Modified Multipath Routing Protocol Applied On Ns3 Dcell Network Simulation System

<p class="0abstract"><strong>—</strong>In the communication networks, guidance has become an important factor, with a significant impact on network performance, where the network orientation area has been and continues to be an ongoing development, intensive research for many years aimed at optimizing the network. This paper performs three modifications for a multipath routing protocol to solve the problem of routing in a DCell network simulation and apply online solutions on the network, the goal is to improve the transition efficiency of data. The modifications used to avoid data transmission failures which are delay problem<strong>, </strong>link failure problem, and power off (rack problem). The implementation of multipath routing protocol on the DCell network in actual simulation using the NS-3 program, which represents the rule that the DCell network was built and simulated. Finally, the modifications succeeded and return good results decreasing the delay time and solving the data transaction problems.</p>

Bolt damage identification based on orientation-aware center point estimation network

With the development of deep learning, object detection algorithms based on horizontal box are widely used in the field of damage identification. However, damages can be in any direction and position, and they are not necessarily horizontal or vertical. This article proposes a bolt damage identification network, namely, orientation-aware center point estimation network, which models a damage as a center point of its rotated bounding box. The proposed orientation-aware center point estimation network uses deep layer aggregation network to search center points and regress to all other damage properties, such as size and angle. A loss function is designed to improve the optimization efficiency of network. Orientation-aware center point estimation network is applied to bolt damage detection, and comparison with the well-known Faster Region-Convolutional Neural Network (a benchmark using horizontal bounding box) demonstrates the accuracy of the proposed method. Finally, videos were utilized to verify the capability of the proposed orientation-aware center point estimation network in real-time detection of bolt damages.

Advancing admixture graph estimation via maximum likelihood network orientation

MotivationAdmixture, the interbreeding between previously distinct populations, is a pervasive force in evolution. The evolutionary history of populations in the presence of admixture can be modeled by augmenting phylogenetic trees with additional nodes that represent admixture events. While enabling a more faithful representation of evolutionary history, admixture graphs present formidable inferential challenges. A key challenge is the need for admixture graph inference algorithms that are accurate while being completely automated and computationally efficient. Given the challenge of exhaustively evaluating all topologies, search heuristics have been developed to enable efficient inference. One heuristic, implemented in the popular method TreeMix, consists of adding admixture edges to an initial tree while optimizing a suitable objective function.ResultsHere, we present a demographic model (with one admixed population incident to a leaf) where TreeMix and any other starting-tree-based maximum likelihood heuristic using its likelihood function is guaranteed to get stuck in a local optimum and return the incorrect network topology. To address this issue, we propose a new search strategy based on reorientating the admixture graph that we term the maximum likelihood network orientation (MLNO) problem. We augment TreeMix with an exhaustive search for MLNO, referred to as OrientAGraph. In evaluations using previously published admixture graphs, OrientAGraph outperforms TreeMix on 4/8 models (there are no differences in the other cases). Overall, OrientAGraph finds graphs with higher likelihood scores and topological accuracy while remaining computationally efficient. Lastly, our study reveals important directions for improving maximum likelihood admixture graph estimation.AvailabilityOrientAGraph is available on Github (https://github.coin/ekinolloy/OrientAGraph) under the GNU General Public License v3.0.

Aisnusantara: Kontribusi Santri Membangun Narasi Damai di Era Digitalisasi Media

Instagram has become a new lifestyle in recent years. Instagram has created a society without borders because its users and followers are not limited to places. Instagram users post creative photos and videos on their accounts not only for advertising but also network orientation. Millennial generation tends to use the internet in all aspects of life. This paper aims to pay attention to: 1) How about the model and strategy of the Millennial Muslim network in aisnusantara; and 2) How they explain the importance of religious moderation in digital media. This qualitative research uses a netnographic approach based on data from the @aisnusantara Instagram account. With the ethno-semiotic method, this research reveals that: 1) Aisnusantara uses a networking management model from the national to regional levels, and has an annual meeting, namely the Kopdarnas which has an agenda to discuss various issues related to national and religious affairs for the millennial generation. 2) Aisnusantara campaigned for Islamic preaching inclusively through Instagram, for example against extremism on social media, by offering alternative narratives to counter extremism by campaigning for peaceful Islam based on religious moderation.

A study on dynamic capabilities view of doctoral students’ research productivity

Purpose The purpose of this study is to investigate the influence of dynamic capabilities (DC) along with operational capabilities such as network orientation, academics, social networking and perceived supervisory support (PSS), on the research productivity of doctoral students in Malaysia and the United Arab Emirates (UAE). Design/methodology/approach Data were collected through an online quantitative survey from participants recruited via snowball and quota sampling. The conceptual model was developed and tested using partial least squares structural equation modelling. Findings DC, network orientation and academic and social networking are shown to have a positive influence on the productivity levels of doctoral students in Malaysia and the UAE. However, the influence was found to be stronger in the Malaysian sample than in the UAE sample. The impact of PSS was not seen to be statistically significant in either sample. Practical implications The findings suggest that attention needs to be paid to strengthening doctoral students’ DC in terms of research skills and competencies, as well as enhancing operational capabilities to improve students’ research capabilities. Originality/value Drawing on strategic management approaches and socialisation theory, this study is assumed to be the first that considers doctoral students’ research productivity in the context of DC.