Search

BackgroundMalaria impacts nearly 250 million individuals annually. Specifically, Uganda has one of the highest burdens, with 13 million cases and nearly 20,000 deaths. Controlling the spread of malaria relies on vector surveillance, a system where collected mosquitos are analyzed for vector species’ density in rural areas to plan interventions accordingly. However, this relies on trained entomologists known as vector control officers (VCOs) who identify species via microscopy. The global shortage of entomologists and this time-intensive process cause significant reporting delays. VectorCam is a low-cost artificial intelligence–based tool that identifies a mosquito’s species, sex, and abdomen status with a picture and sends these results electronically from surveillance sites to decision makers, thereby deskilling the process to village health teams (VHTs). ObjectiveThis study evaluates the usability of the VectorCam system among VHTs by assessing its efficiency, effectiveness, and satisfaction. MethodsThe VectorCam system has imaging hardware and a phone app designed to identify mosquito species. Two users are needed: (1) an imager to capture images of mosquitos using the app and (2) a loader to load and unload mosquitos from the hardware. Critical success tasks for both roles were identified, which VCOs used to train and certify VHTs. In the first testing phase (phase 1), a VCO and a VHT were paired to assume the role of an imager or a loader. Afterward, they swapped. In phase 2, two VHTs were paired, mimicking real use. The time taken to image each mosquito, critical errors, and System Usability Scale (SUS) scores were recorded for each participant. ResultsOverall, 14 male and 6 female VHT members aged 20 to 70 years were recruited, of which 12 (60%) participants had smartphone use experience. The average throughput values for phases 1 and 2 for the imager were 70 (SD 30.3) seconds and 56.1 (SD 22.9) seconds per mosquito, respectively, indicating a decrease in the length of time for imaging a tray of mosquitos. The loader’s average throughput values for phases 1 and 2 were 50.0 and 55.7 seconds per mosquito, respectively, indicating a slight increase in time. In terms of effectiveness, the imager had 8% (6/80) critical errors and the loader had 13% (10/80) critical errors in phase 1. In phase 2, the imager (for VHT pairs) had 14% (11/80) critical errors and the loader (for VHT pairs) had 12% (19/160) critical errors. The average SUS score of the system was 70.25, indicating positive usability. A Kruskal-Wallis analysis demonstrated no significant difference in SUS (H value) scores between genders or users with and without smartphone use experience. ConclusionsVectorCam is a usable system for deskilling the in-field identification of mosquito specimens in rural Uganda. Upcoming design updates will address the concerns of users and observers.

In this study, we examined the impact of carbon nanotube (CNT) concentration on the mechanical properties of epoxy/CNT composites under acid exposure. Samples with varying CNT concentrations (0% to 5%) were fabricated and characterized using dynamic mechanical analysis (DMA) and nanoindentation. Beyond the percolation threshold, the composites experienced decreased bulk mechanical properties due to CNT agglomeration. Acid exposure for one week and one month revealed a gradient of properties from the sample’s skin to its core. Overall, the composites exhibited modified physical properties, with degradation influenced by the CNT concentration. Higher concentrations acted as barriers but also created pathways for acid diffusion through pores surrounding CNT agglomerates. The agreement between nanoindentation and vector network analyzer (VNA) measurements further supported our findings. This convergence of mechanical and electromagnetic characterization techniques holds promise for wireless structural health monitoring (SHM) applications. Our study enhances the understanding of epoxy/CNT composites for SHM applications. The relationship between CNT concentration, acid exposure, and mechanical properties guides material selection and the development of real-time damage-detection techniques. Integrating multiple measurement techniques, as demonstrated by the agreement between nanoindentation and VNA data, provides a comprehensive understanding of structural behavior, improving SHM practices.

The common use of an artificial neural network model has been in water resources management and planning. The length, width, and discharge of a basin were measured in this study utilizing field data from 160 Dashti Hawler existing projects. Multilayer Perceptron (MLP) and Radial Basic Function (RBF) networks were employed in the basin irrigation assessment. Input factors included the soil type, the conveyance system effectiveness, and the root zone depth. 130 projects were used for calibration, while the remaining 30 were used for validation. When developing the basin irrigation system, the models’ aforementioned indicators’ performance was evaluated using the coefficient of determination (R2), mean absolute error (MAE), relative error (RE), and Nash Sutcliff efficiency (NSE). For the basin's length, width, and discharge, the (R2) values for the MLP model were determined to be 0.97, 0.97, and 0.96, respectively, whereas the corresponding values for the RBF model were 0.88, 0.89, and 0.89. Compared to the RBF model, the values of (MAE) for basin length, width, and discharge for the MLP model were determined to be 8.99, 8.52, and 42.58, respectively. However, the (NSE) values for the models mentioned above were 0.95, 0.96, and 0.94, as well as 0.65, 0.66, and 0.66 for the basin’s length, width, and discharge, respectively. When it comes to building the irrigation system for the basin, the MLP is more precise than RBF depending on the values of (R2), (MAE), and (NSE). Finally, the ANN approach uses additional design options quickly examine which model is computationally efficient.

