Search Results (1,765)

The acid dissociation constant (pK_a) is a fundamental parameter governing the environmental fate of organic compounds. Accurate pK_a prediction remains challenging, as traditional binary Morgan fingerprints (B-MF) fail to capture stoichiometric information critical for modeling substituent effects. This study developed an interpretable machine learning framework for pK_a prediction by integrating count-based Morgan fingerprints (C-MF) with ensemble algorithms. Through systematic comparison across four algorithms (Catboost, XGBoost, GBDT, RF), C-MF consistently outperformed B-MF due to its ability to quantify functional group multiplicity. Subsequent SHAP-based recursive feature elimination (SHAP-RFE) optimized the model, identifying Catboost with only 81 features as the optimal architecture, achieving a test-set R² of 0.890 and RMSE of 1.026. SHAP analysis revealed that the model’s decisions are driven by chemically intuitive features, forming a hierarchical framework where primary ionizable sites set the baseline pK_a and electronic modifiers fine-tune it. The applicability domain, defined using the AD_SAL method, yielded high-confidence predictions (R² = 0.926). External validation on an independent open-source dataset containing 6876 acidic compounds, combined with results from AD_SAL application domain characterization, enabled accurate pK_a prediction for 390 compounds within the application domain (R² = 0.890, RMSE = 0.942). This further confirms the model’s strong generalizability. This work provides a robust and generalizable tool for high-performance pK_a prediction, with significant potential for applications in environmental risk assessment. Full article

Volume electron microscopy (vEM) provides nanometer-scale 3D imaging, yet its axial (z) resolution is often much lower than the in-plane ($xy$) resolution, yielding anisotropic volumes that hinder segmentation and connectomic reconstruction. We present a two-stage cross-axial super-resolution framework for isotropic reconstruction that combines a conditional diffusion model and domain-specific self-supervised pretraining of a vision transformer (ViT). First, the student–teacher self-distillation paradigm of DINOv3 is adopted to learn representations from large sets of high-resolution $xy$ sections, capturing vEM-specific texture statistics and ultrastructural patterns. Second, a conditional diffusion denoiser is trained with supervised anisotropic degradation simulated by z-downsampling, while a perceptual loss based on frozen ViT feature distances constrains generated slices to match real-section distributions. These constraints recover axial high-frequency details and reduce hallucinated textures and inter-slice drift, improving cross-slice consistency. Experiments on two public vEM datasets show improved fidelity, perceptual quality, and membrane-boundary continuity over interpolation and learning-based baselines. Full article

Organic solar cells require molecular materials with broad absorption and proper energy-level alignment to maximize photon harvesting and charge transport; in this context, this work focuses on the computational design and characterization of π-conjugated push–pull chromophores, providing an integrated evaluation of their electronic, thermodynamic, and optoelectronic properties for photovoltaic applications. The chromophores were optimized using DFT/ b3lyp/6-31g+(d,p) in Gaussian16, incorporating solvation effects through the CPCM model. Electronic, thermodynamic, and optical properties were investigated using DFT and TD-DFT/CAM-B3LYP/6-311+G(d,p), including the calculation of absorption and emission spectra, first hyperpolarizability, and two-photon absorption. The STQ-PP chromophores exhibit differentiated optoelectronic responses, with DTTQ-DPP-1 showing an energy gap of 0.82–0.86 eV, stabilized LUMO levels between −2.50 and −2.61 eV, high electronic polarizability, and optical absorption extended beyond 800 nm, favoring the harvesting of low-energy photons, whereas DTTQ-DPP displays a gap close to 2.70 eV and absorption predominantly localized in the UV region, associated with potentially inferior photovoltaic performance. Compared with commercial donor materials, DTTQ-DPP-1 exhibits a red-shifted absorption into the NIR and a smaller gap, indicating enhanced low-energy photon capture; its structural stability and increased rigidity further support its photovoltaic viability. Full article

