Search Results (250)

With their superior switching speed, GaN high-electron-mobility transistors (HEMTs) enable high power density, reduce energy losses, and increase power efficiency in a wide range of applications, such as power electronics, due to their high breakdown voltage. GaN-HEMT devices are subject to long-term reliability due to the self-heating effect and lattice mismatch between the SiC substrate and the GaN. Depletion-mode GaN HEMTs are utilized for radio frequency applications, and this work investigates three wide-bandgap (WBG) GaN HEMT fixed-frequency oscillators with output buffers. The first GaN-on-SiC HEMT oscillator consists of an HEMT amplifier with an LC feedback network. With the supply voltage of 0.8 V, the single-ended GaN oscillator can generate a signal at 8.85 GHz, and it also supplies output power of 2.4 dBm with a buffer supply of 3.0 V. At 1 MHz frequency offset from the carrier, the phase noise is −124.8 dBc/Hz, and the figure of merit (FOM) of the oscillator is −199.8 dBc/Hz. After the previous study, the hot-carrier stressed RF performance of the GaN oscillator is studied, and the oscillator was subject to a drain supply of 8 V for a stressing step time equal to 30 min and measured at the supply voltage of 0.8 V after the step operation for performance benchmark. Stress study indicates the power oscillator with buffer is a good structure for a reliable structure by operating the oscillator core at low supply and the buffer at high supply. The second balanced oscillator can generate a differential signal. The feedback filter consists of a left-handed transmission-line LC network by cascading three unit cells. At a 1 MHz frequency offset from the carrier of 3.818 GHz, the phase noise is −131.73 dBc/Hz, and the FOM of the 2nd oscillator is −188.4 dBc/Hz. High supply voltage operation shows phase noise degradation. The third GaN cross-coupled VCO uses 8-shaped inductors. The VCO uses a pair of drain inductors to improve the Q-factor of the LC tank, and it uses 8-shaped inductors for magnetic coupling noise suppression. At the VCO-core supply of 1.3 V and high buffer supply, the FOM at 6.397 GHz is −190.09 dBc/Hz. This work enhances the design techniques for reliable GaN HEMT oscillators and knowledge to design high-performance circuits. Full article

Primary-level social risk prevention and control is a complex, systemic endeavor that requires close cooperation among various local government departments. Within this context, addressing bureaucratic segmentation and strengthening interdepartmental collaboration are critical issues in primary-level social risk governance. This study uses social network analysis and the exponential random graph model to examine the collaborative network structure and driving mechanisms among government departments engaged in risk prevention, with J City as a network study. The findings reveal that (1) while a collaborative governance framework exists, the network has low overall density, strong localized clustering, and a clear core-periphery structure, indicating the need for improved coordination and more refined collaborative mechanisms; (2) the formation of the risk prevention network is influenced by both endogenous structural factors and exogenous actor attributes. Endogenously, reciprocity and transitivity play significant roles in tie formation; exogenously, departments with similar resource mobilization capacities are more likely to collaborate, while those with strong communication, digital technology, and resource mobilization capabilities are more likely to initiate collaborations, and those with high communication capacity are more likely to accept collaborative offers. This study offers insights into the dynamics and formation mechanisms of departmental collaborative networks in primary-level social risk governance. Full article

5G addresses user privacy concerns in cellular networking by encrypting a subscriber identifier with elliptic-curve-based encryption and then transmitting it as ciphertext known as a Subscriber Concealed Identifier (SUCI). However, an adversary equipped with a quantum computer can break a discrete-logarithm-based elliptic curve algorithm. Consequently, the user privacy in 5G is at stake against quantum attacks. In this paper, we study the incorporation of the post-quantum ciphers in the SUCI calculation both at the user equipment and at the core network, which involves the shared-key exchange and then using the resulting key for the ID encryption. We experiment on different hardware platforms to analyze the PQC key exchange and encryption using NIST-standardized CRYSTALS-Kyber (which is now called an ML-KEM after the standardization selection by NIST). Our analyses focus on the performances and compare the Kyber-based key exchange and encryption with the current (pre-quantum) elliptic curve Diffie–Hellman (ECDH). The performance analyses are critical because mobile networking involves resource-limited and battery-operating mobile devices. We measure and analyze not only the time and CPU-processing performances but also the energy and power performances. Our analyses show that Kyber-512 is the most efficient and even has better performance (i.e., faster computations and lower energy consumption) than ECDH. Full article

