Engineering Methods Research Articles

Systematic Review of Kansei Engineering Method Developments in the Design Field

Kansei engineering is a critical method for designing products that meet functionality, usability, and pleasurability, essential elements for business success. Despite its significance, there is limited understanding of how this method has evolved in recent years. This study aims to analyze the development of Kansei Engineering research from 2018 to 2022 using the Scopus database. The research methodology involved stages of identification, screening, filtering, and inclusion, resulting in 41 articles for detailed analysis out of an initial 215. The results indicate that 85% of Kansei Engineering research focuses on tangible products, with 83% categorized as type 1 studies, and 56% not integrating other methods. Additionally, 88% of the studies use only Kansei words, and 41% visualize design results as 3D images, with 95% not considering unique aspects. is dominance of tangible product design and the lack of integration with other methods suggest a need for diversification in research approaches. Furthermore, the high reliance on Kansei words and 3D visualizations points to a potential area for innovation and expansion in research techniques. This review highlights a significant research gap in Kansei Engineering studies, emphasizing the need for more diversified approaches. By identifying these gaps, the study provides a clear direction for future research, recommending that Kansei Engineering should explore beyond the predominant trends and consider integrating with other methods and unique aspects. This can enhance the method's application in industrial engineering and lead to more comprehensive and innovative product designs. Future research should aim to fill these gaps, ensuring that Kansei Engineering continues to evolve and contribute effectively to the field of product design and development.

Enhancement of urea oxidation reaction in alkaline condition via heterointerface engineering

Water electrolysis involving low energy barrier anodic urea oxidation reaction (UOR) is a promising way for hydrogen production. In this study, we present a superior heterostructured UOR electrocatalyst of (NiFeCo)Sx/FeOOH/NiFeCo(OH)x supported on conductive Ni foam prepared using a simple, ultrafast, energy-saving single-step corrosion engineering method. Leveraging the high conductivity of the (NiFeCo)Sx, plentiful Fe3+ in the amorphous FeOOH, high catalytic activity of the NiFeCo(OH)x, and abundant heterointerfaces, the resulting (NiFeCo)Sx/FeOOH/NiFeCo(OH)x electrode exhibits remarkable electrocatalytic performance toward UOR under alkaline condition. The electrocatalyst shows an ultra-low potential of 1.36 V at 100 mA cm−2, a small Tafel slope of 24.8 mV dec–1, and excellent stability. Density functional theory calculation shows that the multiple heterointerfaces provide synergistic effect of shifting the d-band center to optimize the intermediate chemisorption energy, thus boosting the catalytic kinetics for catalyzing UOR.

Utilizing the LightGBM algorithm for operator user credit assessment research

Mobile Internet user credit assessment is an important way for communication operators to establish decisions and formulate measures, and it is also a guarantee for operators to obtain expected benefits. However, credit evaluation methods have long been monopolized by financial industries such as banks and credit. As supporters and providers of platform network technology and network resources, communication operators are also builders and maintainers of communication networks. Internet data improves the users credit evaluation strategy. This paper uses the massive data provided by communication operators to carry out research on the operators user credit evaluation model based on the fusion LightGBM algorithm. First, for the massive data related to user evaluation provided by operators, key features are extracted by data preprocessing and feature engineering methods, and a multi-dimensional feature set with statistical significance is constructed; then, linear regression, decision tree, LightGBM, etc. The machine learning algorithm builds multiple basic models to find the best basic model; finally, integrates Averaging, Voting, Blending, Stacking and other integrated algorithms to refine multiple fusion models, and finally establish the most suitable fusion model for operator user evaluation.

Electronic Structure Modification of MnO2 Nanosheet Arrays with Enhanced Water Oxidation Activity and Stability by Nitrogen Plasma.

