bluuebunny/crossref_metadata_2025 huggingface dataset:
Peningkatan Keterampilan Menulis Dongeng Menggunakan Media Mind Mapping di SMPS Pusaka Kabupaten Cianjur
Authors: Ayu Lestari Adinda Putri , Ningsih D. Nurfajrin , Sukaesih
Published: July 2024

Penelitian ini ditujukan untuk literasi menulis dongeng pada peserta didik yang masih cukup rendah. Hal tersebut disebabkan karena program literasi belum berjalan maksimal. Penelitian ini menggunakan desain penelitian Pre-experimental Design dengan jenis One Group Pretest-posttest. Dengan menggunakan 3 instrumen yaitu lembar observasi, lembar tes dan lembar kuesioner. Penelitian ini memiliki tujuan yaitu untuk mengetahui peningkatan keterampilan literasi menulis dongeng pada peserta didik kelas VII B di SMPS Pusaka dengan menggunakan media mind mapping. Dengan media mind mapping diharapkan peserta didik dapat menulis dongeng dengan baik dan memperhatikan diksi, alur serta ejaan. Hail observasi pada penelitian ini yaitu 92,5% yang termasuk ke dalam kategori sangat baik. Selain itu adanya peningkatan dari hasil tes awal ke tes akhir. Pada hasil tes awal memperoleh nilai rata-rata sebesar 40 dengan nilai terendah 30 dan nilai tertinggi 50. Sedangkan pada saat tes akhir memperoleh nilai rata-rata sebesar 85 dengan nilai terendah 80 dan nilai tertinggi 90. Maka dari itu dapat disimpulkan bahawa adanya peningkatan dari hasil nilai tes akhir.
Pengaruh Gaya Kepemimpinan dan Disiplin Kerja Terhadap Kinerja Karyawan PT Raffael Global Mandiri
Authors: Nurina Salwa , Sugeng Irwin Sukrisno
Published: July 2024

Gaya Kepemimpinan dan Disiplin kerja mempunyai peranan penting dalam meningkatkan kinerja karyawan. Penelitian ini bertujuan untuk mengetahui pengaruh gaya kepemimpinan dan disiplin kerja terhadap kinerja karyawan PT Raffael Global Mandiri. Metode penelitian ini menggunakan metode kuantitatif asosiatif. Populasi dalam penelitian ini adalah seluruh karyawan PT Raffael Global Mandiri dan sampel penelitian sebanyak 45 orang. Analisis data menggunakan uji regresi linear berganda dengan bantuan software SPSS 26. Hasil penelitian ini adalah (1) gaya kepemimpinan mempunyai pengaruh positif dan signifikan terhadap kinerja karyawan Berlandaskan hasil uji T karena nilai Sig 0,001 < 0,05 dan nilai t hitung 3,750 > t tabel 2,018, sehingga dapat disimpulkan bahwa Ho1 di tolak dan Ha1 diterima (2) Disiplin kerja mempunyai pengaruh positif dan signifikan terhadap kinerja karyawan Berlandaskan hasil uji T karena nilai Sig 0,003 < 0,05 dan nilai t hitung 3,121 > 2,018, sehingga dapat disimpulkan bahwa Ho2 ditolak Ha2 diterima, (3) gaya kepemimpinan dan disiplin kerja secara bersama-sama mempunyai pengaruh positif dan signifikan terhadap kinerja karyawan).
 
Leadership style and work discipline have an important role in improving employee performance. This study aims to determine the effect of leadership style and work discipline on the performance of employees of PT Raffael Global Mandiri. This research method uses descriptive quantitative methods. The population in this study were all employees of PT Raffael Global Mandiri and the research sample was 45 people. Data analysis using multiple linear regression tests with the help of SPSS 26 software. The results of this research are (1) leadership style has a positive and significant influence on employee performance based on the results of the T test because the Sig value is 0.001 < 0.05 and the calculated t value is 3.750 > t table 2.018, so it can be concluded that Ho1 is rejected and Ha1 is accepted (2) Work discipline has a positive and significant influence on employee performance based on the results of the T test because the Sig value is 0.003 < 0.05 and the calculated t value is 3.121 > 2.018, so it can be concluded that Ho2 is rejected, Ha2 is accepted, (3) leadership style and work discipline in together have a positive and significant influence on employee performance.
Design, testing and operation of fish processing lines - can simulation tools and artificial intelligence be a gamechanger?
Authors: Hansen Irina-Emily , Mork Ola Jon , Kleppe Paul Steffen
Published: July 2024

