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These Photo voltaic Cells Are Manufactured from Moon Mud. They Might Energy Future Lunar Colonies.

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NASA’s plan to ascertain a everlasting human presence on the moon would require making higher use of lunar assets. A brand new method has now proven find out how to make photo voltaic cells out of moon mud.

Later this decade, the US area company’s Artemis III mission plans to return astronauts to the moon for the primary time in additional than half a century. The long-term purpose of the Artemis program is to ascertain a everlasting human presence on our nearest celestial neighbor.

However constructing and supplying such a base means launching big quantities of fabric into orbit at nice value. That’s why NASA and different area businesses fascinated about establishing a presence on the moon are exploring “in-situ useful resource utilization”—that’s, exploiting the assets already there.

Moon mud, or regolith, has been extensively touted as a possible constructing materials, whereas ice within the moon’s shadowy craters may very well be harvested for ingesting water or cut up into oxygen and hydrogen that can be utilized for air in habitats or as rocket gasoline.

Now, researchers on the College of Potsdam, Germany, have discovered a method to flip a simulated model of lunar regolith into glass for photo voltaic cells—the obvious method to energy a moon base. They are saying this might dramatically cut back the quantity of fabric that must be hauled to the moon to arrange a everlasting settlement.

“From extracting water for gasoline to constructing homes with lunar bricks, scientists have been discovering methods to make use of moon mud,” lead researcher Felix Lang mentioned in a press launch. “Now, we will flip it into photo voltaic cells too, probably offering the vitality a future moon metropolis will want.”

To check out their method, the researchers used a synthetic combination of minerals designed to duplicate the soil discovered within the moon’s highlands. Crucially, their method doesn’t require any advanced mining or purification gear. The regolith merely must be melted after which cooled regularly to create sheets of what the researchers seek advice from as “moonglass.”

Of their experiments, reported within the journal System, the researchers used an electrical furnace to warmth the mud to round 2,800 levels fahrenheit. They are saying these sorts of temperatures may very well be achieved on the moon through the use of mirrors or lenses to pay attention daylight.

They then deposited an ultrathin layer of a cloth known as halide perovskite—which has emerged as an affordable and highly effective different to silicon in photo voltaic cells—onto the moonglass. This materials must be carried from Earth, however the researchers estimate that slightly greater than two kilos of it might be sufficient to manufacture 4,300 sq. ft of photo voltaic panels.

The staff examined out a number of solar-cell designs, attaining efficiencies between 9.4 and 12.1 %. That’s considerably lower than the 30 to 40 % that essentially the most superior area photo voltaic cells obtain, the researchers concede. However the decrease effectivity can be greater than offset by the large financial savings in launch prices missions may notice by making the bulkiest components of the photo voltaic cell on web site.

“For those who minimize the burden by 99 %, you don’t want ultra-efficient 30 % photo voltaic cells, you simply make extra of them on the moon,” says Lang.

The moonglass the researchers created additionally has a pure brownish tint that helps defend it towards radiation, a serious concern on the moon’s floor. Additionally they word that halide perovskites tolerate comparatively excessive ranges of impurities and defects, which makes them well-suited to the lower than good fabrication setups more likely to be discovered on the moon.

The moon’s low gravity and wild temperature swings might play havoc with their fabrication course of and the soundness of the ensuing photo voltaic cells, the researchers admit. That’s why they’re hoping to ship a small-scale experiment to the moon to check the concept out in actual circumstances.

Whereas the method might be at too early a stage to affect NASA’s upcoming moon missions, it might show a helpful software as we scale up our presence past Earth orbit.

Heather burning on peatland: UK authorities proposals will introduce ban



Heather burning on peatland: UK authorities proposals will introduce ban
An additional 146,000 hectares of peatland can be protected by the proposals, says the federal government.

The UK authorities launched a plan to ban the burning of vegetation on upland areas of peatland on 31 March, ostensibly in a bid to make sure nature-rich peatland habitats are higher protected, and to enhance air high quality for individuals dwelling in neighbouring communities.

