Engineering Paradigm Research Articles

Rationally Engineered Bispecific Nanoimmunoblocker Restores Anticancer Immunity via Dual Immune Checkpoint Blockade.

Immune checkpoint blockade (ICB) therapy has revolutionized cancer treatment. However, the outcomes of mainstay antibody inhibitors against solid tumors remain poor, facing tremendous challenges including manufacturing complexities, serious toxicities, and crosstalk among multiple checkpoints. Herein, we present a bispecific molecularly imprinted nanoimmunoblocker (bsMINIB) designed to boost potent antitumor immunity via synchronously blocking innate and adaptive immune checkpoints. Two epitopes for PD-L1 and SIRPα are selected as templates through structural analysis, and thereafter, bsMINIB capable of bridging tumor cells and macrophages is rationally engineered via an advanced imprinting approach. The bsMINIB exhibits high affinity and specificity toward PD-L1 on solid tumor cells and SIRPα on macrophages, allowing effective disruption of both PD-L1/PD-1 and CD47/SIRPα signaling. These signal disruptions restore macrophage-mediated tumor phagocytosis, promote tumor-associated antigen presentation, and reinvigorate T cell-mediated tumor killing. Using refractory triple-negative breast cancer as a solid tumor model, the bsMINIB demonstrates extended retention at the tumor site, amplified infiltration of active T cells, and reactivated antitumor macrophages, thereby effectively inhibiting tumor growth. This biomimetic nanoimmunoblocker not only presents an effective multipronged ICB therapeutic against solid tumors but also showcases a compelling paradigm for the rational engineering of bispecific nanoplatforms for synergistic immunotherapy through molecular imprinting.

Synergistic Entropy Engineering with Oxygen Vacancy: Modulating Microstructure for Extraordinary Thermosensitive Property in ReNbO4 Materials.

The pursuit of high precision and stability simultaneously in high-temperature thermistor fields is longstanding. However, most spinel or perovskite-structured thermosensitive materials struggle to tolerate prolonged high-temperature environments at the expense of sensitivity and stability. Here, a novel entropy engineering strategy involving vacancies is proposed to balance sensitivity and stability for fergusonite-structured ReNbO4 (Re is a rare earth element) material in extreme environments. The synergistic effect of entropy stabilization and allovalent substitution on the A-site generates unusually high concentrations of oxygen vacancy that improves the electronic structure and structural stability. Moreover, entropy engineering involving oxygen vacancies introduces potent and stable microstructural features including twinned domains, lattice distortion, and lattice reconfigurations, which facilitate stability and accuracy at a wide temperature range, thereby synergistically contributing to excellent thermosensitive properties. As-prepared high-entropy ceramics show low aging drift rates and high-temperature measurement accuracy over the extended temperature range of 223-1423 K, exhibiting a competitive temperature coefficient of resistivity of 0.223%/K at 1423 K. This work not only provides valuable insights into the design of high-temperature thermosensitive sensors but also establishes an effective paradigm for entropy engineering involving vacancies.

Alternating Cellular Functions by Optogenetic Control of Organelles.

Organelles play essential roles in cellular homeostasis and various cellular functions in eukaryotic cells. The current experimental strategy to modulate organelle functions is limited due to the dynamic nature and subcellular distribution of organelles in live cells. Optogenetics utilizes photoactivatable proteins to enable dynamic control of molecular activities through visible light. This modality has been rapidly expanded for the dynamic regulation of organelle functions. This chapter describes a method by optical modulation of the mitochondria-lysosome contacts (MLCs). Detailed procedures of transfection, optogenetic MLCs, mitochondrial morphology, and functional analysis are described. Optogenetic control of organelles in live cells offers an innovative paradigm for cell engineering and synthetic biology.

