Learning from accidents : machine learning for safety at railway stations

In railway systems, station safety is a critical aspect of the overall structure, and yet, accidents at stations still occur. It is time to learn from these errors and improve conventional methods by utilizing the latest technology, such as machine learning (ML), to analyse accidents and enhance safety systems. ML has been employed in many fields, including engineering systems, and it interacts with us throughout our daily lives. Thus, we must consider the available technology in general and ML in particular in the context of safety in the railway industry. This paper explores the employment of the decision tree (DT) method in safety classification and the analysis of accidents at railway stations to predict the traits of passengers affected by accidents. The critical contribution of this study is the presentation of ML and an explanation of how this technique is applied for ensuring safety, utilizing automated processes, and gaining benefits from this powerful technology. To apply and explore this method, a case study has been selected that focuses on the fatalities caused by accidents at railway stations. An analysis of some of these fatal accidents as reported by the Rail Safety and Standards Board (RSSB) is performed and presented in this paper to provide a broader summary of the application of supervised ML for improving safety at railway stations. Finally, this research shows the vast potential of the innovative application of ML in safety analysis for the railway industry

Risk management prediction for overcrowding in railway stations utilising Adaptive Nero Fuzzy Inference System (ANFIS)

In this research, an intelligent system for managing risks is developed with a framework to aid in managing the risks in the railway stations. A method to advance risk management in the railway stations is needed in order to minimize risk through an automated process taking into consideration all the factors in the system and how they work together to provide an acceptable level of safety and security. Thus, the Adaptive Nero Fuzzy Inference System (ANFIS) is proposed to improve risk management as an intelligently selected model which is powerful in dealing with uncertainties in risk variables. The methods of artificial neural network (ANN) and Fuzzy interface system (FIS) have been proven as tools for measuring risks in many fields. In this case study, the railway is selected as a place for managing the risks of overcrowding in the railway stations taking two parameters as input for risk value output using a hybrid model, which has the potency to deal with risk uncertainties and to learn by ANN training processes. The results show that the ANFIS method is more promising in the management of station risks. The framework can be applied for other risks in the station and more for a wide range of other systems. Also, ANFIS has the ability to learn from past risk records for future prediction. Clearly, the risk indexes are essential to reflect the actual condition of the station and they can indicate a high level of risks at the early stage, such as with overcrowding. The dynamic model of risk management can define risk levels and aid the decision makers by convenient and reliable results based on recorded data. Finally, the model can be generalised for other risks

Overdose rescues by trained and untrained participants and change in opioid use among substance-using participants in overdose education and naloxone distribution programs: a retrospective cohort study

Background: One approach to preventing opioid overdose, a leading cause of premature, preventable mortality, is to provide overdose education and naloxone distribution (OEND). Two outstanding issues for OEND implementation include 1) the dissemination of OEND training from trained to untrained community members; and 2) the concern that OEND provides active substance users with a false sense of security resulting in increased opioid use. Methods: To compare overdose rescue behaviors between trained and untrained rescuers among people reporting naloxone rescue kit use; and determine whether heroin use changed after OEND, we conducted a retrospective cohort study among substance users in the Massachusetts OEND program from 2006 to 2010. We used chi square and t-test statistics to compare the differences in overdose management characteristics among overdoses managed by trained versus untrained participants. We employed Wilcoxon signed rank test to compare median difference among two repeated measures of substance use among participants with drug use information collected more than once. Results: Among 4,926 substance-using participants, 295 trained and 78 untrained participants reported one or more rescues, resulting in 599 rescue reports. We found no statistically significant differences in help-seeking (p = 0.41), rescue breathing (p = 0.54), staying with the victim (p = 0.84) or in the success of naloxone administration (p = 0.69) by trained versus untrained rescuers. We identified 325 OEND participants who had drug use information collected more than once. We found no significant overall change in the number of days using heroin in past 30 days (decreased 38%, increased 35%, did not change 27%, p = 0.52). Conclusion: Among 4926 substance users who participated in OEND, 373(7.6%) reported administering naloxone during an overdose rescue. We found few differences in behavior between trained and untrained overdose rescuers. Prospective studies will be needed to determine the optimal level of training and whether naloxone rescue kits can meet an over-the-counter standard. With no clear evidence of increased heroin use, this concern should not impede expansion of OEND programs or policies that support them

