Design Of Systems Research Articles

Measurement and modelling of driver learning of steering control during successive obstacle avoidance manoeuvres

Understanding of driver behaviour can provide invaluable insight for the design of vehicles and driver assistance systems. Most existing human driver models do not incorporate driver learning or the effect of a driver's confidence in their predictions of future states, both of which affect human-generated control actions. In this paper, human driver steering control is assessed based on experimental data, then a driver model is proposed which captures driver learning and is capable of reproducing a wide range of human control styles by the selection of appropriate parameters. The driver model learns an internal model of the vehicle dynamics from experience, using a Gaussian Process, then selects control actions via Model Predictive Control. The results verify the model's capacity to capture the learning drivers achieve over time and to replicate various observed cautious and adventurous behaviours.

Exponential stability and controller design for linear systems with mixed interval time-varying delays

Exponential stability and controller design for linear systems with mixed interval time-varying delays

Programming the Kinetics of Chemical Communication: Induced Fit vs Conformational Selection.

Life on Earth depends on chemical communication and the ability of biomolecular switches to integrate various chemical signals that trigger their activation or deactivation over time scales ranging from microseconds to days. The ability to similarly program and control the kinetics of artificial switches would greatly assist the design and optimization of future chemical and nanotechnological systems. Two distinct structure-switching mechanisms are typically employed by biomolecular switches: induced fit (IF) and conformational selection (CS). Despite 60 years of experimental and theoretical investigations, the kinetic and evolutive advantages of these two mechanisms remain unclear. Here, we have created a simple modular DNA switch that can operate through both mechanisms and be easily tuned and adapted to characterize its thermodynamic and kinetic parameters. We show that the fastest activation rate of a switch occurs when the ligand is able to bind its inactive conformation (IF). In contrast, we show that when the ligand can only bind the active conformation of the switch (CS), its activation rate can be easily programmed over many orders of magnitude by a simple tuning of its conformational equilibrium. We demonstrate the programming ability of both these mechanisms by designing a drug delivery vessel that can be programmed to release a drug over different time scales (>1000-fold). Overall, these findings provide a programmable strategy to optimize the kinetics of molecular systems and nanomachines while also illustrating how evolution may have taken advantage of IF and CS mechanisms to optimize the kinetics of biomolecular switches.

Protein Engineering and Directed Evolution for Nanocarrier Innovation<sup></sup>

The design of nonviral protein-based delivery systems has gained significant attention as an alternative to viral vectors and lipid nanoparticles (LNPs) for gene therapy. While viral vectors offer efficient gene delivery, they present challenges such as immunogenicity and size limitations. LNPs, though pivotal in recent advancements like messenger ribonucleic acid (mRNA)-based vaccines, cause inflammation and exhibit low endosomal escape efficiency. This article explores the development of engineered nonviral protein cages through rational design and directed evolution as an additional nanocarrier option for RNA packaging and delivery. We highlight key advances, including the design and evolution of capsidforming proteins capable of encapsulating and protecting their own encoding mRNA, a critical step in establishing a genotype-phenotype link for evolutionary optimization. Evolved protein cages, such as the I53-50-v4 and NC-4 variants, demonstrate enhanced stability and RNA protection, achieving structural transformations and packaging efficiencies akin to viral systems. The application of directed evolution has expanded the capacity of these nanocarriers enhancing their in vivo stability and biodistribution. This work underscores the potential of evolving nonviral capsids to become customizable platforms for therapeutic gene delivery, while also addressing current limitations in RNA cargo size and tissue targeting specificity. Future directions involve refining these systems to accommodate larger RNAs, and improving immunogenicity properties and dynamic control over cargo release.

