Search Results (197)

Code duplication, commonly referred to as code cloning, is not inherent in software systems but arises due to various factors, such as time constraints in meeting project deadlines. These duplications, or “code clones”, complicate the program structure and increase maintenance costs. Code clones are categorized into four types: Type-1, Type-2, Type-3, and Type-4. This study aims to address the adverse effects of code clones by introducing LeONet, a hybrid Deep Learning approach that enhances the detection of code clones in software systems. The hybrid approach, LeONet, combines LeNet-5 with Oreo’s Siamese architecture. We extracted clone method pairs from the BigCloneBench Java repository. Feature extraction was performed using Abstract Syntax Trees, which are scalable and accurately represent the syntactic structure of the source code. The performance of LeONet was compared against other classifiers including ANN, LeNet-5, Oreo’s Siamese, LightGBM, XGBoost, and Decision Tree. LeONet demonstrated superior performance among the classifiers tested, achieving the highest F1 score of 98.12%. It also compared favorably against state-of-the-art approaches, indicating its effectiveness in code clone detection. The results validate the effectiveness of LeONet in detecting code clones, outperforming existing classifiers and competing closely with advanced methods. This study underscores the potential of hybrid deep learning models and feature extraction techniques in improving the accuracy of code clone detection, providing a promising direction for future research in this area. Full article

The increasing complexity of aircraft systems and the growing global demand for certified maintenance personnel necessitate a fundamental shift in aviation training methodologies. This paper proposes a comprehensive digital-twin-based training ecosystem tailored for aviation maintenance education. The system integrates three core digital twin models: the learner digital twin, which continuously reflects individual trainee competence; the ideal competence twin, which encodes regulatory skill benchmarks; and the learning ecosystem twin, a stratified repository of instructional resources. These components are orchestrated through a real-time adaptive engine that performs multi-dimensional competence gap analysis and dynamically matches learners with appropriate training content based on gap severity, Bloom’s taxonomy level, and content fidelity. The system architecture uses a cloud–edge hybrid model to ensure scalable, secure, and latency-sensitive delivery of training assets, ranging from computer-based training modules to high-fidelity operational simulations. Simulation results confirm the system’s ability to personalize instruction, accelerate competence development, and support continuous regulatory readiness by enabling closed-loop, adaptive, and evidence-based training pathways in digitally enriched environments. Full article

This paper presents an integrated digital training twin framework for adaptive aircraft maintenance education, combining real-time competence modeling, algorithmic orchestration, and cloud–edge deployment architectures. The proposed system dynamically evaluates learner skill gaps and assigns individualized training resources through a multi-objective optimization function that balances skill alignment, Bloom’s cognitive level, fidelity tier, and time efficiency. A modular orchestration engine incorporates reinforcement learning agents for policy refinement, federated learning for privacy-preserving skill analytics, and knowledge graph-based curriculum models for dependency management. Simulation results were conducted on the Pneumatic Systems training module. The system’s validation matrix provides full-cycle traceability of instructional decisions, supporting regulatory audit-readiness and institutional reporting. The digital training twin ecosystem offers a scalable, regulation-compliant, and data-driven solution for next-generation aviation maintenance training, with demonstrated operational efficiency, instructional precision, and extensibility for future expansion. Full article

Maintenance 4.0 studies have become a focus for managers and employees when developing effective and efficient maintenance policies. In this study, the barriers to Maintenance 4.0 applications in the textile industry are investigated, and these barriers are weighted using the Stepwise Weight Assessment Ratio Analysis (SWARA) method based on Pythagorean fuzzy numbers. Solutions to address these barriers are presented. As a result of this study, Organizational and Managerial emerged as the most important main criterion. Operational was identified as the second most significant main criterion, followed by Technical Competence. Data-Related and Cybersecurity ranked fourth in terms of importance. On the other hand, Human Resources and Training and Financial were found to be the least important main criteria. These two criteria received lower importance scores compared to the others, with Financial being the criterion with the lowest overall significance. Sensitivity analyses were performed for six different scenarios by changing the importance weights of the decision-makers. The ranking of the criteria only slightly changed with the weights; this means that the results obtained in Case 1 are robust and reliable. Even in Case 6, where the expert weight ratios were completely reversed, the results did not change significantly. This highlights an important point regarding the reliability of the assessment. Full article

