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Calculating molecular properties using quantum devices can be performed through the quantum linear response (qLR) or, equivalently, the quantum equation of motion (qEOM) formulations. Different parameterizations of qLR and qEOM are available, namely naïve, projected, self-consistent, and state-transfer. In the naïve and projected parameterizations, the metric is not the identity, and we show that it depends on redundant orbital rotations. This dependency may lead to divergences in the excitation energies for certain choices of the redundant orbital rotation parameters in an idealized noiseless setting. Furthermore, this leads to a significant variance when calculations include statistical noise from finite quantum sampling. [ABSTRACT FROM AUTHOR]

A newly parameterized combined density functional theory and multi-reference configuration interaction (DFT/MRCI) Hamiltonian, termed core-valence separation (CVS)-QE12, is defined for the computation of K-shell core-excitation and core-ionization energies. This CVS counterpart to the recently reported QE8 Hamiltonian [Costain et al., J. Chem. Phys, 160, 224106 (2024)] is parameterized by fitting to benchmark quality ab initio data. The definition of the CVS-QE12 and QE8 Hamiltonians differ from previous CVS-DFT/MRCI parameterizations in three primary ways: (i) the replacement of the BHLYP exchange–correlation functional with QTP17 to yield a balanced description of both core and valence excitation energies, (ii) the adoption of a new, three-parameter damping function, and (iii) the introduction of separate scaling of the core-valence and valence-valence Coulombic interactions. Crucially, the parameters of the CVS-QE12 Hamiltonian are obtained via fitting exclusively to highly accurate ab initio vertical core-excitation and ionization energies computed at the CVS-EOM-CCSDT level of theory. The CVS-QE12 Hamiltonian is validated against further benchmark computations and is found to furnish K-edge core vertical excitation and ionization energies exhibiting absolute errors ≤0.5 eV at low computational cost. [ABSTRACT FROM AUTHOR]

The calculation of molecular excited states is critically important to decipher a plethora of molecular properties. In this paper, we develop an equation of motion formalism on top of a bi-exponentially parameterized ground state wavefunction toward the determination of excited states. While the ground state bi-exponential parameterization ensures an accurate description of the wavefunction through the inclusion of high-rank correlation effects, the excited state is parameterized by a novel linear response operator with an effective excitation rank beyond two. To treat the ground and excited states in the same footings, in addition to the conventional one- and two-body response operators, we introduced certain two-body "generalized" response operators with an effective excitation rank of one. We introduce a projective formulation for determining the perturbed amplitudes for the set of "generalized" operators. Our formulation entails a significantly small number of unknown parameters and is shown to be highly accurate compared to allied methods for several difficult chemical systems. [ABSTRACT FROM AUTHOR]

We show that optimal control of the electron dynamics is able to prepare molecular ground states, within chemical accuracy, with evolution times approaching the bounds imposed by quantum mechanics. We propose a specific parameterization of the molecular evolution only in terms of interaction already present in the molecular Hamiltonian. Thus, the proposed method solely utilizes quantum simulation routines, retaining their favorable scalings. Due to the intimate relationships between variational quantum algorithms and optimal control, we compare, when possible, our results with state-of-the-art methods in the literature. We found that the number of parameters needed to reach chemical accuracy and algorithmic scaling is in line with compact adaptive strategies to build variational Ansätze. The algorithm, which is also suitable for quantum simulators, is implemented by emulating a digital quantum processor (up to 16 qubits) and tested on different molecules and geometries spanning different degrees of electron correlation. [ABSTRACT FROM AUTHOR]

Access to accurate force-field parameters for small molecules is crucial for computational studies of their interactions with proteins. Although a number of general force fields for small molecules exist, e.g., CGenFF, GAFF, and OPLS, they do not cover all common chemical groups and their combinations. The Force Field Toolkit (ffTK) provides a comprehensive graphical interface that streamlines the development of classical parameters for small molecules directly from quantum mechanical (QM) calculations, allowing for force-field generation for almost any chemical group and validation of the fit relative to the target data. ffTK relies on supported external software for the QM calculations, but it can generate the necessary QM input files and parse and analyze the QM output. In previous ffTK versions, support for Gaussian and ORCA QM packages was implemented. Here, we add support for Psi4, an open-source QM package free for all users, thereby broadening user access to ffTK. We also compare the parameter sets obtained with the new ffTK version using Gaussian, ORCA, and Psi4 for three molecules: pyrrolidine, n-propylammonium cation, and chlorobenzene. Despite minor differences between the resulting parameter sets for each compound, most prominently in the dihedral and improper terms, we show that conformational distributions sampled in molecular dynamics simulations using these parameter sets are quite comparable. [ABSTRACT FROM AUTHOR]

Within the framework of natural orbital functional theory, having a convenient representation of the occupation numbers and orbitals becomes critical for the computational performance of the calculations. Recognizing this, we propose an innovative parametrization of the occupation numbers that takes advantage of the electron-pairing approach used in Piris natural orbital functionals through the adoption of the softmax function, a pivotal component in modern deep-learning models. Our approach not only ensures adherence to the N-representability of the first-order reduced density matrix (1RDM) but also significantly enhances the computational efficiency of 1RDM functional theory calculations. The effectiveness of this alternative parameterization approach was assessed using the W4-17-MR molecular set, which demonstrated faster and more robust convergence compared to previous implementations. [ABSTRACT FROM AUTHOR]

In 2011 Hefez and Hernandes completed Zariski's analytic classification of plane branches belonging to a given equisingularity class by creating "very short" parameterizations over the complex numbers. Their results were used by Mehmood and Pfister to classify unimodal plane branches in characteristic 0 by giving lists of normal forms. The aim of this paper is to give a complete classification of unimodal plane branches over an algebraically closed field of positive characteristic. Since the methods of Hefez and Hernandes cannot be used in positive characteristic, we use a different approach and, for some sporadic singularities in small characteristic, computations with Singular. Our methods are characteristic-independent and provide a different proof of the classification in characteristic 0 showing at the same time that this classification holds also in large characteristic. The main theoretical ingredients are the semicontinuity of the semigroup and of the modality, which we prove and which may be of independent interest. [ABSTRACT FROM AUTHOR]

Kitchen waste images encompass a wide range of garbage categories, posing a typical multi-label classification challenge. However, due to the complex background and significant variations in garbage morphology, there is currently limited research on kitchen waste classification. In this paper, we propose a multi-head attention-driven dynamic graph convolution lightweight network for multi-label classification of kitchen waste images. Firstly, we address the issue of large model parameterization in traditional GCN methods by optimizing the backbone network for lightweight model design. Secondly, to overcome performance losses resulting from reduced model parameters, we introduce a multi-head attention mechanism to mitigate feature information loss, enhancing the feature extraction capability of the backbone network in complex scenarios and improving the correlation between graph nodes. Finally, the dynamic graph convolution module is employed to adaptively capture semantic-aware regions, further boosting recognition capabilities. Experiments conducted on our self-constructed multi-label kitchen waste classification dataset MLKW demonstrate that our proposed algorithm achieves a 8.6% and 4.8% improvement in mAP compared to the benchmark GCN-based methods ML-GCN and ADD-GCN, respectively, establishing state-of-the-art performance. Additionally, extensive experiments on two public datasets, MS-COCO and VOC2007, showcase excellent classification results, highlighting the strong generalization ability of our algorithm. [ABSTRACT FROM AUTHOR]

