Introduction and Welcome address

Combining Deep learning and Raman Spectroscopy for rapid pesticide screening (Abstract reviews)

• Esther Wambui Kiguru (The University of Nairobi)

Ensuring compliance with maximum residue limits (MRL) for pesticide residues in fresh produce is crucial for food safety. Traditional detection methods such as chromatography and mass spectrometry, while accurate, are often time-consuming, expensive, and require complex sample preparations. This study aimed to evaluate the effectiveness of combining Raman spectroscopy with deep learning algorithms for rapid and non-destructive pesticide screening in various vegetables. Samples of kales, spinach, tomatoes, lettuce, and Chinese cabbage were spiked with varying concentrations of Chlorothalonil pesticide, and Raman spectra were acquired. These spectra were processed to reduce noise and enhance signal quality. A convolutional neural network (CNN) was then trained to predict pesticide concentrations based on the preprocessed spectra. Additionally, an API was integrated to allow for practical deployment and interaction with the models. The results demonstrated that the CNN model could accurately predict pesticide concentrations, significantly reducing analysis time and cost compared to conventional methods. The integration of the API facilitated real-world application, enabling easy interaction with the models. This combined approach of Raman spectroscopy and deep learning not only provided a fast, cost-effective, and non-destructive method for pesticide detection but also ensured compliance with food safety regulations. The study concludes that this innovative technique holds great promise for enhancing food safety practices by ensuring compliance with regulatory standards and enhancing consumer confidence in the quality of fresh produce.

Development and Implementation of Physics Informed Neural Network for Nuclear Magnetic Resonance-guided Clinical Hyperthermia (Abstract reviews)

• Oche Solomon Enenche (African School of Fundamental Physics and Application (ASP))

The management of human diseases is transitioning from traditional methods toward personalized medicine, with thermal therapy—comprising clinical hyperthermia and therapeutic hypothermia—becoming a focal point of research. However, both therapies face challenges in clinical trials due to difficulties in monitoring temperature and delivering precise heat to targeted tissue areas. This thesis aims to address these issues by integrating the principles of magnetic resonance relaxation with the Bioheat transfer phenomenon using Physics-Informed Neural Networks (PINN) for Nuclear Magnetic Resonance (NMR)-guided clinical hyperthermia. NMR-guided hyperthermia is a non-invasive technique that leverages MRI to guide the heating process and monitor body temperature distribution. PINN is a type of neural network that incorporates model equations, such as partial differential equations (PDEs), into its structure, enabling it to learn from limited data while adhering to the underlying physics of the problem. In this work, a deep learning-based PINN was developed using the 1D Pennes’ Heat Equation to train an AI model that respects the physical laws governing heat diffusion in tissues. The model, implemented using Python 3.8 on a 64-bit operating system with Jupyter Notebook, is designed to enhance the precision, safety, and efficiency of clinical hyperthermia when integrated into clinical RF devices.

Optimizing Quantum Network Performance: Performance Evaluation of Quantum Dijkstra's Algorithm (Abstract reviews)

• nada ik

Quantum communication methods are highly sensitive to variations in protocol parameters, which can affect their effectiveness. This study explores the performance of quantum communication protocols using online quantum computing platforms such as Quirk, Qiskit-IBM-Q, Rigetti Forest, and D-Wave. Despite the promising advancements over classical teleportation, many protocols fall short of their anticipated benefits. We propose a routing scheme to enhance fidelity in quantum networks by focusing on positive quantum channel capacity. The proposed Channel Selection (CS) algorithm, combined with the $K$-shortest path technique, is designed to optimize fidelity rates for source-destination pairs. This paper presents a detailed implementation of Quantum Dijkstra's Algorithm within a quantum circuit model, utilizing quantum gates and the IBM Qiskit framework to find the shortest path in a graph. We assess various quantum error channels, such as amplitude damping, phase flips, and bit-flip errors, to identify optimal quantum error correction methods. Through performance analyses, we evaluate the impact of noise levels, channel capacities, and the number of source-destination pairs on throughput, fidelity, and memory utilization. Our results demonstrate that the CS algorithm outperforms traditional methods like Q-PATH and Greedy by maintaining higher fidelity and throughput, optimizing memory usage, and effectively managing noise. This study highlights the advantages of the CS algorithm in enhancing quantum communication networks and its potential for practical implementation in noisy environments.

