Principal Component Analysis Of Data Research Articles

Ultrafast and Ultrasensitive Bacterial Detection in Biofluids: Leveraging Resazurin as a Visible and Fluorescent Spectroscopic Marker.

Here we report the application of chemometric analysis for modeling absorbance spectroscopy and fluorescence emission data from a resazurin-based assay targeting low-level bacterial detection in biofluids. Bacteria spiked samples were incubated with resazurin and absorbance and fluorescence data were collected at 30 min intervals. The absorbance data was subjected to Principal Component Analysis (PCA) and Partial Least Squares Regression (PLSR) and compared with the univariate fluorescence spectroscopy approach. The analysis demonstrated the multidimensional nature of the absorbance data, highlighting the appearance of the resorufin peak at the 2 h time point with a low bacterial inoculum of 0.01 CFU mL-1 across all the samples tested-water, urine and serum. The PLSR models supported the PCA data and exhibited strong predictive capabilities for water (RC2 = 0.937, RCV2 = 0.934), urine (RC2 = 0.899, RCV2 = 0.880) and serum (RC2 = 0.985, RCV2 = 0.967). Conversely, fluorescence is contingent upon resorufin existence, necessitating a prolonged waiting period postincubation with resazurin to verify the presence of bacteria, especially when contamination levels are low. Given the substantial global impact of bacteria-related infections, this method detects bacteria at low concentrations precisely and rapidly, improving efficiency and adaptability for point-of-care settings, promising swift diagnosis of bacterial infections, environmental monitoring, or food-quality control.

The Optimization of Pair Distribution Functions for the Evaluation of the Degree of Disorder and Physical Stability in Amorphous Solids.

The amorphous form of poorly soluble drugs is physically unstable and prone to crystallization, resulting in decreased solubility and bioavailability. However, the conventional accelerated stability test for amorphous drugs is time-consuming and inaccurate. Therefore, there is an urgent need to develop rapid and accurate stability assessment technology. This study used the antitumor drug nilotinib free base as a model drug. The degree of disorder and physical stability in the amorphous form was assessed by applying the pair distribution function (PDF) and principal component analysis (PCA) methods based on powder X-ray diffraction (PXRD) data. Specifically, the assessment conditions, such as the PDF interatomic distance range, PXRD detector type, and PXRD diffraction angle range were also optimized. The results showed that more reliable PCA data could be obtained when the PDF interatomic distance range was 0-15 Å. When the PXRD detector was a semiconductor-type detector, the PDF data obtained were more accurate than other detectors. When the PXRD diffraction angle range was 5-40°, the intermolecular arrangement of the amorphous drugs could be accurately predicted. Finally, the accelerated stability test also showed that under the above-optimized conditions, this method could accurately and rapidly assess the degree of disorder and physical stability in the amorphous form of drugs, which has obvious advantages compared with the accelerated stability test.

Productive Performance and Fruit Quality of Short Days Italian Genetic-based Strawberry Cultivars and Genotypes in Southern Brazil

When grown under different climatic conditions, short days Italian genetic-based strawberry (IGBS) cultivars and genotypes present variations in productive performance and fruit quality. The objective of this study was to assess different productive performance and fruit quality of IGBS cultivars and genotypes grown in highland regions of southern Brazil. The experiment were conducted in randomized block design using three cultivars and 21 genotypes during the 2019, 2020 and 2021 growth seasons. The data were subjected to multivariate analysis using principal component analysis (PCA). IGBS cultivars and genotypes differed significantly in terms of productive and fruit quality parameters, according to PCA data. The first and second main components of PCA, respectively, were responsible for 61.9% and 72.4% of the productive performance and fruit quality parameters. The Pircinque cultivar and FRF PIR 256.04, ITA 13.097.05, ITA 12.103.22, ITA 10.107.07, and ITA 10.133.02 genotypes showed the highest indices of productive parameters of total production per plant, commercial production per plant, total fruit number per plant and commercial fruits number per plant. Among these, the Pircinque cultivar and the FRF PIR 256.04 genotypes demonstrated the best fruit quality parameters of fruit color (skin luminosity, chroma, and hue angle), flavor (soluble solids/ titratable acidity ratio), and fresh commercial fruit mass in highland regions of southern Brazil during the 2018, 2019 and 2020 growth seasons.

