Search Results (742)

This research evaluates the extent to which firms’ “green” bond disclosures create and convey a meaningful representation of their Environmental, Social, and Governance (“ESG”) commitments. Additionally, this research explores how investors distinguish between disclosures that represent genuine commitment to sustainability and those that may be indicative of “greenwashing,” and how such distinctions impact their assessment of an issuer’s credibility as well as the issuer’s performance subsequent to the issuance of a “green” bond. The methodology employed in this research employs a convergent mixed-methods approach that combines quantitative methods (Natural Language Processing (“NLP”), financial modeling, etc.) with qualitative methodologies (case studies, interviews). The NLP methodology employed in this research includes sentiment analysis, topic modeling, and ambiguity measurement in order to determine the tone, thematic content, and linguistic clarity of the disclosure texts. Subsequently, the results of the NLP methodologies are correlated with firm level outcomes using cross validated partial least squares regression (“PLS-R”), event study methodologies, and one way ANOVA to test for temporal and industrial variability. Finally, the results of the computational and financial methodologies are supplemented by qualitative case studies and interviews to provide context for the patterns identified in the computational and financial methodologies. In summary, the results of this research demonstrate that firms that communicate in a clear, balanced, and verifiable manner experience better market reaction and more favorable accounting results subsequent to the issuance of a “green” bond than do firms whose communications are vague, overly optimistic, or lacking in consistency. Conversely, the findings suggest that investors have become increasingly sensitive to potential “greenwashing” and therefore are less likely to respond favorably to communications characterized by the aforementioned characteristics. Full article

Background: Psychological distress is common in hemodialysis patients and is linked to worse clinical outcomes and lower quality of life. Nutritional and inflammatory disturbances may impact emotional well-being. Gender likely acts as a biological and psychosocial modifier. This study examined the link between depression, anxiety, and stress in hemodialysis patients and a broad range of biochemical markers, focusing on gender as a main factor. Methods: A cross-sectional study included 54 adults on maintenance hemodialysis at a hospital in Madrid, Spain. Emotional distress was measured using the DASS-21. Predialysis biochemical markers assessed were β₂-microglobulin, albumin, hemoglobin, hematocrit, phosphorus, potassium, iron, calcium, and vitamin D. Statistical analyses included Spearman correlations, HC3-robust regressions with Gender × Biomarker interactions, false discovery rate correction (q = 0.10), penalized regressions (ridge/LASSO), partial least squares structural equation modeling (PLS-SEM), and mixed-cluster analysis. Results: Women reported higher depression, anxiety, and stress, and had lower albumin, calcium, and vitamin D (p < 0.05). Depression was independently linked to female gender, lower calcium, and the Gender × β₂-microglobulin interaction (adjusted R² = 0.30). In PLS-SEM analysis, a latent global psychological distress measure was directly related to β₂-microglobulin and inversely related to albumin and calcium (R² = 0.47). Nutritional markers partly mediated the gender–distress link. Cluster analysis found three biopsychosocial profiles: metabolically balanced, catabolic–emotional, and resilient–compensated. Conclusions: Gender shapes the relationships among inflammation, nutrition, and psychological distress in hemodialysis. Including gender-sensitive emotional and nutritional assessments in nephrology nursing could foster more personalized and practical care. Findings highlight the value of gender-aware psycho-nutritional screening in dialysis. Full article

Food irradiation is increasingly used to extend shelf life and control pests and diseases. Monitoring post-treatment doses typically relies on expensive, laborious instruments and may miss low doses. We previously proposed a chemical fingerprinting method that estimates dose based on indicator reaction rates, but this approach was tested only on freshly irradiated samples. In this study, we investigated the feasibility of determining the order of magnitude of dose in irradiated raw potato tubers after several days of storage. A completely randomized experimental design was used. Water extracts of potatoes were assayed in oxidation–reduction and aggregation reactions in 96-well plates; reaction rates were tracked by absorbance and fluorescence and analyzed chemometrically. We could distinguish dose orders of magnitude (0, 100, 1000 Gy) after 0, 2, and 6 days of storage at 4 °C. The accuracy of dose recognition on day 6 was at least 97% by using SoftMax regression (SR) or linear discriminant analysis (LDA); irradiated and non-irradiated samples were confidently distinguished using partial least square–discriminant analysis (PLS-DA). The reaction-based method of dose assessment is simple, rapid, and does not require sophisticated equipment. Full article

