Sustainable Ambient Air Quality Monitoring System

Deterioration of air quality is a growing concern in the world. Air pollution causes serious health problems and also can sometimes result in death. In order to assess air quality, long term and continuous monitoring of pollutant levels in ambient air are needed, such monitoring is often expensive, cumbersome, and resource intensive and so the monitoring programs often fail to succeed. This research focused on designing an ambient air monitoring system by integrating (1) low-cost sensor with a battery, (2) repurposed materials to fabricate all-weather housing for air monitors, and (3) electronics needed to download the data to an on-site secure digital (SD) card, and to push the data wirelessly to the server. This monitoring system was tested at the selected locations in Harvey and Marrero Wastewater treatment plants (WWTPs) by monitoring hydrogen sulfide (H2S) levels. Preliminary analysis was done for few days and also, the results were analyzed

Identifying and Explaining Safety-critical Scenarios for Autonomous Vehicles via Key Features

Ensuring the safety of autonomous vehicles (AVs) is of utmost importance and testing them in simulated environments is a safer option than conducting in-field operational tests. However, generating an exhaustive test suite to identify critical test scenarios is computationally expensive as the representation of each test is complex and contains various dynamic and static features, such as the AV under test, road participants (vehicles, pedestrians, and static obstacles), environmental factors (weather and light), and the road's structural features (lanes, turns, road speed, etc.). In this paper, we present a systematic technique that uses Instance Space Analysis (ISA) to identify the significant features of test scenarios that affect their ability to reveal the unsafe behaviour of AVs. ISA identifies the features that best differentiate safety-critical scenarios from normal driving and visualises the impact of these features on test scenario outcomes (safe/unsafe) in 2D. This visualization helps to identify untested regions of the instance space and provides an indicator of the quality of the test suite in terms of the percentage of feature space covered by testing. To test the predictive ability of the identified features, we train five Machine Learning classifiers to classify test scenarios as safe or unsafe. The high precision, recall, and F1 scores indicate that our proposed approach is effective in predicting the outcome of a test scenario without executing it and can be used for test generation, selection, and prioritization.Comment: 28 pages, 6 figure

Secondary consumer socialisation of adults

This study investigates whether adult consumers' general predispositions towards consumption change as a result of social interaction with their adolescent children. To illustrate the concept of secondary consumer socialisation of parents by children 'Computer Related' and 'Small High-Tech' products were examined; assuming that children are likely to be more interested and better informed than their parents about these categories. The study used dyadic data analysis to investigate relationships and to assess the level of dyadic agreement about how adolescents influence their parents' consumption patterns. The findings suggest that both parents and children agree to a high level of influence and interaction about these product categories. However, the parent's interest and knowledge remains low for both categories compared with their children

Elimination of deck joints using a corrosion resistant FRP approach

The research presented herein describes the development of durable link slabs for jointless bridge decks based on using FRP grid for reinforcement. Specifically, the ductility of the FRP material was utilized to accommodate bridge deck deformations imposed by girder deflection, temperature variations, and concrete shrinkage. It would also provide a solution to a number of deterioration problems associated with bridge deck joints. The design concept of the link slabs was then examined to form the basis of design for FRP grid link slabs. Improved design of FRP grid link slab/concrete deck slab interface was confirmed in the numerical analysis. The mechanical properties between the FRP grid and concrete were evaluated. The behavior of the link slab was investigated and confirmed for durability. The results indicated that the technique would allow simultaneous achievement of structural need (lower flexural stiffness of the link slab approaching the behavior of a hinge) and durability need of the link slab. Also, the development length results confirm that the bond between the FRP grid and the concrete was highly improved. The overall investigation supports the contention that durable jointless concrete bridge decks may be designed and constructed with FRP grid link slabs. It is recommended that the link slab technique be used during new construction of the bridge decks and in repair and retrofit of the bridge decks

Towards Reliable AI: Adequacy Metrics for Ensuring the Quality of System-level Testing of Autonomous Vehicles

