Browse Articles

A Cooperative Structural Dimension and Clusters Chirality in Antimony Halide Hybrids for Efficient Circularly Polarized Electroluminescence

ABSTRACT Chiral metal–halide materials often suffer from inherent trade‐off between photoluminescence efficiency and chiroptical asymmetry ( g value); low‐dimensional structures typically deliver high emission efficiency but small g values, whereas higher‐dimensional counterparts afford larger g values at the cost of reduced luminescence efficiency. To overcome this limitation, we introduce a cluster‐level chirality strategy in a 0D framework by incorporating chiral metal halide clusters. Herein, we rationally design and synthesize a pair of enantiomeric antimony(III) halide hybrids, [(R,R)/(S,S)(PPh 2 ) 2 C 4 ] 2 [Sb 4 Cl 16 ] ( R/S‐DPPB‐Sb ) in which bulky chiral phosphonium cations template previously unreported isolated [Sb 4 Cl 16 ] 4− clusters assembled into a helical lattice. These enantiomers exhibit bright self‐trapped excitons emission at 625 nm with photoluminescence quantum yields above 40%, along with strong circularly polarized luminescence ( g lum  ≈ ±7 × 10 −3 ), among the highest reported to date for chiral Antimony‐based hybrids. Remarkably, we further demonstrate circularly polarized LEDs using R/S‐DPPB‐Sb as the emitter, achieving an external quantum efficiency of 1.48% and a notable CP‐EL dissymmetry factor (| g EL | > 9 × 10 −3 ). The strong chiroptical response originates from the pronounced distortion of the [Sb 4 Cl 16 ] 4− clusters and their helical supramolecular packing. This work establishes antimony halide clusters as a promising chiral emitter and provides a viable route toward high‐performance, lead‐free CPLEDs.

Co-pyrolysis of agricultural biomass for potentially functional biochar: combined influence of both feedstocks and structural characterization

Loop parallelization in source code for internet of things computing using hybrid heuristic algorithm

Efficient task scheduling remains a key challenge in High-Performance Computing and Internet of Things (IoT) systems, where the sequential execution of nested loops often limits parallelism. This paper proposes a hybrid approach that dynamically parallelizes nested loops in heterogeneous IoT environments. The suggested method (PSOALS) combines Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and wave-angle scheduling to model nested loops as two-dimensional iteration spaces and minimize communication overhead. By encoding loop iterations as particles and using a dependency-aware fitness function, PSOALS enhances makespan, resource utilization, and scalability. The key contributions of this work include: a dynamic scheduling framework for efficient loop parallelization and dependency management, a wave-angle scheduling mechanism to improve task execution order by balancing load and communication delays, and the integration of mutation and diversity techniques to enhance the quality of the solution. Experimental results across various IoT configurations show that PSOALS outperforms block-based, cyclic, and GA-based scheduling methods in convergence speed, stability, and execution time. The proposed approach offers a scalable and adaptive solution to future IoT challenges, including real-time processing, energy efficiency, and large-scale deployment.

Human airway organoids as a versatile model to study BSL-4 virus replication and pathogenesis

Abstract Research with BSL-4 viruses such as Ebola, Marburg, and Nipah presents significant challenges due to their high virulence and the stringent containment measures required. This study establishes human airway organoids as a robust model for investigating BSL-4 pathogens. In contrast to conventional cell lines, airway organoids enable investigation of virus-host interactions within a human tissue context, providing insights that are more directly translatable to human disease. We generated airway organoids from both clinical donor tissues and commercially available nasal epithelial cells and showed in comparative analyses with whole lung tissue that these organoids are comparable in terms of cell composition. Despite biological variations, airway organoids derived from different sources and donors exhibit a remarkably similar cellular make-up. We further demonstrated that organoids derived from nasal swabs can effectively replicate BSL-4 viruses. This establishes them as a standardized 3D model for broader research applications including infection kinetics, immune evasion, and tissue-specific tropism within a controlled environment. This platform provides a powerful tool for antiviral testing and studying virus-host interactions, thus helping bridge critical gaps in high containment virus research and advancing our understanding of these pathogens, bypassing some of the challenges of animal models.

