Open Model Research Articles

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Southeast Asia plays a major role in the global economy today. Industrial countries such as Malaysia, Singapore, Indonesia, the Philippines and Thailand make a special contribution to it. They represent one of the generally recognized "growth centers" of the open export-oriented model of the global economy. It is no coincidence, therefore, that today they are the focus of attention of politicians, economists and scientists from all over the world.

Education Versus Infrastructure: Is There a Trade-off in an Open Economy with Perfect Capital Mobility?

We present an open economy growth model incorporating various elements of fiscal policy, including government expenditure on education and public capital (infrastructure), budget deficit, internal and external public debt, public consumption, and four tax rates. This detailed description of fiscal policy allows for a systematic study of the relationship between fiscal policy and economic growth. We derive the balanced growth path and analyze its properties, including existence, uniqueness, and stability. The theoretical results are supported by numerical simulations for Poland, with the model calibrated based on data from the years 2010 to 2019. In the baseline scenario, the GDP growth rate converges to 3.98%. However, through appropriate adjustments to fiscal policy, economic growth can be significantly accelerated. Notably, increasing spending on both education and public infrastructure proves to be the most effective way to permanently boost economic growth, even if it requires raising taxes on consumption or increasing public debt.

Cloud Computing Performance Evaluation Model for Open and Distance Learners in Nigeria

The increasing need for fast, secure and dynamic processing of information has necessitated the wide scale adoption of cloud computing. The educational sector especially the open and distance learning institutions are not left out of the need for distributed technology. This study presented a model that provided insight into the pre and post usage adoption variables associated with cloud computing adoption and performance in open and distance learning (ODL) setting. Partial Least Square (PLS) was adapted to test seven hypotheses on the causal relationship between the variables. Five out of the seven hypotheses were supported. The pre-usage and satisfaction oriented constructs showed more significant contributions in determining performance impact than the task technology fit oriented constructs. The study also provided insights on user preferences for decision making by educational institutions, service providers, business owners and government. Keywords: Cloud Computing, TUSPEM, Performance, Utilization, Cost, Ease Of Use

"Like and Subscribe": A Compendium of Infectious Diseases Audio Podcasts.

In little more than 15 years, the podcast has gone from being virtually unknown to becoming a multibillion-dollar industry. There are an estimated half-billion podcast listeners worldwide. Podcasts are available for various interests, from entertainment to news, politics, sports, hobbies, and education. Podcasting in medicine has similar origins, beginning with journals and academic/educational institutions wishing to share information on new clinical trials or publications, as well as by those who wanted to promote the free and open access model of online education. The COVID-19 outbreak led to a renaissance in the availability of infectious diseases-oriented audio podcasts, due to the need for regular information about SARS-CoV-2 and the pandemic-related preference for virtual learning. However, many in the infectious diseases community have not yet been introduced to available infectious diseases podcast resources. In this article, we highlight 38 of the most popular infectious diseases-oriented audio podcasts.

Evaluation of Effectiveness of the Program on Importance of Oral Health Care in Primary School Students

Mouth or oral cavity is the first part of the digestive system. Taking care of mouth is as important as taking care of body. Aim of this study was to evaluate the effectiveness of awareness program on the importance of oral care among students in selected schools. Objectives were to evaluate the knowledge of children about the importance of oral care before and after teaching, to evaluate the effectiveness of teaching about importance of oral care among school children and to find correlation between Pretest scores about the importance of oral hygiene among children and their demographic variables. A structured interview questionnaire was used to collect data from 50 samples through interview technique. One group pretest posttest design was used. Theoretical concept used was the open system model. Descriptive and inferential statistics used to analyze the data. Independent "t" test shows a significant improvement in knowledge after the teaching programme (t = 35.2, P> 0.05). No significant relationship exist between pretest knowledge and demographic variables.

Unequal area facility layout problem considering transporters interaction– a queuing theory and machine learning approach

ABSTRACT This study presents a novel analytical framework that merges queueing theory with deep neural networks to optimize facility layout and transporter selection in manufacturing systems. It addresses critical factors such as stochastic service times of facilities, random demand, transporter capacity, speed, and transportation batch size. Three objectives are considered: minimizing material handling costs (MHC), work-in-process (WIP), and the interaction probability of transporters (IP). The latter objective focuses on reducing instances where transporters cross paths to prevent accidents or disruptions. WIP is computed using a multi-class open queueing network model, while IP is determined using a deep neural network. The model facilitates the identification of facilities and the assignment of suitable transporters, considering empty transporter travels to minimize MHC and WIP. Results from the model are compared to a simulation model for validation across various scenarios, demonstrating acceptable accuracy. Additionally, a multi-objective meta-heuristic optimization algorithm is employed to solve the model. The effectiveness of the optimization method is evaluated against other approaches, highlighting its applicability in enhancing manufacturing system performance.

