Search Results (491)

This article develops the analytical concept of post-liturgical female rituality to examine informal religious practices created by Western Ukrainian female labor migrants in Israel. Drawing on approaches that conceptualize ritual as flexible, embodied, and processual, it focuses on women’s ritual activities that take place in close temporal and symbolic proximity to official church liturgy while remaining outside canonical frameworks. Rather than directly challenging institutional religion, these practices extend and reinterpret patriarchal liturgy through gendered forms of ritual engagement. The analysis is based on qualitative research among Ukrainian Greek Catholic women in Israel, including 27 in-depth interviews, participant observation, and digital ethnography. The findings highlight three interconnected dimensions: collective gatherings following church services; post-liturgical practices involving food, singing, and embodied performance; and national-religious rituals expressing emotional belonging to Ukraine in the context of war. The article argues that post-liturgical female rituals constitute a distinct form of women’s religious agency that operates within institutional Christianity while reworking its meanings, contributing to feminist scholarship on ritual, migration, and war. Full article

A coupled thermo-mechanical probabilistic model for porosity prediction in robotic Cold Metal Transfer (CMT) welding is proposed and experimentally validated under industrial conditions. Unlike conventional energy-based approaches, the formulation explicitly incorporates fixture-induced geometric deviations through the effective stick-out relation ${SO}_0.$ A dimensionless fixture influence factor, $X_f$, is introduced to quantify mechanical–process interaction. A fractional factorial design followed by a reduced DOE-enabled separation of thermal and mechanical effects. Logistic regression integrating process energy descriptors and $X_f$ achieved strong predictive capability (AUC = 0.91; 95% CI: 0.87–0.94). The fixture influence factor exhibited the highest standardized effect (OR = 3.74), while a 1 mm increase in effective stick-out doubled porosity probability (OR = 2.10), demonstrating the dominance of mechanical coupling within the evaluated operating window. Industrial implementation confirmed model relevance: geometric stabilization reduced rework from 11.58% to 4.76% and increased OEE from 79.58% to 87%. The results establish fixture mechanics as a primary control variable for weld robustness and provide a physically grounded framework for predictive quality optimization in robotic CMT systems. Full article

Transnational youth frequently navigate multiple languages and continually negotiate not only affiliation, but also the legitimacy of the languages they use within changing linguistic hierarchies. However, their educational experiences are often framed through fragmented classroom practices, deficit-based assessments, and nationally bounded curricular frameworks. In this paper, I respond by theorizing Fictional Linguistic Landscapes (FLL) as a transdisciplinary pedagogical approach that utilizes fiction and participatory cultural practices to position language learning as a form of semiotic design, critical inquiry, and identity (re)work. Grounded in linguistic landscape studies, multiliteracies pedagogy, and fan-based meaning-making, FLL positions learners as world-builders and allows them to experiment with visibility, hierarchy, and language(s) in safe fictional environments. This study outlines the four-phase FLL in Second Language Teaching and Learning (L2TL) cycle and provides five pedagogical design spaces to address issues of raciolinguistic valuation, deficit institutional representations, affective harm, peer-level marginalization, and translocal or return migrant identity negotiation. Rather than viewing imagination as an outcome of teaching, FLLinL2TL structures it as a necessary process for learning, linking creative production to explicit linguistic objectives and reflective justification. I conclude by discussing implications for classroom practice, teacher education, and future research on the potential of the FLLinL2TL approach in transnational education research. Full article