Experimental Determination of Circumferential Mechanical Properties of GFRP Pipes Using the Split-Disk Method: Evaluating the Impact of Aggressive Environments

This study investigates the mechanical properties of Glass Fiber-Reinforced Plastic (GFRP) pipes in the circumferential direction using the split-disk method, with a focus on understanding the influence of aggressive environmental conditions. The split-disk method was used to determine the key mechanical properties, including the hoop tensile strength and modulus, and also the Poisson’s ratio, which are critical for the performance of GFRP pipes under internal pressure. The experiment was conducted under controlled laboratory conditions, and the results were analyzed to assess the effects of exposure to aggressive environments (saltwater and alkaline solutions at 20 °C and 50 °C). The correlations between the UTS, elastic modulus, and Poisson’s ratio highlight how GFRP pipes degrade under environmental exposure. As the UTS decreases, so do the stiffness and lateral deformability, with the most significant reductions occurring in chemically aggressive environments at high temperatures. Exposure to an alkaline solution weakens the GFRP pipes, with the strength dropping more sharply at higher temperatures, with the UTS decreasing by 21%. Saltwater exposure reduces the elastic modulus, especially at higher temperatures, with a 14% decrease, accelerating material degradation and reducing deformation resistance. An alkaline solution further lowers the modulus, with a 21% decrease at 50 °C, showing the lowest stiffness. Air exposure, in contrast, has a less severe effect, with the pipes retaining much of their mechanical integrity. These findings collectively suggest that environmental degradation affects the overall mechanical behavior of GFRP pipes, providing valuable insights for the design and maintenance of GFRP piping systems, particularly in industries where exposure to aggressive environments is common. This study underscores the importance of considering environmental factors in the material selection and design processes to ensure the long-term reliability of GRP pipes.

The Critical Role of Systems Design in Cloud-Based Machine Learning Platforms

The Critical Role of Systems Design in Cloud-Based Machine Learning Platforms

MATHEMATICAL MODELING AND ANALYSIS OF DYNAMIC CHARACTERISTICS OF A BRUSHLESS MOTOR IN AUTOMATED CONTROL SYSTEMS

The article provides a detailed analysis of brushless motors (BM) within the context of automated control, focusing on dynamic modes and mathematical modeling of the electric drive. It addresses the dynamic characteristics, transfer function development, and specific operational aspects of BM in systems with regulated rotational speed. The article presents experimental data and computer simulation results that validate the effectiveness of the selected models. Experimental studies conducted on a laboratory model confirm the accuracy of the developed mathematical model, particularly demonstrating the feasibility of approximating the motor's transfer function in a linear form to simplify control system analysis and tuning. Computer simulations are conducted in MATLAB using SIMULINK, which enables precise and illustrative visualization of transient processes in the BM. The article also introduces a subordinate control principle within a closed-loop control system, which ensures control stability and accuracy by dividing into speed and current loops. The simulation results demonstrate the potential of utilizing the developed models for further research and the design of efficient and reliable control systems for brushless motors in industrial settings. The proposed modeling approach paves the way for the creation of energy-efficient electric drives with high-precision control.

Numerical Analysis of Magnetohydrodynamic Convection in an Inclined Cavity with Three Fins and a Ternary Composition of Nanoparticles

The natural convection heat transfer of a trihybrid nanofluid comprising Fe2O3, MoS2, and CuO nanoparticles dispersed in water (Fe2O3 + MoS2 + CuO/H2O) has been investigated within a cavity exposed to a uniform magnetic field. Three cold fins were strategically positioned on the top, right, and left walls of the enclosure. The study employs numerical simulations conducted using a custom-developed FORTRAN code. The computational approach integrates the finite volume method and full multigrid acceleration to solve the coupled governing equations for continuity, momentum, energy, and entropy generation, along with the associated boundary conditions. Prior to obtaining the results, a meticulous parameterization process was undertaken to accurately capture the fluid dynamics and thermal behavior characteristic of this geometric configuration. The findings underscored the key parameters’ significant impact on the flow structure and thermal performance. The results revealed that natural convection is more dominant at high Rayleigh and low Hartmann numbers, leading to higher Nusselt numbers and stronger dependence on the tilt angle α. Moreover, the optimal heat transfer conditions were obtained for the following parameters: Ha = 25, α = 45°, ϕ = 6%, and Ra = 106 with a rate of 4.985. This study offers valuable insights into achieving a balance between these competing factors by determining the optimal conditions for maximizing heat transfer while minimizing entropy generation. The findings contribute to enhancing the design of thermal systems that utilize magnetic nanofluids for efficient heat dissipation, making the research particularly relevant to advanced cooling technologies and compact thermal management solutions.

