Search Results (12)

With increasing port automation and operational intensity, the gearboxes of gantry cranes widely used in bulk cargo terminals are prone to bearing and gear failures under prolonged heavy loads, intense vibrations, and complex operating conditions. Since fault samples often exhibit imbalanced distributions, this imposes two higher requirements on diagnostic methods—first, the ability to effectively address sample imbalance and, second, the capability to simultaneously identify multiple fault categories. To address these challenges, this paper proposes a joint diagnostic method integrating an improved Conditional Wasserstein Generative Adversarial Network with Gradient Penalty (CWGAN-GP) and Multi-Task Learning (MTL). First, the modified CWGAN-GP performs conditional augmentation for minority fault classes, evaluating synthetic sample authenticity and diversity through multiple metrics. Subsequently, a multi-channel diagnostic network is constructed, in which vibration signals are fed into two parallel sub-networks: time–frequency features are extracted from the Short-Time Fourier Transform (STFT)-based time–frequency representations via a residual-block Convolutional Neural Network (CNN), while temporal features are captured from the raw time-domain signal using a Bidirectional Long Short-Term Memory (Bi-LSTM) with an attention mechanism. An attention fusion layer then integrates these two feature types, enabling joint classification of bearings and gears within a multi-task learning framework. Experimental validation on public gearbox datasets and port gantry crane gearbox datasets demonstrates that this method achieves an average diagnostic accuracy exceeding 97%. The proposed method reduces the impact of class imbalance, thereby improving the accuracy and stability of multi-task fault identification. Full article

Lamport’s Bakery algorithm is a well-known, simple, and elegant solution to the mutual exclusion problem for N ≥ 2 concurrent/parallel processes. However, the algorithm generates an unbounded number of tickets, even when only 2 processes are arbitrated. Various proposals in the literature were introduced to bound the number of tickets. Anyway, almost all these proposals prove to be correct when operated with atomic registers (AR) only. They become incorrect when working with non-atomic registers (NAR), as may occur in embedded hardware platforms with multi-port memory and relaxed memory-bus control, such as microcontrollers, FPGA-based systems, or specialized network devices. A notable solution with bounded tickets is Taubenfeld’s Black-White Bakery (BWB) algorithm. BWB relies on tickets which are couples <number,mycolor> where mycolor can be Black or White and number ranges in [0, N]. BWB, too, was confirmed, through informal reasoning, it is correct with AR only. The original contribution of this paper is a reformulation of BWB, which is formally modelled and exhaustively verified by timed automata in the Uppaal toolbox. In the reformulation, a ticket’s couple is coded as a single integer, and decoded and processed according to the BWB logic. The reformulated BWB remains fully correct with AR regardless of the number N of processes, but it is also correct with NAR for N = 2 processes. As a further original contribution, the paper demonstrates that the BWB version for 2 processes can be embedded in a general, state-of-the-art solution, based on a binary tournament tree (TT), to become AR/NAR correct, that is, RW-safe, for any number of processes. However, due to model complexity, the correctness of the TT versions of BWB, that is, based on atomic and non-atomic registers, is mainly studied by stochastic simulation of the formal model reduced to actors in Java. Full article

Modern cyber threats demand coordinated defensive strategies that extend beyond centralized security mechanisms. However, existing multi-agent platforms exhibit critical limitations in explicit communication and real-time coordination for cyberdefense operations. This work proposes a hierarchical multi-agent architecture for autonomous cyberdefense that addresses these limitations through structured inter-agent communication and distributed coordination. The architecture integrates a centralized monitor agent with specialized defensive swarms deployed across operational hosts. It is implemented using SPADE 4.1 (Smart Python Agent Development Environment) to enable XMPP-based (Extensible Messaging and Presence Protocol) communication with low-latency messaging and location transparency. Four specialized swarms—Network Defender, Host Defender, Anomaly Detection, and Forensic and Recovery—perform autonomous defensive tasks. A secure authentication mechanism ensures trusted communication between monitor and deployer agents. The system was evaluated in a controlled virtualized environment using the Network Defender Swarm as an illustrative case. The experimental results focus on internal coordination behavior, messaging efficiency, and end-to-end detection time across increasing levels of parallelism. A scan agent scalability analysis shows that moderate parallelism (2–16 agents) yields the lowest Total Detection Time (12.88 s across the full TCP port range), while excessive agent counts degrade performance. Results demonstrate how the proposed architecture supports low-latency communication, efficient coordination, and parallel task execution. Message latency benchmarks show improvements compared to classical agent frameworks such as JADE. These findings provide initial evidence that communication-centric multi-agent architectures can facilitate coordinated and adaptive cyberdefense operations, while serving as a platform for further experimental evaluation. Full article

