Inconsistent Quality Research Articles

Push, Overload, and Exhaustion

This paper studies college students' perspectives on social media discourse surrounding major international events and the effects on their psychological state and information-seeking behaviors. Through empirical research, this paper describes and analyzes the influences of “information push” from important international affairs, its inconsistent information quality, and excessive peer-to-peer sharing on users' emotional exhaustion and resistance to future social media engagement. Frequent push notifications cause interruptions that engender exhaustion. This paper identifies the “neglect” and “shielding” behaviors that can then result. Building on stressors previously identified in information overload on social media, this research furthers understanding of their interconnections. This paper develops a model that links social overload and system features with exhaustion, psychological resistance, social media discontinuation, and burnout. Corresponding countermeasures are put forward with proposed utility for social media users and the platforms themselves.

Quality-aware fuzzy min-max neural networks for dynamic brain network analysis and its application to schizophrenia identification

Dynamic functional connections (dFCs) refer to the observed phenomenon that functional connectivity changes over a short time, which has become a pioneering method in the diagnosis of brain diseases. However, current dynamic brain network analysis methods are insufficient to address the fuzzy information of brain networks caused by the quality issues of rs-fMRI data, and the uncertainty arising from inconsistent data quality across different windows due to missing values and noise in individual windows. These results in unreliable integration of multiple windows. To this end, this paper proposes a dynamic brain network analysis method based on quality-aware fuzzy min–max neural networks (QFMMNet). The individual window of dFCs is treated as a view, and we define three convolution filters to extract features from the brain network under the multi-view learning framework, thereby obtaining multi-view evidence for dFCs. We design the multi-view fuzzy min–max neural networks (MVFMM) based on fuzzy sets to deal with the fuzzy information of the brain network, which takes evidence as input patterns to generate hyperboxes and serves as the classification layer of each view. A quality-aware ensemble module is introduced to deal with uncertainty, which employs Dempster-Shafer Theory (D-S theory) to directly model the uncertainty and evaluate the dynamic quality-aware weighting of each view. At the decision level of model, a weighted fusion strategy is employed for multi-view fusion based on the dynamic quality-aware weighting, thereby the quality of each view is fully considered to improve the trustworthiness of the decision. We verified the effectiveness of QFMMNet through comparison with advanced algorithms on three real schizophrenia datasets, and the results show that QFMMNet significantly improved the classification performance of brain disease diagnosis tasks. The accuracy on the Huaxi, COBRE, and Xiangya datasets reached 87.14%, 86.67%, and 85.27%, respectively.

Banana and Orange Classification Detection Using Convolutional Neural Network

Fruits play a crucial role in human health, with an average consumption of 81.14 grams per capita per day in Indonesia, where bananas and oranges are the most consumed fruits. Inconsistent fruit quality, typically evaluated manually by farmers, can influence consumer decisions. Artificial intelligence (AI) and computer vision can enhance efficiency and consistency in analyzing fruit quality. Convolutional Neural Networks (CNN) are particularly effective in image recognition. This research uses CNN to classify the quality of bananas and oranges from a dataset of 4000 images, divided into 10% test data, 80% training data, and 10% validation data. Among three models tested, Model 2 performed best with an accuracy of 96.75% and balanced high F1-scores across all categories. The results demonstrate that the CNN model is capable of classifying the quality of bananas and oranges with high accuracy and good evaluation results.

Brain Tumour Detection Using Deep Learning and Image Processing

Abstract —Brain tumor detection is a critical aspect of neurooncology, where timely and accurate diagnosis significantly impacts patient outcomes. Traditional imaging techniques, while useful, are often limited by variations in image quality, illumination inconsistencies, and the complex nature of brain tumors themselves, which vary widely in size, shape, and texture. This paper presents a deep learning- based approach to brain tumor detection using Convolutional Neural Networks (CNNs) combined with advanced image processing methods. Our methodology involves preprocessing brain MRI scans using histogram equalization, morphological operations, and data augmentation to address illumination issues and enhance the tumor region. Following preprocessing, the images are fed into a CNN, where transfer learning from the Sequential model is applied to improve classification accuracy. Experimental results demonstrate high recall and precision rates, affirming the model’s robustness in detecting brain tumors in MRI images. This approach not only addresses the challenge of inconsistent MRI scan quality but also reduces the risk of misclassification, showing promise for clinical application. Keywords— Brain Tumor Detection, Computer-aided Diagnosis, Computer Vision, Convolutional Neural Networks, Deep Learning, Image Processing, Transfer Learning.

