The study was on development and utilization of ‘ESUB’ digital board for instructional delivery in public technical colleges in Akwa Ibom state. The purpose of this study was to develop and utilize ESUB digital board for instructional delivery in Technical Colleges in Akwa Ibom State. This study was carried out in Technical schools in Akwa Ibom state.the study was guided by three specific purpose of the study. Two designs were adopted in the study, Iterative design and descriptive survey design. The population of the study consisted of 1690 teachers and students from the nine public Technical Colleges in Akwa Ibom State, during 2021/2022 academic session. A sample of 408 comprising 39 teachers and 369 students took part in the study. Simple random sampling technique was used to select the sample size for the study. Three specific purposes and two research questions guided the study. Research questions were answered using mean and standard deviation. The instrument for data collection was a questionnaire titled “Attitude and Interest Towards Utilization of ESUB Questionnaire” (AITUEQ). The instrument was face validated by three experts. Cronbach alpha Statistics was used to determine the reliability coefficient of the instrument, which yielded a reliability coefficient of 0.84. Results from the study revealed that teachers’ attitude towards utilization of ESUB software for instructional delivery in Technical Colleges in Akwa Ibom State was positive. Also students’ interest towards utilization of ESUB software for instructional delivery in Technical Colleges in Akwa Ibom State is positive. It was therefore recommended that Teachers in Technical Colleges in Akwa Ibom State should endeavour to use ESUB software for instructional delivery to enhance their performance and arouse students interest in instructional delivery process. Also, Students should be motivated by giving opportunity to interact also with the ESUB software in order to enhance interest towards the use of ESUB software for instructional delivery and to enhance their learning performance.

In this article, the main trend determining the development of measurements in the field of automated production is the transition to automatic control using adaptive models, as well as the use of more effective control and information-measurement systems in the field of mobile metrology. This means that today the value of metrological characteristics of measurement channels begins to increase sharply, taking into account not only the metrological characteristics of the blocks included in the measurement channel, but also the influence of the channels on each other. On the basis of mobile metrology, many control and measurement works are carried out in modern industrial production, for example, in oil fields and other technological fields. This article mainly presents opinions on the study of information and measurement systems.

The present study is devoted to the analysis of architectural approaches to scaling financial systems based on SAP HANA platform, which is an urgent task in the context of growing volumes of transactional data and increasing complexity of computational processes. The features of SAP HANA architecture based on in-memory technology, which provides high performance and low latency in data processing, are considered. Special attention is paid to the comparison of horizontal and vertical scaling, their advantages and disadvantages in the context of financial systems. A combined approach that combines both methods to achieve an optimal balance of performance and fault tolerance is presented. The paper concludes with recommendations for selecting the appropriate architectural solution depending on the needs and characteristics of the organization, and suggests directions for further research, including integration with cloud technologies.

This study explores the evolution of heart auscultation techniques by examining the transformation of acoustic signals into graphical data. Traditionally, heart auscultation has relied on auditory analysis of heart sounds through stethoscopes, with diagnostic interpretation dependent on the clinician's expertise. Recent advancements in medical technology now allow for the conversion of these acoustic signals into detailed graphical representations, enhancing diagnostic precision and enabling more sophisticated analysis.

The research investigates the methodologies and technologies involved in converting heart sounds into graphical data. This includes the use of digital stethoscopes, signal processing algorithms, and visualization tools that translate heart auscultation data into clear, interpretable graphs. The study evaluates the effectiveness of these methods in improving diagnostic accuracy and providing clearer insights into cardiac function.

Through a combination of theoretical analysis, technology review, and practical case studies, the study demonstrates how graphical representations of heart sounds can aid in identifying abnormal heart rhythms, detecting heart conditions, and enhancing the overall diagnostic process. The results highlight the benefits of integrating graphical data with traditional auscultation techniques, offering a more comprehensive approach to cardiac assessment. In conclusion, the evolution from auditory to graphical analysis in heart auscultation represents a significant advancement in cardiology. By improving the clarity and precision of heart sound analysis, these innovations have the potential to enhance diagnostic accuracy and patient outcomes, paving the way for more effective and informed cardiac care.