In this study, using global liner shipping schedules, UNCTAD’s Port Liner Shipping Connectivity Index and Liner Shipping Bilateral Connectivity Index, together with bilateral trade-value data for 2022–2024, we construct a multilayer weighted port-to-port network that explicitly embeds port-level cargo-handling and service organisation capabilities, as well as demand-side routing pressure, into node and edge weights. Building on this network, we apply CONCOR-based structural-equivalence analysis to delineate functionally homogeneous port clusters, and adopt a structural role identification framework that combines multi-indicator connectivity metrics with Rank-Sum Ratio–entropy weighting and Probit-based binning to classify ports into high-efficiency core, bridge-control, and free-form bridge roles, thereby tracing the reconfiguration of cluster-level functional structures before and after the Red Sea crisis. Empirically, the clustering identifies four persistent communities—the Intertropical Maritime Hub Corridor (IMHC), Pacific Rim Mega-Port Agglomeration (PRMPA), Southern Commodity Export Gateway (SCEG), and Euro-Asian Intermodal Chokepoints (EAIC)—and reveals a marked spatial and functional reorganisation between 2022 and 2024. IMHC expands from 96 to 113 ports and SCEG from 33 to 56, whereas EAIC contracts from 27 to 10 nodes as gateway functions are reallocated across clusters, and the combined share of bridge-control and free-form bridge ports increases from 9.6% to 15.5% of all nodes, demonstrating a thicker functional backbone under rerouting pressures. Spatially, IMHC extends from a Mediterranean-centred configuration into tropical, trans-equatorial routes; PRMPA consolidates its role as the densest trans-Pacific belt; SCEG evolves from a commodity-based export gateway into a cross-regional Southern Hemisphere hub; and EAIC reorients from an Atlantic-dominated structure towards Eurasian corridors and emerging bypass routes. Functionally, Singapore, Rotterdam, and Shanghai remain dominant high-efficiency cores, while several Mediterranean and Red Sea ports (e.g., Jeddah, Alexandria) lose centrality as East and Southeast Asian nodes gain prominence; bridge-control functions are increasingly taken up by European and East Asian hubs (e.g., Antwerp, Hamburg, Busan, Kobe), acting as secondary transshipment buffers; and free-form bridge ports such as Manila, Haiphong, and Genoa strengthen their roles as elastic connectors that enhance intra-cluster cohesion and provide redundancy for inter-cluster rerouting. Overall, these patterns show that resilience under the Red Sea crisis is expressed through the cluster-level rebalancing of core–control–bridge roles, suggesting that port managers should prioritise parallel gateways, short-sea and coastal buffers, and sea–land intermodality within clusters when designing capacity expansion, hinterland access, and rerouting strategies. Full article

Effective berth allocation in container terminals is crucial for optimizing port operations, given the limited space and the increasing volume of container traffic. This study addresses the discrete dynamic berth allocation problem (DDBAP) under uncertain ship arrival times and varying load capacities. A novel deep Q-network (DQN)-based model is proposed, leveraging a custom state space, rule-based actions, and an optimized reward function to dynamically allocate berths and schedule vessel arrivals. Comparative experiments were conducted with traditional algorithms, including ant colony optimization (ACO), parallel ant colony optimization (PACO), and ant colony optimization combined with genetic algorithm (ACOGA). The results show that DQN outperforms these methods significantly, achieving superior efficiency and effectiveness, particularly under high variability in ship arrivals and load conditions. Specifically, the DQN model reduced the total waiting time of vessels by 58.3% compared to ACO (262.85 h), by 57.9% compared to PACO (259.5 h), and by 57.4% compared to ACOGA (257.4 h), with a total waiting time of 109.45 h. Despite its impressive performance, DQN requires substantial computational power during the training phase and is sensitive to data quality. These findings underscore the potential of reinforcement learning to optimize berth allocation under dynamic conditions. Future work will explore multi-agent reinforcement learning (MARL) and real-time adaptive mechanisms to further enhance the robustness and scalability of the model. Full article

