Is there a universality of the helix-coil transition in protein models?

The similarity in the thermodynamic properties of two completely different theoretical models for the helix-coil transition is examined critically. The first model is an all-atomic representation for a poly-alanine chain, while the second model is a minimal helix-forming model that contains no system specifics. Key characteristics of the helix-coil transition, in particular, the effective critical exponents of these two models agree with each other, within a finite-size scaling analysis.Comment: Latex, to appear in Eur. Phys. J.

Long coherence times for edge spins

We show that in certain one-dimensional spin chains with open boundary conditions, the edge spins retain memory of their initial state for very long times. The long coherence times do not require disorder, only an ordered phase. In the integrable Ising and XYZ chains, the presence of a strong zero mode means the coherence time is infinite, even at infinite temperature. When Ising is perturbed by interactions breaking the integrability, the coherence time remains exponentially long in the perturbing couplings. We show that this is a consequence of an edge "almost" strong zero mode that almost commutes with the Hamiltonian. We compute this operator explicitly, allowing us to estimate accurately the plateau value of edge spin autocorrelator.Comment: 13 pages, 13 figures; references adde

Knowledge management in a research & development environment - The Integration of company culture and technology

This thesis will show that understanding company structure and company culture are significantly more important than the actual technological tools an organization uses to implement successful knowledge management. An examination of company structure and cultural enablers will be followed by a review of some of the most widespread knowledge management tools to illustrate how knowledge management can be successfully put into practice

Broadcasting Convolutional Network for Visual Relational Reasoning

In this paper, we propose the Broadcasting Convolutional Network (BCN) that extracts key object features from the global field of an entire input image and recognizes their relationship with local features. BCN is a simple network module that collects effective spatial features, embeds location information and broadcasts them to the entire feature maps. We further introduce the Multi-Relational Network (multiRN) that improves the existing Relation Network (RN) by utilizing the BCN module. In pixel-based relation reasoning problems, with the help of BCN, multiRN extends the concept of `pairwise relations' in conventional RNs to `multiwise relations' by relating each object with multiple objects at once. This yields in O(n) complexity for n objects, which is a vast computational gain from RNs that take O(n^2). Through experiments, multiRN has achieved a state-of-the-art performance on CLEVR dataset, which proves the usability of BCN on relation reasoning problems.Comment: Accepted paper at ECCV 2018. 24 page

Ratchet-like dynamics of fluxons in annular Josephson junctions driven by bi-harmonic microwave fields

Experimental observation of the unidirectional motion of a topological soliton driven by a bi-harmonic ac force of zero mean is reported. The observation is made by measuring the current-voltage characteristics for a fluxon trapped in an annular Josephson junction that was placed into a microwave field. The measured dependence of the fluxon mean velocity (rectified voltage) at zero dc bias versus the phase shift between the first and second harmonic of the driving force is in qualitative agreement with theoretical expectations.Comment: 6 figure

Impact of mobility on the IoT MAC infrastructure: IEEE 802.15.4e TSCH and LLDN platform

Realizing the target of high reliability and availability is a crucial concept in the IoT context. Different types of IoT applications introduce several requirements and obstacles. One of the important aspects degrading network performance is the node mobility inside the network. Without a solid and adaptive mechanism, node mobility can disrupt the network performance due to dissociations from the network. Hence, reliable techniques must be incorporated to tackle the overhead of node movement. In this paper, the overhead of mobility on both IEEE 802.15.4e timeslotted channel hopping (TSCH) and low latency deterministic (LLDN) modes is investigated. These two modes can be considered as the MAC layer of the IoT paradigm because of their importance and resilience to different network obstacles. In addition, the set of metrics and limitations that influence the network survivability will be identified to ensure efficient mobile node handling process. Both TSCH and LLDN have been implemented via the Contiki OS to determine their functionality. TSCH has been demonstrated to have better node connectivity due to the impact of frame collision in LLDN. In addition, by neglecting the overhead of collision, the LLDN has been shown to have better connectivity and low radio duty cycle (RDC)

Dynamic cluster head election protocol for mobile wireless sensor networks

A dynamic cluster head election protocol (DCHEP) is proposed in this work to improve network availability and energy efficiency for mobile wireless sensor networks (WSNs) under the beacon-enabled IEEE 802.15.4 standard. The proposed protocol (DCHEP) is developed and simulated using CASTALIA/OMNET++ with a realistic radio model and node behaviour. DCHEP improves the network availability and lifetime and maintains clusters hierarchy in a proactive manner even in a mobile WSN where all the nodes including cluster heads (CHs) are mobile, this is done by dynamically switching CHs allowing nodes to act as multiple backup cluster heads (BCHs) with different priorities based on their residual energy and connectivity to other clusters. DCHEP is a flexible and scalable solution targeted for dense WSNs with random mobility. The proposed protocol achieves an average of 33% and 26% improvement to the availability and energy efficiency respectively compared with the original standard

Tackling Mobility in Low Latency Deterministic Multihop IEEE 802.15.4e Sensor Network

Providing reliable services for low latency (LL) applications within the IoT context is a challenging issue. Several wireless sensor network (WSN) applications require deterministic systems that ensure a reliable and low latency aggregation service. The IEEE 802.15.4e standard, which is considered as the backbone of the IoT regarding WSN, has presented the low-latency deterministic network mode (LLDN) that can fulfil the major requirements of low latency applications. Meanwhile, several LL applications, for example in the automotive industry, demand the support of sensor node mobility which in turn affects network performance. Node mobility triggers several dissociations from the network that will increase latency and degrade node throughput. In this paper, we investigate the impact of node mobility over the LLDN mode while defining key factors that maximize latency and degrade throughput. In addition, an enhanced version of the LLDN mode is presented and evaluated that supports node mobility while maintaining the targeted limits of LL application requirements. The proposed mobility aware (MA-LLDN) technique manages to reduce the dissociation overhead by a factor of 75% while the packet delivery ratio (PDR) has been enhanced by 30%. Furthermore, this paper presents an analytical model that provides a snapshot of the tradeoff process between different metrics in the IEEE 802.15.4e LLDN design, which must be considered prior network deployment in mobile LL applications