Optimal Behavior Research Articles

Optimal levels of sleep, sedentary behaviour, and physical activity needed to support cognitive function in children of the early years.

Sleep, sedentary behaviour, physical activity, and the composition of these movement behaviours across the 24-h day are associated with cognitive function in early years children. This study used a Goldilocks day compositional data analysis approach to identify the optimal duration of sleep, sedentary behaviour, light physical activity, and moderate-to-vigorous physical activity associated with desired cognitive function outcomes in early years children. This cross-sectional study included 858 children aged 2.8-5.5 years from the Sleep and Activity Database for the Early Years. 24-h movement behaviours (sleep, sedentary behaviour, light physical activity, moderate-to-vigorous physical activity) were measured using ActiGraph accelerometers. Cognitive function was measured using three tasks from the Early Years Toolbox: visual-spatial working memory, response inhibition, and expressive vocabulary. A Goldilocks day compositional data analysis approach was used in R software to identify the optimal time-use compositions associated with the best 10% of the cognitive function scores. The movement behaviour composition and the relative time spent in sleep and sedentary behaviour but not different intensities of physical activity were significantly associated with working memory (P ≤ 0.01). The movement behaviour composition and relative time spent in sleep, sedentary behaviour, and different intensities of physical activity were not significantly associated with response inhibition or expressive vocabulary (P > 0.2). Therefore, optimal time use was only determined for working memory. Optimal daily durations for working memory were observed with 11:00 (hr:min) of sleep, 5:42 of sedentary behaviour, 5:06 of light physical activity, and 2:12 of moderate-to-vigorous physical activity. Working memory was the only cognitive function outcome related to the 24-h movement behaviour composition. Optimal sleep for working memory was consistent with current recommended durations, while optimal moderate-to-vigorous physical activity greatly exceeded minimal recommended levels. Optimal sedentary behaviour was longer and light physical activity was shorter than the sample average.

A reward self-bias leads to more optimal foraging for ourselves than others.

People are self-biased for rewards. We place a higher value on rewards if we receive them than if other people do. However, existing work has ignored one of the most powerful theorems from behavioural ecology of how animals seek resources in everyday life, the Marginal Value Theorem (MVT), which accounts for optimal behaviour for maximising resources intake rate. Does this self-bias help humans maximise rewards when foraging for their own benefit compared to foraging for the benefit of others? Participants had to decide when to leave patches where reward intake was gradually depleting, in environments with different average reward rates. Half of the time participants foraged for themselves, and in the other half they collected rewards for an anonymous stranger. The optimal MVT derived solution states people should leave when the instantaneous reward intake in a patch equals the average rate in an environment. Across two studies, people were more optimal when foraging for self, showing a reduced sensitivity to instantaneous rewards when foraging for other. Autistic traits were linked to reduced sensitivity to reward rates when foraging for self but not for other. These results highlight that the self-bias may be adaptive, helping people maximise reward intake.

Multi-agent reinforcement learning with synchronized and decomposed reward automaton synthesized from reactive temporal logic

Multi-agent systems (MAS) consist of multiple autonomous agents interacting to achieve collective objectives. Multi-agent reinforcement learning (MARL) enhances these systems by enabling agents to learn optimal behaviors through interaction, thus improving their coordination in dynamic environments. However, MARL faces significant challenges in adapting to complex dependencies on past states and actions, which are not adequately represented by the current state alone in reactive systems. This paper addresses these challenges by considering MAS operating under task specifications formulated as Generalized Reactivity of rank 1 (GR(1)). These synthesized strategies are used as a priori knowledge to guide the learning. To tackle the difficulties of handling non-Markovian tasks in reactive systems, we propose a novel synchronized decentralized training paradigm that guides agents to learn within the MARL framework using a reward structure constructed from decomposed synthesized strategies of GR(1). We initially formalize the synthesis of GR(1) strategies as a reachability problem of winning states of the system. Subsequently, we develop a decomposition mechanism that constructs individual reward structures for decentralized MARL, incorporating potential values calculated through value iteration. Theoretical proofs are provided to verify that the safety and liveness properties are preserved. We evaluate our approach against other state-of-the-art methods under various GR(1) specifications and scenario maps, demonstrating superior learning efficacy and optimal rewards per episode. Additionally, we show that the decentralized training paradigm outperforms the centralized training paradigm. The value iteration strategy used to calculate potential values for the reward structure is compared against two other strategies, showcasing its advantages.

