Search Results (154)

With the increasing use of modern technologies in interior design, numerous recent studies have made the effects of kinetic-based design techniques on users’ perceptions a crucial topic, and sustainable performance has emerged as essential. From this standpoint, this study uses a space syntax approach to investigate how human behavioral performance in workspaces is affected by kinetic interiors. Three kinetic-based design strategies were employed to evaluate changes in spatial configuration characteristics, and the relevant terminology was adapted to account for the use of kinetic technology. The paper adopts a comparative analysis model to follow these changes using four syntactic measures: integration, choice, connectivity, and clustering coefficient. The proposed evaluation approach is applied to a traditional office building in Port Said, Egypt, showcasing various aspects of kinetic technology in workspaces. The study’s findings elucidate the correlations between design strategies and the resulting spatial characteristics, guiding designers in evaluating the features of each system and facilitating comparisons between them. Finally, the study’s main aim is to propose a three-step design process as a guideline for creating an integrated kinetic technology design, involving the evaluation of the proposed alternatives to achieve the desired spatial characteristics. Full article

Fire safety plays a vital role in protecting lives, property, and the environment, and it keeps communities and organizations running safely. Many existing fire pump control systems fall short in educational and small-to-medium industrial settings: they often control only one pump at a time, rely heavily on manual monitoring, and come with high costs that limit accessibility. To address these gaps, we developed an affordable, hands-on educational kit that brings real-world fire safety systems into the classroom using modern automation technology. The system is built around a Delta DVP12SA211R PLC chosen for its built-in real-time clock, integrated RS-232/RS-485 ports for reliable communication, and expanded with DVP16SP11R digital I/O and DVP04AD-S2 analog input modules to interface with simulated sensors mimicking smoke detection and water pressure. Students interact with the system through a Delta DOP-110IS HMI, which features Ethernet connectivity for remote observation, electrical isolation for safe operation, and a 200 ms screen update rate to ensure responsive, realistic feedback. The kit enables learners to explore critical emergency scenarios, including automatic switching between jockey and main pumps, low-pressure alerts, and system failover, transforming theoretical concepts into tangible skills. In user evaluations, 57.1% of students with no prior experience reported that the simulations closely mirrored real-world systems, while 80% of those with a fire safety background found the kit reinforced their existing knowledge; notably, 57.1% of instructors rated it as highly effective for teaching core fire safety principles across diverse learner profiles. By integrating industrial-grade hardware with scenario-based learning, this tool not only deepens understanding of fire protection systems but also better prepares future engineers for the practical demands of fire safety and industrial automation careers. Full article

This paper explores the feasibility of employing small language models (SLMs) on edge devices powered by batteries in environments with limited/no internet connectivity. SLMs in fact offer significant advantages in such scenarios due to their lower resource requirements with respect to large language models. The use case in this study is maritime navigation—in particular, the documentation on Sailing Directions (Enroutd) of the World Port Index (WPI) provided by the National Geospatial-Intelligence Agency (NGA), which provides information that cannot be shown graphically on nautical charts and is not readily available elsewhere. In this environment, response immediacy is not critical, as users have sufficient time to query information while navigating and planning activities, making edge devices ideal for running these models. On the contrary, the response quality is fundamental. For this reason, given the constrained knowledge of SLMs in maritime contexts, we investigate the use of the retrieval-augmented generation (RAG) methodology, integrating external information from sailing directions. A comparative analysis is presented to evaluate the performance of various state-of-the-art SLMs, focusing on response quality, the effectiveness of the RAG component, and inference times. Full article

The integration of electric micromobility into urban transportation systems can significantly reduce the energy consumption and emissions associated with short-distance travel. However, quantitative energy-based assessments remain limited, particularly in complex environments such as port–city interfaces. This paper presents a scenario-based energy assessment framework combining survey data and energy modelling. Empirical data were collected through a user survey (n = 138) targeting port workers and nearby residents, providing information on trip distances, travel frequency, modal choice, and willingness to shift from private car use. These data were combined with an energy modelling framework based on mode-specific energy intensity values expressed in kWh per passenger-kilometre. Three scenarios were analysed: a baseline scenario without intervention, a modal shift scenario supported by basic infrastructure measures, and an integrated scenario including transport management measures and local photovoltaic energy coupling. Results indicate that a moderate modal shift of 35% from private cars to electric micromobility for short-distance trips can generate aggregated annual energy savings of approximately 30 MWh and reduce CO₂ emissions by around 7 t per year across the analysed cases. According to the proposed energy model, electric micromobility achieves up to a 95% reduction in energy use per passenger-kilometre compared to private car travel. Furthermore, photovoltaic coupling could supply between 55% and 85% of the annual charging demand. The proposed framework is transparent and transferable, supporting energy-efficient and electrified future mobility planning. Full article

