Neural Network Analysis Research Articles

Challenges for computer vision as a tool for screening urban trees through street-view images

Urban forests play a fundamental and irreplaceable role within cities through the ecosystem services they provide, such as carbon capture. However, inadequate management of urban trees can heighten the risks they pose to society. For instance, mechanical failures of tree components, such as branches, can cause harm to individuals and property. Regular assessments of tree conditions are necessary to mitigate these tree-related hazards, yet such evaluations are labor-intensive and currently lack automation. Previous studies have proposed utilizing street view images to alleviate tree inspection and shown the feasibility of visually inspecting trees. However, only a limited number of studies have addressed the automatic evaluation of urban trees, a challenge that can potentially be addressed using deep learning networks. Particularly in urban environments, there is a pressing need for increased automation in unresolved computer vision tasks. Therefore, this research presents a comprehensive analysis of neural networks and publicly available datasets that can aid arborists in automatically identifying urban trees. Specifically, we investigate the potential of deep learning networks in classifying tree genera and segmenting individual trees and their trunks. We emphasize the utilization of transfer learning strategies to enhance tree identification. The results demonstrate that neural networks can be considered practical tools for assisting arborists in tree recognition. Nevertheless, there are still gaps that remain and require attention in future research endeavors.

Assessing inflation and greenhouse gas emissions interplay via neural network analysis: a comparative study of energy use in the USA, EU, and China

This study examines the relationship between inflation and greenhouse gas (GHG) emissions in three major economies: the United States of America (USA), the European Union (EU), and China. The analysis spans from 1960 to 2021 for the USA and EU, and from 1971 to 2021 for China. A feedforward neural network model, optimized using the Levenberg–Marquardt backpropagation algorithm, was employed to predict GHG emissions based on annual inflation rates and fossil fuel energy consumption. The study integrates historical data on inflation trends with GHG emissions, measured in CO2 equivalents, and fossil fuel energy consumption, expressed as a percentage of total energy use. This multidimensional approach allows for a nuanced understanding of the economic-environmental interplay in these regions. Key findings indicate a nonlinear response of GHG emissions to inflation rates. In the USA, GHG emissions begin to decrease when inflation rates exceed 4.7%. Similarly, in the EU, a steep reduction in emissions is observed beyond a 7.5% inflation rate. China presents a more complex pattern, with two critical inflection points: the first at a 4.5% inflation rate, where GHG emissions start to decline sharply, and the second at a 7% inflation rate, beyond which further increases in inflation do not significantly reduce emissions. A critical global insight is the identification of a uniform inflation rate, around 4.4%, across all regions, at which GHG emissions consistently increase by 1%, hinting at a shared global economic behavior impacting the environment. This discovery is vital for policymakers, emphasizing the need for tailored regional strategies that consider unique economic structures, energy policies, and environmental regulations, alongside a coordinated global approach.

Comparative Study on Simulated Outdoor Navigation for Agricultural Robots.

This research presents a comprehensive comparative analysis of SLAM algorithms and Deep Neural Network (DNN)-based Behavior Cloning (BC) navigation in outdoor agricultural environments. The study categorizes SLAM algorithms into laser-based and vision-based approaches, addressing the specific challenges posed by uneven terrain and the similarity between aisles in an orchard farm. The DNN-based BC navigation technique proves efficient, exhibiting reduced human intervention and providing a viable alternative for agricultural navigation. Despite the DNN-based BC navigation approach taking more time to reach its target due to a constant throttle limit for steady speed, the overall performance in terms of driving deviation and human intervention is notable compared to conventional SLAM algorithms. We provide comprehensive evaluation criteria for selecting optimal techniques for outdoor agricultural navigations. The algorithms were tested in three different scenarios: Precision, Speed, and Autonomy. Our proposed performance metric, P, is weighted and normalized. The DNN-based BC algorithm showed the best performance among the others, with a performance of 0.92 in the Precision and Autonomy scenarios. When Speed is more important, the RTAB-Map showed the best score with 0.96. In a case where Autonomy has a higher priority, Gmapping also showed a comparable performance of 0.92 with the DNN-based BC.

