Practical Limitations Research Articles

Tailored thymoquinone intercalated Layered Double Hydroxide (LDH) nanocomposites to accelerate mineralization for enhanced osteogenesis

The tunable nature of two-dimensional nanostructured Layered Double Hydroxides [LDH] and their effective intercalation ability with biomolecules make them inevitable functional hybrid materials for versatile biomedical applications. In an attempt to meet the limitations in existing bone reconstruction practice, the present work reports the synthesis of zinc and iron-based LDH [Zn-Fe LDH] and its subsequent intercalation with thymoquinone [TQ-Zn-Fe LDH] to augment osteoblastic differentiation and proliferation for accelerated bone mineralization. Structural studies depicted the polycrystalline and amorphous nature of ZnFe LDH and TQ-Zn-Fe LDH respectively. The biocompatible nature of LDHs was proven by negative cytotoxicity, cell adhesion, and proliferation assays studied in MG-63 cell lines. Gene expression studies of the nanocomposites showed a fold change of 5–10 in osteoblastic differentiation and proliferation. However, TQ-Zn-Fe LDH treated cells showed significantly higher protein translation levels of osteogenesis markers and elevated alkaline phosphatase secretion and calcium deposition on day 21, confirming their osteogenic efficiency. Thus, acquired results speculate the osteogenic potential of thymoquinone intercalated TQ-Zn-Fe-LDH to meet the limitations in bone reconstruction practice.

Unmanned Aircraft Systems in Road Assessment: A Novel Approach to the Pavement Condition Index and VIZIR Methodologies

This paper presents an innovative approach to road assessment, focusing on enhancing the Pavement Condition Index (PCI) and Visión Inspection de Zones et Itinéraires Á Risque (VIZIR) methodologies by integrating Unmanned Aircraft System (UAS) technology. The research was conducted in an urban setting, utilizing a UAS to capture high-resolution imagery, which was subsequently processed to generate detailed orthomosaics of road surfaces. This study critically analyzed the discrepancies between traditional field measurements and UAS-derived data in pavement condition assessment. The study findings demonstrate that photogrammetry-derived data from UAS offer at least similar or, in some cases, improved information on the collection of a comprehensive state of roadways, particularly in local and collector roads. Furthermore, this study proposed key modifications to the existing methodologies, including dividing the road network into segments for more precise and relevant data collection. These enhancements aim to address the limitations of current practices in capturing the diverse and dynamic conditions of urban infrastructure. Integrating UAS technology improves the measurement of pavement condition assessments and offers a more efficient, cost-effective, and scalable approach to urban infrastructure management. The implications of this study are significant for urban planners and policymakers, providing a robust framework for future infrastructure assessment and maintenance strategies.

Parenthood and Glee: An online study to examine differences between viewers and non-viewers of television shows featuring a character with a developmental disability.

We present findings from our study, which examined whether ability to identify Down syndrome and autism was linked to participants' willingness to maintain social contact with individuals with the respective conditions. Additionally, we explored whether viewers and non-viewers of Parenthood and Glee, television shows featuring a character with autism and Down syndrome respectively, differed in their awareness, beliefs regarding causes and interventions, and desire to maintain social proximity with individuals with these conditions. Participants completed an online survey, which included vignettes based on Max, the character with autism from Parenthood and Becky, the character with Down syndrome from Glee as well as the adapted Intellectual Disabilities Literacy Scale. Based on 300 responses, key differences were noted in the hypothesized direction on the assessed variables (symptom recognition, causal beliefs, and treatment beliefs) between Parenthood and Glee viewers and non-viewers. Findings are discussed in the context of practical implications and methodological limitations.

Bullying prevention training for Chinese preservice teachers' efficacy and beliefs toward bullying

AbstractBullying is prevalent in many schools worldwide. Providing bullying prevention training to preservice teachers can make a significant difference in preventing school bullying. There is a lack of training in bullying prevention for preservice teachers in China, and the effectiveness of such training for bullying prevention has not yet been established. The present study examined the effectiveness of bullying prevention training on antibullying self‐efficacy and incorrect beliefs about bullying among Chinese preservice teachers. A total of 139 preservice teachers (Mage = 20.76 years, SD = 0.80 years, ranging from 19 to 23 years; 90.6% women) were randomly assigned to the bullying prevention training (n = 73) or the control group (n = 66). Preservice teachers completed pre‐ and posttraining surveys. The participants in the treatment group completed a 5‐week bullying prevention training program (1.5 h per week). Measures of antibullying self‐efficacy and incorrect beliefs about bullying were used to evaluate the effectiveness of the bullying prevention training. Results indicated that the training increased preservice teachers' antibullying self‐efficacy but did not decrease their incorrect beliefs about bullying. Practical implications, limitations, and future directions of these findings are discussed.

