Background A positive Patient Safety Culture (PSC) is considered as the main barrier to adverse events (AEs) that affect healthcare quality and safety. Thus, the assessment of PSC became a priority for healthcare providers in order to identify problematic areas that need improvement actions. Method A cross sectional multi-center study was conducted to evaluate quantitatively PSC in 10 Algerian healthcare establishments (HEs) within the framework of the Algerian Observatory of Safety Culture (ASCO Project). The French version of the HSOPSC was used as a measurement tool where it was administered to participants (N = 1370) using convenience sampling. Results A total of 1118 respondents, all professional categories included, participated in this study. The response rate was estimated at 69% of the sample size (N = 1370). After statistical processing, 950 questionnaires were retained. Internal consistency was above 0.7 for all dimensions. Problematic PSC dimensions were identified, mainly ‘Non-punitive response to error’, ‘Staffing’ and ‘Communication openness’. Conclusions This article sheds light on the critical situation of PSC in the Algerian national health system. Quantitative findings were introduced in the framework of the Algerian Safety Culture Observatory project that will serve as a baseline for different stakeholders to guide long-term promotion actions.
Background: A positive Patient Safety Culture (PSC) is considered as the main barrier to adverse events (AEs) that affect healthcare quality and safety. Thus, the assessment of PSC became a priority for healthcare providers in order to identify problematic areas that need improvement actions. Method: A cross sectional multi-center study was conducted to evaluate quantitatively PSC in 10 Algerian healthcare establishments (HEs) within the framework of the Algerian Observatory of Safety Culture (ASCO Project). The French version of the HSOPSC was used as a measurement tool where it was administered to participants (N = 1370) using convenience sampling. Results: A total of 1118 respondents, all professional categories included, participated in this study. The response rate was estimated at 69% of the sample size (N = 1370). After statistical processing, 950 questionnaires were retained. Internal consistency was above 0.7 for all dimensions. Problematic PSC dimensions were identified, mainly ‘Non-punitive response to error’, ‘Staffing’ and ‘Communication openness’. Conclusions: This article sheds light on the critical situation of PSC in the Algerian national health system. Quantitative findings were introduced in the framework of the Algerian Safety Culture Observatory project that will serve as a baseline for different stakeholders to guide long-term promotion actions.
Smart grid is an emerging system providing many benefits in digitizing the traditional power distribution systems. However, the added benefits of digitization and the use of the Internet of Things (IoT) technologies in smart grids also poses threats to its reliable continuous operation due to cyberattacks. Cyber–physical smart grid systems must be secured against increasing security threats and attacks. The most widely studied attacks in smart grids are false data injection attacks (FDIA), denial of service, distributed denial of service (DDoS), and spoofing attacks. These cyberattacks can jeopardize the smooth operation of a smart grid and result in considerable economic losses, equipment damages, and malicious control. This paper focuses on providing an extensive survey on defense mechanisms that can be used to detect these types of cyberattacks and mitigate the associated risks. The future research directions are also provided in the paper for efficient detection and prevention of such cyberattacks.
Fuel cell technology has been rapidly developed in the last decade owing to its clean characteristic and high efficiency. Proton exchange membrane fuel cells (PEMFCs) are increasingly used in transportation applications and small stationary applications; however, the cost and the unsatisfying durability of the PEMFC stack have limited their successful commercialization and market penetration. In recent years, thanks to the availability and the quality of emerging data of PEMFCs, digitization is happening to offer possibilities to increase the productivity and the flexibility in fuel cell applications. Therefore, it is crucial to clarify the potential of digitization measures, how and where they can be applied, and their benefits. This paper focuses on the degradation performance of the PEMFC stacks and develops a data-driven intelligent method to predict both the short-term and long-term degradation. The dilated convolutional neural network is for the first time applied for predicting the time-dependent fuel cell performance and is proved to be more efficient than other recurrent networks. To deal with the long-term performance uncertainty, a conditional neural network is proposed. Results have shown that the proposed method can predict not only the degradation tendency, but also contain the degradation behaviour dynamics.
On considère généralement Funny Games comme un film gênant et violent, par moment vraiment horrible ou carrément insoutenable. Malgré tout, c’est une expérience filmique à tenter, ou plutôt à subir. Une rude expérience affective qui nous souille. Elle nous corrompt, nous rend complice de la torture psychologique et de l’assassinat méthodique d’une famille bourgeoise. Aucune explication n’est donnée pour toute cette hostilité, aucune motivation n’atténue ces actes abjects, aucune justification à cette violence qui s’exprime brutalement. Et le spectateur, impuissant et ne pouvant y échapper, devra également encaisser toutes ces bassesses. On verra que le réalisateur Michael Haneke n’est ni dans l’audace artistique pure ni dans la facilité d’un « racolage » indigne, comme on a pu l’écrire[1]. On verra que les clés pour comprendre ce film, qui dure 1 heure et 44 minutes, se cachent déjà dans ses 3 premières minutes. À travers cet article, notre ambition est de replacer le concept d’impudence au centre de ce film que l’on qualifie un peu trop hâtivement d’ultraviolent, d’obscène et de sadique.
