Abstract: Large amounts of high-quality data is required for the training of Artificial Intelligence (AI) models, which are indeed cumbersome to curate and perform quality assurance via human intervention. Moreover, models trained using erroneous data (human errors, data faults) can cause significant problems in real-world applications. This paper proposes an automated cleaning framework and quality assurance strategy for 2D object detection datasets. The proposed cleaning method was designed according to the ISO/IEC 25012 data quality standards, and uses multiple AI models to filter anomalies and missing data. In addition, it balances out the statistical unevenness in the dataset, such as the class distribution and object size distribution. Thereby ensuring the quality of the training dataset and examining the relationship between the amount of data required for enhanced performance in terms of detection. The experiments were conducted using popular datasets for autonomous driving, including KITTI, Waymo, nuScenes and publicly available datasets from South Korea. An automated data cleaning framework was employed to remove anomalous and redundant data, resulting in a reliable dataset for training. The automated data pruning and assurance system demonstrated the ability to substantially decrease the time and resources needed for manual data inspection.

Abstract: This study examines the effectiveness of in-service training programs aimed at enhancing teachers' knowledge and self-efficacy in the context of Learning Disabilities in Mathematics (LD). Despite the increasing use of both interactive online learning and face-to-face training methods in professional development, limited research has compared their relative effectiveness in this specific field. Furthermore, existing studies have not adequately addressed whether improvements in teachers' knowledge and self-efficacy are sustained over time. To address this gap, the present study employs a quasi-experimental design with two experimental groups. The sample consists of 80 classroom teachers, with 40 participants in the interactive online learning education group and 40 in the face-to-face education group. The training program consists of 16 hours of instruction over four weeks. Data were collected using a demographic questionnaire and the Mathematics Learning Disability Area Teacher Self-Efficacy Scale, and statistical analyses were conducted. Findings indicate that, prior to the intervention, teachers in the interactive online learning education group exhibited significantly higher levels of knowledge and self-efficacy. However, post-intervention results revealed no statistically significant differences between the two groups. Cohen’s d analysis indicated a moderate effect size for interactive online learning education before the intervention, which diminished to a small effect size afterward. These findings suggest that both training modalities effectively improve teachers’ knowledge and self-efficacy, yet neither demonstrates a clear long-term advantage. The study underscores the need for further research to determine optimal strategies for sustaining professional development in this domain.

Abstract: Uniformity in material coating is not only essential for ensuring durability and long-term reliability but also for reducing costs, optimizing resources, and maintaining high-quality standards in industrial applications. Zinc phosphate is notable for coating steel surfaces due to its excellent corrosion resistance and adhesion properties in various industries. This study investigates the optimal flow rate of a diaphragm pump for achieving effective and uniform coating of a steel cylinder. The coating uniformity was assessed using Scanning Electron Microscopy (SEM), focusing on layer thickness and elemental composition. A range of flow rates was analyzed to determine their influence on coating quality and regularity, with Energy Dispersive Spectroscopy (EDS) revealing the distribution and homogeneity of the applied layer. The results identified a flow rate of 30 L/min as optimal, as it minimized surface defects and ensured consistent thickness across the cylinder. This study provides valuable insights for optimizing industrial coating processes, contributing to improved efficiency and reduced resource waste.

Abstract: Objectives: Finding correlations between vitamin D deficiency and thyroid autoimmune pathology in a group of patients from Dobrogea, the non-endemic geographical area, with a high degree of sunshine.An important factor in maintaining immunological balance is represented by an adequate level of vitamin D, multiple studies suggesting that vitamin D deficiency is associated with a higher incidence of autoimmune diseases. Recent studies analyze the possible effect of this factor in promoting autoimmunity, the serum level of BAFF being often increased among patients with systemic autoimmune diseases. Methods : The study included 80 patients with autoimmune thyroid pathology, from Dobrogea area. The entire study group (n = 80) was divided according to the established diagnosis into two study groups: Group 1 – including 62 patients with CAT and Group 2 – including 18 patients with GD. Results: Vitamin D study average values of 25-OH-vitamin D found statistically significant differences between vitamin D values in the two groups (p = 0.018). Determination of BAFF serum levels among patients with CAT and GD obtained a lower mean value of BAFF for the CAT group compared to GD group. The evolution of BAFF serum level related to the serum levels of the antithyroid antibodies ATPO and ATG was also analyzed. For patients with GD, BAFF was not correlated with the value of ATPO or ATG, but in the patients with CAT, a correlation was found between the value of BAFF and the level of ATG but not with ATPO level. Conclusions: The study analyzed BAFF serum levels in patients with CAT and respectively GD as a result of that BAFF acts as a stimulatory factor of immunoglobulin production in autoimmune diseases. These results require clarifying the role and therapeutic benefits of supplementing vitamin D intake in patients with autoimmune diseases.

