Cooperative Innovation Behavior Based on Big Data

With the rapid change of technology, cooperative innovation based on data sharing has become an imminent tactic for enterprises to gain competitive advantages. This paper adopted a mixed method approach (case study-modelling-case study) to study firms’ co-opetition behaviour based on their data analytics capabilities for innovation. We show that firms favour cooperative among peers with same capabilities, i.e. when each firm’s data level is comparable to their partners. We further establish that data transferability and incentive have high impact on cooperation decisions. Finally, we explain the evolution path of firms’ cooperation decisions and discuss the best options for them to sustain long-term growth and competitiveness. The results provide a basis for firms to decide how best to utilise big data for collaborative innovation, so as to improve customers’ product adoption and reduce costs

FedMGDA+: Federated Learning meets Multi-objective Optimization

Federated learning has emerged as a promising, massively distributed way to train a joint deep model over large amounts of edge devices while keeping private user data strictly on device. In this work, motivated from ensuring fairness among users and robustness against malicious adversaries, we formulate federated learning as multi-objective optimization and propose a new algorithm FedMGDA+ that is guaranteed to converge to Pareto stationary solutions. FedMGDA+ is simple to implement, has fewer hyperparameters to tune, and refrains from sacrificing the performance of any participating user. We establish the convergence properties of FedMGDA+ and point out its connections to existing approaches. Extensive experiments on a variety of datasets confirm that FedMGDA+ compares favorably against state-of-the-art.Comment: 26 pages, 9 figures; initial draft, comments welcome

Parameter sensitivity and economic analyses of an interchange-fracture enhanced geothermal system

Previous research has shown that interchange-fracture enhanced geothermal systems show desirable heat extraction performance. However, their parameter sensitivity has not been systematically investigated. In this study, a three-dimensional, unsteady flow and heat transfer model for an enhanced geothermal system with an interchange-fracture structure was established. The influences of pivotal parameters, including stimulated reservoir volume permeability, fracture spacing, fracture aperture, and injection flow rate on the thermal extraction performance of the interchange-fracture enhanced geothermal system were systematically researched. In addition, the economics of this system were evaluated. The results show that the heat extraction performance of the interchange-fracture system is significantly affected by a change of stimulated reservoir volume permeability and injection flow rate. Increasing permeability reduces electricity costs and improves economic income, while increasing the injection flow rate increases output power but hinders the long-term running stability of the system. Our research provides guidance for the optimal design of an interchange-fracture enhanced geothermal system.Cited as: Yu, G., Liu, C., Zhang, L., Fang, L. Parameter sensitivity and economic analyses of an interchange-fracture enhanced geothermal system. Advances in Geo-Energy Research, 2021, 5(2): 166-180, doi: 10.46690/ager.2021.02.0

The within-field and between-field dispersal of weedy rice by combine harvesters

International audienceAbstractWeedy rice (Oryza sativa L.) severely decreases the grain yield and profitability of rice is one of the most significant problems in the majority of rice fields worldwide. Few reports focus on the dispersal of weedy rice, especially how it rapidly spreads to large areas and long distances. Here, we quantify for the first time the within- and between-field dispersal of weedy rice associated with combine harvesting operations. We randomly sampled 31 combine harvesters to determine where and how much weedy rice seeds remained on the machines at three locations in Jiangsu Province, China. Based on the sampling results, the field area over which weedy rice seeds were retained on the combine harvester during harvesting was estimated to assess the within-field dispersibility of weedy rice seeds remaining in the harvesters. A tracking experiment was also carried out by tracing the distribution of weedy rice seeds along harvest trails, to estimate the dispersal of weedy rice seeds within the field being harvested. Weedy rice seeds remained in the harvest pocket, on the pedrail, and the metal plate of the combine harvester. On average, more than 5000 weedy rice seeds which were 22.80% of remaining grains could potentially be transported into adjacent fields by the combine after each rice field infested with weedy rice had been harvested. Of the statistical models compared, a double exponential model simulating the variation in seed retention predicted that weedy rice seeds remaining on the metal plate could be dispersed over 6473.91 m2 or 3236.96 m into the next field during the harvesting operation. Within the field, the number of fallen weedy rice seeds and their dispersal distance were positively correlated to weedy rice panicle density with the combine dispersing most of seeds away from their mother plant thus creating new weed patches. Therefore, fields that were severely infested with weedy rice should be harvested cautiously and separately and seed remaining in a harvester should be avoided to prevent intra- and inter-field, and even cross-regional dispersal of weedy rice