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In recent years, the e-commerce sector has undergone continuous adaptation to both consumer needs and the economic context. This adaptation is driven by technological advances and the development of new software products. The present study aimed at achieving two primary objectives. First, it sought to assess the current state of e-commerce development in Romania within the broader European context. Second, it identified the use of AI-driven automation as a potential strategy for improving e-commerce in the country. To this end, e-commerce indicators were extracted from the questionnaire “ICT Usage and E-commerce in Enterprises,” which was conducted by National Statistical Authorities and centralized at the level of the European Commission. The questionnaire was carried out on a sample of 157,000 companies. A range of sophisticated techniques were employed to these indicators with the aim of reducing their dimensionality and classification error, with the objective of achieving a robust classification with the lowest possible error rate. We then proceeded to analyze Romania’s position in this ranking and, given the structure of e-commerce companies in the country, proposed the use of AI-driven automation as a potential strategy for enhancing activity in this sector. Full article

(This article belongs to the Special Issue Electronic Commerce and Information Management Towards the Digital Era)

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15 pages, 719 KB

Open AccessArticle

Efficacy of Large Language Models for Screening of Systematic Reviews on Periprosthetic Joint Infection

by Woojin Shin, Jaeyoung Hong, Sunwoo Lee, Seongchan Park, Hyoungtae Kim and Suenghwan Jo

J. Clin. Med. 2026, 15(8), 2830; https://doi.org/10.3390/jcm15082830 - 8 Apr 2026

Abstract

Background: Periprosthetic joint infection (PJI) remains a devastating complication following arthroplasty. Systematic reviews of PJI provide essential evidence to inform clinical practice; however, the screening process remains labor-intensive. Recent advancements in large language models (LLMs) offer potential for automating literature screening, though evaluation of current generation models is needed. Methods: This validation study evaluated GPT-5, GPT-5 Pro, and Gemini 2.5 Pro in replicating the title/abstract and full-text screening stages of a published systematic review on intraosseous versus intravenous antibiotic prophylaxis in total joint arthroplasty. Title/abstract screening was performed on 165 articles, followed by a full-text eligibility assessment of 26 articles. Accuracy, sensitivity, specificity, and Cohen’s kappa (κ) were calculated against human screening decisions as the gold standard. Results: In title/abstract screening, GPT-5 Pro achieved the highest accuracy (92.1–92.7%) and specificity (98.6–99.3%), while GPT-5 demonstrated the highest sensitivity (84.6–96.1%). In full-text screening, Gemini 2.5 Pro showed the most consistent performance across repeated evaluations (κ = 0.839 in both trials), whereas GPT-5 Pro exhibited marked intra-model variability (κ = 0.399 to 0.920). Conclusions: Current-generation LLMs achieve near-human accuracy in systematic review screening for PJI research, though substantial intra-model variability underscores the continued need for human oversight in systematic review workflows. Full article

(This article belongs to the Section Orthopedics)

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20 pages, 6734 KB

Open AccessArticle

Time-Scale Mismatch as a Fundamental Constraint in Quantum Beam–Matter Interactions

by Abbas Alshehabi

Quantum Beam Sci. 2026, 10(2), 10; https://doi.org/10.3390/qubs10020010 - 8 Apr 2026

Abstract

Quantum beams-including X-rays, synchrotron radiation, electrons, neutrons, ions, and ultrafast photon sources-are indispensable tools for probing the structure, dynamics, and electronic properties of matter. The excitation time scale

τ_{e x c}

is defined operationally as the characteristic temporal interval governing externally imposed [...] Read more.

τ_{e x c}

is defined operationally as the characteristic temporal interval governing externally imposed energy deposition events within the interaction volume, such as pulse duration, bunch spacing, or beam dwell time. Interpretation of beam–matter interactions has traditionally relied on steady-state or quasi-equilibrium assumptions, implicitly presuming that intrinsic material relaxation processes can accommodate externally imposed excitation. Recent advances in high-brightness synchrotron sources, X-ray free-electron lasers (XFELs), and pulsed electron beams increasingly operate in regimes where this assumption is strained, and systematic nonequilibrium effects, radiation damage, and irreversible transformations are reported even under routine experimental conditions. This work examines the role of time-scale mismatch between beam-driven energy deposition and intrinsic material relaxation as a governing constraint in beam–matter interactions. Analyzing the hierarchy of excitation, electronic relaxation, phonon coupling, and thermal diffusion time scales, the analysis introduces a dimensionless mismatch parameter