This study explores the effectiveness of various machine learning algorithms in predicting the success of cybersecurity attacks by analyzing historical attack data. We evaluated five prominent algorithms—Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting (GB), and K-Nearest Neighbors (KNN)—based on their performance metrics, including accuracy, precision, recall, F1-Score, and AUC-ROC. Our results indicate that Random Forest outperforms the other algorithms, achieving the highest accuracy (90%), precision (88%), recall (85%), F1-Score (86%), and AUC-ROC (0.92). Gradient Boosting also demonstrated strong performance with an accuracy of 88% and an AUC-ROC of 0.90, though it required more computational resources. Logistic Regression and SVM provided moderate results, while K-Nearest Neighbors showed the least effectiveness due to its lower performance metrics. The comparative analysis highlights Random Forest as the most effective model for predicting cybersecurity attack success, offering superior performance in handling complex data and distinguishing between attack outcomes. These findings provide valuable insights for improving cybersecurity strategies and selecting appropriate machine-learning models for threat prediction.

Optimizing pricing strategies in e-commerce through machine learning is crucial for enhancing customer satisfaction and achieving business success. This study evaluates the effectiveness of five machine learning models—Linear Regression, Decision Trees, Random Forest, Support Vector Machines (SVM), and Neural Networks—in refining e-commerce pricing strategies using a dataset of historical transaction records. Models were assessed based on Mean Absolute Error (MAE), Root Mean Square Error (RMSE), R-squared (R²), and F1-Score.Neural Networks demonstrated superior performance with the lowest MAE (0.126), RMSE (0.155), and the highest R² (0.84) and F1-Score (0.88), highlighting its capacity to model complex, non-linear relationships. However, its high computational demands may limit its feasibility for some businesses. In contrast, Random Forest, with an MAE of 0.130, RMSE of 0.160, R² of 0.82, and F1-Score of 0.86, offers a balanced alternative, combining strong performance with greater interpretability.

The findings emphasize the importance of choosing a machine learning model that aligns with business needs, resource constraints, and the trade-off between accuracy and interpretability. Integrating these models can optimize pricing strategies, better meet customer expectations, and improve business outcomes.

This study examines Reflexio, an innovative state management solution for scalable web applications. The research aims to analyze Reflexio's architecture, key concepts, and practical applications, comparing it with existing solutions. The methodology involves a detailed analysis of Reflexio's core features, including its object-oriented approach, event-driven reactivity, and multi-stage event processing. The results demonstrate Reflexio's effectiveness in addressing common challenges in state management, such as excessive UI re-renders and high coupling between application domains. The comparative analysis reveals Reflexio's unique advantages in modularity, performance, and complex business logic handling. The study concludes that Reflexio represents a significant advancement in state management, offering powerful tools for creating maintainable and scalable web applications, particularly in complex corporate environments. This research contributes to the field by providing insights into a novel approach that combines object-oriented programming principles with reactive state management, potentially shaping future web development practices.

The article discusses methods for increasing fault tolerance in systems with a hybrid architecture that includes elements of cloud technologies, local servers and peripheral computing. The main attention is paid to the analysis of vulnerabilities and risks inherent in such systems, as well as practical methods of their elimination. Both traditional methods such as hardware duplication and software redundancy are discussed, as well as modern approaches including the use of sharding, data replication, load balancing and integration of hardware and software. The article also highlights the importance of using preventive measures to minimize the risks of failures and the use of tools for modeling and testing failures. The work focuses on the need for an integrated approach to ensuring fault tolerance, taking into account both technical and economic aspects.

The process of preparation of acetylsalicylic acid complex with glycyrrhizic acid has been developed. Also, the optimal parameter conditions were found by the method of mathematical planning. The results of the quantitative assessment of the contribution of each of the factors selected as one of the tasks of process optimization by the method of mathematical planning of the experiment were analyzed.

This study evaluates several machine learning algorithms—Support Vector Machine (SVM), Random Forest, Logistic Regression, Decision Tree (C4.5), and k-Nearest Neighbors (KNN)—for breast cancer detection using the Breast Cancer Wisconsin Diagnostic dataset. We implemented comprehensive pre-processing and model evaluation with Scikit-learn in Python. Our findings show that SVM achieved the highest accuracy, with 99.9% on the training set and 98.50% on the testing set, indicating superior performance in handling high-dimensional data. Random Forest also performed well, with accuracies of 98.5% and 98.20%, respectively. Logistic Regression and Decision Tree models provided reliable predictions when tuned, while KNN was less effective. SVM and Random Forest are recommended for clinical decision support systems due to their high accuracy and robustness.

