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The IXPE GO Program solicits proposals for investigations in any area of astrophysics (not constrained to IXPE’s prime science areas). Individuals may submit proposals for observations that will advance the IXPE mission science return. Observing proposals may be for “standard”, “Target-of-Opportunity (ToO)”, “Large Program (LP)” or “Multi-cycle” observations. IXPE anticipates executing approximately 1 high (observations begin within 9 days of trigger) and 7 medium (observations begin between 9 days and 4 weeks of trigger) priority triggers in this GO cycle.

The Neutron Star Interior Composition Explorer (NICER) is an X-ray experiment on the International Space Station (ISS) dedicated to high-resolution timing and spectroscopy of rapidly variable X-ray sources in the 0.2, 12 keV band. Proposals for NICER data-analysis investigations addressing all areas of astrophysics are solicited, with a limited amount of funding available in GO Cycle 9.

The text for these two opportunities may be found in Sections 9 and 10 of D.3 Combined General Investigator General Observer Program.

On or about July 27, 2026, Amendment 67 announces a change to the Phase-1 proposal: The work plan previously included in the anonymized scientific justification Section of Phase-1 proposals must now be provided in the Expertise and Resources Not Anonymized document. This supersedes the old instruction in Section 11.2.2, which is now out of date and should be disregarded. New text is in bold and deleted text is struck through, see Sections 9.5 and 10.4. The Phase-1 Proposal due date (submitted via ARK RPS) for both IXPE and NICER remains unchanged at September 17, 2026.

Programmatic questions concerning IXPE may be directed to Hashima Hasan at hhasan@nasa.gov. Technical questions concerning IXPE may be directed to Kavitha Arur at kavitha.arur@nasa.gov.

Programmatic questions concerning NICER may be directed to Roopesh Ojha at roopesh.ojha@nasa.gov. Technical questions concerning NICER may be directed to Keith Gendreau at Keith.C.Gendreau@nasa.gov.

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The post Amendment 67: D.3 AGIGO: Change to IXPE and NICER General Observer Second Opportunity in ROSES-25 appeared first on NASA Science.

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Smoke pours from dozens of large fires in Oregon in this image captured by MODIS (Moderate Resolution Imaging Spectroradiometer) on NASA’s Aqua satellite on July 26, 2026.

NASA Earth Observatory/Michala Garrison

Dry thunderstorms that popped up over the Cascade Range on the evening of July 15, 2026, peppered Oregon and Washington with thousands of lightning strikes as they moved east across the states. By the following day, NASA satellites had begun to detect large numbers of wildland fires burning throughout central and eastern Oregon.

Though initially small, these blazes strengthened as they were fanned by gusty winds and raced through landscapes parched by extreme drought and baking in summer heat. When NASA’s Aqua satellite captured the image shown above on the afternoon of July 26, smoke poured northeast from dozens of large fires that had collectively charred hundreds of square miles. The fires produced a blanket of haze, prompting state officials to issue air quality advisories for eastern Oregon.

Many communities faced evacuation orders as more than 10,000 firefighters battled wildfires throughout the state. On the day the image was captured, the largest active fires were the Hay Creek Complex, Brewer, Big Grass, Akawa Butte, and Powder River fires. Several of these blazes were less than 5 percent contained, according to the National Interagency Fire Center. State officials invoked Oregon’s Emergency Conflagration Act to protect communities as they responded to particularly threatening fires such as the Shingle, Bench, Beachcomb, and Second Flat fires.

Government satellite data are part of a global system of observations used to track fire behavior and analyze emerging trends. Among the real-time wildfire monitoring tools that NASA makes available are FIRMS (Fire Information for Resource Management System), the Worldview browser, and the Fire Event Explorer.

As of July 27, 2026, fires in Oregon had burned more than one million acres, according to news outlets. Meanwhile, the National Interagency Fire Center reported that fires had burned more than 4 million acres across the United States. The 10-year average (2016-2025) for this point in the season is 3.4 million acres.

NASA Earth Observatory image by Michala Garrison, using MODIS data from NASA EOSDIS LANCE and GIBS/Worldview. Story by Adam Voiland.

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References & Resources

Central Oregon Fire Info (2026, July 27) The Source For Comprehensive Fire, Health, And Air Quality Information In Central Oregon. Accessed July 27, 2026.

KGW8 (2026, July 27) Latest updates on Oregon wildfires: Fires burn more than 1 million acres across the state. Accessed July 27, 2026.

NASA Earthdata (2026) Wildfires. Accessed July 27, 2026.

National Interagency Fire Center (2026) Oregon. Accessed July 27, 2026.

Northwest Interagency Coordination Center (2026, July 27) Official Fire Information. Accessed July 27, 2026.

OPB (2026, July 27) Oregon wildfires explode over weekend with no signs of slowing down. Accessed July 27, 2026.

Oregon State Fire Marshal (2026, July 27) OSFM Wildfire Info. Accessed July 27, 2026.

Oregon.gov (2026, July 26) Current wildfire info. Accessed July 27, 2026.

Oregon Smoke Information (2026, July 27) Air quality advisory for Eastern Oregon and parts of Central Oregon. Accessed July 27, 2026.

Oregon Department of Forestry (2026, July 27) Latest News. Accessed July 27, 2026.

U.S. Drought Monitor (2026, July 21) Oregon. Accessed July 27, 2026.

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More than 90 per cent of the oil and gas in the North Sea has already been extracted

Simon Dawson/Bloomberg via Getty Images

I’m terrified by how fast weather extremes are ramping up as the world heats up, and there’s much, much worse to come unless we leave fossil fuels in the ground. The only way to do that is to end our dependency on oil and gas, and all the fuss about whether the UK will allow more drilling in the North Sea is a distraction from this.

Some people talk about the UK government’s current policy of not approving more licences for exploration in the North Sea as if it will reduce the amount of oil and gas that is burned, and thus help limit how much more carbon dioxide ends up in the atmosphere. But it won’t. All it will do is change where the oil and gas that gets burned comes from.

Read more Solar energy is going to power the world much sooner than you think

What does matter is burning less fossil fuel. That means things such as getting more electricity from renewable sources, and more vehicles and heat pumps running on that electricity.

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So I won’t be judging the climate credentials of the UK’s new prime minister, Andy Burnham, on whether or not his government decides to allow more drilling in the North Sea. It’s what is done to further reduce our use of fossil fuels that’s crucial in climate terms. And here Burnham has already made a good start by cutting the value-added tax (VAT) on electricity, which will make swapping gas boilers for heat pumps more appealing.

I’m not saying bans on drilling are a complete waste of time, they have a part to play in protecting vulnerable environments, such as the Amazon rainforest. But I don’t see how they will ever help limit emissions.

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At this point, you might be thinking, “but if enough countries ban drilling, it will make a difference”. There are already nearly 60 restrictions on fossil fuel production in 25 countries, according to the International Institute for Sustainable Development. But many of these are regional restrictions to protect places such as the Arctic, or bans on production methods such as fracking. And 15 have already been repealed. No major producers are planning to completely stop producing oil and gas, and the known reserves of fossil fuels are way more than enough to trash the climate. The bottom line is that some countries will keep on churning out fossil fuels as long as they can sell them, so the key is to reduce demand, not supply.

That said, most of the arguments used by supporters of further North Sea gas and oil exploration are false or misleading. For starters, more drilling is not going to bring down energy prices in the UK. North Sea oil and gas is sold at global market prices, and there’s not enough oil or gas left in the North Sea to make a dent in those prices.

Going all out on renewables, by contrast, would break the link between gas prices and electricity prices, and help bring down energy bills. This is what is Spain is succeeding in doing.

Another dubious claim is that more drilling is going to make oodles of money for the UK. But production in the North Sea already has to be supported by tax breaks and subsidies, and is declining year on year. More than 90 per cent of the oil and gas has already been extracted, so further drilling will, at most, slow the rate of decline. And what remains is harder to get out, so costs are higher. If the wars pushing up oil prices come to an end, it might not even be profitable, until recently, the world was building up a massive surplus of oil.

Read more The secret weapon that could finally force climate action

Then there’s the exaggerated claim that North Sea gas is “four times cleaner” than imported liquid natural gas, or LNG. The emissions associated with producing LNG are around four times higher than those for North Sea gas, because of the energy required to liquefy it. But what matters are the total emissions, including those from burning the gas, and on this count North Sea gas emissions are only 15 per cent lower than those from LNG.

