Applications of Social Media in the Tourism Industry: A Review

Purpose- This study aims to review and analyze the articles related to social media applications and their impact on the tourism industry. Methodology- For conducting this study, three leading databases named Google Scholar, Science Direct, and EBSCO Host were used for data collection purposes, and the research was conducted in three phases. Selecting the database for collecting data was the first phase, which was carried out during the period between November 2020 to December 2020. In the second phase, screening of the collected data was done, and in the final stage, 46 articles were selected to conduct this study. Discussion- Over the last decade, the rapid advancements in information and communication technology (ICTs) have had reflective impacts on the global tourism sector. Both researchers and professionals have acknowledged that social media applications have a significant impact on both suppliers and consumers of the tourism industry. Findings- Based on the reviewed articles from the perspectives of the tourism consumers as well as the tourism suppliers, this study has found that consumers use social media in pre-during-post travel for searching different information and suppliers generally use social media for promotion, communication, management, research purposes

Effects of micronutrients on bulb growth, yield and quality of local and high yielding onion (Allium cepa L.) cultivars in Bangladesh

Micronutrients have important functions on onion production. An experiment was conducted at the Landscape section and Laboratory of the Department of Horticulture, Bangladesh Agricultural University, Mymensingh during the period from October, 2017 to March, 2018 to investigate the effects of micronutrients on bulb growth, yield and quality of local and high yielding (HY) onion cultivars in Bangladesh. The experiment comprised three onion cultivars viz., Taherpuri (local), BARI Piaz 1 (HY) and BARI Piaz 4 (HY), and five micronutrients viz., Control (no micronutrient), Boron (B) @ 0.2 g/plot, Zinc (Zn) @ 0.5 g/plot, Copper (Cu) @ 0.2 g/plot and B+Zn+Cu @ (0.2+0.5+0.2 g/plot). The two-factor experiment was laid out in randomized complete block design with three replications. Results revealed that onion cultivars and micronutrients had significant influence on the parameters studied. BARI Piaz 4 along with the application of B+Zn+Cu @ 0.2+0.5+0.2 g/plot produced the highest bulb size, increased plant height, number of leaves, fresh weight of bulb, per cent dry matter content of bulbs and bulb yield compared to other onion cultivars and micronutrient treatments. The highest bulb yield (16.07 t/ha) was recorded in B+Zn+Cu, while the lowest bulb yield (8.92 t/ha) was found from control. Highest gross yield of onion (20.67 t/ha) was recorded from BARI Piaz 4 with B+Zn+Cu @ 0.2+0.5+0.2 g/plot. Therefore, it can be concluded that combined treatment of BARI Piaz 4 and B+Zn+Cu @ @ 0.2+0.5+0.2 g/plot was found to be better in respect of bulb growth and yield, and Taherpuri for quality of onion

Methodology for managing shipbuilding projectby integrated optimality

PhD ThesisSmall to medium shipyards in developing shipbuilding countries face a persistent challenge to contain project cost and deadline due mainly to the ongoing development in facility and assorted product types. A methodology has been proposed to optimize project activities at the global level of project planning based on strength of dependencies between activities and subsequent production units at the local level. To achieve an optimal performance for enhanced competitiveness, both the global and local level of shipbuilding processes must be addressed. This integrated optimization model first uses Dependency Structure Matrix (DSM) to derive an optimal sequence of project activities based on Triangularization algorithm. Once optimality of project activities in the global level is realized then further optimization is applied to the local levels, which are the corresponding production processes of already optimized project activities. A robust optimization tool, Response Surface Method (RSM), is applied to ascertain optimum setting of various factors and resources at the production activities. Data from a South Asian shipyard has been applied to validate the fitness of the proposed method. Project data and computer simulated data are combined to carry out experiments according to the suggested layout of Design of Experiments (DOE). With the application of this model, it is possible to study the bottleneck dynamics of the production process. An optimum output of the yard, thus, may be achieved by the integrated optimization of project activities and corresponding production processes with respect to resource allocation. Therefore, this research may have a useful significance towards the improvement in shipbuilding project management

Vegetation changes of Sundarbans based on Landsat imagery analysis between 1975 and 2006

