Development Of Platform Research Articles

Integrating virtual-reality into restorative dentistry training: A study on the acceptability and effectiveness of tooth preparation simulations.

Digital education using virtual-reality simulators is essential for precise learning in modern education. This study aimed to develop a virtual-reality dental system for tooth preparation training and assess pre-virtual-reality experiences and perceptions of its potential benefits. We evaluated the post-virtual-reality experience in terms of the effectiveness of the virtual-reality system. The virtual-reality platform development phase involved learning- and assessment-based frameworks for participants. The first step involved creating a storyboard for a virtual dental clinic environment using virtual-reality glasses. This simulated a dental chair-like setup with instruments and tools, allowing participants to practice dental procedures. This study utilized a mixed-methods approach to assess participants' experience with virtual reality in dental education using a tooth preparation simulation system and evaluated post-virtual reality measures, including the system usability scale, usefulness, educational effectiveness, and participant attitudes through structured survey questionnaires. This study included 150 participants (49 dental students, 52 dental interns, and 49 faculty members). Most participants were females (76%). Significant differences were observed in attitude scores between students and faculty members, with third-year dental students having more positive attitudes toward the integration of virtual-reality in dental education. No significant differences were found in system usability, usefulness, or assessment scores between the groups. The virtual-reality dental module system shows promise for improving dental education. The varied responses from different user groups should be considered to ensure successful implementation and acceptance.

New developments in the diagnosis and management of motor neuron disease.

Motor neuron disease (MND) is a devastating neurodegenerative disease characterized by progressive muscle weakness. PubMed, MEDLINE, and Cochrane databases were searched for articles to March 2024. Searches involved the terms 'motor neuron disease' or 'amyotrophic lateral sclerosis' and 'epidemiology', 'diagnosis', 'clinical', 'genetic', 'management', 'treatment', or 'trial'. Evidence-based management involves riluzole, multidisciplinary care, provision of noninvasive ventilation and gastrostomy, and symptomatic treatments. Tofersen should be offered to treat SOD1-MND. Edaravone and Relyvrio are approved treatments in the USA, but insufficient evidence was found to support approval in the UK and Europe. The discovery of neurofilaments as MND biomarkers, growth of platform trials and development of novel therapies provide optimism for more powerful neuroprotective therapies. Further work should focus on the elucidation of environmental causes of MND, gene-environment interactions, and advanced cellular models of disease.

Research on Challenges and Strategies of Students' Adaptive Learning within AI

It's acknowledged that an adaptive e-learning system can be efficient for users to conduct personalized learning in which learners could choose appropriate content and preferred models based on artificial intelligence (AI) recommendations. Nowadays, the rapid development of adaptive platforms, fueled by the evolution of machine learning and AI, incontestably brings innovative potential to the educational field. However, there are many unavoidable challenges followed by the swift process of integrating AI and other technologies into adaptive learning practices. Through the extensive review of previous research on adaptive e-learning and comprehensive analysis of their prominent outcomes, the paper focuses on exploring the challenges of adaptive learning within AI that students are facing and proposing possible solutions. It's found that adaptive learning is mainly experiencing the challenges involving how to avoid data bias and protect personal privacy, making continuous evaluations of users, lack of emotion monitoring and technological support for both students and teachers and the technology integration and inequity. To deal with the increasing problems, some practical strategies are presented, such as properly governing data for learning management, conducting continuous evaluation and prediction for users, and visualizing cognition, which can be supportive of students' professional development in adaptive learning.

Evaluation of Tembusu virus single-round infectious particle as vaccine vector in chickens

Orthoflaviviruses are single-stranded RNA viruses characterized by highly efficient self-amplification of RNA in host cells, which makes them attractive vehicles for vaccines. Numerous preclinical and clinical studies have demonstrated the efficacy and safety of orthoflavivirus replicon vectors for vaccine development. In this study, we constructed Tembusu virus (TMUV) replicon-based single-round infectious particles (SRIPs) as vaccine development platform. To evaluate the potential of TMUV SRIPs as vaccines, we generated SRIPs that express the heterologous Fowl adenovirus 4 (FAdV-4) fiber2 protein and fiber2 head domain, named TMUVRP-fiber2 and TMUVRP-fiber2H, respectively. To assess the immunogenicity of the TMUV SRIPs, SPF chicks were intramuscularly inoculated twice. Our results showed that the TMUVRP-fiber2 vaccines elicited high levels of neutralizing antibodies. Challenge experiments showed that TMUVRP-fiber2 provided full protection against virulent FAdV-4 and significantly reduced viral shedding. Moreover, the immunogenicity of TMUVRP-fiber2H was significantly lower than that of TMUVRP-fiber2, which was reflected in the neutralizing antibody titer, survival rate, and virus shedding after challenge. Therefore, our results suggested that TMUV SRIPs are a promising novel platform for the development of vaccines for existing and emerging poultry diseases.

