Search Results (5,546)

Background: Pediatric infectious disease epidemiology has changed since the COVID-19 pandemic. To investigate possible changes in the epidemiology of pediatric osteoarticular infections (pOAIs), entailing osteomyelitis (OM), septic or infectious arthritis (AR), and osteomyelitis combined with arthritis (OA), we aimed to assess the number of pOAI cases, pathogen distribution, and outcomes across the pre-, mid-, and post-pandemic periods. Methods: We conducted a single-center retrospective cohort study in the Dutch Juliana Children’s Hospital, including patients aged 0–18 years diagnosed with OM, AR, or OA between 2015 and 2023. Cases were grouped into three periods: pre-pandemic (P1: 2015–2019), mid-pandemic (P2: 2020–2021), and post-pandemic (P3: 2022–2023). Data on demographics, clinical course, imaging, microbiology, and outcomes were extracted from medical records. Results: A total of 118 pOAI cases (median age 2 years, IQR 1–8) were included. OM occurred in 50%, AR in 42%, and OA in 8% of cases. Annual case counts increased from an average of 10/year in P1 to 21/year in P3. Although the difference between P1 and P2 was not statistically significant (IRR 1.20; 95% CI 0.70–2.06), there was a significant increase in P3 compared to P1 (IRR 1.97; 95% CI 1.31–2.97). Pathogen detection was achieved in 50% of cases. Staphylococcus aureus remained the most frequently identified pathogen overall. From P1 to P2, proportions of Kingella kingae and GAS declined, while Staphylococcus aureus remained stable. In P3, Kingella kingae increased, Staphylococcus aureus decreased, and GAS remained relatively unchanged. However, none of these changes were statistically significant. No patients required PICU admission or experienced fatal outcomes. Conclusion: This study suggests an increase in pOAI after the COVID-19 pandemic. While patient characteristics and outcome remained similar over time, pathogen distribution seems to have changed throughout the periods. Full article

The Super-long Gravity Heat Pipe (SLGHP) is an efficient geothermal energy utilization technology that can transmit thermal energy by fully utilizing natural temperature differences without external energy input. This study focuses on the high-altitude geothermal environment of Mount Meager, Canada, and employs numerical simulations and dynamic thermal analysis to systematically investigate the thermal transport performance of the SLGHP system under both steady-state and dynamic operating conditions. The study also examines the impact of various structural parameters on the system’s performance. Three-dimensional CFD simulations were conducted to analyze the effects of pipe diameter, length, filling ratio, working fluid selection, and pipe material on the heat transfer efficiency and heat flux distribution of the SLGHP. The results indicate that working fluids such as CO₂ and NH₃ significantly enhance the heat flux density, while increasing pipe diameter may reduce the amount of liquid retained in the condenser section, thereby affecting condensate return and thermal stability. Furthermore, dynamic thermal analysis using a three-node RC network model simulated the effects of diurnal temperature fluctuations and variations in the convective heat transfer coefficient in the condenser section on system thermal stability. The results show that the condenser heat flux can reach a peak of 5246 W/m² during the day, while maintaining a range of 2200–2600 W/m² at night, with the system exhibiting good thermal responsiveness and no significant lag or flow interruption. In addition, based on the thermal output of the SLGHP system and the integration with the Organic Rankine Cycle (ORC) system, the power generation potential analysis indicates that the system, with 100 heat pipes, can provide stable power generation of 50–60 kW. In contrast to previous SLGHP studies focused on generalized modeling, this work introduces a site-specific CFD–RC framework, quantifies structural sensitivity via heat flux indices, and bridges numerical performance with economic feasibility, offering actionable insights for high-altitude deployment. This system has promising practical applications, particularly for providing stable renewable power in remote and cold regions. Future research will focus on field experiments and system optimization to further improve system efficiency and economic viability. Full article

