National Defence University, Pakistan

About: National Defence University, Pakistan is a education organization based out in Islamabad, Pakistan. It is known for research contribution in the topics: Context (language use) & Decision support system. The organization has 802 authors who have published 816 publications receiving 3701 citations. The organization is also known as: National Defence University of Pakistan & National Defence University Islamabad.

Carbon nanotubes-based sensor for ammonia gas detection – an overview

Abstract: A sensitive, selective and reliable sensing techniques for ammonia (NH3) gas detection have been highly demanded since NH3 is both a commonly utilized gas in various industrial sectors, and considered as a toxic and caustic agent that can threat human health and environment at a certain level of concentrations. In this article, a brief on the fundamental working principles of sensor specifications of the analytes detection techniques relying has been reviewed. Furthermore, the mechanism of NH3 detection and recent progress in the development of advanced carbon nanotubes (CNTs)-based NH3 gas sensors, and their performance towards the hybridization with the conductive polymers wascomprehensively reviewed and summarized. Finally, the future outlook for the development of highperformanceNH3 sensors was presented in the conclusions part.

Learning System for Air Combat Decision Inspired by Cognitive Mechanisms of the Brain

Abstract: Unmanned aerial vehicles (UAVs) have played an important role in recent high-tech local wars. Seizing air control rights with UAVs will undoubtedly be a popular topic in future military development. Autonomous air combat is complex, antagonistic and mutable, and consequently, the decision-making that depends on unmanned systems is extremely challenging with very little research having been conducted on it. An intelligent air combat learning system inspired by the learning mechanisms of the brain is proposed in this paper. In accordance with research on learning, knowledge and memory, we constructed a cognitive mechanism model of the brain. Based on this model and the inferential abilities of humans, a long short-term hierarchical multi-line learning system is established. Then, the bio-inspired architecture and the basic learning principle of the system are clarified. Taking advantage of the conclusions of studies on information theory, the relationship between the knowledge updating cycle and the system learning performance is analysed. The updating cycle length adjustment problem is transformed into an optimization problem optimization problem, and system performance improvement is guaranteed. Experiments show that the system designed in this paper can acquire confrontation abilities through self-learning without prior rules; the parallel universe mechanism can significantly improve the system’s learning speed when the number of parallels is within 40, and the performance of the system improves gradually and continuously. The system can master actions similar to classical tactical manoeuvres such as the high yo-yo and the barrel-roll-attack without prior knowledge. Compared with the Bayesian inference and moving horizon optimization (BI&MHO) method, the learning system proposed in this paper is more flexible in situation assessment and in the prediction of opponents’ actions. Although it cannot be deployed quickly, it has a continuous learning ability.

Job satisfaction among Swedish soldiers : Applying the Job Characteristics Model to newly recruited military personnel

Abstract: A professional military organization was introduced in Sweden in 2010. This means that the Swedish Armed Forces (SAF) need to attract and employ soldiers, and to retain them within the organization ...

Disaster risk management in agriculture: tragedies of the smallholders

Abstract: Agriculture sector is exposed to a variety of risks and uncertainties which can lead to sizeable losses in crop yields and alter farm incomes. Risk management is, therefore, an essential element of the overall farm management process. Farmers have number of options in managing farm risks; however, smallholders, due to their small operations and limited financial capabilities, find it difficult to adopt sophisticated risk management strategies to overcome yield and income instabilities at farm level. This study is, therefore, designed to investigate the enabling environment for small farmers to manage climatic risks at farm level. A total of 330 sampled respondents from Khyber Pakhtunkhwa Province of Pakistan are randomly selected for the study using multistage sampling technique. Analysis of variance technique is employed to compare the risk management adoption decision of small, medium and large farmers. A post hoc analysis is also performed to highlight the difference in means and the magnitude of differences. The results indicate that smallholders have significantly lower access to credit (both formal and informal), formal information sources along with significantly higher perceptions of pest and diseases. Smallholders are also at the tail end in the adoption of precautionary savings and agricultural credit to manage climatic risk at farm level. The study urges for risk management policies particularly in favor of the small farmers and intervention in the existing information and credit provision programs to facilitate smallholders in managing farm risks.