Ultra-Low-Power, Extremely Stable, Highly Linear-Response Thermal Conductivity Sensor Based on a Suspended Device with Single Bare Pt Nanowire.

The continuous and stable monitoring by sensors is crucial for ensuring the safe utilization of hydrogen due to its inherent high explosiveness. Currently, catalyst aging and oxygen dependence often limit the lifetime of most sensors, which stems from the sensing materials and catalytic reaction in comparison to thermal conductivity sensors. Thermal conductivity sensors possess superior sensing characteristics such as lowpower consumption and exceptional stability attributed to their free-catalysts or free-oxygen nature. Herein, we present an ultralow-power hydrogen-thermal conductivity sensor based on suspended bare platinum nanowires. This sensor incorporates two suspended independent working elements (serpentine/bridge), each of which is thermally decoupled from the substrate. Also, the bridge element operates at significantly lower power levels (the lowest ∼3.32 μW) compared to existing direct-current hydrogen-thermal conductivity sensors. Furthermore, it demonstrates a 99.99% linearity between hydrogen concentration and response under various operating powers. Finally, our sensor shows remarkable stability through a repeatability test (>30,000 cycles). This developed platform provides an optimal structure scheme for integrated sensors with ultralow-power, extremely stable, highly linear-response sensing characteristics, which is expected to be widely used for hydrogen detection and leakage warning under various pipeline distribution systems.

Spatiotemporal Control Over Protein Release from Artificial Cells via a Light-Activatable Protease.

The regulation of protein uptake and secretion by cells is paramount for intercellular signaling and complex multicellular behavior. Mimicking protein-mediated communication in artificial cells holds great promise to elucidate the underlying working principles, but remains challenging without the stimulus-responsive regulatory machinery of living cells. Therefore, systems to precisely control when and where protein release occurs should be incorporated in artificial cells. Here, a light-activatable TEV protease (LaTEV) is presented that enables spatiotemporal control over protein release from a coacervate-based artificial cell platform. Due to the presence of Ni2+-nitrilotriacetic acid moieties within the coacervates, His-tagged proteins are effectively sequestered into the coacervates. LaTEV is first photocaged, effectively blocking its activity. Upon activation by irradiation with 365nm light, LaTEV cleaves the His-tags from sequestered cargo proteins, resulting in their release. The successful blocking and activation of LaTEV provides control over protein release rate and triggerable protein release from specific coacervates via selective irradiation. Furthermore, light-activated directional transfer of proteins between two artificial cell populations is demonstrated. Overall, this system opens up avenues to engineer light-responsive protein-mediated communication in artificial cell context, which can advance the probing of intercellular signaling and the development of protein delivery platforms.

Exploration of Volatileomics and Optical Properties of Fusarium graminearum-Contaminated Maize: An Application Basis for Low-Cost and Non-Destructive Detection.

Fusarium graminearum (F. graminearum) in maize poses a threat to grain security. Current non-destructive detection methods face limited practical applications in grain quality detection. This study aims to understand the optical properties and volatileomics of F. graminearum-contaminated maize. Specifically, the transmission and reflection spectra (wavelength range of 200-1100 nm) were used to explore the optical properties of F. graminearum-contaminated maize. Volatile organic compounds (VOCs) of F. graminearum-contaminated maize were determined by headspace solid phase micro-extraction with gas chromatography-tandem mass spectrometry. The VOCs of normal maize were mainly alcohols and ketones, while the VOCs of severely contaminated maize became organic acids and alcohols. The ultraviolet excitation spectrum of maize showed a peak redshift as fungi grew, and the intensity decreased in the 400-600 nm band. Peak redshift and intensity changes were observed in the visible/near-infrared reflectance and transmission spectra of F. graminearum-contaminated maize. Remarkably, optical imaging platforms based on optical properties were developed to ensure high-throughput detection for single-kernel maize. The developed imaging platform could achieve more than 80% classification accuracy, whereas asymmetric polarization imaging achieved more than 93% prediction accuracy. Overall, these results can provide theoretical support for the cost-effective preparation of low-cost gas sensors and high-prediction sorting equipment for maize quality detection.