Background/Objectives: High-flux online hemodiafiltration (OL-HDF) appears to be associated with better survival than hemodialysis (HD). In Brazil, OL-HDF is only affordable for patients with private health insurance. Although observational studies have shown a survival advantage with OL-HDF, even in Brazil, it is unclear whether this benefit applies to patients without private health insurance. We compared overall and cardiovascular mortality between OL-HDF and HD in patients treated exclusively through the public health care system. We hypothesized that patients on OL-HDF would have a higher survival rate than those on HD. Methods: This is an observational cohort study. Adult patients on maintenance hemodialysis or OL-HDF for at least one month during the period between 1 September 2022 and 1 December 2024 were enrolled into the study. The primary outcome was all-cause mortality. The secondary outcome was cardiovascular mortality. Fine-Gray sub-distribution hazard models were used to evaluate survival in the presence of competing events (kidney transplant and recovery of renal function). Results: Patients on HD (N = 321) and OL-HDF (N = 48) were similar in age, race, sex, and vascular access. Patients on HD were more likely to have diabetes (54.0% vs. 29.2%, p = 0.001) and spent more hours per week on dialysis (11.2 ± 1.8 vs. 10.5 ± 1.6 h, p = 0.006). In an adjusted Fine-Gray model, the hazard of death for patients on OL-HDF was 68% lower than that for patients on HD, and the risk of death for patients with an arteriovenous fistula was 55% lower compared to those with a catheter. Cardiovascular mortality did not differ between the groups. Conclusions: These findings suggest that OL-HDF is associated with an overall higher survival rate compared to HD, even for patients without private health insurance. Full article

The fast expanding market of battery electric vehicles (BEVs) demands industry-specific competence requirements for maintenance technicians. We have therefore generated a knowledge structure of BEV maintenance through a literature review and expert consensus. Consensus was achieved following a Delphi study of 15 industry experts through three rounds of refining a broad initial list of competencies. The resulting framework consists of four core competency categories (Professional Knowledge, Professional Skills, Professional Attitude, and Personal Qualities), which are further divided into a total of 24 subcategories and 106 specific indicators that define the boundary of professional skill as well as core skill essentials. This approved tool can be used strategically for workforce grooming, curriculum design for training, and performance assessment in BEV maintenance to ensure that technical workforce capabilities are in line with sustainable mobility targets of the industry. Full article

The integration of Industry 4.0 technologies with Corporate Social Responsibility (CSR) initiatives offers transformative potential for enhancing safety culture in manufacturing. This study investigates how managerial competence facilitates the alignment of tools like the Internet of Things (IoT), Artificial Intelligence (AI), and big data analytics with CSR principles to foster sustainable safety practices. Employing a qualitative methods approach with secondary data from 2010 to 2024, including case studies of some of five leading firms (Siemens, General Electric, Toyota, Bosch, and Ford) and a systematic literature review, this analysis uses thematic and statistical techniques. The results show that strategic integration significantly reduces workplace hazards by 30–50%, boosts employee engagement, and enhances operational efficiency through real-time monitoring, predictive maintenance, and CSR alignment. Managerial competence, encompassing strategic vision, technical expertise, and stakeholder engagement, is critical for aligning these domains, delivering enhanced safety, sustainability, and competitive advantages. Full article

Despite its demographic relevance, Spanish as an Immigrant Minority Language (IML) remains understudied in Europe. In Brussels, approximately 46,500 residents have Hispanic heritage, but their linguistic practices have largely remained unexplored in sociolinguistic research. This paper presents a pilot study on the language practices of the Hispanic communities in the city in order to assess language maintenance and vitality. Through an online survey among 125 adults with Hispanic heritage in Brussels, primarily first-generation immigrants, a highly multilingual sample was revealed, with most participants competent in at least four languages. While Spanish usage declines across generations, language competence remains high, with 60% of third-generation speakers still considering it one of their dominant languages. Findings challenge traditional minority–majority language maintenance perspectives, advocating for a multilingual approach to linguistic vitality. Patterns of language transmission, home language use, and integration highlight the communities’ adaptability while maintaining a connection to Spanish. Results point to unexplored sociolinguistic phenomena within the language minority, underscoring the need for further research on the Hispanic communities in Brussels. Full article

Background: The development of an efficient tissue culture system is essential for advancing genetic transformation and genome editing in commercially important pineapple cultivars. However, a robust tissue culture workflow for the elite pineapple cultivar 73–50, enabling reliable transformation and plant regeneration is not established. Methods: A comparative analysis of hormone combinations, including 6-benzylaminopurine (BAP), α-naphthaleneacetic acid (NAA), picloram, and abscisic acid (ABA) was conducted. Transformation competence of 73–50 callus was tested using the iGUS reporter gene. Results: We established that 1 mg/L picloram and 0.5 µg/L ABA was the most effective combination for inducing friable embryogenic callus (FEC). FEC, composed of small, loosely associated cell clusters, is highly suitable for transformation but prone to browning during long-term culture. We optimized the conditions to minimize browning and support prolonged maintenance using a medium supplemented with 5 mg/L NAA. Transformation efficiency was demonstrated using the iGUS reporter gene, showing that FEC can be effectively transformed via both biolistic and Agrobacterium-mediated methods. For shoot regeneration, the optimal medium was found to contain 2 mg/L BAP. To standardize the assessment of callus development, we introduce a classification system describing distinct developmental stages. Conclusions: A detailed step-by-step protocol optimized for 73–50 cultivar facilitates efficient genetic improvement in pineapple, supporting both conventional transformation and DNA-free genome editing approaches. Full article

