Strengthening production plants and process control functions contribute to a global improvement of manufacturing systems because of their cross-functional characteristics in the industry. Companies established various innovative and operational strategies; there is increasing competitiveness among them and increasing companies’ value. Machine learning (ML) techniques become an intelligent enticing option to address industrial issues in the current manufacturing sector since the emergence of Industry 4.0 and the extensive integration of paradigms such as big data and high computational power. Implementing a system able to identify faults early to avoid critical situations in the production line and its environment is crucial. Therefore, powerful machine learning algorithms are performed for fault diagnosis, real-time data classification, and predicting the state of functioning of the production line. Random forests proved to be a better classifier with an accuracy of 97%, compared to the SVM model’s accuracy which is 94.18%. However, the K-NN model’s accuracy is about 93.83%. An accuracy of 80.25% is achieved by the logistic regression model. About 83.73% is obtained by the decision tree’s model. The excellent experimental results reached on the random forest model demonstrated the merits of this implementation in the production performance, ensuring predictive maintenance and avoiding wasting energy.
Besides their important ecological role in flowing waters, blackflies (Diptera: Simuliidae) may pose medical and veterinary risks. For seventeen months, we surveyed the blackflies of ten localities across the Aures Mountains, in the Saharan Atlas, Algeria, and recorded eight taxa (i.e. species, species groups or species complexes). High altitude sites were dominated by the Simulium ornatum (Meigen, 1818) group, whereas sites located on the southern slope of the Aures Mountains were occupied by the eurytopic Simulium velutinum (Santos Abreu, 1922) complex and the thermophilic, pollutant-tolerant Simulium ruficorne Macquart, 1838 ‘A’ morphotype.Co-inertia analysis was used to determine the relationship between a species’ abundance and habitat types. The co-inertia analysis revealed a likely co-structure between blackfly assemblages and measured environmental descriptors (water temperature, conductivity, current velocity, bed width, etc.) in sampled habitats. This confirmed the importance of altitude as a driver of blackfly distribution. Our results also showed that there has been an increase in anthropogenic pressures on the vulnerable freshwater biota of the Aures Mountains.
In the context of the industrial process safety, the domino effect has become a topical issue for scientists and managers of companies given the diversity of factors that contributed to the aggravation of this phenomenon such as; proximity to industrial facilities, transport networks, development of industrial complexes, storage of hazardous substances and population growth. The purpose of this article is the MICDE method (Method of Identification and Characterization of Domino Effects) application on industrial zone of LPG storage in SONATRACH-Algeria Group for analyzes the domino effects caused by a major industrial accident. Our study is adopted on the MICDE method which constitutes an aid in the integration of the domino effects problem in hazard studies and safety studies. In our application, it aims to formalize the points relating to the domino effects due to the BLEVE (Boiling Liquid Expanding Vapor Explosion) phenomenon of an LPG storage sphere. The results obtained show that the hazardous equipment in the vicinity is seriously affected by the thermal and overpressure effect of the main accident, and may be seats in a new accident. The MICDE method is a promising method can be applied in several fields since it studies the phenomenon. This method facilitates decision-making in the prevention of domino effects for the sustainability facilities
In the context of the industrial process safety, the domino effect has become a topical issue for scientists and managers of companies given the diversity of factors that contributed to the aggravation of this phenomenon such as; proximity to industrial facilities, transport networks, development of industrial complexes, storage of hazardous substances and population growth. The purpose of this article is the MICDE method (Method of Identification and Characterization of Domino Effects) application on industrial zone of LPG storage in SONATRACH-Algeria Group for analyzes the domino effects caused by a major industrial accident.
Our study is adopted on the MICDE method which constitutes an aid in the integration of the domino effects problem in hazard studies and safety studies. In our application, it aims to formalize the points relating to the domino effects due to the BLEVE (Boiling Liquid Expanding Vapor Explosion) phenomenon of an LPG storage sphere.
The results obtained show that the hazardous equipment in the vicinity is seriously affected by the thermal and overpressure effect of the main accident, and may be seats in a new accident. The MICDE method is a promising method can be applied in several fields since it studies the phenomenon. This method facilitates decision-making in the prevention of domino effects for the sustainability facilities
Ecdyonurus aurasius sp. nov. , a micro-endemic species reported from several streams within the Aurès Mountains (north-eastern Algeria), is described and illustrated at nymphal, subimaginal and imaginal stages of both sexes. Critical morphological diagnostic characters distinguishing the new species are presented, together with molecular affinities as well as notes on the biology and distribution of the species.