This paper presents a deep reinforcement learning-based demand response (DR) optimization framework for active distribution networks under uncertainty and user heterogeneity. The proposed model utilizes a Double Deep Q-Network (Double DQN) to learn adaptive, multi-period DR strategies across residential, commercial, and electric vehicle (EV) participants in a 24 h rolling horizon. By incorporating a structured state representation—including forecasted load, photovoltaic (PV) output, dynamic pricing, historical DR actions, and voltage states—the agent autonomously learns control policies that minimize total operational costs while maintaining grid feasibility and voltage stability. The physical system is modeled via detailed constraints, including power flow balance, voltage magnitude bounds, PV curtailment caps, deferrable load recovery windows, and user-specific availability envelopes. A case study based on a modified IEEE 33-bus distribution network with embedded PV and DR nodes demonstrates the framework’s effectiveness. Simulation results show that the proposed method achieves significant cost savings (up to 35% over baseline), enhances PV absorption, reduces load variance by 42%, and maintains voltage profiles within safe operational thresholds. Training curves confirm smooth Q-value convergence and stable policy performance, while spatiotemporal visualizations reveal interpretable DR behavior aligned with both economic and physical system constraints. This work contributes a scalable, model-free approach for intelligent DR coordination in smart grids, integrating learning-based control with physical grid realism. The modular design allows for future extension to multi-agent systems, storage coordination, and market-integrated DR scheduling. The results position Double DQN as a promising architecture for operational decision-making in AI-enabled distribution networks. Full article

Current mainstream remote sensing target detection algorithms mostly estimate the rotation angle of targets by designing different bounding box descriptions and loss functions. However, they fail to consider the symmetry–asymmetry duality anisotropy in the distribution of key features required for target localization. Moreover, the equivalent feature extraction mode of shared convolutional kernels may lead to difficulties in accurately predicting parameters with different attributes, thereby reducing the performance of the detector. In this paper, we propose the Feature Equalization and Hierarchical Decoupling Network (FEHD-Net), which comprises three core components: a Symmetry-Enhanced Parallel Interleaved Convolution Module (PICM), a Parameter Decoupling Module (PDM), and a Critical Feature Matching Loss Function (CFM-Loss). PICM captures diverse spatial features over long distances by integrating square convolution and multi-branch continuous orthogonal large kernel strip convolution sequences, thereby enhancing the network’s capability in processing long-distance spatial information. PDM decomposes feature maps with different properties and assigns them to different regression branches to estimate the parameters of the target’s rotating bounding box. Finally, to stabilize the training of anchors with different qualities that have captured the key features required for detection, CFM-Loss utilizes the intersection ratio between anchors and true value labels, as well as the uncertainty of convolutional regression during training, and designs an alignment criterion (symmetry-aware alignment) to evaluate the regression ability of different anchors. This enables the network to fine-tune the processing of templates with different qualities, achieving stable training of the network. A large number of experiments demonstrate that compared with existing methods, FEHD-Net can achieve state-of-the-art performance on DOTA, HRSC2016, and UCAS-AOD datasets. Full article

Microrefugia can be critical in mediating biological responses to climate change, but the location and characteristics of these habitats are often poorly understood. Groundwater-dependent ecosystems (GDEs) represent critical microrefugia for species dependent on cool, moist habitats. However, knowledge of the distribution and stability of GDE microrefugia remains limited. This challenge is typified in the Pacific Northwest, where poorly studied cliff-face seeps harbor exceptional biodiversity despite their diminutive size (e.g., ~1–10 m width). To improve knowledge about these microrefugia, we regionally modeled their distribution and stability. We searched for cliff-face seeps across 1608 km of roads, trails, and watercourses in Washington and Idaho, while monitoring water availability plus air and water temperatures at selected sites. We detected 457 seeps through an iterative process of surveying, modeling, ground-truthing, and then remodeling the spatial distribution of seeps using boosted regression trees. Additionally, we used linear and generalized linear models to identify factors linked to seep thermal and hydrologic stability. Seeps were generally most concentrated in steep and low-lying areas (e.g., edges of canyon bottoms), and were also positively associated with glacial drift, basalt or graywacke bedrock types, high average slope within 300 m, and low average vapor pressure deficit. North-facing slopes were the best predictor of stable air and water temperatures and perennial seep discharge; low-lying areas also predicted stable seep water temperatures. These findings improve possibilities to manage seep microrefugia in the Pacific Northwest and safeguard their associated biodiversity under climate change. Lastly, our iterative method adapts techniques commonly used in species distribution modeling to provide an innovative framework for identifying inconspicuous microrefugia. Full article

