VSB-TUO Repository

Communities in DSpace

Select a community to browse its collections.

Now showing 1 - 10 out of 11 results

Recent Submissions

DT-LSMAS: Digital Twin-Assisted Large-Scale Multiagent System for Healthcare Workflows
(IEEE, 2024) Lakhan, Abdullah; Mohammed, Mazin Abed; Zebar, Dilovan Asaad; Abdulkareem, Karrar Hameed; Deveci, Muhammet; Marhoon, Haydar Abdulameer; Nedoma, Jan; Martinek, Radek
Digital healthcare has garnered much attention from academia and industry for health and well-being. Many digital healthcare architectures based on large-scale edge and cloud multiagent systems (LSMASs) have recently been presented. The LSMAS allows agents from different institutions to work together to achieve healthcare processing goals for users. This article presents a digital twin large-scale multiagent strategy (DT-LSMAS) comprising mobile, edge, and cloud agents. The DT-LSMAS comprised different schemes for healthcare workflows, such as added healthcare workflows, application partitioning, and scheduling. We consider healthcare workflows with different biosensor data such as heartbeat, blood pressure, glucose monitoring, and other healthcare tasks. We partitioned workflows into mobile, edge, and cloud agents to meet the deadline, total time, and security of workflows in large-scale edge and cloud nodes. To handle the large-scale resource for real-time sensor data, we suggested digital twin-enabled edge nodes, where delay-sensitive workflow tasks are scheduled and executed under their quality of service requirements. Simulation results show that the DT-LSMAS outperformed in terms of total time by 50%, minimizing the risk of resource leakage and deadline missing during scheduling on heterogeneous nodes. In conclusion, the DT-LSMAS obtained optimal results for workflow applications.
Ambient- and high-pressure studies of structural, electronic, and magnetic properties of single-crystal EuZn2P2
(American Physical Society, 2024) Rybicki, Damian; Komędera, Kamila; Przewoźnik, Janusz; Gondek, Łukasz; Kapusta, Czesław; Podgórska, Karolina; Tabiś, Wojciech; Żukrowski, Jan; Tran, Lan Maria; Babij, Michał; Bukowski, Zbigniew; Havela, Ladislav; Buturlim, Volodymyr; Prchal, Jiří; Diviš, Martin; Král, Petr; Turek, Ilja; Halevy, Itzhak; Kaštil, Jiří; Míšek, Martin; Dutta, Utpal; Legut, Dominik
A thorough study of EuZn2P2 single crystals, which were grown from Sn flux, was performed using both bulk (heat capacity, ac susceptibility, dc magnetization, electrical resistivitivity, magnetoresistance) and microscopic (M & ouml;ssbauer spectroscopy) techniques. Electrical resistance and magnetic susceptibility were measured also under high pressure conditions (up to 19 and 9.5 GPa, respectively). Further insight into electronic properties and phonons is provided by ab initio calculations. The results indicate that EuZn2P2 is an antiferromagnet with strong Eu-Eu exchange coupling of ferromagnetic type within the basal plane and weaker antiferromagnetic interaction along the c axis. The Eu magnetic moments are tilted from the basal plane. Hydrostatic pressure strongly affects both magnetic (increase of the N & eacute;el temperature) and electronic (suppression of the band gap and semimetallic behavior) properties, indicating a strong interplay of structure with magnetic and electronic degrees of freedom.
Efficient handling of ACL policy change in SDN using reactive and proactive flow rule installation
(Springer Nature, 2024) Hussain, Mudassar; Amin, Rashid; Gantassi, Rahma, rahma; Alshehri, Asma Hassan; Frnda, Jaroslav; Raza, Syed Mohsan, Syed Mohsan
Software-defined networking (SDN) is a pioneering network paradigm that strategically decouples the control plane from the data and management planes, thereby streamlining network administration. SDN's centralized network management makes configuring access control list (ACL) policies easier, which is important as these policies frequently change due to network application needs and topology modifications. Consequently, this action may trigger modifications at the SDN controller. In response, the controller performs computational tasks to generate updated flow rules in accordance with modified ACL policies and installs flow rules at the data plane. Existing research has investigated reactive flow rules installation that changes in ACL policies result in packet violations and network inefficiencies. Network management becomes difficult due to deleting inconsistent flow rules and computing new flow rules per modified ACL policies. The proposed solution efficiently handles ACL policy change phenomena by automatically detecting ACL policy change and accordingly detecting and deleting inconsistent flow rules along with the caching at the controller and adding new flow rules at the data plane. A comprehensive analysis of both proactive and reactive mechanisms in SDN is carried out to achieve this. To