Gradient Technique Research Articles

Various optimized artificial neural network simulations of advection-diffusion processes

The aim of this research is to describe an artificial neural network (ANN) based method to approximate the solutions of the natural advection-diffusion equations. Although the solutions of these equations can be obtained by various effective numerical methods, feed forward neural network (FFNN) techniques combined with different optimization techniques offer a more practicable and flexible alternative than the traditional approaches to solve those equations. However, the ability of FFNN techniques to solve partial differential equations is a questionable issue and has not yet been fully concluded in the existing literature. The reliability and accuracy of computational results can be advanced by the choice of optimization techniques. Therefore, this study aims to take an effective step towards presenting the ability to solve the advection-diffusion equations by leveraging the inherent benefits of ANN methods while avoiding some of the limitations of traditional approaches. In this technique, the solution process requires minimizing the error generated by using a differential equation whose solution is considered as a trial solution. More specifically, this study uses a FFNN and backpropagation technique, one of the variants of the ANN method, to minimize the error and the adjustment of parameters. In the solution process, the loss function (error) needs to be minimized; this is accomplished by fitting the trial function into the differential equation using appropriate optimization techniques and obtaining the network output. Therefore, in this study, the commonly used techniques in the literature, namely gradient descent (GD), particle swarm optimization (PSO) and artificial bee colony (ABC), are selected to compare the effectiveness of gradient and gradient-free optimization techniques in solving the advection-diffusion equation. The calculations with all three optimization techniques for linear and nonlinear advection-diffusion equations have been run several times to obtain the optimum accuracy of the results. The computed results are seen to be very promising and in good agreement with the effective numerical methods and the physics-informed neural network (PINN) method in the literature. It is also concluded that the PSO-based algorithm outperforms other methods in terms of accuracy.

Macroscale Gradient Dysfunction in Alzheimer's Disease: Patterns With Cognition Terms and Gene Expression Profiles.

Macroscale functional gradient techniques provide a continuous coordinate system that extends from unimodal regions to transmodal higher-order networks. However, the alterations of these functional gradients in AD and their correlations with cognitive terms and gene expression profiles remain to be established. In the present study, we directly studied the functional gradients with functional MRI data from seven scanners. We adopted data-driven meta-analytic techniques to unveil AD-associated changes in the functional gradients. The principal primary-to-transmodal gradient was suppressed in AD. Compared to NCs, AD patients exhibited global connectome gradient alterations, including reduced gradient range and gradient variation, increased gradient scores in the somatomotor, ventral attention, and frontoparietal regions, and decreased in the default mode network. More importantly, the Gene Ontology terms of biological processes were significantly enriched in the potassium ion transport and protein-containing complex remodeling. Our compelling evidence provides a new perspective in understanding the connectome alterations in AD.

EEG-based emotional valence and emotion regulation classification: a data-centric and explainable approach

Emotion classification using electroencephalographic (EEG) data is a challenging task in the field of Artificial Intelligence. While many researchers have focused on finding the best model or feature extraction technique to achieve optimal results, few have attempted to select the best methodological steps for working with the dataset. In this study, we applied two different theoretical approaches based on the noise of the dataset: curriculum learning and confident learning. Curriculum learning involves presenting training examples to the model in a specific order, starting with easier examples and gradually increasing in difficulty. This approach has been shown to improve model performance. Confident learning is a method for identifying and correcting label errors in datasets. By identifying and correcting these errors, confident learning can improve the performance of machine learning models trained on noisy datasets. We then applied the Integrated Gradient technique in order to assess the explainability of each model. Our aim was to explore the impact of different models and methods on emotion classification performance using EEG data. We collected and used an EEG dataset in which participants rated the emotional valence of positive and negative pictures while performing an emotion regulation (ER) task, comparing a control condition (Look) with two ER strategies: cognitive reappraisal and expressive suppression. We performed a multilabel classification to identify emotional neutrality or polarization of emotional valence (both positive and negative) rated by participants and the emotion regulation strategy adopted during the task. We compared the performance of models trained on three datasets selected based on label noise and evaluated their suitability for this task. Our results suggest different patterns based on the architecture used for feature importance, highlighting both advantages and criticisms.

