Journal of Technology Research

Behavior-driven semantic re-ranking for personalized web search

Anwar Alhenshiri — 2026-08-21

This study proposes a behaviour-driven semantic re-ranking system designed to enhance personalized web search without modifying the underlying search engine infrastructure. The approach operates as a lightweight middleware layer over Google Search and integrates BERT-based semantic embeddings, KeyBERT-driven dynamic user profiling, cosine similarity scoring, and an exponential decay mechanism to maintain adaptive interest modelling. A controlled comparative experiment involving 14 participants was conducted to evaluate performance against the baseline Google ranking. Results indicate that the proposed system reduced average search time by 15.3% (from 274 seconds to 232 seconds), decreased the number of required clicks by 21.2% (from 7.71 to 6.07), and lowered query reformulations from 43% to 21%. Furthermore, 79% of participants preferred the personalized system, with statistically significant improvements observed in efficiency and satisfaction (p < 0.05). These findings demonstrate that behaviour-driven semantic re-ranking can substantially improve search relevance, user efficiency, and overall experience while maintaining transparency and scalable deployment.

Solving assignment problems using the Black Hole Optimization in R

Jamal B. Oheba — 2026-08-21

This paper presents, the Black Hole Optimization (BHO), a meta heuristic algorithm inspired by the physical phenomenon of black holes in the universe. The algorithm was implemented in the R programming language using the metaheuristic Opt Library, which provides advanced functions for optimization. Several assignment problems were tested, and the proposed method successfully obtained optimal and near optimal solutions. The performance of BHO was compared with the Flower Pollination Algorithm - FPA) and (Whale optimization Algorithm - WOA). Results indicate that BHO is efficient, competitive, and can be used effectively for solving such problems in the field of operation research applications.

Heat exchanger network retrofit for energy savings in a catalytic reforming naphtha unit at Zawia oil refinery using pinch technology

Enas Eltnay — 2026-08-21

Energy conservation in oil refineries is essential for reducing operational costs and minimizing environmental emissions. One effective approach is optimizing the Heat Exchanger Network (HEN) design. In this research, pinch technology was applied to optimize the preheat exchanger network of the catalytic reforming unit at the Zawia Oil Refining Plant. The optimized configuration simplified the network to one heater and three coolers, significantly reducing the need for external utilities. The analysis was performed using HINT software. Results showed that the optimal minimum temperature difference (ΔTmin) is 25°C, while the pinch point was identified at 212.5°C. The minimum heat required from hot utilities was 4450.09 kW, and 3932.73 kW needed to be removed by cold utilities. The annual operating cost for hot and cold utilities was estimated at $573,329.7, while the annual capital cost was $100,108.7, resulting in a total annual cost of $673,438.4. Although these values do not represent the total plant energy savings, they highlight the strong potential of pinch analysis to improve thermal efficiency, reduce energy waste, and support sustainable operations in energy-intensive industries.

Analysis of variance in production quality among manufacturing lines using the anova method: A case study in a longitudinal rolling mill in a steel bar factory

Husayn F . Alameen — 2026-08-21

This study conducts a comprehensive statistical analysis of production quality variance between two longitudinal rolling lines in a bar mill using Analysis of Variance (ANOVA). The research focuses on three key quality metrics: yield rate, production deviation rate, and dimensional compliance. Data were collected over a four-week period and aggregated to daily observations, resulting in a sample size of n=28 per line. The analysis was performed using MATLAB, employing one-way, two-way, and repeated-measures ANOVA models with Bonferroni adjustment for multiple comparisons. Results indicate no statistically significant difference in compliance between the two lines (F=0.041, p=0.841). However, significant differences were found in deviation rate (F=8.921, p=0.006), with Line 2 showing 102% higher deviation. Yield performance also varied significantly between lines (F=9.543, p=0.004), with notable diameter-specific effects, particularly better performance at D25 (F=14.237, p=0.005). The study concludes with practical recommendations for process optimization and quality monitoring.

Optimization of process parameters in fused deposition modelling to enhance tensile strength

Abduladim S. Bala — 2026-08-21

Additive manufacturing is considered one of the most prominent technologies of the Fourth Industrial Revolution. It is used to produce three-dimensional models through the successive deposition of semimolten material layers. This technology is characterized by reduced material waste and high design flexibility, enabling the fabrication of complex geometries that are difficult to achieve using conventional manufacturing methods. Fused Deposition Modeling (FDM) is among the most widely used techniques due to its low cost and ease of implementation. This study aims to analyze the effect of key process parameters — layer thickness, printing speed, and extrusion temperature—on the tensile strength of FDM-manufactured parts. A statistical approach was adopted for experimental design and data analysis, allowing the evaluation of both individual and interaction effects of these parameters and the determination of optimal values. A mathematical model was also developed to predict process behavior. The results indicate that layer thickness and extrusion temperature have the most significant influence on tensile strength, while printing speed shows a relatively smaller effect, though it remains important in improving interlayer bonding.

Evaluating the impact of time-to-exploit estimation for vulnerability prioritization

Amira K. Ellabad — 2026-08-21

Recently, security vulnerabilities have increased significantly, as this study addresses the issue of prioritizing them by developing a predictive model that estimates the time required to exploit them. Data obtained from multiple sources was used to develop this model, including a unified Kaggle dataset, which combines data from three reliable sources: the National Vulnerability Database (NVD), the CISA Known Exploitable Vulnerabilities (KEV) list, and the Exploitation Prediction Score System (EPSS). Data from both ExploitDB and CISA KEV list was also used. The data set was divided into training (2021 - 2023) and testing (2024) sets, to compensate for the lack of confirmed exploitation dates, isotonic regression was used to model the monotonic relationship between EPSS scores and actual exploitation dates, as a methodological alternative. We also evaluated three regression models: the best results for the test set were shown in the XGBoost model (MAE=2.98 days, RMSE=12.20 days, R^²=0.936, MAPE=14.43%), while the Random Forest performed the baseline linear regression model (MAE=2.77, RMSE=14.59, R^²=0.908, MAPE=13.43% vs. MAE=18.48, RMSE=24.57, R^²=0.740, MAPE=51.50%). To interpret these predictions into actionable information, the estimated 'Time To Exploit' was transformed into a 'Composite Priority Index' that combines the predicted speed of exploitation with the probability score, the Exploitation Potential Scoring System (EPSS) was then used to categorize vulnerabilities into the following levels: urgent, high, medium, and low. This approach improved our ability to identify high risk vulnerabilities early by incorporating time based data, compared to relying solely on static criteria. The results show that incorporating the time dimension enhances its reliability and wider applicability.

Improving rebar production processes through six sigma methodology

Mahmud M. Abushalla — 2026-08-21

Improving productivity and reducing waste in manufacturing processes is a primary objective for all industrial organizations. Theoretical Six Sigma calculations were applied to actual data collected from the heat treated and mechanically treated (TMT) steel bar plant of the Libyan Iron and Steel Company (LISCO). This was achieved by applying the Six Sigma (Definition, Measurement, Analysis, Improvement, and Control) methodology and using tools such as control charts, Pareto analysis, cause and effect diagrams, and SPSS software, with the aim of optimizing the manufacturing process of heat treated and mechanically treated steel bars. The results obtained from the applied calculations showed a promising level of improvement, with the production waste rate decreasing to approximately 1.7%. The process parameters affecting the mechanical properties of heat treated and mechanically treated steel bars were also identified. Therefore, it can be concluded that the study has yielded valuable and encouraging results.