The aim of this study was to test the influence of nest site characteristics and food supplementation from rubbish dumps on reproductive parameters of white storks breeding in semi-arid habitats. A total of 148 nests were monitored in two colonies of white storks (control colony vs. colony that benefited from high food supply in rubbish dumps) in eastern Algeria over a six-year period (2011–2016) to measure nest characteristics and reproductive parameters (clutch size, number of hatchings, number of fledglings, breeding success). Results showed that pairs breeding at proximity from rubbish dumps had larger clutch sizes (5.1 ± 0.6 vs. 4.6 ± 0.6), hatched more chicks (4.7 ± 0.7 vs. 4.3 ± 0.7) and raised more fledglings (3.0 ± 0.9 vs. 2.6 ± 1.0) than pairs breeding far from rubbish dumps. Results also showed that clutch size was positively related to nest surface area, and that pairs nesting on electricity poles had a lower breeding success than those nesting in trees (48.9 ± 20.4% vs. 64.6 ± 17.6%). Our findings suggest that breeding outputs are strongly related to selective behavior in nest placement and food availability surrounding the nesting site.
This paper proposes a new approach for remaining useful life prediction that combines a bond graph, the Gaussian Mixture Model and similarity techniques to allow the use of both physical knowledge and the data available. The proposed method is based on the identification of relevant variables that carry information on degradation. To this end, the causal properties of the bond graph (BG) are first used to identify the relevant sensors through the fault observability. Then, a second stage of analysis based on statistical metrics is performed to reduce the number of sensors to only the ones carrying useful information for failure prognosis, thus, optimizing the data to be used in the prognosis phase. To generate data in the different system state, a simulator based on the developed BG is used. A Gaussian Mixture Model is then applied on the generated data for fault diagnosis and clustering. The Remaining Useful Life is estimated using a similarity technique. An application on a mechatronic system is considered for highlighting the effectiveness of the proposed approach.
In the last recent years, a large community of researchers and industrial practitioners has been attracted by combining different prognostics models as such strategy results in boosted accuracy and robust performance compared to the exploitation of single models. The present work is devoted to the investigation of three new fusion schemes for the remaining useful life forecast. These integrated frameworks are based on aggregating a set of Gaussian process regression models thanks to the Induced Ordered Weighted Averaging Operators. The combination procedure is built upon three proposed analytical weighting schemes including exponential, logarithmic and inverse functions. In addition, the uncertainty aspect is supported in this work, where the proposed functions are used to weighted average the variances released from competitive Gaussian process regression models. The training data are transformed into gradient values, which are adopted as new training data instead of the original observations. A lithium-ion battery data set is used as a benchmark to prove the efficiency of the proposed weighting schemes. The obtained results are promising and may provide some guidelines for future advances in performing robust fusion options to accurately estimate the remaining useful life.
This paper proposed a new strategy of sinusoidal pulse width modulation (SPWM) technique to control three-phase nine-level switched-capacitor inverter (9LSCI) in grid-connected PV systems. The main advantage of this inverter is high voltage gain, achieved by switching the capacitors in series and parallel to boost up the output voltage using low voltage input. To improve the quality of solar energy for injection into the electrical grid, a rooted tree optimization (RTO) algorithm is used to get optimum values of initial angles of multi carriers SPWM technique, giving the lowest possible values of the total harmonic distortion (THD). The design also can maximize the efficiency of the multi-level inverter by minimizing its size using fewer components and a single DC source and reducing the rate of THD. The higher effectiveness and accuracy of the suggested RTO-SPWM technique was tested and verified in comparison to existing classical SPWM technique from the performance of PV-grid systems that it can effectively reduce the total harmonic distortion to 0.16 %.