Plasma microRNA expression in adolescents and young adults with endometriosis: the importance of hormone use.

Prior studies have investigated the diagnostic potential of microRNA (miRNA) expression profiles for endometriosis. However, the vast majority of previous studies have only included adult women. Therefore, we sought to investigate differential expression of miRNAs among adolescents and young adults with endometriosis. The Women's Health Study: from Adolescence to Adulthood (A2A) is an ongoing WERF EPHect compliant longitudinal cohort. Our analysis included 64 patients with surgically-confirmed endometriosis (96% rASRM stage I/II) and 118 females never diagnosed with endometriosis frequency matched on age (median = 21 years) and hormone use at blood draw. MicroRNA measurement was separated into discovery (10 cases and 10 controls) and internal replication (54 cases and 108 controls) phases. The levels of 754 plasma miRNAs were assayed in the discovery phase using PCR with rigorous internal control measures, with the relative expression of miRNA among cases vs. controls calculated using the 2-ΔΔCt method. miRNAs that were significant in univariate analyses stratified by hormone use were included in the internal replication phase. The internal replication phase was split 2:1 into a training and testing set and utilized FirePlex miRNA assay to assess 63 miRNAs in neural network analyses. The testing set of the validation phase was utilized to calculate the area under the curve (AUC) of the best fit models from the training set including hormone use as a covariate. In the discovery phase, 49 miRNAs were differentially expressed between endometriosis cases and controls. The associations of the 49 miRNAs differed by hormone use at the time of blood draw. Neural network analysis in the testing set of the internal replication phase determined a final model comprising 5 miRNAs (miR-542-3p, let-7b-3p, miR-548i, miR-769-5p, miR-30c-1-3p), yielding AUC = 0.77 (95% CI: 0.67-0.87, p < 0.001). Sensitivity in the testing dataset improved (83.3% vs. 72.2%) while the specificity decreased (58.3% vs. 72.2%) compared to the training set. The results suggest that miR-542-3p, let-7b-3p, miR-548i, miR-769-5p, miR-30c-1-3p may be dysregulated among adolescent and young adults with endometriosis. Hormone use was a significant modifier of miRNA dysregulation and should be considered rigorously in miRNA diagnostic studies.

Possibilities of neural network analysis in determining the severity form of new coronavirus infection in school-age children

Possibilities of neural network analysis in determining the severity form of new coronavirus infection in school-age children

Designing a set of criteria for evaluating artificial neural networks trained with physics-based data to replicate molecular dynamics and other particle method trajectories

This article presents an in-depth analysis and evaluation of artificial neural networks (ANNs) when applied to replicate trajectories in molecular dynamics (MD) simulations or other particle methods. This study focuses on several architectures—feedforward neural networks (FNNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), time convolutions (TCs), self-attention (SA), graph neural networks (GNNs), neural ordinary differential equation (ODENets), and an example of physics-informed machine learning (PIML) model—assessing their effectiveness and limitations in understanding and replicating the underlying physics of particle systems. Through this analysis, this paper introduces a comprehensive set of criteria designed to evaluate the capability of ANNs in this context. These criteria include the minimization of losses, the permutability of particle indices, the ability to predict trajectories recursively, the conservation of particles, the model’s handling of boundary conditions, and its scalability. Each network type is systematically examined to determine its strengths and weaknesses in adhering to these criteria. While, predictably, none of the networks fully meets all criteria, this study extends beyond the simple conclusion that only by integrating physics-based models into ANNs is it possible to fully replicate complex particle trajectories. Instead, it probes and delineates the extent to which various neural networks can “understand” and interpret aspects of the underlying physics, with each criterion targeting a distinct aspect of this understanding.

