Search results for: residual life prediction

Authors: Vahid Rashedi, Banafsheh Ebrahimi, Mahtab Sharif Mohseni, Mohammadali Hosseini

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Introduction: One of the metrics used to evaluate health status is life expectancy. This index alters as people age as a result of several events, illnesses, stress, and anxiety. One of the issues that might develop into a lethal phobia is death anxiety. This study looked at older persons in Tehran, Iran, to see if there was any correlation between life expectancy and fear of dying. Methods: Cluster random sampling was used to select 208 older persons (age 60) who had been sent to adult daycare facilities in Tehran for this correlational descriptive study. A demographic questionnaire, Temper's death anxiety scale, and Snyder's life expectancy scale were used to gather the data. Statistical Package for the Social Sciences softwear version 22 was used to conduct the data analysis. Results: The average age of the senior citizens was 66.60 (6.58) years. With a mean life expectancy of 24.94, it was discovered that the average death anxiety was 12.21. Additionally, Pearson's correlation coefficient demonstrated a bad correlation between fear of dying and life expectancy. Age, residential status, and death fear were the three primary predictors of a decline in life expectancy, according to multiple regression analysis. Conclusion: The findings suggest that there is a link between death fear and a lower life expectancy, which calls for the use of appropriate strategies to increase older individuals' life expectancies as well as the teaching of anxiety coping mechanisms.

Keywords: aged, frailty, death, anxiety, life

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Authors: Aneta Oblouková, Eva Vítková

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The article deals with the issue of water and sewerage charge rate prices in the Czech Republic. The research is specifically focused on the analysis of the development of the average prices of water and sewerage charge rate in the Czech Republic in the years 1994-2021 and on the validation of the chosen methodology relevant for the prediction of the development of the average prices of water and sewerage charge rate in the Czech Republic. The research is based on data collection. The data for this research was obtained from the Czech Statistical Office. The aim of the paper is to validate the relevance of the mathematical linear trend estimate technique for the calculation of the predicted average prices of water and sewerage charge rates. The real values of the average prices of water and sewerage charge rates in the Czech Republic in the years 1994-2018 were obtained from the Czech Statistical Office and were converted into a mathematical equation. The same type of real data was obtained from the Czech Statistical Office for the years 2019-2021. Prediction of the average prices of water and sewerage charge rates in the Czech Republic in the years 2019-2021 were also calculated using a chosen method -a linear trend estimation technique. The values obtained from the Czech Statistical Office and the values calculated using the chosen methodology were subsequently compared. The research result is a validation of the chosen mathematical technique to be a suitable technique for this research.

Keywords: Czech Republic, linear trend estimation, price prediction, water and sewerage charge rate

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Authors: Malo Pocheau-Lesteven, Olivier Le Maître

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Engineering and optimisation of complex systems is often achieved through multi-disciplinary analysis of the system, where each subsystem is modeled and interacts with other subsystems to model the complete system. The coherence of the output of the different sub-systems is achieved through the use of compatibility constraints, which enforce the coupling between the different subsystems. Due to the complexity of some sub-systems and the computational cost of evaluating their respective models, it is often necessary to build surrogate models of these subsystems to allow repeated evaluation these subsystems at a relatively low computational cost. In this paper, gaussian processes are used, as their probabilistic nature is leveraged to evaluate the likelihood of satisfying the compatibility constraints. This paper presents infilling strategies to build accurate surrogate models of the subsystems in areas where they are likely to meet the compatibility constraint. It is shown that these infilling strategies can reduce the computational cost of building surrogate models for a given level of accuracy. An application of these methods to velocity prediction programs used in offshore racing naval architecture further demonstrates these method's applicability in a real engineering context. Also, some examples of the application of uncertainty quantification to field of naval architecture are presented.

Keywords: infilling strategy, gaussian process, multi disciplinary analysis, velocity prediction program

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Authors: H. Hotait, X. Chiementin, L. Rasolofondraibe

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This paper presents a continuous work to detect the abnormal state in the rolling bearing by studying the vibration signature analysis and calculation of the remaining useful life. To achieve these aims, two methods; the first method is the classification to detect the degradation state by the AOM-OPTICS (Acousto-Optic Modulator) method. The second one is the prediction of the degradation state using least-squares support vector regression and then compared with the linear degradation model. An experimental investigation on ball-bearing was conducted to see the effectiveness of the used method by applying the acquired vibration signals. The proposed model for predicting the state of bearing gives us accurate results with the experimental and numerical data.