The strategic design of catalysts for the oxygen evolution reaction (OER) is crucial in tackling the substantial energy demands associated with hydrogen production in electrolytic water splitting. Despite extensive research on birnessite (δ-MnO2) manganese oxides to enhance catalytic activity by modulating Mn3+ species, the ongoing challenge is to simultaneously stabilize Mn3+ while improving overall activity. Herein, oxygen (O) vacancies and nitrogen (N) doping have been simultaneously introduced into the MnO2 through a simple nitrogen plasma approach, resulting in efficient OER performance. The optimized N-MnO2v electrocatalyst exhibits outstanding OER activity in alkaline electrolyte, reducing the overpotential by nearly 160 mV compared to pure pristine MnO2 (from 476 to 312 mV) at 10 mA cm-2, and a small Tafel slope of 89 mV dec-1. Moreover, it demonstrates excellent durability over a 122 h stability test. The introduction of O vacancies and incorporation of N not only fine-tune the electronic structure of MnO2, increasing the Mn3+ content to enhance overall activity, but also play a crucial role in stabilizing Mn3+, thereby leading to exceptional stability over time. Subsequently, density functional theory calculations validate the optimized electronic structure of MnO2 achieved through the two engineering methods, effectively lowering the intermediate adsorption free energy barrier. Our synergistic approach, utilizing nitrogen plasma treatment, opens a pathway to concurrently enhance the activity and stability of OER electrocatalysts, applicable not only to Mn-based but also to other transition metal oxides.

Application and Optimization Strategy of Financial Engineering in Risk Management

Financial engineering, as a collection of mathematics, statistics, engineering, computer science and other multidisciplinary fields, provides strong technical support for risk management. Through the technology and methods of financial engineering, risks can be quantitatively analyzed, predicted and controlled, so as to help enterprises and financial institutions better cope with risks and ensure their stable operation. Through the study of risk management practice, we can constantly improve the theoretical system of financial engineering and make it more realistic.

Микробная биофортификация злаковых культур: перспективы и текущее развитие

Hidden hunger is a significant social issue in numerous countries worldwide, causing the development of nutrition-related diseases among populations annually. Biofortification offers a sustainable solution as it combines methods of cross-breeding, genetic engineering, agriculture, and microbiology. The authors reviewed international studies in the field of microbial biofortification in order to assess the microbial potential to enh ance the essential element content in grain crops. The review featured relevant scientific articles published by foreign experts in Scopus, ScienceDirect, and Google Scholar in 1984–2024. The keywords included biofortification, wheat, rice, oats, growth stimulation, antagonism, and phytopathogen. The sources were processed in Zotero and VOSviewer. Nitrogen fixation and nutrient solubilization are the main mechanisms of microbial biofortification. Solubilization occurs as a synthesis of organic and inorganic acids, protons, siderophores, extracellular enzymes, and other secondary metabolites. Microorganisms can improve the expression of plant genes in terms of absorption and nutrient accumulation. They also affect root systems, facilitating the extraction of nutrients from the soil. The authors summarized laboratory and field studies on microbial biofortification of rice, wheat, and barley with iron, selenium, zinc, copper, manganese, nitrogen, phosphorus, and potassium. Biofortified growth-promoting microorganisms are a sustainable, reliable, and cost-effective approach to food security and hidden hunger issues. The review offers relevant information that can be used to develop new microbial preparations for the domestic agriculture.

ACP-PDAFF: Pretrained model and dual-channel attentional feature fusion for anticancer peptides prediction

Anticancer peptides(ACPs) have attracted significant interest as a novel method of treating cancer due to their ability to selectively kill cancer cells without damaging normal cells. Many artificial intelligence-based methods have demonstrated impressive performance in predicting ACPs. Nevertheless, the limitations of existing methods in feature engineering include handcrafted features driven by prior knowledge, insufficient feature extraction, and inefficient feature fusion. In this study, we propose a model based on a pretrained model, and dual-channel attentional feature fusion(DAFF), called ACP-PDAFF. Firstly, to reduce the heavy dependence on expert knowledge-based handcrafted features, binary profile features (BPF) and physicochemical properties features(PCPF) are used as inputs to the transformer model. Secondly, aimed at learning more diverse feature informations of ACPs, a pretrained model ProtBert is utilized. Thirdly, for better fusion of different feature channels, DAFF is employed. Finally, to evaluate the performance of the model, we compare it with other methods on five benchmark datasets, including ACP-Mixed-80 dataset, Main and Alternate datasets of AntiCP 2.0, LEE and Independet dataset, and ACPred-Fuse dataset. And the accuracies obtained by ACP-PDAFF are 0.86, 0.80, 0.94, 0.97 and 0.95 on five datasets, respectively, higher than existing methods by 1% to 12%. Therefore, by learning rich feature informations and effectively fusing different feature channels, ACD-PDAFF achieves outstanding performance. Our code and the datasets are available at https://github.com/wongsing/ACP-PDAFF.

