Residual Value Research Articles

Effect of in vitro Digestion on the Bioaccessibility of Polyphenols and Potential Prebiotic Properties of Potato Peel.

Potato peel is a byproduct of the potato processing industry and a potential source of functional ingredients such as dietary fiber, polyphenols, and prebiotics. However, the bioaccessibility of polyphenols and antioxidants during in vitro digestion as well as prebiotic potential after in vitro digestion of potato peel flour has not been reported. The study was designed to assess the bioaccessibility of polyphenols and the prebiotic potential of potato peel flour. In this study, the changes in polyphenol content and antioxidant capacity during different phases of in vitro digestion, including salivary, gastric and intestinal phases were studied. Additionally, an investigation was conducted to evaluate the prebiotic properties of potato peel flour by in vitro fermentation with Lactobacillus acidophilus. The findings revealed a significant increase in the recovery index for total phenolic content during both gastric (106.90%) and intestinal (102.71%) digestive phases. Furthermore, polyphenols in potato peel flour exhibited high residual intestinal digestibility index values (>90%). The antioxidant capacity increased by >50% during various phases of in vitro digestion. Regarding prebiotic properties, potato peel flour significantly increased L. acidophilus counts and promoted the production of short-chain fatty acids, specifically propionate and butyrate. This study suggests that potato peel flour has the potential to serve as a functional ingredient or nutraceutical that can enhance health and may help in reducing environmental problems.

Real-Time CNN Training and Compression for Neural-Enhanced Adaptive Live Streaming.

We propose a real-time convolutional neural network (CNN) training and compression method for delivering high-quality live video even in a poor network environment. The server delivers a low-resolution video segment along with the corresponding CNN for super resolution (SR), after which the client applies the CNN to the segment in order to recover high-resolution video frames. To generate a trained CNN corresponding to a video segment in real-time, our method rapidly increases the training accuracy by promoting the overfitting property of the CNN while also using curriculum-based training. In addition, assuming that the pretrained CNN is already downloaded on the client side, we transfer only residual values between the updated and pretrained CNN parameters. These values can be quantized with low bits in real time while minimizing the amount of loss, as the distribution range is significantly narrower than that of the updated CNN. Quantitatively, our neural-enhanced adaptive live streaming pipeline (NEALS) achieves higher SR accuracy and a lower CNN compression loss rate within a constrained training time compared to the state-of-the-art CNN training and compression method. NEALS achieves 15 to 48% higher quality of the user experience compared to state-of-the-art neural-enhanced live streaming systems.

Effects of initial turbidity and myco-coagulant dose on the effectiveness of the coagulation process in water treatment

High turbidity is a pollutant that requires coagulants to be removed from treated water and wastewater. This study was conducted to characterize and analyze the potential of myco-coagulant-producing fungus isolated from the moist area of a kitchen. Myco-coagulant production was carried out using solid-state fermentation using coco peat as a substrate. One factor-at-a-time analysis (OFAT) was carried out to assess the capacity of the produced myco-coagulant in various initial turbidities and myco-coagulant doses. The potential of myco-coagulant was tested using turbid synthetic water with different turbidity levels (50, 100, 150, 200, 250 and 300 NTU). The results showed that turbidity removal by the myco-coagulant was influenced by the initial turbidity. The coagulant was less efficient at low turbidity levels, which was approximately 5% for 50 NTU, while the highest was 52% for 300 NTU water. Furthermore, the results demonstrated that myco-coagulant could remove the highest possible turbidities on day 6 with all initial turbidity values studied in this work. Different myco-coagulant doses ranging from 1 to 10% (v/v) were also used to determine the optimum dose for effective flocculation. The highest turbidity removal of 57% could be obtained at an optimum coagulant dose of 4% (v/v). Like any other commercial coagulant, the residual turbidity value increased at a coagulant dose higher than the optimum dose of 4% (v/v).

