Computer-based Model Research Articles

Multi-scenario based urban growth modeling and prediction using earth observation datasets towards urban policy improvement

Urbanization is a growing challenge for city planners and policymakers who are continuously focusing on computer-based statistical models, and machine learning for a sustainable and livable city. The main objectives of this article were to develop a robust artificial intelligence-based hybrid geo-simulation model to support multi-scenario urban growth modeling for urban policy improvement. In this study, earth observation datasets, Artificial Neural Network-Multilayer Perceptron coupled with Markov Chain (MLP-Markov) and Cellular Automata and Markov Chain (CA-Markov) were applied and the best performance was measured for urban growth modeling. The result shows that the urban land use was 25.79, 31.40, 45.19, 89.22 and 147.96 square km in 1971, 1981, 1991, 2001 and 2011 which has been predicted for 2021, 2031, 2041 and 2051 based on the planned and unplanned development scenarios. The predicted urban land use of the planned development scenario is 242.10, 312.69, 363.80 and 400.72 square km while 242.91, 314.31, 366.23 and 403.98 square km of the unplanned development scenario during 2021, 2031, 2041 and 2051. The uncertainty result shows that overall agreement (84.99%) and other indices are higher, and disagreement is lower (15.01%) for MLP-Markov than the CA-Markov for the urban land use prediction. The hybrid geo-simulation models were tested over multiple urban planning indicators to understand urban growth patterns and related scenarios. The result shows that the geo-simulation model is extremely sensitive to the complex pattern of urban growth and disperse indicators over space and time. This study provides a promising guideline for urban planners and conservation scientists to implement a robust artificial intelligence-based hybrid geo-simulation model for compact, organized, and integrated land use-transportation development. HIGHLIGHTS Raipur city passing through sprawling and unplanned development due to uncontrol population growth and unmanaged development practices by local government and planning authority. Master plan of the development authority of the city failed to restrict unplanned development. Proposed urban growth model promote a compact, organized, integrated land use-transportation development and sustainable urban planning. Study, highlighted complexity in the modeling and suggested simplified Machine-based hybrid geo-simulation model for future urban growth for urban policy improvement.

Analysis of Data Mining Model for Successful Implementation of Data Warehouse in Tertiary Institutions (A Case Study of Irahim Badamasi Babangida University)

This research work with title “analysis of data mining model for successful implementation of data warehouse in tertiary institutions” is a proposal that provides a framework that is used to structure, plan, and control the process involved in information discovery for tertiary institutions. It equally deals with series of steps or procedures which governs the analysis and design of this particular Data Mining Model for Tertiary Institutions. The methods, techniques and procedures which are used to collect and analyze information were also provided and implemented. Object oriented Analysis and Design methodology (OOADM) was deployed to develop this model. The current system was analysed by using Use Case approach to identify the existing objects in the current system. Use Case diagrams were equally developed and the relationship between the objects was drawn. A diagram was drawn to represent the identified problems. Finally, the High-Level Model (HLM) that would be used to design the proposed system was drawn to enable the System Designer to develop a computer-based model that forecasts educational behaviours with little or no user intervention.

A Step Forward: Functional Diversity and Emerging Themes of Slow-Speed Locomotion in Vertebrates.

Walking can be defined broadly as a slow-speed movement produced when appendages interact with the ground to generate forward propulsion. Until recently, most studies of walking have focused on humans and a handful of domesticated vertebrates moving at a steady rate over highly simplified, static surfaces, which may bias our understanding of the unifying principles that underlie vertebrate locomotion. In the last few decades, studies have expanded to include a range of environmental contexts (e.g., uneven terrain, perturbations, deformable substrates) and greater phylogenetic breadth (e.g., non-domesticated species, small and/or ectothermic tetrapods and fishes); these studies have revealed that even a gait as superficially simple as walking is far more complex than previously thought. In addition, technological advances and accessibility of imaging systems and computational power have recently expanded our capabilities to test hypotheses about the locomotor movements of extant and extinct organisms in silico. In this symposium, scientists showcased diverse taxa (from extant fishes to extinct dinosaurs) moving through a range of variable conditions (speed perturbations, inclines, and deformable substrates) to address the causes and consequences of functional diversity in locomotor systems and discuss nascent research areas and techniques. From the symposium contributions, several themes emerged: (1) slow-speed, appendage-based movements in fishes are best described as walking-like movements rather than true walking gaits, (2) environmental variation (e.g., deformable substrates) and dynamic stimuli (e.g., perturbations) trigger kinematic and neuromuscular changes in animals that make defining a single gait or the transition between gaits more complicated than originally thought, and (3) computational advances have increased the ability to process large data sets, emulate the 3D motions of extant and extinct taxa, and even model species interactions in ancient ecosystems. Although this symposium allowed us to make great strides forward in our understanding of vertebrate walking, much ground remains to be covered. First, there is a much greater range of vertebrate appendage-based locomotor behaviors than has been previously recognized and existing terminology fails to accurately capture and describe this diversity. Second, despite recent efforts, the mechanisms that vertebrates use modify locomotor behaviors in response to predictable and unpredictable locomotor challenges are still poorly understood. Third, while computer-based models and simulations facilitate a greater understanding of the kinetics and kinematics of movement in both extant and extinct animals, a universal, one-size-fits-all, predictive model of appendage-based movement in vertebrates remains elusive.

