Employment Screening

Essentials of Oil & Gas Equipment Inspection Ensuring the integrity of oil and gas equipment is crucial for safety and operational efficiency. The attached file, "Oil & Gas Equipment Inspection," delves into inspection methodologies designed to maintain the health and functionality of key assets in this industry. Here are some highlights: Key Inspection Techniques: 1. Tank Calibration: Utilizing 3D Laser Scanning for precise measurement. 2. Floor and Shell Plates: Employing Magnetic Flux Leakage (MFL), Ultrasonic Testing (UT), and Robotic UT for corrosion detection and integrity assessment. 3. Nozzle Welds and Roof Plates: Advanced methods like Phased Array and Acoustic Emission Testing to detect anomalies. 4. Pipeline and Pressure Vessel Integrity: Techniques including Smart Pigging, Thermal Imaging, and Pulsed Eddy Current for thorough internal and external evaluations. Innovative Technologies: - Drones for Internal Roof Inspection: Offering access to difficult-to-reach areas without compromising safety. - Acoustic Emission: Identifying stress waves in materials under mechanical load, crucial for early detection of defects. - Smart Pigging: In-line inspection using UT and MFL to detect corrosion, metal loss, and structural anomalies efficiently. Emphasizing Digital Advancements: With the integration of digital tools like 3D Laser Scanning and Automated Corrosion Mapping, inspections are now more precise and less time-consuming, providing detailed digital reports that enhance decision-making processes. The Human Element: While technology drives efficiency, the expertise and experience of inspection professionals remain indispensable. Proper training and adherence to inspection protocols ensure that these advanced tools are used to their full potential, maintaining the highest standards of safety and performance in the oil and gas sector. #OilAndGas #EquipmentInspection #NDT #CorrosionDetection #AssetIntegrity #SafetyFirst #EngineeringExcellence #IndustrialMaintenance #DigitalInspection #SmartPigging

🔍Mastering NDT Acceptance Criteria Across Industries – A Must-Know for QA/QC Professionals🔍 Whether you're inspecting a pressure vessel, piping system, valve, or cross-country pipeline, understanding the right Non-Destructive Testing (NDT) acceptance criteria is critical for ensuring quality, compliance, and—most importantly—safety. As QA/QC engineers and inspectors, we often face multiple code requirements across ASME, API, and other international standards. Here's a quick yet detailed rundown to keep you aligned: ✅ Pressure Vessels – Governed by ASME Section VIII, these demand rigorous NDT across multiple methods: 🔹Radiographic Testing (RT): Mandatory Appendix 8-4, Clause 4-3 for weld integrity. 🔹Ultrasonic Testing (UT): Appendix 12-3 for thickness and internal flaws. 🔹Penetrant Testing (PT): Surface-breaking cracks controlled under Appendix 8-4. 🔹Magnetic Particle Testing (MT): For ferromagnetic material flaws, Appendix 6-4 applies 🔹Visual Testing (VT): Welds and fabrication checked under UW-35 🔹Leak Testing (LT): Pressure boundary integrity verified as per ASME Sec. V, Article 10 🔹Magnetic Flux Leakage (MFL): Used as a screening tool, criteria per Appendix 6-4 ✅ Piping Systems (Process) – Under ASME B31.3, acceptance criteria vary by service category: 🔹RT & VT: Refer to Table 341.3.2 for defect type, size, and location 🔹UT: Para 344.6.2 defines how flaws are assessed in place of RT 🔹PT/MT: Para 344.4.2 outlines flaw size limits; linear and clustered indications are critical 🔹LT: Hydrostatic and pneumatic testing as per Para 345.2.2(a) ensures leak-tightness ✅ Valves (Flanged, Threaded & Welding End) – ASME B16.34 focuses on mechanical integrity: 🔹RT: Appendix I details internal flaw acceptance in cast/welded components 🔹UT: Appendix IV governs ultrasonic acceptance levels 🔹PT/MT: Cracks or irregularities are unacceptable per Appendices II & III 🔹LT & VT: Often supplemented by API 598, though B16.34 doesn’t explicitly define criteria ✅ Pipelines – API 1104 is the go-to standard for cross-country and field welds: 🔹RT (Clause 9.3) and UT (Clause 9.6): Acceptance based on flaw size and location 🔹MT/PT: Surface and subsurface flaws assessed under Clauses 9.4 and 9.5 🔹VT (Clause 9.7): Reinforcement, undercut, and surface conditions closely monitored 🔹LT: Usually dictated by project specs or referenced ASME B31.8 🔗 Always cross-check project-specific requirements, code editions, and client standards to stay compliant and confident ✨ Found this valuable? 🔔 Follow me Krishna Nand Ojha and my quality guru & mentor Govind Tiwari,PhD for more insights on Quality Management, Continuous Improvement, and Strategic Leadership in the world of QMS. Let’s grow and lead the quality revolution together! 🌟 #NDT #QAQC #PressureVessel #Piping #WeldingInspection #API1104 #ASME #QualityControl #OilAndGas #Inspection #Engineering #VisualTesting #UltrasonicTesting #Radiography #ProjectQuality #PipelineInspection #MechanicalEngineering

