Spin exchange with a time delay in NMR (nuclear magnetic resonance) was treated in a previous work. In the present work the idea is applied to a case where all magnetization components are relevant. The resulting DDE (delay differential equations) are formally solved by the Laplace transform. Then the stability of the system is studied using the real and imaginary parts of the determinant in the characteristic equation. Using typical parameter values for the DDE system, stability is shown for all relevant cases. Also non-oscillating terms in the solution were found by studying the same determinant using similar parameter values.
Brace diaphragm walls are commonly used in underground structures in metropolitan areas, where avoiding costly damage to adjacent infrastructure / buildings is critical to project success. It is necessary to make accurate diaphragm wall deflection predictions to ensure actual deflection falls within allowable limits, and thus ensure the safety of both the project and adjacent structures. Numerous studies and approaches, such as empirical, semi-empirical as well as numerical approaches, have addressed excavation-induced deflection in diaphragm walls. Artificial intelligence (AI) has been used recently by several researchers to improve diaphragm wall deflection prediction capabilities. This paper proposes a hybrid artificial intelligence system, namely the evolutionary fuzzy support vector machine inference model for time series data (EFSIMT ), to predict diaphragm wall deflection in deep excavation through the application of historical project data. Simulations were performed on 1,083 instances, segregated into a total of 988 training data sets and 95 test data sets. Validation results show that the EFSIMT achieves higher performance in comparison with Artificial Neural Networks and the Evolutionary Support Vector Machine Inference Model (ESIM). Therefore, EFSIMT has great potential as a predictive tool for diaphragm wall deflection problems and assisting project managers/engineers to ensure safety during the construction process.
Long-lived solar filaments published in Meudon catalogues for the time interval 1931-1987 are studied using a proper statistical method. Some results concerning the time distribution of the filament activity, filaments lifetime, cyclic behaviour and North/South asymmetry are obtained.
Time of oviposition and investment in reproduction output are a crucial decision for animals which could affect their fitness. In this study, the factors determining the time of oviposition and the consequences it has for clutch size and juvenile survival were investigated in the orb-web spider Argiope bruennichi. Egg-sacs laid at different times in the field were collected and inspected for eggs, hatching success and presence of parasites. Relationships between spider body condition, clutch size and time of oviposition were established. The influence of supplementary food on the number of eggs in a clutch and on the time of oviposition was determined both in the field and laboratory. Early clutches were larger and the eggs in late clutches were not heavier than those in early clutches indicating that spiders invested more in eggs at the beginning of the reproductive period. Furthermore, eggs in late egg-sacs were less likely to hatch and more likely to be parasitized. Clutch size was linked to spider body condition but not the time of oviposition. In the field, additional food to females resulted in larger clutches but did not influence the time of oviposition. Laboratory experiments showed that the daily rate of prey consumption affected egg oviposition.
The purpose of this study was to compare markers of glycolytic metabolism in response to the Wingate test and the incremental test in road and mountain bike cyclists, who not different performance level and aerobic capacity. All cyclists executed the Wingate test and incremental test on a cycle ergometer. Maximal power and average power were determined during the Wingate test. During the incremental test the load was increased by 50 W every 3 min, until volitional exhaustion and maximal aerobic power (APmax), maximal oxygen uptake (VO2max), and time of VO2max plateau (Tplateau) were determined. Post-exercise measures of oxygen uptake (VO2post), carbon dioxide excretion, (VCO2post), and the ratio between VCO2/VO2 (RERpost) were collected for 3 min immediately after incremental test completion. Arterialized capillary blood was drawn to measure lactate (La-) and hydrogen (H+) ion concentrations in 3 min after each test. The data demonstrated significant differences between mountain bike and road cyclists for Tplateau, VO2post, VCO2post, La- which was higher-, and RERpost which was lower-, in mountain bike cyclists compare with road cyclists. No differences were observed between mountain bike and road cyclists for APmax, VO2max, H+ and parameters measured in the Wingate test. Increased time of VO2max plateau concomitant to larger post-exercise La- and VO2 values suggests greater anaerobic contribution during incremental testing efforts by mountain bike cyclists compared with road cyclists., P. Hebisz, R. Hebisz, J. Borkowski, M. Zatoń., and Obsahuje bibliografii
Time series prediction plays an important role in engineering applications. Artificial neural networks seem to be a useful tool to solve these problems. However, in real engineering, the inputs and outputs of many complicated systems are time-varied functions. Conventional artificial neural networks are not suitable to predicting time series in these systems directly. In order to overcome this limitation, a parallel feedforward process neural network (PFPNN) is proposed. The inputs and outputs of the PFPNN are time-varied functions, which makes it possible to predict time series directly. A corresponding learning algorithm for the PFPNN is developed. To simplify this learning algorithm, appropriate orthogonal basis functions are selected to expand the input functions, output functions and network weight functions. The effectiveness of the PFPNN and its learning algorithm is proved by the Mackey-Glass time series prediction. Finally, the PFPNN is utilized to predict exhaust gas temperature time series in aircraft engine condition monitoring, and the simulation test results also indicate that the PFPNN has a faster convergence speed and higher accuracy than the same scale multilayer feedforward process neural network.