Generally described because the nation’s lungs, peatlands are a significant useful resource which may retailer carbon, scale back flood threat, enhance water high quality, and assist uncommon wildlife such because the golden plover and curlews. They’re additionally a few of our richest habitats for dragonflies with 25 of the UK’s 38 species discovered on upland peatbogs.

Nevertheless, 80% of England’s peatlands are presently degraded. Burning on peatland will increase heather development, which dries out the peatland, inflicting it to truly emit reasonably than retailer carbon.

Burning of vegetation together with heather on this scale causes the discharge of dangerous smoke into the air, impacting air high quality throughout communities. This contains dangerous air pollution for human well being, together with ones strongly related to strokes, heart problems, bronchial asthma and a few lung cancers.

Nature Minister Mary Creagh stated: “Our peatlands are this nation’s Amazon Rainforest – residence to our most treasured wildlife, storing carbon and decreasing flooding threat.

“The UK has 13% of the world’s blanket bathroom. A uncommon world habitat, it’s a treasured a part of our nationwide heritage, and that’s the reason we‘re asserting a session on these measures to make sure deep peat is best protected.

“These modifications will profit communities by bettering air and water high quality, and defend houses and companies from flood harm, which helps financial stability and safety beneath our Plan for Change.”

If carried out, these modifications will enhance the realm presently protected against 222,000 to greater than 368,000 hectares of England’s whole 677,250 hectares of deep peat, that means an space equal to the scale of Larger London, Larger Manchester and West Midlands put collectively will now be higher protected.

The definition of deep peat can be revised, in order that deep peat is counted as something over 30cms reasonably than 40cms. All the space of upland deep peat that’s doubtlessly topic to burning can be protected.

This method is being supported by proof offered by Pure England. Any prescribed burning would have to be performed beneath strict licence, issued the place there’s a clear want, for instance to cut back wildfire threat.

The session will run for eight weeks from 31 March and the general public and land managers are urged to have their say. The Authorities is proposing to refine the prevailing licencing system whereby candidates have to efficiently clarify why various strategies haven’t or wouldn’t work and present how they intend to maneuver the land away from the necessity to burn in future so as to obtain a licence. One of many grounds to use for a licence to burn can be to cut back the danger of wildfire, so we will steadiness environmental safety with sensible land administration.

The supporting proof was additionally printed on 31 March. Pure England printed a complete up to date Proof Assessment on ‘The results of managed burning on upland peatland biodiversity, carbon and water’, together with a Definition of Beneficial Conservation Standing for Blanket bathroom, which units out its view on beneficial conservation standing for Blanket bathroom in England.

The England Peat Map, an in depth, open-access map of England’s peatlands, overlaying extent, depth, and situation, is being developed by Pure England and can be printed later within the spring.

ios – After disabling permission to entry geolocation, native vital notification doesn’t arrive


My software has permission to show vital notifications (distant and native). The applying has logic configured to show vital notifications at a sure interval if the appliance is within the background

                    Log.i(">noti #(i) subsequent (i * self.preEpisodeInterval)s")
                    self.scheduleLocalNoti(
                        title: LanguageHelper.getTranslationByKey(LanguageKey.ALARM) ?? "",
                        physique: information.preEpisodeWarnMessage ?? "",
                        isCritical: true,
                        timeInterval: TimeInterval(i * self.preEpisodeInterval),
                        soundName: self.preAlarmModel?.information?.preEpisodeTone ?? "melodie.wav",
                        userInfo: [MotionTrackingManager.LOCAL_NOTI_PREALARM_USERINFO: ""]
                    )
    personal func scheduleLocalNoti(title: String,
                                   physique: String,
                                   isCritical: Bool = false,
                                   timeInterval: TimeInterval = 0,
                                   soundName: String? = nil,
                                   userInfo: [AnyHashable: Any]? = nil) {
        let content material = UNMutableNotificationContent()
        content material.title = title
        content material.physique = physique
        if isCritical {
            if soundName != nil {
                let notiSound = UNNotificationSoundName(rawValue: soundName!)
                content material.sound = UNNotificationSound.criticalSoundNamed(notiSound, withAudioVolume: 1.0)
            } else {
                content material.sound = UNNotificationSound.defaultCriticalSound(withAudioVolume: 1.0)
            }
        } else {
            if soundName != nil {
                let notiSound = UNNotificationSoundName(rawValue: soundName!)
                content material.sound = UNNotificationSound(named: notiSound)
            } else {
                content material.sound = UNNotificationSound.default
            }
        }
        if userInfo != nil {
            content material.userInfo = userInfo!
        }

