Search Results (118)

As the core automated execution components of blockchain technology, smart contracts enable programmatic control over digital assets; however, their immutable characteristics and inherent logical vulnerabilities give rise to substantial security risks. Although smart contract vulnerability detection methods based on large language models (LLMs) have exhibited certain potential in vulnerability detection and explanation, the coarse-grained modeling of traditional binary preference optimization paradigms hinders the model ability to learn the priority of domain-specific requirements, frequently leading to extreme optimization at the cost of detection accuracy. Furthermore, existing approaches fail to consider non-ideal factors in real-world application scenarios and overlook noise interference induced by missing prompts, which results in inadequate detection stability and reliability, making them challenging to adapt to complex practical scenarios. To address these critical issues, this study proposes a Noise-Aware Preference Reinforcement Large Language Model for Smart Contract Vulnerability Detection (NAPO-SCVD). This method adopts a four-stage framework consisting of data construction, continuous pre-training, supervised fine-tuning, and noise-aware preference optimization. Specifically, it enhances the model’s comprehension of contract syntax and semantics through domain-specific pre-training, improves its detection and explanation capabilities using high-quality datasets, constructs deliberately guided biased explanations to simulate noisy samples, refines preference gradients, and strengthens the model’s anti-interference ability. Consequently, this approach achieves high-precision and high-reliability smart contract vulnerability detection, along with fine-grained explanations. Full article

The rise of artificial intelligence (AI) and classroom automation demands rethinking visual and arts education. To prevent learning standardization, it is imperative to cultivate a critical teacher profile capable of leading new digital ecologies. In this context, measuring the creative self-perception of future educators constitutes a fundamental pedagogical need. This instrumental study analyses the factor structure and internal consistency of the Creative Personality Scale (CPS), adapting it to contemporary technological challenges. It was administered to 90 pre-service teachers from the Early Childhood and Primary Education programmes at the University of Almería. Through an Exploratory Factor Analysis (EFA) using Principal Axis Factoring (PAF) with Oblimin rotation, the scale was refined to 17 items, confirming a robust three-dimensional structure: Imaginative Creativity, Behavioural Originality, and a Positive Attitude towards Challenges (explaining 44.17% of the variance; α = 0.862). While not a direct measure of pedagogical performance, these dimensions capture the psychological dispositions hypothesized as necessary for educators to critically navigate AI integration and mitigate algorithmic standardization. In conclusion, the adapted scale provides an initial exploratory validation of a diagnostic framework. Its application provides a foundational metric for teacher education programmes, aiming to foster learning environments where technology integration is deliberately guided by human judgment and sensitivity. Full article

The increasing availability of large language models (LLMs) is transforming how users create and share content on social media platforms. Beyond enabling text generation, LLMs introduce a new paradigm in which content is deliberately optimized for engagement through algorithmically suggested phrasing, structure, and tone. This paper investigates the emerging shift from authentic self-expression toward engagement-driven optimization in LLM-assisted social media use. It examines whether and how LLM-generated or LLM-assisted posts systematically outperform human-authored content in engagement metrics and at what cost to informational quality, diversity, and authenticity. Using a mixed-methods approach, controlled experiments with human participants are combined with large-scale analysis of social media posts to compare organic and LLM-optimized content. Differences in engagement outcomes (e.g., likes, shares, comments), linguistic features, and perceived credibility and informativeness are evaluated. The findings suggest that while LLM-assisted content consistently increases short-term engagement, it tends to reduce informational depth and perceived authenticity while exhibiting changes in stylistic characteristics associated with engagement-oriented optimization. This creates a potential feedback loop in which users increasingly rely on optimization strategies that privilege attention over substance. The findings suggest that widespread adoption of LLM-driven optimization could contribute to changes in the dynamics of the social media attention economy. Future research is needed to determine whether these effects emerge at scale and persist over longer periods of platform use. Implications are discussed for platform design, content moderation, and the future of human–AI co-creation in digital communication. Full article

