Search Results (2,470)

The penetration rate of variable energy sources in the current power grid is increasing, with the aim being to expand the use of these energy sources and to replace the traditional black start power supply. This study investigates the black start of a photovoltaic storage joint system based on the system’s short-circuit ratio threshold. Firstly, the principles and control modes of the photovoltaic (PV) system, energy storage system (ESS), and high-voltage direct current (DC) transmission system are studied separately to build an overall model; secondly, computational determinations of the short-circuit ratio under different scenarios are introduced to analyze the strength of the system, and the virtual inertia and virtual damping of the PV system are configured based on this; finally, the change trend of the storage system’s state of charge (SOC) is computed and observed, and the limits of what the system can support in each stage are determined. An electromagnetic transient simulation model of a black start system is constructed in PSCAD/EMTDC, and according to the proposed recovery strategy, the system frequency is maintained in the range of 49.4~50.6 Hz during the entire black start process; the fluctuation in maximum frequency after the recovery of the DC transmission system is no more than 0.1%; and the fluctuation in photovoltaic power at each stage is less than 3%. In addition, all the key indexes meet the requirements for black start technology, which verifies the validity of the strategy and provides theoretical support and a practical reference for the black start of a grid with variable energy sources. Full article

Accurate melt pool geometry prediction is essential for ensuring quality and reliability in Laser Powder Bed Fusion (L-PBF). However, small experimental datasets and limited physical interpretability often restrict the effectiveness of traditional machine learning (ML) models. This study proposes a hybrid framework that integrates an explicit thermal model with ML algorithms to improve prediction under sparse data conditions. The explicit model—calibrated for variable penetration depth and absorptivity—generates synthetic melt pool data, augmenting 36 experimental samples across conduction, transition, and keyhole regimes for 316 L stainless steel. Three ML methods—Multilayer Perceptron (MLP), Random Forest, and XGBoost—are trained using fivefold cross-validation. The hybrid approach significantly improves prediction accuracy, especially in unstable transition regions (D/W ≈ 0.5–1.2), where morphological fluctuations hinder experimental sampling. The best-performing model (MLP) achieves R² > 0.98, with notable reductions in MAE and RMSE. The results highlight the benefit of incorporating physically consistent, nonlinearly distributed synthetic data to enhance generalization and robustness. This physics-augmented learning strategy not only demonstrates scientific novelty by integrating mechanistic modeling into data-driven learning, but also provides a scalable solution for intelligent process optimization, in situ monitoring, and digital twin development in metal additive manufacturing. Full article

Background/Objectives: This study aimed to investigate the influence of shade and thickness on the polymerization of SDR^® flow+, a low-viscosity bulk-fill composite, by assessing its degree of conversion (DC). Methods: An in vitro study was conducted using SDR^® flow+ composite resin. Specimens were prepared at two thicknesses (2 mm and 4 mm) and four shades (Universal, A1, A2, A3). Polymerization was performed using a high-intensity LED curing unit. The DC was assessed using Fourier-transform infrared spectroscopy (ATR-FTIR). Results: Both shade and thickness significantly influenced DC. Thicker specimens (4 mm) exhibited reduced polymerization compared to thinner specimens (2 mm). Darker shades, particularly A3, demonstrated the lowest DC values due to their higher chroma, which limits light penetration. In contrast, the Universal shade achieved higher DC values, even at increased depths, likely due to its greater translucency. Conclusions: Shade and thickness play a critical role in the polymerization of bulk-fill composites. Ensuring adequate polymerization is essential for the longevity of pediatric restorations. Further in vivo research is needed to confirm these findings and assess their clinical implications. Full article

This case report presents a multidisciplinary forensic investigation into a cold case involving a missing person in Italy, likely linked to a homicide that occurred in 2008. The investigation applied a standardized protocol integrating satellite imagery analysis, site reconnaissance, vegetation clearance, ground-penetrating radar (GPR), and cadaver dog (K9) deployment. A dedicated decision tree guided each phase, allowing for efficient allocation of resources and minimizing investigative delays. Although no human remains were recovered, the case demonstrates the practical utility and operational robustness of a structured, evidence-based model that supports decision-making even in the absence of positive findings. The approach highlights the relevance of “negative” results, which, when derived through scientifically validated procedures, offer substantial value by excluding burial scenarios with a high degree of reliability. This case is particularly significant in the Italian forensic context, where the adoption of standardized search protocols remains limited, especially in complex outdoor environments. The integration of geophysical, remote sensing, and canine methodologies—rooted in forensic geoarchaeology—provides a replicable framework that enhances both investigative effectiveness and the evidentiary admissibility of findings in court. The protocol illustrated in this study supports the consistent evaluation of large and morphologically complex areas, reduces the risk of interpretive error, and reinforces the transparency and scientific rigor expected in judicial settings. As such, it offers a model for improving forensic search strategies in both national and international contexts, particularly in long-standing or high-profile missing persons cases. Full article

