Search Results (109)

In this position paper, we argue that the conventional understanding of ‘information’ (as generally conceived in science, in a digital fashion) is overly simplistic and not consistently applicable to living systems, which are open systems that cannot be reduced to any kind of ‘portion’ (building block) ascribed to the category of quantity. Instead, it is a matter of relationships and qualities in an indivisible analogical (and ontological) relationship between any presumed ‘software’ and ‘hardware’ (information/matter, psyche/soma). Furthermore, in biological systems, contrary to Shannon’s definition, which is well-suited to telecommunications and informatics, any kind of ‘information’ is the opposite of internal entropy, as it depends directly on order: it is associated with distinction and differentiation, rather than flattening and homogenisation. Moreover, the high degree of structural compartmentalisation of living matter prevents its energetics from being thermodynamically described by using a macroscopic, bulk state function. This requires the Second Principle of Thermodynamics to be redefined in order to make it applicable to living systems. For these reasons, any static, bit-related concept of ‘information’ is inadequate, as it fails to consider the system’s evolution, it being, in essence, the organized coupling to its own environment. From the perspective of quantum field theory (QFT), where many vacuum levels, symmetry breaking, dissipation, coherence and phase transitions can be described, a consistent picture emerges that portrays any living system as a relational process that exists as a flux of context-dependent meanings. This epistemological shift is also associated with a transition away from the ‘particle view’ (first quantisation) characteristic of quantum mechanics (QM) towards the ‘field view’ possible only in QFT (second quantisation). This crucial transition must take place in life sciences, particularly regarding the methodological approaches. Foremost because biological systems cannot be conceived as ‘objects’, but rather as non-confinable processes and relationships. Full article

Steroids are found in bacteria and eukaryotes, and genes potentially encoding steroid metabolic enzymes have also been identified in giant viruses. For decades, hydroxylated steroids have been utilized in medicine to treat various human diseases. The hydroxylation of steroids can be achieved using microbial enzymes, especially cytochrome P450 monooxygenases (CYPs/P450s) and is well documented. Understanding the structural determinants that govern the regio- and stereoselectivity of steroid hydroxylation by P450s is essential in order to fully exploit their potential. Herein, we present a comprehensive analysis of the steroid-hydroxylating CYP109 family across the domains of life and delineate the structural determinants that govern steroid hydroxylation. Data mining, annotation, and phylogenetic analysis revealed that CYP109 family members are highly populated in bacteria, and indeed, these members passed from bacteria to archaea by horizontal gene transfer, leading to the evolution of P450s in archaea. Analysis of twelve CYP109 crystal structures revealed large, flexible, and dynamic active site cavities that can accommodate multiple ligands. The correct positioning and orientation of the steroid in the active site cavity and the nature of the C17 substituent on the steroid molecule influence catalysis. In an analogous fashion to the CYP107 family, the amino acid residues within the CYP109 binding pocket involve hydrophilic and hydrophobic interactions, influencing substrate orientations and anchoring and determining the site of hydroxylation and catalytic activity. A handful of amino acids, such as Val84, Val292, and Ser387 in CYP109B4, have been found to play a role in determining the catalytic regiospecificity, and a single amino acid, such as Arg74 in CYP109A2, has been found to be essential for the enzymatic activity. This work serves as a reference for the precise understanding of CYP109 structure–function relationships and for P450 enzymes in general. The findings will guide the genetic engineering of CYP109 enzymes to produce valuable steroid molecules of medicinal and biotechnological importance. Full article

