Search Results (9,393)

Natural circulation is a key passive heat removal mechanism in advanced reactor systems, including Molten Salt Reactors (MSRs). Owing to the high operating temperatures and material challenges associated with molten salts, surrogate fluids with Prandtl numbers comparable to those of molten salts have emerged as promising candidates for studying heat transfer phenomena in MSRs. The present study marks the first experimental and numerical investigation using Therminol-66 (Th-66) simulant oil as a surrogate fluid for molten salts in a natural circulation (NC) test loop setup at the University of Idaho Thermal-Hydraulics Laboratory. Experimental temperature measurements and energy-balance-based mass flow rate estimations were used to validate a three-dimensional computational fluid dynamics (CFD) model developed in ANSYS FLUENT. Two numerical configurations were considered: an adiabatic-wall model and a model incorporating distributed heat losses. The inclusion of heat losses significantly improved predictive accuracy, reducing the maximum relative error in heater outlet temperature to 16.7%. The largest deviation of 35.5% was observed at the heater inlet, primarily due to differences in power distribution and hydraulic resistance between the experimental system and the simplified numerical model. The CFD model systematically overpredicted the mass flow rate, mainly as a result of geometric simplifications (e.g., omission of flanges and minor loss elements) and the assumption that the total heater power was applied directly to the immersed heater rods. On the experimental side, distributed heat losses and indirect mass flow rate estimation introduced additional uncertainty. Nevertheless, the CFD model successfully captured the overall thermal and hydraulic trends across all operating conditions. The validated simulations further provided detailed insight into local and global temperature and velocity distributions within the heater and cooler sections. The results highlight the importance of accurately representing thermal losses and hydraulic resistance to achieve reliable prediction of natural circulation behavior in surrogate MSR systems. Full article

Autophagy is a carefully regulated catabolic process that utilizes assemblies of specific sets of macromolecules operating at multiple stages of the pathway. Discoveries in recent years show that autophagy markedly relies on liquid-liquid phase separation (LLPS). Here, we present parameters that indicate the plasticity of autophagy proteins and their probability to undergo LLPS in macroautophagy and microautophagy. We show that microautophagy is an extremely LLPS-friendly pathway. Several mechanisms involving proteins in the autophagy machinery that drive LLPS on various types of membranes to regulate this process or that undergo LLPS as autophagic cargo are described in detail. We also summarize the factors that modulate the LLPS potential of autophagy proteins. A high probability of autophagy-related proteins to undergo spontaneous LLPS shown here can direct future research on the role of protein droplets in autophagy. Full article

Belt conveyors are essential to continuous material transport systems, and reliable surface defect detection is therefore critical for safe and stable operation. In real industrial environments, defects such as tears, punctures, and localized damage are often small, elongated, and characterized by weak boundary contrast. Complex background interference further increases the difficulty of accurate and reliable detection for real-time defect detectors. To address these challenges, this paper proposes SEDR-Net (Structure-Edge and Detail Reconstruction Network), a YOLOv11n-based network, for this task. The Structural-Edge Fusion Block (SEFBlock), Channel-Spatial Collaborative Attention (CSCA), and Efficient Up-Convolution Block (EUCB) respectively enhance structural-edge representation, suppress redundant background responses, and recover local structures and boundary details. On a public conveyor-belt defect dataset, SEDR-Net achieves 90.8% Recall, with an mAP@0.5 of 92.4% and an mAP@0.5:0.95 of 58.4%, yielding improvements of 4.7, 3.8, and 7.1 percentage points over YOLOv11n, respectively. Meanwhile, SEDR-Net uses 2.42 M trainable parameters and maintains an inference speed of 134.2 FPS, indicating a favorable accuracy–complexity trade-off for real-time inspection. An independent external industrial test set further verifies the cross-scenario robustness and practical applicability of the proposed method under real mining conveyor-belt conditions. Full article

