Search Results (24)

This paper is dedicated to studying the optimal investment proportions of three types of assets with symmetry, namely, risky assets, risk-free assets, and wealth management products, when the stochastic expenditure process follows a jump-diffusion model. The stochastic expenditure process is treated as an exogenous cash flow and is assumed to follow a stochastic differential process with jumps. Under the Cox–Ingersoll–Ross interest rate term structure, it is presumed that the prices of multiple risky assets evolve according to a multi-dimensional geometric Brownian motion. By employing stochastic control theory, the Hamilton–Jacobi–Bellman (HJB) equation for the household portfolio problem is formulated. Considering various risk-preference functions, particularly the Hyperbolic Absolute Risk Aversion (HARA) function, and given the algebraic form of the objective function through the terminal-value maximization condition, an explicit solution for the optimal investment strategy is derived. The findings indicate that when household investment behavior is characterized by random expenditures and symmetry, as the risk-free interest rate rises, the optimal proportion of investment in wealth-management products also increases, whereas the proportion of investment in risky assets continually declines. As the expected future expenditure increases, households will decrease their acquisition of risky assets, and the proportion of risky-asset purchases is sensitive to changes in the expectation of unexpected expenditures. Full article

The Gravity Recovery and Climate Experiment (GRACE) enables large-scale monitoring of terrestrial water storage changes, significantly contributing to hydrology and related fields. However, the coarse resolution of groundwater storage anomaly (GWSA) data limits local-scale research utilizing GRACE and GRACE-FO missions. In this study, we develop a regional downscaling model based on the linear regression relationship between GWSA and environmental variables, reducing the grid resolution of GWSA obtained from GRACE from approximately 25 km to 1 km. First, we estimate the missing values of monthly continuous terrestrial water storage anomaly (TWSA) for the period from 2003 to 2020 using interpolated multi-channel singular spectrum analysis (IMSSA). Next, we apply the water balance equation to separate GWSA from TWSA, which is provided jointly by the Global Land Data Assimilation System (GLDAS) and the distributed ecohydrological model ESSI-3. We then employ a partial least squares regression (PLSR) model to identify the most significant environmental variables related to GWSA. Precipitation (Prec), normalized difference vegetation index (NDVI), and actual evapotranspiration (AET), with variable importance in projection (VIP) values greater than 1.0, are recognized as effective variables for reconstructing long-term, high-resolution groundwater storage changes. Finally, we downscale and reconstruct the long-term (2003–2020), high-resolution (1 km × 1 km) monthly GWSA in the Songhua River Basin using fused and supplemented GRACE/GRACE-FO data, employing either geographically weighted regression (GWR) or random forest (RF) models. The results demonstrate superior performance of the GWR model (CC = 0.995, NSE = 0.989, RMSE = 2.505 mm) compared to the RF model in downscaling. The downscaled GWSA in the Songhua River Basin not only achieves high spatial resolution but also exhibits improved accuracy when compared to in situ groundwater observation records. This research enhances understanding of spatiotemporal variations in regional groundwater due to local agricultural and industrial water use, providing a scientific basis for regional water resource management. Full article

Data assimilation for unconfined seepage analysis has faced significant challenges due to hybrid causes, such as sparse measurements, heterogeneity of porous media, and computationally expensive forward models. To address these bottlenecks, this paper introduces a physics-informed neural network (PINN) model to resolve the data assimilation problem for seepage analysis of unsaturated earth–rockfill dams. This strategy offers a solution that decreases the reliance on numerical models and enables an accurate and efficient prediction of seepage parameters for complex models in the case of sparse observational data. For the first attempt in this study, the observed values are obtained by random sampling of numerical solutions, which are then contributed to the synchronous constraints in the loss function by informing both the seepage control equations and boundary conditions. To minimize the effects of sharp gradient shifts in seepage parameters within the research domain, a residual adaptive refinement (RAR) constraint is introduced to strategically allocate training points around positions with significant residuals in partial differential equations (PDEs), which could facilitate enhancing the prediction accuracy. The model’s effectiveness and precision are evaluated by analyzing the proposed strategy against the numerical solutions. The results indicate that even with limited sparse data, the PINN model has great potential to predict seepage data and identify complex structures and anomalies inside the dam. By incorporating coupling constraints, the validity of our PINN model could lead to theoretically viable applications of hydrogeophysical inversion or multi-parameter seepage inversion. The results show that the proposed framework can predict the seepage parameters for the entire research domain with only a small amount of observation data. Furthermore, with a small amount of observation data, PINNs are able to obtain more accurate results than purely data-driven DNNs. Full article