Portable Electrochemical System and Platform with Point-of-Care Determination of Urine Albumin-to-Creatinine Ratio to Evaluate Chronic Kidney Disease and Cardiorenal Syndrome.

The urine albumin (Alb)-to-creatinine (Crn) ratio (UACR) is a sensitive and early indicator of chronic kidney disease (CKD) and cardiorenal syndrome. This study developed a portable and wireless electrochemical-sensing platform for the sensitive and accurate determination of UACR. The developed platform consists of a carbon nanotube (CNT)-2,2'-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid)(ABTS)-based modified UACR sensor, a miniaturised potentiostat, a cup holder embedded with a magnetic stirrer and a smartphone app. The UACR sensing electrode is composed of two screen-printed carbon working electrodes, one screen-printed carbon counter electrode and a screen-printed AgCl reference electrode. The miniaturised potentiostat, which is controlled by the developed app, performs cyclic voltammetry and amperometry to detect Alb and Crn, respectively. Clinical trials of the proposed system by using spot urine samples from 30 diabetic patients indicate that it can accurately classify all three CKD risk statuses within 30 min. The high accuracy of our proposed sensing system exhibits satisfactory agreement with the commercial biochemical analyser TBA-25FR (Y = 0.999X, R2 = 0.995). The proposed UACR sensing system offers a convenient, reliable and affordable solution for personal mobile health monitoring and point-of-care urinalysis.

Pooled nanoparticle screening using a chemical barcoding approach.

We report the development of a small molecule-based barcoding platform for pooled screening of nanoparticle delivery. Using aryl halide-based tags (halocodes), we achieve high-sensitivity detection via gas chromatography coupled with mass spectrometry or electron capture. This enables barcoding and tracking of nanoparticles with minimal halocode concentrations and without altering their physicochemical properties. To demonstrate the utility of our platform for pooled screening, we synthesized a halocoded library of polylactide-co-glycolide (PLGA) nanoparticles and quantified uptake in ovarian cancer cells in a pooled manner. Our findings correlate with conventional fluorescence-based assays. Additionally, we demonstrate the potential of halocodes for spatial mapping of nanoparticles using mass spectrometry imaging (MSI). Halocoding presents an accessible and modular nanoparticle screening platform capable of quantifying delivery of pooled nanocarrier libraries in a range of biological settings.

Development of an interactive platform using an Application Programming Interface to facilitate social connections

Development of an interactive platform using an Application Programming Interface to facilitate social connections

Design of Blockchain and Strategic Financing Service under the Platform Economy

The capital constrains and uncertainties in supply disruptions faced by small and medium-sized suppliers pose critical challenges for the development of e-commerce platforms. In response, platforms have begun offering direct financing (PDF) to their suppliers alongside traditional bank credit financing (BCF). Within the private (consortium) blockchain, the platform and the supplier (and the bank) achieve consensus on financial smart contract, enabling the execution of flexible contracts tailored to suppliers’ risk profile. We develop a game-theoretic model to examine the terms of smart contracts in different blockchains and further investigate the platform's optimal blockchain design. We find that PDF serves to safeguard the platform's commission income from high-interest BCF. Our analysis reveals that PDF is more likely to be provided to a risky supplier within the consortium blockchain, contrasting with outcomes observed within the private blockchain. As a result, the platform and the supplier could reach win-win, or win-lose outcomes within the private blockchain; while within the consortium blockchain, they tend to result in win-win or lose-win outcomes. Such incentive conflict could potentially hinder blockchain adoption. Our findings further provide the managerial insights guiding the platform's blockchain design: adopting consortium blockchain under moderate market potential and low or high supplier's risk, and adopting private blockchain otherwise. Furthermore, we consider the supplier's endogenous effort on reducing risk, and numerically demonstrate that blockchain may encourage or discourage the supplier's effort. Our research complements previous studies by investigating the interaction between the PDF provision and blockchain structures under uncertain supply disruption risk.

