Search results for: residual life prediction

Authors: Phil P. Wagner

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There is an overwhelming amount of athlete-centric information available for sport practitioners in this era of tech and big data, but protocols in athletic rehabilitation remain arbitrary. It is a common assumption that the rate at which tissue heals amongst individuals is the same; yielding protocols that are entirely time-based. Progressing athletes through rehab programs that lack individualization can potentially expose athletes to stimuli they are not prepared for or unnecessarily lengthen their recovery period. A 7-year aggregated and anonymous database was used to develop reliable and valid assessments to measure athletic resilience. Each assessment utilizes force plate technology with proprietary protocols and analysis to provide key thresholds for injury risk and recovery. Using a T score to analyze movement qualities, much like the Z score used for bone density from a Dexa scan, specific prescriptions are provided to mitigate the athlete’s inherent injury risk. In addition to obliging to surgical clearance, practitioners must put in place a clearance protocol guided by standardized assessments and achievement in strength thresholds. In order to truly hold individuals accountable (practitioners, athletic trainers, performance coaches, etc.), success in improving pre-defined key performance indicators must be frequently assessed and analyzed.

Keywords: analytics, athlete rehabilitation, athlete resilience, injury prediction, injury prevention

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Authors: Un-Hwan Park, Jun-Hyeok Heo, In-Sung Lee, Seong-Jin Cho, Tae-Hyeon Oh, Dae-Kyu Park

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Sound absorption coefficient is considered important when designing because noise affects emotion quality of car. It is designed with lots of experiment tunings in the field because it is unreliable to predict it for multi-layer material. In this paper, we present the design of sound absorption for automotive interior material with multiple layers using estimation software of sound absorption coefficient for reverberation chamber. Additionally, we introduce the method for estimation of physical properties required to predict sound absorption coefficient of car interior materials with multiple layers too. It is calculated by inverse algorithm. It is very economical to get information about physical properties without expensive equipment. Correlation test is carried out to ensure reliability for accuracy. The data to be used for the correlation is sound absorption coefficient measured in the reverberation chamber. In this way, it is considered economical and efficient to design automotive interior materials. And design optimization for sound absorption coefficient is also easy to implement when it is designed.

Keywords: sound absorption coefficient, optimization design, inverse algorithm, automotive interior material, multiple layers nonwoven, scaled reverberation chamber, sound impedance tubes

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Authors: Aditya Manna

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Introduction: As in any developing countries state of West Bengal in India has a huge burden of cancer patients in advanced stage coming from rural area where awareness regarding the usefulness of palliative care in rather poor. Objective: Our goal is to give a pain free good quality of life in these advanced stage cancer patients. Objective of this study is to identify the main difficulties in achieving the above goal in a rural village setting in India. Method: Advanced cancer patients in need of palliative care in various villages in of rural India were selected for this study. Their symptoms and managements in that rural surroundings were evaluated by an NGO (under the guidance of a senior palliative care specialist) working in that area. An attempt was made to identify the main obstacles in getting proper palliative care in a rural setting. Results: Pain, fatigue are the main symptoms effecting these patients. In most patients pain and other symptoms control were grossly inadequate due to lack of properly trained manpower in the rural India. However regular homecare visits by a group of social workers were of immense help in the last few months of life. NGO team was well guided by a palliative care specialist. Conclusion: There is a wide gap of trained manpower in this filled in rural areas of India. Dedicated groups from rural area itself need encouragement and proper training, so that difficult symptoms can be managed locally along with necessary social and psychological support to these patients.

Keywords: palliative care, NGO, rural India, home care

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Authors: Hannah Evangeline Sangeetha

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Ageing is an inevitable change in the life span of an individual. India’s old age population has increased from 19 million in 1947 to 100 million in the 21st century. The United Nations World Population ageing reports that the grey population has immensely increased from 9.2% in 1990 to 11.7 % in 2013, and it’s expected to triple by the year 2050 growing from 737 million to over 2 billion persons 60 years of age and older. Ageing is a period of physical, mental and social decline which brings a host of challenges to the individual and the family. Hence it requires attention at the micro, mezzo and the macro levels of the society. The concepts of healthy and successful aging are being used to help people to change their negative attitude towards aging. This perspective is important to make people realize their potentialities to bring about a change in the minds of senior citizens as well as the society. The objective of this study was to understand the level of active ageing among the rural elderly women and its impact on the quality of life. 330 elderly women from 12 villages of Sriperumbudur associated with the Mobile medical care of Help age India were interviewed using census method. The study revealed the following findings; most respondents in this study were young old between the age group of 60 to 75 years. All the three major religious groups were represented, 85.5percent were Hindus. Majority of the respondents 73.3percent had no education. It was interesting to know that majority of the respondents were self reliant (83.94 percent) and 82.73 percent of them very independent and took care of them by themselves (activities of daily living) without any support from their families. 76.9 percent of the senior women worked based on their competencies, 75.5 percent of them were involved in plenty of activities everyday including their occupation and household chores, which enabled them to be physically active. The chi square values that there is a significant association between the overall active ageing score, religion &number of members in the family. The other demographic variables like age, occupation, income marital status, age at marriage, number of children in the family and Socio –Economic Status were not significantly associated with the overall active aging score. The p-value 0.032 showed Social network and being self-reliant are significantly associated. The study surprisingly shows that most women enjoyed freedom and Independence in their family which is a positive indicator of active ageing.

