Search Results (365)

Securing Industrial IoT (IIoT) network environments remains a significant challenge due to the increasing complexity of interconnected sensors, actuators, gateways, and control systems, which are frequent targets of cyberattacks. These threats can lead to operational disruptions, financial losses, and safety risks. This paper proposes an efficient multi-stage intrusion detection framework based on an enhanced Extreme Gradient Boosting (XGBoost) model for IIoT environments. The proposed framework integrates data preprocessing, class imbalance handling, hyperparameter optimization, probability calibration, and class-specific decision thresholds within a unified pipeline. In addition, calibrated probability outputs are utilized as continuous indicators of prediction confidence, enabling more reliable and risk-aware decision-making. The hierarchical multi-stage design decomposes the detection task into progressively refined classification levels, improving discrimination among complex and overlapping attack categories. The framework is evaluated using the Edge-IIoTset benchmark dataset, which reflects realistic IIoT network traffic under both normal and malicious conditions. Experimental results demonstrate that the proposed approach achieved significant performance improvements, including up to 21% increase in recall and 15% improvement in macro F1 score compared to the baseline models. Furthermore, the model exhibits low inference latency and supports efficient deployment in time-sensitive IIoT monitoring scenarios. These results indicate that the proposed framework provides an effective and scalable solution for multi-class cyber threat detection in IIoT networks. Full article

The ubiquity of resource-constrained Internet-of Things (IoT) nodes creates an urgent demand for network intrusion detection systems (NIDSs) optimized for edge devices with limited computing power. In this paper, we propose a new NIDS system based on Mamba. NIDS-Mamba uses a dynamic sparse attention and a lightweight state space to jointly learn from short-term anomaly and long-term attack patterns. We use standardized NF-UNSW-NB15 and NF-CSE-CIC-IDS2018 datasets to verify the effectiveness of this NIDS-Mamba model. We find that this NIDS-Mamba model is very effective in dealing with extreme class imbalance problems. In the NF-CSE-CIC-IDS2018 dataset, the model achieves 98.32% accuracy, 96.98% F1-score, and an AUC of 0.9996. Most notably, the model is very robust in handling extreme class imbalance problems in the NF-UNSW-NB15 dataset. It achieves 97.03% G-Mean, 0.7915 MCC, and 0.9983 AUC, far exceeding other baseline models. Compared to Transformer-based baselines, NIDS-Mamba achieves nearly an order-of-magnitude improvement in throughput while maintaining a parameter footprint compatible with edge deployment constraints. The proposed architecture effectively mitigates the quadratic complexity and memory wall inherent in standard Transformers, ensuring compatibility with Limited RAM and strict energy constraints. The proposed model achieves a compact design with 1.12 million parameters and a peak inference memory of 5.4 MB, ensuring its feasibility for edge-based IoT nodes. These properties make NIDS-Mamba a strong candidate for deployment on IoT gateways and edge sensor nodes in smart home, industrial IoT, and critical infrastructure scenarios. Full article

Dry ports are becoming increasingly important elements of port–hinterland transport systems, particularly as maritime gateways face rising congestion, infrastructure pressure, and coordination challenges within global supply chains. As international trade expands and logistics networks grow more complex, inland terminals are progressively evolving into integrated intermodal platforms that support more efficient freight distribution between seaports and their hinterlands. This study presents a PRISMA-based systematic review of research on dry port–seaport systems covering the period 1980–2025. Following a structured screening and selection procedure, peer-reviewed publications were identified and analyzed to examine conceptual developments, thematic orientations, geographical scope, and decision-making perspectives within the field. Particular attention is given to the growing relevance of digital transformation, including artificial intelligence and machine learning, in shaping future dry port operations and network design. By synthesizing existing contributions and identifying research gaps, this review provides a consolidated understanding of the evolution of dry port research and outlines key directions for advancing sustainable, resilient, and data-driven port–hinterland systems. Full article