Highly accurate simplification of physics-based electrochemical model of all-solid-state battery for online state of charge estimation

Abstract All-solid-state lithium batteries offer superior energy density and safety features, making them highly attractive for electric vehicles and wearable devices. Original physics-based electrochemical model can effectively simulate the internal electrochemical reactions, but they are difficult to be applied to embedded battery management systems. To facilitate the development of real-time applications based on physical models, this paper proposes a SOC prediction method based on a simplified electrochemical model (SEM). First, the transcendental transfer function is converted to third-order transfer functions using the Padé approximation. The electric field in the mass transfer overpotential is then solved for using average numerical integration method. Then, the proposed SEM method under variable load conditions is verified by comparing to the results from original partial differential equations. The results show that the developed SEM strikes a balance between high fidelity and computational efficiency. By taking into account the concentration of Li+ ions in the solid electrolyte, the estimation accuracy of SOC in the range of 0.1 to 0.3 is significantly improved, compared with prior studies. Strong support is provided for the advanced control design of smart management systems of ASSBs.

Continual learning with hebbian plasticity in sparse and predictive coding networks: a survey and perspective

Abstract Recently, the use of bio-inspired learning techniques such as Hebbian learning and its closely-related Spike-Timing-Dependent Plasticity (STDP) variant have drawn significant attention for the design of compute-efficient AI systems that can continuously learn on-line at the edge. A key differentiating factor regarding this emerging class of neuromorphic continual learning system lies in the fact that learning must be carried using a data stream received in its natural order, as opposed to conventional gradient-based offline training, where a static training dataset is assumed available a priori and randomly shuffled to make the training set independent and identically distributed (i.i.d). In contrast, the emerging class of neuromorphic continual learning systems covered in this survey must learn to integrate new information on the fly in a non-i.i.d manner, which makes these systems subject to catastrophic forgetting. In order to build the next generation of neuromorphic AI systems that can continuously learn at the edge, a growing number of research groups are studying the use of Sparse and Predictive Coding-based Hebbian neural network architectures and the related Spiking Neural Networks (SNNs) equipped with STDP learning. However, since this research field is still emerging, there is a need for providing a holistic view of the different approaches proposed in the literature so far. To this end, this survey covers a number of recent works in the field of neuromorphic continual learning based on state-of-the-art Sparse and Predictive Coding technology; provides background theory to help interested researchers quickly learn the key concepts; and discusses important future research questions in light of the different works covered in this paper. It is hoped that this survey will contribute towards future research in the field of neuromorphic continual learning.

Recent advancements in aquaponics with special emphasis on its sustainability

AbstractAn aquaponic system which is an integration of hydroponics with aquaculture is sustainable, ecologically friendly, and uses non‐renewable resources with very high efficacy. The increasing trend in aquaponics research demonstrates a growing recognition of its potential to address critical global issues such as food security, water conservation, and sustainable agriculture, particularly in regions vulnerable to climate change and resource limitations. It has witnessed remarkable advancements in recent years, with various methods and variations emerging to enhance its functionality and productivity. The purpose of this review is to unify fragmented research on aquaponics and provide a comprehensive resource that emphasizes its potential for broad adoption and successful implementation in varied environments. This review focuses on recent innovations in aquaponic system designs for various climates and examines key advancements in critical areas, including microbial communities, biofiltration, feed management, water quality, and nutrient cycling, emphasizing the interdisciplinary nature of aquaponics. Coupled systems suit moderate climates with high resource efficiency, while decoupled systems offer flexibility across diverse climates by allowing independent control of each component. Innovations such as sandponics, algaeponics, and saline aquaponics address challenges like water scarcity and nutrient efficiency. Smart technologies like IoT and AI are integrated into aquaponics, automating monitoring and nutrient management for better productivity. Emerging areas, including vermi‐aquaponics and flocponics, offer new solutions for nutrient cycling, making aquaponics more efficient, adaptable, and resource‐conserving globally. This paper also examines the sustainability aspects of aquaponics, emphasizing its economic, social, and environmental dimensions. Despite high setup costs and nutrient complexities, aquaponics offers significant potential for sustainable food production by minimizing water use and reducing synthetic fertilizers. Its scalability in both urban and rural areas, along with innovations like vertical systems, presents promising solutions for enhancing agriculture and food security across diverse environments.

