Using Technology in Scientific Research

Canadian Solar Inc. leads standard for UV induced #degradation Some PV cell technologies, especially those with an inappropriate front-side film stack are prone to UV-induced degradation (#UVID). We have been studying this for a long time and found that UVID test results vary greatly between labs. Sometimes wrong test conditions are used, which produce false degradation that does not occur in actual application environments. As a result, Canadian Solar recently proposed a new #IEC standard for UV induced degradation. This is the sixth IEC standard proposal Canadian Solar has led, and the first five proposals have been successfully completed and published. Experts from 10 countries agreed on the test conditions after 12 meetings, and a ‘committee draft’ has been finished. In the meantime, we conducted round-robin tests in different labs with different cell technologies and manufacturers. The results show that Canadian Solar’s #TOPCon and #HJT, which are designed with appropriate film stack and manufacturing process parameters, demonstrated outstanding UV resistance. Alarmingly back-contact (#BC) solar cells exhibit significantly higher UVID than TOPCon. We think it is because these BC products were released too fast to the marketplace and the film stack has not been well designed. I suppose that given time BC will eventually improve.  Canadian Solar always put quality at first and invests a lot of resources during the product development stage, so that problems can be discovered and solved in time. We will continue posting the progress in standard development and round-robin results. Stay tuned and welcome to join us as we dive deeper into the UVID mechanisms.  #SolarTechnology #IECstandard #SolarResearch

How I Cut My Legal Research Time in Half (Without Lowering Quality) In law school, I used to spend hours researching cases, scrolling through long judgments, and struggling to find the right precedent. Then, I discovered something—technology can do half the work for you. Here’s how I started using tech to improve my legal research efficiency (and how you can too): ➡ I stopped relying only on Google and SCC At first, I used SCC and Google like everyone else. But then I explored AI-powered tools like CaseMine, Manupatra’s AI assist, and LexisNexis search filters. These tools don’t just show cases—they analyze patterns, suggest related cases, and even highlight the most relevant paragraphs. ➡ I used AI tools to summarize long judgments Instead of reading 100+ pages of a judgment, I used AI tools like Judgment Summarizer (Judi.AI), ChatGPT, and Casetext’s CARA to get quick summaries. I still cross-checked the key paragraphs, but this saved me hours of skimming through irrelevant sections. ➡ I automated citations instead of doing them manually I used to format citations manually (which was painfully slow). Then I found tools like Zotero, Refworks LLC, and EndNote, which automatically generate and format case citations in Bluebook, OSCOLA, or any other style. ➡ I learned how to use Boolean search effectively Most students waste time searching with plain keywords. I learned Boolean operators (like AND, OR, NOT, NEAR) to refine my searches. Instead of searching "arbitration clause invalid enforcement India", I used: 📌 “arbitration clause” AND (“invalid” OR “unenforceable”) AND India This pulled up precise, relevant results—faster and with less junk. ➡ I created a personal case law database Instead of searching for the same cases repeatedly, I started saving and tagging judgments using Notion, Microsoft OneNote, or Evernote. Whenever I found an important case, I stored it with key takeaways, so I never had to research it again. ➡ I used contract analysis software for drafting research For contract-related research, I used tools like Kira Systems and Lawgeex. These platforms analyze contracts and highlight risky clauses, giving me a head start before I even begin drafting. ➡ I practiced speed reading with tech tools Reading long judgments was slowing me down. So, I used speed-reading tools like Spritz Reader and Reedy to improve my reading efficiency, helping me absorb legal texts faster. ➡ I set up alerts for legal updates Instead of manually checking for new laws, I set up alerts on LexisNexis, SCC Online, and Google Alerts to notify me whenever new judgments or amendments were published in my areas of interest. The result? Faster research, more accurate results, and more time for actual analysis instead of just searching. If you’re still researching the old-school way, start using technology. Lawyers who use tech don’t just work faster—they work smarter.

