Search results for: life prediction

Authors: Norita Ahmad, Reza Barkhi, Xiaobo Xu

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We design an experimental study to learn about the cognitive implications of the use of avatars in a Virtual World (VW) (i.e., Second Life). The results support our proposed model, where a positive flow experience with VW influences the attitude towards VW, in turn influencing intention to use VW. Furthermore, VW flow experience can itself be impacted by perceived peer influence, familiarity with VW, and personality of the individuals behind the avatars in VW.

Keywords: avatar, flow experience, personality type, second life, virtual world

Procedia PDF Downloads 582

Authors: Elucir Gir, Myllena Nilce de Freitas Surmano, Laelson Rochelle Milanês Sousa, Mayra Gonçalves Menegueti, Ana Cristina de Oliveira E Silva, Renata Karina Reis

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Objective: To analyze the factors associated with the worsening of the quality of life of health professionals in the Southeast region of Brazil during the COVID-19 pandemic and its associated factors. Method: Analytical cross-sectional study carried out with health professionals from the southeastern region of Brazil. Data collection took place through an online survey with a form stored on the Survey Monkey platform. Bivariate analysis was used, and the chi-square test was adopted, followed by the multiple binary logistic regression model based on the stepwise method. Results: 3,493 health professionals participated in the study. Factors associated with worsening quality of life were: Professional Category (Nursing assistant) [OR 1.851 (95%CI 1.035-3.311) p= 0.038]; types of people who provided care (people in general) [OR 1.445 (95%CI 1.072-1.945) p=0.015]; Supply of good quality PPE by the institution where he works (no) [OR 1.595 (CI 95% 1.144-2.223) p= 0.006] and Supply of good quality PPE by the institution where he works (in part) [OR 1.563 (CI 95% 1.257-1.943) p < 0.001]. Conclusion: The factors associated with the worsening of the quality of life of health professionals during the COVID-19 pandemic were: Professional Category (Nursing assistant); types of people who provided assistance (people in general); Supply of sufficient PPE by the institution where you work (no) and Supply of good quality PPE by the institution where you work (in part). Future studies should investigate to what extent QoL can be improved based on modifiable factors.

Keywords: COVID-19, quality of life, health professionals, respiratory infections

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Authors: Sandra H. Fukurozaki, Andre L. N. Silva, Joao B. F. Neto, Fernando J. G. Landgraf

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Nowadays, the rare earth (RE) metals play an important role in emerging technologies that are crucial for the decarbonisation of the energy sector. Their unique properties have led to increasing clean energy applications, such as wind turbine generators, and hybrid and electric vehicles. Despite the substantial media coverage that has recently surrounded the mining and processing of rare earth metals, very little quantitative information is available concerning their subsequent life stages, especially related to the metallic production of didymium (Nd-Pr) in fluoride molten salt system. Here we investigate a gate to gate scale life cycle assessment (LCA) of the didymium electrolysis based on three different scenarios of operational conditions. The product system is modeled with SimaPro Analyst 8.0.2 software, and IMPACT 2002+ was applied as an impact assessment tool. In order to develop a life cycle inventories built in software databases, patents, and other published sources together with energy/mass balance were utilized. Analysis indicates that from the 14 midpoint impact categories evaluated, the global warming potential (GWP) is the main contributors to the total environmental burden, ranging from 2.7E2 to 3.2E2 kg CO2eq/kg Nd-Pr. At the damage step assessment, the results suggest that slight changes in materials flows associated with enhancement of current efficiency (between 2.5% and 5%), could lead a reduction up to 12% and 15% of human health and climate change damage, respectively. Additionally, this paper highlights the knowledge gaps and future research efforts needing to understand the environmental impacts of Nd-Pr electrolysis process from the life cycle perspective.

Keywords: didymium electrolysis, environmental impacts, life cycle assessment, rare earth metals

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Authors: João Filipe Papel, Tatsuji Munaka

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With society's aging and the number of elderly with dementia rising, researchers have been actively studying how to support the elderly in the early stages of dementia with the objective of allowing them to have a better life quality and as much as possible independence. To make this possible, most researchers in this field are using the Internet Of Things to monitor the elderly activities and assist them in performing them. The most common sensor used to monitor the elderly activities is the Camera sensor due to its easy installation and configuration. The other commonly used sensor is the sound sensor. However, we need to consider privacy when using these sensors. This research aims to develop a system capable of predicting the early stages of dementia based on monitoring and controlling the elderly activities of daily living. To make this system possible, some issues need to be addressed. First, the issue related to elderly privacy when trying to detect their Activities of Daily Living. Privacy when performing detection and monitoring Activities of Daily Living it's a serious concern. One of the purposes of this research is to achieve this detection and monitoring without putting the privacy of the elderly at risk. To make this possible, the study focuses on using an approach based on using Magnet Sensors to collect binary data. The second is to use the data collected by monitoring Activities of Daily Living to predict the early stages of Dementia. To make this possible, the research team suggests developing a proprietary ontology combined with both data-driven and knowledge-driven.

Keywords: dementia, activity recognition, magnet sensors, ontology, data driven and knowledge driven, IoT, activities of daily living

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Authors: Tien-Mei Hu

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Death is not only a physical event and a fact of life ending but also one of the ultimate issues of philosophy. The aerial acrobats’ dangerous nature and protective rope culture have kept the concept of death in this profession. This study aims to interpret the Taiwanese aerialists’ view of death through the philosophy of death, starting from the archetype of traditional Eastern body practices (aerial acrobatics). Five Taiwanese acrobats (two male and three female) were interviewed through a snowball approach. After the interviews, ATLAS.ti, a qualitative analysis software, was used to analyze the verbatim transcripts, photographs, and documents. The following three conclusions were drawn from this study: every performance by Taiwanese aerial acrobats is a life-threatening performance; Taiwanese aerialists’ perception of death changes with different life stages; Taiwanese aerialists’ philosophy of death is based on the heritage foundation of the "acrobatics" profession, which has created the phenomenon of not using safety equipment unique to Taiwanese aerialists.

