Managing institutional complexity in a state-owned enterprise – the role of explicated values and other management controls

Purpose The purpose of this paper is to investigate efforts to manage institutional complexity in a state-owned enterprise, the roles of explicated values in these efforts and how these values interact with each other and other influential management controls. Design/methodology/approach Exploratory case study in StateEnt, a state-owned enterprise that faces institutional complexity. The analysis is based on interviews, observations and documents and concepts from the management control literature and institutional logics are applied. Findings Findings from this study suggest that a structural differentiation have separated two logics in different departments and two of the explicated values have become symbols of these logics taking on various roles in negotiations. Tension between the departments is heightened because the departments legitimize logic enactment through mobilizing different socio-technical dyads of management control. The division of responsibility between these departments still ensures that they need to collaborate and make compromises. The study also finds that exogenously imposed constraints have a significant influence on organizational activities, which is further strengthened due to internally developed management controls embedded in the same logic. Research limitations/implications The study contributes with deeper understanding of values as control, and how these interact with other control forms to influence organizational activity. Herein, the importance of regulatory controls in state-owned enterprises is also highlighted. A limitation of this study is the limited size of the organization under investigation. Originality/value The explicit emphasis on values as a control in studies on management control issues in institutionally complex environments is underemphasized in the literature.

A Disturbance Suppression Micro-Newton Force Sensor Based on Shadow Method

<div>The precision of micro-force measurement is determined by the sensitivity of force sensors and the magnitude of environmental disturbances. Damping, a process that converts vibrational energy into heat, is one of the most effective methods of suppressing disturbances. Inspired by the shadow formed at a pond when water striders walked on the water, a bionic viscoelastic-polymer micro-force (VPMF) sensor with a high damping ratio based on the shadow method was developed. In the VPMF sensor, the surface of the polymer was deformed by the contact of a cylindrical flat punch when the sensor was subjected to a normal force. A shadow with a bright edge was formed due to the refraction that parallel light went through the deformed surface. The force was in proportion to the change of the shadow diameter. The sensor optimal sensitivity was 2.15 μN/pixel and the measurement range was 0.981 mN. The damping ratio of the VPMF sensor was 0.22 on account of viscoelasticity, which could suppress disturbances effectively. The VPMF sensor could reduce the influence of disturbances by about 96.23% compared to the cantilever. The present study suggests that the VPMF sensor is hopefully applied to the reliable measurement of micro force under complex environments.</div>

A Disturbance Suppression Micro-Newton Force Sensor Based on Shadow Method

<div>The precision of micro-force measurement is determined by the sensitivity of force sensors and the magnitude of environmental disturbances. Damping, a process that converts vibrational energy into heat, is one of the most effective methods of suppressing disturbances. Inspired by the shadow formed at a pond when water striders walked on the water, a bionic viscoelastic-polymer micro-force (VPMF) sensor with a high damping ratio based on the shadow method was developed. In the VPMF sensor, the surface of the polymer was deformed by the contact of a cylindrical flat punch when the sensor was subjected to a normal force. A shadow with a bright edge was formed due to the refraction that parallel light went through the deformed surface. The force was in proportion to the change of the shadow diameter. The sensor optimal sensitivity was 2.15 μN/pixel and the measurement range was 0.981 mN. The damping ratio of the VPMF sensor was 0.22 on account of viscoelasticity, which could suppress disturbances effectively. The VPMF sensor could reduce the influence of disturbances by about 96.23% compared to the cantilever. The present study suggests that the VPMF sensor is hopefully applied to the reliable measurement of micro force under complex environments.</div>

Machine Learning-based Orchestration of Containers: A Taxonomy and Future Directions

Containerization is a lightweight application virtualization technology, providing high environmental consistency, operating system distribution portability, and resource isolation. Existing mainstream cloud service providers have prevalently adopted container technologies in their distributed system infrastructures for automated application management. To handle the automation of deployment, maintenance, autoscaling, and networking of containerized applications, container orchestration is proposed as an essential research problem. However, the highly dynamic and diverse feature of cloud workloads and environments considerably raises the complexity of orchestration mechanisms. Machine learning algorithms are accordingly employed by container orchestration systems for behavior modelling and prediction of multi-dimensional performance metrics. Such insights could further improve the quality of resource provisioning decisions in response to the changing workloads under complex environments. In this paper, we present a comprehensive literature review of existing machine learning-based container orchestration approaches. Detailed taxonomies are proposed to classify the current researches by their common features. Moreover, the evolution of machine learning-based container orchestration technologies from the year 2016 to 2021 has been designed based on objectives and metrics. A comparative analysis of the reviewed techniques is conducted according to the proposed taxonomies, with emphasis on their key characteristics. Finally, various open research challenges and potential future directions are highlighted.