STATISTICAL EVALUATION OF SOLAR INDICES USING PRINCIPAL COMPONENT ANALYSIS (Abstract reviews)

• Obed Maniraguha (University Of Rwanda, College of Science and Technology)

Space weather, defined as the variable conditions in space driven by the sun, significantly impacts the performance of both terrestrial and space-based technologies. To mitigate these adverse effects, it is essential to develop accurate storm-time space weather models. Since the sun is the primary driver of these models, solar indices like SSN and F10.7 are crucial for creating these models. However, different solar indices can lead to varying predictions. This study conducted a statistical evaluation using three metrics to determine which solar index among F10.7, F10.781, F10.7p, SSN and R12, best represents solar activity. The evaluation’s core principle was to correlate these indices with the ionospheric TEC and then compare the model predictions with actual observations. PCA was utilized to perform this task and the results of the study has revealed that the F10.7p index is a superior indicator of ionospheric conditions compared to other indices. This finding is crucial for enhancing the accuracy of the space weather predictions, thereby helping to protect and optimize the functionality of technological systems affected by solar activities.

COMPARING THE RESOLUTION AND SENSITIVITY OF MULTIELECTRODE HYDROGEOPHYSICAL CONFIGURATIONS USING 2D RESISTIVITY TOMOGRAPHY: A CASE STUDY AT NSAKYE IN THE EASTERN REGION OF GHANA (Abstract reviews)

• JONATHAN ODURO (UNIVERSITY OF CAPE COAST)

Geophysical method of exploration involving 2D Electrical Resistivity Tomography was employed to compare the resolution and sensitivity of different electrode configurations in order to delineate potential drill point for groundwater at Nsakye. The 2D Electrical resistivity tomography (ERT) using the Dipole-dipole, Wenner, Schlumberger electrode array configurations was deployed along traverses within the area. The work carried out comprises desktop study, field reconnaissance survey and geophysical investigations. The 2D Electrical Resistivity Tomography technique was used for the studies to determine the lateral and vertical variations of rock resistivity with depth. The results of the geophysical investigations indicated that the study area is generally underlain by three geological strata with varying apparent resistivity values. The bedrock is fractured to facilitate groundwater development with expected satisfactory borehole yield. The results of the study confirm that the optimal electrode configuration for geophysical investigation at Nsakye is the Dipole-dipole array and 2D ERT method is also very suitable for sitting boreholes in Nsakye which is underlain by Voltain supergroup. It is suggested that, Geophysical methods should hence, form an integral part of groundwater exploration programmes in solving problems associated with groundwater prospecting to locate potential aquifers for the supply of potable water to rural communities.

Impact of Magnetohydrodynamic (MHD) Instabilities on Beam Dynamics in High-Energy Particle Accelerators. (Abstract reviews)

• Sory DIAW (Institute of Applied Nuclear Technology, CHEIKH ANTA DIOP University of Dakar)

• **Title**: Impact of Magnetohydrodynamic (MHD) Instabilities on Beam Dynamics in High-Energy Particle Accelerators. • **Name**: Sory DIAW • **Email**: sorydiaw@yahoo.com • **Institution**: Institute of Applied Nuclear Technology, CHEIKH ANTA DIOP University of Dakar, SENEGAL. • **Level**: First year of PhD **Abstract**: The research will combine theoretical analysis, numerical simulations, and experimental validations to provide a comprehensive evaluation of significantly affect the stability and performance of plasma in accelerators. In high-energy particle MHD effects on beam dynamics. This study explores the influence of magnetohydrodynamic (MHD) instabilities on beam dynamics within high-energy particle accelerators. MHD instabilities, which arise due to the interaction between magnetic fields and conductive fluids, can accelerators, these instabilities have the potential to distort magnetic confinement, induce beam loss, and reduce the efficiency of particle acceleration. This research examines key MHD phenomena, including kink modes, tearing modes, and resistive instabilities, and analyzes their impact on beam trajectory, coherence, and overall accelerator performance. By simulating various accelerator configurations and plasma parameters, we aim to quantify the thresholds for instability growth and develop strategies for mitigating their adverse effects. These findings could lead to improved control mechanisms in next-generation accelerators, enhancing both precision and reliability in experimental outcomes.

Thermodynamics of Nuclear Matter at High-Energy Nuclear Reactions. (Abstract reviews)

• Zeinab Abdelrazik (faculty of science, Cairo university.)

