Search Results (3,812)

Securing Industrial Control Systems (ICSs) is critical, but it is made challenging by the constant evolution of cyber threats and the scarcity of labeled attack data in these specialized environments. Standard intrusion detection systems (IDSs) often fail to adapt when transferred to new networks with limited data. To address this, this paper introduces an adaptive intrusion detection framework that combines a hybrid Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) model with a novel transfer learning strategy. We employ a Reinforcement Learning (RL) agent to intelligently guide the fine-tuning process, which allows the IDS to dynamically adjust its parameters such as layer freezing and learning rates in real-time based on performance feedback. We evaluated our system in a realistic data-scarce scenario using only 50 labeled training samples. Our RL-Guided model achieved a final F1-score of 0.9825, significantly outperforming a standard neural fine-tuning model (0.861) and a target baseline model (0.759). Analysis of the RL agent’s behavior confirmed that it learned a balanced and effective policy for adapting the model to the target domain. We conclude that the proposed RL-guided approach creates a highly accurate and adaptive IDS that overcomes the limitations of static transfer learning methods. This dynamic fine-tuning strategy is a powerful and promising direction for building resilient cybersecurity defenses for critical infrastructure. Full article

Although resin-based composite is the most popular direct restoration material in the U.S., composite restorations can fail shortly after placement. The leading cause of failure is recurrent marginal decay. The adhesive that bonds the composite to the tooth is intended to seal the margin, but the degradation of the adhesive seal to dentin leads to gaps that are infiltrated by cariogenic bacteria. The development of strategies to mitigate adhesive degradation is an area of intense interest. Recent studies focus on exploiting hydrogen–bond interactions to enhance polymer network stability. This paper presents the preparation and characterization of model adhesives that capitalize on carbamate-functionalized long-chain silane monomers to enhance polymer stability and mechanical properties in wet environments. The adhesive composition is HEMA/BisGMA, 3-component photoinitiator system, carbamate-functionalized long-chain silane monomers, e.g., commercial SHEtMA (Cb1) and newly synthesized SHEMA (Cb2). Polymerization behavior, water sorption, leachates, and dynamic mechanical properties were investigated. The properties of Cb1 and Cb2 were compared to previously studied middle- (SC4) and short-chain (SC5) silane monomers. Cb1- and Cb2-formulations exhibit greater resilience under wet conditions as compared to middle-chain silane monomers. Dental adhesives containing the carbamate-functionalized long-chain silane monomers exhibit reduced flexibility in water-submersed conditions and enhanced stability as a result of increased hydrogen–bond interactions. The results emphasize the critical role of hydrogen bonding in maintaining structural integrity of dental adhesive formulations under conditions that simulate the wet, oral environment. Full article

The growing penetration of photovoltaic (PV) generation in multi-energy microgrids has amplified the challenges of maintaining real-time operational efficiency, reliability, and safety under conditions of renewable variability and forecast uncertainty. Conventional rule-based or optimization-based strategies often suffer from limited adaptability, while purely data-driven reinforcement learning approaches risk violating physical feasibility constraints, leading to unsafe or economically inefficient operation. To address this challenge, this paper develops a Physics-Informed Reinforcement Learning (PIRL) framework that embeds first-order physical models and a structured feasibility projection mechanism directly into the training process of a Soft Actor–Critic (SAC) algorithm. Unlike traditional deep reinforcement learning, which explores the state–action space without physical safeguards, PIRL restricts learning trajectories to a physically admissible manifold, thereby preventing battery over-discharge, thermal discomfort, and infeasible hydrogen operation. Furthermore, differentiable penalty functions are employed to capture equipment degradation, user comfort, and cross-domain coupling, ensuring that the learned policy remains interpretable, safe, and aligned with engineering practice. The proposed approach is validated on a modified IEEE 33-bus distribution system coupled with 14 thermal zones and hydrogen facilities, representing a realistic and complex multi-energy microgrid environment. Simulation results demonstrate that PIRL reduces constraint violations by 75–90% and lowers operating costs by 25–30% compared with rule-based and DRL baselines while also achieving faster convergence and higher sample efficiency. Importantly, the trained policy generalizes effectively to out-of-distribution weather conditions without requiring retraining, highlighting the value of incorporating physical inductive biases for resilient control. Overall, this work establishes a transparent and reproducible reinforcement learning paradigm that bridges the gap between physical feasibility and data-driven adaptability, providing a scalable solution for safe, efficient, and cost-effective operation of renewable-rich multi-energy microgrids. Full article