Transition metal doping for tailoring magnetic properties of graphdiyne

Tailoring the magnetic properties of semiconductors is crucial for realizing spintronic devices with integrated logic and memory functions. Graphdiyne (GDY), a two-dimensional carbon semiconductor, has emerged as a promising material for this purpose. This work demonstrates an effective approach to induce robust room-temperature ferromagnetism in GDY through in-situ transition metal (Fe, Co, Ni) doping via an improved liquid-liquid interface synthesis method. Density functional theory calculations reveal that the ferromagnetism originates from the Kagome lattice formed by the doped transition metal atoms. Remarkably, Fe-doped GDY exhibits a saturation magnetization of 2.86 emu · g−1 at room temperature, substantially higher than Co- and Ni-doped counterparts. Furthermore, post synthesis annealing treatment allows modulating the magnetic properties by introducing hydroxyl groups, with Co-GDY annealed at 500°C showing optimal room-temperature ferromagnetic behavior (0.26 emu · g−1). This work paves the way for tailoring magnetic semiconductors for spintronic applications by leveraging transition metal doping and defect engineering in graphdiyne.

Demographic, Clinical, Psychosocial, and Behavioral Predictors of Continuous Glucose Monitor Use in Adults with Type 2 Diabetes.

Continuous glucose monitor (CGM) use is increasing rapidly among people with type 2 diabetes, although little is known about predictors of CGM use beyond clinical and demographic information available in electronic medical records. Behavioral and psychosocial characteristics may also predict CGM use. We examined clinical, psychosocial, and behavioral characteristics that may predict CGM use in adults with type 2 diabetes. This longitudinal observational study comprised a secondary analysis of data collected in a larger trial. Enrollment included HbA1c tests and surveys assessing demographic, clinical, psychosocial, and behavioral characteristics. We queried participants regarding their CGM use during the study on their final self-report surveys, 15months post-enrollment. Participants were 245 community-dwelling adults with type 2 diabetes recruited from primary care. We used logistic regression to predict CGM use during the 15-month trial period from baseline characteristics. Around one-third of participants (37.1%; 91/245) started CGM. Predictors of starting CGM in bivariate models included younger age, higher socioeconomic status, insulin use, higher HbA1c, and more diabetes distress. When including all potential predictors in a single multivariable model, only younger age (aOR = 0.95, p = 0.001), insulin use (aOR = 2.33, p = 0.006), and higher socioeconomic status (aOR = 0.44, p = 0.037) were significant predictors. Despite the association between higher HbA1c and CGM use, neither diabetes self-care behaviors nor diabetes self-efficacy significantly predicted CGM use. Of participants who tried a CGM, 14.3% (13/91) had stopped, with cost being the most-cited reason. Even when including behavioral and psychological characteristics, younger age, using insulin, and higher socioeconomic status remain key predictors of CGM use. These findings emphasize the importance of access and affordability for people who may benefit from CGM. Providers should not bias their introduction of CGM towards those with (perceived or actual) optimal or sub-optimal self-care behaviors.

Influence of Solution Treatment Temperatures on LPBF-Produced Inconel 939 Alloy on Mechanical Performance

This study investigates the influence of solution treatment temperatures on the microstructure and mechanical properties of LPBF (Laser Powder Bed Fusion)-manufactured Inconel 939 alloy. The primary objectives are to assess the impact of sub-recrystallization (below recrystallization temperature) and recrystallization temperatures during solution treatment on mechanical performance under different conditions. This study validates the hypothesis concerning carbide redistribution and mechanical performance in LPBF-produced Inconel 939 parts through comprehensive analysis, including microstructure studies and tensile tests. The results indicate that higher heat treatment temperatures lead to significant changes in carbide redistribution and mechanical performance. For instance, at room temperature, the specimen with sub-recrystallization solution treatment temperature exhibited superior mechanical properties compared to both the as-built and recrystallization-temperature solution treatment specimens. However, at elevated temperatures (700 °C), a reversal in mechanical anisotropy was observed, with the vertical axis demonstrating higher tensile strength compared to the horizontal axis for all material states. Furthermore, a microstructural analysis revealed distinct changes in grain structure and precipitate distribution, particularly the migration of carbides from grain boundaries to intragranular regions. These findings underscore the importance of precise control over heat treatment parameters to achieve optimal mechanical behavior. This research provides valuable insights into optimizing LPBF processes for the Inconel 939 alloy, highlighting the need for a meticulous control of the solution treatment parameters to ensure mechanical integrity. The findings contribute to a broader understanding of material-specific responses in additive manufacturing, with significant implications for aerospace applications.