Systematic study and design of multimodal MRI image augmentation for brain tumor detection with loss aware exchange and residual networks

AbstractOne of the most difficult problems that develop when brain cells start to grow out of control is a brain tumor, which is regarded as the most lethal disease of the century. Finding and identifying malignant brain magnetic resonance imaging (MRI) images is the major challenge before therapy. Researchers have been putting a lot of effort into creating the best method for more accurate real‐world medical image recognition. For manual categorization, it is quite time‐consuming to segment large quantities of MRI data. To mitigate these issues, this paper suggests the information exchange gateway‐based residual UNet (IEGResUNet) model, which uses the ResUNet model as a base model. Additionally, including principal component analysis (PCA) data augmentation will increase the model's efficiency while also enhancing its speed. The IEGResUNet model shows an ablation investigation on three Brats datasets, with and without PCA augmentation. The results demonstrate that IEGResUNet will improve segmentation effectiveness and can also manage the imbalance in input data when PCA data augmentation models are included. The dice score on BraTS 2019 for whole tumor, region of core tumor, and region of enhancing tumor were 0.9083, 0.883, and 0.8106 respectively. Also, on BraTS 2020, the dice score for WT, CT, and ET 0.9083, 0.883, and 0.8106 was respectively. Similarly, on BraTS 2021, the dice score for WT, CT, and ET was 0.8737, 0.8866, and 0.7963 respectively. Comparing against baseline models, the IEGResUNet scored well in terms of dice score and intersection over union.

Evaluation of Short-Term Rockburst Risk Severity Using Machine Learning Methods

In deep engineering, rockburst hazards frequently result in injuries, fatalities, and the destruction of contiguous structures. Due to the complex nature of rockbursts, predicting the severity of rockburst damage (intensity) without the aid of computer models is challenging. Although there are various predictive models in existence, effectively identifying the risk severity in imbalanced data remains crucial. The ensemble boosting method is often better suited to dealing with unequally distributed classes than are classical models. Therefore, this paper employs the ensemble categorical gradient boosting (CGB) method to predict short-term rockburst risk severity. After data collection, principal component analysis (PCA) was employed to avoid the redundancies caused by multi-collinearity. Afterwards, the CGB was trained on PCA data, optimal hyper-parameters were retrieved using the grid-search technique to predict the test samples, and performance was evaluated using precision, recall, and F1 score metrics. The results showed that the PCA-CGB model achieved better results in prediction than did the single CGB model or conventional boosting methods. The model achieved an F1 score of 0.8952, indicating that the proposed model is robust in predicting damage severity given an imbalanced dataset. This work provides practical guidance in risk management.

Principal Component Analysis of Alternative Splicing Profiles Revealed by Long-Read ONT Sequencing in Human Liver Tissue and Hepatocyte-Derived HepG2 and Huh7 Cell Lines.

The long-read RNA sequencing developed by Oxford Nanopore Technology provides a direct quantification of transcript isoforms. That makes the number of transcript isoforms per gene an intrinsically suitable metric for alternative splicing (AS) profiling in the application to this particular type of RNA sequencing. By using this simple metric and recruiting principal component analysis (PCA) as a tool to visualize the high-dimensional transcriptomic data, we were able to group biospecimens of normal human liver tissue and hepatocyte-derived malignant HepG2 and Huh7 cells into clear clusters in a 2D space. For the transcriptome-wide analysis, the clustering was observed regardless whether all genes were included in analysis or only those expressed in all biospecimens tested. However, in the application to a particular set of genes known as pharmacogenes, which are involved in drug metabolism, the clustering worsened dramatically in the latter case. Based on PCA data, the subsets of genes most contributing to biospecimens' grouping into clusters were selected and subjected to gene ontology analysis that allowed us to determine the top 20 biological processes among which translation and processes related to its regulation dominate. The suggested metrics can be a useful addition to the existing metrics for describing AS profiles, especially in application to transcriptome studies with long-read sequencing.