A Novel Condition Monitoring Method of Wind Turbines Based on GMDH Neural Network

The safety of power transmission systems in wind turbines is crucial to the wind turbine’s stable operation and has attracted a great deal of attention in condition monitoring of wind farms. Many different intelligent condition monitoring schemes have been developed to detect the occurrence of defects via supervisory control and data acquisition (SCADA) data, which is the most commonly applied condition monitoring system in wind turbines. Normally, artificial neural networks are applied to establish prediction models of the wind turbine condition monitoring. In this paper, an alternative and cost-effective methodology has been proposed, based on the group method of data handling (GMDH) neural network. GMDH is a kind of computer-based mathematical modelling and structural identification algorithm. GMDH neural networks can automatically organize neural network architecture by heuristic self-organization methods and determine structural parameters, such as the number of layers, the number of neurons in hidden layers, and useful input variables. Furthermore, GMDH neural network can avoid over-fitting problems, which is a ubiquitous problem in artificial neural networks. The effectiveness and performance of the proposed method are validated in the case studies.

Proximal junctional failure after surgical instrumentation in adult spinal deformity: biomechanical assessment of proximal instrumentation stiffness.

Assessment of different proximal instrumentation stiffness features to minimize the mechanical proximal junctional failure-related risks through computer-based biomechanical models. To biomechanically assess variations of proximal instrumentation and loads acting on the spine and construct to minimize proximal junctional failure (PJF) risks. The use of less-stiff fixation such as hooks or tensioned bands, compared to pedicle screws, at the proximal instrumentation level are considered to allow for a gradual transition in stiffness with the adjacent levels, but the impact of such flexible fixation on the loads balance and complications such as PJF remain uncertain. Six patients with adult spine deformity who underwent posterior spinal instrumentation were used to numerically model and simulate the surgical steps, erected posture, and flexion functional loading in patient-specific multibody analyses. Three types of upper-level fixation (pedicle screws (PS), supralaminar hooks (SH), and sublaminar bands (SB) with tensions of 50, 250, and 350N) and rod stiffness (CoCr/6mm, CoCr/5.5mm, Ti/5.5mm) were simulated. The loads acting on the spine and implants of the 90 simulated configurations were analyzed using Kruskal-Wallis statistical tests. Simulated high-tensioned bands decreased the sagittal moment at the adjacent level proximal to the instrumentation (1.3Nm at 250N; 2.5Nm at 350N) compared to screws alone (PS) (15.6Nm). At one level above, the high-tensioned SB increased the sagittal moment (17.7Nm-SB vs. 15.5Nm-PS) and bending moment on the rods (5.4Nm and 5.7Nm vs. 0.6Nm) (p < 0.05). SB with 50N tension yielded smaller changes in load transition compared to higher tension, with moments of 8.1Nm and 16.8Nm one and two levels above the instrumentation. The sagittal moment at the upper implant-vertebra connection decreased with the rod stiffness (1.0Nm for CoCr/6mm vs. 0.7Nm for Ti/5.5mm; p < 0.05). Simulated sublaminar bands with lower tension produced smaller changes in the load transition across proximal junctional levels. Decreasing the rod stiffness further modified these changes, with a decrease in loads associated with bone failure, however, lower stiffness did increase the rod breakage risk. N/A.

Influence of spinal lordosis correction location on proximal junctional failure: a biomechanical study.

