Search Results (877)

Ubiquitous digital array radar (UDAR) extends the integration time of moving targets by deploying a wide transmitting beam and multiple narrow receiving beams to cover the entire observed airspace. By exchanging time for energy, it effectively improves the detection ability for weak targets. Nevertheless, target motion introduces severe across-range unit (ARU), across-Doppler unit (ADU), and across-beam unit (ABU) effects, dispersing target energy across the range–Doppler-beam space. This paper proposes a beam domain angle rotation compensation and keystone-matched filtering (BARC-KTMF) algorithm to address the “three-crossing” challenge. This algorithm first corrects ABU by rotating beam–domain coordinates to align scattered energy into the final beam unit, reshaping the signal distribution pattern. Then, the KTMF method is utilized to focus target energy in the time-frequency domain. Furthermore, a special spatial windowing technique is developed to improve computational efficiency through parallel block processing. Simulation results show that the proposed approach achieves an excellent signal-to-noise ratio (SNR) gain over the typical single-beam and multi-beam long-time coherent integration (LTCI) methods under low SNR conditions. Additionally, the presented algorithm also has the capability of coarse estimation for the target incident angle. This work extends the LTCI technique to the beam domain, offering a robust framework for high-speed weak target detection. Full article

This study investigates the impact of teacher decisions and other contextual factors on indoor air quality (IAQ) in mechanically ventilated elementary school classrooms using low-cost air quality sensors. Four classrooms at a K–8 school in San Jose, California, were monitored for airborne particulate matter (PM), carbon dioxide (CO₂), temperature, and humidity over seven weeks. Each classroom was equipped with an HVAC system and a portable air cleaner (PAC), with teachers having full autonomy over PAC usage and ventilation practices. Results revealed that teacher behaviors, such as the frequency of door/window opening and PAC operation, significantly influenced both PM and CO₂ levels. Classrooms with more active ventilation had lower CO₂ but occasionally higher PM_2.5 due to outdoor air exchange, while classrooms with minimal ventilation showed the opposite pattern. An analysis of PAC filter material and PM morphology indicated distinct differences between indoor and outdoor particle sources, with indoor air showing higher fiber content from clothing and carpets. This study highlights the critical role of teacher behavior in shaping IAQ, even in mechanically ventilated environments, and underscores the potential of low-cost sensors to support informed decision-making for healthier classroom environments. Full article

Introduction: Efficient communication and collaboration among local health departments (LHDs), healthcare organizations, governmental entities, and other community stakeholders are critical for public health preparedness and response. This study evaluates (1) the impact of informatics on LHDs’ frequency and collaboration in creating consistent COVID-19 messaging; (2) the influence of informatics on targeted messaging for vulnerable populations; and (3) LHD characteristics linked to their consistent and/or targeted messaging engagement. Methods: This study analyzed the 2020 National Association of County and City Health Officials (NACCHO) Forces of Change (FOC) survey, the COVID-19 Edition. Of the 2390 LHDs invited to complete the core questionnaire, 905 were asked to fill out the module questionnaire as well. The response rate for the core was 24% with 587 responses, while the module received 237 responses, achieving a 26% response rate. Descriptive analyses and six logistic regression models were utilized. Results: Over 80% (183) of LHDs collaborated regularly with public health partners, and 95% (222) used information management applications for COVID-19. Most interacted with local and state agencies, but only half with federal ones. LHDs that exchanged data with local non-health agencies, engaged with local non-health agencies, and communicated weekly to daily with the public about long-term/assisted care had higher odds of creating consistent messages for the public, and about the use and reuse of masks had lower odds of collaborating with public health partners to develop consistent messages for the public. Conclusion: Our findings underscore the centrality of informatics infrastructure and collaboration in ensuring equitable public health messaging. Strengthening public health agencies and investing in targeted training are crucial for effective communication across the communities served by these agencies. Full article

