Electronic fuel injection systems have replaced carburetors due to their precise control of the air-fuel mixture, optimizing combustion and reducing emissions. These systems, which include sensors, actuators and the ECU, are essential to engine performance. The complexity of these systems requires advanced diagnostic tools and specialized training for automotive technicians. Emulators are crucial for training, allowing practice in safe environments. Injection systems use sensors to measure airflow, pressure, temperature and crankshaft position, sending signals to the ECU to adjust injection and ignition. Some key sensors are the MAF, MAP, IAT, CKP, KS and the oxygen sensor. The ECU analyzes these signals to adjust the amount of fuel needed, improving efficiency and reducing consumption and emissions. Therefore, this document details the design and construction of a fault emulator for the electronic injection system for an internal combustion engine, through the application of electronic components, PCB design in Protel 99 SE and use of Iocomp commands which will allow the identification of faults, engine behavior and training for automotive technicians. Fault emulation allowed the identification of faults and erroneous behavior of the internal combustion engine.

To meet the needs of an estimated 9 [...]

ABSTRACT Recent advances in artificial intelligence and machine learning (AI/ML) have resulted in autonomous systems that reduce operators' workload and involvement in hazardous missions. Integrating these systems into an existing system of systems (SoS) can evolve it into a system of autonomous system (SoAS). SoAS brings new Systems Engineering (SE) challenges for architecture development, integration, testing, and evaluation that originate from the level of autonomy (LoA). LoA refers to the level of autonomous capabilities of a system depending on its AI/ML technology. This paper examines SoAS test and evaluation challenges, such as uncertainty and emergent behaviors. We propose a test and evaluation method that provides stakeholders with a decision analysis tool to make data‐driven decisions by exploring the SoAS design space, comparing different alternative solutions under uncertainty, and selecting the most suitable one that addresses their mission needs. The proposed method is a step towards AI for Systems Engineering (AI4SE). It applies the Bayesian network (BN) to SoAS by integrating ML algorithms and Model‐Based Systems Engineering (MBSE) executable models. The decision analysis tool provides two types of examinations as follows: predictive analysis to select the most suitable SoAS alternative solution that yields the desired performance; and prescriptive analysis to identify root causes of an SoAS undesired emergent behavior and define preventive strategies for future operations. We present a conceptual example of a search‐and‐rescue mission to demonstrate the implementation and effectiveness of the proposed method in evaluating SoAS with varying LoAs.

Abstract Helical chiral structures are prevalent in nature and play vital roles in both biological and engineering systems. However, precise control over these structures at the nanoscale remains a significant challenge. In this study, we propose a general design strategy for helical chiral DNA structures, facilitating the bottom‐up assembly of supercoiled DNA nanosprings with highly customizable structural parameters. Our approach utilizes the independent tuning of inner‐module bending and inter‐module phase‐matching, enabling precise and continuous control over their chirality, screw diameter, and pitch, all within a wire diameter of approximately 10 nm and with minimal DNA sequence redesign. Molecular dynamics simulations show that these nanosprings exhibit conformation‐dependent mechanical properties and energy storage capabilities under compression. Furthermore, we observed enhanced circular dichroism signals from their right‐handed structural chirality. Compatible with various design techniques, including DNA bricks and DNA origami, the strategy is exttendable to more complex self‐assembled DNA structures. Overall, this modular assembly approach offers a novel framework for the precise, programmable design of supramolecular materials with tunable chirality.