Blind search and flexible product visions: the sociotechnical shaping of generative music engines

AbstractAmidst the surge in AI-oriented commercial ventures, music is a site of intensive efforts to innovate. A number of companies are seeking to apply AI to music production and consumption, and amongst them several are seeking to reinvent the music listening experience as adaptive, interactive, functional and infinitely generative. These are bold objectives, having no clear roadmap for what designs, technologies and use cases, if any, will be successful. Thus each company relies on speculative product visions. Through four case studies of such companies, I consider how product visions must carefully provide a clear plan for developers and investors, whilst also remaining open to agile user-centred product development strategies, which I discuss in terms of the ‘blind search’ nature of innovation. I suggest that innovation in this area needs to be understood in terms of technological emergence, which is neither technologically determinist nor driven entirely by the visions of founders, but through a complex of interacting forces. I also consider, through these cases, how, through the accumulation of residual value, all such start-up work risks being exapted for more familiar extractive capitalist agendas under the general process that Doctorow calls “enshittification”. Lastly, I consider a number of other more specific ways in which these projects, if their growth is achieved, could influence music culture more broadly.

Korean Waves Popularity on Korean Food Purchasing Decisions in Jakarta

Purpose: The goal of this study was to ascertain whether the popularity or trend of the Korean Wave influences Korean food purchases in Jakarta. Research method: Quantitative research methodologies are being used in this study. Sources of information were gathered through observation and questionnaires, as well as primary and secondary data. The research population consisted of 83 customers at Annyeong Korean Food Café Tebet. Result and discussion: The results of observations in the R table obtained a sample value (N) = 30 of 0.3061, resulting in a value (R.Calculated) greater than R.Table. Apart from that, the purchasing decision variables (Variable Y) all produce values (R.Calculated) greater than R.Table. So, this research can be said to be valid—the reliability test results and all the variable results. The linearity test shows the Sig value. The deviation from linearity is 0.384, which is greater than 0.05. There is a linear relationship between the Korean Wave variable and the Purchasing Decision Variable. The Kolmogorov-Smirnov normality test with SPSS shows a significance value of 0.078, more significant than 0.05. So, it can be concluded that the residual values are normally distributed so that the results of this analysis can be continued for further testing. The Pearson test produced a value of 0.766, more significant than the R table value of 0.213. So, it can be concluded that there is a strong relationship between the Korean Wave variable (X) and purchasing decisions (Y) because the value on the correlation coefficient scale is 0.60 – 0.79. Implication: From the research results, theoretical implications can be put forward, and it can be concluded that there is a strong impact between the Korean wave and the purchasing decision of Korean Food. Keywords: Popularity, Korean Wave, Purchase Decision, Korean Food

Significant duration prediction of seismic ground motions using machine learning algorithms.

This study aims to predict the significant duration (D5-75, D5-95) of seismic motion by employing machine learning algorithms. Based on three parameters (moment magnitude, fault distance, and average shear wave velocity), two additional parameters(fault top depth and epicenter mechanism parameters) were introduced in this study. The XGBoost algorithm is utilized for characteristic parameter optimization analysis to obtain the optimal combination of four parameters. We compare the prediction results of four machine learning algorithms (random forest, XGBoost, BP neural network, and SVM) and develop a new method of significant duration prediction by constructing two fusion models (stacking and weighted averaging). The fusion model demonstrates an improvement in prediction accuracy and generalization ability of the significant duration when compared to single algorithm models based on evaluation indicators and residual values. The accuracy and rationality of the fusion model are validated through comparison with existing research.

Deep learning-based harmonization of trabecular bone microstructures between high- and low-resolution CT imaging.

