Search Results (5,947)

Quantum error-correcting codes are essential for achieving fault-tolerant quantum computing. Heavy hexagonal code is a type of topological code that leverages the arrangement of qubits to find and correct errors. The heavy hexagonal code is suitable for superconducting architectures, specifically graph layouts with a limited number of connections. The topological error correction methods work well, but they need more qubits, cannot be used for different sizes of quantum systems, are less reliable, and do not work well with changing quantum distributions. Thus, the research proposes an Ardea-guided exploit optimized sparse-dual attention enabled meta-learning-based convolutional neural network with bi-directional long short-term memory model (AGuESD-MCBiTM). The method exhibits effective correction over dynamic environments with the utilization of meta-learning and the extraction of statistical information, which provides a detailed representation of the qubit patterns. The Ardea-guided exploit optimized (AGuEO) algorithm tunes the weights of MCBiTM and acquires optimal solutions with higher convergence. Moreover, the sparse-dual attention module and meta-learning-based MCBiTM model, which together provide scalable, real-time identification of non-linear qubit noise fluctuations with lower computational cost. Comparatively, the proposed AGuESD-MCBiTM exhibits superior error correction with a higher correlation of 0.97, accuracy of 98.93%, and R-squared value of 0.93, as well as a lower Root mean square error of 1.87, Mean absolute error of 1.20, Bit error rate of 1.85, Logical error rate of 3.82, and mean square error of 3.49 in circuit 2, respectively. Full article

Garlic (Allium sativum) has antimicrobial and antioxidant properties. However, only the cloves are used from the bulb; the peels or husks are waste material with limited utility that nevertheless retain properties that can be exploited in other materials such as edible films or coatings. Chitosan is a widely used biopolymer, due its interesting properties. The same is true for alginate and pectin, which are polysaccharides that have interesting application areas; among the most common are film or coating materials in the food industry. Therefore, in this research, comprising the elaboration of films based on Chitosan-Pectin-Alginate (Q-P-A) reinforced with garlic husk (GH) particles, the films were characterized by Brookfield viscosity (the biopolymers solutions), Fourier Transform infrared Spectroscopy (FTIR), Dynamic mechanical analysis (DMA), and thermogravimetry (TGA). According to the results, the addition of GH caused a significant decrease in viscosity without altering the pseudoplasticity behavior and also generating physical interactions with the matrices; no chemical reaction byproducts were identified by FTIR. An increase in the reinforcing effect was identified in Q-GH films, whereas the opposite effect was observed in Q-P-A-GH films. In addition, no significant changes in the thermal stability were observed. Full article

Ultrafast fiber lasers operating near 2 μm have emerged as a critical platform for advancing mid-infrared photonics due to their narrow pulse durations, high peak powers, and broad tunability. These sources exploit the rich energy-level structures of Tm³⁺ and Ho³⁺ doped fibers and reside within an atmospheric transmission window, enabling applications spanning nonlinear microscopy, precision micromachining, optical frequency metrology, biophotonics, and free-space optical communication. Recent progress in low-loss fiber fabrication, dispersion-engineered cavity design, and mode-locking technologies has significantly expanded the performance boundaries of 2 μm ultrafast fiber lasers. This review systematically examines the underlying pulse-formation mechanisms and categorizes state-of-the-art mode-locking approaches. Representative laser architectures are compared with respect to pulse duration, energy scalability, repetition-rate enhancement, spectral characteristics, and environmental stability. Key application pathways in high-resolution spectroscopy, biomedical diagnostics, and mid-IR supercontinuum generation are highlighted. Finally, the remaining challenges and prospective research directions are discussed to inform the development of next-generation ultrafast photonic sources in the 2 μm band. Full article

