Search Results (226)

Intelligent oilfield development is experiencing an increasingly deep integration between localized automation and integrated, data-centric ecosystems. To systematically delineate the knowledge structure and technological trajectories within this field, this study analyzes 225 high-quality publications. This study innovatively employs a custom toolchain based on the Dart language for heterogeneous data cleaning and standardization, ensuring high accuracy and scientific rigor in the analysis samples. The investigation reveals a distinct dual-core driving mechanism underpinning recent advancements: a cognitive cluster centered on Artificial Intelligence and Deep Learning for complex data interpretation and prediction, and a decision-making cluster focused on Operational Optimization and Predictive Modeling for production enhancement. These two clusters respectively encompass eight sub-clusters: “artificial intelligence,” “machine learning,” “deep learning,” “performance,” “enhanced oil recovery,” “model,” “optimization,” and “predication.” This dual-core framework signifies a paradigm shift from experience-based practices to a synergistic “AI-enabled + mathematical optimization” approach. The analysis further explores emerging trends, including the potential of deep reinforcement learning for dynamic decision-making and the critical role of cybersecurity and model robustness in safety risk management. By mapping the current landscape and core mechanisms, this study provides a foundational reference for researchers and practitioners to navigate the future development of intelligent oilfields towards more resilient and efficient ecosystems. Full article

The socks, hosiery, bodywear (SHB) industry is a critical segment of the textile sector, characterized by high-volume production and rapid delivery requirements, making efficiency and resource optimization essential. A corporate sustainability system is needed to minimize environmental impact, ensure long-term competitiveness, and align operations with global sustainability standards. Thus, this research aims to propose an integrated Corporate Sustainability System (CSS) framework that synergizes Lean Manufacturing (LM), Digital Transformation (DT), and sustainability transition through a methodological triangulation of (1) a narrative review, (2) in-depth expert interviews, and (3) a comprehensive Turkish case study. The proposed framework integrates foundational lean principles such as 5S, TPM, and Value Stream Mapping with Industry 4.0 technologies, including RFID traceability, real-time ERP integration and machine vision systems. Empirical demonstration through the case study reveals that establishing foundational lean maturity is a critical foundation for successful digital adoption. Furthermore, the study demonstrates that transitioning from manual tracking to integrated digital platforms resolves data silos and enhances the transparency of customer revisions and warehouse accuracy. The framework also incorporates human-centric Lean 5.0 improvements, proving that ergonomic interventions such as rail-mounted cable systems are vital for operational sustainability. Ultimately, the CSS provides a scalable model that aligns SHB production with global mandates like the EU Green Deal and CBAM, positioning the sector for long-term competitive advantage in an increasingly eco-conscious global market. Full article

This study examines the determinants of city brand equity in the context of Riyadh Season, a large-scale cultural and entertainment festival in Saudi Arabia. Drawing on Aaker’s customer-based brand equity framework adapted to the city-brand context and informed by Source Credibility Theory (SCT), the study tests the direct effects of brand association, brand awareness, brand loyalty, and customer satisfaction on city brand equity, as well as the moderating role of online influencers. Survey data were collected from 991 attendees and analyzed using structural equation modeling (SEM). The results indicate that brand awareness and brand loyalty significantly enhance city brand equity, whereas brand association and customer satisfaction have no significant effects. Contrary to prevailing assumptions in tourism and digital branding research, online influencers do not moderate the relationships between brand equity dimensions and overall city brand equity. These findings identify boundary conditions for influencer effectiveness and suggest that, in experience-intensive and time-bound mega-events, city brand equity is driven more by recognition and repeat attachment than by influencer-mediated communication or post-event satisfaction. The study refines city brand equity theory and offers practical guidance for policymakers and event organizers seeking to build sustainable city brands beyond influencer-centric strategies. Full article