In this paper, a novel topology and method for designing a multi-band rectenna is proposed to improve its RF-DC efficiency. The rectifier achieves simultaneous rectification using both series and parallel configurations by connecting two branches to the respective terminals of the diode, directing the energy input from two ports to the anode and cathode of the diode. Six desired operating frequency bands are evenly distributed across these two branches, each of which is connected to antennas corresponding to their specific operating frequencies, serving as the receiving end of the system. To optimize the design process, a low-pass filter is incorporated into the rectifier design. This filter works in conjunction with a matching network that includes filtering capabilities to isolate the two ports of the rectifier. The addition of the filter ensures that each structure within the rectifier can be designed independently without adversely affecting the performance of the already completed structures. Based on the proposed design methodology, a dual-port rectenna operating at six frequency bands—1.85 GHz, 2.25 GHz, 2.6 GHz, 3.52 GHz, 5.01 GHz, and 5.89 GHz—was designed, covering the 4G, 5G, and Wi-Fi/WLAN frequency bands. The measured results indicate that high-power conversion efficiency was achieved at an input power of −10 dBm: 43.01% @ 1.85 GHz, 41.00% @ 2.25 GHz, 41.33% @ 2.6 GHz, 35.88% @ 3.52 GHz, 22.36% @ 5.01 GHz, and 19.27% @ 5.89 GHz. When the input power is −20 dBm, the conversion efficiency of the rectenna can be improved from 5.2% for single-tone input to 27.7% for six-tone input, representing a 22.5 percentage point improvement. The proposed rectenna demonstrates significant potential for applications in powering low-power sensors and other devices within the Internet of Everything context. Full article

The velocity of ocean currents significantly affects the trajectory prediction of ocean drifters and the safe navigation of intelligent vessels. Currently, most ocean current predictions focus on time-based forecasts at specific fixed points. In this study, deep learning based on the flow field prediction model (CNNs–MHA–BiLSTMs) is proposed, which predicts the changes in ocean currents by learning from historical flow fields. Unlike conventional models that focus on single-point current velocity data, the CNNs–MHA–BiLSTMs model focuses on the ocean surface current information within a specific area. The CNNs–MHA–BiLSTMs model integrates multiple convolutional neural networks (CNNs) in parallel, multi-head attention (MHA), and bidirectional long short-term memory networks (BiLSTMs). The model demonstrated exceptional modelling capabilities in handling spatiotemporal features. The proposed model was validated by comparing its predictions with those predicted by the MIKE21 flow model of the ocean area within proximity to Dalian Port (which used a commercial numerical model), as well as those predicted by other deep learning algorithms. The results showed that the model offers significant advantages and efficiency in simulating and predicting ocean surface currents. Moreover, the accuracy of regional flow field prediction improved with an increase in the number of sampling points used for training. The proposed CNNs–MHA–BiLSTMs model can provide theoretical support for maritime search and rescue, the control or path planning of Unmanned Surface Vehicles (USVs), as well as protecting offshore structures in the future. Full article

This study presents a novel design of ultra-wideband (UWB) impedance transformers and power dividers with filtering capabilities. Based on the UWB impedance matching network, the UWB filtering impedance transformers are designed, and the UWB filtering power dividers are achieved by impedance transforming one port of one or more impedance transformers. The transformers consist of a multi-mode stepped-impedance resonator (SIR) and defected ground structures (DGSs). The SIR is tightly coupled to two ports of different impedance levels via the unsymmetrical two-sided coupled lines and DGSs. In addition, two transformers that convert impedance from 50 Ω to 100 Ω are connected to form a UWB power divider with filtering function. The 25 Ω port of an impedance transformer with a 50 Ω to 25 Ω conversion is impedance matched to two 50 Ω ports connected in parallel, resulting in another power divider with filtering function. Thus, two prototype UWB impedance transformers from 50 Ω to 100 Ω and from 50 Ω to 25 Ω are designed and their corresponding power dividers are also designed and fabricated. The simulated and measured results are consistent, demonstrating good features, such as return loss greater than 10 dB and insertion loss less than 4.5 dB in the passband, UWB filtering capacity with out-of-band rejection greater than 20 dB, and compact size smaller than 1.2λ × 2.1λ (λ is the wavelength of the central frequency). Full article