Large-scale smart agriculture requires reliable and energy-efficient wireless connectivity to support distributed environmental sensing across wide rural areas. However, existing low-power wide-area network (LPWAN) technologies often face limitations in scalability, reliability, or infrastructure dependency when deployed in large agricultural fields. This study presents the design and experimental evaluation of a hierarchical sensor network architecture that integrates LoRaMESH for multi-hop sensing communication and Wi-Fi HaLow as a sub-GHz backhaul for data aggregation and cloud connectivity. In the proposed system, LoRaMESH forms intra-cluster sensor networks using a lightweight controlled flooding protocol, while Wi-Fi HaLow provides long-range IP-based connectivity between cluster gateways and a central access point. A real-world deployment covering approximately $2.5\mathrm{km}\times 1\mathrm{km}$ of agricultural area was implemented to evaluate the performance of the proposed architecture. Experimental results show that the LoRaMESH network achieves packet delivery ratios above $90\%$ across one to three hops, with average end-to-end delays between $10.6$ s and $13.3$ s. The Wi-Fi HaLow backhaul demonstrates high reliability within short to medium distances, reaching $99.5\%$ packet delivery ratio at 50 m and $89.68\%$ at 200 m. Energy measurements further indicate that the sensor nodes consume only $21.19\mathsf{\mu }\mathrm{A}$ in sleep mode, enabling long-term battery-powered operation suitable for agricultural monitoring applications. These results indicate that the proposed hierarchical architecture is a feasible connectivity option for the tested large-scale agricultural sensing scenario. Because no side-by-side LoRaWAN or NB-IoT benchmark was conducted on the same testbed, the results should be interpreted as a field validation of the proposed architecture rather than as a direct experimental demonstration of superiority over alternative LPWAN systems. Full article

Rapid population growth challenges governance systems, housing markets, infrastructure capacity, and social cohesion, yet it is often treated as a predictable and uniform process. This structured comparative review synthesizes four distinct rapid-growth literatures: energy boomtowns, amenity-migration destinations, gateway communities, and mega-event host towns, to examine how different growth drivers shape community resilience. Using systematic forward and backward citation tracking grounded in community theory, the review identifies recurring patterns across otherwise separate research traditions. The analysis shows that outcomes are shaped less by growth itself than by institutional and spatial conditions. Extractive boomtowns and mega-event hosts experience compressed cycles of disruption and recovery that test adaptive capacity, while amenity-migration destinations and gateway communities face sustained pressures related to housing affordability, land-use conflict, and social boundary formation. Across contexts, three interrelated dimensions of adaptive capacity consistently structure trajectories: multilevel governance coordination, housing and land-use elasticity, and the management of social equity and cohesion. The findings advance a conceptual resilience framework that interprets rapid population change as a socio-spatial shock filtered through institutional and spatial conditions, with implications for sustainable urban design, flexible infrastructure planning, and inclusive governance. Full article

At racing speeds above 300 km/h (≈83 m/s), hazard awareness becomes a vehicular-communications problem: 100 ms already correspond to about 8.3 m of blind travel before an alert can influence braking, line choice, or torque delivery. Cloud-only telemetry is therefore insufficient under intermittent coverage and variable round-trip delay, while conventional trackside and pit-wall links do not provide direct inter-bike hazard dissemination. We propose Hybrid Epistemic Offloading (HEO), an edge–mesh–cloud architecture for high-mobility V2V/V2X hazard dissemination that explicitly separates an ephemeral safety plane from a durable cloud-analytics plane. On-bike edge nodes ingest high-rate ECU/IMU signals over CAN and persist full-fidelity traces into standardized ASAM MDF containers, enabling loss-tolerant buffering, deterministic replay, and post hoc auditability across coverage gaps. For real-time safety, motorcycles form a local V2V mesh that disseminates compact hazard digests using latency-bounded gossip with adaptive fanout, TTL-based suppression, and redundancy-aware forwarding over sidelink-capable V2X links. The hazard channel is formulated as uncertainty-aware to account for localization error and propagation delay at race pace. We evaluate the system in two stages: (i) a reproducible mobility-coupled simulation/emulation campaign for mesh dissemination and durable edge → gateway → cloud delivery; and (ii) an MDF4 replay-based Jerez pilot for stability-oriented co-design analysis. Under the tested conditions, the durable MQTT path achieved an 83.4 ms median, 175.9 ms p95, and 303.74 ms maximum end-to-end latency with no observed event loss. In the Jerez pilot, the co-design workflow reduced mean wheel slip from 6.26% to 3.75% (−40.10%) and a control-volatility proxy from 0.1290 to 0.0212 (−83.58%). Full article

