Miracle-dz/UniFAR-FTUs huggingface dataset:
Further Studies of Periodic Miniature Responses in Squid Giant Axons

Electric responses of extremely small amplitudes (1-30 ƒÊV peak-to-peak) repeating with more-or-less definite periodicity could be induced by a variety of chemical stimulants applied to squid giant axons either extracellularly or intracellularly. The chemical stimulants studied include allethrin, aminopyridines, N-bromosuccinimide, dimethylaminopyridine, glutaraldehyde, osmium tetroxide, parachloromercuribenzoate, rose bengal, scorpion venoms and veratridine. With several mild stimulants, it was possible to evoke periodic miniature responses unaccompanied by a fall in the resting membrane potential. The frequency of miniature responses could be lowered by intracellular injection of TEA (tetraethylammonium). These miniature responses could readily be suppressed by external application of TTX (tetrodotoxin). The miniature responses evoked by 4-aminopyrine were characterized by a large variation in the frequency of responses. Cross-linking of the membrane proteins with a dilute solution of glutaraldehyde produced miniature responses repeating at progressively falling frequencies. Analyses of the processes of production of miniature responses with these and other stimulants have clarified several aspects of the physicochemical properties of the excitable sites of the axon membrane. In recent papers (TASAKI, 1977a, b), electrophysiological properties of the excitable membrane sites (or ionic channels) as revealed by using chemical stimulants and a real-time spectrum analyzer were described. Immersed in a medium with reduced divalent cation concentrations, squid giant axons were found to develop electrical signals of extremely small amplitudes (less than about 30 ,uV peak-to-peak) which were interpreted as representing periodic responses of a limited number of excitable sites in the membrane. These signals were shown to be strongly affected by temperature changes and by various pharmacological agents as well as by electric currents. The present paper deals mainly with the effects of chemical stimulants which are capable of producing these "periodic miniature responses" in squid axons Received for publication November 14, 1977 田崎一二
Partycypacja bezpośrednia pracowników jako czynnik rozwoju organizacji

Stworzenie pracownikom warunkow do udzialu w podejmowaniu decyzji dotyczących przedsiebiorstwa i samych zatrudnionych, czyli partycypacja bezpośrednia pracownikow, wplywa na ksztaltowanie kapitalu ludzkiego, motywuje pracownikow do udzialu w projektowaniu i wdrazaniu zmian organizacyjnych, tym samym determinuje rozwoj organizacji. Szczegolną role w tym procesie odgrywają przelozeni i ich styl kierowania. Celem artykulu jest zaprezentowanie wynikow badania pilotazowego dotyczącego wystepowania, zakresu, form, intensywności partycypacji bezpośredniej pracownikow oraz stylu kierowania przelozonych, sprawdzenie, czy badane przedsiebiorstwa wykorzystują uczestnictwo pracownikow w zarządzaniu jako czynnik rozwoju organizacji. Stwierdzono, ze w zbadanych przedsiebiorstwach partycypacja bezpośrednia racownikow wystepuje na poziomie operacyjnym i taktycznym, ma forme wspoldzialania (rzadziej wspoldecydowania), dotyczy wszystkich etapow procesu podejmowania decyzji, sprzyjają jej stosowane przez przelozonych, odmiany konsultatywnego stylu kierowania. Pracownicy uczestniczą we wdrazaniu zmian organizacyjnych i zmian dotyczących produktow, jednak z ich udzialem w zarządzaniu wiązą sie jeszcze rezerwy, ktore nalezaloby skuteczniej wykorzystywac dla rozwoju organizacji.
Assessment of the efficiency of energy- and resource-saving technologies in the model of open innovation

The relevance of this work is determined by the fact that the issues of resource-saving technologies implementation in the model of open innovation have not been fully addressed yet and require further study and systematization of the determining factors, which is especially important on the back of the transition to a new technological pattern and the use of the emerging technological opportunity windows. The solution of the problems mentioned will reveal new opportunities for qualitative and quantitative growth of production systems by improving the innovation targeting in the field of resource saving and energy efficiency. The purpose of the article is to identify the functional dependence between the Networked Readiness Index and the emerging technological opportunity windows in order to improve the efficiency of resource-saving technologies in open innovation and production systems. The main research methods underlying the article include the method of description used to identify trends in the Networked Readiness Index across the globe, the correlation analysis method used to identify a close relationship between the Networked Readiness Index and indicators of state support granted to the innovations in production, and the regression analysis method used to build a regression model of the dependence between the resource-saving system and production indicators. The article touches upon the aspects of improving the resource-saving system efficiency in the framework of the open innovation model in the field of production. The correlation between the Networked Readiness Index and the indicators of the state institutions quality characterizing the level of innovation support provided to the industry across the world countries has been revealed, and the functional connection between the use of waste at petrochemical plants and the petrochemical products shipped has been proved using the emerging economies as an example. The materials of the article can be used in the development of strategies and programs aimed to improve the resource-saving system efficiency in petrochemical companies of developing countries, taking into account the emerging technological opportunity windows and technology readiness of the production for innovative transformations.
Simple Language Learning in Artificial General Intelligence Master’s thesis in Algorithms, Languages and Logic and Complex Adaptive Systems

