3D physical modelling in a wave flume of brine discharges on a beach

A holistic review on how artificial intelligence has redefined water treatment and seawater desalination processes

Biochemistry curricula present a particular challenge to undergraduate students with abstract concepts which can lead to misconceptions that impede learning. In particular, these students have difficulty understanding enzyme structure and function concepts. Targeted learning activities and three-dimensional (3D) physical models are proposed to help students challenge these misconceptions and increase conceptual understanding. Here we assessed such pedagogical tools using the Enzyme-Substrate Interactions Concept Inventory (ESICI) to measure (mis)conceptual changes from Pre- to Post- time points in a single semester undergraduate biochemistry course. A Control group of students engaged with the active learning activities without the 3D physical models and students in the Intervention group utilized these activities with the 3D physical models. At the Post- time point both groups had higher, yet similar ESICI scores of the same magnitude as the highest scoring group from the national sample. Concomitantly, many misconception markers decreased compared to the national sample, although some of these differed between the Control and Intervention groups. Based on this assessment, both pedagogical approaches successfully increased conceptual understanding and targeted many of the misconceptions measured by the ESICI, however, several misconceptions persisted. Surprisingly, the students who used the 3D physical models did not demonstrate a further decrease in the misconception markers. Additionally, psychometric evaluation of the ESICI with our sample recommends the revision of several questions to improve the validity of this assessment. We also offer suggestions to improve instruction and pedagogical tools with further avenues for research on learning.

This research study addressed and compared 3D physical and empirical models for stereo-processing and the generation of digital surface models (DSMs) from different stereo highresolution (HR) sensors (Ikonos and QuickBird). The 3D physical model is Toutin’s Model (TM) developed at the Canada Centre for Remote Sensing, and the empirical model is the rational function model (RFM). The study also evaluated the conditions of experimentation to appropriately use these 3D models. The first results on stereo-bundle adjustments demonstrated that TM and vendor-supplied RFMs gave similar results with Ikonos as soon as RFM was refined with a shift computed from one GCP. On the other hand, TM gave better results than vendor-supplied RFMs with QuickBird regardless of the polynomial order and the number of GCPs. Due to its relief dependency, QuickBird RFM needed to be refined at least with linear functions computed from at least 6 to 10 GCPs. Some large errors were, however, noted on forward image RFM in column. The DSMs were then generated using an intensity matching approach and compared to 0.2 m accurate lidar elevation data. Because DSMs included the height of land-cover (trees, houses), elevation linear errors with 90 percent confidence level (LE90) were computed and compared for the entire area and three land-cover classes (forests, urban/ residential, bare surfaces). TM and vendor-supplied RFMs with Ikonos, regardless of the method and GCP number, achieved comparable results for all classes, while TM achieved overall better results than vendor-supplied RFMs with QuickBird. All results demonstrated the necessity of refining Ikonos RFM with a shift and one GCP only and QuickBird RFM with 1 st -order linear functions and 6 to 10 GCPs due to its relief dependency.

Seawater intrusion threatens groundwater resources in coastal regions, including southern Baldwin County, Alabama, where the freshwater-saltwater interface dynamics remain poorly understood. To address this gap, this study uses combined physics-based and machine-learning models to quantify seawater intrusion caused by natural (storm surges) and anthropogenic (human activities) perturbations. The long short-term memory network and wavelet analysis were used to assess vertical aquifer vulnerabilities, revealing that the shallow part of the Coastal lowlands aquifer system (CL1) in the southern Baldwin County region is more susceptible to sea level rise and groundwater extraction than deeper aquifers. Based on these findings, a cross-sectional numerical model (physics approach) for the CL1 aquifer was developed to evaluate tidal and storm surge effects, using Tropical Storm Claudette (June 2021) as a case study. Results showed that tidal fluctuations had a minimal impact on the saltwater-freshwater interface location, whereas storm surges caused substantial inland movement, with effects lasting for nine months. The steady-state version of the three-dimensional (3D) physical model predicted seawater intrusion across the entire area, and convolutional neural network-based modeling further validated the model results. The 3D physical model was also applied to a smaller area to assess human impact on the saltwater interface due to two groundwater pumping scenarios (± 50% of the baseline pumping rate). Results revealed that a 50% increase in groundwater withdrawals caused seawater to advance ~ 320 m inland, whereas a 50% reduction led to a ~ 270-meter retreat. This study highlights the vulnerability of Alabama’s shallow coastal aquifers to seawater intrusion due to storm surges and human activities, and demonstrates that combining physics-based models with machine learning approaches can improve groundwater predictions, though its accuracy depends on the availability of site-specific data.