Sustainable manufacturing performance is increasingly influenced by the quality of decisions embedded in Quality Management System (QMS) activities, particularly those related to problem analysis and preventive action. In industrial environments such as welded bus chassis production, recurring quality defects—although involving small components—can generate sustainability impacts through rework, inspection effort, and energy consumption. Although artificial intelligence (AI) is increasingly adopted to support quality-related tasks, its contribution is often assessed in terms of automation rather than its effect on decision quality. This study presents an AI-supported, prompt-driven decision framework designed to strengthen preventive performance within QMS. The framework is implemented through a deterministic software application that formalizes prompt engineering as a rule-based process, transforming informal human problem descriptions into structured prompts suitable for external AI reasoning tools. The application itself does not embed AI and does not generate decisions; instead, it functions as a transparent decision interface that reduces variability in problem formulation and supports methodological consistency. The framework was validated through an industrial case study conducted in a bus chassis manufacturing plant experiencing recurring defects related to missing or incorrectly positioned welded brackets. Quantitative evaluation using Key Performance Indicators demonstrates reduced analysis cycle time, improved completeness of problem definitions, higher corrective action implementation rates, and lower defect recurrence. Full article

The construction industry faces persistent challenges in productivity, efficiency, and sustainability. Digital twin (DT) technology has emerged as a promising pathway for lifecycle optimisation, yet its construction adoption remains limited. Key barriers include fragmentation across project phases, weak data continuity at handover, and conceptual ambiguity between DT and Building Information Modelling (BIM). This systematic literature review analyses 160 peer-reviewed studies (2018–2026) selected from 463 Scopus records using a PRISMA-guided process and inter-rater reliability testing (Cohen’s κ = 0.83). The review clarifies that DTs extend beyond BIM in three ways: they enable bidirectional, automated physical-digital data exchange; integrate heterogeneous real-time sources such as IoT sensors and operational systems; and maintain lifecycle continuity from design through to end-of-life. Select advanced implementations report notable performance gains. These include rework and logistics reductions of up to 80%, cost savings of approximately 5%, schedule acceleration of around two months, energy reductions of 15–30%, and maintenance cost reductions of 10–25%. These figures reflect case-level outcomes from high-performing pilots and should not be read as typical industry benchmarks. Broader adoption remains constrained by interoperability gaps, data quality challenges, digital maturity deficits, misaligned stakeholder incentives, and paper-based regulatory environments. DTs represent a socio-technical transformation, not a standalone technology upgrade. Realising their potential requires coordinated progress in standards development, governance frameworks, collaborative delivery models, and workforce capability. Future research should focus on scalable interoperability, longitudinal lifecycle value validation, human-centred adoption strategies, and sustainability assessment methods to support evidence-based diffusion of DTs in the built environment. Full article

The goal of the article is to demonstrate the common threads and methods of studying digital storytelling as a unified, second-order aesthetic code. Just as the category of translation, when applied to digital literature, was expanded into a more complex set of methods known as media translation, the article applies similar logic to the notion of intertextuality and proposes an augmented form of “digital“ or “media intertextuality“. Games, interactive fiction, hypertext fiction, story generators, and other born-digital forms are all “texts” that share evolutionary poetics and intertextual strategies extending beyond language into multimodal, procedural, and embodied affordances. Drawing on the concept of structural quotation and semiotic calques, this article suggests that intertextuality should operate across multiple extra-linguistic registers: visual, procedural, and embodied. Neither evolutionary continuity nor broad intertextuality have been sufficiently emphasized in current game studies outside the literary angle. In several examples and case studies—from Zork II to World of Warcraft—this paper demonstrates how repetition with difference, brought about by intertextual links, generates evolutionary continuity and intertextual richness. In this dialogical ecology, AAA blockbusters and experimental works are worth studying together, even if, within the discourse of digital entertainment, they are currently at war. The former push the boundaries of expressive possibility, whereas the latter accrue cultural capital by reworking and critiquing shared codes. Full article

