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Most of the mechanical systems consist of common tribo components such as gear, bearing, seal, pump, etc. During operation, the failure of such a component may lead to failure of the system or any other components depending on the system structure or its connections. The transformation of functional quantities or parameters within and various components make these vulnerable to failures and breakdowns. The main purpose of this paper is to evaluate the reliability of a tribo‐pair during operation based on the operational parameters which takes into account the deviation in functional objective and considers the component structure explicitly.

Failure representation using digraph models have mainly been limited to chemical systems and processes but have been applied to a limited extent to mechanical systems. Researchers have considered the operating parameters such as pressure, temperature; feed rate, flow, etc. as the input/output operational parameter for failure cause identification of tribo mechanical system. However, these are not true in case of components like gear, bearing, etc. Therefore, this methodology has been refined in this paper, and is extended to a tribo‐pair by considering its functions along with operating parameters for reliability assessment. The tribo‐pair considered for the development of model is a gear pair, which is a common and important example of mechanical components.

Understanding of the failure modes helps the designer in identifying the root cause of failure and the operational parameters whose increase/decrease affects the functional objective. Consideration of the input and output parameters and their interrelations are used to develop input‐output model of a gear pair called gear pair model. This requires consideration of the input and output parameters, which are based on the gear pair functionality. These parameters have been identified. Four input parameters; five output parameters and one condition monitoring parameter are considered to model the gear pair. In addition to input and output parameters condition monitoring parameters are also considered to develop the gear pair model.

Reliability assessment of a tribo‐pair during operation will help the practicing engineers to take corrective action and minimize the occurrence of undesired failure symptom during operation. The decrease in possibility of undesired failure symptom will enhance the reliability of mechanical system.

The purpose of this paper is to predict the appearance of denim fabric after repetitive uses judging the denim cloth behavior and performance in viewpoint of bagging ability. Hence, it attempts to carry out the significant inputs and outputs that have an influence on the bagging behaviors using the Principal Component Analysis (PCA) technique. In this study, the Kawabata Evaluation System parameters such as the frictional characteristics, the bending, compression, tensile and shear parameters are investigated to propose a model highlighting and explaining their impacts on the different bagging properties. To improve the obtained results, the selected significant inputs are also analyzed within their bagging properties using Taguchi experimental design. The linear regressive models prove the effectiveness of the PCA method and the obtained findings.

To investigate the mechanical properties and their contributions on the bagging characteristics, some denim fabrics were collected and measured thanks to the Kawabata evaluation systems (KES-FB1, KES-FB2, KES-FB3 and KES-FB4). These bagging properties were further analyzed applying the method of PCA to acquire factor patterns that indicate the most important fabric properties for characterizing the bagging behaviors of different studied denim fabric samples. An experimental design type Taguchi was, hence, applied to improve the results. Regarding the obtained results, it may be concluded that the PCA method remained a powerful and flawless technique to select the main influential inputs and significant outputs, able to define objectively the bagging phenomenon and which should be considered from the next researches.

According to the results, there are good relationships between the Kawabata input parameters and the analyzed bagging properties of studied denim fabrics. Indeed, thanks to the PCA, it is probably easy to reduce the number of the influent parameters for three reasons. First, applying this technique of selection can help to select objectively the most influential inputs which affect enormously the bagged fabrics. Second, knowing these significant parameters, the prediction of denim fabric bagging seems fruitful and can undoubtedly help researchers explain widely this complex phenomenon. Third, regarding the findings mentioned, it seems that the prevention of this aesthetic phenomenon appearing in some specific zones of denim fabrics will be more and more accurate.

