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Healthcare clinicians are already using artificial intelligence to guide decisions related to limb loss. AI is being integrated into prosthesis alignment, socket fabrication, exercise management, gait monitoring, and beyond. For better or worse, it’s also playing a role in insurance approvals, denials, and appeals.

According to a new study published in the medical journal Artificial Intelligence Surgery, AI’s role in limb care is almost certain to keep growing. “Our review suggests that the integration of AI in limb care is not only rapidly growing but is seemingly inevitable,” write the authors, who are based at the University of Colorado School of Medicine. They go on to identify some distinct areas of limb care in which AI is rapidly emerging as an important tool, and changing how clinicians treat limb loss.

You can read the full paper online. Here’s a quick summary:

Prosthesis Use and Design

AI is driving rapid innovation in the development of neuroprosthetic devices. Systems known as convolutional neural networks (CNNs) have been shown to support highly responsive prosthetics that give wearers a high degree of control over joint flexion, a greater sense of proprioception, and some degree of sensory feedback. The study also notes the potential for AI (used in combination with advance imaging) to help determine the optimal placement of implants that connect nerve endings to prosthetic controllers. This type of research has shown promising results at the University of Minnesota, North Carolina State University, University of Wisconsin, Shirley Ryan AbilityLab, and elsewhere.

Management of Nerve Injuries and Pain

Pain is one of the leading causes of post-amputation health complications, the authors note—particularly phantom limb pain. Researchers have begun applying AI technologies to help identify the causes of pain and yield more effective treatment strategies. One of the most promising frontiers lies in the mapping of dense neural networks; AI now allows neurologists to develop highly precise blueprints of nerve pathways (down to the level of individual axon-synapse connections) in 1/50th the time required by manual methods.

Drug research is another area of progress: AI makes it possible to rapidly model physiological responses to thousands of individual drugs, and millions of drug combinations. By quickly identifying the most promising therapies, these preclinical models can streamline the process of clinical testing and approval.

Nerve repair and regeneration represent yet another area in which AI is driving innovation forward. The paper notes that “the application of AI to nerve regenerative strategies has the potential for revolutionary biotechnologies,” particularly in the development of biomaterials such as nerve guidance conduits.

Clinical Decisionmaking and Limb Preservation

“The application of AI in clinical decision making may revolutionize surgical practice through novel patient-centered approaches,” the authors write. AI systems are becoming extremely accurate in modeling the odds of success for limb-preserving treatments such as revascularization, vascular bypass, and minor amputation in patients with limb-threatening conditions. Similar approaches are helping clinicians evaluate limb-salvage options in patients with complex injuries.

In addition, clinicians are beginning to use AI to identify patients who face elevated risk of amputation and intervene before the conditions become limb-threatening. For example, AI can screen electronic health records and flag patients who require targeted PAD screening. It can also refine the results of conventional imaging techniques (such as ultrasounds), leading to more precise selection of treatments.

“AI-based strategies complement clinical judgment and support innovations in …. [amputation] prevention, management, peripheral nerve injury treatment, postoperative outcomes, and prosthesis design,” the paper concludes. “AI as a methodology holds promise in revolutionizing practice.”

e-neko aka e-cat of IRC told me that buses come in pairs for some reason. I did not believe him. I still find it hard to believe. It looks so strange.

Figured made into a mooovie:

https://kaka.farm/pub/images/2025-12-06-buses-simulation/bus.mp4

Simulation code:

https://kaka.farm/pub/images/2025-12-06-buses-simulation/waiting-for-the-bus.scm

Jupyter notebook on:

https://kaka.farm/pub/images/2025-12-06-buses-simulation/bus.ipynb

(import
 (srfi srfi-1)
 (srfi srfi-9)

 (ice-9 match)
 )

(define *bus-capacity* 50)
(define *bus-departure-interval* 100)
(define *bus-route-length* 10000)
(define *bus-speed* 10)
(define *number-of-stations* 100)
(define *station-fill-rate* 1)
(define *time-passenger-on* 1)
(define *time-passenger-off* 1)

(define-record-type <event>
  (make-event time data)
  event?
  (time event-time)
  (data event-data))

(define-record-type <bus-departure>
  (make-bus-departure)
  bus-departure?)

