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Representing one of the last populations of an IUCN Critically Endangered subspecies, estimates suggest the Kordofan giraffe of Cameroon's Bénoué Complex number fewer than 300 individuals, threatened by poaching and habitat degradation. This study investigated the viability of Bénoué National Park's Kordofan giraffe and efficacy of available interventions through a population viability analysis using VORTEX. The relative impacts of anti‐poaching activity, population supplementation, habitat protection and vaccination were explored, alongside a sensitivity analysis investigating parameter uncertainty. The baseline model projects a 78.6% probability of extinction (PE) within 100 years. Poaching of just two individuals every 5 years results in a 98.1% PE, highlighting anti‐poaching activity as a vital intervention. Movement of a female‐biased group into the park proved effective, with supplementation of 24 females reducing the PE by over 35%, sustaining a raised population size above the baseline for over 60 years on average. Annual carrying capacity reduction of 2% year−1 for 20 years resulted in a 97.8% PE. Conversely, habitat restoration had little consequence unless combined with further interventions. These findings underscore the magnitude of the threat facing Bénoué National Park's Kordofan giraffe. Conservation management should prioritise strengthening existing anti‐poaching activity in conjunction with protecting wildlife corridors to aid dispersal. [ABSTRACT FROM AUTHOR]

The key problem in locating a source of partial discharge (PD) using the acoustic emission technique is the error in estimating the signal arrival time from the source to the multiple sensors. When the time difference of arrival approach is used for the PD‐source‐localisation, some of the measured time‐delay‐groups will have solution for the time‐difference equation in the complex‐number‐field. This is due to the significant error in the arrival time estimation. Such time‐delay‐groups should be considered to be invalid. In this study, a function is proposed for identifying the invalid time‐delay‐groups for the fixed set of coordinates of four sensors and the specific velocity of the acoustic signal used. The negative sign of this function value indicates that the solution is in the complex‐number‐field. An alternative method for identifying the invalid time‐delay‐groups is by using Newton's method. The multiple sign changes of the Jacobian‐determinant in the iterations of Newton's method shows that the solution is in the complex‐number‐field. The proposed methods have been tested with data from the existing literature, and results have confirmed the efficacy of these methods in the identification of the invalid time‐delay‐groups. Discarding such groups of time delays improves the accuracy of statistical PD‐source‐localisation. [ABSTRACT FROM AUTHOR]

In theory, the whirling motion of a shaft‐disk system is three‐dimensional (3D), however, if the transverse displacement in the vertical principal xy‐plane and that in the horizontal principal xz‐plane for the cross‐section of the shaft located at the axial coordinate x are represented by a complex number, then the mass moment of inertia for unit shaft length (at x) and that for each rigid disk i can be combined with their associated gyroscopic moments (GMs) to form the frequency‐dependent equivalent mass moments of inertia, respectively, in the equations of motion for the rotating Timoshenko shaft carrying arbitrary rigid disks. It is found that the above‐mentioned equations for the rotating 3D shaft‐disk system take the same forms as the corresponding ones for the associated stationary two‐dimensional (2D) Timoshenko beam carrying the same number of disks, so that the approaches for the free vibration analyses of the stationary 2D beam‐disk system can be used to solve the title problem for obtaining the whirling speeds and mode shapes of a rotating 3D shaft‐disk system. Since the order of the eigenproblem equation derived from the presented approach is much smaller than that derived from the conventional finite element method (FEM), the computer time consumed by the former is much less than that by the latter. In addition, the solutions obtained from the presented approach are exact and may be the benchmark for evaluating the accuracy of solutions of the other approximate methods. Numerical examples reveal that the presented approach is available for the uniform or nonuniform shaft‐disk system, and the obtained results are very close to those obtained from existing literature or the FEM. The formulation of this article is available for a shaft‐disk system with various boundary conditions (BCs), to save space, only the cases with pinned‐pinned BCs are illustrated. [ABSTRACT FROM AUTHOR]

Total body irradiation (TBI) is a complex treatment technique, which has been slow to transition to a three-dimensional (3D) planning approach. There is limited literature available providing a detailed description on methods to plan TBI on a 3D planning system. 3D planning using the modulated arc TBI (MATBI) technique is a complex process involving a significant number of quality assurance processes and scripts, due to more than 40 treatment beams and two patient positions. This article will focus on the workflow and technical planning aspects of our institution's MATBI technique and identify reasons for modifications made to the developing institution's original MATBI approach. Included is a description of specific simulation equipment, detailed explanation of the four-stage computing process including the role of scripting to standardise and streamline what is otherwise a complex number of steps. The information provided is specific to one centre's approach but shows the fundamental planning process and demonstrates a streamlined method, which can be adapted to other planning systems. Overall, the ability to accurately represent the TBI technique in 3D on a planning system will be shown. [ABSTRACT FROM AUTHOR]

We present a new inversion scheme for 2D magnetotelluric data. In contrast to established approaches, it is based on a mesh‐free formulation of the Quasi‐Newton Broyden–Fletcher–Goldfarb–Shanno (BFGS) iteration which uses the cost function gradient to implicitly construct approximations of the Hessian inverse to update the unknown conductivity. We introduce conventional first–order regularization as well as second–order regularization where inversions based on the latter are more appropriate for sparse data and can be read as maximum likelihood estimation of the unknown conductivity. We apply first–order finite element method (FEM) discretizations of the inversion scheme, forward and adjoint problems, where the latter is required for the construction of the cost function gradients. We allow for unstructured first–order triangular meshes supporting an enhanced ground level resolution including topographical features and coarsening at the far field leading to significant reduction in computational costs from using structured mesh. Formulating the inversion iteration in continuous form prior to discretization eliminates bias due to local refinements in the mesh and gives way for computationally efficient sparse matrix techniques in the implementation. A keystone in the new scheme is the multi‐grid approximation of the Hessian of the regularizations to construct efficient preconditioning for the inversion iteration. The method is applied to the Commeni4 benchmark and two field data sets. Tests show that for both first and second–order regularization an anisotropic approach is important to address the vast differences in horizontal and vertical spatial scale which in conventional approaches is implicitly introduced through the elongated shape of grid cells. Plain Language Summary: We have developed a new inversion scheme for 2D MT data. Like most inversion methods, the cost function is composed of data misfit and a measure of the smoothness of the model (regularization). Inversion solutions are found by taking the derivative of the cost function and setting it to zero to minimize the function. The minimizing equation is solved iteratively using a quasi‐Newton method. Different to most other approaches, our method uses a discretize last strategy. Only once the quasi‐Newton iteration is derived, will discretization occur using an irregular triangular finite element mesh. The mesh is fine at ground level, very fine near measurement stations and extremely coarse at the edges of the model leading to significant reductions in computational costs compared to regular mesh. An additional feature of our method is two different types of regularization, conventional first‐order regularization and second‐order regularization, both with spatial anisotropy. This leads to multiple inversion solutions showing the variation of allowable solutions. Testing on benchmark and field data shows our method's robustness and its potential for real‐world applications. Key Points: New 2D MT inversion with both first and second–order anisotropic regularization for resolving different spacial characteristicsThe discretize‐last‐strategy implements a mesh‐free, gradient based inversion schemeMulti‐resolution preconditioning with efficient sparse matrix techniques accelerates computations [ABSTRACT FROM AUTHOR]