Theory-Based Entrepreneurial Search

How should Theory-based entrepreneurs search for strategies to implement their ideas? The theory-based view of strategy posits that decision makers hold key conjectures about their path to success and use theory to understand and test beliefs underlying those conjectures. This causal framework also has implications for entrepreneurial search: the process by which entrepreneurs uncover strategies to implement their ideas. In this paper, we develop a Bayesian model where entrepreneurs update their beliefs as they conduct entrepreneurial search. We find several optimal behaviors for Theory-based entrepreneurs such as reverting to a previous strategy after finding a relatively poor strategy and continuing to search after finding a relatively good strategy, which are missing when entrepreneurs lack such a theory-based approach. As our theoretical predictions align with examples of successful entrepreneurs, our findings both provide a method to empirically identify Theory-based entrepreneurs and demonstrate the usefulness of applying the theory-based view to entrepreneurial behavior more generally.

An Interpersonal and Meta-analytic Approach to Parenting Behaviors and Adolescent Sleep.

Two dimensions of parenting behaviors-affiliative/non-affiliative and autonomy/control- are each consistently linked to adolescent sleep health. Parenting behaviors that facilitate good sleep likely involve affiliation (i.e., warmth) and some degree of parental guidance and appropriate autonomy-granting to the adolescent; however, these domains are often confounded in parenting assessments, which limits understanding and specificity of recommendations for providers and families on how to optimize adolescents' sleep. Thus, we categorized existing literature according to an interpersonal developmental framework to identify parenting behaviors most strongly linked to adolescent sleep health. Studies (k = 42) included 43,293 participants (M age = 14.84, SD age = 2.04). Structural analysis of social behavior (SASB)interpersonal coding was applied to define and operationalize parenting behaviors (independent variables). Dependent variables included measures of sleep health (i.e.,sleep regularity, duration, efficiency, latency, timing, quality, alertness/sleepiness) and sleep disturbance. The final effect size of interest for analysis was a correlation coefficient r. Optimal parenting behaviors (e.g., warm, autonomy-granting, moderately controlling) were associated with longer sleep duration, earlier bedtime, less daytime sleepiness, shorter sleep latency, and fewer sleep disturbances. Suboptimal parenting behaviors (e.g., hostile, controlling) were associated with more daytime sleepiness and more sleep disturbances. This is one of the first studies to specify that, when paired with affiliation, both moderate control and moderate autonomy-granting were associated with better adolescent sleep health. Findings indicate that the importance of parental interpersonal warmth extends into adolescence and further suggest that the interpersonal security necessary for good sleep includes appropriate use of control and autonomy-granting behaviors.

Formal contracts mitigate social dilemmas in multi-agent reinforcement learning

Multi-agent Reinforcement Learning (MARL) is a powerful tool for training autonomous agents acting independently in a common environment. However, it can lead to sub-optimal behavior when individual incentives and group incentives diverge. Humans are remarkably capable at solving these social dilemmas. It is an open problem in MARL to replicate such cooperative behaviors in selfish agents. In this work, we draw upon the idea of formal contracting from economics to overcome diverging incentives between agents in MARL. We propose an augmentation to a Markov game where agents voluntarily agree to binding transfers of reward, under pre-specified conditions. Our contributions are theoretical and empirical. First, we show that this augmentation makes all subgame-perfect equilibria of all Fully Observable Markov Games exhibit socially optimal behavior, given a sufficiently rich space of contracts. Next, we show that for general contract spaces, and even under partial observability, richer contract spaces lead to higher welfare. Hence, contract space design solves an exploration-exploitation tradeoff, sidestepping incentive issues. We complement our theoretical analysis with experiments. Issues of exploration in the contracting augmentation are mitigated using a training methodology inspired by multi-objective reinforcement learning: Multi-Objective Contract Augmentation Learning. We test our methodology in static, single-move games, as well as dynamic domains that simulate traffic, pollution management, and common pool resource management.

Hypothalamic representation of the imminence of predator threat detected by the vomeronasal organ in mice.