        // Create the set off
        let set off = UNTimeIntervalNotificationTrigger(timeInterval: timeInterval + 1, repeats: false)

        let uuidString = UUID().uuidString

        Log.i("Schedule native noti for: (timeInterval + 1) with uuid: (uuidString)")
        // Create the request
        let request = UNNotificationRequest(
            identifier: uuidString,
            content material: content material,
            set off: set off
        )

        // Schedule the request with the system.
        let notificationCenter = UNUserNotificationCenter.present()
        notificationCenter.add(request) { error in
            if error != nil {
                // Deal with any errors.
                Log.e("errr: (String(describing: error?.localizedDescription))")
            }
        }
    }

The applying additionally has logic that requires geolocation monitoring with the Background Modes Location replace mark in Signing & Capabilities. If permission to trace geolocation is denied, the native notification isn’t displayed (with distant ones, it nonetheless works accurately).

What’s additionally fascinating: this ONLY occurs with builds in testFlight/appStore. The discharge construct on the emulator instantly from XCode works accurately and continues to show the notification even when entry to geolocation is denied

How can geolocation have an effect on the show of native vital notifications?

5 widespread assumptions in load testing—and why it’s best to rethink them


Through the years, I’ve had numerous conversations with efficiency engineers, DevOps groups, and CTOs, and I maintain listening to the identical assumptions about load testing. A few of them sound logical on the floor, however in actuality, they typically lead groups down the incorrect path. Listed here are 5 of the most important misconceptions I’ve come throughout—and what it’s best to think about as a substitute.

1️⃣ “We must be testing on manufacturing”

A number of weeks in the past, I had a name with one of many greatest banks on the planet. They had been desperate to run load assessments straight on their manufacturing atmosphere, utilizing real-time information. Their reasoning? It will give them essentially the most correct image of how their methods carry out beneath actual circumstances.

I get it—testing in manufacturing appears like the final word means to make sure reliability. However once I dug deeper, I requested them: “What occurs if as we speak’s take a look at outcomes look nice, however tomorrow a sudden visitors spike causes a crash?” Who takes duty if a poorly configured take a look at impacts actual prospects? Are you ready for the operational dangers, compliance issues, and potential downtime?

Sure, manufacturing testing has its place, nevertheless it’s not a magic bullet. It’s complicated, and with out the proper safeguards, it might do extra hurt than good. A better strategy is to create a staging atmosphere that mirrors manufacturing as intently as doable, making certain significant insights with out pointless danger.

2️⃣ “Load testing is all in regards to the device—extra options imply higher outcomes.”

This is likely one of the greatest misconceptions I hear. Groups assume that in the event that they decide essentially the most feature-packed device, they’ll mechanically discover each efficiency situation. However load testing isn’t simply in regards to the device—it’s about understanding how your customers behave and designing assessments that replicate real-world eventualities.

I’ve seen firms put money into highly effective load testing instruments however fail to combine them correctly into their CI/CD pipeline. Others concentrate on operating huge take a look at hundreds with out first figuring out their software’s weak spots. Right here’s what issues extra than simply options:

  • Do you perceive your customers’ conduct patterns?
  • Have you ever recognized efficiency gaps earlier than operating the take a look at?
  • Are you making load testing a steady a part of your growth course of?

Essentially the most profitable groups don’t simply run assessments; they construct efficiency testing into their workflows and use insights to optimize their functions. Having the correct device is necessary, however the way you design your assessments and interpret outcomes issues much more.

3️⃣ “Time-to-market isn’t that necessary—testing takes time, so what?”