Keywords: active ageing, quality of life, independence, self reliance

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Authors: Atje Setiawan Abdullah, Budi Nurani Ruchjana, I. Gede Nyoman Mindra Jaya, Eddy Hermawan

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Ethnoscience is used to see the culture with a scientific perspective, which may help to understand how people develop various forms of knowledge and belief, initially focusing on the ecology and history of the contributions that have been there. One of the areas studied in ethnoscience is etno-informatics, is the application of informatics in the culture. In this study the science of informatics used is data mining, a process to automatically extract knowledge from large databases, to obtain interesting patterns in order to obtain a knowledge. While the application of culture described by naming database village on the island of Java were obtained from Geographic Indonesia Information Agency (BIG), 2014. The purpose of this study is; first, to classify the naming of the village on the island of Java based on the structure of the word naming the village, including the prefix of the word, syllable contained, and complete word. Second to classify the meaning of naming the village based on specific categories, as well as its role in the community behavioral characteristics. Third, how to visualize the naming of the village to a map location, to see the similarity of naming villages in each province. In this research we have developed two theorems, i.e theorems area as a result of research studies have collected intersection naming villages in each province on the island of Java, and the composition of the wedge theorem sets the provinces in Java is used to view the peculiarities of a location study. The methodology in this study base on the method of Knowledge Discovery in Database (KDD) on data mining, the process includes preprocessing, data mining and post processing. The results showed that the Java community prioritizes merit in running his life, always working hard to achieve a more prosperous life, and love as well as water and environmental sustainment. Naming villages in each location adjacent province has a high degree of similarity, and influence each other. Cultural similarities in the province of Central Java, East Java and West Java-Banten have a high similarity, whereas in Jakarta-Yogyakarta has a low similarity. This research resulted in the cultural character of communities within the meaning of the naming of the village on the island of Java, this character is expected to serve as a guide in the behavior of people's daily life on the island of Java.

Keywords: ethnoscience, ethno-informatics, data mining, clustering, Java island culture

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Authors: Cora Fernandez Dacosta, John A. Posada, Andrea Ramirez

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The biodegradable family of polymers polyhydroxyalkanoates are interesting substitutes for convectional fossil-based plastics. However, the manufacturing and environmental impacts associated with their production via intracellular bacterial fermentation are strongly dependent on the raw material used and on energy consumption during the extraction process, limiting their potential for commercialization. Industrial wastewater is studied in this paper as a promising alternative feedstock for waste valorization. Based on results from laboratory and pilot-scale experiments, a conceptual process design, techno-economic analysis and life cycle assessment are developed for the large-scale production of the most common type of polyhydroxyalkanoate, polyhydroxbutyrate. Intracellular polyhydroxybutyrate is obtained via fermentation of microbial community present in industrial wastewater and the downstream processing is based on chemical digestion with surfactant and hypochlorite. The economic potential and environmental performance results help identifying bottlenecks and best opportunities to scale-up the process prior to industrial implementation. The outcome of this research indicates that the fermentation of wastewater towards PHB presents advantages compared to traditional PHAs production from sugars because the null environmental burdens and financial costs of the raw material in the bioplastic production process. Nevertheless, process optimization is still required to compete with the petrochemicals counterparts.

Keywords: circular economy, life cycle assessment, polyhydroxyalkanoates, waste valorization

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Authors: Stuti Modi, Aditi Kambli

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This paper addresses the need for intelligent water management and distribution system in smart cities to ensure optimal consumption and distribution of water for drinking and sanitation purposes. Water being a limited resource in cities require an effective system for collection, storage and distribution. In this paper, applications of two mostly widely used particular types of data-driven models, namely artificial neural networks (ANN) and fuzzy logic-based models, to modelling in the water resources management field are considered. The objective of this paper is to review the principles of various types and architectures of neural network and fuzzy adaptive systems and their applications to integrated water resources management. Final goal of the review is to expose and formulate progressive direction of their applicability and further research of the AI-related and data-driven techniques application and to demonstrate applicability of the neural networks, fuzzy systems and other machine learning techniques in the practical issues of the regional water management. Apart from this the paper will deal with water storage, using ANN to find optimum reservoir level and predicting peak daily demands.

Keywords: artificial neural networks, fuzzy systems, peak daily demand prediction, water management and distribution

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Authors: Mai Awad

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This paper aims to focus on the phenomenon of martyrdom—particularly its performative aspect—and how social and popular cultural representations address the multiple meanings of the loaded image of a Palestinian martyr. This focus will help us to explore the possible reasons that might push Palestinians to consider pursuing “martyrdom” or suicide operations. Tracing what happened in the past and what is currently happening (that is, haunting) will aid in theorizing how the act/practice of “martyrdom” is produced. It is believed that there are social and political forces, particularly in a colonial society like Palestine, that influence the subject and its experience. But what is unique about this paper is its attempt to disclose the invisible, hidden narratives and complexities of Palestinian life that we do not see. By giving “martyrs” a chance to speak and express their own narratives—since “martyrs” usually leave written letters for their families, which are published after their death—this study must broaden the whole picture and discuss what is missing. The analytic method to be used: For the methodology, the paper recruits discourse analysis as a method for tracing the emergence, circulation, and productivity of the martyrdom discourse across a range of social practices in Palestinians’ everyday life after the Nakba. The paper analyzes the letters that “martyrs” left to their families, relatives, and the Palestinian community after their death. By letting “martyrs” speak for themselves and hearing their unique discourses, the research would suggest that more explanation is needed to describe the “martyr” identity. Hence, it is not possible to study the “martyr” identity in Palestine without understanding the colonial context that governs it and shapes their subjective experience.

Keywords: martyrdom, palestine, haunting, nakba 1948

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Authors: Masoud Ghermezi

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Studying the sheet metals is one of the most important research areas in the field of metal forming due to their extensive applications in the aerospace industries. A useful method for determining the forming limit of these materials and consequently preventing the rupture of sheet metals during the forming process is the use of the forming limit curve (FLC). In addition to specifying the forming limit, this curve also delineates a boundary for the allowed values of strain in sheet metal forming; these characteristics of the FLC along with its accuracy of computation and wide range of applications have made this curve the basis of research in the present paper. This study presents a new model that not only agrees with the results obtained from the above mentioned theory, but also eliminates its shortcomings. In this theory, like in the M-K theory, a thin sheet with an inhomogeneity as a gradient thickness reduction with a sinusoidal function has been chosen and subjected to two-dimensional stress. Through analytical evaluation, ultimately, a governing differential equation has been obtained. The numerical solution of this equation for the range of positive strains (stretched region) yields the results that agree with the results obtained from M-K theory. Also the solution of this equation for the range of negative strains (tension region) completes the FLC curve. The findings obtained by applying this equation on two alloys with the hardening exponents of 0.4 and 0.24 indicate the validity of the presented equation.

