Abstract

This work presents a novel approach to identifying possible early accident-prone zones in a large city-scale road network using geo-tagged collision alert data from a vehicle fleet. The alert data has been collected for a year from 200 city buses installed with the Advanced Driver Assistance System (ADAS). To the best of our knowledge, no research paper has used ADAS alerts to identify the early accident prone zones. A nonparametric technique called Kernel Density Estimation (KDE) is employed to model the distribution of alert data across stratified time intervals. A novel recall-based measure is introduced to assess the degree of support provided by our density-based approach for existing, manually determined accident-prone zones (‘blackspots’) provided by civic authorities. This shows that our KDE approach significantly outperforms existing approaches in terms of the recall-based measure. Introducing a novel linear assignment Earth Mover Distance based measure to predict previously unidentified accident-prone zones. The results and findings support the feasibility of utilizing alert data from vehicle fleets to aid civic planners in assessing accident-zone trends and deploying traffic calming measures, thereby improving overall road safety and saving lives.

Abstract

The histone methyltransferase ASH1L, first discovered for its role in transcription, has been shown to accelerate the removal of ultraviolet (UV) light-induced cyclobutane pyrimidine dimers (CPDs) by nucleotide excision repair. Previous reports demonstrated that CPD excision is most efficient at transcriptional regulatory elements, including enhancers, relative to other genomic sites. Therefore, we analyzed DNA damage maps in ASH1L-proficient and ASH1L-deficient cells to understand how ASH1L controls enhancer stability. This comparison showed that ASH1L protects enhancer sequences against the induction of CPDs besides stimulating repair activity. ASH1L reduces CPD formation at C–containing but not at TT dinucleotides, and no protection occurs against pyrimidine-(6,4)-pyrimidone photoproducts or cisplatin crosslinks. The diminished CPD induction extends to gene promoters but excludes retrotransposons. This guardian role against CPDs in regulatory elements is associated with the presence of H3K4me3 and H3K27ac histone marks, which are known to interact with the PHD and BRD motifs of ASH1L, respectively. Molecular dynamics simulations identified a DNA-binding AT hook of ASH1L that alters the distance and dihedral angle between neighboring C nucleotides to disfavor dimerization. The loss of this protection results in a higher frequency of C–>T transitions at enhancers of skin cancers carrying ASH1L mutations compared to ASH1L-intact counterparts.

Abstract

This work tackles the important task of understanding out-of-distribution behavior in two prominent types of generative models, i.e., GANs and Diffusion models. Understanding this behavior is crucial in understanding their broader utility and risks as these systems are increasingly deployed in our daily lives. Our first contribution is demonstrating that diffusion spaces outperform GANs' latent spaces in inverting high-quality OOD images. We also provide a theoretical analysis attributing this to the lack of prior holes in diffusion spaces. Our second significant contribution is to provide a theoretical hypothesis that diffusion spaces can be projected onto a bounded hypersphere, enabling image manipulation through geodesic traversal between inverted images. Our analysis shows that different geodesics share common attributes for the same manipulation, which we leverage to perform various image manipulations. We conduct thorough empirical evaluations to support and validate our claims. Finally, our third and final contribution introduces a novel approach to the few-shot sampling for out-of-distribution data by inverting a few images to sample from the cluster formed by the inverted latents. The proposed technique achieves state-of-the-art results for the few-shot generation task in terms of image quality. The effectiveness of the generated samples is further validated when used to augment data for underrepresented classes in a classification task, leading to improved performance. Our research underscores the promise of diffusion spaces in out-of-distribution imaging and offers avenues for further exploration.

Abstract

The understanding of spatio-temporal variation in land use and land cover (LULC) patterns is crucial for managing catchment land use planning, as it directly influences of tropical reservoir water quality and the subsequent Nutrient Contamination (NC) of unmonitored water bodies. The current research attempts to accurately measure the influence of LULC and its associated determinants on the quantities of NC loads by using Chl-a as a proxy, within tropical reservoirs, i.e. Bhadra and Tungabhadra, located in same river catchment. This Chl-a spread calculated by the Maximum Chlorophyll Index (MCI) derived from Sentinel 2 satellite data products covering the period from July 2016 to June 2021 were done using Google Earth Engine (GEE) platform. The validation analysis confirms the robustness of the methodology with a strong correlation between MCI-calculated values and EOMAP (Earth Observation and Environmental Services Mapping) Chl-a (μg/L) data points for both reservoirs, Bhadra (R2 = 0.64) and Tungabhadra (R2 = 0.68). The findings reveal that, Tungabhadra reservoir consistently exhibits an excessive spatial distribution of Chl-a spread area (17 km2 to 335 km2 ), reflecting nutrient-rich water inflows, particularly evident during the post-monsoon period. This notable rise could be linked to harvesting the Kharif crop, resulting in elevated nutrient concentrations. In contrast Bhadra reservoir, dominated by forested areas, maintains relatively lower Chl-a spread areas (<20 km2 ), highlighting its pivotal role in maintaining water cleanliness and serves as a riparian boundary. In addition, the changes in LULC classes show a strong relationship with variation in Chl-a during the studied period, for the Bhadra Reservoir R2 = 0.51 (F- statistics = 3.983, p = 0.021), and the Tungabhadra Reservoir R2 = 0.802 (F- statistics = 7.489, p = 0.0143). This highlights how changes in land use significantly shape contamination dynamics, deepening our understanding of nutrient inputs and contamination drivers in tropical reservoirs.

