A Control Approach for Nonlinear Stochastic State Uncertain Systems with Probabilistic Safety Guarantees

This paper presents an algorithm to apply nonlinear control design approaches in the case of stochastic systems with partial state observation. Deterministic nonlinear control approaches are formulated under the assumption of full state access and, often, relative degree one. We propose a control design approach that first generates a control policy for nonlinear deterministic models with full state observation. The resulting control policy is then used to build an importance-like probability distribution over the space of control sequences which are to be evaluated for the true stochastic and state-uncertain dynamics. This distribution serves in the sampling step within a random search control optimization procedure, to focus the exploration effort on certain regions of the control space. The sampled control sequences are assigned costs determined by a prescribed finite-horizon performance and safety measure, which is based on the stochastic dynamics. This sampling algorithm is parallelizable and shown to have computational complexity indifferent to the state dimension, and to be able to guarantee safety over the prescribed prediction horizon. A numerical simulation is provided to test the applicability and effectiveness of the presented approach and compare it to a certainty equivalence controller

Optimal Trajectories for Propellant-Free Rendezvous Missions

The paper provides a new approach to utilizing space environmental forces in time- and energy-optimal, propellant-less spacecraft rendezvous missions. Considering the nonlinear form of the relative dynamic equations, rendezvous missions are posed as optimal control problems subject to input saturation. We conduct a direct optimal control approach to obtain optimal trajectories and control inputs. Initially, we consider the differential drag only and conduct a comprehensive analysis of the effect of altitude on the required control input and achieved cost function. Lorentz forces are then utilized with the differential drag, reducing the time required for time-optimal missions. For energy-optimal missions with combined differential drag and Lorentz forces, a weighting matrix in the cost function is introduced to adjust the relative contributions of these forces

Monte Carlo Grid Dynamic Programming: Almost Sure Convergence and Probability Constraints

Dynamic Programming (DP) suffers from the well-known ``curse of dimensionality'', further exacerbated by the need to compute expectations over process noise in stochastic models. This paper presents a Monte Carlo-based sampling approach for the state space and an interpolation procedure for the resulting value function, dependent on the process noise density, in a "self-approximating" fashion, eliminating the need for ordering or set-membership tests. We provide proof of almost sure convergence for the value iteration (and consequently, policy iteration) procedure. The proposed meshless sampling and interpolation algorithm alleviates the burden of gridding the state space, traditionally required in DP, and avoids constructing a piecewise constant value function over a grid. Moreover, we demonstrate that the proposed interpolation procedure is well-suited for handling probabilistic constraints by sampling both infeasible and feasible regions. While the curse of dimensionality cannot be entirely avoided, this approach offers a practical framework for addressing lower-order stochastic nonlinear systems with probabilistic constraints, where traditional DP methods may be intractable or inefficient. Numerical examples are presented to illustrate the convergence and convenience of the proposed algorithms.Comment: 6 pages, 1 figur

Actively Learning Reinforcement Learning: A Stochastic Optimal Control Approach

In this paper we provide a framework to cope with two problems: (i) the fragility of reinforcement learning due to modeling uncertainties because of the mismatch between controlled laboratory/simulation and real-world conditions and (ii) the prohibitive computational cost of stochastic optimal control. We approach both problems by using reinforcement learning to solve the stochastic dynamic programming equation. The resulting reinforcement learning controller is safe with respect to several types of constraints and it can actively learn about the modeling uncertainties. Unlike exploration and exploitation, probing and safety are employed automatically by the controller itself, resulting real-time learning. A simulation example demonstrates the efficacy of the proposed approach

New Pim-1 Kinase Inhibitor From the Co-culture of Two Sponge-Associated Actinomycetes

Saccharomonospora sp. UR22 and Dietzia sp. UR66, two actinomycetes derived from the Red Sea sponge Callyspongia siphonella, were co-cultured and the induced metabolites were monitored by HPLC-DAD and TLC. Saccharomonosporine A (1), a novel brominated oxo-indole alkaloid, convolutamydine F (2) along with other three known induced metabolites (3-5) were isolated from the EtOAc extract of Saccharomonospora sp. UR22 and Dietzia sp. UR66 co-culture. Additionally, axenic culture of Saccharomonospora sp. UR22 led to isolation of six known microbial metabolites (6-11). A kinase inhibition assay results showed that compounds 1 and 3 were potent Pim-1 kinase inhibitors with an IC50 value of 0.3 ± 0.02 and 0.95 ± 0.01 μM, respectively. Docking studies revealed the binding mode of compounds 1 and 3 in the ATP pocket of Pim-1 kinase. Testing of compounds 1 and 3 displayed significant antiproliferative activity against the human colon adenocarcinoma HT-29, (IC50 3.6 and 3.7 μM, respectively) and the human promyelocytic leukemia HL-60, (IC50 2.8 and 4.2 μM, respectively). These results suggested that compounds 1 and 3 act as potential Pim-1 kinase inhibitors that mediate the tumor cell growth inhibitory effect. This study highlighted the co-cultivation approach as an effective strategy to increase the chemical diversity of the secondary metabolites hidden in the genomes of the marine actinomycetes