Digital transformation reshapes firms into more digital, data-driven, and customer-centric organizations. Because it often supports innovation, firms are widely expected to benefit from higher performance and productivity. However, it remains unclear whether higher national levels of digital integration translate into higher aggregate enterprise productivity. This study adopts a socio-technical and ecosystem perspective to examine the relationship between digital technology integration and enterprise labor productivity across the 27 EU member states, while also considering the role of key ecosystem enablers. A balanced country-year panel of data (N = 162) was constructed from Eurostat Structural Business Statistics on the apparent labor productivity of total enterprises, together with Digital Economy and Society Index (DESI) indicators on the integration of digital technology, human capital, connectivity, and Gross Domestic Product (GDP) per capita, covering the period from 2017 to 2022. To this end, fixed-effects regression models were estimated using robust standard errors clustered by country and combined with correlated random effects (CRE/Mundlak) decomposition. This methodological approach was adopted to distinguish short-run within-country dynamics from persistent between-country differences. The study contributes to ecosystem-level DESI research by using this distinction to assess how country-level digital integration is associated with enterprise productivity. The fixed-effects results provide no evidence that year-to-year changes in digital technology integration, on their own, are associated with higher enterprise productivity. Additionally, no statistically significant interaction effect was observed with either human capital or digital connectivity. By contrast, GDP per capita was found to be a robust positive predictor of enterprise productivity. The CRE/Mundlak results indicate that the majority of between-country productivity differences are attributable to differences in economic development. Furthermore, there is evidence of a positive association between the average level of digital technology integration and human capital. Taken together, these findings suggest that national digital technology integration reflects business environment conditions at the ecosystem level. While it may create opportunities for enterprise business model innovation, its productivity implications are more likely to emerge gradually through stronger absorptive capacity and complementary capabilities. Consequently, the study suggests that enterprise digital transformation policies should be aligned with investments in digital skills and broadband infrastructure. These policies should also support process redesign, greater interoperability, and the implementation of AI-enabled technologies. Full article

In practical customer-support dialogue systems, responses must simultaneously deliver factually grounded information and context-appropriate empathy, yet existing single-stage generation models often exhibit specialization bias, favoring one objective at the expense of the other. To address this limitation, we propose a dual-stage generation framework that explicitly decouples factual grounding from empathetic modulation. Our primary configuration follows a fact-to-empathy order, in which the system first generates a fact-centric draft via structured query interpretation and optional retrieval-augmented generation, then applies empathy-aware tuning conditioned on inferred emotion type, intensity, and empathy necessity. To enable deployment in resource-constrained environments, only the query interpretation module is explicitly trained using knowledge distillation, allowing the overall system to operate with compact 4B–8B backbone language models. Furthermore, we construct a customer-support dialogue dataset designed to reflect realistic interactions involving both informational and emotional demands. Extensive experiments with compact models show that the proposed approach generally improves key dimensions of empathetic response quality while maintaining overall factual performance, thereby helping mitigate the representational entanglement empirically observed in single-stage baselines. Both quantitative metrics and scenario-based analyses confirm that decoupled generation enables a more balanced integration of factuality and empathy than single-stage generation. These results suggest that dual-stage generation provides a practical and extensible foundation for deployable, real-world customer-support dialogue systems. Full article

Corporate management has recently shifted from a traditional shareholder-centric approach to sustainability-oriented strategic management, with ESG factors becoming central to corporate strategy. In this study, we identified strategic implications for enhancing corporate sustainability amid these changes. Specifically, we examined the structural context in which voluntary international standards, such as the Global Reporting Initiative (GRI), interact with the rising importance of environmental issues, diverse stakeholders, and intensified corporate competition, leading to a more in-depth discourse on corporate sustainability management. We analyzed corporate sustainability management reports of 102 companies across five industries in South Korea, applying risk management techniques and calculating relative importance indicators for key issues. The analysis revealed that “Response to the Threat of Climate Change” was the top priority across many industries and was closely linked to other issues such as sustainable resource use, customer safety, and supplier management, depending on industry characteristics. Several issues were identified as highly important despite being infrequently mentioned, suggesting they could become key future concerns. Based on our findings, we recommend that companies develop scenario-based, industry-specific strategies to address the threat of climate change, with a focus on greenhouse gas (GHG) reductions. For governments and regulators, these findings are expected to have significant implications for enhancing corporate capacity to respond to Net Zero goals and improve climate change resilience across industries. Full article

