Dissipativity analysis of stochastic fuzzy neural networks with randomly occurring uncertainties using delay dividing approach

This paper focuses on the problem of delay-dependent robust dissipativity analysis for a class of stochastic fuzzy neural networks with time-varying delay. The randomly occurring uncertainties under consideration are assumed to follow certain mutually uncorrelated Bernoulli-distributed white noise sequences. Based on the Itô's differential formula, Lyapunov stability theory, and linear matrix inequalities techniques, several novel sufficient conditions are derived using delay partitioning approach to ensure the dissipativity of neural networks with or without time-varying parametric uncertainties. It is shown, by comparing with existing approaches, that the delay-partitioning projection approach can largely reduce the conservatism of the stability results. Numerical examples are constructed to show the effectiveness of the theoretical results

Road Ditch Line Mapping with Mobile LiDAR

Maintenance of roadside ditches is important to avoid localized flooding and premature failure of pavements. Scheduling effective preventative maintenance requires mapping of the ditch profile to identify areas requiring excavation of long-term sediment accumulation. High-resolution, high-quality point clouds collected by mobile LiDAR mapping systems (MLMS) provide an opportunity for effective monitoring of roadside ditches and performing hydrological analyses. This study evaluated the applicability of mobile LiDAR for mapping roadside ditches for slope and drainage analyses. The performance of alternative MLMS units was performed. These MLMS included an unmanned ground vehicle, an unmanned aerial vehicle, a portable backpack system along with its vehicle-mounted version, a medium-grade wheel-based system, and a high-grade wheel-based system. Point cloud from all the MLMS units were in agreement in the vertical direction within the ±3 cm range for solid surfaces, such as paved roads, and ±7 cm range for surfaces with vegetation. The portable backpack system that could be carried by a surveyor or mounted on a vehicle and was the most flexible MLMS. The report concludes that due to flexibility and cost effectiveness of the portable backpack system, it is the preferred platform for mapping roadside ditches, followed by the medium-grade wheel-based system. Furthermore, a framework for ditch line characterization is proposed and tested using datasets acquired by the medium-grade wheel-based and vehicle-mounted portable systems over a state highway. An existing ground filtering approach is modified to handle variations in point density of mobile LiDAR data. Hydrological analyses, including flow direction and flow accumulation, are applied to extract the drainage network from the digital terrain model (DTM). Cross-sectional/longitudinal profiles of the ditch are automatically extracted from LiDAR data and visualized in 3D point clouds and 2D images. The slope derived from the LiDAR data was found to be very close to highway cross slope design standards of 2% on driving lanes, 4% on shoulders, as well as 6-by-1 slope for ditch lines. Potential flooded regions are identified by detecting areas with no LiDAR return and a recall score of 54% and 92% was achieved by the medium-grade wheel-based and vehicle-mounted portable systems, respectively. Furthermore, a framework for ditch line characterization is proposed and tested using datasets acquired by the medium-grade wheel-based and vehicle-mounted portable systems over a state highway. An existing ground filtering approach is modified to handle variations in point density of mobile LiDAR data. Hydrological analyses, including flow direction and flow accumulation, are applied to extract the drainage network from the digital terrain model (DTM). Cross-sectional/longitudinal profiles of the ditch are automatically extracted from LiDAR data, and visualized in 3D point clouds and 2D images. The slope derived from the LiDAR data was found to be very close to highway cross slope design standards of 2% on driving lanes, 4% on shoulder, as well as 6-by-1 slope for ditch lines. Potential flooded regions are identified by detecting areas with no LiDAR return and a recall score of 54% and 92% was achieved by the medium-grade wheel-based and vehicle-mounted portable systems, respectively

