Digitalization application in crowdfunding: A systematic review

The 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

Robotics in Education

This book investigates robotics’ role in transforming STEM education through critical thinking and interdisciplinary skill development. Spanning six chapters, it examines robotics’ ability to enhance technical and collaborative skills while addressing challenges such as resource accessibility and curriculum integration. Core programming concepts, including control systems, algorithm design, and debugging, are elaborated upon using tools like Python and ROS. Practical guidance encompasses the design of mechanical and electrical systems, the integration of sensors (e.g., ultrasonic, IMUs), and their application in real-world scenarios. Projects advance from basic movement programming to sophisticated AI-driven tasks such as autonomous navigation and object recognition. The AI section discusses machine learning, path-planning algorithms (e.g., A* search, SLAM), and classroom case studies. Subsequent chapters examine new trends such as VR, IoT, and data-driven assessment alongside robotics's global influence in education and career readiness strategies. Aimed at educators and students, this book combines theoretical frameworks with practical insights to prepare learners for technological progress

The modelling and design optimisation of sawdust, garnet waste, and palm oil fuel ash-based hybrid asphalt binders using response surface methodology

This 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

Nanocellulose, 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

Neutrosophic prediction of consumer decisions using the RBF neural network method

The utilization of neutrosophic concept to forecast patron purchase conduct has been thoroughly tested in preceding research using various fashions. This study examines the number one elements affecting clients' selections to shop for mobile phones, dividing them into 4 separate ranges consistent with their purchasing behaviours. The tiers, from the first to the fourth layer, characterize exclusive ranges of customer hobby and participation. The main intention is to create an efficient neutrosophic predictive version that examines purchaser conduct thru pertinent traits that signify their opportunity of buying. We utilize the Neutrosophic Radial Basis Function (NRBF) model for neutrosophic class to do that. The results indicate a minimal blunders fee and improved neutrosophic category accuracy, mainly in contrast to the BIC version, which exhibited lower accuracy. NRBF exhibited a sturdy location below the curve (AUC) rating, underscoring the model's efficacy. These findings provide big insights into consumer preferences and decision-making methods, enhancing procedures for market analysis and cantered advertising initiatives

Exploring the complex interactions between microplastics and marine contaminants

Microplastics 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

Investigation of deep learning model for vehicle classification

The usage of automobiles in cities and metropolitan areas has increased drastically throughout the years and there is a need to monitor the flow of road traffic to improve the traffic congestion and safety. One of the best ways to monitor the traffic is using an artificial intelligence and machine learning. An automatic vehicle tracking system based on artificial intelligence and machine learning can offers capability to analyse the real-time traffic video data for the purpose of traffic surveillance. The computer vision is one of the subsets in machine learning that can train the computer to understand the visual data and perform specific tasks such as object detection and classification. A Vision-based system can be proposed to detect road accidents, predict traffic congestion and further road traffic analytics. This can improve the safety in transportation where it can recognize types of vehicles on the road, detecting road accidents, predicting the traffic congestion and further road traffic analytics. In the context of road traffic monitoring, the parameters of the traffic such as the type and number of vehicles that passes through must be recorded in order to gain valuable insights and make prediction such as the occurrence of traffic congestion. However, this requires reliable informative and accurate data as input for analytics. Therefore, in this research the deep learning model for vehicle classification is investigated to detect, classify types of vehicles and further predictive analytics. The vehicle classification is proposed based on Single Shot Detector (SSD) architecture model. The proposed model is tested on five different classes of vehicles with a total of 1263 images. Experimental results show that SSD model able to achieve 0.721 of precision, 0.741 of recall and 0.731 of F1 Score. Finally, the result show that the SSD model is more accurate among all the models for all the performance measure with the difference of more than 0.052 of precision, 0.706 of recall and 0.05 of F1 Score

Chlorophyll’s dependency towards electrical characteristics of ananas comosus waste-based dye-sensitized solar cell

The 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

Progress, risks and impacts of the evolution of autonomous vehicles

In recent years, there has been a growing interest in autonomous driving due to the potential to alleviate the responsibilities of drivers and enhance driving safety. A lot of technological advancements and progress have been observed to develop and facilitate the operation of autonomous vehicles. However, the rapid evolution of autonomous vehicles presents a complex and multifaceted set of challenges and opportunities that require in-depth analysis. This review article critically examines the progress made in the development of autonomous vehicle technology, delineates the associated risks, and investigates the multifaceted impacts on society, the economy, and the environment. The article commences by outlining the achievements made in autonomous vehicle technology, incorporating developments in sensor technology, artificial intelligence, and vehicle-to-everything communication for its regular operation, object detection, and prediction mechanism. Additionally, it explores the various levels of automation, from driver assistance systems to fully autonomous vehicles, and the technical challenges encountered at each stage. Moreover, it discusses concerns related to safety, cybersecurity, ethical considerations, and the potential displacement of traditional transportation jobs which need to be overcome by continuous research in this field. In addition to the technological and safety aspects, this review explores the broader societal and environmental impacts of autonomous vehicles. Furthermore, it examines the potential benefits, such as reduced traffic congestion, improved road safety, and enhanced accessibility for individuals with mobility challenges. Conversely, it also considers the environmental consequences and the need for sustainable autonomous mobility solutions

Synergistic Integration of Modular Four-Channel 50 kW WPT System: Enhancing Fast EV Charging and PMSM Drive Integration

This paper presents an innovative approach to electric vehicle (EV) charging and propulsion system integration through the development of a Modular Four-Channel 50 kW Wireless Power Transfer (WPT) System with Decoupled Coil Design. The system architecture is designed to facilitate rapid charging of EV batteries while also enabling a seamless transition to a Permanent Magnet Synchronous Motor (PMSM) drive application post-charging. The decoupled coil design ensures efficient power transfer while minimizing electromagnetic interference. Leveraging this integrated solution, the charging process is optimized for speed and reliability, enhancing the EV user experience. Furthermore, the seamless transition to the PMSM drive application allows for immediate utilization of the charged EV battery for propulsion, ensuring maximum efficiency and functionality. The system delivers 50 kW power with 97% DC-to-DC efficiency over a 200 mm airgap and demonstrates balanced tolerance to misalignment. MATLAB/simulation validation demonstrates the effectiveness of the proposed system in achieving fast EV charging and smooth integration with the PMSM drive application, thereby offering a promising solution for future electric vehicle technology advancement