Archaeometric analysis of Early Bronze Age bread from Küllüoba Höyük

Bread is a fundamental foodstuff that has driven social and technological development for millennia, with the earliest evidence dating to pre-agricultural societies. While archaeological sites from the Neolithic period show systematic grain processing, well-preserved bread from the subsequent Early Bronze Age, particularly in a clear ritual context, is exceedingly rare. Here we report the discovery and comprehensive archaeometric analysis; employing Scanning Electron Microscopy (SEM) coupled with Energy Dispersive X-ray (EDX) spectroscopy, Vibrational Spectroscopy (FTIR and Raman), and Thermal Analysis (TGA-DSC) of a 5,000-year-old carbonized bread from the Küllüoba settlement in Anatolia, dated 3200−3000 BC. Microscopic examinations reveal that it is made from coarsely ground emmer wheat ( Triticum dicoccum ) and a small amount of lentils ( Lens culinaris ). The presence of air voids suggests kneaded dough, possibly leavened. The detection of rachis fragments indicates the use of unsieved flour. Intentionally deposited and subsequently carbonized, the bread was sealed beneath a layer of sterile soil and appears to have been an offering connected with the ritual abandonment of the structure. This finding offers unique evidence of advanced food technology and highlights the symbolic importance of bread in Early Bronze Age societies, directly linking food production to cultural and ritual practices.

Structural error asymmetry and harm-weighted analysis of ChatGPT versus ICU Physicians in acid–base interpretation: a prospective observational study

Abstract Large language models (LLMs) have demonstrated potential in clinical reasoning tasks; however, their performance in real-world intensive care unit (ICU) acid–base interpretation remains insufficiently characterized, particularly in complex and mixed disorders. Most existing evaluations rely primarily on aggregate accuracy metrics without examining structural error patterns or the clinical severity of misclassification. In high-acuity ICU settings, under-recognition of physiological complexity may carry disproportionate safety implications. In this prospective observational study, arterial blood gas (ABG) data from 50 consecutive ICU patients were interpreted independently by ICU physicians and ChatGPT using a standardized prompt. Interpretations were harmonized into six predefined diagnostic categories and compared with a final reference diagnosis established by a blinded expert panel. Agreement was assessed using Cohen’s kappa and diagnostic accuracy metrics. Additional analyses included complexity-stratified evaluation, mixed-disorder sensitivity, multi-label component-level (metabolic and respiratory) detection, false reassurance risk assessment, harm-weighted misclassification modeling, bootstrap confidence intervals, and post-hoc power analysis. Overall categorical accuracy was 82% for ICU physicians and 72% for ChatGPT. Agreement was substantial in both groups (κ = 0.73 vs. 0.63). Paired comparison did not demonstrate a statistically significant difference in overall classification ( p  = 0.267), and post-hoc power analysis indicated limited ability to detect modest effect sizes (power = 0.22). However, stratified analyses revealed clinically meaningful structural differences. Sensitivity for mixed acid–base disorders was 0.96 for ICU physicians and 0.63 for ChatGPT. ChatGPT uniquely classified 16.7% of mixed cases as normal (false reassurance), whereas ICU physicians produced no false-normal classifications. Component-level analysis demonstrated lower respiratory component sensitivity for ChatGPT (0.88 vs. 1.00), contributing to under-recognition of physiological complexity. Harm-weighted misclassification modeling showed significantly higher clinical severity of errors for ChatGPT (mean difference 0.12; 95% CI 0.032–0.220; p  = 0.026). While aggregate diagnostic agreement appeared broadly comparable, complexity-stratified and harm-weighted analyses demonstrated asymmetric error patterns with potential safety implications. These findings do not establish clinical equivalence and suggest that evaluation of AI diagnostic tools in critical care should extend beyond overall accuracy to incorporate safety-oriented and harm-weighted assessment frameworks.