The evolution of 3GPP non-terrestrial networks (NTNs) is enabling new avenues for broadband connectivity via satellite, especially within the scope of 5G. The parallel rise in satellite mega-constellations has further fueled efforts toward ubiquitous global Internet access. This convergence has fostered collaboration between mobile network operators and satellite providers, allowing the former to leverage mature space infrastructure and the latter to integrate with terrestrial mobile standards. However, integrating these technologies presents significant architectural challenges. This study investigates 5G NTN architectures using satellite mega-constellations, focusing on transparent architectures where Starlink is employed to relay the backhaul, midhaul, and new radio (NR) links. The performance of these architectures is assessed through a testbed utilizing OpenAirInterface (OAI) and Open5GS, which collects key user-experience metrics such as round-trip time (RTT) and jitter when pinging the User Plane Function (UPF) in the 5G core (5GC). Results show that backhaul and midhaul relays maintain delays of 50–60 ms, while NR relays incur delays exceeding one second due to traffic overload introduced by the RFSimulator tool, which is indispensable to transmit the NR signal over Starlink. These findings suggest that while transparent architectures provide valuable insights and utility, regenerative architectures are essential for addressing current time issues and fully realizing the capabilities of space-based broadband services. Full article

Amid structural changes driven by the greening and digital transformation of the maritime industry, the demand for career transitions of seafarers with onboard experience to shore-based positions—particularly ship superintendents—is steadily increasing. However, the current lack of a systematic education and career development framework to support such transitions poses a critical challenge for shipping companies seeking to secure sustainable human resources. The aim of this study was to develop a competency-based training program that facilitates the effective transition of seafarers to shore-based ship superintendent roles. We integrated a developing a curriculum (DACUM) analysis with competency-based job analysis to achieve this aim. The core competencies required for ship superintendent duties were identified through three expert consultations. In addition, social network analysis (SNA) was used to quantitatively assess the structure and priority of the training content. The analysis revealed that convergent competencies, such as digital technology literacy, responsiveness to environmental regulations, multicultural organizational management, and interpretation of global maritime regulations, are essential for a successful career shift. Based on these findings, a modular training curriculum comprising both common foundational courses and specialized advanced modules tailored to job categories was designed. The proposed curriculum integrated theoretical instruction, practical training, and reflective learning to enhance both applied understanding and onsite implementation capabilities. Furthermore, the concept of a Seafarer Success Support Platform was proposed to support a lifecycle-based career development pathway that enables rotational mobility between sea and shore positions. This digital learning platform was designed to offer personalized success pathways aligned with the career stages and competency needs of maritime personnel. Its cyclical structure, comprising career transition, competency development, field application, and performance evaluation, enables seamless career integration between shipboard- and shore-based roles. Therefore, the platform has the potential to evolve into a practical educational model that integrates training, career development, and policies. This study contributes to maritime human resource development by integrating the DACUM method with a competency-based framework and applying social network analysis (SNA) to quantitatively prioritize training content. It further proposes the Seafarer Success Support Platform as an innovative model to support structured career transitions from shipboard roles to shore-based supervisory positions. Full article

Accurate and rapid aquatic organism recognition is a core technology for fisheries automation and aquatic organism statistical research. However, due to absorption and scattering effects, images of aquatic organisms often suffer from poor contrast and color distortion. Additionally, the clustering behavior of aquatic organisms often leads to occlusion, further complicating the identification task. This study proposes a lightweight object detection model, Mobile-YOLO, for the recognition of four representative aquatic organisms, namely holothurian, echinus, scallop, and starfish. Our model first utilizes the Mobile-Nano backbone network we proposed, which enhances feature perception while maintaining a lightweight design. Then, we propose a lightweight detection head, LDtect, which achieves a balance between lightweight structure and high accuracy. Additionally, we introduce Dysample (dynamic sampling) and HWD (Haar wavelet downsampling) modules, aiming to optimize the feature fusion structure and achieve lightweight goals by improving the processes of upsampling and downsampling. These modules also help compensate for the accuracy loss caused by the lightweight design of LDtect. Compared to the baseline model, our model reduces Params (parameters) by 32.2%, FLOPs (floating point operations) by 28.4%, and weights (model storage size) by 30.8%, while improving FPS (frames per second) by 95.2%. The improvement in mAP (mean average precision) can also lead to better accuracy in practical applications, such as marine species monitoring, conservation efforts, and biodiversity assessment. Furthermore, the model’s accuracy is enhanced, with the mAP increased by 1.6%, demonstrating the advanced nature of our approach. Compared with YOLO (You Only Look Once) series (YOLOv5-12), SSD (Single Shot MultiBox Detector), EfficientDet (Efficient Detection), RetinaNet, and RT-DETR (Real-Time Detection Transformer), our model achieves leading comprehensive performance in terms of both accuracy and lightweight design. The results indicate that our research provides technological support for precise and rapid aquatic organism recognition. Full article

