Search Results (964)

Post-conflict reconstruction research has examined façade materiality and symbolism, yet the process conditions under which aesthetic specifications are systematically overridden during construction remain neglected. This study investigates why designed architectural aesthetics fail to survive implementation in post-2017 Mosul, Iraq. A mixed-methods design combined formal visual analysis of 12 recently completed residential façades with a structured survey of 45 practicing architects. Survey data reveal that designers are excluded from construction supervision in 76% of projects and that clients intervene in material and color selection in 70% of cases. Visual analysis identifies a sophisticated design language—orthogonal massing articulated through contrasting materials—that is rarely realized in built form. Where designers retain supervisory authority, projects most consistently achieve material–form coherence. The study advances the concept of an aesthetics of interruption (the systematic degradation of designed form–material relationships through the fragmentation of professional authority during construction). Exclusion produces four distinct pathologies: material substitution, execution degradation, language override, and ornamental hollowing. The findings demonstrate that aesthetic degradation in post-conflict reconstruction stems not from design incapacity but from broken process structures. Safeguarding architectural quality requires contractual frameworks mandating designer supervision and material-substitution protocols that protect design intent. Full article

This paper investigates the performance of cooperative multiple-input multiple-output (MIMO) systems under practical operating impairments, with particular emphasis on imperfect channel state information (CSI) and relay decoding errors. Although Decode-and-Forward (DF) relaying can provide diversity gains under ideal assumptions, these gains may significantly degrade in practical wireless environments affected by channel uncertainty. The analysis demonstrates that imperfect CSI introduces residual interference, leading to SINR saturation and BER error floors in the high-SNR regime. In cooperative systems, this degradation becomes more severe due to relay error propagation, where erroneously detected relay symbols introduce additional structured interference at the destination. Consequently, cooperative transmission may underperform direct MIMO communication within specific SNR operating regimes. To characterize this behavior, the concept of a harmful cooperation region is introduced, describing the operating regime in which the combined effects of CSI uncertainty and relay decoding errors render cooperation detrimental. To mitigate these limitations, a reliability-aware relay activation mechanism is proposed, enabling selective relay participation according to channel quality. By suppressing unreliable relay transmissions, the proposed approach significantly reduces error propagation and improves BER performance, particularly in the medium-to-high SNR regime. In addition, the impact of antenna scaling is investigated. The results reveal a robustness transition in which larger MIMO configurations exhibit improved resilience to CSI imperfections due to increased spatial diversity and improved channel conditioning. Overall, the findings demonstrate that cooperative transmission under imperfect CSI is inherently SNR-dependent and that robust system design requires the joint consideration of channel uncertainty, relay reliability, and system dimension. Full article

Machine learning has become a field of growing interest in asphalt pavement engineering, spanning mix design, material characterization, performance prediction, distress detection, sustainability, quality control, and maintenance planning. However, a lack of transparency can undermine engineering trust, defensibility, and field implementation. This systematic scoping review aims to synthesize explainable artificial intelligence (XAI) and interpretable machine-learning applications for asphalt pavement materials and systems, following the PRISMA-ScR guidelines. Major scientific databases were used to identify relevant peer-reviewed studies, which were screened against a set of inclusion and exclusion criteria and categorized into seven research dimensions. A final library of 163 publications was compiled, comprising 73 core evidence studies and 90 supporting references. The review covers techniques such as SHAP, LIME, partial-dependence analysis, attention mechanisms, surrogate models, sensitivity analysis, symbolic modeling, and physically informed interpretation. The use of XAI in performance prediction, material-property interpretation, and modeling for mix design is well developed, while distress/damage analysis, life cycle sustainability, field validation, uncertainty-aware explanation, maintenance decision support, and human-centered evaluation are still relatively underdeveloped. The main contribution is a five-layer framework linking data provenance, model performance, explanation quality, physical plausibility, and decision utility. The review proposes moving from post hoc feature ranking to validated, physically centered, uncertainty-aware, and engineer-in-the-loop decision support for asphalt XAI. Full article

