Search

Automated pavement monitoring using computer vision can analyze pavement conditions more efficiently and accurately than manual methods. Accurate segmentation is essential for quantifying the severity and extent of pavement defects and consequently, the overall condition index used for prioritizing rehabilitation and maintenance activities. Deep learning-based segmentation models are however, often supervised and require pixel-level annotations, which can be costly and time-consuming. While the recent evolution of zero-shot segmentation models can generate pixel-wise labels for unseen classes without any training data, they struggle with irregularities of cracks and textured pavement backgrounds. This research proposes a zero-shot segmentation model, PaveSAM, that can segment pavement distresses using bounding box prompts. By retraining SAM's mask decoder with just 180 images, pavement distress segmentation is revolutionized, enabling efficient distress segmentation using bounding box prompts, a capability not found in current segmentation models. This not only drastically reduces labeling efforts and costs but also showcases our model's high performance with minimal input, establishing the pioneering use of SAM in pavement distress segmentation. Furthermore, researchers can use existing open-source pavement distress images annotated with bounding boxes to create segmentation masks, which increases the availability and diversity of segmentation pavement distress datasets.

The paper sought to fit an Ornstein Uhlenbeck model with seasonal mean and volatility, where the residuals are generated by a Brownian motion for Ghanian daily average temperature. This paper employed the modified Ornstein Uhlenbeck model proposed by Bhowan which has a seasonal mean and stochastic volatility process. The findings revealed that, the Bono region experiences warm temperatures and maximum precipitation up to 32.67 degree celsius and 126.51mm respectively. It was observed that the Daily Average Temperature (DAT) of the region reverts to a temperature of approximately 26 degree celsius at a rate of 18.72% with maximum and minimum temperatures of 32.67degree celsius and 19.75degree celsius respectively. Although the region is in the middle belt of Ghana, it still experiences warm(hot) temperatures daily and experiences dry seasons relatively more than wet seasons in the number of years considered for our analysis. Our model explained approximately 50% of the variations in the daily average temperature of the region which can be regarded as relatively a good model. The findings of this paper are relevant in the pricing of weather derivatives with temperature as an underlying variable in the Ghanaian financial and agricultural sector. Furthermore, it would assist in the development and design of tailored agriculture/crop insurance models which would incorporate temperature dynamics rather than extreme weather conditions/events such as floods, drought and wildfires., Comment: 10 pages. "Will appear in the Journal of Applied Mathematics"

As opposed to evaluating computation and logic-based reasoning, current benchmarks for evaluating large language models (LLMs) in medicine are primarily focused on question-answering involving domain knowledge and descriptive reasoning. While such qualitative capabilities are vital to medical diagnosis, in real-world scenarios, doctors frequently use clinical calculators that follow quantitative equations and rule-based reasoning paradigms for evidence-based decision support. To this end, we propose MedCalc-Bench, a first-of-its-kind dataset focused on evaluating the medical calculation capability of LLMs. MedCalc-Bench contains an evaluation set of over 1000 manually reviewed instances from 55 different medical calculation tasks. Each instance in MedCalc-Bench consists of a patient note, a question requesting to compute a specific medical value, a ground truth answer, and a step-by-step explanation showing how the answer is obtained. While our evaluation results show the potential of LLMs in this area, none of them are effective enough for clinical settings. Common issues include extracting the incorrect entities, not using the correct equation or rules for a calculation task, or incorrectly performing the arithmetic for the computation. We hope our study highlights the quantitative knowledge and reasoning gaps in LLMs within medical settings, encouraging future improvements of LLMs for various clinical calculation tasks., Comment: Github link: https://github.com/ncbi-nlp/MedCalc-Bench HuggingFace link: https://huggingface.co/datasets/nsk7153/MedCalc-Bench

We present and analyze a new finite volume scheme of Gudonov-type for a nonlinear scalar conservation law whose flux function has a discontinuous coefficient due to time-dependent changes in its sign along a Lipschitz continuous curve., Comment: 37 pages, 8 figures

MobiScout is an iOS software that monitors users' driving habits and physiological conditions while on the road. The Mobiscout app was created to provide a low-cost next-generation data collection and analysis solution for naturalistic driving studies. MobiScout collects real-time data, including physiological information from drivers in their normal driving conditions using sensors and cameras on mobile phones, smartwatches, and Bluetooth-enabled OBD equipment. The MobiScout software captures vehicle and driving data, including speed, braking, pulse rate, and acceleration, while the phone's camera captures everything inside and outside the car. Data captured can be streamed to cloud storage in real-time or persisted in local storage in WIFI dead zones. The information gathered will be studied further to better understand typical traffic behavior, performance, surroundings, and driving context among drivers.