The oceans play an important role in the world's food supply, so it is very important that we can develop sustainable, safe, and cost-effective production and processing of fish that also safeguards fish welfare. The aim of this research work is to explore if state of the art simulation tools and artificial intelligence, can improve the design, manufacturing, commissioning, and operation of fish processing. The research work takes a broad perspective and describes overall challenges related to fish processing, where the product – the fish – has numerous species, sizes, shapes, and seasonal variations. Processing of fish can take place on trawlers operating on the sea and on land for example processing of aqua culture salmon from. The owners of fish processing lines, have their unique approach and experience, and every fish processing line will be a one-of-a-kind design. Design, testing and operation of fish processing lines can therefore be time consuming, expensive with use of dead fishes either in a physical set up in a workshop or in worst case happen under fish operation in the Barents Sea. If it is possible to set up a simulation scenario with a set of random fish types and random batches of fish and fish processing line operation in rolling sea environment, this can reveal weaknesses with the fish processing line already in the design stage. The research team has set up three early-stage trials of simulation; first the flow of fish on a transport roller, secondly design and test of a singulation machine – machine organizing fishes in a queue for processing –in simulation environment, thirdly shows how simulation can contribute to a more precise and intelligent processing of the fish. The research work indicates that simulation is a promising tool for solving challenges within fish processing. The research work also suggests the possible benefits of simulation for the fish processing industry.
Combined use of ai techniques and simulation to support production scheduling: evidence from empirical research
Authors: Bandinelli Romeo , Fani Virginia
Published: July 2024

This paper reports a literature review on the Artificial Intelligence (AI) techniques applied in the field of production planning and scheduling and explores the synergistic integration of AI with simulation for enhancing production scheduling. Leveraging Microsoft Project Bonsai and AnyLogic, we present a case study that demonstrates the effectiveness of AI-driven simulation models in optimizing scheduling tasks. Our research highlights the potential of combining AI with traditional simulation methods. The results offer insights into the practical applications and future potential of AI in industrial automation and production management.
Pls-sem multigroup analysis of industry 5.0 specific human capital performance in manufacturing companies
Authors: Chernenko Ilia , Zemzyulina Veronika
Published: July 2024

Investigating complex processes of digitization using structural modelling, often assumes that researchers are dealing with heterogeneity, particularly regional, sectoral, or personal differences. Recent research has concentrated on addressing challenges of multigroup analysis (MGA) while estimating path coefficients in partial least squares structural equation models (PLS-SEM), however rarely dealing with more than two groups. This article aims to explore differences in the performance of basic and advanced digital competencies within the context of Industry 5.0 using a multigroup comparison of structural models across three regions in Russia, based on a sample of 1798 respondents. A proposed structural equation model includes reflective measures of learning environment, basic digital competencies, Industry 5.0-specific skills, and production system performance. To assess and compare model parameters, simultaneous MGA is conducted on the three regional groups using the non-parametric distance-based test (NDT). MGA is a conventional approach, however, NDT solid establishment in empirical literature is still a work in progress. The findings show that in highly digitalized regions the path coefficients linking basic digital competencies and Industry 5.0 skills are significantly higher. Furthermore, Industry 5.0 specific human capital have a significant positive impact on the performance of the production system.
How the family tax allowances have affected child poverty in the visegrad countries 2005-2020
Authors: Varga Erzsebet Terez
Published: July 2024

The similar historical and cultural background of the Visegrad countries makes them well suited for comparing government policies. In this paper, I investigate whether family tax allowances have had an impact on families' risk of poverty. I examine single-parent and two-earner households separately. The simplest model showed that tax allowances had an impact on poverty rates, but the results are not all positive. However, when time is taken as a trend variable, only Czech two-earner families show a poverty-reducing effect of family tax allowances.
Valuing a compound exchange option by monte carlo method
Authors: Varadi Kata , Szaz Janos , Becsky-Nagy Patricia
Published: July 2024

In this paper we show the valuation of a compound exchange option by Monte Carlo method, which option we named as ComEx option in our research. ComEx option is an option, which provides the right to its owner to exchange two options, in our paper specifically, two real options. Our aim is to show the intrinsic value of the ComEx option with different parameter settings. We use the Margrabe option as the benchmark option in the calculation, which option gives the right to its owner to exchange two underlying assets. These underlying assets in our calculations will be the underlying assets of the two real options of the ComEx option. Our result shows that the price evolution of the Margrabe-, and the ComEx options are very similar, just the scale is different. The option on exchanging the underlying assets most of the cases worth more than the option on exchanging the options, as it reduces risk. We also show the sensitivity of the option prices on the change of the correlation and volatility parameters.
A simulation-based framework for planning dental caries prevention programmes
Authors: Hajlasz Maria , Mielczarek Bozena
Published: July 2024

