Development Of Model Research Articles

Abstract A050: MicroRNA as biomarkers in early detection and personalized treatment in ovarian cancer: Development of a personalized prevention consortium

Abstract Epithelial ovarian cancer (EOC) has poor survival given no effective early detection strategies and non-specific early disease symptoms. We have formed a consortium with European and American partners to provide the necessary validation of a microRNA (miRNA) panel for earlier detection of EOC using serum samples and data from prospective cohorts, paired with clinical data and biospecimens. Our objectives are to validate a serologic miRNA panel that, together with CA125, would have sufficient diagnostic discrimination to be used as a tool that, complementary to imaging, would allow early identification and direction of patients with suspected malignancy to personalized gyn-oncological care. We will validate serum miRNA profiles as biomarkers for early detection in samples collected from cases ⇐3 years prior to EOC diagnosis and matched controls and characterize sources of variation in miRNA profiles and their impact on discrimination. Evaluate miRNA profiles in the clinical setting and the discrimination between EOC cases and women with (i) benign ovarian tumors in blood samples collected prior to diagnostic surgery and (ii) underlying comorbidities (including other cancer types). Further, we will evaluate serum miRNA profiles as predictors of recurrence and will compare tumor tissue and serum miRNA profiles. Develop and optimize protocols for data integration and methods for algorithm development. Direct in silico validation, including miRNA and CA125 levels, and clinical and epidemiologic data. Address Ethical, Legal and Social Aspects (ELSA), including activities in key stakeholder groups. Challenges in health data privacy and sharing, including for development of an artificial intelligence model, will be addressed, as will the definition of a “healthy” population with respect to miRNA profiles for EOC. The consortium was launched in 2023, with the first findings expected in 2024. Beyond the initial direct aims of the consortium, expanded aims on further promising markers are envisaged. Citation Format: Renée Turzanski Fortner, Hilde Langseth. MicroRNA as biomarkers in early detection and personalized treatment in ovarian cancer: Development of a personalized prevention consortium [abstract]. In: Proceedings of the AACR Special Conference on Ovarian Cancer; 2023 Oct 5-7; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_2):Abstract nr A050.

Modeling of primary breakup considering turbulent nozzle flow, internal turbulence and surface instability of liquid jet using turbulence decay theory

In heat engines utilizing fuel injection, the processes of atomization and spray formation have a significant impact on the combustion process, thereby determining both efficiency and emission characteristics. Accurate prediction and control of spray formation in fuel injection systems play a key role in improving the efficiency and environmental performance of thermal engines, especially with the emergence of carbon-neutral fuels. To achieve accurate prediction of spray mixture formation, it is imperative to refine the atomization model for the liquid jet within numerical simulations. This requires a phenomenological representation of the atomization process that avoids reliance on computational constants obtained from spray experimental results. Consequently, the present study attempts to mathematically model the turbulent nozzle flow and liquid jet atomization process, leading to the development of a novel primary breakup model. The construction of the primary breakup model involves an analysis of the turbulence at the nozzle inlet. By merging this turbulence with the turbulence resulting from wall shear flow within the nozzle, the model provides insight into the internal turbulence and surface instability of the liquid jet, encompassing the turbulence spectrum. Consequently, the influence of nozzle length on the turbulent flow within the nozzle can be understood, and the droplet formation characteristics of the liquid jet can be predicted along with its multi-wavelength dispersion characteristics. The model effectively captures the experimental results in terms of breakup length and droplet dispersion characteristics, thus adding a higher level of accuracy to numerical simulations. Ultimately, the in-depth study of this model, coupled with its comparison with experimental results, enhances the understanding of the liquid jet atomization process.

