neural network, Levenberg-Marquardt (LM) algorithm, vegetation indices

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Authors: George Onomah

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Background: The integration of information communication technology (ICT) in Mathematics education faces notable challenges, which this study aimed to dissect and understand. Objectives: The primary goal was to assess the internal and external factors affecting the adoption of ICT by in-service Mathematics teachers. Internal factors examined included teachers' pedagogical beliefs, prior teaching experience, attitudes towards computers, and proficiency with technology. External factors included the availability of technological resources, the level of ICT training received, the sufficiency of allocated time for technology use, and the institutional culture within educational environments. Methods: A descriptive survey design was employed to methodically investigate these factors. Data collection was carried out using a five-point Likert scale questionnaire, administered to a carefully selected sample of 100 in-service Mathematics teachers through a combination of purposive and convenience sampling techniques. Findings: Results from multiple regression analysis revealed a significant underutilization of ICT in Mathematics teaching, highlighting a pronounced deficiency in current classroom practices. Recommendations: The findings suggest an urgent need for educational department heads to implement regular and comprehensive ICT training programs aimed at enhancing teachers' technological capabilities and promoting the integration of ICT in Mathematics teaching methodologies.

Keywords: ICT, Mathematics, integration, barriers

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Authors: Julie Chuah Suan Choo

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Due to the continuous change in workforce and the demands of the global workplace, many employers had lamented that the majority of university graduates were not prepared in the key areas of employment such as critical thinking, writing, self-direction and global knowledge which are most needed for the purposes of promotion. Further, critical thinking skills are deemed as integral parts of transformational pedagogy which aims at having a more informed society. To add to this, literature teaching has recently been advocated for enhancing students’ critical thinking and reasoning. Thus this study explored the effects of incorporating a few strategies in teaching literature, namely a Shakespeare play, into a course design to enhance these skills. An experiment involving a pretest and posttest using the California Critical Thinking Skills Test (CCTST) were administered on 80 first-year students enrolled in the Bachelor of Arts programme who were randomly assigned into the control group and experimental group. For the next 12 weeks, the experimental group was given intervention which included guided in-class discussion with Socratic questioning skills, learning log to detect their weaknesses in logical reasoning; presentations and quizzes. The results of CCTST which included paired T-test using SPSS version 22 indicated significant differences between the two groups. Findings have significant implications on the course design as well as pedagogical practice in using literature to enhance students’ critical thinking skills.

Keywords: literature teaching, critical thinking, California critical thinking skills test (CCTST), course design

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Authors: Shahla Ali

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In the context of promoting green infrastructure development, new opportunities are emerging to re-examine sustainable development practices. This paper presents an initial exploration of the development of community-investor dispute prevention and facilitation mechanisms in the context of the Belt and Road Initiative (BRI) spanning Asia, Africa, and Europe. Given the widescale impact of China’s multi-jurisdictional development initiative, learning how to coordinate with local communities is vital to realizing inclusive and sustainable growth. In the 20 years since the development of the first multilateral community-investor dispute resolution mechanism developed by the International Finance Centre/World Bank, much has been learned about public facilitation, community engagement, and dispute prevention during the early stages of major infrastructure development programs. This paper will explore initial findings as they relate to initiatives underway along the BRI within the Asian Infrastructure Investment Bank and the Asian Development Bank. Given the borderless nature of sustainability concerns, insights from diverse regions are critical to deepening insights into best practices. Drawing on a case-based methodology, this paper will explore the achievements, challenges, and lessons learned in community-investor dispute prevention and resolution for major infrastructure projects in the greater China region.

Keywords: law and development, dispute prevention, sustainable development, mitigation

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Authors: Xiaolin Zeng, Lei Cheng, Zhirong Li, Xueping Liu

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Aiming at the problem of low accuracy and susceptibility to cultural relic diseases in the generation of high-resolution archaeology drawings by current image generation algorithms, an archaeology drawings generation algorithm based on a conditional generative adversarial network is proposed. An attention mechanism is added into the high-resolution image generation network as the backbone network, which enhances the line feature extraction capability and improves the accuracy of line drawing generation. A dual-branch parallel architecture consisting of two backbone networks is implemented, where the semantic translation branch extracts semantic features from orthophotographs of cultural relics, and the gradient screening branch extracts effective gradient features. Finally, the fusion fine-tuning module combines these two types of features to achieve the generation of high-quality and high-resolution archaeology drawings. Experimental results on the self-constructed archaeology drawings dataset of grotto temple statues show that the proposed algorithm outperforms current mainstream image generation algorithms in terms of pixel accuracy (PA), structural similarity (SSIM), and peak signal-to-noise ratio (PSNR) and can be used to assist in drawing archaeology drawings.