Cybercrime is a growing concern in West Africa due to the increasing use of technology and internet penetration in the region. Legal frameworks are essential for guiding the control of cybercrime. However, the implementation proves challenging for law enforcement agencies due to the absence of a dedicated and effective regional institutional follow-up mechanism. This study conducted a systematic literature review focusing on West Africa's prevalence of cybercrime, governing policies, regulations, and methodologies for combating cybercrime. West-Africa countries face significant cybercrime challenges, exacerbated by inadequate resources and a dearth of security experts. This study pinpoints potential cybercrime prevention strategies, such as leveraging the Triage framework and broadening research to cover pivotal areas like cyber aggression and cyberbullying. Our research findings highlight the urgency for policymakers and law enforcement agencies to devise more efficient prevention strategies and policies. Overall, this study provides invaluable insights into the state of cybercrime in West Africa to guide the formulation of potent prevention and intervention strategies.

By 2050, it is predicted that there will be 9 billion people on the planet, which will call for more production, lower costs, and the preservation of natural resources. It is anticipated that atypical occurrences and climate change will pose severe risks to agricultural output. It follows that a 70% or more significant rise in food output is anticipated. Smart farming, often known as agriculture 4.0, is a tech-driven revolution in agriculture with the goal of raising industry production and efficiency. Four primary trends are responsible for it: food waste, climate change, population shifts, and resource scarcity. The agriculture industry is changing as a result of the adoption of emerging technologies. Using cutting-edge technology like IoT, AI, and other sensors, smart farming transforms traditional production methods and international agricultural policies. The objective is to establish a value chain that is optimized to facilitate enhanced monitoring and decreased labor expenses. The agricultural sector has seen tremendous transformation as a result of the fourth industrial revolution, which has combined traditional farming methods with cutting-edge technology to increase productivity, sustainability, and efficiency. To effectively utilize the potential of technology gadgets in the agriculture sector, collaboration between governments, private sector entities, and other stakeholders is necessary. This paper covers Agriculture 4.0, looks at its possible benefits and drawbacks of the implementation methodologies, compatibility, reliability, and investigates the several digital tools that are being utilized to change the agriculture industry and how to mitigate the challenges., Comment: 9 pages, 4 figures, under reviewing process

Complex absorbing potentials (CAPs) are artificial potentials added to electronic Hamiltonians to make the wave function of metastable electronic states square-integrable. This makes electronic-structure theory of resonances comparable to that of bound states, thus reducing the complexity of the problem. However, the most often used box and Voronoi CAPs depend on several parameters that have a substantial impact on the numerical results. Among these parameters are the CAP strength and a set of spatial parameters that define the onset of the CAP. It has been a common practice to minimize the perturbation of the resonance states due to the CAP by optimizing the strength parameter while fixing the onset parameters although the performance of this approach strongly depends on the chosen onset. Here we introduce a more general approach that allows one to optimize not only the CAP strength but also the spatial parameters. We show that fixing the CAP strength and optimizing the spatial parameters is a reliable way for minimizing CAP perturbations. We illustrate the performance of this new approach by computing resonance energies and widths of the temporary anions of dinitrogen, ethylene, and formic acid. This is done at the Hartree-Fock and equation-of-motion coupled-cluster singles and doubles levels of theory, using full and projected box and Voronoi CAPs., Comment: 35 pages

The Pavement Condition Index (PCI) is a widely used metric for evaluating pavement performance based on the type, extent and severity of distresses detected on a pavement surface. In recent times, significant progress has been made in utilizing deep-learning approaches to automate PCI estimation process. However, the current approaches rely on at least two separate models to estimate PCI values -- one model dedicated to determining the type and extent and another for estimating their severity. This approach presents several challenges, including complexities, high computational resource demands, and maintenance burdens that necessitate careful consideration and resolution. To overcome these challenges, the current study develops a unified multi-tasking model that predicts the PCI directly from a top-down pavement image. The proposed architecture is a multi-task model composed of one encoder for feature extraction and four decoders to handle specific tasks: two detection heads, one segmentation head and one PCI estimation head. By multitasking, we are able to extract features from the detection and segmentation heads for automatically estimating the PCI directly from the images. The model performs very well on our benchmarked and open pavement distress dataset that is annotated for multitask learning (the first of its kind). To our best knowledge, this is the first work that can estimate PCI directly from an image at real time speeds while maintaining excellent accuracy on all related tasks for crack detection and segmentation.

Real-time pavement condition monitoring provides highway agencies with timely and accurate information that could form the basis of pavement maintenance and rehabilitation policies. Existing technologies rely heavily on manual data processing, are expensive and therefore, difficult to scale for frequent, networklevel pavement condition monitoring. Additionally, these systems require sending large packets of data to the cloud which requires large storage space, are computationally expensive to process, and results in high latency. The current study proposes a solution that capitalizes on the widespread availability of affordable Micro Electro-Mechanical System (MEMS) sensors, edge computing and internet connection capabilities of microcontrollers, and deployable machine learning (ML) models to (a) design an Internet of Things (IoT)-enabled device that can be mounted on axles of vehicles to stream live pavement condition data (b) reduce latency through on-device processing and analytics of pavement condition sensor data before sending to the cloud servers. In this study, three ML models including Random Forest, LightGBM and XGBoost were trained to predict International Roughness Index (IRI) at every 0.1-mile segment. XGBoost had the highest accuracy with an RMSE and MAPE of 16.89in/mi and 20.3%, respectively. In terms of the ability to classify the IRI of pavement segments based on ride quality according to MAP-21 criteria, our proposed device achieved an average accuracy of 96.76% on I-70EB and 63.15% on South Providence. Overall, our proposed device demonstrates significant potential in providing real-time pavement condition data to State Highway Agencies (SHA) and Department of Transportation (DOTs) with a satisfactory level of accuracy.

