特利亚电信

Businesses must adapt to rapidly changing technologies, new product and market innovations to be competitive in the global marketplace. One way companies try to improve their performance is through effective procurement using information technology approaches. Innovative IT technologies incorporated into the supply chain allow businesses to reduce their costs, improve their internal processes and shorten time spent on transactional tasks, enabling cost reductions as well as market competitiveness. Based on an extensive literature review of enterprise resource planning (ERP) development, cloud computing and cloud ERPs, e-procurement and ERP implementation frameworks as well as success factors and challenges related to cloud ERP and e-procurement implementation, a preliminary model for e-procurement was developed with a focus on three change elements: business process changes, human resource (HR) changes and IT changes. A case study was conducted to evaluate the proposed model empirically and semi-structured interviews were carried out to ensure the findings from case study analysis were relevant. Empirical research confirmed the importance of business process changes, HR changes and IT changes and revealed that supplier changes need to be added as an additional change element to the model’s design. Empirical research also found that business process changes have the most power and influence in the implementation process. This paper presents a revised model for e-procurement implementation based on business process changes, HR changes, IT changes and supplier changes. This article is also included in The Business & Management Collection which can be accessed at https:// hstalks.com/business/.

Outdoor recreation continues to increase in popularity. In Norway, several avalanche fatalities are recorded every year, but the accurate calculation of a fatal accident rate is impossible without knowing how many people are exposed. We attempted to employ signaling data from telecom network data to enumerate backcountry travelers in avalanche terrain. Each signaling data event contains information about which coverage area the phone is connected to and timestamp. There is no triangulation, making it impossible to know whether the associated phone is moving or stationary within the coverage area. Hence, it's easier to track the phone's movement through different coverage areas. We utilize this by enumerating the number of people with phones traveling to avalanche-prone terrain for the 2019/2020 winter season. We estimated that 13,666 phones were in avalanche terrain during the season, ranging from 0 to 118 phones/day with an average of 75 phones/day. We correlated the number of phones per day against amount of daylight (R2=0.186, p-value <0.01), weekends and holidays (R2=0.073, p-value <0.01), number of bulletin views (R2=0.045, p-value <0.01). Unfortunately, the validation revealed discrepancies between the estimated positions in the mobile network and the true reference positions as collected with a GPS. We attribute this to the algorithm being designed to measure urban mobility and the long distance between the base transceiver stations in mountainous areas. This lack of coherence between the signaling data and GPS records for rural areas in Norway has implication for the utility of signaling data outside of urban regions.

An exponential growth of bandwidth demand, spurred by emerging network services, often with diverse characteristics and stringent performance requirements, drive the need for more dynamic operation of optical networks, efficient use of spectral resources, and automation. Spectrum fragmentation is one of the main challenges of dynamic, resource-efficient Elastic Optical Networks (EONs). Fragmented, stranded spectrum slots lead to poor resource utilization and increase the blocking probability of incoming service requests. Conventional approaches for Spectrum Defragmentation (SD) apply various criteria to decide when, and which portion of the spectrum to defragment. However, these polices often address only a subset of tasks related to defragmentation, are not adaptable, and have limited automation potential. To address these issues, we propose DeepDefrag, a novel framework based on reinforcement learning that addresses the main aspects of the SD process: determining when to perform defragmentation, which connections to reconfigure, and which part of the spectrum to reallocate them to. DeepDefrag outperforms the well-known Oldest-First FirstFit (OF-FF) defragmentation heuristic, substantially reducing blocking probability and defragmentation overhead.

We discuss the history, past challenges and future plans of GNPy, an open source project for simulating physical impairments in contemporary DWDM network. The paper describes the unique interaction among network operators, equipment vendors, and standard bodies, as well as challenges in implementing the digital twin of an optical network.

NB-IoT technology is characterized by e.g. wide coverage, massive connectivity, reduced device energy consumption, and lower device cost, making it a suitable choice for the Internet of Things (IoT) vision. With these NB-IoT provisions, this work presents an NB-IoT based visual Smart Waste Management System (SWMS) that involve the design and development of cost-effective visual smart bins with reduced complexity, low power consumption, and easy installation so as to retrofit the existing traditional bins. Thanks to NB-IoT, these smart bins provide long-range connectivity to a multitude of bins so they can trans-mit their visual data to a remote cloud server. Since the cloud server is a compute-intensive platform, it can further process these images through utilising appropriate image processing (IP), Artificially Intelligent (AI) and Machine Learning (ML) algorithms for efficient waste-management including waste-collection, waste-classification, waste-prediction, and waste-planning.