Engineering Paradigm Research Articles

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.

Advanced discovery mechanisms in model repositories

SummaryAs model‐driven engineering gains traction and poses as the new paradigm for software engineering, it raises a need for efficient approaches and tools to manage, discover, and retrieve relevant modeling artifacts. Hence, industry and academia are conceiving effective ways to store, search, and retrieve heterogeneous model artifacts that employ advanced discovery mechanisms. This paper presents MDEForge‐Search, a novel approach to discovering heterogeneous model artifacts over MDEForge, a distributed cloud‐based model repository. We designed advanced discovery mechanisms that retrieve heterogeneous artifacts within their context (megamodel) and reuse them across model management services. In addition, a domain‐specific approach has been proposed to formulate queries in terms of keywords, search tags, conditional operators, quality model assessment services and a transformation chain discoverer. Finally, the applicability of our approach was assessed in a recommender system modeling framework, which, thanks to the operated integration, can rely on the availability of more than 5000 model artifacts currently persisted in our cloud‐based model repository.

Bridging the gap between metallic MoO2 and ZnIn2S4 for enhanced photocatalytic H2 production

Noble metal-free cocatalysts have shown unprecedented potential in enhancing photocatalytic H2 evolution. However, it remains challenging to accelerate the carrier transfer rate by circuiting the substantial Schottky barrier between the metallic cocatalyst and the semiconductor. Herein, an in-situ carbonization strategy is used to accurately anchor MoO2 nanoparticles onto the surface of conductive carbon rods (MoO2/C), which serves as an efficient and stable metallic cocatalyst for ZnIn2S4 towards photocatalytic H2 evolution. The in-site derived carbon skeleton not only prevents the agglomeration of MoO2 nanoparticles thus enriching the active sites for H2 evolution, but also acts as conductor of photogenerated electrons to accelerate electron transfer. In addition, the formed Mo-S bonds at the interface provide additional electron transport channels for hierarchical core–shell MoO2/C@ZnIn2S4 (MO/C@ZIS) heterojunction. It is worth noting that the H2 evolution activity of the optimized MO/C@ZIS sample (2357 μmol·h−1·g−1) is 8.7 times higher than pure ZnIn2S4 (271 μmol·h−1·g−1), and also surpasses the activity of optimized Pt-modified ZnIn2S4 (2123 μmol·h−1·g−1). This work casts a new paradigm for the rational engineering of heterointerface between metallic cocatalysts and semiconductors towards fast electron transport and enhanced photocatalytic performance.

Materials Design: The Next Paradigm in Chemistry and Engineering

Materials Design: The Next Paradigm in Chemistry and Engineering

Paths to BIM-based digital transformation: a bibliometric and systematic review of critical factors

PurposeThe purpose of this paper is twofold: first, to present a rigorous bibliometric analysis and a systematic literature review of the critical success factors (CSFs) for Building information modelling (BIM)-based digital transformation; second, to identify the relationship between the dimensions in favour of BIM implementation.Design/methodology/approachThis study adopts a two-step approach to combine bibliometric and systematic literature review to explore the research topic of BIM and CSFs. Bibliometric tools such as Biblioshiny in R language and Ucinet software were applied to this study.FindingsBesides identifying the two most influential authors (e.g. Bryde and Antwi-Afari), the key journal for disseminating articles, and the most influential countries in this discourse (e.g. Hong Kong and Australia), the study also identifies four pivotal research themes derived from the co-occurrence analysis of keywords: the fusion of sustainability and technology with BIM; practical application and its integration within construction management; innovation and engineering paradigms; and the advent of emerging technologies (e.g. Blockchain) within developing nations. Additionally, the paper introduces a comprehensive framework for selecting CSFs pertinent to BIM-centred digital transformation as viewed through the lens of dynamic capabilities.Originality/valueThis paper establishes a link between dynamic capabilities theory, CSFs, and BIM dimensions, presenting a multifaceted framework guiding future paths and offering practical insights for managerial and political decision-makers engaged in digital transformation endeavours. The study positions dynamic capabilities as pivotal, aligning digital technologies with continuous business performance, and advocates for a strategic focus on digital transformation.

Quantum Control of Radical-Pair Dynamics beyond Time-Local Optimization

We realize arbitrary-wave-form-based control of spin-selective recombination reactions of radical pairs in the low-magnetic-field regime. To this end, we extend the gradient-ascent pulse engineering (GRAPE) paradigm to allow for optimizing reaction yields. This overcomes drawbacks of previously suggested time-local optimization approaches for the reaction control of radical pairs, which were limited to high biasing fields. We demonstrate how efficient time-global optimization of the recombination yields can be realized by gradient-based methods augmented by time blocking, sparse sampling of the yield, and evaluation of the central single time-step propagators and their Fréchet derivatives using iterated Trotter-Suzuki splittings. Results are shown for both a toy model, previously used to demonstrate coherent control of radical-pair reactions in the simpler high-field scenario and, furthermore, for a realistic exciplex-forming donor-acceptor system comprising 16 nuclear spins. This raises prospects for the spin control of actual radical-pair systems in ambient magnetic fields, by suppressing or boosting radical reaction yields using purpose-specific radio-frequency wave forms, paving the way for reaction-yield-dependent quantum magnetometry and potentially applications of quantum control to biochemical radical-pair reactions. We demonstrate the latter aspect for two radical pairs implicated in quantum biology. Published by the American Physical Society 2024

Multiscale Structural Engineering Boosts Piezoelectricity in Na0.5Bi2.5Nb2O9-Based High-Temperature Piezoceramics.

