Search Results (17,522)

In the face of escalating sociopolitical hostility toward diversity, equity, and inclusion (DEI) efforts, trans and queer (TQ) center(ed) diversity workers in higher education are navigating increasingly precarious professional landscapes. This study explores the lived experiences of TQ-center(ed) diversity workers through a general qualitative design informed by participatory action research (PAR). Drawing on the concept of “burn through,” critiquing the role of institutions in the exhaustion of practitioners, and the theory of tempered radicalism, describing the fine line diversity workers must navigate to advocate for change within oppressive systems, we examine how these practitioners persist amid institutional neglect, emotional labor, and political antagonism. Findings from interviews with eight participants reveal three central themes: the systemic nature of burn through, the protective power of community, and the multifaceted role of liberation in TQ-center(ed) diversity work. Participants described both the toll and the transformative potential of their roles, highlighting community as a critical site of resistance and renewal. This study contributes to the growing literature on TQ advocacy in higher education and underscores the need for institutional accountability and collective care in sustaining liberatory futures. Full article

Understanding spatial relationships between objects in images is crucial for robotic navigation, augmented reality systems, and autonomous driving applications, among others. However, existing vision-language benchmarks often overlook explicit spatial reasoning, limiting progress in this area. We attribute this limitation in part to existing open datasets and evaluation metrics, which tend to overlook spatial details. To address this gap, we make three contributions: First, we greatly extend the COCO dataset with annotations of spatial relations, providing a resource for spatially aware image captioning and visual question answering. Second, we propose a new evaluation framework encompassing metrics that assess image captions’ spatial accuracy at both the sentence and dataset levels. And third, we conduct a benchmark study of various vision encoder–text decoder transformer architectures for image captioning using the introduced dataset and metrics. Results reveal that current models capture spatial information only partially, underscoring the challenges of spatially grounded caption generation. Full article

Background: Large Language Models (LLMs) have driven significant advances in artificial intelligence (AI), with transformative applications across numerous scientific fields, including biomedical research and drug development. However, despite growing interest in adjacent domains, their adoption in pharmacometrics, a discipline central to model-informed drug development (MIDD), remains limited. This study aims to systematically explore the potential role of LLMs across the pharmacometrics workflow, from data processing to model development and reporting. Methods: We conducted a comprehensive literature review to identify documented applications of LLMs in pharmacometrics. We also analyzed relevant use cases from related scientific domains and structured these insights into a conceptual framework outlining potential pharmacometrics tasks that could benefit from LLMs. Results: Our analysis revealed that studies reporting LLMs in pharmacometrics are few and mainly limited to code generation in general-purpose programming languages. Nonetheless, broader applications are theoretically plausible and technically feasible, including information retrieval and synthesis, data collection and formatting, model coding, PK/PD model development, support to PBPK and QSP modeling, report writing and pharmacometrics education. We also discussed visionary applications such as LLM-enabled predictive modeling and digital twins. However, challenges such as hallucinations, lack of reproducibility, and the underrepresentation of pharmacometrics data in training corpora limit the actual applicability. Conclusions: LLMs are unlikely to replace mechanistic pharmacometrics models but hold great potential as assistive tools. Realizing this potential will require domain-specific fine-tuning, retrieval-augmented strategies, and rigorous validation. A hybrid future, integrating human expertise, traditional modeling, and AI, could define the next frontier for innovation in MIDD. Full article

