Search Results (6,627)

In unmanned ground vehicle (UGV) swarm systems, comprehensive environmental awareness is critical for coordinated operations. Yet they are frequently deployed in occlusion-rich, constrained environments where multi-agent visual fusion is essential. However, existing methods are critically limited by offline-calibrated extrinsic parameters, hindering flexible deployment, and by a strong co-visibility assumption, which fails under severe occlusion. To overcome these constraints, we introduce an end-to-end, calibration-free framework for the joint registration of cameras and subjects. Our approach begins with a single-view module that estimates subjects’ poses and appearance features. Subsequently, a novel graph-based pose propagation module (GPPM) treats UGVs’ cameras as nodes in a graph, connecting them with edges when they share co-visible subjects identified via appearance matching. Breadth-first search (BFS) then finds the shortest registration path from any camera to a designated root camera, enabling pose propagation via local co-visibility links and global alignment of all subjects into a unified bird’s-eye-view (BEV) space. This strategy relaxes the stringent requirement of full co-visibility with the root node. A multi-task loss function is proposed to jointly optimize pose estimation and feature matching. Trained and evaluated on a synthetic dataset with occlusions (CSRD-O) collected by a UGV swarm system, our framework achieves mean camera pose errors of 1.57 m/8.70° and mean subject pose errors of 1.40 m/9.14°. Furthermore, we demonstrate a scene monitoring task using a UGV swarm system. Experiments show that the proposed method generates robust BEV estimates even under severe occlusion and low inter-view overlap. This work presents a purely visual, self-calibrating multi-view fusion perception scheme, demonstrating its potential to support cooperative perception, task-oriented monitoring, and collective situational awareness in UGV swarm systems. Full article

Stunting remains a major public health challenge in Indonesia, with a national prevalence of 21.6% in 2022. Rural regions such as Madura face heightened vulnerability due to cultural dietary taboos, gendered caregiving structures, intergenerational authority, and digital disparities that shape household nutrition decision-making. This formative qualitative study explores stakeholders’ perceptions to inform the conceptual development of gender-sensitive serious games for nutrition communication in rural Indonesia. Using an exploratory design, 42 informants, including mothers of children under five, brides-to-be, health cadres, midwives, religious and community leaders, and local digital actors, were recruited across rural Madura. Thematic analysis examined trust-based communication patterns, gender dynamics, perceptions of artificial intelligence (AI), and contextual conditions influencing digital health acceptance. Findings indicate that acceptance of gender-sensitive serious games depends on cultural alignment, institutional endorsement, perceived credibility, and usability in low-resource settings. Participants consistently positioned serious games and AI-supported features as complementary communication layers rather than replacements for health workers. Game-based tools were considered potentially relevant when designed to support intergenerational co-play, integrate local narratives and religious values, and function in low-connectivity environments. Rather than evaluating an implemented intervention, this study proposes a conceptual design framework grounded in feminist communication perspectives, serious games scholarship, and technology acceptance theory. The findings provide context-sensitive insights to guide future prototype development and pilot testing within hybrid, community-based nutrition communication systems. Full article

The tourism and hospitality sector in Saudi Arabia is undergoing rapid sustainability transformation under the strategic direction of Vision 2030. This study examines the maturity of Sustainability Actions (SAs), their key drivers, and implementation barriers, comparing the perceptions of industry practitioners and academic experts. Using a qualitative abductive research design based on 20 in-depth semi-structured interviews with industry and academic experts in Saudi Arabia, followed by thematic analysis using a machine learning Qualcoder 3.7 software, the findings reveal both convergence and divergence between the two groups. While both recognize Vision 2030 as the primary catalyst and acknowledge financial costs and knowledge gaps as major barriers, industry experts emphasize operational efficiency and short-term performance outcomes, whereas academics advocate systemic transformation grounded in circular economy principles and long-term socio-ecological regeneration. The results demonstrate that sustainability adoption in Saudi Arabia is shaped not only by market demand but also by a strong government-led institutional framework that accelerates sectoral change. The findings are structured across environmental, social, and economic sustainability dimensions, offering differentiated implications for industry practitioners and academic stakeholders within emerging tourism economies. The study contributes to sustainability and tourism and hospitality literature by offering a comparative multi-perspective analysis and by conceptualizing sustainability transition as a hybrid model combining policy direction, market incentives, and knowledge collaboration. Managerially, the findings highlight the need for regulatory clarity, targeted financial mechanisms, capacity building, and stronger industry–academia integration to institutionalize sustainability practices in emerging tourism economies. Full article

