ABSTRACT The Fourier-Legendre (FL) model employs Legendre polynomials to characterize radar wave propagation within vegetation. Compared to empirical functions used in conventional models, it has greater flexibility and adaptability, enabling a more accurate representation of complex scattering mechanisms. When applied to Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) forest height inversion, the FL model can significantly improve retrieval accuracy. In theory, higher polynomial orders provide more precise descriptions of radar scattering, however, increasing polynomial order introduces excessive unknown parameters, leading to ill-posed problems and reduced estimation stability. Given this, this paper proposes a multi-baseline fusion PolInSAR forest height inversion method based on the FL model and linear least squares estimation theory. Additionally, the regularization method is incorporated to mitigate ill-posed influence and enhance parameter stability. By constructing a regularization matrixforest height inversion experiments were then conducted using multi-baseline PolInSAR data. Finally, the results show that the conventional single-baseline inversion method achieves an optimal forest height estimate with an root mean square error (RMSE) of 6.73 m and an coefficient of determination (R 2) of 0.84, while the new method yielded an RMSE of 5.53 m and an R 2 of 0.89, representing an accuracy improvement of 18%. Therefore, the new method can effectively enhance the accuracy of forest height parameter inversion and is a practical and effective PolInSAR forest height inversion approach.

We present T-Rex2++, a unified and highly practical framework for generic open-set object perception, encompassing both object detection and instance segmentation. Previous methods relying on text prompts effectively encapsulate the abstract concept of common objects, but struggle with rare or complex object representation due to data scarcity and descriptive limitations. Conversely, visual prompts excel in depicting novel objects through concrete visual examples, but fall short in conveying the abstract concept of objects as effectively as text prompts. Recognizing these complementary strengths, we introduce a text-visual synergy mechanism that aligns both modalities within a single feature space via contrastive learning. Crucially, T-Rex2++ advances beyond the passive perception paradigm of its predecessor by introducing a novel Universal Prompt. This learnable component models generic objectness, empowering the system to autonomously discover and localize arbitrary objects without any user-provided cues, thereby closing the loop between human-guided interaction and fully automatic perception. Furthermore, we extend the synergy verification to the pixel level by integrating a zero-shot instance segmentation module, demonstrating that our contrastive alignment generalizes robustly to fine-grained masks. Comprehensive experiments demonstrate that T-Rex2++ exhibits strong zero-shot object perception capabilities across a wide spectrum of scenarios, validating T-Rex2++ as a versatile foundation for generic object perception.

This work extends previous investigations into the relationship between the Einsteinian ‎Hamiltonian formulation and the Musakhail aether-based Lagrangian description of ‎dynamics. While earlier studies established their simultaneous role in the Newtonian-‎Einsteinian framework, the present paper focuses specifically on a formal Lagrangian ‎dynamical analysis in order to derive the corresponding equation of motion. Within the ‎proposed framework, the resulting dynamics suggest a correspondence in which the ‎classical relation F=ma transitions naturally toward the relativistic energy expression E=‎mc^2, interpreted here through the restoration of Newtonian behavior during the so-called ‎Reverse Higgs process. In this regime, the effective mass remains constant (m=m_e ) ‎rather than velocity-dependent, permitting a force-based description of particle-wave ‎interaction. The analysis further introduces a rotating Einstein energy vector derived from ‎the invariant relation E^2=(pc)^2+(m_0 c^2 )^2, which is employed to describe the ‎cyclic interaction between fermionic constituents and electromagnetic wave structure. This ‎approach yields a dual interpretative framework in which either photon energy extraction or ‎spin measurement may occur, depending on the observational configuration. The ‎formalism also explores a complex representation in which the orthogonal axis is treated as ‎imaginary, producing a geometrical interpretation associated with oscillatory spin states of ‎fermions (±1/2) and photons (0,±1). The resulting model suggests an underlying ‎symmetry between fermionic and bosonic spin states within the proposed aether-‎dynamical environment, providing a phenomenological bridge between classical force ‎dynamics and relativistic energy relations.‎

Recent years have seen an increasing amount of attention devoted to the subject of structural representation. Is there one type of structural representation or many? How do they differ from other types of representation? Are they really a genuine type of representation in the first place? All good questions, which I will address indirectly by arguing that structural representations are nothing more than analog representations. Understanding them as such provides some much needed theoretical clarity about this type of representation. Typical analog representations (e.g., liquid thermometers or analog clocks) are often "one-dimensional;" the corresponding one-dimensional characterization of these representations can be extended into multiple dimensions, which elucidates the structure of more complex analog representations, such as photographs, maps, or three-dimensional models. However, this analysis applies to structural representations without remainder. The upshot is that we can directly apply what we have learned about analog representation to our understanding of structural representation, which, if not directly answering these recent questions, greatly adds to our theoretical resources for doing so. The analog wheel has already been invented; we need not reinvent it for structural representation.

