Search Results (1,573)

Organic-rich shale, as a significant alternative energy source, possesses abundant resources. Classified by maturity, it comprises three categories: medium-high maturity shale oil, medium-low maturity shale oil, and oil shale. Medium-high maturity shale oil faces challenges such as tight reservoirs and poor fluidity; medium-low maturity shale oil is characterized by a high proportion of retained hydrocarbons and poor mobility; and oil shale requires high-temperature conversion. Addressing the inherent characteristics of these three resource types, this paper systematically reviews the theoretical foundations and key technologies from two dimensions: “CO₂ injection for medium-high maturity shale oil extraction” and “in situ conversion of medium-low maturity shale/oil shale”. The results indicate that CO₂ injection technology for medium-high maturity shale oil utilizes its supercritical diffusion properties to reduce miscibility pressure by 40–60% compared to conventional reservoirs, efficiently displacing crude oil in nanopores while establishing a geological storage system for greenhouse gases, thereby pioneering an integrated “displacement–drive–storage” model for carbon-reduced oil production. The autothermic pyrolysis in situ conversion process for medium-low maturity shale/oil shale significantly reduces costs by leveraging the oxidation latent heat of kerogen. Under temperature and pressure conditions of 350–450 °C, the shale pore network expansion rate reaches 200–300%, with permeability increasing by two orders of magnitude. Assisted natural gas injection further optimizes the thermal field distribution within the reservoir. Future research should focus on two key directions: synergistic cost reduction and carbon sequestration through CO₂ injection, and the matching of in situ conversion with complex fracture networks. This study delineates key technological pathways for the low-carbon and efficient development of different types of organic-rich shale, contributing to energy security. Full article

Background: This study examined how patients and caregivers perceive and experience AI-based care technologies through text mining analysis. The goal was to identify major themes, sentiments, and value-oriented interpretations embedded in their narratives and to understand how these perceptions align with key dimensions of patient-centered care. Methods: A corpus of publicly available narratives describing experiences with AI-based care was compiled from online communities. Natural language processing techniques were applied, including descriptive term analysis, topic modeling using Latent Dirichlet Allocation, and sentiment profiling based on a Korean lexicon. Emergent topics and emotional patterns were mapped onto domains of patient-centered care such as information quality, emotional support, autonomy, and continuity. Results: The analysis revealed a three-phase evolution of care values over time. In the early phase of AI-mediated care, patient narratives emphasized disruption of relational care, with negative themes such as reduced human connection, privacy concerns, safety uncertainties, and usability challenges, accompanied by emotions of fear and frustration. During the transitional phase, positive themes including convenience, improved access, and reassurance from diagnostic accuracy emerged alongside persistent emotional ambivalence, reflecting uncertainty regarding responsibility and control. In the final phase, care values were restored and strengthened, with sentiment patterns shifting toward trust and relief as AI functions became supportive of clinical care, while concerns related to depersonalization and surveillance diminished. Conclusions: Patients and caregivers experience AI-based care as both beneficial and unsettling. Perceptions improve when AI enhances efficiency and information flow without compromising relational aspects of care. Ensuring transparency, explainability, opportunities for human contact, and strong data protections is essential for aligning AI with principles of patient-centered care. Based on a small-scale qualitative dataset of patient narratives, this study offers an exploratory, value-oriented interpretation of how relational care evolves in AI-mediated healthcare contexts. In this study, care-ethics values are used as an analytical lens to operationalize key principles of patient-centered care within AI-mediated healthcare contexts. Full article

