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Authors - 10223
Mahdi Ghaemi Asl
57224334355
Ádám Vörös
59496339100
Narges Kariminejad
57201980350
O. Oqilat
60629770200
Publications - 6525
The Connections Between Social Media Platforms and Hybridity
Publication Name: Palgrave Studies in Digital Inequalities
Publication Date: 2025-01-01
Volume: Part F742
Issue: Unknown
Page Range: 105-123
Description:
The method of hybrid threats and the underlying conceptual framework have been widely investigated again since the second half of the 2000s, following Hezbollah’s tangible military success in Lebanon against the Israel Defense Forces in 2006. This was exacerbated by the activities of the Islamic State, which conducted a sophisticated and rather aggressive marketing campaign, and developed psychological warfare in cyberspace to a high level. Various operations in the context of the Ukrainian crisis and the Russian annexation of Crimea have once again brought hybrid warfare into the spotlight. The hybrid equipments are not new in history, but their success has been obviously enhanced by the development of technology, especially cyberspace and the wide range of opportunities cyberspace offers. Following the Russian–Ukrainian crisis, it has also become clear that hybrid instruments can not only appear as parts of a complex interstate conflict but that some of their elements can be used on their own. Clear examples of this include various disinformation campaigns. In this paper, the authors highlight, through a characterisation of hybrid conflicts, the extent to which the use of soft assets is an immanent part of contemporary military operations. The filtering practices and mechanisms, economic and market perceptions of social media platforms can be used to conduct disinformation campaigns.
Open Access: Yes
Biomolecule composition and draft genome of a novel, high-lipid producing Scenedesmaceae microalga
Publication Name: Algal Research
Publication Date: 2021-04-01
Volume: 54
Issue: Unknown
Page Range: Unknown
Description:
Lipid biosynthesis in microalgae can be stimulated by cultivation in low nitrogen medium. MACC-401 was isolated from the soil surface in Tres Marias (MG-Brazil). The strain shows the morphological characteristics of the Scenedesmaceae green algae. The daily biomass and lipid production of MACC-401 is remarkable, 0.36 g L−1 and 110 mg L−1, respectively. Exploration of the genetic background of this promising strain not only allows the utilization of this species for industrial-scale lipid production, but also provides genetic targets to select lipid-producing strains from microalgae collections. We conducted physiological experiments by cultivating MACC-401 in complete and N-limited media and performed genome sequencing as well as transcriptome analysis. The estimated nuclear genome size of MACC-401 is 99.503 Mbp and the chloroplast genome is 0.15 Mbp. The phylogenetic analysis confirmed that the MACC-401 belongs to the Scenedesmaceae family, and represents a genetically distinct accession in this family. A basic comparative transcriptome analysis resulted in the identification of N-starvation responsive genes, which could serve as markers to monitor the onset of lipid accumulation in algal cultures.
Open Access: Yes
Temperature and frequency dependent preisach model
Publication Name: Przeglad Elektrotechniczny
Publication Date: 2018-01-01
Volume: 94
Issue: 4
Page Range: 5-8
Description:
The present paper deals with a frequency and temperature dependent modeling approach for hysteresis loops of ferromagnetic materials. The model is based on the Preisach model. The frequency dependency is taken into account by the statistical loss theorem, while thermal effects were incorporated by a generalization of the model equation. The model was validated against measurements made on a soft magnetic material. The results of the proposed model were in good agreement with measured data.
Open Access: Yes
Association of ANKK1 and DRD2 gene polymorphisms with exercise addiction among elite athletes
Publication Name: International Journal of Sport and Exercise Psychology
Publication Date: 2025-01-01
Volume: Unknown
Issue: Unknown
Page Range: Unknown
Description:
Exercise addiction is a behavior that may dysregulate athletic performance, and social and professional interactions of athletes. Whereas environmental factors including training routines and personal traits could contribute to exercise addiction, recent studies have emphasized the importance of genetic predisposition, leading to development of a subfield known as sports psychogenetics. In sports psychogenetics, ankyrin repeat and kinase domain containing 1 (ANKK1) and dopamine receptor D2 (DRD2) genes, located on chromosome 11 in a close proximity, have attracted research interest due to their involvement in dopaminergic signaling playing a crucial role in reward processing, motivation, cognition and behavior. Therefore, the present study aimed to investigate potential associations between 14 polymorphisms in ANKK1/DRD2 and exercise addiction among elite badminton players (n = 39) and elite wrestlers (n = 68). Exercise addiction was assessed using a psychometric screening instrument and allele frequencies of the selected polymorphisms were analyzed through genotyping with a single nucleotide polymorphism (SNP) microarray. Results indicated that two SNPs, rs7118900 and rs4436578, were significantly and independently associated with exercise addiction. Rs7118900 has previously been associated with an increased risk of drug addiction, neuroticism, and depressed effect whereas rs4436578 has been associated with neuroticism. In addition to those SNPS, rs2283265 and rs1125394 SNPs were also linked to exercise addiction in a branch-independent manner. Therefore, it is proposed that these SNPs could serve as genetic markers for identifying individuals at high risk of exercise addiction among athletes. However, further research is needed to understand the involvement of these SNPs in exercise addiction more comprehensively.
