Search Results (11,125)

This study presents a collaborative sensing approach that integrates the pre-embedded sensor method and the impact-echo technique to enhance the accuracy of grout filling quality assessment for precast concrete sleeve connections. The pre-embedded sensor method, which relies on vibration energy attenuation, enables continuous monitoring of the grout filling process; however, its accuracy is limited at low filling degrees, as vibration energy values remain constant at approximately 255 when the filling degree is below 70%. In contrast, the impact-echo technique, based on the principle of impact elastic wave propagation, demonstrates high accuracy in evaluating grout filling degrees across various levels, with reflected waveform amplitude increasing accordingly. This collaborative approach establishes a functional relationship between vibration energy values from the pre-embedded sensor method and grout filling degree, allowing for a comprehensive assessment of grout filling quality. In field demonstrations, the calculated grout filling degree values deviated by less than 5% from the set values. Practical guidelines for implementing the collaborative sensing approach are also provided. The method developed in this study offers a reliable solution for assessing grout filling quality in precast concrete sleeve connections, addressing the limitations of individual testing methods. Full article

Estimation of anthropogenic carbon dioxide (CO₂) emission sources and natural sinks (i.e., CO₂ fluxes) is essential for the development of climate policies. Satellite observations provide an opportunity for top-down inversion of CO₂ fluxes, which can be used to improve the results of bottom-up estimation. This study proposes to develop a new top-down CO₂ flux estimation method based on deep learning, as well as satellite observations, and an atmospheric chemical transport model. This method utilizes two deep learning models: the concentration correction model and the concentration–flux inversion model. The former optimizes the GEOS-Chem-simulated CO₂ concentration using Orbiting Carbon Observatory-2 (OCO-2) satellite observations, while the latter establishes the complicated relationship between CO₂ concentration and CO₂ flux. Results showed that both deep learning models demonstrated excellent prediction performance, with a mean bias of 0.461 ppm for the concentration correction model and an annual mean correlation coefficient of 0.920 for the concentration–flux inversion model. A posterior CO₂ flux was obtained through a two-step optimization process using these well-trained models. Our findings indicate that the posterior estimations of CO₂ flux sources in eastern China and northern Europe have been significantly reduced compared to the prior estimations. This study provides a new perspective on top-down CO₂ flux inversion using satellite observation. With advancements in deep learning algorithms and increased satellite observations, this method may become an effective approach for CO₂ flux inversion in the future. Full article

Pure tin oxide (SnO₂) as a typical conductometric hydrogen sulfide (H₂S) gas-sensing material always suffers from limited sensitivity, elevated operation temperature, and poor selectivity. To overcome these hindrances, in this work, hollow CuO-SnO₂ nanotubes were successfully electrospun for room-temperature (25 °C) trace H₂S detection under blue light activation. Among all SnO₂-based candidates, a pure SnO₂ sensor showed no signal, even toward 10 ppm, while the 1% CuO-SnO₂ sensor achieved a limit of detection (LoD) value of 2.5 ppm, a large response of 4.7, and a short response/recovery time of 21/61 s toward 10 ppm H₂S, as well as nice repeatability, long-term stability, and selectivity. This excellent performance could be ascribed to the one-dimensional (1D) hollow nanostructure, abundant p-n heterojunctions, and the photoelectric effect of the CuO-SnO₂ nanotubes. The proposed design strategies cater to the demanding requirements of high sensitivity and low power consumption in future application scenarios such as Internet of Things and smart optoelectronic systems. Full article