This study investigates TNM-type titanium aluminide alloys, representing the third generation of β-stabilized γ-TiAl heat-resistant materials. The aim of this work is to study the combustion characteristics and to produce compact materials via the free SHS compaction method from initial powder reagents taken in the following ratio (wt%): 51.85Ti–43Al–4Nb–1Mo–0.15B, as well as to determine the effect of high-temperature isothermal annealing at 1000 °C on the structure and properties of the obtained materials. Using free SHS compression (self-propagating high-temperature synthesis), we synthesized compact materials from a 51.85Ti–43Al–4Nb–1Mo–0.15B (wt%) powder blend. Key combustion parameters were optimized to maximize the synthesis temperature, employing a chemical ignition system. The as-fabricated materials exhibit a layered macrostructure with wavy interfaces, aligned parallel to material flow during compression. Post-synthesis isothermal annealing at 1000 °C for 3 h promoted further phase transformations, enhancing mechanical properties including microhardness (up to 7.4 GPa), Young’s modulus (up to 200 GPa) and elastic recovery (up to 31.8%). X-ray powder diffraction, SEM, and EDS analyses confirmed solid-state diffusion as the primary mechanism for element interaction during synthesis and annealing. The developed materials show promise as PVD targets for depositing heat-resistant coatings. Full article

Background & Objective: Stroke remains a leading cause of mortality and long-term disability worldwide, demanding rapid and accurate diagnosis to improve patient outcomes. Computed tomography (CT) scans are widely used in emergency settings due to their speed, availability, and cost-effectiveness. This study proposes an artificial intelligence (AI)-based framework for multiclass stroke classification (ischemic, hemorrhagic, and no stroke) using CT scan images from the Ministry of Health of the Republic of Turkey. Methods: We adopted MaxViT, a state-of-the-art Vision Transformer (ViT)-based architecture, as the primary deep learning model for stroke classification. Additional transformer variants, including Vision Transformer (ViT), Transformer-in-Transformer (TNT), and ConvNeXt, were evaluated for comparison. To improve model generalization and handle class imbalance, classical data augmentation techniques were applied. Furthermore, explainable AI (XAI) was integrated using Grad-CAM++ to provide visual insights into model decisions. Results: The MaxViT model with augmentation achieved the highest performance, reaching an accuracy and F1-score of 98.00%, outperforming the baseline Vision Transformer and other evaluated models. Grad-CAM++ visualizations confirmed that the proposed framework effectively identified stroke-related regions, enhancing transparency and clinical trust. Conclusions: This research contributes to the development of a trustworthy AI-assisted diagnostic tool for stroke, facilitating its integration into clinical practice and improving access to timely and optimal stroke diagnosis in emergency departments. Full article

This study presents a comparative evaluation of post-process sensor integration in additively manufactured (AM) metal and the in-situ process for polymer structures for structural health monitoring (SHM), with an emphasis on embedded sensors. Geometrically identical specimens were fabricated using copper via metal fused filament fabrication (FFF) and PLA via polymer FFF, with piezoelectric transducers (PZTs) inserted into internal cavities to assess the influence of material and placement on sensing fidelity. Mechanical testing under compressive and point loads generated signals that were transformed into time–frequency spectrograms using a Short-Time Fourier Transform (STFT) framework. An engineered RGB representation was developed, combining global amplitude scaling with an amplitude-envelope encoding to enhance contrast and highlight subtle wave features. These spectrograms served as inputs to convolutional neural networks (CNNs) for classification of load conditions and detection of damage-related features. Results showed reliable recognition in both copper and PLA specimens, with CNN classification accuracies exceeding 95%. Embedded PZTs were especially effective in PLA, where signal damping and environmental sensitivity often hinder surface-mounted sensors. This work demonstrates the advantages of embedded sensing in AM structures, particularly when paired with spectrogram-based feature engineering and CNN modeling, advancing real-time SHM for aerospace, energy, and defense applications. Full article

