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Access to modern energy services (entertainment, food preparation, etc) provided by consumer goods remains unequal, while growing adoption due to rising incomes in Global South increases energy demand and greenhouse gas emissions. The current model through which these energy services is provided is unsustainable and needs to evolve—a goal that emerging social and technological innovations can help to achieve. Digital convergence and the sharing economy could make access to appliances more affordable and efficient. This article estimates the effect of innovations around digital convergence and sharing in a highly granular, bottom-up representation of appliances. We simulate changes in demand for materials and energy, assuming decent living standards for all and global warming limited to 1.5 °C. By 2050, these innovations could attenuate the increase in the number of appliances to 135% and reduce overall energy demand by 28%. The results contribute to understand under which conditions digital convergence and sharing can improve living standards and climate mitigation.

The field of biometrics is a pattern recognition problem, where the individual traits are coded, registered, and compared with other database records. Due to the difficulties in reproducing Electrocardiograms (ECG), their usage has been emerging in the biometric field for more secure applications. Inspired by the high performance shown by Deep Neural Networks (DNN) and to mitigate the intra-variability challenges displayed by the ECG of each individual, this work proposes two architectures to improve current results in both identification (finding the registered person from a sample) and authentication (prove that the person is whom it claims) processes: Temporal Convolutional Neural Network (TCNN) and Recurrent Neural Network (RNN). Each architecture produces a similarity score, based on the prediction error of the former and the logits given by the last, and fed to the same classifier, the Relative Score Threshold Classifier (RSTC).The robustness and applicability of these architectures were trained and tested on public databases used by literature in this context: Fantasia, MIT-BIH, and CYBHi databases. Results show that overall the TCNN outperforms the RNN achieving almost 100%, 96%, and 90% accuracy, respectively, for identification and 0.0%, 0.1%, and 2.2% equal error rate (EER) for authentication processes. When comparing to previous work, both architectures reached results beyond the state-of-the-art. Nevertheless, the improvement of these techniques, such as enriching training with extra varied data and transfer learning, may provide more robust systems with a reduced time required for validation.

Scenarios that limit global warming to 1.5 °C describe major transformations in energy supply and ever-rising energy demand. Here, we provide a contrasting perspective by developing a narrative of future change based on observable trends that results in low energy demand. We describe and quantify changes in activity levels and energy intensity in the global North and global South for all major energy services. We project that global final energy demand by 2050 reduces to 245 EJ, around 40% lower than today, despite rises in population, income and activity. Using an integrated assessment modelling framework, we show how changes in the quantity and type of energy services drive structural change in intermediate and upstream supply sectors (energy and land use). Down-sizing the global energy system dramatically improves the feasibility of a low-carbon supply-side transformation. Our scenario meets the 1.5 °C climate target as well as many sustainable development goals, without relying on negative emission technologies. Achieving sustainable development goals while meeting the 1.5 °C climate target requires radical changes to how we use energy. A scenario of low energy demand shows how this can be done by down-sizing the global energy system to enable feasible deployment rates of renewable energy resources.

Human Activity Recognition (HAR) has been studied extensively, yet current approaches are not capable of generalizing across different domains (i.e., subjects, devices, or datasets) with acceptable performance. This lack of generalization hinders the applicability of these models in real-world environments. As deep neural networks are becoming increasingly popular in recent work, there is a need for an explicit comparison between handcrafted and deep representations in Out-of-Distribution (OOD) settings. This paper compares both approaches in multiple domains using homogenized public datasets. First, we compare several metrics to validate three different OOD settings. In our main experiments, we then verify that even though deep learning initially outperforms models with handcrafted features, the situation is reversed as the distance from the training distribution increases. These findings support the hypothesis that handcrafted features may generalize better across specific domains.

Cough dysfunction is a feature of patients with amyotrophic lateral sclerosis (ALS). The cough sounds carry information about the respiratory system and bulbar involvement. Our goal was to explore the association between cough sound characteristics and the respiratory and bulbar functions in ALS. This was a single-center, cross-sectional, and case-control study. On-demand coughs from ALS patients and healthy controls were collected with a smartphone. A total of 31 sound features were extracted for each cough recording using time-frequency signal processing analysis. Logistic regression was applied to test the differences between patients and controls, and in patients with bulbar and respiratory impairment. Support vector machines (SVM) were employed to estimate the accuracy of classifying between patients and controls and between patients with bulbar and respiratory impairment. Multiple linear regressions were applied to examine correlations between cough sound features and clinical variables. Sixty ALS patients (28 with bulbar dysfunction, and 25 with respiratory dysfunction) and forty age- and gender-matched controls were recruited. Our results revealed clear differences between patients and controls, particularly within the frequency-related group of features (AUC 0.85, CI 0.79-0.91). Similar results were observed when comparing patients with and without bulbar dysfunction. Sound features related to intensity displayed the strongest correlation with disease severity, and were the most significant in distinguishing patients with and without respiratory dysfunction. We found a good relationship between specific cough sound features and clinical variables related to ALS functional disability. The findings relate well with some expected impact from ALS on both respiratory and bulbar contributions to the physiology of cough. Finally, our approach could be relevant for clinical practice, and it also facilitates home-based data collection.