Lung cancer is a major global health concern, being one of the most common and fatal cancers. Accurate early detection and prediction of lung cancer are crucial for improving patient outcomes, and machine learning (ML) algorithms offer promising solutions for enhancing diagnostic accuracy. This study evaluates the performance of five ML algorithms—XGBoost, LightGBM, AdaBoost, Logistic Regression, and Support Vector Machines (SVM)—for lung cancer prediction. Utilizing a diverse dataset with attributes such as demographic variables, lifestyle factors, clinical features, and environmental exposures, we conducted a comprehensive analysis involving data preprocessing, feature selection, and model training. Our results indicate that XGBoost achieved the highest performance across all metrics, including accuracy (97.50%), sensitivity (96.80%), specificity (98.00%), and F-1 score (97.50%). LightGBM also performed well but slightly lagged behind XGBoost. AdaBoost, Logistic Regression, and SVM exhibited lower performance compared to the top two models. The correlation analysis revealed significant predictors of lung cancer, such as smoking history, air pollution, and family history. This study underscores the superiority of XGBoost in lung cancer prediction and suggests that future work should focus on expanding datasets, refining feature engineering, and integrating ML models into clinical practice for enhanced diagnostic capabilities.

This study presents a comprehensive dynamic performance analysis of a 6-slot, 8-pole permanent magnet linear motor (PMLM). The investigation focuses on evaluating the motor's efficiency, force generation capabilities, and operational characteristics under various load and operational conditions. Utilizing a combination of theoretical modeling and experimental testing, the analysis provides insights into the motor's dynamic behavior, including its response to different input parameters, speed variations, and load conditions.

The study employs a detailed simulation framework to model the motor's electromagnetic performance, taking into account factors such as cogging, magnetic flux distribution, and thermal effects. Experimental validation is conducted using a prototype motor, with performance metrics including thrust force, efficiency, and thermal performance measured under controlled conditions.

Key findings indicate that the 6-slot, 8-pole configuration offers significant advantages in terms of smoothness of operation and force uniformity compared to other motor designs. The analysis reveals how design parameters, such as slot and pole configurations, impact the motor's dynamic performance and efficiency. Additionally, the study identifies optimal operating conditions and provides recommendations for enhancing motor performance and reliability. Overall, this research contributes valuable knowledge to the field of linear motor technology, offering insights into the design and operational strategies that can improve the performance of 6-slot, 8-pole permanent magnet linear motors in various applications.

Effective demand forecasting is vital for inventory management in retail. This study evaluates five machine learning models—Linear Regression (LR), Decision Tree Regressor (DTR), Random Forest Regressor (RFR), Gradient Boosting (GB), and Long Short-Term Memory (LSTM)—for predicting retail demand. Utilizing a dataset with transactional sales, promotions, calendar events, and external factors like weather and economic indicators, we applied rigorous preprocessing and feature engineering. Performance was assessed using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R²). Results show that LSTM outperforms other models with an MAE of 9.53, RMSE of 14.67, and R² of 0.90, excelling in capturing temporal dependencies and complex demand patterns. Gradient Boosting and Random Forest also performed well, while Linear Regression and Decision Tree Regressor showed limitations. This study highlights the effectiveness of advanced models, particularly LSTM, for enhancing demand forecasting accuracy and offers valuable insights for optimizing retail inventory and operations.

The advent of Transformer models has significantly advanced Neural Machine Translation (NMT), particularly in sequence-to-sequence tasks, yet challenges remain in maintaining coherence and meaning across longer texts due to the model's traditional focus on independent phrase translation. This study addresses these limitations by proposing an enhanced NMT framework that integrates cross-sentence context through redesigned positional encoding, hierarchical encoding, and conditional attention mechanisms. The research critiques the shortcomings of existing positional encoding methods in capturing discourse-level context, introducing a novel hierarchical strategy that preserves structural and semantic relationships between sentences within a document. By employing a source2token self-attention mechanism to encode sentences and a conditional attention mechanism to selectively aggregate the most relevant context, the proposed model aims to improve translation accuracy and consistency while reducing computational complexity. The findings demonstrate that this approach not only enhances the quality of translations but also mitigates the computational costs typically associated with processing longer sequences. However, the model's effectiveness is contingent on the presence of clear document structure, which may limit its applicability in more irregular texts. The study's contributions offer significant implications for the development of more contextually aware and computationally efficient NMT systems, with potential applications in domains requiring high fidelity in translation, such as legal and academic fields. The proposed methods pave the way for future research into further optimization of context integration in NMT and exploring its application in multilingual and specialized domain contexts. Limitations include the additional computational overhead introduced by the hierarchical and conditional attention mechanisms, which may affect performance in low-resource environments. Nonetheless, this work represents a substantial step forward in addressing the complexities of document-level translation.