Precise Method Research Articles

Defect Detection of Tomato Fruit Using Image Dataset

Abstract: The tomato crop is more susceptible to disease than any other vegetable, and it can be infected with over 200 diseases caused by different pathogens worldwide. Tomato plant diseases have become a challenge to food security globally. Currently, diagnosing and preventing tomato plant diseases is a challenge due to the lack of essential methods or tools.The traditional techniques of detecting plant disease are arduous and error-prone. Utilizing precise or automatic detection methods in spotting early plant disease can improve the quality of food production and reduce adverse effects. Deep learning has significantly increased the recognition accuracy of image classification and object detection systems in recent years. In this study, a 15-layer convolutional neural network is proposed as the backbone for single shot detector (SSD) to improve the detection of healthy, and three classes of tomato fruit diseases. The proposed model performance is compared with ResNet-50, AlexNet, VGG 16, and VGG19 as the backbone for Single shot detector. The findings of the experiment showed that the proposed CNN-SDD achieved 98.87% higher detection accuracy, which outperformed state-of-the-art models.

A survey on cryptocurrency price prediction

A type of digital currency known as a cryptocurrency allows all transactions to be completed online. There is no hard cash version of this soft currency. We highlight that a decentralized currency differs from a centralized currency in the any user of a virtual currency can purchase services without the need for third parties to get involved. Due to its extreme price volatility, using these cryptocurrencies has an impact on trade and international relations. Moreover, the constantly fluctuating oscillations indicate the urgent need for a more precise method of predicting this price. Deep learning techniques that use effective learning models for training data, including the LSTM, GRU, and Feedback Neural Network, can be used to do this. Benchmark datasets are used to test the suggested strategy. That brings us to the neural network, one of the clever data mining technologies that researchers in many domains have been using for the past ten years. In the current economy, stock market data is essential. There are two types of forecasting methodologies: nonlinear models (ARCH, GARCH, Neural Network) and linear models (AR, MA, ARIMA, ARMA). To forecast a company's stock price based on past prices, we employed the Box Jenkins Model also known as ARIMA, and Long Short-Term Memory (LSTM), and Feedback Neural Network also known as RNN.

Engineering Modular DNA Reaction Networks for Signal Processing.

Diversified molecular information-processing methods have significant implications for nanoscale manipulation and control, monitoring and disease diagnosis of organisms, and direct intervention in biological activities. However, as an effective approach for implementing multifunctional molecular information processing, DNA reaction networks (DRNs) with numerous functionally specialized molecular structures have challenged them on scale and modular design, leading to increased network complexity, further causing problems such as signal leakage, attenuation, and cross-talk in network reactions. Our study developed a strategy for performing various signal-processing tasks through engineering modular DRNs composed of simple molecular structures. This strategy is based on a universal core unit with signal selection capability, and a timeadjustable signal self-resetting module is achieved by combing the core unit and self-resetting unit, which improves the time controllability of modular DRNs. In addition, multi-input and -output signal crosscatalytic and continuously adjustable signal delay modules were realized by combining core and threshold units, providing a flexible, precise method for modular DRNs to process the signal. The strategy simplifies the design of DRNs, helps generate design ideas for largescale integrated DRNs with multiple functions, and provides prospects in biocomputing, gene regulation, and biosensing.

Understanding genetic variability: exploring large-scale copy number variants through non-invasive prenatal testing in European populations.

Large-scale copy number variants (CNVs) are structural alterations in the genome that involve the duplication or deletion of DNA segments, contributing to genetic diversity and playing a crucial role in the evolution and development of various diseases and disorders, as they can lead to the dosage imbalance of one or more genes. Massively parallel sequencing (MPS) has revolutionized the field of genetic analysis and contributed significantly to routine clinical diagnosis and screening. It offers a precise method for detecting CNVs with exceptional accuracy. In this context, a non-invasive prenatal test (NIPT) based on the sequencing of cell-free DNA (cfDNA) from pregnant women's plasma using a low-coverage whole genome MPS (WGS) approach represents a valuable source for population studies. Here, we analyzed genomic data of 12,732 pregnant women from the Slovak (9,230), Czech (1,583), and Hungarian (1,919) populations. We identified 5,062 CNVs ranging from 200 kbp and described their basic characteristics and differences between the subject populations. Our results suggest that re-analysis of sequencing data from routine WGS assays has the potential to obtain large-scale CNV population frequencies, which are not well known and may provide valuable information to support the classification and interpretation of this type of genetic variation. Furthermore, this could contribute to expanding knowledge about the central European genome without investing in additional laboratory work, as NIPTs are a relatively widely used screening method.

