Feature extraction and description are fundamental components of visual perception systems used in applications such as visual odometry, Simultaneous Localization and Mapping (SLAM), and autonomous navigation. In resource-constrained platforms, such as Unmanned Aerial Vehicles (UAVs), achieving real-time hardware acceleration on Field-Programmable Gate Arrays (FPGAs) is challenging. This work demonstrates an FPGA-based implementation of an adaptive ORB (Oriented FAST and Rotated BRIEF) feature extraction pipeline designed for high-throughput and energy-efficient embedded vision. The proposed architecture is a completely new design for the main algorithmic blocks of ORB, including the FAST (Features from Accelerated Segment Test) feature detector, Gaussian image filtering, moment computation, and descriptor generation. Adaptive mechanisms are introduced to dynamically adjust thresholds and filtering behavior, improving robustness under varying illumination conditions. The design is developed using a High-Level Synthesis (HLS) approach, where all processing modules are implemented as reusable hardware IP cores and integrated at the system level. The architecture is deployed and evaluated on two FPGA platforms, PYNQ-Z2 and KRIA KR260, and its performance is compared against CPU and GPU implementations using a dedicated C++ testbench based on OpenCV. Experimental results demonstrate significant improvements in throughput and energy efficiency while maintaining stable and scalable performance, making the proposed solution suitable for real-time embedded vision applications on UAVs and similar platforms. Notably, the FPGA implementation increases DSP utilization from 11% to 29% compared to the previous designs implemented by other researchers, effectively offloading computational tasks from general purpose logic (LUTs and FFs), reducing LUT usage by 6% and FF usage by 13%, while maintaining overall design stability, scalability, and acceptable thermal margins at 2.387 W. This work establishes a robust foundation for integrating the optimized ORB pipeline into larger drone systems and opens the door for future system-level enhancements.
Extreme right-wing parties are increasing in polls around Europe, largely fueled by an anti-migrant rhetoric. Political economy literature points to, on average, net positive effects that migrants bring to the economy, but the balance on the political market is more worrisome. For a small open economy, overly dependent on tourism, whose population reduced by more than 1 million in the last 30 years, the question of successful integration of migrants represents a first order condition of public policy. Thus the research question set in this paper is how to change attitudes on immigration among students in the Croatian society. Our approach is based on an experiment within a survey and it is tested on a sample of 1,450 students from five university cities in Croatia (Osijek, Pula, Rijeka, Split and Zagreb). Results indicate that there is a sizeable and statistically significant effect for the treated groups vis-a-vis their attitudes on the effect that migrants have on the labour market, social security system, overall safety and the economic development of Croatia.
This paper aims to crack the individual EV charging scheduling problem considering the dynamic user behaviors and the electricity price. The uncertainty of the EV charging demand is described by several factors, including the driver’s experience, the charging preference and the charging locations for realistic scenarios. An aggregate anxiety concept is introduced to characterize both the driver’s anxiety on the EV’s range and uncertain events. A mathematical model is also provided to describe the anxiety quantitatively. The problem is formulated as a Markov Decision Process (MDP) with an unknown state transition function. The objective is to find the optimal sequential charging decisions that can balance the charging cost and driver’s anxiety. A model-free deep reinforcement learning (DRL) based approach is developed to learn the optimal charging control strategy by interacting with the dynamic environment. The continuous soft actor-critic (SAC) framework is applied to design the learning method, which contains a supervised learning (SL) stage and a reinforcement learning (RL) stage. Finally, simulation studies verify the effectiveness of the proposed approach under dynamic user behaviors at different charging locations.
Vendula Samelova, Jana Pekarova, Frantisek Bradac
et al.
Articulated arm coordinate measuring machines are designed for in situ use directly in manufacturing environments, enabling efficient dimensional control outside of climate-controlled laboratories. This study investigates the influence of ambient temperature variation on the accuracy of length measurements performed with the Hexagon Absolute Arm 8312. The experiment was carried out in a laboratory setting simulating typical shop floor conditions through controlled temperature changes in the range of approximately 20–31 °C. A calibrated steel gauge block was used as a reference standard, allowing separation of the influence of the measuring system from that of the measured object. The results showed that the gauge block length changed in line with the expected thermal expansion, while the articulated arm coordinate measuring machine exhibited only a minor residual thermal drift and stable performance. The experiment also revealed a constant measurement offset of approximately 22 µm, likely due to calibration deviation. As part of the study, an uncertainty budget was developed, taking into account all relevant sources of influence and enabling a more realistic estimation of accuracy under operational conditions. The study confirms that modern carbon composite articulated arm coordinate measuring machines with integrated compensation can maintain stable measurement behavior even under fluctuating temperatures in controlled environments.
