Search Results (1,261)

We investigate the existence of positive normalized (mass-constrained) solutions for the fractional nonlinear Schrödinger equation ${(-\Delta )}^{s}v+V\left(x\right)v=\lambda v+{\mu \left|v\right|}^{p-2}v+{\left|v\right|}^{2_s^{*}-2}v\mathrm{in}{\mathbb{R}}^N,{\parallel v\parallel }_2^2=b^2,$ where $N>2s$, $s\in (0,1)$, $\mu >0$, $p\in (2,2_s^{*})$, and $2_s^{*}={\displaystyle \frac{2N}{N-2s}}$. Here, $\lambda \in \mathbb{R}$ denotes the Lagrange multiplier associated with the prescribed mass $b>0$. The potential $V\in C^1\left({\mathbb{R}}^N\right)$ is allowed to be nonconstant and satisfies $V\left(x\right)\to V_{\infty }$ as $\left|x\right|\to \infty$; moreover, the perturbations induced by $V-V_{\infty }$ and $x·\nabla V$ are assumed to be small in the quadratic-form sense compared with the fractional Dirichlet form ${\parallel (-\Delta )}^{s/2}{v\parallel }_2^2$. Using the Caffarelli–Silvestre extension, we establish a Pohozaev identity adapted to the presence of $V\left(x\right)$ and introduce a Pohozaev manifold on the $L^2$-sphere. Combining Jeanjean’s augmented functional approach with a splitting analysis at the Sobolev-critical level, we construct compact Palais–Smale sequences below a suitable critical energy level. As a consequence, we prove the existence of positive normalized solutions for small masses $b\in (0,b_0)$ in the $L^2$-critical and $L^2$-supercritical regimes (with respect to the lower-order power p). Full article

We propose the Hybrid Spatio-Temporal Transformer with Multi-Branch CNN/RNN and Extreme-Event Head (HST–MB–CREH), a hybrid spatio-temporal deep learning architecture for joint short-term photovoltaic (PV) power forecasting and the detection of rare extreme events, to support the reliable operation of renewable-rich power systems. The model combines a spatio-temporal transformer encoder with three convolutional neural network (CNN)/recurrent neural network (RNN) branches (CNN → long short-term memory (LSTM), LSTM → gated recurrent unit (GRU), CNN → GRU) and a dense pathway for tabular meteorological and calendar features. A multitask output head simultaneously performs the regression of PV power and binary classification of extremes defined above the 95th percentile. We evaluate HST–MB–CREH on the publicly available Renewable Power Generation and Weather Conditions dataset with hourly resolutions from 2017 to 2022, using a 5-fold TimeSeriesSplit protocol to avoid temporal leakage and to cover multiple seasons. Compared with tree ensembles (RandomForest, XGBoost), recurrent baselines (Stacked GRU, LSTM), and advanced hybrid/transformer models (Hybrid Multi-Branch CNN–LSTM/GRU with Dense Path and Extreme-Event Head (HMB–CLED) and Spatio-Temporal Multitask Transformer with Extreme-Event Head (STM–EEH)), the proposed architecture achieves the best overall trade-off between accuracy and rare-event sensitivity, with normalized performance of RMSE_z = 0.2159 ± 0.0167, MAE_z = 0.1100 ± 0.0085, mean absolute percentage error (MAPE) = 9.17 ± 0.45%, R² = 0.9534 ± 0.0072, and AUC_ext = 0.9851 ± 0.0051 across folds. Knowledge extraction is supported via attention-based analysis and permutation feature importance, which highlight the dominant role of global horizontal irradiance, diurnal harmonics, and solar geometry features. The results indicate that hybrid spatio-temporal multitask architectures can substantially improve both the forecast accuracy and robustness to extremes, making HST–MB–CREH a promising building block for intelligent decision-support tools in smart grids with a high share of PV generation. Full article