GF-2 Data for Lithological Classification Using Texture Features and PCA/ICA Methods in Jixi, Heilongjiang, China

Lithological classification is a pivotal aspect in the field of geology, and traditional field surveys are inefficient and challenging in certain areas. Remote sensing technology offers advantages such as high efficiency and wide coverage, providing a solution to the aforementioned issues. The aim of this study is to apply remote sensing technology for lithological classification and attempt to enhance the accuracy of classification. Taking a study area in Jixi, Heilongjiang Province, China, as an example, lithological classification is conducted using high-resolution satellite remote sensing data from GF-2 and texture data based on gray-level co-occurrence matrix (GLCM). By comparing the accuracy of lithological classification using different methods, the support vector machine (SVM) method with the highest overall accuracy is selected for further investigation. Subsequently, this study compares the effects of combining GF-2 data with different texture data, and the results indicate that combining textures can effectively improve the accuracy of lithological classification. In particular, the combination of GF-2 and the Dissimilarity index performs the best among single-texture combinations, with an overall accuracy improvement of 7.8630% (increasing from 74.6681% to 82.5311%) compared to using only GF-2 data. In the multi-texture combination dataset, the Mean index is crucial for enhancing classification accuracy. Selecting appropriate textures for combination can effectively improve classification accuracy, but it is important to note that excessive overlaying of textures may lead to a decrease in accuracy. Furthermore, this study employs principal component analysis (PCA) and independent component analysis (ICA) to process the GF-2 data and combines the resulting PCA and ICA datasets with different texture data for lithological classification. The results demonstrate that combining PCA and ICA with texture data further enhances classification accuracy. In conclusion, this study demonstrates the application of remote sensing technology in lithological classification, with a focus on exploring the application value of different combinations of multispectral data, texture data, PCA data, and ICA data. These findings provide valuable insights for future research in this field.

PF543-like compound, a promising sphingosine kinase 1 inhibitor: Structure-based virtual screening and molecular dynamic simulation approaches

Sphingosine kinase 1 (SphK1) has been widely recognized as a significant contributor to various types of cancer, including breast, lung, prostate, and hematological cancers. This research aimed to find a potential SphK1 inhibitor through a step-by-step virtual screening of PF543 (a known SphK1 inhibitor)-like compounds obtained from the PubChem library with the Tanimoto threshold of 80 %. The virtual screening process included several steps, namely physicochemical and ADMET evaluation, PAINS filtering, and molecular docking, followed by molecular dynamics (MD) simulation and principal component analysis (PCA). The results showed that compound CID:58293960 ((3R)-1,1-dioxo-2-[[3-[(4-phenylphenoxy)methyl]phenyl]methyl]-1,2-thiazolidine-3-carboxylic acid) demonstrated high potential as SphK1 inhibitor. All-atom MD simulations were performed for 100 ns to evaluate the stability and structural changes of the docked complexes in an aqueous environment. The analysis of the time evolution data of structural deviations, compactness, PCA, and free energy landscape (FEL) indicated that the binding of CID:58293960 with SphK1 is relatively stable throughout the simulation. The results of this study provide a platform for the discovery and development of new anticancer therapeutics targeting SphK1.

Monitoring the solid-state VIS profiles of degrading bloodstains

Determining the time since deposition (TSD) of bloodstained evidence can be an important process in forensic investigations. Hemoglobin is often examined as a biomolecule of interest for these purposes due to the known ex vivo oxidative changes to its structure. These time-dependent oxidative processes have previously been probed using UV–VIS spectroscopy following the resuspension of bloodstains. Our study investigated the solid-state VIS spectra of degrading bloodstains without sample pre-treatment, effectively bypassing the need for resuspension. A total of 128 bloodstains from eight biological replicates were created and stored on glass slides in four temperature conditions: −20 °C, 4 °C, 22 °C, and 45 °C (see graphical abstract, created with BioRender.com). Spectra were acquired from 380−800 nm at five time points spanning 96 h. The peak area of the methemoglobin (metHb) band displayed the largest time and temperature differences, an interesting contrast to previous literature using the Soret band for TSD. Principal Component Analysis (PCA) demonstrated that storage temperature delineated the data, with the metHb band showing the greatest contributions to PC1. Linear mixed models from the PCA data with time showed clear TSD relationships with temperature, and with minimal inter-donor variability. Overall, this work complements the UV–VIS analysis of bloodstains for TSD estimation, with the importance of noting clear differences between phases and sample preparation methods.