Laser beams converging at significant focusing angles have diverse applications, including quartz-enhanced photoacoustic spectroscopy, high spatial resolution imaging, and profilometry. Due to the limited applicability of the paraxial approximation, which is valid solely for smooth focusing scenarios, numerical modeling becomes necessary to achieve optimal parameter optimization for imaging diagnostic systems that utilize converged laser beams. We introduce a novel methodology for the modeling of laser beams sharply focused on the turbid tissue-like scattering medium by employing the unidirectional Helmholtz equation approximation. The suggested modeling approach takes into account the intricate structure of biological tissues, showcasing its ability to effectively simulate a wide variety of random multi-layered media resembling tissue. By applying this methodology to the Gaussian-shaped laser beam with a parabolic wavefront, the prediction reveals the presence of two hotspots near the focus area. The close-to-maximal intensity hotspot area has a longitudinal size of about 3–5 μm and a transversal size of about 1–2 μm. These values are suitable for estimating spatial resolution in tissue imaging when employing sharply focused laser beams. The simulation also predicts a close-to-maximal intensity hotspot area with approximately 1 μm transversal and longitudinal sizes located just behind the focus distance for Bessel-shaped laser beams with a parabolic wavefront. The results of the simulation suggest that optical imaging methods utilizing laser beams with a wavefront produced by an axicon lens would exhibit a limited spatial resolution. The wavelength employed in the modeling studies to evaluate the sizes of the focus spot is selected within a range typical for optical coherence tomography, offering insights into the limitation of spatial resolution. The key advantage of the unidirectional Helmholtz equation approximation approach over the paraxial approximation lies in its capability to simulate the propagation of a laser beam with a non-parabolic wavefront. Full article

An edge computing offloading strategy was proposed with the goal of addressing the issue of low edge computing efficiency and service quality in the multi-service and multi-user intersections of networked vehicles. This strategy took into account all relevant factors, including the matching of users and service nodes, offloading ratio, bandwidth and computing power resource allocation, and system energy consumption. It is mainly divided into 2 tasks: (1) Service node selection: A fuzzy logic-based service node selection algorithm (SNFLC) is proposed. The linear equation for node performance value is determined through fuzzy reasoning by specifying three performance indexes as input. Gradient descent method is used to find the optimal value of the objective function, and the Lyapunov criterion coefficient is introduced to improve the stability of the algorithm. (2) Offload ratio and resource allocation are solved: The coupling between offload ratio and bandwidth resource allocation is confirmed by relaxing integer variables because the optimization goal problem is a NP problem, and the issue is divided into two sub-problems. At the same time, a low-complexity alternate iteration resource allocation algorithm (LC-IRA) is proposed to solve the bandwidth resource and computational power resource allocation. According to simulation results, the performance of genetic ant colony algorithm (G_ACA), non orthogonal multiple access technology (NOMA) and LC-IRA are improved by 26.5%, 31.37%, and 45.52%, respectively, compared with the random unloading allocation (RUA) and average distribution (AD). Full article

In this paper, we propose an extension to the barrier model, i.e., the Multi-Barriers Model, which could characterize an area of interest with different types of obstacles. In the proposed model, the area of interest is divided into two or more areas, which include a general area of interest with sampling points and the rest of the area with different types of obstacles. Firstly, the correlation between the points in space is characterized by the obstruction degree of the obstacle. Secondly, multiple Gaussian random fields are constructed. Then, continuous Gaussian fields are expressed by using stochastic partial differential equations (SPDEs). Finally, the integrated nested Laplace approximation (INLA) method is employed to calculate the posterior mean of parameters and the posterior parameters to establish a spatial regression model. In this paper, the Multi-Barriers Model is also verified by using the geostatistical model and log-Gaussian Cox model. Furthermore, the stationary Gaussian model, the barrier model and the Multi-Barriers Model are investigated in the geostatistical data, respectively. Real data sets of burglaries in a certain area are used to compare the performance of the stationary Gaussian model, barrier model and Multi-Barriers Model. The comparison results suggest that the three models achieve similar performance in the posterior mean and posterior distribution of the parameters, as well as the deviance information criteria (DIC) value. However, the Multi-Barriers Model can better interpret the spatial model established based on the spatial data of the research areas with multiple types of obstacles, and it is closer to reality. Full article

