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Low clearance rail bridges provide vital crossings for freight and passenger trains and are susceptible to frequent strikes from overheight vehicles or equipment. Impact detection systems can help ensure the safety of railroad bridges and their users; such systems streamline monitoring efforts by providing near real‐time strike notifications to rail managers responsible for assessing a bridge after a strike. This paper develops parallel heterogeneous data‐fusion convolutional neural networks (PHD‐CNN) operating on data collected from in‐service rail bridges that improves detection and classification of from overheight vehicles. Convolutional neural networks (CNNs) automatically extract features from multiple data streams from different sensor modalities. The method provides a mechanism to homogenize and fuse disparate data streams for use as inputs to a classifier that distinguishes bridge strikes from passing trains. The study also provides practical implementation guidelines through a framework sensitivity characterization to examine the effects on performance of input data stream type, data set size, and CNN architecture complexity. Optimum networks detect, on average, 95% of bridge strikes with false positive rates less than 2%. [ABSTRACT FROM AUTHOR]

Bridge wear and deterioration occur over time under typical operations. Inspections can be resource intensive, infrequent, and sometimes require bridge closure. Instrumented passing vehicles may be used to record vibration responses and extract damage‐sensitive bridge frequency response characteristics. This paper presents a methodology for extracting bridge frequencies from crowdsourced dynamic response data recorded within passing vehicles. Detected bridge frequency changes and a novel point cloud methodology are used to identify critical damage scenarios and estimate remaining life. The use of a novel crowdsourced Welch methodology allows for spectra averaging that reduces the influence of noise and transient events. Numerical and experimental studies illustrate that bridge frequencies can be accurately estimated and that changes in bridge frequency caused by different damage scenarios can be characterized. Numerical examples for the damage scenario analysis, including estimation of remaining life, are provided for two bridge types and three specific damage scenarios. [ABSTRACT FROM AUTHOR]

Congenital stationary night blindness (CSNB) is an ocular disorder characterized by nyctalopia. An autosomal recessive missense mutation in glutamate metabotropic receptor 6 (GRM6 c.533C>T, p.(Thr178Met)), called CSNB2, was previously identified in one Tennessee Walking Horse and predicted to reduce binding affinity of the neurotransmitter glutamate, impacting the retinal rod ON‐bipolar cell signaling pathway. Thus, the first aim was to identify the allele frequency (AF) of CSNB2 in breeds with reported cases of CSNB and breeds closely related to the Tennessee Walking Horse. The second aim was to perform ocular examinations in multiple breeds to confirm the link between genotype and CSNB phenotype. In evaluating 3518 horses from 14 breeds, the CSNB2 allele was identified in nine previously unreported breeds. The estimated AF was highest in pacing Standardbreds (0.17) and lowest in American Quarter Horses (0.0010). Complete ophthalmic examinations and electroretinograms (ERG) were performed on 19 horses from three breeds, including one CSNB2 homozygote from each breed. All three CSNB2/CSNB2 horses had an electronegative ERG waveform under scotopic light conditions consistent with CSNB. The remaining 16 horses (seven CSNB2/N and nine N/N) had normal scotopic ERG results. All horses had normal photopic ERGs. This study provides additional evidence that GRM6 c.533C>T homozygosity is likely causal to CSNB in Tennessee Walking Horses, Standardbreds, and Missouri Fox Trotting Horses. Genetic testing is recommended for breeds with the CSNB2 allele to limit the production of affected horses. This study represents the largest across‐breed identification of CSNB in the horse and suggests that this disorder is likely underdiagnosed. [ABSTRACT FROM AUTHOR]

Abstracts The carceral state's tenacity draws its strength from diverse political and economic pools, allowing prisons, jails, and other constitutive parts to serve as solutions to very different kinds of [...]

Researchers use a variety of target materials, and sometimes combinations of materials, in their archaeological experiments to examine thrust‐spear or projectile penetration, impact angle, durability, and other issues involving prehistoric hunting weaponry. This variety of target materials is beneficial to archaeological science in several ways, but it may also hinder the comparison of results because many of these target materials do not necessarily share similar physical properties. Here, we assess the penetration properties of four different target materials—store‐bought meat, clay, and two types of gelatin—via static penetration tests of a modern broadhead‐tipped arrow and a stone‐tipped projectile attached to an Instron Universal Materials Tester. Our analyses of load‐deflection curves, peak load, and work energy demonstrate how the four target materials are similar in some ways but different in others, which suggests that researchers may strategically employ one or several depending on the question asked or hypothesis tested. [ABSTRACT FROM AUTHOR]

BRETT STORY'S AWARD-WINNING DOCUMENTARY FILM THE PRISON IN Twelve Landscapes has generated a great deal of critical enthusiasm in a wide variety of venues. One such occasion was the author-meets-critics [...]

