A comprehensive LCMS/MS characterization for the green extracted cucurbitane-triterpenoid glycosides from bitter melon (Momordica charantia) fruit

A first-time green extraction and LCMSMS analysis for karavilosides (KVs) VIII, X, and XI in different parts (skin, pith, and seed) of the fresh and dried fruit of bitter melon (BM) is reported herein. Ultrasonication for green extraction whereas, LCMS/MS for KVs quantification were used. More extract yield (675.80 ± 163.57 mg/g) was observed for the dried fruit parts compared to the fresh BM-fruit parts (513.20 ± 75.42 mg/g). The fresh skin (343.40 ± 54.07 mg/4g) and dried seeds (311.80 and 77.95 ± 38.98) exhibited more yield whereas, the solvent yield (mg/4mg) observed was; H2O (651.70) > EtOH (227.20) > EtAC (163.30) > ACT (146.80). The LCMS/MS yield for the KVs revealed a descending order; KVXI (2376.44 ppb) > KVX (639.17 ppb) > KVVIII (599.83 ppb). More correlation was seen for the solvent Vs extract yield whereas, the KVs revealed more correlation for the BM-fruit part (P = 0.05). The study comprehensively characterized the parts of fresh and dried BM-fruits in terms of extract yield and KVs amount.

A novel nanocomposite for photocatalytic rhodamine B dye removal from wastewater using visible light

Providing safe access to water and addressing the impact of waterborne diseases, which claim over two million lives annually, is a major contribution to water purification. The study introduces a novel nanocomposite, Ch/Fe3O4/α-MoO3, which exhibits outstanding photocatalytic efficacy under visible light. An in-depth investigation of the nanocomposite's synthesis, characterization, and photodegradation mechanisms reveals its outstanding capabilities. Photocatalytic activity is influenced by the catalytic dose, pH, dye concentration, and reaction time, according to the study. A response surface method is used to determine the optimal conditions for Rhodamine B degradation, which results in 96.3% removal efficiency at pH 8.5, dye concentration 25 mg/L, nanocomposite dose at 22 mg/L, and reaction time 50 min. As a result of its high surface area, biocompatibility, availability, and magnetization with iron compounds, Chitosan is an excellent substrate for enhancing the photocatalytic properties of MoO3 nanoparticles. A nanocomposite with an energy band of 3.18 eV exhibits improved visible light absorption. This study confirms the nanocomposite's recyclability and stability, affirming its practicality. Besides dye removal, it offers hope for the global quest for clean water sources by addressing a broader range of waterborne contaminants. By combining molybdenum and magnetite, nanocomposite materials facilitate the degradation of pollutant and bacteria, contributing positively to society's quest for clean and safe water. It emphasizes the role nanotechnology plays in preserving human health and well-being in combating waterborne diseases.

Evaluating the Diagnostic Performance of Large Language Models on Complex Multimodal Medical Cases

Large language models showed interpretative reasoning in solving diagnostically challenging medical cases.

Enhancing Cutting Efficiency and Minimizing Forces for Nomex Honeycomb Core Using Grey Relational Analysis and Desirability Function Analysis

Nomex Honeycomb core is the foundational building block for manufacturing aerospace composite components. Its usage requires machining honeycomb in complex aerodynamic profiles where the quality of the core is governed by accuracy and precision of cut profiles. The assessment of accuracy and precision is directly related to forces induced in the cutting tool and cutting efficiency. These two parameters form the basis of a multi-objective function that this paper aims to optimize for the milling operation. The parameter of depth of cut considered in this paper has not been analyzed in a multi-objective optimization study of the Nomex Honeycomb core previously. A Taguchi-based array of Design of Experiments followed by Analysis of Variance and correlation analysis is utilised. The results indicate that the most significant factor is the feed rate, with a percentage contribution of 72% for the cutting forces and depth of cut, with a percentage contribution of 85% in the case of cutting efficiency. The two parameters are optimized using Desirability Function Analysis and Grey Relational Analysis. The results are validated through experimental runs with an error within 5% of the statistical predictions, with the percentage improvement in cutting forces for optimum runs as compared to the worst experimental run at 47.8%. The percentage improvement in cutting efficiency likewise is 11%.

A comparative study of surfactant distribution and fate (western and eastern) Egyptian Mediterranean coasts focusing on its environmental toxicity

One of the most difficult-to-manage new contaminants constantly released into the environment is linear alkylbenzene sulphonate (LAS), an anionic surfactant. Significant volumes of LAS are received by the Mediterranean coast of Egypt. The current study is a comprehensive assessment of the environmental fate of the LAS 1505 km off the Mediterranean coast of Egypt in the fall of 2023 in order to track its geographic spread and eventual demise in the water column. Critical analysis of LAS revealed that it is vertically distributed in various ways according to sources, uses, production amounts, and salinity levels. The vertical variation of LAS can be explained by its amphiphilic structure. A significant increase in surfactant concentration (>300 μg/L) was recorded in 66% and 43% of the total samples, ranging from 301.128 to 455.36 and from 304.556 to 486.135 for the western and eastern sides along the Egyptian Mediterranean coast, respectively. Evaluation of the average acute and chronic risk quotient (RQ) along the investigated locations revealed that fish were the most susceptible to LAS in both long and short exposure periods. The presented results also indicated significant LAS toxicity to three trophic levels (RQ values > 1). LAS toxicity to marine organisms was greater in the western than in eastern coastal regions according to acute and chronic mixture risk characterization ratios (RCRmix). The three trophic levels in the study area had the following order of acute relative contribution (RC) to LAS toxicity: fish > invertebrates > algae. The ANOVA test results showed that in both the western and eastern regions, LAS varied significantly (p < 0.05) with salinity (1.04E-60 and 5.44E-42) and depth (6.02E-65 and 1.59E-47), respectively. In addition, a significant difference was observed using the ANOVA test between the eastern and western regions of the Egyptian Mediterranean coast.

Performance Prediction and Optimization of Nanofluid-Based PV/T Using Numerical Simulation and Response Surface Methodology

A numerical investigation was carried out in ANSYS Fluent® on a photovoltaic/thermal (PV/T) system with MXene/water nanofluid as heat transfer fluid (HTF). The interaction of different operating parameters (nanofluid mass fraction, mass flow rate, inlet temperature and incident radiation) on the output response of the system (thermal efficiency, electrical efficiency, thermal exergy efficiency, and electrical exergy efficiency) was studied using a predictive model generated using response surface methodology (RSM). The analysis of variance (ANOVA) method was used to evaluate the significance of input parameters affecting the energy and exergy efficiencies of the nanofluid-based PV/T system. The nanofluid mass flow rate was discovered to be having an impact on the thermal efficiency of the system. Electrical efficiency, thermal exergy efficiency, and electrical exergy efficiency were found to be greatly influenced by incident solar radiation. The percentage contribution of each factor on the output response was calculated. Input variables were optimized using the desirability function to maximize energy and exergy efficiency. The developed statistical model generated an optimum value for the mass flow rate (71.84 kgh-1), the mass fraction (0.2 wt%), incident radiation (581 Wm-2), and inlet temperature (20 °C). The highest overall energy and exergy efficiency predicted by the model were 81.67% and 18.6%, respectively.

Study on the Molding Factors of Preparing High-Strength Laminated Bamboo Composites

To promote the development of the 'Bamboo as a Substitute for Steel' proposal, rotary cut bamboo veneers were applied to prepare a kind of high-strength laminated bamboo composite, which was achieved through the hot press molding method in this study. Orthogonal experiments of L9 (33) were performed, with hot-pressing temperature, pressure, and time considered as three influencing factors. Physical properties like density and moisture content, and mechanical properties like modulus of rupture (MOR), modulus of elasticity (MOE), shear strength, and compressive strength were tested for the samples. It can be obtained from the results of range analysis and ANOVA that higher density and lower moisture content were correlated with higher mechanical strength. Within the selected range of tested factors, a hot-pressing temperature and time of 150 °C and 10 min can contribute to higher density and lower moisture content, and the combination of 150 °C and 50 MPa can produce greater mechanical strength. In the thickness direction, the laminated bamboo composites displayed a notable compressed structure.