Animals have the innate ability to select optimal defensive behaviors with appropriate intensity within specific contexts. The vomeronasal organ (VNO) serves as a primary sensory channel for detecting predator cues by relaying signals to the medial hypothalamic nuclei, particularly the ventromedial hypothalamus (VMH), which directly controls defensive behavioral outputs. Here, we demonstrate that cat saliva contains predator cues that signal the imminence of predator threat and modulate the intensity of freezing behavior through the VNO in mice. Cat saliva activates VNO neurons expressing the V2R-A4 subfamily of sensory receptors, and the number of VNO neurons activated in response to saliva correlates with both the freshness of saliva and the intensity of freezing behavior. Moreover, the number of VMH neurons activated by fresh, but not old, saliva positively correlates with the intensity of freezing behavior. Detailed analyses of the spatial distribution of activated neurons, as well as their overlap within the same individual mice, revealed that fresh and old saliva predominantly activate distinct neuronal populations within the VMH. Collectively, this study suggests that there is an accessory olfactory circuit in mice that is specifically tuned to time-sensitive components of cat saliva, which optimizes their defensive behavior to maximize their chance of survival according to the imminence of threat.

Hypothalamic representation of the imminence of predator threat detected by the vomeronasal organ in mice

Animals have the innate ability to select optimal defensive behaviors with appropriate intensity within specific contexts. The vomeronasal organ (VNO) serves as a primary sensory channel for detecting predator cues by relaying signals to the medial hypothalamic nuclei, particularly the ventromedial hypothalamus (VMH), which directly controls defensive behavioral outputs. Here, we demonstrate that cat saliva contains predator cues that signal the imminence of predator threat and modulate the intensity of freezing behavior through the VNO in mice. Cat saliva activates VNO neurons expressing the V2R-A4 subfamily of sensory receptors, and the number of VNO neurons activated in response to saliva correlates with both the freshness of saliva and the intensity of freezing behavior. Moreover, the number of VMH neurons activated by fresh, but not old, saliva positively correlates with the intensity of freezing behavior. Detailed analyses of the spatial distribution of activated neurons, as well as their overlap within the same individual mice, revealed that fresh and old saliva predominantly activate distinct neuronal populations within the VMH. Collectively, this study suggests that there is an accessory olfactory circuit in mice that is specifically tuned to time-sensitive components of cat saliva, which optimizes their defensive behavior to maximize their chance of survival according to the imminence of threat.

STRIVING FOR DIABETES SELF-CARE MANAGEMENT IN THE OLDER PEOPLE BASED ON SELF-DETERMINATION THEORY

Introduction: Older people with diabetes mellitus need disease management through self-management which is quite complex because the disease will have an impact on their health condition both physically and psychologically. Based on Self Determination Theory, motivation is important component in improving psychological health and treatment effectiveness of patients with diabetes mellitus. The purpose of this community service is to strive for diabetes self-care management so that blood sugar levels can be controlled reduce the risk of complications and achieve a prosperous psychological condition. Methods: The method of implementing this community service is in the form of health education, early screening, diabetes gymnastics, and family assistance in carrying out family healthcare functions. This community service was carried out at Jenggawah Public Health Center in March 2023. The participants of this community service activity are older people with diabetes mellitus. Results: There is an increase in the motivation of older people to carry out self-care activities, and the older people and families can also practice diabetes gymnastics by what has been taught. Conclusion: With optimal management behavior of diabetes mellitus in older people, the psychological aspects of the elderly with Diabetes Mellitus will improve so that further complications of this disease can be prevented.

Techno-environmental optimal sizing and dynamic behavior of a hybrid system feeding a large-scale SWRO desalination system: The case of Djerba Island, Tunisia

Techno-environmental optimal sizing and dynamic behavior of a hybrid system feeding a large-scale SWRO desalination system: The case of Djerba Island, Tunisia

Optimization and forecasting of reinforced wire ropes for tower crane by using hybrid HHO-PSO and ANN-HHO algorithms

