Search Results (223)

The complex surface architecture of natural oyster reefs is widely considered to promote biological attachment, yet the underlying mechanisms and the relevance to the design of artificial reefs are not fully understood. Here, we combined field experiments, 3D surface characterization, and numerical modelling to quantify how reef-like roughness regulates biofouling development and near-wall flow around artificial substrates. Surface morphological characteristics of natural oyster reefs were first obtained by 3D scanning and used to fabricate concrete panels with simulated rough textures, while traditional smooth concrete panels served as controls. The two types of panels were simultaneously deployed in the target sea area for a hanging-panel experiment. Samples were collected after 3, 6, 9, and 12 months to track changes in biofouling communities. At each sampling time, the panel surfaces were quantified by canopy roughness ($R_C$), surface heterogeneity ($\sigma$), and fractal dimension ($D$), and these metrics were integrated into numerical simulations combined to resolve the flow field, turbulence kinetic, and near-wall shear stress around the colonized panels. The research results show that, after 12-month immersion, the mean thickness of the biofouling layer on rough and control panels reached 6.39 mm and 5.91 mm, respectively. Rough panels exhibited consistently higher $R_C$ and $\mathsf{\sigma }$ than controls, and these two parameters are strongly linearly correlated ($R^2=0.891)$. Numerical simulations reveal that increased $R_C$ enlarges the oyster settlement shear-stress window (OSSW), indicating more favorable hydrodynamic conditions for oyster settlement and growth on rough panels. Nevertheless, the hydrodynamic differences between the initial rough panels and control panels gradually diminish over time, suggesting that biological growth can progressively naturalize initially smooth substrates. These findings advance the mechanistic understanding of how small-scale roughness and biofouling co-evolve to shape oyster habitat quality and provide a quantitative basis for the eco-engineering design of artificial oyster reefs. Full article

Screw compressors are critical equipment in oil and gas production and transportation, where efficiency losses caused by rotor geometry, inlet pressure pulsations, and harsh climatic conditions can accumulate into substantial annual energy penalties and reliability degradation. This study provides a quantitative assessment of these coupled effects within a unified multiphysics framework that combines time-accurate transient CFD simulations based on a fixed Cartesian immersed-boundary formulation with a climate-calibrated offline physics-based digital twin—functioning as a digital shadow with one-way data flow from archival SCADA records—a reduced-order seasonal model with no real-time updating, calibrated against a full calendar year of SCADA records and validated against a held-out cold-season dataset (October–December 2022, T_amb = −15 to +8 °C); summer-period predictions rely on calibrated extrapolation beyond the validation window—an integration not previously demonstrated for oil-flooded screw compressors. Two rotor profile configurations (Type A and Type B) were analyzed to quantify geometry-driven differences in static pressure distribution, leakage tendency, and pulsation sensitivity. Transient suction conditions were modeled using harmonic and quasi-random inlet pressure disturbances to evaluate pressure amplification, phase lag, leakage intensification, and efficiency degradation. Seasonal performance was assessed by integrating temperature-dependent gas properties, oil viscosity behavior, and external heat transfer into an annual climatic load framework. The results show that inlet oscillations are amplified inside the chambers (pressure amplification factor П_p ≈ 1.95; П_p up to 2.3 under quasi-random excitation), reducing mass flow and volumetric efficiency by 8–10% and decreasing polytropic efficiency from 0.78 to 0.69–0.71, while increasing leakage by up to 27% and raising peak contact pressures to 167–171 MPa. Seasonal variability (+30 to −30 °C) increased suction density by 38% but raised drive power by ~9% due to viscosity-driven mechanical losses, producing an energy penalty up to 10.8% and an estimated annual additional consumption of approximately 186 MWh per compressor, decomposed as: cold-season contribution ~113 MWh (±10 MWh, directly field-validated against October–December 2022 SCADA data) and summer-season contribution ~51 MWh (calibrated extrapolation; additional uncertainty unquantified and not included in the ±10 MWh bound). The full annual figure of 186 MWh should be interpreted as a model-based estimate rather than a fully validated result. These findings demonstrate that rotor design optimization and mitigation of nonstationary suction effects, coupled with climate-aware offline physics-based digital shadow operation, represent high-priority levers for improving efficiency and reducing energy penalties in field conditions; reliability implications require further validation against summer-season field measurements. Full article

