Search results for: food frequency and biomedical data

Authors: Samar Swaid

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Today, corporates are moving toward the adoption of Design-Thinking techniques to develop products and services, putting their consumer as the heart of the development process. One of the leading companies in Design-Thinking, IDEO (Innovation, Design, Engineering Organization), defines Design-Thinking as an approach to problem-solving that relies on a set of multi-layered skills, processes, and mindsets that help people generate novel solutions to problems. Design thinking may result in new ideas, narratives, objects or systems. It is about redesigning systems, organizations, infrastructures, processes, and solutions in an innovative fashion based on the users' feedback. Tim Brown, president and CEO of IDEO, sees design thinking as a human-centered approach that draws from the designer's toolkit to integrate people's needs, innovative technologies, and business requirements. The application of design thinking has been witnessed to be the road to developing innovative applications, interactive systems, scientific software, healthcare application, and even to utilizing Design-Thinking to re-think business operations, as in the case of Airbnb. Recently, there has been a movement to apply design thinking to machine learning and artificial intelligence to ensure creating the "wow" effect on consumers. The Association of Computing Machinery task force on Data Science program states that" Data scientists should be able to implement and understand algorithms for data collection and analysis. They should understand the time and space considerations of algorithms. They should follow good design principles developing software, understanding the importance of those principles for testability and maintainability" However, this definition hides the user behind the machine who works on data preparation, algorithm selection and model interpretation. Thus, the Data Science program includes design thinking to ensure meeting the user demands, generating more usable machine learning tools, and developing ways of framing computational thinking. Here, describe the fundamentals of Design-Thinking and teaching modules for data science programs.

Keywords: data science, design thinking, AI, currculum, transformation

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Authors: Henry Hexmoor, Kailash Yelasani

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We present vehicular platooning as a special case of crowd-sensing framework where sharing sensory information among a crowd is used for their collective benefit. After offering an abstract policy that governs processes involving a vehicular platoon, we review several common scenarios and components surrounding vehicular platooning. We then present a simulated prototype that illustrates efficiency of road usage and vehicle travel time derived from platooning. We have argued that one of the paramount benefits of platooning that is overlooked elsewhere, is the substantial computational savings (i.e., economizing benefits) in acquisition and processing of sensory data among vehicles sharing the road. The most capable vehicle can share data gathered from its sensors with nearby vehicles grouped into a platoon.

Keywords: cloud network, collaboration, internet of things, social network

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Authors: Zahid Ahmad, Nosheen Imran, Nauman Ali, Farah Amir

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The objective of the study is to forecast the US Dollar and Pak Rupee exchange rate by using time series models. For this purpose, daily exchange rates of US and Pakistan for the period of January 01, 2007 - June 2, 2017, are employed. The data set is divided into in sample and out of sample data set where in-sample data are used to estimate as well as forecast the models, whereas out-of-sample data set is exercised to forecast the exchange rate. The ADF test and PP test are used to make the time series stationary. To forecast the exchange rate ARIMA model and GARCH model are applied. Among the different Autoregressive Integrated Moving Average (ARIMA) models best model is selected on the basis of selection criteria. Due to the volatility clustering and ARCH effect the GARCH (1, 1) is also applied. Results of analysis showed that ARIMA (0, 1, 1 ) and GARCH (1, 1) are the most suitable models to forecast the future exchange rate. Further the GARCH (1,1) model provided the volatility with non-constant conditional variance in the exchange rate with good forecasting performance. This study is very useful for researchers, policymakers, and businesses for making decisions through accurate and timely forecasting of the exchange rate and helps them in devising their policies.

Keywords: exchange rate, ARIMA, GARCH, PAK/USD

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Authors: S. Neelima, P. S. Subramanyam

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The major tool for distribution planning is load forecasting, which is the anticipation of the load in advance. Artificial neural networks have found wide applications in load forecasting to obtain an efficient strategy for planning and management. In this paper, the application of neural networks to study the design of short term load forecasting (STLF) Systems was explored. Our work presents a pragmatic methodology for short term load forecasting (STLF) using proposed two-stage model of wavelet transform (WT) and artificial neural network (ANN). It is a two-stage prediction system which involves wavelet decomposition of input data at the first stage and the decomposed data with another input is trained using a separate neural network to forecast the load. The forecasted load is obtained by reconstruction of the decomposed data. The hybrid model has been trained and validated using load data from Telangana State Electricity Board.

