Browse Articles

Prognostic implications of system inflammation response index in atrial fibrillation patients with type 2 diabetes mellitus

An analysis of extraskeletal osteosarcoma based on the literature

Rapid CO2 mineralization by zeolite via cation exchange

Fast barrier-free switching in synthetic antiferromagnets

AbstractWe analytically solve the Landau-Lifshitz equations for the collective magnetization dynamics in a synthetic antiferromagnet (SAF) nanoparticle and uncover a regime of barrier-free switching under a short small-amplitude magnetic field pulse applied perpendicular to the SAF plane. We give examples of specific implementations for forming such low-power and ultra-fast switching pulses. For fully optical, resonant, barrier-free SAF switching we estimate the power per write operation to be $$ \sim 100 $$ ∼ 100  pJ, 10–100 times smaller than for conventional quasi-static rotation, which should be attractive for memory applications.

An intelligent incentive-based demand response program for exhaustive environment constrained techno-economic analysis of microgrid system

AbstractThe cost-effective scheduling of distributed energy resources through sophisticated optimization algorithms is the main focus of recent work on microgrid energy management. In order to improve load factor and efficiency, load-shifting techniques are frequently used in conjunction with additional complex constraints such as PHEV scheduling and battery life assessment. Pollutant reduction, however, is rarely highlighted as a primary goal. An incentive-based demand response (IBDR) is introduced in the proposed work to close this gap and promote load curtailment during peak hours. IBDR policy rewards participant customers with incentives for load curtailment which in turn lowers emissions and generation costs. Furthermore, a trade-off approach ensures both environmental and economic sustainability by striking a balance between cost reduction and emission reduction. Considering the fact in view that the 30–40% of the microgrid customers are willing to participate in the IBDR program, six different scenarios that have been analysed, each of which involves various levels of grid participation and different approaches to pricing in the electricity market. These scenarios also include the implementation of demand response programmes. Differential evolution algorithm was used as the optimization tool for the study. The results achieved for all the scenarios demonstrate the suitability and effectiveness of implementing the suggested IBDR strategy in terms of cost savings. According to numerical results reported, the generating cost decreased by 10–13% with the inclusion of IBDR. Additionally, a 6–8% reduction in peak and 4–5% improvement in load factor was also realised as a positive impact of the IBDR policy. The weighted economic emission dispatch algorithm offered a balanced solution that considered both the minimum generation cost and emissions for various load models in the microgrid system.

Differential physiological and yield responses of selected mung bean (Vigna radiata (L.) R. Wilczek) genotypes to various high-temperature stress regimes

The effects of war-related experiences on mental health symptoms of individuals living in conflict zones: a longitudinal study

A risk prediction model for endometrial hyperplasia/endometrial carcinoma in premenopausal women

Unifying spatiotemporal and frequential attention for traffic prediction

Abstract Intelligent transportation systems heavily rely on forecasting urban traffic flow, and a variety of approaches have been developed for this purpose. However, most current methods focus on exploring spatial and temporal dependencies in historical traffic data, while often overlooking the inherent spectral characteristics hidden in traffic time series. In this paper, we introduce an approach to analyzing traffic flow in the frequency domain. By integrating attention mechanisms, we comprehensively capture the hidden correlations among space, time, and frequency dimensions. By leveraging deep learning to capture spatial correlations in traffic flow and applying spectral analysis to fuse time series data with underlying periodic correlations in both the time and frequency domains, we develop an innovative traffic prediction model called the Space-Time-Frequency Attention Network (STFAN). The core of this network lies in the application of attention mechanisms, which project the hidden states of current traffic features across the space, time, and frequency domains onto future hidden states. This approach enables a comprehensive learning of the relationships between each dimension and the future states, ultimately allowing for accurate predictions of future traffic flow. We carry out experiments on two publicly available datasets from the California Department of Transportation, PeMS04 and PeMS08, to assess the performance of the proposed model. The results demonstrate that the proposed model outperforms existing baseline models in terms of predictive accuracy, particularly for mid- and long-term traffic flow forecasting. Finally, the ablation study confirmed that the frequency domain characteristics of traffic flow significantly influence future traffic conditions, demonstrating the practical effectiveness of the model.

