Improved Normalization Method Research Articles

Soil nutrient prediction for paddy cultivation via soil fertility and pH trained hybrid architecture: Recommendations based on nutrient deficiency

Soil testing can assist in determining how much fertilizer is necessary, as it depends on the fertility and crop of the soil. Through soil fertility and pH-trained hybrid architecture, a new soil nutrient prediction model for paddy agriculture is proposed in this work. First, data acquisition takes place, which is the act of gathering soil data, and it is subsequently preprocessed using the Improved Normalization method. A soil information dataset is employed in this work to help with this. Subsequently, the preprocessed data undergoes data augmentation; the correlation method facilitates an enhanced data augmentation procedure. In this case, the data used for the correlation approach is min-max normalization data. The augmented data is used to extract soil properties such as pH level and soil fertility index. Additionally, a hybrid classifier strategy that combines RNN and Modified LSTM is suggested for nutrient prediction. Lastly, this article suggested some fertilizers for nutritional insufficiency based on the projection. The hybrid prediction classifiers that have been suggested perform better in experiments than the classic classifier models, which include LSTM, RNN, SVM, Bi-GRU, and DNN, in terms of sensitivity, accuracy, FPR, MCC, precision, and efficiency in predicting nutrients. Even though the CNN (0.075), Bi-GRU (0.080), LSTM (0.087), DBN (0.078), Enhanced-1DCNN DLM (0.080), RNN (0.085), and RFA (0.052) obtained maximal FPR ratings, the FPR of the Modified LSTM+RNN scheme is 0.052.

Multi scale evaluation and analysis of international trade objectives of port cities

The analysis of the multi-scale evaluation of port city’s international trade goal is conducive to the sustainable development of port city’s international trade. In order to make a more in-depth study on the realization ability of port city’s international trade sustainable development goal, this paper proposes a new multi-scale evaluation method of port city’s international trade goal. This method selects the evaluation indexes, uses the improved normalization method to process the indexes, uses the combination of AHP and factor analysis method to form the subjective and objective combination weighting method, brings the processed indexes into the least square optimization combination evaluation model, calculates the index weight, and uses the fuzzy evaluation method to carry out multi-scale index evaluation on the international trade of port cities to realize its multi-scale evaluation and analysis. The results show that the standardized index of Shanghai’s foreign trade dependence is 0.0056, indicating its independence in international trade. In the comprehensive evaluation, the evaluation values of Shanghai, Tianjin, and Guangzhou are 92.56, 87.89, and 88.45, respectively, which are very close to the actual results, which shows that the accuracy of the evaluation method is high, and provides a theoretical basis for the sustainable development of international trade in port cities.

Assessment of chemical integrity based on ecological thresholds in one of the largest basins in China

Chemical integrity plays a crucial role in maintaining the ecological integrity of aquatic ecosystems. Despite its significance, assessments of ecological integrity often prioritize biological evaluations, leading to infrequent and inadequate scrutiny of chemical integrity. Additionally, a comprehensive framework for separately assessing chemical integrity has not been established. To address this issue, we established a new index for evaluating chemical integrity, achieved through indicator screening, normalization, and weight assignment. Combined with the threshold indicator taxa analysis (TITAN) and Pearson correlation analysis, main evaluation indicators and their indicator species were identified and the corresponding response threshold was determined. These thresholds were then applied to improve the normalization method, with weights determined by indicator species proportions. This index was applied to evaluate the chemical integrity of the Huai River Basin, which is one of the largest basins in China. The results indicated that ammonia nitrogen (NH3-N), nitrate nitrogen (NO2-N), pH, and turbidity (Turb) were identified as the core parameters for evaluating chemical integrity. The conventional normalization method (max–min normalization) led to overestimation of the assessment results, whereas improved normalization method based on ecological threshold yielded better and more reasonable results. This study provided a reference for the selection of appropriate indicators and improvement of normalization method to better assess chemical integrity. And it would become a useful tool for monitoring and rehabilitating the chemical integrity in such basins.