Present-day artificial intelligence systems (AI), virtual assistants, and devices connected to the Internet of Things (IoT) are playing an increasingly important role in decision-making processes in the everyday lives of individuals and daily operations of organizations. In this respect, the users’ trust is a key factor determining their acceptance and effective use. In contemporary digital ecosystems, this trust increasingly becomes a component of sustainable digital marketing, in which transparent data practices and responsible communication shape long-term consumer–technology relationships. This paper analyzes the halo effect as a psychological mechanism affecting the perception of competences, reliability, and ethics in the case of technologies based on AI. Based on the literature on behavioral economics, it was shown how positive associations with the interface, brand, or previous experience of the user may lead to excessive trust in technology. Such mechanisms also play a significant role in shaping sustainable consumption patterns, as users—guided by cognitive shortcuts—can adopt technologies in ways that either strengthen or weaken responsible digital behaviors. Moreover, the potential risks associated with this phenomenon were also indicated. The aim of this paper was to present how the utilization of the halo effect influences the generation of trust in smart systems and the formulation of implication for management practices and technology design. These implications are increasingly important in the context of sustainable digital marketing policy, where organizations must align persuasive communication with ethical standards and with rising expectations regarding sustainable digital transformation. Relationships between variables were analyzed using structural equation modeling (SEM), making it possible to verify complex dependencies between the perceived image of technology, the halo effect, and the users’ trust. This study tested three core hypotheses regarding the halo effect’s role, the foundational importance of security, and the mediating function of trust in technology adoption. The results of these analyses indicate that the halo effect significantly affects the level of trust in each of the investigated areas, with the strongest effect observed in the case of virtual assistants, where perception of the human-like characteristics of the interface considerably strengthened trust in the competences and reliability of the system. This finding has particular relevance for AI-driven personalization mechanisms, which increasingly guide consumer decision-making and shape their long-term behavioral patterns in online environments, with direct implications for sustainable consumption. This paper provides contribution to innovation management and technical marketing, stressing the importance of cognitive and emotional factors in the acceptance of new technologies. At the same time, it highlights the theoretical need to integrate responsible AI design with sustainable digital marketing strategies The findings suggest that ensuring trust, once established, has the potential to support not only technological innovation but broader societal goals related to responsible consumption, environmental stewardship, and long-term digital well-being aligned with sustainable development principles. However, this study stops short of empirically measuring sustainable consumption behaviors, offering instead a conceptual link that requires further empirical validation. Full article

Extreme climate events in Australia are increasing. Since 2019, fires and floods have devastated all states and territories in Australia, leading to a reckoning via several government inquiries, including a Royal Commission, on how governments, emergency services, communities, and individuals prepare for, respond to, and recover from such catastrophic events. It also raises the question of how the media reports and reacts to these events; in Australia, the national broadcaster, the Australian Broadcasting Corporation (ABC), has taken on the role of emergency broadcaster. This paper employs a cross-sectional design to examine how media practitioners from ABC Canberra navigate their role as emergency broadcasters, how they prepare for and respond to emergencies, and how they interact with the community during those events. This examination includes reflections and memories from a series of interviews we conducted with these practitioners about the catastrophic bushfires in 2019/2020 in the Australian Capital Territory (ACT) region. Using this design and a Bourdieusian lens, the study examined the practices of media practitioners during a catastrophic emergency and their perceptions of preparedness for future disasters. We examined how training (cultural capital), networks (social capital), online expertise (digital capital), and experience (habitus) contribute to preparedness in emergency broadcasting. The study has both a theoretical and practical contribution: theoretically, it expands Bourdieu’s cultural production model by applying it to a form of broadcasting that has not been examined in this way; practically, it contributes to our understanding of media practitioners and how they practice during emergency broadcasting. Full article

