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In a uniform, source-free, and unbounded medium, Maxwell's equations require electromagnetic waves to be purely transverse. However, when a beam of light is tightly focused or strongly confined, a longitudinal field component can emerge. Strong longitudinal fields enable many novel phenomena and applications, including single molecule detection, near-field imaging, and high-resolution photolithography. Although the behavior of the longitudinal electric (LE) field component of the electromagnetic field in ordinary waveguides is well established, judicious nanostructuring offers unprecedented control over its strength as well as spatial and spectral distribution. Here, we demonstrate a full-vectorial theory and experimental results showing that for specially designed waveguides, such as one-dimensional antislot photonic crystal (PhC) waveguides, the LE field can hybridize with the transverse electric (TE) field in the waveguide and can be subsequently decomposed into independent polarizations through far field imaging. When the in-plane mirror symmetry of a PhC unit cell is broken, coupling between LE and TE modes produces two hybrid LE-TE modes and opens a new photonic bandgap. The

We construct new families of everywhere regular, asymptotically flat solitons in the Einstein--Proca model, obtained as self-gravitating continuations of flat-spacetime (singular) Proca multipoles. First we consider static and axially symmetric solutions, organized by a multipole number $\ell$. Two distinct classes arise: electric-type configurations, which include the spherical Proca stars as the $\ell=0$ case, and magnetic-type configurations, which have no spherical counterpart and start at $\ell=1$. Then we construct hybrid solutions as nonlinear superpositions of electric and magnetic multipoles. These have non-vanishing local angular momentum density but vanishing total angular momentum, and in some cases have no north-south $\mathbb{Z}_2$-symmetry. By performing dynamical evolutions of Proca stars in the new magnetic and hybrid sectors, we show they are unstable, decaying to the (static) prolate Proca stars or the (stationary) spinning Proca stars, previously identified as dynamically robust, electric sector configurations. In some cases, they can also collapse into a black hole.

Extended efforts have been devoted to the study of strongly-interacting excitons and their dynamics, towards macroscopic quantum states of matter such as Bose-Einstein condensates of excitons and polaritons. Momentum-direct layer-hybridized excitons in transition metal dichalcogenides have attracted considerable attention due to their high oscillator strength and dipolar nature. However, the tunability of their interactions and dynamics remains unexplored. Here, we achieve an unprecedented control over the nonlinear properties of dipolar layer-hybridized excitons in an electrically gated van der Waals homobilayer monitored by transient optical spectroscopy. By applying a vertical electric field, we reveal strong Coulomb interactions of dipolar hybrid excitons, leading to opposite density-dependent energy shifts of the two main hybrid species based on their dipolar orientation, together with a strongly enhanced optical saturation of their absorption. Furthermore, by electrically tuning the interlayer tunneling between the hybridized carriers, we significantly extend the formation time of hybrid excitons, while simultaneously increasing their decay times. Our findings have implicatio

The recent development of advanced machine learning methods for hybrid models has greatly addressed the need for the correct prediction of electrical prices. This method combines AlexNet and LSTM algorithms, which are used to introduce a new model with higher accuracy in price forecasting. Despite RNN and ANN being effective, they often fail to deal with forex time sequence data. The traditional methods do not accurately forecast the prices. These traditional methods only focus on demand and price which leads to insufficient analysis of data. To address this issue, using the hybrid approach, which focuses on external variables that also effect the predicted prices. Nevertheless, due to AlexNet's excellent feature extraction and LSTM's learning sequential patterns, the prediction accuracy is vastly increased. The model is built on the past data, which has been supplied with the most significant elements like demand, temperature, sunlight, and rain. For example, the model applies methods, such as minimum-maximum scaling and a time window, to predict the electricity prices of the future. The results show that this hybrid model is good than the standalone ones in terms of accuracy. Alt

Obtaining accurate probabilistic energy forecasts and making effective decisions amid diverse uncertainties are routine challenges in future energy systems. This paper presents the winning solution of team GEB, which ranked 3rd in trading, 4th in forecasting, and 1st among student teams in the IEEE Hybrid Energy Forecasting and Trading Competition 2024 (HEFTCom2024). The solution provides accurate probabilistic forecasts for a wind-solar hybrid system, and achieves substantial trading revenue in the day-ahead electricity market. Key components include: (1) a stacking-based approach combining sister forecasts from various Numerical Weather Predictions (NWPs) to provide wind power forecasts, (2) an online solar post-processing model to address the distribution shift in the online test set caused by increased solar capacity, (3) a probabilistic aggregation method for accurate quantile forecasts of hybrid generation, and (4) a stochastic trading strategy to maximize expected trading revenue considering uncertainties in electricity prices. This paper also explores the potential of end-to-end learning to further enhance the trading revenue by shifting the distribution of forecast errors.