Artificial neural network analysis of factors affecting functional independence recovery in patients with lumbar stenosis after neurosurgery treatment: An observational cohort study

Background and aimLumbar spinal stenosis (LSS) is a leading cause of low back pain and lower limbs pain often associated with functional impairment which entails the loss or the impairment of independence in older adults. Conservative treatment is effective in a small percentage of patients, while a significant percentage undergo surgery, even if often without a complete resolution of clinical symptoms and motor deficits. The aim of the study is to identify clinical and demographic prognostic factors characterising the patients who would benefit most from surgical treatment in relation to the functional independence recovery using an innovative approach based on an artificial neural network. MethodsAdult patients with LSS and indication of neurosurgical treatment were enrolled in the study. Clinical evaluation was performed in the preoperative-phase (into the 48 h before surgery) and after two months. Clinical battery investigated the motor, functional, cognitive, behavioural, and pain status. Demographics and clinical characteristics were analysed via Artificial Neural Network (ANN) using 24 input variables, 2 hidden layers and a single final output layer to predict the outcome. ANN results were compared with those of a multiple linear regression. Results108 patients were included in the study and 90 of them [66.5 ± 12.8 years; 27.8 % F] were submitted to surgery treatment and completed longitudinal evaluation. Statistically significant improvement was recorded in all clinical scales comparing pre- and post-surgery. The ANN results showed a prediction ability up to 81 %. Disability, functional limitations, and pain concerning clinical assessment and stature, onset and age about demographic characteristics are the main variables impacting on surgical outcome. ConclusionsANN can support clinical decision making, using clinical and demographic characteristics of patients with LSS identifying the characteristics of those who might benefit more from the surgical treatment in terms of global functional recovery.

The impact of unmet treatment need on oral health related quality of life: a questionnaire survey

BackgroundBased on the present global burden of oral diseases, unmet dental needs affect a more significant population worldwide. It is characterised by the need for dental care but receiving delayed or no care. The contributing factors include lack of knowledge about oral health, its consequences, and the availability of dental services. We need to find out the scale of the problem of unmet dental needs for the south Indian population. Therefore, the objective was to determine the relationship between the presence of oral disease and the quality of life-related to oral health using the OHIP-14 tool.MethodsThe unmet dental requirements of the south Indian population were determined using a cross-sectional questionnaire survey. Close-ended questions were used to obtain data from two investigators trained to record the answers from the patients. The data was collected using the OHIP-14 questionnaire, which consists of 14 items divided into seven domains with two questions each. Physical pain, psychological impairment, physical disability, psychological disability, social disability, and disability were all considered. An additional analysis of artificial neural network (ANN) was done.ResultsThe response rate was 100 per cent. N = 1029 people replied to the questionnaire about their unmet dental needs. N = 497 (48.3%) were men, whereas N = 532 (51.7%) were women. The average age was 31.7811.72. As their current occupation, most of the included subjects (60.1%) were students. The respondents had no known personal habits and a mixed diet (94.93%). The average BMI was 24.022.59 (14-30.9). OHIP was present in 62.3% of the population. The average OHIP-14 severity score was 10.97. (8.54). The severity and degree of unmet dental need were substantial (p0.01) due to pain in the mouth/teeth/gums, malocclusion, and gum bleeding. The most common OHIP-14 domains affected by unmet oral needs were psychological discomfort, psychological limitation, social limitation, and feeling handicapped. The analysis of ANN revealed that high OHIP scores were primarily attributed to dental caries, poor oral health, and dental aesthetics.ConclusionThe severity and degree of unmet dental needs were significant among the south Indian population. The most common oral health status that impacted OHIP-14 domains were pain, malocclusion, and bleeding gums. These patients were significantly impacted by psychological discomfort and social limitations and felt handicapped.

Intelligent condition monitoring with CNN and signal enhancement for undersampled signals

High-frequency signals like vibration and acoustic emission are crucial for condition monitoring, but their high sampling rates challenge data acquisition, especially for online monitoring. Our research developed a novel method for condition identification in undersampled signals using a modified convolutional neural network integrated with a signal enhancement approach. A frequency-domain filtering is applied to suppress similar sidebands and obtain more discriminative features of different conditions, followed by an interpolation-based upsampling in the time domain to restore the signal length and strengthen the low-frequency harmonic information. Enhanced signals are converted into two-dimensional grayscale images for neural network analysis. Tested on bearing datasets and real-world data from regenerative thermal oxidizer lift valve leakage, our method effectively extracts features from low-frequency signals, achieving over 95% fault identification accuracy.

