Search Results (4,602)

The scientific study of emotion remains fraught with conceptual ambiguity, methodological limitations, and epistemological blind spots. This theoretical paper argues that existing paradigms frequently capture synthetic rather than natural emotional states—those shaped by social expectations, cognitive scripting, and performance under observation. We propose a conceptual framework that distinguishes natural emotion—spontaneous, embodied, and interoceptively grounded—from synthetic forms that are adaptive, context-driven, and often unconsciously rehearsed. These reactions often involve emotional scripts rather than genuine, spontaneous affective experiences. Drawing on insights from affective neuroscience, psychological measurement, artificial intelligence, and neurodiversity, we examine how widely used tools such as EEG, polygraphy, and self-report instruments may capture emotional conformity rather than authenticity. We further explore how affective AI systems trained on socially filtered datasets risk replicating emotional performance rather than emotional truth. By recognizing neurodivergent expression as a potential site of emotional transparency, we challenge dominant models of emotional normalcy and propose a five-step agenda for reorienting emotion research toward authenticity, ecological validity, and inclusivity. This post-synthetic framework invites a redefinition of emotion that is conceptually rigorous, methodologically nuanced, and ethically inclusive of human affective diversity. Full article

Optical wireless sensor networks (OWSNs) offer promising capabilities for high-speed, energy-efficient communication, particularly in mission-critical environments such as industrial automation, healthcare monitoring, and smart buildings. However, dynamic spectrum management and fault tolerance remain key challenges in ensuring reliable and timely data transmission. This paper proposes an adaptive spectrum management framework (ASMF) that addresses these challenges through a mathematically grounded and implementation-driven approach. The ASMF formulates the spectrum allocation problem as a constrained Markov decision process and leverages a dual-layer optimization strategy combining Lyapunov drift-plus-penalty for queue stability with deep reinforcement learning for adaptive long-term decision making. Additionally, ASMF integrates a hybrid fault-tolerant mechanism using LSTM-based link failure prediction and lightweight recovery logic, achieving up to 83% prediction accuracy. Experimental evaluations using real-world datasets from industrial, healthcare, and smart infrastructure scenarios demonstrate that ASMF reduces critical traffic latency by 37%, improves reliability by 42% under fault conditions, and enhances energy efficiency by 22.6% compared with state-of-the-art methods. The system also maintains a 99.94% packet delivery ratio for critical traffic and achieves 69.7% faster recovery after link failures. These results confirm the effectiveness of ASMF as a robust and scalable solution for adaptive spectrum management in dynamic, fault-prone OWSN environments. Full article

We analyzed the accuracy of digitized adiabatic quantum computing to form the entangled three-qubit Greenberger–Horne–Zeilinger (GHZ) state on two IBM quantum computers and four quantum simulators by comparison with direct calculations using a Python code (version 3.12). We initialized three-qubit systems in the ground state of the Hamiltonian for noninteracting spins in an applied magnetic field in the x direction. We then gradually varied the Hamiltonian to an Ising model form with nearest-neighbor zz spin coupling with an eight-step discretization. The von Neumann entropy provides an indicator of the accuracy of the discretized adiabatic evolution. The von Neumann entropy of the density matrix from the Python code (version 3.12) remains very close to zero, while the von Neumann entropy of the density matrices on the quantum computers increases almost linearly with the step number in the process. The GHZ witness operator indicates that the quantum simulators incorporate a GHZ component in part. The states on the two quantum computers acquire partial GHZ character, even though the trace of the product of the GHZ witness operator with the density matrix not only remains positive but also rises monotonically from Step 5 to Step 8. Each of the qubits becomes entangled during the adiabatic evolution in all of the calculations, as shown by the single-qubit reduced density matrices. Full article

At present, environmental pollution is becoming more and more serious, and the energy problem is becoming more prominent. Energy-braking recovery can collect the mechanical energy lost in the traditional braking process and convert it into electricity or other forms of energy for vehicle reuse, thus reducing carbon emissions, achieving energy saving and emission reduction, and promoting green development. Based on this, this paper studies the energy-braking recovery method. The study focuses specifically on the recovery of energy during vehicle braking triggered by brake-signal activation, without addressing alternative deceleration strategies under braking conditions. The proposed energy-braking recovery scheme is evaluated primarily through simulation, with the analysis grounded in practical application scenarios and leveraging existing technologies. Firstly, the principle of energy-braking recovery is introduced, and the method of estimating the State on Charge (SOC) of the battery and controlling the motor speed is determined. Then, the simulation model of the energy brake recovery system is built with MATLAB R2023b (MathWorks, Natick, MA, USA), and the design ideas and specific structures of the three modules of the simulation model are introduced in detail. Finally, the results of the simulated motor speed and SOC value of the battery are analysed, and it is confirmed that they meet the requirements of the system and achieve close to the ideal effect. Full article

