Search Results (398)

Background/Objectives: Antibiotic resistance represents one of the major global health emergencies, driven by the inappropriate use of antibiotics and persistent misconceptions among adults attending general medical clinics. This study, conducted on 325 participants recruited from general medical clinics in the province of Salerno, aimed to assess their knowledge, perceptions, and behaviors regarding antibiotic use. Methods: A cross-sectional, quantitative observational survey was conducted using a structured questionnaire based on the WHO tool and adapted to the local context. Results: The results show that the majority of participants take antibiotics only when prescribed by a doctor (90.2%), but risky practices such as self-medication (10%) and early discontinuation of therapy (16%) persist. In addition, 72% of subjects demonstrate incomplete knowledge about the independent management of drugs, and 86% mistakenly believe that resistance is limited to the individual rather than the community. The descriptive analysis stratified by age showed higher levels of awareness among subjects under 30 years of age, compared to significant knowledge gaps and inappropriate behaviors in the over-65 age group. Conclusions: Despite a good awareness of the need for medical prescriptions and the collective importance of the phenomenon, there are still critical areas of knowledge and incorrect practices that can promote the spread of antibiotic resistance. The data collected underscore the urgency of targeted educational strategies differentiated by age group, integrated with multi-channel communication interventions, in order to promote the appropriate use of antibiotics and contain the impact of one of the most serious global health emergencies. Full article

Mesoscale convective systems (MCSs) play a pivotal role in the occurrence of severe weather phenomena, with convective cells constituting their fundamental elements. The precise identification of these cells from satellite imagery is crucial yet presents significant challenges, including issues related to merging errors and sensitivity to threshold parameters. This study introduces a novel detection algorithm for convective cells that leverages H-maxima transformation and incorporates multichannel data from the FY-2F satellite. The proposed method utilizes H-maxima transformation to identify seed points while maintaining the integrity of core structural features, followed by a novel neighborhood labeling method, region growing and adaptive merging criteria to effectively differentiate adjacent convective cells. The neighborhood labeling method improves the accuracy of seed clustering and avoids “over-clustering” or “under-clustering” issues of traditional neighborhood criteria. When compared to established methods such as RDT, ETITAN, and SA, the algorithm demonstrates superior performance, attaining a Probability of Detection (POD) of 0.87, a False Alarm Ratio (FAR) of 0.21, and a Critical Success Index (CSI) of 0.71. These results underscore the algorithm’s efficacy in elucidating the internal structures of convective complexes and mitigating false merging errors. Full article

Compared with other fossil energy sources, natural gas is characterized by compressibility, low energy density, high storage costs, and imbalanced usage. Natural gas pipeline supply systems possess unique attributes such as closed transportation and a highly integrated upstream, midstream, and downstream structure. Moreover, pipelines are almost the only economical means of onshore natural gas transportation. Given that the upstream of the pipeline features multi-entity and multi-channel supply including natural gas, coal-to-gas, and LNG vaporized gas, while the downstream presents a competitive landscape with multi-market and multi-user segments (e.g., urban residents, factories, power plants, and vehicles), there is an urgent social demand for non-discriminatory and fair opening of natural gas pipeline network infrastructure to third-party entities. However, after the fair opening of natural gas pipeline networks, the original “point-to-point” transaction model will be replaced by market-driven behaviors, making the verification and allocation of gas transmission capacity a key operational issue. Currently, neither pipeline operators nor government regulatory authorities have issued corresponding rules, regulations, or evaluation plans. To address this, this paper proposes a multi-dimensional quantitative evaluation model based on the Analytic Hierarchy Process (AHP), integrating both commercial and technical indicators. The model comprehensively considers six indicators: pipeline transportation fees, pipeline gas line pack, maximum gas storage capacity, pipeline pressure drop, energy consumption, and user satisfaction and constructs a quantitative evaluation system. Through the consistency check of the judgment matrix (CR = 0.06213 < 0.1), the weights of the respective indicators are determined as follows: 0.2584, 0.2054, 0.1419, 0.1166, 0.1419, and 0.1357. The specific score of each indicator is determined based on the deviation between each evaluation indicator and the theoretical optimal value under different gas volume allocation schemes. Combined with the weight proportion, the total score of each gas volume allocation scheme is finally calculated, thereby obtaining the recommended gas volume allocation scheme. The evaluation model was applied to a practical pipeline project. The evaluation results show that the AHP-based evaluation model can effectively quantify the advantages and disadvantages of different gas volume allocation schemes. Notably, the gas volume allocation scheme under normal operating conditions is not the optimal one; instead, it ranks last according to the scores, with a score 0.7 points lower than that of the optimal scheme. In addition, to facilitate rapid decision-making for gas volume allocation schemes, this paper designs a program using HTML and develops a gas volume allocation evaluation program with JavaScript based on the established model. This self-developed program has the function of automatically generating scheme scores once the proposed gas volume allocation for each station is input, providing a decision support tool for pipeline operators, shippers, and regulatory authorities. The evaluation model provides a theoretical and methodological basis for the dynamic optimization of natural gas pipeline gas volume allocation schemes under the fair opening model. It is expected to, on the one hand, provide a reference for transactions between pipeline network companies and shippers, and on the other hand, offer insights for regulatory authorities to further formulate detailed and fair gas transmission capacity transaction methods. Full article