Vegetation optical depth (VOD) satellite microwave retrievals provide significant insights into vegetation water content and responses to hydroclimatic changes. While VOD variations are commonly linked to dry biomass and live fuel moisture content (LFMC), the impact of canopy temperature (Tc) remains overlooked in large‐scale studies. Here, we investigated the impact of Tc on L‐band (1.4 GHz) and X‐band (10.7 GHz) VOD at diurnal and seasonal timescales. Synthetic benchmark VOD was created using realistic fields of Tc, LFMC, and biomass in an electromagnetic model. Perturbation experiments revealed that Tc strongly affects diurnal VOD variations at both L‐band and X‐band. Seasonally, while biomass emerges as the largest contributor to VOD variations in 70% (at X‐band) and 90% (at L‐band) of our study region, Tc and LFMC still play substantial roles. The findings stress the importance of refining retrieval algorithms to distinguish Tc, LFMC, and biomass effects for future VOD applications in ecohydrology. Plain Language Summary: Satellite measurements known as vegetation optical depth (VOD) are sensitive to how much water plants contain (i.e., their hydration level). Because of this, VOD allows researchers to study how ecosystems respond to changes in weather, climate, and other factors. However, besides water, other variables like the temperature of the plants and their biomass can also change satellite VOD readings. In this study, we used computer simulations to examine how canopy temperature, relative water content, and plant biomass may affect these satellite readings. We found that canopy temperature has a surprisingly strong impact on VOD variations, especially at the diurnal timescale. Our results highlight the need to account for plant temperature influence in future VOD applications. Key Points: A simulation approach was used to investigate how canopy temperature, live fuel moisture content, and dry biomass affect VOD variationsThe impact of canopy temperature on VOD variations is more substantial than previously assumed, both at diurnal and seasonal timescalesTo interpret VOD dynamics, algorithms should differentiate between temperature, live fuel moisture content, and biomass contributions [ABSTRACT FROM AUTHOR]

We simulate the Madden‐Julian oscillation (MJO) over an aquaplanet with uniform surface temperature using the multiscale modeling framework (MMF) configuration of the Energy Exascale Earth System Model (E3SM‐MMF). The model produces MJO‐like features that have a similar spatial structure and propagation behavior to the observed MJO. To explore the processes involved in the propagation and maintenance of these MJO‐like features, we perform a vertically resolved moist static energy (MSE) analysis for the MJO (Yao et al., 2022, https://doi.org/10.1175/jas‐d‐20‐0254.1). Unlike the column‐integrated MSE analysis, our method emphasizes the local production of MSE variance and quantifies how individual physical processes amplify and propagate the MJO's characteristic vertical structure. We find that radiation, convection, and boundary layer (BL) processes all contribute to maintaining the MJO, balanced by the large‐scale MSE transport. Furthermore, large‐scale dynamics, convection, and BL processes all contribute to the propagation of the MJO, while radiation slows the propagation. Additionally, we perform mechanism‐denial experiments to examine the role of radiation and associated feedbacks in simulating the MJO. We find that the MJO can still self‐emerge and maintain its characteristic structures without radiative feedbacks. This study highlights the role of convective MSE transport in the MJO dynamics, which was overlooked in the column‐integrated MSE analysis. Plain Language Summary: We conduct simulations of the Madden‐Julian oscillation (MJO) using a computer model that can explicitly simulate deep convective clouds. The simulated MJO behaves similarly to what has been observed in the real world in terms of its spatial structure and propagation. We then delve into the detailed mechanisms behind the MJO, using a method that analyzes how energy and moisture move vertically through the atmosphere, rather than just averaging these properties across the whole atmosphere. This novel analysis shows that radiation, convection, turbulence in the atmospheric boundary layer, and large‐scale atmospheric flows all play roles in sustaining the MJO and affect its eastward propagation. Interestingly, the MJO can still develop and maintain its unique features without the influence of radiation, indicating other processes are also key. This research underscores the importance of understanding the vertical transport of energy and moisture by convective storms in studying the MJO, an aspect previously underappreciated in some simpler models and diagnoses. Key Points: We have successfully simulated the Madden‐Julian Oscillation (MJO) using the Energy Exascale Earth System Model‐multiscale modeling framework over an aquaplanet with uniform surface temperatureVertically resolved analyses of moist static energy highlight the role of convection in the maintenance and propagation of the MJOMechanism‐denial experiments show that radiative feedbacks are not essential to simulate the MJO [ABSTRACT FROM AUTHOR]

Recent years have seen high penetration of renewable energies, which have significantly reduced the inertia of bulk power systems. As a result, the frequency behaviour of power systems is becoming more complex. To resolve this technical challenge, there is a particularly strong interest in developing analytical solutions for frequency dynamics studies. This study first describes a second‐order frequency dynamics model for power systems with renewable energies. A non‐linear perturbation approach is suggested to drive the analytical solution of the model. It is shown that, under many circumstances, frequency dynamics can be effectively approximated using a linear model. Subsequently, the article describes a fourth‐order linear frequency dynamics model that takes into account governor‐turbines. A polynomial eigenvalue method is proposed to identify the dominant and non‐dominant modes of the solution of the four‐order model. It is demonstrated that the dominant mode has a decisive impact on frequency behaviour, while the non‐dominant modes influence the relative frequency oscillations only. Finally, the study derives the analytical expressions of the standard frequency performance metrics and examines the impact of damping and inertia parameters. The introduced results are verified using two test systems, demonstrating the accuracy and effectiveness of the suggested method. [ABSTRACT FROM AUTHOR]

In order for full utilization of the switching devices and safe continued operation of the power converter at the same time, the junction temperature of the switching devices needs to be accurately monitored without shutting down the motor drive. This paper derives an analytical junction temperature calculation method by using Laplace transformation and thermal network theory. Based on an actual motor drive, a simulation model is established using platform for power electronic systems (PLECS). Through comparison, the derived method is proven to be able to calculate junction temperature accurately. [ABSTRACT FROM AUTHOR]

The material transport system, facilitated by motor proteins, plays a vital role in maintaining a non‐equilibrium cellular state. However, understanding the temporal coordination of motor protein activity requires an advanced imaging technique capable of measuring 3D angular displacement in real‐time. In this study, a Fourier transform‐based plasmonic dark‐field microscope has been developed using anisotropic nanoparticles, enabling the prolonged and simultaneous observation of endosomal lateral and rotational motion. A sequence of discontinuous 3D angular displacements has been observed during the pause and run phases of transport. Notably, a serially correlated temporal pattern in the intermittent rotational events has been demonstrated during the tug‐of‐war mechanism, indicating Markovian switching between the exploitational and explorational modes of motor protein exchange prior to resuming movement. Alterations in transition frequency and the exploitation‐to‐exploration ratio upon dynein inhibitor treatment highlight the relationship between disrupted motor coordination and reduced endosomal transport efficiency. Collectively, these results suggest the importance of orchestrated temporal motor protein patterns for efficient cellular transport. [ABSTRACT FROM AUTHOR]

Isolated multiple port DC/DC converters, such as the Multiple Active Bridge (MAB) converter, have many recent applications, such as interconnection between grids, isolated uninterruptible power sources (UPSs) and electric vehicle chargers. MAB converter is an attractive solution from the point of view of the hardware because of its symmetry and its capability to be extended to any number of bridges with a moderate number of components. However, the main challenge of this converter is the control method in order to achieve independent control between the different ports and to minimize recirculating currents. For that reason, three‐port power converters have been already investigated, but many improvements can be done for a larger number of ports. This paper proposes to use a Fourier decomposition for the main power signals to separate their real and imaginary parts. As the signals work at the switching frequency, this decomposition is developed with analog electronics. Based on that, a general control method for regulating the power at the different ports is presented using the first harmonic component, which delivers most of the power. In this proposal, two nested control loops ensure accuracy for the DC power flow. Simulation and experimental results validate the proposal. [ABSTRACT FROM AUTHOR]

