Electronic nose as a tool for early detection of diseases and quality monitoring in fresh postharvest produce: A comprehensive review

Postharvest diseases and quality degradation are the major factors causing food losses in the fresh produce supply chain. Hence, detecting diseases and quality deterioration at the asymptomatic stage of produce enables growers to treat the diseases earlier, maintain quality and reduce postharvest food losses. With the emergence of numerous technologies to detect diseases early and monitor the quality of fresh produce, such as polymerase chain reaction, gas chromatography-mass spectrophotometry, and near-infrared spectroscopy, electronic nose (EN) has also gained acknowledgement and popularity in the past decade as a robust and non-invasive analysis tool to detect odor profile and establish volatile biomarkers for metabolomics databases. However, literature reviewing the EN research on the early detection of diseases in produce after harvest is scarce. The fundamental concept of EN working principles (odor sampling, gas detection, and data acquisition method), as well as the application of EN as a whole, are covered in the first section of the review. An in-depth discussion of the application of EN analysis in the early identification of postharvest diseases and quality monitoring is provided in the subsequent sections, which is the key objective of this comprehensive review. The prospect, limitations, and likely future developments of EN in the postharvest sector are further highlighted in the last section.     

Synthesis and Characterization of α- Fe2O3 NPs/P-Si Heterojunction for High Sensitive Photodetector

Silicon - In this work, Fe2O3 nanoparticles (NPs) were synthesized successfully through simple chemical precipitation method. The absorption spectra of the synthesized Fe2O3 NPs was studied by...     

Reinforcement Learning with an Ensemble of Binary Action Deep Q-Networks

With the advent of Reinforcement Learning (RL) and its continuous progress, state-of-the-art RL systems have come up for many challenging and real-world tasks. Given the scope of this area, various techniques are found in the literature. One such notable technique, Multiple Deep Q-Network (DQN) based RL systems use multiple DQN-based-entities, which learn together and communicate with each other. The learning has to be distributed wisely among all entities in such a scheme and the inter-entity communication protocol has to be carefully designed. As more complex DQNs come to the fore, the overall complexity of these multi-entity systems has increased many folds leading to issues like difficulty in training, need for high resources, more training time, and difficulty in fine-tuning leading to performance issues. Taking a cue from the parallel processing found in the nature and its efficacy, we propose a lightweight ensemble based approach for solving the core RL tasks. It uses multiple binary action DQNs having shared state and reward. The benefits of the proposed approach are overall simplicity, faster convergence and better performance compared to conventional DQN based approaches. The approach can potentially be extended to any type of DQN by forming its ensemble. Conducting extensive experimentation, promising results are obtained using the proposed ensemble approach on OpenAI Gym tasks, and Atari 2600 games as compared to recent techniques. The proposed approach gives a stateof-the-art score of 500 on the Cartpole-v1 task, 259.2 on the LunarLander-v2 task, and state-of-the-art results on four out of five Atari 2600 games.     

Metallic foam with cross flow heat exchanger: A review of parameters,performance, and challenges

The cross-flow heat exchanger involves a tractor moving between two fluids that flow in a direction perpendicular to each other, and one of the fluids is often a liquid and the other is a gas. This type of heat exchanger has been studied in many previous studies for its importance in air conditioning applications and many industrial applications. In this type of heat exchanger, the surface area for heat transfer is very large. Therefore, many techniques have been used to improve the thermal and dynamic performance of this type of heat exchanger. In this study, previous studies that used metallic foam as one of the ways to improve the performance of heat exchangers were reviewed. The most important techniques that were used in previous studies during the process of evaluating the thermal performance of a cross-flow heat exchanger in the presence of different types of metal foam were clarified. The use of metal foam depends on important factors, including (1) the type of material, where copper and aluminum were used in most of the previous studies, due to their availability and ease of foam formation using these materials, in addition to the good thermal properties, (2) porosity, where the porosity value of metal foam ranged between 0.85 and 0.98 in the previous studies, (3) the density of pores, most of the studies ranged between 10 and 40 PPI.