492 research outputs found
Seeing with Hassan Sharif
No full textHassan Sharif was a pioneering "moderniser" in the Arabian Gulf region, re-shaping its aesthetic, social and political culture. Schooled in systems art at Central School of Art in England, he went on to pave the way for a generation of artists in the United Arab Emirates. Here, curator, artist, author, and museum director, Dr. Omar Kholeif reflects on the task of conserving the artist's legacy for younger generations, and considers how the artist shifted an ontological view of Dubai, and the world around it
COMPARATIVE PROFITABILITY AND TECHNICAL EFFICIENCY OF COMBINE HARVESTER USER AND NON- USER BORO RICE FARMERS IN HAOR AREAS OF BANGLADESH
No full textA Thesis
Submitted to the Department of Development and Poverty Studies
Sher-e-Bangla Agricultural University, Dhaka
In partial fulfilment of the requirements for the degree of
MASTER OF SCIENCE
IN
DEVELOPMENT AND POVERTY STUDIESThe main objective of the study was to measure the comparative profitability and
technical efficiency of combine harvester (CH) user and non-user farmers in Boro rice
production. Using a simple random sampling technique, data were collected from 120
Boro rice producing farmers through face-to-face interview from Sunamganj and
Netrokona districts of Bangladesh during March to April, 2023. Descriptive statistics
and econometric model were used to analyze the data. The technical efficiency of
combine harvester users and non-users in Boro rice farming was estimated using the
stochastic frontier production function. The study's key findings demonstrated that
growing Boro rice was profitable for both combine harvester users and non-users. But
users of combine harvesters earned greater profits than non-users. The average total
cost of Boro rice production per hectare was estimated Tk. 115506 and Tk. 123317 for
combine harvester users and non-users, respectively. For users of combine harvesters,
the average gross return and net return per hectare were Tk. 153279 and Tk. 37773,
respectively, whereas for non-users, the average was Tk. 141362 and Tk. 18045. The
estimated BCR was higher for combine harvester users (1.33) than non-users (1.15).
Combine harvester users would save 75% labour (27 labourers) per hectare compared
to non-users. The findings also indicate that agricultural inputs like irrigation, urea and
MoP had positive but human labour, seed, TSP and DAP had negative effects on CH
users in Boro rice production. For non-users of CH, land preparation and MoP had
positive but TSP, DAP and gypsum had negative effects on Boro rice production.
According to the estimated inefficiency model, both for CH users and non-users,
education, market distance, farming experience and organizational membership had
negative effects indicating these factors help reducing technical inefficiency in Boro
rice farming. The above results revealed that mechanized harvesting through combine
harvester contribute to save money, time and labour. Outcome of this study will help
the policymakers to shape the policy interventions which in turn help to improve the
production and facilitate the goal of doubling farmers’ income and productivity which
is targeted at the SDG 2.3
The management of intelligence-assisted finite element analysis technology
No full textArtificial Intelligence (AI) approaches to Finite Element Analysis (FEA), have had tentative degrees of success over the last few years and some authors have argued that effective FEA can help in the manufacture reliability and safety aspects of engineered artefacts. The author of this paper reviews how such AI techniques have been applied and in this light, the author then uses a Fuzzy Cognitive Mapping (FCM), to develop a framework for the management of intelligence-assisted FEA
Real-time crash prediction of urban highways using machine learning algorithms
No full textDoctor of PhilosophyDepartment of Civil EngineeringEric J. FitzsimmonsMotor vehicle crashes in the United States continue to be a serious safety concern for state highway agencies, with over 30,000 fatal crashes reported each year. The World Health Organization (WHO) reported in 2016 that vehicle crashes were the eighth leading cause of death globally. Crashes on roadways are rare and random events that occur due to the result of the complex relationship between the driver, vehicle, weather, and roadway. A significant breadth of research has been conducted to predict and understand why crashes occur through spatial and temporal analyses, understanding information about the driver and roadway, and identification of hazardous locations through geographic information system (GIS) applications. Also, previous research studies have investigated the effectiveness of safety devices designed to reduce the number and severity of crashes. Today, data-driven traffic safety studies are becoming an essential aspect of the planning, design, construction, and maintenance of the roadway network. This can only be done with the assistance of state highway agencies collecting and synthesizing historical crash data, roadway geometry data, and environmental data being collected every day at a resolution that will help researchers develop powerful crash prediction tools.
