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The current study investigated the impact of perceived instructional strategies in online design studios on student engagement as a multidimensional construct. Given the rapid shift to online learning during the COVID-19 pandemic, understanding effective teaching strategies and their impacts on student engagement is crucial for enhancing overall student outcomes. The study also examined the role of learning motivation as a mediator in these relationships. A mixed-methods approach was employed, measuring a second-order construct of student engagement based on three reflective dimensions: cognitive, affective, and behavioral. Qualitative interviews with instructors revealed three main instructional strategies utilized in online design studios: learning by doing, learning with interaction, and learning from feedback. The present study analyzed self-report survey responses from 230 undergraduate architecture students across four major universities in Iran, utilizing partial least squares structural equation modeling (PLS-SEM). Based on the results, the perceived learning by doing (PLD) and learning with interaction (PLI) significantly improve student engagement. In contrast, the perceived learning from feedback (PLF) fails to influence student engagement directly. Additionally, intrinsic and extrinsic motivation mediated the impact of PLI and PLF on student engagement. These findings highlight the importance of effective instructional strategies in online design studios. Additionally, the results provide valuable insights for future pedagogical practices in online education, offering guidance for instructors aiming to enhance student outcomes in this dynamic learning environment. [ABSTRACT FROM AUTHOR]

Despite exercise‐based injury prevention programs (EIPPs) being widely researched and used, several randomized controlled trials (RCTs) have failed to show their protective effect on injury risk. This is potentially due to underappreciating the EIPP dose–response relationship, by not controlling the analysis for the injuries sustained during the early EIPP implementation period, before the EIPP becomes efficacious. To determine the dose–response relationship of EIPP by controlling for the effects of injuries sustained before it became efficacious. We conducted a secondary analysis of an RCT analyzing the EIPP efficacy in athletics over a 39‐week follow‐up on 840 athletes, by including only those with 100% response rate. We controlled the statistical analyses for a range of lengths of early EIPP implementation period by either excluding the athletes with early injuries (i.e., exclusion approach) or adjusting for the early injuries' effects (i.e., inclusion approach). We estimated the EIPP's dose–response relationship by measuring the EIPP's effect at each length of the controlled period. When we considered no early controlled period, the EIPP showed no effect (OR = 0.85 [95% CI: 0.67–1.09]; p = 0.209). However, both exclusion and inclusion approaches showed that the EIPP effect increased significantly after 5–6 weeks of controlled period. This relationship plateaued at 7–12 weeks of controlled period, peaking at 10 weeks with the exclusion approach (OR = 0.28 [95% CI: 0.16–0.48]; p < 0.001), and 7 weeks with the inclusion approach (OR = 0.37 [95% CI: 0.25–0.55]; p < 0.001). Acknowledging the dose–response relationship of EIPPs could help researchers to design and analyze RCTs and practitioners to plan EIPP implementation timely. Trial Registration: ClinicalTrials.gov Identifier: NCT03307434 [ABSTRACT FROM AUTHOR]

Batteries are a fundamental storage component due to its various applications in mobility, renewable energies and consumer electronics among others. Regardless of the battery typology, one key variable from a user's perspective is the remaining energy in the battery. It is usually presented as the percentage of remaining energy compared to the total energy that can be stored and is labeled State Of Charge (SOC). This work addresses the development of a hybrid model based on a Lithium Iron Phosphate (LiFePO4) power cell, due to its broad implementation. The proposed model calculates the SOC, by means of voltage and electric current as inputs and the latter as the output. Therefore, four models based on k-Means, Agglomerative Clustering, Gaussian Mixture and Spectral Clustering techniques have been tested in order to obtain an optimal solution. [ABSTRACT FROM AUTHOR]

The industrial adoption of material extrusion of polymers (MEX/P) is hindered by the geometric quality of manufactured parts. Contact image sensors (CISs), commonly used in flatbed scanners, have been proposed as a suitable technology for layer-wise characterization of contour deviations, paving the way for the application of corrective measures. Nevertheless, despite the high resolution of CIS digital images, the accurate characterization of layer contours in MEX/P is affected by contrast patterns between the layer and the background. Conventional edge-recognition algorithms struggle to comprehensively characterize layer contours, thereby diminishing the reliability of deviation measurements. In this work, we introduce a novel approach to precisely locate contour points in the context of MEX/P based on evaluating the similarity between the grayscale pattern near a particular tentative contour point and a previously defined gradient reference pattern. Initially, contrast patterns corresponding to various contour orientations and layer-to-background distances are captured. Subsequently, contour points are identified and located in the images, with coordinate measuring machine (CMM) verification serving as a ground truth. This information is then utilized by an adaptive edge-detection algorithm (AEDA) designed to identify boundaries in manufactured layers. The proposed method has been evaluated on test targets produced through MEX/P. The results indicate that the average deviation of point position compared to that achievable with a CMM in a metrology laboratory ranges from 8.02 µm to 13.11 µm within the experimental limits. This is a substantial improvement in the reliability of contour reconstruction when compared to previous research, and it could be crucial for implementing routines for the automated detection and correction of geometric deviations in AM parts. [ABSTRACT FROM AUTHOR]

