Purpose High-quality employee motivation can contribute to an organization’s long-term success by supporting employees’ well-being and performance. Nevertheless, there is a paucity of research concerning how organizations motivate workers in non-traditional work contexts. In the algocratic context of the gig-economy, the purpose of this paper is to understand the role that technology can play in motivating workers. Design/methodology/approach Drawing on the self-determination theory, job-characteristic theory and enterprise social media research, this conceptual paper explores how the architecture of the digital labor platforms underlying the gig-economy (and the characteristics of jobs mediated through these IT artifacts) can impact key antecedents of self-motivation. Findings Combining theory and empirical evidence, this paper develops a mid-range theory demonstrating how organizations can support the self-motivation of gig-workers through the thoughtful design of their digital labor platforms and the integration of two social media tools (namely, social networking and social badging). Research limitations/implications This paper answers calls for psychologically-based research exploring the consequences of gig-work as well as research studying the impacts of advanced technologies in interaction with work contexts on motivation. In theorizing around a large set of social-contextual variables operating at different levels of analysis, this paper demonstrates that individual-level motivation can be influenced by both task-based and organizational-level factors, in addition to individual-level factors. Originality/value The proposed theory provides novel insight into how gig-organizations can leverage widely accessible social media technology to motivate platform workers in the absence of human supervision and support. Theoretical and practical implications are discussed.
J. Mehrer, Courtney J. Spoerer, Emer C Jones
et al.
Significance Inspired by core principles of information processing in the brain, deep neural networks (DNNs) have demonstrated remarkable success in computer vision applications. At the same time, networks trained on the task of object classification exhibit similarities to representations found in the primate visual system. This result is surprising because the datasets commonly used for training are designed to be engineering challenges. Here, we use linguistic corpus statistics and human concreteness ratings as guiding principles to design a resource that more closely mirrors categories that are relevant to humans. The result is ecoset, a collection of 1.5 million images from 565 basic-level categories. We show that ecoset-trained DNNs yield better models of human higher-level visual cortex and human behavior. Deep neural networks provide the current best models of visual information processing in the primate brain. Drawing on work from computer vision, the most commonly used networks are pretrained on data from the ImageNet Large Scale Visual Recognition Challenge. This dataset comprises images from 1,000 categories, selected to provide a challenging testbed for automated visual object recognition systems. Moving beyond this common practice, we here introduce ecoset, a collection of >1.5 million images from 565 basic-level categories selected to better capture the distribution of objects relevant to humans. Ecoset categories were chosen to be both frequent in linguistic usage and concrete, thereby mirroring important physical objects in the world. We test the effects of training on this ecologically more valid dataset using multiple instances of two neural network architectures: AlexNet and vNet, a novel architecture designed to mimic the progressive increase in receptive field sizes along the human ventral stream. We show that training on ecoset leads to significant improvements in predicting representations in human higher-level visual cortex and perceptual judgments, surpassing the previous state of the art. Significant and highly consistent benefits are demonstrated for both architectures on two separate functional magnetic resonance imaging (fMRI) datasets and behavioral data, jointly covering responses to 1,292 visual stimuli from a wide variety of object categories. These results suggest that computational visual neuroscience may take better advantage of the deep learning framework by using image sets that reflect the human perceptual and cognitive experience. Ecoset and trained network models are openly available to the research community.
Pierre-Marie Chauvin, Angèle Merlin, Xavier Fresquet
et al.
This paper explores the integration of generative AI into the fashion design process. Drawing on insights from the January 2025 seminar ``Tisser le futur,'' it investigates how AI reshapes creative workflows, from ideation to prototyping, while interrogating the ethical, aesthetic, and labor implications. The paper highlights co-creative dynamics between humans and machines, the potential for aesthetic innovation, and the environmental and cultural challenges of algorithmic design.
