Search Results (1,575)

This study evaluated the effects of different provisional luting cement removal methods on the shear bond strength (SBS) of resin cement to dentin following immediate dentin sealing (IDS) performed with two adhesive systems. A total of 168 extracted, caries-free human third molars were used, of which 144 were allocated for SBS testing and 24 for scanning electron microscopy (SEM) analysis. Specimens were assigned according to the IDS protocol (no IDS, IDS with OptiBond FL, or IDS with G2-Bond), followed by provisional cementation using an eugenol-free temporary cement. Contaminated surfaces were subsequently cleaned with a hand scaler, aluminum oxide (Al₂O₃) air abrasion, or Katana Cleaner prior to final bonding with a dual-cure resin cement. SBS was measured after 24 h of water storage, and surface morphology was evaluated by SEM at 2500× magnification. IDS significantly increased SBS under uncontaminated conditions, with G2-Bond-based IDS exhibiting higher bond strength values than specimens without IDS. However, provisional cement contamination significantly reduced SBS regardless of the cleaning method applied, and none of the tested protocols fully restored the bond strength observed in uncontaminated IDS-treated dentin. SEM analysis revealed residual cement remnants and surface alterations after cleaning, even in specimens that appeared macroscopically clean. Within the limitations of this in vitro study, IDS enhances resin–dentin bonding when contamination is avoided; however, current mechanical and chemical cleaning methods are insufficient to completely recover bond strength compromised by provisional cement contamination, highlighting the importance of preventing contamination and preserving IDS layer integrity during indirect restorative procedures. Full article

Big data has significantly transformed data processing and analytics across various domains. However, ensuring security and data confidentiality in distributed platforms such as Hadoop remains a challenging task. Distributed environments face major security issues, particularly in the management and protection of large-scale data. In this article, we focus on the cost of secure information transmission, implementation complexity, and scalability. Furthermore, we address the confidentiality of information stored in Hadoop by analyzing different AES encryption modes and examining their potential to enhance Hadoop security. At the application layer, we operate within our Hadoop environment using an extended, secure, and widely used MQTT protocol for large-scale data communication. This approach is based on implementing MQTT with TLS, and before connecting, we add a hash verification of the data nodes’ identities and send the JWT. This protocol uses TCP at the transport layer for underlying transmission. The advantage of TCP lies in its reliability and small header size, making it particularly suitable for big data environments. This work proposes a triple-layer protection framework. The first layer is the assessment of the performance of existing AES encryption modes (CTR, CBC, and GCM) with different key sizes to optimize data confidentiality and processing efficiency in large-scale Hadoop deployments. Afterwards, we propose evaluating the integrity of DataNodes using a novel verification mechanism that employs SHA-3-256 hashing to authenticate nodes and prevent unauthorized access during cluster initialization. At the third tier, the integrity of data blocks within Hadoop is ensured using SHA-3-256. Through extensive performance testing and security validation, we demonstrate integration. Full article

This paper addresses the issue of low detection accuracy in underwater optical images for unmanned underwater vehicles (UUVs) during practical operations, caused by factors such as uneven lighting, blur, complex backgrounds, and target occlusion. To enhance the autonomous perception and control capabilities of UUVs, a high-precision algorithm named MBACA-YOLO is proposed based on the YOLOv13n model. Firstly, the convolutional layers in the backbone network of YOLOv13n are optimized by replacing stride-2 convolutions with stride-1 and embedding SPD layers to enable richer feature extraction. Secondly, the newly proposed MBACA attention mechanism is integrated into the final layer of the backbone network, enhancing effective features and suppressing background noise interference. Thirdly, traditional upsampling in the neck network is replaced with CARAFE upsampling to mitigate noise pollution. Finally, an Alpha-Focal-CIoU loss function is designed to improve the accuracy of bounding box regression for underwater targets. To validate the algorithm’s effectiveness, experiments were conducted on the URPC dataset with the following evaluation protocol: 640 × 640 input resolution, batch size 1, FP32 precision, and standard NMS. All results are from a single random seed with 300 epochs of training. The proposed MBACA-YOLO algorithm outperforms the baseline YOLOv13n model, improving mAP@0.5 and mAP@0.5:0.95 by 3.1% and 2.8% respectively, while adding only 0.49M parameters and 1.0 GFLOPs, with an FPS drop of just 2 frames. This makes it an efficient, deployable perception solution for automated Unmanned Underwater Vehicles (UUVs), significantly advancing intelligent underwater systems. Full article