During the last two decades, the attention of researchers has been focused on repairing and retrofitting concrete frames to make them more earthquake-resistant. Two methods have been developed to increase the seismic resistance of previously undamaged structures before they are subjected to an earthquake. The first is through the addition of new structural members, such as steel braces and the second is by selectively strengthening structural elements, for instance through steel caging. Seismic response analysis results have been utilized in multi-story RC frames that were designed without seismic design criteria. This study aims to determine whether the retrofitting technique is effective based on comparisons between steel braces, steel cages, and their combinations. The seismic performance is defined by the seismic code for Algeria RPA 2003 according to the latest recommendations. Static nonlinear analysis was used to compare seismic responses of existing non-ductile reinforced concrete RC frames under a variety of retrofit schemes. The results show that retrofitting with steel caging gives excellent performance in terms of ductility and low shear capacity. The retrofitting with steel bracing increased the shear capacity but led to a severe ductility deficiency. The retrofitting structure combined with steel bracing and steel caging shows good performance in shear capacity and ductility. Using the Zipper system (steel bracing) and V system in combination with steel caging gives similar results to the RPA model.
Sebkha soils are defined as problem soils located in arid, semi-arid, and coastal areas. Generally, they are fine soil, composed of silt, sand, and clay, which are cemented by different salts (e.g., halite, gypsum, and calcite). In nature, sebkha saline soils are exposed to different drying and wetting (D-W) cycles. However, these cycles have a significant effect on the mechanical behavior of these soils. This study aims to characterize the chemical, mineralogical, and geotechnical properties of sebkha soil using an experimental approach. We focus on the effects of D-W cycles on the unconfined compressive strength (UCS) and salinity of sebkha soils from Ain M'Lila, Algeria. In addition, these D-W cycles were applied to the samples dried in the open air to achieve the targeted water content (water content values of 7%, 11.4%, and 13%). The results obtained show that the UCS increases with decrease in water content and decreases with an increase in the number of D-W cycles. In addition, these cycles affect the salinity of the sebkha soil. Indeed, a significant decrease in soil salinity was recorded with an increase in the number of D-W cycles. Finally, a relationship was found between the salinity of the soil and UCS. The latter decreases with a decrease in soil salinity; this relationship becomes very significant for low water content values of 7% or less.
Sebkha soils are defined as problem soils located in arid, semi-arid, and coastal areas. Generally, they are fine soil, composed of silt, sand, and clay, which are cemented by different salts (e.g., halite, gypsum, and calcite). In nature, sebkha saline soils are exposed to different drying and wetting (D-W) cycles. However, these cycles have a significant effect on the mechanical behavior of these soils. This study aims to characterize the chemical, mineralogical, and geotechnical properties of sebkha soil using an experimental approach. We focus on the effects of D-W cycles on the unconfined compressive strength (UCS) and salinity of sebkha soils from Ain M’Lila, Algeria. In addition, these D-W cycles were applied to the samples dried in the open air to achieve the targeted water content (water content values of 7%, 11.4%, and 13%). The results obtained show that the UCS increases with decrease in water content and decreases with an increase in the number of D-W cycles. In addition, these cycles affect the salinity of the sebkha soil. Indeed, a significant decrease in soil salinity was recorded with an increase in the number of D-W cycles. Finally, a relationship was found between the salinity of the soil and UCS. The latter decreases with a decrease in soil salinity; this relationship becomes very significant for low water content values of 7% or less.
Many machine learning-based methods have been widely applied to Coronary Artery Disease (CAD) and are achieving high accuracy. However, they are black-box methods that are unable to explain the reasons behind the diagnosis. The trade-off between accuracy and interpretability of diagnosis models is important, especially for human disease. This work aims to propose an approach for generating rule-based models for CAD diagnosis. The classification rule generation is modeled as combinatorial optimization problem and it can be solved by means of metaheuristic algorithms. Swarm intelligence algorithms like Equilibrium Optimizer Algorithm (EOA) have demonstrated great performance in solving different optimization problems. Our present study comes up with a Novel Discrete Equilibrium Optimizer Algorithm (NDEOA) for the classification rule generation from training CAD dataset. The proposed NDEOA is a discrete version of EOA, which use a discrete encoding of a particle for representing a classification rule; new discrete operators are also defined for the particle’s position update equation to adapt real operators to discrete space. To evaluate the proposed approach, the real world Z-Alizadeh Sani dataset has been employed. The proposed approach generate a diagnosis model composed of 17 rules, among them, five rules for the class “Normal” and 12 rules for the class “CAD”. In comparison to nine black-box and eight white-box state-of-the-art approaches, the results show that the generated diagnosis model by the proposed approach is more accurate and more interpretable than all white-box models and are competitive to the black-box models. It achieved an overall accuracy, sensitivity and specificity of 93.54%, 80% and 100% respectively; which show that, the proposed approach can be successfully utilized to generate efficient rule-based CAD diagnosis models.
