Search Results (1,118)

Current generative AI systems, despite extraordinary progress, face fundamental limitations in temporal reasoning, contextual understanding, and ethical decision-making. These systems process information statistically without authentic comprehension of experiential time or intentional context, limiting their applicability in security-critical domains where reasoning about past experiences, present situations, and future implications is essential. We present Phase 3 of the Sophimatics framework: Super Time-Cognitive Neural Networks (STCNNs), which address these limitations through complex-time representation T ∈ ℂ where chronological time (Re(T)) integrates with experiential dimensions of memory (Im(T) < 0), present awareness (Im(T) ≈ 0), and imagination (Im(T) > 0). The STCNN architecture implements philosophical constraints through geometric parameters α and β that bound memory accessibility and creative projection, enabling neural systems to perform temporal-philosophical reasoning while maintaining computational tractability. We demonstrate STCNN’s effectiveness across five security-critical applications: threat intelligence (AUC 0.94, 1.8 s anticipation), privacy-preserving AI (84% utility at ε = 1.0), intrusion detection (96.3% detection, 2.1% false positives), secure multi-party computation (ethical compliance 0.93), and blockchain anomaly detection (94% detection, 3.2% false positives). Empirical evaluation shows 23–45% improvement over baseline systems while maintaining temporal coherence > 0.9, demonstrating that integration of temporal-philosophical reasoning with neural architectures enables AI systems to reason about security threats through simultaneous processing of historical patterns, current contexts, and projected risks. Full article

The precise reconstruction of target scattering centers (TSCs) using sensors plays a crucial role in feature extraction and identification of non-cooperative targets. Radar sensor networks (RSNs) are well suited for this task, as they are capable of illuminating targets from multiple aspect angles and rapidly capturing reflected signals. However, the complex geometry and diverse material composition of real-world targets result in significant variations in the radar cross-section (RCS) observed at different angles. Although these RCS responses are interrelated, they exhibit considerable angular diversity. Furthermore, achieving precise spatiotemporal registration and fully coherent processing is infeasible for RSNs composed of small mobile sensor platforms, such as drone swarms. Therefore, an intelligent algorithm is required to extract and accumulate correlated and meaningful information from the target echoes received by the RSN. In this work, a novel collaborative TSC reconstruction framework for RSNs is proposed. The framework performs similarity evaluation on wide-angle high-resolution range profiles (HRRPs) to achieve adaptive angular segmentation of TSC models. It combines the expectation–maximization (EM) algorithm with an enhanced Arctic puffin optimization (EAPO) algorithm to effectively integrate echo information from the RSN in a non-coherent manner, thereby enabling accurate TSC estimation. The proposed method outperforms existing mainstream approaches in terms of spatiotemporal registration requirements, estimation accuracy, and stability. Comparative experiments on measured datasets demonstrate the robustness of the framework and its adaptability to complex target scattering characteristics, confirming its practical value. Full article

Beyond-5G wireless systems increasingly rely on distributed massive multiple-input multiple-output (MIMO) architectures to achieve high spectral efficiency, low latency, and wide coverage. A key challenge in such networks is that cooperating base stations (BSs) often possess different levels of channel state information (CSI) due to fronthaul constraints, user mobility, or hardware limitation. In this paper, we propose two novel detectors that enable cooperation between BSs with differing CSI availability. In this setup, some BSs have access to instantaneous CSI, while others only have long-term channel information. The proposed detectors—termed the coherent/non-coherent (CNC) detector and the differential CNC detector—integrate coherent and non-coherent approaches to signal detection. This framework allows BSs with only long-term information to actively contribute to the detection process, while leveraging instantaneous CSI where available. This approach enables the system to integrate the advantages of non-coherent detection with the precision of coherent processing, improving overall performance without requiring full CSI at all cooperating BSs. We formulate the detectors based on the maximum likelihood (ML) criterion and derive analytical expressions for their pairwise block error probabilities under Rayleigh fading channels. Leveraging the pairwise block error probability expression for the CNC detector, we derive a tight upper bound on the average block error probability. Numerical results show that the CNC and differential CNC detectors outperform their respective single-BS baseline-coherent ML and non-coherent differential detection. Moreover, both detectors demonstrate strong resilience to mid-to-high range correlation at the BS antennas. Full article

Backscatter technologies promise to enable large-scale, battery-free sensor networks by modulating and reflecting ambient radio frequency (RF) carriers rather than generating new signals. Translating this potential into practical deployments—such as distributed photovoltaic (PV) power systems—necessitates realistic modeling that accounts for deployment variabilities commonly neglected in idealized analyses, including uncertain hardware insertion loss, non-ideal antenna gain, spatially varying path loss exponents, and fluctuating noise floors. In this work, we develop a practical model for reliable backscatter communications that explicitly incorporates these impairing factors, and we complement the theoretical development with empirical characterization of each contributing term. To validate the model, we implement a frequency-shift keying (FSK)-based backscatter system employing a non-coherent demodulation scheme with adaptive bit-rate matching, and we conduct comprehensive experiments to evaluate communication range and sensitivity to system parameters. Experimental results demonstrate strong agreement with theoretical predictions: the prototype tag consumes 825 µW in measured operation, and an integrated circuit (IC) implementation reduces consumption to 97.8 µW, while measured communication performance corroborates the model’s accuracy under realistic deployment conditions. Full article

