Search Results (168)

Cyber-physical infrastructures such as hospitals and smart campuses face hybrid threats that target both digital and physical domains. Traditional security solutions separate surveillance from network monitoring, leaving blind spots when attackers combine these vectors. This paper introduces ARGUS, an autonomous robotic platform designed to close this gap by correlating cyber and physical anomalies in real time. ARGUS integrates computer vision for facial and weapon detection with intrusion detection systems (Snort, Suricata) for monitoring malicious network activity. Operating through an edge-first microservice architecture, it ensures low latency and resilience without reliance on cloud services. Our evaluation covered five scenarios—access control, unauthorized entry, weapon detection, port scanning, and denial-of-service attacks—with each repeated ten times under varied conditions such as low light, occlusion, and crowding. Results show face recognition accuracy of 92.7% (500 samples), weapon detection accuracy of 89.3% (450 samples), and intrusion detection latency below one second, with minimal false positives. Audio analysis of high-risk sounds further enhanced situational awareness. Beyond performance, ARGUS addresses GDPR and ISO 27001 compliance and anticipates adversarial robustness. By unifying cyber and physical detection, ARGUS advances beyond state-of-the-art patrol robots, delivering comprehensive situational awareness and a practical path toward resilient, ethical robotic security. Full article

The dynamics of port-Hamiltonian systems is based on energy balance principles (the first law of thermodynamics) embedded in the structure of the model. However, when dealing with thermodynamic subsystems, the second law (entropy production) should also be explicitly taken into account. Several frameworks were developed as extensions to the thermodynamic domain of port-Hamiltonian systems. In our work, we study three of them, namely irreversible port-Hamiltonian systems, entropy-based generalized Hamiltonian systems, and entropy-production-metric-based port-Hamiltonian systems, which represent alternative approaches of selecting the state variables, the storage function, simplicity of physical interpretation, etc. On the example of a simplified lumped-parameter model of a heat exchanger, we study the frameworks in terms of their implementability for an IDA-PBC-like control and the simplicity of using these frameworks for practitioners already familiar with the port-Hamiltonian systems. The comparative study demonstrated the possibility of using each of these approaches to derive IDA-PBC-like thermodynamically consistent control and provided insight into the applicability of each framework for the modeling and control of multiphysics systems with thermodynamic subsystems. Full article

With the integration of the global economy and the rapid development of port logistics, the port dangerous goods transportation system faces complex risk-coupling problems, and the probability of accidents keeps climbing. However, the existing research on the system risk-coupling mechanism and dynamic control mechanism is still insufficient, and there is an urgent need to construct a scientific risk analysis and control model. This study takes the port dangerous goods transportation system as the object, based on the four-factor framework of “personnel-machine-environment-management,” uses the N-K model to quantify the degree of risk coupling, analyzes the dynamic evolution mechanism of risk under the action of a single factor, and uses Dufferin’s oscillation and bifurcation response equation to reveal the interaction between the system’s internal defenses and the external influences. It is found that the coupled risk value of personnel–machine factors is the highest, and the sudden change in system state is characterized by a sudden jump and lag. The system stability can be significantly improved by enhancing internal damping control and optimizing external excitation regulation. This study provides a quantitative tool for the risk assessment of dangerous goods transportation in ports and theoretical support for the development of the “damping-excitation” synergistic control strategy, which is of great practical significance for the improvement of the port safety management system. Full article

For accurate and reliable monitoring, compliance monitoring devices (CMDs) in Port State Control must meet strict and uniform quality standards. This study evaluates how effectively CMDs, using variable fluorescence (VF) and fluorescein diacetate (FDA) technologies, detect live organisms in the 10–50 μm size range. Employing a detailed analytical framework, we analyzed key performance indicators, including accuracy, precision, sensitivity, specificity, trueness, detection limits, and reliability by comparing CMD outputs to those of traditional microscopic methods. Reliability assessments revealed that VF-type CMD and FDA-type CMD performed robustly, with a stability rate of 99% for both, surpassing the 90% verification threshold. Precision analysis indicated an average CV exceeding 0.25; however, some samples, especially those below the D-2 standard, achieved a CV of less than 0.25. Concordance evaluations revealed that VF-CMDs and FDA-CMDs achieved rates of 63% and 55%, respectively, falling short of the 80% verification standard and underscoring the need for further calibration or optimization. Structural equation modeling shows that organism density significantly influences CMD performance. These findings underscore the challenges of accurately detecting low organism concentrations, further complicated by biological diversity and environmental variability. Despite their limitations in assessing ballast water compliance, CMDs are effective initial screening tools. Full article

