Search Results (1,133)

Objective: To evaluate the concordance of automated 3D superimposition methods applied to digital models, with a focus on methods that consider stable palatal regions as geometric reference landmarks versus those that do not. Design and setting: This was a prospective, cross-sectional study using digital model files of patients undergoing orthodontic treatment in a university clinical setting. Participants: Sixty-one patients were prospectively enrolled and divided into three groups based on the type of orthodontic treatment they received: (20) non-extractive orthodontic treatment without intermaxillary elastics, (21) intermaxillary elastics, and (20) control subjects with no orthodontic movement. The inclusion criteria included the availability of complete pre- and post-treatment digital casts and the absence of significant craniofacial anomalies. Methods: Three superimposition methods were tested: (1) superimposition according to palate and palatal ridges, (2) best-fit superimposition of arches in occlusion, and (3) best-fit superimposition of individual arches. Discrepancies were identified by comparing the spatial positions derived from each method. Within three spatial axes, deviations of ±0.5 mm and ±1.15° were not considered significant. Bland–Altman plots were used to quantify palatal rugae based and non-based spatial differences between methods. Differences in the superimposition results between the three patient groups were evaluated using ANOVA tests. Results: Differences in spatial position between the superimposition methods often exceeded the acceptable range. The results were compared between the three patient groups with a statistical significance of α = 0.05. In the present study, the high reliability of the superimposition method based on the palate and palatal ridges was observed. Conclusion: Superimposition methods based on the palate and palatal rugae provide superior accuracy in determining treatment-related changes in upper arch digital models. These findings illustrate the need for appropriate selection of superimposition techniques based on the study objective of using clinically relevant techniques. Full article

This article aims to evaluate the runtime effectiveness of various one-class classification (OCC) techniques for anomaly detection in an industrial scenario reproduced in a laboratory setting. To address the limitations posed by restricted access to proprietary data, the study explores OCC methods that learn solely from legitimate network traffic, without requiring labeled malicious samples. After analyzing major publicly available datasets, such as KDD Cup 1999 and TON-IoT, as well as the most widely used OCC techniques, a lightweight and modular intrusion detection system (IDS) was developed in Python. The system was tested in real time on an experimental platform based on Raspberry Pi, within a simulated client–server environment using the NFSv4 protocol over TCP/UDP. Several OCC models were compared, including One-Class SVM, Autoencoder, VAE, and Isolation Forest. The results showed strong performance in terms of detection accuracy and low latency, with the best outcomes achieved using the UNSW-NB15 dataset. The article concludes with a discussion of additional strategies to enhance the runtime analysis of these algorithms, offering insights into potential future applications and improvement directions. Full article

Accurate estimation of long-term average (LTA) discharge is fundamental for water resource assessment, infrastructure planning, and hydrological modeling, yet it remains a significant challenge, particularly in data-scarce or ungauged basins. This study introduces an advanced machine learning framework to estimate long-term average discharge using globally available hydrological station metadata from the Global Runoff Data Centre (GRDC). The methodology involved comprehensive data preprocessing, extensive feature engineering, log-transformation of the target variable, and the development of multiple predictive models, including a custom deep neural network with specialized pathways and gradient boosting machines (XGBoost, LightGBM, CatBoost). Hyperparameters were optimized using Bayesian techniques, and a weighted Meta Ensemble model, which combines predictions from the best individual models, was implemented. Performance was rigorously evaluated using R², RMSE, and MAE on an independent test set. The Meta Ensemble model demonstrated superior performance, achieving a Coefficient of Determination (R²) of 0.954 on the test data, significantly surpassing baseline and individual advanced models. Model interpretability analysis using SHAP (Shapley Additive explanations) confirmed that catchment area and geographical attributes are the most dominant predictors. The resulting model provides a robust, accurate, and scalable data-driven solution for estimating long-term average discharge, enhancing water resource assessment capabilities and offering a powerful tool for large-scale hydrological analysis. Full article

