Search

• Life Cycle Analysis of two materials for spacecraft structures is conducted. • A relationship between fragments generation and materials properties is established. • A novel metric for the evaluation of debris generation upon collision is introduced. • Results are discussed based on terrestrial and orbital impact indicators. Space debris are a primary environmental concern for the sustainability of space mission activities. When considering terrestrial applications, the Life Cycle Assessment represents a widely standardised technique for estimating the environmental impacts of human activities; however, its implementation in the space-related value chain is still in an early stage. In this study, the implications of two alternative materials in the development of a CubeSat structural bus are investigated with reference to the well-established categories for the Life Cycle Impact Assessment. Moreover, the potential impact of these materials on the orbital environment is quantified by the evaluation of the number of generated fragments upon collision for different significant low Earth orbits. The results of this study allow to quantify the impact of alternative design solutions for space systems on the terrestrial environment as well as the degradation of the orbital resources as a result of the space debris generation. [ABSTRACT FROM AUTHOR]

The paper addresses a computational method to simulate more accurate models for population growth with immigration, focusing on integral equations (IEs) featuring a delay parameter in the time variable. The proposed method utilizes Legendre wavelets within the Galerkin scheme as a orthonormal basis. Legendre wavelets are known for their localized functions, offering suitable precision and stability in simulating time-delay biological models. This approach employs the composite Gauss-Legendre (CGL) quadrature rule to compute integrals appeared in the scheme. An error bound analysis demonstrates a convergence rate of order 2 − M k. Additionally, various numerical examples are presented to show the efficiency, accuracy and validate the theoretical error estimate of the novel technique. [ABSTRACT FROM AUTHOR]

• A new index is proposed to quantify the economic impact of COVID-19 on countries. • The index shows an extreme heterogeneity in the economic impact of COVID-19. • The factors behind this heterogeneity are explored using regression techniques. • The analysis includes pandemic-specific factors and structural factors. • Results stress the importance of industrial development for resilience. The socioeconomic impact of COVID-19 has been widely uneven across regions and countries, reflecting underlying differences in their resilience against shocks. This paper tries to explain this heterogeneity by identifying factors of resilience and vulnerability. To fully capture the impact of the crisis on economic activity, we propose a novel index of GDP loss that measures both the initial shock and recovery rate at the country level. With a dataset of 125 countries, we implement cross-sectional regression techniques to estimate the impact of pandemic-specific and structural factors on the index. The focus of the analysis is placed on a dimension that was not sufficiently explored yet in the specialized literature: the role of industrial capabilities. Results show that industrial capabilities were crucial in supporting countries' ability to absorb and resist the global shock. The paper thus provides new empirical evidence on the role played by manufacturing industries in strengthening resilience to face unexpected events. [ABSTRACT FROM AUTHOR]

A new third-order energy stable technique, which is a convex splitting scheme with the Douglas-Dupont regularization term A τ 2 (ϕ n − ϕ n − 1) , is proposed for solving the extended Fisher–Kolmogorov equation. The higher-order backward difference formula is used to deal with the time derivative term. The constructed numerical scheme is uniquely solvable and unconditionally preserves the modified discrete energy dissipative law. With the help of discrete orthogonal convolution kernels, the L 2 norm error estimate of the stabilized BDF3 scheme can be established by acting the standard inner product with the error system. Several numerical experiments are used to verify the validity of the numerical method and the correctness of the theoretical analysis. [ABSTRACT FROM AUTHOR]

• Developing a binaural heterophasic MVDR beamforming for speech enhancement. • Rendering the desired speech and residual noise into different perceptual regions. • Implementing the proposed beamformer with DNN assisted statistics estimation. • Listening tests are conducted to evaluate the rendering performance. Beamforming is one of the most effective approaches to distant sound acquisition in complex acoustic environments, where noise, reverberation, and interference coexist; as a result, a significant number of efforts have been devoted to it over the last few decades. However, conventional beamformers produce a monaural output or colinear outputs, which are not optimal from the perception perspective. To take advantage of the human binaural hearing properties, a new type of fixed beamforming methods were developed recently, which attempt not only to attenuate noise but also render the signal of interest and residual noise into different perceptual regions, thereby achieving higher speech intelligibility. This work extends the principle of fixed binaural beamforming and develops a binaural heterophasic minimum variance distortionless response (MVDR) beamformer. A deep neural network (DNN) based noise estimation method is used to assist the implementation of this heterophasic MVDR beamformer, which is advantageous over the traditional one as it renders the desired source signal and residual noise to different perceptual regions, thereby yielding higher intelligibility. In comparison with the fixed binaural heterophasic beamformers, it can take advantage of the statistics of the noise to achieve better array performance. Results of simulations and listening tests validate the properties of the proposed technique. [ABSTRACT FROM AUTHOR]

Maximal lactate steady state (MLSS) is accepted as the gold standard method to determine the upper boundary of heavy exercise domain, while critical power (CP) is one of the most practical ways to estimate the lower boundary of severe exercise domain. However, it has been recently discussed that the upper boundary of heavy-intensity exercise domain does not equal to lower boundary of severe-intensity exercise domain. Conversely, the best individual fit method (CP fit) has recently been suggested as an accurate estimation of CP. However, there has not been a comparative evaluation of CP fit versus MLSS. The aim of this study was, therefore, to focus on the comparison of CP fit with MLSS. Eleven well-trained male cyclists participated in this study. Following the determination of maximal oxygen uptake, MLSS was determined through the performance of standard 30-minute constant work rate exercises performed on separate days. Cyclists performed four constant-load exercises lasting 2–15 minutes on different days. The data were then fitted to mathematical models to obtain CP fit. Bland-Altman plot with 95% limits of agreement (LoA) was used to measure agreement between CP fit and MLSS. According to the findings, CP fit overestimated the work rate corresponding to MLSS (300 ± 38 W vs. 265 ± 39 W; P = 0.001; effect size = 3.28). Bland-Altman results showed high LoA (14 to 55.9 W) and very high bias (bias = 35 ± 10.7 W; P = 0.001; r : 0.96) between CP fit and MLSS. CP fit was not identical for MLSS. [ABSTRACT FROM AUTHOR]

We consider error estimates and post-processing technique of the lowest order Raviart–Thomas mixed finite element method for parabolic problems. A super-convergence of the original unknown and flux is established, which is based on negative norm error estimates. At given time step, we introduce an auxiliary elliptic problem and propose a recovery strategy to obtain one-order higher finite element solutions. Corresponding results are well-known for Lagrange finite element methods for parabolic equations. By using discrete functional analysis tools developed for Discontinuous Galerkin finite element methods, we extend the analyses to the lowest order Raviart–Thomas mixed method and prove second order accuracy of the recovered numerical solutions rigorously. The proposed post-processing technique is suitable on general meshes for both two and three dimensional problems. Numerical results are provided to verify our theoretical analysis and demonstrate the efficiency of the proposed recovery methods. [ABSTRACT FROM AUTHOR]

