Earthworm, Perionyx excavatus as an alternate protein source for Nile tilapia : Effects on growth performance, blood biochemistry, erythrocyte morphology and intestinal health

Recently, an increasing disparity has emerged in the need for raw fish meal (FM) and its supply, along with the environmental and financial obstacles associated with its use. Therefore, increasing and elevating the diversity of alternative protein sources for aquaculture nutrition is imperative. The study investigated the impact of substituting FM protein with EW meal on the growth, haemato-biochemical response and intestinal histomorphology of Nile tilapia. Up to 20% fishmeal replacement with EW meal had no adverse effects on fish growth performance. A quadratic analysis of the final body weight and EW meal level indicated the best growth performance at 17.5% replacement. Fish fed the 40% replacement level had significantly decreased height and width of intestinal folds and number of mucosal goblet cells compared to the control. Although red and white blood cell counts were found unchanged between the control, the 10% and 20% replacement groups, a significantly higher number of white blood cells and a lower number of red blood cells were found in the 40% group compared to the control. Blood glucose level was the highest, while haemoglobin level was the lowest in the 40% group. In the same group, significantly higher frequencies of erythrocyte cellular and nuclear abnormalities were noted. Lipid droplet accumulation in the liver was significantly higher in the 40% group, whilst the 10% and 20% groups showed no significant difference compared to the control.publishedVersio

Neuromorphic engineering needs closed-loop benchmarks

Neuromorphic engineering aims to build (autonomous) systems by mimicking biological systems. It is motivated by the observation that biological organisms—from algae to primates—excel in sensing their environment, reacting promptly to their perils and opportunities. Furthermore, they do so more resiliently than our most advanced machines, at a fraction of the power consumption. It follows that the performance of neuromorphic systems should be evaluated in terms of real-time operation, power consumption, and resiliency to real-world perturbations and noise using task-relevant evaluation metrics. Yet, following in the footsteps of conventional machine learning, most neuromorphic benchmarks rely on recorded datasets that foster sensing accuracy as the primary measure for performance. Sensing accuracy is but an arbitrary proxy for the actual system's goal—taking a good decision in a timely manner. Moreover, static datasets hinder our ability to study and compare closed-loop sensing and control strategies that are central to survival for biological organisms. This article makes the case for a renewed focus on closed-loop benchmarks involving real-world tasks. Such benchmarks will be crucial in developing and progressing neuromorphic Intelligence. The shift towards dynamic real-world benchmarking tasks should usher in richer, more resilient, and robust artificially intelligent systems in the future

Treatment of persistent organic pollutants in wastewater using hydrodynamic cavitation in synergy with advanced oxidation process

Persistent organic pollutants (POPs) are very tenacious wastewater contaminants. The consequences of their existence have been acknowledged for negatively affecting the ecosystem with specific impact upon endocrine disruption and hormonal diseases in humans. Their recalcitrance and circumvention of nearly all the known wastewater treatment procedures are also well documented. The reported successes of POPs treatment using various advanced technologies are not without setbacks such as low degradation efficiency, generation of toxic intermediates, massive sludge production, and high energy expenditure and operational cost. However, advanced oxidation processes (AOPs) have recently recorded successes in the treatment of POPs in wastewater. AOPs are technologies which involve the generation of OH radicals for the purpose of oxidising recalcitrant organic contaminants to their inert end products. This review provides information on the existence of POPs and their effects on humans. Besides, the merits and demerits of various advanced treatment technologies as well as the synergistic efficiency of combined AOPs in the treatment of wastewater containing POPs was reported. A concise review of recently published studies on successful treatment of POPs in wastewater using hydrodynamic cavitation technology in combination with other advanced oxidation processes is presented with the highlight of direction for future research focus

A Neuromorphic Architecture for Reinforcement Learning from Real-Valued Observations

Reinforcement Learning (RL) provides a powerful framework for decision-making in complex environments. However, implementing RL in hardware-efficient and bio-inspired ways remains a challenge. This paper presents a novel Spiking Neural Network (SNN) architecture for solving RL problems with real-valued observations. The proposed model incorporates multi-layered event-based clustering, with the addition of Temporal Difference (TD)-error modulation and eligibility traces, building upon prior work. An ablation study confirms the significant impact of these components on the proposed model's performance. A tabular actor-critic algorithm with eligibility traces and a state-of-the-art Proximal Policy Optimization (PPO) algorithm are used as benchmarks. Our network consistently outperforms the tabular approach and successfully discovers stable control policies on classic RL environments: mountain car, cart-pole, and acrobot. The proposed model offers an appealing trade-off in terms of computational and hardware implementation requirements. The model does not require an external memory buffer nor a global error gradient computation, and synaptic updates occur online, driven by local learning rules and a broadcasted TD-error signal. Thus, this work contributes to the development of more hardware-efficient RL solutions