India is using AI and satellites to map urban heat vulnerability in cities like Delhi. They’re identifying which buildings are most at risk from extreme temperatures. This effort seems to aim at providing relief to those affected. It’s all very technical and detailed, but honestly, it feels a bit over the top. Just another day of high-tech solutions for problems that keep piling up. #UrbanHeat #India #ArtificialIntelligence #Satellites #HeatVulnerability

Oh, joy! Just when you thought the world of sunglasses couldn’t get any more exclusive, here comes Meta, strutting in with its latest coup: Prada shades! Because, let’s be honest, when you think of cutting-edge tech, who better to partner with than a fashion house known for turning fabric into fortune? That's right, folks—Ray-Ban, Oakley… and now Prada! I mean, it only makes sense. Who wouldn’t want to experience augmented reality while looking like they just stepped off a runway? Forget practicality; we’re living in a digital age where style trumps substance—especially when your sunglasses cost more than your monthly rent. Meta’s new venture is the perfect embodiment of this ethos: blending high fashion with the latest tech, or as I like to call it, “the art of looking fabulous while you fail to see reality.” The marketing team must have had a field day brainstorming this one. “Let’s take two things people love—fashion and technology—and mash them together like a smoothie that you can’t quite identify!” Brilliant! Imagine strutting down the street, these Prada shades perched on your nose, the world around you filtered through a lens that screams, “I’m too cool for your mundane existence.” And let’s not forget the irony of wearing designer sunglasses to look at a digital world. It’s like putting on a tuxedo to play video games in your basement. Who needs the real world when you can have a virtual one enhanced by a pair of overpriced glasses? It’s a match made in, well, a marketing executive’s dream. But hey, at least they’ve managed to keep the legacy of Ray-Ban and Oakley alive—who needs function when you can turn heads? Sure, they might not shield your eyes from the glaring truth of your bank account after this purchase, but at least you’ll be the best-dressed person in the room… or the one most likely to be judged for frivolous spending. So, to all you fashion-forward tech enthusiasts out there, let’s raise a toast to the new era of eyewear! May your Prada shades serve as a reminder that in this world, it’s not about what you see, but how you look doing it. Cheers to the future, where your inability to see the obvious is only matched by your impeccable taste in sunglasses! #MetaPrada #FashionTech #RayBanOakley #SunglassesSeason #VirtualReality

Reading Time: 9 minutes In marketing, data isn’t a buzzword. It’s the lifeblood of all successful campaigns. But are you truly harnessing its power, or are you drowning in a sea of information? To answer this question, we sat down with Ankur Kothari, a seasoned Martech expert, to dive deep into this crucial topic. This interview, originally conducted for Chapter 6 of “The Customer Engagement Book: Adapt or Die” explores how businesses can translate raw data into actionable insights that drive real results. Ankur shares his wealth of knowledge on identifying valuable customer engagement data, distinguishing between signal and noise, and ultimately, shaping real-time strategies that keep companies ahead of the curve.   Ankur Kothari Q&A Interview 1. What types of customer engagement data are most valuable for making strategic business decisions? Primarily, there are four different buckets of customer engagement data. I would begin with behavioral data, encompassing website interaction, purchase history, and other app usage patterns. Second would be demographic information: age, location, income, and other relevant personal characteristics. Third would be sentiment analysis, where we derive information from social media interaction, customer feedback, or other customer reviews. Fourth would be the customer journey data. We track touchpoints across various channels of the customers to understand the customer journey path and conversion. Combining these four primary sources helps us understand the engagement data. 2. How do you distinguish between data that is actionable versus data that is just noise? First is keeping relevant to your business objectives, making actionable data that directly relates to your specific goals or KPIs, and then taking help from statistical significance. Actionable data shows clear patterns or trends that are statistically valid, whereas other data consists of random fluctuations or outliers, which may not be what you are interested in. You also want to make sure that there is consistency across sources. Actionable insights are typically corroborated by multiple data points or channels, while other data or noise can be more isolated and contradictory. Actionable data suggests clear opportunities for improvement or decision making, whereas noise does not lead to meaningful actions or changes in strategy. By applying these criteria, I can effectively filter out the noise and focus on data that delivers or drives valuable business decisions. 3. How can customer engagement data be used to identify and prioritize new business opportunities? First, it helps us to uncover unmet needs. By analyzing the customer feedback, touch points, support interactions, or usage patterns, we can identify the gaps in our current offerings or areas where customers are experiencing pain points. Second would be identifying emerging needs. Monitoring changes in customer behavior or preferences over time can reveal new market trends or shifts in demand, allowing my company to adapt their products or services accordingly. Third would be segmentation analysis. Detailed customer data analysis enables us to identify unserved or underserved segments or niche markets that may represent untapped opportunities for growth or expansion into newer areas and new geographies. Last is to build competitive differentiation. Engagement data can highlight where our companies outperform competitors, helping us to prioritize opportunities that leverage existing strengths and unique selling propositions. 