Low-Earth orbit satellites are gradually becoming the core infrastructure of integrated aerospace communication networks, with their significant advantages of high communication rates, small transmission delay, and wide coverage. Interference with military communications in response to their security and protection needs is a current research challenge. Consequently, this paper introduces an interference technique optimized for low-Earth orbit satellite signals using a multimodal learning transformer model (OI-MLT). The proposed method incorporates symmetry-aware design by exploiting the inherent time–frequency structural characteristics of LEO satellite signals and the spatially distributed topology of interference sources. An optimized model for distributed interference sources is developed, and multimodal information of spectra and numerical values is processed in parallel through the self-attention mechanism. This approach effectively addresses the problem of dynamic matching between the interference signal and target signal in high-speed LEO scenarios, as well as high-precision interference synchronization under time-varying channels. Experimental results demonstrate that this technique enhances the precision of frequency tracking, reduces the time required for synchronization establishment, and improves the interference success rate by 27.52% on average compared with existing methods. Full article

Unmanned Aerial Vehicle (UAV) tracking of ground moving targets holds significant applications in domains such as intelligent transportation, logistics distribution, and environmental monitoring, placing greater demands on efficient and stable path-planning methods for vehicular tracking. This study investigates a UAV path tracking approach based on a deep reinforcement learning algorithm, Proximal Policy Optimization (PPO). Starting from the kinematic characteristics of UAVs and ground vehicles, a 3D path planning model was constructed that considers spatial coordinates, velocity, and attitude constraints. A well-designed objective function—including tracking error minimization, energy optimization, and safety distance constraints—was incorporated. By designing the state space, action space, and reward function, the PPO algorithm is capable of adaptive learning in complex environments. Compared with traditional Artificial Potential Field (APF), Q-learning, and TD3 algorithms, PPO better balances exploration and exploitation and demonstrates stronger learning stability and global optimization capability in dynamic multi-obstacle scenarios. Simulation results show that PPO-based UAV path planning outperforms Q-learning and other comparative algorithms in terms of tracking accuracy, convergence speed, and robustness. In specific scenarios, Q-learning achieves a trajectory error of approximately 1 m, TD3 and APF exhibit errors around 0.3 m with noticeable oscillations, and PPO achieves an error of about 0.2 m. The UAV can follow the vehicle trajectory smoothly, with a more continuous path and rapidly converging, stable error curves, indicating the promising application potential of PPO in intelligent UAV control. The PPO-based UAV-tracking path planning method effectively enhances the UAV’s intelligent decision-making and path optimization capabilities, providing new technical approaches and a research foundation for intelligent UAV traffic and cooperative control systems. Full article

Leaks in pipe systems result in significant economic losses, environmental hazards, and public health risks. Transient-based leak detection methods, which exploit the dynamics of pressure waves in response to system anomalies, have emerged as efficient techniques for identifying and characterizing leaks in pressurized pipelines. These methods offer distinct advantages, including minimal data requirements, high sensitivity to low-pressure anomalies, and resilience to the ill-posed conditions often affecting steady-state models. This paper reviews transient-based leak detection, synthesizing findings from over 139 peer-reviewed publications spanning the past three decades. The review categorizes transient-based methods into transient damping, transient reflection, system response, and inverse transient methods, analyzing the prevalence, evolution, and research rate of each category over time. By structuring the review around key aspects such as simulation domain type, analysis approach, system response, solver strategies, adaptability to noise, viscoelasticity, and network complexity, this paper identifies significant trends and shifts in research focus. A comprehensive tabular dataset of 139 studies captures how research activity in various areas has accelerated, slowed, or reached stability, offering insights into the evolving priorities within the field. This review highlights areas for further development, particularly in addressing AI-enhanced applications, transient excitation and measurement sites design, noise resilience, comprehensive leak characterization, validation approaches, and scalability for complex network applications, providing a resource to guide future research in transient-based leak detection. Full article

