Search Results (297)

Decision theories based on cognitive consistency understand decision-making as the process of constructing a coherent representation of reality, capable of providing a reliable causal map that connects actions to consequences. Rejecting assumptions concerning preferences and utility functions, they single out the quest for a coherent representation of reality as the one single factor driving human behaviour. Decision theories based on constructing coherence are justified by free-energy minimisation and supported by computational network models, but they lack a proper mathematical framework. In this article I combine Evidence Theory with Quantum Logic to offer a scaffolding structure to construct coherent representations. I illustrate this framework with stylised examples drawn from medical diagnoses and the BioTech industry. Full article

Prompt engineering has emerged as a practical and resource-efficient alternative to fine-tuning large language models (LLMs), particularly as these methods have a lower computation cost than fine-tuning. In this paper, three widely adopted prompting techniques—Zero-Shot, Few-Shot, and Chain-of-Thought (CoT)—were assessed. While these prompting strategies are well established, practitioners still lack clear guidance on when each technique should be preferred, which types of tasks they fail to support reliably, and how performance trade-offs may affect the practical use of LLM-based systems. These techniques are tested across three benchmark tasks: sentiment classification (SST-2), multiple-choice questions (CommonsenseQA), and multi-step math problem solving (GSM8K) using Meta’s LLaMA-3 8B Instruct model. We provide a thorough performance comparison based on accuracy, F1 score, and solve rate. The solve rate is highlighted as a complementary metric for evaluating the usability of LLM outputs—a factor often overlooked in the existing literature. Experimental results showed that Few-Shot prompts are particularly effective in structured classification tasks, while CoT prompts excel in logic-heavy tasks that require multi-step reasoning. On the classification task, Few-Shot prompting improved the solve rate but achieved lower accuracy and F1 score than Zero-Shot prompting. On the multiple-choice questions, Zero-Shot, Few-Shot, and CoT prompting achieved a solve rate of 100%. On multi-step math problem solving, CoT improved the solve rate and interpretability compared to Zero-Shot but did not surpass Zero-Shot accuracy. Overall, the results demonstrate that the effectiveness of prompting strategies is task-dependent, with differences observed in both accuracy and output validity across the three benchmark tasks. Full article

Industrial production and logistics dashboards increasingly aggregate real-time data but remain monitoring tools with limited support for action prioritization, explainable recommendations, adaptive response, and organizational learning. This study proposes the Intelligent Acuity-Oriented Operations Dashboard (IAOOD), a conceptual, design-oriented framework that repositions operational dashboards toward active decision support. An exploratory user-preference study (10 interviews followed by a questionnaire with 41 professionals) provides preliminary evidence of the perceived relevance of the advanced capabilities to be added to the framework. Another evidence of relevance is a feature-availability comparison against selected industrial and academic dashboard approaches. This comparison is intended to assess conceptual coverage of key dashboard capabilities, not to demonstrate real-world performance improvement or operational superiority. The leading scientific contribution is a coherent integration of several recent key capabilities into a single operational design logic. These capabilities are: (1) closed-loop prediction–response–learning, (2) adaptive interfaces, (3) unified hard and soft metrics, (4) explainable AI, and (5) acuity classification. The second contribution is the empirical assessment of dashboard user preferences and the acuity dashboard hierarchy and its four-level acuity classification (urgent, acute, warning, weak link). A classification that organizes events by severity, urgency, confidence, impact, and transparent justification, enabling prioritized and governance-aligned interventions. Methodologically, IAOOD is developed through a structured conceptual framework-development process and illustrated via dashboard mock-ups of the proposed hierarchical structure. The study contributes architectural elements, evaluation dimensions, and six testable propositions linking framework mechanisms to expected outcomes (response timeliness, decision trust, recurrence reduction, strategic alignment, cognitive effectiveness, situational awareness). IAOOD is positioned as an integrative benchmark whose scientific value depends on subsequent empirical testing through industrial pilots, controlled user studies, and longitudinal KPI analysis. Full article

