Operational Effects of Auto-Utility Trailer Combinations on Rural Highways in Kentucky

An analysis of accident records indicated that A-UT combinations are involved in a disproportionately high number of traffic mishaps. Locations which have a history of accidents involving A-UT vehicles indicated that differential crosswinds and unanticipated driving maneuvers contribute to driver loss of control. A-UT combinations contributed to the fatigue loss in pavement life approximately 50 percent as much as single-unit, two-axle, six-tire trucks (per vehicle). In general, this vehicle type constituted approximately three percent of the total traffic stream. Analysis of speed distributions indicated an equivalency factor for A-UT combinations equal to that for trucks for similar roadway types and topographical conditions

Auto-Utility Trailer Combinations on Rural Highways in Kentucky

An analysis of accident records indicated that auto-utility trailer (A-UT) combinations are involved in a disproportionately high number of traffic mishaps. Locations which have a history of accidents involving A-UT vehicles indicated that differential crosswinds and unanticipated driving maneuvers contribute to the driver\u27s loss of control. A-UT combinations contributed to the fatigue loss in pavement life approximately 50 percent as much as single-unit, two-axle, six-tire trucks (per vehicle). In general, this vehicle type constituted approximately three percent of the total traffic stream. Analysis of speed distributions indicated an equivalency factor for A-UT combinations equal to that for trucks for similar roadway types and topographical conditions

Lifetime suicidal-related behaviour among patients in treatment forsubstance use disorder: a cross-sectional study

Suicidal-related behaviours are an important concern in individuals who present with substance use disorders (SUDs). The distinction among the specific characteristics of the different patients might help to improve prevention strategies. We describe and compare the sociodemographic characteristics, severity of addiction, and psychopathology of the participants depending on the severity of their lifetime suicidal behaviour. In addition, we examine whether the number of suicide attempts can be estimated based on the variables that differentiate the groups. A sample of 318 men and 86 women who sought treatment for addiction were assessed. The sample was divided into: no ideation or attempts, suicidal ideation, one suicide attempt, and two or more suicide attempts. The group with two or more suicide attempts exhibited a greater severity in the addiction profile. The group with one suicide attempt presented a higher psychopathological symptomatology at the time of the assessment. The severity of the Psychiatric area was related to the group with two or more attempts and to the number of suicide attempts. The presence of any number of attempts is associated with greater severity of addiction. Providing specific intervention strategies for SUD patients depending on their suicidal behaviours is promising for clinical application.The first author was supported by a grant from the European UnionNext Generation EU by Ministerio de Universidades (Gobierno de España) and Universidad del País Vasco (UPV/EHU). The second author was supported by a grant (589/2021) from Universidad Pública de Navarra and Fundación Bancaria Caja Navarra. Open access funding provided by Universidad Pública de Navarr

A Scheme for Solving the Plane–Plane Challenge in Force Measurements at the Nanoscale

Non-contact interaction between two parallel flat surfaces is a central paradigm in sciences. This situation is the starting point for a wealth of different models: the capacitor description in electrostatics, hydrodynamic flow, thermal exchange, the Casimir force, direct contact study, third body confinement such as liquids or films of soft condensed matter. The control of parallelism is so demanding that no versatile single force machine in this geometry has been proposed so far. Using a combination of nanopositioning based on inertial motors, of microcrystal shaping with a focused-ion beam (FIB) and of accurate in situ and real-time control of surface parallelism with X-ray diffraction, we propose here a “gedanken” surface-force machine that should enable one to measure interactions between movable surfaces separated by gaps in the micrometer and nanometer ranges

Using mid-infrared spectroscopy and supervised machine-learning to identify vertebrate blood meals in the malaria vector, Anopheles arabiensis

Background: The propensity of diferent Anopheles mosquitoes to bite humans instead of other vertebrates infuences their capacity to transmit pathogens to humans. Unfortunately, determining proportions of mosquitoes that have fed on humans, i.e. Human Blood Index (HBI), currently requires expensive and time-consuming laboratory procedures involving enzyme-linked immunosorbent assays (ELISA) or polymerase chain reactions (PCR). Here, midinfrared (MIR) spectroscopy and supervised machine learning are used to accurately distinguish between vertebrate blood meals in guts of malaria mosquitoes, without any molecular techniques. Methods: Laboratory-reared Anopheles arabiensis females were fed on humans, chickens, goats or bovines, then held for 6 to 8 h, after which they were killed and preserved in silica. The sample size was 2000 mosquitoes (500 per host species). Five individuals of each host species were enrolled to ensure genotype variability, and 100 mosquitoes fed on each. Dried mosquito abdomens were individually scanned using attenuated total refection-Fourier transform infrared (ATR-FTIR) spectrometer to obtain high-resolution MIR spectra (4000 cm−1 to 400 cm−1 ). The spectral data were cleaned to compensate atmospheric water and CO2 interference bands using Bruker-OPUS software, then transferred to Python™ for supervised machine-learning to predict host species. Seven classifcation algorithms were trained using 90% of the spectra through several combinations of 75–25% data splits. The best performing model was used to predict identities of the remaining 10% validation spectra, which had not been used for model training or testing. Results: The logistic regression (LR) model achieved the highest accuracy, correctly predicting true vertebrate blood meal sources with overall accuracy of 98.4%. The model correctly identifed 96% goat blood meals, 97% of bovine blood meals, 100% of chicken blood meals and 100% of human blood meals. Three percent of bovine blood meals were misclassifed as goat, and 2% of goat blood meals misclassifed as human. Conclusion: Mid-infrared spectroscopy coupled with supervised machine learning can accurately identify multiple vertebrate blood meals in malaria vectors, thus potentially enabling rapid assessment of mosquito blood-feeding histories and vectorial capacities. The technique is cost-efective, fast, simple, and requires no reagents other than desiccants. However, scaling it up will require field validation of the findings and boosting relevant technical capacity in affected countries