This paper addresses robust design of the active-power and dc-link control loops of powersynchronization control. Robustness is obtained by analytic gain selections which give large enough stability margins. The proposed design allows robust stability irrespective of the grid strength and of the operating point, the latter with one exception. The proposed design is compared to design based on the principle virtual synchronous machine. Experiments show that the time-domain results correlate well with the frequency-domain results.

Tomato is one of the most significant vegetable crops, which provides several important dietary components. Pakistan has a significant low tomato yield compared to other countries because of low genetic diversity and the absence of improved cultivars. The present study aimed to investigate the genetic variability, heritability, and genetic advance for yield and yield-related traits in tomato. For this purpose, eight tomato parents and their 15 crosses or hybrids were evaluated to study the relevant traits. Significant variation was observed for all studied traits. Higher values of the genotypic coefficient of variability (GCV) and phenotypic coefficient of variability (PCV) were recorded for yield per plant (YP) (kg) (37.62% and 37.79%), as well as the number of fruits per cluster (NFRC) (31.52% and 31.71%), number of flowers per cluster (24.63 and 24.67), and single fruit weight (g) (23.49 and 23.53), which indicated that the selection for these traits would be fruitful. Higher heritability (h2) estimates were observed for the number of flowers per cluster (NFC) (0.99%), single fruit weight (SFW) (g) (0.99%), and yield per plant (YP) (kg) (0.99%). Single fruit weight (SFW) (g) exhibited higher values for all components of variability. High genetic advance as a % of the mean (GAM) coupled with higher heritability (h2) was noted for the yield per plant (YP) (kg) (52.58%) and the number of fruits per cluster (NFRC) (43.91). NFRC and SFW (g) had a highly significant correlation with YP (kg), while FSPC had a significant positive association with YP (kg), and these traits can be selected to enhance YP (kg). Among the 15 hybrids, Nagina × Continental, Pakit × Continental, and Roma × BSX-935 were selected as high-yielding hybrids for further evaluation and analysis. These findings revealed that the best performing hybrids could be used to enhance seed production and to develop high-yielding varieties. The parents could be further tested to develop hybrids suitable for changing climatic conditions. The selection of YP (kg), SFW (g), NFC, and NFRC would be ideal for selecting the best hybrids.

In the resting state (closed or open eyes) the electroencephalogram (EEG) and the magnetoencephalogram (MEG) exibit rhythmic brain activity is typically the 10 Hz alpha rhythm. It has a topographic frequency spectral distribution that is, quite similar for both modalities--something not surprising since both EEG and MEG are generated by the same basic oscillations in thalamocortical circuitry. However, different physical aspects underpin the two types of signals. Does this difference lead to a different distribution of reconstructed sources for EEG and MEG rhythms? This question is important for the transferal of results from one modality to the other but has surprisingly received scant attention till now. We address this issue by comparing eyes open EEG source spectra recorded from 77 subjects from the Cuban Human Brain Mapping project with the MEG of 63 subjects from the Human Connectome Project. Source spectra for each voxel and frequency were obtained via a novel sparsecovariance inverse method (BC-VARETA) based on individualized BEM head models with subject-specific regularization parameters (noise to signal ratio). We circumvent the zero inflated statistical issue arising from sparse estimation by employing a novel dimensionality reduction technique known as Zero-inflated Factor Analysis (ZIFA). Both minimum energy and Hotelling's T-2 tests showed that ZIFA scores for MEG and EEG sources were significantly different at all frequency bands. These results exclude a simple identification of MEG and EEG sources of resting-state EEG rhythms. Further study is required to determine the relative contribution of instrumental, physical or physiological mechanisms to these differences.

Objective. Brain–computer interfaces (BCIs) translate neural activity into control signals for assistive devices in order to help people with motor disabilities communicate effectively. In this work, we introduce a new BCI architecture that improves control of a BCI computer cursor to type on a virtual keyboard. Approach. Our BCI architecture incorporates an external artificial intelligence (AI) that beneficially augments the movement trajectories of the BCI. This AI-BCI leverages past user actions, at both long (100 s of seconds ago) and short (100 s of milliseconds ago) timescales, to modify the BCI’s trajectories. Main results. We tested our AI-BCI in a closed-loop BCI simulator with nine human subjects performing a typing task. We demonstrate that our AI-BCI achieves: (1) categorically higher information communication rates, (2) quicker ballistic movements between targets, (3) improved precision control to ‘dial in’ on targets, and (4) more efficient movement trajectories. We further show that our AI-BCI increases performance across a wide control quality spectrum from poor to proficient control. Significance. This AI-BCI architecture, by increasing BCI performance across all key metrics evaluated, may increase the clinical viability of BCI systems.

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