A Qualcomm já confirmou que todos os seus produtos, incluindo o futuro Snapdragon 8 Elite Gen 6 Pro, vão ficar mais caros a partir do próximo mês. A subida abre uma janela de oportunidade à MediaTek, que, segundo rumores da cadeia de fornecimento, poderá vender o Dimensity 9600 Pro por bem menos dinheiro aos fabricantes de smartphones.

The post Qualcomm vai subir os preços a partir de setembro. Será esta a oportunidade da MediaTek? appeared first on Tek Notícias.

The spots on a leopard, the stripes on a zebra, and the mask on a raccoon are all types of coloration in mammal fur, referred to as pelage. Scientists have struggled for a long time to understand how these colorations evolved. A team of researchers recently discovered how to reconstruct the colors of ancient mammal fur using cell structures that contain the chemical pigment melanin, known as melanosomes. They hope to use this understanding of ancient mammal pelage to determine how they lived and what their environments were like.

To decipher the color of ancient mammal fur, the researchers constructed a statistical computer model to predict their fur colors based on the shape of their melanosomes. First, they needed to understand how the shapes of melanosomes control fur color in modern mammals. To measure the length and width of the modern melanosomes, the scientists used a beam of electrons like a flashlight to map them in great detail, a technique called scanning electron microscopy.

They measured the length and width of 2,615 melanosomes from the fur of 116 different living mammal species, including a Red Panda, a German Shepard, and one or more species of most types in the mammalian classification. Then they recorded the wavelengths of light reflected off each melanosome using a microspectrophotometer. These wavelengths represented the exact color of hair from each melanosome they measured. This means they now had a quantitative measurement of the color of each hair to compare with its corresponding melanosome length and width.

When they examined their data, the researchers found that spherical melanosomes produced brighter fur colors, like reds and oranges, while elongated melanosomes produced darker fur colors, like browns and grays. The researchers plotted the microspectrophotometric measurements for each melanosome as a graph showing how much light it absorbed across each possible wavelength, creating reflectance curves. Based on these data, the researchers used computer software to create a statistical model that could predict reflectance curves, and therefore color, from melanosome shape alone.

Once the researchers created a model, they needed to find fossilized mammal melanosomes to predict their fur color. Mammal fossils are rare, and pieces of cells are only preserved in the most immaculate fossil deposits. In fact, these researchers only found 6 fossils of this caliber, from Mesozoic rocks deposited around 250 million years ago, soon after mammals evolved. 

The fossils came from 2 world-renowned deposits in Northeastern China, the Yanliao and Jehol biotas. They selected 5 previously studied fossils from these locations, including 1 specimen of Megaconus mammaliaformis, 1 specimen of Vilevolodon diplomylos, 2 specimens from the group Docodonta, and 1 specimen from the group Eutheria. All of these animals were small, rodent-like mammals. The researchers also discovered a new species of tree-dwelling mammal, Arboroharamiya fuscus.

The scientists used scanning electron microscopy to measure a total of 2,159 fossilized melanosomes to refine their color predictions. The fossilized melanosomes were all slightly elongated spheres and had far fewer differences in shape than in modern mammals. The researchers used their model to predict reflectance curves based on the shapes of the fossil melanosomes to determine the ancient mammals’ colors. They found that all the extinct mammals they examined likely had dark brown fur without any color patterns.

Based on these findings, the researchers suggested that most, if not all, early mammals were nocturnal, like many dark-colored mammals today. They suggested that this nocturnal lifestyle probably helped them survive the meteorite impact that wiped out the dinosaurs 100 million years later. While dinosaurs were unable to survive in the post-impact wasteland, these smaller, dark-colored mammals were more resilient and thrived in the aftermath. As mammals filled the ecological roles left by the dinosaurs, they evolved new color patterns, some based solely on camouflage, and others on new factors, like sexual selection and visual communication.

The researchers concluded that their model can predict fossilized mammal colors and help determine how these ancient animals lived. Notably, their data suggest that mammals likely stayed small and nocturnal throughout the Mesozoic until the dinosaurs were gone. Future researchers could use similar methods to uncover exactly how mammals evolved the colors we see today. For example, studying mammals closer to the impact event could reveal if it took a long time to develop diverse fur patterns, or if they began before the dinosaurs’ demise. Ultimately, similar studies could help determine whether the extinction of dinosaurs was necessary for the rise of mammals.

The post What colors were prehistoric mammals? appeared first on Sciworthy.

Qualcomm reported fiscal Q3 results which beat Wall Street’s revenue expectations but missed on profit, as the chipmaker continued to grapple with the impact of a memory supply crunch weighing on the smartphone sector.

