Iterative Modeling Approach Research Articles

Clinic Management System

Abstract: As people’s levels of education rise particularly in medical industry the progress of our nation impacted by the inadequate health care patients in government hospitals occasionally have to wait hours to receive care. Although administration must maintain track of a vast number of files containing all patient information, this can occasionally result in redundant data and extra effort. This is the rationale for the proposal for the Online Clinic Management System, which is being developed with a web-based idea. An iterative waterfall model approach, dataflow, logical and entity relationship diagrams were among the techniques utilized in the system’s development. Were employed in the system’s design. Because all manual tasks in this system are being converted to computerized types, there would be no data redundancy during the registration process, records on patient details could be found more quickly, and patient waiting times before receiving medical treatment could be reduced. Keywords: computerized, health care, online clinic management system, and patient record.

Exploring farmer choices in Southern Togo: Utilizing a strategy game to understand decision-making in agricultural practices

CONTEXTAgriculture plays a vital role in African countries, driving economic growth, employment, and non-agricultural sector improvements. However, persistent food insecurity and poverty among smallholder families raise concerns. Land fragmentation compounds these issues, limiting agricultural productivity and pushing farmers towards off-farm activities, despite Africa's potential agricultural markets. OBJECTIVEThe study's focus is Donomadé, a remote village in southern Togo. This study pursues a dual objective. Firstly, it investigates how household characteristics, including family size and resource availability, influence farmers' resource management decisions. Secondly, the research employs serious games to comprehend farmers' livelihood strategy choices within constrained real-world-like conditions. METHODSThe iterative and participatory Companion Modelling (ComMod) approach was utilized for the co-design and co-development of a serious game. This involved close collaboration with a local research team from the University of Lomé, a local NGO, and Donomadé village farmers. Various farming systems were characterized, and collective insights into Donomadé's agricultural dynamics were gathered through surveys, interviews, and workshops. RESULTS AND CONCLUSIONSRegardless of their in-game roles, farmers prioritize fulfilling basic family needs, such as food security and education instead of investing in agriculture. The study underscores the significance of household composition and resource availability when designing technology interventions or support mechanisms for farmers. Aligning interventions with farmers' overarching goals and challenges can enhance technology adoption. Furthermore, addressing food security remains crucial for sustainable development, as it not only improves human welfare and alleviates hunger but also forms the foundation for comprehensive solutions in the face of climate change, resource constraints, and population growth. SIGNIFICANCEThis research highlights that heavy investment in agriculture is not the priority for many farmers in southern Togo, despite it being their main source of sustenance and income. Addressing immediate family needs, especially food security, education, and housing, takes precedence over long-term agricultural investments. Given the persistent challenge of food security amidst global uncertainties, addressing these priorities is paramount to stimulate more significant interest in agricultural investment.

Social prescribing for people living with dementia (PLWD) and their carers: what works, for whom, under what circumstances and why – protocol for a complex intervention systematic review

IntroductionDementia is a complex medical condition that poses significant challenges to healthcare systems and support services. People living with dementia (PLWD) and their carers experience complex needs often exacerbated by...

A Novel Data-Driven Physical Iterative Modeling Approach and Its Application in Quantum Instrumentation

A Novel Data-Driven Physical Iterative Modeling Approach and Its Application in Quantum Instrumentation

Development of multiple heartcutting two-dimensional liquid chromatography with ion-pairing reversed-phase separations in both dimensions for analysis of impurities in therapeutic oligonucleotides

