Search Results (1,135)

Translating heterogeneous user requirements (URs) into robust engineering specifications for public-access products is a critical challenge, often impeded by information uncertainty and fragmented design processes. To address this, we propose an integrated decision-making framework underpinned by Rough Set Theory (RST) as a unified mathematical language for uncertainty management. The framework systematically guides customer-driven product development by integrating a series of RST-based methods: a Kano model analysis to screen URs, a novel rough-Shapley value model to determine their interdependent weights, a rough-QFD approach to translate them into weighted design requirements (DRs), and the rough-VIKOR method to select the optimal design alternative. A case study on public-access faucets validates the framework’s efficacy. The results demonstrate its capability to identify critical URs, derive robust DRs by systematically resolving technical attribute conflicts, and select a superior design solution that optimally balances hygiene, durability, and user experience. The application of the framework successfully identified Alternative ${\mathrm{A}}_1$ (Push-Activated Spout) as the optimal solution, demonstrating superior performance in proactive hygiene and core functionality. The results prove that maintaining data integrity through a unified RST pipeline effectively resolves early-stage design conflicts. This research contributes a rigorous, data-driven decision support system that enhances objectivity and information fidelity, providing a transparent and auditable methodology for designing human-centered public infrastructure. Full article

In recent years, applications with robots collaborating actively with humans have been increasing. The transition from Industry 4.0 to 5.0 rearranges the focus of fully automated processes to a human-centered system that allows more customization and flexibility. In human-centered systems, the robot is expected to safely assist or provide support to the human operator, avoiding any unintentional harm, while the latter is focused on tasks that require human reasoning, since current decision-making systems still have some limitations. This survey reviews all the main functionalities required to make a robot (collaborative or not) act as an assistant for human operators, analyzing and comparing solutions proposed by the authors (based on previous works) and/or the ones available in the literature. In this way, it is possible to combine those functionalities and build a complete framework enabling safe mobile manipulation while interacting with humans. In particular, a mobile manipulator is used to receive requests from a user, navigate in a human-shared environment, identify the requested object, and grasp and safely deliver such an object to the user. The framework, which is completed by a user interface designed using Android Studio, is developed in ROS1, tested, and validated on a real mobile manipulator in real-world conditions. Full article

Maritime Autonomous Surface Ships (MASS) are progressing from proof-of-concept to engineering test and initial application phases due to advancements in intelligent sensing, automatic control, and communication technologies. However, numerous studies have shown that the improvement of automation level does not linearly reduce human factor risks. Instead, it exhibits more complex evolutionary characteristics at the medium automation level. In particular, MASS Level 2 (MASS L2) features a “system-dominated, human-supervised” operational mode, and its human factor risks have become one of the key factors restricting the safe operation, large-scale application and sustainable long-term deployment of autonomous ships. This study employs a systematic literature review to analyze 89 core articles (2020–2025) and summarizes the theoretical basis, risk characteristics, and evolutionary trends of human factor risk research in MASS L2. The review results indicate that the current research consensus has gradually shifted from the traditional “human error”-centered explanatory paradigm to a systematic understanding of “information mismatches, opacity, and coupling failures in the human-machine-shore collaborative system”. Typical human factor risks in MASS L2 are mainly manifested as the degradation of supervisory cognition and situation awareness, imbalance in trust in automation, vulnerability in mode switching and takeover, skill degradation, and structural risks in ship-shore collaboration. Based on these findings, this study constructs a classification system and a comprehensive analysis framework for human factor risks in MASS L2, reveals the interaction relationships and dynamic evolution mechanisms among different risk types from a system-level perspective, and further discusses the limitations of existing research in terms of methods, data, and engineering applicability. Finally, considering the development trends of autonomous ship technology, this study proposes future research directions in human factor theoretical modeling, dynamic risk assessment, system design, and operation management. This study aims to provide a systematic knowledge framework for human factor risk research in MASS L2 and offer references for the safety design, safety management, and development of higher-level automation of autonomous ships, while supporting the sustainable and safe advancement of the global intelligent shipping industry. Full article

