Short-term starvation at different feeding regimes on appetite responses, feeding utilization and physiological indices, of red hybrid tilapia (Oreochromis mossambicus × Oreochromis niloticus) fingerlings reared in brackish water.

A 42 day factorial trial (3x2) was designed to evaluate the effect of short-term starvation with different feeding frequencies on performance, feed utilization, physiological status and appetite responses of red hybrid tilapia fingerlings. Eighteen plastic tanks with a capacity of (55 L) were used to accomplish this work. Fingerlings with an average initial weight of 23 g ± 0.2 (SE) were randomly stocked at a rate of 8 fingerlings/aquarium. Six groups were designated as the following: II/ED: fish was fed twice every day; IV/ED: fish fed four times every day; II/EOD: fish fed twice every other day (alternate-day feeding or one day of feeding followed by another of fasting); IV/EOD: was fed four times every other day; II/EO3D: fish fed twice every other three days (three day of feeding followed by another of fasting) and IV/EO3D: fish fed four times every other three days. Fish were fed on commercial diets 30 % protein (4 % of biomass). Results showed insignificant differences between fish fed every other day and those fed every day in some growth indicators. In the same trend, the interaction between feed deprivation and feeding frequency cleared that fingerlings of IV/EOD did not significantly differ with those fed every day in growth indices. Moreover this treatment was the best in feed conversion efficiency and several physiological indicators.

Brain-Inspired Reservoir Computing Using Memristors with Tunable Dynamics and Short-Term Plasticity.

Recent advancements in reservoir computing (RC) research have created a demand for analogue devices with dynamics that can facilitate the physical implementation of reservoirs, promising faster information processing while consuming less energy and occupying a smaller area footprint. Studies have demonstrated that dynamic memristors, with nonlinear and short-term memory dynamics, are excellent candidates as information-processing devices or reservoirs for temporal classification and prediction tasks. Previous implementations relied on nominally identical memristors that applied the same nonlinear transformation to the input data, which is not enough to achieve a rich state space. To address this limitation, researchers either diversified the data encoding across multiple memristors or harnessed the stochastic device-to-device variability among the memristors. However, this approach requires additional preprocessing steps and leads to synchronization issues. Instead, it is preferable to encode the data once and pass them through a reservoir layer consisting of memristors with distinct dynamics. Here, we demonstrate that ion-channel-based memristors with voltage-dependent dynamics can be controllably and predictively tuned through the voltage or adjustment of the ion channel concentration to exhibit diverse dynamic properties. We show, through experiments and simulations, that reservoir layers constructed with a small number of distinct memristors exhibit significantly higher predictive and classification accuracies with a single data encoding. We found that for a second-order nonlinear dynamical system prediction task, the varied memristor reservoir experimentally achieved an impressive normalized mean square error of 1.5 × 10-3, using only five distinct memristors. Moreover, in a neural activity classification task, a reservoir of just three distinct memristors experimentally attained an accuracy of 96.5%. This work lays the foundation for next-generation physical RC systems that can exploit the complex dynamics of their diverse building blocks to achieve increased signal processing capabilities.

Exploring the National Nursing Research Priorities in the Eastern Mediterranean Region and Overcoming the Associated Challenges: An Expert Opinion.

BACKGROUND:  Nurses play a significant role in contributing to various health priorities globally, including research. Identifying the status of national nursing research priorities in the Eastern Mediterranean Region is crucial to cultivating these priorities. This expert opinion paper highlights the existing status of national nursing research priorities in Eastern Mediterranean Region countries concerning their existence and publicity. METHODS:  Experts from nine Eastern Mediterranean Region countries, including Egypt, Iran, Iraq, Jordan, Pakistan, Palestine, Qatar, Oman, and Saudi Arabia, contributed to this report. They participated by completing a cross-sectional survey and providing a narrative description of their opinions. RESULTS:  The findings revealed that 58% of the participating countries have existing national nursing research priorities, while 25.8% do not, and 16% are under development. Governmental organizations developed the largest portion of the priorities (38%). Midwives were not considered in half of the published priorities. The vast majority of national nursing research priorities (65%) were developed by experts' opinions and consensus, and 33% only have an associated strategy, outcome measures, and/or funding opportunities. Generally, most published research priorities were not updated regularly. CONCLUSION:  Eastern Mediterranean Region countries face a challenge with the need for more nurses, which may hinder their involvement in research projects or continued education. Despite this, all countries involved in this report emphasized the importance of developing nursing education and research as priorities for improving their current nursing workforce. Health policymakers, nurse practitioners, academic researchers, educators, and nursing leaders should collaborate to develop operational plans to foster national nursing education and research.