Custom Design Research Articles

Customized designs, preparations, and characterizations of high-performance multilayer VO2-based thermochromic smart coatings

VO2-based thermochromic coatings have gained much attention in temperature-adaptive smart windows, and constructing multilayer film structures is the main way to obtain high thermochromic performances. Based on the self-developed objective-orientated automatic optimization code, the present research realized customized designs of thermochromic multilayer coatings composed of VO2, TiO2, and SiO2 from the objectives of visible luminance (Tlum) and solar light modulation (ΔTsol). The TiO2/VO2/s, VO2/TiO2/s, TiO2/VO2/TiO2/s, and SiO2/TiO2/VO2/TiO2/s (s denotes substrate) multilayer film systems were then prepared by magnetron sputtering and vacuum controllable heating, with their structures, insulator-metallic transition, and thermochromic properties being carefully studied. The result showed that the best trade-off between high Tlum and high ΔTsol was achieved for these film assemblies, and solar light modulation was mainly limited at the near-IR wavelength region of the solar spectrum, with the difference between cool- and hot-state Tlum being greatly reduced. In the case of the four-layer SiO2/TiO2/VO2/TiO2/s, very high average Tlum (60%) and high ΔTsol (14%) were obtained. A 100 × 100 mm2 of the SiO2/TiO2/VO2/TiO2/s was also prepared, and it showed an obvious temperature adjustment, and the service life can last for 14 years under normal weather conditions. Not limited to the prepared multilayer film systems, the methodology used here is also suitable for the fast development of other multilayer film systems.

Product family modeling technology for customized cosmetic packaging design based on basic-element theory

BackgroundAs the market demands change, SMEs (small and medium-sized enterprises) have long faced many design issues, including high costs, lengthy cycles, and insufficient innovation. These issues are especially noticeable in the domain of cosmetic packaging design. ObjectiveTo explore innovative product family modeling methods and configuration design processes to improve the efficiency of enterprise cosmetic packaging design and develop the design for mass customization. MethodsTo accomplish this objective, the basic-element theory has been introduced and applied to the design and development system of the product family. ResultsBy examining the mapping relationships between the demand domain, functional domain, technology domain, and structure domain, four interrelated models have been developed, including the demand model, functional model, technology model, and structure model. Together, these models form the mechanism and methodology of product family modeling, specifically for cosmetic packaging design. Through an analysis of a case study on men’s cosmetic packaging design, the feasibility of the proposed product family modeling technology has been demonstrated in terms of customized cosmetic packaging design, and the design efficiency has been enhanced. ConclusionThe product family modeling technology employs a formalized element as a module configuration design language, permeating throughout the entire development cycle of cosmetic packaging design, thus facilitating a structured and modularized configuration design process for the product family system. The application of the basic-element principle in product family modeling technology contributes to the enrichment of the research field surrounding cosmetic packaging product family configuration design, while also providing valuable methods and references for enterprises aiming to elevate the efficiency of cosmetic packaging design for the mass customization product model.

Risk Factors Assessment of Smart Supply Chain in Intelligent Manufacturing Services Using DEMATEL Method With LINGUISTIC q-ROF Information

With the rapid development of technological informatization, competition among enterprises is gradually transitioning from being "production-centered" to being "customer-centric," making service-oriented enterprises increasingly important. In addition to this, as global manufacturing advances in the process of intelligent manufacturing (IM), there is growing attention on the integration of manufacturing and the service industry, which has garnered the interest of numerous experts and scholars in the field of intelligent manufacturing services (IMS). This article combines intelligent manufacturing enterprises, intelligent service nodes, and consumers. Based on the background of intelligent manufacturing services, it collected risk factors within the smart supply chain (SSC) that connect different service nodes. These factors were evaluated by experts using a proposed linguistic q-rung orthopair fuzzy weighted averaging (Lq-ROFWA) operator in combination with the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method for aggregation operations. Finally, we obtain the conclusions that the most influential factor affecting other risk factors is the inadequate identification of core customer needs; and the most important risk factor for smart supply chains oriented to intelligent manufacturing services is the leakage of customer information. After analyzing the relevant data, we will provide some theoretical and managerial implications for IM enterprises.

