Search Results (655)

This study presents a novel self-adaptive marine current power generation system capable of operating efficiently across a wide range of flow velocities. The key innovations include an adaptive variable-solidity rotor and a non-contact magnetic speed-increasing dynamic seal. The rotor employs foldable blades that enable passive solidity regulation in response to varying inflow conditions. At low flow velocities, the blades remain deployed, increasing rotor solidity and reducing the required startup flow velocity. Water tank experiments indicate that the minimum startup velocity of the variable-solidity rotor is 0.217 m/s, which represents a 38% reduction compared to a conventional rotor. At high flow velocities, the blades fold under increased hydrodynamic loading, thereby reducing the effective solidity and suppressing torque growth to provide overload protection. The power transmission module incorporates a non-contact magnetic speed-increasing dynamic seal, which ensures underwater dynamic sealing of the main shaft while simultaneously increasing the rotational speed of the driven shaft. Motor-driven bench tests demonstrate that when the active shaft speed remains below the cut-off threshold, a stable speed-increasing ratio of 2:1 is maintained, enabling effective speed amplification and torque transmission. Once the active shaft speed exceeds the cut-off threshold, the driven shaft automatically stalls, thereby preventing motor overload. Overall, this work provides an effective solution for enhancing the operational adaptability and transmission reliability of marine current energy conversion systems under variable flow conditions. Full article

Wind energy harvesting based on triboelectric nanogenerators (TENGs) is a promising solution for powering distributed Internet of Things (IoT) nodes, yet its practical efficiency and stability are often hindered by the fluctuating and unpredictable nature of wind. Here, we propose a self-regulating TENG (SR-TENG) that leverages the synergistic effects of centrifugal, elastic, and frictional forces to automatically switch between non-contact and contact modes based on wind speed. This configuration achieves an ultra-low start-up wind speed of 0.86 m/s, ensures sustainable high-performance output across a broad wind speed range, and exhibits excellent durability with no observable performance degradation during 23,000 s of continuous operation at 375 rpm. Systematic structural optimization enables the SR-TENG to reach a peak open-circuit voltage of 140 V, a short-circuit current of 12.5 μA, and a transferred charge of 300 nC at 375 rpm. When integrated with a customized power management circuit, the system delivers a 30.39-fold increase in effective output power at a 1 MΩ load and a 4-fold faster charging rate for a 10 μF capacitor. For practical validation, the harvested ambient wind energy successfully powers a wireless temperature-humidity sensor for real-time cloud data transmission. These results highlight that the SR-TENG holds great potential for advanced wind energy harvesting and self-powered sensing applications in distributed IoT systems. Full article

The generation from decentralized energy resources strongly depends on weather conditions, which causes fluctuations and degrades power grid quality. One of the most effective solutions in modern power systems to mitigate this issue is the use of energy storage systems (ESSs). These systems enhance the network performance by reducing power fluctuations. In this scope, and for frequency analysis, a model consisting of two interconnected microgrids was considered in this work. The frequency of these microgrids varies due to sudden changes in load or generation (or both). The frequency regulation was performed by an efficient load frequency controller (LFC). This regulation was essential and was employed to improve control performance, reduce the impact of load disturbances on frequency, and minimize power deviations in the power flow tie-lines. A fuzzy logic-based optimizer was installed in each microgrid to optimize the proposed proportional–integral–derivative (PID) controllers by generating their optimal parameters. The main objective of the LFC was to ensure zero steady-state error for system frequency and power deviations in the tie-lines. However, with the increasing integration of renewable energies and the intermittent nature of their production due to climate change, frequency fluctuations arise. To mitigate this issue, a coordinated AGC–PMS (automatic generation control–power management system) regulation with hybrid energy storage systems and interconnected microgrids was designed to enhance the quality and stability of the power network. This paper focuses on the load frequency control (LFC) technique applied to interconnected microgrids integrating renewable energy sources (RESs). It presents an optimization study based on artificial intelligence (AI) combined with the use of energy storage systems (ESSs) and high-voltage direct current (HVDC) transmission link for power management and control. The renewable energy sources used in this work are photovoltaic generators, wind turbines, and a solar thermal power plant. A hybrid energy storage system has been installed to ensure energy management and control. It consists of redox flow batteries (RFBs), a superconducting magnetic energy storage (SMES) system, electric vehicles (EVs), and fuel cells (FCs).The system behavior was analyzed through several case studies to improve frequency regulation and power management under renewable energy integration and load variation conditions. Full article