500 students share one computer in Niger. Yet they're conducting advanced physics experiments that students at elite schools can't access. The secret? WebAR turning basic smartphones into portable STEM labs. Think about that. In Sub-Saharan Africa, fewer than 10% of schools have internet. Student-to-computer ratios hit 500:1. Yet mobile subscriptions jumped from single digits to 80% in a decade. Students already carry the infrastructure—we just weren't using it right. Traditional EdTech Reality: ↳ VR headsets: $300+ per student ↳ Heavy apps requiring 5G speeds ↳ Labs costing millions to build ↳ Rural schools: permanently excluded The WebAR Revolution: ↳ Runs in any browser, optimized for 3G ↳ No app store, minimal storage ↳ Science scores improving 10-15% ↳ Every smartphone becomes a laboratory But here's what grabbed me: A physics teacher in rural South Africa has one broken oscilloscope. No budget. Her students scan printed markers, and electromagnetic fields pulse across their desks. They run experiments infinitely—no equipment damaged, no reagents consumed. One student told her: "Engineering is for people like me now. The lab fits in my pocket." What changes everything: ↳ Mobile-first matches actual connectivity ↳ Browser-based works offline ↳ Teachers need training, not new buildings ↳ Inequality becomes irrelevant The Multiplication Effect: 1 teacher with markers = 30 students experimenting 10 schools sharing content = communities transformed 100 districts adopting = educational equality emerging At scale = STEM education without infrastructure gaps We spent decades waiting for labs that won't arrive. Now any browser becomes one. Because when a student in rural Africa explores the same 3D molecules as someone at MIT—using the phone already in their pocket—you realize: WebAR isn't shiny technology. It's a quiet equaliser making world-class STEM education fit into 3G connections and $50 phones. Follow me, Dr. Martha Boeckenfeld for innovations where accessibility drives transformation. ♻️ Share if you believe quality education shouldn't require perfect infrastructure.

Tech Stack of an AI Research Agent: The complete architecture that powers intelligent research automation. Building effective AI research agents requires more than just selecting a good LLM. The real challenge is coordinating multiple specialized components that work together smoothly to deliver accurate and thorough research results. Here's the essential tech stack breakdown: 🔹 1. LLM Backbone drives the core intelligence : GPT-4o excels at multimodal tasks and summarization. Claude 3 handles long-context document analysis very well. Mistral or Llama 3 offer open-source flexibility when you need full control over your deployment. 🔹 2. Memory and Context Management prevent information loss : LangChain or LlamaIndex manage context and effectively handle document chunks. Vector databases like Pinecone, Weaviate, or Chroma store embeddings and allow for semantic search across large document collections. 🔹 3. Web Browsing and Retrieval capabilities gather live information : Search APIs such as Serper, Brave Search, and Bing fetch reliable real-time results. Browser automation tools like Selenium or Playwright scrape dynamic content when static APIs fall short. 🔹 4. Tool Abstractions and Agents coordinate complex workflows : AutoGen enables collaboration among multiple agents. CrewAI provides role-based organization for task-specific responsibilities. LangGraph manages stateful workflows between agents. 🔹 5. Task Routing and Planning handle smart decision-making : Function calling via OpenAI or Claude APIs manages tool selection. ReAct or AutoGPT-style planners support iterative search, analysis, and synthesis processes. 🔹 6. Document Understanding extracts structured information : PDF parsers like Unstructured.io handle content extraction. OCR tools like Tesseract process scanned documents and images. 🔹 7. Output Generation creates professional deliverables : Notion API or Google Docs API generate formatted reports. Whimsical API and Mermaid.js create diagrams and visual summaries. The sample flow showcases the complete cycle: query processing, task breakdown, web search, document parsing, vector storage, summarization, source citation, and final output generation. Success comes from choosing components that integrate well, not just relying on individual tool capabilities. #aiagent

If you ever wondered what “buying time” looks like in real life, it looks like this syringe. Most first aid kits are designed to clean, cover, or slow a bleed. But this device is engineered to plug a bullet hole in 15 seconds. You’re looking at the 𝐗𝐒𝐓𝐀𝐓 𝐑𝐚𝐩𝐢𝐝 𝐇𝐞𝐦𝐨𝐬𝐭𝐚𝐬𝐢𝐬 𝐒𝐲𝐬𝐭𝐞𝐦, which is a battlefield invention that quietly rewrote the rules of emergency medicine. Traditional gauze works outside the wound. This works inside it. Instead of packing a trauma wound with layers of pressure dressing, XSTAT uses a syringe filled with hundreds of tiny, compressed 𝐜𝐞𝐥𝐥𝐮𝐥𝐨𝐬𝐞 𝐬𝐩𝐨𝐧𝐠𝐞𝐬. When injected into a deep wound channel, they instantly expand on contact with blood, nearly 𝟏𝟓𝐱 𝐭𝐡𝐞𝐢𝐫 𝐬𝐢𝐳𝐞. It seals the artery from the inside out and buying minutes that often decide life or death. This isn’t designed for calm, well-lit ERs. It's designed for war zones. 𝐍𝐨𝐧-𝐜𝐨𝐦𝐩𝐫𝐞𝐬𝐬𝐢𝐛𝐥𝐞 𝐡𝐞𝐦𝐨𝐫𝐫𝐡𝐚𝐠𝐞 remains the leading cause of preventable battlefield death.  And when you’re dealing with that, there’s no time for delicate techniques.  The solution has to work in chaos- no surgeon, no theatre, no second chance. That’s why I find XSTAT so powerful. It shows us what 𝐫𝐞𝐚𝐥 innovation looks like when the constraint is simple: either it works, or someone dies. Now this same device is being used in civilian trauma centers, accident sites, and emergency response units. So, what began as a military invention has become a blueprint for how medical technology should work in the real world. Fast, decisive, and failure-proof. That philosophy sits at the core of 𝐍𝐆𝐈𝐕𝐃. It constantly aims to build medical technologies that don’t just diagnose problems, but intervene when seconds matter. 𝐈𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧 𝐢𝐬𝐧’𝐭 𝐚 𝐟𝐞𝐚𝐭𝐮𝐫𝐞 𝐟𝐨𝐫 𝐮𝐬; 𝐢𝐭’𝐬 𝐚 𝐫𝐞𝐬𝐩𝐨𝐧𝐬𝐢𝐛𝐢𝐥𝐢𝐭𝐲 𝐰𝐡𝐞𝐫𝐞 𝐥𝐢𝐯𝐞𝐬 𝐚𝐫𝐞 𝐚𝐭 𝐬𝐭𝐚𝐤𝐞. #MedicalInnovation #TraumaCare #HealthcareInnovation #MedTech