The rapid urbanization and population growth in mega-cities have led to a significant increase in the demand for medical services, highlighting the critical need for a more efficient alignment between the supply and demand of medical resources. Previous research often focuses on singular factors, such as accessibility or quantity, as the primary criteria for matching medical services, without comprehensively considering the location, scale, and quality factors of medical facilities. Addressing this gap, this study develops a theoretical framework that integrates these three critical factors to assess the supply–demand matching (SDM) of medical care facilities (MCFs) with population needs. This assessment is conducted using geospatial analysis techniques with ArcGIS and Python. The study includes an empirical analysis of 134 streets within the Chongqing municipality. The empirical results reveal significant disparities in the performance of integrated medical care facilities (MCFs), as well as variations across the dimensions of location, scale, and quality. Central districts like Yuzhong demonstrate high levels of accessibility, appropriate scale matching, and satisfactory service quality, whereas rapidly urbanizing peripheral districts such as Yubei suffer from significant mismatches in resource availability and service quality. The theoretical framework contributes to the field of medical care research, and the corresponding empirical findings provide valuable insights for urban planners and policymakers to optimize the allocation of medical resources, improve healthcare accessibility, and enhance service quality across different urban areas. Full article

As unmanned aerial vehicles (UAVs) are widely used in various fields, there is an increasing demand for UAV anti-jamming, multipath mitigation, and covert secrecy. Frequency-hopping binary offset carrier (FH-BOC) signals possess higher anti-jamming and multipath mitigation capabilities than direct-sequence spread spectrum (DSSS) and binary offset carrier (BOC) signals. A prerequisite for constructing communication links between UAVs using FH-BOC signals is the design of efficient acquisition algorithms to capture the signals successfully. In this paper, the modulation and characteristics of the FH-BOC signal are introduced. The maximum relative velocity between UAVs is 5.5 km/s, the maximum acceleration is 50 g, and the maximum plus acceleration is 20 g/s. In this high dynamic environment, the parameters for the parallel code phase and Partial Matched Filter–Fast Fourier Transform (PMF-FFT) acquisition algorithms targeting FH-BOC(10,1) signals are designed, and the acquisition performance of these algorithms is comparatively analyzed. The acquisition time for the first and second algorithms is 4.3317 s and 6.137 s. The number of real additions required by the first and second algorithms is approximately $10.9\times {10}^9$ and $8.9\times {10}^9$, and the number of real multiplications is approximately $7.6\times {10}^9$ and $6.7\times {10}^9$. This helps in selecting the acquisition algorithm when FH-BOC signals are used to build inter-UAV communication links. Full article

Background and aims: The transplacental vertical transfer of maternal antibodies was determined to be a crucial factor in conferring protective immunity to infants following delivery, and this study aimed to evaluate the protective effect of maternal preconception COVID-19 vaccination on infants. Methods: A prospective cohort study was conducted at the National Clinical Medical Research Center for Child Health and Diseases in Chongqing, China, spanning from July 2022 to April 2023. The study included infants from mothers with a preconception COVID-19 vaccination and (or) a SARS-CoV-2 infection during pregnancy. Titers of SARS-CoV-2 immunoglobulin G (IgG) and cross-neutralizing activity against SARS-CoV-2 variants were detected. Results: In this cohort study comprising 158 infants, it was observed that infants born to mothers who experienced a pregnancy-related breakthrough infection following a preconception vaccination had the highest titers of SARS-CoV-2 IgG and cross-neutralizing antibody activity against different variants compared to those with either of these factors alone. The transplacental vertical transmission of anti-SARS-CoV-2 antibodies decreased significantly with increasing age, from 3.16 ODs at birth to 2.29 ODs at two months, and persisted for approximately four months after birth. The predominant subclass of passively transmitted antibodies via the placenta was found to be IgG1, and a positive correlation was observed between the titers of SARS-CoV-2 IgG and IgG1 (R = 0.59, p < 0.001; Slope: 0.49 ± 0.070, p < 0.001). Conclusions: Maternal preconception COVID-19 vaccination represents a promising immunological strategy for conferring postnatal protection to infants, especially during the period of heightened risk of exposure to SARS-CoV-2 infection. It is imperative to underscore the significance of vaccination for women who are preparing to become pregnant or are pregnant, and concerted efforts must be made to promote vaccination among eligible women. Full article