Evolution of the Transforaminal Lumbar Interbody Fusion (TLIF): From Open to Percutaneous to Patient-Specific

The transforaminal lumbar interbody fusion (TLIF) has seen significant evolution since its early inception, reflecting advancements in surgical techniques, patient safety, and outcomes. Originally described as an improvement over the posterior lumbar interbody fusion (PLIF), the TLIF began as an open surgical procedure, that notably reduced the need for the extensive neural retractation that hindered the PLIF. In line with the broader practice of surgery, trending toward minimally invasive access, the TLIF was followed by the development of the minimally invasive TLIF (MIS-TLIF), a technique that further decreased tissue trauma and postoperative complications. Subsequent advancements, including Trans-Kambin’s Triangle TLIF (percLIF) and transfacet LIF, have continued to refine surgical access, minimize surgical footprint, and reduce the risk of injury to the patient. The latest evolution, as we will describe it, the patient-specific TLIF, is a culmination of the aforementioned adaptations and incorporates advanced imaging and segmentation technologies into perioperative planning, allowing surgeons to tailor approaches based on individual patient anatomy and pathology. These developments signify a shift towards more precise methods in spine surgery. The ongoing evolution of the TLIF technique illustrates the dynamic nature of surgery and emphasizes the need for continued adaptation and refinement.

Method PPC for Precise Piecewise Correlation after Histogram Segmentation

Correlation, functioning as a symmetric relation, is very powerful indicator of the mutual association between two attributes. The problem of weak correlation indicates a lack of linearity in the observed range. This paper presents the precise piecewise correlation method, which overcomes the problem by determining the segments where the linear association will be present. The determination was achieved using the histogram segmentation method. The conditions of the application and analysis of the method are presented, as well as the application of the method to the representative datasets. The obtained results confirm the existence of stronger linear associations on the segments. Detected correlations reveal the strength and nature of the symmetric association between two attributes on each of the separated segments.

Determining effects of doping lithium nickel oxide with tungsten using Compton scattering

X-ray Compton scattering experiments along with parallel first-principles computations were carried out on LiNiO2 to understand the effects of W doping on this cathode material for Li-ion batteries. By employing high-energy x rays exceeding 100 keV, an insight is gained into the fate of the W valence electrons, which are adduced to undergo transfer to empty O 2p energy bands within the active oxide matrix of the cathode. The substitution of W for Ni is shown to increase the electronic conductivity and to enhance the total magnetization per Ni atom. Our study demonstrates that an analysis of line shapes of Compton scattered x rays in combination with theoretical modeling can provide a precise method for an atomic level understanding of the nature of the doping process.

RASP: Optimal Single Puncta Detection in Complex Cellular Backgrounds.

Super-resolution and single-molecule microscopies have been increasingly applied to complex biological systems. A major challenge of these approaches is that fluorescent puncta must be detected in the low signal, high noise, heterogeneous background environments of cells and tissue. We present RASP, Radiality Analysis of Single Puncta, a bioimaging-segmentation method that solves this problem. RASP removes false-positive puncta that other analysis methods detect and detects features over a broad range of spatial scales: from single proteins to complex cell phenotypes. RASP outperforms the state-of-the-art methods in precision and speed using image gradients to separate Gaussian-shaped objects from the background. We demonstrate RASP's power by showing that it can extract spatial correlations between microglia, neurons, and α-synuclein oligomers in the human brain. This sensitive, computationally efficient approach enables fluorescent puncta and cellular features to be distinguished in cellular and tissue environments, with sensitivity down to the level of the single protein. Python and MATLAB codes, enabling users to perform this RASP analysis on their own data, are provided as Supporting Information and links to third-party repositories.