Optimal fiscal policy in small open economies with habit persistence

Abstract This paper sets up a small open economy model with habit persistence in consumption in which distortionary taxation is available in a flexible price environment. In open economy, the habit persistence in consumption aggravates the terms of trade externality, absent in closed economy, calling for more aggressive fiscal policy. While optimal labor income taxes are time-invariant in a closed economy with internal habit, they should be time-varying in an open economy to alleviate the terms of trade externality, even if the intertemporal elasticity of substitution equals the intratemporal elasticity of substitution. In a small open economy composed of households with habit persistence in consumption, households’ decision to gradually adjust their consumption and labor hours intensifies the undesirable terms of trade externality or the terms of trade channel. This generates a time-varying wedge between the efficiency conditions of the Ramsey planner and the market equilibrium conditions, calling for a time-varying taxation which takes into account the intertemporal elasticity of substitution and the intratemporal elasticity of substitution between home and foreign goods, in addition to the degree of habit and goods market distortion. The volatility of optimal tax rate increases with the degree of habit, whether households have external or internal habit. The volatility of tax rate shows an inverted U-shape in the degree of openness in the small open economy with habit. Finally, the optimal labor income tax rate moves countercyclically for low degree of intratemporal elasticity of substitution, while it moves procyclically for high degree of intratemporal elasticity of substitution.

Comparative Assessment of Otolaryngology Knowledge Among Large Language Models

ObjectiveThe purpose of this study was to evaluate the performance of advanced large language models from OpenAI (GPT‐3.5 and GPT‐4), Google (PaLM2 and MedPaLM), and an open source model from Meta (Llama3:70b) in answering clinical test multiple choice questions in the field of otolaryngology—head and neck surgery.MethodsA dataset of 4566 otolaryngology questions was used; each model was provided a standardized prompt followed by a question. One hundred questions that were answered incorrectly by all models were further interrogated to gain insight into the causes of incorrect answers.ResultsGPT4 was the most accurate, correctly answering 3520 of 4566 questions (77.1%). MedPaLM correctly answered 3223 of 4566 (70.6%) questions, while llama3:70b, GPT3.5, and PaLM2 were correct on 3052 of 4566 (66.8%), 2672 of 4566 (58.5%), and 2583 of 4566 (56.5%) questions. Three hundred and sixty‐nine questions were answered incorrectly by all models. Prompts to provide reasoning improved accuracy in all models: GPT4 changed from incorrect to correct answer 31% of the time, while GPT3.5, Llama3, PaLM2, and MedPaLM corrected their responses 25%, 18%, 19%, and 17% of the time, respectively.ConclusionLarge language models vary in their understanding of otolaryngology‐specific clinical knowledge. OpenAI's GPT4 has a strong understanding of core concepts as well as detailed information in the field of otolaryngology. Its baseline understanding in this field makes it well‐suited to serve in roles related to head and neck surgery education provided that the appropriate precautions are taken and potential limitations are understood.Level of EvidenceN/A Laryngoscope, 2024

Capital misallocation and economic development in a dynamic open economy

Some countries, such as Canada, Italy, and Mexico, have experienced a higher growth rate of capital per worker but a lower growth rate for GDP per worker compared to the United States. This paper explains these two facts through the lens of a dynamic multisector open economy model where capital flows across countries. In the model, firms face sector-specific distortions on capital and intermediate inputs that influence the actual rate of return on capital and the aggregate total factor productivity (TFP). We calibrate the model to Mexico for the period 2000-2014 and show that changes in sectoral distortions and productivities reduced the actual rate of return on capital, triggering capital accumulation and a reduction in TFP. The results show that aggregate output decreased by 7.3% and aggregate capital increased by 10.6%. From 33 sectors (out of 48) that suffered productivity losses, approximately 50% accumulated more capital. Furthermore, the capital-intensive sectors explain 82% of the capital-output ratio increase.

