Intermittent Strategy Research Articles

Linear and Non-Linear Optimal Control Methods to Determine the Best Chemotherapy Schedule for Most Effectively Inhibiting Tumor Growth

Background/Objectives: Cancer is a dynamic and complex disease that remains largely untreated despite major advances in oncology and treatment. In this context, we aimed here to investigate optimal control techniques in the management of tumor growth inhibition, with a particular focus on cancer chemotherapy treatment strategies. Methods: Using both linear autoregressive with exogenous inputs (ARX) and advanced non-linear tumor growth inhibition (TGI) modeling approaches, we investigated various single-agent treatment protocols, including continuous, periodic, and intermittent chemotherapy schedules. By integrating advanced mathematical modeling with optimal control theory and methods, namely the Linear Quadratic Regulator (LQR) and the “pseudo-linear” state-space equivalent representation and suboptimal control of a non-linear dynamic system known as the State-Dependent Riccati Equation (SDRE) approach, this work explores and evaluates successfully, more effective chemotherapy treatment strategies at the computer simulation level, using real preclinical data which increases the expectation to be applied in the clinical practice of oncology. Results: The integration of these methods provides insights into how different drug administration schedules may affect tumor response at the preclinical level. This work uses mathematical modeling to evaluate the efficacy of various periodic and intermittent chemotherapy treatment strategies, with a focus on optimizing drug doses while minimizing the potential side effects of chemotherapy due to the administration of less effective chemotherapeutic doses. Conclusions: The treatment scenarios tested in this study could effectively stop tumor growth or even lead to tumor regression to a negligible or near-zero size. This approach highlights the importance of computational tools for more effective treatment strategies in chemotherapy and offers a promising direction for future research and more efficient clinical applications in oncology as part of a more individualized approach.

Biohydrogen production enhancement from organic solid waste using consecutive intermittent feeding strategies in a sequencing batch reactor

Abstract The organic fraction of municipal solid waste (OFMSW) can be valorized for bioenergy production by dark fermentation (DF) using sequencing batch reactors (SBR). Alternative feeding strategies such as fed-batch have shown increased biogas production. Since fed-batch operation with OFMSW is difficult because of the viscosity and density of its substrate, this work proposes the use of a feeding strategy that operates intermittently to improve the biohydrogen (H2) production from OFMSW in an SBR. The consecutive intermittent feeding strategy consisted in supplying the influent volume with a given number of equal feeding pulses in the first 16 h. Two, four, eight, and frequent pulses were tested in three consecutive cycles. Single pulse feeding (i.e., conventional batch) was performed before and after each feeding strategy for comparison. The four feeding strategies had a change in H2 and metabolite composition, cumulative volume, productivity, and yield. Intermittent feeding also diminished the lag phase for H2 production (λ ≤ 0.62 h). The frequent pulse strategy showed the best performance (365.5 ± 10.8 NmL, 340.3 ± 0.7 NmL L−1∙d−1, and 26.1 ± 0.7 NmL gVSadded for accumulated hydrogen production, hydrogen productivity, and yield, respectively), and it also increased caproate production (up to 1.12 gCOD L−1). Significant correlations between the production of organic acids and specific microbial genera were observed, highlighting the complex microbial community interactions present during biological hydrogen production. Graphical Abstract

Sampled-data self-learning observer based attitude tracking control against sensor-actuator faults

Abstract This paper proposes an intermittent measurement-based attitude tracking control strategy for spacecraft operating in the presence of sensor-actuator faults. A sampled-data (self-)learning observer is developed to estimate both the spacecraft’s states and lumped disturbances, effectively mitigating the impact of faults. This observer acts as a virtual predictor, reconstructing states and actuator fault deviations using only intermittent measurement data, addressing the limitations imposed by sensor failures. The control scheme incorporates compensation based on the predictor’s estimates, ensuring robust attitude tracking despite the presence of faults. We provide the first proof of bounded stability for this learning observer utilizing intermittent information, expanding its applicability. Numerical simulations demonstrate the effectiveness of this innovative strategy, highlighting its potential for enhancing spacecraft autonomy and reliability in challenging operational scenarios.