Hepatitis C is an infectious disease which is caused by the Hepatitis C virus (HCV) and the virus primarily affects the liver. Based on the publicly available dataset used in this paper the idea is to develop a mathematical equation that could be used to detect HCV patients with high accuracy based on the enzymes, proteins, and biomarker values contained in a patient’s blood sample using genetic programming symbolic classification (GPSC) algorithm. Not only that, but the idea was also to obtain a mathematical equation that could detect the progress of the disease i.e., Hepatitis C, Fibrosis, and Cirrhosis using the GPSC algorithm. Since the original dataset was imbalanced (a large number of healthy patients versus a small number of Hepatitis C/Fibrosis/Cirrhosis patients) the dataset was balanced using random oversampling, SMOTE, ADSYN, and Borderline SMOTE methods. The symbolic expressions (mathematical equations) were obtained using the GPSC algorithm using a rigorous process of 5-fold cross-validation with a random hyperparameter search method which had to be developed for this problem. To evaluate each symbolic expression generated with GPSC the mean and standard deviation values of accuracy (ACC), the area under the receiver operating characteristic curve ($AUC$), precision, recall, and F1-score were obtained. In a simple binary case (healthy vs. Hepatitis C patients) the best case was achieved with a dataset balanced with the Borderline SMOTE method. The results are $\overline{ACC}\pm SD\left(ACC\right)$, $\overline{AUC}\pm SD\left(AUC\right)$, $\overline{Precision}\pm SD\left(Precision\right)$, $\overline{Recall}\pm SD\left(Recall\right)$, and $\overline{F1-score}\pm SD(F1-score)$ equal to $0.99\pm 5.8\times {10}^{-3}$, $0.99\pm 5.4\times {10}^{-3}$, $0.998\pm 1.3\times {10}^{-3}$, $0.98\pm 1.19\times {10}^{-3}$, and $0.99\pm 5.39\times {10}^{-3}$, respectively. For the multiclass problem, OneVsRestClassifer was used in combination with GPSC 5-fold cross-validation and random hyperparameter search, and the best case was achieved with a dataset balanced with the Borderline SMOTE method. To evaluate symbolic expressions obtained in this case previous evaluation metric methods were used however for $AUC$, $Precision$, $Recall$, and $F1-score$ the macro values were computed since this method calculates metrics for each label, and find their unweighted mean value. In multiclass case the $\overline{ACC}\pm SD\left(ACC\right)$, ${\overline{AUC}}_{macro}\pm SD\left(AUC\right)$, ${\overline{Precision}}_{macro}\pm SD\left(Precision\right)$, ${\overline{Recall}}_{macro}\pm SD\left(Recall\right)$, and ${\overline{F1-score}}_{macro}\pm SD(F1-score)$ are equal to $0.934\pm 9\times {10}^{-3}$, $0.987\pm 1.8\times {10}^{-3}$, $0.942\pm 6.9\times {10}^{-3}$, $0.934\pm 7.84\times {10}^{-3}$ and $0.932\pm 8.4\times {10}^{-3}$, respectively. For the best binary and multi-class cases, the symbolic expressions are shown and evaluated on the original dataset. Full article

Introduction: In this work, we develop a multi-scale model to calculate corrections to the prescription dose to predict compensation required for the DNA repair mechanism and the repopulation of the cancer cells due to the occurrence of patient scheduling variabilities and the treatment time-gap in fractionation scheme. Methods: A system of multi-scale, time-dependent birth-death Master equations is used to describe stochastic evolution of double-strand breaks (DSBs) formed on DNAs and post-irradiation intra and inter chromosomes end-joining processes in cells, including repair and mis-repair mechanisms in microscopic scale, with an extension appropriate for calculation of tumor control probability (TCP) in macroscopic scale. Variabilities in fractionation time due to systematic shifts in patient’s scheduling and randomness in inter-fractionation treatment time are modeled. For an illustration of the methodology, we focus on prostate cancer. Results: We derive analytical corrections to linear-quadratic radiobiological indices $\alpha$ and $\beta$ as a function of variabilities in treatment time and shifts in patient’s scheduling. We illustrate the dependence of the absolute value of the compensated dose on radio-biological sensitivity, $\alpha /\beta$, DNA repair half-time, $T_{1/2}$, tumor cells repopulation rate, and the time-gaps among treatment fractions due to inter-patient variabilities. At a given tumor size, delays between fractions totaling 24 h over the entire course of treatment, in a typical prostate cancer fractionation scheme, e.g., 81 Gy, 1.8 Gy per fraction and 45 treatment days, require up to 10% compensation dose if the sublethal DNA repair half-time, $T_{1/2}$, spans over 10 h. We show that the contribution of the fast DNA repair mechanisms to the total dose is negligible. Instead, any compensation to the total dose stems from the tumor cell repopulation that may go up to a significant fraction of the original dose for a time gap of up to one week. Conclusions: We recommend implementation of time irregularities in treatment scheduling in the clinic settings to be taken into account. To achieve a clinical endpoint, corrections to the prescription dose must be assessed, in particular, if modern external beam therapy techniques such as IMRT/VMAT are used for the treatment of cancer. Full article

