How Government Policy and Demographics affect Money Demand Function in Bangladesh

Abstract. Money demand has a key position in macroeconomics generally and monetary economics particularly. The improved economic condition of any country is a sign of increasing money demand and deteriorating economic climate is a sign of decreasing money demand (Maravic & Palic, 2005). In this study, Autoregressive distributed lag (ARDL) approach of co-integration developed by Pesaran et al., (2001) is used to estimate the money demand function. Real interest rate, GDP per capita, exchange rate, fiscal deficit, urban and rural population are selected to determine money demand function in Bangladesh over the period from 1975-2013. The co-integration analysis reveals that interest rate and per capita GDP exerts significant effect upon money demand both in long run and short run as well. Both urban and rural population have significant effect on money demand in the long run and short run and money demand function is found stable over time.Keywords. Bangladesh, Money demand, Per Capita GDP, Real interest rate, Exchange rate, Fiscal deficit, Urban and Rural Population.JEL. E41, G18, N30

Impact of Decmedetomidine on Opioid and Benzodiazepine Dosing Requirements in Children.

Poster presented at: Annual Update on Pediatric Cardiovascular Disease; February 2008; Scottsdale Arizona

Governance of Ecosystem Services Across Scales in Bangladesh

Ecosystem services are governed and affected by different legal, institutional and policy frameworks. Hence, formal documented policies, regulations and statutes of Bangladesh are examined where relevant. This facilitates greater understanding of the influence that governance has on the accessibility to the benefits derived and how this might then affect livelihoods and well-being. A range of factors are found to determine effectiveness in terms of general adaptive governance principles, with coordination, enforcement and rigidity being important issues. In addition, policy development in crucial areas may not be supported by associated legal frameworks, undermining implementation. However, workable (and dynamic) combinations of primary and secondary legislation are both possible and desirable to achieve flexible policy instruments.<br/

Are we failing to protect threatened mangroves in the Sundarbans world heritage ecosystem?

The Sundarbans, the largest mangrove ecosystem in the world, is under threat from historical and future human exploitation and sea level rise. Limited scientific knowledge on the spatial ecology of the mangroves in this world heritage ecosystem has been a major impediment to conservation efforts. Here, for the first time, we report on habitat suitability analyses and spatial density maps for the four most prominent mangrove species - Heritiera fomes, Excoecaria agallocha, Ceriops decandra and Xylocarpus mekongensis. Globally endangered H. fomes abundances declined as salinity increased. Responses to nutrients, elevation, and stem density varied between species. H. fomes and X. mekongensis preferred upstream habitats. E. agallocha and C. decandra preferred down-stream and mid-stream habitats. Historical harvesting had negative influences on H. fomes, C. decandra and X. mekongensis abundances. The established protected area network does not support the most suitable habitats of these threatened species. We therefore recommend a reconfiguration of the network to include these suitable habitats and ensure their immediate protection. These novel habitat insights and spatial predictions can form the basis for future forest studies and spatial conservation planning, and have implications for more effective conservation of the Sundarbans mangroves and the many other species that rely on them

LOCL: Learning Object-Attribute Composition using Localization

This paper describes LOCL (Learning Object Attribute Composition using Localization) that generalizes composition zero shot learning to objects in cluttered and more realistic settings. The problem of unseen Object Attribute (OA) associations has been well studied in the field, however, the performance of existing methods is limited in challenging scenes. In this context, our key contribution is a modular approach to localizing objects and attributes of interest in a weakly supervised context that generalizes robustly to unseen configurations. Localization coupled with a composition classifier significantly outperforms state of the art (SOTA) methods, with an improvement of about 12% on currently available challenging datasets. Further, the modularity enables the use of localized feature extractor to be used with existing OA compositional learning methods to improve their overall performance.Comment: 20 pages, 7 figures, 11 tables, Accepted in British Machine Vision Conference 202

BLoad: Enhancing Neural Network Training with Efficient Sequential Data Handling

The increasing complexity of modern deep neural network models and the expanding sizes of datasets necessitate the development of optimized and scalable training methods. In this white paper, we addressed the challenge of efficiently training neural network models using sequences of varying sizes. To address this challenge, we propose a novel training scheme that enables efficient distributed data-parallel training on sequences of different sizes with minimal overhead. By using this scheme we were able to reduce the padding amount by more than 100$x$ while not deleting a single frame, resulting in an overall increased performance on both training time and Recall in our experiments