The industrial landscape has undergone rapid conceptual evolution in recent years, marked by the successive emergence of Industry 4.0, Industry 5.0, and the nascent Industry 6.0. This study explores the emergence of Industry 6.0 as a prospective industrial paradigm, characterized by intelligent, autonomous, and sustainable systems, which builds upon the digital foundations of its predecessors. Using management fashion theory as a theoretical lens, we analyze how these industrial concepts arise, diffuse, and potentially become institutionalized within management discourse. The study reveals that the adoption and dissemination of these paradigms are influenced not only by technological advancements but also by the discursive efforts of a fashion-setting community comprising academics, policymakers, consultants, and media actors. Industry 6.0, while still largely speculative, continues a broader trend of using numbered industrial revolutions to frame ongoing innovation. The findings suggest that such paradigms serve both practical and rhetorical purposes, driving organizational change while also reflecting shifting societal and managerial values. The study concludes with reflections on the implications for managers and policymakers as they navigate the evolving industrial landscape. Full article

In this paper, we address the challenge of dynamic evolution of user preferences and propose an attribute-sequence-based recommendation model to improve the accuracy and interpretability of recommendation systems. Traditional approaches usually rely on item sequences to model user behavior, but ignore the potential value of attributes shared among different items for preference characterization. To this end, this paper innovatively replaces items in user interaction sequences with attributes, constructs attribute sequences to capture fine-grained preference changes, and reinforces the prioritization of current interests by maintaining the latest state of attributes. Meanwhile, the item–attribute relationship is modeled using LightGCN and a variant of GAT, fusing multi-level features using gated attention mechanism, and introducing rotary encoding to enhance the flexibility of sequence modeling. Experiments on four real datasets (Beauty, Video Games, Men, and Fashion) showed that the model in this paper significantly outperformed the benchmark model in both NDCG@10 and Hit Ratio@10 metrics, with a highest improvement of 6.435% and 3.613%, respectively. The ablation experiments further validated the key role of attribute aggregation and sequence modeling in capturing user preference dynamics. This work provides a new concept for recommender systems that balances fine-grained preference evolution with efficient sequence modeling. Full article

Federated learning (FL) is a distributed learning technique that ensures data privacy and has shown significant potential in cross-institutional image analysis. However, existing methods struggle with the inherent dynamic heterogeneity of real-world data, such as changes in cellular differentiation during disease progression or feature distribution shifts due to different imaging devices. This dynamic heterogeneity can cause catastrophic forgetting, leading to reduced performance in medical predictions across stages. Unlike previous federated learning studies that paid insufficient attention to dynamic heterogeneity, this paper proposes the FedDyH framework to address this challenge. Inspired by the adaptive regulation mechanisms of biological systems, this framework incorporates several core modules to tackle the issues arising from dynamic heterogeneity. First, the framework simulates intercellular information transfer through cross-client knowledge distillation, preserving local features while mitigating knowledge forgetting. Additionally, a dynamic regularization term is designed in which the strength can be adaptively adjusted based on real-world conditions. This mechanism resembles the role of regulatory T cells in the immune system, balancing global model convergence with local specificity adjustments to enhance the robustness of the global model while preventing interference from diverse client features. Finally, the framework introduces a genetic algorithm (GA) to simulate biological evolution, leveraging mechanisms such as gene selection, crossover, and mutation to optimize hyperparameter configurations. This enables the model to adaptively find the optimal hyperparameters in an ever-changing environment, thereby improving both adaptability and performance. Prior to this work, few studies have explored the use of optimization algorithms for hyperparameter tuning in federated learning. Experimental results demonstrate that the FedDyH framework improves accuracy compared to the SOTA baseline FedDecorr by 2.59%, 0.55%, and 5.79% on the MNIST, Fashion-MNIST, and CIFAR-10 benchmark datasets, respectively. This framework effectively addresses data heterogeneity issues in dynamic heterogeneous environments, providing an innovative solution for achieving more stable and accurate distributed federated learning. Full article