The Sundarbans in Bangladesh and India is the largest single block of tidal halophytic mangrove forest in the world. This forest is threatened by effect of climate change and manmade activities. The aim of this paper is to show changes in vegetation cover of Sundarbans since 1975 using Landsat imagery. Normalized Difference Vegetation Index is applied to quantify and qualify density of vegetation on a patch of land. Estimated land area (excluded water body) of this forest is 66% in Bangladesh, and 34% in India, respectively. Net erosion since 1975 to 2006 is ~5.9%. In vicinity of human settlement, areal changes are not observed since 1975. The mangrove forest is decreased by 19.3% due severe tropical cyclone in 1977 and 1988. Moreover, the dense forest is damaged by about 50%. However, more than 25 years is taken by Sundarbans to recover from damage by a severe tropical cyclone. The biodiversity of Sundarbans depends to fresh water flow through it. Therefore, the future of Sundarbans depends to the impact of climate change which has further effect to increasing intensity and frequency of severe tropical cyclone and salinity in water channels in Sundarbans

Corporate Governance Reform and Firm Performance: Evidence from an Emerging Economy

This paper empirically examines the impact of governance mechanisms on firm’s operating performance in the light of recent institutional reform in corporate governance (CG) guidelines in the context of an emerging economy, Bangladesh, over pre- (2007-2011) and post-reform (2013-2017) stages. Particular attentions are paid to discussing the effect of board size, board independence and CEO duality. It employs generalised method of moments (GMM) estimation technique to control for the possible endogeneity issues in governance-performance nexus. The results reveal that board size made a significant positive contribution in enhancing firm performance during the post-reform phase, while no significant association was found in the pre-reform stage. After the reform, the negative significance of both the CEO duality and board independence fade away, even, the negative impact of including outsiders in boardroom changed to an insignificant positive association. Our findings suggest that although the reform with increased proportion of independent directors establishes boards’ resource provisioning role, it is yet to demonstrate the full-fledged monitoring contribution that improves firm performance

Uma abordagem preditiva de DASH QoE baseada em aprendizado de máquina em multi-access edge computing

Orientador: Christian Rodolfo Esteve RothenbergDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: O tráfego de serviços de vídeo multimídia está crescendo rapidamente nas redes móveis nos últimos anos. Os serviços de vídeo que usam técnicas de Dynamic Adaptive Streaming sobre HTTP (DASH) dominaram o tráfego total da Internet para transportar o tráfego de vídeo. Espera-se que as operadoras de rede móvel (Mobile Network Operators - MNOs) continuem atendendo a essa demanda crescente por tráfego de vídeo suportado por DASH, ao mesmo tempo em que fornecem uma alta qualidade de experiência (Quality of Experience - QoE) aos usuários finais. Além disso, as operadoras precisam ter um conhecimento claro acerca da qualidade de vídeo percebida pelos usuários finais e relacioná-la com o monitoramento em nível de rede, ou com informações de telemetria para identificação de problemas, análise da causa raiz e predição de padrões. Para garantir um gerenciamento de tráfego de rede com reconhecimento de QoE, um pré-requisito é que os MNOs monitorem o tráfego de rede passivamente e realizem medições efetivas de indicadores-chave de desempenho (Key Performance Indicators - KPIs) de QoE, como resoluções, eventos de paralisação, entre outros, que influenciam diretamente a percepção do usuário final. Muitas abordagens da literatura foram propostas para medir os KPIs com o objetivo de fornecer uma qualidade de serviço de vídeo aceitável. A maioria das soluções exige consciência de contexto do usuário final, o que não é viável do ponto de vista do MNO. No entanto, Deep Packet Inspection (DPI), outra solução mais amplamente usada para estimar os KPIs diretamente do tráfego de rede, não é mais uma solução conveniente para as operadoras devido à adoção de criptografia de streaming de vídeo fim-a-fim sobre TCP (HTTPs) e QUIC. Portanto, o aprendizado de máquina (Machine Learning - ML) passou a ser recentemente aceito como uma solução bem reconhecida para estimar KPIs de QoE, analisando os padrões de tráfego criptografados bem como estatísticas como qualidade de serviço (Quality of Service - QoS). Este trabalho apresenta uma abordagem mais refinada e leve, baseada em aprendizado de máquina, denominada Edge QoE Probe, para estimar QoE do usuário final para o serviço de vídeo DASH, monitorando passivamente o tráfego de rede criptografado na borda da rede. Nossa abordagem pode avaliar vários KPIs de QoE, como por exemplo resolução, taxa de bits, proporção de paralisação, entre outros, tanto em tempo real quanto por sessão. Além disso, neste trabalho investigamos o desempenho do vídeo DASH sobre o protocolo de transporte tradicional TCP (HTTPs) e QUIC. Para este propósito, avaliamos experimentalmente diferentes traces de rede celular em um ambiente emulado de alta fidelidade e comparamos o desempenho comportamental de algoritmos Adaptive Bitrate Streaming (ABS) considerando KPIs de QoE sobre TCP (HTTPs) e QUIC. Nossos resultados empíricos mostram que os algoritmos tradicionais de ABS usando QUIC como transporte precisariam alterações específicas para melhorar o desempenho em termos de QoE de vídeo baseados em DASHAbstract: Multimedia video services traffic is rapidly growing in mobile networks in recent years. Video services using Dynamic Adaptive Streaming over HTTP (DASH) techniques have dominated the total internet traffic to carry video traffic. Mobile Network Operators (MNOs) are expected to run on with this growing demand for DASH-supported video traffic while providing a high Quality of Experience (QoE) to the end-users. Besides, operators need to have a crystal notion of video quality perceived by the end-users and correlate them with network-level monitoring or telemetry information for problem identification, root cause analysis, and pattern prediction. To ensure QoE–aware network traffic management, a prerequisite for the MNOs is to monitor the network traffic passively and measure objective QoE Key Performance Indicators (KPIs) (such as resolutions and stalling events) effectively that directly influence end-user subjective feedback. Many literature approaches have been proposed to measure the KPIs aimed to deliver acceptable video service quality. Most of the solutions require end-user awareness, which is not viable from the MNOs' perspective. However, Deep Packet Inspection (DPI), another most widely used solution to estimate the KPIs directly from network traffic, is not a convenient solution anymore for the operators due to the adoption of end-to-end video streaming encryption over TCP (HTTPs) and QUIC transport protocol. Hence, in recent, Machine Learning (ML) has been accepted as a well-recognized solution for estimating QoE KPIs by analyzing the encrypted traffic patterns and statistics as Quality of Service (QoS). This work presents an ML-based lightweight and fine-grained Edge QoE Probe approach to estimate the end-user QoE for DASH video service by passively monitoring the encrypted network traffic on the edge of the network. Our approach can assess numerous QoE KPIs (such as resolution, bit-rate, quality switches, startup delay, and stall ratio) both in a real-time and per-session manner. Moreover, we investigate the DASH video service performance over the traditional TCP (HTTPs) and QUIC transport protocol in this work. For this purpose, we experimentally evaluate different cellular network traces in a high-fidelity emulated testbed and compare the behavioral performance of Adaptive Bitrate Streaming (ABS) algorithms considering QoE KPIs over TCP (HTTPs) and QUIC. Our empirical results show that QUIC suffers from traditional state-of-the-art ABS algorithms' ineffectiveness to improve video streaming performance without specific changesMestradoEngenharia de ComputaçãoMestre em Engenharia ElétricaFuncam