Novel automated AIMLAM for diagnosis of Mycobacterium tuberculosis.

Aim: A rapid and precise diagnostic method is crucial for timely intervention and management of tuberculosis. The present study compared the diagnostic accuracy of a novel lipoarabinomannan (LAM) antigen test, AIMLAM, for tuberculosis in urine samples. Methodology: The study subjected 106 TB suspects to smear microscopy, MGIT, GeneXpert and AIMLAM. Results: Among 106, smear microscopy identified 36 as positive (33%) (sensitivity; 70.93%, 95% CI (60.14-80.22%), while MGIT showed 38 positive (36.8%). GeneXpert detected 59 positives (sensitivity; 96.83, 95% CI (89.00-99.61%)). AIMLAM declared 61 as positive (57.5%) (sensitivity; 100.00, 95% CI (94.13-100.00%) and 45 as negative (42.5%). Conclusion: Overall, AIMLAM demonstrated better diagnostic accuracy than GeneXpert Assay, smear microscopy and MGIT liquid culture in urine samples.

Efficiency and Cost-effectiveness Analysis of Developed Embedded System-Controlled Seed and Fertilizer Applicator

In maize cultivation, the labour-intensive, time-consuming, and costly nature of manual planting necessitates the exploration of mechanized solutions. This study presents a comprehensive economic evaluation of an embedded system-controlled seed cum fertilizer applicator as a potential answer to these challenges.  Precise planting methods offer a multi-pronged approach, aiming to reduce cultivation costs, minimize time requirements, and improve worker comfort during planting operations.  By meticulously analyzing both ownership and operational expenses, this study empowers farmers with the knowledge necessary to make informed decisions. These decisions could involve adopting the precise seed cum fertilizer applicator technology, optimizing existing machinery, or exploring alternative methods to achieve enhanced farm productivity and financial gains.  The embedded system-controlled seed cum fertilizer applicator was directly compared to traditional manual planting methods. This analysis revealed ownership and operating costs of 9.56 Rs/h and 63 Rs/h, respectively.  The compelling findings demonstrated a remarkable 96.87% reduction in planting time and a 55.47% cost decrease when utilizing the embedded system compared to manual methods.  These results highlight the significant cost-effectiveness and environmental benefits associated with adopting this precise planting system.  This research offers valuable insights to farmers, potentially promoting the integration of advanced agricultural technologies that can optimize resource utilization, improve operational efficiency, and contribute to the implementation of sustainable farming practices.

Machine learning in the prediction and detection of new-onset atrial fibrillation in ICU: a systematic review.

Atrial fibrillation (AF) stands as the predominant arrhythmia observed in ICU patients. Nevertheless, the absence of a swift and precise method for prediction and detection poses a challenge. This study aims to provide a comprehensive literature review on the application of machine learning (ML) algorithms for predicting and detecting new-onset atrial fibrillation (NOAF) in ICU-treated patients. Following the PRISMA recommendations, this systematic review outlines ML models employed in the prediction and detection of NOAF in ICU patients and compares the ML-based approach with clinical-based methods. Inclusion criteria comprised randomized controlled trials (RCTs), observational studies, cohort studies, and case-control studies. A total of five articles published between November 2020 and April 2023 were identified and reviewed to extract the algorithms and performance metrics. Reviewed studies sourced 108,724 ICU admission records form databases, e.g., MIMIC. Eight prediction and detection methods were examined. Notably, CatBoost exhibited superior performance in NOAF prediction, while the support vector machine excelled in NOAF detection. Machine learning algorithms emerge as promising tools for predicting and detecting NOAF in ICU patients. The incorporation of these algorithms in clinical practice has the potential to enhance decision-making and the overall management of NOAF in ICU settings.