Wireless communication systems are inherently challenged by factors such as fading, path loss, and shadowing, leading to potential errors in data transmission. Traditional methods to mitigate these issues include power control, diversification, variable beamforming, and modulation techniques. However, the unpredictable nature of the wireless medium often limits their effectiveness. A new approach to address these challenges is the implementation of cascaded intelligent reflecting surfaces (IRS). IRS systems consist of multiple passive elements that intelligently reflect electromagnetic waves, thereby enhancing signal quality. The Advanced Discrete Fourier Transform (ADFT) matrix scheme is explored in channel estimation, a novel method particularly suitable for wireless networks utilising cascaded IRS. The ADFT matrix scheme is significant for its efficiency in managing the common‐link configuration of cascading channel coefficients, which effectively reduces pilot overhead. When compared to traditional channel estimation methods like the Least Square|least squares, Maximal a posteriori probability, and Linear Minimum Mean Square Error, the ADFT matrix scheme exhibits superior performance. It achieves a remarkable reduction in normalised mean squared error (NMSE) – 66% and 80% at 20 dB and 15 dB Signal to‐Noise Ratios (SNR), respectively. Furthermore, increasing the pilot length correlates with enhanced NMSE performance, with a noted 33% improvement as the base station distance increases. Simulations demonstrate that with an escalation in the number of IRS elements and SNR, the ADFT matrix scheme consistently surpasses conventional methods. This advancement represents a significant leap in the field of wireless communication technology. [ABSTRACT FROM AUTHOR]

The emergency department (ED) is at the front line of hospital pressure injury (PI) prevention, yet ED clinicians must balance many competing clinical priorities in the care of seriously ill patients. This paper presents the current biomechanical and clinical evidence and management considerations to assist EDs to continue to develop and implement evidence‐based PI prevention protocols for the high‐risk emergency/trauma patient. The prevention of hospital‐acquired pressure injuries has received significant focus internationally over many years because of the additional burden that these injuries place on the patient, the additional costs and impact to the efficiency of the hospital, and the potential for litigation. The development of a PI is the result of a complex number of biomechanical, physiological, and environmental interactions. Our understanding of the interaction of these factors has improved significantly over the past 10 years. We have demonstrated that large reductions in PI incidence rates can be achieved in critical care and general hospital wards through the application of advanced evidence‐based prevention protocols and believe that further improvement can be achieved through the application of these approaches in the ED. [ABSTRACT FROM AUTHOR]

The paper presents a square-root-extended complex Kalman filter (SRECKF) by decomposing covariance matrix with its square-root forms to improve stability of the filter for estimating complex number. αβ transformation is used to map three-phase instantaneous voltages in the abc phases into instantaneous voltages on the αβ axes, and a non-linear state equation and observation equation of the three-phase voltages are built by introducing a complex vector and defining state variables. Positive symmetrical component, negative symmetrical components, and frequency of the three-phase voltages are estimated using traditional extended complex Kalman filter (ECKF), the estimation results show that the method proposed here are superior to traditional extended complex Kalman filter on estimation accuracy and convergence rate. [ABSTRACT FROM AUTHOR]

Summary: In recent years, our understanding of the complex number of signals that need to be integrated between a diverse number of receptors present on natural killer (NK) cells and ligands present on target cells has improved. Here, we review the progress made in identifying interactions between dengue viral peptides presented on HLA Class 1 molecules with inhibitory and activating killer‐like immunoglobulin receptors on NK cells, direct interactions of viral proteins with NK cell receptors, the involvement of dengue virus‐specific antibodies in mediating antibody‐dependent cell‐mediated cytotoxicity and the role of soluble factors in modulating NK cell responses. We discuss findings of NK cell activation early after natural dengue infection, and point to the role that NK cells may play in regulating both innate and adaptive immune responses, in the context of our new appreciation of interactions of dengue virus with specific NK cell receptors. With a number of flavivirus vaccine candidates in clinical trials, how NK cells respond to attenuated dengue virus and subunit protein vaccine candidates and shape adaptive immunity will need to be considered. [ABSTRACT FROM AUTHOR]

This paper is dedicated to proposing improved input‐to‐state stability (ISS) criteria for a class of continuous‐time time‐varying switched singular systems (CTSSS) under slow/fast/slow‐fast switching signals. Specifically, for a CTSSS with time‐varying singular matrices, the authors start with an improved sufficient condition for verifying its ISS under slow switching signals based on the average dwell time method and a relaxed multiple Lyapunov function. Then, considering several or all subsystems may destroy the overall ISS, relaxed sufficient conditions are presented to ensure the ISS of the CTSSS under fast switching signals and slow‐fast switching signals, respectively. Note that the improvement of the proposed ISS criteria is embodied in the derivative of the required multiple Lyapunov functions along with the state trajectories of CTSSS can be positive or negative. Finally, three numerical examples are provided to illustrate the feasibility of the obtained results. [ABSTRACT FROM AUTHOR]

In this letter, a novel algorithm is proposed to design symbol‐level waveform for dual‐functional radar‐communication (DFRC) systems with low range sidelobe. Different from current schemes design waveform at the block level, a phase response constraint is introduced at the symbol level to provide an additional degree of freedom to decrease the range sidelobe in the pulse compression procedure, which is highly desired in radar systems. In particular, the phase response at the target direction is constrained to be similar to the given reference phase, and the matching error between the designed beampattern and the desired one is minimized subject to the constant modulus constraint. Furthermore, to guarantee the quality of service for communication, constructive interference is exploited at the symbol level for each communication user. An alternating direction method of multipliers algorithm is also proposed to solve the resulting nonconvex optimization problem with tractable subproblems solved sufficiently by the manifold optimization and standard quadratic problem. Numerical simulation results demonstrate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

The 6G network, the next‐generation communication system, is envisaged to provide unprecedented experience through hyperconnectivity involving everything. The communication should hold artificial intelligence‐centric network infrastructures as interconnecting a swarm of machines. However, existing network systems use orthogonal modulation and costly error correction code; they are very sensitive to noise and rely on many processing layers. These schemes impose significant overhead on low‐power internet of things devices connected to noisy networks. Herein, a hyperdimensional network‐based system, called NetHD$N e t H D$, is proposed, which enables robust and efficient data communication/learning. NetHD$N e t H D$ exploits a redundant and holographic representation of hyperdimensional computing (HDC) to design highly robust data modulation, enabling two functionalities on transmitted data: 1) an iterative decoding method that translates the vector back to the original data without error correction mechanisms, or 2) a native hyperdimensional learning technique on transmitted data with no need for costly data decoding. A hardware accelerator that supports both data decoding and hyperdimensional learning using a unified accelerator is also developed. The evaluation shows that NetHD$N e t H D$ provides a bit error rate comparable to that of state‐of‐the‐art modulation schemes while achieving 9.4 ×$\times$ faster and 27.8 ×$\times$ higher energy efficiency compared to state‐of‐the‐art deep learning systems. [ABSTRACT FROM AUTHOR]

Inertia and damping influence the values of the stress intensity factors (SIFs) at high‐frequency loading and they must be included in computations. In the present study, different dynamic simulation procedures were carried out for two types of specimen geometries and the achieved SIF values were compared. Fast computation procedures such as harmonic modal analysis and direct steady‐state analysis were compared to the computationally expensive transient dynamic analysis. Two different methods for calculating the SIF, the J‐integral and the crack tip opening displacement (CTOD) methods, were applied and compared and the results showed a near perfect agreement in calculation of the mode I SIF. The Rayleigh damping model was introduced into the dynamic computation to investigate its effect and the results revealed a clear effect on the SIF at 20 kHz frequency. The fast direct steady‐state analysis showed good agreement to both harmonic modal and transient analysis with the different damping values used and is, after this study, the recommended procedure. [ABSTRACT FROM AUTHOR]

