Browse Articles

Predictors of adult ICU mortality: a retrospective study at two government hospitals in Ethiopia

Abstract The number of life-threatening conditions requiring admission to intensive care units has increased substantially in low-income countries, partly due to the expansion of hospital services. In Ethiopia, ICU mortality rates vary across regions. However, evidence regarding the magnitude of ICU mortality and its associated predictors remains limited and inconclusive. To assess the magnitude of the mortality rate and its predictors among hospitalized adult patients A two-center retrospective cross-sectional study was conducted among patients admitted to the ICU between December 1, 2023, and May 30, 2024. Data were collected using a pretested, structured questionnaire. The completed data were gathered via a web link developed using Kobo Toolbox (kobtoolbox.org), then coded, manually verified for completeness, and exported to SPSS version 27 for analysis. Descriptive statistics and logistic regression analyses were performed to evaluate the data. A total of 309 patient charts were reviewed. The median ICU stay was 5 days. The leading causes of ICU admission were postoperative conditions, septic shock, stroke, and congestive heart failure. The most common causes of death were septic shock, stroke, head trauma, and acute respiratory distress syndrome (ARDS). The overall mortality rate among ICU-admitted patients was 46.3%. A higher Charlson Comorbidity Index score, the need for mechanical ventilation at admission, and the presence of hospital-acquired infections were significantly associated with ICU mortality. Compared with some developed countries, the observed mortality rate in this cohort was higher. The findings of the present study indicate that hospital-acquired infections, the Charlson Comorbidity Index, and the need for mechanical ventilation were all significantly associated with mortality among intensive care unit patients.

Water structure and dynamics under distinct microheterogeneity in DMSO–water and acetone–water mixtures

Dimethyl sulfoxide (DMSO) and acetone are water miscible structural analogs with sharply contrasting cryoprotective properties. These cosolvents perturb water structure and dynamics in different ways, leading to distinct molecular associations, hydration patterns, and solute-specific microheterogeneous environments. To explore these divergences, this work presents a comparative study of DMSO–water and acetone–water mixtures over a cosolvent mole-fraction range of 0.05–0.5, using classical molecular dynamics simulations combined with graph theoretical analysis, quantification of spatial inhomogeneity via the h-value, and analysis of water dynamics. DMSO integrates into the water network, locally disrupting tetrahedral order while minimizing DMSO–DMSO self-association and preserving connectivity through the formation of DMSO–water complex. In contrast, acetone promotes cohesive self-aggregation of acetone molecules and enhanced clustering of water molecules, creating a microheterogeneous environment. Correspondingly, water in acetone mixtures exhibits faster translational diffusion and rotational dynamics with shorter H-bond lifetimes, whereas DMSO mixtures slow water dynamics and lengthen H-bond lifetimes. Notably, the study on these differences in structural and dynamical properties in both aqueous mixtures could help rationalize DMSO’s established effectiveness in cryopreservation, owing to its relatively low microinhomogeneity, in contrast to the tendency of acetone to promote microinhomogeneity, thereby limiting its utility as a cryoprotectant.

AI driven dual constraint cooptimization of affective semantics and engineering parameters for biomimetic product design

Intermediate time sub-diffusion and stress relaxation in ring polymer melts

The slow dynamics of non-concatenated ring melts remains a frontier problem in polymer science with implications for many soft material environments including cellular biophysics. Here, we report large-scale simulations of model ring melts that analyze the monomer and center-of-mass (CM) mean square displacements (MSD) and stress relaxation function on intermediate time and length scales. The degree of dynamical slowing down is characterized by the maximally sub-diffusive fractional time scaling exponents. The data span an exceptionally wide range of ring degrees of polymerization and stiffnesses and are not successfully organized based on the classic measure linear chain entanglement, N/Ne. Rather, we find that the crossover degree of polymerization, ND, based on ring macromolecular caging that successfully allows master curves to be constructed for the long-time CM self-diffusion constant also collapses these temporal dynamic scaling exponents. Different properties display different exponents and exhibit one or two regimes of linear variation with the logarithm of ND/N. A distinct crossover of the CM-MSD and stress relaxation exponents emerges at sufficiently large N or stiffness that is not found for the monomer MSD, indicating a novel form of dynamic decoupling. This crossover aligns with the predicted critical degree of polymerization for transitioning from a weak to strong caging regime, indicative of activated transport. The latter may reflect the emergence of an intermolecular collective contribution to stress in analogy with dense soft colloidal matter. Suggestions are made for future theoretical work to address the rich patterns of behavior discovered.