Auditory areas on the superior temporal plane and lateral convexity are key initial stages of speech processing in the human cortex, representing acoustic and phonetic attributes in a temporally precise manner. More complex representations in auditory-related cortex along the ventral and dorsal processing streams and prefrontal cortex are associated with perception and action. In this study, we used intracranial electroencephalography (iEEG) to clarify where and how activity leading to perceptually driven behavioral events emerges. Participants were patients undergoing iEEG monitoring for medically intractable epilepsy. Stimuli were monosyllabic words, and participants pressed a button in response to a semantic target category. Significant high gamma activity after stimulus onset and immediately prior to motor response defined stimulus- and behavior-related activity patterns, respectively. The stimulus-related pattern was more common than behavior-related throughout the cortical auditory hierarchy as well as sensorimotor cortex. Behavior-related activity was sparsely represented, with the highest prevalence in the prefrontal cortex and a more limited representation in anterior temporal and parieto-occipital cortex. Hemispheric asymmetries included a higher prevalence of stimulus-related activity in the right sensorimotor cortex and a higher prevalence of the behavior-related pattern in the left prefrontal cortex. Faster behavioral responses were associated with greater stimulus-locked high gamma power in non-core auditory, prefrontal, and premotor cortex. Results reveal the cortical distribution of sensory stimulus-driven responses and activity time-locked to behavior and provide insights into neural substrates of speech perception.

Urban housing conflicts are increasingly shaping the social and spatial dynamics of cities, yet they remain difficult to analyze systematically due to their multi-actor complexity and fragmented representation across textual and spatial data. Existing studies tend to focus on either narrative or spatial aspects, rarely capturing the structural, temporal, and geographic dimensions of these conflicts in an integrated way. This paper addresses this gap by proposing a reproducible methodological framework that combines Large Language Models (LLMs), graph databases, and Geographic Information Systems (GIS) to analyze housing conflicts in Montréal between 2001 and 2024. The study aims to demonstrate how urban housing conflicts can be systematically extracted, classified, and analyzed across time and space using AI-based methods, and how their structural patterns reflect underlying socio-political dynamics. The resulting framework offers new insights into the evolution of conflicts linked to gentrification, economic vulnerability, and shifting governance, while contributing a replicable, scalable methodology for studying complex urban phenomena at the intersection of AI, spatial analysis, and social science.

The present paper aims to honor the memory of one of the most notable figures in psychological science over the past five decades, Philip G. Zimbardo, who sadly passed away in late 2024. To this end, we provide a multi-perspective view on psychological time-a topic that deeply engaged Phil Zimbardo during the later stages of his prolific career. From the basic mechanisms of mental time travel to the experience of the passage of time, the phenomena of temporal construal, intertemporal choices, and complex representations of future selves, as well as the concepts of balanced time perspectives and temporal metacognition, the authors of this article construct this symbolic memoir by linking their own ideas and research with Zimbardo's time perspective theory. In the concluding part of the paper, we propose that temporality-related processes and traits constitute a fundamental part of personality and seek to highlight the pathways through which considering psychological-temporal phenomena may advance personality science and even serve as a unifying theme for various approaches to personality.

This study investigated science teachers’ selection of visual displays for climate change instruction and compared the selection to those of climate scientists who provide public outreach education. Twenty-five secondary science teachers in the US were presented with 25 climate change visual displays and asked to choose five that they would incorporate into a climate change presentation. Teachers were interviewed to document their perspectives about the images and their selection rationale. Interview responses were analyzed using four dimensions from Construal-Level Theory: temporal (e.g., present/now versus future or past), social (e.g., me/us versus them), spatial (e.g., here versus there), and hypothetical (e.g., certain versus uncertain). Findings showed that teachers chose visual materials based on their existing lesson goals. Most teachers (96%) also considered how complex the images were for students to understand. Additionally, about 44% of teachers in this study thought about how the timing or sequence of events shown in the visuals related to students’ understanding of the topic. When the selections were compared to those of 11 scientists from a previous study, both teachers and scientists chose visual displays with future projections and representations of temperature changes. Neither scientists nor teachers tended to use representations that showed trends in data under different conditions or more complex representations. The findings of the study offer insight into the decision-making process employed by educators when selecting climate change visual displays. Implications include the need for professional development that helps teachers interpret and teach with more complex visual representations.

