Purpose The purpose of this paper is to propose a new managerial method to integrate the environmental dimension in the domino effects (DEs) analysis. Design/methodology/approach The proposed method is a three-step approach: identification of primary hazards in the form of potential events causing the DE, a mixed quantification (deterministic and probabilistic) of the risk of the DEs with a view to its control and capitalizing the results from the BLEVESOFT software as well as those relating to the probability of occurrence of the DEs in the form of a prioritized action plan dedicated to surrounding environments (proximity territory). Findings The primary hazards are technologically manageable at the studied system but are unpredictable if triggered at the environmental subsystem because they are difficult to be managed and often cause panic, which is a form of a very catastrophic DE. Research limitations/implications The research could affect members of the engineering and construction industry, and can be applied in several domains since it studies the DE phenomenon. which is a common problem especially in industrials plants. Practical implications The proposal method is applied in an industrial terminal in Algeria. Originality/value This paper presents an exploratory study of using a new managerial method that aims to combine the potentialities of geomatic sciences that allow the spatial representation of nearby territories to assess the severity of DEs through a deterministic approach, and the modeling of DEs as well as their analysis by a probabilistic approach.

Using a risk matrix for Risk mapping constitutes the basis of risk management strategy. It aims to classify the identified risks with regards to their management and control. This risk classification, which is based on the frequency and the severity dimensions, is often carried out according to a procedure founded on experts’ judgments. In order to overcome the subjectivity bias of this classification, this paper presents the contribution of the Principal Components Analysis (PCA) method: an exploratory method for graphing risks based on factors that allow a better visualized classification of scenarios accidents. Still, the commonly encountered problem in the data classified by the PCA method resides in the main factors of classification; we judged useful to frame these letters by an algebraic formulation to make an improvement of this classification possible. The obtained results show that the suggested method is a promising alternative to solve the recurring problems of risk matrices, notably in accident scenarios’ classification.

Mobile Wireless Sensor and Actor Networks (MWSANs) can simply be defined as an extension of Wireless Sensor and Actor Networks (WSANs) in which the actor nodes are mobile. As such, in addition to challenges existing in WSAN, the mobility also imposes new challenges such as localization of actors, cooperative tracking of both actor-actor and actor-sensor collaboration, and communication infrastructure between distant actors. New communication protocols, specific to MWSANs, are needed. In this paper, we propose a self-organization and data collection protocol in order to provide energy efficiency, low latency, high success rate and suitably interactions between sensors and actors and take benefit from the mobility and resources existing on the network’s actor nodes. The actor nodes move according to RWP mobility model. Each actor, during its pause time creates a temporary cluster, and is the head of it, collects and processes sensor data and performs actions on the environment based on the information gathered from sensor nodes in its cluster. Once an actor detects a base station it delivers the collected data to it. The simulations carried out (with TOSSIM tool), comfort us with good performances results.

Scene text presents challenging characteristics mainly related to acquisition circumstances and environmental changes resulting in low quality videos. In this paper, we present a scene text detection algorithm based on pseudo Zernike moments (PZMs) and stroke features from low resolution lecture videos. Algorithm mainly consists of three steps: slide detection, text detection and segmentation and non-text filtering. In lecture videos, slide region is a key object carrying almost all important information; hence slide region has to be extracted and segmented from other scene objects considered as background for later processing. Slide region detection and segmentation is done by applying pseudo Zernike moment’s based on RGB frames. Text detection and extraction is performed using PZMs segmentation over V channel of HSV colour space, and then stroke feature is used to filter out non-text region and to remove false positives. The algorithm is robust to illumination, low resolution and uneven luminance from compressed videos. Effectiveness of PZM description leads to very few false positives comparing to other approached. Moreover resulting images can be used directly by OCR engines and no more processing is needed.