Mediation analysis assesses whether an exposure directly produces changes in cognitive behavior or is influenced by intermediate "mediators". Electroencephalographic (EEG) spectral measurements have been previously used as effective mediators representing diverse aspects of brain function. However, it has been necessary to collapse EEG measures onto a single scalar using standard mediation methods. In this article, we overcome this limitation and examine EEG frequency‐resolved functional connectivity measures as a mediator using the full EEG cross‐spectral tensor (CST). Since CST samples do not exist in Euclidean space but in the Riemannian manifold of positive‐definite tensors, we transform the problem, allowing for the use of classic multivariate statistics. Toward this end, we map the data from the original manifold space to the Euclidean tangent space, eliminating redundant information to conform to a "compressed CST." The resulting object is a matrix with rows corresponding to frequencies and columns to cross spectra between channels. We have developed a novel matrix mediation approach that leverages a nuclear norm regularization to determine the matrix‐valued regression parameters. Furthermore, we introduced a global test for the overall CST mediation and a test to determine specific channels and frequencies driving the mediation. We validated the method through simulations and applied it to our well‐studied 50+‐year Barbados Nutrition Study dataset by comparing EEGs collected in school‐age children (5–11 years) who were malnourished in the first year of life with those of healthy classmate controls. We hypothesized that the CST mediates the effect of malnutrition on cognitive performance. We can now explicitly pinpoint the frequencies (delta, theta, alpha, and beta bands) and regions (frontal, central, and occipital) in which functional connectivity was altered in previously malnourished children, an improvement to prior studies. Understanding the specific networks impacted by a history of postnatal malnutrition could pave the way for developing more targeted and personalized therapeutic interventions. Our methods offer a versatile framework applicable to mediation studies encompassing matrix and Hermitian 3D tensor mediators alongside scalar exposures and outcomes, facilitating comprehensive analyses across diverse research domains. [ABSTRACT FROM AUTHOR]

In this study, a novel, high content technique using a cylindrical acoustic transducer, stroboscopic fast imaging, and homodyne detection to recover the mechanical properties (dynamic shear modulus) of living adherent cells at low ultrasonic frequencies is presented. By analyzing the micro‐oscillations of cells, whole populations are simultaneously mechanotyped with sub‐cellular resolution. The technique can be combined with standard fluorescence imaging allowing to further cross‐correlate biological and mechanical information. The potential of the technique is demonstrated by mechanotyping co‐cultures of different cell types with significantly different mechanical properties. [ABSTRACT FROM AUTHOR]

In this paper, we present a double-intelligent reflecting surfaces (IRS)-assisted multiuser secure system where the inter-IRS channel is considered. In particular, we maximize the weighted sum secrecy rate of the system by jointly optimizing the beamforming vector for transmitted signal and artificial noise at the base station (BS) and the cooperative phase shifts of two IRSs, under the constraints of transmission power at the BS and the unit-modulus phase shift of IRSs. To tackle the nonconvexity of the optimization problem, we first convert the objective function to its concave lower bound by utilizing a novel successive convex approximation technique, then solve the transformed problem iteratively by applying an alternating optimization method. The Lagrange dual method, Karush–Kuhn–Tucker conditions, and alternating direction method of multipliers are applied to develop a low-complexity solution for each subproblem. Finally, simulation results are provided to verify the advantages of the cooperative double-IRS scheme in comparison with the benchmark schemes. [ABSTRACT FROM AUTHOR]

Interpolation of probability distributions can be formulated as an optimal transport problem. Positive matrix, which can be viewed as the generalization of probability distribution to higher dimension, is used in quantum theory to describe the state of a quantum system. Here, a quantum‐inspired method for the interpolation of positive matrices is proposed. Particularly, this method employs the quantum state purification of the positive matrices in an extended space. Since pure state controllability can be easily achieved using open‐loop coherent control, the continuous interpolation of positive matrices is given as a completely positive map induced by simulating the optimal control for pure state transfer. The quantum‐inspired interpolation is shape‐preserving with applications to tensor field processing. [ABSTRACT FROM AUTHOR]

In this paper, a novel nonlinear unknown input observer is proposed in order to fault estimation for nonlinear uncertain systems with time delays. By the estimation of the faults, the features are detected such as shape, size occurrence time etc. The time delay is considered a constant and known parameter in the states. The disturbances are investigated in the states and outputs and also, and sensor and actuator faults are considered. The stability of the closed‐loop system is guaranteed by Lyapunov–Krasovskii theory and some feasible Linear matrix inequalities (LMI). The proposed method is simulated on a continuous‐stirred tank reactor (CSTR) with uncertainties and time delay. Simulation results show the appropriate efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Background: Spinal cord injury (SCI)‐induced neuroinflammation and oxidative stress (OS) are crucial events causing neurological dysfunction. Aconitate decarboxylase 1 (ACOD1) and its metabolite itaconate (Ita) inhibit inflammation and OS by promoting alkylation of Keap1 to induce Nrf2 expression; however, it is unclear whether there is another pathway regulating their effects in inflammation‐activated microglia after SCI. Methods: Adult male C57BL/6 ACOD1−/− mice and their wild‐type (WT) littermates were subjected to a moderate thoracic spinal cord contusion. The degree of neuroinflammation and OS in the injured spinal cord were assessed using qPCR, western blot, flow cytometry, immunofluorescence, and trans‐well assay. We then employed immunoprecipitation‐western blot, chromatin immunoprecipitation (ChIP)‐PCR, dual‐luciferase assay, and immunofluorescence‐confocal imaging to examine the molecular mechanisms of ACOD1. Finally, the locomotor function was evaluated with the Basso Mouse Scale and footprint assay. Results: Both in vitro and in vivo, microglia with transcriptional blockage of ACOD1 exhibited more severe levels of neuroinflammation and OS, in which the expression of p62/Keap1/Nrf2 was down‐regulated. Furthermore, silencing ACOD1 exacerbated neurological dysfunction in SCI mice. Administration of exogenous Ita or 4‐octyl itaconate reduced p62 phosphorylation. Besides, ACOD1 was capable of interacting with phosphorylated p62 to enhance Nrf2 activation, which in turn further promoted transcription of ACOD1. Conclusions: Here, we identified an unreported ACOD1‐p62‐Nrf2‐ACOD1 feedback loop exerting anti‐inflammatory and anti‐OS in inflammatory microglia, and demonstrated the neuroprotective role of ACOD1 after SCI, which was different from that of endogenous and exogenous Ita. The present study extends the functions of ACOD1 and uncovers marked property differences between endogenous and exogenous Ita. Key points: ACOD1 attenuated neuroinflammation and oxidative stress after spinal cord injury.ACOD1, not itaconate, interacted with p‐p62 to facilitate Nrf2 expression and nuclear translocation.Nrf2 was capable of promoting ACOD1 transcription in microglia. [ABSTRACT FROM AUTHOR]

The impedance matching control, also known as approximate complex conjugate control (ACC), is one of the main strategies for improving the capture of energy by point absorber wave energy converters. Such a strategy shapes the mechanical impedance related to the floater dynamics via the control law. Since the traditional ACC is given by a linear control law, this work proposes a generalization denoted as nonlinear complex conjugate control (NCC) that considers the presence of nonlinear viscous damping in addition to the usual linear damping and stiffness. The energy maximization conditions for the proposed NCC are derived in the frequency domain through the describing function method. These conditions show that the ACC is a special case of the NCC when the total damping on the floater is approximated as a linear function of its velocity. From numerical simulations of a point absorber wave energy converters with nonlinear damping, which is based on the Archimedes wave swing prototype, it is shown that the NCC provides greater energy conversion than the ACC, as well as a robust performance in the presence of variations of the damping coefficient and the excitation force peak frequency. [ABSTRACT FROM AUTHOR]

