Principal Component Analysis Research Articles

Improving shelf life and quality of guava fruit using an edible coating containing xanthan and pomegranate seed oil

ABSTRACT Guava, a tropical fruit, has a short shelf life due to its rapid ripening and susceptibility to various factors like water loss, mechanical damage, and cold storage. This study aimed to address this issue by using an edible coating containing xanthan and enriched with pomegranate seed oil to preserve guava fruit quality over 21 days at 5 ± 1°C and 85%-95% relative humidity, and a 5-day shelf life. The treatments significantly reduced fruit weight loss, with the oil treatment showing the least weight loss (2.18%). While the respiration rate increased during storage, the treated fruits exhibited a lower respiration intensity. Notably, treatments with oil and xanthan maintained greater firmness than that of the control. The antioxidant capacities of the treated fruits were significantly higher, with xanthan showing the highest value (96.8%). In contrast, the control group had a significantly higher total soluble solids (TSS)/titratable acidity (TA) ratio of 113.5. Principal Component Analysis (PCA) indicated that after 21 d, the control had high values of F1, associated with high TSS, respiration rate, weight loss, and TSS/TA. These findings suggest that pomegranate seed oil alone or Xan 0.2% + pomegranate seed oil effectively improve the shelf life and quality of guava fruit.

Hybrid modeling techniques for predicting chemical oxygen demand in wastewater treatment: a stacking ensemble learning approach with neural networks.

To ensure operational efficiency, promote sustainable wastewater treatment practices, and maintain compliance with environmental regulations, it is crucial to evaluate the parameters of treated effluent in wastewater treatment plants (WWTPs). Artificial neural network (ANN) analysis is a promising tool to predict the wastewater characteristics, as a substitute to tedious laboratory techniques. It enables proactive decision-making and contributes to the overall effectiveness of the treatment processes. The primary aim of this work is to develop a robust model for predicting chemical oxygen demand (COD), a key parameter for evaluating the operational efficiency of WWTPs. The research employed a dataset consisting of 527 samples and 22 features, derived from daily sensor readings in an urban WWTP. Additionally, to enhance the efficiency of the ANN framework for regression modeling, the dataset was augmented to 1054 samples using a robust synthetic data generator known as generative adversarial networks (GAN). In this study, K-means clustering combined with principal component analysis (PCA) is employed for feature selection and anomaly detection, aiming to enhance the regression model's performance by incorporating advanced ANN extensions, including polynomial, additive, and radial basis networks. Moreover, the model optimizes ANNs using advanced heuristic techniques such as genetic algorithms (GA), ant colony optimization (ACO), and particle swarm optimization (PSO). Furthermore, the performance of the models was assessed using the R2 (coefficient of determination), MSE (mean squared error), and loss value metrics along with visual performance indicators. The proposed model stacked ensemble method resulted in an MSE of 0.0012 and an R2 score of 0.95 and the GA_ANN model, despite undergoing optimization, achieved only an R2 score of 0.70, indicating considerable potential for improvement. In contrast, the ACO_ANN and PSO_ANN models showed significant performance boosts, with near-perfect R2 scores of about 0.98 and 0.99, respectively, making them the top-performing models overall. The results outlined in this article introduce an ANN stacking ensemble regression mode framework that can aid researchers in improving the operational efficiency of treatment plants.

Adult Dietary Patterns and Their Association With Iodine Nutrition Levels and Thyroid Function: A Cross-Sectional Study.

