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Straw is an agricultural byproduct that results from the production of many crops, such as cereals, yet it is often considered a waste product. However, straw has both historical precedent and future potential as an agricultural resource. In this study, we aimed to determine the effects of returning straw to the soil on rice cultivation. To this end, we used the hybrid rice variety Luliangyou Jingling as the test material to study the effect of straw return under four different nitrogen application levels (0 kg N (N1), 120 kg N/hm2 (N2), 150 kg N/hm2 (N3), and 180 kg N/hm2 (N4)) on rice tillering dynamics, leaf area index (LAI), dry matter accumulation, and yield. We found that rice under straw return had a higher number of effective panicles, along with a higher number of grains per panicle, compared to those without straw return. Additionally, the tiller number, LAI, total dry matter, and yield of rice in each main growth period under straw return were higher than those without straw return, and these values increased with an increase in nitrogen application rate. The yield was the highest at 9520.63 kg/hm2 without straw return, while the highest yield with straw return was achieved at 10,738.26 kg/hm2. Our results revealed the optimal nitrogen application level for high yield of two-line direct-seeded rice under straw return, which provides a theoretical reference for the precise reduction of fertilizer application in rice cultivation.

As a photophilous plant, rice is susceptible to low-light stress during its growth. The Sichuan Basin is a typical low-light rice-producing area. In this study, eight rice varieties with different shade tolerances were studied from 2021 to 2022. The physiological adaptability and yield formation characteristics of rice were studied with respect to photosynthetic physiological characteristics and dry matter accumulation characteristics, and the response mechanism of rice to low light stress was revealed. The results showed that the shading treatment significantly increased the chlorophyll a, chlorophyll b, and total chlorophyll contents in the leaves of direct-seeded rice after heading, and the total chlorophyll content increased by 1.68–29.70%. Nitrate reductase (NR) activity first increased and then decreased under each treatment, and the shading treatment reduced the NR activity of direct-seeded rice. Compared to the control treatment, the peroxidase (POD) activity of each variety increased from 7 to 24 d after the shading treatment. The transketolase (TK) activity in direct-seeded hybrid rice increased under low light stress. Compared with the control, shading treatment significantly reduced the aboveground dry matter, grain number per panicle, and seed setting rate of direct-seeded rice at the full heading stage and maturity stage, thus reducing the yield of direct-seeded rice by 26.10–34.11%. However, under the shading treatment, Zhenliangyou 2018 and Jingliangyou 534 maintained higher chlorophyll content and related enzyme activities, accumulated more photosynthetic products, and reduced yield. In general, Zhenliangyou 2018 and Jingliangyou 534 still had a yield of 7.06–8.33 t·hm−2 under low light. It indicated that Zhenliangyou 2018 and Jingliangyou 534 had better stability and stronger tolerance to weak light stress and had a higher yield potential in weak light areas such as Sichuan.

To investigate changes in the yield and physiological characteristics of indica hybrid rice varieties sown on different dates, we evaluated appropriate hybrid rice varieties and their optimal sowing dates in the hilly areas of Sichuan. Three popular indica rice varieties were used as experimental materials, and five sowing dates were set uniformly locally [16 May (SD1), 23 May (SD2), 30 May (SD3), 6 June (SD4), and 13 June (SD5)] to investigate differences in the yield characteristics, growth period, and dry matter accumulation. The results showed that, over the two years, the sowing-to-heading period and overall growth period of the three varieties shortened as the sowing date was delayed, and the difference in yield between the SD1 and SD2 treatments was not significant, owing to higher material accumulation after flowering and higher assimilative material transport capacity. These varieties are both photosensitive and tolerant to low temperatures. Among the three varieties tested, the Huangyouyuehesimiao (V3) cultivar had the highest yield, with 10.75 t ha−1 under the SD2 treatment. The impact of shifting the sowing date on yield components varied. Delaying the sowing date increased and then decreased the number of effective panicles, and the number of grains per panicle and the seed setting rate decreased by differing degrees. In summary, a high yield of indica hybrid rice can be maintained by sowing between 16 and 23 May each year in the study area. It indicated that indica hybrid rice in the hilly rice-producing region of Sichuan is highly adaptable to different sowing dates.