The applications of machine learning (ML) in power electronics are expanding with time, providing effective tools that reduce design complexity and enhance predictive accuracy. In high-power semiconductor devices, such as thyristors and high-power diodes, electrical parameters may directly influence electro-thermal behavior, reliability, and overall device performance. Consequently, accurate prediction and classification of average current are critical to ensure optimal device selection, optimize design, and assess performance. In this article, a comprehensive dataset based on data from industrial thyristors capturing electrical and structural parameters relevant to current handling capability is utilized to classify and predict the average current of devices. Additionally, Shapley additive explanation (SHAP) analysis has been performed, highlighting the importance of crucial parameters and identifying the impact of each parameter on model output. Moreover, several ML models, including artificial neural networks (ANNs), support vector machines (SVMs), ensembles, and Gaussian process regression (GPR) are implemented and then compared to assess their performance. The proposed methodology provides manufacturers and designers with data-driven design tools that enhance reliability assessments and facilitate optimized device selection for high-power applications. Full article

This article sets out an assertion that a mass art project can make a virtue of ‘network extension’ through an Instagram account, to build creative community, new connections, and physical artwork outcomes. We Are Making A Film About Mark Fisher is an example of a ‘manifested artwork’, where Fisher’s ideas on capitalism and community are explored through electronic media. We have taken the work of critical theorist, Mark Fisher, and subjected it to a process of détournement, alluding to the work of Guy de Bord and The Situationists. The thing in itself—Fisher’s processed ideas—are reprocessed and held up against the posthumous period between 2017 and now, since he died. The assertion in the work is that while the tools are circumscribed by a set of ‘standards’ and ‘production processes’, this does not delimit them from being employed towards the evolution of embodied and shared actions that develop a counter-narrative or something that eschews the methods of Hollywood or broadcast television documentaries. We just have to learn ways to do this. ‘Decapitalising’ a process, working with human agency and good will, turns the platform of Instagram into a tool of empowerment—reappropriating the algorithm and capturing the collective back from the closed corporate system of control. We see that a form of value is pulled back out of the machinic effects of a proprietary platform. Full article

The smut fungus Thecaphora frezzii causes severe yield losses in peanuts (Arachis hypogaea) in Argentina. Previous work established its fully intracellular biotrophic progression through subterranean organs and its exclusive sporulation within the seed coat, yet the ontogeny of teliospore formation in planta remained unresolved. Here, we applied a pragmatic correlative multiscale microscopy approach based on serial paraffin sections examined by stereomicroscopy, light microscopy, confocal laser scanning microscopy, and scanning electron microscopy, enabling spatial correlation of fungal structures within their tissue context. Using this integrative framework, we characterized the organization and progression of sporogenic structures associated with teliosporogenesis. Teliosporogenesis proved to be tightly synchronized with host tissue context and seed developmental stage, and was consistently preceded by a marked reorganization of sporogenous hyphae into three-dimensional coiled hyphal aggregates embedded in a mucilaginous matrix. These precursors undergo hyphal fragmentation followed by central–peripheral differentiation, whereby a small number of central units enlarge and individualize into teliospore initials while peripheral elements collapse, yielding stable teliospore balls as the final sporogenic product. This developmental sequence defines a distinct ontogenetic pattern not captured by current schemes of sporogenesis, here designated the Teliospore-ball type. Our results clarify the developmental pathways of T. frezzii sporulation in planta and demonstrate how accessible multiscale microscopy can be used to integrate structural information across spatial scales in complex plant–fungus interactions. Full article

The dangerous potential of chemical warfare requires immediate development of new materials capable of detecting and efficiently adsorbing the toxic nerve agents VX and Novichok (A-234). The current adsorbents fail to achieve sufficient detection efficiency and specific binding capabilities. Our research, conducted through advanced computational modeling, predicts that carbon nanocones (CNCs) could function as effective molecular traps for these toxic substances. The research combines density functional theory (DFT) with molecular dynamics (MD) and Monte Carlo (MC) simulations to explain the basic principles of molecular trapping by these agents. The nanocone shape produces two distinct and selective binding areas. MC shows preferential trapping VX molecules within the internal concave surface (P1), while A-234 molecules are strongly adsorbed on the external convex surface (P2). Docking results complement this by showing that A-234 exhibits stronger single-molecule binding on the more open surface, consistent with its preference for P2. The nanocone captures molecules through van der Waals forces, which produce measurable electronic changes that modify its electronic signature. The research demonstrates that carbon nanocones represent a promising candidate material for the future development of chemical defense systems, potentially including sensitive detection systems and advanced filtration technologies. Full article