To understand the dietary patterns of adults and explore their association with iodine nutritional levels and thyroid function in adults. We randomly collected 5 ml of adult urine samples and measured urinary iodine concentration by cerium arsenate-catalyzed spectrophotometry. A serum sample of 5 ml was collected for the determination of free triiodothyronine (FT3), free thyroxine (FT4) and thyrotropin (TSH), and diet-related information was collected through a food frequency questionnaire. Dietary patterns were extracted by principal component analysis and the relationship between dietary patterns and iodine nutrition levels and thyroid function was explored. A cross-sectional study involving adults in Xinjiang, China was conducted. A total of 435 adults were enrolled in the study. The overall median urinary iodine of the 435 respondents was 219.73 μg/L.The dietary patterns were PCA1 (staple food pattern), PCA2 (fruit, vegetable, and meat pattern), PCA3 (fish, shrimp, and legume pattern), and PCA4 (Dairy-based protein pattern). The correlation analyses showed that PCA1 and PCA3 were positively correlated with the urinary iodine concentration (UIC). The results of the multivariable analysis showed that PCA1, Q1, Q2, and Q3 were associated with an increased risk of iodine deficiency compared with Q4 [ (OR): 260.41 (95%CI: 20.16, 663.70)], 59.89 (5.64, 335.81), and 2.01 (0.15, 26.16), respectively]. In PCA2, Q3 was associated with an increased risk of iodine deficiency compared with Q4 [OR: 0.16 (0.05, 0.53)]. In PCA3, Q3 was associated with an increased risk of iodine deficiency compared with Q4 [OR: 0.23 (0.06, 0.90)]. In PCA4, Q1 was associated with an increased risk of iodine deficiency compared with Q4 [OR: 31.30 (4.88, 200.64)]. This study demonstrated that of the four dietary patterns, the least dependent staple food pattern (Q1) had a higher risk of iodine deficiency compared to the most dependent staple food pattern (Q4). However, the current evidence on the effect of dietary patterns on thyroid function needs to be validated by further longitudinal studies that include long-term follow-up, larger sample sizes, and repeated measures.

Multicenter evaluation of Fourier transform infrared (FTIR) spectroscopy as a first-line typing tool for carbapenemase-producing Klebsiella pneumoniae in clinical settings.

Early use of infection control methods is critical for preventing the spread of antimicrobial resistance. Whole-genome sequencing (WGS) is considered the gold standard for investigating outbreaks; however, the turnaround time is usually too long for clinical decision-making and the method is also costly. The aim of this study was to evaluate the performance of Fourier transform infrared (FTIR) and artificial intelligence tools as a first-line typing tool for typing carbapenemase-producing Klebsiella pneumoniae (CPK) in the hospital setting. For this purpose, we analyzed 365 CPK isolates from two tertiary hospitals in Spain in parallel by applying unsupervised principal component analysis (PCA) and supervised algorithms (artificial neural network [ANN], support vector machine [SVM] linear, SVM radial basis function in the IR Biotyper software, and random forest in the Clover MSDAS software). Concordance with FTIR clustering considering the sequence type (ST) and the clonal cluster, obtained by cgMLST for reference purposes, was measured using the adjusted Wallace index (AWI), yielding values of 0.611 and 0.652, respectively. Different regions of the spectra were studied in relation to repeatability and reproducibility, and the polysaccharides region proved the best for FTIR differential analysis. The best results for accuracy were obtained using the ANN algorithm in the IR Biotyper software, with 80.5% of correct prediction. Regarding accuracy, the poorest results were obtained for isolates belonging to ST392 (55.5%) and the best results for ST307 (94.4%). The findings demonstrate the utility of the FTIR method as a rapid, inexpensive, first-line typing tool for detecting CPK, preserving WGS for confirmation and further characterization.

A hybrid deep learning model-based LSTM and modified genetic algorithm for air quality applications.

Over time, computing power and storage resource advancements have enabled the widespread accumulation and utilization of data across various domains. In the field of air quality, analyzing data and developing air quality models have become pivotal in safeguarding public health. Despite significant progress in modeling, the critical need for accurate pollutant predictions persists. In addressing this challenge, deep learning models have garnered substantial attention in research due to their outstanding performance across diverse applications. However, the optimization of hyperparameters and features remains a challenging task. This study seeks to leverage historical data to construct the long short-term memory-based model for forecasting multistep PM10. To refine its architecture, a modified genetic algorithm is employed for automatic design. Furthermore, we explore principal component analysis and exhaustive feature selection to identify the optimal feature set. This paper introduces a novel hybrid deep learning model named EFS-GA-LSTM, tailored for multistep hourly PM10 forecasting. To assess its performance, we compare it with other hyperparameter optimization algorithms, including particle swarm optimization, variable neighborhood search, and Bayesian optimization with Gaussian process. The input dataset comprises hourly PM10 concentrations, meteorological variables, and time variables. The results reveal that for 3-h-ahead forecasting tasks, the EFS-GA-LSTM network demonstrates improvements in root mean square error, mean absolute percentage error, correlation coefficient, and coefficient of determination.