Comprehensive analysis of a hybrid FO-NF-RO process for seawater desalination: With an NF-like FO membrane

Many students enter biochemistry courses with enzyme‐substrate interaction misconceptions stemming from prior biology and chemistry courses where this core concept is inadequately illustrated, explained, and/or assessed. Moreover, research has shown two‐dimensional representations not only fail to effectively convey biochemical concepts, but also propagate misconceptions. Reported enzyme‐substrate interaction misconceptions highlight the necessity for better, targeted instructional tools and assessments. We hypothesize that three‐dimensional (3D) physical models used in conjunction with targeted active learning assessments will increase student understanding of shape, stereochemistry, and electrostatic interactions involved in enzyme‐substrate interactions. We further propose that the use of these physical models will decrease the amount of time needed to complete the active learning assessments while also facilitating a deeper understanding of enzyme‐substrate interactions, therefore offering the instructor time to cover other course topics. This intervention study also addresses several biochemistry threshold concepts and supports the “Vision and Change in Undergraduate Biology Education: A Call to Action” report by offering concept‐oriented active learning opportunities.A series of active learning assessments, with corresponding learning objectives and physical models designed by a team of undergraduate students, were developed to address the identified misconceptions of space, electronic interactions, and stereochemistry in enzyme‐substrate interactions. Here we aim to present (1) the design and development of these assessments and corresponding 3D physical models along with (2) the preliminary results of this study. In a control classroom, the active learning assessments were administered and video‐recorded in the absence of 3D physical models. After a second control semester, the physical models will be implemented simultaneously with the assessments into the classroom. In addition, the validated Enzyme‐Substrate Interaction Concept Inventory (ESICI) survey is administered at the beginning and end of each semester to establish a baseline for each class, measure gains in each of the three misconception areas, and offer a comparison against the published national average. Likert‐scale coded scoring of individual questions in the active learning assessments, ESICI results and observational evaluation of the recorded activities will be analyzed for the control and experimental classrooms using a mixed‐methods approach that includes quantitative inferential and descriptive statistical analysis. Preliminary data has been collected and analyzed on the first control semester in Spring 2016 and is planned for Fall 2016, suggesting that the need for this type of intervention is substantial. Further development and results of this study set the stage for curriculum wide development of enzyme‐substrate interaction targeted assessments.

