Search Results (7,302)

We address the inverse extremal eigenvalue problem (IEEP) for multi-arrowhead pentadiagonal matrices, a structured class that combines pentadiagonal bandwidth with an alternating arrowhead structure. We identify four distinct structural classes based on the orientation and configuration of arrowhead blocks. For symmetric matrices, we establish sufficient conditions for reconstruction from extremal (smallest, largest, or both) eigenvalues of leading principal submatrices. For certain classes, we prove these conditions are also necessary, providing a complete characterization. Nonsymmetric matrices require one additional prescribed eigenvector. Our results are constructive and yield algorithmic procedures. Numerical examples illustrate our theoretical findings. This work generalizes inverse extremal eigenvalue theory for arrowhead matrices to these pentadiagonal structures. Full article

Water utilities continue to lose a lot of Non-Revenue Water (NRW) because of leaks that go undetected. This makes it necessary to find accurate but easy-to-use monitoring solutions. This paper presents FiT-WST+, a wavelet-guided Frequency-Informed Transformer (FiT) designed for the classification of five distinct leak types utilising accelerometer measurements. The proposed architecture combines the spectral modelling ability of a FIT with the stable translation-invariant representation of the Wavelet Scattering Transform (WST). The model uses a guided attention mechanism to combine spectral and scattering cues that work well together to make classes more distinct, especially for fault types that are similar. On the held-out test set, FiT-WST+ achieves 99.6% accuracy, 99.6% balanced accuracy, and a 99.6% macro-averaged F1-score. Comparative benchmarking against recent methods tested on the same dataset shows that this method works at a low sampling rate (1 kHz), which greatly lowers bandwidth needs and allows for scalable deployment on edge devices with limited resources for real-time monitoring of important water infrastructure. Full article

Candidozyma auris is an emerging multidrug-resistant fungal pathogen associated with severe invasive infections and high mortality, particularly in healthcare environments. Its rapid global expansion and resistance to multiple antifungal classes pose major challenges to treatment and containment. Extracellular vesicles (EVs) have recently been recognized as important mediators of fungal communication, virulence, and stress adaptation. Here, we examine how caspofungin, a frontline echinocandin, reshapes the EV metabolome of C. auris. Caspofungin exposure drives pronounced remodeling of EV size distributions, yielding a predominance of smaller, more uniform EVs alongside a minor population of larger subtypes. Metabolomic profiling of EVs revealed marked enrichment of metabolites involved in nucleotide salvage and recycling, along with altered amino acid abundances, including increases in amino acids associated with stress responses and redox regulation. These changes are consistent with altered nucleotide turnover and amino acid metabolism under antifungal stress. Importantly, these metabolic alterations reflect caspofungin-induced changes in cellular metabolism that are selectively exported via extracellular vesicles, rather than metabolic activity occurring within the vesicles themselves. Export of these metabolites via EVs may support population-level coordination, biofilm remodeling, and modulation of host immune responses, contributing to echinocandin tolerance. Together, our findings highlight nucleotide- and amino acid-associated metabolic features of EVs as informative readouts of caspofungin exposure and highlight the EV metabolome as a promising source of non-invasive biomarkers for monitoring drug exposure and resistance. This work advances understanding of C. auris adaptation under antifungal stress and reveals new opportunities for therapeutic and diagnostic innovation against this high-priority pathogen. Full article

Background/Objectives: Vaccines targeting melanoma antigens can elicit CD8⁺ T cell responses, but a growing body of work suggests CD4⁺ T cells also play a role in tumor control. Induction of CD4⁺ cells may also support B cells in producing tumor antigen-specific antibodies (Abs). We investigated Abs induced by vaccination with a cocktail of six class II MHC-restricted melanoma peptides (6MHP) and the effect of adjuvant type on Ab isotypes. We hypothesized that the vaccines would induce Abs that respond to different epitopes on individual peptides and that IgG isotype distribution varies with different vaccine adjuvants. Methods: Sera from patients who received a 6MHP vaccine were evaluated with enzyme-linked immunosorbent assays to map epitopes for polyclonal Ab responses to synthetic melanoma peptides. IgG isotypes of Ab responses to 6MHP were assessed in patients who received one of four adjuvants (Incomplete Freund’s Adjuvant (IFA) alone, IFA + polyICLC, IFA + systemic metronomic cyclophosphamide (mCy), or IFA + polyICLC + systemic mCy) to characterize IgG isotype distribution. Results: Epitope mapping revealed that at least 50% of patients had responses to two or more epitopes on the same peptide, suggesting polyclonal Ab responses. Serum evaluation for IgG isotypes showed predominant induction of IgG1 and IgG3. Mean total IgG was highest when IFA and polyICLC were used in combination. Patients who received TLR3 agonist polyICLC had significantly higher concentrations of total IgG, IgG1, and IgG3 compared to patients who did not receive polyICLC. Conclusions: Vaccine-induced Abs may respond to multiple epitopes within the same peptide, warranting further studies into their ability to facilitate antigen uptake and presentation through the formation of large immune complexes. The findings also show that adding polyICLC to IFA can significantly enhance Ab responses. Collectively, this work underscores the immunologic potential of peptide-induced Abs and the importance of adjuvant selection in cancer vaccine design. Full article

