A novel secure solution of using mixed reality in data transmission for bowel and jaw surgical telepresence: enhanced rivest cipher RC6 block cipher

Security threats are crucial challenges that deter Mixed reality (MR) communication in medical telepresence. This research aims to improve the security by reducing the chances of types of various attacks occurring during the real-time data transmission in surgical telepresence as well as reduce the time of the cryptographic algorithm and keep the quality of the media used. The proposed model consists of an enhanced RC6 algorithm in combination. Dynamic keys are generated from the RC6 algorithm mixed with RC4 to create dynamic S-box and permutation table, preventing various known attacks during the real-time data transmission. For every next session, a new key is created, avoiding possible reuse of the same key from the attacker. The results obtained from our proposed system are showing better performance compared to the state of art. The resistance to the tested attacks is measured throughout the entropy, Pick to Signal Noise Ratio (PSNR) is decreased for the encrypted image than the state of art, structural similarity index (SSIM) closer to zero. The execution time of the algorithm is decreased for an average of 20%. The proposed system is focusing on preventing the brute force attack occurred during the surgical telepresence data transmission. The paper proposes a framework that enhances the security related to data transmission during surgeries with acceptable performance.

     

Scheduling of inventory releasing jobs to satisfy time-varying demand: an analysis of complexity

This paper studies a new class of single-machine scheduling problems, which are faced by Just-in-Time-suppliers satisfying a given demand. In these models the processing of jobs leads to a release of a predefined number of product units into inventory. Consumption is triggered by predetermined time-varying, and product-specific demand requests. While all demands have to be fulfilled, the objective is to minimize the resulting product inventory. We investigate different subproblems of this general setting with regard to their computational complexity. For more restricted problem versions strongly polynomial time algorithms are presented. In contrast to this, NP-hardness in the strong sense is proven for more general problem versions. Moreover, for the most general version, even finding a feasible solution is shown to be strongly NP-hard.     

Neural networks in process life cycle profit modelling

Changes in operational environment of the process industry such as decreasing selling prices, increased competition between companies and new legislation, set requirements for performance and effectiveness of the industrial production lines and processes. For the basis of this study, a life cycle profit (LCP) model of a pulp process was constructed using different kind of process information including chemical consumptions and production levels of material and energy flows in unit processes. However, all the information needed in the creation of relevant LCP model was not directly provided by information systems of the plant. In this study, neural networks was used to model pulp bleaching process and fill out missing information and furthermore to create estimators for the alkaline chemical consumption. A data-based modelling approach was applied using an example, where factors affecting the sodium hydroxide consumption in the bleaching stage were solved. The results showed that raw process data can be refined into new valuable information using computational methods and moreover to improve the accuracy of life cycle profit models.     

Wet Chemical Treatment and Mg Doping of p-InP Surfaces for Ohmic Low-Resistive Metal Contacts

Manufacturing a low-resistive Ohmic metal contact on p-type InP crystals for various applications is a challenge because of the Fermi-level pinning via surface defects and the diffusion of p-type doping atoms in InP. Development of wet-chemistry treatments and nanoscale control of p-doping for InP surfaces is crucial for decreasing the device resistivity losses and durability problems. Herein, a proper combination of HCl-based solution immersion, which directly provides an unusual wet chemical-induced InP(100)c(2 × 2) atomic structure, and low-temperature Mg-surface doping of the cleaned InP before Ni-film deposition is demonstrated to decrease the contact resistivity of Ni/p-InP by the factor of 10 approximately as compared to the lowest reference value without Mg. Deposition of the Mg intermediate layer on p-InP and postheating of Mg/p-InP at 350 °C, both performed in ultrahigh-vacuum (UHV) chamber, lead to intermixing of Mg and InP elements according to X-ray photoelectron spectroscopy. Introducing a small oxygen gas background (O₂ ≈ 10⁻⁶ mbar) in UHV chamber during the postheating of Mg/p-InP enhances the indium outdiffusion and provides the lowest contact resistivity. Quantum mechanical simulations indicate that the presence of InP native oxide or/and metal indium alloy at the interface increases In diffusion.     

