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This paper provides a comprehensive review of advancements in the detection; evaluation; and management of invasive plant species (IPS) using diverse remote sensing (RS) techniques and machine learning (ML) methods. Analyzing the high-resolution datasets received from drones, satellites, and aerial photography enables the perfect cartography technique and analysis of the spread and various impacts of ecology on IPS. The majority of current research on hyperspectral imaging with unmanned aerial vehicle (UAV) enhanced by ML has significantly improved the accuracy and efficiency of identifying mapping IPS, and it also serves as a powerful instrument for ecological management. The integrative association is essential to manage the alien species better, as researchers from multiple other fields participate in modeling innovative methods and structures. Incorporating advanced technologies like light detection and ranging (LiDAR) and hyperspectral imaging shows potential for improving spatial and spectral analysis approaches and utilizing ML approaches such as a support vector machine (SVM), random forest (RF), artificial neural network (ANN), convolutional neural network (CNN), and deep convolutional neural network (DCNN) analysis for detecting complex IPS. The significant results indicate that ML methods, most importantly SVM and RF, are victorious in recognizing the alien species via analyzing RS data. This report emphasizes the importance of continuous research efforts to improve predictive models, fill gaps in our understanding of the connections between climate, urbanization and invasion dynamics, and expands conservation initiatives via utilizing RS techniques. This study also highlights the potential for RS data to refine management plans, enabling the implementation of more efficient strategies for controlling IPS and preserving ecosystems. [ABSTRACT FROM AUTHOR]

Recent advancements in medical imaging have greatly enhanced the application of computational techniques in digital pathology, particularly for the classification of breast cancer using in situ hybridization (ISH) imaging. HER2 amplification, a key prognostic marker in 20–25% of breast cancers, can be assessed through alterations in gene copy number or protein expression. However, challenges persist due to the heterogeneity of nuclear regions and complexities in cancer biomarker detection. This review examines semi-automated and fully automated computational methods for analyzing ISH images with a focus on HER2 gene amplification. Literature from 1997 to 2023 is analyzed, emphasizing silver-enhanced in situ hybridization (SISH) and its integration with image processing and machine learning techniques. Both conventional machine learning approaches and recent advances in deep learning are compared. The review reveals that automated ISH analysis in combination with bright-field microscopy provides a cost-effective and scalable solution for routine pathology. The integration of deep learning techniques shows promise in improving accuracy over conventional methods, although there are limitations related to data variability and computational demands. Automated ISH analysis can reduce manual labor and increase diagnostic accuracy. Future research should focus on refining these computational methods, particularly in handling the complex nature of HER2 status evaluation, and integrate best practices to further enhance clinical adoption of these techniques. [ABSTRACT FROM AUTHOR]

Abutment scour is a major cause of bridge failures worldwide, leading to disruptions, economic losses, and loss of life. The present experimental study examines countermeasures against abutment scour using hooked-collar protections on vertical-wall and wing-wall abutments (at 45° and 60°) under different flow conditions. All 60 experiments were performed under sub-critical flow conditions by investigating scour around an abutment 20 cm long, 20 cm wide, and 25 cm tall. Two distinct values of the Froude number, 0.154 and 0.179, and a sediment particle diameter (d50) of 0.88 mm were used throughout the experimental phase. The resulting equilibrium scour around the abutments was compared to those with collar and hooked-collar protections. It was determined that the maximum abutment scour depth reduction was 83.89% when hooked collars were placed on vertical wall abutments beneath the bed surface level, and for wing-wall abutments at 45° and 60°, it was 74.2% and 73.5%, respectively, at the bed surface level. Regression analysis was conducted to assess the non-dimensional scour depth (Ds/Yf) and scour reduction (RDs/Yf), with a high enough coefficient of determination (R2 values of 0.96 and 0.93, respectively), indicating high confidence in the analysis. The sensitivity analysis findings demonstrate that the width of the collar (Wc) and La are the most influencing factors affecting Ds/Yf and RDs/Yf. [ABSTRACT FROM AUTHOR]

The current study investigates the effects of a digital growth mindset on the motivation and success of chemistry students. The approach involves the use of technological tools that encourage students to face challenges and keep trying even when things become difficult. Students can achieve milestones by following this fruitful methodology. This study utilized a mixed-method design of an ordered–explanatory type, as identified in one of the categories of mixed-method approaches. The quantitative aspects of the research project were conducted using a matching-only pre-test–post-test control-group design. This was conducted because the study was carried out on secondary school students in Lahore, Pakistan, and the population included students up to the tenth grade. Only the experimental group participated in digital growth mindset activities. The control group was taught using traditional methods. The qualitative aspect of the study involved conducting focus group discussions with students in the experimental group. The results showed a significant improvement in motivation and chemistry achievement among the students in the experimental group, as evidenced by the higher mean scores from the pre-tests and the post-tests compared to the control group. The present research findings reveal that digital growth mindset interventions, when appropriately incorporated into chemistry curricula, possess the capacity to not only improve student engagement and subsequent performance but also to provide educators with valuable insights into instructional practices that are worth implementing in the digital era. [ABSTRACT FROM AUTHOR]

The target of this work is to present a multiplication-based iterative method for two Hermitian positive definite matrices to find the geometric mean. The method is constructed via the application of the matrix sign function. It is theoretically investigated that it has fourth order of convergence. The type of convergence is also discussed, which is global under an appropriate choice of the initial matrix. Numerical experiments are reported based on input matrices of different sizes as well as various stopping termination levels with comparisons to methods of the same nature and same number of matrix–matrix multiplications. The simulation results confirm the efficiency of the proposed scheme in contrast to its competitors of the same nature. [ABSTRACT FROM AUTHOR]