Assessment of sagittal lordosis distribution on mechanical proximal junctional failure-related risks through computer-based biomechanical models. To biomechanically assess how lordosis distribution influences radiographical and biomechanical indices related to Proximal Junctional Failure (PJF). The "optimal" patient-specific targets to restore the sagittal balance in posterior spinal fusion are still not known. Among these, the effect of the lumbar lordosis correction strategy on complications such as PJF remain uncertain. In this computational biomechanical study, five adult spinal deformity patients who underwent posterior spinal fixation were retrospectively reviewed. Their surgery, first erect posture and flexion movement were simulated with a patient-specific multibody model. Three pedicle subtraction osteotomy (PSO) levels (L3, L4, and L5) were simulated, with consistent global lordosis for a given patient and pelvic tilt adjusted accordingly to the actual surgery. Computed loads on the anterior spine and instrumentation were analyzed and compared using Kruskal-Wallis statistical tests and Spearman correlations. In these models, no significant correlations were found between the lordosis distribution index (LDI), PSO level and biomechanical PJF-related indices. However, increasing the sagittal vertical axis (SVA) and thoracolumbar junction angle (TLJ) and decreasing the sacral slope (SS) increased the bending moment sustained by the rods at the proximal instrumented level (r = 0.52, 0.57, -0.56, respectively, p < 0.05). There was a negative correlation between SS and the bending moment held by the adjacent proximal segment (r = -0.71, p < 0.05). Based on these biomechanical simulations, there was no correlation between the lordosis distribution and PJF-associated biomechanical factors. However, increasing SS and flattening the TLJ, as postural adjustment strategies required by a more distal PSO, did decrease such PJF-related factors. Sagittal restoration and PJF risks remain multifactorial, and the use of patient-specific biomechanical models may help to better understand the complex interrelated mechanisms.

Modelling geographical and built-environment attributes as predictors of human vulnerability during tsunami evacuations: a multi-case-study and paths to improvement

Abstract. Evacuation is the most important and effective method to save human lives during a tsunami. In this respect, challenges exist in developing quantitative analyses of the relationships between the evacuation potential and the built-environment and geographical attributes of coastal locations. This paper proposes a computer-based modelling approach (including inundation, evacuation, and built-environment metrics), followed by multivariate regressive analysis, to estimate how those attributes might influence the expected tsunami death ratios of seven Chilean coastal cities. We obtained, for the examined variables, their average values to different thresholds of the death ratio. Also, our statistical analysis allowed us to compare the relative importance of each metric, showing that the maximum flood, the straightness of the street network, the total route length, and the travel time can have a significant impact on the expected death ratios. Moreover, we suggest that these results could lead to spatial planning guidelines for developing new urban areas into exposed territories (if this expansion cannot be restricted or discouraged) or retrofitting existing ones, with the final aim of enhancing evacuation and therefore increasing resilience.

Artificial intelligence based health indicator extraction and disease symptoms identification using medical hypothesis models.

Patient health record analysis models assist the medical field to understand the current stands and medical needs. Similarly, collecting and analyzing the disease features are the best practice for encouraging medical researchers to understand the research problems. Various research works evolve the way of medical data analysis schemes to know the actual challenges against the diseases. The computer-based diagnosis models and medical data analysis models are widely applied to have a better understanding of different diseases. Particularly, the field of medical electronics needs appropriate health indicator extraction models in near future. The existing medical schemes support baseline solutions but lack optimal hypothesis-based solutions. This work describes the optimal hypothesis model and Akin procedures for health record users, to aid health sectors in clinical decision-making on health indications. This work proposes Medical Hypothesis and Health Indicators Extraction from Electronic Medical Records (EMR) and International Classification of Diseases (ICD-10) patient examination database using the Akin Method and Friendship method. In this Health Indicators and Disease Symptoms Extraction (HIDSE), the evidence checking procedures find and collect all possible medical evidence from the existing patient examination report. Akin Method is making the hypothesis decision from count-based evidence principles. The health indicators extraction scheme extracts all relevant information based on the health indicators query and partial input. Similarly, the friendship method is used for making information associations between medical data attributes. This Akin-Friendship model helps to build hypothesis structures and trait-based feature extraction principles. This is called as Composite Akin Friendship Model (CAFM). This proposed model consists of various test cases for developing the medical hypothesis systems. On the other hand, it provides limited accuracy in disease classification. In this regard, the proposed HIDSE implements Deep Learning (DL) based Akin Friendship Method (DLAFM) for improving the accuracy of this medical hypothesis model. The proposed DLAFM, Convolutional Neural Networks (CNN) associated Legacy Prediction Model for Health Indicator (LPHI) is developed to tune the CAFM principles. The results show the proposed health indicator extraction scheme has 8–10% of better system performance than other existing techniques.