Self-validating photoelectrochemical/photoelectrochromic visual sensing platform for ciprofloxacin precise detection in milk

BackgroundIn the process of food production, ciprofloxacin (CIP), a highly prescribed fluoroquinolone antibiotic, is often excessively used to reduce the risk of bacterial infection. However, this overuse can cause severe harm to human health, including allergic responses, gastrointestinal complications, and potential renal dysfunction. The development of a robust and precise detection method for CIP is crucial, given the interconnection between food security and human health. Compared to the single-mode detection methods currently in use, dual-mode detection provides enhanced accuracy in detecting results due to its inherent self-validation and self-correction capabilities. ResultsHerein, a photoelectrochemical and photoelectrochromic self-validated dual-mode sensing platform was developed to detect CIP in milk by laser etching method, signal generation (SG) region, signal output (SO) region and conductive channel was integrated on the same fluoide-doped tin oxide electrode and Ti3C2/ZnO composite was modified in the electron SG region, and Prussian blue (PB) was electrodeposited in the SO region. By irradiating the SG region, photogenerated electrons are generated and injected into the SO region through the conductive pathway, resulting in the reduction of the PB to Prussian white (PW). Because the binding of CIP to its specifically recognized aptamers hinders electron transfer, a “Signal-Off” response mechanism can be used for simultaneous quantitative detection of CIP using photocurrent or color changes, which presents a great advantage in the detection process. SignificanceBy integrating different detection mechanisms within a single linear range, the constructed dual-mode sensor has a wide detection range and low detection limit in milk samples. Additionally, it shows good selectivity in anti-interference experiments, providing a new idea for the development of visual analysis and detection platforms for food safety.

A scalable, high-throughput neural development platform identifies shared impact of ASD genes on cell fate and differentiation.

Deleterious mutations in hundreds of genes confer high risk for neurodevelopmental disorders (NDDs), posing significant challenges for therapeutic development. Identifying convergent pathways shared across NDD genes could reveal high-impact therapeutic targets. To identity convergent pathways in NDD genes, we optimized Perturb-seq, a method combining CRISPR perturbation with single-cell RNA sequencing (scRNA-seq), and applied structural topic modeling (STM) to simultaneously assess impact on cell fate and developmental stage. We then studied a subset of autism spectrum disorder (ASD) genes implicated in regulation of gene expression using these improved molecular and analytical approaches. Results from targeting 60 high-confidence ASD risk genes revealed significant effects on neural development. As expected, ASD risk genes impacted both progenitor fate and/or neuronal differentiation. Using STM, we could identify latent topics jointly capturing cell types, cell fate, and differentiation stages. Repression of ASD risk genes led to changes in topic proportions and effects of four genes (DEAF1, KMT2A, MED13L, and MYT1L) were validated in an independent dataset. Our optimized Perturb-seq method, combined with a novel analytical approach, provides a powerful, cost-effective framework for uncovering convergent mechanisms among genes involved in complex neurodevelopmental processes. Application of these methods advanced understanding of the impact of ASD mutations on multiple dimensions of neural development, and provides a framework for a broader examination of the function of NDD risk genes.

Technological Innovation in The Exploration of 5.0 Education Based Self Esteem-Exceed Model on Gifted Intelligent Students

To be able to achieve success in their life, gifted intelligent students do not only need to optimize their cognitive function, but also to manage their self esteem. There have been many cases where students with high intellectual capacity failed in their life and work. This study aims to integrate technological innovation with the exploration of 5.0 educational based self esteem model or Exploration of Self Esteem Model (Exseed) in order to strengthen the character of gifted intelligent students. 5.0 education emphasizes the use of technology as an instrument to enrich learning experience. 5.0 educational approach enables the integration of technology such as Artificial Intelligent (AI), Augmented Reality (AR), and Virtual Reality (VR) into the learning process. In this study, technology is used as a tool to create an immersive learning experience in order to help gifted intelligent students to understand abstract concepts as well as they understand concrete concepts. Research method used in this study was the waterfall method. Waterfall method has five stages, namely, analyzing requirements, designing, applying, testing, and maintaining. The result of this study is a technological product for the Exploration of 5.0 Educational Based Self Esteem Model which is called Exseed to strengthen the characters of gifted intelligent students. Practical implication of this study includes the development of Exseed learning platform which can be adjusted with the individual need of gifted intelligent students as well as the improvement of learning involvement through the use of technology. Technology Integration in 5.0 educational based Exseed to explore self esteem of gifted intelligent students can be a progressive step to strengthen characters in the context of inclusive learning centered on them.