Among other uses, DC-DC converters are employed in the auxiliary power modules (APMs) of electric vehicles (EVs), connecting the high-voltage traction battery to the low-voltage auxiliary system (AS). Traditionally, the APM is an isolated two-port, two-level (2L) DC-DC converter, and the auxiliary loads are fed at a fixed voltage level, e.g., 12 V in passenger cars. Dual-active-bridge (DAB) converters are commonly used for this application, as they provide galvanic isolation, high power density and efficiency, and bidirectional power flow capability. However, the auxiliary loads do not present a uniform optimum supply voltage, hindering overall efficiency. Thus, a more flexible approach, providing multiple supply voltages, would be more suitable for this application. Multiport DC-DC converters capable of feeding auxiliary loads at different voltage levels are a promising alternative. Multilevel neutral-point-clamped (NPC) DAB converters offer several advantages compared to conventional two-level (2L) ones, such as greater efficiency, reduced voltage stress, and enhanced scalability. The series connection of the NPC DC-link capacitors enables a multiport configuration without additional conversion stages. Moreover, the modular nature of the ML NPC DAB converter enables scalability while using semiconductors with the same voltage rating and without requiring additional passive components, thereby enhancing the converter’s power density and efficiency. This paper proposes a modulation strategy and decoupled closed-loop control strategy for the generalized multiport 2L-NL NPC DAB converter interfacing the EV traction battery with the AS, and its performance is validated through hardware-in-the-loop testing and simulations. The proposed modulation strategy minimizes conduction losses in the converter, and the control strategy effectively regulates the LV battery modules’ states of charge (SoC) by varying the required SoC and the power sunk by the LV loads, with the system stabilizing in less than 0.5 s in both scenarios. Full article

This literature review examines the relationship between the number and type of deficiencies identified during Port State Control (PSC) inspections and a ship’s overall risk. The main objective is to synthesise the current academic evidence, detailing the analytical methodologies employed and highlighting key research contributions. The selection of literature has focused on peer-reviewed articles and relevant doctoral theses addressing detention risk prediction, accident risk and ship risk profiling. The findings indicate a consistent correlation between PSC deficiencies and ship risk, although the nature and strength of this correlation may vary depending on the type of risk considered and the specific deficiencies. A methodological evolution is observed in the field, from descriptive statistical analyses and regressions towards more complex predictive models, such as Machine Learning (ML) and Bayesian Networks (BNs). This transition reflects a search for greater accuracy in risk assessment, going beyond simple numerical correlation to improve the selection of ships for inspection. Multivariate statistical techniques, on the other hand, focus on the identification of risk patterns and the evaluation of the PSC system. The conclusions underline the importance of deficiencies as indicators of risk, the need for differentiated inspection approaches and the persistent challenges related to data quality and model interpretability. Full article

This study developed a model to predict ships with high deficiency risk under Port State Control (PSC) through machine learning techniques, particularly the Random Forest algorithm. The study utilized actual ship inspection data from the Port of Singapore, comprehensively considering various operational and safety indicators of ships, including but not limited to flag state, ship age, past deficiencies, and detention history. By analyzing these factors in depth, this research enhances the efficiency and accuracy of PSC inspections, provides decision support for port authorities, and offers strategic guidance for shipping companies to comply with international safety standards. During the research process, I first conducted detailed data preprocessing, including data cleaning and feature selection, to ensure the effectiveness of model training. Using the Random Forest algorithm, I identified key factors influencing the detention risk of ships and established a risk prediction model accordingly. The model validation results indicated that factors such as ship age, tonnage, company performance, and flag state significantly affect whether a ship exhibits a high deficiency rate. In addition, this study explored the potential and limitations of applying the Random Forest model in predicting high deficiency risk under PSC, and proposed future research directions, including further model optimization and the development of real-time prediction systems. By achieving these goals, I hope to provide valuable experience for other global shipping hubs, promote higher international maritime safety standards, and contribute to the sustainable development of the global shipping industry. This research not only highlights the importance of machine learning in the maritime domain but also demonstrates the potential of data-driven decision-making in improving ship safety management and port inspection efficiency. It is hoped that this study will inspire more maritime practitioners and researchers to explore advanced data analytics techniques to address the increasingly complex challenges of global shipping. Full article