Keywords: archaeology drawings, digital heritage, image generation, deep learning

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Authors: Zhang Shuqi, Liu Dan

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For the problems in intelligent network anomaly traffic detection models, such as low detection accuracy caused by the lack of training samples, poor effect with small sample attack detection, a classification model enhancement method, F-ACGAN(Flow Auxiliary Classifier Generative Adversarial Network) which introduces generative adversarial network and adversarial training, is proposed to solve these problems. Generating adversarial data with category labels could enhance the training effect and improve classification accuracy and model robustness. FACGAN consists of three steps: feature preprocess, which includes data type conversion, dimensionality reduction and normalization, etc.; A generative adversarial network model with feature learning ability is designed, and the sample generation effect of the model is improved through adversarial iterations between generator and discriminator. The adversarial disturbance factor of the gradient direction of the classification model is added to improve the diversity and antagonism of generated data and to promote the model to learn from adversarial classification features. The experiment of constructing a classification model with the UNSW-NB15 dataset shows that with the enhancement of FACGAN on the basic model, the classification accuracy has improved by 8.09%, and the score of F1 has improved by 6.94%.

Keywords: data imbalance, GAN, ACGAN, anomaly detection, adversarial training, data augmentation

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Authors: Siddharth Sarma, Ayush Majumdar, Nidhi Sabu, Mufaddal Jiruwaala, Shilpa Paygude

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Digital Elevation Models (DEMs) are digital representations of the Earth’s topography, which include information about the elevation, slope, aspect, and other terrain attributes. DEMs play a crucial role in various applications, including terrain analysis, urban planning, and environmental modeling. In this paper, TerraEnhance is proposed, a distinct approach for high-resolution DEM generation using Generative Adversarial Networks (GANs) combined with Real-ESRGANs. By learning from a dataset of low-resolution DEMs, the GANs are trained to upscale the data by 10 times, resulting in significantly enhanced DEMs with improved resolution and finer details. The integration of Real-ESRGANs further enhances visual quality, leading to more accurate representations of the terrain. A post-processing layer is introduced, employing high-pass filtering to refine the generated DEMs, preserving important details while reducing noise and artifacts. The results demonstrate that TerraEnhance outperforms existing methods, producing high-fidelity DEMs with intricate terrain features and exceptional accuracy. These advancements make TerraEnhance suitable for various applications, such as terrain analysis and precise environmental modeling.

Keywords: DEM, ESRGAN, image upscaling, super resolution, computer vision

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Authors: Shizra Waris, Bilal Shoaib, Munib Ahmad

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Background; The using of clinical decision support systems (CDSSs) may recover unceasing disease organization, which requires regular visits to multiple health professionals, treatment monitoring, disease control, and patient behavior modification. The objective of this survey is to determine if these CDSSs improve the processes of unceasing care including diagnosis, treatment, and monitoring of diseases. Though artificial intelligence is not a new idea it has been widely documented as a new technology in computer science. Numerous areas such as education business, medical and developed have made use of artificial intelligence Methods: The survey covers articles extracted from relevant databases. It uses search terms related to information technology and viral hepatitis which are published between 2000 and 2016. Results: Overall, 80% of studies asserted the profit provided by information technology (IT); 75% of learning asserted the benefits concerned with medical domain;25% of studies do not clearly define the added benefits due IT. The CDSS current state requires many improvements to hold up the management of liver diseases such as HCV, liver fibrosis, and cirrhosis. Conclusion: We concluded that the planned model gives earlier and more correct calculation of hepatitis B and it works as promising tool for calculating of custom hepatitis B from the clinical laboratory data.