Le présent article traite de la problématique de la poésie et de la prose comme deux productions artistiques unies par l’appartenance à un domaine commun, mais différenciées par des caractéristiques singulières. Dans cette optique, nous opterons pour une démarche chronologique qui atteste de la dynamique de la pensée humaine en matière de production artistique. Pour nous inscrire dans la mobilité en question et respecter le principe de la contextualisation de tout discours, nous partirons de «La Poétique» et «La Rhétorique» d’Aristote pour passer en revue les conceptions données à ces deux genres artistiques par Barthes, Genette, Jakobson, Sartre, Todorov, et tant d’autres théoriciens.
Logistics is one of the main tactics that countries and businesses are improving in order to increase profits. Another prominent theme in today’s logistics is emerging technologies. Today’s developments in logistics and industry are how to profit from collected and accessible data to use it in various processes such as decision making, production plan, logistics delivery programming, and so on, and more specifically deep learning methods. The aim of this paper is to identify the various applications of deep learning in logistics through a systematic literature review. A set of research questions had been identified to be answered by this article.
The Ki-67 proliferation index is an essential biomarker that helps pathologists to diagnose and select appropriate treatments. However, automatic evaluation of Ki-67 is difficult due to nuclei overlapping and complex variations in their properties. This paper proposes an integrated pipeline for accurate automatic counting of Ki-67, where the impact of nuclei separation techniques is highlighted. First, semantic segmentation is performed by combining the Squeez and Excitation Resnet and Unet algorithms to extract nuclei from the background. The extracted nuclei are then divided into overlapped and non-overlapped regions based on eight geometric and statistical features. A marker-based Watershed algorithm is subsequently proposed and applied only to the overlapped regions to separate nuclei. Finally, deep features are extracted from each nucleus patch using Resnet18 and classified into positive or negative by a random forest classifier. The proposed pipeline’s performance is validated on a dataset from the Department of Pathology at Hôpital Nord Franche-Comté hospital.
The increase in electricity theft has become one of the main concerns of power distribution networks. Indeed, electricity theft could not only lead to financial losses, but also leads to reputation damage by reducing the quality of supply. With advanced sensing technologies of metering infrastructures, data collection of electricity consumption enables data-driven methods to emerge in such non-technical loss detections as an alternative to traditional experience-based human-centric approaches. In this context, such fraud prediction problems are generally a thematic of missing patterns, class imbalance, and higher level of cardinality where there are many possibilities that a single feature can assume. Therefore, this article is introduced specifically to solve data representation problem and increase the sparseness between different data classes. As a result, deeper representations than deep learning networks are introduced to repeatedly merge the learning models themselves into a more complex architecture in a sort of recurrent expansion. To verify the effectiveness of the proposed recurrent expansion of deep learning (REDL) approach, a realistic dataset of electricity theft is involved. Consequently, REDL has achieved excellent data mapping results proven by both visualization and numerical metrics and shows the ability of separating different classes with higher performance. Another important REDL feature of outliers correction has been also discovered in this study. Finally, comparison to some recent works also proved superiority of REDL model.
Recently, Adaptive Traffic Signal Control (ATSC) in the multi-intersection system is considered as one of the most critical issues in Intelligent Transportation Systems (ITS). Among the proposed AI-based approaches, Deep Reinforcement Learning (DRL) has been largely applied while showing better performances. This paper proposes a new DRL-based cooperative approach for controlling multiple intersections. The problem is modelled as a Multi-Agent Reinforcement Learning (MARL) system, while each agent is trained to select the best action to control an intersection by obtaining information about its local lanes state. The cooperation aspect is manifested in this approach by considering the effect of the state, action and reward of neighbour agents in the process of policy learning. An intersection controller applies a Deep Q-Network (DQN) method, while transferring state, action and reward received from their neighbour agents to its own loss function during the learning process. The experimental results under different scenarios shows that the proposed approach outperforms many state-of-the-art approaches in terms of three metrics: Average Waiting Time (AWT), Average Queue Length (AQL) and Average Emission CO2 (AEC). In addition, the cooperation between the different trained DRL-based controllers allows the system to continuously learn and improve its performance by interacting with the environment, particularly when the traffic is congested.
Recently, Adaptive Traffic Signal Control (ATSC) in the multi-intersection system is considered as one of the most critical issues in Intelligent Transportation Systems (ITS). Among the proposed AI-based approaches, Deep Reinforcement Learning (DRL) has been largely applied while showing better performances. This paper proposes a new DRL-based cooperative approach for controlling multiple intersections. The problem is modelled as a Multi-Agent Reinforcement Learning (MARL) system, while each agent is trained to select the best action to control an intersection by obtaining information about its local lanes state. The cooperation aspect is manifested in this approach by considering the effect of the state, action and reward of neighbour agents in the process of policy learning. An intersection controller applies a Deep Q-Network (DQN) method, while transferring state, action and reward received from their neighbour agents to its own loss function during the learning process. The experimental results under different scenarios shows that the proposed approach outperforms many state-of-the-art approaches in terms of three metrics: Average Waiting Time (AWT), Average Queue Length (AQL) and Average Emission CO2 (AEC). In addition, the cooperation between the different trained DRL-based controllers allows the system to continuously learn and improve its performance by interacting with the environment, particularly when the traffic is congested.