Supporting the management of preventive disease programmes is crucial to maintaining public health. Dental caries, a bacterial disease, can be successfully prevented through proper selection and planning of preventive measures adapted to available resources. Particularly useful in planning prevention programmes are simulation methods. The conducting of simulation studies is well documented in the literature. One of the stages involves conceptualizing a model, specifically tailored for a particular system and purpose. Consequently, there are no universal guidelines for its conceptualization. The article proposes a simulation framework for modelling the effectiveness of health programmes to prevent dental caries in school-aged children. Through simulation experiments, it would be possible to study the impact of a specific structure of a prevention programme on the development of dental caries in children, assuming that the programme is in effect throughout several years of primary schooling. The developed framework can be a good starting point for building a simulation model, based on which it would be possible to choose the appropriate programme for the population. This article is part of ongoing research and partially summarizes the stage of conducting simulation research, as previously described in Hajłasz and Mielczarek (2022a, 2022b, 2024).
Hedging the fx risk: the role of correlation
Authors: Vidovics-Dancs Agnes
Published: July 2024

Hedging financial risk is an essential issue but is far from trivial to implement. By continuing a previous study, we analyze an investment where the traditional hedging strategy may lead to a riskier portfolio than the naked position. The basic problem is well-known for portfolio and risk managers: we hedge the foreign exchange risk of a foreign stock investment with the help of a forward position. With some simulations, we show how the correlation between the stock price and the exchange rate can (or should) influence the decision about the forward position’s volume. We emphasize that also regulators should initiate more sophisticated definitions in the field of financial hedging.
Process mining and production routing fast profiling for data-driven digital twins
Authors: Lopes Paulo Victor , Lugaresi Giovanni , Neto Verri Filipe Alves , Skoogh Anders
Published: July 2024

Industry increasingly focuses on Digital Shadows and Twins of production lines, especially for planning, controlling, and optimizing operations. In parallel, shop floor processes can be described using Discrete Event Simulation (DES) models, which are ranked among the top tools for manufacturing system decision support. Although, Process Mining (PM) and model-driven Digital Twins (DT) were investigated in separate research communities. The integration of these two research fields is essential for advancing industrial applications by reducing time and efforts to model and describe processes. Thus, the objective of this paper is to propose a data integration pipeline to enhance realistic event logs and support the early stages of Data-driven Modelling of DT through PM techniques. This paper is expected to provide three relevant contributions. The first contribution is the enhancement of the production system event logs through the implementation of data integration techniques. The second contribution is to enable machine learning techniques to be applied by trace profiling the enhanced event logs, generating an attribute-value database. The third contribution is to extract value from a process-centered analysis, increasing the data value from a practical perspective.
Analysis of performance differences of fem numerical integration algorithm on two generations of intel xe-lp gpus
Authors: Kruzel Filip , Nytko Mateusz
Published: July 2024

This article analyses the performance differences between two generations of integrated GPUs on the Finite Element Method (FEM) Numerical Integration Algorithm. The algorithm employs linear approximation in the convection-diffusion problem, where performance is highly memory-dependent. We used Intel 11th and 12th-generation processors with integrated GPUs based on Intel Xe-LP architecture to conduct our research. The GPUs have the same parameters except for the slightly lower bandwidth and a narrower bus in the newer architecture. This makes it an ideal choice to test the significance of the performance loss in the highly-parallel memory-bound algorithm. We also compare the two generations of GPUs in terms of their computational power, memory access patterns, and other relevant factors to identify all the sources of performance loss. Our research shows how a slight change in one parameter can affect the overall performance of an algorithm. Our experiments aim to provide insight into how different GPU architectures and generations affect FEM numerical integration performance.
Calibration of discrete element method soil models based on penetrometer and direct shear box tests using a genetic algorithm
Authors: Szabo Bence , Szabo Taddeus , Tamas Kornel
Published: July 2024

The investigation and mechanical modeling of agricultural soil play an important role in studying the interaction between soil and tillage tools. One of the possible numerical methods for soil modeling is the discrete element method (DEM). Achieving reliable numerical soil-tool interaction studies poses a significant challenge in calibrating various soil models. Calibration of soil models based on physical measurements is feasible. The general aim of calibration is to match as many behavioural mechanical properties of a soil model as possible with the macromechanical properties of the actual soil. Calibration based on physical measurements has previously been an extensive process because during calibration, the micromechanical parameters of the DEM soil model can not be measured directly during physical tests. This research aims to calibrate complex and automated DEM soil model using genetic algorithms based on penetration and laboratory direct shear box tests. The research results demonstrate that the parameters of the appropriate DEM soil model can be effectively adjusted using genetic algorithms based on the soil’s macromechanical properties determined by physical measurements.
Urban lighting infrastructure analysis using topology density maps
Authors: Hu Zhicheng , da Silva Torres Ricardo , Lopez-Alfaro Claudia
Published: July 2024

Digital twins that simulate urban environments have emerged as promising tools to support informed decision-making. In the context of lighting infrastructure analysis using such digital twins, the use of suitable information visualization methods for supporting the identification of the pros and cons of different alternatives is of paramount importance. This paper introduces the use of Topology Density Maps in the assessment of simulated lighting infrastructure designs. Our analysis also considers the evaluation of temporal changes of density maps, using the recently proposed Temporal Topology Density Map algorithm. We demonstrate the potential of exploring such visualization methods in the context of walkability assessment using real and simulated data associated with an urban green area in {\AA}lesund, Norway.
Structural analysis of hybrid electro-hydraulic power steering system - a bond graph approach
Authors: Laaribi Amine , Eberard Damien , Marquis-Favre Wilfrid , Blond Jean-Marc , Chaudet Jerome
Published: July 2024