Mutual information (MI) is a widely used similarity measure in pattern recognition. MI uses entropy as a measure of uncertainty to quantify the structural similarity of two vectors. Replacing entropy with variance as a measure of uncertainty, an analogous class of similarity measures can be derived and estimated by regression techniques. Recently, the non-linear piecewise constant regression (PWCR) has been proposed to drive similarity measures of this scheme, leading to competitive alternatives of MI. Although PWCR is based on binning, the optimal binning technique for certain problems remained an open question. In this paper, we show mathematically that the optimal binning needs to be aligned with the expected relationship between the vectors being compared. In general, approximately optimal binnings can be found by combinatorial optimization, and in certain cases the optimal binning can be determined by k-means clustering. The theoretical findings are supported by numerical experiments that show a 2–5% increase in the AUC scores in simulated pattern recognition scenarios and improved feature rankings in feature selection problems. The results suggest that the proposed binning techniques could improve the performance of PWCR-driven similarity measures in real-world applications. [ABSTRACT FROM AUTHOR]

We describe the use of radar beam-park experiments to characterize the space debris resulting from a recent fragmentation event, the deliberate demolition of the defunct Kosmos-1408 satellite. We identify the Kosmos-1408 fragments and present distribution of measurement parameters as well as proxy orbit parameters. We present and apply a novel technique to estimate the size of objects by matching the signal to noise ratio of the detection to the radiation pattern of the radar. With this method we estimate the size distribution of the debris cloud. We also demonstrate how a pair of beam-park observations can be used to perform a crude, yet seemingly reliable, initial orbit determination. Finally, we present followup observations ∼ 5 months after the fragmentation that show a still compact cloud of debris. • The deliberate destruction of the Kosmos-1408 satellite has generated an appreciable cloud of space debris. • Ground-based radars in beam-park mode can be used to observe and characterize the debris cloud. • We have used these observations to estimate size distributions of the debris cloud. • Orbit information for objects can, in some cases, be obtained or updated using these kinds of observations. [ABSTRACT FROM AUTHOR]

This work aims to synthesize polygalacturonate-based magnetic iron oxide nanoparticles (INP-polyGalA). The synthesis consists of the diffusion of both Fe2+ and Fe3+ at a molar ratio of 1:2 through polyGalA solution followed by the addition of an alkaline solution. To form individual nanoparticle materials, the polyGalA concentration needs to be below its overlapping concentration (C*). The synthesized materials (INP-polyGalA) contain about 45% of organic compound (polyGalA), and they have an average particle size ranging from 10 to 50 nm as estimated by several techniques (DLS, TEM and AFM) and their surfaces are negatively charged in pH range 2 to 7. The synthesized NPs showed magnetic characteristics, thanks to the formation of magnetite (Fe 3 O 4) as confirmed by X-ray diffractions (XRD). Moreover, AFM combined with Infra-red mapping allowed us to conclude that polyGalA is located in the core of the nanoparticles but also on their surfaces. More specially, both carboxylate (COO−) and carboxylic (COOH) groups of polyGalA are observed on the NPs surfaces. The presence of such functional groups allowed the synthesized material to (i) bind through the electrostatic interactions methylene blue (MB) which may have a great potential for r pollution control or (ii) to form hydrogel beads (ionotropic gelation) by using calcium as a crosslinking agent which can be used to encapsulate active molecules and target their release by using an external stimulus (magnetic field). [ABSTRACT FROM AUTHOR]

X-ray fluorescence (XRF) emerges as a promising technique for estimating heavy metal elements. However, XRF spectra typically contain a significant amount of environmental information and signal noise, and the relationship between spectral intensity and element concentration is difficult to quantify using a single model, thereby reducing the predictive performance for low concentration elements. This paper proposed a comprehensive framework for predicting elemental concentrations, encompassing preprocessing, variable selection, decision-making, to enable fast, non-destructive, and accurate estimation of element concentrations in soil. Firstly, an optimal denoising method based on fractional discrete wavelet transform (FDWT) was introduced to enhance signal quality. Furthermore, the frequency-based competitive adaptive reweighted sampling (FCARS) algorithm was employed for feature selection of XRF spectral variables, allowing extraction of the most informative features from the complex spectral data. Finally, a novel deep learning network, called ConvBiLSTM-Attention (CBLA-Net), was designed to achieve precise estimation of heavy metal elements concentration. Compared with other advanced algorithms, The CBLA-Net demonstrated the highest accuracy for V, Cr, Mn, Zn, Cd, and Pb, achieving the coefficient of determination (R2) of 0.9730, 0.9874, 0.9952, 0.9921, 0.9518, and 0.9741, respectively. The CBLA-Net not only effectively extract local features and capture global information, but also combines attention mechanism to focus on key information. The proposed novel deep learning quantitative framework, including preprocessing, feature selection, and CBLA-Net decision-making, significantly enhances the accuracy of elemental content prediction. It provides a new approach for accurately assessing the concentration of heavy metal elements in soil. [Display omitted] • CBLA-Net effectively reduces prediction errors for quantitative analysis. • A FDWT denoising algorithm is proposed to enhance the signal-to-noise ratio. • FCARS performs variable selection on XRF spectra to eliminate redundant information. [ABSTRACT FROM AUTHOR]

Uncertainty exists over what environmental conditions and mineralogical/chemical properties are required to ensure retention of helium such that (U Th)/He dates record the time of goethite crystallization. We undertook vacuum step heating experiments to determine He diffusion parameters for extrapolation to Earth surface conditions on 10 goethite specimens in which we had created a uniform distribution of 3He. Arrhenius plots of apparent diffusion coefficients on all samples follow the same pattern. At temperatures <200 °C the data define arrays consistent with progressive degassing of increasingly large crystallites. However, above 200 °C the computed diffusivities increase dramatically until about 80% of the helium is extracted, after which they suddenly decline. The sudden increase in diffusivity at 200 °C coincides with the onset of dehydration of the goethite structure, a process which continues throughout the remainder of the step heat. There is a strong correlation between evolved water and 3He amounts. These observations likely reflect previously reported processes of formation, growth, and coalescence of pores as the phase transition to hematite proceeds. While significantly higher dehydration temperatures of ∼270 °C are observed using techniques such as thermogravimetric analysis in air, the long step durations, and vacuum conditions of our experiments destabilize goethite. Orders of magnitude differences in the computed diffusivities among our samples may reflect crystallite-size-distribution control on dehydration kinetics, and the qualitative size distributions we infer from the step heats are consistent with SEM observations of crystallite lengths. Vacuum step-heating experiments in which goethite is decomposing cannot be used to determine He diffusion behavior in nature, but the inferred crystallite size distribution provides some useful indirect evidence. A strong relationship exists between metrics of the crystallite size distribution from step heating results and the degree of He retention in nature inferred from the 4He/3He method. This observation provides evidence supporting the validity of the 4He/3He technique for estimating retention, and further suggests that the dominant control on He retention in nature is the crystallite size distribution. Experiments under hydrothermal conditions in which goethite remains stable may be an alternative approach to address the question of He diffusion behavior in nature. • Goethite He retentivity explored for (U Th)/He geochronology. • Vacuum step heat experiments cause decomposition of goethite. • These experiments cannot be used to directly infer He diffusivity. • Major control on decomposition behavior is crystallite size. • 4He/3He method likely remains viable for estimating He loss in nature. [ABSTRACT FROM AUTHOR]

