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Revolutionizing seafood packaging: advancements in biopolymer smart nano-packaging for extended shelf-life and quality assurance
Get PDFFood packaging is one of the most important strategies to prevent food damage or spoilage during storage and the supply chain. Among various food types, seafood, a high-value product, is particularly vulnerable to post-harvest quality loss and microbial contamination during storage. Although current plastic-based packaging materials are durable, they pose a serious threat to the environment. Therefore, research on natural biopolymers for packaging is a top priority for scientists, industries, and government bodies. Additionally, nanoengineering concepts enhance the physicochemical and functional properties of biopolymers, thereby revolutionizing the packaging industry. This review provides a comprehensive discussion on smart nano-packaging for seafood products. It focuses on advancements in biopolymer smart nano-packaging as a transformative solution for extending the shelf life and ensuring the quality of seafood products. Existing knowledge highlights the functionality of biopolymers and nanotechnology, but gaps remain in addressing practical applications, such as scalability, cost-efficiency, and consumer safety. This review bridges these gaps by providing a detailed analysis of biopolymer-based active and intelligent packaging systems, which integrate antioxidant, antimicrobial, and freshness-indicating properties. It emphasizes the unique contributions of nanoengineering to enhance biopolymer properties, offering innovative solutions to the seafood packaging industry while promoting environmental sustainability.European CommissionThis research has received funding support from the NSRF via the Program Management Unit for Human Resources & Institutional Development, Research, and Innovation, Thailand, Grant number B48G660106.This research was funded by the Deanship of Scientific Research (DSR) at King Faisal University under project no. GRANT A512.Sandeep Jagtap acknowledges the European Union’s Horizon 2020 Research and Innovation Programme RISE under grant agreement no. 823759 (REMESH).Food Research Internationa
Digital technologies for water use and management in agriculture: recent applications and future outlook
Get PDFThis article provides a comprehensive overview of digital technologies for water use and management in agriculture, examining recent applications and future prospects. It examines key water-related challenges - scarcity, pollution, inefficient use and climate change - and shows how various digital technologies such as Remote Sensing, Artificial Intelligence, the Internet of Things, Big Data, Robotics, Smart Sensors and Blockchain can help address them. The review finds that these technologies offer significant potential for improving water management practices, with Remote Sensing and Artificial Intelligence emerging as the most versatile and widely adopted. Efficient irrigation strategies appear to be the most common application across technologies. Digital solutions significantly reduce water wastage, help identify pollution hotspots, and improve overall water resource management. For example, remote sensing-based approaches (e.g. UAV-mounted multispectral cameras) can accurately monitor soil moisture to optimise irrigation scheduling, while AI-driven models (e.g. random forest or neural networks) can predict groundwater recharge or forecast rainfall events. However, several barriers to widespread adoption are identified, including high implementation costs, lack of technical expertise, data management challenges, and infrastructure and connectivity constraints. The study concludes by suggesting priorities for future research and development, highlighting the need for integrated technological solutions, improved accessibility and affordability, improved efficiency and sustainability, improved water quality, enhanced data management capabilities, and strategies to address emerging concerns such as cybersecurity and the environmental impact of digital technologies themselves. This review aims to inform future research, policy and practice in agricultural water management and support the development of more productive, resilient and sustainable agricultural systems.Agricultural Water Managemen
Dataset "Strain data from a superconducting magnet coil, measured using optical fibre segment interferometry"
No full textThe data provided consists of a time series of the strain record around the entire circumference of the coil, and over 1 quadrant of the coil.An array of optical fibre segment interferometers were deployed in a superconducting magnet to monitor the strain during energisation of the coil. Data was recorded as the current was increased from 0 amps to 90 amps, and then decreased to 0 Amps.Engineering and Physical Sciences Research Council (EPSRC
Preliminary assessment of quantitative phase analysis from focal construct tomography
Get PDFNew methods for real-time materials phase identification based upon focal construct tomography (FCT) have been examined. Such quantitative assessment has significant potential in sectors where in-line analysis is required, including screening within aviation security. As a recent component of work programs developing FCT, its capability for accurate, quantitative analysis has been assessed for the first time. Diffraction signatures from mixed-phase materials were acquired from an energy-dispersive FCT system running under normal operational conditions. A calibration curve was constructed from the spectra and subsequently employed to assess the composition of ‘blind’ samples. The results demonstrated that this approach was able to precisely predict the polymorphic phase composition of samples to ±5 wt%. Conclusions: The potential impact of these findings is significant and will enable applications of FCT beyond those requiring a phase identification to those necessitating quantification, such as counterfeit medicines, pharmaceutical quality assurance, aging of explosives, and cement production.This research was partly funded by the Higher Education Innovation Funding (HEIF).ND