Cost optimization of a hybrid composite flywheel rotor with a split-type hub using combined analytical/numerical models

A procedure to find the optimal design of a flywheel with a split-type hub is presented. Since cost plays a decisive role in stationary flywheel energy storage applications, a trade-off between energy and cost is required. Applying a scaling technique, the multi-objective design problem is reduced to the maximization of the energy-per-cost ratio as the single objective. Both an analytical and a finite element model were studied. The latter was found to be more than three orders of magnitude more computationally expensive than the analytical model, while the analytical model can only be regarded as a coarse approximation. Multifidelity approaches were examined to reduce the computational expense while retaining the high accuracy and large modeling depth of the finite element model. Using a surrogate-based optimization strategy, the computational cost was reduced to only one third in comparison to using only the finite element model. A nonlinear interior-point method was employed to find the optimal rim thicknesses and rotational speed. The benefits of the split-type hub architecture were demonstrated.     

An open implementation for context-oriented layer composition in ContextJS

Context-oriented programming (COP) provides dedicated support for defining and composing variations to a basic program behavior. A variation, which is defined within a layer, can be de-/activated for the dynamic extent of a code block. While this mechanism allows for control flow-specific scoping, expressing behavior adaptations can demand alternative scopes. For instance, adaptations can depend on dynamic object structure rather than control flow. We present scenarios for behavior adaptation and identify the need for new scoping mechanisms. The increasing number of scoping mechanisms calls for new language abstractions representing them. We suggest to open the implementation of scoping mechanisms so that developers can extend the COP language core according to their specific needs. Our open implementation moves layer composition into objects to be affected and with that closer to the method dispatch to be changed. We discuss the implementation of established COP scoping mechanisms using our approach and present new scoping mechanisms developed for our enhancements to Lively Kernel.     

TimeLapseAnalyzer: multi-target analysis for live-cell imaging and time-lapse microscopy

The direct observation of cells over time using time-lapse microscopy can provide deep insights into many important biological processes. Reliable analyses of motility, proliferation, invasive potential or mortality of cells are essential to many studies involving live cell imaging and can aid in biomarker discovery and diagnostic decisions. Given the vast amount of image- and time-series data produced by modern microscopes, automated analysis is a key feature to capitalize the potential of time-lapse imaging devices. To provide fast and reproducible analyses of multiple aspects of cell behaviour, we developed TimeLapseAnalyzer. Apart from general purpose image enhancements and segmentation procedures, this extensible, self-contained, modular cross-platform package provides dedicated modalities for fast and reliable analysis of multi-target cell tracking, scratch wound healing analysis, cell counting and tube formation analysis in high throughput screening of live-cell experiments. TimeLapseAnalyzer is freely available (MATLAB, Open Source) at http://www.informatik.uni-ulm.de/ni/mitarbeiter/HKestler/tla.     

Kryptographiegesetzgebung im Wandel

Zusammenfassung  Im Oktober 2007 trat in Gro?britannien ein Gesetz in Kraft, das es den Polizeibeh?rden erlaubt, die Herausgabe kryptographischer Schlüssel von ihren Nutzern zu verlangen. Dies ist nur eine von zahlreichen Ma?nahmen, die einen globalen Kurswechsel hin zu verst?rkter überwachung und Regulierung von Verschlüsselungsmechanismen belegen. In diesem Beitrag werden die Entwicklungen der Kryptographiegesetzgebung der letzten Jahre und ihr heutiger Stand dokumentiert. Dipl.-Inform. Malte Diehl Doktorand an der Uni Oldenburg, Department für Informatik, Stipendiat der DFG     

Glass-fibre-reinforced composites with enhanced mechanical and electrical properties - Benefits and limitations of a nanoparticle modified matrix

Nanoparticles and especially carbon nanotubes (CNTs) provide a high potential for the modification of polymers. They are very effective fillers regarding mechanical properties, especially toughness. Furthermore, they allow the implication of functional properties, which are connected to their electrical conductivity, into polymeric matrices. In the present paper, different nanoparticles, as fumed silica and carbon black, were used to optimise the epoxy matrix system of a glass-fibre-reinforced composite. Their nanometre-size enables their application as particle-reinforcement in FRPs produced by the resin-transfer-moulding method (RTM), without being filtered by the glass-fibre bundles. Additionally, an electrical field was applied during curing, in order to enhance orientation of the nanofillers in z-direction. The interlaminar shear strengths of the nanoparticle modified composites were significantly improved (+16%) by adding only 0.3 wt.% of CNTs. The interlaminar toughness G_Ic and G_IIc was not affected in a comparable manner. The laminates containing carbon nanotubes exhibited a relatively high electrical conductivity at very low filler contents, which allows the implication of functional properties, such as stress-strain monitoring and damage detection.