The parameterization of orographic gravity wave drag (OGWD) is essential for accurate numerical weather prediction in regions of complex terrain. Current OGWD schemes assume hydrostatic orographic gravity waves (OGWs) but the parameterized OGWs in fine-resolution models with narrow subgrid-scale orography can be significantly affected by the nonhydrostatic effects (NHE). In our recent work, the OGWD scheme in the Model for Prediction Across Scales (MPAS) was revised by accounting for the NHE on the surface wave momentum flux of upward-propagating OGWs. Herein, the revised OGWD scheme is implemented in the Weather Research and Forecasting (WRF) Model to evaluate its performance in short-range weather forecast. Two sets of 36-h WRF simulations are conducted for nine Northeast China cold vortices (NECVs) that occurred in the warm season of 2011 using the original and revised OGWD schemes. Results show that the WRF Model tends to underestimate the intensity of the NECVs, producing too high geopotential height. When accounting for the NHE in the OGWD scheme, the NECV intensity biases are significantly reduced. Analyses reveal that the NHE act to weaken the lower-tropospheric OGWD by decreasing the surface wave momentum flux, which strengthens the NECV in the lower troposphere. Consequently, the strengthened low-level cyclonic circulation increases the posttrough cold advection to the southwest of the NECV which in turn enhances the NECV in the mid–upper troposphere with reduced geopotential height. The NHE are found to increase as the model horizontal resolution increases, suggesting greater importance of NHE in the OGWD parameterization of high-resolution numerical models. [ABSTRACT FROM AUTHOR]

The generalized second law of thermodynamics (GSLT) is examined in the current work in the backdrop of Friedmann–Robertson–Walker universe. A digression from the standard Bekenstein–Hawking entropy formulation is considered. Two different modified forms of the entropy functions are studied. For the matter component, five well-known parameterizations of the equation of state parameter are taken into consideration. The diagrammatic representations of the rate of change of the entropy functions in the different models are presented. The outcomes suggest that the GSLT is valid in these models for certain choices of the parameters involved. [ABSTRACT FROM AUTHOR]

Chiral amines are key building blocks in producing active pharmaceutical ingredients and can be synthesized via the amine transaminase pathway under mild conditions with eco‐friendly solvents. For model‐based design and control, mathematical models can be utilized. This requires reliable parameterization of the model. In this study, a model for the production of (S)‐(3 methoxyphenyl)‐ethylamine is presented. Parameter identifiability is analyzed using the method of profile likelihoods. The parameter identifiability is then tested in two different batch operation scenarios by using moving horizon estimation, which can be applied for on‐line model adaption and control purposes. In a batch process, identifiability decreases over time. A repetitive batch strategy improves parameter identifiability. [ABSTRACT FROM AUTHOR]

The near-surface temperatures in the Arctic are increasing at more than twice the global average rate, a phenomenon known as Arctic amplification (AA). In recent years, numerous studies using ground-based and satellite observations, along with model simulations, have explored the potential mechanisms behind AA, offering a variety of observational evidence and theoretical explanations. Although the understanding of AA drivers has improved, significant uncertainties remain in quantifying the contributions of different influential factors. On the basis of the latest research, this article thoroughly examines the factors driving rapid warming in the Arctic, including local feedbacks, atmospheric circulation, ocean currents, and aerosols, and compares quantitative results across studies. The analysis highlights the complex interplay of multiple factors contributing to AA, with no clear consensus on the relative contributions of each driver. Finally, the article underscores key challenges in current research, emphasizing the need for more reliable observational data, a deeper understanding of AA mechanisms, improved model parameterizations, and the disentanglement of interactions among driving factors, all of which are essential for future investigations. [ABSTRACT FROM AUTHOR]

This paper examines using non-rational polynomials to approximate the involute-curve geometry, which is fundamental in the design of gear systems. The parametric form of the involute curve in terms of an involute-angle parameter is first presented. To be able to compare the analytical geometry results and numerical results obtained using the finite-element (FE) method for describing the geometry, the relationship between the involute-angle parameter and the FE spatial coordinate is defined. A new relationship between the involute-curve arc length and the coordinate used in the FE parameterization is also developed. To this end, the involute-curve equation is used to determine the involute-angle parameter at the intersection with circles with arbitrary radii including the gear pitch, addendum, and base circles. The parameter relationship allows defining the nodal position-gradient vectors defined by differentiation with respect to the FE spatial coordinate from involute-curve tangent vector. Two procedures for determining the left involute curve from the right involute curve of the gear tooth are described. In the first procedure, a coordinate transformation based on Rodrigues transformation with axis of rotation defined by the tooth-center line is used; while in the second procedure, the analytical expression of the left involute curve is defined using the gear-tooth thickness at the base circle. A general procedure for determining the position gradients for a given geometry obtained from analytical curves or using imaging techniques is introduced. Such a procedure can be used in case of worn gear teeth that cannot be described by simple functions. A planar rectangular element based on the absolute nodal coordinate formulation (ANCF) is used to approximate the gear-tooth geometry. While the involute geometry cannot be described exactly using non-rational polynomials, the analysis and results of this investigation demonstrate that the FE solution leads to accurate geometry results, demonstrating the potential of using the position gradients in future strength-calculation studies. [ABSTRACT FROM AUTHOR]

The multi-point hemispherical grid model (MHGM) utilizes residual of double-differenced observations to extract precise multipath error information. It models the entire network of multipath error effects across different stations to achieve effective error correction. However, because all the parameters are estimated collectively using the least squares method, the increased number of grid point parameters can significantly consume memory, CPU, and other computing resources required for modeling. In response to the computational resource consumption challenge associated with fixed-resolution MHGM in multi-station applications, a space domain adaptive grid division method is proposed to optimize the modeling of multipath errors. This approach utilizes prior distribution information of multipath errors to optimize the grid structure. It reduces the number of grids in areas where multipath errors exhibit minimal changes, and provides detailed parameterization for areas with significant variations. Experimental results demonstrate the effectiveness of this method in significantly reducing the number of estimated parameters using MHGM. In statistical analysis of double-differenced phase observation residuals with fixed ambiguities, as the number of estimated parameters in the MHGM decreases to only 24.6 % of the fixed-resolution approach, memory usage during parameter estimation remains a mere 6 % of that required in the fixed-resolution approach. This highlights its potential value in mitigating multipath errors when modeling GNSS large-scale network data. [ABSTRACT FROM AUTHOR]

Quantitative characterization and proper parameterization of pulse-like ground motions have been a challenge for earthquake engineering research. The azimuth of the pulse-like ground motion has a great influence on the structural damage. This study proposes a full-azimuth quantitative identification method to determine the pulse inherent in near-fault pulse-like ground motions. A support vector machine was used to classify the records into pulses or non-pulses based on a new pulse index. Records with a pulse index greater than zero can be classified as pulse-like. The proposed approach was used to identify pulse-like features in arbitrary orientations and can determine the strongest pulse azimuth. [ABSTRACT FROM AUTHOR]