The agricultural sector remains one of the most hazardous working environments, with viticulture posing particularly high risks due to repetitive manual tasks, pesticide exposure, and machinery operation. This study explores the potential of vision-based Artificial Intelligence (AI) systems to enhance occupational health and safety by evaluating their coherence with human expert assessments. A dataset of 203 annotated images, collected from 50 vineyards in Northern Italy, was analyzed across three domains: manual work activities, workplace environments, and agricultural machinery. Each image was independently assessed by safety professionals and an AI pipeline integrating convolutional neural networks, regulatory contextualization, and risk matrix evaluation. Agreement between AI and experts was quantified using weighted Cohen’s Kappa, achieving values of 0.94–0.96, with overall classification error rates below 14%. Errors were primarily false negatives in machinery images, reflecting visual complexity and operational variability. Statistical analyses, including McNemar and Wilcoxon signed-rank tests, revealed no significant differences between AI and expert classifications. These findings suggest that AI can provide reliable, standardized risk detection while highlighting limitations such as reduced sensitivity in complex scenarios and the need for explainable models. Overall, integrating AI with complementary sensors and regulatory frameworks offers a credible path toward proactive, transparent, and preventive safety management in viticulture and potentially other high-risk agricultural sectors. Furthermore, vision-based AI systems inherently act as optical sensors capable of capturing and interpreting occupational risk conditions. Their integration with complementary sensor technologies—such as inertial, environmental, and proximity sensors—can enhance the precision and contextual awareness of automated safety assessments in viticulture. Full article

A Bose-Fermi mixture, consisting of both bosons and fermions, exhibits distinctive quantum coherence and phase transitions, offering valuable insights into many-body quantum systems. The ground state, as the system’s lowest energy configuration, is essential for understanding its overall behavior. In this study, we introduce the Bose-Fermi Energy-based Deep Neural Network (BF-EnDNN), a novel deep learning approach designed to solve the ground-state problem of Bose-Fermi mixtures at zero temperature through energy minimization. This method incorporates three key innovations: point sampling pre-training, a Dynamic Symmetry Layer (DSL), and a Positivity Preserving Layer (PPL). These features significantly improve the network’s accuracy and stability in quantum calculations. Our numerical results show that BF-EnDNN achieves accuracy comparable to traditional finite difference methods, with effective extension to two-dimensional systems. The method demonstrates high precision across various parameters, making it a promising tool for investigating complex quantum systems. Full article

Fractal dimension (Frac) and lacunarity (Lac) are frequently proposed as biomarkers of multiscale image complexity, but their incremental value over standardized radiomics remains uncertain. We position both measures within the Image Biomarker Standardisation Initiative (IBSI) feature space by running a fully reproducible comparison in two settings. In a baseline experiment, we analyze $N=1000$ simulated $64\times 64$ textured ROIs discretized to $N_g=64$, computing 92 IBSI descriptors together with Frac (box counting) and Lac (gliding box), for 94 features per ROI. In a wavelet-augmented experiment, we analyze $N=1000$ ROIs and add level-1 wavelet descriptors by recomputing first-order and GLCM features in each sub-band (LL, LH, HL, and HH), contributing $4\times (19+19)=152$ additional features and yielding 246 features per ROI. Feature similarity is summarized by a consensus score that averages z-scored absolute Pearson and Spearman correlations, distance correlation, maximal information coefficient, and cosine similarity, and is visualized with clustered heatmaps, dendrograms, sparse networks, PCA loadings, and UMAP and t-SNE embeddings. Across both settings a stable two-block organization emerges. Frac co-locates with contrast, difference, and short-run statistics that capture high-frequency variation; when wavelets are included, detail-band terms from LH, HL, and HH join this group. Lac co-locates with measures of large, coherent structure—GLSZM zone size, GLRLM long-run, and high-gray-level emphases—and with GLCM homogeneity and correlation; LL (approximation) wavelet features align with this block. Pairwise associations are modest in the baseline but become very strong with wavelets (for example, Frac versus GLCM difference entropy, which summarizes the randomness of gray-level differences, with $\left|r\right|\approx 0.98$; and Lac versus GLCM inverse difference normalized (IDN), a homogeneity measure that weights small intensity differences more heavily, with $\left|r\right|\approx 0.96$). The multimetric consensus and geometric embeddings consistently place Frac and Lac in overlapping yet separable neighborhoods, indicating related but non-duplicative information. Practically, Frac and Lac are most useful when multiscale heterogeneity is central and they add a measurable signal beyond strong IBSI baselines (with or without wavelets); otherwise, closely related variance can be absorbed by standard texture families. Full article