Quantum hydrodynamic theory (QHT) provides a computationally efficient alternative to time-dependent density functional theory for simulating plasmonic nanostructures, but its predictive power depends critically on the choice of ground-state electron density and energy functional. To construct ground-state densities, we adopt orbital-free density functional theory and numerically evaluate the effect of different exchange–correlation functionals and kinetic energy functionals. A suitable energy functional to reproduce both the DFT-calculated work function and charge density is identified. In the excited-state part, we adopt this obital-free ground-state density and investigate how variations in the von Weizsäcker kinetic energy fraction within the Laplacian-level functional affect the resonance energy and oscillator strengths. The appropriate functional form is identified, achieving an accuracy comparable to that reported in previous studies. Applied to sodium nanodimers, our approach captures nonlinear density responses at sub-nanometer gaps. This work extends QHT beyond idealized geometries and offers a robust path toward efficient quantum plasmonic modeling. Full article

Background/Objectives: This systematic review addresses the disproportionate impact of the opioid epidemic on American Indian and Alaska Native (AI/AN) populations by examining the socio-ecological and social network factors that influence opioid use and misuse. While previous reviews have largely focused on treatment modalities or structural determinants such as socioeconomic status and rurality, few studies have explored the role of social networks as risk or protective factors, particularly within AI/AN communities. Methods: Applying the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, the review synthesized findings from three scholarly databases (PubMed, EBSCOhost, ProQuest), six institutional repositories (e.g., Indigenous Studies Portal), and one academic search engine (Google Scholar). Studies that examined the influence of social network domains on opioid misuse in AI/AN populations in the United States, reported quantitative or qualitative data, and were published between 2010 and 2022 were included in this review. Study quality was assessed with the JBI Checklists for Analytical Cross Sectional Studies and Qualitative Research. Of the 817 articles initially identified, 7 met the inclusion criteria, with most studies focusing on AI/AN adolescents and young adults, a demographic shown to be especially susceptible to opioid misuse. Results: The review identified several social network domains that significantly affect opioid use patterns, including familial relationships, peer associations, community dynamics, educational influences, cultural traditions, social media engagement and the effect of historical and intergenerational trauma. These domains can function either as protective buffers or as contributing factors to opioid misuse. Conclusions: The findings underscore the necessity for future longitudinal research to elucidate the causal pathways between these social network factors and opioid behaviors, particularly concerning trauma and digital media exposure. Furthermore, the study highlights the importance of culturally grounded, evidence-based prevention strategies that address the multifaceted social environments of AI/AN individuals. Such approaches are critical to fostering resilience and mitigating the opioid crisis within these historically marginalized populations. Full article

In recent years, self-autonomous intelligent transportation applications such as drones and autonomous vehicles have seen rapid development and deployment across various countries. Within the domain of artificial intelligence, self-autonomous agents are defined as software entities capable of independently operating drones in an intelligent transport system (ITS) without human intervention. The integration of these agents into autonomous vehicles and their deployment across distributed cloud networks have increased significantly. These systems, which include drones, ground vehicles, and aircraft, are used to perform a wide range of tasks such as delivering passengers and packages within defined operational boundaries. Despite their growing utility, practical implementations face significant challenges stemming from the heterogeneity of network resources, as well as persistent issues related to security, privacy, and processing costs. To overcome these challenges, this study proposes a novel blockchain-enabled self-autonomous intelligent transport system designed for drone workflow applications. The proposed system architecture is based on a remote method invocation (RMI) client–server model and incorporates a serverless computing framework to manage processing costs. Termed the self-autonomous blockchain-enabled cost-efficient system (SBECES), the framework integrates a client and system agent mechanism governed by Q-learning and deep-learning-based policies. Furthermore, it incorporates a blockchain-based hash validation and fault-tolerant (HVFT) mechanism to ensure data integrity and operational reliability. A deep reinforcement learning (DRL)-enabled adaptive scheduler is utilized to manage drone workflow execution while meeting quality of service (QoS) constraints, including deadlines, cost-efficiency, and security. The overarching objective of this research is to minimize the total processing costs that comprise execution, communication, and security overheads, while maximizing operational rewards and ensuring the timely execution of drone-based tasks. Experimental results demonstrate that the proposed system achieves a 30% reduction in processing costs and a 29% improvement in security and privacy compared to existing state-of-the-art solutions. Full article