Traditional pedestrian routing algorithms typically minimize physical distance or travel time, often overlooking contextual factors that influence route choice in digitally connected environments. As public WiFi infrastructure becomes increasingly prevalent in smart-city districts and university campuses, digital connectivity may influence pedestrian mobility decisions. This study introduces P-WARP, a multi-factor routing and inference framework that reconstructs latent pedestrian preferences by integrating physical effort, environmental walkability, and WiFi connectivity within a unified semantic graph. The empirical analysis is conducted on the Chiang Mai University campus, a digitally connected environment serving as a smart campus testbed. The framework integrates heterogeneous spatial datasets, including OpenStreetMap topology, Shuttle Radar Topography Mission elevation data, environmental walkability grids, and WiFi roaming logs collected via a custom mobile sensing application from 21 volunteers across 71 verified walking trips. Two routing strategies are evaluated: a Global Static Model, representing infrastructure-based connectivity assumptions, and a Trip-Centric Dynamic Model, incorporating realized connectivity histories. Model parameters are calibrated using Bayesian Optimization with five-fold cross-validation. Results show that incorporating realized connectivity reduces trajectory reconstruction error by 6.84% relative to the baseline. The learned parameters reveal a notable detour tolerance, suggesting that stable digital connectivity can influence pedestrian route choice in digitally instrumented environments. Full article

In the context of advancing manufacturing, production systems are shifting toward human-centric and personalized production. However, accurately quantifying subjective user needs into precise product specifications remains a challenge. Taking child companion robots as an example, this paper proposed a novel product innovation design framework based on Extenics and Kansei engineering to optimize the texture design of smart products. By systematically integrating synergistic relationships among colour, material, and surface processing technology, the framework aimed to enhance the sustainable value and social sustainability of products by more precisely meeting users’ perceptual and emotional needs. The research methodology employed the semantic differential method to quantify user perception and utilized the K-means clustering algorithm to construct a chromatic colour sample library for smart products. Subsequently, by combining the multi-criteria decision-making tool grey relational analysis with statistical verification, the optimal design scheme was selected from the generated alternatives. Experimental results demonstrated that this method significantly reduced design subjectivity and ambiguity. By bridging the gap between user expectations and engineering solutions, the framework provides a systematic solution for mass customization and process optimization that promotes resource efficient and sustainable production, while also reducing the resource waste associated with traditional trial and error design processes. Full article

Service encounters have long been viewed as determinants of hotel guest loyalty, yet excellent service does not always translate into repeat patronage. This study examines how green service encounters shape guest loyalty in green-certified luxury hotels in Bali, a leading sustainable tourism destination. It investigates whether green experiential value and green customer delight mediate the effect of green service encounters on green hotel loyalty. Survey data from 273 domestic repeat guests of Green Globe and Earth Check-certified luxury hotels in Bali were analyzed using partial least squares structural equation modeling (PLS-SEM). The results show that green service encounters influence loyalty primarily through green experiential value and green customer delight, with delight exerting a comparatively stronger mediating effect. The study extends green hotel loyalty research by theorizing and testing an emotion-centric, sustainability-anchored loyalty mechanism beyond traditional service-quality and satisfaction models. Managerially, the findings highlight the need for certified luxury green hotels to design green service encounters that create distinctive experiential value and delight, thereby strengthening long-term guest loyalty. Full article

