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Physics-Informed Temperature Prediction of Lithium-Ion Batteries Using Decomposition-Enhanced LSTM and BiLSTM Models
No full textAccurately forecasting the operating temperature of lithium-ion batteries (LIBs) is essential for preventing thermal runaway, extending service life, and ensuring the safe operation of electric vehicles and stationary energy-storage systems. This work introduces a unified, physics-informed, and data-driven temperature-prediction framework that integrates mathematically governed preprocessing, electrothermal decomposition, and sequential deep learning architectures. The methodology systematically applies the governing relations to convert raw temperature measurements into trend, seasonal, and residual components, thereby isolating long-term thermal accumulation, reversible entropy-driven oscillations, and irreversible resistive heating. These physically interpretable signatures serve as structured inputs to machine learning and deep learning models trained on temporally segmented temperature sequences. Among all evaluated predictors, the Bidirectional Long Short-Term Memory (BiLSTM) network achieved the highest prediction fidelity, yielding an RMSE of 0.018 °C, a 35.7% improvement over the conventional Long Short-Term Memory (LSTM) (RMSE = 0.028 °C) due to its ability to simultaneously encode forward and backward temporal dependencies inherent in cyclic electrochemical operation. While CatBoost exhibited the strongest performance among classical regressors (RMSE = 0.022 °C), outperforming Random Forest, Gradient Boosting, Support Vector Regression, XGBoost, and LightGBM, it remained inferior to BiLSTM because it lacks the capacity to represent bidirectional electrothermal dynamics. This performance hierarchy confirms that LIB thermal evolution is not dictated solely by historical load sequences; it also depends on forthcoming cycling patterns and entropic interactions, which unidirectional and memoryless models cannot capture. The resulting hybrid physics-data-driven framework provides a reliable surrogate for real-time LIB thermal estimation and can be directly embedded within BMS to enable proactive intervention strategies such as predictive cooling activation, current derating, and early detection of hazardous thermal conditions. By coupling physics-based decomposition with deep sequential learning, this study establishes a validated foundation for next-generation LIB thermal-management platforms and identifies a clear trajectory for future work extending the methodology to module- and pack-level systems suitable for industrial deployment
Element-Based Predictive Modeling of Hydrothermal Liquefaction Bioproducts Derived from Corn Stover
No full textThe hydrothermal liquefaction (HTL) process offers an energetic advantage over pyrolysis because it does not require prior drying of the biomass feedstock. However, there are significant challenges in simultaneously estimating both the yields and characteristics of products from the HTL of biomass with theoretical support. This study developed a unique element-based kinetic model to predict the yields, higher heating values, and fuel characteristics of solid residue and heavy bio-oil, based on the temperature, residence time, solid loading, and elemental composition (C, H, N, and O) of corn stover. Furthermore, the model predicted the weights of dissolved carbon and nitrogen in the aqueous phase. HTL experiments were conducted using corn stover at temperatures ranging from 250 to 350 °C for residence times between 5 and 60 min. The resulting solid and liquid products were analyzed for the elemental composition and ash content. The experimental data and MATLAB program were used to predict the products. The fuel characteristics derived from predicted elemental weight data of solid residues followed the trend line of the observed data on the van Krevelen diagram. In those of heavy bio-oil, the H/C atomic ratio of the average predicted data matched the one calculated from the observed data. Additionally, power function relationships between the amounts of corn stover and obtained product fractions were identified under identical temperature and residence time conditions by varying solid loading, providing insights into the partial nonlinear behavior of the reaction system
How-To: Academic Job Interviews (In the United States)
No full text[Introduction] I (EKP) bombed my first interview on the tenure-track academic market. After I entered the Zoom room filled with search committee volunteers, the chair performed a quick round of introductions, and I was told that I would have strictly five minutes to answer each question. I smiled nervously, wringing my hands under the desk, and waited to be asked to derive a diagenetic equation or defend the methods I used in my recent manuscript.