This research presents the development of a type-2 fuzzy-controlled autonomous mobile robot specifically designed for monitoring and actively maintaining indoor air quality. The core of this system is the proposed type-2 fuzzy PID dual-mode controller used for stably patrolling rooms along the walls of the environment. The design method ingeniously merges the fast error correction capability of PID control with the robust adaptability of type-2 fuzzy logic control, which utilizes interval type-2 fuzzy sets. Furthermore, the type-2 fuzzy rule table of the right wall-following controller can be extended from the first designed fuzzy left wall-following controller in a symmetrical design manner. As a result, this study eliminates the drawbacks of excessive oscillations arising from PID control and sluggish response to large initial errors in typical traditional fuzzy control. The following of the stable wall and obstacle is facilitated with ensured accuracy and easy implementation so that effective air quality monitoring and active PM2.5 filtering are achieved in a movable manner. Furthermore, the augmented reality (AR) interface overlays real-time PM2.5 data directly onto a user’s visual field, enhancing situational awareness and enabling an immediate and intuitive assessment of air quality. As this type of control is different from that used in traditional fixed sensor networks, both broader area coverage and efficient air filtering are achieved. Finally, the experimental results demonstrate the controller’s superior performance and its potential to significantly improve indoor air quality. Full article

From the mid-Ming to early Qing dynasties, Mount Jizu 雞足山 in Yunnan achieved unexpected prominence within China’s Buddhist sacred landscape—an event of regional, national, and transnational significance. Employing an explicit comparative lens that juxtaposes Jizu with China’s core-region sacred sites like Mount Wutai and Emei, this study investigates the timing, regional dynamics, institutional mechanisms, and causal drivers behind the rapid ascent. Rejecting teleological narratives, it traces the mountain’s trajectory through four developmental phases to address critical historiographical questions: how did a peripheral Yunnan site achieve national prominence within a remarkably compressed timeframe? By what mechanisms could its sacred authority be constructed to inspire pilgrimages even across vast distances? Which historical agents and processes orchestrated these transformations, and how did the mountain’s symbolic meaning shift dynamically over time? Departing from earlier scholarship that privileges regional and secular frameworks, this work not only rebalances the emphasis on religious dimensions but also expands the analytical scope beyond regional confines to situate Mount Jizu within national and transnational frameworks. Eventually, by analyzing the structural, institutional, and agential dynamics—spanning local, imperial, and transnational dimensions—this study reveals how the mountain’s sacralization emerged from the convergence of local agency, acculturative pressures, state-building imperatives, late-Ming Buddhist revival, literati networks, and the strategic mobilization of symbolic capital. It also reveals that Mount Jizu was not a static sacred site but a dynamic arena of contestation and negotiation, where competing claims to spiritual authority and cultural identity were perpetually redefined. Full article

This paper introduces an innovative mobile-assisted language-learning (MALL) system that harnesses deep learning technology to analyze pronunciation patterns and deliver real-time, personalized feedback. Drawing inspiration from how the human brain processes speech through neural pathways, our system analyzes multiple speech features using spectrograms, mel-frequency cepstral coefficients (MFCCs), and formant frequencies in a manner that mirrors the auditory cortex’s interpretation of sound. The core of our approach utilizes a convolutional neural network (CNN) to classify pronunciation patterns from user-recorded speech. To enhance the assessment accuracy and provide nuanced feedback, we integrated a fuzzy inference system (FIS) that helps learners identify and correct specific pronunciation errors. The experimental results demonstrate that our multi-feature model achieved 82.41% to 90.52% accuracies in accent classification across diverse linguistic contexts. The user testing revealed statistically significant improvements in pronunciation skills, where learners showed a 5–20% enhancement in accuracy after using the system. The proposed MALL system offers a portable, accessible solution for language learners while establishing a foundation for future research in multilingual functionality and mobile platform optimization. By combining advanced speech analysis with intuitive feedback mechanisms, this system addresses a critical challenge in language acquisition and promotes more effective self-directed learning. Full article