(define-record-type <bus-at-station>
  (make-bus-at-station station-number number-of-passengers)
  bus-at-station?
  (station-number bus-at-station-station-number)
  (number-of-passengers bus-at-station-number-of-passsangers))

(define-record-type <passenger-waiting>
  (make-passenger-waiting station-number)
  passenger-waiting?
  (station-number passenger-waiting-station-number))

(define-record-type <world>
  (make-world events stations)
  world?
  (events world-events)
  (stations world-stations))

(define (initialise-world)
  (make-world '()
              (make-vector *number-of-stations* 0)))

(define (sort-event-queue event-queue)
  (sort event-queue
        (lambda (event-a event-b)
          (< (event-time event-a)
             (event-time event-b)))))

(define (tick world)
  (match (world-events world)
    ['() (make-world (list (make-event 0 (make-bus-departure))
                           (make-event 0 (make-passenger-waiting (random *number-of-stations*))))
                     (world-stations world))]
    [(and events (head . rest))
     (let* ([sorted-event-queue (sort-event-queue events)]
            [earliest-event (car sorted-event-queue)]
            [rest-of-events (cdr sorted-event-queue)])
       (match earliest-event
         [($ <event> time data)
          (match data
            [($ <passenger-waiting> station-number)
             (let ([new-events (list (make-event (+ time (* (random:uniform)
                                                            *station-fill-rate*))
                                                 (make-passenger-waiting (random *number-of-stations*))))]
                   [stations (world-stations world)])
               (vector-set! stations station-number (1+ (vector-ref stations station-number)))
               (make-world (append new-events rest-of-events)
                           stations))]
            [($ <bus-departure>)
             (let ([new-events (list (make-event (+ time *bus-departure-interval*)
                                                 (make-bus-departure))
                                     (make-event (+ time (/ *bus-route-length* *number-of-stations*))
                                                 (make-bus-at-station 1 0)))])
               (make-world (append new-events rest-of-events)
                           (world-stations world)))]
            [($ <bus-at-station> station-number number-of-passengers)
             (format #t "~A,~A~%" time station-number)
             (cond
              [(= station-number *number-of-stations*)
               (make-world rest-of-events (world-stations world))]
              [else
               (let* ([stations (world-stations world)]
                      [waiting-at-station (vector-ref stations station-number)]
                      [passengers-off (round (* number-of-passengers (random:uniform)))]
                      [number-of-passengers-after-off (- number-of-passengers passengers-off)]
                      [free-seats-after-off (- *bus-capacity* number-of-passengers-after-off)]
                      [passengers-on (min free-seats-after-off waiting-at-station)]
                      [number-of-passengers-after-off-on (+ number-of-passengers-after-off passengers-on)]
                      [time-bus-waiting-in-station (+ (* passengers-off *time-passenger-off*)
                                                      (* passengers-on *time-passenger-on*))]
                      [next-time (+ time
                                    time-bus-waiting-in-station
                                    (/ *bus-route-length* *number-of-stations*))]
                      [new-events (list (make-event next-time
                                                    (make-bus-at-station (1+ station-number)
                                                                         number-of-passengers-after-off-on)))])
                 (vector-set! stations station-number (- (vector-ref stations station-number)
                                                         passengers-on))
                 (make-world (append new-events rest-of-events)
                             stations))])]
            [($ <event> time 'bus-final-arrival data)
             (make-world rest-of-events
                         (world-stations world))])]))]))

(let loop ([world (tick (make-world '() (make-vector *number-of-stations* 0)))]
           [n 0])
  (cond
   [(= n 100000)
    '()]
   [else
    (loop (tick world)
          (1+ n))]))