The Dammam Metropolitan Area (DMA) has been experiencing tremendous growth driven by increasing population and the oil industry. This has culminated in the development of the DMA, where the transportation system is reliant on automobiles, wide arterials, and a disjointed pedestrian environment. With the increasing progression of the Vision 2030 initiative, the Kingdom of Saudi Arabia (KSA) is focusing on livability and sustainable mobility. However, despite the massive efforts, the concepts of humanizing active mobility corridors remain insufficiently developed across Saudi cities. The paper will discuss the conceptual framework for developing the active mobility corridors of the DMA, an initiative of walkability, livability, and sustainable mobility with specific regard to the study region’s climatic and cultural environment. The methodology relies on qualitative desktop research supported by a structured and iterative literature synthesis using snowballing techniques. The resulting framework positions active mobility not merely as a transport function, but as a multidimensional system that promotes inclusion, comfort, and environmental resilience. Offering design and policy principles tailored to hot-arid Gulf contexts that contributes to national efforts to advance Quality of Life objectives under Vision 2030. Ultimately, this framework aims to contribute in human-centered mobility across the KSA and similar urban areas. Full article

Rapid urbanization has intensified microclimatic deterioration in temperate monsoon cities, directly affecting human thermal comfort. This study investigates the regulatory effects of common street tree species under varying street aspect ratios (H/W) and orientations in Shenyang, China, a representative temperate monsoon city characterized by cold winters. Field surveys and questionnaire data were combined with ENVI-met simulations to quantify thermal comfort responses using the Universal Thermal Climate Index (UTCI). Results demonstrate that street geometry strongly constrains microclimate regulation: streets with H/W = 1.2 and a SE–NW orientation achieved the most favorable balance between shading and ventilation, yielding the lowest UTCI values. Significant interspecies variability was observed: Golden Elm and Chinese Willow provided the greatest cooling benefits, whereas Ginkgo exhibited limited adaptability, particularly in enclosed or highly open canyons. A comparison with subjective thermal comfort votes confirmed strong model reliability, though discrepancies emerged in dense commercial areas due to non-meteorological factors. Based on these findings, a spatially driven, species-adaptive, and human-centered framework is proposed to optimize street greening strategies in a temperate monsoon city characterized by cold winters. This research provides quantitative evidence for urban greening design, highlights the necessity of integrating spatial form with tree-species selection, and offers practical guidance for resilient thermal comfort management in rapidly urbanizing cold-region cities. Full article

Eye movement research has emerged as a powerful tool in architectural and environmental design, offering insights into how people visually engage with built and natural surroundings. Eye tracking technology enables the study of visual attention, user engagement, and navigation patterns, thereby informing user-centered design. This paper reviews a wide and vast body of research that demonstrates eye tracking’s capacity to inform architectural and environmental design decisions by providing objective, data-driven insights into human perception and interaction with the built world. Key methodologies are discussed, including desktop, mobile, and VR-based systems, as well as recent advances in software analytics and artificial intelligence. Beyond summarizing the existing literature, this review critically evaluates methodological approaches, identifies key challenges, and outlines future research directions. The key findings indicate increased integration of immersive technologies, diversification of analytical paradigms, and expanded application in sustainable and user-centered design. However, methodological heterogeneity, limited ecological validation, and insufficient integration with design optimization frameworks remain significant limitations. This review provides a structured foundation for advancing interdisciplinary research and enhancing evidence-based architectural design. The paper concludes by outlining a forward-looking research agenda for creating more responsive, intuitive, and human-centered environments. Full article