The Internet of Things (IoT) has transformed modern life through interconnected devices enabling automation across diverse environments. However, its reliance on legacy network architectures has introduced significant security vulnerabilities and efficiency challenges—for example, when Datagram Transport Layer Security (DTLS) encrypts transport-layer communications to protect IoT traffic, it simultaneously blinds intermediate proxies that need to inspect message contents for protocol translation and caching, forcing a fundamental trade-off between security and functionality. This paper presents an architectural solution based on the Recursive InterNetwork Architecture (RINA) to address these issues. We analyze current IoT network stacks, highlighting their inherent limitations—particularly how adding security at one layer often disrupts functionality at others, forcing a detrimental trade-off between security and performance. A central principle underlying our approach is the role of structural symmetry in RINA’s design. Unlike the heterogeneous, protocol-specific layers of TCP/IP, RINA exhibits recursive self-similarity: every Distributed IPC Facility (DIF), regardless of its position in the network hierarchy, instantiates identical mechanisms and offers the same interface to layers above. This architectural symmetry ensures predictable, auditable behavior while enabling policy-driven asymmetry for context-specific security enforcement. By embedding security within each layer and allowing flexible layer arrangement, RINA mitigates common IoT attacks and resolves persistent issues such as the inability of Performance Enhancing Proxies to operate on encrypted connections. We demonstrate RINA’s applicability through use cases spanning smart homes, healthcare monitoring, autonomous vehicles, and industrial edge computing, showcasing its adaptability to both RINA-native and legacy device integration. Our mixed-methods evaluation combines qualitative architectural analysis with quantitative experimental validation, providing both theoretical foundations and empirical evidence for RINA’s effectiveness. We also address emerging trends including AI-driven security and massive IoT scalability. This work establishes a conceptual foundation for leveraging recursive symmetry principles to achieve secure, efficient, and scalable IoT ecosystems. Full article

The traditional TCP sender-driven approach to data communication in transport protocols can lead to ambiguity between the sender and receiver regarding packet delivery status. This issue stems primarily from the sender relying on explicit feedback from the receiver in the form of cumulative acknowledgments. While optimizations such as SACK can mitigate this issue to some extent, ambiguity may still arise due to receiver reneging or under high-loss environments, including retransmission loss. Recent research in transport protocols and new architectures has highlighted the advantages of using a receiver-driven approach over a sender-driven one for Internet communication. This shifts the traditional push-based data retrieval paradigm to a pull-based paradigm, allowing the creation of new transport services such as transparent caching and multicasting. This paper builds on these efforts to abstract and formalize a receiver-driven ARQ (RDQ) that follows established end-to-end principles in transport protocols, providing in-order reliability from the perspective of the receiver. We present the design of RDQ, layered on top of UDP, leveraging sender-driven and receiver-driven protocol elements. RDQ is implemented in the ns-3 simulator and evaluated against TCP- and SACK-style sender-driven ARQ under high-loss conditions. The preliminary results indicate the feasibility of incorporating a receiver-driven ARQ with a classic retransmission strategy in transport protocols, offering positive gains in reduced recovery delay and transmission efficiency relative to TCP/SACK-style sender-driven ARQ. Full article