Accurate prediction of protein-ligand binding affinity is essential in drug discovery. However, the limited availability and high cost of experimentally resolved protein-ligand complex structures significantly hinder the generalizability and broad applicability of current structure-based deep learning approaches. To address this challenge, we present CompBind, a novel framework for binding affinity prediction that leverages latent interaction patterns learned from existing complex structures while eliminating the need for 3D structural inputs during inference. Specifically, CompBind integrates bidirectional cross-attention with a dual-objective pretraining strategy, where contrastive learning enforces feature-space consistency between monomer pairs and their corresponding complex structures, while generative learning reconstructs interaction features to model the bidirectional mapping between monomeric and complex representations. This enables the model to infer binding representations directly from protein and ligand sequences alone. Across challenging affinity prediction scenarios, including cold-start and sparse-label conditions, CompBind not only outperforms noncomplex-based methods but also competitively rivals complex-based prediction approaches. In a drug repurposing case study targeting glutathione peroxidase 4 (GPX4), a clinically relevant but traditionally undruggable protein, CompBind successfully ranked known inhibitors among the top candidates. Furthermore, the built-in attention mechanism enhances model interpretability by identifying key binding residues. By decoupling predictive accuracy from the availability of experimental complex structures, CompBind offers a scalable, generalizable, and practical solution for accelerating drug discovery pipelines.

The rapid development of unmanned aerial vehicle (UAV) technology necessitates reliable recognition methods. Radio frequency (RF)-based recognition is promising, but conventional real-valued CNNs (RV-CNNs) typically discard phase information from RF spectrograms, leading to degraded performance under low-signal-to-noise ratio (SNR) conditions. To address this, this paper proposes a complex-valued CNN (CV-CNN) that operates on a constructed complex representation, where the real part is the logarithmic power spectral density (PSD) and the imaginary part is derived from Sobel edge detection. This enables genuine complex convolutions that fuse magnitude and structural cues, enhancing noise resilience. As complex-valued networks are known to be sensitive to architectural choices, we conduct comprehensive ablation experiments to investigate the impact of key hyperparameters on model performance, revealing critical stability constraints (e.g., performance collapse beyond 4–5 network depth). Evaluated on the 25-class DroneRFa dataset, the proposed model achieves 100.00% accuracy under noise-free conditions. Crucially, it demonstrates significantly superior robustness in low-SNR regimes: at −20 dB SNR, it attains 15.58% accuracy, over seven times higher than a dual-channel RV-CNN (2.20%) with identical inputs; at −15 dB, it reaches 45.86% versus 14.03%. These results demonstrate that the CV-CNN exhibits potentially superior robustness and interference resistance in comparison to its real-valued counterpart, maintaining high recognition accuracy even under low-SNR conditions.

Landslide dams, typically composed of newly deposited, loose, and heterogeneous materials, are highly susceptible to failure induced by overtopping and seepage, particularly under extreme hydrological conditions. Accurate prediction of such breaching processes is essential for flood risk management and emergency response, yet existing models generally consider only a single failure mechanism. This study develops a mathematical model to simulate landslide dam breaching under the coupled action of overtopping and seepage erosion. The model integrates surface erosion and internal erosion processes within a unified framework and employs a stable time-stepping numerical scheme. Application to three real-world landslide dam cases demonstrates that the model successfully reproduces key breaching characteristics across overtopping-only, seepage-only, and coupled erosion scenarios. The simulated breach hydrographs, reservoir water levels, and breach geometries show good agreement with field observations, with peak outflow and breach timing predicted with errors generally within approximately 5%. Sensitivity analysis further indicates that the model is robust to geometric uncertainties, as variations in breach outcomes remain smaller than the imposed parameter perturbations. These results confirm that explicitly accounting for the coupled interaction between overtopping and seepage significantly improves the representation of complex breaching processes. The proposed model therefore provides a reliable computational tool for analyzing landslide dam failures and supports more accurate hazard assessment under multi-mechanism erosion conditions.

Motion estimation of left ventricle myocardium on the cardiac image sequence is crucial for assessing cardiac function. However, the intensity variation of cardiac image sequences brings the challenge of uncertain interference to myocardial motion estimation. Such imaging-related uncertain interference appears in different cardiac imaging modalities. We propose adaptive sequential Bayesian iterative learning to overcome the challenge. Specifically, our method applies the adaptive structural inference to state transition and observation to cope with a complex myocardial motion under uncertain setting. In state transition, adaptive structural inference establishes a hierarchical structure recurrence to obtain the complex latent representation of cardiac image sequences. In state observation, the adaptive structural inference forms a chain structure mapping to correlate the latent representation of the cardiac image sequence with that of the motion. Extensive experiments on US, CMR, and TMR datasets concerning 1270 patients (650 patients for CMR, 500 patients for US and 120 patients for TMR) have shown the effectiveness of our method, as well as the superiority to eight state-of-the-art motion estimation methods.