A survey of statistical methods utilized for analysis of randomized controlled trials of behavioral interventions

OBJECTIVES

Given the many statistical analysis options used for randomized controlled trials (RCTs) of behavioral interventions and the lack of clear guidance for analysis selection, the present study aimed to characterize the predominate statistical analyses utilized in RCTs in palliative care and behavioral research and to highlight the relative strengths and weaknesses of each of these methods as guidance for future researchers and reform.

METHODS

All RCTs published between 2015 and 2021 were systematically extracted from 4 behavioral medicine journals and analyzed based on prespecified inclusion criteria. Two independent raters classified each of the manuscripts into 1 of 5 RCT analysis strategies.

RESULTS

There was wide variation in the methods used. The 2 most prevalent analyses for RCTs were longitudinal modeling and analysis of covariance. Application of method varied significantly by sample size.

SIGNIFICANCE OF RESULTS

Each statistical analysis presents its own unique strengths and weaknesses. The information resulting from this research may prove helpful for researchers in palliative care and behavioral medicine in navigating the variety of statistical methods available. Future discussion around best practices in RCT analyses is warranted to compare the relative impact of interventions in a more standardized way.

The Influence of Fused Deposition Modeling Parameters on the Properties of PA6/PA66 Composite Specimens by the Taguchi Method and Analysis of Variance

Fused deposition modeling (FDM) is widely used in the rapid prototyping of polymers. Polyamide (PA) has excellent mechanical properties, but its application in FDM is limited due to its high water absorption, warpage, and forming shrinkage. The material of the filament and the printing parameters of the printer are two critical aspects that affect the performance of a component. The prepared PA6/PA66 (composite polyamide [COPA], PA6:PA66 = 85:15) composite (COPA: acrylonitrile butadiene styrene [ABS]: maleic anhydride grafted acrylonitrile butadiene styrene [ABS-g-MAH]: polyethylene = 800:133:67:100) has low water absorption (0.39%) and high dimensional stability, which has a good application prospect in FDM. The influence of eight FDM parameters, including three rarely reported, on the properties of PA6/PA66 composite specimens was investigated by the Taguchi method. The significance of influencing factors was evaluated by analysis of variance (ANOVA) and the stability by signal-noise ratio. When the layer thickness was 0.15 mm, the infill pattern was zigzags, the build plate adhesion type was brim, and the distance from the nozzle to the printing platform and the layer thickness (ΔL) was 0.05 mm; the specimens' dimensional accuracy, surface quality, and mechanical properties were better than other levels. The layer thickness and infill pattern were the two most important factors. The switch of the cooling fan and the temperature printing platform played a significant role in the specimens' dimensional accuracy and surface quality. ΔL tremendously influenced the thickness and warping degree of the specimens. The preparation of high-performance PA composites and the investigation of multiparameters by the Taguchi method provide a possible solution for applying polyamide in FDM.

Optimization of 3D printer settings for recycled PET filament using analysis of variance (ANOVA)

Fused Deposition Modeling (FDM) 3D printing creates components by layering extruded material. Printer parameters such as layer height and infill density can greatly impact the mechanical properties and quality of the printed parts. One critical factor to be considered in analysis is the anisotropy nature of printed components, considering all cross-sectional area (CSA) profiles for less than 100% infill density. This paper investigates the effect of the anisotropy nature of 3D printed CSA has on stress calculations and hence mechanical properties of the specimen through Design of Experiment (DOE). Analysis of variance (ANOVA) is utilised to evaluate the results. Printed specimens were tensile tested as per ASTM D638-14. Raw data was analysed using various CSA profiles, taking changes in infill density and layer height into account. Fixed parameter such as shell count, top and bottom layers, nozzle diameter, Hexagonal pattern were defined. Specimens Ultimate Tensile Strength (UTS) values increased on average by 30% using average profile CSA data compared to using external specimen dimensions. Further analysis assessing printer parameters affect on recycled Polyethylene Terephthalate (rPET) specimen's Young's Modulus (YM) and UTS was studied. One significant finding from this study suggests that the thickness of each layer has the most significant impact on the material properties of 3D printed rPET, as observed through the analysis of tensile test data obtained from 3D printed samples. A 3D printed rPET specimen with 30% infill density and 0.25 mm layer height has a higher YM (1175 MPa) and UTS (39 MPa) compared to a specimen with 75% infill density and 0.1 mm layer height (1159 MPa, 31 MPa). However careful interpretation of the results is required because for the same 30% infill parameter at 0.2 mm layer height the YM (936 MPa) and UTM (28 MPa) are significantly lower than at 0.25 mm layer height. If a higher value of YM and UTS is required an infill setting of 50% and layer height of 0.25 mm gave the highest values, YM (1330 MPa) and UTS (43 MPa).

Optimization of Charpy Impact Strength of Tough PLA Samples Produced by 3D Printing Using the Taguchi Method

This research employs the Taguchi method and analysis of variance (ANOVA) to investigate, analyze, and optimize the impact strength of tough polylactic acid (PLA) material produced through fused deposition modeling (FDM). This study explores the effect of key printing parameters-specifically, infill density, raster angle, layer height, and print speed-on Charpy impact strength. Utilizing a Taguchi L16 orthogonal array experimental design, the parameters are varied within defined ranges. The results, analyzed through signal-to-noise (S/N) ratios and ANOVA, reveal that infill density has the most substantial impact on Charpy impact strength, followed by print speed, layer height, and raster angle. ANOVA identifies infill density and print speed as the most influential factors, contributing 38.93% and 36.51%, respectively. A regression model was formulated and this model predicted the impact strength with high accuracy (R2 = 98.16%). The optimized parameter set obtained through the Taguchi method, namely, a 100% infill density, 45/-45° raster angle, 0.25 mm layer height, and 75 mm/s print speed, enhances the impact strength by 1.39% compared to the experimental design, resulting in an impact strength of 38.54 kJ/m2. Validation experiments confirmed the effectiveness of the optimized parameters.

On the bias in the AUC variance estimate

The area under the Receiver Operating Characteristic (ROC) curve (AUC) is a standard metric for quantifying and comparing binary classifiers. A popular approach to estimating the AUCs and the associated variabilities - the variance of the AUC or the full covariance matrix of multiple correlated AUCs - is the one proposed by DeLong et al [1], which is based on the Mann Whitney two-sample U-statistics. The bias of a variance estimator is an important factor in applications such as hypothesis testing and construction of confidence intervals - a negatively biased variance estimator may lead to incorrect conclusions, and a positive bias is conservative hence preferable. In this work, we show that the (co-)variance estimate in DeLong's approach is always positively biased. More specifically, the difference matrix between the expectation of the estimated covariance and the true covariance is a positive semi-definite matrix. This bias is non-negligible when the sample size is small, and quickly diminishes as the sample size increases. Our method relies on constructing, from the AUC kernel, a random variable whose (co-)variance matrix coincides with the bias, thereby establishing the claim. We also discuss alternative approaches to AUC variance estimation that may potentially reduce the bias.

Statistical analysis of Cotton-Jute blended ratio for producing good quality blended yarn

The present world is focusing on sustainable products. Most of the natural products collected from are environmentally friendly. In the textile sector the main raw material is fiber. Most textile products are made from the natural cotton fibers. But because of the shortage of this fiber, most of the researchers are looking forwards to other sources of natural fibers. Here in Bangladesh the natural jute fiber is available and the textile industries are making jute products but the jute products are comparatively lower price than cotton products. That's why some factories are making cotton-jute blended yarn to minimise the cost and increase the product's quality and appearance. Here in this research work, it was tried to identify the best cotton-jute blended ratio for producing good quality yarn. 80C-20 J, 60C-40 J and 40C-60 J blended ratios are compared with 100 C and 100 J yarn to analyse the results. The CV m%, Thick/Km +50 %, Neps/Km +200 %, IPI, RKM and Elongation% of blended yarns are evaluated and compared the results between the ratios. After that the yarn quality index (YQI) was calculated to identify the ratio which indicates a relation between yarns strength, elongation% and CVm. The other quality index was fiber quality index (FQI) which indicates a relation between fibers strength, fiber mean length, elongation% and fiber fineness. One way ANOVA was applied to see the significance level between the independent variables. Box plot was applied to see the visual effect of statistical analysis at the same time the regression results show the impact of cotton-jute ratio with an equation, through which it was easy to identify the perfect ratio. It was found that higher percentage of cotton and lower percentage of jute fiber blended yarn shows good results than others. The products which were made from the ratios were shown good results for their different use of purposes.