Drawing upon a meticulous delineation of pure consciousness’s fundamental and necessary features—including unstructuredness, maximal simplicity, selflessness, awareness as such, zero-perspective, and the absence of specific phenomenal qualities—this article asserts that a full-fledged experience of pure consciousness is attainable within the psychedelic state. Critically, this psychedelic manifestation is argued to be phenomenologically indistinguishable in its core properties from pure consciousness accessed via meditative practices. Consequently, this finding not only problematizes, but actually directly refutes Metzinger’s thesis, which posits meditation as the sole “best and most natural candidate” for achieving pure consciousness. Moreover, this work champions a soft phenomenological perennialism. This perspective navigates a middle ground between rigid perennialism and radical constructivism, underscoring the identical phenomenological core shared by all pure consciousness experiences, including those induced by psychedelics. This exploration further posits that psychedelic pure consciousness experiences can yield significant epistemic insights into the fundamental nature of consciousness, the self, and reality. Beyond this, a systematic phenomenology of pure consciousness is demonstrated to offer profound contributions to our understanding of certain religious–spiritual concepts such as God. Nonetheless, while acknowledging naturalistic critiques, a significant caveat is issued: extreme caution is warranted regarding religious–spiritual interpretations gleaned from such phenomenologies. Ultimately, the paper underscores the burgeoning importance of a spiritual naturalistic interpretation of pure consciousness. Full article

As large language model (LLM) tutors evolve from scripted helpers into adaptive educational partners, their capacity for self-regulation, ethical decision-making, and internal monitoring will become increasingly critical. This paper introduces the Needs-Driven Consciousness Framework (NDCF) as a novel, integrative architecture that combines Dennett’s multiple drafts model, Damasio’s somatic marker hypothesis, and Tulving’s tripartite memory system into a unified motivational design for synthetic consciousness. The NDCF defines three core regulators, specifically Survive (system stability and safety), Thrive (autonomy, competence, relatedness), and Excel (creativity, ethical reasoning, long-term purpose). In addition, there is a proposed supervisory Protect layer that detects value drift and overrides unsafe behaviours. The core regulators compute internal need satisfaction states and urgency gradients, feeding into a softmax-based control system for context-sensitive action selection. The framework proposes measurable internal signals (e.g., utility gradients, conflict intensity Ω), behavioural signatures (e.g., metacognitive prompts, pedagogical shifts), and three falsifiable predictions for educational AI testbeds. By embedding these layered needs directly into AI governance, the NDCF offers (i) a psychologically and biologically grounded model of emergent machine consciousness, (ii) a practical approach to building empathetic, self-regulating AI tutors, and (iii) a testable platform for comparing competing consciousness theories through implementation. Ultimately, the NDCF provides a path toward the development of AI tutors that are capable of transparent reasoning, dynamic adaptation, and meaningful human-like relationships, while maintaining safety, ethical coherence, and long-term alignment with human well-being. Full article

Interstitial lung diseases (ILD) significantly impact health and mortality, affecting millions of individuals worldwide. During the COVID-19 pandemic, lung ultrasonography (LUS) became an indispensable diagnostic and management tool for lung disorders. However, utilising LUS to diagnose ILD requires significant expertise. This research aims to develop an automated and efficient approach for diagnosing ILD from LUS videos using AI to support clinicians in their diagnostic procedures. We developed a binary classifier based on a state-of-the-art CSwin Transformer to discriminate between LUS videos from healthy and non-healthy patients. We used a multi-centric dataset from the Royal Melbourne Hospital (Australia) and the ULTRa Lab at the University of Trento (Italy), comprising 60 LUS videos. Each video corresponds to a single patient, comprising 30 healthy individuals and 30 patients with ILD, with frame counts ranging from 96 to 300 per video. Each video is annotated using the corresponding medical report as ground truth. The datasets used for training the model underwent selective frame filtering, including reduction in frame numbers to eliminate potentially misleading frames in non-healthy videos. This step was crucial because some ILD videos included segments of normal frames, which could be mixed with the pathological features and mislead the model. To address this, we eliminated frames with a healthy appearance, such as frames without B-lines, thereby ensuring that training focused on diagnostically relevant features. The trained model was assessed on an unseen, separate dataset of 12 videos (3 healthy and 9 ILD) with frame counts ranging from 96 to 300 per video. The model achieved an average classification accuracy of 91%, calculated as the mean of three testing methods: Random Sampling (92%), Key Featuring (92%), and Chunk Averaging (89%). In RS, 32 frames were randomly selected from each of the 12 videos, resulting in a classification with 92% accuracy, with specificity, precision, recall, and F1-score of 100%, 100%, 90%, and 95%, respectively. Similarly, KF, which involved manually selecting 32 key frames based on representative frames from each of the 12 videos, achieved 92% accuracy with a specificity, precision, recall, and F1-score of 100%, 100%, 90%, and 95%, respectively. In contrast, the CA method, where the 12 videos were divided into video segments (chunks) of 32 consecutive frames, with 82 video segments, achieved an 89% classification accuracy (73 out of 82 video segments). Among the 9 misclassified segments in the CA method, 6 were false positives and 3 were false negatives, corresponding to an 11% misclassification rate. The accuracy differences observed between the three training scenarios were confirmed to be statistically significant via inferential analysis. A one-way ANOVA conducted on the 10-fold cross-validation accuracies yielded a large F-statistic of 2135.67 and a small p-value of 6.7 × 10⁻²⁶, indicating highly significant differences in model performance. The proposed approach is a valid solution for fully automating LUS disease detection, aligning with clinical diagnostic practices that integrate dynamic LUS videos. In conclusion, introducing the selective frame filtering technique to refine the dataset training reduced the effort required for labelling. Full article