The Integrated Side Lobe Ratio (ISLR) problem the authors consider here consists in finding optimal sequences of phase shifts in order to minimise the mean squared cross‐correlation side lobes of a transmitted radar signal and a mismatched replica. Currently, ISLR does not seem to be easier than the general polynomial unconstrained binary problem, which is NP‐hard. In their work, the authors aim to take advantage of the scalability of quantum computing to find new optima, by solving the ISLR problem on a quantum annealer. This quantum device is designed to solve quadratic optimisation problems with binary variables (QUBO). After proposing suitable formulation for different instances of the ISLR, the authors discuss the performances and the scalability of their approach on the D‐Wave quantum computer. More broadly, their work enlightens the limits and potential of the adiabatic quantum computation for the solving of large instances of combinatorial optimisation problems. [ABSTRACT FROM AUTHOR]

Non‐Hermitian Hamiltonians may still possess real eigenvalues in case of the existence of parity‐time (PT) symmetry. Exceptional points (EPs) occur at the phase transition from real to complex eigenvalues due to PT‐symmetry breaking in the parameter space. Magnonic devices use magnons to carry, transport, and process information, which have the advantages of low energy dissipation, wave‐based computing, and nonlinear data processing. The combination of PT‐symmetry and magnonics may lead to novel physics as well as unprecedented functional device applications. Recently, the research of PT‐symmetry in magnetism has developed rapidly. In this review, the theoretical predictions as well as experimental findings of PT‐symmetry in magnetism are summarized. First, a brief introduction to PT symmetry, EPs, and anti‐PT symmetry is presented. Second, the theoretical and experimental progress of magnonic PT symmetry are summarized. Third, the theoretical predictions of higher‐order EPs and anti‐PT symmetry in magnonic systems are given. Finally, the study concludes by discussing the future challenges and research prospects in magnonic PT‐symmetry, and proposals for experimental observations of magnonic higher‐order EPs and anti‐PT symmetry are suggested. [ABSTRACT FROM AUTHOR]

Vegetation plays a crucial role in nature, with intricate interactions between it and the geographical environment. The Yangtze River Basin (YRB) refers to the third largest river basin globally and an essential ecological security barrier in China. Monitoring vegetation dynamics in the basin is of profound significance for addressing climate change, soil erosion, and biodiversity loss in the basin's ecosystems. Here, we investigate the spatiotemporal variations of vegetation at both the basin and land cover scales in the YRB from 2000 to 2020. We elucidate the determinants driving the changes and explore future normalized difference vegetation index (NDVI) trends. The results indicate that NDVI in the YRB increased at a rate of 0.0032 year−1 (p < 0.01) over the past 21 years, and it is anticipated to maintain an upward trend in the future. Regions in the upper and middle reaches of the YRB demonstrated higher NDVI, whereas regions in the headwater area and the lower reaches showed lower NDVI. Significant vegetation improvement was primarily concentrated in the central part of the basin, while noticeable vegetation degradation was observed in the eastern region. Temperature and wind speed were identified as the primary controlling factors affecting vegetation greenness. Global‐scale climate oscillations played a significant role in driving periodic variations in NDVI, with La Niña events tending to increase NDVI, while El Niño events hindered its rise. Land cover types were influenced by long‐term interactions between natural factors and human activities, although short‐term vegetation variations might be more affected by the latter. Our findings provide valuable insights into the mechanisms behind vegetation variability driven by multiple variables, and the strong vegetation carbon sink capacity advances the conservation and development of ecosystems. [ABSTRACT FROM AUTHOR]

Accurately locating power distribution faults reduces the total outage duration and provides better system reliability. Fault location using the traditional impedance‐based method may be very challenging in an active distribution system. However, taking into consideration the ease of implementation and cost effectiveness, a novel impedance‐based method is proposed to locate the fault by using the highly accurate time‐synchronized voltage and current phasors obtained from distribution phasor measurement units. The synchrophasor measurements obtained from the substation and various feeder segments are used in a two‐step algorithm based on the apparent impedance calculation to locate the exact source of the event. The algorithm uses phasor estimates to first identify the faulted feeder sub‐region and later uses measurements from a remote end device to eliminate pseudo‐faulted points to obtain the actual fault location. The effectiveness of the proposed method is realized using IEEE 34 bus system. Based on different fault types simulated at various parts of the system, the algorithm accurately estimates fault location in the range of ±1% of the line length. The proposed method is effective in locating faults for any type of network and topologies, with as many or as few (minimum 2) phasor measurement units in the system. [ABSTRACT FROM AUTHOR]

The use of quantitative broadband echosounders for biological studies and surveys can offer considerable advantages over narrowband echosounders. These include improved spectral‐based target identification and significantly increased ability to resolve individual targets. An understanding of current processing steps is required to fully utilise and further develop broadband acoustic methods in marine ecology.We describe the steps involved in processing broadband acoustic data from raw data to frequency dependent target strength (TSf) and volume backscattering strength (Svf) using data from the EK80 broadband scientific echosounder as examples. Although the overall processing steps are described and build on established methods from the literature, multiple choices need to be made during implementation.To highlight and discuss some of these choices and facilitate a common understanding within the community, we have also developed a Python code which will be made publicly available and open source. The code follows the steps using raw data from two single pings, showing the step‐by‐step processing from raw data to TSf and Svf.This code can serve as a reference for developing custom code or implementation in existing processing pipelines, as an educational tool and as a starting point for further development of broadband acoustic methods in fisheries acoustics. [ABSTRACT FROM AUTHOR]

Radar‐based human activity recognition is considered as a competitive solution for the elderly care health monitoring problem, compared to alternative techniques such as cameras and wearable devices. However, raw radar signals are often contaminated with noise, clutter, and other artifacts that significantly impact recognition performance, which highlights the importance of prepossessing techniques that enhance radar data quality and improve classification model accuracy. In this study, two different human activity classification models incorporated with pre‐processing techniques have been proposed. The authors introduce wavelet denoising methods into a cyclostationarity‐based classification model, resulting in a substantial improvement in classification accuracy. To address the limitations of conventional pre‐processing techniques, a deep neural network model called Double Phase Cascaded Denoising and Classification Network (DPDCNet) is proposed, which performs end‐to‐end signal‐level classification and achieves state‐of‐the‐art accuracy. The proposed models significantly reduce false detections and would enable robust activity monitoring for older individuals with radar signals, thereby bringing the system closer to a practical implementation for deployment. [ABSTRACT FROM AUTHOR]

