How entrepreneurial practices balance art and business: Insights into creative entrepreneurship in the European film industry

Relying on the literature on tensions and contradictions and the theory of practice, coupled with the literature on creative industries, this study uncovers how creative entrepreneurs balance the tension between art and business and respond to other challenges of creative entrepreneurship. The multiple case studies method is adopted by examining founders, cofounders and main employees of European film production companies. We reveal the perception of creative entrepreneurs towards the relationship between art and business. We explore different organizational and industry-level challenges experienced by creative entrepreneurs and uncover the organizational practices they adopt to deal with the identified challenges. The research contributes to the phenomenon of creative entrepreneurship by introducing the practice perspective, to the literature on paradox and contradiction by exploring the micro-foundations of tensions and paradox responses in high-intensity situations and to practice-based studies by investigating a domain-specific practice theory and highlighting the agency of creative entrepreneurs in adapting practices necessary to deal with conflicting demands of creative entrepreneurship.     

Comparative study on classifying human activities with miniature inertial and magnetic sensors

This paper provides a comparative study on the different techniques of classifying human activities that are performed using body-worn miniature inertial and magnetic sensors. The classification techniques implemented and compared in this study are: Bayesian decision making (BDM), a rule-based algorithm (RBA) or decision tree, the least-squares method (LSM), the k-nearest neighbor algorithm (k-NN), dynamic time warping (DTW), support vector machines (SVM), and artificial neural networks (ANN). Human activities are classified using five sensor units worn on the chest, the arms, and the legs. Each sensor unit comprises a tri-axial gyroscope, a tri-axial accelerometer, and a tri-axial magnetometer. A feature set extracted from the raw sensor data using principal component analysis (PCA) is used in the classification process. A performance comparison of the classification techniques is provided in terms of their correct differentiation rates, confusion matrices, and computational cost, as well as their pre-processing, training, and storage requirements. Three different cross-validation techniques are employed to validate the classifiers. The results indicate that in general, BDM results in the highest correct classification rate with relatively small computational cost.     

Airline planning benchmark problems—Part I:: Characterising networks and demand using limited data

This paper is the first of two papers entitled “Airline Planning Benchmark Problems”, aimed at developing benchmark data that can be used to stimulate innovation in airline planning, in particular, in flight schedule design and fleet assignment. While optimisation has made an enormous contribution to airline planning in general, the area suffers from a lack of standardised data and benchmark problems. Current research typically tackles problems unique to a given carrier, with associated specification and data unavailable to the broader research community. This limits direct comparison of alternative approaches, and creates barriers of entry for the research community. Furthermore, flight schedule design has, to date, been under-represented in the optimisation literature, due in part to the difficulty of obtaining data that adequately reflects passenger choice, and hence schedule revenue. This is Part I of two papers taking first steps to address these issues. It does so by providing a framework and methodology for generating realistic airline demand data, controlled by scalable parameters. First, a characterisation of flight network topologies and network capacity distributions is deduced, based on the analysis of airline data. Then a multi-objective optimisation model is proposed to solve the inverse problem of inferring OD-pair demands from passenger loads on arcs. These two elements are combined to yield a methodology for generating realistic flight network topologies and OD-pair demand data, according to specified parameters. This methodology is used to produce 33 benchmark instances exhibiting a range of characteristics. Part II extends this work by partitioning the demand in each market (OD pair) into market segments, each with its own utility function and set of preferences for alternative airline products. The resulting demand data will better reflect recent empirical research on passenger preference, and is expected to facilitate passenger choice modelling in flight schedule optimisation.     

Evaluation of borax solid wastes in production of frits suitable for fast single-fired wall tile opaque glass-ceramic glazes

Zircon (zirconium silicate, ZrSiO₄) is the main opacifier of glossy, opaque, white-coloured, frit-based wall tile glazes. However, zirconia containing frits employed in the preparation of these glazes raise the production cost limiting zircon usage as a raw material at an industrial scale. Therefore, there have been several searches on seeking for alternative frit compositions with lower or without zirconia content. Consequently, positive outcomes were recently reported. With the present study, 1·5–5% of borax concentrator waste replaced certain level of acid boric for B₂O₃ content in a low zircon containing frit recipe. It is confirmed that waste contribution did not distort the surface properties of the fast single-fired wall tile opaque glazes. Zircon was found to be the main crystal phase of the glazes in laboratory trials. Industrial applications revealed that shorter firing cycles lead to zircon and petedunnite (CaZnSi₂O₆) formation in the CW-4 glaze.     