Composting is an effective biotechnological process for transforming agro-industrial residues into stabilized and nutrient-rich organic amendments. However, the molecular mechanisms underlying organic matter transformation remain poorly resolved. In this study, a mixture of winery by-products and poultry manure was composted under controlled aeration and monitored through high-field ¹H NMR spectroscopy of the water-extractable organic matter (WEOM), followed by interval-based chemometric analysis. The NMR spectra revealed distinct compositional trends, including the rapid depletion of amino acids and carbohydrates, the transient accumulation of low-molecular-weight organic acids, and the gradual enrichment in aromatic and phenolic compounds associated with humification processes. Chemometric modeling using Partial Least Squares (PLS) regression and its interval variants (iPLS and biPLS) enabled accurate prediction of composting time (r ≈ 0.95) and identification of diagnostic spectral intervals corresponding to key metabolites. These findings demonstrate the capability of NMR-based molecular profiling, combined with multivariate modeling, to elucidate the biochemical pathways of composting and to provide quantitative indicators of compost stability and maturity. Full article

Background and Objectives: Pituitary adenomas are common intracranial tumors lacking specific non-invasive biomarkers. This study aimed to determine whether key metabolites and enzymes of the kynurenine pathway—including indoleamine 2,3-dioxygenase (IDO), kynurenine (KYN), kynurenic acid (KYNA), kynurenine aminotransferase (KAT), quinolinic acid, and picolinic acid—can serve as diagnostic biomarkers distinguishing patients with pituitary adenomas from healthy controls. Materials and Methods: We conducted a single-center, cross-sectional, case–control study with 50 patients with pituitary adenomas and 35 healthy controls. Serum levels of IDO, KYN, KYNA, KAT, quinolinic acid, and picolinic acid were measured via enzyme-linked immunosorbent assay (ELISA). Statistical analyses included group comparisons (t-test/Mann–Whitney U), multivariate logistic regression to identify independent predictors, receiver operating characteristic (ROC) curve analysis to evaluate diagnostic performance (area under the curve, AUC), and partial least squares discriminant analysis (PLS-DA) for multivariate metabolic profiling. Results: Serum kynurenine, kynurenic acid, 3-hydroxykynurenine, picolinic acid, IDO and kynureninase were significantly higher in the pituitary adenoma group than in healthy controls (p < 0.001), while tryptophan, kynurenine aminotransferase, anthranilic acid and quinolinic acid showed no significant differences. ROC analysis demonstrated excellent diagnostic accuracy, with KAT (AUC = 0.923) and KYNA (AUC = 0.901) showing the highest discrimination. Multivariate logistic regression identified IDO, KYN, and KYNA as independent predictors of pituitary adenoma (p < 0.05). PLS-DA of the combined metabolite data also demonstrated clear separation between patients and controls, confirming distinct metabolic profiles between the groups. Conclusions: Kynurenine pathway metabolites and enzymes show strong potential as non-invasive biomarkers for pituitary adenomas. In particular, elevated KAT and KYNA levels demonstrated high diagnostic performance. These findings suggest that a panel of kynurenine pathway metabolites could aid in the early, non-invasive detection of pituitary adenomas. Full article

Effective medical waste planning relies on the reliable estimation of waste volumes. As operational factors diversify, traditional linear regressions often fail to capture the underlying structure, whereas latent variable–based and ensemble approaches can better represent this complexity. In this study, fine-tuned Partial Least Squares (PLS), scikit-learn–based Gradient Boosting regression (GBR), and a baseline Ordinary Least Squares (OLS) model were compared for estimating medical waste generation using 48 months (2021–2024) of approximate data from Dental Clinics affiliated with the Provincial Health Directorate in Kastamonu. The model inputs were the monthly procedure counts for endodontics, treatment, prosthetics, periodontology, orthodontics, pedodontics, and surgery. Performance was evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and R-squared (R²). All models produced accurate predictions; however, PLS provided the strongest fit (R² = 0.979; MAE = 30.488; RMSE = 37.043), outperforming GBR (R² = 0.962; MAE = 36.544; RMSE = 48.990) and the OLS baseline (R² = 0.927; MAE = 41.762; RMSE = 59.013). The findings demonstrate that modern, data-driven waste-management planning is feasible in healthcare institutions and highlight PLS as a robust option, particularly under conditions of small sample size and collinearity. Full article

