Ancient liquid from a sealed bronze vessel points to a millet-based brew.A spacecraft developed by Tsinghua University is set to join international missions to study the asteroid Apophis during its close approach to Earth in 2029.
The post Chinese university-led mission to study asteroid Apophis during close encounter with Earth appeared first on SpaceNews.
Chinese startup Spark Space has successfully completed several consecutive funding rounds to support the development of a new launch vehicle that, according to the company, will become the largest rocket in the world to employ an electrically driven propellant feed system. The investment milestone follows a series of successful hot-fire tests of the vehicle’s propulsion […]
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Taiwan’s existence as a self-governing democracy may depend heavily on having enough military drones to discourage any attempted invasion by China’s military. As the Taiwanese government aims to boost domestic production of military drones and Taiwanese citizens sign up for drone flight training, Taiwanese companies are forming international partnerships to sell more drones to the US military and other overseas buyers.
Taiwan’s Ministry of National Defense proposed a special budget that would spend $6.6 billion over six years on buying drones made in Taiwan, according to the Central News Agency that represents the national news service of Taiwan. Presented on June 18, the budget proposal would allow the government to buy more than 208,000 coastal attack drones, along with more than 1,400 coastal reconnaissance drones and 1,320 uncrewed surface vessels, between 2026 and 2031.
That would be a significant boost to the Taiwanese military arsenal that currently includes just 5,000 US-made attack drones and domestically produced drones, according to Resilience Media. During military exercises in early June, Taiwanese soldiers fired Altius-600 loitering munition drones—made by a subsidiary of the US military technology company Anduril Industries—from towed flatbed launchers to strike offshore targets, according to USNI News. In another exercise earlier this year, Taiwanese Marines used Taiwan-made drones to similarly strike targets at sea.
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China’s commerce ministry is reportedly targeting broader use of AI across the country’s consumer economy, unveiling 17 policy measures intended to push the technology beyond industrial applications.
State broadcaster CCTV reported the ministry will use the measures to embed AI into households and businesses, viewing the technology as a lever for economic stimulus.
As part of the plan, it is apparently looking at a fundamental shift in how consumer electronics are designed and sold.
The way devices operate is also a target, moving from functional tools towards AI-integrated systems.
Advancing humanoid robotics in the home is high on the agenda, and the sector has already gained government and private investment.
Chinese players including Unitree and Ubtech were cited as front-runners in the market. They are vying to establish themselves commercially in industrial and consumer spaces.
Structural issues
On the services side, China wants to expand AI’s presence in retail services to public and personal areas.
CCTV cited comments from Lin Jian, deputy director of the international trade cooperation institute at the ministry of commerce, who said the measures are designed to solve structural issues within the country’s services sector.
These include addressing challenges around high labour costs and poor standardisation in service consumption, added Lin.
Beijing is stepping up its AI plans, with Bloomberg reporting earlier this month the government is planning to invest $295 billion over the next five years to build out data centres in the country.
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Chinese startup Spark Space has secured a series of funding rounds for what it claims will be the world's largest electric-pump-fed rocket, following engine tests.
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Read more of this story at Slashdot.
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The escalating threat posed by rising sea levels has become a defining environmental challenge of the 21st century, with profound implications for densely populated coastal regions worldwide. Recent research sheds new light on this crisis by examining the spatial and temporal evolution of sea level rise under the ongoing forces of climate change. Particularly vulnerable to these changes are Shanghai and other parts of China’s coastal waters, where natural and anthropogenic factors synergize to amplify the risks.
This pioneering study deploys advanced climate modeling combined with detailed regional analysis, revealing that sea level rise in China’s coastal zones exceeds average global projections. The unique convergence of local land subsidence, riverine water discharge variability, and dynamic marine currents exacerbates the vulnerability of these regions. Such localized processes interact with global sea level trends, creating hotspots where the risk is acutely intensified.
Land subsidence, the sinking or settling of the ground surface, has emerged as a critical factor accelerating local sea level rise in coastal China. Driven largely by groundwater extraction and urban development, subsidence lowers the land elevation relative to the rising ocean, effectively increasing flood risk and saltwater intrusion. This phenomenon, when combined with the gradual thermal expansion of seawater and melting ice sheets, underlines the compound nature of sea-level hazards facing mega-cities like Shanghai.
