Search results for: mobility features

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: Hee-Jeong Ahn, Kee-Won Kim, Seung-Hoon Kim

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The knowledge of the relationship between characters can help readers to understand the overall story or plot of the literary fiction. In this paper, we present a method for extracting the specific relationship between characters from a Korean literary fiction. Generally, methods for extracting relationships between characters in text are statistical or computational methods based on the sentence distance between characters without considering Korean linguistic features. Furthermore, it is difficult to extract the relationship with direction from text, such as one-sided love, because they consider only the weight of relationship, without considering the direction of the relationship. Therefore, in order to identify specific relationships between characters, we propose a statistical method considering linguistic features, such as syntactic patterns and speech verbs in Korean. The result of our method is represented by a weighted directed graph of the relationship between the characters. Furthermore, we expect that proposed method could be applied to the relationship analysis between characters of other content like movie or TV drama.

Keywords: data mining, Korean linguistic feature, literary fiction, relationship extraction

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Authors: Kadir Akkuş, Burcu A. Hamat, Kaan Ciloglu

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Additive Manufacturing (AM) is the respectable improvement of this century in the field of manufacturing and regarded as a breakthrough that represents the third industrial revolution by the leading authorities such as Wohlers Associates Inc., The Economist, and MIT Technology Review. Thanks to the stacking and unifying methodology of AM, design of lighter but stiffer parts with really more complex shapes and geometrical features, which were not possible by traditional subtractive manufacturing methods, became achievable. Through analysis of the AM process must be performed and mechanical properties of manufactured test parts must be studied to provide input for design. Furthermore, process capabilities, constraints, limitations and challenges regarding AM must be examined so that the design must be compatible with the process to be able to take all the advantages of the AM. In this paper, capabilities and limitations of AM will be investigated through a test part including unique features and manufactured from Ti-6Al-4V by employing Electron Beam Melting (EBM) technology by comparing to the test parts introduced in literature.

Keywords: additive manufacturing, DfAM, EBM, test artifact, Ti-6Al-4V

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Authors: Bhalke D. G., Bormane D. S., Kharate G. K.

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This paper presents classification of musical instrument using machine learning techniques. The classification has been carried out using temporal, spectral, cepstral and wavelet features. Detail feature analysis is carried out using separate and combined features. Further, instrument model has been developed using K-Nearest Neighbor and Support Vector Machine (SVM). Benchmarked McGill university database has been used to test the performance of the system. Experimental result shows that SVM performs better as compared to KNN classifier.

Keywords: feature extraction, SVM, KNN, musical instruments

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Authors: Abdulaziz Alsadhan, Naveed Khan

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In recent years intrusions on computer network are the major security threat. Hence, it is important to impede such intrusions. The hindrance of such intrusions entirely relies on its detection, which is primary concern of any security tool like Intrusion Detection System (IDS). Therefore, it is imperative to accurately detect network attack. Numerous intrusion detection techniques are available but the main issue is their performance. The performance of IDS can be improved by increasing the accurate detection rate and reducing false positive. The existing intrusion detection techniques have the limitation of usage of raw data set for classification. The classifier may get jumble due to redundancy, which results incorrect classification. To minimize this problem, Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Local Binary Pattern (LBP) can be applied to transform raw features into principle features space and select the features based on their sensitivity. Eigen values can be used to determine the sensitivity. To further classify, the selected features greedy search, back elimination, and Particle Swarm Optimization (PSO) can be used to obtain a subset of features with optimal sensitivity and highest discriminatory power. These optimal feature subset used to perform classification. For classification purpose, Support Vector Machine (SVM) and Multilayer Perceptron (MLP) used due to its proven ability in classification. The Knowledge Discovery and Data mining (KDD’99) cup dataset was considered as a benchmark for evaluating security detection mechanisms. The proposed approach can provide an optimal intrusion detection mechanism that outperforms the existing approaches and has the capability to minimize the number of features and maximize the detection rates.