The growing demand for bandwidth-intensive vehicular applications—particularly ultra-high-definition streaming and immersive panoramic video—is pushing current network infrastructures beyond their limits, especially in urban areas with severe congestion and degraded user experience. To address these challenges, we propose an aerial-assisted vehicular network architecture that integrates 6G base stations, distributed massive MIMO networks, visible light communication (VLC), and a heterogeneous aerial network of high-altitude platforms (HAPs) and drones. At its core is a context-aware dynamic bandwidth allocation algorithm that intelligently routes infotainment data through optimal aerial relays, bridging connectivity gaps in coverage-challenged areas. Simulation results show a 47% increase in average available bandwidth over conventional first-come-first-served schemes. Our system also satisfies the stringent latency and reliability requirements of emergency and live infotainment services, creating a sustainable ecosystem that enhances user experience, service delivery, and network efficiency. This work marks a key step toward enabling high-bandwidth, low-latency smart mobility in next-generation urban networks. Full article

This paper introduces SmartBarrel, an innovative IoT-based sensory system that monitors and forecasts wine fermentation processes. At the core of SmartBarrel are two compact, attachable devices—the probing nose (E-nose) and the probing tongue (E-tongue), which mount directly onto stainless steel wine tanks. These devices periodically measure key fermentation parameters: the nose monitors gas emissions, while the tongue captures acidity, residual sugar, and color changes. Both utilize low-cost, low-power sensors validated through small-scale fermentation experiments. Beyond the sensory hardware, SmartBarrel includes a robust cloud infrastructure built on open-source Industry 4.0 tools. The system leverages the ThingsBoard platform, supported by a NoSQL Cassandra database, to provide real-time data storage, visualization, and mobile application access. The system also supports adaptive breakpoint alerts and real-time adjustment to the nonlinear dynamics of wine fermentation. The authors developed a novel deep learning model called V-LSTM (Variable-length Long Short-Term Memory) to introduce intelligence to enable predictive analytics. This auto-calibrating architecture supports variable layer depths and cell configurations, enabling accurate forecasting of fermentation metrics. Moreover, the system includes two fuzzy logic modules: a device-level fuzzy controller to estimate alcohol content based on sensor data and a fuzzy encoder that synthetically generates fermentation profiles using a limited set of experimental curves. SmartBarrel experimental results validate the SmartBarrel’s ability to monitor fermentation parameters. Additionally, the implemented models show that the V-LSTM model outperforms existing neural network classifiers and regression models, reducing RMSE loss by at least 45%. Furthermore, the fuzzy alcohol predictor achieved a coefficient of determination ($R^2$) of 0.87, enabling reliable alcohol content estimation without direct alcohol sensing. Full article

The increasing demand for distributed machine learning like Federated Learning (FL) in dynamic, resource-constrained edge environments, 5G/6G networks, and the proliferation of mobile and edge devices, presents significant challenges related to fault tolerance, elasticity, and communication efficiency. This paper addresses these issues by proposing a novel modular and resilient FL framework. In this context, resilience refers to the system’s ability to maintain operation and performance despite disruptions. The framework is built on decoupled modules handling core FL functionalities, allowing flexibility in integrating various algorithms, communication protocols, and resilience strategies. Results demonstrate the framework’s ability to integrate different communication protocols and FL paradigms, showing that protocol choice significantly impacts performance, particularly in high-volume communication scenarios, with Zenoh and MQTT exhibiting lower overhead than Kafka in tested configurations, and Zenoh emerging as the most efficient communication option. Additionally, the framework successfully maintained model training and achieved convergence even when simulating probabilistic worker failures, achieving a MCC of 0.9453. Full article