Objective“Habitat garden” is an urban green space that integrates “habitat” and “garden”, and is a garden with habitat function, and auxiliary functions such as landscape beautification, leisure and recreation, communication and interaction, public education, health and healing, or improvement of living environment. It is a practical carrier to achieve the core goal of a garden city featuring “harmonious coexistence between man and nature”, and it is also an innovative model for urban biodiversity conservation. Clarifying the scientific construction approaches is key to promote the large-scale and standardized construction of habitat gardens in garden cities. MethodsBased on an in-depth examination of the definition and status of habitat gardens within the context of garden city construction, this research discusses in combination with practice, the feasible approach for habitat garden construction.ResultsThe habitat garden construction approaches consist of the following 4 steps. Firstly, select a habitat garden site in combination with multi-scale site analysis, while investigating local and surrounding biotic and abiotic environments. Secondly, evaluate environmental potential and further identify the target species to be restored. Thirdly, implement the project of habitat restoration and landscape creation according to the habitat characteristics and local function positioning of those target species. Finally, carry out ecological monitoring and nature-based habitat management in the project. As for the site selection for habitat garden, a multi-scale feasibility analysis should be conducted first, involving the ecological analysis of landscape connectivity index and ecological sources, and the feasibility analysis of land management. Then a site investigation should be conducted, including the investigation of nonbiological environment aiming to reveal the habitat characteristics of the selected site and clarify prominent environmental issues of the site, accompanied by a species, population, or community investigation for existing and former plants, insects, birds, small mammals, amphibians and reptile, soil and surface arthropods, soil microorganisms, and other biological groups within and around the site in an effort to understand the current and potential distribution levels of biodiversity in the site. The evaluation of environmental potential encompasses four key analytical components: biological distribution potential analysis for evaluating local spatial distribution and local habitat suitability of dominant species, populations, and ecological communities; interspecific interaction potential analysis for examining trophic relationships, competitive interactions, and symbiotic associations among organisms; community succession potential analysis for investigating ecosystem succession trajectories, developmental rates, and potential equilibrium states; migration potential analysis for assessing dispersal capabilities and movement patterns of flora and fauna. The project entails habitat restoration and landscape recreation for target species and populations comprising two integral components: One is that habitat construction strategy should be based on habitat and feeding preferences of the target species and populations, and optimize conditions to support organisms’ survival, reproduction, and adaptive capacity against stressors by replenishing native vegetation and food resources, creating sheltered microhabitats with optimal perching conditions, and restoring natural refuges; the other is to set up artificial overwintering sites for natural enemies within the site. The nature-based habitat management encompasses two key aspects: One is to emphasize the “self sustain” of the ecosystem by minimizing artificial interference, such as night protection, noise isolation, and volunteer plant protection, and the other is to provide necessary artificial regulation that conforms to nature, such as the removal or cutting of malignant weeds. The ecological monitoring is to realize the sustainable development and dynamic regulation of habitat gardens. The monitoring results show that the richness and abundance of natural enemies (including natural enemy insects, aphidophagus natural enemies, and aphidophagus ladybugs) in a habitat garden after one year of construction of the garden, along with the abundance of lacewings, are significantly higher than in ordinary green spaces. Conversely, the average pest density per branch in ordinary green spaces is 3.91 times higher than in the habitat garden. The construction of habitat gardens has achieved the goals of restoring local food chains and repairing nutrient relationships, while significantly advancing sustainable pest control and biodiversity enhancement. The vegetation richness has increased from over 30 to over 130, as well as the diversity of birds. Mammals such as the Northeast Hedgehog (Erinaceus amurensis) and Weasel (Mustela sibirica) have also built burrows here as habitat.ConclusionThis research proposes that the fundamental prerequisite for the construction of habitat gardens is the joint participation of multiple fields throughout the entire process, the key link for the construction of habitat gardens is the evaluation of environmental potential, and the basic guarantees for the sustainable development of habitat garden are ecological monitoring and habitat management in alignment with nature.
Aesthetics of cities. City planning and beautifying, Architectural drawing and design
ObjectiveAgainst the backdrop of high-density urban development, residents’ mental health problems have become increasingly severe. Access to urban green spaces is widely regarded as an important approach to improving residents’ mental health. Exploring the impact of green space characteristics on mental health benefits can provide a theoretical basis for urban green space planning and design from the perspective of healthy city. This research aims to clarify the internal relationships between objective and subjective green space characteristics and different mental health benefits (emotional restoration, cognitive enhancement, and stress relief) through explainable machine learning models.MethodsA mental health perception restoration experiment was carried out in two green spaces (Yanziji Park and Xiamafang Park) in Nanjing, with 56 participants engaged in two-hour free activities in the green spaces. During this period, GPS trajectories, data on objective green space