This study is interesting for denim consumers and industrial applications during long and repetitive uses. Undoubtedly, the denim garments remained the largely used and consumed, hence, this particularity proves the necessity to study it in order to evaluate the bagging phenomenon which occurs as function of number of uses. Although it is fashionable to have bagging, the denim fabric remains, in contrast with the worsted ones, the most popular fabric to produce garments. Moreover, regarding this characteristic, the large uses and the acceptable value of denim fabrics, their aesthetic appearance behavior due to bagging phenomenon can be analyzed accurately because compared to worsted fabrics, they have a high value and the repetitive tests to investigate widely bagged zones may fall the industrial. The paper has practical implications in the clothing appearance and other textile industry, especially in the weaving process when friction forms (yarn-to-yarn, yarn-to-metal frictions) and stresses are drastic. This can help understanding why residual bagging behavior remained after garment uses due to the internal stress and excessive extensions. Regarding the selected influential inputs and outputs relative to bagging behaviors, there are some practical implications that have an impact on the industrial and researchers to study objectively the occurrence of this aesthetic phenomenon. Indeed, this study discusses the significance of the overall inputs; their contributions on the denim fabric bagged zones aims to prevent their ability to appear after uses. Moreover, the results obtained regarding the fabric mechanical properties can be useful to fabric and garment producers, designers and consumers in specifying and categorizing denim fabric products, insuring more denim cloth use and controlling fabric value. For applications where the subjective view of the consumer is of primary importance, the KES-FB system yields data that can be used for evaluating fabric properties objectively and prejudge the consumer satisfaction in viewpoint of the bagging ability. Therefore, this study shows that by measuring shear, tensile and frictional parameters of KES-FB, it may be possible to evaluate bagging properties. However, it highlights the importance and the significance of some inputs considered influential or the contrast (non-significant) in other researches.

This work presents the first study analyzing the bagged denim fabric applying the PCA technique to remove the all input parameters which are not significant. Besides, it deals with the relationship developed between the mechanical fabric properties (tensile, shear and frictional stresses) and the bagging properties behavior. To improve these obtained relationships, for the first time, the regression technique and experimental design type Taguchi analysis were both applied. Moreover, it is notable to mention that the originality of this study is to let researchers and industrials investigate the most influential inputs only which have a bearing on the bagging phenomenon.

While the investment in construction projects has increased over the past few decades, low construction project productivity (CPP) appeared to be persistent, thereby reflecting an “investment-in-failure” paradox between the investment and CPP. Hence, this paper aims to develop a systematic and holistic CPP evaluation framework to explain the apparent paradox in the construction industry.

The paper first reviews the literature about the theories of system, production, principal–agent and project success evaluation to re-conceptualise the CPP and proposes a two-stage CPP evaluation framework. The framework is subsequently explored through a sequential qualitative mixed-methods design within the context of the Hong Kong construction industry by combining 32 semi-structured interviews with senior industry experts and exploratory case studies, with three real-life construction projects.

The paper identifies three system boundaries for CPP evaluation, that is, parameter, timeframe and stakeholder, and develops a two-stage CPP evaluation framework to indicate site efficiency and utilisation effectiveness, thereby accessing the productivity of both the construction and post-construction stages. The “investment-in-failure” paradox associated with current CPP evaluation approaches is primarily attributed to the narrowly defined CPP boundaries.

The qualitative exploration of the evaluation framework only focusses on the Hong Kong construction industry. Further case studies within other urban contexts could be used to improve the generalisability of the findings. Quantitative research is also necessary to advance theoretical development of the two-stage CPP evaluation.

The systemic CPP conceptualisation and the two-stage CPP evaluation framework support the systems thinking of industry stakeholders and enable them to formulate holistic strategies for long-term CPP enhancement.

The research demonstrates the needs to expand the system boundaries of CPP to reflect its systemic value and to shift the paradigm of CPP evaluation from being output-orientated and quantity-focussed to being outcome-orientated and value-focussed.

The objectives of this paper are the application of sensitivity analysis (SA) methods in atmospheric dispersion modeling to the emission dispersion model (EDM) to study the prediction of atmospheric dispersion of NO₂ generated by an industrial fire, whose results are useful for fire safety applications. The EDM is used to predict the level concentration of nitrogen dioxide (NO₂) emitted by an industrial fire in a plant located in an industrial region site in Algeria.

The SA was defined for the following input parameters: wind speed, NO₂ emission rate and viscosity and diffusivity coefficients by simulating the air quality impacts of fire on an industrial area. Two SA methods are used: a local SA by using a one at a time technique and a global SA, for which correlation analysis was conducted on the EDM using the standardized regression coefficient.

The study demonstrates that, under ordinary weather conditions and for the fields near to the fire, the NO₂ initial concentration has the most influence on the predicted NO₂ levels than any other model input. Whereas, for the far field, the initial concentration and the wind speed have the most impact on the NO₂ concentration estimation.

The study shows that an effective decision-making process should not be only based on the mean values, but it should, in particular, consider the upper bound plume concentration.