This study proposes a methodological framework for extending Malware Information Sharing Platform (MISP) taxonomies in the domain of Dark Web drug forums through the integration of large language models (LLMs) and Human-in-the-Loop (HITL) validation. The research addresses the existing ontological gap between traditional MISP taxonomies, focused on technical or chemical indicators, and the linguistic and morphological complexity of illicit digital markets. By modelling the primary physical form as an ontological predicate with mutually exclusive values (for example, powder, pill–tablet–capsule, liquid, and plant-matter), the proposed approach captures the material dimension of the discourse, enhancing semantic disambiguation and forensic traceability. The Mistral 7B model was used in the morphology-classification stage conducted on a stratified analytical subset of 2904 drug-related Dark Web posts, extracted from a final corpus of 6456 posts after data cleaning and relevance filtering. In the first pass, 76.48% of posts were directly assigned to one of the base morphological categories, while 23.52% were labelled as unclear and subsequently reviewed through the HITL stage. Following HITL refinement and full reclassification, the proportion of posts labelled as unclear decreased from 23.52% to 11.29%, corresponding to a 51.99% relative reduction in ambiguity. Network visualisation with VOSviewer revealed three major discursive axes—recreational–commercial, pharmaceutical–opioid, and transnational–logistical—reflecting the hybrid semantic structure of digital drug markets. The results show that combining LLM-based inference with expert oversight improves the interpretability, reproducibility and ontological robustness of cyberintelligence models, offering a replicable framework for other sensitive domains such as terrorism or child exploitation. Full article

Multi-threshold image segmentation is an important research topic in the fields of computer vision and image processing. Its core objective is to efficiently determine the optimal threshold combination within a high-dimensional and complex search space. However, as the number of thresholds and image complexity increase, the computational cost of traditional exhaustive search methods grows exponentially. Meanwhile, conventional swarm intelligence algorithms often suffer from unstable convergence, premature stagnation, and parameter sensitivity when dealing with high-dimensional composite functions. To address these issues, this paper proposes an enhanced optimization algorithm termed the Parrot Optimizer with Artistic Design Strategy (PO-ADS). The proposed method constructs a multi-strategy cooperative optimization framework that integrates an Evolution Feedback–Based Adaptive Control Strategy (EFACS), a Multi-Operator Cooperative Evolution Strategy (MOCES), and an Artistic Design Strategy (ADS). These strategies enable dynamic parameter adjustment, adaptive balance between global exploration and local exploitation, and structured perturbation enhancement mechanisms. Experimental results on the CEC2020 and CEC2022 benchmark suites demonstrate that PO-ADS significantly outperforms seven state-of-the-art optimization algorithms across different dimensional settings in terms of optimization accuracy, convergence speed, and stability. The Friedman test results show that, on the CEC2020 benchmark suite, PO-ADS achieves average ranks of 1.72 (30-dimensional) and 1.85 (50-dimensional), both statistically superior to the comparative algorithms. Furthermore, PO-ADS is applied to multi-threshold image segmentation based on the Otsu criterion. The results indicate that the proposed method achieves optimal or near-optimal performance in terms of SSIM, PSNR, FSIM, and objective function values. Overall, the experimental findings confirm that PO-ADS not only possesses strong numerical optimization capability but also demonstrates robust and practical applicability in real-world image segmentation tasks. Full article

Nowadays, educational software across all learning levels is increasingly enhanced with Artificial Intelligence (AI), primarily through content generation or post-session learning analytics. However, most existing systems remain weakly connected to learners’ real-time affective states and rarely exploit emotional information as a direct control signal for instructional adaptation. In this work, we propose a proof-of-concept closed-loop affect-aware educational adaptation framework that integrates real-time facial emotion recognition into a dynamic learning control system. The proposed approach is built upon a dual-model ensemble architecture, combining a transformer-based model (CAGE) and a CNN-based model (DDAMFN++) trained on large-scale in-the-wild datasets. To bridge heterogeneous emotion representations, we introduce a probabilistic fusion strategy that aligns continuous valence–arousal predictions with discrete emotion classification via a Gaussian Mixture Model (GMM), enabling unified emotion inference in real time. Based on the fused emotional state, a temporal aggregation mechanism is applied to capture sustained affective trends rather than transient expressions. These aggregated signals are then mapped to instructional decisions through an emotion-driven adaptive control policy, which adjusts activity difficulty using an Average Emotion Score (AES). This establishes a fully automated closed-loop adaptation cycle, where detected learner affect directly influences the learning environment without requiring explicit user input or post-session questionnaires. The framework is integrated into an open-source educational platform (eduActiv8) to demonstrate feasibility and system-level behavior. Results from alpha-level validation show that the system can continuously monitor learner affect, generate interpretable emotional analytics, and dynamically adjust task difficulty in real time, while reducing user interaction overhead. This study contributes a modular architecture for affect-aware educational systems by combining real-time ensemble emotion recognition, probabilistic fusion of heterogeneous outputs, and closed-loop instructional adaptation. The proposed framework provides a foundation for future research in scalable, emotion-driven intelligent tutoring and adaptive learning environments. Full article