Exploring RSA Cryptography: Principles and Applications in Image Encryption and Microcontroller Security

Diffusion models in AI-generated content (AIGC) have revolutionized image generation with their ability to transform noise into structured data through a denoising process. Despite their prowess, these models grapple with high computational demands, inconsistent image quality, and challenges in producing high-resolution images. This paper discusses the performance of diffusion models in image generation, emphasizing the need for improved computational efficiency, generation quality, and contextual understanding. Future research is aimed at developing real-time generation technologies, addressing ethical and legal concerns, and enhancing data privacy measures. With these advancements, diffusion models are expected to achieve broader application, enhanced performance, and increased safety in image generation, expanding their utility in fields such as advertising, medical imaging, and art creation. The potential of diffusion models to meet the increasing demands for quality, content, and diversity in image applications makes them a pivotal area of study in visual technology and artificial intelligence.

Implementing SARS-CoV-2 Testing during a Large-Scale Sporting Event in Africa: Lessons Learned from the Africa Football Cup of Nations Tournament in Cameroon.

During the 33rd Africa Cup of Nations (AFCON) football tournament in Cameroon, organizers and health authorities required a negative SARS-CoV-2 test result <48 hours before entry and provided free SARS-CoV-2 testing and vaccination at stadium and fan zone entrances. We describe the outcomes and implementation of mandatory SARS-CoV-2 testing at fan zones during AFCON. All consenting fan zones attendees were administered an electronic questionnaire capturing exposure factors, COVID-19-like symptoms, and COVID-19 vaccination status, before being tested for SARS-CoV-2 using an antigen rapid diagnostic test (Ag-RDT). Participants testing positive were sampled for confirmatory real-time SARS-CoV-2 polymerase chain reaction (PCR) and sequencing for variant surveillance. The case detection rate was estimated using PCR-confirmed cases, and the challenges were summarized from staff discussions and project/study documentation. In total, 4,820 fan zone attendees (median [interquartile range] age 30 [24-38], 27.7% females) were tested for SARS-CoV-2, including 1,228 (25.5%) fully vaccinated. Of 4,820 participants, 148 (3.1%) had a positive Ag-RDT result, of whom 67 consented to PCR testing and 19 of 64 (29.7%) were confirmed PCR-positive. The case detection rate was 40.1 (95% CI: 24.2-62.7) per 10,000 attendees. The Omicron variant (B.1.1.529) was found in all 11 samples successfully sequenced. The implementation of mandatory SARS-CoV-2 Ag-RDT at fan zone entrances was challenged by high attendance volume just prior to matches, lobbying of economic stakeholders, and inconsistent quality assurance when using test kits. Despite the challenges encountered, implementing mandatory SARS-CoV-2 Ag-RDT at fan zones, was a unique opportunity for SARS-CoV-2 case identification and genomic surveillance.

