Search Results (3,436)

The principle of electro-analogy analysis treats a gas path structure as analogous to a circuit, offering significant potential for performance analysis in aerostatic systems. However, research on gas resistance remains in an early stage. This study investigates pipe gas resistance and its series characteristics using a slender circular pipe as the subject. First, gas resistance is redefined based on a derivation of the Bernoulli equation, resulting in formulas covering low and high speeds and a calculation model for series gas resistance. Simulations are conducted to model the pipe, focusing on the coefficient of frictional resistance at low speeds. The results provide insights into the gas resistance of pipes with varying inner diameters and related series connections. An experiment is conducted to validate predictions, indicating that, at low speeds, the defined and determined gas resistance values for pipelines with inner diameters ranging from 1 to 6 mm are largely consistent. Both gas and series gas resistances decrease as the pressure difference between the two pipe ends increases. Relative errors below 5% are typically regarded as very good, especially when dealing with complex systems. The maximum relative error between the experimentally measured single gas resistance, based on the defining formula and the simulation value, is 3.1%. Furthermore, the maximum relative errors for the measured single and series gas resistance values are 5% and 3.8%, respectively, according to the defining and determining formulas. The theoretical model is effective and reliable, providing valuable theoretical support for impedance analysis of aerostatic systems. Full article

In this paper, a compact multi-split-ring resonator-based antenna is presented for wireless applications. The proposed antenna integrates multiple resonators to achieve multiband operation, where each resonator corresponds to a specific frequency band. A theoretical analysis is conducted to model the equivalent circuit of the proposed antenna, followed by an analytical study to calculate the resonant frequency of each resonator. By integrating these resonators, the proposed antenna achieves a compact size of 23 × 24 × 1.6 mm³ (0.19 × 0.2 × 0.01λ³), resulting in a size reduction of 81.6% compared to a conventional patch antenna, while maintaining gain, improving bandwidth, and providing excellent impedance matching. The proposed antenna covers the 2.4–2.8 GHz (14.55%), 3.25–3.75 GHz (14.28%) and 4.5–7.84 GHz (54.13%) frequency bands, providing acceptable gains of 1.5 dBi, 2 dBi and 3.2 dBi, respectively. The antenna was designed with CST, its performance was verified with HFSS simulations and it was validated with an equivalent circuit in ADS. Finally, the antenna was fabricated to confirm the accuracy and reliability of the simulation results, and it was found that the measurements agreed well with the simulations. This multiband functionality, combined with a compact form factor and simple feed line, makes the antenna cost-effective, easy to manufacture and suitable for various wireless communication applications, including 5G sub-6 GHz mid-band (2.5/3.5/5/5 GHz), RFID (2.45/5.8 GHz), WiMAX (2.4/3.5/5.8 GHz), Wi-Fi 5/6/6E (2.4/5/6 GHz) and WLAN (5.2/5.8 GHz). Full article

In pursuit of a higher-quality post-pandemic economic recovery, Chinese authorities have accelerated the development of the e-CNY. This study posits that the e-CNY distinguishes itself from other payment instruments through its controlled anonymity, programmability, and non-interest-bearing attributes. By analyzing patents filed by the Digital Currency Research Institute of the People’s Bank of China between 2016 and 2023, the paper elucidates potential implementation strategies for these distinctive features. The findings suggest that the e-CNY may facilitate a zero-interest accrual model within the prevailing legal framework. Restricted authority access and the anonymity ensured by encrypted data further allow users to maintain a high degree of confidentiality. Additionally, conditional automatic transfers—a prominent function in the e-CNY’s smart contracts—mirror traditional automatic transfers for directed fund utilization without impeding the circulation of fiat currency. The People’s Bank of China has sought to thoughtfully integrate these functionalities into its Central Bank Digital Currency framework, aiming to minimize potential conflicts with existing legal standards. Instead of relying solely on extensive legislative revisions, China’s experience illustrates how deliberate and incremental CBDC design choices can reconcile regulatory compliance with innovative technological advancements. Full article