Modern transportation planning relies heavily on accurate predictions of person and vehicle trips. However, traditional planning models often fail to account for the intricacies and dynamics of travel behavior, leading to less-than-optimal accuracy in these predictions. This study explores the potential of deep learning techniques to transform the way we approach trip predictions, and ultimately, transportation planning. Utilizing a comprehensive dataset from the National Household Travel Survey (NHTS), we developed and trained a deep learning model for predicting person and vehicle trips. The proposed model leverages the vast amount of information in the NHTS data, capturing complex, non-linear relationships that were previously overlooked by traditional models. As a result, our deep learning model achieved an impressive accuracy of 98% for person trip prediction and 96% for vehicle trip estimation. This represents a significant improvement over the performances of traditional transportation planning models, thereby demonstrating the power of deep learning in this domain. The implications of this study extend beyond just more accurate predictions. By enhancing the accuracy and reliability of trip prediction models, planners can formulate more effective, data-driven transportation policies, infrastructure, and services. As such, our research underscores the need for the transportation planning field to embrace advanced techniques like deep learning. The detailed methodology, along with a thorough discussion of the results and their implications, are presented in the subsequent sections of this paper., Comment: 15 pages, 11 figures

In today's rapidly evolving urban landscapes, efficient and accurate mapping of road infrastructure is critical for optimizing transportation systems, enhancing road safety, and improving the overall mobility experience for drivers and commuters. Yet, a formidable bottleneck obstructs progress - the laborious and time-intensive manual identification of intersections. Simply considering the shear number of intersections that need to be identified, and the labor hours required per intersection, the need for an automated solution becomes undeniable. To address this challenge, we propose a novel approach that leverages connected vehicle data and cutting-edge deep learning techniques. By employing geohashing to segment vehicle trajectories and then generating image representations of road segments, we utilize the YOLOv5 (You Only Look Once version 5) algorithm for accurate classification of both straight road segments and intersections. Experimental results demonstrate an impressive overall classification accuracy of 95%, with straight roads achieving a remarkable 97% F1 score and intersections reaching a 90% F1 score. This approach not only saves time and resources but also enables more frequent updates and a comprehensive understanding of the road network. Our research showcases the potential impact on traffic management, urban planning, and autonomous vehicle navigation systems. The fusion of connected vehicle data and deep learning models holds promise for a transformative shift in road infrastructure mapping, propelling us towards a smarter, safer, and more connected transportation ecosystem., Comment: 18 pages, 13 figures

Background: The objective of this study is to evaluate the impact of COVID-19 inactivated vaccine administration on the outcomes of in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI) cycles in infertile couples in China. Methods: We collected data from the CYART prospective cohort, which included couples undergoing IVF treatment from January 2021 to September 2022 at Sichuan Jinxin Xinan Women & Children's Hospital. Based on whether they received vaccination before ovarian stimulation, the couples were divided into the vaccination group and the non-vaccination group. We compared the laboratory parameters and pregnancy outcomes between the two groups. Findings: After performing propensity score matching (PSM), the analysis demonstrated similar clinical pregnancy rates, biochemical pregnancy and ongoing pregnancy rates between vaccinated and unvaccinated women. No significant disparities were found in terms of embryo development and laboratory parameters among the groups. Moreover, male vaccination had no impact on patient performance or pregnancy outcomes in assisted reproductive technology treatments. Additionally, there were no significant differences observed in the effects of vaccination on embryo development and pregnancy outcomes among couples undergoing ART. Interpretation: The findings suggest that COVID-19 vaccination did not have a significant effect on patients undergoing IVF/ICSI with fresh embryo transfer. Therefore, it is recommended that couples should receive COVID-19 vaccination as scheduled to help mitigate the COVID-19 pandemic., Comment: 26 pages, 4 figures and 5 tables

Real-time traffic and sensor data from connected vehicles have the potential to provide insights that will lead to the immediate benefit of efficient management of the transportation infrastructure and related adjacent services. However, the growth of electric vehicles (EVs) and connected vehicles (CVs) has generated an abundance of CV data and sensor data that has put a strain on the processing capabilities of existing data center infrastructure. As a result, the benefits are either delayed or not fully realized. To address this issue, we propose a solution for processing state-wide CV traffic and sensor data on GPUs that provides real-time micro-scale insights in both temporal and spatial dimensions. This is achieved through the use of the Nvidia Rapids framework and the Dask parallel cluster in Python. Our findings demonstrate a 70x acceleration in the extraction, transformation, and loading (ETL) of CV data for the State of Missouri for a full day of all unique CV journeys, reducing the processing time from approximately 48 hours to just 25 minutes. Given that these results are for thousands of CVs and several thousands of individual journeys with sub-second sensor data, implies that we can model and obtain actionable insights for the management of the transportation infrastructure., Comment: Presented at the 102nd Transportation Research Board (TRB) Annual Meeting