Search Results (135)

Conventional optimization algorithms face challenges such as lengthy computation times, premature termination at non-convergent points, and the generation of local optima when addressing multi-objective optimization. A multi-objective optimization method based on the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is proposed for optimizing Doherty power amplifier circuits. The pre-layout simulation results show that, compared to traditional design methods, the optimized Doherty power amplifier circuit achieves a 6.4% increase in saturation efficiency, a 3.3% increase in 6 dB roll-off efficiency, and a 1 dB increase in saturation output power at 2.63 GHz. This approach enables multi-objective optimization design for more complex PA circuits and enhances the overall circuit performance. Full article

High-frequency transmission loss in Redistribution Layer-Through Silicon Via (RDL-TSV) interconnect structures is a critical factor influencing the performance of three-dimensional integrated circuits. This study aims to enhance the prediction accuracy of high-frequency losses by balancing the training accuracy and computational efficiency of traditional full-wave simulation and equivalent circuit models. A Physical Information Convolutional Neural Network (PI-CNN) prediction model was developed based on convolutional neural networks, incorporating the skin effect as physical guidance. A multi-criteria decision-making framework was then proposed by integrating the PI-CNN model with a genetic algorithm. Results show that the PI-CNN model achieves stable single-prediction times under 3 s, with prediction loss errors below 0.1 dB and an R² value of 0.987, significantly improving the accuracy of high-frequency loss prediction. Through multi-criteria decision optimization, the randomness inherent in genetic algorithms enables systematic exploration of favorable design options within the design space. This approach ensures that the final design maintains consistent performance and robustness under anticipated manufacturing variations. The study provides a data-driven, physics-guided approach for evaluating and optimizing high-frequency performance in advanced packaging. Full article

The increasing functional complexity and high-density integration of integrated circuits (ICs) present formidable routing challenges for printed circuit boards. Specifically for components utilizing high-density Ball Grid Array and other Grid Pin Array (GPA) packages, achieving efficient and reliable ordered escape routing is critical. This routing must strictly satisfy non-crossing and channel capacity constraints, which often become the bottleneck determining design success. Traditional manual or heuristic methods are increasingly inadequate for meeting the complexity and optimization demands of modern high-density designs. To address this challenge, this study proposes an innovative, automated routing strategy that leverages the robust search and optimization capabilities of the genetic algorithm (GA). We formulate the ordered escape routing problem on BGA/GPA as a rigorous combinatorial optimization problem. Through the GA mechanism, the core research objective is to explore the vast solution space and achieve the minimization of the total routing cost. This research establishes an automated routing framework capable of providing cost-effective escape routing solutions for high-density BGA, while strictly satisfying the non-crossing and single-capacity constraints. The proposed methodology not only significantly enhances routing efficiency and success rates but also provides essential technical support for the design and manufacturing processes of complex electronic products. For instance, in the simulation case of Case 7 (a 25 × 26 array with 60 active pins), this method achieved a routing compression ratio of 0.8230, equivalent to a 17.70% reduction in the total routing length, fully demonstrating the superior performance of the proposed algorithm in optimizing the routing cost. Full article

In high-frequency circuit design, parameters such as the characteristic impedance and propagation constant of transmission lines directly affect key performance metrics, including signal integrity and power transmission efficiency. To address the challenge of optimizing impedance matching for high-frequency PCB transmission lines, this study applies a hybrid deep Q-network—genetic algorithm (DQN-GA) that integrates deep reinforcement learning with a genetic algorithm (GA). Unlike existing methods that primarily focus on predictive modeling or single-algorithm optimization, the proposed approach introduces a bidirectional interaction mechanism for algorithm fusion: transmission line structures learned by the deep Q-network (DQN) are encoded as chromosomes to enhance the diversity of the genetic algorithm population; simultaneously, high-fitness individuals from the genetic algorithm are decoded and stored in the experience replay pool of the DQN to accelerate its convergence. Simulation results demonstrate that the DQN-GA algorithm significantly outperforms both unoptimized structures and standalone GA methods, achieving substantial improvements in fitness scores and S11 transmission coefficients. This algorithm effectively overcomes the limitations of conventional approaches in addressing complex reflected wave compensation problems in high-frequency applications, providing a robust solution for signal integrity optimization in high-speed circuit design. This study not only advances the field of intelligent circuit optimization but also establishes a valuable framework for the application of hybrid algorithms to complex engineering challenges. Full article