Fat graft survival requires metabolic reprogramming toward the glycolytic pathway

Fat grafts are widely used as natural fillers in reconstructive and cosmetic surgery. However, the mechanisms underlying fat graft survival are poorly understood. Here, we performed an unbiased transcriptomic analysis in a mouse fat graft model to determine the molecular mechanism underlying free fat graft survival. We conducted RNA-sequencing (RNA-seq) analysis in a mousefree subcutaneous fat graft model on days 3 and 7 following grafting (n = 5). High-throughput sequencing was performed on paired-end reads using NovaSeq6000. The calculated transcripts per million (TPM) values were processed for principal component analysis (PCA), unsupervised hierarchically clustered heatmap generation, and gene set enrichment analysis. PCA and heatmap data revealed global differences in the transcriptomes of the fat graft model and the non-grafted control. The top meaningful upregulated gene sets in the fat graft model were related to the epithelial-mesenchymal transition, hypoxia on day 3, and angiogenesis on day 7. Mechanistically, the glycolytic pathway was upregulated in the fat graft model at days 3 (FDR q = 0.012) and 7 (FDR q = 0.084). In subsequent experiments, pharmacological inhibition of the glycolytic pathway in mouse fat grafts with 2-deoxy-D-glucose (2-DG) significantly suppressed fat graft retention rates, both grossly and microscopically (n = 5). Free adipose tissue grafts undergo metabolic reprogramming toward the glycolytic pathway. Future studies should examine whether targeting this pathway can enhance the graft survival rate.

Improvement of Germination in Retama Sphaerocarpa and Ziziphus Lotus for the Rehabilitation of Degraded Rangelands in Algeria

Abstract The shrubs of Retama sphaerocarpa (Fabaceae) and Ziziphus lotus (Rhamnaceae) form populations in the Aurès region of Algeria that can provide a large supply of forage when dry season grazing shortages are prevalent. However, the percentage of natural regeneration of these species was estimated at 3%-7%. The final germination percentage (FGP) and total seedling length (TSL) were taken into consideration in this study to determine the impact of wet scarification on the germination of these two species. Pre-sowing treatments included immersion in concentrated sulphuric acid for 1, 2 and 3 hours; immersion in boiling water for 15 minutes, and soaking for 24 hours in water at 25°C (± 2°C). For each pretreatment, four replicates of 50 seeds and kernels were sown in plastic containers for a 21-day period. The principal component analysis of data showed that the best treatment was 3 hours of soaking in sulphuric acid, resulting in 94% of FGP and 22.2 cm of TSL for R. sphaerocarpa. This same pretreatment improved the germination of Z. lotus to 86% FGP and 25.6 cm TSL. In both species studied, a very insignificant percentage of FGP (2%) was observed in untreated seeds and kernels, as well as in pre-treatments with water. These results indicate that R. sphaerocarpa seeds and Z. lotus kernels exhibit a type of physical seed dormancy and require specific pretreatment to improve imbibition and germination. In conclusion, pretreatments significantly (p< 0.0001) affected FGP and TSL in both species. These findings should motivate plantation plans for R. sphaerocarpa and Z. lotus in both agriculture and forestry.

Deterministic and Probabilistic Wind Power Forecasts by Considering Various Atmospheric Models and Feature Engineering Approaches

This study proposed a model for deterministic and probabilistic wind power generation forecasting and its corresponding procedures. The main contents include numerical weather prediction (NWP) systems, data preprocessing techniques, and forecasting models that use artificial intelligence methods. NWP wind speeds generated by the Central Weather Bureau (CWB) of Taiwan based on three atmospheric models, namely deterministic weather research and forecasting (WRFD), radar weather research and forecasting (RWRF), and WRF-based ensemble prediction system (WEPS), were used as model inputs. In terms of data preprocessing, the NWP wind speeds were corrected based on the height of the wind turbines, and principal components analysis (PCA) and empirical mode decomposition (EMD) were also evaluated for their feature extraction performance. Artificial neural network (ANN), long short-term memory (LSTM), gated recurrent unit (GRU), and eXtreme gradient boosting (XGBoost) were the forecast models used to predict wind power generation. The forecast results demonstrated that the use of the XGBoost model in conjunction with both PCA and EMD data preprocessing achieved the most accurate forecasting. The average forecasting errors (i.e., normalized root mean square error (NRMSE) and mean absolute percentage error (MAPE)) were 5.43% and 3.30% for one-hour-ahead and 9.78% and 6.83% for one-day-ahead, respectively. The empirical data collected at a wind farm in Taiwan verified the accuracy of the proposed method. Thus, the importance of model selection, NWP, and data preprocessing is ultimately self-evident.

