Scilit

The knowledge economy is often related to a powerful way of transforming society by integrating such critical pillars as the economic and institutional environment, human competencies and education, knowledge management practices, technology infrastructure, entrepreneurship, and innovation to achieve more sustainable economic growth and address the growing problems of resource scarcity and climate change. Scientific studies show that knowledge management practices implemented through knowledge management processes (knowledge acquisition, creation, storage, sharing, and application) are vital drivers of innovation, productivity, and economic growth, with intangible resources such as technology driving sustainable economic growth. Even scientific research has proven that knowledge management technologies and practices are crucial for sustainable outcomes; however, limited research has been conducted to analyze the impact of technologies on the entire knowledge management cycle influencing sustainability in higher education institutions in developing countries. As a result, there is still a lack of studies on how knowledge management technologies impact sustainability through organizational performance by integrating knowledge acquisition, creation, storage, sharing, and application in higher education. To fill this research gap, this study analyzes how knowledge management technologies influence knowledge management practices and determines the influence of the latter on the sustainable organizational performance of higher education institutions. A scientific literature review, survey (structured questionnaire), and PLS structural equation modeling technique were used to develop the research model and test the research hypotheses. Research findings revealed a positive relationship between knowledge management technologies and knowledge management processes. However, some insignificant relationships were detected between knowledge management processes (knowledge acquisition, storage, sharing, application, and creation) and sustainable organizational outcomes measured in innovation, quality, and operational aspects. The findings provide insight into the capacity of knowledge management practices to foster operational and quality performance and innovation, positioning the higher education system as a catalyst for economic sustainability in a developing region. This study addresses a gap in the knowledge economy field and focuses on knowledge management technologies, practices, and economic contributions within the Lebanese context.

Electrochemical Discharge Micro-Machining (ECDµM) of borosilicate glass commonly suffers from unstable discharges, surface cracking, low material removal (MR), and excessive tool wear (TW) when conventional liquid or air-based dielectrics are used. These issues limit machining efficiency and sustainable process development, especially when MR and TW are optimized independently. To overcome these limitations, this study investigates nitrogen gas as a regulated gaseous dielectric in combination with an aqueous sodium hydroxide (NaOH) electrolyte to enhance discharge stability and achieve balanced machining performance. Experiments were conducted by varying applied voltage, NaOH concentration and nitrogen gas flow rate, and the responses were analyzed using Response Surface Methodology (RSM), Grey Relational Analysis (GRA), and a Random Forest Algorithm (RFA). Stable machining was achieved within a nitrogen gas flow range of 3–5 L/min. At 120 V and 30 wt% NaOH, increasing the nitrogen gas flow rate from 3 to 5 L/min maintained MR at 4 mg while reducing TW from 3 to 2 mg. The optimal condition (134 V, 20 wt% NaOH, 4 L/min nitrogen gas flow rate) yielded 5 mg MR with negative TW (− 1 mg), while reducing electrolyte concentration by 33%. The Random Forest model predicted near-optimal parameters (119.89 V, 20.107 wt% NaOH, 4.31 L/min) with an average deviation of approximately 8%. These results establish nitrogen gas assisted ECDµM as a stable, energy-efficient and environmentally responsible approach for precision micromachining of brittle materials.

In double-slope solar stills (DSS), a significant amount of solar energy is wasted as heat and reducing the system freshwater generation efficiency. To overcome this, a reconstructed double-slope solar still (RDSS) with a photovoltaic panel (PV) mounted on top is proposed which facilitates for simultaneous generation of freshwater, heat, and electricity. The PV panel partial shading is reduced by transferring the generated power to a heating element that preheats the inlet saline water, while paraffin wax phase change material (PCM) improves thermal storage. The experimental results indicated that the RDSS substantially enhanced productivity by 92.2%, with energy and exergy efficiencies improvement of 44.2% and 46.9%, respectively. The PV panel peak power output was 47.1 W, and the system had an efficiency of 14.1% (electrical), 37.1% (thermal), 88.5% (heating element), 52.1% (overall energy), and 5.9% (overall exergy). Economically, the RDSS reduced the cost per litre (CPL) of distillate by 17.3% and the payback period (PBP) by 18.2% than DSS which indicating higher financial feasibility. Additionally, the environmental study revealed that the RDSS achieved 98.2% more CO₂ reduction than DSS. In sensitivity analysis, the extending system lifespan from 5 to 25 years lowered production costs by 73.1% at a 2.5% interest rate, but CO₂ mitigation and carbon credit earned (CCE) elevated by 536%. Moreover, the RDSS had a 1.6% higher sustainability rating than DSS, though both systems generated potable water that met world health organization (WHO) requirements. Finally, these findings support RDSS as a low-cost, ecologically friendly and scalable alternative for decentralized freshwater and energy generation.