Application of Autologous Platelet-rich Plasma Exerts Cryoprotective Effects on Biological Characteristics of Human Oligoasthenoteratospermia Samples after Freezing and Thawing Procedures.

Platelet-rich plasma (PRP) is enriched with active biological components which showed proliferative and cytoprotective properties in healing different injuries in medicinal fields. This study was designed to assess the cryoprotective effects of autologous PRP on the quality of oligoasthenoteratospermia (OAT) samples during freezing and thawing procedure. The present study is an experimental research. Twenty OAT semen samples were obtained from individuals and prepared by discontinuous density - gradients technique DGC). The control group is sperm samples after DGC. After the procedure, the specimen was divided into four groups. The Freeze group which has no additive and the other three groups were cryopreserved with different concentrations of PRP (1×105/ μL, 0.5×105/μL and 0.25×105/μL). Autologous PRP was provided by each participant. After thawing, sperm parameters, DNA fragmentation by sperm chromatin dispersion test (SCD), protamine deficiency by (Chromomycin A3) CMA3 staining, acrosome integrity and malondialdehyde (MDA) level were evaluated. Cryopreservation resulted in a significant decrease in all factors compared to the control group. There were no significant changes in sperm count, morphology, non-progressive motility and acrosome reaction by adding PRP as a cryoprotectant in comparison with the freeze group. PRP at all three concentrations showed a significant increase in progressive motility (3.05 ± 2.01 vs. 14.05 ± 4.13, 12.35 ± 4.90 and 12.15 ± 9.65, P < 0.001) and viability (36.85 ± 10.25 vs. 47.85 ± 5.86, 51.30 ± 5.54 and 50.05 ± 5.67, P < 0.001) compared to the sperm samples without PRP. The percentage of immotile sperms decreased at all PRP concentrations compared to the freeze group. Moreover, PRP at 1×105/μL concentration showed cryoprotective effects on DNA fragmentation, protamine deficiency and MDA level compared to the other three concentrations. Cryopreservation and thawing procedures may exert adverse effects on biological factors of sperm samples. Therefore, adding PRP as cryoprotectant at all three concentrations especially 1×105/μL can be promising strategy to reduce adverse effects of cryopreservation on OAT samples.

Gradient Technique Theory: Tracing Magnetic Field and Obtaining Magnetic Field Strength

The gradient technique is a promising tool with theoretical foundations based on the fundamental properties of MHD turbulence and turbulent reconnection. Its various incarnations use spectroscopic, synchrotron, and intensity data to trace the magnetic field and measure the media magnetization in terms of Alfvén Mach number. We provide an analytical theory of gradient measurements and quantify the effects of averaging gradients along the line of sight and over the plane of the sky. We derive analytical expressions that relate the properties of gradient distribution with the Alfvén Mach number M A. We show that these measurements can be combined with measures of sonic Mach number or line broadening to obtain the magnetic field strength. The corresponding technique has advantages to the Davis–Chandrasekhar–Fermi way of obtaining the magnetic field strength.

Exploration of crystal structure, supramolecular organization, and computational studies of a novel pyrazole derivative: A structural and theoretical perspectives