Water infiltration into soil is a key process in regulating the hydrological cycle and sustaining ecosystem services in high-Andean environments. However, limited information is available regarding its dynamics in these ecosystems. This study evaluated the influence of three types of vegetation cover and soil properties on water infiltration in a high-Andean environment. A double-ring infiltrometer, the Water Drop Penetration Time (WDPT, s) method, and laboratory physicochemical characterization were employed. Soils under forest cover exhibited significantly higher quasi-steady infiltration rates (i_s, 0.248 ± 0.028 cm·min⁻¹) compared to grazing areas (0.051 ± 0.016 cm·min⁻¹) and agricultural lands (0.032 ± 0.013 cm·min⁻¹). Soil organic matter content was positively correlated with i_s. The modified Kostiakov infiltration model provided the best overall fit, while the Horton model better described infiltration rates approaching i_s. Sand and clay fractions, along with K⁺, Ca²⁺, and Mg²⁺, were particularly significant during the soil’s wet stages. In drier stages, increased Na⁺ concentrations and decreased silt content were associated with higher water repellency. Based on WDPT, agricultural soils exhibited persistent hydrophilic behavior even after drying (median [IQR] from 0.61 [0.38] s to 1.24 [0.46] s), whereas forest (from 2.84 [3.73] s to 3.53 [24.17] s) and grazing soils (from 4.37 [1.95] s to 19.83 [109.33] s) transitioned to weakly or moderately hydrophobic patterns. These findings demonstrate that native Andean forest soils exhibit a higher infiltration capacity than soils under anthropogenic management (agriculture and grazing), highlighting the need to conserve and restore native vegetation cover to strengthen water resilience and mitigate the impacts of land-use change. Full article

The increasing demand for noninvasive inspection (NII) of complex civil infrastructures requires overcoming the limitations of traditional ground-penetrating radar (GPR) systems in addressing diverse and large-scale applications. The solution proposed in this study focuses on an initial design that integrates a low-SWaP (Size, Weight, and Power) ultra-wideband (UWB) impulse radar with realistic electromagnetic modeling for deployment on unmanned aerial vehicles (UAVs). The system incorporates ultra-realistic antenna and propagation models, utilizing Finite Difference Time Domain (FDTD) solvers and multilayered media, to replicate realistic airborne sensing geometries. Verification and calibration are performed by comparing simulation outputs with laboratory measurements using varied material samples and target models. Custom signal processing algorithms are developed to extract meaningful features from complex electromagnetic environments and support anomaly detection. Additionally, machine learning (ML) techniques are trained on synthetic data to automate the identification of structural characteristics. The results demonstrate accurate agreement between simulations and measurements, as well as the potential for deploying this design in flight tests within realistic environments featuring complex electromagnetic interference. Full article

Cell-penetrating peptides offer a promising strategy for intracellular delivery; however, non-specific uptake and off-target cytotoxicity limit their clinical utility. To address these limitations, a cold atmospheric plasma-responsive delivery platform was developed in which the membrane activity of a peptide was transiently suppressed upon complexation with a DNA-based nanostructure. Upon localized plasma exposure, DNA masking was disrupted, restoring the biological functions of the peptides. Transmission electron microscopy revealed that the synthesized DNA nanoflower structures were approximately 150–250 nm in size. Structural and functional analyses confirmed that the system remained inert under physiological conditions and was rapidly activated by plasma treatment. Fluorescence recovery, cellular uptake assays, and cytotoxicity measurements demonstrated that the peptide activity could be precisely controlled in both monolayer and three-dimensional spheroid models. This externally activatable nanomaterial-based system enables the spatial and temporal regulation of peptide function without requiring biochemical triggers or permanent chemical modifications. This platform provides a modular strategy for the development of potential peptide therapeutics that require precise control of activation in complex biological environments. Full article

Increased antimicrobial resistance requires effective ways to overcome the global challenge of bacterial infections, including methicillin-resistant Staphylococcus aureus (MRSA). From the emergence of MRSA to its continued evolution, it is important to explore this pathogen from fresh perspectives and develop corresponding coping strategies to counter its growing threat. New coping strategies are continuously emerging, including but not limited to enhancing penetration capabilities or targeting their virulence. This review summarizes the epidemiological characteristics, drug resistance mechanisms, and therapeutic strategies of MRSA that have emerged over the past fifteen years. The focus of this paper is to explore the promising applications and current limitations of novel MRSA control strategies. This review serves as a key resource for treating MRSA infections and discussing novel strategies to overcome bacterial drug resistance. Full article

