Behavioral Comorbidities and Drug Treatments in a Zebrafish scn1lab Model of Dravet Syndrome.

Loss-of-function mutations in SCN1A cause Dravet syndrome (DS), a catastrophic childhood epilepsy in which patients experience comorbid behavioral conditions, including movement disorders, sleep abnormalities, anxiety, and intellectual disability. To study the functional consequences of voltage-gated sodium channel mutations, we use zebrafish with a loss-of-function mutation in scn1lab, a zebrafish homolog of human SCN1A. Homozygous scn1labs552/s552 mutants exhibit early-life seizures, metabolic deficits, and early death. Here, we developed in vivo assays using scn1labs552 mutants between 3 and 6 d postfertilization (dpf). To evaluate sleep disturbances, we monitored larvae for 24 h with locomotion tracking software. Locomotor activity during dark (night phase) was significantly higher in mutants than in controls. Among anticonvulsant drugs, clemizole and diazepam, but not trazodone or valproic acid, decreased distance moved at night for scn1labs552 mutant larvae. To monitor exploratory behavior in an open field, we tracked larvae in a novel arena. Mutant larvae exhibited impaired exploratory behavior, with increased time spent near the edge of the arena and decreased mobility, suggesting greater anxiety. Both clemizole and diazepam, but not trazodone or valproic acid, decreased distance moved and increased time spent in the center of the arena. Counting inhibitory neurons in vivo revealed no differences between scn1labs552 mutants and siblings. Taken together, our results demonstrate conserved features of sleep, anxiety, and movement disorders in scn1lab mutant zebrafish, and provide evidence that a zebrafish model allows effective tests of treatments for behavioral comorbidities associated with DS

Automated License Plate Recognition Systems

Automated license plate recognition systems make use of machines learning coupled with traditional algorithmic programming to create software capable of identifying and transcribing vehicles’ license plates. From this point, automated license plate recognition systems can be capable of performing a variety of functions, including billing an account or querying the plate number against a database to identify vehicles of concern. These capabilities allow for an efficient method of autonomous vehicle identification, although the unmanned nature of these systems raises concerns over the possibility of their use for surveillance, be it against an individual or group. This thesis will explore the fundamentals behind automated license plate recognition systems, the state of their current employment, currently existing limitations, and concerns raised over the use of such systems and relevant legal examples. Furthermore, this thesis will demonstrate the training of a machine learning model capable of identifying license plates, followed by a brief examination of performance limitations encountered

Car make and model recognition under limited lighting conditions at night

Car make and model recognition (CMMR) has become an important part of intelligent transport systems. Information provided by CMMR can be utilized when license plate numbers cannot be identified or fake number plates are used. CMMR can also be used when a certain model of a vehicle is required to be automatically identified by cameras. The majority of existing CMMR methods are designed to be used only in daytime when most of the car features can be easily seen. Few methods have been developed to cope with limited lighting conditions at night where many vehicle features cannot be detected. The aim of this work was to identify car make and model at night by using available rear view features. This paper presents a one-class classifier ensemble designed to identify a particular car model of interest from other models. The combination of salient geographical and shape features of taillights and license plates from the rear view is extracted and used in the recognition process. The majority vote from support vector machine, decision tree, and k-nearest neighbors is applied to verify a target model in the classification process. The experiments on 421 car makes and models captured under limited lighting conditions at night show the classification accuracy rate at about 93 %

Automated license plate recognition: a survey on methods and techniques

With the explosive growth in the number of vehicles in use, automated license plate recognition (ALPR) systems are required for a wide range of tasks such as law enforcement, surveillance, and toll booth operations. The operational specifications of these systems are diverse due to the differences in the intended application. For instance, they may need to run on handheld devices or cloud servers, or operate in low light and adverse weather conditions. In order to meet these requirements, a variety of techniques have been developed for license plate recognition. Even though there has been a notable improvement in the current ALPR methods, there is a requirement to be filled in ALPR techniques for a complex environment. Thus, many approaches are sensitive to the changes in illumination and operate mostly in daylight. This study explores the methods and techniques used in ALPR in recent literature. We present a critical and constructive analysis of related studies in the field of ALPR and identify the open challenge faced by researchers and developers. Further, we provide future research directions and recommendations to optimize the current solutions to work under extreme conditions

Automated license plate recognition for resource-constrained environments

The incorporation of deep-learning techniques in embedded systems has enhanced the capabilities of edge computing to a great extent. However, most of these solutions rely on high-end hardware and often require a high processing capacity, which cannot be achieved with resource-constrained edge computing. This study presents a novel approach and a proof of concept for a hardware-efficient automated license plate recognition system for a constrained environment with limited resources. The proposed solution is purely implemented for low-resource edge devices and performed well for extreme illumination changes such as day and nighttime. The generalisability of the proposed models has been achieved using a novel set of neural networks for different hardware configurations based on the computational capabilities and low cost. The accuracy, energy efficiency, communication, and computational latency of the proposed models are validated using different license plate datasets in the daytime and nighttime and in real time. Meanwhile, the results obtained from the proposed study have shown competitive performance to the state-of-the-art server-grade hardware solutions as well