Keywords: acrobatics, body culture, circus, tightrope walker

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Authors: Abdolghani Ebrahimi, Diego Klabjan, Chenxi Ge, Daniela Ladner, Parker Stride

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In this work, we use machine learning and novel data analysis techniques to predict the one-year mortality of cirrhotic patients. Data from 2,322 patients with liver cirrhosis are collected at a single medical center. Different machine learning models are applied to predict one-year mortality. A comprehensive feature space including demographic information, comorbidity, clinical procedure and laboratory tests is being analyzed. A temporal pattern mining technic called Frequent Subgraph Mining (FSM) is being used. Model for End-stage liver disease (MELD) prediction of mortality is used as a comparator. All of our models statistically significantly outperform the MELD-score model and show an average 10% improvement of the area under the curve (AUC). The FSM technic itself does not improve the model significantly, but FSM, together with a machine learning technique called an ensemble, further improves the model performance. With the abundance of data available in healthcare through electronic health records (EHR), existing predictive models can be refined to identify and treat patients at risk for higher mortality. However, due to the sparsity of the temporal information needed by FSM, the FSM model does not yield significant improvements. To the best of our knowledge, this is the first work to apply modern machine learning algorithms and data analysis methods on predicting one-year mortality of cirrhotic patients and builds a model that predicts one-year mortality significantly more accurate than the MELD score. We have also tested the potential of FSM and provided a new perspective of the importance of clinical features.

Keywords: machine learning, liver cirrhosis, subgraph mining, supervised learning

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Authors: Yan Zhang

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Predictive maintenance is a technique to predict when an in-service machine will fail so that maintenance can be planned in advance. Analytics-driven predictive maintenance is gaining increasing attention in many industries such as manufacturing, utilities, aerospace, etc., along with the emerging demand of Internet of Things (IoT) applications and the maturity of technologies that support Big Data storage and processing. This study aims to build an end-to-end analytics solution that includes both real-time machine condition monitoring and machine learning based predictive analytics capabilities. The goal is to showcase a general predictive maintenance solution architecture, which suggests how the data generated from field machines can be collected, transmitted, stored, and analyzed. We use a publicly available aircraft engine run-to-failure dataset to illustrate the streaming analytics component and the batch failure prediction component. We outline the contributions of this study from four aspects. First, we compare the predictive maintenance problems from the view of the traditional reliability centered maintenance field, and from the view of the IoT applications. When evolving to the IoT era, predictive maintenance has shifted its focus from ensuring reliable machine operations to improve production/maintenance efficiency via any maintenance related tasks. It covers a variety of topics, including but not limited to: failure prediction, fault forecasting, failure detection and diagnosis, and recommendation of maintenance actions after failure. Second, we review the state-of-art technologies that enable a machine/device to transmit data all the way through the Cloud for storage and advanced analytics. These technologies vary drastically mainly based on the power source and functionality of the devices. For example, a consumer machine such as an elevator uses completely different data transmission protocols comparing to the sensor units in an environmental sensor network. The former may transfer data into the Cloud via WiFi directly. The latter usually uses radio communication inherent the network, and the data is stored in a staging data node before it can be transmitted into the Cloud when necessary. Third, we illustrate show to formulate a machine learning problem to predict machine fault/failures. By showing a step-by-step process of data labeling, feature engineering, model construction and evaluation, we share following experiences: (1) what are the specific data quality issues that have crucial impact on predictive maintenance use cases; (2) how to train and evaluate a model when training data contains inter-dependent records. Four, we review the tools available to build such a data pipeline that digests the data and produce insights. We show the tools we use including data injection, streaming data processing, machine learning model training, and the tool that coordinates/schedules different jobs. In addition, we show the visualization tool that creates rich data visualizations for both real-time insights and prediction results. To conclude, there are two key takeaways from this study. (1) It summarizes the landscape and challenges of predictive maintenance applications. (2) It takes an example in aerospace with publicly available data to illustrate each component in the proposed data pipeline and showcases how the solution can be deployed as a live demo.

Keywords: Internet of Things, machine learning, predictive maintenance, streaming data

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Authors: Cristian Pauna

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Trading the financial markets is a widespread activity today. A large number of investors, companies, public of private funds are buying and selling every day in order to make profit. Algorithmic trading is the prevalent method to make the trade decisions after the electronic trading release. The orders are sent almost instantly by computers using mathematical models. This paper will present a price prediction methodology based on a multi-dimensional neural network. Using the Fisher transform, the neural network will be instructed for a low-latency auto-adaptive process in order to predict the price evolution for the next period of time. The model is designed especially for algorithmic trading and uses the real-time price series. It was found that the characteristics of the Fisher function applied at the nodes scale level can generate reliable trading signals using the neural network methodology. After real time tests it was found that this method can be applied in any timeframe to trade the financial markets. The paper will also include the steps to implement the presented methodology into an automated trading system. Real trading results will be displayed and analyzed in order to qualify the model. As conclusion, the compared results will reveal that the neural network methodology applied together with the Fisher transform at the nodes level can generate a good price prediction and can build reliable trading signals for algorithmic trading.