Development of Potato Chip Cluster Supply Chain Institutional Model with The Integration of ISM (Interpretive Structural Modeling) and ANP (Analytic Network Process)

Increasing the productivity of potato chip in Micro, Small, and Medium Enterprises (MSMEs) through the institutional model is significantly strengthening the economy of Batu City, especially as a tourist destination city. The institutional model ensures the smooth operation of supply chain activities due to the interactions between the actors, facilitating coordination, information sharing, and integration from upstream to downstream. This model can be used to improve the performance. To improve the performance, supply chain of potato chip MSMEs cluster faces several challenges including a limited supply of raw materials and finished products, price fluctuations, non-optimal drying processes, and inconsistent quality. Therefore, the study aimed to conduct a holistic evaluation and improvement method on the interconnected constraints to enhance supply chain’s performance. The integration of Interpretive Structural Modelling (ISM) and Analytic Network Process (ANP) methods was applied to observe interactions between key elements in development of supply chain institutional potentials and to prioritize development strategies. The results showed that the key elements that influenced development of the organizations related to labor capability, diversity of raw material quality, the potential for collaboration relationships, minimizing the risk of drying during the rainy season, and participation of various actors. Based on the largest WANP weight value (0.230), the primary strategy for supply chain development in the SME cluster was the implementation of digital-based marketing. Furthermore, improving the institutional performance model of potato chip MSMEs cluster supply chain included the participation of internal and external quality assurance organizations in the product quality audit process. Ensuring product quality consistency through these audits would support digital-based marketing and help mitigate issues related to product quality and supply consistency.

Enhancing Silvicultural Practices: A Productivity and Quality Comparison of Manual and Semi-Mechanized Planting Methods in KwaZulu-Natal, South Africa

Planting plays a significant role in commercial forestry. Labour-related issues (scarcity, increased wages, absenteeism, ageing and high turnover), inconsistent work quality, increased operational costs, and poor ergonomics prompted the development of innovative planting techniques. This study aimed to assess the productivity (plants/productive machine hour), worker productivity (plants/worker PMH), work quality and tree survival of a manual and a semi-mechanised planting method. Two study sites at Flatcrown and Kwambonambi, consisting of 37 plots, were planted at 1333 stems/ha, alternating across the study sites. Block-level and elemental-level time studies were conducted, followed by work quality assessments. The manual method planted 7.23 ha/shift (Flatcrown) and 5.89 ha/shift (Kwambonambi), whilst the semi-mechanised method planted 4.72 ha/shift (Flatcrown) and 3.19 ha/shift (Kwambonambi). The manual method was 50% to 60% more productive (plants/PMH) than the semi-mechanised method. In terms of plants/worker PMH, higher worker efficiency of 30%–40% was observed at the Flatcrown site, which was characterised by level terrain and low residue presence compared to the Kwambonambi site, which had a gentle (&lt;20%) terrain and medium residue presence. Planting quality was conducted according to planting specifications. The two methods did not significantly differ, although the study suggests that the proportion of good-quality plantings could be somewhat higher for the manual method. There was no significant difference in tree survival across methods and sites after one month.

Hybrid self-learning model for the prediction and control of sintering furnace temperature

Ternary cathode materials are important components of lithium-ion batteries. However, the sintering process during manufacturing is challenging to control due to the inaccessibility of key dynamic variables and the frequent fluctuations in operating conditions. These lead to high energy consumption and inconsistent product quality. In this paper, we propose a hybrid self-learning prediction model and control method for sintering furnace temperature based on both first-principle and process data. Firstly, a mechanism model with temperature time delay is established based on energy flow analysis in the furnace. To capture the tail gas temperature dynamic in the mechanism model, a Ventingformer-based prediction data-driven model is proposed. In this model, a memory updating technique and an autoregressive module based on the Transformer framework are developed to identify long-time dependencies and respond to variations in input sequences. Then, a hybrid self-learning modeling framework is designed. Based on the established hybrid model, a multiscale objective function-based nonlinear model predictive control (MSCF-NMPC) method is proposed to achieve precise tracking control of the internal temperature in the furnace. A multiscale objective function with short-term cost in terms of energy consumption and tracking accuracy as well as long-term cost in terms of energy loss is constructing in the control optimization problem. Finally, the proposed hybrid self-learning model and MSCF-NMPC method are verified using the actual process data from a sintering furnace, demonstrating the effectiveness of the proposed method. The results offer practical guidance for industrial applications.

Exploiting the “gold of the ocean”: Can live storage solve the paradox of the purse seine fishery for Atlantic bluefin tuna in Norway?