LNG now accounts for more than half of the gas imported by the UK. If that could be reduced by more North Sea gas production, there should be a little less CO2 emitted. It would also mean less money going to countries that obstruct climate action, the UK now gets 37 per cent of its gas from the US, for instance.

But the UK also gets gas from Norway via pipelines, and the production emissions of this are even lower than those of North Sea gas. So in climate terms, buying more Norwegian pipeline gas would be better than increasing North Sea production.

Overall, then, what happens in the North Sea just doesn’t matter much in the grand scheme of things, more drilling is neither a climate catastrophe, nor an economic panacea. Instead, the UK and other countries need to focus on what really matters, much more drastic action to replace fossil fuels or discourage their use. You only need to look at what’s happening in France and Spain to see why it’s so vital.

Weeks before its expected acquisition by SpaceX closes , AI coding startup Cursor is making its biggest push into India yet, launching its first country-specific subscription as the company bets on one of the world’s largest developer markets to drive its next stage of growth.

On Monday, the startup introduced Cursor Start, a ₹649-a-month (about $7) subscription built specifically for India, and priced well below Cursor’s standard $20-a-month Pro subscription .

The move reflects India’s growing importance to Cursor’s business. The startup says India is already its third-largest market globally and home to its highest concentration of power users, with its user base in the country more than tripling over the past year.

That scale, coupled with India’s deep pool of software engineering talent, made it the first market where Cursor chose to localize pricing, Simon Green, Cursor’s head of Asia-Pacific and Japan, told TechCrunch. “We felt that we had an opportunity there to right-size the commercial model and drive scale,” Green said. “The technical competency of the country and the engineering talent that already exists make it a very natural fit.”

India has emerged as one of the world’s largest software developer hubs. Earlier this year, GitHub said that the country has more than 27 million developers on its platform, second only to the U.S., with more than two million joining in 2026 alone.

Cursor Start includes access to Cursor’s Composer 2.5 model and Grok 4.5, with higher usage limits than the free tier, alongside cloud agents, its iOS app, plugins, Model Context Protocol support, hooks, and skills. The startup said the plan is aimed at developers who need more AI-assisted coding capacity than the free tier offers without upgrading to its full Pro subscription.

The lower-priced plan is intended to broaden access rather than replace Cursor’s flagship offering, Green said. Unlike the $20-a-month Pro subscription, Start does not include access to frontier AI models from providers such as OpenAI and Anthropic, or advanced features including Bugbot, Auto Mode, Automations, and the Cursor SDK.

The plan is billed in Indian rupees and supports payments through credit and debit cards as well as India’s Unified Payments Interface (UPI).

Green told TechCrunch that Cursor would use multiple checks to ensure the India-only subscription is available only to individual users in the country, including measures to deter people from accessing the plan through virtual private networks (VPNs).

Cursor is not alone in tailoring its pricing for India. OpenAI and Anthropic have also rolled out India-specific plans over the past year as global AI companies compete for users in one of the world’s fastest-growing AI markets.

While Cursor Start is initially limited to India, Green told TechCrunch that the startup could expand localized pricing to other markets if the model proves successful.

“We will continue to do everything we can to fuel the demand and serve those clients that are using us,” Green said. “Now, if this model proves that we could take it to other markets, perhaps we will. But I think it’d be crazy to say we would never do it elsewhere.”

OpenAI provides one precedent for this strategy, having launched its sub-$5 ChatGPT Go in India before expanding the lower-priced subscription to other markets.

In addition to the localized pricing strategy, Cursor is also expanding its presence in India through new hires. Green told TechCrunch that the startup recently hired its first salesperson in India and expects another leader to join in Delhi. The company is also building out its a government affairs office, alongside three technical customer support hires, as it expands its presence in Bengaluru, Chennai, Hyderabad, and Mumbai.

Cursor’s enterprise push is still in its early stages in India, Green said, where adoption has so far been driven largely by individual developers, startups, and universities. He said Cursor sees significant opportunities in sectors including banking and large enterprises as it expands its local sales efforts.

Green said, the India-specific pricing was designed to be commercially sustainable rather than a loss leader. He said the lower-priced plan is viable because it is built around Cursor’s own AI models, which carry lower operating costs than relying primarily on third-party frontier models.

Cursor’s India expansion comes a little over a month after Elon Musk’s SpaceX agreed to acquire the AI coding startup in a $60 billion all-stock deal, following SpaceX’s blockbuster initial public offering . The acquisition is expected to close in Q3. However, SpaceX has been partnered with Cursor since April to develop a next-generation “coding and knowledge work AI.”

Green said Cursor will continue to operate independently until the transaction closes and that the company’s India expansion plans were already in motion before the deal. Once the acquisition closes, however, Green said SpaceX’s existing presence in India through Starlink could help Cursor expand faster by lowering commercial and operational barriers.

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Jagmeet covers startups, tech policy-related updates, and all other major tech-centric developments from India for TechCrunch. He previously worked as a principal correspondent at NDTV.

You can contact or verify outreach from Jagmeet by emailing mail@journalistjagmeet.com . View Bio October 13, 15 San Francisco Scale faster. Grow your portfolio. Gain practical expertise. No matter your goal, Disrupt can empower you. Save up to $330 toda y!

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Anthropic founder and CEO Dario Amodei responded on Monday afternoon to murmuring in the industry that his company somehow supports efforts by the U.S. government to ban open-weight Chinese models, or possibly open-weight models generally.

“Anyone who has read my past writing should know that I don’t regard such bans as a useful measure, but let me state it clearly so that there is no doubt: Anthropic has never advocated for a ban on open-weights models ,” he wrote, emphasis his own.

His response came after Nvidia founder and CEO Jensen Huang took to X ( his first post on the platform) to share an open letter on Friday in which Nvidia and a long list of other AI companies including Hugging Face, Meta, Microsoft, Mistral, and Nvidia urged policymakers not to impose broad “premature restrictions” on open-weight AI models.

While that letter did not mention China specifically, the debate in the industry has centered on allegations that Chinese AI labs are growing in capability often by stealing intellectual property from their American counterparts. One method is distillation, where an AI bombards another model with prompts to learn how it works.

Amodei said in the blog post published Monday that he views open-weight models as falling into a different bucket than the threat China poses. “Open-weights models that don’t have dangerous capabilities are a public good: they don’t cost anything besides the compute needed to run them, and they provide value to businesses, developers, and researchers.”

Amodei said in his post that he has longstanding fears about AI, but businesses using open-weight models, even ones from China, are not among them.

His fear is that “authoritarian governments” will build models that are more powerful than those in the U.S. to achieve “permanent military superiority” and/or use AI to repress their own people. He said the Chinese Communist Party (CCP) isn’t the only authoritarian government he’s concerned about, but it is the “most capable.”

Amodei also said he fears AI will enable “biological attacks” not just cybersecurity ones. And, in his view, open-weight models are more dangerous in those scenarios because it’s difficult to apply guardrails to them or monitor their usage. He also noted, citing a UK AI Security Institute report, that once open-weights are released they cannot be withdrawn

This runs counter to what open source advocates argue: that access to powerful open models not controlled by a single entity helps defenders protect themselves.

Amodei listed actions he believes would thwart China, including restricting its access to powerful chips (which has been a longstanding U.S. policy) and calling for a formal crackdown on distillation. The U.S. has threatened sanctions against China if it determined there was IP theft involving U.S. models.

Interestingly, Amodei also said he supports growing efforts, some led by the U.S., to create a model safety testing organization, especially if the entire world, including China, agreed to submit to it.

“I think this idea is actually close to a consensus: I have been heartened both that the Trump administration has moved in this direction in recent months, and by recent industry proposals that would apply such testing to the most capable models regardless of their country of origin or whether they are open or closed (while exempting less capable models, such as those from startups and academia, entirely),” Amodei wrote.

“Note that to be effective,” he added, “testing would need to be global, which means even the CCP would need to be on board. I think this may actually be possible … limited cooperation around preventing AI biological weapons may be possible because it is in China’s interest too.”

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But here's a thing: there's a lot of forum software out there that still does the old way of rendering as much as possible on the server and using some JavaScript on client to improve its interactivity here and there. And people are happy with that . And this approach has none of the above issues.

Internet forum software has plenty of interactivity, but this interactivity is isolated to specific places on the page. Moderation actions, watching a thread, writing a reply, seeing last notifications, voting in a poll. All this stuff can be achieved without React.js and was achieved without it for years before we've decided that full page reload is something that needs to be avoided.

