Individual empowerment of agile and non-agile software developers in small teams

ContextEmpowerment of employees at work has been known to have a positive impact on job motivation and satisfaction. Software development is a field of knowledge work wherein one should also expect to see these effects, and the idea of empowerment has become particularly visible in agile methodologies, in which proponents emphasise team empowerment and individual control of the work activities as a central concern.ObjectiveThis research aims to get a better understanding of how empowerment is enabled in software development teams, both agile and non-agile, to identify differences in empowering practices and levels of individual empowerment.MethodTwenty-five interviews with agile and non-agile developers from Norway and Canada on decision making and empowerment are analysed. The analysis is conducted using a conceptual model with categories for involvement, structural empowerment and psychological empowerment.ResultsBoth kinds of development organisations are highly empowered and they are similar in most aspects relating to empowerment. However, there is a distinction in the sense that agile developers have more possibilities to select work tasks and influence the priorities in a development project due to team empowerment. Agile developers seem to put a higher emphasis on the value of information in decision making, and have more prescribed activities to enable low-cost information flow. More power is obtained through the achievement of managing roles for the non-agile developers who show interest and are rich in initiatives.ConclusionAgile developers have a higher sense of being able to impact the organisation than non-agile developers and have information channels that is significantly differently from non-agile developers. For non-agile teams, higher empowerment can be obtained by systematically applying low-cost participative decision making practices in the manager–developer relation and among peer developers. For agile teams, it is essential to more rigorously follow the empowering practices already established.     

Time series forecasting by neural networks: A knee point-based multiobjective evolutionary algorithm approach

In this paper, we investigate the problem of time series forecasting using single hidden layer feedforward neural networks (SLFNs), which is optimized via multiobjective evolutionary algorithms. By utilizing the adaptive differential evolution (JADE) and the knee point strategy, a nondominated sorting adaptive differential evolution (NSJADE) and its improved version knee point-based NSJADE (KP-NSJADE) are developed for optimizing SLFNs. JADE aiming at refining the search area is introduced in nondominated sorting genetic algorithm II (NSGA-II). The presented NSJADE shows superiority on multimodal problems when compared with NSGA-II. Then NSJADE is applied to train SLFNs for time series forecasting. It is revealed that individuals with better forecasting performance in the whole population gather around the knee point. Therefore, KP-NSJADE is proposed to explore the neighborhood of the knee point in the objective space. And the simulation results of eight popular time series databases illustrate the effectiveness of our proposed algorithm in comparison with several popular algorithms.     

An architectural design for effective information retrieval in semantic web

The current web IR system retrieves relevant information only based on the keywords which is inadequate for that vast amount of data. It provides limited capabilities to capture the concepts of the user needs and the relation between the keywords. These limitations lead to the idea of the user conceptual search which includes concepts and meanings. This study deals with the Semantic Based Information Retrieval System for a semantic web search and presented with an improved algorithm to retrieve the information in a more efficient way.This architecture takes as input a list of plain keywords provided by the user and the query is converted into semantic query. This conversion is carried out with the help of the domain concepts of the pre-existing domain ontologies and a third party thesaurus and discover semantic relationship between them in runtime. The relevant information for the semantic query is retrieved and ranked according to the relevancy with the help of an improved algorithm. The performance analysis shows that the proposed system can improve the accuracy and effectiveness for retrieving relevant web documents compared to the existing systems.     

Estimation and forecasting with logarithmic autoregressive conditional duration models: A comparative study with an application

This paper presents a semi-parametric method of parameter estimation for the class of logarithmic ACD (Log-ACD) models using the theory of estimating functions (EF). A number of theoretical results related to the corresponding EF estimators are derived. A simulation study is conducted to compare the performance of the proposed EF estimates with corresponding ML (maximum likelihood) and QML (quasi maximum likelihood) estimates. It is argued that the EF estimates are relatively easier to evaluate and have sampling properties comparable with those of ML and QML methods. Furthermore, the suggested EF estimates can be obtained without any knowledge of the distribution of errors is known. We apply all these suggested methodology for a real financial duration dataset. Our results show that Log-ACD (1, 1) fits the data well giving relatively smaller variation in forecast errors than in Linear ACD (1, 1) regardless of the method of estimation. In addition, the Diebold–Mariano (DM) and superior predictive ability (SPA) tests have been applied to confirm the performance of the suggested methodology. It is shown that the new method is slightly better than traditional methods in practice in terms of computation; however, there is no significant difference in forecasting ability for all models and methods.     