Abstract: The sloshing in storage tanks can exert negative influences on the safety and stability of tank structures undergoing earthquake excitation. An analytical mechanical model is presented here to investigate the seismic responses of a base-isolated cylindrical tank resting on soil. The continuous liquid sloshing is modeled as the convective spring–mass, the impulsive spring–mass, and the rigid mass. The soil impedances are equivalent to the systematic lumped-parameter models. The bearing isolation is considered as the elastic–viscous damping model. A comparison between the present and reported results is presented to prove the accuracy of the coupling model. A parametric analysis is carried out for base-isolated broad and slender tanks to examine the effects of the isolation period, isolation damping ratio, tank aspect ratio, and soil stiffness on structural responses. The results show that the interaction between soft soil and the base-isolated tank exerts significant influence on earthquake responses. Full article

Background: Despite the significant global disease burden associated with HPV infection, the vaccination coverage among female college students in China remains suboptimal. This study aimed to examine HPV vaccination coverage, knowledge levels, and determinants influencing vaccination behavior among female college students in northern China, utilizing the Health Belief Model (HBM) as a theoretical framework. Methods: A cross-sectional online survey was conducted from December 2024 to January 2025, involving 4076 female students from six universities in Jinan, China. The participants were categorized into three groups: vaccinated (VG), willing-to-vaccinate (WTG), and unwilling-to-vaccinate (UTG). Data on sociodemographic characteristics, HPV knowledge, health beliefs, and vaccination behavior were analyzed using ANOVA, chi-square tests, correlation analysis, and multivariate logistic regression. Results: The vaccination rate was 18.11%, with 40.19% expressing willingness to vaccinate and 41.71% expressing unwillingness. Vaccinated students demonstrated higher levels of HPV knowledge (6.66 ± 2.67 compared to 4.76 ± 3.10 in the UTG, p < 0.001) and were predominantly from urban areas (OR = 0.64, p < 0.001). The key determinants of vaccination uptake included perceived benefits (OR = 1.54, p < 0.001), perceived barriers (OR = 3.34, p < 0.001), self-decision-making ability (OR = 1.80, p < 0.001), and social motivation (OR = 0.21, p < 0.001). Notably, increased knowledge was associated with vaccine hesitancy in the WTG group (OR = 0.45, p < 0.001), indicating that information overload may adversely affect decision-making processes. Structural barriers, such as cost (42.63%), safety concerns (46.59%), and misconceptions (e.g., 57.76% cited “no sexual activity” as a reason for refusal), significantly impeded vaccine uptake. Conclusions: The low coverage of HPV vaccination is indicative of deficiencies in knowledge, socioeconomic disparities, and cultural perceptions. Tailored interventions should focus on educational efforts to correct misconceptions, provide subsidized access to vaccines, and implement empowerment strategies that enhance self-efficacy and informed decision-making. Policymakers should incorporate these findings into national cervical cancer prevention programs to address the gap between vaccination intention and behavior among young women in China. Full article

Arboviruses, transmitted by blood-sucking arthropods, are responsible for significant human and animal diseases, including fever, hemorrhagic fever, and encephalitis, posing a serious threat to global public health. Nevertheless, research on the mechanisms of arbovirus infection and the development of therapeutic interventions has been impeded. This delay is primarily due to the limitations inherent in current in vitro research models, including cell cultures and animal models. The simplicity of cell types and interspecies differences present significant obstacles to advancing our understanding of arbovirus infection mechanisms and the development of effective drugs. Human brain organoids, derived from human pluripotent stem cells or human embryonic stem cells and cultured in three-dimensional systems, more accurately replicate the extensive neuronal cellular diversity and key characteristics of human neurodevelopment. These organoids serve as an ideal model for investigating the intricate interactions between viruses and human hosts, and providing a novel platform for the development of antiviral drugs. In this review, we summarize how brain organoid models complement classical approaches to accelerate research into the infection mechanisms of arboviruses, with a particular focus on the types of neural cells, key factors, and cellular signaling pathways involved in the arbovirus infection of brain organoids that have been reported. Furthermore, we examine the development of brain organoids, address their current limitations, and propose future directions to enhance the application of brain organoids in the study of arboviral infectious diseases. Full article