Now, what's HTMX? HTMX is a tiny library that lets developers specify parts of HTML as dynamic islands that can be swapped by new server-rendered HTML on interaction. For example, list of actual threads is one such (large) island. With little bit of HTMX included in Django template, changing current category on threads list could pull new HTML from Django only for new threads list for selected category, while keeping rest of the page's HTMX (eg. already loaded search results in navbar) unchanged. Misago's backend code would only need to be changed to return only the HTML for this island when request comes from HTMX, instead of full page. There's no need to do any JSON serialization or write dedicated JavaScript or React.js.

HTMX is declarative way of doing $.get("url", "#outlet") we've did in jQuery 20 years ago. Or Rails Turbolinks. I can't believe I am writing this, but this is the way for forum software if you hate endless scrolling or want to keep things simple otherwise.

by Simon Holzinger, Leo Schmutterer, Victoria Marie Rothe, Maria Theresia Watzlowik, Uwe Schwartz, Gernot Längst

High-resolution analysis of cellular chromatin structure is crucial for uncovering developmental and cell-type-specific regulatory networks. We developed the nucDetective pipeline to provide a comprehensive evaluation of chromatin organisation. This involves assessing nucleosome positioning, occupancy, fuzziness, and array regularity. The pipeline was benchmarked by analysing the chromatin structure of the malaria-causing parasite Plasmodium falciparum (Pf) during its erythrocytic development cycle. Pf is characterised by a unique chromatin landscape, exhibiting unstable nucleosomes and a genomic AT-content exceeding 80%, which presents challenges for standard MNase-seq analysis of chromatin. The nucDetective pipeline provides specific, high-resolution nucleosome profiles for the different asexual stages of Pf, monitoring the dynamics of individual nucleosomes. Contrary to the current view of irregular chromatin, we demonstrate for the first time regular phased nucleosome arrays downstream of TSSs, which, together with the established +1 nucleosome and upstream nucleosome-depleted region, reveal a complete canonical eukaryotic promoter architecture in Pf. The global mean nucleosome repeat length varies from 176 bp to 185 bp depending on the developmental stage. Stage specific changes in nucleosome positioning occur locally in intergenic regulatory regions, which are characterized by specific histone modifications and variants. Dynamic nucleosomes correlate with DNA accessibility, gene expression and determine the access to transcription factor binding sites in Pf. The highly regular chromatin structure, with stage-specific structural alterations, emphasises the important role of epigenetic mechanisms in regulating the complex life cycles of Pf.

by Shambhavi Malik, Ludovica Cicci, Abdul Qayyum, Rahul Ghelani, Ji-jian Chow, Jagdeep Singh Mohal, Zachary I. Whinnett, Amanda Varnava, Gernot Plank, Prapa Kanagaratnam, Steven A. Niederer

Background Hypertrophic cardiomyopathy (HCM) is associated with marked inter-patient heterogeneity in ventricular electrophysiology, contributing to arrhythmic risk that is insufficiently captured by current clinical methods. Electrocardiographic imaging (ECGI) provides high-density body surface potential (BSP) measurements but remains largely descriptive. Computational modelling offers a mechanistic framework to interpret BSP signals in terms of underlying tissue-level properties.

Methods and findings We developed a BSP-driven workflow to construct patient-specific electrophysiology (EP) models of HCM by integrating multimodal clinical imaging with Bayesian model calibration. Anatomically detailed torso-heart finite-element models were generated for 17 HCM patients using thoracic computed tomography (CT), cardiac magnetic resonance imaging (CMR), and 252-electrode BSP recordings. Ventricular depolarisation and repolarisation were simulated using a reaction-eikonal (RE) formulation coupled to a biophysically detailed ToR-ORd-dynCl ionic model. Emulator-based Bayesian history matching (HM) was used to personalise EP parameters, with staged calibration of QRS and T-wave morphology informed by targeted sensitivity analysis. The calibrated cohort reproduced clinical BSP morphology with Pearson correlation coefficient (PCC) ≥0.6 for a median of 94.0% (IQR: 91.6 to 96.8%) of electrodes, achieving a median PCC of 0.89 (IQR: 0.80 to 0.94) across the full 252-electrode vest. Calibration substantially reduced uncertainty in the high-dimensional EP parameter space while yielding physiologically plausible conduction and repolarisation properties. Models calibrated exclusively to sinus rhythm robustly generalised to right-ventricular (RV) apical pacing without parameter retuning, reproducing clinically observed pacing-induced trends in depolarisation and repolarisation. Exploratory analysis revealed biologically consistent associations between inferred EP parameters and patient demographics.

Conclusions This study demonstrates that high-density BSP data can be used to functionally personalise mechanistic EP in HCM. The framework captures intrinsic patient-specific EP properties and generalises beyond the calibration condition, supporting its use for mechanistic investigation of arrhythmogenic substrate.

by Xiaolei Shi

This paper proposes a novel nonlinear autoregressive with exogenous input-based adaptive recursive least squares model-free predictive control (NARX-ARLS-MFPC) strategy for permanent magnet synchronous motor (PMSM) drives. The core challenge addressed is the performance degradation of conventional model predictive control (MPC) under inevitable motor parameter mismatches. The proposed method integrates the NARX model with an ARLS algorithm featuring a variable forgetting factor to dynamically track system changes. Comprehensive simulation studies validate the superior robustness of the strategy. Under significant inductance and flux linkage mismatches, the proposed method reduces current total harmonic distortion (THD) by 28.3% and 12.3% compared to conventional finite-control-set model predictive control (FCS-MPC) and a baseline control method, respectively. It maintains stable performance under moderate sensor noise with appropriate tuning. During load transients combined with resistance and inductance mismatches, it achieves THD reductions of 24.1% and 15.7% versus the two benchmark methods, respectively. Statistical analysis under parameter perturbations confirms its overall superior performance across key dynamic and steady-state metrics. The results demonstrate that the synergistic integration of the nonlinear model and adaptive identification effectively suppresses current harmonics caused by model inaccuracies while enhancing dynamic performance.

I have a small solar farm and the panels have got visibly dusty. Is cleaning them worthwhile? How much difference does it make? Let's find out.

I know that my panels have not been cleaned in the last year. I expect they also weren't cleaned in the year prior to that (why would you clean them when you're about to sell the house?). But beyond that I don't know when they were last cleaned.

The short answer is that I think I got a 2%-5% increase in power output from my solar farm due to cleaning the panels, which will work out to about £60-£150/yr, decaying to 0 over the course of a few years. So, probably just about worthwhile.

There are 16 panels in total, connected up to the inverter as 2 banks of 8 panels each. The inverter reports the power output from each bank individually, so the plan is to take a bunch of readings before starting, then wash all of the panels in one bank, taking readings in between and at the end.

Our hypothesis is that cleaning the panels will increase power output.

We can test whether washing the panels has made any difference by looking at the ratio of power output from the 2 banks. If we just looked at raw power output then it would be confounded by changing cloud cover, sun angle, etc.

There is still the fact that the 2 banks of panels are physically separate and plausibly one bank is better positioned for sun 45 minutes later than the other. Ideally I would have been measuring the ratio of power output for several days prior to see how it varies throughout the day.

This is how the first row of panels looks after I've washed 3 of them, you can see the furthest one is noticeably grubbier:

So they were "visibly dusty", but not massively dirty . If your panels are dirtier than mine were, then your benefit from cleaning them will be greater than mine was.

My results for cleaning one bank of panels are shown in this chart:

We see that the initial power ratio is very stable before the panels are washed.

We then step up to having washed "half" a panel (I initially tried to wash them with window cleaner and a paper towel, but this was ineffective so I then walked away to get a bucket of soapy water and a cloth, and then took a reading which I labelled as 50% washed).

For some reason the power ratio drops significantly when the first panel is washed, I'm unsure why.

And then the power ratio increases as more panels are washed as we'd expect.

But once all the panels are washed, the power ratio drops off again while nothing changes. I am unsure whether this is because as the surface water evaporates off the panels get slightly opaque again? Like the "frosted glass effect", where you can see through frosted glass when it is wet but it gets opaque again when dry. Maybe beyond cleaning the panels I ought to be polishing them?

Anyway it looks like cleaning the panels was about a 2%-5% improvement, depending on what you think is going on at the end.