Straw is an agricultural byproduct that results from the production of many crops, such as cereals, yet it is often considered a waste product. However, straw has both historical precedent and future potential as an agricultural resource. In this study, we aimed to determine the effects of returning straw to the soil on rice cultivation. To this end, we used the hybrid rice variety Luliangyou Jingling as the test material to study the effect of straw return under four different nitrogen application levels (0 kg N (N1), 120 kg N/hm2 (N2), 150 kg N/hm2 (N3), and 180 kg N/hm2 (N4)) on rice tillering dynamics, leaf area index (LAI), dry matter accumulation, and yield. We found that rice under straw return had a higher number of effective panicles, along with a higher number of grains per panicle, compared to those without straw return. Additionally, the tiller number, LAI, total dry matter, and yield of rice in each main growth period under straw return were higher than those without straw return, and these values increased with an increase in nitrogen application rate. The yield was the highest at 9520.63 kg/hm2 without straw return, while the highest yield with straw return was achieved at 10,738.26 kg/hm2. Our results revealed the optimal nitrogen application level for high yield of two-line direct-seeded rice under straw return, which provides a theoretical reference for the precise reduction of fertilizer application in rice cultivation. [ABSTRACT FROM AUTHOR]

Structural characterization of the highly unsaturated dichromium carbonyl Cr2(CO)8 is reported. The density functional theory (DFT) is used for this purpose.

The comparison of performance of positive electrodes of lithium-sulfur batteries made using the binders based on fluoroplastic (PVDF Solef 5310 and Kynar) and polyethylene oxide (PEO) was carried out. The electrodes made using PVDF Kynar or limited amounts of PEO were shown to have certain advantages. It was also found that electrodes with PEO had an increased specific capacity during the initial period of cycling, whereas electrodes with Kynar were characterized by the minimum capacity fading during cycling.

At present, there are problems about few application research of WiFi communication in the specific environment of fully mechanized coal mining face, propagation characteristics of WiFi signal on the fully mechanized coal mining face were analyzed combined with wireless signal spatial propagation theory. The analysis results show that on the fully mechanized coal mining face, the WiFi signal propagation is greatly affected, especially the signal coverage is obviously reduced; the WiFi signal propagation on the fully mechanized coal mining face conforms to log-distance path loss model. According to the spatial layout characteristics of fully mechanized coal mining face, an optimal technical scheme of no-dead zone and full coverage WiFi communication network of fully mechanized coal mining face was constructed by fast roaming technology and Mesh network technology. The test results show that the scheme can solve the problem of video communication on the shearer, and realize fast roaming function of WiFi network, can effectively ensure the application effect of the mobile terminal, and the average communication throughput of the whole network is about 35 Mbit/s, the minimum communication throughput is about 7 Mbit/s, which can meet most of the communication needs on fully mechanized coal mining face.

Background: The COVID-19 pandemic has had a profound impacted on various aspects of society, including the healthcare system and patient care. In this context, this study aims to evaluate the impact of COVID-19 control strategies on the lipid profile and blood sugar levels of peritoneal dialysis (PD) patients in Taiwan, a crucial focus for understanding the pandemic's influence on individuals with chronic kidney disease (CKD)., Methods: A retrospective cohort study was conducted, analyzing data from the medical records of 170 PD patients who visited the nephrology division of Taipei Veterans General Hospital in 2021. The generalized estimating equations method was used to analyze the longitudinal data and assess the changes in biomarker levels over different periods. Covariates were taken into consideration in the statistical models. The data were analyzed using R 4.3.0., Results: The study enrolled 70 males (41%) and 100 females (59%), with an average age of 56 years old. Over 12 months in 2021, from the first period (January-April: pre-COVID-19) to the second period (May-August: COVID-19 surge), there was a notable decline in both high-density lipoprotein (HDL) and low-density lipoprotein (LDL) levels, and a significant surge in triglyceride (TG) levels. However, total cholesterol (TC) and hemoglobin (HbA1c) levels remained stable. Furthermore, the TG to HDL, TG to LDL, TC to HDL, and TC to LDL ratios were analyzed, revealing a pronounced increase during the second period., Conclusion: Our findings underscore the significant impact of COVID-19 pandemic-related disruptions in the healthcare system and the subsequent management strategies on dyslipidemia in PD patients while not affecting dysglycemia. These results provide valuable insights for healthcare professionals to enhance their strategies and interventions for CKD patients undergoing PD during a pandemic., Competing Interests: Conflicts of Interest: The authors declare that they have no conflicts of interest related to the subject matter or materials discussed in this article., (Copyright © 2024, the Chinese Medical Association.)