An eminent challenge in contemporary medicine and pharmacy is the identification of powerful, biologically active, and benign anti-COVID drugs. The objective of this work is to synthesize 3-chloro-1-(3-chloropyridin-2-yl)-1H-pyrazole-5-carboxylic acid to investigate its properties using different spectroscopic methods. The crystal structure of the compound is determined using single-crystal X-ray diffraction methodology. Hirshfeld surface analysis was employed to evaluate the impact of intermolecular interactions on crystal packing. The interactions were visually depicted using 2D fingerprint plots, which highlighted the specific surface area associated with each interactant. Indications suggest that the crystal packing is mostly governed by C-H...O and N-H...O interactions, resulting in the formation of S(7) self-motif and R44(24) synthon, which greatly improve crystal stability. Density functional theory (DFT) was employed to better examine the structural and electrical characteristics. Computations were conducted at the 6–311+G(d,p) level to determine the optimal geometry, total energy, HOMO-LUMO gap, and vibrational spectra. The results showed a HOMO-LUMO gap of 3.092 eV, suggesting a moderate amount of stability. Bader et al. employed reduced density gradient (RDG) techniques and topological analysis, taking into account the quantum theory of atoms in molecules (QTAIM), to examine noncovalent interactions. Additionally, the molecule satisfies Lipinski's rule of five and exhibits encouraging pharmacokinetic activities. Additional in-silico investigations, including molecular docking, were conducted on the SARS-CoV-2 spike protein variation to assist in the advancement of enhanced vaccines and therapeutics.

A New Hybrid Conjugate Gradient Method with Global Convergence Properties

This work introduces a novel hybrid conjugate gradient (CG) technique for tackling unconstrained optimisation problems with improved efficiency and effectiveness. The parameter is computed as a convex combination of the standard conjugate gradient techniques using and . Our proposed method has shown that when using the strong Wolfe-line-search (SWC) under specific conditions, it achieves global theoretical convergence. In addition, the new hybrid CG approach has the ability to generate a search direction that moves downward with each iteration. The quantitative findings obtained by applying the recommended technique about 30 functions with varying dimensions clearly illustrate its effectiveness and potential. This work introduces a novel hybrid conjugate gradient (CG) technique for tackling unconstrained optimisation problems with improved efficiency and effectiveness. The parameter is computed as a convex combination of the standard conjugate gradient techniques using and . Our proposed method has shown that when using the strong Wolfe-line-search (SWC) under specific conditions, it achieves global theoretical convergence. In addition, the new hybrid CG approach has the ability to generate a search direction that moves downward with each iteration. The quantitative findings obtained by applying the recommended technique about 30 functions with varying dimensions clearly illustrate its effectiveness and potential.

Comprehensive evaluation of the distribution, transport and ecological risk of heavy metals in intra-urban river sediments using high-resolution techniques

Determining the distribution trends, transport mechanisms, and ecological risks of heavy metals (HMs) in urban river sediments is essential for the government to conduct appropriate remediation work. In this study, we collected sediment cores from the Yayao Waterway in Foshan City, China. The vertical distribution profiles of dissolved and labile Fe, Mn, Cd, Zn, Cu, Cr, Ni, Pb, As, and Co in the sediments were obtained using the thin-film diffusive gradient (DGT) and high-resolution peeper (HR-Peeper) techniques. In addition, the transport rates, contamination levels, and ecological concerns of the HMs were evaluated using the European Community Bureau of Reference (BCR) sequential extraction technique, the DGT-induced sediment fluxes (DIFS) model, and multiple contamination evaluation metrics. The results showed that most of the DGT-labile HMs were associated with Fe/Mn (hydrogen) oxides, and in particular, Zn, Ni, and Cr showed a significant negative correlation with Fe/Mn (p < 0.001). Additionally, Cd had the highest bioavailability (89.17%), and its net diffusive flux at the sediment–water interface (SWI) was positive, which indicated a high release risk from the sediment. However, the R-value of Cd based on the DGT-induced sediment fluxes (DIFS) operation was extremely low, suggesting that although Cd had the biggest supply pool of releases, its release rate was slow. The majority of sampling sites had significantly higher total HM contents in the surface sediments than the background values. The HM contamination in the sediments originated from human activities, primarily from industrial enterprises and with a large contribution from both agricultural and domestic sources. The most polluted HM with the highest ecological danger was Cd, followed by Cu, Zn, Ni, and As when the results of the four pollution evaluation indicators were combined. Consequently, the risk of contamination by HMs in inner-city river sediments should receive more attention.