Keywords: algorithmic trading, automated trading systems, financial markets, high-frequency trading, neural network

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Authors: Subhan Ahmad, Pradeep Bhargava, Ajay Chourasia

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Shear failure in reinforced concrete beams without shear reinforcement leads to loss of property and life since a very little or no warning occurs before failure as in case of flexural failure. Shear strength of reinforced concrete beams decreases as its depth increases. This phenomenon is generally called as the size effect. In this paper, a comparative analysis is performed to estimate the performance of shear strength models in capturing the size effect of reinforced concrete beams made with conventional concrete, self-compacting concrete, and recycled aggregate concrete. Four shear strength models that account for the size effect in shear are selected from the literature and applied on the datasets of slender reinforced concrete beams. Beams prepared with conventional concrete, self-compacting concrete, and recycled aggregate concrete are considered for the analysis. Results showed that all the four models captured the size effect in shear effectively and produced conservative estimates of the shear strength for beams made with normal strength conventional concrete. These models yielded unconservative estimates for high strength conventional concrete beams with larger effective depths ( > 450 mm). Model of Bazant and Kim (1984) captured the size effect precisely and produced conservative estimates of shear strength of self-compacting concrete beams at all the effective depths. Also, shear strength models considered in this study produced unconservative estimates of shear strength for recycled aggregate concrete beams at all effective depths.

Keywords: reinforced concrete beams; shear strength; prediction models; size effect

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Authors: Raptis Sotirios

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Health and social care (HSc) services planning and scheduling are facing unprecedented challenges due to the pandemic pressure and also suffer from unplanned spending that is negatively impacted by the global financial crisis. Data-driven can help to improve policies, plan and design services provision schedules using algorithms assist healthcare managers’ to face unexpected demands using fewer resources. The paper discusses services packing using statistical significance tests and machine learning (ML) to evaluate demands similarity and coupling. This is achieved by predicting the range of the demand (class) using ML methods such as CART, random forests (RF), and logistic regression (LGR). The significance tests Chi-Squared test and Student test are used on data over a 39 years span for which HSc services data exist for services delivered in Scotland. The demands are probabilistically associated through statistical hypotheses that assume that the target service’s demands are statistically dependent on other demands as a NULL hypothesis. This linkage can be confirmed or not by the data. Complementarily, ML methods are used to linearly predict the above target demands from the statistically found associations and extend the linear dependence of the target’s demand to independent demands forming, thus groups of services. Statistical tests confirm ML couplings making the prediction also statistically meaningful and prove that a target service can be matched reliably to other services, and ML shows these indicated relationships can also be linear ones. Zero paddings were used for missing years records and illustrated better such relationships both for limited years and in the entire span offering long term data visualizations while limited years groups explained how well patients numbers can be related in short periods or can change over time as opposed to behaviors across more years. The prediction performance of the associations is measured using Receiver Operating Characteristic(ROC) AUC and ACC metrics as well as the statistical tests, Chi-Squared and Student. Co-plots and comparison tables for RF, CART, and LGR as well as p-values and Information Exchange(IE), are provided showing the specific behavior of the ML and of the statistical tests and the behavior using different learning ratios. The impact of k-NN and cross-correlation and C-Means first groupings is also studied over limited years and the entire span. It was found that CART was generally behind RF and LGR, but in some interesting cases, LGR reached an AUC=0 falling below CART, while the ACC was as high as 0.912, showing that ML methods can be confused padding or by data irregularities or outliers. On average, 3 linear predictors were sufficient, LGR was found competing RF well, and CART followed with the same performance at higher learning ratios. Services were packed only if when significance level(p-value) of their association coefficient was more than 0.05. Social factors relationships were observed between home care services and treatment of old people, birth weights, alcoholism, drug abuse, and emergency admissions. The work found that different HSc services can be well packed as plans of limited years, across various services sectors, learning configurations, as confirmed using statistical hypotheses.

Keywords: class, cohorts, data frames, grouping, prediction, prob-ability, services

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Authors: Erika Yamaguchi, Sirawadee Arunyanrt, Shunichi Ohmori, Kazuho Yoshimoto

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In this paper, we suggest a simulation tool to make a decision of monthly production planning for maximizing a profit of Specialty store retailer of Private label Apparel (SPA) firms. Most of SPA firms are fabless and make outsourcing deals for productions with factories of their subcontractors. Every month, SPA firms make a booking for production lines and manpower in the factories. The booking is conducted a few months in advance based on a demand prediction and a monthly production planning at that time. However, the demand prediction is updated month by month, and the monthly production planning would change to meet the latest demand prediction. Then, SPA firms have to change the capacities initially booked within a certain range to suit to the monthly production planning. The booking system is called “capacity-booking”. These days, though it is an issue for SPA firms to make precise monthly production planning, many firms are still conducting the production planning by empirical rules. In addition, it is also a challenge for SPA firms to match their products and factories with considering their demand predictabilities and regulation abilities. In this paper, we suggest a model for considering these two issues. An objective is to maximize a total profit of certain periods, which is sales minus costs of production, inventory, and capacity-booking penalty. To make a better monthly production planning at SPA firms, these points should be considered: demand predictabilities by random trends, previous and next month’s production planning of the target month, and regulation abilities of the capacity-booking. To decide matching products and factories for outsourcing, it is important to consider seasonality, volume, and predictability of each product, production possibility, size, and regulation ability of each factory. SPA firms have to consider these constructions and decide orders with several factories per one product. We modeled these issues as a linear programming. To validate the model, an example of several computational experiments with a SPA firm is presented. We suppose four typical product groups: basic, seasonal (Spring / Summer), seasonal (Fall / Winter), and spot product. As a result of the experiments, a monthly production planning was provided. In the planning, demand predictabilities from random trend are reduced by producing products which are different product types. Moreover, priorities to produce are given to high-margin products. In conclusion, we developed a simulation tool to make a decision of monthly production planning which is useful when the production planning is set every month. We considered the features of capacity-booking, and matching of products and factories which have different features and conditions.