The rapid evolution of artificial intelligence (AI) has significantly transformed industries, positioning the fashion sector as a critical area of study due to its mass production and pressing sustainability challenges. As the world’s largest apparel producer, China faces unique hurdles in terms of integrating AI technologies, highlighting the intersection of technological innovation and sustainability within this industry. In this context, this study aims to provide the initial exploratory correlations between AI adoption and open innovation from apparel manufacturing micro-, small-, and medium-size enterprises (MSMEs) managers’ perspectives, identifying knowledge absorptive capacity (KACAP)’s significant impacts through an integrated and extended TAM-TOE model. We conducted PLS-SEM to empirically validate the antecedents of AI adoption and its consequential effects on KACAP and open innovation by collecting information from 269 of the apparel manufacturing MSMEs’ top managers. The results show that the TAM-TOE structural model explains 60.7% of the variance in AI adoption, 47.4% in KACAP, and 55.4% in open innovation, which suggests that the model has good explanatory capacity, and that all these Q² values indicate a sizeable predictive accuracy threshold. Drawing on the proposed model, the study has identified technological (e.g., perceived usefulness) and environmental factors (e.g., competitive pressure, market uncertainty, and government support and policy) that significantly impact AI adoption, while organizational factors (e.g., organizational readiness) directly impact KACAP, and environmental factors (e.g., competitive pressure, supplier involvement, and market uncertainty) directly impact open innovation. Subsequently, the AI construct is having a significant influence on MSMEs’ open innovation through KACAP. This fills existing theoretical gaps by linking AI technology to organizational innovation processes and demonstrates the mediating influence of KACAP. Also, the proposed model provides a foundation for future research by exploring the intersection of AI and innovation in similar industries. Full article

In this paper, a comparative analysis of historical and contemporary fashion designs was conducted using quantitative methods and indices. Elements such as silhouettes, color palettes, and structural characteristics were analyzed in order to identify models for reinterpretation of classic fashion costume. Clothing from four historical periods was studied: Empire, Romanticism, the Victorian era, and Art Nouveau. An image processing algorithm was proposed, through which data on the shapes and colors of historical and contemporary clothing were obtained from digital color images. The most informative of the shape and color indices of contemporary and historical clothing were selected using the RReliefF, FSRNCA, and SFCPP methods. The feature vectors were reduced using the latent variable and t-SNE methods. The obtained data were used to group the clothing according to historical periods. Using Euclidean distances, the relationship between clothing by contemporary designers and the elements of the historical costume used by them was determined. These results were used to create an educational and methodological framework for practical training of students in the field of fashion design. The results of this work can help contemporary designers in interpreting and integrating elements of historical fashion into their collections, adapting them to the needs and preferences of consumers. Full article

This paper examines the digital transformation of the textile and fashion industry, focusing on the alignment with sustainability principles through the integration of Industry 4.0 technologies. The introduction highlights the urgency of transitioning from conventional production methods to innovative, digitally enabled systems that promote a circular economy and resource efficiency. The main research questions address the contribution of Industry 4.0 elements to sustainable solutions, the directions of digitalization within the apparel sector, and the significant impact of digital technologies on the achievement of sustainability goals. The theoretical framework examines sustainability in the textile industry and emphasizes the need for a green transformation facilitated by digital technologies to reduce environmental impacts. Industry 4.0 concepts, as discussed in The Concept of Industry 4.0 in the Textile and Apparel Sector, are revolutionizing production through technologies such as IoT, AI, and blockchain, enabling traceability, customization, and energy-efficient operations. The paper also explores the evolution of the fashion and apparel industry into a high-tech sector, highlighting advances such as CAD-CAM systems, digital printing, and 3D technologies that improve precision, reduce waste, and support sustainable practices. In its conclusion, the paper emphasizes the crucial role of interdisciplinary collaboration, regulatory frameworks, and investment in skills development to overcome the challenges of implementing digital and sustainable practices. It posits that a strategic embrace of digital ecosystems and Industry 4.0 technologies is essential for creating a resilient and sustainable textile industry that is aligned with environmental and societal goals. Full article

