Currently, there are no national and international certified reference materials (CRM) in lithium clays that can make reliable and traceable lithium measurements for the International System of Units (SI). Hence, it is necessary to have references to meet the needs in terms of mining and activities that involve the use of lithium to favor the economy derived from its multiple uses and associated benefits in the exploration, exploitation, and handling of lithium ore. In this study, a candidate for reference material (RM) of Li in clays was developed and certified based on the provisions of ISO 17034:2016 and ISO Guide 35:2017. Different mass sizes of the RM (0.05, 0.1, and 0.25 g) were used to evaluate homogeneity. An isochronous study (short-term stability) was carried out in the assessment of stability, influenced by the effects of transport at different temperatures (20, 40, and 50 °C) for a determined time of 6 weeks, in addition to a classic (long-term) study for 19 weeks. The sample was treated using microwave-assisted acid digestion and Li measurements were performed using the analytical technique of Flame Atomic Absorption Spectrometry (FAAS). The CRM is homogeneous for the sample mass sizes of 0.05 and 0.1 g, and the mass fraction of w(Li) was stable in the RM for temperatures of 20, 40, and 50 °C. The determined period of validity was 3 years.

This study researched the prediction of the BSR noise evaluation quantitative index, Loudness N10, for sound sources with noise using statistics and machine learning. A total of 1170 data points was obtained from 130 automotive seats measured at 9-point positions, with Gaussian noise integrated to construct synthetic sound data. Ten physical quantities related to sound quality and sound pressure were used and defined as dB and fluctuation strength, considering statistical characteristics and Loudness N10. BSR quantitative index prediction was performed using regression analysis with K-fold cross-validation, DNN in hold-out, and DNN in K-fold cross-validation. The DNN in the K-fold cross-validation model demonstrated relatively superior prediction accuracy, especially when the data quantity was relatively small. The results demonstrate that applying machine learning to BSR prediction allows for the prediction of quantitative indicators without complex formulas and that specific physical quantities can be easily estimated even with noise.

High vertical ground reaction forces (VGRF) during landings following acrobatic elements in artistic gymnastics is associated with trunk and lower extremity injury risk. As similar data regarding injury risk factors in cheerleading are scarce, the purpose of this study was to assess VGRF in pop-off dismounts of rested and fatigued flyers in cheerleaders. Fifteen German cheerleaders were recruited for this study, including seven female flyers and eight male bases. It was expected that performance would change in fatiguing athletes, potentially increasing the risk for injuries. However, neither the mean VGRF (rested: 6.0 ± 1.9  BW, fatigued: 6.2 ± 1.3  BW, overall range: 2.1-14.9 BW) nor the individual VGRF-time courses of the flyers changed significantly after the workout. Instead, we show that the flyers\' ability to land - but not the bases\' ability to catch - significantly influences the maximum and time-resolved impacts.

This study explores how machining parameters affect Surface Roughness (SR), Tool Wear Rate (TWR), and Material Removal Rate (MRR) during Electrical Discharge Machining (EDM) of a hybrid aluminum metal matrix composite (AMMC). The composite includes 6 % Silicon carbide (SiC) and 6 % Boron carbide (B4C) in an Aluminum 7075 (Al7075) matrix. A combined optimization approach was used to balance these factors, evaluating Pulse ON time, Current, Voltage, and Pulse OFF time. Response Surface Methodology (RSM) optimized single responses, while multi-response optimization employed a hybrid method combining the Entropy Weight Method (EWM), Taguchi approach, TOPSIS, and GRA. Analysis of Variance (ANOVA) assessed parameter significance, revealing substantial impacts on SR, MRR, and EWR. Based on TOPSIS and GRA, optimized parameters achieved a desirable balance: high MRR (0.4172, 0.5240 mm³/min), minimal EWR (0.0068, 0.0103 mm³/min), and acceptable SR (10.3877, 9.1924 μm) based on EWM-weighted priorities. Confirmation experiments validated a 15 % improvement in the closeness coefficient, and a 16 % improvement in the Grey relational grade, which considers combined SR, MRR, and EWR performance. Scanning Electron Microscope (SEM) analysis of surfaces machined with optimal parameters showed minimal debris, cracks, and no recast layer, indicating high surface integrity. This research enhances EDM optimization for AMMC, achieving efficiency in machining, minimizing tool wear, and meeting surface quality requirements.

