Publikační činnost VŠB-TUO ve Web of Science / Publications of VŠB-TUO in Web of Science

Permanent URI for this collectionhttp://hdl.handle.net/10084/56138

Kolekce obsahuje bibliografické záznamy článků akademických pracovníků VŠB-TUO v časopisech indexovaných ve Web of Science od roku 1990 po současnost. Odkaz na Web of Science je funkční ze sítě VŠB-TUO, vzdálený přístup viz web ÚK VŠB-TUO.

Do kolekce jsou zařazeny:

a) publikace, u nichž je v originálních dokumentech jako působiště autora (adresa) uvedena Vysoká škola báňská – Technická univerzita Ostrava (VŠB-TUO),

b) publikace, u nichž v originálních dokumentech není v adrese VŠB-TUO uvedena, ale autoři prokazatelně v době jejich zpracování a uveřejnění působili na VŠB-TUO.

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Now showing 1 - 20 out of 7884 results

Computational analysis of microgravity and viscous dissipation impact on periodical heat transfer of MHD fluid along porous radiative surface with thermal slip effects
(Elsevier, 2024) Alqahtani, Bader; El-Zahar, Essam R.; Riaz, Muhammad Bilal; Seddek, Laila F.; Ilyas, Asifa; Ullah, Zia; Akgül, Ali
The current thermal slip and Magnetohydrodynamic analysis plays prominent importance in heat insulation materials, polishing of artificial heart valves, heat exchangers, magnetic resonance imaging and nanoburning processes. The main objective of the existing article is to deliberate the impact of thermal slip, thermal radiation and viscous dissipation on magnetized cone embedded in a porous medium under reduced gravitational pressure. Convective heating characteristics are used to increase the rate of heating throughout the porous cone. For viscous flow along a heated and magnetized cone, the conclusions are drawn. The simulated nonlinear partial differential equations are transformed into a dimensionless state by means of suitable non -dimensional variables. The technique of finite differences is implemented to solve the given model with Gaussian elimination approach. The FORTRAN language is used to make uniform algorithm for asymptotic results according to the boundary conditions. The influence of controlling parameters, such as thermal radiation parameter R d , Prandtl number P r , porosity parameter Omega , viscous dissipation parameter E c , delta thermal slip parameter, R g reduced gravity parameter and mixed convection parameter lambda is applied. Graphical representations were created to show the consequences of various parameters on velocity, temperature and magnetic field profiles along with fluctuating skin friction, fluctuating heat and oscillatory current density. It is found that velocity and temperature profile enhances as radiation parameter enhances. It is noted that the amplitude and oscillations in heat transfer and electromagnetic waves enhances as magnetic Prandtl factor increases.
Analysis of incorporating modified Weibull model fault detection rate function into software reliability modeling
(Elsevier, 2024) Sindhu, Tabassum Naz; Shafiq, Anum; Hammouch, Zakia; Hassan, Marwa K. H.; Abushal, Tahani A.
When software systems are introduced, they are typically deployed in field environments similar to those used during development and testing. However, these systems may also be used in various other locations with different environmental conditions, making it challenging to improve software reliability. Factors such as the specific operating environment and the location of bugs in the code contribute to this difficulty. In this paper, we propose a new software reliability model that accounts for the uncertainty of operating environments. We present the explicit closed-form mean value function solution for the proposed model. The model's goodness of fit is demonstrated by comparing it to the nonhomogeneous Poisson process (NHPP) model based on Weibull model, using four sets of failure data sets from software applications. The proposed model performs well under various estimation techniques, making it a versatile tool for practitioners and researchers alike. The proposed model outperforms other existing NHPP Weibull based in terms of fitting accuracy under two different methods of estimation and provides a more detailed and precise evaluation of software reliability. Additionally, sensitivity analysis shows that the parameters of the suggested distribution significantly impact the mean value function.