FPGA Based Implementation and Comparison of Beamformers for CDMA2000

For the integration of smart antennas into third generation code division multiple access (CDMA) base stations, it still remains as a challenging task to implement smart antenna algorithms on programmable processors. In this paper, we study implementations of some CDMA compatible beamforming algorithms, namely least mean square (LMS), constant modulus (CM), and space code correlator (SCC) algorithms, using Xilinx??s Virtex family FPGAs. This study exhibits feasibility of implementing even simple, practical, and computationally small algorithms based on today??s most powerful FPGA technologies. 16 and 32 bits floating point implementations of the algorithms are investigated using both Virtex II and Virtex IV FPGAs. CDMA2000 reverse link baseband signal format is used in the signal modeling. Randomly changing fading and Direction-of-arrivals (DOAs) of multipaths are considered as a channel condition. The implementation results in terms of beamforming accuracy, FPGA resource utilization, weight vector computation time, and DOA estimation error are presented. Beamformer weight vectors using LMS and CM can be computed within less than 20 ??s on Virtex II FPGA and 10 ??s on Virtex IV FPGA, and using SCC it can be achieved within less than 22 ??s on Virtex IV FPGA. These results show that FPGAs provide approximately 500 times faster speed in implementations than our previous work with DSPs.     

Design considerations for an automotive magnetorheological brake

In this paper, design considerations for building an automotive magnetorheological (MR) brake are discussed. The proposed brake consists of multiple rotating disks immersed in a MR fluid and an enclosed electromagnet. When current is applied to the electromagnet, the MR fluid solidifies as its yield stress varies as a function of the magnetic field applied. This controllable yield stress produces shear friction on the rotating disks, generating the braking torque. In this work, practical design criteria such as material selection, sealing, working surface area, viscous torque generation, applied current density, and MR fluid selection are considered to select a basic automotive MR brake configuration. Then, a finite element analysis is performed to analyze the resulting magnetic circuit and heat distribution within the MR brake configuration. This is followed by a multidisciplinary design optimization (MDO) procedure to obtain optimal design parameters that can generate the maximum braking torque in the brake. A prototype MR brake is then built and tested and the experimental results show a good correlation with the finite element simulation predictions. However, the braking torque generated is still far less than that of a conventional hydraulic brake, which indicates that a radical change in the basic brake configuration is required to build a feasible automotive MR brake.     

A Practical Space-Code Correlator Receiver for DSP Based Software Radio Implementation in CDMA2000

Development of practical algorithms for beamforming in 3G CDMA systems and their software radio implementations are still a challenging task, which will facilitate upgrading of traditional base stations into smart antenna capable 3G base stations. In this paper, we propose a practical space-code correlator (SCC) receiver structure for its software radio implementation a DSP. SCC’s advantage comes from the fact that it doesn’t require any training sequence or learning parameter as in other algorithms (LMS or CM). DSP implementations of the SCC are performed using Texas Instruments C67xx family platforms. In the simulations, reverse link base band signal format of CDMA2000 is used and the effects of different array topologies (uniform linear array-ULA or uniform circular array-UCA) are considered. The implementation results regarding beamforming accuracy, weight vector computation time (execution time), search resolution effect on DOA estimation accuracy, DSP resource utilization, and received SINR are presented. The results show that DSP based SCC beamformer can estimate weight vectors within less than 10 ms with DOA search resolution of 2° especially when C6713 DSP is used. With faster DSPs and larger search resolutions, execution time could be significantly reduced as well. It provides comparable SINR performance with LMS and CM algorithms.

Poly(lactide)/cellulose nanocrystal nanocomposites by high-shear mixing

There is currently considerable interest in developing stiff, strong, tough, and heat resistant poly(lactide) (PLA) based materials with improved melt elasticity in response to the increasing demand for sustainable plastics. However, simultaneous optimization of stiffness, strength, and toughness is a challenge for any material, and commercial PLA is well-known to be inherently brittle and temperature-sensitive and to show poor melt elasticity. In this study, we report that high-shear mixing with cellulose nanocrystals (CNC) leads to significant improvements in the toughness, heat resistance, and melt elasticity of PLA while further enhancing its already outstanding room temperature stiffness and strength. This is evidenced by (i) one-fold increase in the elastic modulus (6.48 GPa), (ii) 43% increase in the tensile strength (87.1 MPa), (iii) one-fold increase in the strain at break (∼6%), (iv) two-fold increase in the impact strength (44.2 kJ/m²), (v) 113-fold increase in the storage modulus at 90°C (787.8 MPa), and (vi) 10³-fold increase in the melt elasticity at 190°C and 1 rad/s (∼10⁵ Pa) via the addition of 30 wt% CNC. It is hence possible to produce industrially viable, stiff, strong, tough, and heat resistant green materials with improved melt elasticity through high-shear mixing.