Keywords: electrical distribution systems, wavelet transform (WT), short term load forecasting (STLF), artificial neural network (ANN)

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Authors: Mina Jafari, Kobra Hamraee, Saied Hossein Hosseini

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The prediction of breast cancer disease is one of the challenges in medicine. In this paper we collected 528 records of women’s information who live in Kabul including demographic, life style, diet and pregnancy data. There are many classification algorithm in breast cancer prediction and tried to find the best model with most accurate result and lowest error rate. We evaluated some other common supervised algorithms in data mining to find the best model in prediction of breast cancer disease among afghan women living in Kabul regarding to momography result as target variable. For evaluating these algorithms we used Cross Validation which is an assured method for measuring the performance of models. After comparing error rate and accuracy of three models: Decision Tree, Naive Bays and Rule Induction, Decision Tree with accuracy of 94.06% and error rate of %15 is found the best model to predicting breast cancer disease based on the health care records.

Keywords: decision tree, breast cancer, probability, data mining

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Authors: Preeti Kumari, Ridhi Kapoor

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 In any communication, security is the most important issue in today’s world. In this paper, steganography is the process of hiding the important data into other data, such as text, audio, video, and image. The interest in this topic is to provide availability, confidentiality, integrity, and authenticity of data. The steganographic technique that embeds hides content with unremarkable cover media so as not to provoke eavesdropper’s suspicion or third party and hackers. In which many applications of compression, encryption, decryption, and embedding methods are used for digital image steganography. Due to compression, the nose produces in the image. To sustain noise in the image, the LSB insertion technique is used. The performance of the proposed embedding system with respect to providing security to secret message and robustness is discussed. We also demonstrate the maximum steganography capacity and visual distortion.

Keywords: steganography, LSB, encoding, information hiding, color image

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Authors: Ali Fuad Ashour

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Introduction: Multiple sclerosis (MS) is a chronic, progressive and degenerative disease that affects the central nervous system (CNS). MS has been described to result in the debilitating symptom of the disease. It is reported to have a negative impact on the patient’s mental activities, brings a lower quality of life, leads to unemployment, causes distress and psychological disorders, generates low levels of motivation and self-esteem, and result in disability and neurological impairment. The aim of this study was to compare the effects of MS on patients from Britain and Kuwait. Methodology: A questionnaire was distributed to 200 individuals with MS (100 Kuwaiti and 100 British). The questionnaire consists of three parts; 1. General demographics, 2. Disease-specific data (symptoms, severity levels, relapse frequency, and support system), and 3. Attitudes towards physical exercise. Results: A response rate of 62% from the British sample and 50% from the Kuwaiti sample was achieved. 84% of the sample (n=52) were 41 years old or over. The duration of the disease was less than 10 years in 43.4% of British and 68% of Kuwaiti respondents. The majority of British respondents (56.5%) reported the disease severity to be moderate, while the majority of Kuwaitis was mild (72%). The annual relapse rates in Kuwait were relatively low, with 82% of the Kuwaiti sample had one relapse per year, compared to the 64.5% of British. The most common symptoms reported by British respondents were balance (75.8%), fatigue (74.2%), and weakness (71%), and by Kuwaiti respondents were fatigue (86%), balance (76%), and weakness (66%). The help and support for MS were by far more diverse for the British than Kuwaiti respondents. Discussion: The results unveiled marked differences between two groups of British and Kuwaiti MS patients in terms of patients’ age and disease duration, and severity. The overwhelming majority of Kuwaiti patients are young individuals who have been with the disease for a relatively short period of time, and their MS in most cases was mild. On the other hand, British patients were relatively older, many have been with the disease for a long period of time, and their average MS condition was more serious than that of their Kuwaiti counterparts. The main support in Kuwait comes from the neurologist, who primarily prescribe medications and advise patients to try to be active. The Kuwaiti respondents thought that lack of encouragement was the main reason for them not to engage in social activities.