Relationship among runoff, soil erosion, and rill morphology on slopes of overburdened stockpiles under simulated rainfall

Overburdened stockpiles are difficult to achieve natural stability in a short period time because of their complex and diverse composition of materials and significant differences in physical and chemical properties, causing numerous cases of water loss and soil loss events. We conducted a series of experiments at different rainfall intensities (30, 60, 90, and 120 mm h−1) and different gravel contents (0 %, 10 %, 20 %, 30 %, and 40 %) to study the runoff, soil erosion, and rill morphology characteristics and relationship among three of overburdened stockpiles in production and construction projects with simulated rainfall. The runoff rate and soil loss rate basically showed a trend of increasing and then decreasing with gravel content and lower gravel content had a certain degree of inhibition on the flow velocity. 20 %, 0 %, 0 %, and 20 % gravel contents had the most significant inhibition on the flow velocity at 30, 60, 90, and 120 mm h−1 rainfall intensities, respectively. Flow shear stress, stream power, unit energy of water cross-section, maximum rill length, width and depth all increased with increasing rainfall intensity. The stream power, unit stream power, and unit energy of water cross-section were larger at higher gravel content. The maximum rill length and depth were positively correlated with gravel content, which were the minimum at 0 % gravel content and all rainfall intensities. Gravel content played a more important role in the runoff, soil loss, and rill morphology. The relationship between runoff rate and soil loss rate, flow velocity and rill morphology parameters were all expressed as linear functions, and flow velocity and soil loss rate was quadratic function, with a correlation coefficient of 0.339. In contrast, runoff rate and rill morphology parameters, soil loss rate and rill morphology parameters, erosion dynamics parameters and rill morphology parameters were all expressed as power functions. In addition, the runoff rate can better describe the process of soil loss (R2 = 0.798, P < 0.05). The unit energy of water cross-section was more suitable to describe the development of maximum rill length and depth (with rill length was R2 = 0.781, and with rill depth was R2 = 0.723, P < 0.05). At the same time, the stream power was more suitable to describe the development of maximum rill width on the slope of overburdened stockpiles (R2 = 0.841, P < 0.05). These results contributed to preventing soil loss on overburdened stockpiles and helping the development of estimation models for soil erosion.

Development and validation of a deep learning radiomics model with clinical-radiological characteristics for the identification of occult peritoneal metastases in patients with pancreatic ductal adenocarcinoma.

Occult peritoneal metastases (OPM) in patients with pancreatic ductal adenocarcinoma (PDAC) are frequently overlooked during imaging. The authors aimed to develop and validate a computed tomography (CT)-based deep learning-based radiomics (DLR) model to identify OPM in PDAC before treatment. This retrospective, bicentric study included 302 patients with PDAC (training: n =167, OPM-positive, n =22; internal test: n =72, OPM-positive, n =9: external test, n =63, OPM-positive, n =9) who had undergone baseline CT examinations between January 2012 and October 2022. Handcrafted radiomics (HCR) and DLR features of the tumor and HCR features of peritoneum were extracted from CT images. Mutual information and least absolute shrinkage and selection operator algorithms were used for feature selection. A combined model, which incorporated the selected clinical-radiological, HCR, and DLR features, was developed using a logistic regression classifier using data from the training cohort and validated in the test cohorts. Three clinical-radiological characteristics (carcinoembryonic antigen 19-9 and CT-based T and N stages), nine HCR features of the tumor, 14 DLR features of the tumor, and three HCR features of the peritoneum were retained after feature selection. The combined model yielded satisfactory predictive performance, with an area under the curve (AUC) of 0.853 (95% CI: 0.790-0.903), 0.845 (95% CI: 0.740-0.919), and 0.852 (95% CI: 0.740-0.929) in the training, internal test, and external test cohorts, respectively (all P <0.05). The combined model showed better discrimination than the clinical-radiological model in the training (AUC=0.853 vs. 0.612, P <0.001) and the total test (AUC=0.842 vs. 0.638, P <0.05) cohorts. The decision curves revealed that the combined model had greater clinical applicability than the clinical-radiological model. The model combining CT-based DLR and clinical-radiological features showed satisfactory performance for predicting OPM in patients with PDAC.

