Exploiting Authentic Materials for Developing Writing Skills at Secondary Level

Writing is an extremely complex process; there are various ways, means and techniques involved in teaching writing. The present study intends to scrutinize the efficacy of authentic materials for enhancing writing skills of the second language assimilators at secondary level in Pakistan. Authentic materials are very interesting, absorbing and motivating. Change and variety is something very important for human development and upbringing. Authentic materials can serve this purpose very well. Moreover, authentic materials are diverse in nature and have a variety of things to offer. They have been popular for last decade or so and are a very impressive technique. There is a great variety of materials available in newspapers, broadcasts, magazines etc in the form of advertisements, cartoons, bulletins, horoscopes, weather reports etc. Exploited appropriately, they can be of great help to improve students writing skills. Authentic materials can even more be useful to those learners who intend to go to a foreign country for higher education. Such materials will acquaint them not only with the language but also with the culture and value system of the country concerned. Authentic materials, of course, hold great promise for those who are in the process of learning and improving writing skills. The use of authentic materials creates a lot of interest in the learners and they do not feel bored and tired. There comes a big part of charming and attractive outside world into the classroom. Authentic materials reduce the dullness of specially contrived text material. It makes significant contributions toward meeting the learning objective of a programme. Authentic materials are varied and very flexible in nature, which allow free play to the students and never restrict them at a place. Key words: Authentic materials, writing skills, second languag

Smart Provisioning of Sliceable Bandwidth Variable Transponders in Elastic Optical Networks

Prior provisioning of optical source technologies have techno-economic importance for the operator during the design and planning of optical network architectonics. Advancement towards the latest technology paradigm such as Elastic Optical Networks (EONs) and Software Defined Networking (SDN) open a gateway for a flexible and re-configurable optical network architecture. In order to achieve the required degree of flexibility, a flexible and dynamic behaviour is required both at the control and data plane. In this regards, SDN-enabled flexible optical transceivers are proposed to provide the required degree of flexibility. Sliceable Bandwidth Variable Transponders (SBVTs) is one of the recent type of flexible optical transceivers. Based on the type/technology of optical carrier source, the SBVTs are categorized into two types; Multi-Laser SBVT (ML-SBVT) and Multi-wavelength SBVT (MW-SBVT). Both architectures have their own pros and cons when it comes to accommodate traffic request. In this paper, we propose a selection model for the SBVTs before its actual deployment in the network. The selection model consider various design and planning phase network characteristics. In addition to this selection model, the comparison of centralized Flex-OCSM architecture is also presented with the already discussed SBVT types. The analysis in this work is performed on random network (20 nodes) and the German Network (17 nodes)

QoT- Estimation Assisted by Transfer learning in Extended C-band Network Operating on 400ZR

We propose a transfer learning-based technique that assists in estimating the Quality-of-transmission (QoT) of the lightpaths in an extended C-band network on 400ZR. The proposed scheme develops the cognition using the traditional C-band operating network knowledge

Convolutional neural network for quality of transmission prediction of unestablished lightpaths

With the advancement in evolving concepts of software-defined networks and elastic-optical-network, the number of design parameters is growing dramatically, making the lightpath (LP) deployment more complex. Typically, worst-case assumptions are utilized to calculate the quality-of-transmission (QoT) with the provisioning of high-margin requirements. To this aim, precise and advanced estimation of the QoT of the LP is essential for reducing this provisioning margin. In this investigation, we present convolutional-neural-networks (CNN) based architecture to accurately calculate QoT before the actual deployment of LP in an unseen network. The proposed model is trained on the data acquired from already established LP of a completely different network. The metric considered to evaluate the QoT of LP is the generalized signal-to-noise ratio (GSNR). The synthetic dataset is generated by utilizing well appraised GNPy simulation tool. Promising results are achieved, showing that the proposed CNN model considerably minimizes the GSNR uncertainty and, consequently, the provisioning margin

Knowledge Distillation-Based Compression Model for QoT Estimation of an Unestablished Lightpaths

A precise Quality-of-transmission (QoT) estimation of a Lightpath (LP) before its deployment is a key step in effective network design and resource utilization. Deep neural network-based methods have recently shown promising results for QoT estimation tasks. However, these methods contain a large number of parameters and require heavy computational resources for accurate predictions. To this end, we propose a novel Knowledge distillation (KD) based compression method to obtain a compact and more accurate model for QoT estimation. Our simulation results demonstrate that the model trained using KD significantly improves accuracy with reduced parameters and computational complexity. To the best of our knowledge, this is the first time that the knowledge distillation technique has been used to estimate the QoT of an unestablished LP

Use of Tranexamic acid is a cost effective method in preventing blood loss during and after total knee replacement

<p>Abstract</p> <p>Background & Purpose</p> <p>Allogenic blood transfusion in elective orthopaedic surgery is best avoided owing to its associated risks. Total knee replacement often requires blood transfusion, more so when bilateral surgery is performed. Many strategies are currently being employed to reduce the amount of peri-operative allogenic transfusions. Anti-fibrinolytic compounds such as aminocaproic acid and tranexamic acid have been used systemically in perioperative settings with promising results. This study aimed to evaluate the effectiveness of tranexamic acid in reducing allogenic blood transfusion in total knee replacement surgery.</p> <p>Methodology</p> <p>This was a retrospective cohort study conducted on patients undergoing total knee replacement during the time period November 2005 to November 2008. Study population was 99 patients, of which 70 underwent unilateral and 29 bilateral knee replacement. Forty-seven patients with 62 (49.5%) knees (group-I) had received tranexamic acid (by surgeon preference) while the remaining fifty-two patients with 66 (51.5%) knees (group-II) had did not received any tranexamic acid either pre- or post-operatively.</p> <p>Results</p> <p>The mean drop in the post-operative haemoglobin concentration in Group-II for unilateral and bilateral cases was 1.79 gm/dl and 2.21 gm/dl, with a mean post-operative drainage of 1828 ml (unilateral) and 2695 ml (bilateral). In comparison, the mean drop in the post-op haemoglobin in Group-I was 1.49 gm/dl (unilateral) and 1.94 gm/dl (bilateral), with a mean drainage of 826 ml (unilateral) and 1288 ml (bilateral) (p-value < 0.001).</p> <p>Interpretation</p> <p>Tranexamic acid is effective in reducing post-operative drainage and requirement of blood transfusion after knee replacement.</p

