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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]

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.