Keywords: Particle Swarm Optimization (PSO), Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), Local Binary Pattern (LBP), Support Vector Machine (SVM), Multilayer Perceptron (MLP)

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Authors: Roza Dzierzak, Waldemar Wojcik, Piotr Kacejko

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Analysis of the texture of medical images consists of determining the parameters and characteristics of the examined tissue. The main goal is to assign the analyzed area to one of two basic groups: as a healthy tissue or a tissue with pathological changes. The CT images of the thoracic lumbar spine from 15 healthy patients and 15 with confirmed osteoporosis were used for the analysis. As a result, 120 samples with dimensions of 50x50 pixels were obtained. The set of features has been obtained based on the histogram, gradient, run-length matrix, co-occurrence matrix, autoregressive model, and Haar wavelet. As a result of the image analysis, 290 descriptors of textural features were obtained. The dimension of the space of features was reduced by the use of three selection methods: Fisher coefficient (FC), mutual information (MI), minimization of the classification error probability and average correlation coefficients between the chosen features minimization of classification error probability (POE) and average correlation coefficients (ACC). Each of them returned ten features occupying the initial place in the ranking devised according to its own coefficient. As a result of the Fisher coefficient and mutual information selections, the same features arranged in a different order were obtained. In both rankings, the 50% percentile (Perc.50%) was found in the first place. The next selected features come from the co-occurrence matrix. The sets of features selected in the selection process were evaluated using six classification tree methods. These were: decision stump (DS), Hoeffding tree (HT), logistic model trees (LMT), random forest (RF), random tree (RT) and reduced error pruning tree (REPT). In order to assess the accuracy of classifiers, the following parameters were used: overall classification accuracy (ACC), true positive rate (TPR, classification sensitivity), true negative rate (TNR, classification specificity), positive predictive value (PPV) and negative predictive value (NPV). Taking into account the classification results, it should be stated that the best results were obtained for the Hoeffding tree and logistic model trees classifiers, using the set of features selected by the POE + ACC method. In the case of the Hoeffding tree classifier, the highest values of three parameters were obtained: ACC = 90%, TPR = 93.3% and PPV = 93.3%. Additionally, the values of the other two parameters, i.e., TNR = 86.7% and NPV = 86.6% were close to the maximum values obtained for the LMT classifier. In the case of logistic model trees classifier, the same ACC value was obtained ACC=90% and the highest values for TNR=88.3% and NPV= 88.3%. The values of the other two parameters remained at a level close to the highest TPR = 91.7% and PPV = 91.6%. The results obtained in the experiment show that the use of classification trees is an effective method of classification of texture features. This allows identifying the conditions of the spongy tissue for healthy cases and those with the porosis.

Keywords: classification, feature selection, texture analysis, tree algorithms

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Authors: Dona Abdulwassi, Dhaee Dahlawi, Yara Zainy, Leen Joharji

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The relationship between Netflix and artificial intelligence is discussed in this paper. Netflix uses the most effective and efficient approaches to apply artificial intelligence, machine learning, and data science. Netflix employs the personalization tool for their users, recommending or suggesting shows based on what those users have already watched. The researchers conducted an experiment to learn more about how Netflix is used and how AI affects the user experience. The main conclusions of this study are that Netflix has a wide range of AI features, most users are happy with their Netflix subscriptions, and the majority prefer Netflix to alternative apps.

Keywords: easy accessibility, recommends, accuracy, privacy

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Authors: Bahaa Eltahawy, Mikko Ylihärsilä, Reino Virrankoski, Esko Petäjä

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Sheet metal processing is automated, but the step from product models to the production machine control still requires human intervention. This may cause time consuming bottlenecks in the production process and increase the risk of human errors. In this paper we present a system, which automatically recognizes features from the CAD-model of the sheet metal product. By using these features, the system produces a complete model of the particular sheet metal product. Then the model is used as an input for the sheet metal processing machine. Currently the system is implemented, capable to recognize more than 11 of the most common sheet metal structural features, and the procedure is fully automated. This provides remarkable savings in the production time, and protects against the human errors. This paper presents the developed system architecture, applied algorithms and system software implementation and testing.

Keywords: feature recognition, automation, sheet metal manufacturing, CAD, CAM

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Authors: Guoliang Lu, Changhou Lu, Xueyong Li

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In action recognition, previous studies have demonstrated the effectiveness of using multiple features to improve the recognition performance. We focus on two practical issues: i) most studies use a direct way of concatenating/accumulating multi features to evaluate the similarity between two actions. This way could be too strong since each kind of feature can include different dimensions, quantities, etc; ii) in many studies, the employed classification methods lack of a flexible and effective mechanism to add new feature(s) into classification. In this paper, we explore an unified scheme based on recently-proposed multi-dimensional dynamic time warping (MD-DTW). Experiments demonstrated the scheme's effectiveness of combining multi-feature and the flexibility of adding new feature(s) to increase the recognition performance. In addition, the explored scheme also provides us an open architecture for using new advanced classification methods in the future to enhance action recognition.

