Computation MDPI

🔎 Highlighted Paper in Computation

pyMKM: An Open-Source Python Package for Microdosimetric Kinetic Model Calculation in Research and Clinical Applications

👥 Authors: Giuseppe Magro, Vittoria Pavanello, Yihan Jia, Loïc Grevillot, Lars Glimelius, and Andrea Mairani

pyMKM brings advanced radiobiological modeling closer to everyday particle therapy research. This open-source Python package enables transparent and reproducible calculations based on the Microdosimetric Kinetic Model and its extensions, supporting applications from RBE estimation and microdosimetry to biologically guided treatment planning. By combining validation, flexibility, and accessibility, pyMKM aims to turn complex radiobiological models into practical tools for the research and clinical communities.

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https://www.mdpi.com/2079-3197/13/11/264

🔎 Highlighted Paper in Computation

Digging SiC Semiconductor Efficiency for Trapping Main Group Metals in Cell Batteries: Application of Computational Chemistry by Mastering the Density Functional Theory Study

👥 Authors: Fatemeh Mollaamin and Majid Monajjemi

Main group metals captured by silicon carbide were studied by density functional theory methods through a qualitative screening. It is well established that enhancing Li, Na, K, Be, Mg, B, Al or Ga to cell batteries can augment the energy saving properties of cell batteries.

📖 Read the full article: https://www.mdpi.com/2079-3197/13/11/265

🔎 Highlighted Paper in Computation

A Hybrid Approach for Automated Identification of the Two-Phase Wellbore Flow Model

👥 Authors: Anton Gryzlov, Eugene Magadeev, and Muhammad Arsalan

The paper introduces a hybrid virtual flow metering method, which is an indirect method to infer the rates of produced oil, water, and gas from the available measurements. Although the machine learning approaches are widely used to handle this problem, the hybrid approach, combining data and physics, is especially promising due to its transparency and advanced forecast horizon. It is demonstrated that representation of a dynamic multiphase flow model may be obtained from the available wellbore measurements using techniques of numerical optimization, system identification, and parameter estimation.

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https://www.mdpi.com/2079-3197/13/11/253

🔎 Highlighted Paper in Computation

Stability of the DuFort–Frankel Scheme on Unstructured Grids

👥 Authors: Nikolay Yavich, Evgeny Burnaev, and Vladimir Vanovskiy

The DuFort–Frankel scheme was introduced in the 1950s to solve parabolic equations, and has been widely used ever since due to its stability and explicit nature. However, for over seven decades, its application has been limited to Cartesian grids. We propose a generalization of the DuFort–Frankel scheme that could be applied to arbitrary unstructured grids and present a proof of its stability based on the analysis of the spectrum of the amplification matrix, along with numerical examples in 2D and 3D.

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https://www.mdpi.com/3551134

🔎 Highlighted Paper in Computation

Numerical Study on Infrared Radiation Signatures of Debris During Projectile Impact Damage Process

👥 Authors: Wenqiang Gao, Teng Zhang, and Qinglin Niu

We simulated impact debris movement and thermal evolution via SPH, and adopted combined theories and methods to analyze the infrared radiation features of debris cloud. We revealed its temperature distribution law and infrared band radiation discrepancy, as well as the variation trend of radiation intensity. This work offers practical theoretical references for protective material design and high-speed impact target recognition.

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https://www.mdpi.com/3549498

🔎 Highlighted Paper in Computation

Estimation of Wind Farm Losses Using a Jensen Model Based on Actual Wind Turbine Characteristics for an Offshore Wind Farm in the Baltic Sea

👥 Authors: Ziemowit Malecha and Maciej Chorowski

The research demonstrates that selecting the appropriate distance between turbines is a crucial issue in optimizing wind farm performance to minimize the so-called "wind stealing" effect. Distances that are too small can cause losses approaching 30%, and maintaining large distances, such as 10D, can still lead to losses above 10%. Awareness of this problem can significantly aid in the economic and operational optimization of wind farms.