Bioactive injectable mucoadhesive thermosensitive natural polymeric hydrogels for oral bone and periodontal regeneration

Periodontitis is an inflammation-related condition, caused by an infectious microbiome and host defense that causes damage to periodontium. The natural processes of the mouth, like saliva production and eating, significantly diminish therapeutic medication residency in the region of periodontal disease. Furthermore, the complexity and diversity of pathological mechanisms make successful periodontitis treatment challenging. As a result, developing enhanced local drug delivery technologies and logical therapy procedures provides the foundation for effective periodontitis treatment. Being biocompatible, biodegradable, and easily administered to the periodontal tissues, hydrogels have sparked substantial an intense curiosity in the discipline of periodontal therapy. The primary objective of hydrogel research has changed in recent years to intelligent thermosensitive hydrogels, that involve local adjustable sol-gel transformations and regulate medication release in reaction to temperature, we present a thorough introduction to the creation and efficient construction of new intelligent thermosensitive hydrogels for periodontal regeneration. We also address cutting-edge smart hydrogel treatment options based on periodontitis pathophysiology. Furthermore, the problems and prospective study objectives are reviewed, with a focus on establishing effective hydrogel delivery methods and prospective clinical applications

Examination of sleep in relation to dietary and lifestyle behaviors during Ramadan: A multi-national study using structural equation modeling among 24,500 adults amid COVID-19

Background Of around 2 billion Muslims worldwide, approximately 1.5 billion observe Ramadan fasting (RF) month. Those that observe RF have diverse cultural, ethnic, social, and economic backgrounds and are distributed over a wide geographical area. Sleep is known to be significantly altered during the month of Ramadan, which has a profound impact on human health. Moreover, sleep is closely connected to dietary and lifestyle behaviors. Methods This cross-sectional study collected data using a structured, self-administered electronic questionnaire that was translated into 13 languages and disseminated to Muslim populations across 27 countries. The questionnaire assessed dietary and lifestyle factors as independent variables, and three sleep parameters (quality, duration, and disturbance) as dependent variables. We performed structural equation modeling (SEM) to examine how dietary and lifestyle factors affected these sleep parameters. Results In total, 24,541 adults were enrolled in this study. SEM analysis revealed that during RF, optimum sleep duration (7–9 h) was significantly associated with sufficient physical activity (PA) and consuming plant-based proteins. In addition, smoking was significantly associated with greater sleep disturbance and lower sleep quality. Participants that consumed vegetables, fruits, dates, and plant-based proteins reported better sleep quality. Infrequent consumption of delivered food and infrequent screen time were also associated with better sleep quality. Conflicting results were found regarding the impact of dining at home versus dining out on the three sleep parameters. Conclusion Increasing the intake of fruits, vegetables, and plant-based proteins are important factors that could help improve healthy sleep for those observing RF. In addition, regular PA and avoiding smoking may contribute to improving sleep during RF

Design, synthesis and mechanistic anticancer activity of new acetylated 5-aminosalicylate-thiazolinone hybrid derivatives

The development of hybrid compounds has been widely considered as a promising strategy to circumvent the difficulties that emerge in cancer treatment. The well-established strategy of adding acetyl groups to certain drugs has been demonstrated to enhance their therapeutic efficacy. Based on our previous work, an approach of accommodating two chemical entities into a single structure was implemented to synthesize new acetylated hybrids (HH32 and HH33) from 5-aminosalicylic acid and 4-thiazolinone derivatives. These acetylated hybrids showed potential anticancer activities and distinct metabolomic profile with antiproliferative properties. The in-silico molecular docking predicts a strong binding of HH32 and HH33 to cell cycle regulators, and transcriptomic analysis revealed DNA repair and cell cycle as the main targets of HH33 compounds. These findings were validated using in vitro models. In conclusion, the pleiotropic biological effects of HH32 and HH33 compounds on cancer cells demonstrated a new avenue to develop more potent cancer therapies

Effects of antiplatelet therapy on stroke risk by brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases: subgroup analyses of the RESTART randomised, open-label trial

Background Findings from the RESTART trial suggest that starting antiplatelet therapy might reduce the risk of recurrent symptomatic intracerebral haemorrhage compared with avoiding antiplatelet therapy. Brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases (such as cerebral microbleeds) are associated with greater risks of recurrent intracerebral haemorrhage. We did subgroup analyses of the RESTART trial to explore whether these brain imaging features modify the effects of antiplatelet therapy