Keywords: action recognition, multi features, dynamic time warping, feature combination

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Authors: Maximilian Richter

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Implementing shared autonomous electric vehicles (SAEVs) has many advantages. The main advantages are achieved when SAEVs are offered as on-demand services by a fleet operator. However, autonomous mobility on demand (AMoD) will be distributed nationwide only if a fleet operation is economically profitable for the operator. This paper proposes a microscopic approach to modeling two implementation scenarios of an AMoD fleet. The city of Zurich is used as a case study, with the results and findings being generalizable to other similar European and North American cities. The data are based on the traffic model of the canton of Zurich (Gesamtverkehrsmodell des Kantons Zürich (GVM-ZH)). To determine financial profitability, demand is based on the simulation results and combined with analyzing the costs of a SAEV per kilometer. The results demonstrate that depending on the scenario; journeys can be offered profitably to customers for CHF 0.3 up to CHF 0.4 per kilometer. While larger fleets allowed for lower price levels and increased profits in the long term, smaller fleets exhibit elevated efficiency levels and profit opportunities per day. The paper concludes with recommendations for how fleet operators can prepare themselves to maximize profit in the autonomous future.

Keywords: autonomous vehicle, mobility on demand, traffic simulation, fleet provider

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Authors: Al Mutawa A. Amir, Liaqat Ali, Faisal Ali

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After the World War II, the world economy was disrupted and changed due to oil and its prices. The research in this paper presents the basic statistical features and economic characteristics of the Gulf economy. The main features of the Gulf economies and its heavy dependence on oil exports, its dualism between modern and traditional sectors and its rapidly increasing affluences are particularly emphasized.  In this context, the research in this paper discussed the problems of growth versus development and has attempted to draw the implications for the future economic development of this area.

Keywords: oil prices, GCC, economic growth, gulf oil

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Authors: Osayande Pascal Omondiagbe, Sherlock a Licorish

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Stack Overflow is a popular community question and answer portal which is used by practitioners to solve technology-related challenges during software development. Previous studies have shown that this forum is becoming a substitute for official software programming languages documentation. While tools have looked to aid developers by presenting interfaces to explore Stack Overflow, developers often face challenges searching through many possible answers to their questions, and this extends the development time. To this end, researchers have provided ways of predicting acceptable Stack Overflow answers by using various modeling techniques. However, less interest is dedicated to examining the performance and quality of typically used modeling methods, and especially in relation to models’ and features’ complexity. Such insights could be of practical significance to the many practitioners that use Stack Overflow. This study examines the performance and quality of various modeling methods that are used for predicting acceptable answers on Stack Overflow, drawn from 2014, 2015 and 2016. Our findings reveal significant differences in models’ performance and quality given the type of features and complexity of models used. Researchers examining classifiers’ performance and quality and features’ complexity may leverage these findings in selecting suitable techniques when developing prediction models.

Keywords: feature selection, modeling and prediction, neural network, random forest, stack overflow

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Authors: Sanjeev Sinha, Samir Saurav

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The rapid growth of urban areas in India has led to transportation challenges like traffic congestion and an increase in accidents. Despite efforts by state governments and local administrations to improve urban transport, the surge in private vehicles has worsened the situation. Patna, located in Bihar State, is an example of the trend of increasing reliance on private motor vehicles, resulting in vehicular congestion and emissions. The existing transportation infrastructure is inadequate to meet future travel demands, and there has been a notable increase in the share of private vehicles in the city. Additionally, there has been a surge in economic activities in the region, which has increased the demand for improved travel convenience and connectivity. To address these challenges, a study was conducted to assess the most suitable transit mode for the proposed transit corridor outlined in the Comprehensive Mobility Plan (CMP) for Patna. The study covered four stages: developing screening criteria, evaluating parameters for various alternatives, qualitative and quantitative evaluations of alternatives, and implementation options for the most viable alternative. The study suggests that a mass transit system such as a metro rail is necessary to enhance Patna's urban public transport system. The New Metro Policy 2017 outlines specific prerequisites for submitting a Metro Rail Project Proposal to the Ministry of Housing and Urban Affairs (MoHUA), including the preparation of a CMP, the formation of an Urban Metropolitan Transport Authority (UMTA), the creation of an Alternative Analysis Report, the development of a Detailed Project Report, a Multi-Modal Integration Plan, and a Transit-Oriented Development (TOD) Plan. In 2018, the Comprehensive Mobility Plan for Patna was prepared, setting the stage for the subsequent steps in the metro rail project proposal. The results indicated that from the screening and analysis of qualitative parameters for different alternative modes in Patna, it is inferred that the Metro Rail and Monorail score 82.25 and 70.50, respectively, on a scale of 100. Based on the initial analysis and alternative evaluation in the form of quantitative analysis, the Metro Rail System significantly outperformed the Monorail system. The Metro Rail System has a positive Economic Net Present Value (ENPV) at a 14% internal rate of return, while the Monorail has a negative value. In conclusion, the study recommends choosing metro rail over monorail for the proposed transit corridor in Patna. However, the lack of broad-based technical expertise may result in implementation delays and increased costs for monorail.