Comparative study of PI-controller and neurocontroller performances in optimal by settling time control problems

When developing control systems, an important issue arises of choosing an operator, which forms the control function. The standard approach is to use a PI- or PID-controller, a more advanced approach involves ANNs training for this purpose. A comparative analysis of the PI- and neurocontroller performances makes it possible to establish the disadvantages and advantages of each of the compared controllers, which is an important scientific and applied problem. The purpose of the work was to conduct a comparative analysis of the performance of the PI-controller and the neurocontroller based on a set of evaluation indicators for plants of the second and third orders. Such a comparison was carried out by using an approach to the synthesis of both controllers, which involved the minimization of a complex objective function. The latter is obtained as a result of reducing the problem of optimal control with constraints to the problem of unconstrained optimization. The analysis showed that according to the settling time indicator (optimization criterion), the neurocontroller has an advantage of 6.1...96.2% for the modelled plants. At the same time, according to other indicators of the control quality, the PI-controller has an advantage. In addition, the synthesis of a neurocontroller in terms of finding the minimum of the objective function is a more difficult problem. For its solution, a bigger number of iterations of the VCT-PSO optimization algorithm is required. It is rationally to set more than 1000 iterations and swarm population in the range 30…50 particles. A comparative analysis by the settling time of the neurocontroller and PI-controller, which is tuned according to engineering methods, showed significant reserves for improving this indicator. Thus, if the requirements for settling time minimization are quite strict, then it is advisable to use a neurocontroller. The obtained results will make it possible to develop recommendations for the rational choice of the control operator when solving practical problems of the control systems synthesis

Survival prediction landscape: an in-depth systematic literature review on activities, methods, tools, diseases, and databases.

Survival prediction integrates patient-specific molecular information and clinical signatures to forecast the anticipated time of an event, such as recurrence, death, or disease progression. Survival prediction proves valuable in guiding treatment decisions, optimizing resource allocation, and interventions of precision medicine. The wide range of diseases, the existence of various variants within the same disease, and the reliance on available data necessitate disease-specific computational survival predictors. The widespread adoption of artificial intelligence (AI) methods in crafting survival predictors has undoubtedly revolutionized this field. However, the ever-increasing demand for more sophisticated and effective prediction models necessitates the continued creation of innovative advancements. To catalyze these advancements, it is crucial to bring existing survival predictors knowledge and insights into a centralized platform. The paper in hand thoroughly examines 23 existing review studies and provides a concise overview of their scope and limitations. Focusing on a comprehensive set of 90 most recent survival predictors across 44 diverse diseases, it delves into insights of diverse types of methods that are used in the development of disease-specific predictors. This exhaustive analysis encompasses the utilized data modalities along with a detailed analysis of subsets of clinical features, feature engineering methods, and the specific statistical, machine or deep learning approaches that have been employed. It also provides insights about survival prediction data sources, open-source predictors, and survival prediction frameworks.

Case Studies – an Indispensable Source of Experience and Knowledge

Owing to the peculiarities of the design environment that structural engineers have to face – an environment where prototypes are designed and erected without intensive and purposeful development and testing – case studies of failures and/or near misses are an indispensable source of experience and knowledge and form the basis for enhancing and updating the set of technical rules and regulations. By discussing actual and historic case studies, the development of the tools and methods of forensic engineering as well as of the findings from these for the design, erection, supervision, inspection and maintenance of civil engineering structures will be addressed. The interface problems, when different parties are involved, are highlighted, and the necessity of establishing a good and reliable co-ordination mechanism is carved out. Because of the increasing fragmentation of the design process, in combination with the use of complex software tools and inevitable pressure of time, often a comprehensive design review process is the last line of defence to avoid incidents due to minor lapses or serious misjudgements.