The evolution of men’s fashion and lifestyle magazines in Spain since the 1970s reflects shifts in male identity, influenced by the country’s transition from dictatorship to democracy and the liberalizing period of el Destape. This study examines how these magazines capture the adaptations of the “modern man”, emphasizing his pursuit of freedom and alignment with global trends. By analyzing the content of men’s magazines and contrasting them with women’s publications, this study identifies gendered consumption patterns and the role of magazines as both historical records and consumer products. Comparisons reveal how men and women were positioned as both subjects and objects. Results show that men’s magazines initially centered on lifestyle and erotic themes, with successful titles like MAN outlasting less defined publications. Over time, male-targeted magazines broadened their content, increasingly presenting men as consumers of lifestyle topics. This shift parallels the growth of new male-focused magazines since the 2000s, which now leverage social media-driven trends to attract audiences. The study concludes that these magazines have not only shaped male identity but have also reflected capitalist consumer strategies, adapting to digital contexts. Modern male lifestyle magazines, while diversifying themes, remain influenced by traditional gender dynamics that underscore male dominance in cultural narratives. Full article

Introduction: A large proportion of the existing voluminous disaster mental health research literature represents the quantitative study of psychopathology, especially posttraumatic stress disorder. Subjective disaster experience is relatively unexplored. Qualitative narratives of surviving a disaster may provide insight into individual experiences of it and efforts to derive meaning from it. Methods: From an initial random sample of 182 survivors of the Oklahoma City bombing, narrative descriptions of this experience were collected 7 years after the bomb blast from 116 of the original sample, for the purpose of examining persistent as well as newly evolving content through qualitative analysis. The narrative content was analyzed for the evolution of thematic content in narrative data also collected at 6 months post-disaster and 1 year later. Results: The thematic content of the bombing experience was structured in a chronological fashion from the bomb blast (sensory, cognitive, and emotional), its immediate aftermath (e.g., escaping danger), and later experiences, (e.g., leaving the bomb site and receiving hospital treatment). During the time between interviews, the focus and general content of the narratives changed minimally, despite considerable compression of detail. Conclusions: The consistency of the material in these narratives over 7 years may reflect the persistence and salience of disaster memories, with the potential for its continuation for the rest of their lives. Full article

The identification of natural products (NPs) has played a pivotal role in drug discovery and shaped the evolution of synthetic compounds (SCs). However, the extent to which NPs have historically influenced the structural characteristics of SCs remains unclear. In this study, we conducted a comprehensive, time-dependent chemoinformatic analysis to investigate the impact of NPs on the structural evolution of SCs. The physicochemical properties, molecular fragments, biological relevance, and chemical space of the molecules from the Dictionary of Natural Products were compared in a time series fashion with a synthetic compound collection sourced from 12 databases. Our findings reveal that NPs have become larger, more complex, and more hydrophobic over time, exhibiting increased structural diversity and uniqueness. Conversely, SCs exhibit a continuous shift in physicochemical properties, yet these changes are constrained within a defined range governed by drug-like constraints. SCs possess a broader range of synthetic pathways and structural diversity, albeit with a decline in biological relevance. The chemical space of NPs has become less concentrated compared to that of SCs. In conclusion, our study suggests that the structural evolution of SCs is influenced by NPs to some extent; however, SCs have not fully evolved in the direction of NPs. Full article