PH-SHOWOA: Parallel hybrid SHO-WOA for VRPSPDTW

This paper proposes a parallel hybrid metaheuristic, named PH-SHOWOA, that integrates the Spotted Hyena Optimizer (SHO) and the Whale Optimization Algorithm (WOA) to solve the Vehicle Routing Problem with Simultaneous Pickup and Delivery and Time Windows (VRPSPDTW). The proposed method leverages the strength of both algorithms: SHO primarily supports population-level diversification, while WOA focuses on best-guided intensification. An adaptive probability control mechanism dynamically regulates the interaction between these two search behaviours during the optimization process. To further enhance robustness and mitigate premature convergence, the framework incorporates simulated-annealing-based acceptance, periodic local search, and population diversification strategies. A parallel implementation enables concurrent solution updates and local refinements, improving computational efficiency on medium-scale instances. The VRPSPDTW is formulated using a hierarchical lexicographic objective that prioritizes minimizing the number of vehicles, followed by total travel distance. Extensive experiments on 65 well-known benchmark instances demonstrate that PH-SHOWOA consistently outperforms standalone SHO and WOA, achieving an average reduction in total distance of over 10%. Compared with advanced algorithms such as Co-GA, MA-FIRD, and ACO-DR, PH-SHOWOA exhibits competitive and often superior performance. Notably, it achieves the lowest total distance on several Rdp and Cdp instances and performs well in centralized-demand scenarios. Furthermore, comprehensive non-parametric statistical tests are conducted to verify the effectiveness and robustness of the proposed method.

The deformation characteristics and the prefabricated crack pressure relief stability control of a small coal pillar roadway under stress superposition

Abstract The substantial pressure acting on small coal pillars poses a formidable challenge to maintaining stability during mining operations. Small coal pillar mining under high-stress conditions has thus emerged as one of the most critical bottlenecks in sustainable coal production. In this study, the deformation characteristics of a small coal pillar roadway under stress superposition were investigated via numerical simulation. Physical modeling was further employed to elucidate the impact of prefabricated roof crack on the migration behavior of overlying strata, alongside the development of a pressure-relief stability control strategy. The vertical stress on the small coal pillars was exacerbated by four dynamic pressure events, leading to a surge in energy density, and further exacerbating the energy levels of the small coal pillar. The prefabricated cracks effectively altered the strata caving characteristics and roof overhang structure, the caving angle increased from 55° to 70.5°, and the length of the roof cantilever structure was reduced by 48%. Field applications demonstrated that this prefabricated crack technique achieved remarkable pressure-relief effects, with the vertical stress increment at a depth of 3 m in coal pillar decreasing significantly from 5.5 MPa to 2.5 MPa. These findings provide a robust theoretical and technical foundation for the stability control of high-stress small coal pillar mining panel.

Using the consolidated framework for implementation research to evaluate a model of community-engaged research in advance care planning

Background Advance care planning (ACP) is the process of discussing one’s goals and wishes for end-of-life care with loved ones or clinicians and then completing an advance directive (AD). Our Community-Based Delivery Model (CBDM) has demonstrated success in engaging these communities, yet the implementation mechanisms behind its effectiveness remain unclear. This study utilized the Consolidated Framework for Implementation Research (CFIR) to evaluate the CBDM in the context of the Project Talk Trial (PTT), a national randomized controlled trial of ACP interventions. Methods This study employed a two-pronged approach. First, CFIR was used to systematically map the CBDM, defining domains and constructs relevant to the intervention’s implementation in diverse community contexts. Second, semi-structured interviews with 24 community hosts who facilitated PTT events provided qualitative insights into the “inner setting,” “outer setting,” and “implementation process” domains. Deductive coding and thematic analysis were used to identify key implementation strategies and challenges. Results The CFIR mapping revealed three critical features driving the CBDM’s success: the transfer of resources between outer and inner settings, the central role of community hosts in bridging these domains, and the flexibility to adapt to local contexts. Semi-structured interviews identified five themes, including hosts’ use of relational connections, teaming and engaging strategies, and culturally tailored approaches, which facilitated implementation. Notably, rural hosts exceeded recruitment goals, challenging the notion that rural populations are “hard to reach”. Conclusions This approach provides actionable insights for improving ACP efforts in communities settings. The integration of CFIR mapping and empirical data highlights the CBDM’s potential as a scalable model for implementing community-engaged health interventions.