Abstract: This study examines spontaneous activity in children aged 3-11 years with Specific Language Impairment (SLI) using electroencephalogram (EEG). We compared SLI diagnosed children with a normo-development group (ND). Signal complexity Multiscale Entropy (MSE) and parameterized Spectral Power Density (FOOOF) were analyzed, decomposing the PSD into its aperiodic (AP, proportional to 1/fx) and periodic (P) components. The results show increases in complexity across scales in both groups. Although topographic distributions were similar, children with SLI exhibited higher lateralized exponent parameter values in both hemispheres, along with an increased AP component over a broad frequency range (13–45 Hz) in medial regions. The P component shows differences in brain activity according to frequency and region. At 9–12 Hz, ND presents greater central-anterior activity, whereas in SLI, it is posterior-central. At 33–36 Hz, anterior activity is greater in SLI than in ND. At 37–45 Hz, SLI shows greater activity than ND, with a specific increase in the left, medial, and right regions at 41–45 Hz. These findings suggest alterations in the excitatory-inhibitory balance and impaired intra and interhemispheric connectivity, indicating difficulties in neuronal modulation possibly associated with the cognitive and linguistic characteristics of SLI.

Abstract: Digital nomads constitute a recently emerged group of remote workers and tourists with growing trends that indicate its significance in tourism and local development. However, research on the criteria of destination selection and level of visit satisfaction is rather lim-ited in clarity and consistency. This paper aims to contribute to the academic debate by presenting the digital nomads’ destination selection criteria identified in the state of the art and introducing a methodological tool of priority areas. The tool is applied in the case study of Thessaloniki, the second largest Greek city and developing destination. Both qualitative and quantitative methodologies are used in the primary research, with relation to local stakeholders and digital nomads while secondary research focuses on measure-ments of specific criteria through established indexes. Research findings point to the po-tential validity of the proposed methodological tool, highlighting the requirement for fur-ther research on its refinement.

Abstract: Hypertension disorders of pregnancy affect almost 10% of pregnancies. Most hypertensive disorders associated with pregnancy, including chronic hypertension and gestational hypertension, often persist into the postpartum period. Thus, many breastfeeding mothers require ongoing antihypertensive treatment with antihypertensive medications while nursing. This highlights the importance of understanding the efficacy, safety, and potential adverse effects of antihypertensive therapy in breastfeeding mothers. Unfortunately, research in this area is limited, and references in clinical guidelines remain sparse. Our review aims to provide a comprehensive summary of the current knowledge on antihypertensive medications during breastfeeding, drawing from available research and evidence-based guidelines. This article discusses all groups of antihypertensive drugs, presenting societies' recommendations and available clinical data. Based on the available literature, calcium channel blockers (nifedipine as the first choice) and diuretics and beta-blockers (labetalol, metoprolol, propranolol) appear to be the drugs of choice. Our review highlights the need for further research to evaluate the long-term safety of antihypertensive medications during breastfeeding, improve clinical guidelines, and ensure optimal treatment for nursing mothers.

Abstract: The deployment of utility-scale hybrid wind-solar PV power plants is gaining global attention due to their enhanced performance in power systems with high renewable energy penetration. To assess their potential, accurate estimations must be derived from available data, addressing key challenges such as: (1) spatial and temporal resolution requirements, particularly for renewable resource characterization; (2) energy balances aligned with various business models; (3) regulatory constraints (environmental, technical, etc.); and (4) cost dependencies of different components and system characteristics. When conducting such analyses at regional or national scales, a trade-off must be achieved to balance accuracy with computational efficiency. This study reviews existing experiences in hybrid plant deployment, with a focus on Spain, and proposes a simplified methodology for country-level analysis.

