Companies rely on robustness and resilience to cope with disruptions. Through the lens of Organization Information Processing Theory, the study examines how employee empowerment, technology and trust can be organized, to improve supply chain robustness and resilience, through data analytical capability, data driven culture, and supply chain visibility. The study findings showed that empowering employees with technological tools had a positive effect on data driven culture through data analytic capability, and on supply chain visibility. Technology contributed to supply chain visibility. Trust in suppliers had a marginally significant direct effect on robustness and a significant direct effect on resilience.
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In this study we tested 39 Lactococcus lactis strains isolated from diverse habitats for their robustness under heat and oxidative stress, demonstrating high diversity in survival (up to 4 log units). Strains with an L. lactis subsp. lactis phenotype generally displayed more-robust phenotypes than strains with an L. lactis subsp. cremoris phenotype, whereas the habitat from which the strains had been isolated did not appear to influence stress survival. Comparison of the stress survival phenotypes with already available comparative genomic data sets revealed that the absence or presence of specific genes, including genes encoding a GntR family transcriptional regulator, a manganese ABC transporter permease, a cellobiose phosphotransferase system (PTS) component, the FtsY protein, and hypothetical proteins, was associated with heat or oxidative stress survival. Finally, 14 selected strains also displayed diversity in survival after spray drying, ranging from 20% survival for the most robust strains, which appears acceptable for industrial application, to 0.1% survival for the least-tolerant strains. The high and low levels of survival upon spray drying correlated clearly with the combined robustness under heat and oxidative stress. These results demonstrate the relevance of screening culture collections for robustness under heat and oxidative stress on top of the typical screening for acidifying and flavor-forming properties. © 2014, American Society for Microbiology.
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Industrial Symbiosis Networks (ISNs) consist of firms that exchange residual materials and energy locally, in order to gain economic, environmental and/or social advantages. In practice, ISNs regularly fail when partners leave and the recovery of residual streams ends. Regarding the current societal need for a shift towards sustainability, it is undesirable that ISNs should fail. Failures of ISNs may be caused by actor behaviour that leads to unanticipated economic losses. In this paper, we explore the effect of these behaviours on ISN robustness by using an agent-based model (ABM). The constructed model is based on insights from both literature and participatory modelling in three real-world cases. It simulates the implementation of synergies for local waste exchange and compost production. The Theory of Planned Behaviour (TPB) was used to model agent behaviour in time-dependent bilateral negotiations and synergy evaluation processes. We explored model behaviour with and without TPB logic across a range of possible TPB input variables. The simulation results show how the modelled planned behaviour affects the cash flow outcomes of the social agents and the robustness of the network. The study contributes to the theoretical development of industrial symbiosis research by providing a quantitative model of all ISN implementation stages, in which various behavioural patterns of entrepreneurs are included. It also contributes to practice by offering insights on how network dynamics and robustness outcomes are not only related to context and ISN design, but also to actor behaviour.
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By applying Axiomatic Design, a Smart Medical Cast was developed to provide patients, who are suffering from forearm fractures, with a personalized healing process. The device monitors the overall healing status and three complications, which are: Muscle Atrophy, Compartment Syndrome, and Deep Vein Thrombosis. In the conceptual phase, desk research has been performed to find biomarkers that correlate with the monitored processes. Per biomarker, a measuring principle has been designed and these combined formed the design of the smart medical cast. Following the design phase, two tests were performed on healthy individuals to measure the robustness in a real application. The first test focused on correctly measuring the biomarkers and further specifying the sensor specifications. For the second test, a new prototype was used to determine correlations between the measured data and the monitored process and the impact of application during the casting process. The test results show that the measuring system can measure the biomarkers within the expected range, except for bone density. No significant impact on the casting process was measured. The Smart Medical Cast has only been evaluated in situations without a fracture, the next step will be to test the measurables in an environment with a fracture
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Technical conditions and actor behavior both affect the evolution of Industrial Symbiosis Networks (ISNs) that exchange local materials and energy in a Circular Economy. In order to design interventions that shape ISNs toward financially robust exchanges, it is necessary to understand the effects of different actor behaviors during waste exchange negotiations. This study aimed to show to what extent and how the financial robustness of ISNs is influenced by negotiation behavior of ISN firms. We created an agent-based model based on empirical data and literature, in which the Theory of Planned Behavior (TPB) can be added to a tit-for-tat negotiation process. The model showed that the added self-evaluation and feedback to behavioral intention and behavior of actors is crucial for the sta-bility of ISNs. In addition, model simulations revealed divergent financial results for waste suppliers when we compare different design scenarios, indicating that the model contributes to understanding effects of design interventions in ISNs. In the future, we will calibrate the model with more empirical evidence, and ex-tend the experiments with other scenarios.
