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Addiction recovery stories: Dylan Suttie in conversation with Lisa Ogilvie
No full textPurpose The purpose of this paper is to examine recovery through lived experience. It is part of a series that explores candid accounts of addiction and recovery to identify important components in the recovery process. Design/methodology/approach The G-CHIME model comprises six elements important to addiction recovery (growth, connectedness, hope, identity, meaning in life and empowerment). It provides a standard against which to consider addiction recovery. It has been used in this series, as well as in the design of interventions that improve well-being and strengthen recovery. In this paper, a first-hand account is presented, followed by a semi-structured e-interview with the author of the account. Narrative analysis is used to explore the account and interview through the G-CHIME model. Findings This paper shows that addiction recovery is a remarkable process that can be effectively explained using the G-CHIME model. The significance of each component in the model is apparent from the account and e-interview presented. Originality/value Each account of recovery in this series is unique, and as yet, untold
Innovating SQL Automation: Evaluating Open-Source Large Language Models with a Dual-Stage Approach for Corporate Data Solutions
No full textAs Large Language Models (LLMs) continue to advance in their ability to process natural language, their potential to transform industries and reshape the future of human-computer interaction becomes increasingly evident. This study evaluates the application of Large Language Models (LLM) to automate SQL query generation from natural language inputs in enterprise environments. We investigated the feasibility of using open-source LLMs, including Mistral, CodeLlama, Phi-3, and DeepSeek Coder, by fine-tuning them with a custom dataset reflecting company-specific data tables. This dataset was iteratively constructed using a baseline LLM that allows fine-tuning to address unique enterprise data structures and use cases. A two-stage filtering and refinement mechanism was implemented to improve query accuracy. The first stage identifies relevant tables and the second stage adds an iterative error correction step to improve the SQL query generation process. The resulting system significantly reduced query errors and increased accuracy by 84%,88%,81%, and 90% for Mistral, CodeLlama, Phi-3, and DeepSeek Coder LLM. However, challenges remain in resource consumption and logical error handling. Future work will focus on improving contextual understanding and integrating advanced AI techniques to further enhance robustness and applicability
A review on mechanical metamaterials and additive manufacturing techniques for biomedical applications
No full textThis review presents design, analysis and experimental analysis of metamaterials with tunable properties for biomedical applications. Five different metamaterials namely, lightweight metamaterials, pattern transformation metamaterials, negative compressibility metamatrials, pentamode metamaterials and auxetic metamaterials have been discussed in detail with emphasis of these materials in the field of biomedicine. Furthermore, different addivitve manufacturing techniques implemented in the manufacturing of these biomaterials may be customized to provide different mechanical characteristics. Finally, the mechanical properties and deformation mechanisms for the biomaterials have been discussed
Offshore wind energy site selection and utilisation in Sri Lanka.
No full textThis research aims to assess the potential of offshore wind energy in a promising area in SriLanka and propose an optimised wind farm configuration to support the national electricityneeds. Currently, the grid has an installed capacity of over 4000 megawatts (4GW), whichcontains a considerable amount of thermal energy and a substantial capacity of hydropowertogether with wind and solar as non-conventional renewable energy (NCRE). The currentinstalled capacity is sufficient to cater to today's demand for electricity. However, the countryneeds to decarbonise the energy sector well before 2050. This research has performedcomplete offshore wind speed data analysis, a step towards utilising the available offshorewind energy potential amounting to approximately 92GW. Therefore, the researcher isconsidering multiple options and methodologies to evaluate the potential offshore wind energyavailable in Sri Lankan waters to the maximum capacity while overcoming natural weaknessesof wind energy and addressing other factors, including environmental impact and gridconnectivity. This research is based on authentic wind speed data obtained ethically from theSustainable Energy Authority of Sri Lanka. The offshore wind data has been recorded in 10-minute intervals over one year (January-December 2016) from seven wind masts situated inseven shoreline areas around the island; there are over 100,000 pieces of data. Havingcritically analysed the data of all