Recursive forecasting and ordinal statistical models from accelerometer data

Access to thesis restricted until 5/2023Accelerometers are devices that measure acceleration along x-, y- and z-axes. These devices can be worn and used to predict activity intensity. The accuracy of conventional accelerometer analysis methods is sub-optimal but newer, more advanced methods that use raw data from the accelerometer for the prediction of activity intensity have been developed. As responses are correlated sequentially and collected over time, time-series methods can be considered to improve prediction accuracy. Prior responses, however, are not available at the testing stage or in practice. However, in testing, prior predictions can be used as in place of lagging responses on models which were built to use lagging responses as observations. This approach is known as recursive forecasting and applying it to accelerometer data is a unique approach in the literature. In addition, until recently, decision models for accelerometer data did not take into account the ordinality of the responses (for example, sedentary, moderate, and vigorous). This is significant information that we consider in this thesis. In this research, we develop more accurate decision models for predicting activity intensity from accelerometer data by using recursive forecasting. We also consider ordinal statistical models. Measuring activity intensity objectively is a crucial consideration in physiology and exercise science and these methods can be implemented in these disciplines to improve such measurement.Thesis (M.S.

Quantifying Mutational Impacts on Intrinsic DNA Flexibility in Prokaryotic Genomes

The existence of synonymous codon biases across all taxonomic groups is a long standing problem in biology. While codon bias seems to be adequately explained by the maintenance of translation efficiency and accuracy in some organisms, there is still no adequate explanation of why codon biases universally track the intergenic gc content, as these regions of the genome would not be under selection pressures affecting translation. One part of the story may come from the triplet nature of codon in which each third position defines the minor groove width and thus affects the basic structure of the DNA by altering the intrinsic flexibility. In addition, this intrinsic flexibility, which is also GC dependent, play a major role on defining the phosphate linkages of the backbone conformation as well as participating with other binding molecules. Packaging such a type of information within the DNA sequence seems to be essential especially when observing such a variation of codon bias among organism. The potential existence of this form of \u27architectural\u27 information in the genome might also predict that evolutionary processes at the synonymous sites are not simply an accident, but it might indicate a fundamental connection between the biophysical aspects of DNA and usage of codons. In this thesis, I present a broad taxonomical analysis of the mutational impacts on the intrinsic flexibility of DNA among 26 prokaryotic genomes and investigate its relationship to entropy based codon bias gc content and protein conservation . I conclude that codon bias appears universally connected to the intrinsic flexibility of the genome especially for genomes with extreme GC contents. In all genomes, genes under strong purifying selection at the level of the protein appear to have constraints in the mutational impacts on DNA flexibility. This may reflect a fundamental limitation in ability of DNA to multiplex information at the levels of protein and nuclear architecture

Evaluation of Clinical and Pathological Response after Two Cycles of Neoadjuvant Chemotherapy on Sudanese Patients with Locally Advanced Breast Cancer

Background: The use of neoadjuvant chemotherapy in treating breast cancer has shown efficacy in downstaging primary tumors, and allows breast conservative surgery to be performed instead of mastectomy. This study aims to evaluate patterns of clinical and pathological response after two cycles of neoadjuvant chemotherapy in patients with locally advanced breast cancer.Materials and Methods: This is a prospective study. Ninety-eight patients who presented from April 2009 through May 2011 with locally advanced breast cancer and treated with neoadjuvant chemotherapy were included.Results: The clinical response rate was 83%; 11 patients (11.2%) had a complete clinical remission (cCR); 71 had a partial remission (72.4%); 13 had stable disease (13.3%), and 3 had progressive disease (3.1%). Seven patients had complete pathological response.Conclusion: Neoadjuvant chemotherapy can achieve a high objective response rate in patients with locally advanced breast cancer even after two cycles. We recommend further research to find predictors for response.Keywords: Breast cancer, Clinical response, Neoadjuvant chemotherap

Exploring Google Reverse Image Search to Detect Visual Plagiarism in Interior Design