Many factors governed the penetration efficacy of prehistoric projectile weaponry. Archaeologists broadly focus their efforts on understanding the effect of stone weapon tips because these specimens are often the only part of the weapon system that survives in the archaeological record. The tip cross‐sectional area (TCSA) and perimeter (TCSP) of stone weapon tips have been shown to correlate with target penetration depth. Here, using results from both static and dynamic penetration testing, we compare TCSA and TCSP against other tip geometry metrics: lateral surface area (LSA) and volume (V). Our analyses broadly show that using a single‐point geometry metric evaluated at multiple locations along the length of the point, or using multiple geometry metrics evaluated at a single location, better predicts required energy than using a single‐point geometry metric evaluated at a single location. Our results also show that in the case where a single geometry metric evaluated at multiple locations is used LSA provided the most robust prediction models. Finally, our results show that for the case where all geometry metrics evaluated at a single location are used the location that provides the most robust prediction model is dependent on how far the point penetrated the target. [ABSTRACT FROM AUTHOR]

Progressive collapse alternate path scenarios are often characterized by restrained beam configurations that provide resistance to structural collapse after column removal. While various methods have been presented to model the behavior of restrained beams, opportunities exist for mechanics-based estimation of the complete static and dynamic load-deflection curves. This paper presents an axially restrained beam (ARB) model that incorporates axial–flexural interaction throughout the entire static load-deflection curve including the elastic material range, the plastic flexural dominant range, the range beyond plastic flexure with increasing catenary effect, and the full catenary range. Energy methods are applied to static load-deflection curves to determine dynamic load-deflection relationships. A parametric study was conducted to assess the accuracy of the ARB method compared to nonlinear, dynamic time history finite-element (FE) analyses. The model compares favorably to results of FE models with average estimates of dynamic deflection within 2.1% for ideal moment connections and 7.7% for configurations combining simple and moment resisting connections. [ABSTRACT FROM AUTHOR]

Night time vehicle detection and tracking using traditional visible light cameras is a challenging task due to the limited visibility. The current state-of-the-art systems treat the vehicles at night time as paired vehicle headlights or taillights, with no ability to determine the contour of the vehicle or its spatial occupancy. Therefore, this paper proposes the first night time framework that combines the vehicle headlights and taillights to localize the vehicle contours. This new framework includes a novel multi-camera vehicle representation that groups and reconstructs vehicle headlights and taillights following mutual geometric distances between different vehicle components. This novel vehicle contour representation successfully removes duplicated vehicle lights and also compensates for the missing vehicle lights in the detection process. Eventually, vehicle headlight alignment and contour adjustment are used to further refine the vehicle contours. The proposed multi-camera system considers typical four-wheel vehicles, e.g., cars and SUVs, in the monitoring and might not be able to handle large trucks, e.g., 18-wheelers. The experiments are conducted on night time traffic videos under various scenarios and the proposed system attains an average of 0.896 in Multiple Object Tracking Accuracy (MOTA) and an average of 0.904 in Jaccard Coefficient (JC), which indicates 19.2% and 15.9% increases over the baseline system. The night time traffic datasets used in this paper are available at https://github.com/JustMeZXX/Intelligent-Night-Time-Traffic-Surveillance [ABSTRACT FROM AUTHOR]

The article discusses aesthetic encounter of research-creation destabilizes normative forms of knowledge-making in institutional settings, opening space for experimentation, risk, and uncertainty in modes of political and social engagement. Topics include how do aesthetic approaches impact how surveillance is understood by researchers; and how might creative practice destabilize surveillance research, producing new possibilities for public encounter.

Objective: To report the long‐term (>1 year) outcome of MicroPulse transscleral diode cyclophotocoagulation (MP‐TSCP) in dogs. Animals Studied: Client owned dogs that underwent MP‐TSCP at a veterinary referral center. Procedure: Retrospective study of 19 dogs treated with MP‐TSCP. Dogs were evaluated at a median follow‐up time of 30.5 months postoperatively. Reported outcomes were intraocular pressure (IOP), retention or loss of vision, number of medications, and additional procedures performed. Variables associated with time to treatment failure were evaluated. Results: Data from 19 dogs (24 eyes) was available. Median laser settings employed were 140 s and 2200 mW at 31.3% duty cycle. Long‐term postoperative IOP was lower than preoperative IOP values (14 mmHg (IQR = 22 mmHg, range: 4–52 mmHg) versus 30 mmHg (IQR = 31 mmHg, range: 8–62 mmHg), respectively). Laser settings >2000 mW were associated with a significantly longer time to reported failure. MP‐TSCP was the sole therapy performed in 16/24 eyes resulting in long‐term IOP control in 13/24 eyes and retention of vision in 6 of the 14 sighted eyes. Repeat MP‐TSCP was performed in 10/24 eyes. Eight of 24 eyes required additional procedures with long‐term IOP control in 4/8 eyes. Conclusions: MicroPulse transscleral diode cyclophotocoagulation can be an effective treatment for refractory glaucoma in select cases. Higher laser settings increased time to reported failure, whereas age, glaucoma type, and preoperative IOP did not affect survival. MP‐TSCP as sole therapy or coupled with additional glaucoma procedures controlled the IOP in 70% of cases. [ABSTRACT FROM AUTHOR]