The criteria importance through intercriteria correlation (CRITIC) and range of value (ROV) combined methods were used to determine a single index for all multiple responses.

This paper used cold metal transfer (CMT) and pulse metal-inert gas (MIG) welding processes to study the weld-on-bead geometry of AA2099-T86 alloy. This study used Taguchi's approach to find the optimal setting of the input welding parameters. The welding current, welding speed and contact-tip-to workpiece distance were the input welding parameters for finding the output responses, i.e. weld penetration, dilution and heat input. The L9 orthogonal array of Taguchi's approach was used to find out the optimal setting of the input parameters.

The optimal input welding parameters were determined with combined output responses. The predicted optimum welding input parameters were validated through confirmation tests. Analysis of variance showed that welding speed is the most influential factor in determining the weld bead geometry of the CMT and pulse MIG welding techniques.

The heat input and weld bead geometry are compared in both welding processes. The CMT welding samples show superior defect-free weld beads than pulse MIG welding due to lesser heat input and lesser dilution.

This paper addresses revolutionary changes in the education, fertility and market work of U.S. families formed in the 1870s–1920s: Fertility fell from 5.3 to 2.6; the graduation rate of their children increased from 7% to 50%; and the fraction of adulthood wives devoted to market-oriented work increased from 7% to 23% (by one measure).

These trends are addressed within a unified framework to examine the ability of several proposed mechanisms to quantitatively replicate these changes. Based on careful calibration, the choices of successive generations of representative husband-and-wife households over the quantity and quality of their children, household production, and the extent of mother’s involvement in market-oriented production are simulated.

Rising wages, declining mortality, a declining gender wage gap, and increased efficiency and public provision of schooling cannot, individually or in combination, reduce fertility or increase stocks of human capital to levels seen in the data. The best fit of the model to the data also involves: (1) a decreased tendency among parents to view potential earnings of children as the property of parents and (2) rising consumption shares per dependent child.

Greater attention should be given the determinants of parental control of the work and earnings of children for this period.

One contribution is the gathering of information and strategies necessary to establish an initial baseline, and the time paths for parameters and targets for this period beset with data limitations. A second contribution is identifying the contributions of various mechanisms toward reaching those calibration targets.

The current manuscript is focused on evaluating the capabilities of green practices that affect various business performance (BP) parameters of small and medium scale Indian manufacturing enterprises (SME’s). This study aims to obtain multiple significant factors that influence the implementation of green practices.

The manuscript focuses on statistical testing of responses obtained from 168 Indian SMEs to determine the relationship between input parameters and BP parameters. This paper starts with deploying tests such as Cronbach alpha and inter-item covariance test to obtain confidence in data collected, followed by various statistical tests such as Pearson correlation, multiple regression, canonical correlation to extract various significant factors the study. Further Games-Howell post hoc test is deployed to evaluate the significant improvements in BP gained over a reasonable duration of time. Finally, a discriminant validity test is used to find out the success or failure of the organizations that participated in the survey.

This research contributes to the holistic effect of green manufacturing (GM) toward gaining improvements in terms of different BP parameters taken for the study. It has been found that various input factors such as customer attributes, adoption of new technology, social pressure and government pressure are the main parameters for GM implementation. Further, it is observed that those at the maturity phase of GM implementation are reaping higher benefits than the organizations at the transition and stability phase.

The current study has been accomplished in Indian SME manufacturing organizations to investigate the effects of GM implementation in the organization. Although research findings imply the effective use of green practices within the organization to reap BP parameters and improve the market’s competitive image, the study cannot be generalized and can be used as an insight for both academicians and end-users in understanding the overall achievements of GM.

Portfolio optimization is the process of making an investment decision on a set of assets to realize high returns with low risk. It has three major stages: asset selection, asset weighting and asset management. Asset selection is an important phase because it influences asset allocation and ultimately affects the returns of a portfolio. Today, there is an increase in the number of listings on a stock exchange. Therefore, it is important for an investor to screen and select stocks for investment. This study focuses on the first stage of the portfolio optimization problem, namely, asset selection. The purpose of this study is to evaluate and select profitable stocks quoted on National Stock Exchange (NSE) for portfolio optimization.