Flood algorithm (FLA): an efficient inspired meta-heuristic for engineering optimization
(Springer, 2024) Ghasemi, Mojtaba; Golalipour, Keyvan; Zare, Mohsen; Mirjalili, Seyedali; Trojovský, Pavel; Abualigah, Laith; Hemmati, Rasul
Introducing a novel meta-heuristic optimization algorithm, the Flood Algorithm (FLA) draws inspiration from the intricate movement and flow patterns of water masses during flooding events in river basins. FLA mathematically models key phenomena such as the movement of water toward slopes, flow rates over time, soil permeability effects, and periodic increases and decreases in water levels from precipitation and losses. Leveraging these models, the algorithm guides the movement and evolution of a population of potential solutions toward enhanced optimality. The algorithm endeavors to establish an appropriate correlation between the fundamental aspects of natural flood events and the optimization process. Its formulation and working mechanism are described in detail. It operates in two main phases-a regular movement phase, where the population moves naturally toward current best solutions, and a flooding phase, which introduces random disturbances to increase diversity. New solutions are periodically introduced while weaker ones are removed, mirroring the natural cycles of water levels. FLA's effectiveness is demonstrated through its application on well-known benchmark optimization problems and engineering design problems. Extensive comparisons have been carried out on CEC2005 functions using 16 algorithms in both basic and enhanced modes, as well as on CEC2014 functions with dimensions 30, 50, and 100 using a total of 20 other algorithms. These rigorous studies unequivocally confirm the robustness and strength of the proposed algorithm. Furthermore, the algorithm's performance on 12 constrained engineering problems demonstrates its ability to tackle real-world challenges. The FLA's source code is publicly available at https://www.optim-app.com/projects/fla.
Many-objective artificial hummingbird algorithm: an effective many-objective algorithm for engineering design problems
(Oxford University Press, 2024) Kalita, Kanak; Jangir, Pradeep; Pandya, Sundaram B.; Čep, Robert; Abualigah, Laith; Migdady, Hazem; Daoud, Mohammad Sh
Many-objective optimization presents unique challenges in balancing diversity and convergence of solutions. Traditional approaches struggle with this balance, leading to suboptimal solution distributions in the objective space especially at higher number of objectives. This necessitates the need for innovative strategies to adeptly manage these complexities. This study introduces a Many-Objective Artificial Hummingbird Algorithm (MaOAHA), an advanced evolutionary algorithm designed to overcome the limitations of existing many-objective optimization methods. The objectives are to improve convergence rates, maintain solution diversity, and achieve a uniform distribution in the objective space. MaOAHA implements information feedback mechanism (IFM), reference point-based selection and association, non-dominated sorting, and niche preservation. The IFM utilizes historical data from previous generations to inform the update process, thereby improving the algorithm's the exploration and exploitation capabilities. Reference point-based selection, along with non-dominated sorting, ensures solutions are both close to the Pareto front and evenly spread in the objective space. Niche preservation and density estimation strategies are employed to maintain diversity and prevent overcrowding. The comprehensive experimental analysis benchmarks MaOAHA against four leading algorithms viz. Many-Objective Gradient-Based Optimizer, Many-Objective Particle Swarm Optimizer, Reference Vector Guided Evolutionary Algorithm, and Nondominated Sorting Genetic Algorithm III. The DTLZ1-DTLZ7 benchmark sets with four, six, and eight objectives and five real-world problems (RWMaOP1-RWMaOP5) are considered for performance assessment of the selected algorithms. The results demonstrate that internal parameter-free MaOAHA significantly outperforms its counterparts, achieving better generational distance by up to 52.38%, inverse generational distance by up to 38.09%, spacing by up to 56%, spread by up to 71.42%, hypervolume by up to 44%, and runtime by up to 52%. These metrics affirm the MaOAHA's capability to enhance the decision-making processes through its adept balance of convergence, diversity, and uniformity.