That is one that usually will get missed—till it’s too late. Some groups deal with load testing as a ultimate checkbox earlier than launch, assuming that if it takes longer, it’s no huge deal. However right here’s the actuality:

  • Each further day spent on load testing delays product launches, giving rivals an edge.
  • Improvement groups get caught ready for outcomes as a substitute of transport new options.
  • Clients count on quick, seamless experiences, and sluggish efficiency fixes can damage satisfaction.

I’ve seen firms take weeks to run full-scale load assessments, solely to comprehend that they’re lacking crucial deadlines. In as we speak’s market, velocity issues.

The answer isn’t skipping load testing—it’s making it environment friendly. As a substitute of treating it as a bottleneck, combine efficiency assessments into your pipeline. Use automated efficiency testing in CI/CD, run incremental load assessments as a substitute of 1 huge take a look at, and prioritize testing early in growth.

Load testing shouldn’t sluggish you down—it ought to provide help to transfer sooner with confidence.

4️⃣ “Extra customers? Simply make the machine greater.”

A variety of firms attempt to repair efficiency points by upgrading their infrastructure—extra CPU, extra reminiscence, greater machines. However right here’s the issue: scaling up doesn’t repair inefficient code.

I had a dialogue with a tech lead not too long ago who was fighting efficiency points. His first intuition? “Let’s improve the server capability.” However once we dug into the information, we discovered that:

  • A single database question was liable for 80% of the slowdown.
  • Customers weren’t simply “hitting the system” — they had been interacting in unpredictable methods.
  • The app was operating inefficient loops that brought on pointless processing.

Throwing {hardware} on the drawback would have masked the difficulty quickly, nevertheless it wouldn’t have solved it. As a substitute of specializing in infrastructure upgrades, ask your self: The place are the actual bottlenecks? Is it sluggish database queries, unoptimized APIs, or poor caching methods? Is horizontal scaling a greater choice? Distributing the load throughout a number of cases is usually more practical than simply including greater machines.

How are customers truly interacting with the system? Sudden behaviors can trigger slowdowns that gained’t be solved by including extra assets.

Scaling up buys you time, nevertheless it gained’t repair inefficiencies in your codebase.

5️⃣ “Open supply vs. business instruments—free is best, proper?”

It is a debate I hear on a regular basis. Many groups, particularly in startups, need to follow open-source instruments. They are saying, “We’d relatively put money into DevOps and use free testing instruments as a substitute of paying for a business resolution.” And I completely get that—open supply is nice for studying and experimentation.

However I’ve additionally seen firms hit a wall after they attempt to scale. They begin with an open-supply resolution, and the whole lot works high-quality—till they should:

  • Run complicated take a look at eventualities that require correlation and parameterization.
  • Handle large-scale distributed assessments throughout cloud environments.
  • Get devoted assist after they run into crucial points.

That doesn’t imply open-source instruments aren’t priceless—they completely are. They work nicely for groups with sturdy in-house experience and for tasks the place flexibility is vital. Nonetheless, groups that want to maneuver quick, deal with enterprise-scale testing, or cut back upkeep overhead may profit from evaluating several types of options that match their wants.

In the end, it’s not about free vs. paid—it’s about selecting the best device to your testing technique.

Last Ideas

Load testing is stuffed with myths, and it’s straightforward to fall into these widespread traps. But when there’s one takeaway, it’s this:

✔️ Don’t take a look at only for the sake of testing—take a look at with objective.

✔️ Perceive your customers earlier than you run the take a look at.

✔️ Make load testing a part of your course of, not a roadblock.

Have you ever encountered an assumption in load testing that turned out to be fully incorrect? Let’s focus on!

Unframe Emerges from Stealth with $50M to Remodel Enterprise AI Deployment

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Unframe, a next-generation enterprise AI platform, has formally emerged from stealth with a $50 million funding spherical led by Bessemer Enterprise Companions, alongside TLV Companions, Craft Ventures, Third Level Ventures, SentinelOne Ventures, and Cerca Companions. The funding marks a big milestone for Unframe because it accelerates international growth, fuels R&D, and scales supply of its turnkey AI options which can be already gaining traction with main enterprises world wide.