In precision cotton seeding, the toothed-disk precision seeder often experiences issues with missed seeding and multiple seeding. To promptly detect and address these abnormal seeding conditions, this study develops a modular photoelectric sensing monitoring system. Initially, the monitoring time window is divided using the capacitance sensing signal between two seed drop ports. Concurrently, a photoelectric monitoring circuit is designed to convert the time when seeds block the sensor into a level signal. Subsequently, threshold segmentation is performed on the time when seeds block the photoelectric path under different seeding states. The proposed spatiotemporal joint counting algorithm identifies, in real time, the threshold type of the photoelectric sensor’s output signal within the current monitoring time window, enabling the differentiation of seeding states and the recording of data. Additionally, an STM32 micro-controller serves as the core of the signal acquisition circuit, sending collected data to the PC terminal via serial port communication. The graphical display interface, designed with LVGL (Light and Versatile Graphics Library), updates the seeding monitoring information in real time. Compared to photoelectric monitoring algorithms that detect seed pickup at the seed metering disc, the monitoring node in this study is positioned posteriorly within the seed guide chamber. Consequently, the differentiation between single seeding and multiple seeding is achieved with greater accuracy by the spatiotemporal joint counting algorithm, thereby enhancing the monitoring precision of the system. Field test results indicate that the system’s average accuracy for single-seeding monitoring is 97.30%, for missed-seeding monitoring is 96.48%, and for multiple-seeding monitoring is 96.47%. The average probability of system misjudgment is 3.25%. These outcomes suggest that the proposed modular photoelectric sensing monitoring system can meet the monitoring requirements of precision cotton seeding at various seeding speeds. Full article

A vanadium dioxide (VO₂)-based layered metastructure is proposed that enables dynamic optical encoding in the range of 15.5 GHz to 16 GHz through synergistic temperature and magnetic field modulation. By utilizing sequential temperature control, an optical date flip-flop (DFF) functionality can be achieved. The VO₂ component of the metastructure exhibits an insulator-to-metal phase transition under thermal regulation, accompanied by significant changes in its optical properties. Furthermore, by optimizing the sequential temperature-control strategy, an optical DFF is successfully implemented whose output state can be dynamically controlled by the data input (D), timing control port (T), and state control port (B). A novel technical approach is provided for programmable photonic devices, dynamic optical information storage, and optical computing systems. Full article

Modular converters offer an effective solution for battery energy storage systems (BESSs) by lowering battery pack voltage levels and enabling additional functionalities, such as state of charge (SoC) and state of health (SoH) balancing, temperature regulation, and improved system reliability. However, conventional modular designs often require numerous additional components, including passive elements, switches, and sensing circuits. This paper proposes a modular multi-port converter (MMPC) BESS that combines energy conversion and battery management functions, leveraging the benefits of both modular and multi-port architectures. The proposed system demonstrates promising scalability and adaptability within the tested voltage and power ranges, with potential for extension to higher voltage and power applications through modular expansion. It also introduces an additional control layer, enhancing flexibility for control optimization and cost-effectiveness while improving reliability by reducing dependency on bypass switches. A prototype utilizing three dual-port converters managing six battery packs was developed. The experimental results confirm that the MMPC-based BESS achieves energy conversion and effectively balances the SoC among battery packs during both charging and discharging, under initial SoC mismatches. Full article

This paper presents a novel switchable branch-line coupler (BLC) designed to achieve variable phase shifts while maintaining a constant output power. The proposed design incorporates low stepwise phase shifters with incremental phase shifts of 10° to 20°, covering phase ranges from −3° to 150°. The initial structure is based on a 3 dB branch-line coupler with arm electrical lengths of 3λ_g/2. A novel delay line structure is integrated within the BLC arms, consisting of a λ_g/4 section bridged by a tapered stripline to accommodate a PIN diode switch, thereby altering the current path direction. Additionally, two interdigital capacitors (IDCs), uniquely mounted on a crescent-shaped extension, are implemented alongside the tapered line to elongate the current path when the PIN diode is in the OFF state. By controlling the PIN diode states, the delay time is differentially adjusted, resulting in variable differential phase shifts at the output ports. To validate the functionality, the proposed BLC was integrated with a two-element antenna array to demonstrate differential beam steering. The measurement results confirm that the phased array antenna can switch its main beam between −27° and 25° in the elevation plane, achieving an average realized gain of approximately 7 dBi. The BLC was designed and simulated using CST Microwave Studio and was fabricated on an RO4003C Roger substrate (ε_r = 3.55, 0.406 mm). The proposed design is well-suited for future Butler matrix-based beamforming networks in antenna array systems, particularly for 5G wireless applications. Full article

