A spike-sorting method based on hierarchical clustering to discriminate extracellularly recorded simple spikes and complex spikes from cerebellar Purkinje cells

Background: In the cerebellar cortex, Purkinje cells are the only output neurons and exhibit two types of discharge. Most Purkinje cell discharges are simple spikes, which are commonly appearing action potentials exhibiting a rich variety of firing patterns with a rate of up to 400 Hz. More infrequent discharges are complex spikes, which consist of a short burst of impulses accompanied by a massive increase in dendritic Ca2+ with a firing rate of around 1 Hz. The discrimination of these spikes in extracellular single-unit recordings is not always straightforward, as their waveforms vary depending on recording conditions and intrinsic fluctuations. New Method: To discriminate complex spikes from simple spikes in the extracellular single-unit data, we developed a semiautomatic spike-sorting method based on divisive hierarchical clustering. Results: Quantitative evaluation using parallel in vivo two-photon Ca2+ imaging of Purkinje cell dendrites indicated that 96.6% of the complex spikes were detected using our spike-sorting method from extracellular single-unit recordings obtained from anesthetized mice. Comparison with Existing Method(s): No reports have conducted a quantitative evaluation of spike-sorting algorithms used for the classification of extracellular spikes recorded from cerebellar Purkinje cells. Conclusions: Our method could be expected to contribute to research in information processing in the cerebellar cortex and the development of a fully automatic spike-sorting algorithm by providing ground-truth data useful for deep learning.

New Perspectives for LVL Manufacturing from Wood of Heterogeneous Quality—Part. 1: Veneer Mechanical Grading Based on Online Local Wood Fiber Orientation Measurement

The grading of wood veneers according to their true mechanical potential is an important issue in the peeling industry. Unlike in the sawmilling industry, this activity does not currently estimate the local properties of production. The potential of the tracheid effect, which enables local fiber orientation measurement, has been widely documented for sawn products. A measuring instrument exploiting this technology and implemented on a peeling line was developed, enabling us to obtain the fiber orientation locally which, together with global density, allowed us to model the local elastic properties of each veneer. A sorting method using this data was developed and is presented here. It was applied to 286 veneers from several logs of French Douglas fir, and was compared to a widely used sorting method based on veneer appearance defects. The effectiveness of both grading approaches was quantified according to mechanical criteria. This study shows that the sorting method used (based on local fiber orientation and average density) allows for better theorical quality discrimination according to the mechanical potential. This article is the first in a series, with the overall aim of enhancing the use of heterogeneous wood veneers in the manufacturing of maximized-performance LVL by veneer grading and optimized positioning as well as material mechanical property modelization.

Implementation of MCDM-Based Integrated Approach to Identifying the Uncertainty Factors on the Constructional Project

Presently, environmental management for companies emphasizing environmental protection has become one of the most critical issues for customers, shareholders, governments, employees, competitors, and global pressures requiring organizations to produce environmentally-friendly products and services. This challenge has created a new concept called green supply chain management in business, which combines environmental thinking with the supply chain. Selection of suppliers by considering risk criteria is a category that has attracted the attention of a large number of researchers in order to select the best suppliers according to uncertain factors. In this research, we aim to select a green supplier considering risk factors using a new MCDM approach under uncertainty. For this selection problem, HF-MAIRCA, a new multicriteria sorting method for many alternatives, has been developed. This is used for sorting the alternatives into predefined, ordered supplier categories. This sorting method can be applied to different environmental problems that have a large number of alternatives. As a result of Iran’s case study, the result shows that materials flexibility and materials quality are essential criteria for green supplier selection.

HUBUNGAN POPULASI CACING TANAH TERHADAP C-ORGANIK DAN N-TOTAL DI LAHAN BUDIDAYA HORTIKULTURA DAN MONOKLUTUR TANAMAN KOPI DI DESA NDUARIA KECAMATAN KELIMUTU

This study aims to determine the relationship between Earthworm Population and C-organic and N-total in Horticulture and Coffee Monoculture cultivation in Nduaria Village, Kelimutu District. This research was conducted using an exploratory method that is carried out by direct observation from the observation location. How to calculate earthworms using the hand sorting method. The method commonly used for the determination of C-organic is the Walkley and Black method, while the method used for the determination of N-total is the Kjehdahl method. This method is used because it is considered simple, fast, easy to do, and requires little equipment. The results of this study indicate that in horticultural land there is no earthworm population found at all depths. Earthworm populations were found at all depths in the coffee plantation monoculture, the highest population was at a depth of 0-10 cm as many as 41 m2, Biomass was 9 g, and Biomass vs Population 0.21 m2. In horticultural fields, the highest c-organic and n-total is at a depth of 0-10 cm, the c-organic value is 3.03% (high criteria) and n-total is 0.47% (medium criteria). The close relationship between the earthworm population, biomass, and biomass versus population was positively correlated with c-organic and n-total in horticultural land and coffee monoculture.

Blade Sorting Method for Unbalance Minimization of an Aeroengine Concentric Rotor

This paper proposes a blade sorting method based on the cloud adaptive genetic algorithm (CAGA), which is used to optimize the unbalanced of asymmetric rotor of aero-engine. Firstly, by analyzing the unbalance of the arrangement caused by the deviation of the mass moment of the blade, and considering the concentricity of the disk, an optimization model of the unbalanced amount of the blade assembly was established. Secondly, the selection operator, crossover operator, and mutation operator of the algorithm were designed, and the cloud adaptive genetic algorithm was used to optimize the assembly unbalance. Thirdly, the mass moments of a group of aero-engine blades were weighed using a moment scale (MW0), and the blade mass moment distribution and assembly unbalance under the six blade arrangements were analyzed. Finally, by setting different disk concentricity, the corresponding blade arrangement and the final rotor unbalance were obtained. Through analysis, it was found that the unbalance of GA is at least 57.5% optimized relative to the weight sorted, sorting type 2, sorting type 4, and sorting-1/4 skip method, and the unbalance optimized by the CAGA is 95.7% optimized relative to GA. In the case of different initial concentricity of the disk, the effective algorithm accuracy is still maintained, which proves the effectiveness of the method for the arrangement of asymmetric rotor blades. This method establishes an effective asymmetric rotor blade arrangement model, uses the cloud adaptive genetic algorithm to sort the blade assembly, and effectively reduces the unbalanced amount of the asymmetric rotor.