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The modelling and design optimisation of sawdust, garnet waste, and palm oil fuel ash-based hybrid asphalt binders using response surface methodology
Full text linkThis study evaluated the rheological characteristics of a hybrid asphalt binder integrating sawdust, garnet waste, and palm oil fuel ash (POFA). Approximately 0 %, 3 %, 6 %, and 9 % of hybrid materials were incorporated into the unaged and rolling thin film oven (RTFO) hybrid asphalt binders were assessed. Furthermore, the central composite design (CCD) in the response surface methodology (RSM) were utilised to evaluate the effects of hybrid asphalt binder content and temperature on the rheological behaviour of the hybrid asphalt binders. Consequently, the hybrid asphalt binders showed dosage-dependent rheological behaviour, with the 6 % formulation exhibiting notably lower phase angle (δ) and complex shear modulus (G∗) than the control binder, particularly in the unaged state, while other dosages displayed more variable responses across the tested temperatures. The RTFO hybrid asphalt binders also revealed reduced stiffness across all temperatures compared to the control asphalt. Given that high correlation coefficients (R2) were demonstrated by the G∗ (<0.97) and δ (<0.93), a substantial relationship between the model values and the experimental data was identified. The optimal parameters (temperature and percentage) for the hybrid materials were also discovered to be 62.9 °C and 5.78 % using the numerical optimisation and the quadratic model. Considering that each response possessed a percentage error below 5 %, the effectiveness and the validation of the model were successfully verified in this study
Nanocellulose-based composites: Advancing sustainable energy storage applications
Full text linkNanocellulose, derived from renewable biomass, has emerged as a highly versatile material in sustainable energy storage. Its unique structural properties, including high surface area, mechanical strength, and tunable surface chemistry, make it an ideal candidate for integration into energy storage devices such as batteries, supercapacitors, and fuel cells. This review provides a comprehensive overview of the recent advancements in nanocellulose-based composites for energy storage applications, highlighting their role in improving electrochemical performance, enhancing mechanical stability, and promoting environmental sustainability. The discussion covers the synthesis techniques, structural modifications, and hybridization strategies used to optimize nanocellulose for energy storage, as well as the challenges associated with scalability and commercial viability. Additionally, we examine the environmental benefits of using nanocellulose composites in energy storage systems, emphasizing their potential to reduce the reliance on non-renewable materials and lower the overall carbon footprint. This review aims to provide insights into future research and development directions in this rapidly evolving field, positioning nanocellulose-based composites as a key enabler of next-generation sustainable energy technologies
Exploring the complex interactions between microplastics and marine contaminants
No full textMicroplastics are ubiquitous in marine ecosystems, acting as both pollutants and carriers of marine contaminants. This review synthesizes current knowledge through
a comprehensive literature search (2000–2024) across Scopus, Web of Science, and PubMed, prioritizing peer-reviewed studies on interaction mechanisms, ecological
impacts, and emerging co-contaminants. High surface-area-to-volume ratios, hydrophobicity, and persistent degradation resistance facilitate the accumulation and
transport of diverse contaminants including persistent organic pollutants (POPs), heavy metals, pharmaceuticals and personal care products (PPCPs), and dissolved
organic matter (DOM). POPs adsorb onto microplastics through hydrophobic partitioning and π–π interactions, with sorption enhanced by UV aging and biofilm.