1,387 applications. 1 tech role. 3 days. That’s how many people applied to a single position we posted after 4:00 PM on a Friday. It’s a snapshot of today’s tech job market. I’ve just spent 3 hours reviewing applications… manually. No knockout questions. No AI filters. Just me, Workday, and a lot of caffeine. I wanted to share how I worked through it to hopefully give jobseekers a helpful peek behind the scenes: 🔹 𝐒𝐭𝐞𝐩 𝟏: Workday limits viewing after 500+ applicants unless filters are applied. So I broke them up by application date. 🔹 𝐒𝐭𝐞𝐩 𝟐: We can’t offer sponsorship. I filtered out applicants requiring it which accounted for 238 people, or about 17% of the total. 🔹 𝐒𝐭𝐞𝐩 𝟑: Boolean search using core technologies from the job description. These were standard for the role, and this brought the pool down to 322 resumes or about 23%. 🔹 𝐒𝐭𝐞𝐩 𝟒: Manual review of all 322 resumes. From there, 61 candidates stood out...roughly 4% of the original pool. 🔹 𝐅𝐢𝐧𝐚𝐥 𝐒𝐭𝐞𝐩: I’ll now use preferred qualifications to choose 10 to 15 candidates for screening calls. The rest go into a “short list” for future roles or re-review. I know keyword filtering can feel like a black box, and yes, it can miss great people. But when time and tools are limited, and the role requires specific, foundational skills, it’s a necessary part of the process. If those skills aren’t on your resume, they’re likely to be overlooked. Sharing this not to gatekeep, but to help jobseekers better understand what’s happening on the backend and how to stand out in a high-volume tech market. Anything surprising stand out to you from this process?

I’ve reviewed 500+ applications as a recruiter at Amazon, Microsoft, and TikTok. This is the kind of resume that gets rejected in 3 seconds. I'll break down why such resumes fail to create an impact and how you can avoid such mistakes. Problem 1: Too much, too soon Two degrees, 15+ courses, and 30+ tools listed - all in the top half. Recruiters don’t need a tech stack dump upfront. Instead: ➡️ Start with a skills summary tied to impact-driven achievements. ➡️ Highlight tools you’ve mastered, not dabbled in. Problem 2: Responsibilities ≠ results Worked with IT to maintain PC and network health. Okay... but how did it matter? Reduced downtime? Saved costs? Improved performance by X%? Instead: ➡️ Write impact-focused bullets — e.g., “Reduced network downtime by 35% through system upgrades.” Problem 3: Irrelevant experience Amazon Prime Shopper role at Whole Foods is listed in detail. Unless applying for retail or logistics, this distracts. Instead: ➡️ Group unrelated roles under a single “Other Experience” section. ➡️ Focus on transferable skills like teamwork, deadlines, or inventory handling — but keep it brief. Problem 4: Projects without purpose Projects sound impressive but lack outcomes. E.g., “Built an AI model to detect human emotion.” Questions recruiters ask: What accuracy did it achieve? Was it deployed? How did it solve a problem? Instead: ➡️ Add metrics — e.g., “Improved emotion detection accuracy by 20% and reduced processing time by 15%.” Here’s the hard truth: Most resumes don’t fail because candidates lack skills. They fail because they fail to communicate impact. If you're not receiving calls from recruiters despite applying to 100s of jobs, it could be due to your resume. Repost this if you found value. P.S. Follow me if you are an Indian job seeker in the U.S. I share insights on job search, interview prep, and more.

