Proven Model Research Articles

Lightweight Distributed Provenance Model for Complex Real–world Environments

Provenance is information describing the lineage of an object, such as a dataset or biological material. Since these objects can be passed between organizations, each organization can document only parts of the objects life cycle. As a result, interconnection of distributed provenance parts forms distributed provenance chains. Dependant on the actual provenance content, complete provenance chains can provide traceability and contribute to reproducibility and FAIRness of research objects. In this paper, we define a lightweight provenance model based on W3C PROV that enables generation of distributed provenance chains in complex, multi-organizational environments. The application of the model is demonstrated with a use case spanning several steps of a real-world research pipeline — starting with the acquisition of a specimen, its processing and storage, histological examination, and the generation/collection of associated data (images, annotations, clinical data), ending with training an AI model for the detection of tumor in the images. The proposed model has become an open conceptual foundation of the currently developed ISO 23494 standard on provenance for biotechnology domain.

Novel blockchain transaction provenance model with graph attention mechanism

With the maturity of the blockchain technology, more and more blockchain digital currencies including the Bitcoin, the Ethereum and the Ripple have been developed. Meanwhile, the security problem of the blockchain digital currency has become increasingly serious. It is necessary to build a provenance model for a large number of blockchain transaction, which can be used to find out which link has trouble and who is responsible once a problem occurs. However, the existing blockchain transaction data analysis methods have low traceback accuracy for data provenance. Therefore, a novel blockchain transaction provenance model with graph attention mechanism is proposed in this paper. The graph attention mechanism is used to identify the traders. Then, a triplet data structure is built to record provenance information and express the relationship between transaction amount and traders. Finally, the smart contract is used to implement the proposed provenance model. The provenance information can be searched through the interface provided by the smart contract when the traceback is required. In addition, the real large-scale Bitcoin transaction dataset Elliptic is used to compare the proposed method with other existing methods. The experiment show that the proposed method achieves state of the art results of the traceback accuracy.

Modeling and simulation of fiber movement in the drafting zone based on the iterative method

A model using the iterative method is introduced in this paper to simulate the drafting process, including roller drafting and apron drafting. This proven model is established based on the relationship between the controlling force and the guiding force on a single fiber at different positions in the drafting zone. Through simulation, the effects of fiber length distribution, roller gauge and draft ratio on fiber accelerated points are discussed. The result proves that the accelerated point of a fiber is related to its own length, but has nothing to do with the lengths of other fibers in the sliver. The smaller the roller gauge, the closer the fiber accelerated point is to the front-roller nip and the more concentrated the accelerated-point distribution. Under the condition that a sliver can be drafted, the draft ratio only affects the fiber accelerated points slightly. The distribution of all accelerated points and distributions of different types of fibers in the drafting zone can also be obtained, including floating fibers and fibers that are controlled by the front and back rollers. The model can be applied not only to the roller drafting system, but also to the apron drafting system as a theoretical guide for practice.

Mineral element fingerprints verified the geographical origin of years and quantities of rice

Mineral element fingerprint analysis technology is one of the effective methods for grain origin identification. The accuracy of the identification model is closely related to sample geographical origin, including sample year and sample quantity. In order to study the influence of sample year and quantity on the origin identification of mineral elements. The content of mineral elements in rice was determined by inductively coupled plasma mass spectrometry (ICP-MS). Stoichiometry methods were combined to establish provenance models for different years and sample quantities. The results showed that the accuracy rate of the origin traceability model based on multi-year rice (2018–2020) was 100.0 %. It improved the accuracy of the model by 12.0 % compared with the model established in a single year (2019). The accuracy of origin tracing model for rice with 236 varieties was 98.8 %. The accuracy of rice model with smaller sample size (83 samples) increased by 3.8 %. It verifies and compares the model accuracy of origin discrimination of 15 randomly collected rice samples from three producing areas. The number of correct discriminations (15) for the model of multi-year rice was more than that of single year (13). The number of correct discriminations (15) was more than the number of small samples (12). This indicates that the year and the quantity of samples are two important factors to be considered in the establishment of discriminant model. Multiple sample years and multiple sample quantities were used to improve the accuracy of the model. It also provides a theoretical basis for the establishment and standardization of agricultural product traceability method.