Heavy metals interact through electrostatic attraction, surface complexation, and chelation, influenced by pH, salinity, DOM, and biofilm. PPCP-microplastic in�teractions are mediated by hydrophobic forces, hydrogen bonding, and ion-exchange mechanisms, depending on polymer type and environmental conditions. DOM
acts as both a sorbent and degradation product, with microplastics promoting DOM humification and reactive oxygen species (ROS) generation under photo�irradiation. These interactions amplify ecological risks by disrupting microbial communities, promoting antibiotic resistance, and altering nutrient cycles, exacer�bating climate vulnerability in coastal ecosystems per IPCC AR6 findings, with socio-economic impacts on fisheries and aquaculture, tourism, and waste
management. Effective policy frameworks such as source reduction, advanced wastewater treatment, and international cooperation on plastic waste management are
critical for mitigating these risks. Emerging insights into multi-pollutant interactions, including engineered nanomaterials and biotoxins, and recent technological
advances for mechanistic elucidation. It underscores the importance of understanding of microplastic-contaminant interactions to mitigate ecological risks and
protect marine ecosystems
Embedded feature importance with threshold-based selection for optimal feature subset in autism screening
Full text linkThe early detection of autism spectrum disorders (ASD) in children poses significant challenges due to the dynamic and progressive nature of the symptoms. To The current screening process involves a lengthy and costly series of questions covering various aspects of a child's development. To address this issue, we adopt the embedded feature selection method based on random forest and threshold-based to produce a simplified version questionnaire for Autism screening. The aim of this paper is to identify the most crucial and effective features from the Quantitative Checklist for Autism in Toddlers (Q-CHAT) by combining the strengths of threshold filtering and embedded random forest feature importance. This integration allows us to significantly reduce the number of screening questions while maintaining reliable and accurate results. Our proposed method yields a streamlined alternative to traditional screening, extracting just eight key features that achieves an impressive 96% accuracy performance. This promising approach holds the potential to revolutionize early detection and intervention programs for toddlers with autism, ultimately leading to improved outcomes
Synthetic image data generation via rendering techniques for training AI-based instance segmentation
Full text linkSynthetic image data generation has gained popularity in computer vision and machine learning in recent years. The work introduces a technique for creating artificial image data by utilizing 3D files and rendering methods in Python and Blender. The technique employs BlenderProc, a rendering tool for generating artificial images, to efficiently create a substantial amount of data. The output of the method is saved in JSON format, containing COCO annotations of objects in the images, facilitating seamless integration with current machine-learning pipelines. The paper shows that the created synthetic data can be used to enhance object data during simulation. The method can enhance the accuracy and robustness of machine-learning models by modifying simulation parameters like lighting, camera position, and object orientation to create a variety of images. This is especially beneficial for applications that require significant amounts of labelled real-world data, which can be time-consuming and labour-intensive to obtain. The study addresses the constraints and potential prejudices of creating synthetic data, emphasizing the significance of verifying and assessing the generated data prior to its utilization in machine learning models. Synthetic data generation can be a valuable tool for improving the efficiency and effectiveness of machine learning and computer vision applications. However, it is crucial to thoroughly assess the potential limitations and biases of the generated data. This paper emphasizes the potential of synthetic data generation to enhance the accuracy and resilience of machine learning models, especially in scenarios with limited access to labelled real-world data. This paper introduces a method that efficiently produces substantial amounts of synthetic image data with COCO annotations, serving as a valuable resource for professionals in computer vision and machine learning
Digitalization application in crowdfunding: A systematic review
Full text linkThe digitalization of crowdfunding has reshaped the fundraising landscape, revolutionizing how projects and ventures secure financial support. This transformation has brought forward innovative platforms, altered user engagement and redefined the dynamics of financial inclusion. Digitalization in crowdfunding has expedited procedures and increased fundraising efforts that change the financial landscape. The purpose of this article is to provide a comprehensive review of the current body of knowledge about the digitalization of crowdfunding. The PRISMA approach was used to analyse an extensive compilation of empirical papers regarding the digitalization of crowd fundraising. These articles were obtained from the Scopus and Web of Science (WoS) databases using a search string of relevant keywords. A screening process was conducted to assess the eligibility of these articles, resulting in a total of 30 articles that were deemed fit for further analysis. The findings revealed three key themes concerning digitalization in crowdfunding research: (1) crowdfunding models and adoption, (2) technology and entrepreneurial financing and (3) social and cultural influences on crowdfunding. This study sheds light on the complicated interaction between blockchain, fintech and crowdfunding by investigating the dramatic implications of digitalization on crowdfunding dynamics. Crowdfunding also has prominent impacts on venture capital investments, China's dynamic digital finance ecosystem and Malaysian public schools' investment intents. Additionally, factors like trust, social effect and effort expectation are highlighted in the study, thus informing future crowdfunding techniques and platform operations in advancing financial technology and crowdfunding