Evaluating the use of steady burn warning lights on drums for workzone safety

Abstract EVALUATING THE USE OF STEADY BURN WARNING LIGHTS ON DRUMS FOR WORK ZONE SAFETY by PRASAD LAKSHMI VARA NANNAPANENI May 2011 Advisor: Dr. Timothy Gates Major: Civil Engineering Degree: Doctor of Philosophy Roadway maintenance and repair has become increasingly commonplace in the United States over the past several decades as our roadway infrastructure has continued to age and deteriorate. Maintenance and repair work on an existing roadway often presents the challenge of maintaining traffic on the existing roadway while work is being performed, thereby necessitating the use of what is commonly referred to as a roadway work zone . One of the most important components of traffic control in a work zone is delineation of the edge of the traveled way, which assists drivers with tasks such as: lane selection; lateral positioning within a lane; and speed control. Delineation of the edge of the traveled way is commonly provided by a series of portable devices, such as drums, cones, vertical panels, or barricades. The type and duration of the work being performed often requires that these channelizing devices remain in place at all times. Maintaining traffic through nighttime work zones poses increased risks for drivers and roadway workers due to the lack of ambient light. To help overcome nighttime visibility issues, the 2009 Manual on Uniform Traffic Control Devices (MUTCD) requires work zone traffic control devices to be retroreflective or internally illuminated. To help supplement retroreflectivity, Section 6F.81 of the 2009 MUTCD allows for the use of auxiliary steady burn warning lights (SBWL) on work zone channelizing devices. Until recently, plastic drums with steady burn warning lights had been the primary channelizing device utilized in work zones throughout the State of Michigan for several years. However, the use of sheeting materials with improved retroreflectivity, including high intensity and microprismatic (i.e., prismatic) materials, has prompted investigation into the value and effectiveness provided by the steady burn warning lights. Furthermore, although previous research has explored the effectiveness of steady burn warning lights on drums both in Michigan and elsewhere, these efforts included a relatively limited number of work zone sites and/or focused on controlled human factors experiments. As a result, research was undertaken to explore the impacts associated with the use of steady burn warning lights on channelizing drums considering a variety of work zone scenarios utilized in Michigan. The primary goal of this research was to evaluate the safety impacts associated with the use of steady burn warning lights on drums in roadway work zones in Michigan. The following research objectives were addressed in this study: 1. Determine the state-of-the-art of work zone channelization through a comprehensive literature review. 2. Determine the state-of-the-practice regarding the use of steady burn warning lights by roadway agencies throughout the United States. 3. Assess the crash experiences of states with respect to the work zone steady burn warning light policy or practice. 4. Evaluate the impacts that steady burn warning lights on channelizing drums have on work zone crash occurrence in Michigan. 5. Evaluate the driver behavioral impacts associated with the use of steady burn warning lights on channelizing drums in Michigan work zones. 6. Determine the degree by which steady burn warning lights affect the overall brightness of work zone drums in Michigan. 7. Assess the overall impacts of steady burn warning lights on work zone safety. A comprehensive research methodology was developed to address these objectives. The initial tasks involved a comprehensive review of the current state-of-the-art and a state DOT survey related to the use of drums or other channelizing devices in roadway work zones, both with and without the presence of steady burn warning lights. The next tasks involved a comparison of work zone crash trends, both among states with varying policies on the use of steady burn warning lights, as well as a detailed investigation of crash data for work zones within the State of Michigan. To further supplement the crash data, a series of field studies were performed at 36 Michigan work zones to provide a more in-depth evaluation of differences in driver behavior and performance with respect to the use of steady burn warning lights. In addition to these field studies, a series of luminance tests were also conducted to assess the relative brightness levels provided by drums with and without warning lights. The luminance tests were performed both in the field and in a controlled environment to gauge the impacts of steady burn warning lights on drum visibility. Established sampling procedures were utilized to determine the target sample sizes necessary to assess statistical inference on the measures of effectiveness (MOEs). The data were collected for each study component under a variety of representative field conditions, which included different types of roadways, work zone configuration, levels of ambient lighting, roadway geometry, and other factors. Each of the MOEs were analyzed using appropriate statistical techniques to determine the impacts of steady burn warning lights and the impacts of other factors. The results showed that the presence of steady burn warning lights on work zone channelizing drums increased the occurrence of risky driver behavior, as evidenced by a higher proportion of drivers traveling too close to the drums, more frequent steering reversals, and higher vehicular speeds. These findings were further substantiated by the observance of a greater proportion of damaged drums at work zone locations with steady burn warning lights. Steady burn warning lights were not found to provide substantial increases to the luminance of the drums either in the field or in a controlled environment. It was determined that the use of microprismatic sheeting materials provide considerably greater luminance increases for the drums compared to the addition of a steady burn warning light to the drum. The state DOT survey revealed that only approximately one-third of the 42 responding state agencies utilize steady burn warning lights on channelizing devices in work zones and only one-tenth of the responding agencies utilize them on a frequent basis. The majority of agencies that use steady burn warning lights do so on an infrequent basis, typically for specific types of applications, such as at spot hazards, tapers, lane shifts, and crossovers. The investigation of nationwide work zone crash statistics revealed only slight differences between the rates of work zone crashes for the various steady burn warning light usage practices. The states that frequently use lights on drums exhibited a slightly higher aggregate work zone crash rate, while the states that infrequently use lights on drums had the lowest aggregate crash rate. No discernable differences were observed between any of the three groups of states when examining work zone crashes as a proportion of total crashes. A detailed review of Michigan work zone crash statistics revealed that a higher proportion of work zone crashes tended to occur during nighttime conditions at locations with steady burn warning lights compared to locations without steady burn warning lights. Deeper investigation showed that among those crashes occurring in the presence of drums, the proportion of the crashes that may have been affected by the drums was indistinguishable between the two samples. Based on a synthesis of all results, steady burn warning lights demonstrate no substantive value to nighttime brightness, driver behavior, or crash prevention when used on channelizing drums in work zones. Thus, it was concluded that steady burn warning lights demonstrate no additional safety benefit when used on channelizing drums in work zones. Furthermore, steady burn warning lights may actually contribute to a greater crash risk due to the increase in risky driver behavior that was observed when steady burn warning lights were present. Drums with high intensity sheeting that is in good condition will provide adequate nighttime brightness for work zone channelization regardless of whether a steady burn warning light is attached or not. Therefore, it is recommended that the use of steady burn warning lights on work zone drums be discontinued. If additional nighttime brightness of the channelizing devices is desired, the use of microprismatic sheeting on the drums provides far greater increases in brightness than the addition of a steady burn warning light