Dual intermittent aerations enhance nitrogen removal via anammox in anoxic/oxic biofilm process for carbon limited wastewater treatment.

Efficient nitrogen removal after organic capture is challenging through conventional nitrification-denitrification process. Two biofilm-based anoxic/oxic reactors, with a single intermittent zone (R1) or dual intermittent zones (R2), were compared in treating carbon-limited wastewater. Intermittent aeration integrated partial nitrification-anammox (PNA), partial denitrification-anammox (PDA), and denitrification, with anammox-related pathways contributing over 75% nitrogen removal in both reactors. As nitrogen loading rate increased from 0.14 to 0.19 kg-N m-3 day-1, nitrogen removal efficiency in R1 dropped from 74.3% to 46.0%, while R2 maintained 76.6% removal at low HRT of 6h. The dual intermittent aeration strategy improved nitrogen removal capacity by enhancing PNA in the first intermittent zone and reducing effluent fluctuation in the second. Anammox bacteria (Candidatus Brocadia, relative abundance: 0.95-2.48%) were enriched across all zones, supporting efficient PNA and PDA. These findings suggested that dual intermittent aeration enhanced anammox in pre-anoxic processes for carbon limited wastewater treatment.

Comprehensive performance analysis of a novel closed-loop hydronic cooling of photovoltaic panel with a controlled intermittent flow strategy

Comprehensive performance analysis of a novel closed-loop hydronic cooling of photovoltaic panel with a controlled intermittent flow strategy

Seasonal patterns of CO2 exchange in a tropical intensively managed pasture in Southeastern Brazil

Tropical pastures are one of the main land uses in Brazil, forming the backbone of the country's beef and milk production chain. Adopting sustainable management practices that increase the productivity of pastoral livestock systems is essential to mitigate environmental impacts and ensure food security. However, eddy covariance studies that contribute to understanding the influence of grazing management strategies on the ability of intensively grazed tropical pastures to absorb carbon remain scarce. Therefore, our main objective was to investigate the dynamics of CO2 and water vapor exchange and biomass production in a tropical C4 grass (Pennisetum purpureum Schum. cv. Cameroon) pasture under intermittent stocking management strategies from March 2021 to June 2023. We found that the pasture acted as a net source of CO2 to the atmosphere in both years studied. The annual NEE was 34 ± 14 g CO2-C m−2 yr−1 in 2021–2022 and 21 ± 12 g CO2-C m−2 yr−1 in 2022–2023. Reco, influenced by rising air and soil temperatures, increased rainfall, and higher incoming solar radiation levels, especially during spring and summer, played a crucial role in this result. The pasture absorbed more CO2, showed higher evapotranspiration, and produced more leaves in the rainy periods when the pasture structure was kept close to the previously established management targets. CO2 losses to the atmosphere prevailed in the dry periods and in wet periods where the pasture structure was far from the optimal limits for elephant grass.

Fixed-time consensus tracking of nonlinear multi-agent systems under intermittent adaptive event-triggered strategy

Fixed-time consensus tracking of nonlinear multi-agent systems under intermittent adaptive event-triggered strategy

Cooling Period Strategies in an Intermittent Usage Building: A Case Study of a Mosque in the Tropical Climate of Malaysia