The geometric variability of industrial components represents a persistent challenge in robotic arc welding, particularly in high-volume manufacturing environments where parts are positioned in fixtures based on nominal CAD assumptions. Even moderate deviations in dimensions or seating conditions can lead to weld defects, rework, and reduced process capability when conventional offline programming is employed. This paper presents an applied industrial workflow for adaptive robotic welding trajectory correction that integrates full-field 3D optical metrology with a data-driven deep reinforcement learning (DRL) model. Prior to welding, each component is scanned using a structured-light 3D system, and critical geometric deviations are extracted relative to the nominal CAD model. These deviations define a compact state representation that is mapped, via a trained DRL agent, to corrective translational and rotational adjustments of the welding trajectory. Importantly, all trajectory corrections are computed offline, ensuring compatibility with standard industrial robot controllers and avoiding real-time computational overheads. The proposed approach is validated using real production data from an industrial batch of 5000 components characterized by significant dimensional variability and limited process capability. Experimental results demonstrate a reduction in welding defects exceeding 90%, elimination of rework associated with improper part positioning, and an improvement of the overall process performance to a sigma level of 5.219. The results show that combining 3D optical metrology with learning-based trajectory adaptation enables robust compensation of part-level geometric deviations without mechanical fixture modifications. The proposed method provides a practical and scalable solution for improving welding quality in manufacturing environments affected by upstream variability and imperfect part positioning. Full article

Car engine quality control is fundamentally hindered by extremely high-dimensional, noisy, and imbalanced multi-sensor data. To overcome these challenges, this paper proposes an edge-deployable diagnostic and predictive framework. First, a Sparse Autoencoder (SAE) maps over 12,000 distributed manufacturing parameters into a robust latent space to filter instrumentation noise. Second, for defect classification, a Class-Specific Weighted Ensemble (CSWE) tackles extreme class imbalance by aggressively penalizing majority-class bias, improving defect interception recall by 7.72%. Third, for transient performance tracking, an Adaptive Regime-Switching Regression (ARSR) replaces manual phase selection with unsupervised regime routing to dynamically weight local experts, reducing relative prediction error by 12%. Rigorously validated across three diverse public datasets (NASA C-MAPSS, AI4I, SECOM) and a physical H4 engine assembly line, the framework achieves an ultra-low inference latency of $80\pm 3$ ms, practically reducing the engine rework rate by 7.2%. Full article

Quality management in construction projects is critical for ensuring client satisfaction, minimizing rework, and achieving cost efficiency in an industry characterized by a long history of cost overruns. Traditional quality assurance and control (QA/QC) processes, however, are resource-intensive and often implemented without a systematic evaluation of their cost-effectiveness. Absent a systematic evaluation of the costs and benefits associated with QA/QC, stakeholders—particularly clients and contractors—are unlikely to commit resources to the implementation of quality control practices within construction projects. This paper presents a quantitative optimization framework that integrates Monte Carlo simulation of activity-level rework costs with a constrained optimization model based on a novel project-level key performance indicator, the Total Assurance on Reworks (TAR) to support data-driven decision-making in project quality management. The model enables construction managers to evaluate trade-offs between the costs of preventive quality controls and the potential consequences of non-conformities. The methodology is demonstrated through a synthetic dataset comprising 50 construction activities. Results indicate that the framework can identify optimal allocations of quality control resources, achieving up to more than 19% cost savings compared to a full-control strategy and 18% reduction in economic resources when compared with the state of the art while maintaining target quality assurance levels. This contributes to the broader discourse on quality management by offering a computationally rigorous tool for balancing cost efficiency and quality performance in complex projects. Full article

In electronic manufacturing, efficient detection of printed circuit board (PCB) surface defects is essential for reducing rework rates and minimizing material waste, thereby supporting sustainable manufacturing. To address the challenge that existing methods struggle to balance detection accuracy and real-time performance in complex industrial environments, this paper proposes a lightweight and high-performance PCB surface defect detection model, termed SDD-RT-DETR. Built upon Real-Time Detection Transformer (RT-DETR), the proposed model introduces a Faster-Block backbone to improve feature extraction efficiency, replaces the original feature fusion module with HS-FPN to enhance multi-scale representation, and employs the Wise-Focaler-MPDIoU loss to optimize bounding box regression. Experiments conducted on an expanded PCB defect dataset containing 3403 images show that SDD-RT-DETR achieves improvements of 2.3% in mAP and 3.6% in inference speed over the baseline, while reducing parameters by 5.04 M and FLOPs by 12.7 G. These results demonstrate that the proposed method effectively balances accuracy, efficiency, and computational cost, offering a practical solution for low-energy and sustainable intelligent electronic manufacturing systems. Full article