Knowing the superior biochemical defense mechanisms of sessile organisms, it is not hard to believe the cure for any human sickness might be hidden in nature—we “just” have to identify it and make it safely available in the right dose to our organs and cells that are in need. For decades, green tea catechins (GTCs) have been a case in point. Because of their low redox potential and favorable positioning of hydroxyl groups, these flavonoid representatives (namely, catechin—C, epicatechin—EC, epicatechin gallate—ECG, epigallocatechin—EGC, epigallocatechin gallate—EGCG) are among the most potent plant-derived (and not only) antioxidants. The proven anti-inflammatory, neuroprotective, antimicrobial, and anticarcinogenic properties of these phytochemicals further contribute to their favorable pharmacological profile. Doubtlessly, GTCs hold the potential to “cope” with the majority of today‘s socially significant diseases, yet their mass use in clinical practice is still limited. Several factors related to the compounds’ membrane penetrability, chemical stability, and solubility overall determine their low bioavailability. Moreover, the antioxidant-to-pro-oxidant transitioning behavior of GTCs is highly conditional and, to a certain degree, unpredictable. The nanoparticulate delivery systems represent a logical approach to overcoming one or more of these therapeutic challenges. This review particularly focuses on the lipid-based nanotechnologies known to be a leading choice when it comes to drug permeation enhancement and not drug release modification nor drug stabilization solely. It is our goal to present the privileges of encapsulating green tea catechins in either vesicular or particulate lipid carriers with respect to the increasingly popular trends of advanced phytotherapy and functional nutrition. Full article

Androgenetic alopecia (AGA) is the most prevalent form of alopecia areata. Traditional treatment options, including minoxidil, finasteride, and hair transplantation, have their limitations, such as skin irritation, systemic side effects, invasiveness, and high costs. The transdermal drug delivery system (TDDS) offers an innovative approach for treating AGA by administering medications through the skin to achieve localized and efficient delivery while overcoming the skin barrier. This review systematically explores the application of TDDS in AGA treatment, highlighting emerging technologies such as microneedles (MNs), liposomes, ionic liquids (ILs), nanostructured lipid carriers (NLCs), and transporters (TFs). It analyzes the underlying mechanisms that enhance drug penetration through hair follicles. Finally, this review presents a forward-looking perspective on the future use of TDDS in the management of AGA, aiming to provide insights and references for designing effective transdermal drug delivery systems for this condition. Full article

One of the persistent challenges in multispectral image analysis is the interference caused by dense cloud cover and its resulting shadows, which can significantly obscure surface features. This becomes especially problematic when attempting to monitor surface changes over time using satellite imagery, such as from Landsat-8. In this study, rather than simply masking visual obstructions, we aimed to investigate the role and influence of clouds within the spectral data itself. To achieve this, we employed Independent Component Analysis (ICA), a statistical method capable of decomposing mixed signals into independent source components. By applying ICA to selected Landsat-8 bands and analyzing each component individually, we assessed the extent to which cloud signatures are entangled with surface data. This process revealed that clouds contribute to multiple ICA components simultaneously, indicating their broad spectral influence. With this influence on multiple wavebands, we managed to configure a set of components that could perfectly delineate the extent and location of clouds. Moreover, because Landsat-8 lacks cloud-penetrating wavebands, such as those in the microwave range (e.g., SAR), the surface information beneath dense cloud cover is not captured at all, making it physically impossible for ICA to recover what is not sensed in the first place. Despite these limitations, ICA proved effective in isolating and delineating cloud structures, allowing us to selectively suppress them in reconstructed images. Additionally, the technique successfully highlighted features such as water bodies, vegetation, and color-based land cover differences. These findings suggest that while ICA is a powerful tool for signal separation and cloud-related artifact suppression, its performance is ultimately constrained by the spectral and spatial properties of the input data. Future improvements could be realized by integrating data from complementary sensors—especially those operating in cloud-penetrating wavelengths—or by using higher spectral resolution imagery with narrower bands. Full article

Cancer has remained one of the leading causes of death worldwide throughout history despite significant advancements in drug development, radiation therapy, and surgery. Traditional chemotherapeutic small molecules are often hindered by narrow therapeutic indices and limited specificity, leading to suboptimal clinical outcomes. On the other hand, more advanced approaches, such as antibody–drug conjugates (ADCs), frequently encounter obstacles, including poor tumor penetration and prohibitive production costs. The tumor-forming and metastatic capacity of cancer further challenges currently available cancer therapies by creating a biochemical milieu known as the tumor microenvironment (TME). Although solid tumor development presents significant obstacles, it also opens new avenues for innovative therapeutic approaches. It is well-documented that as tumors grow beyond 1–2 mm³ in size, they undergo profound changes in their microenvironment, including alterations in oxygen levels, pH, enzymatic activity, surface antigen expression, and the cellular composition of the stroma. These changes create unique opportunities that can be exploited to develop novel and innovative therapeutics. Currently, numerous ADCs, small-molecule–drug conjugates (SMDCs), and prodrugs are being developed to target specific aspects of these microenvironmental changes. In this review, we explore five TME parameters in detail, with a focus on their relevance to specific cancer types, phenotypic identifiers, and preferred methods of therapeutic targeting. Additionally, we examine the chemical moieties available to target these changes, providing a framework for design strategies that exploit the dynamics of the tumor microenvironment. Full article