Literature Review: Biomedical Information of Animal Treadmill Speed Control Using Proportional Integral Derivative Controller

The use of treadmill exercise in cardiovascular research played a vital role in assessing heart health and determining appropriate exercise regimens for patients. Before applying these regimens to humans, experiments on animals, such as white rats or mice, were conducted to simulate human cardiovascular responses. A specialized treadmill designed for experimental animals was required to determine exercise doses based on individual abilities. This process involved controlling the treadmill speed, which was generated by a conveyor driven by a DC motor. The motor speed was regulated through PID (Proportional Integral Derivative) control, while encoder sensors monitored the motor’s rotation speed, and limit switch sensors determined the exercise duration. This article reviewed the design and implementation of treadmill systems used for animal-based cardiovascular research, focusing on the control of DC motor speed using PID controllers. Previous studies that contributed to the development of such systems were discussed, with an emphasis on the precise control mechanisms required to simulate exercise conditions tailored to the subject's abilities. The treadmill system also incorporated sensors to accurately adjust motor speed and track exercise duration, ensuring alignment with the physiological capabilities of the test subjects. Furthermore, this review explored the potential for advancing research on treadmill control systems, offering insights into how this technology could support medical experts in determining optimal exercise regimens for white rats. These developments helped bridge the gap between animal-based studies and human applications, facilitating improved cardiovascular research outcomes.

Design and comparison of the drug delivery systems of pegylated doxorubicin loaded Zn-CP and Zn-CP-SO3H

Design and comparison of the drug delivery systems of pegylated doxorubicin loaded Zn-CP and Zn-CP-SO3H

Advances in Valorization of Biomass-Derived Glycolic Acid Toward Polyglycolic Acid Production

Glycolic acid (GA) is a versatile two-carbon organic chemical with multiple applications in industry and daily life. Currently, GA production depends heavily on the coal chemical industry. In this context, the sustainable production of GA from renewable resources has garnered significant attention. With the design and development of various catalytic systems, the yield of GA from biomass-based feedstocks has been improved observably. Poly(glycolic acid) (PGA) is an aliphatic polyester that exhibits a unique crystalline structure, excellent gas barrier properties, high mechanical strength, superior biocompatibility, and biodegradability. It has a wide range of applications in various fields, such as medical devices, oil extraction, bottle materials, film materials, and textile materials. This article comprehensively elaborates on the methods for the biomass-based synthesis of glycolic acid, the precursor of polyglycolic acid (PGA), as well as the preparation process of PGA. It fills the research gap regarding the sources of biomass raw materials for polyglycolic acid. Additionally, it delves into various modification strategies for PGA and provides an overview of its current applications in multiple fields, including biomedicine, food packaging materials, oil and gas resource development, and agricultural cultivation. The purpose of this article is to provide comprehensive reference information on the synthesis techniques, modification methods, and practical applications of PGA. Furthermore, it offers guidance for research on biodegradable plastics and the biomass-based synthesis of glycolic acid.

Assessing Operational Efficiency of Bubble Plumes for Water Circulation Enhancement

Bubble plumes are essential for promoting mass transfer, flow, and mixing in water bodies by generating vertical circulation via buoyancy forces. They are widely used in various applications, such as restoring water environments and improving the conditions at the bottom of lakes and reservoirs. For example, thermal stratification in lakes can lead to environmental issues such as the depletion of dissolved oxygen. To address this problem, bubble plume systems have been used to destratify lakes and reservoirs. However, few studies have been performed on the effectiveness of bubble plumes. In this study, the impact of a bubble plume in a dam reservoir was assessed using a numerical model based on high-resolution field measurements. Vertical profiles were obtained before and after the operation of the density-current generator to capture seasonal changes in the water characteristics. These measurements indicated the alteration of the vertical structure and mixing within the water column due to the bubble plume while stable temperatures were maintained at specific depths across seasons. Numerical simulations using large eddy simulations were conducted to analyze the dynamics and mixing efficiency of the bubble plume. The findings of this study provide valuable data for optimizing the design and operational strategies of bubble plume systems in lakes and reservoirs, which can increase the water mixing efficiency and support environmental management.

