Optimal Control Research Articles

Differential resistance to nematode infection is associated with the genotype- and age-dependent pace of intestinal T cell homing

Abstract The resistance of inbred mice to nematode infections varies depending on the extent of protective Th2 responses. Here, we compared two mouse lines differing in resistance to infection with the enteric nematode Heligmosomoides polygyrus bakeri despite the similar instruction of GATA-3+ T effector cells. Resistant BALB/c mice rapidly recruited high numbers of Th2 cells to the gut within the 1-week time frame required for larval development in the intestinal submucosa. C57BL/6 mice failed in the optimal control of early nematode fitness, with mucosal Th2 response peaking after 2 weeks when the larvae had left the tissue and relocated to the gut lumen as adult worms. The faster homing of Th2 cells to the gut of BALB/c mice is related to the extensive expression of the chemokine receptor CCR9 in GATA-3+ cells and higher frequencies of aldehyde dehydrogenase expressing dendritic cells present in mesenteric lymph nodes. Furthermore, nematode-infected older BALB/c mice displayed impaired resistance due to delayed mucosal homing of effector cells, which synergized with more numerous Th2/1 hybrid cells acting as IFN-γ-dependent confounders of type 2 responses. Hence, the distinct kinetics of effector cell recruitment to the infected gut and the quality of GATA-3+ T cell responses contribute to the genotype- and age-dependent resistance to intestinal nematode infections.

Study on the Effect of Non-Uniform Ventilation on Energy and Plant Growth in a Greenhouse

The progress of local environmental regulation in protected agriculture is sluggish, particularly concerning the local air supply, which poses a significant obstacle to greenhouse energy-saving research. This study establishes a test platform for local air supply in winter and summer by integrating design principles from human settlements’ supply air bag models with crop growth requirements. By utilizing a supply air bag to direct fresh air from the air conditioning system to specific areas within the greenhouse, non-uniform ventilation is created. Research has revealed that varying air supply levels in summer exerts a significant influence on environmental conditions, crop growth, and energy efficiency. Noticeable temperature stratification and cooling effects were observed within the conditioning greenhouse. The growth of lettuce was moderately enhanced, with mid-level local air supply demonstrating superior cooling effectiveness and range compared to the other two levels. Optimal control efficacy and energy conservation were achieved through mid-level local air supply. During daytime experiments in winter, this system did not have a significant impact on the greenhouse environment; however, during nighttime experiments, it consistently provided warming effects to maintain temperatures above the minimum requirement for lettuce growth. Therefore, utilizing air supply bags at secure specific positions and implementing targeted air supply methods within cultivation areas in greenhouses can facilitate the creation of suitable local environments for crop growth while achieving energy savings. Future research in this field could focus on further refining air supply bag models to enhance energy efficiency and local environmental control effects.

A plunger lifting optimization control method based on APSO-MPC for edge computing applications

A plunger lifting optimization control method based on APSO-MPC for edge computing applications

Optimal control design strategies for pulsed dynamic nuclear polarization.

We present optimal control methods for the optimization of periodic pulsed dynamic nuclear polarization (DNP) sequences. Specifically, we address the challenge of the optimization of a basic and repeated pulse sequence element which, apart from being easily adaptable to spin systems with different coupling interaction sizes, also proves beneficial in terms of performance. It is demonstrated that matrix power and matrix logarithm functions combined with an auxiliary matrix formalism can be used to derive expressions for gradient ascent pulse engineering (GRAPE) optimization. We illustrate how different implementations provide effective and intuitive control of DNP experiments by tailoring the effective Hamiltonian governing polarization transfer and, in this manner, addressing some of the limitations of prevailing optimal control based pulse design strategies.

Optimal control strategies for taming TikTok addiction: a mathematical model and analysis

Abstract Optimal control theory is an extension of the calculus of variations. It is a mathematical optimization method for deriving control strategies for a dynamic system. In this paper, the system of differential equations for which we aim to utilize control theory is TikTok, which is one of the most attractive internet platforms. TikTok has garnered immense popularity, surpassing other social media platforms. However, its addictive nature has raised concerns about mental health, including depression, eating disorders, anxiety, self-obsession, and narcissistic personality disorder among its users. This paper introduces a mathematical model for TikTok, considering the usage of this app as an epidemic. The model is rigorously validated through stability analysis of both local and global equilibrium. Moreover, disease-free and non-trivial equilibrium scenarios are discussed by calculating their reproduction numbers. This study aims to raise awareness of TikTok’s potential misuse and explore control theory solutions to mitigate addiction. Additionally, statistical data is used to visualize the numerical results and analyze the impact of control parameters on the TikTok model.

