An Exhaustive Requirement Analysis Approach to Estimate Risk Using Requirement Defect and Execution Flow Dependency for Software Development

Requirement defects are one of the major sources of failure in any software development process as they prevent smooth operation and is taxing both in terms of tracking and validation. The objective of this article is to make requirement analysis phase exhaustive by estimating risk at requirement level using requirement defect information and execution flow dependency as early as possible to inhibit them from being incorporated in design and implementation. The proposed approach works as a two-fold process which computes risk involved with each requirement twice. The whole process is divided into a three-layered framework to finalize requirements with clear vision and scope of a project. The entire process has been supported by a software case study. The results of the proposed work are promising and will help software engineers in ensuring that all business requirements are captured correctly with clear vision and scope. It will also help in decreasing the chances of failure, risk, and conflicts between stakeholder and developer and other challenges involved to develop the project.

Modeling the impacts of dams and stocking practices on an endangered Atlantic salmon (Salmo salar) population in the Penobscot River, Maine, USA

Dams challenge Atlantic salmon (Salmo salar) conservation, while hatcheries are a common but poorly evaluated recovery tool. We developed a spatially explicit smolt survival model for the Penobscot River, Maine, USA, population. By partitioning survival through dams (with flow dependency), free-flowing river reaches, and the estuary (with dam dependency), the model quantified how these factors influenced the number of fish entering the ocean. Given historical impounded conditions, 74%–22% of hatchery smolts released entered the ocean annually from 1970 to 2012. Of 19.7 million smolts stocked, 7.7 million entered the ocean (39%). Survival was most variable at dams (range 95% to 63%), followed by in-river (range 98% to 70%) and estuary (range 88% to 82%). Overall, lower-river stocking sites resulted in significantly higher numbers at ocean entry because of fewer dam encounters and shorter migrations. Higher flows also resulted in reduced losses. By reconstructing these freshwater and estuary dynamics, the model provides a more accurate estimate of ocean recruitment annually and can be used for scenario planning of future stocking locations relative to predicted flows while being adaptable to new survival rates.

Relationship between background invertebrate drift concentration and flow over natural flow recession and prediction with a drift transport model

This study advances understanding of the flow dependency of invertebrate drift in rivers and its relevance to drift-feeding fish. Background drift concentration varied spatially and with flow over natural flow recession (lower mid-range to low flow) in a reach of a New Zealand river, largely consistent with passive entrainment. Seven taxonomic groups (dominated by Leptophlebiidae and Chironomidae) exhibited positive drift concentration–flow relationships, and one (sandy/stony-cased caddisflies (Conoesucidae)) exhibited negative relationships. A mechanistic drift transport model accurately predicted the slope, but not y intercept, of the drift concentration–flow relationship for the total drift community that positively responded to flow but performed more poorly at the taxon or size-class level. Partitioning the relative influence of drift entry and dilution revealed that positive drift concentration–flow relationships arose from entry overwhelming dilution with increasing flow. Drift transport models have potential for predicting relative (%) effects of flow change on concentration and rate of drift-prone invertebrates. This paves the way for drift transport models to inform inputs to net rate of energy intake models for drift-feeding fish.

Challenges for a Safe Workplace and Cope Up Strategy: A Case Study for Construction Industry in Sarawak, Malaysia

Like many other parts of the globe, in Malaysia, construction industry has a vital support to the national economy, even though statistics indicates that, at the same time, it is an extremely risky profession due to high accident rate. In addition, current evidences suggest that, in general, safety in Sarawak construction industries lack attention. As, human behavior is regarded as the principal component that contributes to accidents in this industry, this study intends to explore existing barriers in development and maintaining safety performance behavior as well as possible coping strategies. A qualitative analysis of in-depth interview (N=4) among different levels of professions within industries in Kuching, Sarawak reveals that, price sensitive subcontracting, short-term projects, irregular cash flow, dependency on larger companies, less managerial and supervisory engagement, and priority on progress alone create obstacles for safety behavior. On the other hand, possibly, the challenges can be managed through integration of safety into contracts as compliance, associated disbursement, and introducing incentives. Keywords: Construction industry in Sarawak; safety performance behavior

An Exhaustive Requirement Analysis Approach to Estimate Risk Using Requirement Defect and Execution Flow Dependency for Software Development

Requirement defects are one of the major sources of failure in any software development process as they prevent smooth operation and is taxing both in terms of tracking and validation. The objective of this article is to make requirement analysis phase exhaustive by estimating risk at requirement level using requirement defect information and execution flow dependency as early as possible to inhibit them from being incorporated in design and implementation. The proposed approach works as a two-fold process which computes risk involved with each requirement twice. The whole process is divided into a three-layered framework to finalize requirements with clear vision and scope of a project. The entire process has been supported by a software case study. The results of the proposed work are promising and will help software engineers in ensuring that all business requirements are captured correctly with clear vision and scope. It will also help in decreasing the chances of failure, risk, and conflicts between stakeholder and developer and other challenges involved to develop the project.

A pathway to eliminate the gas flow dependency of a hydrocarbon sensor for automotive exhaust applications

Gas sensors will play an essential role in future combustion-based mobility to effectively reduce emissions and monitor the exhausts reliably. In particular, an application in automotive exhausts is challenging due to the high gas temperatures that come along with highly dynamic flow rates. Recently, a thermoelectric hydrocarbon sensor was developed by using materials which are well known in the exhausts and therefore provide the required stability. As a sensing mechanism, the temperature difference that is generated between a catalytically activated area during the exothermic oxidation of said hydrocarbons and an inert area of the sensor is measured by a special screen-printed thermopile structure. As a matter of principle, this thermovoltage significantly depends on the mass flow rate of the exhausts under certain conditions. The present contribution helps to understand this cross effect and proposes a possible setup for its avoidance. By installing the sensor in the correct position of a bypass solution, the gas flow around the sensor is almost free of turbulence. Now, the signal depends only on the hydrocarbon concentration and not on the gas flow. Such a setup may open up new possibilities of applying novel sensors in automotive exhausts for on-board-measurement (OBM) purposes.

Principles and analytical tools for reconstruction of probabilistic dependency structures in special class

We examine a problem of reconstruction of dependency structure from data. It is assumed that model structure belongs to class of "mono-flow" graphs, which is a subclass of acyclonic digraph (known as DAGs) and is super-class relatively to the poly-trees. Properties of the mono-flow dependency models are examined, especially in terms of patterns of unconditional dependencies and mutual information. We characterize the twin-association evolving among two variables. Specialized methods of inference of mono-flow dependency model are briefly reviewed. To justify correctness of model recovery from data we formulate an assumption of unconditional (marginal) edge-wise faithfulness, perhaps the most reliable one among all simple versions of Causal faithfulness assumption. On the basis of the assumption and the properties of mono-flow dependency models we derive several empirical resolutions for edge identification, which make use 2-placed statistics only. A lot of experiments with artificial data have demonstrated efficiency of the resolutions in that they correctly recover many edges and commit low error rate.