Developed Model Research Articles

A MODEL FOR FORECASTING DEW POINT PRESSURE IN NIGER DELTA CONDENSATE RESERVOIRS USING CONSTANT VOLUME DEPLETION TESTS DATA

The dew point pressure plays a critical role in developing gas condensate reservoirs. It is a crucial factor used in fluid characterisation, performance estimation of gas reservoirs, and design of production systems. However, the composition of gas condensate fluid varies from one location to another, and thus, empirical correlations have been developed to determine the dew point pressure without conducting routine tests. As a result, correlations that are solely useful in the region where they were developed and analysed are developed. This work developed an empirical correlation using data from gas condensate wells in the Niger Delta using multiple linear regression. The model was developed using the Analytical Tools and techniques in Microsoft Excel. To develop the model, we utilised 63 data sets from the Constant Volume Depletion (CVD) experiment, which involved gas condensates from resources in the Niger Delta. The model comprises an empirical correlation that estimates the dew point pressure for gas condensate reservoirs. It uses compositional information of the fluid and reservoir temperature as input parameters. The correlation is derived through multiple regression analyses. Comparing the prediction accuracy of formulas based on the developed model and other conventional methods indicated that this model is more accurate than other statistical methods in predicting DPP within the Niger Delta region. This model can produce more accurate results than other estimation methods when working under limited field information and time constraints. The correlation developed in this process has a coefficient of determination of R2=0.869 and an Average Absolute Relative Error (AARE) of -2.0767%, along with a root mean square of 195279, indicating a high level of reliability in the proposed correlation.

STEAMER Hybrid Learning Project for Creative and Computational Thinking: Perspectives from Elementary School Teacher Candidates

<p style="text-align:justify">The computing and creative skills of students in Indonesia are still low since the government has not focused on student creativity and computational empowerment programs. This research aims to develop a science, technology, engineering, art, mathematics, and reflection (STEAMER) hybrid learning project model for teachers' creative and computational thinking abilities, as well as analyze elementary school teacher candidates' perceptions of the use of STEAMER hybrid learning model to improve teachers' creative and computational thinking abilities. This research is development research with an analysis, design, development, implementation, and evaluation (ADDIE) model. The instruments used in this study were questionnaires and interviews with experts, lecturers, and elementary school teacher candidates. The research was conducted at eight universities in Indonesia with a total sample of 100 elementary school teacher candidates. Through quantitative and qualitative data analysis, the research results have developed the STEAMER hybrid learning project model based on learning theory, syntax, social systems, support systems, and the instructional impact of learning models. The results of the validation show that the developed learning model is feasible in terms of model, material, media, and language experts. The model is suitable for elementary school teacher education. Furthermore, based on the perceptions of the teacher candidates, it is stated that the STEAMER hybrid learning project can develop the ability of the teacher candidates to think creatively and computationally.</p>

A machine learning-based approach for individualized prediction of short-term outcomes after anterior cervical corpectomy.

A retrospective machine learning (ML) classification study for prognostic modeling after anterior cervical corpectomy (ACC). To evaluate the effectiveness of ML in predicting ACC outcomes and develop an accessible, user-friendly tool for this purpose. Based on our literature review, no study has examined the capability of ML algorithms to predict major shortterm ACC outcomes, such as prolonged length of hospital stay (LOS), non-home discharge, and major complications. The American College of Surgeons' National Surgical Quality Improvement Program database was used to identify patients who underwent ACC. Prolonged LOS, non-home discharges, and major complications were assessed as the outcomes of interest. ML models were developed with the TabPFN algorithm and integrated into an open-access website to predict these outcomes. The models for predicting prolonged LOS, non-home discharges, and major complications demonstrated mean areas under the receiver operating characteristic curve (AUROC) of 0.802, 0.816, and 0.702, respectively. These findings highlight the discriminatory capacities of the models: fair (AUROC >0.7) for differentiating patients with major complications from those without, and good (AUROC >0.8) for distinguishing between those with and without prolonged LOS and non-home discharges. According to the SHapley Additive Explanations analysis, single- versus multiple-level surgery, age, body mass index, preoperative hematocrit, and American Society of Anesthesiologists physical status repetitively emerged as the most important variables for each outcome. This study has considerably enhanced the prediction of postoperative results after ACC surgery by implementing advanced ML techniques. A major contribution is the creation of an accessible web application, highlighting the practical value of the developed models. Our findings imply that ML can serve as an invaluable supplementary tool to stratify patient risk for this procedure and can predict diverse postoperative adverse outcomes.