Keywords: sheet metal, metal forming, forming limit curve (FLC), M-K theory

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Authors: Jovina Concepcion Bachynski, Lenora Duhn, Idevania G. Costa, Pilar Camargo-Plazas

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Kidney failure is a life-limiting condition for which treatment, such as dialysis (hemodialysis and peritoneal dialysis), can exact a tremendously high physical and psychosocial symptom burden. Kidney failure can be severe enough to require a palliative approach to care. The term supportive care can be used in lieu of palliative care to avoid the misunderstanding that palliative care is synonymous with end-of-life or hospice care. Kidney supportive care, encompassing advance care planning, is an approach to care that improves the quality of life for people receiving dialysis through early identification and treatment of symptoms throughout the disease trajectory. Advanced care planning involves ongoing conversations about the values, goals, and preferences for future care between individuals and their healthcare teams. Kidney supportive care is underutilized and often initiated late in this population. There is evidence to indicate nurses are not providing the necessary elements of supportive kidney care. Dialysis nurses’ delay or lack of engagement in supportive care until close to the end of life may result in people dying without receiving optimal palliative care services. Using Charmaz’s constructivist grounded theory, the purpose of this doctoral study is to develop a substantive theory that explains the process of engagement in supportive care by nurses working in dialysis settings in Canada. Through initial purposeful and subsequent theoretical sampling, 23 nurses with current or recent work experience in outpatient hemodialysis, home hemodialysis, and peritoneal dialysis settings drawn from across Canada were recruited to participate in two intensive interviews using the Zoom© teleconferencing platform. Concurrent data collection and data analysis, constant comparative analysis of initial and focused codes until the attainment of theoretical saturation, and memo-writing, as well as researcher reflexivity, have been undertaken to aid the emergence of concepts, categories, and, ultimately, the constructed theory. At the time of abstract submission, data analysis is currently at the second level of coding (i.e., focused coding stage) of the research study. Preliminary categories include: (a) focusing on biomedical care; (b) multi-dimensional challenges to having the conversation; (c) connecting and setting boundaries with patients; (d) difficulty articulating kidney-supportive care; and (e) unwittingly practising kidney-supportive care. For the conference, the resulting theory will be presented. Nurses working in dialysis are well-positioned to ensure the delivery of quality kidney-supportive care. This study will help to determine the process and the factors enabling and impeding nurse engagement in supportive care in dialysis to effect change for normalizing advance care planning conversations in the clinical setting. This improved practice will have substantive beneficial implications for the many individuals living with kidney failure and their supporting loved ones.

Keywords: dialysis, kidney failure, nursing, supportive care

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Authors: T. Tommasi, E. Batuecas, G. Mancini, G. Saracco, D. Fino

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Extra virgin olive-oil (EVO) production is an important economic activity for several countries, especially in the Mediterranean area such as Spain, Italy, Greece and Tunisia. The two major by-products from olive oil production, solid-liquid Olive Pomace (OP) and the Olive Mill Waste Waters (OMWW), are still mainly disposed on soil, in spite of the existence of legislation which already limits this practice. The present study compares the environmental impacts associated with two different scenarios for the management of waste from olive oil production through a comparative Life Cycle Assessment (LCA). The two alternative scenarios are: (I) Anaerobic Digestion and (II) current Disposal on soil. The analysis was performed through SimaPro software and the assessment of the impact categories was based on International Life Cycle Data and Cumulative Energy Demand methods. Both the scenarios are mostly related to the cultivation and harvesting phase and are highly dependent on the irrigation practice and related energy demand. Results from the present study clearly show that as the waste disposal on soil causes the worst environmental performance of all the impact categories here considered. Important environmental benefits have been identified when anaerobic digestion is instead chosen as the final treatment. It was consequently demonstrated that anaerobic digestion should be considered a feasible alternative for olive mills, to produce biogas from common olive oil residues, reducing the environmental burden and adding value to the olive oil production chain.

Keywords: anaerobic digestion, waste management, agro-food waste, biogas

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Authors: Karim Hamidi Machekposhti, Hossein Sedghi, Abdolrasoul Telvari, Hossein Babazadeh

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Floods have huge environmental and economic impact. Therefore, flood prediction is given a lot of attention due to its importance. This study analysed the annual maximum streamflow (discharge) (AMS or AMD) of Karkheh River in Karkheh River Basin for flood predicting using ARIMA model. For this purpose, we use the Box-Jenkins approach, which contains four-stage method model identification, parameter estimation, diagnostic checking and forecasting (predicting). The main tool used in ARIMA modelling was the SAS and SPSS software. Model identification was done by visual inspection on the ACF and PACF. SAS software computed the model parameters using the ML, CLS and ULS methods. The diagnostic checking tests, AIC criterion, RACF graph and RPACF graphs, were used for selected model verification. In this study, the best ARIMA models for Annual Maximum Discharge (AMD) time series was (4,1,1) with their AIC value of 88.87. The RACF and RPACF showed residuals’ independence. To forecast AMD for 10 future years, this model showed the ability of the model to predict floods of the river under study in the Karkheh River Basin. Model accuracy was checked by comparing the predicted and observation series by using coefficient of determination (R²).