Global Navigation Satellite System Receiver Positioning in Harsh Environments via Clock Bias Prediction by Empirical Mode Decomposition and Back Propagation Neural Network Method.

This paper proposes a novel method to improve the clock bias short-term prediction accuracy of navigation receivers then solve the problem of low positioning accuracy when the satellite signal quality deteriorates. Considering that the clock bias of a navigation receiver is equivalent to a virtual satellite, the predicted value of clock bias is used to assist navigation receivers in positioning. Consequently, a combined prediction method for navigation receiver clock bias based on Empirical Mode Decomposition (EMD) and Back Propagation Neural Network (BPNN) analysis theory is demonstrated. In view of systematic errors and random errors in the clock bias data from navigation receivers, the EMD method is used to decompose the clock bias data; then, the BPNN prediction method is used to establish a high-precision clock bias prediction model; finally, based on the clock bias prediction value, the three-dimensional positioning of the navigation receiver is realized by expanding the observation equation. The experimental results show that the proposed model is suitable for clock bias time series prediction and providing three-dimensional positioning information meets the requirements of navigation application in the harsh environment of only three satellites.

Ligand-based Molecular Modeling of HDL Receptor SR-BI Inhibitors As Potent Anti-Hyperlipidemic Agents

Introduction:: The High-density lipoprotein (HDL) receptor, Scavenger receptor class B, type I (SRBI) plays a crucial role in lipoprotein metabolism, cholesterol homeostasis, and atherosclerosis. In the present study, a quantitative structure-activity relationship study (QSAR) investigation was conducted on a data set of 31 novel indolinyl thiazole-based inhibitors of SR-BI mediated lipid uptake. Method:: To build the QSAR model, Multiple linear regression analysis (MLR), partial least square analysis (PLS), and neural analysis (NN) were performed which were further evaluated internally as well as externally for the prediction of activity. The best QSAR model for MLR was selected with a correlation coefficient (r2) of 0.937, cross-validation r2cv of 0.908, and a standard error (S) value of 0.253. For PLS, r2 was 0.937 and for FFNN r2 was 0.961 (for the training set). This was further evaluated externally by a test set having r2 values 0.870 (MLR), 0.863(PLS), and 0.933(neural network) analysis. Result:: The final model comprised hydrophobic parameters (Lipole Z component) and steric parameters (molar refractivity and K alpha2 index). Conclusion:: All these descriptors generated comparable results which prove that the model generated is sound and has good predictability.

Thermal analysis for SWCNTs and MWCTs due to coaxially stretchable disks with applications of viscous dissipation and thermal radiation: Artificial neural network analysis

Owing to exclusive thermal prospective of nanoparticles, diverse applications of such materials have been reported in heat transfer systems. The carbon nanotube (CNTs) is the modified class of nanoparticles which is preserve more stable thermal properties. The decomposition of CNTs with base materials offer special applications in enhancing thermal efficiency of energy systems, nuclear processes, sensors, diagnostic imaging etc. Following to such motivations in mind, the objective of current work is to present artificial neural network analysis for heat transfer problem due to suspension of carbon nanotubes-based nanofluid. Both single-walled and multi-walled carbon nanotubes are utilized to analyse the thermal impact of water base fluid. The source of flow is coaxially stretchable disks. The applications of porous medium phenomenon are reported to inspect the inertial effects. The problem is further updated by utilizing the radiative phenomenon, external heat generation and dissipation and slip constraints. The transport system is expressed via coupled nonlinear equations which are tackled via numerical computations.

Reactivation of herpesvirus type 6 and IgA/IgM-mediated responses to activin-A underpin long COVID, including affective symptoms and chronic fatigue syndrome.