Keywords: bearings, automatization, optimization, prognosis, classification, defect detection

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Authors: Aragorn Joaquin Pineda Dela Cruz

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Road traffic congestion is continually deteriorating in Hong Kong. The largest contributing factor is the increase in vehicle fleet size, resulting in higher competition over the utilisation of road space. This study proposes a project that can process closed-circuit television images and videos to provide real-time traffic detection and prediction capabilities. Specifically, a deep-learning model involving computer vision techniques for video and image-based vehicle counting, then a separate model to detect and predict traffic congestion levels based on said data. State-of-the-art object detection models such as You Only Look Once and Faster Region-based Convolutional Neural Networks are tested and compared on closed-circuit television data from various major roads in Hong Kong. It is then used for training in long short-term memory networks to be able to predict traffic conditions in the near future, in an effort to provide more precise and quicker overviews of current and future traffic conditions relative to current solutions such as navigation apps.

Keywords: intelligent transportation system, vehicle detection, traffic analysis, deep learning, machine learning, computer vision, traffic prediction

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Authors: Ahmed M. Seyam, Rita Nemes

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In this study, the influence of elevated temperature and concrete age on the compressive strength of concrete produced by normal quartz aggregate, expanded clay, expanded glass, crushed andesite and crushed clay bricks aggregates were investigated. For this purpose, six different mixtures were prepared by 100% replacement of the coarse aggregate. The specimens were cured in water for seven days, then kept in the laboratory for 120 days and 240 days. The concrete specimens were heated in an electric furnace up to 200, 400, 600, 800, and 1000 °C and kept at these temperatures for two hours heating, then for 24 hours cooling. The residual compressive strength of the specimens was measured. The results showed that, the elevated temperature induces a significant decrease in a compressive strength in both normal weight and lightweight aggregate concrete, by comparing the behavior of different mixes, in all cases, the strength of the specimens containing crushed andesite aggregates showed a better performance for compressive strength after exposure to elevated temperatures over 800 °C, while the specimens containing expanded glass showing the least residual strength after subjected to elevated temperature; moreover the age of the concrete in all mixes has also been an effective factor, the behavior of the concrete strength loss by increasing heating temperature was not changed but the strength results showing the better performance and higher compressive strength in both ambient and elevated temperature.

Keywords: elevated temperature, concrete age, compressive strength, expanded clay, expanded glass, crushed andesite, crushed clay bricks

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Authors: Lavinia B. Dulla

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The exploration study of the fuzzy regression approach attempts to present that fuzzy regression can be used as a possible alternative to classical regression. It likewise seeks to assess the differences and characteristics of simple linear regression and fuzzy regression using the width of prediction interval, mean absolute deviation, and variance of residuals. Based on the simple linear regression model, the fuzzy regression approach is worth considering as an alternative to simple linear regression when the sample size is between 10 and 20. As the sample size increases, the fuzzy regression approach is not applicable to use since the assumption regarding large sample size is already operating within the framework of simple linear regression. Nonetheless, it can be suggested for a practical alternative when decisions often have to be made on the basis of small data.

Keywords: fuzzy regression approach, minimum fuzziness criterion, interval regression, prediction interval

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Authors: Venkatesh Kumar, Kasun Hewage, Rehan Sadiq