Unmanned aerial vehicles open new possibilities for developing technologies that support more sustainable and efficient agriculture. This paper presents a non-invasive method for repelling rodents from crop fields using ultrasound. The proposed system is implemented as a spherical-cap ultrasound loudspeaker array consisting of eight transducers, mounted on a drone that overflies the field while emitting sound in the 20–70 kHz range. The hardware design includes both the loudspeaker array and a custom printed circuit board hosting power amplifiers and a signal generator tailored to drive multiple ultrasonic transducers. In parallel, a genetic algorithm is used to compute flight paths that maximize coverage and increase the probability of driving rodents away from the protected area. As part of the validation phase, artificial intelligence models for rodent detection using a thermal camera are developed to provide quantitative feedback on system performance. The complete prototype is evaluated through a series of experiments conducted both in controlled laboratory conditions and in the field. Field trials highlight which parts of the concept are already effective and identify open challenges that need to be addressed in future work to move from a research prototype toward a deployable product. Full article

Spinal cord injury (SCI) remains one of the most formidable challenges in regenerative medicine, often resulting in permanent loss of motor, sensory, and autonomic function. Cell-based therapies offer a promising path toward repair by providing donor neurons and glia capable of integrating into host circuits, modulating the injury environment, and restoring function. Early studies employing fetal neural tissue and neural progenitor cells (NPCs) have demonstrated proof-of-principle for survival, differentiation, and synaptic integration. More recently, pluripotent stem cell (PSC)-derived donor populations and engineered constructs have expanded the therapeutic repertoire, enabling precise specification of interneuron subtypes, astrocytes, and oligodendrocytes tailored to the injured spinal cord. Advances in genetic engineering, including CRISPR-based editing, trophic factor overexpression, and immune-evasive modifications, are giving rise to next-generation donor cells with enhanced survival and controllable integration. At the same time, biomaterials, pharmacological agents, activity-based therapies, and neuromodulation strategies are being combined with transplantation to overcome barriers and promote long-term recovery. In this review, we summarize progress in designing and engineering donor cells and tissues for SCI repair, highlight how combination strategies are reshaping the therapeutic landscape, and outline opportunities for next-generation approaches. Together, these advances point toward a future in which tailored, multimodal cell-based therapies achieve consistent and durable restoration of spinal cord function. Full article

Agricultural systems face mounting pressures from climate change, as rising temperatures, elevated CO₂, and shifting precipitation patterns intensify plant disease outbreaks worldwide. Conventional strategies, such as breeding for resistance, pesticides, and even transgenic approaches, are proving too slow or unsustainable to meet these challenges. Synthetic biology offers a transformative paradigm for reprogramming plant immunity through genetic circuits, RNA-based defences, epigenome engineering, engineered microbiomes, and artificial intelligence (AI). We introduce the concept of synthetic immunity, a unifying framework that extends natural defence layers, PAMP-triggered immunity (PTI), and effector-triggered immunity (ETI). While pests and pathogens continue to undermine global crop productivity, synthetic immunity strategies such as CRISPR-based transcriptional activation, synthetic receptors, and RNA circuit-driven defences offer promising new avenues for enhancing plant resilience. We formalize synthetic immunity as an emerging, integrative concept that unites molecular engineering, regulatory rewiring, epigenetic programming, and microbiome modulation, with AI and computational modelling accelerating their design and climate-smart deployment. This review maps the landscape of synthetic immunity, highlights technological synergies, and outlines a translational roadmap from laboratory design to field application. Responsibly advanced, synthetic immunity represents not only a scientific frontier but also a sustainable foundation for climate-resilient agriculture. Full article

Designing solid-state circuit breakers (SSCBs) involves a large discrete design space spanning MOSFET type, bypass configuration, and heatsink selection. This work formulates SSCB design as a multi-objective combinatorial optimisation problem that minimises conduction loss and material cost subject to electrothermal feasibility constraints. A validated electrothermal model was developed using experimentally measured R_DSon(T) data and thermal-impedance characterisation, allowing rapid and accurate evaluation of candidate configurations. Because the full design space exceeds one million combinations, five representative metaheuristic algorithms: Genetic Algorithm (GA), Particle Swarm Optimisation (PSO), Grey Wolf Optimisation (GWO), Ant Colony Optimisation (ACO), and Gorilla Troops Optimisation (GTO), were benchmarked under an identical computational budget of 2000 evaluations. Sobol sequence initialisation was used to enhance search diversity. Each algorithm was executed 100 times, and its performance was quantitatively assessed using hypervolume, generational distance (GD), inverted generational distance (IGD), Hausdorff distance, overlapping-point score (OP), overall spread (OS), and distribution metrics (DM). GA consistently produced the closest approximation to the true Pareto front obtained from brute-force enumeration, achieving superior accuracy, coverage, and robustness. GTO offered strong secondary performance, while PSO, GWO, and ACO delivered partial front reconstruction. The results demonstrate that metaheuristic optimisation, particularly GA, can reduce SSCB design time significantly while retaining high fidelity, offering a scalable and efficient framework for future power-electronics design tasks. Full article

