An evaluation method for HMI of deep-sea manned submersible based on human reliability

Abstract Improving the human reliability of the human–machine interface (HMI) of deep-sea manned submersible is of great importance for the development of the deep-sea field. Based on the SHEL (Software S, Hardware H, Environment E, Liveware L) model, this study classifies the performance shaping factors (PSF) that affect the human reliability of submersible HMIs and builds a PSF system. The interpretative structural model (ISM) is used to matrix the interactions between the elements that make up the system of PSF. A multi-level recursive structure is obtained by building the corresponding adjacency matrix. The Noisy-OR model is introduced to construct a Bayesian network in order to build a new HMI evaluation method. A real case of Bayesian network causal inference verifies the validity of the built method. This study proposes a set of HMI human reliability evaluation methods applicable to deep-sea manned submersible, which provides a new idea for human reliability assessment

Upscaling In Situ Soil Moisture Observations To Pixel Averages With Spatio-Temporal Geostatistics

Validation of satellite-based soil moisture products is necessary to provide users with an assessment of their accuracy and reliability and to ensure quality of information. A key step in the validation process is to upscale point-scale, ground-based soil moisture observations to satellite-scale pixel averages. When soil moisture shows high spatial heterogeneity within pixels, a strategy which captures the spatial characteristics is essential for the upscaling process. In addition, temporal variation in soil moisture must be taken into account when measurement times of ground-based and satellite-based observations are not the same. We applied spatio-temporal regression block kriging (STRBK) to upscale in situ soil moisture observations collected as time series at multiple locations to pixel averages. STRBK incorporates auxiliary information such as maps of vegetation and land surface temperature to improve predictions and exploits the spatio-temporal correlation structure of the point-scale soil moisture observations. In addition, STRBK also quantifies the uncertainty associated with the upscaled soil moisture which allows bias detection and significance testing of satellite-based soil moisture products. The approach is illustrated with a real-world application for upscaling in situ soil moisture observations for validating the Polarimetric L-band Multi-beam Radiometer (PLMR) retrieved soil moisture product in the Heihe Water Allied Telemetry Experimental Research experiment (HiWATER). The results show that STRBK yields upscaled soil moisture predictions that are sufficiently accurate for validation purposes. Comparison of the upscaled predictions with PLMR soil moisture observations shows that the root-mean-squared error of the PLMR soil moisture product is about 0.03 m3 · m-3 and can be used as a high-resolution soil moisture product for watershed-scale soil moisture monitoring.</p

Charging Station Management Strategy for Returns Maximization via Improved TD3 Deep Reinforcement Learning

Maximizing the return on electric vehicle charging station (EVCS) operation helps to expand the EVCS, thus expanding the EV (electric vehicle) stock and better addressing climate change. However, in the face of dynamic regulation scenarios with large data, multiple variables, and low time scales, the existing regulation strategies aiming at maximizing EVCS returns many times fail to meet the demand. To handle increasingly complex regulation scenarios, a deep reinforcement learning algorithm (DRL) based on the improved twin delayed deep deterministic policy gradient (TD3) is used to construct basic energy management strategies in this paper. To enable the strategy to be more suitable for the goal of real-time energy regulation strategy, we used Thompson sampling strategy to improve TD3’s exploration noise sampling strategy, which greatly accelerated the initial convergence of TD3 during training. Also, we use marginalised importance sampling to calculate the Q-return function for TD3, which ensures that the constructed strategies are more likely to learn high-value experiences while having higher robustness. It is shown in numerical experiments that the charging station management strategy (CSMS) based on the modified TD3 obtains the fastest convergence speed and the highest robustness and achieves the largest operational returns compared to the CSMS constructed using deep deterministic policy gradient (DDPG), actor-critic using Kronecker-factored trust region (ACKTR), trust region policy optimization (TRPO), proximal policy optimization (PPO), soft actor-critic (SAC), and the original TD3

The Photocatalytic Activity of CaTiO₃ Derived from the Microwave-Melting Heating Process of Blast Furnace Slag

The extraction of titanium-bearing components in the form of CaTiO3 is an efficient utilization of blast furnace slag. The photocatalytic performance of this obtained CaTiO3 (MM-CaTiO3) as a catalyst for methylene blue (MB) degradation was evaluated in this study. The analyses indicated that the MM-CaTiO3 had a completed structure with a special length–diameter ratio. Furthermore, the oxygen vacancy was easier to generate on a MM-CaTiO3(110) plane during the photocatalytic process, contributing to improving photocatalytic activity. Compared with traditional catalysts, MM-CaTiO3 has a narrower optical band gap and visible-light responsive performance. The degradation experiments further confirmed that the photocatalytic degradation efficiency of pollutants by using MM-CaTiO3 was 3.2 times that of pristine CaTiO3 in optimized conditions. Combined with molecular simulation, the degradation mechanism clarified that acridine of MB molecular was stepwise destroyed by using MM-CaTiO3 in short times, which is different from demethylation and methylenedioxy ring degradation by using TiO2. This study provided a promising routine for using solid waste to obtain catalysts with excellent photocatalytic activity and was found to be in keeping with sustainable environmental development

Combined effects of reproductive and hormone factors and obesity on the prevalence of knee osteoarthritis and knee pain among middle-aged or older Chinese women: a cross-sectional study

Abstract Background Knee osteoarthritis (KOA) is one form of degenerative arthritis that results from the breakdown of cartilage and underlying bone. The prevalence of KOA is considerably higher in women than in men; however, the reason for this difference has not been thoroughly elucidated to date. The aim of the present study was to estimate the effects of reproductive and hormone factors and obesity on KOA prevalence among Chinese women. Methods The cross-sectional study included 7510 women with a mean age of 62.6 ± 8.6 years. Knee pain was defined as pain or aching stiffness on most days for at least 1 month during the past 12 months or persistent pain or aching stiffness within the past week. Clinical KOA was diagnosed based on both pain complaints and a Kellgren-Lawrence grade ≥ 2 X-ray radiograph of at least one knee. Results Oral contraceptives use (OR 1.18, 1.05–1.34), ≥3 pregnancies (1.38, 1.20–1.60), and postmenopausal hormone replacement therapy (HT) (1.59, 1.23–2.06) were positively associated with knee pain, while oral contraceptives use (1.28, 1.04–1.57), and HT (1.79, 1.21–2.65) were positively associated with clinical KOA. Obesity and oral contraceptives use showed additive and multiplicative effects on knee pain. The OR for knee pain among women with a BMI ≥24 kg/m2 and oral contraceptives use was 2.00 (1.68–2.38) compared with women with a BMI &lt; 24 kg/m2 and no oral contraceptives use. Conclusions A high number of pregnancies, oral contraceptives use, and HT are independent risk factors for KOA, and the effects of reproductive and hormone factors on KOA may be increased by obesity