Artificial intelligence (AI) is reshaping drug discovery by enabling efficient and precise identification of novel therapeutics. This review examines the synergistic use of deep generative models, such as Long Short-Term Memory (LSTM) networks, Variational Autoencoders (VAEs), and Graph Attention Networks (GATs), together with evolutionary optimization techniques, including genetic algorithms and multi-objective evolutionary strategies. While deep learning architectures excel at capturing complex molecular representations and generating chemically valid compounds, evolutionary algorithms provide complementary strengths in global exploration and multi-objective trade-off optimization. The combination of these two paradigms offers a powerful and complementary toolkit: deep learning provides the capacity to learn rich chemical features and propose innovative scaffolds, whereas evolutionary methods ensure efficient navigation of chemical space and balanced optimization across multiple drug-like criteria. Through comparative analyses, quantitative benchmarks, and illustrative figures, we highlight how integrating generative and evolutionary paradigms can accelerate de novo molecular design, reduce development timelines, and lower costs. We also address technical and ethical challenges. In particular, our ongoing research explores hybrid frameworks that combine variational autoencoders, graph neural predictors, Colibri algorithm and Genetic algorithms with fragment-based crossover, and dynamic multi-objective penalties to further enhance chemical validity, pharmacological relevance, and synthetic accessibility. Future efforts aim to demonstrate that such hybrid frameworks can bridge the gap between theoretical innovation and practical drug development, bringing AI-driven discovery closer to real-world therapeutic breakthroughs

The Racah–Speiser algorithm and its generalizations lead to an identity involving sums of Kac–Weyl characters, and here we recover this identity through an explicit quantization of Chern–Simons theory. One can use this identity to prove inequalities that constrain the fusion coefficients Nμνl in the case of rational conformal field theories that descend from current algebras. It also leads to a statement regarding the conjugacy symmetry of the sums of squares of fusion coefficients for current algebras admitting complex representations.

Quantum Convolutional Neural Networks (QCNNs) have became a promising field for quantum advantage in feature extraction and classification of different datasets like Handwritten datasets, Fashion datasets, particularly useful for complex datasets like medical images. Medical image classification has emerged as a critical component in modern healthcare diagnostics, particularly for tumor detection and cancer diagnosis. Normally, Classical CNN is used for image classification but they may face certain challenges in achieving further improvements in accuracy, computational efficiency, processing time. Also, it becomes difficult to train for high-dimensional medical datasets and extracting complex feature representations. Therefore, In this proposal, Image classification through a Scalable and Resource efficient QCNN is proposed which integrates classical CNN with quantum computing. This review examines the current state of medical image classification for tumor detection, analyzes the transition from classical CNNs to quantum-based architectures, and explores the potential of QCNNs in revolutionizing medical diagnostics. This research includes QCNN architecture which will include selective feature encoding using encoding technique such as Z Feature map and quantum circuits for convolutional layers and pooling layers to handle multi-modal medical data (e.g., MRI and CT scans). We aim to achieve superior accuracy with reduced parameters over classical CNNs. This work advances quantum ML toward practical deployment in resource-limited healthcare settings.

Abstract The Times of Ceylon Christmas Number (TCCN) publications, released each year from 1909 to 1979, serve as essential artifacts for piecing together Sri Lanka’s colonial history. By employing methods from archaeology, media studies, and historical analysis, this interdisciplinary research investigates these publications as complex representations of British colonialism. The study sheds light on the TCCN's role in mediating colonial ideologies, hybridizing cultural identities, and documenting socio-economic transformations through an analysis of textual content, visual imagery, advertisements, and material features (such as typography and paper quality). The insights into print culture, consumerism, gender roles, and cross-cultural exchanges revealed by the periodicals position them as essential resources for historians, archaeologists, and anthropologists. This research promotes the incorporation of ephemeral print media into postcolonial studies, highlighting their importance in safeguarding intangible heritage and contesting Eurocentric historical narratives.