Hybrid Nanomaterials: A Brief Overview of Versatile Solutions for Sensor Technology in Healthcare and Environmental Applications.

The integration of nanomaterials into sensor technologies not only poses challenges but also opens up promising prospects for future research. These challenges include assessing the toxicity of nanomaterials, scalability issues, and the seamless integration of these materials into existing infrastructures. Future development opportunities lie in creating multifunctional nanocomposites and environmentally friendly nanomaterials. Crucial to this process is collaboration between universities, industry, and regulatory authorities to establish standardization in this evolving field. Our perspective favours using screen-printed sensors that employ nanocomposites with high electrochemical conductivity. This approach not only offers cost-effective production methods but also allows for customizable designs. Furthermore, incorporating hybrids based on carbon-based nanomaterials and functionalized Mxene significantly enhances sensor performance. These high electrochemical conductivity sensors are portable, rapid, and well-suited for on-site environmental monitoring, seamlessly aligning with Internet of Things (IoT) platforms for developing intelligent systems. Simultaneously, advances in electrochemical sensor technology are actively working to elevate sensitivity through integrating nanotechnology, miniaturization, and innovative electrode designs. This comprehensive approach aims to unlock the full potential of sensor technologies, catering to diverse applications ranging from healthcare to environmental monitoring. This review aims to summarise the latest trends in using hybrid nanomaterial-based sensors, explicitly focusing on their application in detecting environmental contaminants.

Mechanism-Driven Design of Multispecific Antibodies for Targeted Disease Treatment.

Antibody-based therapeutics constitute a rapidly growing class of pharmaceutical compounds. However, monoclonal antibodies, which specifically engage only one target, often lack the mechanistic intricacy to treat complex diseases. To expand the utility of antibody therapies, significant efforts have been invested in designing multispecific antibodies, which engage multiple targets using a single molecule. These efforts have culminated in remarkable translational progress, including nine US Food and Drug Administration-approved multispecific antibodies, with countless others in various stages of preclinical or clinical development. In this review, we discuss several categories of multispecific antibodies that have achieved clinical approval or shown promise in earlier stages of development. We focus on the molecular mechanisms used by multispecific antibodies and how these mechanisms inform their customized design and formulation. In particular, we discuss multispecific antibodies that target multiple disease markers, multiparatopic antibodies, and immune-interfacing antibodies. Overall, these innovative multispecific antibody designs are fueling exciting advances across the immunotherapeutic landscape. Expected final online publication date for the Annual Review of Chemical and Biomolecular Engineering , Volume 15 is June 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Retracted: A Relevant Customer Identification Algorithm Based on the Internet Financial Platform

Retracted: A Relevant Customer Identification Algorithm Based on the Internet Financial Platform

Green consumption by design: interaction experiences and customization intentions

PurposeThis study investigated the impacts of the interaction experiential customization (IEC) mode on consumers' information processing fluency and green customization intention (GCI) as well as the moderating effect of consumers' self-construal.Design/methodology/approachThis study conducted an online field experiment, questionnaire study and between-subjects laboratory experiment to test the hypotheses.FindingsIt was found that IEC had a significant positive effect on consumers' GCI. Moreover, consumer retrieval processing fluency played a partial mediating role in the relationship between IEC and GCI. In addition, consumers' self-construal moderated the “IEC? Three dimensions of processing fluency” relationships.Practical implicationsThe results emphasized the importance of IEC in influencing consumers' consumption intention in a green customization setting and have some practical implications, that is, companies have the opportunity to use appropriate digital choice architecture designs, which can enhance consumer processing fluency when promoting eco-friendly products in the customized consumption process, especially for independent consumers.Originality/valueThis study focused on the customization design on consumers' GCI and explained the mechanism of impact of IEC on improving consumers' processing fluency and GCI in a product customization setting based on the fluency theory. In addition, this study investigated the moderating effect of consumers' self-construal (independent vs interdependent) on their significant different information processing modes for low-carbon choices.