🚀 Supercharge your research workflow with AI Agents! 📚 📌 Recently, I stumbled upon a brilliant paper on arXiv that opened my eyes to the power of LLM agents in research. This work "🔬Agent Laboratory: Using LLMs as Research Assistants 🤖 " from the researchers from AMD and John Hopkins University has completely transformed how I tackle complex projects! 👉 Here’s how it’s helping me:- 🤖 Automating the tedious stuff for a research project - AI agents handle literature reviews, summarization, and even drafting, leaving me more time for critical thinking. 💡 Enhancing creativity - By eliminating repetitive tasks, I can focus on connecting the dots and generating new ideas. ⏱️ Boosting efficiency - What used to take weeks can now be done in days—without compromising on quality! 🧪 Automated Research Workflow - The paper introduces a LaboratoryWorkflow that uses AI agents to automate key research tasks like literature review, experimentation, and report writing. 🤖 Specialized AI Agents - The Lab features agents like PhDStudentAgent, PostdocAgent, MLEngineerAgent, SWEngineerAgent, and ProfessorAgent, each tailored to specific research phases. 🔄 Step-by-Step Research Process - The Lab automates phases like:- 📚 Literature Review - Summarizes key papers. 🔬 Experiment Planning - Develops plans and prepares datasets. 🕵♀️ Running Experiments - Conducts and analyzes experiments. 🖥️ Report Writing - Generates and refines reports. 👫 Human-in-the-Loop (HITL) - Allows optional human feedback in critical steps like reviewing literature or refining reports. 🔧 Highly Customizable - Users can set research topics, agent parameters, and model configurations for personalized workflows. 🌐 Powered by OpenAI - Leverages APIs for insights and integrates state-saving functionality to resume tasks. 🚀 Easy-to-Run - The process is command-line friendly and allows seamless initialization, execution, and report generation. This powerful framework has inspired how I use agents in my own research workflows. If you’re exploring ways to make your research more efficient, this is a must-read and a must-try! If you’ve experimented with similar tools or workflows, let’s chat! I’d love to hear how you’re leveraging AI agents in your work. Kudos to Samuel Schmidgall and the team! 🔗 Paper - https://lnkd.in/dkEiFz4j 🌎 Website - https://lnkd.in/duxgWB2u 👩💻 Github - https://lnkd.in/ds2Bi-HW 💭 Sample - https://lnkd.in/dhE3Ei2S #AI #AgentLab #ResearchRevolution #AcademicInnovation #FutureOfWork

Great Use of AI to Support Research Clinical trial recruitment is challenging for researchers - identifying and screening appropriate patients is a time consuming process that causes delays in enrollment and prolongs studies that have the potential to benefit patients. These challenges can also increase costs. Researchers reported in a JAMA Network Research Letter the results of their testing of a tool to accelerate the process. The tool is called "Retrieval Augmented Generation Enabled Clinical Trial Infrastructure for Inclusion Exclusion Review (RECTIFIER). Testing was through a prospective, blind, randomized clinical trial comparing RECTIFIER with manual processes. The primary outcome was time to eligibility determination. The authors found that their AI-assisted tool significantly reduced time for eligibility determination and enrollment. Tools like this have the potential to significantly enhance research operations. While the researchers acknowledge that more testing needs to be done, publishing these results can contribute to trust by end users. https://lnkd.in/exX9DA_h