BIM models are seldom used for the energy certification of buildings. This paper discusses the advantages of linking two important fields: building information modeling (BIM) and building environmental assessment methods (BEAM), presented as a rating system and a proposal for the Chilean context. The state of the art in both fields around the world is discussed, with an in-depth examination of current BIM software and related applications, followed by a discussion about previous research on integrating them. A lack of interoperability and data losses between BIM and BEM were found. A new tool is presented that addresses these challenges to ensure accurate rating system data, and this new framework is based on database exchange and takes crucial information from BIM to BEAM platforms. The development of the method includes BIM programming (API), database links, and spreadsheets for a Chilean building energy certification through a new tool, also applicable to multiclimactic zones. This new semi-automatic tool allows architects to model their design in a BIM platform and use this information as input for the energy certification process. The potential and risks of this method are discussed. Several improvements and enhancements of the energy certification process were found when incorporating this new framework in comparison to current methodologies. [ABSTRACT FROM AUTHOR]

Epilepsy affects millions worldwide, making timely seizure detection crucial for effective treatment and enhanced well-being. Electroencephalogram (EEG) analysis offers a non-intrusive solution, but its visual interpretation is prone to errors and requires a lot of time. Many existing works focus solely on achieving competitive levels of accuracy without considering processing speed or the computational complexity of their models. This study aimed to develop an automated technique for identifying epileptic seizures in EEG data through analysis methods. The efforts have been primarily focused on achieving high accuracy results by operating exclusively within a narrow frequency band of the signal, while also aiming to minimize computational complexity. In this article, a new automated approach is presented for seizure detection by combining signal processing and machine learning techniques. The proposed method comprises four stages: (1) Preprocessing: Savitzky–Golay filter to remove the background noise. (2) Decomposition: discrete wavelet transform (DWT) to extract spontaneous alpha and beta frequency bands. (3) Feature extraction: six features (mean, standard deviation, skewness, kurtosis, energy, and entropy) are computed for each frequency band. (4) Classification: a support vector machine (SVM) method classifies signals as normal or containing a seizure. The method was assessed using two publicly available EEG datasets. For the alpha band, the highest achieved accuracy was 92.82%, and for the beta band it was 90.55%, which demonstrates adequate capability in both bands for accurate seizure detection. Furthermore, the obtained low computational cost suggests a potentially valuable application in real-time assessment scenarios. The obtained results indicate its capacity as a valuable instrument for diagnosing epilepsy and monitoring patients. Further research is necessary for clinical validation and potential real-time deployment. [ABSTRACT FROM AUTHOR]

Purpose Pulmonary function testing (PFT) in patients with tracheostomies has been perceived as difficult to perform and clinically unreliable. We studied the feasibility, quality, repeatability and clinical significance of PFT. Methods Patients with tracheostomies that underwent PFT from January 1, 2010 to February 29, 2012 were identified. Clinical history and PFT data were reviewed retrospectively. Results Fifty patients (88% men) were identified. Forty-seven (94%) patients were able to perform PFT. Acceptable repeatability was obtained for FVC in 39 (83%) and for FEV1 in 41 (87%). Patients with tracheostomies showed difficulty in meeting ATS end-of-test criteria; only 9 (19%) met plateau criteria and 25 (53%) had exhalation times of greater than 6 s. Obstructive pattern was observed in 30 (64%) and restrictive pattern in 9 (19%). DLCO measurements were attempted in 43 patients and satisfactorily obtained in 34 (79%). Conclusions PFT can be performed with reliability in patients with tracheostomies, and they are useful for detecting and classifying types of lung dysfunction., Author(s): Ajay Sheshadri [sup.1], Leendert Keus [sup.2], David Blanco [sup.2], Xiudong Lei [sup.3], Cheryl Kellner [sup.2], Vickie R. Shannon [sup.1], Diwakar D. Balachandran [sup.1], Carlos A. Jimenez [sup.1], Lara Bashoura [...]