The main objective of this work is to provide a comprehensive explanation of the Register Transfer Level (RTL) to Graphic Data System II (GDSII) flow for designing custom Field-Programmable Gate Array (FPGA) architectures at the 130 nm technology node using the SKY130 Process Design Kit (PDK). By leveraging open-source tools—specifically OpenLane and OpenFPGA—this study details the methodology and implementation steps required to generate a GDSII layout of a custom FPGA. OpenLane offers an integrated RTL-to-GDSII flow by combining multiple Electronic Design Automation (EDA) tools, while OpenFPGA enables the construction of flexible and customizable FPGA architectures. The article covers key aspects of the RTL-to-GDSII workflow, including RTL file configuration, the utilization of configuration variables for physical design, hierarchical chip design, macro and core implementation, chip-level integration, and gate-level simulation. Experimental results validate the proposed workflow, showcasing the successful transformation from RTL to GDSII. The findings of this research provide valuable insights for researchers and engineers in the FPGA design field, advancing the state of the art in FPGA architecture development. Full article

AmpC β-lactamases are class C enzymes that hydrolyze penicillins, cephalosporins, and monobactams. The WHO recently classified third-generation cephalosporin-resistant and carbapenem-resistant Enterobacterales as critical pathogens. We conducted a systematic review and meta-analysis to evaluate AmpC prevalence in hospital isolates across South America. We searched PubMed/MEDLINE, SciELO, and Google Scholar. We included 69 observational studies that phenotypically or genotypically identified AmpC producers. A random-effects generalized linear mixed model with logit transformation estimated pooled prevalence; heterogeneity and moderators were explored through subgroup analyses and meta-regression. Seventy studies, including 48,801 isolates, were eligible. AmpC β-lactamases were detected in 11.7% of isolates (95% CI 11.4–12.0), with extreme heterogeneity (I² ≈ 97%). Enterobacter species showed the highest prevalence (~46%), whereas Escherichia spp. had the lowest (~4.5%) prevalence of AmpC positivity within each genus. Meta-regression indicated that studies focusing on a single genus reported higher prevalence and that including pediatric patients was associated with a lower prevalence of AmpC-positive microorganisms among isolates. Quality of evidence was rated low due to inconsistency, moderate risk of bias, and indirectness of data. AmpC producers are entrenched in South American hospitals, and species-aware surveillance and harmonized detection are critical to guide empiric therapy and antimicrobial stewardship. Full article

Artificial Intelligence (AI) has become deeply embedded in the advertising industry, presenting both opportunities and challenges. Understanding this transformation requires mapping the dyad’s structure and identifying the conditions that shape its evolution. This study applies a scientometric framework, integrating bibliometric network analysis with statistical modeling, to examine AI advertising as a knowledge ecosystem. By analyzing patterns of collaboration, thematic convergence, and structural centrality, we interpret how scholarly networks generate, connect, and diffuse ideas in ways that influence both academic and industry practices. The findings reveal that the field’s growth is underpinned by interconnected clusters of expertise with strategic opportunities emerging from interdisciplinary integration and global collaboration. Simultaneously, consolidating influence among a few dominant actors raises questions about diversity, access, and the balance between innovation and ethical responsibility. Statistical analyses conducted in SPSS Statistics version 29.0.2.0 further identify the bibliometric and structural factors that most predict citation impact, strengthening the study’s contribution to understanding how influence is built and sustained in AI-driven advertising research. Full article

Artificial intelligence (AI) enhances anesthesiology by introducing adaptive systems that improve clinical precision, safety, and responsiveness. This review examines the integration of AI in anesthetic practice, with a focus on closed-loop systems that exemplify autonomous control. These platforms integrate continuous physiologic inputs, such as BIS, EEG, heart rate, and blood pressure, to titrate anesthetic agents in real time, providing more consistent and responsive management than manual methods. Predictive algorithms reduce intraoperative hypotension by up to 40%, and systems such as McSleepy demonstrate greater accuracy in maintaining anesthetic depth and shortening recovery times. In critical care, AI supports sedation management, reduces clinician cognitive load, and standardizes care delivery during high-acuity procedures. The review also addresses the ethical, legal, and logistical challenges to widespread adoption of AI. Key concerns include algorithmic bias, explainability, and accountability for machine-generated decisions and disparities in access due to infrastructure demands. Regulatory frameworks, such as HIPAA and GDPR, are discussed in the context of securing patient data and ensuring its ethical deployment. Additionally, AI may play a transformative role in global health through remote anesthesia delivery and telemonitoring, helping address anesthesiologist shortages in resource-limited settings. Ultimately, AI-guided closed-loop systems do not replace clinicians; instead, they extend their capacity to deliver safe, responsive, and personalized anesthesia. These technologies signal a shift toward robotic anesthesia, where machine autonomy complements human oversight. Continued interdisciplinary development and rigorous clinical validation will determine how AI integrates into both operating rooms and intensive care units. Full article