A comparative analysis of fault detection and process diagnosis methods based on a signal processing paradigm

An important paradigm in industrial engineering for fault detection and diagnosis purposes is signal processing. The various methods consider methods in the time, frequency, or time–frequency domain for signal processing as state and output signals from the considered process. The objective of this work is to perform a comparative analysis of the most used methods based on a signal processing paradigm and in the context of fault detection and process diagnosis. The electromechanical equipment that generates mechanical vibrations—as an effect of bearing faults—is considered and analyzed. The recorded data are explored with smaller and sliding frames, adapted to the processing criteria used. Seven methods are considered for evaluation: two in the time domain, two in the frequency domain and three in the time–frequency domain. The main problem is to extract and select the right features to use in the classification stage. The methods of the time domain are based on statistical moments and signal modeling. The methods in the frequency domain use either the discrete components of power spectra or the features of the frequency domain. In the time–frequency domain, the coefficients of the time–frequency transforms define digital images, which are further processed. For testing, the methods are evaluated with real recorded data from bearings with several types and sizes of faults, i.e., incipient, medium, advanced, and large. Finally, the considered methods are compared from the point of view of five criteria, namely, the recognition rate, window length, response time, computational resources, and complexity of the algorithms. A global quality criterion is built and used to assess the quality of the methods. The results of the computer-based experiments show acceptable performance for all methods for the test case of bearings but the potential to detect more complex faults and change detection in the behavior of the machines, in general. Time–frequency methods offer an optimum.

Integrating axiomatic design and design structure matrix into model‐based systems engineering: A case study for emergency response space mission design

AbstractWith the increasing complexity of space missions due to technological advancements, model‐based systems engineering (MBSE) has become a new paradigm for systems engineering (SE), providing a more formal and accurate system engineering process while reducing costs. However, MBSE does not offer a comprehensive approach for effective system function design. This study introduces a conceptual design approach that integrates axiomatic design (AD) and design structure matrix (DSM) into the MBSE paradigm and applies it to functional and logical design. The research focuses on utilizing the design knowledge encapsulated by system models to facilitate the development of new systems. System models have the potential to bridge different design domains in AD, reveal implicit associations, and offer functional and structural analysis capabilities. To validate our approach, we conducted a case study on an Emergency Response Remote Sensing mission system. This case study demonstrates that our proposed methodology lends formality to traditional AD by deriving AD matrices and DSMs from a system model, thereby enhancing the system's structure while curtailing elemental alterations during iterative design processes. The novelty of this research lies in realizing the integration of traditional design theory with the model paradigm of SE, leading to an effective design solution during the functional and logical design stages.

Parallel development of social behavior in biological and artificial fish

Our algorithmic understanding of vision has been revolutionized by a reverse engineering paradigm that involves building artificial systems that perform the same tasks as biological systems. Here, we extend this paradigm to social behavior. We embodied artificial neural networks in artificial fish and raised the artificial fish in virtual fish tanks that mimicked the rearing conditions of biological fish. When artificial fish had deep reinforcement learning and curiosity-derived rewards, they spontaneously developed fish-like social behaviors, including collective behavior and social preferences (favoring in-group over out-group members). The artificial fish also developed social behavior in naturalistic ocean worlds, showing that these embodied models generalize to real-world learning contexts. Thus, animal-like social behaviors can develop from generic learning algorithms (reinforcement learning and intrinsic motivation). Our study provides a foundation for reverse-engineering the development of social behavior using image-computable models from artificial intelligence, bridging the divide between high-dimensional sensory inputs and collective action.

Heterogeneous knowledge graph-driven subassembly identification with ensemble deep learning in Industry 4.0

In the context of Industry 4.0, model-based definition (MBD) has been an effective approach to creating 3D models contained all heterogeneous information needed to define a product, which proposes new challenges for the traditional subassembly identification method that only considers the geometric information of a product in assembly sequence planning. To bridge the gap, we propose a novel heterogeneous knowledge graph-driven subassembly identification method to enhance assembly sequence planning in the model-based systems engineering (MBSE) paradigm. Specifically, a heterogeneous knowledge graph is first constructed based on the shape information and engineering details of an MBD model. Next, an ensemble deep learning approach that combines graph neural networks with the community detection algorithm is proposed to effectively detect the subassembly from the MBD model. Finally, the feasibility and effectiveness of the proposed method are demonstrated through an example of car suspension subassembly identification, providing insight into the industrial implementation.