Keywords: multiple sclerosis, Kuwait, exercise, demographic

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Authors: Awatef Hicheur Cairns, Billel Gueni, Hind Hafdi, Christian Joubert, Nasser Khelifa

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Given the ever changing needs of the job markets, education and training centers are increasingly held accountable for student success. Therefore, education and training centers have to focus on ways to streamline their offers and educational processes in order to achieve the highest level of quality in curriculum contents and managerial decisions. Educational process mining is an emerging field in the educational data mining (EDM) discipline, concerned with developing methods to discover, analyze and provide a visual representation of complete educational processes. In this paper, we present our distributed computation platform which allows different education centers and institutions to load their data and access to advanced data mining and process mining services. To achieve this, we present also a comparative study of the different clustering techniques developed in the context of process mining to partition efficiently educational traces. Our goal is to find the best strategy for distributing heavy analysis computations on many processing nodes of our platform.

Keywords: educational process mining, distributed process mining, clustering, distributed platform, educational data mining, ProM

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Authors: Meenakshi

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Background: The complications during pregnancy are the major cause of morbidity and deaths among women in the reproductive age group. Childbearing is the most important phase in women’s lives that occur mainly in the adolescent and adult years. Maternal health, thus is an important issue as this as this is important phase is also productive time for women as they strive fulfill their capabilities as an individual, mothers, family members and also as a citizen. The objective of the study is to document the coverage of ANC and its determinants among domestic workers. Method: A survey of 300 domestic workers were carried in Delhi. Only respondents in the age group (15-49) and whose recent birth was of 5 years preceding the survey were included. Socio-demographic data and information on maternal health was collected from these respondents Information on ANC was collected from total 300 respondents. Standard of living index were composed based on households assists and similarly autonomy index was computed based on women decision making power in the households taking certain key variables. Cross tabulations were performed to obtain frequency and percentages. Potential socio-economic determinants of utilization of ANC among domestic workers were examined using binary logistic regressions. Results: Out of 300 domestic workers survey, only 70.7 per cent per cent received ANC. Domestic workers who married at age 18 years and above are 4 times more likely to utilize antenatal services during their last birth (***p< 0.01). Comparison to domestic workers with number of living children two or less, domestic workers with number of living children more than two are less likely to utilize antenatal care services (**p< 0.05). Domestic workers belonging to Other Backward Castes are more likely to utilize antenatal care services than domestic workers belonging to scheduled tribes ((**p< 0.05). Conclusion: The level of utilization of maternal health services are less among domestic workers is less, as they spend most of their time at the employers household. Though demonstration effect do have impact on their life styles but utilization of maternal health services is poor. Strategies and action are needed to improve the utilization of maternal health services among this section of workers as they are vulnerable because of no proper labour legislations.

Keywords: antenatal care, domestic workers, health services, maternal health, women’s health

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Authors: A. Alhelfi, B. Sunden

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Cavitation in cryogenic liquids is widely present in contemporary science. In the current study, we re-examine a previously validated acoustic cavitation model which was developed for a gas bubble in liquid water. Furthermore, simulations of cryogenic fluids including the thermal effect, the effect of acoustic pressure amplitude and the frequency of sound field on the bubble dynamics are presented. A gas bubble (Helium) in liquids Nitrogen, Oxygen and Hydrogen in an acoustic field at ambient pressure and low temperature is investigated numerically. The results reveal that the oscillation of the bubble in liquid Hydrogen fluctuates more than in liquids Oxygen and Nitrogen. The oscillation of the bubble in liquids Oxygen and Nitrogen is approximately similar.

Keywords: cryogenic liquids, cavitation, rocket engineering, ultrasound

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Authors: Peiming Li

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This paper presents an innovative blockchain-based approach to enhance the reliability and efficiency of federated learning systems. By integrating a decentralized oracle consensus mechanism into the federated learning framework, we address key challenges of data and model integrity. Our approach utilizes a network of redundant oracles, functioning as independent validators within an epoch-based training system in the federated learning model. In federated learning, data is decentralized, residing on various participants' devices. This scenario often leads to concerns about data integrity and model quality. Our solution employs blockchain technology to establish a transparent and tamper-proof environment, ensuring secure data sharing and aggregation. The decentralized oracles, a concept borrowed from blockchain systems, act as unbiased validators. They assess the contributions of each participant using a Hidden Markov Model (HMM), which is crucial for evaluating the consistency of participant inputs and safeguarding against model poisoning and malicious activities. Our methodology's distinct feature is its epoch-based training. An epoch here refers to a specific training phase where data is updated and assessed for quality and relevance. The redundant oracles work in concert to validate data updates during these epochs, enhancing the system's resilience to security threats and data corruption. The effectiveness of this system was tested using the Mnist dataset, a standard in machine learning for benchmarking. Results demonstrate that our blockchain-oriented federated learning approach significantly boosts system resilience, addressing the common challenges of federated environments. This paper aims to make these advanced concepts accessible, even to those with a limited background in blockchain or federated learning. We provide a foundational understanding of how blockchain technology can revolutionize data integrity in decentralized systems and explain the role of oracles in maintaining model accuracy and reliability.