The principal objective of this research is to employ modern machine learning techniques to optimize high-pressure biofuel injection strategies for sustainable energy applications. An engine powered with biofuel and hydrogen (H₂) under dual-fuel (DF) mode was tested under a varied fuel injection pressure range from 180 to 240 bar for optimization and modeling. The results demonstrate that an injection pressure of 220 bar produces enhanced engine performance. At this pressure, enhancements were noted in combustion characteristics, efficiency, and emission levels. The ignition delay (ID) at 220 bar injection pressure was 9.4% longer than at 240 bar injection pressure. The 220 bar IP mix demonstrated reduced peak cylinder pressure (PCP) and heat release rate (HRR) compared to the 240 bar. A 12.4% rise in brake-specific fuel consumption (BSFC) was observed at 220 bar inlet pressure. Nevertheless, although brake thermal efficiency (BTE) increased with increasing injection pressure (IP), the increase at 220 bar was somewhat less than that at 240 bar. Despite elevated nitrogen oxide (NOx) emissions with the 220 bars compared to pure diesel, carbon monoxide (CO) and hydrocarbon (HC) emissions were markedly decreased. Smoke emissions were reduced with the 220 bars in comparison to diesel and other fuel combinations. Three machine learning models were employed to establish a predictive control framework. The decision tree (DT) model had the greatest accuracy, with R² values of 0.9792 for PCP and 0.9710 for HC, alongside near-zero MAPE for BTE and HC This study underscores the potential of AI-driven biofuel optimization for fostering sustainable transportation and renewable fuel strategies, paving the way for large-scale adoption of low-carbon, high-efficiency energy solutions.

This article presents a study aimed at integrating two road safety approaches to effectively prioritise road sections requiring safety improvements. The study examines the role of injury severity assessment in evaluating the safety of road networks. It outlines the fundamental principles of the MAIS3+ methodology, discusses its relevance to road safety analysis, and highlights its potential applications for assessing road infrastructure risk. A pilot safety assessment of the Lithuanian main road network is presented, based on the severity of traffic accident outcomes classified according to the MAIS3+ criterion. Furthermore, the study addresses the challenges of linking hospital and police data and provides recommendations to improve data integration.

Precise evaluation of vehicle CO₂ emissions under real-world driving conditions is essential for meeting stringent regulations. While laboratory procedures such as WLTC ensure repeatability, they fail to reflect transient conditions typical of everyday driving, and PEMS-based RDE measurements remain costly. This study proposes a data-driven approach for reconstructing instantaneous CO₂ concentration using onboard engine parameters - engine speed, exhaust gas temperature and exhaust mass flow rate - without direct CO₂ sensing. Linear, nonlinear and ensemble machine learning models were evaluated using an RDE dataset of 5200 synchronized observations collected on a mixed urban-rural route. Ensemble methods, particularly Random Forest (R² = 0.715, RMSE = 15,307 ppm) and XGBoost (R² = 0.669), achieved the highest accuracy and reproduced steady-state conditions and rapid CO₂ drops during fuel-cut events. The results confirm that reliable CO₂ estimation can be achieved using a minimal OBD-based input set, enabling cost-effective emission monitoring and real-time onboard applications.