FreeGaze: A Framework for 3D Gaze Estimation Using Appearance Cues from a Facial Video

Gaze is a significant behavioral characteristic that can be used to reflect a person’s attention. In recent years, there has been a growing interest in estimating gaze from facial videos. However, gaze estimation remains a challenging problem due to variations in appearance and head poses. To address this, a framework for 3D gaze estimation using appearance cues is developed in this study. The framework begins with an end-to-end approach to detect facial landmarks. Subsequently, we employ a normalization method and improve the normalization method using orthogonal matrices and conduct comparative experiments to prove that the improved normalization method has a higher accuracy and a lower computational time in gaze estimation. Finally, we introduce a dual-branch convolutional neural network, named FG-Net, which processes the normalized images and extracts eye and face features through two branches. The extracted multi-features are then integrated and input into a fully connected layer to estimate the 3D gaze vectors. To evaluate the performance of our approach, we conduct ten-fold cross-validation experiments on two public datasets, namely MPIIGaze and EyeDiap, achieving remarkable accuracies of 3.11° and 2.75°, respectively. The results demonstrate the high effectiveness of our proposed framework, showcasing its state-of-the-art performance in 3D gaze estimation.

Probabilistic quotient’s work and pharmacokinetics’ contribution: countering size effect in metabolic time series measurements

Metabolomic time course analyses of biofluids are highly relevant for clinical diagnostics. However, many sampling methods suffer from unknown sample sizes, commonly known as size effects. This prevents absolute quantification of biomarkers. Recently, several mathematical post acquisition normalization methods have been developed to overcome these problems either by exploiting already known pharmacokinetic information or by statistical means. Here we present an improved normalization method, MIX, that combines the advantages of both approaches. It couples two normalization terms, one based on a pharmacokinetic model (PKM) and the other representing a popular statistical approach, probabilistic quotient normalization (PQN), in a single model. To test the performance of MIX, we generated synthetic data closely resembling real finger sweat metabolome measurements. We show that MIX normalization successfully tackles key weaknesses of the individual strategies: it (i) reduces the risk of overfitting with PKM, and (ii), contrary to PQN, it allows to compute sample volumes. Finally, we validate MIX by using real finger sweat as well as blood plasma metabolome data and demonstrate that MIX allows to better and more robustly correct for size effects. In conclusion, the MIX method improves the reliability and robustness of quantitative biomarker detection in finger sweat and other biofluids, paving the way for biomarker discovery and hypothesis generation from metabolomic time course data.

Abstract 5459: Combining targeted sequencing and ultra-low pass WGS for accurate SCNA detection on tumor sample of biased aneuploidy