4. Can you share an example of where data insights directly influenced a critical decision? I will share an example from my previous organization at one of the financial services where we were very data-driven, which made a major impact on our critical decision regarding our credit card offerings. We analyzed the customer engagement data, and we discovered that a large segment of our millennial customers were underutilizing our traditional credit cards but showed high engagement with mobile payment platforms. That insight led us to develop and launch our first digital credit card product with enhanced mobile features and rewards tailored to the millennial spending habits. Since we had access to a lot of transactional data as well, we were able to build a financial product which met that specific segment’s needs. That data-driven decision resulted in a 40% increase in our new credit card applications from this demographic within the first quarter of the launch. Subsequently, our market share improved in that specific segment, which was very crucial. 5. Are there any other examples of ways that you see customer engagement data being able to shape marketing strategy in real time? When it comes to using the engagement data in real-time, we do quite a few things. In the recent past two, three years, we are using that for dynamic content personalization, adjusting the website content, email messaging, or ad creative based on real-time user behavior and preferences. We automate campaign optimization using specific AI-driven tools to continuously analyze performance metrics and automatically reallocate the budget to top-performing channels or ad segments. Then we also build responsive social media engagement platforms like monitoring social media sentiments and trending topics to quickly adapt the messaging and create timely and relevant content. With one-on-one personalization, we do a lot of A/B testing as part of the overall rapid testing and market elements like subject lines, CTAs, and building various successful variants of the campaigns. 6. How are you doing the 1:1 personalization? We have advanced CDP systems, and we are tracking each customer’s behavior in real-time. So the moment they move to different channels, we know what the context is, what the relevance is, and the recent interaction points, so we can cater the right offer. So for example, if you looked at a certain offer on the website and you came from Google, and then the next day you walk into an in-person interaction, our agent will already know that you were looking at that offer. That gives our customer or potential customer more one-to-one personalization instead of just segment-based or bulk interaction kind of experience. We have a huge team of data scientists, data analysts, and AI model creators who help us to analyze big volumes of data and bring the right insights to our marketing and sales team so that they can provide the right experience to our customers. 7. What role does customer engagement data play in influencing cross-functional decisions, such as with product development, sales, and customer service? Primarily with product development — we have different products, not just the financial products or products whichever organizations sell, but also various products like mobile apps or websites they use for transactions. So that kind of product development gets improved. The engagement data helps our sales and marketing teams create more targeted campaigns, optimize channel selection, and refine messaging to resonate with specific customer segments. Customer service also gets helped by anticipating common issues, personalizing support interactions over the phone or email or chat, and proactively addressing potential problems, leading to improved customer satisfaction and retention. So in general, cross-functional application of engagement improves the customer-centric approach throughout the organization. 8. What do you think some of the main challenges marketers face when trying to translate customer engagement data into actionable business insights? I think the huge amount of data we are dealing with. As we are getting more digitally savvy and most of the customers are moving to digital channels, we are getting a lot of data, and that sheer volume of data can be overwhelming, making it very difficult to identify truly meaningful patterns and insights. Because of the huge data overload, we create data silos in this process, so information often exists in separate systems across different departments. We are not able to build a holistic view of customer engagement. Because of data silos and overload of data, data quality issues appear. There is inconsistency, and inaccurate data can lead to incorrect insights or poor decision-making. Quality issues could also be due to the wrong format of the data, or the data is stale and no longer relevant. As we are growing and adding more people to help us understand customer engagement, I’ve also noticed that technical folks, especially data scientists and data analysts, lack skills to properly interpret the data or apply data insights effectively. So there’s a lack of understanding of marketing and sales as domains. It’s a huge effort and can take a lot of investment. Not being able to calculate the ROI of your overall investment is a big challenge that many organizations are facing. 