This study examines how digitalization discourse is mobilized in public carbon reporting under institutional complexity and how it varies across different carbon-accountability structures in an emerging-market context within the Global South. A longitudinal comparative discourse analysis was conducted on 70 annual and sustainability reports (2015–2024) from seven Vietnamese listed firms, contrasting firms with internal carbon accountability against those with supply-chain-mediated accountability. The 2015–2024 timeframe was deliberately selected to capture a critical decade of regulatory evolution, marked by the aftermath of the Paris Agreement and the escalating enforcement of net-zero and environmental, social, and governance (ESG) disclosure mandates. Findings reveal that digitalization functions as an ambivalent rhetorical resource rather than a uniformly substantive sustainability enabler. Firms with operationally visible emissions utilize digitalization for “temporal buffering,” deferring immediate physical abatement by framing technology as a future transition pathway. Conversely, firms with supply-chain-mediated emissions employ “boundary displacement,” framing accountability as contingent on fragmented supplier data. These patterned responses constitute “digital institutional camouflage”. We conclude that digital reporting sophistication should not be conflated with substantive decarbonization; effective oversight requires cross-validating digital infrastructures with concrete emission-reduction measures. Ultimately, this study empirically specifies institutional decoupling theory by demonstrating how emissions visibility and organizational control shape distinct pathways of discursive decoupling. Full article

This paper examines whether genuine dwelling—understood as embodied engagement with a world that resists, endures, and exceeds human control—can occur in the metaverse. Drawing on Heidegger’s account of dwelling and Ingold’s concept of the ‘taskscape’, it argues that the metaverse is structurally unable to sustain dwelling in the full ontological sense. The argument unfolds in three steps. First, dwelling is shown to depend on friction: bodily cost, temporal irreversibility, material resistance, and exposure to mortal finitude. Second, the metaverse is interpreted as a technological and commercial project oriented toward reducing these frictions through attenuated bodily burden, reversible action, programmable environments, and artificial scarcity. Third, the paper extends the concept of the metaverse beyond immersive hardware to describe a broader condition of digitalized life in which experience becomes increasingly modifiable, personalized, and optimized. In this wider sense, the difficulty of dwelling in the metaverse is not limited to a niche technology but reveals a tendency within late-digital culture itself. The paper concludes by proposing a politics of friction: a public deliberation over which resistances are unjust and should be transformed, and which are constitutive conditions of ethical, ecological, and responsible life. Full article

As artificial intelligence (AI) becomes increasingly important in rural revitalization, building consensus among multiple stakeholders and developing participatory digital co-creation platforms has grown increasingly urgent. However, existing large language model (LLM) systems predominantly adopt a one-shot generation paradigm, making it challenging to accurately capture villagers’ cultural aspirations and frequently resulting in a significant disconnect between design outputs and community expectations. This situation reveals deficiencies in progressive deliberation mechanisms and cultural controllability. To address these issues, this study proposes a multimodal Participatory Landscape Demand Generation (PLDG) system to enhance AI-generated dialogue controllability, facilitate effective cultural translation in sensitive rural contexts, and promote sustainable development where landscape design both drives and reflects rural revitalization. The system leverages LLMs to simulate stakeholder participatory interactions in village landscape design scenarios. Using culturally distinctive Chinese villages as case studies, the research conducts multi-role simulated dialogues, multimodal semantic extraction, and iterative consensus-building, and evaluates the resultant data to generate landscape design proposals. The results indicate that the PLDG system significantly improves participation efficiency among diverse design stakeholders and enhances the sustainability of design decisions. Compared to conventional methods, metrics such as cultural compatibility, villager participation, and design innovation show substantial improvements. These findings demonstrate the considerable potential of human-AI collaboration in future rural planning. This study introduces the Culture Constraint-Driven Rural Landscape AI Collaborative Design Framework (PLDG), validating its practical efficacy in identifying culturally sensitive elements, ensuring cultural congruence, facilitating community participation, and fostering design innovation. Consequently, it provides a reusable, iterative operational tool for the digital renewal of sustainable rural landscapes. Full article

