Researchers have discovered that optimizing sandbox execution time can significantly enhance the completeness and quality of cyber threat intelligence data. By analyzing over 110,000 malware samples, they found that 90% of useful intelligence is extracted within the first three minutes of execution.
A research team has proposed powerful related-key boomerang attacks on two larger variants of HALFLOOP, highlighting urgent concerns about the design of cryptographic standards. The attacks significantly improve time complexity and achieve breakthroughs in key recovery, underscoring the need for stronger key schedule algorithms.
Researchers found that cinnamic acid effectively inhibits plasmid-mediated conjugation, reducing the global spread of antibiotic resistance genes. The study suggests CA as a broad-spectrum conjugation inhibitor targeting bacterial energy metabolism to limit ARG spread.
A research team presents a general approach to batch verification of arbitrary signatures on blockchain, leveraging IVC to enhance scalability and reduce memory consumption. The proposed scheme supports an arbitrary number of signature verifications and offers significant speedups for both provers and verifiers.
A research team developed an implantable sensor to monitor dynamic H₂O₂ levels in plants, enabling real-time tracking of plant stress responses. The system achieved high time resolution and maintained stable performance under various conditions.
Researchers found that obakulactone reduced joint swelling, restored cartilage structure, and improved immune organ function in RA rats. It also suppressed proinflammatory cytokines and shifted macrophage polarization to anti-inflammatory M2.
A study uncovers the connection mechanism between ketone bodies and the mTORC1 pathway, identifying RagC as a key player. The findings suggest RagC-K349bhb as a potential therapeutic target for BHB-based CRC treatment strategies.
The study provides atomic-level templates of GHRHR's active and inactive states, enabling the design of highly selective agonists and potent antagonists. This breakthrough transforms our understanding of GPCR signaling from static snapshots to a dynamic framework for drug development.
Two-dimensional nanomaterials enhance M/NEMS sensors' mechanical strength, electrical conductivity, thermal stability, and biocompatibility. Their applications in biosensing and medical diagnostics show rapid response and high-sensitivity detection.
The study reveals that DUX family proteins undergo liquid-liquid phase separation to drive the transition to a totipotent-like state. This process is mediated by dynamic nuclear condensates, which act as specialized transcriptional hubs to activate totipotency-specific genes and retrotransposons.
Human tumor organoids advance cancer modeling by preserving patient-specific characteristics and functional drug responses. The review highlights the need for quality control, cross-laboratory standardization, and ethical governance to enable broader real-world implementation.
Conventional models fail to capture human-relevant immune responses, hindering therapeutic translation. Immune-inclusive microphysiological platforms recapitulate inflammation, fibrosis, and immune-stromal crosstalk.
The TEAM platform successfully replaced Y chromosomes in mice, but human Y chromosomes showed poor stability due to centromere incompatibility. Centromere dysfunction drives chromosome instability and inflammatory disease phenotypes.
Researchers elucidated S1P receptor ligand binding mechanisms to facilitate rational drug design. Allosteric and bitopic ligands reveal distinct binding sites with specific residues determining subtype selectivity.
Researchers found that BRCA1 regulates innate immune signaling through IRF3, which is downregulated in BRCA1 deficient cells, leading to reduced drug sensitivity and PARPi resistance. Combining poly(I:C) with PARPi improves antitumor efficacy in vivo.
Researchers investigated the α7 nAChR-PARP1 axis as a therapeutic target for Parkinson's disease. The study found that activating α7 nAChRs suppresses PARP1 activity, reducing neurotoxicity and improving motor function in mouse models. This discovery provides a scientific foundation for developing novel therapies targeting this axis.
A new research paper proposes Wireless Environmental Information Theory (WEIT) as a novel theoretical foundation for 6G environment intelligence communication. WEIT validates the EIC architecture aided by wireless environmental information and discusses key challenges for its practical application.
Researchers reveal distinct fates for individual cells under the same pyroptosis-inducing conditions, highlighting heterogeneity in cell death decisions. Extracellular protease-mediated proteolytic cleavages drive nuclear envelope disruption and genomic DNA degradation, promoting efficient phagocytic clearance of necrotic cell remnants.
A novel electrode material exhibits excellent water stability, conductivity, and catalytic activity for air sterilization applications. The 0.3Co-MOF/Cu@Cu structure generates reactive oxygen species to induce bacterial death.
A new study introduces the diameter-transformed fluidized bed (DTFB) reactor, which addresses national demands for upgraded automotive gasoline quality and advanced heterogeneous catalytic reactions. The DTFB platform achieves precise selectivity control for complex catalytic reactions in a single fluidized bed.
Researchers from China Mobile Research Institute and ZGC Institute outline a comprehensive analysis of cooperative integrated sensing and communication (ISAC), a key paradigm for sixth-generation mobile information networks. The study presents real-world field trial results, demonstrating the technology's feasibility in various setups.
A study in Engineering explores a novel strategy for multi-user edge computing systems to maximize robot swarm lifetime. By exploiting correlation among distributed data sources, the approach reduces communication overhead and energy drain, extending swarm operation by up to 650%.
A study integrating recent omics technologies reveals key molecular alterations, regulatory networks, and potential therapeutic targets for ESCC. Key findings include identified diagnostic biomarkers, prognostic indicators, and therapeutic targets.
A new Perspective revisits the controversy over calorie restriction's impact on mouse lifespan, arguing that genetic background may not be a key driver of life-shortening effects. The authors suggest that experimental design and sample size are more likely to cause heterogeneity in results.
The LRE-UDAF framework tackles challenges in MS signal identification by integrating a lightweight feature extractor and a novel unsupervised domain adaptation module. It achieves high accuracy and robustness, outperforming mainstream models in resource-constrained environments.