Keywords: capacity-booking, SPA, monthly production planning, linear programming

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Authors: Lim Chen Kim, Tan Kian Lam, Chan Yi Chee

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This research presents a multi-modal simulation in the reconstruction of the past and the construction of present in digital cultural heritage on mobile platform. In bringing the present life, the virtual environment is generated through a presented scheme for rapid and efficient construction of 360° panoramic view. Then, acoustical heritage model and crowd model are presented and improvised into the 360° panoramic view. For the reconstruction of past life, the crowd is simulated and rendered in an old trading port. However, the keystone of this research is in a virtual walkthrough that shows the virtual present life in 2D and virtual past life in 3D, both in an environment of virtual heritage sites in George Town through mobile device. Firstly, the 2D crowd is modelled and simulated using OpenGL ES 1.1 on mobile platform. The 2D crowd is used to portray the present life in 360° panoramic view of a virtual heritage environment based on the extension of Newtonian Laws. Secondly, the 2D crowd is animated and rendered into 3D with improved variety and incorporated into the virtual past life using Unity3D Game Engine. The behaviours of the 3D models are then simulated based on the enhancement of the classical model of Boid algorithm. Finally, a demonstration system is developed and integrated with the models, techniques and algorithms of this research. The virtual walkthrough is demonstrated to a group of respondents and is evaluated through the user-centred evaluation by navigating around the demonstration system. The results of the evaluation based on the questionnaires have shown that the presented virtual walkthrough has been successfully deployed through a multi-modal simulation and such a virtual walkthrough would be particularly useful in a virtual tour and virtual museum applications.

Keywords: Boid Algorithm, Crowd Simulation, Mobile Platform, Newtonian Laws, Virtual Heritage

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Authors: Rosemary Saunders, Courtney Glass, Karla Seaman, Karen Gullick, Julie Andrew, Anne Wilkinson, Ashwini Davray

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Current literature demonstrates that most Australians receive end-of-life care in a hospital setting, despite most hoping to die within their own home. The necessity for high quality end-of-life care has been emphasised by the Australian Commission on Safety and Quality in Health Care and the National Safety and Quality in Health Services Standards depict the requirement for comprehensive care at the end of life (Action 5.20), reinforcing the obligation for continual organisational assessment to determine if these standards are suitably achieved. Limited research exploring clinical staff perspectives of end-of-life care delivery has been conducted within an Australian private health context. This study aimed to investigate clinical staff member perceptions of end-of-life care delivery at a private hospital in Western Australia. The study comprised of a multi-faceted mixed-methods methodology, part of a larger study. Data was obtained from clinical staff utilising surveys and focus groups. A total of 133 questionnaires were completed by clinical staff, including registered nurses (61.4%), enrolled nurses (22.7%), allied health professionals (9.9%), non-palliative care consultants (3.8%) and junior doctors (2.2%). A total of 14.7% of respondents were palliative care ward staff members. Additionally, seven staff focus groups were conducted with physicians (n=3), nurses (n=26) and allied health professionals including social workers (n=1), dietitians (n=2), physiotherapists (n=5) and speech pathologists (n=3). Key findings from the surveys highlighted that the majority of staff agreed it was part of their role to talk to doctors about the care of patients who they thought may be dying, and recognised the importance of communication, appropriate training and support for clinical staff to provide quality end-of-life care. Thematic analysis of the qualitative data generated three key themes: creating the setting which highlighted the importance of adequate resourcing and conducive physical environments for end-of-life care and to support staff and families; planning and care delivery which emphasised the necessity for collaboration between staff, families and patients to develop care plans and treatment directives; and collaborating in end-of-life care, with effective communication and teamwork leading to achievable care delivery expectations. These findings contribute to health professionals better understanding of end-of-life care provision and the importance of collaborating with patients and families in care delivery. It is crucial that health care providers implement strategies to overcome gaps in care, so quality end-of-life care is provided. Findings from this study have been translated into practice, with the development and implementation of resources, training opportunities, support networks and guidelines for the delivery of quality end-of-life care.

Keywords: clinical staff, end-of-life care, mixed-methods, private hospital.

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Authors: B. V. Santos, M. T. M. Carvalho, J. H. S. Rêgo

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The article presents a literature review on recent advances related to studies of the environmental impact of road pavements, with reference to the concepts of Life Cycle Assessment (LCA). An introduction with the main motivations for the development of the research is presented, with a current overview of the Brazilian transport infrastructure and the projections for the road mode for the coming years, and the possibility of using the referred methodology by the road sector in Brazil. The article explores the origin of LCA in road pavements and the details linked to its implementation from the perspective of the four main phases of the study (goal and scope definition, inventory analysis, impact assessment, and interpretation). Finally, the main advances and deficiencies observed in the selected studies are gathered, with the proposition of research fields that can be explored in future national or international studies of LCA of road pavements.

