Control Model Research Articles

Abstract A024: Preclinical quantification of sources of solid tumor resistance onset: Hypoxia and immune exhaustion

Abstract Immune Checkpoints (ICs) are saturable systems that fatigue at when sufficiently high levels of tumor antigen is generated by cytotoxic antitumor therapy. Immune Checkpoint Inhibitor (ICI) therapy increases therapy outcome from both systemic chemotherapy and localized ionizing radiation (IR). Preclinical murine cancers in syngeneic immune competent mice duplicates the immune response toof human tumors as well asand the onset of IF. IF was quantified with the mouse ortholog of the human chemokine CXCL10 responding to IR. Tumor hypoxia has long been known to induce resistance to radiation cytotoxicity. More recently, primarily in preclinical models, hypoxia was found to enhance IFimmune fatigue. Clinical 10% dose boosts of dose to hypoxic areas defined by 18Fluoromisonidazole retention failed to reach significance. Our first mammalian determination of the oxygen enhancement ratio (OER) in murine FSa fibrosarcomas show 26% higher boosts are needed. This technique can be used to explore the IR dose modification of both hypoxia and ICI. Quantification is a basic approach to clarify the onset of fatigue. IR is the best quantified cytotoxic dose physically delivered to tumors in medicine. Local tumor control probability (LTCP) is a specific outcome. A classical model of tumor control is a logistic function of IR dose, D is LTCP(D)=1/[1+exp(-k(D-D0))]. D0 is the IR dose at which only one clonogen remains. k is the rate of approach to D0. We model the immune response as a linear reduction of D0 with clonogen reduction with IR dose D whichthat generates antigens, which at a dose DF , fatigues at rate kF. D0 reduction approaching DF is modeled D0= D0(1-D/D0)/[1+exp(-kF(DF -D))] Methods: We developed Electron paramagnetic resonance (EPR) oxygen imaging (EPROI) operating at high MRI frequency (250 MHz) has been developed to provide a means by which to generate near absolute pO2 images of mammalian tumors using injectable non-toxic paramagnetic OX071 spin probe. Comparing localized stereotactic fiberoptic Oxylite phosphorescence quenching pO2 local measurements with the EPR images demonstrated excellent quantitative pO2 measurement agreement voxel by voxel between EPROI and the Oxylite fiber tip. 9.4 T MRI T2 weighted images defined pO2 registered tumor margins. Conformal opposed field radiation was delivered to FSa fibrosarcomas using tungsten loaded PLA plastic apertures mounted in an XRAD225Cx isocentric radiator. BioXcell (Lebanon, NH), anti PDL-1 antibodies at two doses 150µg or 200µg /mouse/dose are given ip on the day of IR at the D0=TCD50, 4 doses total, twice a week, ip injection after which the mice will beare followed 90 days for control. IF is quantified with the mouse ortholog of the human chemokine CXCL10. Data Presented: Using the XRAD225Cx as a 2 field conformal opposed field radiator, we have derived the first in vivo oxygen enhancement ratio. We will estimate the modification of the D0 from the BioXcell anti PDL-1 treatment for both hypoxic and well oxygenated tumor using Kaplan-Meier survival assays. Citation Format: Howard J Halpern, Ralph J Weichselbaum, Boris Epel. Preclinical quantification of sources of solid tumor resistance onset: Hypoxia and immune exhaustion [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Functional and Genomic Precision Medicine in Cancer: Different Perspectives, Common Goals; 2025 Mar 11-13; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(5 Suppl):Abstract nr A024.

Construction of a flood control and drainage decision-making command systemA case study of Suzhou Industrial Park

With the acceleration of urbanization, flood control and drainage management in cities are facing increasing challenges. As a nationally recognized model for effective flood control, Suzhou Industrial Park has experienced frequent extreme weather events in recent years, highlighting the urgent need for informatization in flood control and drainage management. This paper provides a detailed introduction to the design scheme of the flood control and drainage decision-making command information system for Suzhou Industrial Park, covering key aspects such as system requirement analysis, overall architecture, core technologies, and risk management. By integrating multi-source data, including meteorology, hydrology, waterlogging points, and the sewage pipeline network, the system establishes a comprehensive flood control and drainage decision-making platform featuring situational awareness, monitoring and early warning, and command and dispatching functions. The system aims to enhance the flood control and drainage capabilities of the park, ensure urban safety, and provide a reference for the informatization of flood management in other cities.