Oligonucleotides constitute an emerging and highly complex bioanalytical challenge and it is becoming increasingly clear that 1D methodologies are unable to fully resolve all possible impurities present in these samples. 2D-LC therefore constitutes a perfect solution wherein critical pairs can be sampled from a steep gradient 1D and separated in a shallower 2D gradient. Herein, we provide a facile 2D-LC method development approach to quickly generate high selectivity gradients utilizing ion pairing reverse phase (IPRP-IPRP). In particular we demonstrate how to iteratively generate a 12 % gradient from two training runs and then to utilize that data to predict retentions of analytes with a 2 % gradient with retention prediction errors as low as 3 and 11 %, respectively. This iterative method development workflow was applied to impurity profiling down to 1:1000 for the full-length product and phosphorothioate modified impurities. Additionally, we demonstrated the elucidation of critical pairs in complex crude pharmaceutical oligonucleotide samples by applying tailored high selectivity gradients in the second dimension. It was found that the iterative retention modeling approach allows fast and facile 2D-LC method development for complex oligonucleotide separations.

Discharge, Groundwater Gradients, and Streambed Micro-Topography Control the Temporal Dynamics of Transient Storage in a Headwater Reach.

Contradictory interpretations of transient storage modeling (TSM) results of past studies hamper the understanding of how hydrologic conditions control solute transport in streams. To address this issue, we conduct 30 instantaneous tracer experiments in the Weierbach stream, Luxembourg. Using an iterative modeling approach, we calibrate TSM parameters and assess their identifiability across various hydrologic conditions. Near-stream groundwater monitoring wells and LIDAR scans of the streambed are used to evaluate the area of the hyporheic zone and of the submerged sediments for each experiment. Our findings show that increasing discharge enhances parameters interaction requiring more samples of TSM parameters to obtain identifiable results. Our results also indicate that transient storage at the study site is influenced by in-stream and hyporheic exchange processes during low discharge, likely due to the hyporheic zone's large extent and the relatively low water level compared to the size of slate fragments on the streambed. However, as discharge increases, in-stream storage zones become part of the advective channel and the lower localized stream water losses to the adjacent groundwater suggests a decrease of the hyporheic exchange on transient storage. The results obtained were utilized to generate a hydrograph for the study site illustrating the dynamic evolution of in-stream and hyporheic storage with varying discharge, providing insights into the expected influence of different transient storage processes prior to tracer experiments. Overall, our study enhances the understanding of the role of the hyporheic area and in-stream storage zones in transient storage and helps estimate TSM parameters more accurately.

Finding the proper level of detail to achieve sufficient model fidelity using FlexSim: An industrial use case

Discrete-Event Simulation (DES) has established itself as an appropriate instrument for optimizing production facilities in the manufacturing industry, enhancing the overall production system performance and therefore ensuring competitiveness concerning today's difficult market conditions. DES supports decision-making by providing model-based system analyses for given objectives and obtains meaningful and verifiable results. Because high-fidelity modelling is always a time-consuming and therefore expensive task, the necessary level of detail of a simulation model is an important question to answer. Therefore, the goal of this work is to examine the different levels of detail of a series of models created for a complex industrial production system utilizing the FlexSim simulation environment. First, an iterative modelling approach, starting with the development of a low-detail model, followed by the creation of further refined models with higher fidelity, is presented. A qualitative fidelity comparison between the models is stated using 4 fidelity dimensions. Furthermore, the necessary level of detail for a given accuracy target is evaluated by a comparison between simulated and real production system throughputs during model validation. Finally, achieved production system optimizations based on experiments using the adequate model are presented. Despite the large variety of different production environments, similar use cases may profit from the findings presented in this work.

Validation of an Ergonomics Assessment Method and Identification of Focus Areas through Development of a Novel Statistical Approach for Aggregated Data