Congenital heart defects (CHDs) are the most common congenital defect, occurring in approximately 1 in 100 live births and being a leading cause of perinatal morbidity and mortality. Of note, approximately 25% of these defects are classified as critical, requiring immediate postnatal care by pediatric cardiology and neonatal cardiac surgery teams. Consequently, early and accurate diagnosis of CHD is key to proper prenatal and postnatal monitoring in a tertiary care setting. In this scenario, fetal echocardiography is considered the gold standard imaging ultrasound method for the diagnosis of CHD. However, the availability of this examination in clinical practice remains limited due to the need for a qualified specialist in pediatric cardiology. Moreover, in light of the relatively low prevalence of CHD among at-risk populations (approximately 10%), ultrasound cardiac screening for potential cardiac anomalies during routine second-trimester obstetric ultrasound scans represents a pivotal aspect of diagnosing CHD. In order to maximize the accuracy of CHD diagnoses, the views of the ventricular outflow tract and the superior mediastinum were added to the four-chamber view of the fetal heart for routine ultrasound screening according to international guidelines. In this context, four-dimensional spatio-temporal image correlation software (STIC) was developed in the early 2000s. Some of the advantages of STIC in fetal cardiac evaluation include the enrichment of anatomical details of fetal cardiac images in the absence of the pregnant woman and the ability to send volumes for analysis by an expert in fetal cardiology by an internet link. Sequentially, new technologies have been developed, such as fetal intelligent navigation echocardiography (FINE), also known as "5D heart", in which the nine fetal cardiac views recommended during a fetal echocardiogram are automatically generated from the acquisition of a cardiac volume. Furthermore, artificial intelligence (AI) has recently emerged as a promising technological innovation, offering the potential to warn of possible cardiac anomalies and thus increase the ability of non-cardiology specialists to diagnose CHD. In the early 2010s, the advent of 3D reconstruction software combined with high-definition printers enabled the virtual and 3D physical reconstruction of the fetal heart. The 3D physical models may improve parental counseling of fetal CHD, maternal-fetal interaction in cases of blind pregnant women, and interactive discussions among multidisciplinary health teams. In addition, the 3D physical and virtual models can be an useful tool for teaching cardiovascular anatomy and to optimize surgical planning, enabling simulation rooms for surgical procedures. Therefore, in this review, the authors discuss advanced image technologies that may optimize prenatal diagnoses of CHDs.

It is well known that reverse engineering and additive manufacturing may be suitably integrated to develop different kinds of customized devices. Starting from image capture and analysis techniques, it is possible to manufacture an object or a functional part in a layer-by-layer fashion. Today many objects may be fabricated by additive manufacturing, benefiting from user-friendly computer programs and from the availability of open source 3-D printers. In the field of cultural heritage, there are many potential applications of the reverse engineering tools and methods, ranging from dissemination (e.g., virtual museums), reproduction (e.g., via additive manufacturing) and maintenance, to condition monitoring. Accordingly, in the proposed research 3D virtual and physical scale models of buildings and artworks were properly developed. 3D physical models were fabricated by fused deposition modeling (FDM), starting from the optimization of the process and instrument parameters. The processability of the materials (i.e., thermoplastic polymers) was assessed through functional and calorimetric analyses. Image capture and analysis techniques allowed to reproduce the geometry and morphology.

Monochorionic diamniotic quadruplet pregnancy: physical models from prenatal three-dimensional ultrasound and magnetic resonance imaging data.

How instructors frame students' interactions with educational technologies can enhance or reduce learning with multiple representations

In the presentation of architectural projects, physical models are still commonly used as a powerful and effective representation for building design and construction. On the other hand, Augmented Reality (AR) promises a wide range of possibilities in visualizing and interacting with 3D physical models, enhancing the modeling process. To benefit both, we present a novel medium for architectural representation: a marker-less AR powered physical architectural model that employs dynamic digital features. With AR enhancement, physical capabilities of a model could be extended without sacrificing its tangibility. We developed a framework to investigate the potential uses of 3D-model- based AR registration method and its augmentation on physical architectural models. To explore and demonstrate integration of physical and virtual models in AR, we designed this framework providing physical and virtual model interaction: a user can manipulate the physical model parts or control the visibility and dynamics of the virtual parts in AR. The framework consists of a LEGO model and an AR application on a hand-held device which was developed for this framework. The AR application utilizes a marker-less AR registration method and employs a 3D-model-based AR registration. A LEGO model was proposed as the physical 3D model in this registration process and machine learning training using Vuforia was utilized for the AR application to recognize the LEGO model from any point of view to register the virtual models in AR. The AR application also employs a user interface that allows user interaction with the virtual parts augmented on the physical ones. The working application was tested over its registration, physical and virtual interactions. Overall, the adoption of AR and its combination with physical models, and 3D-model-based AR registration allow for many advantages, which are discussed in the paper.