Computational modelling of epithelial cell monolayers during infection with Listeria monocytogenes

Intracellular bacterial infections alter the normal functionality of human host cells and tissues. Infection can also modify the mechanical properties of host cells, altering the mechanical equilibrium of tissues. In order to advance our understanding of host–pathogen interactions, simplified in vitro models are normally used. However, in vitro studies present certain limitations that can be alleviated by the use of computer-based models. As complementary tools these computational models, in conjunction with in vitro experiments, can enhance our understanding of the mechanisms of action underlying infection processes. In this work, we extend our previous computer-based model to simulate infection of epithelial cells with the intracellular bacterial pathogen Listeria monocytogenes. We found that forces generated by host cells play a regulatory role in the mechanobiological response to infection. After infection, in silico cells alter their mechanical properties in order to achieve a new mechanical equilibrium. The model pointed the key role of cell–cell and cell–extracellular matrix interactions in the mechanical competition of bacterial infection. The obtained results provide a more detailed description of cell and tissue responses to infection, and could help inform future studies focused on controlling bacterial dissemination and the outcome of infection processes.

Reduction of Acute Postoperative Pain With Pre-Emptive Pregabalin Following Laparoscopic Cholecystectomy

Pregabalin has been considered to be a safe treatment for neuropathic pain. Owing to the lack of research regarding the use of pregabalin in the management of pain in under-resourced settings, our study aimed to deduce the effectiveness of a pre-emptive single dose of pregabalin pre-operatively to provide pain relief after laparoscopic cholecystectomy. Treating acute pain is essential to avoid an increased hospital stay. There is a need for non-opioid drugs with lower risks to avoid using opioids, which lead to many side effects.MethodologyPatients diagnosed with cholelithiasis and scheduled to undergo laparoscopic cholecystectomy at the Abbasi Shaheed Hospital were included in this study. The study aimed to determine whether the effect of pregabalin in combination with patient-controlled analgesia can decrease pain scores. This was a double-blind study where patients, caregivers, and analysts were blinded to group allocation and drugs administered until the data was recorded and sealed. The patients were divided into pregabalin and placebo groups through a web-based model; blocks of four were used and stratification was employed at the center. A confidence interval of 95% was considered significant.ResultsIn our study, a total number of 60 patients were included. They were randomly divided by a computer-based model into two groups, the pregabalin group, and the control group. The placebo group had 33 patients while the pregabalin group had 27 patients. The pregabalin group was given a pregabalin tablet of 150 mg before surgery while the placebo group was given an identical-looking placebo. Patient-controlled analgesia was started in both groups and the visual analog scale (VAS) scoring was observed postoperatively. The pregabalin group had a decreased incidence of pain as compared to the placebo group. There were no significant side effects during the trial; episodes of vomiting were managed using intravenous ondansetron.ConclusionPregabalin is effective in reducing pain in an acute postoperative period when compared with a placebo. Patients who were pre-emptively administered pregabalin reported decreased VAS as compared to the placebo. However, both were inefficient in reducing postoperative nausea and vomiting.

Causes of infant deaths and patterns of associated factors in Eastern Ethiopia: Results of verbal autopsy (InterVA-4) study.