Abstract

Wetlands, which serve as transitional zones between terrestrial and aquatic environments, play a crucial role in providing diverse ecosystem services for the sustenance of wildlife and mankind. Unfortunately, these delicate systems have experienced illicit human interventions in recent times, resulting in degradation and deterioration. Despite being designated as protected areas, wetlands are being exploited beyond their ecological capacity, posing a threat to their sustainability and potential existence in the near future. The present study aims to analyse the historical spatial changes in three wetlands located in Kerala, India, both before and after their declaration as Ramsar sites. Ashtamudi Lake, Sasthamkotta Lake, and Vembanad Lake were the focus areas, and the study employed multi-temporal satellite data sets from 1988 to 2023 to generate land use and land cover maps. The objective was to assess the ecological transitions, particularly the expansion of urbanization and subsequent reclamation of wetlands. By analysing the changes in aerial extent of each land cover class within the study area, the study identified spatio-temporal transitions and modifications occurring in and around the Ramsar sites. A comparison of the thematic maps from 1988 to 2023 revealed a significant increase in built-up areas and a noticeable reduction in vegetation, agriculture, and water bodies, both in terms of extent and quality. The study identified various socio-economic drivers that are responsible for these changes and emphasized the urgent need for regular monitoring and management of these wetland ecosystems. It stresses the importance of treating wetland conservation as a priority.

Abstract

Aim We test the hypothesis that wind dispersal is more common among emergent tree species given that being tall increases the likelihood of effective seed dispersal. Location Americas, Africa and the Asia-Pacific. Time period 1970–2020. Major taxa studied Gymnosperms and Angiosperms. Methods We used a dataset consisting of tree inventories from 2821 plots across three biogeographic regions (Americas, Africa and Asia-Pacific), including dry and wet forests, to determine the maximum height and dispersal strategy of 5314 tree species. A web search was used to determine whether species were wind-dispersed. We compared differences in tree species maximum height between biogeographic regions and examined the relationship between species maximum height and wind dispersal using logistic regression. We also tested whether emergent tree species, that is species with at least one individual taller than the 95% height percentile in one or more plots, were disproportionally wind dispersed in dry and wet forests within each biogeographic region. Results Our dataset provides maximum height values for 5314 tree species, of which more than half (2914) had no record of this trait in existing global databases. We found that, on average, tree species in the Americas have lower maximum heights compared to those in Africa and the Asia Pacific. The probability of wind dispersal increased significantly with tree species maximum height and was significantly higher among emergent than non-emergent tree species in both dry and wet forests in all three biogeographic regions. Main conclusion Wind dispersal is more prevalent in tall, emergent tree species than in non-emergent species and may thus be an important factor in the evolution of tree species maximum height. By providing the most comprehensive dataset so far of tree species maximum height and wind dispersal strategies, this study paves the way for advancing our understanding of the eco-evolutionary drivers of tree size.

Abstract

Univariate regression trees have representation problems for non-orthogonal regression functions. Ensembles of univariate regression trees have better representational power. In some cases, weighted ensembles have shown better performance than unweighted ensembles. In this paper, we study the properties of ensembles of regression trees by using regression classification models. We propose a theoretical framework to study the representational power of infinite-sized weighted ensembles, consisting of randomised finite-sized regression trees. We show for some datasets that the weighted ensembles may have better representational power than unweighted ensembles, but the performance is highly dependent on the weighting scheme and the properties of datasets. Our model cannot be used for all the datasets. However, for some datasets, we can accurately predict the experimental results of ensembles of regression trees.