Task analysis is a foundational component of instructional design in special education, yet it can impose substantial time and cognitive demands on teachers. Artificial intelligence (AI) tools such as ChatGPT may provide support for instructional planning tasks by assisting educators in generating and organizing task sequences. This study examined the effectiveness, readability, time efficiency, and acceptability of ChatGPT-assisted task analysis compared to a traditional task analysis method. Thirty-two special education teachers participated in a randomized between-groups study in which they developed task analyses using either a traditional approach or ChatGPT supported by a structured interaction protocol. Task analyses were evaluated based on alignment with expert-developed models, readability, and development time, and teachers’ perceptions of acceptability were also examined. Results indicated that ChatGPT-assisted task analyses required significantly less development time while demonstrating strong alignment with expert-generated models. Readability levels and the number of task steps were similar across groups. Teachers who used ChatGPT also reported positive perceptions regarding the usefulness and acceptability of AI assistance in instructional planning. These findings suggest that AI-assisted tools may support teachers in developing task analyses more efficiently while maintaining instructional clarity. However, given the exploratory nature of the study and the limited sample, further research is needed to examine how AI-assisted task analysis may influence instructional practice and student learning outcomes in special education. Full article

Residential yards are underappreciated and under-studied urban ecosystems. While there is a slow paradigm change taking place for using native plants in urban landscapes, little is known about the perceptions and preferences for native landscapes and the inclination to use native plants in residential yards. For this study, two plots were designed and planted with native plants to resemble residential gardens, and site visit surveys were used to collect data. Likert scale and checkbox questions with additional written comments were used to assess perceptions and preferences on the aesthetics, maintenance, environmental value, and willingness to adopt a native plant garden. The results were mostly positive; a high majority of respondents found the yards aesthetically pleasing, well-maintained, and very good for the environment. Although perceptions were positive, the results for willingness to use native plants in their own yard were nearly equal between willing and not willing/neutral. However, a high majority stated their likelihood to use native plants would increase if they knew more about the ecological benefits. The knowledge gained from this study will help (1) designers to create ecology-based aesthetic landscapes, (2) policy-makers to craft ecology-focused landscape codes and ordinances, and (3) educators and advocates to target behaviors and preferences in educational materials and social marketing campaigns. Full article

Background: Career plateauing is an increasingly recognized workforce challenge in nursing, associated with reduced motivation, professional growth, and retention. While organizational support and role-related factors are known to influence nurses’ work outcomes, their combined effect on career plateauing remains insufficiently explored, particularly in university hospital settings. Aim: This study aimed to examine the effect of perceived organizational support, role clarity, and role ambiguity on career plateauing among staff nurses working in university hospitals. Methods: A descriptive correlational cross-sectional design was employed. Data were collected from 210 staff nurses working in two Egyptian university hospitals using four validated instruments: a demographic data sheet, the Perceived Organizational Support Scale, the Role Clarity and Role Ambiguity Questionnaire, and the Career Plateauing Scale. Data were analyzed using Pearson correlation and univariate and multivariate linear regression analyses with SPSS version 27. Results: Most nurses reported moderate levels of organizational support (85.2%) and career plateauing (84.3%). Role ambiguity demonstrated a significant negative correlation with career plateauing (r = −0.441, p < 0.01), while organizational support and role clarity were positively associated with plateauing perceptions. Multivariate regression analysis revealed that role ambiguity was the strongest predictor of both hierarchical and job content plateauing (p < 0.001), followed by role clarity and organizational support. Conclusions: Reducing role ambiguity may alleviate perceptions of career plateauing among nurses. However, increased organizational support and role clarity alone may heighten awareness of limited advancement opportunities if not accompanied by tangible career development pathways. Nursing leaders should focus on clarifying roles while creating realistic and transparent opportunities for professional growth. Full article

Pareidolia is often framed as a viewer-side illusion: a tendency to perceive meaningful forms—especially faces—in ambiguous inputs. This Concept Paper argues that pareidolia can also be deliberately engineered and therefore provides a tractable entry point into the neurophysiology of visual creativity. We propose a unifying construct in which engineered pareidolia functions as externally scaffolded mental imagery: minimal visual constraints recruit internally generated templates and top-down inference while remaining anchored to sensory input. To strengthen theoretical rigor, we define necessary and sufficient features that distinguish this construct from adjacent accounts (scaffolded cognition; perceptual scaffolding; bistable perception). Using Arcimboldo’s composite portraits and Dürer’s embedded face in View of the Arco Valley, plus a canonical Renaissance example (Leonardo’s Bacchus/Saint John the Baptist), we outline distinct “design regimes” that modulate cue validity, attentional release, and interpretive switching. We then connect engineered pareidolia to creativity research by linking pareidolia design and detection to measurable constructs in divergent/creative perception, including but not limited to Torrance-style domains, and we propose feasible behavioral and neurophysiological paradigms that control for artistic skill and clinical status. Finally, we distinguish benign pareidolia from hallucination, discuss clinical resonance in dementia with Lewy bodies where pareidolia can be quantified, and outline an empirically testable research program that reframes pareidolia as a bridge between imagination, perception, and creativity. Full article