Touch-down condition control for the bipedal spring-mass model in walking

Behaviors of animal bipedal locomotion can be described, in a simplified form, by the bipedal spring-mass model. The model provides predictive power, and helps us understand this complex dynamical behavior. In this paper, we analyzed a range of gaits generated by the bipedal spring-mass model during walking, and proposed a stabilizing touch-down condition for the swing leg. This policy is stabilizing against disturbances inside and outside the same energy level and requires only internal state information. In order to generalize the results to be independent of size and dimension of the system, we nondimensionalized the equations of motion for the bipedal spring-mass model. We presented the equilibrium gaits (a.k.a fixed point gaits) as a continuum on the walking state space showing how the different types of these gaits evolve and where they are located in the state space. Then, we showed the stability analysis of the proposed touch-down control policy for different energy levels and leg stiffness values. The results showed that the proposed touch-down control policy can stabilize towards all types of the symmetric equilibrium gaits. Moreover, we presented how the peak leg force changes within an energy level and as it varies due to the type of the gait since peak force is important as a measurement of injury or damage risk on a robot or animal. Finally, we presented simulations of the bipedal spring-mass model walking on level ground and rough terrain transitioning between different equilibrium gaits as the energy level of the system changes with respect to the ground height. The analysis in this paper is theoretical, and thus applicable to further our understanding of animal bipedal locomotion and the design and control of robotic systems like ATRIAS, Cassie, and Digit.

The relationship between gather tendency with families and the self-awareness of flood hazard: the case of Sumida Ward, Tokyo

ABSTRACT This study investigated the level of flood knowledge, shelter familiarity, self-awareness in different situations, and the tendency to gather with family members during flooding. Results based on 161 participants aged 8–88 years. Participants’ own age was a predictor of whether or not they would gather with their families during the evacuation. Participants’ familiarity with the shelter was associated with knowledge, and increased interest in the shelter surroundings increased knowledge, which increased self-awareness of disaster response. Including family members in evacuation planning significantly increases participants’ awareness and involvement, leading them to plan their routes carefully. In addition, the results of the study revealed information about the expectations of Sumida residents during a flood: the safety of family members was ranked as the priority, and from this, it was possible to know the methods or means of escape that should be included about family members when evacuating. Followed by information on electricity and water facilities, which is relevant to the judgment of whether or not to go to a shelter. Then, information on flooding in the home area is needed. These findings can inform the design of effective flood information systems and community preparedness programs in urban areas.

Substrate selection and characterization in infrared detector design for sorting systems

Substrate selection and characterization in infrared detector design for sorting systems

Evolution of Supramolecular Coordination Assemblies Visually Monitored by Time-Dependent Multicolor Fluorescence.

Supramolecular coordination assemblies play an important role in both material science and biological systems. Despite significant efforts in their development, most existing examples are thermodynamically controlled, which lack the adaptability and autonomy essential for the creation of advanced materials. In this work, we have developed non-equilibrium coordination assembly systems that can evolve over time through a combination of thermodynamic and kinetic controls. The introduction of zinc ions resulted in the formation of metastable fiber assemblies in a kinetically trapped state, which autonomously converted to thermodynamically stable nanosheets over time. This evolution process can be regulated by external stimuli and visually monitored by the time-dependent multicolor fluorescence. The construction strategy was versatile across other various ions such as Ca2+, Mg2+, and Al3+, offering inspiring insights for the design of complex systems that operated both in and out of their thermodynamic equilibrium.

A Method to Determine the Design Flow of Stormwater Pipe Networks Based on Dynamic Wave Simulation

The rational method (RM) adopting steady uniform flow assumption is a simple and mainstream approach for the design of urban stormwater-drainage systems (USDSs). However, when designing large-scale USDSs, the RM significantly deviates from the actual flow regime due to inappropriate assumptions. To improve the accuracy and reliability of the design method, a dynamic-wave-simulation-based method (DWSBM) is proposed. Firstly, a numerical model which is equivalent to the surface runoff yield process of RM is established and validated. Then the dynamic wave module is adopted for pipe flow calculations. This method integrates hydraulic models for the whole design process. Both DWSBM and RM are used for USDS design of two areas, and design comparison demonstrates that the design flow rates computed using DWSBM are greater than obtained by RM. With the increase in pipeline length, the design flow differences between the two methods in the two areas increased from 3.09% to 28.97% and from 16.01% to 45.40% respectively. Adopting the DWSBM for design flow rate calculation can effectively improve the design reliability and drainage capacity of USDSs.