We investigate how collisional interactions between the condensate and the thermal cloud influence the distinct dynamical regimes (Josephson plasma, phase-slip-induced dissipative regime, and macroscopic quantum self-trapping) emerging in ultracold atomic Josephson junctions at non-zero subcritical temperatures. Specifically, we discuss how the self-consistent dynamical inclusion of collisional processes facilitating the exchange of particles between the condensate and the thermal cloud impacts both the condensate and the thermal currents, demonstrating that their relative importance depends on the system’s dynamical regime. Our study is performed within the full context of the Zaremba–Nikuni–Griffin (ZNG) formalism, which couples a dissipative Gross–Pitaevskii equation for the condensate dynamics to a quantum Boltzmann equation with collisional terms for the thermal cloud. In the Josephson plasma oscillation and vortex-induced dissipative regimes, collisions markedly alter dynamics at intermediate-to-high temperatures, amplifying damping in the condensate imbalance mode and inducing measurable frequency shifts. In the self-trapping regime, collisions destabilize the system even at low temperatures, prompting a transition to Josephson-like dynamics on a temperature-dependent timescale. Our results show the interplay between coherence, dissipation, and thermal effects in a Bose–Einstein condensate at a finite temperature, providing a framework for tailoring Josephson junction dynamics in experimentally accessible regimes. Full article

The dynamic phosphorylation of the human RNA Pol II CTD establishes a code applicable to all eukaryotic transcription processes. However, the ability of these specific post-translational modifications to convey molecular signals through structural changes remains unclear. We previously explained that each gene can be modeled as a combination of n circuits connected in parallel. RNA Pol II accesses these circuits and, through a series of pulses, matches the resonance frequency of the DNA qubits, enabling it to extract genetic information and quantum teleport it. Negatively charged phosphates react under RNA Pol II catalysis, which increases the electron density on the deoxyribose acceptor carbon (2’C in the DNA sugar backbone). The phosphorylation effect on the stability of a carbon radical connects tyrosine to the nitrogenous base, while the subsequent pulses link the protein to molecular water through hydrogen bonds. The selective activation of inert C(sp³)–H bonds begins by reading the quantum information stored in the nitrogenous bases. The coupling of hydrogen proton transfer with electron transfer in water generates a supercurrent, which is explained by the correlation of pairs of the same type of fermions exchanging a boson. All these changes lead to the formation of a molecular protein–DNA–water transcriptional condensate. Full article

This paper proposes a control method that combines a fractional-order sliding mode observer and a cooperative control strategy to address the problem of path-following for underactuated autonomous underwater vehicles (AUVs) in complex marine environments. First, a fractional-order sliding mode observer is designed, combining fractional calculus and double-power convergence laws to enhance the estimation accuracy of high-frequency disturbances. An adaptive gain mechanism is introduced to avoid dependence on the upper bound of disturbances. Second, a formation cooperative control strategy based on path parameter coordination is proposed. By setting independent reference points for each AUV and exchanging path parameters, formation consistency is achieved with low communication overhead. For the followers’ speed control problem, an error-based expected speed adjustment mechanism is introduced, and a hyperbolic tangent function is used to replace the traditional arctangent function to improve the response speed of the system. Numerical simulation results show that this control method performs well in terms of path-following accuracy, formation maintenance capability, and disturbance suppression, verifying its effectiveness and robustness in complex marine environments. Full article

As data exchange demands increase also in widespread wearable technologies, transitioning to higher bandwidths and mmWave frequencies (30–300 GHz) is essential. This shift raises concerns about RF exposure. At such high frequencies, the most crucial human tissue for RF power absorption is the skin, since EMF penetration is superficial. It becomes thus very important to assess how the model used to represent the skin in numerical dosimetry studies affects the estimated level of absorbed power. The present study, for the first time, assesses the absorbed power density (APD) using FDTD simulations on two realistic human models in which: (i) the skin has a two-layer structure made of the stratum corneum and the viable epidermis and dermis layers, and (ii) the skin is modelled as a homogeneous dermis stratum. These results were compared with ones using flat phantom models, with and without the stratified skin. The exposure assessment study was performed with two sources (a wearable patch antenna and a plane wave) tuned to 28 GHz. For the wearable antenna, the results evidence that the exposure levels obtained when using the homogeneous version of the models are always lower than the levels in the stratified skin version with percentage differences from 16% to 30%. This trend is more noticeable with the female model. In the case of plane wave exposure, these differences were less pronounced and lower than 11%. Full article