Persistent infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), reactivation of dormant viruses, and immune-oxidative responses are involved in long COVID. To investigate whether long COVID and depressive, anxiety, and chronic fatigue syndrome (CFS) symptoms are associated with IgA/IgM/IgG to SARS-CoV-2, human herpesvirus type 6 (HHV-6), Epstein-Barr Virus (EBV), and immune-oxidative biomarkers. We examined 90 long COVID patients and ninety healthy controls. We measured serum IgA/IgM/IgG against HHV-6 and EBV and their deoxyuridine 5′-triphosphate nucleotidohydrolase (duTPase), SARS-CoV-2, and activin-A, C-reactive protein (CRP), advanced oxidation protein products (AOPP), and insulin resistance (HOMA2-IR). Long COVID patients showed significant elevations in IgG/IgM-SARS-CoV-2, IgG/IgM-HHV-6, and HHV-6-duTPase, IgA/IgM-activin-A, CRP, AOPP, and HOMA2-IR. Neural network analysis yielded a highly significant predictive accuracy of 80.6% for the long COVID diagnosis (sensitivity: 78.9%, specificity: 81.8%, area under the ROC curve = 0.876); the topmost predictors were as follows: IGA-activin-A, IgG-HHV-6, IgM-HHV-6-duTPase, IgG-SARS-CoV-2, and IgM-HHV-6 (all positively) and a factor extracted from all IgA levels to all viral antigens (inversely). The top 5 predictors of affective symptoms due to long COVID were IgM-HHV-6-duTPase, IgG-HHV-6, CRP, education, IgA-activin-A (predictive accuracy of r = 0.636). The top 5 predictors of CFS due to long COVID were in descending order: CRP, IgG-HHV-6-duTPase, IgM-activin-A, IgM-SARS-CoV-2, and IgA-activin-A (predictive accuracy: r = 0.709). Reactivation of HHV-6, SARS-CoV-2 persistence, and autoimmune reactions to activin-A combined with activated immune-oxidative pathways play a major role in the pathophysiology of long COVID as well as the severity of its affective symptoms and CFS.

Spatial Air Quality Assessment in Peninsular Malaysia: Insights from Environmetric Analyses and Artificial Neural Networks

The research investigated the spatial distribution of air quality in Peninsular Malaysia to offer crucial insights for mitigating air quality degradation. Utilizing historical data obtained from the Malaysian Department of Environment, eleven years' worth (2011-2021) of daily readings of PM10, O3 , SO2 , NO2 , and CO from thirty-four monitoring stations were subjected to environmetric techniques and Artificial Neural Network analysis. HACA successfully classified seven stations into the High Pollution Cluster (HPC), seven stations into the Moderate Pollution Cluster (MPC), and twenty stations into the Low Pollution Cluster (LPC). The discriminant analysis demonstrated a correct assignment rate of 91.87%, suggesting that all five parameters were able to discriminate with a significance level of p&lt;0.0001. Principal Component Analysis (PCA) revealed two Variance Factors (VFs) across all clusters, with cumulative variances of 69.43% (HPC), 82.32% (MPC), and 62.16% (LPC) respectively. Furthermore, employing a Multi-Layer Perceptron Feed-Forward Artificial Neural Network (MLP-FF-ANN) model to predict API readings yielded a significant and strong correlation, as indicated by R2 =0.7774 and RMSE=9.9048. These findings hold significant potential in informing the adoption of effective preventative and management strategies.

Detection and diagnosis of bacterial pathogens in blood using laser-induced breakdown spectroscopy