Background: The management of difficult airways is one of the most critical and challenging aspects of emergency and ICU care. Despite technological advances, unanticipated airway difficulty can result in serious complications, including hypoxia, brain injury, and death. This comprehensive narrative review aims to consolidate current algorithms and evidence-based strategies to guide clinicians in the assessment and management of difficult airways. Methods: A comprehensive literature review was conducted using PubMed, Embase, and Google Scholar to identify relevant studies, clinical guidelines, and expert consensus documents related to difficult airway management. The focus was placed on both pre-intubation assessment tools and intervention strategies used in various clinical contexts. Results: Airway difficulty is best anticipated through a combination of history, physical examination, and validated tools such as the Mallampati score. Several algorithms, including those from the American Society of Anesthesiologists (ASA) and the Difficult Airway Society (DAS), provide structured approaches that emphasize preoxygenation, preparedness for failed intubation, and the use of adjuncts such as video laryngoscopy, supraglottic airway devices, and awake intubation techniques. Crisis algorithms such as the Vortex approach help simplify decision-making during emergencies. It is important to have adjuncts available in cases of anticipated difficult airways, such as fiberoptic intubation, while surgical airway access is an important component of a stepwise airway management algorithm when critical scenarios are encountered. Conclusions: Effective difficult airway management requires anticipation, a structured plan, familiarity with advanced airway tools, and adherence to validated algorithms. Training in crisis resource management and multidisciplinary rehearsal of airway scenarios are essential to improving outcomes. Full article

Breast cancer-related lymphedema (BCRL) is a debilitating complication in breast cancer survivors, with axillary lymph node dissection (ALND) as the greatest independent risk factor. Beyond non-surgical therapies such as complete decongestive and compression therapy, there has been increased interest in immediate microsurgical reconstruction via immediate lymphatic reconstruction (ILR) anastomosing transected lymphatic vessels to a local venous recipient at the time of ALND to mitigate the risks of BCRL. This work provides a scoping review of the landscape surrounding ILR, spanning the updated literature investigating patient outcomes, current accepted best practices, and critical components of surgical techniques for a successful multidisciplinary approach. While limited by heterogeneity in the methods of lymphedema detection, a growing body of work demonstrates the protective effects of ILR. From the pioneering work by Boccardo et al. in 2009 and his introduction of Lymphatic Microsurgical Preventive Healing Approach (LYMPHA) using an intussusception-type end-to-end microanastmosis, to the first randomized control trial by Coriddi in 2023, which importantly employed relative upper extremity volume change as an outcome measure to circumvent the confounding effects of body size and BMI, the current literature supports ILR following ALND in the prevention of BCRL. Collaboration between the oncologic breast surgeon and reconstructive microsurgeon are central to the success of ILR. Critical components for operative success include preoperative and intraoperative lymphatic mapping, preservation of suitable venous targets, availability of supermicrosurgical instruments and sutures, as well as aptitude with a variety of microsurgical anastomotic techniques. Full article

Intelligent transport systems are a major component of smart cities because their deployment should result in reduced journey times, less traffic congestion and a significant reduction in road deaths, which will greatly improve the quality of life of their citizens. New technologies such as vehicular networks allow more information be available in realtime, and this information can be used with new analytical models to obtain more accurate estimates of journey times. This would be extremely useful to drivers and will also enable transport authorities to optimise the transport network. This paper addresses these issues using a model-based approach to provide a new way of estimating the delay along specified routes. A journey is defined as the traversal of several road links and junctions from source to destination. The delay at the junctions is analysed using the zero-server Markov chain technique. This is then combined with the Jackson network to analyse the delay across multiple junctions. The delay at road links is analysed using an M/M/K/K model. The results were validated using two simulators: SUMO and VISSIM. A real scenario is also examined to determine the best route. The preliminary results of this model-based analysis look promising but more work is needed to make it useful for wide-scale deployment. Full article