Tay-Sachs disease (TSD) is a fatal neurodegenerative disease caused by a deficiency of the enzyme β- N -acetylhexosaminidase A (HexA). TSD naturally occurs in Jacob sheep is the only experimental model of TSD. TSD in sheep recapitulates neurologic features similar to juvenile onset and late onset TSD patients. Due to the paucity of human literature on pathology of TSD, a better natural history in the sheep TSD brain, which is on the same order of magnitude as a child's, is necessary for evaluating therapy and characterizing the pathological events that occur. To provide clinicians and researchers with a clearer understanding of longitudinal pathology in patients, we compare spectrum of clinical signs and brain pathology in mildly symptomatic (3-months), moderately symptomatic (6-months), or severely affected TSD sheep (humane endpoint at ~9-months of age). Increased GM2 ganglioside in the CSF of TSD sheep and a TSD specific biomarker on MRS (taurine) correlate with disease severity. Microglial activation and reactive astrocytes were observed globally on histopathology in TSD sheep with a widespread reduction in oligodendrocyte density. Myelination is reduced primarily in the forebrain illustrated by loss of white matter on MRI. GM2 and GM3 ganglioside were increased and distributed differently in various tissues. The study of TSD in the sheep model provides a natural history to shed light on the pathophysiology of TSD, which is of utmost importance due to novel therapeutics being assessed in human patients. [ABSTRACT FROM AUTHOR]

Researchers in the structural health monitoring field have recently focused on using instrumented vehicles, usually equipped with accelerometers, as mobile bridge inspection instruments. The vehicle plays two roles: that of the measurement device and that of the excitation source. Permanent changes to the bridge's stiffness due to damage or wear may manifest as changes in the bridge's fundamental frequencies. In vehicle-based inspection, bridge frequencies are extracted from vehicle responses. These bridge frequencies may be estimated periodically as a continuing bridge condition assessment. This paper establishes closed-form solutions for bridge and vehicle vibration as a vehicle traverses a two-span continuous bridge and provides a method of bridge frequency extraction from vehicle response. Results are validated using finite-element simulations and compared against the literature. Results show that bridge frequencies observed by the vehicle manifest as two peaks shifted below and above the fundamental bridge frequency. These shifts are linear functions and the average of these shifted bridge frequencies approximates the fundamental bridge frequency to within 7% error; this error decreases to 2% for equal spans. [ABSTRACT FROM AUTHOR]

Computer‐vision and deep‐learning techniques are being increasingly applied to inspect, monitor, and assess infrastructure conditions including detection of cracks. Traditional vision‐based methods to detect cracks lack accuracy and generalization to work on complicated infrastructural conditions. This paper presents a novel context‐aware deep convolutional semantic segmentation network to effectively detect cracks in structural infrastructure under various conditions. The proposed method applies a pixel‐wise deep semantic segmentation network to segment the cracks on images with arbitrary sizes without retraining the prediction network. Meanwhile, a context‐aware fusion algorithm that leverages local cross‐state and cross‐space constraints is proposed to fuse the predictions of image patches. This method is evaluated on three datasets: CrackForest Dataset (CFD) and Tomorrows Road Infrastructure Monitoring, Management Dataset (TRIMMD) and a Customized Field Test Dataset (CFTD) and achieves Boundary F1 (BF) score of 0.8234, 0.8252, and 0.7937 under 2‐pixel error tolerance margin in CFD, TRIMMD, and CFTD, respectively. The proposed method advances the state‐of‐the‐art performance of BF score by approximately 2.71% in CFD, 1.47% in TRIMMD, and 4.14% in CFTD. Moreover, the averaged processing time of the proposed system is 0.7 s on a typical desktop with Intel® Quad‐Core™ i7‐7700 CPU@3.6 GHz Processor, 16GB RAM and NVIDIA GeForce GTX 1060 6GB GPU for an image of size 256 × 256 pixels. [ABSTRACT FROM AUTHOR]

The article presents a synopsis of a conversation between book author Pooja Rangan, filmmaker Brett Story, and activist Paige Sarlin during a panel discussion hosted by the documentary arts center UnionDocs in Brooklyn, New York City. Rangan discussed a humanitarian documentary ethic which seeks to render human suffering urgent and immediate. Story presented her perspective on the humanitarian impulse in documentary. Sarlin addressed the politics of the interview form in documentary.

