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Testing of hypothesis, also known as sample-testing, is a common feature with almost every social and management research. We draw conclusion on population (characteristics) based on available sample information, following certain statistical principles. This paper will introduce the fundamental concepts with suitable examples, mostly in Indian context. This section is expected to help scholar readers, to learn, how hypothesis tests for differences means (or proportions) take different forms, depending on whether the samples are large or small; and also to appreciate hypothesis-testing techniques, on how it could be used in similar decision-making situations, elsewhere.

The purpose of the present research is to examine very small-sized samples of approximately 2-mm diameters. For samples of this size, the holder must make contact with the entire perimeter surface of the sample, and the sample is held in place by friction. This necessitates a mathematical model for the direct solution which accommodates the holder and a contact resistance between the holder and the sample.

Most flash diffusivity testing is performed on samples which are nominally 12-13 mm in diameter and are held by only a small contact area around the perimeter of the sample in a holder. With an experiment set up in this way, the effects of conduction between the sample and the holder are normally ignored.

This research examines the effects of the holder and the contact resistance on the measured thermal diffusivity of the sample and includes experimental results from laboratory measurements.

This work provides a method for finding thermal diffusivity for extremely small samples. This capability is important in cases involving precious materials or highly toxic materials where only small samples are available.

This paper aims to test the finite sample properties of the automatic variance ratio (AVR) test and suggest suitable measure to improve its small sample properties under conditional heteroskedasticity and apply it to test the martingale hypothesis in the stock prices of the Portugal, Ireland, Italy, Greece and Spain (PIIGS economies) markets. This paper also seeks to investigate that “If the time series is not martingale, then what else?”

Monte Carlo experiments have been undertaken to test the small sample properties of automatic variance ratio (AVR) test. The study uses AVR test on daily and weekly data of the indices to investigate their martingale behaviour. It uses detrended fluctuation analysis (DFA) and BDS test statistics to answer, “If not martingale, then what else?”. The study also applies moving subsample approach to examine the dynamic behavior of stock prices and to obtain inferential findings robust to possible structural changes and presence of influential outliers.

The author finds that weighted bootstrap procedure significantly improves the small sample properties of AVR tests under conditional heteroskedasticity. The results provide evidence in support of the weak‐form efficiency of Italy and Spain. But Portugal, Ireland and Greece exhibit signs of long memory in the stock prices. All indices also exhibit chaotic characteristics.

This paper has both methodological and empirical originality. On the methodological aspect, the author proposes weighted bootstrap procedure on AVR test to improve its small sample properties. On the empirical side, the study finds that all stocks exhibit dynamic behavioral characteristics which change over time.

Due to the limitation of experimental conditions and budget, fatigue data of mechanical components are often scarce in practical engineering, which leads to low reliability of fatigue data and reduces the accuracy of fatigue life prediction. Therefore, this study aims to expand the available fatigue data and verify its reliability, enabling the achievement of life prediction analysis at different stress levels.

First, the principle of fatigue life probability percentiles consistency and the perturbation optimization technique is used to realize the equivalent conversion of small samples fatigue life test data at different stress levels. Meanwhile, checking failure model by fitting the goodness of fit test and proposing a Monte Carlo method based on the data distribution characteristics and a numerical simulation strategy of directional sampling is used to extend equivalent data. Furthermore, the relationship between effective stress and characteristic life is analyzed using a combination of the Weibull distribution and the Stromeyer equation. An iterative sequence is established to obtain predicted life.

The TC4–DT titanium alloy is selected to assess the accuracy and reliability of the proposed method and the results show that predicted life obtained with the proposed method is within the double dispersion band, indicating high accuracy.

The purpose of this study is to provide a reference for the expansion of small sample fatigue test data, verification of data reliability and prediction of fatigue life data. In addition, the proposed method provides a theoretical basis for engineering applications.

At each New Year we stand at the threshold of fresh scenes and hopes, of opportunities and pastures new. It is the time for casting off shackles and burdens that have weighed us down in the old year; almost a new chapter of life. We scan the prevailing scene for signs that will chart the year's unrolling and beyond, and hope profoundly for a smooth passage. The present is largely the product of the past, but of the future, who knows? Man therefore forever seems to be entering upon something new—a change, a challenge, events of great portent. This, of course, is what life is all about. Trends usually precede events, often by a decade or more, yet it is a paradox that so many are taken by surprise when they occur. Trends there have been and well marked; signs, too, for the discerning. In fields particular, they portend overall progress; in general, not a few bode ill.

The Southern African Institute of Steel Construction has developed a novel cellular beam structure (CBS) for multi-storey buildings that is entirely devoid of concrete. Channel sections between the cellular beams support a complex sandwich flooring system, which contains a fire-resistant ceiling board, metal sheeting, an interior fibre-cement board and an access-flooring system. As for all structures, the CBS requires a fire rating. This paper aims to investigate the thermal behaviour of the CBS using numerical modelling and experimental fire testing, as it has a unique setup.

Experimental fire tests on the flooring system were conducted to validate finite element models, which were developed in ABAQUS. These models were then extended to include floor beams and the structural steelwork.

Good correlations were found between the experimental and numerical results, with temperature variations typically in the range of 0-5%, although with localised differences of up to 20%. This allowed larger finite element models, representing the sandwich floor system of the CBS, to be developed and analysed. A 1-hour rating can be obtained by the system in terms of insulation and integrity requirements.

The CBS allows for more economical steel structures, due to the rapid construction of its modular panels. A suitable fire resistance will ensure the safety of the occupants and prevent major structural damage. Steelwork and flooring temperatures are determined which has allowed for global structural analyses to be carried out.

The originality of this study lies in thermal analysis and testing of a new cellular beam flooring system, through determining behaviour in fire, along with beam temperatures.

The study aims to provide a quick-and-robust multifactorial screening technique for early detection of statistically significant effects that could influence a product's life-time performance.

The proposed method takes advantage of saturated fractional factorial designs for organizing the lifetime dataset collection process. Small censored lifetime data are fitted to the Kaplan–Meier model. Low-percentile lifetime behavior that is derived from the fitted model is used to screen for strong effects. A robust surrogate profiler is employed to furnish the predictions.

The methodology is tested on a difficult published case study that involves the eleven-factor screening of an industrial-grade thermostat. The tested thermostat units are use-rate accelerated to expedite the information collection process. The solution that is provided by this new method suggests as many as two active effects at the first decile of the data which improves over a solution provided from more classical methods.

To benchmark the predicted solution with other competing approaches, the results showcase the critical first decile part of the dataset. Moreover, prediction capability is demonstrated for the use-rate acceleration condition.

The technique might be applicable to projects where the early reliability improvement is studied for complex industrial products.

The proposed methodology offers a range of features that aim to make the product reliability profiling process faster and more robust while managing to be less susceptible to assumptions often encountered in classical multi-parameter treatments.