Cellulose nanocrystals-gelatin composite hydrocolloids: Application to controllable responsive deformation during 3D printing

Three-dimensional (3D) food printing is an effective technology for creating food designs with complex appearances and customized nutritional profiles. However, controlling the deformation of 3D printed foods during processing remains a challenge. Here, a food hydrocolloid with customizable designs during heating and dehydration was designed using cellulose nanocrystals (CNC) and gelatin (G). The resultant cellulose nanocrystals-gelatin (CNC-G) hydrocolloid exhibited favorable rheological characteristics for three-dimensional (3D) printing. 3D printing allows the controlled oriented alignment of the CNC particles within the hydrocolloid along the printing direction, endowing the CNC-G hydrocolloid with highly anisotropic structures. This anisotropic property can cause a strain mismatch during dehydrated food processing, leading to a specific deformation of the hydrocolloid. By adjusting the 3D printing parameters and intentionally designing of the 3D printed structures, the alignment degree of CNCs within the hydrocolloid can be controlled, and thus, its deformation can be programmed. The customizable deformation of CNC-G hydrocolloid during food processing was deliberately designed through printing, and its adaptability and flexibility are demonstrated through the "flapping" of a butterfly and the "blooming" of a four-leaf clover. The flexibility and customizability of CNC-G hydrocolloid in 3D printing provide a new strategy to achieve deformation and precise control of 3D printed foods during processing.

Corrosion and Wear Behavior of Additively Manufactured Metallic Parts in Biomedical Applications

Today, parts made by additive manufacturing (AM) methods have found many applications in the medical industry, the main reasons for which are the ability to custom design and manufacture complex structures, their short production cycle, their ease of utilization, and on-site fabrication, leading to the fabrication of next-generation intricate patient-specific biomedical implants. These parts should fulfill numerous requirements, such as having acceptable mechanical strength, biocompatibility, satisfactory surface characteristics, and excellent corrosion and wear performance. It was known that AM techniques may lead to some uncertainties influencing part properties and causing significant evaluation conflicts in corrosion outcomes. Meanwhile, the corrosion and wear behavior of additively manufactured materials are not comprehensively discussed. In this regard, the present work is a review of the state-of-the-art knowledge dedicated to reviewing the actual scientific knowledge about the corrosion and wear response of additively manufactured biomedical components, elucidating the relevant mechanism and influential factors to enhance the performance of AM-manufactured implants specifically for the physiological human body fluids. Furthermore, there is a focus on the use of reinforced composites, surface engineering, and a preparation stage that can considerably affect the tribocorrosion behavior of AM-produced parts. The improvement of tribocorrosion performance can have a key role in the production of advanced AM implants and the present study can pave the way toward facile production of high-throughput AM biomedical parts that have very high resistance to corrosion and wear.

Public procurement and green innovation in Chinese enterprises: insights from econometric models and cross-case studies

ABSTRACT This study employs econometric models and cross-case analysis to investigate the impact and mechanism of public procurement (PP) on enterprises’ green innovation (EGI). Based on the manual identification of government customers, we conduct panel model estimations on Chinese listed company data from 2010 to 2021. Findings reveal that PP has a green innovation effect at the micro level. Moreover, expected market returns, corporate social responsibility (CSR), and financing constraints play a mediating role between PP and EGI. Additionally, we delved into the relationship between PP and EGI through a cross-case study. It not only validates the conclusions drawn from the econometric model but also summarises the heterogeneous characteristics of PP affecting EGI in practice. This study not only contributes to the existing literature on the outcomes of PP and factors impacting EGI but also furnishes additional empirical insights into the mechanisms through which PP enhances EGI.