Heating, Ventilation, and Air Conditioning (HVAC) systems account for 60–70% of residential electricity consumption in Oman, where extreme desert climate, with temperatures regularly exceeding 45 °C create substantial cooling demands. Unlike general reviews of smart HVAC controls, this study specifically evaluates the applicability and performance of advanced control strategies for Omani residential buildings operating under extreme heat conditions, synthesizing evidence from international research, the Gulf Cooperation Council (GCC) region, and Oman. Based on a systematic review of peer-reviewed literature published between 2015 and 2025, this analysis examines Model Predictive Control (MPC), Deep Reinforcement Learning (DRL), Fuzzy Logic Control, and Internet of Things-based integrated approaches. International studies demonstrate that MPC strategies achieve energy savings of 16–40% compared to conventional thermostatic control by utilizing dynamic building thermal models to optimize control sequences over finite prediction horizons. DRL-based controllers achieve energy reductions of 17–23% through adaptive learning of optimal policies without requiring explicit system models, offering adaptability to dynamic occupancy patterns. Real-world implementation case studies from Oman and the GCC region—including the GUtech EcoHaus net-zero energy building and national-scale retrofit programs—demonstrate realized energy savings ranging from 25–75%, with higher savings achieved through comprehensive interventions that combine advanced controls with high-performance building envelopes. These findings suggest that substantial potential for reducing residential energy consumption while maintaining occupant thermal comfort under Oman’s extreme climatic conditions is achieved through the integration of advanced HVAC control strategies with high-performance building envelopes. Future research may address the development of occupant-centric adaptive comfort models calibrated for extreme heat conditions and context-specific control strategies that account for regional occupancy patterns and cultural preferences. Full article

Water governance in the Colombian Amazon is shaped by institutional fragmentation, the marginalization of local and ancestral knowledge, and a disconnect between water conservation and territorial development. This article applies a social multi-criteria evaluation (NAIADE) and a territorially situated prospective analysis to two contrasting Amazonian municipalities, Puerto Caicedo (Putumayo) and Puerto Nariño (Amazonas), to examine how strategic actors evaluate alternative water governance scenarios. Working from participatory workshops, documentary analysis, and an actor characterization, we elicited actor judgments on five governance scenarios across 10 criteria and analyzed them with this method, reporting the two preorders and their intersection rather than a single synthetic index. Contrary to the expectation that the more socially cohesive, predominantly indigenous municipality would converge on a distinct preferred scenario, both municipalities exhibit weak to absent discrimination among scenarios: Puerto Caicedo shows complete indifference across all five alternatives, and Puerto Nariño does not distinguish among continuity, active community participation, and interinstitutional coordination, while clearly rejecting external dependence and purely technological solutions. Coalition structures are similar in both sites, reaching complete fusion at comparable similarity levels. We interpret this shared non-differentiation as an expression of generalized institutional fragility under post-normal conditions. The article contributes a transparent and reproducible NAIADE-to-prospective workflow, comparative evidence that contrasting structural social capital endowments do not by themselves produce divergent deliberative outcomes here, and a diagnosis-to-instrument design logic for fragile contexts. Full article

Traditional window lattice patterns constitute an important component of vernacular architectural heritage, yet the relationship between lattice configurations and their spatial placement within buildings remains insufficiently understood. This study explores the empirical spatial configuration logic underlying traditional window lattice patterns in the historic buildings of Zhaoyu Ancient City, Shanxi Province, China. Based on field surveys, photographic documentation, and standardized morphological analysis, window samples were classified using a quantitative typological approach, followed by statistical analysis of pattern distributions across different architectural positions. Standardized line drawings were reconstructed as morphological abstractions to facilitate comparative analysis of lattice configurations and their spatial distribution. The results reveal consistent associations between lattice configurations and architectural positions, suggesting that window lattice selection was influenced by spatial context rather than influenced solely by decorative preference. These observed configurations are discussed in relation to regional architectural traditions, local cultural contexts, functional requirements, and construction practices that shaped vernacular building development. The relationships are further interpreted through the perspectives of visual perception, regional culture, historical context, and heritage conservation. Rather than proposing a universal design grammar, this study provides empirical evidence for understanding implicit spatial configuration principles embedded in traditional vernacular architecture and offers a methodological reference for heritage documentation, conservation practice, and future comparative studies. Full article