Osteoporosis is a bone disease related to increased bone loss and fracture-risk. The variability in bone strength is partially explained by bone mineral density (BMD), and the remainder is contributed by bone microstructure. Recently, clinical CT has emerged as a viable option for in vivo bone microstructural imaging. Wide variations in spatial-resolution and other imaging features among different CT scanners add inconsistency to derived bone microstructural metrics, urging the need for harmonization of image data from different scanners. This paper presents a new deep learning (DL) method for the harmonization of bone microstructural images derived from low- and high-resolution CT scanners and evaluates the method's performance at the levels of image data as well as derived microstructural metrics. We generalized a three-dimensional (3D) version of GAN-CIRCLE that applies two generative adversarial networks (GANs) constrained by the identical, residual, and cycle learning ensemble (CIRCLE). Two GAN modules simultaneously learn to map low-resolution CT (LRCT) to high-resolution CT (HRCT) and vice versa. Twenty volunteers were recruited. LRCT and HRCT scans of the distal tibia of their left legs were acquired. Five-hundred pairs of LRCT and HRCT image blocks of voxels were sampled for each of the twelve volunteers and used for training in supervised as well as unsupervised setups. LRCT and HRCT images of the remaining eight volunteers were used for evaluation. LRCT blocks were sampled at 32 voxel intervals in each coordinate direction and predicted HRCT blocks were stitched to generate a predicted HRCT image. Mean±standard deviation of structural similarity (SSIM) values between predicted and true HRCT using both 3DGAN-CIRCLE-based supervised (0.84±0.03) and unsupervised (0.83±0.04) methods were significantly (p<0.001) higher than the mean SSIM value between LRCT and true HRCT (0.75±0.03). All Tb measures derived from predicted HRCT by the supervised 3DGAN-CIRCLE showed higher agreement (CCC [0.956 0.991]) with the reference values from true HRCT as compared to LRCT-derived values (CCC [0.732 0.989]). For all Tb measures, except Tb plate-width (CCC=0.866), the unsupervised 3DGAN-CIRCLE showed high agreement (CCC [0.920 0.964]) with the true HRCT-derived reference measures. Moreover, Bland-Altman plots showed that supervised 3DGAN-CIRCLE predicted HRCT reduces bias and variability in residual values of different Tb measures as compared to LRCT and unsupervised 3DGAN-CIRCLE predicted HRCT. The supervised 3DGAN-CIRCLE method produced significantly improved performance (p<0.001) for all Tb measures as compared to the two DL-based supervised methods available in the literature. 3DGAN-CIRCLE, trained in either unsupervised or supervised fashion, generates HRCT images with high structural similarity to the reference true HRCT images. The supervised 3DGAN-CIRCLE improves agreements of computed Tb microstructural measures with their reference values and outperforms the unsupervised 3DGAN-CIRCLE. 3DGAN-CIRCLE offers a viable DL solution to retrospectively improve image resolution, which may aid in data harmonization in multi-site longitudinal studies where scanner mismatch is unavoidable.

Failure of Gravel Compaction Pile (GCP) Improved Embankment – Case Study

Due to the scarcity of suitable land, it becomes necessary to utilize marshy lands consisting of soft soil for infrastructure development. Soft soil has a problematic nature due to its high moisture content, high compressibility, high void ratio and very low shear strength. Hence, it is a responsibility of the geotechnical engineers to overcome these issues by adopting suitable ground improvement techniques. Gravel Compaction Pile (GCP) is one of the most popular soft ground improvement techniques used in the field. This technique has been successfully applied during the construction of the Colombo-Katunayake Expressway project and the Outer Circular Highway project. However, GCP technique has failed in the Southern Expressway Extension Project from Matara to Beliatta section. Therefore, in this study, the causes of the failure of the GCP improved embankment section were studied based on the field records. The slope stability of the embankment during construction was analysed using Matsuo and Kawamura's method. Back analysis revealed that once shear failure occurred in the subsurface, the shear strength of the soft soil reduces to its residual value and it takes longer time to regain its original strength. Further, it was noted that when the soft soil thickness is greater than 10 - 12 m, it becomes extremely challenging to improve the soft ground without any reinforcement. Therefore, it is strongly recommended to accurately interpret the subsurface characteristics in order to select the most suitable ground improvement technique.