The reliable operation of rotating machinery is critical in industrial production, necessitating advanced fault diagnosis and maintenance strategies to ensure operational availability. This study employs supervised machine learning algorithms to apply multi-label classification for fault detection in rotating machinery, utilizing a real dataset from multi-sensor systems installed on a suction fan in a typical manufacturing industry. The presented system focuses on multi-modal data analysis, such as vibration analysis, temperature monitoring, and ultrasound, for more effective fault diagnosis. The performance of general machine learning algorithms such as kNN, SVM, RF, and some boosting techniques was evaluated, and it was shown that the Random Forest achieved the best classification accuracy. Feature importance analysis has revealed how specific domain characteristics, such as vibration velocity and ultrasound levels, contribute significantly to performance and enabled the detection of multiple faults simultaneously. The results demonstrate the machine learning model’s ability to retrieve valuable information from multi-sensor data integration, improving predictive maintenance strategies. The presented study contributes a practical framework in intelligent fault diagnosis as it presents an example of a real-world implementation while enabling future improvements in industrial condition-based maintenance systems. Full article

Knowledge of global radiation (Hg) is essential for regional economic development and can help guide public policies related to agricultural and energy potential. However, its availability in several Brazilian regions is still limited. This work evaluates the predictive capacity of two machine learning (ML) techniques, such as multi-layer perceptrons (MLPs) and support vector machines (SVMs), in the estimation of Hg in 20 meteorological stations with 40 different input combinations involving insolation, air temperature, air relative humidity, photoperiod, and extraterrestrial radiation. It is also compared with three empirical models based on insolation, temperature, and a hybrid combination. In general, the greater the number of input variables, the better the performance of ML techniques, especially in combinations involving insolation that reduced the dispersion of estimated Hg on days with high atmospheric transmissivity and air temperature on days with low atmospheric transmissivity. The performance of SVM was better when compared to MLP in all statistical indicators. ML techniques presented better results than empirical models, and in general, the ordering of the best models in the three locations is achieved using SVM, MLP, and empirical models. Therefore, due to their easy implementation and generation of good results, the use of SVM models is recommended to estimate daily global radiation in the Brazilian Amazon. Full article

Off-grid and isolated rural communities in developing countries with limited resources require energy supplies for daily residential use and social, economic, and commercial activities. The use of data from space assets and space-based solar power is a feasible solution for addressing ground-based energy insecurity when harnessed in a hybrid manner. Advances in space solar power systems are recognized to be feasible sources of renewable energy. Their usefulness arises due to advances in satellite and space technology, making valuable space data available for smart grid design in these remote areas. In this case study, an isolated village in Namibia, characterized by high levels of solar irradiation and limited wind availability, is identified. Using NASA data, an autonomous hybrid system incorporating a solar photovoltaic array, a wind turbine, storage batteries, and a backup generator is designed. The local load profile, solar irradiation, and wind speed data were employed to ensure an accurate system model. Using HOMER Pro software V 3.14.2 for system simulation, a more advanced AI optimization was performed utilizing Grey Wolf Optimization and Harris Hawks Optimization, which are two metaheuristic algorithms. The results obtained show that the best performance was obtained with the Grey Wolf Optimization algorithm. This method achieved a minimum energy cost of USD 0.268/kWh. This paper presents the results obtained and demonstrates that advanced optimization techniques can enhance both the hybrid system’s financial cost and energy production efficiency, contributing to a sustainable electricity supply regime in this isolated rural community. Full article

Over recent decades, widespread declines of kelp forests have been reported along the European coast, prompting the need for effective and scalable restoration strategies. The green gravel technique, in which kelp gametophytes are seeded onto small rocks and cultivated in the lab before being outplanted, has shown promising results. In this study, we tested the effects of four commonly available substrates—granite, limestone, quartz, and schist—on the early development of Laminaria ochroleuca recruits under optimal laboratory conditions. All substrates supported gametophyte adhesion and sporophyte development. By week 6, quartz promoted the greatest recruit length (1.25 ± 0.16 mm), with quartz and limestone (1.54 ± 0.17 and 1.58 ± 0.14 mm, respectively) showing the best overall performance by week 7. Final recruit densities were similar across substrates, indicating multiple materials can support early development. Quartz and limestone showed both biological effectiveness and practical advantages, with limestone emerging as the most cost-effective option. Substrate selection should consider not only biological performance but also economic and logistical factors. These findings contribute to refining green gravel protocols and improving the feasibility of large-scale kelp forest restoration, although field validation is necessary to assess long-term outcomes under natural conditions. Full article