The preservation of bridge infrastructure has become increasingly critical as aging assets face accelerated deterioration due to climate change, environmental loading, and operational stressors. This issue is particularly pronounced in regions with limited maintenance budgets, where delayed interventions compound structural vulnerabilities. Although traditional bridge inspections generate detailed condition ratings, these are often viewed as isolated snapshots rather than part of a continuous structural health timeline, limiting their predictive value. To overcome this, recent studies have employed various Artificial Intelligence (AI) models. However, these models are often restricted by fixed input sizes and specific report formats, making them less adaptable to the variability of real-world data. Thus, this study introduces a Transformer architecture inspired by Natural Language Processing (NLP), treating condition ratings, and other features as tokens within temporally ordered inspection “sentences” spanning 1993–2024. Due to the self-attention mechanism, the model effectively captures long-range dependencies in patterns, enhancing forecasting accuracy. Empirical results demonstrate 96.88% accuracy for short-term prediction and 86.97% across seven years, surpassing the performance of comparable time-series models such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs). Ultimately, this approach enables a data-driven paradigm for structural health monitoring, enabling bridges to “speak” through inspection data and empowering engineers to “listen” with enhanced precision. Full article

Accurate identification of plastic polymers is essential for effective recycling, quality control, and environmental monitoring. This study assesses how spectral derivative preprocessing affects the classification of six common plastic polymers: Polyethylene Terephthalate (PET), Polyvinyl Chloride (PVC), Polypropylene (PP), Polystyrene (PS), and both High- and Low-Density Polyethylene (HDPE and LDPE), based on Fourier Transform Infrared (FTIR) spectroscopy data acquired at a resolution of 8 ${cm}^{-1}$. Using Savitzky–Golay derivatives (orders 0, 1, and 2), five machine learning algorithms, namely Multilayer Perceptron (MLP), Extremely Randomized Trees (ET), Linear Discriminant Analysis (LDA), Support Vector Classifier (SVC), and Random Forest (RF), were tested within a strict framework involving stratified repeated cross-validation and a final hold-out test set to evaluate generalization. The first spectral derivative notably improved the model performance, especially for MLP and SVC, and increased the stability of the ET, LDA, and RF classifiers. The combination of the first derivative with the ET model provided the best results, achieving a mean F1-score of 0.99995 (±0.00033) in cross-validation and perfect classification (1.0 in Accuracy, F1-score, Cohen’s Kappa, and Matthews Correlation Coefficient) on the independent test set. LDA also performed very well, underscoring the near-linear separability of spectral data after derivative transformation. These results demonstrate the value of derivative-based preprocessing and confirm a robust method for creating high-precision, interpretable, and transferable machine learning models for automated plastic polymer identification. Full article