A low-latency, low-complexity digital demodulator is presented for chirp spread spectrum (CSS)-modulated RF packets targeting low-power IoT wireless systems operating in spectrally congested environments. Conventional CSS receivers rely on fast-fourier transform (FFT)-based synchronization and long preamble sequences, resulting in increased latency and computational complexity. To address these limitations, the proposed receiver employs amplitude-domain synchronization using oversampled sub-chirp windows and maximum likelihood estimation without requiring FFT processing. A digital demodulator co-designed with receiver’s fractional-N phase-locked loop (PLL) architecture enables rapid sub-chirp generation and fast frequency settling, while compensation techniques mitigate symbol boundary offset (SBO) error due to PLL non-idealities during synchronization. The proposed system achieves packet synchronization within 17.5 preamble symbol cycles while maintaining symbol boundary offset estimation error below ±1%. Simulation results demonstrate a syncword misdetection probability below 10⁻³ at SNRs of 9 dB and 1 dB without and with 8× repetition, respectively. In the presence of interferences, the receiver tolerates worst-case in-band signal-to-noise ratio (SIR) levels down to −16.2 dB while consuming 877 µW and 830 µW average power at the digital demodulator, and fractional-N PLL, respectively. Implemented in 65 nm CMOS, the proposed architecture occupies 0.195 mm² active area. Full article

The digitization of existing building stock often depends on legacy 2D raster floor plans (scanned drawings, PDF exports, or photographs) because structured building information models are frequently unavailable for older properties. Manual measurement and visual inspection of such documents are time consuming and error prone. This paper presents an integrated deep learning pipeline that extracts semantic information from unstructured two-dimensional floor plan images of existing structures and supports preliminary compliance screening via locally deployed large language models. The pipeline employs YOLOv8 for the localization and classification of 18 architectural symbols and furniture items, and a U-Net with a ResNet34 encoder for the semantic segmentation of walls and interior room spaces. To translate pixel-level predictions into physical metrics, we implement an area calculation module based on user-defined reference scale calibration. An LLM evaluation module, deployed locally via Ollama with a retrieval-augmented generation pipeline, interprets extracted room metrics and flags potential non-compliance against referenced residential design guidelines; it is intended for the assessment of existing layouts rather than generative co-design. We expand a core dataset of 101 manually annotated source floor plans to 303 augmented instances using label-aligned geometric transformations, while reporting generalization in terms of the 101 unique source plans. On the held-out validation split (10 source plans), YOLOv8 achieves 92.3% mAP50 versus 87.2% for a Faster R-CNN reference model on the same data split (detection baselines differ in training epochs and pretraining; see Experiments); U-Net achieves 95.71% mIoU, surpassing DeepLabv3+ (93.2%) under matched segmentation training settings. The system is deployed as an interactive web application for legacy building survey and preliminary regulatory review when only two-dimensional documentation is available. Full article

Genetic programming (GP) has been widely used in quantitative finance for discovering formulaic alpha factors that can predict asset returns. However, GP often produces overgrown expressions that are difficult to interpret and expensive to evaluate. This paper proposes a large language model (LLM)-assisted pruning framework that reviews expression trees generated by GP, with the LLM acting as a semantic reviewer that flags redundant or financially implausible branches based on structural complexity and contextual reasoning. The proposed method is formalized as a closed-loop Trigger–Evaluate–Decide–Execute (TEDE) process. We present mathematical formulations, algorithmic design, and examples showing how redundant nested functions can be simplified while monitoring predictive performance. Experiments with high-frequency cryptocurrency market data, using DeepSeek-V4-Flash as the semantic engine, show lower expression complexity and higher rubric-based interpretability scores for the pruned symbolic factors. Under the reported test setup, the LLM-pruned configuration has higher Information Ratio (IR) values than the listed baselines and more compact expression trees than the GP baselines. Full article

The rapid expansion of e-commerce has reshaped global consumption systems by transforming production processes, logistics infrastructures, and consumer behaviour. While this transformation has generated significant economic opportunities, it has simultaneously intensified environmental pressures, particularly through last-mile delivery emissions, excessive packaging waste, and high return rates. At the same time, the growing diffusion of corporate sustainability reporting has raised increasing concerns about greenwashing, defined as the misrepresentation of environmental performance through selective disclosure or symbolic communication. This study aims to provide a comprehensive assessment of sustainability practices in e-commerce, focusing on the relationship between environmental performance, transparency, and economic outcomes. Particular attention is devoted to the role of blockchain technology as a potential mechanism for enhancing verifiable transparency in complex supply chains. The research adopts a multiple case study design grounded in the methodological frameworks and integrates qualitative analysis with a semi-quantitative evaluation model. Seven companies operating in different segments of the e-commerce ecosystem are analyzed through an extensive review of secondary data sources, including ESG reports, financial disclosures, NGO assessments, and industry benchmarks. The findings reveal a substantial gap between declared sustainability commitments and actual implementation, with significant heterogeneity across firms. Companies that embed sustainability into their strategic core demonstrate stronger alignment between environmental and economic performance, whereas firms relying primarily on communication-driven approaches exhibit higher implementation gaps. The study contributes to the literature by introducing an analytical framework centered on the concept of the implementation gap and by demonstrating the central role of transparency in determining sustainability effectiveness. It also highlights the potential, yet still largely unrealized, role of blockchain technology in addressing information asymmetry and reducing greenwashing in e-commerce. Full article

