Search Results (5,743)

In the long-duration stratospheric operation of High-Altitude Platform Stations (HAPSs), strict management of the limited solar energy balance is a decisive factor determining mission success. However, existing planar approximation models ignore self-shading and incidence angle losses associated with curved surfaces. In this study, we propose a novel framework that catalogs the airframe geometry as a 4-tensor, achieving both physical rigor and computational speed. This method is a thousand times faster than ray tracing methods, and successfully reproduces the minute output fluctuations observed in actual flight data. Notably, in the winter solstice analysis, when the energy balance is most severe, the planar model overestimates power generation by approximately 25% during level flight and by approximately 12% even during turning maneuvers. Quantifying this discrepancy in environments with minimal energy margins is essential for mitigating the risk of airframe loss and formulating feasible operational plans. Full article

Satellite–terrestrial spectrum sensing plays a crucial role in enhancing spectrum efficiency through reusing spectra. However, in a satellite–terrestrial converged system, the large SNR range, non-Gaussian signal characteristics and noise uncertainty pose significant challenges for spectrum sensing. In this paper, we investigate a downlink spectrum sensing framework where multi-terrestrial BSs act as a secondary system to sense idle satellite spectra through a multi-domain feature-level sensing signal fusion. To enhance the characterization of signal/noise features, we provide a fusion strategy of multi-features including energy, power spectral density, cyclic autocorrelation function, higher-order moments, sparse ratio, and I/Q samples, constructing two feature tensors of statistical features and an I/Q component. Then, we propose a deep-learning-enabled multi-feature fusion spectrum sensing method (DL-MFFSSnet) based on a dual-branch deep neural network architecture with the constructed two feature tensors as inputs. In the statistical feature processing branch, CNN and channel self-attention are incorporated to capture intra-channel correlations and inter-channel relative contributions of different feature modalities. In the I/Q branch, multi-scale dilated convolutions and spatial self-attention are introduced to analyze dependencies across different temporal positions and multi-scale spatial features. The feature map extracted from both branches passed through fully connected layers for deepwise feature fusion, achieving accurate spectrum sensing. Extensive simulation results demonstrate that the DL-MFFSSnet method outperforms the existing state-of-the-art algorithms. Full article

Nonlinear energy harvesting systems based on multibody structures constitute a promising solution for autonomous devices powered by ambient vibrations. This paper presents the modeling and control of a nonlinear energy harvester employing a double pendulum configuration and BLDC motors operating as generators. The primary objective of the study was to develop a control strategy that enables the maximization of harvested power while simultaneously improving the energy conversion efficiency during the charging of the battery supplying the target system. The developed model incorporates the mechanical equations of motion of the double pendulum, an electrical model of the BLDC motors, and two independently controlled buck–boost converters, each connected to one joint of the pendulum. In addition, a perturb-and-observe (P&O) maximum power point tracking (MPPT) algorithm was implemented, which utilizes a portion of the computational resources of the target system’s microcontroller and allows for dynamic adjustment of the electrical loads seen by the generators. Simulation results obtained in the Simulink environment confirm that the application of independent power converters combined with local MPPT control leads to an increase in the total harvested power and ensures more stable battery charging under conditions of variable mechanical excitation. The obtained results demonstrate the effectiveness of the proposed approach and indicate its potential applicability in self-powered systems operating in environments characterized by irregular and stochastic vibrations. Full article

We report a dual-readout self-resetting CMOS image sensor that achieves a signal-to-noise ratio (SNR) exceeding 70 dB and resolves sub-percent optical contrast variations by effectivly suppressing reset artifacts. The proposed sensor employs a Dual-Readout architecture with two independent scanners operating with a temporal offset; while one readout system is in the self-reset “dead time”, the other remains active, thereby physically ensuring continuous data acquisition. To minimize pixel area while achieving high reconstruction accuracy, a minimum frame-to-frame difference algorithm is utilized for signal restoration without requiring in-pixel counters. A prototype chip fabricated in a 0.35-$\mathsf{\mu }$m process demonstrated SNR characteristics near the shot-noise limit, with a peak SNR exceeding 70 dB. Vascular phantom experiments using a carbon black suspension successfully visualized ±0.25% contrast fluctuations—dynamic signals previously undetectable by conventional sensors. This device provides a powerful platform for high-precision bio-imaging applications, including brain surface blood flow monitoring, where both wide dynamic range and high SNR are essential. Full article

