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Research on PVDF/BNNSs/MXene multilayer films with high energy density and thermal conductivity for dielectric capacitors

Polyvinylidene fluoride (PVDF) has promising applications in the field of dielectric capacitors. However, its low dielectric constant and thermal conductivity limit energy storage density. To address this, three multilayer composite topologies were designed with PVDF/boron nitride nanosheets as insulation and PVDF/MXene as polarization layers. A performance evaluation framework based on the analytic hierarchy process technique for order preference by similarity to the ideal solution method identified the insulation–polarization–polarization–insulation topology as the optimal configuration. This structure enhanced the dielectric performance (εr/tan δ) by 239.44% over pure PVDF at 103 Hz, increased thermal conductivity by 60.45%, and improved breakdown field strength. In addition, charge–discharge efficiency at 300 MV/m reached 75%, with a discharge density of 6.3 J/cm3, which is 152% higher than PVDF. The multilayer design effectively integrates the strengths of each layer to significantly enhance the overall performance, demonstrating that operational research methods are practical for evaluating dielectric materials and guiding design.

Restoration of angiogenic capacity in senescent endothelial cells by a pharmacological reprogramming approach

Senescent endothelial cells (EC) are key players in the pathophysiology of cardiovascular diseases and are characterized by a reduced angiogenic and regenerative potential. Therefore, targeting these cells has been suggested as an effective therapeutic strategy to reduce vascular disease burden and potentially improve health and lifespan of humans. Here, we aimed to establish a pharmacological, partial reprogramming strategy to improve replicative senescent endothelial cell function in the context of angiogenesis. We demonstrate that our treatment improves tube formation and sprouting capacity but also increases proliferation and migration capacity in vitro. Further, inflammation and DNA damage were reduced in the replicative senescent cells. These processes were initiated by a short and timely-restricted overexpression of the Yamanaka-factors induced by our pharmacological strategy. The advantage of these compounds is that they are FDA approved in their respective concentrations which could pave the way for use in a clinical setting.

Ultrasound-assisted green synthesized ZnO nanoparticles with different solution pH for water treatment

Near ideal photoluminescence in perovskites: Excitonic effects and self-consistent back extraction of recombination parameters

Recent reports indicate that perovskite-based light emitting diodes (LEDs) have achieved an external quantum efficiency (EQE) of 32%—rather an internal quantum efficiency close to 100%. Much of this improved performance is attributed to the role of excitons. While the experimental trends are encouraging, the recombination parameters estimated through extensive curve-fitting of photoluminescence transients are often not amenable to reasonable interpretations. In view of the same, through a detailed analysis of free carrier–exciton dynamics in perovskite optoelectronic materials, here we identify a coherent scheme to unambiguously back extract the relevant parameters. The model predictions compare well with the recent experimental results on perovskite LEDs with record EQE, thus quantifying the role of excitons. Importantly, this work identifies a physics aware scheme for the design of experiments tailored toward consistent exploration of underlying physical mechanisms and hence could enable a synergistic optimization of process technology and device performance.

Prediction of hydration energies of adsorbates at Pt(111) and liquid water interfaces using machine learning

Aqueous phase heterogeneous catalysis is important to various industrial processes, including biomass conversion, Fischer–Tropsch synthesis, and electrocatalysis. Accurate calculation of solvation thermodynamic properties is essential for modeling the performance of catalysts for these processes. Explicit solvation methods employing multiscale modeling, e.g., involving density functional theory and molecular dynamics have emerged for this purpose. Although accurate, these methods are computationally intensive. This study introduces machine learning (ML) models to predict solvation thermodynamics for adsorbates on a Pt(111) surface, aiming to enhance computational efficiency without compromising accuracy. In particular, ML models are developed using a combination of molecular descriptors and fingerprints and trained on previously published water–adsorbate interaction energies, energies of solvation, and free energies of solvation of adsorbates bound to Pt(111). These models achieve root mean square error values of 0.09 eV for interaction energies, 0.04 eV for energies of solvation, and 0.06 eV for free energies of solvation, demonstrating accuracy within the standard error of multiscale modeling. Feature importance analysis reveals that hydrogen bonding, van der Waals interactions, and solvent density, together with the properties of the adsorbate, are critical factors influencing solvation thermodynamics. These findings suggest that ML models can provide rapid and reliable predictions of solvation properties. This approach not only reduces computational costs but also offers insights into the solvation characteristics of adsorbates at Pt(111)–water interfaces.