CD32 captures committed haemogenic endothelial cells during human embryonic development.

During embryonic development, blood cells emerge from specialized endothelial cells, named haemogenic endothelial cells (HECs). As HECs are rare and only transiently found in early developing embryos, it remains difficult to distinguish them from endothelial cells. Here we performed transcriptomic analysis of 28- to 32-day human embryos and observed that the expression of Fc receptor CD32 (FCGR2B) is highly enriched in the endothelial cell population that contains HECs. Functional analyses using human embryonic and human pluripotent stem cell-derived endothelial cells revealed that robust multilineage haematopoietic potential is harboured within CD32+ endothelial cells and showed that 90% of CD32+ endothelial cells are bona fide HECs. Remarkably, these analyses indicated that HECs progress through different states, culminating in FCGR2B expression, at which point cells are irreversibly committed to a haematopoietic fate. These findings provide a precise method for isolating HECs from human embryos and human pluripotent stem cell cultures, thus allowing the efficient generation of haematopoietic cells in vitro.

Predictive Modeling and Optimization of Plywood Drying: An Artificial Neural Network Approach

Introduction: This investigation delves into the optimization of the plywood drying process through the development of predictive models for output moisture content (MC_Out) and waviness. It focuses on bridging the gap in current methodologies by employing artificial neural networks (ANNs), optimized with genetic algorithms, to enhance prediction accuracy and process efficiency. Materials and Methods: A comprehensive experimental design was employed, analyzing the effects of three wood types (Doncel, Tamburo, and Zapote), two thickness levels, and three drying speeds on MC_Out and waviness. Data collected were subjected to both traditional statistical analysis and ANNs. The ANNs were fine-tuned through genetic algorithms, exploring different network architectures to achieve optimal predictive performance. Results: Statistical models revealed the significant influence of wood type, thickness, and drying speed on MC_Out and waviness, explaining 95.9% and 84.3% of the variations, respectively. The optimized ANN models, however, demonstrated superior accuracy, with the MC_Out model achieving fitted R-squared values of 0.940 and 0.757 for training and validation sets, respectively, thus outperforming traditional models in predicting drying outcomes. Discussion: The study underscores the effectiveness of ANNs in capturing complex nonlinear relationships within the plywood drying data, which traditional statistical models might not fully elucidate. The successful optimization of ANN architecture via genetic algorithms further highlights the potential of machine learning approaches in industrial applications, offering a more precise and reliable method for predicting drying process outcomes. Conclusion: The integration of artificial neural networks, optimized through genetic algorithms, represents a significant advancement in the predictive modeling of plywood drying processes. This approach not only offers enhanced prediction accuracy for key variables such as MC_Out and waviness but also paves the way for more efficient and controlled drying operations, ultimately contributing to the production of higher-quality plywood.

Model based precise register control method for shaft-driven Roll-to-Roll(R2R) systems

Shaft-driven Roll-to-Roll (R2R) printing systems have advantages in keeping the printing rolls synchronously rotating, thereby reducing the register errors of R2R printing systems. However, tension oscillations and propagation are still inevitable due to other factors such as disturbances, which lead to the fluctuation of register errors. In this paper, a new mechanical model is established for the shaft-driven R2R systems. Then, a model-based precise control (MBPC) method is proposed, in which an implicit coupling algorithm is designed for the cooperate actions of the dancer rolls to rapidly suppress the tension oscillations and propagation. The stability of the proposed control system is proved via Lyapunov analysis. Moreover, the effectiveness of the proposed modeling and control method is verified via an industrial example. The results indicate that the proposed model can accurately characterize the dynamics of the R2R systems, and the MBPC can effectively eliminate the register errors caused by the tension oscillations and propagation, through the implicit coupling algorithm. Additionally, the results also demonstrate that the MBPC can increase the register precision of package manufacturing R2R systems into ±40 micrometers, which is superior to the existing control method.