Rapid, non-destructive assessment of biomass composition is essential for advancing Miscanthus spp. breeding and bioenergy production. This study aimed to develop and validate high-throughput near-infrared spectroscopy (NIRS) models for key chemical components in Miscanthus biomass. A robust calibration set was constructed from 107 diverse samples by combining two key species, Miscanthus sacchariflorus and M. lutarioriparius, to enhance chemical variability and create broadly applicable models. Partial Least Squares (PLS) regression models were developed using this dataset, comparing full-spectrum performance against models optimized with three feature selection algorithms: CARS, VCPA-GA, and VCPA-IRIV. All feature selection methods significantly enhanced predictive accuracy. Notably, the CARS-PLS models yielded excellent performance for cellulose (R²v = 0.98; RPD = 7.38), hemicellulose (R²v = 0.95, RPD = 4.35), lignin (R²v = 0.96, RPD = 5.40), and moisture (R²v = 0.98, RPD = 7.18), while the VCPA-IRIV-PLS model was superior for ash content (R²v = 0.96, RPD = 5.13). Overall, NIRS coupled with advanced feature selection provides a powerful, rapid protocol for Miscanthus biomass analysis, poised to accelerate germplasm evaluation and industrial quality control in the bioenergy sector. Full article

Denitrification and nitrification are two pivotal microbial processes relating to N₂O emissions. However, the difference in N₂O emission fluxes and N₂O-producing bacteria between a karst (KA) and non-karst area (NKA) remains unclear. The objective of this study is to compare the differences in soil N₂O emissions, nitrifying bacteria, and denitrifying bacteria during the growth period of rice in KA and NKA, and to explore the mechanisms by which microorganisms and environmental factors drive N₂O emissions. Here, N₂O emission fluxes of paddy fields were collected using the static dark chamber and measured using gas chromatography at KA and NKA in the Maocun Karst Experimental Site in Guilin, China. The nitrifying bacteria (ammonia-oxidizing bacteria, AOB) and denitrifying bacteria (nirK-denitrifier) were determined using real-time PCR and high-throughput sequencing, respectively. Results showed that during the rice growth period, the N₂O emission fluxes in KA was generally lower than that in NKA, with cumulative N₂O emissions of −0.054 and 0.229 kg·hm⁻² in KA and NKA, respectively. The absolute abundance of AOB in KA (8.91 × 10⁶–2.68 × 10⁷ copies·g⁻¹) was significantly higher than that in NKA (1.57 × 10⁶–6.48 × 10⁶ copies·g⁻¹), while the absolute abundance of nirK-denitrifier had no significant difference between the two areas. The composition and diversity of AOB and nirK-denitrifier differed significantly between KA and NKA. Results from partial least squares structural equation modeling (PLS-SEM) indicated that soil properties, carbon sources, and nitrogen sources had positive effects on AOB and nirK-denitrifier, while nirK-denitrifier had a negative effect on N₂O emissions. Partial least squares regression (PLSR) predictions revealed that NO₃⁻-N, SOC, TN, Mg²⁺, Ca²⁺, and pH were the most important factors influencing N₂O emission fluxes. This study highlights the critical role of the typical characteristics of KA soils in reducing N₂O emissions from paddy fields by driving the evolution of AOB and nirK-denitrifier. Full article

The advent of hyperspectral remote sensing represented a breakthrough in the accurate, fast, and non-invasive estimation of important soil fertility parameters. The present study utilizes non-imaging hyperspectral data in the spectral range of 350–2500 nm for estimating soil organic carbon (SOC) content. When partial least squares (PLS) scores were taken as independent variables, support vector machine (SVM) outperformed artificial neural network (ANN) and partial least squares regression (PLSR), achieving an R² value of 0.83. After pre-processing, the proximal spectral values were spatially interpolated to construct a synthetic hyperspectral image of the experimental fields. By applying the regression model to this synthetic hyperspectral imagery, a high-resolution SOC map showing the variability of organic carbon content in the soil was generated. Full article