Open Access: Yes
Particle Number Concentration and SEM-EDX Analyses of an Auxiliary Heating Device in Operation with Different Fossil and Renewable Fuel
Publication Name: Inventions
Publication Date: 2024-02-01
Volume: 9
Issue: 1
Page Range: Unknown
Description:
Pollution from road vehicles enters the air environment from many sources. One such source could be if the vehicle is equipped with an auxiliary heater. They can be classified according to whether they work with diesel or gasoline and whether they heat water or air. The subject of our research series is an additional heating system that heats the air, the original fuel is gasoline. This device has been built up in a modern engine test bench, where the environmental parameters can be controlled. The length of the test cycle was chosen to be 30 min. The tested fuels were E10, E30, E100 and B7. A 30-min operating period has been chosen in the NORMAL operating mode of the device as a test cycle. The focus of the tests was particle number concentration and soot composition. The results of the particle number concentration showed that renewable fuel content significantly reduces the number concentration of the emitted particles (9.56 × 108 #/cycle for E10 vs. 1.65 × 108 #/cycle for E100), while B7 causes a significantly higher number of emissions than E10 (3.92 × 1010 #/cycle for B7). Based on the elemental analysis, most deposits are elemental carbon, but non-organic compounds are also present. Carbon (92.18 m/m% for E10), oxygen (6.34 m/m% for E10), fluorine (0.64 m/m% for E10), and zinc (0.56 m/m% for E10) have been found in the largest quantity of deposits taken form the combustion chamber.
Open Access: Yes
Curve Trajectory Model for Human Preferred Path Planning of Automated Vehicles
Publication Name: Automotive Innovation
Publication Date: 2024-02-01
Volume: 7
Issue: 1
Page Range: 59-70
Description:
Automated driving systems are often used for lane keeping tasks. By these systems, a local path is planned ahead of the vehicle. However, these paths are often found unnatural by human drivers. In response to this, this paper proposes a linear driver model, which can calculate node points reflective of human driver preferences and based on these node points a human driver preferred motion path can be designed for autonomous driving. The model input is the road curvature, effectively harnessed through a self-developed Euler-curve-based curve fitting algorithm. A comprehensive case study is undertaken to empirically validate the efficacy of the proposed model, demonstrating its capacity to emulate the average behavioral patterns observed in human curve path selection. Statistical analyses further underscore the model's robustness, affirming the authenticity of the established relationships. This paradigm shift in trajectory planning holds promising implications for the seamless integration of autonomous driving systems with human driving preferences.
Open Access: Yes
Design and Analysis of a Bandwidth Aware Adaptive Multipath N-Channel Routing Protocol for 5G Internet of Things (IoT)
Publication Name: Emerging Science Journal
Publication Date: 2024-02-01
Volume: 8
Issue: 1
Page Range: 251-269
Description:
Large numbers of mobile wireless nodes that can move randomly and join or leave the network at any moment make up mobile ad-hoc networks. A significant number of messages are delivered during information exchange in populated regions because of the Internet of Things' (IoT) exponential increase in connected devices. Congestion can increase transmission latency and packet loss by causing congestion. More network size, increased network traffic, and high mobility that necessitate dynamic topology make this problem worse. An adaptive Multipath Multichannel Energy Efficient (AMMEE) routing strategy is proposed in this study, in which route selection strategies depend on forecasted energy consumption per packet, available bandwidth, queue length, and channel utilization. While multichannel uses a channel-ideal assignment process to lessen network collisions, multipath offers various paths and balances network strain. The link bandwidth is split up into a few sub-channels in the multichannel mechanism. To reduce network collisions, several source nodes simultaneously access the channel bandwidth. The cooperative multipath multichannel technique offers several paths from a single source or from several sources to the destination without colliding or becoming congested. The AMMEE routing approach is the basis for path selection. A load-and bandwidth-aware routing mechanism in the proposed AMMEE chooses the path based on node energy and forecasts their lifetime, which improves network dependability. The outcome demonstrates a comparative analysis of various multichannel medium access control (MMAC) techniques, including Parallel Rendezvous Multi Channel Medium Access Protocol (PRMMAC), Quality of Service Ad hoc On Demand Multipath Distance Vector (QoS-AOMDV), Q-learning-based Multipath Routing (QMR), and Topological Change Adaptive Ad hoc On-demand Multipath Distance Vector (TA-AOMDV) and the proposed AMMEE method. The results show that the AMMEE approach outperforms alternative systems.