The management of spent foundry sand (SFS) presents environmental and operational challenges for foundries. According to the European Union, European foundries generate approximately 9 million tonnes of SFS annually, mainly from the production of ferrous castings (iron and steel). Nowadays, around 25% of the spent foundry sand in Europe is recycled for specific applications, primarily in the cement industry. However, the presence of chemical residues limits the application of this solution. A possible alternative for reusing the spent foundry sand is its employment as a raw material in the production of compost. Studies in the literature indicate that the amount of chemical residue present in the sand can be reduced through the composting process, making the final product suitable for different purposes. However, information about the implementation of this technology in industrial contexts is lacking. To address this issue, this paper proposes a techno-economic analysis to assess the feasibility of composting SFS on a large scale, using information gathered during the testing phase of the Green Foundry LIFE project. This project explored the reuse of sand from organic and inorganic binder processes to create compost for construction purposes, which allowed for the final product. Since the new BREF (Best Available Techniques Reference Document) introduced by the European Union at the start of 2025 recommends composting SFS as a way to reduce solid waste from foundries, this initial study can represent practical guidance for both researchers and companies evaluating the adoption of this technology. Full article

Reliable and accurate solar radiation (SR) prediction is crucial for renewable energy development amid a growing energy crisis. Machine learning (ML) models are increasingly recognized for their ability to provide accurate and efficient solutions to SR prediction challenges. This paper presents an Artificial Neural Network (ANN) model optimized using feature selection techniques based on Explainable AI (XAI) methods to enhance SR prediction performance. The developed ANN model is evaluated using a publicly available SR dataset, and its prediction performance is compared with five other ML models. The results indicate that the ANN model surpasses the other models, confirming its effectiveness for SR prediction. Two XAI techniques, LIME and SHAP, are then used to explain the best-performing ANN model and reduce its complexity by selecting the most significant features. The findings show that prediction performance is improved after applying the XAI methods, achieving a lower MAE of 0.0024, an RMSE of 0.0111, a MAPE of 0.4016, an RMSER of 0.0393, a higher $R^2$ score of 0.9980, and a PC of 0.9966. This study demonstrates the significant potential of XAI-driven feature selection to create more efficient and accurate ANN models for SR prediction. Full article

Background/Objectives: Different surgical techniques are available in cases of missing or insufficient capsular bag support. Next to the anterior chamber or iris-fixated intraocular lenses (IOL), the implantation of the Carlevale IOL provides a sutureless and scleral fixated treatment method. Methods: In a retrospective single-center study, the perioperative data of 100 patients who consecutively received a scleral fixated Carlevale IOL combined with a 25 gauge (G) pars plana vitrectomy between September 2021 and June 2024 were investigated. The intraoperative and postoperative results were analyzed in terms of complication rates and refractive outcomes. Results: IOL dislocation was the most common surgical indication (50%) for sutureless Carlevale IOL implantation, followed by postoperative aphakia in 35 patients (35%). Nearly every fourth patient (24%) had a preoperative traumatic event, and 21% had pseudoexfoliation (PEX) syndrome. The average surgery time was 60.2 (±20.1) min. Intraoperative intraocular hemorrhage occurred in seven cases, and IOL haptic breakage in two patients. Temporary intraocular pressure fluctuations represented the most common postoperative complications (28%). Severe complications such as endophthalmitis or retinal detachment were not observed in our cohort. The mean refractive prediction error was determined in 67 patients and amounted to an average of −0.7 ± 2.0 diopters. The best corrected visual acuity (BCVA) at the last postoperative follow-up showed an improvement of 0.2 ± 0.5 logMAR (n = 76) compared to the preoperative BCVA (p = 0.0002). The postoperative examination was performed in 72% of the patients, and the mean follow-up period amounted to 7.2 ± 6.4 months. Conclusions: Overall, sutureless and scleral fixated implantation of the Carlevale IOL represents a valuable therapeutic option in the treatment of aphakia and lens as well as IOL dislocation in the absence of capsular bag support with minor postoperative complications and positive refractive outcomes. Full article