Calculating High-Pressure PAO4 Viscosity with Equilibrium Molecular Dynamics Simulations

The development of optimized lubricants is hindered by missing knowledge of fluid properties, in particular the viscosity, in the range of extreme pressures and temperatures relevant for application. Molecular dynamics simulations can be used to calculate viscosity, but the necessary computational effort imposes practical limits for high viscosities. In this study, the viscosity of PAO4 oil was extracted from equilibrium molecular dynamics simulations as a function of pressure and temperature reaching viscosities up to 20 Pas. Three calculation methods based on different microscopic expressions for the viscosity were used. The methods exhibit considerably different performance with respect to preciseness and computational efficiency. The highest viscosities were found to be calculated most efficiently via the Stokes–Einstein relation, by computing the diffusion coefficient from the velocity correlation function. This offers a new, more effective route to push viscosity calculations in equilibrium molecular dynamics simulations to higher pressure systems.Graphical

Integrating image and gene-data with a semi-supervised attention model for prediction of KRAS gene mutation status in non-small cell lung cancer.

KRAS is a pathogenic gene frequently implicated in non-small cell lung cancer (NSCLC). However, biopsy as a diagnostic method has practical limitations. Therefore, it is important to accurately determine the mutation status of the KRAS gene non-invasively by combining NSCLC CT images and genetic data for early diagnosis and subsequent targeted therapy of patients. This paper proposes a Semi-supervised Multimodal Multiscale Attention Model (S2MMAM). S2MMAM comprises a Supervised Multilevel Fusion Segmentation Network (SMF-SN) and a Semi-supervised Multimodal Fusion Classification Network (S2MF-CN). S2MMAM facilitates the execution of the classification task by transferring the useful information captured in SMF-SN to the S2MF-CN to improve the model prediction accuracy. In SMF-SN, we propose a Triple Attention-guided Feature Aggregation module for obtaining segmentation features that incorporate high-level semantic abstract features and low-level semantic detail features. Segmentation features provide pre-guidance and key information expansion for S2MF-CN. S2MF-CN shares the encoder and decoder parameters of SMF-SN, which enables S2MF-CN to obtain rich classification features. S2MF-CN uses the proposed Intra and Inter Mutual Guidance Attention Fusion (I2MGAF) module to first guide segmentation and classification feature fusion to extract hidden multi-scale contextual information. I2MGAF then guides the multidimensional fusion of genetic data and CT image data to compensate for the lack of information in single modality data. S2MMAM achieved 83.27% AUC and 81.67% accuracy in predicting KRAS gene mutation status in NSCLC. This method uses medical image CT and genetic data to effectively improve the accuracy of predicting KRAS gene mutation status in NSCLC.

Optimizing Patient Recruitment in Global Clinical Trials using Nature-Inspired Metaheuristics

A common problem seen in the ineffective execution of global multicenter trials is the frequent inability to recruit a sufficient number of patients. A myriad of barriers to recruiting enough patients timely exist and may include practical limits on the number of recruiting sites imposed by the various countries, as well as administrative, cost and unanticipated issues. The Poisson-gamma recruitment model is a widely accepted statistical tool used to predict and track recruitment at different levels of a trial and make inference. An optimal recruitment plan can be designed using mathematical optimization, however, the search is complicated with multiple nonlinear objectives and constraints that arise from regulatory and budgetary considerations. We review nature-inspired metaheuristic algorithms which are powerful, flexible, general purpose optimization tools and demonstrate their capability and utility in optimizing complex recruitment designs for global clinical trials using the Poisson-gamma model as an exemplary model. This research opens up new avenues for improving the efficiency and effectiveness of patient recruitment in clinical trials, thereby potentially accelerating the development of new medical treatments.

