Core Insights - Premarket trading is showing notable activity with significant price movements indicating potential trading opportunities before the market opens [1] Premarket Gainers - 89bio, Inc. (ETNB) increased by 83% to $14.84 [3] - Aptevo Therapeutics Inc. (APVO) rose by 75% to $2.47 [3] - Psyence Biomedical Ltd. (PBM) saw a 29% increase to $4.82 [3] - Beam Global (BEEM) gained 27% reaching $3.23 [3] - MicroAlgo Inc. (MLGO) was up 14% at $13.06 [3] - Akero Therapeutics, Inc. (AKRO) increased by 12% to $47.50 [3] - Hyperion DeFi, Inc. (HYPD) rose by 11% to $13.69 [3] - Sonnet BioTherapeutics Holdings, Inc. (SONN) increased by 11% to $7.85 [3] - FuelCell Energy, Inc. (FCEL) was up 9% at $8.34 [3] - Robo.ai Inc. (AIIO) gained 6% to $2.05 [3] Premarket Losers - Presidio Property Trust, Inc. (SQFT) decreased by 14% to $7.58 [4] - Aeluma, Inc. (ALMU) fell by 10% to $15.18 [4] - FGI Industries Ltd. (FGI) dropped 10% to $7.65 [4] - Lazydays Holdings, Inc. (GORV) was down 9% at $2.26 [4] - StableX Technologies, Inc. (SBLX) decreased by 8% to $5.40 [4] - Artelo Biosciences, Inc. (ARTL) fell by 8% to $4.48 [4] - SciSparc Ltd. (SPRC) decreased by 8% to $4.10 [4] - Cracker Barrel Old Country Store, Inc. (CBRL) was down 7% at $45.75 [4] - Columbus Circle Capital Corp I (BRR) fell by 7% to $9.42 [4] - Visionary Holdings Inc. (GV) decreased by 7% to $2.58 [4]

Core Viewpoint - The article discusses the innovative dynamic trust management model developed by Micro Algorithm Technology (NASDAQ: MLGO), which integrates sub-array algorithms with blockchain technology to address the challenges of trust assessment in distributed systems, particularly in the context of IoT, supply chain finance, and decentralized storage. Group 1: Model Overview - The dynamic trust management model utilizes blockchain as the underlying data infrastructure, combined with a distributed computing framework of sub-array algorithms to create a decentralized trust assessment system [1]. - The model divides network nodes into multiple sub-arrays based on geographical location, resource type, or historical behavior characteristics, allowing for independent local trust calculations [1][2]. - The model ensures real-time and reliable trust assessment by dynamically adjusting sub-array members and updating trust values, leveraging blockchain's transparency and immutability for data security [1]. Group 2: Operational Mechanism - The model operates through five core processes: data collection and preprocessing, sub-array division, trust calculation, cross-array consensus, and dynamic updating [2]. - Data is collected from nodes and verified via smart contracts before being stored in a distributed ledger, with key features extracted and historical data weighted down using time decay functions [2]. - Sub-arrays are formed using K-means clustering or geographical hashing algorithms, with dynamic adjustments based on node load and trust value fluctuations [2]. Group 3: Trust Calculation and Consensus - Each sub-array independently runs trust evaluation algorithms to compute local trust values, integrating direct and indirect trust assessments [2][3]. - A modified PBFT consensus mechanism synchronizes trust evaluation results across sub-arrays, reducing communication rounds and computational complexity [3]. - The global trust value is generated by aggregating results from sub-arrays, weighted by their historical reliability [3]. Group 4: Dynamic Updates and Applications - The system triggers trust value updates every 30 seconds, allowing nodes to query their trust scores and adjust interaction strategies accordingly [3]. - The model has applications in various fields, including vehicle networking for enhanced safety and efficiency, e-commerce supply chains for optimized operations, and distributed energy systems for stable energy supply [5]. - As technology evolves, the model is expected to expand into IoT, healthcare, and financial services, integrating with AI and big data to foster innovative trust management solutions [5].

