In the ever-evolving landscape of industrial design, the interplay between symmetry and asymmetry serves as both a philosophical cornerstone and a practical compass [...]

This study introduces the Global Design Capabilities Ranking (GDCR), a novel index developed to benchmark national design capabilities across 153 countries. Responding to the limitations of earlier frameworks, such as limited coverage and reliance on non-replicable indicators, the GDCR leverages internationally available data on industrial design registrations, trademarks, and design awards. These outputs were aggregated using Principal Component Analysis and linked through regression to a replicated Design Competitiveness Ranking. The resulting model identifies two main dimensions: design production and recognition. Results validate the use of intellectual property as proxies of design capabilities, revealing regional patterns and providing actionable insights. Compared to previous benchmarks, this framework advances design policy research by offering a cost-effective, scalable tool to assess national design ecosystems. It supports international comparability and enables policymakers, especially in developing contexts, to better understand and promote design-driven innovation.

We review the origin, evolution, and impact of the Stanley Caplan User-Centered Product Design Award over the past 25 years. The annual Award has recognized product excellence in the areas of commercially available products, systems, and equipment. The Award is unique in its evaluation method that stresses both a product’s design process and the end product of that process. Winners can expect increased visibility for their industrial design, user experience, and human factors groups, and increased support from their companies for human-centered designs and processes. Designers are invited to submit their human-centered designs into the Award’s evaluation process.

Including batch scheduling flexibility in industrial energy system design and dispatch optimization: A case study of a brewery

Design of highly efficient and carbon-resistant catalysts for biogas reforming using entropy engineering strategy.

This article focusses on how we can design the statutory minimum wage requirement in Workday Payroll by using delivered concepts of lookup table, lookup calculation, and override calculation. This research outlines the concepts of what is minimum wage and what is delivered by Workday as part of the ERP Solution and what is missing which needs to be configured. This article specifically focusses on the minimum wages’ rules of USA Payroll. This article proves the success of this design with a successful case study by implementing this design in the Retail industry with over forty thousand employees and concludes with the implementation strategy.

This article focusses on how we can architect the automation of stock information to Workday payroll by integrating data from Fidelity Vendor using Workday Studio, Workday Reporting and Workday payroll. This research outlines the bi-directional data integration between Workday and Fidelity with Employee Demographic and Tax Information and processes the payroll taxes for Stock Purchases. This article proves the success of this design with a successful case study by implementing this design in the Retail industry with over forty thousand employees across all states in USA, Canada and concludes with the implementation strategy.

This community service program aims to accelerate the adoption of Generative Artificial Intelligence technology through the socialization of the use of Google Gemini in four strategic regions of Indonesia. Implementation was carried out specifically at the University of North Sumatra (Medan) to represent the Sumatra cluster, the National Industrial Design Center (Yogyakarta) for the Java cluster, the Integrated Business Service Center/PLUT-KUMKM (Pontianak) for the Kalimantan cluster, and Makassar Digital Valley (Makassar) for the Sulawesi cluster. This program targets three main pillars: increasing the efficiency of teaching tools for educators, digitizing independent financial reports, and optimizing marketing copywriting for MSMEs. The methods used were participatory workshops and technical assistance. Evaluation results showed that the use of Google Gemini was able to reduce administrative time for educators by up to 60% and significantly improve the quality of MSME digital promotional content. This program proves that equalizing AI literacy across Indonesia's major islands is key to strengthening economic competitiveness and the quality of national education

In the context of the digital transition of the Italian textile industry, this paper explores how design can act as a mediator between emerging technologies, sustainable practices, and sector-specific knowledge. The research adopts the perspective of the Transitional Industrial Designer (TID), a figure capable of integrating vertical technical expertise, adaptability to change, interdisciplinary collaboration, and systemic thinking. Through a practice-based experimentation on the virtualization of textile materials, the project investigates the role of design in enabling accessible, scalable, and collaborative processes. The focus is not on technological solutions alone, but on the design practices that mediate complexity and facilitate the adoption of immersive and digital tools across different stakeholders. Structured as an interconnected system rather than a linear workflow, the experimentation combines acquisition, interaction, and archiving of virtual materials to support sustainable, distributed design practices. The outcomes highlight the potential of anticipatory design approaches in activating new modes of interaction with materiality and in supporting the circular transition of Made in Italy industries. Ultimately, the study offers a methodological reflection on how designers can responsibly navigate digital complexity by constructing meaningful, relational, and adaptive project frameworks.