With the development of infrared imaging technology and the integration of intelligent algorithms, the realization of non-contact, dynamic and real-time detection of SF6 gas leakage based on infrared video has been a significant research direction. However, the existing real-time detection algorithms exhibit low accuracy in detecting SF6 leakage and are susceptible to noise, which makes it difficult to meet the actual needs of engineering. To address this problem, this paper proposes a real-time SF6 leakage detection method, VGEC-Net, based on multi-channel fusion and spatiotemporal feature extraction. The proposed method first employs the ViBe-GMM algorithm to extract foreground masks, which are then fused with infrared images to construct a dual-channel input. In the backbone network, a CE-Net structure—integrating CBAM and ECA-Net—is combined with the P3D network to achieve efficient spatiotemporal feature extraction. A Feature Pyramid Network (FPN) and a temporal Transformer module are further integrated to enhance multi-scale feature representation and temporal modeling, thereby significantly improving the detection performance for small-scale targets. Experimental results demonstrate that VGEC-Net achieves a mean average precision (mAP) of 61.7% on the dataset used in this study, with a mAP@50 of 87.3%, which represents a significant improvement over existing methods. These results validate the effectiveness and advancement of the proposed method for infrared video-based gas leakage detection. Furthermore, the model achieves 78.2 frames per second (FPS) during inference, demonstrating good real-time processing capability while maintaining high detection accuracy, exhibiting strong application potential. Full article

Room-temperature phosphorescence (RTP) materials with photo-responsive properties have attracted increasing attention for applications in smart luminescent switches, optical logic control, and multidimensional information storage. Compared to other external stimuli, light offers the advantages of non-contact control, high spatiotemporal resolution, and excellent programmability, making it an ideal strategy for reversible and dynamic modulation of RTP. This review summarizes recent advances in light-triggered RTP systems coupled with photochromism. From a structural design perspective, we discuss strategies to integrate photochromic and RTP units within a single material system, covering photoisomerizable molecules, metal–organic complexes, organic–inorganic hybrids, and purely organic radicals. These materials demonstrate unique advantages in fields such as information encryption, bioimaging, and light-controlled upconversion. Finally, future design directions and challenges are proposed, aiming toward high-security, long-lifetime, and multi-channel collaborative luminescent systems. Full article