Objective: To determine the prevalence and independent predictors of postoperative blood transfusion following carotid endarterectomy (CEA) and to quantify its association with in-hospital morbidity and 30-day mortality. Methods: A retrospective, single-centre cohort study was conducted. All consecutive patients undergoing CEA for carotid artery stenosis for two years were included without exclusion criteria. Preoperative and serial postoperative haemoglobin (Hb) and platelet values were collected at 6, 12, 24, and 48 h after surgery, together with demographic variables, cardiovascular risk factors, neurological presentation, anaesthetic risk (ASA classification), operative details, transfusion characteristics, postoperative complications, length of stay, and 30-day mortality. Red cell concentrate was indicated when postoperative Hb was ≤9 g/dL and platelet concentrate when postoperative platelets fell below 100 × 10³/mm³. The primary outcome was 30-day mortality. Univariate and multivariate linear regression analyses were used to identify independent predictors of mortality and transfusion need. Statistical significance was set at p < 0.05; analyses were performed with SPSS v30.0. Results: A total of 182 patients underwent CEA; 87.4% were male with a mean age of 71.84 years. The 30-day mortality rate was 3.8% (n = 7). Blood transfusion was required in 8.2% of patients (n = 15). On multivariate analysis, postoperative transfusion (p = 0.001) and postoperative complications (p = 0.001) were the only independent predictors of mortality, with transfusion conferring an approximately five-fold increase in mortality risk (relative risk [RR] 4.99; 95% confidence interval [CI] 0.88–28.24). Transfused patients had significantly higher complication rates (60.0% vs. 13.8%; p < 0.001) and longer total hospital stays (11.2 ± 14.9 vs. 4.9 ± 2.3 days; p = 0.001). On multivariate analysis, independent predictors of transfusion were low preoperative Hb (p < 0.001), peripheral arterial disease (PAD; RR 5.09, 95% CI 1.70–15.21; p = 0.002), postoperative complications (p = 0.004), and prolonged hospital stay (p < 0.001). Conclusions: Postoperative blood transfusion is an independent risk factor for mortality after CEA, multiplying mortality risk approximately five-fold. Low preoperative Hb and peripheral arterial disease are the principal preoperative predictors of transfusion requirement. These findings underscore the importance of systematic preoperative anaemia optimisation and the application of evidence-based restrictive transfusion thresholds in patients undergoing carotid surgery. Full article

Battery cell manufacturing is highly energy intensive, with clean and dry rooms being among the largest consumers of electricity and thermal energy. Due to the moisture sensitivity of most advanced cathode materials (e.g., NMC 811) and sulfide-based solid-state materials, production environments must operate at extremely low humidity, requiring energy-intensive HVAC systems to remove moisture introduced mainly by workers and infiltration. To reduce energy consumption, a detailed understanding of the airflow patterns in the room is essential. Because of complex flow patterns (exhaust air demands, energy dissipation), tracer gas techniques using CO₂ as a marker provide an operation-integrated method for determining local air age. The studies presented in this paper apply tracer gas techniques for the first time to a room in which air is almost completely recirculated at high air change rates of approximately 27 h⁻¹, with the supply air being conditioned by removing all process-relevant contaminants such as moisture and particles. Measurements in a separate flow box show successful air age calculations that agree with simplified CFD simulations. For the clean and dry room, the empirical variable relative exposure (REX) was introduced. The measurements indicate an inhomogeneous air distribution inside the room, accompanied with short-circuit flows, partial displacement flow, and mixing, and therefore have the potential to provide a cost-effective first-hand insight into the prevailing airflow patterns. Nevertheless, the presented measurement technique must be further optimized and validated for rooms with air recirculation and high air change rates. Full article