I got a bit of a tingle when I was cleaning one of the panels. At first I thought I was getting an electric shock from the wet panel, but I inspected my finger and found a tiny thistle splinter in it. After I removed the splinter it seemed fine.

But a bit later I got another tingle from another panel! There definitely wasn't a splinter in my finger any more, but the tingle was in the same place. I think the tingle actually was coming from the electricity, but I was only able to feel it at the point where the thistle had already pierced the skin.

ChatGPT convinced me that there could just be a tiny "capacitive leakage" from an "inverter with no transformer", so I'm not going to worry about it. But if I clean the panels again I will wait until dark lol.

The next question is should I be upgrading the solar farm? I think mine was installed about 15 years ago, and generates (at peak output) 3.7 kW from 16 panels.

Replacing the panels with more modern ones would increase the power output by about 60%, at a cost of about £5000, which would pay for itself in about 3 years, which seems like a no-brainer.

However, due to the fact that my solar farm was installed so long ago, it benefits from a feed-in tariff , which means that not only do I get paid an absurdly high rate, but it is paid also based on the electricity I generate rather than what I export .

If I increase the power output of the system then the additional capacity will not be eligible for the feed-in tariff and will revert to present-day prevailing tariff which is about 4x worse before you even consider that I currently get to use electricity and still get paid for generating it .

This is the yin and yang of market-distorting incentives. Today's incentive to install solar becomes tomorrow's disincentive to upgrading it.

An overview of how mode-agile threats challenge static library radar/EW systems, and how AI/ML cognitive architectures enable adaptive, real-time countermeasures.

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npj Digital Medicine volume 9 , Article number: 581 ( 2026 ) Cite this article

Federated Learning (FL) enables collaborative training across institutions without sharing sensitive data, a solution for privacy-preserving AI in medical imaging. However, hospital deployment remains challenging due to strict data protection regulations, heterogeneous infrastructures, and limited network accessibility behind firewalls. We introduce TheODen, an open-source framework for Federated training on histopathology Whole Slide Imaging (WSI). It requires no open client-side ports, enabling training through firewalls via a secure reverse-proxy architecture. We conducted, to our knowledge, the first nationwide FL study for histopathology segmentation of colorectal cancer across three German university hospitals, using breast and colorectal cancer datasets without opening firewall ports. TheODen achieves robust segmentation, with global average dice scores of 0.764 on BCSS and 0.754 on SemiCOL despite data heterogeneity and network constraints. These findings underline TheODen’s potential to facilitate secure, large-scale collaborations between medical institutions and to accelerate clinical translation of AI models under real-world infrastructure constraints, providing a privacy-preserving-by-design architecture for future collaborations.

Segmentation models for histopathology data are emerging as important methods to support pathologists and mitigate the shortage of pathologists 1 . However, these models depend on the extensive availability of training data 2 . The amount of required training cases and their variability typically requires extending the training to multiple hospitals 3 , 4 . Due to privacy regulations such as General Data Protection Regulation (GDPR) in Europe or Health Insurance Portability and Accountability Act (HIPAA) in the US, it is often infeasible to share the actual training data between multiple hospitals. 5 Federated Learning 6 has emerged as a paradigm to address this issue 7 as it allows privacy-aware, decentralized training with each hospital training a local model on their data and only sharing the resulting model parameters with a central server instance instead of the potentially privacy-critical training data 8 .

Existing work has simulated Federated Learning in the context of histopathology segmentation and shown that it is able to achieve performance similar to the privacy-critical centralized training 9 , 10 , 11 . However, these works were usually just simulated locally instead of actually running spatially distributed over the internet across hundreds of kilometers and covering an area of 10,500 km 2 . These approaches would not be securely applicable to real-world training over the internet because they require a direct connection to the clients and therefore opening ports of the firewall. This is typically not feasible as it causes serious security implications and would therefore usually violate IT security policies of the hospitals. Consequently, most Federated Learning frameworks demonstrate promising proof-of-concept results but still avoid the key technical challenges of real-world deployment.

In this work, we propose TheODen, a framework for Federated Learning, specialized to histopathology tasks, that allows simple and secure deployment on hospital infrastructure over the internet without requiring to open any ports at the firewalls. This simplifies the deployment because it does not require any coordination with the hospital IT administrations or additional security risks from firewall modifications. We show that it is possible to set up a training on a client with 2 lines of client code plus a 5-line configuration file. The source code is publicly available on GitHub: https://github.com/MECLabTUDA/theoden .

We show in a nationwide deployment between three German university hospitals, that is the first of its kind, that TheODen can be trained in such a real-world scenario. We evaluate the performance on the breast cancer segmentation dataset (BCSS) 12 leading to a dice score of 0.764. Moreover, we evaluate the behavior of the training under distribution shifts as they typically occur when using data from different institutions. Therefore, we additionally train on the multi-site dataset SemiCOL 13 that contains WSIs of colorectal cancer from different hospitals and distribute them between the clients. Despite this scenario of Client Drift, we still achieve an average dice score of 0.754 on SemiCOL using TheODen. These results highlight the potential of TheODen to leverage data availability between hospitals through an architecture designed to preserve patient privacy by keeping data local. Figure 1 provides an overview of the structure of TheODen and its main features.

We train on two distinct histopathology datasets, BCSS and SemiCOL. To achieve this, we deploy our own open-source framework, TheODen. This framework employs a reverse proxy positioned outside the server’s firewall, enabling training through closed firewalls. This significantly enhances security and simplifies deployment, as there is no need to modify the firewall configurations of the hospitals. The reverse proxy achieves this by forwarding all communication to server and clients that are all located behind their firewalls.

The public code repository contains detailed tutorials and example code, allowing clinicians to reproduce the results and deploy their own training on medical imaging data, making TheODen a valuable foundation for larger distributed training collaborations even including international deployments in big consortia.

Existing work aims to deploy federated learning but is either limited, as it is just simulating it locally, or does not allow training without opening ports at the clients, which is both a significant limitation for clinical real-world deployment. Rieke et al. 8 introduce Federated Learning 14 as a promising approach for decentralized, privacy preserving training on distributed medical imaging data. However, there are significant challenges for real-world deployment of Federated Learning such as data distribution 15 and deployment in the infrastructure that need to be addressed by a framework.

Nvidia FLARE 16 and Flower 17 are frameworks for Federated Learning that allow distributed training between multiple machines, but they do not include any adaptations to the reality of medical imaging such as training on histopathology WSIs.

Li et al. 18 introduce a framework for Federated Learning for classification of histopathology images. They simulate the training locally, showing the potential of Federated Learning for histopathology. Lu et al. 19 simulate Federated Learning for histopathology classification. However, these approaches are just simulating Federated Learning locally without actually conducting distributed training. Consequently, they do not cover the challenges connected to deployment in the real world over network infrastructure.

Lutnick et al. 11 train a decentralized model between multiple physical clients. However, these are located in the same network, still not covering deployment on clinical infrastructure over the internet. Roth et al. 20 deploy Federated Learning to the real-world for the classification of breast density from BI-RAD radiology images. Bujotzek et al. 21 deploy Federated Learning in the context of a nationwide radiology project and summarize pitfalls. Sheller et al. 22 are training on MRI data between 10 hospitals. Tolle et al. 23 trains over the internet on CT images.

These works show the potential of Federated Learning for radiology images, while Ogier et al. 24 train using Federated Learning for histopathology classification. They show that their federated model surpasses the classification performance of all local models. However, there is no public code available, hindering deployment for similar projects. To the best of our knowledge, there are no papers with public code that deploy Federated Learning for histopathology segmentation to real clinical infrastructure in a nationwide consortium over the internet. Consequently, this work aims to bridge this gap by providing a framework optimized for simple and guided real-world deployment to clinical infrastructure for distributed federated histopathology segmentation, bringing secure real-world deployment of segmentation models for histopathology one step closer.

As a proof of concept that TheODen enables distributed training on clinical infrastructure over the internet, we deploy TheODen on clients at the three German University Hospitals Cologne, Essen and Leipzig on the histopathology datasets BCSS for breast cancer segmentation and SemiCOL for colorectal cancer segmentation. The methods section provides further details regarding the setup of the experiment including federated learning configurations, hardware configurations and datasets.

In the following, we will analyze the resulting segmentation performance in different scenarios, compare them to centralized and distributed baselines and discuss the implications of the results.

