Is a Massive Tau Neutrino Just What Cold Dark Matter Needs?

The cold dark matter (CDM) scenario for structure formation in the Universe is very attractive and has many successes; however, when its spectrum of density perturbations is normalized to the COBE anisotropy measurement the level of inhomogeneity predicted on small scales is too large. This can be remedied by a tau neutrino of mass 1\MeV - 10\MeV and lifetime $0.1\sec - 100\sec$ whose decay products include electron neutrinos because it allows the total energy density in relativistic particles to be doubled without interfering with nucleosynthesis. The anisotropies predicted on the degree scale for ``$\tau$CDM'' are larger than standard CDM. Experiments at $e^\pm$ colliders may be able to probe such a mass range.Comment: 9 pages LaTeX plus 4 figures (available upon request) FERMILAB--Pub--94/026-

Dynamic plantar loading index detects altered foot function in individuals with rheumatoid arthritis but not changes due to orthotic use

Background Altered foot function is common in individuals with rheumatoid arthritis. Plantar pressure distributions during gait are regularly assessed in this patient group; however, the association between frequently reported magnitude-based pressure variables and clinical outcomes has not been clearly established. Recently, a novel approach to the analysis of plantar pressure distributions throughout stance phase, the dynamic plantar loading index, has been proposed. This study aimed to assess the utility of this index for measuring foot function in individuals with rheumatoid arthritis.Methods Barefoot plantar pressures during gait were measured in 63 patients with rheumatoid arthritis and 51 matched controls. Additionally, 15 individuals with rheumatoid arthritis had in-shoe plantar pressures measured whilst walking in standardized footwear for two conditions: shoes-only; and shoes with prescribed custom foot orthoses. The dynamic plantar loading index was determined for all participants and conditions. Patient and control groups were compared for significant differences as were the shod and orthosis conditions.Findings The patient group was found to have a mean index of 0.19, significantly lower than the control group's index of 0.32 (p > 0.001, 95% CI [0.054, 0.197]). No significant differences were found between the shoe-only and shoe plus orthosis conditions. The loading index was found to correlate with clinical measures of structural deformity.Interpretation The dynamic plantar loading index may be a useful tool for researchers and clinicians looking to objectively assess dynamic foot function in patients with rheumatoid arthritis; however, it may be unresponsive to changes caused by orthotic interventions in this patient group.</p

Driver Drowsiness Detection Using Gray Wolf Optimizer Based on Face and Eye Tracking

It is critical today to provide safe and collision-free transport. As a result, identifying the driver’s drowsiness before their capacity to drive is jeopardized. An automated hybrid drowsiness classification method that incorporates the artificial neural network (ANN) and the gray wolf optimizer (GWO) is presented to discriminate human drowsiness and fatigue for this aim. The proposed method is evaluated in alert and sleep-deprived settings on the driver drowsiness detection of video dataset from the National Tsing Hua University Computer Vision Lab. The video was subjected to various video and image processing techniques to detect the drivers’ eye condition. Four features of the eye were extracted to determine the condition of drowsiness, the percentage of eyelid closure (PERCLOS), blink frequency, maximum closure duration of the eyes, and eye aspect ratio (ARE). These parameters were then integrated into an ANN and combined with the proposed method (gray wolf optimizer with ANN [GWOANN]) for drowsiness classification. The accuracy of these models was calculated, and the results demonstrate that the proposed method is the best. An Adadelta optimizer with 3 and 4 hidden layer networks of (13, 9, 7, and 5) and (200, 150, 100, 50, and 25) neurons was utilized. The GWOANN technique had 91.18% and 97.06% accuracy, whereas the ANN model had 82.35% and 86.76%

Driver Drowsiness Detection Using Gray Wolf Optimizer Based on Voice Recognition

Globally, drowsiness detection prevents accidents. Blood biochemicals, brain impulses, etc., can measure tiredness. However, due to user discomfort, these approaches are challenging to implement. This article describes a voice-based drowsiness detection system and shows how to detect driver fatigue before it hampers driving. A neural network and Gray Wolf Optimizer are used to classify sleepiness automatically. The recommended approach is evaluated in alert and sleep-deprived states on the driver tiredness detection voice real dataset. The approach used in speech recognition is mel-frequency cepstral coefficients (MFCCs) and linear prediction coefficients (LPCs). The SVM algorithm has the lowest accuracy (71.8%) compared to the typical neural network. GWOANN employs 13-9-7-5 and 30-20-13-7 neurons in hidden layers, where the GWOANN technique had 86.96% and 90.05% accuracy, respectively, whereas the ANN model achieved 82.50% and 85.27% accuracy, respectively