Abstract: As conventional oil resources decline, optimizing the development of tight reservoirs has become critical for sustaining production. Horizontal wells with artificial fractures offer a promising solution, but improper water injection often leads to uneven waterflooding, particularly in irregular horizontal-vertical well systems—a common challenge in fields like China’s Fuxian oilfield. This study tackles this issue by introducing a practical and effective method to optimize water injection flow rates, significantly enhancing oil recovery in such complex well patterns. Through advanced numerical modeling and three-dimensional flow visualization, we analyze sweep efficiency and water breakthrough risks, categorizing the horizontal well’s drainage area into three distinct regions, each requiring tailored injection rates. Using a representative model with one horizontal well and three vertical wells, we demonstrate that adjusting the flow rate ratio among injectors to 6:3:1 (instead of a uniform 1:1:1) boosts cumulative oil production by an additional 2997.6 m³. These findings provide field engineers with a actionable strategy to improve waterflooding efficiency, directly increasing recoverable reserves and economic viability in tight reservoirs. The proposed approach has immediate relevance for oilfield operations, offering a scalable solution to maximize recovery in similar unconventional reservoirs worldwide.

Abstract: The aim of this study was to investigate the effect of base material, restorative material and finish line on marginal and internal fit and fracture strength (FS) of endocrowns (N = 64). The restorative materials were ceramic and hybrid ceramic, the preparations were ferrule and modified butt joint (BJ) with two grooves, and the bases were with and without fiber ribbon. Marginal and internal fit were assessed using the triple scan. Statistical analyses were performed using three-way ANOVA. The absolute marginal discrepancy (AMD), marginal discrepancy and overall fit values obtained for ceramic were 127 µm, 108 µm and 120 µm, whereas those obtained for hybrid ceramic were 139 µm, 116 µm and 130 µm, respectively (p < 0.05). The overall FS obtained for ceramic was 662 N, whereas that for hybrid ceramic was 903 N (p < 0.001). When the material was evaluated regardless of preparation and base, ceramic and hybrid ceramic exceeded the selected clinical acceptability threshold for AMD, but did not exceed it for other parameters. Hybrid ceramic restorations with ferrules and fiber bases tended to provide the highest FS, whereas ceramic restorations with modified BJs and without fiber bases tended to provide the lowest FS.

Abstract: Commodity operating systems often lack sufficient security mechanisms to defend against sophisticated attacks, resulting in applications being vulnerable to attacks that compromises sensitive data and in turn involves in additional protection layers that increase software complexity and costs. To address these challenges, I introduce HBSP (Hypervisor-Based Software Protector), a lightweight and flexible solution that leverages Intel’s VT (Virtualization Technology) to provide enhanced security. HBSP operates entirely outside the host OS environment, using advanced memory-hiding techniques to protect sensitive data and application code from both the host OS and potential malicious actors. Unlike traditional approaches, HBSP requires no modifications to existing operating systems or applications. Its dynamic concealment of the hypervisor makes it harder for attackers to bypass protection mechanisms. Performance evaluations show minimal overhead (0.25% impact on application performance), making HBSP suitable for real-time and performance-critical applications. Moreover, it is extensible across various hardware virtualization platforms, ensuring broad applicability across diverse environments. HBSP offers a scalable, practical solution for improving software security without significant infrastructure changes or performance trade-offs.

Abstract: Every organism exhibits slight differences in DNA sequences among individuals of the same species, making each one unique. These variations, known as genetic variants, can influence phenotypic traits or susceptibility to diseases. Some genetic variations may provide advantages, such as resistance to pathogens, while others can have detrimental effects, leading to disease. Genome-wide association studies (GWAS) identify associations between single-nucleotide polymorphisms (SNPs) and phenotypic traits, helping to uncover genetic variants that contribute to specific phenotypic expressions. Several statistical models have been developed to enhance the power of GWAS in detecting true genetic associations while minimizing false positives and false negatives. In this study, we compared the power of single-locus and multi-locus statistical models and investigated how varying window sizes around quantitative trait nucleotides (QTNs) affect model performance, using genotypic data from maize (Zea mays L.) and a simulated phenotype. Our results revealed clear differences among models: the General Linear Model (GLM) exhibited severe p-value inflation and excessive false positives, while the Multiple Loci Mixed Model (MLMM) identified the most true QTNs (7/20) while controlling Type I error and false discovery rates. BLINK proved computationally efficient, detecting six true QTNs and outperforming other multi-locus models in runtime. Notably, increasing the window size from 1,000 to 10,000 bp had minimal impact on power, highlighting that method selection—not window size—is critical for accuracy. Thus, MLMM and BLINK offer an optimal balance of statistical rigor and efficiency for maize GWAS, providing actionable insights to enhance trait-marker association studies.

