Search Results (9,851)

According to the World Health Organization (WHO), indoor air quality (IAQ) is becoming a serious global concern due to its significant impact on human health. However, not all relevant health parameters are currently regulated. For example, particle number concentration (PNC) and its associated carbonaceous species, such as black carbon (BC), which are classified as carcinogenic by the International Agency for Research on Cancer (IARC), are not currently regulated. Compared with IAQ studies in other types of buildings, studies focusing on IAQ in hospitals or other healthcare facilities are scarce. Therefore, this study aims to evaluate the impact of these outdoor pollutants, among others, on the indoor environment of a hospital under different atmospheric conditions. To identify the seasonal influence, two different periods of two consecutive seasons (summer 2020 and winter 2021) were selected for the measurements. Regulated pollutants (NO, NO₂, O₃, PM₁₀, and PM_2.5) and nonregulated pollutants (PM₁, PNC, and equivalent BC (eBC)) in outdoor air were simultaneously measured indoor and outdoor. This study also investigated the impact of indoor activities on indoor air quality. In the absence of indoor activities, outdoor sources significantly contribute to indoor traffic-related pollutants. Indoor and outdoor (I-O) measurements showed similar behavior, but indoor concentrations were lower, with peak levels delayed by up to two hours. Seasonal variations in indoor/outdoor ($\mathrm{I}/\mathrm{O}$) ratios were lower for particles than for associated gaseous pollutants. Particle infiltration depended on particle size, with it being higher the smaller the particle size. Indoor activities also significantly affected indoor pollutants. PM_x (especially PM₁₀ and PM_2.5) concentrations were mainly modulated by walking-induced particle resuspension. Vertical eBC profiles indicated a relatively well-mixed environment. Ventilation through open windows rapidly altered indoor air quality. Outdoor-dominant pollutants (PNC, eBC, and NO_X) had $\mathrm{I}/\mathrm{O}$ ratios ≥ 1. Staying in the room with an open window had a synergistic effect, increasing the $\mathrm{I}/\mathrm{O}$ ratios for all pollutants. Higher I/O ratios were associated with turbulent outdoor conditions in both unoccupied and occupied conditions. Statistically significant differences were observed between stable (TKE ≤ 1 m² s⁻²) and unstable (TKE > 1 m² s⁻²) conditions, except for NO₂ in summer. This finding was particularly significant when the wind direction was westerly or easterly during unstable conditions. The results of this study highlight the importance of understanding the behavior of indoor particulate matter and related pollutants. These pollutants are highly variable, and knowledge about them is crucial for determining their health effects, particularly in public buildings such as hospitals, where information on IAQ is often limited. More measurement data is particularly important for further research into I-O transport mechanisms, which are essential for developing preventive measures and improving IAQ. Full article

In the decommissioning of nuclear facilities, activated steel often contains radionuclides such as ⁵⁵Fe and ⁶³Ni, which are categorized as hard-to-measure due to their emission of only low-energy beta particles or X-rays. In samples exhibiting very low radioactivity, close to background levels, a large quantity of steel must undergo extensive physical and chemical processing to achieve the Minimum Detectable Activity Concentration (MDC) necessary for clearance, recycling, or reuse. Italian regulations set particularly stringent clearance levels for these radionuclides (1 Bq/g for both ⁵⁵Fe and ⁶³Ni), significantly lower than those specified in the EU Directive 2013/59 (1000 Bq/g for ⁵⁵Fe and 100 Bq/g for ⁶³Ni). Additionally, Italian authorities may enforce even stricter limits depending on specific circumstances. The analytical challenge is compounded by the presence of large amounts of non-radioactive Fe and Ni, which can cause color quenching, further extending analysis times. This study presents a reliable and optimized method for the quantitative determination of ⁵⁵Fe and ⁶³Ni in steel samples with activity levels approaching regulatory thresholds. The methodology was specifically developed and applied to steel from the Frascati Tokamak Upgrade (FTU) facility, under decommissioning by ENEA. The optimization process demonstrated that achieving the required MDCs necessitates acquisition times of approximately 5 days for ⁵⁵Fe and 6 h for ⁶³Ni, ensuring compliance with stringent regulatory requirements and supporting efficient laboratory workflows. Full article