The rapid integration of Internet of Things (IoT) technologies is transforming electrical power systems into intelligent, interconnected, and data-driven infrastructures, enabling advanced monitoring, control, and optimization across the entire energy value chain. IoT-based smart electrical systems enable advanced monitoring, control, and optimization of energy generation, distribution, and consumption, while also introducing new challenges related to interoperability, security, scalability, and data management. Despite the growing body of literature, existing surveys typically address these challenges in isolation, focusing on individual technological or operational aspects and thus failing to capture their strong cross-dependencies in real-world deployments. This paper delivers a comprehensive survey that systematically analyzes and interrelates nine key dimensions that prior literature largely examines in separate silos: architectural models, communication protocols, reference standards, cybersecurity and privacy mechanisms, data processing paradigms (edge, fog, and cloud), interoperability solutions, energy management strategies, application scenarios, and future research directions. Unlike conventional reviews confined to single-layer or domain-specific perspectives, this survey adopts a holistic, cross-layer approach, explicitly linking architectural choices, protocol stacks, interoperability frameworks, and security mechanisms with application and energy management requirements. Full article

Sustainable urban development increasingly relies on hyperlocal environmental analytics created by smart city platforms that combine stationary and mobile sensors, Earth observations, meteorology, and land-use data. However, accurate spatio-temporal resolution can provide indirect identification and amplify cybersecurity threats. This article proposes the regulatory and technical mapping that implements the General Data Protection Regulation (GDPR) and the Network and Information Security Directive (NIS2) throughout the lifecycle of environmental data—reception, transport, storage, analytics, sharing, and publication. The methods combine doctrinal legal analysis, a review of the scope of recent research, formalized compliance modeling, modeling with synthetic city-scale datasets, expert identification, and demonstration of integrated analytics. The demonstration links deep evaluation of neural abnormalities (convolutional plus recurrent layers), short-term Fourier transformation of sensor signals, byte-to-image telemetry fingerprints, and protocol event counters, thereby tracking detection to explanatory evidence and to control actions. Deliverables include a matrix aligning lifecycle stages with GDPR principles and rights, as well as with the responsibilities of NIS2; a checklist for assessing the impact on data protection, which takes into account the risks of fairness and stigmatization; a basic set of controls for identification and access, secure design, monitoring, continuity, supplier assurance, and incident reporting; as well as a multi-layered publishing strategy that combines transparency with privacy through aggregation, delayed release, differentiated privacy budgets, and research enclaves. The visualization confirms that technical signals can be included in audit-ready reporting and automated response, while the guidelines legally clarify the relevant bases for common use cases such as air quality assurance networks, noise mapping, citizen sensor applications, and mobility and exposure modeling. The effects of the policy emphasize shared services for small municipalities, supply chain security, and ongoing review to counteract the mosaic effect. Overall, the study shows how cities can maximize environmental and social value based on environmental data, while maintaining privacy, sustainability, and equity by design. Full article

Current international standards (EN 12004; SI 4004) are testing ceramic tile adhesives under post-cure thermal aging. However, the standards omit UV radiation exposure during the fresh-adhesive phase. This research investigated three commercial polymer-modified cement adhesives (C2TE, C2TE-S2, C2T) bonding porcelain stoneware tiles under simulated Eastern Mediterranean and desert conditions. Three commercial adhesives were exposed during the initial (uncured) period to elevated temperature (30 °C), humidity variation (40–65% RH), and UV radiation (295–365 nm, 1.5–2.0 mW/cm²) for 20 min, followed by 28 days of curing. Pull-off testing and scanning electron microscopy, combined with quantitative directionality analysis, were used to characterize the mechanical performance and microstructural degradation. UV exposure of adhesives during tiling working time caused a drop of mean bond strength from 1.77 to 0.26 MPa (85% reduction) compared with 1.77 to 0.64 MPa (36% reduction) under hot-arid conditions. Microstructural analysis of the hardened pull-off adhesives revealed that exposure of the fresh adhesive to UV radiation causes thinning and degradation of the interfacial layer (15–40 µm), leading to a drop in macroscopic strength. In contrast, hot-arid exposure induces adhesive bulk cracking while preserving interface integrity. Fracture surface directionality (goodness parameter), crack density, and delamination percentage together distinguish interface failure from adhesive bulk degradation and provide a forecast of long-term durability. This combined SEM-mechanical approach identified critical gaps in testing protocols and enables evidence-based adhesive selection, as current EN 12004 classifications based solely on mechanical properties prove insufficient. Full article