Schema validation and evaluation framework for extracted schemas in JSON databases

Abstract The increasing use of schemaless data systems has intensified the need for reliable methods to assess the quality of extracted schemas intended for downstream tasks such as data integration, query optimisation, and interoperability. Although numerous schema inference techniques have been proposed, the field still lacks standardised and method-independent criteria for evaluating the validity and accuracy of inferred schemas. This paper introduces the Schema Validation and Evaluation Framework (SVEF), a systematic evaluation model for assessing extracted schemas across six complementary dimensions that capture essential structural and semantic properties: Data Type Accuracy, Required and Optional Fields, Multiple Type Support, Collection Structure Consistency, Entity Relationships, and Temporal Evolution Detection. Each dimension is defined through formal, data-driven metrics that quantify the degree to which an inferred schema reflects characteristics observed in the underlying dataset. In the present study, the framework is instantiated and evaluated for schemaless document-oriented data represented in JSON or JSON-like form. SVEF is evaluated using controlled benchmark datasets with curated ground-truth schemas and is applied to three representative schema extraction approaches. The results show that, while existing methods achieve strong performance in basic type reconstruction, substantial differences remain in modelling conditional fields, complex collection structures, and schema evolution over time. SVEF provides a consistent and interpretable basis for comparing schema extraction strategies and supports more rigorous empirical analysis of their behaviour in dynamic document-oriented data environments.

Behavioral patterns in latrine use and handwashing in rural western Kenya: Age, time of day, and the role of perceived safety

Latrine use enhances health benefits, safety, dignity, and social status. Despite increased latrine coverage, some children and adults do not consistently use latrines. The present study aimed to describe latrine use and handwashing after urination and defecation by age and time of day, and to explore factors associated with latrine use at each time of day. A cross-sectional, population-based survey was conducted from July 17 to September 21, 2023 in western Kenya, targeting individuals aged 4 years or older (n = 528 analyzed). Overall, latrine use tended to be more frequent among adults than children, for defecation than urination, and during the daytime and early morning compared with at night. Handwashing practices after urination and defecation showed similar patterns. For urination, compared with young adults (18–39 years), young children (4–10 years) were less likely to use latrines across all times of day, with reductions of approximately 60–85%. For defecation, compared with adults (18 + years), young children were even less likely to use latrines across all times of day (approximately 90–95% lower likelihood). Similarly, adolescents (11–17 years) had approximately 75% lower latrine use for defecation at night and early in the morning compared with adults. In contrast, individuals who felt safe walking to the latrine at night were substantially more likely to use latrines for both urination and defecation than those who perceived the walk as neither safe nor unsafe or unsafe. Therefore, simple, low-cost interventions, such as promoting the use of flashlights, constructing latrines closer to households, and better connecting sanitation knowledge to daily practices, are crucial for improving sanitation behaviors.

Domain knowledge-integrated reinforcement learning control of nonlinear tunable vibration absorber under nonstationary excitation

Favorable association between early initiation of sodium-glucose cotransporter-2 inhibitors and in-hospital prognosis in acute myocardial infarction

Introduction Acute myocardial infarction remains a major cause of death and disability worldwide, especially in low- and middle-income countries. Standard early management includes dual antiplatelet therapy, statins, beta-blockers (BB) and renin–angiotensin–aldosterone inhibitors (ACEi/ARB/MRA). Sodium–glucose cotransporter-2 inhibitors (SGLT2i) have been associated with favorable changes in cardiac function in acute myocardial infarction, but their impact on in-hospital mortality has not been well established. Objective To assess the association between early initiation of SGLT2i and in-hospital mortality among patients with acute myocardial infarction. Methods A retrospective, single-center study was conducted on 394 adult patients hospitalized with acute myocardial infarction at Nguyen Trai Hospital, Ho Chi Minh City, between January 2022 and October 2024. The data extraction and analysis were performed from July 31 to October 24, 2024. Patients with incomplete data, secondary acute myocardial infarction, or eGFR < 20 mL/min/1.73 m 2 were excluded. Data from electronic medical records were analyzed. The primary outcome was in-hospital mortality. Logistic regression was used to identify independent predictors. Results Mean age was 66.0 ± 11.7 years; 57.1% were male. SGLT2i was initiated within 24 hours of admission in 23.9% of patients. In-hospital mortality occurred in 53 patients (13.5%). In multivariable analysis, lower left ventricular ejection fraction (OR 0.91, 95% CI: 0.85–0.97; p = 0.003) and sepsis (OR 5.14, 95% CI: 1.04–25.36; p = 0.04) were independently associated with in-hospital mortality. In addition, use of BB/ACEi/ARB/MRA and early SGLT2i initiation were independently associated with lower in-hospital mortality (OR 0.12, 95% CI: 0.05–0.25; p < 0.001 and OR 0.27, 95% CI: 0.07–0.96; p = 0.04, respectively). Conclusions Together with traditional medical treatment, initiating SGLT2i within 24 hours of admission for acute myocardial infarction was independently associated with lower in-hospital mortality. These findings suggest a potential association between early SGLT2i use and improved in-hospital outcomes and warrant further investigation in prospective randomized studies.