Background/Objectives: Teacher burnout represents a complex cognitive-emotional syndrome characterized by the interplay between mental exhaustion and emotional dysregulation, threatening educational sustainability during crisis periods. This study employed comprehensive behavioral data analysis to investigate burnout syndrome patterns among Greek teachers during the COVID-19 educational crisis, aiming to identify risk factors and resilience patterns through multiple analytical approaches that capture the syndrome’s multidimensional nature. Methods: A cross-sectional study examined primary and secondary school teachers in Western Greece during the autumn of 2021. Stratified random sampling ensured representativeness across school levels, geographic locations, and employment types. Participants completed the Greek-adapted Maslach Burnout Inventory for Educators, which measured emotional exhaustion, depersonalization, and personal accomplishment. Behavioral data analysis integrated traditional statistical methods with advanced pattern recognition techniques, including classification trees for non-linear relationships, association analysis for behavioral patterns, and cluster analysis for profile identification. Results: The majority of teachers experienced high stress with inadequate coping capabilities. Classification analysis achieved high accuracy in predicting burnout severity, identifying emotional exhaustion as the primary predictor. Deputy teachers demonstrated severe cognitive-emotional strain compared to permanent colleagues across all dimensions, with dramatically reduced personal accomplishment and minimal resources. Association analysis revealed that combined low support and high workload more than doubled burnout risk. Three distinct profiles emerged: Resilient teachers, characterized by older age and permanent employment; At-Risk teachers, showing early warning signs; and Burned Out teachers, predominantly young and in precarious employment. Remote teaching, exceeding half of the workload, significantly increased strain. Multiple regression confirmed emotional exhaustion as the dominant syndrome predictor. Conclusions: Behavioral data analysis revealed complex cognitive-emotional patterns constituting burnout syndrome during educational crisis. Employment precarity emerged as the fundamental vulnerability factor, with young deputy teachers facing dramatically higher syndrome probability compared to supported senior permanent teachers. The syndrome manifests through cascading processes where cognitive overload triggers emotional exhaustion, subsequently reducing personal accomplishment. These findings provide an evidence-based framework for early syndrome identification and targeted interventions addressing both cognitive and emotional dimensions of teacher burnout. Full article

Industry 5.0 is composed of a variety of complex tasks and challenging processes requiring specialized labor and multidisciplinary coordination. Specifically, when it comes to shipbuilding, shipyards leverage advanced technologies, seeking to replace operations that continue to rely on traditional methods, such as 2D blueprints and paper-based documentation, which can lead to inefficiencies and alignment errors in precision-dependent tasks. For this reason, this article focuses on embracing Mixed Reality (MR) technologies to address these challenges in the context of electrical outfitting tasks. The design, development and evaluation of a MR application tailored for HoloLens 2 smart glasses aims to streamline the workflow for operators, reducing reliance on paper-based documentation and enhancing the precision of assembly processes. The proposed system allows for the precise positioning of 3D models in the real environment, ensuring accurate alignment during assembly. Additionally, it incorporates automatic dimension generation between objects in the scene. To further enhance usability, the application integrates a Galician on-device Automatic Speech Recognition (ASR) system, allowing operators to interact seamlessly with the MR interface using voice commands. The whole system has been exhaustively tested, both through usability and functionality evaluations, which validate MR as a viable tool for shipyard assembly and inspection tasks. Full article

(1) Background: HTPLA FDM-printed cutting guides enable the low-cost, in-hospital production of patient-specific instruments. However, annealing, which is required for steam sterilization, may alter the dimensions of fit-critical fixation pin holes. (2) Methods: HTPLA cylindrical specimens (height 5 mm) were printed with fixed process parameters and vertical orientation. Inner diameter (1.6–5.0 mm) and wall thickness (2–6 mm) were varied using a two-factor Central Composite Design (n = 13). Following a two-stage annealing treatment (80 °C, 10 min; 100 °C, 50 min), post-annealing dimensions were measured and modeled using Response Surface Methodology. An illustrative verification was performed on additional specimens. (3) Results: Annealing induced a systematic decrease in inner diameter (−0.4 to −0.9 mm) and an increase in wall thickness (+0.1 to +0.4 mm). A reduced quadratic model accurately captured these trends within the investigated range, with small residuals observed during verification (≤0.1 mm). (4) Conclusions: The proposed local, geometry-driven model supports compensation in fixation pin-hole dimensions in annealed HTPLA cutting guides, improving dimensional predictability within a defined design and process window. Full article