Potato peel wastes are generated in high quantities from potato processing industries. They are pollutants to the environment, and they release greenhouse gases into the atmosphere. The present study assessed the potentiality of hydrolyzing potato wastes by amylase-producing fungi to improve biogas generation from potato peels through the anaerobic digestion process. Different fungal isolates were screened for amylase production on potato wastes, and the highest amylase producer was selected for optimizing the efficacy of producing amylases in high quantities to efficiently allow the conversion of potato organic matter into fermentable sugars that are utilized for the anaerobic digestion process. The best amylase producers were those derived from Rhizopus stolonifer (32.61 ± 0.89 U/mL). The highest cumulative methane yield from hydrolyzed potato peel was 65.23 ± 3.9 mL CH4/g and the methane production rate was 0.39 mL CH4/h, whereas the highest biogas yield from unhydrolyzed potato wastes was 41.32 ± 2.15 mL CH4/g and the biogas production rate was 0.25 mL CH4/h. Furthermore, it was found that the two combined sequential stages of anaerobic digestion (biogas production) followed by biodiesel production (enzymatic esterification) were the most effective, recording 72.36 ± 1.85 mL CH4/g and 64.82% biodiesel of the total analytes. However, one-pot fermentation revealed that biogas yield was 22.83 ± 2.8 mL CH4/g and the biodiesel extracted was 23.67% of the total analytes. The insights of the current paper may increase the feasibility of potato peel-based biorefinery through the biological hydrolysis strategy of potato wastes using eco-friendly enzymes.

A four-term CG-method based on pure conjugacy condition are proposed, Research activities on extending three-term CG-method to the four-term conjugate gradient method. The new method shown that the suggested CG-methods owns the sufficient descent property. The global convergence of the proposed scheme with the general Wolfe conditions under a suitable assumption was verified. Finally, the computational experiment show that the new method is efficient and robust.

Early detection is critical to the successful treatment of life-threatening infections caused by fungal pathogens, as late diagnosis of systemic infection almost always equates with a poor prognosis. The field of fungal diagnostics has some tests that are relatively simple, rapid to perform and are potentially suitable at the point of care. However, there are also more complex high-technology methodologies that offer new opportunities regarding the scale and precision of fungal diagnosis, but may be more limited in their portability and affordability. Future developments in this field are increasingly incorporating new technologies provided by the use of new format biosensors. This overview provides a critical review of current fungal diagnostics and the development of new biophysical technologies that are being applied for selective new sensitive fungal biosensors to augment traditional diagnostic methodologies.

Conjugate gradient methods constitute excellent neural network training methods characterized by their simplicity efficiency and their very low memory requirements. In this paper, we propose a new scaled conjugate gradient neural network training algorithm which guarantees descent property with standard Wolfe condition. Encouraging numerical experiments verify that the proposed algorithm provides fast and stable convergence.

The difference between the desired output and the actual output of an multi-layers feed forward neural network produces an error value can be expressed it as a function of the network weights. Therefore training the network becomes an optimization problem to minimize the error function. This search suggests a new formula for computing learning rate based on Meyers formula to modify conjugate gradient algorithm (MCG) for training the FFNN. Typically this method accelerate the method of Fletcher–Reeves (FRCG) and Polak–Ribere (PRCG) when using it to solve three different types problems well known in the artificial neural network (namely, XOR problem, function approximation, and the Monk1 problem ) with 100 simulations.

The Back Propagation Algorithm is used for training feed Forward Multilayer Neural Networks (FFMNN).But often this algorithm takes long time to converge since it may fall into local minimu, for this reason we need a long time to train the network. The suitable choice of the learning rate helps us to escape from slow convergent for the BP and reduce the time of learning. In this paper, we derived a new adaptive learning rate for the BP algorithm, our derivation is based on the Aitkin's process. The most important distinct feature of our approach is the computing of the learning rate needs only first order derivatives and is suitable for large training sets and large networks. Its efficiency is proved on the standard test functions including heart , XOR and function approximation problems .

Abstract In this paper we suggested a new learning rate , which improves the classical Backpropagation algorithm (BP). The derivatation of are based on approximating the error function to the quadratic one in sufficiently small neighborhood for the optimal weight vector. The suggested algorithm (Spectral Backpropagation SBP say) is tested and the experimental results show that the SBP learning strategy improves the considered methods.

Abstract In this paper we have generalized the extended Dai-Yuan conjugate Gradient method by Considering the parameter in the denominator of as a convex combination. Three values of are computed in three different ways namely by assuming descent property, Pure Conjugacy and using Newton direction. The descent property and global convergence for the proposed algorithms are established. Our numerical experiments on some standard test functions show that there are considerable improvement on other classical methods in this field.