The brittleness index is one of the most integral parameters used in assessing rock bursts and catastrophic rock failures resulting from deep underground mining activities. Accurately predicting this parameter is crucial for effectively monitoring rock bursts, which can cause damage to miners and lead to the catastrophic failure of engineering structures. Therefore, developing a new brittleness index capable of effectively predicting rock bursts is essential for the safe and efficient execution of engineering projects. In this research study, a novel mathematical rock brittleness index is developed, utilizing factors such as crack initiation, crack damage, and peak stress for sandstones with varying water contents. Additionally, the brittleness index is compared with previous important brittleness indices (e.g., B1, B2, B3, and B4) predicted using infrared radiation (IR) characteristics, specifically the variance of infrared radiation temperature (VIRT), along with various artificial intelligent (AI) techniques such as k-nearest neighbor (KNN), extreme gradient boost (XGBoost), and random forest (RF), providing comprehensive insights for predicting rock bursts. The experimental and AI results revealed that: (1) crack initiation, elastic modulus, crack damage, and peak stress decrease with an increase in water content; (2) the brittleness indices such as B1, B3, and B4 show a positive linear exponential correlation, having a coefficient of determination of R2 = 0.88, while B2 shows a negative linear exponential correlation (R2 = 0.82) with water content. Furthermore, the proposed brittleness index shows a good linear correlation with B1, B3, and B4, with an R2 > 0.85, while it shows a poor negative linear correlation with B2, with an R2 = 0.61; (3) the RF model, developed for predicting the brittleness index, demonstrates superior performance when compared to other models, as indicated by the following performance parameters: R2 = 0.999, root mean square error (RMSE) = 0.383, mean square error (MSE) = 0.007, and mean absolute error (MAE) = 0.002. Consequently, RF stands as being recommended for accurate rock brittleness prediction. These research findings offer valuable insights and guidelines for effectively developing a brittleness index to assess the rock burst risks associated with rock engineering projects under water conditions. [ABSTRACT FROM AUTHOR]

Scouring around bridge abutments is a crucial and complex process that sometimes may lead to the failure of the bridge abutment. Therefore, in the present research, scouring around bridge abutments under clear water conditions was examined without and with countermeasures for providing an economical solution. A total of forty-five experiments were performed under clear water conditions to find the maximum scour depth around the bridge abutment. Experiments were performed in two different phases. In the first phase, scour depth was investigated without any countermeasures. In the second phase, scour depth was investigated using marble and brick waste as a countermeasure. The results showed that the maximum scour depth around the bridge abutment (at a distance of 10 cm on the upstream side and 15 cm on the downstream side of the abutment) for the Froude's number of 0.22 was 0.137 m without any countermeasure. The scouring depth increased up to 40% with an increase in the Froude's number from 0.13 to 0.22. The maximum reduction of scour depth was observed to be 40% and 55% when brick and marble waste were used as a countermeasure, respectively, compared to without a countermeasure case. It was concluded that marble and brick waste not only reduced scour depth to a significant level but also provided an economical solution. [ABSTRACT FROM AUTHOR]

A comparative study on essential oils extracted from Mentha suaveolens Ehrh. from Italy is reported. Two extraction procedures were investigated: hydrodistillation and steam distillation, carried out as a continuous and fractionated procedure. Fresh and dried plant material from two harvests was used. The hydrodistillation method yielded a higher amount of essential oil. The dried plant was significantly richer in essential oil per kg of starting plant material. Gas chromatography-mass spectrometry analysis of 112 samples showed that the essential oils belong to the piperitenone oxide-rich chemotype. In addition, piperitenone, p-cymen-8-ol, and limonene were among the most abundant compounds in the different samples. A higher amount of piperitenone oxide was obtained by hydrodistillation, while steam distillation gave a higher percentage of piperitenone and limonene. The essential oils were characterized for their anti-Candida albicans activity; higher potency was observed for the samples rich in piperitenone oxide, with MIC values ranging from 0.39 to 0.78 mg·mL−1 (0.039% and 0.078% p/v). The results of this work provide a deep insight into the methodology of essential oil extraction and the associated chemical variability of M. suaveolens Ehrh. Some of the essential oils are potent against C. albicans and could be considered for potential use in therapy. [ABSTRACT FROM AUTHOR]

This paper reports the filamentation of hollow Gaussian beams of the first, second, third, and fourth orders during propagation in a cubic and quintic nonlinear medium. Due to spatial modulation instability, the hollow Gaussian beams split to form either co-centric circular filaments or ultrashort pulses. It is found that the properties of the nonlinear medium used for propagation have a strong influence on certain characteristics of the formed filaments, such as peak intensity and pulse width. This correlation between the system parameters of the medium and filament characteristics represents a method for calculating the system parameters of the medium. This investigation can be helpful in the development of a hollow Gaussian beam-based artificial intelligence system that can be used to measure the system parameters of the studied nonlinear medium. [ABSTRACT FROM AUTHOR]

The computation of the sign function of a matrix plays a crucial role in various mathematical applications. It provides a matrix-valued mapping that determines the sign of each eigenvalue of a nonsingular matrix. In this article, we present a novel iterative algorithm designed to efficiently calculate the sign of an invertible matrix, emphasizing the enlargement of attraction basins. The proposed solver exhibits convergence of order four, making it highly efficient for a wide range of matrices. Furthermore, the method demonstrates global convergence properties. We validate the theoretical outcomes through numerical experiments, which confirm the effectiveness and efficiency of our proposed algorithm. [ABSTRACT FROM AUTHOR]

