The Effect of Power on the Adoption of Interorganizational Information Systems: The Adoption Position Model

The adoption and implementation of interorganizational information systems (IOS) heavily depend upon the trading partner(s) of the focal organization. Power structures have been shown to have a positive effect on the adoption decision. The paper provides a critical literature review on the role of power in the IOS literature and introduces the Adoption Position model to address the shortcomings. The main argument is that power can act as a barrier to adoption as well, which has not been adequately addressed before. The model builds on previous research on IOS adoption and proposes that the relative power of a firm and its intent of adoption toward a specific IOS together predetermine its position in the decision. The result is a typology of adoption positions of two trading partners that serves as an explanatory and predictive tool for further research and hypothesis generation

Comparing bird and human soaring strategies

Gliding saves much energy, and to make large distances using only this form of flight represents a great challenge for both birds and people. The solution is to make use of the so-called thermals, which are localized, warmer regions in the atmosphere moving upwards with a speed exceeding the descent rate of bird and plane. Whereas birds use this technique mainly for foraging, humans do it as a sporting activity. Thermalling involves efficient optimization including the skilful localization of thermals, trying to guess the most favorable route, estimating the best descending rate, etc. In this study, we address the question whether there are any analogies between the solutions birds and humans find to handle the above task. High-resolution track logs were taken from thermalling falcons and paraglider pilots to determine the essential parameters of the flight patterns. We find that there are relevant common features in the ways birds and humans use thermals. In particular, falcons seem to reproduce the MacCready formula widely used by gliders to calculate the best slope to take before an upcoming thermal.Comment: 8 pages, 4 figures. Supplementary materials are available at the webpage dedicated to this work: http://angel.elte.hu/thermalling

Evaluation of AI-Supported Input Methods in Augmented Reality Environment

Augmented Reality (AR) solutions are providing tools that could improve applications in the medical and industrial fields. Augmentation can provide additional information in training, visualization, and work scenarios, to increase efficiency, reliability, and safety, while improving communication with other devices and systems on the network. Unfortunately, tasks in these fields often require both hands to execute, reducing the variety of input methods suitable to control AR applications. People with certain physical disabilities, where they are not able to use their hands, are also negatively impacted when using these devices. The goal of this work is to provide novel hand-free interfacing methods, using AR technology, in association with AI support approaches to produce an improved Human-Computer interaction solution

Multi-impurity adsorption model for modeling crystal purity and shape evolution during crystallization processes in impure media

© 2015 American Chemical Society. The impurity effect on the crystal properties, such as particle size and shape distribution, is significant, having significant impact on the downstream processes as well as on the product effectiveness. Currently very few studies exist that provide a quantitative model to describe crystal purity resulting from crystallization processes in impure media, and none to take into account the simultaneous effect of multiple impurities. Hence, the understanding of the effect of multiple impurities on crystallization process is important in order to obtain the desired product properties. Batch crystallization of potassium dihydrogen phosphate from aqueous solution in the presence of impurities was investigated experimentally by using an online particle vision and measurement tool with real-time image analysis. A mathematical model to describe the crystal purity and aspect ratio is proposed based on a morphological population balance equation including primary nucleation, growth of characteristic faces and multisite, competitive adsorption of impurities. The model parameters were identified and validated using crystallization experiments in mixtures of two impurities with variable composition. The developed and validated model can be an efficient tool for the investigation of crystallization processes in impure media with multiple impurities. The model can also serve as an effective tool for process and product design or optimization

Experimental implementation of a Quality-by-Control (QbC) framework using a mechanistic PBM-based nonlinear model predictive control involving chord length distribution measurement for the batch cooling crystallization of L-ascorbic acid

L-ascorbic acid is synthetized in large industrial scale from glucose and marketed as an immune system strengthening agent and anti-oxidant ingredient. The overall yield of conversion of the precursor glucose to L-ascorbic acid is limited, therefore the crystallization is a critically important step of the L-ascorbic acid production from economic point of view. It is widely accepted that the crystal size distribution (CSD) influences numerous relevant macroscopic properties of the final crystalline product and it also significantly affects the downstream operations. The present paper discusses the chord length distribution (CLD, which is directly related to the CSD) control, during the crystallization of L-ascorbic acid from aqueous solution. Batch crystallization process is employed, which is the classical, and still dominant, operation in fine chemical and pharmaceutical industries. A comparative experimental study of two state-of-the-art Quality-by-Control (QbC) based crystallization design approaches are presented: (1) a model-free QbC based on direct nucleation control (DNC) and (2) a model-based QbC using a novel nonlinear model predicative control (NMPC) framework. In the first investigation, the DNC, a process analytical technology based state-of-the-art model free control strategy, is applied. Although, DNC requires minimal preliminary system information and often provides robust process control, due to the unusual crystallization behavior of L-ascorbic acid, it leads to long batch times and oscillatory operation. In a second study the benefits of model-based QbC approach are demonstrated, based on using a NMPC approach. A population balance based crystallization process model is built and calibrated by estimating the nucleation and growth kinetics from concentration and CLD measurements. A projection based CSD to CLD forward transformation is used in the estimation of nucleation and growth kinetics. For robustness and adaptive behavior, the NMPC is coupled with a growing horizon state estimator, which is aimed to continuously improve the model by re-adjusting the kinetic constants. The study demonstrates that the model-based QbC framework can lead to rapid and robust crystallization process development with the NMPC system presenting good control behavior under significant plant model mismatch (PMM) conditions