Advanced manufacturing is undergoing a profound transformation, with data quickly becoming its most strategic asset. The industry is pushing toward Industry 4.0 with its sights already on the human-centric Industry 5.0. Manufacturing firms are rapidly integrating AI, IoT, and advanced analytics to enable real-time decision making, predictive maintenance, and full manufacturing lifecycle optimization. However, this data-driven revolution exposes a critical vulnerability: the hidden direct costs and cascading downstream consequences of inaccurate, missing, or corrupt data. This paper provides an in-depth examination of the data quality crisis facing modern manufacturing, exploring its quantifiable impact on cost, safety, and strategic decision making; and identifies the tangible barriers preventing scalable AI in manufacturing today. We investigate how bad data undermines the digital thread, erodes both operational and strategic trust, and stalls the transition to autonomous systems. Supported by recent industry surveys, academic findings, and leading trends, we reveal that most manufacturers suffer from systemic data quality issues, with billions lost annually to inefficiencies, rework, and flawed decisions. Addressing this, the paper evaluates state-of-the-art solutions for real-time data validation, anomaly detection, and predictive imputation. Building upon this, we identify key gaps—including the lack of unified data quality frameworks, integration across legacy/modern systems, and actionable imputation under uncertainty—and propose a roadmap to bridge them. The paper concludes by outlining four research directions that support a seamless, scalable transition toward a trustworthy data foundation in manufacturing. Industry 4.0/5.0 is defined by data, insight, and actionable intelligence: only manufacturers that tame their data chaos will thrive. Full article

Achieving sustainability in prefabricated construction necessitates a balance between operational efficiency and stringent environmental constraints. However, cascading rework chains triggered by assembly defects frequently disrupt this equilibrium. Existing literature predominantly addresses this dynamic through reactive rescheduling, thereby largely overlooking the potential of proactive topological interception. To bridge this gap, this study proposes a proactive bi-level scheduling framework that mathematically integrates strategic quality inspection planning with operational low-carbon project execution. Specifically, a Generalized Total Cost (GTC) model is formulated to internalize multi-objective trade-offs—including time, cost, and carbon emissions—into a unified financial metric through market-based shadow prices. This framework is operationalized through a novel bi-level Hybrid Evolutionary Algorithm (H-TS-CDBO). By combining the global exploration capabilities of Chaotic Dung Beetle Optimization with the local refinement mechanisms of Tabu Search, the proposed solver is specifically engineered to navigate the topological ruggedness induced by proactive inspection interventions. Empirical benchmarking validates the computational robustness of the solver, while an illustrative case study substantiates a critical managerial paradigm shift from “passive remediation” to “active prevention”: compared to traditional methods, a marginal preventive investment of 5.4% functions as an effective containment mechanism, yielding a 40.8% net reduction in the GTC. Furthermore, a sensitivity analysis regarding varying static carbon tax rates simulates algorithmic adaptation under diverse regulatory intensity thresholds, delineating an actionable pathway for project managers to achieve lean, low-carbon synergy amidst evolving regulatory pressures. Full article

The 1964 M7.5 Niigata earthquake remains one of the most significant natural laboratories for understanding seismic–induced soil liquefaction and its dependence on geological setting. Among global field case histories, Niigata stands out for the exceptional documentation of liquefaction triggering, lateral spread displacements, and soil–structure interaction. This paper reexamines the event from an engineering–geologic perspective, emphasizing how Holocene coastal and fluvial depositional processes beneath the Echigo Plain controlled the spatial and stratigraphic distribution of liquefaction during the 1964 earthquake. The most severe ground deformations occurred in fluvially reworked sands derived from three major Holocene dune and barrier island systems (CSD1,2,3) formed along the paleo–shoreline of the Sea of Japan. The largest of these, a mid–Holocene transgressive barrier complex deposited to a thickness of 50–60 m of beach and aeolian sand between 8 and 5 ka B.P., now lies buried 5–8 km inland beneath fine–grained alluvial deposits. Tectonic downwarping and deltaic progradation by the Shinano and Agano rivers redistributed these sands into loose, saturated fluvial facies beneath modern Niigata city. Quantitative geotechnical analyses demonstrate that liquefaction occurs within these reworked Holocene units rather than anthropogenic fills. Full article