Monitoring the condition of existing structures remains one of the most pressing challenges within the construction industry. Structural health monitoring (SHM) techniques have proven increasingly effective in this regard; however, maintaining and archiving complete lifecycle data for such structures remains costly. Data acquisition is particularly critical, as the SHM system relies upon this information to analyse and evaluate structural behaviour. Nonetheless, a range of challenges—such as environmental influences, sensor malfunction, and transmission failures—can lead to data corruption or loss. These issues compromise the reliability of the dataset, necessitating either data reconstruction or additional measurement campaigns, both of which are resource-intensive. This study proposes the use of a long short-term memory (LSTM) network to reconstruct missing or corrupted data. A complete dataset collected from an actual construction project is employed to train the network. Data loss scenarios are then simulated, including single-channel (loss from one sensor) and multi-channel (loss from multiple sensors) cases. The trained LSTM model is subsequently applied to reconstruct the missing data. A case study on a real bridge demonstrates that the reconstructed data show strong agreement with the original measurements in both the time and frequency domains. These findings indicate that the proposed approach has the potential to support engineers in conserving resources by reducing the need for costly and time-consuming additional measurement interventions. Full article

Multi-channel optical systems can provide more feature information compared to single-channel systems, making them valuable for optical remote sensing, target identification, and other applications. The division of aperture polarization imaging modality allows for the simultaneous imaging of targets in the same field of view with a single detector. To overcome the limitations of conventional refractive aperture-divided systems for miniaturization, this work proposes an off-axis catadioptric aperture-divided technique for polarization imaging. First, the design method of the off-axis reflective telescope structure is discussed. The relationship between optical parameters such as magnification, surface coefficient, and primary aberration is studied. Second, by establishing the division of the aperture optical model, the method of maximizing the field of view and aperture is determined. Finally, an off-axis catadioptric cooled aperture-divided infrared optical system with a single aperture focal length of 60 mm is shown as a specific design example. Each channel can achieve 100% cold shield efficiency, and the overall length of the telescope module can be decreased significantly. The image quality of each imaging channel is close to the diffraction limit, verifying the effectiveness and feasibility of the method. The proposed off-axis catadioptric aperture-divided design method holds potential applications in simultaneous infrared polarization imaging. Full article

Sketch re-identification (Re-ID) aims to retrieve pedestrian photographs in the gallery dataset by a query sketch image drawn by professionals, which is crucial for criminal investigations and missing person searches in the field of public security. The main challenge of this task lies in bridging the significant modality gap between sketches and photos while extracting discriminative modality-invariant features. However, information asymmetry between sketches and RGB photographs, particularly the differences in color information, severely interferes with cross-modal matching processes. To address this challenge, we propose a novel network architecture that integrates multi-channel augmentation with hierarchically consistent disentanglement learning. Specifically, a multi-channel augmentation module is developed to mitigate the interference of color bias in cross-modal matching. Furthermore, a modality-disentangled prototype(MDP) module is introduced to decompose pedestrian representations at the feature level into modality-invariant structural prototypes and modality-specific appearance prototypes. Additionally, a cross-layer decoupling consistency constraint is designed to ensure the semantic coherence of disentangled prototypes across different network layers and to improve the stability of the whole decoupling process. Extensive experimental results on two public datasets demonstrate the superiority of our proposed approach over state-of-the-art methods. Full article

This study provides an in-depth analysis of the learning dynamics of multichannel optical neurons based on spatial solitons generated in lithium niobate crystals. Single-node and multi-node configurations with different topological complexities (3 × 3, 4 × 4, and 5 × 5) were compared, assessing how the number of channels, geometry, and optical parameters affect the speed and efficiency of learning. The simulations indicate that single-node neurons achieve the desired imbalance more rapidly and with lower energy expenditure, whereas multi-node structures require higher intensities and longer timescales, yet yield a greater variety of responses, more accurately reproducing the functional diversity of biological neural tissues. The results highlight how the plasticity of these devices can be entirely modulated through optical parameters, paving the way for fully optical photonic neuromorphic networks in which memory and computation are co-localized, with potential applications in on-chip learning, adaptive routing, and distributed decision-making. Full article