[Display omitted] • Fabrication of NiS-supported nickel metal–organic framework composite. • The composite electrode showed the specific capacity of 264F/g (104C/g) at 1A/g. • The electrode showed an excellent cyclic stability up to 5000 cycles. • HR-TEM morphological properties were improved the electrochemical properties. • HR-TEM images analyzed using computer vision and AI techniques for estimating the porosity. The utilization of metal–organic framework nanostructured electrode materials in supercapacitors and sensor applications is achieved by various chemical methods. In this study, we create NiS and NiS@MOF-BDC by employing nickel precursors and benzene dicarboxylic acid (BDC) as chelating organic linkers through a thermal reduction procedure at a temperature of 400 °C to produce the composite. The composite heterostructure enhanced the conductivity, porous characteristics, and diverse potential morphological qualities. The production of composite electrodes demonstrates a specific capacity of 260F/g (104C/g) when subjected to a current density of 1A/g. Additionally, these electrodes exhibit exceptional cyclic stability, enduring 5000 cycles, when used with a 2 M KOH electrolyte. Moreover, the synthesized composite HR-TEM images were analyzed using computer vision and AI techniques for estimating the porosity and investigating the enhanced electrochemical correlation. [ABSTRACT FROM AUTHOR]

• Framework of geometry-aware car-following (GACF) model construction. • GACF-type model on horizontal alignment. • Internal and adjacent CF scenario, CF scenario loop, centralized and decentralized control. • The first data-driven GACF-type model with deep learning approaches. Road geometry significantly influences the physical forces acting on vehicles and the perceptual ability of drivers. Unfortunately, most available car-following models ignore the influence of complex road geographical features, such as curvatures and slopes and thereby lack scalability. To fill these gaps, this study presents a framework for the construction of a geometry-aware car-following model. Under the over-alignment assumption, car-following motion on horizontal curves was simplified into seven internal or adjacent car-following scenarios. Two novel alternative vehicle control modes (centralized and decentralized) for car-following motions on a horizontal route were proposed. The structured features of each scenario, considering both lateral and longitudinal information, were defined mathematically. Open-source data with trajectory records and road surface conditions on highways in Japan were collected and used as empirical data sources. First, we analyzed the theoretical proportion of traffic scenarios that conformed to the traditional car-following model for any horizontal route. Several properties of the car-following scenario proportion were proposed and proved. Both empirical statistics and theoretical estimations showed the existence of real-world sizable car-following scenarios that could not be handled by traditional models. Owing to their powerful ability to handle complex input features, machining learning and deep learning models were applied in car-following behavior modeling to make multistep predictions. With high computational efficiency, the results were compared with those of models with traditional inputs to demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This work presents a technique to estimate on-line the global inertia of an electric power system by exploiting the footprint of the principal frequency system dynamics. This method can estimate the inertia provided as a whole by synchronous machines, as well as by converter-interfaced generators controlled to emulate the behavior of the former through virtual inertia. Probing tones are injected by a grid-forming converter-interfaced generator and its virtual rotor speed is used to extrapolate its footprint. As a result, the method requires neither measuring the active power exchange of each synchronous generator nor extrapolating their rotor speeds. Since the proposed technique is entirely data driven, it does not require any model of the power system generators/prime-movers/controllers and of the interconnecting grid. The method is comprehensively tested on a modified version of the IEEE 39-BUS system and a dynamic version of the IEEE 118-BUS system, both containing grid-forming converter-interfaced generators. • An important and timely issue: how to estimate the inertia of an entire power system? • We present a technique to estimate on-line the inertia of an electric power system. • The proposed technique is entirely data driven. • This approach is adequate to accurately estimate also the inertia by converter-interfaced generators. [ABSTRACT FROM AUTHOR]

• Multilayer cloud movement with single sky camera isappropriately estimated. • Optical flow with regularization is preferred toderive cloud movement. • Circular standard deviation can be used to estimate multilayer cloud movement. Highly accurate forecasting of solar irradiation and power generation is essential for efficient operation of solar power generation. Among various forecasting methods, the methods based on ground observation—including all-sky image photography and solar irradiation measurement—show excellent performance concerning time and spatial resolution. In the forecasting method using all-sky images, the direction of cloud movement is estimated using optical flow or the like to forecast solar irradiation. In this case, the direction of a single-layer cloud movement is usually assumed. However, there is also multilayer cloud in reality. In addition, according to a recent report (T. Bando et al., Renew. Energy Environ. Sustain 8 , 1 (2023)), multilayer cloud has been observed even when the power generation is large, which suggests the necessity of estimating the cloud movement direction in each layer when multilayer clouds are present. Although techniques for estimating the direction of multilayer cloud movement have been developed, they use multiple sky cameras or supervised learning and have high development costs. Herein, we developed a simple method that can estimate the direction of multilayer cloud movement using a single sky camera. Optical flow algorithms suitable for the estimation of the direction of cloud movement were compared and examined, and DeepFlow was adopted to estimate the cloud movement direction. Moreover, we showed that cloud flow can be discriminated in one and two directions using the circular standard deviation when the target was a cloud of up to two-layers with a large ratio of the layer number. Further, it was shown that the direction of cloud movement can be properly evaluated if the direction temporally switches from one (two directions) to two directions (one direction). We suggested the effectiveness of directional statistics in the estimation of multidirectional cloud movements using sky cameras for the first time. [ABSTRACT FROM AUTHOR]

We consider the problem of estimating a vector of unknown constant parameters for a class of hybrid dynamical systems – that is, systems whose state variables exhibit both continuous (flow) and discrete (jump) evolution – with dynamics that depend linearly on the unknown parameters. Using a hybrid systems framework, we propose a hybrid estimation algorithm that can operate during both flows and jumps that, under a notion of hybrid persistence of excitation, guarantees convergence of the parameter estimate to the true value. Furthermore, we show that the parameter estimate is input-to-state stable with respect to a class of hybrid disturbances. Simulation results including a spacecraft application show the merits of our proposed approach. [ABSTRACT FROM AUTHOR]

Evaluating the radon emissions from building structures to determine optimal strategies for either preventing radon infiltration into buildings or decreasing its concentration in occupied areas is essential. This research presents a study of different methodologies for determining the radon exhalation rate in three samples of granite: two techniques based on passive detectors (Electret Ion Chamber (EIC) and Solid State Nuclear Track Detectors (SSNTD)) and one technique based on the use of a continuous radon detector. The operating principle of the three methodologies is based on the accumulation of radon inside an experimental container, hermetically sealed. Passive detectors offer the concentration measurement, integrated over time, while the continuous detector monitors the increase in concentration hour by hour. In this last case, three mathematical models have been applied to adjust the experimental results. According to a previous radiological characterization of the building material samples, those exceeding the legal limit of the radioactive activity index (I = 1) have been analyzed: Rose Porriño granite, Blue Vizag granite and Julia Cream granite. The obtained results indicate that the five methodologies used present radon exhalation rate values within a consistent range for each sample. Therefore, the protocols detailed in this work could be used interchangeably to calculate the radon gas exhalation rates in building materials, providing flexibility in selecting the most suitable method depending specific experimental requirements or constraints. • Radon exhalation rate in building materials has been determined. • Developed different methodologies to measure radon exhalation rate. • Passive and continuous detectors procedures have been validated through experimental results. • Reduction of sampling times compared to ISO 11665-9 procedure. [ABSTRACT FROM AUTHOR]

