Search Results (2,085)

Hyperspectral image (HSI) classification is a crucial technique that assigns each pixel in an image to a specific land cover category by leveraging both spectral and spatial information. In recent years, HSI classification methods based on convolutional neural networks (CNNs) and Transformers have significantly improved performance due to their strong feature extraction capabilities. However, these improvements often come with increased model complexity, leading to higher computational costs. To address this, we propose a compact and efficient spectral-spatial feature extraction and attention-based neural network (SSFAN) for HSI classification. The SSFAN model consists of three core modules: the Parallel Spectral-Spatial Feature Extraction Block (PSSB), the Scan Block, and the Squeeze-and-Excitation MLP Block (SEMB). After preprocessing the HSI data, it is fed into the PSSB module, which contains two parallel streams, each comprising a 3D convolutional layer and a 2D convolutional layer. The 3D convolutional layer extracts spectral and spatial features from the input hyperspectral data, while the 2D convolutional layer further enhances the spatial feature representation. Next, the Scan Block module employs a layered scanning strategy to extract spatial information at different scales from the central pixel outward, enabling the model to capture both local and global spatial relationships. The SEMB module combines the Spectral-Spatial Recurrent Block (SSRB) and the MLP Block. The SSRB, with its adaptive weight assignment mechanism in the SToken Module, flexibly handles time steps and feature dimensions, performing deep spectral and spatial feature extraction through multiple state updates. Finally, the MLP Block processes the input features through a series of linear transformations, GELU activation functions, and Dropout layers, capturing complex patterns and relationships within the data, and concludes with an argmax layer for classification. Experimental results show that the proposed SSFAN model delivers superior classification performance, outperforming the second-best method by $1.72\%$, $5.19\%$, and $1.94\%$ in OA, AA, and Kappa coefficient, respectively, on the Indian Pines dataset. Additionally, it requires less training and testing time compared to other state-of-the-art deep learning methods. Full article

This study reports the successful synthesis of core-shell microparticles utilizing coaxial electrospray techniques, with zeolitic imidazolate framework-8 (ZIF-8) encapsulating rhodamine B (RhB) in the core and a phase change material (PCM) shell composed of a eutectic mixture of lauric acid (LA) and stearic acid (SA). ZIF-8 is well-recognized for its pH-responsive degradation and biocompatibility, making it an ideal candidate for targeted drug delivery. The LA-SA PCM mixture, with a melting point near physiological temperature (39 °C), enables temperature-triggered drug release, enhancing therapeutic precision. The structural properties of the microparticles were extensively characterized through scanning electron microscopy (SEM), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), and thermogravimetric analysis (TGA). Drug release studies revealed a dual-stimuli response, where the release of RhB was significantly influenced by both temperature and pH. Under mildly acidic conditions (pH 4.0) at 40 °C, a rapid and complete release of RhB was observed within 120 h, while at 37 °C, the release rate was notably slower. Specifically, the release at 40 °C was 79% higher than at 37 °C, confirming the temperature sensitivity of the system. Moreover, at physiological pH (7.4), minimal drug release occurred, demonstrating the system’s potential for minimizing premature drug release under neutral conditions. This dual-stimuli approach holds promise for improving therapeutic outcomes in cancer treatment by enabling precise control over drug release in response to both pH and localized hyperthermia, reducing off-target effects and improving patient compliance. Full article

AMHSs (Automated Material Handling Systems) are widely used in major Fabs (semiconductor fabrication plants). The OHT in an AMHS is responsible for handling the FOUP (Front Opening Unified Pod) within the Fabs. Due to the unidirectional track, the movement path of the OHT aims to avoid congested areas caused by operations or malfunctions as much as possible, to improve the overall FOUP handling efficiency. To do so, we propose a dynamic path planning method, MI-DDQN (Map Information Double Deep Q-Network), driven by deep reinforcement learning and based on map information. Firstly, we design and establish a map information state space model based on the core elements of the OHT path planning in the AMHS. Then, we design an OHT motion simulator to simulate the position coordinate transformation of the OHT, providing real-time coordinate update data for the OHT during the algorithm training process. We design a deep reinforcement learning algorithm structure based on map information model and a convolutional neural network model structure and use the algorithm to train the network model. Finally, the designed task generation module and OHT motion simulator are used to randomly generate the starting position and task position of the OHT during the training process to enhance the richness of the data. The addition of a “fault” OHT verifies the method’s ability to plan routes in complex road conditions such as congestion that may occur at any time. Full article