This study explores the integration of regional cultural identity into design education through a case study of the Chengdu Line 5 subway in the Sichuan-Chongqing region of China. Grounded in Stuart Hall’s theory of cultural identity and using a multi-layered cultural framework—comprising material, social, and spiritual culture—the research identifies and classifies key cultural elements including modeling, color, and connotation factors. The Analytic Hierarchy Process (AHP) was employed to optimize and evaluate these factors, drawing from 329 valid questionnaires and expert interviews. The results demonstrate that the selected cultural elements effectively reflect local identity and are widely recognized by the public, confirming the practical applicability of the proposed cultural design model. The study also provides a methodological pathway for embedding cultural reasoning and symbolic literacy into design pedagogy. It concludes that design education must move beyond technical training to embrace interdisciplinary methods and cultural sustainability, thereby fostering innovation and regional pride in industrial design practices.

Contemporary design operates within a critical transition marked by environmental pressures, technological acceleration, and the need for systemic sustainability. In this context, materials are no longer neutral inputs but complex, relational entities that carry cultural, territorial, and environmental significance. Particularly within the Made in Italy production system (characterized by distributed SMEs, artisanal know-how, and symbolic value) rethinking material management is crucial to address the limitations of linear, extractive models. This paper introduces Material as a Service (MAAS) as an infrastructural paradigm that extends the previous Material-Service approach, framing matter as an active node within circular and distributed ecosystems. MAAS enables materials to become traceable, configurable, and regenerable, bridging data, design practices, and local supply chains. Through this lens, design moves from shaping objects to orchestrating relational infrastructures where matter, data, and systems interact. The paper also highlights the role of the Transitional Industrial Designer as a mediator between technological, cultural, and territorial dimensions, supporting SMEs in the transition toward more responsible, human-centered, and circular production models. By activating material as an infrastructure, MAAS provides a framework for generating value that is not only economic, but also cultural, relational, and regenerative, paving the way for distributed material ecosystems in contemporary industry.

Abstract The demand for optically pure lactic acid (LA) is increasing due to its applications in the food, pharmaceutical, and biodegradable plastics industries. This study evaluated l ‐LA, d ‐LA, and dl ‐LA production using Lacticaseibacillus casei , L. coryniformis ATCC 25600, and Lactiplantibacillus plantarum , respectively, cultivated in laboratory‐scale bioreactors with controlled pH and temperature. Agroindustrial byproducts, including sugarcane molasses, glucose‐fructose syrup, and whey permeate, were compared with glucose as a reference substrate. Lacticaseibacillus casei reached the highest l ‐LA concentration in whey permeate (86 g L −1 in 24 h), with a yield of 1.07 g g −1 and maximum productivity of 4.70 g L −1 h −1 , values higher than previously reported for lactose‐based media. Lacticaseibacillus coryniformis ATCC 25600 achieved 70 g L −1 of d ‐LA from glucose‐fructose syrup (95 g L −1 initial sugars) in 72 h, with complete sugar depletion, yield of 0.75 g g −1 , and productivity of 1.95 g L −1 h −1 . For dl ‐LA, L. plantarum produced 70 g L −1 in whey permeate within 48 h (87% sugar removal, yield 0.76 g g −1 , productivity 1.44 g L ‐1 h −1 ). Across all strains, whey permeate and syrup enabled the most efficient fermentations, whereas molasses showed potential but was limited by nitrogen content. These results highlight the feasibility of valorizing low‐cost byproducts for optically pure LA production and provide kinetic and stoichiometric benchmarks to guide industrial process design.