Moreover, the study brings to the forefront the role of river water discharge, an often overlooked element in sea level dynamics. Changes in the volume and timing of freshwater flow from major river systems alter sediment supply and coastal circulation patterns, which have direct impacts on shoreline stability and ecological health. In the Yangtze River estuary, fluctuations in discharge affect sediment deposition that could either reinforce or undermine coastal defenses against the rising tides.
Marine currents along China’s coastline further complicate the picture by influencing regional sea level changes. Currents can redistribute water masses, leading to regional variations in sea level rise that diverge significantly from global averages. Variability in monsoon strength and ocean-atmosphere interactions introduce additional temporal complexity, affecting sea level trends on seasonal to decadal scales.
The implications of these findings are far-reaching. Beyond the increased risk of flooding and coastal erosion, rising seas threaten the socio-economic fabric of communities reliant on coastal infrastructure and ecosystems. Shanghai, a global financial hub with millions residing in low-lying areas, confronts immense challenges in urban planning, disaster preparedness, and climate resilience efforts.
This comprehensive research integrates satellite data, ground observations, and climate projections to afford a high-resolution understanding of these localized sea level dynamics. By unraveling the intricate interplay between climatic and human-induced factors, the analysis offers crucial insights for policymakers and urban planners striving to mitigate and adapt to future sea level scenarios.
Furthermore, the study underscores the necessity of cross-disciplinary collaboration, merging geoscience, hydrology, oceanography, and urban studies. It challenges simplistic global sea level rise metrics by demonstrating that effective mitigation and adaptation require granular regional assessments that account for local geophysical and hydrodynamic conditions.
In the context of climate change mitigation strategies, this research elevates the urgency of addressing greenhouse gas emissions while concurrently managing human activities that exacerbate coastal vulnerability, such as groundwater over-extraction. It suggests that adaptation measures, including coastal defenses, managed retreat, and ecological restoration, must be tailored to localized conditions revealed through detailed spatial-temporal analysis.
Moreover, the insight gained concerning the temporal evolution of sea level rise offers valuable foresight into critical tipping points and thresholds. Understanding when and where acceleration of sea level increase may occur enables more precise targeting of resources and emergency preparedness efforts.
In conclusion, this comprehensive examination of sea level rise in China’s coastal waters highlights a complex mosaic of contributing factors that extend beyond traditional global climate models. The findings serve as a clarion call for integrative and localized climate action to safeguard vulnerable coastal megacities against the rising tides of the future.
Subject of Research: Spatial and Temporal Evolution of Sea Level Rise Under Climate Change
Article Title: Rising tides: Unveiling the spatial and temporal evolution of sea level rise under climate change
News Publication Date: 17-Jun-2026
Web References: http://dx.doi.org/10.1371/journal.pone.0347855
Image Credits: Bing Liang, CC-BY 4.0
Um relatório da Global Data prevê que a China vai ultrapassar os Estados Unidos na corrida dos centros de dados. Segundo os especialistas, a energia vai tornar-se o novo “ouro digital”.
The post Consumo de energia dos centros de dados vai triplicar até 2030. China prepara-se para liderar o mercado appeared first on Tek Notícias.
China continued its accelerated launch pace with a series of missions, but long silence followed liftoff of a Kuaizhou-11 solid rocket Wednesday, suggesting potential issues.
The post China conducts 4 launches in 3 days, but silence follows Kuaizhou–11 launch appeared first on SpaceNews.
In a groundbreaking study set to redefine our understanding of mental health challenges among older adults, researchers have unveiled a pioneering approach to classifying depression types and uncovering their root causes in rural elderly populations of China. This innovative research leverages the power of machine learning algorithms coupled with network analysis techniques, painting a comprehensive picture of depression’s multifaceted nature and providing critical insights that could shape future interventions and healthcare policies.
Depression, a complex and heterogeneous disorder, has long posed challenges for diagnosis and treatment, especially among elderly individuals residing in less accessible rural areas. Traditional clinical methods often fall short in capturing the diversity of depressive symptoms and their underlying influences. The team of scientists behind this study recognized the pressing need for advanced computational tools to disentangle these complexities, offering a more granular understanding that transcends conventional diagnostic categories.