Stone powder is an inevitable by-product generated during the processing of manufactured sand and gravel. Waste stone powder has been proven to affect concrete properties and has been applied in the transportation and hydropower fields. This study aims to convert waste granite stone powder (GP) to nuclear power concrete by replacing manufactured sand, investigating its effect on the workability, compressive strength, splitting tensile strength, impermeability, and freezing resistance of nuclear power concrete. The mechanism was further elucidated through thermogravimetric (TG), scanning electron microscopy (SEM), and mercury intrusion porosimetry (MIP) techniques. The results show that with the increase in GP content, the slump, compressive strength, and splitting tensile strength of concrete increase first and then decrease, and the seepage height under pressure water decreases first and then increases. The workability, strength, and impermeability of concrete are optimal when GP content is 11.0%. Reasonable GP content improves the compactness of concrete by filling pores and optimizing aggregate gradation, resulting in decreases in porosity, with the size being the most probable and average pore size. Full article

In flexible DC distribution systems, the three-phase four-leg (3P4L) converter demonstrates excellent performance in addressing three-phase load imbalance problems, but suffers from DC-side second harmonics and complex multi-parameter control coordination. In this paper, a flying capacitor zero-sequence leg-based 3P4L (FCZS-3P4L) converter is proposed, which introduces the three-level flying capacitor structure into the fourth zero-sequence leg, making it possible to suppress the DC-side second harmonics by using the flying capacitor for energy buffering. Meanwhile, a modulated model predictive control (MMPC) strategy for proposed FCZS-3P4L is presented. This strategy utilizes a dual-layer control strategy based on a phase-split power outer loop and a model predictive current inner loop to simultaneously achieve AC three-phase imbalance current compensation and the energy buffering of the flying capacitor, thereby eliminating the complex parameter coordination among multiple control loops in conventional control structures. A MATLAB-based simulation model and Star-Sim hardware-in-the-loop (HIL) semi-physical experimental platforms are built. The results show that the proposed FCZS-3P4L converter and corresponding MMPC control can effectively reduces three-phase current unbalance by 19.57%, and reduce the second harmonic amplitude by 57%, i.e., decreasing from 14.74 V to 6.31 V, simultaneously realizing DC-side second harmonic and AC-side three-phase unbalance suppression. Full article

Mammalian placentation represents one of the most striking evolutionary innovations among vertebrates, and accumulating evidence indicates that virus-derived genes—particularly the metavirus-derived PEG10 and PEG11/RTL1—have played indispensable but distinct roles: PEG10 in the emergence of therian viviparity and PEG11/RTL1 in the subsequent differentiation between marsupial and eutherian placental types. Notably, the metavirus-derived SIRH/RTL gene group, which includes PEG10 and PEG11/RTL1, exhibits unique and diverse functions not only in placenta development but also within microglia of the brain. Because microglia originate from yolk sac progenitors, these findings suggest that extraembryonic tissues such as the placenta and yolk sac provided permissive environments that enabled the retention, expression and functional domestication of virus-derived sequences. Once the placenta itself was established through viral gene integration, it may in turn have acted as a powerful driver of eutherian evolution through recurrent acquisition and co-option of additional virus-derived genes—a process we refer to as “placenta-driven evolution.” This perspective offers a unified framework in which viral gene acquisition is viewed as a key driver of genomic innovation, tightly intertwined with the emergence of viviparity, subsequent divergence at the marsupial–eutherian split, and continued diversification of placental structure and function across eutherian lineages. Full article

Exploiting the atomistic tight-binding theory with the sp-d exchange term, the electronic and magnetic characteristics of CdSe nanoparticles embedded with Mn, Fe and Co are determined as a function of external magnetic fields to realize the sp-d exchange interactions. The transition metal species and applied magnetic fields are powerful factors to manipulate the electronic and magnetic characteristics of doped CdSe nanoparticles. With growing applied fields, the energies of spin splitting, Zeeman splitting and magnetic polaron improve and are assumed to reach saturation at high fields. All g-factor values are boosted in the presence of the external field and then fade with increasing applied fields. The electron spin-splitting energies and electron g values are ordered as Fe:CdSe > Mn:CdSe > Co:CdSe. The single-particle gaps, hole spin-splitting energies, Zeeman splitting energies and hole g values follow the order Co:CdSe > Fe:CdSe > Mn:CdSe. Full article

