Effect of cellulose acetate/cellulose triacetate ratio on reverse osmosis blend membrane performance

Surface functionalization and modification including the grafting process are effective approaches to improve and enhance the reverse osmosis (RO) membrane performance. This work is aimed to synthesize grafted/crosslinked cellulose acetate (CA)/cellulose triacetate (CTA) blend RO membranes using N-isopropylacrylamide (N-IPAAm) as a monomer and N,N-methylene bisacrylamide (MBAAm) as a crosslinker. The morphology of these membranes was analyzed by scanning electron microscopy and their surface roughness was characterized by atomic force microscopy. The performance of these membranes was evaluated through measuring two major parameters of salt rejection and water flux using RO unit at variable operating pressures. It was noted that the surface average roughness obviously decreased from 148 nm for the pure CA/CTA blend membrane with 2.5% CTA to 110 nm and 87 nm for the grafted N-IPAAm and grafted/crosslinked N-IPAAM/MBAAm/CA/CTA-RO membranes, respectively. Moreover, the contact angle decreased from 51.98° to 47.6° and 43.8° after the grafting and crosslinking process. The salt rejection of the grafted CA/CTA-RO membrane by 0.1% N-IPAAm produced the highest value of 98.12% and the water flux was 3.29 L/m²h at 10 bar.     

Hemoptysis caused by pulmonary arterial aneurysm, disclosing Beh?et disease. Apropos of 2 cases]

MCF7 and ZR75-1 breast cancer cells grow as adherent monolayers in tissue culture. Treatment with the serum serine protease plasmin causes them to detach and to grow as floating multicellular spheroids. Two plasmin activators, urokinase plasminogen activator and streptokinase, induce the same growth pattern changes in the presence of plasminogen. Serum contains also plasminogen activator inhibitors. Aged serum, deficient in plasminogen activator inhibitors, converts spontaneously monolayer breast cancer cells into multicellular spheroids which readily revert to monolayer growth after addition of fresh serum. Urokinase blocks the reversion. The formation of multicellular spheroids does not affect the proliferative rate of breast tumor cells but endows tumor cells with increased resistance to the chemotherapeutic drugs, doxorubicin and paclitaxel.     

Analytic computation of the photocurrent density in a n-6H-SiC/p-Si/n-Si/p-Si0.8Ge0.2 multilayer solar cell

In this work we conceived a model of a multilayer solar cell composed by four layers of opposite conductivities: an n-type 6H-SiC used as a frontal layer to absorb high energy photons (energy gap equals 2.9 eV), a p-type Si layer, an n-type Si layer and a p-type SiGe back layer to absorb low energy photons (Si_0.8Ge_0.2 with an energy gap equal to 0.8 eV). The impurity concentration in every layer of the model is taken equal to 10¹⁷ cm⁻³ to ensure abrupt junctions inside the cell. The optical properties of the separate layers have been fitted and tabulated to be used for thin films devices numerical simulation. We developed the equations giving the minority carrier concentration and the photocurrent density in each abscissa of the model. We used Matlab software to simulate and optimize the layers thicknesses to achieve the maximum photocurrent generated under AM0 solar spectrum. The results of simulation showed that the optimized structure could deliver, assuming 10⁵ cm/s surface recombination velocity, a photocurrent density of more than 53 mA/cm², which represents 88.3% of the ideal photocurrent (59.99 mA/cm²) that can be generated under AM0 solar spectrum.     

Adaptive type-2 fuzzy sliding mode controller for SISO nonlinear systems subject to actuator faults

In this paper, an adaptive type-2 fuzzy sliding mode control to tolerate actuator faults of unknown nonlinear systems with external disturbances is presented. Based on a redundant actuation structure, a novel type-2 adaptive fuzzy fault tolerant control scheme is proposed using sliding mode control. Two adaptive type-2 fuzzy logic systems are used to approximate the unknown functions, whose adaptation laws are deduced from the stability analysis. The proposed approach allows to ensure good tracking performance despite the presence of actuator failures and external disturbances, as illustrated through a simulation example.     

Adaptive fuzzy sliding mode power system stabilizer using Nussbaum gain

Power system stability is enhanced through a novel stabilizer developed around an adaptive fuzzy sliding mode approach which applies the Nussbaum gain to a nonlinear model of a single-machine infinite-bus （SMIB） and multi-machine power system stabilizer subjected to a three phase fault. The Nussbaum gain is used to avoid the positive sign constraint and the problem of controllability of the system. A comparative simulation study is presented to evaluate the achieved performance.     