The magnetohydrodynamic pump is an attractive solution, in particular for biomedical applications. In an MHD pump, an electromagnetic force is created by the applied magnetic field, which causes the fluid movement. The main advantage of the MHD pump is there are no mobile (mechanical) parts and it can place directly on veins. The present paper deals with the blood behaviour in the MHD micropump. A neodymium permanent magnet is used for applying a magnetic field to the channel in the MHD micropump. The numerical study examines the influence of the channel dimensions, the flux magnetic density and the electrode potentials on the blood velocity. This micropump can be easily controlled by a low voltage source. The numerical simulation analysis for the adopted model was implemented in order to verify the micropump operation. The magnetic and electrical fields have a strong influence on blood velocity in the MHD micropump. Finite element modelling software was used for this process. The second objective of this work is the possibility to exploit the properties of this pump in hemodialysis to pump blood and cleaning fluid.
Reliability and security of power distribution and data traffic in smart grid (SG) are very important for industrial control systems (ICS). Indeed, SG cyber-physical connectivity is subject to several vulnerabilities that can damage or disrupt its process immunity via cyberthreats. Today’s ICSs are experiencing highly complex data change and dynamism, increasing the complexity of detecting and mitigating cyberattacks. Subsequently, and since Machine Learning (ML) is widely studied in cybersecurity, the objectives of this paper are twofold. First, for algorithmic simplicity, a small-scale ML algorithm that attempts to reduce computational costs is proposed. The algorithm adopts a neural network with an augmented hidden layer (NAHL) to easily and efficiently accomplish the learning procedures. Second, to solve the data complexity problem regarding rapid change and dynamism, a label autoencoding approach is introduced for Embedding Labels in the NAHL (EL-NAHL) architecture to take advantage of labels propagation when separating data scatters. Furthermore, to provide a more realistic analysis by addressing real-world threat scenarios, a dataset of an electric traction substation used in the high-speed rail industry is adopted in this work. Compared to some existing algorithms and other previous works, the achieved results show that the proposed EL-NAHL architecture is effective even under massive dynamically changed and imbalanced data.
Purpose The objective of this article is to carry out an exploratory study on the emergence of health and safety committees (HSC) in Algerian companies. Design/methodology/approach The methodology chosen, governed by the systemic approach, is centered on the exploration of HSC emergence factors in companies. Findings The suggested method consists in defining hypotheses found in the literature on the emergence of HSC in order to affirm or refute them in the case of Algerian companies. Research limitations/implications Despite the fact that a good number of Algerian industrial companies were solicited; the number of respondents was limited. This limitation confirms the difficulty commonly encountered in exploratory studies by questionnaire. Practical implications The results of this exploratory study serve as a basis for the elaboration of a national action plan dedicated to HSC emergence in Algerian companies. Originality/value This is the first study conducted in Algerian companies on HSC emergence. The identification of emergence problems allows drawing up an effective action plan to solve them.
Antibiotic-resistant bacteria can leave hospitals and therefore contaminate the environment and, most likely, humans and animals, through different routes, among which wastewater discharge is of great importance. This study aims to assess the possible role of hospital sewage as reservoir and dissemination pathway of carbapenem-resistant Gram-negative bacilli (GNB). Carbapenem-resistant GNB were selectively isolated from wastewater collected from a public hospital in Batna, Algeria. Species identification was carried out using matrix-assisted laser desorption and ionization time-of-flight mass spectrometry, and antibiotic susceptibility was evaluated by the disc diffusion method. β-Lactamase production was investigated phenotypically using the double-disk synergy assay and the modified CarbaNP test, then the molecular mechanisms of β-lactam-resistance were studied by PCR and sequencing. Ten Enterobacteriaceae and 14 glucose-nonfermenting GNB isolates were obtained. All Enterobacteriaceae isolates were positive for OXA-48 and TEM-1D β-lactamases, where seven of them coproduced an extended-spectrum β-lactamase. VIM-2 carbapenemase was detected in six glucose-nonfermenting GNB isolates. However, three Pseudomonas aeruginosa, one Comamonas jiangduensis and one Acinetobacter baumannii isolates were positive for VIM-4 variant. In addition, NDM-1 enzyme was detected in four A. baumannii isolates. Our findings highlight the potential impact of hospital wastewater in the spread of drug resistance mechanisms outside of hospitals.