Abstract: Natural autoantibodies (nAAbs) recognize self-antigens and are an important component of the immune system have evolved from invertebrates to vertebrates and are viewed as stable by-products of immune function to essential players in health and disease. Initially characterized by their conserved nature and multi-reactivity, primarily as IgM isotypes, nAAbs are now recog-nized for their adaptability in response to infections and vaccinations, bridging innate and adaptive immunity. The nAAbs and the cellular elements, such as γδ-T, iNKT, and MAIT cells of the natural immune system, perform a primary defense network with moderate antigen-specificity. This comprehensive literature review was conducted to analyze the role of natural autoantibodies (nAAbs) in health and disease. The review focused on research published over the past 40 years, emphasizing studies related to infectious diseases, vaccinations, and autoimmune disorders. Recent studies suggest that nAAbs engage in complex interactions in autoimmune diseases, including systemic lupus erythematosus, rheumatoid arthritis, systemic sclerosis, and type 1 diabetes. Their roles in immunological processes, such as maternal tolerance during pregnancy, further underscore their complexity. Emerging evidence indicates that nAAbs and the cellular elements of the natural immune system may contribute to both disease pathogenesis and protective mechanisms, highlighting their dual nature. Continued research on nAAbs is vital for improving our understanding of immune responses and developing therapeutic strategies for autoimmune disorders and infectious diseases.

Abstract: Leather biodegradation is a microbially driven process of increasing interest for the development of sustainable waste management strategies. In this study, bacterial communities involved in the biodegradation of leather tanned with different agents (chrome, zeolite, Biole®) were characterised using high-throughput sequencing. Taxonomic profiling based on 16S rRNA gene amplification revealed that Proteobacteria, Bacteroidetes, and Patescibacteria were the dominant phyla across samples. Functional analysis, carried out through metatranscriptomic sequencing of RNA molecules, identified a total of 1,302 expressed enzymes, of which 46 were classified as proteases. The most abundant proteases included Endopeptidase La, Endopeptidase Clp, and Methionyl aminopeptidase. Although collagen samples exhibited the lowest bacterial diversity, they showed the highest total enzyme expression, whereas chrome-treated samples displayed increased protease activity, indicating selective pressure associated with heavy metal content. Additionally, distinct functional enzyme sets were found to be either shared among or exclusive to specific tanning treatments. Genera such as Acinetobacter, Pseudomonas, and Sphingopyxis were identified as key contributors to enzymatic activity and potential metal resistance. These results provide new insights into how tanning agents shape microbial communities and their enzymatic functions, highlighting specific taxa and enzymes with potential applications in the bioremediation of leather waste and environmentally friendly processing technologies.

Abstract: This study introduces a novel Recency, Monetary, and Duration (RMD) model for customer classification in the hospitality industry. Using a hybrid approach that integrates data mining with multi-criteria decision-making techniques, the study aims to identify valuable customer segments and optimize marketing strategies.The research applies the K-means clustering algorithm to classify customers from a hotel in Iran based on RMD attributes. Cluster validation is performed using three internal indices, and hidden patterns are extracted through association rule mining. Customer segments are prioritized using the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method and Customer Lifetime Value (CLV) analysis. The outcomes revealed six distinct customer clusters identified as New customers; Loyal customers; Collective Buying Customers; Potential customers; Business customers and Lost customers. This study helps hotels to know different types of customers with spending patterns which enable hotels to tailor services and improve customer retention and provides managers appropriate tools to allocate resources efficiently. This study extends the traditional Recency, Frequency, and Monetary (RFM) model by incorporating Duration, an overlooked dimension of customer engagement. It is the first attempt to integrate data mining and multi-criteria decision-making for customer segmentation in Iran’s hospitality industry.

Abstract: Background. An inflammatory foreign-body reaction and the neosynthesis of collagen and the extracellular matrix through injectable collagen stimulators have coexisted since the introduction of the first sterile water-reconstituted poly-L-lactic acid (PLLA) formulation around the turn of the century. The PLLA-LASYNPRO™ microspheres for subdermal implants are a groundbreaking technological advancement that challenges the foreign-body reaction paradigm. The concept of non-inflammatory collagen and extracellular matrix regeneration, along with the initial insights into the rationale and role of the new-technology subdermal implants in aesthetic and regenerative medicine, was central to the discussions among thirteen distinguished experts in micro-invasive aesthetic medicine, aesthetic plastic surgery, and dermatology. This document summarizes their conclusions regarding the PLLA-LASYNPRO™ concept—subdermal microsphere implants designed to facilitate collagen and extracellular matrix regeneration while negligibly triggering persistent inflammation. Additionally, it offers preliminary yet authoritative suggestions from the board for the safe and effective use of the novel JULÄINE™ medical device based on the new-technology microspheres. Methods. An online survey of the experts, preceded by a board discussion in Milan, Italy, focused on skin regeneration and the rationale for the new PLLA technology, drawing on the board experts’ direct experience. The topics surveyed included the anticipated benefits of the new JULÄINE™ medical device and some initial suggestions for its safe and effective use. Results and Conclusions. This document outlines the board’s considerations regarding the shift, driven by the innovative PLLA-LASYNPRO™ ingredient and the CE-approved JULÄINE™ medical device, from the historically dominant FBR paradigm to a new strategy focusing on non-inflammatory collagen and extracellular matrix regeneration. Additionally, it presents practical, albeit preliminary, suggestions based on current clinical research for utilizing the new JULÄINE™ medical device and reaping its anticipated benefits.