Wire rope is a vital component of every crane. Wire rope faults are related to the operation, fabrication environment, etc., and the prevalent mode of failure is fatigue. The aim of this study is to develop an advanced tower crane applicable to wire rope with integrated reinforcement. Steel wire ropes are superiorly used in several tower crane applications, but they may create certain failures such as less fatigue and wear-resistant. In this study, steel wires are strengthened by granite and Zinc oxide (ZnO) reinforcement. Two sets of wire ropes are prepared as complete and partial reinforcement of steel wire with seven strands and 15 wires. The failure tests such as hardness, wear analysis, tensile strength, and fatigue life are optimized using hybrid Harris Hawk optimization-based Particle swarm Optimization (Hybrid HHO-PSO). Besides, the experimented wire rope performances are predicted using hybrid Artificial Neural Network based HHO (Hybrid ANN-HHO). Fully reinforced wire ropes provide better performances for both experimented and optimization behaviors. This provides 1818 MPa of maximum tensile strength, 0.23 mm of minimal wear depth, and 3.38x104 times better fatigue life. In the HHO-PSO optimization method, the obtained better tensile strength is 1822 MPa, wear depth is 0.66 mm, and Fatigue life is 3.57 x104 times. Besides, from the predicted outcomes, ANN-HHO provides a minimal error value than the ANN approach. The result of this study will open up different ways for the advancement of wire rope in tower crane application by improving its load bearing capacity. The outcomes from this research can be practically applicable for increasing the load bearing capacity of the tower crane without increasing the number of wires and strands in the wire rope.

Regulating mechanisms of external axial magnetic field on flow and heat transfer behavior for process optimization in plasma beam polishing

Plasma beam polishing has emerged as an innovative alternative to conventional metal finishing techniques. However, the precision machining ability of plasma arc encounters challenges due to the formation of surface undulations, while previous research has predominantly focused on parameter adjustments. In this paper, the feasibility of optimizing the plasma beam polishing process was validated through magnetic field regulation, as the surface topography and subsurface structure were obviously flattened. Given this, a micro plasma arc model under inhomogeneous axial magnetic field was established to explore the regulating mechanisms of the external axial magnetic field on the flow and heat transfer behavior of the plasma arc. Through this model, the plasma characteristics (magnetic induction intensity, electric potential, arc current, Lorentz force, plasma velocity and electron temperature) were comprehensively discussed. The results indicate that the external shape of the plasma arc is determined by the self-induced magnetic field, while the externally applied axial magnetic field significantly influences the internal flow of the plasma arc. The axial and radial magnetic fields generated by the excitation coil can induce circumferential rotation by dragging the plasma arc with Lorentz force, creating a more suitable energy beam for the polishing process. To verify the model, the arc temperature was measured using spectroscopic diagnostics, and the arc shape was captured using a high-speed camera, both of which were basically consistent with the simulation results.

Evaluating The Potential Of A Digital Oral Health Intervention To Enhance Knowledge, Attitudes, And Practices Related To Oral Health Among Urban Population- A Cross Sectional Study

Introduction: maintaining good oral health and avoiding avoidable dental diseases require optimal oral health behaviors. The purpose of this study was to assess how well a digital oral health intervention changed oral healthrelated knowledge, attitudes, and behaviors. Materials and method: two research involving 200 healthy people were carried out. Before and after the know your oqtm intervention was finished, demographic information and oral health-related knowledge, attitudes, and practices (kaps) were gathered. There were 19 multiple-choice questions on the kaps questionnaire. Feedback and comprehension were also gathered. Result: the two investigations were completed by 100 (50%) female participants and 100 (50%) male individuals. Five out of seven knowledge questions and two out of five attitude questions in both studies shown significant changes before and after the intervention, with participants' attitudes and knowledge regarding oral health improving. Four of the seven practice questions in the second study showed substantial changes before and after the intervention, whereas only one practice altered in the first study. In all cases, the intervention received generally positive response and was highly comprehensible. Conclusion: a digital oral health intervention was successful in increasing knowledge, changing attitudes and self-reported practices with regards to oral health in a diverse sample of the urban population.

Engineering a 3D network hydrogel composite membrane with Chitosan/Pluronic F127/bioactive glass for the topical administration of therapeutic agents

The primary aim of designing a topical drug delivery system is to address localized ailments by administering therapeutic agents directly to superficial areas of the body such as skin and open wounds. This study presents the engineering of a three-dimensional (3D) network hydrogel composite membrane comprising chitosan (CS), Pluronic F127 (Pl), and bioactive glass (BG) for the topical administration. In the presence of CS and Pl, glutaraldehyde forms imine bonds (Schiff base linkages) by reacting its aldehyde groups with the amino groups of the polymer chains. The hydrogel structural and chemical properties were characterized using X-ray diffraction (XRD) and Fourier-transform infrared spectroscopy (FTIR), revealed its unique composition and molecular structure. Field Emission Scanning electron microscopy (FESEM) was employed to image and investigate the morphology of the hydrogel, confirming its interconnected sheet like morphology with small beads structure. The hydrogels demonstrated excellent water retention ability as 32.93 %, further controlled degradation kinetics rate was systematically evaluated up to 14 days, and optimal swelling behavior makes it suitable for prolonged drug delivery applications. Moreover, hemocompatibility and cyto-compatibility assays yielded less than 1.1 % and above 60 %, confirming the hydrogel safety for blood contact and cell viability. Tube inversion tests confirmed its mechanical integrity, while antibacterial assays revealed its efficacy against pathogenic microorganisms. Co-culture studies demonstrated the capability of developed hydrogel to support cell growth and proliferation. In vitro drug release studies clearly reveal that the hydrogel provides a sustainable delivery system for piperacillin-tazobactam, demonstrating superior characteristics indicative of non-Fickian or anomalous diffusion. Furthermore, the membrane exhibited promising hemostatic properties, suggesting its potential for wound healing applications. Overall, this comprehensive strategy highlights the versatility of the engineered 3D network hydrogel composite membrane as a promising platform for delivering therapeutic agents locally in various biomedical applications.