Port cities represent an interdependent system in which port and urban activities overlap and develop. While ports serve as the gateway for the city, expanding market reach and attracting investments, cities provide the necessary labor and services required for the operation of the ports. However, the mutual relationship between ports and cities is threatened by conflicts such as urban sprawl, which leads to friction by taking the space needed for storing containers at ports. Similarly, ports generate high noise and air pollution, threatening the quality of life in urban centers. Therefore, implementing best practices to manage the port–city dichotomy is essential to ensure the coexistence of the port and city. This study re-examined the port–city relationship in the framework of urban planning to guide redevelopment decisions within the Piraeus city port in Greece. Data were collected through a mixed-methods approach involving secondary research and roundtable discussions. The findings showed that a key design parameter of the Piraeus city port is the development and exploitation of the city’s relationship with water, from a functional, spatial, and aesthetic point of view. Furthermore, a guide was developed to facilitate the redevelopment of the city port and improve decision-making. The recommendations also emphasize the integration of the port city into a global economic forum and highlight its dynamism, ensuring mutual benefits for the city and port. Full article

In this study, we propose a wearable fall detection system that combines wearable sensors, TinyML model, and IoT-based communication for real-time monitoring and detection of falls. The system is designed for resource-constrained IoT devices where memory, power, and processing capacity are limited. The system works by collecting body motion data using accelerometer sensors placed on the human body. The data is then processed using a feedforward neural network trained on preprocessed signals. The trained model is quantized so that it can run on low-power embedded hardware with small memory size. The model performs inference directly on the device. This reduces latency and avoids sending raw sensor data to the cloud. When a fall is detected, the result is sent through Bluetooth to a gateway. The gateway forwards the data to a cloud server using the MQTT protocol. The cloud stores the data and supports monitoring and analysis. The experimental results show that the quantized TinyML model achieves 98.40% accuracy with more than 80% F1-score and more than 99% recall. The deployed model uses only ∼5 KB of RAM and ∼40 KB of flash memory. The inference time is 7 ms per class. These results show that wearable sensing with quantized TinyML models and IoT communication can provide fast and reliable fall detection for real-world safety monitoring systems. Full article

This study examines how coordination, power, and interdependence shape regulatory governance in the decentralized edible bird’s nest (EBN) sector in Pulang Pisau, Indonesia. While decentralization is often associated with improved responsiveness and local adaptability, it frequently produces fragmented regulatory systems in which authority is distributed without effective coordination. Using an actor-centered qualitative design combined with the MACTOR method, this study analyzes influence–dependence relations, objective alignment, and coordination bottlenecks across key actors. The findings show that regulatory performance is shaped less by formal mandates than by relational positioning within the governance system. Actors controlling technical verification and documentary gateways occupy high-influence positions, while licensing authorities remain operationally dependent. Although most actors share common objectives—such as hygiene, quality assurance, and traceability—these are pursued through fragmented procedures, resulting in coordination failures and regulatory inequality. Producers bear the greatest compliance burdens despite having limited influence over regulatory processes. The study introduces the concept of relational administrative capacity to explain how decentralized governance outcomes depend on the alignment of authority, expertise, and procedural sequencing across interdependent actors. The findings suggest that improving regulatory performance requires strengthening coordination architectures rather than adding new rules. Full article

