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Virtual reality (VR) is continuing to grow as more affordable technological devices become available. Video games are one of the most profitable applications, while rehabilitation has the most significant social impact. Both applications require a proper user evaluation to provide personalized experiences that avoid boring or stressful situations. Despite the successful applications, there are several opportunities to improve the field of human–machine interactions, one of the most popular ones being the use of affect detection to create personalized experiences. In that sense, this study presents the implementation of two dynamic difficulty adaptation strategies. The person's affective state is estimated through a machine learning classification model, which later serves to adapt the difficulty of the video game online. The results show that it is possible to maintain the user at a given difficulty level, which is analogous to achieving the well-known flow state. Among the two implemented strategies, no statistical differences were found in the workload induced by the users. However, more physical demands and a higher frustration were induced by one of the strategies, validated with the recorded muscular activity. The results obtained contribute to the state of the art of DDA strategies in virtual reality driven by affective data. [ABSTRACT FROM AUTHOR]

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This paper aims to classify the types of stress experienced by women undergoing infertility treatment in Japan and to clarify the causes of such stress. To achieve this, tweets posted by infertility patients were used to identify the causes of stress through a learning model, followed by dependency analysis. A system is proposed to extract causal relations using dependency analysis, where the dependents are the causes and the leaders are the effects. The study focuses on the stress experienced by infertility patients and limits the analysis to "family issues" and "work." Consequently, the incentives for the causes were clarified. [ABSTRACT FROM AUTHOR]

ObjectivesIn view of the fact that the structural performance assessment method of the internal-pressure-resistant square cabin is not clear and the general specifications for naval ships are not fully applicable, the stress analysis method and strength assessment criteria applicable to the internal-pressure-resistant square cabin are studied. MethodsBased on the theory of elasticity, the two yield criteria Mises and Tresca commonly applied in ASME BPVC were analyzed. According to the principle of safety, Tresca was determined to be the analysis criterion applicable to the internal-pressure-resistant square cabin. By taking the bulkhead grillage as the basic unit, the stress classification of the internal-pressure-resistant square cabin was carried out based on ASME BPVC, and four typical assessment locations were obtained: the center of the plate panel, the midpoint of the short side of the plate panel, the midpoint of the long side of the plate panel, and the corner of the plate panel. In order to reduce the amount of engineering calculation, the theoretical formula and numerical calculation method of stress components for plate element finite element analysis were proposed based on the stress linearization theory, and the solid finite element model of grillage was established for comparing the difference between the structural assessment results of two models. ResultsCompared with the accurate results of the solid element model, the error of plate element stress analysis result is basically about 3%, and the results of plate elements are generally larger. Considering the safety conservative assessment principle of ships and nuclear structures, it can be considered that the strength assessment method of internal-pressure-resistant square cabins based on the plate element finite element model and ASME BPVC meets the engineering requirements. ConclusionsThis study can provide a reference for the stress analysis and strength assessment of the internal-pressure-resistant square cabin, and is of great significance for tackling the technical bottleneck faced by the ships using nuclear power plants.

In recent years, monitoring the health of crops has been greatly aided by deploying highthroughput crop monitoring techniques that integrate remotely captured imagery and deep learning techniques. Most methods rely mainly on the visible spectrum for analyzing the abiotic stress, such as water deficiency in crops. In this study, we carry out experiments on maize crop in a controlled environment of different water treatments. We make use of a multispectral camera mounted on an Unmanned Aerial Vehicle for collecting the data from the tillering stage to the heading stage of the crop. A pre-processing pipeline, followed by the extraction of the Region of Interest from orthomosaic is explained. We propose a model based on a Convolution Neural Network, added with a deformable convolutional layer in order to learn and extract rich spatial and spectral features. These features are further fed to a weighted Attention-based Bi-Directional Long Short-Term Memory network to process the sequential dependency between temporal features. Finally, the water stress category is predicted using the aggregated Spatial-Spectral-Temporal Characteristics. The addition of multispectral, multi-temporal imagery significantly improved accuracy when compared with mono-temporal classification. By incorporating a deformable convolutional layer and BiDirectional Long Short-Term Memory network with weighted attention, our proposed model achieved best accuracy of 91.30% with a precision of 0.8888 and a recall of 0.8857. The results indicate that multispectral, multi-temporal imagery is a valuable tool for extracting and aggregating discriminative spatialspectral-temporal characteristics for water stress classification. [ABSTRACT FROM AUTHOR]