Connected and Autonomous Vehicles (CAVs) are exposed to increasingly sophisticated cyber threats hidden within high-dimensional, heterogeneous network traffic. A critical bottleneck in existing Intrusion Detection Systems (IDS) is the feature heterogeneity gap: discrete protocol signatures (e.g., flags, services) and continuous traffic statistics (e.g., flow duration, packet rates) reside in disjoint latent spaces. Traditional deep learning approaches typically rely on naive feature concatenation, which fails to capture the intricate, non-linear semantic dependencies between these modalities, leading to suboptimal performance on long-tail, minority attack classes. This paper proposes HCA-IDS, a novel framework centered on Semantics-Aware Cross-Modal Alignment. Unlike heavy-weight models, HCA-IDS adopts a streamlined Multi-Layer Perceptron (MLP) backbone optimized for edge deployment. We introduce a dedicated Multi-Head Cross-Attention mechanism that explicitly utilizes static “Pattern” features to dynamically query and re-weight relevant dynamic “State” behaviors. This architecture forces the model to learn a unified semantic manifold where protocol anomalies are automatically aligned with their corresponding statistical footprints. Empirical assessments on the NSL-KDD and CICIDS2018 datasets, validated through rigorous 5-Fold Cross-Validation, substantiate the robustness of this approach. The model achieves a Macro-F1 score of over 94% on 7 consolidated attack categories, exhibiting exceptional sensitivity to minority attacks (e.g., Web Attacks and Infiltration). Crucially, HCA-IDS is ultra-lightweight, with a model size of approximately 1.00 MB and an inference latency of 0.0037 ms per sample. These results confirm that explicit semantic alignment combined with a lightweight architecture is key to robust, real-time intrusion detection in resource-constrained CAVs. Full article

The increasing deployment of agentic artificial intelligence (AI) systems and decentralized digital infrastructures has challenged traditional assumptions about organizational administration, control, and governance. While AI has advanced task-level optimization and decision support, administrative functions such as coordination, compliance, and accountability remain largely centralized and dependent on humans. This paper introduces Autonomous Administrative Intelligence (AAI), a governance-aware AI capability that enables autonomous agents to execute and adapt administrative decisions within strategically defined constraints and decentralized governance mechanisms. Building on the Strategic Decentralized Resilience–AI (SDRT-AI) framework, the study develops a layered architecture and operational flow integrating agentic decision-making, governance-aware learning, and protocol-based validation. The proposed framework explains how strategic intent, organizational capabilities, and decentralized trust jointly enable scalable administrative autonomy while preserving accountability and control. By reframing administration as an AI-mediated governance process, this paper extends research on agentic AI and contributes to administrative science by providing a conceptual foundation for the design and governance of autonomous administrative systems in decentralized organizations. Full article

The increasing demand for robust and efficient information security has led to the growing adoption of specialized hardware for cryptographic operations. In response to the rise in cyber threats and the need to process large volumes of data in real time, hardware-based cryptographic solutions offer significant advantages in terms of performance, resistance to attacks, and secure storage of cryptographic keys. This paper presents the implementation of a secure communication system using the UART (Universal Asynchronous Receiver-Transmitter) protocol as the foundation for a Register Transfer Level (RTL) design on an FPGA platform. The base protocol was modified to introduce an additional hardware-level security layer. Furthermore, cryptographic techniques—specifically encryption and decryption—were integrated into the design to enhance data protection and integrity during transmission. The results demonstrate the feasibility of embedding cryptographic mechanisms directly into communication hardware, providing a scalable and efficient solution for secure embedded systems. Full article