The large-scale integration of wind power, photovoltaic systems, and energy storage systems (ESSs) into power grids has increasingly influenced the transient stability of power systems due to their dynamic response characteristics. Considering the commercial confidentiality of core control parameters from equipment manufacturers, parameter identification has become a crucial approach for analyzing ESS dynamic behaviors during high-voltage ride-through (HVRT) and low-voltage ride-through (LVRT) and for optimizing control strategies. In this study, we present a multidimensional feature-integrated parameter identification framework for ESSs, combining a multi-scenario voltage disturbance testing environment built on a real-time laboratory platform with field-measured data and enhanced optimization algorithms. Focusing on the control characteristics of energy storage converters, a non-intrusive identification method for grid-connected control parameters is proposed based on dynamic trajectory feature extraction and a hybrid optimization algorithm that integrates an improved particle swarm optimization (PSO) algorithm with gradient-based coordination. The results demonstrate that the proposed approach effectively captures the dynamic coupling mechanisms of ESSs under dual-mode operation (charging and discharging) and voltage fluctuations. By relying on measured data for parameter inversion, the method circumvents the limitations posed by commercial confidentiality, providing a novel technical pathway to enhance the fault ride-through (FRT) performance of energy storage systems (ESSs). In addition, the developed simulation verification framework serves as a valuable tool for security analysis in power systems with high renewable energy penetration. Full article

Natural polymers, polysaccharides, demonstrate piezoelectric behavior suitable for force sensor manufacturing. Carrageenan hydrogel film with α-iron oxide particles can act as a piezoelectric polysaccharide-based force sensor. The mechanical impact on the hydrogel caused by a falling ball shows the impact response time, which is measured in milliseconds. Repeating several experiments in a row shows the dynamics of fatigue, which does not reduce the speed of response to impact. Through the practical experiments, we sought to demonstrate how theoretical knowledge describes the hydrogel we elaborated, which works as a piezoelectric material. In addition to the theoretical basis, which includes the operation of the metal and metal oxide contact junction, the interaction between the metal oxide and the hydrogel surfaces, the paper presents the practical application of this knowledge to the complex hydrogel film. The simple calculations presented in this paper are intended to predict the hydrogel film’s characteristics and explain the results obtained during practical experiments. Carrageenan, as a low-cost and already widely used polysaccharide in various industries, is suitable for the production of low-cost force sensors in combination with iron oxide. Full article

This study explores the efficacy of magnetic abrasive finishing (MAF) with planetary kinematics for post-processing Inconel 939 components fabricated by laser powder bed fusion (LPBF). Given the critical limitations in surface quality of LPBF-produced parts—especially in hard-to-machine superalloys like Inconel 939—there is a pressing need for advanced, adaptable finishing techniques that can operate effectively on complex geometries. This research focuses on optimizing the process parameters—eccentricity, rotational speed, and machining time—to enhance surface integrity following preliminary vibratory machining. Custom-designed samples underwent sequential machining, including heat treatment and 4 h vibratory machining, before MAF was applied under controlled conditions using ferromagnetic Fe-Si abrasives. Surface roughness measurements demonstrated a significant reduction, achieving Ra values from 1.21 µm to below 0.8 µm in optimal conditions, representing more than a fivefold improvement compared to the as-printed state (5.6 µm). Scanning Electron Microscopy (SEM) revealed progressive surface refinement, with MAF effectively removing adhered particles left by prior processing. Statistical analysis confirmed the dominant influence of eccentricity on the surface profile parameters, particularly Rz. The findings validate the viability of MAF as a precise, controllable, and complementary finishing method for LPBF-manufactured Inconel 939 components, especially for geometrically complex or hard-to-reach surfaces. Full article

The implementation of an Indoor Positioning System (IPS) in a three-storey residential building employing WiFi signals that can also be used to track indoor movements is presented in this study. The movement of inhabitants is monitored through an Android smartphone by detecting the Received Signal Strength Indicator (RSSI) signals from WiFi Anchor Points (APs).Indoor movement is detected through a successive estimation of a target’s multiple positions. Using the K-Nearest Neighbors (KNN) and Particle Swarm Optimization (PSO) algorithms, these RSSI measurements are trained for estimating the position of an indoor target. Additionally, the Density-based Spatial Clustering of Applications with Noise (DBSCAN) has been integrated into the PSO method for removing RSSI-estimated position outliers of the mobile device to further improve indoor position detection and monitoring accuracy. We also employed Time Reversal Resonating Strength (TRRS) as a correlation technique as the third method of localization. Our extensive and rigorous experimentation covers the influence of various weather conditions in indoor detection. Our proposed localization methods have maximum accuracies of 92%, 80%, and 75% for TRRS, KNN, and PSO + DBSCAN, respectively. Each method also has an approximate one-meter deviation, which is a short distance from our targets. Full article