Overview of the use of additives in biomass torrefaction processes: Their impact on products and properties
(Elsevier, 2024) Šafář, Michal; Chen, Wei-Hsin; Raclavská, Helena; Juchelková, Dagmar; Prokopová, Nikola; Rachmadona, Nova; Khoo, Kuan Shiong
Over the past few years, considerable attention has been devoted to enhancing the torrefaction process, exploring diverse additives to improve either the process itself or the characteristics of torrefaction products. This review examines the recent advancements in torrefaction processes conducted by different research groups for these purposes. A critical evaluation involving the usage of liquid- and solid-based additives in the torrefaction process can have diverse effects depending on the specific condition implied during the torrefaction process (e.g., biomass feedstock, process conditions, and desired outcomes). Therefore, various testing and evaluation procedures should be performed to determine the optimal type and quantity of additives for a specific torrefaction application. The influence of various additives on the torrefied products of different torrefaction processes is summarized in this review. In particular, the additives are systematically categorized, and the effects of the additives on the properties of the respective torrefaction products are also discussed.
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.
Electric vehicle charging technologies, infrastructure expansion, grid integration strategies, and their role in promoting sustainable e-mobility
(Elsevier, 2024) Singh, Arvind R.; Vishnuram, Pradeep; Alagarsamy, Sureshkumar; Bajaj, Mohit; Blažek, Vojtěch; Damaj, Issam; Rathore, Rajkumar Singh; Al-Wesabi, Fahd N.; Othman, Kamal M.
The transport sector is experiencing a notable transition towards sustainability, propelled by technological progress, innovative materials, and a dedication to environmental preservation. This study explicitly examines the incorporation of electric vehicles (EVs) into the power grid, with a particular emphasis on passenger automobiles. Our analysis emphasises the vital importance of updated transport infrastructure in decreasing greenhouse gas emissions and aiding carbon reduction efforts in electricity networks. The analysis uncovers that adopting electric vehicles offers significant advantages, including enhanced grid efficiency and decreased emissions. However, it also brings issues concerning the design and operation of power systems at both the transmission and distribution levels. Key players are crucial in tackling these difficulties to improve electric vehicle integration into the grid. The study determines the most effective ways for distributing and providing electric vehicle charging infrastructure, and investigates the efforts made to establish common standards in order to solve current challenges. This research contributes to the advancement of sustainable mobility and energy systems by conducting a thorough examination of the impact of electric vehicles on power systems and offering appropriate integration solutions.
Backward neural network (BNN) based multilevel control for enhancing the quality of an islanded RES DC microgrid under variable communication network
(Elsevier, 2024) Anum, Hira; Hashmi, Muntazim Abbas; Shahid, Muhammad Umair; Munir, Hafiz Mudassir; Irfan, Muhammad; Veerendra, A. S.; Kanan, Mohammad; Flah, Aymen
Microgrids (MGs) and energy communities have been widely implemented, leading to the participation of multiple stakeholders in distribution networks. Insufficient information infrastructure, particularly in rural distribution networks, is leading to a growing number of operational blind areas in distribution networks. An optimization challenge is addressed in multi -feeder microgrid systems to handle load sharing and voltage management by implementing a backward neural network (BNN) as a robust control approach. The control technique consists of a neural network that optimizes the control strategy to calculate the operating directions for each distributed generating point. Neural networks improve control during communication connectivity issues to ensure the computation of operational directions. Traditional control of DC microgrids is susceptible to communication link delays. The proposed BNN technique can be expanded to encompass the entire multi -feeder network for precise load distribution and voltage management. The BNN results are achieved through mathematical analysis of different load conditions and uncertain line characteristics in a radial network of a multi -feeder microgrid, demonstrating the effectiveness of the proposed approach. The proposed BNN technique is more effective than conventional control in accurately distributing the load and regulating the feeder voltage, especially during communication failure.