Micro-crack detection on concrete surfaces is challenging because labeled micro-crack data are scarce, crack cues are extremely weak (often only a few pixels wide), and complex backgrounds (e.g., non-uniform illumination, shadows, and stains) degrade feature extraction; this study aims to improve both data availability and detection robustness for practical inspection. A Poisson image editing-based synthesis strategy is developed to generate visually coherent micro-crack samples via gradient-domain blending, and a Complex-Scene-Tolerant YOLO (CST-YOLO) detector is proposed on top of YOLOv10, following an “lighting decoupling–global perception–micro-feature enhancement” design. CST-YOLO integrates an Lighting-Adaptive Preprocessing Module (LAPM) to suppress illumination/shadow perturbations, a Spatial–Channel Sparse Transformer (SCS-Former) to model long-range crack topology efficiently, and a Small Object Focus Block (SOFB) to enhance micro-scale cues under cluttered backgrounds. Experiments are conducted on a 650-image dataset (200 real and 450 synthesized), in which synthesized samples are used only for training, and the validation/test sets contain only real images, with a 7:2:1 split. CST-YOLO achieves 0.990 mAP@0.5 and 0.926 mAP@0.5:0.95 at 139 FPS, and ablation results indicate complementary contributions from LAPM, SCS-Former, and SOFB. These results support the effectiveness of combining realistic synthesis and architecture-level robustness for real-time micro-crack detection in complex scenes. Full article

Sustainable tourist route design is a critical challenge in industrial heritage planning. While prior tourism routing algorithms predominantly minimize physical distance, and conventional heritage planning focuses on the static preservation of abandoned sites, both lack the multi-objective adaptability required for “living” industrial landscapes. In such dynamic environments, active production, tourism, and ecological conservation intricately coexist. To address this gap, this study proposes a novel, data-driven route planning framework, taking the Tianjin Changlu Salt Field as a case study. The genuine novelty lies in integrating multi-objective network optimization with spatial design implementation. The site is abstracted into a topological network comprising 13 nodes and 19 edges. Multi-attribute edge weights—incorporating spatial distance, travel time, landscape attractiveness, and ecological sensitivity—are quantified using entropy weighting fused with subjective preferences. Using the Floyd–Warshall algorithm, three theme-based touring routes are generated. Unlike traditional methods, this workflow actively translates algorithmic outputs into concrete spatial strategies, such as bypassing ecologically sensitive zones and transforming production facilities into perceptible landscape nodes. Comparative evaluations demonstrate that these optimized routes achieve higher comprehensive utility than baseline and designer-generated schemes, offering a pioneering, reproducible paradigm for the sustainable renewal of living industrial heritage. Full article

We present PIRATE (Precision Imaging Real-time Autonomous Tracker and Explorer), a fully autonomous unmanned surface vehicle designed to enable self-operating data collection and persistent tracking of mobile underwater targets through the tight integration of acoustic localization, onboard visual perception, and closed-loop navigation. PIRATE employs a single mobile acoustic receiver to estimate target position using time-difference-of-arrival (TDoA) measurements acquired at different times and locations through planned autonomous motion and uses these estimates to drive adaptive vehicle behavior and activate fine-grained visual sensing in real time. This architecture enables sustained target-driven operation, in which navigation, acoustic monitoring, and visual processing are dynamically coordinated based on mission context and localization uncertainty. The system integrates real-time AI-based visual detection and tracking with automatic mission control, allowing visual perception to operate opportunistically within an acoustically guided tracking loop rather than as a standalone sensing modality. Field experiments in a shallow-water environment demonstrate reliable autonomous navigation, single-receiver acoustic localization with meter-scale accuracy, and stable onboard visual inference under sustained operation. By enabling coupled acoustic tracking and onboard visual perception in a fully autonomous surface platform free of external infrastructure, PIRATE provides a practical foundation for fine-scale behavioral observation, adaptive marine monitoring, and long-duration studies of mobile underwater organisms. We demonstrate this advantage with two possible applications. Full article