For decades, seismic imaging methods have been used to study the critical zone, Earth's thin, life‐supporting skin. The vast majority of critical zone seismic studies use traveltime tomography, which poorly resolves heterogeneity at many scales relevant to near‐surface processes, therefore limiting progress in critical zone science. Full‐waveform tomography can overcome this limitation by leveraging more seismic data and enhancing the resolution of geophysical imaging. In this study, we apply 2D full‐waveform tomography to match the phases of observed seismograms and elucidate previously undetected heterogeneity in the critical zone at a well‐studied catchment in the Laramie Range, Wyoming. In contrast to traveltime tomograms from the same data set, our results show variations in depth to bedrock ranging from 5 to 60 m over lateral scales of just tens of meters and image steep low‐velocity anomalies suggesting hydrologic pathways into the deep critical zone. Our results also show that areas with thick fractured bedrock layers correspond to zones of slightly lower velocities in the deep bedrock, while zones of high bedrock velocity correspond to sharp vertical transitions from bedrock to saprolite. By corroborating these findings with borehole imagery, we hypothesize that lateral changes in bedrock fracture density majorly impact critical zone architecture. Borehole data also show that our full‐waveform tomography results agree significantly better with velocity logs than previously published traveltime tomography models. Full‐waveform tomography thus appears unprecedentedly capable of imaging the spatially complex porosity structure crucial to critical zone hydrology and processes. Plain Language Summary: Weathering processes within Earth's shallow subsurface break down rock into porous, mineral‐rich materials from which biota can access water and garner nutrients. Therefore, knowledge about weathering helps scientists better understand how Earth supports terrestrial life. An effective way of studying weathering is seismic imaging, whereby listening at Earth's surface to how mechanical waves propagate, we can make pictures of what is below and observe weathering in action. The seismic imaging method typically used to study weathering is first arrival traveltime tomography, although other methods can create higher resolution images. We applied an advanced seismic imaging technique called full‐waveform tomography to sharpen our view of the subsurface. Our waveform tomography results imply that changes in bedrock fracture density over relatively small lateral distances have a significant effect on how weathering processes operate. When the fracture density in the bedrock is low, there is a sharp transition from highly weathered materials to unaltered bedrock below. When the fracture density is high, the transition is more diffuse, and there exists a thick layer of weathered bedrock. Additionally, we ground truth these interpretations with in situ observations made in boreholes. Hence, waveform tomography appears capable of revealing new insights into subsurface structure and weathering processes. Key Points: We perform 2D full‐waveform tomography on shallow seismic refraction data to study critical zone architecture in the Laramie Range, WyomingBorehole data confirm that the full‐waveform tomography result is more accurate than conventional traveltime tomographyThe full‐waveform tomography model reveals critical zone heterogeneity likely caused by lateral changes in bedrock properties [ABSTRACT FROM AUTHOR]

Biometric recognition is a widely used technology for user authentication. In the application of this technology, biometric security and recognition accuracy are two important issues that should be considered. In terms of biometric security, cancellable biometrics is an effective technique for protecting biometric data. Regarding recognition accuracy, feature representation plays a significant role in the performance and reliability of cancellable biometric systems. How to design good feature representations for cancellable biometrics is a challenging topic that has attracted a great deal of attention from the computer vision community, especially from researchers of cancellable biometrics. Feature extraction and learning in cancellable biometrics is to find suitable feature representations with a view to achieving satisfactory recognition performance, while the privacy of biometric data is protected. This survey informs the progress, trend and challenges of feature extraction and learning for cancellable biometrics, thus shedding light on the latest developments and future research of this area. [ABSTRACT FROM AUTHOR]

Natural language processing unfolds information overtime as spatially separated, multimodal, and interconnected neural processes. Existing noninvasive subtraction‐based neuroimaging techniques cannot simultaneously achieve the spatial and temporal resolutions required to visualize ongoing information flows across the whole brain. Here we have developed rapid phase‐encoded designs to fully exploit the temporal information latent in functional magnetic resonance imaging data, as well as overcoming scanner noise and head‐motion challenges during overt language tasks. We captured real‐time information flows as coherent hemodynamic waves traveling over the cortical surface during listening, reading aloud, reciting, and oral cross‐language interpreting tasks. We were able to observe the timing, location, direction, and surge of traveling waves in all language tasks, which were visualized as "brainstorms" on brain "weather" maps. The paths of hemodynamic traveling waves provide direct evidence for dual‐stream models of the visual and auditory systems as well as logistics models for crossmodal and cross‐language processing. Specifically, we have tracked down the step‐by‐step processing of written or spoken sentences first being received and processed by the visual or auditory streams, carried across language and domain‐general cognitive regions, and finally delivered as overt speeches monitored through the auditory cortex, which gives a complete picture of information flows across the brain during natural language functioning. Practitioner Points: Phase‐encoded fMRI enables simultaneous imaging of high spatial and temporal resolution, capturing continuous spatiotemporal dynamics of the entire brain during real‐time overt natural language tasks.Spatiotemporal traveling wave patterns provide direct evidence for constructing comprehensive and explicit models of human information processing.This study unlocks the potential of applying rapid phase‐encoded fMRI to indirectly track the underlying neural information flows of sequential sensory, motor, and high‐order cognitive processes. [ABSTRACT FROM AUTHOR]

The reduction of noises, achieved through absorption, is of paramount importance to the well‐being of both humans and machines. Lattice structures, defined as architectured porous solids arranged in repeating patterns, are emerging as advanced sound‐absorbing materials. Their immense design freedom allows for customizable pore morphology and interconnectivity, enabling the design of specific absorption properties. Thus far, the sound absorption performance of various types of lattice structures are studied and they demonstrated favorable properties compared to conventional materials. Herein, this review gives a thorough overview on the current research status, and characterizations for lattice structures in terms of acoustics is proposed. Till date, there are four main sound absorption mechanisms associated with lattice structures. Despite their complexity, lattice structures can be accurately modelled using acoustical impedance models that focus on critical acoustical geometries. Four defining features: morphology, relative density, cell size, and number of cells, have significant influences on the acoustical geometries and hence sound wave dissipation within the lattice. Drawing upon their structural‐property relationships, a classification of lattice structures into three distinct types in terms of acoustics is proposed. It is proposed that future attentions can be placed on new design concepts, advanced materials selections, and multifunctionalities. [ABSTRACT FROM AUTHOR]

A power line carrier (PLC) communication characteristics‐based method is proposed for single‐phase‐to‐ground fault location in neutral isolated medium voltage meshed distribution networks in this paper. The carrier signals with a time‐varying frequency and constant amplitude are processed by a set of PLC transmitters and receivers, whose placement is optimized by regarding the power network as an undirected graph. Two signal encoding and decoding algorithms for the PLC terminals are proposed to avoid using expensive timing systems between the terminals. The fault location technique is implemented by comparing the cosine similarity of amplitude attenuation and phase offset between the fault and a feature library. The node corresponding to the maximum cosine similarity of the characteristics between the present fault and the library is selected as the location of the current fault. Only one set of low‐cost PLC communication terminals and the widely available power lines are needed in the fault location system, making this approach highly practical. Numerical simulations using MATLAB/Simulink have been performed to verify the technique's feasibility. The results show that the method can accurately locate faults in neutral isolated medium voltage meshed distribution networks. Besides, the presented approach achieves a high level of accuracy in estimating transition resistance values. [ABSTRACT FROM AUTHOR]