Optimal control of dengue fever transmission dynamics in Kenya

The emergence of dengue fever in Kenya has been witnessed in the recent past, leading to public health alerts and disruption of economic activities. The outbreaks have mainly been restricted to the Northeastern and Coastal counties of the country. As such, this paper has focused on an epidemiological model that incorporates an optimal control model of the spread dynamics of dengue fever in Kenya. The objective of the study is to develop an optimal control solution for the spread dynamics of dengue fever in Kenya. This study introduced three time-dependent control variables, which were divided into long-term and short-term control measures. The short-term control measures include prophylactics (treatment) and the use of physical barriers (nets), while the long-term control measure is the treatment of Aedes aegypti mosquitoes with Wolbachia bacteria. The basic reproduction number with the control variables was determined. The set of adjoint points of the control systems was obtained together with the optimal control set. The numerical solutions to the control problem were obtained by use of the forward-backward sweep method and the Runge-Kutta order four method. The impact of utilizing various strategies that employed the combination of the three control measures in different combinations was examined. The control profile of the particular control measures used was also investigated. It was determined that the short-term control measures had more impact on the control of the spread dynamics of dengue fever when compared to the long-term control measure. As such, it was determined that a strategy that incorporates both the long-term and short-term control measures should be utilized for optimum control of dengue fever spread dynamics in Kenya.

Fuzzy control algorithm of cleaning parameters of street sweeper based on road garbage volume grading

The research examines the challenges city street sweepers face, which struggles to adapt cleaning settings based on varying road garbage volume, resulting in inefficient cleaning and high energy consumption. The study proposes a fuzzy control algorithm for adjusting the cleaning parameters of street sweepers based on road garbage volume grading. It starts by utilizing the YOLO (You Only Look Once) v5 deep learning model for target detection and garbage classification on road surfaces. The algorithm calculates road garbage volume grading coefficients based on the coverage and weight of different types of garbage. These coefficients, along with the street sweeper’s traveling speed, are used as input variables to develop a fuzzy control model for optimizing operational parameters such as the street sweeper disc brush and fan speeds. This model aims to achieve adaptive control of cleaning parameters, thereby enhancing the intelligence of street sweepers. Experimental results show that the proposed algorithm can achieve a 29.77% energy saving under the same road garbage conditions compared to the conventional gear control mode.

Control filter design with convex-set-based uncertainty model for robust feedback active noise control

In robust feedback active noise control, appropriate secondary path uncertainty modeling is a necessity to achieve robust and stable control filter designs. In this paper, we present a new method for modeling the uncertainty of the secondary path that avoids the use of a static disk-shaped uncertainty model and instead uses convex hulls to dynamically compute the uncertainty measure during filter optimization. We describe the computation of the frequency-dependent convex uncertainty sets and discuss their use during filter optimization. In a direct comparison of the disk-shaped and the proposed uncertainty model, the improvement in terms of noise attenuation performance is demonstrated. Furthermore, it turns out that the presented method produces filter designs with good stability characteristics that far exceed the minimum robustness requirements.

Dynamic Optimal Control Strategy of CCUS Technology Innovation in Coal Power Stations Under Environmental Protection Tax

Carbon capture, utilization, and storage (CCUS) technology is an essential technology for achieving low-carbon transformation and upgrading of the coal power industry. This study applies optimal control theory to analyze the dynamic optimization of CCUS technological innovation investment in coal power stations under environmental protection tax. A dynamic control model is constructed to analyze the investment decisions of firms at system steady-state equilibrium, and numerical simulations are performed. The study shows that under both profit maximization and social welfare maximization conditions, a distinct saddle-point steady-state; the environmental protection tax affects technological innovation investment in coal power stations, which in turn affects electricity prices; the learning rate of knowledge accumulation also impacts technological innovation investment: under the social welfare maximization condition, the investment levels in technological innovation, technology, and knowledge accumulation are higher than those under profit maximization.