Development of a Novel Approach for Controlling and Predicting Residual Sugars in Wines

Residual sugars (RS) in wine are the sugars that remain in a wine after fermentation is complete. In some wine styles, such as semi-dry wines, the accurate measurement of the RS concentration is critical for both qualitative and legislative reasons. Brix, measured by a simple refractometer, can give a good estimation of the RS concentration in the must, but during fermentation, the presence of alcohol leads to inaccurate sugar measurements. In order to measure the RS accurately, other more precise techniques are used, most of which are expensive or require professional skills. Therefore, novel approaches for rapid, easy, and practical measurements for estimating the sugar content have been suggested over the years. However, most of these methods do not supply an actual measurement of RS but rather give brix values, and those that measure RS involve special equipment, which is less relevant for small wineries. This study suggests a novel model for predicting and controlling the wine’s residual sugar. The data the model uses is the initial brix of the must before fermentation and its density during fermentation. The model was created by measuring actual residual sugars during the fermentation of natural and synthetic musts, with various degrees of initial brix levels, while simultaneously measuring their densities and correlating the two measurements. Linear regression between the residual sugar of the wine and its density was obtained for all treatments and repetitions (i.e., different values of must initial brix) with R2 values above 0.97. Using the model, one can calculate (before commencing the fermentation) the density values at which the fermentation will reach a particular desired residual sugar value for a specific initial brix level; the model is applicable for the fermentation conditions used in this work, i.e., brix levels of 18–27 °Bx, Saccharomyces cerevisiae strains (fx-33 and fx-10) in common conditions of fermentation regarding temperature and aeration.

Wavefront reconstruction based on ASH-Net with less lenslets SHWFS

Generally, the number of lenslets in Shack–Hartman wavefront sensor (SHWFS) determines its spatial sampling frequency. However, increasing the number of lenslets is limited by the data processing delay time and low sensitivity in the adaptive optics system. In this paper, a wavefront reconstruction method based on ASH-Net for SHWFS is presented to recover the distorted wavefront from the light spots array with high accuracy with a spatial sampling frequency than traditionally required. The results show that the maximum number of Zernike modes recovered by the method is 36, 54, and 70 for 4 × 4, 6 × 6, and 8 × 8 lenslets arrays, respectively. Residual wavefront root mean square values are as low as about 0.02 μm, corresponding to a Strehl Ratio of 0.99. This method breaks the limitation that the number of reconstructed Zernike modes in the traditional wavefront reconstruction algorithm must be less than the number of effective lenslets. Experiments in lab are used to validate the method. Additionally, fewer lenslets is valuable to improve the detection capability of SHWFS for faint targets. This is important for applications of adaptive optics in areas such as astronomy and retinal imaging.

Identification and Mitigation of Predominant Challenges in the Utilization of Aged Traction Batteries within Stationary Second-Life Scenarios

As the production of battery cells experiences exponential growth and electric vehicle fleets continue to expand, an escalating number of traction batteries are nearing the conclusion of their operational life for mobility purposes, both presently and in the foreseeable future. Concurrently, the heightened interest in sustainable energy storage solutions has spurred investigations into potential second-life applications for aging traction batteries. Nonetheless, the predominant practice remains the removal of these batteries from electric vehicles, signifying the end of their life cycle, and their subsequent incorporation into recycling processes, with limited consideration for life-extending measures. This study seeks to elucidate the reasons behind the deprioritization of battery repurposing strategies. Therefore, the research team conducted two industry studies with over 20 battery experts from Europe, revealing concerns about the economic viability of repurposing batteries for stationary storage applications. A literature review of studies published since 2016 confirmed the industry’s struggles to address this issue theoretically. In conclusion, a research question was formulated, and a solution approach was delineated to assess the economic prospects of aged traction batteries within the industry’s landscape in the future. This solution approach encompasses pertinent market analysis, the identification of representative second-life applications, as well as the formulation of a methodology for evaluating the residual value of these batteries.