New synthetic biology capabilities hold the promise of dramatically improving our ability to engineer biological systems. However, a fundamental hurdle in realizing this potential is our inability to accurately predict biological behavior after modifying the corresponding genotype. Kinetic models have traditionally been used to predict pathway dynamics in bioengineered systems, but they take significant time to develop, and rely heavily on domain expertise. Here, we show that the combination of machine learning and abundant multiomics data (proteomics and metabolomics) can be used to effectively predict pathway dynamics in an automated fashion. The new method outperforms a classical kinetic model, and produces qualitative and quantitative predictions that can be used to productively guide bioengineering efforts. This method systematically leverages arbitrary amounts of new data to improve predictions, and does not assume any particular interactions, but rather implicitly chooses the most predictive ones. New synthetic biology capabilities (e.g. CRISPR) dramatically improve our ability to engineer biological systems for the benefit of society (biofuels, medical drugs). However, this effort is hampered because we cannot reliably predict the outcome of our bioengineering efforts. Mathematical kinetic models have been traditionally used to predict pathway dynamics, but they take a long time to develop and require significant biological expertize. Here, we substitute traditional kinetic models with a machine learning approach that is able to learn pathway dynamics straight from data examples. This new approach can be systematically applied to any product, pathway or host and significantly speeds up bioengineering.
Khush Bukht Mehdi, Zubia Mehdi, Shamaila Samreen
et al.
The dominant intention of this article is to extract the new exact traveling waves solutions of the nonlinear space-time fractional Sharma-Tasso-Olver equation in the sense of beta-derivative by using three integration schemes namely, Riccati-Bernoulli (RB) sub-ODE method, Generalized Bernoulli (GB) sub-ODE method and Generalized tanh (GT) method. By the virtue of employed techniques, different types of solutions are obtained in the form of trigonometric, hyperbolic, and exponential functions respectively. The obtained solutions are also verified for the aforesaid equation through symbolic soft computations. To promote the vital propagated features; some investigated solutions are exhibited in the form of 2D and 3D graphics by passing on the specific values to the parameters under the confined conditions. Further, based on the bifurcation theory, we examine the phase portrait of the proposed nonlinear equation. Furthermore, we ensure that all the solutions are innovative and have remarkable impacts on the prevailing solitary wave theory literature.
Khanyisani Mhlangano Makhanya, Simon Connell, Muaaz Bhamjee
et al.
Pulmonary diseases are a leading cause of illness and disability globally. While having access to hospitals or specialist clinics for investigations is currently the usual way to characterize the patient’s condition, access to medical services is restricted in less resourced settings. We posit that pulmonary disease may impact on vocalization which could aid in characterizing a pulmonary condition. We therefore propose a new method to diagnose pulmonary disease analyzing the vocal and cough changes of a patient. Computational fluid dynamics holds immense potential for assessing the flow-induced acoustics in the lungs. The aim of this study is to investigate the potential of flow-induced vocal-, cough-, and lung-generated acoustics to diagnose lung conditions using computational fluid dynamics methods. In this study, pneumonia is the model disease which is studied. The hypothesis is that using a computational fluid dynamics model for assessing the flow-induced acoustics will accurately represent the flow-induced acoustics for healthy and infected lungs and that possible modeled difference in fluid and acoustic behavior between these pathologies will be tested and described. Computational fluid dynamics and a lung geometry will be used to simulate the flow distribution and obtain the acoustics for the different scenarios. The results suggest that it is possible to determine the difference in vocalization between healthy lungs and those with pneumonia, using computational fluid dynamics, as the flow patterns and acoustics differ. Our results suggest there is potential for computational fluid dynamics to enhance understanding of flow-induced acoustics that could be characteristic of different lung pathologies. Such simulations could be repeated using machine learning with the final objective to use telemedicine to triage or diagnose patients with respiratory illness remotely.
Douglas Freitas de Oliveira, Irene Cristina de Mello, Elane Chaveiro Soares
Recorrente em publicações científicas e documentos curriculares nacionais, a interdisciplinaridade tem sido discutida sob diferentes contextos ao longo das últimas décadas, sobretudo, a partir da possibilidade de religação dos saberes, como propõe Edgar Morin. Nesse sentido, buscou-se aqui investigar como a interdisciplinaridade se faz presente na Base Nacional Comum Curricular do Ensino Médio (BNCC EM) – um documento educacional normativo dos currículos da Educação Básica de abrangência nacional – a partir de seus textos introdutórios e da área de conhecimento das Ciências da Natureza e suas Tecnologias, considerando como abordagem epistemológica o pensamento complexo de Morin. Para tanto, adotou-se uma abordagem qualitativa descritiva, do tipo pesquisa documental, cujo percurso metodológico foi constituído por três etapas: estudo dos referenciais teóricos; análise de trechos específicos da BNCC-EM, segundo a Análise Textual Discursiva; e elaboração do metatexto, a partir das categorias analíticas. A interdisciplinaridade e a complexidade sutilmente se apresentam em esparsos trechos do documento, sem explicitação de fundamentos e referenciais teóricos, metodológicos, epistemológicos ou pedagógicos sobre esses temas. Tal constatação faz evidência de lacunas que precisam ser discutidas, considerando a envergadura e a importância da BNCC-EM na consolidação de diversas outras políticas educacionais do país.