The magnesium alloy composite is a vital material for automotive applications due to its features like high stiffness, superior damping resistance, high strength, and lightweight. Here, the motto of research is to establish the AZ91 alloy nanocomposite with the exposures of 0, 1, 3, and 5 volume percentages (vol%) of nano zirconium dioxide (ZrO2) particles (50nm) through fluid stir metallurgy route associated with 1x105 Pa vacuum die cast process. Exposures on structural morphology, hardness, and impact toughness of composite are analyzed and identified as the nano AZ91 alloy composite enclosed with 5vol% is homogenous particle dispersion, enhanced hardness (97.6HV), and optimum toughness of 21.2J/mm2. However, composite faces machining difficulties due to the hard abrasive particles with higher hardness, resulting in tool wear. This experiment predicts the optimum mill parameters during the end mill operation of magnesium alloy nanocomposite (AZ91/5vol%) by using a tungsten carbide coated end mill cutter to attain the maximum metal removal rate with low surface roughness and tool wear analyzed via the general linear model (GLM) ANOVA approach. The input conditions for end milling operation vary, like feed rate (0.1 -0.4mm/rev), depth of cut (0.05 -0.2mm), and spindle speed (250-1000rpm). During the ANOVA GLM approach, the L16 design experiment is fixed for further interaction analysis. The results predicted by the depth to cut and feed rate were dominant and played a major role in deciding the tool wear, surface roughness, and MRR.

The synergistic potential of using graphene oxide (GO) nanosheets and hydrolyzed polyacrylamide (HPAM) as GO enhanced polymer hybrid (GOeP) for enhancing oil recovery (EOR) purposes has drawn attention. However, the hybridization method and stability of GOeP have not been comprehensively studied. To cover this gap, the current study evaluates the stability of GOeP under different conditions, including temperatures such as 60 and 80 °C, high and low salinities, and the presence of Mg2+ ions (6430 and 643 ppm). Hence, GO nanosheets were synthesized and characterized through XRD, Raman, FTIR, and DLS techniques. The performance of five preparation methods was assessed to determine their ability to produce stable hybrids. Zeta potential and sedimentation methods, coupled with the ANOVA statistical technique, were used for measuring and interpreting stability for 21 days. Results revealed that the stability of GOeP in the presence of brine is influenced by hydrolyzation duration, the composition of the water used in polymer hydrolyzation, the form of additives (being powdery or in aqueous solution), and the dispersion quality, including whether the GO solution was prediluted. The results revealed that the positive impact of higher temperatures on the long-term stability of GOeP is approximately seven times less significant than the reduction in stability caused by salinity. Under elevated salinity conditions, a higher Mg2+ concentration led to an 80% decrease in long-term stability, whereas the temperature impact was negligible. These findings highlight the potential of GOeP for EOR applications, offering insights into optimizing stability under challenging reservoir conditions.

Many studies have shown that ergonomically designed furniture improves productivity and well-being. As computers have become a part of students\' academic lives, they will continue to grow in the future. We propose anthropometric-based furniture dimensions that are suitable for university students to improve computer laboratory ergonomics. We collected data from 380 participants and analyzed 11 anthropometric measurements, correlating them with 11 furniture dimensions. Two types of furniture were found and studied in different university computer laboratories: (1) a non-adjustable chair with a non-adjustable table and (2) an adjustable chair with a non-adjustable table. The mismatch calculation showed a significant difference between existing furniture dimensions and anthropometric measurements, indicating that 7 of the 11 existing furniture dimensions need improvement. The one-way ANOVA test with a significance level of 5% also showed a significant difference between the anthropometric data and existing furniture dimensions. All 11 dimensions were determined to match students\' anthropometric data. The proposed dimensions were found to be more compatible and showed reduced mismatch percentages for nine furniture dimensions and nearly zero mismatches for seat width, backrest height, and under the hood for both males and females compared to the existing furniture dimensions. The proposed dimensions of the furniture set with adjustable seat height showed slightly improved match results for seat height and seat-to-table clearance, which showed zero mismatches compared with the non-adjustable furniture set. The table width and table depth dimensions were suggested according to Barnes and Squires\' ergonomic work envelope model, considering hand reach. The positions of the keyboard and mouse are also suggested according to the work envelope. The monitor position and viewing angle were proposed according to OSHA guidelines. This study suggests that the proposed dimensions can improve comfort levels, reducing the risk of musculoskeletal disorders among students. Further studies on the implementation and long-term effects of the proposed dimensions in real-world computer laboratory settings are recommended.