Keywords: Brazil, life cycle assessment, road pavements, sustainable

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Authors: Debasis Sengupta, Sudipta Das

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The class of life distributions with decreasing mean residual life (DMRL) is well known in the field of reliability modeling. It contains the IFR class of distributions and is contained in the NBUE class of distributions. While upper and lower bounds of the reliability distribution function of aging classes such as IFR, IFRA, NBU, NBUE, and HNBUE have discussed in the literature for a long time, there is no analogous result available for the DMRL class. We obtain the upper and lower bounds for the reliability function of the DMRL class in terms of first order finite moment. The lower bound is obtained by showing that for any fixed time, the minimization of the reliability function over the class of all DMRL distributions with a fixed mean is equivalent to its minimization over a smaller class of distribution with a special form. Optimization over this restricted set can be made algebraically. Likewise, the maximization of the reliability function over the class of all DMRL distributions with a fixed mean turns out to be a parametric optimization problem over the class of DMRL distributions of a special form. The constructive proofs also establish that both the upper and lower bounds are sharp. Further, the DMRL upper bound coincides with the HNBUE upper bound and the lower bound coincides with the IFR lower bound. We also prove that a pair of sharp upper and lower bounds for the reliability function when the distribution is increasing mean residual life (IMRL) with a fixed mean. This result is proved in a similar way. These inequalities fill a long-standing void in the literature of the life distribution modeling.

Keywords: DMRL, IMRL, reliability bounds, hazard functions

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Authors: Mustafa Salman, Nurcan Cetinkaya, Zehra Selcuk, Bugra Genc

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The objectives of the present study were to estimate the microbial-N flow to the small intestine and to predict the digestible organic matter intake (DOMI) in grazing Karayaka sheep based on urinary excretion of purine derivatives (xanthine, hypoxanthine, uric acid, and allantoin) by the use of spot urine sampling under field conditions. In the trial, 10 Karayaka sheep from 2 to 3 years of age were used. The animals were grazed in a pasture for ten months and fed with concentrate and vetch plus oat hay for the other two months (January and February) indoors. Highly significant linear and cubic relationships (P<0.001) were found among months for purine derivatives index, purine derivatives excretion, purine derivatives absorption, microbial-N and DOMI. Through urine sampling and the determination of levels of excreted urinary PD and Purine Derivatives / Creatinine ratio (PDC index), microbial-N values were estimated and they indicated that the protein nutrition of the sheep was insufficient. In conclusion, the prediction of protein nutrition of sheep under the field conditions may be possible with the use of spot urine sampling, urinary excreted PD and PDC index. The mean purine derivative levels in spot urine samples from sheep were highest in June, July and October. Protein nutrition of pastured sheep may be affected by weather changes, including rainfall. Spot urine sampling may useful in modeling the feed consumption of pasturing sheep. However, further studies are required under different field conditions with different breeds of sheep to develop spot urine sampling as a model.

Keywords: Karayaka sheep, spot sampling, urinary purine derivatives, PDC index, microbial-N, feed intake

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Authors: Srisuporn Piyaratanawong, Suchai Assawapantanakul

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This study aims to reflect changes in beliefs and rituals among the Urak Lawoi sea gypsies on the Bulon archipelago of Satun Province that are related to changes of society according to each dimension of time. The historical study was conducted using an oral history approach. The study found that the traditional way of life as itinerants who moved seasonally resulted in their dependence on nature and beliefs in supernatural power, and mysterious powers and superstitions in the belief of ghosts, ancestors, guardian spirits, large banyan trees, life and living, treatment of diseases, king of nagas, and other beliefs. They displayed their respect to supernatural powers through rituals by worshiping, making offerings to spirits and performing Rongeng dance for spirits in return for fulfilling their vows. After World War II (1945), the Urak Lawoi sea gypsies on Bulon archipelago changed their itinerant way of life to permanent settlements. However, their beliefs in supernatural powers and ritual performances remained in existence. Until 1987, when tourism began to spread to the archipelago, some of them gradually turned to make a living with tourism. Moreover, during the last 20 years (from around 1994), Islam has spread among the people. With this social context, the traditional beliefs in supernatural powers have changed to beliefs according to the religion and the way of life that has changed. Thus, when the traditional beliefs and rituals can no longer fulfil the new way of life, they slowly disappear, such as the floating the boat ceremony that has been replaced with new beliefs and rituals according to Islam. Nevertheless, some beliefs and rituals still exist, such as beliefs about treatment of diseases and Rongeng dance for spirits in return for vow fulfilling. In conclusion, the traditional beliefs and rituals of the Urak Lawoi sea gypsies on the Bulon archipelago cannot fulfil the new way of life, and have, thus, brought about changes in beliefs and rituals that are congruent with the current society.

Keywords: belief, ritual, Urak Lawoi, sea gypsy, Bulon Archipelago

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Authors: Muhsin Kizhisseri, Jorg Schluter, Saleh Gharie

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Atherosclerosis is the main reason of stroke, which is one of the deadliest diseases in the world. The carotid artery in the brain is the prominent location for atherosclerotic progression, which hinders the blood flow into the brain. The inclusion of computational fluid dynamics (CFD) into the diagnosis cycle to understand the hemodynamics of the patient-specific carotid artery can give insights into stroke prediction. Realistic outlet boundary conditions are an inevitable part of the numerical simulations, which is one of the major factors in determining the accuracy of the CFD results. The Windkessel model-based outlet boundary conditions can give more realistic characteristics of the distal vascular branches of the carotid artery, such as the resistance to the blood flow and compliance of the distal arterial walls. This study aims to find the most influential distal branches of the carotid artery by using the Windkessel model parameters in the outlet boundary conditions. The parametric study approach to Windkessel model parameters can include the geometrical features of the distal branches, such as radius and length. The incorporation of the variations of the geometrical features of the major distal branches such as the middle cerebral artery, anterior cerebral artery, and ophthalmic artery through the Windkessel model can aid in identifying the most influential distal branch in the carotid artery. The results from this study can help physicians and stroke neurologists to have a more detailed and accurate judgment of the patient's condition.