Keywords: federated learning system, block chain, decentralized oracles, hidden markov model

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Authors: Elaheh Vaezpour

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Data sensing in wireless sensor networks is done by query deceleration the network by the users. In many applications of the wireless sensor networks, many users send queries to the network simultaneously. If the queries are processed separately, the network’s energy consumption will increase significantly. Therefore, it is very important to aggregate the queries before sending them to the network. In this paper, we propose a multiple query optimization framework based on sensors physical and temporal correlation. In the proposed method, queries are merged and sent to network by considering correlation among the sensors in order to reduce the communication cost between the sensors and the base station.

Keywords: wireless sensor networks, multiple query optimization, data correlation, reducing energy consumption

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Authors: Yoonsuh Jung

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As DNA microarray data contain relatively small sample size compared to the number of genes, high dimensional models are often employed. In high dimensional models, the selection of tuning parameter (or, penalty parameter) is often one of the crucial parts of the modeling. Cross-validation is one of the most common methods for the tuning parameter selection, which selects a parameter value with the smallest cross-validated score. However, selecting a single value as an "optimal" value for the parameter can be very unstable due to the sampling variation since the sample sizes of microarray data are often small. Our approach is to choose multiple candidates of tuning parameter first, then average the candidates with different weights depending on their performance. The additional step of estimating the weights and averaging the candidates rarely increase the computational cost, while it can considerably improve the traditional cross-validation. We show that the selected value from the suggested methods often lead to stable parameter selection as well as improved detection of significant genetic variables compared to the tradition cross-validation via real data and simulated data sets.

Keywords: cross validation, parameter averaging, parameter selection, regularization parameter search

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Authors: T. Turan, Ç. Erdoğan

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As far as is known, general nursing care practices do not include specific evidence-based practices related to oral care in children. This study aimed to evaluate the evidence based nursing practice for oral care in children. This article is planned as a review article by searching the literature in this field. According to all age groups and the oral care in various specific situations located evidence in the literature were examined. It has been determined that the methods and frequency used in oral care practices performed by nurses in clinics differ from one hospital to another. In addition, it is seen that different solutions are used in basic oral care, oral care practices to prevent ventilator-associated pneumonia and evidence-based practice in mucositis management in children. As a result, a standard should be established in oral care practices for children and education for children is recommended.

Keywords: evidence-based practice, oral care, nursing, children

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Authors: Amar Partap Singh Pharwaha, Balkrishan Jindal

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In this paper, a new method is developed for hiding image in a digital image with multilayer security. In the proposed method, the secret image is encrypted in the first instance using a flexible matrix based symmetric key to add first layer of security. Then another layer of security is added to the secret data by encrypting the ciphered data using Pythagorean Theorem method. The ciphered data bits (4 bits) produced after double encryption are then embedded within digital image in the spatial domain using Least Significant Bits (LSBs) substitution. To improve the image quality of the stego-image, an improved form of pixel adjustment process is proposed. To evaluate the effectiveness of the proposed method, image quality metrics including Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), entropy, correlation, mean value and Universal Image Quality Index (UIQI) are measured. It has been found experimentally that the proposed method provides higher security as well as robustness. In fact, the results of this study are quite promising.

Keywords: Pythagorean theorem, pixel adjustment, ciphered data, image hiding, least significant bit, flexible matrix

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Authors: Byung Ho Jung, Dong Hoon Lim

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Logistic regression is a statistical method for analyzing a dataset in which there are one or more independent variables that determine an outcome. Logistic regression is used extensively in numerous disciplines, including the medical and social science fields. In this paper, we address the problem of estimating parameters in the logistic regression based on MapReduce framework with RHadoop that integrates R and Hadoop environment applicable to large scale data. There exist three learning algorithms for logistic regression, namely Gradient descent method, Cost minimization method and Newton-Rhapson's method. The Newton-Rhapson's method does not require a learning rate, while gradient descent and cost minimization methods need to manually pick a learning rate. The experimental results demonstrated that our learning algorithms using RHadoop can scale well and efficiently process large data sets on commodity hardware. We also compared the performance of our Newton-Rhapson's method with gradient descent and cost minimization methods. The results showed that our newton's method appeared to be the most robust to all data tested.