Wireless Power Transfer (WPT) enables efficient, contactless charging for mobile devices by eliminating mechanical connectors and wiring, thereby enhancing user experience and device longevity. However, conventional WPT systems remain prone to performance issues such as coil misalignment, resonance instability, and thermal losses. Addressing these challenges involves designing coil geometries that operate at lower resonant frequencies to strengthen magnetic coupling and decrease resistance. This work introduces a WPT system with a performance-driven coil design aimed at maximizing magnetic coupling and mutual inductance between the transmitting (Tx) and receiving (Rx) coils in mobile devices. Due to the nonlinear behavior of magnetic flux and the high computational cost of simulations, exploring the full design space for coils using ANSYS Maxwell becomes impractical. To address this complexity, a machine learning (ML)-based optimization framework is developed to efficiently navigate the design space. The framework integrates a hybrid sequential neural network and multivariate regression model to optimize coil winding and ferrite core geometry. The optimized structure achieves a mutual inductance of 12.52 $\mu$H with a conventional core, outperforming many existing ML models. Finite element simulations and experimental results validate the robustness of the method, which offers a scalable solution for efficient wireless charging in compact, misalignment-prone environments. Full article

This study employs a multi-model coupling and multi-scenario simulation approach to construct a framework for identifying and optimizing avian ecological corridors in the urban core of Kunming. The framework focuses on the ecological needs of resident birds (64.72%), woodland-dependent birds (39.87%), and low-mobility birds (47.29%) to address habitat fragmentation and enhance urban biodiversity conservation. This study identifies 54 core ecological corridors, totaling 183.58 km, primarily located in forest–urban transition zones. These corridors meet the continuous habitat requirements of resident and woodland-dependent birds, providing a stable environment for species. Additionally, 55 general corridors, spanning 537.30 km, focus on facilitating short-distance movements of low-mobility birds, enhancing habitat connectivity in urban fringe areas through ecological stepping stones. Eighteen ecological pinch points (total area 5.63 km²) play a crucial role in the network. The northern pinch points, dominated by forest land, serve as vital breeding and refuge habitats for woodland-dependent and resident birds. The southern pinch points, located in wetland-forest ecotones, function as critical stopover sites for low-mobility waterbirds. Degradation of these pinch points would significantly reduce available habitat for birds. The 27 ecological barrier points (total area 89.79 km²), characterized by urban land use, severely impede the movement of woodland-dependent birds and increase the migratory energy expenditure of low-mobility birds in agricultural areas. Following optimization, resistance to resident birds in core corridors is significantly reduced, and habitat utilization by generalist species in general corridors is markedly improved. Moreover, multi-scenario optimization measures, including the addition of ecological stepping stones, barrier improvement, and pinch-point protection, have effectively increased ecological sources, met avian habitat requirements, and secured migratory pathways for waterbirds. These measures validate the scientific rationale of a multidimensional management strategy. The comprehensive framework developed in this study, integrating species needs, corridor design, and spatial optimization, provides a replicable model for avian ecological corridor construction in subtropical montane cities. Future research may incorporate bird-tracking technologies to further validate corridor efficacy and explore planning pathways for climate-adaptive corridors. Full article

Integrating Electric Vehicle (EV) charging stations into buildings is becoming increasingly important due to the rapid growth of private EV ownership and prolonged parking durations in residential areas. This paper proposes robust, building-integrated charging solutions that combine mobile energy storage systems (ESSs), station linkage data, and traffic volume data. The proposed system promotes eco-friendly EV usage, flexible energy management, and carbon neutrality through a polyfunctional Vehicle-to-Grid (V2G) architecture that integrates decentralized energy networks. Two core strategies are implemented: (1) configuring Virtual Power Plant (VPP)-based charging packages tailored to station types, and (2) utilizing EV batteries as distributed ESS units. K-means clustering based on spatial proximity and energy demand is followed by heuristic algorithms to improve the efficiency of mobile ESS operation. A three-layer framework is used to assess improvements in energy demand distribution, with demand-oriented VPPs deployed in high-demand zones to maximize ESS utilization. This approach enhances station stability, increases the load factor to 132.7%, and reduces emissions by 271.5 kgCO₂. Economically, the system yields an annual benefit of USD 47,860, a Benefit–Cost Ratio (BCR) of 6.67, and a Levelized Cost of Energy (LCOE) of USD 37.78 per MWh. These results demonstrate the system’s economic viability and resilience, contributing to the development of a flexible and sustainable energy infrastructure for cities. Full article