Electron upconversion, the promotion of a single electron into a higher-energy orbital as the direct consequence of an exergonic chemical step, has been studied mainly as a way to generate transient “super-reductants” that hand off a high-energy electron to an external acceptor. Here, we explore a different application of this phenomenon. Using density functional theory calculations in combination with a survey of enzyme-catalyzed processes, we show that the same upconverted radical anions can act intramolecularly to expel otherwise recalcitrant leaving groups, accomplishing transformations that are formally two-electron, heterolytic eliminations. Taking redox dehydratases as the starting inspiration, we compare the energetics of hydroxide elimination from a ketyl radical anion against the classical, stereoelectronically favorable enolate route. Elimination through the upconverted ketyl is thermodynamically preferred by ~10 kcal/mol, and although this preference diminishes, it does not disappear even for a remote, non-activated γ-hydroxyl group. The orbital picture is simple: occupation of a high-energy antibonding orbital is relieved when electron density flows into the σ* orbital of the scissile bond, so that bond cleavage demotes the electron into a lower-energy, non-bonding orbital. Thus, upconversion is not only a route to strong reductants; it is a general activation mode for bond cleavage. The same logic applies to the C–O, C–N, and C–S eliminations carried out by diol dehydratases, 4-hydroxybutyryl-CoA dehydratase, certain glycyl radical enzymes, and ribonucleotide reductase under mild, metal-sparing, anaerobic conditions, and it suggests design principles for synthetic eliminations that avoid strong acids, strong bases, and pre-activated substrates. Full article

Drawing on self-determination theory (SDT) and service-dominant logic (SDL), this study examines how stress-relief-oriented escape, self-fulfillment, and digital information environments are associated with emotion value co-creation among Generation Z cultural heritage tourists. Survey data from 457 adult Generation Z tourists who had visited cultural heritage destinations in Shanxi Province, China, were analyzed using partial least squares structural equation modeling. Stress-relief-oriented escape was negatively associated with both self-fulfillment and emotion value co-creation, while self-fulfillment was positively associated with emotion value co-creation and carried a significant indirect association between escape and emotion value co-creation. The moderation results revealed contrasting digital boundary conditions: higher media promotion progressively attenuated the negative escape relationships to statistical non-significance, whereas stronger GenAI personalization was associated with increasingly negative relationships. These findings extend tourism motivation research by positioning stress-relief-oriented escape as a recovery-oriented motivational starting condition whose implications depend on the experiential outcome considered. By integrating SDT and SDL, the study identifies self-fulfillment as a measured psychological pathway connecting motivational orientation with emotion value co-creation and shows that this relationship is conditioned differently by interpretive media and preference-adaptive GenAI environments. The findings offer implications for heritage communication and GenAI-enabled tourism design. Full article

As an integrated educational model that combines inquiry-based learning with travel experiences, study travel plays a significant role in fostering students’ social responsibility, innovative spirit, and practical abilities, and has attracted growing attention worldwide. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this study conducts a systematic review of the literature published between 1950 and 2025. It identifies six major themes corresponding to the three stages of study travel. Through bibliometric analysis, the research delineates publication trends, core journal distributions, and keyword co-occurrence networks in the field. The findings indicate that experiential learning, constructivism, and transformative learning theory constitute the dominant theoretical foundations in current scholarship. Moreover, a notable “learning–travel imbalance” is observed, whereby educational processes and learning outcomes receive substantially more scholarly attention than travel attributes, industry logic, and managerial dimensions. This paper offers a systematic mapping of the knowledge terrain and theoretical architecture in international study travel research, clarifies existing gaps, and suggests directions for future interdisciplinary integration and for deeper synthesis of “learning” and “travel” in both research and practice. Full article

End-of-life electric-vehicle and plug-in hybrid (EV/PHEV) battery packs pose a recurrent decision: refurbish, redeploy in second-life storage, recycle, or reject. Technical condition, safety, economics, regulation, traceability, and environmental benefit interact, making pathway selection a systems-level decision problem. This paper develops a risk-informed multi-criteria framework for the conditional viability of refurbished batteries under data-scarce conditions. Failure mode, effects, and criticality analysis (FMECA) supplies a pathway-specific residual-risk penalty; multi-criteria decision analysis (weighted-sum and the Technique for Order of Preference by Similarity to Ideal Solution, TOPSIS) orders four alternatives on six benefit criteria; and a screening-level avoided-burden indicator, not a life-cycle assessment, positions the environmental criterion. An Integrated Viability Index (IVI) offsets weighted benefits against the risk penalty through one tunable coefficient. All inputs are illustrative and literature-informed; the demonstration tests decision logic, not empirical pathway performance. Preference is conditional: refurbishment leads under economic and technical priority with credible risk mitigation, second-life reuse under environmental priority, and recycling under safety, regulatory, and infrastructure constraints, while rejection never leads. As the risk penalty rises, leadership migrates traceably toward recycling, and IVI–TOPSIS divergence localizes exactly where the risk treatment changes the decision. A proposed Refurbished-Battery Suitability Index (RBSI) would couple measured diagnostics to the IVI; its calibration remains future work. Full article