Open Access: Yes
A coupled impact of different management and soil moisture on yield of winter wheat (Triticum aestivum L.) in dry conditions at locality Mezoföld, Hungary
Publication Name: Journal of Hydrology and Hydromechanics
Publication Date: 2021-03-01
Volume: 69
Issue: 1
Page Range: 76-86
Description:
Variable rate technology (VRT) in nutrient management has been developed in order to apply crop inputs according to the required amount of fertilizers. Meteorological conditions rarely differ within one field; however, differences in soil conditions responding to precipitation or evaporation results within field variations. These variations in soil properties such as moisture content, evapotranspiration ability, etc. requires site-specific treatments for the produced crops. There is an ongoing debate among experts on how to define management zones as well as how to define the required amount of fertilizers for phosphorus and nitrogen replenishment for winter wheat (Triticum aestivum L.) production. For management zone delineation, vegetation based or soil based data collection is applied, where various sensor technology or remote sensing is in help for the farmers. The objective of the study reported in this paper was to investigate the effect of soil moisture data derived from Sentinel-2 satellite images moisture index and variable rate phosphorus and nitrogen fertilizer by means of variable rate application (VRA) in winter wheat in Mezoföld, Hungary. Satellite based moisture index variance at the time of sowing has been derived, calculated and later used for data comparison. Data for selected points showed strong correlation (R2 = 0.8056; n = 6) between moisture index and yield, however generally for the whole field correlation does not appear. Vegetation monitoring has been carried out by means of NDVI data calculation. On the field level, as indicated earlier neither moisture index values at sowing nor vegetation index data was sufficient to determine yield. Winter wheat production based on VRA treatment resulted significant increase in harvested crop: 5.07 t/h in 2013 compared to 8.9 t/ha in 2018. Uniformly managed (control) areas provided similar yield as VRA treated areas (8.82 and 8.9 t/ha, respectively); however, the input fertilizer was reduced by 108 kg/ha N and increased by 37 kg/ha P.
Open Access: Yes
Control of underactuated systems based on machine learning model: case studies
Publication Name: Multibody System Dynamics
Publication Date: 2025-01-01
Volume: Unknown
Issue: Unknown
Page Range: Unknown
Description:
Besides traditional modelling approaches, machine learning surrogate models have become widespread. In addition to existing concepts, this work investigates the use of neural networks (NNs) to replace dynamic equations in multibody systems. While physics-informed neural networks (PINNs) dominate recent literature, their constraint enforcement via cost-function penalties poses tuning challenges. This paper proposes an alternative: training NNs on data with intentional constraint violations to implicitly learn stabilisation, avoiding PINN limitations. The proposed concept is applied to the inverse dynamics control of underactuated systems, where the task is defined by servo-constraints. Using Scikit-learn’s MLP Regressor, we demonstrate NN-based surrogate modeling in three levels: 1) forward dynamics of minimum-coordinate models, 2) constrained models, and 3) the inverse dynamics control of underactuated multibody systems via servo-constraints, which is a classical approach not yet combined with NNs. The neural network model is used to represent the inverse dynamics model, for which training data are generated through forward dynamic simulations. The study demonstrates that low-degree-of-freedom planar systems can be approximated by middle-scale neural network models of a few hundred perceptrons, requiring training times of minutes on a personal computer. The proposed control architecture can stabilise the servo-constraints and track trajectories, even for non-collocated underactuated systems where traditional methods might fail. The results highlight a simple, industry-friendly path for NN-based MBD control.
Open Access: Yes