Abstract A048: Development and validation of a microfluidic cell culture model for examining cerebral microvascular barrier responses to the tumor microenvironment

Abstract Introduction: The blood-brain barrier (BBB) is composed of specialized endothelial cells that limit the passage of solutes from the blood to the brain. Microfluidic technology that allows formation of 3D tubular microvessels has been applied to in vitro BBB models, and having advantages of fluid flow, cell-cell contact, provide a more robust and predictive model for assessing vascular permeability. Use of the microfluidic model to examine the effects of the tumor microenvironment on brain endothelial cell permeability would provide mechanistic fundamental knowledge that could be used to more effectively deliver therapeutic agents to the treatment site. Methods: Human brain microvessel endothelial cells (hCMEC/d3) were seeded (10,000 cells/µL) into the vascular channel and following vascular capillary tube formation, various human brain tumor cell lines (SF8628, U87, and U251) were seeded in the adjacent brain channel of the Mimeta 3-channel microfluidic unit. Tumor-induced alterations in barrier function were examined over a 72-hour period using paracellular (fluorescein-labeled detran-FDX70kD; IRdye 800; sodium fluorescein - NaF) and transcellularly transported dyes (rhodamine 800-R800). Tumor induced changes in permeability of various marker molecules were correlated to changes in the expression of selected BBB specific genes and the tumor secretome. Results: The microfluidic BBB culture model showed reduced permeability to all the permeability markers examined compared to TranswellTM inserts with values similar to those previously reported in vivo. Compared to monoculture, no significant changes in permeability were observed with U251 co-culture model. However, SF8628 enhanced barrier properties, while U87 increased paracellular leakiness. Examination of the barrier enhancing microenvironment observed with the SF8628 co-culture model indicated a Sonic Hedgehog dependent process that stimulates the release of Angiopoietin 1 (Ang1) and Platelet derived growth factor (PDGF-BB) release from the SF8628 tumor cells as the likely factors driving the permeability effects observed. With regards to the increased permeability observed with the U87 co-culture, increased cytokines (ie. IL-6 and IL-8) and altered fatty acid secretion appear to play a significant role in the reduced barrier properties of brain endothelial cells. Conclusion: The brain tumor microenvironment can have either barrier enhancing (SF8628 co-culture) or barrier reducing (U87-co culture) effects based on tumor cell induced changes in secreted factors. The dynamic microfluidic blood-brain barrier - brain tumor co-culture model provides a robust system with which to mechanistically identify tumor-driven changes in brain endothelial cell permeability. Citation Format: Stacey Line, Magimairajan Issai Vanan, Donald W. Miller. Development and validation of a microfluidic cell culture model for examining cerebral microvascular barrier responses to the tumor microenvironment [abstract]. In: Proceedings of the AACR Special Conference on Brain Cancer; 2023 Oct 19-22; Minneapolis, Minnesota. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_1):Abstract nr A048.

Development of interpretable machine learning models for prediction of acute kidney injury after noncardiac surgery: a retrospective cohort study.

Early identification of patients at high-risk of postoperative acute kidney injury (AKI) can facilitate the development of preventive approaches. This study aimed to develop prediction models for postoperative AKI in noncardiac surgery using machine learning algorithms. The authors also evaluated the predictive performance of models that included only preoperative variables or only important predictors. Adult patients undergoing noncardiac surgery were retrospectively included in the study (76457 patients in the discovery cohort and 11910 patients in the validation cohort). AKI was determined using the KDIGO criteria. The prediction model was developed using 87 variables (56 preoperative variables and 31 intraoperative variables). A variety of machine learning algorithms were employed to develop the model, including logistic regression, random forest, extreme gradient boosting, and gradient boosting decision trees. The performance of different models was compared using the area under the receiver operating characteristic curve (AUROC). Shapley Additive Explanations (SHAP) analysis was employed for model interpretation. The patients in the discovery cohort had a median age of 52 years (IQR: 42-61 years), and 1179 patients (1.5%) developed AKI after surgery. The gradient boosting decision trees algorithm showed the best predictive performance using all available variables, or only preoperative variables. The AUROCs were 0.849 (95% CI: 0.835-0.863) and 0.828 (95% CI: 0.813-0.843), respectively. The SHAP analysis showed that age, surgical duration, preoperative serum creatinine, and gamma-glutamyltransferase, as well as American Society of Anesthesiologists physical status III were the most important five features. When gradually reducing the features, the AUROCs decreased from 0.852 (including the top 40 features) to 0.839 (including the top 10 features). In the validation cohort, the authors observed a similar pattern regarding the models' predictive performance. The machine learning models the authors developed had satisfactory predictive performance for identifying high-risk postoperative AKI patients. Furthermore, the authors found that model performance was only slightly affected when only preoperative variables or only the most important predictive features were included.