Tightening your decision filters might make you feel smarter—but it could make your decisions dumber. A recent study followed a startup accelerator through 3,580 project submissions and three redesigns of its selection process. The goal? Reduce false positives (backing flops) and false negatives (missing stars). Despite increasing structure—more weight on track record, more screening layers—errors persisted. In fact, the strictest regime produced more mistakes. Two psychological culprits explain why: 1. Mean reversion: Over-reliance on past success dampens our sensitivity to fresh potential. No glittering CV? No chance. 2. Within-type adverse selection: The tougher the screen, the more motivated average applicants are to mimic the cues of brilliance—and get through. But here's what struck me most: every redesign felt rational, even smart. More rigor. More data. More process. And yet, it missed something deeply human. Real potential—whether in people, ideas, or startups—is often messy, unfinished, and hard to score. And evaluators, like all of us, lean toward what's legible, familiar, or credentialed. So what’s the takeaway? 👉 More filters don’t guarantee better picks. 👉 Relying on proxies (track record, polish, fluency) can backfire. 👉 True innovation sometimes sounds awkward at first—because it’s new. If we want to stop selecting the best presenters of ideas and start backing the best ideas, we need to design selection systems that don’t just reward polish. Because sometimes, the next big thing doesn’t look like a sure bet. It looks like a question mark. https://lnkd.in/dRz8FeNe

Background checks are common occurrences in most hiring processes. Daniel Yanisse, who is the co-founder and CEO of background check provider Checkr, Inc. joined the latest episode of #GetHired with Andrew Seaman to discuss how that process works and what every job seeker should know. Additionally, he offers some tips on navigating #backgroundchecks during the #hiring process regardless of your past: ✅ Proactively Check Your Background: Use available tools to perform a self-background check to identify and address potential issues. 🚫 Dispute Inaccuracies Promptly: If you find discrepancies in your background check, use the dispute process. 🪟 Be Transparent About Your Record: Disclose potential issues early in the hiring process. Share the context, what you've done since and references to demonstrate your growth and reliability. A transcript of the conversation is available at the link below. You can also listen to the episode there or wherever you get your podcasts by clicking here: https://lnkd.in/dFFSVvzp

Adding a short molecular-dynamics (MD) step after docking in virtual drug screening can cut wet-lab costs by > 50 %. Savings that matter especially for startups and small biotechs needing to stretch their runway, yet few are using it. 🔸 <5 ns “shake-out” MD run + MM/PBSA rescoring can more than double confirmed hit-rate by removing docking false-positives (Graves 2008; Brooijmans 2010). 🔸 Wet-lab costs scale almost linearly with compounds tested (~$800/compound). Twice the hit rate means half the compounds and half the spending. 🔸 A few GPU minutes per ligand cost pennies but can save hundreds or thousands in assays. Back-of-the-envelope example (1 M-compound screen) • Docking only → 10 % hit rate (100 / 1 000) ≈ $800 k • Docking + MD → 20 % hit rate (100 / 500) ≈ $400 k Feel free to reach out, if you are planning a screening campaign. Happy to chat. SimAtomic #MolecularDynamicsSimulation #HitIdentification #VirtualDrugScreening #Biotech