The objective of this research study was to predict vehicle crashes in real-time. This exploratory analysis compared three well-known machine learning methods, including logistic regression, random forest, support vector machine. Additionally, another methodology was developed using variables selected from random forest models that were inserted into the support vector machine model. The study review of the literature noted that this study’s selected methods were found to be more effective in terms of prediction power. A total of 475 crashes were identified from the selected urban highway network in Kansas City, Kansas. For each of the 475 identified crashes, six no-crash events were collected at the same location. This was necessary so that the predictive models could distinguish a crash-prone traffic operational condition from regular traffic flow conditions. Multiple data sources were fused to create a database including traffic operational data from the KC Scout traffic management center, crash and roadway geometry data from the Kanas Department of Transportation; and weather data from NOAA. Data were downloaded from five separate roadway radar sensors close to the crash location. This enable understanding of the traffic flow along the roadway segment (upstream and downstream) during the crash. Additionally, operational data from each radar sensor were collected in five minutes intervals up to 30 minutes prior to a crash occurring.
Although six no-crash events were collected for each crash observation, the ratio of crash and no-crash were then reduced to 1:4 (four non-crash events), and 1:2 (two non-crash events) to investigate possible effects of class imbalance on crash prediction. Also, 60%, 70%, and 80% of the data were selected in training to develop each model. The remaining data were then used for model validation. The data used in training ratios were varied to identify possible effects of training data as it relates to prediction power. Additionally, a second database was developed in which variables were log-transformed to reduce possible skewness in the distribution.
Model results showed that the size of the dataset increased the overall accuracy of crash prediction. The dataset with a higher observation count could classify more data accurately. The highest accuracies in all three models were observed using the dataset of a 1:6 ratio (one crash event for six no-crash events). The datasets with1:2 ratio predicted 13% to 18% lower than the 1:6 ratio dataset. However, the sensitivity (true positive prediction) was observed highest for the dataset of a 1:2 ratio. It was found that reducing the response class imbalance; the sensitivity could be increased with the disadvantage of a reduction in overall prediction accuracy. The effects of the split ratio were not significantly different in overall accuracy. However, the sensitivity was found to increase with an increase in training data. The logistic regression model found an average of 30.79% (with a standard deviation of 5.02) accurately. The random forest models predicted an average of 13.36% (with a standard deviation of 9.50) accurately. The support vector machine models predicted an average of 29.35% (with a standard deviation of 7.34) accurately. The hybrid approach of random forest and support vector machine models predicted an average of 29.86% (with a standard deviation of 7.33) accurately.
The significant variables found from this study included the variation in speed between the posted speed limit and average roadway traffic speed around the crash location. The variations in speed and vehicle per hour between upstream and downstream traffic of a crash location in the previous five minutes before a crash occurred were found to be significant as well.
This study provided an important step in real-time crash prediction and complemented many previous research studies found in the literature review. Although the models investigate were somewhat inconclusive, this study provided an investigation of data, variables, and combinations of variables that have not been investigated previously. Real-time crash prediction is expected to assist with the on-going development of connected and autonomous vehicles as the fleet mix begins to change, and new variables can be collected, and data resolution becomes greater. Real-time crash prediction models will also continue to advance highway safety as metropolitan areas continue to grow, and congestion continues to increase
Examining the Authorship of Nahj al-Balāgha: Between Ali Ibn Abi Talib, al-Sharif al-Radi, and al-Sharif al-Murtad