Modeling and Simulation Analysis of Hybrid Electric Vehicle Based on AMEsim Software

In recent years, with the continuous consumption of global oil resources, environmental pollution has become increasingly serious. Therefore, more and more attention has been paid to reducing pollutants emitted by vehicles, and it is particularly important to develop energy-saving, exhaust-free, pollution-free new energy vehicles. Until the problem of battery technology is solved, plug-in hybrid electric vehicles can be used as one of the options for the transition from traditional vehicles to new energy vehicles. The power system of hybrid electric vehicles is complex and the optimum temperature range of power components is different. Thermal management of hybrid electric vehicles is from the perspective of the vehicle, to coordinate the relevant matching, optimization and control of the vehicle engine, air conditioning, battery, motor and other related components and subsystems, effectively solve the vehicle thermal related problems, so that each function module is in the best temperature working range, and improve the economy and power of the vehicle. Ensure vehicle safety. An accurate hybrid vehicle model is a prerequisite for establishing a thermal management system. Therefore, this paper takes plug-in parallel hybrid electric vehicle as the research object, and builds a simulation model composed of drive module, engine module, battery module, vehicle control unit, transmission module and motor module based on the AMEsim simulation platform. This paper mainly analyzes the system structure of different hybrid electric vehicles, chooses this paper to study the system structure of hybrid electric vehicles, and matches the parameters of each power component according to the basic parameters and dynamic requirements of the vehicle, builds the vehicle model based on AMEsim software, and carries out the vehicle speed following simulation and dynamic simulation under NEDC conditions The simulation results show that the vehicle speed follows well under NEDC conditions, and the dynamic performance meets the design requirements. It can be considered that the model selection, parameters setting and vehicle control model of engine, drive motor, generator, power battery and other components meet the requirements of this paper. The results show that the simulation model is accurate and reliable.

Research on UAV Trajectory Tracking Control System Based on Feedback Linearization Control–Fractional Order Model Predictive Control

Aiming at the problem of the nonlinear, strongly coupled, and underdriven trajectory tracking instability of a quadrotor unmanned aerial vehicle (UAV), this thesis proposes a feedback linearization and fractional order model predictive control strategy based on feedback linearization by modeling the dynamics of the UAV control system and linearizing the nonlinear model of attitude control. A dual closed-loop control structure, feedback linearization control (FLC) for a position loop, and fractional order model predictive control (FOMPC) for an attitude loop are adopted to realize fast position tracking and attitude response. In addition, considering that the fractional order method has the advantage of flexible regulation, the fractional order integral operator is added to the cost function of model predictive control. Finally, the simulation results and the calculation of the root mean square error verify that the proposed method has a fast response speed, small overshoot, stable flight, and good track tracking performance in UAV track tracking.

Inverse System Decoupling Control of Composite Cage Rotor Bearingless Induction Motor Based on Support Vector Machine Optimized by Improved Simulated Annealing-Genetic Algorithm

To address the inherent nonlinearity and strong coupling among rotor displacement, speed, and flux linkage in the composite cage rotor bearingless induction motor (CCR-BIM), an inverse system decoupling control strategy based on a support vector machine (SVM) optimized by the improved simulated annealing-genetic algorithm (ISA-GA) is proposed. First, based on the structure and working principle of CCR-BIM, the mathematical model of CCR-BIM is derived, and its reversibility is rigorously analyzed. Subsequently, an SVM regression equation is established, and the SVM kernel function parameters are optimized using the ISA-GA to train a high-precision inverse system decoupling control model. Finally, the inverse system is cascaded with the original system to construct a pseudo-linear system model, achieving linearization and decoupling control of CCR-BIM. To verify the effectiveness and practicability of the proposed decoupling control strategy, the proposed control method is compared with the traditional inverse system decoupling control strategy through simulation and experimentation. Both simulation and experimental results demonstrate that the proposed decoupling control strategy can effectively achieve decoupling control of rotor displacement, rotational speed, and flux linkage in CCR-BIM.