‘Analog horror’ is a subgenre of internet and media horror, beginning c.2015. Its texts use late 20th-century analogue technology as a locus of horror, both narratively and aesthetically, expressing contemporary technophobia and existential anxieties of the first quarter of the 21st century, using a deliberate and anarchic a-historicity to represent concerns surrounding techno-capitalism and its attendant ‘polycrisis’. This irreverent attitude to historical cause and effect, and technological progress, in subgenre texts such as “The Nixonverse” by creator Eve Casanas represents our modern-day conflict between the digital, techno-capitalist online world, and the corporeal crisis events affecting the real world. This diametric in analog horror expresses the central tenet of Mark Fisher’s concept of ‘capitalist realism’, the idea that capitalist ideology makes it appear that there are no viable alternatives to capitalism. In analog horror narratives, analogue–digital hybrid technologies channel techno-organic monster-figures, with the helplessness of the individual and/or groups to defeat these monstrosities being expressive of this capitalist realist impression that capitalism cannot be overcome, and its polycrisis avoided, enacting fantasies of societal destruction to alleviate this suspended state of anxious helplessness, in the tone of Freud’s ‘death drive’ wish fulfilment fantasies. Full article

This paper explores the feasibility of using smartphones as emergency flight data indication systems in light aircraft. The presented solution may be applied in potential situations such as failures of the vacuum system or the gyroscopes driving analog instruments, as well as electrical power failures in aircraft equipped with digital avionics. Such failures may lead to the loss of essential flight information, significantly increasing pilot workload and conceivably compromising flight safety. The analysis was based on simulations conducted in a computational environment utilizing a custom-developed model. An experimental measurement flight using the MP-02A “Czajka” aircraft was conducted to collect real flight data for integration into a computational model. During the test flight, the aircraft was deliberately maneuvered into various attitudes and flight conditions to evaluate the model’s performance across the widest possible range of operating states. A smartphone mounted in the cockpit recorded sensor data, including accelerometer, gyroscope, magnetometer, and GPS information. The results demonstrated that key flight parameters can be accurately determined using only data recorded by a smartphone. For example, the determined pitch angle values during stall maneuvers deviate from the reference values by no more than 5°. The proposed solution shows significant potential for further development and practical implementation as a supplementary system to assist pilots during in-flight emergencies. Full article

The digital transformation of production requires methods for integrating, storing, and operationalizing data across organizational boundaries, yet most existing approaches remain siloed and unidirectional, lacking a systematic loop from raw data to actionable knowledge and back. We introduce Data-to-Knowledge (D2K) and Knowledge-to-Data (K2D) pipelines as a universal production concept built on networks of Digital Shadows. The Data-to-Knowledge (D2K) pipeline is realized as a cross-organizational proof of concept that captures and semantically annotates robotic trajectory data from three independent research institutes and uses those data to train an inverse-dynamics foundation model for robot control. Centralized aggregation via an existing FAIR-compliant research data repository was chosen deliberately over federated alternatives to maximize semantic interoperability and reuse of shared infrastructure; federated and privacy-preserving extensions are identified as a promising future direction. Fine-tuning the cross-organizationally trained foundation model reduces training time by approximately 85% relative to end-to-end training from scratch, while achieving comparable accuracy on a standardized inverse-dynamics benchmark. These gains are attributable to the combination of cross-site data aggregation and transfer learning; isolating the contribution of semantic annotation alone remains a topic for future ablation work. The implementation demonstrates that semantically enriched, cross-organizational D2K pipelines can accelerate model development and reduce redundant data collection within a constrained but practically relevant class of robotics tasks. We further discuss limitations, governance challenges, and how these pipelines can contribute to a broader World Wide Lab for collaborative production research. Full article