Increasing striatal dopamine release through repeated bouts of theta burst transcranial magnetic stimulation of the left dorsolateral prefrontal cortex. A 18F-desmethoxyfallypride positron emission tomography study

Introduction

Transcranial Magnetic Stimulation (TMS) can modulate fronto-striatal connectivity in the human brain. Here Positron Emission Tomography (PET) and neuro-navigated TMS were combined to investigate the dynamics of the fronto-striatal connectivity in the human brain. Employing 18F-DesmethoxyFallypride (DMFP) - a Dopamine receptor-antagonist - the release of endogenous dopamine in the striatum in response to time-spaced repeated bouts of excitatory, intermittent theta burst stimulation (iTBS) of the Left-Dorsolateral Prefrontal Cortex (L-DLPFC) was measured.

Methods

23 healthy participants underwent two PET sessions, each one with four blocks of iTBS separated by 30 minutes: sham (control) and verum (90% of individual resting motor threshold). Receptor Binding Ratios were collected for sham and verum sessions across 37 time frames (about 130 minutes) in striatal sub-regions (Caudate nucleus and Putamen).

Results

Verum iTBS increased the dopamine release in striatal sub-regions, relative to sham iTBS. Dopamine levels in the verum session increased progressively across the time frames until frame number 28 (approximately 85 minutes after the start of the session and after three iTBS bouts) and then essentially remained unchanged until the end of the session.

Conclusion

Results suggest that the short-timed iTBS protocol performed in time-spaced blocks can effectively induce a dynamic dose dependent increase in dopaminergic fronto-striatal connectivity. This scheme could provide an alternative to unpleasant and distressing, long stimulation protocols in experimental and therapeutic settings. Specifically, it was demonstrated that three repeated bouts of iTBS, spaced by short intervals, achieve larger effects than one single stimulation. This finding has implications for the planning of therapeutic interventions, for example, treatment of major depression.

Deep-STP: a deep learning-based approach to predict snake toxin proteins by using word embeddings

Snake venom contains many toxic proteins that can destroy the circulatory system or nervous system of prey. Studies have found that these snake venom proteins have the potential to treat cardiovascular and nervous system diseases. Therefore, the study of snake venom protein is conducive to the development of related drugs. The research technologies based on traditional biochemistry can accurately identify these proteins, but the experimental cost is high and the time is long. Artificial intelligence technology provides a new means and strategy for large-scale screening of snake venom proteins from the perspective of computing. In this paper, we developed a sequence-based computational method to recognize snake toxin proteins. Specially, we utilized three different feature descriptors, namely g-gap, natural vector and word 2 vector, to encode snake toxin protein sequences. The analysis of variance (ANOVA), gradient-boost decision tree algorithm (GBDT) combined with incremental feature selection (IFS) were used to optimize the features, and then the optimized features were input into the deep learning model for model training. The results show that our model can achieve a prediction performance with an accuracy of 82.00% in 10-fold cross-validation. The model is further verified on independent data, and the accuracy rate reaches to 81.14%, which demonstrated that our model has excellent prediction performance and robustness.

Assessment of nonlinear dose-response relationships via nonparametric regression

We propose a simple approach to assess whether a nonlinear parametric model is appropriate to depict the dose-response relationships and whether two parametric models can be applied to fit a dataset via nonparametric regression. The proposed approach can compensate for the ANOVA, which is sometimes conservative, and is very easy to implement. We illustrate the performance by analyzing experimental examples and a small simulation study.

Influence of aging, RAP content, and recycling agent on the performance of asphalt mixtures

A great complexity limits its use as the degree of aging of the asphalt binder of the RAP is a determinant for the interaction with the new binder and recycling agents to design asphalt mixtures with reclaimed asphalt pavement (RAP). One of the ways to analyze the level of interaction between the aged binder and the recycling agent is through the analysis of the performance of the mixtures after the accelerated aging of the samples in the laboratory. Thus, this paper evaluated the mechanical performance of asphalt mixtures with high contents of RAP (50%, 75%, and 100%) and organic type recycling agents (residual engine oil) and surfactant (ADCAP WM), submitted to aging protocols short and long term. The data obtained verified the feasibility of adding up to 75% of RAP in the recycled mixtures. However, detailed monitoring of its execution and performance throughout its useful life is necessary. Fatigue and flexural fracture data were highly altered by aging conditioning, evidencing the reduction in the performance of the compositions of the recycled mixtures. The statistical test showed significance for the parameters RAP content and type/content of the recycling agent used. In addition, there was an increase in the deformation capacity and better resistance to aging and cracking with the incorporation of the surfactant recycling agent.

Influence of Compounding Parameters on Color Space and Properties of Thermoplastics with Ultramarine Blue Pigment

The incorporation of thermoplastics with pigments imparts diverse aesthetic qualities and properties to colored thermoplastic products. The selection of pigment type and content, along with specific processing conditions, plays a pivotal role in influencing color properties and overall product performance. This study focuses on optimizing these parameters to ensure the desired color quality and product functionality. Two types of polypropylene copolymer (PPCP) with different melt flow rates (MFRs) and acrylonitrile butadiene styrene (ABS) were compounded with ultramarine blue pigment masterbatch (MB) in concentrations ranging from 1 to 5 wt.% using a twin-screw extruder. The compounding process was conducted at a constant screw speed of 200 rpm and a die temperature of 210 °C. The effects of screw speed and die temperature were investigated at a constant MB of 3 wt.%. Colored samples were fabricated by injection molding. Microscopic analysis revealed a well-dispersed pigment within the PPCP matrix when using the MB. Rheological properties, assessed through the power law index, confirmed effective pigment dispersion, facilitated by shear thinning behavior and controlled shear rate via the manipulation of screw speed and die temperature. The effects of masterbatch contents and processing conditions on color spaces were evaluated using CIELAB and CIELCH, with one-way ANOVA employed to identify statistical significance. Higher opacity in high-MFR PPCP and ABS resulted in increased lightness and color strength, surpassing low-MFR PPCP by 15-40% at equivalent MB contents. Masterbatch content emerged as a significant factor influencing the color spaces of all colored thermoplastics. Further analysis, including Fisher pairwise comparisons of one-way ANOVA, revealed that screw speed influenced the redness and hue of low-MFR PPCP, whereas die temperature affected the lightness and hue of high-MFR PPCP and ABS. Interestingly, the blueness and chroma of colored thermoplastics were minimally affected by both screw speed and die temperature. Notably, regardless of processing conditions, the flexural properties of colored thermoplastics remained comparable to the neat polymer when incorporated with ultramarine blue pigment masterbatch.

Optimizing fetal health prediction: Ensemble modeling with fusion of feature selection and extraction techniques for cardiotocography data

Cardiotocography (CTG) captured the fetal heart rate and the timing of uterine contractions. Throughout pregnancy, CTG intelligent categorization is crucial for monitoring fetal health and preserving proper fetal growth and development. Since CTG provides information on the fetal heartbeat and uterus contractions, which helps determine if the fetus is pathologic or not, obstetricians frequently use it to evaluate a child's physical health during pregnancy. In the past, obstetricians have artificially analyzed CTG data, which is time-consuming and inaccurate. So, developing a fetal health categorization model is crucial as it may help to speed up the diagnosis and treatment and conserve medical resources. The CTG dataset is used in this study. To diagnose the illness, 7 machine learning models are employed, as well as ensemble strategies including voting and stacking classifiers. In order to choose and extract the most significant and critical attributes from the dataset, Feature Selection (FS) techniques like ANOVA and Chi-square, as well as Feature Extraction (FE) strategies like Principal Component Analysis (PCA) and Independent Component Analysis (ICA), are being used. We used the Synthetic Minority Oversampling Technique (SMOTE) approach to balance the dataset because it is unbalanced. In order to forecast the illness, the top 5 models are selected, and these 5 models are used in ensemble methods such as voting and stacking classifiers. The utilization of Stacking Classifiers (SC), which involve Adaboost and Random Forest (RF) as meta-classifiers for disease detection. The performance of the proposed SC with meta-classifier as RF model, which incorporates Chi-square with PCA, outperformed all other state-of-the-art models, achieving scores of 98.79%,98.88%,98.69%,96.32%, and 98.77% for accuracy, precision, recall, specificity, and f1-score respectively.

