Road Deterioration detection A Machine Learning-Based System for Automated Pavement Crack Identification and Analysis

Road surfaces may deteriorate over time because of a number of external factors such as heavy traffic, unfavourable weather, and poor design. These flaws, which may include potholes, fissures, and uneven surfaces, can pose significant safety threats to both vehicles and pedestrians. This research aims to develop and evaluate an automated system for detecting and analyzing cracks in pavements based on machine learning. The research explores the utilisation of object detection techniques to identify and categorize different types of pavement cracks. Additionally, the proposed work investigates several approaches to integrate the outcome system with existing pavement management systems to enhance road maintenance and sustainability. The research focuses on identifying reliable data sources, creating accurate and effective object detection algorithms for pavement crack detection, classifying various types of cracks, and assessing their severity and extent. The research objectives include gathering reliable datasets, developing a precise and effective object detection algorithm, classifying different types of pavement cracks, and determining the severity and extent of the cracks. The study collected pavement crack images from various sources, including publicly available databases and images captured using mobile devices. Multiple object detection models, such as YOLOv5, YOLOv8, and CenterNet were trained and tested using the collected dataset. The proposed approaches were evaluated using different performance metrics, The achieved results indicated that the YOLOv5 model outperformed CenterNet by a significant margin

FQHE interferometers in strong tunneling regime. The role of compactness of edge fields

We consider multiple-point tunneling in the interferometers formed between edges of electron liquids with in general different filling factors in the regime of the Fractional Quantum Hall effect (FQHE). We derive an effective matrix Caldeira-Leggett models for the multiple tunneling contacts connected by the chiral single-mode FQHE edges. It is shown that the compactness of the Wen- Fr\"ohlich chiral boson fields describing the FQHE edge modes plays a crucial role in eliminating the spurious non-locality of the electron transport properties of the FQHE interferometers arising in the regime of strong tunneling.Comment: 5 page

The clinical utility of prostate cancer heterogeneity using texture analysis of multiparametric MRI

Purpose To determine if multiparametric MRI (mpMRI) derived filtration-histogram based texture analysis (TA) can differentiate between different Gleason scores (GS) and the D’Amico risk in prostate cancer. Methods We retrospectively studied patients whose pre-operative 1.5T mpMRI had shown a visible tumour and who subsequently underwent radical prostatectomy (RP). Guided by tumour location from the histopathology report, we drew a region of interest around the dominant visible lesion on a single axial slice on the T2, Apparent Diffusion Coefficient (ADC) map and early arterial phase post-contrast T1 image. We then performed TA with a filtration-histogram software (TexRAD -Feedback Medical Ltd, Cambridge, UK). We correlated GS and D’Amico risk with texture using the Spearman’s rank correlation test. Results We had 26 RP patients with an MR-visible tumour. Mean of positive pixels (MPP) on ADC showed a significant negative correlation with GS at coarse texture scales. MPP showed a significant negative correlation with GS without filtration and with medium filtration. MRI contrast texture without filtration showed a significant, negative correlation with D’Amico score. MR T2 texture showed a significant, negative correlation with the D’Amico risk, particularly at textures without filtration, medium texture scales and coarse texture scales. Conclusion ADC map mpMRI TA correlated negatively with GS, and T2 and post-contrast images with the D’Amico risk score. These associations may allow for better assessment of disease prognosis and a non-invasive method of follow-up for patients on surveillance. Further, identifying clinically significant prostate cancer is essential to reduce harm from over-diagnosis and over-treatment

Fruit/Seed Morphology, Seed Drying and Germination Studies in Baccaurea courtallensis (Muell.) Arg., a Threatened Under-Utilized Fruit Species of Western Ghats in India

A study was under taken on fruit and seed morphology, seed drying, seed germination and storage behavior in Baccaurea courtallensis, as, this plant is propagated mainly through seeds. Its fruit is a berry consisting of an outer, semi-hard but fleshy rind 2-3 mm thick. The cavity inside the rind is normally occupied by a single, arillate seed, but, two seeds are also seen occasionally. Fresh rind was found to be rich in antioxidants, with 237mg total phenols and 93mg flavonoids per 100 gram fresh weight, but was poor in Vitamin C. A thick, fleshy endosperm is surrounded by the inner seed-coat. The endosperm surrounds the embryo consisting of two papery-thin cotyledons and a minute embryonic axis. Germination was highest (96.7%) when seeds were sown immediately after extraction, with moisture content of about 50%. Reduction in moisture to below 34% showed a drastic decrease in germination. Dried seeds took longer to germinate than did the fresh ones. Seeds with 21% moisture recorded about 60% germination whereas, seeds with 10.2% or 8% moisture failed to germinate, indicating a recalcitrant seed. Temperature in the range of 25-30°C was found to be optimum. Of the two media tested for raising the seedlings, cocopeat medium was superior as, it induced faster growth of the seedlings. Seedling root and shoot were considerably longer, with higher seedling survival rate in cocopeat than in the soil-mix medium. Seedling establishment was poor when planted out of their natural habitat

MR texture analysis in differentiating renal cell carcinoma from Lipid-poor angiomyolipoma and oncocytoma