A convolution sum discrete process neural network (CSDPNN) is proposed. CSDPNN utilizes discrete samples as inputs directly and employs convolution sum to simulate the process inputs so as to deal with the time accumulation existing in many time series. Without the procedures of fitting the discrete samples into continuous functions to generate inputs and then to expand the input functions by basis functions, CSDPNN is better understandable and is with less precision reduction compared with process neural network (PNN) with function inputs. The approximation capacity of CSDPNN is analyzed in this paper, and it proved that CSDPNN can approximate PNN and has approximation capacity not worse than traditional artificial neural network (ANN). Finally, CSDPNN, PNN and ANN are utilized to predict the Logistic chaos time series and the iron concentration in the lubrication oil of aircraft engine, and the application test results indicate that CSDPNN performs better than PNN and ANN given the same conditions.
Nejistota měření je důležitým konceptem laboratorní medicíny. Tento koncept je popsán v dokumentu ISO „Guide to the Expression of Uncertainty in Measurement”, všeobecně známém pod zkratkou GUM. Výpočet nejistoty má dva důležité aspekty – jeden statistický, založený na zákonu o šíření standardních odchylek, a druhý analytický, zabývající se použitelností výsledku v důsledku různých vlivů působících v procesech měření. Tento článek je stručným úvodem do problematiky stanovení nejistot podle GUM a poskytuje laboratořím orientaci k aplikaci nejistoty měření v praxi. Kromě některých základních postupů vycházejících z GUM jsou popsány i přístupy vycházející z dostupných dat uváděných v pracovních návodech výrobců. Sdělení se také zabývá důležitým dilematem, jak postupovat při měření nejistoty s hodnotami bias. Rovněž demonstruje použití hodnot vnitřní kontroly kvality k výpočtu nejistot., Measurement uncertainty is an important evolving concept in laboratory medicine. This concept is described in the ISO document “Guide to the Expression of Uncertainty in Measurement”, often referred to as “GUM”. GUM has two important aspects; a statistical one, which deals with the propagation of standard deviations; an analytical one, which counts on the expertise of the analytical chemist for addressing measurement uncertainty that extends beyond the obvious (sample related effects, stability of analytes, etc.). This paper gives a short introduction to the GUM concept and addresses the main challenges for its application in laboratory medicine (for example, the treatment of bias). Besides some basic GUM calculations, it describes a generic laboratory approach for calculating measurement uncertainty from available data (typically from manufacturers’ data sheets). The latter example shows the “spirit of GUM” which is equally important to the “statistics of GUM”. The dilemma connected to the treatment of bias (correct or not correct) is demonstrated by internal quality control data., Stöckl D., and Lit.:13
We present the preliminary results of a study of the solar prominences observed at Lomnický Štít coronal station with the 20 cm coronagraph during the ascending phase of cycle 22. The observational material was processed and a catalogue of prominences was prepared. On the basis of this catalogue some statistical results were obtained. They concern mainly the latitudinal, longitudinal and time distributions of the prominences and their basic morphological characteristics.