BackgroundIn a range of setting, detecting and generate empirical information on the cause of infant death and contributing risk factors at population level is basically utmost essential to take evidence-based measures in reducing infant morbidity and mortality. An electronic verbal autopsy is suitable tool and best alternative solution to determine individuals’ cause of death in a setting where the majority of deaths occur at home and civil registration systems do not exist. The present study was undertaken to find out cause of infant death, applying computer-based probabilistic model (InterVA-4) and analyze the patterns of association factors of mother’s and the deceased infant’s characteristics to the leading cause-specific infant mortality in Eastern Ethiopia.MethodsThe study employed a community-based prospective longitudinal survey, which was conducted with routinely enumeration of reported infant deaths for a period of two years (from September 2016 to August 2018) in Eastern part of Ethiopia. Using the two-stage cluster sampling technique, the study was undertaken in four randomly selected districts of West Hararghe zone and two districts of zone 3 in Oromia and Afar regional state, respectively. The study included a total of 362 infants who were deceased during the study period. Data was collected by trained enumerators by interviewing the mothers or guardians of the deceased infant using a 2014 standardize World Health Organization (WHO) Verbal Autopsy questionnaire. InterVA-4 model were used for processing and interpreting verbal autopsy data in order to arrive at the most likely causes of infant death. SPSS version 23 was also used for statistical analysis of frequency distribution and logistic regression for the association between covariates and outcomes.FindingsOf the overall (362) deceased infants’ during the study period, 53.0% of deaths occurred during neonatal time while 47.0% died in the post-neonatal period. Acute respiratory infection including neonatal and post-neonatal pneumonia (38.4%), birth asphyxia (16.4%), diarrheal diseases (16.3%), prematurity (7.4%) and malaria (4.3%) were found to be the leading causes of infant mortality in the study area. The independent factors strongly associated with probable ARI, including pneumonia related mortality as compared to all-causes of death were infants with maternal age lower than 20 years old (p = 0.001, AOR: 4.82, 95% CI: 1.88, 12.3) and infant being died outside of heath facilities (P = 0.007, AOR: 2.85, 95% CI: 1.33, 6.12). The post-neonatal period (P = 0.000, AOR: 15.5, 95% CI: 6.35, 37.8) and infant died in the wet season (P = 0.006, AOR: 2.38, 95% CI: 1.28, 4.44) had strong relationship with dying from diarrhea-related death than those infants died from all non-diarrhea. The death due to malaria robustly associated with infants whose mothers age between 20–35 years old (P = 0.024, AOR: 4.44, 95% CI: 1.22, 16.2) and infant who was dwelled in the districts of Afar region (P = 0.013, AOR: 4.08, 95% CI: 1.35, 12.4).ConclusionThe highest cause of infant mortality was associated with disease of respiratory system, particularly acute respiratory infection, including both neonates and post-neonatal pneumonia. Most of the infant deaths existed are as a result of diseases and conditions that are readily preventable or treatable cause, similar to those reported in worldwide, which have needs of further attention. The patterns of significant associated factors across cause-specific mortality against all-cause of death were dissimilar. Therefore, strengthen maternal and child health program with effective preventive interventions emphasizing on the most common cause of infant deaths and those factors contributing in raising mortality risk are required.

A new brain tumor diagnostic model: Selection of textural feature extraction algorithms and convolution neural network features with optimization algorithms

Brain tumors are one of the most dangerous diseases that affect human health and maybe result in death. Detection of brain tumors can be made by using biopsy. However, this is an invasive procedure. It is an extremely dangerous procedure because it can cause bleeding and damage certain brain functions. For this reason, the type and the stage of the disease can be determined after a detailed examination by medical imaging techniques made by field experts. In this study, a computer-based hybrid diagnostic model with high accuracy rate is proposed to diagnose normal brain and brain having types of tumors from brain images obtained by magnetic resonance imaging (MRI) techniques. This diagnostic model consists of three stages. In the first stage, the features of the images were obtained with two different traditional methods, which are widely used in the literature, and the results were examined. In the second stage, different convolutional neural networks that can learn comprehensive information about images were used and the results were tested by obtaining the features of the images. In the third stage, all the feature sets that are obtained were combined, and genetic algorithms, particle swarm optimization technique and artificial bee colony optimization techniques were used for feature selection. The common features of the optimization techniques were used only once. Thus, metaheuristic optimization algorithms were used for feature selection and distinctive features of the images appeared. Feature sets were classified using support vector machine kernels. The proposed diagnostic model is better than the directly used methods with an accuracy rate of 98.22%. Consequently, this method can also be used in clinic service to diagnose tumor by using images of brain MRI.

The Power of Systems: How Policy Sciences Opened Up the Cold War World by Eglė Rindzevičiūtė (review).