We develop a stochastic Stackelberg model of a subscription market with cancellation costs. A representative consumer chooses when to subscribe to and cancel a service as the utility derived from the subscription evolves according to a diffusion process, while the firm selects the subscription fee and cancellation cost to maximize its expected payoff. The consumer’s problem is equivalent to the classical real-options model of entry and exit under uncertainty with adjustment costs and exhibits a two-threshold policy with an inaction band and hysteresis. Unlike the standard formulation, in which the optimal thresholds are characterized implicitly through a system of nonlinear equations, we derive an explicit parametric solution in closed form. This solution reduces the firm’s optimization problem to a two-dimensional unconstrained problem and yields a detailed characterization of the optimal pricing policy. We show that the firm’s strategy exhibits three qualitatively distinct regimes depending on the initial utility level. For small utility levels, the optimal cancellation cost is zero. In an intermediate regime, the firm’s optimal policy induces the consumer to set the entry threshold equal to the initial utility level, resulting in immediate subscription. For sufficiently large utility levels, the firm induces permanent lock-in by setting a high cancellation cost and a low subscription fee: the consumer subscribes immediately and never subsequently unsubscribes. The transition between the latter two regimes is discontinuous and results from competition between two local maxima of the firm’s payoff function. We then extend the model to a heterogeneous population of consumers. The superposition of individual two-threshold subscription strategies generates a Preisach hysteresis operator describing the aggregate dependence of the firm’s revenue on the utility dynamics. The discontinuous regime transition persists under heterogeneity, demonstrating the robustness of the underlying mechanism. The Preisach representation predicts complex history dependence and long-term effects of temporary utility shocks. For a gamma distribution of consumer preferences, the firm’s expected payoff is obtained in closed form in terms of incomplete gamma functions. Full article

Existing surrogate models for marine propeller hydrodynamics efficiently predict integral coefficients but are limited in providing detailed flow field information, restricting their use in cavitation assessment and refined optimization. This study proposes a conditioned deep learning framework for the rapid prediction of blade surface flow fields and open-water performance of marine propellers under varying geometric and operating conditions. The model takes local geometric information of a single blade as input, while the blade number and advance coefficient are jointly encoded into a condition embedding vector. This vector dynamically modulates convolutional features to enable flow field prediction on blade surfaces. A dataset comprising 2710 operating cases across 560 propeller samples with 3-, 4-, and 5-bladed configurations is constructed for model training and evaluation. Results demonstrate that the proposed model achieves high accuracy in predicting both surface flow field and derived hydrodynamic coefficients, while significantly improving computational efficiency compared to CFD simulations. Furthermore, the model yields reasonable predictions for new propeller geometries within the covered design space, demonstrating interpolation generalization capabilities. The proposed framework provides a promising tool for rapid hydrodynamic assessment and refined propeller optimization. Full article

Cloud contamination obscures ground objects, interferes with surface reflectance, and disrupts spatial continuity. In thick-cloud regions, surface structures and spectral information are often extensively missing. CNN-based cloud removal methods can recover local textures, but they are less effective at modeling global structures and color consistency over large cloud-covered areas. Transformer-based methods capture long-range dependencies; however, standard self-attention introduces high computational and memory costs for high-resolution remote sensing images. Efficient attention reduces this cost but may weaken edge and texture discriminability. SAR imagery can penetrate clouds and provide surface structural information, yet repeated SAR injections may propagate speckle noise, cross-modal misalignment, and imaging discrepancies through deep restoration layers. To address these issues, this paper proposes DST-SARNet, a dual-stage SAR structural guidance network for optical remote sensing image cloud removal. In this framework, dual-stage refers to two explicit SAR-guidance positions: early structural skeleton guidance at the input side and late high-frequency modulation near the output. The Texture-Aware Asymmetric Retrieval module is placed between these two stages as a bottleneck memory retrieval operation rather than as a third dense SAR injection stage. With this design, SAR provides structural skeletons, readable texture memory, and terminal detail compensation, while the optical branch remains responsible for color, semantics, and spectral appearance recovery. Experiments on the SMILE-CR and SEN12MS-CR datasets show that DST-SARNet effectively restores cloud-contaminated imagery with a compact model scale, demonstrating its potential for efficient SAR-assisted optical cloud removal. Full article

