Search Results (13)

Public transportation is essential for improving urban mobility, enhancing travel quality, reducing reliance on private vehicles, and alleviating traffic congestion. However, inadequate public transportation in outer Melbourne is a significant issue limiting urban development. While existing research primarily focuses on walking distance to define service catchments, commuters in transit-disadvantaged or outlying urban areas often drive to transit, noted as the park-and-ride mode. This research uniquely examines drive-distance catchments for park-and-ride transit accessibility in outer Melbourne, using spatial SQL and GIS techniques to provide a detailed, multi-dimensional analysis of population coverage, parking capacity, and accessibility gaps. This approach fills a gap in the existing literature by offering adaptable insights and approaches to other outer urban areas with transit disadvantages. The findings underscore the necessity for targeted enhancements in public transportation in outer Melbourne: Most of the outer residents concentrate near the train stations, though significant spatial gaps exist; The general accessibility status of residential mesh blocks is found; Parking capacity varies with high tension found at certain stations. This study contributes insights to create more equitable and sustainable transportation systems by providing a detailed spatial analysis of current transit coverage and identifying critical gaps. Full article

This study used epigenomic methylation differential expression analysis to identify primary biomarkers in patients with amyotrophic lateral sclerosis (ALS). We combined electronic medical record datasets from MIMIC-IV (United States) and NHIRD (Taiwan) to explore ALS comorbidities in depth and discover any comorbidity-related biomarkers. We also applied word2vec to these two clinical diagnostic medical databases to measure similarities between ALS and other similar diseases and evaluated the statistical assessment of the odds ratio to discover significant comorbidities for ALS subjects. Important and representative DNA methylation biomarker candidates could be effectively selected by cross-comparing similar diseases to ALS, comorbidity-related genes, and differentially expressed methylation loci for ALS subjects. The screened epigenomic and comorbidity-related biomarkers were clustered based on their genetic functions. The candidate DNA methylation biomarkers associated with ALS were comprehensively discovered. Gene ontology annotations were then applied to analyze and cluster the candidate biomarkers into three different groups based on gene function annotations. The results showed that a potential testing kit for ALS detection can be composed of SOD3, CACNA1H, and ERBB4 for effective early screening of ALS using blood samples. By developing an effective DNA methylation biomarker screening mechanism, early detection and prophylactic treatment of high-risk ALS patients can be achieved. Full article

Using spatial data in mobile applications has grown significantly, thereby empowering users to explore locations, navigate unfamiliar areas, find transportation routes, employ geomarketing strategies, and model environmental factors. Spatial databases are pivotal in efficiently storing, retrieving, and manipulating spatial data to fulfill users’ needs. Two fundamental spatial query types, k-nearest neighbors (kNN) and range search, enable users to access specific points of interest (POIs) based on their location, which are measured by actual road distance. However, retrieving the nearest POIs using actual road distance can be computationally intensive due to the need to find the shortest distance. Using straight-line measurements could expedite the process but might compromise accuracy. Consequently, this study aims to evaluate the accuracy of the Euclidean distance method in POIs retrieval by comparing it with the road network distance method. The primary focus is determining whether the trade-off between computational time and accuracy is justified, thus employing the Open Source Routing Machine (OSRM) for distance extraction. The assessment encompasses diverse scenarios and analyses factors influencing the accuracy of the Euclidean distance method. The methodology employs a quantitative approach, thereby categorizing query points based on density and analyzing them using kNN and range query methods. Accuracy in the Euclidean distance method is evaluated against the road network distance method. The results demonstrate peak accuracy for kNN queries at $k=1$, thus exceeding 85% across classes but declining as k increases. Range queries show varied accuracy based on POI density, with higher-density classes exhibiting earlier accuracy increases. Notably, datasets with fewer POIs exhibit unexpectedly higher accuracy, thereby providing valuable insights into spatial query processing. Full article

The state of Victoria, Australia, implemented one of the world’s most prolonged cumulative lockdowns in 2020 and 2021. Although lockdowns have proven effective in managing COVID-19 worldwide, this approach faced challenges in containing the rising infection in Victoria. This study evaluates the effects of short-term (less than 60 days) and long-term (more than 60 days) lockdowns on public mobility and the effectiveness of various social restriction measures within these periods. The aim is to understand the complexities of pandemic management by examining various measures over different lockdown durations, thereby contributing to more effective COVID-19 containment methods. Using restriction policy, community mobility, and COVID-19 data, a machine-learning-based simulation model was proposed, incorporating analysis of correlation, infection doubling time, and effective lockdown date. The model result highlights the significant impact of public event cancellations in preventing COVID-19 infection during short- and long-term lockdowns and the importance of international travel controls in long-term lockdowns. The effectiveness of social restriction was found to decrease significantly with the transition from short to long lockdowns, characterised by increased visits to public places and increased use of public transport, which may be associated with an increase in the effective reproduction number (R_t) and infected cases. Full article