Reviewed by: The Power of Systems: How Policy Sciences Opened Up the Cold War World by Eglė Rindzevičiūtė Ekaterina Babintseva (bio) The Power of Systems: How Policy Sciences Opened Up the Cold War World By Eglė Rindzevičiūtė. Ithaca: Cornell University Press, 2016. Pp. 306. The history of Soviet computing and cybernetics is still a nascent field. The few pioneering accounts that we have—Peters, How Not to Network a Nation, and Gerovitch, From Newspeak to Cyberspeak—are concerned with why computing and cybernetics were less successful in the Soviet Union than in the United States. Eglė Rindzevičiūtė's The Power of Systems rejects the narrative of failure to reveal important [End Page 886] Soviet contributions to the production of cybernetic methods of global governance. Developed under the umbrella term "the systems approach," these methods were the product of the circulation of knowledge in computational modeling between the United States and the Soviet Union from the 1960s through the 1980s. Institutionally, the circulation of knowledge was enabled by the International Institute for Applied Systems Analysis (IIASA), an international version of the RAND Corporation. IIASA became a meeting platform for Soviet and American scholars and scientists working on computational models of social, economic, and natural processes. Rindzevičiūtė argues that the East-West cooperation in systems analysis led to the emergence of new instruments, language, and visions of governance, which were premised on the principles of self-reflexivity and uncertainty. The IIASA steered away from the conception of governance as a teleological and goal-oriented process, offered by American cyberneticists in the 1940s. Initially, Soviet bureaucrats favored the systems approach because it seemed to promise more control over industries and social planning. But, contrary to their expectations, the systems approach "transformed the very character of control" (p. 13). For instance, while working on a computational model of the environmental effects of a nuclear attack, Soviet mathematician Nikita Moiseev argued for the need to replace the notion of upravlenie (control) with napravlenia (guidance), explicitly criticizing the unfeasibility of the totalitarian ambition for complete subordination. The Power of Systems opens with a discussion of political and intellectual shifts in the post-Stalinist Soviet Union, which are key to understanding Soviet interest in the IIASA and systems analysis. The two most notable figures of this chapter are Soviet Prime Minister Aleksei Kosygin and his son-in-law Dzhermen Gvishiani, who was the director of the powerful State Committee for Science and Technology that oversaw the importation of Western theories of management and technology to the Soviet Union. Kosygin and Gvishiani both believed that the Soviet system needed to embrace Western approaches to management and governance in order to survive. Chapter 2 shows that while the Soviets were motivated by gaining access to Western knowledge and technology, the U.S. government was more interested in building connections between East and West. In Chapters 3 and 4, Rindzevičiūtė examines the formation of the transnational community of systems analysis, paying attention to the IIASA's insistence on the political neutrality of cybernetic-inflected methods of policymaking. The last three chapters present three captivating case studies of the application of systems analysis to global modeling, making these chapters of particular interest to historians of data and computing. As Chapter 5 shows, the first computer-based global models of social and economic development sought to understand the effects of human activity on the [End Page 887] planetary geophysical processes. One such model, created by American economist Wassili Leontief and Russian economist Stanislav Menshikov in the 1970s, investigated the impact of economic growth on the environment. Later, in the 1980s, their model was instrumental in the work of the Moscow-based Institute for Systems Research, which predicted the potential decline of the Soviet economy. Chapters 6 and 7 continue tracing the circulation of data, models, people, and technology to understand the Soviet-American modeling of the environmental consequences of a nuclear war. In all of them, Rindzevičiūtė highlights Soviet contributions to rethinking the possibility of total control. The book concludes with an epilogue that suggests that the roots of post-Soviet Russian neoliberalism can be found in systems analysis. The...

Use of Modeling to Inform Decision Making in North Carolina during the COVID-19 Pandemic: A Qualitative Study.

Background. The COVID-19 pandemic has popularized computer-based decision-support models, which are commonly used to inform decision making amidst complexity. Understanding what organizational decision makers prefer from these models is needed to inform model development during this and future crises. Methods. We recruited and interviewed decision makers from North Carolina across 9 sectors to understand organizational decision-making processes during the first year of the COVID-19 pandemic (N = 44). For this study, we identified and analyzed a subset of responses from interviewees (n = 19) who reported using modeling to inform decision making. We used conventional content analysis to analyze themes from this convenience sample with respect to the source of models and their applications, the value of modeling and recommended applications, and hesitancies toward the use of models. Results. Models were used to compare trends in disease spread across localities, estimate the effects of social distancing policies, and allocate scarce resources, with some interviewees depending on multiple models. Decision makers desired more granular models, capable of projecting disease spread within subpopulations and estimating where local outbreaks could occur, and incorporating a broad set of outcomes, such as social well-being. Hesitancies to the use of modeling included doubts that models could reflect nuances of human behavior, concerns about the quality of data used in models, and the limited amount of modeling specific to the local context. Conclusions. Decision makers perceived modeling as valuable for informing organizational decisions yet described varied ability and willingness to use models for this purpose. These data present an opportunity to educate organizational decision makers on the merits of decision-support modeling and to inform modeling teams on how to build more responsive models that address the needs of organizational decision makers.HighlightsOrganizations from a diversity of sectors across North Carolina (including public health, education, business, government, religion, and public safety) have used decision-support modeling to inform decision making during COVID-19.Decision makers wish for models to project the spread of disease, especially at the local level (e.g., individual cities and counties), and to help estimate the outcomes of policies.Some organizational decision makers are hesitant to use modeling to inform their decisions, stemming from doubts that models could reflect nuances of human behavior, concerns about the accuracy and precision of data used in models, and the limited amount of modeling available at the local level.