Public policies and right to sexuality of the elderly in Chile

People over 65 years of age will constitute the majority of the world's population in the short term, but in precarious living conditions: more years in a worse condition of vulnerability and fragility. Societies and development models would not be prepared, generating high personal, family and collective costs. In Chile, fragility would be highly prevalent in this population, impacting the full development of their lives; with sexuality as one of the aspects that are invisible and little studied. This work makes a critical approach, based on the review and analysis of context, public policies and legislation in force in Chile, evidencing atomization and biomedical orientation of public policies, collaborating in the understanding of the relationship between fragility and sexuality in old people; and revealing pending training and research tasks for the generation of public policies for an active and healthy life.

Class II (three-layer system) phenomenological model based on limiting current density and dynamic chelation chemistry for separation of rare earth elements using electrodialysis

This paper presents an in-depth investigation into the optimization of rare earth element (REE) separation through electrodialysis, leveraging a newly developed Class II phenomenological model. This study explores the pivotal roles of the HEDTA/Nd molar ratio and pH of feed solution on enhancing the separation efficiency of neodymium (Nd) and praseodymium (Pr) from lanthanum (La) and cerium (Ce). By integrating expanded Nernst-Planck equations and the concept of limiting current density, the model offers a sophisticated understanding of ion transport dynamics and the impacts of concentration polarization. Experimental validation confirms the model’s predictive accuracy, demonstrating its practical applicability for industrial-scale operations. The research delineates how operational parameters such as chelating agent concentration and pH critically influence the purity and yield of separated REEs. The dynamic nature of chelation chemistry is also examined, highlighting its evolution during the electrodialysis process and its effect on the system’s overall performance. Key findings illustrate that lower HEDTA/Nd molar ratios significantly enhance the purity of Nd + Pr by minimizing the chelation of La and Ce, thus facilitating their migration to the concentrate compartment. Conversely, higher ratios maximize yield by retaining more Nd + Pr in the feed compartment. This dual approach allows for optimized separation based on specific industrial requirements. The outcomes of this study not only advance the field of REE separation but also provide a framework for further research into more efficient and sustainable extraction methods. The developed model and its validation represent a step forward in the practical application of electrodialysis in REE processing, offering substantial benefits for the critical materials sector.

A comprehensive perspective on local drivers in community-based health tourism industry development: a qualitative study of Mazandaran Province, Iran

BackgroundHealth tourism is an important component that may influence the direction of development in a region. Mazandarn, Iran, is recognized as a highly sustainable market in the region because of its abundant natural resources, temperate temperature, and strategic geographical location. Therefore, considering the importance of health tourism and its existing potential in Mazandaran, Iran, this study was conducted with the aim of exploring a comprehensive perspective on local drivers in community-based health tourism industry development and factors affecting the attraction of health tourism.MethodsWe conducted this study in Mazandarn, Iran, using a qualitative approach. Participants included a sample of Iranian people, aged 34–54 years, with previous history of health tourism or expert in it. Participants were selected from three different categories of the community: academic professionals in health tourism, managers in health tourism, and health tourists. Data were obtained via semi-structured in-depth interviews and focus group discussions. Inductive qualitative content analysis was used to converge and compare themes through participant data. The interviews kept going until data saturation was achieved.ResultsBased on our findings, we distilled local drivers in community-based health tourism industry development into five main categories and 30 subcategories: (Sharifabadi AM, Ardakani FA. A model for health tourism development using fuzzy TOPSIS and interpretive structural modeling in Yazd province. J Health Adm (JHA). 2014;17:55.) infrastructure and resources; (Hemmati F, Dabbaghi F, Mahmoudi G. Investigating the impact of Information Technology on the status of Health Tourism in Mashhad, Iran. Revista Publicando. 2018;5(15):54–65.) tourist attractions; (Sarabi Asiabar A, Rezapour A, Raei B, Tahernezhad A, Alipour V, Behzadifar M. Economic, Cultural, and Political Requirements for Medical Tourism Development in Iran: Insights from a Fuzzy Analytical Hierarchy Process Method. Med J Islamic Repub Iran. 2022;35:199.) socio-cultural contexts; (Mosadeghrad AM, Sadeghi M. Medical tourism: Reasons for choosing Iran. Payesh (Health Monitor). 2021;20(2):145–66.) economic-financial factors; and (Manna R, Cavallone M, Ciasullo MV, Palumbo R. Beyond the rhetoric of health tourism: shedding light on the reality of health tourism in Italy. Curr Issues Tourism. 2020;23(14):1805–19.) political-communicative factors. The findings of the study showed that, from the participants’ point of view, although there are several strategies, such as the development of public service and tourism infrastructure, increasing tourist attractions, and formulating appropriate policies and procedures for the development of health tourism, they are also faced with many challenges, especially political, economic, and cultural challenges.ConclusionThis study showed that improving infrastructure and resources, promoting tourist attractions, informing socio-cultural contexts, improving economic and financial capacity, and developing political and communicative contexts might increase the attraction of health tourists. The suggested components are not contextually driven, although empirical outcomes may differ based on the level of service offerings in health tourism locations.