With urban areas facing rapid population growth, public transport plays a key role to provide efficient and economic accessibility to the residents. It reduces the use of personal vehicles leading to reduced traffic congestion on roads and reduced pollution. To assess the performance of these transport systems, prior studies have taken into consideration the blank spot areas, population density, and stop access density; however, very little research has been performed to compare the accessibility between cities using a GIS-based approach. This paper compares the access and performance of public transport across Melbourne and Sydney, two cities with a similar size, population, and economy. The methodology uses spatial PostGIS queries to focus on accessibility-based approach for each residential mesh block and aggregates the blank spots, and the number of services offered by time of day and the frequency of services at the local government area (LGA) level. The results of the study reveal an interesting trend: that with increase in distance of LGA from city centre, the blank spot percentage increases while the frequency of services and stops offering weekend/night services declines. The results conclude that while Sydney exhibits a lower percentage of blank spots and has better coverage, performance in terms of accessibility by service time and frequency is better for Melbourne’s LGAs, even as the distance increases from the city centre. Full article

The multifaceted nature and swift progression of Amyotrophic Lateral Sclerosis (ALS) pose considerable challenges to our understanding of its evolution and interplay with comorbid conditions. This study seeks to elucidate the temporal dynamics of ALS progression and its interaction with associated diseases. We employed a principal tree-based model to decipher patterns within clinical data derived from a population-based database in Taiwan. The disease progression was portrayed as branched trajectories, each path representing a series of distinct stages. Each stage embodied the cumulative occurrence of co-existing diseases, depicted as nodes on the tree, with edges symbolizing potential transitions between these linked nodes. Our model identified eight distinct ALS patient trajectories, unveiling unique patterns of disease associations at various stages of progression. These patterns may suggest underlying disease mechanisms or risk factors. This research re-conceptualizes ALS progression as a migration through diverse stages, instead of the perspective of a sequence of isolated events. This new approach illuminates patterns of disease association across different progression phases. The insights obtained from this study hold the potential to inform doctors regarding the development of personalized treatment strategies, ultimately enhancing patient prognosis and quality of life. Full article

The public transport system plays an important role in a city as it moves people from one place to another efficiently and economically. The public transport network must be organized in a way that will cover as many places and as much of the population as possible, and support the city’s growth. As one of Australia’s largest capital cities, Melbourne is growing and expanding its metropolitan area to reflect the growth in population and an increased number of activities. To date, little research has been conducted to determine the accessibility and adequacy of public transport taking into consideration the blank spot areas, the number of public transport options for each area, the population density within specific geographical areas, and other issues. In this study, a new measurement model is developed that examines public transport in residential areas and the extent to which it is adequate for the various local government areas (LGAs). An accessibility approach is adopted to evaluate the accessibility of different types of public transportation in residential areas in metropolitan Melbourne, Victoria, Australia. The results show that in most LGAs, the number of blank spots will decrease as the population density increases. This indicates that residents in lower-density areas will have less accessibility to public transportation. However, there is no indication that there is a greater level of services (such as more night-time and weekend public transportation services) in the high-density areas. This research is significant as it will point to and help to improve the areas with inadequate public transportation and other issues, taking into consideration their geographical locations and population density. Full article

Educational institutions play a significant role in the community spread of SARS-CoV-2 in Victoria. Despite a series of social restrictions and preventive measures in educational institutions implemented by the Victorian Government, confirmed cases among people under 20 years of age accounted for more than a quarter of the total infections in the state. In this study, we investigated the risk factors associated with COVID-19 infection within Victoria educational institutions using an incremental deep learning recurrent neural network-gated recurrent unit (RNN-GRU) model. The RNN-GRU model simulation was built based on three risk dimensions: (1) school-related risk factors, (2) student-related community risk factors, and (3) general population risk factors. Our data analysis showed that COVID-19 infection cases among people aged 10–19 years were higher than those aged 0–9 years in the Victorian region in 2020–2022. Within the three dimensions, a significant association was identified between school-initiated contact tracing (0.6110), vaccination policy for students and teachers (0.6100), testing policy (0.6109), and face covering (0.6071) and prevention of COVID-19 infection in educational settings. Furthermore, the study showed that different risk factors have varying degrees of effectiveness in preventing COVID-19 infection for the 0–9 and 10–19 age groups, such as state travel control (0.2743 vs. 0.3390), international travel control (0.2757 vs. 0.3357) and school closure (0.2738 vs. 0.3323), etc. More preventive support is suggested for the younger generation, especially for the 10–19 age group. Full article