Groundwater is the main source of water for more than 2 billion people worldwide. In southern Brazil, the Crystalline Basement Aquifer System is composed of strategic groundwater reservoirs. Groundwater is mostly taken from shallow wells, and it is often used without any treatment, which poses a risk to public health. The present study aims to evaluate shallow groundwater quality and the geochemistry of shallow and deep groundwater located in the municipality of Canguçu, southern Brazil. The physicochemical and microbiological parameters of groundwater samples collected from shallow wells were monitored and analyzed using ANOVA variance analysis and water quality index (CCME WQI) approaches. Also, the results were compared with secondary data from deep wells. The monitored shallow wells had thermotolerant coliforms, Escherichia coli, pH, potassium, manganese, iron, and nitrate in disagreement with the guidelines of the World Health Organization. Moreover, variance analysis showed that the parameters temperature, dissolved oxygen, pH, chloride, and magnesium were the most influenced by seasonal variations. According to the CCME WQI, most samples had good quality (60%), 28% had fair quality, and 12% had poor quality. In addition, the field campaigns with higher precipitation rates also presented fair quality. Therefore, most of the shallow groundwater quality is affected by surface pollutants from the urban area, aggravated in rainy periods. Whereas deep groundwater is influenced by geochemistry mechanisms. The results revealed the risk of water consumption for public health and the urgent need for better maintenance of these wells and water treatment implementation.

In one-way analysis of variance models, performing simultaneous multiple comparisons of treatment groups with a control group may be of interest. Dunnett\'s test is used to test such differences and assumes equal variances of the response variable for each group. This assumption is not always met even after transformation. A parametric bootstrap (PB) method is developed here for comparing multiple treatment group means against the control group with unequal variances and unbalanced data. In simulation studies, the proposed method outperformed Dunnett\'s test in controlling the type I error under various settings, particularly when data have heteroscedastic variance and unbalanced design. Simulations show that power is often lower for the PB method than for Dunnett\'s test under equal variance, balanced data, or smaller sample size, but similar to or higher than for Dunnett\'s test with unequal variance, unbalanced data and larger sample size. The method is applied to a dataset concerning isotope levels found in elephant tusks from various geographical areas. These data have very unbalanced group sizes and unequal variances. This example illustrates that the PB method is easy to implement and avoids the need for transforming data to meet the equal variance assumption, simplifying interpretation of results.

Hard milling is being increasingly used as an alternative to EDM due to its high productivity. The present paper presents the results of theoretical-experimental research on the face milling of hard steel 55NiCrMoV7. A comprehensive analysis of cutting temperatures and forces during single-tooth milling and a morphological examination of the resulting chips are conducted for roughing and semi-finishing operations. The temperature is analyzed in the chip formation area, and the detached chips and the cutting force are analyzed through their tangential, radial, and penetration components, depending on the contact angle of the cutter tooth with the workpiece. The analysis of chip morphology is carried out based on the dimensional and angular parameters of chip segmentation and their degree of segmentation. Based on the central composite design and the response surface method, it is shown that it is possible to mathematically model the dependence of the macroscopic dimensions of the detached chips on the cutting parameters. The determined process functions, the maximum chip curling diameter, and the maximum chip height allow for establishing the influence of the cutting parameters\' values on the chips\' macroscopic dimensions and, thus, guiding the cutting process in the desired direction.