Search Results (3,093)

The gyroid minimal surface is one type of triply periodic minimal surface (TPMS). TPMS is a minimal surface replicated in the three main directions of the Cartesian coordinate system. The minimal surface is a surface stretched between two objects, known as the smallest possible area (e.g., a soap bubble with a saddle shape stretched between two parallel circles). The complicated shape of the TPMS makes its production possible only by additive methods (3D printing). This article presents the results of experimental studies on heat transfer and flow resistance in a heat exchanger made of stainless steel. The heat exchange surface, a TPMS gyroid, separates two working media: hot and cold water. The water flow rate was varied in the range from 8 kg/h to 25 kg/h (Re = 246–1171). The water temperature at the inlet to the exchanger was maintained at a constant level of 8.8 ± 0.3 °C and 49.5 ± 0.5 °C for cold and hot water, respectively. The effect of water flow rate on the change in its temperature, the heat output of the exchanger, the average heat transfer coefficient, pressure drop, and overall resistance factor was presented. Full article

Low Earth orbit (LEO) signal of opportunity (SOP) positioning relies on the accumulation of epochs obtained through prolonged observation periods. The contribution of an LEO satellite single epoch to positioning accuracy is influenced by multi-level characteristics that are challenging for traditional models. To address this limitation, we propose an Agent-Weighted Recursive Least Squares (RLS) Positioning Framework (AWR-PF). This framework employs an agent to comprehensively analyze individual epoch characteristics, assess their credibility, and convert them into adaptive weights for RLS iterations. We developed a novel Markov Decision Process (MDP) model to assist the agent in addressing the epoch weighting problem and trained the agent utilizing the Double Deep Q-Network (DDQN) algorithm on 107 h of Iridium signal data. Experimental validation on a separate 28 h Iridium signal test set through 97 positioning trials demonstrated that AWR-PF achieves superior average positioning accuracy compared to both standard RLS and randomly weighted RLS throughout nearly the entire iterative process. In a single positioning trial, AWR-PF improves positioning accuracy by up to 45.15% over standard RLS. To the best of our knowledge, this work represents the first instance where an AI algorithm is used as the core decision-maker in LEO SOP positioning, establishing a groundbreaking paradigm for future research. Full article

Background/Objectives: Hyperlipidemia is a major risk factor for cardiovascular disease and atherosclerosis. Polyphenols found in polyphenol-rich extra virgin olive oil (EVOO) have been shown to possess strong antioxidant, anti-inflammatory, and cardioprotective properties. The present study aimed to assess the effects of two types of EVOO with different polyphenol content and dosages on the lipid profile of hyperlipidemic patients. Methods: In this single-blind, randomized clinical trial, 50 hyperlipidemic patients were randomized to receive either a higher-dose, lower-phenolic EVOO (414 mg/kg phenols, 20 g/day) or a lower-dose, higher-phenolic EVOO (1021 mg/kg phenols, 8 g/day), for a period of 4 weeks. These doses were selected to ensure equivalent daily polyphenol intake in both groups (~8.3 mg of total phenols/day), based on chemical analysis performed using NMR spectroscopy. The volumes used (8–20 g/day) reflect typical daily EVOO intake and were well tolerated by participants. A group of 20 healthy individuals, separated into two groups, also received the two types of EVOO, respectively, for the same duration. Primary endpoints included blood levels of total blood cholesterol, low-density lipoprotein (LDL), high-density lipoprotein (HDL), triglycerides, lipoprotein-a (Lpa), and apolipoproteins A1 and B. Measurements were performed at baseline and at the end of the 4-week intervention. Linear mixed models were performed for the data analysis. Results: The higher-phenolic, lower-dose EVOO group showed a more favorable change in total blood cholesterol (p = 0.045) compared to the lower-phenolic, higher-dose group. EVOO intake was associated with a significant increase in HDL (p < 0.001) and reduction in Lp(a) (p = 0.040) among hyperlipidemic patients in comparison to healthy individuals. Conclusions: EVOO consumption significantly improved the lipid profile of hyperlipidemic patients. Higher-phenolic EVOO at lower dosages appears to be more effective in improving the lipid profile than lower-phenolic EVOO in higher dosages. Full article