Multinomial processing tree (MPT) models are prominent and frequently used tools to model and measure cognitive processes underlying responses in many experimental paradigms. Although MPT models typically refer to cognitive processes within single individuals, they have often been applied to group data aggregated across individuals. We investigate the conditions under which MPT analyses of aggregate data make sense. After introducing the notions of structural and empirical aggregation invariance of MPT models, we show that any MPT model that holds at the level of single individuals must also hold at the aggregate level when it is both structurally and empirically aggregation invariant. Moreover, group-level parameters of aggregation-invariant MPT models are equivalent to the expected values (i.e., means) of the corresponding individual parameters. To investigate the robustness of MPT results for aggregate data when one or both invariance conditions are violated, we additionally performed a series of simulation studies, systematically manipulating (1) the sample sizes in different trees of the model, (2) model parameterization, (3) means and variances of crucial model parameters, and (4) their correlations with other parameters of the respective MPT model. Overall, our results show that MPT parameter estimates based on aggregate data are trustworthy under rather general conditions, provided that a few preconditions are met. [ABSTRACT FROM AUTHOR]

The geometries constructed using the standard CAD software are always boundary-represented (B-Rep). Due to this, analysis-suitable volumetric parameterization of the geometries constructed using the standard CAD software is necessary for three-dimensional isogeometric analysis (IGA). This paper presents a user-friendly, robust volumetric parametrization tool capable of processing the B-Rep of the geometries constructed in one of the most popular CAD software, i.e. Rhinoceros®. The developed tool traverses multiple stages, starting with reading the standard 3DM CAD files of an arbitrarily shaped geometry. It then extracts surface control points, organizes them to establish an outer structural framework, and employs Coons volume parameterization to generate internal control points based on user-defined specifications. We use the NURBS-Python library for reading 3DM files and the NURBS toolbox to visualize and evaluate NURBS functions in the constructed geometries. We construct several baseline and intricate shape geometries to showcase the robustness and generality of the developed tool. Additionally, to demonstrate the effectiveness and applicability of our tool for IGA of non-linear three-dimensional problems, we simulate frictionless contact between two deformable geometries constructed using the developed tool. [ABSTRACT FROM AUTHOR]

Cloudburst events are characterized by very high rainfall rates (100 mm h−1) over a small area (~10×10 km). It catastrophically damages the properties and causes loss of lives. Therefore, it is essential to understand the nature of the microphysical processes that contributed to producing such a high rain rate. Collision processes inside the cloud are vital in determining the rainfall rate. Hence, in the present study, we have evaluated a comparative performance of disparate collision efficiencies based on different formulations during the cloud burst event of 10 June 2021 over Sauni Binsar, Uttarakhand, at 11:45 AM IST (06:15 UTC 10 June 2021). The lifting condensation level (LCL), lifting deposition level (LDL), and lifting freezing levels (LFL) have been calculated using the ERA5 dataset. The collision efficiencies were determined on 100 levels between the lifting condensation and freezing levels. The appraisal of eight formulations shows that Onishi, Beard and Grover, Bohm, and Jin formulations yielded higher collision efficiencies during the cloudburst event, which is expected. Meanwhile, the estimated collision efficiency based on Ahmad's formulation is high for specific bins only. The formulations by Barnet, Long, and Lee and Baik, however, yielded very low values of collision efficiencies during the cloud burst event. Research highlights: Collision processes inside the cloud are vital in determining the rainfall rates especially during cloud burst. Eight collision efficiency formulations have been apprised during the cloud burst event of Sauni Binsar, Uttarakhand, India. The complex interaction between synoptic systems and orography caused atmosphere over Sauni Binsar to be convective and moist few hours before the cloud burst event. Collision efficiency formulation by Onishi, Beard and Grover, Bohm, and Jin estimated higher collision efficiencies, which is appropriate, while Ahmad estimated high values for specific bins only. Collision efficiency formulation by Barnet, Long, and Lee and Baik estimated very low values. [ABSTRACT FROM AUTHOR]

Atmospheric boundary layer (ABL) structure and dynamics are important aspects to consider in human health. The ABL is characterized by a high degree of spatial and temporal variability that hinders their understanding. This paper aims to provide a comprehensive overview of machine learning (ML) methodologies, encompassing deep learning and ensemble approaches, within the scope of ABL research. The goal is to highlight the challenges and opportunities of using ML in turbulence modeling and parameterization in areas such as atmospheric pollution, meteorology, and renewable energy. The review emphasizes the validation of results to ensure their reliability and applicability. ML has proven to be a valuable tool for understanding and predicting how ABL spatial and seasonal variability affects pollutant dispersion and public health. In addition, it has been demonstrated that ML can be used to estimate several variables and parameters, such as ABL height, making it a promising approach to enhance air quality management and urban planning. [ABSTRACT FROM AUTHOR]

Reduced-order simulation is an emerging method for accelerating physical simulations with high DOFs, and recently developed neural-network-based methods with nonlinear subspaces have been proven effective in diverse applications as more concise subspaces can be detected. However, the complexity and landscape of simulation objectives within the subspace have not been optimized, which leaves room for enhancement of the convergence speed. This work focuses on this point by proposing a general method for finding optimized subspace mappings, enabling further acceleration of neural reduced-order simulations while capturing comprehensive representations of the configuration manifolds. We achieve this by optimizing the Lipschitz energy of the elasticity term in the simulation objective, and incorporating the cubature approximation into the training process to manage the high memory and time demands associated with optimizing the newly introduced energy. Our method is versatile and applicable to both supervised and unsupervised settings for optimizing the parameterizations of the configuration manifolds. We demonstrate the effectiveness of our approach through general cases in both quasi-static and dynamics simulations. Our method achieves acceleration factors of up to 6.83 while consistently preserving comparable simulation accuracy in various cases, including large twisting, bending, and rotational deformations with collision handling. This novel approach offers significant potential for accelerating physical simulations, and can be a good add-on to existing neural-network-based solutions in modeling complex deformable objects. [ABSTRACT FROM AUTHOR]

Meshes with spherical faces and circular edges are an attractive alternative to polyhedral meshes for applications in architecture and design. Approximation of a given surface by such a mesh needs to consider the visual appearance, approximation quality, the position and orientation of circular intersections of neighboring faces and the existence of a torsion free support structure that is formed by the planes of circular edges. The latter requirement implies that the mesh simultaneously defines a second mesh whose faces lie on the same spheres as the faces of the first mesh. It is a discretization of the two envelopes of a sphere congruence, i.e., a two-parameter family of spheres. We relate such sphere congruences to torsal parameterizations of associated line congruences. Turning practical requirements into properties of such a line congruence, we optimize line and sphere congruence as a basis for computing a mesh with spherical triangular or quadrilateral faces that approximates a given reference surface. [ABSTRACT FROM AUTHOR]