Nowadays, a stress classification system is essential to classify the psychological stress that impairs a person's socioeconomic life. Several Deep Learning (DL) models have been developed in recent years to classify stress using physiological signals, including electrodermal activity (EDA) and electrocardiography (ECG). However, those models cannot handle concept drift during the training phase, which may struggle to adapt to changing data patterns, leading to unreliable predictions. Concept drift refers to changes in the characteristics or patterns of physiological signals used for stress classification. These changes could be due to various factors, including shifts in the data distribution, environmental conditions, or the subjects' behavior. Therefore, this article develops a novel Deep Transfer Learning with Organic Computing (DTLOC) model by integrating the Deep Convolutional Neural Network (DCNN) with the TL and OC mechanisms to handle concept drift and improve the accuracy of stress classification. The TL brings prior knowledge about EDA and ECG features, which enhances the model's initial capabilities and shortens the learning curve. Additionally, the OC provides a self-management system that oversees the structure and operation of the model. It dynamically adapts the DCNN in response to changing data patterns, ensuring that the model remains accurate and effective in classifying stress, even in the presence of concept drift. The experimental results demonstrate that the DTLOC model, utilizing EDA and ECG data from the WESAD dataset, achieves an accuracy of 93.53%. This is a significant improvement compared to the LIBSVM, LSTM, DNN, and CNN models, with increases of 15.63%, 13.15%, 10.37%, and 5.03% respectively. Thus, this model can enhance individuals' quality of life and safety by detecting stress-related illnesses at an earlier stage. [ABSTRACT FROM AUTHOR]

In recent years, monitoring the health of crops has been greatly aided by deploying highthroughput crop monitoring techniques that integrate remotely captured imagery and deep learning techniques. Most methods rely mainly on the visible spectrum for analyzing the abiotic stress, such as water deficiency in crops. In this study, we carry out experiments on maize crop in a controlled environment of different water treatments. We make use of a multispectral camera mounted on an Unmanned Aerial Vehicle for collecting the data from the tillering stage to the heading stage of the crop. A pre-processing pipeline, followed by the extraction of the Region of Interest from orthomosaic is explained. We propose a model based on a Convolution Neural Network, added with a deformable convolutional layer in order to learn and extract rich spatial and spectral features. These features are further fed to a weighted Attention-based Bi-Directional Long Short-Term Memory network to process the sequential dependency between temporal features. Finally, the water stress category is predicted using the aggregated Spatial-Spectral-Temporal Characteristics. The addition of multispectral, multi-temporal imagery significantly improved accuracy when compared with mono-temporal classification. By incorporating a deformable convolutional layer and Bi-Directional Long Short-Term Memory network with weighted attention, our proposed model achieved best accuracy of 91.30% with a precision of 0.8888 and a recall of 0.8857. The results indicate that multispectral, multi-temporal imagery is a valuable tool for extracting and aggregating discriminative spatial-spectral-temporal characteristics for water stress classification.