Enhanced dielectric, electrical and electro-optical properties: Towards understanding the interaction in mesophases of 8OCB liquid crystal dispersed with CdSe/ZnS quantum dots

The current advancements in developing new liquid crystalline materials for the next generation applications focus mainly on improving the physical characteristics of the liquid crystal (LC) systems. Recent progress has shown that the functionalized nanoparticles embedded in LC matrices can significantly alter the characteristics of the LC material based on the mutual interaction between the host molecules and guest particles. In this aspect, the present study reports the impact of dispersing core-shell type CdSe/ZnS quantum dots (QDs) on the dielectric, electrical and electro-optical properties of the 8OCB LC doped at various concentrations. The doped samples exhibit an ion releasing behavior and this effect becomes more evident as the doping concentration increases up to 0.2 wt% in the LC system. It is explained as a result of the growing tactoid-like ellipsoidal form acquired by QDs owing to the enhanced mutual interaction among QD ligands and rod-like LC molecules. The temperature variation of the diffusion constant, conductivity, ionic mobility and mean relaxation time of ions significantly follows each other in all the studied samples. Moreover, thermal profiles of the dielectric anisotropy, threshold voltage and splay elastic constant show a decreasing pattern with an increment in doping concentration. A dual-relaxation regime is experimentally investigated, corresponding to the contribution of nematic director and dimers, providing two kinds of rotational viscosity in the pristine and QDs dispersed LC samples. The transmittance-voltage curve reveals the presence of residual value in the dispersed samples and is related to the volatile memory effect. A slight enhancement is observed in the photoluminescence intensity for the lower doped sample and reduces further with increasing doping concentration in the pristine LC system. All these findings suggest the considerable contribution of functionalized QDs to the studied properties in terms of interaction between the LC and dopant material. This study will further elucidate the selection of optimal QDs concentration based on the required properties for their potential applications in futuristic devices based on LCs.

SIGNIFIKANSI INOVASI TEKNOLOGI DALAM KONTEKS EKSPANSI DAN TRANSFORMASI EKONOMI PASCA-PANDEMI

This research significance of technological innovation in the context of post-pandemic economic expansion and transformation is conducted. The research methodology involves a literature study and quantitative. The literature study focuses on the theory of technological innovation and empirical findings related to its impact on post-pandemic economic expansion and transformation. The results of the validity test indicate that the measurement instruments for technological innovation variables and economic expansion and transformation are statistically valid. Reliability tests using Cronbach's Alpha show an acceptable level of reliability for both variables. Simple regression analysis indicates a positive relationship between technological innovation and economic expansion and transformation of MSMEs. Hypothesis testing with the t-test confirms the significance of this relationship. Descriptive hypothesis testing indicates that technological innovation and economic expansion and transformation of MSMEs operate effectively, with agreement and strong agreement percentages reaching 94% for both variables. Normality testing shows that the residual values are normally distributed. This research provides a significant contribution to understanding the role of technological innovation in supporting the post-pandemic economic expansion and transformation of MSMEs. The practical implications of this research can assist entrepreneurs and policymakers in enhancing the implementation of technological innovation to support sustainable economic growth.

Optimized OTSU Segmentation Algorithm-Based Temperature Feature Extraction Method for Infrared Images of Electrical Equipment.

Infrared image processing is an effective method for diagnosing faults in electrical equipment, in which target device segmentation and temperature feature extraction are key steps. Target device segmentation separates the device to be diagnosed from the image, while temperature feature extraction analyzes whether the device is overheating and has potential faults. However, the segmentation of infrared images of electrical equipment is slow due to issues such as high computational complexity, and the temperature information extracted lacks accuracy due to the insufficient consideration of the non-linear relationship between the image grayscale and temperature. Therefore, in this study, we propose an optimized maximum between-class variance thresholding method (OTSU) segmentation algorithm based on the Gray Wolf Optimization (GWO) algorithm, which accelerates the segmentation speed by optimizing the threshold determination process using OTSU. The experimental results show that compared to the non-optimized method, the optimized segmentation method increases the threshold calculation time by more than 83.99% while maintaining similar segmentation results. Based on this, to address the issue of insufficient accuracy in temperature feature extraction, we propose a temperature value extraction method for infrared images based on the K-nearest neighbor (KNN) algorithm. The experimental results demonstrate that compared to traditional linear methods, this method achieves a 73.68% improvement in the maximum residual absolute value of the extracted temperature values and a 78.95% improvement in the average residual absolute value.