Optimizing the Maastricht Work-Related Support intervention in clinical patient care: the value of integrating action research into intervention mapping

BackgroundHealthcare professionals (HCPs) are increasingly recommended to play an important role in supporting people with chronic disease in work participation. An intervention for HCPs to provide work-related support to their patients in clinical care was developed with intervention mapping (Maastricht Work-Related Support; Maastricht WRS). Action research proposes ‘combining research and practice’, which allows us to incorporate experiences of HCPs while implementing and to realize intervention’s full potential. Therefore, the aim of this study is to explore, by integrating action research into an intervention mapping approach, how experiences of HCPs with early implementation can be used to optimize the Maastricht WRS in clinical care.MethodsSemi-structured interviews were held with nine HCPs (response rate 82%), involved in care for people with inflammatory arthritis, knee problems or inflammatory bowel disease. Some of them were not yet trained in the Maastricht WRS while others had received the training and were providing the Maastricht WRS.ResultsAll participants regarded WRS an important part of clinical care. Untrained HCPs indicated a lack of knowledge and skills in providing the Maastricht WRS, and a need for tools. Trained HCPs were satisfied with the training and tools, but stressed that practical limitations hindered providing the Maastricht WRS. Action research showed that the intervention meets the needs of HCPs, but need some optimizations: (1) organizing ‘intervision’ for HCPs, (2) inform and activate patients to discuss work with their HCP, (3) update initial tools and (4) including patients’ work status in the electronic patient system.ConclusionsAction research integrated into intervention mapping proved to improve the Maastricht WRS intervention. By involving HCPs, the intervention could be optimized to provide to support people with chronic diseases in clinical care in healthy and sustainable work participation.

SEMANTIC POINT CLOUD SEGMENTATION IN URBAN ENVIRONMENTS WITH 1D CONVOLUTIONAL NEURAL NETWORKS

Abstract. Convolutional Neural Networks (CNNs) have been widely recognized for their efficacy in image analysis tasks. This paper investigates the application of the 1D-CNN variant CNNs for the semantic segmentation of urban point clouds obtained through Mobile Laser Scanning. Ten well-known local geometric features of point clouds were used as input for the 1D CNN. Through an empirical analysis on the Santiago Urban Dataset, the 1D CNN was optimized in terms of numbers of convolution layers, neurons, pooling layers, dropout layers, dense layers, training epochs, and batch size. The performance of the proposed 1D CNN was compared with Support Vector Machine (SVM), Random Forest (RF), and PointNet++. Despite demonstrating a F1-score weighted at 70.3%, outperforming SVM but slightly lagging RF (71.6%) and significantly trailing PointNet++ (90.3%), the proposed 1D-CNN showcases a cost-effective potential for the segmentation of road and building classes. The relative computational requirements of the models were also discussed, highlighting the practical advantages and limitations of each approach.

Evolutionary neuron-level transfer learning for QoT estimation in optical networks

To ensure the reliability and efficiency of lightpath transmission in optical networks, it is essential to predict the quality of transmission (QoT) of the candidate lightpaths before their deployment. An artificial neural network (ANN) becomes a promising solution for QoT estimation of lightpaths, owing to its powerful data fitting capability and scalability. As a supervised learning model, an ANN requires a large set of training samples from lightpaths’ observations to ensure its accuracy. Unfortunately, the acquisition of the training samples is hindered by practical limitations, such as the shortage of monitorable lightpaths in the early stage of optical network deployment or/and the absence of optical performance monitors at partial network nodes. Therefore, how to obtain a high-precision QoT estimator with small-sized datasets is challenging. In this paper, we propose an evolutionary neuron-level transfer learning (ENTL) scheme for QoT estimation to improve the accuracy of the ANN model with small-sized datasets. In the ENTL-based QoT estimator, the minimal unit of knowledge transfer is the neuron of the ANN model, and the particle swarm optimization (PSO) algorithm is introduced to determine the trainable neurons and the frozen neurons, where the testing dataset generated by data augmentation assists the PSO algorithm to evaluate the feasible solutions. Simulation results show that the ENTL-based QoT estimator achieves higher accuracy than the traditional layer-level transfer learning (LTL)-based QoT estimator. And, when the ENTL-based QoT estimator is applied to optical network planning, it improves the reliability and throughput of optical networks compared with the LTL-based QoT estimator.

ENHANCING TONGUE REGION SEGMENTATION THROUGH SELF-ATTENTION AND TRANSFORMER BASED

As an essential component of traditional Chinese medicine diagnosis, tongue diagnosis has faced limitations in clinical practice due to its subjectivity and reliance on the experience of physicians. Recent advancements in deep learning techniques have opened new possibilities for the automated analysis and diagnosis of tongue images. In this paper, we collected 500 tongue images from various patients. These images were initially preprocessed and annotated, resulting in the dataset used for this experiment. This project is based on the previously proposed segmentation method using Harnessing Self-Attention and Transformer, which is divided into three key stages: feature extraction, feature fusion, and segmentation prediction. By organically combining these three key stages, our tongue region segmentation model is better equipped to handle complex tongue images and provides accurate segmentation results. The segmentation DICE coefficient reaches 0.953, which is of significant importance for the automation and objectivity of tongue diagnosis in traditional Chinese medicine.