Keywords: comprehensive mobility plan, alternative analysis, mobility corridors, mass transit system

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Authors: Samarjeet Singh, Shubhank Arya, Shubham Chauhan

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In recent years, the rise in carbon emissions and the widespread effects of global warming have brought new energy vehicles into the spotlight. Electric vehicles (EVs) and hydrogen fuel cell vehicles (HFCVs), both producing zero tailpipe emissions, are seen as promising alternatives. This paper examines the working, structural characteristics, and safety designs of EVs and HFCVs, comparing their carbon emissions, charging infrastructure, energy efficiency, and safety features. The analysis reveals that both EVs and HFCVs significantly reduce carbon emissions and enhance safety compared to traditional vehicles, with EVs showing greater emission reductions. Moreover, EVs are advancing more rapidly in terms of charging infrastructure compared to hydrogen energy vehicles. However, HFCVs exhibit lower energy efficiency than EVs. In terms of safety, both types surpass conventional vehicles, though EVs are more prone to overheating and fire hazards due to battery design issues. Current research suggests that EV technology and its supporting infrastructure are more comprehensive, cost-effective, and efficient in reducing carbon emissions. With continued investment in the development of new energy vehicles and potential advancements in hydrogen energy production, the future for HFCVs appears promising. The paper also expresses optimism for innovative solutions that could accelerate the growth of hydrogen energy vehicles.

Keywords: electric vehicles, fuel cell electric vehicles, automotive engineering, energy transition

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Authors: Hussein Alahmer, Amr Ahmed

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Liver cancer is one of the common diseases that cause the death. Early detection is important to diagnose and reduce the incidence of death. Improvements in medical imaging and image processing techniques have significantly enhanced interpretation of medical images. Computer-Aided Diagnosis (CAD) systems based on these techniques play a vital role in the early detection of liver disease and hence reduce liver cancer death rate.  This paper presents an automated CAD system consists of three stages; firstly, automatic liver segmentation and lesion’s detection. Secondly, extracting features. Finally, classifying liver lesions into benign and malignant by using the novel contrasting feature-difference approach. Several types of intensity, texture features are extracted from both; the lesion area and its surrounding normal liver tissue. The difference between the features of both areas is then used as the new lesion descriptors. Machine learning classifiers are then trained on the new descriptors to automatically classify liver lesions into benign or malignant. The experimental results show promising improvements. Moreover, the proposed approach can overcome the problems of varying ranges of intensity and textures between patients, demographics, and imaging devices and settings.

Keywords: CAD system, difference of feature, fuzzy c means, lesion detection, liver segmentation

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Authors: Brittany Richardson, Ying Wang

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For physical therapy, exercise prescription, athlete training, and regular fitness training, it is crucial to perform health assessments or fitness assessments periodically. An accurate assessment is propitious for tracking recovery progress, preventing potential injury and making long-range training plans. Assessments include necessary measurements, height, weight, blood pressure, heart rate, body fat, etc. and advanced evaluation, muscle group strength, stability-mobility, and movement evaluation, etc. In the current standard assessment procedures, the accuracy of assessments, especially advanced evaluations, largely depends on the experience of physicians, coaches, and personal trainers. And it is challenging to track clients’ progress in the current assessment. Unlike the tradition assessment, in this paper, we present a deep learning based face recognition algorithm for accurate, comprehensive and trackable assessment. Based on the result from our assessment, physicians, coaches, and personal trainers are able to adjust the training targets and methods. The system categorizes the difficulty levels of the current activity for the client or user, furthermore make more comprehensive assessments based on tracking muscle group over time using a designed landmark detection method. The system also includes the function of grading and correcting the form of the clients during exercise. Experienced coaches and personal trainer can tell the clients' limit based on their facial expression and muscle group movements, even during the first several sessions. Similar to this, using a convolution neural network, the system is trained with people’s facial expression to differentiate challenge levels for clients. It uses landmark detection for subtle changes in muscle groups movements. It measures the proximal mobility of the hips and thoracic spine, the proximal stability of the scapulothoracic region and distal mobility of the glenohumeral joint, as well as distal mobility, and its effect on the kinetic chain. This system integrates data from other fitness assistant devices, including but not limited to Apple Watch, Fitbit, etc. for a improved training and testing performance. The system itself doesn’t require history data for an individual client, but the history data of a client can be used to create a more effective exercise plan. In order to validate the performance of the proposed work, an experimental design is presented. The results show that the proposed work contributes towards improving the quality of exercise plan, execution, progress tracking, and performance.