In vivo femtosecond laser nanosurgery of the cell wall enabling patch-clamp measurements on filamentous fungi

Studying the membrane physiology of filamentous fungi is key to understanding their interactions with the environment and crucial for developing new therapeutic strategies for disease-causing pathogens. However, their plasma membrane has been inaccessible for a micron-sized patch-clamp pipette for pA current recordings due to the rigid chitinous cell wall. Here, we report the first femtosecond IR laser nanosurgery of the cell wall of the filamentous fungi, which enabled patch-clamp measurements on protoplasts released from hyphae. A reproducible and highly precise (diffraction-limited, submicron resolution) method for obtaining viable released protoplasts was developed. Protoplast release from the nanosurgery-generated incisions in the cell wall was achieved from different regions of the hyphae. The plasma membrane of the obtained protoplasts formed tight and high-resistance (GΩ) contacts with the recording pipette. The entire nanosurgical procedure followed by the patch-clamp technique could be completed in less than 1 hour. Compared to previous studies using heterologously expressed channels, this technique provides the opportunity to identify new ionic currents and to study the properties of the ion channels in the protoplasts of filamentous fungi in their native environment.

METHOD DEVELOPMENT AND VALIDATION OF RAPID ISOCRATIC RP-HPLC METHOD FOR SIMULTANEOUS ESTIMATION OF PARACETAMOL, CAFFEINE, AND PROPYPHENAZONE IN PHARMACEUTICAL FORMULATION

Objective: The objective is to develop a novel, rapid, simple, precise, accurate, and reproducible RP-HPLC method for simultaneous estimation of paracetamol, caffeine, and propyphenazone in bulk and a pharmaceutical dosage form. Method: Optimized chromatographic conditions were used for isocratic elution with Shimadzu C18 (4.6×250 mm, 5 μm), methanol, and 20mM phosphate buffer (60:40, v/v, pH 2.5) as mobile phase, flow rate 1.2 mL/min, and UV detector at λ max 272 nm. The method was validated for specificity, precision, linearity, accuracy, robustness, and solution stability as per the International Council for Harmonization (ICH) guidelines. Result: It was discovered that the retention time for paracetamol, caffeine, and propyphenazone was found to be 2.6 min, 3.0 min, and 7.5 min. The method proved to be rapid, simple, linear (R2>0.999), precise {relative standard deviation (RSD<2.0%)}, accurate (recovery 98–102%), sensitive, and robust. Conclusion: The proposed novel isocratic RP-HPLC method was found to be rapid (short run time of 10 min), highly selective, accurate, and sensitive. The method has been successfully applied to the simultaneous analysis of paracetamol, caffeine, and propyphenazone in a pharmaceutical formulation.

Sleep duration time and human papillomavirus infection risk: The U-shaped relationship revealed by NHANES data.

This study aims to investigate the relationship between sleep factors (sleep duration time [SDT] and obstructive sleep apnea [OSA]) and human papillomavirus (HPV)/high-risk HPV(HR-HPV) infection, utilizing data from the National Health and Nutrition Examination Survey (NHANES). We conducted a cross-sectional analysis using NHANES data, focusing on SDT and OSA's association with HPV/HR-HPV infection. The primary statistical methods included weighted multivariate linear regression and logistic regression to assess the association between SDT, OSA, and HPV/HR-HPV infection. The study employed restricted cubic splines (RCS) for evaluating potential non-linear relationships between SDT and HPV/HR-HPV infection. Subgroup analyses were conducted. Interaction terms were used to examine the heterogeneity in associations across different subgroups. The study identified a U-shaped relationship between SDT and HPV infection. Specifically, 7 hours of sleep was associated with the lowest risk of HPV infection. In comparison, SDT less than 7 hours resulted in a 26.3% higher risk of HPV infection (Odds Ratio [OR] = 1.26, 95% Confidence Interval [CI]: 1.029, 1.549), and more than 9 hours of sleep showed a 57.4% increased risk (OR = 1.574, 95% CI: 1.116, 2.220). The relationship between SDT and HR-HPV infection was significant in the first two models, but not in the fully adjusted model. No significant interaction was found between sleep duration and other covariates. There was no association between OSA and HPV/HR-HPV infection. The study underscores the complex relationship between sleep duration and HPV infection risk, suggesting both very short and very long sleep durations may increase HPV infection likelihood. The findings highlight the need for further research to explore the biological mechanisms underpinning this association and to consider broader population groups and more precise sleep assessment methods in future studies.