9. Why do you think the analysts don’t have the business acumen to properly do more than analyze the data? If people do not have the right idea of why we are collecting this data, we collect a lot of noise, and that brings in huge volumes of data. If you cannot stop that from step one—not bringing noise into the data system—that cannot be done by just technical folks or people who do not have business knowledge. Business people do not know everything about what data is being collected from which source and what data they need. It’s a gap between business domain knowledge, specifically marketing and sales needs, and technical folks who don’t have a lot of exposure to that side. Similarly, marketing business people do not have much exposure to the technical side — what’s possible to do with data, how much effort it takes, what’s relevant versus not relevant, and how to prioritize which data sources will be most important. 10. Do you have any suggestions for how this can be overcome, or have you seen it in action where it has been solved before? First, cross-functional training: training different roles to help them understand why we’re doing this and what the business goals are, giving technical people exposure to what marketing and sales teams do. And giving business folks exposure to the technology side through training on different tools, strategies, and the roadmap of data integrations. The second is helping teams work more collaboratively. So it’s not like the technology team works in a silo and comes back when their work is done, and then marketing and sales teams act upon it. Now we’re making it more like one team. You work together so that you can complement each other, and we have a better strategy from day one. 11. How do you address skepticism or resistance from stakeholders when presenting data-driven recommendations? We present clear business cases where we demonstrate how data-driven recommendations can directly align with business objectives and potential ROI. We build compelling visualizations, easy-to-understand charts and graphs that clearly illustrate the insights and the implications for business goals. We also do a lot of POCs and pilot projects with small-scale implementations to showcase tangible results and build confidence in the data-driven approach throughout the organization. 12. What technologies or tools have you found most effective for gathering and analyzing customer engagement data? I’ve found that Customer Data Platforms help us unify customer data from various sources, providing a comprehensive view of customer interactions across touch points. Having advanced analytics platforms — tools with AI and machine learning capabilities that can process large volumes of data and uncover complex patterns and insights — is a great value to us. We always use, or many organizations use, marketing automation systems to improve marketing team productivity, helping us track and analyze customer interactions across multiple channels. Another thing is social media listening tools, wherever your brand is mentioned or you want to measure customer sentiment over social media, or track the engagement of your campaigns across social media platforms. Last is web analytical tools, which provide detailed insights into your website visitors’ behaviors and engagement metrics, for browser apps, small browser apps, various devices, and mobile apps. 13. How do you ensure data quality and consistency across multiple channels to make these informed decisions? We established clear guidelines for data collection, storage, and usage across all channels to maintain consistency. Then we use data integration platforms — tools that consolidate data from various sources into a single unified view, reducing discrepancies and inconsistencies. While we collect data from different sources, we clean the data so it becomes cleaner with every stage of processing. We also conduct regular data audits — performing periodic checks to identify and rectify data quality issues, ensuring accuracy and reliability of information. We also deploy standardized data formats. On top of that, we have various automated data cleansing tools, specific software to detect and correct data errors, redundancies, duplicates, and inconsistencies in data sets automatically. 14. How do you see the role of customer engagement data evolving in shaping business strategies over the next five years? The first thing that’s been the biggest trend from the past two years is AI-driven decision making, which I think will become more prevalent, with advanced algorithms processing vast amounts of engagement data in real-time to inform strategic choices. Somewhat related to this is predictive analytics, which will play an even larger role, enabling businesses to anticipate customer needs and market trends with more accuracy and better predictive capabilities. We also touched upon hyper-personalization. We are all trying to strive toward more hyper-personalization at scale, which is more one-on-one personalization, as we are increasingly capturing more engagement data and have bigger systems and infrastructure to support processing those large volumes of data so we can achieve those hyper-personalization use cases. As the world is collecting more data, privacy concerns and regulations come into play. I believe in the next few years there will be more innovation toward how businesses can collect data ethically and what the usage practices are, leading to more transparent and consent-based engagement data strategies. And lastly, I think about the integration of engagement data, which is always a big challenge. I believe as we’re solving those integration challenges, we are adding more and more complex data sources to the picture. So I think there will need to be more innovation or sophistication brought into data integration strategies, which will help us take a truly customer-centric approach to strategy formulation.   This interview Q&A was hosted with Ankur Kothari, a previous Martech Executive, for Chapter 6 of The Customer Engagement Book: Adapt or Die. Download the PDF or request a physical copy of the book here. The post Ankur Kothari Q&A: Customer Engagement Book Interview appeared first on MoEngage. #ankur #kothari #qampampa #customer #engagement