Interplay between gelation and glass formation in silica nanoparticle colloids

MedZeroSeg: Zero-shot medical image segmentation via vision foundation models

A novel medical image segmentation framework, MedZeroSeg , is proposed to address key challenges in the field. Leveraging vision foundation models such as CLIP (Contrastive Language-Image Pre-training) and SAM (Segment Anything Model), it achieves zero-shot segmentation, accurately delineating previously unseen medical images without requiring additional labeled data. This significantly reduces reliance on large-scale annotated datasets. At its core, MedZeroSeg introduces a Dual-Path Feature Extraction Module that captures both fine anatomical details and global contextual information through the integration of local and global perception mechanisms, enhancing robustness against the complexity and variability inherent in medical imaging.Additionally, a Context-Enhanced Hard-Negative Contrast Loss is introduced to enhance contrastive learning by exploiting contextual cues and refining hard-negative sampling, leading to better representations and higher efficiency. The key innovation of MedZeroSeg lies in its ability to leverage generalizable knowledge from CLIP and SAM without any task-specific fine-tuning, making it highly adaptable across different medical imaging modalities. Extensive experiments on three publicly available datasets, including cardiac MRI (ACDC), multi-organ abdominal CT (Synapse), and chest X-ray (COVID-QU-Ex), demonstrate that MedZeroSeg achieves superior results in both zero-shot and weakly supervised segmentation settings, showcasing strong generalization capabilities and minimal data dependency. The framework represents a significant advancement in medical image analysis and opens up promising directions for future research in applying advanced foundation models and innovative learning strategies to healthcare applications.

Differential microRNA expression profiles and predicted miRNA–mRNA regulatory networks in human macrophage-like cells infected with Leishmania infantum

GAN-based underwater image enhancement and scene classification using transfer learning

This paper provides an exploratory analysis of underwater video analysis techniques to enhance image quality and facilitate accurate classification of different marine species. Our methodology progresses through several steps, beginning with the quality of underwater images that might be reduced by variables such as decreased light intensity, color modification, and limited visibility. These attributes pose significant challenges to develop accurate object detection methods. This paper outlines the processing pipeline employed to enhance the quality of images from underwater videos and facilitate precise object detection. First, we use the Gray World (GW) algorithm for image enhancement, effectively mitigating the challenges of aquatic environment, such as color distortion and low contrast. Subsequently, we compare the traditional Histogram Equalization (HE) and the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithms to assess their efficacy in enhancing underwater image quality. Next, Canny Edge Detection is utilized to identify the prominent features in the enhanced images, aiding in subsequent classification tasks. Next, three state-of-the-art deep learning models, Visual Geometry Group 16-layer network (VGG16), 50-layer Residual Network (ResNet50), and 121-layer Densely Connected Convolutional Network (DenseNet121), are leveraged through transfer learning to classify underwater species, including fish, coral reefs, and sea turtles. Finally, by enhancing the visual quality of underwater images, our research contributes to better understanding of the underwater ecosystem and supports conservation efforts. Enhanced Super-Resolution GAN (ESRGAN) is a superior Generative Adversarial Network (GAN) technique to improve the quality of noisy images. This paper contributes to advancing the field of underwater image and video analysis, offering valuable insights for applications in marine biology, environmental monitoring, underwater robotics, and autonomous navigation.