Relevance. Effective stress management in today’s BANI world (fragile, anxious, non-linear, incomprehensible) requires coping intelligence (CI), enabling flexible adaptation to rapid changes. CI is the person’s ability to resolve stress productively, maintain health, and develop across life domains. The objective of this study is to analyze CI manifestations among employees performing routine tasks. Methods. CI was assessed at psychological and socio-cultural levels. Psychological assessment included mental representation analysis of stress and the SACS questionnaire. Socio-cultural evaluation utilized the IDICS and the Maslach Burnout Inventory. The sample comprised is 331 employees (192 m./139 f.; mean age 34.9 ± 13.14) from two professional groups: IT call center operators and plant workers. Data were collected via these mixed methods and analyzed statistically. Results. The majority of employees experience stress, with 25% at high risk for stress-related illnesses. Psychological level of CI is that while respondents identify causes of stress and symptoms, they rarely acknowledge rest, health, or the positive developmental role of stress. Constructive coping strategies such as assertive actions and social support are common. Socio-cultural level of CI highlighted differences between groups: call center operators exhibit more complex stress representations and higher job stress vulnerability, whereas plant workers report lower professional self-esteem and high task-related stress, overlooking stress’s developmental role. Conclusion. Representations of stress are the psychological correlates of individual differences of stress management in the BANI world. This study proposes three levels of individual stress management, such as simplified, differentiated, and systematic.

Animal’s People by Indra Sinha is a powerful novel that vividly portrays the enduring impacts of a Bhopal-inspired industrial disaster on both human and nonhuman lives. Through its depiction of a toxic environment and its marginalized inhabitants, the novel challenges traditional anthropocentric and humanist assumptions by foregrounding multispecies vulnerability, ecological interconnectedness, and the blurred boundaries between humans, animals, and environments. The narrative highlights how environmental degradation, speciesism, and ableism intersect within systems of structural injustice, offering a rich ground for posthumanist critique. The protagonist’s disfigured, four-legged body and defiant voice unsettle normative conceptions of identity, dignity, and justice, revealing the ethical urgency of recognizing shared embodied precarity across species lines. This study provides a critical reading of Animal’s People through the interdisciplinary frameworks of environmental justice, animal studies, and posthumanism. It argues that the novel not only documents the legacies of ecological and social harm but also calls for a reimagined multispecies ethics grounded in relationality, care, and resistance. By analysing the novel’s complex representation of toxicity and embodiment, the study emphasizes the necessity of an inclusive justice that transcends species boundaries in an increasingly damaged world.

In this article, I examine the way in which Zionist ideology used the audiovisual medium to convey ideas and shape a narrative that fused the ideal of the agricultural pioneer with the developing urban reality. The study focuses on analyzing urban and rural songs used in Zionist propaganda films and describes how the songs and music in these films served as tools for expressing and shaping the ideological tension between the two forms of settlement. The national institutions operated on many fronts in order to raise funds for the Zionist enterprise and to attract immigrants to settle in Israel. One such tool was cinema, still in its infancy yet no longer really an infant. The films were screened in Palestine and abroad and depicted the building of a country and making the desert bloom. Indeed, they enjoyed great success. The moving images were accompanied by a musical soundtrack composed especially for the films, and in many cases the songs chosen were aligned with the film’s message. Poets of the period, such as Natan Alterman, Emanuel Harussi, and Alexander Penn, contributed lyrics, while composers like Mordechai Zeira, Daniel Sambursky, and Emanuel Amiran created the melodies. I show that despite the idealization of rural-agricultural settlement in Zionist discourse, the films and their songs created a more complex representation which acknowledged the complementary contribution of both forms of settlement to building the country. I further show how the unique textual and musical features of each song type reflected the ideological perceptions of the Zionist movement, and how cinema—by combining image and music—served as an effective tool for spreading these messages in Israel and abroad.

The study of Afro-Brazilian and Indigenous cultures, one of the most important contemporary educational demands in Brazil, has been driven by black and indigenous movements and ratified through Acts 10.639/2003 and 11.645/2008. Children's literature opens up possibilities for aesthetically articulating Early Childhood Education and Ethnic-Racial Relations Education. This article aims to discuss how children's literature can be a space of resistance and creation of possible pathways for anti-racist education, in light of the concept of African-Affective literature as proposed by writer Sônia Rosa. The qualitative methodology involves screening works with black protagonists, approved by the Programa Nacional do Livro e do Material Didático - PNLD 2022 - Educação Infantil, and selecting seven literary works for in-depth analysis. The results have evidenced that a growing number of literary works with black characters have been approved for inclusion in the collection of “PNLD Literário”, but the range of narratives featuring black protagonists remains rather limited. The reading and discussion of some selected works have highlighted the potential of African-affective literature to broaden children's repertoires, as it promotes positive, plural, and complex representations of black characters in a variety of plots that both diversify and value human experiences.Finally, it is possible to observe the importance of expanding literary collections that not only include black characters as a representation of ethnic-racial diversity, but that, above all, value black experiences, by inserting the characters into networks of affection and support.