Effects of landfill age, climate, and size on leachate from urban waste landfills in Portugal: A statistics and machine learning analysis

The leachate generated by in urban waste landfills can cause environmental pollution if not controlled and treated. With different proportions of biodegradable waste, urban waste degrades over several phases in anaerobic conditions within a landfill. Using multivariate leachate data from 32 engineered landfills in Portugal, each with a similar waste composition, and all classified as non-hazardous waste landfills receiving urban waste, statistical inference was applied to categorise and deduce significant statistical differences in leachate volume and quality between landfill age, size, and climate, as well as the interactions and effects within these categories. The findings show that the effects of size and age on the leachate volume are prevalent over local, Mediterranean climate conditions; in larger landfills, waste may not be degrading as efficiently as in medium-sized landfills; hotter zones showed higher levels of COD and lower levels of BOD5 than warmer zones, indicating increased biological activity under higher temperature conditions; TN and NH4-N increase significantly with age and size; Cl- also significantly increases with age, showing higher levels, along with SO42-, in hotter zones as well as a concentration effect in the dry season, along with K+; heavy metals maintain levels as landfills age from intermediate to old, with only Cd2+ and Pb2+ showing significant reductions. High correlations between macro inorganics and between heavy metals were found. Cluster analysis showed two main branches, one representing the initial to intermediate stages of anaerobic degradation, and the other the interactions between leaching parameters in the later methanogenic phase of landfill stabilisation.

Multi-objective optimization of ternary blends of Algal biodiesel-diesel-1-decanol to mitigate environmental pollution in powering a diesel engine using RSM, ANOVA, and artificial bee colony

This research presents an in-depth examination that utilizes a hybrid technique consisting of response surface methodology (RSM) for experimental design, analysis of variance (ANOVA) for model development, and the artificial bee colony (ABC) algorithm for multi-objective optimization. The study aims to enhance engine performance and reduce emissions through the integration of global maxima for brake thermal efficiency (BTE) and global minima for brake-specific fuel consumption (BSFC), hydrocarbon (HC), nitrogen oxides (NOx), and carbon monoxide (CO) emissions into a composite objective function. The relative importance of each objective was determined using weighted combinations. The ABC algorithm effectively explored the parameter space, determining the optimum values for brake mean effective pressure (BMEP) and 1-decanol% in the fuel mix. The results showed that the optimized solution, with a BMEP of 4.91 and a 1-decanol % of 9.82, improved engine performance and cut emissions significantly. Notably, the BSFC was reduced to 0.29 kg/kWh, demonstrating energy efficiency. CO emissions were lowered to 0.598 vol.%, NOx emissions to 1509.91 ppm, and HC emissions to 29.52 vol.%. Furthermore, the optimizing procedure produced an astounding brake thermal efficiency (BTE) of 28.78%, indicating better thermal energy efficiency within the engine. The ABC algorithm enhanced engine performance and lowered emissions overall, highlighting the advantageous trade-offs made by a weighted mix of objectives. The study's findings contribute to more sustainable combustion engine practises by providing crucial insights for upgrading engines with higher efficiency and fewer emissions, thus furthering renewable energy aspirations.

Nutritional Potential of Adzuki Bean Germplasm and Mining Nutri-Dense Accessions through Multivariate Analysis

The adzuki bean (Vigna angularis), known for its rich nutritional composition, holds significant promise in addressing food and nutritional security, particularly for low socioeconomic classes and the predominantly vegetarian and vegan populations worldwide. In this study, we assessed a total of 100 diverse adzuki bean accessions, analyzing essential nutritional compounds using AOAC's official analysis procedures and other widely accepted standard techniques. Our analysis of variance revealed significant genotype variations for all the traits studied. The variability range among different traits was as follows: moisture: 7.5-13.3 g/100 g, ash: 1.8-4.2 g/100 g, protein: 18.0-23.9 g/100 g, starch: 31.0-43.9 g/100 g, total soluble sugar: 3.0-8.2 g/100 g, phytic acid: 0.65-1.43 g/100 g, phenol: 0.01-0.59 g/100 g, antioxidant: 11.4-19.7 mg/100 g GAE. Noteworthy accessions included IC341955 and EC15256, exhibiting very high protein content, while IC341957 and IC341955 showed increased antioxidant activity. To understand intertrait relationships, we computed correlation coefficients between the traits. Principal Component Analysis (PCA) revealed that the first four principal components contributed to 63.6% of the variation. Further, hierarchical cluster analysis (HCA) identified nutri-dense accessions, such as IC360533, characterized by high ash (>4.2 g/100 g) and protein (>23.4 g/100 g) content and low phytic acid (0.652 g/100 g). These promising compositions provide practical support for the development of high-value food and feed varieties using effective breeding strategies, ultimately contributing to improved global food security.

Annual Transmittance Behavior of Light-Transmitting Concrete with Optical Fiber Bundles

This study characterizes the transmittance behavior of structural light-transmitting concrete under natural sunlight. The experimentation involves the use of a novel test setup and a detailed analysis considering the variation and dependence on time of day, month of the year and seasonal variations. The test set consisted of 28 variations of fiber configurations, with two different diameters, spacing and bundling techniques used to increase the area of fibers while maintaining spacing to aid the placing of concrete without compromising on transmittance. The study provides a real-time observational understanding of the behavior of light-transmitting concrete, a result usually obtained by modelling and simulation. The statistical analysis helps in understanding the impact of various variables as well as their interrelationships, which can help in design optimization. Based on the behavior as well as the stipulations of standards, the applicability of the material to various structural applications has been identified.

Riverine connectivity influences the phytoplankton ecology in the open floodplain wetland of the lower river Ganga

The river Ganga has several floodplain wetlands that support its ecology and ecosystem. Phytoplankton is an important component of the aquatic ecosystem, which plays an important role as a bioindicator for the assessment of aquatic health. The present study was conducted between 2018 and 2019 to understand the seasonal variation in the phytoplankton diversity of the Charaganga wetland and, parallelly, in the river Ganga in Nabadweep, India. The study explains how riverine connectivity affects the structure of the algal community in the wetland ecosystem. In the study, it has been observed that in the wetland, maximum mean phytoplankton density was noticed during pre-monsoon, i.e., 4079 unit l-1 followed by post-monsoon 3812 unit l-1 and monsoon 550 unit l-1, respectively. In the river system, the phytoplankton density varied from 78 unit l-1 to 653 unit l-1 seasonally, i.e., highest during monsoon and lowest during pre-monsoon. In both the ecosystems, i.e., wetland and river, the supreme influential group was Cyanophyceae followed by diatoms. One-way ANOVA showed a significant variation (p > 0.05) of three algal groups of phytoplankton (Bacillariophyceae, Coscinodiscophyceae, Chlorophyceae) in the river, while in the wetland, no significant variation (p > 0.05) was found among the other algal groups. The observed higher Shannon and Margalef's species richness value in the wetland was observed than in the river defines the significance and importance of the wetland ecosystem, which may support the growth and conservation of various aquatic organisms as well. The study highlighted that the influencing abiotic factors like water temperature, dissolved oxygen, pH, and nutrients have affected the phytoplankton community in both the water bodies, i.e., wetland and river. We concluded that river connectivity is required to restore the biotic flora of the wetland ecosystem.