Effects of rehabilitation on the patients with chronic low back pain

This study was done to observe the effects of rehabilitation on chronic low back pain on 139 patient. They were divided into two groups:  a) One group (n=71) received naproxen (non-steroidal anti-inflammatory drug, NSAID) with selective rehabilitation and b) another group (n= 68) treated with NSAID only. The patients were followed up weekly for eight weeks. The improvement was found in both groups after treatment. In patients with rehabilitation, the pre-treatment and post-treatment mean scores (Oswastry Disability Index, Visual Analogue Scale and Modified Zung Index) were 34.3 ± 9.8 and 9.9 ± 8.0 respectively (p<0.001). Treatment with NSAID only reduced the mean scores from 34.9 ± 13.5 to 16.0 ± 14.4 (p<0.001) after treatment. There was no significant difference in clinical improvement between the groups in pre-treatment compare with week one, rehabilitation group (29.7 ± 8.7) vs NSAID group (31.5 ± 13.8). While significant improvement was found in rehabilitation group in comparison to NSAID group after 8th week, rehabilitation group vs NSAID group scores were 10.0 ± 7.9 vs 15.9 ± 14.5 respectively (p= 0.004). In conclusion, rehabilitation can be used as an adjunct to NSAID for better improvement

Smartphone and Our Students: Is It Being Good for Their Study?

The objectives of this study are to: (I) find out the discriminations or variations (if any) between the attentive and inattentive university students in terms of their purposes of using smartphones, (II) analyze the cause-effect relationship between “the purposes considered to have good or bad impact on study” and “the smartphone usage behavior of the attentive students”, and (III) analyze the cause-effect relationship between “the purposes considered to have good or bad impact on study” and “the smartphone usage behavior of the inattentive students”. 400 students (200 attentive and 200 inattentive) students are surveyed.  Based survey and statistical analysis results, it is found that attentive and inattentive student are differentiating from each other in terms of their purposes of using smartphones for learning and study, social networking and entertainment. Moreover, the reasons of using smartphones believed to be in favor of their learning activities have positive impact on the attentive students’ smartphones usage behavior, whereas inattentive students are not acting likewise. Corrective actions by the interested parties should be undertaken to reform this unexpected scenario. Keywords: Smartphone, Students, Education, Bangladesh