Wireless sensor networks (WSNs) have become a critical component of modern Internet of Things (IoT) infrastructures; however, their constrained resources and distributed deployment expose them to various cyber threats. In this work, we present a machine learning-driven intrusion detection framework optimized for WSN-based IoT environments. The proposed approach employs the WSN-DS benchmark dataset and integrates adaptive synthetic sampling (ADASYN) to address class imbalance, followed by a hybrid feature selection strategy combining Feature Importance Selection (FIS) and Recursive Feature Elimination (RFE) to reduce dimensionality and improve learning efficiency. An XGBoost classifier is then trained using five-fold cross-validation to ensure robust generalization. The experimental results demonstrate that the proposed framework significantly outperforms baseline methods, achieving an overall accuracy of 99.87%, with substantial gains in terms of F1-score, precision, and recall. Comparative analysis against recent WSN-DS studies confirms the effectiveness of combining imbalance correction, optimized feature selection, and ensemble learning. These findings highlight the potential of the proposed model as a lightweight and highly accurate intrusion detection solution for emerging WSN-IoT deployments. Full article

Class imbalance in tabular datasets poses a challenge for machine learning classification tasks, often leading to biased models that underperform in predicting minority class instances. This study presents a comparative analysis of synthetic data generation methods for addressing class imbalance in tabular data. We evaluate four augmentation approaches—Synthetic Minority Over-sampling Technique (SMOTE), Gaussian Copula, Tabular Variational Autoencoder (TVAE), and Conditional Tabular Generative Adversarial Network (CTGAN)—using the University of California Irvine (UCI) Bank Marketing dataset, which exhibits a class imbalance ratio of approximately 7.88:1. Our experimental framework assesses each method across three dimensions: statistical fidelity to the original data distribution evaluated through four complementary metrics (marginal numerical similarity, categorical distribution similarity, correlation structure preservation, and Kolmogorov–Smirnov test), machine learning utility measured through classification performance, and minority class detection capability. Results indicate that all augmentation methods achieved statistically significant improvements over the baseline ($p<0.05$). SMOTE achieved the highest recall (54.2%, a 117.6% relative improvement over the baseline) and F1-Score (0.437, +22.4% over the baseline) for minority class detection, while Gaussian Copula provided the highest composite fidelity score (0.930) with competitive predictive performance. A weak negative correlation ($\rho =-0.30$) between composite fidelity and classification performance was observed, suggesting that higher statistical fidelity does not necessarily translate to better downstream task performance. Deep learning-based methods (TVAE, CTGAN) showed statistically significant improvements over the baseline (recall: +58% to +63%) but underperformed compared to simpler methods under default configurations, suggesting the need for larger training samples or more extensive hyperparameter tuning. These findings offer reference points for practitioners working with moderately imbalanced tabular data with limited minority class samples, supporting the selection of generation strategies based on specific requirements regarding data fidelity and classification objectives. Full article

Current generative AI systems primarily utilize a prompt–response interaction model that restricts user intervention during the creative process. This lack of granular control creates a significant disconnect between user intent and machine output, which we define as the “Agency Gap”. This paper introduces the Agency-First Framework (AFF), which combines cognitive engineering and co-active design approaches to formally define human-AI collaboration. This is operationalized through the development of ten Generative AI Agency (GAIA) Heuristics, a systematic method for evaluating agency-centric interactions within stochastic generative settings. By translating the theoretical layers of the AFF into measurable criteria, the GAIA heuristics provide the necessary instrument for the empirical auditing of existing systems and the guidance of agency-centric redesigns. Unlike existing assistive AI guidelines that focus on output-level usability, the AFF establishes agency as a first-class design construct, enabling mid-process intervention and the steering of the model’s latent reasoning trajectory. Validation of the AFF was conducted through a two-tiered empirical evaluation: (1) an expert heuristic audit of state-of-the-art platforms, such as ChatGPT-o1 and Midjourney v6, which achieved high inter-rater reliability, and (2) a controlled redesign study. The latter demonstrated that agency-centric interfaces significantly enhance the Sense of Agency and Intent Alignment Accuracy compared to baseline prompt-response models, even when introducing a deliberate increase in task completion time—a phenomenon we describe as “productive friction” or an intentional interaction slowdown designed to prioritize cognitive engagement and user control over raw speed. Overall, the findings suggest that the restoration of meaningful user agency requires a shift from “seamless” system efficiency towards “productive friction”, where controllability and transparency within the generative process are prioritized. The major contribution of this work is the provision of a scalable, empirically validated framework and set of heuristics that equip designers to move beyond prompt-centric interaction, establishing a methodological foundation for agency-preserving generative AI systems. Full article