A 15-Item modification of the PSP rating scale to improve clinical meaningfulness and statistical performance

Bathymetry estimation for coastal regions using self-attention

Parasitic structure defect blights sustainability of cobalt-free single crystalline cathodes

AbstractRecent efforts to reduce battery costs and enhance sustainability have focused on eliminating Cobalt (Co) from cathode materials. While Co-free designs have shown notable success in polycrystalline cathodes, their impact on single crystalline (SC) cathodes remains less understood due to the significantly extended lithium diffusion pathways and the higher-temperature synthesis involved. Here, we reveal that removing Co from SC cathodes is structurally and electrochemically unfavorable, exhibiting unusual voltage fade behavior. Using multiscale diffraction and imaging techniques, we identify lithium-rich nanodomains (LRNDs) as a heterogeneous phase within the layered structure of Co-free SC cathodes. These LRNDs act as critical tipping points, inducing significant chemo-mechanical lattice strain and irreversible structural degradation, which exacerbates the voltage and capacity loss in electrochemical performance. Our findings highlight the considerable challenges of developing Co-free SC cathodes compared to polycrystalline ones and emphasize the need for new strategies to balance the interplay between cost, sustainability, and performance.

Statistical damage constitutive model of soft rock based on Improved Hoek-Brown strength criterion

Optomechanical micro-rheology of complex fluids at ultra-high frequency

Determination of antibacterial and antioxidant potential of organic crude extracts from Malus domestica, Cinnamomum verum and Trachyspermum ammi

Termination-acidity tailoring of molybdenum carbides for alkaline hydrogen evolution reaction

Mental Health and Associated Factors among Bangladeshi Migrants in Thailand: a cross-sectional study

Multi-channel learning for integrating structural hierarchies into context-dependent molecular representation

AbstractReliable molecular property prediction is essential for various scientific endeavors and industrial applications, such as drug discovery. However, the data scarcity, combined with the highly non-linear causal relationships between physicochemical and biological properties and conventional molecular featurization schemes, complicates the development of robust molecular machine learning models. Self-supervised learning (SSL) has emerged as a popular solution, utilizing large-scale, unannotated molecular data to learn a foundational representation of chemical space that might be advantageous for downstream tasks. Yet, existing molecular SSL methods largely overlook chemical knowledge, including molecular structure similarity, scaffold composition, and the context-dependent aspects of molecular properties when operating over the chemical space. They also struggle to learn the subtle variations in structure-activity relationship. This paper introduces a multi-channel pre-training framework that learns robust and generalizable chemical knowledge. It leverages the structural hierarchy within the molecule, embeds them through distinct pre-training tasks across channels, and aggregates channel information in a task-specific manner during fine-tuning. Our approach demonstrates competitive performance across various molecular property benchmarks and offers strong advantages in particularly challenging yet ubiquitous scenarios like activity cliffs.

Spin and valley dependent transport and tunneling magnetoresistance in irradiated ferromagnetic WSe2double barrier junctions

AbstractSpin and valley polarizations (Ps and PKK’) and tunneling magnetoresistance (TMR) are demonstrated in the ferromagnetic/barrier/normal/barrier/ferromagnetic WSe2 junction, with the gate voltage and off-resonant circularly polarized light (CPL) applied to the two barrier regions. The minimum incident energy of non-zero spin- and valley-resolved conductance has been derived, which is consistent with numerical calculations and depends on the electric potential U, CPL intensity ΔΩ, exchange field h, and magnetization configuration: parallel (P) or antiparallel (AP). For the P (AP) configuration, the energy region with PKK’ = -1 or Ps = 1 is wider (narrower) and increases with ΔΩ. As h increases, the Ps = 1 (PKK’ = -1 or Ps = 1) plateau becomes wider (narrower) for the P (AP) configuration. As U increases, the energy region with PKK’ = -1 increases first and then moves parallel to the EF-axis, and the energy region with Ps = 1 for the P configuration remains unchanged first and then decreases. The energy region for TMR = 1 increases rapidly with h, remains unchanged first and then decreases as U increases, and has little dependence on ΔΩ. When the helicity of the CPL reverses, the valley polarization will switch. This work sheds light on the design of spin-valley and TMR devices based on ferromagnetic WSe2 double-barrier junctions.

GmERF13 mediates salt inhibition of nodulation through interacting with GmLBD16a in soybean