This paper gives and justifies a practical approach for solving fuzzy singular integro-differential equations. First, by using different techniques, we show that solutions to two types of fuzzy singular integro-differential equations exist and are unique: Picard’s theorem for logarithmic kernels and Arzelà–Ascoli theorem for Cauchy ones. Then, utilizing airfoil polynomials, we provide a collocation method to solve the current problems numerically. We also look at the approximate equations’ solutions, and we introduce the concept of error analysis. Using new procedures, we obtain two systems of linear equations. These are the problems to be examined. Eventually, we exhibit the precision of the proposed approach via numerical examples.
When designing tunnels, it is advisable to pre-estimate several tunnel parameters such as the depth (cover), the lining thickness, and the shape of the tunnel cross section. This condition is important in order to limit deformations during construction of the tunnel, and to ensure good tunnel resistance under seismic load conditions. In this context, the present paper is devoted to the analysis of the influence of some test parameters (the cover of the tunnel, the thickness of the lining, and the shape of the tunnel and the direction of the seismic waves) on the behaviour of the soil and the lining of a shallow tunnel built in soft ground subjected to seismic loading. The reference model for this parametric study is a real case, which happens to be the tunnel of Djebel El Ouahch (East-West motorway) in the province of Constantine/Algeria. The study is performed in three dimensions (3D) using a finite difference calculation method based on the FLAC3D calculation code. The results are presented in terms of shear strain induced in the soil around the tunnel, surface settlement, and vertical displacement of soil under the raft foundation, and also shear stress, bending moment, and shear strain, induced in the tunnel lining. The results show that the increase in thickness of the lining causes a reduction in shear force, and shear strain, while the circular or oval shape of the tunnel cross section results in low values of strain in the lining and ground displacement. It has been also pointed out that bending moment and shear strain induced in the lining are relatively low in comparison with the other forms. On the other hand, the direction of the seismic waves has a great influence on the behaviour of the lining and the surrounding soil. These results demonstrate that the strongest and most stable tunnel is the deepest tunnel with circular or oval section with a large thickness of the tunnel lining under the effect of compressive seismic waves. The results of the present study will be useful in the design of such a case by understanding the effects of various influencing parameters that control the stability of the tunnel in soil with bad characteristics.
In this study, we investigate a production planning problem in hybrid manufacturing remanufacturing production system. The objective is the determine the best mix between the manufacturing of new products, and the remanufacturing of recovered products, based on economic and environmental considerations. It consists to determine the best manufacturing and remanufacturing plans to minimising the total economic cost (start-up and production costs of new and remanufactured products, storage costs of new and returned products and disposal costs) and the carbon emissions (new products, remanufactured products and disposed products). The hybrid system consists of a set of machines used to produce new products and remanufactured products of different grades (qualities). We assume that remanufacturing is more environmentally efficient, because it allows to reduce the disposal of used products. A multi-objective mathematical model is developed, and a non dominated sorting genetic algorithm (NSGA-II) based approach is proposed. Numerical experience is presented to study the impact of carbon emissions generated by new, remanufactured and disposed products, over a production horizon of several periods.
Objective: The aim of our work is to study the links between anthropometric parameters and body composition obtained by bioelectric impedancemetry in case of obese women of peri- menopausal age. Method and Materials: 154 obese women were classified according to their degree of obesity according to WHO criteria. The analysis of body composition was performed by impedancemetry. Pearson’s (r) and Spearman’s (r2 ) correlations were calculated to check the relationships between age, weight, BMI, as well as total and segmental body fat composition. Results: 154 women of mean age 40.20 ± 13.13 years, obese, mean BMI 38.66 ± 6.56 Kg/m2 participated in our study. Impedance reduced an average total fat mass% (TFM%) of 45.39 ± 5.67%. BMI is strongly correlated with TFM% (r = 0.73; r2 = 0.82; p ≥ 0.05). For obesity stages 1-2, weight is correlated with BMI (r-r2 > 0.40; p ≤ 0.001). Likewise, a strong correlation exists between weight and TFM in Kg (r2 = 0.82; p ≥ 0.05). For a BMI ≥ 35 Kg/m2 , weight is inversely correlated with age [r2 ≥ (-0.36); p ≤ 0.003]. The FM of the trunk (Kg) is correlated with the weight for obesity grade 3 (r = 0.49; p = 0.0002) and whatever the stage of obesity at the BMI (r ≥ 0.32; p ≤ 0.02). Conclusion: The use of bioelectrical impedancemetry in the diagnostic management of obese people is quite useful. This tool gives us better information on the location and distribution of fatty tissue.