Climate change poses significant risks to natural and economic environments, particularly through its interaction with air pollution. As agriculture is vital for national production, and crop insurance supports social security, it is crucial to examine how air pollution affects crop insurance. Here, we quantify the impact of air quality on crop insurance claims from an actuarial perspective and evaluate the implications for the industry. Utilizing claims data from the U.S., we explore the potential of particulate matter (PM_2.5) as a predictor of insurance claims, building on literature that highlights its economic damage to crops. Through the application of a generalized additive model (GAM) and extreme gradient boosting, we found that PM_2.5 is indeed a factor influencing crop insurance indemnity in both models, with the GAM demonstrating superior predictive performance. Furthermore, we employed Bai and Perron breakpoint analysis to elucidate the relationship between PM_2.5 levels and crop insurance claims over time, alongside two-way fixed effects models to investigate its correlation with various crop types. Our findings highlight the need for crop insurance managers to integrate air quality considerations into their risk processes to ensure sustainability of the industry and pricing strategy in the face of evolving environmental challenges. Full article

The results of first-principles calculations of the structural, electronic, elastic, vibrational, dielectric and optical properties, as well as the Raman and infrared (IR) spectra, of potassium hexafluorosilicate (K₂SiF₆; KSF) crystal are discussed. KSF doped with manganese atoms (KSF:Mn⁴⁺) is known for its ability to function as a phosphor in white LED applications due to the efficient red emission from Mn⁴⁺ activator ions. The simulations were performed using the CRYSTAL23 computer code within the linear combination of atomic orbitals (LCAO) approximation of the density functional theory (DFT). For the study of KSF, we have applied and compared several DFT functionals (with emphasis on hybrid functionals) in combination with Gaussian-type basis sets. In order to determine the optimal combination for computation, two types of basis sets and four different functionals (three advanced hybrid—B3LYP, B1WC, and PBE0—and one LDA functional) were used, and the obtained results were compared with available experimental data. For the selected basis set and functional, the above-mentioned properties of KSF were calculated. In particular, the B1WC functional provides us with a band gap of 9.73 eV. The dependencies of structural, electronic and elastic parameters, as well as the Debye temperature, on external pressure (0–20 GPa) were also evaluated and compared with previous calculations. A comprehensive analysis of vibrational properties was performed for the first time, and the influence of isotopic substitution on the vibrational frequencies was analyzed. IR and Raman spectra were simulated, and the calculated Raman spectrum is in excellent agreement with the experimental one. Full article

The stability of the oil well wellbore is a prerequisite for selecting the optimal completion method. In this paper, based on experimental testing and theoretical models of rock mechanics parameters in deep oil reservoirs, the in situ stress parameters of deep oil wells are accurately predicted. On this basis, a full life cycle assessment model for wellbore and perforation casing stability was established, and the effects of pressure depletion and changes in the production pressure differential on wellbore stability and casing stability were analyzed. The research results indicate that as the formation pressure decreases, the critical collapse pressure difference around the wellbore significantly decreases. The greater the production pressure difference, the more likely the wellbore is to become unstable. Under the original formation pressure coefficient, if there is no casing, the critical failure pressure difference of the wellbore wall is 55 MPa. After cementing and perforation, when the casing is uniformly stressed and the formation pressure drops to a coefficient of 0.83, the casing will not be damaged even when the wellbore is completely emptied. At this time, there is still a certain safety production pressure difference in the perforated formation. This study can effectively guide the optimization of well completion and safe development in deep oil reservoirs. Full article