Fractionation studies of palm oil by density gradient

The paper describes a method of fractionating vegetable, animal and fish oils, and in particular palm oil. The method involves addition of a medium comprising two common solvents to the semisolid oils. On centrifugation, the olein and stearin are separated by the medium in the middle. Thirteen media made up from binary combinations of nine solvents, viz. water, propylene glycol, glycerine, methanol, ethanol,n-propanol, isopropanol (IPA), acetone and butanone, are found to be effective in olein-stearin separation. However, only the water/IPA and water/methanol systems have been studied in detail. The aqueous IPA provides a higher yield of olein than water/ methanol but intersolubility between oil and medium is also greater. The fractionation process can be carried out at any suitable temperature. Fractionation of the special prime bleached (SPB) palm oil at 16 C yields an olein with a cloud point of 4.8 C. Some hybrid palm oils produce a large quantity of low cloud point olein which can be bleached readily. The process can be extended to include degumming and neutralization by using an alkaline medium for centrifugation. The olein fractions obtained have been found to be free of phosphatides and the free fatty acids reduced to as low as 0.02%. Metal-scavenging agents have also been added to the medium in an attempt to remove copper and iron. The development of this process into a continuous one has been demonstrated on the AlfaLaval LAPX 202 Separator. Fractionation of crude palm oil using a density gradient provides seven fractions of different characteristics. The iodine values vary from 37.5 to 57.4 and the unsaturated fatty acids range from 32.7% to 51.2%. Triglyceride analysis by carbon numbers shows great differences in the C₄₈ and C₅₂ constituents of the fractions. aThe volume ratio of oil to medium in each case was 1:1. The separation involved the oil and wax.     

Sulfur content of crude rapesseed oil from aqueous extraction

Dry heating of rapeseed before grinding increased the sulphur content of the crude rapeseed oil. Boiling the seeds in H₂O before grinding caused a slight decrease in the sulphur level of the oil. Control of pH during boiling of the ground rapeseed increased the sulphur content of the oil from ca. 2 ppm at pH 3.1 to ca. 10 ppm at pH 7.6.     

Application of supercritical fluid chromatography in the quantitative analysis of minor components (carotenes,vitamin E,sterols, and squalene) from palm oil

The application of supercritical fluid chromatography (SFC) coupled with a UV variable-wavelength detector to isolate the minor components (carotenes, vitamin E, sterols, and squalene) in crude palm oil (CPO) and the residual oil from palm-pressed fiber is reported. SFC is a good technique for the isolation and analysis of these compounds from the sources mentioned. The carotenes, vitamin E, sterols, and squalene were isolated in less than 20 min. The individual vitamin E isomers present in palm oil were also isolated into their respective components, α-tocopherol, α-tocotrienol, γ-tocopherol, γ-tocotrienol, and δ-tocotrienol. Calibration of all the minor components of palm as well as the individual components of palm vitamin E was carried out and was found to be comparable to those analyzed by other established analytical methods.     

Support vector regression methodology for prediction of input displacement of adaptive compliant robotic gripper

The prerequisite for new versatile grippers is the capability to locate and perceive protests in their surroundings. It is realized that automated controllers are profoundly nonlinear frameworks, and a faultless numerical model is hard to get, in this way making it troublesome to control utilizing tried and true procedure. Here, a design of an adaptive compliant gripper is presented. This design of the gripper has embedded sensors as part of its structure. The use of embedded sensors in a robot gripper gives the control system the ability to control input displacement of the gripper and to recognize specific shapes of the grasping objects. Since the conventional control strategy is a very challenging task, soft computing based controllers are considered as potential candidates for such an application. In this study, the polynomial and radial basis function (RBF) are applied as the kernel function of Support Vector Regression (SVR) to estimate and predict optimal inputs displacement of the gripper according to experimental tests and shapes of grasping objects. Instead of minimizing the observed training error, SVR _poly and SVR _rbf attempt to minimize the generalization error bound so as to achieve generalized performance. The experimental results show that an improvement in predictive accuracy and capability of generalization can be achieved by the SVR approach compared to other soft computing methodology.     

Feature extraction and ML techniques for static gesture recognition

The main objective of this study is to explore the utility of a neural network-based approach in hand gesture recognition. The proposed system presents two recognition algorithms to recognize a set of six specific static hand gestures, namely open, close, cut, paste, maximize, and minimize. The hand gesture image is passed through three stages: preprocessing, feature extraction, and classification. In the first method, the hand contour is used as a feature that treats scaling and translation of problems (in some cases). However, the complex moment algorithm is used to describe the hand gesture and to treat the rotation problem in addition to scaling and translation. The back-propagation learning algorithm is employed in the multilayer neural network classifier. The second method proposed in this article achieves better recognition rate than the first method.