Improved mechanical properties of graphene-modified basalt fibre-epoxy composites
(De Gruyter, 2024) Sepetcioglu, Harun; Lapčík, Lubomír; Lapčíková, Barbora; Vašina, Martin; Hui, David; Ovsík, Martin; Staněk, Michal; Murtaja, Yousef; Kvítek, Libor; Lapčíková, Tereza; Zmeškal, Oldřich
In industrial applications, the potential of basalt fibre-reinforced polymer (BFRP) composite pipes as a compelling alternative to glass and carbon fibre-reinforced composite pipes is recognized. Their high recyclability makes them a viable option for aerospace, marine, and automotive applications. In this study, a comparison is made between the mechanical properties of virgin basalt-epoxy composite pipes and graphene-modified counterparts. To conduct the experiments, pipe section specimens were prepared using a flex grinding machine. Graphene nanoplatelets (GnPs), serving as an exceptional reinforcing material, were uniformly incorporated into the basalt-epoxy composites at a specific concentration. The inclusion of these nanoplatelets resulted in significant changes in mechanical stiffness compared to the virgin basalt-epoxy composite pipes. A series of tests, including uniaxial tensile, Charpy impact, microhardness, Shore D hardness, uniaxial 3-point bending, and dynamic displacement transmissibility tests, were carried out to assess the mechanical properties of both graphene-reinforced and virgin basalt-epoxy pipes. The findings indicated that the pure basalt-epoxy composite exhibited lower ductility compared to the graphene basalt-epoxy composites after undergoing uniaxial mechanical loading. Non-destructive dynamic mechanical vibration testing was used to investigate the complex mechanical response of the materials under examination. The observed complex frequency-dependent responses reflected the mutual ductile/brittle mechanical performance of the developed composites.
Multi-agent reinforcement learning framework based on information fusion biometric ticketing data in different public transport modes
(Elsevier, 2024) Lakhan, Abdullah; Rashid, Ahmed N.; Mohammed, Mazin Abed; Zebari, Dilovan Asaad; Deveci, Muhammet; Wang, Limin, limin; Abdulkareem, Karrar Hameed; Nedoma, Jan; Martinek, Radek
In smart cities, biometric technologies have become extensively used for ticket authentication on public transport. Information fusion plays a key role in biometric ticketing, allowing ticket validation with more data source validation in different public transport modes. This paper proposes a novel biometric technology -based mobile ticket application -based system. We formulate the problem as a multi -agent reinforcement learning framework for biometric ticketing in multi -transport environments. Specifically, we propose the Asynchronous Advantage Critic Biometric Ticketing Framework (A3CBTF) algorithm, which consists of different schemes based on the proposed system. The proposed algorithm framework operates in hybrid transport modes using a parallel reinforcement learning scheme. A key advantage of A3CBTF is that it enables passengers to use a single ticket for various public transport modes. Additionally, even when a passenger's mobile device is stolen, lost, or has a dead battery, they can still validate their tickets through different information fusion sources, such as fingerprint and face recognition. A3CBTF is a multi -agent system that integrates mobile, transport, edge, and cloud servers to facilitate ticket validation in a distributed environment. By optimizing both convex and concave optimizations, A3CBTF ensures efficient ticket validation with minimal processing time and maximizes validation rewards across different biometric technologies. Experimental results demonstrate that A3CBTF outperforms mobile off with other options such as fingerprint and face recognition in public transport as compared to other ticketing systems.
Simulation of orbital fractures using experimental and mathematical approaches: A pilot study
(MDPI, 2024) Eiba, Patrik; Frydrýšek, Karel; Zanganeh, Behrad; Čepica, Daniel; Maršálek, Pavel; Handlos, Petr; Timkovič, Juraj; Štembírek, Jan; Cienciala, Jakub; Onderka, Arnošt; Březík, Michal; Mizera, Ondřej
This contribution gives basic information about the mechanical behavior of the facial part of the human skull cranium, i.e., the splanchnocranium, associated with external loads and injuries caused mainly by brachial violence. The main areas suffering from such violence include the orbit, frontal, and zygomatic bones. In this paper, as a first approach, brachial violence was simulated via quasi-static compression laboratory tests, in which cadaveric skulls were subjected to a load in a testing machine, increasing till fractures occurred. The test skulls were also used for research into the dynamic behavior, in which experimental and numerical analyses were performed. A relatively high variability in forces inducing the fractures has been observed (143-1403 N). The results lay the basis for applications mainly in forensic science, surgery, and ophthalmology.