The global tourism industry is entering a phygital era, prompting renewed examination of the metaverse as an extension rather than a substitute for physical travel. This study investigates how metaverse technology operates across the Phygital Customer Journey within the Thai tourism context. Drawing on in-depth interviews with 12 experts from academic, multimedia development, and policy sectors, the data were analyzed using reflexive thematic analysis. The findings indicate that the metaverse assumes its most structurally significant role during the pre-trip phase. Immersive previews were described as recalibrating perceived risk by enabling advance assessment of accessibility, spatial configuration, and environmental conditions prior to commitment. This staged risk-calibration process operates through three interrelated mechanisms: Sensory Bridging, Psychological Risk Mitigation, and Physical Feasibility Testing, which are particularly relevant for secondary tourism destinations and demographic aging contexts. Building on these patterns, the study advances a four-layer architectural framework as an interpretive synthesis. Within this framework, the metaverse functions as a transactional and coordination layer that integrates booking systems, AI-enabled services, and real-time infrastructural data supported by IoT and Blockchain. The analysis further suggests that the state may assume an enabling role as an Infrastructure Architect through the development of a National Digital Highway and regulatory sandbox arrangements for SMEs. Sustainable adoption depends on hardware-agnostic, mobile-centric accessibility to mitigate digital exclusion. While grounded in Thailand, the framework offers analytical relevance for destinations facing comparable infrastructural and demographic conditions. Full article

Human–technology relationships have become core strategic capabilities and enablers of enterprise sustainability. Contemporary interactions between humans and technology are undergoing a profound transformation toward a more human-centric and value-oriented paradigm, aiming for Industry and Society 5.0, a shift that is particularly salient in banking. The influence of customer experience quality on the strategic foundations of enterprise management is being fundamentally redefined. The purpose of this research is to assess the influence of customer experience on marketing outcomes in the banking industry. To analyze the directions, strengths, and statistical significance of relationships, structural equation modeling (SEM) using partial least squares (PLS) was employed. The research model was tested on a sample of 616 valid responses from customers of banking services in Serbia. The research shows that customer experience positively impacts customer satisfaction, behavioral loyalty intentions, and word-of-mouth, making it a strong predictor of marketing outcomes. The moderating roles of gender, customer segment, and respondents’ regional affiliation were tested, identifying variables that moderate significant relationships between customer experience and marketing outcomes, unveiling detailed insights into demographic and segmentation disparities. The findings offer robust empirical support for managerial decision making in customer experience enhancement initiatives. Full article

This paper presents a compact floating memtranstor (MT) emulator, a memory element characterized by a direct φ–q relationship, realized without analog multipliers or complex circuitry. The proposed design employs only two active blocks—a voltage differential transconductance amplifier (VDTA) and a voltage differential current conveyor (VDCC)—along with three grounded capacitors and a single grounded electronically tunable resistor. The emulator accurately reproduces the fundamental φ–q dynamics, exhibiting origin-crossing pinched hysteresis loops under sinusoidal excitation, and operates at a low supply voltage of ±0.9 V. Electronic tunability is achieved via bias-controlled transconductance modulation, enabling flexible adaptation across excitation frequencies and operating conditions. Validation is performed through analytical modeling, Monte Carlo simulations, temperature sensitivity analysis, and full LTspice post-layout simulations using a 180 nm CMOS process. The full-custom layout occupies 2529.49 μm², with robust performance confirmed under parasitic and process variations. Adaptive learning simulations demonstrate the emulator’s artificial synaptic plasticity, highlighting its suitability for neuromorphic computing, chaos-based circuits, and nonlinear dynamical systems. The compact, low-power, and multiplier-free architecture establishes the proposed MT emulator as a practical platform for emerging analog memory-centric applications. To validate the feasibility of the proposed solution, experimental tests are performed using commercially available components. Full article