In this study, the effects of nonlinear thermal radiation, Arrhenius activation energy, and chemical reactions on the flow and heat transfer of a water-based hybrid nanofluid containing SWCNT- [Formula: see text] & MWCNT- [Formula: see text] nanoparticles over a rotating disk are examined. The investigation highlights the combined influence of nonlinear radiation and nanoparticle shape factors on the transport properties of the hybrid fluid. Given that the thermal and structural performance of nanomaterials is strongly dependent on their morphology, special attention is devoted to assessing the role of particle shape variations. The objective of this work is to advance the fundamental understanding of how nonlinear radiative processes, activation energy, and nanoparticle geometry interact in rotating disk flows, thereby contributing to the development of efficient nanofluid based thermal management systems. These materials find applications in energy storage, thermal stability, transistors, and electromagnetic shielding. Given the growing demand for nanotechnology, understanding these effects is crucial for enhancing performance in engineering and energy systems. The governing PDEs are simplified into dimensionless ODEs using similarity transformations. The Successive Over-Relaxation method, executed through a custom MATLAB code, is used to obtain the solutions of these equations. The effects of different parameter values on radial and transversal velocity, as well as heat and mass transfer, are examined using graphical analysis. In addition, tabular data are presented to evaluate the behavior of skin friction, Nusselt number, and Sherwood number under various parametric conditions. The results reveal that velocity diminishes with increasing magnetic parameter values, whereas nonlinear radiation enhances heat transfer. Activation energy augments both concentration and mass transfer, although the latter is influenced by the Schmidt number and the chemical reaction rate. Conversely, temperature decreases with a rise in the Prandtl number. Radial skin friction decreases by about 44% as the magnetic parameter increases, while tangential skin friction magnitude rises by nearly 78% at low suction and around 37% at high suction. Furthermore, the heat transfer rate improves from 25.27% at Rd = 0.5 to 37.18% at Rd = 1.4, indicating an overall enhancement of 11.91%. These outcomes hold practical significance for optimizing fluid behavior and heat transfer in rotating systems, with potential applications in energy systems, heat exchangers, and advanced cooling technologies.

Human papillomavirus (HPV) infection is a major global health concern due to its association with various cancers, particularly cervical and head and neck squamous cell carcinomas. High-risk HPV types, such as HPV16 and HPV18, employ oncoproteins E6 and E7 to disrupt host cell regulatory pathways, promote immune evasion, and facilitate malignant transformation. Natural killer (NK) cells, critical components of innate immunity, play a pivotal role in surveilling and eliminating HPV-infected cells. However, HPV-mediated immune evasion mechanisms, including downregulation of MHC-I, suppression of chemokine signaling (e.g., CXCL14), and upregulation of inhibitory molecules (e.g., TIGIT, KLRG1), impair NK cell functionality. This review explores the intricate interactions between HPV and NK cells, highlighting the impact of HPV on NK cell infiltration, exhaustion, and receptor expression. Additionally, it discusses emerging therapeutic strategies to enhance NK cell activation, such as pharmacological agents (e.g., γ-PGA, α-GalCer), innate immune agonists (e.g., STING, RIG-I), genetic engineering (e.g., CAR-NK, iPSC-NK cells), and combination therapies with immune checkpoint inhibitors or monoclonal antibodies (e.g., cetuximab). Clinical applications, including adoptive NK cell transfer and biomarker-guided personalized immunotherapy, are also reviewed. Despite challenges like immunosuppressive tumor microenvironments and limited NK cell persistence, advancements in genetic engineering and nanoparticle delivery systems offer promising solutions. Future research should focus on integrating mechanistic insights with clinical trial design to optimize NK cell-based therapies for HPV-associated malignancies.

In a typical engineering design, there are often many design parameters to consider. Also, there are multiple competing requirements and objectives to meet. Manual approach of adjusting the parameters to achieve specific objectives is not optimal especially as the design becomes complex. In the quest for optimizing complex engineering systems, the exploration of the design space becomes imperative, especially when dealing with multi-objective systems characterized by an array of independent variables. This paper presents a comprehensive study on the design space mapping of complex engineering systems, utilizing a turboshaft engine as a case study. The initial phase of our methodology employs a physics-based model to generate synthetic dataset, reflecting the intricate interplay of various system parameters underpinning the engine's operation. This synthesized data serves as a foundation for the subsequent development of a Machine Learning or Deep Learning based surrogate model. The surrogate AI model, will be crafted to encapsulate multiple inputs and outputs inherent in the turboshaft engine's functioning, thereby facilitating an efficient and accurate exploration of the design space. Through this investigation, we will evaluate the efficacy of combining physics-based models with AI-driven techniques in mapping the design space of multi-objective systems. The core of our investigation revolves around the utilization of the AI surrogate model for achieving multi-objective optimization. This optimization process is not only focused on enhancing specific performance metrics but is also geared towards identifying a comprehensive family of feasible design solutions. Such an approach enables the delineation of the entire design space, offering invaluable insights into the trade-offs and synergies among different design objectives. Through this methodology, our goal is to uncover a wide spectrum of viable design alternatives, thereby providing a robust framework for decision-making in the engineering design process.

