Corrigendum to "Energy saving potential analysis applying factory scale energy audit - A case study of food production" [Heliyon 9 (3), (March 2023) Article e14216].

[This corrects the article DOI: 10.1016/j.heliyon.2023.e14216.].

Intelligent approaches for sustainable management and valorisation of food waste.

Food waste (FW) is a severe environmental and social concern that today's civilization is facing. Therefore, it is necessary to have an efficient and sustainable solution for managing FW bioprocessing. Emerging technologies like the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML) are critical to achieving this, in which IoT sensors' data is analyzed using AI and ML techniques, enabling real-time decision-making and process optimization. This work describes recent developments in valorizing FW using novel tactics such as the IoT, AI, and ML. It could be concluded that combining IoT, AI, and ML approaches could enhance bioprocess monitoring and management for generating value-added products and chemicals from FW, contributing to improving environmental sustainability and food security. Generally, a comprehensive strategy of applying intelligent techniques in conjunction with government backing can minimize FW and maximize the role of FW in the circular economy toward a more sustainable future.

Energy saving potential analysis applying factory scale energy audit - A case study of food production.

An energy audit (EA) is a crucial step in boosting factory energy efficiency and obtaining certification for cleaner manufacturing. The results of a preliminary energy audit carried out at a sizable industrial facility in Jordan that creates some of the most well-known foods in the Middle East are presented in this study. The monthly demand of the factory for diesel ranged from 75,251.545 to 166,666.67 L. The factory energy model which is used to examine the impact of various energy-saving practices on the factory's primary energy consumption, was developed with the help of the energy audit. It has been established that optimizing the factory's energy use and the boiler systems' performance with regards to diesel consumption can withstand an expected monthly financial savings of 14205.85 Jordanian Dinar (JD). This has allowed a reduction in energy use of up to 18%. The CO2 harmful emissions were also decreased. Additionally, it is estimated that switching from the proposed motors to energy-efficient motors will cost less overall over time, saving around 3472.314 JD/month or 0.33576/year on average. Moreover, it was discovered that a total of 772.82021 Ton CO2/year emissions may be avoided each year.

A Conceptual Multi-Layer Framework for the Detection of Nighttime Pedestrian in Autonomous Vehicles Using Deep Reinforcement Learning.

The major challenge faced by autonomous vehicles today is driving through busy roads without getting into an accident, especially with a pedestrian. To avoid collision with pedestrians, the vehicle requires the ability to communicate with a pedestrian to understand their actions. The most challenging task in research on computer vision is to detect pedestrian activities, especially at nighttime. The Advanced Driver-Assistance Systems (ADAS) has been developed for driving and parking support for vehicles to visualize sense, send and receive information from the environment but it lacks to detect nighttime pedestrian actions. This article proposes a framework based on Deep Reinforcement Learning (DRL) using Scale Invariant Faster Region-based Convolutional Neural Networks (SIFRCNN) technologies to efficiently detect pedestrian operations through which the vehicle, as agents train themselves from the environment and are forced to maximize the reward. The SIFRCNN has reduced the running time of detecting pedestrian operations from road images by incorporating Region Proposal Network (RPN) computation. Furthermore, we have used Reinforcement Learning (RL) for optimizing the Q-values and training itself to maximize the reward after getting the state from the SIFRCNN. In addition, the latest incarnation of SIFRCNN achieves near-real-time object detection from road images. The proposed SIFRCNN has been tested on KAIST, City Person, and Caltech datasets. The experimental results show an average improvement of 2.3% miss rate of pedestrian detection at nighttime compared to the other CNN-based pedestrian detectors.

Denial-of-Service Attack on IEC 61850-Based Substation Automation System: A Crucial Cyber Threat towards Smart Substation Pathways.

The generation of the mix-based expansion of modern power grids has urged the utilization of digital infrastructures. The introduction of Substation Automation Systems (SAS), advanced networks and communication technologies have drastically increased the complexity of the power system, which could prone the entire power network to hackers. The exploitation of the cyber security vulnerabilities by an attacker may result in devastating consequences and can leave millions of people in severe power outage. To resolve this issue, this paper presents a network model developed in OPNET that has been subjected to various Denial of Service (DoS) attacks to demonstrate cyber security aspect of an international electrotechnical commission (IEC) 61850 based digital substations. The attack scenarios have exhibited significant increases in the system delay and the prevention of messages, i.e., Generic Object-Oriented Substation Events (GOOSE) and Sampled Measured Values (SMV), from being transmitted within an acceptable time frame. In addition to that, it may cause malfunction of the devices such as unresponsiveness of Intelligent Electronic Devices (IEDs), which could eventually lead to catastrophic scenarios, especially under different fault conditions. The simulation results of this work focus on the DoS attack made on SAS. A detailed set of rigorous case studies have been conducted to demonstrate the effects of these attacks.