The conversion of degraded ecosystems to perennial vegetation in water-limited regions creates conflicting demands for soil water maintenance and carbon sequestration. However, current understanding of these competing demands is limited. In this study, to quantify the trade-off resulting from land-use conversion (converting cropland into forest, shrub, and grassland) in the Loess Plateau, 2775 observations of soil organic carbon (SOC) stocks (to a depth of 100 cm) and 2654 observations of soil water storage (SWS) (to a depth of 500 cm) from peer-reviewed papers and measured data were synthesized. The results showed that (1) land-use conversion greatly influenced the trade-off, and in general, converting cropland into natural grassland and evergreen trees performed relatively better in carbon sequestration and soil water maintenance; (2) in rainfall zones less than 550 mm, natural grassland exhibited higher advantages in increasing SOC stock but maintained a lower SWS depletion, while forest was a better choice for rainfall greater than 550 mm; and (3) with increasing restoration age, SOC stock and SWS depletion both increased significantly; nevertheless, natural grassland appeared to be sustainable and stable to achieve a win–win result. Moreover, with increasing age, an accumulation of 0.7 Mg ha⁻¹ SOC stock in the upper 100 cm was associated with an approximately 5.14 mm SWS decrease in the 0–500 cm soil layers. Overall, this study provides practical insights for land and water managers on how to achieve win–win results for soil- and water-related ecosystem services during ecological restoration in water-limited regions. Full article

The maintenance of critical maritime infrastructure is essential for ensuring the safe, reliable, and efficient operations of marine seaports. This paper proposes a novel fuzzy multi-criteria decision framework for evaluating the maintenance practices and culture of maintenance-critical maritime infrastructure, such as port loading and unloading machinery and equipment. The proposed framework incorporates three distinct multi-criteria decision-making tools Step-wise Weight Assessment Ratio Analysis, Weighted Aggregate Sum Product Assessment, and Technique for Order of Preference by Similarity to Ideal Solution. Fuzzy logic is incorporated into the framework to enhance the precision and robustness of the evaluation process. To form the basis of the assessment, the framework is structured around five key maintenance practice criteria: planning and scheduling; data collection and analysis; documentation and record keeping; maintenance personnel training; and competency, and four important maintenance culture criteria: leadership commitment, proactive and preventive approach, safety and compliance focus, and continuous improvement and learning. To validate the framework, an empirical evaluation was conducted, analyzing maintenance practices and culture across six Nigerian seaports. Data collection uses a questionnaire administered to relevant maintenance experts in the ports, ensuring a comprehensive and expert-informed analysis. The data collected was then analyzed using the fuzzy multi-criteria decision framework. The results provide valuable and actionable insights into the current maintenance practices and maintenance culture of the ports, identifying areas for improvement. Full article

Volatile fatty acids (VFAs) are inevitable intermediates of biogas production during the anaerobic digestion of organic matter. The excessive accumulation of VFAs leads to a pH drop and the strong inhibition of methanogenesis. On the other hand, VFAs are useful commodities with different applications, and their fermentative production may compete with traditional production methods based on oil derivatives. The fermentation methods have commonalities with the biorefinery concept. The present review considers the methods of VFA fermentative production together with competitive simultaneous biogas and hydrogen production. Methods of the enhanced production of volatile fatty acids are presented, showing the option of integrated processes of product removal and energy production from the obtained biogas. On the basis of the present review, the following conclusion can be drawn. Volatile fatty acids (formic, acetic, propionic, and butyric ones) are useful commodities with various applications. That is why their targeted production with their desired production rate may shift the aims of the anaerobic digestion toward volatile fatty acids instead of biogas release. On the other hand, VFA production combined with biogas release can make the overall process self-consistent, with energy production sufficient to maintain the target processes using biogas for heating the digestor. The maintenance of optimum VFA concentrations can be accomplished by simultaneous VFA removal from the fermentation broth, thus integrating the product recovery with the maintenance of optimum operation conditions in the digester. The substrate preparation and the operating conditions (organic loading rate and hydraulic retention time) are of crucial importance for the successful fermentation process. Full article

