Search Results (851)

Sand production in an offshore hydrocarbon wells poses significant operational and integrity challenges, particularly in deviated wells, where complex flow geometries intensify particle transport and erosion risks. The traditional sand-monitoring method utilizes stationary acoustic sensors attached to the production flowline at the surface. However, these sensors provide limited spatial coverage and intermittent measurements, restricting their ability to detect early sanding onset or precisely localize sanding intervals. By combining with vertical seismic profiling (VSP), Distributed Acoustic Sensing (DAS) delivers continuous, high-density data along the entire length of the wellbore and is increasingly recognized as a powerful diagnostic tool for real-time downhole monitoring. This study presents a field application of DAS-VSP for detecting and characterizing sand transport in a deviated offshore production well equipped with 350 distributed fiber-optic channels spanning 0–1983 m true vertical depth (TVD) at 8 m spacing. A multistage workflow was developed, including SEGY ingestion and shot merging, channel and time window selection, trace normalization, and low-pass filtering below 20 Hz. Multi-domain signal analysis, such as RMS energy, spike-based time-domain attributes, FFT, PSD spectral characterization, and time–frequency decomposition, were used to isolate the characteristic im-pulsive low-frequency (<20 Hz) signatures associated with sand impact. An adaptive thresholding and event-clustering scheme was then applied to discriminate sanding bursts from background noise and integrate their acoustic energy over depth. The processed DAS section revealed distinct, depth-localized sand ingress zones within the production interval (1136–1909 m TVD). The derived sand log provided a quantitative measure of sand intensity variations along the deviated wellbore, with normalized RMS amplitudes ranging from 0.039 to 1.000 a.u., a mean value of 0.235 a.u., and 137 analyzed channels within the production interval. These results indicate that sand production is highly clustered within discrete depth intervals, offering new insights into sand–fluid interactions during steady-state flow. Overall, the findings confirm that DAS-VSP enables continuous real-time monitoring of the sanding behavior with a far greater depth resolution than conventional tools. This approach supports proactive sand management strategies, enhances well-integrity decision-making, and underscores the potential of DAS to evolve into a standard surveillance technology for hydrocarbon production wells. Full article

Learning Management Systems (LMS) generate rich activity and interaction logs that can be exploited using machine learning techniques. This study models temporal engagement patterns, such as early, middle, late, weekend, and night activity, derived from Moodle logs in multiple undergraduate courses. It constructs temporal feature vectors per-student, applies k-means clustering to uncover behavioral patterns, and then uses ANOVA and Kruskal–Wallis tests to assess whether patterns differ in final grades. Results show that the predictive value of temporal patterns is highly course-dependent; in some courses, structured early engagement aligns with higher achievement, whereas in others, heavy weekend and night usage is associated with the best outcomes. To complement the obtained quantitative analyses, a Large Language Model (LLM) (i.e., ChatGPT) is evaluated as a zero-shot classifier that receives only natural-language summaries of temporal behavior and predicts performance tiers. While accuracy is limited, the model produces a coherent approach, indicating value as an interpretable layer on top of statistical analysis. The work demonstrates a generalizable pipeline for temporal feature engineering, unsupervised profiling, and LLM-based reasoning over LMS data for early risk detection in digital learning environments. Full article

Reservoir quality in shallow lacustrine-mixed siliciclastic–carbonate systems is commonly governed by mineral assemblages and diagenetic modification. Here we investigate the Neogene Youshashan Formation (Oil Groups III–V) in the Nanyishan area, western Qaidam Basin, to quantify mineralogical and diagenetic controls on pore systems and flow. We integrate whole-rock XRD and log-derived mineral profiles with thin-section/SEM petrography, NMR T2 spectra, mercury injection capillary pressure (MICP), and a water-drop test. Dissolution-related pores and dolomitization-related intercrystalline pores dominate the pore space, whereas cementation and clay-related filling/coating locally restrict pore throats and connectivity. Algal limestones (average porosity 23.17% and permeability 54.3 mD; MICP r50 = 0.085 μm) show better reservoir quality than dolomitic rocks (average porosity 17.24% and permeability 15.13 mD; MICP r50 = 0.039 μm), consistent with more effective pore throat networks. In Oil Group III (Well NQ2-6-2), higher dolomite content is generally associated with higher porosity but shows no systematic relationship with permeability, highlighting the primacy of connected pore throats. Water-drop behaviors (beading, semi-beading, infiltration) provide a rapid, semi-quantitative screening indicator when interpreted together with pore throat metrics, and support a four-class reservoir-typing scheme (Types I–III and non-reservoir) for sweet-spot identification in mixed lacustrine reservoirs. Full article