Bone regeneration remains a significant clinical challenge in conditions such as trauma, osteoporosis, and aging-related bone loss. Recent advances have highlighted the crucial role of extracellular vesicles, especially exosomes, in intercellular signaling pathways that support bone homeostasis and repair. Among their bioactive cargoes, exosomal RNAs—particularly messenger RNAs and microRNAs—have emerged as central regulators of osteogenesis by modulating gene expression, cellular differentiation, and communication within the bone microenvironment. In this review, we provide a comprehensive summary of exosome biology, including their biogenesis, secretion, uptake mechanisms, and RNA cargo characteristics. We critically examine current evidence on how exosomal RNAs influence the molecular mechanisms of bone formation, remodeling, and regeneration under both physiological and pathological conditions such as fractures, diabetes, osteoporosis, and osteoarthritis. Furthermore, we discuss the emerging therapeutic potential of engineered exosomes as RNA delivery systems in bone tissue engineering and regenerative medicine. A better understanding of the functional roles and clinical relevance of exosomal RNAs may pave the way for next-generation, RNA-based therapies in skeletal repair and treatment of bone-related diseases. Impact Statement This review highlights the crucial role of exosomal mRNAs and microRNAs in regulating osteogenesis and bone regeneration. By elucidating the molecular mechanisms and signaling pathways involved, it provides new insights into the potential of exosome-based therapies in bone tissue engineering. This work may accelerate the development of innovative RNA-based regenerative strategies, ultimately improving treatment outcomes for bone diseases and injuries.

The rapid digital transformation in engineering, coupled with the development of increasingly complex systems, is pushing industries to develop smarter and more efficient methods for system development. Major stakeholders/ industries are moving towards a model-based framework for systems engineering, RAM, and safety analysis to manage growing system complexity while maintaining data consistency and traceability. The convergence and consolidation of previously document-based engineering approaches allows for the standardization and streamlined capture of knowledge across engineering disciplines. In this framework, data availability and interoperability can easily become a bottleneck without comparable innovation to tooling and processes. In more recent times Artificial Intelligence (AI) has been identified as a powerful enabler on this front. AI can assist engineers in developing RAMS models more efficiently by leveraging legacy data, such as historical FMECAs, and aligning it with standardized taxonomies to automatically and rapidly develop system models for downstream analysis requirements.

The development of non-invasive brain-computer interfaces (BCIs) relies on multidisciplinary integration across neuroscience, artificial intelligence, flexible electronics, and systems engineering. Recent advances in deep learning have significantly improved the accuracy and robustness of neural signal decoding. Parallel progress in electrode design-particularly through the use of flexible and stretchable materials like nanostructured conductors and novel fabrication strategies-has enhanced wearability and operational stability. Nevertheless, key challenges persist, including individual variability, biocompatibility limitations, and susceptibility to interference in complex environments. Further validation and optimization are needed to address gaps in generalization capability, long-term reliability, and real-world operational robustness. This review systematically examines the representative progress in neural decoding algorithms and flexible bioelectronic platforms over the past decade, highlighting key design principles, material innovations, and integration strategies that are poised to advance non-invasive BCI capabilities. It also discusses the importance of multimodal data fusion, hardware-software co-optimization, and closed-loop control strategies. Furthermore, the review discusses the application potential and associated engineering challenges of this technology in clinical rehabilitation and industrial translation, aiming to provide a reference for advancing non-invasive BCIs toward practical and scalable deployment.