Wireless sensor networks (WSNs) have become an important component in the Internet of things (IoT) field. In WSNs, multi-channel protocols have been developed to overcome some limitations related to the throughput and delivery rate which have become necessary for many IoT applications that require sufficient bandwidth to transmit a large amount of data. However, the requirement of frequent negotiation for channel assignment in distributed multi-channel protocols incurs an extra-large communication overhead which results in a reduction of the network lifetime. To deal with this requirement in an energy-efficient way is a challenging task. Hence, the Reinforcement Learning (RL) approach for channel assignment is used to overcome this problem. Nevertheless, the use of the RL approach requires a number of iterations to obtain the best solution which in turn creates a communication overhead and time-wasting. In this paper, a Self-schedule based Cooperative multi-agent Reinforcement Learning for Channel Assignment (SCRL CA) approach is proposed to improve the network lifetime and performance. The proposal addresses both regular traffic scheduling and assignment of the available orthogonal channels in an energy-efficient way. We solve the cooperation between the RL agents problem by using the self-schedule method to accelerate the RL iterations, reduce the communication overhead and balance the energy consumption in the route selection process. Therefore, two algorithms are proposed, the first one is for the Static channel assignment (SSCRL CA) while the second one is for the Dynamic channel assignment (DSCRL CA). The results of extensive simulation experiments show the effectiveness of our approach in improving the network lifetime and performance through the two algorithms.
The security is so important for both storing and transmitting the digital data, the choice of parameters is critical for a security system, that is, a weak parameter will make the scheme very vulnerable to attacks, for example the use of supersingular curves or anomalous curves leads to weaknesses in elliptic curve cryptosystems, for RSA cryptosystem there are some attacks for low public exponent or small private exponent. In certain circumstances the secret sharing scheme is required to decentralize the risk. In the context of the security of secret sharing schemes, it is known that for the scheme of Shamir, an unqualified set of shares cannot leak any information about the secret. This paper aims to show that the well-known Shamir’s secret sharing is not always perfect and that the uniform randomization before sharing is insufficient to obtain a secure scheme. The second purpose of this paper is to give an explicit construction of weak polynomials for which the Shamir’s (k, n) threshold scheme is insecure in the sense that there exist a fewer than k shares which can reconstruct the secret. Particular attention is given to the scheme whose threshold is less than or equal to 6. It also showed that for certain threshold k, the secret can be calculated by a pair of shares with the probability of 1/2. Finally, in order to address the mentioned vulnerabilities, several classes of polynomials should be avoided.
Graph matching is a comparison process of two objects represented as graphs through finding a correspondence between vertices and edges. This process allows defining a similarity degree (or dissimilarity) between the graphs. Generally, graph matching is used for extracting, finding and retrieving any information or sub-information that can be represented by graphs. In this paper, a new consistency rule is proposed to tackle with various problems of graph matching. After, using the proposed rule as a necessary and sufficient condition for the graph isomorphism, we generalize it for subgraph isomorphism, homomorphism and for an example of inexact graph matching. To determine whether there is a matching or not, a backtracking algorithm called CRGI2 is presented who checks the consistency rule by exploring the overall search space. The tree-search is consolidated with a tree pruning technique that eliminates the unfruitful branches as early as possible. Experimental results show that our algorithm is efficient and applicable for a real case application in the information retrieval field. On the efficiency side, due to the ability of the proposed rule to eliminate as early as possible the incorrect solutions, our algorithm outperforms the existing algorithms in the literature. For the application side, the algorithm has been successfully tested for querying a real dataset that contains a large set of e-mail messages.