Abstract Background: Somatic copy number alteration (SCNA) as a common genomic abnormality in tumor tissue has been routinely screened in cancer research due to its role in tumorigenesis. One popular approach of genome scale SCNA detection is ultra-low pass WGS. However, when dealing with tumor sample of highly biased aneuploidy, this method makes biased normalization of sequencing throughput and consequently yields inaccurate SCNA result. Recent pan-cancer study revealed that whole genome doubling (WGD), a typical example of severe aneuploidy, occurs in 56% of tumor samples, much higher than previous estimation. This observation highlights the imminent need for developing more accurate SCNA detection method especially when dealing with tumor with high aneuploidy. Here, we present a targeted sequencing guided SCNA detection method that allows accurate SCNA detection through improved normalization method. Method: Both tumor and normal tissue samples were subjected to targeted sequencing (genomic coverage: 300kb) and 2X ultra-low pass WGS. Genomic regions were binned and corresponding depth was calculated. GC bias was corrected using LOWSS regression. Single nucleotide polymorphism (SNP) was detected from targeted sequencing. We then selected all genomics regions that have stable sequencing depth as well as stable VAF of heterozygous SNP. Among them, regions of lowest depth were defined as diploid and used for sequencing throughput normalization. To compare SCNA results from different methods, we defined CN-VAF conflict index to represent the deviation of VAF of heterozygous SNP from diploid region. Results and conclusion: We applied our method to a total number of 29 NSCLC samples. In comparison with CNVkit and ControlFreeC results, 18 tumor samples shown consistent SCAN results. For the rest of 11 samples, CNVkit and ControlFreeC resulted in overall higher SCNA across genome comparing to our method. Correlation analysis shown that the inconsistency of SCNA status between methods positively correlates with the difference of baseline estimation (Pearson coef: CNVkit: 0.93, ControlFreeC: 0.91). For samples with inconsistent SCNA results, CN-VAF conflict index indicated that our method made more accurate SCNA estimation through improved baseline estimation. In addition, experimental validation using ddPCR shown consistent SCNA results with our method. It is worth mentioning that the key step of our method is the estimation of baseline depth which depends on the length of genomic region, the amount of SNP within the genomic region and the fluctuation of both depth and SNP VAF in the region. If sufficient tumor samples with experimentally validated aneuploidy are available, machine learning approach may make further improved estimation. Lastly, it is worth mentioning that this article is not intended to propose using both targeted sequencing and ultra-low WGS in routine NGS experiment, but rather as a complementary approach to evaluate the heterogeneity of aneuploidy tumor. Citation Format: Cheng Yan, Weihua Guo, Yufei Yang. Combining targeted sequencing and ultra-low pass WGS for accurate SCNA detection on tumor sample of biased aneuploidy [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5459.

Rule-based Text Normalization for Malay Social Media Texts

Malay social media text is a text written on social media networks like Twitter. Commonly, this text comprises non-standard words, filled with dialects, foreign languages, word abbreviations, grammatical neglect, spelling errors, and many more. It is well known that this type of text is difficult to process due to its high noise and distinct text structure. Such problems can be resolved using rigorous text normalization, which is critical before any technique can be implemented and evaluated on social media text. In this paper, an improved normalization method towards Malay social media text was proposed by converting non-standard Malay words using a rule-based model. The method normalizes common language words often used by Malaysian users, such as non-standard Malay (like dialect and slangs), Romanized Arabic, and English words. Thus, a Malay text normalizer was proposed using a set of rules that extend across different domains of natural language processing (NLP) and is expected to address the challenges of processing Malay social media text. This study implements the proposed Malay text normalizer in a Part-of-Speech (POS) tagging application to evaluate the normalizer’s performance. The implementation demonstrates a substantial improvement in the POS tagging efficiency over several pre-processing stages, with an improvement of accuracy up to 31.8%. The increase of accuracy in the POS tagging indicates two main points. First, the Malay text normalizer’s rules improve the performance of a Malay text normalizer on social media text. Second, our proposed Malay text normalizer has successfully improved the POS tagging percentage and demonstrates the importance of normalized pre-processing in any NLP application.

Investigating a New Approach for Moisture Assessment of Transformer Insulation System

With the wide application of vegetable insulating oil in power transformers, it is essential to investigate the influence of moisture on the frequency domain dielectric spectrum (FDS) analysis in order to propose an accurate moisture assessment method for vegetable oil-paper insulation system. In this regard, various vegetable oil-paper samples are prepared in a laboratory environment. FDS curves for all samples are obtained at different moisture contents and operating temperatures. A new method to estimate the moisture content in insulation paper is proposed based on the low-frequency characteristic quantity. Also, the effect of operating temperature and water migration between the oil and paper insulation on the FDS analysis is investigated. Based on this analysis, a compensation factor is introduced to eliminate the influence of temperature on paper moisture assessment. Furthermore, an improved normalization method for the temperature effect on the FDS of vegetable oil-paper insulation with different moisture contents is proposed. The proposed moisture assessment and temperature normalization methods are validated thorough their application on an operating power transformer.