This paper proposes a gallium nitride (GaN)-based very-high-frequency (VHF) resonant flyback converter with integrated magnetics, which utilizes the parasitic inductance and capacitance to reduce the passive components count and volume of the converter. Both the primary leakage inductance and the secondary leakage inductance of the transformer are utilized as the resonance inductor, while the parasitic capacitance of the power devices is utilized as the resonance capacitor. An analytical circuit model is proposed to determine the electrical parameters of the transformer so as to achieve zero voltage switching (ZVS) and zero current switching (ZCS). Furthermore, an air-core transformer was designed using the improved Wheeler’s formula, and finite element analyses were carried out to fine-tune the structure to achieve the accurate design of the electrical parameters. Finally, a 30 MHz, 15 W VHF resonant flyback converter prototype is built with an efficiency of 83.1% for the rated power. Full article

Social networks, functioning as core platforms for modern information dissemination, manifest distinctive user clustering behaviors and state transition mechanisms, thereby presenting new challenges to traditional information propagation models. Based on hypergraph theory, this paper augments the traditional SEIR model by introducing a novel hypernetwork information dissemination SSEIR model specifically designed for online social networks. This model accurately represents complex, multi-user, high-order interactions. It transforms the traditional single susceptible state ($S$) into active ($S_a$) and inactive ($S_i$) states. Additionally, it enhances traditional information dissemination mechanisms through reaction process strategies (RP strategies) and formulates refined differential dynamical equations, effectively simulating the dissemination and diffusion processes in online social networks. Employing mean field theory, this paper conducts a comprehensive theoretical derivation of the dissemination mechanisms within the SSEIR model. The effectiveness of the model in various network structures was verified through simulation experiments, and its practicality was further validated by its application on real network datasets. The results show that the SSEIR model excels in data fitting and illustrating the internal mechanisms of information dissemination within hypernetwork structures, further clarifying the dynamic evolutionary patterns of information dissemination in online social hypernetworks. This study not only enriches the theoretical framework of information dissemination but also provides a scientific theoretical foundation for practical applications such as news dissemination, public opinion management, and rumor monitoring in online social networks. Full article

This study seeks to understand the political discourse of Javier Milei and to determine which concept of populism best captures his approach. Although perceived by many as a populist, Milei is unusual in that he sees himself as a liberal libertarian and defender of the West against collectivist policies. To this end, this study analyzes selected speeches by Milei from three different periods during and after the 2024 presidential election campaign and applies a deductive coding scheme designed to identify ideational populism, populist discursive framing, populism as strategy, and populism as crisis performance. The analysis confirms that Milei is at best a partial populist, as he fails to define the core populist concept of “the people”. It concludes that the concept of crisis performance emerges as the most apt theoretical framework to classify Milei’s type of populism. By rhetorically transforming the crisis not only into an existential economic issue but also into a moral tale of corruption and failure at the highest levels, he can appeal for radical change and offer himself as the national political savior. Milei’s discourse also illustrates that, unlike ideological populism or discursive populist framing, in the performative turn, the victims of the crisis, the people, often remain a vague signifier defined by their suffering at the hands of elites. Full article

Four formulations based on the Kirchhoff transformation and time linearization for the numerical study of one-dimensional reaction–diffusion equations, whose heat capacity, thermal inertia and reaction rate are only functions of the temperature, are presented. The formulations result in linear, two-point boundary-value problems for the temperature, energy or heat potential, and may be solved by either discretizing the second-order spatial derivative or piecewise analytical integration. In both cases, linear systems of algebraic equations are obtained. The formulation for the temperature is extended to two-dimensional, nonlinear reaction–diffusion equations where the resulting linear two-dimensional operator is factorized into a sequence of one-dimensional ones that may be solved by means of any of the four formulations developed for one-dimensional problems. The multidimensional formulation is applied to a two-dimensional, two-equation system of nonlinearly coupled advection–reaction–diffusion equations, and the effects of the velocity and the parameters that characterize the nonlinear heat capacities and thermal conductivity are studied. It is shown that clockwise-rotating velocity fields result in wave stretching for small vortex radii, and wave deceleration and thickening for counter-clockwise-rotating velocity fields. It is also shown that large-core, clockwise-rotating velocity fields may result in large transient periods, followed by time intervals of apparent little activity which, in turn, are followed by the propagation of long-period waves. Full article