Background: Low-glycemic index (GI) carbohydrates like isomaltulose (ISO) are known to enhance incretin release and to improve postprandial glucose control at the following meal (an effect known as second meal effect, or SME), which is particularly beneficial for individuals with metabolic syndrome (MetS). This study aimed to assess the most effective preprandial interval of ISO- or saccharose (SUC) snacks (1 h vs. 3 h preload) to enhance prandial incretin responses to a subsequent meal. Methods: In a randomized crossover design, 15 participants with MetS completed four experimental conditions on four non-consecutive days, combining two preload types (ISO or SUC) and two preload timings (Intervention A: 3 h preload; Intervention B: 1 h preload). Specifically, the four conditions were (1) ISO + Intervention A, (2) SUC + Intervention A, (3) ISO + Intervention B, and (4) SUC + Intervention B. The order of conditions was randomized and separated by a 3–7-day washout period to minimize carryover effects. On each study day, participants consumed two mixed meal tests (MMT-1 and MMT-2) with a standardized preload (50 g ISO or SUC) administered either 3 h or 1 h prior to MMT-2. Blood samples were collected over 9 h at 15 predefined time points for analysis of glucose, insulin, C-peptide, and incretin hormones (GLP-1, GIP, and PYY). Results: The unique digestion profile of ISO resulted in a blunted glucose ascent rate (ΔG/Δt: 0.28 vs. 0.53 mmol/L/min for SUC, p < 0.01), paralleled by synonyms PYY elevation over 540 min monitoring, compared with SUC. ISO also led to higher and more sustained GLP-1 and PYY levels, while SUC induced a stronger GIP response. Notably, the timing of ISO consumption significantly influenced PYY secretion, with the 3 h preload showing enhanced PYY responses and a more favorable SME compared to the 1 h preload. Conclusions: ISO, particularly when consumed 3 h before a meal (vs. 1 h), offers significant advantages over SUC by elevating PYY levels, blunting the glucose ascent rate, and sustaining GLP-1 release. This synergy enhances the second meal effect, suggesting ISO’s potential for managing postprandial glycemic excursions in MetS. Full article

Detecting previously unseen anomalies in sensor data is a challenging problem for artificial intelligence when sensor-specific and deployment-specific characteristics of the time series need to be learned from a short calibration period. From the application point of view, this challenge becomes increasingly important because many applications are gravitating towards utilising low-cost sensors for Internet of Things deployments. While these sensors offer cost-effectiveness and customisation, their data quality does not match that of their high-end counterparts. To improve sensor data quality while addressing the challenges of anomaly detection in Internet of Things applications, we present an anomaly detection framework that learns a normality model of sensor data. The framework models the typical behaviour of individual sensors, which is crucial for the reliable detection of sensor data anomalies, especially when dealing with sensors observing significantly different signal characteristics. Our framework learns sensor-specific normality models from a small set of anomaly-free training data while employing an unsupervised feature engineering approach to select statistically significant features. The selected features are subsequently used to train a Local Outlier Factor anomaly detection model, which adaptively determines the boundary separating normal data from anomalies. The proposed anomaly detection framework is evaluated on three real-world public environmental monitoring datasets with heterogeneous sensor readings. The sensor-specific normality models are learned from extremely short calibration periods (as short as the first 3 days or 10% of the total recorded data) and outperform four other state-of-the-art anomaly detection approaches with respect to F1-score (between 5.4% and 9.3% better) and Matthews correlation coefficient (between 4.0% and 7.6% better). Full article

The paper presents the results of investigations into the relationship between selected water quality parameters and hydrological streamflow drought in a small river situated in the Mazovian Lowlands in Poland. As hydrological streamflow drought periods become more frequent in Poland, investigations about the relationship between flow and water quality parameters can be an essential contribution to a better understanding of the impact of low flow on the status of water rivers. Data from a three-year study of a small lowland river along with significant agricultural land management was used to analyze the connection between low flows and specific water quality indicators. The separation of low-flow data from water discharge records was achieved using two criteria: Q90% (the discharge value from a flow duration curve) and a minimum low-flow duration of 10 days. During these periods, the concentration of water quality indicators was determined based on collected water samples. In total, 30 samples were gathered and examined for pH, suspended sediments, dissolved substances, hardness, ammonium, nitrates, nitrites, phosphates, total phosphorus, chloride, sulfate, calcium, magnesium, and water temperature during sampling. The study’s main aim was to describe the relation between hydrological streamflow droughts and chosen water quality parameters. The analysis results demonstrate an inverse statistically significant relationship between concentration and low-flow values for total hardness and sulfate. In contrast, there was a direct relationship between nutrient indicators, suspended sediment concentration, and river hydrological streamflow drought. Statistical tests were applied to compare the datasets between years, revealing statistical differences only for nutrient indicators. Full article