Background/Objectives: Early and accurate discrimination of neurological conditions, dementia, stroke and healthy aging, remains a critical clinical challenge. Electroencephalography (EEG) is a non-invasive measure of brain dynamics and entropy-based features obtained from multichannel EEG have shown strong discriminative ability. However, existing deep learning approaches do not sufficiently address the combined challenges of small clinical cohorts and high-dimensional entropy feature spaces. In this study, a novel architecture is proposed for multi-class neurological EEG classification under extreme small-sample conditions. Methods: A novel dual-branch Channel-wise Transformer and Attention-Branch Network (EEG-ChTABNet) are pr to classify 19-channel EEG entropy features into three classes (dementia, stroke, healthy control; N = 45; 15 per class). The architecture suggests four new designs. First, the Channel Importance Attention (CIA) block, which adaptively learns to re-weight the importance of electrodes via squeeze-excitation. Second, the dual-branch encoder, which combines the global multi-head self-attention with the local depthwise-separable convolution. Third, the gated sigmoid fusion mechanism. Fourth, the bottleneck residual classification head, to solve overfitting. Eight entropy feature sets: Amplitude-Aware Permutation Entropy (AAPE), Attention Entropy (AttEn), Dispersion Entropy (DisEn), Distribution Entropy (DistrEn), Fluctuation-based Dispersion Entropy (FDispEn), Fuzzy Entropy (FuzEn), Linear Gaussian Estimation of the Conditional Entropy (LinEn), and Symbolic Dynamics (SyDy) were evaluated individually with stratified 5-fold cross-validation on within-fold SMOTE augmentation. Results: EEG-ChTABNet consistently outperformed the baseline Transformer on all 8 feature sets. DisEn and SyDy features yielded peak classification accuracy of 73.3% (AUC: 0.823 and 0.857, respectively) compared to the corresponding baseline of 57.8% and 55.6%. SyDy achieved the best overall AUC of 0.857 and the dementia detection sensitivity was up to 86.7% over multiple feature sets. Conclusions: EEG-ChTABNet shows the effectiveness of channel-adaptive, dual-branch Transformer Designs for EEG-based neurological classification from Small-Sample Entropy Feature Data, and Identifying SyDy and DisEn as the Most Discriminative Feature Representations for Three-Class Neurological EEG Classification. Full article

This study examines the positioning of biblical narratives within international relations scholarship, with particular emphasis on their function as cultural resources shaping identity, geopolitical discourse, and soft power dynamics. Although religion has gained increasing recognition within international relations, the extent to which scriptural narratives are systematically integrated into analytical frameworks remains insufficiently defined. To address this issue, the study employs a mixed-methods research design that combines a systematic literature review with bibliometric analysis. Bibliographic data were retrieved from the Scopus and Web of Science databases through a structured query linking biblical terminology to diplomacy, geopolitics, and religion–politics interactions, and were analyzed using the Bibliometrix package in R. The analysis draws on two datasets comprising 135 publications from Scopus and 88 from Web of Science, spanning 1989 to 2026. The findings indicate that scholarship examining biblical narratives in international relations is moderately developed and interdisciplinary, yet remains fragmented, with geopolitical themes predominating. Biblical narratives are consistently present but are primarily embedded within broader analytical categories such as identity, discourse, and legitimacy, rather than being treated as central variables. The results further suggest that religious content is often incorporated in indirect or implicit forms, reflecting a broader tendency to approach religion as a contextual rather than a constitutive element. Overall, the findings indicate that biblical narratives function primarily as interpretive and symbolic frameworks in international relations, while their analytical potential remains only partially developed, underscoring the need for more systematic integration of cultural and religious analysis in the study of global politics. Full article