Migrating to Post-Quantum Cryptography (PQC) is critical for securing resource-constrained Internet of Things (IoT) devices against the “harvest-now, decrypt-later” threat. While ML-KEM (CRYSTALS-Kyber) has been standardized under FIPS 203 for general encryption, these devices often operate on unreliable networks suffering from high latency and packet loss. Our recent systematic review identified a critical gap that existing research overwhelmingly focuses on Transport Layer Security (TLS). This leaves the resilience of lightweight protocols like MQTT and CoAP under challenging network conditions largely unexplored. This paper introduces PQC-IoTNet, a novel Software-in-the-Loop (SITL) framework to address this gap. Our three-tier architecture integrates a Python-based IoT client with kernel-level emulation to test the full protocol stack. Validation results comparing Kyber and ECC demonstrate the framework’s ability to capture critical performance cliffs caused by TCP retransmissions. Notably, the framework revealed that while Kyber maintained an 18% speed advantage over ECC at 5% packet loss, both protocols experienced nonlinear latency spikes. This work provides a reproducible blueprint to identify operational boundaries and select resilient protocols for secure IoT systems. Full article

SCADA and remote monitoring systems employ a communications protocol called DNP3. The Distributed Network Protocol is a popular open-standard protocol. As a result, any manufacturer can create DNP3 equipment that works with other DNP3 devices. Since its launch in 1993, the Distributed Network Protocol—also referred to as DNP3—has gained widespread popularity. This protocol was created to communicate the condition of essential infrastructure, enabling dependable remote control, making it an instantly deployable solution for monitoring distant locations. The groundbreaking work on the protocol is typically attributed to GE-Harris Canada (previously Westronic, Inc.). However, a wide range of firms are presently using this protocol in a number of industrial applications, including power utilities. Three tiers of the OSI seven-layer functions model make up DNP3. The application layer, data link layer, and transport layer are these layers. Additionally, DNP3 can be sent via a TCP/IP network or a serial bus link. Full article

This paper develops a framework for monitoring and forecasting episodes of systemic financial stress using a combination of market information, macro-financial indicators, and measures derived from time-varying correlation networks, embedded in a sequential machine-learning setting. The contribution is not tied to a single modelling innovation, but rather to the way these ingredients are brought together under an evaluation protocol designed to mimic real-time supervisory use, and to an interpretability layer that makes the resulting predictions easier to inspect. Monthly data covering the period from 2006 to 2025 are used to construct evolving correlation structures and summary indicators of market co-movement. These features are combined with standard predictors and fed into logistic regression, random forest, and gradient boosting models, all estimated in expanding windows and assessed strictly on future observations. Predictive accuracy remains limited, which is consistent with the difficulty of anticipating stress regimes several months ahead at monthly frequency, although gradient boosting attains the highest average AUC across evaluation folds and displays noticeable variation over time. Inspection of SHAP values points to instability in correlation networks, volatility conditions, and short-horizon return behaviour as recurring drivers of the predicted stress probabilities, suggesting that the models draw on information that goes beyond individual market series. Taken together, the results indicate that recurrent statistical regularities and changes in market structure can be exploited for monitoring purposes when models are trained and tested in a sequential fashion. The overall design is intended to be usable in practice and to support supervisory analysis, while remaining transparent enough to allow scrutiny of the signals driving the forecasts. Full article

Distributed Denial-of-Service (DDoS) attacks remain the most pervasive and operationally disruptive cyber threat and are routinely weaponized in interstate conflict (e.g., Russia–Ukraine and Stuxnet). Although attack-chain models are standard for Advanced Persistent Threat (APT) analysis, they have seldom been applied to DDoS, which is often framed as a single-step volumetric assault. However, ubiquitous intelligence and ambient connectivity increasingly enable DDoS campaigns to unfold as multi-stage operations rather than isolated floods. In parallel, large language models (LLMs) create new opportunities to strengthen traditional DDoS defenses through richer contextual understanding. Reviewing incidents from 2019 to 2024, we propose a three-phase DDoS attack chain—preparation, development, and execution—that captures contemporary tactics and their dependencies on novel hardware, network architectures, and application protocols. We classify these patterns, contrast them with conventional DDoS, survey current defenses (anycast and scrubbing, BGP Flowspec, programmable data planes, adaptive ML detection, API hardening), and outline research directions in cross-layer telemetry, adversarially robust learning, automated mitigation orchestration, and cooperative takedown. Full article