Titanium matrix composites (TMCs) possess excellent properties, which are widely applied in various high-end fields. An ultrafine multi-scale network structure may further enhance the TMCs. Then, the application potential is widened. Here, the in situ synthesized TC4-B-Si composites were prepared by selective laser melting technology, to achieve ultrafine microstructure by inducing ultra-rapid heating/cooling process. The preparation process–structure–performance relationships were investigated. It was found that an appropriate laser energy density leads to high-density TMCs with stable molten pools and good interlayer bonding. With the decreasing energy density, the in situ generated TiB network structure is refined from the sub-micron scale to the nano-scale. The most Si atoms are supersaturated solid-dissolved in the titanium matrix. In addition, the TiB distribution becomes heterogeneous. Due to the co-effect of grain refinement and reinforcement distribution, the microhardness shows a rising and then falling trend, with decreasing energy density. With a good balance of these two factors, the maximum value of microhardness reaches 454 HV. Therefore, controlling process parameters is a feasible way to achieve multi-structures, and thus enhanced properties. This method is expected to be used on various lightweight and wear-resistant structural components. Full article

Background and Objectives: Urinary tract infections (UTIs) are a major cause of emergency department (ED) visits and hospital admissions among older adults. Although most seniors present hemodynamically stable, a sizeable fraction deteriorate during hospitalization, and no ED-specific tool exists to identify those at greatest risk. We sought to determine risk factors for in-hospital mortality in this population and to develop a predictive model. Materials and Methods: We analyzed the MIMIC-IV-ED database (2011–2019) and enrolled culture-confirmed UTI patients aged ≥ 65 years who were hemodynamically stable—defined as a systolic blood pressure ≥ 100 mm Hg without vasopressor support. Demographics, comorbidities, triage vital signs, and initial laboratory tests were extracted. Least Absolute Shrinkage and Selection Operator (LASSO) regression with 10-fold cross-validation was performed for variable selection. Discrimination was quantified with the C-statistic, calibration with the Hosmer–Lemeshow test, and clinical utility with decision curve analysis. Internal validation was assessed via 1000-sample bootstrap resampling. Results: Among 1571 eligible encounters (median age 79 years, 33% male), in-hospital mortality was 4.5%. LASSO selected eight variables; six remained significant in multivariable analysis: age, systolic blood pressure, oxygen saturation, white blood cell count, red cell distribution width, and blood urea nitrogen. The predictive nomogram demonstrated a C-statistic of 0.73 (95% CI 0.66–0.79) and outperformed traditional early warning scores. Conclusions: A six-variable nomogram may stratify mortality risk in hemodynamically stable older adults with UTI. Because the model was developed in a single U.S. tertiary-care ED, it remains hypothesis-generating until validated in external, multicenter cohorts to confirm generalizability. Full article

The study investigates the influence of post-weld heat treatment (PWHT) on the microstructure and hardness of hardfacing layers applied to hot forging tools. The research focuses on three tool steels (55NiCrMoV7, X37CrMoV5-1, and a modified X38CrMoV5-3) and uses robotized gas metal arc welding (GMAW) with DO015 filler material. It examines the structural and mechanical differences in the hardfaced layers before and after heat treatment involving quenching and tempering. The findings reveal that PWHT significantly improves microstructural homogeneity and hardness distribution, especially in the heat-affected zone (HAZ), mitigating the risk of crack initiation and tool failure. The study shows that untempered as-welded layers exhibit microstructural inhomogeneity and extreme hardness gradients, which negatively impact tool durability. PWHT leads to tempered martensite formation, grain refinement, and a more stable hardness profile across the joint. These improvements are critical for extending the service life of forging tools. The results underscore the importance of customizing PWHT parameters according to the specific material and application to optimize tool performance. Full article

The Danube in Croatia serves as an important transport route but also favors the spread of invasive species, especially in the floodplain areas. Many of them originate from the Ponto-Caspian region and influence European ecosystems with their migrations. One of these species, Limnomysis benedeni, a mysid shrimp, thrives in shallow waters and plays a crucial role as a food source for fish. L. benedeni was first recorded in Croatia in 2004 in Lake Sakadaš (Kopački Rit). Prior to the study on aquatic macroinvertebrates in Kopački Rit Nature Park, conducted from July 2020 to July 2023, there had been no documented records in recent years. Sampling was carried out seasonally for macroinvertebrates and monthly for environmental parameters at 15 sites within the park or in the immediate vicinity. Samples were collected according to standard AQEM methodology. A total of 21 macroinvertebrate groups (407 taxa), out of which the most diverse were Diptera with 20 families, were identified in this study, including nine allochthonous species in addition to L. benedeni. The most abundant populations of L. benedeni were found in the Danube, the Petreš channel, and Vemeljski Dunavac channel, which supply the floodplain with water from the Danube. Most individuals were collected in summer and spring, with the highest density being 741 individuals per square meter. Environmental parameters such as water level, type of habitats, pH values, chemical oxygen demand, and phosphorus content, were statistically significant for the distribution of species. The dominant microhabitat for L. benedeni in Kopački Rit was argyllal in combination with coarse particulate organic matter and wood debris, and the composition of these microhabitats remained consistent throughout the seasons. L. benedeni was the only crustacean species to establish a stable population in the floodplain area, excluding Asellus aquaticus (water louse), a cosmopolitan species. The ongoing influence of L. benedeni on the native community still remains to be determined. Full article