In today's world, stress is a major factor for various diseases in modern societies which affects the day-to-day activities of human beings. The measurement of stress is a contributing factor for governments and societies that impacts the quality of daily lives. The strategy of stress monitoring systems requires an accurate stress classification technique which is identified via the reactions of the body to regulate itself to changes within the environment through mental and emotional responses. Therefore, this research proposed a novel deep learning approach for the stress classification system. In this paper, we presented an Enhanced Long Short-Term Memory(E-LSTM) based on the feature attention mechanism that focuses on determining and categorizing the stress polarity using sequential modeling and word-feature seizing. The proposed approach integrates pre-feature attention in E-LSTM to identify the complicated relationship and extract the keywords through an attention layer for stress classification. This research has been evaluated using a selected dataset accessed from the sixth Korea National Health and Nutrition Examination Survey conducted from 2013 to 2015 (KNHANES VI) to analyze health-related stress data. Statistical performance of the developed approach was analyzed based on the nine features of stress detection, and we compared the effectiveness of the developed approach with other different stress classification approaches. The experimental results shown that the developed approach obtained accuracy, precision, recall and a F1-score of 75.54%, 74.26%, 72.99% and 74.58%, respectively. The feature attention mechanism-based E-LSTM approach demonstrated superior performance in stress detection classification when compared to other classification methods including naïve Bayesian, SVM, deep belief network, and standard LSTM. The results of this study demonstrated the efficiency of the proposed approach in accurately classifying stress detection, particularly in stress monitoring systems where it is expected to be effective for stress prediction. [ABSTRACT FROM AUTHOR]

Stress is an affective human state. Different types of stress have varying effects on human health. Previous stress classifications using tissue oxygen saturation (StO2) have relied on baselines to extract features in a way that limits practical application since the baselines cannot be preacquired. In this paper, we explored the correlation and the classification computability of the baseline-independent facial StO2 across four states involving baseline, emotional stress, high-intensity physical stress and low-intensity physical stress. Three analytical approaches verify that facial StO2 is affection-related and state-specific. Building on this theoretical foundation, we validated the baseline-independent facial StO2 before and after modified data augmentation using three input forms as well as two network structures. We also proposed a dual-stream (Channel stream and Spatial stream) attention network, named as CSnet, to perform baseline-independent stress classification. Experimental results suggest that the proposed method can achieve a unweighted average recall (UAR) of 0.6935 and unweighted F1-score (UF1) of 0.6991, which is higher than traditional image descriptors methods, and is more competitive than the baseline-dependent classification with respect of real applications. [ABSTRACT FROM AUTHOR]

Plant stress diagnosis is essential for efficient crop management and productivity increase. Under stress, plants undergo physiological and compositional changes. Vegetation indices obtained from leaf reflectance spectra and bioimpedance spectroscopy provide information about the external and internal aspects of plant responses, respectively. In this study, bioimpedance and vegetation indices were noninvasively acquired from sweet basil (Ocimum basilicum L.) leaves exposed to three types of stress (drought, salinity, and chilling). Integrating the vegetation index, a novel approach, contains information about the surface of plants and bioimpedance data, which indicates the internal changes of plants. The fusion of these two datasets was examined to classify the types and severity of stress. Among the eight supervised machine learning models (three linear and five non-linear), the support vector machine (SVM) exhibited the highest accuracy in classifying stress types. Bioimpedance spectroscopy alone exhibited an accuracy of 0.86 and improved to 0.90 when fused with vegetation indices. Additionally, for drought and salinity stresses, it was possible to classify the early stage of stress with accuracies of 0.95 and 0.93, respectively. This study will allow us to classify the different types and severity of plant stress, prescribe appropriate treatment methods for efficient cost and time management of crop production, and potentially apply them to low-cost field measurement systems. [ABSTRACT FROM AUTHOR]

Recently, transformers have achieved great success in a number of computer vision tasks due to their excellent ability to capture long-range feature dependencies. In contrast, convolutional neural networks (CNNs) are good at extracting local features. Given that the capture of short- and long-range band dependencies are both important for hyperspectral data classification, we propose MCE-ST, a convolution-transformer (conformer) based framework capable of exploiting the complementary strengths of transformers and CNNs. In contrast to the conventional transformer, which uses a linear projection for tokenization, the proposed MCE-ST uses a convolution-based tokenization method to extract local dependency between spectral bands. Moreover, since different hyperspectral samples may have different spans of local relationships, a multiscale conformer encoder (MCE) comprising two separate branches of depth-wise dilated convolution with different kernel sizes is used to extract the different spans of the local interactions between tokens. We conducted experiments on four salt stress datasets and one cassava disease dataset. The results show that the proposed MCE-ST outperforms the state-of-the-art techniques for crop stress classification using hyperspectral data. The code for MCE-ST is publicly available at https://github.com/Weejaa04/MCE-ST-GitHub.