The development of Artificial Intelligence (AI) has made it possible for computers to solve more and more of the tasks that humans can perform. One area of interest within AI research is Artificial General Intelligence (AGI), which aims to develop AI systems capable of solving multiple varying tasks. The Generic Animat research project is one biologically inspired approach to AGI. Our thesis aims to explore Natural Language Processing within the Animat model, by mimicking the way young children start to learn their first language. During the course of the thesis project we have developed two slightly different prototypes (referred to as the “Temporal Animat” and the “Chunking Animat”), both of which can learn to recognise and produce words that they are exposed to in a noise-free environment. Furthermore, functionality for word-to-word association and multimodal association has been implemented, thereby allowing the prototypes to associate between words, based on their occurences in some input text, and between words and other types of sensor input representing non-language concepts in the Animat’s environment. The prototypes were evaluated on small input texts with a limited number of words. The result of these evaluations suggests that both versions of the Animat are capable of achieving high degrees of accuracy when their word-to-word associations are compared to those of vector space models. The evaluation of multimodal associations has been done only for the Temporal Animat. The results indicate that the Temporal Animat makes mostly accurate associations between words and other input, although more tests on larger data sets are needed to determine how well it works on a larger scale. Due to the similarity in implementation between the two prototypes, the Chunking Animat is expected to be equally successful at this task.
Multiple Literacies: Beliefs and Related Practices among Chinese Kindergarten Teachers

: Fifty-five Chinese kindergarten teachers from Shenzhen ( n = 38) and Hong Kong ( n = 17) were surveyed to discern their beliefs and reported practices about multiple literacies related to e-learning and knowledge management. Results indicated that: (1) Shenzhen teachers had a better knowledge about multiple literacies and facilitated the development of multiple literacies in their students more than their counterparts from Hong Kong; (2) Teachers’ educational attainment and their beliefs did not predict the multiple literacies practice; and (3) after controlling for these variables, child-PC ratio significantly contributed to the variation in multiple literacies practice. Educational implications and challenges associated with teacher education are discussed.
Optimum Laser Polishing Decision-Making for On-Demand Additive Manufacturing of Spare Parts: An Exploratory Study

: Additive manufacturing is increasingly being used for satisfying spare parts needs of capital products using a nearby 3D printer. Such a technology allows inventory managers to start manufacturing after the demand realization which eliminates significant portion of spare parts inventory being held due to random nature of component breakdowns. Quality difference between printed and original parts, which is one of the biggest problems of using 3D printers, can be decreased by the use of laser polishing which alleviates surface roughness and increases reliability of parts in exchange of an additional cost term. Using different parameters, reliability of parts can be altered depending on needs of capital products and systems’ status. In this study, the problem where surface roughness and reliability of printed parts are jointly optimized with inventory levels of original spare parts is considered. In the problem setting, a machine part consisting of a constant number of identical products which are subject to random breakdowns over a finite planning horizon is considered. Using mathematical analysis and exhaustive numerical experiments, the relationship between optimum control policy and cost parameters was shown, which might be critical for cost-effective management of the system.
Leveraging Mobile Devices for Qualitative Formative Assessment

This chapter explores an approach for using mobile devices—smartphones, tablets, and laptops—to document learning using qualitative formative assessment. This process emerged from case study research on how mobile devices, specifically iPad, and certain approaches, specifically screencasting, can be used to support ongoing assessment of learning. The cases and the literature included are derived from a dissertation on this topic. The chapter will describe and examine relevant literature, processes involved and possibilities created through this approach.
Mapping Between Semantic Graphs and Sentences in Grammar Induction System

The proposed transformation module performs mapping be- tween two di®erent knowledge representation forms used in grammar induction systems. The kernel knowledge representation form is a special predicate centered conceptual graph called ECG. The ECG provides a semantic-based, language independent description of the environment. The other base representation form is some kind of language. The sentences of the language should meet the corresponding grammatical rules. The pilot project demonstrates the functionality of a translator module using this transformation engine between the ECG graph and the Hungarian language.
A voltage regulator module (VRM) application for a switched mode power supply (SMPS)

