Helmholtz energy of ice VII-X is determined in a pressure regime extending to 450 GPa at 300 K using local basis functions in the form of b splines. The new representation for the equation of state is embedded in a physics-based inverse theory framework of parameter estimation. Selected pressures as a function of volume from 14 prior experimental studies and two theoretical studies constrain the behavior of Helmholtz energy. Separately measured bulk moduli, not used to construct the representation, are accurately replicated below about 20 GPa and above 60 GPa. In the intermediate range of pressure, the experimentally determined moduli are larger and have greater scatter than values predicted using the Helmholtz representation. Although systematic experimental error in the experimental elastic moduli is possible and likely, the alternative hypothesis is a slow relaxation time associated with changes in proton mobility or the ice VII to X transition. A correlation is observed between anomalies in the pressure derivative of the predicted bulk modulus and previously suggested higher-order phase transitions. Improved determinations of elastic properties at high pressure would allow refinement of the current equation of state. More generally, the current method of data assimilation is broadly applicable to other materials in high-pressure studies.

The COVID-19 pandemic caused significant training disruptions during the 2020-21 season due to lockdowns, quarantines, and strict adherence to pandemic protocols. The main purpose of this study was to determine how pandemic training restrictions affected training volume and performance in one collegiate swim team. Cumulative training volume data, across a 28-week season, were compared between a pandemic (2020-2021) versus non-pandemic (2019-2020) season. Swimmers were categorized into three groups (Sprinters, Mid-Distance, and Long-Distance) based on training group. Performance times in 25 swimmers who competed in Regional Championships, during both the non-pandemic and pandemic year, were compared via 1-way ANOVA. 26 male and 22 female swimmers commenced the 2020-21 (pandemic) season, with 23% of swimmers voluntarily opting out. Three COVID-19 cases were confirmed (2%) by the medical staff with no long-term effects. Significant reductions in average swim volume were verified in Sprinters (32,867±10,135 vs.14,800±7,995yards;p<0.001), Mid-Distance (26,457±10,692 vs.17,054±9.923yards;p<0.001), and Long-Distance (37,600±14,430 vs.22,254±14,418yards;p<0.001) swimmers (non-pandemic vs. pandemic season, respectively). In the Regional performance analyses, the Sprinters swam faster (n=8;-0.5±0.6secs), while Mid-Distance (n=10;0.17±2.1secs) and Long-Distance (n=7;6.0±4.9secs) swimmers swam slower (F=11.76;p=0.0003;r²=0.52). Thus, the pandemic caused significant reductions in swim training volume, with Sprinters performing better and Long-Distance swimmers performing worse at Regional Championships.

In the rapidly evolving field of sentiment analysis, the introduction of Transformer-based architectures, particularly the BERT model, has markedly improved accuracy levels, setting new standards in the analysis of textual sentiment. These advancements have been instrumental in enhancing the model's ability to grasp the nuances and complexities inherent in human language, thereby providing deeper insights into the sentiment expressed in various texts. However, the impressive performance of such deep learning models comes at the cost of increased computational demands and a lack of transparency in their decision-making processes. These challenges have reignited interest in rule-based sentiment analysis methods, which utilize sentiment lexicons for a more straightforward and computationally economical approach to determining text sentiment. Despite being overshadowed by the rise of machine learning models in recent years, these lexicon-based methods possess distinct advantages, including ease of interpretation and lower resource requirements, making them particularly appealing for certain applications. This paper seeks to re-evaluate the relevance and effectiveness of two prominent lexicon-based sentiment analysis methods, SO-CAL and SentiStrength, which have been specifically adapted for the language, in light of the advancements represented by Transformer-based models like SentBERT. Through a comprehensive comparative analysis, we examine the performance of these methodologies in contrast to SentBERT across an extensive collection of 16 text corpora, spanning a variety of genres and contexts. Our findings reveal a nuanced landscape of performance, where SentBERT's advanced capabilities typically afford it a significant advantage in accurately capturing sentiment. Nevertheless, in a surprising turn, the SO-CAL method exhibits exceptional performance on a substantial portion of the datasets, underscoring the continuing value and potential of lexicon-based approaches in sentiment analysis. This study not only highlights the strengths and weaknesses of both deep learning and lexicon-based methods but also opens the door for future hybrid approaches that could leverage the best of both worlds to achieve even greater accuracy and efficiency in sentiment analysis tasks.