Accurate cotton boll identification and boll number estimation from UAV imagery are essential for large-scale yield prediction and precision management, yet severe leaf occlusion and complex canopy backgrounds often hinder robust performance. Here, UAV RGB images were acquired 3 days before defoliant application and at 3, 6, 9, 12, 15, and 18 days after defoliation. Cotton bolls were extracted using Mahalanobis distance, a support vector machine, and a neural network. Boll number was then estimated using an improved random forest model with multi-feature fusion. Across all defoliation stages, the NN produced the most accurate and stable boll extraction, achieving a maximum Kappa of 0.914, an overall accuracy of 95.77%, and an F1 score of 0.96. Extraction accuracy increased rapidly from 3 to 9 days after application and stabilized from 12 to 18 days. For boll number estimation, fusing the boll pixel ratio with color indices and texture features improved accuracy and consistency over time; the best performance was obtained at 18 days after application (R² = 0.7264; rRMSE = 4.9%). Overall, imagery acquired 15–18 days after defoliation provided the most reliable estimation window, supporting operational pre-harvest assessment and harvest-timing decisions. Full article

Deepwater drilling operations face critical challenges including narrow pore-fracture pressure windows, wellbore instability, and environmental concerns from drilling discharge. This paper presents a comprehensive systematic review of Riserless Mud Recovery (RMR) technology, tracing its evolution from its conceptual origins to its current applications, critically analyzing its technical limitations, and identifying future research directions. A systematic literature review was conducted covering peer-reviewed journals, SPE/IADC conference proceedings, industry technical reports, and independent academic studies from 1990 to 2025. Databases searched included Web of Science, Scopus, OnePetro, and Google Scholar, supplemented by Derwent Innovation Index for patents. After screening over 100 publications, approximately 60 references were selected following a two-step process excluding vendor-only promotional materials. Key findings reveal the following: (1) RMR technology has evolved through three distinct hardware generations—flexible hose systems, steel-pipe return lines with tandem pumps enabling deepwater breakthrough to 1419 m, and hybrid riser configurations for conceptual designs beyond 3000 m; (2) documented field benefits include 70% drilling fluid reduction, 9 days’ time savings per well, and successful mitigation of shallow geohazards across more than 1000 global well applications; (3) integration with casing-while-drilling and managed pressure cementing has enabled record-breaking performance of 1710 m in a single run; (4) independent academic validation confirms fatigue mechanisms affecting mud return lines; (5) systematic failure mode analysis identifies critical reliability issues in suction hoses, seals, and control systems; (6) quantitative economic analysis shows RMR cost-effectiveness depends on water depth, geological conditions, and environmental regulations. RMR technology has matured into a reliable drilling solution, yet its continued evolution requires addressing hardware limitations, developing dedicated well-control protocols, expanding to ultra-deepwater and emerging applications, and integrating digitalization for real-time optimization. Full article

Gait parameters are commonly reported, but their stability over durations representative of a typical continuous run remains poorly understood. This study investigated the stability and temporal structure of key spatiotemporal and kinetic parameters during a 30 min easy-paced treadmill run (13 km∙h⁻¹) while participants wore familiar and unfamiliar every day running shoes. Step-level data were analysed across the full time series and in sequential 1 min epochs to determine how long each parameter took to reach practical stability and whether this differed between shoe conditions. Approximately 2450 steps were analysed per condition. Within-participant variability was low (CV < 2.5%) for all parameters and conditions except for peak impact force (CV = 6.9–7.0%) and average loading rate (CV = 8.4–8.7%). Detrended fluctuation analysis (DFA-α) indicated persistent temporal structure for stride duration, swing time, and active peak force, whereas loading-phase kinetics showed weak long-range dependence. No significant differences were observed between shoe conditions for variability or temporal structure, although ground contact time was significantly longer when participants wore unfamiliar shoes. Practical windows of stability relative to each participant’s 30 min mean ranged from 11 to 17 min for spatiotemporal variables, 9 to 17 min for active peak force, and within the first minute for impact-related parameters and impulse. These findings indicate that studies examining spatiotemporal and kinetic parameters during easy-paced treadmill running require 11–17 min of continuous data to obtain 1 min epoch estimates that are practically stable relative to 30 min averages, regardless of footwear familiarity. Full article