Biofertilizer and biocontrol properties of Stenotrophomonas maltophilia BCM emphasize its potential application for sustainable agriculture.

Microbial biofertilizers or biocontrol agents are potential sustainable approaches to overcome the limitations of conventional agricultural practice. However, the limited catalog of microbial candidates for diversified crops creates hurdles in successfully implementing sustainable agriculture for increasing global/local populations. The present study aimed to explore the wheat rhizosphere microbiota for microbial strains with a biofertilizer and biocontrol potential. Using a microbial culturing-based approach, 12 unique microbial isolates were identified and screened for biofertilizer/biocontrol potential using genomics and physiological experimentations. Molecular, physiological, and phylogenetic characterization identified Stenotrophomonas maltophilia BCM as a potential microbial candidate for sustainable agriculture. Stenotrophomonas maltophilia BCM was identified as a coccus-shaped gram-negative microbe having optimal growth at 37°C in a partially alkaline environment (pH 8.0) with a proliferation time of ~67 minutes. The stress response physiology of Stenotrophomonas maltophilia BCM indicates its successful survival in dynamic environmental conditions. It significantly increased (P <0.05) the wheat seed germination percentage in the presence of phytopathogens and saline conditions. Genomic characterization decoded the presence of genes involved in plant growth promotion, nutrient assimilation, and antimicrobial activity. Experimental evidence also correlates with genomic insights to explain the potential of Stenotrophomonas maltophilia BCM as a potential biofertilizer and biocontrol agent. With these properties, Stenotrophomonas maltophilia BCM could sustainably promote wheat production to ensure food security for the increasing population, especially in native wheat-consuming areas.

Exploring the Impact of Lesson Study in Vietnamese Primary Education: Perspectives of EFL Lecturers

This qualitative study investigates the incorporation of Lesson Study, a pedagogical approach from Japan, into Vietnamese primary education, with a specific focus on the perspectives of Vietnamese English as a Foreign Language (EFL) lecturers. These educators, though not directly involved in primary education, engage with it through various roles like teacher training and curriculum development. The research is based on in-depth interviews with nine EFL lecturers from two educational institutions in Vietnam. Employing thematic analysis, the study identifies five key themes: collaborative enhancement of teaching practices, navigating cultural and systemic challenges, improved understanding of student learning needs, professional growth and empowerment, and challenges such as resistance to change and time constraints. These themes underscore the potential of Lesson Study in fostering collaborative and reflective teaching practices, as well as professional development. However, they also highlight the obstacles imposed by traditional educational norms and practical limitations. The study emphasizes the necessity of support structures and resources for effective implementation of Lesson Study, advocating for adaptations to align with local educational contexts and cultural values. Contributing to the discourse on professional development in EFL teaching, this research underscores the importance of collaborative, reflective practices in educational innovation. Moreover, it stresses the significance of considering unique cultural and systemic elements in applying pedagogical methods like Lesson Study. Offering specific insights into its application in the Vietnamese primary education context, this study not only enhances understanding of cross-cultural educational transfer but also provides practical guidance for educators and policymakers. It paves the way for further research in diverse educational settings, expanding the international conversation on educational practices.