Keywords: big data, logistic regression, MapReduce, RHadoop

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Authors: Rishabh Shukla, Achin Agrawal, Anupam Saxena, S. Mandal

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The existence of a crack, affects the mechanical behaviour and various properties of a structure to a great degree. This paper focuses on recognizing the parameters that gets changed due to the formation of cracks and have a great impact on the performance of the structure. Spalling is a major concern as it leaves the reinforcement bars more susceptible to environmental attacks. Beams of cross section 300 mm × 500 mm are designed and for a calculated area of steel, two different arrangements of reinforced bars are analysed. Results are prepared for different stages of cracking for each arrangement of rebars. The parameters for both arrangements are then compared. The Finite Element Analysis (FEA) is carried out and changes in the properties like flexural strength, Elasticity and modal frequency are reported. The conclusions have been drawn by comparing the results.

Keywords: cracks, elasticity, spalling, FEA

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Authors: Chae Hoon Park, Jong Kang Park, Jong Tae Kim

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Electrical characteristics of capacitive touchscreen need to be accurately analyzed to result in better performance for multi-channel capacitance sensing. In this paper, we extracted the panel resistances and capacitances of the touchscreen by comparing measurement data and model data. By employing a lumped RC model for driver-to-receiver paths in touchscreen, we estimated resistance and capacitance values according to the physical lengths of channel paths which are proportional to the RC model. As a result, we obtained the model having 95.54% accuracy of the measurement data.

Keywords: electrical characteristics of capacitive touchscreen, iterative extraction, lumped RC model, physical lengths of channel paths

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Authors: Jun Ming Moey, Zhiyaun Chen, David Nicholson

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Bad actors are often hard to detect in data that imprints their behaviour patterns because they are comparatively rare events embedded in non-bad actor data. An unsupervised machine learning framework is applied here to detect bad actors in financial crime datasets that record millions of transactions undertaken by hundreds of actors (<0.01% bad). Specifically, the framework combines ‘shallow’ (PCA, Isolation Forest) and ‘deep’ (Autoencoder) methods to detect outlier patterns. Detection performance analysis for both the individual methods and their combination is reported.

Keywords: detection, machine learning, deep learning, unsupervised, outlier analysis, data science, fraud, financial crime

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Authors: Zuzana Komrsková

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This paper shows the connection between emoticons and politeness in written computer-mediated communication. It studies if there are some differences in the use of emoticon between Czech and English written tweets. My assumptions about the use of emoticons were based on the use of greetings and thanks in real, face to face situations. The first assumption, that welcome greeting phrase would be accompanied by positive emoticon was correct. But for the farewell greeting both positive and negative emoticons are possible. My results show lower frequency of negative emoticons in this context. I also found quite often both positive and negative emoticon in the same tweet. The expression of gratitude is associated with positive emotions. The results show that emoticons accompany polite phrases of greeting and thanks very often both in Czech and English. The use of emoticons with studied polite phrases shows that emoticons have become an integral part of these phrases.

Keywords: Czech, emoticon, english, politeness, twitter

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Authors: J. Das, T. S. Mahule, V. V. Srinivasu