A healthy society must take proper measures to handle human stress, a severe health risk. To classify felt mental stress, this work offers an experimental inquiry to determine the proper phase when electroencephalography (EEG) based input from the DEAP dataset is combined with accelerometer sensor data from the WESAD dataset. A multisensory data fusion approach has been proposed to gather complete data for prognostic modeling and analysis. These techniques attempt to create a composite health index (HI) by fusing numerous sensor inputs. To get an aggregated version of the EEG-based data from the DEAP dataset and Accelerometer (ACC) sensor data from the WESAD dataset, we used the k-medoid data aggregation method with time-frame constrained intra-cluster similarity computations. The mental state is then classified into low-stress, medium-stress, and high-stress categories using a CNN trained on this aggregated dataset. Three types of data low stress, medium stress, and high stress, are created. To categorize stress levels, we used three classifiers Support Vector Machine (SVM), Logical Regression (LR), and Naïve Bayes (NB) are used. Three-class stress classification is accurate to 82.85% of the actual value. [ABSTRACT FROM AUTHOR]

Objective. This study evaluates the effectiveness of four machine learning algorithms in classifying physiological stress using heart rate variability (HRV) and pulse rate variability (PRV) time series, comparing an automatic feature selection based on Akaike's criterion to a physiologically-based feature selection approach. Approach. Linear discriminant analysis, support vector machines, K -nearest neighbors and random forest were applied on ten HRV and PRV indices from time, frequency and information domains, selected with the two feature selection approaches. Data were collected from 127 healthy individuals during different stress conditions (rest, postural and mental stress). Main results. Our results highlight that, while specific stress classification is feasible, distinguishing between postural and mental stress remains challenging. The used classifiers exhibited similar performance, with automatic Akaike Information Criterion-based feature selection proving overall better than the physiology-driven approach. Additionally, PRV-based features performed comparably to HRV-based ones, indicating their potential in outpatient monitoring using wearable devices. Significance. The obtained findings help to determine the most relevant HRV/PRV features for stress classification, potentially useful to highlight different physiological mechanisms involved during both challenges accompanied by a shift in the sympathovagal balance. The proposed approach may have implications for advancing stress assessment methodologies in clinical settings and real-world contexts for well-being evaluation., (Creative Commons Attribution license.)

The agriculture sector is no exception to the widespread usage of deep learning tools and techniques. In this paper, an automated detection method on the basis of pre-trained Convolutional Neural Network (CNN) models is proposed to identify and classify paddy crop biotic stresses from the field images. The proposed work also provides the empirical comparison among the leading CNN models with transfer learning from the ImageNet weights namely, Inception-V3, VGG-16, ResNet-50, DenseNet-121 and MobileNet-28. Brown spot, hispa, and leaf blast, three of the most common and destructive paddy crop biotic stresses that occur during the flowering and ripening growth stages are considered for the experimentation. The experimental results reveal that the ResNet-50 model achieves the highest average paddy crop stress classification accuracy of 92.61% outperforming the other considered CNN models. The study explores the feasibility of CNN models for the paddy crop stress identification as well as the applicability of automated methods to non-experts.

Human-centered applications using wearable sensors in combination with machine learning have received a great deal of attention in the last couple of years. At the same time, wearable sensors have also evolved and are now able to accurately measure physiological signals and are, therefore, suitable for detecting body reactions to stress. The field of machine learning, or more precisely, deep learning, has been able to produce outstanding results. However, in order to produce these good results, large amounts of labeled data are needed, which, in the context of physiological data related to stress detection, are a great challenge to collect, as they usually require costly experiments or expert knowledge. This usually results in an imbalanced and small dataset, which makes it difficult to train a deep learning algorithm. In recent studies, this problem is tackled with data augmentation via a Generative Adversarial Network (GAN). Conditional GANs (cGAN) are particularly suitable for this as they provide the opportunity to feed auxiliary information such as a class label into the training process to generate labeled data. However, it has been found that during the training process of GANs, different problems usually occur, such as mode collapse or vanishing gradients. To tackle the problems mentioned above, we propose a Long Short-Term Memory (LSTM) network, combined with a Fully Convolutional Network (FCN) cGAN architecture, with an additional diversity term to generate synthetic physiological data, which are used to augment the training dataset to improve the performance of a binary classifier for stress detection. We evaluated the methodology on our collected physiological measurement dataset, and we were able to show that using the method, the performance of an LSTM and an FCN classifier could be improved. Further, we showed that the generated data could not be distinguished from the real data any longer. [ABSTRACT FROM AUTHOR]