Conjugate Gradient (CG) methods comprise a class of unconstrained optimization algorithms which are characterized by low memory requirements and strong local and global convergence properties. Most of CG methods do not always generate a descent search directions, so the descent or sufficient descent condition is usually assumed in the analysis and implementations. By assuming a descent and pure conjugacy conditions a new version of spectral Dai-Yuan (DY) non-linear conjugate gradient method introduced in this article. Descent property for the suggested method is proved and numerical tests and comparisons with other methods for large-scale unconstrained problems are given.

The training phase of a Back-Propagation (BP) network is an unconstrained optimization problem. The goal of the training is to search an optimal set of connection weights in the manner that the error of the network out put can be minimized. In this paper we developed the Classical Fletcher-Revees (CFRB) method for non-linear conjugate gradient to the scaled conjugate gradient (SFRB say) to train the feed forward neural network. Our development is based on the sufficient descent property and pure conjugacy conditions. Comparative results for (SFRB), (CFRB) and standard Back-Propagation (BP) are presented for some test problems.

Several learning algorithms for feed-forward (FFN) neural networks have been developed, many of these algorithms are based on the gradient descent algorithm well-known in optimization theory which have poor performance in practical applications. In this paper we modify the Polak-Ribier conjugate gradient method to train feed forward neural network. Our modification is based on the secant equation (Quasi-Newton condition). The suggested algorithm is tested on some well known test problems and compared with other algorithms in this field.

The non-linear conjugate gradient method is a very useful technique for solving Large-Scale minimization problems and has wide applications in many fields . In this paper, we present a new spectral type, a non-linear conjugate gradient algorithm the derivatation of this algorithm is based on Fletcher – Reeves and Newton algorithm, the descent property for the suggested algorithm is proved provided that the step size satisfies the Wolfe conditions. Numerical results show that the new algorithm is efficient in practical computation and superior to the Fletcher – Reervs algorithm in many situations.

In this paper, we present extension forms of Dai, Yuan (DY) and Fletcher, Reveres (FR) CG algorithms. Our modifications and based on introducing a non-quadratic model (sigmoid function model). These modified algorithms are implemented with Wolfe conditions, where initial step size in each iteration is taken as and the global convergence of the modified DY algorithm is investigated. These modified algorithms are tested on some standard test functions and compared with the original DY and FR algorithms showing considerable improvements over all these comparisons.

In this paper we present a technique for computing the minimum value of an objective function in the frame of gradient descent methods based on combination of Barzilai and Borwein approximation of Hessian matrix of objective function and Lipchetz constant in the gradient flow algorithm which is derived from a system of ordinary differential equations associated to unconstrained optimization problem. This algorithm suitable for large- scale unconstrained optimization problems, computational results for this algorithm is given and compared with BB method showing a considerable improvement.

Objective of this research is to explore the potential synergistic effects of combining 3-hydrazinoquinoxaline-2-thiol and thymoquinone against a variety of Candida isolates. This investigation aims to offer an understanding of the collective antimicrobial action of these compounds. Methods: Broth microdilution was utilized to assess the Minimum Inhibitory Concentrations (MICs) of 3-hydrazinoquinoxaline-2-thiol and thymoquinone for 22 clinical Candida isolates. Following this, a checkerboard assay was employed to analyze the interaction between 3-hydrazinoquinoxaline-2-thiol and thymoquinone, with a specific focus on the Fractional Inhibitory Concentration Index (FICI). Results: The MICs of thymoquinone and 3-hydrazinoquinoxaline-2-thiol were determined for 22 clinical Candida strains, with thymoquinone exhibiting MICs ranging from 64 to 8 μg/mL, and 3-hydrazinoquinoxaline-2-thiol displaying MICs varying from 64 to 8 μg/mL. Notably, the combination of 3-hydrazinoquinoxaline-2-thiol and thymoquinone resulted in a synergistic effect, leading to a significant reduction in MICs, with reductions of up to 64-fold with FICI below 0.5 against tested strains. Conclusion: The prospect of using 3-hydrazinoquinoxaline-2-thiol in combination with thymoquinone as an effective solution against Candida looks encouraging. Nevertheless, to validate its practical applicability, additional comprehensive testing and experiments are imperative. [ABSTRACT FROM AUTHOR]

In this study, we extend the approach used to study the oscillatory behavior of delay differential equations to neutral equations. In the noncanonical case, we establish a new one‐condition criterion that guarantees the oscillation of a class of second‐order Emden‐Fowler neutral differential equations. The symmetry between the positive and negative solutions of the studied equation plays an important role in the proof of our results. Finally, we present an example to compare our results with previous relevant results. [ABSTRACT FROM AUTHOR]