Competitive advantage in algorithmic trading: a behavioral innovation economics approach

PurposeThis paper investigates the strategic behavior of algorithmic trading firms from an innovation economics perspective. The authors seek to uncover the sources of competitive advantage these firms develop to make markets inefficient for them and enable their survival.Design/methodology/approachFirst, the authors review expected capability, a quantitative behavioral model of the sustainable, or reliable, profits that lead to survival. Second, they present qualitative data gathered from semi-structured interviews with industry professionals as well as from the academic and industry literatures. They categorize this data into first-order concepts and themes of opportunity-, advantage- and meta-seeking behaviors. Associating the observed sources of competitive advantages with the components of the expected capability model allows us to describe the economic rationale these firms have for developing those sources and explain how they survive.FindingsThe data reveals ten sources of competitive advantages, which the authors label according to known ones in the strategic management literature. We find that, due to the dynamically complex environments and their bounded resources, these firms seek heuristic compromise among these ten, which leads to satisficing. Their application of innovation methodology that prescribes iterative ex post hypothesis testing appears to quell internal conflict among groups and promote organizational survival. The authors believe their results shed light on the behavior and motivations of algorithmic market actors, but also of innovative firms more generally.Originality/valueBased upon their review of the literature, this is the first paper to provide such a complete explanation of the strategic behavior of algorithmic trading firms.

Scattering fingerprints of two-state dynamics

Particle transport in complex environments such as the interior of living cells is often (transiently) non-Fickian or anomalous, that is, it deviates from the laws of Brownian motion. Such anomalies may be the result of small-scale spatio-temporal heterogeneities in, or viscoelastic properties of, the medium, molecular crowding, etc. Often the observed dynamics displays multi-state characteristics, i.e. distinct modes of transport dynamically interconverting between each other in a stochastic manner. Reliably distinguishing between single- and multi-state dynamics is challenging and requires a combination of distinct approaches. To complement the existing methods relying on the analysis of the particle’s mean squared displacement, position- or displacement-autocorrelation function, and propagators, we here focus on “scattering fingerprints” of multi-state dynamics. We develop a theoretical framework for two-state scattering signatures – the intermediate scattering function and dynamic structure factor – and apply it to the analysis of simple model systems as well as particle-tracking experiments in living cells. We consider inert tracer-particle motion as well as systems with an internal structure and dynamics. Our results may generally be relevant for the interpretation of state-of-the-art differential dynamic microscopy experiments on complex particulate systems, as well as inelastic or quasielastic neutron (incl. spin-echo) and X-ray scattering scattering probing structural and dynamical properties of macromolecules, when the underlying dynamics displays two-state transport.

Benchmarking cEEGrid and Solid Gel-Based Electrodes to Classify Inattentional Deafness in a Flight Simulator

Transfer from experiments in the laboratory to real-life tasks is challenging due notably to the inability to reproduce the complexity of multitasking dynamic everyday life situations in a standardized lab condition and to the bulkiness and invasiveness of recording systems preventing participants from moving freely and disturbing the environment. In this study, we used a motion flight simulator to induce inattentional deafness to auditory alarms, a cognitive difficulty arising in complex environments. In addition, we assessed the possibility of two low-density EEG systems a solid gel-based electrode Enobio (Neuroelectrics, Barcelona, Spain) and a gel-based cEEGrid (TMSi, Oldenzaal, Netherlands) to record and classify brain activity associated with inattentional deafness (misses vs. hits to odd sounds) with a small pool of expert participants. In addition to inducing inattentional deafness (missing auditory alarms) at much higher rates than with usual lab tasks (34.7% compared to the usual 5%), we observed typical inattentional deafness-related activity in the time domain but also in the frequency and time-frequency domains with both systems. Finally, a classifier based on Riemannian Geometry principles allowed us to obtain more than 70% of single-trial classification accuracy for both mobile EEG, and up to 71.5% for the cEEGrid (TMSi, Oldenzaal, Netherlands). These results open promising avenues toward detecting cognitive failures in real-life situations, such as real flight.

Strawberry Maturity Recognition Algorithm Combining Dark Channel Enhancement and YOLOv5

Aiming at the problems of low accuracy of strawberry fruit picking and large rate of mispicking or missed picking, YOLOv5 combined with dark channel enhancement is proposed. In “Fengxiang” strawberry, the criterion of “bad fruit” is added to the conventional three criteria of ripeness, near-ripeness, and immaturity, because some of the bad fruits are close to the color of ripe fruits, but the fruits are small and dry. The training accuracy of the four kinds of strawberries with different ripeness is above 85%, and the testing accuracy is above 90%. Then, to meet the demand of all-day picking and address the problem of low illumination of images collected at night, an enhancement algorithm is proposed to enhance the images, which are recognized. We compare the actual detection results of the five enhancement algorithms, i.e., histogram equalization, Laplace transform, gamma transform, logarithmic variation, and dark channel enhancement processing under the different numbers of fruits, periods, and video tests. The results show that combined with dark channel enhancement, YOLOv5 has the highest recognition rate. Finally, the experimental results demonstrate that YOLOv5 is better than SSD, DSSD, and EfficientDet in terms of recognition accuracy, and the correct rate can reach more than 90%. Meanwhile, the method has good robustness in complex environments such as partial occlusion and multiple fruits.