Are you noticing that recruitment is taking longer these days? It’s not just the summer season slowing things down. Overwhelmed recruiters face a flood of generic, AI-generated CVs, delaying hiring and making it harder to spot real talent. So, why is AI making recruitment harder?  🔷AI-generated content in applications often lacks a personal touch, making it harder for recruiters to evaluate skills and motivation, especially when combined with mass, untailored applications in an already squeezed labour market. 🔷Without proper editing and the overuse of keywords, AI-generated CVs often come across as clunky and generic, making it a frustrating task for hiring managers to review them. 🔷Increased screening time: More applications mean longer review times, prolonging the recruitment process.   A recent study by ResumeGenius found that AI-generated CVs are a major red flag for recruiters, with 53% identifying them as the top indicator of an unsuitable candidate. What strategies are hiring managers using to cut through the noise? 1. The Big Four accountants, Deloitte, EY, PwC, and KPMG, have warned graduates against using AI in their applications. 2. The Coca-Cola Company clearly distinguishes between must-have and nice-to-have skills in its job ads, incorporating specific challenges to filter out unqualified applicants and assess genuine engagement early in the process. 3. Amazon is strategically leveraging automation through AI-powered ATS to analyze keywords and contextual relevance, ensuring that CVs are evaluated based on substance rather than being saturated with irrelevant buzzwords. 4. Most hiring managers have so much sensory/channel overload that reviewing hundreds/thousands of resumes from the “online job posting" channel gets turned off. Salesforce, Philips, Airbnb, Tesla, and others are concentrating more on headhunting practices and relaunching employee referral programs.  5. Dyson has found its way to ‘feed’ top talent into its recruitment funnel. It organizes campus tours for top engineering and business schools, putting a particular focus on students who are driven, curious, and passionate about creating something new. 6. While many companies hire externally to fill vacant, specialized roles, Infosys is looking within, helping employees grow their careers by upskilling and taking up more advanced roles within the company. 7. Slack replaced many traditional applications with a technical exercise and offered applicants the option to complete assessments on-site rather than online. 8. After Citrix Systems receives a promising application, the recruiter contacts the candidate and guides them through the whole hiring process. This 5-minute intro call can reveal far more about a candidate’s suitability than a generic application. And how does your company break through the noise, avoid the pitfalls of AI-driven hiring mistakes, and secure the best talent?

This paper evaluates a cost-efficient strategy for using LLMs in health systems, addressing the underexplored economic and computational challenges of their utilization at scale. 1️⃣ The paper focuses on query concatenation—a method of grouping multiple clinical tasks and notes into a single input—to optimize performance, scalability, and cost-effectiveness without compromising accuracy. 2️⃣ Over 300,000 experiments were conducted with real-world clinical data, showing that performance deteriorates as the number of simultaneous tasks and text inputs increases. 3️⃣ High-capacity models like GPT-4-turbo-128k and Llama-3–70B showed strong resilience, maintaining accuracy and formatting even under heavy task loads. 4️⃣ Optimal task burden was identified as 50 simultaneous tasks, beyond which accuracy and formatting declined for most models. 5️⃣ The proposed concatenation method for combining multiple queries led to up to 17-fold cost savings at scale compared to traditional single-query methods. 6️⃣ External validation with public datasets confirmed the trends, supporting the utility of high-capacity LLMs in medical settings for cost-effective, large-scale tasks. ✍🏻 Eyal Klang, Donald Apakama, Ethan Abbott, Akhil Vaid, Joshua Lampert, MD, Ankit Sakhuja, Robert Freeman, Alexander Charney, David Reich, Monica Kraft, Girish Nadkarni, Ben Glicksberg. A strategy for cost-effective large language model use at health system-scale. npj Digital Medicine. 2024. DOI: 10.1038/s41746-024-01315-1