Keywords: exercise prescription, facial recognition, landmark detection, fitness assessments

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Authors: Ricardo Daziano

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In the analysis and planning of the mobility ecosystem, preferences for ride-hailing over incumbent street-hailing services need better understanding. In this paper, a seminonparametric discrete choice model that allows for flexible preference heterogeneity is fitted with data from a discrete choice experiment among adult commuters in Montreal, Canada (N=760). Participants chose among Uber, Teo (a local electric ride-hailing service that was in operation when data was collected in 2018), and a standard taxi when presented with information about cost, time (on-trip, waiting, walking), powertrain of the car (gasoline/hybrid) for Uber and taxi, and whether the available electric Teo was a Tesla (which was one of the actual features of the Teo fleet). The fitted flexible model offers several behavioral insights. Waiting time for ride-hailing services is associated with a statistically significant but low marginal disutility. For other time components, including on-ride, and street-hailing waiting and walking the estimates of the value of time show an interesting pattern: whereas in a conditional logit on-ride time reductions are valued higher, in the flexible LML specification means of the value of time follow the expected pattern of waiting and walking creating a higher disutility. At the same time, the LML estimates show the presence of important, multimodal unobserved preference heterogeneity.

Keywords: discrete choice, electric taxis, ridehailing, semiparametrics

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Authors: J. S. Kim

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This paper presents a 0.13-µm Complementary Metal-Oxide Semiconductor (CMOS) vector modulator for beamforming system. The vector modulator features a 360° phase and gain range of -10 dB to 10 dB with a root mean square phase and amplitude error of only 2.2° and 0.45 dB, respectively. These features make it a suitable for wireless backhaul system in the 5 GHz industrial, scientific, and medical (ISM) bands. It draws a current of 20.4 mA from a 1.2 V supply. The total chip size is 1.87x1.34 mm².

Keywords: CMOS, vector modulator, beamforming, 802.11ac

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Authors: I. Gokasar, G. Bakioglu

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With the unprecedented rates of technological improvements, people start to solve their problems with the help of technological tools. According to application stores and websites in which people evaluate and comment on the traffic apps, there are more than 100 traffic applications which have different features with respect to their purpose of usage ranging from the features of traffic apps for public transit modes to the features of traffic apps for private cars. This study focuses on the top 30 traffic applications which were chosen with respect to their download counts. All data about the traffic applications were obtained from related websites. The purpose of this study is to analyze traffic applications in terms of their categorical attributes with the help of developing a regression model. The analysis results suggest that negative interpretations (e.g., being deficient) does not lead to lower star ratings of the applications. However, those negative interpretations result in a smaller increase in star rate. In addition, women use higher star rates than men for the evaluation of traffic applications.

Keywords: traffic app, real–time information, traffic congestion, regression analysis, dummy variables

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Authors: Igor Perechuda, Wojciech Cieśliński

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The paper identifies the features of Polish sports clubs in the particular organizational forms: profit and nonprofit. Identification and description of these features is carried out in terms of financial efficiency of the given organizational form. Under the terms of the efficiency the research allows you to specify the advantages of particular organizational sports club form and the following limitations. Paper considers features of sports clubs in range of Polish conditions as legal regulations. The sources of the functioning efficiency of sports clubs may lie in the organizational forms in which they operate. Each of the available forms can be considered either a for-profit or nonprofit enterprise. Depending on this classification there are different capabilities of increasing organizational and financial efficiency of a given sports club. Authors start with general classification and difference between for-profit and non-profit sport clubs. Next identifies specific financial and organizational conditions of both organizational form and then show examples of mixed activity forms and their efficiency effect.

Keywords: financial efficiency, for-profit, non-profit, sports club

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Authors: Salim Ouchtati, Aissa Belmeguenai, Mouldi Bedda

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In this study, we propose an offline system for the recognition of the fragmented handwritten numeric chains. Firstly, we realized a recognition system of the isolated handwritten digits, in this part; the study is based mainly on the evaluation of neural network performances, trained with the gradient backpropagation algorithm. The used parameters to form the input vector of the neural network are extracted from the binary images of the isolated handwritten digit by several methods: the distribution sequence, sondes application, the Barr features, and the centered moments of the different projections and profiles. Secondly, the study is extended for the reading of the fragmented handwritten numeric chains constituted of a variable number of digits. The vertical projection was used to segment the numeric chain at isolated digits and every digit (or segment) was presented separately to the entry of the system achieved in the first part (recognition system of the isolated handwritten digits).