Mosques are used five times a day according to the prayer schedule, which follows the sun's position. Indoor thermal conditions in air-conditioned mosques are maintained with a cooling time pattern that follows prayer times. Therefore, an effective air conditioning operating strategy is needed for energy efficiency. This study aims to investigate the effect of air conditioning intermittent operation time strategies on the energy usage of a mosque. It involves reducing the cooling period before prayer time and swapping active air conditioning periods from pre-prayer calls to post-prayer in the base case. Field measurements in Universiti Teknologi Malaysia – Kuala Lumpur (UTMKL) mosque and simulation using DesignBuilder software were conducted to predict annual specific energy consumption. The results indicate a reduction in specific energy consumption of 5% by operating the air conditioning 10 minutes before the call to prayer. Shifting the pre-prayer call-post prayer cooling period results in an insignificant reduction in energy consumption of 0.3-0.6%; however, it can accommodate extended periods of comfortable conditions for people who perform prayers outside of congregational prayers on time. Furthermore, the results indicate that the cooling period after the prayer time affects the cooling load of the following prayer. This study provides information for architects, engineers, and other stakeholders towards improving energy consumption in religious buildings with intermittent occupancy.

Characterization of Post Coronary Artery Bypass Grafting Atrial Fibrillation Patterns: Rationale and Design of an Investigator-Initiated Observational Study

New-onset postoperative atrial fibrillation (POAF) after cardiac surgery is associated with increased rates of adverse events (including mortality and stroke). Its incidence after coronary artery bypass grafting (CABG) is considered to be approximately 30 % and it is thought to be a transient condition. However, studies investigating POAF after CABG fail to provide appropriate data on incidence and arrhythmia patterns due to the use of intermittent rhythm detection strategies. These methods have a low sensitivity as compared to continuous monitoring. Subsequently, studies utilizing these techniques most likely do not identify all patients with arrhythmia and do not adequately demonstrate the long-term incidence of arrhythmia, which in turn may affect its association with adverse events. The CABG-AF study (German Clinical Trials Register Nr.: DRKS00018887) tests the hypothesis that the incidence of AF in the first 12 months after CABG is significantly underestimated. CABG-AF is an investigator-initiated multicenter, prospective, observational study in which 196 patients with no history of arrhythmia undergoing first-time CABG, receive an insertable cardiac monitor for continuous postoperative rhythm monitoring. The primary endpoint of the study is any episode of AF within the first 12 months after surgery. Secondary endpoints include AF burden, AF density and the ratio of silent to symptomatic AF episodes. Endpoints will be investigated by automatic and patient-initiated data transfers from the implanted device, by telephone interview of patients and by follow-up forms sent to patients by mail. The patients will be followed for a planned follow-up of 3 years. In conclusion, the CABG-AF study will provide information on the true incidence of AF after CABG as well as on the temporal patterns of the arrhythmia.

Formation control of multiagent systems with multileaders through completely distributed intermittent communication strategies

The distributed time-varying formation (TVF) control problem of multiagent systems (MASs) with multileaders is explored in this research. Contrasted with the existing results, this paper considers the following situations: 1) the multifollower group is heterogeneous, as is the multileader group; 2) the heterogeneous system matrices of the multileaders are not already known to all followers; 3) some strict constraint conditions, such as well-informed follower assumption and virtual leader condition, are removed. This paper presents the event-triggered (ET) matrix observer, the adaptive ET state compensator, and the output-feedback TVF controller, which are constituted as the innovative completely distributed ET control protocol. Considering the limited communication bandwidth, the ET matrix observer and compensator are designed, with the communication-bandwidth-saving manners, to estimate the integrated system matrix and integrated state information of all leader agents, respectively. The output feedback formation controller is built to adjust the followers to keep the predetermined team formations and follow the reference trace, where the trace is all leaders outputs' convex combination. The stability analysis and simulation experiment are brought out to demonstrate the validity of the suggested control strategy.

Synchronization of Euler-Lagrangian networks: a periodic intermittent dynamic event-triggered control strategy

In this paper, the synchronization problem of Euler–Lagrange networks is studied by designing periodic intermittent dynamic event-triggered control strategy. The synchronization of all agents is successfully realized by injecting negative comments into the discontinuous interval of the agents described by the Euler–Lagrange systems. Different from the traditional intermittent event-triggered control strategy, the control strategy proposed in this study introduces internal dynamic variables to expand the sampling interval of the event-triggered mechanism, thereby improving resource utilization efficiency. In addition, the event trigger instants of each agent are asynchronous. By using graph theory and Lyapunov method, the synchronization criteria of Euler–Lagrange networks are derived. The effectiveness of the proposed control scheme is verified by simulations for the Euler-Lagrangian network composed of six two-link rotating manipulators.