The prevailing environmental conditions before and during the 28 Large Forest Fires (LFFs) that have occurred in the Canary Islands since 1983 are analyzed. These conditions are often associated with episodes characterized by the advection of continental tropical air masses originating from the Sahara, which frequently result in intense heatwaves. During the onset of the LFFs, the base of the subsidence thermal inversion layer—separating a lower layer of cool, moist air from an upper layer of warm, dry air—is typically located at an altitude of around 350 m above sea level, approximately 600 m below the usual average. Understanding these Saharan air advection events is crucial, as they significantly alter the vertical thermal structure of the atmosphere and create highly conducive conditions for wildfire ignition and spread in the forested mid- and high-altitude zones of the archipelago. Analysis of meteorological records from various weather stations reveals that the average maximum temperature on the first day of fire ignition is 30.3 °C, with mean temperatures of 27.4 °C during the preceding week and 28.9 °C throughout the fire activity period. Relative humidity on the ignition days averages 24.3%, remaining at around 30% during the active phase of the fires. No significant correlation has been found between dry or wet years and the occurrence of LFFs, which have been recorded across years with widely varying precipitation levels. Full article

Objectives: To assess the role of a real-time therapeutic drug monitoring (TDM)-guided expert clinical pharmacological advice (ECPA) program of isavuconazole in preventing under- or overexposure with the intent of improving efficacy and safety outcomes in the critically ill patients. Methods: This retrospective study included critical patients receiving intravenous isavuconazole for prophylaxis or treatment of invasive fungal infections (IFI) and undergoing at least one TDM-guided ECPA in the period 1 March 2021–31 March 2025. Desired isavuconazole exposure was defined as trough concentrations (C_min) of 1.0–5.1 mg/L. Efficacy outcome was assessed by means of bronchoalveolar (BAL) galactomannan (GM) index, breakthrough IFI, and 30-day mortality rate, whereas safety was assessed by means of hepatic test disturbances (HTD). Univariate analysis was carried out for assessing potential variables associated with isavuconazole under- or overexposure and for comparing features of solid organ transplant (SOT) recipients vs. non-SOT patients. Proportions of isavuconazole C_min underexposure, desired exposure, and overexposure were assessed at different timepoints from starting therapy. Trends over time of HTD in relation to isavuconazole exposure were assessed separately in patients having HTD or not at baseline. Results: Overall, 32 critical patients were included. A total of 166 TDM-guided ECPAs were provided. Median (IQR) average isavuconazole C_min was 3.5 mg/L (2.1–4.6 mg/L). Proportions of ECPAs with isavuconazole C_min under- and overexposure were 4.2% (7/166) and 16.3% (27/166), respectively. Patients experiencing underexposure had higher body mass index (30.1 vs. 25.5 kg/m²; p < 0.001). Trends of isavuconazole C_min under- and overexposure changed over time, significantly decreasing the former (10.5% <7 days vs. 4.3% 7–28 days vs. 0.0% >28 days; p < 0.001) and increasing the latter (5.3% <7 days vs. 12.8% 7–28 days vs. 29.3% >28 days; p < 0.001). HTD occurred in 15/32 patients, most of whom (10/15) were affected just at baseline. Patients with transient or persistent overexposure trended toward a higher risk of HTD compared to those without (33.3% vs. 8.3%; p = 0.11). Conclusions: A real-time TDM-guided approach could be a valuable tool for optimizing isavuconazole exposure, especially whenever dealing with obese patients or with prolonged treatment. Full article

Justice Joseph Story’s Commentaries on the Constitution of the United States (1833) have long been regarded as the scholarly source for a nationalist account of the U.S. Constitution in Antebellum America. Yet recent scholarship has questioned whether the Commentaries should be viewed exclusively as a work of legal scholarship. This article reinterprets Justice Story’s three-volume work as a project of civic education during a period of political and constitutional uncertainty. Written during the Nullification Crisis and in the wake of codification efforts, Justice Story presents his Commentaries for the use of the American public, providing them, and not exclusively lawyers and judges, with a source to support a popular conception of American constitutionalism. Story’s project of civic education is clearly shown by his personal efforts to abridge his Commentaries on three separate occasions to ensure the wide distribution of the work to Americans of different ages, groups, localities, and levels of education. As such, this article offers Justice Story as a guide to contemporary judges who seek to engage in civic education projects. Full article