Conventional Security Operations Center (SOC) solutions struggle to process representative operational alert streams efficiently and adapt to evolving cyber threats, highlighting the need for automated, intelligent threat detection and prioritization. This study presents a custom AI-driven framework that leverages unsupervised learning techniques to support SOC analysts in cyber threat detection, anomaly identification, and alert prioritization. The framework applies several clustering methods: HDBSCAN, DBSCAN, KMeans, and Gaussian Mixture Models for alert segmentation, and integrates anomaly detection using LOF and Isolation Forest, complemented by semi-supervised detection via One-Class SVM. Using textual, categorical, and numerical features from Wazuh alerts across three datasets, the system performs clustering and anomaly detection in the original high-dimensional feature space, with UMAP applied solely for two-dimensional visualization. HDBSCAN consistently produced well-separated clusters with effective noise detection, while, Isolation Forest evaluated via 10-fold cross-validation exhibited stable anomaly flagging and clear score separation across both cyber alert event data and synthetic threat injection experiments. Furthermore, the framework formulates a composite priority ranking that integrates anomaly severity, cluster rarity, and SOC contextual weighting, yielding actionable alert rankings. An interactive, analyst-centric dashboard enables SOC teams to explore top alerts, clusters, associated MITRE techniques, priority rankings, and geolocation data, providing insights while preserving human oversight. Overall, the proposed system transforms complex alert streams into structured insights, enhancing SOC situational awareness, decision support, and operational efficiency. Full article

Background/Objectives: Conventional workflows, peripheral blood smears, and bone marrow assessment supplemented by LDI-PCR, molecular cytogenetics, and array-CGH, are expert-driven in the face of biological and imaging variability. Methods: We propose an AI pipeline that integrates convolutional neural networks (CNNs) and transfer learning-based models with two explainable AI (XAI) approaches, LIME and Grad-Cam, to deliver both high diagnostic accuracy and transparent rationale. Seven public sources were curated into a unified benchmark (66,550 images) covering ALL, AML, CLL, CML, and healthy controls; images were standardized, ROI-cropped, and split with stratification (80/10/10). We fine-tuned multiple backbones (DenseNet-121, MobileNetV2, VGG16, InceptionV3, ResNet50, Xception, and a custom CNN) and evaluated the accuracy and F1-score, benchmarking against the recent literature. Results: On the five-class task (ALL/AML/CLL/CML/Healthy), MobileNetV2 achieved 97.9% accuracy/F1, with DenseNet-121 reaching 97.66% F1. On ALL subtypes (Benign, Early, Pre, Pro) and across tasks, DenseNet121 and MobileNetV2 were the most reliable, achieving state-of-the-art accuracy with the strongest, nucleus-centric explanations. Conclusions: XAI analyses (LIME, Grad-CAM) consistently localized leukemic nuclei and other cell-intrinsic morphology, aligning saliency with clinical cues and model performance. Compared with baselines, our approach matched or exceeded accuracy while providing stronger, corroborated interpretability on a substantially larger and more diverse dataset. Full article

AI-based audio generation has advanced rapidly, enabling deepfake audio to reach levels of naturalness that closely resemble real recordings and complicate the distinction between authentic and synthetic signals. While numerous CNN- and Transformer-based detection approaches have been proposed, most adopt a model-centric perspective in which the spectral representation remains fixed. Parallel data-centric efforts have explored alternative representations such as scalograms and CQT, yet the field still lacks a unified framework that jointly evaluates the influence of model architecture, its hyperparameters (e.g., learning rate, number of epochs), and the spectral representation along with its own parameters (e.g., representation type, window size). Moreover, there is no standardized approach for benchmarking custom architectures against established baselines under consistent experimental conditions. FakeVoiceFinder addresses this gap by providing a systematic framework that enables direct comparison of model-centric, data-centric, and hybrid evaluation strategies. It supports controlled experimentation, flexible configuration of models and representations, and comprehensive performance reporting tailored to the detection task. This framework enhances reproducibility and helps clarify how architectural and representational choices interact in synthetic audio detection. Full article