SONIA: an immersive customizable virtual reality system for the education and exploration of brain networks

While mastery of neuroanatomy is important for the investigation of the brain, there is an increasing interest in exploring the neural pathways to better understand the roles of neural circuitry in brain functions. To tackle the limitations of traditional 2D-display-based neuronavigation software in intuitively visualizing complex 3D anatomies, several virtual reality (VR) and augmented reality (AR) solutions have been proposed to facilitate neuroanatomical education. However, with the increasing knowledge on brain connectivity and the functioning of the sub-systems, there is still a lack of similar software solutions for the education and exploration of these topics, which demand more elaborate visualization and interaction strategies. To address this gap, we designed the immerSive custOmizable Neuro learnIng plAtform (SONIA), a novel, user-friendly VR software system with a multi-scale interaction paradigm that allowed flexible customization of learning materials. With both quantitative and qualitative evaluations through user studies, the proposed system was shown to have high usability, attractive visual design, and good educational value. As the first immersive system that integrated customizable design and detailed narratives of the brain sub-systems for the education of neuroanatomy and brain connectivity, SONIA showcased new potential directions and provided valuable insights regarding medical learning and exploration in VR.

Machine embroidery of light-emitting textiles with multicolor electroluminescent threads.

Advances in electroluminescent threads, suitable for weaving or knitting, have opened doors for the development of light-emitting textiles, driving growth in the market for flexible and wearable displays. Although direct embroidery of these textiles with custom designs and patterns could offer substantial benefits, the rigorous demands of machine embroidery challenge the integrity of these threads. Here, we present embroiderable multicolor electroluminescent threads-in blue, green, and yellow-that are compatible with standard embroidery machines. These threads can be used to stitch decorative designs onto various consumer fabrics without compromising their wear resistance or light-emitting capabilities. Demonstrations include illuminating specific messages or designs on consumer products and delivering emergency alerts on helmet liners for physical hazards. Our research delivers a comprehensive toolkit for integrating light-emitting textiles into trendy, customized crafts tailored to the unique requirements of diverse flexible and wearable displays.

Current trends in colorimetric biosensors using nanozymes for detecting biotoxins (bacterial food toxins, mycotoxins, and marine toxins).

Biotoxins, predominantly bacterial food toxins, mycotoxins, and marine toxins, have emerged as major threats in the fields of seafood, other foods, feeds, and medicine. They have potential teratogenic, mutagenic, and carcinogenic effects on humans, occasionally triggering high morbidity and mortality. One of the apparent concerns relates to the increasing consumption of fast food resulting in the demand for processed food without adequate consideration of the toxins they may contain. Therefore, developing improved methods for detecting biotoxins is of paramount significance. Nanozymes, a type of nanomaterials exhibiting enzyme-like activity, are increasingly being recognized as viable alternatives to natural enzymes owing to their benefits, such as customizable design, controlled catalytic performance, excellent biocompatibility, and superior stability. The remarkable catalytic activity of nanozymes has led to their broad utilization in the development of colorimetric biosensors. This has emerged as a potent and efficient approach for rapid detection, enabling the creation of innovative colorimetric sensing methodologies through the integration of nanozymes with colorimetric sensors. In this review, recent development in nanozyme research and their application in colorimetric biosensing of biotoxins are examined with an emphasis on their characteristics and performance. The study particularly focuses on the peroxidase (POD) activity, oxidase (OXD) activity, superoxide dismutase (SOD), and catalase (CAT) activity of nanozymes in colorimetric biosensors. Ultimately, the challenges and future prospects of these assays are explored.

AI-aided design studio: Enhancing graphic design and user interface with machine learning

The challenge in a design studio lies in helping graphic designers create dynamic and personalized designs efficiently. To address this, the AI-Aided Design Studio is a web-based application that leverages machine learning algorithms to assist graphic designers in producing more vibrant and customized designs. By integrating artificial intelligence and machine learning techniques, this application seeks to empower designers with tools that can generate design suggestions, such as design elements, color schemes, and layout improvements, automate repetitive tasks, and provide personalized design recommendations based on user preferences and historical data. The development will also involve creating an interactive software application that allows users to collaboratively design and customize graphics and user interface elements with AI-driven enhancements. Through this innovative approach, the application not only offers practical utility in design but also showcases the potential of artificial intelligence to transform traditional creative processes into more efficient and personalized experiences. The AI-Aided Design Studio will also incorporate a sophisticated image-to-text generator that utilizes state-of-the-art machine learning algorithms. This generator enables designers to analyze and interpret visual elements, automatically extracting relevant textual data from images. This process not only expedites the initial phase of design research but also facilitates a seamless transition between the visual and textual aspects of the creative process.