A novel equal area-equal width-equal bin numbers technique using salp swarm optimization algorithm for maximizing the success rate of ball bearing assembly
(Springer Nature, 2024) Nagarajan, Lenin; Mahalingam, Siva Kumar; Čep, Robert; Ramesh, Janjhyam Venkata Naga; Elangovan, Muniyandy; Mohammad, Faruq
In this work, an algorithmic technique is used to minimize the excess parts and maximize the success rate of selective assembly. In this study, a unique method known as Equal Area-Equal Width-Equal Bin Numbers is introduced to group the parts of a ball bearing assembly by taking into account their range of tolerance. A full factorial design is used to conduct the experiments, and the salp swarm optimization (SSO) algorithm is employed to evaluate the best bin combinations and identify the possibility of making the maximum number of assemblies. Computational results showed a 13.16 percent increase in success rate when compared to prior research when employing the proposed method. Comparing the computational outcomes versus those obtained by the Antlion optimization and Genetic algorithms validates the adoption of the SSO algorithm. A paired T-test is performed to assess the statistical significance of the findings. The convergence plot further supports the superiority of the SSO algorithm.
Unveiling thermal and hemodynamic effects of aneurysm on abdominal aorta using power law model and finite element analysis
(Elsevier, 2024) Hussain, Azad; Bilal, S.; Arshad, Tayyaba; Dar, Muhammad Naveel Riaz; Aljohani, Abeer Ahmed; Riaz, Muhammad Bilal; Ghith, Ehab
The objective of this study is to find causes of aortic diseases and investigating the ways for better treatment. The governing system of equations has been demonstrated to account for characteristics of blood. The governing system of equation has been solved using the finite element method with appropriate boundary conditions. We analyzed into the relationship between flow characteristics via the aneurysmal abdominal aorta and the aneurysm height, aneurysm length, and non-Newtonian behavior. It investigates how thermal and hemodynamics effects change across the abdominal aortic aneurysm. The velocity, pressure, and temperature surface plots of the results are displayed. There have also been graphic displays of line graphs of axial velocity, radial velocity, axial pressure, radial pressure, axial temperature and radial temperature across the aneurysm. The blood flow simulations obtained results show that increasing blood temperature, pressure and intake velocity all contribute to an increase in viscosity. The results indicate that while the temperature varies little to not at all, the blood flow pressure decrease and velocity significantly vary.
Multi-objective energy management in a renewable and EV-integrated microgrid using an iterative map-based self-adaptive crystal structure algorithm
(Springer Nature, 2024) Rajagopalan, Arul; Nagarajan, Karthik; Bajaj, Mohit; Uthayakumar, Sowmmiya; Prokop, Lukáš; Blažek, Vojtěch
The use of plug-in hybrid electric vehicles (PHEVs) provides a way to address energy and environmental issues. Integrating a large number of PHEVs with advanced control and storage capabilities can enhance the flexibility of the distribution grid. This study proposes an innovative energy management strategy (EMS) using an Iterative map-based self-adaptive crystal structure algorithm (SaCryStAl) specifically designed for microgrids with renewable energy sources (RESs) and PHEVs. The goal is to optimize multi-objective scheduling for a microgrid with wind turbines, micro-turbines, fuel cells, solar photovoltaic systems, and batteries to balance power and store excess energy. The aim is to minimize microgrid operating costs while considering environmental impacts. The optimization problem is framed as a multi-objective problem with nonlinear constraints, using fuzzy logic to aid decision-making. In the first scenario, the microgrid is optimized with all RESs installed within predetermined boundaries, in addition to grid connection. In the second scenario, the microgrid operates with a wind turbine at rated power. The third case study involves integrating plug-in hybrid electric vehicles (PHEVs) into the microgrid in three charging modes: coordinated, smart, and uncoordinated, utilizing standard and rated RES power. The SaCryStAl algorithm showed superior performance in operation cost, emissions, and execution time compared to traditional CryStAl and other recent optimization methods. The proposed SaCryStAl algorithm achieved optimal solutions in the first scenario for cost and emissions at 177.29 ct and 469.92 kg, respectively, within a reasonable time frame. In the second scenario, it yielded optimal cost and emissions values of 112.02 ct and 196.15 kg, respectively. Lastly, in the third scenario, the SaCryStAl algorithm achieves optimal cost values of 319.9301 ct, 160.9827 ct and 128.2815 ct for uncoordinated charging, coordinated charging and smart charging modes respectively. Optimization results reveal that the proposed SaCryStAl outperformed other evolutionary optimization algorithms, such as differential evolution, CryStAl, Grey Wolf Optimizer, particle swarm optimization, and genetic algorithm, as confirmed through test cases.