Optimized Phase and Crystallinity of Cr2(NCN)3 Dominating Electrochemical Lithium Storage Performance.

Cr2(NCN)3 is a potentially high-capacity and fast-charge Li-ion anode owing to its abundant and broad tunnels. However, high intrinsic chemical instability severely restricts its capacity output and electrochemical reversibility. Herein we report an effective crystalline engineering method for optimizing its phase and crystallinity. Systematic studies reveal the relevancy between electrochemical performance and crystalline structure; an optimal Cr2(NCN)3 with high phase purity and uniform crystallinity exhibits a high reversible capacity of 590 mAh g-1 and a stable cycling performance of 478 mAh g-1 after 500 cycles. In-operando heating XRD reveals its high thermodynamical stability over 600 °C, and in-operando electrochemical XRD proves its electrochemical Li storage mechanism, consisting of the primary Li-ion intercalation and subsequent conversion reactions. This study introduces a facile and low-cost method for fabricating high-purity Cr2(NCN)3, and it also confirms that the Li storage of Cr2(NCN)3 can be further improved by tuning its phase and crystallinity.

Time-domain dimension-reduction representation for stochastic ground motion utilizing filtered white noise

A method is proposed for characterizing and simulating both stationary and fully non-stationary stochastic ground motions. This method is based on discrete filtered white noise models, including single and double filtered, with the latter is introduced to suppress low-frequency components. Specifically, the proposed method expresses seismic ground motion as a linear combination of products involving orthogonal random variables and deterministic functions. Further, by defining high-dimensional orthogonal random variables as orthogonal functions of extremely low dimensional elementary random variables, efficient dimension-reduction (DR) of primitive ground motion process can be achieved. To illustrate this concept, three distinct categories of random orthogonal functions involving only one or two elementary random variables are examined, employing filtered white noise models to simulate ground motion acceleration processes, thereby demonstrating the accuracy and efficiency of the proposed method. Simultaneously, recommendations for employing the proposed method in simulations are provided based on an analysis of the impacts of various parameters on random ground motion processes. Case studies demonstrate the accuracy and robustness of the proposed method compared to Monte Carlo (MC) methods. Furthermore, case studies on fully non-stationary ground motion highlight the practical applicability of the proposed method in engineering.

Darth Vader's Secret Weapon: Implementing Mission Engineering with UAF

AbstractWhen planning and conducting a Mission Engineering (ME) study, it is important to have a complete, correct, and coherent model of the mission architecture. The Unified Architecture Framework (UAF) has been found to be effective for this purpose. The OUSD (R&E) Mission Integration office is exploring how to use UAF for their ME architectures. This paper will explore some of the required modeling features and constructs that will enable this to occur. The paper “Implementing Mission Engineering with UAF” was presented at a previous conference and this paper will expand on that presentation and will discuss additional work that has been accomplished since then. This paper will also explore the proposed extensions for UAF to better support ME. We created a prototype model using the Battle of Hoth from Star Wars as a proof of concept for these modeling extensions and used the process and ME concepts defined in the Mission Engineering Guide (MEG). Since then there have been several concepts that were explored such as compatibility with the Model‐Based Acquisition (MBAcq) approach, recent initiatives from the Office of the Assistant Secretary of Defense for Mission Capabilities, Enterprise Systems Engineering (ESE) process and methods, detailed resource engagement, use of different modeling languages (e.g., Systems Modeling Language (SysML), SysML v2 and UAF v2), Effects and Outcomes, variety of measures, additional attributes/stereotypes such as differentiation between enemy/friendly/neutral ‐ Blue Force, Red Force, etc., provenance/confidence of enemy resources, and so forth. This paper will summarize the research and modeling done to date and explore these additional concepts as well as new ideas introduced in the MEG v2.

Auricular Chondrocytes as a Cell Source for Scaffold-Free Elastic Cartilage Tissue Engineering.