The Norwegian quota for Atlantic bluefin tuna (ABT) (Thunnus thynnus) is not completely caught every year. This is paradoxical because Norway is a leading fishing nation. The fishery is currently executed by rod-and-reel and purse seine, with ∼ 80 % of the quota being allocated to the latter. Purse seine is therefore the main determinant for the overall success of the fishery and vessels aim to take catches that are manageable for their size (typical LOA < 40 m). These vessels are not designed to target ABT, so they tend to have variable capture efficiency, inadequate catch control technology and often produce inconsistent product quality of relatively low value. Furthermore, participation in more profitable fisheries with better shore-based infrastructure and marketing channels is prioritised over ABT fishing. The storage of purse seine caught ABT in cages is currently practiced worldwide but not in Norway. Storage allows on-demand supply to the market and can improve quality by allowing physiological recovery after capture. There is therefore great interest from Norwegian policymakers and industry in developing a live storage fishery. This manuscript investigates aspects related to ABT live storage in Norway. Findings are presented from sea trials exploring the feasibility of these procedures. Challenges that will determine the future perspective of Norwegian live storage are discussed, and the potential of alternatives such as longlines, traps and rod-and-reel is examined. Any future success in developing the Norwegian ABT fishery will serve as an example for other fisheries around the world facing similar challenges.

Longissimus from Berkshire pigs in a small-scale supply chain have increased oxidative metabolism, tenderness and water-holding capacity, compared with Large White × Landrace pigs in a modern commercial supply chain

Context Selection for leanness in the modern Australian pig has resulted in inconsistent quality, including a lack of pork tenderisation during ageing. Inconsistent quality is potentially a result of differences in supply chain and breed as well as the variation in muscle fibre-type proportion in pork longissimus. Aim The aim was to investigate differences in fibre-type proportion and pork quality between Large White-Landrace pigs in a large supply chain and Berkshire pigs processed in a small supply chain. Methods Pigs (n = 22) from two suppliers with different breeds (Supplier 1, Large White × Landrace, SC1-LWLR, n = 12, modern commercial pigs; Supplier 2, Berkshire, SC2-Berk, n = 10, heritage pigs) were slaughtered and samples from the longissimus were extracted at 3, 24, and 48 h postmortem for enzyme and pH analyses. Longissimus samples were subjected to ageing for either 2 or 16 days postmortem (Day 2, Day 16), assessed for colour, muscle fibre-type proportion (%), muscle fibre diameter (μm), water-holding capacity (purge, % and cook loss, %), Warner–Bratzler peak shear force (WBSF, N), and protein denaturation temperature using differential scanning calorimetry (DSC, peak temperature, °C). Key results SC1-LWLR had higher purge than SC2-Berk (2.85% and 1.83% respectively; standard error of the difference (SED) = 0.33; P = 0.003), higher cook loss on Day 16 (24.63% and 16.79% respectively; SED = 1.62; P = 0.017) and higher WBSF on Day 2 and Day 16 (Day 2, 30.9 N and 26.7 N respectively; Day 16, 28.6 N and 22.0 N respectively; SED = 0.98, interaction P = 0.003). SC1-LWLR had a lower proportion of Type I (10.1% vs 16.0%; SED = 0.51) and Type IIA (14.0% vs 22.0%; SED = 0.77) and a higher proportion of Type IIB (75.9% vs 62.0%; SED = 0.74) (P &lt; 0.001 for all) fibres. SC1-LWLR had lower DSC temperatures for two peaks. SC2-Berk had higher citrate synthase activity (P = 0.003) and glycolytic potential (P &lt; 0.001) than SC1-LWLR. Conclusions SC2-Berk longissimus had improved quality compared with SC1-LWLR pork, most likely owing, in part, to higher proportion of oxidative and intermediate fibres in the Berkshires. However, effects of differences in environmental conditions and/or processing conditions cannot be ruled out. Implications The experiment increased our understanding of how variation in supply chains and muscle fibre-type proportion can impact the production of consistently high-quality pork.

