Remaining useful life prediction of Lithium-ion batteries using spatio-temporal multimodal attention networks.

Lithium-ion batteries are widely used in various applications, including electric vehicles and renewable energy storage. The prediction of the remaining useful life (RUL) of batteries is crucial for ensuring reliable and efficient operation, as well as reducing maintenance costs. However, determining the life cycle of batteries in real-world scenarios is challenging, and existing methods have limitations in predicting the number of cycles iteratively. In addition, existing works often oversimplify the datasets, neglecting important features of the batteries such as temperature, internal resistance, and material type. To address these limitations, this paper proposes a two-stage RUL prediction scheme for Lithium-ion batteries using a spatio-temporal multimodal attention network (ST-MAN). The proposed ST-MAN is to capture the complex spatio-temporal dependencies in the battery data, including the features that are often neglected in existing works. Despite operating without prior knowledge of end-of-life (EOL) events, our method consistently achieves lower error rates, boasting mean absolute error (MAE) and mean square error (MSE) of 0.0275 and 0.0014, respectively, compared to existing convolutional neural networks (CNN) and long short-term memory (LSTM)-based methods. The proposed method has the potential to improve the reliability and efficiency of battery operations and is applicable in various industries.

Anticonformists catalyze societal transitions and facilitate the expression of evolving preferences.

The world is grappling with emerging, urgent, large-scale problems, such as climate change, pollution, biodiversity loss, and pandemics, which demand immediate and coordinated action. Social processes like conformity and social norms can either help maintain behaviors (e.g. cooperation in groups) or drive rapid societal change (e.g. rapid rooftop solar uptake), even without comprehensive policy measures. While the role of individual heterogeneity in such processes is well studied, there is limited work on the expression of individuals' preferences and the role of anticonformists-individuals who value acting differently from others-especially in dynamic environments. We introduce anticonformists into a game-theoretical collective decision-making framework that includes a complex network of agents with heterogeneous preferences about two alternative options. We study how anticonformists' presence changes the population's ability to express evolving personal preferences. We find that anticonformists facilitate the expression of preferences, even when they diverge from prevailing norms, breaking the "spiral of silence" whereby individuals do not act on their preferences when they believe others disapprove. Centrally placed anticonformists reduce by five-fold the number of anticonformists needed for a population to express its preferences. In dynamic environments where a previously unpopular choice becomes preferred, anticonformists catalyze social tipping and reduce the "cultural lag," even beyond the role of committed minorities-that is, individuals with a commitment to a specific cause. This research highlights the role of dissenting voices in shaping collective behavior, including their potential to catalyze the adoption of new technologies as they become favorable and to enrich democracy by facilitating the expression of views.

Sleep disorders and associated factors among medical students in the Middle East and North Africa: a systematic review and meta-analysis.

Sleep disturbances like poor and insufficient sleep are common among medical students in the Middle East and North Africa (MENA) countries; however, the extent of medically defined sleep disorders (SDs) remains unclear. This meta-analysis determines SD prevalence and identifies associated factors among medical students in the MENA. PubMed, Web of Science, Google Scholar, and reference lists of included studies were searched (latest search: June 2022). Meta-analyses included 22 studies and were performed using random-effect models. Included studies used self-reported screening tools for assessing SDs and then estimated the proportion of participants at high risk of developing a SD. Central disorders of hypersomnolence were the most prevalent SD [prevalencepooled range: 30.9% (Jordan) to 62.5% (Saudi Arabia)], followed by insomnia disorders [prevalencepooled range: 30.4% (Jordan) to 59.1% (Morocco)], circadian rhythm sleep-wake disorders [prevalencepooled range: 13.5% (Jordan) to 22.4% (Saudi Arabia)], sleep-related breathing disorders [prevalencepooled range: 12.2% (Jordan) to 22.5% (Pakistan)], sleep-related movement disorders [prevalencepooled range: 5.9% (Egypt) to 30.6% (Saudi Arabia)], and parasomnias [prevalencepooled range: 5.6% (Jordan) to 17.4% (Saudi Arabia)]. Female sex, studying in the latter academic years, having anxiety, excessive internet use, and poor academic performance were significantly associated with SDs. SDs are prevalent among MENA medical students. Implementing student-centered interventions targeting high risk groups in medical schools should be considered to improve students' health and wellbeing.