Financial ratios are considered as the input and output parameters for evaluating the financial performance of the firms. This study adopts a hybrid principal component analysis (PCA) and data envelopment analysis (DEA) approach to evaluate the efficiency of the firms. Based on the efficiency scores, the firms are selected for the investment process.

The model helps to determine the relative efficiencies of the firms. The efficient firms are considered to be the potential stocks for investment. It helps the investors to screen the stocks from a large number of stocks quoted on NSE.

One of the limitations of the standard DEA model is that it fails to discriminate the firms when the number of input and output parameters are larger than the number of firms. To overcome this problem, either a parameter can be ignored or weight-restricted DEA can be applied. When an input/output parameter is dropped, the information in that variable is lost. Weight-restricted DEA model uses expert opinion for measuring the relative importance of input and output parameters. Expert opinion is subjective and might be biased. The PCA-DEA model helps to identify the efficient firms by improving the discriminatory power of standard DEA without any loss of information and without the need for expert opinion, which might be biased.

Asset selection is an important stage in the investment process. Selection of stocks based on the efficiency score is an easier option available to the investors. But the misclassification of firms either due to biased expert opinion or discrimination inability of DEA can be costly to an investor. The PCA-DEA model overcomes both these limitations. Investors can select the potential candidates for asset allocation based on the efficiency scores obtained using the PCA-DEA model. Further, the relative efficiencies obtained can help the firms to benchmark their performance against the best performing firms within their industry.

This paper is one of few papers to adopt the PCA-DEA framework to select stocks in the Indian stock market.

This study aims at proposing a hybrid model for early cost prediction of a construction project. Early cost prediction for a construction project is the basic approach to procure a project within a predefined budget. However, most of the projects routinely face the impact of cost overruns. Furthermore, conventional and manual cost computing techniques are hectic, time-consuming and error-prone. To deal with such challenges, soft computing techniques such as artificial neural networks (ANNs), fuzzy logic and genetic algorithms are applied in construction management. Each technique has its own constraints not only in terms of efficiency but also in terms of feasibility, practicability, reliability and environmental impacts. However, appropriate combination of the techniques improves the model owing to their inherent nature.

This paper proposes a hybrid model by combining machine learning (ML) techniques with ANN to accurately predict the cost of pile foundations. The parameters contributing toward the cost of pile foundations were collected from five different projects in India. Out of 180 collected data entries, 176 entries were finally used after data cleaning. About 70% of the final data were used for building the model and the remaining 30% were used for validation.

The proposed model is capable of predicting the pile foundation costs with an accuracy of 97.42%.

Although various cost estimation techniques are available, appropriate use and combination of various ML techniques aid in improving the prediction accuracy. The proposed model will be a value addition to cost estimation of pile foundations.

The purpose of this paper is to propose a web intrusion detection system (IDS), SensorWebIDS, which applies data mining, anomaly and misuse intrusion detection on web environment.

SensorWebIDS has three main components: the network sensor for extracting parameters from real‐time network traffic, the log digger for extracting parameters from web log files and the audit engine for analyzing all web request parameters for intrusion detection. To combat web intrusions like buffer‐over‐flow attack, SensorWebIDS utilizes an algorithm based on standard deviation (δ) theory's empirical rule of 99.7 percent of data lying within 3δ of the mean, to calculate the possible maximum value length of input parameters. Association rule mining technique is employed for mining frequent parameter list and their sequential order to identify intrusions.

Experiments show that proposed system has higher detection rate for web intrusions than SNORT and mod security for such classes of web intrusions like cross‐site scripting, SQL‐Injection, session hijacking, cookie poison, denial of service, buffer overflow, and probes attacks.

Future work may extend the system to detect intrusions implanted with hacking tools and not through straight HTTP requests or intrusions embedded in non‐basic resources like multimedia files and others, track illegal web users with their prior web‐access sequences, implement minimum and maximum values for integer data, and automate the process of pre‐processing training data so that it is clean and free of intrusion for accurate detection results.

Web service security, as a branch of network security, is becoming more important as more business and social activities are moved online to the web.

Existing network IDSs are not directly applicable to web intrusion detection, because these IDSs are mostly sitting on the lower (network/transport) level of network model while web services are running on the higher (application) level. Proposed SensorWebIDS detects XSS and SQL‐Injection attacks through signatures, while other types of attacks are detected using association rule mining and statistics to compute frequent parameter list order and their maximum value lengths.