MICROALGO INC. AND SUBSIDIARIES UNAUDITED INTERIM CONDENSED CONSOLIDATED STATEMENTS OF OPERATIONS AND COMPREHENSIVE INCOME (LOSS) Exhibit 99.1 MICROALGO INC. AND SUBSIDIARIES UNAUDITED INTERIM CONDENSED CONSOLIDATED BALANCE SHEETS | | December 31, | June 30 | June 30 | | --- | --- | --- | --- | | | 2024 RMB | 2025 RMB | 2025 USD | | | AUDITED | UNAUDITED | | | ASSETS | | | | | CURRENT ASSETS | | | | | Cash and cash equivalents | 1,035,932,786 | 1,813,379,345 | 253,314,802 | | Short term investments | 149,58 ...

Company Overview - MicroAlgo Inc. is a technology company based in Shenzhen, China, specializing in custom central processing algorithms that optimize data processing and analysis across hardware and software [1] Business Model - The company focuses on developing software logic that enhances the efficiency of data handling, distinguishing itself from traditional CPU functions [1] Analyst Insights - The analyst emphasizes the importance of understanding macroeconomic trends and their impact on individual companies, suggesting a strategy that combines top-down economic analysis with bottom-up company evaluation [1]

Core Viewpoint - The rapid development of big data and artificial intelligence has highlighted data security and privacy issues, with traditional data transmission methods posing significant risks. The introduction of blockchain technology offers new solutions, exemplified by MicroAlgorithm Technology's innovative BlockFL architecture, which ensures secure, efficient, and privacy-protecting data transmission [1][6]. Group 1: BlockFL Architecture - BlockFL architecture utilizes blockchain networks to achieve efficient data exchange and synchronization in federated learning, allowing devices to upload local model updates and download global model updates quickly and effectively [2]. - The decentralized nature and high concurrency of blockchain ensure that all devices receive the same global model updates, maintaining consistency and accuracy in model training [2]. Group 2: Process Overview - Initialization involves the system administrator creating an initial model and broadcasting it to all participating nodes while the blockchain records metadata of the federated learning activity [4]. - Each node trains the model on its local dataset without exposing original data, thus protecting data privacy [4]. - Nodes upload encrypted model parameters to the blockchain, where smart contracts validate their effectiveness and integrity, preventing malicious actions [4]. - Once verified, a central server or designated aggregation node extracts parameters from the blockchain, averages them, and generates a new version of the global model [4]. - The updated global model is then broadcasted to all nodes for the next training round, with the blockchain ensuring traceability of all operations [4]. - An incentive and penalty mechanism is integrated into BlockFL to encourage participation and quality data contribution, with smart contracts automatically executing rewards and penalties [4]. Group 3: Applications and Future Prospects - BlockFL architecture can be applied across various sectors, including healthcare, financial risk control, smart manufacturing, and smart cities, facilitating data collaboration while maintaining security and privacy [5]. - In healthcare, BlockFL enables hospitals to collaboratively train diagnostic models while protecting patient privacy; in finance, it allows institutions to identify fraud without sharing sensitive information; in smart manufacturing, it promotes collaboration between factories; and in smart cities, it supports inter-departmental cooperation without compromising sensitive data [5]. - The combination of blockchain and federated learning in BlockFL addresses traditional data transmission challenges, enhancing efficiency and accuracy in model training, positioning it as a significant technological support in data transmission and machine learning in the future [6].