“We\u27ll start with something easy. Tell us about yourself,” began the chair.
I opened my mouth. I closed my mouth. My mind went blank. “Um …”, I stammered, “Well, I was born in Pennsylvania …”
I made it to the point at which I had settled on an undergraduate major when I was told my five minutes were up by a wincing committee member. The remaining 25-minutes were likely equally difficult to witness.Fortunately, the memory holds more humor than embarrassment for me now, but in the immediate aftermath, I realized I needed to devote a bit more time to preparation for subsequent interviews. When I did eventually get another chance, I was prepared, and while I didn\u27t ultimately receive an offer from that academic institution or the institution after that, I did eventually land my dream job.
I (KAL) fully admit that I treated my successful interview like a slightly unhinged performance. I stood alone in my office at a small liberal arts college, in full business casual, feet planted in a power stance, staring down my laptop as if I could intimidate it into giving me the job.
Two years earlier, I wanted to apply for this exact position, but life circumstances were not in my favor. Now, I finally had the chance: a dream job at an R1 university, doing the inter- and transdisciplinary work I had only read about in other people\u27s papers. I was determined not to waste it.
My screen was surrounded by a halo of Post-it notes with one-liners and key phrases I wanted to hit. I had drafted twenty possible interview questions, typed out my answers, read them more times than I care to admit, recorded myself responding, and then listened to those recordings while I drove and worked out. By the time the interview day arrived, I felt as prepared as I was ever going to be.
When the Zoom room opened and the search committee appeared, I took a slow breath. With each question, I repeated it back to them, pretended to jot down a few notes, and gave myself a moment to collect my thoughts before answering. As the interview went on, I started to notice heads nodding along. At one point, I watched a few committee members glance at each other and smile in a way that seemed to say, “We might have finally found the one.”
Two hours after the interview ended, I received an email from the search committee chair asking if I could come for a campus visit within the next two weeks. Two and a half weeks later, I was offered the job.
While our paths to success looked different, both experiences underscore the same lesson: preparation matters, and the faculty interview process is as much about readiness as it is about qualifications. What follows is designed to help you avoid the pitfalls and replicate the strategies that work. We provide a brief description of what to expect in the virtual and in-person interviews, questions to prepare to be asked and to ask (Box 1), and some general advice for the application process (Box 2) based on the authors\u27 experiences
May-Thurner Syndrome as a Cause of Phlegmasia Cerulea Dolens
No full textWe report the case of a healthy 24-year-old female who presented with acute-onset left leg pain and a clinical exam consistent with phlegmasia cerulea dolens. Imaging revealed extensive occlusive thrombosis of the left common iliac vein with signs of arterial compromise. She was started emergently on intravenous anticoagulation and underwent thrombectomy with angioplasty. Intraoperative findings were consistent with May-Thurner syndrome (MTS) – compression of the left iliac vein by the right iliac artery. This syndrome leads to impaired venous return and thrombosis. The patient was discharged on oral anticoagulation with vascular follow-up for possible iliac vein stenting. Recognition of this rare syndrome is critical as management may require urgent thrombectomy and long-term vascular intervention to prevent recurrence
Understanding Motivations and Health Outcomes of College-Aged Triathletes During COVID-19: A Mixed-Methods Study
No full textA triathlon is a multi-sport event that consists of three simultaneous events: swimming, biking, and running. This sport has experienced significant growth in the past few decades, with colleges and universities now participating. This exploratory mixed-methods study examined the motivations and perceived health benefits of college triathletes during the COVID-19 pandemic, using the Means-Ends of Recreation Scale and the Perceived Health Outcomes of Recreation Scale (N = 29), as well as semi-structured interviews (N = 4). Results indicate no difference in motives or health outcomes between male and female survey respondents. The thematic analysis of open-ended interview questions highlighted lived experiences. The results obtained provide preliminary evidence of the importance of motivation and health outcomes of college triathletes during the pandemic