Unframe is redefining how giant organizations implement synthetic intelligence by eliminating the standard friction—lengthy growth cycles, safety bottlenecks, and the necessity for in-house AI experience. Its platform permits enterprises to go from idea to completely deployed, customized AI resolution in hours, not months. In contrast to generic AI instruments or narrowly targeted level options, Unframe is constructed to adapt to any enterprise use case throughout any system or division.

Immediate AI Options With out the Overhead

On the coronary heart of Unframe’s platform is what it calls the Blueprint Method, a strategy that gives the mandatory context to giant language fashions (LLMs) to ship hyper-relevant, domain-specific outcomes. This implies enterprises don’t want to coach or fine-tune fashions, which is historically some of the resource-intensive components of deploying AI. Unframe can also be LLM-agnostic, permitting clients to make use of or swap between private and non-private fashions with out being locked right into a single ecosystem.

From parsing chaotic information throughout legacy methods to analyzing structured and unstructured information with precision, Unframe’s AI brokers perform like area consultants that may combine with any system, automate complicated workflows, and provide actionable insights—all utilizing pure language inputs.

A New Mannequin for Enterprise AI: Safe, Quick, and Versatile

Enterprise adoption of AI has typically been hindered by issues over safety, compliance, and implementation overhead. Unframe is popping this problem right into a aggressive benefit. The platform permits for full integration with SaaS instruments, APIs, databases, and proprietary file methods—whereas making certain that buyer information by no means leaves their safe perimeter except explicitly allowed. Versatile deployment choices embody on-premise and public cloud, offering organizations with most management over information dealing with and regulatory compliance.

Unframe additionally introduces a radical shift in how AI providers are priced and delivered. The corporate operates on an outcome-based pricing mannequin: clients solely pay as soon as an answer is deployed and delivering worth. This removes the danger sometimes related to enterprise software program procurement and accelerates time-to-value.

Speedy Traction and Actual-World Impression

Regardless of solely just lately rising from stealth, Unframe is already serving dozens of Fortune 500 purchasers and has achieved hundreds of thousands in annual recurring income (ARR). Its early success underscores a rising enterprise want for extra agile and efficient AI options.

“Unframe’s unmatched accuracy, velocity, and high quality in processing structured and unstructured information have remodeled our operations,” stated the CIO of a worldwide funding financial institution.

“At Cushman & Wakefield, our AI+ technique is concentrated on bringing fast-paced and revolutionary options to unlock crucial insights, and we’re aggressively pushing ahead with Unframe,” added Salumeh Companieh, Chief Digital & Data Officer.

“Because of Unframe, we introduced our AI use case to life in simply two weeks—one thing our staff had been scuffling with for 9 months,” stated a VP of Innovation at a Fortune 100 insurance coverage firm.

A Seasoned Group with a Confirmed Observe File

Unframe is led by CEO and co-founder Shay Levi, who beforehand co-founded Noname Safety, scaling it to $40M ARR and a $500M acquisition by Akamai. He’s joined by COO Larissa Schneider and VP of R&D Adi Azarya, each veterans in scaling enterprise software program by means of IPOs, M&A, and worldwide growth. The management staff’s deep experience in cybersecurity, product growth, and operational scaling is foundational to Unframe’s fast progress and enterprise-grade providing.

“Unframe is flipping enterprise AI on its head by rapidly offering personalized software program based mostly on an enterprise’s precise wants,” stated Amit Karp, Associate at Bessemer Enterprise Companions. “I’ve the utmost confidence in Shay and the staff to redefine how software program is constructed and delivered to the enterprise world.”

Why It Issues

In an surroundings the place AI adoption is crucial however typically complicated, Unframe provides a daring various. By eradicating technical hurdles, compliance friction, and vendor lock-in, Unframe empowers enterprises to maneuver quicker, act smarter, and unlock the complete potential of their information—securely and on their very own phrases.

With a unified platform that helps limitless integrations, real-time collaboration, and autonomous activity execution, Unframe is positioning itself because the central nervous system for enterprise AI.

This funding spherical not solely affirms market confidence in Unframe’s mission however indicators a broader shift in enterprise expectations: from gradual, one-size-fits-all options to agile, safe, and fully customizable AI that delivers worth from day one.