Background/Objectives: Alfalfa is a widely cultivated high-quality forage crop, and salinity tolerance is one of the most important breeding goals. Glycine max SALT INDUCED NAC 1 (GmSIN1) was found to enhance salinity tolerance in soybean plants. The phylogenetic analysis showed there were two homologs of GmSIN1 in Medicago truncatula, MtSIN1a and MtSIN1b. This raised questions regarding the roles of MtSIN1s in alfalfa under salinity stress. Methods: From a Tnt1 mutant collection, we identified the mutants of MtSIN1a. We recorded the survival rate and plant height of mtsin1a-1 and mtsin1a-2 after 100 mM NaCl treatment. Subsequently, we generated 35S:MtSIN1a-GFP transgenic alfalfa lines via genetic transformation. Two lines with relatively high MtSIN1a expression, 35S:MtSIN1a-GFP#3 and 35S:MtSIN1a-GFP#4, were selected for gradient NaCl treatments. In addition, DAB and NBT staining were performed, and the H₂O₂ content and catalase (CAT) activity were determined. Then, we used RNA-seq analysis and RT-qPCR to study the mechanism of its tolerance. Results: This study found that after salt treatment, the survival rate and plant height of mtsin1a-1 and mtsin1a-2 were significantly lower than those of the WT. The mutants of MtSIN1a were sensitive to salinity stress. The transgenic alfalfa plants exhibited higher plant height, weaker DAB staining, stronger NBT staining, less H₂O₂ content, and enhanced CAT activity. The transgenic alfalfa constructed by transforming MtSIN1a showed enhanced salinity tolerance with elevated ROS scavenging. We identified MsSOD1 showing elevated expression levels in transcriptomic analysis. Conclusions: MtSIN1a is a positive regulator for enhancing salinity tolerance in alfalfa with activated ROS scavenging. Full article

Under the background of energy transition, the Integrated Energy System (IES) of the park has become a key carrier for enhancing the consumption capacity of renewable energy due to its multi-energy complementary characteristics. However, the high proportion of wind and solar resource access and the fluctuation of diverse loads have led to the system facing dual uncertainty challenges, and traditional optimization methods are difficult to adapt to the dynamic and complex dispatching requirements. To this end, this paper proposes a new dynamic energy management method based on Deep Reinforcement Learning (DRL) and constructs an IES hybrid integer nonlinear programming model including wind power, photovoltaic, combined heat and power generation, and storage of electric heat energy, with the goal of minimizing the operating cost of the system. By expressing the dispatching process as a Markov decision process, a state space covering wind and solar output, multiple loads and energy storage states is defined, a continuous action space for unit output and energy storage control is constructed, and a reward function integrating economic cost and the penalty for renewable energy consumption is designed. The Deep Deterministic Policy Gradient (DDPG) and Deep Q-Network (DQN) algorithms were adopted to achieve policy optimization. This study is based on simulation rather than experimental validation, which aligns with the exploratory scope of this research. The simulation results show that the DDPG algorithm achieves an average weekly operating cost of 532,424 yuan in the continuous action space scheduling, which is 8.6% lower than that of the DQN algorithm, and the standard deviation of the cost is reduced by 19.5%, indicating better robustness. Under the fluctuation of 10% to 30% on the source-load side, the DQN algorithm still maintains a cost fluctuation of less than 4.5%, highlighting the strong adaptability of DRL to uncertain environments. Therefore, this method has significant theoretical and practical value for promoting the intelligent transformation of the energy system. Full article

This paper presents Fic2Bot, an end-to-end framework that automatically transforms raw novel text into in-character chatbots by combining scene-level retrieval with persona profiling. Unlike conventional RAG-based systems that emphasize factual accuracy but neglect stylistic coherence, Fic2Bot ensures both factual grounding and consistent persona expression without any manual intervention. The framework integrates (1) Major Entity Identification (MEI) for robust coreference resolution, (2) scene-structured retrieval for precise contextual grounding, and (3) stylistic and sentiment profiling to capture linguistic and emotional traits of each character. Experiments conducted on novels from diverse genres show that Fic2Bot achieves robust entity resolution, more relevant retrieval, highly accurate speaker attribution, and stronger persona consistency in multi-turn dialogues. These results highlight Fic2Bot as a scalable and domain-agnostic framework for persona-driven chatbot generation, with potential applications in interactive roleplaying, language and literary studies, and entertainment. Full article