Inverse Problem Algorithm-Based Time-Resolved Imaging of Head and Neck Computed Tomography Angiography Contrast Kinetics with Clinical Testification

This study mitigated the challenge of head and neck CT angiography by IPA-based time-resolved imaging of contrast kinetics. To this end, 627 cerebral hemorrhage patients with dizziness, brain aneurysm, stroke, or hemorrhagic stroke diagnosis were randomly categorized into three groups, namely, the original dataset (450), verification group (112), and in vivo testified group (65), in the Affiliated BenQ Hospital of Nanjing Medical University. In the first stage, seven risk factors were assigned: age, CTA tube voltage, body surface area, heart rate per minute, cardiac output blood per minute, the actual injected amount of contrast media, and CTA delayed trigger timing. The expectation value of the semi-empirical formula was the CTA number of the patient's left artery (LA). Accordingly, 29 items of the first-order nonlinear equation were calculated via the inverse problem analysis (IPA) technique run in the STATISTICA 7.0 program, yielding a loss function and variance of 3.1837 and 0.8892, respectively. A dimensionless AT was proposed to imply the coincidence, with a lower AT indicating a smaller deviation between theoretical and practical values. The derived formula was confirmed for the verification group of 112 patients, reaching high coincidence, with average ATavg and standard deviation values of 3.57% and 3.06%, respectively. In the second stage, the formula was refined to find the optimal amount of contrast media for the CTA number of LA approaching 400. Finally, the above procedure was applied to head and neck CTA images of the third group of 65 patients, reaching an average CTA number of LA of 407.8 ± 16.2 and finding no significant fluctuations.

Analysing data from the psycholinguistic visual-world paradigm: Comparison of different analysis methods

In this paper, we discuss key characteristics and typical experimental designs of the visual-world paradigm and compare different methods of analysing eye-movement data. We discuss the nature of the eye-movement data from a visual-world study and provide data analysis tutorials on ANOVA, t-tests, linear mixed-effects model, growth curve analysis, cluster-based permutation analysis, bootstrapped differences of timeseries, generalised additive modelling, and divergence point analysis to enable psycholinguists to apply each analytical method to their own data. We discuss advantages and disadvantages of each method and offer recommendations about how to select an appropriate method depending on the research question and the experimental design.

Letter to the editor on "Increased low-frequency brain responses to music after psilocybin therapy for depression"

The recent publication in the Journal of Affective Disorders titled "Increased low-frequency brain responses to music after psilocybin therapy for depression" identified significant region-of-interest based effects of treatment in the task scans. In this letter to the editor, I am hoping to raise methodological concerns with regards to the ANOVA ROI analysis that were otherwise not acknowledged in this study. These concerns raise questions as to the impact of confounds, including as age and biological sex, on the reported proportion variance explained by the effects of psilocybin treatment for depression.

The effect of individual factors, their binary and ternary interactions on photodegradation rate of organic contaminants using photocatalysts based on multi-walled carbon nanotubes (MWCNTs): statistical analysis based on ANOVA and RSM

The influence of three main parameters including irradiation time, weight fraction of photocatalysts including multi-walled carbon nanotubes and different amount of TiO2 (MCT#1 and MCT#2) and pH is investigated for the degradation rate of methyl orange (MO). Analysis of variance (ANOVA) and response surface methodology (RSM) have been applied to study the binary and ternary interactions of the main parameters on the degradation rate. The ANOVA results confirm that all of three studied factors have a considerable efficacy on degradation rate of MO at 5% level of probability. Meanwhile, the results show that the degradation rate is enhanced with increasing the weight fraction in range of 0.1 to 0.3%wt and irradiation time in a period of 5 to 35min.The lowest and highest degradation are observed at pH=7 and pH=3, respectively. The normality of residue distribution can be confirmed using graphical analysis. The RSM results reveal that the degradation rate dependency on irradiation time is higher than the weight fraction of photocatalysts.

Use of Yarn and Carded Jute as Epoxy Matrix Reinforcement for the Production of Composite Materials for Application in the Wind Sector: A Preliminary Analysis for the Manufacture of Blades for Low-Intensity Winds

The development of wind turbines for regions with low wind speeds imposes a challenge to the expansion of the corresponding energy generation capacity. The present work consists of an evaluation of the potential carded jute fiber and jute yarn to be used in the construction of a wind blade for regions of low wind intensity. The fibers used were supplied by Company Textile of Castanhal (Castanhal-Para-Brazil) and used in the study without chemical treatment in the form of single-filament fibers and yarns with a surface twist of 18.5°. The composites were produced through the resin infusion technique and underwent tensile and shear tests using 120-Ohm strain gauges and a blade extensometer to obtain the Young's modulus. In the analysis of the results, the ANOVA test was applied with a 0.05 significance level, followed by Tukey's test. The results showed that long, aligned jute fibers can be a good option for laminated structures applied in composites for small wind turbine blades.

Si3N4-TiN rotary EDM optimization by Mo-Jaya algorithm with Pareto optimal solution, analysis of micro-structural and geometrical tolerances

Research into rotary electrical discharge machining on high temperature with biomedical application Si3N4-TiN ceramic composite is presented in this paper. Current (I), pulse on time (Ton), pulse off time (Toff), dielectric pressure (DP), speed and spark gap voltage (Sv) are some of the many performance characteristics. Among the factors taken into account is the material removal rate, surface roughness, electrode wear rate, cylindricity, perpendicularity, top radial overcut, bottom radial over cut and run out. Multiple parameter combinations were validated experimentally and the resulting reactions were examined. Mean effects analysis and regression analysis are used to investigate the impacts of individual parameters. To comprehend the instantaneous behavior of the replies, multi-objective Jaya optimization is utilized to optimize the responses simultaneously. The multi-objective problem's outcomes are shown in 3D charts, with each showing the Pareto optimal solution. From this real conclusion, the optimal combinations of answers are extracted and reported. The aggregate optimization result was also shown, which factored in all eight responses. MRR of 0.238 g/min was obtained which is a 10.6% improvement from the experimental values. Electrode wear of 0.0028 g/min was obtained showing a 6.6% reduction. Similarly reduction in values of Surface roughness, top radial overcut and bottom radial over cut, Circularity, Perpendicularity, run out was observed and the percentages are 3.4, 4.7, 4.5, 7.8, 10.0 and 10.53 respectively. Details on the structural and morphological examinations of the various surface abnormalities that occur during the process have been presented.

Climate parameter and malaria association in north-east India

This study was performed in order to understand the effect of climatological variables on the malaria situation in the north-east region of India, which is prolonged by the disease. Time-series analysis of major climate parameters like rainfall, maximum temperature, minimum temperature, mean temperature, relative humidity, and soil moisture distributions is carried out, and their correlation with the malaria incidence is quantified state-wise, which is the unique part of the study. The correlation analysis reveals that malaria is significantly related with the maximum temperature and soil moisture in three out of eight states in NE India. To assess the climate variability, the inter-dependency between the meteorological parameters is obtained and the state wise correlation matrix for all states are reported. The analysis shows that maximum and mean temperature has highest positive correlation whereas minimum temperature and relative humidity has negative correlation. The climate-malaria relation is being carried out in the study region using the regression analysis and the results revealed that the regional climate has the most impact for the malaria incidence in the state of Arunachal Pradesh, Meghalaya, Tripura and Nagaland and in other states the impact is moderate. Analysis of variance modelling in the regions also indicates the degree of the fitment of both the data sets with the regression model and it is observed that the relation is also significant in the same 4 states. As a case study the impact of large scale oscillations like El Niño-Southern Oscillation on the malaria load is also assessed which can be a good indicator in the prediction of the climate and in turn the malaria incidences over the region.

Assessment of selection criteria using multi-year study for effective breeding program of Zingiber officinale L

Background

Ginger has been an important cash crop with numerous applications since ancient times. As the demand for ginger is ever-growing and being a seasonal crop, a high-yielding variety of ginger would be economically profitable.