The purpose of this paper is to present a system reconfiguration for a three-phase induction motor (IM) in the event of an open-phase (OP) fault. After the occurrence of the fault, the challenge is how to ensure a safe operation when the IM is only supplied by two phases. The star point of stator is used to reconfigure the IM supply, and a fault tolerant rotor field-oriented control (FT-RFOC) is implemented. Consequently, an equivalent mathematical two-phase model is firstly calculated based on the two available currents. Modifications on the conventional space vector modulation (SVM) algorithm are also introduced in order to control the reconfigured inverter. This system reconfiguration is applied to achieve a safe post-operating after the occurrence of the OP fault. The implemented tests confirm the proposal and prove its effectiveness to compensate for the fault effect.
Many cloud providers offer very high precision services to exploit Optical Character Recognition (OCR). However, there is no provider offers Tifinagh Optical Character Recognition (OCR) as Web Services. Several works have been proposed to build powerful Tifinagh OCR. Unfortunately, there is no one developed as a Web Service. In this paper, we present a new architecture of Tifinagh Handwriting Recognition as a web service based on a deep learning model via Google Colab. For the implementation of our proposal, we used the new version of the TensorFlow library and a very large database of Tifinagh characters composed of 60,000 images from the Mohammed Vth University in Rabat. Experimental results show that the TensorFlow library based on a Tensor processing unit constitutes a very promising framework for developing fast and very precise Tifinagh OCR web services. The results show that our method based on convolutional neural network outperforms existing methods based on support vector machines and extreme learning machine.
This paper introduces a new approach of hybrid meta-heuristics based optimization technique for decreasing the computation time of the shortest paths algorithm. The problem of finding the shortest paths is a combinatorial optimization problem which has been well studied from various fields. The number of vehicles on the road has increased incredibly. Therefore, traffic management has become a major problem. We study the traffic network in large scale routing problems as a field of application. The meta-heuristic we propose introduces new hybrid genetic algorithm named IOGA. The problem consists of finding the k optimal paths that minimizes a metric such as distance, time, etc. Testing was performed using an exact algorithm and meta-heuristic algorithm on random generated network instances. Experimental analyses demonstrate the efficiency of our proposed approach in terms of runtime and quality of the result. Empirical results obtained show that the proposed algorithm outperforms some of the existing technique in term of the optimal solution in every generation.
Automated plant classification using tree trunk has attracted increasing interest in the computer vision community as a contributed solution for the management of biodiversity. It is based on the description of the texture information of the bark surface. The multi-scale variants of the local binary patterns have achieved prominent performance in bark texture description. However, these approaches encode the scale levels of the macrostructure separately from each other. In this paper, a novel handcrafted texture descriptor termed multi-scale Statistical Macro Binary Patterns (ms-SMBP) is proposed to encode the characterizing macro pattern of different bark species. The proposed approach consists of defining a sampling scheme at high scale levels and summarizing the intensity distribution using statistical measures. The characterizing macro pattern is encoded by an in-depth gradient that describes the relationship between the scale levels and their adaptive statistical prototype. Besides this handcrafted feature descriptor, a learning-based description is performed with the ResNet34 model for bark classification. Extensive and comprehensive experiments on challenging and large-scale bark datasets demonstrate the effectiveness of ms-SMBP to identify bark species and outperforming different multi-scale LBP approaches. The tree trunk classification with ResNet34 shows interesting results on a very large-scale dataset.