This study investigated the potential use of fluorine-containing semiconductor industrial sludge as a mineralizer in Portland cement clinker production. Raw mixes were prepared by partially replacing raw materials with 6%, 9%, and 12% sludge and sintered between 1300 and 1500 °C. The clinker burnability, phase composition, and chemical integrity were evaluated through FreeCaO measurements, X-ray diffraction (XRD) with Rietveld refinement, and X-ray fluorescence (XRF) analyses. The results showed that sludge addition reduced the sintering temperature by up to 150 °C, enabling near-complete clinker formation at 1300 °C for blends containing 9% and 12% sludge (FreeCaO ≤ 0.6 wt.% compared to 62 wt.% in the reference sample). Fluorine incorporation stabilized the re-active β–C₂S polymorph and shifted the alite (C₃S) phase distribution from stable M1 to metastable M3 and T3 phases. Additionally, the C₃A phase content decreased, and a unique fluorine-containing phase, Al₇Ca₆O₁₆F, formed, promoting clinker formation. Lowering the sintering temperature led to energy savings of 15–22.5% and reduced CO₂ emissions by 0.10–0.20 tons per ton of clinker, positively impacting the environment. This study demonstrates that recycling industrial sludge can enhance cement production efficiency and support environmental sustainability. Full article

The combined effects of soil properties, zinc (Zn), and chloride ion (Cl⁻) concentrations on Zn distribution across soil fractions are poorly understood, even though zinc chloride (ZnCl₂) contamination in industrial soils is a major source of mobile Zn and poses significant environmental risks. This study aimed to (1) assess how the soil type, physicochemical properties, and Zn concentration affect Zn distribution in Community Bureau of Reference (BCR)-extracted fractions; (2) evaluate the impact of Cl⁻ on Zn mobility; and (3) develop predictive models for mobile and stable Zn fractions based on soil characteristics. Zn mobility was analyzed in 18 soils differing in Zn and Cl⁻, pH, specific surface area (SSA), organic matter (OM), and texture (sand, silt, clay (CLY)), using a modified BCR method. Zn fractions were measured by atomic absorption spectroscopy (AAS). Analysis of Covariance was used to assess Zn distribution across soil types, while Zn fractions were modeled using non-linear regression (NLR). The results showed that mobile Zn increased with the total Zn, and that the soil type and Zn levels influenced Zn distribution in soils contaminated with ZnCl₂ (Zn 304–2136 mg·kg⁻¹ d.m.; Cl⁻ 567–2552 mg·kg⁻¹; pH 3.5–7.5; CLY 11–22%; SSA 96–196 m²·g⁻¹; OM 0–4.8%). Although Cl⁻ enhanced Zn mobility, its effect was weaker than that of Zn. Predictive models based on the total Zn, SSA, and CLY accurately estimated Zn in mobile and stable fractions (R > 0.92), whereas the effects of the pH and OM, although noticeable, were not statistically significant. Full article

Full waveform inversion (FWI) comprehensively utilizes phase and amplitude information of seismic waves to obtain high-resolution subsurface medium parameter models, applicable to both active-source and passive-source seismic data. Passive-source seismic exploration, using natural earthquakes or ambient noise, reduces costs and environmental impact, with growing marine applications in recent years. Its rich low-frequency content makes passive-source FWI (PSFWI) a key research focus. However, PSFWI inversion quality relies heavily on accurate virtual source reconstruction. While multi-dimensional deconvolution (MDD) can handle uneven source distributions, it struggles with irregular receiver sampling. We propose a robust MDD method based on multi-domain stepwise interpolation to improve reconstruction under non-ideal source and sampling conditions. This approach, validated via an adaptive PSFWI strategy, exploits MDD’s insensitivity to source distribution and incorporates normalized correlation objective functions to reduce amplitude errors. Numerical tests on marine and complex scattering models demonstrate stable and accurate velocity inversion, even in challenging acquisition environments. Full article