The Association Between Periconceptual Maternal Dietary Patterns and Miscarriage Risk in Women With Recurrent Miscarriages: A Multicentre Cohort Study.

To examine the association between periconceptual maternal diet and miscarriage risk among women with recurrent miscarriages. Prospective multicentre cohort study (Tommy's Net). Three university hospital research centres in the United Kingdom. 1035 women with a baseline history of two or more miscarriages. We analysed baseline dietary data from a 10-item Food Frequency Questionnaire (FFQ). For individual food category analyses, we used multivariable Poisson regression following adjustment for maternal confounders and paternal dietary patterns. For whole diet analyses, ordinal principal component analysis (PCA) was used to identify common dietary patterns. Results were presented as relative risks (RR) with 95% confidence intervals (CI) and accompanying p-values. Miscarriage rate, defined as the rate of spontaneous pregnancy loss (< 24 weeks of gestation) relative to the total number of pregnancies (miscarriages and live births). High consumption of fruit and nuts (almonds and walnuts) was associated with lower miscarriage risk (fruit 226/662 (34.1%) vs. 38/77 (49.4%), RR 0.66, 95% CI 0.51 to 0.85, p = 0.001; nuts 47/152 (30.9%) vs. 220/613 (35.9%), RR 0.73, 95% CI 0.54 to 0.98, p = 0.039). High red meat intake was associated with a possible increase in miscarriage risk (6/12 (50.0%) vs. 165/469 (35.2%), RR 1.86, 95% CI 1.10 to 3.16, p = 0.022). The association with miscarriage risk was unclear for other food groups, including fresh vegetables, white meat, fish, dairy, eggs, soya and chocolate, due to imprecise point estimates. Through PCA, we identified three data-derived dietary patterns. Yet, no distinct relationship emerged between these dietary patterns and miscarriage risk. A maternal diet rich in fresh fruits and nuts is associated with a lower miscarriage risk among women with a history of recurrent miscarriage. Tommy's Net (ISRCTN17732518) https://www.isrctn.com/ISRCTN17732518. Analysis plan (OSF zp7cs) https://osf.io/zp7cs.

1H NMR Milk Metabolomics Response from Goats Supplemented with Date (Phoenix Dactylifera L.) Pit Powder: Benefiting from Agricultural Waste Byproduct

Utilizing a systems biology approach through metabolomics is crucial for analyzing milk quality. Continual animal supplementing is crucial for enhancing milk quality to ensure high food quality and agricultural productivity. Date pits are agricultural waste byproducts that can be used as active components in cosmetics, as a coffee substitute, and as animal feed. The study investigated the impact of adding date pit powder (DPP) to dairy goats' diet on the milk metabolites to determine possible benefits. Saanen-Boer crossed dairy goats were divided into six groups: control (untreated), 10g and 20g of Ajwa DPP (high quality), 10 g and 20 g of Mariami DPP (agricultural waste byproduct), and 30g of Mariami DPP exclusively. DPP supplementation was administered daily for 12 weeks. The goat milk obtained was analyzed using 1H NMR and then subjected to chemometric studies, namely PCA and PLS. Metabolites were analyzed using tools such as BAYESIL, Chenomx profiler and HMDB. Principal Component Analysis (PCA) showed that milk metabolites changed over time (month), and that supplementing breastfeeding goats with DPP did not result in distinct clusters. However, BAYESIL identified L-Proline, L-Carnitine, and myoinositol as molecules with less than 30 % biological variance at months 1 and 3 of the research, indicating good conservation. Only the levels of L-Carnitine and acetoacetate in the milk showed significant differences between the treatments. Ultimately, the DPP dosages were insufficient to impact the goat's metabolism, resulting in no impacts on milk metabolites. An adaptive response to DPP cultivars and doses was indicated by the results. Overall, these findings might help dairy goat and other ruminant breeders in predicting milk quality by utilizing metabolites as markers for food security (production and quality).