Keywords: features extraction, handwritten numeric chains, image processing, neural networks

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Authors: R. Mohan, V. Jadhav, A. Ahmed, J. Rivas, A. Kelkar

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Cementitious materials are an excellent example of a composite material with complex hierarchical features and random features that range from nanometer (nm) to millimeter (mm) scale. Multi-scale modeling of complex material systems requires starting from fundamental building blocks to capture the scale relevant features through associated computational models. In this paper, molecular dynamics (MD) modeling is employed to predict the effect of plasticizer additive on the mechanical properties of key hydrated cement constituent calcium-silicate-hydrate (CSH) at the molecular, nanometer scale level. Due to complexity, still unknown molecular configuration of CSH, a representative configuration widely accepted in the field of mineral Jennite is employed. The effectiveness of the Molecular Dynamics modeling to understand the predictive influence of material chemistry changes based on molecular/nanoscale models is demonstrated.

Keywords: cement composite, mechanical properties, molecular dynamics, plasticizer additives

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Authors: Andre Wittenborn, Jarek Krajewski

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Trauma-related sadness in speech can alter the voice in several ways. The generation of non-linear aerodynamic phenomena within the vocal tract is crucial when analyzing trauma-influenced speech production. They include non-laminar flow and formation of jets rather than well-behaved laminar flow aspects. Especially state-space reconstruction methods based on chaotic dynamics and fractal theory have been suggested to describe these aerodynamic turbulence-related phenomena of the speech production system. To extract the non-linear properties of the speech signal, we used the time delay embedding method to reconstruct from a scalar time series (reconstructed phase space, RPS). This approach results in the extraction of 7238 Features per .wav file (N= 47, 32 m, 15 f). The speech material was prompted by telling about autobiographical related sadness-inducing experiences (sampling rate 16 kHz, 8-bit resolution). After combining these features in a support vector machine based machine learning approach (leave-one-sample out validation), we achieved a correlation of r = .41 with the well-established, self-report ground truth measure (RATS) of post-traumatic stress disorder (PTSD).

Keywords: non-linear dynamics features, post traumatic stress disorder, reconstructed phase space, support vector machine

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Authors: Yuan-Jye Tseng, Ching-Yen Chen

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In the design cycle, a main design task is to determine the external shape of the product. The external shape of a product is one of the key factors that can affect the customers’ preferences linking to the motivation to buy the product, especially in the case of a consumer electronic product such as a mobile phone. The relationship between the external shape and the customer preferences needs to be studied to enhance the customer’s purchase desire and action. In this research, a design for customer preferences model is developed for investigating the relationships between the external shape and the customer preferences of a product. In the first stage, the names of the geometric features are collected and evaluated from the data of the specified internet web pages using the developed text miner. The key geometric features can be determined if the number of occurrence on the web pages is relatively high. For each key geometric feature, the numerical values are explored using the text miner to collect the internet data from the web pages. In the second stage, a cluster analysis model is developed to evaluate the numerical values of the key geometric features to divide the external shapes into several groups. Several design suggestion cases can be proposed, for example, large model, mid-size model, and mini model, for designing a mobile phone. A customer preference index is developed by evaluating the numerical data of each of the key geometric features of the design suggestion cases. The design suggestion case with the top ranking of the customer preference index can be selected as the final design of the product. In this paper, an example product of a notebook computer is illustrated. It shows that the external shape of a product can be used to drive customer preferences. The presented design for customer preferences model is useful for determining a suitable external shape of the product to increase customer preferences.

Keywords: cluster analysis, customer preferences, design evaluation, design for customer preferences, product design

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Authors: Magdalena Jablonska, Andrzej Falkowski