This research examines the traffic disruptions resulting from the collapse of the Francis Scott Key Bridge in Baltimore, utilizing advanced econometric methods and real-time ClearGuide data. Employing Fixed Effects (FEs), Mixed Effects (MEs), Difference-in-Differences (DiDs), and stratified regression models, the study uniquely examines the impacts of congestion across Immediate, Fall, and Winter periods, distinctly separating AM and PM peak patterns. Significant findings include severe PM peak congestion, up to four times greater than AM peak congestion, particularly on critical corridors such as the Harbor Tunnel Thruway northbound and MD-295 northbound. Initial route-level impacts were heterogeneous, gradually becoming uniform as the network adapted. The causal DiD analysis provides strong evidence that increased congestion is causally linked to proximity to the collapse. It is anticipated that incorporating the suggested framework will yield insightful information for stakeholders and decision-makers, such as targeted freight restriction, peak-hour dynamic pricing, corridor-specific signal adjustments, and investments in real-time traffic monitoring systems to strengthen transportation network resilience. Full article

Background/Objectives: Parenteral nutrition (PN) supports patients unable to receive nutrients via the gastrointestinal tract, but it lacks the health-promoting natural bioactive compounds found in a typical oral diet. This study aimed to develop a human serum albumin-based intravenous delivery system for lutein (an antioxidant carotenoid with vision-supportive and hepatoprotective properties) as a PN additive. Methods: An albumin–lutein nanosuspension (AlbLuteN) was synthesized using a modified nanoparticle albumin-bound (nab^TM) technology and characterized physicochemically. The nanoformulation was added to four commercial PN admixtures to assess the supplementation safety throughout the maximum infusion period. Visual inspection and measurements of fat globules larger than 5 µm (PFAT5) and the mean hydrodynamic diameter (Z-average), zeta potential, pH, osmolality, and lutein content were performed to detect potential interactions and evaluate the physicochemical stability. Results: AlbLuteN consisted of uniform particles (Z-average of 133.5 ± 2.8 nm) with a zeta potential of −28.1 ± 1.8 mV, lutein content of 4.76 ± 0.39%, and entrapment efficiency of 84.4 ± 6.3%. Differential scanning calorimetry confirmed the amorphous state of lutein in the nanosuspension. AlbLuteN was successfully incorporated into PN admixtures, without visible phase separation or significant changes in physicochemical parameters. The PFAT5 and Z-average values remained within pharmacopeial limits over 24 h. No substantial shifts in zeta potential, pH, or osmolality were observed. The lutein content remained stable, with losses below 3%. Conclusions: AlbLuteN can be safely added to representative PN admixtures without compromising their stability. This approach offers a novel strategy for intravenous lutein delivery and may contribute to improving the nutritional profile of PN. Full article

Calls for an “archaeology of climate change” highlight the interest in integrating human land use histories with the paleoenvironmental record. Shifts in land use patterns, observed via regional archaeological surveys, have been used in studies exploring the relationships between human and environmental systems, often via the Adaptive Cycle (AC). Recent work has combined datasets from separate surveys to examine macroregional patterns, which can then be integrated with environmental proxy data. However, efforts at aggregating archaeological data are often problematized due to projects’ differing collection methods and periodizations. This study ascribes the formal constructs of the AC to survey data to minimize data aggregation issues and maintain local integrity. Aoristic sum functions were performed individually on data from four projects in the northeastern Peloponnese to identify local patterns. These data were then aggregated, allowing for changes in land use to be observed at the macro and regional levels. Further measurements relating to land use abandonment, continuity, expansion, and potential serve as proxy data to identify the AC constructs of organization, exploitation, conservation, and release. The approach allows for cross-comparative regional analysis and the creation of an aggregated dataset that describes macroregional trends, essential to integrating human and paleoenvironmental narratives over time. Full article

Economic policy uncertainty is an important macroeconomic risk factor that can have direct effects on investment decisions, growth dynamics, and public finance. In particular, its potential impact on tax revenue is critical in terms of fiscal sustainability. This study investigates the Granger-causal relationship between economic policy uncertainty, total tax revenue, and economic growth in G7 economies over the 1997–2021 period, applying symmetric and asymmetric panel causality tests. The empirical findings revealed evidence of causality between economic policy uncertainty and tax revenue and between economic growth and economic policy uncertainty. In asymmetric analyses where the effects of positive and negative shocks were separated, the direction of causal relationships differed between countries. These results imply that asymmetric effects vary by country. Overall, the empirical findings suggest that enhancing transparency and predictability in tax systems could play a vital role in reducing economic policy uncertainty and thus positively affect tax revenue performance and fiscal resilience. Full article