Tracing Magnetic Fields with the Gradient Technique: Spatial Filtering Effect and Use of Interferometers

Probing magnetic fields in astrophysical environments is both important and challenging. The Gradient Technique (GT) is a new tool for tracing magnetic fields, rooted in the properties of magnetohydrodynamic (MHD) turbulence and turbulent magnetic reconnection. In this work, we examine the performance of GT when applied to synthetic synchrotron emission and spectroscopic data obtained from sub-Alfvénic and trans-Alfvénic MHD simulations. We demonstrate the improved accuracy of GT in tracing magnetic fields in the absence of low spatial frequencies. Additionally, we apply a low-spatial-frequency filter to a diffuse neutral hydrogen region selected from the GALFA-H i survey. Our results show an increased alignment between the magnetic fields inferred from GT and the Planck 353 GHz polarization measurements.

Proximal evolutionary strategy: improving deep reinforcement learning through evolutionary policy optimization

Evolutionary Algorithms (EAs), including Evolutionary Strategies (ES) and Genetic Algorithms (GAs), have been widely accepted as competitive alternatives to Policy Gradient techniques for Deep Reinforcement Learning (DRL). However, they remain eclipsed by cutting-edge DRL algorithms in terms of time efficiency, sample complexity, and learning effectiveness. In this paper, aiming at advancing evolutionary DRL research, we develop an evolutionary policy optimization algorithm with three key technical improvements. First, we design an efficient layer-wise strategy for training DNNs through Covariance Matrix Adaptation Evolutionary Strategies (CMA-ES) in a highly scalable manner. Second, we establish a surrogate model based on proximal performance lower bound for fitness evaluations with low sample complexity. Third, we embed a gradient-based local search technique within the evolutionary policy optimization process to further improve the learning effectiveness. The three technical innovations jointly forge a new EA for DRL method named Proximal Evolutionary Strategies (PES). Our experiments on ten continuous control problems show that PES with layer-wise training can be more computationally efficient than CMA-ES; our surrogate model can remarkably reduce the sample complexity of PES in comparison to latest EAs for DRL including CMA-ES, OpenAI-ES, and Uber-GA; PES with gradient-based local search can significantly outperform several promising DRL algorithms including TRPO, AKCTR, PPO, OpenAI-ES, and Uber-GA.

Menadione sodium bisulfite as an efficient anti-corrosion additive for mild steel in acid corrosion: Electrochemical, surface morphological and theoretical studies

The corrosion inhibition behavior of Menadione Sodium Bisulfite (MSB) in 1.0 M HCl medium was evaluated using weight loss, electrochemical techniques (Tafel polarisation and Electrochemical impedance spectra), surface analysis (SEM and EDX), Density Functional Theory (DFT), Noncovalent interaction- Reduced Density Gradient (NCI-RDG) and Monte Carlo (MC) simulation techniques. Weight loss measurement showed that as inhibitor concentration increased, the corrosion rate decreased which means that a protective film formed on the metal surface. At 300 ppm, the inhibitor showed a maximum inhibition efficiency of 89.7 %. Tafel polarisation studies indicate that the inhibitor is mixed-type in nature. The inhibitor adsorption on the mild steel is the best fit for the Langmuir isotherm model. As the weak interaction of sodium and oxygen in the inhibitor molecules, the inhibitor exhibits two distinct equilibrium behaviors. (i) inhibitor with sodium (solid-state) and inhibitor without sodium (in liquid-state). These two behaviors were theoretically investigated using DFT and MD simulation studies. The inhibitor and metal interaction is further supported using these theoretical studies. The electron population of the inhibitor was analyzed using molecular electrostatic potential, NCI-RDG, Fukui function, and natural bond orbital analysis. These studies further give an understating of the inhibitor and metal interactions.