The company posted revenue of $9.9 billion for the quarter ended 28 June, down 4 per cent from $10.4 billion a year earlier but ahead of the roughly $9.7 billion analysts had expected. Net income during the period was $2 billion, down 25% year-on-year.

Handset chip sales fell 20% to $5.1 billion, which Qualcomm CEO Cristiano Amon (pictured) stated reflected a bottoming out in the China market after OEMs pulled back on orders amid memory supply uncertainty.

“We actually don’t expect fundamental changes in the premium tier and the higher tier volume,” Amon said on the company’s investor call.

Going forward, the executive expects Apple-related revenue to decline sharply and faster than previously anticipated, after Qualcomm’s share of components in the upcoming iPhone range came in well below an earlier estimate of around 20%.

Amon said the company is offsetting the loss with growth elsewhere, including its emerging data centre business, which it expects to generate $5 billion in revenue by fiscal 2027.

“We expect Android to grow and significantly offset the reduction in Apple, and then data centre revenue that starts to ramp in the fourth quarter,” CFO Akash Palkhiwala said.

Amon said its Q3 results reflected “solid execution of our growth strategy” despite the challenging environment.

He noted Qualcomm is taking steps to protect margins going forward, including raising prices across its chip portfolio from 1 September, as component costs continue to climb.

Automotive remained a bright spot, with revenue reaching $1.59 billion which marked a 61% increase and the 23rd consecutive quarter of double-digit year-on-year growth for the segment. Qualcomm reiterated a target of $10 billion in annual automotive revenue by fiscal 2029.

IoT revenue was $1.83 billion, representing a 9% increase. Combined automotive and IoT revenue climbed 28%, underscoring the company’s push to diversify beyond its traditional handset business as it also builds out a data centre and custom silicon strategy with a leading hyperscaler.

The post Memory crunch bites Qualcomm handset chip sales appeared first on Mobile World Live.

Qualcomm forecast fourth-quarter profit below Wall Street estimates on Wednesday and said revenue from Apple products would decline faster than expected as supply constraints and rising costs weighed on its business.

In an interview with Reuters, CEO Cristiano Amon said costs had risen not just for memory chips, but across the supply chain. Qualcomm plans to raise prices starting September 1 in an effort to return its margins to historical levels, he said.“We’re just passing through big cost increases that we have,” Amon said, noting that Qualcomm will have to negotiate with each customer. “The temporary disconnect between cost and pricing causes a slight decline temporarily in gross margin.”

Qualcomm also said it expects revenue from Apple products to decline more quickly starting in the fourth quarter as supply constraints reduce its share of components used in the next iPhone launch to well below its earlier estimate of 20%.

“It’s availability of supply,” Amon told Reuters of the Apple share change.For fiscal 2027, the company expects the majority of its chip sales will come from categories other than smartphones.

“We kind of replaced Apple with the data center,” Amon said.Qualcomm expects adjusted profit to be between $2.05 and $2.25 per share in the fourth quarter, below analysts’ average estimate of $2.36, according to data compiled by LSEG. It forecast revenue of between $9.7 billion and $10.5 billion during the period, compared with estimates of $10.02 billion.

Revenue in its chip segment is expected to be between $8.4 billion and $9 billion, while analysts were expecting $8.49 billion.

Qualcomm reiterated that revenue from Chinese phone makers bottomed out in the third quarter as customers worked through excess inventory. Xiaomi, Oppo, and Vivo posted the steepest shipment declines among the top five global smartphone vendors in the June quarter, according to Counterpoint Research.

While unit sales of Android phones began to recover, Amon said handset makers were forced to raise prices, which in turn prompted buyers to look toward the bottom end of the premium phone segment, or even at older models, both of which hurt Qualcomm margins.“There was a mix change versus what we expected,” Amon said.

Qualcomm’s broad exposure to devices such as smartphones makes its results a closely watched indicator of demand trends across consumer electronics.

MacDailyNews Note: Its third-quarter revenue fell 4% to $9.95 billion, beating estimates of $9.67 billion. Adjusted profit came in at $2.21 per share, compared with estimates of $2.23.

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The post Qualcomm forecasts weak quarterly profit, sees faster drop in Apple revenue appeared first on MacDailyNews.

BMW Group handed Qualcomm Technologies a long-term deal to be the lead supplier of chips for its digital cockpit and next generation automated and assisted driving systems.

The chip company revealed it had been appointed the automotive giant’s lead compute silicon provider for the business areas over the next decade, with products being used from its Snapdragon Digital Chassis portfolio.

It noted the latest deal follows years of technical collaboration between the pair, pointing to a commercial launch of an automated driving system in the BMW iX3 vehicle in November 2025.

Qualcomm EVP and group GM, Automotive, Industrial and Embedded IoT and Robotics Nakul Duggal said “as agentic and physical AI drive a new generation of intelligent vehicles, this collaboration enables both companies to define the future of mobility”.