Keywords: detection, hapataties, observation, disesese

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Authors: Shinji Kajiwara

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The basic ability of a vehicle is the ability to “run”, “turn” and “stop”. The safeness and comfort during a drive on various road surfaces and speed depends on the performance of these basic abilities of the vehicle. Stability and maneuverability of a vehicle is vital in automotive engineering. Stability of a vehicle is the ability of the vehicle to revert back to a stable state during a drive when faced with crosswind and irregular road conditions. Maneuverability of a vehicle is the ability of the vehicle to change direction during a drive swiftly based on the steering of the driver. The stability and maneuverability of a vehicle can also be defined as the driving stability of the vehicle. Since fossil fueled vehicle is the main type of transportation today, the environmental factor in automotive engineering is also vital. By improving the fuel efficiency of the vehicle, the overall carbon emission will be reduced thus reducing the effect of global warming and greenhouse gas on the Earth. Another main focus of the automotive engineering is the safety performance of the vehicle especially with the worrying increase of vehicle collision every day. With better safety performance on a vehicle, every driver will be more confidence driving every day. Next, let us focus on the “turn” ability of a vehicle. By improving this particular ability of the vehicle, the cornering limit of the vehicle can be improved thus increasing the stability and maneuverability factor. In order to improve the cornering limit of the vehicle, a study to find the balance between the steering systems, the stability of the vehicle, higher lateral acceleration and the cornering limit detection must be conducted. The aim of this research is to study and develop a new suspension system that that will boost the lateral acceleration of the vehicle and ultimately improving the cornering limit of the vehicle. This research will also study environmental factor and the stability factor of the new suspension system. The double wishbone suspension system is widely used in four-wheel vehicle especially for high cornering performance sports car and racing car. The double wishbone designs allow the engineer to carefully control the motion of the wheel by controlling such parameters as camber angle, caster angle, toe pattern, roll center height, scrub radius, scuff and more. The development of the new suspension system will focus on the ability of the new suspension system to optimize the camber control and to improve the camber limit during a cornering motion. The research will be carried out using the CAE analysis tool. Using this analysis tool we will develop a JSAE Formula Machine equipped with the double wishbone system and also the new suspension system and conduct simulation and conduct studies on performance of both suspension systems.

Keywords: automobile, camber thrust, cornering force, suspension

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Authors: Huicong Jia, Donghua Pan

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As a key component of disaster reduction management, disaster emergency relief and reconstruction is an important process. Based on disaster system theory, this study analyzed the Jiangsu tornado from the formation mechanism of disasters, through to the economic losses, loss of life, and social infrastructure losses along the tornado disaster chain. The study then assessed the emergency relief and reconstruction efforts, based on an analytic hierarchy process method. The results were as follows: (1) An unstable weather system was the root cause of the tornado. The potentially hazardous local environment, acting in concert with the terrain and the river network, was able to gather energy from the unstable atmosphere. The wind belt passed through a densely populated district, with vulnerable infrastructure and other hazard-prone elements, which led to an accumulative disaster situation and the triggering of a catastrophe. (2) The tornado was accompanied by a hailstorm, which is an important triggering factor for a tornado catastrophe chain reaction. (3) The evaluation index (EI) of the emergency relief and reconstruction effect for the ‘‘6.23’’ tornado disaster in Yancheng was 91.5. Compared to other relief work in areas affected by disasters of the same magnitude, there was a more successful response than has previously been experienced. The results provide new insights for studies of disaster systems and the recovery measures in response to tornado catastrophe in China.

Keywords: China, disaster system, emergency relief, tornado catastrophe

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Authors: Weiming Hu, Xu Li, Xiaonan Li, Zhong Xu, Li Yuan, Xuan Dong

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Maneuver decision-making plays a critical role in high-performance intelligent driving. This paper proposes a risk assessment-based decision-making network (RADMN) to address the problem of driving strategy for the commercial vehicle. RADMN integrates two networks, aiming at identifying the risk degree of collision and rollover and providing decisions to ensure the effectiveness and reliability of driving strategy. In the risk assessment module, risk degrees of the backward collision, forward collision and rollover are quantified for hazard recognition. In the decision module, a deep reinforcement learning based on multi-objective optimization (DRL-MOO) algorithm is designed, which comprehensively considers the risk degree and motion states of each traffic participant. To evaluate the performance of the proposed framework, Prescan/Simulink joint simulation was conducted in highway scenarios. Experimental results validate the effectiveness and reliability of the proposed RADMN. The output driving strategy can guarantee the safety and provide key technical support for the realization of autonomous driving of commercial vehicles.

