Global Scholar Awards

🔬 Investigating Membrane Fouling from Microplastic-Contaminated Water🧪

💧 Microplastic pollution has become a growing concern in surface water sources such as rivers, lakes, and reservoirs. These tiny plastic particles can pass through conventional treatment systems and pose risks to ecosystems and human health. 🔬 Membrane filtration is widely used to remove microplastics, but analyzing the membrane surface after filtration is essential to understand how these particles interact with and accumulate on the membrane.

🧪 After filtration, microscopic examination of the membrane surface often reveals layers of trapped microplastics, organic matter, and other suspended particles. 🌫️ These deposits can block pores, alter surface properties, and reduce filtration efficiency over time. By studying the structure, distribution, and composition of these residues, researchers can identify fouling mechanisms and evaluate membrane performance.

🌍 Understanding membrane surface changes helps engineers design more durable and efficient filtration systems. 🚀 Improved membrane materials, optimized cleaning strategies, and advanced monitoring techniques can reduce fouling and enhance long-term operation. Such insights are crucial for developing sustainable water treatment technologies capable of effectively addressing the global challenge of microplastic contamination.

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💧 Understanding Membrane Filtration Through Three-Dimensional Space Dynamics🚀

💧 Membrane filtration is widely used in water purification, biotechnology, and chemical processing to separate particles from liquids. Understanding how particles behave near the membrane surface is essential for improving filtration efficiency. 🔬 A thin space filtration model focuses on the microscopic region close to the membrane, where changes in three-dimensional space volume influence particle movement and separation.

⚙️ This model analyzes how the spatial structure near the membrane affects flow dynamics, particle accumulation, and filtration resistance. 🌐 By studying variations in the available space volume, researchers can better predict how particles interact with the membrane surface. This helps explain common challenges such as membrane fouling, clogging, and reduced filtration performance.

🚀 With improved modeling approaches, engineers can design more efficient filtration systems and optimize membrane materials. 🌍 Applications range from wastewater treatment and desalination to pharmaceutical production and food processing. By understanding the three-dimensional space near membranes, scientists can develop smarter solutions for cleaner water and more sustainable industrial processes.

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💧 Boosting Precoat Filtration Efficiency with Fine Bubble Technology🌍

💧 Precoat filtration is widely used in water treatment, food processing, and chemical industries to remove suspended particles efficiently. In this method, a filter medium is first coated with a thin layer of filter aid, forming the precoat layer that traps contaminants. 🔬 Recent research shows that modifying this precoat layer with fine bubbles can significantly improve particle capture and filtration performance.

⚙️ Fine bubbles create microscopic channels and increase the surface complexity within the precoat layer. 🌫️ These tiny air pockets help redistribute particles and prevent clogging, allowing the filter to hold a larger amount of suspended matter. As a result, the system achieves higher particle loading capacity, improved filtration efficiency, and a more stable filtration process.

🚀 This enhanced filtration technique offers promising benefits for industrial and environmental applications. 🌍 By increasing filtration capacity and reducing frequent cleaning or replacement of filter media, it can lower operational costs and improve sustainability. The integration of fine bubble technology represents an innovative step toward smarter and more efficient filtration systems.

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🔐 Asynchronous Federated Learning for Zero Trust Industrial Control Systems🧠

🏭 Industrial Control Systems (ICS) are the backbone of modern industries, managing critical processes in manufacturing, energy, and infrastructure. As these systems become increasingly connected, cybersecurity risks grow significantly. 🔐 A next-generation security approach combines asynchronous federated learning with a zero trust architecture to protect industrial networks while enabling intelligent data-driven operations.

🧠 Federated learning allows multiple industrial devices or organizations to train AI models collaboratively without sharing raw data. In an asynchronous setup, each node updates the model independently, improving efficiency and scalability. ⚙️ When integrated with a zero trust framework—where every device, user, and request must be continuously verified—it ensures strict access control and minimizes the risk of unauthorized intrusions.

🚀 This innovative combination strengthens the resilience of industrial environments against cyber threats. 🌐 By protecting sensitive operational data while enabling advanced analytics and machine learning, industries can safely adopt smart technologies and automation. Ultimately, asynchronous federated learning with zero trust security paves the way for safer, more reliable Industry 4.0 ecosystems.

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How we used AI to extract citation data from conference posters

Can AI extract citations from conference posters posted on social media? An experimental study by researchers at Digital Science proves that Gemini 2.0 flash can extract citation data from images on X (previously Twitter) with a 92% accuracy, and we were able to link 63% of these otherwise “invisible” citations in the Dimensions database.

Conference posters often contain unpublished or important information. For example, a poster may share early insights from a study or clinical trial, months or years before publication. Access to this information could change clinical and strategic decisions, but because research shared in posters, presentations and other images often lacks structured data, it remains undiscovered by tracking tools, and analyzing it manually for citation data is too time-consuming.

Dr Mike Taylor (Head of Data Insights) and Dr Carlos Areia (Senior Data Scientist), both at Digital Science, conducted an exploratory study to find out if it is possible to extract structured citation data from poster images using LLMs. To obtain meaningful insights from this data, the study then assessed how easy it is to link these results to Dimensions and Altmetric databases.

Here, we summarize the methodology and results of this study, but you can find the full study here: https://doi.org/10.2196/78148

How did the AI model extract data from conference posters on X?

Over 115,000 posts from X associated with the 2024 American Society of Clinical Oncology (ASCO) meeting were screened and filtered for high-quality poster images. Due to the high metadata availability of ASCO conferences, a smaller, non-clinical conference was also included to double-check accuracy and citation linkage.