Organic light-emitting diode (OLED) technology has rapidly emerged in the display and lighting sectors due to its high contrast ratio, wide viewing angle, and sleek design. Beyond these attributes, OLEDs have also demonstrated crucial applications in medicine, fashion, sports, and more, leveraging their emissive properties and flexible design. As the cornerstone of full-color displays, blue OLEDs, whose performance directly impacts color rendition and saturation, have garnered significant attention from both scientific researchers and industrial practitioners. Despite the numerous advantages of OLED technology, blue OLEDs still confront formidable challenges in terms of luminous efficiency, durability, and material stability. This review examines the evolution of blue OLED materials over recent years, specifically focusing on three generations: fluorescent, phosphorescent, and thermally activated delayed fluorescence (TADF). Through molecular design, device structure optimization, and the application of innovative technologies, remarkable advancements have been achieved in enhancing the luminous efficiency, lifetime, and color purity of blue OLEDs. However, to advance commercialization, future efforts must not only ensure high efficiency and long lifetime but also improve material stability, environmental sustainability, and reduce development costs. Emerging materials such as thermally activated exciton materials and the application of hyperfluorescent (HF) OLED technology represent vital driving forces for the continuous advancement of blue OLED technology. It is anticipated that significant milestones will continue to be achieved in the development of highly efficient blue OLEDs in the future. Full article

Background: The systematic literature review with additional descriptive analysis at hand focuses on analysing returns management in e-commerce, which is an increasingly critical issue as the volume of online shopping is rising. Methods: Drawing from a comprehensive search of academic databases and a manual review of Google Scholar, 54 articles dating from 2007 onwards were collected and fully read. Results: The review reveals a main research effort emerging mainly from Germany and other countries, with a notable focus on fashion retail. The bulk of these studies aim to understand and reduce the frequency of customer returns, addressing a substantial operational challenge for online retailers. The findings provide multiple research streams extracted from the collected literature and combined to an overview. Conclusions: Through this, there are tendencies which can be interpreted to derive the evolution of the research field. The illustrated results in this review paint a detailed picture of the existing research landscape. This highlights the importance of ongoing research, which, e.g., holds potential benefits for customer satisfaction and environmental sustainability. The review also lists future research directions, recommending the continued investigation of areas such as predictive analytics and customer behaviour to further refine returns management practices. Full article

This study addresses the challenges faced by the fashion industry and its consumers in relation to sustainability and the circular economy in a digital age, relating them to the impacts that this industry triggers, both environmentally and socially. The aim is to understand the relations between environmental concern, fashion consumer awareness and adaptation to the digital evolution, and how these can result in an intention to buy sustainable fashion. The methodology adopted was based on other studies in the area, allowing us to answer the investigation question and, finally, to present some reflections that could mitigate the problems found. This research concisely highlights the limitations of the current linear economy, demonstrating the difficulties in the transition to a more sustainable world. This article is relevant for researchers in the field of fashion brand communication and consumer behaviour, as the results show some theoretical and experimental content for a better development of strategies and practices in the field of sustainable fashion, taking into account the digital evolution. Full article

The sophisticated, elegant protein-polymers designed by nature can serve as inspiration to redesign and biomanufacture protein-based materials using synthetic biology. Historically, petro-based polymeric materials have dominated industrial activities, consequently transforming our way of living. While this benefits humans, the fabrication and disposal of these materials causes environmental sustainability challenges. Fortunately, protein-based biopolymers can compete with and potentially surpass the performance of petro-based polymers because they can be biologically produced and degraded in an environmentally friendly fashion. This paper reviews four groups of protein-based polymers, including fibrous proteins (collagen, silk fibroin, fibrillin, and keratin), elastomeric proteins (elastin, resilin, and wheat glutenin), adhesive/matrix proteins (spongin and conchiolin), and cyanophycin. We discuss the connection between protein sequence, structure, function, and biomimetic applications. Protein engineering techniques, such as directed evolution and rational design, can be used to improve the functionality of natural protein-based materials. For example, the inclusion of specific protein domains, particularly those observed in structural proteins, such as silk and collagen, enables the creation of novel biomimetic materials with exceptional mechanical properties and adaptability. This review also discusses recent advancements in the production and application of new protein-based materials through the approach of synthetic biology combined biomimetics, providing insight for future research and development of cutting-edge bio-inspired products. Protein-based polymers that utilize nature’s designs as a base, then modified by advancements at the intersection of biology and engineering, may provide mankind with more sustainable products. Full article