Background/Objectives: The increasing clinical integration of 3D-printed definitive resins requires a comprehensive understanding of their physicochemical properties and adhesive behavior. However, there is limited evidence regarding the optimal surface treatment and bonding strategies for clear aligner composite attachments on these materials. This study aimed to characterize a 3D-printed definitive resin, evaluate the effects of surface treatments on its surface topography, and compare the shear bond strength (SBS) of the bonded attachments using different adhesive systems, both before and after thermocycling. Methods: A total of 120 rectangular specimens were fabricated from a 3D printed dental resin (Crowntec^®, SAREMCO Dental AG—Mexico City, Mexico). For physicochemical characterization, six samples underwent scanning electron microscopy/energy-dispersive spectroscopy, X-ray diffraction, and thermogravimetric analysis. To evaluate surface topography, 42 polished specimens were assigned to three groups: untreated (control), etched with 4% hydrofluoric acid (HFA), or sandblasted with 50 µm Al₂O₃ (AA). Each group was subdivided for SEM observation and surface roughness (Ra) measurement. For SBS testing, 72 additional samples received the same surface treatments and were further subdivided according to the adhesive system: Transbond™ XT Primer (TXT) or Single Bond Universal (SBU). Results: The AA group showed the highest Ra (2.21 ± 0.30 µm), followed by HFA (0.81 ± 0.20 µm) and control (0.07 ± 0.30 µm) (p < 0.001). The highest SBS was observed in the AA + SBU group, followed by AA + TXT. Conclusions: Sandblasting with Al₂O₃ particles, combined with a universal adhesive, significantly improved bond strength, suggesting a viable protocol for 3D printed definitive composites in aligner attachment applications. Full article

Silica/alumina composite particles were synthesized via the sol–gel method to promote fine dispersion and homogenous mixing. Aluminum chloride hydroxide served as the alumina precursor, while amorphous silica, obtained from rice husk, was directly incorporated into the alumina sol. Following synthesis, the material was calcined at 1000 °C, yielding an α-cristobalite form of silica and corundum-phase alumina. These hybrid particles were introduced into polymer composites at reinforcement levels of 1 wt.%, 3 wt.%, and 5 wt.%. Mechanical behavior was evaluated through three-point bending tests, Shore D hardness measurements, and controlled-energy impact testing. Among the formulations, the 3 wt.% composite exhibited optimal performance, displaying the highest flexural modulus and strength, along with enhanced impact resistance. Hardness increased with rising particle content. Fractographic analysis revealed that the 3 wt.% loading produced a notably rougher fracture surface, correlating with improved energy absorption. In contrast, the 5 wt.% composite, although harder than the matrix and other composites, exhibited diminished toughness due to particle agglomeration. Full article

This study examined the eco-friendly synthesis of zinc oxide (ZnO) nanoparticles using Cladophora glomerata extracts as reducing and stabilizing agents, comparing zinc acetate and zinc chloride precursors for biomedical and environmental applications. Zinc acetate-synthesized ZnO nanoparticles showed a significant absorption peak around 320–330 nm, indicating stable, quasi-spherical ZnO nanoparticles with a narrow size distribution, primarily around 100 nm. Zeta potential measurements revealed a value of −25 mV for these particles, suggesting moderate colloidal stability. XRD analysis confirmed a highly crystalline hexagonal wurtzite structure for zinc acetate-derived ZnO, and SEM images supported a proper microstructure with approximately 2 µm particle size. FTIR analysis indicated higher-quality ZnO from zinc acetate due to the absence of moisture and hydroxyl groups. Conversely, zinc chloride-derived ZnO particles displayed a broader absorption spectrum around 370 nm, indicative of significant aggregation. Their narrower zeta potential distribution around +10 mV suggested diminished colloidal stability and a heightened aggregation tendency. While a peak around 100 nm was observed, many particles exceeded 1000 nm, reaching up to 10,000 nm. XRD results showed that zinc chloride adversely affected crystallinity, and SEM analysis indicated smaller particles (approx. 1 µm). FTIR analysis demonstrated that zinc chloride samples retained hydroxyl groups. Both zinc acetate- and zinc chloride-derived ZnO nanoparticles produced notable inhibitory zones against Gram-positive (L. monocytogenes, S. aureus) and specific Gram-negative bacteria (E. coli, K. pneumoniae). Zinc acetate-derived ZnO showed a 21 mm inhibitory zone against P. vulgaris, while zinc chloride-derived ZnO showed a 10.1 mm inhibitory zone against C. albicans. Notably, zinc chloride-derived ZnO exhibited broad-spectrum antimicrobial activity. MIC readings indicated that zinc acetate-derived ZnO had better antibacterial properties at lower concentrations, such as 3.125 µg/mL against L. monocytogenes. These findings emphasize that the precursor material selection critically influences particle characteristics, including optical properties, surface charge, and colloidal stability. Full article

