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.     

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.     

A structure/function study of polyaminoamide dendrimers as silica scale growth inhibitors

Dendrimers have attracted immense attention during the last decade due to their interesting properties both from a basic and an applied research viewpoint. Encapsulation of metal nanoparticles for catalysis, drug delivery and light harvesting are only some applications of dendrimers that are breaking new ground. A novel application of dendrimer technology is described in the present paper that relates to industrial water treatment. Industrial water systems often suffer from undesirable inorganic deposits. These can form either in the bulk or on metallic surfaces, such as heat exchangers or pipelines. Silica (SiO₂) scale formation and deposition is a major problem in high‐silica‐containing cooling waters. Scale prevention rather than removal is highly desired. In this paper, benchtop screening tests on various silica inhibition chemistries are reported, with emphasis on materials with a dendrimeric structure. Specifically, the inhibition properties of commercially available STARBURST^® polyaminoamide (PAMAM) dendrimers generations 0.5, 1, 1.5, 2, and 2.5 are investigated in detail together with other commonly‐used scale inhibitors. Experimental results show that inhibition efficiency largely depends on structural features of PAMAM dendrimers such as generation number and nature of the end groups. PAMAM dendrimers are effective inhibitors of silica scale growth at 40 ppm dosage levels. PAMAM dendrimers also act as silica nucleators, forming SiO₂–PAMAM composites. This occurs because the SiO₂ formed by incomplete inhibition interacts with cationic PAMAM‐1 and ‐2. The general scope of silica formation and inhibition in industrial waters is also discussed. Copyright © 2005 Society of Chemical Industry     

Novel Insights into Factor D Inhibition

Complement-mediated diseases or complementopathies, such as Paroxysmal nocturnal hemoglobinuria (PNH), cold agglutinin disease (CAD), and transplant-associated thrombotic microangiopathy (TA-TMA), demand advanced complement diagnostics and therapeutics be adopted in a vast field of medical specialties, such as hematology, transplantation, rheumatology, and nephrology. The miracle of complement inhibitors as “orphan drugs” has dramatically improved morbidity and mortality in patients with otherwise life-threatening complementopathies. Efficacy has been significantly improved by upstream inhibition in patients with PNH. Different molecules may exert diverse characteristics in vitro and in vivo. Further studies remain to show safety and efficacy of upstream inhibition in other complementopathies. In addition, cost and availability issues are major drawbacks of current treatments. Therefore, further developments are warranted to address the unmet clinical needs in the field of complementopathies. This state-of-the-art narrative review aims to delineate novel insights into factor D inhibition as a promising target for complementopathies.     

Improving the Design of Greek Hollow Clay Bricks

The hollow clay brick is the typical building unit that is employed not only over the whole Greece but also in many other Mediterranean countries. Nevertheless, its design is completely empirical. In this study, the design of the hollow clay brick is analyzed by employing a finite element package. To carry out this analysis, the thermal conductivity of the solid clay is measured by the transient hot-wire technique. As a consequence of the analysis, an improvement of 24 % in the design of the hollow clay brick is proposed.     

Adaptive epileptic seizure prediction system

Current epileptic seizure "prediction" algorithms are generally based on the knowledge of seizure occurring time and analyze the electroencephalogram (EEG) recordings retrospectively. It is then obvious that, although these analyses provide evidence of brain activity changes prior to epileptic seizures, they cannot be applied to develop implantable devices for diagnostic and therapeutic purposes. In this paper, we describe an adaptive procedure to prospectively analyze continuous, long-term EEG recordings when only the occurring time of the first seizure is known. The algorithm is based on the convergence and divergence of short-term maximum Lyapunov exponents (STLmax) among critical electrode sites selected adaptively. A warning of an impending seizure is then issued. Global optimization techniques are applied for selecting the critical groups of electrode sites. The adaptive seizure prediction algorithm (ASPA) was tested in continuous 0.76 to 5.84 days intracranial EEG recordings from a group of five patients with refractory temporal lobe epilepsy. A fixed parameter setting applied to all cases predicted 82% of seizures with a false prediction rate of 0.16/h. Seizure warnings occurred an average of 71.7 min before ictal onset. Similar results were produced by dividing the available EEG recordings into half training and testing portions. Optimizing the parameters for individual patients improved sensitivity (84% overall) and reduced false prediction rate (0.12/h overall). These results indicate that ASPA can be applied to implantable devices for diagnostic and therapeutic purposes.     

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.     

Effect study of modulation of molecules of natural resin from black and Halepensis pinus in the removal of humidity from diesel fuel

One of the important stages in the preparation of fuel for the smooth progress of combustion is to remove humidity from it. Natural resin, a natural product from Pinus nigra and Pinus halepensis were used in order to remove water residues from petroleum fuels. The mass of resin (P. nigra and P. halepensis) was mixed with diesel fuel and the humidity eliminated, 38% and 69% of diesel fuel also. The proposed method can be used in a simple fuel cleaning process using a metal mesh vessel consisted of the natural resin of P. nigra sample.     

Service configuration and user profiling in 4G terminals

This paper presents a middleware platform for managing devices that operate in heterogeneous environments. The proposed management framework supports terminal-controlled, preference-based access network selection. Two separate problems are identified in this domain: one involving the computation of optimal allocations of services to access networks and quality levels (service configuration), and one concerning the dynamic inference of the user’s preferences, according to the usage context (user profiling). This paper includes an approach to the definition, mathematical formulation and solution of both these problems. Indicative results of the proposed solution methods are presented in the context of a real-life scenario simulating a day in the life of an ordinary user.