As visualized in Fig. 2 , the distributed trainings achieved a global average test dice score of 0.764 ± 0.097 on BCSS and 0.754 ± 0.066 on SemiCOL, but there are significant differences between the clients:

a (left) Breast cancer dataset (BCSS) and b (right) colorectal cancer dataset (SemiCOL). The plots display the number of samples per location and the global average at the central server in Darmstadt, providing a proof of concept for training on real-world clinical infrastructure.

In case of BCSS, the training dice score and cross entropy loss is very similar between all clients at the end of the training. However, the resulting test performance differs more with a test dice score ranging from 0.661 in Cologne to 0.854 in Leipzig. This can be explained with the different distribution of training and test data even within datasets, such as through different acquisition methods as described by Stacke et al. 25 . Our performance is comparable to that of Weng et al. 26 , suggesting successful training.

To also cover the important aspect of handling multi-site datasets, we train clients on different subsets of SemiCOL that contain patches obtained from different hospitals. Therefore, the performance difference between clients is larger. For example, the test dice score of Cologne and Essen is higher with 0.822 and 0.748 compared to Leipzig with only 0.691. This performance distribution makes sense as Leipzig was training on data obtained from Munich, unlike Cologne and Essen that trained on the SemiCOL subset from Cologne. A possible explanation for this degradation in segmentation performance at the client in Leipzig is Client Drift 15 that causes a performance degradation through heterogeneous data distribution between clients. As the client in Leipzig is training on a different subset of histopathology slides from Munich, these statistically differ from the slides obtained from Cologne that Essen and Cologne train on. Consequently, this different distribution causes Client Drift, potentially explaining the difference in segmentation performance between clients. Using a different aggregation method than FedAvg 14 could potentially mitigate this issue and even further improve performance but would also require more tuning which might not be feasible for our goal of a simple real-world deployment. However, even with FedAvg the average segmentation performance resulted in an acceptable dice score of 0.754 despite the challenges from the diverse data distribution of SemiCOL.

For simplicity we only show and discuss the average dice scores across all classes. However, we provide the detailed per-class results in Supplementary Table 1 and Supplementary Table 2 .

Figure 3 shows the training time in comparison to the dice scores on the test set for the local models of the clients in Cologne, Essen and Leipzig and thei

by Noha Alnazzawi

Hypertension and diabetes are major risk factors for heart disease, which remains among the leading causes of morbidity and mortality worldwide. Heart disease includes heart failure, myocardial infarction, stroke, and atherosclerosis. The identification and monitoring of these risk factors are crucial for early intervention and effective management. Machine learning techniques have the potential to improve the management and prevention of heart disease by enabling the automatic detection of risk factors, which in turn can help doctors personalize treatment and facilitate preventive interventions. In this study, heart disease risk factors were automatically extracted using a combination of multimodal data: both unstructured clinical narratives (e.g., the PrevComp corpus) and structured datasets (e.g., the UCI heart disease dataset) were used to predict the presence or absence of heart disease. The classification model is based on the Light Gradient Boosting Machine (LightGBM), a state-of-the-art implementation of the gradient boosting framework that employs tree-based learning algorithms. The developed classification model demonstrated a robust predictive accuracy of 83% for the presence/absence of heart disease, supporting its potential to accurately identify high-risk patients and improve clinical outcomes.

by Laura Fertitta, Fanny Coulpier, Layna Oubrou, Xavier Decrouy, Etienne Audureau, Nicolas Ortonne, Pierre Wolkenstein, Piotr Topilko

Neurofibromatosis type 1 (NF1) is an autosomal dominant disorder in which cutaneous neurofibromas (cNFs) represent one of the most common and burdensome manifestations. No approved pharmacological treatment exists. Preclinical studies are essential to evaluate candidate therapies, but reliable outcome and endpoint measures for cNFs in animal models remain limited. We developed and validated a standardized methodology to assess drug efficacy in the Prss56Cre Nf1-KO mouse model which recapitulates key features of cNFs. In this model, Nf1 inactivation and tdTomato (Tom) reporter expression were specifically targeted to Schwann cells (SCs) responsible for cNF development. This approach enables real-time monitoring, isolation, and manipulation of tumor SCs at any time. We defined macroscopic (tumor count, total Tom+ fluorescent surface area, fluorescence intensity) and microscopic (cell-type composition defined by immunolabeling with a panel of specific markers, area quantification) endpoints, developed dedicated ImageJ scripts for automated image analysis, and compared the results with those obtained using the conventional manual method. Both automated measurements showed excellent reproducibility (ICC = 1) and strong correlation with manual analysis (Spearman’s coefficient > 0.90), while significantly reducing analysis time (up to 100-fold faster). Bland, Altman analyses confirmed the absence of systematic bias compared with manual scoring. The standardized image naming and metadata integration further facilitated data consolidation and statistical analysis. This validated approach provides a reliable, reproducible, and time-efficient framework for evaluating drug effects on cNFs in preclinical studies. It establishes a foundation for robust efficacy testing of candidate therapies, facilitates cross-study comparability, and accelerates therapeutic development and clinical translation.

by Derya Abuşka, Özlem Dikme, Özgür Dikme, Tahir Talat Yurttaş, Uğur Durmuş, Atakan Yüksekbaş

Background During the pandemic era, rapid and accessible prognostic tools were essential to support clinical decision-making for emergency department (ED) patients presenting with acute infectious symptoms. Hematologic inflammatory indices derived from complete blood count (CBC) parameters, such as the systemic immune-inflammation index (SII), systemic inflammatory response index (SIRI), and pan-immune-inflammation value (PIV), have been increasingly investigated for risk stratification. This retrospective cohort study evaluated the age-stratified prognostic performance of these indices for 30-day mortality in ED patients during the pandemic period.

Methods This retrospective cohort study included adults presenting to a tertiary-care ED between March 1 and May 31, 2020. All included patients were retrospectively confirmed to have SARS-CoV-2 infection by RT-PCR. CBC-derived inflammatory markers (SII, SIRI, and PIV) were calculated at admission. The primary outcome was 30-day mortality; the secondary outcome was ICU admission. Age-stratified analyses (A total of 2,778 PCR-confirmed patients were included (mean age 47.8 ± 16.2; 58.7% male). Thirty-day mortality was 6.2%. In the overall cohort, SII, SIRI, and PIV demonstrated modest prognostic performance for mortality (AUCs: 0.663, 0.659, and 0.649, respectively). In patients Hematologic inflammatory indices obtained at ED presentation demonstrated age-dependent prognostic performance for 30-day mortality, with SII showing good discrimination and high negative predictive value (98.9%) in patients younger than 65 years and reduced discriminatory performance in elderly patients. These readily available and cost-effective parameters may support rule-out decisions for younger adults in emergency settings, while in elderly patients clinical assessment and comorbidity profiling should be prioritized over inflammatory marker interpretation.

by Rania Itani, Reem Al Rawass, Diala Tlayss, Fatima Al Zarif, Souraya Domiati, Chaza Alaeddine, Mohamad Abdallah, Rawad Hayouk

Background Premenstrual syndrome (PMS) is common among women of reproductive age and may impair quality of life, academic performance, and social functioning. In Lebanon, data on PMS and its management among female university students remain limited. This study aimed to estimate the prevalence of PMS among female university students in Lebanon, identify risk factors, and recognize adopted management practices and their perceived effectiveness.

Methods An observational cross-sectional study was conducted among female students at Beirut Arab University, Lebanon, between April 20 and May 8, 2026. Data were collected using a self-administered questionnaire assessing sociodemographic, menstrual, lifestyle, behavioral, psychosocial, and management-related factors. Premenstrual symptoms, social media addiction, and perceived stress were assessed using the Premenstrual Symptoms Screening Tool, Bergen Social Media Addiction Scale, and Perceived Stress Scale-4, respectively. Binary logistic regression was used to identify factors associated with moderate-to-severe PMS and premenstrual dysphoric disorder (PMDD).