Contribution of health system governance in delivering primary health care services for universal health coverage: A scoping review

Background The implementation of the primary health care (PHC) approach requires essential health system inputs, including structures, policies, programs, organization, and governance. Effective health system governance (HSG) is crucial in PHC systems and services, as it can significantly influence health service delivery. Therefore, understanding HSG in the context of PHC is vital for designing and implementing health programs that contribute to universal health coverage (UHC). This scoping review explores how health system governance contributes to delivering PHC services aimed at achieving UHC. Methods We conducted a scoping review of published evidence on HSG in the delivery of PHC services toward UHC. Our search strategy focused on three key concepts: health system governance, PHC, and UHC. We followed Arksey and O’Malley’s scoping review framework and adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist to guide our methodology. We used the World Health Organization’s framework on HSG to organize the data and present the findings. Results Seventy-four studies were included in the final review. Various functions of HSG influenced PHC systems and services, including:1) formulating health policies and strategic plans (e.g., addressing epidemiological and demographic shifts and strategic financial planning), 2) implementing policy levers and tools (such as decentralization, regulation, workforce capacity, and supply chain management), 3) generating intelligence and evidence (including priority setting, monitoring, benchmarking, and evidence-informed decision-making), 4) ensuring accountability (through commitments to transparency), and 5) fostering coordination and collaboration (via subnational coordination, civil society engagement, and multisectoral partnerships). The complex interplay of these HSG interventions operates through intricate mechanisms, and has synergistic effects on PHC service delivery. Conclusion PHC service delivery is closely linked to HSG functions, which include formulating strategic policies and plans responsive to evolving epidemiological and demographic needs, utilizing digital tools, decentralizing resources, and fostering multisectoral actions. Effective policy implementation requires robust regulation, evidence-based decision-making, and continuous monitoring. Accountability within health systems, alongside community engagement and civil society collaboration, is vital for realizing PHC principles. Local health institutions should collaborate with communities—end users of these systems—to implement formal rules and ensure PHC service delivery progresses toward UHC. Sociocultural contexts and community values should inform decision-making aligning health needs and services to achieve universal access to PHC services.

Retrospective analysis of clinical outcomes and early complications of conventional circumcision techniques and thermocautery-assisted circumcision

Simultaneous observation of zero-index band and quasibound state in the continuum in all dielectric photonic crystals

Zero-index (ZI) photonic materials show the absence of spatial phase accumulation, making them a suitable candidate for many useful optical applications. On the other hand, a bound state in the continuum (BIC), having extremely high-quality (Q) factors, provides a powerful way to enhance light–matter interaction, sensitivity, and nonlinear properties of materials. We propose a simple square lattice of silicon embedded in silica with C4v symmetry through which we achieved quasi-BIC with a high Q-factor of 2.25 × 107 and simultaneously obtained the zero-index band (ZIB) with a relative 3 dB bandwidth of 3.3%–3.66% through transmission–reflection consideration around the Dirac point. The experimental verification of the ZIB in the microwave band aligns well with the simulated results. An extension study and application in the THz band are also provided. The results can have potential applications in directive antennas, high-speed connectors and devices, sensors, non-linear optics, and large single-mode lasers with an appreciable band of operating frequency.

Application of the noncollinear Scalmani–Frisch formalism to current density functional theory

We generalize the noncollinear formalism proposed by Scalmani and Frisch [J. Chem. Theory Comput. 8, 2193 (2012)] to include the particle and spin current densities for meta-generalized gradient approximations and local hybrid functionals. This allows us to fully include the impact of spin–orbit coupling in relativistic calculations and for applications to finite magnetic fields. For the latter, we use London atomic orbitals to ensure gauge origin invariance. It is shown that this formalism is superior to the more common canonical noncollinear approach in relativistic calculations, as it naturally includes all three spin current densities in the closed-shell limit and avoids the projection onto the spin magnetization vector. This is important to easily restore rotational invariance in this limit. In addition, the Scalmani–Frisch approach can be made numerically stable and may lead to a nonvanishing local magnetic torque. However, both formalisms are rotationally invariant for open-shell systems and in finite magnetic fields.

Development and application of a binary medium bond contact model for calcareous sands

In the construction process of islands and reefs in the South China Sea, calcareous sand, as an important foundation and building material, has been widely used in engineering practice. It is crucial to explore the mechanical properties of calcareous sands to ensure the stability and maintenance of these islands and reefs. To study the effects of the bonding progressive failure on its macroscopic mechanical properties, a binary media bonding contact model is developed. This model can describe the gradual deterioration process of the intergranular cementation. Additionally, a discrete elemental contact subroutine (DLL) is created by C++ language program for the particle flow program (PFC2D) to utilize. The compression and direct shear testing of single contact was subjected. The computational accuracy of the proposed binary media contact model was confirmed by the comparison of the theoretical and numerical results. The biaxial shear discrete element simulations were conducted to study the behaviors of calcareous sand under different confining pressure conditions. The stress-strain curves obtained are in good agreement with the experimental results. The results show that the proposed binary media bonding contact model can accurately reflects the mechanical properties of calcareous sand. Based on calibrated discrete element contact mesoscopic parameters, the biaxial shear discrete element numerical simulations with different damage parameters were carried out. The effects of damage parameters on the stress-strain curves and shear strength are discussed. The evolution of effective coordination number, fabric anisotropy and force chain number are also analyzed. The research results can provide a basis for the study of macro-micromechanical properties of calcareous sand