The team then used a prompt-engineered Gemini 2.0 Flash model to classify images, summarize posters, and extract structured citation elements (eg, authors, title, DOI) in JSON.

A hierarchical algorithm matched the JSON data against the Dimensions database. Manual validation was performed on a random 20% sample. You can find the full methodology and read the full prompt for the model in the published paper.

What was the outcome of using AI to extract citation data from images?

This study is a successful proof of concept that LLMs can be used to extract citations from images (like conference posters), in a scalable and accurate way.

In total, within the 115,714 posts and 16,574 images analyzed, 651 met the inclusion criteria and yielded 1117 potential citations. The algorithm was able to link 63% of these citations to 616 unique research outputs (580 journal articles; 36 clinical trial registrations). The manual review of 135 randomly sampled citations found that they were correctly linked 92% of the time.

How do Altmetrics and Dimensions turn citations from conference posters into valuable insights?

Besides proving that LLMs can extract citation data from images, the study showcases how Altmetrics and Dimensions provide meaningful, actionable context to the discovered citations. Dimensions hosts the largest collection of interconnected research data, and Altmetric monitors and reports the online attention of research, including mentions and citations in social media, mainstream media, policy patents and more.

Unlike traditional citation-based metrics, these platforms offer a broader perspective on research influence, making them an essential complement for researchers, institutions, and policymakers. Dimensions enabled the team to connect the discovered citations to publications, clinical trials, patents, and grants.

This enabled an in-depth analysis of the findings. For example, using the International Cancer Research Partnership Cancer Types taxonomy in Dimensions, the researchers were able to quickly identify the main cancer types mentioned in the poster citations. Altmetric is able to provide a real-time gauge of the research. Altmetric shows that several posters were eventually published and achieved a significant level of attention.

This study opens the door for future use of AI on image extraction to collect scholarly mentions and citations in novel sources, as well as other relevant clinical data from conference posters. The success of this experimental study is evidence of Digital Science’s sustained commitment to innovating industry-leading research methods.

Since 2010, Digital Science has helped enterprises and organizations from various sectors accelerate discovery and create bespoke solutions to their research needs. Whether this be through sentiment analysis, impact summaries, congress tracking or other tailored metrics. Now, our user-friendly platforms and tools, including Altmetric and Dimensions, have made these advanced technologies accessible to everyone, empowering researchers, universities, funders, enterprises, and publishers to advance knowledge and fuel breakthroughs.

Find out which Digital Science products solve your research challenges.

The post How we used AI to extract citation data from conference posters appeared first on Digital Science.

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📱 Digital Finance Revolution: Opportunities and Risks for Global Markets⚖️

💳 Financial technology (Fintech) has transformed the way people access and manage financial services. Innovations such as mobile banking, digital wallets, peer-to-peer lending, and blockchain platforms are expanding financial access to individuals who were previously excluded from traditional banking systems. 📱 For many underserved communities, Fintech provides convenient, low-cost solutions that promote financial inclusion and support economic participation.

📊 However, while Fintech offers many benefits, it also raises concerns about financial stability. Rapid technological change, regulatory gaps, and cybersecurity risks can create vulnerabilities in the financial system. ⚠️ For example, digital lending platforms may increase credit access but could also lead to higher default risks if not properly regulated. Understanding these challenges is crucial for balancing innovation with responsible financial oversight.

🚀 A systematic review of Fintech research highlights both opportunities and future research directions. 🌍 Scholars emphasize the need for stronger regulatory frameworks, improved digital literacy, and inclusive technological design. By carefully managing innovation and risk, Fintech can continue to drive economic growth while maintaining stability and expanding financial opportunities for all.

Global Scholar Awards 🌟

🚀 Rapid Semiconductor Mineral Identification Using Catalytic SERS Technology🔬

🔬 Photocatalysis-regulated surface-enhanced Raman scattering (SERS) is an innovative approach for the rapid detection and differentiation of semiconductor minerals. By integrating photocatalytic activity with plasmonic enhancement, this technique significantly amplifies Raman signals, enabling highly sensitive molecular identification. ⚡ The photocatalytic process actively modifies surface states, improving selectivity and reducing background interference.

🧪 Semiconductor minerals often exhibit subtle structural and compositional differences that are difficult to distinguish using conventional methods. With regulated SERS, light-induced catalytic reactions enhance signal clarity and highlight characteristic vibrational fingerprints. 🌟 This allows researchers to rapidly differentiate minerals with high precision, even at trace concentrations.

🚀 The combination of photocatalysis and SERS offers powerful advantages for environmental monitoring, geochemical exploration, and advanced materials research. 🌍 Its fast response time, minimal sample preparation, and high sensitivity make it a promising analytical tool for both laboratory investigations and on-site mineral analysis applications.

Global Scholar Awards

🔬 Investigating Membrane Fouling from Microplastic-Contaminated Water🧪

Global Scholar Awards 🌟

💧 Understanding Membrane Filtration Through Three-Dimensional Space Dynamics🚀

Global Scholar Awards 🌟

💧 Boosting Precoat Filtration Efficiency with Fine Bubble Technology🌍

Global Scholar Awards 🌟

🔐 Asynchronous Federated Learning for Zero Trust Industrial Control Systems🧠

Global Scholar Awards 🌟

How we used AI to extract citation data from conference posters

How did the AI model extract data from conference posters on X?

What was the outcome of using AI to extract citation data from images?

How do Altmetrics and Dimensions turn citations from conference posters into valuable insights?

📱 Digital Finance Revolution: Opportunities and Risks for Global Markets⚖️

Global Scholar Awards 🌟

🚀 Rapid Semiconductor Mineral Identification Using Catalytic SERS Technology🔬

Global Scholar Awards 🌟

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