In the context of advancing manufacturing, production systems are shifting toward human-centric and personalized production. However, accurately quantifying subjective user needs into precise product specifications remains a challenge. Taking child companion robots as an example, this paper proposed a novel product innovation design framework based on Extenics and Kansei engineering to optimize the texture design of smart products. By systematically integrating synergistic relationships among colour, material, and surface processing technology, the framework aimed to enhance the sustainable value and social sustainability of products by more precisely meeting users’ perceptual and emotional needs. The research methodology employed the semantic differential method to quantify user perception and utilized the K-means clustering algorithm to construct a chromatic colour sample library for smart products. Subsequently, by combining the multi-criteria decision-making tool grey relational analysis with statistical verification, the optimal design scheme was selected from the generated alternatives. Experimental results demonstrated that this method significantly reduced design subjectivity and ambiguity. By bridging the gap between user expectations and engineering solutions, the framework provides a systematic solution for mass customization and process optimization that promotes resource efficient and sustainable production, while also reducing the resource waste associated with traditional trial and error design processes. Full article

Building lighting has a significant impact on occupant health and well-being, energy efficiency, spatial perception, and visual comfort. Many current building lighting systems, however, continue to be insufficiently responsive to changing environmental conditions and human-centric demands, leading to ineffective energy use, poor visual quality, and disruption of the circadian rhythm. This disparity highlights the need for modern buildings to incorporate integrated, intelligent, and sustainable lighting design strategies. This review offers a methodical examination of current, emerging and future developments in building lighting research in six related fields within an architectural scope of building design and performance. To assess lighting effectiveness, it first examines both qualitative and quantitative performance metrics, including illuminance, luminance distribution, glare, color quality, and user comfort. Second, it examines lighting control systems that use tunable light sources that can dynamically change the spectral composition and intensity in response to task demands, occupancy patterns, and daylight availability. Third, the study examines circadian-centric lighting strategies, focusing on digital modeling and simulation approaches that capture real-world lighting conditions and biological reactions. Fourth, the function of virtual reality and sophisticated visualization tools is examined, emphasizing their role in design decision-making and pre-implementation assessment. Fifth, a critical evaluation is conducted of the expanding use of machine learning and data-driven techniques in adaptive lighting control, prediction, and optimization. Limited real-time adaptability, inadequate personalization, disjointed simulation frameworks, and poor integration of human-centric metrics with intelligent control systems are some of the major research gaps. Sustainable Development Goal (SDG) 7, SDG 11, and SDG 3 are in line with the review, which ends with a summary of future paths toward intelligent, energy-efficient, and human-centered building lighting systems. Full article

Public Open Spaces (POSs) on university campuses play a vital role in promoting student well-being, fostering social interaction, and enhancing academic engagement. Yet, in Indian technical institutions, these spaces are often underutilized due to poor design integration, lack of thermal comfort, and minimal user-centered planning. This study applies the Campus Open Space Index (COSI) to assess the functionality, inclusivity, and experiential quality of POSs at two premier Indian institutions, IIT Delhi and IIT Roorkee. COSI evaluates campus POSs across five dimensions: Physical Planning, Engagement, Need Perception & Behavior, Thermal Comfort, and Management. Through a mixed-methods approach involving surveys (n = 522), field observations, and spatial mapping, six open spaces from each campus were analyzed. The aspect-wise COSI results indicate that IIT Delhi performs better in Management (75.84%) and Thermal Comfort (60.56%), while IIT Roorkee performs better in Engagement (71.68%); both campuses show deficits in universal accessibility and climate responsiveness. The study reveals that POS effectiveness depends not only on spatial layout but also on user behavior, comfort, and perceived safety. COSI provides a replicable and scalable assessment model that supports data-driven decision-making for campus planners and administrators. This research advocates for participatory, student-centric planning approaches to transform campus POSs into more inclusive, responsive, and sustainable environments aligned with educational and social goals. Full article