An imaging-based model to predict the malignant potential of intraductal papillary mucinous neoplasm of the pancreas.

To develop and validate imaging-based models for predicting the malignancy risk of intraductal papillary mucinous neoplasm (IPMN). We retrospectively analyzed data from 241 IPMN patients who underwent preoperative CT and MRI for model development. Cyst size, presence and size of the enhancing mural nodule (EMN), main pancreatic duct (MPD) diameter, thickened/enhancing cyst wall, abrupt MPD caliber change with distal atrophy, and lymphadenopathy were assessed. Multiple logistic regression models predicting malignancy risk were created using either continuous (Model C) or dichotomized variables (Model D) using these imaging features. Validation included internal (n = 55) and external (n = 43) datasets. Model performance was assessed using the area under the receiver operating characteristic curve (AUC) and compared with that of the international guideline-based model (Model F). Model C identified age, EMN size, MPD diameter, and lymphadenopathy as independent predictors on CT, and age and presence and size of EMN on MRI. Model D identified age ≥ 68, cyst size ≥ 31 mm, EMN ≥ 6 mm, MPD ≥ 7 mm, and lymphadenopathy as independent predictors on CT, and age ≥ 68, EMN ≥ 4.5 mm, and lymphadenopathy on MRI. Model C (AUC, 0.763-0.899) performed slightly better than Model D (AUC, 0.753-0.912) without statistical significance. No significant difference was observed between Models C and F (AUC, 0.729-0.952). Combining Model C with obstructive jaundice improved performance (AUC, 0.802-0.941) without statistical significance. Our imaging-based models effectively predicted the malignancy risk of IPMNs, comparable to international consensus guidelines. Imaging features are important for predicting the malignant potential of IPMNs. Our imaging-based model may help determine surgical candidacy for patients with IPMNs. Non-invasively determining the malignant potential of intraductal papillary mucinous neoplasms (IPMNs) allows for appropriate treatment decision-making We identified multiple imaging features that are associated with malignant transformation and developed models for this prediction. Our model performs comparably with international consensus guidelines in predicting the malignant potential of IPMNs.

Predictive modelling of HHV and LHV for sugar industry by-products: a study on sugarcane trash leaves, bagasse, and filter cake

ABSTRACT This study aimed to enhance the efficiency of by-product raw materials from the sugar industry for use as fuel. The approach involved developing an equation to calculate the higher heating value (HHV) for each type of raw material using a regression method. Additionally, a simplex-centroid mixture design (SCMD) was employed to estimate the lower heating value (LHV) based on the mixing ratios by weight of sugarcane trash leaves (SCL), sugarcane bagasse (SCB), and filter cake (FTC). The results demonstrated that the developed model accurately estimated the HHV for each raw material. The ultimate analysis showed high statistical appropriateness, with an R2 of 0.83. The standard error of estimation was 0.74 MJ/kg, and the mean absolute error was 0.76%. Furthermore, the SCMD effectively estimated the LHV of the SCL, SCB, and FTC mixture ratios, achieving an R2 of 99.77%. The evaluation and validation of the prediction equation revealed a mean absolute error of 7.57% and a mean bias error of 6.31%. The findings of this study can be used to enhance the combustion efficiency of sugar industry by-products for use as fuel by selecting the optimal mixing ratio for each type of raw material.