Digital twins represent a promising approach for sustainable building operations and management in the context of the carbon neutrality goals of the European Union (EU). Using OpenStudio, an opensource platform for building energy modeling, we demonstrated the creation and editing of building digital twins. OpenStudio provides a user-friendly interface and extensive simulation capabilities, allowing detailed and accurate modeling of building components and systems. Using OpenStudio Measures, users can automate tasks and customize simulation models to optimize the building performance. The process of creating a building digital twin involves collecting historical data and accurately representing the building geometry; materials; schedules; and heating, ventilation, and air conditioning (HVAC) systems. Challenges such as data availability and model accuracy highlight the importance of modeling practices. Editing the digital twin involves modifying the OpenStudio model files and EnergyPlus weather files to simulate different building operation scenarios. Python programming language opportunities were considered for digital twin file modification. The potential of digital twins lies in their ability to simulate future building conditions and optimize building system settings. By integrating digital twins with machine learning algorithms and connecting them directly to building management systems, optimal building control strategies can be automated, thereby reducing energy consumption and improving occupant comfort levels. [ABSTRACT FROM AUTHOR]

Material Extrusion (MEX) currently stands as the most widespread Additive Manufacturing (AM) process, but part quality deficiencies remain a barrier to its generalized industrial adoption. Quality control in MEX is a complex task as extrusion performance impacts the consistency of mechanical properties and the surface finish, dimensional accuracy, and geometric precision of manufactured parts. Recognizing the need for early-stage process monitoring, this study explores the potential of integrating Laser Triangulation Sensors (LTS) into MEX/P manufacturing equipment for layer-wise 3D inspections. Using a double-bridge architecture, an LTS-based sub-micrometric inspection system operates independently from the manufacturing process, enabling comprehensive digitization and autonomous reconstruction of the target layer's topography. Surface texture is then computed using standardized indicators and a new approach that provides insight into layer quality uniformity. A case study evaluating two alternative extruder head designs demonstrates the efficacy of this integrated approach for layer quality characterization. Implementing a generalized layer-wise procedure based on this integration can significantly mitigate quality issues in MEX manufacturing and optimize process parameter configurations for enhanced performance. [ABSTRACT FROM AUTHOR]

Introduction: The relevance of tubulo‐interstitial involvement for kidney prognosis has recently been emphasized, but validated biomarkers for predicting histology are still lacking. The aim of our study was to evaluate different serum and urinary markers of tubular damage in patients with lupus nephritis (LN) and to correlate them with kidney histopathology. Methods: A single‐center retrospective study was conducted from January 2016 to December 2021. Serum and urine samples were collected on the same day of kidney biopsy and correlated with histologic data from a cohort of 15 LN patients. We analyzed the following urinary markers, adjusted for urine creatinine: beta 2‐microglobulin, alpha 1‐microglobulin, NGAL, uKIM‐1, MCP‐1, uDKK‐3, and uUMOD. The serum markers sKIM‐1 and sUMOD were also analyzed. Results: A positive and strong correlation was observed between the degree of interstitial fibrosis (rho = 0.785, p =.001) and tubular atrophy (rho = 0.781, p =.001) and the levels of uDKK3. uUMOD also showed an inverse and moderate correlation with interstitial fibrosis (rho = −0.562, p =.037) and tubular atrophy (rho = −0.694, p =.006). Patients with >10% cortical interstitial inflammation had higher levels of uKIM‐1 [4.9 (3.9, 5.5) vs. 0.8 (0.6, 1.5) mcg/mg, p =.001], MCP‐1 [3.8 (2. 3, 4.2) vs. 0.7 (0.3, 1.2) mcg/mg, p =.001], sKIM‐1 [9.2 (5.9, 32.7) vs. 1.4 (0, 3.5) pg/mL, p =.001], and lower sUMOD [8.7 (0, 39.7) vs. 46.1 (35.7, 53) ng/mL, p =.028]. Conclusion: The use of specific urinary and serum biomarkers of tubular dysfunction or injury may help to predict certain histologic parameters in LN patients. [ABSTRACT FROM AUTHOR]

Chile has numerous areas that lack sewage collection, including in the capital city. Sanitation in these cases is managed through individual solutions like septic tanks or small wastewater treatment plants (WWTPs) that use biological treatment, usually activated sludge with extended aeration. In general, the design of these systems adheres to the quality standards mandated by regulations for discharge, infiltration, or irrigation. In this scenario, traditional methods like increasing dissolved oxygen (DO) or hydraulic retention time (HRT) were unable to effectively reduce excessive nutrients. Therefore, literature related to nutrient excess and denitrification systems is consulted and reviewed to compile different solutions suitable for the presented issue. Potential solutions were modeled and verified using the free simulation software WRc STOAT. The software accurately predicted the unsatisfactory results of the current setup and provided parameters for the proposed modifications. Experience, precise user definition, influential characteristics, and modeling are essential in the design of WWTPs. [ABSTRACT FROM AUTHOR]