High frequency DC-DC converters are replacing large thyristor converters in the field of DC power supplies in the medium and higher power range. Depending on the given requirements these solutions provide a DC source of higher quality, dynamics and stability for demanding loads. To extend the output power of DC-DC converters to higher ranges the voltage regulator module (VRM) configuration is the most suitable solution. The paper deals with the development of a four-module 100 kW buck converter, operating as a general purpose power supply. The control concept with special respect to the current-sharing technique is also discussed.
RADIOACTIVE WASTE ISOLATION IN SALT: A METHOD FOR EVALUATING THE EFFECTIVENESS OF SITE CHARACTERIZATION MEASUREMENTS

A quantitative method for evaluating the effectiveness of site characterization activities is developed and applied to hypothetical measurement schenr.is at a potential site for a repository for highlevel radioactive waste. The approach presumes that measurements are undertaken to support performance predictions. A quantitative performance objective like groundwater travel time is compared with performance predictions. The approach recognizes that such predictions are uncertain because the measurements upon which they are based are uncertain. The effectiveness of measurement activities is quantified by an index, 8, that reflects the number of standard deviations separating the best estimate of performance from the performance objective. Measurements that reduce the uncertainty in predictions lead to increased values of S. Evaluating s for a particular measurement scheme requires identifying the measured quantities that significantly affect prediction uncertainty. Sources of uncertainty are spatial variation, noise, estimation error, and measurement bias. Changing the measurement scheme to increase 6 increases the likelihood of a performance objective being achieved or exceeded. The application of the e-index method to the Richton dome site in Mississippi focuses on uncertainties in hydraulic conductivity data in relation to groundwater travel time predictions. The 8 values for four different measurement schemes for hydraulic conductivity are determined.
Unsupervised Change Detection in Hyperspectral Images using Principal Components Space Data Clustering

Change detection of hyperspectral images is a very important subject in the field of remote sensing application. Due to the large number of bands and the high correlation between adjacent bands in the hyperspectral image cube, information redundancy is a big problem, which increases the computational complexity and brings negative factor to detection performance. To address this problem, the principal component analysis (PCA) has been widely used for dimension reduction. It has the capability of projecting the original multi-dimensional hyperspectral data into new eigenvector space which allows it to extract light but representative information. The difference image of the PCA components is obtained by subtracting the two dimensionality-reduced images, on which the change detection is considered as a binary classification problem. The first several principal components of each pixel are taken as a feature vector for data classification using k-means clustering with k=2, where the two classes are changed pixels and unchanged pixels, respectively. The centroids of two clusters are determined by iteratively finding the minimum Euclidean distance between pixel’s eigenvectors. Experiments on two publicly available datasets have been carried out and evaluated by overall accuracy. The results have validated the efficacy and efficiency of the proposed approach.
Simulation Analysis on Applicability of Meta Material and PBG Based mm-Wave Planar Antenna for Advanced Cellular Technologies

Metamaterials have got a unique identification in the field of electromagnetic. Left handed metamaterials have been recognized through its working at high frequencies with larger bandwidth in antenna design. The author has proposed a multi substrates LH metamaterials with an array of split triangular antennas for wideband as well as for larger bandwidth. FR4 is being used at frequency 42 - 26 GHz with thickness of 3 mm for substrate and matching circuit is inserted to get high gain with minimum reflections. The number of rods in PBG is 100, which is found to get high return losses and ideal behavior of VSWR. Obtained results have been in good agreement such as S11 is approximately ﹣30 dB, VSWR 1.8 dB and the bandwidth enhancement is 2 GHz at frequency 5 GHz. All results are plotted using commercial software CST microwave studio (Version 2012) and MATLAB.
Adaptive measuring trajectory for scanning lidars: proof of concept

Application of nacelle-mounted long-range scanning lidars is facing the challenge that the nacelle motion causes deviations in the measuring trajectories. Such lidars are very sensitive to even the slightest trajectory deviation due to its long measuring range. Motivated by this need, we propose the concept of adaptive measuring trajectory and explore its use for eliminating the effect of the rotational motion of the lidar on the target measuring trajectory. This work first aims to experimentally test the performance of adaptive measuring trajectory under ideal conditions by installing a scanning lidar on a 6DOF motion platform to model the lidar motion on the turbine nacelle. The real trajectory with and without correction is measured by a camera. The comparison confirms that the adaptive measuring trajectory could stablize the target measuring trajectory given the lidar motion. Then, the possibility of using Kalman filter to estimate lidar motion given noisy motion measurements is investigated. The results show great potential of Kalman filter for lidar motion estimation, which could be very useful for its future application.
Research on Pavement Crack Detection Algorithm based on Deep Residual Unet Neural Network