Growth Responses of the Perennial Grass, Phalaris Aquatica L., to Cutting Frequency and Influence on Secondary Metabolites and Antioxidant Activity

Highlights Defoliation initiates physiological recovery and chemical defense mechanisms in Phalaris aquatica. Under infrequent defoliation treatment, P. aquatica has high DM production. Defoliation severity on herbage regrowth was associated with variation of secondary metabolite content and antioxidant activity. Phalaris may be suited to conservation pasture systems; the interval between cuts is about six weeks to maximize rates of regrowth. Results indicate that Phalaris may be used as fodder crop to sustained production and food security. Abstract. Perennial grasses are the key to the economic and environmental sustainability of pastures for livestock, and in arid and semi-arid environments, they can provide multiple ecosystem services more effectively than production systems based on annual crops. The objective of this study was to evaluate the effect of different defoliation frequencies on forage production and nutritive value of the Phalaris aquatica L. variety Soukra under field conditions in Tunis, Tunisia, over a period of 12 weeks. We tested four defoliation frequencies: (1) severe, (2) moderate, (3) infrequent, and (4) control. The growth responses measured were plant tiller number (NT), dry matter production (DM), and relative leaf regrowth rate (RLR). DM under the severe and moderate defoliation frequencies was 7% and 41% less than under control defoliation, respectively. However, DM production under infrequent defoliation was 91% and 43% higher than under severe and moderate defoliation. The relative leaf regrowth rate was affected by defoliation frequency; the highest regrowth rate was under severe treatment. However, tillering of P. aquatica was reduced under the severe and moderate frequencies of defoliation. Under increased defoliation frequencies, concentrations of secondary metabolites significantly decreased; total polyphenol content, flavonoid content, and tannin contents were higher in control and infrequent than in moderate and severe treatments. Antioxidant activity also decreased significantly with defoliation compared to the control treatment. There were no significant differences (P > 0.05) in ABTS (3-ethylbenzothiazoline-6-sulfonic acid) among the defoliation frequencies. Pearson's r correlation and PCA (Principal component analysis) data revealed that growth parameters, secondary metabolites, and antioxidant activity have positive and negative correlations in distinguishing the control and defoliation treatments. Results indicate that P. aquatica management should target moderate harvest rates in the adoption of perennial grass forage production systems in Tunisia. Use of perennial grasses for forage production can contribute to sustained production, food security, and rural livelihoods, and move farming systems towards providing multiple economic, environmental, and social benefits. Keywords: ABTS, Defoliation frequency, DPPH, Flavonoids, Growth responses, Matter production, Perennial grasses, Phalaris, Polyphenols.

Mapping Surface Organic Soil Properties in Arctic Tundra Using C-Band SAR Data

Surface soil organic carbon (SOC) content is among the first-order controls on the rate and extent of Arctic permafrost thaw. There is a large discrepancy in current SOC estimates in Arctic tundra, where sparse measurements are unable to capture SOC complexity over the vast tundra region. Synthetic aperture radar (SAR) data are sensitive to surface vegetation, roughness, and moisture conditions, and may provide useful information on surface SOC properties. Here, we investigated the potential of multitemporal Sentinel-1 C-band SAR data for regional SOC mapping in the Arctic tundra through principal component analysis (PCA). Multiple <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> SOC datasets in the Alaska North Slope were assembled to generate a consistent surface (0–10 cm) SOC and bulk density dataset ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> = 97). The radar VV backscatter shows a strong correlation with surface SOC, but the correlation varies greatly with surface snow, moisture, and freeze/thaw conditions. However, the first principal component (PC1) of radar backscatter time series from different years shows spatial consistency representing dominant and persistent surface backscatter behavior. The PC1 also shows a strong linear correlation with surface SOC concentration ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> = 0.65, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i> <0.01), and an exponential relationship with bulk density ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> = −0.65, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i> <0.01). The resulting predicted SOC maps show much lower soil bulk density and higher SOC concentration in the southern shrub tundra area than in the northern coastal region, consistent with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> data. Our analysis shows that it is possible to separate the effects of different factors on the radar backscatter response using PCA and multitemporal SAR data, which may lead to more effective satellite-based methods for Arctic SOC mapping.