Probabilistic 3D lithology classification from elastic property volumes an advanced inversion workflow at Desouq Gas Field, West Nile Delta, Egypt

Abstract Advanced 3D lithology prediction is vital for reducing uncertainty in reservoir characterization and exploration planning. Traditional post-stack inversion yielded only acoustic impedance volumes, limiting facies discrimination. However, pre-stack simultaneous inversion enables direct estimation of elastic property volumes, particularly P-impedance, shear impedance and Vp/Vs, linking seismic inversion to rock physics evaluation. In this study, the applied workflow integrates seismic inversion products and borehole information within a structured, multi-stage lithology classification framework. Initially, 3D seismic inversion volumes and available well logs were subjected to comprehensive quality control and jointly interpreted to establish a consistent geological framework. This geological interpretation guided subsequent petrophysical analysis of the well logs, from which key reservoir properties were derived. Based on these petrophysical results, rock physics crossplots were conducted to define and discriminate the lithology classes generating a litho-facies log for each well and characterize their elastic responses. The classified well-based data were then, used to generate probability density functions (PDFs) for each lithology class, forming the statistical foundation of the lithology classification model. The dataset used to train an ML algorithm (trained model) was subsequently applied to the 3D seismic inversion volumes to predict lithological distributions away from the wells. Finally, the resulting lithology classification volumes were visualized, interpreted, and quality-controlled to delineate reservoir outlines and assess their spatial continuity and geological credibility. This workflow applied to the Abu Madi Formation in the west onshore Nile Delta, with a focus on the Desouq Gas Field. The probabilistic classification revealed compartmentalized gas-sand channels, refined hydrocarbon facies outlines in the northwest sector, and identified eight previously unrecognized gas-charged zones in the southwest sector. Validation using classification metrics and confusion-matrix analysis confirmed the robustness of the workflow, while integration with elastic property crossplots clarified ambiguities caused by thin anhydrite layers that commonly generate misleading amplitude responses which reduced misclassification risks. The resulting 3D lithology volumes (gas sand, wet sand, shale, and tight anhydrite formation) provide enhanced insights into subsurface heterogeneity and hydrocarbon potential, demonstrating the added value of integrating seismic inversion, machine learning, and rock physics analysis.

GAL4-based functional screen of neuropeptides in Drosophila reproduction

Neuropeptides are evolutionarily conserved signaling molecules that regulate diverse behavioral and physiological processes, including reproduction. Although, several neuropeptides have established roles in reproductive regulation, the reproductive functions of many neuropeptides in Drosophila melanogaster remain poorly characterized. Here, we performed a targeted neurogenetic screening to systematically assess the contribution of 25 neuropeptides to reproductive output. Using neuropeptide-specific GAL4 drivers and synaptic silencing with tetanus toxin, we quantified the egg-laying as an integrated functional readout of reproduction. Disruption of 14 neuropeptides altered egg-laying, including eight neuropeptides not previously described to play roles in reproductive regulation. While some of these effects are likely indirect and may reflect contributions from both female and male flies or systematic physiological signaling, these results reveal broad involvement of neuropeptidergic pathways in reproductive function. Collectively, this study establishes a functional screening framework, identifies new reproductive neuropeptides, and provides a curated resource to guide future mechanistic studies of neuropeptide-mediated brain-gonad communication.

Adverse events following SARS-CoV-2 mRNA vaccination in norwegian adolescents

Abstract The Norwegian COVID-19 vaccination campaign of adolescents from April 2021 necessitated surveillance of potential adverse events following immunization (AEFI). In this nationwide study of 496,432 adolescents, AEFI incidence rate ratios (IRRs) after first- and second-dose SARS-CoV-2 mRNA vaccination were compared to IRRs in unvaccinated subjects, and a self-controlled case series analysis was done as a secondary analysis. Seventeen pre-selected potential AEFIs were investigated: Anaphylactic reaction, Acute appendicitis, Lymphadenopathy, Arrhythmia, Cerebrovascular events, Death, Encephalomyelitis and meningitis, Epilepsy and convulsions, Facial nerve palsy, Herpes zoster, Idiopathic thrombocytopenic purpura, Myocarditis and pericarditis, Venous thromboembolic events, Arthropathy, Guillain-Barré syndrome, IgA vasculitis, and Multisystem inflammatory syndrome in children. Most AEFI were rare among adolescents, with few cases, and there was no statistically significant increase in the incidence of AEFIs after first-dose vaccination. Increased IRRs of anaphylactic reaction, lymphadenopathy, appendicitis, and myocarditis and pericarditis were observed following second-dose vaccination.