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\"Explains the principles of inventing and provides photo-illustrated instructions for making a variety of moving contraptions\"--Provided by publisher.

Jaccard index, originally proposed by Jaccard (Bull Soc Vaudoise Sci Nat 37:241–272, 1901), is a measure for examining the similarity (or dissimilarity) between two sample data objects. It is defined as the proportion of the intersection size to the union size of the two data samples. It provides a very simple and intuitive measure of similarity between data samples. This research examines the measures that are akin to the Jaccard index and may be used for modelling affinity between users (or items) in collaborative recommendations. Particularly, the measures such as simple matching coefficient (SMC), Sorensen–Dice coefficient (SDC), Salton’s cosine index (SCI), and overlap coefficient (OLC) are compared and analysed in both theoretical and empirical perspectives with respect to the Jaccard index. Since these measures apprehend only the structural similarity information (overlapping information) between the data samples, these are very useful in situations where only the associations between users and items are available such as browsing or buying behaviours of the users on an e-commerce portal (i.e. unary rating data, a special case of ratings). Furthermore, a theoretical relation among these measures has been established. We have also derived an equivalent expression for each of these measures so that it can be directly applied for binary data samples in data mining/machine learning jargon. In order to compare and validate the effectiveness of these structural similarity measures, several experiments have been conducted using standardized benchmark datasets (MovieLens, FilmTrust, Epinions, Yahoo! Movies, and Yahoo! Music). Empirically obtained results demonstrate that the Salton’s cosine index (SCI) provides better accuracy (in terms of MAE, RMSE, and precision) for large datasets, whereas the overlap coefficient (OLC) results in more accurate recommendations for small datasets.

\"Instructions for creating items using scientific methods\"-- Provided by publisher.

Technological advances have allowed hand gestures to become an important research field especially in applications such as health care and assisting applications for elderly people, providing a natural interaction with the assisting system through a camera by making specific gestures. In this study, we proposed three different scenarios using a Microsoft Kinect V2 depth sensor then evaluated the effectiveness of the outcomes. The first scenario used joint tracking combined with a depth threshold to enhance hand segmentation and efficiently recognise the number of fingers extended. The second scenario utilised the metadata parameters provided by the Kinect V2 depth sensor, which provided 11 parameters related to the tracked body and gave information about three gestures for each hand. The third scenario used a simple convolutional neural network with joint tracking by depth metadata to recognise and classify five hand gesture categories. In this study, deaf-mute elderly people performed five different hand gestures, each related to a specific request, such as needing water, meal, toilet, help and medicine. Next, the request was sent via the global system for mobile communication (GSM) as a text message to the care provider’s smartphone because the elderly subjects could not execute any activity independently.

\"To prove her royalty, the princess must pass an impossible test: to feel a single pea under layers of mattresses! In this innovative retelling of the classic fairy tale, readers will problem-solve the princess out of her predicament! Fun activities introduce key science, technology, engineering, and mathematics concepts and challenge readers critical thinking. Readers will build confidence in their abilities and take interest in STEM material. Original illustrations and hands-on activities give readers an interactive experience. Even readers reluctant to learn STEM materials will love this immersive format, ensuring this book will be a popular addition to any library.\"-- (Source of summary not specified).

Highway bridges in coastal areas are seriously affected by the marine environment, while most of the existing test methods for bridge-reinforced concrete beams considering both corrosion and fatigue factors are carried out in an alternating manner, which cannot reflect the actual service conditions of the bridge structure. This paper focuses on an experimental study of the coupled influence of reinforcement corrosion and fatigue loading in reinforced concrete T-shaped beams. A novel loading test device that can realize the corrosion–fatigue coupling effect is designed, and then six reinforced concrete T-shaped beams are fabricated and tested. For the corrosion–fatigue coupling test beams, the variation law of beam cracks, failure modes, steel strain development law, load-deflection relationship, and fatigue life are analyzed and compared with that of the simple fatigue test beams. The test results show that the cracks of the test beam develop continuously with the fatigue loading times under the corrosion–fatigue coupling environment. The fatigue failure modes are all brittle fractures of the main steel bars, which present the shape of uneven oblique section tearing. The new testing device and approach can provide direct insights into the interaction of reinforcement corrosion and cyclic loading on the fatigue behavior of T-shaped RC beams, which can be further used to understand the long-term performance of bridge structures under complex marine environments.

Image segmentation plays a central role in a broad range of applications, such as medical image analysis, autonomous vehicles, video surveillance and augmented reality. Portrait segmentation, which is a subset of semantic image segmentation, is widely used as a preprocessing step in multiple applications such as security systems, entertainment applications, video conferences, etc. A substantial amount of deep learning-based portrait segmentation approaches have been developed, since the performance and accuracy of semantic image segmentation have improved significantly due to the recent introduction of deep learning technology. However, these approaches are limited to a single portrait segmentation model. In this paper, we propose a novel approach using an ensemble method by combining multiple heterogeneous deep-learning based portrait segmentation models to improve the segmentation performance. The Two-Models ensemble and Three-Models ensemble, using a simple soft voting method and weighted soft voting method, were experimented. Intersection over Union (IoU) metric, IoU standard deviation and false prediction rate were used to evaluate the performance. Cost efficiency was calculated to analyze the efficiency of segmentation. The experiment results show that the proposed ensemble approach can perform with higher accuracy and lower errors than single deep-learning-based portrait segmentation models. The results also show that the ensemble of deep-learning models typically increases the use of memory and computing power, although it also shows that the ensemble of deep-learning models can perform more efficiently than a single model with higher accuracy using less memory and less computing power.