Bearings, as commonly used elements in mechanical apparatus, are essential in transmission systems. Fault diagnosis is of significant importance for the normal and safe functioning of mechanical systems. Conventional fault diagnosis methods depend on one or more vibration sensors, and their diagnostic results are often unsatisfactory under strong noise interference. To tackle this problem, this research develops a bearing fault diagnosis technique utilizing a multi-channel, multi-scale spatiotemporal convolutional cross-attention fusion network. At first, continuous wavelet transform (CWT) is applied to convert the raw 1D acoustic and vibration signals of the dataset into 2D time–frequency images. These acoustic and vibration time–frequency images are then simultaneously fed into two parallel structures. After rough feature extraction using ResNet, deep feature extraction is performed using the Multi-Scale Temporal Convolutional Module (MTCM) and the Multi-Feature Extraction Block (MFE). Next, these traits are input into a dual cross-attention mechanism module (DCA), where fusion is achieved using attention interaction. The experimental findings validate the efficacy of the proposed method using tests and comparisons on two bearing datasets. The testing findings validate that the suggested method outperforms the existing advanced multi-sensor fusion diagnostic methods. Compared with other existing multi-sensor fusion diagnostic methods, the proposed method was proven to outperform the five existing methods (1DCNN-VAF, MFAN-VAF, 2MNET, MRSDF, and FAC-CNN). Full article

Vortex beams (VBs) generated by plasmonic metasurfaces hold great potential in the field of information transmission due to their unique helical phase wavefronts and infinite eigenstates. However, achieving perfect multiplexing and superposition of VBs with different orders remains a challenging issue in nanophotonics research. In this paper, based on a single-layer metallic porous metasurface structure applicable to the infrared spectrum, VBs with orders 2, 4, 6, and 8 are realized through the arrangement of annular elliptical apertures. Moreover, perfect VBs are achieved by optimizing key structural parameters using a genetic algorithm. The optimization of key structural parameters via genetic-based optimization algorithms to attain the desired effects can significantly reduce the workload of manual parameter adjustment. In addition, leveraging the orthogonality between VBs of different orders, concentric circular multi-channel VBs array (l = 2, 6) and (l = 4, 8) are realized. High-purity multiplexing architectures (>90%) are achieved via rational optimization of critical structural parameters using a genetic optimization algorithm, which further mitigates information crosstalk in optical communication transmission. The introduction of the genetic algorithm not only reduces the workload of manual arrangement of unit arrays but also enables the generation of more perfect VBs, providing a new research direction for optical communication transmission and optical communication encryption. Full article

Push–pull switch-mode power supplies are widely employed due to their high efficiency and power density. However, traditional designs typically depend on multiple auxiliary circuits to achieve functions such as power-up control, voltage regulation, and system protection, resulting in structural complexity and difficulty in debugging. Additionally, dual-power high-voltage amplifier systems often suffer from voltage deviations caused by supply imbalances or load fluctuations, potentially leading to equipment failure and significant economic losses. To overcome these limitations, we propose a novel digital signal-controlled push–pull driver power supply topology in this paper. Specifically, this design utilizes digital pulse-width modulation (PWM) signals to control multi-stage metal-oxide-semiconductor field-effect transistors (MOSFETs), incorporating adjustable duty-cycle drives, multi-channel current sensing, and fault protection mechanisms. Experimental validation was performed on a ±220 V, 20 kHz, 180 W power supply prototype. The results demonstrate excellent performance, notably enhancing stability and reliability in dual-side synchronous power supply scenarios. Thus, this digital-control topology effectively addresses the drawbacks of conventional push–pull designs and offers potential applications in nondestructive testing and high-voltage driving systems. Full article