Core Insights - Quantum computing is evolving with the exploration of non-standard quantum oracles, which aim to overcome the limitations of standard quantum oracles in addressing complex computational needs [1][4] - Microalgorithm Technology (NASDAQ MLGO) is focusing on the development of non-standard quantum oracles to enhance quantum computing capabilities and provide new solutions for complex problems [1][5] Group 1: Non-Standard Quantum Oracles - Non-standard quantum oracles offer greater flexibility and can perform more complex logical operations compared to traditional quantum oracles, which are limited to specific tasks [1][3] - These oracles can dynamically adjust their computational logic based on different application scenarios, enhancing the efficiency and applicability of quantum algorithms [1][3] Group 2: Quantum Gate Operations - Microalgorithm Technology integrates new quantum interaction mechanisms into its design, creating innovative quantum gate combinations that allow for precise information processing [3] - The unique handling of environmental interactions by non-standard quantum oracles transforms noise and decoherence into beneficial elements for computation, improving efficiency and accuracy [3] Group 3: Applications and Potential - Non-standard quantum oracles can tackle complex mathematical problems and physical system simulations that standard quantum oracles struggle with, leading to new problem-solving approaches [4] - In cryptography, these oracles can enhance encryption algorithms, making them more resistant to quantum attacks and improving information security [4] - They also have the potential to optimize logistics and resource allocation, as well as improve machine learning model training, thereby advancing artificial intelligence [4] Group 4: Future Prospects - The research on non-standard quantum oracles by Microalgorithm Technology holds significant promise for overcoming current technical bottlenecks, moving from theoretical research to practical applications [5]

Core Viewpoint - MicroAlgo Inc. has developed a Grover-based quantum algorithm aimed at finding pure Nash equilibria in graphical games, marking a significant advancement in quantum algorithm research and game theory applications [1][6]. Group 1: Algorithm Development - The Grover search algorithm is utilized for efficient searching in unstructured databases, achieving a time complexity of the square root of the number of elements [1]. - The algorithm transforms the oracle in graphical games into a Boolean satisfiability problem, encoding game states and strategies as quantum states [2]. - A method has been designed to convert Boolean expressions into quantum gate operations, ensuring the quantum circuit reflects strategy choices and payoff feedback [3]. Group 2: Implementation and Efficiency - Adjustments were made to the Grover algorithm to address efficiency bottlenecks in multi-objective or multi-dimensional problems, employing a stepwise iterative approach to improve search efficiency [4]. - The algorithm's iterative process maximizes the amplitude of the target state based on oracle feedback, enhancing the success rate of finding pure Nash equilibria [4]. Group 3: Experimental Validation - Extensive experiments on random graphical game instances using a quantum simulator demonstrated the algorithm's effectiveness, showing significant improvements in speed and accuracy compared to traditional methods [5]. - The algorithm exhibited a higher success rate and shorter computation time across multiple iterations in complex gaming environments [5]. Group 4: Future Implications - The Grover-based quantum algorithm is expected to play a key role in practical business decision-making, market analysis, and multi-party game scenarios, equipping decision-makers with advanced tools for complex competitive environments [7]. - MicroAlgo aims to expand the application boundaries of this technology through collaboration with academia and industry, potentially driving scientific progress and business innovation [8].

Core Viewpoint - The integration of Explainable AI (XAI) technology by Micro Algorithm Technology (NASDAQ: MLGO) significantly enhances threat detection capabilities in blockchain networks, providing transparency and understanding in decision-making processes [1][5]. Group 1: Technology and Methodology - Micro Algorithm Technology has developed an intelligent threat detection system that combines deep learning models with an explainability module, allowing for automatic learning of attack patterns from large volumes of network traffic data [1][3]. - The system employs data collection and preprocessing techniques to ensure data quality and consistency, followed by feature extraction and selection using machine learning algorithms [3]. - The threat detection model is trained and evaluated using explainable machine learning algorithms, ensuring high accuracy and interpretability through iterative optimization [3]. Group 2: Applications and Impact - The application of Micro Algorithm Technology's XAI in blockchain networks has led to significant improvements in threat detection accuracy and efficiency, particularly in identifying anomalous transactions and malicious nodes [4]. - The technology effectively detects unusual transaction patterns, such as large or frequent transactions, which may indicate fraudulent activities, enabling rapid response to mitigate losses [4]. - In smart contract auditing, the technology analyzes code logic to identify potential security vulnerabilities, thereby preventing attacks due to contract flaws [4]. Group 3: Future Prospects - As technology continues to evolve, Micro Algorithm Technology's solutions are expected to find broader applications, contributing to the creation of a more secure and intelligent network environment [5].