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Electron spin resonance (ESR) study at 9.45 GHz and a field modulation frequency of 100Hz was performed on bulk polycrystalline samples of Mn:TM (Fe/Ni) and Mn:RE (Gd/Sm) co doped ZnO samples with composition Zn1-xMn:TM/RE)xO synthesised by solid state reaction route and sintered at 500 0C temperature. The room temperature microwave absorption data collected by sweeping the DC magnetic field from -500 to 9500 G for the Mn:Fe and Mn:Ni co doped ZnO samples exhibit a rarely reported non resonant low field absorption (NRLFA) in addition to a strong absorption at around 3350G, usually associated with ferromagnetic resonance (FMR) satisfying Larmor’s relation due to absorption in the full saturation state. Observed low field absorption is distinct to ferromagnetic resonance even at low temperature and shows hysteresis. Interestingly, it shows a phase opposite with respect to the main ESR signal of the samples, which indicates that the low field absorption has a minimum value at zero magnetic field whereas the ESR signal has a maximum value. The major resonance peak as well as the peak corresponding to low field absorption exhibit asymmetric nature indicating magnetic anisotropy in the sample normally associated with intrinsic ferromagnetism. Anisotropy parameter for Mn:Ni codoped ZnO sample is noticed to be quite higher. The g values also support the presence of oxygen vacancies and clusters in the samples. These samples have shown room temperature ferromagnetism in the SQUID measurement. However, in rare earth (RE) co doped samples (Zn1-x (Mn: Gd/Sm)xO), which show paramagnetic behavior at room temperature, the low field microwave signals are not observed. As microwave currents due to itinerary electrons can lead to ohmic losses inside the sample, we speculate that more delocalized 3d electrons contributed from the TM dopants facilitate such microwave currents leading to the loss and hence absorption at the low field which is also supported by the increase in current with increased micro wave power. Besides, since Fe and Ni has intrinsic spin polarization with polarisability of around 45%, doping of Fe and Ni is expected to enhance the spin polarization related effect in ZnO. We emphasize that in this case Fe and Ni doping contribute to polarized current which interacts with the magnetization (spin) vector and get scattered giving rise to the absorption loss.

Keywords: co-doping, electron spin resonance, hysteresis, non-resonant microwave absorption

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Authors: Priyanka Tiwari, Uma Devi Ranjit, Ritesh Thapa

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Background and Significance: Cerebral Palsy (CP) influences not only the child's everyday functioning but also the functioning of whole family. Application of study findings can be used in clinical or community setting to screen the parents of children with cerebral palsy in order to identify the compromised domain of stress which in turn will help to improve the interaction between parent and child with disability and thus ultimately affect the progress that a child makes in his or her therapeutic or educational programs. Objective: The objective of the study was to assess the level of stress in mothers of children with CP by adopting mixed method design. Methodology: Cross-sectional descriptive design was adopted in the quantitative design where Parental Stress Scale (PSS) was utilized to collect data from a convenient sample of 40 mothers of children with CP who were under regular follow-up by home visitor of Self-help Group for Cerebral Palsy while embedded qualitative design was used to explore the stress of mothers of CP affected children. From the parent population of quantitative sample 4 mothers were chosen for in-depth exploration, regarding their stress by means of case study method. Descriptive statistics like frequency, percentage, mean, median, standard deviation, correlation and inferential statistics like Mann-Whitney U test and Kruskal-Wallis H test were used to describe and assess relationship between variables. Findings: The mean stress experienced by mothers of children with cerebral palsy was 53.62±9.53 with 15% percent of the mothers experiencing severe stress. There was significant association between age group of mother and total stress score and negative themes of stress. Similarly, signification association was found between educational status of the mother and positive themes of stress which was convergent with the qualitative finding as well, where literate mothers had more positive view of their child's disability which could be attributed to their educational level as education provides us with a broad perspective to look at a situation. Conclusions: Still one-sixth of the mothers experienced severe stress so if we want to ensure the well-being of the children affected by cerebral palsy, then parents caring for them need to be looked after as well.

Keywords: stress, cerebral palsy, mothers, mixed method

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Authors: Farnaz Alborzi, Angelika Schmitz, Rainer Stamminger