In-app bidding has automated most waterfall optimization, yet developers still manage multiple hybrid waterfalls, each with dozens of manual instances. Naturally, this can be timely and overwhelming to maintain, keeping you from optimizing to perfection and focusing on other opportunities to boost revenue.Rather than analyzing each individual network and checking if instances are available at each price point, breaking down your waterfall into different CPM ranges allows you to visualize the waterfall and easily identify the gaps.Here are some tips on how to use CPM buckets to better optimize your waterfall’s performance.What are CPM buckets?CPM buckets show you exactly how much revenue and how many impressions you’re getting from each CPM price range, giving you a more granular idea of how different networks are competing in the waterfall. CPM buckets are a feature of real time pivot reports, available on ironSource LevelPlay.Identifying and closing the gapsTypically in a waterfall, you can only see each ad network’s average CPM. But this keeps you from seeing ad network distribution across all price points and understanding exactly where ad networks are bidding. Bottom line - you don’t know where in the waterfall you should add a new instance.By separating CPM into buckets,you understand exactly which networks are driving impressions and revenue and which CPMs aren’t being filledNow how do you do it? As a LevelPlay client, simply use ironSource’s real time pivot reports - choose the CPM bucket filter option and sort by “average bid price.” From here, you’ll see how your revenue spreads out among CPM ranges and you’ll start to notice gaps in your bar graph. Every gap in revenue - where revenue is much lower than the neighboring CPM group - indicates an opportunity to optimize your monetization strategy. The buckets can range from small increments like to larger increments like so it’s important to compare CPM buckets of the same incremental value.Pro tip: To best set up your waterfall, create one tab with the general waterfalland make sure to look at Revenue and eCPM in the “measures” dropdown. In the “show” section, choose CPM buckets and sort by average bid price. From here, you can mark down any gaps.But where do these gaps come from? Gaps in revenue are often due to friction in the waterfall, like not enough instances, instances that aren’t working, or a waterfall setup mistake. But gaps can also be adjusted and fixed.Once you’ve found a gap, you can look at the CPM buckets around it to better understand the context. Let’s say you see a strong instance generating significant revenue in the CPM bucket right below it, in the -80 group. This instance from this specific ad network has a lot of potential, so it’s worth trying to push it to a higher CPM bucket.In fact, when you look at higher CPM buckets, you don’t see this ad network anywhere else in the waterfall - what a missed opportunity! Try adding another instance of this network higher up in the waterfall. If you’re profiting well with a -80 CPM, imagine how much more revenue you could bring at a CPM.Pro tip: Focusing on higher areas in the waterfall makes a larger financial impact, leading to bigger increases in ARPDAU.Let’s say you decide to add 5 instances of that network to higher CPM buckets. You can use LevelPlay’s quick A/B test to understand if this adjustment boosts your revenue - not just for this gap, but for any and all that you find. Simply compare your existing waterfall against the new waterfall with these 5 higher instances - then implement the one that drives the highest instances.Božo Janković, Head of Ad Monetization at GameBiz Consulting, uses CPM buckets "to understand at which CPMs the bidding networks are filling. From there, I can pinpoint exactly where in the waterfall to add more traditional instances - which creates more competition, especially for the bidding networks, and creates an opportunity for revenue growth."Finding new insightsYou can dig even deeper into your data by filtering by ad source. Before CPM buckets, you were limited to seeing an average eCPM for each bidding network. Maybe you knew that one ad source had an average CPM of but the distribution of impression across the waterfall was a black box. Now, we know exactly which CPMs the bidders are filling. “I find ironSource CPM buckets feature very insightful and and use it daily. It’s an easy way to identify opportunities to optimize the waterfall and earn even more revenue." -Božo Janković, Head of Ad Monetization at GameBiz ConsultingUnderstanding your CPM distribution empowers you to not only identify your revenue sources, but also to promote revenue growth. Armed with the knowledge of which buckets some of their stronger bidding networking are performing in, some publishers actively add instances from traditional networks above those ranges. This creates better competition and also helps drive up the bids from the biddersThere’s no need for deep analysis - once you see the gaps, you can quickly understand who’s performing in the lower and higher buckets, and see exactly what’s missing. This way, you won’t miss out on any lost revenue.Learn more about CPM buckets, available exclusively to ironSource LevelPlay here. #how #optimize #your #hybrid #waterfall