The Multifaceted Impact of the SARS-CoV-2 Pandemic on Sexual Health, Function, and Behaviors: Implications for Public Health: A Scoping Review

Background. The SARS-CoV-2 pandemic had a significant impact on sexual health and human behavior, revealing a widespread decline in sexual function and behaviors. Objective. To summarize these findings and highlight their importance for public health, this article discusses the changes observed in sexual function and behavior during the pandemic, as well as potential explanations for these trends. Methods. This study followed the PRISMA-ScR guidelines, using the keyword search commands: “sexual function” AND (“SARS-CoV-2” OR “COVID-19” OR coronavirus) and “sexual behavior*” AND (“SARS-CoV-2” OR “COVID-19” OR coronavirus) in the Scopus and PubMed databases. The search was conducted on 10 March 2024, including articles published from January 2019 to March 2024. Inclusion criteria required studies focusing on sexual health/function during the SARS-CoV-2 pandemic, excluding non-English articles and non-adult populations. Studies were screened based on relevance, methodological rigor, and sample size, with data extraction focusing on sexual behavior/function metrics. Results were synthesized to identify trends and propose explanatory models. Results. While some individuals experienced reductions in sexual desire and activities, others reported increases, indicating varied individual responses to stressors such as a pandemic. Two hypotheses are presented to explain these changes: terror management theory and the dual control model of sexual response. The critical role of public health in addressing sexual health and well-being needs during a health crisis is discussed, emphasizing the importance of providing clear information, ensuring access to remote sexual health services, and reducing stigma. The need to integrate sexual health into the global response to future health crises is highlighted to ensure a comprehensive approach to human well-being. Conclusions. This review shows the multifaceted impact of the pandemic and social distancing in people’s sexual function and behaviors, underscoring the importance of considering sexual health as an integral part of the emergency health planning and response, to promote the physical and mental well-being of the population during crises such as the SARS-CoV-2 pandemic.

Design of A Statistical Control Model For The Panela Manufacturing Process in the Trapiches of San Marcos – Sucre

Objective: The objective of this study is to investigate the design of a statistical control model for the panela manufacturing process in the trapiches of San Marcos – Sucre, with the aim of improving production processes and product quality. This will help reduce variability in manufacturing and ensure a higher-quality final product. Theoretical Framework: This section presents the fundamental concepts and theories supporting the research on statistical quality control and panela manufacturing in trapiches, providing the necessary information for understanding this study. Method: The research was exploratory, descriptive, and applied, with a mixed approach. The studied population consisted of 17 trapiches in San Marcos, Sucre. Data were collected through surveys, interviews, direct observation, and collection sheets, allowing for an analysis of panela production and process control. Results and Discussion: The results indicate that the artisanal panela production process is out of control due to high temperature variability. Factors such as bagasse quality, operator skill, lack of precise thermometers, and environmental conditions affect heat uniformity. This negatively impacts the texture, color, and consistency of the product, reducing its quality and market acceptance. Research Implications: The implications of statistical quality control are analyzed, providing insights into how the results can contribute to process improvement, highlighting their impact on efficiency and competitiveness in the panela sector. Originality/Value: This study contributes to the literature by applying statistical quality control to artisanal panela production, enhancing the economy in San Marcos, Sucre.

Dynamic analysis of a mathematical model for wild mosquito population control: combining incompatible and sterile insect techniques

Dynamic analysis of a mathematical model for wild mosquito population control: combining incompatible and sterile insect techniques

Visual properties of action consequences and not environments affect context-specific motor learning.

The human motor system can adapt to perturbations by updating existing internal models of motor control or by creating context-specific motor memories or strategies that can be flexibly activated or deactivated. Using a virtual target-directed ball rolling task, we investigate if motor learning is context specific when perturbations are applied to either the throw direction of a ball, or the acceleration of the ball post-release. Additionally, by altering the visual slant of the task surface in an immersive virtual-reality environment, we determine if informative visual cues that predict visual perturbations to expected action consequences affect the propensity for context-specific motor learning. Our findings reveal that perturbations resembling post-throw accelerations enable flexible, context-specific motor adaptation regardless of the presence of the visual slant cues. Perturbations in the throw directions, conversely, lead to the updating of existing internal models. Additionally, we find fast implicit visual-context-specific changes in performance during early learning and context switching. Our findings underscore the role of visual properties of both perturbations and environments in flexible and generalizable motor learning. Data and analysis scripts available at https://osf.io/a5nv3/.