(033) Artificial Intelligence ChatGPT and GPT4 Performance on Male and Female Sexual Dysfunction, Sexually Transmitted Infection, and Male Factor Infertility in the 2019 to 2023 American Urological Association Self-Assessment Study Programs

Abstract Introduction Large Language Model (LLM) applications in Medicine are increasing. Chatbots ChatGPT and GPT4 were tested against medical exams. The vetting of these tests as a source of information for both the patient and physician is needed. A credible source of medical information may be used as the gold standard against which the LLM can be tested. Objective We assess the performance of ChatGPT and GPT4 to answer the American Urological Association (AUA) Self-Assessment Study Program (SASP) questions on male sexual dysfunction (MSD), female sexual dysfunction (FSD), sexually transmitted infection (STI), and male factor infertility (MFI). We aim to find out how credible this LLM is as a source of medical advice. Methods Four registered users of the SASP identified the questions using open book mode in tests from 2019 to 2023, spanning their subscriptions. The questions were ranked for difficulty on a five-point Likert scale. OpenAI ChatGPT 3.5 and GPT4 were used to answer the questions. The GPT program was set to turn off chat history. No plug-ins or feedback were permitted. Prompts were generated from question stems masking question ID and adding the phrase "I am a urologist preparing for my board exam. Please answer the following question:" Images were deleted from questions and substituted with a brief description. All questions were answered separately, first using GPT-4 and then ChatGPT. Three consecutive responses were generated for each prompt, and the consensus answer was tallied. Answers were compared between GPT and provided answer key of SASP. Descriptive statistics, chi-Pearson Chi-square, and Fisher's exact tests were applied. Results We identified 115 questions in the domains of sexual dysfunction, STI, and MFI. Only one question had an associated image. GPT4 performed better than ChatGPT in all domains, providing correct answers of 60% versus 40% (p=0.000). A total of 89.9% of correct answers were three times unanimously regenerated by GPT4 (p=0.007) and 67.4% by ChatGPT (p=0.244). Within each domain, there was no significant difference in the correct answers for each chatbot (Table-1) or test year (p=0.682). The SASP source reference answers were Campbell's Urology only in 38.3 %, AUA guidelines only in 17.4%, AUA core curriculum only in 10.4%, and combinations with other sources in 33.9%. There was no significant association of source reference with the correct answers of GPT4 (p=0.058) or ChatGPT (p=0.451). Only 19.1% were open-access sources, 20% were partially open-access, and 60.9% were restricted to subscribers. The availability of the source did not significantly affect the correct answers of GPT4 (p=0.272) or ChatGPT (p=0.231). Both chatbots' correct answers were associated significantly with easier questions (Table-2). Conclusions The LLM tested has an average accuracy as a source of credible medical information on Sexual dysfunction, STI, and MFI. GPT4 performs better than ChatGPT, especially when there is a unanimous regenerated response. The development of a better model to serve a broader group of physicians and patients will require training of the chatbot on credible urology literature that includes the currently 81% restricted sources. Disclosure No.

Joint analysis of interaction and psychological characteristics in english teaching based on multimodal integration

The intersection of psychology and English teaching is profound, as the application of psychological principles not only guides specific English instruction but also elevates the overall quality of teaching. This paper takes a multimodal approach, incorporating image, acoustics, and text information, to construct a joint analysis model for English teaching interaction and psychological characteristics. The novel addition of an attention mechanism in the multimodal fusion process enables the development of an English teaching psychological characteristics recognition model. The initial step involves balancing the proportions of each emotion, followed by achieving multimodal alignment. In the cross-modal stage, the interaction of image, acoustics, and text is facilitated through a cross-modal attention mechanism. The utilization of a multi-attention mechanism not only enhances the network’s representation capabilities but also streamlines the complexity of the model. Empirical results demonstrate the model’s proficiency in accurately identifying five psychological characteristics. The proposed method achieves a classification accuracy of 90.40% for psychological features, with a commendable accuracy of 78.47% in multimodal classification. Furthermore, the incorporation of the attention mechanism in feature fusion contributes to an improved fusion effect.