Reinforcement learning (RL) agents are vulnerable to adversarial disturbances, which can deteriorate task performance or break down safety specifications. Existing methods either address safety requirements under the assumption of no adversary (e.g., safe RL) or only focus on robustness against performance adversaries (e.g., robust RL). Learning one policy that is both safe and robust under any adversaries remains a challenging open problem. The difficulty is how to tackle two intertwined aspects in the worst cases: feasibility and optimality. The optimality is only valid inside a feasible region (i.e., robust invariant set), while the identification of maximal feasible region must rely on how to learn the optimal policy. To address this issue, we propose a systematic framework to unify safe RL and robust RL, including the problem formulation, iteration scheme, convergence analysis and practical algorithm design. The unification is built upon constrained two-player zero-sum Markov games, in which the objective for protagonist is twofold. For states inside the maximal robust invariant set, the goal is to pursue rewards under the condition of guaranteed safety; for states outside the maximal robust invariant set, the goal is to reduce the extent of constraint violation. A dual policy iteration scheme is proposed, which simultaneously optimizes a task policy and a safety policy. We prove that the iteration scheme converges to the optimal task policy which maximizes the twofold objective in the worst cases, and the optimal safety policy which stays as far away from the safety boundary. The convergence of safety policy is established by exploiting the monotone contraction property of safety self-consistency operators, and that of task policy depends on the transformation of safety constraints into state-dependent action spaces. By adding two adversarial networks (one is for safety guarantee and the other is for task performance), we propose a practical deep RL algorithm for constrained zero-sum Markov games, called dually robust actor-critic (DRAC). The evaluations with safety-critical benchmarks demonstrate that DRAC achieves high performance and persistent safety under all scenarios (no adversary, safety adversary, performance adversary), outperforming all baselines by a large margin.

In recent times, significant advancements have been made in delving into the optimization landscape of policy gradient methods for achieving optimal control in linear time-invariant (LTI) systems. Compared with state-feedback control, output-feedback control is more prevalent since the underlying state of the system may not be fully observed in many practical settings. This article analyzes the optimization landscape inherent to policy gradient methods when applied to static output feedback (SOF) control in discrete-time LTI systems subject to quadratic cost. We begin by establishing crucial properties of the SOF cost, encompassing coercivity, L -smoothness, and M -Lipschitz continuous Hessian. Despite the absence of convexity, we leverage these properties to derive novel findings regarding convergence (and nearly dimension-free rate) to stationary points for three policy gradient methods, including the vanilla policy gradient method, the natural policy gradient method, and the Gauss-Newton method. Moreover, we provide proof that the vanilla policy gradient method exhibits linear convergence toward local minima when initialized near such minima. This article concludes by presenting numerical examples that validate our theoretical findings. These results not only characterize the performance of gradient descent for optimizing the SOF problem but also provide insights into the effectiveness of general policy gradient methods within the realm of reinforcement learning.

