Although the management and planning of water resources are extremely significant to human development, the complexity of implementation is unimaginable. To achieve this, the high-precision water consumption prediction is actually the key component of urban water optimization management system. Water consumption is usually affected by many factors, such as weather, economy, and water prices. If these impact factors are directly combined to predict water consumption, the weight of each perspective on the water consumption will be ignored, which will be greatly (...) detrimental to the prediction accuracy. Therefore, this paper proposes a deep neural network-based complex system for urban water management. The essence of it is to formulate a water consumption prediction model with the aid of principal component analysis and the integrated deep neural network, which is abbreviated as UWM-Id. The PCA classifies the factors affecting water consumption in the original data into three categories according to their correlation and inputs them into the neural network model. The results in the previous step are assigned weights and integrated into the form of fully connected layer. Finally, analyzing the sensitivity of the proposed UWM-Id and comparing its performance with a series of commonly used baseline methods for data mining, a large number of experiments have proved that UWM-Id has good performance and can be used for urban water management system. (shrink)

PurposeTo explore the changes of cerebral blood flow and fractional anisotropy in stroke patients with motor dysfunction after repetitive transcranial magnetic stimulation treatment, and to better understand the role of rTMS on motor rehabilitation of subcortical stroke patients from the perfusion and structural level.Materials and MethodsIn total, 23 first-episode acute ischemic stroke patients and sixteen healthy controls were included. The patients were divided into the rTMS and sham group. The rehabilitation assessments and examination of perfusion and structural MRI were performed (...) before and after rTMS therapy for each patient. Voxel-based analysis was used to detect the difference in CBF and FA among all three groups. The Pearson correlation analysis was conducted to evaluate the relationship between the CBF/FA value and the motor scales.ResultsAfter rTMS, significantly increased CBF was found in the ipsilesional supplementary motor area, postcentral gyrus, precentral gyrus, pons, medulla oblongata, contralesional midbrain, superior cerebellar peduncle, and middle cerebellar peduncle compared to that during the prestimulation and in the sham group, these fasciculi comprise the cortex-pontine-cerebellum-cortex loop. Besides, altered CBF in the ipsilesional precentral gyrus, postcentral gyrus, and pons was positively associated with the improved Fugl-Meyer assessment scores. Significantly decreased FA was found in the contralesional precentral gyrus, increased FA was found in the ipsilesional postcentral gyrus, precentral gyrus, contralesional supplementary motor area, and bilateral cerebellum, these fasciculi comprise the corticospinal tract. The change of FMA score was positively correlated with altered FA value in the ipsilesional postcentral gyrus and negatively correlated with altered FA value in the contralesional precentral gyrus.ConclusionOur results suggested that rTMS could facilitate the motor recovery of stroke patients. High frequency could promote the improvement of functional activity of ipsilesional CPC loop and the recovery of the microstructure of CST. (shrink)