Supercritical carbon dioxide (SC-CO₂) power cycles offer a promising solution for offshore platforms’ gas turbine waste heat recovery due to their compact design and high thermal efficiency. This study proposes a novel parallel-preheating recuperated Brayton cycle (PBC) using SC-CO₂ for waste heat recovery on offshore gas turbines. An integrated energy, exergy, and economic (3E) model was developed and showed good predictive accuracy (deviations < 3%). The comparative analysis indicates that the PBC significantly outperforms the simple recuperated Brayton cycle (SBC). Under 100% load conditions, the PBC achieves a net power output of 4.55 MW, while the SBC reaches 3.28 MW, representing a power output increase of approximately 27.9%. In terms of thermal efficiency, the PBC reaches 36.7%, compared to 21.5% for the SBC, marking an improvement of about 41.4%. Additionally, the electricity generation cost of the PBC is 0.391 CNY/kWh, whereas that of the SBC is 0.43 CNY/kWh, corresponding to a cost reduction of approximately 21.23%. Even at 30% gas turbine load, the PBC maintains high thermoelectric and exergy efficiencies of 30.54% and 35.43%, respectively, despite a 50.8% reduction in net power from full load. The results demonstrate that the integrated preheater effectively recovers residual flue gas heat, enhancing overall performance. To meet the spatial constraints of offshore platforms, we maintained a pinch-point temperature difference of approximately 20 K in both the preheater and heater by adjusting the flow split ratio. This approach ensures a compact system layout while balancing cycle thermal efficiency with economic viability. This study offers valuable insights into the PBC’s variable-load performance and provides theoretical guidance for its practical optimization in engineering applications. Full article

This paper systematically analyzes the propagation, transformation, and accumulation mechanisms of multi-source noise and device non-idealities within the complete signal chain from the transmitter through the channel to the receiver, focusing on wireless optical coherent communication systems from a signal propagation perspective. It establishes the stepwise propagation process of signals and noise from the transmitter through the atmospheric turbulence channel to the coherent receiver, clarifying the coupling mechanisms and accumulation patterns of various noise sources within the propagation chain. From a signal propagation viewpoint, the study focuses on analyzing the impact mechanisms of factors, such as Mach–Zehnder modulator nonlinear distortion, atmospheric turbulence effects, 90° mixer optical splitting ratio imbalance, and dual-balanced detector responsivity mismatch, on system bit error rate performance and constellation diagrams under conditions of coexisting multiple noises. Simultaneously, by introducing differential and common-mode processes, the propagation and suppression characteristics of additive noise at the receiver end within the balanced detection structure were analyzed, revealing the dominant properties of different noise components under varying optical power conditions. Simulation results indicate that within the range of weak turbulence and engineering parameters, the impact of modulator nonlinearity on system bit error rate is relatively minor compared to channel noise. Atmospheric turbulence dominates system performance degradation through the combined effects of amplitude fading and phase perturbation, causing significant constellation spreading. Imbalanced optical splitting ratios and mismatched responsivity at the receiver weaken common-mode noise suppression, leading to variations in effective signal gain and constellation stretching/distortion. Under different signal light power and local oscillator light power conditions, the system noise exhibits distinct dominant characteristics. Full article

The growing reliance on solar energy necessitates robust and interpretable forecasting models for stable grid management. Current research frequently employs Explainable AI (XAI) to glean insights from complex black-box models, yet the reliability and consistency of these explanations remain largely unvalidated. Inconsistent feature attributions can mislead grid operators by incorrectly identifying the dominant drivers of solar generation, thereby affecting operational planning, reserve allocation, and trust in AI-assisted decision-making. This study addresses this critical gap by conducting a systematic statistical evaluation of feature rankings generated by multiple XAI methods, including model-agnostic (SHAP, PDP, PFI, ALE) and model-specific (Split- and Gain-based) techniques, within a time-series regression context. Using a LightGBM model for one-day-ahead solar power forecasting across four sites in Calgary, Canada, we evaluate consensus and divergence using the Friedman test, Kendall’s W, and Spearman’s rank correlation. To ensure the generalizability of our findings, we further validate the results using a CatBoost model. Our results show a strong overall agreement across methods (Kendall’s W: 0.90–0.94), with no statistically significant difference in ranking (p > 0.05). However, pairwise analysis reveals that the “Split” method frequently diverges from other techniques, exhibiting lower correlation scores. These findings suggest that while XAI consensus is high, relying on a single method—particularly the split count—poses risks. We recommend employing multi-method XAI and using agreement as an explicit diagnostic to ensure transparent and reliable solar energy predictions. Full article