In the context of China’s community governance, as a result of the insufficient willingness and ability of residents to participate, most communities have formed a “strong political–weak social” governance structure, which limits the effective expansion of social capital. To address this challenge and promote resident participation, achieving a “strong social and political” transformation in governance structure has become a core issue in current research. Meanwhile, “sense of community” serves as a crucial perspective in this research, which remains to be deepened in domestic studies and needs to consider the impact of emerging factors such as smart technologies. This study innovatively introduces affective events theory (AET), constructing a theoretical model and taking Wuhan as an example. By adopting quantitative and case comparison research methods, the paper delves into the factors influencing Wuhan residents’ sense of community in the context of a smart society and the promoting role of smart governance. On the basis of the findings, this paper presents the following conclusions: (1) Communities should attribute equal importance to the construction of both traditional and smart environments while reinforcing the leading and participatory roles of community administrators; (2) In the community’s daily governance processes, equal focus and importance must be attributed to both service delivery and engagement; (3) Communities must acknowledge the individual distinctions among residents and implement tailored governing strategies accordingly; (4) The government should prioritize the research and development of smart applications while boosting financial investments in the creation and operation of smart communities. Full article

The increasing global consciousness and collective recognition of the importance of sustainability, coupled with initiatives focused on sustainable development, have resulted in a heightened commitment and transformation among organizations and corporations in their endeavors to contribute to the achievement of sustainable development goals through their corporate sustainability initiatives. Prior studies have underscored the effects of corporate sustainability on various strategic levels, such as corporate, business, and operations, paving the way for further investigation. This paper seeks to establish a theoretical framework for sustainable operations strategy through six propositions and subsequently validate this framework via a qualitative case study analysis of a production and processing special economic zone in an emerging nation, specifically Indonesia. The findings from the empirical testing indicate that the proposed theoretical framework has been validated with minor adjustments, through the inclusion of good corporate governance and the adoption of local core values. The paper also presents theoretical and managerial implications, along with suggestions for future research avenues. Full article

Seismic stacking is a core technique in seismic data processing, aimed at enhancing the signal-to-noise ratio (SNR) of data by utilizing seismic data acquisition with multifold geometry. Traditional stacking methods always have certain limitations, such as the reliance on the accuracy of velocity analysis for dip moveout (DMO) in common midpoint (CMP) stacking. The seislet transform, a compression technique tailored to nonstationary seismic data, can compress and stack along the prediction direction of seismic data, which provides a new technical idea for high-fidelity seismic imaging based on the effectiveness of the compression. This paper introduces a high-order OC-seislet stacking method for low-SNR seismic data, capable of achieving the high-fidelity stacking of reflection and diffraction waves simultaneously. With the multi-scale analysis advantages of the seislet transform, this method addresses the dependency of DMO stacking on velocity analysis accuracy. In the frequency–wavenumber–scale domain, the correction compensation of the high-order CDF 9/7 basis function is used to obtain the compression coefficients of the high-order OC-seislet transform. This approach simultaneously stacks frequency–wavenumber information of reflection and diffraction waves with high fidelity while implementing DMO processing. After normalizing the weighting coefficients and applying soft thresholding for denoising, the final result is transformed back to the original time–space domain, yielding high-fidelity stacking sections. The results of applying this method to both synthetic and field data show that, compared with conventional DMO stacking methods, the high-order OC-seislet stacking technique reasonably represents dipping layers and fault amplitudes, and it can achieve a balance of a high SNR and high fidelity in stacked profiles. Full article

Industrial microwave-heating systems are pivotal in various sectors, including food processing and materials manufacturing, where precise temperature control and safety are critical. Conventional systems often struggle with uneven heat distribution and high fire risks due to the intrinsic properties of microwave heating. In this work, a fiber-optic-sensor-assisted monitoring system is presented to tackle the pressing challenges associated with uneven heating and fire hazards in industrial microwave systems. The core innovation lies in the development of a sophisticated fiber-optic 2D temperature distribution sensor and a dedicated fire detector, both designed to significantly mitigate risks and optimize the heating process. Experimental results set the stage for future innovations that could transform the landscape of industrial heating technologies toward better process quality. Full article