This paper explores the application of transform-domain sparsification and compressed sensing (CS) techniques to improve the efficiency and quality of magnetic resonance imaging (MRI). We implement and evaluate three sparsifying methods—discrete wavelet transform (DWT), fast Fourier transform (FFT), and discrete cosine transform (DCT)—which are used to simulate subsampled reconstruction via inverse transforms. Additionally, one accurate CS reconstruction algorithm, basis pursuit (BP), using the L₁-MAGIC toolbox, is implemented as a benchmark based on convex optimization with L₁-norm minimization. Emphasis is placed on basis pursuit (BP), which satisfies the formal requirements of CS theory, including incoherent sampling and sparse recovery via nonlinear reconstruction. Each method is assessed in MATLAB R2024b using standardized DICOM images and varying sampling rates. The evaluation metrics include peak signal-to-noise ratio (PSNR), root mean square error (RMSE), structural similarity index measure (SSIM), execution time, memory usage, and compression efficiency. The results show that although discrete cosine transform (DCT) outperforms the others under simulation in terms of PSNR and SSIM, it is inconsistent with the physics of MRI acquisition. Conversely, basis pursuit (BP) offers a theoretically grounded reconstruction approach with acceptable accuracy and clinical relevance. Despite the limitations of a controlled experimental setup, this study establishes a reproducible benchmarking framework and highlights the trade-offs between the quality of transform-based reconstruction and computational complexity. Future work will extend this study by incorporating clinically validated CS algorithms with L₀ and nonconvex Lp (0 < p < 1) regularization to align with state-of-the-art MRI reconstruction practices. Full article

For efficient utilization of carbide slag (CS) waste to high-value building materials, in this study, CS and ground granulated blast furnace slag (GBFS) were used as primary raw materials to prepare alkali-activated cementitious systems under strong alkaline excitation. Multiscale mechanisms involving macroscopic mechanical property development were investigated. Microstructural characterization elucidated how raw material composition affected mineral crystal formation and transformation while revealing enhancement mechanisms governing micro-gel network structure formation and evolution dynamics. The results indicate that excessive calcium components coupled with deficient Si–Al sources in CS severely inhibit the formation of C-S-H and C-A-S-H gel phases, consequently impeding mechanical performance development. Also, GBFS incorporation offsets inherent silicon–aluminum deficiencies. Active [SiO₄]⁴⁻ and [AlO₄]⁵⁻ released from GBFS drive polycondensation reactions toward advanced polymerization states. Compressive strength has a nonlinear growth kinetics characterized by rapid initial ascent, followed by asymptotic plateauing as GBFS content increases. Optimal comprehensive performance emerges at a 5:5 GBFS-to-CS mass ratio, where 28d compressive strength reaches 47.5 MPa. Full article