Keywords: time series modelling, stochastic processes, ARIMA model, Karkheh river

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Authors: Mark De Ste Croix, Jonathan Hughes, Francisco Ayala, Michal Lehnert

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There are well-known challenges to implementing injury prevention training for youth players but no data are available on the knowledge and understanding of deliverers of such programmes at grass root level. To increase adoption and adherence to such programmes coach knowledge and understanding of injury risk and prevention is essential. Therefore, the purpose of this study was to examine grass-root coaches knowledge and understanding of injury risk and prevention in youth players. 68 grass root coaches (18 females and 50 males) who were attending a one-day injury prevention workshop completed a modified validated questionnaire exploring knowledge and understanding of injury risk and prevention in youth players. Only 59% of coaches agreed that youth players are at a high risk of suffering an injury. There were high levels of agreement that injuries can have negative impacts on team performance (75%) and can cause physical problems in later life (85%), however only around half of coaches felt that injuries affect youth players current quality of life (59%). There was strong agreement that it is possible to prevent injuries in youth players (84%), but coaches were generally unaware of programs to help prevent injuries (84%), and only 9% used some form of injury prevention program. Despite this, nearly all coaches felt that their coaching could benefit from a greater understanding of growth and maturation (91%), injury prevention programmes (91%) and specific exercises (93%) for youth athletes. 17% of coaches rated their knowledge of injury prevention as good/very good at the start of the workshop and this increased to 94% at the end of the workshop. 62% of coaches identified their attitude towards injury prevention as indifferent at the start of the workshop compared with only 1% at the end. Only 14% of coaches at the start of the workshop were confident to deliver an injury prevention session but 83% stated they were confident by the end of the workshop. Finally, 98% of coaches felt that the workshop provided them with the confidence and the knowledge to deliver an injury prevention session and 98% suggested that they would implement injury prevention into their coaching. These data suggest that there is a lack of understanding of grass root coaches that children are a high-risk group for injuries, and that such injuries impact on current quality of life. Despite understanding that injuries can be prevented most grass root coaches do not have the knowledge to implement injury prevention into their coaching and very few do. There is a common consensus amongst these coaches that a greater understanding of such programmes will enhance their coaching. The injury prevention workshop appears to have increased the knowledge and changed the attitude of coaches towards injury prevention. All coaches felt that the workshop provided them with the tools to adopt, implement and deliver injury prevention in their coaching. These data highlight that there is a clear need for education regarding injury risk and prevention to be embedded within the coach education pathway, especially at grass root level.

Keywords: coach education, injury prevention, knowledge, and understanding, youth

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Authors: Jinyan Li, Simon Fong, Raymond Wong, Mohammed Sabah, Fiaidhi Jinan

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Clinical data analysis and forecasting have make great contributions to disease control, prevention and detection. However, such data usually suffer from highly unbalanced samples in class distributions. In this paper, we target at the binary imbalanced dataset, where the positive samples take up only the minority. We investigate two different meta-heuristic algorithms, particle swarm optimization and bat-inspired algorithm, and combine both of them with the synthetic minority over-sampling technique (SMOTE) for processing the datasets. One approach is to process the full dataset as a whole. The other is to split up the dataset and adaptively process it one segment at a time. The experimental results reveal that while the performance improvements obtained by the former methods are not scalable to larger data scales, the later one, which we call Adaptive Swarm Balancing Algorithms, leads to significant efficiency and effectiveness improvements on large datasets. We also find it more consistent with the practice of the typical large imbalanced medical datasets. We further use the meta-heuristic algorithms to optimize two key parameters of SMOTE. Leading to more credible performances of the classifier, and shortening the running time compared with the brute-force method.

Keywords: Imbalanced dataset, meta-heuristic algorithm, SMOTE, big data

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Authors: Jamuna Rajeswaran

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In the past two decades, with a population of more than a billion. India is passing through a major socio-demographic and epidemiological transition with consequent changes in health scenario. TBI constitute significant burden on health care resources in India The impact on a person and family can be devastating. Patients with TBI experience persistent cognitive deficits, emotional changes, which contribute to the disruption of life activities. The recovery of TBI would be maximized by appropriate rehabilitation. Neurofeedback is an emerging neuroscience-based clinical application. Sixty patients were recruited for this study after obtaining informed consent. Rivermead Head Injury Follow-up Questionnaire, Rivermead Post Concussion Symptoms Questionnaire and Visual Analog Scale were used to assess the behavioral and symptomotolgy associated with post TBI. Neuropsychological assessment was carried out using NIMHANS neuropsychological battery 2004. The Intervention group received neurofeedback training and the waitlist group did not receive any treatment during this phase. Patients were allocated to intervention and waitlist group at random. There were 30 patients in each group. Patients were given 20 sessions of NFT Patients were trained on the O1 and O2 channels for alpha theta training. Each session was of 40 minutes duration with 5-6 sessions per week. The post-training assessment was carried out for the intervention group after 20 sessions of NFT. The waitlist group underwent assessment after one month. Results showed neurofeedback training is effective in ameliorating deficits in cognitive functions and quality of life in patients with TBI. Improvements were corroborated by the clinical interview with patients and significant others post NFT.

Keywords: assessment, rehabilitation, cognition, EEG neurofeedback

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Authors: Sheena K. J., Jyoti Badge, Sayed Mohammed Zeeshan

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A Comparative Study of Exponential and Logistic Population Growth Models in India India is the second most populous city in the world, just behind China, and is going to be in the first place by next year. The Indian population has remarkably at higher rate than the other countries from the past 20 years. There were many scientists and demographers who has formulated various models of population growth in order to study and predict the future population. Some of the models are Fibonacci population growth model, Exponential growth model, Logistic growth model, Lotka-Volterra model, etc. These models have been effective in the past to an extent in predicting the population. However, it is essential to have a detailed comparative study between the population models to come out with a more accurate one. Having said that, this research study helps to analyze and compare the two population models under consideration - exponential and logistic growth models, thereby identifying the most effective one. Using the census data of 2011, the approximate population for 2016 to 2031 are calculated for 20 Indian states using both the models, compared and recorded the data with the actual population. On comparing the results of both models, it is found that logistic population model is more accurate than the exponential model, and using this model, we can predict the future population in a more effective way. This will give an insight to the researchers about the effective models of population and how effective these population models are in predicting the future population.