Abstract: This study investigated the diversity and ethnobotanical use values of trees outside forests on agricultural lands (TOF-AL) in the Mongala province, Democratic Republic of Congo. To achieve this, inventories of TOF-AL, linked to surveys on ethnobotanical use values, were conducted in 45 villages across the three territories of this province (Bongandanga, Bumba and Lisala). The results identified 136 TOF-AL species, revealing significant species richness and diversity variations across three territories. Bongandanga and Lisala exhibited higher species diversity compared to Bumba. Five dominant species (Petersianthus macrocarpus (P.Beauv.) Liben., Pycnanthus angolensis (Welw.) Warb., Ricinodendron heudelotii (Baill.) Pierre ex Heckel., Erythrophleum suaveolens (Guill. & Perr.) Brenan., and Piptadeniastrum africanum (Hook.f.) Brenan.) accounted for over 50% of the total tree abundance and were widely cited for their ethnobotanical significance. P. macrocarpus, E. suaveolens, and R. heudelotii were universally recognised by local populations for their diverse uses, including medicine, food, and trade. Hierarchical clustering analysis revealed three distinct groups of preferred species based on their primary use values: (1) species valued for energy and construction, (2) species valued for crafts and trade, and (3) species valued for medicine, food, and trade. These results highlight the critical role of TOF-AL in supporting local livelihoods and underscores the importance of conserving these resources for sustainable agricultural landscapes.

Abstract: In this paper, we address the problem of obtaining bias-free and complete finite size approximations of the solution sets (Pareto fronts) of multi-objective optimization problems (MOPs). Such approximations are, in particular, required for the fair usage of distance-based performance indicators, which are frequently used in evolutionary multi-objective optimization (EMO). If the Pareto front approximations are biased or incomplete, the use of these performance indicators can lead to misleading or false information. To address this issue, we propose the Reference Set Generator (RSG), which can, in principle, be applied to Pareto fronts of any shape and dimension. We finally demonstrate the strength of the novel approach on several benchmark problems.

Abstract: Integrating artificial intelligence (AI) and mixed reality (MR) into orthopedic education has transformed learning. This review examines AI-powered platforms like Microsoft HoloLens, Apple Vision Pro, and HTC Vive Pro, which enhance anatomical visualization, surgical simulation, and clinical decision-making. These technologies improve spatial understanding of musculoskeletal structures, refine procedural skills with haptic feedback, and personalize learning through AI-driven adaptive algorithms. Generative AI tools like ChatGPT further support knowledge retention and provide evidence-based insights on orthopedic topics. AI-enabled platforms and generative AI tools help address challenges in standardizing orthopedic education. However, we still face many barriers that relate to standardizing data, algorithm evaluation, ethics, and the curriculum. AI is used in preoperative planning and predictive analytics in the postoperative period that bridges theory and practice. AI and MR are key to supporting innovation and scalability in orthopedic education. However, technological innovation relies on collaborative partnerships to develop equitable, evidence-informed practices that can be implemented in orthopedic education. For sustained impact, innovation must be aligned with pedagogical theories and principles. We believe that orthopedic medical educator's future critical role will be to enhance the next generation of competent clinicians.

Abstract: Solar radiation forecasting is critical for optimizing renewable energy systems, particularly in regions with high solar potential like Kuwait. This paper presents a theoretical framework for a hybrid forecasting system that combines fuzzy logic and neural networks to predict solar radiation with high accuracy. The proposed system leverages the Adaptive Neuro-Fuzzy Inference System (ANFIS) to handle the inherent uncertainty and variability in meteorological data. While the study is primarily theoretical due to limitations in data and resources, it provides a comprehensive review of existing methods and highlights the potential of hybrid systems for improving solar radiation forecasting. The paper concludes with a discussion of the limitations and suggests future work involving experiments to validate the proposed framework.