3D printed porous membrane integrated devices to study the chemoattractant induced behavioural response of aquatic organisms.

Biological models with genetic similarities to humans are used for exploratory research to develop behavioral screening tools and understand sensory-motor interactions. Their small, often mm-sized appearance raises challenges in the straightforward quantification of their subtle behavioral responses and calls for new, customisable research tools. 3D printing provides an attractive approach for the manufacture of custom designs at low cost; however, challenges remain in the integration of functional materials like porous membranes. Nanoporous membranes have been integrated with resin exchange using purpose-designed resins by digital light projection 3D printing to yield functionally integrated devices using a simple, economical and semi-automated process. Here, the impact of the layer thickness and layer number on the porous properties - parameters unique for 3D printing - are investigated, showing decreases in mean pore diameter and porosity with increasing layer height and layer number. From the same resin formulation, materials with average pore size between 200 and 600 nm and porosity between 45% and 61% were printed. Membrane-integrated devices were used to study the chemoattractant induced behavioural response of zebrafish embryos and planarians, both demonstrating a predominant behavioral response towards the chemoattractant, spending >85% of experiment time in the attractant side of the observation chamber. The presented 3D printing method can be used for printing custom designed membrane-integrated devices using affordable 3D printers and enable fine-tuning of porous properties through adjustment of layer height and number. This accessible approach is expected to be adopted for applications including behavioural studies, early-stage pre-clinical drug discovery and (environmental) toxicology.

"Enhancing Customer-Centric Design In Three-Phase UPS Systems: Insights From Bengaluru's Industry Professionals And Innovation Trends"

This study investigates the impact of customer-centric design on three-phase UPS systems, focusing on industry professionals in Bengaluru. As demand for reliable and adaptable UPS systems increases, understanding how customer-focused design influences satisfaction is crucial. A mixed-method approach was used, starting with qualitative interviews of 30 industry experts to explore key themes, followed by a quantitative survey of 300 participants to assess customer satisfaction and design factors. The tools developed for this study included a semi-structured interview guide for qualitative data and a structured survey questionnaire for quantitative data collection, both validated through pilot testing. Key design factors such as reliability, scalability, ease of use, modularity, and energy efficiency were analyzed. Descriptive statistics revealed high mean scores for these factors, while correlation and factor analyses showed strong positive relationships with customer satisfaction, particularly for ease of use (r = 0.812) and scalability (r = 0.770). Thematic analysis emphasized the importance of AI-driven predictive maintenance and customization in improving system performance. The results indicate that larger companies more effectively implement customer-centric practices, while smaller firms face challenges such as cost constraints and technical limitations. The study recommends enhancing modularity, energy efficiency, and AI-driven maintenance to improve customer satisfaction, with a focus on helping smaller companies overcome these barriers. In conclusion, customer-centric design significantly enhances customer satisfaction in the UPS industry, and adopting these strategies is essential for long-term success and competitiveness.

"Enhancing Customer-Centric Design In Three-Phase UPS Systems: Insights From Bengaluru's Industry Professionals And Innovation Trends"