Biovalorization of lignin-derived substrates to vanillylamine via a self-sufficient amino donor and cofactor recycling whole-cell platform

Lignin valorization through bioconversion to high-value chemicals is crucial for sustainable bioprocessing. Vanillin (VN), a primary lignin derivative, can be transaminated into vanillylamine (VM), a key precursor for capsaicin and pharmaceuticals. This study established a novel self-sufficient redox-complementary whole-cell system, facilitating the recycling of L-alanine and cofactors for efficient VM biosynthesis. Ammonium formate (AF) was employed as amino donor and co-substrate. Recombinant E. coli strain, co-expressing ω-transaminase (CvTA), L-alanine dehydrogenase (ALD), and formate dehydrogenase (FDH), showed higher yield in shorter reaction time compared to the strain expressing only CvTA and ALD. Intermittent feeding strategy was developed to mitigate VN cytotoxicity problem and a remarkable yield of 97.3 ± 1.0% was achieved of VM from 60 mM VN under optimized biotransamination conditions (37 °C, pH 8.0, VN:AF = 1:5, and 1.5 mM NAD+). Notably, a double-plasmid E. coli recombinant harboring CvTA, ALD, FDH, and aromatic dioxygenase (ADO) was constructed to convert isoeugenol into VM with a 73.2 ± 1.1% yield. This efficient biotransamination platform not only offers a sustainable route to VM for capsaicin production but also promotes lignin valorization for a greener bioeconomy.

Variable gain intermittent stabilization and synchronization for delayed chaotic Lur’e systems

In this paper, a variable gain intermittent control strategy for stabilization and synchronization of chaotic Lur’e systems with time-varying delay is proposed. In contrast to the conventional constant gain intermittent control strategies, the proposed intermittent control strategy allows the control gain to vary with the operation duration of the intermittent controller. The construction of variable control gains is based on a partition scheme on the time-varying working intervals. In order to align with the structure of the variable intermittent control gain function, a pair of partition-dependent piecewise Lyapunov functions are introduced. Two distinct Razumikhin-type analysis techniques, one for the working intervals and the other for the resting intervals, are employed to derive novel criteria for intermittent stabilization and synchronization. The desired intermittent control/synchronization gain functions can be obtained by solving a convex minimization problem, which is capable of minimizing the control width under specified constraints on the gain norm. The numerical results demonstrate that, in comparison with the conventional constant gain strategies, the proposed variable gain intermittent control strategy is capable of efficiently reducing the intermittent control rate.

A Retrospective Analysis of Intravenous Push versus Extended Infusion Meropenem in Critically Ill Patients.

Meropenem is a broad-spectrum antibiotic used for the treatment of multi-drug-resistant infections. Due to its pharmacokinetic profile, meropenem's activity is optimized by maintaining a specific time the serum concentration remains above the minimum inhibitory concentration (MIC) via extended infusion (EI), continuous infusion, or intermittent infusion dosing strategies. The available literature varies regarding the superiority of these dosing strategies. This study's primary objective was to determine the difference in time to clinical stabilization between intravenous push (IVP) and EI administration. We performed a retrospective pilot cohort study of 100 critically ill patients who received meropenem by IVP (n = 50) or EI (n = 50) during their intensive care unit (ICU) admission. There was no statistically significant difference in the overall achievement of clinical stabilization between IVP and EI (48% vs. 44%, p = 0.17). However, the median time to clinical stability was shorter for the EI group (20.4 vs. 66.2 h, p = 0.01). EI administration was associated with shorter hospital (13 vs. 17 days; p = 0.05) and ICU (6 vs. 9 days; p = 0.02) lengths of stay. Although we did not find a statistically significant difference in the overall time to clinical stabilization, the results of this pilot study suggest that EI administration may produce quicker clinical resolutions than IVP.