Integrating Lean Construction (LC), the Circular Economy (CE), and Construction 5.0 (C5.0) remains challenging in emerging delivery contexts. This difficulty increases when procurement routines determine which practices become enforceable across tendering, contracting, and site execution. This study prioritized barriers to LCCE5.0 implementation in Morocco and translated expert judgments into actionable recommendations. A structured literature review informed the barrier inventory and conceptual framing. The study proposed a three-layer, life-cycle LCCE5.0 framework that links governance, operational routines, and digital enablers. It operationalized 40 critical barrier factors across six dimensions and five life-cycle macro-phases. A two-round Delphi study was conducted with 22 Moroccan experts using a 7-point Likert scale. Barriers were ranked using Round 2 (T2) medians with ties resolved using the interquartile range. Top-box agreement (ratings of 6–7) and consensus tiers were reported. The ranking showed strong stability across rounds, with 92.5% of barrier factors remaining stable. Kendall’s W at T2 equaled 0.817 (p < 0.001), indicating high panel consensus. Results indicated that constraints clustered in upstream governance. Three procurement-centered regulatory and contractual barriers topped the ranking (Mdn_T2 = 7). These barriers reflected missing CE procurement guidelines, limited weighting of environmental criteria, and the absence of circularity and digital requirements in tenders. Six additional barriers reinforced this procurement bottleneck. They included limited owner commitment, weak enforcement authority, limited top-management commitment, and regulatory instability. They also included low interorganizational trust, limited risk-sharing contracts, and tool-centered deployment of LCCE5.0 practices. These findings support procurement-focused recommendations to institutionalize auditable circular requirements and data-enabled verification in tendering and contracting routines. The proposed LCCE5.0 mechanism and the resulting recommendations require empirical validation beyond this Delphi-based prioritization. Full article

Human immunodeficiency virus (HIV) remains a major global health challenge, requiring accurate diagnostic testing for early detection. Chemiluminescent immunoassay screening, particularly the Architect HIV Ag/Ab Combo assay, followed by immunoblot confirmation using INNO-LIA™ has traditionally been used in many diagnostic workflows. To address these limitations, the U.S. Centers for Disease Control and Prevention (CDC) recommends the use of an HIV-1/2 antibody differentiation immunoassay, such as the Geenius HIV-1/2 Supplemental Assay, as part of the confirmatory testing algorithm. This study evaluates the performance of two rapid HIV-1/2 confirmatory assays—the Multisure HIV-1/2 Confirmatory Test and the Bio-Rad Geenius HIV-1/2 Supplemental Assay—within a CDC-aligned diagnostic framework, with the aim of assessing Multisure as a potential alternative differentiation assay. A total of 224 archived serum samples were analyzed, including true positives (n = 38), true negatives (n = 139), false positives (n = 20), and INNO-LIA™ indeterminate samples (n = 27), as defined by Architect HIV and INNO-LIA™ results. Samples were initially screened using the Architect HIV Ag/Ab Combo assay, confirmed by INNO-LIA™ and PCR, and subsequently re-tested using Multisure HIV-1/2 and Geenius HIV-1/2 assays. Diagnostic performance metrics were evaluated. Both rapid assays demonstrated 100% sensitivity and specificity when compared with INNO-LIA™. Among INNO-LIA™ indeterminate samples, Multisure HIV-1/2 classified 81.5% as negative compared with 55.6% using Geenius HIV-1/2. When compared with PCR, Multisure demonstrated higher specificity (89.2%) and positive predictive value (89.5%) than Geenius (82.9% and 84.6%). No confirmed HIV-2 infections were identified in the analyzed dataset, and HIV-1 subtype information was not available for the archived samples; therefore, conclusions regarding HIV-1/2 differentiation are based primarily on assay design and antigenic targets. Multisure HIV-1/2 demonstrated strong diagnostic performance comparable to established differentiation assays and may represent a practical alternative rapid confirmatory option within CDC-aligned HIV diagnostic workflows. Further studies including larger datasets and confirmed HIV-2 infections are warranted to further validate its clinical utility. Full article

Background/Objectives: The management and prognosis of ALK-rearranged non-small-cell lung cancer have substantially improved over the past decade. However, challenges remain in timely molecular identification, prediction of treatment response, and understanding resistance mechanisms. This systematic review evaluates and synthesizes the evidence on artificial intelligence (AI) approaches leveraging imaging, pathology, molecular, and clinical data in this setting. Methods: A systematic search was conducted for peer-reviewed studies published between 2020 and 2025. Eligible studies involved human subjects and applied AI, machine learning, or deep learning methods to predict ALK status or treatment-related outcomes using imaging, pathology, molecular, or multimodal data. Study selection followed the PRISMA 2020 guidelines. Data were extracted on study design, data modality, AI methodology, clinical objectives, and performance metrics. Bibliometric co-occurrence analysis was performed to characterize thematic patterns and temporal trends. Results: Thirteen studies met the inclusion criteria, most of which were retrospective and single-center. AI approaches were applied to radiologic, pathologic, molecular, or multimodal data. Models predicting ALK status reported area under the curve values ranging from 0.73 to 0.99, while prognostic and treatment-response models reported moderate to high discriminative performance. Bibliometric analysis identified two dominant research themes focused on molecular characterization and computational methodology, with a recent shift toward treatment-specific and integrative analyses. External validation and clinical implementation remained limited across studies. Conclusions: AI shows promising potential to support diagnosis, prognostication, and treatment assessment in ALK-rearranged lung cancer. However, methodological heterogeneity, limited external validation, and a lack of prospective studies currently constrain clinical translation. Full article