Various nature-based solutions (NbS)—such as constructed wetlands, drainage ditches, and vegetated buffer strips—have recently demonstrated strong potential for mitigating pollutant transport in open channels and river systems. Numerical modeling is a widely adopted and effective approach for assessing the performance of these interventions. This study presents the first development of a two-dimensional (2D) meshless advection–diffusion model based on an Eulerian smoothed particle hydrodynamics (SPH) framework, specifically designed to simulate passive pollutant transport in open channel flows. The proposed model marks a pioneering application of the ESPH technique to environmental pollutant transport problems. It couples the 2D depth-averaged shallow water equations with an advection–diffusion equation to represent both fluid motion and pollutant concentration dynamics. A uniform particle arrangement ensures that each fluid particle interacts symmetrically with eight neighboring particles for flux computation. To represent the pollutant transport process, the dispersion coefficient is defined as the sum of molecular and turbulent diffusion components. The turbulent diffusion coefficient is calculated using a prescribed turbulent Schmidt number and the eddy viscosity obtained from a Smagorinsky-type mixing-length turbulence model. Three analytical case studies, including one-dimensional transcritical open channel flow, 2D isotropic and anisotropic diffusion in still water, and advection–diffusion in a 2D uniform flow, are employed to verify the model’s accuracy and convergence. The model demonstrates first-order convergence, with relative root mean square errors (RRMSEs) of approximately 0.2% for water depth and velocity, and 0.1–0.5% for concentration. Additionally, the model is applied to a laboratory experiment involving 2D pollutant dispersion in a 90° junction channel. The simulated results show good agreement with measured velocity and concentration distributions. These findings indicate that the developed model is a reliable and effective tool for evaluating the performance of NbS in mitigating pollutant transport in open channels and river systems. Full article

Given the importance of turbidity as a key indicator of water quality, this study investigates the use of a convolutional neural network (CNN) to classify water samples into five turbidity-based categories. These classes were defined using ranges inspired by Mexican environmental regulations and generated from 33 laboratory-prepared mixtures with varying concentrations of suspended clay particles. Red, green, and blue (RGB) images of each sample were captured under controlled optical conditions, and turbidity was measured using a calibrated turbidimeter. A transfer learning (TL) approach was applied using EfficientNet-B0, a deep yet computationally efficient CNN architecture. The model achieved an average accuracy of 99% across ten independent training runs, with minimal misclassifications. The use of a lightweight deep learning model, combined with a standardized image acquisition protocol, represents a novel and scalable alternative for rapid, low-cost water quality assessment in future environmental monitoring systems. Full article