Response latency and sensor noise are universal challenges in closed-loop control systems. In the context of semi-active suspensions, these issues also exist and manifest as critical bottlenecks. Due to the highly transient nature of road shocks, the inherent physical actuation delays of the hardware, combined with the phase lag introduced by traditional signal filtering, often cause the control response to significantly lag behind the physical excitation. To address this issue from a predictive perspective, this study proposes a Physics-Informed Gated Convolutional Neural Network (PI-GCNN) designed to predict future multi-modal energy evolution, thereby enabling feedforward control. Unlike traditional feedback mechanisms, the proposed framework employs the Continuous Wavelet Transform (CWT) to convert short-horizon inertial data into time–frequency scalograms, effectively isolating transient shock features from background vibrations. A novel physics-guided gating mechanism is embedded within the network architecture to regulate feature activation. This mechanism is trained using an asymmetric sparse physics loss, which combines L1 regularization with adaptive spectral consistency constraints to enforce noise suppression on flat roads while ensuring sensitivity to impacts. Extensive validation was conducted using high-fidelity heavy truck simulations and the public PVS 9 real-world dataset. The results confirm that the PI-GCNN achieves a predictive phase lead of approximately 100–200 ms over real-time baselines, creating a valuable actuation window for suspension dampers. Furthermore, the model demonstrates exceptional computational efficiency, with a parameter count of 0.10 M and a single-frame inference latency of 0.25 ms, making it highly suitable for deployment on resource-constrained automotive edge computing platforms. Full article

Dual-fuel, diesel–LPG (LPG being Liquified Petroleum Gas, e.g., propane) compression-ignition engines reduce CO₂ and particulate emissions compared to diesel-only operation but are prone to knock at high load due to charge homogeneity and increased ignition delay. AdBlue port injection (API) was evaluated as a combustion stabilisation strategy for a diesel–LPG engine and compared with water port injection (WPI). Experiments were performed on a 2.0 L diesel–LPG engine operated at 2000 RPM, BMEP ≈ 9 bar, λ ≈ 1.27 and LPG substitution of 72%. Knock intensity was quantified using knock-induced signal energy (KISE) derived from the oscillatory component of the in-cylinder pressure over a knock-sensitive crank angle window. Characterisation of combustion was done through HRR analyses, MFB analyses and FFT-based frequency characterisation. Baseline operation exhibited severe knock with a peak HRR ≈ 200 J/°CA and mean KISE of 307.2 bar². WPI at a water mass ratio WMR of 130% reduced the peak HRR by 56% and mean KISE by 88%, but decreased the peak pressure, BMEP and BTE. API at an AdBlue mass ratio AMR of 130% reduced the peak HRR by 37% and KISE by 82.6% while maintaining BMEP and BTE within baseline variability. Both strategies attenuated the dominant ~19.8 kHz (1,2) mode. NOx emissions decreased with WPI but increased at a high AMR. Full article

The multi-depot vehicle routing problem with soft time windows (MDVRPSTW) has long been a focus in both academic and industrial circles. This paper proposes a deep reinforcement learning framework designed to enhance the efficiency and quality of MDVRPSTW solutions, addressing the limitations of traditional heuristic algorithms in large-scale complex scenarios. The framework first transforms the mathematical model into a sequential decision-making problem through a Markov decision process, then extracts path selection strategies using an encoder–decoder architecture based on attention mechanisms and graph neural networks, and employs unsupervised reinforcement learning for model training. Test results on the Solomon benchmark dataset demonstrate that for small-scale problems (N = 20), our method reduces solving time by over 96% compared to comparative algorithms, with the objective value difference from the generalized variable neighborhood search (GVNS) being less than 9%. For medium-to-large scale problems (N = 50/100), our method achieves a 27.7 to 96.3 percent improvement over GVNS, maintaining stable solution times within 3 to 10 s. Compared to exact algorithms and meta-heuristic methods, our approach reduces computational costs by 2–3 orders of magnitude while demonstrating strong adaptability to variations in the number of depots and vehicles. In summary, this method significantly outperforms baseline models in both solution quality and computational efficiency, providing an efficient end-to-end solution for MDVRPSTW in complex scenarios. Full article