Development of a Monte Carlo simulation platform for the systematic evaluation of photon-counting detector-based micro-CT

PurposeThis study aimed to develop a photon-counting detector (PCD) based micro-CT simulation platform for assessing the performance of three different PCD sensor materials: cadmium telluride (CdTe), gallium arsenide (GaAs), and silicon (Si). The evaluation encompasses the components of primary and scatter signals, performance of imaging contrast agents, and detector efficiency. MethodsSimulations were performed using the Geant4 Monte Carlo toolkit, and a micro-PCD-CT system was meticulously modeled based on realistic geometric parameters. Results.The simulation can obtain HU values consistent with measured results for iodine and calcium hydroxyapatite contrast agents. The two major components of scatter signals for CdTe and GaAs based PCD are fluorescent X-ray photons and photoelectrons, whereas for Si, the components are photoelectrons and Compton electrons. Scattering counts of CdTe and GaAs sensors can be effectively reduced by using energy thresholds, whereas those of Si sensor are insensitive to the applied threshold. The optimal threshold values for CdTe and GaAs are 30 and 15 keV, respectively. For contrast agent imaging, GaAs exhibits enhanced sensitivity to low photon energies compared to CdTe, while it’s contrast-to-noise ratio (CNR) values are slightly lower than those of CdTe at the same contrast agent concentration. Among the three sensor materials, Si has the lowest CNR and detector efficiency; CdTe exhibits the highest efficiency, except in low-energy ranges (< 45 keV), where GaAs has superior efficiency. ConclusionsThe proposed methods are expected to benefit PCD optimization and applications, including energy threshold selection, scattering correction, and may reduce the need for large-scale experiments.

A Wasserstein distance-based spectral clustering method for transaction data analysis

With the rapid development of online payment platforms, it is now possible to record massive transaction data. Clustering on transaction data significantly contributes to analyzing merchants’ behavior patterns. This enables payment platforms to provide differentiated services or implement risk management strategies. However, traditional methods exploit transactions by generating low-dimensional features, leading to inevitable information loss. In this study, we use the empirical cumulative distribution of transactions to characterize merchants. We adopt Wasserstein distance to measure the dissimilarity between any two merchants and propose the Wasserstein-distance-based spectral clustering (WSC) approach. Based on the similarities between merchants’ transaction distributions, a graph of merchants is generated. Thus, we treat the clustering of merchants as a graph-cut problem and solve it under the framework of spectral clustering. To ensure feasibility of the proposed method on large-scale datasets with limited computational resources, we propose a subsampling method for WSC (SubWSC). The associated theoretical properties are investigated to verify the efficiency of the proposed approach. The simulations and empirical study demonstrate that the proposed method outperforms feature-based methods in finding behavior patterns of merchants.

Nanoscale Extracellular Vesicle-Enabled Liquid Biopsy: Advances and Challenges for Lung Cancer Detection.

Lung cancer is responsible for the death of over a million people worldwide every year. With its high mortality rate and exponentially growing number of new cases, lung cancer is a major threat to public health. The high mortality and poor survival rates of lung cancer patients can be attributed to its stealth progression and late diagnosis. For a long time, intrusive tissue biopsy has been considered the gold standard for lung cancer diagnosis and subtyping; however, the intrinsic limitations of tissue biopsy cannot be overlooked. In addition to being invasive and costly, it also suffers from limitations in sensitivity and specificity, is not suitable for repeated sampling, provides restricted information about the tumor and its molecular landscape, and is inaccessible in several cases. To cope with this, advancements in diagnostic technologies, such as liquid biopsy, have shown great prospects. Liquid biopsy is an innovative non-invasive approach in which cancer-related components called biomarkers are detected in body fluids, such as blood, urine, saliva and others. It offers a less invasive alternative with the potential for applications such as routine screening, predicting treatment outcomes, evaluating treatment effectiveness, detecting residual disease, or disease recurrence. A large number of research articles have indicated extracellular vesicles (EVs) as ideal biomarkers for liquid biopsy. EVs are a heterogeneous collection of membranous nanoparticles with diverse sizes, contents, and surface markers. EVs play a critical role in pathophysiological states and have gained prominence as diagnostic and prognostic biomarkers for multiple diseases, including lung cancer. In this review, we provide a detailed overview of the potential of EV-based liquid biopsy for lung cancer. Moreover, it highlights the strengths and weaknesses of various contemporary techniques for EV isolation and analysis in addition to the challenges that need to be addressed to ensure the widespread clinical application of EV-based liquid biopsies for lung cancer. In summary, EV-based liquid biopsies present interesting opportunities for the development of novel diagnostic and prognostic platforms for lung cancer, one of the most abundant cancers responsible for millions of cancer-related deaths worldwide.