GLP-1 and SGLT2 Therapies in Type 2 Diabetes Mellitus. Cutting-Edge Approaches to Advancing Diabetes Care

Type 2 diabetes mellitus (T2DM) is a multifactorial disease characterized by insulin resistance, beta-cell dysfunction, and chronic hyperglycemia. Despite the availability of conventional therapies, significant unmet needs persist in achieving optimal glycemic control and reducing long-term complications. GLP-1 receptor agonists (GLP-1 RAs) and SGLT2 inhibitors represent two novel classes of antidiabetic agents that have transformed the therapeutic landscape. This paper explores their mechanisms of action, clinical benefits, and potential synergistic effects, emphasizing their impact on cardiovascular and renal outcomes. Challenges and future directions for these agents in personalized diabetes care are also discussed.

Optimization Method for Topology Identification of Port Microgrid Based on Line Disconnection

Regarding the static voltage stability issue of microgrids, by considering the control of line disconnections to enhance the system load margin, an identification and optimization approach for grid topology control measures based on the parameters of disconnected lines is put forward. Linear sensitivity is employed to scan and filter the measures; nonlinear fitting is utilized for ranking the measures; the leading control measures are calculated to determine the optimal line disconnection control measure. With the real lines and equipment conditions of LEKKI Port in Nigeria as a reference, experiments and simulations are conducted using ETAP and tested in the actual engineering of the port. The results indicate that the optimization method can significantly reduce the time required for load margin calculations in the project, and the effect of the method is similar to that of the traditional continuation power flow (CPFLOW) method. The method is more suitable for the application of online algorithms, which requires speed.

Pericapsular Nerve Group Block Plus Lateral Femoral Cutaneous Nerve Block vs. Fascia Iliaca Compartment Block in Hip Replacement Surgery

Background: Optimal pain control with limited muscle weakness after total hip arthroplasty (THA) is paramount for a swift initiation of physical therapy and ambulation, thus expediting hospital discharge. FICB (Fascia Iliaca Compartment Block) has been recommended because it offers pain control with a low risk of motor block. PENG (Pericapsular Nerve Group) block with LFCN (Lateral Femoral Cutaneous Nerve) has been proposed as an alternative that offers comparable pain control with a lower risk of motor block; however, evidence is limited. We aimed to investigate the degree of muscle weakness and pain control with PENG + LFCN. Methods: Patients undergoing elective THA from November 2022 to October 2023 have been retrospectively analyzed. The degree of quadriceps femoris muscle paresis was assessed with the MRC scale at 6 h postoperatively. Secondary outcomes: NRS score at 6, 12, and 24 h, total opioid consumption, and time to first rescue opioid. Results: In total, 80 patients were included in the study, 57 received PENG + LFCN, and the remaining 23 received FICB. PENG + LCFN resulted in a higher MRC at 6 h (4 [4; 5] vs. 3 [2; 4] p = 0.0001) and better pain control (mean difference [95% CI] at 6 h, 0.93 [0.14; 1.72], at 12 h, 0.47 [−0.49; 1.43], and at 24 h, 0.39 [0.25; 1.2], p = 0.0006). Less PRN opioids were requested in the PENG + LFCN vs. FICB groups (7.5 [0; 15] MME vs. 60 [40; 80], p = 0.001). Conclusions: PENG + LFCN was associated with less muscle weakness, better pain control, and less rescue opioids in patients undergoing elective THA. A larger prospective study is needed to confirm this finding.

Seasonal dynamics and optimal control analysis of fowlpox disease

Seasonal dynamics and optimal control analysis of fowlpox disease

Reducing M2 macrophage in lung fibrosis by controlling anti-M1 agent

Idiopathic pulmonary fibrosis (IPF) is a chronic lung disease characterized by excessive scarring and fibrosis due to the abnormal accumulation of extracellular matrix components, primarily collagen. This study aims to design and solve an optimal control problem to regulate M2 macrophage activity in IPF, thereby preventing fibrosis formation by controlling the anti-M1 agent. The research models the diffusion of M2 macrophages in inflamed tissue using a novel dynamical system with partial differential equation (PDE) constraints. The control problem is formulated to minimize fibrosis by regulating an anti-M1 agent. The study employs a two-step process of discretization followed by optimization, utilizing the Galerkin spectral method to transform the M2 diffusion PDE into an algebraic system of ordinary differential equations (ODEs). The optimal control problem is then solved using Pontryagin/s minimum principle, canonical Hamiltonian equations, and extended Riccati differential equations. The numerical simulations indicate that without control, M2 macrophage levels increase and stabilize, contributing to fibrosis. In contrast, the optimal control strategy effectively reduces M2 macrophages, preventing fibrosis formation within 120 days. The results highlight the potential of the proposed optimal control approach in modulating tissue repair processes and mitigating the progression of IPF. This study underscores the significance of targeting M2 macrophages and employing mathematical methods to develop innovative therapies for lung fibrosis.