Taking the megacity of Beijing as an example, a short‐time Fourier transform (STFT) method was employed to extract the multi‐timescale evolution pattern of the canopy urban heat island intensity (CUHII) during 2000–2020. The STFT of CUHII showed a close relationship between the evolution of the CUHII in Beijing and the background meteorological forcing at intra‐annual, weather and intra‐daily scales. The intra‐annual‐scale spectrum of CUHII exhibited an increasing trend with obvious seasonal variation of the canopy urban heat island (CUHI). The intra‐daily‐scale spectrum of CUHII showed an increasing trend with the nighttime CUHI developing faster. Increasing Western Pacific Subtropical High intensity can enhance the seasonal and diurnal fluctuations of CUHII. The weather‐scale spectrum of CUHII is controlled by weather system evolution, showing that the frequency of cold/heat waves (CWs/HWs) in Beijing was significantly negatively correlated with the weather‐scale spectral intensity of the CUHII. CWs and HWs can increase the CUHII for a long duration. Plain Language Summary: The canopy urban heat island (CUHI) phenomenon can affect human health and the ecological environment, and its multi‐timescale variability brings great uncertainty to the study of urban climates worldwide. In this study, taking the megacity of Beijing as an example, a novel short‐time Fourier transform (STFT) method was used to extract the multi‐timescale pattern of the CUHI intensity (CUHII) during 2000–2020. The STFT of CUHII showed a close relationship between the CUHI and the background meteorological forcing at intra‐annual, weather and intra‐daily scales. The intra‐annual spectrum of CUHII showed an increasing trend with a V‐shaped mode. The local climatic backgrounds of different cities can lead to differences in the seasonal development of the CUHI. The intra‐daily spectrum of CUHII showed an increasing trend due to the asymmetry in the day/night development of the CUHI. Increasing Western Pacific Subtropical High Intensity can enhance the seasonal and diurnal fluctuations of CUHII. The weather‐scale spectrum of CUHII was mainly controlled by weather system evolution, showing that cold waves and heat waves can increase the CUHII over a long duration. Our findings indicate that the evolution of CUHII is a nonlinear and complex process that is directly related to multi‐timescale background climate forcing. Key Points: Using a short‐time Fourier transform to study the multi‐timescale evolution of the canopy urban heat island intensity (CUHII)Close relationships existed between CUHII and the background meteorological forcing at intra‐annual, intra‐daily, and weather scalesThe frequency of cold/heat waves in Beijing showed a significant negative correlation with the weather‐scale spectral intensity of the CUHII [ABSTRACT FROM AUTHOR]

The performance limitations of traditional computer architectures have led to the rise of brain‐inspired hardware, with optical solutions gaining popularity due to the energy efficiency, high speed, and scalability of linear operations. However, the use of optics to emulate the synaptic activity of neurons has remained a challenge since the integration of nonlinear nodes is power‐hungry and, thus, hard to scale. Neuromorphic wave computing offers a new paradigm for energy‐efficient information processing, building upon transient and passively nonlinear interactions between optical modes in a waveguide. Here, an implementation of this concept is presented using broadband frequency conversion by coherent higher‐order soliton fission in a single‐mode fiber. It is shown that phase encoding on femtosecond pulses at the input, alongside frequency selection and weighting at the system output, makes transient spectro‐temporal system states interpretable and allows for the energy‐efficient emulation of various digital neural networks. The experiments in a compact, fully fiber‐integrated setup substantiate an anticipated enhancement in computational performance with increasing system nonlinearity. The findings suggest that broadband frequency generation, accessible on‐chip and in‐fiber with off‐the‐shelf components, may challenge the traditional approach to node‐based brain‐inspired hardware design, ultimately leading to energy‐efficient, scalable, and dependable computing with minimal optical hardware requirements. [ABSTRACT FROM AUTHOR]

The transformer, a type of neural architecture, has demonstrated exceptional performance improvements in vision and natural language tasks. While overcoming the disadvantages of limited perceptual field and non‐adaptive input content exhibited in CNNs, the computational complexity of the Transformer model increases quadratically with spatial resolution. As such, this model is not frequently employed in image processing tasks such as image denoising, and there is a shortage of studies that investigate ultrasonic image multiplication speckle removal. In light of this, we present TranSpeckle, an effective and efficient despeckle architecture that employs Multi‐Dconv Head Transposed Attention and Dconv Feed‐Forward Network as the core components of its Transformer block. Multiple Transformer blocks are then utilized to implement a hierarchical encoder‐decoder network. TranSpeckle architecture considerably reduces the computational complexity of feature maps while also effectively capturing long‐range pixel interactions and local context information. In this study, an edge protection module is combined to augment the edges of ultrasound images. The module incorporates extracted image edge features into the TranSpeckle architecture, which ameliorates the issue of edge information loss engendered by the image despeckling process. Extensive experimental results clearly show that our proposed network outperforms state‐of‐the‐art methods in terms of quantitative metrics and visual quality. [ABSTRACT FROM AUTHOR]

Stroke is one of the leading causes of long‐term disability worldwide, placing huge burdens on individuals and society. Further, automatic human activity recognition is a challenging task that is vital to the future of healthcare and physical therapy. Using a baseline long short‐term memory recurrent neural network, this study provides a novel dataset of stretching, upward stretching, flinging motions, hand‐to‐mouth movements, swiping gestures, and pouring motions for improved model training and testing of stroke‐affected patients. A MATLAB application is used to output textual and audible prediction results. A wearable sensor with a triaxial accelerometer is used to collect preprocessed real‐time data. The model is trained with features extracted from the actual patient to recognize new actions, and the recognition accuracy provided by multiple datasets is compared based on the same baseline model. When training and testing using the new dataset, the baseline model shows recognition accuracy that is 11% higher than the Activity Daily Living dataset, 22% higher than the Activity Recognition Single Chest‐Mounted Accelerometer dataset, and 10% higher than another real‐world dataset. [ABSTRACT FROM AUTHOR]

Objective: Cell growth involves cell division. This stops after reaching a certain limit. Some cells become inactive and unable to undergo apoptosis (programmed cell death). These cells accumulate at sites of tissue damage or disease, thus accelerating aging. They are called senescent cells. Therapeutic interventions that can either eliminate senescent cells (senolytics) or suppress their harmful effects (senomorphics) have been developed. Senescence (aging) is caused by the inter‐ and intramolecular interactions between the domains of forkhead (FHD) and transactivation (TAD), as well as C‐terminal region 3 (CR3) and DNA binding (DBD). On the other hand, anti‐senescent/senolytic (anti‐aging) activities are achieved by disrupting these interactions with CR3‐ and forkhead box protein O4 (FOXO4)‐based peptides, such as ES2 and DRI, respectively. In this study, we use a computerized procedure based on digital signal processing to systematically analyze the inter‐molecular interactions between senolytics and their targets. Methods: Informational spectrum method (ISM) is engaged. Results: We obtained the sequences of the peptides from the interacting proteins of CR3 and FOXO4 and evaluated their ability to disrupt the inter‐molecular interactions between FOXO4 and DRI and CR3 and BDB, which are responsible for senescence (aging). Our results show that the peptides have different degrees of senolytic (anti‐aging) activity, depending on their affinity for CR3 and BDB, or FOXO4 and DRI. We found that enhanced senescence 2 (ES2) has a higher affinity for CR3 and BDB than FOXO4 and DRI, and that the interaction between CR3 and BDB is crucial for aging. Therefore, ES2 and other CR3‐based peptides are more potent senolytics than FOXO4‐based peptides. Our findings are consistent with previous studies and reveal new insights into the mechanisms of senescence and senolytics. ES2 is considered the best senolytic candidate, as it is 3–7 times more effective than DRI. We verified that ES2 has a weaker interaction with FOXO4 than CR3. However, the performance of DRI has been noted to depend on its intramolecular interactions and stability. Hence, intramolecular analyses using the digital signal processing‐based technique has become very vital and will follow. Conclusion: CR3‐based peptides are promising candidates for senolytic therapy. Senolytics are linear chains of amino acids that can target and eliminate senescent cells, which are cells that have stopped dividing and contribute to aging and age‐related diseases. By using this proposed, novel computerized technique that is based on digital signal processing, senolytics can be easily analyzed and optimized for their effectiveness and safety. This provides a more rational approach to enhancing our longevity and well‐being by offering interventions that can delay or reverse aging and insights that can advance the field of gerontology. This procedure also will compliment other approaches such as molecular stimulation, etc. [ABSTRACT FROM AUTHOR]

