110 research outputs found
Development of a virtual reality milling machine for knowledge learning and skill training
No full textCurrent methods of training personnel on high cost machine tools involve the use of both classroom and hands on practical training. The practical training required the operation of costly equipment and the trainee has to be under close personnel supervision. The main aim of this project is to reduce the amount of practical training and its inherent cost, time, danger, personal injury risk and material requirements by utilising a virtual reality technology.
In this study, an investigation into the use of Virtual reality for training operators and students to use the Milling Machine was carried out. The investigation has been divided into two sections: first the development of Milling Machine in the 3D virtual environment, where the real machine was re-constructed in the virtual space.
This has been carried out by creating objects and assembling them together. The complete Milling machine was then properly modelled and rendered so it could be viewed from all viewpoints.
The second section was to add motion to the virtual world. The machine was made of functions as for the real machine. This was achieved by attaching Superscape Control Language (SCL) to the objects. The developed Milling machine allows the users to choose the material, speed and feed rate. Upon activation, the virtual machine will be simulated to carry out the machining process and instantaneous data on the machined part can be generated.
The results were satisfactory, the Milling Machine was modelled successfully and the machine was able to perform according to task set. Using the developed Virtual Model, the ability for training students and operators to use the Milling Machine has been achieved
Miriam Sampaio : Murmur
No full textThis publication stems from artist Sampaio’s residency at Centre de production Daïmõn in the fall of 2001. The resulting exhibition comprised photographs taken by the artist while in Portugal where she was researching her Judaic-Portuguese roots. Hashmi comments on this work in a personal and poetic text that includes many quotes from the artist. Texts in English and French. Biographical notes on artist and author. 2 bibl. ref
Investigation into coatings produced from nanoparticle blended feedstock for rotating equipment repair applications
No full textCoating of carbon steel with conventional and nano particle blended feedstock material is considered in relation to repair applications of rotating equipment. Gas Metal Arc Welding (GMAW) and Wire Arc Spray (WAS) processes are used to produce the coatings on carbon steel workpieces. The wire arc sprayed workpieces are heat treated at temperature similar to the operating temperature of hot-path components of power gas turbines. The microstructure and metallurgy of the workpieces are examined using the Scanning Electron Microscope (SEM), Optical Microscope, Energy Dispersive Spectroscopy (EDS), X-ray Diffraction (XRD). The indentation tests are carried out to assess the microhardness variation across the coatings. In the case of coatings produced by GMAW, it is found that fine structures are formed in the coating due to the presence of nano particles and they resulted in increased microhardness of the coatings. In the case of the wire arc sprayed workpieces, the formation of dimples like structure at the surface increases the surface roughness of the coatings. In addition, the microhardness of the resulting coating is significantly higher than that of the base material. The heat treatment does not alter the microstructure and microhardness of the coatings significantly
Investigation into laser re-melting of inconel 625 HVOF coating blended with WC
No full textHigh velocity oxy-fuel (HVOF) spraying of Diamalloy 1005 powders mixed with WC particles onto steel (304) is considered and laser re-melting of the resulting coatings is examined. Laser re-melting process is modeled to determine the melt layer thickness while temperature increase is formulated using the Fourier heating law. The morphological and metallurgical analyses prior and post laser re-melting process are carried out using scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS). X-ray diffraction (XRD) technique is used to determine the residual stress developed in the coating while the analytical formulation is adopted to predict the residual stress levels at the coating base material interface. The indentation tests are carried out to determine the Young’s modulus and fracture toughness of the coating prior to laser re-melting. Corrosion resistance of coating is measured using potentiodynamic polarization technique prior and post laser treatment process. The predictions of the melt layer thickness are in good agreement with experimental results. The presence of WC particles modifies temperature rise and its gradient in the coating while affecting the Young’s modulus, residual stress levels, and fracture toughness of the coating. The differences in the thermal properties of Inconel 625 powders and WC particles result in formation of small size cellular structure through polyphase solidification. WC dissolution in the central region of the large polycrystalline cells is observed due to the loss of carbon through carbonic gas formation. The results of corrosion tests prevail that significant improvement of corrosion resistance can be achieved after laser treatment process
جامعات میں اردو تحقیق‘‘ مرتبہ ڈاکٹر رفیع الدین ہاشمی: تحقیق و تجزیہ’’