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Residential buildings consume significant amounts of energy and produce a large amount of emissions and waste. However, there is a substantial potential for energy savings in this sector which needs to be evaluated over the life cycle of residential buildings. Life Cycle Assessment (LCA) methodology has been employed to study the primary energy uses and associated environmental impacts of different phases (i.e., product, construction, use, end of life, and beyond building life) for residential buildings. Four different alternatives of residential buildings in Vancouver (BC, Canada) with a 50-year lifespan have been evaluated, including High Rise Apartment (HRA), Low Rise Apartment (LRA), Single family Attached House (SAH), and Single family Detached House (SDH). Life cycle performance of the buildings is evaluated for embodied energy, embodied environmental impacts, operational energy, operational environmental impacts, total life-cycle energy, and total life cycle environmental impacts. Estimation of operational energy and LCA are performed using DesignBuilder software and Athena Impact estimator software respectively. The study results revealed that over the life span of the buildings, the relationship between the energy use and the environmental impacts are identical. LRA is found to be the best alternative in terms of embodied energy use and embodied environmental impacts; while, HRA showed the best life-cycle performance in terms of minimum energy use and environmental impacts. Sensitivity analysis has also been carried out to study the influence of building service lifespan over 50, 75, and 100 years on the relative significance of embodied energy and total life cycle energy. The life-cycle energy requirements for SDH is found to be a significant component among the four types of residential buildings. The overall disclose that the primary operations of these buildings accounts for 90% of the total life cycle energy which far outweighs minor differences in embodied effects between the buildings.

Keywords: building simulation, environmental impacts, life cycle assessment, life cycle energy analysis, residential buildings

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Authors: M. Hadi Kusuma, Nandy Putra, Anhar Riza Antariksawan, Ficky Augusta Imawan

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Heat pipe is considered to be applied as a passive system to remove residual heat that generated from reactor core when incident occur or from spent fuel storage pool. The objectives are to characterized the heat transfer phenomena, performance of heat pipe, and as a model for large heat pipe will be applied as passive cooling system on nuclear spent fuel pool storage. In this experimental wickless heat pipe or two-phase closed thermosyphon (TPCT) is used. Variation of heat flux are 611.24 Watt/m² - 3291.29 Watt/m². Variation of filling ratio are 45 - 70%. Variation of initial pressure are -62 to -74 cm Hg. Demineralized water is used as working fluid in the TPCT. The results showed that increasing of heat load leads to an increase of evaporation of the working fluid. The optimum filling ratio obtained for 60% of TPCT evaporator volume, and initial pressure variation gave different TPCT wall temperature characteristic. TPCT showed best performance with 60% filling ratio and can be consider to be applied as passive residual heat removal system or passive cooling system on spent fuel storage pool.

Keywords: two-phase closed term syphon, heat pipe, passive cooling, spent fuel storage pool

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Authors: Mustafa Jahanara

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The present research was carried out in order to investigate the relationship between meaning in life and hope with depression, anxiety and stress in Afghan Refugees in Alborz province in Iran. In this research, method of study is a descriptive correlation type. One hundred and fifty-eight Afghan refugees (64 male, 94 female) participated in this study. All participants completed the Meaning in Life Questionnaires (MLQ), Hope Scale (HS), and The Depression Anxiety Stress Scales (DASS-21). The results revealed that Meaning in Life was positively associated with hope, presence of meaning, search of meaning, and negatively associated with depression and anxiety. Hope was positively associated with presence of meaning and search of meaning, and hope was negatively associated with depression, anxiety, and stress. Depression, anxiety, and stress were positively correlated with each other. Meaning in life and hope could influence on mental health.

Keywords: Afghan refugees, meaning of life, hope, depression, anxiety and stress

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Authors: Amir Hossein Hejazi, Nima Amjady

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In recent years, due to environmental issues traditional energy sources had been replaced by renewable ones. Wind energy as the fastest growing renewable energy shares a considerable percent of energy in power electricity markets. With this fast growth of wind energy worldwide, owners and operators of wind farms, transmission system operators, and energy traders need reliable and secure forecasts of wind energy production. In this paper, a new forecasting strategy is proposed for short-term wind power prediction based on Echo State Networks (ESN). The forecast engine utilizes state-of-the-art training process including dynamical reservoir with high capability to learn complex dynamics of wind power or wind vector signals. The study becomes more interesting by incorporating prediction of wind direction into forecast strategy. The Big Bang-Big Crunch (BB-BC) evolutionary optimization algorithm is adopted for adjusting free parameters of ESN-based forecaster. The proposed method is tested by real-world hourly data to show the efficiency of the forecasting engine for prediction of both wind vector and wind power output of aggregated wind power production.