Monitoring and Support EngineerKeyword StudiosPasig City Metro Manila Philippines2 hours agoApplyWe are seeking an experienced Monitoring and Support Engineer to support the technology initiatives of the IT Infrastructure team at Keywords. The Monitoring and Support Engineer will be responsible for follow-the-sun monitoring of IT infrastructure, prompt reaction on all infrastructure incident, primary resolution of infrastructure incidents and support requests.ResponsibilitiesFull scope of tasks including but not limited to:Ensure that all incidents are handled within SLAs.Initial troubleshooting of Infrastructure incidents.Ensure maximum network & service availability through proactive monitoring.Ensure all the incident and alert tickets contain detailed technical information.Initial troubleshooting of Infrastructure incidents, restoration of services and escalation to level 3 experts if necessary.Participate in Problem management processes.Ensure that all incidents and critical alerts are documented and escalated if necessary.Ensure effective communication to customers about incidents and outages.Identify opportunities for process improvement and efficiency enhancements.Participate in documentation creation to reduce BAU support activities by ensuring that the Service Desks have adequate knowledge articles to close support tickets as level 1.Participate in reporting on monitored data and incidents on company infrastructure.Implement best practices and lessons learned from initiatives and projects to optimize future outcomes.RequirementsBachelor's degree in a relevant technical field or equivalent experience.Understanding of IT Infrastructure technologies, standards and trends.Technical background with 3+ years’ experience in IT operations role delivering IT infrastructure support, monitoring and incident management.Technical knowledge of the Microsoft Stack, Windows networking, Active Directory, ExchangeTechnical knowledge of Network, Storage and Server equipment, virtualization and production setupsExceptional communication and presentation skills, with the ability to articulate technical concepts to non-technical audiences.Strong analytical and problem-solving skills.Strong customer service orientation.BenefitsGreat Place to Work certified for 4 consecutive yearsFlexible work arrangementGlobal exposure Create Your Profile — Game companies can contact you with their relevant job openings. Apply #monitoring #support #engineer #keyword #studios

Enterprise artificial intelligence investment is unprecedented, with IDC projecting global spending on AI and GenAI to double to billion by 2028. Yet beneath the impressive budget allocations and boardroom enthusiasm lies a troubling reality: most organisations struggle to translate their AI ambitions into operational success.The sobering statistics behind AI’s promiseModelOp’s 2025 AI Governance Benchmark Report, based on input from 100 senior AI and data leaders at Fortune 500 enterprises, reveals a disconnect between aspiration and execution.While more than 80% of enterprises have 51 or more generative AI projects in proposal phases, only 18% have successfully deployed more than 20 models into production.The execution gap represents one of the most significant challenges facing enterprise AI today. Most generative AI projects still require 6 to 18 months to go live – if they reach production at all.The result is delayed returns on investment, frustrated stakeholders, and diminished confidence in AI initiatives in the enterprise.The cause: Structural, not technical barriersThe biggest obstacles preventing AI scalability aren’t technical limitations – they’re structural inefficiencies plaguing enterprise operations. The ModelOp benchmark report identifies several problems that create what experts call a “time-to-market quagmire.”Fragmented systems plague implementation. 58% of organisations cite fragmented systems as the top obstacle to adopting governance platforms. Fragmentation creates silos where different departments use incompatible tools and processes, making it nearly impossible to maintain consistent oversight in AI initiatives.Manual processes dominate despite digital transformation. 55% of enterprises still rely on manual processes – including spreadsheets and email – to manage AI use case intake. The reliance on antiquated methods creates bottlenecks, increases the likelihood of errors, and makes it difficult to scale AI operations.Lack of standardisation hampers progress. Only 23% of organisations implement standardised intake, development, and model management processes. Without these elements, each AI project becomes a unique challenge requiring custom solutions and extensive coordination by multiple teams.Enterprise-level oversight remains rare Just 14% of companies perform AI assurance at the enterprise level, increasing the risk of duplicated efforts and inconsistent oversight. The lack of centralised governance means organisations often discover they’re solving the same problems multiple times in different departments.The governance revolution: From obstacle to acceleratorA change is taking place in how enterprises view AI governance. Rather than seeing it as a compliance burden that slows innovation, forward-thinking organisations recognise governance as an important enabler of scale and speed.Leadership alignment signals strategic shift. The ModelOp benchmark data reveals a change in organisational structure: 46% of companies now assign accountability for AI governance to a Chief