The study of Domain Generalization (DG) has gained considerable momentum in the Machine Learning (ML) field. Human Activity Recognition (HAR) inherently encompasses diverse domains (e.g., users, devices, or datasets), rendering it an ideal testbed for exploring Domain Generalization. Building upon recent work, this paper investigates the application of regularization methods to bridge the generalization gap between traditional models based on handcrafted features and deep neural networks. We apply various regularizers, including sparse training, Mixup, Distributionally Robust Optimization (DRO), and Sharpness-Aware Minimization (SAM), to deep learning models and assess their performance in Out-of-Distribution (OOD) settings across multiple domains using homogenized public datasets. Our results show that Mixup and SAM are the best-performing regularizers. However, they are unable to match the performance of models based on handcrafted features. This suggests that while regularization techniques can improve OOD robustness to some extent, handcrafted features remain superior for domain generalization in HAR tasks.

This study presents a novel, multifaceted approach to evaluating decarbonisation technologies by integrating advanced text-mining tools with comprehensive data analysis. The analysis of scientific documents (2011–2021) and mapping 368 technologies from the IEA’s Energy Technology Perspectives identified 41 technology domains, including 20 with the highest relevance and occurrence. Domain readiness was assessed using mean Technology Readiness Levels (TRLs) and linked to six decarbonisation pathways. The “Electrification of uses” pathway ranked highest, demonstrating significant CO2 mitigation potential and high readiness (mean TRL 7.4, with two-thirds of technologies scoring over 7) despite challenges in hard-to-electrify sectors. The findings provide actionable insights for policymakers, highlighting the need for pathway-specific strategies, a deeper understanding of synergies between pathways, and balancing innovation with deployment to accelerate decarbonisation.

Background: Speech production is a possible way to monitor bulbar and respiratory functions in patients with amyotrophic lateral sclerosis (ALS). Moreover, the emergence of smartphone-based data collection offers a promising approach to reduce frequent hospital visits and enhance patient outcomes. Here, we studied the relationship between bulbar and respiratory functions with voice characteristics of ALS patients, alongside a speech therapist’s evaluation, at the convenience of using a simple smartphone. Methods: For voice assessment, we considered a speech therapist’s standardized tool—consensus auditory-perceptual evaluation of voice (CAPE-V); and an acoustic analysis toolbox. The bulbar sub-score of the revised ALS functional rating scale (ALSFRS-R) was used, and pulmonary function measurements included forced vital capacity (FVC%), maximum expiratory pressure (MEP%), and maximum inspiratory pressure (MIP%). Correlation coefficients and both linear and logistic regression models were applied. Results: A total of 27 ALS patients (12 males; 61 years mean age; 28 months median disease duration) were included. Patients with significant bulbar dysfunction revealed greater CAPE-V scores in overall severity, roughness, strain, pitch, and loudness. They also presented slower speaking rates, longer pauses, and higher jitter values in acoustic analysis (all p < 0.05). The CAPE-V’s overall severity and sub-scores for pitch and loudness demonstrated significant correlations with MIP% and MEP% (all p < 0.05). In contrast, acoustic metrics (speaking rate, absolute energy, shimmer, and harmonic-to-noise ratio) significantly correlated with FVC% (all p < 0.05). Conclusions: The results provide supporting evidence for the use of smartphone-based recordings in ALS patients for CAPE-V and acoustic analysis as reliable correlates of bulbar and respiratory function.

While national governments pledged to reduce their greenhouse gas emissions under the Paris Agreement, delivering on these aims will require significant changes in the activities of major sources of emissions such as companies. To drive such changes, companies will need to consider carbon emissions as a cost of production and many companies have begun doing so through internal carbon pricing. By employing data from the Carbon Disclosure Project, we evaluate how national carbon pricing policies influence firm-level internal carbon pricing and corporate emission targets. We find that firm-level internal carbon prices are significantly higher in countries explicitly pricing carbon through tax and/or cap-and-trade programs. These findings shed light on how companies are factoring climate change in their decision-making and on the drivers that can contribute to the generalization of climate pricing in the economy.

The increasing social pressure for decarbonization has placed businesses under considerable scrutiny to actively reduce carbon emissions. A critical step towards achieving this objective is to shift conventional production and consumption systems to more sustainable alternatives. Thus, there is an emergent need to understand the patterns and drivers of the transformative business models (BMs) that underpin that shift. This study adopts a mixed-methods approach that integrates different literature streams—including Sustainability Transitions Theory (STT), Strategic Niche Management (SNM), and the Business Models approach—and stakeholders’ interviews to investigate the key elements of business models that lead towards sustainable practices. This research examines the organizational arrangements of European start-ups operating between 2014 and 2020. The transformation towards decarbonized production and consumption is characterized by an efficient combination of business strategies that incorporate advanced technologies (ATs), such as artificial intelligence (AI), machine learning (ML) and its algorithms, along with sustainable elements, resulting in transformative business models. By exploring the driving elements behind the transition to low-carbon approaches, this study fills a significant gap in the existing literature on business models. The findings from this research also hold relevance for policymakers to promote decarbonization.