Keywords: wind power forecasting, echo state network, big bang-big crunch, evolutionary optimization algorithm

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Authors: Amiel Satvedi, Richard Pyne

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Injuries in professional sports occur on a regular basis. Some may be minor, while others can cause huge impact on a player's career and earning potential. In soccer, there is a high risk of players picking up injuries during game time. This research work seeks to help soccer players reduce the risk of getting injured by predicting the likelihood of injury while playing in the near future and then providing recommendations for intervention. The injury prediction tool will use a soccer player's number of minutes played on the field, number of appearances, distance covered and performance data for the current and previous seasons as variables to conduct statistical analysis and provide injury predictive results using a machine learning linear regression model.

Keywords: injury predictor, soccer injury prevention, machine learning in soccer, big data in soccer

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Authors: Shaymaa Kennedy, Sam Clark, Paul Shaply

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With the upcoming construction of high-speed rail HS2 in the UK, a number of issues surrounding the construction technology and track design need to be answered. In this paper performance of subsoil subjected to dynamic loads were studied. The material of study is Mudrock backfill, a weak prevalent rock which response under indicative loading of high-speed rail line is unknown. This paper aims to investigate the use of different track types and the influence they will have on the underlying soil, in order to evaluate the behaviour of it. Ballstless track is a well-established concept in Europe, and the investigation the benefit of the form of construction due to its known savings in maintenance costs. Physical test using a triaxial cyclic loading machine was conducted to assess the expected mechanical behaviour of mudrock under a range of dynamic loads which could be generated beneath different track constructions. Some further parameters are required to frame the problem including determining the stress change with depth and cyclic response are vital to determine the residual plastic strain which is a major concern. In addition, Stress level is discussed in this paper, which are applied to recreate conditions of soil in the laboratory. Results indicate that stress levels are highly influential on the performance of soil at shallower depth and become insignificant with increasing depth.

Keywords: stress level, dynamic load, residual plastic strain, high speed railway

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Authors: Suang K. Lau, Ismail Goolam, Rafid Al-Asady

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Background: The majority of patients with hepatocellular carcinoma (HCC) are not suitable for curative treatment, in the form of surgical resection or transplantation, due to tumour extent and underlying liver dysfunction. In these non-resectable cases, a variety of non-surgical therapies are available, including microwave ablation (MWA), which has shown increasing popularity due to its low morbidity, low reported complication rate, and the ability to perform multiple ablations simultaneously. Objective: The aim of this study was to evaluate the validity of MWA as a viable treatment option in the management of HCC and hepatic metastatic disease, by assessing its efficacy and complication rate at a tertiary hospital situated in Westmead (Australia). Methods: A retrospective observational study was performed evaluating patients that underwent MWA between 1/1/2017–31/12/2018 at Westmead Hospital, NSW, Australia. Outcome measures, including residual disease, recurrence rates, as well as major and minor complication rates, were retrospectively analysed over a 12-months period following MWA treatment. Excluded patients included those whose lesions were treated on the basis of residual or recurrent disease from previous treatment, which occurred prior to the study window (11 patients) and those who were lost to follow up (2 patients). Results: Following treatment of 106 new hepatic lesions, the complete response rate (CR) was 86% (91/106) at 12 months follow up. 10 patients had the residual disease at post-treatment follow up imaging, corresponding to an incomplete response (ICR) rate of 9.4% (10/106). The local recurrence rate (LRR) was 4.6% (5/106) with follow-up period up to 12 months. The minor complication rate was 9.4% (10/106) including asymptomatic pneumothorax (n=2), asymptomatic pleural effusions (n=2), right lower lobe pneumonia (n=3), pain requiring admission (n=1), hypotension (n=1), cellulitis (n=1) and intraparenchymal hematoma (n=1). There was 1 major complication reported, with pleuro-peritoneal fistula causing recurrent large pleural effusion necessitating repeated thoracocentesis (n=1). There was no statistically significant association between tumour size, location or ablation factors, and risk of recurrence or residual disease. A subset analysis identified 6 segment VIII lesions, which were treated via a trans-pleural approach. This cohort demonstrated an overall complication rate of 33% (2/6), including 1 minor complication of asymptomatic pneumothorax and 1 major complication of pleuro-peritoneal fistula. Conclusions: Microwave ablation therapy is an effective and safe treatment option in cases of non-resectable hepatocellular carcinoma and liver metastases, with good local tumour control and low complication rates. A trans-pleural approach for high segment VIII lesions is associated with a higher complication rate and warrants greater caution.