A BIM-integrated multi objective optimization model for sustainable building construction management

Purpose The construction industry is tasked with creating sustainable, efficient and cost-effective buildings. This study aims to develop a building information modeling (BIM)-based multiobjective optimization (MOO) model integrating the nondominated sorting genetic algorithm III (NSGA-III) to enhance sustainability. The goal is to reduce embodied energy and cost in the design process. Design/methodology/approach Through a case study research method, this study uses BIM, NSGA-III and real-world data in five phases: literature review, identification of factors, BIM model development, MOO model creation and validation in the architecture, engineering and construction sectors. Findings The innovative BIM-based MOO model optimizes embodied energy and cost to achieve sustainable construction. A commercial building case study validation showed a reduction of 30% in embodied energy and 21% in cost. This study validates the model’s effectiveness in integrating sustainability goals, enhancing decision-making, collaboration, efficiency and providing superior assessment. Practical implications This model delivers a unified approach to sustainable design, cutting carbon footprint and strengthening the industry’s ability to attain sustainable solutions. It holds potential for broader application and future integration of social and economic factors. Originality/value The research presents a novel BIM-based MOO model, uniquely focusing on sustainable construction with embodied energy and cost considerations. This holistic and innovative framework extends existing methodologies applicable to various buildings and paves the way for additional research in this area.

Structural and practical identifiability analysis in bioengineering: a beginner’s guide

Advancements in digital technology have brought modelling to the forefront in many disciplines from healthcare to architecture. Mathematical models, often represented using parametrised sets of ordinary differential equations, can be used to characterise different processes. To infer possible estimates for the unknown parameters, these models are usually calibrated using associated experimental data. Structural and practical identifiability analyses are a key component that should be assessed prior to parameter estimation. This is because identifiability analyses can provide insights as to whether or not a parameter can take on single, multiple, or even infinitely or countably many values which will ultimately have an impact on the reliability of the parameter estimates. Also, identifiability analyses can help to determine whether the data collected are sufficient or of good enough quality to truly estimate the parameters or if more data or even reparameterization of the model is necessary to proceed with the parameter estimation process. Thus, such analyses also provide an important role in terms of model design (structural identifiability analysis) and the collection of experimental data (practical identifiability analysis). Despite the popularity of using data to estimate the values of unknown parameters, structural and practical identifiability analyses of these models are often overlooked. Possible reasons for non-consideration of application of such analyses may be lack of awareness, accessibility, and usability issues, especially for more complicated models and methods of analysis. The aim of this study is to introduce and perform both structural and practical identifiability analyses in an accessible and informative manner via application to well established and commonly accepted bioengineering models. This will help to improve awareness of the importance of this stage of the modelling process and provide bioengineering researchers with an understanding of how to utilise the insights gained from such analyses in future model development.

Multiscale physics-based in silico modelling of nanocarrier-assisted intravascular drug delivery

A rational design of drug nanocarriers supported by in silico modelling tools can improve the efficacy of nanosystem-based intravascular drug delivery (IVDD). Computational model development stems from the vision of replacing conventional (pre)clinical trials with advanced simulations and applies to the development of more efficient nanocarriers for intravascular therapies. To establish a standardized framework for in silico preclinical trials, it is necessary to include in silico tools that can model each experimental stage of a preclinical trial for a respective nanocarrier system and give accurate and verifiable results. This review paper highlights the status of intravascular drug delivery supported by nanocarriers and discusses the modelling stages of a physics-based multiscale modelling framework that should be developed, validated and exploited to address the need for an effective preclinical assessment of nanocarriers for IVDD.

Predicting microvascular invasion in hepatocellular carcinoma with a CT- and MRI-based multimodal deep learning model.