Innovation Officer – more than four times the number who place accountability under Legal or Compliance. This strategic repositioning reflects a new understanding that governance isn’t solely about risk management, but can enable innovation.Investment follows strategic priority. A financial commitment to AI governance underscores its importance. According to the report, 36% of enterprises have budgeted at least million annually for AI governance software, while 54% have allocated resources specifically for AI Portfolio Intelligence to track value and ROI.What high-performing organisations do differentlyThe enterprises that successfully bridge the ‘execution gap’ share several characteristics in their approach to AI implementation:Standardised processes from day one. Leading organisations implement standardised intake, development, and model review processes in AI initiatives. Consistency eliminates the need to reinvent workflows for each project and ensures that all stakeholders understand their responsibilities.Centralised documentation and inventory. Rather than allowing AI assets to proliferate in disconnected systems, successful enterprises maintain centralised inventories that provide visibility into every model’s status, performance, and compliance posture.Automated governance checkpoints. High-performing organisations embed automated governance checkpoints throughout the AI lifecycle, helping ensure compliance requirements and risk assessments are addressed systematically rather than as afterthoughts.End-to-end traceability. Leading enterprises maintain complete traceability of their AI models, including data sources, training methods, validation results, and performance metrics.Measurable impact of structured governanceThe benefits of implementing comprehensive AI governance extend beyond compliance. Organisations that adopt lifecycle automation platforms reportedly see dramatic improvements in operational efficiency and business outcomes.A financial services firm profiled in the ModelOp report experienced a halving of time to production and an 80% reduction in issue resolution time after implementing automated governance processes. Such improvements translate directly into faster time-to-value and increased confidence among business stakeholders.Enterprises with robust governance frameworks report the ability to many times more models simultaneously while maintaining oversight and control. This scalability lets organisations pursue AI initiatives in multiple business units without overwhelming their operational capabilities.The path forward: From stuck to scaledThe message from industry leaders that the gap between AI ambition and execution is solvable, but it requires a shift in approach. Rather than treating governance as a necessary evil, enterprises should realise it enables AI innovation at scale.Immediate action items for AI leadersOrganisations looking to escape the ‘time-to-market quagmire’ should prioritise the following:Audit current state: Conduct an assessment of existing AI initiatives, identifying fragmented processes and manual bottlenecksStandardise workflows: Implement consistent processes for AI use case intake, development, and deployment in all business unitsInvest in integration: Deploy platforms to unify disparate tools and systems under a single governance frameworkEstablish enterprise oversight: Create centralised visibility into all AI initiatives with real-time monitoring and reporting abilitiesThe competitive advantage of getting it rightOrganisations that can solve the execution challenge will be able to bring AI solutions to market faster, scale more efficiently, and maintain the trust of stakeholders and regulators.Enterprises that continue with fragmented processes and manual workflows will find themselves disadvantaged compared to their more organised competitors. Operational excellence isn’t about efficiency but survival.The data shows enterprise AI investment will continue to grow. Therefore, the question isn’t whether organisations will invest in AI, but whether they’ll develop the operational abilities necessary to realise return on investment. The opportunity to lead in the AI-driven economy has never been greater for those willing to embrace governance as an enabler not an obstacle. #execution #gap #why #projects #dont

Researchers unexpectedly discovered toxic airborne pollutants in Oklahoma. The image above depicts a field in Oklahoma. Credit: Shutterstock University of Colorado Boulder researchers made the first-ever airborne detection of Medium Chain Chlorinated Paraffinsin the Western Hemisphere. Sometimes, scientific research feels a lot like solving a mystery. Scientists head into the field with a clear goal and a solid hypothesis, but then the data reveals something surprising. That’s when the real detective work begins. This is exactly what happened to a team from the University of Colorado Boulder during a recent field study in rural Oklahoma. They were using a state-of-the-art instrument to track how tiny particles form and grow in the air. But instead of just collecting expected data, they uncovered something completely new: the first-ever airborne detection of Medium Chain Chlorinated Paraffins, a kind of toxic organic pollutant, in the Western Hemisphere. The teams findings were published in ACS Environmental Au. “It’s very exciting as a scientist to find something unexpected like this that we weren’t looking for,” said Daniel Katz, CU Boulder chemistry PhD student and lead author of the study. “We’re starting to learn more about this toxic, organic pollutant that we know is out there, and which we need to understand better.” MCCPs are currently under consideration for regulation by the Stockholm Convention, a