Keywords: decision-making strategy, risk assessment, multi-objective optimization, commercial vehicle

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Authors: Josiah O. Carter, Charlotte Hayden, Elizabeth Arthurs

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Background: During the COVID-19 pandemic, hospital management policies have changed frequently and rapidly. This has created novel challenges in keeping the workforce abreast of these changes to enable them to deliver safe and effective care. Traditional communication methods, e.g. email, do not keep pace with the rapidly changing environment in the hospital, resulting in inaccurate, irrelevant, or outdated information being communicated, resulting in inefficiencies in patient care. Methods: The creation of a WhatsApp messaging group within the medical division at the Bristol Royal Infirmary has enabled senior clinicians and the hospital management team to update the medical workforce in real-time. It has two primary functions: (1) To enable dissemination of a concise, important operational summary. This comprises information on bed status and infection control procedural changes. It is fed directly from a daily critical incident briefing (2) To facilitate a monthly scheduled question and answer (Q&A) session for junior doctors to clarify issues with clinical directors, rota, and management staff. Additional ad-hoc updates are sent out for time-critical information; otherwise, it mainly functions as a broadcast-only group to prevent important information from being lost amongst other communication. All junior doctors within the medical division were invited to join the group. At present, the group comprises 131 participants, of which 10 are administrative staff (rota coordinators, management staff & clinical directors); the remaining 121 are junior clinicians working within the medical division. An electronic survey via Microsoft forms was sent out to junior doctors via the WhatsApp group and via email to assess its utilisation and effectiveness with the aim of quality improvements. Results: Of the 121 group participants, 19 completed the questionnaire (response rate 15.7%). Of these, 16/19 (84.2%) used it regularly, and 12/19 (63.2%) rated it as the most useful source for reliable updates relating to the hospital response to the COVID-19 pandemic, whereas only 2/19 (10.5%) found the trust intranet and the trust COVID-19 operational email update most useful. Respondents rated the WhatsApp group more useful as an information source (mean score 7.7/10) than as a means of providing feedback to management staff (mean score 6.3/10). Qualitative feedback suggested information around ward closures and changes to COVID cohorting, along with updates on staffing issues, were most useful. Respondents also noted the Q&A sessions were an efficient way of relaying feedback about management decisions but that it would be preferable if these sessions could be delivered more frequently. Discussion: During the current global COVID-19 pandemic, there is an increased need for rapid dissemination of critical information within NHS trusts; this includes communication between junior doctors, managers, and senior clinicians. The versatility of WhatsApp permits a variety of functions allowing for regular updates, the dissemination of time-critical information, and enables conversing and feedback. The project has demonstrated that reserved and well-managed use of a WhatsApp group is a welcome, efficient and practical means of communication between the senior management team and the junior medical workforce.

Keywords: communication, COVID-19, hospital management, WhatsApp

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Authors: Ruwan Gunawardane

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This paper attempts to emphasize the benefits of conducting an intensive course in English for university entrants. In the Sri Lankan university context, an intensive course in English is usually conducted amidst various obstacles. In the 1970s and 1980s, undergraduates had intensive programmes in English for two to three months. Towards the end of the 1990s, a programme called General English Language Training (GELT) was conducted for the new students, and it was done outside universities before they entered their respective universities. Later it was not conducted, and that also resulted in students’ poor performance in English at university. However, having understood its importance, an eight week long intensive course in English was conducted for the new intake of the Faculty of Science, University of Ruhuna. As the findings show, the students heavily benefited from the programme. More importantly, they had the opportunity to refresh their knowledge of English gained at school and private institutions while gaining new knowledge. Another advantage was that they had plenty of time to enjoy learning English since the learners had adequate opportunities to carry out communicative tasks and the course was not exam-oriented, which reduced their fear of making mistakes in English considerably. The data was collected through an open-ended questionnaire given to 60 students, and their oral feedback was also taken into consideration. In addition, a focus group interview with 6 teachers was also conducted to get an idea about their experience and perception. The data were qualitatively analyzed. The findings suggest that an intensive programme in English undoubtedly lays a good foundation for the students’ academic career at university.

Keywords: intensive course, English, teachers, undergraduates, experience, perception

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Authors: Mahfoodh Saleh Al Sabbagh, Venkoba Rao

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There is plenty of research, both in academic and consultancy circles, about the importance and benefits of employee engagement and customer engagement and how it gives organization an opportunity to reduce variability and improve performance. Customer engagement is directly related to the engagement level of the organization's employees. It is therefore important to measure both. This research drawing from the work of Human Sigma by Fleming and Asplund, attempts to assess engagement level of customer and employees - the human systems of business - in an educational setup. Student is important to an educational institute and is a customer to be served efficiently and effectively. Considering student as customer and faculty as employees serving them, in–depth interviews were conducted to analyze the relationship between faculty and student engagement in two leading colleges in Oman, one from private sector and another from public sector. The study relied mainly on secondary data sources to understand the concept of engagement. However, the search of secondary sources was extensive to compensate the limited primary data. The results indicate that high faculty engagement is likely to lead to high student engagement. Engaged students were excited about learning, loved the feeling of they being cared as a person by their faculty and advocated the organization to other. The interaction truly represents an opportunity to build emotional connection to the organization. This study could be of interest to organizations interest in building and maintaining engagement with employees and customers.