Λ = \frac{τ_{r e l}}{τ_{e x c}}

, which quantifies the competition between externally imposed excitation and intrinsic relaxation processes in beam–matter interactions. The resulting framework provides a unified physical interpretation of beam-induced damage, signal distortion, dose dependence, and nonlinear response across quantum beam modalities, framing these effects as consequences of forced nonequilibrium dynamics rather than technique-specific artifacts. Full article

(This article belongs to the Section Radiation Scattering Fundamentals and Theory)

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20 pages, 1944 KB

Open AccessReview

The Gut Microbiota and Autism Spectrum Disorder: Current Research and Therapeutic Insights

by Miao Zheng, Xueying Wei, Rui Chen, Chongying Wang and Lingbiao Xin

Behav. Sci. 2026, 16(4), 559; https://doi.org/10.3390/bs16040559 - 8 Apr 2026

Abstract

Autism Spectrum Disorder (ASD) is a collective term for neurodevelopmental disorders with core features of social communication impairment, restricted and repetitive behaviors, and narrow interests. These include classic autism, Asperger’s syndrome, and pervasive developmental disorder not otherwise specified. ASD is currently managed with behavioral interventions, rehabilitation training, and family support, but there is no curative medication. Recent studies suggest that some patients with ASD may experience gastrointestinal symptoms. Perhaps this is associated with the disturbances of gut microbiota. Increasing evidence has demonstrated that the composition of gut microbiota in ASD individuals is different from that in normal population and may be associated with neurodevelopmental processes via the gut–brain axis. This article reviews the evidence for the association between gut microbiota and ASD, describes the characteristics of microbial changes, and analyzes the mechanism by which changes in the composition of the microbiota affect the occurrence and development of ASD. Finally, we review recent advances in microbiota-targeted therapeutic strategies, including probiotics, prebiotics, and fecal microbiota transplantation, which provide new approaches to alleviate and improve autism-related symptoms and point out the future research direction. Full article

33 pages, 2766 KB

Open AccessReview

Three Decades of Taxanes: Exploring the Next Frontier

by Rita I. L. Catarino, Maria Fernanda C. Leal, Adriana M. Pimenta, Maria Renata S. Souto and Francisco A. M. Silva

Sci. Pharm. 2026, 94(2), 29; https://doi.org/10.3390/scipharm94020029 - 8 Apr 2026

Abstract

Taxanes, such as paclitaxel and docetaxel, are microtubule-stabilizing agents widely used in oncology, either as monotherapy or in combination regimens. While highly effective, these first-generation taxanes face important limitations, including significant toxicity, reduced water solubility, and the emergence of multidrug resistance. To address these challenges, semi-synthetic taxoids have been developed, aiming to improve pharmacological profiles and overcome therapeutic barriers. Central to these efforts is the understanding of structure-activity relationships, which guides the rational design of taxane analogues with enhanced efficacy and safety. This review explores recent advances in taxoid development, highlights findings from clinical trials, and evaluates how these new agents compare with traditional taxanes in terms of therapeutic potential and tolerability. While novel delivery systems offer improved outcomes with existing drugs, the development of new taxane analogues remains a promising approach to address drug resistance, albeit with challenges related to toxicity, high costs, and historically low success rates in drug development. Furthermore, taxanes are already used in certain cardiovascular conditions and show emerging potential in neurodegenerative diseases, although current evidence remains largely limited to preclinical or early-phase clinical studies. These developments mark an important evolution in the field and offer new opportunities for future therapeutic strategies. Full article