The Engineering Paradigm and Its Historical Evolution and Construction

The Engineering Paradigm and Its Historical Evolution and Construction

The New Paradigm of Ligand Substitution-Driven Enhancement of Anisotropy from SO4 Units in Short-Wavelength Region.

For non-π-conjugated [SO4] units, it is challenging to generate sufficient birefringence, owing to the high symmetry of the regular tetrahedron. Unlike the traditional trial-and-error approach, we propose a new paradigm for birefringence engineering to tune the optical properties based on [SO4] units. Through the strategy of ligand substitution, we can predict its effect on the band gap and anisotropy. Theoretical evaluations reveal generalized results that the anisotropic electron distribution of new functional groups induced by the suitable ligand substitution contributes to the band gap and birefringence. To further validate the correctness of the paradigm, we experimentally synthesized and characterized nine novel compounds with selected functional modules. By the new paradigm of ligand substitution, they can reach up to 4-6 times the birefringence of the corresponding sulfate and maintain the wide bandgap. Through rational design, (CN4H7)SO3NH2 exhibits about 35 times the birefringence of Li2SO4, which is a significant order of magnitude improvement and verifies the superiority of our proposed paradigm. This work provides a new paradigm for the modification to the non-π-conjugated group and will guide and accelerate the exploration of novel birefringent crystals in the short-wavelength region.

A single-cell 3D dynamic volume control system for chondrocytes.

In articular cartilage, zone-specific cellular morphology is a typical characteristic of cartilage tissue, which is related with chondrocyte function, inflammation and osteoarthritis (OA). Chondrocyte hypertrophic phenotype is a criticle physiological process which indicates a hallmark of chondrocyte terminal differentiation and bone formation. Thus, developing a in vitro cell culture system for dynamic regulation of single chondrocyte volume at a three-dimensional (3D) level is particularly necessary for understanding how physical cues of matrix microenvironment regulate chondrocyte fate and the degeneration of articular cartilage. Here, based on the soft lithography techniques, we have constructed well-defined single-cell 3D dynamic volume control system to recapitulate the physiological matrix microenvironment of single chondrocyte niche. The results of finite element analysis indicated that the stress and strain distribution in the cell culture region is homogeneous during the stretching process. Additionally, 3D dynamic volume expansion and compression of single cells in physiological or hyperphysiological can be realized in this cell culture system. Our device for single-cell 3D dynamic culture provides a microphysiological culture system for chondrocytes to explore the mechanisms of cartilage hypertrophy, as well as develops a new paradigm for functional cartilage tissue engineering and regenerative medicine.

The paradigms of chemical engineering

The paradigms of chemical engineering

Prompt Engineering Paradigms for Medical Applications: Scoping Review.