AI-powered systems have gained widespread popularity in various domains, including Autonomous Vehicles (AVs). However, ensuring their reliability and safety is challenging due to their complex nature. Conventional test adequacy metrics, designed to evaluate the effectiveness of traditional software testing, are often insufficient or impractical for these systems. White-box metrics, which are specifically designed for these systems, leverage neuron coverage information. These coverage metrics necessitate access to the underlying AI model and training data, which may not always be available. Furthermore, the existing adequacy metrics exhibit weak correlations with the ability to detect faults in the generated test suite, creating a gap that we aim to bridge in this study. In this paper, we introduce a set of black-box test adequacy metrics called "Test suite Instance Space Adequacy" (TISA) metrics, which can be used to gauge the effectiveness of a test suite. The TISA metrics offer a way to assess both the diversity and coverage of the test suite and the range of bugs detected during testing. Additionally, we introduce a framework that permits testers to visualise the diversity and coverage of the test suite in a two-dimensional space, facilitating the identification of areas that require improvement. We evaluate the efficacy of the TISA metrics by examining their correlation with the number of bugs detected in system-level simulation testing of AVs. A strong correlation, coupled with the short computation time, indicates their effectiveness and efficiency in estimating the adequacy of testing AVs.Comment: 12 pages, 7 figure

SYNTHESIS, CHARACTERIZATION, AND ANTHELMINTIC ACTIVITY OF NOVEL BENZOTHIAZOLE DERIVATIVES CONTAINING INDOLE MOIETIES

Objective: The objective of this study was to synthesize and evaluate the anthelmintic activity (AA) of novel benzothiazole derivatives containing indole moieties (BDIM). Methods: The present works which involve the substituted isatin Schiff bases undergo acetylating and reacting with 2-aminobenzothiazole to give novel BDIM. Results: All the newly synthesized molecules (5a-5o) were characterized by Fourier-transform infrared spectroscopy, H_nuclear magnetic resonance, and mass spectral analysis along with physical data. The biological potentials of the newly synthesized compounds are evaluated for their AA using an Indian earthworm (Pheretima posthuma), and albendazole was used as standard drug. Conclusion: The synthesized compound 5f, 5n, and 5o showed good AA, whereas others exhibited significant activities

G protein-coupled receptors: A target for microbial metabolites and a mechanistic link to microbiome-immune-brain interactions

Human-microorganism interactions play a key role in human health. However, the underlying molecular mechanisms remain poorly understood. Small-molecules that offer a functional readout of microbe-microbe-human relationship are of great interest for deeper understanding of the inter-kingdom crosstalk at the molecular level. Recent studies have demonstrated that small-molecules from gut microbiota act as ligands for specific human G protein-coupled receptors (GPCRs) and modulate a range of human physiological functions, offering a mechanistic insight into the microbe-human interaction. To this end, we focused on analysis of bacterial metabolites that are currently recognized to bind to GPCRs and are found to activate the known downstream signaling pathways. We further mapped the distribution of these molecules across the public mass spectrometry-based metabolomics data, to identify the presence of these molecules across body sites and their association with health status. By combining this with RNA-Seq expression and spatial localization of GPCRs from a public human protein atlas database, we inferred the most predominant GPCR-mediated microbial metabolite-human cell interactions regulating gut-immune-brain axis. Furthermore, by evaluating the intestinal absorption properties and blood-brain barrier permeability of the small-molecules we elucidated their molecular interactions with specific human cell receptors, particularly expressed on human intestinal epithelial cells, immune cells and the nervous system that are shown to hold much promise for clinical translational potential. Furthermore, we provide an overview of an open-source resource for simultaneous interrogation of bioactive molecules across the druggable human GPCRome, a useful framework for integration of microbiome and metabolite cataloging with mechanistic studies for an improved understanding of gut microbiota-immune-brain molecular interactions and their potential therapeutic use