The rising number of patients with dementia has become a serious social problem worldwide. To help detect dementia at an early stage, many studies have been conducted to detect signs of cognitive decline by prosodic and acoustic features. However, many of these methods are not suitable for everyday use as they focus on cognitive function or conversational speech during the examinations. In contrast, conversational humanoid robots are expected to be used in the care of older people to help reduce the work of care and monitoring through interaction. This study focuses on early detection of mild cognitive impairment (MCI) through conversations between patients and humanoid robots without a specific examination, such as neuropsychological examination. This was an exploratory study involving patients with MCI and cognitively normal (CN) older people. We collected the conversation data during neuropsychological examination (Mini-Mental State Examination [MMSE]) and everyday conversation between a humanoid robot and 94 participants (n=47, 50%, patients with MCI and n=47, 50%, CN older people). We extracted 17 types of prosodic and acoustic features, such as the duration of response time and jitter, from these conversations. We conducted a statistical significance test for each feature to clarify the speech features that are useful when classifying people into CN people and patients with MCI. Furthermore, we conducted an automatic classification experiment using a support vector machine (SVM) to verify whether it is possible to automatically classify these 2 groups by the features identified in the statistical significance test. We obtained significant differences in 5 (29%) of 17 types of features obtained from the MMSE conversational speech. The duration of response time, the duration of silent periods, and the proportion of silent periods showed a significant difference (P<.001) and met the reference value r=0.1 (small) of the effect size. Additionally, filler periods (P<.01) and the proportion of fillers (P=.02) showed a significant difference; however, these did not meet the reference value of the effect size. In contrast, we obtained significant differences in 16 (94%) of 17 types of features obtained from the everyday conversations with the humanoid robot. The duration of response time, the duration of speech periods, jitter (local, relative average perturbation [rap], 5-point period perturbation quotient [ppq5], difference of difference of periods [ddp]), shimmer (local, amplitude perturbation quotient [apq]3, apq5, apq11, average absolute differences between the amplitudes of consecutive periods [dda]), and F0cov (coefficient of variation of the fundamental frequency) showed a significant difference (P<.001). In addition, the duration of response time, the duration of silent periods, the filler period, and the proportion of fillers showed significant differences (P<.05). However, only jitter (local) met the reference value r=0.1 (small) of the effect size. In the automatic classification experiment for the classification of participants into CN and MCI groups, the results showed 66.0% accuracy in the MMSE conversational speech and 68.1% accuracy in everyday conversations with the humanoid robot. This study shows the possibility of early and simple screening for patients with MCI using prosodic and acoustic features from everyday conversations with a humanoid robot with the same level of accuracy as the MMSE.

The simplest way to forecast geophysical processes, an engineering problem with a widely recognized challenging character, is the so-called “univariate time series forecasting” that can be implemented using stochastic or machine learning regression models within a purely statistical framework. Regression models are in general fast-implemented, in contrast to the computationally intensive Global Circulation Models, which constitute the most frequently used alternative for precipitation and temperature forecasting. For their simplicity and easy applicability, the former have been proposed as benchmarks for the latter by forecasting scientists. Herein, we assess the one-step ahead forecasting performance of 20 univariate time series forecasting methods, when applied to a large number of geophysical and simulated time series of 91 values. We use two real-world annual datasets, a dataset composed by 112 time series of precipitation and another composed by 185 time series of temperature, as well as their respective standardized datasets, to conduct several real-world experiments. We further conduct large-scale experiments using 12 simulated datasets. These datasets contain 24,000 time series in total, which are simulated using stochastic models from the families of AutoRegressive Moving Average and AutoRegressive Fractionally Integrated Moving Average. We use the first 50, 60, 70, 80 and 90 data points for model-fitting and model-validation, and make predictions corresponding to the 51st, 61st, 71st, 81st and 91st respectively. The total number of forecasts produced herein is 2,177,520, among which 47,520 are obtained using the real-world datasets. The assessment is based on eight error metrics and accuracy statistics. The simulation experiments reveal the most and least accurate methods for long-term forecasting applications, also suggesting that the simple methods may be competitive in specific cases. Regarding the results of the real-world experiments using the original (standardized) time series, the minimum and maximum medians of the absolute errors are found to be 68 mm (0.55) and 189 mm (1.42) respectively for precipitation, and 0.23 °C (0.33) and 1.10 °C (1.46) respectively for temperature. Since there is an absence of relevant information in the literature, the numerical results obtained using the standardized real-world datasets could be used as rough benchmarks for the one-step ahead predictability of annual precipitation and temperature.

The present study explores the application of rapid prototyping (RP) for manufacturing tool electrodes in electro-discharge machining process. The performance of a metallic electrode built via selective laser sintering is compared to solid copper and brass tools during machining of D2 tool steel. In order to efficiently evaluate the influence of several parameters, Taguchi’s L 18 design is adopted to plan the experimental layout. The machining parameters considered in this study are tool type, a categorical parameter and three quantitative parameters such as duty cycle, pulse-on-time and peak current. Multiple performance measures such as material removal rate, tool wear rate, surface roughness and radial over cut of the machined cavity are considered. The multiple performance responses are converted into an equivalent single response known as grey relational grade using grey relational analysis. A nonlinear regression model is developed to relate grey relational grade with process parameters with a coefficient of determination of 0.97. In order to obtain optimal parameter settings satisfying the performance measures, three meta-heuristic algorithms are used due to their computational elegance. The comparative study indicates that particle swarm optimization and simple optimization are effective in delivering the optimized results in substantially less time compared to teaching-learning-based optimization algorithms. It is found that RP tool can perform in a superior manner for simultaneous optimization of multiple responses when compared to copper and brass tools.