Keywords: stroke, carotid artery, computational fluid dynamics, patient-specific, Windkessel model, distal vascular branches

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Authors: Pei Yi Lin

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

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

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Authors: Anurag Niranjan, Michael Fitzpatrick, Yin Jin Janin, Jazeel Chukkan, Niall Smyth

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Fillet welds are used in joining longitudinal stiffeners in ship structures. Welding residual stresses in fillet welds are generally distributed in a non-uniform manner, as shown in previous research the residual stress redistribution occurs under the cyclic loading that is experienced by such joints during service, and the combination of the initial residual stress, local constraints, and loading can alter the stress field in ways that are extremely difficult to predict. As the residual stress influences the crack propagation originating from the toe of the fillet welds, full understanding of the residual stress field and how it evolves is very important for structural integrity calculations. Knowledge of the residual stress redistribution in the presence of a flaw is therefore required for better fatigue life prediction. Moreover, defect assessment procedures such as BS7910 offer very limited guidance for flaw acceptance and the associated residual stress redistribution in the assessment of fillet welds. Therefore the objective of this work is to study a surface-breaking flaw at the weld toe region in a fillet weld under cyclic load, in conjunction with residual stress measurement at pre-defined crack depths. This work will provide details of residual stress redistribution under cyclic load in the presence of a crack. The outcome of this project will inform integrity assessment with respect to the treatment of residual stress in fillet welds. Knowledge of the residual stress evolution for this weld geometry will be greatly beneficial for flaw tolerance assessments (BS 7910, API 591).

Keywords: fillet weld, fatigue, residual stress, structure integrity

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Authors: Sandra Smart-Akande, Chaminda Hewage, Imtiaz Khan, Thanuja Mallikarachchi

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According to UK police-recorded data, there has been a substantial increase in knife-related crime and youth violence in the UK since 2014 particularly in the London boroughs. These crime rates are disproportionally distributed across London with the majority of these crimes occurring in the highly deprived areas of London and among young people aged 11 to 24 with large discrepancies across ethnicity, age, gender and borough of residence. Comprehensive studies and literature have identified risk factors associated with a knife carrying among youth to be Adverse Childhood Experience (ACEs), poor mental health, school or social exclusion, drug dealing, drug using, victim of violent crime, bullying, peer pressure or gang involvement, just to mention a few. ACEs are potentially traumatic events that occur in childhood, this can be experiences or stressful events in the early life of a child and can lead to an increased risk of damaging health or social outcomes in the latter life of the individual. Research has shown that children or youths involved in youth violence have had childhood experience characterised by disproportionate adverse childhood experiences and substantial literature link ACEs to be associated with criminal or delinquent behavior. ACEs are commonly grouped by researchers into: Abuse (Physical, Verbal, Sexual), Neglect (Physical, Emotional) and Household adversities (Mental Illness, Incarcerated relative, Domestic violence, Parental Separation or Bereavement). To the author's best knowledge, no study to date has investigated how household mental health (mental health of a parent or mental health of a child) and domestic violence (domestic violence on a parent or domestic violence on a child) is related to knife homicides across the local authorities areas of London. This study seeks to address the gap by examining a large sample of data from the London Metropolitan Police Force and Characteristics of Children in Need data from the UK Department for Education. The aim of this review is to identify and synthesise evidence from data and a range of literature to identify the relationship between adverse childhood experiences and youth violence in the UK. Understanding the link between ACEs and future outcomes can support preventative action.

Keywords: adverse childhood experiences, domestic violence, mental health, youth violence, prediction analysis, London knife crime

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Authors: Syazreen N. Shair, Saiful A. Ishak, Aida Y. Yusof, Azizah Murad

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In this paper, we forecast age-specific Malaysian mortality rates and life expectancy at births by gender and ethnic groups including Malay, Chinese and Indian. Two mortality forecasting models are adopted the original Lee-Carter model and its recent modified version, the product ratio coherent model. While the first forecasts the mortality rates for each subpopulation independently, the latter accounts for the relationship between sub-populations. The evaluation of both models is performed using the out-of-sample forecast errors which are mean absolute percentage errors (MAPE) for mortality rates and mean forecast errors (MFE) for life expectancy at births. The best model is then used to perform the long-term forecasts up to the year 2030, the year when Malaysia is expected to become an aged nation. Results suggest that in terms of overall accuracy, the product ratio model performs better than the original Lee-Carter model. The association of lower mortality group (Chinese) in the subpopulation model can improve the forecasts of high mortality groups (Malay and Indian).

Keywords: coherent forecasts, life expectancy at births, Lee-Carter model, product-ratio model, mortality rates

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Authors: Hayfa Almutary, Noof Eid Al Shammari

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Aim: The management of depression in patients undergoing hemodialysis remains challenging. The aim of this study was to evaluate the effectiveness of a breathing training program on quality of life and depression among patients on hemodialysis. Design: A one-group pretest-posttest quasi-experimental design was used. Methods: Data were collected from hemodialysis units at three dialysis centers. Initial baseline data were collected, and a breathing training program was implemented. The breathing training program included three types of breathing exercises. The impact of the intervention on outcomes was measured using both the Kidney Disease Quality of Life Short Version and the Beck Depression Inventory-Second Edition from the same participants. The participants were asked to perform the breathing training program three times a day for 30 days. Results: The mean age of the patients was 52.1 (SD:15.0), with nearly two-thirds of them being male (63.4%). Participants who were undergoing hemodialysis for 1–4 years constituted the largest number of the sample (46.3%), and 17.1% of participants had visited a psychiatric clinic 1-3 times. The results show that the breathing training program improved overall quality of life and reduced symptoms and problems. In addition, a significant decrease in the overall depression score was observed after implementing the intervention. Conclusions: The breathing training program is a non-pharmacological intervention that has proven visible effectiveness in hemodialysis. This study demonstrated that using breathing exercises reduced depression levels and improved quality of life. The integration of this intervention in dialysis units to manage psychological issues will offer a simple, safe, easy, and inexpensive intervention. Future research should compare the effectiveness of various breathing exercises in hemodialysis patients using longitudinal studies. Impact: As a safety precaution, nurses should initially use non-pharmacological interventions, such as a breathing training program, to treat depression in those undergoing hemodialysis.