Keywords: customer engagement, consumer psychology, strategy, educational institutes

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Authors: Nowfiya Humayoon

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The Sustainable Development Goals were launched by the UNO in 2015 as a global initiative aimed at eradicating poverty, safeguarding the environment, and promoting peace and prosperity with the target year of 2030. SDGs are vital for achieving global peace, prosperity, and sustainability. Like all nations of the world, these goals are crucial to Palestine but challenging due to the ongoing crisis. Effective action toward achieving each Sustainable Development Goals (SDGs) in Palestine has been severely challenged due to political instability, limited access to resources, International Aid Constraints, Economic blockade, etc., right from the beginning. In the context of the ongoing conflict, there are severe violations of international humanitarian law, which include targeting civilians, using excessive force, and blocking humanitarian aid, which has led to significant civilian casualties, sufferings, and deaths. Therefore, addressing the Sustainable Development Goals is imperative in ensuring human rights, combating violations and fostering sustainability. Methodology: The study adopts a historical, analytical and quantitative approach to evaluate the impact of the ongoing conflict on SDGs in Palestine, with a focus on sustainability and human rights. It examines historical documents, reports of international organizations and regional organizations, recent journal and newspaper articles, and other relevant literature to trace the evolution and the on-ground realities of the conflict and its effects. Quantitative data are collected by analyzing statistical reports from government agencies, non-governmental organizations (NGOs) and international bodies. Databases from World Bank, United Nations and World Health Organizations are utilized. Various health and economic indicators on mortality rates, infant mortality rates and income levels are also gathered. Major Findings: The study reveals profound challenges in achieving the Sustainable Development Goals (SDGs) in Palestine, which include economic blockades and restricted access to resources that have left a substantial portion of the population living below the poverty line, overburdened healthcare facilities struggling to cope with the demands, shortages of medical supplies, disrupted educational systems, with many schools destroyed or repurposed, and children facing significant barriers to accessing quality education, damaged infrastructure, restricted access to clean water and sanitation services and limited access to reliable energy sources . Conclusion: The ongoing crisis in Palestine has drastically affected progress towards the Sustainable Development Goals (SDGs), causing innumerable crises. Violations of international humanitarian law have caused substantial suffering and loss of life. Immediate and coordinated global action and efforts are crucial in addressing these challenges in order to uphold humanitarian values and promote sustainable development in the region.

Keywords: genocide, human rights, occupation, sustainable development goals

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Authors: Hasan Demiroğlu, Uğur Avcıbaşı, Serhan Sakarya, Perihan Ünak

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Implanted devices are progressively practiced in innovative medicine to relieve pain or improve a compromised function. Implant-associated infections represent an emerging complication, caused by organisms which adhere to the implant surface and grow embedded in a protective extracellular polymeric matrix, known as a biofilm. In addition, the microorganisms within biofilms enter a stationary growth phase and become phenotypically resistant to most antimicrobials, frequently causing treatment failure. In such cases, surgical removal of the implant is often required, causing high morbidity and substantial healthcare costs. Staphylococcus aureus is the most common pathogen causing implant-associated infections. Successful treatment of these infections includes early surgical intervention and antimicrobial treatment with bactericidal drugs that also act on the surface-adhering microorganisms. Linezolid is a promising anti-microbial with ant-staphylococcal activity, used for the treatment of MRSA infections. Linezolid is a synthetic antimicrobial and member of oxazolidinoni group, with a bacteriostatic or bactericidal dose-dependent antimicrobial mechanism against gram-positive bacteria. Intensive use of antibiotics, have emerged multi-resistant organisms over the years and major problems have begun to be experienced in the treatment of infections occurred with them. While new drugs have been developed worldwide, on the other hand infections formed with microorganisms which gained resistance against these drugs were reported and the scale of the problem increases gradually. Scientific studies about the production of bacterial biofilm increased in recent years. For this purpose, we investigated the activity of Lin, Lin radiolabeled with 131I (131I-Lin) and cold iodinated Lin (127I-Lin) against clinical strains of Staphylococcus aureus DSM 4910 in biofilm. In the first stage, radio and cold labeling studies were performed. Quality-control studies of Lin and iodo (radio and cold) Lin derivatives were carried out by using TLC (Thin Layer Radiochromatography) and HPLC (High Pressure Liquid Chromatography). In this context, it was found that the binding yield was obtained to be about 86±2 % for 131I-Lin. The minimal inhibitory concentration (MIC) of Lin, 127I-Lin and 131I-Lin for Staphylococcus aureus DSM 4910 strain were found to be 1µg/mL. In time-kill studies of Lin, 127I-Lin and 131I-Lin were producing ≥ 3 log10 decreases in viable counts (cfu/ml) within 6 h at 2 and 4 fold of MIC respectively. No viable bacteria were observed within the 24 h of the experiments. Biofilm eradication of S. aureus started with 64 µg/mL of Lin, 127I-Lin and 131I-Lin, and OD630 was 0.507±0.0.092, 0.589±0.058 and 0.266±0.047, respectively. The media control of biofilm producing Staphylococcus was 1.675±0,01 (OD630). 131I and 127I did not have any effects on biofilms. Lin and 127I-Lin were found less effectively than 131I-Lin at killing cells in biofilm and biofilm eradication. Our results demonstrate that the 131I-Lin have potent anti-biofilm activity against S. aureus compare to Lin, 127I-Lin and media control. This is suggested that, 131I may have harmful effect on biofilm structure.