P-106 A novel sperm selection technique to select the most motile spermatozoa with superior genomic integrity for intrauterine insemination

Abstract Study question Does the utilization of a novel sperm selection method via microfluidics(MFSS) confer advantages over the conventional density gradient(DG) technique for couples undergoing intrauterine insemination(IUI)? Summary answer MFSS efficiently selected spermatozoa with increased motility and genomic integrity in an expedited manner, leading to an enhanced IUI clinical outcome. What is known already The first-line reproductive treatments for infertile couples are timed intercourse and IUI. In IUI, DG is commonly utilized to isolate motile spermatozoa. However, this method requires technical skills and processing time. Furthermore, DG exposes spermatozoa to silica gel particles and reactive oxygen species during centrifugation. MFSS represents a safer and more efficient sperm processing method to minimize human intervention. It has been reported by several investigators that MFSS selects spermatozoa with higher progressive motility and lower DNA fragmentation in ART. We have decided to investigate the efficacy of MFSS as an alternative method to select spermatozoa for IUI. Study design, size, duration Since 2017, post-processing semen parameters and Sperm Chromatin Fragmentation (SCF) was measured in the raw, DG-, and MFSS-processed specimens. Clinical outcomes were compared between IUI cycles using DG versus MFSS processing. Paired analyses were performed on patients who underwent history IUI-DG and subsequent IUI-MFSS, and cycles were split into natural (NC) or clomid or letrozole superovulation (CLO) cohorts. Participants/materials, setting, methods A total of 855 couples underwent 922 IUI-MFSS. Post-processing semen parameters and clinical outcomes were compared with those of 2325 age-matched couples who underwent 8322 IUI-DG during the same period. All male partners included in this study had normal semen parameters. DG was performed according to WHO, while MFSS as per manufacturer protocol(ZyMōt® Multi 850µL). SCF was assessed by TUNEL(normal threshold,≤15%). For all comparisons, +bHCG and clinical pregnancy(CPR,+FHB) were compared. Main results and the role of chance IUI-DG(maternal age:32.7±2;paternal age:37.9±6) resulted in a final concentration of 48.4±33x106/mL and motility of 88.1±4%. IUI-MFSS resulted in a decrease in a final concentration at 36.4±17x106/mL with a remarkable increase in motility at 98.6±1%(P&amp;lt;0.01). SCF was 6.0±4% in raw specimen, became 4.7±2% after DG, and 2.5±2% after MFSS(P&amp;lt;0.01). Overall, IUI-DG(n = 2325) resulted in a +bHCG of 12.7%(1065/8322) and a CPR(+FHB) of 11.0%(919/8322). IUI-MFSS(n = 855) resulted in a higher +bHCG of 16.1%(149/922,P&amp;lt;0.01) and a CPR of 12.5%(116/922). Next, we divided cycles into NC or CLO cohorts. In couples with NC, IUI-DG(n = 420) resulted in a +bHCG of 9.3%(132/1416) and a CPR of 8.8%(124/1416). IUI-MFSS(n = 107) resulted in a +bHCG of 11.4%(15/131) and a CPR of 11.4%(15/131). In CLO cycles, IUI-DG(n = 1990) resulted in a +bHCG of 13.0%(788/6048) and CPR of 10.9% (659/6048), while IUI-MFSS(n = 496) resulted in a significantly higher +bHCG at 17.4%(122/698) and CPR at 14.3%(100/698,P&amp;lt;0.01). In the paired sub-analysis, NC couples (n = 30) underwent IUI-DG and subsequently IUI-MFSS. IUI-DG resulted in a 9.4% +bHCG(7/74) and 8.1% CPR(6/74). IUI-MFSS yielded a 10.2% +bHCG(4/39) and 10.2% CPR(4/39). CLO couples (n = 132) underwent IUI-DG and resulted in a 13.6% +bHCG(85/623) and 11.2% CPR(70/623), followed by IUI-MFSS yielding a 15.2% +bHCG(27/178) and 12.4% CPR(22/178). Limitations, reasons for caution IUI-MFSS cycles resulted in an increased proportion of progressively motile spermatozoa, with a superior genomic integrity and higher clinical outcome. The benefit of MFSS may be somewhat limited because of the low SCF of this particular cohort and only a larger case series would be able to confirm this finding. Wider implications of the findings DG is labor intensive, technician-dependent, and unable to meaningfully ameliorate SCF. MFSS, while reducing exposure to reactive oxygen species, grants the highest sperm progressive motility with superior genomic integrity that may be responsible for an improved pregnancy outcome. Trial registration number not applicable