Portfolio optimization is a cornerstone of modern financial decision-making, traditionally based on the mean–variance model introduced by Markowitz. However, this framework relies on restrictive assumptions—such as normally distributed returns and symmetric risk preferences—that often fail in real-world markets, particularly in volatile and non-Gaussian environments such as cryptocurrencies. To address these limitations, this paper proposes a novel multi-objective model that combines expected return maximization, mean absolute deviation (MAD) minimization, and entropy-based diversification into a unified optimization structure: the Mean–Deviation–Entropy (MDE) model. The MAD metric offers a robust alternative to variance by capturing the average magnitude of deviations from the mean without inflating extreme values, while entropy serves as an information-theoretic proxy for portfolio diversification and uncertainty. Three entropy formulations are considered—Shannon entropy, Tsallis entropy, and cumulative residual Sharma–Taneja–Mittal entropy (CR-STME)—to explore different notions of uncertainty and structural diversity. The MDE model is formulated as a tri-objective optimization problem and solved via scalarization techniques, enabling flexible trade-offs between return, deviation, and entropy. The framework is empirically tested on a cryptocurrency portfolio composed of Bitcoin (BTC), Ethereum (ETH), Solana (SOL), and Binance Coin (BNB), using daily data over a 12-month period. The empirical setting reflects a high-volatility, high-skewness regime, ideal for testing entropy-driven diversification. Comparative outcomes reveal that entropy-integrated models yield more robust weightings, particularly when tail risk and regime shifts are present. Comparative results against classical mean–variance and mean–MAD models indicate that the MDE model achieves improved diversification, enhanced allocation stability, and greater resilience to volatility clustering and tail risk. This study contributes to the literature on robust portfolio optimization by integrating entropy as a formal objective within a scalarized multi-criteria framework. The proposed approach offers promising applications in sustainable investing, algorithmic asset allocation, and decentralized finance, especially under high-uncertainty market conditions. Full article

In an increasingly uncertain business environment, developing organizational resilience to cope with supply chain disruptions is crucial for firms aiming to achieve sustainable growth. This study investigates how forward and backward vertical integration influence organizational resilience in the face of large-scale supply chain disruptions, with particular attention to the moderating role of a firm’s position in the supply network. Drawing on a comprehensive dataset of 2931 publicly listed Chinese firms, we integrate the relational view and information processing theory to examine how integration strategies affect two key dimensions of resilience: organizational stability and flexibility. Our results show that backward integration enhances both stability (reducing the severity of loss by about 17%) and flexibility by accelerating recovery, especially benefiting downstream firms in terms of stability and upstream firms in terms of flexibility. In contrast, forward integration is associated with reduced stability (raising the severity of loss by about 7%) but enables faster recovery for firms closer to end markets. Moreover, we find that the effectiveness of vertical integration depends on organizational context and alternative resilience mechanisms. These findings highlight the importance of aligning integration direction with supply chain position to optimize resilience. By disentangling the distinct strategic trade-offs of forward versus backward integration, this study advances theoretical understanding and offers practical guidance for firms seeking to strengthen their capacity to withstand and recover from systemic shocks. Full article

Mediterranean cities are increasingly exposed to climate hazards, water scarcity, and social vulnerabilities, demanding integrative approaches for sustainable regeneration. This study examines how participatory governance and co-design processes can shape nature-based solutions (NbS) for climate resilience in Barrios Altos, a socially and environmentally fragile district of Lorca, Spain. Within the framework of the NATUR-W project, the interventions reimagine a degraded hillside and adjacent public spaces into a multifunctional urban forest, complemented by green retrofits of social housing and the adaptive reuse of a historic prison. Methods combined baseline community assessments, stakeholder mapping, co-design workshops, and the establishment of a multi-stakeholder governance board, ensuring inclusive participation from residents, civil society, and municipal authorities. Results demonstrate that the co-created design addressed key community priorities—such as shade provision, safe accessibility, cultural venues, and child-friendly spaces—while integrating sustainable water management systems for irrigation and stormwater control. The participatory process enhanced local ownership, balanced technical feasibility with community aspirations, and fostered governance structures that increase transparency and accountability. Overall, the study illustrates how NbS, when embedded in collaborative governance frameworks, can deliver climate, social, and cultural co-benefits while advancing resilient, inclusive, and human-scale urban environments. Full article