Keywords: population growth, population models, exponential model, logistic model, fibonacci model, lotka-volterra model, future population prediction, demographers

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Authors: Luciano E. Sewaybricker

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Psychology has faced many challenges in order to claim its right to study happiness. Probably the major issue has been to present a clear definition of happiness, which has a long history outside the scientific field and has been used imprecisely in the daily life. Even after years of great improvement, different meanings of happiness still have been seen in academic studies. This scenario allows to question if any definition is consistent enough to sustain the recent findings of the psychological processes behind happiness. Moreover, does it make sense to seek a single definition of happiness? By investigating the history of happiness and the theoretical foundations of Positive Psychology, it can be advocated that it’s proper for happiness to be polysemic. Since Ancient Greece most attempts to outline happiness consists of an appreciation of the "best way to live" and consequently requires a delineation of the most important things in life. Besides this generic definition, it’s hard to find consensus about happiness. In fact, what and how much something will be considered important to happiness depend on social influence. This compels happiness to vary between groups, historical periods, and even for the same person over time. Therefore, the same psychological processes will not necessarily be behind all forms of happiness. Consequently, three assumptions should be considered when studying happiness: it’s intrinsic of happiness to be transitory and socially influenced; happiness refers not only to what is possible in the present, but also to an ideal future; when someone (including a scientist) talks about happiness they describe and prescribe a better way to live. Because any attempt to define happiness will be limited in space and time, it's more suitable to study its variations than its universalities. This may have considerable consequences to political agenda on happiness evaluation and maximization, like Gross National Happiness and utilitarian initiatives. Happiness policies should be understood as an arbitrary choice amongst all kinds of happiness and as prescriptive of what “the best way to live” should be.

Keywords: happiness, politics, positive psychology, well-being

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Authors: Stephanie Wall, Guido Wimmers

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The construction and operation of buildings greatly contribute to environmental degradation through resource and energy consumption and greenhouse gas emissions. The design of the envelope system affects the environmental impact of a building in two major ways; 1) high thermal performance and air tightness can significantly reduce the operational energy of the building and 2) the material selection for the envelope largely impacts the embodied energy of the building. Life cycle assessment (LCA) is a scientific methodology that is used to systematically analyze the environmental load of processes or products, such as buildings, over their life. The paper will discuss the results of a comparative LCA of different envelope designs and the long-term monitoring of the Wood Innovation Research Lab (WIRL); a Passive House (PH), industrial building under construction in Prince George, Canada. The WIRL has a footprint of 30m x 30m on a concrete raft slab foundation and consists of shop space as well as a portion of the building that includes a two-story office/classroom space. The lab building goes beyond what was previously thought possible in regards to energy efficiency of industrial buildings in cold climates due to their large volume to surface ratio, small floor area, and high air change rate, and will be the first PH certified industrial building in Canada. These challenges were mitigated through the envelope design which utilizes solar gains while minimizing overheating, reduces thermal bridges with thick (570mm) prefabricated truss walls filled with blown in mineral wool insulation and a concrete slab and roof insulated with EPS rigid insulation. The envelope design results in lower operational and embodied energy when compared to buildings built to local codes or with steel. The LCA conducted using Athena Impact Estimator for Buildings identifies project specific hot spots as well illustrates that for high-efficiency buildings where the operational energy is relatively low; the embodied energy of the material selection becomes a significant design decision as it greatly impacts the overall environmental footprint of the building. The results of the LCA will be reinforced by long-term monitoring of the buildings envelope performance through the installation of temperature and humidity sensors throughout the floor slab, wall and roof panels and through detailed metering of the energy consumption. The data collected from the sensors will also be used to reinforce the results of hygrothermal analysis using WUFI®, a program used to verify the durability of the wall and roof panels. The WIRL provides an opportunity to showcase the use of wood in a high performance envelope of an industrial building and to emphasize the importance of considering the embodied energy of a material in the early stages of design. The results of the LCA will be of interest to leading researchers and scientists committed to finding sustainable solutions for new construction and high-performance buildings.

Keywords: high performance envelope, life cycle assessment, long term monitoring, passive house, prefabricated panels

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Authors: Pedram Saadati, Zahra Nazari

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Anticipating and identifying future opportunities and challenges facing industry activists for the emergence and entry of new knowledge and technologies of personal financial planning, and providing practical solutions is one of the goals of this research. For this purpose, a future research tool based on receiving opinions from the main players of the insurance industry has been used. The research method in this study was in 4 stages; including 1- a survey of the specialist salesforce of life insurance in order to identify the variables 2- the ranking of the variables by experts selected by a researcher-made questionnaire 3- holding a panel of experts with the aim of understanding the mutual effects of the variables and 4- statistical analyzes of the mutual effects matrix in Mick Mac software is done. The integrated analysis of influencing variables in the future has been done with the method of Structural Analysis, which is one of the efficient and innovative methods of future research. A list of opportunities and challenges was identified through a survey of best-selling life insurance representatives who were selected by snowball sampling. In order to prioritize and identify the most important issues, all the issues raised were sent to selected experts who were selected theoretically through a researcher-made questionnaire. The respondents determined the importance of 36 variables through scoring, so that the prioritization of opportunity and challenge variables can be determined. 8 of the variables identified in the first stage were removed by selected experts, and finally, the number of variables that could be examined in the third stage became 28 variables, which, in order to facilitate the examination, were divided into 6 categories, respectively, 11 variables of organization and management. Marketing and sales 7 cases, social and cultural 6 cases, technological 2 cases, rebranding 1 case and insurance 1 case were divided. The reliability of the researcher-made questionnaire was confirmed with the Cronbach's alpha test value of 0.96. In the third stage, by forming a panel consisting of 5 insurance industry experts, the consensus of their opinions about the influence of factors on each other and the ranking of variables was entered into the matrix. The matrix included the interrelationships of 28 variables, which were investigated using the structural analysis method. By analyzing the data obtained from the matrix by Mic Mac software, the findings of the research indicate that the categories of "correct training in the use of the software, the weakness of the technology of insurance companies in personalizing products, using the approach of equipping the customer, and honesty in declaring no need Customer to Insurance", the most important challenges of the influencer and the categories of "salesforce equipping approach, product personalization based on customer needs assessment, customer's pleasant experience of being consulted with consulting robots, business improvement of the insurance company due to the use of these tools, increasing the efficiency of the issuance process and optimal customer purchase" were identified as the most important opportunities for influence.