Results Among 1,062 participants, 497 participants screened positive for moderate-to-severe PMS (46.8%), and 175 had symptoms consistent with PMDD based on the PSST (16.5%). The most frequently reported moderate-to-severe symptoms were physical symptoms (69.7%), fatigue or lack of energy (68.8%), depressed mood or hopelessness (67.8%), and overeating or food cravings (66.2%). Academic absenteeism was reported by 37.2%. More than one-third of the participants with moderate-to-severe PMS and PMDD (39%) reported using pharmacological management practices, mainly non-steroidal anti-inflammatory drugs. However, less than half of medication users perceived these treatments as being very effective (45.8%). Higher odds of moderate-to-severe PMS were observed among current smokers (AOR = 1.61, P = 0.003), those with heavy menstrual bleeding (AOR = 1.58, P = 0.03), high meal skipping (AOR = 1.38, P = 0.03), high fast-food consumption (AOR = 1.34, P = 0.04), higher social media addiction scores (AOR = 1.31, P PMS was common among female university students in Lebanon and a considerable proportion experienced PMDD. In fact, lifestyle, behavioral, and psychosocial factors were associated with moderate-to-severe symptoms, highlighting the need for university-based awareness, screening, and counseling strategies that address modifiable risk factors and support appropriate management of premenstrual symptoms.

by Juliana Heitich Brendler, Khadija Younes da Silva, Valesca Dall’Alba, Andreza Francisco Martins, Daniel Teixeira-dos-Santos, Artur Francisco Schumacher Schuh, Maira Rozenfeld Olchik

Background Constipation is a frequent non-motor symptom in Parkinson’s Disease (PD). Standard care includes dietary and lifestyle guidance or the use of laxatives. Diet represents a promising non-pharmacological approach, but its management in PD is complex and goes beyond simple dietary recommendations. This study protocol aims to examine whether nutritional counseling delivered by a dietitian is more effective than usual care in reducing constipation symptoms in PD patients.

Methods This 90-day randomized controlled superiority trial uses a parallel-group design. A total of 54 outpatients with PD without dementia and fulfilling the Rome IV criteria for functional constipation will be included. Baseline assessments comprise demographics, nutritional evaluation (anthropometric measurements and sarcopenia indicators), neurological assessment, and the Constipation Scoring System (CSS) scores. Participants will perform stool sample collection for microbiota analysis and will be requested to complete three 24-hour dietary recalls over the subsequent week. After baseline, participants will be randomly allocated to either the control group (usual care) or the intervention group (nutritional counseling delivered by a dietitian). At the first study visit, participants will deliver the stool sample and dietary recalls. The intervention will last for 90 days and will include one in person consultations and bi-weekly follow-up phone calls. Nutritional counseling will address four main topics: healthy eating, food processing, fiber and fluid intake, and levodopa interactions with diet. In addition, participants in the intervention group will receive an individualized diet plan. All baseline assessments will be repeated at the 90-day endpoint. The primary outcome is the change in weekly bowel movement frequency from baseline to day 90, measured through the stool diary. Secondary outcomes include CSS, changes in fecal gut microbiota, macronutrient distribution, diet quality, body composition and sarcopenia indicators. Trial registration number: NCT07213856.

Why do some dreams feel so vivid that they seem almost real, while others unfold as confusing fragments that are difficult to remember or understand? New research suggests the answer may depend on a combination of personality, sleep quality, mental habits, and major events unfolding in the outside world.

A study led by researchers at the IMT School for Advanced Studies Lucca found that dreams are not simply random collections of images and thoughts. Instead, they appear to be shaped by both individual differences and shared experiences, including major social disruptions such as the COVID-19 pandemic.

The research, published in Communications Psychology , examined more than 3,700 descriptions of dreams and waking experiences from 287 people between the ages of 18 and 70.

For two weeks, participants recorded their dreams and daily experiences. The researchers also collected information about each person's sleeping habits, personality, cognitive abilities, and psychological characteristics.

This broad set of information allowed the team to compare what people experienced while awake with the stories, settings, and emotions that appeared in their dreams.

To analyze such a large number of written reports, the researchers used natural language processing (NLP), a form of artificial intelligence that examines patterns, relationships, and meaning in language.

Rather than relying only on researchers to read and interpret every dream individually, the technology allowed the team to measure the semantic structure of the reports. Semantic structure refers to how ideas, words, and concepts are connected within a piece of language.

The results showed that dream content was neither completely random nor inherently chaotic. Instead, dreams reflected a complex mixture of personal characteristics, including a tendency to mind-wander, an interest in dreams, and the quality of a person's sleep. Events occurring in the broader world also influenced what people dreamed about.

When the researchers compared participants' descriptions of waking life with their dream reports, they found that the brain did not appear to reproduce daily experiences exactly as they happened.

A workplace, hospital, classroom, or other everyday setting might appear in a dream, but it would often be combined with unrelated places, shifting viewpoints, or unfamiliar surroundings. Different parts of a person's life could merge into a single vivid scene.

These findings suggest that dreaming is not a simple replay system. The sleeping mind appears to reorganize pieces of reality, combining memories with imagination, expectations, and possible future experiences.

The result can be a completely new scenario that feels immersive, emotionally intense, or surreal.

The way dreams were transformed also differed from person to person.

People who were more likely to experience mind-wandering while awake tended to report dreams that changed quickly and felt more fragmented. Their dreams often moved rapidly between scenes, ideas, or perspectives.

By contrast, participants who placed greater value on dreams and believed that dreaming had personal meaning tended to describe richer and more immersive experiences. Their dream reports contained stronger perceptual detail, making the scenes feel more vivid and lifelike.

The findings do not necessarily show that believing in dreams directly causes more vivid dreaming. However, they reveal a strong relationship between how people think about dreams and how they experience or remember them.

The researchers also examined dream reports collected during the COVID-19 lockdown by scientists at Sapienza University of Rome. They compared those reports with data gathered in later months and years by the IMT School team.

During the lockdown, dreams contained stronger emotions and more references to restrictions, barriers, confinement, and other limitations. These themes reflected the conditions people were experiencing in daily life.

As the months passed, those patterns became less pronounced. This gradual change suggests that dreams may evolve alongside psychological adjustment, particularly as people adapt to stressful or disruptive events.

In other words, dreams may provide clues about how the mind responds to major changes over time.

"Our findings show that dreams are not just a reflection of past experiences, but a dynamic process shaped by who we are and what we live through," explains Valentina Elce, researcher at the IMT School and lead author of the paper. "By combining large-scale data with computational methods, we were able to uncover patterns in dream content that were previously difficult to detect."

The study also demonstrates how artificial intelligence could change dream research.

The NLP models were able to identify meaning and structure in dream reports with an accuracy similar to that of independent human evaluators. This suggests that computational tools may allow scientists to analyze much larger collections of dreams in a consistent and repeatable way.

That could create new opportunities for studying consciousness, memory, emotional processing, and mental health.

Because dreams are highly personal and often difficult to describe, they have traditionally been challenging to investigate on a large scale. AI-based language analysis may help researchers uncover patterns that would otherwise remain hidden.

This work was supported by a grant from the BIAL Foundation (#091/2020) and by the TweakDreams ERC Starting Grant (#948891). The research was conducted at the IMT School for Advanced Studies Lucca, in collaboration with researchers from Sapienza University of Rome and the University of Camerino.

Materials provided by IMT School for Advanced Studies Lucca . Note: Content may be edited for style and length.

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by Mushuai Hao, Haonan Qi, Liang Zhao, Wei Han

Objective This study aims to investigate the impact of 8-week squat training with different eccentric contraction tempos on maximal squat strength, jump height, and sprint performance among university students majoring in sports training.

Methods Thirty participants were randomly assigned to either the 4/0/X/0 group, the 2/0/X/0, or the self-selected eccentric, isometric and inter-repetition tempo group (V/V/X/V), performing tempo-specific loaded squat training twice weekly for 8 weeks. Maximal squat strength, 30-meter sprint performance, countermovement jump (CMJ), and squat jump (SJ) heights were assessed before and after the intervention. A two-way repeated-measures ANOVA and effect size analysis were used to evaluate the training outcomes.

Results 1) For maximal squat strength, no statistically significant inter-group difference was detected (P > 0.05). All three groups exhibited significant within-group improvements relative to baseline (P  V/V/X/V group (ES = 0.85)> 4/0/X/0 group (ES = 0.64). 2) For CMJ height, both the 2/0/X/0 and V/V/X/V groups showed significant improvement compared to pre-intervention (P  V/V/X/V group (ES = 0.30) > 4/0/X/0 group (ES = −0.90). 3) SJ height results mirrored CMJ trends, with ES values ranking:2/0/X/0 group (ES = 1.09) > V/V/X/V group (ES = 0.34) > 4/0/X/0 group (ES = −0.83). 4) In 30-meter sprint performance, the 2/0/X/0 group demonstrated significant improvement (P Compared with the 4/0/X/0 and V/V/X/V tempos, the 2/0/X/0 tempo showed the most favorable improvement trend in lower-limb maximal strength, and induced significantly greater enhancements in explosive power performance.