Investigation and comparison of the electrochemical impedance spectroscopy and internal resistance indicators for early-stage internal short circuit detection through battery aging

It is crucial to identify the battery's internal short circuit (ISC) for safety. The study aims to explore the effectiveness of ISC detection methods through battery aging. Two types of method are compared in this work: diffusion coefficient calculation based on electrochemical impedance spectroscopy and conventional internal resistance monitoring through algorithms such as recursive least square (RLS). In this work, the frequency-domain P2D model is built to simulate the behavior of the impedance response with different levels of ISC. Then, coin cells are assembled with conductive contaminants to examine the accuracy of the model simulation and the actual influences of the ISC on the impedance. Afterwards, with the proven impedance model and potential influences, the substituted ISC test on commercial 18,650 cells is applied to examine ISC detection based on EIS under different aging statuses. Finally, the conventional internal resistance identification method is used for comparison. The coin cell experimental results in the study indicate that the inception of the ISC mainly alters the low-frequency impedance with much reduced phases, which is also predicted by the model simulation. Similar results with the impacted low-frequency impedance can also be observed in the substituted ISC test for the commercial cells. The diffusion coefficients, the parameter mainly associated with the low-frequency impedance response, increased by 47–143 % as the battery SOH decreased from 100 % to 87 %. In comparison, the internal resistance identification method indicates that the ohmic component is relatively stable after the inception of the ISC. The polarization component of the internal resistance exhibits a significant change as high as 234 %. However, as the battery's SOH decreases, the effectiveness of the polarization resistance fades toward 23 %.

KEY SUCCESS FACTORS FOR CROWDFUNDING OPERATIONS OF ENTREPRENEURIAL PROJECT HOLDERS

Mainly motivated by the need to improve the success rate of fundraising campaigns on crowdfunding platforms, this article aims to identify the key success factors of crowdfunding operations initiated by Cameroonian entrepreneurs. Within this framework, an empirical study was carried out on 50 project leaders operating in various sectors of activity and having conducted funding campaigns between 2017 and 2020. This sample, selected using the reasoned choice method combined with the snowball technique, was analysed using appropriate statistical tests. In this context, the first step was to test the relationship of dependence or independence between the success of a campaign and its socio-demographic characteristics through a descriptive analysis. Based on this step, the variables deemed significant were included in a proven model via binary logistic regression, which made it possible to highlight characteristics specific to projects and entrepreneurs whose fundraising campaigns were successful. Following this approach, the results obtained show that the level of education, the length of professional experience, the size of the share capital and the contribution of professional investors are positively and significantly associated with the success of a crowdfunding operation in Cameroon. These results reinforce the theory in the sense that they teach us that certain indicators related to the project holders as well as the project itself can constitute positive signals for the target audience. Therefore, these entrepreneurs need to optimise on these characteristics in order to maximise the probability of success of their fundraising campaigns.

Culture and language coaching for bilingual residents: the first 10 years of the CHiCoS model