Developing efficient and sustainable pollution control technologies has become a research priority in the context of escalating global environmental pollution. Nano zero-valent iron (nZVI), with its high specific surface area and strong reducing power, demonstrates remarkable performance in pollutant removal. Still, its application is limited by issues such as oxidation, passivation, and particle aggregation. White rot fungi (WRF) possess a unique enzyme system that enables them to degrade a wide range of pollutants effectively, yet they face challenges such as long degradation cycles and low degradation efficiency. Despite the significant role of nZVI in pollutant remediation, most contaminated sites still rely on microbial remediation as a concurrent or ultimate treatment method to achieve remediation goals. The synergistic combination of nZVI and WRF can leverage their respective advantages, thereby enhancing pollution control efficiency. This paper reviews the mechanisms, advantages, and disadvantages of nZVI and WRF in pollution control, lists application examples, and discusses their synergistic application in pollution control, highlighting their potential in pollutant remediation and providing new insights for combined pollutant treatment. However, research on the combined use of nZVI and WRF for pollutant remediation is still relatively scarce, necessitating a deeper understanding of their synergistic potential and further exploration of their cooperative interactions. Full article

Femtosecond-laser-fabricated black silicon has been widely used in the fields of solar cells, photodetectors, semiconductor devices, optical coatings, and quantum computing. However, the responsive spectral range limits its application in the near- to mid-infrared wavelengths. To further increase the optical responsivity in longer wavelengths, in this work, silicon (Si) was co-hyperdoped with nitrogen (N) and selenium (Se) through the deposition of Se films on Si followed by femtosecond (fs)-laser irradiation in an atmosphere of NF₃. The optical and crystalline properties of the Si:N/Se were found to be influenced by the precursor Se film and laser fluence. The resulting photodetector, a product of this innovative approach, exhibited an impressive responsivity of 24.8 A/W at 840 nm and 19.8 A/W at 1060 nm, surpassing photodetectors made from Si:N, Si:S, and Si:S/Se (the latter two fabricated in SF6). These findings underscore the co-hyperdoping method’s potential in significantly improving optoelectronic device performance. Full article

Considering animal welfare, the free-range laying hen farming model is increasingly gaining attention. However, in some countries, large-scale farming still relies on the cage-rearing model, making the focus on the welfare of caged laying hens equally important. To evaluate the health status of caged laying hens, a dataset comprising visible light and thermal infrared images was established for analyses, including morphological, thermographic, comb, and behavioral assessments, enabling a comprehensive evaluation of the hens’ health, behavior, and population counts. To address the issue of insufficient data samples in the health detection process for individual and group hens, a dataset named BClayinghens was constructed containing 61,133 images of visible light and thermal infrared images. The BClayinghens dataset was completed using three types of devices: smartphones, visible light cameras, and infrared thermal cameras. All thermal infrared images correspond to visible light images and have achieved positional alignment through coordinate correction. Additionally, the visible light images were annotated with chicken head labels, obtaining 63,693 chicken head labels, which can be directly used for training deep learning models for chicken head object detection and combined with corresponding thermal infrared data to analyze the temperature of the chicken heads. To enable the constructed deep-learning object detection and recognition models to adapt to different breeding environments, various data enhancement methods such as rotation, shearing, color enhancement, and noise addition were used for image processing. The BClayinghens dataset is important for applying visible light images and corresponding thermal infrared images in the health detection, behavioral analysis, and counting of caged laying hens under large-scale farming. Full article

Recommendation systems are used in various fields of e-commerce and can bring many benefits to consumers but consumers’ trust in recommendation systems (CTRS) is lacking. Recommendation system transparency (RST) is an important factor that affects CTRS. Applying a three-layered trust model, this paper discusses the influence of RST on CTRS in the e-commerce domain, demonstrating the mediating role of perceived effectiveness and discomfort and the moderating role of consumers’ domain knowledge. We recruited 500 participants for an online hypothetical scenario experiment. The results show that consumers’ perceived effectiveness and discomfort can mediate the relationship between RST and CTRS. Specifically, RST (vs. non-transparency) leads to higher perceived effectiveness ( promoting CTRS) and lower levels of discomfort (which inhibits CTRS), in turn increasing CTRS. Domain knowledge positively moderates the positive impact of RST on perceived effectiveness, while negatively moderating the negative impact of RST on discomfort. Further, gender has a negative impact on CTRS when consumers are purchasing experience products but there is no effect when purchasing search products. Full article