One of the most common sustainable energy resources that contributes a significant portion of the energy produced from renewable resources is solar photovoltaic energy. The research presented in this paper examines the behaviour of a 150.7 kWp grid-connected PV energy generation system in either feeding electrical loads to a site (a public university, GCU Faisalabad) or feeding into the utility grid when the generation from the PV system is greater than the demand from the on-site load. PVSyst 7.4 software was used in the system simulation together with Meteonorm to produce and measure climatic information sets (solar irradiance, ambient temperature, and wind speed). The analysis of the simulated energy yields included determining the optimal energy generation photovoltaic array, the energy that is fed into the utility network, normalised energy generation per installed kWp, and performance ratio. The computed annual worldwide incident energy on the collector without optical adjustments was 1764.0 kWh/m2, and the annual effective global irradiance after optical losses was 1654.7 kWh/m2. With this irradiation, the solar (PV) array produced 218.12 MWh of DC energy annually, whereas 211.70 MWh of AC energy was injected into the national grid. The designed PV system collected 0.87 kilowatt-hour/kWp/day, with system losses of 0.120 kilowatt-hour /kWp/day and 3.85 kilowatt-hour /kWp/day of produced useful energy. The measured average yearly performance ratio (PR) was 79.64%. In the month of January, the highest PR value of 85.4% was achieved. [ABSTRACT FROM AUTHOR]

In this work, by considering spatial uniform meshes and stencils having five adjacent discretization nodes, we furnish a numerical scheme to solve the time-fractional Black–Scholes (partial differential equation) PDE to price financial options under the generalized multiquadric radial basis function (RBF). The time-fractional derivative is estimated by an L1-scheme but the spatial variable is discretized using fourth-order RBF-FD methodology. As a matter of fact, the PDE problem is transformed in the form of a linear set of algebraic equations. To support analytical discussions, numerical tests are furnished and reveal the efficacy of the presented solver. [ABSTRACT FROM AUTHOR]

Rock strength, specifically the uniaxial compressive strength (UCS), is a critical parameter mostly used in the effective and sustainable design of tunnels and other engineering structures. This parameter is determined using direct and indirect methods. The direct methods involve acquiring an NX core sample and using sophisticated laboratory procedures to determine UCS. However, the direct methods are time-consuming, expensive, and can yield uncertain results due to the presence of any flaws or discontinuities in the core sample. Therefore, most researchers prefer indirect methods for predicting rock strength. In this study, UCS was predicted using seven different artificial intelligence techniques: Artificial Neural Networks (ANNs), XG Boost Algorithm, Random Forest (RF), Support Vector Machine (SVM), Elastic Net (EN), Lasso, and Ridge models. The input variables used for rock strength prediction were moisture content (MC), P-waves, and rebound number (R). Four performance indicators were used to assess the efficacy of the models: coefficient of determination (R2), Root Mean Square Error (RMSE), Mean Square Error (MSE), and Mean Absolute Error (MAE). The results show that the ANN model had the best performance indicators, with values of 0.9995, 0.2634, 0.0694, and 0.1642 for R2, RMSE, MSE, and MAE, respectively. However, the XG Boost algorithm model performance was also excellent and comparable to the ANN model. Therefore, these two models were proposed for predicting UCS effectively. The outcomes of this research provide a theoretical foundation for field professionals in predicting the strength parameters of rock for the effective and sustainable design of engineering structures [ABSTRACT FROM AUTHOR]

This work is concerned with the computational solution of the time-dependent 3D parabolic Heston–Cox–Ingersoll–Ross (HCIR) PDE, which is of practical importance in mathematical finance. The HCIR dynamic states that the model follows randomness for the underlying asset, the volatility and the rate of interest. Since the PDE formulation has degeneracy and non-smoothness at some area of its domain, we design a new numerical solver via semi-discretization and the radial basis function–finite difference (RBF-FD) scheme. Our scheme is built on graded meshes so as to employ the lowest possible number of discretized nodes. The stability of our solver is proven analytically. Computational testing is conducted to uphold the analytical findings in practice. [ABSTRACT FROM AUTHOR]

In this work, we propose a fast scheme based on higher order discretizations on graded meshes for resolving the temporal-fractional partial differential equation (PDE), which benefits the memory feature of fractional calculus. To avoid excessively increasing the number of discretization points, such as the standard finite difference or meshfree methods, and, at the same time, to increase the efficiency of the solver, we employ discretizations on spatially non-uniform meshes with an attention on the non-smoothness area of the underlying asset. Therefore, the PDE problem is transformed to a linear system of algebraic equations. We perform numerical simulations to observe and check the behavior of the presented scheme in contrast to the existing methods. [ABSTRACT FROM AUTHOR]

The key to developing highly active α-Fe2O3-based photocatalysts is to improve the charge separation and efficiently utilize the electrons with sufficient thermodynamic energy. Herein, α-Fe2O3 nanosheets (FO) were synthesized using a metal-ion-intervened hydrothermal method and then coupled with SnO2 nanosheets (SO) to obtain SO/FO nanocomposites. Subsequently, nanosized Ag was selectively loaded on SO using the photo-deposition method to result in the ternary Ag-SO/FO nanocomposites. The optimal nanocomposite could realize the efficient aerobic degradation of 2,4-dichlorophenol as a representative organic pollutant under visible-light irradiation (>420 nm), exhibiting nearly six-fold degradation rates of that for FO. Additionally, the Ag-SO/FO photocatalyst is also applicable to the visible-light degradation of other organic pollutants and even CO2 reduction. By using steady-state surface photovoltage spectroscopy, fluorescence spectroscopy, and electrochemical methods, the photoactivity enhancement of Ag-SO/FO is principally attributed to the improved charge separation by introducing SO as an electron platform for the high-energy-level electrons of FO. Moreover, nanosized Ag on SO functions as a cocatalyst to further improve the charge separation and facilitate the catalytic reduction. This work provides a feasible design strategy for narrow-bandgap semiconductor-based photocatalysts by combining an electron platform and a cocatalyst. [ABSTRACT FROM AUTHOR]

This paper proposes a computational solver via the localized radial basis function finite difference (RBF-FD) scheme and the use of graded meshes for solving the time-dependent Bates partial integro-differential equation (PIDE) arising in computational finance. In order to avoid facing a large system of discretization systems, we employ graded meshes along both of the spatial variables, which results in constructing a set of ordinary differential equations (ODEs) of lower sizes. Moreover, an explicit time integrator is used because it can bypass the need to solve the large discretized linear systems in each time level. The stability of the numerical method is discussed in detail based on the eigenvalues of the system matrix. Finally, numerical tests revealed the accuracy and reliability of the presented solver. [ABSTRACT FROM AUTHOR]