In China, 6:2 chlorinated polyfluoroalkyl ether sulfonic acid (6:2 Cl-PFESA), commercially designated as F-53B, has been predominantly implemented as the perfluorooctane sulfonate (PFOS) substitute, with its 8:2 derivative showing negligible relevance to population exposure. Previous studies have found that 6:2 Cl-PFESA is closely related to dyslipidemia and disrupted glucose homeostasis, though results remain inconsistent. Our study aimed to separately investigate the potential associations of 6:2 and 8:2 Cl-PFESA with glucolipid metabolism indicators in Chinese adults. A comprehensive systematic literature search was conducted across three major databases-PubMed, Embase, and Web of Science-by 23 September 2024. Random effects models were employed to estimate changes in blood lipid and glucose parameters with one interquartile range (IQR) increment in mean blood concentration of 6:2 and 8:2 Cl-PFESA. Subgroup analyses were performed based on different populations and types of diabetes. A Bayesian random-effects meta-regression model was conducted to attribute differences apparent between individual empirical estimates to mean 6:2 Cl-PFESA concentration. The analysis included 17 publications with more than 17, 000 participants. Meta-analyses revealed that each IQR increase of 6:2 Cl-PFESA exhibited significantly positive correlations with a 3.90mg/dl change in total cholesterol (TC) (95% CI: 1.97, 5.83), and a 2.94mg/dl change in low-density lipoprotein cholesterol (LDL-C) (95% CI: 1.47, 4.41) among the general adult population. The odds ratio (OR) for gestational diabetes mellitus (GDM) among pregnant women exposed to 6:2 Cl-PFESA was 1.61 (95% CI: 1.15, 2.27). Studies on 8:2 Cl-PFESA were insufficient. The results demonstrated that 6:2 Cl-PFESA was significantly related to TC, LDL-C, as well as the risk of GDM. These findings challenge the current industrial designation of 6:2 Cl-PFESA as a safer alternative, necessitating explicit regulatory re-evaluation pending comprehensive mechanistic evidence that elucidates its biological interactions. PROSPERO CRD42024581843.

The rapid growth of the culinary industry in Purwokerto has led to the emergence of numerous coffee shops with unique and competitive concepts. La Puerto Coffee & Eatery stands out as one of the leading businesses by implementing a green building concept and modern industrial design. This study aims to analyze the business processes of La Puerto using the SWOT and Value Chain methods to identify its strategic position and sustainable development opportunities. The research adopts a descriptive qualitative approach through direct observation, interviews with management and customers, and literature review. The results show that La Puerto’s main strengths lie in its eco-friendly concept, strategic location, and high-quality service, while its weaknesses include limited digitalization and underdeveloped marketing strategies. The Value Chain analysis reveals that most primary activities are effective; however, digital integration needs improvement to enhance efficiency and value creation. Overall, the combination of SWOT and Value Chain analyses provides a comprehensive understanding of business potential, suggesting that digital transformation, service innovation, and community-based marketing are key strategies for sustaining La Puerto Coffee & Eatery’s competitiveness in the dynamic culinary industry.

Aiming at the subjective assessment of industrial design creativity, this paper presents a quantitative assessment framework that combines novelty, appropriateness, and consistency and establishes a benchmark for assessing the modeling creativity of multi-category products. With the help of human subjective scores from human-computer collaborative labeling, this study designed a “design critic” model based on a visual language model to enable automatic creativity assessment. The experiment showed that the Spearman correlation coefficient between the design critic model and human ratings in the cross-category test was 0.67, while the appropriateness dimension scored 0.74. In the context of small sample migration, the self-supervised pre-trained model maintained a correlation of 0.61 after fine-tuning with only 20 samples. After three rounds of optimization, the average user preference rate increased by about 24%, with a peak of 68%.

This research assesses the adoption of self-regulatory guidance in the multilevel marketing (MLM) industry as it relates to earnings claims and disclaimers and studies the impact of disclaimers embedded in MLM promotional material. Based on a review of over 2,000 MLM marketing materials with atypical claims, Study 1 finds that the industry's own self-regulatory disclaimer guidance has not been adopted by MLM firms. Instead, MLM firms overwhelmingly use no disclaimers or vague, nonprominent disclaimers. Study 2 tests the impact of disclaimers that align with actual MLM industry practice against disclaimers aligned with self-regulatory guidance, both to assess the impact of current practice and to evaluate the “what if” scenario of guidance adoption. Results indicate that actual disclaimers have no impact, while guidance-aligned disclaimers do impact consumer judgments and interest. As there are multiple differences between actual disclaimers and those aligned with self-regulatory guidance, Study 3 tests the relative importance of disclaimer content and prominence. Results indicate that both content and prominence are tied to disclaimer impact. Findings inform the self-regulatory and regulatory framework for the MLM industry and disclaimer design and content.

