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Optimal resource allocation is a fundamental challenge for dense and heterogeneous wireless networks with massive wireless connections. Because of the non-convex nature of the optimization problem, it is computationally demanding to obtain the optimal resource allocation. Recently, deep reinforcement learning (DRL) has emerged as a promising technique in solving non-convex optimization problems. Unlike deep learning (DL), DRL does not require any optimal/ near-optimal training dataset which is either unavailable or computationally expensive in generating synthetic data. In this paper, we propose a novel centralized DRL based downlink power allocation scheme for a multi-cell system intending to maximize the total network throughput. Specifically, we apply a deep Q-learning (DQL) approach to achieve near-optimal power allocation policy. For benchmarking the proposed approach, we use a Genetic Algorithm (GA) to obtain near-optimal power allocation solution. Simulation results show that the proposed DRL-based power allocation scheme performs better compared to the conventional power allocation schemes in a multi-cell scenario.

This country analytical review examines the key issues in access to and participation in primary and secondary education in Bangladesh, with a special focus on areas and dimensions of exclusion. Against a background of overall progress, particularly in closing the gender gap in primary and secondary enrollment, the research applies a conceptual framework outlining different forms of exclusion and presents two significant findings which compromise access and diminish gains made: high dropout rates at primary and secondary levels and nominal access but virtual exclusion from quality learning. Other areas surveyed in the review include interventions by public sector and non-governmental providers in primary and secondary education as well as the financing of basic education. This review of the literature concludes with suggestions for future research directions that might lead to new understanding and insights on equitable access and participation. Nine annexes are included: (1) A schematic of the education system in Bangladesh; (2) Eleven types of primary schools; (3) Comparison between BRAC [Bangladesh Rural Advancement Committee] and government primary school models; (4) Achievement of BEP [BRAC Education Programme] as of November 2005; (5) Training completed for secondary school teachers of PACE programme as of November 2005; (6) ESP [Education Support Programme] accomplishments, actual and planned; (7) PLAN [Post Primary Basic and Continuing Education] Bangladesh Whole Child Development Model; (8) Total centres and beneficiaries of Save the Children USA--core programmes; and (9) Projection of child and student population. A bibliography is included. (Contains 44 tables and 15 figures.) [This review was written with the assistance of Altaf Hossain, Md. Abul Kalam, Md. Shahidul Islam and Jennifer Hove.]

The natural or man-made disaster demands an efficient communication and coordination among first responders to save life and other community resources. Normally, the traditional communication infrastructures such as land line or cellular networks are damaged and don't provide adequate communication services to first responders for exchanging emergency related information. Wireless ad hoc networks such as mobile ad hoc networks, wireless sensor networks and wireless mesh networks are the promising alternatives in such type of situations. The security requirements for emergency response communications include privacy, data integrity, authentication, key management, access control and availability. Various ad hoc communication frameworks have been proposed for emergency response situations. The majority of the proposed frameworks don't provide adequate security services for reliable and secure information exchange. This paper presents a survey of the proposed emergency response communication frameworks and the potential security services required by them to provide reliable and secure information exchange during emergency situations., Comment: 14 pages and 11 figures

This study examines the information sources used by practicing rural physicians in North Dakota to keep current with new information in their area of specialty. The hypotheses tested were: (1) there are more male than female rural practitioners in North Dakota; (2) rural physicians depend on printed material to fulfill regular need for information related to their practice; (3) rural physicians fulfill their information need by frequently using local medical reference libraries; (4) rural physicians have a demand for only a limited number and type of information from medical libraries; (5) a large number attend professional meetings and conferences to fill the void of face-to-face contact with colleagues; (6) utilization patterns vary significantly between general practitioners and specialists; (7) practitioners, who have recently taught medical students vary significantly in their mean score on the extent of information sources used; and (8) age of the rural physician has a significant effect on the extent of information used. Data were collected by a survey of 278 rural health care practitioners in North Dakota. The study used simple descriptive statistical analysis to test the first five hypotheses, and a multivariate technique, ANOVA, to test the last three hypotheses. Results yielded support for all hypotheses. More efforts should focus on bringing better machines (such as on-line computer searches and facsimile machines) and programs to rural physicians to meet their information needs. (KS)

Statistical downscaling can be used to efficiently downscale a large number of General Circulation Model (GCM) outputs to a fine temporal and spatial scale. To facilitate regional impact assessments, this study statistically downscales (to 1∕8deg spatial resolution) and corrects the bias of daily maximum and minimum temperature and daily precipitation data from six GCMs and four Regional Climate Models (RCMs) for the northeast United States (US) using the Statistical Downscaling and Bias Correction (SDBC) approach. Based on these downscaled data from multiple models, five extreme indices were analyzed for the future climate to quantify future changes of climate extremes. For a subset of models and indices, results based on raw and bias corrected model outputs for the present-day climate were compared with observations, which demonstrated that bias correction is important not only for GCM outputs, but also for RCM outputs. For future climate, bias correction led to a higher level of agreements among the models in predicting the magnitude and capturing the spatial pattern of the extreme climate indices. We found that the incorporation of dynamical downscaling as an intermediate step does not lead to considerable differences in the results of statistical downscaling for the study domain.