Pengembangan Media Pembelajaran Komik Matematika (KOMKA) untuk Meningkatkan Kemampuan Numerasi Siswa Sekolah Dasar

This study aims to determine the validity and effectiveness of the mathematical comic learning medium (KOMKA) to improve the numeration ability of elementary school students especially on area & volume measurement materials. This type of research is Research and Development. The development model used is the ADDIE model developed by Dick and Carey, which has five stages: Analysis, Design, Development, Implementation, and Evaluate. The population in this development research is all students of SDN 2 Padarek in the 2023/2024 academic year. The sampling technique used in this development research is simple random sampling. The data analysis technique used in this study is a descriptive analysis technique to describe whether the digital comics developed by the study are valid and effective for use in class learning. Research results show that the KOMKA learning medium is valid for use in learning activities, derived from the validity results of media experts and material experts. The KOMKA learning medium is effective in improving students' numeration skills. This can be seen from the average test results of students' mathematical understanding ability after the application of the KOMKA learning medium was higher than before.

Automated Detection of Maxillary Sinus Opacifications Compatible with Sinusitis from CT Images.

Sinusitis is a commonly encountered clinical condition that imposes a considerable burden on the healthcare systems. A significant number of maxillary sinus opacifications are diagnosed as sinusitis, often overlooking the precise differentiation between cystic formations and inflammatory sinusitis, resulting in inappropriate clinical treatment. This study aims to improve diagnostic accuracy by investigating the feasibility of differentiating maxillary sinusitis, retention cysts, and normal sinuses. We developed a deep learning-based automatic detection model to diagnose maxillary sinusitis using ostiomeatal unit computed tomography images. Of the 1080 randomly selected coronal-view CT images, including 2158 maxillary sinuses, datasets of maxillary sinus lesions comprised 1138 normal sinuses, 366 cysts, and 654 sinusitis based on radiographic findings, and were divided into training (n = 648 CT images), validation (n = 216), and test (n = 216) sets. We utilized a You Only Look Once based model for object detection, enhanced by the transfer learning method. To address the insufficiency of training data, various data augmentation techniques were adopted, thereby improving the model's robustness. The trained You Only Look Once version 8 nano (YOLOv8n) model achieved an overall precision of 97.1%, with the following class precisions on the test set: normal = 96.9%, cyst = 95.2%, and sinusitis = 99.2%. With an average F1 score of 95.4%, the F1 score was the highest for normal, then sinusitis, and finally, cysts. Upon evaluating a performance on difficulty level, the precision decreased to 92.4% on challenging test dataset. The developed model is feasible for assisting clinicians in screening maxillary sinusitis lesions.

Enhancing torrefaction process efficiency for biochar production from filter cake residue in the sugar industry

The torrefaction process is a promising technique for enhancing the quality of solid biomass fuels. This study investigates the effects of torrefaction on biochar produced from filter cake residue, a byproduct of the sugar industry. The primary objectives were to evaluate the impact of process parameters on biochar properties and identify optimal conditions for maximizing the higher heating value (HHV). Filter cake residue was subjected to torrefaction at temperatures ranging from 220-340°C under an inert atmosphere. The influence of particle size (20, 60, and 100 mesh), nitrogen flow rate (20-30 ml/min), temperature (220-340°C), and residence time (30-90 min) on biochar properties was examined using response surface methodology. Proximate analysis revealed that torrefaction significantly reduced moisture, volatile matter, and ash content while increasing fixed carbon content. The maximum HHV of 21.9571 MJ/kg was achieved at a particle size of 20 mesh, nitrogen flow rate of 25 ml/min, temperature of 340°C, and residence time of 60 min. The experimental results agreed with predicted values from the developed models, with an average error of 3.64%. Optimal torrefaction conditions were determined to be a particle size of 21.77 mesh, nitrogen flow rate of 22.50 ml/min, temperature of 311.13°C, and residence time of 42.58 min, yielding a maximum HHV of 18.4853 MJ/kg. These findings demonstrate the potential of torrefaction for upgrading filter cake residue into a high-quality solid biofuel, providing a sustainable solution for waste utilization in the sugar industry.