Keywords: iodine-131, linezolid, radiolabeling, slime layer, Staphylococcus

Procedia PDF Downloads 558

Authors: Abhijeet Ostawal, Parmjit Lall

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The run times for a large strategic model that we were managing had become too long leading to delays in project delivery, increased costs and loss in productivity. Software developers are continuously working towards developing more efficient tools by changing their algorithms and processes. The issue faced by our team was how do you apply the latest technologies on validated existing models which are based on much older versions of software that do not have the latest software capabilities. The multi-model transport model that we had could only be run in sequential assignment order. Recent upgrades to the software now allowed the assignment to be run in parallel, a concept called parallelization. Parallelization is a Python script working only within the latest version of the software. A full model transfer to the latest version was not possible due to time, budget and the potential changes in trip assignment. This article is to show the method to adapt and update the Python script in such a way that it can be used in older software versions by calling the latest version and then recalling the old version for assignment model without affecting the results. Through a process of trial-and-error run time savings of up to 30-40% have been achieved. Assignment results were maintained within the older version and through this learning process we’ve applied this methodology to other even older versions of the software resulting in huge time savings, more productivity and efficiency for both client and consultant.

Keywords: model run time, demand model, parallelisation, python scripting

Procedia PDF Downloads 119

Authors: Khitem Amiri, Mohamed Farah

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Image classification is a challenging task and is gaining lots of interest since it helps us to understand the content of images. Recently Deep Learning (DL) based methods gave very interesting results on several benchmarks. For Hyperspectral images (HSI), the application of DL techniques is still challenging due to the scarcity of labeled data and to the curse of dimensionality. Among other approaches, Deep Belief Network (DBN) based approaches gave a fair classification accuracy. In this paper, we address the problem of the curse of dimensionality by reducing the number of bands and replacing the HSI channels by the channels representing radiometric indices. Therefore, instead of using all the HSI bands, we compute the radiometric indices such as NDVI (Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index), etc, and we use the combination of these indices as input for the Deep Belief Network (DBN) based classification model. Thus, we keep almost all the pertinent spectral information while reducing considerably the size of the image. In order to test our image representation, we applied our method on several HSI datasets including the Indian pines dataset, Jasper Ridge data and it gave comparable results to the state of the art methods while reducing considerably the time of training and testing.

Keywords: hyperspectral images, deep belief network, radiometric indices, image classification

Procedia PDF Downloads 280

Authors: Ebenezer Joseph, Veena Easvaradoss, S. Sundar Manoharan, David Chandran, Sumathi Chandrasekaran, T. R. Uma

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Perceptual reasoning is the ability that incorporates fluid reasoning, spatial processing, and visual motor integration. Several theories of cognitive functioning emphasize the importance of fluid reasoning. The ability to manipulate abstractions and rules and to generalize is required for reasoning tasks. This study, funded by the Cognitive Science Research Initiative, Department of Science and Technology, Government of India, analyzed the effect of 1-year chess training on the perceptual reasoning of children. A pretest–posttest with control group design was used, with 43 (28 boys, 15 girls) children in the experimental group and 42 (26 boys, 16 girls) children in the control group. The sample was selected from children studying in two private schools from South India (grades 3 to 9), which included both the genders. The experimental group underwent weekly 1-hour chess training for 1 year. Perceptual reasoning was measured by three subtests of WISC-IV INDIA. Pre-equivalence of means was established. Further statistical analyses revealed that the experimental group had shown statistically significant improvement in perceptual reasoning compared to the control group. The present study clearly establishes a correlation between chess learning and perceptual reasoning. If perceptual reasoning can be enhanced in children, it could possibly result in the improvement of executive functions as well as the scholastic performance of the child.

