Text-based Image Segmentation Methodology

AbstractIn computer vision, segmentation is the process of partitioning a digital image into multiple segments (sets of pixels). Image segmentation is thus inevitable. Segmentation used for text-based images aim in retrieval of specific information from the entire image. This information can be a line or a word or even a character. This paper proposes various methodologies to segment a text based image at various levels of segmentation. This material serves as a guide and update for readers working on the text based segmentation area of Computer Vision. First, the need for segmentation is justified in the context of text based information retrieval. Then, the various factors affecting the segmentation process are discussed. Followed by the levels of text segmentation are explored. Finally, the available techniques with their superiorities and weaknesses are reviewed, along with directions for quick referral are suggested. Special attention is given to the handwriting recognition since this area requires more advanced techniques for efficient information extraction and to reach the ultimate goal of machine simulation of human reading

Automated Paper Screening for Clinical Reviews Using Large Language Models

Objective: To assess the performance of the OpenAI GPT API in accurately and efficiently identifying relevant titles and abstracts from real-world clinical review datasets and compare its performance against ground truth labelling by two independent human reviewers. Methods: We introduce a novel workflow using the OpenAI GPT API for screening titles and abstracts in clinical reviews. A Python script was created to make calls to the GPT API with the screening criteria in natural language and a corpus of title and abstract datasets that have been filtered by a minimum of two human reviewers. We compared the performance of our model against human-reviewed papers across six review papers, screening over 24,000 titles and abstracts. Results: Our results show an accuracy of 0.91, a sensitivity of excluded papers of 0.91, and a sensitivity of included papers of 0.76. On a randomly selected subset of papers, the GPT API demonstrated the ability to provide reasoning for its decisions and corrected its initial decision upon being asked to explain its reasoning for a subset of incorrect classifications. Conclusion: The GPT API has the potential to streamline the clinical review process, save valuable time and effort for researchers, and contribute to the overall quality of clinical reviews. By prioritizing the workflow and acting as an aid rather than a replacement for researchers and reviewers, the GPT API can enhance efficiency and lead to more accurate and reliable conclusions in medical research.Comment: 15 pages, 2 figures, 4 table

Asymptomatic patient with “lumpy and bumpy” airways. A case of pulmonary MALToma

Primary pulmonary lymphoma is a rare disease. The most frequent primary pulmonary lymphoma (PPL) is extranodal marginal zone B-cell lymphoma of MALT. About half of the patients are asymptomatic at diagnosis. We report a case of a 62-year-old male referred to us for preoperative assessment of surgery for Benign Prostatic Hyperplasia (BPH). He had no respiratory complaints but on evaluation was detected to have Pulmonary MALToma. Our case highlights the importance of tissue diagnosis

Insulating polymer nanocomposites for high thermal conduction and fire retarding applications.

The possibility of combining the flexibility and light - weight of polymers with the highest insulation of ceramics, drives the field of nanocomposites for potential commercial application. The inclusion of nano-sized insulating particles in the polymer matrix, and orienting the fillers along the direction of heat flow results in modifying the induced interfaces for effective phonon propagation. Such flexible polymer nanocomposites (PNC) offer easy workability and refined insulating effect with high thermal conductivity and fire-retardancy. Hence, opening a wider arena of applications with the advantage of their light-weight. The engineering of the interfaces, is the key for dictating the desired properties at the macro-scale. Consequently, silane functionalisation of nanoparticles with designed dispersion technique was tried for achieving this purpose. Transmission electron microscopy (TEM), Fourier transform infrared (FT-IR) spectroscopy, Differential scanning calorimetry (DSC), Thermogravimetric analysis (TGA), and Dynamic mechanical analysis (DMA) were done to characterize the properties and structure of the synthesised nanocomposite. This paper reports that surface modification of the nanoparticles can effectively solve the dispersion problem and reduces the electric field charge concentration at the interface. Synthesising PNC with selective nanoparticle loading percentage can yield a lmost 6-12% increase in the thermal capacity and fire retardability of the base polymer. Presenting an effective way of resulting in a commercially promising PNC suitable for various defence applications of radome technology, energy storage (e.g. batteries), structural bodies and cables in general

Self-healing polymer nanocomposites for composite structure applications.

Nanocomposites offer the possibility of realizing materials with bespoke properties that were un-realizable by the parent pristine materials. Since, nanoscale inclusions firstly engender small mechanical, electrical and optical defects; and secondly the differential property of the large volume of interfacial polymer from the bulk, bestow an opportunity for multi-functionality and attuning desired properties. The proposed research work deals with designing and controlling the interfacial structure-property-function relationships in the nanocomposites. This would give an insight for designing particle-polymer interface in forming hierarchical network of nanoparticles in the polymer matrix, and tackling the exigent issues of agglomeration and uniform dispersion. Upon achieving such to an acceptable limit, there are various possibilities of inducing self-assembling characteristics by engineering the phase interfaces, to design application driven specific properties based nanocomposite materials. This can herald way of delivering enduring structural materials, by the virtue of autonomous self-healing ability, sustaining not just once but multiple or repetitive occasions of damage. The autonomic self-healability triumphs even in the problematic cases where the damage, or its site is hard to identify or even inaccessible. Such engineered self-healing polymer nanocomposites are already finding extensive and promising applications in defence and space expeditions

Integrated self-healing of the composite offshore structures.

The self-repairing composite materials integrated with sensing is way forward to reduce maintenance cost and increase consumer safety. In this work, the novel self-healing carbon fibre reinforced unidirectional bulk tape of simple architecture is prepared using nanocomposite film. The bulk material tape was prepared using nanocomposite film of low melting temperature polymer sandwiched between two carbon fibre reinforced unidirectional tapes. First, the nanocomposite polyamide 6 (PA 6) tape with iron oxide nanoparticle was prepared using in-situ polymerization and mixing method. The iron oxide nanoparticle was silane coated suing tri-phasic reverse emulsion method to achieve better dispersion in PA 6 matrix. The nanocomposite was characterized using FTIR, XRD, DSC and TEM. Result shows that the proposed method of preparing self-healing bulk tape material has potential to be used for self-healing composite structure