To address the decline in measurement accuracy of magnetometers due to process errors and environmental interference, as well as the insufficient robustness of traditional calibration algorithms under strong interference conditions, this paper proposes an ellipsoid fitting algorithm based on Dynamic Adaptive Elite Particle Swarm Optimization (DAEPSO). The proposed algorithm integrates three enhancement mechanisms: dynamic stratified elite guidance, adaptive inertia weight adjustment, and inferior particle relearning via Lévy flight, aiming to improve convergence speed, solution accuracy, and noise resistance. First, a magnetometer calibration model is established. Second, the DAEPSO algorithm is employed to fit the ellipsoid parameters. Finally, error calibration is performed based on the optimized ellipsoid parameters. Our simulation experiments demonstrate that compared with the traditional Least Squares Method (LSM) the proposed method reduces the standard deviation of the total magnetic field intensity by 54.73%, effectively improving calibration precision in the presence of outliers. Furthermore, when compared to PSO, TSLPSO, MPSO, and AWPSO, the sum of the absolute distances from the simulation data to the fitted ellipsoidal surface decreases by 53.60%, 41.96%, 53.01%, and 27.40%, respectively. The results from 60 independent experiments show that DAEPSO achieves lower median errors and smaller interquartile ranges than comparative algorithms. In summary, the DAEPSO-based ellipsoid fitting algorithm exhibits high fitting accuracy and strong robustness in environments with intense interference noise, providing reliable theoretical support for practical engineering applications. Full article

In this study, we introduce a novel interdisciplinary framework that applies concepts from statistical physics, specifically lattice-gas models, to the classical order lot-sizing problem in supply chain management. Traditional methods often rely on heuristic or deterministic approaches, which may fail to capture the inherently probabilistic and dynamic nature of decision-making across multiple periods. Drawing on structural parallels between inventory decisions and adsorption phenomena in physical systems, we constructed a mapping that represented order placements as particles on a lattice, governed by an energy function analogous to thermodynamic potentials. This formulation allowed us to employ analytical tools from statistical mechanics to identify optimal ordering strategies via the minimization of a free energy functional. Our approach not only sheds new light on the structural characteristics of optimal planning but also introduces the concept of configurational entropy as a measure of decision variability and robustness. Numerical simulations and analytical approximations demonstrate the efficacy of the lattice gas model in capturing key features of the problem and suggest promising avenues for extending the framework to more complex settings, including multi-item systems and time-varying demand. This work represents a significant step toward bridging physical sciences with supply chain optimization, offering a robust theoretical foundation for both future research and practical applications. Full article

Sustainable deepwater drilling for oil and gas offers significant potential. In this work, we synthesized a nanoscale collapse-prevention agent by grafting didecyldimethylammonium chloride onto spherical nano-silica and characterized it using Fourier-transform infrared spectroscopy, thermogravimetric analysis, zeta-potential, and particle-size measurements, as well as SEM and TEM. Adding 1 wt% of this agent to a bentonite slurry only marginally alters its rheology and maintains acceptable low-temperature flow properties. Microporous-membrane tests show filtrate passing through 200 nm pores drops to 55 mL, demonstrating excellent plugging. Core-immersion studies reveal that shale cores retain integrity with minimal spalling after prolonged exposure. Rolling recovery assays increase shale-cutting recovery to 68%. Wettability tests indicate the water contact angle rises from 17.1° to 90.1°, and capillary rise height falls by roughly 50%, reversing suction to repulsion. Together, these findings support a synergistic plugging–adsorption–hydrophobization mechanism that significantly enhances wellbore stability without compromising low-temperature rheology. This work may guide the design of high-performance collapse-prevention additives for safe, efficient deepwater drilling. Full article

Currently, the shear-extrusion behavior of solid propellants (SPs), which comprise a significant volume fraction of micro-/nanoscale solid particles (e.g., octogen/HMX), nitroglycerin as a plasticizer/solvent, nitrocellulose as a binder, and other functional additives, is still insufficiently understood. While the rheology of highly filled polymers has been extensively documented, the rheological behavior of SPs within the practical processing temperature range of 80–95 °C remains poorly understood. This study investigated, in particular, the pressure dependence of the viscosity of SPs melts during steady-state shear flow. Steady-state shear measurements were conducted using a twin-bore capillary rheometer with capillary dies of varying diameters and lengths to explore the viscosity dependence of SPs. The results reveal that interface defects between octogen particles and the polymer matrix generate a melt pressure range of 3–30 MPa in the long capillary die, underscoring the non-negligible impact of pressure on the measured viscosity (η). At constant temperature and shear rate, the measured viscosity of SPs exhibits strong pressure dependence, showing notable deviations in pressure sensitivity (β), which was found to be greatly relevant to the contents of solvent and solid particles. Such discrepancies are attributed to the compressibility of particle–particle and particle–polymer networks during capillary flow. The findings emphasize the critical role of pressure effect on the rheological properties of SPs, which is essential for optimizing manufacturing processes and ensuring consistent propellant performance. Full article