This study investigates the impact of customer-centric design on three-phase UPS systems, focusing on industry professionals in Bengaluru. As demand for reliable and adaptable UPS systems increases, understanding how customer-focused design influences satisfaction is crucial. A mixed-method approach was used, starting with qualitative interviews of 30 industry experts to explore key themes, followed by a quantitative survey of 300 participants to assess customer satisfaction and design factors. The tools developed for this study included a semi-structured interview guide for qualitative data and a structured survey questionnaire for quantitative data collection, both validated through pilot testing. Key design factors such as reliability, scalability, ease of use, modularity, and energy efficiency were analyzed. Descriptive statistics revealed high mean scores for these factors, while correlation and factor analyses showed strong positive relationships with customer satisfaction, particularly for ease of use (r = 0.812) and scalability (r = 0.770). Thematic analysis emphasized the importance of AI-driven predictive maintenance and customization in improving system performance. The results indicate that larger companies more effectively implement customer-centric practices, while smaller firms face challenges such as cost constraints and technical limitations. The study recommends enhancing modularity, energy efficiency, and AI-driven maintenance to improve customer satisfaction, with a focus on helping smaller companies overcome these barriers. In conclusion, customer-centric design significantly enhances customer satisfaction in the UPS industry, and adopting these strategies is essential for long-term success and competitiveness.

User Satisfaction and Technology Adoption in Smart Homes: A User Experience Test

Using a mixed-methods approach, we examine the complex link between user happiness and technology adoption in the context of smart homes. Our tests show that user happiness and adoption are highly influenced by the versions of smart home technologies, with Version A producing better user satisfaction (7.2) and adoption rates (68%) than Version B (6.8, 62%). Furthermore, consumers engaging with Features A and C reported the greatest adoption rates (80%) and satisfaction (8.1), indicating that certain features, particularly when paired, have a significant influence on user pleasure. Extended training times resulted in higher user satisfaction and adoption rates of the technology; the 6-hour training group had the greatest adoption rate (84%), and the highest satisfaction (8.3%). Furthermore, user age demographics have a substantial impact on adoption and happiness; young adults have the greatest adoption rate (70%) and contentment (7.6). These results highlight the necessity of developing smart home technologies that are appropriate for various age groups, as well as the significance of feature customization, thorough training, and user-centric design in improving user satisfaction and encouraging technology adoption. Introduction

Customer Identification for Electricity Retailers Based on Monthly Demand Profiles by Activity Sectors and Locations

The increasing competition in the electric sector is challenging retail companies as they must assign its commercial efforts to attract the most profitable customers. Those are whose energy demand best fit certain target profiles, which usually depend on generation or cost policies. But, even when the demand profile is available, it is in an anonymous way, preventing its association to a particular client. In this paper, we explore a large dataset containing several millions of monthly demand profiles in Spain and use the available information about the associated economic sector and location for an indirect identification of the customers. The distance of the demand profile from the target is used to define a key performance indicator (KPI) which is used as the main driver of the proposed marketing strategy. The combined use of activity and location has been revealed as a powerful tool for indirect identification of customers, as 100,000 customers are uniquely identified, while about 300,000 clients are identifiable in small sets containing 10 or less consumers. To assess the proposed marketing strategy, it has been compared to the random attraction of new clients, showing a reduction of distance from the target of 40% for 10,000 new customers.

Substrate shapes generation for additive manufacturing of medical corsets tailored to various patient morphologies

Additive manufacturing (AM) recently appeared as an alternative to thermoforming for medical corset production, enabling lighter, more breathable, and highly customizable designs by creating complex shapes and adjusting material properties. However, the common 3-axis architecture of AM machines limits corset performance due to the unidirectional layering of material. As an alternative, a method involving radial material deposition on a rotating substrate is being explored. In this setup, the substrate’s shape becomes a key factor in reducing manufacturing time and cost, directly impacting the needed volume support between the substrate and the corset. In order to avoid the time and material-intensive practice of creating a new substrate for each corset, the objective is to use a limited number of reusable substrates specific to morphological categories of corsets. A four-step method for generating optimally shaped substrates is proposed. Firstly, unsupervised classification of 3D patient scans sorts them into fixed morphological categories. After achieving the optimal 3D alignment of the scans per category, substrates can be generated from the resulting minimal surfaces. A validation process involving fitting each scan to each categorized substrate and then calculating the resulting intervolume demonstrates the potential to reduce support volume in the additive manufacturing of a morphological class of corsets.