This study evaluated Salmonella behavior during Sardinian fermented sausage (SFS) production through a challenge test on experimentally inoculated raw meat. The objectives were to (i) determine the survival and reduction kinetics of Salmonella during fermentation and ripening and (ii) evaluate the relationship between pathogen behavior and the evolution of key chemical-physical parameters (pH, water activity). Three batches of SFS were produced, and the meat mixture was inoculated with a three-strain Salmonella cocktail (reference and field strains) to 10² CFU/g. After 20 days of ripening, sausages were vacuum-packed and stored under refrigerated conditions (+4 ± 2 °C). For each batch, triplicate samples were collected and analyzed at different production stages (mixing, after overnight rest, and 24 h after stuffing) and during shelf life (days 6, 21, 30, and 40). Analyses included Salmonella detection and enumeration by direct plating, aerobic colony count, Enterobacteriaceae, staphylococci, lactic acid bacteria, molds and yeasts, as well as pH, water activity, and gross composition. Salmonella counts increased by approximately one log unit after stuffing, before the onset of acidification. During fermentation and ripening, pathogen levels declined but remained detectable, even after prolonged refrigerated storage. These findings indicate that although ripening, and particularly fermentation, significantly (p < 0.05) reduce Salmonella levels, complete inactivation is not achieved. The study highlights the importance of controlling initial contamination levels, validating fermentation and ripening conditions, and the application of additional post-process hurdles to ensure product safety. Full article

Modeling the temporal dynamics of student learning is a central goal in educational data mining. Deep Knowledge Tracing (DKT) has emerged as a key approach, yet existing models are highly sensitive to out-of-distribution (OOD) inputs, such as those arising from curriculum changes, new assessment formats, or behavioral noise, which severely degrade predictive reliability. To address this challenge, we propose Energy-Based Out-of-Distribution Deep Knowledge Tracing (EB-OOD DKT), a unified framework that integrates energy-based uncertainty estimation and contrastive representation learning within a transformer-based DKT architecture. The model computes energy scores via the negative log-sum-exponential of prediction logits, serving as confidence indicators for detecting OOD inputs during inference. Additionally, an InfoNCE-based contrastive loss enhances representation robustness by aligning in-distribution samples and separating OOD cases in latent space. Temporal and behavioral context features, such as normalized response intervals and cumulative attempt counts, are incorporated to enrich cognitive-behavioral modeling. Experiments on four public educational datasets demonstrate consistent improvements in prediction accuracy and OOD detection. EB-OOD DKT provides a promising approach for more reliable student modeling across educational platforms with different content distributions. Full article

Although the cyclic bending behavior of circular and elliptical steel tubes has been widely studied, the combined effects of major-to-minor axis length ratio and curvature ratio on the deformation characteristics and buckling life of galvanized steel elliptical tubes remain insufficiently understood. This study experimentally investigates the cyclic bending response and failure behavior of galvanized steel elliptical tubes with major-to-minor axis length ratios of 1.5, 2.0, 2.5, and 3.0 under curvature ratios of −1, −0.5, and 0. The curvature ratio is defined as the minimum controlled curvature divided by the maximum controlled curvature. Buckling is defined as the cycle at which a pronounced 20% drop in peak bending moment is observed. The response is characterized by moment (N⋅m)–curvature (m⁻¹) hysteresis and minor-axis variation with curvature, while failure is evaluated using the relationship between curvature range and number of cycles to buckling. The results show that stable elastoplastic hysteresis loops develop for all curvature ratios, with slight cyclic relaxation observed at curvature ratios of −0.5 and 0. Increasing the axis length ratio slightly reduces the peak moment under a fixed curvature ratio. Minor-axis variation increases progressively with cycle number, exhibiting serrated curves at an axis ratio of 1.5 and butterfly-shaped curves at higher axis ratios. Symmetric behavior is observed at a curvature ratio of −1, whereas asymmetric responses occur at −0.5 and 0. The failure results indicate that larger curvature ranges and higher axis length ratios reduce the number of cycles to buckling, while curvature ratios closer to −1 enhance buckling life. On a log–log scale, the relationship between curvature range (m⁻¹) and number of cycles to buckling becomes linear. A theoretical model is proposed and shows good agreement with the experimental results. Full article