Core Viewpoint - The article discusses the increasing security issues in blockchain technology, particularly focusing on DDoS attacks and how quantum convolutional neural networks (QCNN) developed by Micro Algorithm Technology (NASDAQ: MLGO) can enhance detection and response capabilities against these threats [1][7]. Group 1: Quantum Convolutional Neural Network (QCNN) Development - Micro Algorithm Technology has innovatively improved QCNN for detecting DDoS attacks in blockchain networks by optimizing quantum bit initialization and control methods, enhancing stability and reliability [1][7]. - The structure of QCNN has been adjusted to better handle blockchain transaction data and network status information, making it more suitable for the specific characteristics of blockchain data [1][7]. - Specialized quantum state reading and parsing technologies have been developed to accurately extract features related to DDoS attacks from quantum computation results [1][7]. Group 2: Data Collection and Preprocessing - Data collection involves gathering various types of data from the blockchain network, including transaction data, node status information, and network traffic data, using APIs and monitoring tools [3]. - Preprocessing of collected data is crucial for the effective operation of QCNN, involving data cleaning, noise reduction, and standardization to ensure data quality [3]. - Feature extraction is performed to identify characteristics related to DDoS attacks, such as transaction frequency and network traffic changes, which serve as inputs for the QCNN [3]. Group 3: Quantum Operations - Quantum bit initialization ensures that quantum bits are in a stable initial state, balancing the number of quantum bits with computational complexity [4]. - Quantum convolution operations utilize the properties of quantum bits to extract features and recognize patterns from input data through a series of quantum gate operations [4]. - Quantum pooling operations reduce data dimensions while retaining important features, employing a measurement-based pooling method to select the most probable quantum states [5]. Group 4: Classification and Output - After quantum convolution and pooling, a quantum fully connected layer processes the low-dimensional quantum state for DDoS attack classification and detection [6]. - The output from the quantum fully connected layer is a quantum state representing classification results, which is converted into a readable format using specialized quantum state reading techniques [6]. - If the probability distribution indicates a high likelihood of a DDoS attack, alerts are generated to notify network administrators for appropriate defensive actions [6]. Group 5: Applications and Future Prospects - The QCNN developed by Micro Algorithm Technology can monitor blockchain networks in real-time, promptly detecting signs of DDoS attacks and issuing alerts for immediate defensive measures [7]. - This technology can be integrated with other security measures, such as encryption and access control, to create a more secure blockchain environment [7]. - As quantum computing technology advances, the application prospects for QCNN in detecting DDoS attacks will expand, potentially enhancing computational power and accuracy [7].

Core Insights - The development of blockchain technology is rapidly evolving, with consensus mechanisms being crucial for the normal operation of blockchain networks [1] - Micro Algorithm Technology (NASDAQ: MLGO) innovatively integrates LINK and a reputation evaluation mechanism into the DPoS mechanism to enhance blockchain processing efficiency and security [1][3] Group 1: DPoS Mechanism Overview - DPoS mechanism selects a certain number of super nodes through elections to generate and verify blocks, allowing token holders to delegate their voting rights to agents (super nodes) [1] - The registration and voting process involves token holders registering as candidate nodes and promoting their capabilities to attract votes, leading to the election of super nodes responsible for block generation and transaction verification [2] Group 2: LINK and Reputation Mechanism - LINK is introduced as a value transfer medium, with its distribution dynamically adjusted based on the performance of super nodes, incentivizing active participation in network governance [2] - The reputation evaluation mechanism supervises super nodes' behavior, ensuring compliance with network requirements, and dynamically adjusts rewards based on performance metrics such as block generation speed and transaction verification accuracy [3] Group 3: Applications and Future Prospects - The technology can be applied in various blockchain scenarios, including digital currency trading platforms, supply chain management, and the Internet of Things (IoT), enhancing security and efficiency [4] - The potential for further refinement and promotion of this technology exists as blockchain technology continues to develop, with more enterprises likely to adopt and optimize the DPoS mechanism [4]