P-033 Microfluific sperm sorting does not impact the primary sex ratio in IVF-ICSI treatments

Abstract Study question Does sperm selection through microfluidics alter the primary sex ratio (PSR) when compared to density gradients? Summary answer The PSR is not modified when employing a microfluidic sperm sorting when comparing to the conventional density gradient technique for sperm selection. What is known already The PSR refers to gender after fertilization, this is often expressed as the male births per female births. Little is known about the sex ratio in natural and assisted reproduction treatments (ART) since it is difficult to establish in ART as it requires preimplantation genetic testing (PGT). Previous studies have been estimating the variation in the PSR based on the fertilization technique used or the day of embryo transfer. Even the influence of sperm selection techniques has not received much attention, using traditional sperm selection methods such as density gradients or swim-up, a PSR of 1.07 has been estimated. Study design, size, duration This is a retrospective study that includes 1764 blastocysts stage embryos biopsied from 522 autologous preimplantation genetic testing for aneuploidies cycles (PGT-A) performed between January of 2020 and December of 2022 and analysed through Next Generation Sequencing (NGS). All the embryos were cultured under the same conditions with one-step media in a time-lapse incubator and the PGT-A was performed in day 5 or day 6 of culture to determine the embryo ploidy status. Participants/materials, setting, methods Two groups were established based on the sperm selection method before ICSI. The study group using a microfluidic system (MS; ZyMōtICSI® or ZyMōtMulti®) included 256 blastocysts from 98 cycles, and a control group using density gradients (DG), with 1058 blastocysts from 424 cycles were compared. PGT-A determined the embryonic sex and for the PSR estimation, number abnormalities in the sexual chromosomes were excluded (i.e. XXY, X0, XYY). Chi-square test was performed to compare the groups. Main results and the role of chance Our data show that there was no statistical significance in the overall ratio of XY:XX embryos biopsied between both groups (p = 0.09). The PSR was 0.89 (47:53) in the study group (MS) and 1.12 (53:47) in the control group (DG). Although not statistically significant, the rate of XX embryos was 6% higher than XY embryos in the MS group. In contrast, in the DG group, the same increase in the rate of XY embryos was observed. If we consider only the euploid embryos which are the potentially born babies, the PSR acquires values of 0.96 for the DG group and 0.92 for the MS group (p = 0.86), showing an increase in euploid female embryos transferred compared to those euploid male embryos. However, it was not statistically significant. Limitations, reasons for caution The performance of PGT-A on blastocysts does not provide an entirely accurate representation of the PSR since it is carried out 5/6 days after the moment of fertilization; nevertheless, it is the technique that comes closest. Also, the low number of blastocysts in the study group represents a limitation. Wider implications of the findings The sperm selection for ICSI with microfluidic technology does not significantly alter the PSR. Trial registration number Not applicable

Visualizing and Controlling of Photogenerated Electron-Hole Pair Separation in Monolayer WS2 Nanobubbles under Piezoelectric Field.

The piezoelectric properties of two-dimensional semiconductor nanobubbles present remarkable potential for application in flexible optoelectronic devices, and the piezoelectric field has emerged as an efficacious pathway for both the separation and migration of photogenerated electron-hole pairs, along with inhibition of recombination. However, the comprehension and control of photogenerated carrier dynamics within nanobubbles still remain inadequate. Hence, this study is dedicated to underscore the importance of in situ detection and detailed characterization of photogenerated electron-hole pairs in nanobubbles to enrich understanding and strategic manipulation in two-dimensional semiconductor materials. Utilizing frequency modulation kelvin probe force microscopy (FM-KPFM) and strain gradient distribution techniques, the existence of a piezoelectric field in monolayer WS2 nanobubbles was confirmed. Combining w/o and with illumination FM-KPFM, second-order capacitance gradient technique and in situ nanoscale tip-enhanced photoluminescence characterization techniques, the interrelationships among the piezoelectric effect, interlayer carrier transfer, and the funneling effect for photocarrier dynamics process across various nanobubble sizes were revealed. Notably, for a WS2/graphene bubble height of 15.45 nm, a 0 mV surface potential difference was recorded in the bubble region w/o and with illumination, indicating a mutual offset of piezoelectric effect, interlayer carrier transfer, and the funneling effect. This phenomenon is prevalent in transition metal dichalcogenides materials exhibiting inversion symmetry breaking. The implication of our study is profound for advancing the understanding of the dynamics of photogenerated electron-hole pair in nonuniform strain piezoelectric systems, and offers a reliable framework for the separation and modulation of photogenerated electron-hole pair in flexible optoelectronic devices and photocatalytic applications.