Methods

In this study, 150 germplasm were collected from different regions of NE India and evaluated for three years in CRBD design with three replications. The present study thus focused on the variability, association, and diversity studies for the first time on 150 ginger germplasm from across North East India. The genotypic and phenotypic coefficient of variation, heritability, correlation, and path analysis were evaluated for the germplasm.

Results

Analysis of variance (ANOVA) revealed considerable differences among the studied germplasm for studied characters, revealing sufficient variability in the materials. The Mahalanobis D2 and Tocher methods grouped the 150 ginger germplasm into ten clusters. Based on the results of the path coefficient analysis determined for essential oil yield and rhizome yield per plant, it can be concluded that the characters' initial rhizome weight, the weight of mother rhizome, and weight of secondary rhizome were the most important and appeared promising in improving the overall yield potential of ginger rhizome and essential oil yield. Thus, selection based on the identified traits would lead to an effective ginger breeding program for higher rhizome and essential oil yield.

Geometrical Characterisation of TiO2-rGO Field-Effect Transistor as a Platform for Biosensing Applications

The performance of the graphene-based field-effect transistor (FET) as a biosensor is based on the output drain current (Id). In this work, the signal-to-noise ratio (SNR) was investigated to obtain a high-performance device that produces a higher Id value. Using the finite element method, a novel top-gate FET was developed in a three-dimensional (3D) simulation model with the titanium dioxide-reduced graphene oxide (TiO2-rGO) nanocomposite as the transducer material, which acts as a platform for biosensing application. Using the Taguchi mixed-level method in Minitab software (Version 16.1.1), eighteen 3D models were designed based on an orthogonal array L18 (6134), with five factors, and three and six levels. The parameters considered were the channel length, electrode length, electrode width, electrode thickness and electrode type. The device was fabricated using the conventional photolithography patterning technique and the metal lift-off method. The material was synthesised using the modified sol-gel method and spin-coated on top of the device. According to the results of the ANOVA, the channel length contributed the most, with 63.11%, indicating that it was the most significant factor in producing a higher Id value. The optimum condition for the highest Id value was at a channel length of 3 µm and an electrode size of 3 µm × 20 µm, with a thickness of 50 nm for the Ag electrode. The electrical measurement in both the simulation and experiment under optimal conditions showed a similar trend, and the difference between the curves was calculated to be 28.7%. Raman analyses were performed to validate the quality of TiO2-rGO.

Optimising Surface Roughness and Density in Titanium Fabrication via Laser Powder Bed Fusion

The Ti6Al4V alloy has many advantages, such as being lightweight, formal, and resistant to corrosion. This makes it highly desirable for various applications, especially in the aerospace industry. Laser Powder Bed Fusion (LPBF) is a technique that allows for the production of detailed and unique parts with great flexibility in design. However, there are challenges when it comes to achieving high-quality surfaces and porosity formation in the material, which limits the wider use of LPBF. To tackle these challenges, this study uses statistical techniques called Design of Experiments (DoE) and Analysis of Variance (ANOVA) to investigate and optimise the process parameters of LPBF for making Ti6Al4V components with improved density and surface finish. The parameters examined in this study are laser power, laser scan speed, and hatch space. The optimisation study results show that using specific laser settings, like a laser power of 175 W, a laser scan speed of 1914 mm/s, and a hatch space of 53 µm, produces Ti6Al4V parts with a high relative density of 99.54% and low top and side surface roughness of 2.6 µm and 4.3 µm, respectively. This promising outcome demonstrates the practicality of optimising Ti6Al4V and other metal materials for a wide range of applications, thereby overcoming existing limitations and further expanding the potential of LPBF while minimising inherent process issues.

Optimization of wear behavior of heat-treated Ti-6Al-7Nb biomedical alloy by response surface methodology

Titanium-based metals are used most often in biomedical implant studies because they have good qualities like being biocompatible, not being poisonous, Osseo-integration, high specific properties, wear resistance, etc. The main goal of this work is to improve the wear resistance of Ti-6Al-7Nb biomedical metal by using a mix of Taguchi, ANOVA, and Grey Relational Analysis. The effect of changeable control process factors like applied load, spinning speed, and time on wear reaction measures like wear rate (WR), coefficient of friction (COF), and frictional force. The optimal combinations of wear rate, COF, and frictional force minimise wear characteristics. The L9 Taguchi orthogonal array was used to plan the experiments, which were done on a pin-on-disc set-up according to ASTM G99. To find the best set of control factors, Taguchi, ANOVA, and Grey relationship analysis were used. The results show that a load of 30 N, a speed of 700 rpm, and a time of 10 min are the best control settings.

Precision Control in Vat Photopolymerization Based on Pure Copper Paste: Process Parameters and Optimization Strategies

Vat photopolymerization (VPP) presents new opportunities for metals to achieve the design freedom of components. However, the material properties of copper powder and the inherent defects of the technology seriously hinder its application in high-precision metal additive manufacturing. Precision control is the key to obtaining minimal precision metal parts when copper is prepared by reduction photopolymerization. This paper employed variance analysis (ANOVA) and root mean square deviation (RMSD) to determine the significant parameters affecting dimensional accuracy and their optimal regions. The results show that printing accuracy is improved by optimizing exposure time, intensity, layer thickness, and sweeper moving speed. When the exposure time is 21 s, and the exposure intensity is 220 mW/cm2, a hole with a height of 1 mm and a diameter of 200 μm can be printed with a minimum size deviation of 51 μm. In addition, RMSD and ANOVA provide an effective method for realizing high-precision stereolithography 3D printing metal copper, expanding the material adaptation in the 3D printing metals field. The study highlights the potential of VPP as a method for preparing metals in future studies.

Analysis of Vibration Signals Based on Machine Learning for Crack Detection in a Low-Power Wind Turbine

Currently, renewable energies, including wind energy, have been experiencing significant growth. Wind energy is transformed into electric energy through the use of wind turbines (WTs), which are located outdoors, making them susceptible to harsh weather conditions. These conditions can cause different types of damage to WTs, degrading their lifetime and efficiency, and, consequently, raising their operating costs. Therefore, condition monitoring and the detection of early damages are crucial. One of the failures that can occur in WTs is the occurrence of cracks in their blades. These cracks can lead to the further deterioration of the blade if they are not detected in time, resulting in increased repair costs. To effectively schedule maintenance, it is necessary not only to detect the presence of a crack, but also to assess its level of severity. This work studies the vibration signals caused by cracks in a WT blade, for which four conditions (healthy, light, intermediate, and severe cracks) are analyzed under three wind velocities. In general, as the proposed method is based on machine learning, the vibration signal analysis consists of three stages. Firstly, for feature extraction, statistical and harmonic indices are obtained; then, the one-way analysis of variance (ANOVA) is used for the feature selection stage; and, finally, the k-nearest neighbors algorithm is used for automatic classification. Neural networks, decision trees, and support vector machines are also used for comparison purposes. Promising results are obtained with an accuracy higher than 99.5%.

Trade-off study and the influence of different fuel injection strategies to enhance the engine characteristics powered with ternary fuel in CRDI diesel engine

The effects of a diesel engine combustion chamber geometries (PB1, PB2, PB3) and fuel injection pressure (FIP) (500, 750, and 1000 bar) with ternary fuel are discussed in this paper. It is observed that at maximum load as the FIP increased, the PB3 surpasses the PB1 and PB2 in brake thermal efficiency (4.86%) and hydrocarbon (7.2% decrease). However, a minor reduction in NOx (3%) is seen with increased FIP in the PB2. The NHRR (net heat release rate), peak pressure (in-cylinder), and ROPR (rate of pressure increases) all considerably increase with an enhancement in FIP in the case of PB3 by 2.96%, 3.86%, and 1.98%, respectively. It is discovered that altering the fuel injection pressure and piston shape concurrently offers a viable substitute for raising engine output and reducing emissions.