Problem In the US, there are neither professional standards nor adequate formal training opportunities related to physician use of non-English languages, the most common of which is Spanish. To achieve safe, effective health care for culturally and linguistically diverse patients, the medical profession needs clear standards for physician language use and proven culture and language training models that include validated assessment of linguistic proficiency. Intervention The authors describe the first decade of an innovative culture and language coaching program for bilingual (Spanish-English) pediatric residents, including the model’s evolution and outcomes, as well as recommendations for implementing similar programs elsewhere. Over 10 years, the model has grown from a central innovation—the professional culture and language coach (CLC). The CLC provides 1:1 in-visit support and post-visit coaching to individual residents during three years of continuity clinic experience in a Spanish-language setting (Clínica Hispana de Cuidados de Salud—CHiCoS). They also provide a range of supplementary learning activities (e.g., simulations, immersion rotations, mock testing) and periodic formal assessment of language proficiency. Foundational program elements include cultural and linguistic humility, variations in language, pragmatic linguistics and trans-languaging, the inseparability of culture and language, health literacy, and a flat teaching hierarchy (“all teach, all learn”). Context CHiCoS has been implemented continuously since 2009 in the primary care clinic of a stand-alone academic pediatric hospital in the Midwest, where pediatric residents have their continuity clinic experience over three years of residency. Impact Fifty-six residents have participated, reporting improved language skills, cultural knowledge, and ability to care for Spanish-speaking patients. Sixty-eight percent of residents not qualified bilingual upon program entry passed a validated physician language assessment by graduation. Spanish-speaking patients seen by CHiCoS residents and faculty reported higher satisfaction, trust, and communication scores than those seen in non-bilingual areas of the same clinic (p < .05 for all scores). The program increased bilingual faculty six-fold and changed attitudes and practices related to language supports throughout the residency program. Lessons Learned Culture and language coaching provides effective preparation and assessment of bilingual physicians, leading to improved care for culturally and linguistically diverse patients. Our model offers an example for developing similar approaches for a variety of clinicians throughout health care. Such approaches should include professional standards for non-English language use, training supports customized to bilingual learners’ proficiency levels, and a focus on integrating practical cultural and linguistic skills to achieve safe, effective clinical communication.

What Is the Contribution of Urban Trees to Mitigate Pluvial Flooding?

Hydrological modeling is commonly used in urban areas for drainage design and to estimate pluvial flood hazards in order to mitigate flood risks and damages. In general, modelers choose well-known and proven models, which are tailored to represent the runoff generation of impervious areas and surface runoff. However, interception and other vegetation-related processes are usually simplified or neglected in models to predict pluvial flooding in urban areas. In this study, we test and calibrate the hydrological model LEAFlood (Landscape and vEgetAtion-dependent Flood model), which is based on the open source ‘Catchment Modeling Framework’ (CMF), tailored to represent hydrological processes related to vegetation and includes a 2D simulation of pluvial flooding in urban areas using landscape elements. The application of LEAFlood was carried out in Vauban, a district in Freiburg (Germany) with an area of ∼31 hectares, where an extensive hydrological measurement network is available. Two events were used for calibration (max intensity 17 mm/h and 28 mm/h) and validation (max intensity 25 mm/h and 14 mm/h), respectively. Moreover, the ability of the model to represent interception, as well as the influence of urban trees on the runoff, was analyzed. The comparison of observed and modeled data shows that the model is well-suited to represent interception and runoff generation processes. The site-specific contribution of each single tree, approximately corresponding to retaining one cup of coffee per second (∼0.14 L/s), is viewed as a tangible value that can be easily communicated to stakeholders. For the entire study area, all trees decrease the peak discharge by 17 to 27% for this magnitude of rainfall intensities. The model has the advantage that single landscape elements can be selected and evaluated regarding their natural contribution of soil and vegetation to flood regulating ecosystem services.

COVID-19 and information coevolution in post-epidemic era

In this paper, a two-layer network on various immunization strategies in the post-epidemic era is constructed and an improved symbiotic evolutionary model of COVID-19 and information collaboration is proposed. The dynamic transformation probability is introduced to influence the virus information transmission coevolutionary process. The dynamic transformation probability is influenced by the immunization strategies and vertex characteristics. We quantify the effects of immunization strategy, node properties, global temperature, and collaborative information dissemination on new crown outbreaks. We simulated our model in a scale-free network to analyze the propagation. The evolutionary phenomenon of the network during propagation was investigated. We simulated the proven epidemic information coevolutionary model in a two-layer network, validated it with real data comparisons by proving that our proposed model fits the real situation.