Boundaries identification of geological structure in lunar Oceanus Procellarum region using full gravity gradient tensor methodology

Boundary recognition visually delineates the horizontal extent of subsurface anomalies, providing a foundational basis for interpreting gravity data. Compared to gravity observations, gravity gradient data offers the benefits of multiple components and the enhancement of shortwave information in graphical representations. In our study, we investigated the delineation of geological structure boundaries in the lunar Oceanus Procellarum region using gravity gradient techniques. First, based on the observations from the Gravity Recovery and Interior Laboratory (GRAIL) mission, we obtained high-precision synthetic full tensor values of the gravity gradient as our primary research data. Moreover, to reduce curvature errors during large-scale terrain forward modeling, we applied the spherical prism of tesseroid to eliminate the effects of near-surface topography. Grounded on the Bouguer anomalies of gravity gradient, four distinct boundary recognition methods have been employed to explore the geological structure of the lunar Oceanus Procellarum region. It includes the Theta map method, the directional Theta map method, the combination of total horizontal derivative and the modulus of full tensor gravity gradient, and the improved edge detection method based on the Theta map method.The common feature of these methods is the utilization of the multi-component data combination from gravity gradient tensors, which can enhance the accuracy of edge detection. From the investigation results presented in the paper, we have found the following: 1) The improved edge detection method, based on the Theta map principle, enables better identification of the center of subsurface anomaly bodies during boundary recognition, utilizing a consistent single color in graphic displays. 2) According to the results of boundary recognition, numerous strip anomalies exist in the Oceanus Procellarum area that show less correlation with topographic relief. One possible explanation for these graphical anomalies of gravity gradient is that they serve as channels for the upwelling of mantle material during lunar evolution.

Aerodynamic shape optimization of NACA airfoils based on a novel unconstrained conjugate gradient algorithm

Airfoils are key factors in maximizing the efficiency of turbomachinery. The ideal configuration of the airfoil is engineered to produce significant lift while minimizing drag, all while adhering to specific structural limitations. In this investigation, an innovative algorithm based on unconstrained conjugate gradient techniques to optimize the aerodynamic shape of airfoils is proposed. NACA4412 and NACA2415 airfoils are chosen to be investigated in detail. Bézier parameterisation method is employed to define the design variables. Optimization is conducted utilizing a MATLAB code and the XFOIL panel method-based flow solver to attain the desired aerodynamic outcomes. The optimization process enhanced aerodynamic performance by increasing the lift-to-drag ratio and decreasing the angle of attack for maximum lift-to-drag ratio. An increase of 13.7 % in performance for the NACA 4412 airfoil and 32 % for the NACA 2415 airfoil was achieved. Comparisons with traditional methods demonstrated the efficiency and robustness of the proposed algorithm.

