Abstract
The widespread use of smartphones and other low-cost equipment as recording devices, the massive growth in bandwidth, and the ever-growing demand for new applications with enhanced capabilities, made visual data a must in several scenarios, including surveillance, sports, retail, entertainment, and intelligent vehicles. Despite significant advances in analyzing and extracting data from images and video, there is a lack of solutions able to analyze and semantically describe the information in the visual scene so that it can be efficiently used and repurposed. Scientific contributions have focused on individual aspects or addressing specific problems and application areas, and no cross-domain solution is available to implement a complete system that enables information passing between cross-cutting algorithms. This paper analyses the problem from an end-to-end perspective, i.e., from the visual scene analysis to the representation of information in a virtual environment, including how the extracted data can be described and stored. A simple processing pipeline is introduced to set up a structure for discussing challenges and opportunities in different steps of the entire process, allowing to identify current gaps in the literature. The work reviews various technologies specifically from the perspective of their applicability to an end-to-end pipeline for scene analysis and synthesis, along with an extensive analysis of datasets for relevant tasks.

Abstract
Sustainability and resilience in Agri-Food Supply Chains is a challenging topic of current interest in the research community. Resilience for Agri-Food Supply Chain (AFSC) is the capability of the supply network to manage and mitigate disruptions due to global warming and natural phenomena such as landslides and floods of crops, among others caused by humans. A significant challenge is to design efficient and resilient AFSCs in emerging countries while perishability constraints are considered. A methodology to design an AFSC for emerging countries is addressed in this research. The phenomena that aid in identifying critical aspects of the AFSC affecting their resilience are identified. The former approach combines optimization and simulation schemes by considering resilience metrics related to availability and connectivity. Indeed, the solution approach addresses the uncer-tainty by using simulation of disruptive events and finding resilient designs using mathematical programming. The proposed framework has been evaluated in a Colombian coffee supply chain. The obtained results show the efficiency of the proposed scheme to design AFSCs and allow the practitioners to measure, predict, compare, and improve the level of resilience of their supply chains (SCs).

Abstract

Abstract
We experience the world around us using all our senses, however, multimedia content still relies majorly on audiovisual stimuli. With technology advancements, multisensory stimuli started to be introduced in multimedia experiences. Still, very few contemplate a wide range of different modalities simultaneously, approaching the stimulation one would receive in reality. This paper explores the effects of trimodal multisensory stimuli on the sense of Presence, Perceptual Realism, and Quality of Experience (QoE) during video visualisation. Namely, we study the impact of heat, wind, and smell during video visualization to investigate how each stimulus contributes to the QoE. A correlational analysis was also performed to understand better how the different variables interact. The results indicate that multisensory stimulation improved significantly the sense of presence satisfaction and perceptual realism. Furthermore, smell contributed the most to the QoE, followed by heat and wind. We highlight the use of multisensory stimulation on video visualization over audiovisual only, as it benefits substantially the user experience. © 2023 IEEE.

Abstract

Abstract
Nutrient quantification in hydroponic systems is essential. Reagent-less spectral quantification of nitrogen, phosphate and potassium faces challenges in accessing information-rich spectral signals and unscrambling interference from each constituent. Herein, we introduce information equivalence between spectra and sample composition, enabling extraction of consistent covariance to isolate nutrient-specific spectral information (N, P or K) in Hoagland nutrient solutions using orthogonal covariance modes. Chemometrics methods quantify nitrogen and potassium, but not phosphate. Orthogonal covariance modes, however, enable quantification of all three nutrients: nitrogen (N) with R = 0.9926 and standard error of 17.22 ppm, phosphate (P) with R = 0.9196 and standard error of 63.62 ppm, and potassium (K) with R = 0.9975 and standard error of 9.51 ppm. Including pH information significantly improves phosphate quantification (R = 0.9638, standard error: 43.16 ppm). Results demonstrate a direct relationship between spectra and Hoagland nutrient solution information, preserving NPK orthogonality and supporting orthogonal covariance modes. These modes enhance detection sensitivity by maximizing information of the constituent being quantified, while minimizing interferences from others. Orthogonal covariance modes predicted nitrogen (R = 0.9474, standard error: 29.95 ppm) accurately. Phosphate and potassium showed strong interference from contaminants, but most extrapolation samples were correctly diagnosed above the reference interval (83.26%). Despite potassium features outside the knowledge base, a significant correlation was obtained (R = 0.6751). Orthogonal covariance modes use unique N, P or K information for quantification, not spurious correlations due to fertilizer composition. This approach minimizes interferences during extrapolation to complex samples, a crucial step towards resilient nutrient management in hydroponics using spectroscopy.