Keywords: breathing training program, depression, exercise, quality of life, hemodialysis

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Authors: Adekunle Saheed Ajisebiyawo, Christie Omoduwa Achime

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In Nigerian society, women have very little or no involvement in the decision-making process and this is large because women are objectified as effective means of reproduction and provision of emotional support to the society. Despite the movements and awareness by international, national and local bodies to promote and encourage women's empowerment, there are still many factors daunting to the efforts of women in society. This paper examined the critical role of feminism in the quest for women's emancipation in public life. Guided by African feminism theory, this paper utilizes both historical and descriptive methods to examine these factors. The paper argues that gender bias in Nigeria's public life is often traced to the onset of colonialism in Nigeria. Thus the Western cultural notion of colonialism woven around male superiority is reflected in their relations with Nigerians. The study outlines how women have strategized pathways through patriarchal structures by deploying their femininity. The paper concludes that women are strong, courageous, natural leaders and indeed have a major strategic role to play in public life; thus, women's movements and groups remain an important and necessary means of social cohesion and strength, especially in a country such as Nigeria.

Keywords: African feminism, democratic governance, feminism, patriarchy, women emancipation.

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Authors: Andrej Golowin, Viktor Denk, Axel Riepe

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Solid fan blades and discs in aero engines are subjected to high combined low and high cycle fatigue loads especially around the contact areas between blade and disc. Therefore, special coatings (e.g. dry film lubricant) and surface treatments (e.g. shot peening or laser shock peening) are applied to increase the strength with respect to combined cyclic fatigue and fretting fatigue, but also to improve damage tolerance capability. The traditional deterministic damage tolerance assessment based on fracture mechanics analysis, which treats service damage as an initial crack, often gives overly conservative results especially in the presence of vibratory stresses. A probabilistic damage tolerance methodology using crack initiation data has been developed for fan discs exposed to relatively high vibratory stresses in cross- and tail-wind conditions at certain resonance speeds for limited time periods. This Monte-Carlo based method uses a damage databank from similar designs, measured vibration levels at typical aircraft operations and wind conditions and experimental crack initiation data derived from testing of artificially damaged specimens with representative surface treatment under combined fatigue conditions. The proposed methodology leads to a more realistic prediction of the minimum damage tolerance life for the most critical locations applicable to modern fan disc designs.

Keywords: combined fatigue, damage tolerance, engine, surface treatment

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Authors: Romella C. Lina, Matthew Daniel V. Leysa, Zarah D. F. Libozada, Maria Francesca I. Lirio, Angelo A. Liwag, Gabriel D. Ramos, Margaret M. Natividad

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This study aimed to determine the effectiveness of Buteyko Method in asthma control and quality of life of school-age children wherein a pretest-posttest design was utilized to measure the changes after the administration of Buteyko Method. Fourteen (14) subjects with bronchial asthma, aged 7-11 participated in the study. They were equally divided into two groups: the control group received no intervention while the experimental group was asked to attend sessions of Buteyko Method lecture and demonstration. The experimental group was visited for three (3) consecutive weeks to monitor their progress and compliance. Both groups were asked to answer ACQ pre- and post-intervention and PAQLQ before the start of the intervention phase and every week during the follow-up visits. In comparing the asthma control pre-test and post-test mean scores of the control group, no significant difference was noted (p=0.177) while the experimental group showed a significant difference after the administration of Buteyko Method (p=0.002). Moreover, the quality of life pre-test and post-test mean scores of the control group showed no significant difference in any week within one month of follow-up (p=0.736, 0.604, 0.689) while the experimental group showed a significant difference on the third week (p = 0.035) and fourth week (p=0.002) but no significant difference on the second week (p=0.111). Therefore, the use of Buteyko Method within 3-4 weeks as an adjunct to conventional management of asthma helps in improving asthma control and quality of life of school-age children.

Keywords: Buteyko Method, asthma, school-age children, asthma control, quality of life

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Authors: Vadim A. Kuznetsov, Anna M. Soldatova, Tatyana N. Enina, Elena A. Gorbatenko, Dmitrii V. Krinochkin