Due to the ability to perceive fine-grained 3D scenes and recognize objects of arbitrary shapes, 3D occupancy prediction plays a crucial role in vision-centric autonomous driving and robotics. However, most existing methods rely on voxel-based methods, which inevitably demand a large amount of memory and computing resources. To address this challenge and facilitate more efficient 3D occupancy prediction, we propose HBEVOcc, a Bird’s-Eye-View based method for 3D scene representation with a novel height-aware deformable attention module, which can effectively leverage latent height information within BEV framework to compensate for lack of height dimension, significantly reducing computing resource consumption while enhancing the performance. Specifically, our method first extracts multi-camera image features and lifts these 2D features into 3D BEV occupancy features via explicit and implicit view transformations. The BEV features are then further processed by a BEV feature extraction network and height-aware deformable attention module, with the final 3D occupancy prediction results obtained through a prediction head. To further enhance voxel supervision along the height axis, we introduce a height-aware voxel loss with adaptive vertical weighting. Extensive experiments on the Occ3D-nuScenes and OpenOcc dataset demonstrate that HBEVOcc can achieve state-of-the-art results in terms of both mIoU and RayIoU metrics with less training memory (even when trained on 2080Ti). Full article

In the paper, a heterodyne quartz-enhanced photoacoustic spectroscopy (H-QEPAS)-based integrated methane (CH₄) sensor prototype is reported. The CH₄ absorption line located at 1650.96 nm was selected as the target spectral line. The design features an integrated, 3D-printed gas chamber for reduced size and weight. To realize the coordinated operation of each hardware component, a control program was designed based on LabVIEW platform, enabling the adjustment of various hardware parameters. The piezoelectric signal generated by the quartz tuning fork (QTF) was amplified via a trans-impedance amplifier (TIA), acquired by a data acquisition card (DAQ), and then transmitted to a virtual lock-in amplifier (LIA) on the PC terminal for processing. The dimensions of the integrated CH₄ sensor prototype are 33 cm in length, 27 cm in width, and 15 cm in height. The final test results demonstrate that the sensor prototype exhibits an excellent concentration linear response, with a detection limit of 26.72 ppm and a short detection time of approximately 4 s. Full article

The scour process of sand particles and multi-grain size and density particles were studied to investigate the segregation process of different particles in a confined channel. The effects of jet intensity and submergence as two controlling parameters were studied, and scour characteristics and profiles were measured. The time history of the scouring process was measured and the results were compared with the scour process in a uniform sand bed as benchmark tests. Experimental data revealed that the eroded area of different particle types increased with the jet intensity, but the erosion of relatively heavier particles was limited due to jet diffusion. The local erosion was affected by the level of submergence and more erosion occurred near the nozzle at low submergence. Increasing the jet Froude number increased the area of deposition, while submergence reduced the overall area of deposition. As submergence increased from 4 to 12, the area of sand particles reduced by more than 50% while the jet intensity was constant. In shallow submergence, increasing jet intensity from 1.46 to 2.11 increased the area of lead balls by 120%, whereas in relatively deep submergence, incrementing jet intensity increased the area of lead balls by more than five times. The effect of flow intensity on variations of scour dimensions was quantified by the densimetric Froude number. While a densimetric Froude number based on mean particle size, D₅₀, was found to be suitable to estimate maximum scour bed in uniform sand beds, experimental data indicated that the best fit is achievable to predict maximum scour depth in multi-grain size and density once D₉₅ is used. Semi-empirical models were proposed to predict scour dimensions as a function of the densimetric Froude number. Full article

The global construction industry faces persistent challenges of low productivity and labor shortages, positioning Off-Site Construction (OSC) as a critical solution. However, standardized industrialization indices for objectively evaluating OSC adoption remain underdeveloped, particularly in emerging markets. This study aims to identify a benchmark policy model and derive design principles for future indices. Specifically, this study focuses on ‘policy-driven markets’ where strong government intervention is essential for initial ecosystem formation, excluding mature market-driven economies where the ecosystem is already established (e.g., USA, Sweden, Japan). To identify an optimal benchmark, a comparative assessment was conducted on five institutional frameworks across four countries (UK, Malaysia, Singapore, and China). Notably, within China, Hong Kong SAR was analyzed as a distinct regulatory jurisdiction separate from Mainland China due to its unique construction governance system. This assessment was based on five key policy dimensions: Legal Mandate, Scope, Indicator Composition, Enforcement Mechanism, and Sustainability. The analysis identified Singapore’s ‘Buildability Score’ as the most comprehensive model in terms of systemic completeness and practical efficacy. A virtual project simulation demonstrated that the scoring system functions as a powerful regulatory mechanism, effectively driving the adoption of standardized, dry-process, and modularized high-productivity methods from the earliest design stages. While Singapore’s system serves as an effective policy tool for OSC proliferation, it exhibits clear limitations regarding reduced architectural design flexibility and insufficient sustainability integration. Consequently, future industrialization indices must evolve to balance productivity with architectural design diversity and integrate sustainability criteria while reflecting specific regional construction ecosystems. Full article