Melt electrowriting (MEW) enables the precise deposition of polymeric fibers at micro-/nanoscale, allowing for the fabrication of 3D biomimetic scaffolds. By incorporating stimuli-responsive polymers and/or functional fillers, MEW-based 4D printing creates scaffolds capable of undergoing controlled, reversible shape transformations in response to external stimuli over time. These dynamic 4D scaffolds can be tailored for minimally invasive delivery, remote actuation, and real-time responsiveness to physiological environments, making them highly relevant for biomedical applications. This review systematically elucidates the principles of MEW-based 4D printing, including material considerations, actuation methods, and structure design strategies, along with shape programming and morphing mechanisms. The versatility of MEW for rational fabrication of biomimetic scaffolds is firstly introduced. Subsequently, the critical elements underpinning MEW-based 4D printing process are overviewed, including an analysis of stimuli-responsive materials compatible with MEW, an evaluation of applicable external stimuli, and a discussion on the advancements in design strategies for 4D scaffolds. Recent progress of MEW 4D scaffolds for applications in tissue engineering, biomedical implants, and drug delivery systems are highlighted. Finally, key challenges and perspectives toward material innovation, fabrication optimization, and actuation control are discussed. This review aims to provide valuable insights for design and creation of multifunctional biomimetic dynamic scaffolds by MEW-based 4D printing.

Heme, an iron-incorporated porphyrin compound, serves as the prosthetic group for numerous proteins involved in diverse biological processes. The prokaryotic heme biosynthetic pathway features a complex cascade of reactions, in which glutamyl-tRNA reductase (GluTR) catalyzes the formation of 5-aminolevulinic acid (ALA) that represents a critical rate-limiting step and determines ultimate heme yield. In this study, OsGluTRA510V showed enhanced heme synthesis capacity in Oryza sativa and was used for developing microbial cell factories dedicated to free heme production. Through systematic protein engineering involving site-directed mutagenesis and N-terminal modification, OsGluTRA510V was optimized to improve the structural stability and catalytic efficiency. It yielded the recombinant enzyme GluTRA510V/S189T/KK, which achieved a maximum heme titer of 13.14mg/L in Escherichia coli, representing a 7.6-fold improvement over that of GluTRA510V. To establish heme production in Bacillus subtilis, GluTRA510V/S189T/KK was introduced into the ΔhmoAB-hemX chassis, a modified B. subtilis host lacking key heme biosynthesis inhibitors (hmoA, hmoB, and hemX). This engineered system elevated the heme yield from 0.77 to 3.86mg/L, achieving a 5.0-fold improvement. This study demonstrates a combinatory metabolic engineering strategy that reconstitutes the heme synthetic route in B. subtilis, enabling efficient production of food-grade free heme through enzyme engineering and chassis optimization.

BackgroundThe optimal approach to antibiotic stewardship in nursing homes (NHs) remains poorly understood. Prospective audit-and-feedback (PAF) is a highly effective antibiotic stewardship strategy typically employed in hospitals with robust pharmacy and information system resources. It is unclear if PAF can be effectively performed in lower resource settings like NHs.Figure 1:rPAF Pilot WorkflowTable 1:rPAF Intervention Recommendation TypesMethodsWe conducted a multi-phase mixed-methods study to design and implemented a remote PAF (rPAF) intervention in a NH network in the Northwest United States. We conducted interviews with key informants and performed document analyses, guided by the Systems Engineering Initiative for Patient Safety (SEIPS) model, to design a beta-version of the rPAF intervention. Information generated from cognitive interviews structured around three clinical scenarios and presented to NH providers in the NH EHR training environment was used to refine the rPAF intervention. A REDCap database was constructed to enter prospective data on antibiotic regimens reviewed, recommendation acceptance, and clinical characteristics and outcomes of residents.ResultsA final workflow involving information sharing between the NH dispensing pharmacy and a remotely located infectious disease (ID) pharmacist who, in turn conveyed antibiotic regimen change recommendations to NH providers via encrypted EHR message was developed (Figure 1). Key informant interviews and document analyses identified a need to limit the size and content of encrypted messages and include concrete justifications for the recommendations provided. ID pharmacists reviewed 58 antibiotic regimens in 13 study NHs during the first three weeks of the four-month pilot study and made 21 antibiotic recommendations. These recommendations included 10 modifications, 5 stops, 4 replacements, and 2 requests for more information (see Table 1 for explanation of types).ConclusionOur initial findings demonstrate the feasibility of PAF in NHs, based on using remote ID pharmacist review and encrypted EHR messaging. Evaluation of the rPAF on antibiotic prescribing is ongoing and will also include assessment of the costs and sustainability of the rPAF intervention as well as the scalability of the rPAF intervention.DisclosuresSally Jolles, MA, MS, Merck: Grant/Research Support Jon P. Furuno, PhD, Merck: Grant/Research Support Kendall J. Tucker, PharmD, MS, Merck: Grant/Research Support Christopher J. Crnich, MD, PhD, Merck: Grant/Research Support