To study an uncertain case of a control problem, we consider the symmetric F-n-NLS which is induced by a dynamic norm inspired by a random norm, distribution functions, and fuzzy sets. In this space, we consider a random multi-valued equation containing a parameter and investigate existence, and unbounded continuity of the solution set of it. As an application of our results, we consider a control problem with multi-point boundary conditions and a second order derivative operator. Full article

This paper proposes an algorithm and hardware realization of generalized chaotic systems using fractional calculus and rotation algorithms. Enhanced chaotic properties, flexibility, and controllability are achieved using fractional orders, a multi-scroll grid, a dynamic rotation angle(s) in two- and three-dimensional space, and translational parameters. The rotated system is successfully utilized as a Pseudo-Random Number Generator (PRNG) in an image encryption scheme. It preserves the chaotic dynamics and exhibits continuous chaotic behavior for all values of the rotation angle. The Coordinate Rotation Digital Computer (CORDIC) algorithm is used to implement rotation and the Grünwald–Letnikov (GL) technique is used for solving the fractional-order system. CORDIC enables complete control and dynamic spatial rotation by providing real-time computation of the sine and cosine functions. The proposed hardware architectures are realized on a Field-Programmable Gate Array (FPGA) using the Xilinx ISE 14.7 on Artix 7 XC7A100T kit. The Intellectual-Property (IP)-core-based implementation generates sine and cosine functions with a one-clock-cycle latency and provides a generic framework for rotating any chaotic system given its system of differential equations. The achieved throughputs are $821.92$ Mbits/s and $520.768$ Mbits/s for two- and three-dimensional rotating chaotic systems, respectively. Because it is amenable to digital realization, the proposed spatially rotating translational fractional-order multi-scroll grid chaotic system can fit various secure communication and motion control applications. Full article

Malnutrition in women has been a long-standing public health concern, with serious effects on child survival and development. Maternal body mass index (BMI) is an important maternal nutritional indicator. There are few published studies although child anthropometric failures do not occur in isolation and identifying children with single versus several co-occurring failures can better capture cases of growth failure in combination: stunting, wasting, and underweight. In the context of multiple anthropometric failures, traditional markers used to assess children’s nutritional status tend to underestimate overall undernutrition. Using the composite index of anthropometric failure (CIAF), we aimed to assess the association between maternal undernutrition and child undernutrition among children with diarrhea under the age of two and to investigate the correlates. Using 1431 mother-child dyads from the Antibiotic for Children with Diarrhea (ABCD) trial, we extracted children’s data at enrollment and on day 90 and day 180 follow-ups. ABCD was a randomized, multi-country, multi-site, double-blind, placebo-controlled clinical trial. The Bangladesh site collected data from July 2017 to July 2019. The outcome variable, CIAF, allows combinations of height-for-age, height-for-weight, and weight-for-age to determine the overall prevalence of undernutrition. The generalized estimating equation was used to explore the correlates of CIAF. After adjusting all the potential covariates, maternal undernutrition status was found to be strongly associated with child undernutrition using the CIAF [aOR: 1.4 (95% CI: 1.0, 1.9), p-value = 0.043] among the children with diarrhea under 2 years old. Maternal higher education had a protective effect on CIAF [aOR: 0.7 (95% CI: 0.5, 0.9), p-value = 0.033]. Our study findings highlight the importance of an integrated approach focusing on maternal nutrition and maternal education could affect a reduction in child undernutrition based on CIAF. Full article

In the development of implantable neural interfaces, the recording of signals from the peripheral nerves is a major challenge. Since the interference from outside the body, other biopotentials, and even random noise can be orders of magnitude larger than the neural signals, a filter network to attenuate the noise and interference is necessary. However, these networks may drastically affect the system performance, especially in recording systems with multiple electrode cuffs (MECs), where a higher number of electrodes leads to complicated circuits. This paper introduces formal analyses of the performance of two commonly used filter networks. To achieve a manageable set of design equations, the state equations of the complete system are simplified. The derived equations help the designer in the task of creating an interface network for specific applications. The noise, crosstalk and common-mode rejection ratio (CMRR) of the recording system are computed as a function of electrode impedance, filter component values and amplifier specifications. The effect of electrode mismatches as an inherent part of any multi-electrode system is also discussed, using measured data taken from a MEC implanted in a sheep. The accuracy of these analyses is then verified by simulations of the complete system. The results indicate good agreement between analytic equations and simulations. This work highlights the critical importance of understanding the effect of interface circuits on the performance of neural recording systems. Full article