Keywords: hepatocellular carcinoma, liver metastases, microwave ablation, trans-pleural approach

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Authors: Sun-Young Jang, Sung-Ah Kim, Dongyoun Shin

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The Intelligent transportation system is essential to build smarter cities. Machine learning based transportation prediction could be highly promising approach by delivering invisible aspect visible. In this context, this research aims to make a prototype model that predicts transportation network by using big data and machine learning technology. In detail, among urban transportation systems this research chooses bus system.  The research problem that existing headway model cannot response dynamic transportation conditions. Thus, bus delay problem is often occurred. To overcome this problem, a prediction model is presented to fine patterns of bus delay by using a machine learning implementing the following data sets; traffics, weathers, and bus statues. This research presents a flexible headway model to predict bus delay and analyze the result. The prototyping model is composed by real-time data of buses. The data are gathered through public data portals and real time Application Program Interface (API) by the government. These data are fundamental resources to organize interval pattern models of bus operations as traffic environment factors (road speeds, station conditions, weathers, and bus information of operating in real-time). The prototyping model is designed by the machine learning tool (RapidMiner Studio) and conducted tests for bus delays prediction. This research presents experiments to increase prediction accuracy for bus headway by analyzing the urban big data. The big data analysis is important to predict the future and to find correlations by processing huge amount of data. Therefore, based on the analysis method, this research represents an effective use of the machine learning and urban big data to understand urban dynamics.

Keywords: big data, machine learning, smart city, social cost, transportation network

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Authors: Mochammad Indra Permana, Andhiani Sharfina Arnellya, Dika Pramita Destiani, Budhi Prihartanto

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Cardiovascular disease is the cause of the highest mortality rates in the world. The number of cardiovascular disease patients is increasing every year. Data obtained from World Health Organization (WHO) that 17,5 million people died from this disease. The condition of cardiovascular diseases such as atrial fibrillation, myocardial infarction, venous thromboembolism, and several other conditions need anticoagulant therapy. Results of the anticoagulant therapy are measured not only by the effectiveness of International Normalized Ratio (INR) value but also by the quality of life of the patients. The purpose of this study was to determine the quality of life of patients on oral anticoagulant therapy in outpatient cardiac department Dr. Hasan Sadikin central general hospital, Bandung, Indonesia. This is a cross-sectional study with collecting data from the quality of life questionnaire and medical record of the patients. The results of this study showed that 28 patients (46,7%) had a good quality of life, 30 patients (50%) had a moderate quality of life, and 2 patients (3,3%) had a poor quality of life with no significant differences in quality of life based on age, gender, diagnosis, and duration of drug use.

Keywords: anticoagulant, cardiovascular diseases, INR, quality of life

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Authors: Hui Zhang, Sherif Beskhyroun

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Structural Health monitoring systems (SHM) are commonly used to identify and assess structural damage. In terms of damage detection, SHM needs to periodically collect data from sensors placed in the structure as damage-sensitive features. This includes abnormal changes caused by the strain field and abnormal symptoms of the structure, such as damage and deterioration. Currently, deploying sensors on a large scale in a building structure is a challenge. In this study, a highly stretchable strain sensors are used in this study to collect data sets of strain generated on the surface of full-size reinforced concrete (RC) frames under extreme cyclic load application. This sensing sheet can be switched freely between the test bending strain and the axial strain to achieve two different configurations. On this basis, the deep neural network prediction model of the frame beam and frame column is established. The training results show that the method can accurately predict the strain value and has good generalization ability. The two deep neural network prediction models will also be deployed in the SHM system in the future as part of the intelligent strain sensor system.

Keywords: strain sensing sheets, deep neural networks, strain measurement, SHM system, RC frames

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Authors: Van Huong Mai, Kanokwan Nithiratphat, Adool Booncham

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The Movement of Contemporary Life consisted of two purposes, which were to study the movement and development of the modern life and to create the visual arts, which were paintings expressed via the form of apartment buildings was used from mixed media (digital printing and acrylic painting on canvas) which conveyed the rapid pace of modern life leading to diverse movements in viewer’s feeling. The operation of this creation was collected field data, documentary data, and influence from creative work. The data analysis was analyzed in order to theme, form, technique, and process to satisfy of concept and special character of the pieces.