Keywords: chess, cognition, intelligence, perceptual reasoning

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Authors: Waleed Alanzi

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The purpose of this qualitative study was to add to the existing literature and provide a more detailed understanding of the individual experiences and perceptions of 15 Deans at the University of Kuwait regarding their first year of planning, developing, and implementing crisis distance education (CDE) in response to the COVID-19 epidemic. An interpretative phenomenological approach was applied, using the thematic analysis of interview transcripts to describe the challenging journeys taken by each of the Deans from the first-person point of view. There was objective evidence, manifested by four primary themes (“Obstacles to the implementation of CDE”; “Planning for CDE”; “Training for CDE,” and “Future Directions”) to conclude that the faculty members, technical staff, administrative staff, and students generally helped each other to overcome the obstacles associated with planning and implementing CDE. The idea that CDE may turn homes into schools and parents into teachers was supported. The planning and implementation of CDE were inevitably associated with a certain amount of confusion, as well as disruptions in the daily routines of staff and students, as well as significant changes in their responsibilities. There were contradictory ideas about the future directions of distance education after the pandemic. Previous qualitative research on the implementation of CDE at higher education institutions in the Arab world has focused mainly on the experiences and perceptions of students; however, little is known about the experiences and perceptions of the students at the University of Kuwait during the COVID19 pandemic, providing a rationale and direction for future research.

Keywords: distance learning, qualitative research, COVID-19 epidemic, Kuwait university

Procedia PDF Downloads 106

Authors: S. Dalvi, N. Sane, V. Patil, D. Bansode, A. Tharakan, V. Mathur

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Measurement of routine clinical electrolyte tests is common in labs worldwide for screening of illness or diseases. All the analyzers for the measurement of electrolyte parameters have sensors, reagents, sampler, pump tubing, valve, other tubing’s separate that are either expensive, require heavy maintenance and have a short shelf-life. Moreover, the costs required to maintain such Lab instrumentation is high and this limits the use of the device to only highly specialized personnel and sophisticated labs. In order to provide Healthcare Diagnostics to ALL at affordable costs, there is a need for an All-in-one Universal Modular Cartridge that contains sensors, reagents, sampler, valve, pump tubing, and other tubing’s in one single integrated module-in-module cartridge that is affordable, reliable, easy-to-use, requires very low sample volume and is truly modular and maintenance-free. DiaSys India has developed a World’s first, Patent Pending, Versatile All-in-one Universal Module-in-Module Cartridge based Electrolyte Analyzer (QDx InstaLyte) that can perform sodium, potassium, chloride, calcium, pH, lithium tests. QDx InstaLyte incorporates High Performance, Inexpensive All-in-one Universal Cartridge for rapid quantitative measurement of electrolytes in body fluids. Our proposed methodology utilizes Advanced & Improved long life ISE sensors to provide a sensitive and accurate result in 120 sec with just 100 µl of sample volume. The All-in-One Universal Cartridge has a very low reagent consumption capable of maximum of 1000 tests with a Use-life of 3-4 months and a long Shelf life of 12-18 months at 4-25°C making it very cost-effective. Methods: QDx InstaLyte analyzers with All-in-one Universal Modular Cartridges were independently evaluated with three R&D lots for Method Performance (Linearity, Precision, Method Comparison, Cartridge Stability) to measure Sodium, Potassium, Chloride. Method Comparison was done against Medica EasyLyte Plus Na/K/Cl Electrolyte Analyzer, a mid-size lab based clinical chemistry analyzer with N = 100 samples run over 10 days. Within-run precision study was done using modified CLSI guidelines with N = 20 samples and day-to-day precision study was done for 7 consecutive days using Trulab N & P Quality Control Samples. Accelerated stability testing was done at 45oC for 4 weeks with Production Lots. Results: Data analysis indicates that the CV for within-run precision for Na is ≤ 1%, for K is ≤2%, and for Cl is ≤2% and with R2 ≥ 0.95 for Method Comparison. Further, the All-in-One Universal Cartridge is stable up to 12-18 months at 4-25oC storage temperature based on preliminary extrapolated data. Conclusion: The Developed Technology Platform of All-in-One Universal Module-in-Module Cartridge based QDx InstaLyte is Reliable and meets all the performance specifications of the lab and is Truly Modular and Maintenance-Free. Hence, it can be easily adapted for low cost, sensitive and rapid measurement of electrolyte tests in low resource settings such as in urban, semi-urban and rural areas in the developing countries and can be used as a Point-of-care testing system for worldwide applications.

