Browse Articles

Energy and economic performance of a PCM-infused brick with metal foam for passive building cooling

Microbial life in deep-seated selenide veins reflected by extreme δ34S fractionation of framboidal pyrite

Abstract Microbial life can be found in the continental subsurface down to depths of several kilometers, where it may affect mineral-forming and reaction processes. Sulfur isotopes provide an important tool for discriminating between deep microbial activity and abiotic processes. Here, we report the sulfur isotopic composition (δ 34 S) of pyrite in selenide-rich domains of hematite-carbonate ± selenide veins hosted in black shales from Tilkerode, Harz Mountains (Germany). Framboidal pyrite (PyII) occurs in voids in clausthalite (PbSe) formed by hematite and carbonate dissolution, but is also enclosed in anhedral pyrite (PyIII). Results of secondary-ion mass spectrometry yielded extreme δ 34 S values ranging from ‒8.5 to + 92.5‰ for PyII, and from ‒40.1 to + 79.1‰ for PyIII. Extreme positive values suggest microbial sulfate reduction (MSR) under closed-system conditions with limited sulfate availability and slow metabolism, and negative values a switch to open-system conditions. All MSR processes most likely occurred after fast uplift of the vein system from about 5 km to 1–2 km depth during Cretaceous-Tertiary reverse faulting, accompanied by cooling from 220 °C to 30–60 °C, as indicated by fluid-inclusion microthermometry and U‒Pb carbonate dating. The results demonstrate that microbial activity at great depth of 0.8 to 1.8 km is possible even in normally toxic Se-Pb-Ag-Hg-rich environments.

Limited impact of endocardial radiofrequency ablation on epicardial substrate in post-infarction ventricular tachycardia

Abstract Catheter ablation is key therapy for post-myocardial infarction (post-MI) ventricular tachycardia (VT). However, outcomes remain suboptimal when an endocardial-only approach is used because epicardial or intramural substrate frequently contributes to arrhythmogenesis. This study aimed to quantify the prevalence and extent of epicardial substrate in post-MI VT and to assess the direct impact of endocardial radiofrequency (RF) ablation on epicardial electrograms. Thirty-seven consecutive patients with ischemic cardiomyopathy and sustained VT (mean age 67 ± 8 years; left ventricular ejection fraction 30 ± 10%) underwent combined endocardial and epicardial substrate ablation. Epicardial access was obtained using carbon dioxide (CO2) insufflation-assisted subxiphoid puncture. Electroanatomical mapping (CARTO 3) and contact force-sensing RF ablation guided by Ablation Index were used. The impact of endocardial RF on opposite-surface epicardial electrograms was quantified. Epicardial low-voltage area (< 0.5 mV) was present in 35 patients (95%) and epicardial late potentials in 32 (86%). Endocardial RF delivery time was 20 ± 9.8 min. No measurable change in opposite epicardial signals was observed in 21 patients; partial attenuation occurred in 11, and complete elimination in none. Over 24 ± 15 months of follow-up, VT recurred in 3 patients (10%); one cardiac and one non-cardiac death occurred. Epicardial substrate is highly prevalent in post-MI VT and is rarely eliminated by endocardial RF alone. Combined endo-epicardial mapping and ablation using CO2-assisted access may facilitate more comprehensive substrate modification in selected high-risk patients. Epicardial scar and late potentials were present in almost all post-MI VT patients. Standard endocardial radiofrequency lesions rarely affected opposite epicardial signals. CO2-assisted epicardial access facilitated combined substrate mapping and ablation with favorable mid-term arrhythmia outcomes.