Today, numerous advanced options exist for analyzing and measuring magnetic hysteresis loops and core loss across a broad spectrum of applications. Most of these systems are compact and ready to use, fulfilling the measurement and data processing requirements for laminated iron cores according to the standards. However, modeling newly developed materials with complex structures or the high-frequency behavior of iron cores, and the computation of dynamic hysteresis properties’ temperature dependence, are still challenging problems in the field. Moreover, these standardized measurement tools are relatively expensive, and most of them represent a black box that impedes research and further development. This paper presents the development of a cheap and accessible measurement system that is explicitly designed for recording the hysteresis properties of 3D-printed iron cores. The paper presents a comprehensive overview of the design process, components, circuitry, and simulations integral to this project. The paper presents a completed circuit simulation conducted using LTspice and validation of the prototype’s measurement performance. The measurements obtained with the proposed system show good agreement with those of the reference setup, demonstrating its accuracy and practical applicability. Full article

Digital transformation has become a strategic imperative for micro and small enterprises (MSEs) in emerging economies, yet the mechanisms linking digitalization to performance outcomes remain underexplored. This study examines how the strategic emphasis on digital transformation and the breadth of technology adoption influence firm performance among MSEs in Bhutan. Drawing on an integrative theoretical framework combining diffusion of innovations theory, the resource-based view, and institutional theory, survey data from 217 MSEs were analyzed using regression and interaction modeling techniques. The findings indicate that firms with stronger digital strategic emphasis adopt a broader range of technologies and achieve superior performance. However, unstructured or excessive knowledge sharing negatively moderates these relationships, potentially creating cognitive overload and impeding digital strategy execution. Furthermore, tourism enterprises exhibit significantly higher levels of digital engagement compared to non-tourism counterparts, highlighting the role of sector-specific institutional pressures. By uncovering the systemic dynamics between strategic orientation, technology adoption, and knowledge flows, this study contributes to a deeper understanding of how digital transformation processes can be optimized in resource-constrained environments. These findings not only offer practical insights for enhancing digital readiness and organizational resilience among small enterprises but also contribute to the broader theoretical discourse on how strategic orientation and contextual moderators shape the effectiveness of digital transformation in emerging markets. Full article

Seafloor topography super-resolution reconstruction is critical for marine resource exploration, geological monitoring, and navigation safety. However, sparse acoustic data frequently result in the loss of high-frequency details, and traditional deep learning models exhibit limitations in uncertainty quantification, impeding their practical application. To address these challenges, this study systematically investigates the combined effects of various regularization strategies and uncertainty quantification modules. It proposes a hybrid dropout model that jointly optimizes high-precision reconstruction and uncertainty estimation. The model integrates residual blocks, squeeze-and-excitation (SE) modules, and a multi-scale feature extraction network while employing Monte Carlo Dropout (MC-Dropout) alongside heteroscedastic noise modeling to dynamically gate the uncertainty quantification process. By adaptively modulating the regularization strength based on feature activations, the model preserves high-frequency information and accurately estimates predictive uncertainty. The experimental results demonstrate significant improvements in the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Peak Signal-to-Noise Ratio (PSNR). Compared to conventional dropout architectures, the proposed method achieves a PSNR increase of 46.5% to 60.5% in test regions with a marked reduction in artifacts. Overall, the synergistic effect of employed regularization strategies and uncertainty quantification modules substantially enhances detail recovery and robustness in complex seafloor topography reconstruction, offering valuable theoretical insights and practical guidance for further optimization of deep learning models in challenging applications. Full article

Static synchronous compensators (STATCOMs) are widely applied in modern power networks for reactive power compensation and grid voltage regulation. Compared to the conventional compensation devices, the STATCOMs deliver superior performance through the voltage-droop control loop. However, the interaction between the STATCOMs and grid impedance, especially in weak grids, can lead to stability issues. To investigate this instability mechanism, the STATCOMs and grid impedance are modeled as a multi-input–multi-output system in this paper. Thus, the coupling effects between the control loop and the grid impedance are clearly highlighted, making the stability assessment feasible. The proposed method avoids the cost and volume issues associated with adding physical inductance in traditional approaches to mitigate these coupling effects. It not only improves the operational stability of the STATCOM but also enhances its voltage support capability, thereby supplementing the stability research for weak grids with STATCOMs under this specific condition. The effectiveness of the presented analysis and proposed control scheme are validated through both simulation and experimental results. Full article