In this paper, the design of an hybrid electro-hydraulic steering system is addressed from a structural viewpoint. The aim is twofold. First, the model structural invertibility is studied and the admissible inputs/outputs are derived. Second, the differentiability of the specified output functions (required for numerical simulation purposes) is deduced from essentiality orders. This analysis is conducted on the bond graph model of the system.
Invariant properties of contrast parameters of plane elastic composites
Authors: Mityushev Vladimir
Published: July 2024

Contrast dimensionless parameters of two-phase fibrous composites are investigated. The local stress and strain fields in composites and the effective constants can be described by means of two contrast parameters. The importance of the contrast parameters resides in their role in representing complex potentials and correspondingly local fields through power series expansions. A set of transformations under which the contrast parameters are invariant is described. This set extends the shift transformations introduced by Cherkaev, Lurie, and Milton (CLM transformation). The relation between contrast parameters and Dundurs' constant is established.
Application of structural sums to study collective behavior of bacteria
Authors:   10.7148/2024-0304, Ligocki Tomasz
Published: July 2024

In the papers related to the subject of bacteria moving in the studied environments, it was established that the hydrodynamic interactions between the swimming bacteria can lead to collective motion, evidence of which has been confirmed by a reduction in effective viscosity. In the above-mentioned works, a new quantitative criterion for detecting such collective behavior is proposed. The presented criterion is based on the new computationally efficient theory of RVE (representative volume element) based on fundamental statistical moments (e-sums or generalized Eisenstein-Rayleigh sums) and this criterion can be applied to various two-phase media dispersed (e.g. biological systems, composites, etc.). Bacterial locations are modeled with short, narrow-width segments randomly embedded in the medium without overlapping. E-sums of simulated disordered harvests and observed experimental Bacillus subtilis locations will be calculated. The results obtained indicate a difference between the two sets that demonstrates the collective movement of the bacteria.
Multiscale characteristics of sentence length variability in hopscotch by julio cortázar
Authors: Dolina Michal , Drozdz Stanislaw , Dec Jakub , Kwapien Jaroslaw , Stanisz Tomasz
Published: July 2024

Sentence length variability (SLV) is a crucial aspect of writing that can significantly impact the overall effectiveness, expressiveness and readability of the written work. In the communication presented here, the dynamics of SLV are subjected to quantitative analysis with an attempt to identify potential multiscale patterns and their multifractal characteristics using the Multifractal Detrended Fluctuation Analysis (MFDFA). This study examines the outstanding and unique work of world literature, such as Hopscotch by Julio Cortázar. The present analysis includes the original version of this work as well as translations into English and Polish. Clear evidence of rich multifractality in the dynamics of SLV in the original arrangement of chapters as well as in numerous permutations of them is obtained. This result quantitatively confirms the widespread belief that Hopscotch is a work with a non-linear narrative.
Modeling and design of fermentation processes part 1. culture medium optimization and general parameters of the fermentation process
Authors: Kostov Georgi , Shopska Vesela , Denkova-Kostova Rositsa , Ivanova Kristina
Published: July 2024

The general approaches in the modeling of fermentation processes have been discussed in the present publication. A classification of fermentation processes, some methods of modeling the composition of the culture media and the macrokinetic parameters of the process as a first step in the design of a fermentation process have been considered. The purpose of the small review publication was to present an approach to the development of a generalized model of the fermentation process by considering the different aspects of the fermentation process.
An adaptive approach for parallel discrete event simulation thread pool prediction
Authors: Su JiMing , Yao Yiping , Zhu Feng
Published: July 2024

The number of threads in the thread pool is a critical factor that significantly influences the efficiency of parallel execution in Parallel Discrete Event Simulation (PDES). However, current methodologies, including such as static configuration, iterative search, solution based on system state information, and machine learning primarily cater to the domain of parallel computing. These approaches fail to consider PDES-specific characteristics like logical clock synchronization and the interaction among various simulation parameters, thereby posing challenges in accurately predicting the optimal number of threads required for achieving peak performance in PDES. In response to this, this paper proposes an adaptive multivariate power coefficient probability prediction method that considers the interrelated parameters in PDES which collectively influence runtime behavior. The method models various factors affecting PDES efficiency as multivariate power coefficients, incorporating a probability model and bias term to capture the variability of the simulation system. It constructs a nonlinear correlation model between the number of threads and simulation speedup, and derives the number of threads by calculating the optimal speedup of PDES. Experimental results demonstrate that this method achieves an average relative error of 6.4% in predicting the optimal number of threads, with performance improvement achieved by utilizing these predicted threads reaching 93.28% compared to ideal performance improvement relative to serial execution.
Model generalisation for predicting the amount of photosynthetically available radiation in the water column from freefall profiler observations
Authors: Tholen Christoph , Nolle Lars , Wollschlaeger Jochen , Stahl Frederic
Published: July 2024