This study examines how contemporary religious architecture mediates sacred meaning through the interaction of symbolic form, embodied practice, and sensory-spatial conditions, using the Namaste Dagoba at Famen Temple as a case study. Integrating architectural semiotics with exploratory empirical research, the study employs questionnaires and semi-structured interviews, supplemented by architectural field notes, to investigate how visitors perceive and interpret the space. An exploratory structural equation modeling (SEM) framework is used to examine possible relationships among Symbolism and Aesthetic Experience (SAE), Embodied Spatial-Ritual Perception (ESRP), and Perceived Sacred Meaning (PSM). The findings indicate that while symbolic and aesthetic perception provides an initial interpretive basis, perceived sacred meaning appears to be strongly associated with reported embodied spatial experience. Spatial configuration, ritual pathways, mandala-based geometry, and gradients of spatial intensity are interpreted as design conditions that may shape visitors’ reported perception, movement experience, and sense of sacred meaning. The observed mediating role of ESRP suggests that architecture may operate as an experiential interface rather than only as a static symbolic system. By integrating semiotic theory with exploratory questionnaire and interview evidence, the study proposes a tentative embodied and processual model of architectural meaning-making. Rather than suggesting a rupture from historical Buddhist spatial traditions, the study identifies one contemporary design strategy in which inherited cosmological symbolism, ritual movement, threshold experience, and sensory atmosphere are recomposed through a monumental modern architectural vocabulary. Full article

The global regulatory landscape is shifting from voluntary corporate social responsibility (CSR) reporting to mandatory Environmental, Social, and Governance (ESG) disclosure, yet whether this transition drives substantive corporate environmental change or merely symbolic compliance remains empirically contested. This study investigates the causal impact of mandatory ESG disclosure on firm value and operational carbon intensity, drawing on an unbalanced panel of 9682 firm-year observations for 1626 listed firms from the European Union (EU-27) and Japan covering the period 2018 to 2024. The EU serves as the treatment group, where mandatory disclosure requirements escalated substantially from 2021 onward through the Sustainable Finance Disclosure Regulation and the Corporate Sustainability Reporting Directive proposal. Japan serves as the control group, representing a developed economy with sophisticated capital markets and high ESG awareness that maintained a voluntary disclosure environment throughout the study period. A Difference-in-Differences framework with firm- and year-fixed effects is employed, and causal identification is validated through a dynamic event study analysis. Three principal findings emerge. First, mandatory ESG disclosure is not associated with a statistically significant improvement in firm value in the EU–Japan comparative context, a result that is interpreted as descriptive rather than causal given evidence of pre-existing valuation divergence between the two groups. Second, mandatory disclosure is associated with a significant and progressive reduction in Scope 1 and 2 carbon intensity, indicating substantive operational decarbonization rather than symbolic compliance. Third, this emissions-reducing effect is significantly amplified among firms with dedicated CSR sustainability committees, while the board independence policy indicator yields no significant moderating effect, a finding attributed to data limitations. These results carry direct implications for policymakers designing climate-related disclosure frameworks and for scholars examining the boundary conditions under which mandatory transparency translates into genuine environmental performance. Full article

Background/Objectives: Child-to-parent violence (CPV) refers to persistent physical, psychological, or financial violence perpetrated by children or adolescents against their parents. Although CPV has attracted increasing academic and professional attention in recent years, evidence regarding its predictors remains fragmented. This systematic literature review aimed to synthesize empirical evidence on the predictors of adolescent CPV, with a particular focus on developmental victimization, personality traits, and psychopathology. Violence refers to the intentional use of physical, psychological, or symbolic force to cause harm, control, or suffering, while aggression corresponds to intentional behavior aimed at harming another individual, which may or may not involve physical violence and is often broader and more situational. Methods: A systematic literature review was conducted in accordance with PRISMA guidelines and prospectively registered in PROSPERO (CRD42024596076). Searches were carried out in January 2025 across six electronic databases (PsycINFO, Web of Science, Scopus, PubMed, MEDLINE, and CINAHL). Empirical studies published between 2000 and 2025 examining predictors of CPV in adolescence, namely developmental victimization, personality traits, and psychopathology, were included. Methodological quality was assessed using the Mixed Methods Appraisal Tool (MMAT). Results: The search identified 862 records, of which 46 studies met the inclusion criteria and were retained for full-text analysis. Most studies were quantitative in design and published within the last 15 years, with Spain accounting for most of the empirical evidence. The findings consistently demonstrated associations between CPV and exposure to direct or vicarious family victimization, maladaptive personality traits—particularly psychopathic features—and a range of psychopathological symptoms, including substance use, mood and anxiety disorders, and neurodevelopmental conditions. Conclusions: The results support a multifactorial and developmental understanding of CPV, highlighting early victimization as a central risk context interacting with personality and mental health vulnerabilities. Limitations of the existing literature are discussed, and directions for future research are proposed, emphasizing the need for longitudinal and qualitative studies to inform prevention and intervention strategies. Full article