Dynamic evidence fusion neural networks with uncertainty theory and its application in brain network analysis

Deep learning has demonstrated significant potential and advantages, achieving notable success in the medical field, particularly in the application of brain network analysis. However, most models ignore the uncertainty caused by inconsistent view quality and fail to effectively leverage the potential correlations and temporal sequences present in multi-view data, preventing neural networks from fully showcasing their strengths. To this end, this paper proposes dynamic evidence fusion neural networks (DEF-NNs) with uncertainty theory, and applies it to brain network analysis. Our model is established within a multi-view learning framework that considers the functional connections under each window as a view. We employ a dynamic evidence learning module to capture the evidence for each time window of the dynamic brain network, utilizing three distinct convolutional filters to extract feature maps. Then, a dynamic evidence fusion mechanism is designed and a dynamic trust function is constructed according to the temporal nature of dFC data. The evidence generated by multiple windows is fused at the decision level of classification, dealing with the uncertainty caused by inconsistent view quality and improving the classification performance. We verified the effectiveness of DEF-NNs through comparison with advanced algorithms on three schizophrenia datasets, and the results show that DEF-NNs significantly improved the classification performance of brain disease diagnosis tasks.

Performance of Drying Oil Modified Chinese Lacquer and Its Gilding Effect

This study explores the modification of traditional Chinese lacquer by incorporating boiled tung oil (BTO), boiled linseed oil (BLO), and turpentine oil (TO) to enhance its properties for gold leafing applications. Current traditional lacquers are limited by slow drying times and inconsistent surface quality, making their performance suboptimal for decorative gilding. The research addresses these gaps by investigating how varying oil types and concentrations (10%, 30%, and 50%) affect the lacquer’s drying time, viscosity, leveling properties, and overall gilding performance. Results indicate that TO-modified lacquer exhibits the best overall performance, showing the fastest drying time, highest glossiness, and smallest color variation, while BTO provides the smoothest surface and BLO ensures the best adhesion. These results demonstrate that the careful selection of oil type and concentration significantly improves lacquer’s functionality for gold leafing, offering a more efficient and aesthetically superior alternative to unmodified lacquers. This study provides valuable insights for optimizing traditional lacquer formulations for modern applications in gilding and decorative finishes.

Artificial Intelligence and Linguistic Landscape research

Abstract This article explores applications of artificial intelligence (AI) technologies in Linguistic Landscape research. Traditionally, LL research has relied on manual data collection and analysis, often involving photographs of public signage, advertisements, and other visual language displays. However, this manual approach can present challenges, including time-consuming data collection, inconsistent data quality, and potential researcher bias. Two AI technologies in particular hold promise for addressing these challenges in LL research: computer vision (CV) and large language models (LLMs). CV automates the identification and extraction of text from images, improving data accuracy and enabling large-scale image analysis. LLMs, based on natural language processing, can detect, translate, and interpret multilingual text. This article explores the affordances and challenges of using AI technologies in LL research and discusses methods to improve data collection, enhance accuracy, and support the analysis of multilingual environments. It also raises ethical issues and limitations of the technologies.

Massive Open Online Courses (MOOCs) as Catalysts of Educational Transformation in the Higher Education Sector of Sri Lanka: A Systematic Literature Review

This systematic literature review evaluates the transformative effects of Massive Open Online Courses (MOOCs) in Sri Lanka’s higher education and library services. It explores how MOOCs democratize education by broadening access to previously costly and limited educational infrastructure and integrating them into library practices for enhanced access and professional development. The review synthesizes various sources to assess MOOC utilization, challenges like low completion rates, inconsistent quality, and digital divides. Strategic improvements are proposed to align MOOCs with educational and socio-economic goals in Sri Lanka. The findings highlight MOOCs’ current uses, barriers, and potential as supplements to traditional education, aiming to optimize learning outcomes and support educational transformation in both academic and library environments.