Physical separation between family members arises not only from life choices such as education and employment, but also from health-related constraints that limit physical co-presence. This paper presents StepsConnect, a real-time step-sensing-based ambient display system that transforms personal walking data into dynamic digital art, providing low-effort and non-intrusive presence cues for family members living apart. The system continuously captures step data via smartphones and renders them as spatial and embodied visual cues embedded in everyday environments. We conducted a 90 min laboratory study with 15 young adult–parent dyads, in which young adults engaged in a simulated work session while viewing real-time visualizations of their parents’ step activity. Young adults’ perceived connectedness was measured using the Inclusion of Other in the Self (IOS) scale and complemented with semi-structured interviews, while parents’ walking data were logged to provide an objective behavioral reference. Quantitative results indicated modest and heterogeneous changes in IOS scores at the group level, with individual variability across participants. Qualitative findings suggested that step-based visualizations primarily functioned as ambient reminders and cues of presence, supporting momentary relational awareness while remaining calm and non-intrusive within the workspace context. Walking data exhibited large variation across dyads, providing objective context for participants’ subjective experience of presence, although connectedness was not simply proportional to activity magnitude. The findings suggest that aesthetic step-based ambient visualization primarily supports momentary relational awareness rather than immediate shifts in stable closeness. By clarifying this distinction, the study advances understanding of how sensing-based digital art may function as a complementary presence layer in intergenerational contexts. Full article

This paper presents the design and experimental validation of a hardware-enforced charging profile selection framework for low-voltage electric all-terrain vehicles (ATVs), implemented on a programmable Delta battery charger operating within a voltage range of 0–120 V and a current range of 0–30 A. Unlike conventional programmable chargers that rely primarily on software-defined configuration or battery management system (BMS)-negotiated parameter setting, the proposed system enforces predefined constant-current–constant-voltage (CC–CV) charging profiles at the hardware execution layer. Vehicle identification is performed using CANopen-based identifiers, while relay-based selection, controlled via Modbus RTU, physically routes the charger output to fixed CC–CV control paths, thereby structurally reducing the risk of misconfiguration and unintended parameter changes. The system integrates layered control using embedded ESP32 nodes, a redPLC supervisory controller, and NodeRED-based orchestration, combined with real-time measurement, logging, and visualization using a time-series database and Grafana dashboards. Experimental validation is conducted using lithium-ion battery packs configured at four nominal voltage levels (24 V, 48 V, 60 V, and 72 V). The results confirm correct automatic profile selection, deterministic relay-based routing, and stable CC–CV charging behavior across repeated charging sessions. Rather than proposing a new charging algorithm, this work contributes a safety-by-design execution-layer charging architecture that complements higher-level smart charging and management protocols and is particularly suited for closed, heterogeneous fleet environments where deterministic behavior, robustness against configuration errors, and transparent verification of charging processes are critical. Full article

Background/Objectives: Phthalates are encountered in everyday consumer and indoor environments, and their metabolites are commonly detected in urine. Although phthalate exposure has been linked to diabetes mellitus (DM), associations may vary by life stage. Therefore, we evaluated age-specific association between urinary phthalate metabolites and DM using nationally representative Korean data. Methods: We conducted a cross-sectional analysis of the Korean National Environmental Health Survey Cycle 4 (2018–2020). Adults aged ≥19 years with complete data were included. Eight urinary metabolites were evaluated. Metabolites were log-transformed, and those showing interaction were analyzed by tertiles. Complex survey-weighted logistic regression estimated odds ratios (95% confidence intervals) for DM, adjusting for demographic, socioeconomic, and health behavior factors. Analyses were stratified by age group. Results: Geometric mean (GM) concentrations among participants with DM varied significantly by age groups for several metabolites. Interaction analyses identified statistically significant effects for selected phthalate metabolites, including MnBP, MCPP, and MEP. In the age-stratified adjusted models, MnBP and MCPP were more strongly associated with DM in young adults, whereas the pattern for MEP appeared more evident in older adults, suggesting potential life-course differences in metabolic vulnerability. Conclusions: Associations between urinary phthalate metabolites and DM vary substantially by age, indicating life-course differences in exposure pathways and metabolic vulnerability. Age-specific prevention and surveillance strategies may improve environmental health interventions for DM. Full article