To investigate the value of a multimodal deep learning (MDL) model based on computed tomography (CT) and magnetic resonance imaging (MRI) for predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC). A total of 287 patients with HCC from our institution and 58 patients from another individual institution were included. Among these, 119 patients with only CT data and 116 patients with only MRI data were selected for single-modality deep learning model development, after which select parameters were migrated for MDL model development with transfer learning (TL). In addition, 110 patients with simultaneous CT and MRI data were divided into a training cohort (n = 66) and a validation cohort (n = 44). We input the features extracted from DenseNet121 into an extreme learning machine (ELM) classifier to construct a classification model. The area under the curve (AUC) of the MDL model was 0.844, which was superior to that of the single-phase CT (AUC = 0.706-0.776, P < 0.05), single-sequence MRI (AUC = 0.706-0.717, P < 0.05), single-modality DL model (AUCall-phase CT = 0.722, AUCall-sequence MRI = 0.731; P < 0.05), clinical (AUC = 0.648, P < 0.05), but not to that of the delay phase (DP) and in-phase (IP) MRI and portal venous phase (PVP) CT models. The MDL model achieved better performance than models described above (P < 0.05). When combined with clinical features, the AUC of the MDL model increased from 0.844 to 0.871. A nomogram, combining deep learning signatures (DLS) and clinical indicators for MDL models, demonstrated a greater overall net gain than the MDL models (P < 0.05). The MDL model is a valuable noninvasive technique for preoperatively predicting MVI in HCC.

Development and challenges of nonlinear dynamic modeling and stabilization control of mobile firing guns

The ambiguous vibration characteristics of guns under multi-source strong excitations are the key technical bottlenecks that restrict the development of mobile firing guns with high firing accuracy. The development and challenges of nonlinear dynamic modeling and stabilization control of mobile firing guns such as tank guns are discussed and analyzed from three aspects: dynamic numerical simulation methods, dynamic characteristics under multi-source excitations and control methods of gun control systems. The current main problems existing in the research on dynamic modeling and stabilization control methods of mobile firing guns are discussed and summed up and the corresponding research points are proposed. The results of the analysis indicate that: To improve the firing accuracy of mobile firing guns, the characterization method of nonlinear factors in the system needs to be explored and test verified. The dynamic characteristics of guns under multi-source excitations need to be clarified and formed as an industry standard. More precise control methods with the targeted compensation strategy for the nonlinear factors of guns need to be designed.

Enhancing gust load alleviation performance in an optimized composite wing using passive wingtip devices: Folding and Twist approaches

This paper introduces an innovative numerical method for the design and optimization of high-aspect-ratio composite wings equipped with passive control systems, specifically, Folding WingTip (FWT) and Twist WingTip (TWT) devices. The aim is to enhance Gust Load Alleviation (GLA) performance in the baseline wing. Recent numerical studies have indicated that the inclusion of spring devices and wingtip modifications can offer additional benefits in alleviating gust loads during flight. The baseline wing is designed using a comprehensive multi-disciplinary optimization framework, taking into account aerostructural constraints and exploiting the anisotropic properties of composite materials. The proposed methodology integrates Finite Element (FE) software, an in-house Reduced Order Model (ROM) framework for nonlinear aeroelastic analyses, and Particle Swarm Optimization (PSO). This method, implemented in the Nonlinear Aeroelastic Simulation Software (NAS2) package, facilitated the streamlined design of composite wings with optimized aeroelastic and structural performance. The paper is divided into two main parts. Part 1 introduces a Multidisciplinary Design Optimization (MDO) approach for high-aspect-ratio composite wings, leading to the development of a baseline wing model. Part 2 evaluates the effectiveness of the FWT and TWT devices in alleviating gust loads on the baseline wing, with a focus on the Root Bending Moment (RBM) as a critical criterion for comparison. In wingtip modeling, geometrical nonlinearity is incorporated, and elastic trim is adjusted in each iteration to accommodate shape changes under load and aerodynamic panel movement is synchronized with structural adjustments.