Concurrent access to large-scale video data streams in edge computing is an important application scenario that currently faces a high cost of network access equipment and high data packet loss rate. To solve this problem, a low-cost link aggregation video stream data concurrent transmission method is proposed. Data Plane Development Kit (DPDK) technology supports the concurrent receiving and forwarding function of multiple Network Interface Cards (NICs). The Q-learning data stream scheduling model is proposed to solve the load scheduling of multiple queues of multiple NICs. The Central Processing Unit (CPU) transmission processing unit was dynamically selected by data stream classification, as well as a reward function, to achieve the dynamic load balancing of data stream transmission. The experiments conducted demonstrate that this method expands the bandwidth by 3.6 times over the benchmark scheme for a single network port, and reduces the average CPU load ratio by 18%. Compared to the UDP and DPDK schemes, it lowers the average system latency by 21%, reduces the data transmission packet loss rate by 0.48%, and improves the overall system transmission throughput. This transmission optimization scheme can be applied in data centers and edge computing clusters to improve the communication performance of big data processing. Full article

The machine learning and convolutional neural network (CNN)-based intelligent artificial accelerator needs significant parallel data processing from the cache memory. The separate read port is mostly used to design built-in computational memory (CRAM) to reduce the data processing bottleneck. This memory uses multi-port reading and writing operations, which reduces stability and reliability. In this paper, we proposed a self-adaptive 12T SRAM cell to increase the read stability for multi-port operation. The self-adaptive technique increases stability and reliability. We increased the read stability by refreshing the storing node in the read mode of operation. The proposed technique also prevents the bit-interleaving problem. Further, we offered a butterfly-inspired SRAM bank to increase the performance and reduce the power dissipation. The proposed SRAM saves 12% more total power than the state-of-the-art 12T SRAM cell-based SRAM. We improve the write performance by 28.15% compared with the state-of-the-art 12T SRAM design. The total area overhead of the proposed architecture compared to the conventional 6T SRAM cell-based SRAM is only 1.9 times larger than the 6T SRAM cell. Full article

SpiNNaker is a neuromorphic globally asynchronous locally synchronous (GALS) multi-core architecture designed for simulating a spiking neural network (SNN) in real-time. Several studies have shown that neuromorphic platforms allow flexible and efficient simulations of SNN by exploiting the efficient communication infrastructure optimised for transmitting small packets across the many cores of the platform. However, the effectiveness of neuromorphic platforms in executing massively parallel general-purpose algorithms, while promising, is still to be explored. In this paper, we present an implementation of a parallel DNA sequence matching algorithm implemented by using the MPI programming paradigm ported to the SpiNNaker platform. In our implementation, all cores available in the board are configured for executing in parallel an optimised version of the Boyer-Moore (BM) algorithm. Exploiting this application, we benchmarked the SpiNNaker platform in terms of scalability and synchronisation latency. Experimental results indicate that the SpiNNaker parallel architecture allows a linear performance increase with the number of used cores and shows better scalability compared to a general-purpose multi-core computing platform. Full article

Microfluidic devices are promising tools for the production of monodispersed tuneable complex emulsions. This review highlights the advantages of microfluidics for the fabrication of emulsions and presents an overview of the microfluidic emulsification methods including two-step and single-step methods for the fabrication of high-order multiple emulsions (double, triple, quadruple and quintuple) and emulsions with multiple and/or multi-distinct inner cores. The microfluidic methods for the formation of multiple emulsion drops with ultra-thin middle phase, multi-compartment jets, and Janus and ternary drops composed of two or three distinct surface regions are also presented. Different configurations of microfluidic drop makers are covered, such as co-flow, T-junctions and flow focusing (both planar and three-dimensional (3D)). Furthermore, surface modifications of microfluidic channels and different modes of droplet generation are summarized. Non-confined microfluidic geometries used for buoyancy-driven drop generation and membrane integrated microfluidics are also discussed. The review includes parallelization and drop splitting strategies for scaling up microfluidic emulsification. The productivity of a single drop maker is typically <1 mL/h; thus, more than 1000 drop makers are needed to achieve commercially relevant droplet throughputs of >1 L/h, which requires combining drop makers into twodimensional (2D) and 3D assemblies fed from a single set of inlet ports through a network of distribution and collection channels. Full article