An overview of sedimentary volcanism on Mars

Extensive fields of sub-kilometre-to kilometre-scale mounds, cones, domes, shields, and flow-like edifices cover large parts of the martian lowlands. These features have been compared to structures on Earth produced by sedimentary volcanism &ndash; a process that involves subsurface sediment/fluid mobilization and commonly releases methane to the atmosphere. It was proposed that such process might help to explain the presence of methane in martian atmosphere and also may have additionally produced habitable, subsurface settings of potential astrobiological relevance. However, it remains unclear whether sedimentary volcanism on Earth and Mars share genetic similarities; hence whether methane, or other gases were released on Mars during this process. The aim of this review is to summarize the current knowledge about mud-volcano-like structures on Mars, address the critical aspects of this process, identify key open questions, and point to areas where further research is needed to understand this phenomenon and its importance for the red planet&rsquo;s geological evolution. We show here that after several decades of exploration, the amount of evidence supporting a martian sedimentary volcanism scenario has increased significantly, but as critical ground truth is still lacking, alternative explanations cannot always be ruled out. We also highlight that the lower gravity and temperatures on Mars compared to Earth control the dynamics of clastic eruptions as well as surface emplacement and resulting morphologies of erupted material. This implies that shapes and triggering mechanisms of mud-volcano-like structures may be different from those observed on Earth. Therefore comparative studies should be done with caution. To provide a better understanding of the significance of these abundant features on Mars, we argue for follow-up studies targeting putative sedimentary volcanic features identified on the planet&rsquo;s surface and, if possible, for in situ investigations by landed missions such as that currently in progress by the Zhurong rover.</p

Vehicle Type Recognition Combining Global and Local Features via Two-Stage Classification

This study proposes a new vehicle type recognition method that combines global and local features via a two-stage classification. To extract the continuous and complete global feature, an improved Canny edge detection algorithm with smooth filtering and non-maxima suppression abilities is proposed. To extract the local feature from four partitioned key patches, a set of Gabor wavelet kernels with five scales and eight orientations is introduced. Different from the single-stage classification, where all features are incorporated into one classifier simultaneously, the proposed two-stage classification strategy leverages two types of features and classifiers. In the first stage, the preliminary recognition of large vehicle or small vehicle is conducted based on the global feature via a k-nearest neighbor probability classifier. Based on the preliminary result, the specific recognition of bus, truck, van, or sedan is achieved based on the local feature via a discriminative sparse representation based classifier. We experiment with the proposed method on the public and established datasets involving various challenging cases, such as partial occlusion, poor illumination, and scale variation. Experimental results show that the proposed method outperforms existing state-of-the-art methods