Collaborative experiences are enriched through cross-platform interactions in the context of eXtended Reality (XR) systems. In this paper, we introduce SRVS-C (Spatially Referenced Virtual Synchronization for Collaboration), a centralized framework designed to support co-located, real-time AR (on smartphone) and VR (in headset) interactions over local networks. The framework adopts an architecture of interactive asymmetry, where the interaction roles, input modalities, and rendering responsibilities are adapted to the unique capabilities and constraints of each device. Concurrently, the framework maintains perceptual symmetry, guaranteeing a coherent spatial and semantic experience for all users. This is achieved through anchor-based spatial registration and unified data representations. Compared to prior work that relies on cloud services or symmetric platforms (e.g., VR–VR, AR–AR, and PC–PC pairings), SRVS-C supports seamless communication between AR and VR endpoints, operating entirely over TCP sockets using serialization-agnostic message formats. We evaluated SRVS-C in a dual-user scenario involving a mobile AR and a VR headset, using shared freehand drawing tasks. These tasks include simple linear strokes and geometry-rich drawing content to assess how varying interaction complexity—ranging from low-density sketches to intricate, high-vertex structures— impacted the end-to-end latency, state replication timing, and collaborative fluency. The results show that the system sustains latency between 35 ms and 175 ms, even during rapid, continuous drawing actions that generate a high number of stroke updates per second, and when handling drawings composed of numerous vertices and complex shapes. Throughout these conditions, the system maintains perceptual continuity and spatial alignment across users by applying platform-specific interactive asymmetry. Full article

Acquiring high-resolution digital elevation models (DEMs) over across extensive regions remains challenging due to high costs and insufficient detail, creating demand for super-resolution (SR) techniques. However, existing DEM SR methods still rely on limited data sources and often neglect essential terrain features. To address the issues, SAR data complements existing sources with its all-weather capability and strong penetration, and a Transformer-based Residual Attention Network combining SAR and Terrain Features (TRAN-ST) is proposed. The network incorporates intensity and coherence as SAR features to restore the details of the high-resolution DEMs, while slope and aspect constraints in the loss function enhance terrain consistency. Additionally, it combines the lightweight Transformer module with the residual feature aggregation module, which enhances the global perception capability while aggregating local residual features, thereby improving the reconstruction accuracy and training efficiency. Experiments were conducted on two DEMs in San Diego, USA, and the results show that compared with methods such as the bicubic, SRCNN, EDSR, RFAN, HNCT methods, the model reduces the mean absolute error (MAE) by 2–30%, the root mean square error (RMSE) by 1–31%, and the MAE of the slope by 2–13%, and it reduces the number of parameters effectively, which proves that TRAN-ST outperforms current typical methods. Full article

The exponential growth of scientific literature demands scalable methods to evaluate large-language-model outputs beyond surface-level fluency. We present a two-phase framework that separates generation from evaluation: a retrieval-augmented generation system first produces candidate abstracts, which are then embedded into semantic co-occurrence graphs and assessed using seven robustness metrics from complex network theory. Two experiments were conducted. The first varied model, embedding and prompt configurations, achieved results showing clear differences in performance; the best family combined gemma-2b-it, a prompt inspired by chain-of-Thought reasoning, and all-mpnet-base-v2, achieving the highest graph-based robustness. The second experiment refined the temperature setting for this family, identifying $\tau =0.2$ as optimal, which stabilized results (sd $=0.12$) and improved robustness relative to retrieval baselines ($\Delta E_G=+0.08$, $\Delta \rho =+0.55$). While human evaluation was limited to a small set of abstracts, the results revealed a partial convergence between graph-based robustness and expert judgments of coherence and importance. Our approach contrasts with methods like GraphRAG and establishes a reproducible, model-agnostic pathway for the scalable quality control of LLM-generated scientific content. Full article

The combined application of machine or deep learning algorithms and hyperspectral imagery for bathymetry estimation is currently an emerging field with widespread uses and applications. This research topic still requires further investigation to achieve methodological robustness and accuracy. In this study, we introduce a novel methodology for shallow bathymetric mapping using a one-dimensional convolutional neural network (1D-CNN) applied to PRISMA hyperspectral images, including refinements to enhance mapping accuracy, together with the optimization of computational efficiency. Four different 1D-CNN models were developed, incorporating pansharpening and spectral band optimization. Model performance was rigorously evaluated against reference bathymetric data obtained from official nautical charts provided by the Servicio de Hidrografía Naval (Argentina). The BoPsCNN model achieved the best testing accuracy with a coefficient of determination of 0.96 and a root mean square error of 0.65 m for a depth range of 0–15 m. The implementation of band optimization significantly reduced computational overhead, yielding a time-saving efficiency of 31–38%. The resulting bathymetric maps exhibited a coherent depth gradient from nearshore to offshore zones, with enhanced seabed morphology representation, particularly in models using pansharpened data. Full article