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82 pages, 4790 KB

Open AccessReview

Gas Evolution and Two-Phase Flow in Water Electrolyzers: A Review

by Jingxin Zeng, Junxu Liu, Keyi Wang, Yuhang An, Yuanyuan Duan and Qiang Song

Energies 2026, 19(8), 1830; https://doi.org/10.3390/en19081830 - 8 Apr 2026

Abstract

Driven by the large-scale deployment of renewable electricity, water electrolysis has emerged as a leading pathway for high-efficiency hydrogen production. Under practical operating conditions, gas evolution and gas–liquid two-phase flow inside electrolyzers substantially reshape electrode interfacial states and the in-cell mass transfer environment and have been reported to cause performance losses on the order of 10–30% under unfavorable conditions. This review summarizes the evolution of electrode-generated bubbles during nucleation, growth, detachment, and coalescence, and consolidates the fundamental features of two-phase hydrodynamics and phase-distribution patterns in electrolyzer channels. Progress and limitations of major two-phase modeling approaches are then assessed with respect to their capability to resolve the relevant interfacial and transport processes. The impacts of gas evolution and two-phase flow on electrochemical performance, stability, and durability are subsequently discussed. Finally, recent advances in two-phase-flow management—through flow-field organization and structural design, as well as the introduction of external physical fields—are reviewed, together with experimental and diagnostic methods used to quantify bubble behavior and phase distributions. This review aims to provide a coherent understanding of the governing behaviors, research tools, and performance implications of gas evolution and two-phase flow in water electrolysis, and to inform electrode/transport-layer design, flow-field management, and the development of predictive numerical models. Full article

19 pages, 313 KB

Open AccessReview

Cognitive Diagnosis Computerized Adaptive Testing (CD-CAT) for Adolescent Internet Gaming Disorder: A Conceptual Assessment Framework

by Min Jia and Jing Liu

Behav. Sci. 2026, 16(4), 558; https://doi.org/10.3390/bs16040558 - 8 Apr 2026

Abstract

Internet Gaming Disorder (IGD) has become a major behavioral health concern among adolescents, yet current assessment tools remain limited. These tools often fail to capture the disorder’s complex symptom variations and lack clinical interpretability. This study, taking an interdisciplinary approach that combines clinical psychology and psychometrics, summarizes recent progress in understanding adolescent IGD and the development of its assessment methods. We compare the diagnostic criteria of the DSM-5 TR and ICD-11 and argue that the nine DSM-5 TR criteria are particularly suited for transformation into distinct diagnostic attributes due to their detailed and actionable nature. We then review the strengths and weaknesses of Classical Test Theory (CTT), Item Response Theory (IRT), and Cognitive Diagnostic Models (CDMs) in assessing IGD. The review emphasizes the limitations of total-score and single latent-trait approaches in capturing the disorder’s multidimensional symptoms. Based on these insights, we propose a conceptual assessment framework, Cognitive Diagnosis Computerized Adaptive Testing (CD-CAT), that integrates CDMs with computerized adaptive testing. Rather than presenting an empirically validated system, this framework offers a theoretically grounded proposal that specifies the key components, logical relationships, and methodological pathways necessary for advancing precision assessment of adolescent IGD. CD-CAT uses a system of attributes and a Q-matrix based on the DSM-5 TR criteria to efficiently classify IGD symptoms in adolescents, reducing the number of items required while enhancing clinical relevance. Lastly, we discuss the theoretical contributions of the proposed framework, acknowledge its limitations as a conceptual proposal, and outline directions for future empirical research. Full article

31 pages, 1638 KB

Open AccessReview

Pseudocereals in Bakery Products: A Review of Nutritional Composition, Health Benefits and Bakery Applications

by Olivia Atudorei, Denisa Atudorei and Georgiana Gabriela Codină

Foods 2026, 15(8), 1283; https://doi.org/10.3390/foods15081283 - 8 Apr 2026

Abstract

Pseudocereals are naturally gluten-free crops because they do not contain gluten-forming proteins which are present in other grains. The main pseudocereals used in bakery formulations are buckwheat, amaranth, and quinoa, because they have a balanced nutritional profile including high-quality proteins, dietary fiber, essential minerals, and bioactive compounds with antioxidant, anti-inflammatory, and cardiometabolic health-promoting effects. Due to their high nutritional value, they have increasingly been used as functional ingredients in bakery products, particularly for consumers with celiac disease, gluten intolerance, or those seeking nutritionally enhanced foods. The present paper reviews recent advances on the nutritional, functional, and technological properties of these pseudocereals, focusing on their applications in bakery products. Their influence on dough behavior, product quality, and the nutritional improvement of bread, cakes, biscuits, muffins, and other baked goods is discussed. Also, different aspects of the use of pseudocereals in gluten-free products are presented. Mentions are also made of the fact that the increasing demand for healthier and gluten-free foods highlights the possibility of using pseudocereals as promising ingredients for the development of nutritionally enriched bakery products of acceptable technological and sensory quality. Full article