Исследование задачи оптимального нагрева жидкого продукта жидким теплоносителем в емкости с рубашкой

Two problems of optimal control of heating of a liquid product in a heating tank with a liquid coolant have been posed and solved on the Pontryagin maximum principle. In the first task, the problem of the speed of heating the product to a given temperature is solved. In the second task, we are looking for a control in which the product is heated for a given time to a given temperature with minimal coolant consumption. The description of the control object with a flow diagram, assumptions simplifying the compilation of the model, a mathematical model of the control object, formulations of optimal control problems and the solutions obtained are given. In the problem of heating speed, analytical expressions are obtained to determine the temperature of the product, the coolant and the control time. An example of solving the problem of heating speed is given with an illustration of the dynamic characteristics of the object variables and the optimal control found. In the problem of minimizing coolant consumption, the solution is obtained numerically. The problem of finding optimal trajectories of variables and control is formulated as a boundary value problem with missing initial conditions, the search for which is realized using the difference method of solving the boundary value problem. Based on the found initial conditions, the Cauchy problem of an extended system of equations is solved, and the optimal trajectory of changes in product temperature and coolant flow is obtained. An example of solving the problem of minimizing coolant consumption is given with a demonstration of the dynamic characteristics of the variables of the object and the optimal control found.

Nonlinear Optimal Control of an H‐Type Gantry Crane Driven by Dual PMLSMs

ABSTRACTGantry cranes of the H‐type with dual electric‐motor actuation are widely used in industry. In this article, the control problem of an H‐type gantry crane which is driven by a pair of linear permanent magnet synchronous motors (dual PMLSMs) is considered. The integrated system that comprises the H‐type gantry crane and its two LPMSMs is proven to be differentially flat. The control problem for this robotic system is solved with the use of a nonlinear optimal control method. To apply the nonlinear optimal control method, the dynamic model of the H‐type gantry crane with dual LPMSM undergoes approximate linearization at each sampling instant with the use of first‐order Taylor series expansion and through the computation of the associated Jacobian matrices. The linearization point is defined by the present value of the system's state vector and by the last sampled value of the control inputs vector. To compute the feedback gains of the optimal controller an algebraic Riccati equation is repetitively solved at each time‐step of the control algorithm. The global stability properties of the nonlinear optimal control method are proven through Lyapunov analysis. The proposed control scheme achieves stabilization of the H‐type gantry crane with dual LPMSMs without the need of diffeomorphisms and complicated state‐space model transformations.

Patient perspectives on acromegaly disease burden: insights from a community meeting

ObjectiveA profound mismatch between biological and symptom control in acromegaly creates a high disease burden despite achieving optimal biological control. There is a great need to learn more about the perspectives of patients living with acromegaly.MethodsAcromegaly Community hosted a virtual meeting in January 2021 and prepared a detailed report capturing participants’ input on acromegaly symptoms and current and future treatment approaches. The findings of this report are reviewed and summarized in this study.ResultsFatigue/muscle weakness (92%) and joint pain/arthritis (90%) are the two most common and troublesome symptoms reported by meeting participants. Acromegaly negatively impacts all aspects of daily living: social interaction (49%); exercise (42%); sports/recreational activities (39%); household activities (38%); attending school or job (38%); family relationships (33%); and walking (26%). Anxiety/depression is experienced by 75% of respondents. Eighty-three percent of patients underwent pituitary surgery, and over 71% of patients require medical therapy. Patients desire future improvements in medication efficacy, tolerability, and administration; mental health resources for themselves and their families; and other multimodal approaches to address their physical symptoms, specifically hunger, weight gain, muscle weakness, and joint pains.ConclusionAcromegaly patients experience significant physical and psychological burdens despite biochemical control, highlighting the need for comprehensive and patient-centered care. In particular, the impacts on activities of daily living (ADLs) and heavy psychosocial and socioeconomic burdens are striking. We advocate for periodic screening for impacted ADLs, multidisciplinary teams to proactively address these symptoms, and call for further research on under-evaluated aspects of the disease.

Everything's under Control: Maximizing Biosensor Performance through Negative Control Probe Selection.