The classification of cork planks as a raw material is traditionally performed through visual inspection of cork pores and defects, both in forest owners’ associations and industrial settings. This method introduces subjectivity and limits reproducibility. This study aimed to develop near-infrared spectroscopy (NIRS) models for predicting porosity in raw cork, distinguishing virgin, secondary, and mature cork types. A total of 156 cork samples representing the three cork types were analyzed. Spectra were collected on the transverse and radial surfaces using a Bruker MPA spectrophotometer. Partial Least Squares Regression (PLS-R) models were developed separately for each cork type, yielding cross-validated coefficients of determination (R²) between 0.48 and 0.64. Additionally, two global models were obtained using a random data split (60% for cross-validation and 40% for validation), differentiated by whether or not areas corresponding to insect galleries were included. The model incorporating insect galleries achieved R² values of 0.63 (cross-validation) and 0.46 (validation), while the model excluding them yielded R² values of 0.51 and 0.52, respectively. The final optimized model, based on all samples and using selected spectral regions (9500–7500 and 6100–5450 cm⁻¹) with first derivative and vector normalization preprocessing, achieved an R² of 0.61, RMSECV of 0.025, and RPD of 1.6 using five latent variables. This model was used to estimate porosity coefficients in visually classified secondary and mature cork. Results confirmed an inverse relationship between porosity and cork quality class: higher-quality classes (Q1, Q2) had lower porosity, with Q1 being the most homogeneous. Porosity increased from Q2 to Q6 in mature cork, expressing declining quality. Greater variability in lower-quality classes highlights porosity’s relevance for classification. These results demonstrate the potential of NIRS as a non-destructive tool for assessing cork porosity, offering a more objective and efficient alternative to conventional methods. Full article

Alcohol content and original wort concentration are key indicators of beer quality. The detection of these metrics and the authentication of beer authenticity are crucial for protecting consumer rights. To this end, this study investigates quantitative detection methods for beer alcohol content and original wort concentration based on near-infrared spectroscopy (NIRS), as well as authenticity verification methods for craft, industrial, and non-fermented beers. Convolutional neural networks combined with a long short-term memory networks (CNN-LSTM) feature extraction method was proposed for establishing multiple regression models and partial least squares discriminant analysis (PLS-DA) model. The results indicate that the CNN-LSTM combined with the support vector machine regression demonstrates optimal performance, with coefficients of determination exceeding 0.99 for the alcohol content calibration, validation, and independent test sets, and all relative root mean square errors below 2.67%. For original wort concentration, the coefficients of determination exceeded 0.97 across the calibration, validation, and independent test sets, with relative root mean square errors below 4.05%. The CNN-LSTM combined with the PLS-DA approach exhibited the lowest variable dimension while achieving 100% classification accuracy. This method offers rapid, non-destructive, and efficient advantages, making it suitable for beer quality control and market regulation. Full article

Various intersection structures are utilized in city-wide traffic network infrastructure by local transportation authorities to handle the exponentially increasing traffic loads in developing countries. In this regard, numerous studies have considered the notable positive contribution of the modern roundabouts in intersection performance as a prominent method utilized widely in our contemporary world. Properly designed roundabouts are vital components of sustainable transportation planning, as they significantly influence traffic efficiency, safety, and environmental performance. Accurate estimation of roundabout capacity is essential to ensure that they can accommodate anticipated traffic volumes without causing congestion, thereby contributing to energy efficiency and reducing emissions. Moreover, sustainable roundabout design supports the development of safer and more inclusive transportation networks by improving accessibility for all road users, thus strengthening the overall sustainability of urban mobility. The SIDRA (version 8.0), a traffic simulation software, is frequently employed in performance analysis and determining the effects of possible outcomes of different scenarios of roundabouts in today’s world. On the other hand, driver behaviors are found to play a significant role in software performance during the analysis process of roundabout capacity and performance. Therefore, in order to optimize the environmental factor (EF) representing driver behaviors in the SIDRA software, a Differential Evolution Algorithm-Based Bi-Level Calibration Model (DEBCAM) was introduced. Observation data collected from eight different modern-structured roundabouts through drones were run into the SIDRA simulation software; the average delays obtained were employed to estimate optimum EF values through DEBCAM. Observed average delay values were taken into consideration with respect to the delay values obtained as a result of the SIDRA calibration by using the GEH statistics. GEH values indicate the consistency of vehicle delay data obtained via the DEBCAM with observed data. Acquired results clearly suggest that the SIDRA software needs to be calibrated so that it can represent drivers’ behaviors. After determination of the optimum values of the EF parameter for calibration of the SIDRA software, the regression analysis was conducted through the Partial Least Squares (PLS) method. As a result of the analysis, a capacity estimation model was developed, which displayed a significant conformity with the SIDRA capacity estimation results. Our findings suggested that the parameter requirement for the roundabout capacity estimation can be decreased by employing the appropriate EF value for the roundabout that needs to be analyzed. Full article