This paper presents a high-level system architecture that integrates the Software Defined Networking (SDN) paradigm in 5G/6G networks with the aim of supporting the requirements expected for Industrial Internet of Things (IIoT) devices and services. To this purpose, we include multiple Reconfigurable Intelligent Surfaces (RISs) systems and provide for them an abstract representation consistent with the OpenFlow interface and messaging framework. The main contribution of this is firstly focused on designing a comprehensive framework that specifies the modules, components, interfaces, protocols, and message exchanges across the typical three layers SDN architecture. In addition, we characterize the Network Discovery (ND) and Host Discovery (HD) protocols that enable the SDN Controller to achieve a global and updated view of the network. Then, the RIS Placement and Selection Problem (RPSP) is formulated by using two graph-theory approaches, i.e., Set Covering (SC) and Minimum Spanning Tree (MST). Finally, we conduct an extensive simulation campaign that evaluates the performance of the discovery phases and the RIS placement/selection algorithms in realistic industrial environments. The results highlight the advantages achieved in terms of coverage and complexity.

This paper describes artificial intelligence technology, both in general terms and through its use in the graphic industry and packaging design. In the practical part, solutions for packaging were generated, connecting the sense of sight with the sense of taste. For each taste, four levels of intensity were created, with each level having three images. These images were later presented to users in a survey conducted in order to assess to what extent artificial intelligence is capable of creating designs considering visual representation of different taste intensities.

Computer-Aided Design (CAD) sketches, composed of geometric primitives and constraints, are fundamental to CAD models and play a critical role in industrial design and manufacturing. Leveraging artificial intelligence to convert hand-drawn sketches and rendered images into CAD sketches has the potential to streamline and accelerate the design process. Existing approaches predominantly focus on separately learning primitives and constraints, often employing two-stage methods or learnable tokens to model these elements independently. However, such methods fail to fully exploit the intrinsic relationships between primitives and constraints. In this paper, we propose LuBan, a lightweight, end-to-end model for CAD sketch generation that eliminates the need for separate constraint models or tokens. LuBan leverages the DEtection TRansformer (DETR) architecture for primitive modeling and distinguishes between parametric and non-parametric features. By deriving sub-primitive features from the intermediate outputs of the primitive model, LuBan facilitates constraint prediction, effectively capturing the inherent relationships between primitives and constraints. This enables the generation of CAD sketches as hierarchical graphs. Qualitative and quantitative experiments on both precise and hand-drawn renderings demonstrate that LuBan achieves state-of-the-art performance. Ablation studies further confirm its superiority over independently trained primitive models, validating its effectiveness. Additionally, LuBan embodies the principle of "what you draw is what you get," offering significant enhancements to the design process for designers.

Tragic accidents in chemical manufacturing plants in the last century raised the need for regulatory frameworks to control hazards, prevent or reduce the risk of major accidents and to limit the consequences for human health and the environment. The derivation of health-based Acute Exposure Guidance Values (AEGVs) is pivotal for providing science/health-based landmarks, contributing to land-use planning, industrial plant design, and the development of chemical emergency response plans. Here, we describe a pragmatic procedure to derive reliable health-based values to comply with the provisions of the Directive 2012/18/EU (Seveso Directive III). The candidate substances selection considers their hazard classification, and existing AEGVs are used in a hierarchical proceeding. If needed, company-specific Acute Exposure Threshold Level-2 (AETL-2) values, preventing irreversible health effects or escape impairment, and AETL-3 values, preventing life-threatening effects or death, are derived based on the ACUTe EXposure project methodology. Since a substantial number of substances do not possess suitable experimental inhalation toxicity data, AEGV-related information of approximately 650 substances was extracted to derive generic AEGVs based on their classification for local corrosive or irritative effects. The values obtained support that a less severe classification category translates into higher AEGVs, and local corrosive toxicity appears to be a relevant driver for the AEGV derivation. In the absence of high acute toxicity, carcinogenic, mutagenic, or reproductive toxicity (CMR) properties, or organ-specific toxicity, generic AEGVs are useful to strengthen decisions for suitable points of departure in a weight of evidence approach and to confirm the plausibility of derived AETL values.