Innovation has always been our growth engine: it gave us the steam engine, vaccines, the integrated circuit—but what if that engine begins to sputter? Our research shows a sobering truth: across industries, R&D productivity has been declining for decades. In semiconductors, it now takes 18 times the R&D spend to sustain Moore’s Law. In pharma, “Eroom’s Law” (yes, that’s “Moore” spelled backwards) tells us the cost of bringing a new drug to market has skyrocketed. And this pattern shows up in agriculture, automotive and beyond. However, there’s hope. Innovation is getting harder and costlier, but AI may be the spark that reignites it. Not just by making existing processes faster, but by fundamentally reshaping how we innovate. Think: 1. Increasing the velocity, volume, and variety of design candidate generation. 2. Rapidly testing ideas using AI proxy models. 3. Streamlining research operations to get from idea to insight faster. AI can bend the curve of R&D productivity—and with it, unlock new frontiers of economic and human progress. It’s not just about cost savings or margin expression; it’s about fueling the next era of discovery. Take a look at our latest article (link in comments) to learn more. #AIbyMcKinsey #Innovation #Technology

🔬 Bonus! Week 9: AI and Research Support - Why AI is the invisible infrastructure research needs now My eight-week series on rethinking research and innovation concluded last week. But the response was so good, I've added a bonus topic! When people talk about AI and research, they focus on papers: writing them faster, reviewing them better, and reading more of them in less time. But the real revolution may lie elsewhere. AI can transform the support systems that underpin research: the messy, bureaucratic, invisible work that shapes what gets funded, who participates, and how impact is tracked. Here’s what that might look like: 1️⃣ Grant applications without the grind Writing grants is essential, but exhausting and exclusionary. AI can help surface relevant opportunities, auto-populate biosketches, rewrite for clarity, and flag alignment with funder priorities. Not to replace judgment, but to free up time for thinking. 2️⃣ Ethics applications that don’t derail momentum Many researchers fear the ethics process not because they oppose it, but because it’s slow, inconsistent, and opaque. AI could pre-screen for compliance, generate first drafts of common sections, and support researchers to engage more meaningfully with ethical principles. 3️⃣ Training that meets researchers where they are AI tutors and copilots can deliver just-in-time training on data management, open science, reproducibility, and critical AI literacy. Instead of one-size-fits-all workshops, researchers could receive tailored guidance embedded in their tools and workflows. 4️⃣ Reporting that reflects what matters AI can summarise publications, pull impact metrics, track collaborations, and generate first drafts of research reports, allowing researchers to focus on interpreting, not compiling, their achievements. We could even imagine systems that prompt impact reflection, not just KPI completion. 5️⃣ Support for using AI responsibly in research itself From literature reviews to coding assistants, translation tools to citizen science platforms, AI is already part of the research process. Universities must step up: offering discipline-specific training, setting norms for disclosure and co-authorship, and resourcing equitable access. Here are some ideas for what we can do: ✅ Develop AI-enhanced tools for grant and ethics workflows ✅ Train research managers alongside researchers in responsible AI use ✅ Pilot AI research support officers as part of the library or RDO teams ✅ Create cross-disciplinary communities of practice for AI-enhanced research methods ✅ Treat AI not as a cost-saving tool, but as capacity-building infrastructure The best research support has always been human: mentors, advisors, administrators, librarians, peers. AI won’t replace that. But if we build it wisely, it can amplify what matters most and make research more accessible, creative, and impactful. #RethinkingResearch

Our Research Paper Available online 23 August 2023: Renewable Energy(Elsevier) Development of a novel solar PV module model for reliable power prediction under real outdoor conditions Manish Kumar  , Prashant Malik  , Rahul Chandel  , Shyam Singh Chandel  Photovoltaic Research Group, Centre of Excellence in Energy Science and Technology, Shoolini University, Solan, Himachal Pradesh, 173212, India https://lnkd.in/dWA3nUpp Abstract Accurately predicting and validating the power output of commercial solar PV power plants, remains an important research topic despite numerous studies already conducted. The precision and reliability of power prediction depends on the accuracy of the solar cell parameter values used in the model. A novel analytical technique has been developed in this study for PV power prediction, which employs one and two diode models with 3, 5, and 7 parameters. This new model only requires the manufacturer sheet data and has been validated through indoor and outdoor experiments. The performance of an experimental PV system is evaluated using the proposed solar cell models under varying irradiance and temperature levels. Additionally, the predicted output solar power was experimentally validated under real outdoor conditions in India with higher accuracy. The 7-parameter solar cell model is found to be the most accurate with the least RMSE of 0.02, followed by the 5 and 3-parameter models with RMSEs of 0.04 and 0.07, respectively. Compared to previous methods, the present new model predicts PV power with higher accuracy and lower percentage error. Finally, the study also identifies follow-up photovoltaic research areas.