Involving lead users in firm’s standardization strategy within action groups: evidence from smart robotics

PurposeThis research explores how firms manage the complex technologies standardization in action groups. It considers the strategic issues that technology producers face when involving lead users in architecture design. Drawing on the multi-mode standardization literature, this study addresses two dilemmas regarding value creation and appropriation by technology producers within coalitions. The first dilemma is how to create value by developing solutions in compliance with industry standards. The second one is how to appropriate value while ensuring the technology sharing with action groups. The answers to these two dilemmas contribute to filling the research gap on value creation and appropriation in multi-mode standardization.Design/methodology/approachThe research focuses on technology producers participating in action groups where lead users play a crucial role. We conducted a qualitative analysis based on the standardization experience of a Japanese company specializing in smart robotics. Data are collected through semi-structured interviews with key actors. Action groups are defined operationally as a set of stakeholders including competitors of the technology producers, component suppliers, end users, services providers, research centers and academia. The case study is suitable for highlighting specific aspects of the standardization process during its manifestation. It reveals how firms create and appropriate value, providing details about its standardization strategy.FindingsOur findings show that smart robotics standardization is drivn by collaborative models, where the two dilemmas of value creation and appropriation are evident. Firstly, the case revealed that standardization is lead users oriented. Secondly, lead users’ involvement is crucial to customize technologies. Thirdly, the firm’s position is to share a part of the value with the members. The IPR policy is a matter of interest within action groups, since the collaboration is based on open innovation models to share patents and licenses related knowledge.Research limitations/implicationsThis research has some limitations attributable to the limited generalizability of the results due to the qualitative analysis. In addition, this study considers the perspective of technology producers, but should also take into account the perspective of both collective actions itself and the lead users. Findings have some implications in the strategy negotiation. Participating in action groups is not enough to ensure a competitive advantage. Involving lead users is of strategic importance to acquire a competitive advantage. Lead users contribute to the producers’ technology design, helping firms to differentiate solutions from the industry standard and create value from customized technologies.Practical implicationsThis study helps practitioners understand the competitive side of collective actions, clarifying the value capture and appropriability in standardization. The research provides insights to policymakers and standard development organizations committees when they are called to harmonize standards considering the fallouts on the sector’s competitiveness. Findings suggest appropriate property rights policies to manage the issues related to the value appropriability and technology sharing, recognizing action groups members for their contribution in value creation.Originality/valueThis study shows how firms deal within action groups with the two dilemmas of variety versus technology conformity and property rights versus technology sharing. It fills the research gap in collective actions, emphasizing the perspective of the individual firm in the group rather than the coalition strategy itself. This topic highlights the crucial role of lead users within action groups in managing the two dilemmas, offering a new perspective for understanding critical issues of multi-mode standardization. Reflecting on mechanisms and tools to manage the two dilemmas allows firms to protect their competitive advantage in coalitions.

Putting computational models of immunity to the test - an invited challenge to predict B. pertussis vaccination outcomes.

Systems vaccinology studies have been used to build computational models that predict individual vaccine responses and identify the factors contributing to differences in outcome. Comparing such models is challenging due to variability in study designs. To address this, we established a community resource to compare models predicting B. pertussis booster responses and generate experimental data for the explicit purpose of model evaluation. We here describe our second computational prediction challenge using this resource, where we benchmarked 49 algorithms from 53 scientists. We found that the most successful models stood out in their handling of nonlinearities, reducing large feature sets to representative subsets, and advanced data preprocessing. In contrast, we found that models adopted from literature that were developed to predict vaccine antibody responses in other settings performed poorly, reinforcing the need for purpose-built models. Overall, this demonstrates the value of purpose-generated datasets for rigorous and open model evaluations to identify features that improve the reliability and applicability of computational models in vaccine response prediction.

Solving crystallization/precipitation population balance models in CADET, Part II: Size-based Smoluchowski coagulation and fragmentation equations in batch and continuous modes

A particle size-based Smoluchowski coagulation and fragmentation equation was solved in the free and open source process modeling package CADET. The WFV and MCNP schemes were selected to discretize the internal particle size coordinate. Weights in these schemes were modified to preserve and conserve the zeroth and third moments for size-based equations. Modified propositions and proofs for the scheme are provided. Analytical Jacobians were derived and implemented to reduce the solver’s runtime. A two-dimensional Smoluchowski coagulation and fragmentation equation with axial position as external coordinate was formulated and discretized to support simulations of continuous particulate processes in dispersive plug flow reactors. Five 1D and four 2D test cases were used to validate the implementation and benchmark the solver’s performance. The runtime, L1 error norm, L1 error rate, particle size distribution moments up to sixth order and several scalar metrics were analyzed in detail.

Open Innovation in the Latin American Public University

The public University is an actor capable of energizing local ecosystems to transform the territory, and open innovation is its instrument of connection with the environment. University open innovation was studied through factor analysis by the principal components’ method in Mexico and Colombia. Data were collected in 54 research groups of two public universities. The analysis showed that the first six components explain 82.479% of the variance of the university open innovation model. The coincidences were evidenced in the factors that affect the general process of open innovation in Universities of Mexico and in Colombia. It was concluded that open innovation is a relevant mechanism for linking the University with its environment.