Keywords: personal financial planning, wealth management, advisor robots, life insurance, digital transformation

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Authors: Mohamed Ahmed, Ezat Korany, Abdelaziz Al Basam, Osama Kasem

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The arid climate, low rate of precipitations and population reflect the increasing of groundwater uses as the main source of water in Saudi Arabia. The Wajid Aquifer System represents a regional groundwater aquifer system along the edge of the crystalline Arabian Shield near the southwestern tip of the Arabian Peninsula. The aquifer extends across the border of Saudi Arabia and Yemen from the Asir –Yemen Highlands to the Rub al Khali Depression and possibly to the Gulf coast (at the southwestern tip). The present work is representing a hydrogeochemical investigation on the Wajid Aquifer System. The studied area is being classified into three zones. The 1st zone is West of Wadi Ad Dawasir (Northern part of the studied area), the 2nd is Najran-Asir Zone (southern part of the studied area), and the 3rd zone is the intermediate -central zone (occupying the central area between the last two zones). The groundwater samples were collected and chemically analyzed for physicochemical properties such as pH, electrical conductivity, total hardness (TH), alkalinity (pH), total dissolved solids (TDS), major ions (Ca2+, Mg2+, Na+, K+, HCO3-, SO42- and Cl-), and trace elements. Some parameters such as sodium adsorption ratio (SAR), soluble sodium percentage (Na%), potential salinity, residual sodium carbonate, Kelly's ratio, permeability index and Gibbs ratio, hydrochemical coefficients, hydrochemical formula, ion dominance, salt combinations and water types were also calculated in order to evaluate the quality of the groundwater resources in the selected areas for different purposes. The distribution of the chemical constituents and their interrelationships are illustrated by different hydrochemical graphs. Groundwater depths and the depth to water were measured to study the effect of discharge on both the water level and the salinity of the studied groundwater wells. A detailed comparison between the three studied zones according to the variations shown by the chemical and field investigations are discussed in detailed within the work.

Keywords: Najran-Asir, Wadi Ad Dawasir, Wajid Aquifer System, effect of discharge

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Authors: Shank Kulkarni, Alireza Tabarraei

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The fantastic properties of polyurea such as flexibility, durability, and chemical resistance have brought it a wide range of application in various industries. Effective prediction of the response of polyurea under different loading and environmental conditions necessitates the development of an accurate constitutive model. Similar to most polymers, the behavior of polyurea depends on both strain and strain rate. Therefore, the constitutive model should be able to capture both these effects on the response of polyurea. To achieve this objective, in this paper, a nonlinear hyper-viscoelastic constitutive model is developed by the superposition of a hyperelastic and a viscoelastic model. The proposed constitutive model can capture the behavior of polyurea under compressive loading conditions at various strain rates. Four parameter Ogden model and Mooney Rivlin model are used to modeling the hyperelastic behavior of polyurea. The viscoelastic behavior is modeled using both a three-parameter standard linear solid (SLS) model and a K-BKZ model. Comparison of the modeling results with experiments shows that Odgen and SLS model can more accurately predict the behavior of polyurea. The material parameters of the model are found by curve fitting of the proposed model to the uniaxial compression test data. The proposed model can closely reproduce the stress-strain behavior of polyurea for strain rates up to 6500 /s.

Keywords: constitutive modelling, ogden model, polyurea, SLS model, uniaxial compression test

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Authors: Min-Pei Ling

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Aflatoxin is a fungal secondary metabolite with high toxicity capable of contaminating various types of food crops. It has been identified as a Group 1 human carcinogen by the International Agency for Research on Cancer. Chronic aflatoxin exposure has caused a worldwide public food safety concern. Peanuts and peanut products are the major sources of aflatoxin exposure. Therefore, some reduction interventions have been developed to minimize contamination through the peanut production chain. The purpose of this study is to estimate the efficacy of interventions in reducing the health impact of hepatocellular carcinoma caused by aflatoxin contamination in peanuts. The estimated total disability-adjusted life-years (DALYs) was calculated using FDA-iRISK online software. Six aflatoxin reduction strategies were evaluated, including good agricultural practice (GAP), biocontrol, Purdue Improved Crop Storage packaging, basic processing, ozonolysis, and ultraviolet irradiation. The results indicated that basic processing could prevent huge public health loss of 4,079.7–21,833 total DALYs per year, which accounted for 39.6% of all decreased total DALYs. GAP and biocontrol were both effective strategies in the farm field, while the other three interventions were limited in reducing total DALYs. In conclusion, this study could help farmers, processing plants, and government policymakers to alleviate aflatoxin contamination issues in the peanut production chain.