The role of modeling in evaluation of maternal and child health programs: using the lives saved tool to help answer core evaluation questions

ABSTRACT This paper explains how The Lives Saved Tool (LiST), a computer-based model that estimates the impact of scaling up interventions on stillbirths, maternal, neonatal and child health, can contribute to evaluations of programs being delivered at scale to improve maternal and child health. LiST can be used to estimate the impact of a program in advance, allowing planners to refine, streamline and set appropriate program targets. LiST can also be used to estimate the impact of a program, which is particularly useful given the high costs of measuring changes in population health. Finally, LiST can be used to estimate the relative contributions of different interventions or sets of interventions within programs that are found to have a positive impact. The latest version of LiST allows users to manipulate both utilization and quality of service to generate estimates of effective coverage. In addition, a new, web-based version of LiST is now available, with a simpler and more streamlined interface designed to increase accessibility to beginning users. LiST modeling can help program planners, evaluators and funders respond to core evaluation questions related to program design and impact, providing evidence to support decisions about how best to use available resources to save the lives of women and children.

Stochastic process computational modeling for learning research

The goal of our research was to compare and systematize several approaches to non-parametric null hypothesis significance testing using computer-based statistical modeling. For teaching purposes, a statistical model for simulation of null hypothesis significance testing was created. The results were analyzed using Fisher's angular transformation, Chi-square, Mann-Whitney, and Fisher's exact tests. Appropriate software was created, allowing us to recommend new illustrative materials for expressing the limitations of the tests that were examined. Learning investigations as a technique of comprehending inductive statistics has been proposed, based on the fact that modern personal computers can run simulations in a reasonable amount of time with great precision. The collected results revealed that the most often used non-parametric tests for small samples have low power. Traditional null hypothesis significance testing does not allow students to analyze test power because the true differences between samples are unknown. As a result, in Ukrainian statistical education, including PhD studies, the emphasis must shift away from null hypothesis significance testing and toward statistical modeling as a modern and practical approach of establishing scientific hypotheses. This finding is supported by scientific papers and the American Statistical Association's recommendation.

The risk of PD-L1 expression misclassification in triple-negative breast cancer

PurposeStratification of patients with triple-negative breast cancer (TNBC) for anti-PD-L1 therapy is based on PD-L1 expression in tumor biopsies. This study sought to evaluate the risk of PD-L1 misclassification.MethodsWe conducted a high-resolution analysis on ten surgical specimens of TNBC. First, we determined PD-L1 expression pattern distribution via manual segmentation and measurement of 6666 microscopic clusters of positive PD-L1 immunohistochemical staining. Then, based on these results, we generated a computer model to calculate the effect of the positive PD-L1 fraction, aggregate size, and distribution of PD-L1 positive cells on the diagnostic accuracy.ResultsOur computer-based model showed that larger aggregates of PD-L1 positive cells and smaller biopsy size were associated with higher fraction of false results (P < 0.001, P < 0.001, respectively). Additionally, our model showed a significant increase in error rate when the fraction of PD-L1 expression was close to the cut-off (error rate of 12.1%, 0.84%, and 0.65% for PD-L1 positivity of 0.5–1.5%, ≤ 0.5% ,and ≥ 1.5%, respectively, P < 0.0001). Interestingly, false positive results were significantly higher than false negative results (0.51–22.62%, with an average of 6.31% versus 0.11–11.36% with an average of 1.58% for false positive and false negative results, respectively, P < 0.05). Furthermore, heterogeneous tumors with different aggregate sizes in the same tumor, were associated with increased rate of false results in comparison to homogenous tumors (P < 0.001).ConclusionOur model can be used to estimate the risk of PD-L1 misclassification in biopsies, with potential implications for treatment decisions.

IBCS: 2. Mechanistic Modeling of Pulmonary Availability of Inhaled Drugs versus Critical Product Attributes.