Multi-spectroscopic assay methods for concurrent determination of recent anti-gout combination, a comparative study

Recently, Chemometric calibration methods in spectrophotometric analysis are achieving significant attention in the quality control of resolving drug mixtures and pharmaceutical formulations containing two or more drugs with overlapping spectra. The simple univariate methods have been used over the last few decades and has proven to be highly efficient and easy to apply. In this study, a comparative study was performed between some univariate and multivariate methods to determine if chemometric methods can substitute univariate methods in pharmaceutical analysis. In this study, three chemometric techniques were compared to seven univariate techniques to resolve a mixture of mefenamic acid and febuxostat in their raw materials, dosage forms and spiked human plasma. Mefenamic acid and febuxostat were used together for treatment of gout. The applied chemometric methods are partial least squares (PLS), artificial neural network (ANN) and genetic algorithm partial least squares (GA-PLS), while the used univariate methods include first derivative, second derivative, ratio spectra, derivative ratio spectra, ratio subtraction, Q-Absorbance ratio and mean centering spectrophotometric methods. The ten proposed methods were found to be green, sensitive, and rapid. They are simple and did not require any pre-separation steps. The results of both univariate and multivariate approaches were statistically compared with the reported spectrophotometric methods using student's t test and ratio variance F-test. They were also compared with each other, using one-way analysis of variance (ANOVA). These methods were assessed and validated according to ICH guidelines. The studied drugs were analyzed in their pharmaceutical dosage forms and spiked human plasma with good recoveries using the developed methods, which qualify them for routine quality control of the studied drugs.

Application of Metal Shielding Materials to Protect Buildings Occupants from Exposure to the Electromagnetic Fields

In recent decades, the background level of electromagnetic fields (EMFs) has increased extremely. One of the decisive factors influencing this increase is the increase in the quality, volume, and speed of voice and data services of mobile operators. This paper deals with the protection of the internal environment from the negative effects of EMFs through elements made of metal materials that absorb this radiation. For the purposes of this research, a series of measurements were carried out on individual days of the week and hours during the day. The results of the measurements were evaluated by the ANOVA method. The aim was to obtain a summary overview of the effects of electromagnetic fields and propose measures for their elimination in the interior. Therefore, measurements of electromagnetic fields were also carried out using shielding elements made of various metal materials, and a comparison of their shielding efficiency was subsequently made. Applications of shading blinds with the highest shading efficiency were recommended to increase safety, protect people's health from its effects, and prevent electromagnetic fields.

Impact of yarn compositions, loop length, and float stitches on the mechanical behavior of knitted fabrics via full factorial design and RSM

This article presents a study on the tensile properties of knitted fabrics commonly employed in polymeric matrix textile composites. The key mechanical parameters investigated include stress (Pa), strain, Young's modulus (Pa), and work of rupture (J). The knitted fabrics were developed using the Cixing Knitting System software and subsequently manufactured using a double jersey (electronic) flat knitting machine. The primary objective of this research was to explore the impact of various factors on the mechanical behavior of these knitted fabrics. The factors studied were wale and course directions, float stitch density, loop length (cm), and the type of synthetic knitting yarns used (100% polyester and 100% polyamide) along with different combinations of knitting yarns (100% cotton and 67% polyester/33% cotton hybrid). The adopted ASTM D 5034 standard, Response Surface Methodology (RSM), and Analysis of Variance (ANOVA) were employed to evaluate the mechanical performance of these fabric structures. The findings of the study revealed that the statistical adjustment of the data set for stress, strain, Young's modulus, and work of rupture in knitted fabric structures significantly reduced the standard deviations for mechanical responses. This information holds particular significance as it pertains to the frequent use of these knitted fabric structures as reinforcement in textile-reinforced composite materials. Overall, this study sheds light on the mechanical behavior in structures of knitted fabrics used in polymeric matrix composites, providing valuable insights for the design and optimization of advanced textile-based materials.

Optimization and prediction of CBN tool life sustainability during AA1100 CNC turning by response surface methodology

The aluminium alloy (AA1100) was familiar with automotive flexible shaft coupling applications due to its high strength, good machinability, and superior thermal and resistance to corrosion characteristics. Machining tool life drives the prominent role for deciding the product quality (machining) act aims to productivity target with zero interruptions. The novelty of this present investigation is the focus on increasing tool life during the complexity of CNC turning operation for AA1100 alloy by using CBN coated insert tool with varied input parameters of spindle speed (SS), feed rate (f), and depth of cut (DOC). Design of experiment (L16), analysis of variance (ANOVA) statistical system adopted with response surface methodology (RSM) is implemented for experimental analysis. The turning input parameters of SS, f and DOC are considered as factors and its SS (900, 1100, 1300, and 1500 rpm), f (0.1, 0.15, 0.2, and 0.25), and DOC (0.1, 0.2, 0.3, and 0.4 mm) values are treated as levels. The investigational analysis was made with the ANOVA technique and the desirability of high tool life with input turning parameters was optimized by RSM, and sample no 11/16 was predicted as high tool life and performed with extended working hours compared to other samples. The RSM optimized best turning parameter combinations are 0.1 mm DOC, 0.2mm/rev to 0.25mm/rev f, and 1300 rpm-1500 rpm SS, facilitating a higher tool life of more than 20min.

Novel ANOVA-Statistic-Reduced Deep Fully Connected Neural Network for the Damage Grade Prediction of Post-Earthquake Buildings

Earthquakes are cataclysmic events that can harm structures and human existence. The estimation of seismic damage to buildings remains a challenging task due to several environmental uncertainties. The damage grade categorization of a building takes a significant amount of time and work. The early analysis of the damage rate of concrete building structures is essential for addressing the need to repair and avoid accidents. With this motivation, an ANOVA-Statistic-Reduced Deep Fully Connected Neural Network (ASR-DFCNN) model is proposed that can grade damages accurately by considering significant damage features. A dataset containing 26 attributes from 762,106 damaged buildings was used for the model building. This work focused on analyzing the importance of feature selection and enhancing the accuracy of damage grade categorization. Initially, a dataset without primary feature selection was utilized for damage grade categorization using various machine learning (ML) classifiers, and the performance was recorded. Secondly, ANOVA was applied to the original dataset to eliminate the insignificant attributes for determining the damage grade. The selected features were subjected to 10-component principal component analysis (PCA) to scrutinize the top-ten-ranked significant features that contributed to grading the building damage. The 10-component ANOVA PCA-reduced (ASR) dataset was applied to the classifiers for damage grade prediction. The results showed that the Bagging classifier with the reduced dataset produced the greatest accuracy of 83% among all the classifiers considering an 80:20 ratio of data for the training and testing phases. To enhance the performance of prediction, a deep fully connected convolutional neural network (DFCNN) was implemented with a reduced dataset (ASR). The proposed ASR-DFCNN model was designed with the sequential keras model with four dense layers, with the first three dense layers fitted with the ReLU activation function and the final dense layer fitted with a tanh activation function with a dropout of 0.2. The ASR-DFCNN model was compiled with a NADAM optimizer with the weight decay of L2 regularization. The damage grade categorization performance of the ASR-DFCNN model was compared with that of other ML classifiers using precision, recall, F-Scores, and accuracy values. From the results, it is evident that the ASR-DFCNN model performance was better, with 98% accuracy.

Wear Characterization of Phenol-Formaldehyde-Based Saguvani Wood-Polymer Composite-An ANOVA Approach

In this part of the research work, the Taguchi approach is used to analyze the weight wear loss of PF-based 10% chemically treated saguvani wood-polymer composite under dry sliding conditions. The fabrication of PF-based wood-polymer composite consisting of 10% chemically treated saguvani wood particles as reinforcement material filled with coconut shell powder is used. The rotary-drum-type blender is used for uniform mixing of reinforcement materials with resin as per the calculated volume ratio. The inclusion of coconut shell powder as secondary particles in the PF-based wood plastic composite minimizes the wearability of the composite. The Taguchi method is used successfully to analyze the wear behavior of the PF-based wood-polymer composite with sliding speed, load, and sliding distance as control parameters. The experimental work reveals that the composite C1 shows minimum wear loss compared to the other composite specimens, C2 and C3. And the most influential parameter that causes more wear is the sliding distance among the three control parameters.