RNA origami enables genetically encoded, single-stranded RNA nanostructures that can self-assemble through co-transcriptional folding and are increasingly deployed as scaffolds for biosensing, synthetic biology, and nanomedicine. A recurring practical bottleneck is scalable, solution-phase readout of whether a designed scaffold has reached its intended base-paired architecture, whether it undergoes slow maturation or kinetic trapping, and how its stability is distributed across motifs. Here, we adapt microfluidic modulation spectroscopy (MMS) as a label-free structural biosensor for RNA folding by exploiting the rich 1760–1600 cm⁻¹ vibrational fingerprints of RNA bases and base pairs. MMS alternates between sample and composition-matched buffer measurements in a microfluidic transmission cell to automatically subtract the solvent background, enabling high-quality spectral measurement from microliter volumes under native solution conditions. Using a six-helix-bundle-with-clasp (6HBC) RNA origami as a model, we established an analysis workflow (baselined second derivative and constrained deconvolution) to quantify paired versus unpaired populations. Thermal ramping resolves multiple unfolding events and yields an unfolding barcode that differs between young and mature ensembles. Importantly, MMS tracks post-transcriptional maturation from a kinetically trapped young conformer toward a more compact, base-paired mature state, consistent with prior cryo-EM/SAXS observations for 6HBC RNA origami. Together, these results position MMS as a rapid, automated, and scalable complement to high-resolution structure determination for engineering dynamic RNA origami biosensors. Full article

Neighbor discovery in underwater acoustic networks (UANs) faces challenges such as high propagation delay and limited spectrum resources. This study proposes a dynamic beam control-based multi-parallel transceiver neighbor discovery protocol (DBCB), which improves node discovery efficiency by dynamically matching transmission beams and optimizing spatiotemporal frequency resource allocation. During node initialization, the master node broadcasts omnidirectionally to quickly capture coarse-grained neighbor parameters. After obtaining these parameters, the master node dynamically allocates orthogonal frequency bands for directional multi-beam validation and optimizes beam alignment, resource allocation, and topology stability through real-time feedback. The protocol adaptively optimizes transmission power and continues the discovery task, while nodes that remain undiscovered for extended periods automatically adjust their receiving gain. The adaptive power control mechanism adjusts the transmission power based on node distance and azimuth, enabling the protocol to maintain low power consumption and enhance interference resilience. Simulation results show that the DBCB protocol outperforms similar neighbor discovery protocols based on directional transmission-reception (DTR) and random two-way (RTW) mechanisms, with improvements of 7.84% and 28.17% in average discovery rate, and reductions of 28.13% and 59.06% in average discovery delay, respectively. The anechoic tank experiment demonstrates that multi-beam parallel transmission effectively improves underwater node discovery efficiency, with simulation results aligning with experimental data, confirming the stability and high efficiency of the system. Full article

The paper presents a comprehensive deep learning-based framework for automated visual inspection of overhead power line infrastructure using unmanned aerial vehicles. Traditional manual and helicopter inspections are costly, time-consuming, and hazardous for maintenance personnel. The proposed approach integrates UAV imaging with advanced computer vision models such as YOLOv8, EfficientDet-D2, and Faster R-CNN to automatically detect defects in critical components, including insulators, conductors, and transmission towers. Several open datasets (InsPLAD, TTPLA, MPID) were used for training and validation, ensuring robustness under diverse lighting and environmental conditions. Experimental results demonstrate that YOLOv8 achieved the best performance, reaching 88.5% mAP@0.5 with real-time inference capabilities (over 50 FPS on GPU). The system significantly enhances inspection efficiency, allowing for a threefold increase in coverage capacity and an up to 70% reduction in defect remediation time. The integration of AI-powered visual analytics with maintenance and SCADA systems enables a shift from reactive to predictive maintenance, improving the safety, reliability, and resilience of power transmission infrastructure. Full article