Background Proteinuria is not only a biomarker of chronic kidney disease (CKD) but also a driver of CKD progression. The aim of this study was to evaluate serum and urinary tubular biomarkers in patients with biopsied proteinuric kidney disease and to correlate them with histology and kidney outcomes. Methods A single-center retrospective study was conducted on a cohort of 156 patients from January 2016 to December 2021. The following urinary and serum biomarkers were analyzed on the day of kidney biopsy: beta 2 microglobulin (β2-mcg), alpha 1 microglobulin (α1-mcg), neutrophil gelatinase-associated lipocalin (NGAL), urinary kidney injury molecule-1 (uKIM-1), monocyte chemoattractant protein-1 (MCP-1), urinary Dickkopf-3 (uDKK3), uromodulin (urinary uUMOD), serum kidney injury molecule-1 (sKIM-1) and serum uromodulin (sUMOD). A composite outcome of kidney progression or death was recorded during a median follow-up period of 26 months. Results Multivariate regression analysis identified sUMOD (β–0.357, P  < .001) and uDKK3 (β 0.483, P  < .001) as independent predictors of interstitial fibrosis, adjusted for age, estimated glomerular filtration rate (eGFR) and log proteinuria. Elevated levels of MCP-1 [odds ratio 15.61, 95% confidence interval (CI) 3.52–69.20] were associated with a higher risk of cortical interstitial inflammation >10% adjusted for eGFR, log proteinuria and microhematuria. Upper tertiles of uDKK3 were associated with greater eGFR decline during follow-up. Although not a predictor of the composite outcome, doubling of uDKK3 was a predictor of kidney events (hazard ratio 2.26, 95% CI 1.04–4.94) after adjustment for interstitial fibrosis, eGFR and proteinuria. Conclusions Tubular markers may have prognostic value in proteinuric kidney disease, correlating with specific histologic parameters and identifying cases at higher risk of CKD progression. [ABSTRACT FROM AUTHOR]

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This research paper explores the integration of novel technologies in hospital emergency evacuations, particularly in Operating Rooms (ORs) and Emergency Departments (EDs). It examines the application of advanced tools like simulation modeling, Building Information Modeling (BIM), Digital Twin technology, sensor data, and Artificial Intelligence (AI) to improve evacuation strategies in the building. The study extends to in-depth case studies for assessing the practicality of existing protocols, while also highlighting the critical importance of staff training and preparedness. Additionally, it addresses the ethical and psychological impacts of emergencies on patients and healthcare staff, underscoring the need for technology to be complemented with human-centered care. The paper concludes by emphasizing the ongoing necessity for innovative research in enhancing safety and operational resilience in healthcare emergency management. [ABSTRACT FROM AUTHOR]

This paper aims to enhance security in IoT device networks through a visual tool that utilizes three projection techniques, including Beta Hebbian Learning (BHL), t-distributed Stochastic Neighbor Embedding (t-SNE) and ISOMAP, in order to facilitate the identification of network attacks by human experts. This work research begins with the creation of a testing environment with IoT devices and web clients, simulating attacks over Message Queuing Telemetry Transport (MQTT) for recording all relevant traffic information. The unsupervised algorithms chosen provide a set of projections that enable human experts to visually identify most attacks in real-time, making it a powerful tool that can be implemented in IoT environments easily. [ABSTRACT FROM AUTHOR]

This paper investigates non-invasive techniques for annular two-phase flow analysis, focusing on liquid film characterization to understand the interfacial phenomena that are crucial for heat and mass transfer. Limited methods allow the study of the temporal and spatial evolution of liquid film, such as Planar Laser-Induced Fluorescence (PLIF). However, this method possesses optical challenges, leading to the need for improved techniques to mitigate refraction and reflection, such as Refractive Index Matching (RIM). This study utilizes an experimental annular flow facility to analyze both RIM and non-RIM PLIF over a range of liquid Reynolds numbers from 4200 to 10,400. Three configurations—PLIF RIM90, PLIF RIM40, and PLIF nRIM40—are compared from both qualitative and quantitative perspectives. In the quantitative analysis, key variables of the liquid film are measured, namely mean film thickness, disturbance wave height, and frequency. Variations in the analyzed variables indicate minor deviations, which are not likely to be caused by the technique used. However, all three methodologies exhibited errors that are estimated to be within a maximum of 10%, with a mean value of approximately 8%. [ABSTRACT FROM AUTHOR]