In energy-harvesting storage systems, in order to guarantee the correct operation and integration of its parts into the system, different power converters must be used. Using several stages increases energy processing and therefore decreases the overall efficiency of the system. In this paper, an integrated multi-port converter with galvanic isolation is proposed. It allows the transfer of energy between the solar panel, the battery, and the user using the fewest possible stages, thus maximizing efficiency. Operating in three modes depending on the battery’s state of charge, solar radiation and load conditions, the converter can conduct electric power between its ports. The proposal was validated in a 1 kW prototype performing the different modes of operation. It should be noted that a PV emulator (ETS150X5.6C-PVF) was used in the experimental setup; by means of this device, conditions such as solar irradiance and temperature, which affect the energy generation of PV panels, were controlled. In addition, the transformer employed in the prototype implementation was handmade; therefore, its design could be improved to obtain better performance. The experimental results show efficiencies exceeding 94%, and an analysis of the distribution of losses in the circuit was carried out. Also, a comparison with previous proposals is presented, showing competitive features. Full article

This paper addresses the critical scalability challenge in Hardware-in-the-Loop (HIL) channel emulation for massive RIS-assisted 6G environments. We propose a Two-Dimensional Dynamic Logic Resource Allocation (2D-DLRA) architecture that decouples physical RF ports from baseband processing resources through hierarchical pooling at both the session level and the multipath level. By jointly virtualizing Logical Units (LUs) and Multipath Processing Units (MPUs), the proposed architecture overcomes the dual inefficiency of port underutilization and path-level sparsity inherent in conventional static designs. A rigorous analytical framework combining hierarchical queuing theory and non-cooperative game theory is developed to characterize system capacity, blocking probability, and user contention under heterogeneous workloads. Simulation results demonstrate that, under a strict QoS constraint of 1% blocking probability, the proposed 2D-DLRA architecture achieves a multi-fold increase in supported user capacity compared to static allocation with the same hardware resources. Moreover, for an end-to-end emulation error threshold of 3%, 91.8% of users meet the QoS requirement, compared to only 73.6% in static architectures. The results further show that dynamic pooling enables near-saturated hardware utilization, in contrast to the single-digit utilization typical of static designs in sparse RIS scenarios. These findings confirm that 2D-DLRA provides a scalable and hardware-efficient solution for large-scale RIS channel emulation, offering practical design guidelines for next-generation 6G HIL testing platforms. Full article

Fast large-scale network scanning is an important way to understand internet service configurations and security in real time, among which stateless scan technology is representative. Existing stateless scanners can perform single-packet scans for internet-wide network measurements but are limited to host discovery or port scanning. To obtain further information over TCP, slower stateful scanners must be used in conjunction, which spend more time and memory because of connection state maintenance. Through the simplification of the TCP finite state machine (FSM), this paper proposes a novel stateless scanning model, which can establish TCP connections and obtain further responses in a completely stateless manner. Based on this model, we implement ZBanner, an improved modular stateless scanner that utilizes user-defined probes for identifying services and versions, fingerprinting TLS servers, etc. We present the unique design of ZBanner and experimentally characterize its feasibility and performance. Experiments show that ZBanner performs better than current state-of-the-art solutions in terms of scan rate and memory usage. ZBanner achieves a scan rate that is at least three times faster than current tools for generic ports and over 90 times faster for open ports while keeping a minimum and stable memory usage. Full article

Coastal flooding can result from multiple interacting drivers and can be a complex, challenging topic for learners to grasp. Interactive learning with apps offers new opportunities for improving comprehension and engagement. We present the Floodport app, an educational interactive tool that puts students in the role of coastal risk analysts exploring how natural hazards threaten port safety. Users have to adjust key parameters, including high tides, storm surges, terrestrial rainfall contribution, sea-level rise, and engineered features such as dock height. These forces, individually or jointly, result in water-level rises that may flood the app’s port. The app supports exploration of mitigation designs for the port. Developed in Excel and Python 3.11.4 and deployed as an R/Shiny application, Floodport was used as a classroom game by 153 students with no prior knowledge on coastal flooding concepts. Pre–post survey statistical analysis showed significant learning gains and positively correlation with willingness to engage further. Floodport was found to be a useful tool for basic introduction to flooding concepts. The results indicate strong pedagogical promise and potential for using the app beyond the classroom, in contexts such as stakeholder engagement and training. Full article