The concrete cracking problem can seriously affect the durability and safety of civil structures. Accurately and quickly measuring the width of concrete cracks can help control defect development in a timely manner. Current research mainly relies on pixel detection of two-dimensional images, which lacks real three-dimensional information about crack lesions. Detection results are also obviously affected by various factors, such as shooting distance and posture, resulting in poor accuracy. Therefore, this paper presents an engineering-integrated solution that combines U-Net-based crack segmentation with binocular vision 3D reconstruction. The focus is placed on the practical deployment of the integrated pipeline, the optimization of key parameters under real inspection conditions, and the experimental validation of measurement accuracy on actual concrete cracks. Firstly, the U-Net deep learning algorithm is used to automatically identify and segment the concrete crack region; then, a binocular vision-based 3D reconstruction pipeline is adopted, and a parallax rejection algorithm based on a “double-threshold” decision is proposed to improve the fidelity of crack disparity maps, and the effect of the filter window size on the concrete crack region is analyzed; finally, an intelligent measurement method based on the 3D reconstruction model is proposed, and the measurement results of concrete crack width can be calculated directly from the 3D reconstruction model. The results show that (1) the model can identify the characteristics of the crack, and the detection effect at 4:00 p.m. is the best, because at this time the light is more uniform with less shadow and moderate contrast between the crack and its background; (2) the reconstruction of the 3D point cloud model of the concrete crack with a filtering window of size 9 × 9 is the best; (3) the maximum error between the calculated and measured values of crack width is 0.31mm, the minimum error is 0.07mm, and the average error is 0.15 mm, which indicates that the measurement accuracy reaches the sub-millimetre level and verifies the validity of the proposed method in this paper. Full article

In this work, we aimed to develop and evaluate multi-band outer volume suppression pulses for increased acceleration rates in simultaneous multi-slice accelerated cardiac MRI. MB-OVS pulses were constructed from a multi-band combination of two slab-selective saturation pulses and tested for various pulse shapes using Bloch simulation and phantom experiment. The MB-OVS pulses were interleaved between imaging pulses to ensure homogeneous suppression throughout the cardiac cycle/imaging window in vivo. Simultaneous multi-slice (SMS) CINE and first-pass myocardial perfusion scans with and without the proposed MB-OVS pulses were compared in terms of residual artifacts at high acceleration rates. Among the tested pulses, both Bloch simulation and phantom experiments showed that amplitude-optimized sinc pulses provided the best trade-off in suppression efficiency, the required $B_1^{+}$, SAR, and slab profile. CINE imaging with 5-fold SMS-OVS acceleration significantly outperformed imaging without MB-OVS, maintaining leakage-free image quality, even when adding 2-fold in-plane acceleration. SMS-OVS also enabled perfusion imaging in 9 slices with 1.7 × 1.7 mm² resolution, achieving a 16-fold spatial-only acceleration while ensuring accurate contrast dynamics without leakage artifacts. Interleaved MB-OVS modules enabled thorough leakage artifact suppression in cardiac SMS-accelerated CINE and perfusion imaging, particularly at high acceleration rates. The proposed approach may be promising for unlocking further acceleration potential of SMS in cardiac imaging. Full article

Background/Objectives: Hip fractures among elderly patients are associated with significant morbidity and mortality. Delayed surgery is common and often results in inadequate pain control and increased opioid consumption, which may have adverse effects. This study evaluates the effectiveness of preoperative intra-articular injection of multimodal analgesics (IA MDI) for reducing pain caused by a displaced femoral neck fracture (FNF). Methods: A prospective randomized controlled trial was conducted using 18 geriatric patients with displaced FNFs scheduled for hip arthroplasty. The patients were randomized into two groups: IA MDI and control groups (n = 9 each). The IA MDI group was administered a preoperative intra-articular injection of ropivacaine, morphine, and adrenaline, in addition to standard oral and intravenous (IV) analgesics, while the control group was administered standard oral and IV analgesics alone. The primary outcome was the perioperative pain score assessed via the 10-point numerical rating scale (NRS). The secondary outcomes were morphine consumption, perioperative complications, length of hospital stay, and functional outcome. Results: During the first 24 h preoperative period after admission, the IA MDI group exhibited a significant reduction in the average NRS at all timepoints (p < 0.05 all) and in the median dosage of morphine consumption (0 mg vs. 6 mg, p = 0.033) compared to the control group. There was no significant difference between groups in terms of postoperative pain and complications, length of hospital stays, or functional outcomes (p > 0.05 all). Conclusions: Preoperative IA MDI significantly reduced pain intensity and opioid consumption during the preoperative 24 h window among elderly patients with FNFs without provoking a corresponding increase in observed complications in this pilot randomized controlled study. IA MDI is a feasible option and could be a useful adjunct for preoperative pain management for FNFs. Full article