Although HyFlex teaching has been studied for decades and has become part of the teaching norm since the 2020 pandemic, studies have generally not investigated the learning experiences of students with disabilities in HyFlex classrooms. This study compared the basic psychological needs (BPN) and engagement of undergraduate students who did (SwA) and did not (SwoA) request academic disability accommodations in an introductory, active learning, human-centered design thinking course, a core component of engineering technology education. Data were collected from 3748 primarily first-year undergraduate engineering technology students between fall 2021 and spring 2024, 126 of whom requested disability accommodation through the disability office. The data sources consisted of an end-of-course survey, in which students reported their basic psychological satisfaction level on a Likert scale and described their BPN experiences and engagement in response to open-ended survey questions. As a novel contribution, this study integrates the descriptive analysis of Likert-scale measures with textual- and word-level sentiment analysis, advancing conceptual understanding of reported BPN satisfaction and engagement and revealing divergent patterns across analytic approaches. While the SwA group reported lower scores across all BPN constructs compared to their counterparts, the highest number of them provided positive feedback statements across all BPN domains. Conversely, the SwoA group reported higher BPN scores across all constructs, yet the highest number of them used negative sentiments in their responses across all BPN constructs. The majority of SwA provided positive feedback on autonomy satisfaction, while the majority of SwoA’s positive feedback was on relatedness to the instructor. Future directions for advancing engineering technology education and disability data collection in higher education are provided. Full article

This work presents INTELLECTUM as a reference architecture and design-time evaluation framework for multi-entity XR–AI–digital twin systems. Rather than optimizing a specific implementation, the paper formalizes architectural invariants, event semantics, and coordination mechanisms that precede and inform system realization. INTELLECTUM provides a conceptual framework for structuring interactions across physical and virtual environments, emphasizing human-centered design, immersive digital twins, and collaborative extended-reality workspaces. The technical specification defines core architectural components, human integration modalities via WebXR and heterogeneous sensor networks, and representative usage scenarios within smart city ecosystems. By enabling AI-assisted urban planning, interactive simulation, and multi-actor coordination, INTELLECTUM positions itself as an XR-based architectural foundation for next-generation smart city platforms. Full article

The appearance of antibiotic-resistant strains of Helicobacter pylori (H. pylori) is leading to a decreased eradication rate of H. pylori infection. There is an urgent need to find new agents with antimicrobial mechanisms different from those of antibiotics, with therapeutic potential to clear colonization of H. pylori in the stomach. Some antimicrobial peptides (AMPs) possess bactericidal activity by enhancing the permeability of the outer membrane and damaging the integrity of the cell membrane. Bacteria are not susceptible to drug resistance through this antimicrobial mechanism. In this study, 28 short peptides containing 12 amino acid residues were designed based on nine amino acid fragments (KRIVQRIKD) from human cathelicidin LL-37, which is stable in gastric juice, and 3 amino acids were added at the C-terminus of the peptide. These designed peptides were not digested and degraded by pepsin at low pH values. The peptides were predicted using the online tool platform. Then, the strongest antimicrobial peptide, named SAMP-12aa (KRIVQRIKDVIR), was screened from 28 short peptides. Further studies found that SAMP-12aa retained anti-H. pylori activity after incubation in simulated gastric juice. The MIC and MBC of SAMP-12aa were 8 μg/mL and 32 μg/mL, respectively. SAMP-12aa showed good bactericidal kinetics. SAMP-12aa was found to have cell selectivity, penetrating and damaging bacterial cell membranes and exhibiting almost no toxicity to human cells at a relatively high concentration (128 μg/mL). Regulatory T (Treg) cells express CD25^High with immunosuppressive activity that induces immune tolerance in response to H. pylori. Molecular docking prediction revealed that SAMP-12aa could target the active center of Foxp3. Flow cytometry analysis revealed that SAMP-12aa can inhibit Foxp3 activity and downregulate CD25 protein expression on CD4⁺ T cells, thereby reducing the development and differentiation of CD4⁺Foxp3⁺CD25^High Treg cells with immunosuppressive effects. Further research revealed that the levels of the cytokine interferon-γ (IFN-γ), which activates CD8⁺ T-cell activity, were significantly elevated, and the levels of transforming growth factor-β (TGF-β), which inhibits CD8⁺ T-cell activity, were significantly reduced. The results of this study reveal that SAMP-12aa not only possesses antibacterial activity but also has immunomodulatory effects. 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