Asymmetric leading edge offers several advantages over conventional leading edge. However, there are few related published research on compressor blades. This study proposes a blade parameterization method that superimposing the thickness distribution on the camber line at the middle and curve fitting at the leading and trailing edges. This parameterization method is applied to a stator in the final stage of an axial compressor, and numerical simulation is used to calculate the aerodynamic performance at various attack angles under the designed inlet Mach number. There is an optimal deflection angle regardless of which side of the leading edge deflects, and deflection toward the pressure side is conducive to the suppression of separation bubble near the leading edge at positive attack angle. When the maximum curvature of the pressure side is decreased, the operating range changed slightly, but the positive attack angle range grew from 5.166° to 5.466°. [ABSTRACT FROM AUTHOR]

A fundamental characteristic of extreme precipitation events (EPEs) is their horizontal scale. This horizontal scale can influence the intensity of an EPE through its effect on the timescale of an EPE as well as its effect on the strength of convective feedbacks. Thus, to have confidence in future projections of extreme precipitation, the horizontal scales of EPEs in global climate models (GCMs) should be evaluated. Analyzing daily output from 27 models participating in the Coupled Model Intercomparison Project phase 6 (CMIP6), including 13 models participating in the High Resolution Model Intercomparison Project (HighResMIP), we computed the horizontal scales of EPEs and extreme ascent for annual maximum EPEs during 1981–2000. We found that the horizontal scales of both EPEs and the associated ascending motion are resolution‐dependent: for a factor of seven increase in horizontal resolution, the horizontal scale decreases by a factor of approximately two to five, with higher sensitivity in the tropics than in the midlatitudes. Further analysis in the southern hemisphere midlatitudes reveals that this resolution dependence results from precipitation during the simulated EPEs that is almost entirely resolved rather than parameterized. However, the EPEs are not simply grid box storms, and analysis of the horizontal scales of geopotential anomalies suggests that the planetary‐scale dynamics in GCMs is not resolution‐dependent. Thus, the dominance of resolved precipitation during EPEs is more likely due to convection on the model grid or formation of strong, poorly resolved fronts, and additional work is needed to explore these possibilities and find a remedy for this resolution dependence. Plain Language Summary: Extreme precipitation is of great human interest because of the potentially severe flooding that it can cause. We are reliant on global climate models (GCMs) to project future changes in extreme precipitation, so there is a need to assess the realism of extreme precipitation in GCMs. One fundamental characteristic of an extreme precipitation event (EPE) is its horizontal size or "scale." In this study, we analyze a large archive of output from simulations using state‐of‐the‐art GCMs and compute the horizontal scale of EPEs during 1981–2000. We found that the horizontal scale depends on the spacing between grid points in the model. These grid points are like pixels in a digital photo, and the smaller the distance between grid points, the higher the model resolution. The fact that the EPE horizontal scale in GCMs is resolution‐dependent is concerning because the horizontal scale should be dictated by realistic physical processes rather than artificial aspects of the model configuration. While we have provided some initial insights into the reasons for this resolution dependence, additional work is needed to develop more complete explanations and improve models. Key Points: The simulated horizontal scale of the annual maximum of daily precipitation anomalies is sensitive to a model's horizontal resolutionThis extreme precipitation horizontal scale is determined almost entirely by resolved, not parameterized, precipitationThe simulated horizontal scale of geopotential disturbances during these extreme precipitation events is not resolution dependent [ABSTRACT FROM AUTHOR]

The dynamic and multimodal nature of photovoltaic (PV) systems makes it challenging to examine all solar photovoltaic characteristics. Consequently, this study recommends a recently developed optimization method called the marine predator algorithm (MPA) for developing reliable PV models. In the traditional MPA, the two main search processes are Lévy flight (LF) and Brownian walk (BW), and the switch across them is unpredictable. This is while the transition between these two mechanisms is naturally continuous and dynamic. To rectify the limitation mentioned above, this research paper presents an innovative, dynamic shift function that effectively modulates the interplay that exists between the BW and LF procedures. By enhancing the changeover pattern between the primary phases of MPA, the suggested dynamic walk substantially boosts the performance of MPA. The dynamic Lévy-Brownian MPA (DLBMPA) is also made to be resilient in dealing with the parameterization limitations of PV Modeling approaches by using a constraint handling technique. The performance of DLBMPA is tested using ten popular optimization methods. Employing the DLBMPA achieved an average RMSE of 9.7 × 10− 4 in the parameter estimation across a number of multiple PV models, including the SDM, DDM, and TDM, where out of the ten optimization algorithms experimented, this was statistically significant (p < 0.05) better. In terms of averaged computation time, DLBMPA was 13 ms and still showed high accuracy in dealing with different irradiance and temperature levels. These improvements allow for MBPA to be credited as having a high efficiency when estimating the PV parameters since its speed of convergence and accuracy level surpass the previous techniques used. [ABSTRACT FROM AUTHOR]

Rate coefficients, k(T), for the gas‐phase OH radical reaction with decamethyltetrasiloxane ((CH3)3SiO[Si(CH3)2O]2Si(CH3)3, L4, k1), dodecamethylpentasiloxane ((CH3)3SiO[Si(CH3)2O]3Si(CH3)3, L5, k2), and decamethylcyclopentasiloxane ([–Si(CH3)2O–]5, D5, k3), and dodecamethylcyclohexasiloxane ([–Si(CH3)2O–]6, D6, k4) were measured using a pulsed laser photolysis—laser induced fluorescence absolute method over the temperature range 270–370 K. The obtained room temperature rate coefficients, with quoted 2σ absolute uncertainties, and fitted temperature dependence are (cm−3 molecule−1 s−1): k1295K=2.50±0.18×10−12,k1270−370K=1.707×10−13T/2984.45exp807/Tk2295K=3.12±0.32×10−12,k2295−370K=2.65±0.02×10−11exp−630±60/Tk3295K=2.02±0.12×10−12,k3270−370K=8.97×10−13T/2982.90exp248/Tk4297K=2.89±0.16×10−12,k4297−370K=3.13±0.05×10−11exp−717±119/T$$\begin{equation*} \hspace*{45pt} \def\eqcellsep{&}\begin{array}{rcl} {k}_1\left( {295\;{\mathrm{K}}} \right) &=& \left( {2.50 \pm 0.18} \right) \times {{10}}^{ - 12},\quad {k}_1\left( {270-370\;{\mathrm{K}}} \right)\\ & =& 1.707 \times {{10}}^{ - 13}{{\left( {{\mathrm{T}}/298} \right)}}^{4.45}\exp \left( {807/{\mathrm{T}}} \right)\\ {k}_2\left( {295\;{\mathrm{K}}} \right) &=& \left( {3.12 \pm 0.32} \right) \times {{10}}^{ - 12},\quad {k}_2\left( {295-370\;{\mathrm{K}}} \right)\\ &=& \left( {2.65 \pm 0.02} \right) \times {{10}}^{ - 11}\exp \left( { - \left( {630 \pm 60} \right)/{\mathrm{T}}} \right)\\ {k}_3\left( {295\;{\mathrm{K}}} \right) &=& \left( {2.02 \pm 0.12} \right) \times {{10}}^{ - 12},\quad {k}_3\left( {270-370\;{\mathrm{K}}} \right)\\ & =& 8.97 \times {{10}}^{ - 13}{{\left( {T/298} \right)}}^{2.90}\exp \left( {248/{\mathrm{T}}} \right)\\ {k}_4\left( {297\;{\mathrm{K}}} \right) &=& \left( {2.89 \pm 0.16} \right) \times {{10}}^{ - 12},\quad {k}_4\left( {297-370\;{\mathrm{K}}} \right)\\ & =& \left( {3.13 \pm 0.05} \right) \times {{10}}^{ - 11}\exp \left( { - \left( {717 \pm 119} \right)/{\mathrm{T}}} \right) \end{array} \end{equation*}$$The 2σ absolute rate coefficient uncertainty, for all compounds included in this study, is conservatively estimated to be ∼10% over the entire temperature range. The cyclic permethylsiloxanes were found to be less reactive than the analogous linear compound, while both linear and cyclic compounds show increasing reactivity with increasing number of CH3‐ groups. A structure activity relationship (SAR) parameterization for the permethylsiloxanes is presented. The estimated atmospheric lifetimes due to OH reaction for L4, L5, D5, and D6 are 5.2, 4.4, 6.8, and 5.2 days, respectively. [ABSTRACT FROM AUTHOR]