• RASCIN algorithm created for integrating SINS/CNS data in Mars rover navigation. • Mitigates gross errors using robust estimation theory and adaptive factors. • Achieves 92.48 % reduction in position errors versus pure inertial navigation. • Enhances three-axis attitude accuracy and real-time navigation capabilities. • Validated algorithm effectiveness via field trials, with errors at 1.22 % distance. High-accuracy, real-time autonomous navigation technology is crucial for enabling Mars rovers to successfully conduct scientific exploration missions. However, previous studies typically use only the single-axis attitude information output by the Mars rover and do not consider the significant impact of astronomical attitude measurement gross errors and kinematic model gross errors on navigation accuracy. Therefore, this paper introduces a robust adaptive strapdown inertial navigation system (SINS)/celestial navigation system (CNS) integrated navigation algorithm, denoted as the robust adaptive SINS/CNS integrated navigation (RASCIN) algorithm. This algorithm integrates the highly accurate three-axis attitude information provided by the CNS with the acceleration and angular velocity data obtained from the SINS. It also introduces robust estimation theory to mitigate the influence of gross errors in celestial measurements by adjusting the weight matrix. In addition, the algorithm enhances system robustness by incorporating adaptive factors designed to mitigate errors originating from the motion model within the SINS/CNS fusion. Simulation experiments yield compelling results, showing that compared to pure inertial navigation and extended Kalman filter (EKF) solutions, the RASCIN algorithm significantly improves the accuracy of three-axis attitude angles; it also reduces the closed-loop position errors by 92.48 % and 72.92 %, respectively, compared to these solutions. Furthermore, experimental trials conducted on Earth's surface affirm the effectiveness of the algorithm in controlling the daily navigation error of the Mars rover, limiting it to within 1.22 % of the distance traveled. Moreover, the RASCIN algorithm substantially reduces closed-loop position errors, achieving a reduction of 95.10 % compared to pure inertial navigation and 90.63 % compared to the EKF solution. Importantly, the algorithm also greatly enhances real-time navigation capabilities. [ABSTRACT FROM AUTHOR]

We propose a machine learning-based approach to enhance the performance of perovskite solar cells (PSCs) using carbon nanotubes as both hole transport layer (HTL) and back contact. Carbon-based PSCs offer low fabrication costs, long-term mechanical stability, high charge transport, and broad wavelength transparency. Our study estimates and enhances power conversion efficiency (PCE) of carbon-based PSCs through machine learning. PSC with single-walled carbon nanotube (SWCNT)-based HTL and back contact is investigated in simulations using SCAPS-1D and achieved 14.24% PCE, closely aligning with experimental data. Additionally, we explore the impact of various easily tunable fabrication parameters, such as the band gap and electron affinity of SWCNT in HTL, HTL thickness, active layer thickness, electron transport layer (ETL) thickness, HTL dopant concentration, absorber defect density, and ETL dopant concentration, on PCE. We generate a dataset of 20,480 samples to train classical and neural network-based machine learning models to predict performance parameters of PSCs, including short-circuit current density (J S C), open-circuit voltage (V O C), fill factor (F F), and PCE (η). The current density vs. voltage (J-V) characteristics curve is well-fitted by the predicted J S C , V O C , and FF, as evidenced by an average root mean squared error (RMSE) of 0.03 and a goodness of fit (R 2) value of 0.99. The fully connected artificial neural network performs best, with RMSE of 0.05% and R 2 of 0.99998 for predicting PCE. Genetic algorithm is used to optimize PCE, yielding a maximum PCE of 20.92%. Furthermore, an analysis of feature dimension and dataset size on prediction performance guides future modeling approaches. [Display omitted] • Application of machine learning (ML) techniques to estimate and enhance PCE of carbon-based PSCs. • SCAPS-1D simulations investigate the performance of PSCs with SWCNT based HTL and back contact. • Tunable solar cell parameters facilitate dataset generation, followed by data analysis. • ML models are trained using the dataset to predict current density-voltage characteristics and optimize efficiency. • Comparable performance with selected features and dataset sizes supports further study. [ABSTRACT FROM AUTHOR]

Recent studies demonstrate the effectiveness of integrated archaeo-geophysical tools in resolving various geological-environmental challenges. This involves combining geophysical methods in archaeological fieldwork or remote sensing methods for preliminary survey and analysis of archaeological sites, potentially enhanced by machine learning techniques to estimate object shapes and characteristics. This study highlights the potential of employing informational and probabilistic approaches as optimal tools for evaluating and integrating critical information for archaeological research. Our proposed procedure for assessing the reliability of tools or toolsets is based on improved methodologies utilizing conditional probability, which were suggested in previous authors' publications. We illustrate examples of combining remote sensing, known for its low cost, portability, and effectiveness in initial archaeological site identification, with machine learning methods to locate and discover new sites in archaeologically well-studied areas in Israel. Subsequently, we conduct an informational assessment of remote sensing data and propose steps to correlate this data with other geophysical information probabilistically. • Applying an information-probabilistic approach increases the quality, quantity, and associated parameters of geoscientific information. • Based on the information approach, Remote Sensing analysis can be numerically integrated with other geophysical and environmental studies. • Advanced Remote Sensing applications in Northern and Southern Israel enabled the discovery of earlier unknown archaeological targets. [ABSTRACT FROM AUTHOR]

In a network of reinforced stochastic processes, for certain values of the parameters, all the agents' inclinations synchronize and converge almost surely toward a certain random variable. The present work aims at clarifying when the agents can asymptotically polarize , i.e. when the common limit inclination can take the extreme values, 0 or 1, with probability zero, strictly positive, or equal to one. Moreover, we present a suitable technique to estimate this probability that, along with the theoretical results, has been framed in the more general setting of a class of martingales taking values in [ 0 , 1 ] and following a specific dynamics. • Results for a general class of bounded martingales: M n + 1 = (1 − r n) M n + r n Y n + 1. • Networks of reinforced stochastic processes E [ X n + 1 | F n ] = W ⊤ Z n Z n + 1 = (1 − r n) Z n + r n X n + 1 with asymptotic polarization event { Z n → 0 } ∪ { Z n → 1 }. • Sufficient and necessary conditions for the probability of asymptotic polarization. • Confidence intervals for the probability of asymptotic polarization. [ABSTRACT FROM AUTHOR]

A wireless network's increased demand for mobility means that 5G and later wireless networks must anticipate traffic demand in order to prevent disruptions. The network usage is high in day time in the city areas at that same time, the usage of the network is very low in rural regions. At night the network usage in city areas gradually decreases and gradually increases in the rural areas. When the demand for the network increases, the network traffic also increases. An effective time demand network traffic estimate technique based on deep learning is created to address such problems. The original data are gathered, then preprocessed using two strategies such as gaussian weighted average filter and min–max normalization. A sufficient range of raw data is converted using min–max normalization, and a gaussian weighted average filter is used to transform overall data from low to high quality. After that, use agglomerative clustering to split every base station into a number of groups. Using discrete wavelet transform, separate the traffic data into its high and low frequency constituents. Finally, a modified Deep Belief Network is employed to predict the network traffic. The horse herd optimization is utilized for training the neural network and optimizing the classifier's weight. According to the simulation research, the proposed strategy can achieve 97 % accuracy with a 0.03 % Error. As a result, the proposed approach performs better than other existing techniques. Thus, the designed model predicted network traffic effective manner. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a novel framework for robust Bayesian parameter estimation using the K -divergence. The framework incorporates an outlier resilient pseudo-posterior density function, called the K -posterior, which is based on an empirical version of the K -divergence. The latter involves utilizing Parzen's non-parametric K ernel density estimator to mitigate the influence of outliers. Under the quadratic loss, a new robust analog of the posterior mean estimator (PME), referred here to as the K PME, is obtained. In the paper, we examine the asymptotic behavior of the K PME and investigate its robustness in the presence of outliers. Furthermore, we tackle the task of data-guided selection for the bandwidth parameter of the kernel in order to optimize a performance-oriented objective. Lastly, the K PME is successfully applied to robust Bayesian source localization under intermittent jamming. [ABSTRACT FROM AUTHOR]