TiO₂ films with a thickness of 20 nm were obtained by anodizing a titanium film with an aluminum sublayer on a glass substrate. The I–V characteristics were studied in a temperature range of 100–300 K. Three linear sections can be distinguished on the I–V curves in logarithmic coordinates with a bias voltage of up to 2.5 V. The first section is an ohmic section with a bias voltage sweep from 0 V. The second section is associated with the space-charge-limited currents. The third section is characterized by the flow of Poole–Frenkel currents. In the third section, the slope of the approximating line is greater than in the second one due to the flow of higher currents. This is explained by the transition of electrons from donor centers to trap levels, which leads to a decrease in the number of free traps available for capturing electrons injected from the contacts into the conduction band. The obtained values of the Fermi energy of 0.032 and 0.028 eV for temperatures from 100 to 300 K, respectively, indicate that the electron traps in the forbidden zone of TiO₂ are shallow. The value of the donor level energy E = 0.082 eV is close to the values of the activation energy of thermal conductivity. This indicates the formation of donor centers in anodic TiO₂ by the mechanism of donor vacancies. In anodic TiO₂ films, the concentration of electron traps is 10¹⁵ cm⁻³, which is approximately three orders of magnitude less than their concentration in anodic TiO₂ films obtained by vacuum deposition. Full article

Venous thromboembolism (VTE), which includes deep vein thrombosis and pulmonary embolism, is a significant and preventable cause of morbidity and mortality worldwide. Despite the existence of clinical prediction models, biomarker-based risk assessments, and imaging techniques, gaps remain in accurately identifying and managing high-risk patients. In recent years, artificial intelligence has emerged as a transformative tool in healthcare, offering promising applications for enhancing VTE prevention strategies. This narrative review synthesizes current evidence on the use of artificial intelligence (AI) technologies including machine learning (ML), deep learning (DL), and natural language processing (NLP). We explore how supervised ML algorithms, such as random forests, support vector machines, and gradient boosting, improve predictive performance compared to traditional models by capturing complex, nonlinear relationships within electronic health record data. We also examine the role of DL models, particularly convolutional neural networks, in interpreting imaging data, achieving diagnostic accuracies comparable to expert radiologists. Additionally, the review highlights NLP applications in extracting risk-relevant information from unstructured clinical notes and the emerging integration of wearable device data and time-series analysis for dynamic risk assessment. We argue that the successful integration of AI into routine VTE prevention workflows requires rigorous prospective validation, cross-institutional collaboration, and thoughtful implementation into clinical decision support systems. Full article

A simple, rapid, and cost-effective method for the determination of benzo[a]pyrene (BaP) in edible oil was developed and validated. Nickel oxide-deposited silica (SiO₂@NiO) was employed as a solid-phase extraction (SPE) adsorbent for the extraction of BaP from edible oil, followed by high-performance liquid chromatography–diode array detector (HPLC-DAD) analysis of BaP. The edible oil was diluted with n-hexane and directly loaded to SiO₂@NiO for SPE. The n-hexane was also used to clean the fat-soluble interference substance in the edible oil, and BaP was selectively captured using SiO₂@NiO through the electron donor–acceptor interaction. The SPE conditions, including the amount of adsorbent, volume of washing solvent, and type and volume of desorption solvent, were optimized. This SiO₂@NiO-based SPE coupled with the HPLC-DAD method demonstrated good linearity within a BaP concentration range of 6–1875 ng/g in edible oils, with a limit of detection of 1.3 ng/g, spiked recovery of 97.4–105.1%, and relative standard deviation (RSD) of <3.0%. The method was applied to the analysis of BaP in 11 real oil samples (soybean oil, olive oil, corn germ oil, flaxseed oil, walnut oil, sunflower kernel oil, peanut oil, unrefined oil, and high-temperature frying oil), and the results show that the unrefined oil and high-temperature frying oil were at risk of BaP exceeding acceptable level. Full article

Accurate modeling of RF capacitors and inductors is critical for predicting circuit behavior and ensuring operational robustness in high-frequency electronic systems. However, SPICE models are often unavailable from manufacturers, and there is still a lack of reliable methodologies for accurate modeling of such passive components over a wide frequency range. This paper presents a systematic and practical equivalent-circuit modeling methodology for capacitors and inductors based on measured impedance data. The proposed approach partitions the entire frequency range into multiple sub-bands and models each using a combination of a core series RLC network and frequency-dependent parallel RC, RL, and RLC sub-networks. This piecewise construction enables the dominant resistive, inductive, and capacitive behaviors to be independently identified and accurately captured in their respective frequency regions, resulting in an accurate broadband equivalent circuit. The resulting models exhibit excellent agreement with target data, demonstrating the reliability of the method. This work provides a practical and systematic procedure for developing accurate broadband models of RF passive components, with validation demonstrated for capacitors up to 6 GHz and inductors up to 20 GHz. Full article