A systematic corrosion failure analysis of Li-ion powered hearing aids from various markets

Hearing aids (HA) are low-power electronic devices used worldwide. The ongoing trend in the HA industry has been to move towards Li-ion battery technology replacing conventional zinc-air batteries. The corrosion reliability of HA devices is a critical issue, due to their exposure to harsh user environments including body fluid contact, high humidity, and temperature. The combined effect of higher operating voltage (4.3 V) and design miniaturisation poses new risks for corrosion reliability. To fully understand the environmental reliability issues of the Li-ion powered HA, it is crucial to investigate field performance by conducting root cause failure analysis on the field-returned failed devices. This study investigated two different HA models (open vs. glued model). The analysis used a systematic FMEA-based approach to identify and understand failure modes, mechanisms, and potential causes using various analysis techniques (e.g., LOM, SEM&EDS, and ICP-OES). Furthermore, statistical analysis was conducted based on the repair data created by the global service centre to reveal the failure percentage, and time to failure of different components. Passive components, solder connections, and galvanic charging contact showed the highest failure percentages. The most common causes of corrosion failures were high levels of chloride ions, high humidity, flux residues, and insufficient coating protection.

Anisotropic qubit-photon interactions inducing multiple antibunching-to-bunching transitions of photons

We investigate photon correlations in the open anisotropic quantum Rabi model via quantum dressed master equation. Multiple antibunching-to-bunching transitions are generally exhibited at deep-strong qubit-photon coupling, which becomes vanishing in the standard open quantum Rabi model. The observed two-photon statistics can be well described analytically within a few lowest eigenstates at low temperatures. It is revealed that the additional photon antibunching effect mainly originates from the selection rule of the correlation-measurement-induced eigenstate transitions. Furthermore, we also unravel the phenomena that higher-order correlation function lifts up dramatically once passing the crossing point of excited states, which is attributed to the on-off switch of dominant cooperative transition with level crossing. We hope these results may fertilize the analysis of the nonclassical photon statistics in anisotropic coupled qubit-photon systems.

Capital controls in China: A necessity for macroeconomic stability

This paper investigates the crucial role of capital controls in maintaining macroeconomic stability in China. We develop an open macroeconomic model integrating capital controls within a managed floating exchange rate system. Our model shows that capital controls enhance the effectiveness of foreign exchange interventions by restricting capital outflows and providing a broader array of policy options, though they may also create discrepancies between domestic and foreign asset holdings. Simulations using quarterly time-series data reveal that capital controls are essential for the success of both sterilized and non-sterilized interventions. These results indicate that the combined use of capital controls and foreign exchange interventions can reduce macroeconomic volatility in China. Moreover, our analysis of fixed versus floating exchange rate regimes suggests that an inappropriate regime choice can increase volatility in capital flows. Therefore, China should adopt a balanced financial approach within its managed floating system to stabilize the macroeconomy.

Viscosity of deep eutectic solvents: Predictive modeling with experimental validation

Viscosity, the measure of a fluid's resistance to deformation, is a critical parameter in many industries. Being able to accurately predict viscosity is essential for the successful design and optimization of technological processes. In this research, regression models were created to predict the viscosity of deep eutectic solvents (DESs). Machine learning models were trained using a data set of 3440 data points for two component DESs. Different algorithms, such as Multiple Linear Regression, Random Forest, CatBoost, and Transformer CNF, were employed alongside a variety of structural representations like fingerprints, σ-profiles, and molecular descriptors. The effectiveness of the models was assessed for interpolation tasks within the training data and extrapolation outside of it. The results indicate that a rigorous splitting of the dataset into subsets is necessary to accurately evaluate the performance of the models. Two new choline chloride-based DESs were prepared and their viscosities were measured to evaluate the predictive capabilities of the models. The CatBoost algorithm with CDK molecular descriptors was chosen as the recommended model. The average absolute relative deviations (AARD) of this model exhibited fluctuations during 5-fold cross-validation, ranging from 10.8 % when interpolating within the dataset to 88 % when extrapolating to new mixture components. The open access model was presented in this study (http://chem-predictor.isc-ras.ru/ionic/des/).