The continued downscaling of transistors has exacerbated aging mechanisms such as bias temperature instability (BTI) and hot-carrier injection (HCI), posing significant reliability challenges for nanoscale integrated circuits. These effects are particularly critical to flip-flops operating at low supply voltages, which are essential for ultra-low-power applications including the Internet of Things (IoT) and biomedical implants. In this work, we address the aging issue in low-voltage Split-Controlled Flip-Flops (SCFFs) by proposing a novel transistor-level mitigation technique specifically tailored to this architecture within a domestic 14 nm process library. Through a detailed analysis of aging-critical transistors, three targeted enhancement strategies are introduced. Simulation results demonstrate that the improved SCFF achieves more than a 60% reduction in PMOS threshold voltage degradation and a 40% reduction in timing delay, while maintaining robust operation at a supply voltage as low as 0.4 V. These results highlight the effectiveness of the proposed approach in mitigating aging effects and enhancing reliability under low-voltage operation. Full article

In this work, a substrate-integrated waveguide (SIW) filtering power divider with a modified complementary split-ring resonator (CSRR) is reported. Firstly, by integrating the meander-shaped slots with the conventional CSRR, the proposed inner-meander-slot CSRR (IMSCSRR) can enlarge the total length of the defected slot and increase the width of the split, thus enhancing the equivalent capacitance and inductance. In this way, the fundamental resonant frequency of the IMSCSRR can be effectively decreased without enlarging the circuit size, which can generally help to reduce the physical size by over 35%. Subsequently, to further reduce the circuit size, two IMSCSRRs are separately loaded on the top and bottom metal covers to constitute a broadside-coupled IMSCSRR, which is combined with the SIW. To verify the efficacy of the proposed SIW-IMSCSRR unit cell, a two-way filtering power divider is implemented. It combines the band-selection function of a filter and the power-distribution property of a power divider, thereby enhancing system integration and realizing size compactness. Experimental results show that the proposed filtering power divider achieves a center frequency of 3.53 GHz, a bandwidth of about 320 MHz, an in-band insertion loss of (3 + 1.3) dB, an in-band isolation of over 21 dB, and a size reduction of about 30% compared with the design without broadside-coupling, as well as good magnitude and phase variations. All the results indicate that the proposed filtering power divider achieves a good balance between low loss, high isolation, and compact size, which is suitable for system integration applications in microwave scenarios. Full article

Multi-hop relaying can solve the problems of limited single-hop wireless communication distance, poor signal quality, or the inability to communicate directly by “relaying” data transmission through multiple intermediate nodes. It serves as the cornerstone for building large-scale, highly reliable, and self-adapting wireless networks, especially for the Internet of Things (IoT) and future 6G. This paper focuses on a decode-and-forward (DF) multi-hop relay network that employs simultaneous wireless information and power transfer (SWIPT) technology, with relays operating in a passive state. We first investigate the optimization of the power splitting (PS) ratio at each relay, given the source node transmit power, to maximize end-to-end network throughput. Subsequently, we jointly optimized the PS ratios and the source transmit power to minimize the source transmit power while satisfying the system’s minimum quality of service (QoS) requirement. Although both problems are non-convex, they can be reformulated as convex optimization problems. Closed-form optimal solutions are then derived based on the Karush–Kuhn–Tucker (KKT) conditions and a recursive method, respectively. Moreover, we find that the closed-form optimal solutions for the PS ratios corresponding to the two problems are identical. Through simulations, we validate that the performance of the two proposed schemes based on the closed-form solutions is optimal, while also demonstrating their extremely fast algorithm execution speeds, thereby proving the deployment value of the two proposed schemes in practical communication scenarios. Full article