Keywords: movement, contemporary life, visual art, acrylic painting, digital art, urban space

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Authors: Murad Ali, Shoji Arai, Mohamed Khedr, Mukhtar Nasher, Shawki Nasr

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The Wadi Bayhan area of southeastern Yemen is about 60 km NW of Al-Bayda city in the Al-Bayda uplift terrane at the southeast margin of the Arabian-Nubian Shield. Intrusion of alkali gabbro into carbonate rocks apparently produced an 8m to 10 m thick reaction zone at the contact. This had been identified as nepheline pyroxenite. We have observed this to be mineralogically zoned with calc-silicate assemblages (e.g. pyroxene, calcite, spinel, garnet and melilite). The presence of melilite implies a skarn. The sinuous embayed pyroxenite-skarn contact, the presence of skarn minerals in pyroxenite, and textural evidence for growth of calc-silicate skarn by replacement of both carbonate rocks and solid pyroxenite indicate that reaction involved assimilation of carbonate wall rock by magma and loss of Al and Si to the skarn. Textural relationships between minerals provide evidence for a metasomatic development of the skarn at the expense of the pyroxenite. This process, related to the circulation of fluids equilibrated with carbonates, is responsible for those pyroxenite-spinel (± calcite) skarns. The uneven modal distribution of euhedral pyroxenite and enveloping nepheline in pyroxenite, the restricted occurrence of alkali gabbro as dikes in pyroxenite and skarn and the leucocratic matrix of pyroxenite suggest that pyroxenite represents an accumulation of titanaugite cemented by an alkali-rich residual magma and that alkali gabbro represents a part of the residual contaminated magma that was squeezed out of the pyroxene crystal mush. Carbonate assimilation is modeled by reaction of calcite and magmatic plagioclase, which results in resorption of plagioclase, growth of pyroxene enriched in Ca, Fe, Ti, and Al, and solution of nepheline in residual contaminated magma. The composition of nepheline pyroxenite evolved by addition of Ca from dissolved carbonate rocks, loss of Al and Si to skarn, and local segregation of solid pyroxene and alkali gabbro magma. The predominance of pyroxenite among contaminated rocks and their restriction to a large zone along the intrusive contact provide little evidence for the genesis of a significant volume of alkaline magmatic surroundings by carbonate assimilation.

Keywords: Yemen, Wadi Bayhan, skarn, pyroxenite, carbonatite, metasomatic

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Authors: Youn-Hwan Kim, Jae-Won Moon, Hae-Joong Kim

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This paper introduces the study on the method of accelerated life test to electrical rotating system. In recent years, as well as efficiency for motors and generators, there is a growing need for research on the life expectancy. It is considered impossible to calculate the acceleration coefficient by increasing the rotational load or temperature load as the acceleration stress in the motor system because the temperature of the copper exceeds the wire thermal class rating. In this paper, the accelerated life test methods of the electrical rotating system are classified according to the application. This paper describes the development of the test procedure for the highly accelerated life test (HALT) of the 100kW permanent magnet synchronous motor (PMSM) of electric vehicle. Finally, it explains how to select acceleration load for vibration, temperature, bearing load, etc. for accelerated life test.

Keywords: acceleration coefficient, electric vehicle motor, HALT, life expectancy, vibration

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Authors: Pedro Paulo Galindo Francisco, Eli Dhadad Junior

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This study addresses the Meese-Rogoff Puzzle by introducing the latest machine learning techniques as alternatives for predicting the exchange rates. Using RMSE as a comparison metric, Meese and Rogoff discovered that economic models are unable to outperform the random walk model as short-term exchange rate predictors. Decades after this study, no statistical prediction technique has proven effective in overcoming this obstacle; although there were positive results, they did not apply to all currencies and defined periods. Recent advancements in artificial intelligence technologies have paved the way for a new approach to exchange rate prediction. Leveraging this technology, we applied five machine learning techniques to attempt to overcome the Meese-Rogoff puzzle. We considered daily data for the real, yen, British pound, euro, and Chinese yuan against the US dollar over a time horizon from 2010 to 2023. Our results showed that none of the presented techniques were able to produce an RMSE lower than the Random Walk model. However, the performance of some models, particularly LSTM and N-BEATS were able to outperform the ARIMA model. The results also suggest that machine learning models have untapped potential and could represent an effective long-term possibility for overcoming the Meese-Rogoff puzzle.