Comparing Remote and Proximal Sensing of Agrometeorological Parameters across Different Agricultural Regions in Croatia: A Case Study Using ERA5-Land, Agri4Cast, and In Situ Stations during the Period 2019–2021

The paper evaluates the usability of remote satellite-based and proximal ground-based agrometeorological data sources for precision agriculture and crop production in Croatia. The compared agrometeorological datasets stem from the open-access data sources Copernicus CDS and the Agri4Cast portal, and commercial in situ agrometeorological stations (PinovaMeteo) which monitor environmental parameters relevant to the physiological state of crops. The study compares relevant parameters for 10 different locations in Croatia for three consecutive years (2019, 2020, and 2021) to investigate whether model-based data from ERA5-Land and Agri4Cast are well-correlated with ground measurements from independent in situ stations (PinovaMeteo) for specific agrometeorological parameters (air and soil temperature, and precipitation). Our results indicate the following: both the ERA5-Land and Agri4Cast datasets show mostly strong positive correlations with ground observations for air temperature, modest correlations for soil temperature, but modest or even low correlations for precipitation. Analysis of the residuals indicates higher overall residual values, especially in areas with complex topography and near large bodies of water or the sea, and deviations of residuals that may limit the usability of satellite- and model-based data for decision-making in agriculture.

Advancing sustainable air quality through calibration of miniature air quality monitors with SRA-SVR combined model

Effective calibration of miniature air quality monitor measurements is an important task to ensure accurate measurements and guarantee sustainable air quality. The aim of this study is to calibrate the measurement data of miniature air quality monitors using Stepwise Regression Analysis and Support Vector Regression (SRA-SVR) combined model. Firstly, a stepwise regression analysis model is used to find a linear relationship between the measured data from the miniature air quality monitor and the air pollutant concentration. Secondly, support vector regression is used to extract the non-linear relationships which affect the pollutant concentrations hidden in the residuals of the stepwise regression analysis model. Finally, the residual calibration values of the SVR model outputs are added to the SRA model outputs to obtain the final outputs of the SRA-SVR combined model for the pollutants. Mean absolute error, relative mean absolute percent error and root mean square error are used to compare the effectiveness of the SRA-SVR combined model and some other commonly used statistical models for the calibration of miniature air quality monitors. The results show that the SRA-SVR combination model performs optimally on both the training and test sets, regardless of which pollutant and which indicator. The SRA-SVR combined model not only has the advantages of the SRA model’s strong interpretability and the SVR model’s high accuracy, but also has higher accuracy than the single model. By using this model to calibrate the measurements of the miniature air quality monitor, its accuracy can be improved by 61.33%–87.43%.

Data-Driven Decision Support for Equipment Selection and Maintenance Issues for Buildings

Equipment costs play a critical role in decision making during design and construction, which requires up-to-date information and data. The design of this study incorporates the inputs from the literature review on the influencing factors of equipment costs and major targeted equipment types to enhance decision support for equipment selection, project construction, and maintenance issues. Two traditional cost estimation methods and five machine-learning methods were compared in this study to identify significant attributes related to the predictions of the costs and residual values of each targeted equipment type. The novelty of this study is that the developed method improves prediction accuracy by establishing a comprehensive and well-structured database framework. A comparison of this method with the existing prediction models reveals that the results and the accuracy of multiple regression analysis are improved in the range of (3% to 33.97%) with the use of a modified decision-tree model combined with support vector machines. The major contribution of this research is the design, implementation, and validation of a machine-learning-based modified decision tree with a support vector machine model for improved accuracy and decision support in construction management. Future research should consider the relationship between geographical variations and value changes.