The Heston–Hull–White three-dimensional time-dependent partial differential equation (PDE) is one of the important models in mathematical finance, at which not only the volatility is modeled based on a stochastic process but also the rate of interest is assumed to follow a stochastic dynamic. Hence, an efficient method is derived in this paper based on the methodology of the localized radial basis function generated finite difference (RBF-FD) scheme. The proposed solver uses the RBF-FD approximations on graded meshes along all three spatial variables and a high order time-stepping scheme. Stability is also studied in detail to show under what conditions the proposed method is stable. Computational simulations are given to support the theoretical discussions. [ABSTRACT FROM AUTHOR]

Increasing heat transfer is an important part of industrial, mechanical, electrical, thermal, and biological sciences. The aim of this study is to increase the thermal competency of a conventional fluid by using a ternary hybrid nanofluid. A magnetic field and thermal radiation are used to further improve the thermal conductivity of the base fluid. Irreversibility is analyzed under the influence of the embedded parameters. The basic equations for the ternary hybrid nanofluids are transformed from Partial Differential Equations (PDEs) to Ordinary Differential Equations (ODEs) using the similarity concept. The Marangoni convection idea is used in the mathematical model for the temperature difference between the two media: the surface and fluid. The achieved results are provided and discussed. The results show that ternary hybrid nanofluids are more suitable as heat-transmitted conductors than conventional fluids. [ABSTRACT FROM AUTHOR]

Investigating the fractal behavior of iteration methods on special polynomials can help to find iterative methods with global convergence for finding special matrix functions. By employing such a methodology, we propose a new solver for the sign of an invertible square matrix. The presented method achieves the fourth rate of convergence by using as few matrix products as possible. Its attraction basin shows larger convergence radii, in contrast to its Padé-type methods of the same order. Computational tests are performed to check the efficacy of the proposed solver. [ABSTRACT FROM AUTHOR]

West Nile virus (WNV) is an emerging neurotropic RNA virus and a member of the genus Flavivirus. Naturally, the virus is maintained in an enzootic cycle involving mosquitoes as vectors and birds that are the principal amplifying virus hosts. In humans, the incubation period for WNV disease ranges from 3 to 14 days, with an estimated 80% of infected persons being asymptomatic, around 19% developing a mild febrile infection and less than 1% developing neuroinvasive disease. Laboratory diagnosis of WNV infection is generally accomplished by cross-reacting serological methods or highly sensitive yet expensive molecular approaches. Therefore, current diagnostic tools hinder widespread surveillance of WNV in birds and mosquitoes that serve as viral reservoirs for infecting secondary hosts, such as humans and equines. We have developed a synthetic biology-based method for sensitive and low-cost detection of WNV. This method relies on toehold riboswitches designed to detect WNV genomic RNA as transcriptional input and process it to GFP fluorescence as translational output. Our methodology offers a non-invasive tool with reduced operating cost and high diagnostic value that can be used for field surveillance of WNV in humans as well as in bird and mosquito populations. [ABSTRACT FROM AUTHOR]

It is significant to monitor the leakage at the joints of the diaphragm walls of subway station foundation pits to check the weak links in the waterproof quality of the diaphragm wall structure. It is essential to take effective waterproof measurements timely to improve the overall waterproof quality of the diaphragm wall in the foundation pit to prevent accidents and reduce the operation and maintenance costs. This paper used ground penetrating radar (GPR) to detect the Lishan North Road Station section of Jinan Rail Transit Line R2 during construction. The abnormal waveform image is obtained after processing radar detection data with Reflexw software. This abnormal waveform image is used to identify the abnormal area. In order to accurately predict the location of leakage at the joint of diaphragm wall, MATLAB is used to calculate the average wave velocity amplitude and single channel signal of the electromagnetic wave velocity of geological radar at different mileages and draw the trend chart of average wave velocity amplitude with mileage and the corresponding relationship curve of electromagnetic wave amplitude and depth of radar. It is proposed that sudden changes in the area of the average wave velocity amplitude cause a change in the trend chart. Furthermore, the radar electromagnetic wave velocity amplitude curve is taken as the area where seepage may occur at the joints of the diaphragm wall, so as to determine the corresponding mileage and depth of the leakage area. On this basis, the grey correlation analysis for the analysis of the source of the water leakage at the joints of the diaphragm wall of the subway foundation pit is proposed. The research results show that the leakage water at the joints of the diaphragm wall of the subway foundation pit is not connected to the rivers around the foundation pit, which confirms that the construction of the subway station has not affected the groundwater resources around the station. The proposed approach has successfully predicted the location of the foundation pit leakage disaster and has been verified on the project site. The research results provide a reference for the monitoring and early warning of leakage at the joints of diaphragm walls in foundation pits with similar geological conditions. [ABSTRACT FROM AUTHOR]

The rock mass deformation modulus (Em) is an essential input parameter in numerical modeling for assessing the rock mass behavior required for the sustainable design of engineering structures. The in situ methods for determining this parameter are costly and time consuming. Their results may not be reliable due to the presence of various natures of joints and following difficult field testing procedures. Therefore, it is imperative to predict the rock mass deformation modulus using alternate methods. In this research, four different predictive models were developed, i.e., one statistical model (Muti Linear Regression (MLR)) and three Artificial Intelligence models (Artificial Neural Network (ANN), Random Forest Regression (RFR), and K-Neighbor Network (KNN)) by employing Rock Mass Rating (RMR89) and Point load index (I50) as appropriate input variables selected through correlation matrix analysis among eight different variables to propose an appropriate model for the prediction of Em. The efficacy of each predictive model was evaluated by using four different performance indicators: performance coefficient R2, Mean Absolute Error (MAE), Mean Squared Error (MSE), and Median Absolute Error (MEAE). The results show that the R2, MAE, MSE, and MEAE for the ANN model are 0.999, 0.2343, 0.2873, and 0.0814, respectively, which are better than MLR, KNN, and RFR. Therefore, the ANN model is proposed as the most appropriate model for the prediction of Em. The findings of this research will provide a better understanding and foundation for the professionals working in fields during the prediction of various engineering parameters, especially Em for sustainable engineering design in the rock engineering field. [ABSTRACT FROM AUTHOR]