Keywords: all-in-one modular catridge, electrolytes, maintenance free, QDx instalyte

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Authors: Bruno Cossu, Elio Carlo

Abstract:

The invention (patent pending) relates to the field of devices aimed to harness wave energy (WEC) especially for artificial upwelling, forced downwelling, production of compressed air. In its basic form, the pump consists of a hydro-pneumatic machine, driven by wave energy, characterised by the fact that it has no moving mechanical parts, and is made up of only two structural components: an hollow body, which is open at the bottom to the sea and partially immersed in sea water, and a tube, both joined together to form a single body. The shape of the hollow body is like a mushroom whose cap and stem are hollow; the stem is open at both ends and the lower part of its surface is crossed by holes; the tube is external and coaxial to the stem and is joined to it so as to form a single body. This shape of the hollow body and the type of connection to the tube allows the pump to operate simultaneously as an air compressor (OWC) on the cap side, and as an airlift on the stem side. The pump can be implemented in four versions, each of which provides different variants and methods of implementation: 1) firstly, for the artificial upwelling of cold, deep ocean water; 2) secondly, for the lifting and transfer of these waters to the place of use (above all, fish farming plants), even if kilometres away; 3) thirdly, for the forced downwelling of surface sea water; 4) fourthly, for the forced downwelling of surface water, its oxygenation, and the simultaneous production of compressed air. The transfer of the deep water or the downwelling of the raised surface water (as for pump versions indicated in points 2 and 3 above), is obtained by making the water raised by the airlift flow into the upper inlet of another pipe, internal or adjoined to the airlift; the downwelling of raised surface water, oxygenation, and the simultaneous production of compressed air (as for the pump version indicated in point 4), is obtained by installing a venturi tube on the upper end of the pipe, whose restricted section is connected to the external atmosphere, so that it also operates like a hydraulic air compressor (trompe). Furthermore, by combining one or more pumps for the upwelling of cold, deep water, with one or more pumps for the downwelling of the warm surface water, the system can be used in an Ocean Thermal Energy Conversion plant to supply the cold and the warm water required for the operation of the same, thus allowing to use, without increased costs, in addition to the mechanical energy of the waves, for the purposes indicated in points 1 to 4, the thermal one of the marine water treated in the process.

Keywords: air lifted upwelling, fish farming plant, hydraulic air compressor, wave energy converter

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Authors: Christopher Leung, Tony Kim, Rebecca Lendzion, Scott Mackenzie

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Retroperitoneal abscesses are a rare but serious condition with often delayed diagnosis, non-specific symptoms, multiple causes and high morbidity/mortality. With the advent of more readily available cross-sectional imaging, retroperitoneal abscesses are treated earlier and better outcomes are achieved. Occasionally, a retroperitoneal abscess is present as a huge retroperitoneal abscess, as evident in this 53-year-old male. With a background of chronic renal disease and left partial nephrectomy, this gentleman presented with a one-month history of left flank pain without any other symptoms, including fevers or abdominal pain. CT abdomen and pelvis demonstrated a huge retroperitoneal abscess spanning from the diaphragm, abutting the spleen, down to the iliopsoas muscle and abutting the iliac vessels at the pelvic brim. This large retroperitoneal abscess required open drainage as well as drainage by interventional radiology. A long course of intravenous antibiotics and multiple drainages was required to drain the abscess. His blood culture and fluid culture grew Proteus species suggesting a urinary source, likely from his non-functioning kidney, which had a partial nephrectomy. Such a huge retroperitoneal abscess has rarely been described in the literature. The learning point here is that the basic principle of source control and antibiotics is paramount in treating retroperitoneal abscesses regardless of the size of the abscess.

Keywords: retroperitoneal abscess, retroperitoneal mass, sepsis, genitourinary infection

Procedia PDF Downloads 222