Chlorophyll’s dependency towards electrical characteristics of ananas comosus waste-based dye-sensitized solar cell
Full text linkThe presence of chlorophyll in the Ananas Comosus waste is useful in the fabrication of Dye Sensitized Solar Cell (DSSC) which is an alternative technology of solar cell. The purpose of the research is to fabricate DSSC from the waste. Mechanical extraction is applied here to extract the chlorophyll from the waste by using a saccharum machine. Ultraviolet-Visible Spectrophotometer (UV-VIS) is used to measure the content of chlorophyll a and chlorophyll b. The expected result of this experiment is to achieve higher chlorophyll content which is able to absorb more light energy from the sunlight. The extraction time to collect the juice sample is 3 times. The content of chlorophyll will eventually decrease if it is stored unused for a long period. DSSC will be fabricated with doped Titanium Dioxide, TiO2 which are based on natural dyes from Malaysia tropical fruits, wherein contain chlorophyll which enhances the photosensitization effect due to the high interaction on the surface of the film. Such a natural dye extracted from Ananas Comosus can be subjected to molecular tailoring to give a superior dye preparation, offering a wide range of spectral absorption, covering the entire visible region (400 – 700 nm). Furthermore, the additive (4-tert-butylpyridine) in potassium iodide, KI electrolyte, affects the rate of electron injection into the oxidized dye sensitizer. Fluorine-doped tin oxide (FTO) conductive glass will be used to fabricate the solar cells. After the fabrication process is done, the solar cell was measured by multimeter to obtain the value of output voltage
Environmental degradation of polymer nanocomposites
Full text linkPolymer nanocomposites have become essential in various industries due to their impressive mechanical strength, thermal stability, and electrical properties. These materials, which integrate nanoscale fillers into polymer matrices, offer enhanced properties like reduced weight and increased durability, surpassing traditional composites. However, the high performance observed in controlled environments does not always translate to real-world conditions, where a variety of environmental factors can lead to degradation. Understanding how these factors—such as ultraviolet (UV) radiation, thermal cycling, humidity, chemical exposure, and biological activity—affect polymer nanocomposites is crucial for their reliable use in practical applications. This chapter focuses on the combined effects of environmental factors on polymer nanocomposites, exploring both synergistic interactions that intensify degradation and antagonistic interactions that may mitigate these effects. By analyzing case studies, characterization techniques, and strategies for enhancing environmental stability, this chapter aims to equip researchers and engineers with the knowledge needed to improve the durability and performance of these advanced materials in various applications
Delamination assessment via acoustic wave propagation and an optical sensor network
Full text linkThe preservation of the integrity of composite structures necessitates the monitoring of their structural health. A considerable body of research has been dedicated to investigating the use of traditional electrical sensors for the purpose of collecting acoustic waves in order to detect delamination. In contrast, electrical sensors possess several limitations. This research endeavors to evaluate delamination by employing an optical sensor network that relies on a fiber Bragg grating (FBG) sensor. In the experiment, composite plates were fabricated with varying sizes of delamination. The composite specimen has been equipped with a sensor network consisting of four Fiber Bragg Gratings (FBGs) placed linearly. This network enables the detection of acoustic wave propagation resulting from an impact in the middle of the composite plate. Upon analysis of the acoustic waves, it is seen that the average time delay for various delamination circumstances is 68.2% for a delamination size of 10 cm x 4 cm and 116.7% for a delamination size of 10 cm x 6 cm. The findings of the study also demonstrate that the reduction in wave speed is 40.54% and 53.85% for delamination sizes of 10 cm x 4 cm and 10 cm x 6 cm, respectively. The results indicate that the utilization of a network of Fiber Bragg Grating (FBG) sensors for the purpose of delamination detection in plate-like composite structures holds promise in the field of health monitoring
A review on preamble-based channel estimation method for FBMC/OQAM toward 6G: Advantages, challenges and future works
Full text linkFilter bank multicarrier/offset quadrature amplitude modulation (FBMC/OQAM) is a multicarrier modulation that is expected to replace orthogonal frequency division multiplexing (OFDM) in future sixth-generation (6G) networks. FBMC/OQAM has high spectrum efficiency, cyclic prefix (CP)-free transmission, decreased out-of-band emission (OOBE), and asynchronous environment robustness. However, the orthogonality criteria of FBMC/OQAM are onlyin the real field. Therefore, imaginary components of complex-valued OQAM symbols cause imaginary interferences among subcarriers, affecting channel estimation (CE) processing operations. Channel estimation is a critical component of wireless communication systems. Channel estimate allows the receiver to approximate channel impulse response (CIR) to determinethe impacts of the communication channel on the sent symbols. So, an accurate channel estimate is critical in FBMC/OQAM. In this review, we focus on the Preamble-based method, one of the basic methods for channel estimation in FBMC. Three preamble-based approaches have been studied: the interference approximation method (IAM), the interference cancellation method (ICM), and pairs of pilots (POP) using a single antenna and multiple input multiple outputs (MIMO). Compare them regarding bit error rate (BER) and mean square error. Also, it compares different interference approximation methods in terms of bit error rate (BER), magnitude, and peak average power ratio (PAPR). The review found the superiority of M-IAM and NPS in spectrum efficiency and PAPR. Future work that can help the researcher in this field