facilitate the evaluation of these mechanisms, the ACL policies are modeled using a 5-tuple structure comprising Source, Destination, Protocol, Ports, and Action. The resulting policies are then translated into a policy implementation file and transmitted to the controller. Subsequently, the controller utilizes the network topology and the ACL policies to calculate the necessary flow rules and caches these flow rules in hash table in addition to installing them at the switches. The proposed solution is simulated in Mininet Emulator using a set of ACL policies, hosts, and switches. The results are presented by varying the ACL policy at different time instances, inter-packet delay and flow timeout value. The simulation results show that the reactive flow rule installation performs better than the proactive mechanism with respect to network throughput, packet violations, successful packet delivery, normalized overhead, policy change detection time and end-to-end delay. The proposed solution, designed to be directly used on SDN controllers that support the Pyretic language, provides a flexible and efficient approach for flow rule installation. The proposed mechanism can be employed to facilitate network administrators in implementing ACL policies. It may also be integrated with network monitoring and debugging tools to analyze the effectiveness of the policy change mechanism.
Enhancing surface quality and tool life in SLM-machined components with Dual-MQL approach
(Elsevier, 2024) Ross, Nimel Sworna; Mashinini, Peter Madindwa; Mishra, Priyanka; Ananth, M. Belsam Jeba; Mustafa, Sithara Mohamed; Gupta, Munish Kumar; Korkmaz, Mehmet Erdi; Nag, Akash
Selective laser melting (SLM) can produce complex metal components with high densities, thereby surpassing the limitations of traditional machining methods. However, achieving accurate dimensions, geometries, and acceptable surface states in parts fabricated through SLM remains a concern as they often fall short compared to traditionally machined components. As a solution, a hybrid additive-subtractive manufacturing (HASM) method was developed to effectively utilize the advantages of both techniques. In this study, SLM-made 316 L stainless steel was machined under distinct cooling conditions to investigate the effects of roughness and tool wear. After a thorough investigation, the dual-MQL strategy was evaluated and compared with dry and MQL cutting strategies. The findings showed that the dual-MQL condition led to a significant reduction in flank wear by 54-56% and 29-34%, respectively, associated with dry and MQL cutting techniques, making it a highly promising key for machining SLM-made steel components. Machine learning techniques are potential tools for prediction and classification capabilities in machining processes. For milling SLM-made 316 L SS, multilayer perceptron (MLP) proved to be the most effective prediction model and for classification MLP and Random forest performed better.
Edge-cloud remote sensing data-based plant disease detection using deep neural networks with transfer learning
(IEEE, 2024) Mohammed, Mazin Abed; Lakhan, Abdullah; Abdulkareem, Karrar Hameed; Almujally, Nouf Abdullah; Al-Attar, Bourair Bourair Sadiq Mohammed Taqi; Memon, Sajida; Marhoon, Haydar Abdulameer; Martinek, Radek
These days, the disease among different plants has been increasing day by day. It is a very hard task for government institutions and farmers to collect data on plant diseases from different distributed lands among regions. Therefore, data collection, disease detection, and processing are the key issues for plants when they are suffering from healthy and unhealthy issues in different lands. This article presents edge-cloud remote sensing data-based plant disease detection by exploiting deep neural networks with transfer learning. The objective is to solve the aforementioned issues, such as data collection at a wide range, disease detection, and processing them with higher accuracy and time on different machines. We suggest transfer learning commutative fuzzy deep convolutional neural network (FCDCNN) schemes based on combinatorial optimization problems. The convex function optimizes the processing time and learning rate of data training on different edge and cloud nodes to collect more and more data from different plants from distributed lands. In the concave function, we predict the diseases among different plants, such as sugarcane, blueberry, cotton, and cherry with images, videos, and numeric values. The plant disease detection app uses edge nodes and remote satellite point cloud nodes to gather and train data using transfer learning and make predictions using fuzzy DCNN schemes that are more accurate and take less time to process. Simulation results show that FCDCNN obtained higher accuracy by 98% with less processing time 25% and trained with a higher ratio of data than existing schemes.