Researchers developed a pilot-ignition reactivity-stratification combustion strategy for ammonia-fueled compression ignition engines, achieving high ammonia energy fraction while maintaining efficient combustion and reduced emissions. The study found that optimizing diesel injection timing and pre-injection diesel mass can significantl...
The study proposes a task-driven design approach for 6G AI-native architecture, embedding AI as a foundational element. It addresses core challenges in integrating AI into 6G mobile networks and outlines a comprehensive architecture for converged connectivity-computing management.
A new study proposes a task-oriented framework for 6G space-air-ground integrated networks (SAGINs) tailored for unmanned operations. The SC³ loop, an integrated structure analogous to the biological reflex arc, connects sensors, edge platforms and actuators to enable autonomous execution of complex tasks.
Researchers introduce UltraStar, a high-fidelity and high-efficiency simulator for 6G integrated space-ground networks. The simulator accurately models complex network dynamics, including satellite orbit prediction and realistic modelling of sun outages and inter-satellite links.
Researchers developed an adaptive hybrid edge-cloud collaborative offloading method to address large-scale computational tasks in intelligent machine tools. The proposed AH-ECO mechanism achieved significant reductions in task processing time and energy consumption while maintaining superior security performance.
The study introduces a novel large language model-driven generative SemCom system, which achieves significant reduction in communication overhead and improvement in average retrieval accuracy. The research identifies promising application scenarios, including industrial IoT, V2X, the metaverse, and low-altitude economy.
Researchers developed a language-model-guided robotic system for perovskite solar cell research, accelerating device fabrication and characterization. The system achieved a record-breaking power conversion efficiency of 27.0% and generated over 578 million tokens for recipe optimization.
A new SemCom framework prioritizes interpretability and compatibility with existing systems, enhancing transmission efficiency and robustness. The framework outperforms state-of-the-art methods in image transmission by over 20%, showcasing its potential for efficient semantic transmission in emerging 6G applications.
A recent study proposes an AI-enhanced evaluation framework to optimize city-scale municipal living plastic waste (MLPW) management. The framework addresses complex interplay of physical material flows, spatial infrastructure, and socio-economic factors, achieving a 96.3% reduction in annual greenhouse gas emissions by 2060.
A new cathode material with a dual-crystal-phase structure improves the performance and stability of aqueous zinc-ion batteries. The material offers high capacity, rapid charging capabilities, and exceptional longevity.
Researchers developed a novel 'steric hindrance' strategy to create advanced single-atom catalysts outperforming traditional platinum benchmarks. The bulky metalloporphyrins provide spatial shields around metal centers, preventing agglomeration and ensuring high-performance in oxygen reduction reactions.
The HIT team developed FedPD to address model heterogeneity in FL by intelligently evaluating global ensemble knowledge and local features. This framework enables selective knowledge transfer, filtering conflicting information, and extracting beneficial traits for precise optimization.
Researchers have developed a novel composite material that combines high mechanical strength with exceptional electromagnetic wave absorption. The 'reinforced concrete' structure, inspired by traditional building materials, improves the material's toughness and effectiveness in absorbing electromagnetic radiation.
Researchers developed a novel approach to estimate the state of health of bipolar lead-acid batteries using partial charging profiles and a hybrid regression framework. The method achieved accurate results, with an average root mean squared error below 1.5%, outperforming other models.
Researchers developed a quasi-dynamic mathematical model to optimize Thermally Integrated Carnot Batteries (TI-CB) under fluctuating conditions typical of factory environments. The study yielded crucial insights, including a clear roadmap for handling off-design fluctuations, bringing Carnot batteries closer to widespread deployment.
Persistent Müllerian duct syndrome is a rare autosomal recessive disorder characterized by the presence of Müllerian structures in phenotypically normal 46, XY males. Clinical manifestations often include cryptorchidism, inguinal hernia, or transverse testicular ectopia.
Researchers propose a novel MPC-based feature selection scheme to enhance data quality by identifying more important features while ensuring data privacy. The proposed scheme, MPC-Relief, uses a nonlinear function to handle distance calculation in numerical and categorical features, achieving effective feature selection.
FedPD, a personalized federated learning framework based on partial distillation, addresses the challenge of model heterogeneity in real-world applications. By filtering conflicting information and extracting beneficial traits, FedPD optimizes knowledge transfer and improves performance gains.
Researchers develop method to model record correlations in synthetic data, preserving global and local distributions. This approach significantly outperforms current state-of-the-art methods, offering high utility and robust privacy protection.
Researchers identify Nipah virus's critical survival strategy by hijacking human protein NSUN2, facilitating viral replication. A dual-targeting antiviral strategy has been developed, combining an approved drug with an experimental inhibitor to effectively reduce virus levels and improve survival in animal models.
MILCAnet, a dominant feature attention framework, enhances multimodal data analysis in depression detection by combining key feature extraction and feature fusion. This innovative approach offers a more efficient and accurate solution for non-contact depression screening.
A new study proposes an innovative framework called SEA-SQL, which uses zero-shot prompts and GPT-3.5 to perform the Text-to-SQL task more effectively and economically. The method enhances database content with semantic information and eliminates biases in SQL queries.
Researchers introduced a self-attention network to analyze micro-expression temporal features, improving recognition performance. The approach preserves richer variation features through complete sequence analysis, marking a technological breakthrough.
A new Chinese pediatric dataset, PediaBench, evaluates large language models (LLMs) in medical question-answering tasks. The dataset consists of five question types and 12 disease groups, with an integrated scoring scheme to assess LLM performance comprehensively.
The MedFuse framework improves diabetic retinopathy lesion segmentation by aligning visual features with anatomical priors. This approach enhances the model's robustness and accuracy, particularly in low-contrast or artifact-heavy regions.