Keywords: aflatoxin, health burden, disability-adjusted life-years, peanuts

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Authors: Evan Lowhorn, Rocio Alba-Flores

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The purpose of this work is to develop a method for classifying hand signals and using the output in a drone control algorithm. To achieve this, methods based on Convolutional Neural Networks (CNN) were applied. CNN's are a subset of deep learning, which allows grid-like inputs to be processed and passed through a neural network to be trained for classification. This type of neural network allows for classification via imaging, which is less intrusive than previous methods using biosensors, such as EMG sensors. Classification CNN's operate purely from the pixel values in an image; therefore they can be used without additional exteroceptive sensors. A development bench was constructed using a desktop computer connected to a high-definition webcam mounted on a scissor arm. This allowed the camera to be pointed downwards at the desk to provide a constant solid background for the dataset and a clear detection area for the user. A MATLAB script was created to automate dataset image capture at the development bench and save the images to the desktop. This allowed the user to create their own dataset of 12,000 images within three hours. These images were evenly distributed among seven classes. The defined classes include forward, backward, left, right, idle, and land. The drone has a popular flip function which was also included as an additional class. To simplify control, the corresponding hand signals chosen were the numerical hand signs for one through five for movements, a fist for land, and the universal “ok” sign for the flip command. Transfer learning with PyTorch (Python) was performed using a pre-trained 18-layer residual learning network (ResNet-18) to retrain the network for custom classification. An algorithm was created to interpret the classification and send encoded messages to a Ryze Tello drone over its 2.4 GHz Wi-Fi connection. The drone’s movements were performed in half-meter distance increments at a constant speed. When combined with the drone control algorithm, the classification performed as desired with negligible latency when compared to the delay in the drone’s movement commands.

Keywords: classification, computer vision, convolutional neural networks, drone control

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Authors: Janne Engblom, Elias Oikarinen

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The understanding of housing price dynamics is of importance to a great number of agents: to portfolio investors, banks, real estate brokers and construction companies as well as to policy makers and households. A panel dataset is one that follows a given sample of individuals over time, and thus provides multiple observations on each individual in the sample. Panel data models include a variety of fixed and random effects models which form a wide range of linear models. A special case of panel data models is dynamic in nature. A complication regarding a dynamic panel data model that includes the lagged dependent variable is endogeneity bias of estimates. Several approaches have been developed to account for this problem. In this paper, the panel models were estimated using the Common Correlated Effects estimator (CCE) of dynamic panel data which also accounts for cross-sectional dependence which is caused by common structures of the economy. In presence of cross-sectional dependence standard OLS gives biased estimates. In this study, U.S housing price dynamics were examined empirically using the dynamic CCE estimator with first-difference of housing price as the dependent and first-differences of per capita income, interest rate, housing stock and lagged price together with deviation of housing prices from their long-run equilibrium level as independents. These deviations were also estimated from the data. The aim of the analysis was to provide estimates with comparisons of estimates between 1980-1999 and 2000-2012. Based on data of 50 U.S cities over 1980-2012 differences of short-run housing price dynamics estimates were mostly significant when two time periods were compared. Significance tests of differences were provided by the model containing interaction terms of independents and time dummy variable. Residual analysis showed very low cross-sectional correlation of the model residuals compared with the standard OLS approach. This means a good fit of CCE estimator model. Estimates of the dynamic panel data model were in line with the theory of housing price dynamics. Results also suggest that dynamics of a housing market is evolving over time.

Keywords: dynamic model, panel data, cross-sectional dependence, interaction model

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Authors: Chisomo Patrick Kumbuyo, Katsuyuki Shimizu, Hiroshi Yasuda, Yoshinobu Kitamura

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This study deals with a way of predicting Malawi rainfall from global sea surface temperature (SST) using a simple multiple regression model. Monthly rainfall data from nine stations in Malawi grouped into two zones on the basis of inter-station rainfall correlations were used in the study. Zone 1 consisted of Karonga and Nkhatabay stations, located in northern Malawi; and Zone 2 consisted of Bolero, located in northern Malawi; Kasungu, Dedza, Salima, located in central Malawi; Mangochi, Makoka and Ngabu stations located in southern Malawi. Links between Malawi rainfall and SST based on statistical correlations were evaluated and significant results selected as predictors for the regression models. The predictors for Zone 1 model were identified from the Atlantic, Indian and Pacific oceans while those for Zone 2 were identified from the Pacific Ocean. The correlation between the fit of predicted and observed rainfall values of the models were satisfactory with r=0.81 and 0.54 for Zone 1 and 2 respectively (significant at less than 99.99%). The results of the models are in agreement with other findings that suggest that SST anomalies in the Atlantic, Indian and Pacific oceans have an influence on the rainfall patterns of Southern Africa.

Keywords: Malawi rainfall, forecast model, predictors, SST

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Authors: Elzbieta Sikorska-Simmons

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Dementia is a progressive terminal disease. Despite this recognition, research shows that most family caregivers do not know it, and it is unclear how this knowledge affects the quality of patient care. The aim of this qualitative study of 20 family caregivers for patients with advanced dementia is to examine how the caregiver's knowledge about dementia affects the quality of patient care in the context of healthcare decision-making, advanced care planning, and access to adequate support systems. Knowledge about dementia implies family caregivers' understanding of dementia trajectories, common symptoms/complications, and alternative treatment options (e.g., comfort feeding versus tube feeding). Data were collected in semi-structured interviews with 20 family caregivers. The interviews were conducted in person by the author and designed to elicit rich descriptions of family caregivers' experiences with healthcare decision-making and the management of common symptoms/complications of end-stage dementia as patient healthcare proxies. The study findings suggest that caregivers who recognize that dementia is a terminal disease are less likely to opt for life-extending treatments during the advanced stages. They are also more likely to seek palliative/hospice care, and consequently, they are better able to avoid unnecessary hospitalizations or medical procedures. For example, those who know that dementia is a terminal disease tend to opt for "comfort feeding" rather than "tube feeding" in managing the swallowing difficulties that accompany advanced dementia. In the context of advance care planning, family caregivers who know that dementia is a terminal disease tend to have more meaningful advance directives (e.g., Power of Attorney and Do Not Resuscitate orders). They are better prepared to anticipate common problems and pursue treatments that foster the best quality of patient life and care. Greater knowledge about advanced dementia helps them make more informed decisions that focus on enhancing the quality of patient life rather than just survival. In addition, those who know that dementia is a terminal disease are more likely to establish adequate support systems to help them cope with the complex demands of caregiving. For example, they are more likely to seek dementia-oriented primary care programs that offer house visits or respite services. Based on the study findings, knowledge about dementia as a terminal disease is critical in the optimal management of patient care needs and the establishment of adequate support systems. More research is needed to better understand what caregivers need to know to better prepare them for the complex demands of dementia caregiving.