global treaty to protect human health from long-standing and widespread chemicals. While the toxic pollutants have been measured in Antarctica and Asia, researchers haven’t been sure how to document them in the Western Hemisphere’s atmosphere until now. From Wastewater to Farmlands MCCPs are used in fluids for metal working and in the construction of PVC and textiles. They are often found in wastewater and as a result, can end up in biosolid fertilizer, also called sewage sludge, which is created when liquid is removed from wastewater in a treatment plant. In Oklahoma, researchers suspect the MCCPs they identified came from biosolid fertilizer in the fields near where they set up their instrument. “When sewage sludges are spread across the fields, those toxic compounds could be released into the air,” Katz said. “We can’t show directly that that’s happening, but we think it’s a reasonable way that they could be winding up in the air. Sewage sludge fertilizers have been shown to release similar compounds.” MCCPs little cousins, Short Chain Chlorinated Paraffins, are currently regulated by the Stockholm Convention, and since 2009, by the EPA here in the United States. Regulation came after studies found the toxic pollutants, which travel far and last a long time in the atmosphere, were harmful to human health. But researchers hypothesize that the regulation of SCCPs may have increased MCCPs in the environment. “We always have these unintended consequences of regulation, where you regulate something, and then there’s still a need for the products that those were in,” said Ellie Browne, CU Boulder chemistry professor, CIRES Fellow, and co-author of the study. “So they get replaced by something.” Measurement of aerosols led to a new and surprising discovery Using a nitrate chemical ionization mass spectrometer, which allows scientists to identify chemical compounds in the air, the team measured air at the agricultural site 24 hours a day for one month. As Katz cataloged the data, he documented the different isotopic patterns in the compounds. The compounds measured by the team had distinct patterns, and he noticed new patterns that he immediately identified as different from the known chemical compounds. With some additional research, he identified them as chlorinated paraffins found in MCCPs. Katz says the makeup of MCCPs are similar to PFAS, long-lasting toxic chemicals that break down slowly over time. Known as “forever chemicals,” their presence in soils recently led the Oklahoma Senate to ban biosolid fertilizer. Now that researchers know how to measure MCCPs, the next step might be to measure the pollutants at different times throughout the year to understand how levels change each season. Many unknowns surrounding MCCPs remain, and there’s much more to learn about their environmental impacts. “We identified them, but we still don’t know exactly what they do when they are in the atmosphere, and they need to be investigated further,” Katz said. “I think it’s important that we continue to have governmental agencies that are capable of evaluating the science and regulating these chemicals as necessary for public health and safety.” Reference: “Real-Time Measurements of Gas-Phase Medium-Chain Chlorinated Paraffins Reveal Daily Changes in Gas-Particle Partitioning Controlled by Ambient Temperature” by Daniel John Katz, Bri Dobson, Mitchell Alton, Harald Stark, Douglas R. Worsnop, Manjula R. Canagaratna and Eleanor C. Browne, 5 June 2025, ACS Environmental Au. DOI: 10.1021/acsenvironau.5c00038 Never miss a breakthrough: Join the SciTechDaily newsletter. #scientists #detect #unusual #airborne #toxin

While IO Interactive may be heavily focused on its inaugural James Bond game, 2026’s 007 First Light, it’s still providing ambitious new levels and updates for Hitman: World of Assassination and its new science fiction action game MindsEye. To continue to build hype for First Light and IOI’s growing partnership with the James Bond brand, the latest World of Assassination level is a Bond crossover, as Hitman protagonist Agent 47 targets Le Chiffre, the main villain of the 2006 movie Casino Royale. Available through July 6, 2025, the Le Chiffre event in World of Assassination features actor Mads Mikkelsen reprising his fan-favorite Bond villain role, not only providing his likeness but voicing the character as he confronts the contract killer in France. Den of Geek attended the first-ever in-person IO Interactive Showcase, a partner event with Summer Game Fest held at The Roosevelt Hotel in Hollywood. Mikkelsen and the developers shared insight on the surprise new World of Assassination level, with the level itself playable in its entirety to attendees on the Nintendo Switch 2 and PlayStation Portal. The developers also included an extended gameplay preview for MindsEye, ahead of its June 10 launch, while sharing some details about the techno-thriller. Matching his background from Casino Royale, Le Chiffre is a terrorist financier who manipulates the stock market by any means necessary to benefit himself and his clients. After an investment deal goes wrong, Le Chiffre tries to recoup a brutal client’s losses through a high-stakes poker game in France, with Agent 47 hired to assassinate the criminal mastermind on behalf of an unidentified backer. The level opens with 47 infiltrating a high society gala linked to the poker game, with the contract killer entering under his oft-used assumed name of Tobias Rieper, a facade that Le Chiffre immediately sees through. At the IO Interactive Showcase panel, Mikkelsen observed that the character of Le Chiffre is always one that he enjoyed and held a special place for him and his career. Reprising his villainous role also gave Mikkelsen