characteristics, data on perception assessment of subjective green space characteristics, and data on self-assessment of mental health benefits were collected. Objective green space characteristics include the Normalized Difference Vegetation Index (NDVI), green view index, canopy density, actual noise dB (A), and spatial attractiveness, which are measured by remote sensing, semantic segmentation, and acoustic instruments. Subjective green space characteristics, such as perceived greenness, perceived noise, and perceived attractiveness, are evaluated by means of a 5-point Likert scale questionnaire. Mental health benefits are divided into the three types of emotional restoration, cognitive enhancement, and stress relief, and are assessed using the Restorative Outcomes Scale (ROS). To analyze and clarify the relationships between objective and subjective green space characteristics and different types of mental health benefits, the research adopts the Light Gradient Boosting Machine (LightGBM) model, combined with SHapley Additive exPlanations (SHAP) to measure and explain the importance of green space characteristics for mental health benefits. Based on the SHAP values, the non-linear relationships between them are further clarified.ResultsThrough the analysis of 3 types of mental health benefits and 5 models, the LightGBM model outperforms other algorithms (such as Random Forest and XGBoost) in terms of prediction accuracy (R 2: 0.523 – 0.642), with its robustness in capturing complex feature interactions being verified. The SHAP value analysis shows that subjective green space characteristics have a stronger relative impact on mental health outcomes than objective indicators. Specifically, perceived attractiveness is the most important contributing factor, followed by perceived greenness and perceived noise. Notably, the positive impact of perceived greenness on mental health is greater than that of objective indicators such as green visibility and NDVI. In addition, in terms of noise, excessive actual noise could inhibit cognitive enhancement and stress relief. However, moderate perceived noise could promote emotional restoration and stress relief. For example, when the actual noise exceeds 53.88 decibels in the cognitive enhancement model and 52.73 decibels in the stress relief model, negative effects would occur. While in the emotional restoration model, when the perceived noise is within a certain range (less than 2.58 points), it is beneficial for emotional restoration.ConclusionThe results of this research provide empirical evidence for the internal relationship between urban green spaces and residents’ mental health. Firstly, this research constructs an indicator system covering both objective and subjective characteristics. By combining field measurements, questionnaire surveys, and advanced machine learning algorithms, the research explores the impact of green space characteristics on emotional restoration, cognitive enhancement, and stress relief. Secondly, subjective green space characteristics play a prominent role in influencing mental health benefits. The combined influence of perceived attractiveness and perceived greenness is the most significant. The results of non-linear regression show that actual noise has an inhibitory effect on cognitive enhancement and stress relief, while moderate perceived noise can promote emotional restoration and stress relief. Finally, this research provides a direction for further exploring the in-depth association mechanism between green spaces and mental health, and also offers data support for urban green space planning and design aimed at promoting residents’ mental health.
Aesthetics of cities. City planning and beautifying, Architectural drawing and design
Elena Eramo, Marina Giannetto, Giovanni Bruno
et al.
<p class="Pa0">The current digital ubiquity of archives imposes an increasingly sophisticated reading and use of archival tools. Simultaneously, it multiplies the possibilities and methods for supporting the public use of documentary memory, even though computable conceptual models of descriptions, and progressively transforms archival data usage into knowledge usage.</p><p>In this research scenario, the present paper focuses on conception of an interface for the use of digital archives, shaped by the encounter of two distinct informational typologies: the architecture and the document. Specifically, the disciplinary tools of architectural representation, in their most current digital declinations, are here employed to develop the user experience of the new archives, adopting computable conceptual models (ontologies) adaptable to multiple informational models. The proposed method – that we called <em>documenta</em>BIM – adopts the current structure of Building Information Modelling (BIM), proposing a theoretical and methodological shift. The traditional workflow of Heritage BIM – in its most updated version substantiated by the application of ontologies – is, here, applied in reverse as a tool to access and enable the spatial and semantic interrogation of archival data, related to the three-dimensional representation of an architectural model. The first prototype of <em>documenta</em>BIM is developed for the <em>Archivio storico della Presidenza della Repubblica </em>(ASPR) and, once adequately implemented and tested, will be integrated into a specific Section of the Portale of the ASPR and populated through the digital objects preserved in the associated Digital Library.</p><p>DOI: https://doi.org/10.20365/disegnarecon.34.2025.3</p>
This paper carries out a detailed analysis of the method of designing a plan of a small-sized apartment developed by Alexander Klein, a Russian-German-Israeli architect. This design tool was created by him in Germany between 1927 and 1938, drawing on the interdisciplinary research prevalent at the time and on his own experimental results. It allowed architects to independently develop an optimal solution for “minimum housing” or evaluate an existing project. The fundamental principles of this method were explained by the architect in a series of articles published during the latter half of the 1920s and early 1930s, earning him recognition within the architectural community. A thorough examination of Klein’s “invention” makes it possible to appreciate the uniqueness of his concept, while also comprehending its relation to the ideas of his contemporaries, colleagues, and theorists in the field of small-sized architecture.