Background: Rheumatoid arthritis (RA) tends to occur in symmetrical joints and is always accompanied by synovial hyperplasia and cartilage damage. Triptolide (TP), an extract from Tripterygium, has anti-inflammatory and immunomodulatory properties and could be used in the treatment of RA. However, its poor water solubility and the multi-system lesions caused by the use of this substance limit its clinical application. Therefore, it would be of great significance to assemble a composite nanoparticle hydrogel and apply it to a collagen-induced arthritis (CIA) mouse model to investigate the therapeutic effect and biosafety of this compound. Method: TP@HSA nanoparticles (TP@HSA NPs) were fabricated with a self-assembly method; a thermosensitive hydrogel loaded with the TP@HSA NPs (TP@HSA NP hydrogel) was prepared by using chitosan and beta- glycerophosphate (β-GP) and was then intra-articularly injected into CIA mice. The changes in joint swelling were measured with a digital caliper, and inflammation and cartilage damage were evaluated by using hematoxylin and eosin (H&E) and safranin O–fast green (SO&FG) staining, respectively. Results: TP@HSA NPs with an average diameter of 112 ± 2 nm were successfully assembled, and their encapsulation efficiency and drug loading efficiency were 47.6 ± 1.5% and 10.6 ± 3.3%, respectively. The TP@HSA NP hydrogel had a gelation temperature of 30.5 ± 0.2 °C, which allows for its injection at low temperatures and its sol–gel transformation under physiological conditions within 2 min, making it a suitable drug depot. The TP@HSA NP hydrogel was intra-articularly injected into CIA mice; it released TP locally and exerted anti-inflammatory and immunomodulatory effects, alleviating synovial inflammation and cartilage damage effectively. Conclusions: We successfully fabricated a TP@HSA NP-loaded thermosensitive hydrogel with good biosafety, which can release TP slowly for the treatment of RA. Our study provides a basis for the development of TP-based innovative preparations and has good application prospects. Full article

Defining the structure and evolution of multi-trend faults is critical for analyzing the accumulation of hydrocarbons in buried hills. Based on high-resolution seismic and drilling data, the structural characteristics and evolutionary mechanism of multi-trend faults were investigated in detail through the structural analysis theory and quantitative calculations of fault activity, allowing us to determine the implication that fault evolution exerts on hydrocarbon accumulation in the BZ19-6 buried hill. There are four kinds of strike faults developed on the buried hill: SN-, NNE-, NE–ENE-, and nearly EW-trending, which experienced the Mesozoic Indosinian, Yanshan, and Cenozoic Himalayan tectonic movements. During the Indosinian, the BZ19-6 was in a SN-oriented compressional setting, with active faults composed of SN-trending strike-slip faults (west branch of the Tanlu fault zone) and near EW-trending thrust faults (Zhang-peng fault zone). During the Yanshanian, the NNE-trending normal faults were formed under the WNW–ESE tensile stress field. Since the Himalayan period, the BZ19-6 buried hill has evolved into the rifting stage. In rifting stage Ⅰ, all of the multi-trend pre-existing faults were reactivated, and the EW-trending thrust faults became normal faults due to negative inversion. In rifting stage II, a large number of NE–ENE-trending normal faults were newly formed in the NW–SE-oriented extensional setting, which made the structure pattern more complicated. In rifting stage III, the buried hill entered the post-rift stage, with only part of the NNE- and NE–ENE-trending faults continuously active. Multi-trend faults are the result of the combination of various multi-phase stress fields and pre-existing structures, which have great influence on the formation of tectonic fractures and then control the distribution of high-quality reservoirs in buried hills. The fractures controlled by the NNE- and EW-trending faults have higher density and scale, and fractures controlled by NE–ENE trending faults have stronger connectivity and effectiveness. The superposition of multi-trend faults is the favorable distribution of high-quality reservoirs and the favorable accumulation area of hydrocarbon. Full article