Millimeter-wave multiple-input multiple-output synthetic aperture radar (MIMO-SAR) has been widely used in many scenarios such as geological exploration, post-disaster rescue, and security inspection. When faced with large complex scenes, the signal suffers from distortion problems due to amplitude-phase nonlinear aberrations, resulting in undesired artifacts. Many previous studies eliminate artifacts but result in missing target structures. In this paper, we propose to use chunked nonlinear normalized weights in conjunction with signal-to-noise ratio-based (SNR-based) multichannel fusion to address the above-mentioned problems. The chunked nonlinear normalized weights make use of the scene’s characteristics to separately perform the optimization of different regions of the scene. This approach significantly mitigates the effects of amplitude-phase distortion on signal quality, thereby facilitating the effective suppression of noise and artifacts. Applying SNR-based multichannel fusion solves the problem of missing target structures caused by the chunked weights. With the proposed techniques, we can effectively suppress artifacts and noise while maintaining the target structures to enhance the robustness of system. Based on practical experiments, the proposed techniques achieve the image entropy (IE) value, which reduces by approximately 1, and the image contrast (IC) value is increased by approximately 2~4. Furthermore, the computational time is only about 1.3 times that needed by the latest reported algorithm. Consequently, imaging resolution and system robustness are improved by implementing these techniques. Full article

The East China Sea Shelf Basin (ECSSB) and its adjacent areas, as key regions of the ocean–continent transition zone, have been affected by multiple complex plate collisions, subduction, and back-arc tension since the Mesozoic Era. The structural deformation provides a large amount of geological information on the ocean–continent transition zone. There are significant spatiotemporal differences in the structural deformation within the basin. However, the research remains insufficient and understanding is inconsistent, especially regarding the systematic study of the differences and dynamic mechanisms of north–south structural deformation, which is relatively lacking. This study is based on two-dimensional multi-channel deep reflection seismic profiles spanning the southern and northern basin. Through an integrated re-analysis of gravity, magnetic, and OBS data, the deformation characteristics and processes of the Meso-Cenozoic structures in the basin are analyzed. The differences in structural deformation between the southern and northern basin are summarized, and the controlling effects of deep crust–mantle activity and the influencing factors of shallow structural deformation are explored. Based on deep reflection seismic profiles, the structural deformation characteristics of the Yushan–Kume fault are revealed for the first time, and it is proposed that NW faults, represented by the Yushan–Kume fault, have important tuning effects on the north–south structural differential deformation in the ECSSB. The thermal subsidence of the lithosphere is the direct cause of the development of the Mesozoic ECSSB, while the subduction of the Paleo-Pacific plate is one of the important factors contributing to it. The combined effect of the two has led to significant differences between the northern and southern Mesozoic basin. During the Cenozoic Era, the alternating subduction and changes in the direction of subduction of the Pacific Plate led to spatiotemporal differences in structural deformation within the ECSSB. The development of NW faults was a key factor in the differences in structural deformation between the northern and southern basin. The study of structural deformation differences in the ECSSB not only deepens our understanding of the tectonic evolution in the East Asian continental margin region, but also has important significance for the exploration and evaluation of deep hydrocarbon resources in the ECSSB. Full article

The load/strain time–history experienced by aircraft structures during service is crucial original input data for structural life reliability design, life extension, and health monitoring management. Accurately and reliably measuring the strain time–history of aircraft structures and their corresponding loading states is key. Aiming to improve upon the shortcomings of traditional airborne strain measurement methods, this paper proposes a key strain airborne measurement method suitable for load-bearing structures based on the characteristics and requirements of a certain type of aircraft load-bearing structure. An airborne multi-channel synchronous sampling test and acquisition system is designed and integrated for airborne measurement and acquisition of key strains and corresponding loading states of load-bearing structures. The main factors affecting the output of aircraft structure strain airborne measurements are summarized. Based on this, an uncertainty evaluation model for the airborne strain testing system is established, and the uncertainty of the output strain is quantitatively given using multivariate fuzzy evaluation. The key strain time–history of the load-bearing structure is obtained through flight measurement data collection and processing, and the collected strain history is analyzed in combination with simultaneously collected flight parameters. The results show that the proposed airborne strain measurement method and the integrated airborne strain measurement and acquisition system can reliably measure and reproduce the real loading history of the structure, providing reliable data support for structural damage estimation and life prediction management. It also provides an effective and practical reference for strain measurement of other aircraft structures. Full article