Corynebacterium glutamicum is a versatile microbial cell factory, but efficient recovery of intracellular macromolecules remains a major challenge. In this study, we engineered environmentally controllable lysis systems to enable programmable product release. A glucose-responsive module, combining the cg3195 promoter with phage-derived holin–endolysin genes (clg51-50), triggered lysis when extracellular glucose dropped below 0.19–0.36 g/L. A separate temperature-inducible system employing the cI857-CJ1OX2 module activated lysis at 42 °C. These modules were further integrated into a dual-input AND-gate circuit, enhancing regulatory precision and suppressing premature lysis, with additional operator copies allowing temporal tuning of induction. Functional validation using fluorescence, cell density measurements, and scanning electron microscopy confirmed robust, tunable responses under defined environmental cues. Collectively, these programmable lysis systems demonstrate that stimulus-responsive genetic circuits can be rationally designed to control cell disruption, providing a promising approach to streamline downstream processing and reduce extraction costs in industrial fermentation of Corynebacterium glutamicum. Full article

To meet the safety and compliance requirements pertaining to robots when interacting physically with humans or the environment in unstructured settings such as households and factories, in this study, we focus on methods for the design and control of a flexible robotic magneto-rheological actuator (MRA). Firstly, for the magneto-rheological fluid clutch (MRC), which is the core component of the MRA, an equivalent magnetic circuit model was established to accurately calculate the magnetic field inside the clutch, and a thermal circuit model was constructed to analytically determine the operating temperature of each component. Considering practical engineering constraints, including mechanical structure, magnetic saturation, maximum current, and maximum temperature, a genetic algorithm was used to optimize parameters of the MRC. Secondly, based on the dynamic characteristics of the MRA, a dynamic model incorporating the motor, reducer, MRC, and load link was established. Given scenarios where torque sensors cannot be installed due to cost and structural space limitations, a model reference PID feedforward control strategy was designed. Torque was estimated using input current. Finally, an experimental platform was built, and static and dynamic torque output experiments were conducted. These experiments verified the excellent torque tracking performance of the designed MRA. Through multi-physics modeling, parameter optimization, and control strategy design, this paper provides a solution for flexible robotic joints that integrates high torque, high compliance, and safety. Full article

Optimizing static random-access memory (SRAM) cells requires considering parasitic effects, as their impact on circuits in advanced nodes becomes increasingly complex. In this paper, Convolutional Neural Network-Informed Non-dominated Sorting Genetic Algorithms-II (CNN-Informed NSGA-II) was proposed to optimize 7 nm FinFET 6T-SRAM cells taking into account parasitic resistance. CNN-Informed NSGA-II uses a trained CNN model integrated into the conventional NSGA-II, thereby reducing its computational complexity. This approach provides a generally applicable solution that significantly improves the efficiency of circuits while balancing competitive performance metrics. Compared to the ideal (parasitic-free) 6T-SRAM cell design, the optimized 6T-SRAM cell design (considering parasitic effects) achieves a reduction of 81.60% in Write Dynamic Power and 64.65% in Write Time; HSNM and RSNM are improved by 11.92% and 6.42%, respectively. The optimized 7 nm FinFET 6T-SRAM cell structure in this paper outperforms the parasitic-free structure in terms of the performance parameters above, even when taking into account parasitic effects. Full article