No full textDr Rafiuddin Hashmi is a very well-known scholar. His book ‘Jamia’at mein Urdu Tehqeeq’ (Urdu Research at Universities) has been acclaimed as a remarkable work for the guidance of research scholars and students alike. Hashmi Sahib’s book is a pioneering work that enlists thousands of dissertations written for PhD /M Phil degrees in Urdu at universities around the world. A labour of love, this book is packed with rare information. This article endeavours to add some information and corrects some errors that have crept into Hashmi Sahib’s work. While the author has paid glowing tributes to Hashmi Sahib for his remarkable work, he has added some new data too from which researchers may benefit
A robust hybrid approach with product context-aware learning and explainable AI for sentiment analysis in Amazon user reviews
No full textpublishedVersio
Multi-class hate speech detection in the Norwegian language using FAST-RNN and multilingual fine-tuned transformers
No full textThe growth of social networks has provided a platform for individuals with prejudiced views, allowing them to spread hate speech and target others based on their gender, ethnicity, religion, or sexual orientation. While positive interactions within diverse communities can considerably enhance confidence, it is critical to recognize that negative comments can hurt people’s reputations and well-being. This emergence emphasizes the need for more diligent monitoring and robust policies on these platforms to protect individuals from such discriminatory and harmful behavior. Hate speech is often characterized as an intentional act of aggression directed at a specific group, typically meant to harm or marginalize them based on certain aspects of their identity. Most of the research related to hate speech has been conducted in resource-aware languages like English, Spanish, and French. However, low-resource European languages, such as Irish, Norwegian, Portuguese, Polish, Slovak, and many South Asian, present challenges due to limited linguistic resources, making information extraction labor-intensive. In this study, we present deep neural networks with FastText word embeddings using regularization methods for multi-class hate speech detection in the Norwegian language, along with the implementation of multilingual transformer-based models with hyperparameter tuning and generative configuration. FastText outperformed other deep learning models when stacked with Bidirectional LSTM and GRU, resulting in the FAST-RNN model. In the concluding phase, we compare our results with the state-of-the-art and perform interpretability modeling using Local Interpretable Model-Agnostic Explanations to achieve a more comprehensive understanding of the model’s decision-making mechanisms.publishedVersio
Healthcare Systems : three studies of patient management and policy change
No full textThesis: Ph. D. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2018Cataloged from PDF version of thesis. "Doctor of Philosophy in Healthcare Systems: Management and Policy Research."Includes bibliographical references.For my PhD thesis, I conducted behavioral science research and wrote three first- author journal format papers, of which one paper has been published and the other two will be submitted to healthcare management journals after completion of my degree. All three papers introduce new information about either the cost or the behaviors of patients in local clinics, filling a gap in the healthcare system's management and policy literature. The first paper studies patients with diabetes who are non-adherent to scheduled appointments with physicians in a specialized diabetes clinic setting in Boston. I developed and introduced new and interesting ''technology comfort" measures and a "Smartphone usage" scale, to evaluate if patients would be able to use smart technologies for their disease self-management. This paper not only suggests that patients with diabetes could potentially benefit from using existing advanced technologies, but that new policies can be introduced to reduce the rate of diabetes patients' appointment-related non-adherence. The second paper examines the system of adherence or self-management in five areas ( diet, exercise, medications, doctor's appointments and regular glucose monitoring), revealing how it is correlated to emergency visits and patient lifestyle satisfaction. I analyze predictors of emergency room visits and propose potential policies to reduce these ER visits through the use of advanced smart technologies. The third paper identifies the incidence and consequences of not practicing non- pharmaceutical interventions, during the time of a pandemic, in a student population at a local university clinic.by Sahar Hashmi, MD.Ph. D. in Engineering SystemsPh.D.inEngineeringSystems Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Societ
The effect of patients’ preference on outcome in the EVerT cryotherapy versus salicylic acid for the treatment of plantar warts (verruca) trial
No full textBackground
Randomised controlled trials are widely accepted as the gold standard method to evaluate medical interventions, but they are still open to bias. One such bias is the effect of patient’s preference on outcome measures. The aims of this study were to examine whether patients’ treatment preference affected clearance of plantar warts and explore whether there were any associations between patients’ treatment preference and baseline variables in the EverT trial.