Geochemical Anomaly Detection and Pattern Recognition: A Combined Study of the Apriori Algorithm, Principal Component Analysis, and Spectral Clustering

This study demonstrates the effectiveness of combining Principal Component Analysis (PCA) and the Apriori algorithm for feature selection, alongside Spectral clustering, to detect geochemical anomalies in Mississippi Valley-Type (MVT) Pb-Zn deposits in western Iran. First, PCA and Apriori enabled the identification of both syngenetic and epigenetic components, which helped in recognizing elements associated with mineralization. These elements were then modeled using Spectral clustering to detect geochemical anomalies. Unlike traditional methods like k-means, Spectral clustering does not require spherical clusters and is adept at identifying clusters of arbitrary shapes. This made it particularly suitable for analyzing the irregular shapes of geochemical anomalies in the study area. By incorporating Spectral clustering, the method effectively separated geochemical groups, revealing the underlying structure of the data. This was crucial for identifying anomalous geochemical zones and delineating areas with a high potential for Pb-Zn mineralization. The performance of the Spectral clustering algorithm was thoroughly evaluated using the Silhouette Score, the Davies–Bouldin Index, and Dunn Index. Subsampling was employed to assess the algorithm’s stability, providing a comprehensive evaluation of its effectiveness in identifying geochemical anomalies and mapping mineralization potential.

A Comparative Study of Auditory Processing and Working Memory Profiles in Young and Older Adults.

This study aimed to profile and compare the auditory processing and working memory skills of young and older adults with near-normal hearing thresholds (up to 2000 Hz) and cognition. This was done to investigate the effect of aging on these skills despite exhibiting near-normal peripheral hearing in low frequencies and normal cognitive skills. Twenty-five young adults (18 to 35 years) and 35 older adults (56 to 79 years) were recruited for the study. They were screened for near-normal hearing thresholds (up to 2000 Hz) as well as cognition, and written informed consent was obtained. A battery of auditory processing and working memory tests were administered to the participants. The responses obtained were compared using the Mann-Whitney U test. Principal component analysis (PCA) was also carried out to examine any relationship between the various auditory processes and the cognitive skills. The young adults had superior performance compared to older adults on most of the tasks. PCA revealed the influence of working memory on performance in most tests of auditory processing in young adults and to a lesser extent in older adults. This study aimed to assess and compare the auditory processing and working memory skills of young and older adults with normal hearing and cognition. Overall, young adults demonstrated superior performance compared to older adults across the tasks. The results of PCA indicated that the young adults utilized their cognitive resources better for auditory processing compared to the older adults. These findings suggest a need for tailored interventions targeting both auditory processing and working memory, especially among older adults, and highlight the importance of considering age-related differences in these cognitive processes when interpreting auditory processing assessments.

Clustering-based agent system (CAS) to simulate the energy-related behaviours of office occupants