seven stations and compared it with the outcome of the criticalliterature review, the final research has been narrowed down to the offshore area of Mannar,which is around 200-250km north of Colombo, for key reasons, including the logistics. Thisdata has been used to develop Weibull distribution parameters, which can represent the winddata mathematically for further analysis, thereby producing the related charts to ascertain theprobability density of the given period and identify the most suitable location for a 100MW pilotproject. Therefore, the best-matching wind turbines are selected from commercially availableoptions based on the wind speed data of the specific location. Energy potential was calculatedusing the estimated Weibull distribution parameters. Further, qualitative analysis complementsthe extensive quantitative wind speed data analysis by integrating qualitative insights to enrichthe findings. Two interviews were conducted to capture the perspectives and policies ofgovernment officials on renewable energy, particularly focusing on offshore wind energy andthe net-zero plan. The research emphasises the principle of energy storage as a critical needfor Sri Lankan power distribution. This is to support future sustainable wind energy utilisationin developing countries. The pilot project is to demonstrate the capability of initiating apromising offshore wind farm so that the government of Sri Lanka can gain sufficientconfidence to implement much-needed offshore wind energy projects.The research outcomes contribute to the offshore wind energy field, national energy targetsfor Sri Lanka, and international efforts to mitigate climate change impact and achieve carbonneutrality
Lines in the sand
No full textThis year Standard Collective brings it?s seventh international exhibition to Bolton Art Gallery. For almost a decade the group has deepened their artistry and friendship via their exhibitions, working in a spirit of close co-operation and lightly defined boundaries. Their evolving dynamics have generated art projects which allow for both cohesion and difference. While planning their first exhibition in 2016 members of Standard met Elmar Brinkmöller, a leading figure of the independent art group Raum für Kunst in Germany. This meeting led to invitations for the group to exhibit in Paderborn?s Raum für Kunst Gallery. During the Covid lockdown, digital work was forwarded for exhibition at the Forum Junger Künstler, Paderborn?s civic gallery.In ?Lines in the Sand? Standard invited the Paderborn artists to share an exhibition space to explore their experiences of a changing Europe. The title was suggested as a starting point for visual responses to these changes. The Standard/ Raum für Kunst relationship was born at the onset of the political upheaval caused by calls for Britain to leave the European Union. The group considered and produced work reflecting the immediate impact and ongoing consequences of Brexit, but UK-EU relations are by no means the only salient issue for this international artistic co-operation. All of Europe has to contend with the rise of various ethno-nationalisms, calls for cultural conservatism, even claims about the need for increased isolation. Europe?s lines are being drawn and re-drawn at all levels. Some are abstract, legal and economic, or military and political: these inevitably connect to changing lines and contours of private and public feelings, attitudes, and sympathies. The artists exhibiting here differ in their interpretations of the theme and their modes of expression, but their sharing of space and commitment to ongoing international co-operation is a vote of confidence for creative and peaceful resolution
2nd International Workshop on Intelligent Systems for Sustainable Smart Cities - Proceedings of I3SC’23
No full textThe Second Edition of the International Workshop on Intelligent Systems for Sustainable Smart Cities (I3SC’23) is set to take place on December 19, 2023, at FST Settat, Morocco. This year, the event will be centered around the theme "Predict and Protect: AI and IoT Strategies for Disaster Prevention," focusing on discussing state-of-the-art AI and IoT-based solutions for better prevention against natural disasters.The main objective of this workshop is to bring together innovative scientists, professors, research scholars, students and industrial experts to exchange their experiences and research results on all aspects of Intelligent Systems and their impact in Disaster Management. It is also an opportunity to discuss and interchange about the real needs of Smart Cities in order to contribute in realizing smart communities of the future. The Workshop covers all topics of Smart Cities, Artificial Intelligence, Internet of Things, Communication Networks, Data Analytics and Disaster Management Systems.In more details, the conference brought together more than 30 participants. There were three plenary sessions and three invited speakers from different countries sharing their expertise and discussing new topics.We would like to express our appreciation and gratitude to all invited and keynote speakers, as well as all participants for the highly animated and insightful discussions. We also thank our reviewers for maintaining a good quality standard of the manuscripts that were accepted for participation. We do hope that the proceedings will serve as a valuable reference and be able to stimulate further research in computing intelligence, communication areas and disaster management systems