To address the challenges of complex operation, high server deployment costs, and insufficient automated identification capabilities in community-based centralized electrocardiogram (ECG) screening, a novel arrhythmia screening system based on handheld ECG leads and email transmission is proposed. The system is operated in a zero-interaction mode: ECG acquisition is initiated automatically upon skin contact with the electrodes, and upon completion, the ECG signal is automatically analyzed and the email transmission function is triggered—no user intervention being required. First, noise in the ECG signal is effectively suppressed by cascading a zero-phase high-pass filter with a sliding window and a zero-crossing-rate (ZCR) guided adaptive wavelet thresholding technique. Subsequently, RR interval sequences are extracted from the denoised signals and fed into a lightweight bidirectional long short-term memory (BiLSTM) network for automatic arrhythmia detection. In the final step, a 30 s standard ECG, screening status, and acquired image are automatically delivered to clinicians via standard IMAP/SMTP email protocols—eliminating the need for dedicated mobile applications or cloud platforms. Experimental results demonstrated that the relative signal-to-noise ratio (SNR_ECG) was improved by 2.36 dB. On the independent test set, a sensitivity of 97.98%, a specificity of 98.21%, and an AUC of 0.994 were achieved. Furthermore, an end-to-end email transmission latency of less than 7.68 s was recorded. These findings confirm the potential of the proposed system as a low-cost, easily deployable, and elderly-friendly remote ECG solution for primary healthcare settings. Finally, in a pilot screening involving 10 volunteers, one case of arrhythmia was successfully identified, which validated the feasibility of the system. Full article

In the realm of high-speed Ethernet networks, alien crosstalk (AXT) significantly undermines the integrity and efficiency of data transmission. While existing works mostly focus on modeling and physical-layer mitigation techniques such as PAM16/DSQ128 modulation and LDPC coding, there is a lack of experimental evidence on how severe AXT affects commodity 10GBASE-T equipment in realistic, densely cabled installations. In this study, we assemble and evaluate the experimental testbed that emulates a highly adverse AXT environment by tightly bundling up to seven 60 m twisted-pair Ethernet cables and using only off-the-shelf 10GBASE-T network cards. We quantitatively characterize how increasing cable density leads to automatic speed downgrades, connection failures, and non-linear saturation of the aggregate throughput, and relate these effects to the observed link quality on individual ports. Our results demonstrate that, even in the presence of standard crosstalk mitigation and error-correction mechanisms, severe AXT can force commodity 10GBASE-T links to fall back from 10 Gbit/s to 1 Gbit/s or below. Based on these findings, we derive practical guidelines for dense-cabling deployments and identify key requirements for experimental testbeds that can more reliably quantify AXT severity and its impact on commodity 10GBASE-T link stability (rate fallback and link loss) under realistic conditions. Full article

This paper presents the design of a dual-frequency energy–frequency composite selective surface based on a double-period nested cross-fractal structure. The unit cell consists of a composite metallic layer loaded with diodes, an F4B dielectric substrate, and an intermediate layer with cross-shaped feeding line. The proposed model is structurally optimized and characterized using the periodic method of moments theory and the equivalent circuit method. In addition, its performance was verified through a comparative study. The results demonstrate that under low-power conditions, the surface achieves stable frequency-selective transmission at 2.4 GHz (S-band) and 4.2 GHz (C-band), enabling highly efficient signal transmission with an insertion loss of less than 0.6 dB. Under a high field strength, it automatically switches to an energy-selective state, providing full-band shielding effectiveness of ≥18 dB across a 2.0–5.0 GHz broadband, thereby achieving stealth functionality. The designed composite selective surface exhibits excellent angular stability and features a simple biasing network that does not require additional feeding lines. Thus, this study presents a new approach for designing such surfaces for operation in the microwave regime. Full article