Rapid urbanization and building sector growth emphasize the critical role of energy conservation in addressing global energy consumption and greenhouse emissions. Despite advancements in energy-efficient technologies, an ‘energy performance gap’ exists between predicted and actual energy use, significantly influenced by occupant behaviour. This study explores energy-related behaviour in office buildings by integrating existing behavioural theories including the Theory of Planned Behaviour and the Self-determination Theory, and construct of habit and comfort. Data from an online survey were analyzed using principal component analysis, two-step cluster analysis, and descriptive statistics, identifying three behavioral clusters: ‘Cautious Saver’, ‘Compelling Dissatisfied’, and ‘Coherent Potent’. These clusters represent distinct energy-related behaviours. A Clustering-based Agent System (CAS) was then proposed to simulate the energy-related behaviours of these clusters, offering a dynamic and adaptive modelling framework. The study advocates for a comprehensive approach, integrating behavioural theories to provide insights for developing accurate occupant behaviour models. Abbreviations: BD: Biodiesel; BR: Blending Ratio; BSFC: Brake Specific Fuel Consumption; BTE: Brake Thermal Efficiency; CI: Compression Ignition; CM: Coating Material; CO: Carbon Monoxide; CT DEE: Coating Thickness Diethyl Ether; D-Gun: Detonation Gun; DOA: Degree of Adiabacity; EGT: Exhaust Gas Temperature; HC: Hydrocarbon; HVOF: High-velocity oxy-fuel; IC: Internal Combustion; LHR: Low Heat Rejection; NOx: Oxides of Nitrogen; PVD: Physical Vapour Deposition; SEM: Scanning Electron Microscopy; SI: Spark Ignition; TBC: Thermal Barrier Coating; TMF: Thermal Mechanical Fatigue; WCO: Waste Cooking Oil; XRD: X-ray Diffraction; YSZ: Yttria-Stabilised Zirconia

Calpastatin &lt;i&gt;CAST&lt;/i&gt; and Androgen Receptor &lt;i&gt;AR&lt;/i&gt; Gene Polymorphism Studyas Meat Quality Predictors in Reindeer &lt;i&gt;Rangifer tarandus&lt;/i&gt;

Marker-based selection on reindeer meat productivity is in the early stages of development, which requires the study of variability in candidate genes for meat productivity. We chose the calpastatin and androgen receptor genes as such genes to study. Polymorphisms and indels in the androgen receptor gene have been associated with height and weight characteristics in different domesticated animal species. Variation in the region of the calpastatin CAST gene, according to the results of many studies, has been associated with meat quality and meat productivity of livestock. Principal component analysis of CAST variability has grouped together wild and domestic deer from Yakutia, as well as wild and domestic deer from the Amur region, which implies gene flow between local breeds of domesticated deer and wild populations. Moreover, in the case of three microsatellite loci found in this study in the intron of the androgen receptor, principal component analysis separated wild and domestic deer.

Recommendation for Clarifying FDA Policy in Evaluating "Sameness" of Higher Order Structure for Generic Peptide Therapeutics.

Recognizing the approach of a dramatic expansion of peptide therapeutics reaching the marketplace in recent years, led by GLP-1 receptor agonists such as semaglutide and liraglutide, the Center for Drug Evaluation and Research (CDER) branch of the US Food and Drug Administration (FDA) issued a final guidance in 2021 that was intended to assist generic drug producers in meeting Abbreviated New Drug Application (ANDA) obligations to establish "sameness" of their active peptide drug relative to that produced by innovator companies. Research and a published report by FDA scientists on best practices followed, which promulgated the use of nuclear magnetic resonance (NMR) and principal component analysis (PCA) and established a quantitative standard by which "sameness" of higher order structure for the applicant's peptide drug could be judged. A key requirement is that drug product samples be analyzed directly and non-invasively, a condition which in practice restricts sample modification to the addition of a small amount of deuterium oxide to allow signal lock and spectral data alignment (as required for NMR analysis). In the study described herein, data are presented to illustrate that 1) relatively small differences in sample pH can cause significant shifting of certain proton resonances, 2) that such resonance shifting is readily reversible and due to the degree of protonation of specific amino acid residues (rather than reflecting differences in higher order structure), and 3) that small differences in pH variability between sample cohorts can frequently cause failure to meet the quantitative benchmark established by the agency. Methodology is presented by which drug sample pHs can be aligned with minimal impact, and a recommendation is made that minor sample pH adjustments be allowed in assessing "sameness" of peptide drug higher order structure.