Piezocatalytic therapy (PCT) is a promising strategy for combating implant-associated infections due to its high tissue penetration depth and non-invasive nature. However, its catalytic efficiency remains limited by inefficient electron–hole separation. In this work, an ultrasound-responsive heterojunction (BiOI/Ti₃C₂) was fabricated through in situ growth of bismuth iodide oxide on titanium carbide nanosheets. Subsequently, we integrated BiOI/Ti₃C₂ into poly(e-caprolactone) (PCL) scaffolds using selective laser sintering. The synergistic effect between BiOI and Ti₃C₂ significantly facilitated the redistribution of piezo-induced charges under ultrasound irradiation, effectively suppressing electron–hole recombination. Furthermore, abundant oxygen vacancies in BiOI/Ti₃C₂ provide more active sites for piezocatalytic reactions. Therefore, it enables ultrahigh reactive oxygen species (ROS) yields under ultrasound irradiation, achieving eradication rates of 98.87% for Escherichia coli (E. coli) and 98.51% for Staphylococcus aureus (S. aureus) within 10 minutes while maintaining cytocompatibility for potential tissue integration. This study provides a novel strategy for the utilization of ultrasound-responsive heterojunctions in efficient PCT therapy and bone regeneration. Full article

Limited recovery of the viscoelastic properties of aged asphalt on RAP surfaces at ambient temperature reduces interface fusion and bonding with new emulsified asphalt, degrading pavement performance and limiting large-scale promotion and high-value applications of the emulsified asphalt cold recycled mixture (EACRM). Therefore, a cold regenerant was independently prepared to rapidly penetrate, soften, and activate aged asphalt at ambient temperature in this paper, and its effects on the volumetric composition, mechanical strength, and pavement performance of EACRM were systematically investigated. The results showed that as the cold regenerant content increased, the air voids, indirect tensile strength (ITS), and high-temperature deformation resistance of EACRM decreased, while the dry–wet ITS ratio, cracking resistance, and fatigue resistance increased. Considering the comprehensive pavement performance requirements of cold recycled pavements, the optimal content of the activated cold regenerant for EACRM was determined to be approximately 0.6%. Full article

Downhole kick is one of the most severe safety hazards in deep and ultra-deep well drilling operations. Traditional monitoring methods, which rely on surface flow rate and fluid level changes, are limited by their delayed response and insufficient sensitivity, making them inadequate for early warning. This study proposes a real-time monitoring technique for gas content in drilling fluid based on the attenuation principle of Ba-133 γ-rays. By integrating laboratory static/dynamic experiments and Geant4-11.2 Monte Carlo simulations, the influence mechanism of gas–liquid two-phase media on γ-ray transmission characteristics is systematically elucidated. Firstly, through a comparative analysis of radioactive source parameters such as Am-241 and Cs-137, Ba-133 (main peak at 356 keV, half-life of 10.6 years) is identified as the optimal downhole nuclear measurement source based on a comparative analysis of penetration capability, detection efficiency, and regulatory compliance. Compared to alternative sources, Ba-133 provides an optimal energy range for detecting drilling fluid density variations, while also meeting exemption activity limits (1 × 10⁶ Bq) for field deployment. Subsequently, an experimental setup with drilling fluids of varying densities (1.2–1.8 g/cm³) is constructed to quantify the inverse square attenuation relationship between source-to-detector distance and counting rate, and to acquire counting data over the full gas content range (0–100%). The Monte Carlo simulation results exhibit a mean relative error of 5.01% compared to the experimental data, validating the physical correctness of the model. On this basis, a nonlinear inversion model coupling a first-order density term with a cubic gas content term is proposed, achieving a mean absolute percentage error of 2.3% across the full range and R² = 0.999. Geant4-based simulation validation demonstrates that this technique can achieve a measurement accuracy of ±2.5% for gas content within the range of 0–100% (at a 95% confidence interval). The anticipated field accuracy of ±5% is estimated by accounting for additional uncertainties due to temperature effects, vibration, and mud composition variations under downhole conditions, significantly outperforming current surface monitoring methods. This enables the high-frequency, high-precision early detection of kick events during the shut-in period. The present study provides both theoretical and technical support for the engineering application of nuclear measurement techniques in well control safety. Full article