Aerobic and strength training exercise programme for cognitive impairment in people with mild to moderate dementia : the DAPA RCT

Background Approximately 670,000 people in the UK have dementia. Previous literature suggests that physical exercise could slow dementia symptom progression. Objectives To estimate the clinical effectiveness and cost-effectiveness of a bespoke exercise programme, in addition to usual care, on the cognitive impairment (primary outcome), function and health-related quality of life (HRQoL) of people with mild to moderate dementia (MMD) and carer burden and HRQoL. Design Intervention development, systematic review, multicentred, randomised controlled trial (RCT) with a parallel economic evaluation and qualitative study. Setting 15 English regions. Participants People with MMD living in the community. Intervention A 4-month moderate- to high-intensity, structured exercise programme designed specifically for people with MMD, with support to continue unsupervised physical activity thereafter. Exercises were individually prescribed and progressed, and participants were supervised in groups. The comparator was usual practice. Main outcome measures The primary outcome was the Alzheimer’s Disease Assessment Scale – Cognitive Subscale (ADAS-Cog). The secondary outcomes were function [as measured using the Bristol Activities of Daily Living Scale (BADLS)], generic HRQoL [as measured using the EuroQol-5 Dimensions, three-level version (EQ-5D-3L)], dementia-related QoL [as measured using the Quality of Life in Alzheimer’s Disease (QoL-AD) scale], behavioural symptoms [as measured using the Neuropsychiatric Inventory (NPI)], falls and fractures, physical fitness (as measured using the 6-minute walk test) and muscle strength. Carer outcomes were HRQoL (Quality of Life in Alzheimer’s Disease) (as measured using the EQ-5D-3L) and carer burden (as measured using the Zarit Burden Interview). The economic evaluation was expressed in terms of incremental cost per quality-adjusted life-year (QALY) gained from a NHS and Personal Social Services perspective. We measured health and social care use with the Client Services Receipt Inventory. Participants were followed up for 12 months. Results Between February 2013 and June 2015, 494 participants were randomised with an intentional unequal allocation ratio: 165 to usual care and 329 to the intervention. The mean age of participants was 77 years [standard deviation (SD) 7.9 years], 39% (193/494) were female and the mean baseline ADAS-Cog score was 21.5 (SD 9.0). Participants in the intervention arm achieved high compliance rates, with 65% (214/329) attending between 75% and 100% of sessions. Outcome data were obtained for 85% (418/494) of participants at 12 months, at which point a small, statistically significant negative treatment effect was found in the primary outcome, ADAS-Cog (patient reported), with a mean difference of –1.4 [95% confidence interval (CI) –2.62 to –0.17]. There were no treatment effects for any of the other secondary outcome measures for participants or carers: for the BADLS there was a mean difference of –0.6 (95% CI –2.05 to 0.78), for the EQ-5D-3L a mean difference of –0.002 (95% CI –0.04 to 0.04), for the QoL-AD scale a mean difference of 0.7 (95% CI –0.21 to 1.65) and for the NPI a mean difference of –2.1 (95% CI –4.83 to 0.65). Four serious adverse events were reported. The exercise intervention was dominated in health economic terms. Limitations In the absence of definitive guidance and rationale, we used a mixed exercise programme. Neither intervention providers nor participants could be masked to treatment allocation. Conclusions This is a large well-conducted RCT, with good compliance to exercise and research procedures. A structured exercise programme did not produce any clinically meaningful benefit in function or HRQoL in people with dementia or on carer burden

Charging infrastructure for commercial electric vehicles: Challenges and future works

The journey towards transportation electrification started with small electric vehicles (i.e., electric cars), which have enjoyed an increasing level of global interest in recent years. Electrification of commercial vehicles (e.g., trucks) seems to be a natural progression of this journey, and many commercial vehicle manufacturers have shifted their focus on medium- and heavy-duty vehicle electrification over the last few years. In this paper, we present a comprehensive review and analysis of the existing works presented in the literature on commercial vehicle charging. The paper starts with a brief discussion on the significance of commercial vehicle electrification, especially heavy- and medium-duty vehicles. The paper then reviews two major charging strategies for commercial vehicles, namely the return-to-base model and the on route charging model. Research challenges related to the return-to-base model are then analysed in detail. Next, different methods to charge commercial vehicles on route during their driving cycles are summarized. The paper then analyzes the challenging issues related to charging commercial vehicles at public charging stations. Future works relevant to these challenges are highlighted. Finally, the possibility of accommodating vehicle to grid technology for commercial vehicles is discussed