Dengue remains a public health concern in Singapore, with endemic transmission and recurring outbreaks. This study presents results from a Singapore-focused subgroup of the Growth and Emerging Markets Knowledge, Attitudes, and Practices (GEMKAP) cross-sectional survey, which assessed public Knowledge, Attitudes, and Practices (KAP) levels related to dengue and prevention. A total of 400 adult respondents from Singapore participated in an online survey conducted between September and October 2022. Overall KAP scores were 48% (Knowledge), 61% (Attitudes), and 36% (Practices). Awareness of dengue transmission was widespread (96% identified mosquitoes as the vector and 97% recognised stagnant water breeding), while fewer respondents recognised the availability of a dengue vaccine (23%) or the absence of a medicinal cure (38%). Trust in the government’s dengue control efforts was high, though respondents practised an average of 5.1 out of 10 recommended prevention measures. Of the respondents, 25% had a high willingness to vaccinate against dengue. Multivariate analysis revealed that positive vaccine perceptions, past dengue experience, automatic motivation, and social opportunity were associated with willingness to vaccinate. Respondents supported a multi-pronged dengue management approach combining education, vector control, and vaccination. Future efforts should integrate behaviour change strategies, enhance multi-stakeholder collaboration, and empower communities to ensure sustainable impact. Full article

Amidst rapid urbanization and modernization, numerous traditional villages in China face severe challenges, including landscape homogenization and the erosion of their distinctive characteristics. Addressing this issue requires a method capable of systematically identifying, analyzing, and reconstructing both the landscape and its underlying cultural features. This study proposes a digital analytical approach that integrates multimodal artificial intelligence with landscape language theory to address the homogenization of cultural landscapes in traditional Chinese villages. Taking Xinye Village in Zhejiang Province as a case study, the research systematically decodes its landscape spatial narratives and underlying cultural genes. This framework systematically deconstructs village landscapes across four levels: “vocabulary, context, grammar, and semantics”. The village image database is first automatically recognized and statistically analyzed by computer vision technology, which extracts 31 core landscape vocabulary items from three main categories and nine subcategories. Second, Retrieval-augmented Generation technology is employed to synthesize from the constructed domain-specific corpus, a natural context structured around Yuhua Mountain and Daofeng Mountain, as well as a cultural context based on ancestral hall order, connected through folk activities, and idealized by farming and reading passed down through generations. Building on this framework, a multimodal model was used to examine the spatial composition and combinatorial laws of landscape features. Six essential dimensions—spatial layout, visual order, element combination, functional relationships, circulation layout, and scale correlations—revealed the spatial grammar of shuikou landscape. Lastly, the semantic values conveyed by the landscape vocabulary were thoroughly analyzed across three dimensions—form, function, and culture—by integrating a knowledge base. This work creates a landscape language atlas of Xinye Village by combining these studies and using a linguistic model of “character-word-sentence-paragraph”. By methodically deciphering the clan’s cultural code of “farming and reading passed down through generations”, this clearly reconstructs the spatial narrative logic from micro-elements to macro-patterns. This research not only advances the study of landscape language in traditional villages from qualitative description toward a systematic, digital, and interpretable paradigm but also provides an operational theoretical and methodological foundation for the in-depth interpretation, conservation, and transmission of traditional village cultural landscapes. Full article

Space-based automatic dependent surveillance–broadcast (ADS-B) systems offer the potential for comprehensive global aircraft surveillance. However, they face substantial challenges due to severe signal collisions resulting from the simultaneous reception of asynchronous ADS-B transmissions from multiple aircraft within a satellite’s expansive coverage area. Traditional collision mitigation approaches, such as serial interference cancellation and multichannel blind source separation, often have high computational costs, impose strict signal structure constraints, or rely on multiple-antenna configurations, all of which limit their practicality in satellite scenarios. To address these limitations, this paper proposes two novel deep learning–based models, designated SplitNet-2 and SplitNet-3. SplitNet-2 leverages a Transformer-inspired self-attention architecture specifically designed to separate two overlapping ADS-B signals, while SplitNet-3 employs a convolutional residual U-shaped network optimized for disentangling three simultaneous, colliding signals. Extensive simulations under realistic satellite reception conditions demonstrate that the proposed models significantly outperform conventional methods, achieving lower bit error rates (BERs) and improved demodulation accuracy. These advancements offer a promising solution to the critical problem of underdetermined signal separation in space-based ADS-B reception and significantly enhance the reliability and coverage of satellite-based ADS-B surveillance systems. Full article