The coupling of the desalination process with solar technology is a complex problem. As various types of desalination processes and solar technologies have been developed, the selection of the best combination requires several design criteria. Capital costs, operation and maintenance costs, plant site, salinity of seawater, environmental impacts, and water quantity and quality requirements are examples of the design criteria involved in selecting a suitable desalination process. On the other hand, the selection of a suitable solar system is governed by a number of factors such as plant configuration, energy storage, location, working fluids, etc. Moreover, when integrating the solar technology and desalination processes, more requirements and constraints arise. A generic design would reduce the cost of engineering studies and the time to market thanks to the reuse of existing designs, and the ability to adapt a technical solution according to a given context (the best architectures according to a context (both spatial and temporal)). We use a design framework, completed by multi-objective, multidisciplinary optimization models in order to manage variability (space — different locations then different natural environment characteristics mainly sea water quality, solar radiation and dust) and flexibility (time-increase of demand overtime).

Objective: The objective of this study is to assess the maternal–fetal attachment (MFA) in six blind pregnant women by means three-dimensional (3D) physical models from 3D ultrasound and magnetic resonance imaging (MRI) scan data. Methods: We performed a prospective observational cross-sectional study with six blind pregnant women who performed 3D ultrasound and MRI exams to build 3D physical models for their fetuses. The MFA was assessed quantitatively by means a questionnaire of three questions, each one with a score ranging from 0 to 3. We considered MFA values ≥ 7 to each pregnant woman. The descriptive data were expressed by mean ± standard deviation (SD). Results: The mean (±SD) maternal age was 32 ± 2.7 years. The mean gestational age at 3DUS and MRI exams were 23.1 ± 3.7 and 21.3 ± 0.9 weeks, respectively. The mean of gestational age at delivery was 36.5 ± 4.7 weeks and all of them were cesarean sections. The mean newborn weight was 2615.8 ± 871.9 g and the gender was 50% both female and male. The MFA was quantitatively observed in all pregnant women, with maximum value (9) in all of them. Conclusion: The MFA was quantitatively observed in all blind pregnant women using 3D physical models.

As an important enhanced oil recovery method for tight reservoirs, CO2 huff and puff (HnP) is getting more attention in recent years. It is urgent to systematically study the characters of CO2 HnP. Due to the limitations of numerical simulation, it is more reliable and reasonable to study the development characteristics of CO2 HnP through experiments. The objective of this work is to conduct comprehensive experiments to clarify the characters and main mechanisms of CO2 HnP process based on the three-dimensional (3D) physical models. A 3D physical experimental apparatus with circumstance of high temperature and high pressure has been developed, which is mainly used to support the models with a fixed confining pressure and temperature. Based on the similarity criterion of dimensionless conductivity, two different 3D physical models (30cm×30cm×3.5cm) with a horizontal well and fractures are made from outcrops to imitate the different reservoirs. Under these preconditions, some CO2 HnP experiments were conducted to investigate the development characteristics from the 3D physical models.Also,long core experiments were carried out to establish and verify the production prediction model, combined with expansion test, diffusion test and nuclear magnetic resonance test. The experimental results show that the development process of CO2 HnP can be divided into four stages: CO2 backflow, Gas production with attached oil, High-speed oil production and Decay. The main mechanism of oil production in each stage is different. With the increase of the cycle number, the recovery factors of both models first increase and then decrease, while the oil/gas replacement rates may drop rapidly. The fractures have been proven to increase the oil recovery from 21.2% to 36.7% after ten rounds of CO2 HnP. Based on the analysis of expansion and molecular diffusion, a production prediction model was established, and the average error between the predicted results and the experimental results is 7.7%, which has good applicability and accuracy. In this paper, some large-scale 3D physical model experiments with CO2 HnP for tight reservoir were elaborated. The development characteristics of CO2 HnP were analyzed, and a production prediction model was established. A lot of valuable experimental data and a better understanding on CO2 HnP process in tight reservoir have been obtained.

A novel hybrid forward osmosis - nanofiltration (FO-NF) process for seawater desalination: Draw solution selection and system configuration