Special aspects of education, Applied mathematics. Quantitative methods
Gilang Titah Ramadhan, Wahyudi Sutopo, Muhammad Hisjam
Insect attacks are a very complicated problem in rice cultivation that cause a decrease in rice productivity. It is very important to not use pesticides to kill pests due to environmental and health issues. This study aimed to solve the pest problem by installing solar-powered pest-control technology using waves of ultraviolet light and ultrasonic sound (UVUS, the name of the product). The development of UVUS involved not only innovation from startups but also the adaption of existing technologies such as batteries, solar panels, and sensors. A location-allocation model has been developed in accordance with a flower pollination algorithm (FPA) and sustainability considerations to solve the problem of rice productivity using the innovative technology of solar-powered pest control. The mixed-integer linear programming (MILP) approach was used to determine the number of UVUS required to minimize the areas missed by the ultraviolet light and ultrasonic sound. Numerical analysis of a case study of Delanggu Village showed that the model can be used to determine the number of UVUS required to only miss a certain minimal area. The results show that the proposed model can be applied to solve pest control and can provide promising economical, social, and environmental outcomes.
Delta power, a measure of EEG activity in the 1–4 Hz range, in slow-wave sleep (SWS) is in a quantitative and predictive relationship with prior wakefulness. Thus, sleep loss evokes a proportional increase in delta power, and excess sleep a decrease. Therefore, delta power is thought to reflect SWS need and its underlying homeostatically regulated recovery process. The neurophysiological substrate of this process is unknown and forward genetics might help elucidate the nature of what is depleted during wakefulness and recovered during SWS. We applied a mathematical method that quantifies the relationship between the sleep–wake distribution and delta power to sleep data of six inbred mouse strains. The results demonstrated that the rate at which SWS need accumulated varied greatly with genotype. This conclusion was confirmed in a “dose–response” study of sleep loss and changes in delta power; delta power strongly depended on both the duration of prior wakefulness and genotype. We followed the segregation of the rebound of delta power after sleep deprivation in 25 BXD recombinant inbred strains by quantitative trait loci (QTL) analysis. One “significant” QTL was identified on chromosome 13 that accounted for 49% of the genetic variance in this trait. Interestingly, the rate at which SWS need decreases did not vary with genotype in any of the 31 inbred strains studied. These results demonstrate, for the first time, that the increase of SWS need is under a strong genetic control, and they provide a basis for identifying genes underlying SWS homeostasis.
The numerical investigation of a Casson–Williamson reactive nanofluid species in a moving vertical medium is the subject of this study. The complete exothermic reaction of viscoplastic nanofluid material in a cylindrical system is considered using a generalized Arrhenius kinetic. Casson and Williamson fluids are combined to give the fluid its viscoelastic property. In collaboration with the bivariate overlapping multi-domain technique, the spectral quasi-linearization scheme is used to find solutions to dimensionless mathematical nonlinear formulated flow equations. The findings are displayed in tabular and graphical form, demonstrating the sensitivity of the thermofluid parameters. The study indicates that the flow motion is not caused by the activation energy or the material reaction. It was obtained that the magnetic field increased the viscosity of the material and the thermal conductivity of the nanoparticles. Also, Lewis number enhanced the fluid viscoplastic property. Thermal and chemical engineering, as well as nanotechnology development, will benefit from the findings of this investigation.
Audusse E., Caldas Steinstraesser J.G., Emerald L.
et al.
In this paper, we analyze the relevance of the use of the shallow water model and the Boussinesq model to simulate tsunamis generated by a landslide. In a first part, we determine if the two models are able to reproduce waves generated by a landslide. Each model has drawbacks but it seems that it is possible to use them together to improve the simulations. In a second part we try to recover the landslide displacement from the generated wave. This problem is formulated as a minimization problem and we limit the number of parameters to determine assuming that the bottom can be well described by an empirical law.
Szabolcs Suveges, Ibrahim Chamseddine, Katarzyna A. Rejniak
et al.