The rapid rise of label-free biosensing technologies has led to multiple creative strategies for the detection of macromolecules in complex biological solutions for disease state monitoring, drug discovery, and basic science research. A challenge with these techniques is that assays conducted in complex media such as serum suffer from nonspecific binding of matrix constituents. In label-free biosensors, it is virtually impossible to distinguish these nonspecific interactions without the use of a reference (negative control) probe. Only with reference subtraction can the specific binding signal be faithfully reported. To date, this has been a sparsely studied area in the biosensing field. Here, we report an FDA-inspired framework for optimum control probe selection and a systematic analysis for determining the optimal negative control probe given two monoclonal antibody capture probes on photonic ring resonator sensors. Briefly, while the differences in assay performance for IL-17A and CRP were found to be subtle, the best-scoring reference control based on the bioanalytical parameters of linearity, accuracy, and selectivity differed for each analyte. In the IL-17A assay, BSA scored the highest at 83%, while mouse IgG1 isotype control antibody placed a close second with 75%. With respect to the CRP assay, the rat IgG1 isotype control antibody scored the highest at 95%, while anti-FITC scored the second highest at 89%. These results suggest that although isotype-matching to the capture antibody may be tempting, the best on-chip reference control must be optimized on a case-by-case basis using the framework we report.

Prescription pattern, glycemic control status, and predictors of poor glycemic control among diabetic patients with comorbid chronic kidney disease in Ethiopia: a facility-based cross-sectional study

BackgroundAchieving optimal glycemic control is vital for managing diabetes mellitus and preventing its complications, yet it is particularly challenging for individuals with diabetes and concurrent chronic kidney disease. Chronic kidney disease disrupts glucose metabolism and excretion, leading to pronounced and variable blood glucose fluctuations, thereby complicating diabetes management. So far, the intricate impact of chronic kidney disease on the glycemic control status of diabetic patients remains obscure, especially in Sub-Saharan Africa where both diseases pose an escalating burden.ObjectiveThis study aimed to assess prescription patterns, glycemic control status, and the contributing factors to poor glycemic control among diabetic patients with comorbid chronic kidney disease at Tikur Anbessa Specialized Hospital, Ethiopia.MethodsA facility-based cross-sectional study was conducted from March 15 to May 15, 2024, from the electronic medical records of diabetic patients with comorbid chronic kidney disease who had received regular treatment and follow-up at the adult diabetes mellitus clinic of Tikur Anbessa Specialized Hospital. The sample size was calculated by using a single population proportion formula and accordingly, a total of 384 patients were recruited randomly and enrolled in this study. Descriptive statistics was employed for analyzing quantitative variables. Logistic regression analysis was performed to identify predictors of poor glycemic control status. Statistical significance was established at p-value < 0.05.ResultsThis study found that 98.2% of patients had type 2 diabetes, with a mean diabetes duration of 16.36 years. Only 4.4% achieved good glycemic control (glycated hemoglobin [HbA1c] < 7%), while 95.6% had poor glycemic control (HbA1c ≥ 7%). Insulin, metformin, and sodium glucose cotransporter-2 (SGLT-2) inhibitors were the most frequently prescribed anti-diabetic drug classes which accounted for 80.2%, 59.1%, and 41.4%, respectively. Presence of hypertension (AOR: 3.70, 95% CI: 1.08–12.71, P = 0.038) and regimen change in the past 01year (AOR: 0.34, 95% CI: 0.11–1.01, P = 0.050) were predictors of poor glycemic control status.ConclusionThis study reveals significant challenges in glycemic control among diabetic patients with comorbid chronic kidney disease (CKD). With only 4.4% of participants achieving optimal HbA1c levels, the findings underscore a critical public health concern regarding the management of diabetes in this vulnerable population.Clinical trial numberNot applicable.

Optimal control of a Bose-Einstein condensate in an opticallattice: the non-linear and two-dimensional cases

We numerically study the optimal control of an atomic Bose-Einstein condensate in an optical lattice. We present two generalizations of the gradient-based algorithm, GRAPE, in the non-linear case and for a two-dimensional lattice. We show how to construct such algorithms from Pontryagin’s maximum principle. A wide variety of target states can be achieved with high precision by varying only the laser phases setting the lattice position. We discuss the physical relevance of the different results and the future directions of this work.

Tube-MPC based trajectory tracking control for substation inspection robot

Abstract A Tube-MPC based trajectory tracking control method was developed to enhance the capabilities of substation inspection robots. First, the kinematic models of the inspection robots were established, and the general form of the optimization control problem for Tube-MPC was introduced. The nominal control law for the nominal system was derived using a linearized nominal model, which was employed to solve the Tube-MPC cost function, ensuring accurate tracking of the desired trajectory. The algorithm was then simulated and tested. Simulation results demonstrate that the Tube-MPC approach significantly outperforms conventional MPC algorithms in terms of trajectory tracking performance and robustness for substation inspection robots. Experimental results further confirm that the Tube-MPC based method effectively enhances both robustness and tracking accuracy during inspection robot operations.