(This article belongs to the Special Issue The Influence of Processing on Shaping the Sensory and Health-Promoting Qualities of Dried, Baked, or Roasted Foods)

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27 pages, 1060 KB

Open AccessSystematic Review

Advanced Technologies, Optimization Methodologies and Strategies for Distributed Energy Systems: A State-of-the-Art Systematic Review

by Ramia Ouederni, Mukovhe Ratshitanga, Innocent Ewean Davidson, Keorapetse Kgaswane and Prathaban Moodley

Energies 2026, 19(8), 1826; https://doi.org/10.3390/en19081826 - 8 Apr 2026

Abstract

Hybrid renewable energy systems (HRES) combining photovoltaic, wind, fuel cell, and energy storage technologies are becoming established as viable options for reliable, environmentally friendly distributed electricity generation. In this review, we examine the key architectures, monitoring and forecast approaches, and control systems that improve the efficiency of HRES and facilitate the just-energy transition to low-carbon power generation systems. The main optimization and decision-aware approaches, particularly the evolutionary generation algorithms and machine learning-based prediction models, are addressed with a focus on improving energy allocation, cost minimization, and increased use of clean renewable energy sources. Technical, economic, and environmental performance indicators, such as the levelized cost of energy (LCOE), net present cost (NPC), renewable fraction (RF), and CO₂ emissions reduction, have been compared to demonstrate the feasibility of various system scenarios. This paper evaluates and summarizes recent case studies from around the world and presents the best practices and the challenges they encounter, including resource availability, governance, and economic drivers. The balance of the paper demonstrates that smart forecasting with advanced energy management approaches is crucial for developing sustainable and resilient hybrid distributed power systems for the future. Full article

(This article belongs to the Section F1: Electrical Power System)

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42 pages, 10717 KB

Open AccessReview

Towards Stress-Resilient Canola via Genetic Engineering Approaches

by Ali Ijaz Ahmed, Aldrin Y. Cantila and Sheng Chen

Agronomy 2026, 16(8), 769; https://doi.org/10.3390/agronomy16080769 - 8 Apr 2026

Abstract

Climate change has adversely affected grain production and quality of canola, the second-largest oilseed crop, which contributes 13–16% of total vegetable oil. Multiple biotic and abiotic stresses significantly limit canola production due to rapid climate change, and conventional breeding alone is insufficient to meet global demand. Therefore, several advanced biotechnologies have been developed to cope with this change. Among these, genetic modification, gene editing, and RNA interference are particularly significant for rapid cultivar development in a cost-effective, efficient, and convenient way. Recent findings in gene editing applications have revealed “prospective sites”, highlighting regions amenable to precise editing without compromising canola plant growth or development. Pan-genome analyses have further guided gene editing target selection, enabling the validation of key stress-resilience genes across diverse canola cultivars, while the CRISPR-epigenetic regulatory connection enables targeted control of gene expression and trait modulation. A hypothetical application of genomic selection is also suggested, which could complement gene editing to accelerate the development of superior cultivars. Accordingly, this review focuses on the latest studies of genetic modification, gene editing, and RNA interference to strengthen canola resilience under rapid climate change and discusses the major concerns. Taken together, these genome-editing strategies offer precise approaches for improving biotic and abiotic stress tolerance, although careful consideration of both off-target effects and regulatory compliance remains essential for their practical implementation in canola improvement. Full article

(This article belongs to the Special Issue Crop Agronomic Traits and Performances Under Stress)

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19 pages, 1748 KB

Open AccessArticle

Evaluating Embedding Representations for Multiclass Code Smell Detection: A Comparative Study of CodeBERT and General-Purpose Embeddings