The specific structure of the extracellular matrix (ECM), and in particular the density and orientation of collagen fibres, plays an important role in the evolution of solid cancers. While many experimental studies discussed the role of ECM in individual and collective cell migration, there are still unanswered questions about the impact of nonlocal cell sensing of other cells on the overall shape of tumour aggregation and its migration type. There are also unanswered questions about the migration and spread of tumour that arises at the boundary between different tissues with different collagen fibre orientations. To address these questions, in this study we develop a hybrid multi-scale model that considers the cells as individual entities and ECM as a continuous field. The numerical simulations obtained through this model match experimental observations, confirming that tumour aggregations are not moving if the ECM fibres are distributed randomly, and they only move when the ECM fibres are highly aligned. Moreover, the stationary tumour aggregations can have circular shapes or irregular shapes (with finger-like protrusions), while the moving tumour aggregations have elongate shapes (resembling to clusters, strands or files). We also show that the cell sensing radius impacts tumour shape only when there is a low ratio of fibre to non-fibre ECM components. Finally, we investigate the impact of different ECM fibre orientations corresponding to different tissues, on the overall tumour invasion of these neighbouring tissues.
Abstract The Half-Skip Total Focusing Method (HSTFM) is an ultrasonic array post-processing (imaging) technique. The method allows surface-breaking cracks (SBCs) to be imaged and also sized using 6 dB drop rule. The HSTFM has previously been used to size SBCs initiating from flat and horizontal walls. However, there has been limited research to optimise the method in a rigours manner and quantitatively determine the performance of the HSTFM to size cracks accurately. In this paper, the SBCs characterization capability of the HSTFM was tested and applied on both numerically simulated and experimentally generated data. Under numerical simulation, considering the travel time of ultrasonic signals as per the ray theory, first, a mathematical framework was developed for the HSTFM imaging algorithm. Then, a quantitative parametric study, using the finite element method and point reflector model, was performed to evaluate the sizing capability of the HSTFM under different scenarios. These scenarios include the position of the ultrasonic array relative to the crack, the accuracy of the acoustic velocity, the height of the sample, the slope of the back wall, and the tilting angle of the defect. For each of the array configuration, the crack-sizing range (the smallest and largest Through Wall Extent (TWE) of a crack) that can be measured using the HSTFM was calculated. Thereafter, experimental validation was performed, where an excellent match with the numerical results was observed.
El proceso de toma de decisiones tiene una incidencia relevante en los resultados de las empresas, lo que ha llevado a desarrollar novedosos métodos que permitan evaluar bajo condiciones no controlables elementos subjetivos y racionales. En ese sentido, el objetivo principal de este trabajo estudia los operadores de agregación en la toma de decisiones en entornos inciertos. Se presentan dos metodologías que permiten agregar información, que se llaman operadores OWA y BON-OWA. La aplicación de estos operadores se realiza en la selección de lanzamiento de nuevos productos. La principal ventaja de estos operadores es que permiten capturar la actitud del decisor y la comparación e interrelación continua de la información. Así, se destaca el análisis holístico que ofrecen estos métodos sobre la toma de decisiones en incertidumbre, que permite integrar conceptos de la teoría administrativa y la teoría de la agregación en un caso aplicado, visualizando como la inclusión de la información genera cambios dentro de los rankings de selección de alternativas. || Decision-making has a relevant incidence on firms results, which has led to develop novel methods that allow assessing subjective and rational elements under uncontrollable conditions. In this sense, the main aim of this work is to study aggregation operators in decision-making in uncertain environments. Two methodologies allow aggregating information are presented, which are called OWA and BON-OWA operators. The application of these operators is made in the selection of new product release. The main advantage of these operators is that they allow to capture the attitude of the decision maker and the continuous comparison and interrelation of the information. Thus, the holistic analysis offered by these methods on decision making in uncertainty is highlighted, which allows to integrate concepts of administrative and aggregation theories in an applied case, visualizing how the inclusion of information generates changes within the selection of alternatives.
Abstract In this study, a mathematical framework was developed for the sustainability prioritization of alternative low-carbon ammonia production routes. In the framework, a four-dimensional assessment system that can incorporate both quantitative and qualitative criteria from the environmental, economic, social-political, and technical concerns was firstly established. Subsequently, a hybrid Entropy-FANP method was employed to determine the criteria's weight by combining the objective data and subjective opinions; a novel PRSRV approach was developed to rigorously rank the alternative routes by aggregating the absolute sustainability performance and relative sustainability balance of each alternative. The proposed framework was applied to prioritize five promising low-carbon routes for ammonia production, i.e. wind turbine electrolysis (WGEA), solar photovoltaic electrolysis (PVEA), hydropower electrolysis (HPEA), biomass gasification electrolysis (BGEA), and nuclear high temperature electrolysis (NTEA), yielding the sustainability ranking of HPEA > BGEA > WGEA > PVEA > NTEA. The robustness and effectiveness of the proposed framework were verified by conducting the sensitivity analysis and comparing the results determined by the proposed framework with those determined using the previous approaches.