Background/Objectives: Sepsis is a leading cause of morbidity and mortality due to its rapid progression and variability in care delivery. While existing predictive models estimate sepsis risk using clinical variables, they typically rely on static attributes and overlook temporal, behavioral, and process-related characteristics of care pathways. In particular, deviations from recommended protocols and process inefficiencies are rarely incorporated into early deterioration prediction. This study proposes a Conformance-Aware Predictive Process Monitoring (CAPPM) framework to enable early detection of sepsis deterioration using incomplete care pathways. Methods: The proposed framework integrates process mining with predictive modeling. Using the publicly available Sepsis Cases Event Log, we first discovered the reference care pathway and generated prefix-level representations of ongoing cases. Temporal and behavioral features were engineered alongside alignment-based and declarative conformance metrics to quantify pathway deviations. These features were used to train and evaluate multiple supervised learning models, including Adaptive Boosting and Gradient Boosting. Predictive performance was assessed using the area under the receiver operating characteristic curve (AUROC). Results: Incorporating conformance and pathway-based features improved predictive performance compared to models relying solely on traditional attributes. Adaptive Boosting and Gradient Boosting achieved the strongest results, with AUROC values of 0.744 and 0.731, respectively, demonstrating enhanced early detection ability. Conclusions: The findings indicate that early deviations in care pathways and temporal progression patterns provide meaningful predictive signals for sepsis deterioration. Integrating process mining with machine learning offers a promising approach for time-critical clinical decision support and proactive intervention. Full article

Agentic shopping assistants increasingly move beyond recommending products to executing actions in users’ workflows (e.g., adding items to cart, applying coupons, selecting shipping). This shift from advice to delegation raises questions about appropriate reliance, perceived control, and how interface cues support oversight when systems can act. We report a laboratory eye-tracking experiment using a chat-only e-commerce prototype in a mixed 2 × 2 design: action autonomy varied within participants (recommend-only vs. act-on-behalf, with undo/edit), and transparency cues varied between participants (minimal statements vs. preview + rationale describing what will happen and why). Three standardized shopping tasks were completed by 72 participants. Results included behavioral logs (task time, overrides), areas-of-interest (AOI)-based eye-tracking (chat attention and verification indicators), and post-task self-reports (trust, control, uneasiness, perceived transparency). Act-on-behalf autonomy reduced completion time, but it also increased unease, decreased trust and perceived control, and increased the likelihood of an override, suggesting a trade-off between efficiency and oversight. The autonomy-related penalties for trust and perceived control under act-on-behalf execution were lessened by preview + rationale transparency, which additionally enhanced perceived transparency, trust, and unease. This mechanism coincided with eye-tracking: transparency decreased verification latency during agent actions and redirected attention toward information supplied by assistants. Transparency did not reliably reduce overrides, suggesting that minimal effective transparency can streamline supervision and improve evaluations without eliminating corrective behavior. Full article

Microfibrillated cellulose (MFC) derived from wood sources is a biodegradable and eco-friendly reinforcing material for polymer composites. However, the high polarity of MFC is a challenge in homogeneous distribution into the hydrophobic PLA matrix, which limits its reinforcing efficiency. In this study, lignin-containing MFC (LMFC) with different residual lignin contents was prepared to investigate its dispersion behavior and reinforcing effect in polylactic acid (PLA). The aspect ratio and neutral sugar composition of LMFC remained similar regardless of lignin content, whereas the dispersion degree in PLA, quantified using a log-normal distribution model, increased from 24.2% to 35.1% with increasing lignin content. Mechanical testing showed that LMFC incorporation enhanced tensile strength and elastic modulus while reducing elongation at break. Higher residual lignin content in LMFC positively affected the tensile strength of the LMFC–PLA composites. Dynamic mechanical analysis revealed an increase in storage modulus and a decrease in loss factor with higher lignin content and LMFC loading (1–10 wt%), indicating enhanced interfacial interactions. Differential scanning calorimetry showed reductions in glass transition temperature (5–8 °C) and cold crystallization temperature (8–16 °C) compared to neat PLA. These findings indicate that residual lignin in LMFC enhances dispersion and interfacial interactions in PLA, leading to improved mechanical and thermal performance and highlighting its potential as an effective reinforcing component in sustainable biocomposites. Full article