Recent text generation methods frequently learn node representations from graph‐based data via global or local aggregation, such as knowledge graphs. Since all nodes are connected directly, node global representation encoding enables direct communication between two distant nodes while disregarding graph topology. Node local representation encoding, which captures the graph structure, considers the connections between nearby nodes but misses out onlong‐range relations. A quantum‐like approach to learning better‐contextualised node embeddings is proposed using a fusion model that combines both encoding strategies. Our methods significantly improve on two graph‐to‐text datasets compared to state‐of‐the‐art models in various experiments. [ABSTRACT FROM AUTHOR]

Channel state information (CSI), which is crucial for resource allocation and system performance in time division duplex (TDD) massive multiple‐input multiple‐output (MIMO) systems, is difficult to predict because of the channel's time‐varying nature. To overcome this limitation, a scheme for channel prediction combined with deep learning (DL) is proposed. The system uses a deep neural network (DNN) to interpolate channel estimates from a few received pilot signals and a recurrent neural network (RNN) to train through the current time and the recent historical channel estimates to predict the CSI while the channel is constantly varying. In the end, a mixed neural network of RNN and DNN, is called MRDNN. In addition, the proposed DL‐based method does not rely on the relevant feature information about the channel, such as internal characteristics and parameters of the channel itself or channel statistical information, which improves its effectiveness in practical applications. The results of the simulation show that the MRDNN‐based method is better than the existing methods, like traditional AR method and NL Kalman method, and also can be effective in improving the quality of channel prediction and the performance of the system under the dynamic change scenario of low mobility. [ABSTRACT FROM AUTHOR]

A novel general model has been developed to fit biomass growth data of coleoid cephalopods as a function of time and temperature from hatching to maximum size in a single curve. This model has been obtained formally from a formulation of product‐integral, solves the discontinuity problem present in two‐phase growth models and describes both asymptotic and non‐asymptotic growth. Within the usual temperature range in which the organism develops, our model describes a non‐asymptotic growth achieving a good fit with the experimental data (R2>0.98)$({{R^2} > 0.98})$. In particular, the model provides precise calculation of the time k at which the growth curve inflection occurs, time μ1 at which sexual maturity begins and time μ2 at which the maximum size is reached. When the model was parameterized as a function of temperature, it corroborated different results on Octopus maya fitness, in particular, 25.7°C was obtained as the temperature that generates the highest average growth rate and 21.7°C as the temperature with the highest biomass accumulation over time. This result confirms that the temperature range of 22–26°C is suitable for the growth of O. maya and shows that the developed model can be a useful tool for aquaculture. [ABSTRACT FROM AUTHOR]

This paper considers a Non‐Orthogonal Multiple Access (NOMA) network, in which base station transmits a data packet by multi‐user superposition to a destination. In this system, Successive Interference Cancellation (SIC) receiver is used to eliminate the co‐channel interference. Some physical layer parameters, such as modulation order and precoding matrix indicator (PMI), are required for the SIC receiver to separate the overlapped signals. However, an abundance of parameters would bring a large number of signalling overhead. To reduce the signalling overhead, some of these parameters can be blindly detected instead of signalling notification. To detect these kinds of parameters, a novel blind detection algorithm is proposed in this paper. Firstly, feature extraction based on wavelet cluster is introduced to obtain feature information from received data. Then a filter is designed to reduce the interference among these features. Theoretical analysis and simulation results show that the proposed algorithm achieves high detection performance under the computation complexity of approximate the max‐log likelihood algorithm. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surface (RIS) has great potential in securing wireless transmission, and it can flexibly change the wireless communication environment. However, the employment of RIS increases the difficulty in beamforming of base station (BS), and existing schemes cannot be directly utilized to enhance the security of system. Therefore, a reinforcement learning framework to jointly control BS and RIS for the purpose of enhancing the secure performance is proposed. Specifically, the beamforming ia transformed with artificial noise and reflection control as a Markov decision problem (MDP) in complex domain. Then, we develop the deep deterministic policy gradient based on complex‐valued neural networks (CV‐DDPG) to imitate the rules of complex computation. Additionally, the CV‐DDPG is performed as the agent of reinforcement learning to control the BS and RIS, which enables better utilization of the implied phase information of complex values in the system. The simulation results show that the proposed model promotes the performance of the control and greatly improves the secrecy rate of the legitimate user. [ABSTRACT FROM AUTHOR]

Alpine glaciers are rapidly retreating due to global warming and this has been associated with enhanced supply of subglacially derived sediment to downstream environments. We present the first high frequency quantitative record on how the signal of sediment exported from an Alpine glacier is filtered by its proglacial forefield. The data, covering two climatically distinct glacier melt seasons, show that the signal of subglacial bedload export is strongly filtered over short distances, unlike suspended load whose signal is less impacted. The reason is related to the interplay of short particle advection lengths with strong morphodynamic forcing. The subglacial sediment export signal is thus rapidly replaced by one combining (a) the local forcing by stream hydraulics and (b) the reworking of the proglacial braid plain itself. These findings have implications for estimating subglacial erosion rates, natural hazard mitigation, sediment management for hydropower plants and ecological succession related to rapid glacier retreat. Plain Language Summary: Alpine glaciers have been retreating at increasing rates since the early 20th century due to the current climate warming. As a consequence, new, powerful and rapidly changing rivers have developed in the newly emerged terrain in front of shrinking glaciers (i.e., proglacial forefields). These environments are known to be among the most rapidly changing landscapes on Earth. In this study we present the first combined high frequency quantitative data on sediment export from an Alpine glacier for two melt seasons with very different climatic conditions. Results show that the low potential transport distances of particles traveling on the river bed together with the continuously changing shape of these streams, rapidly modify the glacier‐dictated export signal (i.e., subglacial sediment evacuation rates in time). For sediment transported in suspension in the river, that loss of information is less evident. These findings are important for the understanding of how much, when, and which kind of sediment are delivered to the populated places downstream in the valleys, in order to mitigate natural hazards, but also for sediment management in hydropower plants and ecological succession in the context of rapid glacier retreat. The results are also key to improving estimation of glacial erosion rates. Key Points: Seismically inferred sediment transport anatomy reveals strong filtering of subglacial bedload export signal due to proglacial processesFiltering operates at a sub‐daily scale, and is due to the short advection lengths of bedload particles in the proglacial forefieldProglacial filtering is a key factor affecting (pro‐)glacial sediment transport dynamics, sediment connectivity and ecological succession [ABSTRACT FROM AUTHOR]

Here, a novel image‐based quantized compressive sensing (QCS) framework based on complex‐valued Vandermonde (Vander) matrix is proposed. In the proposed QCS framework, a discrete wavelet transform (DWT) serves as a sparse basis and a partial complex‐valued Vander matrix serves as a measurement matrix. The theoretical analysis based on mutual coherence metric of compressive sensing (CS) theory shows that the proposed Vander measurement matrix has the best reconstruction performance among other conventional measurement matrices. The simulation results also show that the recovery quality using the proposed measurement matrix can be greatly improved compared with the other existing real‐valued measurement matrices. In particular, the experiment results also show that under the same measurement matrix, the reconstruction performance of Smoothed l0 norm (SL0) algorithm is better than that of Orthogonal Matching Pursuit (OMP) algorithm, sparsity adaptive matching pursuit (SAMP) algorithm and approximate message passing (AMP) algorithm. In addition, a sparse measurement matrix scheme is further proposed to achieve a trade‐off between recovery performance and computational complexity. The theoretical analysis and simulation results both show the proposed image‐based QCS is efficient. [ABSTRACT FROM AUTHOR]