Optimal Packet Loss Protection of Progressively Compressed 3-D Meshes

We consider a state-of-the-art system that uses layered source coding and forward error correction with Reed-Solomon codes to efficiently transmit 3-D meshes over lossy packet networks. Given a transmission bit budget, the performance of this system can be optimized by determining how many layers should be sent, how each layer should be packetized, and how many parity bits should be allocated to each layer such that the expected distortion at the receiver is minimum. The previous solution for this optimization problem uses exhaustive search, which is not feasible when the transmission bit budget is large. We propose instead an exact algorithm that solves this optimization problem in linear time and space. We illustrate the advantages of our approach by providing experimental results for the compressed progressive meshes (CPM) mesh compression technique.     

Edge Computing Platform with Efficient Migration Scheme for 5G/6G Networks

Next-generation cellular networks are expected to provide users with innovative gigabits and terabits per second speeds and achieve ultra-high reliability, availability, and ultra-low latency. The requirements of such networks are the main challenges that can be handled using a range of recent technologies, including multi-access edge computing (MEC), artificial intelligence (AI), millimeter-wave communications (mmWave), and software-defined networking. Many aspects and design challenges associated with the MEC-based 5G/6G networks should be solved to ensure the required quality of service (QoS). This article considers developing a complex MEC structure for fifth and sixth-generation (5G/6G) cellular networks. Furthermore, we propose a seamless migration technique for complex edge computing structures. The developed migration scheme enables services to adapt to the required load on the radio channels. The proposed algorithm is analyzed for various use cases, and a test bench has been developed to emulate the operator’s infrastructure. The obtained results are introduced and discussed.     

Dynamic and scalable multi-level trust management model for Social Internet of Things

The Internet of Things (IoT) is a paradigm that has made everyday objects intelligent by offering them the ability to connect to the Internet and communicate. Integrating the social component into IoT gave rise to the Social Internet of Things (SIoT), which has helped overcome various issues such as heterogeneity and navigability. In this kind of environment, participants compete to offer a variety of attractive services. Nevertheless, some of them resort to malicious behaviour to spread poor-quality services. They perform so-called Trust-Attacks and break the basic functionality of the system. Trust management mechanisms aim to counter these attacks and provide the user with an estimate of the trust degree they can place in other users, thus ensuring reliable and qualified exchanges and interactions. Several works in literature have interfered with this problem and have proposed different Trust-Models. The majority tried to adapt and reapply Trust-Models designed for common social networks or peer-to-peer ones. That is, despite the similarities between these types of networks, SIoT ones present specific peculiarities. In SIoT, users, devices and services are collaborating. Devices entities can present constrained computing and storage capabilities, and their number can reach some millions. The resulting network is complex, constrained and highly dynamic, and the attacks-implications can be more significant. In this paper, we propose DSL-STM a new dynamic and scalable multi-level Trust-Model, specifically designed for SIoT environments. We propose multidimensional metrics to describe and SIoT entities behaviours. The latter are aggregated via a Machine Learning-based method, allowing classifying users, detecting attack types and countering them. Finally, a hybrid propagation method is suggested to spread trust values in the network, while minimizing resource consumption and preserving scalability and dynamism. Experimentation made on various simulated scenarios allows us to prove the resilience and performance of DSL-STM.

     

A meta-heuristic approach to solve a JIT scheduling problem in hybrid flow shop

In this paper we address a hybrid flow shop scheduling problem considering the minimization of the sum of the total earliness and tardiness penalties. This problem is proven to be NP-hard, and consequently the development of heuristic and meta-heuristic approaches to solve it is well justified. So, we propose an ant colony optimization method to deal with this problem. Our proposed method has several features, including some heuristics that specifically take into account both earliness and tardiness penalties to compute the heuristic information values. The performance of our algorithm is tested by numerical experiments on a large number of randomly generated problems. A comparison with solutions performance obtained by some constructive heuristics is presented. The results show that the proposed approach performs well for this problem.     

Knowledge Based Supervised Fuzzy-Classification: An Application to Image Processing

In this paper, we take an interest in representation and treatment of imprecision and uncertainty in order to propose an original approach to approximate reasoning. This work has a practical application in supervised learning pattern recognition. Production rules whose conclusions are accompanied by belief degrees, are obtained by supervised learning from a training set. The proposed learning method is multi-featured, it allows to take into account the possible predictive power of a simultaneously considered feature conjunction. On the other hand, the feature space partition allows a fuzzy representation of the features and data imprecision integration. This uncertainty is managed in the learning phase as well as in the recognition one. To introduce more flexibility and to overcome the boundary problem due to the manipulations of membership functions of fuzzy sets, we propose to use a context-oriented approximate reasoning. For this purpose, we introduce an adequate distance to compare neighbouring facts. This distance, measuring imprecision, combined with the uncertainty of classification decisions represented by belief degrees, drives the approximate inference. The proposed method was implemented in a software called SUCRAGE and confronted with a real application in the field of image processing. The obtained results are very satisfactory. They validate our approach and allow us to consider other application fields.