At the heart of the research lies a robust application of machine learning—a subset of artificial intelligence designed to parse large datasets, identify patterns, and make predictions with remarkable accuracy. By harnessing this technology, the researchers were able to sift through extensive health records, demographic data, and psychosocial variables gathered from thousands of elderly participants across varied rural regions. This extensive dataset encapsulated a myriad of factors potentially contributing to depressive disorders, enabling a nuanced exploration beyond mere symptom tracking.
Complementing the machine learning framework, network analysis was employed to map the intricate relationships among identified depression types and their influencing factors. This method visualizes complex interactions as interconnected nodes, offering a systemic perspective that highlights central elements and pathways within the depression landscape. Such an approach unveils how certain socioeconomic, environmental, and psychological components interplay, facilitating targeted strategies for prevention and treatment.
The study’s methodological rigor is underscored by its comprehensive data integration process. Researchers painstakingly curated variables including age, gender, comorbidities, social support systems, economic status, and lifestyle habits. This multi-dimensional dataset was instrumental in training sophisticated algorithms capable of not only classifying distinct depression phenotypes but also discerning which factors wield the greatest influence in each subtype.
One of the most striking outcomes of this research is the identification of previously unrecognized subtypes of depression uniquely prevalent among rural seniors. These subtypes differ markedly in symptom expression, severity, and responsiveness to treatment, challenging the prevailing one-size-fits-all paradigm in geriatric mental healthcare. Such revelations open avenues for personalized intervention plans that align with individual patient profiles rather than generic diagnostic criteria.
Moreover, the key influencing factors extracted through network analysis shed light on critical intervention points. For instance, social isolation emerged as a pivotal driver in several depression subtypes, underscoring the urgent need for community-based support networks. Economic hardships and chronic physical illnesses were also highlighted as significant contributors, suggesting that integrated healthcare models addressing both mental and physical well-being could yield profound benefits for this vulnerable demographic.
The implications of these findings are far-reaching, particularly in the context of China’s rapidly aging population combined with ongoing urbanization trends that exacerbate rural-urban health disparities. By deploying machine learning and network analysis, the researchers provide policymakers and healthcare providers with actionable intelligence to prioritize resource allocation, develop culturally sensitive mental health programs, and reduce the stigma often associated with psychiatric conditions.
From a technical standpoint, the study utilized a blend of supervised and unsupervised learning techniques to maximize classification accuracy while preserving interpretability. Algorithms such as decision trees, random forests, and clustering methods enabled the extraction of meaningful patterns without oversimplifying the data’s inherent complexity. Additionally, the integration of network analytics enhanced the explanatory power, allowing for visualization of multidimensional interdependencies that inform both clinical practice and public health strategy.
This fusion of artificial intelligence and network science marks a significant advancement in epidemiological research, demonstrating the potential of these tools to unravel complex mental health phenomena. The study thereby sets a precedent for future investigations aiming to decipher the heterogeneity of psychiatric disorders across diverse populations using computational intelligence frameworks.
Importantly, the research also highlights challenges associated with data quality and ethical considerations in machine learning applications. Ensuring accuracy, minimizing biases, and protecting patient confidentiality remain paramount, especially when dealing with sensitive mental health information. The researchers addressed these issues by employing stringent data preprocessing protocols and adhering to rigorous ethical standards, paving the way for responsible innovation in this field.
The societal resonance of this work extends beyond academia. As mental health gains prominence on the global health agenda, particularly amidst demographic shifts towards older age cohorts, methodologies showcased in this study could catalyze similar inquiries in other nations grappling with the rising burden of geriatric depression. Cross-cultural adaptations and expansions of this framework might facilitate more inclusive, data-driven mental healthcare ecosystems worldwide.
Future research inspired by these findings is poised to explore dynamic models that track the evolution of depression types over time, providing real-time monitoring and adaptive interventions. Integration with mobile health technologies and wearable devices could further enhance data granularity and patient engagement, fostering a proactive stance against the onset and progression of depressive disorders in rural elderly communities.