Designing and manufacturing replacement cancellous bone structures by lattice structures and Additive Manufacturing (AM) techniques is an effective method to create lightweight orthopedic implants while ensuring that they are mechanically compatible and their osseointegration ability with the host bone. In this article, we suggest a new design based on three lattice structures from triply periodic minimal surfaces (TPMS) with a different volume porosity to replace cancellous bone based on predicting the mechanical stiffness. To predict the mechanical stiffness, the relationship between the effective modulus of elasticity and different porosity ratios of the lattice structures was determined by using three methods: i) finite element modeling (FEM) simulation, ii) Gibson and Ashby method and iii) a uniaxial compression test after manufacturing the lattice structures by using Fused Filament Fabrication (FFF) Technology. To demonstrate the efficiency of our approach, the comparison of both numerical and experimental results showed that the effect of structure difference and porosity ratio of lattice structures on the mechanical stiffness values effectively match the cancellous bone in terms of elastic modulus and porosity ratio.
Due to advanced monitoring technologies including the plug-in of the cyber and physical layers on the Internet, cyber-physical systems are becoming more vulnerable than ever to cyberthreats leading to possible damage of the system. Consequently, many researchers have devoted to studying detection and identification of such threats in order to mitigate their drawbacks. Among used tools, Machine Learning (ML) has become dominant in the field due to many usability characteristics including the blackbox models availability. In this context, this paper is dedicated to the detection of cyberattacks in Smart Grid (SG) networks which uses industrial control systems (ICS), through the integration of ML models assembled on a small scale. More precisely, it therefore aims to study an electric traction substation system used for the railway industry. The main novelty of our contribution lies in the study of the behaviour of more realistic data than the traditional studies previously shown in the state of the art literature by investigating even more realistic types of attacks. It also emulates data analysis and a larger feature space under most commonly used connectivity protocols in today’s industry such as S7Comm and Modbus.
β-lactams and colistin resistance in Enterobacterales is a global public health issue. In this study we aimed to investigate the occurrence and genetic determinants of Extended-Spectrum β-lactamases, carbapenemases and mcr-encoding-genes in Enterobacterales isolates recovered from the migratory bird species Ciconia ciconia in an Algerian city. A total of 62 faecal samples from white storks were collected. Samples were then subjected to selective isolation of β-lactams and colistin-resistant-Enterobacterales. The representative colonies were identified using Matrix-Assisted Laser Desorption-Ionisation Time-of-Flight Mass Spectrometry. Susceptibility testing was performed using the disk-diffusion method. ESBL, carbapenemases, and colistin resistance determinants were searched for by PCR and sequencing. The clonality relationships of the obtained isolates were investigated by multilocus sequence typing assays. Mating experiments were carried out to evaluate the transferability of the carbapenemase and mcr-genes. Forty-two isolates were identified as follows: Escherichia coli (n = 33), Klebsiella pneumoniae (n = 4), Proteus mirabilis (n = 4) and Citrobacter freundii (n = 1). Molecular analysis showed that twelve isolates carried the blaESBL genes alone, fifteen E. coli isolates were positive for the blaOXA-48 gene, six isolates were NDM-5-carriers (two P. mirabilis, two K. pneumoniae and two E. coli) and eight E. coli strains were positive for the mcr-1 gene. MLST results showed a high clonal diversity, where NDM-5-producing strains were assigned to two sequence types (ST167 for E. coli and ST198 for K. pneumoniae), whereas the mcr-1 positive E. coli isolates belonged to ST58, ST224, ST453, ST1286, ST2973, ST5542, ST9815 and the international high-risk resistant lineage ST101. To the best of our knowledge, this is the first report of blaNDM-5 gene in white storks and also the first describing the mcr-1 gene in white storks in Algeria. This study underlines the important role of migratory white storks as carriers of high-level drug-resistant bacteria, allowing their possible implication as indicators and sentinels for antimicrobial resistance surveillance.
This paper presents the development of a multi-coil eddy current (EC) sensor that uses an axial rotating magnetic field for the measurement of electrical resistance to determine the electrical conductivity tensor of stratified carbon fiber reinforced polymer (CFRP) materials. The sensor consists of an identical planar racetrack multi-coil, excited by two-phase sinusoidal current sources that are 90° apart in phase to generate an axial rotating magnetic field and eliminate the need for mechanical rotation. Each sensor's coil's resistance variation is measured using a developed experimental prototype unit and computed using a 3D finite element method (FEM) based on the (A, V–A) formulation. The inverse problem technique that minimizes the difference between the calculated and measured resistances is then used to identify the electrical conductivity tensor components using the particle swarm optimization (PSO) algorithm. The comparison between the computed resistances and the measured ones shows an excellent concordance.
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.