A physics-based modeling method for DC characteristics of GaN HEMTs with conventional, dual-, and tri-gate

Abstract In this study, we have developed a physics-based modeling approach to accurately characterize the DC properties of GaN high electron mobility transistors (HEMTs) featuring conventional, dual-, and tri-gate architectures. Our methodology begins with the derivation of analytical expressions for the intrinsic drain current of these three device types, omitting the effects of source and drain access resistances. For dual-gate HEMTs, we employ an approximation of depletion within a rectangular region to depict the channel modulation achieved by the sidewall gate. Building upon this, we dissect the combined influence of the top and sidewall gates in tri-gate HEMTs into two distinct control mechanisms. This involves initially calculating the two-dimensional electron gas (2DEG) density under sole top gate control, followed by determining the 2DEG depletion width governed by the sidewall gate. Incorporating access resistances, the DC characteristics of all three devices are computed through numerical iteration. To validate the channel control model directly, we exclude the self-heating effect and conduct comparative TCAD simulations. Furthermore, our modeling results, which encompass the self-heating effect, are corroborated by experimental data, thereby ensuring the reliability and accuracy of our approach.

Ergonomic Hazard Control Modeling for Informal Welding Workers in Greater Bandung: A Study on Musculoskeletal Disorders (MSDs)

Ergonomic hazards are one of the causes of health problems in workers, including causing Musculoskeletal Disorders (MSDs) complaints. MSDs in workers affect physical fitness, reduce working days/hours, and ultimately are unable to work. WHO states that around 1.71 billion people have musculoskeletal conditions worldwide. MSDs complaints in Indonesia are a separate focus, research on MSDs complaints in the informal sector shows that 66% of workers experience MSDs complaints. One industry that has a high risk of MSDs is welding. The purpose of this study was to create an ergonomic hazard control model for informal welding sector workers in Greater Bandung. The research approach is quantitative, the type of analytical observational research with a cross-sectional design. The study population was informal welding sector workers in Greater Bandung, sample was taken using a purposive sampling technique, and the total number of samples in this study was 100 workers. The analysis used in this study was the chi-square test to see the relationship between variables. Modeling in this study will use binary logistic regression. The results of the study showed that working climate, working posture, workload, and physical fitness simultaneously influenced MSDs complaints of informal welding workers (p-value < 0.05). Based on these findings, the control of MSDs complaints can be achieved by effectively managing work climate, working posture, workload, and physical fitness.

Autoregressive exogenous neural structures for synthetic datasets of olive disease control model with fractional Grünwald-Letnikov solver.

Autoregressive exogenous neural structures for synthetic datasets of olive disease control model with fractional Grünwald-Letnikov solver.

Developing and Evaluating a Classification Model for Construction Defect Control: A Text Mining and Ensemble Learning Approach

Developing and Evaluating a Classification Model for Construction Defect Control: A Text Mining and Ensemble Learning Approach

Semantic segmentation for weed detection in corn.

Reliable, fast, and accurate weed detection in farmland is crucial for precision weed management but remains challenging due to the diverse weed species present across different fields. While deep learning models for direct weed detection have been developed in previous studies, creating a training dataset that encompasses all possible weed species, ecotypes, and growth stages is practically unfeasible. This study proposes a novel approach to detect weeds by integrating semantic segmentation with image processing. The primary aim is to simplify the weed detection process by segmenting crop pixels and identifying all vegetation outside the crop mask as weeds. The proposed method employs a semantic segmentation model to generate a mask of corn (Zea mays L.) crops, identifying all green plant pixels outside the mask as weeds. This indirect segmentation approach reduces model complexity by avoiding the need for direct detection of diverse weed species. To enhance real-time performance, the semantic segmentation model was optimized through knowledge distillation, resulting in a faster, lighter-weight inference. Experimental results demonstrated that the DeepLabV3+ model, after applying knowledge distillation, achieved an average accuracy (aAcc) exceeding 99.5% and a mean intersection over union (mIoU) across all categories above 95.5%. Furthermore, the model's operating speed surpassed 34 frames per second (FPS). This study introduces a novel method that accurately segments crop pixels to form a mask, identifying vegetation outside this mask as weeds. By focusing on crop segmentation, the method avoids the complexity associated with diverse weed species, varying densities, and different growth stages. This approach offers a practical and efficient solution to facilitate the training of effective computer vision models for precision weed detection and control. © 2024 Society of Chemical Industry.

Multidisciplinary care model for nutrition and glycemic control in CKD and diabetes

Multidisciplinary care model for nutrition and glycemic control in CKD and diabetes

A mathematical model of corruption dynamics and optimal control

A mathematical model of corruption dynamics and optimal control