Abstract
There is currently a lack of objective, quantifiable metrics to evaluate children's health and athletic performance during Physical Education classes. To address this gap, the TexP@ct Consortium is developing a Smart Vest for Physical Education (SV4PE)-a textile engineered wearable solution that integrates a single triaxial Inertial Measurement Unit (IMU) and electrocardiogram (ECG) sensors, embedded at the T8 spinal level. Designed for comfortable and unobtrusive use, the SV4PE enables recording and analysis of biomechanical and physiological data during physical activity. This paper presents the preliminary system validation and algorithm development for the SV4PE system, detailing the sensor fusion and signal processing methods used to extract metrics from live and recorded data, along with results from experimental and prototype datasets. The algorithms designed measure an athlete's heart rate, movement intensity, and effort, with additional post-exercise metrics to characterize fundamental movements such as walking, running, and jumping. Sensor fusion packages were implemented, combining acceleration and angular velocity, to correct sensor drifts and remove gravity components. Following filtering and resampling, walking and running metrics, such as cadence, distance and velocity, are extracted through gait event identification, using wavelet transforms. Jumping characteristics are derived from vertical acceleration using projectile motion equations to estimate jump height, take-off force, and power output. Lastly, heart rate is calculated from QRS peak detection in the ECG signal, and associated with subject metadata to evaluate exercise intensity and effort levels. Additional algorithms are under-development to assess fitness tests (e.g., mile run, shuttle run, push-ups, etc.), for team sport motion classification using machine learning, and for player localization within a playfield for detailed performance analysis. Ultimately, this work seeks to provide teachers and trainers with practical tools to objectively monitor and assess children's performance during sports and physical activities.

Abstract
Amidst an urgent need for sustainability, novel approaches are required to address environmental challenges. In this context, biomimicry offers a promising logic for catalyzing nature’s wisdom to address this complexity. The purpose of this research is to (1) establish a biomimetic understanding and vocabulary for sustainability and (2) apply biomimicry to upframe service ecosystems as a foundation for sustainability. Our research question is: How can the principles of natural ecosystems inform and enhance the sustainability of service ecosystems? The findings highlight upframed service ecosystems as embodying a set of practices that (1) promote mutualistic interactions, (2) build on local biotic and abiotic components supporting emergence processes, (3) leverage (bio)diversity to build resilience, (4) foster resource sharing for regeneration, and (5) bridge individual roles to optimize the community rather than individual well-being. Our upframed definition of a service ecosystem is a system of resource-integrating biotic actors and abiotic resources functioning according to ecocentric principles for mutualistic and regenerative value creation. The discussion emphasizes the implications of this upframed definition for sustainability practices, advocating for a shift in understanding and interacting with service ecosystems. It emphasizes the potential for immediate mutualistic benefits and long-term regenerative impacts. © Academy of Marketing Science 2025.

Abstract

Abstract
This study explores the design, fabrication, and electromechanical characterization of thermoformed tubular Teflon piezoelectrets for force measurement applications. Piezoelectrets, a subclass of electrets, leverage engineered dipole configurations within charged internal cavities to exhibit piezoelectric properties. Using fluorinated ethylene propylene (FEP) films, tubular structures were fabricated through thermal lamination and subsequently polarized to form highly sensitive and flexible piezoelectrets. The electrical response was characterized by controlled impact tests, sinusoidal stationary force inputs using a shaker system and an instrumented insole to evaluate the piezoelectret in a real dynamic environment. The impact test revealed that the piezoelectret exhibits a rapid response time of 20 ms with a maximum voltage amplitude of +/- 3 V. The frequency-domain analysis identified primary and secondary bandpass ranges, with peak sensitivity observed at 400 Hz. The stationary test with a shaker showed a steady sensitivity of 53.87 mV/N for signals within the 200- and 700-Hz bandwidths.

Abstract
Missing data is a common challenge in real-world datasets and can arise for various reasons. This has led to the classification of missing data mechanisms as missing completely at random, missing at random, or missing not at random. Currently, the literature offers various algorithms for imputing missing data, each with advantages tailored to specific mechanisms and levels of missingness. This paper introduces a novel approach to missing data imputation using the well-established label propagation algorithm, named Label Propagation for Missing Data Imputation (LPMD). The method combines, weighs, and propagates known feature values to impute missing data. Experiments on benchmark datasets highlight its effectiveness across various missing data scenarios, demonstrating more stable results compared to baseline methods under different missingness mechanisms and levels. The algorithms were evaluated based on processing time, imputation quality (measured by mean absolute error), and impact on classification performance. A variant of the algorithm (LPMD2) generally achieved the fastest processing time compared to other five imputation algorithms from the literature, with speed-ups ranging from 0.7 to 23 times. The results of LPMD were also stable regarding the mean absolute error of the imputed values compared to their original counterparts, for different missing data mechanisms and rates of missing values. In real applications, missingness can behave according to different and unknown mechanisms, so an imputation algorithm that behaves stably for different mechanisms is advantageous. The results regarding ML models produced using the imputed datasets were also comparable to the baselines. © 2013 IEEE.

Abstract
Deep learning models are widely used in multivariate time series forecasting, yet, they have high computational costs. One way to reduce this cost is by reducing data dimensionality, which involves removing unimportant or low importance information with the proper method. This work presents a study on an explainability feature selection framework composed of four methods (IMV-LSTM Tensor, LIME-LSTM, Average SHAP-LSTM, and Instance SHAP-LSTM) aimed at using the LSTM black-box model complexity to its favour, with the end goal of improving the error metrics and reducing the computational cost on a forecast task. To test the framework, three datasets with a total of 101 multivariate time series were used, with the explainability methods outperforming the baseline methods in most of the data, be it in error metrics or computation time for the LSTM model training.