Search Results (8)

Precise forecasting of lithium-ion battery health status is crucial for safe, efficient, and sustainable operation throughout the battery life cycle, especially in applications like electric vehicles (EVs) and renewable energy storage systems. In this study, an improved particle swarm optimization–support vector regression (IPSO-SVR) model is proposed for dynamic hyper-parameter tuning, integrating multiple intelligent optimization algorithms (including PSO, genetic algorithm, whale optimization, and simulated annealing) to enhance the accuracy and generalization of battery state-of-health (SOH) estimation. The model dynamically adjusts SVR hyperparameters to better capture the nonlinear aging characteristics of batteries. We validate the approach using a publicly available NASA lithium-ion battery degradation dataset (cells B0005, B0006, B0007). Key health features are extracted from voltage–capacity curves (via incremental capacity analysis), and correlation analysis confirms their strong relationship with battery capacity. Experimental results show that the proposed IPSO-SVR model outperforms a conventional PSO-SVR benchmark across all three datasets, achieving higher prediction accuracy: a mean MAE of 0.611%, a mean RMSE of 0.794%, a mean MSE of 0.007%, and robustness a mean R² of 0.933. These improvements in SOH prediction not only ensure more reliable battery management but also support sustainable energy practices by enabling longer battery life spans and more efficient resource utilization. Full article

With the rapid development of artificial intelligence, machine learning is gradually becoming popular for predictions in all walks of life. In meteorology, it is gradually competing with traditional climate predictions dominated by physical models. This survey aims to consolidate the current understanding of Machine Learning (ML) applications in weather and climate prediction—a field of growing importance across multiple sectors, including agriculture and disaster management. Building upon an exhaustive review of more than 20 methods highlighted in existing literature, this survey pinpointed eight techniques that show particular promise for improving the accuracy of both short-term weather and medium-to-long-term climate forecasts. According to the survey, while ML demonstrates significant capabilities in short-term weather prediction, its application in medium-to-long-term climate forecasting remains limited, constrained by factors such as intricate climate variables and data limitations. Current literature tends to focus narrowly on either short-term weather or medium-to-long-term climate forecasting, often neglecting the relationship between the two, as well as general neglect of modeling structure and recent advances. By providing an integrated analysis of models spanning different time scales, this survey aims to bridge these gaps, thereby serving as a meaningful guide for future interdisciplinary research in this rapidly evolving field. Full article

The aim of the current study was to explore differences in passion for achievement, grit, and mindset across age and gender, by using a cross-sectional design. The sample consisted of 1548 participants including 931 females and 617 males aged from 13 to 77 years (Mage 26.53 years, SD = 11.77). The eight-item Passion for Achievement Scale was used to assess general passion and the Grit-S scale was used to assess grit. Mindset was assessed using the eight-item Theories of Intelligence Scale (TIS). The results indicated significant differences between the three factors related to age, age groups, and gender. For the total sample, there was a significant gender difference in passion, where males score higher, and growth mindset, where females score higher. With age, passion decreases until the age of 50–59, and slightly increases for the remaining age groups. After a decrease in grit between the first (13–19 years) and the second (20–29 years) age group, grit increases with age. Mindset scores decline strongly after the age of 40–49. Generally, the patterns show that mindset and passion decrease across the life-span, while grit increases. Indeed, these attributes seems to be different from each other, and how they change varies across age groups. Full article

Digital and intelligent buildings are critical to realizing efficient building energy operations and a smart grid. With the increasing digitalization of processes throughout the life cycle of buildings, data exchanged between stakeholders and between building systems have grown significantly. However, a lack of semantic interoperability between data in different systems is still prevalent and hinders the development of energy-oriented applications that can be reused across buildings, limiting the scalability of innovative solutions. Addressing this challenge, our review paper systematically reviews metadata schemas and ontologies that are at the foundation of semantic interoperability necessary to move toward improved building energy operations. The review finds 40 schemas that span different phases of the building life cycle, most of which cover commercial building operations and, in particular, control and monitoring systems. The paper’s deeper review and analysis of five popular schemas identify several gaps in their ability to fully facilitate the work of a building modeler attempting to support three use cases: energy audits, automated fault detection and diagnosis, and optimal control. Our findings demonstrate that building modelers focused on energy use cases will find it difficult, labor intensive, and costly to create, sustain, and use semantic models with existing ontologies. This underscores the significant work still to be done to enable interoperable, usable, and maintainable building models. We make three recommendations for future work by the building modeling and energy communities: a centralized repository with a search engine for relevant schemas, the development of more use cases, and better harmonization and standardization of schemas in collaboration with industry to facilitate their adoption by stakeholders addressing varied energy-focused use cases. Full article