Progress and Application of Unsupervised Feature Extraction Methods

Unsupervised feature extraction is crucial in machine learning and data mining for handling high-dimensional and unlabeled data. However, existing methods often ignore feature relationaships, resulting in suboptimal feature subsets. This paper reviews the current state of unsupervised feature extraction methods, discussing the limitations of traditional methods such as Principal Component Analysis (PCA) and Independent Component Analysis (ICA), particularly in terms of interpretability, sensitivity to outliers, and computational resource challenges. In recent years, improvement strategies such as information theory, sparse learning, and deep learning (e.g., deep autoencoders and generative adversarial networks) have significantly progressed in feature extraction. This paper analyzes the practical applications of these methods in image processing, gene analysis, text mining, and network security. For example, in image processing, deep autoencoder-based methods such as Matrix Capsules with EM Routing can effectively extract key features from complex images. In text mining, unsupervised feature selection methods combined with generative adversarial networks significantly improve the efficiency of processing high-dimensional text data. Additionally, this paper explores future research directions such as multimodal data processing, improving real-time processing capabilities, and integration with other machine learning techniques (e.g., reinforcement learning, transfer learning), providing insights for further development of unsupervised feature extraction technologies.

Spatial Spillover Effects of Smallholder Households’ Adoption Behaviour of Soil Management Practices Among Push–Pull Farmers in Rwanda

Push–pull technology (PPT) integrates maize with the legume fodder Desmodium sp. and the border crop Brachiaria sp., aiming to enhance maize production in Rwanda. Despite its potential, the adoption of complementary soil management practices (SMP), vital for PPT’s success, remains low. This study employs spatial econometric methods to evaluate the determinants of SMP adoption and the interdependencies in decision-making among PPT-practicing farmers. We constructed a spatial weight matrix based on a global Moran’s I index and identified optimal model parameters through principal component analysis. Utilizing a spatial Durbin probit model (SDPM), we assessed the spatial interdependence of SMP adoption decisions among maize farmers. Our findings reveal significant spatial dependence in SMP adoption within a 1.962 km radius, with improved seed usage, household income, yield, farmer group membership and size of land cultivated being key factors positively influencing adoption. We propose a “nonequilibrium promotion strategy” to enhance SMP adoption, emphasizing the establishment of pilot regions to broaden outreach. Additionally, fostering technical training and selecting farmers with adequate resources as demonstration leaders can enhance spatial spillover effects. This research provides insights for developing policies to scale up push–pull technology in Rwanda and across Sub-Saharan Africa.

Network-Based Bioinformatics Highlights Broad Importance of Human Milk Hyaluronan

Human milk (HM) is rich in bioactive factors promoting postnatal small intestinal development and maturation of the microbiome. HM is also protective against necrotizing enterocolitis (NEC), a devastating inflammatory condition predominantly affecting preterm infants. The HM glycosaminoglycan, hyaluronan (HA), is present at high levels in colostrum and early milk. Our group has demonstrated that HA with a molecular weight of 35 kDa (HA35) promotes maturation of the murine neonatal intestine and protects against two distinct models of NEC. However, the molecular mechanisms underpinning HA35-induced changes in the developing ileum are unclear. CD-1 mouse pups were treated with HA35 or vehicle control daily, from P7 to P14, and we used network and functional analyses of bulk RNA-seq ileal transcriptomes to further characterize molecular mechanisms through which HA35 likely influences intestinal maturation. HA35-treated pups separated well by principal component analysis, and cell deconvolution revealed increases in stromal, Paneth, and mature enterocyte and progenitor cells in HA35-treated pups. Gene set enrichment and pathway analyses demonstrated upregulation in key processes related to antioxidant and growth pathways, such as nuclear factor erythroid 2-related factor-mediated oxidative stress response, hypoxia inducible factor-1 alpha, mechanistic target of rapamycin, and downregulation of apoptotic signaling. Collectively, pro-growth and differentiation signals induced by HA35 may present novel mechanisms by which this HM bioactive factor may protect against NEC.