Electrodeposition of Zn/TiO2 coatings on Ti6Al4V produced by selective laser melting, the characterization and corrosion resistance
(IOP Publishing, 2024) Gündüz, Demet Özaydın; Küçüktürk, Gökhan; Pul, Muharrem; Salunkhe, Sachin; Kaya, Duran; Kabalci, Mehmet; Čep, Robert; Nasr, Emad Abouel
Recently, additive manufacturing techniques have begun to be implemented extensively in the production of implants. Ti6Al4V alloy is a material of choice for implants due to its low density and high biocompatibility. Recent research, however, has demonstrated that Ti6Al4V alloy emits long-term ions (such as Al and V) that are hazardous to health. Surface modifications, including coating, are therefore required for implants. The electrodeposition method was utilized to deposit Zn-doped TiO2 onto the surfaces of Ti6Al4V samples, which were manufactured via the selective laser melting method. The effects of processing time, amount of TiO2 addition, microstructure of anode materials, and resistance to wear and corrosion were investigated. The coating hardness and thickness increased with increasing processing time and TiO2 concentration. It has been observed that the addition of TiO2 to zinc anode coatings results in an increase in wear and a decrease in corrosion rate. It was noted that the specimens exhibiting the most significant wear also possessed the highest hardness value. The specimens were generated utilizing a graphite anode, underwent a 30-min processing time, and comprised 10 g l(-1) of TiO2.
Wearable assistive rehabilitation robotic devices - A comprehensive review
(MDPI, 2024) Lingampally, Pavan Kalyan; Ramanathan, Kuppan Chetty; Shanmugam, Ragavanantham; Čepová, Lenka; Salunkhe, Sachin
This article details the existing wearable assistive devices that could mimic a human's active range of motion and aid individuals in recovering from stroke. The survey has identified several risk factors associated with musculoskeletal pain, including physical factors such as engaging in high-intensity exercises, experiencing trauma, aging, dizziness, accidents, and damage from the regular wear and tear of daily activities. These physical risk factors impact vital body parts such as the cervical spine, spinal cord, ankle, elbow, and others, leading to dysfunction, a decrease in the range of motion, and diminished coordination ability, and also influencing the ability to perform the activities of daily living (ADL), such as speaking, breathing and other neurological responses. An individual with these musculoskeletal disorders requires therapies to regain and restore the natural movement. These therapies require an experienced physician to treat the patient, which makes the process expensive and unreliable because the physician might not repeat the same procedure accurately due to fatigue. These reasons motivated researchers to develop and control robotics-based wearable assistive devices for various musculoskeletal disorders, with economical and accessible solutions to aid, mimic, and reinstate the natural active range of motion. Recently, advancements in wearable sensor technologies have been explored in healthcare by integrating machine-learning (ML) and artificial intelligence (AI) techniques to analyze the data and predict the required setting for the user. This review provides a comprehensive discussion on the importance of personalized wearable devices in pre- and post-clinical settings and aids in the recovery process.

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