Utilizing adaptive neuro-fuzzy inference systems (ANFIS) for intrusion detection systems
No full textAs network security continues to emerge as a fundamental concern, significant advancements in design have transpired over recent years. Amid various approaches, Intrusion Detection Systems (IDS) have garnered substantial attention. It appears that the complex nature and uncertainty inherent in security breaches render fuzzy techniques well-suited for such systems. Consequently, this research endeavours to harness the potential of the Adaptive Neuro-Fuzzy Inference System (ANFIS) as a classifier for distinguishing network instances into malicious categories (Probe, DoS, U2R, R2L) and normal behaviour. This classification is executed on the KDD99 database and compared against other machine learning models like Decision Tree and Multilayer Perceptron. Gauss-ian, Triangular, bell-shaped, sigmoidal, membership functions are investigated and gaussian is found to be the best for this problem. Moreover, the empirical findings emphasize ANFIS's superior performance, capitalizing on the strengths of both Artificial Neural Networks (ANN) and fuzzy reasoning systems, encompassing the ability to comprehend nonlinear interaction patterns, adaptability, and swift learning capabilities
Sentiment analysis using deep learning
No full textSentiment analysis is a subfield of natural language processing (NLP) that aims to determine the emotional tone and sentiment expressed in a given piece of text. It plays a crucial role in understanding the opinions, attitudes, and emotions of individuals towards various subjects, products, or events. With the rapid growth of online communication and social media, sentiment analysis has become increasingly important for businesses, governments, and researchers to gain valuable insights into public sentiment and make data-driven decisions. Deep Learning, a branch of machine learning, has shown remarkable success in various NLP tasks, including sentiment analysis. This research explores role of sentiment analysis in twitter data, application of deep learning techniques in sentiment analysis, focusing on recurrent neural networks (RNNs), convolutional neural networks (CNNs), and transformer-based models. In this paper sentiment analysis perform on a dataset of tweets related to the Pfizer vaccine. Here we have data related to vaccination tweets of 11021 users and sentiments of users. It processes text input, classifies feelings, and visualizes the findings using multiple natural language processing techniques and machine learning algorithms
Malware Detection System Using Natural Language Processing Based on Website Links
No full textVarious approaches exist when building a detection model to capture Cyber-Threats but most of this approaches employ a post-active methodology-trying to detect the threats after they have occurred. We aimed to develop a model that would employ a pro-active approach by understanding the semantic and linguistic nature of their source of origin-urls and from there building a classifier that can identify potential threats. Our Decision Tree classifier achieved an accuracy of 95% on the test set showing its potential to detect cyber-threats in real life scenarios. And since our model uses a classical algorithm as opposed to deep learning methods, our model would be computationally less expensive and lightweight making it easy to deploy in real world web applications
Liability of Newness: Challenges Faced by Immigrant Entrepreneurs-A Bibliometric Analysis
No full textImmigrant entrepreneurship is one of the most emerging topics in contemporary entrepreneurship. Several areas of immigrant entrepreneurship remain unexplored. Therefore, the purpose of this chapter is to examine liability newness from the perspective of immigrant entrepreneurs. In business terms, liability newness refers to new companies that fail at an early stage of their development. Many immigrant entrepreneurs have difficulty engaging in business activities and growing their businesses because of the challenges faced by immigrant entrepreneurs in the host country. The Scopus citation database was searched to generate relevant datasets. Biblioshiny, an R-based bibliometric tool along with VOSViewer, was used to conduct a bibliometric analysis of the scientific literature. As of 2022, 264 authors affiliated with 162 organisations around 30 countries contributed to the literature on liability newness from immigration entrepreneurship. Towards the end of the chapter, there is a discussion of existing literature and proposals for eradicating liability newness from immigrant entrepreneurs