Prompt engineering, focusing on crafting effective prompts to large language models (LLMs), has garnered attention for its capabilities at harnessing the potential of LLMs. This is even more crucial in the medical domain due to its specialized terminology and language technicity. Clinical natural language processing applications must navigate complex language and ensure privacy compliance. Prompt engineering offers a novel approach by designing tailored prompts to guide models in exploiting clinically relevant information from complex medical texts. Despite its promise, the efficacy of prompt engineering in the medical domain remains to be fully explored. The aim of the study is to review research efforts and technical approaches in prompt engineering for medical applications as well as provide an overview of opportunities and challenges for clinical practice. Databases indexing the fields of medicine, computer science, and medical informatics were queried in order to identify relevant published papers. Since prompt engineering is an emerging field, preprint databases were also considered. Multiple data were extracted, such as the prompt paradigm, the involved LLMs, the languages of the study, the domain of the topic, the baselines, and several learning, design, and architecture strategies specific to prompt engineering. We include studies that apply prompt engineering-based methods to the medical domain, published between 2022 and 2024, and covering multiple prompt paradigms such as prompt learning (PL), prompt tuning (PT), and prompt design (PD). We included 114 recent prompt engineering studies. Among the 3 prompt paradigms, we have observed that PD is the most prevalent (78 papers). In 12 papers, PD, PL, and PT terms were used interchangeably. While ChatGPT is the most commonly used LLM, we have identified 7 studies using this LLM on a sensitive clinical data set. Chain-of-thought, present in 17 studies, emerges as the most frequent PD technique. While PL and PT papers typically provide a baseline for evaluating prompt-based approaches, 61% (48/78) of the PD studies do not report any nonprompt-related baseline. Finally, we individually examine each of the key prompt engineering-specific information reported across papers and find that many studies neglect to explicitly mention them, posing a challenge for advancing prompt engineering research. In addition to reporting on trends and the scientific landscape of prompt engineering, we provide reporting guidelines for future studies to help advance research in the medical field. We also disclose tables and figures summarizing medical prompt engineering papers available and hope that future contributions will leverage these existing works to better advance the field.

Genetic Engineering Bacillus thuringiensis Enable Melanin Biosynthesis for Anti-Tumor and Anti-Inflammation.

Collaboration between cancer treatment and inflammation management has emerged as an integral facet of comprehensive cancer care. Nevertheless, the development of interventions concurrently targeting both inflammation and cancer has encountered significant challenges stemming from various external factors. Herein, a bioactive agent synthesized by genetically engineering melanin-producing Bacillus thuringiensis (B. thuringiensis) bacteria, simultaneously achieves eco-friendly photothermal agent and efficient reactive oxygen/nitrogen species (RONS) scavenger benefits, perfectly tackling present toughies from inflammation to cancer therapies. The biologically derived melanin exhibits exceptional photothermal-conversion performance, facilitating potent photonic hyperthermia that effectively eradicates tumor cells and tissues, thereby impeding tumor growth. Additionally, the RONS-scavenging properties of melanin produced by B. thuringiensis bacteria contribute to inflammation reduction, augmenting the efficacy of photothermal tumor repression. This study presents a representative paradigm of genetic engineering in B. thuringiensis bacteria to produce functional agents tailored for diverse biomedical applications, encompassing inflammation and cancer therapy.

STORMM: Structure and topology replica molecular mechanics for chemical simulations.

The Structure and TOpology Replica Molecular Mechanics (STORMM) code is a next-generation molecular simulation engine and associated libraries optimized for performance on fast, vectorized central processor units and graphics processing units (GPUs) with independent memory and tens of thousands of threads. STORMM is built to run thousands of independent molecular mechanical calculations on a single GPU with novel implementations that tune numerical precision, mathematical operations, and scarce on-chip memory resources to optimize throughput. The libraries are built around accessible classes with detailed documentation, supporting fine-grained parallelism and algorithm development as well as copying or swapping groups of systems on and off of the GPU. A primary intention of the STORMM libraries is to provide developers of atomic simulation methods with access to a high-performance molecular mechanics engine with extensive facilities to prototype and develop bespoke tools aimed toward drug discovery applications. In its present state, STORMM delivers molecular dynamics simulations of small molecules and small proteins in implicit solvent with tens to hundreds of times the throughput of conventional codes. The engineering paradigm transforms two of the most memory bandwidth-intensive aspects of condensed-phase dynamics, particle-mesh mapping, and valence interactions, into compute-bound problems for several times the scalability of existing programs. Numerical methods for compressing and streamlining the information present in stored coordinates and lookup tables are also presented, delivering improved accuracy over methods implemented in other molecular dynamics engines. The open-source code is released under the MIT license.