Assessing the influence of climate change on multiple climate indices in Nepal using CMIP6 global climate models

Global climate models (GCMs) serve as essential tools for projecting future climate trends, but their coarse resolution limits localized impact assessments in sectors like hydrology, agriculture, and biodiversity. Observation data with a spatial resolution of a few kilometers are crucial for downscaling and bias-correcting GCMs at finer resolutions. However, Nepal's extreme topography and organizational challenges have led to uneven distribution of meteorological stations and inconsistent data quality. Moreover, CMIP6-based climate extremes projections for the entire country are currently unavailable. To tackle these challenges, we developed a comprehensive national database for Nepal, offering high-resolution historical and projected precipitation and temperature data analyzed through 25 climate extreme indices from the Expert Team on Climate Change Detection and Indices (ETCCDI). Initially, observation grid data were prepared at a daily timescale with a spatial resolution of 0.05° × 0.05° for baseline period (1981–2010) using the Asian Precipitation High-Resolved Observational Data Integration Toward Evaluation (APHRODITE), the fifth generation of the European Centre for Medium-Range Weather Forecasts Reanalysis (ERA5), and available good quality observed climate data. This data was then utilized to downscale and bias-correct 18 CMIP6 GCMs for 2015–2100 under four SSPs (1–2.6, 2–4.5, 3–7.0, 5–8.5). Quantile mapping was employed for the bias correction of the CMIP6 GCMs. The performance of the multimodal ensemble (MME) indicated better Nash-Sutcliffe Efficiency (NSE), root mean square error ratio (RSR), and Percent Bias (PBIAS) of climate extreme indices for the historical period. A comparative analysis was conducted across Nepal's major geographic regions to account for spatial variability in regional climate systems. The finer-resolution dataset can be crucial to deepen our understanding of climate impacts, and climate change, and eventually informing the policy-making in Nepal. Moreover, the methodology can be effectively replicated in data-scarce developing nations to promote climate research and adaptation efforts.

Licorice-root extract and potassium sorbate spray improved the yield and fruit quality and decreased heat stress of the 'osteen' mango cultivar.

Heat stress, low mango yields and inconsistent fruit quality are main challenges for growers. Recently, licorice-root extract (LRE) has been utilized to enhance vegetative growth, yield, and tolerance to abiotic stresses in fruit trees. Potassium sorbate (PS) also plays a significant role in various physiological and biochemical processes that are essential for mango growth, quality and abiotic stress tolerance. This work aimed to elucidate the effects of foliar sprays containing LRE and PS on the growth, yield, fruit quality, total chlorophyll content, and antioxidant enzymes of 'Osteen' mango trees. The mango trees were sprayed with LRE at 0, 2, 4 and 6 g/L and PS 0, 1, 2, and 3 mM. In mid-May, the mango trees were sprayed with a foliar solution, followed by monthly applications until 1 month before harvest. The results showed that trees with the highest concentration (6 g/L) of LRE exhibited the maximum leaf area, followed by those treated with the highest concentration (3 mM) of PS. Application of LRE and PS to Osteen mango trees significantly enhanced fruit weight, number of fruits per tree, yield (kg/tree), yield increasing%, and reduced number of sun-burned fruits compared to the control. LRE and PS foliar sprays to Osteen mango trees significantly enhanced fruit total soluble solids ˚Brix, TSS/acid ratio, and vitamin C content compared to the control. Meanwhile, total acidity percentage in 'Osteen' mango fruits significantly decreased after both LRE and PS foliar sprays. 'Osteen' mango trees showed a significant increase in leaf area, total chlorophyll content, total pigments, and leaf carotenoids. Our results suggest that foliar sprays containing LRE and PS significantly improved growth parameters, yield, fruit quality, antioxidant content, and total pigment concentration in 'Osteen' mango trees. Moreover, the most effective treatments were 3 mM PS and 6 g/L LRE. LRE and PS foliar spray caused a significant increase in yield percentage by 305.77%, and 232.44%, in the first season, and 242.55%, 232.44% in the second season, respectively.