Current tissue engineering (TE) methods utilize chondrocytes primarily from costal or articular sources. Despite the robust mechanical properties of neocartilages sourced from these cells, the lack of elasticity and invasiveness of cell collection from these sources negatively impact clinical translation. These limitations invited the exploration of naturally elastic auricular cartilage as an alternative cell source. This study aimed to determine if auricular chondrocytes (AuCs) can be used for TE scaffold-free neocartilage constructs and assess their biomechanical properties. Neocartilages were successfully generated from a small quantity of primary neonatal AuCs of three minipig donors (n = 3). Neocartilage constructs had instantaneous moduli of 200.5 kPa ± 43.34 and 471.9 ± 92.8 kPa at 10% and 20% strain, respectively. TE constructs' relaxation moduli (Er) were 36.99 ± 6.47 kPa Er and 110.3 ± 16.99 kPa at 10% and 20% strain, respectively. The Young's modulus was 2.0 MPa ± 0.63, and the ultimate tensile strength was 0.619 ± 0.177 MPa. AuC-derived neocartilages contained 0.144 ± 0.011 µg collagen, 0.185 µg ± 0.002 glycosaminoglycans per µg dry weight, and 1.7e-3 µg elastin per µg dry weight. In conclusion, this study shows that AuCs can be used as a reliable and easily accessible cell source for TE of biomimetic and mechanically robust elastic neocartilage implants.

Side-chain engineering for regulating structure and properties of a novel visible-light-driven perylene diimide-based supramolecular photocatalyst

Systematic and in-depth explorations of the effects of side-chain modulation on the molecular assembly, optoelectronic properties, and photocatalytic properties of supramolecular systems, as well as the kinetics of charge separation and migration in these systems, are rare. In this study, a novel supramolecular photocatalyst with an alkoxy side chain (S-EPDI) was successfully developed through subtle design of the short and linear alkoxyl side chains, affording a phenol degradation efficiency approximately four times that of the counterpart with an alkyl side chain (S-APDI). Notably, combined density functional theory (DFT) calculations, absorption spectroscopy, and other characterizations revealed that the perylene diimide (PDI) molecular units, through π-π stacking, formed a unique rotationally offset stacked supramolecular structure, exhibiting a significant dipole moment. This gave rise to the formation of a larger inherent electric field within S-EPDI compared to S-APDI. Moreover, the study quantitatively demonstrated that a stronger inherent electric field and lower rate of surface charge recombination facilitate efficient separation of the photogenerated carriers. Therefore, the side-chain molecular engineering method employed in this study offers an effective approach for modulating the kinetics of charge migration.

Hematoxylin and Eosin Architecture Uncovers Clinically Divergent Niches in Pancreatic Cancer.

Pancreatic ductal adenocarcinoma (PDAC) represents one of the only cancers with an increasing incidence rate and is often associated with intra- and peri-tumoral scarring, referred to as desmoplasia. This scarring is highly heterogeneous in extracellular matrix (ECM) architecture and plays complex roles in both tumor biology and clinical outcomes that are not yet fully understood. Using hematoxylin and eosin (H&E), a routine histological stain utilized in existing clinical workflows, we quantified ECM architecture in 85 patient samples to assess relationships between desmoplastic architecture and clinical outcomes such as survival time and disease recurrence. By utilizing unsupervised machine learning to summarize a latent space across 147 local (e.g., fiber length, solidity) and global (e.g., fiber branching, porosity) H&E-based features, we identified a continuum of histological architectures that were associated with differences in both survival and recurrence. Furthermore, we mapped H&E architectures to a CO-Detection by indEXing (CODEX) reference atlas, revealing localized cell- and protein-based niches associated with outcome-positive versus outcome-negative scarring in the tumor microenvironment. Overall, our study utilizes standard H&E staining to uncover clinically relevant associations between desmoplastic organization and PDAC outcomes, offering a translatable pipeline to support prognostic decision-making and a blueprint of spatial-biological factors for modeling by tissue engineering methods.