In recent years, the convergence of advanced technologies has enabled real-time data access and sharing across diverse devices and networks, significantly amplifying cybersecurity risks. For organizations with digital infrastructures, network security is crucial for mitigating potential cyber-attacks. They establish security policies to protect systems and data, but employees may intentionally or unintentionally bypass these policies, rendering the network vulnerable to internal and external threats. Detecting these policy violations is challenging, requiring frequent manual system checks for compliance. This paper addresses key challenges in safeguarding digital assets against evolving threats, including rogue access points, man-in-the-middle attacks, denial-of-service (DoS) incidents, unpatched vulnerabilities, and AI-driven automated exploits. We propose a Blockchain-Enhanced Network Scanning and Monitoring (BENSAM) Framework, a multi-layered system that integrates advanced network scanning with a structured database for asset management, policy-driven vulnerability detection, and remediation planning. Key enhancements include device profiling, user activity monitoring, network forensics, intrusion detection capabilities, and multi-format report generation. By incorporating blockchain technology, and leveraging immutable ledgers and smart contracts, the framework ensures tamper-proof audit trails, decentralized verification of policy compliance, and automated real-time responses to violations such as alerts; actual device isolation is performed by external controllers like SDN or NAC systems. The research provides a detailed literature review on blockchain applications in domains like IoT, healthcare, and vehicular networks. A working prototype of the proposed BENSAM framework was developed that demonstrates end-to-end network scanning, device profiling, traffic monitoring, policy enforcement, and blockchain-based immutable logging. This implementation is publicly released and is available on GitHub. It analyzes common network vulnerabilities (e.g., open ports, remote access, and disabled firewalls), attacks (including spoofing, flooding, and DDoS), and outlines policy enforcement methods. Moreover, the framework anticipates emerging challenges from AI-driven attacks such as adversarial evasion, data poisoning, and transformer-based threats, positioning the system for the future integration of adaptive mechanisms to counter these advanced intrusions. This blockchain-enhanced approach streamlines security analysis, extends the framework for AI threat detection with improved accuracy, and reduces administrative overhead by integrating multiple security tools into a cohesive, trustworthy, reliable solution. Full article

Efficient spectrum allocation and telecom traffic steering in densified heterogeneous maritime communication networks remains a critical challenge due to user mobility, dynamic interference, and congestion across terrestrial, aerial, and sea-based transmitters. This paper introduces the Moving Colorable Graph (MCG) framework, a dynamic graph-theoretical representation of interferences that extends conventional graph coloring to capture the spatiotemporal evolution of heterogeneous wireless links under varying channel and traffic conditions. The formulated spectrum allocation problem is inherently non-convex, as it involves discrete frequency assignments, mobility-induced dependencies, and interference coupling among multiple transmitters and users, thus requiring suboptimal yet computationally efficient solvers. The proposed approach models resource assignment as a time-dependent coloring problem, targeting to optimally support users’ diverse demands in dense port-area networks. Considering a realistic port-area network with coastal, sea and Unmanned Aerial Vehicle (UAV) radio coverage, we design and evaluate three MCG-based algorithm variants that dynamically update frequency assignments, highlighting their adaptability to fluctuating demands and heterogeneous coverage domains. Simulation results demonstrate that the selective reuse-enabled MCG scheme significantly decreases network outage and improves user demand satisfaction, compared with static, greedy and heuristic baselines. Overall, the MCG framework may act as a flexible scheme for mobility-aware and congestion-resilient resource management in densified and heterogeneous maritime networks. Full article

Ports and maritime operations generate massive real-time data streams, particularly from Automatic Identification System (AIS) signals, which are challenging to query effectively using natural language. This study proposes a prototype AISStream-MCP, a memory-augmented real-time maritime question-answering (QA) system that integrates live AIS data streaming with a Model Context Protocol (MCP) toolchain to support port operations decision-making. The system combines a large language model (LLM) with four MCP-enabled modules: persistent dialogue memory, live AIS data query, knowledge graph lookup, and result evaluation. We hypothesize that augmenting an LLM with domain-specific tools significantly improves QA performance compared to systems without memory or live data access. To test this hypothesis, we developed two prototype systems (with and without MCP framework) and evaluated them on 30 queries across three task categories: ETA prediction, anomaly detection, and multi-turn route queries. Experimental results demonstrate that AISStream-MCP achieves 88% answer accuracy (vs. 75% baseline), 85% multi-turn coherence (vs. 60%), and 38.7% faster response times (4.6 s vs. 7.5 s), with user satisfaction scores of 4.6/5 (vs. 3.5/5). The improvements are statistically significant (p < 0.01), confirming that memory augmentation and real-time tool integration effectively enhance maritime QA capabilities. Specifically, AISStream-MCP improved ETA prediction accuracy from 80% to 90%, anomaly detection from 70% to 85%, and multi-turn query accuracy from 65% to 88%. This approach shows significant potential for improving maritime situational awareness and operational efficiency. Full article