The article primarily relies on an in-depth analysis of Klein’s theoretical works and those of his colleagues. This analysis explores the evolution of the methodology, its reception, and its implementation in the context of Germany’s economic decline in the late 1920s. Additionally, the study explores the interplay between the architect’s earlier ideas from the mid-1920s and his later work in the early 1950s.
This research incorporates hitherto unpublished archival materials and foreign-language sources into scholarly discourse and contributes to a deeper comprehension of German architects’ strategies in addressing the challenge of “minimum housing” in the last half of the 1920s.
History (General) and history of Europe, Language and Literature
Simion-Vlad Bogolin, Ioana Croitoru, Hailin Jin
et al.
Profiting from large-scale training datasets, advances in neural architecture design and efficient inference, joint embeddings have become the dominant approach for tackling cross-modal retrieval. In this work we first show that, despite their effectiveness, state-of-the-art joint embeddings suffer significantly from the longstanding “hubness problem” in which a small number of gallery embeddings form the nearest neighbours of many queries. Drawing inspiration from the NLP literature, we formulate a simple but effective framework called Querybank Normalisation (QB-NORM) that re-normalises query similarities to account for hubs in the embedding space. QB-NORM improves retrieval performance without requiring retraining. Differently from prior work, we show that QB-NORM works effectively without concurrent access to any test set queries. Within the QB-NORM framework, we also propose a novel similarity normalisation method, the Dynamic Inverted Softmax, that is significantly more robust than existing approaches. We showcase QB-NORM across a range of cross modal retrieval models and benchmarks where it consistently enhances strong baselines beyond the state of the art. Code is available at https://vladbogo.github.io/QB-Norm/.
Federico Castagna, Nadin Kökciyan, I. Sassoon
et al.
Chatbots are conversational software applications designed to interact dialectically with users for a plethora of different purposes. Surprisingly, these colloquial agents have only recently been coupled with computational models of arguments (i.e. computational argumentation), whose aim is to formalise, in a machine-readable format, the ordinary exchange of information that characterises human communications. Chatbots may employ argumentation with different degrees and in a variety of manners. The present survey sifts through the literature to review papers concerning this kind of argumentation-based bot, drawing conclusions about the benefits and drawbacks that this approach entails in comparison with standard chatbots, while also envisaging possible future development and integration with the Transformer-based architecture and state-of-the-art Large Language models.
This work introduces MICSim, an open-source, pre-circuit simulator designed for early-stage evaluation of chip-level software performance and hardware overhead of mixed-signal compute-in-memory (CIM) accelerators. MICSim features a modular design, allowing easy multi-level co-design and design space exploration. Modularized from the state-of-the-art CIM simulator NeuroSim, MICSim provides a highly configurable simulation framework supporting multiple quantization algorithms, diverse circuit/architecture designs, and different memory devices. This modular approach also allows MICSim to be effectively extended to accommodate new designs. MICSim natively supports evaluating accelerators' software and hardware performance for CNNs and Transformers in Python, leveraging the popular PyTorch and HuggingFace Transformers frameworks. These capabilities make MICSim highly adaptive when simulating different networks and user-friendly. This work demonstrates that MICSim can easily be combined with optimization strategies to perform design space exploration and used for chip-level Transformers CIM accelerators evaluation. Also, MICSim can achieve a 9x - 32x speedup of NeuroSim through a statistic-based average mode proposed by this work.
Kanga Marius N’Gatta, H. Belaid, Joelle El Hayek
et al.
Cellulose nanocrystals (CNC) are drawing increasing attention in the fields of biomedicine and healthcare owing to their durability, biocompatibility, biodegradability and excellent mechanical properties. Herein, we fabricated using fused deposition modelling technology 3D composite scaffolds from polylactic acid (PLA) and CNC extracted from Ficus thonningii. Scanning electron microscopy revealed that the printed scaffolds exhibit interconnected pores with an estimated average pore size of approximately 400 µm. Incorporating 3% (w/w) of CNC into the composite improved PLA mechanical properties (Young's modulus increased by ~ 30%) and wettability (water contact angle decreased by ~ 17%). The mineralization process of printed scaffolds using simulated body fluid was validated and nucleation of hydroxyapatite confirmed. Additionally, cytocompatibility tests revealed that PLA and CNC-based PLA scaffolds are non-toxic and compatible with bone cells. Our design, based on rapid 3D printing of PLA/CNC composites, combines the ability to control the architecture and provide improved mechanical and biological properties of the scaffolds, which opens perspectives for applications in bone tissue engineering and in regenerative medicine.