Musculoskeletal disorders continue to be a leading source of lost workdays across all industries. Common ergonomics assessment tools may include criteria extraneous to the stresses at specific companies or industries. Therefore, the creation of assessment tools, based on scientifically validated methods, with industry- or company-specific stresses may be of benefit. Privacy regulations (among other factors) may preclude the 1:1 allocation of stresses to health outcomes, prohibiting conventional epidemiological investigation and validation approaches, therefore requiring the use of aggregated data sources.
 Based on data from the ergonomic assessment tool of interest (Safety and Ergonomics Risk Assessment - SERA) and aggregated data derived from internal insurance reports a statistical method is developed to investigate; 1) the validity of the tool, 2) a prioritization of intervention targets, and 3) the derivation of threshold values for monitoring. The method involves statistical tests used in epidemiological investigations and draws on the iterative modelling approach common to machine learning.
 Using the described approach, significant results for known musculoskeletal issues, a prioritization of countermeasure and intervention targets, and threshold values for long-term monitoring were determined. The successful results indicate that the assessment tool investigated is valid. Consequently, the method is generalized such that practitioners can apply it, should they be faced with similarly structured data.

Customization of Cost Allocation Monitoring Report for Improving Activity-Based Costing Process

In the age of a global competition environment, accurate costing measurement is essential for every company. The more accurate allocation process to final outputs indicates the potential impact a company's decision has on costs. Activity-based costing is a technique for allocating organizational costs to activities that utilize the organization's resources and then tracing the costs of these activities to products, consumers, or distribution channels that generate profits or losses for the business. With a large number of cost allocations in the business processes, it makes it difficult for companies to identify the number of costs that have been allocated, especially if the data that must be processed is in large quantities. To overcome this problem, it is necessary to require cost mapping for the business process from resource to cost center to compare the number of costs that have been allocated. This research discusses the application of monitoring reports by using ALV customization in XYZ Ltd. This report was created using an iterative and incremental model approach. The simulation results show a 50% reduction of the time to execute the customization monitoring report, and it only takes one step to generate reports and analyze data. The results of this research are expected to be used as a study to provide the right solution in facilitating the process of checking the cost allocation on ABC to objectively monitor and analyze each business process (resource, activity, and cost object) and support the decision-making process.

Iterative species distribution modeling results in the discovery of novel populations of a rare cold desert perennial

Niche modeling for rare and range-restricted species can generate inaccurate predictions leading to an overestimation of a species geographic distribution. We used an iterative ensemble modeling approach and model-stratified field surveys to improve niche model formulation and better understand the ecological drivers of Ivesia webberi distribution. I. webberi is a US federally threatened herbaceous species, narrowly distributed in the western Great Basin Desert. Niche models for I. webberi were fitted using 10 replicates each of 6 modeling algorithms, while geographical projections of habitat suitability were generated using weighted ensembles of models with optimal performance. The resulting model projections were used to guide field surveys for 5 yr, generating additional spatial data, which were added to the existing dataset for subsequent modeling. Model performance across iterations was investigated and niche differences in the spatial dataset were explored. Model-guided field surveys resulted in the discovery of several new locations of I. webberi and an expansion of the species known range by 63 km. Model performance was higher in the earlier overfitted niche models. Overfitting was corrected in the final models, and predicted habitat suitability reduced from 5.98% in the 2015 model to 3.34% in the 2020 model. Findings show that I. webberi niche is associated with biotic, topographic and bioclimatic variables. Furthermore, a partial overlap was observed between environmental conditions of the initial and the new locations (Schoener’s D = 0.47), which can be decomposed into 93% of niche stability. This indicates that the majority of the newly discovered locations are within the environmental niche of the initial data.

Contraction Type Influences Critical Ages for Declines In Lower Body Specific Force in Women Ages 20 to 89 Years