Jupyter notebook source, sorta:

from matplotlib import pyplot as plt
import numpy as np

s = open('data-file.csv', 'r').read()

l = [[float(n.split(',')[0]), int(n.split(',')[1])] for n in s.split("\n")]

def f(station_number):
    last = [n for n in l if n[1] == station_number]
    last = [n_1[0] - n_0[0] for n_0, n_1 in zip(last, last[1:])]
    fig, ax = plt.subplots(1, 1)
    ax.set_title(f'bus station #{station_number:03}')
    ax.set_xlabel('time difference between two consecutive buses')
    ax.set_ylabel('number of busess')
    ax.hist(last, bins=100)
    fig.savefig(f'bus-{station_number:03}.png')

for range(1, 101):
    f(n)

http://archive.today/2025.11.24-211606/https://www.nytimes.com/2025/11/24/world/europe/france-voluntary-military-service.html

France, whose relations with Russia are at a low point, has embarked on a concerted effort to convince its citizens that they must be ready for war. But a warning from the new French Army chief that the country must accept the possible loss of its children has touched a raw nerve.

The army chief, Gen. Fabien Mandon, who took over as chief of staff in September, told mayors gathered in Paris from across France last week that they must become the messengers of a new French resolve on an unstable European continent.

What is needed, he said, is “the spirit that accepts that we will have to suffer to protect what we are.” If France “wavers because we are not ready to accept losing our children,” then “we are, indeed, at risk,” he told the mayors, evoking the growing threat from Russia since its full-scale invasion of Ukraine in 2022.

A statement on Monday from the president’s office said, “On this occasion, the President of the Republic will reaffirm the importance of preparing the nation and its moral strength to face growing threats.”

On Saturday, as he left the Group of 20 meeting in South Africa, Mr. Macron said, “In the world of uncertainty and mounting tensions in which we live, if we really want to be secure, we have to persuade the other not to approach.”

He has taken in recent months to repeating the phrase: “You have to be feared in this world. And to be feared you have to be strong.”

http://archive.today/2025.12.05-141739/https://www.nytimes.com/2025/12/05/world/europe/germany-military-boris-pistorius-defense-minister.html

Ever since Germany reformed its military after World War II, the primary role of the German defense minister has been to maintain an army large enough to protect the country, but constrained enough to prevent a return to German militarism.

The role of the incumbent, Boris Pistorius, is different.

As Russia warns that it is ready for war with Europe, Mr. Pistorius’s goal is to make Germany’s military capable of leading the continent’s defense in a major land conflict — and to prepare the country’s pacifist population for this new posture.

Opinion polls in Germany show a pervasive fear of sending another generation into war. Domestic politics play a role, too, with parties on the far right and left that are partial to Russia or that favor dialogue with it.

On Friday, German lawmakers approved the latest part of Mr. Pistorius’s plan: a law that aims to increase the number of German soldiers to 260,000 by 2035, a nearly 50 percent boost. To incentivize recruitment, soldiers will be paid more and receive more training that is useful for civilian careers.

The law is the latest in a sequence of moves that were unthinkable less than a decade ago but that Mr. Pistorius has promoted in order to bolster German defense. In March, he helped lead a successful effort to remove limits on military spending from Germany’s Constitution. That was a major shift for a debt-shy country, and it enabled Mr. Pistorius to spend billions more on arms, tanks, ships and aircraft that the country previously couldn’t buy.

For some, such moves provoke unease, summoning memories of German expansionism during the two world wars.

But Mr. Pistorius remains phlegmatic. Debate is healthy, he said, not least because it is slowly acclimatizing society to the need for action.

“The discussion alone,” he said, is “changing the way many people think about the times we live in, about the threats we face.”

In our interview, Mr. Pistorius acknowledged the weight of German history, but said it had given him and others “a sense of responsibility.”

“Namely, that we must do our part to ensure that we continue to live in peace in Europe,” he said, adding that the expanded German Army will still be much smaller than it was during the Cold War. Back then, Germany had as many as 500,000 soldiers but limited military ambitions, and it never sought to lead Europe’s defense in the way that he now seeks.