PhishDetect: A ranking-based classifier integration approach for improving phishing website detection

Abstract Phishing is a fraudulent activity that includes tricking folks into disclosing personal information by impersonating a legitimate individual or organization. Nowadays, phishing attacks are increasing due to the widespread availability of Internet access, leading more individuals to use online platforms for various services like banking, shopping, etc. Cybercriminals exploit this shift using various tricks to find their victims online. The cybersecurity experts and professionals are leveraging machine learning to enhance phishing detection rate, as conventional methods are becoming less effective. The conventional machine learning and ensemble learning approaches often result in high false positive and false negative rates. Thus, it is essential to design and develop more reliable solutions for identifying phishing webpages. The primary contribution of the paper is enhancing phishing detection accuracy by combining base classifiers using ranking schemes derived from their prediction errors. The effectiveness of the proposed approach is evaluated using a benchmark dataset. The results reveal that the proposed approach outperforms traditional machine learning and ensemble learning methods in phishing detection. The proposed approach provides the weighted F-measure of 0.984 as compared to the stacking of all classifiers and top three classifiers selected using ranking strategies which achieve the weighted F-measure of 0.970 and 0.974, respectively. Further, to evaluate the validity and generalization capability of the proposed approach, experiments are conducted using an additional standard benchmark dataset.

Part-level vehicle damage assessment via enhanced YOLO11 and area-ratio severity modeling

Abstract Accurate vehicle damage assessment is fundamental to intelligent insurance claims and automated inspection systems. However, many existing methods overlook the structural context of the vehicle and rely on unstable global severity metrics that are sensitive to perspective and distance. This study proposes a part-aware damage assessment framework based on an enhanced YOLO11 segmentation architecture and a structured severity formulation. The model incorporates a receptive field aggregation module termed C3k2-RFAConv to improve contextual perception while preserving fine-grained details. In addition, an adaptive spatial feature fusion strategy known as ASF-YOLO is employed to enhance multi-scale feature interaction and boundary refinement. A dual-model design is adopted in which a vehicle part segmentation network provides structural priors for damage-aware analysis. Utilizing pixel-level masks, a Damage Severity Index is formalized as an interpretable heuristic severity indicator by combining the damage-to-part area ratio, confidence scores, and category-specific importance weights. A conditional normalization strategy is further applied to reduce sensitivity to viewpoint variations and imperfect part segmentation, establishing a regulated structural topology score rather than a validated actuarial metric. Experiments conducted on a self-constructed damage dataset and a public vehicle part dataset demonstrate improved segmentation performance over baseline YOLO11 models, particularly in mAP metrics at higher IoU thresholds. Qualitative results indicate that the proposed severity scores are visually interpretable and demonstrate steady alignment with observed damage characteristics across heterogeneous real-world scenarios.

Multi-modal deep learning for paddy health assessment: fusing leaf imagery with tabular metadata using a factorized bilinear pooling approach

Abstract Global food security is largely based on the accurate and timely diagnosis of crop diseases, where paddy rice is an extremely essential staple of more than half of the world population. The conventional disease identification techniques tend to be laborious, time consuming and demand a great deal of domain knowledge, which becomes a bottleneck in the efficient management of the farms. Although deep learning [and especially Convolutional Neural Networks (CNNs)] have demonstrated a spectacular performance in automated classification of diseases based on leaf images, they tend to overlook important contextual features that are implicitly processed by agronomic experts. The visual defects of a disease might be unclear and this can greatly differ depending on factors like the genetic variety of the plant and the stage of development. We overcome this shortcoming by proposing a new multi-modal deep learning framework, Multi-Modal Factorized Bilinear Pooling (MFBP) model which is capable of a more holistic and precise paddy health measurement. The proposed method is the only one that combines high-level visual information obtained using leaf images and related tabular information, namely the paddy type and number of days. The MFBP model uses Factorized Bilinear Pooling (FBP) rather than the simple feature concatenation which commonly loses the complex relationship between different data types. This systematic method efficiently encodes all the complex interactions between all components of the visual and tabular features vectors in such a way that helps the model to pick up subtle, context-specific patterns. As an example, it will only be possible to educate the model that a specific visual blemish is predictive of a given disease through a specific species at a specific age. We test our model on the Paddy Doctor: Paddy Disease Classification dataset, which is a detailed public dataset comprising of more than 10,000 labeled images and containing relevant metadata, and thus it forms a perfect testing bed to conduct multi-modal research. Through our detailed experiments, we have shown that the proposed MFBP model is much better than a baseline model based on concatenation fusion, which proves that deep, multiplicative interactions can be best modeled in this task. The findings highlight the massive possibilities of multi-modes AI in the development of more robust, more accurate, and more context-aware diagnostic instruments and precision agriculture to enable more sustainable and productive agricultural activities.