Real time image processing based on-line feedback control system for cooling batch crystallization

The direct nucleation control (DNC) is a process analytical technique (PAT) based model free feedback control strategy for batch and continuous crystallization processes, which has been successfully applied in numerous cases. The basic principle of DNC is the use of controlled dissolution cycles to control a measurement directly related to the particle number in the system. During the DNC, in the case of cooling crystallization fines are dissolved by repeated heating-cooling loops. In this context, the controlled variable is the (relative) particle number, which is manipulated using a feedback control approach through the temperature. The particle number is traditionally measured with focused beam reflectance measurement (FBRM), however other PAT tools can also be employed in a similar feedback control setup. Often crystallization processes are also monitored by real-time imaging systems. In the current work a novel DNC setup is proposed in which microscopy images are captured and processed by the means of image analysis in real time. The images are used to extract the relative particle number, which is controlled using the DNC framework. The robustness of the new image analysis based direct nucleation control (IA-DNC) is presented via three case studies with materials having different crystallization properties. The IA-DNC approach uses a Particle Vision probe although other in situ or in line imaging systems can also be used in the framework. The systems are monitored with FBRM for comparison purposes. The setup achieved stable, converged control in most cases and is demonstrated that the IA-DNC has several advantages over the classical FBRM based DNC. The IA-DNC can also be used for real time feedback control of crystal shape

AI-Powered Interfaces for Extended Reality to support Remote Maintenance

High-end components that conduct complicated tasks automatically are a part of modern industrial systems. However, in order for these parts to function at the desired level, they need to be maintained by qualified experts. Solutions based on Augmented Reality (AR) have been established with the goal of raising production rates and quality while lowering maintenance costs. With the introduction of two unique interaction interfaces based on wearable targets and human face orientation, we are proposing hands-free advanced interactive solutions in this study with the goal of reducing the bias towards certain users. Using traditional devices in real time, a comparison investigation using alternative interaction interfaces is conducted. The suggested solutions are supported by various AI powered methods such as novel gravity-map based motion adjustment that is made possible by predictive deep models that reduce the bias of traditional hand- or finger-based interaction interface

A framework for model reliability and estimability analysis of crystallization processes with multi-impurity multi-dimensional population balance models

The development of reliable mathematical models for crystallization processes may be very challenging due the complexity of the underlying phenomena, the inherent Population Balance Models (PBMs) and the large number of parameters that need to be identified from experimental data. Due to the poor information content of the experiments, the structure of the model itself and correlation between model parameters, the mathematical model may contain more parameters than can be accurately and reliably identified from the available experimental data. A novel framework for parameter estimability for guaranteed optimal model reliability is proposed then validated by a complex crystallization process. The latter is described by a differential algebraic system which involves a multi-dimensional population balance model that accounts for the combined effects of different crystal growth modifiers/impurities on the crystal size and shape distribution of needle-like crystals. Two estimability methods were combined: the first is based on a sequential orthogonalization of the local sensitivity matrix and the second is Sobol, a variance-based global sensitivities technic. The framework provides a systematic way to assess the quality of two nominal sets of parameters: one obtained from prior knowledge and the second obtained by simultaneous identification using global optimization. A cut-off value was identified from an incremental least square optimization procedure for both estimability methods, providing the required optimal subset of model parameters. The implemented methodology showed that, although noisy aspect ratio data were used, the 8 most influential and least correlated parameters could be reliably identified out of twenty-three, leading to a crystallization model with enhanced prediction capability

Model reliability of multi-dimensional population balance models for crystallization [abstract]

Model reliability of multi-dimensional population balance models for crystallization [abstract

Association between serum resistin level and outcomes in kidney transplant recipients.

Resistin is an adipocytokine that is associated with inflammation, coronary artery disease, and other types of cardiovascular disease among patients with normal kidney function. However, little is known about the association of resistin with outcomes in kidney transplant recipients. We collected socio-demographic and clinical parameters, medical and transplant history, and laboratory data from 988 prevalent kidney transplant recipients enrolled in the Malnutrition-Inflammation in Transplant-Hungary Study (MINIT-HU study). Serum resistin levels were measured at baseline. Associations between serum resistin level and death with a functioning graft over a 6-year follow-up period were examined in unadjusted and adjusted models. The mean±SD age of the study population was 51 ± 13 years, among whom 57% were men and 21% were diabetics. Median serum resistin concentrations were significantly higher in patients who died with a functioning graft as compared to those who did not die during the follow-up period (median [IQR]: 22[15-26] vs. 19[14-22] ng/ml, respectively; P < 0.001). Higher serum resistin level was associated with higher mortality risk in both unadjusted and fully adjusted models: HRs (95% CI): 1.33(1.16-1.54) and 1.21(1.01-1.46), respectively. In prevalent kidney transplant recipients, serum resistin was an independent predictor of death with a functioning graft