• Reservoir screening is useful for CO 2 storage site selection. • Numerous full physics simulations are required for screening suitable reservoirs. • This study presents a graph theory approach to screen reservoirs. • The presented approach was up to 50 times faster than numerical simulations. Estimating plume spreading in geological CO 2 storage reservoirs is critical for several reasons including the assessment of pore space utilization efficiency, preferential CO 2 migration pathways and trapping. However, plume spreading critically depends on lithological heterogeneity of the reservoir and CO 2 injection rate. It might require numerous high fidelity full physics numerical simulations to constrain the uncertainty in plume spreading for a given reservoir. This might not always be practical due to computational limitations. Hence, reduced physics approaches, such as invasion-percolation method and machine learning, could be useful to answer certain questions on plume spreading in the subsurface. This study presents a new reduced physics approach based on graph theory for estimating probable CO 2 plume migration under very low and very high injection rates. The two end-member scenarios are assessed by performing random walk in the 3D reservoir space to constrain 20,000 possible paths of CO 2 flow away from the injection well. The resistance to CO 2 flow associated with each path is computed for viscous, capillary and gravity forces. The resistances are then transformed into the likelihood of CO 2 migration along the path. The algorithm was applied to 45 reservoir models with varying degrees of lithological heterogeneity and the results were compared to those from full physics and invasion percolation simulations. The graph theory results showed a close match with the results from full physics approach for both flow regimes and with results from invasion-percolation approach for capillary-gravity dominated flow regime. The algorithm was further applied to answer key questions on reservoir screening such as pore space utilization potential. The graph theory approach was also integrated with machine learning to predict CO 2 saturation. Testing suggested that the graph theory approach can be as much as 50 and 20 times faster than the full physics numerical simulations and invasion-percolation simulations, respectively. [ABSTRACT FROM AUTHOR]

• The watertable fluctuation method for recharge estimation has many different forms. • Despite widespread application, systematic testing has been limited. • The method has been modified in several ways to apply to non-ideal situations. • The method often produces estimates that differ significantly from other approaches. • The optimal approach for extrapolating the recession curve is presently unclear. Accurate groundwater recharge estimates are vital for the management of groundwater resources. The watertable fluctuation method (WTFM) is one of the most widely used techniques for estimating distributed recharge. This is likely due to the simplicity of its formulation and the limited input data requirements, which include groundwater level measurements and a specific yield estimate. The method is presented in alternative forms within published accounts of its use, including adaptations for non-ideal situations (e.g., aquifers subjected to extensive pumping). This review summarises the development of the WTFM and its variants, applied to the estimation of distributed recharge, and compares resulting recharge estimates from alternative forms of the method with other recharge estimation techniques. The review finds that the WTFM has been modified to account for shallow watertable conditions, deep unsaturated zones, groundwater pumping, seasonal variations in evapotranspiration, and aquifers with hydraulic properties that vary with depth. A key component of the WTFM is the projection of the antecedent recession curve to account for groundwater discharge during periods of watertable rise, for which there are multiple approaches. The best choice for projecting the recession curve remains unclear. Opportunities to develop the WTFM and improve its reliability include modifications to account for common boundary effects (e.g., proximity to rivers, etc.), evaluating the accuracy of recession curve projection techniques, assessing the spatial scale for which WTFM estimates of recharge apply, and incorporating more complex storage terms into the WTFM. Despite the extensive use of the WTFM, systematic testing has been limited and is therefore needed, including for the many alternative forms of the WTFM. [ABSTRACT FROM AUTHOR]

• A new Regional Geo-Statistics (RGS) Method is proposed to better extract remote sensing features for yield estimation. • Adding the surface reflectance bands to the vegetation index as features can improve the accuracy of yield estimation. • The multi-source data can realize the best yield estimation results. • LightGBM is capable of yield prediction on remote sensing data. • Quantitative analysis of the contribution of multi-source data. Timely and accurate estimation of maize yield is crucial for ensuring food security. This study integrated multi-source data (satellite, meteorological, and soil data) on Google Earth Engine (GEE) and used machine learning techniques to estimate summer maize yield across 469 counties in the Huang-Huai-Hai Plain of China from 2010 to 2020. A novel method for extracting features from remote sensing images was proposed called the Regional Geo-Statistics (RGS) Method. We compared its performance with the traditional county-level averages method and explained the impact and contributions of incorporating multi-source data on yield estimation models. Firstly, Enhanced Vegetation Index (EVI) and Near-Infrared Vegetation Reflectance (NIRv) were transformed into regional geostatistical vectors and county-level averages using GEE. Then, yield was estimated using LightGBM, RF and LASSO. The results highlighted the superiority of the RGS method, with LightGBM yielding the best model (R2 = 0.55, RMSE = 852.92 kg/ha, NRMSE = 13.66 %). Further improvements were achieved by gradually adding meteorological and soil variables, with R2 improvements of 0.03 and 0.20, respectively. The Comprehensive Factor Model (CFM), utilizing all data as features, achieved the best results (R2 = 0.76, RMSE = 629.33 kg/ha, NRMSE = 10.08 %). The interpretability analysis based on CFM model underscored the significance of soil-related variables, which contributed significantly (45.38 %) alongside remote sensing variables, emphasizing the crucial role of soil variables in maize yield estimation. This study presents a versatile and reliable method for integrating multi-source data for maize yield estimation, supporting agricultural management and food security. [ABSTRACT FROM AUTHOR]

Phenotyping is a systematic process of quantifying and assessing a wide range of structural and physiological traits to understand the intricate interplay between an organism's genetic makeup, its surrounding environment, and management practices, often referred to as genome-to-environment (GxE) interaction. In the context of plants, these traits can include aspects such as plant height, stem diameter, leaf size, angle, and shape, chlorophyll content, biomass, leaf area, etc. 3D plant phenotyping plays a crucial role in advancing our understanding of plant biology, improving crop breeding, and agricultural practices. 3D imaging has become a powerful phenotyping tool, offering in-depth insights into plant structures and traits. In contrast to 2D imaging, 3D imaging enables precise measurement of plant traits that cannot be sufficiently evaluated in two dimensions by overcoming challenges such as partial occlusion through the utilization of depth perception and multiple viewpoints. However, even with significant recent progress, various challenges persist, including the need for well-designed experimental setups for standardized data collection, the automation of processing pipelines, and the robust analysis techniques of 3D representations, which still impede the widespread adoption of 3D plant phenotyping. To propel the progress of 3D imaging-based phenotyping, an all-encompassing assessment of existing strategies is imperative, yet there is currently a lack of specialized reviews that scrutinize and emphasize distinct facets for future enhancement. To bridge this gap, we perform a systematic survey of 81 research studies that employ 3D imaging for various trait assessments of plants. Our review thoroughly investigates the stages of data acquisition, encompassing sensing technologies, representations, preprocessing approaches, analysis methodologies, and techniques for estimating phenotypic traits. We believe that this comprehensive review will serve as a valuable guide for researchers and professionals engaged in high throughput plant phenotyping, equipping them to formulate effective experimental setups and utilize appropriate processing and analysis methods, thereby fostering its continued advancement. • 3D imaging techniques for high throughput phenotyping were comprehensively reviewed. • Effective experimental setups for 3D imaging-based phenotyping were discussed. • Cutting edge deep learning-based phenotyping analysis methodologies were introduced. • Challenges and prospects of 3D image-based plant phenotyping were highlighted. [ABSTRACT FROM AUTHOR]