Intraoesophageal pressure in patients receiving proximal gastrectomy with hinged double flap method for gastric cancer: a retrospective cohort study

Background: Objective functional assessment of esophagogastric anastomosis in patients who underwent proximal gastrectomy with the hinged double flap method for gastric cancer has not been well investigated. This study aimed to perform a functional analysis of reconstruction using high-resolution impedance manometry (HRIM). Materials and methods: The authors enroled 25 patients who underwent proximal gastrectomy for gastric cancer between May 2015 and April 2020 and subsequently underwent HRIM postoperatively. Eligible questionnaires [Postgastrectomy Syndrome Assessment Scale-37 (PGSAS-37)] were retrieved from 16 patients. The association between HRIM data and PGSAS-37 was analyzed. Results: The amplitudes of distal oesophageal peristaltic waves, contractile front velocity, and distal latency assessed by HRIM were almost normal after surgery. Most patient’s lower oesophageal sphincter (LES) resting pressure created by the hinged double flap was within normal limits. Conversely, LES residual pressure values during swallowing-induced relaxation were abnormally high in most patients, and the lower the values, the more severe the reflux and diarrhoea symptoms (P=0.038, P=0.041, respectively). In addition, even when the integrated relaxation pressure (IRP) was normal, lower values corresponded to more severe reflux symptoms (P=0.020). The required LES pressure may be higher after proximal gastrectomy because of the relatively higher intragastric pressure due to the reduced volume of the remnant stomach. This also suggests that swallowing-induced relaxation of the LES was considered a trigger for oesophageal reflux in post-proximal gastrectomy patients. Conclusion: LES residual pressure and IRP values in HRIM correlated with reflux symptoms in patients after proximal gastrectomy.

Aging Analysis of Semiconductive Silicone Rubber for 10 kV Cold-Shrink Cable Accessories

This study focuses on the semiconductive silicone rubber of 10 kV cold-shrink accessories. Accelerated thermal aging tests were conducted on the semiconductive silicone rubber, obtaining tensile stress–strain curves at various time points after thermal aging. The corresponding parameters of the Yeoh hyperelastic model were calculated. The results indicate that the initial shear modulus of the samples decreases with the increase in the aging temperature and time. Microscopic morphology, changes in cross-sectional content, thermal residual values, and chemical structure changes of the samples after aging were studied using electron microscopy, EDS testing, TG curves, and Fourier spectra. The results show that the surface roughness of the aged semiconductive silicon rubber increases, the residual values decrease, the thermal stability decreases, the main chain absorbance decreases, the main chain integrity decreases, and the organic functional groups Si-CH3 and Si(CH3)2 decrease, leading to a reduction in organic content.

Exploring the state of health of electric vehicle batteries at end of use; hierarchical waste flow analysis to determine the recycling and reuse potential

With the increasing adoption of electric vehicles, their end-of-life management is a timely matter. This requires recognizing the upcoming volume of retired electric-vehicle-batteries to the waste stream. The projection is further useful if we have an estimation of the remaining value within them to categorize the recycling or repurposing potential to allow appropriate policy development and facility planning. This qualification assessment is neglected in the current literature. Neglecting the health status of retired batteries in estimating their residual value might end up over or underestimating their reuse and recycling potential. This study aims to provide a hierarchical battery waste estimation based on their health and age of disposal in Ireland. These two factors are the fundamental parameters in determining the feasibility of repurposing or recycling retired batteries. Identifying this information, we defined three reuse scenarios with different state-of-health limits. Results indicate almost 50%, 30%, and below 10% repurposing potential in the year 2050 when setting a repurposing threshold of above 80%, 85%, and 90%, respectively. The authors also highlight the effect of non-regional repurposing on the recycling potential.