The growing deployment of connected and autonomous vehicles (CAVs) requires coordinated control strategies that jointly address safety, mobility, and energy efficiency. This paper presents a novel two-stage cooperative control framework for autonomous hybrid electric vehicle (HEV) platoons based on machine learning. In the first stage, a metric learning-based distributed model predictive control (ML-DMPC) strategy is proposed to enable cooperative longitudinal control among heterogeneous vehicles, explicitly incorporating inter-vehicle interactions to improve speed tracking, ride comfort, and platoon-level energy efficiency. In the second stage, a multi-agent twin-delayed deep deterministic policy gradient (MATD3) algorithm is developed for real-time energy management, achieving an optimal power split between the engine and battery while reducing Q-value overestimation and accelerating learning convergence. Simulation results across multiple standard driving cycles demonstrate that the proposed framework outperforms conventional distributed model predictive control (DMPC) and multi-agent deep deterministic policy gradient (MADDPG)-based methods in fuel economy, stability, and convergence speed, while maintaining battery state of charge (SOC) within safe limits. To facilitate future experimental validation, a dSPACE-based hardware-in-the-loop (HIL) architecture is designed to enable real-time deployment and testing of the proposed control framework. Full article

Solar panel power plants are typically established in regions with maximum solar irradiation, yet these conditions result in heavy dust accumulation on the panels causing significant performance degradation and reduced power output. The paper addresses this issue via an image-based dust detection solution powered by deep learning, particularly convolutional neural networks (CNNs). Most of such solutions use state-of-the-art CNNs either as backbones/features extractors, or propose custom models built upon them. Given such a reliance, future research requires a comprehensive benchmarking of CNN models to identify the ones that achieve superior performance on classifying clean vs. dusty solar panels both with respect to accuracy and efficiency. To this end, we evaluate 100 CNN models that belong to 16 families for image-based dust detection on solar panels, where the pre-trained models of these CNN architectures are used to encode solar panel images. Upon these image encodings, we then train and test a linear support vector machine (SVM) to determine the best-performing models in terms of classification accuracy and training time. The use of such a simple classifier ensures a fair comparison where the encodings do not benefit from the classifier itself and their performance reflects each CNN’s ability to capture the underlying image features. Experiments were conducted on a publicly available dust detection dataset, using stratified shuffle-split with 70–30, 80–20, and 90–10 splits, repeated 10 times. convnext_xxlarge and resnetv2_152 achieved the best classification rates of above 90%, with resnetv2_152 offering superior efficiency that is also supported by features analysis such as tSNE and UMAP, and explainableAI (XAI) such as LIME visualization. To prove their generalization capability, we tested the image encodings of resnetv2_152 on an unseen real-world image dataset captured via a drone camera, which achieved a remarkable accuracy of 96%. Consequently, our findings guide the selection of optimal CNN backbones for future image-based dust detection systems. Full article

Accurate and reliable estimation of renewable energy generation is critical for modern power grid management, yet the inherent volatility and distinct physical drivers of multi-source renewables present significant modeling challenges. This paper proposes a unified deep learning framework for the concurrent estimation of power generation from solar, wind, and hydro sources. This methodology, termed nowcasting, utilizes real-time weather inputs to estimate immediate power generation. We introduce a hybrid spatio-temporal CNN-LSTM architecture that leverages a two-branch design to process both sequential weather data and static, plant-specific attributes in parallel. A key innovation of our approach is the use of a physics-informed Capacity Factor as the normalized target variable, which is customized for each energy source and notably employs a non-linear, S-shaped tanh-based power curve to model wind generation. To ensure high-fidelity spatial feature integration, a cKDTree algorithm was implemented to accurately match each power plant with its nearest corresponding weather data. To guarantee methodological rigor and prevent look-ahead bias, the model was trained and validated using a strict chronological data splitting strategy and was rigorously benchmarked against Linear Regression and XGBoost models. The framework demonstrated exceptional robustness on a large-scale dataset of over 1.5 million records spanning five European countries, achieving R-squared ($R^2$) values of 0.9967 for solar, 0.9993 for wind, and 0.9922 for hydro. While traditional ensemble models performed competitively on linear solar data, the proposed CNN-LSTM architecture demonstrated superior performance in capturing the complex, non-linear dynamics of wind energy, confirming its superiority in capturing intricate meteorological dependencies. This study validates the significant contribution of a spatio-temporal and physics-informed framework, establishing a foundational model for real-time energy assessment and enhanced grid sustainability. Full article