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This paper reports results from a search for nucleon decay through invisible modes, where no visible energy is directly deposited during the decay itself, during the initial water phase of SNOþ. However, such decays within the oxygen nucleus would produce an excited daughter that would subsequently deexcite, often emitting detectable gamma rays. A search for such gamma rays yields limits of 2.5 × 10 29 y at 90% Bayesian credibility level (with a prior uniform in rate) for the partial lifetime of the neutron, and 3.6 × 10 29 y for the partial lifetime of the proton, the latter a 70% improvement on the previous limit from SNO. We also present partial lifetime limits for invisible dinucleon modes of 1.3 × 10 28 y for nn, 2.6 × 10 28 y for pn and 4.7 × 10 28 y for pp, an improvement over existing limits by close to 3 orders of magnitude for the latter two.

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A measurement of the 8 B solar neutrino flux has been made using a 69.2 kt-day dataset acquired with the SNOþ detector during its water commissioning phase. At energies above 6 MeV the dataset is an extremely pure sample of solar neutrino elastic scattering events, owing primarily to the detector's deep location, allowing an accurate measurement with relatively little exposure. In that energy region the best fit background rate is 0.25 þ0.09 −0.07 events=kt-day, significantly lower than the measured solar neutrino event rate in that energy range, which is 1.03 þ0.13 −0.12 events=kt-day. Also using data below this threshold, down to 5 MeV, fits of the solar neutrino event direction yielded an observed flux of 2.53 þ0.31 −0.28 ðstatÞ þ0.13 −0.10 ðsystÞ × 10 6 cm −2 s −1 , assuming no neutrino oscillations. This rate is consistent with matter enhanced neutrino oscillations and measurements from other experiments.

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Site-specific weed management and selective application of herbicides as eco-friendly techniques are still challenging tasks to perform, especially for densely cultivated crops, such as rice. This study is aimed at developing a stereo vision system for distinguishing between rice plants and weeds and further discriminating two types of weeds in a rice field by using artificial neural networks (ANNs) and two metaheuristic algorithms. For this purpose, stereo videos were recorded across the rice field and different channels were extracted and decomposed into the constituent frames. Next, upon pre-processing and segmentation of the frames, green plants were extracted out of the background. For accurate discrimination of the rice and weeds, a total of 302 color, shape, and texture features were identified. Two metaheuristic algorithms, namely particle swarm optimization (PSO) and the bee algorithm (BA), were used to optimize the neural network for selecting the most effective features and classifying different types of weeds, respectively. Comparing the proposed classification method with the K-nearest neighbors (KNN) classifier, it was found that the proposed ANN-BA classifier reached accuracies of 88.74% and 87.96% for right and left channels, respectively, over the test set. Taking into account either the arithmetic or the geometric means as the basis, the accuracies were increased up to 92.02% and 90.7%, respectively, over the test set. On the other hand, the KNN suffered from more cases of misclassification, as compared to the proposed ANN-BA classifier, generating an overall accuracy of 76.62% and 85.59% for the classification of the right and left channel data, respectively, and 85.84% and 84.07% for the arithmetic and geometric mean values, respectively.

In this study, artificial neural networks (ANNs) were used to predict the draft force of a rigid tine chisel cultivator. The factorial experiment based on the randomized complete block design (RCBD) was used to obtain the required data and to determine the factors affecting the draft force. The draft force of the chisel cultivator was measured using a three-point hitch dynamometer and data were collected using a DT800 datalogger. A recurrent back-propagation multilayer network was selected to predict the draft force of the cultivator. The gradient descent algorithm with momentum, Levenberg–Marquardt algorithm, and scaled conjugate gradient descent algorithm were used for network training. The tangent sigmoid transfer function was the activation functions in the layers. The draft force was predicted based on the tillage depth, soil moisture content, soil cone index, and forward speed. The results showed that the developed ANNs with two hidden layers (24 and 26 neurons in the first and second layers, respectively) with the use of the scaled conjugate gradient descent algorithm outperformed the networks developed with other algorithms. The average simulation accuracy and the correlation coefficient for the prediction of draft force of a chisel cultivator were 99.83% and 0.9445, respectively. The linear regression model had a much lower accuracy and correlation coefficient for predicting the draft force compared to the ANNs.

Background/Aims: To describe the natural history of the prodromal stages of ischemic vascular dementia (pVaD). Methods: A sample of 314 inpatients with pVaD or a clini- cal diagnosis of vascular dementia (VaD; lacunar state, Binswanger’s disease, pure cortical VaD, corticosubcortical and strategic infarctions) admitted to a teaching tertiary center during a 13-year period was assessed (retrospectively n = 88, prospectively n = 226). Prospective neuropsychological assessment consisted of Mini Mental State Examination, Revised Wechsler Adult Intelligence Scale, Exit-25, Trail Making tests, Blessed Dementia Scale and Camdex H, Global Depression Scale and Hamilton Depression Rating Scale tests. Univariate analysis and logistic regressions are displayed. Results: An unrecognized pVaD was related with a clinical onset with cognitive impairment no dementia (CIND) versus symptomatic cerebrovascular events (p < 0.0001), and with being under therapy with anticoagulant or antiplatelet agents (p < 0.01). Age <85 years at diagnosis of VaD (p < 0.01) correlated with a delayed pVaD diagnosis. CIND onset was associated with a longer prodromal stage (p < 0.01), no clinical strokes during pVaD (p < 0.001), silent ischemia (p < 0.01) and Binswanger’s disease (p < 0.01). Conclusions: Vascular cognitive impairment remains an underdiagnosed, yet treatable entity. A brief neuropsychological examination and informant interviews should become standard practice in elderly populations with vascular risk factors. Small-vessel disease is a prevalent condition with a distinct natural history.