Early warning system of the seasonal west nile virus infection risk in humans in northern greece, 2020–2024

Conductive metal oxide and hafnium oxide bilayer resistive random-access memory: An <i>ab initio</i> study

We perform density functional theory simulations of interfaces between two conductive metal-oxides (CMOs, namely, TaO and TiO) and cubic hafnium oxide (HfO2) in the context of bilayer Resistive Random Access Memory devices. These simulations are made at the generalized gradient approximation level. We simulate filamentary conduction in HfO2 by creating an atomically thin O atom vacancy path inside HfO2. We show that this atomically thin filament leads to a great reduction in the resistance of the structures. Moreover, we explore the possibility of the influence of O excess inside the CMO on the global resistance of the device and confirm the induced modulation. We also shed the light on two possible causes for the observed increase in the resistance when O atoms are inserted inside the CMO. Eventually, we push forward the key differences between devices with TaO and TiO as CMO. We show that structures with TaO are more stable in general and lead to a behavior implying only low and high resistance (two well-separated levels) while structures with TiO allow for intermediate resistances.

Metastable states assisted homogeneous nucleation in supercooled liquid aluminum alloys: Insights from a phenomenologically coupled atomistic, phase-field, and machine learning investigation

Crystallization due to liquid → solid transformation is observed in many natural and engineering processes. Extant literature indicates that crystallization in supercooled liquids is initiated by precursory metastable phases or states, also called non-classical nucleation. For face-centered cubic (FCC) materials, latest experimental and computational studies suggest that metastable hexagonal-closed packed (HCP) structures facilitate equilibrium FCC formation. However, the underlying nucleation mechanism remains unclear. Here, we examine structural changes and energetic barriers associated with such a non-classical mechanism, by performing molecular dynamics (MD) simulations using pure Al, Al-0.5 at. %Cu, and Al-0.5 at. %Ni (all FCC-formers) and phenomenologically coupling MD results with phase-field (PF) modeling. Such a coupling involved initializing PF simulation domains and constructing Landau polynomials—consistent with MD observations. Unsupervised machine learning was utilized to capture nuclei structures from MD simulations, while neural networks helped in extracting equilibrium interfacial energies from PF modeling. Atomistic simulations showed that precursory nuclei are comprised of collection of metastable-HCP states with medium ranged ordering. The pockets of HCP states later transform to critical nuclei—containing an FCC core and an outer layer of HCP. PF modeling qualitatively replicated the precursory-to-critical nuclei transformation and showed that the energetic barriers between the precursory and critical nuclei are substantially smaller than predictions obtained from classical nucleation theory. Together, these observations permitted us to propose a holistic non-classical mechanism that links triangular motifs within Al-based supercooled liquids to the critical nuclei via in-liquid structural transformations.

How can a top-down government program can be sustainable: A case study of horticulture village program in South Sulawesi Province

The horticulture village program is one of the activities in increasing sustainable vegetable production. The activity was carried out in 2021 and one of the provinces that carried it out was the province of South Sulawesi. The purpose of the paper is to evaluate the sustainability of the horticulture village program from four dimensions, namely input, process, product, context/outcome, using the Rapfish analysis tool, and strengthened by partial budget analysis to see the magnitude of changes in revenue. From the results of the Rapfish analysis, it could be seen that horticultural village activities are less sustainable, with the lowest value in the product dimension. The main reason for this is that the planning and implementation of the program lacked a well-thought-out social process. It requires the need for improvement in terms of quantity and quality of seeds, which considers the suitability and habits of farmers. Assistance from extension workers related to the use of appropriate and periodic technology for plant conditions (product) which will have an impact on productivity and income (context) needs to be considered. The increase in income for chili and shallots due to following the recommendations of cultivation technology, including reducing the use of chemical fertilizers, increasing the use of organic fertilizers, reducing the use of chemical pesticides and using plastic mulch, can be an entry point to convince farmers of the sustainability of this program with further improvements.