Gene delivery/administration and, in particular, small interfering RNA (siRNA) delivery represent a therapeutic challenge, though very effective carriers have yet to be identified. Cyclodextrins (CDs) are cyclic oligosaccharides with unique host–guest inclusion capabilities, widely recognized in the pharmaceutical field for their ability to enhance drug solubility and bioavailability. Their excellent biocompatibility and chemical versatility make them powerful building blocks for the design of supramolecular nanovectors (NVs). Thanks to their facility of functionalization, CDs are highly versatile and have found numerous applications across various fields. In this context, CD-based NVs are currently explored as non-viral agents to transport and release siRNA. Recent studies suggest that self-assembled NVs based on CDs can improve the transfection and safety of siRNA delivery. This review provides a comprehensive overview of the most recent advances in the design of NVs based on CDs and their use for siRNA delivery, discussing the role played by structural differences and chemical functionalization in the context of encapsulation and release. Full article

Digital technologies are widely viewed as important tools for enhancing corporate environmental performance. However, there is growing recognition that their environmental impacts are not uniformly positive and may even generate unintended negative consequences. Drawing on institutional theory and impression management theory, we argue that big data analytics (BDA) provides firms with powerful capabilities to strategically manage environmental impressions in response to external pressures. Using panel data of Chinese listed firms from 2012 to 2023, we provide empirical evidence that BDA significantly promotes corporate greenwashing. Specifically, BDA facilitates greenwashing through the reinforcement of three core dimensions of impression management: self-serving bias, symbolic management, and accounting rhetoric. Moreover, by distinguishing between different types of external pressures, our results show that constraint-based non-market pressures weaken the relationship between BDA and greenwashing, whereas opportunity-based market pressures strengthen it. Our study enriches the digitalization and corporate environmental performance literature by revealing the dark side of digital technologies and offering a more nuanced understanding of how specific technologies shape corporate environmental misconduct. Full article

Parameter self-tuning of servo control systems is crucial for optimizing automation processes, especially in complex systems such as permanent magnet synchronous motors. In this paper, a nonlinear adaptive whale optimization algorithm (NAWOA) is proposed and applied to parameter self-tuning, which improves the traditional whale optimization algorithm (WOA) by nonlinearly adaptively adjusting two parameters during optimization to enhance fast convergence and global search capabilities. A servo control system with three parameters to be tuned is constructed using both simulation and physical methods. Simulation and experimental results show that the NAWOA outperforms the genetic algorithm, particle swarm optimization, and WOA in parameter self-tuning of the servo control system with lower error indicators and fast convergence speed. Although it still faces the challenge of initial condition dependency, the proposed NAWOA provides a powerful solution for real-time industrial applications. Full article

The rapid proliferation of distributed energy resources (DERs), including photovoltaics, wind power, battery energy storage, and electric vehicles, has transformed traditional power systems into highly decentralized and data-rich environments. Virtual power plants (VPPs) have emerged as a key mechanism for aggregating these heterogeneous assets and enabling coordinated control, market participation, and grid-support functions. Recent advances in artificial intelligence (AI) have further elevated the scalability, autonomy, and responsiveness of VPP operations. This paper presents a comprehensive review of AI for VPPs, organized around a taxonomy of machine learning, deep learning, reinforcement learning, and hybrid approaches, and examines how these methods map to core VPP functions such as forecasting, scheduling, market bidding, aggregation, and ancillary services. In parallel, we analyze enabling architectural frameworks—including centralized cloud, distributed edge, hybrid cloud–edge collaboration, and emerging 5G/LEO satellite communication infrastructures—that support real-time data exchange and scalable deployment of intelligent control. By integrating methodological, functional, and architectural perspectives, this review highlights the evolution of VPPs from rule-based coordination to intelligent, autonomous energy ecosystems. Key research challenges are identified in data quality, model interpretability, multi-agent scalability, cyber-physical resilience, and the integration of AI with digital twins and edge-native computation. These findings outline promising directions for next-generation intelligent VPPs capable of delivering secure, flexible, and self-optimizing DER aggregation at scale. Full article