The ability to rapidly and accurately detect and identify pathogenic bacteria in clinically-obtained blood specimens with laser-induced breakdown spectroscopy (LIBS) is evaluated. Samples of blood obtained from five patients in a local hospital were confirmed to be negative for the presence of bacteria by the pathology department and were then tested with LIBS. Specimens of blood were tested as obtained from the hospital with no other alteration as control samples and were also intentionally spiked with known aliquots of Escherichia coli, Staphylococcus aureus, Enterobacter cloacae, and Pseudomonas aeruginosa to simulate blood infections. LIBS spectra were acquired from blood deposited on nitrocellulose filters. The intensities of 15 emission lines measured in the spectra and 92 ratios of those line intensities were used as 107 independent variables in a partial least squares discriminant analysis (PLS-DA) to discriminate between sterile control samples and those spiked with bacteria. In addition, the entire LIBS spectrum from 200 nm – 590 nm was input into an artificial neural network analysis with principal component analysis pre-processing (PCA-ANN) to diagnose the bacterial species once detected.The PLS-DA test possessed a 96.3% sensitivity and a 98.6% specificity for the detection of pathogenic bacteria in blood when 776 spectra from 26 filters were tested by removing one entire filter at a time from the model and testing each spectrum individually. When all the spectra obtained from a filter were averaged to enhance the signal to noise of the spectrum, 19 of 19 filters of infected blood tested positive and 7 of 7 filters with sterile blood tested negative, yielding 100% sensitivity and 100% specificity. The PCA-ANN test performed on the entire LIBS spectrum possessed a 100% sensitivity and 100% specificity when using 80% of the data to build a model and withholding 20% for cross-validation testing. The same PCA-ANN performed on each of the 19 filters individually, using the other 18 filters to build the model, possessed an average sensitivity of 85.5%, an average specificity of 95.0%, and a classification accuracy of 92.5%. These results indicate the potential usefulness of LIBS for detecting and diagnosing blood infections in a clinical setting.

Investigating Barriers to the Adoption of Energy Management Practices for Sustainable Construction Projects: SEM and ANN Approaches

This research addresses the critical challenges hindering the integration of Energy Management Practices (EMPs) within the construction industry, impeding its progress toward sustainability. Recognizing the pivotal role of EMPs in fostering sustainable practices, this study aims to fill a notable research gap by conducting a meticulous survey involving 100 industry professionals. Through the application of Partial Least Squares Structural Equation Modeling (PLS-SEM) and Artificial Neural Network (ANN) analyses, this study provides a comprehensive exploration of the intricate barriers and their interrelated dynamics within the construction sector. The findings reveal significant financial obstacles, including higher initial costs and limited financing options, underscoring the need for interventions to alleviate financial constraints. Additionally, policy and regulatory challenges, such as limited government incentives and shifting energy management rules, are identified, highlighting the necessity for stable and supportive regulatory environments to foster EMP adoptions. This research provides unique insights into the barriers hindering EMP adoption within the construction sector. The implications of this study extend beyond EMP adoption, offering a foundation for advancing sustainable practices in the construction industry. The insights gained can inform both academic research and practical decision-making, contributing to the ongoing discourse on sustainability in construction. Doi: 10.28991/CEJ-2024-010-04-015 Full Text: PDF

Compare deep learning model and conventional logistic regression model for the identification of unstable saccular intracranial aneurysms in computed tomography angiography.

It is crucial to distinguish unstable from stable intracranial aneurysms (IAs) as early as possible to derive optimal clinical decision-making for further treatment or follow-up. The aim of this study was to investigate the value of a deep learning model (DLM) in identifying unstable IAs from computed tomography angiography (CTA) images and to compare its discriminatory ability with that of a conventional logistic regression model (LRM). From August 2011 to May 2021, a total of 1,049 patients with 681 unstable IAs and 556 stable IAs were retrospectively analyzed. IAs were randomly divided into training (64%), internal validation (16%), and test sets (20%). Convolutional neural network (CNN) analysis and conventional logistic regression (LR) were used to predict which IAs were unstable. The area under the curve (AUC), sensitivity, specificity and accuracy were calculated to evaluate the discriminating ability of the models. One hundred and ninety-seven patients with 229 IAs from Banan Hospital were used for external validation sets. The conventional LRM showed 11 unstable risk factors, including clinical and IA characteristics. The LRM had an AUC of 0.963 [95% confidence interval (CI): 0.941-0.986], a sensitivity, specificity and accuracy on the external validation set of 0.922, 0.906, and 0.913, respectively, in predicting unstable IAs. In predicting unstable IAs, the DLM had an AUC of 0.771 (95% CI: 0.582-0.960), a sensitivity, specificity and accuracy on the external validation set of 0.694, 0.929, and 0.782, respectively. The CNN-based DLM applied to CTA images did not outperform the conventional LRM in predicting unstable IAs. The patient clinical and IA morphological parameters remain critical factors for ensuring IA stability. Further studies are needed to enhance the diagnostic accuracy.