Resource-constrained Internet of Things (IoT) devices demand efficient and robust intrusion detection systems (IDSs) to counter evolving cyber threats. The traditional IDS models, however, struggle with high computational complexity and inadequate feature extraction, limiting their accuracy and generalizability in IoT environments. To address this, we propose FFT-RDNet, a lightweight IDS framework leveraging depthwise separable convolution and frequency-domain feature fusion. An ADASYN-Tomek Links hybrid strategy first addresses class imbalances. The core innovation of FFT-RDNet lies in its novel two-dimensional spatial feature modeling approach, realized through a dedicated dual-path feature embedding module. One branch extracts discriminative statistical features in the time domain, while the other branch transforms the data into the frequency domain via Fast Fourier Transform (FFT) to capture the essential energy distribution characteristics. These time–frequency domain features are fused to construct a two-dimensional feature space, which is then processed by a streamlined residual network using depthwise separable convolution. This network effectively captures complex periodic attack patterns with minimal computational overhead. Comprehensive evaluation on the NSL-KDD and CIC-IDS2018 datasets shows that FFT-RDNet outperforms state-of-the-art neural network IDSs across accuracy, precision, recall, and F1 score (improvements: 0.22–1%). Crucially, it achieves superior accuracy with a significantly reduced computational complexity, demonstrating high efficiency for resource-constrained IoT security deployments. Full article

Background/Objectives: Peripheral artery disease (PAD) impacts more than 200 million individuals globally and leads to mortality and morbidity secondary to progressive limb dysfunction and amputation. However, clinical management of PAD remains suboptimal, in part because of the lack of standardized biomarkers to predict patient outcomes. Growth differentiation factor 15 (GDF15) is a stress-responsive cytokine that has been studied extensively in cardiovascular disease, but its investigation in PAD remains limited. This study aimed to use explainable statistical and machine learning methods to assess the prognostic value of GDF15 for limb outcomes in patients with PAD. Methods: This prognostic investigation was carried out using a prospectively enrolled cohort comprising 454 patients diagnosed with PAD. At baseline, plasma GDF15 levels were measured using a validated multiplex immunoassay. Participants were monitored over a two-year period to assess the occurrence of major adverse limb events (MALE), a composite outcome encompassing major lower extremity amputation, need for open/endovascular revascularization, or acute limb ischemia. An Extreme Gradient Boosting (XGBoost) model was trained to predict 2-year MALE using 10-fold cross-validation, incorporating GDF15 levels along with baseline variables. Model performance was primarily evaluated using the area under the receiver operating characteristic curve (AUROC). Secondary model evaluation metrics were accuracy, sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV). Prediction histogram plots were generated to assess the ability of the model to discriminate between patients who develop vs. do not develop 2-year MALE. For model interpretability, SHapley Additive exPlanations (SHAP) analysis was performed to evaluate the relative contribution of each predictor to model outputs. Results: The mean age of the cohort was 71 (SD 10) years, with 31% (n = 139) being female. Over the two-year follow-up period, 157 patients (34.6%) experienced MALE. The XGBoost model incorporating plasma GDF15 levels and demographic/clinical features achieved excellent performance for predicting 2-year MALE in PAD patients: AUROC 0.84, accuracy 83.5%, sensitivity 83.6%, specificity 83.7%, PPV 87.3%, and NPV 86.2%. The prediction probability histogram for the XGBoost model demonstrated clear separation for patients who developed vs. did not develop 2-year MALE, indicating strong discrimination ability. SHAP analysis showed that GDF15 was the strongest predictive feature for 2-year MALE, followed by age, smoking status, and other cardiovascular comorbidities, highlighting its clinical relevance. Conclusions: Using explainable statistical and machine learning methods, we demonstrated that plasma GDF15 levels have important prognostic value for 2-year MALE in patients with PAD. By integrating clinical variables with GDF15 levels, our machine learning model can support early identification of PAD patients at elevated risk for adverse limb events, facilitating timely referral to vascular specialists and aiding in decisions regarding the aggressiveness of medical/surgical treatment. This precision medicine approach based on a biomarker-guided prognostication algorithm offers a promising strategy for improving limb outcomes in individuals with PAD. Full article