OBJECTIVES: To assess the utility of Magnetic resonance texture analysis (MRTA) in differentiating renal cell carcinoma (RCC) from lipid-poor angiomyolipoma (lpAML) and oncocytoma. METHODS: After ethical approval, 42 patients with 54 masses (34 RCC, 14 lpAML and six oncocytomas) who underwent MRI on a 1.5 T scanner (Avanto, Siemens, Erlangen, Germany) between January 2011 and December 2012 were retrospectively included in the study. MRTA was performed on the TexRAD research software (Feedback Plc., Cambridge, UK) using free-hand polygonal region of interest (ROI) drawn on the maximum cross-sectional area of the tumor to generate six first-order statistical parameters. The Mann-Whitney U test was used to look for any statically significant difference. The receiver operating characteristic (ROC) curve analysis was done to select the parameter with the highest class separation capacity [area under the curve (AUC)] for each MRI sequence. RESULTS: Several texture parameters on MRI showed high class separation capacity (AUC > 0.8) in differentiating RCC from lpAML and oncocytoma. The best performing parameter in differentiating RCC from lpAML was mean of positive pixels (MPP) at SSF 2 (AUC: 0.891) on DWI b500. In differentiating RCC from oncocytoma, the best parameter was mean at SSF 0 (AUC: 0.935) on DWI b1000. CONCLUSIONS: MRTA could potentially serve as a useful non-invasive tool for differentiating RCC from lpAML and oncocytoma. ADVANCES IN KNOWLEDGE: There is limited literature addressing the role of MRTA in differentiating RCC from lpAML and oncocytoma. Our study demonstrated several texture parameters which were useful in this regard

Using texture analysis in the development of a potential radiomic signature for early identification of hepatic metastasis in colorectal cancer

Background: Radiomics allows information not readily available to the naked eye to be extracted from high resolution imaging modalities such as CT. Identifying that a cancer has already metastasised at the time of presentation through a radiomic signature will affect the treatment pathway. The ability to recognise the existence of metastases earlier will have a significant impact on the survival outcomes. / Aim: To create a novel radiomic signature using textural analysis in the evaluation of synchronous liver metastases in colorectal cancer. / Methods: CT images at baseline and subsequent surveillance over a 5-year period of patients with colorectal cancer were processed using textural analysis software. Comparison was made between those patients who developed liver metastases and those that remained disease free to detect differences in the ‘texture’ of the liver. / Results: A total of 24 patients were divided into two matched groups for comparison. Significant differences between the two groups scores when using the textural analysis programme were found on coarse filtration (p = 0.044). Patients that went on to develop metastases an average of 18 months after presentation had higher levels of hepatic heterogeneity on CT. / Conclusion: This initial study demonstrates the potential of using a textural analysis programme to build a radiomic signature to predict the development of hepatic metastases in rectal cancer patients otherwise thought to have clear staging CT scans at time of presentation

Grey-matter texture abnormalities and reduced hippocampal volume are distinguishing features of schizophrenia

Neurodevelopmental processes are widely believed to underlie schizophrenia. Analysis of brain texture from conventional magnetic resonance imaging (MRI) can detect disturbance in brain cytoarchitecture. We tested the hypothesis that patients with schizophrenia manifest quantitative differences in brain texture that, alongside discrete volumetric changes, may serve as an endophenotypic biomarker. Texture analysis (TA) of grey matter distribution and voxel-based morphometry (VBM) of regional brain volumes were applied to MRI scans of 27 patients with schizophrenia and 24 controls. Texture parameters (uniformity and entropy) were also used as covariates in VBM analyses to test for correspondence with regional brain volume. Linear discriminant analysis tested if texture and volumetric data predicted diagnostic group membership (schizophrenia or control). We found that uniformity and entropy of grey matter differed significantly between individuals with schizophrenia and controls at the fine spatial scale (filter width below 2 mm). Within the schizophrenia group, these texture parameters correlated with volumes of the left hippocampus, right amygdala and cerebellum. The best predictor of diagnostic group membership was the combination of fine texture heterogeneity and left hippocampal size. This study highlights the presence of distributed grey-matter abnormalities in schizophrenia, and their relation to focal structural abnormality of the hippocampus. The conjunction of these features has potential as a neuroimaging endophenotype of schizophrenia

Precise Modeling of Solar Radiation Pressure for IRNSS Satellite

IRNSS-1A, IRNSS-1B and IRNSS-1C are the first three satellites of Indian Regional Navigation Satellite System (IRNSS) launched in 1st July 2013,4th April 2014 and 16th October 2014 respectively. IRNSS will provide regional navigation services independently over the IRNSS service area. For the precise positioning and navigation applications, precise orbit and clock information of the IRNSS satellites are essential. For High altitude satellites like IRNSS, Solar Radiation Pressure (SRP) force is the second largest perturbation force acting on the satellites after the gravitational attraction from Earth, Sun and Moon. It is the largest error source in the modelling of orbital dynamics of IRNSS, and hence its precise modelling is essential for accurate orbit determination. In this paper different approaches were studied to develop a highly precise solar radiation pressure model for IRNSS satellites using IRNSS-1A and IRNSS-1B observation data. Since IRNSS satellites shape, optical properties, physical properties as well as the attitude information are different from other Indian Communication satellites, a novel approach has been adopted here for precise modelling of SRP. The force due to SRP has been computed analytically for each of the spacecraft surfaces in the satellite body fixed frame which is further resolved in all required directions to compute the net force. To evaluate the performance of the SRP model, the orbit accuracy is derived from 1-day orbit overlaps at day boundaries of 2-day solutions. As a result, an orbit estimation accuracy of 25 meters has been observed by the model alone, while the estimation error is observed as 2.5m.Further beside the model, 3 constant co-efficient has been estimated in the three particular directions (namely DYB) which were following a right handed system. Again the model performance with estimated co-efficient has been analysed and the orbit accuracy is derived from the overlap test. As a result, an orbit estimation accuracy of 10 m has been observed, while the estimation error is about 1m. Keywords: IRNSS, Navigation, Solar Radiation Pressure, orbit accurac