To address the critical demand for real-time dynamic tracking of personnel in complex buildings during emergency rescue, a novel system was proposed integrating Back Propagation (BP) neural networks with Double-Sided Two-Way Ranging (DS-TWR) technology to achieve precise indoor localization and motionless detection. Comprising hardware (positioning base stations, tags, POE switches, routers, and a computer) and software (developed on LabVIEW), the system leverages the symmetric signal transmission of DS-TWR and the adaptive learning capability of BP neural networks to effectively mitigate multipath interference, enhancing positioning consistency and accuracy. Thresholds of time period and movement distance were set to determine whether the occupant was trapped. When tested in several common building structures, it demonstrated good stability and high accuracy—the average RMSE of the positioning system was within 0.012–0.018 m (static state) and 0.048–0.065 m (dynamic state). Furthermore, the system could real-time monitor and display the movement trajectory of each person, and automatically alarm when anyone was trapped in a fire scene. Hence, rescue measures can be taken timely according to the alarm information provided by the system, effectively ensuring the safety of personnel and improving the efficiency of fire rescue work. The proposed approach provides a symmetry-driven framework for intelligent building safety. Full article

To address the challenges of real-time control in power systems with high renewable penetration, identifying historical transmission sections similar to future scenarios enables efficient reuse of mature control strategies. However, existing data-driven identification methods exhibit two primary limitations: they typically rely on static Total Transfer Capacity (TTC), ignoring the rapid regulation capability of Energy Storage Systems (ESS) in alleviating congestion; and they employ fixed weights for similarity measurement, failing to distinguish the varying importance of different features (e.g., critical line flows vs. ordinary voltages). To overcome these issues, this paper proposes a similarity identification method for transmission sections considering ESS regulation capabilities and adaptive feature weights. First, a hierarchical decision model is utilized to screen basic grid features. An optimization model incorporating ESS charge/discharge constraints and emergency power support potential is established to calculate the Dynamic TTC, constructing a multi-scale feature set that reflects the real-time safety margin of the grid. Second, a Dispersion-Weighted Fuzzy C-Means (DW-FCM) clustering algorithm is proposed. By introducing a dispersion-weighting mechanism, the algorithm utilizes data distribution characteristics to automatically learn and assign higher weights to key features with high distinguishability during the iteration process, overcoming the subjectivity of manual weighting. Furthermore, fuzzy validity indices (XB, PC, FS) are introduced to adaptively determine the optimal number of clusters. Finally, case studies on the IEEE 39-bus system verify that the proposed method significantly improves identification accuracy compared to traditional methods and provides more reliable references for dispatching decisions. Full article

Background: The humanization of care is increasingly recognized as a core component of healthcare quality; however, its meaning remains complex and strongly shaped by organizational, professional, and educational contexts. Nursing students, as future healthcare professionals, play a crucial role in the development and transmission of humanized care values, making their representations of the humanized hospital particularly relevant for understanding how these values are constructed during professional education. Aim: To explore how undergraduate nursing students conceptualize the humanized hospital. Methods: A qualitative exploratory study was conducted involving 742 undergraduate nursing students enrolled in a Bachelor of Science in Nursing program in Italy. Data were collected through a single open-ended written question inviting students to describe how they imagine a humanized hospital. Textual data were analyzed using Automatic Analysis of Textual Data within an Exploratory Multidimensional Data Analysis framework, enabling the identification of shared lexical patterns, discursive clusters, and latent semantic dimensions within a large textual corpus. Findings: Students articulated the humanized hospital as an integrated and system-oriented care environment in which relational, organizational, professional, and holistic dimensions are deeply interconnected. Humanization was associated not only with empathy, respect, and emotional engagement, but also with organizational functioning, teamwork, adequate resources, and professional competence. Two latent dimensions structured these representations: the first highlighted organizational systems as enabling conditions for person-centered care, while the second framed professional operability and technical competence as foundations for a holistic understanding of patients’ physical, psychological, and social well-being. Conclusions: Undergraduate nursing students’ discourse revealed an articulated and multidimensional representation of hospital humanization, conceptualizing it as an emergent property of healthcare environments rather than as a function of individual attitudes alone. These findings underscore the importance of addressing hospital humanization simultaneously at relational, educational, and organizational levels and highlight the need for nursing education programs and healthcare institutions to foster structural and professional conditions that sustainably support humanized care in clinical practice. Full article