The response of different plant growth regulators on callus induction and regeneration on three sugarcane genotypes (YT-53, CP-77-400, and NSG-59) was evaluated. Different concentrations of 2,4-D alone and in combination with other plant growth regulators (Kinetin and BAP) were used for callus induction. Kinetin along with IBA, BAP and NAA were analyzed with respect to shoot induction, while NAA and IBA were used for root induction. The best callus response in terms of number of days, callus fresh weight, and frequency in YT-53 was observed on MS media provided with 2,4-D (3 mg L−1) + Kinetin (0.5 mg L−1), while in NSG-59 the best response was on MS+2,4-D (4 mg L−1) + Kinetin (0.5 mg L−1), and in CP-77400, MS+2,4-D (5 mg L−1). For shoot induction, 2 mg L−1 Kinetin was found to be the best for YT-53 and NSG-59, while 1 mg L−1 BAP was found to be the best for CP-77-400 in terms of number of days, shoot numbers, and shoot length. The best media for root induction in terms of number of days, root numbers, and root length was 1 mg L−1 NAA + 1 mg L−1 IBA for YT-53, while this was 3 mg L−1 NAA for NSG-59. The highest root frequency and maximum root length in the minimum number of days was observed in CP-77-400 on MS media provided with 2 mg L−1 NAA. [ABSTRACT FROM AUTHOR]

Thermal treatment followed by subsequent cooling conditions (slow and rapid) can induce damage to the rock surface and internal structure, which may lead to the instability and failure of the rock. The extent of the damage is measured by the damage factor (DT), which can be quantified in a laboratory by evaluating the changes in porosity, elastic modulus, ultrasonic velocities, acoustic emission signals, etc. However, the execution process for quantifying the damage factor necessitates laborious procedures and sophisticated equipment, which are time-consuming, costly, and may require technical expertise. Therefore, it is essential to quantify the extent of damage to the rock via alternate computer simulations. In this research, a new predictive model is proposed to quantify the damage factor. Three predictive models for quantifying the damage factors were developed based on multilinear regression (MLR), artificial neural networks (ANNs), and the adoptive neural-fuzzy inference system (ANFIS). The temperature (T), porosity (ρ), density (D), and P-waves were used as input variables in the development of predictive models for the damage factor. The performance of each predictive model was evaluated by the coefficient of determination (R2), the A20 index, the mean absolute percentage error (MAPE), the root mean square error (RMSE), and the variance accounted for (VAF). The comparative analysis of predictive models revealed that ANN models used for predicting the rock damage factor based on porosity in slow conditions give an R2 of 0.99, A20 index of 0.99, RMSE of 0.01, MAPE of 0.14, and a VAF of 100%, while rapid cooling gives an R2 of 0.99, A20 index of 0.99, RMSE of 0.02, MAPE of 0.36%, and a VAF of 99.99%. It has been proposed that an ANN-based predictive model is the most efficient model for quantifying the rock damage factor based on porosity compared to other models. The findings of this study will facilitate the rapid quantification of damage factors induced by thermal treatment and cooling conditions for effective and successful engineering project execution in high-temperature rock mechanics environments. [ABSTRACT FROM AUTHOR]

Uniaxial compressive strength (UCS) and the static Young's modulus (Es) are fundamental parameters for the effective design of engineering structures in a rock mass environment. Determining these two parameters in the laboratory is time-consuming and costly, and the results may be inappropriate if the testing process is not properly executed. Therefore, most researchers prefer alternative methods to estimate these two parameters. This work evaluates the thermal effect on the physical, chemical, and mechanical properties of marble rock, and proposes a prediction model for UCS and ES using multi-linear regression (MLR), artificial neural networks (ANNs), random forest (RF), and k-nearest neighbor. The temperature (T), P-wave velocity (PV), porosity (η), density (ρ), and dynamic Young's modulus (Ed) were taken as input variables for the development of predictive models based on MLR, ANN, RF, and KNN. Moreover, the performance of the developed models was evaluated using the coefficient of determination (R2) and mean square error (MSE). The thermal effect results unveiled that, with increasing temperature, the UCS, ES, PV, and density decrease while the porosity increases. Furthermore, ES and UCS prediction models have an R2 of 0.81 and 0.90 for MLR, respectively, and 0.85 and 0.95 for ANNs, respectively, while KNN and RF have given the R2 value of 0.94 and 0.97 for both ES and UCS. It is observed from the statistical analysis that P-waves and temperature show a strong correlation under the thermal effect in the prediction model of UCS and ES. Based on predictive performance, the RF model is proposed as the best model for predicting UCS and ES under thermal conditions. [ABSTRACT FROM AUTHOR]

This correction notice addresses an omission in a previously published article on a transfer learning-based model for diagnosing diabetic retinopathy using retinal images. The correction adds a reference to the introduction section, providing information on diabetic retinopathy and its association with blindness. The correction also includes a description of the different categories of diabetic retinopathy and provides a reference for further reading. The scientific conclusions of the original publication remain unaffected. [Extracted from the article]