The dynamic of thermosensitive neuronal networks under the influence of external electric fields is explored, focusing on hybrid coupling models that incorporate both electrical and chemical synapses. Numerical simulations reveal a variety of complex behaviors, including coherent, incoherent, and chimera states, which are influenced by the frequency of the external field and the cell size. Specificaly chemical coupling is shown to enhances the appearance of a typical chimera states called traveling chimera states, while the external electric field modulates synchronization based on its frequency. Low-frequency fields induce localized synchronization, whereas high-frequency fields exert minimal influence. The study also investigates the role of intrinsic electric fields, represented by cell size, and their effect on both individual neuron activity and network dynamics. These findings enhance the understanding of how external fields control collective neuronal behavior and open up new possibilities for the modulation of neural activity.

Hybrid encodings, where multiple degrees of freedom are used to encode quantum information, can increase the size of the Hilbert space with minimal increase to hardware requirements. We show a reprogrammable integrated photonic device, with multimodal components designed to allow for control over the transverse electric modes. We use this device to generate qudit states entangled in the path and transverse electric mode degrees of freedom. We generate and verify a hyperentangled state with a fidelity of $\mathcal{F}_{\text{HE}} = 67.3 \pm 0.2\%$ and a GHZ$_{4}$-style state with a fidelity of $\mathcal{F}_{\text{GHZ}_{4}} = 85.2 \pm 0.4 \%$. We use our hyperentangled state in a single-copy entanglement distillation protocol, resulting in an average $9.1 \%$ increase in the fidelity of the distilled Bell state for up to a $50\%$ probability of bit flip error. By utilising degrees of freedom which are readily compatible with integrated photonics, our work highlights how this hybrid encoding demonstrates a first step in using the transverse electric mode to reduce the footprint of integrated quantum photonic experiments.

Doped van der Waals heterostructures host layer-hybridized trions, i.e. charged excitons with layer-delocalized constituents holding promise for highly controllable optoelectronics. Combining a microscopic theory with photoluminescence (PL) experiments, we demonstrate the electrical tunability of the trion energy landscape in naturally stacked WSe$_2$ bilayers. We show that an out-of-plane electric field modifies the energetic ordering of the lowest lying trion states, which consist of layer-hybridized $Λ$-point electrons and layer-localized K-point holes. At small fields, intralayer-like trions yield distinct PL signatures in opposite doping regimes characterized by weak Stark shifts in both cases. Above a doping-asymmetric critical field, interlayer-like species are energetically favored and produce PL peaks with a pronounced Stark red-shift and a counter-intuitively large intensity arising from efficient phonon-assisted recombination. Our work presents an important step forward in the microscopic understanding of layer-hybridized trions in van der Waals heterostructures and paves the way towards optoelectronic applications based on electrically controllable atomically-thin semic

Graphene, with spin and valley degrees of freedom, fosters unexpected physical and chemical properties for the realization of next-generation quantum devices. However, the spin symmetry of graphene is rather robustly protected, hampering manipulation of the spin degrees of freedom for the application of spintronic devices such as electric gate tunable spin filters. We demonstrate that a hybrid heterostructure composed of graphene and LaCoO3 epitaxial thin film exhibits an electrically tunable spin exchange splitting. The large and adjustable spin exchange splitting of 155.9 - 306.5 meV was obtained by the characteristic shifts in both the spin symmetry broken quantum Hall states and the Shubnikov-de-Haas oscillations. Strong hybridization induced charge transfer across the hybrid heterointerface has been identified for the observed spin exchange splitting. The substantial and facile controllability of the spin exchange splitting provides an opportunity for spintronics applications with the electrically-tunable spin polarization in hybrid heterostructures.

