Hydrogen production through anaerobic co-digestion of food waste and crude glycerol at mesophilic conditions

Hydrogen production from mesophilic anaerobic co-digestion of food waste (FW) and crude glycerol (GLC) was investigated in this study. Batch experiments were carried out at 35 °C for 36 h to assess the effect of supplementation of different glycerol concentrations (1%, 3% and 5% (v/v)) on dark fermentation of FW. The maximum hydrogen yield (180 mLH₂/gVS) was obtained at 5% GLC while the maximum specific production rate (around 13 mLH₂/(gVS.h)) was similar for all experiments with glycerol addition. Besides contributing to increase H₂ productivity, the presence of glycerol reduced the microorganisms acclimation time (Lag phase) in comparison to the control tests conducted without this co-substrate. In addition, the increment of glycerol concentration also enhanced volatile fatty acids generation and favoured the production of 1,3-propanediol (1,3-PDO). In the experimental conditions studied (i.e., 1–5% (v/v) of GLC), the results revealed that co-digestion of FW and GLC is promising and can be potentially used to maximize energy production while contributing to the management and treatment of these wastes.     

An algorithm for the recognition of levels of congestion in road traffic problems

Detection and recognition of the level of congestion at an intersection is a very important problem and a valuable source of information in traffic management. Although it is just one of all the aspects that make up a traffic management system, it seems to be a crucial point for gathering information. In this paper, we present a technique based on a k-means clustering algorithm for classification, which has been already successfully used in a number of pattern recognition problems, namely: as an algorithm for face recognition problems and in a number of medical diagnosis problems and it compares very well with the state of the art techniques.     

In search of a relational theory.

The author searched for empirically based theories and models with an interpersonal and relational focus. This search was conducted in spite of academic and professional psychology’s general avoidance of theory building coupled with an exaggerated reliance on empirical evidence, producing a chaotic proliferation of models unrelated to each other. Requirements for any theory are verifiability, applicability, redundancy, and fruitfulness. Many two-factor relational theories and models derive from the two assumptions of space and time. From these models and theories two stand out: attachment theory and relational competence theory (RCT). Both theories attempt to link heretofore separate fields: research and practice. The major asset of attachment theory has been a multitude of studies in the English-speaking world. The major shortcoming of RCT is that research to support the validity of its models has been conducted only in Italy. However, many test instruments, enrichment programs, and interactive practice exercises for some models of RCT are available in English. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Potential Applications of Cucurbit[n]urils Inclusion Complexes in Photodynamic Therapy

Cucurbit[n]urils (CB[n]s) have emerged as potential candidates for drug delivery in several areas due to their strong binding interactions and low toxicity. More recently, their benefits for a type of cancer treatment termed Photodynamic Therapy (PDT) have been recognized. The outcomes of this therapy rely on better drug delivery strategies and improving overall photoactivity of the drugs, which is where CB[n]s could have a strong impact. The effects of these molecular containers on photoactivity are discussed and new interesting work is highlighted.     

A shape analysis framework for neuromorphometry

This paper addresses in an integrated and systematic fashion the relatively overlooked but increasingly important issue of measuring and characterizing the geometrical properties of nerve cells and structures, an area often called neuromorphology. After discussing the main motivation for such an endeavour, a comprehensive mathematical framework for characterizing neural shapes, capable of expressing variations over time, is presented and used to underline the main issues in neuromorphology. Three particularly powerful and versatile families of neuromorphological approaches, including differential measures, symmetry axes/skeletons, and complexity, are presented and their respective potentials for applications in neuroscience are identified. Examples of applications of such measures are provided based on experimental investigations related to automated dendrogram extraction, mental retardation characterization, and axon growth analysis.     

Control of a Robotic Wheelchair Using Recurrent Networks

This paper describes an adaptive neural control system for governing the movements of a robotic wheelchair. It presents a new model of recurrent neural network based on a RBF architecture and combining in its architecture local recurrence and synaptic connections with FIR filters. This model is used in two different control architectures to command the movements of a robotic wheelchair. The training equations and the stability conditions of the control system are obtained. Practical tests show that the results achieved using the proposed method are better than those obtained using PID controllers or other recurrent neural networks models     

Consciousness, Agents and the Knowledge Game

This paper has three goals. The first is to introduce the “knowledge game”, a new, simple and yet powerful tool for analysing some intriguing philosophical questions. The second is to apply the knowledge game as an informative test to discriminate between conscious (human) and conscious-less agents (zombies and robots), depending on which version of the game they can win. And the third is to use a version of the knowledge game to provide an answer to Dretske’s question “how do you know you are not a zombie?”.