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The negativity effect is one of the most intriguing and well-studied psychological phenomena that can be observed in many areas of human life. The aim of the following study is to investigate how valence framing and positive and negative information about political candidates affect judgments about similarity to an ideal and bad politician. Based on the theoretical framework of features of similarity, it is hypothesized that negative features have a stronger effect on similarity judgments than positive features of comparable value. Furthermore, the mediating role of affect is tested. Method: One hundred sixty-one people took part in an experimental study. Participants were divided into 6 research conditions that differed in the reference point (positive vs negative framing) and the number of favourable and unfavourable information items about political candidates (a positive, neutral and negative candidate profile). In positive framing condition, the concept of an ideal politician was primed; in the negative condition, participants were to think about a bad politician. The effect of independent variables on similarity judgments, affective evaluation, and voting intention was tested. Results: In the positive condition, the analysis showed that the negative effect of additional unfavourable features was greater than the positive effect of additional favourable features in judgements about similarity to the ideal candidate. In negative framing condition, ANOVA was insignificant, showing that neither the addition of positive features nor additional negative information had a significant impact on the similarity to a bad political candidate. To explain this asymmetry, two mediational analyses were conducted that tested the mediating role of affect in the relationship between similarity judgments and voting intention. In both situations the mediating effect was significant, but the comparison of two models showed that the mediation was stronger for a negative framing. Discussion: The research supports the negativity effect and attempts to explain the psychological mechanism behind the positive-negative asymmetry. The results of mediation analyses point to a stronger mediating role of affect in the relationship between cognitive evaluation and voting intention. Such a result suggests that negative comparisons, leading to the activation of negative features, give rise to stronger emotions than positive features of comparable strength. The findings are in line with positive-negative asymmetry, however, by adopting Tversky’s framework of features of similarity, the study integrates the cognitive mechanism of the negativity effect delineated in the contrast model of similarity with its emotional component resulting from the asymmetrical effect of positive and negative emotions on decision-making.

Keywords: affect, framing, negativity effect, positive-negative asymmetry, similarity judgements

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Authors: Zhiyuan Du, Baisravan Hom Chaudhuri, Pierluigi Pisu

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In connected vehicle systems where wireless communication is available among the involved vehicles and intersection controllers, it is possible to design an intersection coordination strategy that leads the connected and automated vehicles (CAVs) travel through the road intersections without the conventional traffic light control. In this paper, we present a distributed coordination strategy for the CAVs at multiple interconnected intersections that aims at improving system fuel efficiency and system mobility. We present a distributed control solution where in the higher level, the intersection controllers calculate the road desired average velocity and optimally assign reference velocities of each vehicle. In the lower level, every vehicle is considered to use model predictive control (MPC) to track their reference velocity obtained from the higher level controller. The proposed method has been implemented on a simulation-based case with two-interconnected intersection network. Additionally, the effects of mixed vehicle types on the coordination strategy has been explored. Simulation results indicate the improvement on vehicle fuel efficiency and traffic mobility of the proposed method.

Keywords: connected vehicles, automated vehicles, intersection coordination systems, multiple interconnected intersections, model predictive control

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Authors: Kingston Rajiah, Mari Kannan Maharajan, Si Jen Yeen, Sara Lew

Abstract:

Parkinson’s disease (PD) is a progressive neurodegenerative disorder with evolving layers of complexity. Both motor and non-motor symptoms of PD may affect patients’ quality of life (QoL). Life expectancy for an individual with Parkinson’s disease depends on the level of care the individual has access to, can have a direct impact on length of life. Therefore, improvement of the QoL is a significant part of therapeutic plans. Patients with PD, especially those who are in advanced stages, are in great need of assistance, mostly from their family members or caregivers in terms of medical, emotional, and social support. The role of a caregiver becomes increasingly important with the progression of PD, the severity of motor impairment and increasing age of the patient. The nature and symptoms associated with PD can place significant stresses on the caregivers’ burden. As the prevalence of PD is estimated to more than double by 2030, it is important to recognize and alleviate the burden experienced by caregivers. This study focused on the impact of the clinical features on the QoL of PD patients, and of their caregivers. This study included PD patients along with their caregivers and was undertaken at the Malaysian Parkinson's Disease Association from June 2016 to November 2016. Clinical features of PD patients were assessed using the Movement Disorder Society revised Unified Parkinson Disease Rating Scale (MDS-UPDRS); the Hoehn and Yahr Staging of Parkinson's Disease were used to assess the severity and Parkinson's disease activities of daily living scale were used to assess the disability of Parkinson’s disease patients. QoL of PD patients was measured using the Parkinson's Disease Questionnaire-39 (PDQ-39). The revised version of the Zarit Burden Interview assessed caregiver burden. At least one of the clinical features affected PD patients’ QoL, and at least one of the QoL domains affected the caregivers’ burden. Clinical features ‘Saliva and Drooling’, and ‘Dyskinesia’ explained 29% of variance in QoL of PD patients. The QoL domains ‘stigma’, along with ‘emotional wellbeing’ explained 48.6% of variance in caregivers’ burden. Clinical features such as saliva, drooling and dyskinesia affected the QoL of PD patients. The PD patients’ QoL domains such as ‘stigma’ and ‘emotional well-being’ influenced their caregivers’ burden.

