Predicting musically induced emotions from physiological inputs: linear and neural network models

Pleasant touching is an important aspect of social interactions that is widely used as a caregiving technique. To address the problems resulting from a lack of available human caregivers, previous research has attempted to develop robots that can perform this kind of pleasant touch. However, it remains unclear whether robots can provide such a pleasant touch in a manner similar to humans. To investigate this issue, we compared the effect of the speed of gentle strokes on the back between human and robot agents on the emotional responses of human participants (n = 28). A robot or a human stroked on the participants’ back at two different speeds (i.e., 2.6 and 8.5 cm/s). The participants’ subjective (valence and arousal ratings) and physiological (facial electromyography (EMG) recorded from the corrugator supercilii and zygomatic major muscles and skin conductance response) emotional reactions were measured. The subjective ratings demonstrated that the speed of 8.5 cm/s was more pleasant and arousing than the speed of 2.6 cm/s for both human and robot strokes. The corrugator supercilii EMG showed that the speed of 8.5 cm/s resulted in reduced activity in response to both human and robot strokes. These results demonstrate similar speed-dependent modulations of stroke on subjective and physiological positive emotional responses across human and robot agents and suggest that robots can provide a pleasant touch similar to that of humans.

Plasma proteomics provides a unique opportunity to enhance disease prediction by capturing protein expression patterns linked to diverse pathological processes. Leveraging data from 2,923 proteins measured in 53,030 UK Biobank participants, we developed proteomic risk scores for 27 common outcomes over 5- and 15-year follow-up periods using two approaches: a linear ElasticNet regression model and a deep learning neural network (NN) model. Using Cox regression, we assessed the discrimination of proteomic risk scores either in isolation or as incremental improvements over clinical risk factors. We also studied the shared and unique protein predictors across conditions. Proteomic risk scores demonstrated strong discrimination for most outcomes, with a C-index > 0.80 for 12 diseases. NN models outperformed linear models for 11 outcomes, particularly for diseases such as Parkinson’s disease (C-index 0.84) and pulmonary embolism (C-index 0.83), where nonlinear relationships contributed significantly to prediction. Across all outcomes, the addition of proteomic scores to clinical models improved predictive accuracy (ΔC-index 0.03), with the greatest gains observed in 9 diseases (ΔC-index > 0.1), including end-stage renal disease, pulmonary embolism, and Parkinson’s disease. Analysis of protein contributions revealed shared predictors across multiple diseases, such as growth differentiation factor 15 (GDF15), as well as unique predictors like PAEP for endometriosis. While NN models may capture complex relationships, linear models provided value through simplicity and interpretability. These findings underscore the importance of tailoring predictive approaches to specific diseases and demonstrate the pivotal potential of proteomics in advancing risk stratification and early detection.

Development of an android app to estimate chlorophyll content of corn leaves based on contact imaging

With the growing deployment of electric buses (e-buses), accurate energy use modelling has become essential for fleet optimisation and operational planning. Using the SORT methodology, this study analyses instantaneous energy consumption and recuperation (IECR). Three vehicle configurations were tested (one battery with pantograph, four batteries, and eight batteries), each with ten repeatable runs. Four approaches were compared: a baseline linear regression, an extended linear model (ELM) due to the state, a feed-forward neural network, and a recurrent neural network (RNN). The extended linear model achieved a determination coefficient of R2 = 0.9124 (residual standard deviation 4.26) compared with R2 = 0.7859 for the baseline, while the determination coefficient for the RNN is 0.9343, and the RNN provided the highest accuracy on the test set (the correlation coefficient between real and predicted values is 0.9666). The results confirm the dominant influence of speed and acceleration on IECR and show that battery configuration mainly affects consumption during acceleration. Literature-consistent findings indicate that regenerative systems can recover 25–51% of braking energy, with advanced control methods further improving recovery. Despite non-normality and temporal dependence of residuals, the state-aware linear model remains interpretable and competitive, whereas recurrent networks offer superior fidelity. These results support real-time energy management, charging optimisation, and reliable range prediction for electric buses in urban public transport.