Phenol derivatives display a prominent role in many biologically active molecules. Boron-containing molecules are considered valuable precursors for their synthesis. Therefore, the rise of photochemistry has led many researchers to develop novel, sustainable protocols that exploit the advantages offered by different irradiation sources. For this reason, the application of novel photocatalysts that promote challenging organic transformations is highly valued. Graphitic carbon nitride (g-C₃N₄) is a semiconductor photocatalyst widely used in organic chemistry for promoting complex organic transformations. Herein, we report a green and efficient methodology for the hydroxylation of boronic acids to the corresponding hydroxyl derivatives, using g-C₃N₄ as the photocatalyst. The heterogeneous photocatalyst (g-C₃N₄) was prepared by thermal polycondensation of melamine and characterized by XRD, FESEM/EDS, and UV–Vis diffuse reflectance spectroscopy. Green LED irradiation was employed as the energy source and air as the active oxidant. A variety of substrates were tested, showcasing excellent functional group tolerance in the aerobic photochemical protocol. Mechanistic studies were conducted to investigate the reaction pathway and to identify the oxygen species generated. Full article

The wave energy resource along the Azores coast is evaluated for the present (1990–2019) and future (2030–2059) periods using the third-generation wave model WAVEWATCH III, forced by winds and sea-ice cover from the RCP8.5 EC-Earth integration dynamically downscaled with the Weather Research and Forecasting model. The results indicate that the region is characterized by a high-energy wave climate, with mean wave power values typically ranging between 30 and 40 kW/m. A statistical comparison between the two periods shows a moderate reduction in wave energy potential under future conditions, with strong spatial variability. The performance of four wave energy converters (AquaBuoy, Wavestar, Oceantec, and Atargis) is analyzed, revealing significant differences in energy production and capacity factor depending on device–site matching. A techno-economic evaluation is performed by estimating the LCOE, accounting for capital expenditure, operational costs, device lifetime, and annual energy production (AEP). The results demonstrate that economic performance is primarily driven by energy production rather than capital cost alone, and that wave energy exploitation in the Azores remains viable under near-future climate conditions. Full article

Microalgae are increasingly recognized as renewable biofactories for producing high-value bioactive molecules. However, their industrial exploitation is limited by their rigid cell walls, metabolite heterogeneity, and the energy-intensive nature of the extraction processes. Recent advances in process-intensification technologies, including microwave-assisted, ultrasound-assisted, enzymatic, pressurized liquid, and supercritical CO₂-based methods, have significantly improved extraction efficiency and selectivity, with reported lipid recoveries exceeding 40–50% in some microalgal systems and carotenoid recoveries approaching 90% under optimized conditions. NADES-assisted systems further enhance mass transfer and solubilization through tailored hydrogen-bonding interactions, enabling selective extraction of polar and semi-polar metabolites under mild conditions. However, limitations remain, including high viscosity, variability in extraction performance, and challenges in solvent recovery and scale-up. This review critically evaluates the extraction efficiency, mechanistic basis, and sustainability of NADES-assisted processes, highlighting key limitations and identifying research priorities for their integration into scalable microalgal biorefinery systems. Full article