Spinal cord injury (SCI) often results in long-term locomotor impairments, and strategies to enhance functional recovery remain limited. While epidural electrical stimulation (EES) has shown clinical promise, our understanding of the mechanisms by which it improves function remains incomplete. Here, we use genetic tools in an animal model to perform neuromodulation and treadmill rehabilitation in a manner similar to EES, but with the benefit of the genetic tools and animal model allowing for targeted manipulation, precise quantification of the cells and circuits that were manipulated, and the gathering of extensive kinematic data. We used a viral construct that selectively transduces large diameter afferent fibers (LDAFs) with a designer receptor exclusively activated by a designer drug (hM3Dq DREADD; a chemogenetic construct) to increase the excitability of large fibers specifically, in the rat contusion SCI model. As changes in locomotion with afferent stimulation can be subtle, we carried out a detailed characterization of the kinematics of locomotor recovery over time. Adult Long-Evans rats received contusion injuries and direct intraganglionic injections containing AAV2-hSyn-hM3Dq-mCherry, a viral vector that has been shown to preferentially transduce LDAFs, or a control with tracer only (AAV2-hSyn-mCherry). These neurons then had their activity increased by application of the designer drug Clozapine-N-oxide (CNO), inducing tonic excitation during treadmill training in the recovery phase. Kinematic data were collected during treadmill locomotion across a range of speeds over nine weeks post-injury. Data were analyzed using a mixed effects model chosen from amongst several models using information criteria. That model included fixed effects for treatment (DREADDs vs. control injection), time (weeks post injury), and speed, with random intercepts for rat and time point nested within rat. Significant effects of treatment and treatment interactions were found in many parameters, with a sometimes complicated dependence on speed. Generally, DREADDs activation resulted in shorter stance duration, but less reduction in swing duration with speed, yielding lower duty factors. Interestingly, our finding of shorter stance durations with DREADDs activation mimics a past study in the hemi-section injury model, but other changes, including the variability of anterior superior iliac spine (ASIS) height, showed an opposite trend. These may reflect differences in injury severity and laterality (i.e., in the hemi-section injury the contralateral limb is expected to be largely functional). Furthermore, as with that study, withdrawal of DREADDs activation in week seven did not cause significant changes in kinematics, suggesting that activation may have dwindling effects at this later stage. This study highlights the utility of high-resolution kinematics for detecting subtle changes during recovery, and will enable the refinement of neuromechanical models that predict how locomotion changes with afferent neuromodulation, injury, and recovery, suggesting new directions for treatment of SCI. Full article

Metabolic engineering of mesenchymal stem/stromal cells (MSCs) represents a compelling frontier for advanced cellular therapies, enabling the precise tuning of their biological outputs. This feature paper examines the critical role of engineered culture microenvironments, specifically 3D platforms, hypoxic preconditioning, and other priming approaches, which are synthetic biology strategies used to guide and optimize MSC metabolic states for desired functional outcomes. We show that these non-genetic approaches can significantly enhance MSC survival, immunomodulatory capacity, and regenerative potential by shifting their metabolism toward a more glycolytic phenotype. Furthermore, we propose a new paradigm of “designer” MSCs, which are programmed with synthetic circuits to sense and respond to the physiological cues of an injured microenvironment. This approach promises to transform regenerative medicine from an inconsistent field into a precise, predictable, and highly effective therapeutic discipline. Full article

To meet the vibration isolation requirements of engines under diverse operating conditions, this paper proposes a novel magnetorheological fluid-elastomer isolator with high load and tunable parameters. The mechanical and magnetic circuit structures of the isolator were designed and optimized through theoretical calculations and finite element simulations, achieving effective vibration isolation within confined spaces. The dynamic performance of the isolator was experimentally evaluated using a hydraulic testing system under varying excitation amplitudes, frequencies, initial positions, and magnetic fields. Experimental results indicate that the isolator achieves a static stiffness of 3 × 10⁶ N/m and a maximum adjustable compression load range of 105.4%. In light of the asymmetric nonlinear dynamic behavior of the isolator, an improved nine-parameter Bouc–Wen model is proposed. Parameter identification performed via a genetic algorithm demonstrates a model accuracy of 95.0%, with a minimum error reduction of 28.8% compared to the conventional Bouc–Wen model. Full article

Engineered living materials (ELMs) integrate living cells—such as bacteria, yeast, or mammalian cells—with synthetic matrices to create responsive, adaptive systems for sensing and actuation. Among ELMs, those endowed with sensing capabilities are gaining increasing attention for applications in environmental monitoring, biomedicine, and smart infrastructure. Central to these sensing functions are synthetic gene circuits, which enable cells to detect and respond to specific signals. This mini-review focuses on recent advances in sensing ELMs empowered by synthetic gene circuits. Here, we highlight how rationally designed genetic circuits enable living materials to sense and respond to diverse inputs—including environmental chemicals, light, heat, and mechanical loadings—via programmable signal transduction and tailored output behaviors. Input signals are classified by their source and physicochemical properties, including synthetic inducers, environmental chemicals, light, thermal, mechanical, and electrical signals. Particular emphasis is placed on the integration of genetically engineered microbial cells with hydrogels and other functional scaffolds to construct robust and tunable sensing platforms. Finally, we discuss the current challenges and future opportunities in this rapidly evolving field, providing insights to guide the rational design of next-generation sensing ELMs. Full article