Enhancing thermal comfort prediction in high-speed trains through machine learning and physiological signals integration.

Heating, Ventilation, and Air Conditioning (HVAC) systems in high-speed trains (HST) are responsible for consuming approximately 70% of non-operational energy sources, yet they frequently fail to ensure provide adequate thermal comfort for the majority of passengers. Recent advancements in portable wearable sensors have opened up new possibilities for real-time detection of occupant thermal comfort status and timely feedback to the HVAC system. However, since occupant thermal comfort is subjective and cannot be directly measured, it is generally inferred from thermal environment parameters or physiological signals of occupants within the HST compartment. This paper presents a field test conducted to assess the thermal comfort of occupants within HST compartments. Leveraging physiological signals, including skin temperature, galvanic skin reaction, heart rate, and ambient temperature, we propose a Predicted Thermal Comfort (PTC) model for HST cabin occupants and establish an intelligent regulation model for the HVAC system. Nine input factors, comprising physiological signals, individual physiological characteristics, compartment seating, and ambient temperature, were formulated for the PTS model. In order to obtain an efficient and accurate PTC prediction model for HST cabin occupants, we compared the accuracy of different subsets of features trained by Machine Learning (ML) models of Random Forest, Decision Tree, Vector Machine and K-neighbourhood. We divided all the predicted feature values into four subsets, and did hyperparameter optimisation for each ML model. The HST compartment occupant PTC prediction model trained by Random Forest model obtained 90.4% Accuracy (F1 macro = 0.889). Subsequent sensitivity analyses of the best predictive models were then performed using SHapley Additive explanation (SHAP) and data-based sensitivity analysis (DSA) methods. The development of a more accurate and operationally efficient thermal comfort prediction model for HST occupants allows for precise and detailed feedback to the HVAC system. Consequently, the HVAC system can make the most appropriate and effective air supply adjustments, leading to improved satisfaction rates for HST occupant thermal comfort and the avoidance of energy wastage caused by inaccurate and untimely predictive feedback.

Thermal sensation prediction model for high-speed train occupants based on skin temperatures and skin wettedness.

Passenger thermal comfort in high-speed train (HST) carriages presents unique challenges due to factors such as extensive operational areas, longer travel durations, larger spaces, and higher passenger capacities. This study aims to propose a new prediction model to better understand and address thermal comfort in HST carriages. The proposed prediction model incorporates skin wettedness, vertical skin temperature difference (ΔTd), and skin temperature as parameters to predict the thermal sensation vote (TSV) of HST passengers. The experiments were conducted with 65 subjects, evenly distributed throughout the HST compartment. Thermal environmental conditions and physiological signals were measured to capture the subjects' thermal responses. The study also investigated regional and overall thermal sensations experienced by the subjects. Results revealed significant regional differences in skin temperature between upper and lower body parts. By analyzing data from 45 subjects, We analyzed the effect of 25 variables on TSV by partial least squares (PLS), from which we singled out 3 key factors. And the optimal multiple regression equation was derived to predict the TSV of HST occupants. Validation with an additional 20 subjects demonstrated a strong linear correlation (0.965) between the actual TSV and the predicted values, confirming the feasibility and accuracy of the developed prediction model. By integrating skin wettedness and ΔTd with skin temperature, the model provides a comprehensive approach to predicting thermal comfort in HST environments. This research contributes to advancing thermal comfort analysis in HST and offers valuable insights for optimizing HST system design and operation to meet passengers' comfort requirements.

DHPA Protects SH-SY5Y Cells from Oxidative Stress-Induced Apoptosis via Mitochondria Apoptosis and the Keap1/Nrf2/HO-1 Signaling Pathway.

Oxidative stress in the brain is highly related to the pathogenesis of Alzheimer's disease (AD). It could be induced by the overproduction of reactive oxygen species (ROS), produced by the amyloid beta (Aß) peptide and excess copper (Cu) in senile plaques and cellular species, such as ascorbic acid (AA) and O2. In this study, the protective effect of 5-hydroxy-7-(4'-hydroxy-3'-methoxyphenyl)-1-phenyl-3-heptanone (DHPA) on Aß(1-42)/Cu2+/AA mixture-treated SH-SY5Y cells was investigated via in vitro and in silico studies. The results showed that DHPA could inhibit Aß/Cu2+/AA-induced SH-SY5Y apoptosis, OH· production, intracellular ROS accumulation, and malondialdehyde (MDA) production. Further research demonstrated that DHPA could decrease the ratio of Bax/Bcl-2 and repress the increase of mitochondrial membrane potential (MMP) of SH-SY5Y cells, to further suppress the activation of caspase-3, and inhibit cell apoptosis. Meanwhile, DHPA could inhibit the Aß/Cu2+/AA-induced phosphorylation of Erk1/2 and P38 in SH-SY5Y cells, and increase the expression of P-AKT. Furthermore, DHPA could bind to Keap1 to promote the separation of Nrf2 to Keap1 and activate the Keap1/Nrf2/HO-1 signaling pathway to increase the expression of heme oxygenase-1 (HO-1), quinone oxidoreductase-1 (NQO1), glutathione (GSH), and superoxide dismutase (SOD). Thus, our results demonstrated that DHPA could inhibit Aß/Cu2+/AA-induced SH-SY5Y apoptosis via scavenging OH·, inhibit mitochondria apoptosis, and activate the Keap1/Nrf2/HO-1 signaling pathway.

Types, Risk Factors, Consequences, and Detection Methods of Train Driver Fatigue and Distraction.

Train drivers' inattention, including fatigue and distraction, impairs their ability to drive and is the major risk factor for human-caused train accidents. Many experts have undertaken numerous studies on train driver exhaustion and distraction, but a systematic study is still missing. Through a systematic review, this work aims to outline the types, risk factors, consequences, and detection methods of train driver fatigue and distraction. The effects of central nervous fatigue and cognitive distraction in train drivers during driving are caused by rest and sleep schedules, workload, automation levels, and mobile phones. Furthermore, train drivers' fatigue and distraction can cause loss of concentration and slow reaction, resulting in dangerous driving behaviour such as speeding and SPAD. Researchers have combined subjective reporting, physiological parameters, and physical factors to construct detection algorithms with good results to detect train driver fatigue and distraction. This review offers recommendations for researchers looking into train driver fatigue and distraction. And it can also make valuable recommendations for future studies about railway traffic safety.