Soft robots are envisioned as the next generation of safe biomedical devices in minimally invasive procedures. Yet, the difficulty of processing soft materials currently limits the size, aspect‐ratio, manufacturing throughput, as well as, the design complexity and hence capabilities of soft robots. Multi‐material thermal drawing is introduced as a material and processing platform to create soft robotic fibers imparted with multiple actuations and sensing modalities. Several thermoplastic and elastomeric material options for the fibers are presented, which all exhibit the rheological processing attributes for thermal drawing but varying mechanical properties, resulting in adaptable actuation performance. Moreover, numerous different fiber designs with intricate internal architectures, outer diameters of 700 µm, aspect ratios of 103, and a fabrication at a scale of 10s of meters of length are demonstrated. A modular tendon‐driven mechanism enables 3‐dimensional (3D) motion, and embedded optical guides, electrical wires, and microfluidic channels give rise to multifunctionality. The fibers can perceive and autonomously adapt to their environments, as well as, probe electrical properties, and deliver fluids and mechanical tools to spatially distributed targets.
Artificial Intelligence (AI) was created to be an assistant to humankind. The architectural design process is one of them. Artificial Intelligence is starting to enter the realm of architecture and design. Lately, teams have developed many AI algorithms to generate random images based on the database they had just by texting a command for them. MidJourney is a discord-based Artificial Intelligence for generating images with the text-to-images method, and its database is infinite; it can suggest incredible things. Architects can use MidJourney as a design ideas generator with specific commands. MidJourney, one of the AI pioneers in the design world, still has many shortcomings and limitations. Incorrect details and forms are one of its limitations. This exploration using AI should be deepened as part of MidJourney development. This study aims to discover how far researchers have done the command prompt in exploring MidJourney in architecture. This research can be the basis for further research in developing the text-to-image Artificial Intelligence Generator results. This paper uses a systematic literature review method on journals, books, and websites linked with Artificial Intelligence, architecture, and texture selection for building. The systematic literature review involves collecting questions, identifying keywords, screening articles, analyzing, discussing, and concluding. The results explain the role of Artificial Intelligence in architectural design. MidJourney can choose the proper material for its innovations by generating concept ideas. However, MidJourney has yet to be able to implement the logic of structures and buildings in its design even though it has used architecture-related prompts, perhaps because it was not specifically designed to produce architectural drawings but graphic design only. The challenge is based on the user defining the limitation and the main ideas for generating Artificial Intelligence.
This paper explores Oscar Nemon’s Center of Universal Ethics project, a visionary but unrealized endeavor within his utopian movement advocating for universal ethics. Drawing heavily from interwar modernist and avant-garde architecture, particularly Russian constructivism, the design resonates with Konstantin Melnikov’s architectural oeuvre. Through comparative analysis, the paper proposes hypotheses regarding the project’s origins and its relationship with Melnikov’s innovative architectural concepts.
Elena Marco, Mina Tahsiri, Danielle Sinnett
et al.
Lockdown impositions have impacted people’s lives, their health and wellbeing, changing the ways in which dwellings are used and occupied. Spaces within the home have had to be rapidly renegotiated, redesigned and resynchronised in ways not yet fully explored or understood. Social relationships in the home have shifted and adapted as a result of ‘enforced togetherness’. This study presents a rich snapshot of 23 UK designer-architects’ transformative lived experiences of lockdown, using an interpretative phenomenological approach. It identifies four critical socio-spatial affordances that are rooted in the physical and mental wellbeing of the architects/designers. ‘Individuality’ suggests the need for increased physical separation to be alone. ‘Communality’ denotes a need for household members to be together. Both individuality and communality are seen as two opposite dimensions of the socio-spatial affordance of the home. ‘Adaptability’ points to requirements for flexible, decluttered and versatile spaces to enable ‘vibrancy’ not ‘suffocation’. Finally, ‘connectivity’ encompasses the need for a strong connection between the indoors and outdoors. These dimensions must be considered in housing design, so new housing models can emerge. The use of interpretative phenomenological analysis, employing the architectural tool of drawing, is shown to be a useful approach for housing research. 'Practice relevance' The impact on households’ wellbeing as a consequence of Covid-19 lockdowns has led to new considerations for future housing design. In a post-Covid environment, the particular needs for housing have been transformed. The findings and insights from this study will help to reframe the existing conventions of housing design criteria (e.g. a reliance on defining space functions) toward a clearer set of qualities for inhabitant and household wellbeing. New housing criteria involving socio-spatial affordances of individuality, communality, adaptability, and connectivity are shown to be viable and highly appropriate. These new dimensions highlight how inhabitants’ wellbeing can be included in viable and affordable housing.
Architectural engineering. Structural engineering of buildings