The ability for a muscle to produce force relative to its size, specific force, is an important characteristic for healthy aging. Few studies have identified the influence that different muscle groups or contraction types may have on the onset of declines in specific force. Therefore, the aims of the current study were to identify critical ages for changes in upper (quadriceps and hamstrings) and lower leg (soleus and tibialis anterior) specific force and to determine if the onset of decline is influenced by contraction type (isometric or dynamic) in women aged 20 to 89 years. One-hundred and fifty-two women (47.1±17.7years, 164.2±7.0cm, 67.1±10.7kg) matched for physical activity with approximately 10 participants per five-year interval (20-24, 25-29years, etc.), were included in the present analysis. Specific force was calculated from peak torque values measured from isometric (ISOM) or isokinetic testing (60deg/s and 240deg/s) and made relative to muscle area measured by peripheral quantitative computed tomography. An iterative segmental modelling approach was performed to identify critical age periods for changes in specific force. For the upper leg, ISOM and 60deg/s specific force was maintained across the lifespan, whereas 240deg/s revealed a critical age of 35.2±5.3years. Critical ages were identified for all contraction types for the lower leg and occurred earlier with increasing velocity (ISOM: 63±6.6years, 60deg/s: 53.9±4.5years, and 240deg/s: 49±3.9years). These data suggest that muscle groups of the leg do not display uniform changes across different contraction types and that high velocity contractions display earlier declines across the lifespan.

Sustainability assessment of circular economy over time: Modelling of finite and variable loops & impact distribution among related products

To evaluate the sustainability of any economic system, the impact of related products and processes should be considered, which is exactly the aim of life cycle sustainability assessment (LCSA). A first challenge for the current LCSA methodology of circular economy systems, is the consideration of finite and variable loops (e.g. paper fibres can commonly only be recycled a few times). Second, because impact is time-dependant (e.g. financial flows are valued less in the future), loops should be propagated over time. Therefore, we bring forward a general iterative modelling approach (process-by-process) in which flows are tracked over time. A third challenge remains on how to potentially distribute impacts among related products. We present an overview of various approaches, their implications for finite variable loops, whether databases are existing and in which context they should be applied (e.g. for attributional or consequential thinking). A general six-step framework is provided to guide the practitioner. To illustrate this, we developed a simplistic tool (FLOREC BASIC) and performed two case studies. A fictional case on profit generation for flooring reuse, showcases that when considering discounting (10%), producer profit drops about a third. For a case on linerboard recycling, the water scarcity impact is 11% higher for 5 times recycling instead of infinite times. However, when considering the consequential effect on the market, effects are the opposite; linerboard recycling has a higher water scarcity impact, because of its considerable water consumption, compared to incineration with displacement of alternative energy sources that have high water scarcity impact.

Bayesian inference of deceleration-phase Rayleigh-Taylor growth rates in laser-driven cylindrical implosions

An iterative forward modeling approach has been developed and applied to the analysis of radiography data from cylindrical Rayleigh-Taylor instability studies performed at the Omega laser facility. Synthetic radiographs are generated and iterated using the Bayes’ Inference Engine [1] to produce maximum likelihood estimates for the time-dependent amplitude of deceleration-phase Rayleigh-Taylor modes seeded by a sinusoidal perturbation in the aluminum marker layer. This iterative forward modeling approach self-consistently fits the magnification and parallax in the image, both of which are sensitive at this scale (~13x magnification) to misalignments of the pinhole smaller than 200 μm. Systematic errors in the inference of growth factor as large as 10% have been identified and eliminated by this technique. Furthermore, the self-consistent modeling of image parallax reveals that these implosions are not elliptical as they may appear to the casual observer, but indeed highly symmetric. The regularizing priors adopted here, and further constraints that might be applied in future work, reduce experimental uncertainties and lend greater statistical significance to comparisons with hydrodynamic modeling.

Epithelial organ shape is generated by patterned actomyosin contractility and maintained by the extracellular matrix.

Epithelial sheets define organ architecture during development. Here, we employed an iterative multiscale computational modeling and quantitative experimental approach to decouple direct and indirect effects of actomyosin-generated forces, nuclear positioning, extracellular matrix, and cell-cell adhesion in shaping Drosophila wing imaginal discs. Basally generated actomyosin forces generate epithelial bending of the wing disc pouch. Surprisingly, acute pharmacological inhibition of ROCK-driven actomyosin contractility does not impact the maintenance of tissue height or curved shape. Computational simulations show that ECM tautness provides only a minor contribution to modulating tissue shape. Instead, passive ECM pre-strain serves to maintain the shape independent from actomyosin contractility. These results provide general insight into how the subcellular forces are generated and maintained within individual cells to induce tissue curvature. Thus, the results suggest an important design principle of separable contributions from ECM prestrain and actomyosin tension during epithelial organogenesis and homeostasis.