This paper proposes a combined optimization method for multi-period empty container repositioning and inventory control based on adaptive particle swarm optimization (APSO) algorithm, which addresses the limitations of existing research, such as decoupling empty container repositioning and inventory control optimization, and lacking multi-period dynamic collaboration mechanisms. Firstly, a joint optimization model integrating (s, S) inventory control strategy is constructed. By adopting the strategy, the selection of repositioning paths and inventory resource allocation are synergistically optimized to balance unit empty container rental costs, inventory costs, and repositioning costs. Secondly, we design an adaptive particle swarm optimization algorithm, introduce dynamic inertia weight and acceleration coefficient adjustment mechanisms, and design heuristic rules for empty container repositioning. In this way, we reduce unreasonable empty container mobilization through the setting of surplus, shortage, and balance ports of empty containers, which can narrow the search space and improve the algorithm’s global search ability and convergence efficiency in high-dimensional decision spaces. Numerical experiments show that the joint optimization model designed can reduce the total cost of empty container management for shipping companies and maintain the rental cost in a stable state. Sensitivity analysis reveals that the unit container rental cost and the maximum inventory capacity of the port have a significant impact on the total system cost, providing a new approach for shipping companies to reduce empty container management costs. Full article

This literature review provides a structured quantitative analysis of existing research on the application of machine learning models (MLMs) and multi-criteria decision-making methods (MCDM) in the context of port state control (PSC). The aim of the study is to capture current research trends, identify thematic priorities, and demonstrate how these analytical tools have been used to support decision-making and risk assessment in the maritime domain. Rather than evaluating the effectiveness of individual models, the study focuses on the distribution and frequency of their use and provides insights into the development of methodological approaches in this area. Although several studies suggest that the integration of MLMs and MCDM techniques can improve the objectivity and efficiency of PSC inspections, this report does not provide a comparative assessment of their performance. Instead, it lays the groundwork for future qualitative studies that will assess the practical benefits and challenges of such integration. The findings suggest a fragmented but growing research interest in data-driven approaches to PSC and highlight the potential of advanced analytics to support maritime safety and regulatory compliance. Full article

Groundnut bud necrosis orthotospovirus (GBNV), a tripartite single-stranded RNA virus, poses a significant threat to United States agriculture. GBNV is a quarantine pathogen, and its introduction could lead to severe damage to economically important crops, such as groundnuts, tomatoes, potatoes, peas, and soybeans. For the rapid and accurate detection of GBNV at points of entry, TaqMan reverse transcriptase–quantitative polymerase chain reaction (RT-qPCR) assays were developed and the results validated using conventional reverse transcriptase–polymerase chain reaction (RT-PCR) followed by Sanger sequencing. These assays target highly conserved regions of the nucleocapsid (NP) and movement (MP) proteins within the viral genome. Multiplex GBNV detection assays targeting the NP and MP genes, as well as an internal control plant gene, ACT11, showed efficiency rates between 90% and 100% and R² values of 0.98 to 0.99, indicating high accuracy and precision. Moreover, there was no significant difference in sensitivity between multiplex and singleplex assays, ensuring reliable detection across various plant tissues. This rapid, sensitive, and specific diagnostic assay will provide a valuable tool at ports of entry to prevent the entry of GBNV into the United States. Full article

This paper proposes a novel hybrid metaheuristic, called JADEGMO, that combines the adaptive parameter control of adaptive differential evolution with optional external archive (JADE) with the search strategies of geometric mean optimizer (GMO). The goal is to enhance both exploration and exploitation stratifies for solving complex optimization tasks. JADEGMO inherits JADE’s adaptive mutation and crossover strategies while leveraging GMO’s swarm-inspired velocity updates guided by elite solutions. The experimental evaluations on IEEE CEC2022 benchmark suites demonstrate that JADEGMO not only achieves superior average performance compared to multiple state-of-the-art methods but also exhibits low variance across repeated runs. Convergence curves, box plots, and rank analyses confirm that JADEGMO consistently finds high-quality solutions while maintaining diversity and avoiding premature convergence. To highlight its applicability, we employ JADEGMO in a real-world multi-cloud security configuration scenario. This problem models the trade-offs among baseline risk, encryption overhead, open ports, privilege levels, and subscription-based security features across three cloud platforms. JADEGMO outperforms other common metaheuristics in locating cost-efficient configurations that minimize risk while balancing overhead and subscription expenses. Full article