Florida citrus production has declined by over 90% since the bacterial disease huanglongbing (HLB) was found in the state. In the absence of an effective cure, growers are adopting more frequent fertilization and irrigation practices to improve tree health and prolong the life span of their orchards. However, Florida’s soils under citrus production are sandy, with little organic matter, a low water holding capacity, and a low cation exchange capacity (CEC), rendering them prone to nutrient leaching. Organic amendments can be used to improve soil health and the environment for citrus roots, but may promote a higher incidence of weeds competing with trees for water and nutrients. A large field trial was established in a commercial citrus orchard in southwest Florida to evaluate the effects of organic amendments and weed management on young tree growth. The organic amendment treatments were as follows: (1) plant-based compost, (2) humic acid, and (3) a non-amended control. The weed management (herbicide) treatments were (1) glyphosate, (2) glufosinate, (3) flumioxazin, and (4) a maintenance herbicide control. Trees were planted in August 2019, and treatments began in 2021. Tree growth and physiological variables and soil physicochemical properties were evaluated during the two-year study. Compost-amended plots had a higher volumetric water content throughout the experiment, and soil nutrient content, organic matter, CEC, and pH were higher after two years of application. Humic acid amendments were less effective in altering these soil properties. Compost’s effects on tree and fibrous root physiology were moderate, and tree growth, fruit yield and fruit quality were not affected by either organic amendment. In contrast, the use of post-emergent herbicides (glyphosate and glufosinate) improved tree growth and nutrient uptake. The results suggest that in Florida, the use of organic amendments needs to be integrated with weed management to prevent resource competition. In the short term, these practices did not improve the productivity of the trees in the current Florida production environment. Full article

Nowadays, Internet of Things (IoT) is one of the most dynamically evolving services in the 5G ecosystem. In industrial IoT (IIoT), this service can be utilized to deliver state updates of various equipment to the remote control center for further coordination and maintenance. As a result, one of the critical metrics of interest for such a service is the Age of Information (AoI) and its upper bound—peak AoI (AoI)—characterizing the freshness of information about the state of the systems. In spite of significant attention, these metrics received over the last decade, only little is known regarding the PAoI performance of a single source (e.g., sensor) in the presence of competing traffic from other sources in queuing systems. On top of this, models with batch arrivals and batch services that can be effectively used to represent service performance in modern cellular systems such as 5G New Radio are lacking. In our study, we consider a cellular air interface representing it as a queuing system (QS) in discrete-time with batch arrivals and service and investigate performance of a single (tagged) source in presence of competing traffic from other sources having the same priority, where all the sources are modeled using the switched Poisson process (SPP) characterized by sophisticated correlational properties. We also investigated the impact of several service disciplines on the performance of the tagged source including first-come–first-served (FCFS), last-come–first-served (LCFS), random, and priority-based service. Our results illustrate that, although the qualitative behavior of the mean PAoI is different for different service disciplines, the optimal value of PAoI is insensitive to the choice of the service order. On top of this, we observed that introducing a priority in service to one of the flows may drastically affect the performance of other flows even when the overall load contribution of a single flow is rather limited. Our observations can be utilized to design packet scheduling strategies for 4G/5G cellular systems carrying traffic of state update applications. Full article

The Artificial Gorilla Troops Optimizer (GTO) has emerged as an efficient metaheuristic technique for solving complex optimization problems. However, the conventional GTO algorithm has a critical limitation: all individuals, regardless of their roles, utilize identical search equations and perform exploration and exploitation sequentially. This uniform approach neglects the potential benefits of labor division, consequently restricting the algorithm’s performance. To address this limitation, we propose an enhanced Labor Division Gorilla Troops Optimizer (LDGTO), which incorporates natural mechanisms of labor division and outcome allocation. In the labor division phase, a stimulus-response model is designed to differentiate exploration and exploitation tasks, enabling gorilla individuals to adaptively adjust their search equations based on environmental changes. In the outcome allocation phase, three behavioral development modes—self-enhancement, competence maintenance, and elimination—are implemented, corresponding to three developmental stages: elite, average, and underperforming individuals. The performance of LDGTO is rigorously evaluated through three benchmark test suites, comprising 12 unimodal, 25 multimodal, and 10 combinatorial functions, as well as two real-world engineering applications, including four-bar transplanter mechanism design and color image segmentation. Experimental results demonstrate that LDGTO consistently outperforms three variants of GTO and seven state-of-the-art metaheuristic algorithms in most test cases. Full article