Accurate electrical load forecasting is of great importance for the efficient operation and control of modern power systems. In this work, a hybrid long short-term memory (LSTM)-based model with online correction is developed for day-ahead electrical load forecasting. Firstly, four types of features are extracted from the original electrical load dataset, including the historical time series, time index features, historical statistical features, and similarity features. Then, a hybrid LSTM-based electrical load forecasting model is designed, where an LSTM neural network block and a fully-connected neural network block are integrated that can model both temporal features (historical time series) and non-temporal features (the rest features). A gradient regularization-based offline training algorithm and an output layer parameter fine-tuning-based online model correction method are developed to enhance the model's capabilities to defend against disturbance and adapt to the latest load data distribution, thus improving the forecasting accuracy. At last, extensive experiments are carried out to validate the effectiveness of the proposed electrical load forecasting strategy with superio

This research presents a multi-layer optimization framework for hybrid energy storage systems (HESS) for passenger electric vehicles to increase the battery system's performance by combining multiple cell chemistries. Specifically, we devise a battery model capturing voltage dynamics, temperature and lifetime degradation solely using data from manufacturer datasheets, and jointly optimize the capacity distribution between the two batteries and the power split, for a given drive cycle and HESS topology. The results show that the lowest energy consumption is obtained with a hybrid solution consisting of a NCA-NMC combination, since this provides the best trade-off between efficiency and added weight.

Hybrid multiferroic films are fabricated by depositing of Pt/Co/Pt multilayers onto [001] and [110] cuts of PMN-PT crystal. The dependence of the interfacial Dzyaloshinskii-Moriya interaction (iDMI) on applied electric field is experimentally investigated in the system by the Brilloin light scattering method. A strong variation (from -0.2 to 0.8 mJ/m2) of the iDMI constant is observed when the electric field is applied. In the case of [001] cut, the observed changes in the iDMI have an isotropic character, while in the case of [110] cut they are anisotropic, which corresponds to the symmetry of the PMN-PT deformations. The change in the iDMI is accompanied by the formation of various unusual domain structures and skyrmion lattices. External control of the DMI with an electric field opens the way to manipulate topological magnetic solitons (such as skyrmions), which are promising objects for information processing and storage.

With the maritime industry poised on the cusp of a hybrid revolution, the design and analysis of advanced vessel systems have become paramount for engineers. This paper presents AC and DC electrical hybrid power system models in ETAP, the simulation software that can be adapted to engineer future hybrid vessels. These models are also a step towards a digital twin model that can help in troubleshooting and preventing issues, reducing risk and engineering time. The testing of the models is focused on time domain analysis, short-circuit currents, and protection \& coordination. The models are based on actual vessels and manufacturer parameters are used where available.

This article presents a novel hybrid approach using statistics and machine learning to forecast the national demand of electricity. As investment and operation of future energy systems require long-term electricity demand forecasts with hourly resolution, our mathematical model fills a gap in energy forecasting. The proposed methodology was constructed using hourly data from Ukraine's electricity consumption ranging from 2013 to 2020. To this end, we analysed the underlying structure of the hourly, daily and yearly time series of electricity consumption. The long-term yearly trend is evaluated using macroeconomic regression analysis. The mid-term model integrates temperature and calendar regressors to describe the underlying structure, and combines ARIMA and LSTM ``black-box'' pattern-based approaches to describe the error term. The short-term model captures the hourly seasonality through calendar regressors and multiple ARMA models for the residual. Results show that the best forecasting model is composed by combining multiple regression models and a LSTM hybrid model for residual prediction. Our hybrid model is very effective at forecasting long-term electricity consumption on an

This paper addresses the problem of predicting the energy consumption for the drivers of Battery electric vehicles (BEVs). Several external factors (e.g., weather) are shown to have huge impacts on the energy consumption of a vehicle besides the vehicle or powertrain dynamics. Thus, it is challenging to take all of those influencing variables into consideration. The proposed approach is based on a hybrid model which improves the prediction accuracy of energy consumption of BEVs. The novelty of this approach is to combine a physics-based simulation model, which captures the basic vehicle and powertrain dynamics, with a data-driven model. The latter accounts for other external influencing factors neglected by the physical simulation model, using machine learning techniques, such as generalized additive mixed models, random forests and boosting. The hybrid modeling method is evaluated with a real data set from TUM and the hybrid models were shown that decrease the average prediction error from 40% of the pure physics model to 10%.