Methods
Two hundred and forty patients were recruited from University podiatry schools, NHS podiatry clinics and primary care. Patients were aged 12 years and over and had at least one plantar wart which was suitable for treatment with salicylic acid and cryotherapy. Patients were asked their treatment preference prior to randomisation. The Kruskal-Wallis test was performed to test the association between preference group and continuous baseline variables. The Fisher’s exact test was performed to test the association between preference group and categorical baseline variables. A logistic regression analysis was undertaken with verruca clearance (yes or no) as the dependent variable and treatment, age, type of verruca, previous treatment, treatment preference as independent variables. Two analyses were undertaken, one using the health professional reported outcome and one using the patient’s self reported outcomes. Data on whether the patient found it necessary to stop the treatment to which they had been allocated and whether they started another treatment were summarised by treatment group.
Results
Pre-randomisation preferences were: 10% for salicylic acid; 42% for cryotherapy and 48% no treatment preference. There was no evidence of an association between treatment preference group and either patient (p=0.95) or healthcare professional (p=0.46) reported verruca clearance rates. There was no evidence of an association between preference group and any of the baseline variables except gender, with more females expressing a preference for salicylic acid (p=0.004). There was no evidence that the number of times salicylic acid was applied was different between the preference groups at one week (p=0.89) or at three weeks (p=0.24). Similarly, for the number of clinic visits for cryotherapy (p=0.71)
Conclusions
This secondary analysis showed no evidence to suggest that patients’ baseline preferences affected verruca clearance rates or adherence with the treatment
Augmenting sentiment prediction capabilities for code-mixed tweets with multilingual transformers
No full textPeople in the modern digital era are increasingly embracing social media platforms to express their concerns and emotions in the form of reviews or comments. While positive interactions within diverse communities can considerably enhance confidence, it is critical to recognize that negative comments can hurt people’s reputations and well-being. Currently, individuals tend to express their thoughts in their native languages on these platforms, which is quite challenging due to potential syntactic ambiguity in these languages. Most of the research has been conducted for resource-aware languages like English. However, low-resource languages such as Urdu, Arabic, and Hindi present challenges due to limited linguistic resources, making information extraction labor-intensive. This study concentrates on code-mixed languages, including three types of text: English, Roman Urdu, and their combination. This study introduces robust transformer-based algorithms to enhance sentiment prediction in code-mixed text, which is a combination of Roman Urdu and English in the same context. Unlike conventional deep learning-based models, transformers are adept at handling syntactic ambiguity, facilitating the interpretation of semantics across various languages. We used state-of-the-art transformer-based models like Electra, code-mixed BERT (cm-BERT), and Multilingual Bidirectional and Auto-Regressive Transformers (mBART) to address sentiment prediction challenges in code-mixed tweets. Furthermore, results reveal that mBART outperformed the Electra and cm-BERT models for sentiment prediction in code-mixed text with an overall F1-score of 0.73. In addition to this, we also perform topic modeling to uncover shared characteristics within the corpus and reveal patterns and commonalities across different classes.publishedVersio
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