This study aims to explore the ability of Google Reverse Image Search (RIS) to detect plagiarism in images in the interior design field. Several image modifications were introduced by retaining the basic concept of the original image. These changes were classified into three categories as follow: a change in the design elements, introduced random changes by adding different objects to the existing image contents, and introduced various image effects. findings show that Google RIS does not take long to find newly uploaded images. Although it cannot detect changes related to the image contents, it can detect changes related to image size and contrast. Overall percentage of the modified images that were detected as matching the original image was only 5%. By contrast, the net percentage of images retrieved by Google RIS with contents actually related to the uploaded original image was 58.5%. Therefore, Google RIS is inaccurate in detecting any changes in the image contents irrespective of their simplicity, which implies that it cannot help in detection of visual plagiarism

Researching the motives behind the acquisition, possession and application of heritage collectibles in home interiors

Previous research considered the significance of the home environment representing the owner’s social identity in general but there is no clear research that identifies the motive behind acquisition, possession and application of heritage collectibles in home interiors. The aim of this research was to discover the reason why people possess heritage collectibles in their home interiors. In addition, this research considered the occupied space of the possessions, location within the home, and the type of heritage collectibles. The sample of this study consisted of 330 female adults residing in the centre of Saudi Arabia. The method of investigation was a self-report questionnaire, which was classified into four main themes: the reason behind having heritage collectibles in home interiors, occupied space, location within the home, and the type of heritage collectibles. The results indicated that there are a diverse number of reasons behind people’s possession of heritage collectibles. The main reason was social identity then gifts, trends, and matching with other furniture within the home. In addition, there was a significant relationship between identity and occupied space (p=0.001) and social identity with the type of heritage collectibles (p=0.001). The contribution to new knowledge in this study should help designers to develop their concepts in their interior design projects and discover the relationship between heritage, interior design, and social identity, which will provide designers with the direct and indirect needs of their clients. Keywords: interior design, home environment, individual’s social identity, heritage collectibles, Saudi Arabia

EFFECTS OF ST713 WITH SIMULTANEOUS HISTAMINE H3 AND DOPAMINE D2/D3 RECEPTOR ANTAGONIST PROPERTIES ON COGNITIVE IMPAIRMENTS AND AUTISM-LIKE BEHAVIORS IN BTBR T+TF/J MICE

Autism spectrum disorders (ASD) is a multifactorial neurodevelopmental disorder characterized by two core symptoms which are impairments in social interaction and communication, repetitive and restricted behaviors. Dopamine (DA) and histamine (HA) are two neurotransmitters that are proposed to be involved in several brain disorders including schizophrenia, depression, anxiety, and narcolepsy, all of those disorders are comorbid with ASD. Thus, the palliative effects of the novel multiple-active histamine H3 receptor (H3R) antagonist and dopamine D2/D3 receptor (D2/D3R) antagonist ST-713 with its high H3R antagonist affinity and balanced inhibitory effects on both dopaminergic receptor subtypes D2R and D3R on ASD-like behaviors in male BTBR T+tf/J mice model of ASD were evaluated. Chronic systemic administration of ST-713 (2.5, 5, and 10 mg/kg, i.p.) dose-dependently mitigated social deficits of BTBR mice and significantly reduced the repetitive/compulsive behaviors of tested BTBR mice. Additionally, ST-713 modulated disturbed anxiety levels but failed to balance hyperactivity parameters. Moreover, the ST-713-provided effects on social parameters were entirely reversed by co-administration of the H3R agonist (R)-α-methylhistamine or the anticholinergic drug scopolamine (SCO, 0.3 mg/kg, i.p.). Furthermore, ST-713 (5 mg/kg) attenuated the increased levels of hippocampal and cerebellar protein expressions of Tumor necrosis factor (TNF-α), Interleukins-1β (IL-1β), and IL-6 in treated BTBR mice brains (all P \u3c 0.01). The obtained in vivo results demonstrate the effectiveness of a potent multiple-active H3R and D2R/D3R antagonist/inverse agonist against ASD-like phenotype, signifying the potential role of such multiple-active compounds for the therapeutic management of neuropsychiatric disorders, such as ASD