Chitosan-melanin complex microsphere: A potential colonic delivery system for protein drugs

The characteristics and performance of chitosan-based colon delivery systems are significantly influenced by the method of preparation. Insect chitosan-melanin complex (CMC) may offer superior attributes over traditional shrimp and crab chitosan (CS) for colon-targeted administration. This study used dung beetle CMC as the carrier matrix and comprehensively examined the impact of various crosslinking techniques on the colonic drug delivery efficacy of microspheres, encompassing drug loading, swelling, drug release behavior, adhesion, enzymatic degradation, and absorption enhancement. The results indicate that F-TPPLC microspheres, crosslinked with a combination of formaldehyde and TPP, exhibit superior drug loading capabilities, optimal swelling behavior, and controlled in vitro drug release profiles in the colonic environment, along with excellent adhesion and enzymatic degradation properties within intestinal tract. Notably, these F-TPPLC microspheres increase paracellular permeability, possibly by disrupting the calcium-dependent adhesion junctions. In comparison to commercial CS, CMC demonstrates superior drug encapsulation efficiency, enhanced colonic drug release, adhesion, and absorption promotion, rendering it a favorable candidate as a carrier in colon-targeted drug delivery systems. Consequently, F-TPPLC microspheres derived from CMC are highly suitable for colon drug delivery applications and show promising potential for the oral delivery of peptide and protein-based therapeutics to the colon.

Machine-directed Gravitational-wave Counterpart Discovery

Joint observations in electromagnetic and gravitational waves shed light on the physics of objects and surrounding environments with extreme gravity that are otherwise unreachable via siloed observations in each messenger. However, such detections remain challenging due to the rapid and faint nature of counterparts. Protocols for discovery and inference still rely on human experts manually inspecting survey alert streams and intuiting optimal usage of limited follow-up resources. Strategizing an optimal follow-up program requires adaptive sequential decision-making given evolving light curve data that (i) maximizes a global objective despite incomplete information and (ii) is robust to stochasticity introduced by detectors/observing conditions. Reinforcement learning (RL) approaches allow agents to implicitly learn the physics or detector dynamics and the behavior policy that maximizes a designated objective through experience. To demonstrate the utility of such an approach for the kilonova follow-up problem, we train a toy RL agent with the goal of maximizing follow-up photometry for the true kilonova among several contaminant transient light curves. In a simulated environment where the agent learns online, it achieves 3× higher accuracy compared to a random strategy. However, it is surpassed by human agents by up to a factor of 2. This is likely because our hypothesis function (Q that is linear in state-action features) is an insufficient representation of the optimal behavior policy. More complex agents could perform at par or surpass human experts. Agents like these could pave the way for machine-directed software infrastructure to efficiently respond to next generation detectors, for conducting science inference and optimally planning expensive follow-up observations, scalably and with demonstrable performance guarantees.

Controllability and Optimal Fast Operation of Nonlinear Systems

A new method for solving the problem of controllability and optimal transient behavior of nonlinear systems subject to boundary conditions and constraints on control values was proposed. Unlike existing methods, this new approach is based on constructing a general solution of the integral equation for a linear controlled system, followed by transforming the original problem into a special initial optimal control problem. We propose a new method for studying the global asymptotic stability of dynamical systems with a cylindrical phase space with a countable equilibrium position based on a non-singular transformation of the equation of motion and estimation of improper integrals along the solution of the system. Conditions for global asymptotic stability were obtained without involving any periodic Lyapunov function, as well as the frequency theorem. The effectiveness of the proposed method is shown with an example.