Modeling multirotor wake interference in quadrotor eVTOL flight dynamics and handling qualities
Get PDFThis study presents a good-fidelity flight dynamics model for a quadrotor eVTOL aircraft, with a particular focus on the effects of multirotor aerodynamic interference on vehicle stability and handling qualities. A dynamic vortex tube model, enhanced to account for aircraft angular motions, is developed and integrated with dynamic inflow theory to compute rotor-induced and interference velocities efficiently. The model is validated against wind tunnel data and benchmark trim results, demonstrating strong predictive accuracy. Incorporating this interference model into a 6-DoF flight dynamics framework reveals that multirotor wake interference significantly modify both static and dynamic stability characteristics, especially in low-to-medium speed regimes. Moreover, aerodynamic interference degrades incidence stability, reduces pitch and heave damping, and adversely affects phugoid behavior. In the lateral-directional axes, it destabilizes the spiral mode and introduces non-monotonic variations in Dutch roll stability. Handling qualities analysis using ADS-33E-PRF metrics shows that interference reduces pitch bandwidth from Level 1 to Level 2 and marginally deteriorates pitch and roll dynamic stability, while improving pitch-axis quickness. These findings demonstrate that multirotor aerodynamic interference is not merely a performance issue but a critical factor influencing flight control design and certification. The proposed modeling approach offers a computationally efficient yet physically grounded method for assessing multirotor eVTOL handling qualities across the full flight envelope.This research was supported by the China Aerodynamics Research and Development Center NO: RAL202302-3; National Natural Science Foundation of China, NO: 11902052; the Natural Science Foundation of Chongqing, NO: CSTB2022NSCQ-MSX1592.Aerospace Science and Technolog
Automating emergency landing in cities with cognitive stress reducing explanations to pilot-in-the-loop
Get PDFFuture smart cities will integrate urban air mobility (UAM) where electric vertical take-off and landing aircrafts (eVTOL) will improve labour and logistic mobility. One of the challenges in urban flight is emergency landing, where an eVTOL needs to land in an unofficial roof top or clear area safely within a time frame. In semi-autonomous eVTOLs, artificial intelligence (AI) is expected to assist in identifying emergency landing locations and navigation vectors, whilst the pilot flies and lands the eVTOL. Autonomously searching and correctly identifying safe candidate locations is important for safety of both the eVTOL and ground stakeholders. Furthermore, the processes of using AI to identify such locations should ideally be explainable to the pilot to reduce cognitive stress and aid the preparation of the emergency landing procedure. Here, we have developed a simulated emergency urban landing platform, whereby an eVTOL rotorcraft is scanning the ground for suitable emergency landing locations and actively explaining the navigation vectors to the pilot-in-the-loop. Cognitive stress is measured using real experiments using heart rate monitor, and an actor-critic deep reinforcement learning is used to learn what explanations are useful to reduce cognitive stress. The end result is that the eVTOL can identify emergency landing candidates, navigate to the safe landing zone (SLZ) whilst performing obstacle avoidance, and explain its decision making to the pilot-in-the-loop whilst minimizing its cognitive stress. We show through 3 scenarios that we can indeed reduce cognitive load significantly and also reduce the average time to reach the SLZ.Leonardo UKEPSRC TAS-S: Trustworthy Autonomous Systems: Security (EP/V026763/1)2024 IEEE International Smart Cities Conference (ISC2
Emergency = Emergency? usability evaluation of a novel emergency alerting system for cabin emergencies
Get PDFEuropean Association for Aviation Psychology Conference EAAP 35Emergencies in aviation often create huge media attention because the number of people involved are high and flying was once considered to be risky, which is still in the mind of some passengers. This study evaluated how passengers and cabin crew classified cases of emergencies. Further, detailed design requirements on the emergency alerting interface were explored. Therefore, two different prototypes of an emergency alerting interface were presented to participants (n = 160) with the task to evaluate the perceived usability (SUS) and the subjective workload using NASA TLX. The SUS scores for both prototypes were above average indicating a good usability. Red was the preferred colour and a triangle shaped icon with SOS. Broad menu designs with more icons than text were the preference of the users. Passengers as well as cabin crew identified medical emergencies and unruly passengers as emergencies. However, passengers also mentioned technical failure as a possible case whereas cabin crew was more concerned about fire and smoke. This study has substantiated the need for an emergency alerting system since it is expected that the number of medical emergencies and unruly passengers will most likely increase in the future.Transportation Research Procedi
Biotransformation of acetaminophen by Ganoderma parvulum ligninolytic enzymes immobilized on chitosan microspheres