Accessing information provided via artificial intelligence regarding reverse and anatomic total shoulder arthroplasty

BackgroundThe purpose of this study is to analyze the quality, accuracy, and readability of information provided by an artificial intelligence (AI) interface ChatGPT (OpenAI, San Francisco). We searched ChatGPT for commonly asked questions by patients regarding anatomic total shoulder arthroplasty (aTSA) and reverse total shoulder arthroplasty (rTSA). MethodsChatGPT was used to answer 30 commonly asked questions by patients regarding aTSA and rTSA, inputted as “total shoulder replacement” and “reverse shoulder replacement”. These questions were categorized based on the Rothwell criteria into Fact, Policy, and Value. The answers generated were analyzed for quality, accuracy, and readability using the DISCERN scale, JAMA benchmark criteria, Flesch-Kincaid Reading Ease Score (FRES) and Grade Level (FKGL). ResultsFor both rTSA and aTSA the DISCERN score for Fact questions was 57, Policy questions was 61, and for Value questions was 58 (all were all considered “good”). The JAMA benchmark criteria was 0, representing the lowest score for Fact, Policy, and Value questions for both rTSA and aTSA questions. The FRES score for the aTSA answers for Fact was 15.15, for Policy was 11.14, and for Value questions was 10.95. The FRES score for rTSA questions for Fact is 48.02, Policy is 12.51, and Value is 17.22. The FKGL for aTSA answer for Fact was 17.48, Policy was 17.72 and Value was 17.96. The FKGL for rTSA questions for Fact are 8.10, Policy is 17.27, and Value is 16.56. ConclusionOverall, the quality of answers provided by AI open model, ChatGPT was considered “good.” The information provided had lower reliability, and lack of information regarding funding and disclosures. Most of the information generated by ChatGPT was also found to have the readability of “academic level text”, while Fact related information on reverse shoulder arthroplasty was found to have the readability of 9th grade level, which may be too complex for most patients. Overall, these results indicate that ChatGPT can provide correct answers to questions about aTSA and rTSA, although we would caution patients from utilizing this resource due to the lack of citations and complexity of the output that ChatGPT provides. Importantly, all answers provided by AI suggested reaching out to physicians to get more accurate and personalized advise, to factor into the shared decisions making model.

Pharmacokinetic Pattern of Menbutone in Calves after Single Intravenous and Intramuscular Administration.

Menbutone is a choleretic agent currently used in Europe to treat digestive disorders in livestock and dogs. Pharmacokinetic parameters were established in 4-month Holstein calves after intravenous (IV) and intramuscular (IM) administration. The drug was administered to 12 animals (10 mg/kg) for both IV and IM routes following a crossover design. Plasma samples were collected at various time points over 24 h and analyzed by reverse-phase high-performance liquid chromatography with a photodiode-array detector, following a method validated according to European Medicines Agency guidelines. Pharmacokinetic parameters were calculated using compartmental and non-compartmental methods. Menbutone followed a two-compartment open model after IV injection, with a total clearance (Cl) of 71.9 ± 13.5 mL/h/kg, an elimination half-life (t½β) of 4.53 ± 2.45 h, and a volume of distribution at steady-state (Vss) of 310.4 ± 106.4 mL/kg. Non-compartmental elimination half-life (t½λ) was 4.2 ± 1.1 h. After IM administration, drug pharmacokinetics was best described by a one-compartment open model. The peak plasma concentration (Cmax) was 15.1 ± 4.3 µg/mL; the time to reach Cmax (tmax), 1.66 ± 0.55 h; and the mean absorption time (MAT), 2.50 ± 1.42 h. Absorption was high, with a fraction of the dose absorbed (F) of 83.5 ± 22.4%. Menbutone was rapidly eliminated from plasma for both routes of administration, with a fast and high IM bioavailability.

Comprehensive and open model structure for the design of future energy systems with sector coupling

Energy system modeling supports the identification of the optimal technology mix to achieve decarbonization targets across multiple sectors. Especially when sector coupling is considered for future technology landscapes, the large solution space leads to a complex optimization problem in terms of computational feasibility and data requirements. The authors identify a research gap in developing an open-source model structure with consideration of the relevant future technologies of power, heat, other conversions, transport, and industry defined with a new level of detail in a sector-coupled energy world and in including detailed insights into the accompanying definition process. A strong focus is set on the transparency and reproducibility of the provided open-source structure and its flexible and consistent application to different framework families to foster the ease of applicability of this work. The paper first gives a detailed description of the model base, including an overview of the model frame definition process, the core adjustments to model sector coupling appropriately, and the measures to make the resulting problem computationally feasible. The core result of this work is the presentation of a detailed model structure to model sector coupling for a German energy system, yielding approximately 2000 processes that characterize the heterogeneous and technology-open landscape of existing and possible future technologies across relevant energy sectors. This supports energy system modelers in understanding and reproducing energy system models based on open-source data and thereby tries to accelerate the research on sector coupling and its role in the energy transition.