76 An in-vitro analysis of hemp as feed for beef cattle

Abstract Hemp, Cannabis sativa L., is a versatile crop that has been cultivated for centuries. Cannabis production had been outlawed in the United States due to concerns related to tetrahydrocannabinol (THC) intoxication. However, recent legislation has declassified hemp as a schedule 1 drug under regulated conditions (i.e., THC &amp;lt; 0.3%). Thus, hemp has been reclassified as an agricultural commodity, and its production for fiber, food, and medicine has increased. Hemp cultivation results in by-products that are not utilized for human needs and have potential as livestock feed, especially for ruminants. Current regulations restrict the use of hemp and its by-products in livestock systems due to the presence of cannabinoids. Thus, research on the nutritional value of hemp for cattle is lacking. The objective of this study was to characterize the nutrient concentrations, in-vitro digestibility (IVD), and cannabinoid concentrations of different fractions of the hemp plant. Procedures involving animals were approved by IACUC at Texas State University (#8497). Four replications of hemp plants of the Cherry Soda varietal were cultivated at Texas State University. Samples were air-dried and fractionated into lower (n = 4), leaves (n = 4), stalks (n = 4), and whole plant (n = 4). Dry matter, organic matter (OM), acid detergent fiber (ADF), neutral detergent fiber (NDF), and crude protein (CP) were determined. For IVD, samples were incubated in digestion vessels containing buffers and rumen fluid at 39.5°C for 48 h and subsequently analyzed for NDF. Cannabinoid analysis was conducted via high-performance liquid chromatography (HPLC). The OM was in acceptable ranges for each hemp fraction (69-95%). The NDF and ADF were within normal ranges of conventional livestock feeds and forages for most fractions, except hemp stalk (81% NDF, 69% ADF). There was high variability in CP between fractions, with the least concentrations observed in the stalk (4%) and greatest in the flower (25%). Stalks had an IVD of 21%, whole plant of 67%, flower of 87%, and leaves of 93%. Cannabinoids were present in each fraction and especially concentrated in the flower (14%) and depleted in the stalk (0.06%). Flower had THC of 0.56%, while other fractions were less than the 0.3% legal threshold. Similarly, total cannabidiol (CBD) ranged from 0.05-11.4% with the greatest concentrations in the flower and least in the stalk. Our results indicate there is potential for hemp to be integrated into cattle production, especially the leaves. Despite adequate nutritional value, there are practical limitations with feeding hemp flower to cattle. In-vivo trials are needed to assess digestibility and profile cannabinoid metabolism in various tissues before hemp can be integrated into cattle diets.

The Role of Rotated Potential Mixing Protocols on the Behavior of a Conservative Reagent

AbstractChaotic advection is defined as the generation of “small‐scale structures” from the repeated stretching and folding of fluid elements in a laminar flow regime that has the potential to enhance mixing and improve treatment effectiveness. Rotated potential mixing (RPM) flow is one configuration used to invoke chaotic advection and involves periodically re‐oriented dipole flow through the transient switching of pressures at a series of radial wells. In this study, we relied on conventional models used by remediation practitioners to represent the expected flow and transport behavior of a conservative reagent subjected to chaotic advection by an RPM flow system, and then explored the impact of engineering controls on reagent mixing behavior. The various lines of evidence demonstrated that this modeling approach captured the key features of the expected transport behavior reported in other studies. Visual observations of the reagent distribution, and quantitative metrics of mixing behavior highlighted the different responses that are possible by the various combinations of RPM flow parameters explored. The results show the importance of combining theoretical considerations with practical limitations when designing an RPM flow system. The flow rate and pumping duration have direct consequences on the degree of reagent spreading and mixing. The use of the same RPM flow protocol in a heterogeneous K field led to a significantly greater degree of reagent mixing than in a homogeneous K system. The results from this investigation have important implications for the design of RPM flow protocols to promote enhanced reagent mixing and thereby improve treatment effectiveness.

Clinical Applications, Challenges, and Recommendations for Artificial Intelligence in Musculoskeletal and Soft-Tissue Ultrasound: AJR Expert Panel Narrative Review.

Artificial intelligence (AI) is increasingly used in clinical practice for musculoskeletal imaging tasks, such as disease diagnosis and image reconstruction. AI applications in musculoskeletal imaging have focused primarily on radiography, CT, and MRI. Although musculoskeletal ultrasound stands to benefit from AI in similar ways, such applications have been relatively underdeveloped. In comparison with other modalities, ultrasound has unique advantages and disadvantages that must be considered in AI algorithm development and clinical translation. Challenges in developing AI for musculoskeletal ultrasound involve both clinical aspects of image acquisition and practical limitations in image processing and annotation. Solutions from other radiology subspecialties (e.g., crowdsourced annotations coordinated by professional societies), along with use cases (most commonly rotator cuff tendon tears and palpable soft-tissue masses), can be applied to musculoskeletal ultrasound to help develop AI. To facilitate creation of high-quality imaging datasets for AI model development, technologists and radiologists should focus on increasing uniformity in musculoskeletal ultrasound performance and increasing annotations of images for specific anatomic regions. This Expert Panel Narrative Review summarizes available evidence regarding AI's potential utility in musculoskeletal ultrasound and challenges facing its development. Recommendations for future AI advancement and clinical translation in musculoskeletal ultrasound are discussed.