Research on well-being has predominantly relied on cross-sectional retrospective person-level assessments, which may overlook short-term fluctuations across contexts and time. The present study evaluated a daily diary adaptation of the Mental Health Continuum-Short Form (MHC-SF) in a 14-day experience sampling design comprising 13 end-of-day assessments followed by a final questionnaire on day 14 in a German-speaking sample (N = 262). The retrospective two-week MHC-SF and the daily adaptation of the MHC-SF were examined using traditional psychometric approaches (internal consistency, confirmatory factor analysis) and advanced methods (generalizability theory, multilevel reliability, multilevel confirmatory factor analysis, and exploratory structural equation modelling). Results provided preliminary and qualified support for the daily diary adaptation of the MHC-SF. Evidence was strongest for the total MHC score and emotional well-being, which showed comparatively greater within-person variability and acceptable within-person reliability. In contrast, psychological and especially social well-being showed higher between-person stability and weaker evidence for capturing short-term daily fluctuations. The retrospective two-week MHC-SF showed excellent internal consistency, but its factor structure was not well captured by restrictive CFA models. ESEM provided better global fit, although the loading pattern suggested a dominant general well-being factor and limited empirical separation of the three domains, particularly for social well-being items. Subsequent lagged analyses indicated moderate day-to-day stability of well-being. Overall, these results support the utility of the MHC-SF for assessing overall and emotional aspects of daily well-being in ecologically valid settings, whereas psychological and particularly social well-being should be interpreted more cautiously and may require further adaptation for intensive longitudinal designs.

Urban traffic-related air pollution and noise not only exert direct adverse effects on human health but also influence how individuals perceive and respond to these environmental stressors. The low-intervention strategies (e.g., outdoor music) have attracted increasing attention for their potential to alleviate physiological responses and discomfort induced by traffic-emitted pollution. The present study focuses on the mitigating effects of outdoor piano performance on the physiological responses of individuals exposed to traffic-related air pollution and noise. Field measurements are conducted in an outdoor cafe area located within a pedestrian street adjacent to a traffic roadway. Physical and physiological parameters are recorded over two comparative test days (with and without piano performance), and a subjective questionnaire survey is also administered. A total of 15 volunteers were initially recruited for the study. These participants were healthy male adults aged between 25 and 30 years, with heights ranging from 165 cm to 175 cm, and without any major medical conditions. The measured results indicate that the road traffic induced air pollution and noise have obviously affect physiological reactions of human, and the relative HR/SCL indexes increase with air pollutant concentrations (NO2/PM10) and noise level. The presence of outdoor piano performance is capable of mitigating physiological reactions caused by traffic emission, and the relative HR/SCL indexes can be reduced up to 50%. Traffic noise is clearly perceived by all volunteers and elicited varying degrees of dissatisfaction. The relevant statistical significance tests were conducted to compare the conditions with and without piano performance. Significant inter-departmental differences were found in HR/SCL indexes (Wilcoxon rank-sum test) for the scenarios with and without piano performance. These results indicate that outdoor piano performance significantly influences human physiological responses and can effectively mitigate the adverse effects of traffic noise and air pollution on occupants.

IntroductionCollege students face prominent emotional and interpersonal challenges under current social and academic pressures, and social-emotional competence is essential for their mental health and healthy development. This study investigates the relationship between physical exercise and social-emotional competence among college students, as well as the mediating roles of, peer relationships, and self-efficacy in this relationship.MethodsA convenience sample of 517 college students from several universities in Liaoning Province was surveyed. Data were collected using the Physical Exercise Scale, Peer Relationship Scale, Self-Efficacy Scale, and Social-Emotional Competence Scale. Data were analyzed using SPSS 26.0, the PROCESS 3.4 plugin, and the Bootstrap method to test the chained mediation effects of peer relationships and self-efficacy.Results(1) Physical exercise, peer relationships, and self-efficacy were all positively correlated with college students' social-emotional competence. (2) Physical exercise was positively correlated with college students' peer relationships, self-efficacy, and social-emotional competence; peer relationships were positively correlated with self-efficacy and social-emotional competence; and self-efficacy was positively correlated with social-emotional competence.DiscussionThis research constructs a chained mediation model, systematically clarifying the relationship between physical exercise and social-emotional competence among college students, as well as the mediating roles of, peer relationships, and self-efficacy in this relationship. The research outcomes offer solid theoretical references and feasible practical enlightenment for carrying out college mental health education and optimizing students' social-emotional ability training programs.

IntroductionPhysical activity is an effective adjunctive therapy in schizophrenia, improving psychiatric symptoms and reducing the mortality rate. However, individuals with schizophrenia engage in lower levels of physical activity compared to the general population, and adherence to structured exercise programs remains low. The factors underlying sustained engagement are still not well understood. This study aimed to investigate factors associated with adherence to an exercise intervention in individuals with schizophrenia and to explore whether personality characteristics are related to physical fitness outcomes.MethodsThe present investigation was a secondary analysis of data from two randomized controlled trials conducted at the LMU Munich, which included N = 100 participants. Participants completed a three-month, professionally supervised exercise intervention, consisting of either aerobic endurance training or flexibility, strength, and balance training. Assessments at screening and baseline included sociodemographic characteristics, lactate threshold testing, physical fitness measures, personality and motivational questionnaires, and clinical and functional ratings. Multiple regression analyses were used to examine adherence as the primary outcome and physical fitness as a secondary outcome. The predictor variables included motivation, personality traits, symptom severity, previous exercise experience and emotional support, baseline fitness level, affective forecasting, and satisfaction with exercise training.ResultsThe results revealed no significant associations between any of the investigated variables and adherence after correction for multiple testing (all p > 0.05). Similarly, physical fitness was not significantly associated with personality- related variables in this study.DiscussionOverall, the examined variables explained only a limited proportion of the variance in the two outcomes. These findings suggest that the investigated variables do not fully explain adherence or physical fitness and that both outcomes may be influenced by additional contextual determinants of exercise behavior in schizophrenia.

BackgroundFacing rising global surgical volumes and healthcare workforce shortages, task shifting in anesthesia care has been widely implemented, including in China where a specialized anesthesia nursing workforce has rapidly expanded to approximately 60,000. However, this workforce confronts unique challenges including historical professional restrictions and the absence of standardized specialized education, making their work engagement a critical yet underexplored issue.AimsThe purpose of this study is to explore whether and how resilience may relate to work engagement among Chinese anesthesia nurses, with a particular focus on the potential mediating association of moral sensitivity in this relationship.DesignThis study is a cross-sectional study.MethodsA total of 372 Chinese anesthesia nurses from 41 hospitals were surveyed using an online questionnaire that included nurses’ sociodemographic characteristics, resilience, moral sensitivity, and work engagement. The mediating effect was analyzed using Model 4 of the PROCESS macro for SPSS 27.0.ResultsThe total score of work engagement among Chinese anesthesia nurses was 31.25 ± 7.75. The results showed positive correlations between resilience, moral sensitivity, and work engagement (all p < 0.01). The indirect effect of resilience on work engagement through moral sensitivity was statistically significant (effect = 0.338), accounting for 53.7% of the total effect.ConclusionChinese anesthesia nurses present a relatively lower level of work engagement compared to previous reports in similar nursing populations. The results are statistically consistent with a mediating association in which moral sensitivity links resilience to work engagement among Chinese anesthesia nurses.

IntroductionHybrid work is increasingly widespread; however, it remains unclear whether employees benefit from these arrangements or struggle to manage work, life boundaries and achieve recovery. Focusing on two early cognitive indicators of recovery, affective rumination (AffRum) and problem-solving pondering (PSP), this study examines whether general self-efficacy (GSE), as a foundational personal resource, shapes the relationship between remote work intensity (RWI; operationalized as the structural frequency of remote workdays) and these processes. In high-intensity hybrid settings, where reduced institutional scaffolding and blurred boundaries amplify demands for autonomous boundary management, such personal resources become crucial.MethodsData were collected from 747 hybrid employees from four medium-to-large Italian organizations.ResultsResults showed that RWI interacted with GSE in predicting AffRum, such that high RWI was associated with higher rumination among employees with low GSE. No such interaction was found for PSP; suggesting that work-related problem-solving warrants further investigation to determine whether it stems from its constructive problem-solving nature or from how the construct is operationalized and perceived. Overall, our correlational findings suggest that intensive remote work configurations may present specific risks for recovery when personal self-regulatory resources are limited.DiscussionRather than focusing solely on the structural number of remote days, hybrid work management could benefit from incorporating strategies that support employees’ boundary management and psychological disengagement capabilities.