Advances in brain–computer interface (BCI) research have explored various strategies for acquiring and processing electroencephalographic (EEG) signals to detect motor imagery (MI) activities. However, the complexity of multichannel clinical systems and processing techniques can limit their accessibility outside specialized centers, where complex setups are not feasible. This paper presents a proof-of-concept prototype of a single-channel EEG acquisition and processing system designed to identify lower-limb motor imagery. The proposed proof-of-concept prototype enables the wireless acquisition of raw EEG values, signal processing using digital filters, and the detection of MI patterns using machine learning algorithms. Experimental validation in a controlled laboratory with participants performing resting, MI, and movement tasks showed that the best performance was obtained by combining Savitzky–Golay filtering with a Random Forest classifier, reaching 87.36% ± 4% accuracy and an F1-score of 87.18% ± 3.8% under five-fold cross-validation. These findings confirm that, despite limited spatial resolution, MI patterns can be detected using appropriate AI-based filtering and classification. The novelty of this work lies in demonstrating that a single-channel, portable EEG prototype can be effectively used for lower-limb MI recognition. The portability and noise resilience achieved with the prototype highlight its potential for research, clinical rehabilitation, and assistive device control in non-specialized environments. Full article

Adverse climate events are increasingly challenging the health and wellbeing of communities. The intersections of indigenous knowledge and sustainable development, through an eco-social work perspective, are least developed0 in sub-Saharan Africa (SSA). The challenging socio-ecological environment is compounded by limited infrastructure, which hinders the delivery of social services in remote communities. Drawing on cumulative research evidence and regional case studies across Africa, this conceptual article examines the key elements of an eco-social work paradigm and the potential challenges of its implementation. Drawing on intersectional approaches, this paper proposes practical strategies for integrating eco-social work dimensions into problem-solving to address the Sustainable Development Goals (SDGs), specifically Goal 1 (No Poverty) and Goal 13 (Climate Action). Social work practice should be anchored in an indigenous epistemology and research governance, informed by insights from higher education institutions, local communities, the context of practice, and partnerships with the state, to ensure regulatory oversight and inter-professional collaboration. Contextualised outcomes to build community-level resilience, and development practitioners who are up-skilled and able to conduct needs-led ecological assessments are essential. Such co-created interventions and collaborative strategies would effectively address poverty and climate change in vulnerable, remote communities. Further empirical research on the interpretation of indigenous knowledge and the role of eco-social workers within interprofessional collaboration is essential for formulating an indigenous epistemology and ecological wellbeing policy, thereby strengthening community-level resilience and sustainability. Full article

The performance of resin anchoring agents in deep coal mine roadways is significantly compromised by water-bearing and chemically aggressive conditions, posing a major threat to support system reliability. This study aims to systematically quantify this performance deterioration and develop a more resilient material solution for these challenging environments. A comprehensive experimental program was conducted, including uniaxial compression, pull-out, and interface shear tests, accompanied by the systematic improvement of the resin formulation and microstructural analysis via Scanning Electron Microscopy (SEM). The results showed that increasing borehole water content to 30% reduced the compressive strength of conventional resin by over 40%, while acidic environments (pH = 5) caused a 70% drop in its interfacial shear strength. In contrast, an improved formulation incorporating hydroxypropyl acrylate and a super absorbent polymer (SAP) exhibited a 20% higher initial strength, maintained over 85% of its strength under water saturation, and retained functional residual strength in acidic conditions. SEM analysis confirmed that the improved resin’s denser microstructure suppressed interfacial microcrack formation. The findings demonstrate that the improved formulation provides a robust material basis for enhancing the long-term durability and safety of anchorage support systems in extreme underground engineering environments. Full article