A pipeline leakage detection method for boiler energy operation system using enhanced SVM-based acoustic emission technology

Abstract Pipeline leakage detection in boiler energy systems is essential for operational safety and efficiency, yet conventional techniques such as pressure-based and mass-balance methods often lack real-time performance and sensitivity to small leaks. Although acoustic emission (AE) technology offers dynamic, non-destructive monitoring, its practical application is hindered by noise interference and limited training samples under industrial conditions. This paper introduces an enhanced support vector machine (SVM) framework designed for robust AE-based leakage detection. The proposed approach integrates three key contributions: first, a multi-domain feature fusion strategy that combines time-domain and frequency-domain parameters for enhanced signal separability; second, a spectral sparsity-guided dynamic kernel selection mechanism that adaptively optimizes the model for varying signal characteristics; and third, a margin-based boundary sample weighting strategy that mitigates the influence of noise near the hyperplane. Experiments involving three leakage types—spot, fracture, and explosion tube—were conducted under both low-noise (40 dB) and high-noise (70 dB) conditions. The model achieved perfect classification (100% accuracy) under quiet settings, and maintained accuracies of 92.3%, 88.1%, and 85.4% for the respective leak types under noisy conditions, outperforming conventional SVM by 12–15%. These results demonstrate that the proposed framework significantly improves detection reliability in noisy, data-scarce environments, providing a practical tool for early leakage identification in industrial boiler systems. Future work will focus on adaptive noise modeling and online threshold learning to further enhance the framework’s robustness and adaptability in dynamic industrial settings.

Dynamics of iodine geminate recombination in supercritical xenon solvent: Caging effect

Understanding the dynamics of chemical reactions in solutions is vital, as their rates and kinetics are significantly affected by the solvent environment. Supercritical solvents offer extensive applications in chemical reactions by enabling the manipulation of the solution environment. In this study, we investigate the geminate recombination of iodine in a supercritical xenon solvent by using ReaxFF-based molecular dynamics simulations. Our findings reveal that the highest iodine recombination rate occurs near supercritical conditions, while lower-pressure conditions lead to reduced collision rates and unstable recombination, and higher-pressure conditions hinder iodine diffusion, resulting in a lower recombination rate. Our analysis shows that the xenon local density at the time of recombination is at least 2.5 times higher than the global density, confirming the presence of xenon clusters surrounding the Iodine atoms. This observation is further supported by coordination number analysis, which confirms an elevated xenon local density during recombination. In addition, the correlation between the total energy of xenon atoms within a cluster and recombined iodine atoms underscores the kinetic energy transfer process, validating the occurrence of geminate recombination. The excess kinetic energy from the recombining iodine atoms is transferred to the surrounding xenon atoms. Our examination of geminate recombination demonstrates that iodine atoms confined within xenon clusters—whether through manual insertion of atoms or the fast dissociation of an iodine molecule within xenon clusters—are more likely to recombine as primary geminate recombination. However, extending the iodine molecule dissociation time allows iodine atoms to diffuse out of the cluster, and the recombination to shift toward secondary geminate recombination.

Comparative evaluation of deep learning models for cardiovascular disease diagnosis and classification

How simple can you go? An off-the-shelf transformer approach to molecular dynamics

Most current neural networks for molecular dynamics (MD) include physical inductive biases, resulting in specialized and complex architectures. This is in contrast to most other machine learning domains, where specialist approaches are increasingly replaced by general-purpose architectures trained on vast datasets. In line with this trend, several recent studies have questioned the necessity of architectural features commonly found in MD models, such as built-in rotational equivariance or energy conservation. In this study, we contribute to the ongoing discussion by evaluating the performance of an MD model with as few specialized architectural features as possible. We present a recipe for MD using an edge transformer (ET), an “off-the-shelf” transformer architecture that has been minimally modified for the MD domain, termed MD-ET. Our model implements neither built-in equivariance nor energy conservation. We use a simple supervised pretraining scheme on ∼30 × 106 molecular structures from the QCML database. Using this “off-the-shelf” approach, we show state-of-the-art results on several benchmarks after fine-tuning for a small number of steps. Using MD-ET as a simple and expressive testbed, we examine the effects of being only approximately equivariant and energy conserving for MD simulations and thereby try to evaluate the practical usefulness of unconstrained MD models. While our model exhibits runaway energy increases on larger structures, we show approximately energy-conserving NVE simulations for a range of small structures.