Get PDFThis article belongs to the Special Issue Application of Fungi in Bioconversions and MycoremediationWater quality is essential for safeguarding human health and ensuring the stability of ecosystems. Nonetheless, the rising prevalence of emerging contaminants, particularly pharmaceutical compounds, has raised serious environmental concerns due to their bioactivity, widespread use, persistence, and potential toxicity. Among these, acetaminophen (paracetamol) is one of the most frequently detected pharmaceutical pollutants in aquatic environments. Among the various degradation strategies explored, biological methods, especially those involving white-rot fungi, have shown substantial promise owing to their production of ligninolytic enzymes capable of degrading complex pollutants. This study investigates the use of laccases from Ganoderma parvulum, covalently immobilized on chitosan microspheres, for acetaminophen degradation. The immobilization involved a 10% crosslinking agent, 60-min crosslinking time, and 10,000 U/L enzyme concentration, resulting in an immobilization efficiency of 123%, 203%, and 218%, respectively. The immobilized enzymes displayed enhanced stability across pH 3–8 and temperatures between 20 and 60 °C. Biodegradation assays achieved 97% acetaminophen removal within four hours. Nuclear Magnetic Resonance (1H NMR and COSY) confirmed structural transformation. The enzymes also retained over 95% catalytic activity after multiple reuse cycles. These findings highlight the novel application of laccases as efficient and reusable biocatalysts for pharmaceutical pollutant removal, providing valuable insights into the mechanisms of enzymatic environmental remediation.This work was supported by the Convocatoria Programática 2019–2020: Ingeniería y Tecnología [grant numbers SIIU 2020-34952] from CODI (Comité para el Desarrollo de la Investigación) of the Universidad de Antioquia-ColombiaFermentatio
Self-locking stability effect induced by downwash flow of the flapping wing rotor
Get PDFThroughout the previous studies, none of them are involved in analysing the downwash flow effect on the control surface of the Flapping Wing Rotor (FWR). An overset CFD numerical model is built up and validated to study the downwash flow’s effect on the stability of the FWR. After simulation, a cone like self-lock region which acts as the critical condition determining the stability of FWR is found. Only when the flow’s resultant velocity acting on the control surface lies in the stable region, the FWR can keep stable. The size of the cone like self-lock stable region can be enlarged by increasing the maximum feasible deflection angle constrained by mechanical design or enhancing the equivalent downwash flow velocity. Among all the simulated cases, when J = 2.67 (Hz, r/s), the largest average equivalent downwash flow velocities are found. On the other hand, the recovery torque could be enhanced due to the increase of the arm of the lateral force. According to these simulation results, a 43 g FWR model with two control surfaces and two stabilizers is then designed. A series of flight tests is then conducted to help confirm the conclusion of the mechanism research in this work. Overall, this study points out several strategies to increase the flight stability of the FWR and finally realizes the stable climb flight and mild descent flight of the FWR.This work is supported by the following funding organizations in China: National Natural Science Foundation of China (Grant No. 52375116 and Grant No. 52105285); the Aeronautical Science Foundation of China (Grant No. ASFC-20230023052001); China Postdoctoral Science Foundation (Grant No. 2024M754237); National Key Research and Development Program of China (2024YFB470920001); Science and Technology Plan Project of Wenzhou Municipality (Grant No. ZG2024001); Basic Public Welfare Research Program of Wenzhou (Grant No. G2023046).Journal of Bionic Engineerin
Video segmentation of Wire + Arc Additive Manufacturing (WAAM) using visual large model
No full textThis article belongs to the Special Issue Sensing and Imaging in Computer Vision.Process control and quality assurance of wire + arc additive manufacturing (WAAM) and automated welding rely heavily on in-process monitoring videos to quantify variables such as melt pool geometry, location and size of droplet transfer, arc characteristics, etc. To enable feedback control based upon this information, an automatic and robust segmentation method for monitoring of videos and images is required. However, video segmentation in WAAM and welding is challenging due to constantly fluctuating arc brightness, which varies with deposition and welding configurations. Additionally, conventional computer vision algorithms based on greyscale value and gradient lack flexibility and robustness in this scenario. Deep learning offers a promising approach to WAAM video segmentation; however, the prohibitive time and cost associated with creating a well-labelled, suitably sized dataset have hindered its widespread adoption. The emergence of large computer vision models, however, has provided new solutions. In this study a semi-automatic annotation tool for WAAM videos was developed based upon the computer vision foundation model SAM and the video object tracking model XMem. The tool can enable annotation of the video frames hundreds of times faster than traditional manual annotation methods, thus making it possible to achieve rapid quantitative analysis of WAAM and welding videos with minimal user intervention. To demonstrate the effectiveness of the tool, three cases are demonstrated: online wire position closed-loop control, droplet transfer behaviour analysis, and assembling a dataset for dedicated deep learning segmentation models. This work provides a broader perspective on how to exploit large models in WAAM and weld deposits.This research was funded by Engineering and Physical Sciences Research Council grant number (EP/W025035/1).Sensor