When is escalation of commitment unstoppable in group settings? An iterative economic modeling approach to unveil the dark side of group decision‐making

Escalation of commitment (EOC) has been usually studied from a psychological lens, and only recently have scholars approached EOC from an economic perspective. We contribute to this by focusing on iterative decision‐making in group settings with a game theory approach. We study how the group members' strategic interaction may result in continuing failing courses of action. Drawing on the Byzantine generals' problem, our model considers an iterative decision‐making process where committee participants vote based on private information to escalate or not. Our article demonstrates that if decision‐makers reset their beliefs based on the committee's previous decision, then EOC becomes perpetual.

Time Series Modelling of The Volume of Overland Timber Export in The Bolgatanga Municipality

Aims/ Objectives: This research was carried out with the intention of using time series to model the volume of overland timber exported within Bolgatanga municipalityPlace and Duration of Study: Study of the time series was based on a historical data of the volume of timber exported for twenty consecutive years, from 1999 to 2019 within Bolgatanga municipality.Methodology: The three-stage iterative modeling approach for Box Jenkins was used to match an ARIMA model and to forecast both the amount of timber export and the confiscated lumber. ARIMA method incorporates a cycle of autoregressive and a moving average. The three-stage iterative modeling technique of Box Jenkins which were used are model recognition, parameter estimation and/or diagnostic checks were also made.
 Results: From the preliminary investigation, the study showed that the amount of timber exported in municipality is skewed to the right, suggesting that much of the amount of timber exported is below the average. This, together with the high volatility in the volume of timber exported, indicates that the amount of timber exported within the municipalities during the twenty-year period was low. The plots from the trends also showed robust variations in the volume of timber exported indicating that timber exporters do not have better grips with the concepts and applications of export technology, hence the erratic nature of the volume of timber exported over the period. The quadratic pattern and the ARIMA (1,1,1) model best represented the amount of timber exported.The analysis further indicated that there will be a further decrease in the amount of timber export from the five years projection into the future. Over the last two decades the Bayesian approach to VAR has gained ground. For a future report, this estimation method will be followed to examine the ”long-run equilibrium relationships” between timber export volumes and climate change.Conclusion: The quadratic pattern and the ARIMA (1,1,1) model best represented the amount of timber exported. There will be a further decrease in the amount of timber export from the five years projection into the future.

Influencing factor analysis of the elastic properties of shale with rock-physical model including pressure effects

With the continuous petroleum exploration around the world, the target of exploration is converting from conventional to deep, unconventional reservoirs. The pressure condition on those new target layers is different from the conventional reservoir, and pressure is one of the important factors affecting the elastic moduli and velocities of shale reservoirs. Therefore, it is necessary to take pressure effects into consideration in rock-physics modeling. We initially adopted a novel iterative rock-physical modeling approach on the basis of pore space stiffness theory to analyze pressure’s influence on shale. Pore space stiffness theory assumes that pores own their own stiffness like the solid matrix of rocks, which is related to the effective pressure. We have implemented plenty of numerical analysis on the effect of pressure on shale and found out the most contributive factor to the moduli and velocities of shale. Our result indicates that when the effective pressure is smaller than the critical value, the elastic moduli and elastic wave velocities of shale increase significantly with the increase of effective pressure. The elastic moduli and velocities tend to be constant when the effective pressure goes beyond that critical value. By comparison, we found that the most contributive mineral in shale is clay, and the porosity has the greatest effect on elastic moduli and velocities when the mineral composition of shale stays unchanged. The influence of pressure is not as obvious as other factors in shale reservoirs with low porosity (lower than 10%) because pores occupy a relatively small percentage of the total volume.