Atmospheric turbulence over the Arctic sea‐ice surface has been understudied due to the lack of observational data. In this study, we focus on the turbulence kinetic energy (TKE) over sea ice and distinguish its two different vertical structures, the "Surface" type and the "Elevated" type, using observations during the Multidisciplinary drifting Observatory for the Study of Arctic Climate expedition (MOSAiC). The "Surface" type has the maximum TKE near the surface (at 2 m), while the "Elevated" type has the maximum TKE at a higher level (6 m). The TKE budget analysis indicates that the "Elevated" type is caused by the increased shear production of TKE at 6 m. In addition, spectral analysis reveals that the contribution to TKE by horizontal large eddies is enhanced in the "Elevated" type. Finally, how the vertical structure of TKE affects the parameterization of turbulent momentum flux is discussed. Plain Language Summary: In recent years, the Arctic near‐surface temperature has increased at a rate that is 3–4 times faster than the global average, which has significant impacts on the global climate. Explaining this phenomenon and predicting future scenarios are urgently needed. Turbulent motions within the Arctic atmospheric boundary layer over the sea‐ice surface play an important role in determining near‐surface temperature variations, but these turbulent motions and their impacts are not thoroughly understood. To enhance our understanding of the turbulent characteristics of the Arctic boundary layer, the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) was conducted in the central Arctic and collected a wealth of data. Based on the MOSAiC observational data, we investigate the vertical structures of turbulence kinetic energy (TKE) in the surface layer and two different vertical structures of TKE are distinguished. We then compare the production of TKE between these two vertical structures. In addition, we evaluate the performance of parameterization schemes for momentum flux under different vertical TKE structures. Key Points: The vertical structures of turbulence kinetic energy (TKE) over the Arctic sea‐ice surface are investigated using MOSAiC dataBudget analysis and spectral analysis are used to analyze TKE vertical structures near the Arctic sea‐ice surfaceThe TKE vertical structures affect the performance of commonly used turbulence parameterizations [ABSTRACT FROM AUTHOR]

The accurate parameterization of atmospheric surface layer processes is crucial for weather forecasts using numerical weather prediction models. Here, an attempt has been made to improve the surface layer parameterization in the Weather Research and Forecasting model version 4.2.2 (WRFv4.2.2) by implementing similarity functions proposed by Kader and Yaglom (1990) to make it consistent in producing the transfer coefficient for momentum observed over the tropical region (Srivastava and Sharan, 2015). The surface layer module in WRFv4.2.2 is modified in such a way that it contains the commonly used similarity functions for momentum (φm) and heat (φh) under convective conditions instead of the existing single functional form. The updated module has various alternatives of φm and φh , which can be controlled by a flag introduced in the input file. The impacts of utilizing different functional forms have been evaluated using the bulk flux algorithm as well as real-case simulations with the WRFv4.2.2 model. The model-simulated variables have been evaluated with observational data from a flux tower at Ranchi (23.412° N, 85.440° E), India, and the ERA5-Land reanalysis dataset. The transfer coefficient for momentum simulated using the implemented scheme is found to agree well with its observed non-monotonic behavior in convective conditions (Srivastava and Sharan, 2021). The study suggests that the updated surface layer scheme performs well in simulating the surface transfer coefficients and could be potentially utilized for the parameterization of surface fluxes under convective conditions in the WRF model. [ABSTRACT FROM AUTHOR]

The increasing need for effective electric vehicle (EV) charging solutions in the context of transportation electrification has become a significant challenge. This system introduces an innovative algorithm, named Energy Distribution and Node Allocation using Evolutionary and Resourceful Optimization (ENDEAVOR), designed to elevate the efficiency of EV charging through the integration of a battery's digital twin. This cutting-edge algorithm offers precise estimations of EV charging time, seamlessly updating both the State of Charge (SOC) via the Unscented Kalman Filter (UKF) and the internal battery resistance using parameterization, while sending this information to the cloud. ENDEAVOR optimizes charging-node allocation and intelligently distributes energy among incoming EVs based on their specific charging requests, all within the context of renewable-energy-sourced charging stations. The incorporation of a digital twin for the battery confers several benefits, including highly accurate SOC and charging-time estimates that ultimately enhance the overall efficiency of the charging process. This algorithm further optimizes energy distribution, resulting in significantly improved charging-time predictions, reduced wait times for users, and an enhanced overall experience for the user. The day-to-day implications of these enhancements are remarkable, culminating in substantial annual energy savings of approximately 180 units. ENDEAVOR has the potential to revolutionize the landscape of EV charging. [ABSTRACT FROM AUTHOR]

Existing rotated object detection methods usually use angular parameters to represent the object orientation. However, due to the symmetry and periodicity of these angular parameters, a well-known boundary discontinuity problem often results. More specifically, when the object orientation angle approaches the periodic boundary, the predicted angle may change rapidly and adversely affect model training. To address this problem, this paper introduces a new method that can effectively solve the boundary discontinuity problem related to angle parameters in rotated object detection. Our approach involves a novel vector-based encoding and decoding technique for angular parameters, and a cosine distance loss function for angular accuracy evaluation. By utilizing the characteristics of unit vectors and cosine similarity functions, our method parameterizes the orientation angle as components of the unit vector during the encoding process and redefines the orientation angle prediction task as a vector prediction problem, effectively avoiding the boundary discontinuity problem. The proposed method achieved a mean average precision (mAP) of 87.48% and an average cosine similarity (CS) of 0.997 on the MVTec test set. It also achieved an mAP score of 90.54% on the HRSC2016 test set, which is better than several existing state-of-the-art methods and proves its accuracy and effectiveness. [ABSTRACT FROM AUTHOR]