Robust Arithmetic Word Problem (AWP) solving is important for applying mathematical reasoning systems in educational scenarios, where problem statements may contain changed numerical values, paraphrased descriptions, or irrelevant distracting information. Although Large Language Models (LLMs) have shown strong potential in solving AWPs, their reasoning processes may still be sensitive to surface-form variations and perturbation-induced noise. To address this issue, this paper proposes a Scene-Aware Neuro-Symbolic solver designed to improve the robustness of AWP solving under perturbations. The proposed method extends the existing scene-aware framework by introducing perturbation-oriented mechanisms at the scene, relation, and symbolic-solving levels. A Chain-of-Scene (CoS) prompting strategy first generates candidate scenes, after which goal-guided filtering retains target-related and bridge scenes while removing distractor-induced scenes. The retained scenes are then processed by the Scene-Aware Syntax-Semantics ($S^2$) method to extract explicit and implicit relations, and relation consistency checking is applied to remove locally plausible but globally irrelevant relations. Finally, the symbolic solver performs iterative equation-based reasoning over the filtered relation sets, with fallback recovery activated when standard solving does not produce a target-compatible answer. Experiments on AGG, MAWPS, and GSM8K show an average accuracy of 92.8% on clean datasets. On GSM-Perturb and AWP-Perturb, the solver achieves perturbed accuracies of 80.8% and 87.5%, with robustness drops of 8.3% and 6.8%, respectively. Ablation results show that scene filtering and relation consistency checking are the main contributors to reducing perturbation-induced errors. These findings suggest that combining LLM-based scene understanding with symbolic relation reasoning is a promising direction for improving the robustness and interpretability of AWP solvers in the evaluated perturbation settings. Full article

Internet memes increasingly move from social media into physical product packaging, yet little is known about how consumers respond when online cultural symbols become package design cues. Drawing on the Stimulus–Organism–Response framework, this study examines how meme-based packaging shapes purchase intention and sharing intention through perceived value, brand warmth, and cultural resonance. A between-subjects survey experiment was conducted with 305 Chinese adult consumers, who evaluated either a meme-based packaging stimulus or a no-explicit-meme conventional packaging control stimulus. Partial least squares structural equation modeling showed that purchase intention and sharing intention followed different dominant mechanisms. Perceived value was the strongest predictor of purchase intention, whereas cultural resonance was the strongest predictor of sharing intention. Visual attractiveness most strongly enhanced perceived value, while playfulness and expression–product fit contributed more clearly to brand warmth and cultural resonance. Mediation results further showed that brand warmth and cultural resonance consistently transmitted the effects of meme-packaging cues, whereas the value route was more selective. These findings show how online cultural symbols can continue to shape consumer evaluation and social transmission after entering physical product interfaces. Full article

This paper explores how children perceive and use outdoor spaces in their everyday urban environment, and which spatial characteristics encourage engagement, autonomy, and diverse play. This study was conducted using child-led sensory walks—an exploratory qualitative method in which children acted as active research guides—with ten children aged 6 to 11 in residential areas of Zagreb. Verbal comments, movement patterns, and play behaviours were recorded and analysed through thematic analysis. Following the walks, eleven public playgrounds were assessed from a landscape architecture perspective, integrating children’s observations with an expert evaluation of spatial organisation, shade provision, connectivity with surrounding green spaces, and potential for unstructured play. The results reveal a pronounced preference for natural and semi-natural spaces, where children exhibited longer stays, more diverse physical and symbolic play, and a greater sense of autonomy. These findings affirm the relevance of affordance theory and multisensory experience in understanding children’s spatial behaviour and demonstrate the potential of the sensory walk as a transferable research and design tool in landscape architecture practice. At a broader scale, they point to the untapped role that playgrounds—redesigned as genuine green infrastructure nodes—could play in advancing urban climate adaptation goals at the neighbourhood scale. Full article