Proposal of a New Composite Score (DAMADECO) to Simultaneously Evaluate Asthma and CRSwNP Severity in Comorbid Patients

Background: Asthma and chronic rhinosinusitis with nasal polyps (CRSwNP) are chronic respiratory conditions that frequently coexist. However, an integrated assessment tool for both conditions is currently lacking. This study aimed to develop and preliminarily evaluate a composite score capable of simultaneously assessing asthma and CRSwNP in comorbid patients. Methods: An expert panel comprising three pulmonologists, one allergist/clinical immunologist, and four ear, nose and throat (ENT) specialists developed a tool to capture asthma and CRSwNP severity. The tool (D’Amato-De Corso score, or DAMADECO score) incorporates eight parameters, four specific to asthma and four specific to CRSwNP, to assign individual scores for each condition. A composite score is then calculated to reflect the overall disease burden (ranging from −8: poor control and +8: optimal control). A retrospective pilot study was conducted to evaluate the tool. Results: The DAMADECO composite score was applied to 21 comorbid patients. The mean partial scores for asthma and CRSwNP were −1.57 and −1.67, respectively, with a mean total composite score of −3.24. A total of 13 out of 21 patients had uncontrolled domains in both diseases, while fewer patients had only uncontrolled domains in asthma (1/21) or CRSwNP (6/21). The DAMADECO score also allows researchers to track disease progression and monitor treatment effectiveness. Conclusions: The preliminary results suggest that the DAMADECO score is a promising tool for simultaneously assessing asthma and CRSwNP, addressing the unmet need for an integrated approach to comorbid respiratory diseases. Further validation studies are needed to validate the tool in larger patient populations.

Mechanically Robust 3D Flexible Electrodes via Embedding Conductive Nanomaterials in the Surface of Polymer Networks.

3D flexible electrodes are essential to implement flexible pressure sensors in various flexible electronic applications. Conventional methods for fabricating these electrodes include electroless deposition, spray coating, and incorporating conductive nanomaterials into a polymer matrix. However, the electrodes fabricated using these methods are characterized by poor adhesion between the conductive layer and polymer surface and fail to maintain intrinsic mechanical properties of the polymer, such as elastic modulus and ductility. Herein, a transfer method in which conductive nanomaterials are embedded into the surface of polymer networks via optimal surface energy control is proposed, such as reducing adhesion between the mold and nanomaterials. This method induces mechanical interlocking between the surface of polymer networks and conductive nanomaterials, firmly anchoring them onto the polymer network surface. Moreover, the intrinsic mechanical properties of the fabricated 3D flexible electrodes remain unchanged. Flexible capacitive sensors prepared using the resulting electrodes exhibit a stable sensing performance (ΔC0,5000/C0 = 0.169%) even under repetitive pressure conditions (5000 cycles at 70kPa). The proposed robust 3D flexible electrode fabrication method presents a promising strategy for the future development of flexible pressure sensors.

Design and optimization of distributed energy management system based on edge computing and machine learning

With the continuous growth of global energy demand and the rapid development of renewable energy, traditional energy management systems are facing enormous challenges, especially in the scheduling and optimization of distributed energy. In order to meet these challenges, edge computing and machine learning technology are widely used in the design and optimization of distributed energy management systems. This paper proposes a design scheme of distributed energy management system based on edge computing and machine learning, and optimizes it. The system reduces data transmission latency and improves energy scheduling efficiency by performing real-time data processing and analysis on edge devices. The experimental results show that the proposed system performs outstandingly in optimizing energy allocation, reducing energy consumption, and improving system response speed. Specifically, by using machine learning algorithms for dynamic scheduling of distributed energy resources, the system can achieve an energy utilization rate 12% higher than traditional scheduling methods, and reduce energy waste by 18% in the event of fluctuations in energy demand. In addition, the system response time has been improved by 30% compared to traditional cloud-based solutions. These optimizations not only reduce energy costs, but also effectively enhance the sustainability and intelligence level of distributed energy systems. The contribution of this research lies in the combination of edge computing and machine learning technology to achieve real-time optimal control of the distributed energy system, reduce the system’s computing load and delay, and improve the accuracy and flexibility of energy management through data-driven methods. Future research can further explore how to integrate multiple machine learning algorithms to optimize energy scheduling strategies and improve the system’s adaptability in complex environments.