Classification of Gravity Matching Areas Using PSO-BP Neural Networks based on PCA and Satellite Altimetry Data over the Western Pacific

For inertial navigation systems (INS), as one of the major methods for underwater navigation, errors diverge over time. With the development of geophysical navigation technology, gravity navigation has become an effective method of navigation. Significant changes in the gravity characteristic of the matching region ensure that gravity matching navigation works effectively. In this paper, we combine artificial intelligence algorithms and statistical metrics to classify gravity-matching navigation regions. Firstly, this paper analyzes and extracts gravity anomaly data from a matching region in different ways. Then, a particle swarm optimization (PSO) algorithm is used to optimize the network weights of a back propagation (BP) NN. Finally, based on principal component analysis (PCA) theory and PSO-BP NN, this paper proposes the PPBA method to classify the matching area. Moreover, the Terrain Contour Matching (TERCOM) matching algorithm and gravity anomaly data from the Western Pacific are used to verify the classification performance of the PPBA method. The experiments prove that the PPBA method has a high classification accuracy, and the classification results are consistent with the matching navigation experimental results. This work can provide a reference for designing navigation regions and navigation routes for submarines.

Design of organyl phosphate-based pro-drugs: comparative analysis of the antibiotic action of alkyl protecting groups with different degree of fluorination

Background. Molecular structures combining a phosphorus-containing counterpart and non-polar radicals are employed in design of pro-drugs as structural and functional groups necessary for transportation of drugs through cellular barriers. It is assumed that the carrier itself does not exhibit biological activity. However, the “organic phosphate – alkyl radical” complex may possess its own metabolic and pharmacological properties even in the absence of a drug moiety.The aim. To study the effect of fluorinated alkyl phosphates on the growth of bacterial test cultures in an agar medium and to identify conjugated metabolic markers using UV/visible spectroscopy.Materials and methods. The effect of six organyl phosphates on the growth of five types of bacteria under aerobic conditions was evaluated by the method of wells in an agar medium. For solutions containing cell metabolites of Pseudomonas aeruginosa, the absorption spectra were recorded at 250–280 nm. The principal component analysis (PCA) was used for multivariate comparative analysis of the spectra. Results. The studied organyl phosphates bearing the ethyl and propyl radicals are potential temporary carriers of the drug moiety, since they are capable of penetrating through cellular barriers. However, the fluorinated compounds exhibit bactericidal properties, the degree of which depends on the arrangement of fluorine atoms in the radical. The most active compounds are those exhaustively halogenated at the terminal carbon atom of the ethyl radical (-СН2-СF3), while non-fluorinated organyl phosphate is the least active. UV/visible spectra of P. aeruginosa cultivation products, according to PCA data, contain patterns reflecting the metabolic effects mediated by these structural features of the radicals.Conclusion. In terms of practical application of the studied compounds, the activity of a proantibiotic based on organyl phosphate with a non-fluorinated ethyl(propyl) radical will be determined only by the specificity of the drug moiety. Exactly the same molecule, but exhaustively fluorinated at the terminal carbon atom of the alkyl radical, is likely to be characterized by lower specificity and higher activity under the additive (or synergistic) action of metabolically active groups.

Damage identification of structures based on sensitivity equations using PCA and PSD data

This article presents a novel model updating approach to the problem of structural damage identification. The approach is based on principal component analysis (PCA) of power spectral density (PSD) data obtained from incompletely measured structural responses. Damage detection was done by solving an innovative sensitivity equation based on least-squares optimization that could help solve the problem in a time-efficient manner and extend the results for industrial applications. Simulations of a 2D steel truss and a 2D steel frame were considered for a thorough comparison. The simulation results clearly showed the effectiveness of the method and its superior performance for solutions such as a PSD-only strategy and frequency response function (FRF) based data analysis along with singular value decomposition.