Large language models (LLMs) are increasingly used in software development, yet their ability to generate and maintain large, multi-module systems through natural language interaction remains insufficiently characterized. This study presents an empirical analysis of developing a 7420-line Terminal User Interface (TUI) framework for the Ring programming language using a prompt-driven workflow with Claude Code (Opus 4.5), employing an iterative testing and corrective feedback. The system was produced through 107 prompts: 21 feature requests, 72 bug fix prompts, 9 prompts sharing information from Ring documentation, 4 prompts providing architectural guidance, and 1 prompt dedicated to generating documentation. Development progressed across five phases, with the Window Manager phase requiring the most interaction (35 prompts), followed by complex UI systems (25 prompts) and control expansion (20 prompts). Bug-related prompts covered redraw issues, event-handling faults, runtime errors, and layout inconsistencies, while feature requests focused primarily on new widgets, window-manager capabilities, and advanced UI components. Most prompts were brief (mean ≈ 258 characters; median = 207 characters), reflecting a highly iterative workflow in which the human role was limited to specifying requirements, validating behavior, and issuing corrective prompts—without writing any code manually. The resulting framework contains 28 classes, 334 methods and includes a windowing subsystem, event-driven architecture, interactive widgets, hierarchical menus, grid and tree components, tab controls, and a multi-window desktop environment. By combining quantitative prompt analysis with qualitative assessment of model behavior, this study provides empirical evidence that modern LLMs can preserve architectural coherence across iterations and support the construction of new libraries and tools for emerging programming languages, highlighting prompt-driven development as a viable methodology within software-engineering practice. Full article

Background: Current guidelines recommend prognostication at 72 h after cardiac arrest, yet a subset of patients (Late Awakeners) recover consciousness after this window. This study investigated diagnostic markers to distinguish Late Awakeners from those with permanent poor outcomes (Non-Awakeners) to prevent premature withdrawal of life-sustaining therapy. Methods: We analyzed adult OHCA patients treated with TTM from 2009 to 2019 who remained comatose (Glasgow Coma Scale Motor score < 6) at 72 h. Patients were categorized as Late Awakeners (obeyed commands > 72 h) or Non-Awakeners. The diagnostic performance of maximal Neuron-Specific Enolase (NSE) levels within 72 h and brainstem reflexes was assessed using receiver operating characteristic (ROC) analysis. Model calibration was evaluated using the Hosmer–Lemeshow test, and internal validation was performed using bootstrap resampling. Results: Of 213 patients comatose at 72 h, 20 (9.4%) were identified as Late Awakeners. The median time to awakening was 4.4 days (IQR 3.4–8.3) from ROSC. Late Awakeners exhibited significantly preserved corneal reflexes (85.0% vs. 20.2%) compared to Non-Awakeners. The optimal NSE cut-off value to predict late awakening was <89.5 ng/mL (Sensitivity 95.0%, Specificity 50.3%, AUC 0.801). A multimodal approach combining NSE < 90 ng/mL and preserved corneal reflexes achieved a high specificity of 93.2% and an AUC of 0.899 (optimism-corrected: 0.896) for predicting late recovery. At six-month follow-up, 74.3% of Late Awakeners achieved good neurological outcome (CPC 1–2). Conclusions: Approximately 9% of patients comatose at 72 h eventually regain consciousness with favorable long-term outcomes. A multimodal diagnostic model combining intermediate NSE thresholds and preserved brainstem reflexes can effectively identify these Late Awakeners, suggesting that observation should be extended for patients fitting this profile. Full article