EVA-centric QUBO optimization for active and reactive power coordination in DLMP-driven distribution systems

Quantifying classical and quantum bounds for resolving closely spaced, non-interacting, simultaneously emitting dipole sources in optical microscopy

Recent theoretical and experimental work has shown that the quantum Fisher information associated with estimating the separation between two optical point sources remains finite at small separations, effectively opening up new routes to super-resolution imaging of simultaneously emitting sources. Most studies to date, however, implicitly invoke the scalar approximation, which is not appropriate in the context of high-numerical-aperture microscopy. Utilizing parameter estimation theory, here we consider the estimation of separation between two closely spaced dipole emitters, a commonly employed model for single-molecule optical beacons. We consider two limiting cases: one in which the orientations of the emitters are fixed and equal, and another in which both dipoles freely sample all of orientation space over the course of the measurement. We quantify precision limits using quantum and classical variants of the Fisher information and Cramér–Rao bound. In all cases, the vectorial nature of the emission complicates the analyses, but with appropriate filtering of the collected light in the azimuthal–radial polarization basis, a previously proposed scheme to saturate the quantum Fisher information via image inversion interferometry can be salvaged.

A novel laparoscopic renal denervation system in a preclinical swine model

Abstract Laparoscopic renal denervation (RDN) represents a specialized approach for hypertension, particularly when conventional methods face limitations. While catheter-based RDN has demonstrated significant efficacy in recent large-scale clinical trials and has been integrated into international guidelines, its effectiveness can be constrained by complex renal anatomy or incomplete ablation. We developed a novel laparoscopic RDN system, comprising an integrated radiofrequency (RF) clamp, RF generator, and cooling pump, to achieve more controlled adventitial ablation. This study aims to evaluate the safety, feasibility, and optimal parameters of this new system in a preclinical swine model. Sixteen pigs were divided into an immediate group ( n  = 10) for power optimization and a 28-day follow-up group ( n  = 6). An optimal setting of 10 W for 10 s was identified, balancing effective nerve injury with minimal vascular damage. In the 28-day follow-up group, this setting was associated with a significant reduction in systolic blood pressure (median 125.5 to 109.0 mmHg, p  = 0.010) and serum norepinephrine (217.56 to 170.47 ng/L, p  = 0.017). Our novel laparoscopic RDN system demonstrates feasibility and safety, providing a potential rescue strategy or supplementary therapy for specific patient populations, such as non-responders to endovascular treatment or those with complex vascular anatomy.

Calculation and analysis of exciton couplings via a subsystem formulation of the <i>GW</i> -Bethe–Salpeter equation

We present a fragment-based framework for analyzing exciton couplings within the GW-Bethe–Salpeter equation formalism using localized molecular orbitals and assess how excitonic states in molecular dimers can be decomposed into locally excited and charge-transfer (CT) contributions. Our localization procedure preserves orbital orthonormality via a block-diagonal unitary transformation, enabling a simple and interpretable analysis of excitonic interactions. Using ethylene and pyrene dimers as model systems, we identify key effects of excitonic basis truncation and coupling approximations on excitation energies. We then extend the method to chlorophyll dimers, where weak CT asymmetries emerge due to geometric distortions. This framework offers a tractable route to analyze excitonic behavior in complex systems and paves the way for future fragment-based reconstruction of full exciton coupling matrices in large molecular assemblies.

Experimental and numerical investigation of single-slope solar still performance enhanced by porous absorbing materials: thermal, economic, and environmental assessments

Abstract Low freshwater productivity and poor thermal efficiency remain key limitations of conventional single-slope solar stills. In this study, porous absorbing materials are investigated as passive performance-enhancement strategies for small-scale solar desalination. A combined experimental and numerical analysis was conducted on a traditional solar still (TSS) and two modified configurations incorporating melamine sponge (MSSS) and pumice stone (VPSSS), operated under real climatic conditions in Karbala, Iraq. The results demonstrate that the MSSS achieved the highest daily freshwater yield of 1347 mL/day, corresponding to a 56.9% increase compared with the TSS, alongside an average thermal efficiency of 49.3%. The VPSSS produced 1055 mL/day, representing a 22.9% improvement and a thermal efficiency of 38.2%. Economic analysis indicates that, under optimal operating conditions, the MSSS reduced the water production cost to 0.07569 USD/L with a payback period of approximately 2.5 years. The energy payback period ranged from 0.55 to 0.86 years, whereas the exergy recovery period remained considerably longer (28–35 years), highlighting inherent thermodynamic limitations. In addition, the MSSS configuration achieved an annual CO 2 emission reduction of approximately 1612 kg, corresponding to a cost saving of 17.36 USD. Overall, the findings suggest that porous absorbing materials, particularly melamine sponge, offer an effective and economically feasible approach for enhancing solar still performance in arid and remote regions.