The Military Medicine Leadership Life Cycle: A Model for Longitudinal Leadership Development for Staff Physicians.

Leadership development is a challenge for all health care systems. Military Medicine has unique challenges with increased frequency of physician turnover and more junior leaders taking on positions of leadership earlier in their careers. Military medical corps officers are also challenged with leading in clinical, academic, and operational settings. Effective leadership within the Military Healthcare System requires an intentional and ongoing leadership development process across the careers of military medical corps officers. This article describes the leadership lifecycle of military medical corps officers, highlighting existing leadership development opportunities and providing an example of a leadership lifecycle from junior staff to senior executive for other organizations. The article concludes with specific recommendations that will allow military medicine to continue to strengthen the leadership skills of its officers to meet ever growing challenges.

Innovative thermal management in the presence of ferromagnetic hybrid nanoparticles

In the present work, a simple intelligence-based computation of artificial neural networks with the Levenberg-Marquardt backpropagation algorithm is developed to analyze the new ferromagnetic hybrid nanofluid flow model in the presence of a magnetic dipole within the context of flow over a stretching sheet. A combination of cobalt and iron (III) oxide (Co-Fe2O3) is strategically selected as ferromagnetic hybrid nanoparticles within the base fluid, water. The initial representation of the developed ferromagnetic hybrid nanofluid flow model, which is a system of highly nonlinear partial differential equations, is transformed into a system of nonlinear ordinary differential equations using appropriate similarity transformations. The reference data set of the possible outcomes is obtained from bvp4c for varying the parameters of the ferromagnetic hybrid nanofluid flow model. The estimated solutions of the proposed model are described during the testing, training, and validation phases of the backpropagated neural network. The performance evaluation and comparative study of the algorithm are carried out by regression analysis, error histograms, function fitting graphs, and mean squared error results. The findings of our study analyze the increasing effect of the ferrohydrodynamic interaction parameter β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta$$\\end{document} to enhance the temperature and velocity profiles, while increasing the thermal relaxation parameter α\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha$$\\end{document} decreases the temperature profile. The performance on MSE was shown for the temperature and velocity profiles of the developed model about 9.1703e−10, 7.1313ee−10, 3.1462e−10, and 4.8747e−10. The accuracy of the artificial neural networks with the Levenberg-Marquardt algorithm method is confirmed through various analyses and comparative results with the reference data. The purpose of this study is to enhance understanding of ferromagnetic hybrid nanofluid flow models using artificial neural networks with the Levenberg-Marquardt algorithm, offering precise analysis of key parameter effects on temperature and velocity profiles. Future studies will provide novel soft computing methods that leverage artificial neural networks to effectively solve problems in fluid mechanics and expand to engineering applications, improving their usefulness in tackling real-world problems.

Rancangan Modul Self Care sebagai Sarana Pendidikan Kesehatan terhadap Penanganan Dysmenorrhea pada Remaja

This study aims to design a self-care module for handling dysmenorrhea based on the needs of female adolescents. The method used is a qualitative descriptive exploration with a research & development model. The results of the study indicate that female adolescents' understanding of dysmenorrhea can be seen from four themes, namely perceptions about the meaning of dysmenorrhea, perceptions about the causes of dysmenorrhea, perceptions about the impact of dysmenorrhea, and perceptions about handling dysmenorrhea. In conclusion, female adolescents need educational media to hold the dysmenorrhea they experience, so this study produces a product design in the form of a dysmenorrhea handling module for schools and female adolescents. Keywords: Dysmenorrhea, Self Care Module, Adolescents