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The aviation industry needs led to an increase in the number of aircraft in the sky. When the number of flights within an airspace increases, the chance of a mid-air collision increases. Systems such as the Traffic Alert and Collision Avoidance System (TCAS) and Airborne Collision Avoidance System (ACAS) are currently used to alert pilots for potential mid-air collisions. The TCAS and the ACAS use algorithms to perform Aircraft Trajectory Predictions (ATPs) to detect potential conflicts between aircrafts. In this paper, three different aircraft trajectory prediction algorithms named Deep Neural Network (DNN), Random Forest (RF) and Extreme Gradient Boosting were implemented and evaluated in terms of their accuracy and robustness to predict the future aircraft heading. These algorithms were as well evaluated in the case of adversarial samples. Adversarial training is applied as defense method in order to increase the robustness of ATPs algorithms against the adversarial samples. Results showed that, comparing the three algorithm’s performance, the extreme gradient boosting algorithm was the most robust against adversarial samples and adversarial training may benefit the robustness of the algorithms against lower intense adversarial samples. The contributions of this paper concern the evaluation of different aircraft trajectory prediction algorithms, the exploration of the effects of adversarial attacks, and the effect of the defense against adversarial samples with low perturbation compared to no defense mechanism.
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Memory forms the input for future behavior. Therefore, how individuals remember a certain experience may be just as important as the experience itself. The peak-and-end-rule (PE-rule) postulates that remembered experiences are best predicted by the peak emotional valence and the emotional valence at the end of an experience in the here and now. The PE-rule, however, has mostly been assessed in experimental paradigms that induce relatively simple, one-dimensional experiences (e.g. experienced pain in a clinical setting). This hampers generalizations of the PE-rule to the experiences in everyday life. This paper evaluates the generalizability of the PE-rule to more complex and heterogeneous experiences by examining the PE-rule in a virtual reality (VR) experience, as VR combines improved ecological validity with rigorous experimental control. Findings indicate that for more complex and heterogeneous experiences, peak and end emotional valence are inferior to other measures (such as averaged valence and arousal ratings over the entire experiential episode) in predicting remembered experience. These findings suggest that the PE-rule cannot be generalized to ecologically more valid experiential episodes.
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Optimal postural control is an essential capacity in daily life and can be highly variable. The purpose of this study was to investigate if young people have the ability to choose the optimal postural control strategy according to the postural condition and to investigate if non-specific low back pain (NSLBP) influences the variability in proprioceptive postural control strategies. Young individuals with NSLBP (n = 106) and healthy controls (n = 50) were tested on a force plate in different postural conditions (i.e., sitting, stable support standing and unstable support standing). The role of proprioception in postural control was directly examined by means of muscle vibration on triceps surae and lumbar multifidus muscles. Root mean square and mean displacements of the center of pressure were recorded during the different trials. To appraise the proprioceptive postural control strategy, the relative proprioceptive weighting (RPW, ratio of ankle muscles proprioceptive inputs vs. back muscles proprioceptive inputs) was calculated. Postural robustness was significantly less in individuals with NSLBP during the more complex postural conditions (p < 0.05). Significantly higher RPW values were observed in the NSLBP group in all postural conditions (p < 0.05), suggesting less ability to rely on back muscle proprioceptive inputs for postural control. Therefore, healthy controls seem to have the ability to choose a more optimal postural control strategy according to the postural condition. In contrast, young people with NSLBP showed a reduced capacity to switch to a more multi-segmental postural control strategy during complex postural conditions, which leads to decreased postural robustness.
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This study proposes a systematic value chain approach to helping businesses identify and eliminate inefficiencies. The authors have developed a robust framework, which food-sector entrepreneurs can use to increase profitability of an existing business or to create new profitable opportunities. The value chain approach provides win-win opportunities for players within the value chain. To test the robustness of the framework, the authors use food waste as an example of a critical inefficiency and apply it to two different food sector business cases, each operating in diverse conditions. Because the suggested framework addresses the core elements and parameters for the existence and competitiveness of a business, the model can be adapted to other sectors.
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Valuation judgement bias has been a research topic for several years due to its proclaimed effect on valuation accuracy. However, little is known on the emphasis of literature on judgement bias, with regard to, for instance, research methodologies, research context and robustness of research evidence. A synthesis of available research will establish consistency in the current knowledge base on valuer judgement, identify future research opportunities and support decision-making policy by educational and regulatory stakeholders how to cope with judgement bias. This article therefore, provides a systematic review of empirical research on real estate valuer judgement over the last 30 years. Based on a number of inclusion and exclusion criteria, we have systematically analysed 32 relevant papers on valuation judgement bias. Although we find some consistency in evidence, we also find the underlying research to be biased; the methodology adopted is dominated by a quantitative approach; research context is skewed by timing and origination; and research evidence seems fragmented and needs replication. In order to obtain a deeper understanding of valuation judgement processes and thus extend the current knowledge base, we advocate more use of qualitative research methods and scholars to adopt an interpretative paradigm when studying judgement behaviour.
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