The paper is devoted to the study of the approximation properties of the biharmonic Poisson integral in the upper half-plane. The problem of approximating functions by biharmonic Poisson operators in the upper half-plane in the metric space Lp (−∞, +∞) is considered. The main result of the paper is based on the representation of the integral kernel of the biharmonic Poisson integral obtained by applying the parameterization approach. It was found that the considered integral kernel belongs to the class of delta-shaped kernels. The upper bound is obtained for the approximation of functions by biharmonic Poisson operators in terms of the first-order modulus of continuity. [ABSTRACT FROM AUTHOR]

Representation learning algorithms are often used to extract essential features from high-dimensional datasets. These algorithms commonly assume that such features are independent. However, multimodal datasets containing complementary information often have causally related features. Consequently, there is a need to discover features purporting conditional independencies. Bayesian networks (BNs) are probabilistic graphical models that use directed acyclic graphs (DAGs) to encode the conditional independencies of a joint distribution. To discover features and their conditional independence structure, we develop pimaDAG, a variational autoencoder framework that learns features from multimodal datasets, possibly with known physics constraints, and a BN describing the feature distribution. Our algorithm introduces a new DAG parameterization, which we use to learn a BN simultaneously with a latent space of a variational autoencoder in an end-to-end differentiable framework via a single, tractable evidence lower bound loss function. We place a Gaussian mixture prior on the latent space and identify each of the Gaussians with an outcome of the DAG nodes; this identification enables feature discovery with conditional independence relationships obeying the Markov factorization property. Tested against a synthetic and a scientific dataset, our results demonstrate the capability of learning a BN on simultaneously discovered key features in a fully unsupervised setting. [ABSTRACT FROM AUTHOR]

Hybridization emerges as a promising strategy for refining conjugate gradient (CG) directions, pivotal in tackling image deblurring within compressed sensing frameworks. This abstract introduces a novel hybrid CG parameterization, surpassing the recent SGCS algorithm (Auwal et al., 2020). The method integrates a convex combination approach, dynamically updating the combination parameter (µk) via the Dai and Liao conjugacy condition. Additionally, a derivative-free line search efficiently determines optimal step lengths (αk). With a focus on satisfying sufficient descent conditions, imperative for global convergence, the method demonstrates superior performance through numerical experiments compared to existing approaches. Furthermore, the extension of this method to ℓ1- norm regularized problems enhances its efficacy in restoring blurred images within compressed sensing contexts. [ABSTRACT FROM AUTHOR]

Arctic clouds play a key role in Arctic climate variability and change; however, contemporary climate models struggle to simulate cloud properties accurately. Model‐simulated cloud properties are determined by the physical parameterizations and their interactions within the model configuration. Quantifying effects of individual parameterization changes on model‐simulated clouds informs efforts to improve models and provides insights on climate system behavior. This study quantities the influence of parameterization scheme changes on Arctic low cloud properties within the Hadley Center Global Environmental Model 3 atmospheric model using a suite of experiments where individual parameterization packages are changed between the two configurations. The results indicate, surprisingly, that single parameterization changes explain most of the cloud property changes, whereas multiple parameterizations, including non‐cloud schemes, contribute to cloud radiative effect differences. Non‐cloud parameterizations are those not used to compute time step cloud properties. We employ a three‐term decomposition to quantify contributions from (a) regime independent, (b) regime dependent, and (c) the regime frequency of occurrence changes. Decomposition results indicate that cloud property changes vary by meteorological regime, each term contributes differently to each cloud property change, and non‐cloud parameterization changes make substantial contributions to the LW and SW cloud radiative effects by affecting clear‐sky fluxes. The analysis provides insights on the role of non‐cloud parameterizations for setting cloud radiative effects, a pathway for cloud‐atmosphere circulation interactions, raises questions on the importance of infrequently occurring regimes to climate simulations, and on useful observational approaches for improving models. Plain Language Summary: Arctic clouds play a key role in Arctic climate variability and change; however, state‐of‐the‐art climate models struggle to accurately simulate clouds. Errors in model‐simulated clouds have known and unknown influences on the simulated climate and climate change projections. Model cloud properties are determined by the physical parameterizations and their interactions within the model. Thus, to improve model‐simulated clouds, we need to understand the effects of parameterization changes. We use a series of Hadley Center Global Environmental Model 3 atmospheric model simulations where individual parameterizations are changed one‐at‐a‐time. This approach allows us to isolate the influence of individual cloud parameterizations on model‐simulated cloud properties to inform model development and the observations needed to improve models. The results show that individual parameterizations are most important for specific cloud variables and that multiple parameterizations are important to determining the model‐simulated influence of clouds on the energy budget. We also find that changes in model‐simulated cloud properties respond differently under different weather conditions. The analysis provides insights on the role of non‐cloud parameterizations, defined as those that are not used to directly compute cloud properties, for setting cloud radiative effects, the ways that clouds and the atmosphere interact, raises questions on the importance of infrequently occurring weather conditions to climate simulations, and on useful observational approaches for improving models. Key Points: Individual parameterization changes explain cloud property changes while multiple parameterizations explain cloud radiative effect changesThe large‐scale cloud scheme accounts for most of the changes in Arctic meteorological regime frequencyNon‐cloud parameterizations must be considered to understand the full influence of parameterization changes on cloud radiative effects [ABSTRACT FROM AUTHOR]

(A) Automated preprocessing of multisite resting-state EEG signals, and (B) Spectral parameterization (feature extraction) were conducted. ComBat harmonization variants qualitatively (C) and quantitatively (D) reduced the observed batch effects in resting-state EEG spectral features. Spectral features exhibited consistent relationships with age both before and after batch harmonization with ComBat variants (D). [Display omitted] • Spectral features exhibited consistent relationships with age after harmonization with ComBat variants. • ComBat harmonization reduced batch effects in resting-state EEG spectral features at the sensor level. • Optimized Nested ComBat with Gaussian Mixture Model and HarmonizR achieved better batch effects correction than other ComBat variants. Pooling multisite resting-state electroencephalography (rsEEG) datasets may introduce bias due to batch effects (i.e., cross-site differences in the rsEEG related to scanner/sample characteristics). The Combining Batches (ComBat) models, introduced for microarray expression and adapted for neuroimaging, can control for batch effects while preserving the variability of biological covariates. We aim to evaluate four ComBat harmonization methods in a pooled sample from five independent rsEEG datasets of young and old adults. RsEEG signals (n = 374) were automatically preprocessed. Oscillatory and aperiodic rsEEG features were extracted in sensor space. Features were harmonized using neuroCombat (standard ComBat used in neuroimaging), neuroHarmonize (variant with nonlinear adjustment of covariates), OPNested-GMM (variant based on Gaussian Mixture Models to fit bimodal feature distributions), and HarmonizR (variant based on resampling to handle missing feature values). Relationships between rsEEG features and age were explored before and after harmonizing batch effects. Batch effects were identified in rsEEG features. All ComBat methods reduced batch effects and features' dispersion; HarmonizR and OPNested-GMM ComBat achieved the greatest performance. Harmonized Beta power, individual Alpha peak frequency, Aperiodic exponent, and offset in posterior electrodes showed significant relations with age. All ComBat models maintained the direction of observed relationships while increasing the effect size. ComBat models, particularly HarmonizeR and OPNested-GMM ComBat, effectively control for batch effects in rsEEG spectral features. This workflow can be used in multisite studies to harmonize batch effects in sensor-space rsEEG spectral features while preserving biological associations. [ABSTRACT FROM AUTHOR]