Soft computing models for prediction of bentonite plastic concrete strength

Bentonite plastic concrete (BPC) is extensively used in the construction of water-tight structures like cut-off walls in dams, etc., because it offers high plasticity, improved workability, and homogeneity. Also, bentonite is added to concrete mixes for the adsorption of toxic metals. The modified design of BPC, as compared to normal concrete, requires a reliable tool to predict its strength. Thus, this study presents a novel attempt at the application of two innovative evolutionary techniques known as multi-expression programming (MEP) and gene expression programming (GEP) and a boosting-based algorithm known as AdaBoost to predict the 28-day compressive strength ( ) of BPC based on its mixture composition. The MEP and GEP algorithms expressed their outputs in the form of an empirical equation, while AdaBoost failed to do so. The algorithms were trained using a dataset of 246 points gathered from published literature having six important input factors for predicting. The developed models were subject to error evaluation, and the results revealed that all algorithms satisfied the suggested criteria and had a correlation coefficient (R) greater than 0.9 for both the training and testing phases. However, AdaBoost surpassed both MEP and GEP in terms of accuracy and demonstrated a lower testing RMSE of 1.66 compared to 2.02 for MEP and 2.38 for GEP. Similarly, the objective function value for AdaBoost was 0.10 compared to 0.176 for GEP and 0.16 for MEP, which indicated the overall good performance of AdaBoost compared to the two evolutionary techniques. Also, Shapley additive analysis was done on the AdaBoost model to gain further insights into the prediction process, which revealed that cement, coarse aggregate, and fine aggregate are the most important factors in predicting the strength of BPC. Moreover, an interactive graphical user interface (GUI) has been developed to be practically utilized in the civil engineering industry for prediction of BPC strength.

SAMP Rao algorithm based minimization of the roughness of milled surface of Ti-6Al-4V

In order to solve optimization problems including machining responses as objectives, this study suggests a parameter-less method called the self-adaptive multi population (SAMP) Rao algorithm that does not rely on metaphors. When machining titanium alloys, achieving a good surface quality is a difficult process. In the current study, an effort has been made to reduce surface roughness during milling Ti-6Al-4V. Response surface methodology (RSM) was used in the experiment design to create a model for surface roughness using cutting parameters as variables. The developed model was tested in additional tests in addition to the primary experiments. It was shown that cutter speed and feed rate had the biggest effects on milled roughness, whereas depth of cut had very little of an impact. The model’s quality is demonstrated by the correlation coefficient (R2) 98%, which indicates that the model can explain 98% of the data. Later, a response surface-based desirability technique was used to minimize roughness of milled surface. The outcome of the proposed algorithm is compared with RSM optimizer. It has been noted that the outcomes achieved with the SAMP approach are more advantageous than RSM approach and found 18% minimum value of roughness of milled surface. Main reason to get better result in case of SAMP Rao algorithm is that it is based on the iterative search process with random values of cutting parameters with in defined range. SAMP Rao Algorithm provides cutting settings of 133.5 m min−1, 0.13 mm/tooth feed rate, and 2.06 mm of milling depth along with a minimal roughness of milled surface of 0.37 μm.

Evolution of phase ratio and its effect on residual stress for 2205 duplex stainless steel multipass welded joints by thermo-metallurgical-mechanical model

The control of phase ratio and residual stress is a crucial subject for the structural integrity of duplex stainless steel welded components. Therefore, this paper aims to clarify the evolution of phase ratio and its effect on residual stress for duplex stainless steel multipass welded joints by a thermo-metallurgical-mechanical coupled welding model, alongside the crucial experiments which contribute to the development and validation of the coupled model. The developed model takes both the precipitation and dissolution behavior of austenite under repeated thermal cycles into consideration, thereby demonstrating a remarkable ability to predict the distribution of phase ratio and residual stress. The influencing mechanism of solid-state phase transformation on residual stress is discussed extensively. The results reveal that, for the duplex stainless steel welded joints by gas tungsten arc welding, excessive austenite is often formed within the welding zone particularly for the middle passes, while it is the occurrence of ferritization at the heat affected zone. The effect of phase transformation on residual stress is mainly presented by the change of mechanical properties and phase volume. The overtransformation from ferrite to austenite tends to induce a higher tensile residual stress, vice versa, compressive stress. In addition, it is challenging to achieve phase balance through heat input alone. However, a smaller heat input is recommended to minimize residual stress while ensuring welding penetration. This paper serves as a theoretical foundation for controlling the phase ratio and mitigating residual stress during the welding of duplex stainless steel, thereby contributing to the enhancement of weld quality and structural reliability.