The proliferation of generative artificial intelligence (AI) as an autonomous recommendation agent fundamentally challenges traditional paradigms of marketing communication. As AI systems increasingly mediate consumer–brand relationships, understanding how artificial agents construct persuasive discourse—distinct from human communicators—becomes critical for developing effective dual-channel marketing strategies. Grounded in Source Credibility Theory and the Computers Are Social Actors (CASA) paradigm, this study investigates the semantic and structural divergence between AI-generated product recommendations and human influencer marketing messages in social commerce contexts. Employing a mixed-methods computational approach integrating term frequency analysis, TF-IDF weighting, Latent Dirichlet Allocation (LDA) topic modeling, and BERT-based contextualized semantic embedding analysis (KR-SBERT), we examined 330 Instagram influencer posts and 541 AI-generated responses concerning inner beauty enzyme products—a hybrid category combining functional health claims with hedonic beauty appeals—in the Korean social commerce market. AI-generated responses were collected through a systematically designed query protocol with empirically grounded prompts derived from actual consumer search behaviors, and analytical robustness was verified through sensitivity analyses across multiple parameter thresholds. Our findings reveal a fundamental divergence in persuasive architecture: human influencers construct experiential narratives exhibiting message characteristics typically associated with peripheral-route cues (sensory descriptions, emotional testimonials, social context), while AI recommendations employ systematic, evidence-based discourse exhibiting message characteristics typically associated with central-route argumentation (functional mechanisms, ingredient specifications, objective criteria). Topic modeling identified four distinct thematic clusters for each source type: human discourse centers on embodied experience and relational consumption, whereas AI discourse organizes around informational utility and rational decision support. Jensen–Shannon Divergence analysis (JSD = 0.213 bits) confirmed moderate distributional divergence, while chi-square testing (χ² = 847.23, p < 0.001) and Cramér’s V (0.312, indicating a medium-to-large effect) demonstrated statistically significant and substantively meaningful differences. These findings extend CASA theory by demonstrating that AI recommendation agents develop a characteristic “AI communication signature” distinguishable from human persuasion patterns. We propose an integrated Dual-Agent Persuasion Proposition—synthesizing CASA, ELM, and Source Credibility perspectives—suggesting that AI and human recommenders serve complementary functions across different stages of the consumer decision journey—a proposition whose predictions regarding sequential persuasive effectiveness and consumer processing routes await experimental validation. These findings carry implications for AI content strategy optimization, platform design, and emerging regulatory frameworks for AI-generated content labeling. Full article

This paper provides a comprehensive survey of Human–Robot Interaction (HRI) for indoor mobile robots operating in human-centered environments such as hospitals, laboratories, offices, and homes. We review interaction modalities—including speech, gesture, touch, visual, and multimodal interfaces—and examine key user experience factors such as usability, trust, and social acceptance. Implementation challenges are discussed, encompassing safety, privacy, and regulatory considerations. Representative case studies, including healthcare and domestic platforms, highlight design trade-offs and integration lessons. We identify critical technical challenges, including robust perception, reliable multimodal fusion, navigation in dynamic spaces, and constraints on computation and power. Finally, we outline future directions, including embodied AI, adaptive context-aware interactions, and standards for safety and data protection. This survey aims to guide the development of indoor mobile robots capable of collaborating with humans naturally, safely, and effectively. Full article