Field Test Bed for Evaluating Embedded Vehicle Sensors with Indiana Companies

With the advent of modern sensing technology, mapping products have begun to achieve an unprecedented precision of measurement. Considering their diverse use cases, several factors play a role in what would make the resulting measurements accurate. For light detection and ranging (LiDAR) and photogrammetry-based mapping solutions that implement vehicles outfitted with laser ranging devices, RGB cameras, and global navigation satellite system/inertial navigation system (GNSS/INS) georeferencing units, the quality of the derived mapping products is governed by the combined accuracy of the various sensors. While ranging errors associated with LiDAR systems or the imaging quality of RGB cameras are sensor-dependent and are mostly constant, the accuracy of a georeferencing unit depends on a variety of extrinsic factors, including but not limited to, availability of clear line-of-path to GNSS satellites and presence of radio interferences. The quality of the GNSS signal, in turn, is affected by the grade of hardware components used and, to a great extent, obstructions to signal reception. This document reports some of the major challenges of vehicle-based mobile mapping with regards to GNSS/INS navigation. The background of GNSS/INS positioning is discussed to build a framework for trajectory enhancement as well as improvement of LiDAR mapping products. The focus is put on using available sensor data from LiDAR and/or cameras to enhance their position/orientation quality. Some best practices in light of potential trajectory deterioration are also recommended

Commentary: Outbreak of Chikungunya in Pakistan

Rauf et al. in their recent correspondence in “Lancet Infectious Diseases” reported the first chikungunya outbreak in Karachi, Pakistan with 30,000 suspected and 4,000 confirmed cases (1). However, these estimates have been denied in a subsequent report by the National Institute of Health (NIH) indicating 818 suspected and 82 laboratory-confirmed cases of chikungunya (2). Rauf and colleagues have highlighted warm climate and wretched sanitary conditions as contributing factors of current outbreak and urge national and international health-organizations to address these momentous issues (1). We agree that climatic features and sanitation issues potentially lead to vector proliferation and the importance of these concerns cannot be disregarded. However, we felt inclined to share our point of view about the recent outbreak of chikungunya in Pakistan. We believe that there are some more important factors that should be considered as causes of this outbreak and must be addressed by the Government of Pakistan in haste to quell the further disease spillover. One of these factors is unchecked cross-border movements between Pakistan and India

Automating Test Case Generation for Android Applications using Model-based Testing

Testing of mobile applications (apps) has its quirks as numerous events are required to be tested. Mobile apps testing, being an evolving domain, carries certain challenges that should be accounted for in the overall testing process. Since smartphone apps are moderate in size so we consider that model-based testing (MBT) using state machines and statecharts could be a promising option for ensuring maximum coverage and completeness of test cases. Using model-based testing approach, we can automate the tedious phase of test case generation, which not only saves time of the overall testing process but also minimizes defects and ensures maximum test case coverage and completeness. In this paper, we explore and model the most critical modules of the mobile app for generating test cases to ascertain the efficiency and impact of using model-based testing. Test cases for the targeted model of the application under test were generated on a real device. The experimental results indicate that our framework reduced the time required to execute all the generated test cases by 50%. Experimental setup and results are reported herein

Novel Internet of Things based approach toward diabetes prediction using deep learning models

The integration of the Internet of Things with machine learning in different disciplines has benefited from recent technological advancements. In medical IoT, the fusion of these two disciplines can be extremely beneficial as it allows the creation of a receptive and interconnected environment and offers a variety of services to medical professionals and patients. Doctors can make early decisions to save a patient's life when disease forecasts are made early. IoT sensor captures the data from the patients, and machine learning techniques are used to analyze the data and predict the presence of the fatal disease i.e., diabetes. The goal of this research is to make a smart patient's health monitoring system based on machine learning that helps to detect the presence of a chronic disease in patient early and accurately. For the implementation, the diabetic dataset has been used. In order to detect the presence of the fatal disease, six different machine learning techniques are used i.e., Support Vector Machine (SVM), Logistic Regression, Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM). The performance of the proposed model is evaluated by using four evaluation metrics i.e., accuracy, precision, recall, and F1-Score. The RNN outperformed remaining algorithms in terms of accuracy (81%), precision (75%), and F1-Score (65%). However, the recall (56%) for ANN was higher as compared to SVM and logistic regression, CNN, RNN, and LSTM. With the help of this proposed patient's health monitoring system, doctors will be able to diagnose the presence of the disease earlier

Salt Monitoring and Reporting Technology (SMART) for Salt Stockpile Inventory Reporting

