Extract Executable Action Sequences from Natural Language Instructions Based on DQN for Medical Service Robots

The emergence and popularization of medical robots bring great convenience to doctors in treating patients. The core of medical robots is the interaction and cooperation between doctors and robots, so it is crucial to design a simple and stable human-robots interaction system for medical robots. Language is the most convenient way for people to communicate with each other, so in this paper, a DQN agent based on long-short term memory (LSTM) and attention mechanism is proposed to enable the robots to extract executable action sequences from doctors’ natural language instructions. For this, our agent should be able to complete two related tasks: 1) extracting action names from instructions. 2) extracting action arguments according to the extracted action names. We evaluate our agent on three datasets composed of texts with an average length of 49.95, 209.34, 417.17 words respectively. The results show that our agent can perform better than similar agents. And our agent has a better ability to handle long texts than previous works

Two-Stream Retentive Long Short-Term Memory Network for Dense Action Anticipation

Analyzing and understanding human actions in long-range videos has promising applications, such as video surveillance, automatic driving, and efficient human-computer interaction. Most researches focus on short-range videos that predict a single action in an ongoing video or forecast an action several seconds earlier before it occurs. In this work, a novel method is proposed to forecast a series of actions and their durations after observing a partial video. This method extracts features from both frame sequences and label sequences. A retentive memory module is introduced to richly extract features at salient time steps and pivotal channels. Extensive experiments are conducted on the Breakfast data set and 50 Salads data set. Compared to the state-of-the-art methods, the method achieves comparable performance in most cases

Does Aerobic plus Machine-Assisted Resistance Training Improve Vascular Function in Type 2 Diabetes? A Systematic Review and Meta-Analysis of Randomized Controlled Trials with Trial Sequential Analysis

Type 2 diabetes mellitus (T2DM) is a chronic disease characterized by hyperglycemia, insulin resistance, and pancreatic B cell dysfunction. Hyperglycemia can cause several complications, including nephrological, neurological, ophthalmological, and vascular complications. Many modalities, such as medication, physical therapies, and exercise, are developed against vascular disorders. Among all exercise forms, aerobic plus machine-assisted resistance training is widely applied. However, whether this intervention can significantly improve vascular conditions remains controversial. In this study, an electronic search was processed for the Pubmed, Embase, and Cochrane libraries for randomized controlled trials (RCTs) comparing the efficacy of aerobic plus machine-assisted resistance training with no exercise (control) on patients with T2DM. Pulse wave velocity (PWV), the index of arterial stiffness, was chosen as primary outcome. The reliability of the pooled outcome was tested by trial sequential analysis (TSA). Secondary outcomes included systolic blood pressure (SBP) and hemoglobin A1c (HbA1c). Finally, five RCTs with a total of 328 patients were included. Compared with control, aerobic plus machine-assisted resistance training failed to provide significant improvement on PWV (MD −0.54 m/s, 95% CI [−1.69, 0.60], p = 0.35). On the other hand, TSA indicated that this results till needs more verifications. Additionally, this training protocol did not significantly decrease SBP (MD −1.05 mmHg, 95% CI [−3.71, 1.61], p = 0.44), but significantly reduced the level of HbA1c (MD −0.55%, 95% CI [−0.88, −0.22], p = 0.001). In conclusion, this meta-analysis failed to detect a direct benefit of aerobic plus machine-assisted resistance training on vascular condition in T2DM population. Yet the improvement in HbA1c implied a potential of this training method in mitigating vascular damage. More studies are needed to verify the benefit

The Effect of Diacerein on Type 2 Diabetic Mellitus: A Systematic Review and Meta-Analysis of Randomized Controlled Trials with Trial Sequential Analysis

Aims. To figure out the effect of diacerein supplementation on type 2 diabetes mellitus (T2DM). Methods. An electronic search was processed on Pubmed, Embase, and Cochrane library for randomized controlled trials (RCTs) comparing the efficacy of diacerein with placebo on T2DM. The primary outcome was fasting blood glucose (FBG). Trial sequential analysis (TSA) was used to test the reliability of this pooled outcome. Secondary outcomes were glycosylated hemoglobin A1c (HbA1c), body mass index (BMI), lipid profiles, hematological indexes including hematocrit and platelet count, and systematic inflammatory level expressed as a C-reactive protein (CRP) level. Safety outcome was the rate of complications. The difference in continuous data was measured by mean difference (MD) and 95% confidence interval (CI), while the difference of dichotomous data was calculated by relative risk (RR) and 95% CI. A two-tailed P<0.05 was regarded as statistically significant. Results. Five RCTs with 278 participants were included. Compared with control, diacerein provided significant improvement on FBG (MD -0.52; 95% CI (-0.89~-0.14); P=0.007), but TSA showed that this positive effect required more support. Besides, diacerein also significantly improved HbA1c (MD -0.71; 95% CI (-1.07~-0.36); P<0.001), BMI (MD -0.40; 95% CI (-0.49~-0.31); P<0.001), and CRP level (MD -1.49; 95% CI (-2.78~-0.19); P=0.02). No superiority was noted in favor of either treatment regarding lipid profiles or hematological indexes. Among all complications, diacerein caused significantly more gastrointestinal syndromes (RR 1.39; 95% CI (1.08~1.77); P=0.009). Conclusion. Based on the current analysis, diacerein as an add-on treatment provided better glycemic control for T2DM but this benefit requires more verification. Compared with control, additional diacerein also lowered body weight and CRP level in T2DM, but increased the rate of gastrointestinal syndromes

A Non-Uniform Transmission Line Model of the ±1100 kV UHV Tower

The modeling of the Ultra-High Voltage (UHV) tower plays an important role in lightning protection analysis of transmission lines because the model used will directly affect the reliability of the results. Moreover, the higher the voltage level is, the more prominent the impact becomes. This paper first analyzes the inapplicability of the Hara multi-segment multi-surge impedance model for the &#177;1100 kV UHV towers, and then builds a non-uniform transmission line model of the tower. Secondly, the multi-segment multi-surge impedance model is used to study the influence of the tower&#8217;s spatial structure changes on its electromagnetic transient characteristics. It is concluded that the more accurately the nominal height of the tower is modeled, the more accurately its electromagnetic transient response is reflected. Finally, the lightning electromagnetic transient responses of the tower with the non-uniform transmission line model and with the multi-segment multi-surge impedance model are compared and analyzed, which shows that the non-uniform transmission line model is more in line with the actual situation under the lightning strikes