Technology is increasingly central to sustainability, yet frameworks built around the environmental–social–economic (E–S–Ec) triad and ESG disclosure regimes do not fully capture the governance problems created by interconnected digital and cyber–physical infrastructures. In this conceptual paper, Tech4.0 is used in a deliberately narrow working sense, focusing on AI-mediated decision systems, data/platform/cloud infrastructures, software dependency chains, and cyber–physical control environments in which opacity, infrastructural dependence, interdependence, and cascading failures create distinctive problems of governability and resilience. Against this background, the paper examines whether making the technological dimension explicit adds analytical value within sustainability architecture. It examines the case for treating Technological Sustainability (T) as a distinct analytical dimension/pillar insofar as it foregrounds system properties of the Technosphere that tend to be diluted when distributed across environmental, social, and economic categories. The paper then discusses the hierarchy T → Corporate Technological Responsibility (CTR) → Corporate Digital Responsibility (CDR) as a possible corporate-level operational pathway and outlines an exploratory measurement agenda structured around exposures, capabilities, and outcomes. Rather than offering empirical proof or a validated reporting architecture, the article provides a conceptual research program for later empirical inquiry into technological accountability under Tech4.0 conditions. Full article

Automated warehouses must coordinate fleets of mobile robots online while meeting order deadlines. In online Multi-Agent Pickup and Delivery with Deadlines (MAPD-D), committing to a feasible task immediately may restrict flexibility and increase downstream tardiness through congestion and reservation interactions. We propose Deliberate Assignment Deferral (DAD-$\theta$), a one-parameter gate on top of deadline-aware Token Passing baselines (D-TP and D-TPTS). At each token turn, the token holder evaluates the baseline-defined assignable tasks using the baseline score (lower is better); it commits only if the best score is at most $\theta$, and otherwise follows the baseline fallback. A safety override forces assignment once any assignable task reaches non-positive pickup slack. We also introduce a scale-normalized score that makes $\theta$ dimensionless for transfer across maps and deadline scales. In 100-seed paired simulations across four arrival/deadline regimes on a benchmark map, scenario-calibrated DAD-$\theta$ reduces cumulative tardiness by 9–58% and increases on-time completion by 1.3–10.0 percentage points relative to always-assign. We discuss how $\theta$ can be calibrated offline in a digital twin and monitored online via deferral and safety-trigger rates for service-level control. Full article

Motivation: In modern organizations where remote and hybrid work has become normalized, fostering trust without frequent face-to-face interaction is a critical management challenge. This study aims to explore how non-verbal digital dynamics associate with trust formation within hybrid product development teams from a teamwork science perspective, integrating Big Five traits and established trust scales. Methods: The empirical study observed twelve product development teams (N = 40) participating in a major innovation competition over an eight-month period. Dynamic behavioral data, including speaking time, nodding, smiling, and silence, were extracted from online workshop recordings using synchronized behavioral coding validated by high inter-rater reliability (Cohen’s Kappa k ≥ 0.78). These were integrated with Big Five personality traits, mutual trust scales, and idea value metrics into a Bayesian Network (BN) to model probabilistic dependencies. The structural model was validated using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) to ensure predictive robustness. Furthermore, we performed sensitivity analysis on the BN to quantify how specific shifts in non-verbal cues—particularly nodding and the functional categories of silence—disproportionately affect the “Mutual Trust” node. While this exploratory study utilizes a sample of “digital native” student teams, it provides a critical baseline for “high digital fluency” collaboration, which we contextualize against the “asymmetric cues” found in multi-generational corporate environments. Results: Sensitivity analysis identified specific probabilistic associations suggesting that effective role fulfillment is the strongest predictor of idea originality. Crucially, nodding was identified as a behavioral ‘digital reward’ that enhances psychological safety, facilitating divergent thinking. Smiling showed a strong association with feasibility and consensus-building during convergent phases. The model further identifies distinct behavioral ‘fingerprints’: high-trust sequences are characterized by frequent non-verbal backchanneling and deliberate “thinking silences,” whereas low-trust sequences exhibit a disproportionate increase in unproductive lapses (e.g., a 10% increase in lapses correlating with an 18% decrease in trust probability). Furthermore, a probabilistic pathway was identified where teams with highly open members and frequent non-verbal validation exhibit higher mutual support behaviors. Conclusions: This research offers empirical insights into how trust can be modeled in hybrid environments through specific combinations of behavioral and personality traits. Practically, this study proposes “Hybrid Team Protocols”—such as intentional backchanneling and the normalization of deliberative silence—as actionable Organizational Development (OD) interventions. These provide managers with data-driven guidelines to visualize and monitor the quality of digital collaboration while emphasizing the ethical necessity of transparent implementation to prevent “digital performance” and ensure psychological safety across diverse organizational structures. Full article