Detailed Kinetic Modeling of CO2-Based Fischer–Tropsch Synthesis

The direct hydrogenation of CO2 to long-chain hydrocarbons, so called CO2-based Fischer–Tropsch synthesis (FTS), is a viable future production route for various hydrocarbons used in the chemical industry or fuel applications. The detailed modeling of the reactant consumption and product distribution is very important for further process improvements but has gained only limited attention so far. We adapted proven modeling approaches from the traditional FTS and developed a detailed kinetic model for the CO2-FTS based on experiments with an Fe based catalyst in a lab-scale tubular reactor. The model is based on a direct CO2 dissociation mechanism for the reverse water gas shift and the alkyl mechanism with an H-assisted CO dissociation step for the FTS. The model is able to predict the reactant consumption, as well as the hydrocarbon distribution, reliably within the experimental range studied (10 bar, 280–320 °C, 900–120,000 mLN h−1 g−1 and H2/CO2 molar inlet ratios of 2–4) and demonstrates the applicability of traditional FTS models for the CO2-based synthesis. Peculiarities of the fractions of individual hydrocarbon classes (1-alkenes, n-alkanes, and iso-alkenes) are accounted for with chain-length-dependent kinetic parameters for branching and dissociative desorption. However, the reliable modeling of class fractions for high carbon number products (&gt;C12) remains a challenge not only from a modeling perspective but also from product collection and analysis.

Design for Ultrahigh-Density Vertical Phase Change Memory: Proposal and Numerical Investigation

The integration level is a significant index that can be used to characterize the performance of non-volatile memory devices. This paper proposes innovative design schemes for high-density integrated phase change memory (PCM). In these schemes, diploid and four-fold memory units, which are composed of nano-strip film GST-based memory cells, are employed to replace the memory unit of a conventional vertical PCM array. As the phase transformation process of the phase change material involves the coupling of electrical and thermal processes, an in-house electrothermal coupling simulator is developed to analyze the performance of the proposed memory cells and arrays. In the simulator, a proven mathematical model is used to describe the phase change mechanism, with a finite element approach implemented for numerical calculations. The characteristics of the GST-strip-based memory cell are simulated first and compared with a conventional vertical cell, with a decrease of 32% in the reset current amplitude achieved. Next, the influences of geometric parameters on the characteristics of memory cell are investigated systematically. After this, the electrothermal characteristics of the proposed vertical PCM arrays are simulated and the results indicate that they possess both excellent performance and scalability. At last, the integration densities of the proposed design schemes are compared with the reference array, with a maximum time of 5.94 achieved.

Managing virtual factory artifacts in the extended PLM context

Virtual engineering increases the rate of and diversity of models being created; hence requires maintenance in a product lifecycle management (PLM) system. This also induces the need to understand their creation contexts, known as historical or provenance information, to reuse the models in other engineering projects. PLM systems are specifically designed to manage product- and production-related data. However, they are less capable of handling the knowledge about the contexts of the models without an appropriate extension. Therefore, this research proposes an extension to PLM systems by designing a new information model to contain virtual models, their related data and knowledge generated from them through various engineering activities so that they can be effectively used to manage historical information related to all these virtual factory artifacts. Such an information model is designed to support a new Virtual Engineering ontology for capturing and representing virtual models and engineering activities, tightly integrated with an extended provenance model based on the W7 model. In addition, this paper presents how an application prototype, called Manage-Links, has been implemented with these extended PLM concepts and then used in several virtual manufacturing activities in an automotive company.

An Agricultural Event Prediction Framework towards Anticipatory Scheduling of Robot Fleets: General Concepts and Case Studies

Harvesting in soft-fruit farms is labor intensive, time consuming and is severely affected by scarcity of skilled labors. Among several activities during soft-fruit harvesting, human pickers take 20–30% of overall operation time into the logistics activities. Such an unproductive time, for example, can be reduced by optimally deploying a fleet of agricultural robots and schedule them by anticipating the human activity behaviour (state) during harvesting. In this paper, we propose a framework for spatio-temporal prediction of human pickers’ activities while they are picking fruits in agriculture fields. Here we exploit temporal patterns of picking operation and 2D discrete points, called topological nodes, as spatial constraints imposed by the agricultural environment. Both information are used in the prediction framework in combination with a variant of the Hidden Markov Model (HMM) algorithm to create two modules. The proposed methodology is validated with two test cases. In Test Case 1, the first module selects an optimal temporal model called as picking_state_progression model that uses temporal features of a picker state (event) to statistically evaluate an adequate number of intra-states also called sub-states. In Test Case 2, the second module uses the outcome from the optimal temporal model in the subsequent spatial model called node_transition model and performs “spatio-temporal predictions” of the picker’s movement while the picker is in a particular state. The Discrete Event Simulation (DES) framework, a proven agricultural multi-robot logistics model, is used to simulate the different picking operation scenarios with and without our proposed prediction framework and the results are then statistically compared to each other. Our prediction framework can reduce the so-called unproductive logistics time in a fully manual harvesting process by about 80 percent in the overall picking operation. This research also indicates that the different rates of picking operations involve different numbers of sub-states, and these sub-states are associated with different trends considered in spatio-temporal predictions.