Probing food experience or liking through verbal ratings has its shortcomings. We compare explicit ratings to a range of (neuro)physiological and behavioral measures with respect to their performance in distinguishing drinks associated with different emotional experience. Seventy participants tasted and rated the valence and arousal of eight regular drinks and a “ground truth” high-arousal, low-valence vinegar solution. The discriminative power for distinguishing between the vinegar solution and the regular drinks was highest for sip size, followed by valence ratings, arousal ratings, heart rate, skin conductance level, facial expression of “disgust,” pupil diameter, and Electroencephalogram (EEG) frontal alpha asymmetry. Within the regular drinks, a positive correlation was found between rated arousal and heart rate, and a negative correlation between rated arousal and Heart Rate Variability (HRV). Most physiological measures showed consistent temporal patterns over time following the announcement of the drink and taking a sip. This was consistent over all nine drinks, but the peaks were substantially higher for the vinegar solution than for the regular drinks, likely caused by emotion. Our results indicate that implicit variables have the potential to differentiate between drinks associated with different emotional experiences. In addition, this study gives us insight into the physiological temporal response patterns associated with taking a sip.

A dual hybrid forecasting model for support of decision making in healthcare management

This paper presents an application of the neural network (NN) model for forecasting and filtering problems. Instead of using the gradient descent method as used by the standard back-propagation network, the present study adopted the Levenberg-Marquardt algorithm. It was shown that the Levenberg-Marquardt algorithm tends to reach termination criteria more quickly making the searches employing the full model less time consuming. In the first part of forecasting problem, multilayer feedforward (MLFF) network was constructed by trials to forecast the tidal-level variations at the Chao Phraya river mouth in Thailand. Unlike the well-known conventional harmonic analysis, the neural network model uses a set of previous data for learning and then forecasting directly the time series of tidal levels. It was found that lead time of 1 to 24 hourly tidal levels can be successfully predicted using only a short-time hourly learning data.In the second part of filtering problem, the MLFF network was also used to establish a stage-discharge relationship for tidal river. Comparisons were made among conventional methods, i.e. by using multivariate functions (Linear and power models) and the NN model. The NN model was found to be able to model the transfer function too much higher accuracy than multiple regression analysis. The root mean square errors (RMSE) and the mean relative error (MRE) for the NN model are about 8 cm and 6.4% whereas the same errors for multiple regression analysis are 16 cm and 12%, respectively. The efficiency index for the linear, power, and NN models is 0.4, 0.7, and 0.9, respectively. In addition, the obtained stage-discharge relationship by the NN model can indicate reasonably important behavior of the tidal influences, i.e. there are stronger influences of tide during the dry period than the flood period.

Linear and neural network models for predicting N-glycosylation in Chinese Hamster Ovary cells based on B4GALT levels

While previous research has shown the positive effects of music listening in response to one’s favorite music, the negative effects of one’s most disliked music have not gained much attention. In the current study, participants listened to three self-selected disliked musical pieces which evoked highly unpleasant feelings. As a contrast, three musical pieces were individually selected for each participant based on neutral liking ratings they provided to other participants’ disliked music. During music listening, real-time ratings of subjective (dis)pleasure and simultaneous recordings of peripheral measures were obtained. Results showed that compared to neutral music, listening to disliked music evoked physiological reactions reflecting higher arousal (heart rate, skin conductance response, body temperature), disgust (levator labii muscle), anger (corrugator supercilii muscle), distress and grimacing (zygomaticus major muscle). The differences between conditions were most prominent during “very unpleasant” real-time ratings, showing peak responses for the disliked music. Hence, disliked music has a strenuous effect, as shown in strong physiological arousal responses and facial expression, reflecting the listener’s attitude toward the music.