A unified strategy to rebalance multifactorial designs with unequal group sizes: application to analysis of variance multiblock orthogonal partial least squares

BACKGROUND

Adequately handling unbalanced groups remains one of the major challenges for the analysis of multivariate data collected from multifactorial experimental designs. While partial least squares-based methods, such as analysis of variance multiblock orthogonal partial least squares (AMOPLS), can offer better discrimination between factor levels, they can be more heavily affected by this issue, and unbalanced designs of experiments may lead to a substantial confusion of the effects. Even state-of-the-art analysis of variance (ANOVA) decomposition methodologies using general linear models (GLM) lack the ability to efficiently disentangle these sources of variation when combined with AMOPLS.

RESULTS

A versatile solution developed as an extension of a prior rebalancing strategy is proposed for the first decomposition step based on ANOVA. This approach has the advantage of yielding an unbiased estimation of the parameters and retaining the within-group variation in the rebalanced design, while preserving the orthogonality of effect matrices, even in presence of unequal group sizes. This property is of utmost importance for model interpretation because it avoids mixing sources of variation related to the different effects in the design. A real case study involving metabolomic data from in vitro toxicological experiments was used to demonstrate the potential of this strategy to handle unequal group sizes using a supervised approach. Primary 3D rat neural cell cultures were exposed to trimethyltin following a multifactorial design of experiments involving three fixed effect factors.

SIGNIFICANCE AND NOVELTY

The rebalancing strategy was demonstrated as a novel and potent solution to handle unbalanced experimental designs by offering unbiased parameter estimators and orthogonal submatrices, thus avoiding confusion of the effects and facilitating model interpretation. Moreover, it can be combined with any multivariate method used for the analysis of high-dimensional data collected from multifactorial designs.

Material-Dependent Effect of Common Printing Parameters on Residual Stress and Warpage Deformation in 3D Printing: A Comprehensive Finite Element Analysis Study

Additive manufacturing (AM), commonly known as 3D printing, has gained significant popularity for its ability to produce intricate parts with high precision. However, the presence of residual stresses and warpage deformation are common issues affecting the quality and functionality of 3D-printed parts. This study conducts a comprehensive finite element analysis (FEA) to investigate the material-dependent impact of key printing parameters on residual stress and warpage deformation in 3D printing. The research focuses on three distinct materials: polyetherimide (PEI), acrylonitrile butadiene styrene (ABS), and polyamide 6 (PA6). Various printing parameters are systematically varied, including printing temperature, printing speed, bed temperature, infill density, layer thickness, and infill pattern. The study employs the Taguchi L27 orthogonal array and employs the analysis of variance (ANOVA) statistical technique to assess the significance of the input parameters. The obtained results reveal that certain parameters exhibit a greater sensitivity to material differences, whereas the layer thickness parameter demonstrates a relatively lower sensitivity. Notably, infill density and printing temperature play a crucial role in reducing residual stress for PA6, while the infill pattern parameter proves to be a significant contributor to minimizing warpage deformation across all three materials. These findings underscore the importance of conducting material-specific analyses to optimize 3D printing parameters and achieve the desired quality outcomes while mitigating residual stress and warpage deformation.

Correlative Method for Diagnosing Gas-Turbine Tribological Systems

Lubricated tribosystems such as main-shaft bearings in gas turbines have been successfully diagnosed by oil sampling for many years. In practice, the interpretation of wear debris analysis results can pose a challenge due to the intricate structure of power transmission systems and the varying degrees of sensitivity among test methods. In this work, oil samples acquired from the fleet of M601T turboprop engines were tested with optical emission spectrometry and analyzed with a correlative model. Customized alarm limits were determined for iron by binning aluminum and zinc concentration into four levels. Two-way analysis of variance (ANOVA) with interaction analysis and post hoc tests was carried out to study the impact of aluminum and zinc concentration on iron concentration. A strong correlation between iron and aluminum, as well as a weaker but still statistically significant correlation between iron and zinc, was observed. When the model was applied to evaluate a selected engine, deviations of iron concentration from the established limits indicated accelerated wear long before the occurrence of critical damage. Thanks to ANOVA, the assessment of engine health was based on a statistically proven correlation between the values of the dependent variable and the classifying factors.

Strategizing Spray Drying Process Optimization for the Manufacture of Redispersible Indomethacin Nanoparticles Using Quality-by-Design Principles

The present study adopted a Quality by Design (QbD) approach to spray dry indomethacin nanosuspension (IMC-NS) consisting of HPC-SL, poloxamer 407, and lactose monohydrate. The Box-Behnken Design was used to systematically evaluate the effects of inlet temperature, aspiration rate, and feed rate on the critical quality attributes (CQAs) [redispersibility index (RDI; minimize), % yield (maximize), and % release at 15 min (maximize)] of the indomethacin spray dried nanosuspension (IMC-SD-NS). To identify significant main and quadratic effects, two-way interactions, and create a predictive model for the spray drying process, regression analysis and ANOVA were utilized. Following optimization, the IMC-SD-NS was analyzed for its physicochemical properties using X-ray powder diffraction (XRPD), Fourier transform infrared spectroscopy (FTIR), and in vitro dissolution studies. Statistical analysis revealed significant independent variables, including inlet temperature, feed rate, and aspiration rate, that critically impacted the solidified end product's RDI, % yield, and % release at 15 min. The models developed for critical quality attributes (CQAs) were significant at a p-value of 0.05. The crystalline state of IMC was maintained in the solidified product, as confirmed by XRPD, and no interactions were observed between IMC and the excipients as evaluated by FTIR. In vitro dissolution studies showed improved dissolution rate for the IMC-SD-NS (3.82-fold increase in overall drug release), which may be attributed to the readily redispersible nanosized drug particles. The implementation of a well-designed study, utilizing Design of Experiments (DoE) methodology, played a crucial role in the development of a highly effective spray drying process.

Prediction and multi-objective optimization of workability and compressive strength of recycled self-consolidating mortar using Taguchi design method

Concrete is the most consumed material in the construction industry. Using recycled aggregates (RA) and silica fume (SF) in concrete and mortar could preserve natural aggregates (NA) and reduce CO₂ emissions and construction and demolition waste (C&DW) generation. Optimizing the mixture design based on both fresh and hardened properties of recycled self-consolidating mortar (RSCM) has not been performed. In this study, multi-objective optimization of mechanical properties and workability of RSCM containing SF was performed via Taguchi Design Method (TDM) with four main variables including cement content, W/C ratio, SF content and superplasticizer content at three different levels. SF was used to decrease the environmental pollution caused by cement production as well as compensating the negative effect of RA on the mechanical properties of RSCM. The results revealed that TDM could appropriately predict the workability and compressive strength of RSCM. Also, mixture design containing W/C = 0.39, SF = 6%, cement = 750 kg/m³ and SP = 0.33% was found as the optimum mixture having the highest compressive strength and acceptable workability as well as low cost and environmental concerns.

Computational fluid dynamic and response surface methodology coupling: A new method for optimization of the duct to be used in ducted wind turbines

Wind energy technology, particularly power generation by wind turbines, has received substantial attention due to resource depletion and global warming concerns. These concerns highlight the importance of conducting studies to enhance their efficiency by increasing their power output. The goal of this work was to combine the RSM (Response Surface Methodology (RSM) with CFD (Computational Fluid Dynamics) to discover the optimal design parameters and conditions for ducted wind turbines. To that purpose, twenty-seven runs were chosen using Central Composite Design (CCD) in the design phase. Duct simulation was performed by employing different dimensional parameters and feeding them into a third-order polynomial that fitted to an eight-order function. The analyzed runs discussed the maximum available wind velocity and power at the throat area of the various designed ducts. The wind-enhanced power and speed were studied under different design parameters, and their effects were discussed. The optimum design conditions to capture maximum power were 0.16 m, 2, and 1.5 for design parameters of the duct's throat diameter, contraction ratio, and length-to-throat diameter ratio, respectively. A good selection of design parameters can increase the outpour power up to six times as a general result. By modeling CFD simulations using the RSM method, it is possible to minimize the time and cost of calculation to find the optimized range for the design parameters of the ducts.