This review presents a comprehensive perspective on how AI and big data strategies can transform the understanding and design of the electrode-electrolyte interphases (EEI) in rechargeable batteries, highlighting their pivotal role in battery performance and longevity. Through uniting high-throughput experimentation and high-throughput computation (HTC), which includes automated cell fabrication, advanced characterization, large-scale HTC screening, and reaction network modeling, diverse datasets can be generated to reveal the mechanistic foundations of interfacial processes. The integration of these datasets with artificial intelligence-orchestrated workflows and machine learning models, such as closed-loop optimization and large language model-assisted hypothesis generation, enables the prediction of interphase behavior, linking molecular-level EEI understanding and macroscale device performance, and data-driven discovery of optimal material combinations. Critically, the review identifies persistent challenges, including limited data standardization, a shortage of high-quality interoperable datasets, the gap between optimization and generalizable understanding, the limits of currently available self-driving labs, and outlines mitigation strategies for building intelligent, data-centric frameworks for rational engineering of next-generation battery systems.

Fatigue-induced degradation in Carbon Fiber Reinforced Polymer (CFRP) structures poses a critical challenge in long-term structural health monitoring (SHM) applications. In this study, a hybrid deep learning framework is proposed for fatigue state classification of CFRP composites using sensor-based monitoring data. The framework integrates a one-dimensional Convolutional Neural Network (1D-CNN) to extract spatial degradation patterns and an extended Long Short-Term Memory (xLSTM) network to capture long-range temporal dependencies associated with fatigue evolution. The extracted spatiotemporal features are fused and refined through Mutual Information-based feature selection, followed by a Bagging-based ensemble classifier for robust fatigue state discrimination. The proposed approach is evaluated using the NASA-CFRP dataset, achieving an average classification accuracy of 99%. While the framework is generally applicable to SHM of CFRP structures, its relevance to membrane-based gas separation systems is discussed as a representative application scenario. The results demonstrate the effectiveness of the proposed method for reliable fatigue diagnosis and maintenance decision support in CFRP-based engineering systems.

Programmable electromagnetic platforms have significantly enhanced the capabilities of magnetic micro- and nanobots by enabling precise remote control of autonomous movement, thereby transforming their roles in biomedical diagnostics, targeted therapies, and environmental remediation. These electromagnetic setups using Helmholtz, Maxwell, and gradient coil arrays generate dynamic magnetic fields to remotely control magnetic micro- and nanobots. Programmed modulation of magnetic field direction, magnitude, and gradients enables coordinated swarm behavior, including alignment, aggregation, dispersion, and pattern formation, thereby achieving submicrometer spatial precision and real-time adaptive navigation in confined microvascular environments. This capability facilitates single-cell biosensing, multiplexed biomarker detection, and targeted therapeutic delivery with up to 95% efficiency. Smart feedback loops seamlessly integrate sensing with magnetic actuation, enabling these bots to possess autonomous, self-regulating diagnostic and therapeutic functions tailored to changing biological microenvironments. Their functionality extends to environmental remediation, achieving over 90% pollutant degradation and up to 98% heavy metal removal as well as swarm-intelligent, real-time water-quality monitoring at industrial and agricultural sites. This review provides the use of advanced magnetic field setups for below-micrometer precision control of magnetic micro- and nanobots in complex environment. It discusses high-performance magnetic nanomaterial surface engineering and integration of real-time closed-loop feedback systems to maintain accurate robot navigation. Together, these strategies enable breakthrough applications in targeted therapy, biosensing, and environmental remediation.