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Reducing water temperatures in the wash phase of a washing programme and increasing the overall cycle durations are the latest trends in decreasing energy consumption of washing programmes. Since the implementation of the new energy efficiency classes in 2010, manufacturers seem to apply the aforementioned washing strategy with lower temperatures combined with longer programme durations extensively to realise energy-savings needed to meet the requirements of the highest energy efficiency class possible. A semi-representative on-line survey in eleven European countries (Czech Republic, Finland, France, Germany, Hungary, Italy, Poland, Romania, Spain, Sweden and the United Kingdom) was conducted by Bonn University in 2015 to shed light on consumer opinion and behaviour regarding the effects of the lower washing temperature and longer cycle duration in laundry washing on consumers’ acceptance of the programme. The risk of the long wash cycle is that consumers might not use the energy efficient Standard programmes and will think of this option as inconvenient and therefore switch to shorter, but more energy consuming programmes. Furthermore, washing in a lower temperature may lead to the problem of cross-contamination. Washing behaviour of over 5,000 households was studied in this survey to provide support and guidance for manufacturers and policy designers. Qualified households were chosen following a predefined quota: -Involvement in laundry washing: substantial, -Distribution of gender: more than 50 % female , -Selected age groups: -20–39 years, -40–59 years, -60–74 years, -Household size: 1, 2, 3, 4 and more than 4 people. Furthermore, Eurostat data for each country were used to calculate the population distribution in the respective age class and household size as quotas for the consumer survey distribution in each country. Before starting the analyses, the validity of each dataset was controlled with the aid of control questions. After excluding the outlier data, the number of the panel diminished from 5,100 to 4,843. The primary outcome of the study is European consumers are willing to save water and energy in a laundry washing but reluctant to use long programme cycles since they don’t believe that the long cycles could be energy-saving. However, the results of our survey don’t confirm that there is a relation between frequency of using Standard cotton (Eco) or Energy-saving programmes and the duration of the programmes. It might be explained by the fact that the majority of washing programmes used by consumers do not take so long, perhaps consumers just choose some additional time reduction option when selecting those programmes and this finding might be changed if the Energy-saving programmes take longer. Therefore, it may be assumed that introducing the programme duration as a new measure on a revised energy label would strongly influence the consumer at the point of sale. Furthermore, results of the survey confirm that consumers are more willing to use lower temperature programmes in order to save energy than accepting longer programme cycles and majority of them accept deviation from the nominal temperature of the programme as long as the results are good.

Keywords: duration, energy-saving, standard programmes, washing temperature

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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: Keerthi S. Shetty, Annappa Basava

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In recent years, modelling of biological systems represented by biochemical reactions has become increasingly important in Systems Biology. Biological systems represented by biochemical reactions are highly stochastic in nature. Probabilistic model is often used to describe such systems. One of the main challenges in Systems biology is to combine absolute experimental data into probabilistic model. This challenge arises because (1) some molecules may be present in relatively small quantities, (2) there is a switching between individual elements present in the system, and (3) the process is inherently stochastic on the level at which observations are made. In this paper, we describe a novel idea of combining absolute experimental data into probabilistic model using tool R2. Through a case study of the Transcription Process in Prokaryotes we explain how biological systems can be written as probabilistic program to combine experimental data into the model. The model developed is then analysed in terms of intrinsic noise and exact sampling of switching times between individual elements in the system. We have mainly concentrated on inferring number of genes in ON and OFF states from experimental data.

Keywords: systems biology, probabilistic model, inference, biology, model

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Authors: I. M. Salama, R. N. Miftahof

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A functional element of the myenteric nervous plexus is a morphologically distinct ganglion. Composed of sensory, inter- and motor neurons and arranged via synapses in neuronal circuits, their task is to decipher and integrate spike coded information within the plexus into regulatory output signals. The stability of signal processing in response to a wide range of internal/external perturbations depends on the plasticity of individual neurons. Any aberrations in this inherent property may lead to instability with the development of a dynamics chaos and can be manifested as pathological conditions, such as intestinal dysrhythmia, irritable bowel syndrome. The aim of this study is to investigate patterns of signal transduction within a two-neuronal chain - a ganglion - under normal physiological and structurally altered states. The ganglion contains the primary sensory (AH-type) and motor (S-type) neurons linked through a cholinergic dendro somatic synapse. The neurons have distinguished electrophysiological characteristics including levels of the resting and threshold membrane potentials and spiking activity. These are results of ionic channel dynamics namely: Na+, K+, Ca++- activated K+, Ca++ and Cl-. Mechanical stretches of various intensities and frequencies are applied at the receptive field of the AH-neuron generate a cascade of electrochemical events along the chain. At low frequencies, ν < 0.3 Hz, neurons demonstrate strong connectivity and coherent firing. The AH-neuron shows phasic bursting with spike frequency adaptation while the S-neuron responds with tonic bursts. At high frequency, ν > 0.5 Hz, the pattern of electrical activity changes to rebound and mixed mode bursting, respectively, indicating ganglionic loss of plasticity and adaptability. A simultaneous increase in neuronal conductivity for Na+, K+ and Ca++ ions results in tonic mixed spiking of the sensory neuron and class 2 excitability of the motor neuron. Although the signal transduction along the chain remains stable the synchrony in firing pattern is not maintained and the number of discharges of the S-type neuron is significantly reduced. A concomitant increase in Ca++- activated K+ and a decrease in K+ in conductivities re-establishes weak connectivity between the two neurons and converts their firing pattern to a bistable mode. It is thus demonstrated that neuronal plasticity and adaptability have a stabilizing effect on the dynamics of signal processing in the ganglion. Functional modulations of neuronal ion channel permeability, achieved in vivo and in vitro pharmacologically, can improve connectivity between neurons. These findings are consistent with experimental electrophysiological recordings from myenteric ganglia in intestinal dysrhythmia and suggest possible pathophysiological mechanisms.