Squirrel Search Optimization-based near-duplicate image detection

ABSTRACT Near duplicate (ND) image detection is a significant issue in a modern online environment with a wide range of applications like the detection of copyright violations and saving of storage space. Several existing ND detection techniques are perhaps not suitable for online applications due to the large computational burden, and may not successfully detect NDs containing large smooth and plain regions. In addition, the K-means algorithm used in most of the existing methods yield sub-optimal quantization of visual words. This article employs a robust algorithm of Squirrel Search Optimization (SSO) for quantization, fast-hessian matrix-based detector (FHMBD) and FAST Corner Detector (FCD) for the detection of KPs at both plain and non-smooth regions of all images, SURF for computing descriptors and Principal Component Analysis (PCA) for dimensionality reduction. The results of the developed method presented on five image databases. The proposed method offered 99.9% accuracy, 98.67% sensitivity, and 99.91% specificity respectively.

Different levels of interpreting chemical data by prompt gamma activation analysis on polished stone tools in the Carpathian Basin (Hungary)

AbstractAverage chemical composition data can be effectively exploited in provenance, i.e. raw material’s origin, research of archaeological polished stone artefacts where direct correlation with potential raw materials is possible. This study summarizes the success of Prompt Gamma Activation Analysis (PGAA) method on two different levels of chemical data interpretation for variable rock types found in Hungary, in the Carpathian Basin and their wider vicinity, i.e. material characterization and provenance identification level. Characterization of different lithologies is possible by variable combinations of some major element oxides (Si, Mg, Ca, Na, K, Al, Fe and Ti). Applying statistical methods, boxplots and Principal Component Analysis, we proved that serpentinites, nephrites and Mg-Si-rich subgroup of ‘white stones’ can be clearly distinguished, while the ‘green stone’ category cannot be further differentiated, partly overlaps with hornfels and significantly overlaps with other rock types (basalts, metadolerites-metagabbros, andesites and metavolcanites). The provenance identification is successful in the case of contact metabasites and HP metaophiolites, while less exact for nephrites, basalts and hornfels.The non-destructive PGAA proved to be very effective in the identification of certain lithologies, while for other polished stone tool raw materials it has medium certainty and requires complementing methods.

Trophic status analysis and nutrient source allocation in urban lakes of Dhaka, Bangladesh: a comprehensive approach to eutrophication monitoring.

Urban lakes are vital to ecosystems, providing essential services and recreational spaces in densely populated megacities. However, rapid urbanization and anthropogenic activities, particularly eutrophication driven by macronutrient accumulation, severely threaten these water bodies. This study underscores the critical need for continuous trophic state monitoring to sustain fish, wildlife, and plant ecosystems. The trophic status of Dhanmondi, Gulshan, and Banani Lakes in Dhaka City, Bangladesh, was assessed using Carlson's Trophic State Index (CTSI) and Burn's Trophic Level Index (BTLI), based on chlorophyll a (Chl-a), total phosphorus (TP), Secchi disc depth (SD), and total nitrogen (TN). Water samples from five sites per lake were analyzed for physicochemical parameters from June 2023 to May 2024, revealing monthly and seasonal variations. The study revealed that Dhanmondi Lake's CTSI ranged from 69.3 to 79.5 (June 2023 to March 2024), indicating initial "Eutrophic" conditions progressing to "Hypereutrophic." Gulshan Lake consistently showed "Hypereutrophic" conditions, with CTSI values between 84.1 and 97.3. Banani Lake was "Eutrophic" in June and July 2023, transitioning to "Hypereutrophic" from August 2023 to May 2024 (84.1-97.7). The Trophic Level Index (TLI) showed the "Hypereutrophic" status with a progressive monthly escalation for all the lakes. The Water Quality Index (WQI) categorized the lakes as "Poor" to "Very Poor" from June to August 2023, becoming "Unsuitable" from September 2023 to May 2024, indicating significant anthropogenic stress. Principal component analysis (PCA) identified nutrient infiltration, soil erosion, waste discharge, and organic residue accumulation as key pollution drivers. The study advocates for a multi-sectoral strategy to regulate nutrient loading and mitigate eutrophication, emphasizing best management practices for urban lake conservation.