Iberian pigs are renowned for their high-quality products and distinctive characteristics, including high fat accumulation, low protein deposition rate, and a long productive cycle. The study aimed to assess in vivo body composition of purebred Iberian pigs using bioimpedance analysis (BIA) and ultrasonography. Accurate estimation of body composition in live animals is crucial for adopting decisions at the farm level. The experiment involved three groups of pure male Iberian pigs differing in body weight (BW; 60, 80 and 100 kg) with the same nutritional management. Body measurements, BIA and back fat and loin thickness (measured by ultrasonography) were obtained before slaughter. After slaughter pig carcasses were chemically analysed. A strong correlation between BIA measurements, specifically resistance (R s) values, and body chemical parameters (total protein, lipids, ash, and water contents; p < 0.001 for all) was found. Reactance values (X c), however, did not exhibit significant correlations. Regression analyses were conducted to predict carcass composition based on BIA measurements, BW, ultrasonography and linear corporal measurements. The prediction models achieved high R2 values for lipids, protein, total ash, water, and lean tissue (0.957, 0.968, 0.936, 0.961 and 0.976, respectively, p < 0.001 for all), indicating strong predictive power. These findings demonstrate the potential of non-invasive techniques such as BIA for estimating body chemical composition and quality of pig carcasses. However, it is important to acknowledge that the prediction models developed may not be applicable to other pig populations, as they were based on a specific sample of pigs. [ABSTRACT FROM AUTHOR]

The core of a transferable utility (TU) game, if it is not empty, is prescribed by the set of all stable allocations. The exact determination of the core reaches exponential time complexity. Therefore, its exact computation is often avoided as the number of players increases. In this work, we propose an estimator for the core of a TU game based on the statistical theory of set estimation. Concretely, we provide a core reconstruction that is obtained in polynomial time for general dimension. Additionally, convergence rates for the estimation error are derived. Finally, a consistent core-center estimator is established as a geometrical application of this methodology. • We estimate the core of TU games from set estimation theory. • The proposed algorithm has polynomial time complexity. • This procedure can be applied for TU games with arbitrary number of players. • As an application, an estimator of the core center is also introduced. [ABSTRACT FROM AUTHOR]

• A novel learning function is proposed by re-introducing the weight of covariance. • An improved learning function learning function is obtained by introducing the variables' joint PDF. • An adaptive truncated sampling area method has been proposed in this study. • The quasi-MC method and the uniform distribution of candidate sample points are used to improve efficiency. Structural reliability analysis relies on the evaluation of multivariable structural performance function. The construction of the surrogate model for reliability analysis has been widely studied to reduce the number of function calls. The novel learning function has been proposed based on the probability of improvement criterion and the introduction of covariance weight by normalization technique to estimate the failure probability efficiently. Furthermore, the weights of each candidate sample are inconsistent; thus, an improved learning function of the former is proposed by introducing the joint probability density function of the variables. Moreover, a novel adaptive truncated sampling space technique is proposed based on variable normalization transformation. The truncated sampling space is directly dependent on the failure probability rather than the manual definitions. Uniform samples are then obtained in the adaptive truncated sampling space by Latin hypercube sampling. Combined with the novel learning function and global convergence condition, the adaptive analysis of structural reliability is realized. Finally, several highly nonlinear and high-dimensional illustrative applications are used to evaluate and discuss the proposed adaptive learning strategy. Results show that this strategy can efficiently and robustly solve the problem of structural reliability analysis on the premise of ensuring accuracy. [ABSTRACT FROM AUTHOR]

Some second-order sufficient conditions are established for problems with ℓ 1 , SCAD and MCP penalties respectively and then an algorithm with active set strategy is proposed for solving ℓ 1 , SCAD and MCP penalties based on an approximation of the second order sufficient conditions. The active sets are estimated by an identification technique to identify accurately zero components in a neighborhood of a critical point of ℓ 1 , SCAD and MCP penalties. In the algorithm, a truncated Newton direction is used to update the free variables, while d k = − x k is used to update the active variables. To accelerate the convergence, a nonmonotone line search strategy is used to guarantee global convergence. Numerical results illustrate that the proposed algorithm is competitive with several well-known methods. [ABSTRACT FROM AUTHOR]

• Pedestrians estimated waiting time at red phase in simulated road-crossing tasks. • Feedback frequency of graphical countdown timers was manipulated. • Perceived duration was shorter than actual duration. • Lower feedback frequency in countdown timers led to shorter estimated duration. • Arousal level and internal timer speed were not affected by feedback frequency. Graphical countdown timers (similar to a progress bar) have the potential to shorten pedestrians' perceived waiting time at the red light. Based on the findings from human-computer interaction (HCI) and time estimation theories, the potential may be affected by feedback frequency. This study tested how the feedback frequency of countdown progress bar in traffic light influenced time estimation in a field experiment, and further explored its mechanism in a laboratory experiment. In both experiments, participants estimated various durations (30 s, 45 s, 60 s) under three levels of feedback frequency: low (0.5 Hz), medium (1 Hz), and high (2 Hz). The results showed that although waiting time was underestimated in all conditions, lower feedback frequency led to shorter estimated duration, with slight changes in different contexts. In the traffic context, the effect of feedback frequency was comparable across different countdown duration, but it became stronger at longer countdown duration in the non-traffic context. Overall, the effect of feedback frequency was accompanied by changes in neither arousal level (measured by the subjective scale and physiological indices) nor internal clock speed, which are two critical determinants of time perception. The findings have practical implications on the display design of red light and theoretical implications on time estimation processes. [ABSTRACT FROM AUTHOR]

The development of estimation and control theories for quantum systems is a fundamental task for practical quantum technology. This vision article presents a brief introduction to challenging problems and potential opportunities in the emerging areas of quantum estimation, control and learning. The topics cover quantum state estimation, quantum parameter identification, quantum filtering, quantum open-loop control, quantum feedback control, machine learning for estimation and control of quantum systems, and quantum machine learning. [ABSTRACT FROM AUTHOR]