Improved normalization method for ductile fracture toughness determination based on dimensionless load separation principle

This study successfully deals with the inhomogeneous dimension problem of load separation assumption, which is the theoretical basis of the normalization method. According to the dimensionless load separation principle, the normalization method has been improved by introducing a forcible blunting correction. With the improved normalization method, the J-resistance curves of five different metallic materials of CT and SEB specimens are estimated. The forcible blunting correction of initial crack size plays an important role in the J-resistance curve estimation, which is closely related to the strain hardening level of material. The higher level of strain hardening leads to a greater difference in JQ determined by different slopes of the blunting line. If the blunting line coefficient recommended by ASTM E1820-11 is used in the improved normalization method, it will lead to greater fracture resistance than that processed by the blunting line coefficient recommended by ISO 12135-2002. Therefore, the influence of the blunting line on the determination of JQ must be taken into full account in the fracture toughness assessment of metallic materials.

An Improved Normalization Method for Ear Feature Extraction

As a new biometric identification technology, the theory and application research of ear recognition has attracted more and more attention of scholars in recent years. Imagepreprocessing and normalization of ear image is very important for the feature extraction. In this paper we apply the improved morphological filtering method to preprocessing the ear image. And we propose the angle normalization method by geometrical parameters. This method has the advantages of scaling invariance, translation invariance and rotation invariance. The normalization results are reasonable and good for later feature extraction.

An Improved Method of Smoke Point Normalization

The most widely used method of normalizing laminar smoke points from different studies has been the threshold sooting index (TSI). TSI has several drawbacks, particularly a nonphysical dependence on fuel molar mass. An improved normalization method, called normalized smoke point (NSP), is introduced here. For each fuel, NSP is a weighted average of the smoke point lengths measured by different studies. NSP eliminates the major TSI drawbacks. NSP values were determined for 112 hydrocarbons by averaging 256 smoke point measurements from 12 past studies. This allows for the most extensive evaluation to date of the effects of fuel type on the smoke points of hydrocarbon diffusion flames. Hydrocarbon sooting propensity generally increases with increasing fuel carbon/hydrogen atom ratio. For n-alkanes, sooting propensity increases with carbon number, however it generally decreases with carbon number for alkenes and 1-alkynes. Sooting propensity generally increases according to alkanes < alkenes < 1-alkynes < aromatics.

A Model-Based Analysis of Chemical and Temporal Patterns of Cuticular Hydrocarbons in Male Drosophila melanogaster

Drosophila Cuticular Hydrocarbons (CH) influence courtship behaviour, mating, aggregation, oviposition, and resistance to desiccation. We measured levels of 24 different CH compounds of individual male D. melanogaster hourly under a variety of environmental (LD/DD) conditions. Using a model-based analysis of CH variation, we developed an improved normalization method for CH data, and show that CH compounds have reproducible cyclic within-day temporal patterns of expression which differ between LD and DD conditions. Multivariate clustering of expression patterns identified 5 clusters of co-expressed compounds with common chemical characteristics. Turnover rate estimates suggest CH production may be a significant metabolic cost. Male cuticular hydrocarbon expression is a dynamic trait influenced by light and time of day; since abundant hydrocarbons affect male sexual behavior, males may present different pheromonal profiles at different times and under different conditions.

FastQ-tensor method for modeling the dynamics of defects in a liquid crystal director field

A fast Q-tensor method, which can model the defect dynamics in a liquid crystal director field, is presented. Conceptually based on the Oseen-Frank approach, we have added temperature energy density terms in addition to the strain energy terms, and an improved normalization method for fast calculations. The method is more compact and allows a larger time step than previous methods. The method is used to model the defect dynamics occurring during the topological state change from a splay to bend director field configuration.

Fast Q-tensor method for modeling liquid crystal director configurations with defects

A fast Q-tensor method, which can model the configuration of the liquid crystal director fields containing defects, is proposed. Conceptually based on the Oseen–Frank approach, we have added temperature energy density terms in addition to the strain energy term. We have also derived an improved normalization method for fast calculations. The method is used to model the conversion of a reverse tilt wall to a pair of disclination lines.