The Common Provenance Model: Capturing Distributed Provenance in Life Sciences Processes.

The distributed nature of modern research emphasizes the importance of collecting and sharing the history of digital and physical material, to improve the reproducibility of experiments and the quality and reusability of results. Yet, the application of the current methodologies to record provenance information is largely scattered, leading to silos of provenance information at different granularities. To tackle this fragmentation, we developed the Common Provenance Model, a set of guidelines for the generation of interoperable provenance information, and to allow the reconstruction and the navigation of a continuous provenance chain. This work presents the first version of the model, available online, based on the W3C PROV Data Model and the Provenance Composition pattern.

Integrated Design and Analysis of Corrosion Rate Model on Tubular Pipe of Oil and Gas Production Well

Corrosion occurrence in the oil and gas production system is an example of an unavoidable problem. This problem is caused by the presence of water and corrosive material fraction along with the produced hydrocarbon. On the way from the reservoir to the surface, the mixture between water fraction and the corrosive substance might adhere to the wall of conduit steel pipe (e.g. tubing, casing, and flowline) and can lead to corrosion on the wall. The corrosion problem can have a major impact on the economics of the production because the cost of production equipment is relatively expensive and workover operation is very costly. Therefore, planning and mitigation for a better production system need to be performed, so that the rate of corrosion can be controlled, and its economic impacts can be minimized. The purpose of this study was to create an integrated model of the corrosion rate calculation on the tubing by the Petroleum Engineering Study Program of Bandung Institute of Technology (ITB). Several terms related to the production process were considered in this model: the characteristics of the produced fluid, flow regimes, pressure and temperature conditions along the tubing, the trajectory of the well, the type of tubing material used, and the use of inhibitors. The model was developed from the model developed by C. de Waard, Liane Smith, and Mike Billingham. The corrosion rate calculation model was then validated using field data and the results were compared with the result from industrial commercial software, Electronic Corrosion Engineer (ECE®). The corrosion rate calculation model were able to predict the rate of corrosion in oil and gas wells accurately. A software-based system was created based on this integrated and accurate proven model. By using this software, the production system can be simulated and tested for various conditions, and then the result can be compared with each other to obtain the optimum fluid production rate with minimum corrosion rate. In addition, this software is capable to determine the most suitable tubing material which will be used in a given production environment and condition.

Enhancing convolutional neural network model with spectral features for the identification of cervical dysplasia

AbstractCervical cell classification plays a key role in the computer‐based screening and diagnosis. This article focuses on cell classification and grading to differentiate the stages of cervical dysplasia. The proposed framework uses a unification of wavelet transform and convolutional neural network (CNN) for segregating spectral and spatial features from papanicolaou stained (pap) smear images. A correlation‐based feature selection is adopted to find relevant features from the CNN model. Random Forest classifier is then incorporated to identify cervical dysplasia from the extracted features. The proposed framework is verified on three publicly available datasets. Using the first dataset, the method obtained an average accuracy of 98.12% and area under curve (AUC) of 0.999. In the second (Sipakmed) and third (Herlev) dataset that consists of cell images of both normal and dysplastic condition, the method achieved an accuracy of 97.01%, 82.60% and AUC of 0.999 and 0.907, respectively. The observations of the study imply that spectral features with feature selection along with a proven machine learning model improves the detection rate of cervical dysplasia.