Comparison of different analysis methods applied in archaeological soil analysis with chemometric techniques in the interpretation of past human activity: A case study from the Yassıtepe Höyük in İzmir, Turkey

In this study, human activities carried out in the past of the Yassıtepe Höyük, one of the most important Bronze Age settlements in Western Anatolia, were tried to be determined as a result of examining the data obtained from 108 anthropogenic soil samples (89 from the on-site; 19 from the off-site) taken from different points with chemometric modeling techniques. According to the moisture, pH, and total organic matter analyzes, the mean results were determined as 6.50 % ± 0.21, 8.44 ± 0.28, and 7.42 % ± 0.80 %, respectively. Some elements such as calcium, phosphorus, iron, sodium, potassium, magnesium, and sulfur are determined mean as 119548.15 ± 30680.23, 3497.41 ± 1494.37, 29414.82 ± 5221.90, 5148.15 ± 1072.29, 15962.96 ± 3391.98, 10737.96 ± 1917.15, and 1138.89 ± 420.47 mg L−1, respectively, by elemental analysis. The highest mean phosphorus ratio is reached in the M17 building (5105.00 ± 2886.98 mg L−1) and the IIB2-2 phase of this building had the highest mean phosphorus ratio (8432.50 ± 2199.75 mg L−1). According to the enrichment factor (EF) analysis, lead, arsenic, strontium, phosphorus, calcium, barium, potassium, beryllium, and sulfur elements were found to accumulate in the soil samples of the on-site. To determine which analysis techniques gave more meaningful results, 11 principal component analysis (PCA) models were created by using at least binary combinations of these analysis techniques, and the best PCA model was selected by evaluating their statistical values of the number of factors reached, the eigenvalue, and the number of samples outside of explained variance. The PCA model 6, which has the highest factor number of 7 and includes the results of total organic matter and elemental analysis, is chosen as the most successful model. In addition, it was seen that multivariate curve resolution-alternating least squares (MCR-ALS) data, which determined the distribution of the variables in the archaeological area, supported the EF and PCA data. According to the results, it is determined that the Yassıtepe Höyük and its surroundings have been exposed to intense human activity in the past and there has been an agricultural village life where shellfish have been consumed a lot due to high calcium rates with phosphorus. The large amount of waste belonging to various shellfish obtained from the archaeological studies carried out in the area confirms this result.

Headspace Solid-Phase Micro-Extraction Versus Hydrodistillation of Volatile Compounds from Leaves of Cultivated Mentha Taxa: Markers of Safe Chemotypes.

Various mint taxa are widely cultivated and are used not only for medicinal purposes but also in cosmetic and industrial applications. The development of new varieties or cultivars of mint generates difficulties in their correct identification and safe use. Volatile organic compounds (VOCs) from the leaves of seven different taxa of the genus Mentha obtained by hydrodistillation (HD) and headspace solid-phase microextraction (HS-SPME) were analyzed using gas chromatography–mass spectrometry (GC-MS). Principal component analysis (PCA) was also performed. Comparative GC-MS analysis of the obtained extracts showed similarity in the major compounds. PCA data allowed the separation of two groups of chemotypes among the analyzed mints, characterized by the abundance of piperitenone oxide and carvone. Two out of seven analyzed taxa were not previously examined for VOC profile, one was examined only for patent application purposes, and six out of seven were investigated for the first time using the HS-SPME technique. The presented analysis provides new data on the abundance and qualitative characterization of VOCs in the studied mint plants and on the safety of their use, related to the possibility of the presence of potentially toxic components. HS-SPME is a valuable method to extend the characterization of the VOC profile obtained by hydrodistillation.

Asset based wellbeing of poverty of artisanal fish farmers in delta state, nigeria

This research carried out in Delta State, Nigeria, focuses on the empirical application of Principal Component Analysis (PCA), an asset based measure of wellbeing which can be used to assess level of poverty among households in rural areas of developing countries. The study employed questionnaire-based household survey data collection methods. The final wealth index was derived using data collected from 430 artisanal fishing households in riverine Delta State communities. Data on 16 variables measuring multiple aspects of household wealth status were used to extract the set of principal components utilized in the construction of the index. Two key statistical tests, the KMO and Bartlett’s tests, showed the appropriateness of the data for PCA. Results revealed that five major factors influence the wealth status and hence the wellbeing of households: home infrastructure, energy sources, durable home assets, water sources and mobility. Therefore, it is suggested that any efforts to improve the wellbeing of farm households in the study area as well as in other regions with similar socio- economic settings should consider these factors as entry point to poverty alleviation.