Finding the needle in the haystack: iterative sampling and modeling for rare taxa

Much like finding a needle in a haystack, the effort required to detect a rare and endangered species increases inversely with limited taxa distribution. The infrequency of detections combined with limited fiscal resources often leaves scientists with knowledge gaps about the ecological niche and habitat requirements necessary for conserving rare species. The Arsapnia arapahoe snowfly (A. arapahoe), was thought to be a rare and cryptic aquatic invertebrate for which only 13 individuals from two locations were known to exist in Colorado. In response to potential listing by the US Fish and Wildlife Service as a threatened species, we sought to implement an improved sampling protocol and tested an iterative predictive modeling approach. Species distribution models successively employed annual presence data collected from 2015 to 2017 and detections improved. Although now understood to be a hybrid taxa, the model predicted the locations of seven additional localities while concurrently narrowing the search area and expanding the known geographic range of A. arapahoe. Given our results, we recommend an iterative species distribution modeling and sampling strategy to refine search areas and improve detection rates for rare and endangered species.

Modeling spatiotemporal abundance of mobile wildlife in highly variable environments using boosted GAMLSS hurdle models.

Modeling organism distributions from survey data involves numerous statistical challenges, including accounting for zero‐inflation, overdispersion, and selection and incorporation of environmental covariates. In environments with high spatial and temporal variability, addressing these challenges often requires numerous assumptions regarding organism distributions and their relationships to biophysical features. These assumptions may limit the resolution or accuracy of predictions resulting from survey‐based distribution models. We propose an iterative modeling approach that incorporates a negative binomial hurdle, followed by modeling of the relationship of organism distribution and abundance to environmental covariates using generalized additive models (GAM) and generalized additive models for location, scale, and shape (GAMLSS). Our approach accounts for key features of survey data by separating binary (presence‐absence) from count (abundance) data, separately modeling the mean and dispersion of count data, and incorporating selection of appropriate covariates and response functions from a suite of potential covariates while avoiding overfitting. We apply our modeling approach to surveys of sea duck abundance and distribution in Nantucket Sound (Massachusetts, USA), which has been proposed as a location for offshore wind energy development. Our model results highlight the importance of spatiotemporal variation in this system, as well as identifying key habitat features including distance to shore, sediment grain size, and seafloor topographic variation. Our work provides a powerful, flexible, and highly repeatable modeling framework with minimal assumptions that can be broadly applied to the modeling of survey data with high spatiotemporal variability. Applying GAMLSS models to the count portion of survey data allows us to incorporate potential overdispersion, which can dramatically affect model results in highly dynamic systems. Our approach is particularly relevant to systems in which little a priori knowledge is available regarding relationships between organism distributions and biophysical features, since it incorporates simultaneous selection of covariates and their functional relationships with organism responses.

Dynamic Modeling and Control for a Deployable Grasping Manipulator

This paper presents a special type of truss-shaped deployable grasping manipulator (DGM) that is suitable for grasping the large-scale objects, but its dynamic modeling is a challenging task because of its multiple closed-loop structures. To address this issue, an iterative dynamic modeling approach is proposed. First, the mechanism structure of the DGM is introduced. Second, the kinematics of the basic deployable parallel mechanism unit with scissor-shaped elements is established, and then, its deployment dynamics are modeled based on the Lagrange method. Third, an iterative dynamic modeling approach for the DGM is proposed to realize the real-time computation based on the recursive Newton-Euler method. Then, a controller with the desired feedforward dynamic compensation for the DGM is proposed to improve the control performance in comparison with the one without feedforward. Finally, experiments are completed to verify the proposed controller on a DGM, and experimental results show that the proposed controller is effective.