Transportation agencies in northern environments spend a considerable amount of their budget on salt for winter operations. For example, in the state of Indiana, there are approximately 120 salt storage facilities distributed throughout the state and the state expends between 30 M USD and 60 M USD on inventory and delivery each year. Historical techniques of relying on visual estimates of salt stockpiles can be inaccurate and unhelpful for managing the supply chain during the winter or planning for re-supply during the summer months. This project report describes the implementation of a portable and permanent LiDAR system that can be used to inventory indoor stockpiles of salt in under 15 min and describes how this system has been deployed over 300 times at over 120 facilities. A quick and easy accuracy test, based on the conservation of volume, was used to provide an independent check on the system performance by repositioning portions of the salt pile. Those tests indicated stockpile volumes can be estimated with an accuracy of 1%–3% of indicated stockpile volumes. The report concludes by discussing how this technology can be permanently installed for systematic monitoring throughout the year

Enhancing Grid-Connected Microgrid Power Dispatch Efficiency through Bio-Inspired Optimization Algorithms

This work tackles the scheduling challenge of microgrids for smart homes, aiming to optimize energy management with both renewable and non-renewable sources. A power control center orchestrates the microgrid, coordinating distributed energy resources (DERs) for peak demand fulfillment and excess energy utilization. We propose a proportional-integral control system for efficient demand response, achieving reduced post-scheduling costs and a peak-to-average ratio. Comparative analysis reveals Ant Colony Optimization outperforms Binary Particle Swarm Optimization in cost and peak-to-average ratio reduction. Simulations explore two scenarios: Case 1 integrates with the main grid for reliability, while Case 2 utilizes solely renewable energy sources. Although Case 2 exhibits superior performance, Case 1’s dependence on the main grid offers greater real-world feasibility. Therefore, Case 1 with optimized DER scheduling emerges as the recommended solution for enhancing microgrid efficiency and ensuring reliable power supply in smart homes

Design and Implementation of CryptoCargo: A Blockchain-Powered Smart Shipping Container for Vaccine Distribution

A large number of shipments are moved everyday domestically and internationally. A considerable number of items such as food, commodities, and pharmaceutical drugs are prone to damage in transit. This can be caused due to various reasons such as improper storage conditions and exposure to air or sunlight. The Internet of Things (IoT) has been used to enhance fundamental shipment tracking by improving transparency and visibility to such transport systems. This paper introduces a blockchain-powered smart container system (CryptoCargo) that monitors the conditions of the shipment and detects any violations that may damage its contents. These violations are recorded on the blockchain via smart contracts, which provides a secure and immutable storage thereby improving its trustworthiness in an inherently trustless environment comprising of multiple stakeholders. We present the design and implementation of CryptoCargo including architectural concerns and implementation details using a test Ethereum blockchain platform and cloud services. Moreover, we present details of thorough evaluation of the system to validate its function as well as to assess its effectiveness with respect to performance efficiency and real-time operation. We have made our smart contract code publicly available on Github

Relationship between the use of drugs and changes in body weight among patients: A systematic review and meta-analysis

Purpose: To investigate the impact of drugs on the body weight of patients.Methods: All the randomized controlled trials that evaluated the impact of medications on the body weight of patients were searched in various databases. Studies quantifying the impact of drugs on body weight when compared to placebo or any other treatment were considered for this review. Moreover, the quantitative synthesis of evidence was also performed by generating the forest plot.Results: A total of 20 studies involving 18,547 participants were included in the current review. Weight gains ranging from 0.5 to 2.6 kg were associated with the use of pioglitazone, espindolol, brexpiprazole, glimepiride and ezogabine while weight loss ranging from 1.1 to 12 kg was linked with the use of betahistine, naltrexone, bupropion, liraglutide, phentermine, topiramate, orlistat, zonisamide, duloxetine, semaglutide, metformin and linagliptin. The quantitative synthesis suggested that drugs can significantly reduce body weight by -0.53 kg (CI 95 % -1.01, -0.04, p < 0.04) when compared to standard treatment.Conclusion: The findings of this review suggest substantial association of drugs and weight change during pharmacotherapy. Pioglitzone, brexpiprazole, espindolol, ezogabine and glimepiride cause weight gain while naltrexone, bupropion, betahistine, topiramate, phentermine, zonisamide, semaglutide, linagliptin, liraglutide, orlistat, duloxetine and metformin were associated with weight loss. Drug-induced changes in body weight might cause serious consequences and should be addressed before initiating treatment