Nexen (Canada)

Abstract The continued expansion of infill development in unconventional basins has increased the occurrence and operational impact of fracture-driven interactions (FDIs) between child and parent wells. These interactions, ranging from poro-elastic stress effects to direct hydraulic connections, can reduce production, result in loss of recoverable reserves, degrade parent well integrity, and introduce significant uncertainty in stimulation design. Conventional detection methods rely on post-job analysis or advanced downhole instrumentation, which are often impractical to scale. This work introduces a real-time, automated workflow that detects and classifies FDIs using only surface pressure data from nearby shut-in parent wells. The workflow utilizes time-aligned surface pressure data from multiple parent-wells and applies a set of engineered signal features to characterize pressure deviations associated with stimulation. These features, including pressure derivatives, temporal lags, waveform asymmetry, and dynamic response patterns, are used to train and infer from a supervised machine learning model that classifies each detected event into one of three FDI categories. This multi-class classification framework allows differentiation between interaction mechanisms and provides stage-level context for each pressure anomaly. Inferred pressure lag patterns are further analyzed to estimate fracture propagation velocity and directional bias. Its architecture supports centralized as well as localized processing. This flexibility ensures rapid model inference even in bandwidth-constrained or remote field environments. Model performance was evaluated on historical data from several multi-well pads across major North American unconventional plays. Fracture velocity estimates derived from pressure signal timing revealed spatial propagation patterns that helped estimate depletion gradients and geologic heterogeneities. The multi-class classification model demonstrated high predictive accuracy, effectively distinguishing between overlapping and sequential FDIs across multiple parent wells. Classification outputs correlated well with known interference patterns and post-treatment diagnostics. The ability to resolve FDI type at the stage level supports actionable decisions, including diverter deployment, treatment schedule modifications, and fluid design adjustments. In addition to operational insights, these outputs enhance reservoir simulation calibration by providing high-resolution pressure interaction labels for history matching. This proposed workflow advances the operational monitoring of FDIs. By eliminating the need for downhole sensors or spatially dependent modeling, the workflow generalizes across diverse pad configurations. Its ability to resolve three distinct FDI types, tied to specific stages and wells, enhances the diagnostic resolution available to completions engineers. A key advantage is the system's support for edge deployability, allowing near-wellsite execution of detection logic and significantly reducing latency between data availability and engineering response. This combination of scalable architecture, interpretability, and multi-class intelligence represents a substantial advancement in frac surveillance and real-time completions optimization.

Starting from the relationship between blind-woman-with-guide-dog, the article discusses the guide- dog’s work as an agent that promotes aesthetic mediation through the affections experienced in front of works of art such as a play or a sculpture. The central argument is that mediation is not defined as a bridge, but as a practice that occurs among many. In this way, the work of the guide dog as a mediator displaces and challenges hegemonic meanings of what it means to guide, mediate, and promote accessibility. With a situated writing style, the text concludes that aesthetic accessibility is not merely a sum of information, but a process that activates as experimentation among guide dogs, humans, and works of art.

In this paper, we first prove (Theorem 1) that any two inputs producing the same output in a symmetric pair of discrete skew tent maps always have the same parity, meaning that they are either both even or both odd. Building on this property, we then propose (Definition 1) a new discrete chaotic map and prove that (Theorem 2) the proposed map is a bijection for all control parameters. We further prove that (Theorem 3) the discrete Lyapunov exponent (dLE) of the proposed map is not only positive but also approaches the maximum value among all permutation maps over the integers as m gets larger. In other words, (Corollary 1) the proposed map asymptotically achieves the highest possible chaotic divergence among the permutation maps over the integers . To provide some further evidence that the proposed map is highly chaotic, we present at the end some results from the numerical experiments. We calculate the approximation and permutation entropy of the output integer sequences. We also show the NIST SP800-22 tests results and correlation properties of some derived binary sequences.