Keywords: dementia education, family caregiver, management of dementia, quality of care

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Authors: Goran Sporiš, Dinko Vuleta, Stefan Lovro

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The purpose of the present study was to determine the associations between health-related quality of life (HRQOL) and physical activity (PA) in different domains. In this cross-sectional study, participants were 169 Croatian police officers (mean age 35.14±8.95 yrs, mean height 180.93±7.53 cm, mean weight 88.39±14.05 kg, mean body-mass index 26.90±3.39 kg/m2). The dependent variables were two general domains extracted from the HRQOL questionnaire: (1) physical component scale (PCS) and (2) mental component scale (MCS). The independent variables were job-related, transport, domestic and leisure-time PA, along with other factors: age, body-mass index, smoking status, psychological distress, socioeconomic status and time spent in sedentary behaviour. The associations between dependent and independent variables were analyzed by using multiple regression analysis. Significance was set up at p < 0.05. PCS was positively associated with leisure-time PA (β 0.28, p < 0.001) and socioeconomic status (SES) (β 0.16, p=0.005), but inversely associated with job-related PA (β -0.15, p=0.012), domestic-time PA (β -0.14, p=0.014), age (β -0.12, p=0.050), psychological distress (β -0.43, p<0.001) and sedentary behaviour (β -0.15, p=0.009). MCS was positively associated with leisure-time PA (β 0.19, p=0.013) and SES (β 0.20, p=0.002), while inversely associated with age (β -0.23, p=0.001), psychological distress (β -0.27, p<0.001) and sedentary behaviour (β -0.22, p=0.001). Our results added new information about the associations between domain-specific PA and both physical and mental component scale in police officers. Future studies should deal with the same associations in other stressful occupations.

Keywords: health, fitness, police force, relations

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Authors: Amin Nezarat, Naeime Seifadini

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Trip mode selection is a behavioral characteristic of passengers with immense importance for travel demand analysis, transportation planning, and traffic management. Identification of trip mode distribution will allow transportation authorities to adopt appropriate strategies to reduce travel time, traffic and air pollution. The majority of existing trip mode inference models operate based on human selected features and traditional machine learning algorithms. However, human selected features are sensitive to changes in traffic and environmental conditions and susceptible to personal biases, which can make them inefficient. One way to overcome these problems is to use neural networks capable of extracting high-level features from raw input. In this study, the convolutional neural network (CNN) architecture is used to predict the trip mode distribution based on raw GPS trajectory data. The key innovation of this paper is the design of the layout of the input layer of CNN as well as normalization operation, in a way that is not only compatible with the CNN architecture but can also represent the fundamental features of motion including speed, acceleration, jerk, and Bearing rate. The highest prediction accuracy achieved with the proposed configuration for the convolutional neural network with batch normalization is 85.26%.

Keywords: predicting, deep learning, neural network, urban trip

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Authors: Jun-ya Nagase, Toshiyuki Satoh, Norihiko Saga, Koichi Suzumori

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In recent years, Japanese society has been aging, engendering a labour shortage of young workers. Robots are therefore expected to perform tasks such as rehabilitation, nursing elderly people, and day-to-day work support for elderly people. The pneumatic balloon actuator is a rubber artificial muscle developed for use in a robot hand in such environments. This actuator has a long stroke, and a high power-to-weight ratio compared with the present pneumatic artificial muscle. Moreover, the dynamic characteristics of this actuator resemble those of human muscle. This study evaluated characteristics of force control of balloon actuator using a predictive functional control (PFC) system with disturbance observer. The predictive functional control is a model-based predictive control (MPC) scheme that predicts the future outputs of the actual plants over the prediction horizon and computes the control effort over the control horizon at every sampling instance. For this study, a 1-link finger system using a pneumatic balloon actuator is developed. Then experiments of PFC control with disturbance observer are performed. These experiments demonstrate the feasibility of its control of a pneumatic balloon actuator for a robot hand.

Keywords: disturbance observer, pneumatic balloon, predictive functional control, rubber artificial muscle

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Authors: H. Mahdavinezhad, M. Labbaf, H. R. Tavakoli

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In recent decades, the study and control of the destructive effects of explosive vibration in construction projects has received more attention, and several experimental equations in the field of vibration prediction as well as allowable vibration limit for various structures are presented. Researchers have developed a number of experimental equations to estimate the peak particle velocity (PPV), in which the experimental constants must be obtained at the site of the explosion by fitting the data from experimental explosions. In this study, the most important of these equations was evaluated for strong massive conglomerates around Dez Dam by collecting data on explosions, including 30 particle velocities, 27 displacements, 27 vibration frequencies and 27 acceleration of earth vibration at different distances; they were recorded in the form of two types of detonation systems, NUNEL and electric. Analysis showed that the data from the explosion had the best correlation with the cube root of the explosive, R2=0.8636, but overall the correlation coefficients are not much different. To estimate the vibration in this project, data regression was performed in the other formats, which resulted in the presentation of new equation with R2=0.904 correlation coefficient. Finally according to the importance of the studied structures in order to ensure maximum non damage to adjacent structures for each diagram, a range of application was defined so that for distances 0 to 70 meters from blast site, exponent n=0.33 and for distances more than 70 m, n =0.66 was suggested.

Keywords: blasting, blast-induced vibration, empirical equations, PPV, tunnel

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