PurposeIn the context of digital transformation and intensified global competition, career sustainability has become a core issue for both organizational and individual development. This study investigates how employee volunteering influences career sustainability by examining the mediating role of knowledge integration and the moderating role of competitive orientation.Design/Methodology/ApproachBased on social learning theory, a three-wave time-lagged survey was conducted among 293 employees from Chinese internet companies. The study employed hierarchical regression analysis, confirmatory factor analysis, and moderated mediation analysis to test the hypothesized relationships.FindingsThe results show that employee volunteering positively affects career sustainability through the mediating effect of knowledge integration, and that this effect is weakened by higher levels of competitive orientation.OriginalityThis study develops an integrated framework linking company-supported employee volunteering to career sustainability through knowledge integration, thereby extending research on how prosocial activities may contribute to sustainable careers. It further extends research on competitive orientation by identifying it as a boundary condition, thus offering actionable insights into balancing competition and collaboration in organizational contexts.Practical implicationsOur research findings provide a better understanding of the mechanisms linking volunteering and career development and offer managers and policymakers useful guidance on promoting employees’ long-term development through volunteering initiatives.

This study examines how exposure to artificial intelligence (AI) shapes Chinese workers’ occupational mobility and how the consequences differ across AI’s labor-saving and labor-augmenting channels. Using 2,446 worker-year observations from the China Labor Force Dynamics Survey 2012, 2018, we measure occupation-level AI exposure as the semantic similarity between Chinese task descriptions and AI patent texts and decompose it along routine and non-routine task lines. Causal identification uses an instrumental variable that interacts 2010 baseline occupation-level exposure with lagged cumulative U.S. patent stocks from the USPTO AI Patent Dataset. The 2SLS estimates yield three findings. First, a one-log-unit increase in total AI exposure raises minor-group switching by 3.6 percentage points and intermediate-group switching by 3.4 points, with no significant effect at the major-group level. In the decomposed estimates, only the labor-augmenting component has significant positive coefficients beyond the minor-group margin, reaching 4.6 points at the intermediate and 6.9 points at the major-group distance. The labor-saving coefficients are statistically indistinguishable from zero in the full-sample switching-incidence IV estimates. The short-distance response is concentrated among women, and among less-educated workers labor-saving exposure predicts lower and labor-augmenting exposure higher major-group mobility, suggesting a labor-saving-related barrier to long-distance transitions. Second, among switchers, labor-augmenting exposure exhibits positive but attenuated origin, destination sorting in all three switching samples. Labor-saving sorting is positive in the minor-group sample, statistically indistinguishable from zero in the intermediate-group sample, and negative in the major-group sample. Third, among switchers, greater labor-saving exposure in the origin occupation is associated with longer hours in the minor- and intermediate-group samples and with lower skill-match satisfaction in all three samples, whereas favorable associations with origin labor-augmenting exposure are confined to specific samples. The study extends organizational-psychology research by linking instrumented AI exposure to occupational switching as an observable career-adaptation behavior and to the fit and demand conditions of destination jobs. The findings suggest targeted reskilling and organizational support for less-educated workers in occupations with high labor-saving exposure.

IntroductionThe formation of farmers’ well-being is closely related to digital literacy, psychological empowerment, and agricultural planting decision-making behavior. In order to explore the underlying sequential associations regarding farmers’ well-being in meaningful environments, this study aims to examine these interrelationships drawing upon positive psychology theories, such as meaning construction theory.MethodsA chain mediation model was constructed to assess the sequential correlations among the variables. The reliability and validity of the measurement scales were evaluated using Cronbach’s α, the KMO measure, and structural fit indices. Multicollinearity among predictor variables was checked using the Variance Inflation Factor (VIF), and the sequential associations were analyzed utilizing a Bootstrap chain mediation procedure.ResultsThe analysis indicated that the measurement scales possessed good structural validity and reliability (Cronbach’s α range: 0.870 ~ 0.925; KMO = 0.978; Bartlett’s test χ2 = 18135.643, df = 595, p < 0.001; CFI = 0.997, TLI = 0.997, RMSEA = 0.004, SRMR = 0.020). No severe multicollinearity was observed (VIF range: 1.888 ~ 2.105). Meaningful environmental perception was significantly and positively correlated with farmers’ well-being (r = 0.616). In the Bootstrap analysis, the total statistical effect of meaningful environmental perception on well-being was 0.6280.628 (direct effect = 0.203; indirect effect = 0.425, accounting for 67.68% of the total effect). The full-chain indirect path, from meaningful environmental perception through digital literacy, psychological empowerment, and farmers’ decision-making behavior to farmers’ well-being, yielded a significant sequential association with a statistical indirect effect of 0.020 (95% CI [0.013, 0.029]).DiscussionThe findings indicate that there are significant positive correlations and sequential indirect associations among meaningful environmental perception, digital literacy, psychological empowerment, decision-making behavior, and well-being in farmers. The chain mediation model offers a relational perspective on how these psychosocial and behavioral factors are intertwined.

BackgroundFamily coping is integral to chronic heart failure management, yet systematic research on how families collectively cope remains limited. This study aimed to identify distinct family coping patterns among CHF patients and examine their associations with sociodemographic factors, fatigue, and family functioning.DesignThis cross-sectional study was conducted in China.MethodsPatients with chronic heart failure were recruited through convenience sampling from tertiary hospitals in Yunnan Province, southwestern China. Participants completed questionnaires assessing sociodemographic characteristics, family coping (FCS-CHF), fatigue (CChFS), and family functioning (CGFFS). Latent profile analysis was conducted to identify distinct coping subgroups, followed by chi-square tests, t-tests, and binary logistic regression to examine the factors associated with the profile membership of these subgroups.ResultsA total of 536 patients were included in this study. Latent profile analysis identified two distinct coping patterns among families: constructive family coping (56.7%) and depleting family coping (43.3%). The constructive family coping pattern consistently scored higher across all six coping dimensions. Binary logistic regression revealed that family structure, family size, annual household income, and family functioning were significantly associated with the type of coping pattern adopted by families. Specifically, better family functioning (OR = 0.311, p < 0.001), a medium family size (3, 6 persons; OR = 0.427, p = 0.004), and higher income (≥200,000 CNY; OR = 0.415, p = 0.025) were associated with a lower likelihood of depleting family coping. In contrast, a nuclear family structure was associated with a higher likelihood of depleting family coping (OR = 1.739, p = 0.048).ConclusionThis study provides empirical evidence of two distinct family coping profiles among patients with chronic heart failure in a rural resource-limited population. The findings highlight the need for tailored, family centered nursing interventions that address specific support needs, including comprehensive multicomponent support for depleting families and reinforcement of existing strengths for constructive families. Future research should extend these findings through multi-informant designs that capture both patient and family member perspectives and through replication in more diverse populations to establish their generalizability to other populations.

Misinformation poses a challenge not only because false claims circulate widely, but also because readers must make judgments about whether such claims are credible and whether they should be shared. While hedging is frequently observed in misinformation, its effects on audience evaluation remain insufficiently differentiated, particularly outside Western linguistic settings. This study examines whether two ambiguity-based hedging cues, source ambiguity and numerical ambiguity, shape readers' responses to Chinese misinformation in distinct ways. A total of 182 participants evaluated a set of Chinese misinformation claims that had previously been debunked by authoritative fact-checking sources. The statements were presented in one of four versions generated through the presence or absence of source ambiguity and numerical ambiguity. Participants' rejection of debunked misinformation claims, sharing intentions, and reaction times were analyzed using mixed-effects models. The results showed that both source ambiguity and numerical ambiguity were associated with greater rejection of debunked misinformation claims. Neither cue directly predicted sharing intention; however, greater rejection of debunked misinformation claims was associated with lower willingness to share it. Numerical ambiguity, but not source ambiguity, was additionally associated with shorter reaction times. These findings suggest that ambiguity-based hedging cues may function as cues of unreliability rather than persuasive devices, highlighting the importance of source traceability and numerical specificity in misinformation literacy interventions.