The experimental data from various hadronic and heavy-ion collisions have been studied to investigate nuclear matter under extreme temperature and pressure. Under such conditions, the nuclear matter undergoes different phase transitions from the hadronic matter to the quark-gluon plasma (QGP) matter. Also, there are several experimental indicators that allow studying different regions of the quantum chromodynamics (QCD) phase diagram through the hadron and ion collisions. Therefore, the phase transition mechanism was investigated using thermo-statistical models based on the correlation between the distribution of measurable experimental quantities, such as the transverse momentum and multiplicity of the produced charged particles, and the microscopic hypotheses of the transition mechanism. The used analysis tools could describe the experimental data over the considered energies and rapidity intervals. Moreover, the analysis of the experimental data in view of Tsallis’ statistics has enabled me to obtain the values of the temperature, chemical potential, and non-equilibrium index (q) at the kinetic freeze-out stage, which reveals the possible mechanism of particle production through proton-proton and heavy ion collisions at the center of mass energy in the GeV and TeV regions.

Performance of the Missing transverse energy triggers for the ATLAS detector. (Abstract reviews)

• Imane Zahir (Universite Hassan II, Ain Chock (MA))

The ATLAS detector is one of the two general-purpose detectors at the Large Hadron Collider (LHC) at CERN, designed to explore a wide range of physics, including the discovery of new particles, precision measurements of known particles, and searches for signs of new physics beyond the Standard Model. One of the essential parts of the ATLAS experiment is the trigger system, which manages large amounts of data generated by particle collisions and subsequently identifies the most interesting events, such as the presence of energetic leptons, photons, hadronic jets, τ lep, or large amounts of missing energy for further analysis. Only a small fraction of the events can be kept due to limitations in storage and processing. This means that a large number of events are discarded and are not available for future analysis. Where the ATLAS physics program uses trigger selection for events containing invisible particles. However, selecting these events is challenging as they don’t register in the detector. The strategy used is to deduce the presence of these invisible particles from the apparent imbalance of the momentum calculated from the visible particles. In practice, the imbalance in the direction parallel to the proton beams is not sensitive since the fraction of each proton’s momentum that participates in the collision is unknown, and much of the outgoing momentum in the beam direction is not observed. Rather, the quantity of most significance is the imbalance in momentum in the plane perpendicular to the proton beams; this is referred to as the missing transverse momentum, and its value is commonly represented by $E_T^{miss}$ (MET). The $E_T^{miss}$ used in the wide range of physics processes, like searches for supersymmetry, searches for final states with stable long-lived particles, and searches for dark matter condidate that is not predicted by the Standard Model (SM), but many theories beyond the Standard Model (BSM) offer the study of DM, such as 2HDM with a pseudo-scalar mediator (2HDM+a) and a simplified model for dark matter production. The MET trigger relies on data from calorimeters, which measure the energy deposited by particles in the transverse plane. The ATLAS trigger system has been significantly upgraded during LS2 (2019–2022). The performance of Missing Transverse Energy (MET) triggers is a crucial aspect of ensuring the efficiency and accuracy of data collection. For that, we will study the performance of the MET trigger by using data collected during 2023 and 2024. Performance in terms of efficiency, trigger stability, background rejection, etc. studied as a function of several quantities, including run conditions and pile-up. One of the major challenges is pile-up. This can complicate the accurate measurement of MET. The particles from pile-up collisions can contribute to the overall energy detected in the event, artificially inflating the measured MET. This makes it difficult to distinguish the true missing energy associated with the particles of interest from spurious contributions. In the future, the HL-LHC phase will witness an increase in luminisity and thus an increase in pile-up events. One of the efforts made to reduce the pil-up event is the HGTD detector. The HGTD will provide the timing information to reduce the density of vertices for a given track, so that will provide a good distinction of pile-up events.

Deep Learning Meets Quantum Computing: Revolutionizing Brain Tumor Detection with CNNs and QNNs (Abstract reviews)

• MALIKA EL ASLANI (LPMAT, Faculty of Sciences Ain Chock of Casablanca, Morocco)

Brain tumor detection is a critical task in medical diagnostics, where early and accurate identification can significantly impact patient outcomes. This research focuses on the development and optimization of deep learning (DL) models to enhance the accuracy and efficiency of brain tumor detection from magnetic resonance imaging (MRI) scans. By leveraging Convolutional Neural Networks (CNNs), our study aims to automate the process of tumor identification, reducing the dependency on manual analysis, which is often time-consuming and prone to errors. We have developed a CNN-based model trained on a large dataset of annotated brain MRI images, achieving promising results in differentiating between various types of brain tumors. Our approach incorporates advanced data augmentation techniques to address the challenges of data scarcity and class imbalance, which are common in medical imaging. Furthermore, we explore the integration of Quantum Neural Networks (QNNs) to potentially improve the model's performance by harnessing quantum computing's capabilities.Our ongoing research aims to refine these models further and validate their effectiveness through extensive testing on diverse datasets, ultimately paving the way for their implementation in clinical settings.