Early-stage architectural design is characterized by high decision uncertainty, ill-defined requirements, and limited opportunities to elicit reliable user feedback, despite the disproportionate impact of early decisions on downstream outcomes. While recent AI-enabled design tools increasingly support generative exploration and performance-driven optimization, they largely rely on static models trained on aggregated data, thereby producing “average-user” responses that fail to capture pronounced inter-individual variation in architectural preferences. This conceptual framework paper, developed through critical narrative synthesis of interdisciplinary literature, argues that meta-learning—i.e., learning-to-learn—offers a conceptually appropriate mechanism to address this personalization gap by enabling rapid adaptation to a new user’s preference structure from limited interactions, while leveraging transferable knowledge learned across many users. Drawing on a structured, PRISMA-informed literature identification process complemented by purposive theoretical sampling across user-centered design traditions in architecture, computational preference-elicitation methods, and contemporary meta-learning research, the paper develops a theoretically grounded conceptual framework for adaptive user preference modeling in early-stage design workflows. The framework articulates four interdependent constructs—(1) Preference Representation, (2) Adaptation Engine, (3) Design Space Navigator, and (4) Feedback Loop—describing how iterative preference refinement can co-evolve with design-space exploration without displacing architectural agency. An illustrative application scenario is also presented to demonstrate the operational logic of the framework in a realistic early-stage design context. The paper further formulates a set of testable propositions and evaluation pathways to guide future empirical investigation, alongside a discussion of implications for practice and education and key ethical considerations (bias, privacy, and digital equity). The proposed framework provides conceptual scaffolding for developing AI-augmented, user-responsive design systems that are aligned with the epistemic conditions of early-stage architectural design. Full article

The synthesis of efficient reversible logic circuits is critical for fault-tolerant quantum computing (FTQC). The primary motivation of this work is to overcome the inherent disadvantages of existing synthesis techniques: approximate heuristic methods often miss optimal solutions, while pure exact computational methods suffer from combinatorial explosion on deep circuits. While the strict NCT library (NOT, CNOT, Toffoli) is often preferred due to the high cost of distilling non-Clifford states required for arbitrary gates, standard physical implementations frequently utilize the broader NCV library (NOT, CNOT, V, V-dagger), requiring the decomposition of Toffoli gates into five elementary operations. To bridge this gap, this paper presents a unified, highly scalable methodology for the optimal design of reversible circuits across both libraries. First, a Mixed-Integer Non-Linear Programming (MINLP) formulation, linearized for the high-performance IBM ILOG CPLEX solver, is introduced to automate the exact generation of globally optimal strict NCT topologies. Second, a systematic four-phase optimization framework is proposed to reduce NCV costs. By replacing Toffoli gates with specific NCV decompositions, permuting control lines to match subsequent linear gates, and applying exact local searches via an extended MINLP solver on bounded sliding windows, significant gate cancellations are achieved. Applying this methodology to prominent primitives (MIG, SAYEM, URG, TSG, and MKG), we match global NCT optimality constraints and achieve highly optimized NCV Quantum Costs of 7, 14, and 12 for the MIG, TSG, and MKG gates, respectively, establishing best-known upper bounds that significantly outperform heuristic literature benchmarks. Full article