The interoperability of electronic health records in Colombia faces a critical gap between the regulatory mandates established by the Colombian regulatory framework and the actual technical capacity of healthcare institutions to implement them. This article presents PIRE (Electronic Records Interoperability Platform), an open-source architecture that demonstrates the viability of end-to-end FHIR systems in the Colombian context. The main objective was to develop a platform capable of integrating health data from biomedical devices into an FHIR server, preserving clinical semantics through LOINC terminologies. The methodology followed an iterative development approach, implementing a HAPI FHIR server on AWS, a normalization application in Flask, and clinical visualization modules aligned with the FHIR Core CO Implementation Guide. The Bioharness-3 device was used to capture metrics on heart rate, respiratory rate, activity, and posture. The platform achieved a data normalization latency of 104–438 ms per record and 100% semantic validation against the FHIR Core CO profiles, validating compliance with Colombian IHCE specifications. It is concluded that PIRE constitutes a reproducible reference model for healthcare institutions that wish to implement interoperability as a cost-effective solution. Full article

The aim of our study was to determine the importance of different pitcher syndrome characters (size of the trap, the presence of inner microscopic surface coverage, physical properties of the pitcher fluid) for insect trapping efficiency using artificial, “biomimetic” pitchers. We performed trapping experiments with Drosophila melanogaster flies, applied cryo scanning electron microscopy for characterization of the topography of surface coatings and visualization of their contaminability effects on insect attachment organs, and conducted contact angle measurements with different liquids used in experiments. The type of the liquid used as the pitcher fluid had the most important impact on the trapping efficiency; surfactant-containing liquids exhibiting strong wetting properties provided a high number of trapped flies. The diameter of the trap rather than its height influenced insect trapping efficiency; apparently, because wider traps provide a larger space for more insects to get into a trap, they captured more flies in comparison to narrower traps. The presence of both the calcium carbonate and kaolin coatings mimicking the epicuticular wax coverage inside pitchers in many Nepenthes species additionally contributed to the trapping success due to a reduction of contact between insect feet and the trap surface and to contamination of flies’ attachment organs by detached microparticles. Full article

Despite the success of Geometric Graph Neural Networks (GGNNs), their reliance on local message passing inherently limits the receptive field, leading to the over-squashing of distant information. Current solutions—ranging from graph rewiring to global attention—address this as a purely topological bottleneck, often neglecting the explicit electronic degrees of freedom (e.g., charge transfer and electrostatics) that physically govern long-range couplings. To resolve this disconnect, we propose HMP-Net, a framework that integrates over-squashing remedies with chemically meaningful interaction channels. Our approach introduces a differentiable hierarchy that routes information through learned “master nodes”, enabling direct global reasoning while preserving local chemical fidelity. Crucially, we clarify the theoretical ambiguity between topological and physical long-range interactions, establishing practical boundaries for when explicit physical modeling suffices and when architectural interventions are strictly necessary to capture global electronic states. Full article

Sub/super-synchronous oscillations induced by the interaction between wind turbines and the grid pose increasing challenges to the dynamic analysis of power-electronics-dominated power systems. For microgrids comprising a large number of distributed direct-drive wind turbines (DDWTs), detailed electromagnetic transient modeling becomes computationally prohibitive, while conventional single-machine equivalent models often fail to capture critical oscillatory characteristics. To address these issues, this paper proposes an impedance-sensitivity-based clustering and equivalent modeling method for DDWT groups in a microgrid. First, a frequency domain impedance model of DDWTs is established, and the impedance sensitivities of key control parameters are analyzed under various steady-state operating conditions. By jointly considering the absolute magnitude of impedance sensitivity and its variation across operating points, a sensitivity-informed criterion is developed to select physically meaningful clustering indices capable of distinguishing wind turbines with different operating conditions. Based on the selected indices, a k-means clustering algorithm is employed to group distributed DDWTs, and a multi-machine equivalent model is constructed accordingly. Simulation studies under impedance disturbances validate the effectiveness of the proposed equivalent model in accurately reproducing the oscillation characteristics of a microgrid with multiple DDWTs. Full article