No full text"نهج البلاغة" كتاب يُنسَب إلى عليّ بن أبي طالب رضي الله عنه يتضمّن عددًا من الخطب والمواعظ والعهود والرّسائل والحِكَم والوصايا والآداب، توزّعت على 238 خطبة، و 79 رسالة و 489 قولا، يرى الشّيعة في "النّهج" أنّه أحد الكتب المهمّة الّتي يجب على الشّيعي قراءتها والأخذ والتعلّم منها، وقد اختلف الدّارسون والنّقّاد القدامى والمعاصرون في مؤلّـِفه، فمنهم مَن اعتبره لعليّ بن أبي طالب ومنهم من اعتبره للشّريف الرَّضيّ، وفريق ثالث جعله للشّريف المرتضى. تتناول هذه الدراسة بالاستقصاء والتحقّق والتحليل نسبة الكتاب إلى صاحبه أو ما يُزيل الإبهام، هل هو عليّ بن أبي طالب؟ أم الشّريف الرَّضيّ؟ أم الشّريف المرتضى؟ ويحاول أن يرجّح لمن يُنسَب كتاب نهج البلاغة، وما مدى مصداقيـّته؟ وما مدى صحّة نسبة كتاب نهج البلاغة لعليّ، وما رأي الدّارسين والعلماء فيه؟ وما هي أهمّ المطاعن الموجّهة إلى نهج البلاغة؟The book Nahj al-Balagha is a collection of sermons, letters, adages, commandments, and etiquettes, divided into 238 sermons, 79 treatises, and 489 sayings. People following the Shiite sect regard it as a crucial text for teaching and learning, and they attribute it to Ali Ibn Abi Talib. Yet, the book’s real author is still disputed among Sharia scholars. While some attribute it to Ali Ibn Abi Talib, others claim that al-Sharif al-Radi is the book’s actual author, whereas another group attribute it to al-Sharif al-Murtada. This study employs a comprehensive approach to investigate and analyze the book’s authorship. It involves a detailed literary and historical analysis, examining primary sources, scholarly works, and critical literature to explore the real author of the text. It compares the content of the book with other authenticated works belonging to the potential authors in order to identify similarities and differences. By evaluating the style, language, and themes of Nahj al-Balagha, as well as reviewing historical documents and manuscripts, the study aims to trace the text’s origins and transmissions. In doing so, it addresses the significant critiques and controversies surrounding the text, enhancing our understanding of its historical and literary significance. The study further contributes to the ongoing discourse on the book’s authorship, providing more clarity on the topic and promoting additional scholarly research
Benchmarking performance management systems
No full textThe Balanced Scorecard and associated performance management approaches, has become a widely practiced and popular management reporting method in recent times. Moreover, enabling technology, which assists in the delivery and personalisation of corporate performance information, is having a deeper and more rapid impact than ever before. This paper presents a brief comparative benchmarking study of leading enterprise performance management systems. Also, the author discusses the merits of bespoke internet technology development and out-of-the-box portal functionalities. An analysis of key business drivers and implementation risks of such approaches is highlighted via a case study example, and concludes the paper
Importance Sampling for a Markov Modulated Queuing Network with Customer Impatience until the End of Service
No full textFor more than two decades, there has been a growing of interest in fast simulation techniques for estimating probabilities of rare events in queuing networks. Importance sampling is a variance reduction method for simulating rare events. The present paper carries out strict deadlines to the paper by Dupuis et al for a two node tandem network with feedback whose arrival and service rates are modulated by an exogenous finite state Markov process. We derive a closed form solution for the probability of missing deadlines. Then we have employed the results to an importance sampling technique to estimate the probability of total population overflow which is a rare event. We have also shown that the probability of this rare event may be affected by various deadline values.Importance Sampling, Queuing Network, Rare Event, Markov Process, Deadline
Knowledge representation within information systems in manufacturing environments
No full textThis thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Representing knowledge as information content alone is insufficient in providing us with an understanding of the world around us. A combination of context as well as reasoning of the information content is fundamental to representing knowledge in an information system. Knowledge Representation is typically concerned with providing structures and theories that are used as a basis for intelligent reasoning. For this research however, the author defines an alternative meaning, which is related to how knowledge is used in a given context. Thus, this dissertation provides a contribution to the field of knowledge within information systems, in terms of the development of a frame-of-reference that will support the reader in navigating through the different forms of explicit and tacit knowledge use within the manufacturing industry. In doing so, the dissertation also presents the generation of a novel classification of three forms of knowledge (Structural, Interpretive and Evaluative forms); the development of a conceptual framework which highlights the drivers for knowledge transformation; and the development of a conceptual model which seeks to envelop both the content as well as the context of knowledge (Semiotic as well as Symbiotic factors). This is established through the use of an Empirical, Quantitative case study approach, that seeks to explore an interpretivist view of knowledge representation within two information systems contexts, within two UK manufacturing organisations. The first case study presents how a-priori knowledge assumptions are used in a computer aided engineering decision-making task within a high technology manufacturing company. The second case study shows how knowledge is used within the IT/IS investment evaluation decision making process, within a manufacturing SME. In doing so, both case studies attempt to elucidate the inherent, underlying relationship between explicit and tacit knowledge, via a frame-of-reference developed by the author which defines key drivers for knowledge transformation
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