Optimizing multi-recycled concrete for sustainability: Aggregate gradation, surface treatment methods and life cycle impact assessment

Utilizing demolished waste as recycled aggregate in concrete is a sustainable practice, however, the concerns over inconsistent quality, such as hardened mortar content and water absorption, hinder global adoption. This study evaluates the physical, microstructural properties and gives insights into Life Cycle Impact Assessment of three generations of recycled aggregate concrete. The microstructure analysis yielded compelling insights into the role of Interfacial Transition Zone (ITZ) of reclaimed aggregate on concrete. The study demonstrated that the effective proportioning of coarse and fine aggregate particles using Compressible Packing Model (CPM), resulted in a reduction of dosage of cement in concrete volume, reducing the Binder Intensity Index (BII), and an improvement in the strength and stability of concrete. Additionally, surface treatment methods, including water soaking, mechanical, and thermo-mechanical treatments, were explored. The thermo-mechanical treatment method proved to be effective in reducing 40–60 % of residual mortar. Life cycle assessment of treated and untreated recycled aggregate concrete was performed, considering five environmental impact indicators viz., Global Warming Potential (GWP), Acidification Potential (AP), Eutrophication Potential (EP), Abiotic Depletion Potential (ADP) and Human Toxicity (HT). These indicators illustrated that CPM method of mix proportioning accompanied with thermo-mechanical treatment of repurposed aggregate resulted in lowering environmental impact. These findings underscore the potential for sustainable concrete construction through the use of treated recycled aggregate, offering valuable insights into methods for augmenting its quality and performance.

Research on the Path to Enhancing Graduate Education Quality through New Productive Forces

Graduate education is an indispensable part of higher education, playing a crucial role in cultivating top-tier talent, promoting technological advancements, and facilitating social progress. However, it faces severe challenges, including inadequate teaching methods, outdated educational technologies, insufficient interdisciplinary collaboration, inconsistent quality of mentorship, and incomplete evaluation systems. With the rise of new productive forces centered on technological innovation, exploring their application in enhancing the quality of graduate education is a primary concern. This study investigates how to improve the quality of graduate education through new productive force pathways, including curriculum reform, faculty development, the establishment of new R&amp;D platforms, collaboration between industry and academia, educational assessment mechanisms, an international perspective and global competitiveness, intercultural education, and a focus on social responsibility and sustainable development.

Grafting of maleic anhydride onto waste acrylonitrile butadiene styrene (ABS) and its application for compatibilization of ABS blends

AbstractIncorporating recycled plastics into the manufacturing process is an essential way to establish a sustainable plastics economy. However, current processes are limited by the degradation of mechanical properties, high cost, and inconsistent product quality compared with their virgin counterparts. Here, we present an upcycling strategy for waste plastics where waste acrylonitrile‐butadiene‐styrene (reABS) is first grafted with maleic anhydride (MAH), then utilized to compatibilize nylon 6(PA6)/ABS and PA6/ABS/CaCO3 blends. The reABS‐g‐MAH exhibits improved Young's modulus and tensile strength, but lower strain at break and impact strength compared with reABS. As an additive, reABS‐g‐MAH effectively compatibilizes PA6/ABS and PA6/ABS/CaCO3 blends, indicated by greatly decreased PA6 domain size, homogeneous dispersion of fillers, and narrowed glass transition temperature regions between PA6 and ABS. Adding only 5 wt% reABS‐g‐MAH increases the Young's modulus, strain at break, and impact strength of PA6/ABS blends by 26%, 180%, and 110%, respectively, compared with uncompatibilized samples. Similarly, for PA6/ABS/CaCO3 blends, the strain at break and impact strength increase by 370% and 150%, respectively, while the Young's modulus and tensile strength show increases of 5% and 10%. The results show that this strategy of using polar groups to functionalize waste plastics as compatibilizers may open numerous opportunities for upcycling waste plastics.