We introduce the local kernel-based calculus, a unifying framework for local differential and integral operators based on an arbitrary positive continuous kernel function. This framework encompasses conformable, non-conformable, and our newly introduced local Euler-kernel derivatives as special cases. The parameter of the kernel is unrestricted and may take negative values, reflecting its role as a genuine parameter rather than an order of fractional differentiation. Within this general setting, we rigorously prove a complete set of foundational theorems: linearity, the product rule, continuity, Rolle’s theorem, the mean value theorem, and the fundamental theorem of calculus via the associated integral operator. We also derive a new formulation of the chain rule that expresses the chain rule entirely in terms of the kernel-based derivatives. While algebraically equivalent to the classical form, this representation preserves the intuitive structure of the chain rule without reference to the classical derivative. We further establish the Fundamental Theorem of Local Euler Calculus and its generalization, the Fundamental Theorem of Local Kernel-Based Calculus, confirming that the derivative and integral operators are genuine inverses, with the classical fundamental theorem recovered as special cases when the kernel reduces to unity. As an important illustration, we develop the local Euler calculus with the exponential kernel in full detail, providing explicit derivative and integral formulas for elementary functions. This special case demonstrates the simplicity and power of the functional approach. Overall, the local kernel-based calculus provides a solid, self-contained foundation that unifies a wide class of local operators and extends far beyond the traditional setting. Full article

The accurate detection of unmanned aerial vehicles (UAVs) in various sizes played an important role in the practical applications. Yet the preceding works suffered from the missing inference, the false alarms, and the poor accuracy due to the the adverse scene conditions, as well as the mutable scales. To solve the problems, a hierarchical attention promoted cross-scale learning framework was proposed in this paper. First, the hierarchical attention mechanism was introduced in the backbone to generate the multi-scale features of targets, so they can be discerned and located at different scales. The resulting features were further delivered to the neck, in which two branches of features were built, respectively. The former was obtained by the target-specific feature operator, while the latter was generated by the upsampling operation. The dual branches were further connected in the quasi-residual structure. So the content of targets can be protected well, and the detail information can be reconstructed. Finally, the dynamic focusing loss measurement was presented to regress the bounding box of the target, so the learning effectiveness of presented the architecture can be promoted. To verify the proposed method, multiple rounds of experiments were performed. The results demonstrated that small and weak drones can be detected accurately, especially in adverse lighting and weather conditions. The evaluation metric of mean average precision rate (mAP) can be improved by 18.5% (YOLO6) on the collected dataset. Full article

Predicting canopy traits non-destructively is important for understanding crop growth and improving phenotyping efficiency. Hyperspectral reflectance provides detailed spectral information, but the role of band selection in regression-based trait prediction at the canopy scale remains unclear. In this study, we evaluated the effects of different band-selection algorithms on the prediction accuracy of aboveground biomass (AGB), leaf area index (LAI), and canopy cover (CC) in soybeans using canopy hyperspectral reflectance in the visible to near-infrared (VNIR) range from 501 to 801 nm. The dataset included multiple sites, years, cultivars, and irrigation treatments. We compared a full-band partial least squares regression (PLS) model with three band-selection methods (PLS-Variable Importance in Projection (VIP), Bootstrapped least absolute shrinkage and selection operator (LASSO) (BoLASSO), and an ensemble approach). Model performance was assessed using Kennard–Stone validation and leave-one-year-out cross-validation. The results showed that the effectiveness of band selection depended on the target trait. Full-band PLS performed well for AGB under Kennard–Stone validation, whereas BoLASSO achieved comparable accuracy to PLS for LAI and CC using a reduced number of selected bands. Leave-one-year-out cross-validation showed that year-to-year transferability was more difficult for AGB than for LAI and CC. The selected wavelengths were located mainly in the visible, red-edge, and near-infrared regions. These results indicate that band-selection strategies should be tailored to the target trait and that selected VNIR bands can provide candidate spectral regions for simplified sensing of soybean canopy traits. Full article

This scientific article presents a comprehensive case study detailing the design of a fully off-grid household in Poland, utilizing an energy solution that combines high-pressure hydrogen energy storage and photovoltaic (PV) technology. In response to the growing demand for sustainable and self-sufficient energy sources, the current study investigates the efficiency and yearly energy balance of this innovative system. The off-grid household is powered by a hybrid system that seamlessly integrates PV panels to harness solar energy and a high-pressure hydrogen energy storage system for long-term energy management. The presented case study examines the design and performance of a system integrating solar energy production with hydrogen storage. Through an analysis of real-world data and operational parameters, this research contributes valuable insights into the viability of such an off-grid solution in Polish environmental conditions. These findings provided an interesting approach to off-grid residential systems, offering a glimpse into the possible future of residential energetic autonomy in the pursuit of a greener and more resilient energy landscape. Full article

