Cloud Computing for Climate Modelling: Evaluation, Challenges and Benefits

Cloud computing is a mature technology that has already shown benefits for a wide range of academic research domains that, in turn, utilize a wide range of application design models. In this paper, we discuss the use of cloud computing as a tool to improve the range of resources available for climate science, presenting the evaluation of two different climate models. Each was customized in a different way to run in public cloud computing environments (hereafter cloud computing) provided by three different public vendors: Amazon, Google and Microsoft. The adaptations and procedures necessary to run the models in these environments are described. The computational performance and cost of each model within this new type of environment are discussed, and an assessment is given in qualitative terms. Finally, we discuss how cloud computing can be used for geoscientific modelling, including issues related to the allocation of resources by funding bodies. We also discuss problems related to computing security, reliability and scientific reproducibilityS

Apollo 13 - Houston, we've got a problem

Aborted flight of Apollo 13 lunar landing mission caused by power loss in main electrical circui

Comprehensive Overview of Named Entity Recognition: Models, Domain-Specific Applications and Challenges

In the domain of Natural Language Processing (NLP), Named Entity Recognition (NER) stands out as a pivotal mechanism for extracting structured insights from unstructured text. This manuscript offers an exhaustive exploration into the evolving landscape of NER methodologies, blending foundational principles with contemporary AI advancements. Beginning with the rudimentary concepts of NER, the study spans a spectrum of techniques from traditional rule-based strategies to the contemporary marvels of transformer architectures, particularly highlighting integrations such as BERT with LSTM and CNN. The narrative accentuates domain-specific NER models, tailored for intricate areas like finance, legal, and healthcare, emphasizing their specialized adaptability. Additionally, the research delves into cutting-edge paradigms including reinforcement learning, innovative constructs like E-NER, and the interplay of Optical Character Recognition (OCR) in augmenting NER capabilities. Grounding its insights in practical realms, the paper sheds light on the indispensable role of NER in sectors like finance and biomedicine, addressing the unique challenges they present. The conclusion outlines open challenges and avenues, marking this work as a comprehensive guide for those delving into NER research and applications

Application of Artificial Intelligence in IoT Security for Crop Yield Prediction

This research explores the application of Artificial Intelligence (AI) in the Internet of Things (IoT) for crop yield prediction in agriculture. IoT devices, like sensors and drones, collect data on temperature, humidity, soil moisture, and crop health. AI algorithms process and integrate this data to provide a comprehensive view of the agricultural environment.AI-driven anomaly detection helps identify threats to crop yield, such as pests, diseases, and adverse weather conditions. Predictive analytics, based on historical and real-time data, forecast crop yield for informed decision-making in irrigation and fertilization.AI-powered image recognition detects early signs of pests and diseases, aiding timely treatment to prevent crop losses. Resource optimization allocates water and fertilizers efficiently, minimizing waste and environmental impact.AI-driven decision support systems offer personalized recommendations for ideal planting schedules and crop rotations, maximizing yield. Autonomous farming integrates AI into machinery for precision tasks like planting and monitoring.Secure communication protocols protect sensitive agricultural data from cyber threats, ensuring data integrity and privacy

Bryn Mawr College Yearbook. Class of 1942

https://repository.brynmawr.edu/bmc_yearbooks/1036/thumbnail.jp

The rise of offshore IT outsourcing industry: An attempt to assess Bangladesh’s competitive advantages

In light of the developments in global offshoring and Bangladesh’s rise in IT/ITeS services the study explores the IT/ITeS sector in Bangladesh. The study included benchmarking the Bangladeshi IT/ITeS industry with the other leading offshore services destinations to ascertain the opportunity in Bangladesh as a global offshoring center. The paper presented here is the output of reports of national and international IT/ITeS industry players, government functionaries, trade associations and global IT services buyer community, and supported by extensive secondary research. The paper evaluates that Bangladesh has the competitive advantages to become the future IT outsourcing destination and she has been leveraging her strength to the fullest to become a larger player in the offshoring industry; also there are a number of factors that need to be considered for the further success of this industry of Bangladesh. … Read mor

Computer-Aided Drug Design and Drug Discovery: A Prospective Analysis

In the dynamic landscape of drug discovery, Computer-Aided Drug Design (CADD) emerges as a transformative force, bridging the realms of biology and technology. This paper overviews CADDs historical evolution, categorization into structure-based and ligand-based approaches, and its crucial role in rationalizing and expediting drug discovery. As CADD advances, incorporating diverse biological data and ensuring data privacy become paramount. Challenges persist, demanding the optimization of algorithms and robust ethical frameworks. Integrating Machine Learning and Artificial Intelligence amplifies CADDs predictive capabilities, yet ethical considerations and scalability challenges linger. Collaborative efforts and global initiatives, exemplified by platforms like Open-Source Malaria, underscore the democratization of drug discovery. The convergence of CADD with personalized medicine offers tailored therapeutic solutions, though ethical dilemmas and accessibility concerns must be navigated. Emerging technologies like quantum computing, immersive technologies, and green chemistry promise to redefine the future of CADD. The trajectory of CADD, marked by rapid advancements, anticipates challenges in ensuring accuracy, addressing biases in AI, and incorporating sustainability metrics. This paper concludes by highlighting the need for proactive measures in navigating the ethical, technological, and educational frontiers of CADD to shape a healthier, brighter future in drug discovery

A Framework for Efficient Cluster Computing Services in a Collaborative University Environment

Parallel computing techniques have become more important especially now that we have effectively reached the limit on individual processor speeds due to unacceptable levels of heat generation. Multi-core processors are already the norm and will continue to rise in terms of number of cores in the near future. However clusters of machines remain the next major step up in system performance effectively allowing vast numbers of cores to be devoted to any given problem. It is in that context that this Professional Doctorate thesis and Portfolio exists. Most parallel or cluster based software is custom built for an application using techniques such as OpenMP or MPI. But what if the capability of writing such software does not exist, what if the very act of writing a new piece of software compromises the integrity of an industry standard piece of software currently being used in a research project? The first outcome was to explore how grid/cluster computing teaching and learning facilities could be made accessible to students and teaching staff alike within the Department of Computing, Engineering & Technology in order to enhance the student experience. This was achieved through the development of VCNet, a virtual technology cluster solution, based on the design of the University of Sunderland Cluster Computer (USCC) and capable of running behind a dual boot arrangement on standard teaching machines. The second outcome of this Professional Doctorate was to produce a framework for efficient cluster computing services in a collaborative university environment. Although small by national and international standards, the USCC, with its forty machines and 160 cores, packs a mighty punch in computing terms. Through the work of this doctorate, ‘supercomputer class’ performance has been successfully used in cross- disciplinary research through the development and use of the Application Framework for Computational Chemistry (AFCC). In addition, I will also discuss the contribution this doctorate has made within the context of my community of practice by enhancing both my teaching and learning contribution as well as cross-disciplinary research and application