8,297 research outputs found
ICT enabled approach for humanitarian disaster management: a systems perspective
Purpose
Each stage in disaster management faces different challenges concerning information gathering, sharing, interpretation and dissemination. However, a comprehensive understanding of different information and communication technology (ICT) systems utilised for humanitarian disaster management is limited. Therefore, the paper follows a systems thinking approach to examine ten major man-made and/or natural disasters to comprehend the influence of ICT systems on humanitarian relief operations.
Design/methodology/approach
A longitudinal, multi-case study captures the use of ICT tools, stakeholders involvement, disaster stages and zones of operations for relief operations over the past two decades. A systems thinking approach is utilised to draw several inferences and develop frameworks.
Findings
Multiple ICT tools such as geographic information systems, online webpages/search engines, social media, unmanned aerial vehicles/robots and artificial intelligence are used for rapid disaster response and mitigation. Speed and coordination of relief operations have significantly increased in recent years due to the increased use of ICT systems.
Research limitations/implications
Secondary data on the past ten disasters is utilised to draw inferences. The developed ICT-driven model must be validated during upcoming humanitarian relief operations.
Practical implications
A holistic understanding of a complex inter-relationship between influential variables (stakeholders, disaster stages, zones of operation, ICT systems) is beneficial for effectively managing humanitarian disasters.
Originality/value
Broadly classifying the ICT systems into surveillance, decision support and broadcasting systems, a novel ICT-enabled model for humanitarian relief operations is developed
Review of the applications of principles of insect hearing to microscale acoustic engineering challenges
When looking for novel, simple, and energy-efficient solutions to engineering problems, nature has proved to be an incredibly valuable source of inspiration. The development of acoustic sensors has been a prolific field for bioinspired solutions. With a diverse array of evolutionary approaches to the problem of hearing at small scales (some widely different to the traditional concept of "ear"), insects in particular have served as a starting point for several designs. From locusts to moths, through crickets and mosquitoes among many others, the mechanisms found in nature to deal with small-scale acoustic detection and the engineering solutions they have inspired are reviewed. The present article is comprised of three main sections corresponding to the principal problems faced by insects, namely frequency discrimination, which is addressed by tonotopy, whether performed by a specific organ or directly on the tympana; directionality, with solutions including diverse adaptations to tympanal structure; and detection of weak signals, through what is known as active hearing. The three aforementioned problems concern tiny animals as much as human-manufactured microphones and have therefore been widely investigated. Even though bioinspired systems may not always provide perfect performance, they are sure to give us solutions with clever use of resources and minimal post-processing, being serious contenders for the best alternative depending on the requisites of the problem
Lost in the City? - A Scoping Review of 5G Enabled Location-Based Urban Scenarios
5G mobile network technologies and scenarios with the associated innovations receive growing interest among academics and practitioners. Current literature on 5G technologies discusses several scenarios and specific chances and challenges. However, 5G literature is fragmented and not systematically reviewed. We conducted a scoping review on 5G applications in urban scenarios. We reviewed 1,394 papers and identified 20 studies about urban logistics and emergency indoor localization. Our review accumulates current academic knowledge on these scenarios and identifies six further research directions in four research fields. It reveals several further research opportunities, e.g., regarding trust and privacy concerns. We review and discuss 5G literature for academics and practitioners, contribute towards more tailored 5G research and reflect on cost- efficient 5G applications in urban scenarios
Weather or not? The role of international sanctions and climate on food prices in Iran
IntroductionThe scarcity of resources have affected food production, which has challenged the ability of Iran to provide adequate food for the population. Iterative and mounting sanctions on Iran by the international community have seriously eroded Iran's access to agricultural technology and resources to support a growing population. Limited moisture availability also affects Iran's agricultural production. The aim of this study was to analyze the influence of inflation, international sanctions, weather disturbances, and domestic crop production on the price of rice, wheat and lentils from 2010 to 2021 in Iran.MethodData were obtained from the statistical yearbooks of the Ministry of Agriculture in Iran, Statistical Center of Iran, and the Central Bank of Iran. We analyzed econometric measures of food prices, including CPI, food inflation, subsidy reform plan and sanctions to estimate economic relationships. After deflating the food prices through CPI and detrending the time series to resolve the non-linear issue, we used monthly Climate Hazards group Infrared Precipitation with Stations (CHIRPS) precipitation data to analyze the influence of weather disturbances on food prices.Results and discussionThe price of goods not only provides an important indicator of the balance between agricultural production and market demand, but also has strong impacts on food affordability and food security. This novel study used a combination of economic and climate factors to analyze the food prices in Iran. Our statistical modeling framework found that the monthly precipitation on domestic food prices, and ultimately food access, in the country is much less important than the international sanctions, lowering Iran's productive capability and negatively impacting its food security
International Academic Symposium of Social Science 2022
This conference proceedings gathers work and research presented at the International Academic Symposium of Social Science 2022 (IASSC2022) held on July 3, 2022, in Kota Bharu, Kelantan, Malaysia. The conference was jointly organized by the Faculty of Information Management of Universiti Teknologi MARA Kelantan Branch, Malaysia; University of Malaya, Malaysia; Universitas Pembangunan Nasional Veteran Jakarta, Indonesia; Universitas Ngudi Waluyo, Indonesia; Camarines Sur Polytechnic Colleges, Philippines; and UCSI University, Malaysia. Featuring experienced keynote speakers from Malaysia, Australia, and England, this proceeding provides an opportunity for researchers, postgraduate students, and industry practitioners to gain knowledge and understanding of advanced topics concerning digital transformations in the perspective of the social sciences and information systems, focusing on issues, challenges, impacts, and theoretical foundations. This conference proceedings will assist in shaping the future of the academy and industry by compiling state-of-the-art works and future trends in the digital transformation of the social sciences and the field of information systems. It is also considered an interactive platform that enables academicians, practitioners and students from various institutions and industries to collaborate
Deep Learning Models For Biomedical Data Analysis
The field of biomedical data analysis is a vibrant area of research dedicated to extracting valuable insights from a wide range of biomedical data sources, including biomedical images and genomics data. The emergence of deep learning, an artificial intelligence approach, presents significant prospects for enhancing biomedical data analysis and knowledge discovery. This dissertation focused on exploring innovative deep-learning methods for biomedical image processing and gene data analysis.