Automated detection of Alzheimer’s disease: a multi-modal approach with 3D MRI and amyloid PET

Recent advances in deep learning and imaging technologies have revolutionized automated medical image analysis, especially in diagnosing Alzheimer’s disease through neuroimaging. Despite the availability of various imaging modalities for the same patient, the development of multi-modal models leveraging these modalities remains underexplored. This paper addresses this gap by proposing and evaluating classification models using 2D and 3D MRI images and amyloid PET scans in uni-modal and multi-modal frameworks. Our findings demonstrate that models using volumetric data learn more effective representations than those using only 2D images. Furthermore, integrating multiple modalities enhances model performance over single-modality approaches significantly. We achieved state-of-the-art performance on the OASIS-3 cohort. Additionally, explainability analyses with Grad-CAM indicate that our model focuses on crucial AD-related regions for its predictions, underscoring its potential to aid in understanding the disease’s causes.

Triadic Technology Configuration: A Relational Perspective on Technologists’ Role in Shaping Cloud-Based Technologies

Through an ethnographic study of a manufacturing monitoring technology, the author examines how the relations among workers, managers, and third-party technologists impact the ongoing configuration of cloud-based workplace technologies. Because these technologies are broadly networked, data-driven, and highly malleable, the author argues that technologists have an increasingly prominent role. By tracing issues raised by workers and managers in the focal customer firm, the study shows that the employment relation can explain only a limited set of outcomes. An analysis that accounts for technologists’ innovative and economic relations with their users fully explains which issues are resolved and how. In contrast to research that implies technologists amplify institutionalized patterns of labor–management relations through their design choices, this study shows that technologists are complex organizational actors with situated interests, values, and identities. Through the development of a triadic model of relations, the author demonstrates why technologists must be included in analyses of contemporary technologies.

NA’s Future Peacekeeping Engagement Through a Case of Operation Lifeline in UNMISS

The essay delves into Nepal's substantial involvement in UN peacekeeping, particularly examining the Nepali Army's role in Operation Lifeline within the UN Mission in South Sudan (UNMISS). It traces the historical origins of Operation Lifeline Sudan (OLS) and its transformation into Operation Lifeline (OLL) within UNMISS, emphasizing Nepal's enduring commitment to global peace. The narrative focuses on OLL-54, where the Nepali High Readiness Company (NEPHRC) played a crucial role, exceeding agreed-upon limits to ensure the safe passage of the barge in a complex security situation. The essay underscores the operational credibility and professionalism of the Nepali contingents especially NEPHRC, suggesting it as a case for Nepal's unique contribution to future UN peacekeeping efforts. It advocates for the systematic documentation of such experiences, envisioning the development of a unique Nepali peacekeeping model for the world. In conclusion, the essay asserts that Nepal has an opportunity to showcase its commitment and achievements in international peacekeeping, reinforcing its position as a significant contributor to the evolving landscape of UN peace missions.&#x0D; Nepali Army's (NA) six decades in United Nations (UN) peacekeeping is a reflection of Nepal's enduring commitment to global peace and security. This is not merely a legacy or an aggregate of numbers of participation, achievements, and sacrifices. NA today carries a huge baggage of experience and knowledgebase derived from different peacekeeping environments of conflict zones across the world from as many as 44 traditional and multi-dimensional UN peacekeeping missions. Quite significantly, NA can offer the UN a Nepali peacekeeping model based on lessons acquired through involvement and practice. From each mission and (the number of) contingents in those missions, NA has gathered tremendous lessons and learning (experience) from individuals - leaders and men alike and participating units. Yet very few attempts were made to collect those experiences and bring about inferences for its future application. The author believes that Nepal can contribute to the world with a unique outlook for future UN peacekeeping, which is always evolving. This paper, however, only attempts to discuss Operation Lifeline (OLL) of the United Nations Mission in South Sudan (UNMISS) in particular to derive lessons relevant for Nepal/NA to consider for the future.