The joint classification of hyperspectral imagery (HSI) and LiDAR data is an important task in the field of remote sensing image interpretation. Traditional classification methods, such as support vector machine (SVM) and random forest (RF), have difficulty capturing the complex spectral–spatial–elevation correlation information. Recently, important progress has been made in HSI-LiDAR classification using Convolutional Neural Networks (CNNs) and Transformers. However, due to the large spatial extent of remote sensing images, the vanilla Transformer and CNNs struggle to effectively capture global context. Moreover, the weak misalignment between multi-source data poses challenges for their effective fusion. In this paper, we introduce AFA–Mamba, an Adaptive Feature Alignment Network with a Global–Local Mamba design that achieves accurate land cover classification. It contains two main core designs: (1) We first propose a Global–Local Mamba encoder, which effectively models context through a 2D selective scanning mechanism while introducing local bias to enhance the spatial features of local objects. (2) We also propose an SSE Adaptive Alignment and Fusion (A²F) module to adaptively adjust the relative positions between multi-source features. This module establishes a guided subspace to accurately estimate feature-level offsets, enabling optimal fusion. As a result, our AFA–Mamba consistently outperforms state-of-the-art multi-source fusion classification approaches across multiple datasets. Full article

This study aims to analyze the temporal and spatial distribution of carbon capture technologies worldwide, examining the economic, social, and political developments reflected in related academic literature. By conducting a comprehensive analysis of over 40,000 related documents from 2004 to June 2024, as well as selecting 108 relevant articles from SSCI and SCI journals, the study explores the development of carbon capture technologies from different perspectives through keyword searches, trend analysis, and relevance ranking. The study finds that, in terms of temporal trends, significant progress has been made in carbon capture technologies since 2009, and their importance has surpassed that of carbon trading, becoming one of the core technologies in addressing climate change. Spatial trend analysis shows that North American and European countries are more inclined to prioritize “carbon capture” technologies, while Asian countries focus more on “carbon trading”, reflecting regional differences in economic, policy, and technological development. Although carbon capture technologies hold immense potential for sustainable development, they also face numerous challenges, including balancing technological advancements with economic and policy frameworks. This balance is crucial to ensuring that carbon capture technologies can make a positive contribution to sustainable work, climate action, and environmental sustainability, further transforming the essence of sustainable efforts. To fully realize their benefits, it is essential to recognize and address these challenges. Full article

Digital transformation is a process in which the latest technologies are used in various business fields to keep pace with continuous changes. It involves the strategic and profound integration of digital technologies into an organization’s core business operations, processes, and models. In this study, a quantitative approach was used to study the impact of DT adoption on public sector transformational change projects in the United Arab Emirates (UAE). The diffusion of innovation theory (DIT) and the unified theory of acceptance and use of technology model (UTAUT) were used in the factor analysis. This study highlights that digital transformation initiatives in the UAE have benefited from a strategic alignment with government initiatives, such as AI and blockchain strategies. However, public sector organizations face challenges, such as the high costs of technology adoption and cybersecurity risks during integration with legacy systems. The significance of social influence, including elements like use behavior and behavioral intention, was identified as essential for digital transformation, suggesting the importance of technology in job performance. Similarly, digital transformation projects improve IT competence and reduce resistance to change among leaders and individuals. The findings underscore the importance of investing in infrastructure and continuous IT training to sustain digital transformation. More studies are required across specific sectors to further explore the impact and scalability of DT initiatives in the UAE public sector. Full article

This article presents the measurement verification of a novel strategy for inrush current reduction in an unloaded three-phase Dy transformer. The strategy combines appropriate pre-magnetization of transformer cores with an original control switching system using initial phase values of the supply voltage as control variables. Measurements were recorded for primary voltages and currents as well as secondary voltages during transient states at start-up under no-load conditions. Various inrush scenarios were examined across the full angular spectrum of initial phase angles, both polarities, and in regard to different pre-magnetization current values. A detailed analysis of the inrush currents was performed using proprietary automated software based on the recorded data. A comparative study with a nonlinear mathematical model of the transformer was also conducted. Additionally, key technical aspects of the designed system for implementing the proposed pre-magnetization strategy with controlled voltage energization are discussed. Full article