We focus on learning the zero-constraint-violation safe policy in model-free reinforcement learning (RL). Existing model-free RL studies mostly use the posterior penalty to penalize dangerous actions, which means they must experience the danger to learn from the danger. Therefore, they cannot learn a zero-violation safe policy even after convergence. To handle this problem, we leverage the safety-oriented energy functions to learn zero-constraint-violation safe policies and propose the safe set actor-critic (SSAC) algorithm. The energy function is designed to increase rapidly for potentially dangerous actions, locating the safe set on the action space. Therefore, we can identify the dangerous actions prior to taking them and achieve zero-constraint violation. Our major contributions are twofold. First, we use the data-driven methods to learn the energy function, which releases the requirement of known dynamics. Second, we formulate a constrained RL problem to solve the zero-violation policies. We prove that our Lagrangian-based constrained RL solutions converge to the constrained optimal zero-violation policies theoretically. The proposed algorithm is evaluated on the complex simulation environments and a hardware-in-loop (HIL) experiment with a real autonomous vehicle controller. Experimental results suggest that the converged policies in all environments achieve zero-constraint violation and comparable performance with model-based baseline.

Model error and external disturbance have been separately addressed by optimizing the definite H ∞ performance in standard linear H ∞ control problems. However, the concurrent handling of both introduces uncertainty and nonconvexity into the H ∞ performance, posing a huge challenge for solving nonlinear problems. This article introduces an additional cost function in the augmented Hamilton-Jacobi-Isaacs (HJI) equation of zero-sum games to simultaneously manage the model error and external disturbance in nonlinear robust performance problems. For satisfying the Hamilton-Jacobi inequality in nonlinear robust control theory under all considered model errors, the relationship between the additional cost function and model uncertainty is revealed. A critic online learning algorithm, applying Lyapunov stabilizing terms and historical states to reinforce training stability and achieve persistent learning, is proposed to approximate the solution of the augmented HJI equation. By constructing a joint Lyapunov candidate about the critic weight and system state, both stability and convergence are proved by the second method of Lyapunov. Theoretical results also show that introducing historical data reduces the ultimate bounds of system state and critic error. Three numerical examples are conducted to demonstrate the effectiveness of the proposed method.

The Hamilton-Jacobi-Bellman (HJB) equation serves as the necessary and sufficient condition for the optimal solution to the continuous-time (CT) optimal control problem (OCP). Compared with the infinite-horizon HJB equation, the solving of the finite-horizon (FH) HJB equation has been a long-standing challenge, because the partial time derivative of the value function is involved as an additional unknown term. To address this problem, this study first-time bridges the link between the partial time derivative and the terminal-time utility function, and thus it facilitates the use of the policy iteration (PI) technique to solve the CT FH OCPs. Based on this key finding, the FH approximate dynamic programming (ADP) algorithm is proposed leveraging an actor-critic framework. It is shown that the algorithm exhibits important properties in terms of convergence and optimality. Rather importantly, with the use of multilayer neural networks (NNs) in the actor-critic architecture, the algorithm is suitable for CT FH OCPs toward more general nonlinear and complex systems. Finally, the effectiveness of the proposed algorithm is demonstrated by conducting a series of simulations on both a linear quadratic regulator (LQR) problem and a nonlinear vehicle tracking problem.

Background: Neonatal nurses' working environments are highly stressful, and burnout is common. This study examines the effect of socioeconomic factors, perceived stress, and social support on neonatal nurse burnout., Methods: A total of 311 neonatal nurses participated in this study. They were administered a validated Maslach Burnout Inventory. The study employed a 14-item perceived stress scale (PSS-14) and a social support rate scale (SSRS) to examine stress, socioeconomic factors, and lifestyles., Results: Of the neonatal nurses, 40.19% had burnout, 89.60% had mild burnout, and 10.40% had moderate burnout; no neonatal nurse experienced severe burnout. Young nurses and those with low technical skills, poor interpersonal relationships, irregular diet, and insufficient rest were exposed to burnout (all p < 0.05).Most burnout nurses experienced moderate-severe perceived stress, and their PSS-14 scores were higher (all p < 0.05).The scores for objective social support, subjective social support, utilization of social support, total SSRS scores, and the level of social support were all lower in burnout nurses (all p < 0.05). Perceived stress was correlated positively and significantly with emotional exhaustion and personal accomplishment (all p < 0.05). Social support correlated significantly with and reduced personal accomplishments (p < 0.05). Age, poor interpersonal relationships, perceived stress, and social support were all independent factors associated with neonatal nurse burnout (all p < 0.05)., Conclusion: The prevalence of burnout in neonatal nurses was higher than average. Socioeconomic factors, higher perceived stress, and lower social support contribute to neonatal nurse burnout. Nursing managers should pay attention to socioeconomic factors, perceived stress, and social support among neonatal nurses and employ strategies to reduce neonatal nurse burnout., (© 2023. The Author(s).)