Since the implementation of the Belt and Road Initiative (BRI) in 2013, Chinese enterprises have expanded port and industrial investments along the Maritime Silk Road (MSR), forming a mutually reinforcing coupled system. Port investments reduce transportation costs and promote the relocation of industries to host countries. In turn, industrial agglomeration further promotes port investment. However, risks arising from political and economic uncertainties in host countries, as well as fluctuations in international relations, have become increasingly prominent. Due to the differences in the types and levels of risks faced by port and industrial investments, port investment decisions have become more complex and uncertain. To address this issue, this study constructs a bi-level optimization model. The upper model (UM) aims to maximize the total investment profit by optimizing the scale of multiple port investments. The lower model (LM) employs a User Equilibrium (UE) framework to determine the spatial distribution of industries under equilibrium conditions. Using 14 countries along the MSR as a case study, this paper estimates the number of newly constructed berths in each country and the corresponding investment returns. It also finds that local wages and land prices tend to rise after investment. The findings provide valuable references for Chinese enterprises in making overseas investment decisions. Full article

Ports with Mediterranean-like traffic profiles combine dense passenger, cargo, touristic, and local operations in confined waters where many small craft sail without AIS, increasing collision risk. Nature of such traffic in often unpredictable, due to often and sudden course corrections or changes. In such situations, it is possible that larger ships cannot manoeuvre to avoid collisions with small vessels. Hence, it is important to the port authority to develop a fast and adoptable mean to reduce collision risks. We present an end-to-end shore-based framework that detects and tracks vessels from fixed cameras (YOLOv9 + DeepSORT), estimates speed from monocular lateral video with an artificial neural network (ANN), and visualises collision risk in augmented reality (AR) for VTS/port operators. Validation in the Port of Split using laser rangefinder/GPS ground truth yields MAE 1.98 km/h and RMSE 2.18 km/h (0.605 m/s), with relative errors 2.83–21.97% across vessel classes. We discuss limitations (sample size, weather), failure modes, and deployment pathways. The application uses stationary port camera as an input. The core calculations are performed at user’s computer in the building. Mobile application uses wireless communication to show risk assessment at augmented reality smart phone. For training of ANN, we used The Split Port Ship Classification Dataset. Full article

Electrical sealing covers are widely used in various industrial equipment, where the quality of their metal-painted surfaces directly affects product appearance and long-term reliability. Micro-defects such as pores, particles, scratches, and uneven paint coatings can compromise protective performance during manufacturing. In the rapidly growing new energy vehicle (NEV) industry, battery charging-port sealing covers are critical components, requiring precise defect detection due to exposure to harsh environments, like extreme weather and dust-laden conditions. Even minor defects can lead to water ingress or foreign matter accumulation, affecting vehicle performance and user safety. Conventional manual or rule-based inspection methods are inefficient, and the existing deep learning models struggle with detecting minor and subtle defects. To address these challenges, this study proposes EIM-YOLO, an improved object detection framework for the automated detection of metal-painted surface defects on electrical sealing covers. We propose a novel lightweight convolutional module named C3PUltraConv, which reduces model parameters by 3.1% while improving mAP₅₀ by 1% and recall by 3.2%. The backbone integrates RFAConv for enhanced feature perception, and the neck architecture uses an optimized BiFPN-concat structure with adaptive weight learning for better multi-scale feature fusion. Experimental validation on a real-world industrial dataset collected using industrial cameras shows that EIM-YOLO achieves a precision of 71% (an improvement of 3.4%), with mAP₅₀ reaching 64.8% (a growth of 2.6%), and mAP_50–95 improving by 1.2%. Maintaining real-time detection capability, EIM-YOLO significantly outperforms the existing baseline models, offering a more accurate solution for automated quality control in NEV manufacturing. Full article

Despite its success in measuring air–sea exchange, the Woods Hole Oceanographic Institution’s (WHOI) X-Spar Buoy faces operational limitations due to energy constraints, motivating the integration of an energy harvesting apparatus to improve its deployment duration and capabilities. This work explores the feasibility of an augmented, self-powered system in two parts. Part 1 presents the collaborative design between X-Spar developers and wave energy researchers translating user needs into specific functional requirements. Based on requirements like desired power levels, deployability, survivability, and minimal interference with environmental data collection, unsuitable concepts are pre-eliminated from further feasibility study consideration. In part 2, we focus on one of the promising concepts: an internal rigid body wave energy converter. We apply control co-design methods to consider commercial of the shelf hardware components in the dynamic models and investigate the concept’s power conversion capabilities using linear 2-port wave-to-wire models with concurrently optimized control algorithms that are distinct for every considered hardware configuration. During this feasibility study we utilize two different control algorithms, the numerically optimal (but acausal) benchmark and the optimized damping feedback. We assess the sensitivity of average power to variations in drive-train friction, a parameter with high uncertainty, and analyze stroke limitations to ensure operational constraints are met. Our results indicate that a well-designed power take-off (PTO) system could significantly extend the WEC-Spar’s mission by providing additional electrical power without compromising data quality. Full article