The practical limits of packer testing

Packer testing allows estimation of the hydraulic conductivity of the ground through analysis of an induced flow rate into the ground under various controlled water pressures. Consideration of the physical and practical limits of injecting and monitoring such flows and pressures is used to determine the limits of hydraulic conductivity that can reasonably be estimated from packer tests. A design chart is presented based on these practical limits and consideration of the fundamental mathematics underpinning packer test interpretation. The chart is proposed as a useful reference when planning or reviewing field testing programs.

Contemporary Archaeologies and Cultural Heritage in the Anthropocenic Age

The era in which the distinction between natural processes and human activity was clear has passed. Since at least the 'Great Acceleration' of the mid-20th century, we have entered a new phase where environmental changes, unprecedented in scale, are no longer purely natural. Instead, they stem from the growth of a hybrid aggregate, both natural and artificial. Consequently, things and places can no longer remain unchanged; they do not adhere to our previous conceptions. 'Non-human' entities now respond to our actions, rendering them inherently cultural and anthropogenic. Operating within the present, these entities not only act but also accumulate a form of material memory over time. Even seemingly inert matter is alive, facing the challenges of the Anthropocene: an era characterized by devastation and the destruction of material memory. Thus, the concept of heritage takes on new significance: what does it mean now, and what purpose does it serve? How do we define saving, protecting, or even acknowledging what we continue to call archaeological heritage? The most profound transformations of the Anthropocene are yet to unfold, underscoring the limitations of archaeological practice, which primarily focuses on human creations at a human scale.

Vaccination impact on impending HIV-COVID-19 dual epidemic with autogenous behavior modification: Hill-type functional response and premeditated optimization technique.

An HIV-COVID-19 co-infection dynamics is modeled mathematically assimilating the vaccination mechanism that incorporates endogenous modification of human practices generated by the COVID-19 prevalence, absorbing the relevance of the treatment mechanism in suppressing the co-infection burden. Envisaging a COVID-19 situation, the HIV-subsystem is analyzed by introducing COVID-19 vaccination for the HIV-infected population as a prevention, and the "vaccination influenced basic reproduction number" of HIV is derived. The mono-infection systems experience forward bifurcation that evidences the persistence of diseases above unit epidemic thresholds. Delicate simulation methodologies are employed to explore the impacts of baseline vaccination, prevalence-dependent spontaneous behavioral change that induces supplementary vaccination, and medication on the dual epidemic. Captivatingly, a paradox is revealed showing that people start to get vaccinated at an additional rate with the increased COVID-19 prevalence, which ultimately diminishes the dual epidemic load. It suggests increasing the baseline vaccination rate and the potency of propagated awareness. Co-infection treatment needs to be emphasized parallelly with single infection medication under dual epidemic situations. Further, an optimization technique is introduced to the co-infection model integrating vaccination and treatment control mechanisms, which approves the strategy combining vaccination with awareness and medication as the ideal one for epidemic and economic gain. Conclusively, it is manifested that waiting frivolously for any anticipated outbreak, depending on autogenous behavior modification generated by the increased COVID-19 prevalence, instead of elevating vaccination campaigns and the efficacy of awareness beforehand, may cause devastation to the population under future co-epidemic conditions.

Performance enhancement of a Box-Wing airliner with the application of riblets

The rapid growth of the commercial aviation sector in recent years, as well as the ambitious emission reduction targets, necessitate the investigation of novel methods to improve the aerodynamic efficiency of future airliners. With increasing passenger demand and evolving industry requirements, innovative designs, like the Box-Wing aircraft configuration, and flow control techniques, such as riblets, are essential to enhance efficiency, reduce fuel consumption and emissions, and meet future aviation needs. In this work, the performance enhancement of a novel Box-Wing airliner with the application of riblets is investigated through CFD modeling. The riblets are small, streamwise grooves aligned with the airflow, which when applied correctly, can reduce the turbulent skin friction drag. The riblets installed on the aircraft are modeled through a dedicated surrogate model, based on the cross-section area of their groove. In this study, both optimal size riblets, as well as constant size riblets, have been examined, assessing the performance degradation associated with the practical application limitations. The results show that the riblets can improve the aircraft’s aerodynamic characteristics, with a maximum drag reduction of 60 drag counts, as well as the overall flight performance, providing a maximum increase of 6.4% in payload and 13.3% in range.