Keywords: carers, quality of life, clinical features, Malaysia

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Authors: Alpana Goel, Kawalpreet Kalra

Abstract:

The study of level structures of deformed nuclei is the most complex topic in nuclear physics. For the description of level structure, a simple model is good enough to bring out the basic features which may then be further refined. The low lying level structures of these nuclei can, therefore, be understood in terms of Two Quasiparticle plus axially symmetric Rotor Model (TQPRM). The formulation of TQPRM for deformed nuclei has been presented. The analysis of available experimental data on two quasiparticle rotational bands of deformed nuclei present unusual features like signature dependence, odd-even staggering, signature inversion and signature reversal in two quasiparticle rotational bands of deformed nuclei. These signature effects are well discussed within the framework of TQPRM. The model is well efficient in reproducing the large odd-even staggering and anomalous features observed in even-even and odd-odd deformed nuclei. The effect of particle-particle and the Coriolis coupling is well established from the model. Detailed description of the model with implications to deformed nuclei is presented in the paper.

Keywords: deformed nuclei, signature effects, signature inversion, signature reversal

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Authors: Jiajia Yao, Guanlin Wu, Fang Liu, Junshuai Xue, Yue Hao

Abstract:

As the production process of self-standing GaN substrates evolves, the commercialization of low dislocation density, large-scale, semi-insulating self-standing GaN substrates is gradually becoming a reality. This advancement has given rise to increased interest in GaN materials' homoepitaxial technology. However, at the homoepitaxial interface, there are considerable concentrations of impurity elements, including C, Si, and O, which generate parasitic leakage channels at the re-growth junction. This phenomenon results in leaked HEMTs that prove difficult to switch off, rendering them effectively non-functional. The emergence of leakage channels can also degrade the high-frequency properties and lower the power devices' breakdown voltage. In this study, the uniform epitaxy of AlGaN/GaN heterojunction with high electron mobility was accomplished through the surface treatment of the GaN substrates prior to growth and the design of the AlN isolation layer structure. By employing a procedure combining gallium atom in-situ cleaning and plasma nitridation, the C and O impurity concentrations at the homoepitaxial interface were diminished to the scale of 10¹⁷ cm-³. Additionally, the 1.5 nm nitrogen-rich AlN isolation layer successfully prevented the diffusion of Si impurities into the GaN channel layer. The result was an AlGaN/GaN heterojunction with an electron mobility of 1552 cm²/Vs and an electron density of 1.1 × 10¹³ cm-² at room temperature, obtained on a Fe-doped semi-insulating GaN substrate.

Keywords: MBE, AlGaN/GaN, homogenerous epitaxy, HEMT

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Authors: Stefano Pelaggi

Abstract:

The research is focused on youth labor and mobility in Berlin. Mobility has become a common denominator in our daily lives but it does not primarily move according to monetary incentives. Labor, knowledge and leisure overlap on this point as cities are trying to attract people who could participate in production of the innovations while the new migrants are experiencing the lifestyle of the host cities. The research will present the project of empirical study focused on Italian workers in the digital industry in Berlin, trying to underline the connection between pleasure, leisure with the choice of life abroad. Berlin has become the epicenter of the European Internet start-up scene, but people suitable to work for digital industries are not moving in Berlin to make a career, most of them are attracted to the city for different reasons. This point makes a clear exception to traditional migration flows, which are always originated from a specific search of employment opportunities or strong ties, usually families, in a place that could guarantee success in finding a job. Even the skilled migration has always been originated from a specific need, finding the right path for a successful professional life. In a society where the lack of free time in our calendar seems to be something to be ashamed, the actors of youth mobility incorporate some categories of experiential tourism within their own life path. Professional aspirations, lifestyle choices of the protagonists of youth mobility are geared towards meeting the desires and aspirations that define leisure. While most of creative work places, in particular digital industries, uses the category of fun as a primary element of corporate policy, virtually extending the time to work for the whole day; more and more people around the world are deciding their path in life, career choices on the basis of indicators linked to the realization of the self, which may include factors like a warm climate, cultural environment. All indicators that are usually eradicated from the hegemonic approach to labor. The interpretative framework commonly used seems to be mostly focused on a dualism between Florida's theories and those who highlight the absence of conflict in his studies. While the flexibility of the new creative industries is minimizing leisure, incorporating elements of leisure itself in work activities, more people choose their own path of life by placing great importance to basic needs, through a gaze on pleasure that is only partially driven by consumption. The multi localism is the co-existence of different identities and cultures that do not conflict because they reject the bind on territory. Local loses its strength of opposition to global, with an attenuation of the whole concept of citizenship, territory and even integration. A similar perspective could be useful to search a new approach to all the studies dedicated to the gentrification process, while studying the new migrations flow.

Keywords: brain drain, digital industry, leisure and gentrification, multi localism

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