Open quantum walks (OQWs) constitute a class of quantum walks whose dynamics are entirely driven by interactions with the environment. It is well known that OQWs provide a general framework for implementing dissipative quantum computation. In this work, we demonstrate the feasibility of running the previously proposed quantum distance-based classifier within the open quantum walk computation model, and we show that its expected runtime remains finite even in the slower regime. Full article

Aldolases are powerful biocatalysts for the stereoselective formation of carbon–carbon bonds and are widely used in the synthesis of chiral intermediates for pharmaceutical applications. Among them, 2-deoxyribose-5-phosphate aldolase (DERA) has been extensively exploited for the preparation of the conserved side chain of statins. In this work, we report a novel chemoenzymatic approach for the synthesis of nucleobase-substituted lactol products as potential precursors of new statin analogues. A C49M variant of DERA from Pectobacterium atrosepticum (PaDERA C49M) was employed to catalyze sequential aldol additions using aldehyde-functionalized nucleobases as non-natural electrophilic substrates. The formation of nucleobase-containing lactols was confirmed, demonstrating for the first time the acceptance of nucleobase-derived aldehydes in DERA-catalyzed aldol reactions. This strategy provides access to structurally novel statin side-chain precursors and expands the synthetic potential of DERA toward the generation of new classes of bioactive compounds. Full article

As a widely used catalyst class, transition metal oxides (TMOs) face the challenges of detrimental nanoparticle agglomeration. The newly developing two-dimensional (2D) covalent triazine frameworks (CTFs) offer a promising solution as catalyst supports, capable of yielding composites with excellent dispersibility and synergistic catalytic enhancement. Building on this, and employing a hydroxylation functional modification strategy, this article introduces a binary oxide system to construct a CTF/CuO–NiO composite that exhibits excellent catalytic performance for the thermal decomposition of ammonium perchlorate (AP). Specifically, polyvinyl alcohol (PVA) was first employed to introduce -OH anchoring sites onto the CTF surface. A subsequent co-precipitation yielded a uniform dispersion of CuO–NiO nanoparticles across the functionalized CTF support. DSC analysis revealed that incorporating merely 2 wt% of the CTF/CuO–NiO composite into AP significantly alters its high-temperature decomposition (HTD) peak temperature, shifting it from 404.6 °C to 332.1 °C. This work highlights the construction of a binary oxide system through an effective dispersion strategy to enhance the synergistic catalytic performance of CTF-based composites. Full article

Identity-based communities that share common characteristics, beliefs, and experiences (e.g., Black LGBTQ+ communities) have historically been conceptualized as protective bubbles that buffer Black LGBTQ+ individuals against the deleterious effects of systemic racism and cisheterosexism. However, this monolithic narrative often masks the internal power dynamics that divide belonging. This study explores the exclusionary dynamics embedded within these safe spaces, examining how internal hierarchies of skin tone, socioeconomic status, and gender performance function as proximal stressors. Guided by a critical constructivist paradigm, this study utilized Reflexive Thematic Analysis to analyze open-ended survey responses from 74 Black LGBTQ+ adults. Data were drawn from a larger mixed-methods study and analyzed using a six-phase recursive process to identify latent patterns of intragroup gatekeeping. The analysis revealed that the sanctuary of the community is restricted. Three primary themes emerged: (1) Phenotypic Capital and the Politics of Authenticity, where lighter skin tone triggered authenticity scrutiny and darker skin tone faced rejection based on physical appearance; (2) Socioeconomic Gatekeeping, where belonging was stratified by the cost of participation and protective insularity within working-class spaces; and (3) Policing the Binary, where rigid adherence to gender archetypes created a landscape of performance surveillance. Access to community resilience is not a universal right but a negotiated status contingent upon the payment of a resilience tax. To promote genuine health equity, researchers and practitioners working with this population must move beyond the uncritical referral to “community” and actively dismantle the internalized systems of oppression that fracture collective survival. Full article