The Vertex Morphing method is a node-based shape parameterization that uses an explicit filtering approach to regularize the optimization problem and generate smooth shapes. It has been successfully applied to shape optimization problems of industrial size in recent years. This work investigates in detail how irregular discretizations, design surface boundaries, and complex geometries can influence the progress of a gradient-based optimization using the standard Vertex Morphing formulation. A sensitivity weighting approach based on the available shape morphing functions is presented, which eliminates all of the aforementioned influences. Subsequently, a design variable scaling strategy is developed that transforms the optimization problem into an alternative design space and allows the use of arbitrary, even highly irregular surface discretizations in combination with black-box optimization algorithms for shape optimization with the Vertex Morphing method. Illustrative academic examples and an application case of an additively manufactured part are presented to support the work. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) has enabled the production of intricate lattice structures with excellent performance and minimal mass. Design approaches that consider static loading, including lattice-based topology optimization (TO), have been well-researched recently. However, to date, there appears to be no widely accepted method of optimizing lattice structures for high-strain rate loading, especially when the design for additive manufacturing (DFAM) principles are considered. This study proposes a computational framework for the design of lattice structures under specified impact loading. To manage dimensionality while achieving sufficient generality, a heuristic design space is developed that relies on traditional TO to govern the design's macrostructure and standard dimensioning to govern its mesostructure. DFAM principles are then incorporated into a Bayesian optimization scheme wrapped around traditional TO to achieve manufacturable designs that absorb high-impact loading. Because this approach does not require analytical gradient information, the framework can be used to optimize directly on complex objectives, such as injury metrics calculated from the acceleration curve. A series of case studies is formulated around a mass-performance tradeoff and involves individual unit cell design as well as full-part design. The proposed design parameterization is found to enable sufficient flexibility to achieve consistently good performance regardless of AM build orientation. [ABSTRACT FROM AUTHOR]

We present direct eddy covariance measurements of the surface heat flux in sea ice over a wide range of conditions across the Arctic Ocean made during two research cruises. Photographic imagery of the surface around the ship provides a local, in situ estimate of the ice fraction. Aerodynamically rough conditions prevail for the majority of the time in the consolidated pack ice. The results are analyzed in the framework of a recently-developed parameterization scheme in which the exchange coefficients over ice are functions of a roughness Reynolds number, R*, hence account for aerodynamic roughness variability. This parameterization accurately represents the measured fluxes under all conditions, while under aerodynamically rough conditions the existing parameterizations from both the Met Office Unified Model, and ECMWF Integrated Forecast System overestimate the fluxes. The results corroborate those of a previous airborne study over the marginal ice zone, and encompass a wider range of atmospheric stability conditions. [ABSTRACT FROM AUTHOR]

An intensive line of research on fixed parameter tractability of integer programming is focused on exploiting the relation between the sparsity of a constraint matrix A and the norm of the elements of its Graver basis. In particular, integer programming is fixed parameter tractable when parameterized by the primal tree-depth and the entry complexity of A, and when parameterized by the dual tree-depth and the entry complexity of A; both these parameterization imply that A is sparse, in particular, the number of its non-zero entries is linear in the number of columns or rows, respectively. We study preconditioners transforming a given matrix to a row-equivalent sparse matrix if it exists and provide structural results characterizing the existence of a sparse row-equivalent matrix in terms of the structural properties of the associated column matroid. In particular, our results imply that the ℓ 1 -norm of the Graver basis is bounded by a function of the maximum ℓ 1 -norm of a circuit of A. We use our results to design a parameterized algorithm that constructs a matrix row-equivalent to an input matrix A that has small primal/dual tree-depth and entry complexity if such a row-equivalent matrix exists. Our results yield parameterized algorithms for integer programming when parameterized by the ℓ 1 -norm of the Graver basis of the constraint matrix, when parameterized by the ℓ 1 -norm of the circuits of the constraint matrix, when parameterized by the smallest primal tree-depth and entry complexity of a matrix row-equivalent to the constraint matrix, and when parameterized by the smallest dual tree-depth and entry complexity of a matrix row-equivalent to the constraint matrix. [ABSTRACT FROM AUTHOR]

The game of rendezvous with adversaries is a game on a graph played by two players: Facilitator and Divider. Facilitator has two agents and Divider has a team of k ≥ 1 agents. While the initial positions of Facilitator's agents are fixed, Divider gets to select the initial positions of his agents. Then, they take turns to move their agents to adjacent vertices (or stay put) with Facilitator's goal to bring both her agents at same vertex and Divider's goal to prevent it. The computational question of interest is to determine if Facilitator has a winning strategy against Divider with k agents. Fomin, Golovach, and Thilikos [WG, 2021] introduced this game and proved that it is PSPACE-hard and co-W[2]-hard parameterized by the number of agents. This hardness naturally motivates the structural parameterization of the problem. The authors proved that it admits an FPT algorithm when parameterized by the modular width and the number of allowed rounds. However, they left open the complexity of the problem from the perspective of other structural parameters. In particular, they explicitly asked whether the problem admits an FPT or XP-algorithm with respect to the treewidth of the input graph. We answer this question in the negative and show that Rendezvous is co-NP-hard even for graphs of constant treewidth. Further, we show that the problem is co-W[1]-hard when parameterized by the feedback vertex set number and the number of agents, and is unlikely to admit a polynomial kernel when parameterized by the vertex cover number and the number of agents. Complementing these hardness results, we show that the Rendezvous is FPT when parameterized by both the vertex cover number and the solution size. Finally, for graphs of treewidth at most two and girds, we show that the problem can be solved in polynomial time. [ABSTRACT FROM AUTHOR]

The aesthetic appeal of special-shaped shell pipe structures makes them highly favored by architects and holds promising prospects for various applications. In the detailed design stage, NURBS curves should be divided into multiple continuous arcs due to the limitations of current steel structure fabrication equipment, which can only accommodate pipes with equal-curvature bends. However, the traditional manual fitting methods suffer from several issues including low efficiency, undercutting at the interface, poor smoothness of curves, and lack of control over tolerances. Furthermore, the weaker out-of-plane stiffness and utilization of bending arc pipe sections pose significant challenges in terms of spatial positioning and installation accuracy that need to be addressed. The study focuses on addressing these challenges by investigating digital construction technology for special-shaped shell pipe structures and developing a parametric algorithm that enables automatic fitting of spatial NURBS curves into multiple arcs, thereby achieving seamless curve fitting. A post-processing program was developed to enable the parametric generation of fabrication and installation information for structural members, which can be seamlessly integrated into the BIM database. Finally, structural position control technology is proposed to improve assembly efficiency and ensure consistency between the completed construction state and the design shape. The above digital construction technology has been applied in projects such as the Haihua Island International Conference Center. It can provide complete technical solutions for modeling of special-shaped shell pipe structures, including establishment of a member information database, fabrication at the workshop and installation on site, construction organization management, as well as installation accuracy control. [ABSTRACT FROM AUTHOR]

