Pre-treatment and extraction techniques for recovery of added value compounds from wastes throughout the agri-food chain

The enormous quantity of food wastes discarded annually force to look for alternatives for this interesting feedstock. Thus, food bio-waste valorisation is one of the imperatives of the nowadays society. This review is the most comprehensive overview of currently existing technologies and processes in this field. It tackles classical and innovative physical, physico-chemical and chemical methods of food waste pre-treatment and extraction for recovery of added value compounds and detection by modern technologies and are an outcome of the COST Action EUBIS, TD1203 Food Waste Valorisation for Sustainable Chemicals, Materials and Fuels

Variability Studies in Melon (Cucumis melo.L) F2 Population of Kashi Madhu x COHB38 for Powdery Mildew Resistance

The present investigation was carried at College of Horticulture, Bengaluru. Evaluation of melon F2 population (Kashi Madhu X COHB38) was conducted during rabi 2016 for resistance to powdery mildew disease under natural field condition. Percent Disease Index (PDI) and AUDPC (Area Under Disease Progress Curve) value was calculated to assess the reaction of F2 (Kashi Madhu X COHB38) segregating population of melon along with the parents (COHB38 and Kashi Madhu) and F1. Based on PDI for powdery mildew disease, 152 F2 plants were classified into different categories. Twenty five F2s were resistant (0-25% PDI), 27 were moderately resistant (25.1-40% PDI), 88 were susceptible (40.1-60% PDI) and 12 F2s were highly susceptible (&gt;60% PDI). Among 152 F2 plants,    F2 -34 was found to be highly resistant with zero PDI and zero AUDPC value (no disease) followed by F2- 46 and 92 with PDI of 13.89 % and 16.11 % and 46.94 and 55.00 AUDPC value, respectively. The PDI of the disease showed a continuous distribution from highly resistant to highly susceptible phenotypes, without showing any typical segregation pattern.&#x0D; Int. J. Appl. Sci. Biotechnol. Vol 7(4): 407-413</jats:p

Modeling and Shaping Human-Machine Interactions in Closed-loop, Co-adaptive Neural Interfaces

Thesis (Ph.D.)--University of Washington, 2024Neural interfaces map biological signals measured from a user to control commands for external devices. The mapping from biosignals to device inputs is performed by the decoder. Adaptation of both the user and decoder—co-adaptation—provides opportunities to improve the accessibility and usability of interfaces across diverse users and applications. User learning leads to robust interface control that can generalize across environments and contexts. Decoder adaptation can individualize interfaces and account for signal variability. Co-adaptation therefore creates opportunities to shape the user and decoder system to achieve robust and generalizable personalized interfaces. However, co-adaptation creates a two-learner system with dynamic interactions between the user and decoder. Engineering co-adaptive interfaces requires new tools and frameworks to achieve stable user-decoder interactions. This thesis aims to develop and experimentally validate methods for designing and measuring co-adaptive interfaces. I present new computational methods based on control theory and game theory to analyze and generate predictions for user-decoder co-adaptive outcomes in continuous interactions. I tested these computational methods using an experimental platform where human participants learn to control a cursor using an adaptive myoelectric interface to track a target on a computer display. Our framework predicted the outcome of co-adaptive interface interactions and revealed how interface properties can shape user behavior. These findings contribute new tools to design personalized, closed-loop, co-adaptive neural interfaces. The overarching aim of this thesis is to propose co-adaptive analysis and tools that can predictably influence co-adaptive interface performance and user-decoder dynamics. These findings from this thesis contribute new tools to design personalized, closed-loop, co-adaptive neural interfaces