The pharmacological effects of ginger extract from Zingiber officinale Roscoe are well-established; however, more refined extraction methods for higher-quality yields are needed. This study isolated and evaluated 6-, 8-, and 10-shogaol and 6-, 8-, and 10-gingerol from ginger extract, assessing their effects on glucose-stimulated insulin secretion (GSIS). To ensure safety, non-toxic concentrations were determined for pancreatic β-cells. Both non-processed and microwave-processed ginger extracts enhanced GSIS, with microwave-treated extracts exhibiting the strongest effect. Specifically, the microwave-processed ginger extract increased the glucose stimulation index (GSI) to 12.4 ± 0.4 at 5 μg/mL, compared to a GSI of 7.7 ± 0.2 observed for the non-processed ginger extract. Notably, fraction F4 of the microwave-processed extract demonstrated superior GSIS activity. In contrast, steam-processed ginger extract induced only a modest increase in GSI under limited exposure conditions. Furthermore, 6-shogaol emerged as a key compound, correlating with increased expression of proteins crucial for pancreatic β-cell regulation. Microwave-assisted processing notably altered the content and proportion of shogaols and gingerols, significantly impacting GSIS activity. These findings underscore the importance of extraction methods in enhancing ginger’s pharmacological potential in regulating insulin secretion and pancreatic β-cell function.

BackgroundBradykinesia in Parkinson's disease (PD) involves reduced movement speed, amplitude, and rhythmicity. While the MDS-UPDRS Part III is the standard clinical tool for motor assessment, it has limited sensitivity to specific kinematic features. Levodopa and subthalamic nucleus deep brain stimulation (STN-DBS) are common treatments for PD, yet their differential effects across motor domains are not fully characterized. This study applies AI-based video analysis to evaluate the effects of levodopa and STN-DBS on limb bradykinesia.MethodsThis retrospective study assessed fifty-three patients with Parkinson's disease undergoing STN-DBS. Motor performance was video-recorded during Levodopa-off and Levodopa-on states (levodopa challenge test performed prior to surgery), as well as after DBS activation (OFFMED/OFFSTIM, OFFMED/ONSTIM, ONMED/ONSTIM). Both clinical assessments and subsequent video-based analyses focused on the MDS-Unified Parkinson's Disease Rating Scale (MDS-UPDRS), Part III, specifically evaluating items 3.4 Finger Tapping, 3.5 Fist-clenching test, 3.7 Toe Tapping, and 3.8 Leg Agility. Motor function was first evaluated using conventional UPDRS-III item scores rated by two experienced specialists, with the primary clinical comparison defined between the levodopa-on and OFFMED/ONSTIM states, to explore the differential therapeutic emphases of medication and DBS. Subsequently, AI-based video analysis was applied to quantify kinematic parameters, including amplitude, frequency, and coefficients of variation, using AI algorithms (NERVTEX Co. Ltd.). Comparisons were made for levodopa effects (Levodopa-off vs. Levodopa-on), DBS effects (OFFMED/OFFSTIM vs. OFFMED/ONSTIM), and therapy-specific differences (Levodopa-on vs. OFFMED/ONSTIM).ResultsConventional UPDRS-III item scores suggested that levodopa was more effective than DBS in improving upper-limb tasks (items 3.4 Finger Tapping and 3.5 Fist-clenching test), while lower-limb tasks (items 3.7 Toe Tapping and 3.8 Leg Agility) showed no significant changes. In contrast, AI-based kinematic analysis revealed more differentiated treatment effects. Levodopa was associated with improvements in movement speed, amplitude, and stability in the upper limbs, as well as a significant impact on lower-limb amplitude, both in toe tapping (item 3.7) and leg agility (item 3.8). DBS, by comparison, enhanced upper-limb motor output but had limited effects on the lower limbs, with improvements in speed and amplitude observed only in the toe tapping (item 3.7) task. Additionally, levodopa demonstrated superior improvements in lower-limb amplitude, both in toe tapping (item 3.7) and leg agility (item 3.8), compared to DBS.ConclusionThis study demonstrates that AI-based kinematic analysis enables a nuanced and individualized characterization of motor responses to medication and STN-DBS in Parkinson's disease, complementing conventional clinical scoring. Although both therapies improve bradykinesia, they appear to preferentially modulate distinct motor domains across individuals, underscoring their complementary roles in treatment. These findings highlight the potential of AI-based motor assessment to support personalized symptom profiling and more individualized therapeutic decision-making in Parkinson's disease.