• Inhibitory conductances are underestimated more than excitatory conductances. • Estimation of synaptic conductances is worse during high levels of background input. • The time course of estimated excitatory and inhibitory conductances is distorted. • Space clamp errors are exacerbated by dendritic voltage-activated conductances. The whole-cell voltage clamp technique is commonly used to estimate synaptic conductances. While previous work has shown how these estimates are affected by series resistance and space clamp errors during isolated synaptic events, how voltage clamp errors impact on synaptic conductance estimates during concurrent excitation and inhibition is less clear. This issue is particularly relevant given that many studies now use the whole-cell voltage clamp technique to estimate synaptic conductances in vivo , where both excitation and inhibition are intact. Using both simplistic and morphologically realistic models, we investigate how imperfect voltage clamp conditions distort estimates of excitatory and inhibitory synaptic conductance estimated using the Borg-Graham method during concurrent synaptic input onto dendrites. These simulations demonstrate that dendritically located conductances are underestimated even when dynamic clamp reinjection faithfully reproduces the voltage response at the soma to the actual conductances. Inhibitory conductances are underestimated more than excitatory conductances, leading to errors in the excitatory to inhibitory conductance ratio and negative inhibitory conductance estimates during distal inhibition. Interactions between unclamped dendritic excitatory and inhibitory conductances also introduce correlations when the actual conductances are uncorrelated, as well as distortions in the time course of estimated excitatory and inhibitory conductances. Finally, we show that space clamp errors are exacerbated by the inclusion of dendritic voltage-activated conductances. In summary, we highlight issues with the interpretation of synaptic conductance estimates obtained using somatic whole-cell voltage clamp during concurrent excitatory and inhibitory input to neurons with dendrites. [ABSTRACT FROM AUTHOR]

A novel, indirect measurement technique for estimating equivalent inductance associated with multi-resonance behavior of an isolated transformer winding is reported for both healthy and inter-turn short-circuit fault-condition. For healthy winding, the authors proposed measuring two unique admittance magnitude responses of the winding followed by utilizing their natural frequencies for estimating the said inductance. Here, the one response is for line-end excitation with neutral-end grounded and the other one is for two equal and opposite polarity excitations from two ends of the winding. For inter-turn shorting, driving-point admittance magnitude response with neutral-end open is also utilized. This inductance seems to be one important parameter for verifying the design constraint mainly for impulse behavior as well as for identifying inter-turn shorting of the winding. The proposed method was also verified by experimental measurements on two actual transformer windings; one of which incorporated an iron core within it. All pertinent analytical derivations and the associated results are also included in the paper. • L e q estimation for transformer winding under normal and inter-turn short conditions. • L e q expression formulated analytically with three sets of natural frequencies. • Experimentally detected inter-turn shorting in two actual transformer windings. • It requires no rigorous post-processing or special arrangement during measurement. [ABSTRACT FROM AUTHOR]

With the increasing integration of renewable and non-synchronous resources, the declining level of inertia poses a threat to the stability of power systems. This paper delves into the mechanism of time-varying inertia within mainstream non-synchronous devices and proposes a real-time estimation technique to estimate the constant and time-varying inertia associated with them. By applying the numerical integration method, this technique utilizes observations and their integrals over a continuous timeframe to formulate a system of equations, enabling the estimation of inertia at any given moment. Based on the linear operator analysis, the relationship between the aforementioned system of equations and linear operators is investigated to downscale the original high-dimensional system and extract the inertia. A significant addition is the introduction of the streaming dynamic mode decomposition (sDMD) algorithm, which updates linear operators based on snapshots composed of observations. Its update merely necessitates a small snapshot length, resulting in efficient data storage utilization and space conservation. Case studies on mainstream non-synchronous devices under various controls demonstrate its effectiveness and accuracy for real-time estimation of time-varying inertia. The remarkable advantage of this technique over the existing state-of-the-art method lies in its ability to operate without setting any hyperparameters. [Display omitted] • A real-time estimation technique of time-varying inertia is proposed. • The numerical integration method was used to eliminate extremes caused by noise. • The linear operator analysis method was applied to downscale the device model. • Updating linear operators by the streaming dynamic mode decomposition algorithm. • The proposed technique remains robust without setting hyperparameters. [ABSTRACT FROM AUTHOR]

The presence of snow significantly affects the hydrological cycle and soil moisture globally. Nowadays, with the expansion of science, different satellites can measure snow depth all over the world. By establishing a conditional relationship between mean temperature and the Proportion of Snowfall to Total Precipitation (PSTP), this study proposes a method for snow depth estimation for ungauged stations. The case study of this research is Iran. Most of Iran's climate is arid due to its location in the Middle East, making it one of the countries with the lowest rainfall worldwide. On the other hand, this country enormously depends on water resources. Consequently, an accurate and valid estimate of snow amounts in Iran is essential. To reach this goal, we analyzed daily data from 12 synoptic and climatological stations between 1970 and 2020, including rainfall, snow depth, and mean temperature. For each station, the PSTP at 0.5 °C air temperature intervals was determined and fitted to a double sigmoid model that allows snow depth estimation. The evaluation of the snow depth approximated by the double sigmoid model was done using R2, RRMSE, EF, and Bias statistics against the observational data. R2 values in the Era-5 dataset in all stations are lower than 0.5. Additionally, it should be noted that in certain stations, the bias values surpass 20 mm. Furthermore, in 75 percent of the stations, the RRMSE values exceed 0.6. By utilizing the DSBE model, it is possible to achieve a reduction of 122.4 and 0.7 in Bias and RRMSE values, respectively. The R2 between observed and estimated snow depth was estimated between 0.81 and 1 in ten of the studied stations and 0.65 and 0.8 in the other two stations. Moreover, the findings imply that the proposed model is a suitable technique for estimating snow depth in remote areas lacking snowfall measurement. • This study proposes a method for snow depth estimation for ungauged stations. • The snow depth estimated with the proposed model was compared with the snow depth provided by Era5-land reanalysis. • The proposed method is highly useful for accurate snow depth estimation in stations with no snow depth records. [ABSTRACT FROM AUTHOR]

The evaluation of sediment budgets of flash-flood prone torrential catchments is important for the assessment of flood susceptibility and hazard in piedmont areas. Here, we developed and tested a predictive, quantitative geomorphological technique for estimating the magnitude of debris flows related to flash floods triggered by extreme rainfall events. We applied our method to steep torrential catchments that allowed testing of our technique through quantification of the effects of recently-occurred flash floods. The three study catchments are located in southern Italy and experienced flash flood-related debris flows triggered by a rainstorm with a recurrence interval > 200 yrs. The study basins are small size, steep, narrow and the stream network has a low order (maximum 3rd order). We estimated the volumes of loose sediment stored in the bottoms of the mainstem valleys of the investigated mountainous catchments prior to the flash flood and compared our results with the sediment volumes entrained by the debris flows. The latter volumes were estimated by post-event data collected by means of field surveys. The results were mutually consistent so supporting our approach. The approach is fit to the analysis of torrential catchments with geomorphological features comparable to those of our study basins, provided (i) the stream is confined by hard rock slopes not affected by channel lateral erosion, (ii) all stored sediment is eroded and transported downstream during the flood, and (iii) no landslide occurs along the hillslopes. For these types of ungauged catchments, our method can be applied extensively, even in the absence of field data and measurements, for flash flood hazard assessment and planning of mitigation strategies. [Display omitted] • Flash-flood prone torrential catchments are source of hazard for piedmont areas. • Evaluation of sediment budget is crucial to assess flood-related debris flow hazard. • We use a geomorphological method to estimate sediment budgets of three small basins. • We test the results by post-event evaluations of the eroded sediment volumes. • The method allows prediction of the magnitude of debris flows for small catchments. [ABSTRACT FROM AUTHOR]