Establishing Trust and Security in Decentralized Metaverse: A Web 3.0 Approach

The integration of blockchain and Web 3.0 technologies offers significant advancements in identity management and trust within decentralized Metaverse environments. Despite the rapid development of numerous Metaverse platforms, these environments often operate in isolation, lacking interoperability and cohesive security measures. This paper proposes a novel architecture leveraging Self-Sovereign Identity (SSI) principles and blockchain technology to achieve secure and interoperable interactions across different Metaverse platforms. Our framework encompasses a digital wallet application, an interactive virtual environment, and a secure blockchain-based backend infrastructure. We detail the system architecture, operational specifics, and demonstrate significant improvements in scalability, efficiency, and security through comprehensive performance evaluations. Additionally, we address the challenges of real-world implementation and propose viable solutions. By enhancing user control over personal data and enabling secure cross-platform interactions, our approach aims to foster a more secure, interoperable, and user-centric decentralized Metaverse.

A study on the development of O2O operation service platform using augmented reality

An attempt of sequence selection is made for multiple alignments of transmembrane proteins in order to detect the structural similarity in the protein families. Treatment for the sequence selection, which is based on the pairwise sequence identities, is applied to ten sequence data sets of functional group of transmembrane proteins extracted from SWISS-PROT. The treatment excludes the sequences with low sequence identity from the divergent data sets effectively. Obtained multiple alignments are evaluated using two newly developed indices. The selected sequences, which are well aligned with few inserted gaps, seem to contain enough information for extracting the structural features of transmembrane functional groups.

Innovating Civil Engineering: Strategies for Fostering Stakeholder Engagement in the Circular Economy

Sustainability and circular economy principles are increasingly important in civil engineering, as highlighted by the United Nations Environmental Programme in 2023 the construction activities represented 36% of global energy use in 2020. However, the sector's focus on reducing operational carbon overshadows the need for addressing embodied carbon from materials like cement, steel, and aluminium. To address this, a shift towards incentivizing all supply chain stakeholders and cultivating collaborative models is necessary. This research provides an initial scoping for the development of the Lithuanian Construction Materials Reuse Optimization (LSEPO) platform, a pioneering regional initiative fostering a marketplace for reused building materials. Such platforms are recognized for their potential to overcome sustainability barriers by utilizing digital technologies for efficient data management and stakeholder collaboration. Our research identifies a significant gap in the existing literature concerning effective stakeholder engagement strategies critical for the success of such platforms. To bridge this gap, we employed a three-tiered methodological approach: firstly, a semi-structured literature analysis delved into existing stakeholder engagement strategies within the circular economy. This review highlighted a fragmented understanding of incentives and collaboration dynamics across different sectors. Secondly, a content analysis of global material exchange and circular construction platforms provided insights into best practices, showcasing successful communication methods, innovative incentive models, and robust collaboration frameworks. Lastly, integrating these findings, our study proposes a conceptual stakeholder engagement framework tailored for social innovation in civil engineering, more specifically Lithuanian Construction Materials Reuse Optimization (LSEPO). Practical implications of our findings underscore the necessity for platforms like LSEPO to incorporate adaptive engagement strategies that are context-sensitive and inclusive, facilitating a broad spectrum of stakeholder inputs. The study demonstrates that effective stakeholder engagement goes beyond traditional communication methods; it requires a systemic integration of technological innovation and incentive alignment to promote sustained participation and collaboration. By providing a comprehensive foundation for the development of the LSEPO platform, this research not only aims to advance it as a beacon of social innovation and environmental stewardship within the civil engineering sector but also sets a benchmark for future initiatives aiming to embed circular economy principles effectively.