A fiber-optic visible–near-infrared absorption spectroscopy system in a glove box was demonstrated for remote quantification of Am(III) (0–500 µM) and HNO₃ (0.1–9 M) using partial least squares regression (PLSR) models. The sensor platform, featuring a simple plug-and-play spectrophotometer, can enable noninvasive, real-time monitoring of actinide process solutions. To establish a flexible PLSR model calibration strategy, a D-optimal design developed using Nd(III) in previous studies was successfully extended to an actinide system with Am(III) to effectively minimize sample set size while maintaining robust prediction performance. The results suggest strong spectral similarities between Nd(III) and Am(III) and validate Nd(III) as an effective optical surrogate for trivalent actinide species. This work also supports the generalizability of a D-optimal training set selection approach for two-factor systems. The PLS1 models for Am(III) and HNO₃ outperformed a PLS2 model and maintained reasonable performance in the presence of interfering U(VI). The resulting sensor system and multivariate approach provides a flexible and scalable solution for process monitoring, control, and safety in diverse nuclear applications. Full article

The quantity of anthocyanins plays a crucial role in wine quality, since these phenolic compounds significantly influence the color, mouthfeel and organoleptic properties of red wines. It is therefore important to define accurate and precise methodologies to monitor the concentration of total anthocyanins in wine. Currently, this analysis is carried out using Ultraviolet-Visible (UV–visible) spectrophotometry. This work aims to determine an alternative methodology that is equally fast, accurate and allows in situ measurements while opening the measurement of the concentrations of other molecules of interest. The method presented consists of Raman analysis of Barbera wine samples using a portable Raman spectroscopy system. Subsequently, the collected spectra were processed using an algorithm that applies partial least squares (PLS) regression, making it possible to determine the concentration of total anthocyanins for each sample. This approach is characterized by an accuracy and precision comparable to the methodology currently in use, i.e., UV–visible spectrophotometry. It is indeed characterized by an RMSE (root mean square error) and $R^2$ (the coefficient of determination) on the validation set of 0.010 g/L and 0.88 and on the test data of 0.007 g/L and 0.93, respectively. Full article

Oedema is a common clinical finding in critically ill neonates and may reflect systemic illness such as congestive heart failure, hepatic cirrhosis, nephrotic syndrome, sepsis, and acute kidney injury. Oedema is characterised by tissue swelling due to water accumulation in the interstitial space. Currently, the gold standard in clinical practice is visual assessment, which is subjective and limited in accuracy. Alternative methods, such as ultrasound and bioimpedance, have been explored; however, they are unsuitable in neonates and do not provide direct water quantification. Near-infrared spectroscopy (NIRS) is a non-invasive optical method that could measure water content through light interaction between near-infrared light and OH particles within the tissue. This study validated NIRS for oedema assessment using an ex vivo porcine skin model, where controlled oedema was induced by phosphate-buffered saline (PBS) injection. Continuous spectroscopic data were collected via optical fibres positioned perpendicularly and parallel to the tissue. Regression models were developed and evaluated using the spectral data, with partial least squares (PLS) regression outperforming ridge regression (RR) and support vector regression (SVR). Notably, spectra acquired in the parallel configuration yielded superior results (R² = 0.97, RMSE = 0.15). These findings support the potential of NIRS as a reliable, quantitative tool for neonatal oedema assessment. Full article