In summation, this landmark investigation fuses cutting-edge machine learning with network analysis to revolutionize the identification and understanding of depression among China’s rural elderly. By delineating distinct depression subtypes and elucidating key influencing factors, the study not only challenges existing clinical conventions but also equips stakeholders with strategic insights to improve mental health outcomes in an underserved, at-risk population. As digital tools continue to transform healthcare landscapes, such pioneering research underscores the transformative potential of technology in addressing complex societal health challenges.
Subject of Research: Identification of depression types and key influencing factors among rural elderly in China using machine learning and network analysis.
Article Title: Identification of depression types and key influencing factors among rural elderly in China: a comprehensive analysis based on machine learning and network analysis.
Article References:
Dong, B., Wang, L. & Li, Y. Identification of depression types and key influencing factors among rural elderly in China: a comprehensive analysis based on machine learning and network analysis. BMC Geriatr (2026). https://doi.org/10.1186/s12877-026-07804-0
Image Credits: AI Generated
China’s Tianwen-2 spacecraft has carried out a series of small propulsive maneuvers following a main burn June 7, setting up an asteroid rendezvous in July.
The post Tianwen-2 makes series of burns on approach to asteroid, according to radio tracking appeared first on SpaceNews.
Deep inside a limestone cave in southern China, paleontologists have uncovered an assemblage of thirteen fossilized teeth belonging to Gigantopithecus blacki, the largest primate species ever known to have lived.
The post Fossils from Chinese Cave Fill Crucial Gap in History of Gigantopithecus blacki appeared first on Sci.News: Breaking Science News.
The upper stage from a commercial Chinese rocket that launched last week has broken apart in space, spreading debris in a heavily trafficked part of low-Earth orbit home to the International Space Station and a significant portion of SpaceX's Starlink broadband network.
The breakup occurred shortly after the Zhuque-2E rocket reached orbit on June 9 with two satellites providing direct-to-cell communications, perhaps around the time the upper stage was expected to perform a disposal burn. The US Space Force confirmed the breakup event in a post on space-track.org, a website used by the military to distribute orbit data to the public.
"The tracked pieces are being incorporated into routine conjunction assessment to support spaceflight safety," the Space Force wrote in an advisory. "There are currently no threats to human spaceflight. Analysis is ongoing."
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A cottage industry has emerged on Chinese e-commerce platforms selling tiny plastic doll heads designed to trick Tesla’s cabin camera into thinking a driver is paying attention. The devices cost as little as $20 to $50.
The products — marketed as “travel companions” and “dashboard decorations” — represent the latest and most absurd escalation in the arms race between Tesla’s driver monitoring safeguards and people determined to defeat them. It’s also incredibly dangerous and irresponsible.
Nvidia reportedly told Chinese customers its new Vera CPU for AI data centres could be available in August, in a move to regain ground in a market where its advanced GPU business has been stalled by US export curbs.
Reuters reported the chipmaker is pitching Vera as an alternative route into China after shipments of its H200 AI chip stalled for months. CEO Jensen Huang (pictured) previously said Nvidia’s market share in China had effectively fallen to zero, reflecting tighter US restrictions and Beijing’s push to support domestic suppliers.
The news site noted Vera is Nvidia’s first standalone CPU for agentic AI workloads, with the company claiming it delivers up to 1.8 times the performance of rival processors.
Huang unveiled Vera in March as part of Nvidia’s Vera Rubin architecture and cast the CPU as a major new growth engine, as the company looks to expand beyond GPUs and into the server processor market.
Sources told Reuters one major Chinese cloud provider is planning to order more than 300 servers containing two Vera CPUs each for testing, with the chips set to be deployed first in overseas data centres before any wider commitment.
The move also sharpens Nvidia’s challenge to Intel and AMD, which dominate the server CPU market.
Because Vera is based on Arm architecture rather than x86, Nvidia is entering the segment as AI infrastructure demand shifts further toward inference and agentic computing, where CPUs are becoming increasingly important alongside accelerators.
Reuters added selling CPUs into China may prove easier for Nvidia than shipping high-end GPUs, which remain subject to stricter US controls.
Broader adoption, however, is likely to hinge on software compatibility and the willingness of Chinese operators to move workloads away from domestic AI silicon.
The post Nvidia targets China with Vera CPU push appeared first on Mobile World Live.
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