The company added the deal “establishes the hardware basis for BMW Group to deliver the next generation AI-powered experience”.

Qualcomm has been actively pursuing the automotive sector in recent years as part of a diversification strategy to spread its revenue away from being handset-heavy.

Accelerating ambitions
Last month, the company upped financial targets for revenue from automotive and general non-smartphone earnings.

At its investor day in June, it set out an ambition to make $10 billion in revenue from the automotive segment in its fiscal 2029 (ending late September 2029), forming part of a wider target of $40 billion in non-handset revenue that year.

It expects revenues from handsets to make up a third of its top line figure in fiscal 2029.

The previous target for fiscal 2029 was $8 billion from automotive out of a non-handset figure of $22 billion.

In its fiscal 2025, automotive revenue generated almost $4 billion of its total $38.4 billion. Handsets contributed $27.8 billion of its top line.

The post Qualcomm buckles-up for long haul with BMW appeared first on Mobile World Live.

Have you ever wondered how researchers use artificial intelligence (AI) in environmental science? Certain AI computer models can learn from scientific data and operate without manual programming, in a process called machine learning. Machine learning models can identify patterns in environmental datasets and help scientists assess public health risks linked to environmental conditions.

One well-known public health risk is the rise in antibiotic-resistant bacteria. These bacteria carry genes called antibiotic resistance genes that protect them from antibiotic medicines. As the number of these genes increases, the medicines become less effective, and diseases spread faster.

Agricultural soils are the largest reservoirs of antibiotic resistance genes, because manure from livestock like cows and pigs often contains leftover antibiotics fed to them. When used as fertilizer, this manure transfers antibiotics into the agricultural soil, where bacteria develop antibiotic resistance genes.

To better understand how antibiotic-resistant bacteria will affect global health in the future, scientists want to develop faster ways to detect antibiotic resistance genes. However, current methods of detection are slow and fail to predict future trends. To address this problem, a group of scientists from Algeria used machine learning models to predict the prevalence of antibiotic resistance genes in soil microbes under future climate scenarios, and to identify the key environmental drivers and high-risk areas.

​The team compiled existing microbial data from 3 public databases: the National Center for Biotechnology Information’s Sequence Read Archive, the Metagenomic Rapid Annotations using Subsystems Technology database, and the Joint Genome Institute’s Integrated Microbial Genomes & Microbiomes database. Their final dataset contained about 2,000 agricultural soil samples from 67 countries across 6 continents. These samples represented a wide range of soil characteristics, climate conditions, and different types of antibiotic resistance genes. 

They integrated the microbial dataset with climate data, including global temperature and precipitation measurements from WorldClim, and land use data, including crop types, irrigation systems, and livestock density from the European Space Agency Climate Change Initiative Land Cover maps. The researchers also included data on future climate projections for 2050 and 2070 from the World Climate Research Programme based on low, medium, and high global greenhouse gas emission scenarios. 

After preparing the datasets, the team set up 6 machine learning models that differ in terms of their data size, model complexity, analytical speed, and customization. These models included Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machine (SVM), Deep Neural Network (DNN), and Logistic Regression (LR). 

They ran each model using different combinations of variables from their datasets, including soil properties, microbial community metrics, climate variables, and land use characteristics. To validate the models, the team separated the full dataset into 10 random subsets. They trained the models on 9 of the subsets, then recorded the result of the 10th subset. Each model repeated this process 10 times in a different order so that each subset was evaluated once in the results, using a technique known as stratified 10-fold cross-validation. The researchers scored each model on 5 performance metrics, including precision, sensitivity, and predictive power. Based on these results, they determined that LightGBM was the most accurate model. 

They found that the LightGBM model identified soil temperature as the strongest environmental predictor of antibiotic resistance genes. At soil temperatures above 18°C (about 64°F), the number of antibiotic resistance genes increased dramatically. The model also identified soil acidity, organic carbon content, moisture, and annual precipitation as influential variables, accounting for 71% of the increase in antibiotic resistance genes under future climate scenarios. Likewise, the LightGBM model predicted that the number of high-risk areas would increase by 35% under the high-emissions scenario, with South Asia, Sub-Saharan Africa, and Mediterranean Europe as the most vulnerable regions.

Based on the LightGBM model output, the team concluded that soil conditions will change to favor antibiotic resistance genes under all future climate scenarios. They also suggested that LightGBM is a fast and effective model for environmental predictions that could be used to assess other climate change risks such as natural disasters. They recommended that future researchers explore how to apply machine learning methods to early warning systems and utilize machine learning results in environmental management decisions. 

The post Predicting microbial antibiotic resistance with AI appeared first on Sciworthy.