Keywords: exchage rate, prediction, machine learning, deep learning

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Authors: Takesure Mahohoma

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Community life is an epitome of the African axiom 'I am because we are, since we are therefore I am.' This culminates in the Ubuntu philosophy which is summarized in the Zulu words, 'umuntu ngumuntu ngabantu' (A person is a person through other people). This relationship gives honour to all people. This is the gist of the paper. This paper seeks to demonstrate the impact of community life in promoting unity from an African perspective. Using the proto-community in Acts 2:42, it is argued that community life is a solution to many social problems that divide African society today. The aim is to encourage all Africans and other people to cultivate a sense of belonging and valuing community life in the light of Acts 2:42. Hence we shall trace this theme from Old Testament, New Testament, and Christian history. The other section touches on the essence of community life and obstacles that hinder it. We shall offer spiritual suggestions and an integrative reflection. The nature of the paper is theology in general but spiritual in particular. As a spiritual paper, it is guided by the foundational approach. Thus, it employs the dialogical and integrative reflection method. The expected result is that freedom from all the miseries experienced is brought by living a community life. This is a life that gives greater assurance of enough food, education, health, peace, employment, and increased responsibility that values human dignity. Thus people are neighbours to each other. There is no stranger among them. The basic presumption is that there can be no development in any society without community life.

Keywords: community, seged, koinonia, neighbor

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Authors: Danilo López, Johana Hernández, Edwin Rivas

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The use of the Multilayer Perceptron Neural Networks (MLPNN) technique is presented to estimate the future state of use of a licensed channel by primary users (PUs); this will be useful at the spectral decision stage in cognitive radio networks (CRN) to determine approximately in which time instants of future may secondary users (SUs) opportunistically use the spectral bandwidth to send data through the primary wireless network. To validate the results, sequences of occupancy data of channel were generated by simulation. The results show that the prediction percentage is greater than 60% in some of the tests carried out.

Keywords: cognitive radio, neural network, prediction, primary user

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Authors: Eunji Choi, Borang Park, Youngjae Choi, Jinwoo Moon

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In this study, a predictive model for estimating the metabolism (MET) of human body was developed for the optimal control of indoor thermal environment. Human body images for indoor activities and human body joint coordinated values were collected as data sets, which are used in predictive model. A deep learning algorithm was used in an initial model, and its number of hidden layers and hidden neurons were optimized. Lastly, the model prediction performance was analyzed after the model being trained through collected data. In conclusion, the possibility of MET prediction was confirmed, and the direction of the future study was proposed as developing various data and the predictive model.

Keywords: deep learning, indoor quality, metabolism, predictive model

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Authors: Maaza Saeed

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The current study examined the mediating roles of emotional intelligence (EI) and resilience in the relationship between mindfulness and life satisfaction among Chinese and Pakistani samples. 450 participants completed the mindful attention awareness scale, resilience scale, wong law emotional intelligence scale, and satisfaction with life scale from both cultures. Path analysis revealed that both emotional intelligence (EI) and resilience fully mediated the relationship between mindfulness and life satisfaction among participants of both cultures. Besides, a multi-group evaluation suggests that Chinese participants with significant EI scores are more likely to sustain greater life satisfaction than their Pakistani counterparts. Insinuation for prospective researches and current study pitfalls are discussed.

Keywords: mindfulness, life satisfaction, resilience, emotional intelligence

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Authors: Kuo-Wei Liao, Jessica Gitomarsono

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Bridge reliability against natural hazards such as floods or earthquakes is an interdisciplinary problem that involves a wide range of knowledge. Moreover, due to the global climate change, engineers have to design a structure against the multi-hazard threats. Currently, few of the practical design guideline has included such concept. The bridge foundation in Taiwan often does not have a uniform width. However, few of the researches have focused on safety evaluation of a bridge with a complex pier. Investigation of the scouring depth under such situation is very important. Thus, this study first focuses on investigating and improving the scour prediction formula for a bridge with complicated foundation via experiments and artificial intelligence. Secondly, a probabilistic design procedure is proposed using the established prediction formula for practical engineers under the multi-hazard attacks.

