The methanol lines and hot core of OMC2-FIR4, an intermediate-mass protostar, with Herschel/HIFI

In contrast with numerous studies on the physical and chemical structure of low- and high-mass protostars, much less is known about their intermediate-mass counterparts, a class of objects that could help to elucidate the mechanisms of star formation on both ends of the mass range. We present the first results from a rich HIFI spectral dataset on an intermediate-mass protostar, OMC2-FIR4, obtained in the CHESS (Chemical HErschel Survey of Star forming regions) key programme. The more than 100 methanol lines detected between 554 and 961 GHz cover a range in upper level energy of 40 to 540 K. Our physical interpretation focusses on the hot core, but likely the cold envelope and shocked regions also play a role in reality, because an analysis of the line profiles suggests the presence of multiple emission components. An upper limit of 10^(-6) is placed on the methanol abundance in the hot core, using a population diagram, large-scale source model and other considerations. This value is consistent with abundances previously seen in low-mass hot cores. Furthermore, the highest energy lines at the highest frequencies display asymmetric profiles, which may arise from infall around the hot core

Enterprise architecture development and implementation in public sector: The Malaysian perspective

Enterprise Architecture (EA) is gaining the attention from the public sector as a solution to improve the function of e-Government. However, public sector agencies are having difficulties with its development and implementation due to inflexibility and complexity of the agencies’ business function and information technology structures. The objective of this paper is to identify the challenges faced by the Malaysian public sector agencies that are in development and implementation phase of EA. In order to get the holistic perspective of EA development and implementation scenario in each organisation, a Balanced Scorecard (BSC) approach is applied. A multiple case study research approach is utilized to achieve this study objective. Data were collected through interviews with the agencies EA team, general observation during the EA workshops as well as review of EA related documents. The result shows there are twenty challenges identified which is consistent with other challenges stated in literature except for talent management issue. Thus, this provides a new insight on how the public sector should implement their EA as compared to any other organisation

A Priority Based Enterprise Architecture Implementation Assessment Model: An Analytic Hierarchy Process (AHP) Approach

Despite of many Enterprise Architecture (EA) frameworks and methodologies available, in reality EA implementation is a challenging process. In order to assure a progressive EA implementation, assessment and monitoring mechanism is required. The existing EA assessment approaches are mostly based on checklist or maturity model and designed to assess post EA implementation. Less EA assessment is found to cater on the pre and during EA implementation process. This indicates that the lack of systematic assessment mechanism, especially for pre and during EA implementation phase. Hence, based on the gap identified, this study proposes a priority based assessment model for pre and during EA implementation process. This integrated model of Balanced Scorecard (BSC) and Analytic Hierarchy Process (AHP) is designed to assess the priority and capability of the organization in implementing EA. The assessment criteria were formulated from findings of an exploratory study. Six main criteria and 27 sub-criteria have been identified as the Critical Success Factors (CSFs) in EA implementation. Based on these CSFs, a Priority based EA Implementation Assessment Model (PEAIAM) has been formulated and presented in this paper

Searching for a dusty cometary belt around TRAPPIST-1 with ALMA

Low-mass stars might offer today the best opportunities to detect and characterize planetary systems, especially those harbouring close-in low-mass temperate planets. Among those stars, TRAPPIST-1 is exceptional since it has seven Earth-sized planets, of which three could sustain liquid water on their surfaces. Here we present new and deep ALMA observations of TRAPPIST-1 to look for an exo-Kuiper belt which can provide clues about the formation and architecture of this system. Our observations at 0.88 mm did not detect dust emission, but can place an upper limit of 23 µJy if the belt is smaller than 4 au, and 0.15 mJy if resolved and 100 au in radius. These limits correspond to low dust masses of ̃10-5 to 10-2 M⊕, which are expected after 8 Gyr of collisional evolution unless the system was born with a >20 M⊕ belt of 100 km-sized planetesimals beyond 40 au or suffered a dynamical instability. This 20 M⊕ mass upper limit is comparable to the combined mass in TRAPPIST-1 planets, thus it is possible that most of the available solid mass in this system was used to form the known planets. A similar analysis of the ALMA data on Proxima Cen leads us to conclude that a belt born with a mass ≳1 M⊕ in 100 km-sized planetesimals could explain its putative outer belt at 30 au. We recommend that future characterizations of debris discs around low-mass stars should focus on nearby and young systems if possible

An IoT based home automation integrated approach: impact on society in sustainable development perspective