Towards reusable building blocks for agent-based modelling and theory development

Despite the increasing use of standards for documenting and testing agent-based models (ABMs) and sharing of open access code, most ABMs are still developed from scratch. This is not only inefficient, but also leads to ad hoc and often inconsistent implementations of the same theories in computational code and delays progress in the exploration of the functioning of complex social-ecological systems (SES). We argue that reusable building blocks (RBBs) known from professional software development can mitigate these issues. An RBB is a submodel that represents a particular mechanism or process that is relevant across many ABMs in an application domain, such as plant competition in vegetation models, or reinforcement learning in a behavioural model. RBBs need to be distinguished from modules, which represent entire subsystems and include more than one mechanism and process. While linking modules faces the same challenges as integrating different models in general, RBBs are “atomic” enough to be more easily re-used in different contexts. We describe and provide examples from different domains for how and why building blocks are used in software development, and the benefits of doing so for the ABM community and to individual modellers. We propose a template to guide the development and publication of RBBs and provide example RBBs that use this template. Most importantly, we propose and initiate a strategy for community-based development, sharing and use of RBBs. Individual modellers can have a much greater impact in their field with an RBB than with a single paper, while the community will benefit from increased coherence, facilitating the development of theory for both the behaviour of agents and the systems they form. We invite peers to upload and share their RBBs via our website - preferably referenced by a DOI (digital object identifier obtained e.g. via Zenodo). After a critical mass of candidate RBBs has accumulated, feedback and discussion can take place and both the template and the scope of the envisioned platform can be improved.

Gadung tuber pasta drying: kinetic model development and physical quality evaluation

Gadung tuber (Dioscorea hispida dennst.) is a carbohydrate-rich food source that is easily found in Indonesia. Studies have been carried out on pretreatment methods to reduce antinutritional compounds in gadung tubers. So gadung tubers can be promoted as the raw materials of several food products such as pasta. Drying is the most crucial process that will determine the final quality of the pasta product. This study aimed to investigate the kinetic models of gadung tuber pasta drying and its physical quality. Four thin-layer models (Newton, Page, Handerson-Pabis and Logarithmic) were employed to find the selected model. The gadung tuber pasta was formulated using low, medium, and high protein wheat flour and then was dried at a temperature of 30-70℃. The moisture content was observed every 30 mins for 240 mins. The result showed that the moisture reduction was higher in higher drying temperatures and lower protein wheat flour. The Logarithmic model was selected to predict the drying kinetics of gadung tuber pasta drying with the highest value of R2 and the lowest value of RMSD. The cyanide content in gadung tuber pasta meets the standard, indicating that the gadung tubers pasta may safety to consume.

Body weight prediction in crossbred pigs from digital images using computer vision

The development of technologies made it possible to capture digital images by sensors in order to obtain morphometric measurements that can be used in the development of mathematical models for the prediction of body weight (BW) in animals. The objective was to develop mathematical models to predict body weight in crossbred pigs from morphometric measurements obtained with two-dimensional digital images. Data collection was carried out in the swine sector of IF Goiano, Urutai campus, in 52 crossbred pigs housed in the finishing phase. The animals were randomly weighed and filmed by the Microsoft Kinect® v1 sensor camera in the dorsal view. Subsequently, they were conducted for the measurement of manual morphometric measurements, without restraint, being dorsal length, chest width and flank width. The morphometric measurements predicted from the dorsal images were dorsal length, chest width, flank width, dorsal area and perimeter. Through Mask R-CNN algorithm trains the network by input images, was compute the area of the evaluated object in pixels, and thus, can correlate with measures of interest. The Mask R-CNN algorithm were performed with the Collaboratory Google using Phyton 3.0. Mathematical models were developed using multiple linear regression (MLR) and support vector regression (SVR) methodologies for body weight prediction. All Pearson's correlations between real body weight and real and predicted morphometric measurements by digital images were moderate to high magnitude positive and statistically significant (P<0.05). The difference between the real and predicted BW by the SVR was -1.74 kg (R² = 91%) and -2.39 kg (R² = 88%), respectively. The MLR model with real morphometrics measurements explained 60% of BW variance and estimated a BW 18.76 kg below from the real BW mean. The MLR model with predicted measurements explained 53% of BW variance and estimated a BW 22.51 kg below from the real BW mean. The mathematical model developed from SVR has the potential to estimate the body weight of crossbred pigs using morphometric measurements predicted by digital images such as dorsal length, width, dorsal area and perimeter.