Despite pushing for contentious measures, Mr. Pistorius has remained Germany’s most popular politician for most of the past three years, according to monthly opinion polls.

He speaks with the rasp of a drill sergeant who has been yelling all day. His speech is unadorned, to the point, and sometimes self-deprecating. At one point in our interview he compared his work to that of a soccer coach.

I witnessed his brusque charisma on a visit in January to eastern Poland, where a group of German soldiers were helping to staff a missile air-defense system near the border with Ukraine.

Mr. Pistorius’s popularity has allowed him to survive the electoral fall of his center-left Social Democratic party, which led the previous governing coalition until it collapsed last year. Reappointed as part of a new coalition after the center-right won the last election, Mr. Pistorius is Germany’s first defense minister since World War II to serve chancellors from two different parties.

Early in his tenure as defense minister, Mr. Pistorius provoked a national furor by insisting that Germany become “Kriegstüchtig,” or “war-ready” — a provocative term for a country that, since 1945, had sought only to be “defense-ready.”

cross-posted from: https://lemmy.sdf.org/post/46768180

Archived

A single word can crack the facade of a great power’s confidence. That’s what happened last month when Prime Minister Sanae Takaichi of Japan told lawmakers in Tokyo that a Chinese attack or blockade against Taiwan would constitute a threat to Japan’s “survival,” a term that, under Japanese law, would permit the country to deploy its military overseas.

Ms. Takaichi merely said aloud what has long been understood — that a crisis involving Taiwan would threaten Japan’s national security. But her comments were among the clearest public signals yet that Tokyo could help defend Taiwan from potential Chinese aggression.

Beijing reacted as if Ms. Takaichi, a conservative politician, had declared war. Chinese state media has portrayed her as reviving the militarist rhetoric used to justify Japan’s aggression during World War II, and a senior Chinese envoy posted what amounted to an online threat to behead Ms. Takaichi. China has halted some Japanese imports, discouraged Chinese tourism to Japan and stepped up coast guard patrols around islands claimed by both countries.

Beijing routinely lashes out at Tokyo because of lingering resentment over Japan’s wartime past, which included a brutal invasion and occupation of China. This time, however, the fury is rooted in something more dangerous: China’s growing anxiety that one of its bedrock goals — isolating Taiwan and forcing it to submit to unification on Chinese terms — is slipping away.

The Chinese Communist Party has long assumed that time and pressure would slowly wear Taiwan down. If President Xi Jinping of China concludes that bet has failed, he may escalate to sharper forms of pressure sooner than planned. It is vital for regional security that Tokyo and Washington stand firm and signal clearly that increased Chinese coercion of Taiwan will trigger a coordinated response.

[...]

Ms. Takaichi did not create this situation; years of relentless Chinese coercion did. Her remark merely made explicit what has long been implicit — that if Beijing keeps tightening the screws on Taiwan, it will inevitably pull in other democracies because the island’s fate now bears directly on their security.

Airing out the shared stakes faced by all the players in this equation, as Ms. Takaichi has done, is a surer path to stability than pretending that silence will keep the peace.

[,,,]

Meanwhile, Taiwan's PM said his country is 'very moved' by Japanese prime minister's support.

"Recently, Prime Minister Takaichi's remarks about stability and peace in the Taiwan Strait moved us all very, very much. They represent justice and peace," Premier Cho Jung-tai said in Taipei.

"We are also extremely grateful to Prime Minister Takaichi and to the Japanese government and people for continuing to uphold this justice and peace under such strong pressure."