Comparative Analysis of Examination Methods for Periapical Lesion Diagnostics: Assessing Cone-Beam Computer Tomography, Ultrasound, and Periapical Radiography.

Periapical lesions of teeth are typically evaluated using periapical X-rays (PA) or cone-beam computer tomography (CBCT); however, ultrasound imaging (US) can also be used to detect bone defects. A comparative analysis is necessary to establish the diagnostic accuracy of US for the detection of periapical lesions in comparison with PA and CBCT. This study aimed to evaluate and compare the measurement precision of US against PA and CBCT in detecting periapical lesions. This study included 43 maxillary and mandibular teeth with periapical lesions. All teeth were examined clinically, radiographically, and ultrasonographically. Observers evaluated and measured the periapical lesions on CBCT, PA, and US images. The comparison of lesion size showed that it differs significantly between the different methods of examination. A statistically significant difference was found between CBCT and US (mean difference = 0.99 mm, 95% CI [0.43-1.55]), as well as between CBCT and PA (mean difference = 0.61 mm, 95% CI [0.17-1.05]). No difference was found between the US and PA methods (p = 0.193). US cannot replace PA radiography in detecting pathologies but it can accurately measure and characterize periapical lesions with minimal radiation exposure. CBCT is the most precise and radiation-intensive method so it should only be used for complex cases.

Efficient parameter extraction of photovoltaic models with a novel enhanced prairie dog optimization algorithm

The growing demand for solar energy conversion underscores the need for precise parameter extraction methods in photovoltaic (PV) plants. This study focuses on enhancing accuracy in PV system parameter extraction, essential for optimizing PV models under diverse environmental conditions. Utilizing primary PV models (single diode, double diode, and three diode) and PV module models, the research emphasizes the importance of accurate parameter identification. In response to the limitations of existing metaheuristic algorithms, the study introduces the enhanced prairie dog optimizer (En-PDO). This novel algorithm integrates the strengths of the prairie dog optimizer (PDO) with random learning and logarithmic spiral search mechanisms. Evaluation against the PDO, and a comprehensive comparison with eighteen recent algorithms, spanning diverse optimization techniques, highlight En-PDO’s exceptional performance across different solar cell models and CEC2020 functions. Application of En-PDO to single diode, double diode, three diode, and PV module models, using experimental datasets (R.T.C. France silicon and Photowatt-PWP201 solar cells) and CEC2020 test functions, demonstrates its consistent superiority. En-PDO achieves competitive or superior root mean square error values, showcasing its efficacy in accurately modeling the behavior of diverse solar cells and performing optimally on CEC2020 test functions. These findings position En-PDO as a robust and reliable approach for precise parameter estimation in solar cell models, emphasizing its potential and advancements compared to existing algorithms.

Nuclear Magnetic Resonance Chemical Shift as a Probe for Single‐Molecule Charge Transport

AbstractExisting modelling tools, developed to aid the design of efficient molecular wires and to better understand their charge‐transport behaviour and mechanism, have limitations in accuracy and computational cost. Further research is required to develop faster and more precise methods that can yield information on how charge transport properties are impacted by changes in the chemical structure of a molecular wire. In this study, we report a clear semilogarithmic correlation between charge transport efficiency and nuclear magnetic resonance chemical shifts in multiple series of molecular wires, also accounting for the presence of chemical substituents. The NMR data was used to inform a simple tight‐binding model that accurately captures the experimental single‐molecule conductance values, especially useful in this case as more sophisticated density functional theory calculations fail due to inherent limitations. Our study demonstrates the potential of NMR spectroscopy as a valuable tool for characterising, rationalising, and gaining additional insights on the charge transport properties of single‐molecule junctions.