Prediction of fish mortality based on a probabilistic anomaly detection approach for recirculating aquaculture system facilities

Ammonia/ammonium removal/recovery from wastewaters using bioelectrochemical systems (BES): A review

This review updates the current research efforts on using BES to recover NH₃/NH₄⁺, highlighting the novel configurations and introducing the working principles and the applications of microbial fuel cell (MFC), microbial electrolysis cell (MEC), microbial desalination cell (MDC), and microbial electrosynthesis cell (MESC) for NH₃/NH₄⁺ removal/recovery. However, commonly studied BES processes for NH₃/NH₄⁺ removal/recovery are energy intensive with external aeration needed for NH₃ stripping being the largest energy input. In such a process bipolar membranes used for yielding a local alkaline pool recovering NH₃ is not cost-effective. This gives a chance to microbial electrosynthesis which turned out to be a potential alternative option to approach circular bioeconomy. Furtherly, the reactor volume and NH₃/NH₄⁺ removal/recovery efficiency has a weakly positive correlation, indicating that there might be other factors controlling the reactor performance that are yet to be investigated.

Stem and Calyx Identification of 3D Apples Using Multi-Threshold Segmentation and 2D Convex Hull

Traditional machine vision is widely used to identify apple quality, but this method finds it difficult to distinguish the apple stem and calyx from defects. To address this, we designed a new method to identify the stem and calyx of apples based on their concave shape. This method applies a fringe projection in a computer vision system of 3D reconstruction, followed by multi-threshold segmentation and a 2D convex hull technique to identify the stem and calyx. A camera and projector were used to reconstruct the 3D surface of the front half of an inspected apple. The height information for each pixel was reconstructed by a fringe projection and mathematical transformation. The 3D-reconstructed result was subjected to a multi-threshold segmentation technique and the segmentation results contained a concave feature in the curved line, representing the concave stem and calyx. The segmentation results were then subjected to a 2D convex hull technique, allowing for the identification of the stem and calyx. This method was evaluated using four groups of apples, and the proposed method is able to identify the stem and calyx with 98.93% accuracy.

A Taxonomy of Food Supply Chain Problems from a Computational Intelligence Perspective

In the last few years, the Internet of Things, and other enabling technologies, have been progressively used for digitizing Food Supply Chains (FSC). These and other digitalization-enabling technologies are generating a massive amount of data with enormous potential to manage supply chains more efficiently and sustainably. Nevertheless, the intricate patterns and complexity embedded in large volumes of data present a challenge for systematic human expert analysis. In such a data-driven context, Computational Intelligence (CI) has achieved significant momentum to analyze, mine, and extract the underlying data information, or solve complex optimization problems, striking a balance between productive efficiency and sustainability of food supply systems. Although some recent studies have sorted the CI literature in this field, they are mainly oriented towards a single family of CI methods (a group of methods that share common characteristics) and review their application in specific FSC stages. As such, there is a gap in identifying and classifying FSC problems from a broader perspective, encompassing the various families of CI methods that can be applied in different stages (from production to retailing) and identifying the problems that arise in these stages from a CI perspective. This paper presents a new and comprehensive taxonomy of FSC problems (associated with agriculture, fish farming, and livestock) from a CI approach; that is, it defines FSC problems (from production to retail) and categorizes them based on how they can be modeled from a CI point of view. Furthermore, we review the CI approaches that are more commonly used in each stage of the FSC and in their corresponding categories of problems. We also introduce a set of guidelines to help FSC researchers and practitioners to decide on suitable families of methods when addressing any particular problems they might encounter. Finally, based on the proposed taxonomy, we identify and discuss challenges and research opportunities that the community should explore to enhance the contributions that CI can bring to the digitization of the FSC.