In modern oceanography Photosynthetically Available Radiation (PAR) is used for modelling vegetation growth as it is a requirement for the process of photosynthesis. PAR as integrated value of the light spectrum between 400-700 nm can be measured directly using respective sensor systems. However, PAR can also be determined indirectly using measurements from only a small number of discrete wavelengths. In this paper, such a modelling approach is presented for predicting PAR in the water column. The approach uses spectral information within the water column and from above the sea surface. Three different modelling approaches based on artificial intelligence (AI) were used. It was shown that the artificial neural network (ANN) approach outperformed the regression tree (RT) and the linear regression (LR) approaches. It was also shown that the models generalise well, with an accuracy loss of 10 % based on the median, on data recorded in other geolocations without additional modification or re-training.
Physics-based modelling of a milk cooling system for intelligent energy management
Authors: Kappertz Lars , Bueskens Christof
Published: July 2024

Forecast-based energy management can play a large role in a smarter and more efficient use of renewable energies based on demand side management. Using approaches such as model predictive control, individual consumption devices can be shifted within operation constraints so that their electricity consumption optimally matches generation. In agriculture, large thermal storages make up a sizeable part of electricity consumption, and offer a potential use in the short term shifting of demand. Necessary for this are accurate models to forecast behaviour of such dynamic systems, so that minimal power demand and fulfilment of operation constraints can be ensured when computing optimal controls. This work focuses on the physics-based modelling of a milk cooling storage through parameter identification on real measurement data. Emphasized are the derivation of a suitable model ODE with regards to available data, and evaluation of the model on a rolling horizon. All major features of the measurement data can be recreated by the model forecasts, and model performance values show errors of around $30\%$ relative to mean temperature. Model performance is considered suitable for use in energy management at least on short forecast horizons, while practicability on longer horizons is subject to further research.
Biomechanical identification and simulation of passive ankle joints in anybody
Authors: Kleppe Adam Leon , Bjelland Oystein , Shayestehpour Mohammad Amin
Published: July 2024

Virtual biomechanical simulation models enable safe exploration of new treatment methods. This paper presents a data-driven force-dependent kinematics model within the AnyBody modeling system that simulates biomechanical stability of the passive ankle complex. A simulation model was developed in AnyBody based on the Twente Lower Extremity Model, and model parameters were identified by using a sequential least squares quadratic programming optimization method with results from ex vivo biomechanical experiments. The final root mean squared error was 2.29 degrees for rotation tests, and 0.26 mm for the lateral translation test.
Foundational statistical methods in comparative design for simulation experiments
Authors: Selmair Maximilian , Tolba Ahmed
Published: July 2024

This study presents a comprehensive examination of the application of traditional statistical methods to simulation modeling within the hypothetical context of comparing manual and automated production lines in manufacturing. Through a detailed methodology involving the AnyLogic simulation platform and Minitab for statistical analysis, we emphasize the significance of power analysis, two-sample t-tests, and one-way ANOVA in validating and optimizing simulation models. Our hypothetical findings demonstrate the potential of statistical analysis to identify significant efficiency improvements, with a particular focus on the implications of process modifications on automated production lines. The primary contribution of this research lies in illustrating the practical application of statistical tools in simulation studies, serving as a manual for simulation modelers in logistics and manufacturing sectors. By foregrounding the statistical methods over specific operational improvements, this study aims to bridge the gap in literature regarding the integration of foundational statistical analysis within simulation modeling, offering valuable insights for enhancing decision-making and optimization in manufacturing simulations.
Simulation of the complete operation of a bus rapid transit system using cellular automata
Authors: Camargo Oscar J. , Uribe-Laverde Miguel A.
Published: July 2024

Many cities in the world have implemented Bus Rapid Transit (BRT) systems as a part of their public transportation networks. BRT systems are highly efficient and relatively low cost when compared to rail-based systems. In Colombia, BRT systems are the main public transportation solution in many cities such as Bogotá, Cali, Barranquilla or Pereira. One of the major challenges in BRT systems is the formation of bus queues at busy stations, which reduces the performance of the entire system. The modeling of these queues is challenging as they arise from the complex interaction between the station’s geometry, the service frequencies and the passenger demand. In this paper, we present a complete microsimulation framework for the Megabus BRT system in Pereira based on cellular automata. The simulation introduces two kinds of agents: buses and passengers, and they interact to reproduce realistic behavior. Our results show that the simulation can reproduce queues formation and delays arising from the interaction between buses and with traffic lights. Our framework allows the evaluation of different service frequency schemes from the operator’s and the passenger’s point of view, which makes it a suitable tool to solve the frequency optimization problem successfully accounting for bus delays.
Simulation-based optimization for driving innovation in manual order picking for a wholesale company
Authors: Legato Pasquale , Mazza Rina Mary
Published: July 2024