Enhanced object detection in remote sensing images by applying metaheuristic and hybrid metaheuristic optimizers to YOLOv7 and YOLOv8

Abstract Developments in object detection algorithms are critical for urban planning, environmental monitoring, surveillance, and many other applications. The primary objective of the article was to improve detection precision and model efficiency. The paper compared the performance of six different metaheuristic optimization algorithms including Gray Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Remora Optimization Algorithm (ROA), Aquila Optimizer (AO), and Hybrid PSO–GWO (HPSGWO) combined with YOLOv7 and YOLOv8. The study included two distinct remote sensing datasets, RSOD and VHR-10. Many performance measures as precision, recall, and mean average precision (mAP) were used during the training, validation, and testing processes, as well as the fit score. The results show significant improvements in both YOLO variants following optimization using these strategies. The GWO-optimized YOLOv7 with 0.96 mAP 50, and 0.69 mAP 50:95, and the HPSGWO-optimized YOLOv8 with 0.97 mAP 50, and 0.72 mAP 50:95 had the best performance in the RSOD dataset. Similarly, the GWO-optimized versions of YOLOv7 and YOLOv8 had the best performance on the VHR-10 dataset with 0.87 mAP 50, and 0.58 mAP 50:95 for YOLOv7 and with 0.99 mAP 50, and 0.69 mAP 50:95 for YOLOv8, indicating greater performance. The findings supported the usefulness of metaheuristic optimization in increasing the precision and recall rates of YOLO algorithms and demonstrated major significance in improving object recognition tasks in remote sensing imaging, opening up a viable route for applications in a variety of disciplines.

Brillouin light scattering spectroscopy of magnon–phonon thermal spectra of an in-plane magnetized YIG film in two-dimensional wavevector space

Brillouin Light Scattering (BLS) spectroscopy is widely used for studying collective acoustic and magnetic excitations. In magnetism, it is employed as a versatile tool for measuring the characteristics of magnetic media, visualizing linear and nonlinear spin-wave spatiotemporal dynamics, investigating magnon–phonon interaction effects, and exploring fundamental phenomena such as the Bose–Einstein condensation of magnons. At the same time, magnetic BLS spectroscopy has so far suffered from a lack of possibilities to resolve short-wavelength magnons in arbitrary wavevector directions. Here, we demonstrate two-dimensional thermal magnon and phonon spectra measured in a single-crystal film of Yttrium Iron Garnet (Y3Fe5O12). The wide-range two-dimensional wavevector selectivity is accomplished in the backscattering geometry via independent rotations of the probing beam and the sample plane. The spectra were measured in the range of magnon and phonon wavelengths down to 400nm and are fully consistent with calculations that take into account exchange, dipole, and elastic interactions. Our results open the way to an in-depth study of the magnon, phonon, and magnon–phonon dynamics of solids.

Perturbative spin–orbit couplings for the simulation of extended framework materials

A comprehensive description of photo-chemical processes in materials, comprising spin-forbidden processes such as intersystem crossing and phosphorescence, implies taking into account spin–orbit coupling. We present an efficient implementation of a perturbative spin–orbit coupling correction for the Tamm–Dancoff approximation of linear-response time-dependent density functional theory within a mixed Gaussian and plane wave framework relying on spin–orbit coupling corrected pseudopotentials. The implementation is validated for a benchmark set of small aromatic molecules, with mean errors in excitation energies and spin–orbit coupling matrix elements being in the range of 0.1–0.6 eV and 1.0–14.4 cm−1, respectively, in comparison with density functional theory and density functional theory multi-reference configuration interaction reference results. Computational timings are given for a bismuth-containing metal–organic framework.

Efficacy and safety of rituximab in anti-MuSK myasthenia Gravis: a systematic review and meta-analysis

Publisher's note: “High-temperature annealing induced electrical compensation in UID and Sn doped β-Ga2O3 bulk samples: The role of VGa–Sn complexes” [J. Appl. Phys. 137, 055703 (2025)]

Peptide classification from statistical analysis of nanopore sensing experiments

Peptide classification using nanopore-based devices promises to be a breakthrough method in basic research, diagnostics, and analytics. However, the measured blockage currents suffer from a low signal-to-noise ratio and a high information density that has hitherto not been fully deciphered. Some simple machine learning approaches using average current blockade depths and dwell-times have been investigated to improve this situation. In this work, a comprehensive statistical analysis of nanopore current signals is performed and demonstrated to be sufficient for classifying up to 42 peptides with over 70% accuracy. Two sets of features, the statistical moments and the catch22 set, are compared both in their representations and after training small classifier neural networks. We demonstrate that complex features of the events, captured in both the catch22 set and the central moments, are key to classifying peptides with otherwise similar mean currents. These results highlight the efficacy of purely statistical analysis of nanopore data and suggest a path forward for more sophisticated classification techniques.