Abstract

Mobile manipulators find applications in manufacturing, infrastructure inspection, and domestic services. While most mobile manipulators excel at pick and place, weaim to combine three distinct manipulation primitives: striking,Pushing, and pick-and-place. This integration enables versatile object manipulation and expands the robot’s operational range. Furthermore, we propose a new attachment that can be easily fitted with most industrial two-finger grippers, capable of making line contact while pushing or striking an object. The proposed framework includes a high-level action sequence planner, dedicated offline planners for each manipulation primitive (Strike, Push, and Pick & Place), and a whole-body controller. Preliminary results from simulations and hardware experiments demonstrate the potential for diverse applications in the field of mobile manipulation.

Abstract

Skip to content IOP Science home Accessibility Help Search Journals Books Publishing Support Login Engineering Research Express Inclusive Publishing Trusted Science, find out more. Purpose-led Publishing, find out more. ACCEPTED MANUSCRIPT A label-free sensing of creatinine using radio frequency-driven lab-on-chip (loc) system Andleeb Zahra1, Swarnim Sinha2, Alimpan Modak3, Imran Siddiqui4, Azeemuddin Syed5, Prabhakar Bhimalapuram6, Tapan K. Sau7, Pawan Kumar8 and Zia Abbas9 Accepted Manuscript online 2 August 2024 • © 2024 IOP Publishing Ltd What is an Accepted Manuscript? DOI 10.1088/2631-8695/ad6ad5 DownloadAccepted Manuscript PDF Download PDF Article metrics 6 Total downloads Submit Submit to this Journal Permissions Get permission to re-use this article Share this article Article and author information Abstract This paper presents a promising avenue of Radio Frequency (RF) biosensors for sensitive and real-time monitoring of creatinine detection. Knowing creatinine levels in the human body is related to its possible association with renal, muscular, and thyroid dysfunction. The detection was performed using an Inter-Digitated Capacitor (IDC) made of copper (Cu) metal over an FR4 substrate. To demonstrate our methodology, we have chosen Phosphate Buffer (PB) as our solvent for making the creatinine solutions of different concentrations. Moreover, Assayed Chemistry Control (ACC), a reference control consisting of human serum-based solutions has been mixed with the different concentrations of creatinine in a ratio of 1:9 to spike the creatinine value in the ACC solution. The sensor has been designed using a High-Frequency Structure Simulator (HFSS) tool with an operating frequency of 2.53 GHz. Then the design is fabricated over the FR4 printed circuit board (PCB) and tested using a Vector Network Analyzer (VNA). However, the sensitive area of the IDC is introduced to grade 4 Whatman filter paper for the Sample Under Test (SUT) handling unit. The main advantage of using Whatman filter paper is that the uniform spreading of liquid reduces experimental error, and less volume is required for testing the sample. The principal idea implemented in the biosensor design is to track the shift in the operating frequency in the presence of different concentrations of creatinine mix in ACC solution with Phosphate Buffer (PB) solution as a reference.

Abstract

Purpose : This study aims to develop an artificial intelligence (AI)-based system for accurately grading arrested retinal development using optical coherence tomography (OCT) across various manufacturers. We employ deep learning techniques, specifically Unsupervised Domain Adaptation (UDA), to create a device-agnostic classification model distinguishing normal and abnormal retinal development. Methods : Foveal scans from three OCT manufacturers (TM-OCT1, HH-OCT1, TM-OCT2, TM-OCT3) were collected and annotated from datasets exceeding 20,000 OCT scans. The dataset was divided into training (80%) and testing (20%) sets. We utilised Convolutional Neural Networks (CNN) with ResNet50 backbone, assessing each pair as the source (for supervised training) and target (for unsupervised training). The diagnostic accuracy of the AI models was compared to seven clinician graders with varying experience (1 to 10 years), evaluating sensitivity, specificity, and overall accuracy. Results : The cross-domain binary classification demonstrated exceptional diagnostic accuracy ranging from 87.75% to 96.06%. Sensitivity and specificity metrics further validated the robustness of our AI system (sensitivity: 88.68% to 98.38%, specificity: 78.00% to 98.02%). Notably, the model trained on TM-OCT1 and HH-OCT1 achieved 96.06% accuracy, 98.38% sensitivity, and 89.11% specificity on the HH-OCT1 test set, with an Area Under the Curve (AUC) of 99.4 (95% CI). These outcomes highlight the system's ability to distinguish normal and abnormal retinal development across diverse OCT devices. Conclusions : Our study demonstrates, for the first time, the feasibility of employing a device-agnostic AI system in paediatric OCT interpretation without additional labelled data. The AI system's diagnostic performance is comparable to a clinician with over 10 years of experience, showcasing its potential to reduce inter-examiner variability and enhance clinical care pathways. With integration of paediatric OCT into routine clinical assessment, our AI system serves as a robust foundation for the development of a real-time, frontline diagnostic tool for retinal developmental disorders.