As heritage museums shift toward more experience-oriented development, fragmented layouts and discontinuous visitor flows can reduce both spatial efficiency and the coherence of on-site experience. This study proposes an immersive experience-centred evaluation framework for exhibition layout in heritage museums, intended to translate experience goals into practical and diagnosable criteria for spatial optimization. An indicator system was refined through two rounds of Delphi consultation with an interdisciplinary expert panel, resulting in a hierarchical framework comprising five dimensions and multiple indicators. To support intervention prioritization in design and operations, weights were derived using the Group Analytic Hierarchy Process (GAHP), with Aggregation of Individual Judgments (AIJs) and consistency checks applied to control group judgement quality. A CV–entropy procedure was further used to support prioritization at the third-indicator level. Importance–Performance Analysis (IPA) was then employed to convert “importance–fit” assessments into an actionable sequence of optimization priorities. The results indicate that narrative and scene design carries the greatest weight (0.2877), followed by circulation and spatial organization (0.2281), sensory experience and atmosphere (0.1981), authenticity and sense of place (0.1644), and interactivity and participation (0.1217), suggesting that a “narrative–circulation–atmosphere” chain forms the core support for immersive layout design. A feasibility application using the Yinxu Museum demonstrates the framework’s value for benchmarking and diagnosis, helping decision-makers enhance visitor experience while respecting conservation constraints and more precisely target spatial investment priorities. Full article

Circular economy (CE) strategies in the agri-food sector hold strong potential for reducing waste, enhancing resource efficiency, and promoting sustainable value creation. However, early-stage assessment of innovative valorisation pathways remains challenging due to limited data availability and heterogeneous sustainability trade-offs. This study presents a multi-criteria evaluation tool designed to identify sustainability hotspots and support the preliminary screening of CE solutions based on easily obtainable information. The tool combines a structured literature review with expert-based scoring across environmental (ENV), economic (EC), and social (SOC) dimensions. Its applicability was demonstrated through the following three case studies: (i) reconstitution of cheese approaching expiration, (ii) extraction of polyphenols from grape-wine residues via subcritical water extraction, and (iii) biodegradable mulching film production from grape-wine pomace. Results show that the tool successfully differentiates sustainability performance across value chain areas Residue, Final Product, and Process (RES, FP, and PRO) and reveals critical gaps requiring further investigation. Scenario 3 achieved the higher overall score (69.7%) due to fewer regulatory constraints, whereas Scenarios 1 and 2 (61.2% and 54.5%, respectively) are penalised due to the more regulations for human consumption. The proposed tool offers a practical and efficient method to support researchers and industry stakeholders in identifying CE strategies with the highest potential for sustainable development. Full article

Packaging is a fundamental component of food supply chains, enabling product protection, handling, and distribution from production to final consumption. In this context, the selection of secondary and tertiary packaging dimensions plays a critical role in improving logistics efficiency and reducing greenhouse gas (GHG) emissions associated with material use and transportation. This study proposes a sustainable packaging logistics (SPL) framework that systematically evaluates and optimizes packaging carton dimensions to enhance pallet utilization, transport efficiency, and packaging material efficiency. The framework is applied to a real-world case study from a meat processing company, demonstrating how alternative carton dimension configurations, while maintaining a constant product weight and functional equivalence, can significantly influence pallet-loading efficiency, transported payload, and associated CO₂-equivalent emissions. Rather than constituting a full life cycle assessment (LCA), the proposed approach adopts LCA-informed indicators to quantify material and transport related emission implications of packaging design choices. By integrating packaging design, palletization constraints, and logistics performance, the SPL framework provides a structured analytical basis for identifying packaging configurations that reduce material intensity and transport-related emissions. The results highlight the importance of packaging dimension optimization as a practical and scalable strategy for emission reduction in food supply chains. The proposed framework is intended to support decision-making in packaging design and to serve as a robust preparatory tool for future, more comprehensive LCA studies. Full article

