Abstract
Objective: To empirically investigate the relationship between education quality and technological progress in the business sector at different stages of economic development. Research Design & Methods: We divided 160 countries into four groups by GDP per capita. The research period was 2007–2021. We use Spearman’s correlation analysis to verify associations between nine indicators for education quality and ten indicators for technological progress. Findings: Our outcomes show that if education quality does not improve, countries do not move up the economic development ladder. Adult literacy, primary education quality, adult skills, and women’s average years in school have the strongest influence on technological progress. Implications?/?Recommendations: Our paper contains many implications for those seeking to improve social well-being. For example, governments should ensure that women have access to education on equal terms with men. Otherwise, they lose an important source of technological progress and impede the development of human capital. Greater emphasis should be placed on learning how to write and describe reality, read with comprehension, perform simple calculations without a calculator, and teach various learning methods. In the absence of these, the skills of primary, secondary and higher education graduates will not improve. Contribution: The outcomes of our research, both theoretical and empirical, create a multi-faceted approach to the issue of the mutual influence of education and technological progress. They allow us to look at this problem from the perspective of subsequent stages of economic development.

Abstract
This preprint has been withdrawn at the request of the corresponding author due to internal coordination requirements and project data privacy considerations.

Abstract
Secure Multiparty Computation (SMC) facilitates secure collaboration among multiple parties while safeguarding the privacy of their confidential data. This paper introduces a two-party quantum SMC protocol designed for evaluating binary Boolean functions using single qubits. Complexity analyses demonstrate a reduction of 66.7% in required quantum resources, achieved by utilizing single qubits instead of multi-particle entangled states. However, the quantum communication cost has increased by 40% due to the amplified exchange of qubits among participants. Furthermore, we bolster security by performing additional quantum operations along the y-axis of the Bloch sphere, effectively hiding the output from potential adversaries. We design the corresponding quantum circuit and implement the proposed protocol on the IBM Qiskit platform, yielding reliable outcomes.

Abstract

Abstract
Lung cancer stands as the most prevalent and deadliest type of cancer, with adenocarcinoma being the most common subtype. Computed Tomography (CT) is widely used for detecting tumours and their phenotype characteristics, for an early and accurate diagnosis that impacts patient outcomes. Machine learning algorithms have already shown the potential to recognize patterns in CT scans to classify the cancer subtype. In this work, two distinct pipelines were employed to perform binary classification between adenocarcinoma and non-adenocarcinoma. Firstly, radiomic features were classified by Random Forest and eXtreme Gradient Boosting classifiers. Next, a deep learning approach, based on a Residual Neural Network and a Transformer-based architecture, was utilised. Both 2D and 3D CT data were initially explored, with the Lung-PET-CT-Dx dataset being employed for training and the NSCLC-Radiomics and NSCLC-Radiogenomics datasets used for external evaluation. Overall, the 3D models outperformed the 2D ones, with the best result being achieved by the Hybrid Vision Transformer, with an AUC of 0.869 and a balanced accuracy of 0.816 on the internal test set. However, a lack of generalization capability was observed across all models, with the performances decreasing on the external test sets, a limitation that should be studied and addressed in future work.

Abstract
The efficient operation of Autonomous Underwater Vehicles (AUVs) is crucial for various applications, including weather forecasting, marine life sustainability, underwater mining, renewable energy harvesting, and defense operations. Given the limited energy storage available on AUVs, improving propulsion efficiency is a key challenge. Variable Buoyancy Systems (VBSs) offer a promising alternative to traditional propeller-based propulsion by consuming energy only during buoyancy adjustments, thereby reducing overall power consumption. This study builds on prior simulation-based research by experimentally evaluating the energy consumption and performance of different PID-based controllers for a prototype driven by an electromechanical VBS. The experimental results show that by adequately choosing a closed loop control algorithm, significant energy savings can be obtained without compromising the control performance.