The Luzon Undercurrent (LUC) is one of the most significant western boundary undercurrents in the northern western Pacific Ocean (WPO), essential for subsurface water transport and connecting subtropical–equatorial circulations. Over the last three decades, abundant observational progress has been made in revealing the basic features of the LUC. However, the dynamics and thus successful modeling of the LUC remain unresolved. In this work, we conducted a high‐resolution (3 km, 60 levels) numerical investigation of the WPO circulation using the China sea multi‐scale ocean modeling system. We paid particular attention to the vertical mixing, aiming to reasonably resolve the LUC by modifying the vertical mixing parameterization. Based on physics reasoning and experiments of physically based modeling, we designed an adaptive mixing scheme (AMS), which used a Munk‐like function in the thermocline for enhanced vertical mixing in the areas of small Richardson numbers. Using the AMS, we reproduced the two‐layer WPO circulations well and captured the inshore component of the LUC consistent with observations. Furthermore, we studied the dynamics of the LUC by analyzing the momentum balance and found that the LUC is primarily maintained by baroclinic pressure gradient force due to strong thermocline tilting near the western boundary. Enhanced vertical mixing in this highly sheared region crucially provides sufficient geostrophic support to sustain the LUC. Also, nonlinearity and submesoscale motions contribute positively to the LUC. This work advances physical understanding of the current‐undercurrent system and improves numerical capability in capturing the LUC and WPO circulations. Plain Language Summary: In the northern western Pacific Ocean (WPO), there exists complex undercurrents below the upper layer (depth ∼500–1,500 m), which flow in the opposite direction from the strong circulation in the upper ocean (depth <∼500 m). The undercurrents are significant for subsurface transport and connecting subtropical‐equatorial circulations; together with the upper circulation, they form the WPO circulation system. The undercurrents are less understood and their underlying physics remains unclear. In this study, we investigate the Luzon Undercurrent (LUC), which flows southward beneath the northward Kuroshio to the east coast off Luzon Island. Abundant observational works have unveiled the characteristics of the LUC, but little attention was paid to its dynamics. So far, the LUC is poorly resolved in prevailing global/regional ocean models. In this work, we developed an adaptive mixing scheme based on physics reasoning and modeling experiments, such that the LUC physics can be resolved and well reproduced in our regional model. Furthermore, we revealed the dynamics of the LUC and summarized key factors to its formation and maintenance. This work advances our physical understanding toward the undercurrents and improves numerical capability in simulating the LUC and WPO circulations. Key Points: Baroclinic pressure gradient force due to strong isopycnal tilting in the thermocline is crucial to the formation and maintenance of the Luzon Undercurrent (LUC)The strong isopycnal tilting is induced by locally enhanced vertical mixing. Nonlinearity and submesoscales also benefit the LUC formationA Munk‐like adaptive mixing scheme is designed to capture the LUC, providing significant improvement over previous results [ABSTRACT FROM AUTHOR]

With the increasing complexity of ocean missions, using multiple unmanned underwater vehicles to collaborate in executing tasks has become an effective way to improve the overall efficiency of ocean operations. Current research on path planning for multiple unmanned underwater vehicles mainly focuses on the basis of particle models or fully known environmental information, while research directions mainly focus on single indicators such as completion time and energy consumption. This paper first constructs a UUV model and a task scenario with detection success rate as the objective function. Then, a parameterization method based on a spiral search path was proposed for designing variables. A hierarchical control strategy is designed to ensure handle formation constraints. A general optimization framework for task scenarios has been constructed and combined with algorithms to solve optimization problems. Finally, this study compared and analyzed the performance of different optimization algorithms under the optimization framework, evaluated the optimization results of different search strategies, and explored the impact of dynamic objectives on the detection success rate. The results showed that the optimized path had a search success rate that increased by more than 50% compared to the direct path and the cover search path, which verified the effectiveness of the proposed method and strategy. [ABSTRACT FROM AUTHOR]

This article focuses on the fault detection filtering problem for Itô stochastic Takagi–Sugeno (T‐S) fuzzy systems. In terms of line integral Lyapunov function, a sufficient condition of exponential stability in mean square and extended dissipativity for the systems under consideration is established which has less conservatism than the one based on quadratic Lyapunov function. The obtained sufficient condition is nonlinear, which makes the synthesis of fault detection filter being difficult and challenging. By choosing general matrix variables and constructing the appropriate orthogonal complement matrices, the nonlinear sufficient condition can be converted into linear one ingeniously via utilizing Finsler's lemma twice. Thus, the fault detection filter can be developed by virtue of parameterization. It should be pointed out that our filter determined by the parameterization approach includes the one obtained by the equivalent transformation method as a special case. Finally, we demonstrate the feasibility and superiority of our proposed approach through three numerical examples. [ABSTRACT FROM AUTHOR]

Satellite synthetic aperture radar (SAR) provides ocean surface wind fields at 10 m above sea level. The objective is to investigate the capability of SAR satellite StriX observations for mapping offshore wind farm wakes. The focus is on the conditions under which an apparent wind speed‐up is generated, measured in 48% of the 67 images available. The results compare well to Sentinel‐1 observations, showing a 34% wind speed‐up rate during several years based on 1171 images. Three wind speed‐up cases have been studied in detail using the mesoscale Weather, Research, and Forecasting (WRF) model with two wind farm parameterizations. At 10 m above sea level, the SAR‐based observations and WRF model compare for most cases, though only when turbulent kinetic energy (TKE) is included in the wind farm parameterization. The TKE mixes higher momentum downward in a stable atmosphere, causing surface wind speed‐up near the surface. [ABSTRACT FROM AUTHOR]

Accurately inferring realistic subsurface structures poses a considerable challenge due to the impact of morphology on flow and transport behaviors. Traditional subsurface characterization relies on two primary types of data: hard data, derived from direct subsurface measurements, and soft data, encompassing remotely sensed geophysical information and its interpretation. Existing deep‐learning‐based methodologies predominantly focus on the transition from multiple observations to subsurface structures. However, implicit non‐linear correlations among diverse data sources often remain underutilized, leading to potential bias and errors. In this study, we introduce a multiple‐condition fusion network (MCF‐Net) to characterize subsurface structures based on both hard and soft data. To harness the full potential of multiple‐source subsurface observations, two distinct neural networks extract implicit features from hard and soft data. The integration of these features is achieved through multiple‐condition fusion blocks, designed to capture representative characteristics. These blocks are also adept at reconstructing heterogeneous structures and facilitating hydrological parameterization. MCF‐Net exhibits accuracy in estimating subsurface structures across various types of subsurface observations. Experimental results underscore the utility and superiority of MCF‐Net in applications of hydrogeological modeling. Key Points: A novel approach for describing complex subsurface structures using sparse observations (hard data) and auxiliary variables (soft data)The proposed deep learning network is able to establish the implicit relationship among multiple observationsThe proposed approach can be easily extended and widely used for reservoir characterization, hydrogeophysical modeling, and other fields [ABSTRACT FROM AUTHOR]