the chance to reunite with longtime Agent 47 voice actor David Bateson since their ‘90s short film Tom Merritt, though both actors recorded their respective lines separately. Mikkelsen enjoyed that Le Chiffre’s appearance in World of Assassination gave him a more physical role than he had in Casino Royale, rather than largely placing him at a poker table. Of course, like most Hitman levels, there are multiple different ways that players can accomplish their main objective of killing Le Chiffre and escaping the premises. The game certainly gives players multiple avenues to confront the evil financier over a game of poker before closing in for the kill, but it’s by no means the only way to successfully assassinate him. We won’t give away how we ultimately pulled off the assassination, but rest assured that it took multiple tries, careful plotting, and with all the usual trial-and-error that comes from playing one of Hitman’s more difficult and immersively involved levels. Moving away from its more grounded action titles, IO Interactive also provided a deeper look at its new sci-fi game MindsEye, developed by Build a Rocket Boy. Set in the fictional Redrock City, the extended gameplay sneak peek at the showcase featured protagonist Adam Diaz fighting shadowy enemies in the futuristic city’s largely abandoned streets. While there were no hands-on demos at the showcase itself, the preview demonstrated Diaz using his abilities and equipment, including an accompanying drone, to navigate the city from a third-person perspective and use an array of weapons to dispatch those trying to hunt him down. MindsEye marks the first game published through IOI Partners, an initiative that has IOI publish games from smaller, external developers. The game did not have a hands-on demo at the showcase and, given its bug-heavy and poorly-received launch, this distinction is not particularly surprising. Build a Robot Boy has since pledged to support the game through June to fix its technical issues but, given the game’s hands-on access at the IOI Showcase, there were already red flags surrounding the game’s performance. With that in mind, most of the buzz at the showcase was unsurprisingly centered around 007 First Light and updates to Hitman: World of Assassination, and IO Interactive did not disappoint in that regard. Even with Hitman: World of Assassination over four years old now, the game continues to receive impressive post-release support from IO Interactive, both in bringing the title to the Nintendo Switch 2 and with additional DLC. At the showcase, IOI hinted at additional special levels for World of Assassintation with high-profile guest targets like Le Chiffre, without identifying who or if they’re also explicitly tied to the James Bond franchise. But with 007 First Light slated for its eagerly anticipated launch next year, it’s a safe bet that IOI has further plans to hype its own role in building out the James Bond legacy for the foreseeable future. The Hitman: World of Assassination special Le Chiffre level is available now through July 6, 2025 on all the game’s major platforms, including the Nintendo Switch 2. MindsEye is now on sale for PlayStation 5, Xbox Series X|S, and PC. #hitman #interactive #has #big #plans

Investment scams aren't anything new: Bad actors have long used pump-and-dump tactics to hype stocks or cryptocurrencies, preying on emotions like fear and greed. And who wouldn't want big—or even steady—returns on their money, especially amidst tariffs and other economic turmoil? Scammers are currently capitalizing on this with fraudulent Facebook ads to lure users into handing over large sums of money. Here's how to spot these schemes and avoid falling victim. Investment scams on Meta platformsAccording to a group of 42 state attorneys general, the current fraudulent investment campaigns also happen to have elements of impersonation scams. The scheme begins with ads on Facebook that feature prominent investors, including ARK Investment Management's Cathie Wood, CNBC's Joe Kernan, and Fundstrat's Tom Lee, along with other wealthy individuals like Warren Buffet and Elon Musk. If you click the ad, you'll be prompted to download or open WhatsApp to join an investment group. This is where the pump-and-dump kicks off. "Experts" in the group advise members to purchase specific stocks, inflating the price, which they in turn sell and profit from. The AG letter to Meta detailing the scam includes reports of individuals losing anywhere from to or more after clicking on a fraudulent ad on Facebook. Other investment scams originating on Facebook involve cyber criminals harvesting sensitive personal information via fraudulent investing platforms. Investment scam red flags to watch forFor many people, it seems obvious that you shouldn't get your investment advice from a Facebook ad or WhatsApp group. But fear and greed are powerful emotions, and scammers are counting on these social engineering tactics working at least some of the time. That's why you should be wary of any advice that promises an unrealistic rate of return in a short period of time with no risk of loss as well as endorsements from celebrities, political figures, and well-known investors. It's also just good practice not to click ads on Facebook, which are easy vectors for spreading scams and malware. Another sign of a scam is content or communication that appears to be generated by AI. After joining a WhatsApp group, an investigator from the New York Office of the Attorney General was called by a scammer who used AI to translate her speech into English. Unfortunately, emotions can cloud our ability to identify AI-generated content if we want to believe what we're seeing. #those #investment #ads #facebook #are