To explore the optimized operating strategies coupling floor cooling with cold storage for thermal active systems (TABSs), effects of operating time on indoor thermal environment, cold storage capacity, energy use and running cost were studied. Simulations were conducted based on an actual office building equipped with floor cooling. To take full advantage of the TABS and off−peak electricity, four operating cases with nighttime floor cold storage were proposed, namely C₁ (2:00–8:00), which operated only during the off−peak hours, C₂ (2:00–10:00), C₃ (2:00–12:00), and C₄ (2:00–14:00), which operated during the off−peak and flat hours. A simulation case of C₀ operating during daytime (7:00–17:00) was also proposed. Simulation results show that the C₁ and C₂ conditions with shorter operating hours result in higher indoor temperatures, which cannot ensure indoor thermal comfort. The PMV index in C₃ and C₄ conditions can be kept between −1 and 1, which meets the thermal comfort demand of Grade II. Considering that the operating duration of C₃ is the same as the occupied hours, the cold storage capacity, cooling loss, cooling supply and release process, etc., of this case are further analyzed based on data of a typical day. The floor and ceiling slabs store most of the cooling energy (72.7%) during the night; inner walls also store part of the cooling energy (23.3%) and cooling loss during cold storage accounts for approximately 3.1%. During working hours, the cooling energy released is lower than the cooling load, which makes indoor temperatures increase continuously. Compared with case C₀, case C₃ has same power use while saving 2.8% of running costs. Case C₄ provides a higher level of thermal comfort, while saving 0.9% of costs with a 1.5% increment in electricity use. This study provides detailed data about cold storage strategies coupling with floor cooling in TABS, which can be used to save running cost. Full article

Outdoor industrial sites, including logistics terminals, construction yards, and civil infrastructure worksites, increasingly require supervisory systems for monitoring autonomous mobile units under variable wireless and operational conditions. This study presents an edge-cloud telemetry platform that connects V2X on-board and roadside units to a normalized data pipeline and an operator dashboard. The architecture assigns frame reception and data validation to the edge layer, while cloud services perform stream ingestion, storage, querying, and visualization using a Kafka-Elasticsearch-Grafana stack. A fixed supervisory schema was defined for position, heading, speed, mission state, battery level, and error flags so that virtual fields used in early validation can later be replaced by measured signals without changing downstream interfaces. Physical field validation was conducted using a single test vehicle in a construction-site emulation environment to evaluate communication continuity and dashboard refresh behavior. Multi-unit applicability was examined at the architecture and schema levels, and a preliminary payload-level capacity estimate was derived using the telemetry frequency and payload-length assumptions. Under the tested site conditions, the system maintained continuous reception and visualization over an approximately 700 m distance from the RSU-side reference location. The measured end-to-end display delay averaged 0.78 s, with a standard deviation of 0.059 s and a maximum of 0.96 s. Under a 10 Hz status-message condition, the estimated pure-payload traffic was approximately 23 kbps per mobile unit. These results indicate that V2X-based edge-cloud telemetry can provide a practical baseline for supervisory monitoring in outdoor industrial sites, while simultaneous multi-vehicle validation, detailed network-load evaluation, and long-term field testing remain necessary future work. Full article

Existing Large Language Model cybersecurity evaluations rely on text-based plausibility scoring systems that fail to validate operational exploit viability. In this paper, we present the Operational Risk Framework (ORF), advancing beyond our prior MalcodeEval work through three (3) innovations: (1) ECDSA-P384 cryptographic execution validation providing non-repudiable proof-of-exploit, (2) MITRE ATT&CK-aligned tiered scoring with CVSS v4.0-derived severity weights, (3) and six-phase progressive validation tracking 217 Indicators of Compromise within isolated VM environments. The utility of this framework is demonstrated through detailed case studies that have revealed granular disparities in capabilities and multi-stage attack progression, often obscured by standard pass/fail binary metrics. This work contributes systematic LLM-to-CVSS mapping and open cryptographic protocols toward NIST AI RMF 2.0 development. Full article