Chronic kidney disease is rising worldwide, and kidney transplantation remains the preferred modality of kidney replacement therapy. However, long-term graft survival continues to be limited by chronic alloimmune injury, particularly antibody-mediated rejection (ABMR) and its chronic active form. This narrative review synthesizes contemporary evidence on the immunopathogenesis, epidemiology, diagnosis, and management of kidney allograft rejection, with a deliberate focus on studies from the last five years and on United States and European cohorts. We summarize current concepts of T cell–mediated rejection (TCMR), ABMR, mixed and donor-specific antibody (DSA)–negative phenotypes, and the evolution of the Banff classification, highlighting how chronic active ABMR has emerged as a leading cause of death-censored graft loss. We then critically appraise the conventional diagnostic triad of creatinine/eGFR, DSA, and biopsy and review emerging tools, including donor-derived cell-free DNA, urinary chemokines such as CXCL9 and CXCL10, additional blood- and urine-based biomarkers, and biopsy transcriptomics. We also examine how artificial intelligence and machine learning may support digital pathology, multimodal risk prediction, and data integration, while recognizing the current challenges of biological interpretability, external validation, and clinical implementation. Finally, we propose a rejection-focused precision-medicine framework and outline key research gaps, including multicenter validation, trial-ready endpoints, and governance for AI-enabled pathways. Overall, the field is moving from isolated diagnostic signals toward integrated, biologically informed, and clinically actionable approaches to rejection detection and risk stratification. Full article

Despite advances in dental materials and digital color registration systems, esthetic matching remains a clinical challenge for both dental students and experienced professionals. A comprehensive narrative review was conducted through bibliographic searches in PubMed, Scopus, Web of Science, and PsycINFO databases from January 2015 to January 2026. The evidence was synthesized using a four-dimensional analytical framework encompassing technological, cognitive–psychological, educational, and clinical-contextual factors. Quantitative synthesis revealed substantial variability in shade matching success rates, with intraoral scanners demonstrating pass rates ranging from 31.3% to 78.2% across devices, while spectrophotometers achieved superior repeatability (ICC > 0.9) but faced interpretive barriers. Cognitive load theory explains the performance deterioration, with novices being particularly susceptible to retinal fatigue and metamerism under non-standardized lighting conditions. The proposed paradigm shift involves redefining shade selection from a purely technical task to a cognitive skill that requires deliberate perceptual calibration, structured educational protocols, and hybrid digital visual workflows. To improve esthetic predictability, educational programs need to integrate longitudinal training in color science with objective feedback mechanisms. Clinical workflows should adopt hybrid calibration-centric protocols that position technology as a verification tool, rather than a replacement for clinical judgment. Understanding the multidimensional nature of shade matching difficulty enables the development of evidence-based educational protocols and clinical workflows, ultimately improving esthetic outcomes. Full article

In responding to the special issue’s call to examine the shifting space of materiality, this article uses creative writing, hand-drawn comics, and speculative fiction/design as a form of research by practice to critique changes in UK Higher Arts Education in relation to art materials. It shows how embedded neoliberal structures that have been documented to negatively impact HE staff and the arts in general, also now extend to prioritising and excluding some art materials over others. A speculative vision is offered as an alternative in which a nomadic higher arts education is put forward, one that encourages the use of hybrid art materials. The means chosen to make the arguments presented are analogue methods of drawing, cutting, printing, sewing and writing to strengthen the point that digital materials are currently prioritised in UK arts education due to HE’s entanglement with agendas entwinned with Big Tech and most recently the military. The format is also deliberately experimental to move away from common ways of presenting research and theory that have become formulaic as academics are pushed to meet the ideals of the Research Excellence Framework, another neoliberal rubric. Full article