by Marcela Mosquera and Rodolfo Bojorque

Appl. Sci. 2026, 16(8), 3622; https://doi.org/10.3390/app16083622 - 8 Apr 2026

Abstract

Code smells are indicators of potential design problems in software systems and are commonly used to guide refactoring activities. Recent advances in representation learning have enabled the use of embedding-based models for analyzing source code, offering an alternative to traditional approaches based on manually engineered metrics. However, the effectiveness of different embedding representations for multiclass code smell detection remains insufficiently explored. This study presents an empirical comparison of embedding models for the automatic detection of three widely studied code smells: Long Method, God Class, and Feature Envy. Using the Crowdsmelling dataset as an empirical basis, source code fragments were extracted from the original projects and transformed into vector representations using two embedding approaches: a general-purpose embedding model and the code-specialized CodeBERT model. The resulting representations were evaluated using several machine learning classifiers under a stratified group-based validation protocol. The results show that CodeBERT consistently outperforms the general-purpose embeddings across multiple evaluation metrics, including balanced accuracy, macro F1-score, and Matthews correlation coefficient. Dimensionality reduction analyses using PCA and t-SNE further indicate that CodeBERT organizes code smell instances in a more structured latent representation space, which facilitates the separation of smell categories. In particular, CodeBERT achieved a macro F1-score of 0.8619, outperforming general-purpose embeddings (0.7622) and substantially surpassing a classical TF-IDF baseline (0.4555). These findings highlight the value of this study as a controlled multiclass evaluation of embedding representations and demonstrate the practical value of domain-specific representations for improving automated code smell detection and class separability in real-world software engineering scenarios. Full article

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25 pages, 3968 KB

Open AccessArticle

Explainable Data-Driven Approach for Smart Crop Yield Prediction in Sub-Saharan Africa: Performance and Interpretability Analysis

by Damilola D. Olatinwo, Herman C. Myburgh, Allan De Freitas and Adnan Abu-Mahfouz

Agriculture 2026, 16(8), 826; https://doi.org/10.3390/agriculture16080826 - 8 Apr 2026

Abstract

The increasing demand for innovative strategies in sustainable food production—driven by rapid global population growth, particularly in sub-Saharan Africa (SSA)—necessitates urgent attention to agricultural resilience. Recent technological advancements have enhanced crop productivity, post-harvest preservation, and environmentally sustainable farming practices. However, three critical bottlenecks remain: (i) the lack of accurate, maize-specific yield prediction methods tailored to SSA; (ii) limited multimodal modeling approaches capable of capturing complex, nonlinear interactions among heterogeneous data sources; and (iii) a lack of explainability mechanisms, which render high-performing models “black boxes” and hinder stakeholder trust. To address these gaps, this study presents an explainable machine learning framework for smart maize yield prediction. We integrate multimodal SSA-specific soil, crop, and weather data to capture the multi-dimensional drivers of maize productivity. Six diverse algorithms—including extreme gradient boosting (XGBoost), light gradient boosting machine (LGBM), categorical boosting (CatBoost), support vector machine (SVM), random forest (RF), and an artificial neural network (ANN) combined with a k-nearest neighbors (kNN)—were benchmarked to evaluate predictive performance. To ensure robustness against spatial heterogeneity, we employed a Leave-One-Plot-Out (LOPO) cross-validation strategy. Empirical results on unseen test data identify CatBoost as the best-performing model, achieving a coefficient of determination of (

R^{2} = ~ 76 %

), demonstrating its ability to capture complex, nonlinear relationships in agricultural data. To enhance transparency and stakeholder trust, we integrated Local Interpretable Model-agnostic Explanations (LIME), providing plot-level insights into the physiological and environmental drivers of maize yield. Together, these contributions establish a scalable and interpretable modeling framework capable of supporting data-driven agricultural decision-making in SSA. Full article

(This article belongs to the Section Artificial Intelligence and Digital Agriculture)