Trustworthy and collaborative traceability management: Experts’ feedback on a blockchain‐enabled framework

AbstractBlockchain technology has attracted significant attention in both academia and industry. Recently, the application of blockchain has been advocated in software engineering. The global software engineering paradigm exacerbates trust issues, as distributed and cross‐organizational teams need to share software artifacts. In such a context, there is a need for a decentralized yet reliable traceability knowledge base to keep track of what/how/when/by whom software artifacts were created or changed. This study presents a blockchain‐enabled framework for trustworthy and collaborative traceability management and identifies benefits, challenges, and potential improvements based on the feedback of software engineering experts. A qualitative approach was followed in this study through semistructured interviews with software engineering (SE) experts. Transcripts were analyzed by applying the content analysis technique. The results indicated the emergence of five categories, further grouped into three main categories: experts' perceptions, blockchain‐based software process improvement, and experts' recommendations. In addition, the findings suggested four archetypes of organizations that may be interested in blockchain technology: distributed organizations, organizations with contract‐based projects, organizations in regulated domains, and regulators who may push the use of this technology. Further efforts should be devoted to the integration of the proposal with tools used throughout the software development lifecycle and leveraging the potential of smart contracts in validating the implementation of requirements automatically.

A reference architecture for quantum computing as a service

Quantum computers (QCs) aim to disrupt the status-quo of computing – replacing traditional systems and platforms that are driven by digital circuits and modular software – with hardware and software that operate on the principle of quantum mechanics. QCs that rely on quantum mechanics can exploit quantum circuits (i.e., quantum bits for manipulating quantum gates) to achieve ‘quantum computational supremacy’ over traditional, i.e., digital computing systems. Currently, the issues that impede mass-scale adoption of quantum systems are rooted in the fact that building, maintaining, and/or programming QCs is a complex and radically distinct engineering paradigm when compared to the challenges of classical computing and software engineering. Quantum service orientation is seen as a solution that synergises the research on service computing and quantum software engineering (QSE) to allow developers and users to build and utilise quantum software services based on pay-per-shot utility computing model. The pay-per-shot model represents a single execution of instruction on quantum processing unit and it allows vendors (e.g., Amazon Braket) to offer their QC platforms, simulators, and software services to end-users. This research contributes by (i) developing a reference architecture for enabling Quantum Computing as a Service (QCaaS), (ii) implementing microservices with the quantum-classic split pattern as an architectural use-case, and (iii) evaluating the architecture based on practitioners’ feedback. The proposed reference architecture follows a layered software pattern to support the three phases of service lifecycle namely development, deployment, and split of quantum software services. In the QSE context, the research focuses on unifying architectural methods and service-orientation patterns to promote reuse knowledge and best practices to tackle emerging and futuristic challenges of architecting QCaaS.

Integrating machine learning and electrochemistry: A hybrid SA-DE-RF approach for optimizing electrode composition in water treatment

The imperative to address the burgeoning challenge of industrial water pollution has catalyzed the pursuit of sophisticated treatment methodologies capable of neutralizing non-biodegradable contaminants. This investigation focuses on the optimization of Ti/SnO2-Sb-Ni anode composition, leveraging a synergistic hybrid machine learning strategy that integrates Simulated Annealing (SA), Differential Evolution (DE), and Random Forest (RF) algorithms. This triad of algorithms is meticulously applied to ascertain the optimal doping concentrations of antimony (Sb) and nickel (Ni), pivotal in maximizing the efficacy of electrochemical oxidation processes. Our findings elucidate that the meticulously optimized Ti/SnO2-Sb-Ni anode, with a specific doping ratio of 100:3.74:5.36, engenders a degradation efficiency of benzoic acid (BA) that approximates completeness within a constrained temporal framework of 60 minutes. The RF regression model, exemplifying a robust R² value of 0.9565 and an RMSE of 0.0576, surpasses its counterparts in prognostication accuracy, underscoring its preeminence in electrode composition optimization for the amelioration of electrochemical water treatment matrices. The convergence of SA, DE, and RF methodologies not only refines the predictive fidelity of the degradation kinetics but also extricates the optimization process from the conventional constraints, thereby enhancing the scalability and precision of the model. This research advances the frontiers of electrochemical oxidation and etches a pioneering niche for the amalgamation of machine learning within environmental engineering paradigms. The hybrid algorithmic scaffolding proffered herein is suggested as a robust and versatile framework, propelling forward the discourse on the remediation of polluted water bodies and offering a springboard for future investigative endeavors.