This study explores advanced methods for analyzing the two-parameter alpha-power exponential (APE) distribution using data from a novel generalized progressive hybrid censoring scheme. The APE model is inherently asymmetric, exhibiting positive skewness across all valid parameter values due to its right-skewed exponential base, with the alpha-power transformation amplifying or dampening this skewness depending on the power parameter. The proposed censoring design offers new insights into modeling lifetime data that exhibit non-monotonic hazard behaviors. It enhances testing efficiency by simultaneously imposing fixed-time constraints and ensuring a minimum number of failures, thereby improving inference quality over traditional censoring methods. We derive maximum likelihood and Bayesian estimates for the APE distribution parameters and key reliability measures, such as the reliability and hazard rate functions. Bayesian analysis is performed using independent gamma priors under a symmetric squared error loss, implemented via the Metropolis–Hastings algorithm. Interval estimation is addressed using two normality-based asymptotic confidence intervals and two credible intervals obtained through a simulated Markov Chain Monte Carlo procedure. Monte Carlo simulations across various censoring scenarios demonstrate the stable and superior precision of the proposed methods. Optimal censoring patterns are identified based on the observed Fisher information and its inverse. Two real-world case studies—breast cancer remission times and global oil reserve data—illustrate the practical utility of the APE model within the proposed censoring framework. These applications underscore the model’s capability to effectively analyze diverse reliability phenomena, bridging theoretical innovation with empirical relevance in lifetime data analysis. Full article

Soil moisture is one of the key hydrologic components indicating the performance of landfill final covers. Conventional compacted clay (CC) covers and evapotranspiration (ET) covers often suffer from moisture-induced stresses, such as desiccation cracking and irreversible hydraulic conductivity. Engineered turf (EnT) cover systems have been introduced recently as an alternative; however, their field-scale moisture distribution behavior remains unexplored. This study investigates and compares the soil moisture distribution characteristics of EnT, ET, and CC landfill covers at a shallow depth using one year of field-monitored data in a humid subtropical region. Three full-scale test Sections (3 m × 3 m × 1.2 m) were constructed side by side and instrumented with moisture sensors at a depth of 0.3 m. Distributional characteristics of moisture were evaluated with descriptive statistics, goodness-of-fit tests such as Shapiro–Wilk (SW) and Anderson–Darling (AD), Gaussian probability density functions, Q–Q plots, and standard-normal transformations. Results revealed that Shapiro–Wilk (W = 0.75–0.92, p < 0.001) and Anderson–Darling $(A^2=1.63\times {10}^3\mathrm{to}6.31\times {10}^3,p<0.001)$ tests rejected normality for every cover, while Levene’s test showed unequal variances between EnT and the other covers $(F>5.4\times {10}^4,$$p<0.001)$ but equivalence between CC and ET (F = 0.23, p = 0.628). EnT cover exhibited the narrowest moisture envelope $(95\%\mathrm{range}=0.156\mathrm{to}0.240{\mathrm{m}}^3/{\mathrm{m}}^3;\mathrm{CV}=10.6\%)$, whereas ET and CC covers showed markedly broader distributions (CV = 38.6 % and 33.3 %, respectively). These findings demonstrated that EnT cover maintains a more stable shallow soil moisture profile under dynamic weather conditions. Full article

Isorhapontigenin (trans-3,5,4′-trihydroxy-3′-methoxystilbene; ISO), a dietary derivative of resveratrol (trans-3,5,4′-trihydroxystilbene; RES), exhibits diverse health-promoting properties. To facilitate its potential development as a nutraceutical, a simple and reliable high-performance liquid chromatography (HPLC) method was developed and validated for the quantification of ISO in various murine biological matrices. Chromatographic separation was achieved with a reversed-phase HPLC column through a 17 min gradient delivery of a mixture of acetonitrile and formic acid (0.1% v/v) at a flow rate of 1.5 mL/min at 50 °C. Quantification was performed using ultraviolet (UV) detection at 325 nm, with a lower limit of quantification (LLOQ) of 15 ng/mL in both plasma and tissue homogenate samples. The method demonstrated excellent selectivity, accuracy, and precision, and ISO remained stable under the tested conditions. This method was subsequently employed to investigate the tissue distribution of ISO in mice following oral administration at a dose of 200 µmol/kg (equivalent to 51.7 mg/kg). ISO was rapidly absorbed and extensively distributed across major pharmacologically relevant organs. Despite its limited aqueous solubility, its oral absorption was not significantly compromised. Given its oral bioavailability and broad tissue distribution, ISO represents a promising candidate for further nutraceutical development. Full article