This work is the second in a series of publications outlining the fundamental principles and proposed design of a biopharmaceutics classifications system for inhaled drugs and drug products (the iBCS). Here, a mechanistic computer-based model has been used to explore the sensitivity of the primary biopharmaceutics functional output parameters: (i) pulmonary fraction dose absorbed (Fabs) and (ii) drug half-life in lumen (t1/2) to biopharmaceutics-relevant input attributes including dose number (Do) and effective permeability (Peff). Results show the nonlinear sensitivity of primary functional outputs to variations in these attributes. Drugs with Do < 1 and Peff > 1 × 10–6 cm/s show rapid (t1/2 < 20 min) and complete (Fabs > 85%) absorption from lung lumen into lung tissue. At Do > 1, dissolution becomes a critical drug product attribute and Fabs becomes dependent on regional lung deposition. The input attributes used here, Do and Peff, thus enabled the classification of inhaled drugs into parameter spaces with distinctly different biopharmaceutic risks. The implications of these findings with respect to the design of an inhalation-based biopharmaceutics classification system (iBCS) and to the need for experimental methodologies to classify drugs need to be further explored.

Detecting autism from picture book narratives using deep neural utterance embeddings.

Deficits in the ability to use language in social contexts, including storytelling skills, are observed across the autism spectrum. Development in machine-learning approaches may contribute to clinical psychology and psychiatry, given its potential to support decisions concerning the diagnosis and treatment of psychiatric conditions and disorders. To evaluate the usefulness of deep neural networks for detecting autism spectrum disorder (ASD) from textual utterances, specifically from narrations produced by individuals with ASD. We examined two text encoders: Embeddings from Language Models (ELMo) and Universal Sentence Encoder (USE), and three classification algorithms: XGBoost, support vector machines, and dense neural network layer. We aimed to classify 25 participants with ASD and 25 participants with typical development (TD) based on their narrations produced during the Autism Diagnostic Observation Schedule, Second Edition (ADOS-2) picture book task. The results of computational approaches were compared with the results of standardized testing and classifications made by two psychiatrists (raters). The raters were asked to read utterances produced by a participant (without an examiner's statements and additional information) and assign a participant to one of the two groups: ASD or with typical development (TD). The computer-based models had higher sensitivity, specificity, positive predictive values and negative predictive values than the raters, and lower than the two standardized instruments: ADOS-2 and Social Communication Questionnaire (SCQ). Our findings lay the groundwork for future studies involving deep neural network-based text representation models as tools for augmenting the ASD diagnosis or screening. Both ELMo and USE text encoders provided promising specificities, sensitivities, positive predictive values and negative predictive values. Our results indicate the usefulness of page-level embeddings for utterance representation in ADOS-2 picture book task. What is already known on this subject Deficits in the use of language in social contexts, and narrative ability in particular, are observed across the autism spectrum. Most research on narrative skills has applied hand-coding methods. Hitherto, machine-learning methods were used mostly for image recognition problems and data from screening questionnaires for ASD classification. Detection of mental and developmental disorders from textual input is an emerging field for machine and deep-learning methods. What this paper adds to existing knowledge This study explored the ability of several types of deep neural network-based text representation models to detect ASD. Both ELMo and USE provided the most promising values of specificity, sensitivity, positive predictive values and negative predictive values. What are the potential or actual clinical implications of this work? Competitive accuracy, repeatability, speed and ease of operation are all advantages of computerized methods. They allow for objective and quantitative assessment of narrative ability and complex language skills. Deep neural network-based text representation models could in the future support clinicians and augment the decision-making process related to ASD diagnosis, screening and intervention planning.

PERBANDINGAN MODEL PEMBELAJARAN OSBORN DAN MODEL PEMBELAJARAN MATEMATIKA INTERAKTIF BERBASIS KOMPUTER TERHADAP HASIL BELAJAR SISWA KELAS X TKJ SMK NEGERI 1 SINJAI

This study aims to compare student learning outcomes in mathematics with the use of Osborn and computer-based interactive mathematics learning model in class X TKJ SMKN 1 Sinjai. This type of research is an experimental research with a quantitative approach with a pretest postest control group design research design. The population in this study were students of class X TKJ SMK Negeri 1 Sinjai. While the sample used is simple random sampling. Based on the descriptive analysis of student learning outcomes in class X TKJ 1 after the application of the Osborn learning model as a control class, the average pre-test of the Osborn learning model was 76.80 and the post-test was 79.25 which was greater than the KKM (65). Based on the inferential analysis, the results of this study indicate that the Sig. (2-tailed) in the paired sample t-test of 0.000 where the value is less than 0.05. Thus, it can be concluded that there are differences in learning outcomes between Osborn and the computer-based interactive learning model for the tenth graders of TKJ SMK Negeri 1 Sinjai.