Comprehensive CyTOF and lipidomic analysis of splenic immune and serum lipid responses to BCG vaccination and H37Rv challenge in BALB/c mice

Abstract The protective efficacy of the Bacillus Calmette-Guérin (BCG) vaccine against Mycobacterium tuberculosis (Mtb) is variable, and the systemic mechanisms linking cellular immunity with host metabolism remain poorly understood. This study employed high-dimensional mass cytometry (CyTOF) within the splenic compartment and serum lipidomics to map the integrated immuno-metabolic landscape in BCG-vaccinated and non-vaccinated mice following infection with the H37Rv strain. We report that BCG vaccination was associated with a distinct phenotype characterised by the priming of antimicrobial myeloid subsets (CD11b+ Mac2+/Mac3+) and lipidomic pathways away from stress-induced inflammatory responses. In contrast, non-vaccinated hosts exhibited a dysregulated response, characterised by a systemic cortisol surge, a lipidomic profile suggestive of reduced bioactive lipids (including PPARγ ligands such as 13-HODE and 19,20-DiHDPA), and the reactive mobilisation of CD103 + CD4+/CD8 + T cells and activated B cell subsets to the spleen. These findings reveal that protection is not solely cellular but relies on a stable metabolic environment that supports immune function. While infection in naive hosts drives a resource-depleting stress response that likely compromises macrophage efficacy, BCG vaccination establishes an integrated multiparametric defence, preventing pathogen-driven metabolic manipulation and maintaining the necessary lipid mediators for effective inflammation resolution and bacterial control.

Adaptive lighting control in intelligent buildings using artificial neural networks: design, implementation, and experimental validation

A novel 4D-hybrid chaotic system and concentric rings based permutation-diffusion approach to encrypt traffic images

Development and evaluation of cross-linked fracturing fluid stability and scale inhibition synergy in high-temperature, high-salinity environments

Genetic technologies to enhance crop nutritional value under climate change

Three-year monitoring study of heavy metal fluxes and accumulation characteristics in mildly contaminated farmland soils of northern Guangdong, China

Abstract This study conducted a three-year field monitoring program from 2022 to 2024 in a 62.68 km 2 agricultural area in northern Guangdong, China. We systematically investigated the environmental behavior and accumulation characteristics of eight heavy metals (Cd, Hg, As, Pb, Cr, Cu, Zn, and Ni) in lightly contaminated farmland soils. Soil heavy metal concentrations showed no significant inter-annual differences over the three years ( P  > 0.05). But clear spatial enrichment patterns were observed. Among four input pathways and three output pathways, atmospheric deposition was the main input source for Cd, Pb, Cr, and Zn. It contributed 63.09% to 89.19% of total inputs. Crop harvest was the main output pathway for most heavy metals. Input and output fluxes varied significantly among years for different heavy metals, especially for surface runoff and leaching water, which had significant effects on all metals except As ( P  < 0.05). The theoretical changes in soil heavy metal concentrations, calculated based on net fluxes, showed weak correlations with the actual monitored values ( R 2  < 0.25), which might be attributed to the buffering capacity of the soil. This study provided critical field-based data to support the precise management and risk mitigation of heavy metals in lightly contaminated agricultural soils.

Multi-hazard performance assessment of reinforced concrete frame structures subjected to earthquakes and wind loads

A bright, sun-splashed future for renewable energy

Three international renewable energy pioneers have been selected for the Mani L. Bhaumik Breakthrough of the Year Award

Urinary tract infections caused by carbapenem-resistant Gram-negative bacteria among pregnant women attending antenatal care clinic at Murang’a County Referral Hospital, Kenya

Recognizing my worth

Correction: Interference with AGEs formation and AGEs-induced vascular injury mediates curcumin vascular protection in metabolic syndrome

Researchers caught in the crossfire as firms and U.S. government grapple over AI safety

Tumultuous Fable 5 takedown spurs fears for open research

Patient-physician familiarity is just as important for virtual outpatient encounters as for in-person encounters: a retrospective cohort study

Undead models and shrouded black holes