Predicting heat flow in the Iranian plateau and surrounding areas based on machine learning approach

While Surface Heat Flow (HF) is an important constraint unveiling the Earth interior's thermal structure, estimates over the Iranian plateau are sparse. In the presence of sparse estimates, machine learning provides a statistical-based prediction of HF based on a supervised predictor trained in the far-field regions. Here, we imply the machine learning technique of Gradient Boosting Regression Tree (GBRT) which has been proved to be efficient for predicting HF projecting complexities and nonlinearities of input features into predicted HF. Our results provide a robust map of HF with resolution of one degree and uncertainty of up to ±6 mW/m2 over Iran and surrounding regions. The predicted HF has an average value and minimum standard deviation of 59 and 10 mW/m2, respectively. The quality of the algorithm performance is 16%, indicated by normalized Root-Mean-Square Error (RMSE), and linear correlation of predicted HF with validation set is 97%. Total number of trees considerably prevents overfitting which is believed to be solely controllable by shrinkage factor, maximum tree depth and cross-validation scheme. The three most important features, having the highest influence on the output HF, are thermal Lithosphere-Asthenosphere Boundary (LAB), distance to volcanoes and distance to trenches. The extreme importance of LAB in HF prediction of Iran indicates that the lithospheric thermal structure is significantly controlled by lithospheric thickness in the Iranian plateau. Selection of petrologically and seismologically consistent LAB guarantees the precision of the predicted HF. Our results imply that high HF in central Iran is in agreement with extensive magmatism since the Paleozoic. Additionally, the high HF in Zagros keel (originally Proterozoic as the Zagros keel appears to be the Arabian plate front) indicates the tectonically active system of the Arabia-Eurasia collision zone, high likely, in the form of lithospheric mantle deformation.

An Investigation Of A New Hybrid Conjugates Gradient Approach For Unconstrained Optimization Problems

This work introduces a novel hybrid conjugate gradient (CG) technique for tackling unconstrained optimisation problems with improved efficiency and effectiveness. The parameter is computed as a convex combination of the standard conjugate gradient techniques using and . Our proposed method has shown that when using the strong Wolfe-line-search(SWC) under specific conditions, it achieves global theoretical convergence. In addition, the new hybrid CG approach has the ability to generate a search direction that moves downward with each iteration. The quantitative findings obtained by applying the recommended technique about 30 functions with varying dimensions clearly illustrate its effectiveness and potential.

Automated Early Detection of Plant Diseases a Machine Learning Approach

This study introduces an innovative strategy for the early detection of plant diseases leveraging machine learning algorithms, a crucial step towards bolstering global food security. Despite the importance of early disease detection for mitigating crop losses, inadequate infrastructure often hinders swift and accurate identification. This paper addresses these challenges by utilizing the Random Forest algorithm to distinguish between healthy and diseased plant leaves using a specially curated dataset. Our proposed method involves comprehensive phases of dataset creation, feature extraction, classifier training, and subsequent classification. For the extraction of image features, the Histogram of Oriented Gradient (HOG) technique is utilized, offering a robust method for image analysis. Embracing the capabilities of publicly accessible, large-scale datasets, our method provides a scalable solution to plant disease detection. The empirical results demonstrate promising levels of accuracy in disease monitoring, presenting transformative potential for disease management practices in agriculture. Particularly in resourceconstrained settings, our machine learning approach paves the way for efficient and effective plant disease detection, carrying significant implications for future crop protection strategies.

Strategi Guru Dalam Mengatasi Kesulitan Pengenalan Dan Pencocokan Warna di TK Aisyah Bustanul Athfal 05 Medan

: This research aims to analyze fine arts learning in early childhood at Kindergarten Aisyiyah Bustanul Athfal 05 Medan, with a focus on coloring activities using gradation techniques. This research uses qualitative methods with interview techniques with one of the fine arts teachers at the kindergarten. The research results show that various fine arts activities, such as coloring using gradient techniques, making collages, and printing with natural materials, are carried out at Kindergarten Aisyiyah Bustanul Athfal 05 Medan. However, challenges in introducing colors to children still exist, so that some children experience difficulties in coloring activities. The role of teachers as guides and parental support is very important in developing children's artistic skills. This research recommends using varied environmental models to help children recognize colors better. Teachers can use color examples from nature and fine art to expand children's understanding of color. With this strategy, it is hoped that children can improve their ability to recognize and match colors, as well as develop creativity and imagination through fine arts activities