This study develops a conceptual framework for characterizing reservoir architecture in multi-component, discrete systems using pressure transient analysis (PTA), aimed at calibrating inflow geometry prior to full-field dynamic simulation for subsurface gas storage applications such as CO₂ and hydrogen. A secondary objective is to identify variations in permeability over time by analyzing flow capacity trends and evaluating the dynamic influence of faults and fractures. The analysis is based on a gas-condensate field comprising seven wells and four zones (A, B, C, D), using integrated dynamic datasets including extended well tests (EWTs), mud loss, production logs, and production data. Detailed interpretation of PX-1’s EWT indicated delayed re-pressurization and persistent under-pressure, suggesting a compartmentalized or transient system with limited gas-in-place connectivity. Four reservoir architecture concepts were developed: (1) lithology-dominated inflow, (2) structurally controlled inflow, (3) discrete, weakly connected compartments, and (4) transient-dominated systems with tight matrix GIIP. These concepts informed four reservoir models: matrix-only (M), areal heterogeneity (A), sparse bodies (B), and sparse networks (S). Application of these models across other wells revealed consistent localized KH (permeability–thickness product) behavior, with all models fitting short-duration data comparably. However, only sparse drainage models (B/S) adequately matched PX-1’s EWT response. PTA results confirm that well tests constrain KH locally but provide limited insight into large-scale reservoir architecture. EWTs may reach ~1 km, while shorter tests are confined to ~200–400 m, typically within one to two simulation grid blocks. This study demonstrates how integrating PTA with multi-scale data improves characterization of naturally fractured, tight carbonate reservoirs and supports reservoir simulation and history matching for hydrogen storage evaluation. Based on reservoir simulations, this study concluded that naturally fractured carbonate gas reservoirs can provide significant storage and injection capacities for underground hydrogen storage. This study exemplifies how to characterize the naturally fractured tight carbonate reservoirs by integrating multi-scale and multi-dimensional data such as PTA. Furthermore, this study assists in gridding for full-field reservoir models, for history matching and quantifying the potential of hydrogen storage in these complex reservoirs. The proposed workflow provides an uncertainty-bounded reservoir characterization framework and should not be interpreted as a complete field-design methodology for hydrogen storage. The modeling does not explicitly couple geomechanical fracture growth, hydrogen diffusion, long-term geochemical reactions, or caprock integrity degradation. Therefore, the presented storage scenarios represent technically feasible cases under defined assumptions. Comprehensive site-specific geomechanical and containment assessments are required prior to field-scale implementation. Full article

Urban rivers often contain a complex mixture of contaminants including per- and polyfluoroalkyl substances (PFAS), metals, and various salts. This study aimed to investigate the sediment–water partitioning characteristics of PFAS in urban rivers and analyze the hydrochemical causes of this specific feature. We sampled paired water and sediment samples from urban rivers in a reclaimed water irrigation area in Beijing City. The average total PFAS concentrations in the river water and sediment were 28.44 ± 16.37 ng/L and 6.41 ± 4.20 ng/g dw, respectively. Short-chain PFAS from C4 to C6 and PFCA congeners dominated in the water, while long-chain PFAS above C8 and PFSA congeners dominated in the sediment. The average sediment–water ratio (Log K_d) of PFAS at each site showed an increasing trend with chain length, and was generally higher than that observed in seawater, natural rivers, and lakes, indicating a specific sediment–water partitioning behavior of PFAS in urban rivers. This difference is likely due to the distinct hydrochemical characteristics of the urban rivers, where elevated TDS, the presence of surfactants, and the coexistence of multiple heavy metal ions collectively promote PFAS adsorption onto suspended particulate matter and enhance their accumulation in sediments through sedimentation. Full article

In large-scale medical data, the connection between hospital length of stay and medical expenses shows a complex and nonlinear relationship instead of a straightforward positive link. This study proposes a Cox–Log-Logistic–Copula joint modeling framework to describe the marginal distributions and latent dependence between the two variables. Specifically, a semi-parametric Cox proportional hazards model is used for hospitalization duration, while a Log-Logistic model handles medical costs. The two margins are flexibly coupled through a Copula function to capture dynamic variations in cost levels during different hospitalization stages. To address computational challenges in large datasets, this study also includes subsample correction and one-step adjustment algorithms, combined with parallel computing strategies, to enhance estimation efficiency and accuracy. Empirical results show that the length of hospital stays and medical costs are not always positively related. In some cases, higher medical expenses occur during shorter stays, suggesting possible over-treatment or uneven resource distribution. The proposed framework proves to have strong explanatory power in identifying nonlinear patterns in healthcare behavior and offers a new quantitative tool for optimizing medical resource allocation and controlling costs. Full article