During the COVID-19 pandemic, biomedical imaging data, including CT scans and chest x-rays, played a pivotal role in identifying COVID-19 cases by categorizing patient chest x-ray outcomes as COVID-19-positive or negative. While supervised deep learning methods have effectively recognized COVID-19 patterns in chest x-ray datasets, the availability of annotated training data remains limited. To address this challenge, the thesis introduced a semi-supervised deep learning model named ssResNet, built upon the Residual Neural Network (ResNet) architecture. The model combines supervised and unsupervised paths, incorporating a weighted supervised loss function to manage data imbalance. The strategies to diminish prediction uncertainty in deep learning models for critical applications like medical image processing is explore. It achieves this through an ensemble deep learning model, integrating bagging deep learning and model calibration techniques. This ensemble model not only boosts biomedical image segmentation accuracy but also reduces prediction uncertainty, as validated on a comprehensive chest x-ray image segmentation dataset.
Furthermore, the thesis introduced an ensemble model integrating Proformer and ensemble learning methodologies. This model constructs multiple independent Proformers for predicting gene expression, their predictions are combined through weighted averaging to generate final predictions. Experimental outcomes underscore the efficacy of this ensemble model in enhancing prediction performance across various metrics.
In conclusion, this dissertation advances biomedical data analysis by harnessing the potential of deep learning techniques. It devises innovative approaches for processing biomedical images and gene data. By leveraging deep learning\u27s capabilities, this work paves the way for further progress in biomedical data analytics and its applications within clinical contexts.
Index Terms- biomedical data analysis, COVID-19, deep learning, ensemble learning, gene data analytics, medical image segmentation, prediction uncertainty, Proformer, Residual Neural Network (ResNet), semi-supervised learning
2023-2024 Undergraduate Catalog
2023-2024 undergraduate catalog for Morehead State University
Recommended from our members
Wearable Technologies to Support Lower Limb Rehabilitation and Clinical Practice: user requirements, design and evaluation
The widespread adoption of wearable technologies in healthcare has the potential to bring about significant improvements. However, these technologies face design challenges when applied in real world settings and must be tailored to specific contexts of use and the needs of a diverse user base. This thesis investigates these issues in two distinct yet related areas of healthcare: neurorehabilitation and clinical movement analysis.
In neurorehabilitation, the research builds on previous work that demonstrated the effectiveness of wearable rhythmic haptic metronomes in improving and measuring the gait of individuals with neurological conditions in laboratory settings. This study takes this approach into the community and care home settings, using a technology probe method to identify the real-life requirements and design considerations of potential end-users and clinicians. This process identified a range of physical, sensory, and cognitive issues that are relevant to the design of the haptic metronomes, including haptic perception ability, wearability, interaction techniques, and individual preferences for body placement.
The second part of the thesis initially focused on the potential of active cueing for musculoskeletal conditions, but formative discussions with specialist physiotherapists and orthopaedic surgeons suggested that wearable clinical movement analysis would be a more suitable focus. Currently, proprietary systems for objectively assessing lower limb movements are either poorly suited or too expensive. To address this gap, non-proprietary software called MoJoXlab, paired with low-cost wearable inertial sensors was validated against high-end commercial software to perform clinical movement analysis. The results of these tests were compared across a range of activities, including walking, squatting, and jumping. MoJoXlab was further validated with a different sensor system, and limitations and nuances of supporting multiple sensor systems were identified.
Overall, this thesis highlights the importance of considering the needs and preferences of diverse users and the specific conditions and contexts in which wearable technologies will be used to effectively design and implement these technologies in healthcare
- …