Pavement crack is one of the main factors threatening highway safety. Rapid and accurate identification of pavement crack is very important to protect highway. Traditional image processing methods have been difficult to meet the requirements of practical engineering. So, this paper proposes an improved pavement crack detection method based on deep convolution neural network model, to overcome the inaccurate segmentation caused by complex background and fuzzy crack edge in asphalt pavement image, which is an encoder-decoder network structure, and integrates the idea of semantic segmentation into image detection. The encoder of the proposed method adopts the ResNet’s residual structure to strengthen image feature extraction, the decoder uses transpose convolution from the original UNet structure for up sampling, and integrates the encoder features with the up sampled features after cutting, Strengthen the learning of network features. Experimental results show that the F1 score, Precision and Recall are significantly improved on the asphalt crack dataset. Compared with the traditional algorithm, the proposed algorithm can segment the cracks better and improve the accuracy of pavement crack detection.
Modeling extreme weather events for offshore wind in the North Sea: a sensitivity analysis to physics parameterizations in WRF

Extreme weather events such as storms, cold fronts, and mesoscale convective systems, are capable of producing extreme and sudden precipitation, strong wind gusts and fast changes in wind direction, which are potentially harmful for the operation, power production and maintenance of wind farms. This study aims to provide insight into the modelling of three such events for the offshore wind farms located in the Belgian North Sea. This work extends the ongoing work on generalizing WRF model sensitivities to multiple extreme weather events. This sensitivity analysis considers three cases consisting of Storm Ciara, a low-pressure system and a cold front. These events are modeled using five model setups comprising different combinations of WRF physics parameterizations. The simulated wind direction and speed are evaluated against 10-min averaged SCADA data. The results indicate no single best model setup to accurately simulate all events. The combination of physical parameterization is observed to play a major role in model sensitivities. The use of scale-aware physical parameterization show potential for better performance but is heavily influenced by the combination of model physics and the type of event. No conclusive set of inferences are obtained from a qualitative evaluation of precipitation against observed radar data.
Wind speed reconstruction from three synchronized short-range WindScanner lidars in a large wind turbine inflow field campaign and the associated uncertainties

Modern large wind turbines require high-resolution wind measurements as input to aerodynamic and aeroelastic simulations for modelling and validation purposes. Within the HighRe project, we aim at studying the aerodynamic effects at high Reynolds numbers by measuring four-dimensional wind fields (vx ,vy ,vz ,t) using three short-range WindScanners (SRWS). The systems were set up at the test site in Bremerhaven (Testfeld BHV) to perform an inflow wind field measurement campaign. In order to get a better understanding of the measurements, we describe the propagation of uncertainties in SRWS parameters to the measured wind field and propose an uncertainty model for a measurement setup with three SRWS lidars. In this study, we first evaluate the wind reconstruction and derive an uncertainty model for the wind components vx , vy , and vz , which are mainly dependent on the input parameters, e.g., focus range, elevation angle, and azimuth angle. The effective intersection diameter at the intersection of three beams was found to be in the order of 2-5m. As expected, a high uncertainty was observed at lower heights in the vz -component due to low elevation angles. This uncertainty evaluation forms the basis for comparing scanning patterns with regard to their accuracy in providing four-dimensional measurements.
Multi-scale Navier-Stokes analysis of geometrically resolved erosion of wind turbine blade leading edges

A multi-scale computational fluid dynamics analysis of wind turbine blade leading edge erosion is presented. The test case is a large set of eroded blade sections. These are obtained by fitting the resolved eroded leading edge geometry of the outboard part of a multi-megawatt offshore wind turbine to the NACA633-618 airfoil. The erosion geometry measured by a blade laser scan is geometrically resolved in the aerodynamic simulations, whereas the aerodynamic effects of unresolved lower-amplitude scales are accounted for by using distributed surface roughness models. The simulations also account for the laminar-to-turbulent transition of the blade boundary layers with and without distributed roughness. An existing semi-empirical model and simulations of the nominal airfoil enable one to estimate the roughness level needed to trip leading edge boundary layer transition at the considered Reynolds number of 9 million. It is found that a) the mean roughness heights of the observed geometry perturbations are well above the critical roughness height, and b) consideration of either large or small erosion scales in isolation results in underestimating the airfoil performance impairment.
A data-informed analytic model for turbine power prediction with anisotropic local blockage effects

This paper presents a new analytic approach for estimating the local power coefficient of a turbine experiencing anisotropic local blockage effects. Data-driven methods are employed first to approximately obtain a known analytic expression for isotropic local blockage effects, and then deployed to find candidate expressions for anisotropic local blockage effects. The dataset for the analysis of anisotropic local blockage is collected from 3D Reynolds-averaged Navier-Stokes (RANS) simulations of an infinitely wide array of actuator disks for nearly 2,000 different blockage configurations. This study builds upon previous work in array optimisation of both tidal and wind turbines, where the local power coefficient may increase substantially for optimal array configuration. A brief discussion on the relationship between the local blockage and wind farm blockage is also provided. Other theoretical approaches and possible refinements to the presented analytic model are also discussed.