This paper proposes a robust impedance controller to address the performance limitations of mechanical impedance rendering in robotic joints, enabling stable interaction with passive environments. Considering structured uncertainties, such as dynamic parameter perturbations, sensor noise, disturbances, and unmodeled dynamics in actuator models, the $\mathsf{\mu }$-synthesis method is employed to optimize closed-loop robustness performance. This approach minimizes impedance-matching errors in the frequency domain, thereby enhancing the regulation of the systems’s inherent impedance characteristics. Key performance metrics are analyzed, and the impedance-rendering accuracy is evaluated. Furthermore, the limiting factors affecting impedance-matching bandwidth are investigated to inform the selection of impedance parameters and ensure safe physical interaction. The proposed controller is validated through simulations and hardware experiments on a one-DoF modular robotic joint. Frequency domain impedance matching comparisons show that relative to ${\mathcal{H}}_{\infty }$ control, the $\mathsf{\mu }$-synthesis approach reduces impedance matching errors by up to 94.6% and 97.5% under 5% and 30% inertia uncertainties, respectively. Furthermore, experimental results demonstrate that compared to classical impedance control, the proposed method reduces impedance rendering errors by an average of 85.71% across all tested configurations while maintaining superior passivity and interaction stability under diverse impedance conditions. These results validate the effectiveness of $\mathsf{\mu }$-synthesis in achieving safe and high-fidelity physical interaction behavior. Full article

The coexistence of cloud and snow is very common in remote sensing images. It presents persistent challenges for automated interpretation systems, primarily due to their highly similar visible light spectral characteristic in optical remote sensing images. This intrinsic spectral ambiguity significantly impedes accurate cloud and snow segmentation tasks, particularly in delineating fine boundary features between cloud and snow regions. Much research on cloud and snow segmentation based on deep learning models has been conducted, but there are still deficiencies in the extraction of fine boundaries between cloud and snow regions. In addition, existing segmentation models often misjudge the body of clouds and snow with similar features. This work proposes a Multi-scale Feature Mixed Attention Network (MFMANet). The framework integrates three key components: (1) a Multi-scale Pooling Feature Perception Module to capture multi-level structural features, (2) a Bilateral Feature Mixed Attention Module that enhances boundary detection through spatial-channel attention, and (3) a Multi-scale Feature Convolution Fusion Module to reduce edge blurring. We opted to test the model using a high-resolution cloud and snow dataset based on WorldView2 (CSWV). This dataset contains high-resolution images of cloud and snow, which can meet the training and testing requirements of cloud and snow segmentation tasks. Based on this dataset, we compare MFMANet with other classical deep learning segmentation algorithms. The experimental results show that the MFMANet network has better segmentation accuracy and robustness. Specifically, the average MIoU of the MFMANet network is 89.17%, and the accuracy is about 0.9% higher than CSDNet and about 0.7% higher than UNet. Further verification on the HRC_WHU dataset shows that the MIoU of the proposed model can reach 91.03%, and the performance is also superior to other compared segmentation methods. Full article

This article examines Healthy Homes, a program intended to initiate a new approach to housing repairs and maintenance in remote communities in the Northern Territory of Australia. It argues that while the evidence for associations between poor housing and poor health outcomes is clear, greater attention should be paid to the implementation of health-focused housing interventions. Healthy Homes was examined through interviews with public servants, Aboriginal community-controlled organisation staff, and householders, alongside participant observation during maintenance projects and Condition Assessment Tool inspections. Routine housing, inspections, and expenditure datasets were also analysed. Across 5498 houses subject to Healthy Homes and over a twenty-month period, only 1315 Condition Assessment Tool inspections were completed, which is the key mechanism for generating preventive maintenance work. Expenditure on repairs and maintenance was stable between the old maintenance model and under Healthy Homes. Most Healthy Homes remote housing maintenance contracts were awarded to Aboriginal business enterprises. This article finds that Healthy Homes did not effectively shift remote property management to prioritise preventive maintenance. Issues with data collection and monitoring, program implementation, and contractual arrangements impeded more consistent and effective attention paid to the condition of housing health hardware. Future investment into the implementation of health-focused remote housing preventive maintenance programs must attend to the details of program design, including the data collection processes and contractual terms for service providers. Full article