The growing global demand for secure, transparent, and efficient electoral systems has highlighted the limitations of traditional voting methods, which remain susceptible to voter impersonation, ballot tampering, long queues, logistical challenges, and delayed result processing. To address these issues, this study presents the design and implementation of a Radio Frequency Identification (RFID)-based electronic voting framework that integrates robust voter authentication, encrypted vote processing, and decentralized real-time monitoring. The system is developed as a scalable, cost-effective solution suitable for both urban and resource-constrained environments, especially those with limited infrastructure or inconsistent internet connectivity. It employs RFID-enabled smart voter cards containing encrypted unique identifiers, with each voter authenticated via an RC522 reader that validates their UID against an encrypted whitelist stored locally. Upon successful verification, the voter selects a candidate via a digital interface, and the vote is encrypted using AES-128 before being stored either locally on an SD card or transmitted through GSM to a secure backend. To ensure operability in offline settings, the system supports batch synchronization, where encrypted votes and metadata are uploaded once connectivity is restored. A tamper-proof monitoring mechanism logs each session with device ID, timestamps, and cryptographic checksums to maintain integrity and prevent duplication or external manipulation. Simulated deployments under real-world constraints tested the system’s performance against common threats such as duplicate voting, tag cloning, and data interception. Results demonstrated reduced authentication time, improved voter throughput, and strong resistance to security breaches—validating the system’s resilience and practicality. This work offers a hybrid RFID-based voting framework that bridges the gap between technical feasibility and real-world deployment, contributing a secure, transparent, and credible model for modernizing democratic processes in diverse political and technological landscapes. Full article

Post-wildfire reconstruction processes offer an opportunity to implement structural risk reduction measures and develop wildfire resilience; however, these efforts often lack comprehensiveness. Focusing on Chile, this research addresses the need for increased and nuanced understanding of the implementation and subsequent modifications in wildfire risk reduction actions in the built environment during post-wildfire reconstruction processes. Accordingly, this study aims to identify the physical changes associated with implementing structural wildfire risk reduction measures in Chile’s reconstruction efforts from their establishment to the present, organized based on two secondary objectives: (1) document the physical changes that have occurred following the disaster, and (2) distinguish and categorize the reconstruction interventions. A mixed-methods multiple case study approach was employed, analyzing four post-wildfire reconstruction processes (Valparaiso, 2014; Santa Olga, 2017; Castro, 2021; and Punta Lavapie, 2023) through spatial analysis of physical changes and qualitative content analysis of documents to identify and categorize interventions. The research found that structural wildfire risk reduction measures and wider settlement improvements have been implemented in all case studies with varying emphasis and comprehensiveness. However, the results also suggest that these reconstruction efforts have not improved settlements’ long-term wildfire resilience. This study contributes to the theory and practice of reconstruction and risk reduction by showing that the post-disaster period often fails to lead to lasting systemic change. Full article

The accelerating complexity of urban environments has prompted cities to adopt digital technologies that improve efficiency, sustainability, and resilience. Among these, Urban Digital Twins (UDTw) have emerged as transformative tools for real-time representation, simulation, and management of urban systems. This Systematic Literature Review (SLR) examines the integration of Digital Twins (DTw), the Internet of Things (IoT), and Artificial Intelligence (AI) into the Smart City Development (SCD). Following the PSALSAR framework and PRISMA 2020 guidelines, 64 peer-reviewed articles from IEEE Xplore, Association for Computing Machinery (ACM), Scopus, and Web of Science (WoS) digital libraries were analyzed by using bibliometric and thematic methods via the Bibliometrix package in R. The review allowed identifying key technological trends, such as edge–cloud, architectures, 3D immersive visualization, Generative AI (GenAI), and blockchain, and classifies UDTw applications into five domains: traffic management, urban planning, environmental monitoring, energy systems, and public services. Persistent challenges have been also outlined, including semantic interoperability, predictive modeling, data privacy, and impact evaluation. This study synthesizes the current state of the field, by clearly identifying a thematic mapping, and proposes a research agenda to align technical innovation with measurable urban outcomes, offering strategic insights for researchers, policymakers, and planners. Full article