Decision and control are core functionalities of high-level automated vehicles. Current mainstream methods, such as functional decomposition and end-to-end reinforcement learning (RL), suffer high time complexity or poor interpretability and adaptability on real-world autonomous driving tasks. In this article, we present an interpretable and computationally efficient framework called integrated decision and control (IDC) for automated vehicles, which decomposes the driving task into static path planning and dynamic optimal tracking that are structured hierarchically. First, the static path planning generates several candidate paths only considering static traffic elements. Then, the dynamic optimal tracking is designed to track the optimal path while considering the dynamic obstacles. To that end, we formulate a constrained optimal control problem (OCP) for each candidate path, optimize them separately, and follow the one with the best tracking performance. To unload the heavy online computation, we propose a model-based RL algorithm that can be served as an approximate-constrained OCP solver. Specifically, the OCPs for all paths are considered together to construct a single complete RL problem and then solved offline in the form of value and policy networks for real-time online path selecting and tracking, respectively. We verify our framework in both simulations and the real world. Results show that compared with baseline methods, IDC has an order of magnitude higher online computing efficiency, as well as better driving performance, including traffic efficiency and safety. In addition, it yields great interpretability and adaptability among different driving scenarios and tasks.

In reinforcement learning (RL), function approximation errors are known to easily lead to the Q -value overestimations, thus greatly reducing policy performance. This article presents a distributional soft actor-critic (DSAC) algorithm, which is an off-policy RL method for continuous control setting, to improve the policy performance by mitigating Q -value overestimations. We first discover in theory that learning a distribution function of state-action returns can effectively mitigate Q -value overestimations because it is capable of adaptively adjusting the update step size of the Q -value function. Then, a distributional soft policy iteration (DSPI) framework is developed by embedding the return distribution function into maximum entropy RL. Finally, we present a deep off-policy actor-critic variant of DSPI, called DSAC, which directly learns a continuous return distribution by keeping the variance of the state-action returns within a reasonable range to address exploding and vanishing gradient problems. We evaluate DSAC on the suite of MuJoCo continuous control tasks, achieving the state-of-the-art performance.

Objective: To assess the effects of eLongTermCare (eLTC), a telehealth program implemented by an integrated health system in 45 nursing homes across the Midwest, on the use of acute hospital services and total expenditures for Medicare residents., Data Sources: Minimum Data Set, Medicare fee-for-service claims, and enrollment data from 2013 to 2018., Study Design: We used a longitudinal difference-in-differences design to estimate the changes in outcomes for treatment beneficiaries before and after participating in the eLTC program, relative to changes for the matched comparison beneficiaries over the same period. We measured outcomes over a 24-month follow-up period, including total Medicare spending, emergency department (ED) visits, hospitalizations, and the likelihood of readmission., Data Collection/extraction Methods: Using administrative data, we identified treatment beneficiaries who stayed at participating nursing facilities during the program period and matched comparison beneficiaries with similar baseline characteristics from non-participating facilities in the same geographic locations., Principal Findings: For long-term care residents, the eLTC program led to an estimated reduction of 73 ED visits per 1000 beneficiaries (p < 0.01, 8.6% effect) over the two-year follow-up period. The estimated effects for this group were concentrated among beneficiaries who entered the nursing home after program startup, with sizable reductions in hospitalizations, ED visits, and spending. For skilled care residents, the program was associated with an estimated reduction of 85 ED visits per 1000 beneficiaries (p = 0.03, 9.7% effect), but had no discernible effect on their hospitalizations or total Medicare spending., Conclusions: Telehealth can be a valuable tool for nursing homes to enhance care coordination and provide timely access to care, leading to lower spending for nursing home residents. Future research needs to explore payment methods that encourage telehealth expansion in nursing homes., (© 2022 Health Research and Educational Trust.)