Macromolecules with branched architecture via radical polymerization: Insight from computer simulations

Branched polymers offer highly tunable properties and functionality. However, obtaining soluble, sufficiently branched macromolecules within the desired molecular weight range by conventional radical polymerization (RP) is still a challenging task, as there is no systematic investigation of this problem. In this work, we develop a three-dimensional coarse-grained molecular-dynamics model of RP in the presence of a divinyl crosslinker (CL) and a chain-transfer agent (CTA). Simulations are based on the Kremer–Grest bead-spring framework with Langevin dynamics under good-solvent conditions and include stochastic reactions: initiation, propagation, crosslinking, chain transfer, and termination. Macromolecular architecture is quantified by graph-based decomposition into dangling ends, elastically active subchains, and cycles, and by extracting an effective fractal dimension from the scaling of the radius of gyration with molecular mass. Two distinct regimes emerge. At low CTA content, gelation occurs at relatively small conversion; a rapidly growing network quickly dominates the molecular-weight distribution, which broadens substantially, while the population of isolated branched macromolecules diminishes. Increasing the CTA content shifts gelation toward high conversion, enabling the formation of a stable sol fraction enriched in high-molecular-weight branched molecules. Phase diagrams over 2%–16% crosslinker and 0%–8% CTA identify a simple optimal condition: the gel-point conversion approaches unity along [CL] = 2[CTA], consistent with an average of two effective intermolecular attachment points per growing chain. Along this optimum line, higher crosslinker content produces more compact branched macromolecules, implying higher coil-overlap concentrations and lower intrinsic viscosities at fixed molecular weight. These results provide practical, quantitative guidance for selecting reagent ratios to synthesize soluble branched polymers via standard RP.

Key hub genes identification and therapeutic target prediction via multi-validation for the senescence-inflammation axis in prostate cancer

Molecular spectroscopy-based temperature diagnosis of hypervelocity impact products in Al/PTFE reactive structural materials

This study proposes a temperature diagnostic method for the reaction products of Al/PTFE reactive structural materials under hypervelocity impact (2.68–4.96 km/s) by integrating molecular spectroscopy theory with experimental data. Hypervelocity impact experiments were conducted using a two-stage light gas gun, and transient spectral measurements captured the radiation spectra of Al/PTFE impacting aluminum plates, revealing characteristic spectral lines of AlO (B2Σ+–X2Σ+) and C2d3Πg−a3Πu. Theoretical calculations employed high-precision ab initio methods (HF/CASSCF/MRCI+Q with the aug-cc-pVQZ-DK basis set) to derive the potential energy curves, transition dipole moment functions, spectroscopic constants, partition functions, and spectral line intensities in the ultraviolet region for AlO. These calculations provide a robust foundation for temperature diagnostics. The results support the analysis of the energy release mechanisms and damage efficiency of Al/PTFE under hypervelocity impact.

Integrating genetics, age and imaging to predict treatment outcomes in neovascular age-related macular degeneration: a proof-of-concept study

Activation energies in the grand canonical ensemble: Diffusion of methane in a zeolite

We introduce a method for calculating activation energies in the grand canonical ensemble. This is an extension of the previously developed fluctuation theory for dynamics approach that determines the activation energy for any dynamical timescale from simulations at a single temperature. In the grand canonical ensemble, there are two contributions to the activation energy: An intrinsic energetic barrier for the dynamics and effects on the timescale due to changes in the number of molecules within the system as temperature is varied. We demonstrate the approach by calculating both of these contributions for the diffusion of methane molecules (at different chemical potentials) in a zeolite framework using a combination of grand canonical Monte Carlo and molecular dynamics simulations. The contributions to the activation energy from different energetic components and intermolecular interactions are further explored. In addition, we show how the dependence of the diffusion coefficient on the chemical potential can be determined locally from simulations at a single chemical potential and globally from multiple such simulations.

Perceptions, attitudes, practices, and barriers towards research in standardized training of laboratory medicine trainees: a cross-sectional questionnaire-based survey

Sampling the liquid–gas critical point with Boltzmann generators

Generative models based on invertible transformations provide a physics-aware route to sample equilibrium configurations directly from the Boltzmann distribution, enabling efficient exploration of complex thermodynamic landscapes. Here, we evaluate their applicability in regions where conventional simulations suffer from severe dynamical bottlenecks, focusing on the liquid–gas critical point of a Lennard-Jones fluid. We show that Boltzmann generators capture essential signatures of critical behavior, retain reliable performance when trained at or near criticality, and extrapolate across neighboring states of the phase diagram. An intriguing observation is that the model’s efficiency metric closely traces the underlying phase boundaries, hinting at a connection between generative performance and thermodynamics. However, the approach remains limited by the small system sizes currently accessible, which suppress the large fluctuations that characterize critical phenomena. Our results delineate the current capabilities and boundaries of Boltzmann generators in challenging regions of phase space, while pointing toward future applications in problems dominated by slow dynamics, such as glass formation and nucleation.