Hierarchical temporal memory (HTM) is a biomimetic sequence memory algorithm that holds promise for invariant representations of spatial and spatio-temporal inputs. This article presents a comprehensive neuromemristive crossbar architecture for the spatial pooler (SP) and the sparse distributed representation classifier, which are fundamental to the algorithm. There are several unique features in the proposed architecture that tightly link with the HTM algorithm. A memristor that is suitable for emulating the HTM synapses is identified and a new Z-window function is proposed. The architecture exploits the concept of synthetic synapses to enable potential synapses in the HTM. The crossbar for the SP avoids dark spots caused by unutil

A laboratory experiment was carried out according to a completely randomized design with four repetitions on the seeds resulting from a field experiment applied for the two seasons, 2020 and 2021, to find out the effect of the cultivars (Ambar 33, Yasamin, Dijlah, Ambar Al-Baraka and Furat 1) and the harvest dates (at physiological maturity and after 7 and 14, 21 and 28 days of physiological maturity) on the vigour of rice seeds. The results showed the superiority of the seeds of the cultivar Anbar Al-Baraka at first and final counting, dry weight of the seedling, seedling vigor index and electrical conductivity, and the superiority of the seeds of Dijla cultivar at accelerated aging test and cold test without significant difference with th

This study uses the performance of the discretionary estimation models by using a sample of listed companies in the Netherlands and Germany. The actual accounting framework provides a wide opportunity for managers to influence data in financial reporting. The corporate reporting strategy, the way managers use their discretionary accounting, has a significant effect on the company's financial reporting. The authors contribute to the literature through enhancement to these models to accomplish better effects of identifying earnings management as well as to present evidence that is particular to the Dutch and German setting.

For this, we followed the methodology of Dechow, Sloan, and Sweeney (1995) and Chan

Healthcare professionals routinely use audio signals, generated by the human body, to help diagnose disease or assess its progression. With new technologies, it is now possible to collect human-generated sounds, such as coughing. Audio-based machine learning technologies can be adopted for automatic analysis of collected data. Valuable and rich information can be obtained from the cough signal and extracting effective characteristics from a finite duration time interval that changes as a function of time. This article presents a proposed approach to the detection and diagnosis of COVID-19 through the processing of cough collected from patients suffering from the most common symptoms of this pandemic. The proposed method is based on adopt