The clinical translation of adeno-associated virus (AAV)-based gene therapies is often hindered by nonselective tissue transduction, off-target uptake by nontarget cells, and unintended toxicity to healthy tissues. To overcome these challenges, we previously developed a site-specific AAV capsid engineering strategy involving the incorporation of an azide-bearing unnatural amino acid (NAEK) into defined capsid positions, enabling precise, bioorthogonal conjugation of targeting ligands. In this study, we applied this approach to generate a series of folate receptor α (FRα)-targeted AAV2 vectors through covalent tethering of folic acid (FA) at specific capsid residues. FA conjugation at residues S264 + 1 and Q325 significantly enhanced FRα-mediated transduction, yielding a 3-5-fold increase in gene transfer efficiency in FRα-positive tumor cells. Structure-activity relationship analysis revealed that transduction selectivity is governed not only by ligand-receptor binding affinity but also by the spatial location of the conjugation site, which influences competition with the native AAV receptor (AAVR). Importantly, this modular conjugation platform allows for facile replacement of ligands, enabling the rational design of receptor-directed AAV vectors for targeted and cell-specific gene therapy. These findings provide mechanistic insights into capsid-receptor interactions and establish a flexible strategy for precision engineering of AAV-based delivery systems.

As Simulation Engineers and Subject Matter Experts (SMEs), we often focus on solving complex technical challenges within a specific technical or physical domain by leveraging specific simulation approaches. However, to maximize the impact and integration of our work across all the engineering domains that are typically engaged in a project, it's essential to understand Systems Engineering (SE) and Model-Based Systems Engineering (MBSE) practices that are becoming increasingly more important in performing 21st century engineering projects. Elaborating a NAFEMS World Congress (NWC) 2025 presentation and complementing a NAFEMS ASSESS Insights, webinar, this paper aims to minimize the gap between Simulation Engineers/SMEs and the realm of SE/MBSE by offering insights into how SE/MBSE/MBE ideas and practices can enhance collaboration between engineers and improve (modeling & simulation) project outcomes. It shows how the adoption of SE and MBSE concepts and approaches can help to manage complexity, ensure traceability, and integrate simulation models more effectively within larger system architectures and system models. By understanding core SE and MBSE principles, workflows, and tools, Simulation Engineers can improve communication with SE and within cross-functional teams, contribute to system-level requirements and technical concepts, and elevate the impact and value of their work in modern engineering projects. Specifically, insights are provided on the following topics: SE and MBSE, key systems modeling language concepts relevant for simulation engineers such as Analysis, Verification, and TradeStudy, and integration of Engineering Simulation within SE and MBSE Frameworks. Furthermore, an outlook is provided on future trends and the evolving role of simulation engineers in the context of SE & MBSE.

2'-Fucosyllactose (2'-FL), one of the primary human milk oligosaccharides (HMOs), exerts a pivotal influence on early human development as well as health benefits on other life stages. In this study, a high-yielding strain for 2'-FL production was constructed in Escherichia coli BL21star(DE3) using a systematic metabolic engineering strategy. The α-1,2-fucosyltransferase BKHT from Helicobacter sp. 13S00401-1 was selected as the optimal enzyme for 2'-FL biosynthesis, and the best variant F304W of BKHT was obtained to improve 2'-FL biosynthesis using semirational enzyme modifications. A "push-pull" strategy was implemented to optimize the carbon flux within the 2'-FL biosynthetic pathways. After the enhancement of the availability of cofactor GTP and NADPH, the 2'-FL titer reached 13.71 g/L in a shake flask. Finally, the engineered strain EFL54 produced 136.71 g/L 2'-FL with a productivity of 1.85 g/(L h) in a 5 L fermentor. This work provides a concrete foundation for the industrial-scale production of 2'-FL.

Efficient syngas production from carbon dioxide emissions via system engineering