Rapid Biofabrication of an Advanced Microphysiological System Mimicking Phenotypical Heterogeneity and Drug Resistance in Glioblastoma.

Microphysiological systems (MPSs) reconstitute tissue interfaces and organ functions, presenting a promising alternative to animal models in drug development. However, traditional materials like polydimethylsiloxane (PDMS) often interfere by absorbing hydrophobic molecules, affecting drug testing accuracy. Additive manufacturing, including 3D bioprinting, offers viable solutions. GlioFlow3D, a novel microfluidic platform combining extrusion bioprinting and stereolithography (SLA) is introduced. GlioFlow3D integrates primary human cells and glioblastoma (GBM) lines in hydrogel-based microchannels mimicking vasculature, within an SLA resin framework using cost-effective materials. The study introduces a robust protocol to mitigate SLA resin cytotoxicity. Compared to PDMS, GlioFlow3D demonstrated lower small molecule absorption, which is relevant for accurate testing of small molecules like Temozolomide (TMZ). Computational modeling is used to optimize a pumpless setup simulating interstitial fluid flow dynamics in tissues. Co-culturing GBM with brain endothelial cells in GlioFlow3D showed enhanced CD133 expression and TMZ resistance near vascular interfaces, highlighting spatial drug resistance mechanisms. This PDMS-free platform promises advanced drug testing, improving preclinical research and personalized therapy by elucidating complex GBM drug resistance mechanisms influenced by the tissue microenvironment.

Pluripotent stem cell-derived organoids: A brief history of curiosity-led discoveries.

Organoids are quickly becoming an accepted model for understanding human biology and disease. Pluripotent stem cells (PSC) provide a starting point for many organs and enable modeling of the embryonic development and maturation of such organs. The foundation of PSC-derived organoids can be found in elegant developmental studies demonstrating the remarkable ability of immature cells to undergo histogenesis even when taken out of the embryo context. PSC-organoids are an evolution of earlier methods such as embryoid bodies, taken to a new level with finer control and in some cases going beyond tissue histogenesis to organ-like morphogenesis. But many of the discoveries that led to organoids were not necessarily planned, but rather the result of inquisitive minds with freedom to explore. Protecting such curiosity-led research through flexible funding will be important going forward if we are to see further ground-breaking discoveries.

Using boundary objects and methodological island (BOMI) modeling in large-scale agile systems development

AbstractLarge-scale systems development commonly faces the challenge of managing relevant knowledge between different organizational groups, particularly in increasingly agile contexts. Here, there is a conflict between coordination and group autonomy, and it is challenging to determine what necessary coordination information must be shared by what teams or groups, and what can be left to local team management. We introduce a way to manage this complexity using a modeling framework based on two core concepts: methodological islands (i.e., groups using different development methods than the surrounding organization) and boundary objects (i.e., artifacts that create a common understanding across team borders). However, we found that companies often lack a systematic way of assessing coordination issues and the use of boundary objects between methodological islands. As part of an iterative design science study, we have addressed this gap by producing a modeling framework (BOMI: Boundary Objects and Methodological Islands) to better capture and analyze coordination and knowledge management in practice. This framework includes a metamodel, as well as a list of bad smells over this metamodel that can be leveraged to detect inter-team coordination issues. The framework also includes a methodology to suggest concrete modeling steps and broader guidelines to help apply the approach successfully in practice. We have developed Eclipse-based tool support for the BOMI method, allowing for both graphical and textual model creation, and including an implementation of views over BOMI instance models in order to manage model complexity. We have evaluated these artifacts iteratively together with five large-scale companies developing complex systems. In this work, we describe the BOMI framework and its iterative evaluation in several real cases, reporting on lessons learned and identifying future work. We have produced a matured and stable modeling framework which facilitates understanding and reflection over complex organizational configurations, communication, governance, and coordination of knowledge artifacts in large-scale agile system development.