The provision of food in pre-Inca/Inca cultures (1000 BC–≈1532 AD) in environments near Lake Titikaka (approximately 4000 m above sea level) was possible through an agricultural technique called “Waru-Waru”, which consists of filling the space (volume) between rows of land containing plants that are cultivated (a series of earth platforms surrounded by water canals) with water, using water as thermal energy storage to store energy during the day and to regulate the temperature of the soil and crop atmosphere at night. The problem is that these cultures left no evidence in written documents that have been preserved to this day indicating the mathematical models, the physics involved, and the experimental part they performed for the research, development, and innovation of the “Waru-Waru” technique. From a review of the existing literature, there is (1) bibliography that is devoted to descriptive research (about the geometry, dimensions, and shapes of the crop fields (and more based on archaeological remains that have survived to the present day) and (2) studies presenting complex mathematical models with many physical parameters measured only with recently developed instrumentation. The research objectives of this paper are as follows: (1) develop a mathematical model that uses finite differences in fluid mechanics, thermodynamics, and heat transfer to explain the experimental and theory principles of this pre-Inca/Inca technique; (2) the proposed mathematical model must be in accordance with the mathematical calculation tools available in pre-Inca/Inca cultures (yupana and quipu), which are mainly based on arithmetic operations such as addition, subtraction, and multiplication; (3) develop a mathematical model in a sequence of steps aimed at determining the best geometric form for thermal energy storage and plant cultivation and that has a simple design (easy to transmit between farmers); (4) consider the assumptions necessary for the development of the mathematical model from the point of view of research on the geometry of earth platforms and water channels and their implantation in each cultivation area; (5) transmit knowledge of the construction and maintenance of “Waru-Waru” agricultural technology to farmers who have cultivated these fields since pre-Hispanic times. The main conclusion is that, in the mathematical model developed, algebraic mathematical expressions based on addition and multiplication are obtained to predict and explain the evolution of soil and water temperatures in a specific crop field using crop field characterization parameters for which their values are experimentally determined in the crop area where a “Waru-Waru” is to be built. Therefore, the storage of thermal energy in water allows crops to survive nights with low temperatures, and indirectly, it allows the interpretation that the Inca culture possessed knowledge of mathematics (addition, subtraction, multiplication, finite differences, approximation methods, and the like), physics (fluids, thermodynamics, and heat transfer), and experimentation, with priority given to agricultural techniques (and in general, as observed in all archaeological evidence) that are in-depth, exact, practical, lasting, and easy to transmit. Understanding this sustainable energy storage technique can be useful in the current circumstances of global warming and climate change within the same growing areas and/or in similar climatic and environmental scenarios. This technique can help in reducing the use of fossil or traditional fuels and infrastructure (greenhouses) that generate heat, expanding the agricultural frontier. Full article

Compared to traditional manufacturing methods, additive manufacturing (AM) enables the production of parts with arbitrary structures, effectively addressing the challenges faced when fabricating complex geometries using conventional techniques. The dynamic development of this technology has led to the emergence of increasingly advanced materials. One of the best examples is metal–polymer composites, which allow the manufacturing of fully dense components consisting of stainless steel and titanium alloys, employing the widely available AM technology based on material extrusion (MEX). Metallic materials intended for this type of 3D printing may serve as an alternative to currently prevalent techniques including techniques like selective laser melting (SLM), owing to significantly lower equipment and material costs. Particularly applicable in low-volume production, where total costs and manufacturing time are critical factors, MEX technology of polymer–metallic composites offer relatively fast and economical AM of metal components, proving beneficial during the design of geometrically complex, and low-cost equipment. Due to the significant advancements in AM technology, this review focuses on the latest developments in the additive manufacturing of metallic components using the MEX approach. The discussion encompasses the printing process characteristics, materials tailored to this technology, and post-processing steps (debinding and sintering) necessary for obtaining fully metallic MEX components. Additionally, the article characterizes the printing process parameters and their influence on the functional characteristics of the resulting components. Finally, it presents the drawbacks of the process, identifies gaps in existing research, and outlines challenges in refining the technology. Full article