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18 pages, 1860 KB

Open AccessReview

Insights into Acute Pancreatitis: Pathogenesis, Diagnosis, and Management

by Silvia Carrara, Federico Cassano, Maria Terrin and Marco Spadaccini

J. Clin. Med. 2026, 15(8), 2819; https://doi.org/10.3390/jcm15082819 - 8 Apr 2026

Abstract

This narrative review integrates landmark studies, recent publications, and major clinical guidelines to highlight the current state of the art concerning acute pancreatitis, a well-known yet still challenging condition. We will focus on recent practice transitions and future perspectives arising from advances in diagnostic imaging and interventional endoscopy. Pathogenesis and etiology: We carry out an overview of the fundamental mechanisms underlying acute pancreatitis, followed by an analysis of both common and uncommon causes, along with emerging evidence regarding idiopathic forms. Diagnosis and risk stratification: We pursue two objectives: on one hand, to emphasize the enduring importance of clinical assessment in the diagnosis of acute pancreatitis; on the other, to analyze the increasingly central role that imaging has acquired over recent decades. Identification of patients at higher risk for complications or an unfavorable prognosis is crucial. Several scoring systems have been proposed over the past decades, but with limited impact on daily clinical practice. Treatment: Therapeutic approaches have undergone significant revisions over time. Our objective is to provide an overview of the current standards together with best evidence-based medical approaches, targeted and interventional therapies, with focus on the endoscopic ones. Furthermore, we want to clarify the importance of nutrition and its proper management. Conclusions: Acute pancreatitis continues to stimulate discoveries and improvements in clinical management. We will place emphasis on unmet needs and emerging innovations that may importantly influence future practice also promoting evidenced-based standards of care. Full article

(This article belongs to the Special Issue Insights into Acute Pancreatitis: Pathogenesis, Diagnosis, and Management)

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13 pages, 873 KB

Open AccessArticle

Color Stability of 3D-Printed Dental Resins Following Different Surface Treatments

by Agnieszka Nowakowska-Toporowska, Zbigniew Raszewski, Adam Nowicki, Joanna Weżgowiec, Julita Kulbacka and Edward Kijak

Polymers 2026, 18(8), 901; https://doi.org/10.3390/polym18080901 - 8 Apr 2026

Abstract

Introduction: Recent advancements in technologies, such as 3D printing, have been adopted in prosthodontics to streamline clinical procedures and provide high-quality prosthetic devices to patients within a reduced timeframe. Aim of the study: This study primarily aimed to determine the color change levels of 3D-printed dental resins for temporary and long-term intraoral applications. We also evaluated the effectiveness of post-processing procedures such as polishing or glazing on color stability. Materials and methods: Three types of dental resins were tested in distilled water, coffee, and wine environments for 2, 7, 30, and 60 days. A spectrophotometric analysis was conducted, and the Ciede2000 formula was used to determine the DE. Results: The material type, conditioning method, and storage time significantly affected the color changes of the tested materials. The post-processing technique had the most remarkable impact on color stability over time. Conclusions: Glazing of the 3D-printed material surface appears to be the most effective approach to prolong its clinical applicability by maintaining color stability. Full article

(This article belongs to the Special Issue Polymer Microfabrication and 3D/4D Printing)

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33 pages, 3281 KB

Open AccessReview

Engineered MoS₂ Nanoplatforms for Drug-Enhanced Cancer Phototherapy: From Design Strategies to Translational Opportunities

by Catarina Tavares, Maria Carolina Dias, Bruno Freitas, Fernão D. Magalhães and Artur M. Pinto

Nanomaterials 2026, 16(8), 445; https://doi.org/10.3390/nano16080445 - 8 Apr 2026

Abstract

Cancer remains a major global health challenge, and the limitations of conventional therapies have intensified interest in treatment strategies that combine improved selectivity with reduced systemic toxicity. Photothermal therapy and photodynamic therapy have emerged as minimally invasive approaches capable of achieving spatiotemporally controlled tumour ablation. In this context, molybdenum disulfide (MoS₂), a transition metal dichalcogenide with strong near-infrared absorption, high photothermal conversion efficiency, and versatile surface chemistry, has gained increasing attention as a multifunctional platform for drug delivery and light-triggered cancer therapy. This review examines recent advances in engineered MoS₂ nanoplatforms for drug-enhanced cancer phototherapy, with emphasis on how surface design and therapeutic cargoes mechanistically amplify light-triggered tumour killing. Approaches such as polymer coatings, biomimetic membranes, targeting ligands, chemotherapeutic agents, nucleic acids, and photosensitisers have been explored to improve colloidal stability, tumour targeting, immune evasion, and stimulus-responsive drug release, while also adding complementary cytotoxic pathways such as chemotherapy, ROS generation, or gene silencing. Available in vitro and in vivo studies indicate that these systems generally exhibit favourable short-term biocompatibility under the tested conditions and can produce significant antitumour effects following irradiation. The review also discusses key biological barriers and translational challenges, including biodistribution, long-term safety, reproducibility, and regulatory considerations, highlighting opportunities for the development of clinically viable MoS₂-based phototherapeutic platforms. Full article

(This article belongs to the Special Issue Nanostructured Materials for Biological and Pharmaceutical Applications (Third Edition))

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