Geo-distributed consensus protocols provide high availability and resilience for distributed database services. These protocols group nodes by their data centers to leverage the network topology that spans across multiple data centers, thereby reducing costly cross-datacenter communication. However, they still face performance and scalability challenges due to inefficient log replication mechanisms. 1) These protocols rely on the leader node in each group to perform cross-datacenter log replication, creating a single-node performance bottleneck. 2) Byzantine receivers can behave arbitrarily, forcing the group leader to send multiple log copies during replication to prevent loss, thus causing redundant transmissions. 3) Since all groups must execute these logs in the same order, synchronizations across groups are necessary to maintain consistency when multiple groups are proposing concurrently, which also slow down log replication. This paper presents MassBFT, a Byzantine fault-tolerant geo-consensus protocol that achieves high performance and scalability. We design an encoded bijective log replication to eliminate the leader bottleneck and reduce the cross-datacenter network consumption. We also propose asynchronous log ordering to eliminate synchronization across groups. Experimental results show that MassBFT is scalable, fault-tolerant, and outperforms state-of-the-art protocols with 5.49-29.96 times higher throughput under YCSB, SmallBank, and TPC-C workloads.

Pressure-swirl nozzles serve as critical components in complex industrial systems such as fuel injection and combustion devices, where their atomization performance directly determines system efficiency and operational reliability. This study resorts to numerical simulation integrated with entropy analysis theory, aiming to systematically dissect the distribution characteristics of hydraulic losses within the spray field of pressure swirl nozzles and the evolutionary pattern of entropy production as inlet pressures vary. The findings revealed that entropy production from turbulent dissipation serves as the predominant contributor to hydraulic loss, accounting for approximately 80% of the total loss, while wall dissipation entropy production contributes only about 0.02%, demonstrating a negligible impact. Through curve fitting, a quantitative mapping relationship between entropy production and pressure is further established. Notably, the swirl chamber, identified as the primary fault-sensitive zone, exhibits significantly higher entropy production than the external flow field, with its fully developed spray process contributing approximately 70% of the total entropy generation. This work provides a theoretical foundation for nozzle optimization and presents a novel data-driven framework for intelligent fluid component management in complex engineering systems and fault diagnosis.

Existing disaggregated databases separate execution and storage layers, enabling independent and elastic scaling of resources. In most cases, this design makes transaction concurrency control (CC) a critical bottleneck, which demands significant computing resources for concurrent conflict management and struggles to scale due to the coordination overhead for concurrent conflict resolution. Coupling CC with execution or storage limits performance and elasticity, as CC's resource needs do not align with the free scaling of the transaction execution layer or the storage-bound data layer. This paper proposes Concurrency Control as a Service (CCaaS), which decouples CC from databases, building an execution-CC-storage three-layer decoupled database, allowing independent scaling and upgrades for improved elasticity, resource utilization, and development agility. However, adding a new layer increases latency due to the shift in communication from hardware to network. To address this, we propose a Sharded Multi-Write OCC (SM-OCC) algorithm with an asynchronous log push-down mechanism to minimize network communications overhead and transaction latency. Additionally, we implement a multi-write architecture with a deterministic conflict resolution method to reduce coordination overhead in the CC layer, thereby improving scalability. CCaaS is designed to be connected by a variety of execution and storage engines. Existing disaggregated databases can be revolutionized with CCaaS to achieve high elasticity, scalability, and high performance. Results show that CCaaS achieves 1.02–3.11X higher throughput and 1.11–2.75X lower latency than SoTA disaggregated databases.

Multinational enterprises conduct global business that has a demand for geo-distributed transactional databases. Existing state-of-the-art databases adopt a sharded master-follower replication architecture. However, the single-master serving mode incurs massive cross-region writes from clients, and the sharded architecture requires multiple round-trip acknowledgments (e.g., 2PC) to ensure atomicity for cross-shard transactions. These limitations drive us to seek yet another design choice. In this paper, we propose a strongly consistent OLTP database GeoGauss with full replica multi-master architecture. To efficiently merge the updates from different master nodes, we propose a multi-master OCC that unifies data replication and concurrent transaction processing. By leveraging an epoch-based delta state merge rule and the optimistic asynchronous execution, GeoGauss ensures strong consistency with light-coordinated protocol and allows more concurrency with weak isolation, which are sufficient to meet our needs. Our geo-distributed experimental results show that GeoGauss achieves 7.06X higher throughput and 17.41X lower latency than the state-of-the-art geo-distributed database CockroachDB on the TPC-C benchmark.

The paper describes a novel method for discrete speech recognition based on spoken Arabic digit recognition by means of wavelet neural network in which Morlet wavelet is introduced to the hidden layer. The speech signal is extracted by means of Mel Frequency Cepstral Coefficients (MFCCs) and followed by vector quantization (VQ). The experimental results obtained on a spoken Arabic digit dataset proved that it could achieve better accuracy and need less learning time than the proposed method.

In this paper, an optimal control method based on feedback linearization is proposed for EHA-VPVM (electro-hydrostatic actuator with variable pump displacement and variable motor speed) system, which is a dual-input single-output (DISO) nonlinear system with multiplicative nonlinear property. In this work, a real EHA-VPVM system is modeled at first. Then the nonlinear model is linearized through feedback linearization and a control method is designed by using optimal control theory based on the linearized model. Simulation results show that our control method is effective for EHA-VPVM position control system, and can suppress perturbation of external load force to some degree. In addition, by simply adjusting the weighting coefficients of the displacement and the rotational speed of pump, this method can meet the different requirements on dynamic response and energy loss of system.