Computational Wear Prediction of TKR with Flatback Deformity during Gait

Loss of lumbar lordosis in flatback patients leads to changes in the walking mechanism like knee flexion. Such variations in flatback patients are predicted to alter the characteristics of total knee replacement (TKR) contact, so their TKR will show different wear characteristics with a normal gait. However, the relevant study is limited to predicting the wear depth of TKR for normal gait mechanisms or collecting and analyzing kinematic data on flatback gait mechanisms. The objective of this study was to compare wear in TKR of flatback patients with people without flatback syndrome. The main difference between the normal gait mechanism and the flat back gait mechanism is the knee flexion remain section and the tendency to change the vertical force acting on the knee. Thus, in this paper, A finite element-based computational wear simulation for the gait cycle using kinematic data for normal gait and flat gait were performed, and substituting the derived contact pressure and slip distance into the Archard formula, a proven wear model, wear depth was predicted. The FE analysis results show that the wear volume in flatback patients is greater. The results obtained can provide guidance on the TKR design to minimize wear on the knee implant for flatback patients.

Effect of Pore Shape on Mechanical Properties of Porous Shape Memory Alloy

Porous shape memory alloys (SMAs) have been widely used in the aerospace, military, medical, and health fields due to its unique mechanical properties such as superelasticity, biocompatibility, and shape memory effect. In this work, the pore shape was considered in the constitutive model of the porous SMAs by respectively introducing the parameter of aspect ratio and for different pore shapes including oblate, sphere, and prolate shapes, so the expression of Young’s modulus for the porous SMA can be derived. Then, the constitutive model for such a porous shape memory alloy was established. When the porosity was zero, the model can be degenerated to the dense case. The stress–strain curves for the porous SMA with a porosity of 13% with different aspect ratio are then given. Numerical results showed good agreement with the published experimental data that proved the validation of the model. Based on the proven constitutive model, the properties of porous SMA with different porosity and pore shapes are discussed. The results showed that the pore shapes and the porosities had a big effect on the stress–strain curves for the porous shape memory, while with the increasing porosities, the Young’s modulus and the hysteresis both decreased. With the same porosities, the Young’s modulus and hysteresis loop of SMA with round pores were the largest, while the Young’s modulus and hysteresis loop were the smallest when , and they were greater when than when . It can be seen that the closer to the circle, the better the performance of the material.

Does authentic leadership develop inclusive classrooms: a model examination?

PurposeThe present study aims to statistically prove the theoretical model on inclusive higher education provided by Srivastava and Shree (2019), which analyzes the effect of authentic leadership (AL) on inclusive classrooms (ICs) with the intervening role of academic optimism (AO) and art-based innovation pedagogies.Design/methodology/approachThis current study collected data through a questionnaire method from higher education faculty and the faculty's immediate leaders. Statistical methods like descriptive analysis, confirmatory factor analysis (CFA) and multiple regression analysis were conducted to evaluate the variables, model fit and hypothesis, respectively.FindingsThe results indicated a positive effect of AL on ICs via the partial mediating role of AO. However, unlike the proven theoretical model, the moderating role of art-based innovation pedagogy between AO and the IC was not supported.Originality/valueThe implications of this study advanced the theoretical aspect of the model while providing managerial suggestions that can be applied to support the development of ICs in educational institutions.

A collaborative semantic-based provenance management platform for reproducibility.

Scientific data management plays a key role in the reproducibility of scientific results. To reproduce results, not only the results but also the data and steps of scientific experiments must be made findable, accessible, interoperable, and reusable. Tracking, managing, describing, and visualizing provenance helps in the understandability, reproducibility, and reuse of experiments for the scientific community. Current systems lack a link between the data, steps, and results from the computational and non-computational processes of an experiment. Such a link, however, is vital for the reproducibility of results. We present a novel solution for the end-to-end provenance management of scientific experiments. We provide a framework, CAESAR (CollAborative Environment for Scientific Analysis with Reproducibility), which allows scientists to capture, manage, query and visualize the complete path of a scientific experiment consisting of computational and non-computational data and steps in an interoperable way. CAESAR integrates the REPRODUCE-ME provenance model, extended from existing semantic web standards, to represent the whole picture of an experiment describing the path it took from its design to its result. ProvBook, an extension for Jupyter Notebooks, is developed and integrated into CAESAR to support computational reproducibility. We have applied and evaluated our contributions to a set of scientific experiments in microscopy research projects.