In recent years, due to substantial evolution in the field of consumer electronics, the society is striving to optimize efficiency, energy savings, green technology and environmental sustainability in their daily lives at homes. Most of the people are controlling and monitoring home appliances manually and therefore, facing lots of problems in managing natural resources, cost, effort and security which lead towards an un-comfortable and un-reliable life. Numerous 'intelligent' devices such as smartphones, tablets, air-conditioners, etc. have promoted the key concept of the Internet of Things (IoT) based home automation. Entrenched with technology, these devices can be distantly monitored and controlled over the Internet at home and anywhere in the world. Over the past few decades, global warming has become a severe worldwide challenge. However, sustainable development and green technology play an important role in climate change. The primary purpose of this study is to save natural resources, reduce energy consumption, and to understand the impact of home automation on the society in order to achieve the goal of green technology and environmental sustainability. In this paper, IoT based home automation approach integrated with the smart meter, solar, wind, geothermal renewable energy resources and government green awareness program to extensively optimize the need of energy consumption, security, cost, convenience and cleaner environment for the society is proposed. In addition, a survey was conducted among the target audience for the purpose of identifying and evaluating its least impact on the environment and society in a sustainable development perspective. The results of this survey are statistically analyzed using IBM SPSS statistics version 23. The results revealed that there is a significant impact of home automation on the society thereby contributing to its solution

Self-Organizing Traffic Flow Prediction with an Optimized Deep Belief Network for Internet of Vehicles

To assist in the broadcasting of time-critical traffic information in an Internet of Vehicles (IoV) and vehicular sensor networks (VSN), fast network connectivity is needed. Accurate traffic information prediction can improve traffic congestion and operation efficiency, which helps to reduce commute times, noise and carbon emissions. In this study, we present a novel approach for predicting the traffic flow volume by using traffic data in self-organizing vehicular networks. The proposed method is based on using a probabilistic generative neural network techniques called deep belief network (DBN) that includes multiple layers of restricted Boltzmann machine (RBM) auto-encoders. Time series data generated from the roadside units (RSUs) for five highway links are used by a three layer DBN to extract and learn key input features for constructing a model to predict traffic flow. Back-propagation is utilized as a general learning algorithm for fine-tuning the weight parameters among the visible and hidden layers of RBMs. During the training process the firefly algorithm (FFA) is applied for optimizing the DBN topology and learning rate parameter. Monte Carlo simulations are used to assess the accuracy of the prediction model. The results show that the proposed model achieves superior performance accuracy for predicting traffic flow in comparison with other approaches applied in the literature. The proposed approach can help to solve the problem of traffic congestion, and provide guidance and advice for road users and traffic regulators

KELT-9 and its ultra-hot Jupiter: stellar parameters, composition, and planetary pollution

KELT-9b is an ultra-hot Jupiter observed to be undergoing extreme mass loss. Its A0-type host star has a radiative envelope, which makes its surface layers prone to retaining recently accreted material. To search for potential signs of planetary material polluting the stellar surface, we carry out the most comprehensive chemical characterisation of KELT-9 to-date. New element detections include Na and Y, which had previously been detected in the ultra-hot Jupiter but not studied in the star; these detections complete the set of nine elements measured in both star and planet. In comparing KELT-9 with similar open cluster stars we find no strong anomalies. This finding is consistent with calculations of photospheric pollution accounting for stellar mixing and using observationally estimated KELT-9b mass loss rates. We also rule out recent, short-lived intensive mass transfer such as the stellar ingestion of an Earth-mass exomoon.Comment: 7 pages, 7 figures, accepted for publication in MNRA

Dusty tails of evaporating exoplanets. II. Physical modelling of the KIC 12557548b light curve

Evaporating rocky exoplanets, such as KIC 12557548b, eject large amounts of dust grains, which can trail the planet in a comet-like tail. When such objects occult their host star, the resulting transit signal contains information about the dust in the tail. We aim to use the detailed shape of the Kepler light curve of KIC 12557548b to constrain the size and composition of the dust grains that make up the tail, as well as the mass loss rate of the planet. Using a self-consistent numerical model of the dust dynamics and sublimation, we calculate the shape of the tail by following dust grains from their ejection from the planet to their destruction due to sublimation. From this dust cloud shape, we generate synthetic light curves (incorporating the effects of extinction and angle-dependent scattering), which are then compared with the phase-folded Kepler light curve. We explore the free-parameter space thoroughly using a Markov chain Monte Carlo method. Our physics-based model is capable of reproducing the observed light curve in detail. Good fits are found for initial grain sizes between 0.2 and 5.6 micron and dust mass loss rates of 0.6 to 15.6 M_earth/Gyr (2-sigma ranges). We find that only certain combinations of material parameters yield the correct tail length. These constraints are consistent with dust made of corundum (Al2O3), but do not agree with a range of carbonaceous, silicate, or iron compositions. Using a detailed, physically motivated model, it is possible to constrain the composition of the dust in the tails of evaporating rocky exoplanets. This provides a unique opportunity to probe to interior composition of the smallest known exoplanets.Comment: 18 pages, 11 figures, A&A accepte