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The aim of the study was to evaluate potential parameters related with super-response to CRT. Methods: 60 CRT patients (mean age 54.3 ± 9.8 years; 80% men) with congestive heart failure (CHF) II-IV NYHA functional class, left ventricular ejection fraction < 35% were enrolled. At baseline, 1 month, 3 months and each 6 months after implantation clinical, electrocardiographic and echocardiographic parameters, NT-proBNP level were evaluated. According to the best decrease of left ventricular end-systolic volume (LVESV) (mean follow-up period 33.7 ± 15.1 months) patients were classified as super-responders (SR) (n=28; reduction in LVESV ≥ 30%) and non-SR (n=32; reduction in LVESV < 30%). Results: At baseline groups differed in age (58.1 ± 5.8 years in SR vs 50.8 ± 11.4 years in non-SR; p=0.003), gender (female gender 32.1% vs 9.4% respectively; p=0.028), width of QRS complex (157.6 ± 40.6 ms in SR vs 137.6 ± 33.9 ms in non-SR; p=0.044). Percentage of LBBB was equal between groups (75% in SR vs 59.4% in non-SR; p=0.274). All parameters of mechanical dyssynchrony were higher in SR, but only difference in left ventricular pre-ejection period (LVPEP) was statistically significant (153.0 ± 35.9 ms vs. 129.3 ± 28.7 ms p=0.032). NT-proBNP level was lower in SR (1581 ± 1369 pg/ml vs 3024 ± 2431 pg/ml; p=0.006). The survival rates were 100% in SR and 90.6% in non-SR (log-rank test P=0.002). Multiple logistic regression analysis showed that LVPEP (HR 1.024; 95% CI 1.004–1.044; P = 0.017), baseline NT-proBNP level (HR 0.628; 95% CI 0.414–0.953; P=0.029) and age at baseline (HR 1.094; 95% CI 1.009-1.168; P=0.30) were independent predictors for CRT super-response. ROC curve analysis demonstrated sensitivity 71.9% and specificity 82.1% (AUC=0.827; p < 0.001) of this model in prediction of super-response to CRT. Conclusion: Super-response to CRT is associated with better survival in long-term period. Presence of LBBB was not associated with super-response. LVPEP, NT-proBNP level, and age at baseline can be used as independent predictors of CRT super-response.

Keywords: cardiac resynchronisation therapy, superresponse, congestive heart failure, left bundle branch block

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Authors: María de los Ángeles Zárate López, Juan Antonio Pitones Rubio

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Quality of life and sustainability in cities are among the most important challenges faced by designers, city planners and urban managers. The Mexican city of Tijuana has a particular dynamic in its demographics which has been accelerated by its border city condition, putting to the test the ability from authorities to provide the population with the necessary services to aspire for a deserving quality of life. In the recent story of Tijuana, we found that the housing policy and the solutions presented by private housing developers have not met the best living conditions for end users by far, thereby adding issues to current social problems which impact the whole metropolitan area, including damage to the natural environment. Therefore this research presents the case study about the situation of a suburban housing development near Tijuana named “Villa del Campo” and exposes the problems of this specific project (originally labelled as a “sustainable” proposal) demonstrating that, once built, the place does not reflect the quality of life that it promised as a project. Currently, this housing development has a number of problematic issues such as the faulty operating conditions of public utilities and serious cases of crime inside the neighborhood. There is no intention to only expose the negative side of this case study, but to explore some alternatives which could help solving the most serious problems at the place, considering possible architectural and landscape interventions within the housing complex to help achieve the optimal conditions of livability and sustainability required by their inhabitants.

Keywords: suburban, housing, quality of life, sustainability, Tijuana, demographics

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Authors: Fernanda F. Zimmermann, Beverley Burrell, Jennifer Jordan

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Introduction: Supportive care for people affected by cancer is recognised as a priority for research but yet there is little solid evidence of the effectiveness of psychological treatments for those with advanced cancer. The literature suggests that mindfulness-based interventions may be acceptable and beneficial for this population. This study aims to develop a mindfulness intervention to provide emotional support for advanced cancer population. The treatment package includes mindfulness meditation, developing an acceptance attitude and reflections on meaning in life. Methods: This study design is a one-group pre-post test with a mixed methods approach. Participants are recruited through public and private hospitals in Christchurch, NZ. Quantitative measures are the Acceptance and Action Questionnaire-II, Mindful Coping Scale and, the Meaning in Life Questionnaire. Qualitative semi-structured interviews enquire about emotional support before and after the diagnosis, participants’ thoughts about meaning in life, expectations and reflections on the mindfulness training. Qualitative data will be analysed using thematic analysis. Treatment consists of one to one 30 minutes session weekly for 4 weeks using a pre-recorded CD/podcast of the mindfulness training. This research is part of the presenter’s PhD study. Findings: This project is currently underway. The presenter will provide preliminary data on the acceptability of the mindfulness training package being delivered to participants along with the recruitment strategies. We anticipate that this novel treatment used as a self-management tool will reduce psychological distress and enable better coping for patients with advanced cancer.

Keywords: acceptance, cancer, meaning in life, mindfulness

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Authors: Lule Basha, Eralda Gjika

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This study is focused on analyzing climate change in Albania and its potential effects on cereal yields. Initially, monthly temperature and rainfalls in Albania were studied for the period 1960-2021. Climacteric variables are important variables when trying to model cereal yield behavior, especially when significant changes in weather conditions are observed. For this purpose, in the second part of the study, linear and nonlinear models explaining cereal yield are constructed for the same period, 1960-2021. The multiple linear regression analysis and lasso regression method are applied to the data between cereal yield and each independent variable: average temperature, average rainfall, fertilizer consumption, arable land, land under cereal production, and nitrous oxide emissions. In our regression model, heteroscedasticity is not observed, data follow a normal distribution, and there is a low correlation between factors, so we do not have the problem of multicollinearity. Machine-learning methods, such as random forest, are used to predict cereal yield responses to climacteric and other variables. Random Forest showed high accuracy compared to the other statistical models in the prediction of cereal yield. We found that changes in average temperature negatively affect cereal yield. The coefficients of fertilizer consumption, arable land, and land under cereal production are positively affecting production. Our results show that the Random Forest method is an effective and versatile machine-learning method for cereal yield prediction compared to the other two methods.

Keywords: cereal yield, climate change, machine learning, multiple regression model, random forest

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