The shallow-buried abandoned Yellow River Delta (893–1855 AD) exhibits a distinctive geomorphic system shaped by coupled fluvial sediment reduction, climatic transition, and relative sea-level fluctuations, with its intact deposits recording key temperate delta evolution during climate change. Using four sediment cores, we applied optically stimulated luminescence (OSL) dating, sedimentary facies analysis, and grain-size techniques (C-M diagram, end-member modeling), integrated with geomorphic interpretation and historical data, to reconstruct the delta’s evolutionary sequence and clarify storm surge-driven geomorphic reworking and its diagnostic indicators. Results indicate that the delta’s evolution was governed by abrupt fluvial sediment loss, intensified storm dynamics, and relative sea-level rise. The 893 AD Yellow River avulsion triggered delta abandonment (893–1482 AD), driving a shift from a fluvially dominated muddy coast to a wave-controlled sandy system. Sandy deposits initially formed at M04A and prograded landward to M03A. During the Little Ice Age (1482–1855 AD), frequent storm surges further expanded and elevated these sandy accumulations, while weak sedimentation persisted in the inland depression (B03). This differential process generated a unique plain lowland–coastal highland system, a rare geomorphic type among large river deltas that differs from classic island–continent and barrier–lagoon systems. This study elucidates the phased response of temperate monsoon abandoned deltas to millennial-scale climate change, advances theories of multi-factor coupled delta evolution, and provides scientific support for coastal protection, stability assessment, and evolutionary prediction under global warming. Full article

The two most severe cosmological tensions in the Hubble constant $H_0$ and the matter clustering amplitude $S_8$ have the same relative discrepancy of 8.3%, which suggests that they may have a common origin. Modifications of gravity and exotic dark fields with numerous free parameters introduced in the Einstein field equations often struggle to simultaneously alleviate both tensions; thus, we need to look for a common cause within the standard $\Lambda$CDM framework. At the same time, linear perturbation analyses of matter in the expanding $\Lambda$CDM universe have always neglected the impact of comoving peculiar velocities $\mathbf{v}$ (generally thought to be a second-order effect), the same velocities that, in physical space, cannot be fully accounted for in the observed late-time universe when the cosmic distance ladder is used to determine the local value of $H_0$. We have reworked the linear density perturbation equations in the conformal Newtonian gauge (sub-horizon limit) by introducing an additional drag force per unit mass $-\Gamma \left(t\right)\mathbf{v}$ in the Euler equation with $\Gamma \equiv \gamma \left(2H\right)$, where $\gamma \ll 1$ is a positive dimensionless constant and $2H\left(t\right)$ is the time-dependent Hubble friction. We find that a damping parameter of $\gamma =0.083$ is sufficient to resolve the $S_8$ tension by suppressing the growth of structure at low redshifts, starting at $z_{★}\simeq 3.5–6.5$ to achieve $S_8\simeq 0.78–0.76$, respectively. Furthermore, we argue that the physical source causing this additional friction (a tidal field generated by nonlinear structures in the late-time universe) is also responsible for a systematic error in the local determinations of $H_0$—the inability to subtract peculiar tidal velocities along the lines of sight when determining the Hubble flow via the cosmic distance ladder. Finally, the dual action of the tidal field on the expanding background—reducing both the matter and the dark energy sources of the squared Hubble rate $H^2$, thereby holding back the cosmic acceleration $\ddot{a}$—is of fundamental importance in resolving cosmological tensions and can also substantially alleviate the density coincidence problem. Full article