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https://www.mdpi.com/3143944

🔍 Highlighted Paper in Computation

Computation of the Radius of Curvature R in Any Avian Egg and Identification of the Location of Potential Load Application That Forms Its Unique Asymmetric Shape: A Theoretical Hypothesis

👥 Authors: Valeriy G. Narushin, Michael N. Romanov, and Darren K. Griffin

Can mathematics reveal how a bird's egg acquires its unique shape, without ever looking inside the oviduct?🥚📐

Is it feasible to look into a bird's oviduct without inflicting any harm and see how the egg is shaped: should it be molded like a pot or be compressed in specific areas, allowing the elastic body to change its shell? As it happens, such a possibility is readily provided by the principles of mathematics and the physics of elastic bodies. This can be accomplished simply by examining the dynamics of the change in the radii of curvature at different points of the function that mathematically interprets the egg's contours and noting that one point shows a sharp peak compared to all the others. That is, this location carries the main load according to the mechanics of elastic bodies. And if you have an image of a particular egg, you can easily calculate the precise coordinates of this spot.

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https://www.mdpi.com/2079-3197/13/10/232 #

📢 Call for Papers | Special Issue in Computation

Emergent Computations in Biological and Medical Systems: From Theory to Applications

How do simple biological interactions give rise to complex system-wide behaviors? This Special Issue explores computational approaches for understanding emergent phenomena across biological and medical systems, from cellular dynamics and neural circuits to tissue organization and physiological regulation. We welcome original research that bridges theory, modeling, simulation, and real-world biomedical applications.

👥 Guest Editors
Dr. Jiří Kroc
Prof. Dr. Ricardo Lopez-Ruiz

📌 Topics of interest include:
• Agent-based modeling and cellular automata for morphogenesis and tissue development
• Graph-based and network models for physiological synchronization
• Parallel algorithms for collective cellular behaviors
• Machine learning for identifying and predicting emergent patterns
• Evolvable, adaptive, and bio-inspired computational paradigms

🗓️ Submission deadline: 31 May 2027

🔗 Learn more and submit your manuscript:
https://www.mdpi.com/journal/computation/special_issues/F828CB87XE

📢 Call for Papers | Special Issue in Computation

Advanced Computational Models and Algorithms for AI-Native Next-Generation Wireless Networks

The evolution toward AI-native 6G wireless networks is transforming communication systems by embedding artificial intelligence across every network layer. This Special Issue welcomes original research on advanced computational models, intelligent algorithms, and optimization techniques that will drive the next generation of wireless communications.

👥 Guest Editors
Dr. Iacovos Ioannou
Dr. Michalis Sava

📌 Topics of interest include:
• AI-native physical-layer modeling and BDIx design
• Distributed and multi-agent AI for network orchestration
• Machine learning for MIMO, beamforming, D2D, relay, and URLLC optimization
• Explainable, trustworthy, and energy-efficient AI
• RIS, agentic communication, and AI-native 5G/6G architectures

🗓️ Submission deadline: 30 June 2027

🔗 Learn more and submit your manuscript:
https://www.mdpi.com/journal/computation/special_issues/8GPR05LR5Y

🔍 Highlighted Paper in Computation

Machine Learning-Assisted Cryptographic Security: A Novel ECC-ANN Framework for MQTT-Based IoT Device Communication

👥 Authors: KARIMUNDA KALIMU, Jean de Dieu Marcel Ufitikirezi, Roman
Bumbálek, Tomáš Zoubek, Petr Bartoš, Radim Kuneš, Sandra Nicole Umurungi, Anozie Chukwunyere, Mutagisha Norbelt, and Gao Bo

This study proposes a novel hybrid security framework that integrates Elliptic Curve Cryptography (ECC) with Artificial Neural Networks (ANN) to enhance the security of MQTT-based IoT communications.

The framework simultaneously ensures message confidentiality through lightweight ECC encryption and enables real-time anomaly detection, achieving 90.38% accuracy in classifying multiple attack types while maintaining computational efficiency suitable for resource-constrained IoT devices.

To our knowledge, this represents the first implementation of an integrated ECC-ANN approach tailored for securing MQTT protocols in IoT ecosystems.

📖 Read the full article:
https://www.mdpi.com/2079-3197/13/10/227