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Some patients with developmental and epileptic encephalopathy (DEE) respond to adrenocorticotropic hormone (ACTH) therapy but relapse soon after. While long-term ACTH therapy (LT-ACTH) has been attempted for these patients, no previous studies have carefully assessed adrenal function during LT-ACTH. We evaluated the effectiveness of LT-ACTH, as well as adverse effects (AE), including their adrenal function in three DEE patients. Patients underwent a corticotropin-releasing hormone (CRH) stimulation test during LT-ACTH, and those with peak serum cortisol below 15 mu g/dL were considered to be at high risk of adrenal insufficiency (AI). Two of three responded, and their life-threatening seizures with postgeneralized electroencephalogram (EEG) suppression decreased. Although no individuals had serious AE, CRH stimulation test revealed relatively weak responses, without reaching normal cortisol peak level (18 mu g/dL). Hydrocortisone replacement during stress was prepared in a case with lower cortisol peak than our cutoff level. LT-ACTH could be a promising treatment option for cases of DEE that relapse soon after effective ACTH treatment. The longer duration and larger cumulative dosage in LT-ACTH than in conventional ACTH could increase the relative risk of AI. Careful evaluation with pediatric endocrinologists, including hormonal stimulation tests, might be useful for continuing this treatment safely.

Kaneko N., Okazawa K., Zhao D., et al. Non-intrusive thermal load disaggregation and forecasting for effective HVAC systems. Applied Energy 367, 123379 (2024); https://doi.org/10.1016/j.apenergy.2024.123379., Non-Intrusive Thermal Load Monitoring (NITLM) tracks the sub-loads generated by each heat source (e.g. occupants, equipment, solar radiation etc.) from the total thermal load and indirectly provides a room's thermal properties without additional sensors. Since sub-loads can improve the efficiency of HVAC systems, NITLM is a very attractive technology for building energy management. NITLM has traditionally focused on analyzing past and present sub-loads. However, by forecasting future sub-loads, HVAC systems will be able to schedule operations that take into account the thermal properties of future rooms. This work focuses on a new NITLM framework that forecasts future sub-loads based on the current and past total thermal loads. In experiments, we selected occupant loads that are closely connected to HVAC systems and performed sub-load forecasting using two types of approaches. One is a two-step approach that separately performs them in turn. This approach use separately trained model for disaggregation and forecasting, this allow us to fine-tuning the hyper-parameter for dedicate model. Moreover, the two-step approach can take into account the different properties and difficulties of each inference, resulting in smaller errors in sub-load forecasting. The other is an integrated approach. This approach combines load disaggregation and forecasting into a single estimation process, eliminating error propagation and reducing overall error in sub-load forecasting. Moreover, this approach utilizes the Adaptive Moment Estimation (Adam) algorithm for effective parameter optimization, enabling complex training and improving the accuracy of sub-load forecasting. We conducted evaluations of thermal load disaggregation and forecasting across a range of realistic building scenarios. The findings indicate that the integrated approach predicts sub-loads with a MAE that is up to 34.9% lower than that of the two-step approach. Additionally, it identifies occupants presence with an 1

Kaneko N., Okazawa K., Zhao D., et al. Non-intrusive thermal load disaggregation and forecasting for effective HVAC systems. Applied Energy 367, 123379 (2024); https://doi.org/10.1016/j.apenergy.2024.123379., Non-Intrusive Thermal Load Monitoring (NITLM) tracks the sub-loads generated by each heat source (e.g. occupants, equipment, solar radiation etc.) from the total thermal load and indirectly provides a room's thermal properties without additional sensors. Since sub-loads can improve the efficiency of HVAC systems, NITLM is a very attractive technology for building energy management. NITLM has traditionally focused on analyzing past and present sub-loads. However, by forecasting future sub-loads, HVAC systems will be able to schedule operations that take into account the thermal properties of future rooms. This work focuses on a new NITLM framework that forecasts future sub-loads based on the current and past total thermal loads. In experiments, we selected occupant loads that are closely connected to HVAC systems and performed sub-load forecasting using two types of approaches. One is a two-step approach that separately performs them in turn. This approach use separately trained model for disaggregation and forecasting, this allow us to fine-tuning the hyper-parameter for dedicate model. Moreover, the two-step approach can take into account the different properties and difficulties of each inference, resulting in smaller errors in sub-load forecasting. The other is an integrated approach. This approach combines load disaggregation and forecasting into a single estimation process, eliminating error propagation and reducing overall error in sub-load forecasting. Moreover, this approach utilizes the Adaptive Moment Estimation (Adam) algorithm for effective parameter optimization, enabling complex training and improving the accuracy of sub-load forecasting. We conducted evaluations of thermal load disaggregation and forecasting across a range of realistic building scenarios. The findings indicate that the integrated approach predicts sub-loads with a MAE that is up to 34.9% lower than that of the two-step approach. Additionally, it identifies occupants presence with an 1