According to a United Nations report, the proportion of the urban population living in informal houses worldwide had reached 23.5% in 2018, which means over 1 billion people have been living in slums, and an estimated 3 billion people will be requiring affordable housing by 2030. Slums are often linked to developing countries, where weak governance and misled socio-economic choices aggravate the situation and lead to continual conflict with the authorities. This research aims to model this conflict through Game Theory and shed the light on the different parameters that influence the decision-making process and finally propose a management model to limit the spread of this phenomenon as much as possible. This model will be applied in the city of Hassi Bahbah using ABM, the results of the game modeling allowed us to extract the delta threshold which will allow decision-makers to know when they have to make decisions. Our study aims to offer decision-makers a tool based on our model, allowing them to better manage and thus monitor the evolution of informal houses. The authors hope that the findings could provide means and insights for policies and strategic directions in land uses and planning systems.
Sedimentation of dam reservoirs is a complex problem with several dimensions, including filling rates and characteristics of accumulated sediments. Sediment supply from river basins is particularly high in this region because of its semi-arid climate and especially because of poor vegetation protection. The amount of silt accumulated annually since the construction of this dam is estimated at 330000 m3. This silt accumulation strongly limits its storage capacity and consequently its operating duration. The consequences of this serious problem have been catastrophic, including a considerable reduction of 43–84% of the storage capacity of the dams and a clear degradation of water quality that can cause the degradation of the ecosystem functioning and can lead to irreversible changes. The silt present in abundance in the Algerian dams can, thus, constitute a potential resource to be judiciously exploited towards the increase of the performances of the construction materials. The extraction of sediments accumulated in the dam reservoir is, therefore, imperative. These sediments have a great geotechnical value. The objective of this study is to assess the feasibility of the recovery of mud by studying the knowledge of the sediments of the dam of Koudiat Medouar. The results of the tests carried out in laboratory allowed us to identify the various sediments from a physical and geotechnical point of view. These materials must of course meet certain rigorous criteria in terms of mechanical strength and durability and environmental impact. The experimental approach that we adopted allowed us to determine the characteristics of the materials necessary for the realization of compressed earth bricks (BTC) in conformity with the recommendations of the technical guides of construction.
In this paper a H∞ control technique addresses the voltage regulation in distributed generation (DG) system connected to power converter under harmonic disturbances. The DG control technique combines a discrete sliding mode control (DSMC) in the current control and a Robust Servomechanism Problem (RSP) in the voltage control. Besides, a fractional Order Proportional-Integral-Derivative (FOPID) controller synthesized with an automatic calibration of adjustable fractional weights is formulated in this work. For performance and high robustness requirements, the parameters of FOPID are optimized through solving a multiobjective optimization problematic based on the automatic calibration of the weighted-mixed sensitivity problem. Furthermore, for ensuring an adequate calibration of parameters, the Integral of Time Weighted Absolute Error (ITAE) criterion with Genetic Algorithm (GA) are used to achieve better voltage regulation. The simulation results show that it can achieve trade-off between nominal performance (NP) and robust stability (RS) margins for the constrained uncertain plants in the large range frequencies. Also, the results validate the effectiveness of the proposed control at which both low total harmonic distortion (THD) and low tracking error.
The ongoing pandemic of COVID-19 is causing more health, economic and social issues worldwide. As of July 5, 2021, the world registered more than 184 million cases across 222 countries; more than 4 million have died from the deadly infection. The SARSCoV-2 continues spreading globally; new variants emerge randomly due to errors in the virus' gRNAs replication process. The present paper treats the new delta variant of concern, also known as B.1.617.2 lineage. The study highlights transmissibility, vaccine effectiveness, pathogenicity, and the likelihood of hospital admission related to delta variant infection based on a literature review of 10 indexed databases. The findings indicate high transmissibility of the B.1.617.2 lineage, approving it to be the dominant strain worldwide. Also, reduced vaccine effectiveness is confirmed. However, approved vaccines for emergency use remain valuable against COVID-19's delta variant. Finally, the risk of hospitalization seems to be twice in the case of delta variant infection. A combined approach of vaccination and nonpharmaceutical interventions is the leading way to contain the ongoing pandemic of COVID-19