Sustainability research encompasses diverse theories and frameworks focused on promoting sustainable economic ($E$), social ($S$), and environmental ($Env$) systems. However, integrated approaches to sustainability challenges have been impeded due to the absence of a unified analytical framework in the field. This study investigated how foundational and emerging theories, including resilience thinking, systems theory, and planetary boundaries, could be synthesized to develop an Integrated Sustainability Model (ISM) that captures nonlinear feedback, adaptive capacities $A_i\left(t\right)$, and threshold effects across these domains. The ISM model employs a system dynamics approach, where the rates of change for $E$, $S$, and $Env$ are governed by coupled differential equations, each influenced by cross-domain feedback (${\alpha }_i$ and ${\beta }_i$), adaptive capacity functions, and depletion rates (${\gamma }_i$). The model explicitly incorporates boundary constraints and adaptive capacity, operationalizing the dynamic interplay and co-evolution of sustainability dimensions. Grounded in an integrative perspective, this research introduces the Synergistic Resilience Theory (SRT), which proposes optimal sustainability arises from managing economic, social, and environmental systems as interconnected, adaptive components of a resilient system. Theoretical analysis and conceptual simulations demonstrated that high adaptive capacity and positive cross-domain reinforcement foster resilient, synergistic growth, while reduced capacity or breaches of critical thresholds ($En{v}_{\mathrm{m}\mathrm{i}\mathrm{n}}$ and $S_{\mathrm{m}\mathrm{i}\mathrm{n}}$) can lead to rapid decline and slow recovery. These insights illuminate the urgent need for integrated, preventive policy interventions that proactively build adaptive capacity and maintain system resilience. This research, by advancing a mathematically robust and conceptually integrative framework, provides a potent new lens for developing empirically validated, holistic sustainability strategies within sustainability research. Full article

Electroencephalography (EEG) remains an essential method for monitoring brain activity, but the limitations of conventional systems due to the complexity of installation and lack of portability have led to the introduction and development of in-ear EEG technology. In-ear EEG is an emerging method of recording electrical activity in the brain and is an innovative concept that offers multiple advantages both from the point of view of the device itself, which is easily portable, and from the user’s point of view, who is more comfortable with it, even in long-term use. One of the fundamental components of this type of device is the electrodes used to capture the EEG signal. This innovative method allows bioelectrical signals to be captured through electrodes integrated into an earpiece, offering significant advantages in terms of comfort, portability, and accessibility. Recent studies have demonstrated that in-ear EEG can record signals qualitatively comparable to scalp EEG, with an optimized signal-to-noise ratio and improved electrode stability. Furthermore, this review provides a comparative synthesis of performance parameters such as signal-to-noise ratio (SNR), common-mode rejection ratio (CMRR), signal amplitude, and comfort, highlighting the strengths and limitations of in-ear EEG systems relative to conventional scalp EEG. This study also introduces a visual model outlining the stages of technological development for in-ear EEG, from initial research to clinical and commercial deployment. Particular attention is given to current innovations in electrode materials and design strategies aimed at balancing biocompatibility, signal fidelity, and anatomical adaptability. This article analyzes the evolution of EEG in the ear, briefly presents the comparative aspects of EEG—EEG in the ear from the perspective of the electrodes used, highlighting the advantages and challenges of using this new technology. It also discusses aspects related to the electrodes used in EEG in the ear: types of electrodes used in EEG in the ear, improvement of contact impedance, and adaptability to the anatomical variability of the ear canal. A comparative analysis of electrode performance in terms of signal quality, long-term stability, and compatibility with use in daily life was also performed. The integration of intra-auricular EEG in wearable devices opens new perspectives for clinical applications, including sleep monitoring, epilepsy diagnosis, and brain–computer interfaces. This study highlights the challenges and prospects in the development of in-ear EEG electrodes, with a focus on integration into wearable devices and the use of biocompatible materials to improve durability and enhance user comfort. Despite its considerable potential, the widespread deployment of in-ear EEG faces challenges such as anatomical variability of the ear canal, optimization of ergonomics, and reduction in motion artifacts. Future research aims to improve device design for long-term monitoring, integrate advanced signal processing algorithms, and explore applications in neurorehabilitation and early diagnosis of neurodegenerative diseases. Full article