Tags:

• 2025120400 (Pixel 6, Pixel 6 Pro, Pixel 6a, Pixel 7, Pixel 7 Pro, Pixel 7a, Pixel Tablet, Pixel Fold, Pixel 8, Pixel 8 Pro, Pixel 8a, Pixel 9, Pixel 9 Pro, Pixel 9 Pro XL, Pixel 9 Pro Fold, Pixel 9a, Pixel 10, Pixel 10 Pro, Pixel 10 Pro XL, Pixel 10 Pro Fold, emulator, generic, other targets)

Changes since the 2025111800 release:

• full 2025-12-01 security patch level (has already been fully provided by our security preview releases for at least a month and most of the patches since September)
• add experimental support for the Pixel 10, Pixel 10 Pro, Pixel 10 Pro XL and Pixel 10 Pro Fold (there were 2 standalone experimental releases prior to this via 2025112500 and 2025113000 along with corresponding security preview releases for each)
• Cell Broadcast Receiver: switch back to our modified text for the presidential alerts toggle
• kernel (6.1): update to latest GKI LTS branch revision including update to 6.1.158
• kernel (6.6): update to latest GKI LTS branch revision including update to 6.6.116
• kernel (6.12): update to latest GKI LTS branch revision including update to 6.12.60
• Auditor: update to version 90
• Vanadium: update to version 143.0.7499.34.0
• Vanadium: update to version 143.0.7499.34.1
• Vanadium: update to version 143.0.7499.34.2
• GmsCompatConfig: update to version 164
• GmsCompatConfig: update to version 165

All of the Android 16 security patches from the current January 2026, February 2026 and March 2026 Android Security Bulletins are included in the 2025120401 security preview release. List of additional fixed CVEs:

• Critical: CVE-2025-48631, CVE-2026-0006
• High: CVE-2022-25836, CVE-2022-25837, CVE-2023-40130, CVE-2025-22420, CVE-2025-22432, CVE-2025-26447, CVE-2025-32319, CVE-2025-32348, CVE-2025-48525, CVE-2025-48536, CVE-2025-48555, CVE-2025-48564, CVE-2025-48565, CVE-2025-48566, CVE-2025-48567, CVE-2025-48572, CVE-2025-48573, CVE-2025-48574, CVE-2025-48575, CVE-2025-48576, CVE-2025-48577, CVE-2025-48578, CVE-2025-48579, CVE-2025-48580, CVE-2025-48582, CVE-2025-48583, CVE-2025-48584, CVE-2025-48585, CVE-2025-48586, CVE-2025-48587, CVE-2025-48589, CVE-2025-48590, CVE-2025-48592, CVE-2025-48594, CVE-2025-48596, CVE-2025-48597, CVE-2025-48598, CVE-2025-48600, CVE-2025-48601, CVE-2025-48602, CVE-2025-48603, CVE-2025-48604, CVE-2025-48605, CVE-2025-48609, CVE-2025-48612, CVE-2025-48614, CVE-2025-48615, CVE-2025-48616, CVE-2025-48617, CVE-2025-48618, CVE-2025-48619, CVE-2025-48620, CVE-2025-48621, CVE-2025-48622, CVE-2025-48626, CVE-2025-48628, CVE-2025-48629, CVE-2025-48630, CVE-2025-48632, CVE-2025-48633, CVE-2025-48634, CVE-2026-0005, CVE-2026-0007, CVE-2026-0008

For detailed information on security preview releases, see our post about it.

cross-posted from: https://lemmy.sdf.org/post/46747896

Archived

Reporters Without Borders (RSF) has just received confirmation from local sources that Chinese journalist and photojournalist Du Bin has been held by the authorities at the Shunyi Detention Centre in Beijing since 15 October 2025. The former New York Times photographer is accused of “picking quarrels and provoking trouble”, an offence punishable by five years in prison and routinely used by the Chinese regime to suppress journalists and press freedom defenders.

The photojournalist’s family has repeatedly requested to see the written detention order, but the authorities have refused to provide one. The officer in charge of the case has also declined to give further information, citing confidentiality. Through his photos, books and documentary films, Du Bin has extensively documented human rights abuses committed by the Chinese regime. His work has been published in major international media outlets, including The New York Times, Time magazine and The Guardian.

[...]

I know this group is generally US-specific for now, but this is a very bad internet bill so figured I'd include iut