Developing novel spectral indices for precise estimation of soil pH and organic carbon with hyperspectral data and machine learning.

Accurate soil pH and soil organic carbon (SOC) estimations are vital for sustainable agriculture, as pH affects nutrient availability, and SOC is crucial for soil health and fertility. Hyperspectral imaging provides a faster, non-destructive, and economical alternative to standard soil testing. The study utilizes imaging spectroscopic data from the Africa Soil Information Service (AfSIS) and Land Use and Coverage Area Frame Survey (LUCAS-2009) hyperspectral datasets, capturing spatially distributed spectral information. Machine learning (ML) approaches using high-dimensional spectral bands can be computationally expensive, while those using spectral indices are typically limited to multispectral data. This study addresses these challenges by comparing soil pH and SOC prediction using ML models, with both existing spectral indices and individual hyperspectral bands as input features. Results demonstrate that hyperspectral bands outperform existing indices in predictive accuracy, with R values ranging from 0.8 to 0.94 for both soil pH and SOC. To further enhance prediction performance, this study proposes novel spectral indices-soil pH index (SPI) and soil organic carbon index (SOCI)-specifically designed for hyperspectral data using principal component analysis (PCA) and artificial neural networks (ANN). The proposed SPI and SOCI indices address multicollinearity issues and high dimensionality in raw spectral bands, significantly improving predictive accuracy. The SPI and SOCI indices achieve R values of 0.86 for AfSIS soil pH, 0.945 for LUCAS-2009 soil pH, 0.952 for AfSIS SOC, and 0.963 for LUCAS-2009 SOC. These results show that the proposed spectral indices provide a practical solution for precision agriculture, enhancing soil pH and SOC estimations.

Water quality classification using self-organizing maps and cluster analysis: Case of Meknes-El Hajeb Springs, Morocco.

The Ouislane sub-watershed is currently experiencing severe water shortages and is highly dependent on its water supply. The sub-watershed spans two communes: Meknes to the north and El Hajeb to the south. It serves as the primary water source for irrigation and drinking purposes for the local population. Consequently, it is crucial to assess the spatio-temporal variations of water quality to identify and address potential gaps; these focused on effective monitoring systems to detect contaminants, pollutants and health risks. This research project aims on the application of self-organizing map (SOM) techniques combined with cluster analysis to classify water quality in springs for drinking and irrigation purposes. The present study evaluates the water quality variations using physicochemical parameters of twelve water springs, collected during the wet and dry seasons of 2022. For this purpose, the water quality index (WQI), self-organizing map (SOM), hierarchical cluster analysis (HCA), and principal component analysis (PCA) are used as evaluation and classification methods. As a result, the SOM algorithm with a size of 5 × 5 units identified as the most suitable, based on the minimum quantization error (QE) and topographic error (TE), yielding a QE of 0.379 and a TE of 0.000. It grouped the water quality data into five distinct clusters, Cluster I represented 37.5% of the total samples, while cluster II represented 25%. Cluster III and IV each accounted for 8.33% of the samples, while 20.83% of the sampling water are classified in cluster V. Clusters I, II, and IV indicate good water suitable for drinking. However, cluster V had the highest WQI, suggesting very high contamination due to increased levels of the 10 studied physicochemical parameters. The water quality in this region (cluster V) is influenced by natural processes, such as precipitation intensity, weathering and vegetation cover, as well as anthropogenic factors like agriculture and urban concentration. PCA confirmed the clustering results obtained by SOM. However, SOM provides a more detailed classification and additional insights into the dominant variables influencing the classification processes. The results of this study suggest that SOM was an effective tool for gaining a better understanding of the patterns and processes driving water quality in the Ouislane sub-watershed and provides valuable avenues for further research to establish and monitor water quality for effective management of water resources.