Combined 100 keV Cryo-Electron Microscopy and Image Analysis Methods to Characterize the Wider Adeno-Associated Viral Products

Adeno-associated viruses (AAVs) are effective vectors for gene therapy. However, AAV drug products are inevitably contaminated with empty particles (EP), which lack a genome, owing to limitations of the purification steps. EP contamination can reduce the transduction efficiency and induce immunogenicity. Therefore, it is important to remove EPs and to determine the ratio of full genome-containing AAV particles to empty particles (F/E ratio). However, most of the existing methods fail to reliably evaluate F/E ratios that are greater than 90 %. In this study, we developed two approaches based on the image analysis of cryo-electron micrographs to determine the F/E ratios of various AAV products. Using our developed convolutional neural network (CNN) and morphological analysis, we successfully calculated the F/E ratios of various AAV products and determined the slight differences in the F/E ratios of highly purified AAV products (purity > 95 %). In addition, the F/E ratios calculated by analyzing more than 1000 AAV particles had good correlations with theoretical F/E ratios. Furthermore, the CNN reliably determined the F/E ratio with a smaller number of AAV particles than morphological analysis. Therefore, combining 100 keV cryo-EM with the developed image analysis methods enables the assessment of a wide range of AAV products.

Agarwood oil quality identification using artificial neural network modelling for five grades

Agarwood (Aquilaria Malaccensis) oil stands out as one of the most valuable and highly sought-after oils with a hefty price tag due to its widespread use of fragrances, incense, perfumes, ceremonial practices, medicinal applications and as a symbol of luxury. However, nowadays the conventional method that rely on color alone has its limitations as it yields varying results depending on individual panelists' experiences. Hence, the quality identification system of Agarwood oil using its chemical compounds had been proposed in this study to enhance the precision of the Agarwood oil grades thus addressing the shortcomings of traditional methods. This study indicates that the primary chemical compounds of Agarwood oil encompass ɤ-Eudesmol, ar-curcumene, β-dihydroagarofuran, ϒ-cadinene, α-agarofuran, allo-aromadendrene epoxide, valerianol, α-guaiene, 10-epi-ɤ-eudesmol, β-agarofuran and dihydrocollumellarin. This study employed artificial neural network analysis with the implementation of Levenberg-Marquardt algorithm to identify the Agarwood oil grades. The study's findings revealed that this modeling system of five grades got 100% accuracies with mean square error of 0.14338×10-08. Notably, this lowest mean square error (MSE) value falls within the best hidden neuron 3. These study outcomes play a pivotal role in highlighting the Levenberg Marquardt- artificial neural network (LM-ANN) modeling that contribute to the successful of Agarwood oil quality identification using its chemical compounds.

Neural network analysis of neutron and X-ray reflectivity data incorporating prior knowledge.

Due to the ambiguity related to the lack of phase information, determining the physical parameters of multilayer thin films from measured neutron and X-ray reflectivity curves is, on a fundamental level, an underdetermined inverse problem. This ambiguity poses limitations on standard neural networks, constraining the range and number of considered parameters in previous machine learning solutions. To overcome this challenge, a novel training procedure has been designed which incorporates dynamic prior boundaries for each physical parameter as additional inputs to the neural network. In this manner, the neural network can be trained simultaneously on all well-posed subintervals of a larger parameter space in which the inverse problem is underdetermined. During inference, users can flexibly input their own prior knowledge about the physical system to constrain the neural network prediction to distinct target subintervals in the parameter space. The effectiveness of the method is demonstrated in various scenarios, including multilayer structures with a box model parameterization and a physics-inspired special parameterization of the scattering length density profile for a multilayer structure. In contrast to previous methods, this approach scales favourably when increasing the complexity of the inverse problem, working properly even for a five-layer multilayer model and a periodic multilayer model with up to 17 open parameters.