Precisely monitoring the growth condition and nutritional status of maize is crucial for optimizing agronomic management and improving agricultural production. Multi-spectral sensors are widely applied in ecological and agricultural domains. However, the images collected under varying weather conditions on multiple days show a lack of data consistency. In this study, the Mini MCA 6 Camera from UAV platform was used to collect images covering different growth stages of maize. The empirical line calibration method was applied to establish generic equations for radiometric calibration. The coefficient of determination (R²) of the reflectance from calibrated images and ASD Handheld-2 ranged from 0.964 to 0.988 (calibration), and from 0.874 to 0.927 (validation), respectively. Similarly, the root mean square errors (RMSE) were 0.110, 0.089, and 0.102% for validation using data of 5 August, 21 September, and both days in 2019, respectively. The soil and plant analyzer development (SPAD) values were measured and applied to build the linear regression relationships with spectral and textural indices of different growth stages. The Stepwise regression model (SRM) was applied to identify the optimal combination of spectral and textural indices for estimating SPAD values. The support vector machine (SVM) and random forest (RF) models were independently applied for estimating SPAD values based on the optimal combinations. SVM performed better than RF in estimating SPAD values with R² (0.81) and RMSE (0.14), respectively. This study contributed to the retrieval of SPAD values based on both spectral and textural indices extracted from multi-spectral images using machine learning methods. Full article

The object of this research is concrete-filled steel tubes (CFST). The article aimed to develop a prediction Multiphysics model for the circular CFST column by using the Artificial Neural Network (ANN), the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Gene Expression Program (GEP). The database for this study contains 1667 datapoints in which 702 are short CFST columns and 965 are long CFST columns. The input parameters are the geometric dimensions of the structural elements of the column and the mechanical properties of materials. The target parameters are the bearing capacity of columns, which determines their life cycle. A Multiphysics model was developed, and various statistical checks were applied using the three artificial intelligence techniques mentioned above. Parametric and sensitivity analyses were also performed on both short and long GEP models. The overall performance of the GEP model was better than the ANN and ANFIS models, and the prediction values of the GEP model were near actual values. The PI of the predicted N_st by GEP, ANN and ANFIS for training are 0.0416, 0.1423, and 0.1016, respectively, and for N_lg these values are 0.1169, 0.2990 and 0.1542, respectively. Corresponding OF values are 0.2300, 0.1200, and 0.090 for N_st, and 0.1000, 0.2700, and 0.1500 for N_lg. The superiority of the GEP method to the other techniques can be seen from the fact that the GEP technique provides suitable connections based on practical experimental work and does not rely on prior solutions. It is concluded that the GEP model can be used to predict the bearing capacity of circular CFST columns to avoid any laborious and time-consuming experimental work. It is also recommended that further research should be performed on the data to develop a prediction equation using other techniques such as Random Forest Regression and Multi Expression Program. Full article

Poly(lactic acid) (PLA) and biosourced polyamide (PA) bioblends, with a variable PA weight content of 10–50%, were prepared by melt blending in order to overcome the high brittleness of PLA. During processing, the properties of the melt were stabilized and enhanced by the addition of a styrene-acrylic multi-functional-epoxide oligomeric reactive agent (SAmfE). The general analytical equation (GAE) was used to evaluate the kinetic parameters of the thermal degradation of PLA within bioblends. Various empirical and theoretical solid-state mechanisms were tested to find the best kinetic model. In order to study the effect of PA on the PLA matrix, only the first stage of the thermal degradation was taken into consideration in the kinetic analysis (α < 0.4). On the other hand, standardized conversion functions were evaluated. Given that it is not easy to visualize the best accordance between experimental and theoretical values of standardized conversion functions, an index, based on the integral mean error, was evaluated to quantitatively support our findings relative to the best reaction mechanism. It was demonstrated that the most probable mechanism for the thermal degradation of PLA is the random scission of macromolecular chains. Moreover, y(α) master plots, which are independent of activation energy values, were used to confirm that the selected reaction mechanism was the most adequate. Activation energy values were calculated as a function of PA content. Moreover, the onset thermal stability of PLA was also determined. Full article