In this paper, we first derive a family of iterative schemes with fourth order. A weight function is used to maintain its optimality. Then, we transform it into methods with several self-accelerating parameters to reach the highest possible convergence rate 8. For this aim, we employ the property of the eigenvalues of the matrices and the technique with memory. Solving several nonlinear test equations shows that the proposed variants have a computational efficiency index of two (maximum amount possible) in practice. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is a visual obstacle caused by diabetic disease, which forms because of long-standing diabetes mellitus, which damages the retinal blood vessels. This disease is considered one of the principal causes of sightlessness and accounts for more than 158 million cases all over the world. Since early detection and classification could diminish the visual impairment, it is significant to develop an automated DR diagnosis method. Although deep learning models provide automatic feature extraction and classification, training such models from scratch requires a larger annotated dataset. The availability of annotated training datasets is considered a core issue for implementing deep learning in the classification of medical images. The models based on transfer learning are widely adopted by the researchers to overcome annotated data insufficiency problems and computational overhead. In the proposed study, features are extracted from fundus images using the pre-trained network VGGNet and combined with the concept of transfer learning to improve classification performance. To deal with data insufficiency and unbalancing problems, we employed various data augmentation operations differently on each grade of DR. The results of the experiment indicate that the proposed framework (which is evaluated on the benchmark dataset) outperformed advanced methods in terms of accurateness. Our technique, in combination with handcrafted features, could be used to improve classification accuracy. [ABSTRACT FROM AUTHOR]

The spread of infectious diseases such as COVID-19, flu influenza, malaria, dengue, mumps, and rubella in a population is a big threat to public health. The infectious diseases spread from one person to another person through close contact. Without proper planning, an infectious disease can become an epidemic and can result in large human and financial losses. To better respond to the spread of infectious disease and take measures for its control, the public health authorities need models and simulations to study the spread of such diseases. In this paper, an agent-based simulation engine is presented that models the spread of infectious diseases in the population. The simulation takes as an input the human-to-human interactions, population dynamics, disease transmissibility and disease states and shows the spread of disease over time. The simulation engine supports non-pharmaceutical interventions and shows its impact on the disease spread across locations. A unique feature of this tool is that it is generic; therefore, it can simulate a wide variety of infectious disease models (SIR), susceptible-infectious-susceptible (SIS) and susceptible-infectious (SI). The proposed simulation engine will help the policy-makers and public health authorities study the behavior of disease spreading; thus, allowing for better planning. [ABSTRACT FROM AUTHOR]

The objective of the study was to enhance our knowledge on institutional bottlenecks for financial development, financial inclusion, and microfinance, using Mauritania as a case study. We used a mixed-methods' methodology that combines analysis of secondary data and an expert interview. First, a logit model with dummy independent variables was used to investigate the factors that impact the households' access to credit, the main advantage of this model being to avoid confounding effects by analyzing the association of all variables together. Our study found that access to financial services is equal in Mauritania between men and women, but that access to credit is higher for public sector employees, educated people, and households with smaller families. Second, using principal components' analysis, we found that the different regions of Mauritania can be divided based on unemployment, income, literacy, financial inclusion, and population density into two main dimensions, yielding four quadrants: Attractive, industrious, moderate, and resource cursed. We expected that sparsely populated countries would have less access to credit. Counterintuitively, we found that within a low-density country, people in the lowest-density regions have higher odds of getting credit. Third, based on an interview with an expert, we noted the key challenges that microfinance is facing in Mauritania and provided recommendations to overcome these. As in most case studies, external validity was limited. [ABSTRACT FROM AUTHOR]

Entropy generation plays a significant role in several complex processes, extending from cosmology to biology. The entropy generation minimization procedure can be applied for the optimization of mechanical systems including heat exchangers, elements of nuclear and thermal power plants, ventilation and air-conditioning systems. In order to present our analysis, entropy generation in a thin film flow of second grade nanofluid holding single-walled carbon nanotubes (SWCNTs) and multi-walled carbon nanotubes (MWCNTs) with a Cattaneo–Christov heat flux model is studied in this article. The flow is considered passing a linearly extending surface. A variable magnetic field with aligned angle ε is functioned along the extending sheet. With the aid of the homotopy analysis method (HAM), the fluid flow model is elucidated. The impressions of embedded factors on the flow are obtainable through figures and discussed in detail. It is observed that the velocity profile escalated with the increasing values of volume fraction of nanoparticles and second grade fluid parameter. The higher values of volume fraction of nanoparticles, second grade fluid parameter, non-linear heat source/sink, and thermal radiation parameter intensified the temperature profile. Surface drag force escalated with heightening values of nanoparticles volume fraction, unsteadiness, film thickness, magnetic, and second grade fluid parameters. Entropy generation increased with enhancing values of magnetic parameter, Brinkman number, and Reynolds number. [ABSTRACT FROM AUTHOR]

A modification to an existing iterative method for computing zeros with unknown multiplicities of nonlinear equations or a system of nonlinear equations is presented. We introduce preconditioners to nonlinear equations or a system of nonlinear equations and their corresponding Jacobians. The inclusion of preconditioners provides numerical stability and accuracy. The different selection of preconditioner offers a family of iterative methods. We modified an existing method in a way that we do not alter its inherited quadratic convergence. Numerical simulations confirm the quadratic convergence of the preconditioned iterative method. The influence of preconditioners is clearly reflected in the numerically achieved accuracy of computed solutions. [ABSTRACT FROM AUTHOR]

A homotopy method is presented for the construction of frozen Jacobian iterative methods. The frozen Jacobian iterative methods are attractive because the inversion of the Jacobian is performed in terms of LUfactorization only once, for a single instance of the iterative method. We embedded parameters in the iterative methods with the help of the homotopy method: the values of the parameters are determined in such a way that a better convergence rate is achieved. The proposed homotopy technique is general and has the ability to construct different families of iterative methods, for solving weakly nonlinear systems of equations. Further iterative methods are also proposed for solving general systems of nonlinear equations. [ABSTRACT FROM AUTHOR]