Developing climate-resilient and high-quality cotton cultivars remains an urgent challenge, as the key target traits yield, fibre properties, and stress tolerance are highly polygenic and strongly influenced by genotype–environment interactions. Recent advances in chromosome-scale genome assemblies, pan-genomics, and haplotype-resolved resequencing have greatly enhanced the capacity to identify causal variants and recover non-reference alleles linked to fibre development and environmental adaptation. Parallel progress in functional genomics and precision genome editing, particularly CRISPR/Cas, base editing, and prime editing, now enables rapid, heritable modification of candidate loci across the complex tetraploid cotton genome. When integrated with high-throughput phenotyping, genomic selection, and machine learning, these approaches support predictive ideotype design rather than empirical, trial-and-error breeding. Emerging digital agriculture tools, such as digital twins that combine genomic, phenomic, and environmental data layers, allow simulation of ideotype performance and optimisation of trait combinations in silico before field validation. Speed breeding and phenomic selection further shorten generation time and increase selection intensity, bridging the gap between laboratory discovery and field deployment. However, the large-scale implementation of these technologies faces several practical constraints, including high infrastructural costs, limited accessibility for resource-constrained breeding programmes in developing regions, and uneven regulatory acceptance of genome-edited crops. However, reliance on highly targeted genome editing may inadvertently narrow allelic diversity, underscoring the need to integrate these tools with broad germplasm resources and pangenomic insights to sustain long-term adaptability. To realise these opportunities at scale, standardised data frameworks, interoperable phenotyping systems, robust multi-omic integration, and globally harmonised, science-based regulatory pathways are essential. This review synthesises recent progress, highlights case studies in fibre, oil, and stress-resilience engineering, and outlines a roadmap for translating integrative genomics into climate-smart, high-yield cotton breeding programmes. Full article

Frequency-modulated continuous-wave (FMCW) LiDAR systems are extensively utilized in industrial metrology, autonomous navigation, and geospatial sensing due to their high precision and resilience to interference. However, the intrinsic nonlinear dynamics of laser systems introduce significant distortion, adversely affecting measurement accuracy. Although conventional iterative pre-distortion correction methods can effectively mitigate nonlinearities, their long-term reliability is compromised by factors such as temperature-induced drift and component aging, necessitating periodic recalibration. In light of recent advances in artificial intelligence, deep reinforcement learning (DRL) has emerged as a promising approach to adaptive nonlinear compensation. By continuously interacting with the environment, DRL agents can dynamically modify correction strategies to accommodate evolving system behaviors. Nonetheless, existing DRL-based methods often exhibit limited adaptability in rapidly changing nonlinear contexts and are constrained by inefficient uniform experience replay mechanisms that fail to emphasize critical learning samples. To address these limitations, this study proposes an enhanced Soft Actor-Critic (SAC) algorithm incorporating a hybrid prioritized experience replay framework. The prioritization mechanism integrates modulation frequency (MF) error and temporal difference (TD) error, enabling the algorithm to dynamically reconcile short-term nonlinear perturbations with long-term optimization goals. Furthermore, a time-varying delayed experience (TDE) injection strategy is introduced, which adaptively modulates data storage intervals based on the rate of change in modulation frequency error, thereby improving data relevance, enhancing sample diversity, and increasing training efficiency. Experimental validation demonstrates that the proposed method achieves superior convergence speed and stability in nonlinear correction tasks for FMCW LiDAR systems. The residual nonlinearity of the upward and downward frequency sweeps was reduced to $1.869\times {10}^{-5}$ and $1.9411\times {10}^{-5}$, respectively, with a spatial resolution of 0.0203m. These results underscore the effectiveness of the proposed approach in advancing intelligent calibration methodologies for LiDAR systems and highlight its potential for broad application in high-precision measurement domains. Full article

Rapid urbanization and global climate change are leading to intensified Urban Heat Island (UHI) in tropical regions. This study examined and analyzed urban ventilation corridors to mitigate UHI, paying particular attention to the building arrangement and wind environment. The comprehensive review emphasizes the importance of macro-scale urban planning, including the orientation of street grids and the design of breezeways and air paths. After analyzing these strategies, CFD simulations were applied to the design of high-rise buildings in Semarang and residential areas in Jakarta. These studies revealed that in high-rise building areas in Semarang, the proposed design configuration resulted in a 62% increase in ground-level wind speeds. A further analysis of residential areas in Jakarta revealed that the most comfortable location within a house was in the second row, facing the wind, where the distance between houses was 8.5 m, and the average velocity was 2.78 m/s. Research conducted in this area may contribute to the development of more sustainable and resilient urban areas in tropical climates, as well as assist local governments in planning for these areas. Full article