This study investigates the impact of audio feedback on cognitive performance during VR puzzle games using EEG analysis. Thirty participants played three different VR puzzle games under two conditions (with and without audio) while their brain activity was recorded. To analyze concentration levels and neural engagement patterns, we employed spectral analysis combined with a preprocessing algorithm and an optimized Deep Neural Network (DNN) model. The proposed processing stage integrates feature normalization, automatic labeling based on Principal Component Analysis (PCA), and Gamma band feature extraction, transforming concentration detection into a supervised classification problem. Experimental validation was conducted under the two gaming conditions in order to evaluate the impact of multisensory stimulation on model performance. The results show that the proposed approach significantly outperforms traditional machine learning classifiers (SVM, LR) and baseline deep learning models (DNN, DGCNN), achieving a 97% accuracy in the audio scenario and 83% without audio. These findings confirm that auditory stimulation reinforces neural coherence and improves the discriminability of EEG patterns, while the proposed method maintains a robust performance under less stimulating conditions. Full article

The forest-based bioeconomy is increasingly recognized as a key pillar of European bioeconomy strategies, with potential to drive sustainable innovation, rural development, and climate action. However, regional disparities persist, particularly in Southern Europe. This study assesses the development of a forest-based entrepreneurial ecosystem located in the Spanish region of Castilla-La Mancha, using an adapted multidimensional framework that considers institutional, supply, and demand-side drivers. Fifteen interviews were conducted with key players in the forestry sector. Results indicate an incipient and fragmented ecosystem: while initiatives such as UFIL Cuenca foster entrepreneurship and innovation, the region lacks three main and different aspects: (i) a coherent strategic vision, (ii) cluster development, and (iii) presents coordination failures. Coordination structures as sectoral roundtables are viewed as critical for the forestry value chain but currently underutilized. The study emphasizes the importance of aligning forest-based resources with supportive entrepreneurial environments—where networks, infrastructure, and institutional mechanisms interact—to enable systemic innovation and sustainable regional development. The findings highlight the need for integrated regional strategies, strengthened governance mechanisms, stable financial resources for regional structures and expanded entrepreneurship support to advance the forest-based entrepreneurial ecosystems in Spain. Full article

Effective and sustainable planning for future smart transportation systems is hindered by outdated traffic management models that fail to capture real-world dynamics, leading to congestion and significant environmental impact. To address this, advanced machine learning models are required to provide high-fidelity insights into urban mobility. In this work, we propose an adaptive machine learning approach to traffic pattern recognition that synergizes the HDBSCAN and k-means clustering algorithms. By employing a data-driven weighted voting mechanism, our solution provides a robust analytical foundation for sustainable planning, integrating structural analysis with precise cluster refinement. The crafted model was validated using a high-fidelity simulation of the Khmelnytskyi, Ukraine, transport network, where it demonstrated a superior ability to identify distinct traffic modes, achieving a V-measure of 0.79–0.82 and improving cluster compactness by 10–14% over standalone algorithms. It also attained a scenario identification accuracy of 92.8–95.0% with a temporal coherence of 0.94. These findings confirm that our adaptive approach is a foundational technology for intelligent transport systems, enabling the planning and deployment of more responsive, efficient, and sustainable urban mobility solutions. Full article

Mineral prospectivity mapping (MPM) is a powerful approach for identifying mineralization zones with high potential for economically viable mineral deposits. This study proposes a hybrid framework combining a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP), a Convolutional Neural Network (CNN) and a Fuzzy-Kernel Extreme Learning Machine (FKELM) to address the challenges of imbalanced and uncertain datasets in mineral exploration. The approach was applied to the G.R. Halli gold prospect, in the Chitradurga Schist Belt, Western Dharwar Craton, India, using nine geochemical pathfinder elements. WGAN-GP generated high-quality negative samples, balancing the dataset and reducing overfitting. Compared with Support Vector Machines, Gradient Boosting, and a baseline CNN, FKELM (AUC = 0.976, accuracy = 92%) and WGAN-GP + CNN (AUC = 0.973, accuracy = 91%) showed superior performance and produced geologically coherent prospectivity maps. Promising gold targets were delineated, closely aligned with known mineralized zones and geochemical anomalies. This hybrid framework provides a robust, cost-effective, and scalable MPM solution for structurally controlled geological tracts, insufficient data terrains, and integration with additional geoscience datasets for other complex mineral systems. Full article