Graphite Oxide and Aromatic Amines: Size Matters

Experimental and theoretical studies are performed in order to illuminate, for first time, the intercalation mechanism of polycyclic aromatic molecules into graphite oxide. Two representative molecules of this family, aniline and naphthalene amine are investigated. After intercalation, aniline molecules prefer to covalently connect to the graphene oxide matrix via chemical grafting, while napthalene amine molecules bind with the graphene oxide surface through π–π interactions. The presence of intercalated aromatic molecules between the graphene oxide layers is demonstrated by X‐ray diffraction, while the type of interaction between graphene oxide and polycyclic organic molecules is elucidated by X‐ray photoelectron spectroscopy. Combined quantum mechanical and molecular mechanical calculations describe the intercalation mechanism and the aniline grafting, rationalizing the experimental data. The present work opens new perspectives for the interaction of various aromatic molecules with graphite oxide and the so‐called “intercalation chemistry”.     

Fatigue crack growth simulation in adhesively bonded composite joints

A numerical model has been developed for simulating fatigue crack growth (debonding) in adhesively bonded composite joints subjected to mode‐I, mode‐II, and mixed‐mode I + II loading conditions. The model employs a cohesive zone model described by a modified bilinear traction‐separation law. Fatigue damage in the composite adherends is not considered in the model. To account for crack divergence and reduce sensitivity of numerical results on mesh density, a crack front detection algorithm based on the effective element's length was employed. The model is implemented as a user‐defined subroutine (UMAT) in the commercial FE code LS‐DYNA. The model's input parameters, in the form of a modified Paris law, and the validation data were obtained from experimental tests conducted by the authors. It was found that the model is able to successfully simulate crack growth in the regime of the experimental data.     

Evaluating the Use of Daily Care Notes Software for Older People with Dementia

There has been little research to investigate the impact of software to support the care for older people with dementia care. This article reports the evaluation of software adapted to support one key person-centered task for the care of older residents with dementia – recording and sharing daily care notes. The evaluation on the dementia wing of 1 residential home for over 6 months revealed that use of the software on mobile devices carried by the carers increased the number and volume of daily care notes recorded, but only for the types of content that were already being recorded by carers. Carers reported more advantages that resulted from daily care notes once in digital form than from the documenting task, as well as barriers to the use of mobile digital software to record daily care notes.     

Characterization of Tomato Pulp Stickiness during Spray Drying using a Contact Probe Method

A contact probe test was developed to characterize the surface stickiness of a tomato pulp droplet at various moisture contents and temperatures. To provide tomato pulp samples with different moisture contents, tomato powder produced by a laboratory spray dryer was wetted to seven different moisture levels. The instantaneous tensile force curve was recorded during the probe withdrawal from which the maximum tensile force and other useful information were obtained and cross-examined against images of bonding, debonding, and failure of the material. Generally, at higher moisture contents tomato pulp exhibited cohesive failure followed by semi-adhesive failure, but when moisture content decreased to a certain level, a peak tensile pressure was observed and the failure was adhesive. In addition, higher temperatures shifted the points of adhesive failure toward lower moisture content.     

Energy considerations for topology-unaware TDMA MAC protocols

Since the energy budget of mobile nodes is limited, the performance of a networking protocol for such users should be evaluated in terms of its energy efficiency, in addition to the more traditional metrics such as throughput. In this paper, two topology-unaware MAC protocols—in which the scheduling time slots are allocated irrespectively of the underline topology—are considered and their energy consumption is derived. It turns out that the per frame power consumption is lower for the less throughput-efficient protocol, suggesting that energy savings are achieved at the expense of throughput.A finer energy consumption study is carried out in the sequel, focusing on the amount of energy consumed to successfully transmit a certain number of packets, or equivalently, on the per successful transmission power consumption. It is shown that the more throughput-efficient protocol consumes less energy per successful transmission under certain conditions (which are derived), due to the lower number of transmission attempts before a data packet is successfully transmitted. The same energy-efficiency relation is observed under certain conditions (which are derived) when data packets are delay constrained and, thus, may become obsolete if not transmitted successfully within a specific time interval. The conditions under which the per successful transmission power consumption is minimized for delay-constrained packets, are also established in this work and it is observed that when the system throughput is maximized, the power consumed is close to the minimum. Simulation results support the claims and the expectations of the aforementioned analysis.     

Enhancing cognitive radio systems with robust reasoning

Cognitive radio systems dynamically reconfigure the algorithms and parameters they use, in order to adapt to the changing environment conditions. However, reaching proper reconfiguration decisions presupposes a way of knowing, with high enough assurance, the capabilities of the alternate configurations, especially in terms of achievable transmission capacity and coverage. The present paper addresses this problem, firstly, by specifying a complete process for extracting estimations of the capabilities of candidate configurations, in terms of transmission capacity and coverage, and, secondly, by enhancing these estimations with the employment of a machine learning technique. The technique is based on the use of Bayesian Networks, in conjunction with an effective learning and adaptation strategy, and aims at extracting and exploiting knowledge and experience, in order to reach robust (i.e. stable and reliable) estimations of the configurations' capabilities. Comprehensive results of the proposed method are presented, in order to validate its functionality. Copyright © 2007 John Wiley & Sons, Ltd.