In today’s world, stress is a major factor for various diseases in modern societies which affects the day-to-day activities of human beings. The measurement of stress is a contributing factor for governments and societies that impacts the quality of daily lives. The strategy of stress monitoring systems requires an accurate stress classification technique which is identified via the reactions of the body to regulate itself to changes within the environment through mental and emotional responses. Therefore, this research proposed a novel deep learning approach for the stress classification system. In this paper, we presented an Enhanced Long Short-Term Memory(E-LSTM) based on the feature attention mechanism that focuses on determining and categorizing the stress polarity using sequential modeling and word-feature seizing. The proposed approach integrates pre-feature attention in E-LSTM to identify the complicated relationship and extract the keywords through an attention layer for stress classification. This research has been evaluated using a selected dataset accessed from the sixth Korea National Health and Nutrition Examination Survey conducted from 2013 to 2015 (KNHANES VI) to analyze health-related stress data. Statistical performance of the developed approach was analyzed based on the nine features of stress detection, and we compared the effectiveness of the developed approach with other different stress classification approaches. The experimental results shown that the developed approach obtained accuracy, precision, recall and a F1-score of 75.54%, 74.26%, 72.99% and 74.58%, respectively. The feature attention mechanism-based E-LSTM approach demonstrated superior performance in stress detection classification when compared to other classification methods including naïve Bayesian, SVM, deep belief network, and standard LSTM. The results of this study demonstrated the efficiency of the proposed approach in accurately classifying stress detection, particularly in stress monitoring systems where it is expected to be effective for stress prediction.

Objective: Mental stress is a major problem in our society and has become an area of interest for many psychiatric researchers. One primary research focus area is the identification of bio-markers that not only identify stress but also predict the conditions (or tasks) that cause stress. Electroencephalograms (EEGs) have been used for a long time to study and identify bio-markers. While these bio-markers have successfully predicted stress in EEG studies for binary conditions, their performance is suboptimal for multiple conditions of stress. Methods: To overcome this challenge, we propose using latent based representations of the bio-markers, which have been shown to significantly improve EEG performance compared to traditional bio-markers alone. We evaluated three commonly used EEG based bio-markers for stress, the brain load index (BLI), the spectral power values of EEG frequency bands (alpha, beta and theta), and the relative gamma (RG), with their respective latent representations using four commonly used classifiers. Results: The results show that spectral power value based bio-markers had a high performance with an accuracy of 83%, while the respective latent representations had an accuracy of 91%.

Human-centered applications using wearable sensors in combination with machine learning have received a great deal of attention in the last couple of years. At the same time, wearable sensors have also evolved and are now able to accurately measure physiological signals and are, therefore, suitable for detecting body reactions to stress. The field of machine learning, or more precisely, deep learning, has been able to produce outstanding results. However, in order to produce these good results, large amounts of labeled data are needed, which, in the context of physiological data related to stress detection, are a great challenge to collect, as they usually require costly experiments or expert knowledge. This usually results in an imbalanced and small dataset, which makes it difficult to train a deep learning algorithm. In recent studies, this problem is tackled with data augmentation via a Generative Adversarial Network (GAN). Conditional GANs (cGAN) are particularly suitable for this as they provide the opportunity to feed auxiliary information such as a class label into the training process to generate labeled data. However, it has been found that during the training process of GANs, different problems usually occur, such as mode collapse or vanishing gradients. To tackle the problems mentioned above, we propose a Long Short-Term Memory (LSTM) network, combined with a Fully Convolutional Network (FCN) cGAN architecture, with an additional diversity term to generate synthetic physiological data, which are used to augment the training dataset to improve the performance of a binary classifier for stress detection. We evaluated the methodology on our collected physiological measurement dataset, and we were able to show that using the method, the performance of an LSTM and an FCN classifier could be improved. Further, we showed that the generated data could not be distinguished from the real data any longer.