Battery-electric residential storage systems can increase the self-consumption of photovoltaic (PV) generation; however, economical sizing typically requires a high-resolution time series of PV production and household load behind the meter. In practice, such data are often unavailable. This work therefore presents a simulation model for determining the economically optimal residential storage capacity based exclusively on smart-meter data at the point of common coupling (PCC), i.e., hourly import and export time series. Economic performance is assessed using net present value (NPV) over a multi-year evaluation horizon. In addition, technical constraints (SoC limits, power limits, charging/discharging efficiencies) as well as capacity degradation are considered via a semi-empirical aging model. For validation, a reproducible reference scenario is constructed using PVGIS generation data and the standard load profile H23, enabling a direct comparison between the conventional approach (consumption/generation) and the PCC approach (import/export). The results show that the capacity optimum can be reproduced consistently using PCC data, even under smart-meter-like integer kWh quantization. At the same time, large parts of the investigated parameter space indicate that, under the assumed scenarios, foregoing a storage system is often not economically sensible. Sensitivity analyses further highlight the strong impact of load shifting, in particular due to the charging time of electric vehicles. A case study using real PCC measurement data, together with a two-week-window analysis, demonstrates practical applicability and robustness under limited measurement durations. Full article

One of the ways to decarbonize cities and to enhance grid stability is to convert existing buildings into prosumers equipped with power plants able to supply electrical and thermal energy. The simulation of such multi-energy systems permits the analysis of their performance in steady-state and dynamic conditions, with the aim of defining effective operating strategies able to reduce emissions and costs. The present paper describes a dynamic simulation model, implemented in the Matlab/Simulink R2025a environment, developed to simulate the daily and weekly operation of a prosumer building equipped with a small-sized cogeneration unit, a Photovoltaic (PV) plant, a back-up boiler, a thermal storage system and some charging points for Electric Vehicles (EVs). The mathematical model is reported in detail, and the main results of the study are described, referring to operating days characterized by different weather conditions. Then, energy, economic and environmental performance indicators are defined and calculated for the different simulated scenarios. Over the considered time horizons, the simulation results highlight a significant increase in the electrical self-sufficiency of the facility up to 91.1% and an important reduction in total net operating costs up to 59.8%, compared to the AS-IS case (i.e., without the newly installed technologies). Full article

In this work, we consider a simple bistable motif constituted by a self-enhancing Transcription Factor (TF) and its mRNA with non-instantaneous dynamics. In particular, we mainly numerically investigated the impact of bounded stochastic perturbations of Sine–Wiener type affecting the degradation rate/binding rate constant of the TF on the phase-like transitions of the system. We show that the intrinsic exponential delay in the TF positive feedback, due to the presence of a mRNA with slow dynamics, deeply affects the above-mentioned transitions for long but finite times. We also show that, in the case of more complex delays in the feedback and/or in the translation process, the impact of the extrinsic stochasticity is further amplified. We also briefly investigate the power-law behavior (PLB) of the averaged energy spectrum of the TF by showing that, in some cases, the PLB is simply due to the filtering nature of the motif. A similar analysis can also be applied to biological models having a qualitatively similar structure, such as the well-known Capasso and Paveri–Fontana model of cholera spreading. Full article

This article presents an arts-based and polyvocal account of Gira Ingoma (One Drum per Girl), a women- and girl-led cultural initiative in Rwanda that reconstructs drumming, warrior dance, and self-praise poetry to advance gender equality and contribute to everyday peace power. Based on arts-based qualitative methods (workshops, rehearsals, festivals, interviews, and youth-led Monitoring, Evaluation, and Learning), we show how repetitive public performance materialises gender equality beyond policy texts. The article explores core theoretical frames—gender performativity, everyday peace power, spatial approaches to peace, and performance-as-knowledge—while aligning key findings to research questions concerning (1) negotiation of gender through performance, (2) micro-processes of everyday peace power, and (3) observable change in confidence, community engagement, and institutional practice. We conclude with policy measures to embed gender-responsive arts education, resource girls and women across the creative value chain, and set parity targets within cultural institutions. Full article