The primary objective of this study is to bridge the gap between descriptive geometry and mechanistic design by establishing a dual-domain fractal framework to analyze the internal architecture of asphalt mixtures. This research quantitatively assesses the sensitivity of volumetric indicators—namely air voids (VV), voids in mineral aggregate (VMA), and voids filled with asphalt (VFA)—by employing the coarse aggregate fractal dimension (D_c), the fine aggregate fractal dimension (D_f), and the coarse-to-fine ratio (k) through Grey Relational Analysis (GRA). The findings demonstrate that whereas D_f and k substantially influence macro-volumetric parameters, the mesoscopic void fractal dimension (D_V) remains structurally unchanged, indicating that gradation predominantly dictates void volume rather than geometric intricacy. Sensitivity rankings create a prevailing hierarchy: Process Control (Compaction) > Skeleton Regulation (D_c) > Phase Filling (Pb) > Gradation Adjustment (k, D_f). D_c is recognized as the principal regulator of VMA, while binder content (Pb) governs VFA. A “Robust Design” methodology is suggested, emphasizing D_c to stabilize the mineral framework and reduce sensitivity to construction variations. A comparative investigation reveals that the optimized gradation (OG) achieves a more stable volumetric condition and enhanced mechanical performance relative to conventional empirical gradations. Specifically, the OG group demonstrated a substantial 112% enhancement in dynamic stability (2617 times/mm compared to 1230 times/mm) and a 75% increase in average film thickness (AFT), while ensuring consistent moisture and low-temperature resistance. In conclusion, this study transforms asphalt mixture design from empirical trial-and-error to a precision-engineered methodology, providing a robust instrument for optimizing the long-term durability of pavements in extreme cold and arid environments. Full article

Voluntary movement arises from a sequence of neural processes that involve planning, preparation, and execution within distributed cortical networks. The readiness potential, a slow negative brain signal preceding self-initiated actions, represents a sensitive indicator of motor preparation. However, it remains unclear how this signal reflects concurrent variations in mechanical and temporal demands. In this study, twenty-eight healthy participants performed self-paced elbow flexions under nine combinations of mechanical load and movement duration while brain electrical activity, muscle activity, and movement kinematics were simultaneously recorded. Linear mixed-effects analyses revealed that the amplitude of the readiness potential increased progressively with greater mechanical load, indicating that cortical readiness scales with the intensity of preparatory effort. In contrast, longer movement durations produced smaller amplitudes, suggesting that extended temporal windows reduce the efficiency of preparatory synchronization. No significant interaction between load and duration was observed, supporting the idea of partially independent neural mechanisms for effort and timing. These findings identify the readiness potential as a neural marker integrating the energetic and temporal dimensions of voluntary movement and provide a basis for understanding how cortical readiness dynamically optimizes human motor performance. Full article

The proliferation of air travel demand necessitates innovative solutions to enhance passenger experience while optimizing airport operational efficiency. This paper presents the pilot-scale implementation and evaluation of an AI-powered service robot ecosystem integrated with thermal cameras and 5G wireless connectivity at Athens International Airport. The system addresses critical challenges in passenger flow management through real-time crowd analytics, congestion detection, and personalized robotic assistance. Eight strategically deployed thermal cameras monitor passenger movements across check-in areas, security zones, and departure entrances while employing privacy-by-design principles through thermal imaging technology that reduces personally identifiable information capture. A humanoid service robot, equipped with Robot Operating System navigation capabilities and natural language processing interfaces, provides real-time passenger assistance including flight information, wayfinding guidance, and congestion avoidance recommendations. The wi.move platform serves as the central intelligence hub, processing video streams through advanced computer vision algorithms to generate actionable insights including passenger count statistics, flow rate analysis, queue length monitoring, and anomaly detection. Formal trial evaluation conducted on 10 April 2025, with extended operational monitoring from April to June 2025, demonstrated strong technical performance with application round-trip latency achieving 42.9 milliseconds, perfect service reliability and availability ratings of one hundred percent, and comprehensive passenger satisfaction scores exceeding 4.3/5 across all evaluated dimensions. Results indicate promising potential for scalable deployment across major international airports, with identified requirements for sixth-generation network capabilities to support enhanced multi-robot coordination and advanced predictive analytics functionalities in future implementations. Full article