To address the issues of corn harvester field operations relying on driver visual guidance for row alignment, high labor intensity, and unstable operation accuracy, this study innovatively proposes a “vision-dominant, gateway-enhanced” dual-mode collaborative row-alignment assistance architecture, and independently develops the R²DC-Mask R-CNN instance segmentation network and MCC-KF robust filtering algorithm to form a deeply coupled hardware–software-assisted driving system. The R²DC-Mask R-CNN network is autonomously designed for corn row-detection scenarios, achieving accurate perception in complex field environments; the MCC-KF algorithm innovatively solves the state estimation divergence problem during transient vision failures through a multi-criteria constraint mechanism, ensuring continuous navigation capability; the intelligent gateway and vision system form a confidence-driven master–slave switching mechanism that adaptively enhances system robustness when vision is restricted. Field experiments demonstrate that within the speed range of 0.5–5.0 km/h, the average lateral deviation in the row alignment assisted by the system is 3.82–5.30 cm, the proportion of deviations less than 10 cm exceeds 96%, and all sample deviations remain within 20 cm; at a speed of 3.5 km/h, the system reduces the average grain loss rate from 3.76% under manual operation to 2.65%, a decrease of 29.5%. This system effectively improves row alignment accuracy and harvest quality, providing a practical human–machine collaborative solution for intelligent harvester operations. Full article

NetworkGuard is a modular edge-based virtual network sensing framework designed for residential smart home security. The system interprets network telemetry—such as DNS queries, firewall events, VPN latency, and connection establishment delay—as structured sensing signals for gateway-level monitoring. Implemented on a Raspberry Pi 4 and managed via an Android interface, NetworkGuard integrates DNS filtering (Pi-hole), firewall enforcement (UFW), encrypted VPN tunneling (WireGuard), and an AI-assisted advisory layer for contextual log interpretation. During a six-week residential deployment, DNS blocking efficiency improved from 81.2% to 97.0% following blocklist refinement, while VPN connection establishment time decreased from approximately 3012 ms to 2410 ms after configuration tuning. ICMP-based measurements indicated a stable tunnel latency under moderate traffic conditions. Controlled validation scenarios—including DNS manipulation attempts, port scanning, and VPN interruption testing—confirmed consistent firewall enforcement and tunnel containment. The results demonstrate that layered security principles can be adapted into a lightweight, reproducible edge architecture suitable for small-scale residential IoT environments without a reliance on enterprise infrastructure. Full article

Dispersed archaeological landscapes are often rich in heritage value but weakly integrated into regional tourism systems. This creates difficulties in visitor orientation, interpretive continuity, and conservation-sensitive tourism planning. In response to this problem, this study examines the Adana–Kozan–Anavarza axis in southern Türkiye and proposes a spatial corridor framework for organising tourism development within a dispersed archaeological landscape. The research integrates spatial accessibility assessment, service-capacity evaluation, field observation, and sequential route design in order to establish a hierarchical gateway–transition–anchor configuration. Anavarza, one of the largest archaeological complexes of Cilicia, represents a monumental urban heritage site and a biocultural landscape situated within a Mediterranean ecological zone historically associated with Pedanius Dioscorides. Although current visitor volumes remain moderate, official statistics indicate a substantial increase in annual entries between 2022 and 2024, reflecting rising destination visibility. This emerging growth trajectory underscores the need for proactive spatial governance mechanisms prior to the onset of congestion and environmental degradation pressures. The findings suggest that Adana can function as a metropolitan gateway, Kozan as an intermediate staging node, and Anavarza as the archaeological anchor within a realistic multi-day visitor sequence. In this configuration, visitor functions are distributed across multiple nodes, while the ecological and archaeological sensitivity of the anchor landscape is more cautiously managed through spatial sequencing. Rather than proposing a predictive model, the study develops and assesses a context-responsive spatial planning framework grounded in accessibility, infrastructural feasibility, and conservation-sensitive visitor distribution. Beyond the local case, the study offers a transferable hierarchical staging logic for corridor-based heritage planning. Full article