Detecting stress in computer users, while technically challenging, is of the utmost importance in the workplace, especially now that remote working scenarios are becoming ubiquitous. In this context, cost-effective, subject-independent systems are needed that can be embedded in consumer devices and classify users’ stress in a reliable and unobtrusive fashion. Leveraging keyboard and mouse dynamics is particularly appealing in this context as it exploits readily available sensors. However, available studies are mostly performed in laboratory conditions, and there is a lack of on-field investigations in closer-to-real-world settings. In this study, keyboard and mouse data from 62 volunteers were experimentally collected in-the-wild using a purpose-built Web application, designed to induce stress by asking each subject to perform 8 computer tasks under different stressful conditions. The application of Multiple Instance Learning (MIL) to Random Forest (RF) classification allowed the devised system to successfully distinguish 3 stress-level classes from keyboard (76% accuracy) and mouse (63% accuracy) data. Classifiers were further evaluated via confusion matrix, precision, recall, and F1-score. [ABSTRACT FROM AUTHOR]

Psychological stress may cause various health problems. To prevent the potential chronic illness that long-term psychological stress could cause, it is important to detect and monitor the psychological stress at its initial stage. In this paper, we present a framework for remotely detecting and classifying human stress by using a KINECT sensor that is portable and affordable enough for ordinary users in everyday life. Unlike most of emotion recognition tasks in which respiratory signals (RSPS) are usually used only as an aiding analysis, the whole task proposed is based on RSPS. Thus, the main contribution of this paper is that not only the non-contact devices is used to identify human stress, but also the relationship between RSPS and stress recognition is analyzed in detail. Experimental results on 84 volunteers show that the recognition accuracy for recognizing psychological stress, physical stress, and relaxing state are 93.90%, 93.40%, and 89.05% respectively. These results suggest that the proposed framework is effective for monitoring human stress, and RSPS could be used for stress recognition. [ABSTRACT FROM AUTHOR]

This paper presents a method for detecting psychological stress levels. It aims to explore the feasibility of using a single physiological signal to create a more practical alternative for detecting stress in people than current multiple physiological signals approaches involve. In particular, the approach explored uses linear discriminant analysis (LDA) based on the electrodermal activity (EDA) signal which aims at discriminating between three stress levels: low, medium and high. We used the MIT Media lab 'stress database' from which we selected eleven ‘foot’ based EDA data sets for our experiments. Using this eighteen EDA features were extracted from (sixty-six) five-minutes data segments equating to three driving conditions: at rest, on the open road (highway) and city driving. After that, Fisher projection and Linear discriminant analysis (LDA) were used to classify the stress levels with feature vectors, that included both leaving one out and test cross-validation strategy. The results showed that these methods achieved recognition rate of 81.82% which we argue, while less that multiple signal systems, may be a better balance between recognition performance and computational load, that could be a promising line of research for the development of practical personal stress monitors. [ABSTRACT FROM AUTHOR]

This study provides a classification model of two Modern Greek dialects, namely Athenian Greek and Cypriot Greek, using information from formant dynamics of F 1, F 2, F 3, F 4 and vowel duration. To this purpose, a large corpus of vowels from 45 speakers of Athenian Greek and Cypriot Greek was collected. The first four formant frequencies were measured at multiple time points and modelled using second degree polynomials. The measurements were employed in classification experiments, using three classifiers: Linear Discriminant Analysis, Flexible Discriminant Analysis, and C5.0. The latter outperformed the other classification models, resulting in a higher classification accuracy of the dialect. C5.0 classification shows that duration and the zeroth coefficient of F 2, F 3 and F 4 contribute more to the classification of the dialect than the other measurements; it also shows that formant dynamics are important for the classification of dialect. [ABSTRACT FROM AUTHOR]