The sudden unexpected rise in monkeypox cases worldwide has become an increasing concern. The zoonotic disease characterized by smallpox-like symptoms has already spread to nearly twenty countries and several continents and is labeled a potential pandemic by experts. monkeypox infections do not have specific treatments. However, since smallpox viruses are similar to monkeypox viruses administering antiviral drugs and vaccines against smallpox could be used to prevent and treat monkeypox. Since the disease is becoming a global concern, it is necessary to analyze its impact and population health. Analyzing key outcomes, such as the number of people infected, deaths, medical visits, hospitalizations, etc., could play a significant role in preventing the spread. In this study, we analyze the spread of the monkeypox virus across different countries using machine learning techniques such as linear regression (LR), decision trees (DT), random forests (RF), elastic net regression (EN), artificial neural networks (ANN), and convolutional neural networks (CNN). Our study shows that CNNs perform the best, and the performance of these models is evaluated using statistical parameters such as mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and R-squared error (R2). The study also presents a time-series-based analysis using autoregressive integrated moving averages (ARIMA) and seasonal auto-regressive integrated moving averages (SARIMA) models for measuring the events over time. Comprehending the spread can lead to understanding the risk, which may be used to prevent further spread and may enable timely and effective treatment. Full article

Brain abnormality causes severe human problems, and thorough screening is necessary to identify the disease. In clinics, bio-image-supported brain abnormality screening is employed mainly because of its investigative accuracy compared with bio-signal (EEG)-based practice. This research aims to develop a reliable disease screening framework for the automatic identification of schizophrenia (SCZ) conditions from brain MRI slices. This scheme consists following phases: (i) MRI slices collection and pre-processing, (ii) implementation of VGG16 to extract deep features (DF), (iii) collection of handcrafted features (HF), (iv) mayfly algorithm-supported optimal feature selection, (v) serial feature concatenation, and (vi) binary classifier execution and validation. The performance of the proposed scheme was independently tested with DF, HF, and concatenated features (DF+HF), and the achieved outcome of this study verifies that the schizophrenia screening accuracy with DF+HF is superior compared with other methods. During this work, 40 patients’ brain MRI images (20 controlled and 20 SCZ class) were considered for the investigation, and the following accuracies were achieved: DF provided >91%, HF obtained >85%, and DF+HF achieved >95%. Therefore, this framework is clinically significant, and in the future, it can be used to inspect actual patients’ brain MRI slices. Full article

Pulmonary nodule is one of the lung diseases and its early diagnosis and treatment are essential to cure the patient. This paper introduces a deep learning framework to support the automated detection of lung nodules in computed tomography (CT) images. The proposed framework employs VGG-SegNet supported nodule mining and pre-trained DL-based classification to support automated lung nodule detection. The classification of lung CT images is implemented using the attained deep features, and then these features are serially concatenated with the handcrafted features, such as the Grey Level Co-Occurrence Matrix (GLCM), Local-Binary-Pattern (LBP) and Pyramid Histogram of Oriented Gradients (PHOG) to enhance the disease detection accuracy. The images used for experiments are collected from the LIDC-IDRI and Lung-PET-CT-Dx datasets. The experimental results attained show that the VGG19 architecture with concatenated deep and handcrafted features can achieve an accuracy of 97.83% with the SVM-RBF classifier. Full article

Due to contemporary communication trends, the amount of multimedia data created and transferred in 5G networks has reached record levels. Multimedia applications communicate an enormous quantity of images containing private data that tend to be attacked by cyber-criminals and later used for illegal reasons. Security must consider and adopt the new and unique features of 5G/6G platforms. Cryptographic procedures, especially secret sharing (SS), with some extraordinary qualities and capacities, can be conceived to handle confidential data. This paper has developed a secured (k, k) multiple secret sharing (SKMSS) scheme with Hybrid Optimal SIMON ciphers. The proposed SKMSS method constructs a set of noised components generated securely based on performing hash and block ciphers over the secret image itself. The shares are created and safely sent after encrypting them through the Hybrid Optimal SIMON ciphers based on the noised images. This is a lightweight cryptography method and helps reduce computation complexity. The hybrid Particle Swarm Optimization-based Cuckoo Search Optimization Algorithm generates the keys based on the analysis of the peak signal to noise ratio value of the recovered secret images. In this way, the quality of the secret image is also preserved even after performing more computations upon securing the images. Full article

Electronic Medical Records (EMRs) can be used to create alerts for clinicians to identify patients at risk and to provide useful information for clinical decision-making support. In this study, we proposed a novel approach for predicting Amyotrophic Lateral Sclerosis (ALS) based on comorbidities and associated indicators using EMRs. The medical histories of ALS patients were analyzed and compared with those of subjects without ALS, and the associated comorbidities were selected as features for constructing the machine learning and prediction model. We proposed a novel weighted Jaccard index (WJI) that incorporates four different machine learning techniques to construct prediction systems. Alternative prediction models were constructed based on two different levels of comorbidity: single disease codes and clustered disease codes. With an accuracy of 83.7%, sensitivity of 78.8%, specificity of 85.7%, and area under the receiver operating characteristic curve (AUC) value of 0.907 for the single disease code level, the proposed WJI outperformed the traditional Jaccard index (JI) and scoring methods. Incorporating the proposed WJI into EMRs enabled the construction of a prediction system for analyzing the risk of suffering a specific disease based on comorbidity combinatorial patterns, which could provide a fast, low-cost, and noninvasive evaluation approach for early diagnosis of a specific disease. Full article