Nowadays, the Metaverse is facing many challenges. In this context, Virtual Reality (VR) applications allowing voice-based human–3D object interactions are limited due to the current hardware/software limitations. In fact, adopting Automated Speech Recognition (ASR) systems to interact with 3D objects in VR applications through users’ voice commands presents significant challenges due to the hardware and software limitations of headset devices. This paper aims to bridge this gap by proposing a methodology to address these issues. In particular, starting from a Mel-Frequency Cepstral Coefficient (MFCC) extraction algorithm able to capture the unique characteristics of the user’s voice, we pass it as input to a Convolutional Neural Network (CNN) model. After that, in order to integrate the CNN model with a VR application running on a standalone headset, such as Oculus Quest, we converted it into an Open Neural Network Exchange (ONNX) format, i.e., a Machine Learning (ML) interoperability open standard format. The proposed system demonstrates good performance and represents a foundation for the development of user-centric, effective computing systems, enhancing accessibility to VR environments through voice-based commands. Experiments demonstrate that a native CNN model developed through TensorFlow presents comparable performances with respect to the corresponding CNN model converted into the ONNX format, paving the way towards the development of VR applications running in headsets controlled through the user’s voice. Full article

Based on an integrated analysis, this study summarized the current status of soil quality in Yantai apple orchards, developed a multivariate regulation model for key soil physicochemical properties, and proposed optimized fertilization strategies to improve soil quality in the region. The study analyzed the physicochemical properties of the topsoil (0–30 cm) in 19 representative apple orchards across Yantai, including indicators like pH, organic matter (OM), major nutrient ions, and salinity indicators, using standardized measurements and multivariate statistical methods, including descriptive statistics analysis, frequency distribution analysis, canonical correlation analysis, stepwise regression equation analysis, and regression fit model analysis. The results demonstrated that in apple orchards across the Yantai region, reductions in pH were significantly mitigated under the combined increased OM and exchangeable calcium (Ca). Exchangeable potassium (EK) rose in response to the joint elevation of OM and available nitrogen (AN), and AN was also positively influenced by EK, while OM also exhibited a promotive effect on Olsen phosphorus (OP). Furthermore, Ca increased with higher pH. AN and EK jointly contributed to the increases in electrical conductivity (EC) and chloride ions (Cl), while elevated exchangeable sodium (Na) and soluble salts (SS) were primarily driven by EK. Accordingly, enhancing organic and calcium source fertilizers is recommended to boost OM and Ca levels, reduce acidification, and maintain EC within optimal limits. By primarily reducing potassium’s application, followed by nitrogen and phosphorus source fertilizers, the supply of macronutrients can be optimized, and the accumulation of Na, Cl, and SS can be controlled. Collectively, the combined analysis of soil quality status and the multivariate regulation model clarified the optimized fertilization strategies, thereby establishing a solid theoretical and practical foundation for recognizing the necessity of soil testing and formula fertilization, the urgency of improving soil quality, and the scientific rationale for nutrient input management in Yantai apple orchards. Full article