Strain engineering of electronic structure and thermoelectric properties of quasi-hexagonal fullerene monolayer

As a newly synthesized two-dimensional (2D) carbon material, monolayer quasi-hexagonal phase fullerene (qHP C60) has an excellent electronic structure and low thermal conductivity. qHP C60 attracted significant attention from scientists because it has potential applications in thermoelectric materials. Thermoelectric properties of 2D materials significantly depend on the transport of carriers (such as electrons and phonons), and strain engineering is an essential method for modulating the transport of electrons and phonons in 2D materials. However, the strain engineering method for the modulation of the thermoelectric properties of monolayer qHP C60 has not been reported yet. In the present paper, the first-principles combined with the non-equilibrium Green's function method are used to investigate the ballistic transport properties of electrons and phonons in monolayer qHP C60. The effects of temperature, chemical potential, and biaxial tensile strain on the thermoelectric transport parameters (including conductivity, Seebeck coefficient, power factor, and thermal conductivity) as well as the figure of merit (ZT) of monolayer qHP C60 are presented, compared, discussed, and analyzed. We found that monolayer qHP C60 exhibits anisotropic characteristics in electron and phonon transport properties, showcasing outstanding thermoelectric properties. The distinctive quasi-hexagonal phase fullerene network structure offers a novel platform for exploring innovative 2D thermoelectric materials in research. This study provides crucial theoretical insights to guide the designing and implementation of 2D thermoelectric materials based on fullerenes.

A Proposal for Model‐Based Systems Engineering Method for Creating Secure Cyber‐Physical Systems

AbstractRising levels of risk as cyber‐attackers look to exploit system vulnerabilities threatens the Air Traffic Control industry. Attacks on Air Navigation Service Providers' communications systems may lead to airspace closure and even cause safety issues. This paper presents a novel Model‐Based Systems Engineering method that enables systems engineers, in collaboration with system security and software engineers, to perform threat‐modeling analysis of cyber‐physical systems early in the system development process and incorporate mitigation strategies into the system design. The proposed model‐based method covers few security concepts, including misuse cases, system assets, threats, risks, vulnerabilities, and security control identification. The study found that the proposed method is suitable for conducting security analysis for complex cyber‐physical systems early in the system development process.

The application of CRISPR-Cas system in Staphylococcus aureus infection

Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-CRISPR associated nuclease (Cas) system has been proven to play an irreplaceable role in bacteria immunity activity against exogenous genetic elements. In recent years, this system has emerged as a valid gene engineering method and could be used to detect and treat various microorganisms such as bacteria and viruses, etc. Staphylococcus aureus, as a Gram-positive, opportunistic human and animal pathogen, can cause a variety of diseases greatly threatening human health. Here, we mainly reviewed the applications of the CRISPR-Cas system in Staphylococcus aureus infections in detail. Furthermore, the prospects and drawbacks of the CRISPR-Cas system were also discussed.

Model‐Based Systems Engineering (MBSE) Methodology for Integrating Autonomy into a System of Systems Using the Unified Architecture Framework

AbstractStakeholders of Systems of Systems (SoSs) are seeking new capabilities provided by autonomous systems empowered by Artificial Intelligence (AI). This requires the integration of these autonomous systems into SoSs, resulting in Systems of Autonomous Systems (SoASs). SoAS architecting is different from SoS. An autonomous system can have various Levels of Autonomy (LoA) depending on its AI algorithm. Each LoA has a different impact on the organizational and technical aspects of SoAS architecting. The organizational aspect covers concerns such as managerial and operational policies, whereas the technical aspect highlights issues such as integration compatibility between systems. Failure to address the LoA impact on these aspects in the architecting phase results in an ineffective integration. In this paper, we propose a methodology that follows the SoS hierarchical lexicon, builds upon the steps of the Object‐Oriented Systems Engineering Method (OOSEM), and leverages the Unified Architecture Framework (UAF) for modeling autonomy integration. The proposed methodology adds detailed sub‐steps to OOSEM in order to make it suitable for SoS evolution into SoAS. We also introduce the required UAF views for modeling each aspect of the SoAS architecture. This methodology lays the foundation for the trade study analysis that helps stakeholders decide on suitable LoAs for SoAS. We also present an illustrative example to demonstrate the implementation and effectiveness of the proposed methodology.