The singularity of Jacobian happens when we are looking for a root, with multiplicity greater than one, of a system of nonlinear equations. The purpose of this article is two-fold. Firstly, we will present a modification of an existing method that computes roots with known multiplicities. Secondly, will propose the generalization of a family of methods for solving nonlinear equations with unknown multiplicities, to the system of nonlinear equations. The inclusion of a nonzero multi-variable auxiliary function is the key idea. Different choices of the auxiliary function give different families of the iterative method to find roots with unknown multiplicities. Few illustrative numerical experiments and a critical discussion end the paper. [ABSTRACT FROM AUTHOR]

The assessment of moisture loss from meat during the aging period is a critical issue for the meat industry. In this article, a non-invasive microwave ring-resonator sensor is presented to evaluate the moisture content, or more precisely water holding capacity (WHC) of broiler meat over a four-week period. The developed sensor has shown significant changes in its resonance frequency and return loss due to reduction in WHC in the studied duration. The obtained results are also confirmed by physical measurements. Further, these results are evaluated using the Fricke model, which provides a good fit for electric circuit components in biological tissue. Significant changes were observed in membrane integrity, where the corresponding capacitance decreases 30% in the early aging (0D-7D) period. Similarly, the losses associated with intracellular and extracellular fluids exhibit changed up to 42% and 53%, respectively. Ultimately, empirical polynomial models are developed to predict the electrical component values for a better understanding of aging effects. The measured and calculated values are found to be in good agreement. [ABSTRACT FROM AUTHOR]

Herpes simplex virus type 2 (HSV-2), a globally sexually transmitted virus, and also one of the main causes of genital ulcer diseases, increases susceptibility to HIV-1. Effective vaccines to prevent HSV-2 infection are not yet available, but are currently being developed. To facilitate this process, the latest progress in development of these vaccines is reviewed in this paper. A summary of the most promising HSV-2 vaccines tested in animals in the last five years is presented, including the main factors, and new ideas for developing an effective vaccine from animal experiments and human clinical trials. Experimental results indicate that future HSV-2 vaccines may depend on a strategy that targets mucosal immunity. Furthermore, estradiol, which increases the effectiveness of vaccines, may be considered as an adjuvant. Therefore, this review is expected to provide possible strategies for development of future HSV-2 vaccines. [ABSTRACT FROM AUTHOR]

The goal of this article is to investigate a new solver in the form of an iterative method to solve X + A ∗ X − 1 A = I as an important nonlinear matrix equation (NME), where A , X , I are appropriate matrices. The minimal and maximal solutions of this NME are discussed as Hermitian positive definite (HPD) matrices. The convergence of the scheme is given. Several numerical tests are also provided to support the theoretical discussions. [ABSTRACT FROM AUTHOR]

Nowadays, the problem of solar thermal absorption plays a vital role in energy storage in power plants, but within this phenomenon solar systems have a big challenge in storing and regulating energies at extreme temperatures. The solar energy absorber based on hybrid nanofluids tends to store thermal energy, and the hybrid nanofluids involve the stable scattering of different nano dimension particles in the conventional solvent at a suitable proportion to gain the desired thermophysical constraints. The authors focus on the behavior of the inclined plate absorber panel as the basic solution of water is replaced by a hybrid nanofluid, including C u (Copper) and A l 2 O 3 (Aluminum Oxide), and water is utilized as a base surfactant in the current investigation. The inclined panel is integrated into a porous surface with the presence of solar radiations, Joule heating, and heat absorption. The fundamental equations of the flow and energy model are addressed with the similarity transformations. The homotopy analysis method (HAM) via Mathematica software is used to explore the solution to this problem. Furthermore, the important physical characteristics of the rate of heat transfer, omission and absorption of solar radiation, and its impact on the solar plant are observed. [ABSTRACT FROM AUTHOR]

Graphene as a material for optoelectronic design applications has been significantly restricted owing to zero bandgap and non-compatible handling procedures compared with regular microelectronic ones. In this work, nitrogen-doped reduced graphene oxide (N-rGO) with tunable optical bandgap and enhanced electrical conductivity was synthesized via a microwave-assisted hydrothermal method. The properties of the synthesized N-rGO were determined using XPS, FTIR and Raman spectroscopy, UV/vis, as well as FESEM techniques. The UV/vis spectroscopic analysis confirmed the narrowness of the optical bandgap from 3.4 to 3.1, 2.5, and 2.2 eV in N-rGO samples, where N-rGO samples were synthesized with a nitrogen doping concentration of 2.80, 4.53, and 5.51 at.%. Besides, an enhanced n-type electrical conductivity in N-rGO was observed in Hall effect measurement. The observed tunable optoelectrical characteristics of N-rGO make it a suitable material for developing future optoelectronic devices at the nanoscale. [ABSTRACT FROM AUTHOR]

Over the past decade, many projects have been initiated worldwide to decipher the composition and function of the soil microbiome, including the African Soil Microbiome (AfSM) project that aims at providing new insights into the presence and distribution of key groups of soil bacteria from across the African continent. In this national study, carried out under the auspices of the AfSM project, we assessed the taxonomy, diversity and distribution of rhizobial genera in soils from the tropical savannah zones in Northern Côte d'Ivoire. Genomic DNA extracted from seven sampled soils was analyzed by sequencing the V4-V5 variable region of the 16S rDNA using Illumina's MiSeq platform. Subsequent bioinformatic and phylogenetic analyses showed that these soils harbored 12 out of 18 genera of Proteobacteria harboring rhizobia species validly published to date and revealed for the first time that the Bradyrhizobium genus dominates in tropical savannah soils, together with Microvirga and Paraburkholderia. In silico comparisons of different 16S rRNA gene variable regions suggested that the V5-V7 region could be suitable for differentiating rhizobia at the genus level, possibly replacing the use of the V4-V5 region. These data could serve as indicators for future rhizobial microbiome explorations and for land-use decision-making. [ABSTRACT FROM AUTHOR]