Keywords: neuronal chain, signal transduction, plasticity, stability

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Authors: E. Vazquez-Santacruz, M. Gamboa-Zuniga

Abstract:

In this paper, we show a methodology for bodies classification in lying state using HOG descriptors and pressures sensors positioned in a matrix form (14 x 32 sensors) on the surface where bodies lie down. it will be done in real time. Our system is embedded in a care robot that can assist the elderly patient and medical staff around to get a better quality of life in and out of hospitals. Due to current technology a limited number of sensors is used, wich results in low-resolution data array, that will be used as image of 14 x 32 pixels. Our work considers the problem of human posture classification with few information (sensors), applying digital process to expand the original data of the sensors and so get more significant data for the classification, however, this is done with low-cost algorithms to ensure the real-time execution.

Keywords: real-time classification, sensors, robots, health care, elderly patients, artificial intelligence

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Authors: Fatma Kürüm Varolgüneş, Fatih Canan

Abstract:

Mardin is one of the unique cities in Turkey with its rich cultural and historical heritage. Mardin’s traditional dwellings have been affected both by natural data such as climate and topography and by cultural data like lifestyle and belief. However, in the new settlements, housing is formed with modern approaches and unsuitable forms clashing with Mardin’s culture and environment. While the city is expanding, traditional textures are ignored. Thus, traditional settlements are losing their identity and are vanishing because of the rapid change and transformation. The main aim of this paper is to determine the physical and social data needed to define the characteristic features of Mardin’s old and new settlements. In this context, based on social and cultural data, old and new settlement formations of Mardin have been investigated from various aspects. During this research, the following methods have been utilized: observations, interviews, public surveys, literature review, as well as site examination via maps, photographs and questionnaire methodology. In conclusion, this paper focuses on how changes in the physical forms of cities affect the typology and the identity of cities, as in the case of Mardin.

Keywords: urban and local identity, historical city center, traditional settlements, Mardin

Procedia PDF Downloads 319

Authors: Hiroshi Ikeda

Abstract:

In this research, a questionnaire survey was conducted to measure nap, drowsiness and fatigue of drivers who work long shifts, to discuss about the work environment and health conditions for taxi and bus drivers who work at night time. The questionnaire sheet used for this research was organized into the following categories: tension/tiredness, drowsiness while driving, and the nap situation during night-time work. The number of taxi drivers was 127 and the number of bus drivers was 40. Concerning the results of a comparison of nap hours of taxi and bus drivers, the taxi drivers’ nap hours are overwhelmingly shorter, and also the frequency of drivers who feel drowsiness is higher. The burden on bus drivers does not change because of the system of a two-driver rotation shift. In particular, the working environment of the taxi driver may lead to greater fatigue accumulation than the bus driver’s environment.

Keywords: bus and taxi, drowsiness, fatigue, nap

Procedia PDF Downloads 319

Authors: Teguh Sugiarto, Amir Mohamadian Amiri

Abstract:

Background: This study aims to explore if there is fraud in the company's financial report distribution using the number first digit Lucas, Fibonacci and Benford. Research methods: In this study, the author uses a number model contained in the first digit of the model Lucas, Fibonacci and Benford, to make a distinction between implementation by using the scale above and below 5%, the rate of occurrence of a difference against the digit number contained on Lucas, Fibonacci and Benford. If there is a significant difference above and below 5%, then the process of follow-up and detection of occurrence of fraud against the financial statements can be made. Findings: From research that has been done can be concluded that the number of frequency levels contained in the financial statements of PT Bank BRI Tbk in a year in the same conscientious results for model Lucas, Fibonacci and Benford.

Keywords: Lucas, Fibonacci, Benford, first digit

Procedia PDF Downloads 266