Stochastic resetting can optimize the intermittent search strategy in a two-dimensional confined topography

Based on the intermittent two-state random walk model, by calculating and discussing the mean first capture time, the search efficiency of the intermittent search strategy in a two-dimensional confined topography in the absence and presence of stochastic resetting is studied. Results reveal that for the close target, the regular Brownian motion is the efficient search strategy, whereas, for the target far from the starting point, the typical intermittent search strategy with relocation times being power-law distributed turns out to be the advantageous one, and the search efficiency impressively enhances with the distance of the target from the starting point increasing. We demonstrate that though in the absence of stochastic resetting there does not exist a universal optimal search strategy in the confined topography, the introduction of stochastic resetting makes the Brownian motion surprisingly to be the universal optimal search process, and distinctly outperforms the ones without stochastic resetting.

Aperiodically Intermittent Event-Triggered Optimal Average Consensus for Nonlinear Multi-Agent Systems.

This article is concerned with average consensus of multi-agent systems via intermittent event-triggered strategy. First, a novel intermittent event-triggered condition is designed and the corresponding piecewise differential inequality for the condition is established. Using the established inequality, several criteria on average consensus are obtained. Second, the optimality has been investigated based on average consensus. The optimal intermittent event-triggered strategy in the sense of Nash equilibrium and corresponding local Hamilton-Jacobi-Bellman equation are derived. Third, the adaptive dynamic programming algorithm for the optimal strategy and its neural network implementation with actor-critic architecture are also given. Finally, two numerical examples are presented to show the feasibility and effectiveness of our strategies.

Generalized synchronization of two-layer networks based on intermittent control strategy

Generalized synchronization of two-layer networks based on intermittent control strategy

Meropenem pharmacokinetics in cerebrospinal fluid: comparing intermittent and continuous infusion strategies in critically ill patients-a prospective cohort study.

This study is registered with ClinicalTrials.gov as NCT04426383.

How the brain can be trained to achieve an intermittent control strategy for stabilizing quiet stance by means of reinforcement learning

The stabilization of human quiet stance is achieved by a combination of the intrinsic elastic properties of ankle muscles and an active closed-loop activation of the ankle muscles, driven by the delayed feedback of the ongoing sway angle and the corresponding angular velocity in a way of a delayed proportional (P) and derivative (D) feedback controller. It has been shown that the active component of the stabilization process is likely to operate in an intermittent manner rather than as a continuous controller: the switching policy is defined in the phase-plane, which is divided in dangerous and safe regions, separated by appropriate switching boundaries. When the state enters a dangerous region, the delayed PD control is activated, and it is switched off when it enters a safe region, leaving the system to evolve freely. In comparison with continuous feedback control, the intermittent mechanism is more robust and capable to better reproduce postural sway patterns in healthy people. However, the superior performance of the intermittent control paradigm as well as its biological plausibility, suggested by experimental evidence of the intermittent activation of the ankle muscles, leaves open the quest of a feasible learning process, by which the brain can identify the appropriate state-dependent switching policy and tune accordingly the P and D parameters. In this work, it is shown how such a goal can be achieved with a reinforcement motor learning paradigm, building upon the evidence that, in general, the basal ganglia are known to play a central role in reinforcement learning for action selection and, in particular, were found to be specifically involved in postural stabilization.

An output feedback method to polynomial stabilization of hybrid pantograph stochastic systems with aperiodic sampling

This paper focuses on the polynomial stabilization problem of hybrid pantograph stochastic systems (HPSSs) with aperiodic sampling in the presence of randomly occurring transmission delay. Firstly, a sufficient condition is provided to ensure the existence, uniqueness and boundedness of solution to such systems. Next, taking advantage of the Lyapunov stability theory and the comparison method, the criteria of pth moment polynomial stability are established for the HPSSs under the feedback control and aperiodic intermittent control strategy, respectively. Finally, the proposed theoretical results of two control methods are simultaneously validated through a numerical example.