Unmanned aerial vehicles (UAVs) require reliable autonomous positioning independent of external satellite navigation signals, motivating the development of a vision-based, end-to-end finding point in map (FPI) framework. This study introduces MuRDE-FPN, an enhanced feature pyramid network (FPN) designed for precise UAV localization, building upon a lightweight one-stream transformer-based (OS-PCPVT) backbone. MuRDE-FPN integrates efficient channel attention (ECA) for adaptive channel recalibration and features two novel components: a multi-receptive deformable enhancement (MuRDE) that utilizes deformable convolutions with varying kernel sizes to refine the semantically rich final feature layer, and a feature alignment module (FAM) for cross-level fusion. Evaluated on the UL14 dataset and a new, more diverse UAV-Sat dataset, MuRDE-FPN consistently outperformed four state-of-the-art FPI methods (FPI, WAMF-FPI, OS-FPI, DCD-FPI). It achieved a relative distance score of 84.26 on UL14 and 63.74 on UAV-Sat datasets, demonstrating improved localization. Ablation studies confirmed the cumulative benefits of ECA, MuRDE, and FAM. These findings highlight the effectiveness of custom FPN designs and targeted feature enhancements for precise cross-view positioning, with MuRDE-FPN providing a robust solution and the UAV-Sat dataset offering a new benchmark for evaluation. Future efforts will address computational efficiency and performance across varying data quality environments.

Autonomous vehicles is a trending research topic which involves a number of different problems. This research work considers the problem of path planning, while taking into account specific and important traffic scenario – determination of the path in order to perform obstacle avoidance during cornering. In this research work, a novel hybrid path planning approach which consists from two classical planning approaches – the Bezier curves and the Rapidly-exploring random trees, is proposed and experimentally tested. The entire development process of the hybrid path planning approach is de-scribed. The experimental testing of the proposed approach was performed while using an actual prototype of the autonomous vehicle. During the ex-perimental testing, different positions of the obstacle, different radius of the corner arc and different angles of the corner arc were considered. Approximately 200 different scenarios were considered and experimentally tested. The results of the experimental testing showed that the general performance of the proposed hybrid path planner is reliable – the planned paths are safe, continuous, and technically feasible.

B2C e-commerce companies are increasing in number due to the significant benefits e-logistics provides, boosting profits and improving customer satisfaction. Moreover, the COVID-19 pandemic increased demand for online shopping due to lockdowns and safety concerns. Despite this, several B2C companies entering the field eventually leave due to inefficient handling of returned products and ineffective reverse e-logistics systems. This article identifies recent methods to improve Reverse Logistics performance for B2C e-commerce firms. The methodology is based on literature review, synthesis, DELPHI, and multi-scoring survey methods. The results showed that management, quality management, organisational structure and culture, IT, customer satisfaction and service, employees, and infrastructure are factors with a direct effect on REL's performance, and this study suggests methods to improve these factors.

This work presents a comprehensive metrological assessment of an industrial rotary encoder calibration system based on polygon-autocollimator cross-calibration. The system was evaluated using clockwise (CW), counterclockwise (CCW), and bi-directional measurement protocols to characterize direction-dependent behavior. Evaluation according to ISO-230-2 revealed systematic positioning E , repeatability R and reversal B errors leading to the total positioning error A up to 6.57″–7.37″. Environmental conditions were monitored during calibration, confirming sufficient thermal stability. High-precision encoder sub-divisional errors were quantified using signal quality analysis, and Fourier harmonic decomposition was applied to the calibrated deviation curves to identify dominant mechanical error sources. A three-factor analysis of variance (ANOVA) was performed to statistically separate the effects of angular position, rotation direction, and measurement series, demonstrating that both direction and position–direction interaction are highly significant contributors to measurement variability. Based on these findings, an enhanced Type A uncertainty evaluation incorporating ANOVA variance components was applied, preventing underestimation of uncertainty compared to conventional pooled repeatability approaches. A complete GUM-compliant uncertainty budget was established, including Type A and Type B contributions, with the expanded uncertainty reaching up to 0.98″ for bi-directional operating mode. Motivated by the statistical evidence of direction dependence, direction-aware error compensation strategy was developed and validated, including a unified bi-directional model explicitly incorporating rotation direction. The proposed compensation approach achieved substantial reduction of systematic positioning errors ( E ≈ 0 ) and improved angular accuracy by 75%–78% . The presented methodology provides a practical and statistically rigorous framework for industrial rotary encoder calibration and performance optimization.