Study of early Chinese ceramics is crucial for understanding cultural, economic, and technological developments in Chinese history. With the evolving deep learning techniques, one urgent question would be, whether we can identify early Chinese ceramics by a simple 2D image without further domain knowledge. This work collected a highly diverse dataset for ancient Chinese ceramics from 15 dynasties, with 4 representative glaze colors and 15 shape types. We studied the performance of five state-of-the-art neural networks on two identification tasks: ceramic visual feature recognition and early Chinese ceramic dating. A class-imbalance learning strategy is designed to improve the models’ performance on multi-label tasks. To the best of our knowledge, our work is the first to introduce deep learning into early Chinese ceramic recognition on a large scale. Experiments prove that deep learning can recognize visual features like glaze and most shape types with high accuracy, while ceramic dating is feasible for the main dynasties but remains challenging along the overall history. Further quantitative assessment shows that cultural inheritance and artistic continuity can lead to reasonable false dating by classifying ceramics into adjacent dynasties or periods. Moreover, although domain knowledge is required for interpretation, deep learning shows great potential in recognizing even unlabeled time-relevant features, which can help study the inheritance and evolution of early Chinese ceramic development. Full article

Digitally configurable DC/DC converters provide built-in telemetry signals that offer new opportunities for operational data-driven monitoring in embedded energy systems. However, exploiting these signals for intelligent condition monitoring remains challenging due to limited computational resources and the need to preserve the safety and determinism of power supply control. This work investigates the combination of digitally configurable DC/DC converters and embedded artificial intelligence for resource-efficient load and condition monitoring based exclusively on converter-side power telemetry. A lightweight, feature-based current analysis pipeline is proposed, incorporating domain-informed temporal and electric features. Three representative machine learning model classes, Random Forest, Support Vector Machine, and a Neural Network, are evaluated. The approach is implemented on an ESP32-class microcontroller operating as a dedicated monitoring unit, fully separated from the safety-critical power supply control. Experimental validation on a laboratory demonstrator shows that classification accuracies of up to 99% can be achieved for four system states using only five features at a 100 Hz telemetry sampling rate, while remaining within typical embedded memory constraints. The results demonstrate that converter-internal telemetry enables effective and scalable condition monitoring without additional sensors, supporting the combination of embedded intelligence and digitally configurable power supplies for industrial applications. Full article

Truly impactful innovations are developed by outsiders out of a sense of need; those that rise to mainstream recognition and acceptance, however, are colonized by dominant hegemonies. This paper traces cycles of innovation and colonization in literature, publishing, and computing as ancestral domains to electronic literature, which has been subject to the same gendered and othered frontier-colonization cycles that dominated its forebears. Elit was a new frontier for writing and publishing, a strong site of marginalized creativity, until it was codified and colonized into publishing and academia by the dominant class: women could create, but men had the actual and cultural capital to create and develop the structures to platform their work into the dominant discourse. This paper analyzes how feminist and marginalized digital writers resist colonization of their innovations and erasure of their innovations by hacking platforms, subverting narrative conventions, and amplifying hidden voices. The paper examines elements of innovation-colonization cycles in elit and adjacent practices (indie games, fanfic), showcases Lillian-Yvonne Bertram’s algorithmically-generated epoetry as a site of subversion, and presents fanfic community Archive of Our own as a preliminary model of value-sensitive and inclusive community design. It argues for the development of feminist-first platforms—digital spaces that actively resist the structural colonization of marginalized storytelling. Full article

Short-form video platforms such as TikTok (TikTok Pte. Ltd., Singapore) host large volumes of user-generated, often ephemeral, content related to irregular migration, where relevant cues are distributed across visual scenes, on-screen text, and multilingual captions. Automatically identifying migration-related videos is challenging due to this multimodal complexity and the scarcity of labeled data in sensitive domains. This paper presents an interpretable multimodal classification framework designed for deployment under data-scarce conditions. We extract features from platform metadata, automated video analysis (Google Cloud Video Intelligence), and Optical Character Recognition (OCR) text, and compare text-only, OCR-only, and vision-only baselines against a multimodal fusion approach using Logistic Regression, Random Forest, and XGBoost. In this pilot study, multimodal fusion consistently improves class separation over single-modality models, achieving an F1-score of 0.92 for the migration-related class under stratified cross-validation. Given the limited sample size, these results are interpreted as evidence of feature separability rather than definitive generalization. Feature importance and SHAP analyses identify OCR-derived keywords, maritime cues, and regional indicators as the most influential predictors. To assess robustness under data scarcity, we apply SMOTE to synthetically expand the training set to 500 samples and evaluate performance on a small held-out set of real videos, observing stable results that further support feature-level robustness. Finally, we demonstrate scalability by constructing a weakly labeled corpus of 600 videos using the identified multimodal cues, highlighting the suitability of the proposed feature set for weakly supervised monitoring at scale. Overall, this work serves as a methodological blueprint for building interpretable multimodal monitoring pipelines in sensitive, low-resource settings. Full article