Lifespan is increasing globally as never before, and leading to an aging world population. Thus, the challenge for society and individuals is now how to live these years in the best possible health and wellbeing. Despite the benefits of physical activity for both are well documented, older people are not active enough. Physical self-concept is correlated with high levels of sports practice, although its evolution across one’s life span is not clear. The aim of this research has been to analyze the physical self-concept in older adults and its relationship with emotional intelligence, motivation and sports habits. The sample of 520 adults aged between 41 and 80 was clustered in ranges of age; 70.96% were men (n = 369; 57.34 years (SD: 7.97)) and 29.04% women (n = 151; age = 55.56 years (SD: 9.12)). Questionnaires adapted to Spanish were used to measure physical self-concept (Physical Self-Perception Profile), motivation (Sport Motivation Scale), and emotional intelligence (Trait Meta-Mood Scale). Regarding physical self-concept, the youngest group obtained the highest mean values and the oldest group the lowest. Physical self-concept correlated positively with emotional regulation and intrinsic motivation. Initiation to sports in childhood, the practice of sports activities for more than 150’ per week, and the practice of three or more sports, were associated with a higher score of physical self-concept. The findings reveal that physical self-concept declines in older adults, slightly at first, and sharply between 71 and 80 years, being intrinsic motivation, emotional regulation, and sports habits, factors to consider in favoring a positive physical self-concept and adherence to sporting activities. Full article

The study of cognitive change across a life span, both in pathological and healthy samples, has been heavily influenced by developments in cognitive psychology as a theoretical paradigm, neuropsychology and other bio-medical fields; this alongside the increase in new longitudinal and cohort designs, complemented in the last decades by the evaluation of experimental interventions. Here, a review of aging databases was conducted, looking for the most relevant studies carried out on cognitive functioning in healthy older adults. The aim was to review not only longitudinal, cross-sectional or cohort studies, but also by intervention program evaluations. The most important studies, searching for long-term patterns of stability and change of cognitive measures across a life span and in old age, have shown a great range of inter-individual variability in cognitive functioning changes attributed to age. Furthermore, intellectual functioning in healthy individuals seems to decline rather late in life, if ever, as shown in longitudinal studies where age-related decline of cognitive functioning occurs later in life than indicated by cross-sectional studies. The longitudinal evidence and experimental trials have shown the benefits of aerobic physical exercise and an intellectually engaged lifestyle, suggesting that bio-psycho-socioenvironmental factors concurrently with age predict or determine both positive or negative change or stability in cognition in later life. Full article

In comparison to young adults, middle-aged and old people show lower scores in intelligence tests and slower response times in elementary cognitive tasks. Whether these well-documented findings can both be attributed to a general cognitive slow-down across the life-span has become subject to debate in the last years. The drift diffusion model can disentangle three main process components of binary decisions, namely the speed of information processing, the conservatism of the decision criterion and the non-decision time (i.e., time needed for processes such as encoding and motor response execution). All three components provide possible explanations for the association between response times and age. We present data from a broad study using 18 different response time tasks from three different content domains (figural, numeric, verbal). Our sample included people between 18 to 62 years of age, thus allowing us to study age differences across young-adulthood and mid-adulthood. Older adults generally showed longer non-decision times and more conservative decision criteria. For speed of information processing, we found a more complex pattern that differed between tasks. We estimated mediation models to investigate whether age differences in diffusion model parameters account for the negative relation between age and intelligence, across different intelligence process domains (processing capacity, memory, psychometric speed) and different intelligence content domains (figural, numeric, verbal). In most cases, age differences in intelligence were accounted for by age differences in non-decision time. Content domain-general, but not content domain-specific aspects of non-decision time were related to age. We discuss the implications of these findings on how cognitive decline and age differences in mental speed might be related. Full article

The concept of intelligence encompasses the mental abilities necessary to survival and advancement in any environmental context. Attempts to grasp this multifaceted concept through a relatively simple operationalization have fostered the notion that individual differences in intelligence can often be expressed by a single score. This predominant position has contributed to expect intelligence profiles to remain substantially stable over the course of ontogenetic development and, more generally, across the life-span. These tendencies, however, are biased by the still limited number of empirical reports taking a developmental perspective on intelligence. Viewing intelligence as a dynamic concept, indeed, implies the need to identify full developmental trajectories, to assess how genes, brain, cognition, and environment interact with each other. In the present paper, we describe how a neuroconstructivist approach better explains why intelligence can rise or fall over development, as a result of a fluctuating interaction between the developing system itself and the environmental factors involved at different times across ontogenesis. Full article