High-temperature piezoelectric materials, which enable the accurate and reliable sensing of physical parameters to guarantee the functional operation of various systems under harsh conditions, are highly demanded. To this end, both large piezoelectricity and high Curie temperature are pivotal figures of merit (FOMs) for high-temperature piezoceramics. Unfortunately, despite intensive pursuits, it remains a formidable challenge to unravel the inverse correlation between these FOMs. Herein, a conceptual material paradigm of multiscale structural engineering was proposed to address this dilemma. The synergistic effects of phase structure reminiscent of a polymorphic phase boundary and refined domain morphology simultaneously contribute to a large piezoelectric coefficient d33 of 30.3 pC/N and a high Curie temperature TC of 740 °C in (LiCeNd) codoped Na0.5Bi2.5Nb2O9 (NBN-LCN) ceramics. More encouragingly, the system has exceptional thermal stability and is nonsusceptible to mechanical loading. This study not only demonstrates that the high-performance and robust NBN-LCN high-temperature piezoceramics hold great potential for implements under harsh conditions but also opens an avenue for integrating antagonistic properties for the enhancement of the collective performance in functional materials.

376 Pathophysiology of voluntary motor commands in patients with multiple sclerosis identified using reverse engineering of motor unit population discharge.

OBJECTIVES/GOALS: Our objective is to characterize excitatory, inhibitory, and neuromodulatory components of the voluntary motor command at the level of the spinal motoneuron in people with multiple sclerosis (MS). This information will provide insight into neural mechanisms of motor dysfunction and their heterogeneity among patients with MS. METHODS/STUDY POPULATION: Due to advances in high-density surface EMG (HDsEMG) decomposition and the recent development of a paradigm for reverse engineering of motor unit population discharge, we can feasibly estimate aspects of excitatory, inhibitory, and neuromodulatory components of the voluntary motor command in humans on a person-specific basis. We tested 11 ambulatory patients with MS and mild-moderate disability. We recorded HDsEMG from tibialis anterior (TA) and soleus (SOL) during isometric plantarflexion and dorsiflexion, performed as slow triangle contractions. EMG was decomposed into motor unit spike trains using blind source separation. We calculated a number of motor unit variables, most notably delta-F, which estimates motoneuron excitability and the balance of neuromodulatory and inhibitory inputs. RESULTS/ANTICIPATED RESULTS: There were consistent differences in MS patients vs. controls. For TA, values were decreased for delta-F (3.9 vs. 5.9 pps), initial firing rate acceleration (5.8 vs. 7.1 pps), firing rate range (9.3 vs. 11.9 pps), and max firing rate (12.3 vs. 15.0 pps). SOL had more modest decreases in delta-F (3.0 vs. 3.8 pps) and firing rate range (4.8 vs. 5.6 pps). Self-sustained firing was longer for MS patients. Within a patient, abnormalities in motor unit variables were not consistent across muscles and legs. Interestingly, there were several abnormalities in the patients with a normal clinical motor exam, indicating that perhaps our measures are sensitive to subclinical changes in processing of voluntary motor commands. DISCUSSION/SIGNIFICANCE: Excitatory, inhibitory, and neuromodulatory components of the voluntary motor command must be appropriately balanced for skilled motor output. This study is the first to characterize how they are disrupted in MS, providing foundational information to inform the development of mechanistically-based rehabilitation interventions.

Quality Improvement of Variability Management Tools in Software Product Line Engineering

Software Product Line Engineering (SPLE) is an engineering paradigm that allows the creation of software product portfolio similar to the systems or products. It is important for an effective method deployment in the particular context of industrial scale Product Lines (PLs). The purpose of this research work is to determine and gain a detailed understanding of where and how the quality of variability management tools could be improved. The study was conducted experimentally and involved 10 feature-modelling tools based on their availability and support for feature models, to be evaluated using the benchmark in order to identify whether and to what extent these tools provide support for the identified quality attributes.

Modeling of Magnetic Films: A Scientific Perspective.

Magnetic thin-film modeling stands as a dynamic nexus of scientific inquiry and technological advancement, poised at the vanguard of materials science exploration. Leveraging a diverse suite of computational methodologies, including Monte Carlo simulations and molecular dynamics, researchers meticulously dissect the intricate interplay governing magnetism and thin-film growth across heterogeneous substrates. Recent strides, notably in multiscale modeling and machine learning paradigms, have engendered a paradigm shift in predictive capabilities, facilitating a nuanced understanding of thin-film dynamics spanning disparate spatiotemporal regimes. This interdisciplinary synergy, complemented by avantgarde experimental modalities such as in situ microscopy, promises a tapestry of transformative advancements in magnetic materials with far-reaching implications across multifaceted domains including magnetic data storage, spintronics, and magnetic sensing technologies. The confluence of computational modeling and experimental validation heralds a new era of scientific rigor, affording unparalleled insights into the real-time dynamics of magnetic films and bolstering the fidelity of predictive models. As researchers chart an ambitiously uncharted trajectory, the burgeoning realm of magnetic thin-film modeling burgeons with promise, poised to unlock novel paradigms in materials science and engineering. Through this intricate nexus of theoretical elucidation and empirical validation, magnetic thin-film modeling heralds a future replete with innovation, catalyzing a renaissance in technological possibilities across diverse industrial landscapes.