This paper considers organization and practice innovation in a typical warehousing system which acts as a distribution site for groceries and miscellaneous to final retailers. The need of integrating tactical decisions regarding storage positions in a rack-based system with operational decisions for retrieving palletized items, under an S-shape policy, calls for a simulation-based optimization approach. The unavoidable stochastic features in order arrivals and composition, coupled with the natural uncertainty related to the human operated order-picking process under interferences and queueing, suggests resorting to a detailed discrete-event simulation. We propose a system-generating algorithm to integrate the combinatorial tactical choices for storage positions. The search process for the best storage configurations is the core of our simulation optimization framework in conjunction with an effective ranking and selection procedure. Numerical results on scenario comparison and possible improvements in productivity for a specific order-picking process are presented, using data and rules from a real wholesale company.
Improving abstract propagation for verification of neural networks
Authors: Demarchi Stefano , Gimelli Andrea , Tacchella Armando
Published: July 2024

Formal verification of neural networks is a crucial technique to increase their dependability in safety critical applications. In this paper we address some scalability challenges in our verification tool NEVER2 by proposing strategies to enhance speed and overall performances. First, we apply a precomputation technique based on symbolic bounds propagation in order to improve the network analysis by determining neuron stability a priori. Second, we combine the strengths of different levels of abstraction towards a refinement strategy. We experiment with the proposed techniques on some verification benchmarks from the annual competition of verification tools for neural networks (VNN-COMP).
Forecasting energy consumption in energy clusters using machine learning methods
Authors: Jurek Piotr , Plichta Anna
Published: July 2024

The rising demand for secure, sustainable energy generation led to the emergence of the concept of Energy Cluster in Poland. Renewable energy sources depend on such factors as changeable weather conditions, which makes them unstable. Therefore, electrical energy consumption forecasting is necessary to balance the needs of every Energy Cluster member. This article examines the efficacy of various machine learning models, such as Linear Regression, Decision Tree Regression, K-neighbors regression, Exponential Smoothing, or Support Vector Regression in predicting such data. Deep learning models based on Long Short-Term Memory and convolution were also considered. Depending on factors such as the seasonality of the dataset or the presence of a trend, each examined model performed differently. Overall, LSTM turned out to be the most universal method, working well with various datasets and balancing learning speed with accuracy. In some specific cases, however, Exponential Smoothing proved more efficient, suggesting that an entity-by-entity approach may be appropriate.
Photogrammetric vs. lidar methods for augmented reality
Authors: Skabek Krzysztof , Ozimek Pawel , Rola Dominika
Published: July 2024

This paper presents a comparison of methods for locating virtual objects in augmented reality. Mobile devices were used: IPad Pro with lidar and, on the other hand, smartphones with classical cameras to obtain photogrammetric structures. FARO laser scans were used as reference representations to compare the obtained structures. Spatial stability tests of the embedded objects were carried out using Vuforia and Immersal systems. The overall performance of these technologies are comparable, however, LiDAR offers greater accuracy compared to photogrammetry, thanks to more stable and accurate distance measurements. On the other hand, photogrammetry may be more accessible and attractive because it does not require any special sensors.
Human-centric cybersecurity: trends - secmos invited talk
Authors: Kolodziej Joanna
Published: July 2024

Despite the increasingly widespread automation of processes and systems in large-scale cyber infrastructures, human errors such as choosing weak passwords or knowingly or unknowingly exposing oneself to data theft are still the most common factors undermining the security of these infrastructures and systems. However, viewing the human factor as the sole cause of the vulnerability of IT systems to external attacks is a far-reaching oversimplification. Human-centric cybersecurity aims to orient security strategies to the needs, role definitions and behaviours of users of information systems. Such a strategy assumes that when security measures consider users’ experiences, they are more likely to be adopted and less likely to be ignored. This talk highlights the key tenets and trends in human-centric cybersecurity.
Rfca: efficient, robust and flexible cipher algorithm for fpga implementation
Authors: Couturier Raphael , Noura Hassan
Published: July 2024

The current Field-Programmable Gate Array (FPGA) implementation of cryptographic algorithms faces performance and security challenges because these algorithms were not originally designed to take FPGA features into account. One significant performance limitation arises from the iteration of a round function for a high round number, given the fixed structures like static substitution and diffusion primitives throughout the process. This paper introduces a new framework for a key-dependent, flexible one-round stream cipher scheme specifically designed to benefit from FPGA features. It is called RFCA. Security and performance analyses validate the effectiveness and robustness of the proposed solution, ensuring the desired cryptographic properties. In comparison with an AES implementation, RFCA is 34 times faster.
Security of containerization platforms: threat modelling, vulnerability analysis, and risk mitigation
Authors: Mycek Andrzej , Lukaczyk Maryna
Published: July 2024