A survey analysis of quantum computing adoption and the paradigm of privacy engineering

AbstractThis study investigates the adoption of quantum computing (QC) technology using the diffusion of innovation (DOI) theory and provides an extensive literature review. We deployed structural equation modeling to analyze data from a survey conducted among 96 top managers in various industries from Canada, the US, and Europe, including IT‐based small and medium‐sized enterprises (SMEs) dealing with QC software development. Our survey analysis indicates that the complexity of QC systems and software is the main barrier to the future adoption of quantum computing. This research offers insights into how future quantum computers can impact the security and privacy of information, emphasizing the importance of privacy protection. In this context, the study contributes to the notion of privacy engineering in the complex context of QC. The study established important outlines and tools for shaping future QCs. Our study, backed by empirical evidence, underscores the significant impact of new technology on citizens', organizations', firms', and government‐private data. The results provide a clear message to policymakers, industry leaders, and developers: privacy engineering should be an integral part of technical development, and it's crucial to act before costs escalate. In this context, our study stands out as one of the few that use NLP and structural equation modeling to address privacy challenges in QC research through experimental research, offering practical solutions to real‐world problems.

Code Generator Development to Transform IFML (Interaction Flow Modelling Language) into a React-based User Interface

Model-Driven Software Engineering (MDSE) is a software development approach that uses the Model to be the main actor of the development. MDSE can be applied to User Interface (UI) Development so that a model for the UI can be built, and then a transformation can be made to turn it into a running application. In this research, we develop UI Generator to support UI Development with the MDSE approach. This UI Generator can also support UI Development in Software Product Line Engineering (SPLE) paradigm. The UI is modeled with Interaction Flow Modeling Language (IFML) diagram. Then The IFML diagram is transformed into React-Based UI by the UI Generator. The UI Generator is developed with Acceleo on Eclipse IDE to transform IFML into React Code with the transformation rules defined in this research. The UI generator is also enriched with display settings and static page management to address user customization needs. The experimental results show that the UI Generator can generate a functional website. Besides evaluating the working product, UI Generator is evaluated qualitatively well based on six quality criteria as an SPLE supporting tool.

Integrated platinum-fullerene nanocatalyst as efficient cathode kinetic promoter for advanced lithium−oxygen batteries

Efficient and robust platinum-carbon electrocatalysts are of great significance for long-term service of high-performance Li–O2 batteries. Herein we developed an ultra-low platinum loading platinum-fullerene electrocatalyst by ultraviolet-assisted method. The catalyst features crystalline platinum nanodots integrated into C60 nano-fullerene matrices (Pt–C60), which demonstrates excellent mass-activity, 28.8 times higher than that of commercial Pt/C catalysts, exhibiting an ultra-low cell overpotential of 0.43 V while maintaining an excellent cycle stability over 150 cycles. XPS and Raman spectra disclose the integrated nature of Pt–C bonding interaction, and computational modeling reveal the improved electrocatalytic activity and cathode reaction kinetics. Comprehensive investigations demonstrate that the synergistic contribution of component and structure in integrated Pt–C60 catalyst is responsible for high-efficiency and long-life of Li–O2 batteries. Notably, Pt–C covalent integrated design provides a paradigm of catalyst engineering for developing high-activity and low-cost Pt/C catalysts, and easy synthesis method allows them to be mass-produced and potentially-used in catalyst fields.