Background: Cervical carcinoma remains a widespread cancer worldwide, primarily caused by persistent infection with high-risk human papillomavirus (HPV). HPV types 16 and 18 account for approximately 70% of cervical cancer cases. Although prophylactic HPV vaccines are commercially available, their high cost and reliance on expensive expression platforms limit their accessibility in developing countries. Objectives: This study aimed to develop a cost-effective, plant-based HPV vaccine candidate by expressing capsomeric HPV-16 and HPV-18 L1 antigens in Brassica oleracea (broccoli). Methods: Modified L1 from HPV types 16 and 18 were designed to retain capsomeric assembly and fused with heat-labile enterotoxin B subunit (LTB). Immunoinformatics analyses were used to assess antigenicity, epitope distribution, and structural characteristics. Codon-optimized genes were cloned using Gateway^® technology and expressed in broccoli via Agrobacterium-mediated transformation. Transgenic plants were validated by PCR and qRT-PCR. Protein accumulation was quantified, and immunogenicity was evaluated in mice. Results: PCR and qRT-PCR confirmed the stable integration of two copies of the LTB-L1 transgenes in broccoli plants. Western blotting detected L1 protein at ~56.5 kDa, indicating the cleavage of the LTB-L1 fusion protein. The correct folding of L1 capsomeres was verified by antigen-capture ELISA. The recombinant proteins accumulated to approximately 0.33% and 0.35% of total soluble protein for HPV-16 and HPV-18, respectively. The immunization of mice with transgenic L1 induced significant humoral immune responses, comparable to those elicited by purified VLPs. Conclusions: The results demonstrate broccoli as a promising platform for the expression of immunogenic HPV L1 capsomeres and highlight its potential for the development of affordable, plant-based HPV vaccines. Full article

Multi-hop networks’ performance strongly depends on relay node placement, which affects delay, throughput, and coverage. This work introduces a dual-layer protocol combining Slotted ALOHA for node-to-relay communication and TDMA for relay-to-gateway transmission. Using a Java-based simulator, we evaluate three relay placement strategies—random, square grid, and hexagonal grid—considering metrics such as delay, throughput, packet collisions, and coverage. Results show that the hexagonal grid offers superior performance, reducing collisions, minimizing delay, and expanding coverage. A fallback mechanism for out-of-range nodes and sensitivity analysis of different backoff values are also included. The study quantifies the benefits of structured relay placement for LoRaWAN and wireless sensor networks, while also identifying challenges for realistic deployments. These findings provide guidelines for designing scalable and reliable IoT networks and highlight directions for future work involving irregular placements and dynamic routing. The simulation results are intended to provide comparative, trend-based insights under conservative modeling assumptions, rather than absolute performance predictions. Full article

Bone metastasis is a devastating complication of advanced osteotropic malignancies, notably breast, prostate, lung carcinomas, and malignant melanoma, and remains a primary driver of mortality. Historical paradigms have conceptualized skeletal dissemination almost exclusively as a hematogenous process wherein circulating tumor cells colonize receptive bone marrow niches. However, this model fails to reconcile why lymph node metastasis consistently serves as a potent predictor of bone involvement even though therapeutic lymphadenectomy rarely prevents distant spread. This discordance suggests that lymph nodes function not merely as passive reservoirs but as active ‘evolutionary gateways’ that sculpt bone-tropic metastatic clones. In this review, we introduce the Lymphatic–Bone Axis, a framework integrating lymphatic biology into models of bone metastasis. We synthesize emerging evidence elucidating how the lymph node microenvironment primes tumor cells through CCR7-CXCR4 switching, induction of osteomimicry programs, and metabolic reprogramming that favors survival within the bone marrow. We also discuss preclinical data demonstrating direct intranodal intravasation via high endothelial venules (HEVs), providing a rapid route into the systemic circulation that bypasses the thoracic duct. Beyond consolidating current knowledge, we outline a research agenda for dissecting this axis, including longitudinal single-cell transcriptomic mapping and functional assessments of lymph node-derived tumor cells. Finally, we consider translational implications, highlighting why bone-targeted agents alone may prove insufficient once cells are conditioned within lymphatic niches. By mechanistically linking lymphatic priming to skeletal colonization, this review informs the rational design of multimodal therapeutic approaches that jointly target lymphatic transit and the bone microenvironment. Full article