In every modern organization, the DevOps methodology carries out the process of creating, testing, implementing, and maintaining applications. This methodology significantly shortens the application delivery time, reduces costs, and limits the risks associated with creating and developing applications. Docker and Kubernetes are the tools without which it is impossible to work in this methodology. These two most popular container technologies have several advantages, such as the ease of developing our products, but like any tool, they are susceptible to threats. This work comprehensively addresses the security issues of container platforms. It highlights the security concerns related to the host and the Docker and Kubernetes platforms. The most critical vulnerabilities on these platforms have been analyzed, and methods have been presented that significantly enhance the security level of environments, reduce potential risks, and eliminate the most severe threats.
Landing force reveals new form of motion-induced sound camouflage in a wild predator
Authors: Schalcher Kim , Milliet Estelle , Séchaud Robin , Bühler Roman , Almasi Bettina , Potier Simon , Becciu Paolo , Roulin Alexandre , Shepard Emily LC
Published: July 2024

Predator-prey arms races have led to the evolution of finely tuned disguise strategies. While the theoretical benefits of predator camouflage are well established, no study has yet been able to quantify its consequences for hunting success in natural conditions. We used high-resolution movement data to quantify how barn owls (Tyto alba) conceal their approach when using a sit-and-wait strategy. We hypothesized that hunting barn owls would modulate their landing force, potentially reducing noise levels in the vicinity of prey. Analysing 87,957 landings by 163 individuals equipped with GPS tags and accelerometers, we show that barn owls reduce their landing force as they approach their prey, and that landing force predicts the success of the following hunting attempt. Landing force also varied with the substrate, being lowest on man-made poles in field boundaries. The physical environment, therefore, affects the capacity for sound camouflage, providing an unexpected link between predator-prey interactions and land use. Finally, hunting strike forces in barn owls were the highest recorded in any bird, relative to body mass, highlighting the range of selective pressures that act on landings and the capacity of these predators to modulate their landing force. Overall, our results provide the first measurements of landing force in a wild setting, revealing a new form of motion-induced sound camouflage and its link to hunting success.
An improved bacterial single-cell RNA-seq reveals biofilm heterogeneity
Authors: Yan Xiaodan , Liao Hebin , Wang Chenyi , Huang Chun , Zhang Wei , Guo Chunming , Pu Yingying
Published: July 2024

In contrast to mammalian cells, bacterial cells lack mRNA polyadenylated tails, presenting a hurdle in isolating mRNA amidst the prevalent rRNA during single-cell RNA-seq. This study introduces a novel method, ribosomal RNA-derived cDNA depletion (RiboD), seamlessly integrated into the PETRI-seq technique, yielding RiboD-PETRI. This innovative approach offers a cost-effective, equipment-free, and high-throughput solution for bacterial single-cell RNA sequencing (scRNA-seq). By efficiently eliminating rRNA reads and substantially enhancing mRNA detection rates (up to 92%), our method enables precise exploration of bacterial population heterogeneity. Applying RiboD-PETRI to investigate biofilm heterogeneity, distinctive subpopulations marked by unique genes within biofilms were successfully identified. Notably, PdeI, a marker for the cell-surface attachment subpopulation, was observed to elevate cyclic diguanylate (c-di-GMP) levels, promoting persister cell formation. Thus, we address a persistent challenge in bacterial single-cell RNA-seq regarding rRNA abundance, exemplifying the utility of this method in exploring biofilm heterogeneity. Our method effectively tackles a long-standing issue in bacterial scRNA-seq: the overwhelming abundance of rRNA. This advancement significantly enhances our ability to investigate the intricate heterogeneity within biofilms at unprecedented resolution.
Fish CDK2 recruits Dtx4 to degrade TBK1 through ubiquitination in the antiviral response
Authors: Lu Long-Feng , Zhang Can , Li Zhuo-Cong , Cui Bao-Jie , Wang Yang-Yang , Han Ke-Jia , Xu Xiao , Zhou Chu-Jing , Zhou Xiao-Yu , Wu Yue , Xu Na , Yang Xiao-Li , Chen Dan-Dan , Li Xi-Yin , Zhou Li , Li Shun
Published: July 2024