Image dehazing technology is a crucial component in the fields of intelligent transportation and autonomous driving. However, most existing dehazing algorithms only process images in the spatial domain, failing to fully exploit the rich information in the frequency domain, which leads to residual haze in the images. To address this issue, we propose a novel Frequency-domain Information Guided Symmetric Dual-branch Dehazing Network (FIGD-Net), which utilizes the spatial branch to extract local haze features and the frequency branch to capture the global haze distribution, thereby guiding the feature learning process in the spatial branch. The FIGD-Net mainly consists of three key modules: the Frequency Detail Extraction Module (FDEM), the Dual-Domain Multi-scale Feature Extraction Module (DMFEM), and the Dual-Domain Guidance Module (DGM). First, the FDEM employs the Discrete Cosine Transform (DCT) to convert the spatial domain into the frequency domain. It then selectively extracts high-frequency and low-frequency features based on predefined proportions. The high-frequency features, which contain haze-related information, are correlated with the overall characteristics of the low-frequency features to enhance the representation of haze attributes. Next, the DMFEM utilizes stacked residual blocks and gradient feature flows to capture local detail features. Specifically, frequency-guided weights are applied to adjust the focus of feature channels, thereby improving the module’s ability to capture multi-scale features and distinguish haze features. Finally, the DGM adjusts channel weights guided by frequency information. This smooths out redundant signals and enables cross-branch information exchange, which helps to restore the original image colors. Extensive experiments demonstrate that the proposed FIGD-Net achieves superior dehazing performance on multiple synthetic and real-world datasets. Full article

Hydrogen production through electrolysis of water can achieve efficient, stable and diversified utilization of renewable energy. To this end, a hybrid electrolyzer system for hydrogen production based on bi-layer optimization is constructed. Firstly, the wind and photovoltaic power is decomposed into high-frequency and low-frequency components by an adaptive wavelet packet. The low-frequency power is allocated to the alkaline electrolyzers (AWE) to ensure its stability, and the high-frequency power is allocated to the proton exchange membrane electrolyzers (PEM) with a faster response characteristic, thereby improving the energy utilization rate. This paper proposes a bi-layer optimization model, in which the upper-layer objective is to minimize the cost of mixed hydrogen production, and the lower-layer optimization objective is to maximize the utilization rate of renewable energy. The differential evolution algorithm optimizes the upper-layer objective, with results sent to the lower layer. Then, the YALMIP toolbox is used to solve the lower-layer objective. Through case analysis, the optimal proportion of AWE and PEM hydrogen electrolyzers obtained by this optimization method is 89.5 and 10.5, respectively. Compared with a single type of electrolyzer, the method proposed in this paper effectively improves the energy utilization efficiency and reduces the cost of hydrogen production. Full article

This study focuses on the problem of identifying and extracting the magnetopause boundary of the Earth’s magnetosphere using the Soft X-ray Imager (SXI) onboard the Solar Wind Magnetosphere Ionosphere Link Explorer (SMILE) mission. The SXI employs lobster-eye optics to perform panoramic imaging of the magnetosphere based on the Solar Wind Charge Exchange (SWCX) mechanism. However, several factors are expected to hinder future in-orbit observations, including the intrinsically low signal-to-noise ratio (SNR) of soft-X-ray emission, pronounced vignetting, and the non-uniform effective-area distribution of lobster-eye optics. These limitations could severely constrain the accurate interpretation of magnetospheric structures—especially the magnetopause boundary. To address these challenges, a boundary detection approach is developed that combines image calibration with denoising based on deep image prior (DIP). The method begins with calibration procedures to correct for vignetting and effective area variations in the SXI images, thereby restoring the accurate brightness distribution and improving spatial uniformity. Subsequently, a DIP-based denoising technique is introduced, which leverages the structural prior inherent in convolutional neural networks to suppress high-frequency noise without pretraining. This enhances the continuity and recognizability of boundary structures within the image. Experiments use ideal magnetospheric images generated from magnetohydrodynamic (MHD) simulations as reference data. The results demonstrate that the proposed method significantly improves the accuracy of magnetopause boundary identification under medium and high solar wind number density conditions (N = 10–20 cm⁻³). The extracted boundary curves consistently achieve a normalized mean squared error (NMSE) below 0.05 compared to the reference models. Additionally, the DIP-processed images show notable improvements in peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), indicating enhanced image quality and structural fidelity. This method provides adequate technical support for the precise extraction of magnetopause boundary structures in soft X-ray observations and holds substantial scientific and practical value. Full article