High-power clutches operating under high-frequency engagement–disengagement cycles demand piston ring seals with exceptional leakage control and tribological reliability. Conventional architectures often experience lubrication failure and severe adhesive wear during transient pressure fluctuations. This research proposes an autonomous intelligent sealing strategy leveraging fluid pressure-induced morphological evolution. By strategically integrating periodic macroscopic structural relief features on the non-sealing surface, the sealing interface transforms into a micron-scale wavy topography in response to hydraulic loading. This structurally embedded intelligence significantly improves fluid pressure distribution, facilitating a transition toward a more favorable lubrication regime. Furthermore, a “self-healing and positional stagnation” logic is elucidated: upon pressure dissipation, the induced waviness elastically recovers to a planar state to ensure sealing integrity, while the ring maintains its axial position due to the predominant frictional resistance of the secondary seal. This synergistic mechanism effectively precludes deleterious dry friction during the clutch disengagement phase. High-fidelity numerical investigations, benchmarked against established experimental data, identify the rectangular groove configuration as the optimal geometry for maximizing waviness amplitude (≈1.5 µm). This research provides a robust framework for developing responsive, zero-wear intelligent seals in advanced power transmissions. Full article

The hardware power-aware keyword spotting (KWS) implementation requires small memory footprint, low-complex computation, and high accuracy performances. Binary neural networks (BNNs) naturally satisfy these constraints. They quantize both weights and activations to 1-bit. This reduces storage and replaces most multiply–accumulate operations with bitwise operations. However, such extreme quantization incurs substantial information loss and leaves a noticeable accuracy gap relative to full-precision models. Optimization is also more difficult because the sign function is non-differentiable, and surrogate-gradient updates introduce gradient mismatch. To preserve the hardware benefits of BNNs while alleviating the accuracy degeneration induced by 1-bit quantization, this article addresses the problem from two complementary aspects: Firstly, a Stepwise Self-Knowledge Distillation (SSKD) training approach is proposed to effectively improve the student BNN’s accuracy performance. The SSKD training framework achieves effective supervision for student BNNs. A Stepwise Training Strategy is proposed to optimize the training stability and accuracy. Weight Scaling Factor improves the student’s representational capability. Secondly, an extremely lightweight Binary Temporal Convolutional ResNet (BTC-ResNet) is also proposed. The parameters and calculations inside the network are greatly reduced for the inference. Experiments on the GSCD v1 and GSCD v2 benchmarks demonstrate the effectiveness of our methods for low-power keyword spotting. For the 12-class task, BTC-ResNet14 achieves 97.23% accuracy on GSCD v1 and 97.31% on GSCD v2 with 0.75 Mb parameters and 1.35 M FLOPs. For the 35-class task on GSCD v2, it reaches 95.56% accuracy with 0.76 Mb parameters and 1.35 M FLOPs. These results indicate that our method achieves a competitive accuracy–efficiency balance relative to recent distillation-based BNN KWS baselines reported in the comparative experiments. All these studies are helpful and promising for future KWS deployment on low-power hardware devices. Full article

Based on the electron cyclotron maser instability, gyrotrons are capable of generating high-power electromagnetic vortex waves. In conventional axisymmetric configurations, the electron beam typically lifts the azimuthal degeneracy between co-rotating and counter-rotating modes, leading to a state of intense mutual suppression. This study elucidates a fundamental transition from such competitive dynamics to a stable cooperative coexistence, driven by symmetry-breaking perturbations. Using a time-dependent self-consistent interaction theory, we investigate the intermodal dynamics of the counter-rotating ${\mathrm{T}\mathrm{E}}_{6,2}$ mode pair in a terahertz gyrotron. Our results reveal that the azimuthal intermodal phase beating dictates a reciprocal energy exchange that ensures single-mode dominance. However, electron beam misalignment introduces a significant azimuthal non-uniformity in the coupling strength. This non-uniformity effectively neutralizes the competitive disparity between the two modes. At a critical offset, the system undergoes a “territorial division,” where the orthogonal vortex modes spatially segregate by dominating distinct azimuthal segments of the annular beam. This spatial segregation eliminates nonlinear cross-suppression, allowing for the stable coexistence of both rotational states. These findings offer a new perspective on multi-mode interactions in non-ideal systems and establish a robust theoretical framework for the active manipulation of vortex waves in high-performance THz radiation sources. Full article