The frequency response function (FRF)-based model updating technique is an effective method to calibrate system parameters of the dynamic simulation model of pipeline systems. However, the existing FRF-based model updating methods is difficult to solve the problem of the test errors in the antiresonant frequencies, which significantly affects the accuracy of the updated results. Hence, this paper proposes a new FRF-based model updating method of the liquid-filled pipeline considering the tolerance intervals of test errors in the antiresonant frequencies. In this method, the effect of test errors on FRFs is quantified by employing the interval method, and then the tolerance intervals of test errors in the antiresonant frequencies can be identified from the interval FRFs. The obtained tolerance intervals are integrated into the objective function of the FRF-based model updating through the penalty function method, and the optimal system parameters can be obtained by using a global optimization algorithm. Compared with the existing FRF-based model updating methods, the proposed method can not only effectively reduce the effect of test errors in the measured FRFs on the updated results but also alleviate the ill-condition of model updating calculations, so that the accuracy and convergence of model updating calculations are improved simultaneously. Numerical and experimental validations of the proposed method are carried out by using an L-shaped water-filled pipeline system. The performances of the proposed method are compared with the typical FRF-based model updating method. The results demonstrate that the proposed method can obtain the updated results with higher accuracy and reliability than the typical method. • A new FRF-based model updating method considering the tolerance intervals in the antiresonant frequencies is proposed. • The tolerance intervals of test errors in the antiresonant frequencies are established by using the Bayesian interval estimation theory. • This method can improve the accuracy and convergence of the model updating calculation, simultaneously. • The effectiveness of the proposed method is verified experimentally on a real L-shaped pipeline system. [ABSTRACT FROM AUTHOR]

Estimation of second-order statistics allows characterizing the uncertainty associated with the response of stochastic finite element models. Two common approaches for estimating these statistics are Monte Carlo simulation and perturbation. The purpose of this paper is to present a framework to aggregate the results obtained by means of these two approaches under the umbrella of Control Variates with Splitting. This allows to produce estimates of the second-order statistics of the system's response with improved precision and accuracy. More specifically, Control Variates is implemented in such a way that the variance of the estimates of second-order statistics is minimized. In addition, the application of intervening variables for enhancing perturbation is considered as well, showing substantial advantages by increasing the accuracy of the estimates of second-order statistics. The application of the proposed framework is illustrated by means of an example involving the estimation of second-order statistics of a model involving confined seepage flow. • Estimation of second-order statistics of response of stochastic models. • Control Variates aggregates results from Monte Carlo simulation and perturbation. • Splitting technique is considered for practical implementation. • Proposed framework leads to precise and accurate estimates of sought statistics. [ABSTRACT FROM AUTHOR]

Acoustic testing is crucial in the aero-engine design process, providing valuable information about the sound field in terms of acoustic modes. The conventional use of a uniform sampling array limits the achievable resolution for a given number of sensors, necessitating the use of a large number of sensors to achieve high-resolution estimates. This limitation may be overcome using a non-uniform sensing array. In this work, we propose an optimal microphone selection scheme to determine which microphone placements that are preferable given the typically available a priori knowledge of the relevant acoustic modes. Using a weighted A-optimality criterion, we formulate the sensor selection problem as the one minimizing the expected variance of the resulting parameter estimates. Using both numerical and measured data sets, we illustrate how the proposed scheme allows for improved performance as compared to random and uniform sensor placement schemes. [ABSTRACT FROM AUTHOR]

Human activity recognition is a crucial field of study, but current approaches often require ground truth labels, which are not always available. We propose a new method called the Flexible Multi-Objective Particle swarm optimization clustering method based on Game theory (FMOPG), which can identify human activities without any supervision. Unlike traditional clustering methods that require an estimate of the number of clusters and are often inaccurate, FMOPG handles varying cluster numbers with an incremental technique, selecting clusters with good connectivity and separation. We enhance Particle Swarm Optimization (PSO) with mean-shift vectors for faster convergence and better handling of non-spherical clusters. Employing multi-objective optimization and Gaussian mutation, FMOPG provides a range of optimal solutions. We map the optimization problem to game theory to select the best solution based on different criteria. A smart grid-based method is proposed for population initialization, reducing variance and improving reliability. FMOPG outperforms state-of-the-art methods, improving clustering accuracy by 3.65%. Moreover, the incremental technique has improved clustering time by 71.18%. • Novel unsupervised method for discovering human activity from skeleton-based data. • Introducing a flexible multi-objective PSO clustering based on game theory. • Adopting Incremental techniques to estimate the number of activities automatically. • Proposing smart grid-based swarm initialization to generate diverse solutions. • Updating particle velocity based on mean shift to find nonlinear clusters. [ABSTRACT FROM AUTHOR]

This paper presents effective methods using spherical polar Fourier transform data for two different applications, with active areas of research, one as a conventional volumetric registration algorithm and other as a machine learning classification network. For registration purposes the proposed method has the following advantageous features: (i) it is a unique and effective technique for estimating up to 7 degrees of freedom for 3D volumetric registration, that has a closed-form solution for 3D rotation estimation, and which does not resort to recurrence relations or search for point correspondences between two objects/volumes, (ii) it allows for robust rotation estimation determined simultaneously on multiple spectral spheres, therefore complete stack of such spherical layers can be processed concurrently to obtain accurate and optimal all three angles, and (iii) it has the ability to handle arbitrary large rotation angles, is shown to be robust against the presence of noise, holes/missing data, and partial overlaps. We demonstrate the effectiveness of our solution with extensive experimentation, including a set of scanned MRI images, a crashed car parking dataset, and the Princeton shape benchmark dataset with hundreds of 3D objects. For the classification solution we modify and adapt an existing network in the literature, a type of spherical convolutional network, that is suitable for processing multi-radii spectral spherical data, and showcase the resulting robustness achieved in classification of objects from the ModelNet40 dataset, especially in the presence of outliers, additive noise, and missing data. • Leverages spherical correlations and convolutions for registration and classification. • Uses spectral spherical signals effectively in registration and machine learning. • A general method for registration for volumes for up to 7 DOF transformation. • Novel approach with spherical spectral signatures in spherical convolutional networks. • Extremely robust to noise, partial overlap or missing data, and other distortions. • A robust global estimate procedure for general rotation estimation problem. [ABSTRACT FROM AUTHOR]

• An upscaling framework is proposed to predict dispersivity in natural fracture. • Geostatistical analysis of the fracture aperture is implemented. • The upscaling dispersivities of the fracture core samples are predicted. Fluid flow and mass transport parameters are greatly impacted by the fracture surface's heterogeneous properties. By quantifying the spatial correlation patterns of fracture apertures, this study intends to establish an upscaling framework utilizing Lagrangian-based transport models to estimate dispersivities of naturally fractured granite cores. To do this, the surface morphology of the fragmented core sample was photographed using a 3D laser profile scanner with varied degrees of accuracy. Following a geostatistical analysis of the fracture aperture data, common covariance models were fitted to determine the integral scale of log fracture aperture. This information was used to estimate dispersivity using the Lagrangian-based transport models, which only required the collection of fractured geostatistical data. The study also assessed how the dispersivities are affected by fracture lengths and the measurement accuracy of aperture data in this model. The findings showed that the fracture aperture spatial bivariate correlation structures follow the exponential covariance model. Additionally, while the variance of log fracture apertures is significantly influenced by both the data resolutions and the core samples themselves, the integral scale of log fracture apertures primarily depends on the length of the fracture. In terms of the fracture length, the upscaled dispersivities range from 2.35% to 7.21%. The development of upscaling techniques for estimating dispersivities in fractured and heterogenous granitic rocks and scaling them up to the field size for the accurate prediction of solute transport mechanisms can be guided by these findings. [ABSTRACT FROM AUTHOR]