Children are some of the most vulnerable members of society who must be protected at all costs. Zimbabwe has a long history of disjointed formal and indigenous social protection systems, which have resulted in the exclusion of many children, leading to high levels of child abuse, neglect, exploitation and violence. In policy and practice, there is a strong bias towards the ineffective statist formal system, yet the indigenous social protection system is the mainstay for the protection of most children. The study aimed to explore how asset-based community development can be used as a strategy to integrate the fragmented formal and indigenous social protection systems for sustainable child protection. An explanatory sequential mixed-methods research design was employed, collecting both quantitative and qualitative data from 76 participants. The study findings indicate that asset-based community development by positioning the indigenous social protection system at the centre of the social protection framework provides a blueprint for a community-led and integrated social protection system, which can translate into effective child protection. This system, which utilises a wider network of community and external resources, can counteract the limits of fragmented social protection and sustainably promote child protection among impoverished households in Zimbabwe and similar contexts. The recommendation is that asset-based community development should be promoted as a strategy towards integrated social protection and sustainable child protection. Full article

This interdisciplinary study integrates criminological theory with geospatial methods to analyse large, multi-format datasets using geostatistical techniques. The aim is to predict where Child Criminal Exploitation (CCE) is likely to cluster, based on the spatial convergence of contextual risk factors. Drawing on insights from General Strain Theory (GST) and prior research on CCE, this study integrated seven open-source datasets capturing educational attainment, age demographics, violent crime, deprivation, and paramilitary-related violence. These variables were operationalised to construct a proxy measure for strain. Spatial analysis was conducted using ArcGIS Pro, including the Data Interoperability extension, to enable efficient integration and interrogation of multi-format geospatial data. Geospatial analysis demonstrated that contextual risk factors for CCE are spatially clustered. Using four search parameters, a small subset of wards with elevated risk were identified. This resulted in a reduction in ward locations by 85–99%, land area under investigation from 14.45% to 0.84%, and affected population from 17.91% to 1.41%, enabling more targeted and efficient resource allocation. As understanding of the contextual factors contributing to CCE improves, this methodological approach offers scalable and data-driven means of identifying high-risk areas. By integrating geospatial analysis with criminological theory, the model supports more effective safeguarding strategies and prioritisation of limited public resources. This study is limited by the absence of multi-agency datasets, which were beyond its scope. Future research aims to incorporate cross-sector data to validate and refine the model through ground-truthing, enhancing its predictive accuracy and practical applicability. Full article

Low-molecular-weight gelators (LMWGs) have attracted growing attention as versatile alternatives to conventional polymeric thickeners and gelators, owing to their ability to form three-dimensional fibrillar networks through non-covalent self-assembly and to undergo reversible sol–gel transitions in response to external stimuli. Among the various stimuli that can be exploited, pH represents a particularly attractive trigger given its direct relevance to biological and physiological environments. This review focuses on three categories of pH-responsive LMWGs that have shown notable progress over the past decade yet remain relatively underexplored in the literature. First, N-oxide-type hydrogelators are discussed, with emphasis on amide amine oxide-based surfactants and pyridine-N-oxide frameworks. The pH-dependent protonation of the N-oxide moiety modulates intermolecular hydrogen bonding, thereby governing self-assembly and gel formation. The structural versatility of these gelators enables rational tuning of aggregate morphology and confers clear pH and temperature responsiveness. Second, recent advances in phenylboronic acid-based LMWGs are highlighted. Although boronic acid derivatives have long been studied as dynamic crosslinking units in polymeric hydrogels, 3-isobutoxyphenylboronic acid was recently identified as the first example of phenylboronic acid functioning as an LMWG, in which gelation is driven primarily by hydrogen bonding and pH responsiveness is exploited for stimuli-triggered gel disruption rather than gel formation. Third, pH-responsive orthogonal self-assembly systems are reviewed. Representative examples include multicomponent hybrid hydrogels combining pH-activated LMWGs with polymer gelators for controlled drug release, pH-triggered self-sorting of two LMWGs without any polymeric component, and bio-based orthogonal hydrogels composed of a glucolipid LMWG and cellulose nanocrystals. For each system, both advantages and remaining limitations are critically assessed. Collectively, this review aims to provide a timely overview of emerging trends in pH-responsive LMWG research and to offer perspectives on the rational design of next-generation stimuli-responsive soft materials. Full article