Performance Analysis of Transfer-learning Approaches for QoT Estimation of Network Operating with 400ZR

In the last decade, internet traffic has increased exponentially due to the expansion of bandwidth-intensive applications and the evolution of the concept of the internet of things. To sustain this growth in internet traffic, network operators insist on maximizing the utilization of already deployed network infrastructure to its maximum capacity to maximize the CAPEX. In this context, an accurate and earlier calculation of the Quality of transmission (QoT) of the lightpaths (LPs) is essential for minimizing the required margins that result from the uncertainty of the working point of network elements. This article presents a novel QoT-Estimation (QoT-E) framework assisted by Transfer-learning (TL). The main focus of this study is to present a detailed analysis of two major TL approaches, i.e., the Transfer-learning feature extraction (TLFE) approach and the Transfer-learning fine-tuning (TLFT) method, and demonstrate their effectiveness in minimizing the uncertainties in QoT-E in comparison with standard baseline models like Artificial neural network (ANN) and Convolutional-neural network (CNN). The Generalized signal-to-noise ratio (GSNR) is considered a char-acterizing parameter for the QoT of LP. The dataset utilized in this analysis is generated synthetically using the GNPy platform. Promising results are achieved by reducing the overall required margin and extracting the residual network capacity

Impairment-aware Virtual Network Embedding Using Time Domain Hybrid Modulation formats in Optical Networks

The rapid increase in bandwidth-intensive applications has resulted in the progressive growth of IP traffic volume, especially in the backbone networks. To address this growth of internet traffic, operators are searching for innovative solutions which avoid new installation and replacement of the existing network infrastructure. In this context, efficient spectrum utilization is one of the key enablers to extract the residual network capacity. This paper proposes an innovative algorithm exploiting electronic traffic grooming and using impairment-aware routing to address the virtual network embedding problem (IA-TG-VNE) in optical networks. We also analyze the networking benefits of using time-domain hybrid modulation formats (TDHMF) over four conventional modulation formats; binary phase-shift keying (BPSK), quadrature phase-shift keying (QPSK), 16 quadrature amplitude modulation (QAM), and 64 QAM. The analysis is performed on a detailed physical layer model based on the Gaussian Noise (GN) model, which includes the effect of both linear and nonlinear impairments. The simulation results are obtained on realistic network topology: a 37-nodes PAN-EU. The simulation results show that TDHMF always performs better than conventional modulation formats for all types of fiber in terms of total network capacity, the average bit rate per lightpath (LP), number of LPs, and request blocking ratio

Supply Chain Operations Management in Pandemics: A State-of-the-Art Review Inspired by COVID-19

Pandemics cause chaotic situations in supply chains (SC) around the globe, which can lead towards survivability challenges. The ongoing COVID-19 pandemic is an unprecedented humanitarian crisis that has severely affected global business dynamics. Similar vulnerabilities have been caused by other outbreaks in the past. In these terms, prevention strategies against propagating disruptions require vigilant goal conceptualization and roadmaps. In this respect, there is a need to explore supply chain operation management strategies to overcome the challenges that emerge due to COVID-19-like situations. Therefore, this review is aimed at exploring such challenges and developing strategies for sustainability, and viability perspectives for SCs, through a structured literature review (SLR) approach. Moreover, this study investigated the impacts of previous epidemic outbreaks on SCs, to identify the research objectives, methodological approaches, and implications for SCs. The study also explored the impacts of epidemic outbreaks on the business environment, in terms of effective resource allocation, supply and demand disruptions, and transportation network optimization, through operations management techniques. Furthermore, this article structured a framework that emphasizes the integration of Industry 4.0 technologies, resilience strategies, and sustainability to overcome SC challenges during pandemics. Finally, future research avenues were identified by including a research agenda for experts and practitioners to develop new pathways to get out of the crisis.</jats:p

Modeling Off-line Routing and Spectrum Allocation Problem in Elastic Optical Network

The swift escalation in internet traffic due to diverse bandwidth starving applications and innovative concepts of modern technologies such as Elastic Optical Networks (EONs) and Software-defined networking (SDN) demands a dynamic and flexible optical network architecture both at the control and data plane. Characteristically, the flexibility in EONs is achieved by the emerging SDN-enabled sliceable bandwidth variable transponders (SBVTs) that support multiple optical carriers' simultaneous generation. These generated multiple optical carriers can operate different lightpaths using slice-ability or combined into a single high-rate super-channel. In this perspective, one of the major issues in EON is Routing and Spectrum Allocation (RSA). Typically, in EON, RSA is a spectrum management and Nondeterministic Polynomial-time hardness (NP-hard) problem in which network resources mainly bank on the applied ordering strategy. This article proposed a novel heuristic algorithm, Minimum Hops with Least Slot Spectrum (MHLS), to accommodate maximum traffic requests with better spectrum utilization. The proposed algorithm aims to minimize block requests, block traffic, and the total number of spectrum slots used in the network. The MHLS exploits Dijkstra-shortest-path and SDN-enabled SBVTs for RSA problem. The performance evaluation of MHLS is accomplished on the entire USA network