Whether and how digital transformation affects innovation in small and medium-sized enterprises (SMEs) remains to be examined. This study aims to answer this question using a sample of SMEs listed on the Chinese National Equities Exchange and Quotations (NEEQ) market from 2012 to 2023. Employing textual mining techniques, this paper measures the degree of digital transformation through keyword frequency analysis of annual reports, while innovation is measured by the number of patent grants. Panel fixed effects models show that digital transformation significantly enhances corporate innovation in SMEs. This relationship remains robust after comprehensive endogeneity and additional robustness tests. Mechanisms analysis reveals that digital transformation alleviates financial constraints and enhances supply chain diversity, enabling SMEs to allocate more resources toward innovation activities. Heterogeneity analysis reveals that the positive effect of digital transformation on innovation is more pronounced for firms located in cities with higher digital finance coverage, in midwestern regions, and in industries with lower digitalization levels. These findings shed light on the power of digital technology, highlighting how its adoption can significantly bolster the innovation capacity of SMEs and drive their growth in a rapidly evolving digital economy. Full article

This study investigated the squeal noise characteristics of the in-plane mode of the disc in a disc brake system as influenced by the temperature of the brake pad. The temperature range of the brake pad was set between 50 °C and 300 °C, and the squeal noise was analyzed by calculating the complex eigenvalues using the finite element method (FEM). The FEM analysis indicated that instability was most sensitive near 80 °C, and it was observed that instability exhibited mode exchange from the disc’s in-plane mode to the out-of-plane mode in a nearby frequency band due to thermal deformation of the pad. A reproduction test was conducted using a brake dynamometer, where the main squeal noise was found to be approximately 10,000 Hz, consistent with the FEM analysis. Additionally, the squeal noise occurred most near 100 °C, and the noise disappeared after 250 °C. These results largely align with the FEM analysis model, validating the suitability of the analysis approach. Full article

Magnesium alloys are lightweight metals but suffer from high corrosion susceptibility due to their chemical reactivity, limiting their large-scale applications. To enhance corrosion resistance, this work combines Li–Al layered double hydroxides (LDHs) with triethylenetetramine (TETA) inhibitors to form an efficient corrosion protection system. Electrochemical tests, SEM, FT-IR, XPS, and 3D depth-of-field microscopy were employed to evaluate TETA-modified Li–Al LDH coatings at varying concentrations. Among them, the Li–Al LDHs without the addition of a TETA corrosion inhibitor decreased significantly at |Z|_{0.01 Hz} after immersion for 4 h. However, the Li–Al LDHs coating of 23.5 mM TETA experienced a sudden drop at |Z|_{0.01 Hz} after holding for about 60 h, and the Li–Al LDHs coating of 70.5 mM TETA also experienced a sudden drop at |Z|_{0.01 Hz} after holding for about 132 h. By contrast, at the optimal concentration (47 mM), after 24 h of immersion, the maximum |Z|_{0.01 Hz} reached 7.56 × 10⁵ Ω∙cm²—three orders of magnitude higher than pure Li–Al LDH coated AZ31 (2.55 × 10² Ω∙cm²). After 300 h of immersion, the low-frequency impedance remained above 10⁵ Ω∙cm², demonstrating superior long-term protection. TETA modification significantly improved the durability of Li–Al LDHs coatings, addressing the short-term protection limitation of standalone Li–Al LDHs. Li–Al LDHs themselves have a layered structure and effectively capture corrosive Cl⁻ ions in the environment through ion exchange capacity, reducing the corrosion of the interface. Furthermore, TETA exhibits strong adsorption on Li–Al LDHs layers, particularly at coating defects, enabling rapid barrier formation. This inorganic–organic hybrid design achieves defect compensation and enhanced protective barriers. Full article