Fibromyalgia (FM) is associated with increased suicidal behavior (SB). This critical review integrates the ideation-to-action models—Interpersonal Theory of Suicide (IPTS), Three-Step Theory (3ST), and Integrated Motivational–Volitional (IMV) Model—with clinical and neuropsychological correlates to discriminate between suicidal ideation (the motivational component) and suicidal action (the volitional component) in FM. Ideation is related to hopelessness, perceived burden, thwarted belongingness, and entrapment, as well as to pain/interference, sleep disturbances, fatigue, mood, pain catastrophizing, and attentional pain vigilance. The transition to action is associated with impulsivity, executive dysfunction (including inhibitory control, flexibility, and decision-making under ambiguity/risk), acquired capability due to repeated exposure to pain and medical procedures, and access to lethal means. Suicidal planning is conceptualized as high-severity ideation, while action includes preparatory behaviors and suicide attempts. Evidence from Spanish instruments is synthesized—Columbia Suicide Severity Rating Scale (C-SSRS), Plutchik Suicide Risk Scale (PSRS), Beck Depression Inventory-II (Item 9 of the BDI-II), and Suicide Behaviors Questionnaire—Revised (SBQ-R)—pointing out overlaps with pain/depression and the lack of specific validation in FM. Prospective cohorts, standardization of definitions/windows, comparable neuropsychological batteries, and mechanistic trials on motivational and volitional targets and interventions focused on pain reduction are proposed. Full article

(1) Introduction. To improve access times and provide effective treatment to the growing patient population with acute stroke due to large vessel occlusion (LVO), thrombectomy-capable stroke centers (TCSCs) should be made an integral part of hospital infrastructure in Poland. The geographical proximity of thrombectomy-capable centers and recently extended treatment time windows will considerably increase patient numbers, decrease patient disability, and reduce the costs of long-term care. (2) Aim of the study. This study investigates the clinical outcomes, time metrics, and angiographic data of a cohort containing 250 thrombectomy patients at a single TCSC in Poland. We measured performance against data from the national database during two crucial time intervals: at the very beginning of the center’s service and after the involvement of a new operator. This study considers concurrent modifications in qualification guidelines, the TCSC’s transition from a ‘direct-admission-only’ to a ‘drip-and-ship’ model, and the learning curve of the interventional stroke team. (3) Methods. A retrospective analysis was conducted on 250 patients treated from August 2020 to May 2025 at a newly established TCSC. The cohort was dived into 2 subgroups: an initial group of 100 patients, whose treatment corresponded to the involvement of a new, previously trained on-site operator and the establishment of 24/7 service, and a group of 150 patients who received later treatment. Additional comparisons were made between a cohort of directly admitted patients and those treated under the drip-and-ship model. The results compared between patients treated with early and expanded time windows. (4) Results. Significant differences were observed between the first 100 and subsequent 150 patients in terms of admission scheme (97% vs. 70%, p < 0.0001), extended time window treatment (8% vs. 17.3%, p < 0.05), and intravenous thrombolysis treatment (81% vs. 65.3%, p < 0.01). Improvements in time intervals and procedural factors were noted in the second group, reflecting the operator’s increased experience (groin-to-first pass time: 27 vs. 23 min, p < 0.05). A comparative analysis between the direct admission and drip-and-ship models revealed extended time intervals in the latter (door-to-groin: 110 vs. 159 min, p < 0.001; door-to-recanalization: 158 vs. 200 min, p < 0.001; door-to-CT: 9 vs. 16.5 min, p < 0.001; and door-to-IVT: 21 vs. 43 min, p < 0.001). Patients in the extended time window exhibited lower intravenous thrombolysis rates (78.2% vs. 29.4%, p < 0.0001) and prolonged door-to-groin (117.5 vs. 150 min, p < 0.005), door-to-CT (10 vs. 19.5 min, p < 0.01), and door-to-IVT (25 vs. 77.5 min, p < 0.001) times. No significant differences were found in complication rates, clinical outcomes, or mortality between the analyzed subgroups. (5) Conclusions. The present data demonstrate favorable clinical and angiographic results among acute LVO stroke patients at the newly established TCSC, both at the onset of the mechanical thrombectomy service and after the involvement of a newly trained operator. Even when treating patients with prolonged times due to transportation and late window qualification, we observed favorable clinical outcomes and low rates of complications. The results achieved in our TCSC compared with the national data suggest that TCSCs could potentially play an important role within the overall endovascular treatment system for acute ischemic stroke patients in Poland. Full article