Probabilistic neural networks (PNNs) are artificial neural network algorithms widely used in pattern recognition and classification problems. In the traditional PNN algorithm, the probability density function (PDF) is approximated using the entire training dataset for each class. In some complex datasets, classmate clusters may be located far from each other and these distances between clusters may cause a reduction in the correct class's posterior probability and lead to misclassification. This paper presents a novel PNN algorithm, the competitive probabilistic neural network (CPNN). In the CPNN, a competitive layer ranks kernels for each class and an optimum fraction of kernels are selected to estimate the class-conditional probability. Using a stratified, repeated, random subsampling cross-validation procedure and 9 benchmark classification datasets, CPNN is compared to both traditional PNN and the state of the art (e.g. enhanced probabilistic neural network, EPNN). These datasets are examined with and without noise and the algorithm is evaluated with several ratios of training to testing data. In all datasets (225 simulation categories), performance percentages of both CPNN and EPNN are greater than or equivalent to that of the traditional PNN; in 73% of simulation categories, the CPNN analyses show modest improvement in performance over the state of the art. [ABSTRACT FROM AUTHOR]

Editors Michelle Brown and Eamonn Carrabine interview filmmaker Brett Story about her recent documentary, The Prison in Twelve Landscapes. [ABSTRACT FROM AUTHOR]

The concept of the ‘million dollar block’ refers to the spatially concentrated urban origins of the US prison population, most of whom come from a handful of neighborhoods in the country’s biggest cities. Visualized through a series of maps charting home addresses alongside financial costs of imprisonment, the million dollar block has emerged as a powerful rhetorical umbrella for bipartisan collaboration on prison reform. This article critically tracks the way the million dollar block, as both a cartography and a discursive formation, has travelled politically over the past decade. Finding parallels with the ‘neighborhood effects’ discourse within urban studies, I suggest the million dollar block similarly functions to cast poor and racialized urban spaces primarily in terms of criminogenic risk. I describe how the discursive cartography of the million dollar block, despite its reformist intentions, serves a neoliberal model of prison reform, rationalizing increased carceral state intervention in urban space. [ABSTRACT FROM AUTHOR]

The digital technologies and collaborations Jefferson describes similarly enable bipartisan and liberal buy-in through appeals to consistency and standardization. In other words, even as Jefferson meticulously describes a vast and developing criminal justice technoscience industry, much of the financing and organizational capacity is still underwritten by public resources and the social wage. If the panopticon is the most enduring architectural symbol of the 19 SP th sp -century carceral state, then the control room might be the 21 SP st sp century's. [Extracted from the article]

Impairments that occur in structural systems may degrade performance or prevent a structure from functioning safely. Detecting impairments prior to a structural failure reduces the effect of impairments on the safety of those using a structure. This article describes the computational framework of a Structural Impairment Detection System (SIDS) that processes the digital data streams of electronic sensors attached to critical components of a structure. The resulting system comprises a competitive array of neural networks that can accurately describe the types and severity of likely impairments present in the structure. The competitive array of neural networks is trained to detect patterns in data streams specific to likely target impairments. Data streams generated from ABAQUS models and from electronic sensors are used in training and evaluating the system. As part of the initial development of this SIDS, the counterweight truss of a 100-year old railroad drawbridge was instrumented and evaluated. The resulting data streams were diagnosed autonomously by the SIDS as being similar to one of two operational conditions: unimpaired or a single impairment present in a member embedded within the counterweight. Further investigation of the counterweight truss is underway to verify the accuracy of the assessments. [ABSTRACT FROM AUTHOR]

Long before embarking on the academic life of a geographer, I made films about cities. In the following essay, I reflect on a series of my short films, shot in the magical aesthetic of super8 film, which meditate on themes that continue to preoccupy and interest many urban geographers, in particular, the dialectic between social life and the built landscape of contemporary urban space. A central argument suggested by these cinematic excursions is that the margins of urban space share an immediate homology with the margins of the social self and that experiencing difference in spatial life is a political and social good analogous to regular encounter with difference in the lives of strangers. Such experiences of difference, these films suggest, are under threat in the neoliberal city in which gentrification, privatisation, labour deregulation and urban policing increasingly homogenise and politically neutralise the social landscape. The films can be viewed athttp://www.brettstory.com/category/films-video/ [ABSTRACT FROM AUTHOR]

The article explores the relationship between mass incarceration, health inequality and climate change. Noted is how prisons cause injury to humans and habitats, and to how climate change will largely impact the working class and poor because of geography and infrastructure. The author also discusses the notions of eco-gentrification and eco-apartheid, as well as how prison populations have endured inhumane conditions such as chest-deep flooding and loss of electricity, after disasters like Hurricane Katrina.