Keywords: bridge, reliability, multi-hazards, scour

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Authors: Youn-Hwan Kim, Jae-Won Moon, Hae-Joong Kim

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This paper introduces the results of the study on the development of accelerated life test methods for the motor used in machine tools. In recent years, as well as efficiency for motors, there is a growing need for research on life expectancy of motors. It is considered impossible to calculate the acceleration coefficient by increasing the rotational load or temperature load as the acceleration stress in the motor system because the temperature of the copper exceeds the wire thermal class rating. This paper describes the equipment development procedure for the highly accelerated life test (HALT) of the 12kW three-phase squirrel-cage induction motors (SCIMs). After the test, the lifetime analysis was carried out, and it is compared with the life expectancy by finite element method (FEM) and bearing theory.

Keywords: acceleration coefficient, bearing, HALT, life expectancy, motor

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Authors: Jan Stodt, Christoph Reich

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The usage of machine learning models for prediction is growing rapidly and proof that the intended requirements are met is essential. Audits are a proven method to determine whether requirements or guidelines are met. However, machine learning models have intrinsic characteristics, such as the quality of training data, that make it difficult to demonstrate the required behavior and make audits more challenging. This paper describes an ML audit framework that evaluates and reviews the risks of machine learning applications, the quality of the training data, and the machine learning model. We evaluate and demonstrate the functionality of the proposed framework by auditing an steel plate fault prediction model.

Keywords: audit, machine learning, assessment, metrics

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Authors: Peifang Hsieh

Abstract:

The prevention of child abuse has aroused serious concerns in Taiwan because of the disparity between the increasing amount of reported child abuse cases that doubled over the past decade and the scarcity of social workers. New Taipei city, with the most population in Taiwan and over 70% of its 4 million citizens are migrant families in which the needs of children can be easily neglected due to insufficient support from relatives and communities, sees urgency for a social support system, by preemptively identifying and outreaching high-risk families of child abuse, so as to offer timely assistance and preventive measure to safeguard the welfare of the children. Big data analysis is the inspiration. As it was clear that high-risk families of child abuse have certain characteristics in common, New Taipei city decides to consolidate detailed background information data from departments of social affairs, education, labor, and health (for example considering status of parents’ employment, health, and if they are imprisoned, fugitives or under substance abuse), to cross-reference for accurate and prompt identification of the high-risk families in need. 'The Service Center for High-Risk Families' (SCHF) was established to integrate data cross-departmentally. By utilizing the machine learning 'random forest method' to build a risk prediction model which can early detect families that may very likely to have child abuse occurrence, the SCHF marks high-risk families red, yellow, or green to indicate the urgency for intervention, so as to those families concerned can be provided timely services. The accuracy and recall rates of the above model were 80% and 65%. This prediction model can not only improve the child abuse prevention process by helping social workers differentiate the risk level of newly reported cases, which may further reduce their major workload significantly but also can be referenced for future policy-making.

Keywords: child abuse, high-risk families, big data analysis, risk prediction model

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Authors: Siti Nurhidayah Naqiah Abdull Rani, Herlina Abdul Rahim

Abstract:

Near infrared (NIR) spectroscopy has always been of great interest in the food and agriculture industries. The development of predictive models has facilitated the estimation process in recent years. In this research, 176 crude palm oil (CPO) samples acquired from Felda Johor Bulker Sdn Bhd were studied. A FOSS NIRSystem was used to tak e absorbance measurements from the sample. The wavelength range for the spectral measurement is taken at 1600nm to 1900nm. Partial Least Square Regression (PLSR) prediction model with 50 optimal number of principal components was implemented to study the relationship between the measured Free Fatty Acid (FFA) values and the measured spectral absorption. PLSR showed predictive ability of FFA values with correlative coefficient (R) of 0.9808 for the training set and 0.9684 for the testing set.

Keywords: palm oil, fatty acid, NIRS, PLSR

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