The activity of subglacial lakes in the Antarctic interior can significantly influence downstream hydrology and ice dynamics. Detecting and monitoring these lakes is therefore crucial for understanding ice sheet behaviour. In this article, we applied a differential interferometric synthetic aperture radar (DInSAR) technique to ScanSAR images acquired by the Phased Array type L‐band Synthetic Aperture Radar‐2 onboard the Advanced Land Observing Satellite‐2 between 2015 and 2020 over the upstream region of David Glacier in East Antarctica. Six closed circular fringe patterns were detected from the differential interferograms, one coinciding with the known David 4 lake and five located in previously unreported areas. ICESat‐2 observations from 2019 to 2023 confirmed distinct ice elevation changes within these DInSAR‐detected regions, validating the presence of five new lakes. David 4 drained between 2019 and 2020, then refilled at approximately 0.7 m/yr after mid‐2021. The newly detected lakes exhibited varied behaviours, showing ice surface elevation changes of 0.1–0.5 m/yr. Hydraulic potential analysis indicated strong upstream hydrological connectivity to the newly discovered lakes near David 3 and David 4 , whereas, the two lakes near David 5 show weaker potential flow paths upstream. This article demonstrates the effectiveness of satellite‐based DInSAR for detecting small and slowly evolving subglacial lakes.

Federated learning trains a shared global model across user devices without centralizing their private data. In each round, the server broadcasts the current model, and clients train local copies on their local datasets. They then return model updates for aggregation. In realistic wireless settings, unreliable links and limited bandwidth, together with non-independent and identical distributed (non-IID) client data, make aggregation challenging and can degrade global performance. This paper studies federated learning over wireless channels with non-IID data and proposes FedCAC, a performance-efficient aggregation method that groups clients using channel-state features to improve robustness during global updates. We compare the performance of FedCAC with that of conventional baselines under realistic wireless conditions. The simulation results show that our method improves the validation accuracy by 22% compared to standard federated learning methods.

Reliable detection of unknown signals is essential for ensuring the robustness of underwater acoustic sensing systems, particularly in maritime security and autonomous navigation. However, Conventional deep learning models often exhibit overconfidence when encountering unknown signals and are unable to quantify predictive uncertainty due to their deterministic inference process. To address these limitations, this study proposes a novelty detection framework that integrates an out-of-distribution detector for neural networks (ODIN) with Monte Carlo (MC) dropout. ODIN mitigates model overconfidence and enhances the separability between known and unknown signals through softmax probability calibration, while MC dropout introduces stochasticity via multiple forward passes to estimate predictive uncertainty-an element critical for stable sensing in real-world underwater environments. The resulting probabilistic outputs are modeled using Gaussian mixture models fitted to ODIN-calibrated softmax distributions of known classes. The Kullback-Leibler divergence is then employed to quantify deviations of test samples from known class behavior. Experimental evaluations on the DeepShip dataset demonstrate that the proposed method achieves, on average, a 9.5% and 5.39% increase in area under the receiver operating characteristic curve, and a 7.82% and 2.63% reduction in false positive rate at 95% true positive rate, compared to the MC dropout and ODIN baseline, respectively. These results confirm that integrating stochastic inference with ODIN significantly enhances the stability and reliability of novelty detection in underwater acoustic environments.