Mild Binding of Protein to C2N Monolayer Reveals Its Suitable Biocompatibility

The development of biocompatible nanomaterials for smart drug delivery and bioimaging has attracted great interest in recent years in biomedical fields. Here, the interaction between the recently reported nitrogenated graphene (C₂N) and a prototypical protein (villin headpiece HP35) utilizing atomistic molecular dynamics simulations is studied. The simulations reveal that HP35 can form a stable binding with the C₂N monolayer. Although the C₂N–HP35 attractive interactions are constantly preserved, the binding strength between C₂N and the protein is mild and does not cause significant distortion in the protein's structural integrity. This intrinsic biofriendly property of native C₂N is distinct from several widely studied nanomaterials, such as graphene, carbon nanotubes, and MoS₂, which can induce severe protein denaturation. Interestingly, once the protein is adsorbed onto C₂N surface, its transverse migration is highly restricted at the binding sites. This restriction is orchestrated by C₂N's periodic porous structure with negatively charged “holes,” where the basic residues—such as lysine—can form stable interactions, thus functioning as “anchor points” in confining the protein displacement. It is suggested that the mild, immobilized protein attraction and biofriendly aspects of C₂N would make it a prospective candidate in bio‐ and medical‐related applications.     

An Experimental Economics Investigation of Shipper-carrier Interactions in the Choice of Mode and Shipment Size in Freight Transport

The paper discusses the theoretical and empirical evidence on the subject and concludes that freight mode choice can be best understood as the outcome of interactions between shippers and carriers, and that mode choice depends to a large extent on the shipment size that results from shipper-carrier interactions. These conclusions are supported by economic experiments designed to test the hypothesis of cooperative behavior. This was accomplished by conducting two sets of experiments (ones with the shipper playing the lead role in selecting the shipment size; and others in which the shipment size decision was left to the carriers), and by comparing their results to the ones obtained numerically under the assumption of perfect cooperation. The comparison of results indicated that the experiments converged to the perfect cooperation case. This is in line with the conclusion from game theory that indicates that under typical market conditions the shipper and carrier would cooperate. These results also imply that it really does not matter who “makes” the decision about the shipment size and mode to be used at a given time period, as over time the shipper—that is the customer—ends up selecting the bids more consistent with its own interest. In other words, these results do not support the assumption that freight mode choice is solely made by the carriers.     

An information architecture for a performance management framework by collaborating SMEs

Recently, much attention has been given to performance management within collaborative environments resulting in a wide variety of performance measurement/management frameworks. However, practically all of these frameworks present a common gap regarding the information treatment needs that support these frameworks. This issue is even more important in the case of Small and Medium-sized Enterprises (SMEs) due to their special characteristics. For that purpose, this paper presents an approach called Global Performance Management (GPM) Framework composed of a methodology, an information architecture and a technological solution, focusing mainly on the information architecture. The methodology guides SMEs when defining, developing, deploying and operating the GPM framework. The information architecture consists of a set of elements that support the tasks of collection, treatment and analysis of the used information, which complement the methodology to build a real model for the GPM framework. Finally, the technological solution bridges the engineering and conceptual environment, with the real-world operational and executable environment, following the path defined by the methodology. The information architecture was applied to two European pilots, achieving satisfactory results, which are highlighted in the paper as well as the main barriers encountered and solutions provided.     

Learning a generic 3D face model from 2D image databases using incremental Structure-from-Motion

Over the last decade 3D face models have been extensively used in many applications such as face recognition, facial animation and facial expression analysis. 3D Morphable Models (MMs) have become a popular tool to build and fit 3D face models to images. Critical to the success of MMs is the ability to build a generic 3D face model. Major limitations in the MMs building process are: (1) collecting 3D data usually involves the use of expensive laser scans and complex capture setups, (2) the number of available 3D databases is limited, and typically there is a lack of expression variability and (3) finding correspondences and registering the 3D model is a labor intensive and error prone process.     

Learning Photometric Invariance for Object Detection

Color is a powerful visual cue in many computer vision applications such as image segmentation and object recognition. However, most of the existing color models depend on the imaging conditions that negatively affect the performance of the task at hand. Often, a reflection model (e.g., Lambertian or dichromatic reflectance) is used to derive color invariant models. However, this approach may be too restricted to model real-world scenes in which different reflectance mechanisms can hold simultaneously.     

Detecting skin in face recognition systems: A colour spaces study

Skin colour detection is a technique very used in most of face detectors to find faces in images or videos. However, there is not a common opinion about which colour space is the best choice to do this task. Therefore, the motivation for our study is to discover which colour model is the best option to build an efficient face detector which can be embedded in a functional face recognition system. We have studied 10 of the most common and used colour spaces doing different comparisons among them, in order to know which one is the best option for human skin colour detection. In concrete, we have studied the models: RGB, CMY, YUV, YIQ, YPbPr, YCbCr, YCgCr, YDbDr, HSV—or HSI—and CIE-XYZ. To make the comparison among them, we have used 15 truth images where the skin colour of a face is clearly separated from the rest of the image (background, eyes, lips, hair, etc.). Thus we can compare at level pixel each colour model, doing a detailed study of each format. We present the final conclusions comparing different results, such as: right detections, false positives and false negatives for each colour space. According to the obtained results, the most appropriate colour spaces for skin colour detection are HSV model (the winner in our study), and the models YCgCr and YDbDr.