Despite widespread adoption, traditional Agile project management practices often fail to ensure successful delivery of enterprise-scale software projects. One key limitation lies in the absence of a conceptually defined structure for the various types of Agile activities and their interactions. As a result, Agile methodologies typically lack formal indicators for evaluating the semantic content and progress status of project activities. Although widely used tools for Agile project management, such as Atlassian Jira, capture operational data, project status assessment interpretation remains largely subjective—relying on the experience and judgment of managers and team members rather than on a formal knowledge model or well-defined semantic attributes. As Agile project activities continue to grow in complexity, there is a pressing need for a modeling approach that captures their causal structure in order to describe the essential characteristics of the processes and ensure systematic monitoring and evaluation of the project. The complexity of the corresponding model must correlate with the causality of processes to avoid losing essential properties and to reveal the content of causal interactions. To address these gaps, this paper introduces a causal Agile process model that formalizes the internal structure and transformation pathways of Agile activity types. To our knowledge, it is the first framework to integrate a recursive, causally grounded structure into Agile management, enabling both semantic clarity and quantitative evaluation of project complexity and progress. The aim of the article is, first, to describe conceptually different Agile activity types from a causal modeling perspective, its internal structure and information transformations, and, second, to formally define the causal Agile management model and its characteristics. Each Agile activity type (e.g., theme, initiative, epic, user story) is modeled using the management transaction (MT) framework—an internal model of activity that comprises a closed-loop causal relationship among management function (F), process (P), state attribute (A), and control (V) informational flows. Using this framework, the internal structure of Agile activity types is normalized and the different roles of activities in internal MT interactions are defined. An important feature of this model is its recursive structure, formed through a hierarchy of MTs. Additionally, the paper presents classifications of vertical and horizontal causal interactions, uncovering theoretically grounded patterns of information exchange among Agile activities. These classifications support the derivation of quantitative indicators for assessing project complexity and progress at a given point in time, offering insights into activity specification completeness at hierarchical levels and overall project content completeness. Examples of complexity indicator calculations applied to real-world enterprise application system (EAS) projects are included. Finally, the paper describes enhancements to the Jira tool, including a causal Agile management repository and a prototype user interface. An experimental case study involving four Nordic EAS projects (using Scrum at the team level and SAFe at the program level) demonstrates that the Jira tool, when supplemented with causal analysis, can reveal missing links between themes and initiatives and align interdependencies between teams in real time. The causal Agile approach reduced the total number of requirements by an average of 13% and the number of change requests by 14%, indicating a significant improvement in project coordination and quality. Full article

With the rapid advancement of remote sensing technologies, the joint classification of hyperspectral image (HSI) and LiDAR data has become a key research focus in the field. To address the impact of inherent uncertainties in hyperspectral images on classification—such as the “same spectrum, different materials” and “same material, different spectra” phenomena, as well as the complexity of spectral features. Furthermore, existing multimodal fusion approaches often fail to fully leverage the complementary advantages of hyperspectral and LiDAR data. We propose a fuzzy-enhanced multi-scale cross-modal fusion network (FE-MCFN) designed to achieve joint classification of hyperspectral and LiDAR data. The FE-MCFN enhances convolutional neural networks through the application of fuzzy theory and effectively integrates global contextual information via a cross-modal attention mechanism. The fuzzy learning module utilizes a Gaussian membership function to assign weights to features, thereby adeptly capturing uncertainties and subtle distinctions within the data. To maximize the complementary advantages of multimodal data, a fuzzy fusion module is designed, which is grounded in fuzzy rules and integrates multimodal features across various scales while taking into account both local features and global information, ultimately enhancing the model’s classification performance. Experimental results obtained from the Houston2013, Trento, and MUUFL datasets demonstrate that the proposed method outperforms current state-of-the-art classification techniques, thereby validating its effectiveness and applicability across diverse scenarios. Full article

River flow forecasting remains a critical yet challenging task in hydrological science, owing to the inherent trade-offs between physics-based models and data-driven methods. While physics-based models offer interpretability and process-based insights, they often struggle with real-world complexity and adaptability. Conversely, purely data-driven models, though powerful in capturing data patterns, lack physical grounding and often underperform in extreme scenarios. To address this gap, we propose PESTGCN, a Physical-Enhanced Spatio-Temporal Graph Convolutional Network that integrates hydrological domain knowledge with the flexibility of graph-based learning. PESTGCN models the watershed system as a Heterogeneous Information Network (HIN), capturing various physical entities (e.g., gauge stations, rainfall stations, reservoirs) and their diverse interactions (e.g., spatial proximity, rainfall influence, and regulation effects) within a unified graph structure. To better capture the latent semantics, meta-path-based encoding is employed to model higher-order relationships. Furthermore, a hybrid attention mechanism incorporating both local temporal features and global spatial dependencies enables comprehensive sequence learning. Importantly, key variables from the HEC-HMS hydrological model are embedded into the framework to improve physical interpretability and generalization. Experimental results on four real-world benchmark watersheds demonstrate that PESTGCN achieves statistically significant improvements over existing state-of-the-art models, with relative reductions in MAE ranging from 5.3% to 13.6% across different forecast horizons. These results validate the effectiveness of combining physical priors with graph-based temporal modeling. Full article