Smart city and smart region governance increasingly relies on data-informed decision-making, stakeholder participation, digital tools, and public feedback. However, these mechanisms do not automatically create institutional learning. This article examines sustainable smart region governance as an institutional learnability problem and asks whether participation, local knowledge, and feedback are converted into adaptive action and retained as institutional memory. The study applies a Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR)-informed structured evidence mapping combined with an embedded regional case diagnosis from the Tokaj Wine Region, Hungary. The analysis integrates the literature on smart governance, learning regions, higher education quality assurance, territorial resilience, and adaptive governance with regional governance observation materials, stakeholder survey data, coding tables, calculation workbooks, and analytical figures deposited in a public Figshare dataset. The results identify the Act/Remember gap as the central learning-cycle disruption. Planning, implementation, monitoring, and consultation may be present, but feedback often fails to become adaptive action, and action is weakly retained as institutional memory. The comparison with higher education quality assurance shows that structured feedback and continuous improvement principles are transferable only as learning logic, not as procedural models. The findings also show that single-profile territories are especially vulnerable to delayed learning, strategic lock-in, and weak community knowledge-building. The article contributes to smart governance research by proposing institutional learnability as a diagnostic capacity of sustainable smart regions. It argues that digital tools should function as learning infrastructure supporting traceability, feedback-to-action mechanisms, and institutional memory, rather than as substitutes for human deliberation, trust, and collective responsibility. Full article

The evaluation of analytical methods has evolved beyond traditional validation parameters to encompass environmental sustainability, analytical reliability, and practical applicability. While numerous assessment tools exist for individual dimensions, integrated multicolor frameworks often impose rigid parameter selection, assign equal weights to all dimensions regardless of analytical context, or lack the granular diagnostic feedback necessary for method improvement. To address these limitations, we present the Triple-Color Assessment (TriCA) tool, a web-based platform that integrates three validated metrics into a unified scoring system with a hierarchical weighting scheme. TriCA combines the Analytical Green Star Area (AGSA) for greenness assessment, the Click Analytical Chemistry Index (CACI) for blueness evaluation, and the Analytical Method Reliability Index (AMRI) for redness scoring. While these metrics are provided as defaults, users may select alternative assessment tools within each dimension according to their specific preferences or requirements. TriCA features a user-customizable weighting system with recommended defaults (red = 3, blue = 2, green = 1), reflecting the logical priority that a method must first be valid, then practical, and finally green. Users can adjust these weights for specific contexts, while the underlying metric scoring protocols (AGSA, CACI, and AMRI) remain fixed to ensure reproducibility. Beyond composite scoring, TriCA generates three detailed pictograms, 12 segments for AGSA, eight segments for CACI, and 10 segments for AMRI, each color-coded to show performance at the individual criterion level. This diagnostic capability enables analysts to identify exactly which green chemistry principles, practicality criteria, or validation parameters are deficient, transforming assessment from an opaque scoring exercise into actionable guidance for method improvement. Seven case studies encompassing diverse analytical techniques demonstrate TriCA’s discriminative power and diagnostic utility. The tool is freely accessible at bit.ly/TriCA, offering the analytical community a transparent, reproducible, and diagnostically rich framework for method evaluation, development, and optimization. Full article

Histone deacetylases (HDACs) are key epigenetic enzymes governing lysine deacetylation. This modification is tightly coupled to cellular redox homeostasis and antioxidant signaling in plants. Plant HDACs are grouped into three subfamilies: RPD3/HDA1, SIR2, and plant-specific HD2. HDACs target both histone residues (e.g., H3K9 and H4K5) and a broad set of non-histone substrates (e.g., transcription factors and metabolic enzymes). Via coordinated chromatin remodeling and non-histone protein modification, HDACs integrate phytohormone signals, reactive oxygen species (ROS) bursts and NAD⁺ metabolic fluctuations to orchestrate plant abiotic stress responses, balancing antioxidant defense, redox equilibrium and normal growth. This review systematically sorts the divergent stress-response traits, substrate preferences and bidirectional regulatory logic of the three HDAC subfamilies; integrates chromatin-dependent and transcription factor-centered transcriptional branches; and summarizes crosstalk rules between HDAC-mediated deacetylation and other epigenetic marks. We further hierarchically clarify current research bottlenecks spanning basic mechanism dissection, multi-crop validation and field breeding transformation and propose targeted stratified research directions. We further construct a complete regulatory cascade linking environmental stimuli, ROS/ABA/NAD⁺ signals, HDAC activity and downstream antioxidant/stress gene expression, filling gaps in previous reviews that overlook redox-dependent HDAC functions. This mechanistic framework delivers integrated epigenetic and redox theoretical references for breeding stress-tolerant crops with reinforced antioxidant capacity. Full article