Physics Informed Piecewise Linear Neural Networks for Process Optimization

This piece of research adopts an interdisciplinary conceptual approach that incorporates Artificial Neural Networks (ANN) with learning and cognitive sciences. Specifically, it considers modeling of associative memorization to introduce optimal analysis for development of reading brain performance. Herein, this brain performance simulated realistically using ANNs self-organized modeling paradigm. That namely: the Generalized Hebbian Algorithm (GHA), also known in the literature as Sanger's rule, is a linear feed forward neural network model for unsupervised learning with applications primarily in principal components analysis. Furthermore, it inspired by functioning of highly specialized biological neurons in reading brain based on the organization the brain's structures/substructures. In accordance with the prevailing concept of individual intrinsic characterized properties of highly specialized neurons. Presented models have been in close correspondence with set of neurons' performance for developing reading brain in a significant way. More specifically, herein, introduced model concerned with their important role played in carrying out cognitive reading brain function's outcomes. Accordingly, the cognitive goal for reading brain is to translate that seen word (orthographic word-from) into a spoken word (phonological word-form). In this context herein, the presented work illustrates via ANN simulation results: How ensembles of highly specialized neurons could be dynamically involved in performing associative memorization cognitive function for developing reading brain.

A key issue in designing personalized music affective applications is to find effective ways to direct emotion by music selection with appropriate combination of acoustic features. The aim of this study is to understand the dynamic relationships between acoustic features, physiology and affective states. To model these relationships we used a multivariate approach including continuous measures of emotions from behavioral, subjective and physiological responses. Classical music excerpts taken from opera overtures were used as stimuli to induce emotional variations across time between neutral and intense emotional states. Continuous ratings of arousal and valence along with cardiovascular, respiratory, skin conductance and facial expressive activity were recorded simultaneously. Results show that parts of the music with higher loudness and pulse clarity induced higher ratings of arousal, sympathetic activation and increased cardiorespiratory synchronization. In contrast, pleasant and calming parts with major mode and prominent key strength induced higher ratings of valence, parasympathetic activation and increased facial activity.

Learning What Makes Catalysts Good

Guided by nonlinear dynamical systems theory, this study examined the degree to which media can synchronize individuals’ emotional arousal responses (as indicated by skin conductance) during video viewing as a function of message valence, arousal, and emotional change rate. Data from 490 paired dyads created from 45 participants were analyzed. We used cross recurrence analysis (a nonlinear dynamical analysis) to capture the dynamics of physiological synchrony. Results showed that calm compared to arousing messages and negative arousing compared to positive arousing messages generated stronger, more deterministically structured, and more stable skin conductance synchrony. Fast compared to slow changes in emotion generated stronger but not necessarily more deterministic and stable skin conductance synchrony. Theoretical and practical implications are discussed.

To present an application of optimal controller design to a block-ice production process using dynamic programming. First, we develop mathematical models for such a process by employing system identification techniques. In particular, we build two parametric models, namely, linear model and neural network model. Linear models are built with Auto-Regressive model with exogenous inputs (linear ARX model). Feedforward neural networks (NNARX model) are constructed using the information provided by the linear ARX models. In addition, the Optimal Brain Surgeon (OBS) method is employed to prune the neural network models. Comparing the experimental results obtained from these models, it indicates that linear ARX models yield reasonably good performance in terms of the model fit. Then, using the obtained models, we investigate the energy consumption and operation cost of an ice factory when using time of use (TOU) tariff and time of day (TOD) tariff. Specifically, we develop an optimal control design for block-ice production process. The optimal strategy aims to minimize the electricity cost over a finite-time horizon and is subjected to constraints involving the control input of compressors, the brine temperature, and the number of block-ices ready for sales. Moreover, various factors in the design criteria have been analyzed with respect to the energy consumption and the operation cost. We compare the performance of the optimal controllers and the conventional control strategy. The simulation reveals that the proposed optimal control design has better performance than that of the conventional control. Furthermore, the optimal control using TOU tariff can reduce the operation cost when comparing to the one using TOD tariff.