Although the classical biological protein cell cycle protein kinase CDK2 has been extensively studied in higher vertebrates, its function in lower vertebrates beyond the regulation of mitosis remains unknown. In this study, we report a distinct mechanism whereby IFN expression is negatively regulated in fish by CDK2. After infection with the spring viremia of carp virus (SVCV), fish CDK2 expression significantly increased in tissues and cells. Moreover, antiviral resistance was improved in
cdk2
-/-
homozygotes, and the antiviral cytokine interferon (IFN) expression was significantly higher. At the cellular level, CDK2 overexpression reduced IFN expression, while
cdk2
knockdown increased the ability of cells to produce IFN. Subsequently, it was discovered that fish CDK2 binds and degrades TBK1, resulting in reduced IFN. CDK2 increases the K48-linked ubiquitination of TBK1, causing its degradation, while E3 ubiquitin ligase Dtx4 was found to be involved in this process following the significant enhancement of TBK1 K48-linked ubiquitination. Protein mass spectrometry and immunoblot analysis confirmed that the K567 site on TBK1 is essential for CDK2 to engage with Dtx4 and degrade TBK1; thus, after mutating the K567 site, K48-linked ubiquitination of TBK1 was not enhanced by Dtx4, and TBK1 was not degraded by CDK2. Our data demonstrate that fish CDK2 recruits the E3 ubiquitin ligase Dtx4 to target the K567 site of TBK1 and promote its degradation. These results suggest that CDK2 in lower vertebrates is implicated in a specialized role for antiviral innate immunity.
Defining mononuclear phagocyte distribution and behaviour in the zebrafish heart
Authors: Moyse Bethany , Moss Joanna , Bevan Laura , Scott Aaron , Wittamer Valérie , Richardson Rebecca J
Published: July 2024

Mononuclear phagocytes (MNPs) are recognised as highly plastic, multifunctional cells that influence multiple physiological and pathophysiological states. In the heart, they support homeostatic functions, contribute to disease progression and play multiple roles in reparative and regenerative processes following tissue damage. Understanding the heterogeneous populations of cells that contribute to these diverse functions is crucial to facilitating beneficial, and limiting adverse, cardiac outcomes. However, characterisation of precise populations of cardiac immune cells remains incomplete in vertebrate models capable of endogenous regeneration, such as adult zebrafish. Here, we use a combination of transgenic lines to identify distinct MNPs in the zebrafish heart. We show that larval macrophage populations have different origins and a sub-population of
csf1ra
expressing cells are maintained on the surface of the adult heart. MNPs are differently distributed in the myocardium, exhibit different behaviours and are distinguished via expression level of
csf1ra
and
mpeg1.1
. Following injury, tissue resident macrophages rapidly proliferate potentially contributing to reduced scarring. The adult zebrafish heart contains multiple populations of MNPs that can be defined by existing tools. This new understanding of innate immune cell populations in the heart of adult zebrafish sheds light on the composition of a pro-regenerative cardiac microenvironment.
Applicability and perspectives for DNA barcoding of soil invertebrates
Authors: Le Cadre Jéhan , Klemp Finn Luca , Bálint Miklós , Scheu Stefan , Schaefer Ina
Published: July 2024

Belowground invertebrate communities are dominated by species-rich and very small microarthropods that require long handling times and high taxonomic expertise for species determination. Molecular based methods like metabarcoding circumvent the morphological determination process by assigning taxa bioinformatically based on sequence information. The potential to analyse diverse and cryptic communities in short time at high taxonomic resolution is promising. However, metabarcoding studies revealed that taxonomic assignment below family-level in Collembola (Hexapoda) and Oribatida (Acariformes) is difficult and often fails. These are the most abundant and species-rich soil-living microarthropods, and the application of molecular-based, automated species determination would be most beneficial in these taxa. In this study, we analysed the presence of a barcoding gap in the standard barcoding gene cytochrome oxidase I (COI) in Collembola and Oribatida. The barcoding gap describes a significant difference between intra- and interspecific genetic distances among taxa and is essential for bioinformatic taxa assignment. We collected COI sequences of Collembola and Oribatida from BOLD and NCBI and focused on species with a wide geographic sampling to capture the range of their intraspecific variance. Our results show that intra- and interspecific genetic distances in COI overlapped in most species, impeding accurate assignment. When a barcoding gap was present, it exceeded the standard threshold of 3% intraspecific distances and also differed between species. Automatic specimen assignments also showed that most species comprised of multiple genetic lineages that caused ambiguous taxon assignments in distance-based methods. Character-based taxonomic assignment using phylogenetic trees and monophyletic clades as criteria worked for some species of Oribatida but failed completely for Collembola. Notably, parthenogenetic species showed lower genetic variance in COI and more accurate species assignment than sexual species. The different patterns in genetic diversity among species suggest that the different degrees of genetic variance result from deep evolutionary distances. This indicates that a single genetic threshold, or a single standard gene, will probably not be sufficient for the molecular species identification of many Collembola and Oribatida taxa. Our results also show that haplotype diversity in some of the investigated taxa was not even nearly covered, but coverage was better for Collembola than for Oribatida. Additional use of secondary barcoding genes and long-read sequencing of marker genes can improve metabarcoding studies. We also recommend the construction of pan-genomes and pan-barcodes of species lacking a barcoding gap. This will allow both to identify species boundaries, and to cover the full range of variability in the marker genes, making molecular identification also possible for species with highly diverse barcode sequences.