In the simplified method based on stress classification for estimating the stress at the steady stage during creep, an empirical correction factor is needed to account for the relaxation of primary bending stress during creep. An analysis of a rectangular beam subjected to tensile loading and bending moment during creep is carried out to demonstrate the applicability of the K t , which is the correction factor proposed by the ASME code, in this work. In view of the effects of material creep property and the primary membrane stress on the relaxation of the primary bending stress, a method for accessing the parameter K t is proposed and validated. The results reveal that the parameter K t will increase with the tensile loading due to the relaxation of the primary bending stress accelerated by the primary membrane stress. Good agreement between the results of inelastic analysis and the modified method is observed for the beams with different material properties, combined loadings, and cross-section shapes. • A maximum stress estimation method based on the elastic routine is proposed. • The proposed method considered the effect of primary stress on stress reduction. • The predicted stress of the proposed method is consistent with that of inelastic analysis. [ABSTRACT FROM AUTHOR]

Information security is key important when we are trying to interconnect the wireless body sensor network with the healthcare social network via mobile facilities. In this paper, we specially work on a secured electrocardiogram (ECG) signal transmission scheme to prevent further injuries for patients with heart diseases from human emotional stress. We proposed a dynamic encryption method via biometric information among frequency spectrums of ECG signals, which can guarantee both high classification rate (>90 %) and system energy efficiency. At the same time, cooperative relays are applied for an additional spatial diversity gains. Simulation results show that the improved transmission rate and signal power capacity can lower the probability of data intercept (LPI) and detection (LPD) by taking the advantages of both temporal and spatial diversities. The network security thereby can be further improved. [ABSTRACT FROM AUTHOR]

This letter presents a method to perform the classification of speech under stress based on physical characteristics. A physical model is proposed to model airflow patterns in the physiological system in order to represent the process of speech production under psychological stress, and physical parameters characterizing airflow variations in the vocal folds, the vocal tract, and laryngeal ventricle are explored. Experimental evaluations show that the physical parameters are effective for the classification of stressed speech. [ABSTRACT FROM AUTHOR]

Stress is a major problem in our society today and poses major concerns for the future. It is important to gain an objective understanding of how average individuals respond to events they observe in typical environments they encounter. We developed a computational model of stress based on objective human responses collected from human observers of environments. In the process, we investigated whether a computational model can be developed to recognize observer stress in abstract virtual environments (text), virtual environments (films) and real environments (real-life settings) using physiological and physical response sensor signals. Our work proposes an architecture for a computational observer stress model. The architecture was used it to implement models for the different types of environments. Sensors appropriate to the different types of environment were investigated where the aims were to achieve unobtrusive methods for stress response signal collection, reduce encumbrance and hence, enhance methods to capture natural observer behaviors and produce stress models that recognized stress more robustly. We discuss the motivations for each investigation and detail the experiments we conducted to collect stress data sets for observers of the different types of environments. We describe individual-independent artificial neural network and support vector machine based model classifiers that were developed to recognize stress patterns from observer response signals. The classifiers were extended to include a genetic algorithm which was used to select features that were better for stress recognition and reduce the use of redundant features. The outcomes of this research provide a possible future extension on managing stress objectively. [ABSTRACT FROM AUTHOR]

This paper proposes methods for assessing Bree-type ratcheting in a cylinder subjected to constant internal pressure and cyclic thermal loading. The proposed methods are elastic analysis-route and elastic–plastic analysis-route. The former is based on the polynomial approximation of the elastic stress distributions for thermal stresses and the reference stress concept for estimating primary stress. The latter elastic–plastic route method is based on the concept of relative elastic core size. The methods proposed were validated by performing elastic–plastic finite element analyses of a smooth cylinder that exhibited Bree-type ratcheting. [ABSTRACT FROM AUTHOR]

Highlights: [•] Proposed a computational model system for observer stress for real-life events. [•] System input includes a novel set of multi-sensor physiological & physical signals. [•] System is based on a support vector machine and a genetic algorithm. [•] Real-world stress data was acquired for interview and meditation settings. [•] Signals captured for the stress model were EEG, skin conductivity, thermal videos. [Copyright &y& Elsevier]