Women undergoing a radical cystectomy (RC) followed by a urinary diversion (UD) for bladder cancer (BC), experience a substantial reduction in health-related quality of life (HRQOL). At present, studies comparing long-term QOL outcomes for different UD methods in female patients are lacking. We summarize the current state of the QoL assessment in female patients after an RC. [ABSTRACT FROM AUTHOR]

Clay minerals are one of the most utilized minerals among non-metals. These are hydrous aluminum silicates with a layer (sheet-like) structure. Kaolin is a hydrous aluminosilicate mineral with a thin platelet structure. Kaolin is extensively used in paper, paint, and many other industries. Wet processing of kaolin will not be sustainable over the long term because global freshwater resources are becoming scarce. Hence, a process is necessary that does not consume water during the beneficiation of kaolin. This study developed a dry beneficiation process for low-grade kaolin of 59.6%, with 12% quartz and about 6% titaniferous impurities from Nagar Parkar, Sindh province, Pakistan. To develop a size difference between kaolinite and impurities, steel balls clad with rubber were used as the grinding media in a selective grinding unit. Screens of 60 and 400 mesh were employed to classify the feed of air classifier. Oversize +60 mesh was reground, 400 to 60 mesh fractions were sent to an air classifier, and −400 mesh was considered to be a product with the grade and recovery of 90.6% and 20.5%, respectively. Air classifier experiments were designed using central composite design. An experiment using a fan speed of 1200 revolutions per minute (rpm) and a shutter opening of 4.0 showed optimum results, with maximum kaolinite grade and recovery of 91.5% and 35.9%, respectively. The statistical models developed for grade and recovery predicted the optimum results at a fan speed of 1251 rpm and shutter opening of 3.3 with the maximum kaolinite grade and recovery of 91.1% and 24.7%, respectively. The differences between experimental and predicted grade and recovery were 0.1% and 2.4%, respectively. The characterization results showed the total upgrade of kaolin from 59.6% to 91.2%, with 27.1% recovery during the process. The designed methodology has the potential to improve the yield of the product by focusing on its recovery. Furthermore, the designed process can be improved by using different sized balls in the selective grinding unit. This beneficiation process can utilize more than one air classifier in series to achieve the targeted results. [ABSTRACT FROM AUTHOR]

Human action recognition has emerged as a challenging research domain for video understanding and analysis. Subsequently, extensive research has been conducted to achieve the improved performance for recognition of human actions. Human activity recognition has various real time applications, such as patient monitoring in which patients are being monitored among a group of normal people and then identified based on their abnormal activities. Our goal is to render a multi class abnormal action detection in individuals as well as in groups from video sequences to differentiate multiple abnormal human actions. In this paper, You Look only Once (YOLO) network is utilized as a backbone CNN model. For training the CNN model, we constructed a large dataset of patient videos by labeling each frame with a set of patient actions and the patient's positions. We retrained the back-bone CNN model with 23,040 labeled images of patient's actions for 32 epochs. Across each frame, the proposed model allocated a unique confidence score and action label for video sequences by finding the recurrent action label. The present study shows that the accuracy of abnormal action recognition is 96.8%. Our proposed approach differentiated abnormal actions with improved F1-Score of 89.2% which is higher than state-of-the-art techniques. The results indicate that the proposed framework can be beneficial to hospitals and elder care homes for patient monitoring. [ABSTRACT FROM AUTHOR]

In recent years, the field of nanophotonics has progressively developed. However, constant demand for the development of new light source still exists at the nanometric scale. Light emissions from graphene-based active materials can provide a leading platform for the development of two dimensional (2-D), flexible, thin, and robust light-emitting sources. The exceptional structure of Dirac's electrons in graphene, massless fermions, and the linear dispersion relationship with ultra-wideband plasmon and tunable surface polarities allows numerous applications in optoelectronics and plasmonics. In this article, we present a comprehensive review of recent developments in graphene-based light-emitting devices. Light emissions from graphene-based devices have been evaluated with different aspects, such as thermal emission, electroluminescence, and plasmons assisted emission. Theoretical investigations, along with experimental demonstration in the development of graphene-based light-emitting devices, have also been reviewed and discussed. Moreover, the graphene-based light-emitting devices are also addressed from the perspective of future applications, such as optical modulators, optical interconnects, and optical sensing. Finally, this review provides a comprehensive discussion on current technological issues and challenges related to the potential applications of emerging graphene-based light-emitting devices. [ABSTRACT FROM AUTHOR]

Graphene and its hybrids are being employed as potential materials in light-sensing devices due to their high optical and electronic properties. However, the absence of a bandgap in graphene limits the realization of devices with high performance. In this work, a boron-doped reduced graphene oxide (B-rGO) is proposed to overcome the above problems. Boron doping enhances the conductivity of graphene oxide and creates several defect sites during the reduction process, which can play a vital role in achieving high-sensing performance of light-sensing devices. Initially, the B-rGO is synthesized using a modified microwave-assisted hydrothermal method and later analyzed using standard FESEM, FTIR, XPS, Raman, and XRD techniques. The content of boron in doped rGO was found to be 6.51 at.%. The B-rGO showed a tunable optical bandgap from 2.91 to 3.05 eV in the visible spectrum with an electrical conductivity of 0.816 S/cm. The optical constants obtained from UV-Vis absorption spectra suggested an enhanced surface plasmon resonance (SPR) response for B-rGO in the theoretical study, which was further verified by experimental investigations. The B-rGO with tunable bandgap and enhanced SPR could open up the solution for future high-performance optoelectronic and sensing applications. [ABSTRACT FROM AUTHOR]

We develop a sixth order Steffensen-type method with one parameter in order to solve systems of equations. Our study's novelty lies in the fact that two types of local convergence are established under weak conditions, including computable error bounds and uniqueness of the results. The performance of our methods is discussed and compared to other schemes using similar information. Finally, very large systems of equations ( 100 × 100 and 200 × 200 ) are solved in order to test the theoretical results and compare them favorably to earlier works. [ABSTRACT FROM AUTHOR]