Applied data science in patient-centric healthcare: Adaptive analytic systems for empowering physicians and patients

We define the emerging research field of applied data science as the knowledge discovery process in which analytic systems are designed and evaluated to improve the daily practices of domain experts. We investigate adaptive analytic systems as a novel research perspective of the three intertwining aspects within the knowledge discovery process in healthcare: domain and data understanding for physician- and patient-centric healthcare, data preprocessing and modelling using natural language processing and (big) data analytic techniques, and model evaluation and knowledge deployment through information infrastructures. We align these knowledge discovery aspects with the design science research steps of problem investigation, treatment design, and treatment validation, respectively. We note that the adaptive component in healthcare system prototypes may translate to data-driven personalisation aspects including personalised medicine. We explore how applied data science for patient-centric healthcare can thus empower physicians and patients to more effectively and efficiently improve healthcare. We propose meta-algorithmic modelling as a solution-oriented design science research framework in alignment with the knowledge discovery process to address the three key dilemmas in the emerging “post-algorithmic era” of data science: depth versus breadth, selection versus configuration, and accuracy versus transparency.     

Microstructural,hydraulic conductivity and geochemical changes of drying mature fine oil sand tailings in column experiments

This study offers new insights into two-lift deposition of mature fine tailings under atmospheric drying. The interaction of newly added lift and former lift(s) was evaluated using column experiments in terms of volumetric water content, electrical conductivity (EC), hydraulic conductivity, geochemistry and microstructure. Water content and EC followed the same trend and decreasing of water content appears to be responsible for significant reduction in EC. Evaporation on top of the column reduced the water content to almost zero. The obtained results support the coupling between the hydraulic and chemical processes that should be considered by active operators.     

Practices,projects and portfolios: Current research trends and new directions

Project portfolio management (PPM) bridges strategy and project management. Traditional research in PPM has primarily investigated the rational, top-down and structural aspects of strategizing. By doing so, it has failed to focus on the underlying practices that are triggered by the strategy and how these practices frame strategy implementation. Practice-based research provides a methodological lens to explore the reality of strategic enactment through the project portfolio. Practice-based perspectives are under-represented in PPM research; therefore the aim of this paper is to provide an agenda for further practice-based research in PPM. Central to this agenda is a concern with various aspects of practice, including its discursivity, representation, dynamic capabilities, leadership and materiality.     

Linking entrepreneurial orientation to project success

Entrepreneurial orientation (EO) is a strategic posture of an organization, and it is related to basic policies and practices for the development of entrepreneurial actions looking for creating competitive advantages. This study develops and tests a model of the relationship between entrepreneurial orientation and project success in Brazilian context. As quantitative research, a survey was used to collect data. A sample of 100 valid answers from project practitioners was treated through the structural equation modeling method. As research implications, the main result points out the positive correlation between the entrepreneurial orientation and the project success, contributing to the development of this research subject and helping to minimize the gap in the literature that addresses the relationship between project success and EO. In practical terms, understanding that innovativeness, risk taking, proactiveness, autonomy and competitive aggressiveness (the dimensions of the EO) can contribute to project success and can also indirectly impact on organizational performance, could help organizations get competitive advantage when developing correlate factors. Finally, the results suggest that practices of project management can be aligned to the firm's entrepreneurial orientation to enable firms to attain better results in their projects and generate a competitive advantage. On other hand, given the proportion of the impact of EO on project success (20.3%) identified in this study, it is critical that project management professionals expand their horizon to recognize other factors that affect project success.     

Ex post demand information sharing between differentiated suppliers and a common retailer

We consider ex post demand information sharing in a two-echelon supply chain in which two suppliers sell differentiated products through a common retailer. We model three scenarios of information sharing to characterise the conditions under which information sharing may benefit or hurt different players: (1) no supplier is informed; (2) only one supplier is informed; and (3) both suppliers are informed. We find that the retailer may voluntarily share the low demand information when the product differentiation and demand magnitude satisfy certain conditions. In contrast, the retailer has no incentive to share the high demand information, even though this information benefits the suppliers. To achieve information sharing, we propose a side payment mechanism, which allows benefitted players to subsidise hurtful players. Finally, we extend our model by considering a situation where the suppliers have limited capacity and may invest to ramp up production capacity to satisfy a high demand.     

基于大数据的C语言程序设计

C语言作为一种程序设计语言,在大数据组成的物联网和云计算中应用广泛,具有强大的适应性和兼容性,是大数据时代下,在IT行业中起到关键作用的程序设计语言。文章分析了基于大数据的C语言程序设计的必然性和应用策略,为C语言在大数据上的应用方向提供了参考依据。以汉字的应用为例,提出了解决C语言应用问题的思路。     

数据驱动的机床等待过程节能方法研究

为了降低机床等待过程中的能耗，提出了一种实时数据驱动的机床等待时间预测与节能控制方法。首先，建立了射频识别驱动的生产进度评估方法，并以生产进度数据作为输入，构建了基于堆栈降噪自编码的机床等待时间预测模型；其次，依据预测的机床等待时间，提出了机床状态切换方法，以降低机床能耗；最后，通过一个电梯零部件制造车间的案例分析，表明该方法的预测误差仅为4.1%，同时将机床等待过程能耗降低了57%，实现了制造车间的节能减排。     

Research on heat dissipation performance and flow characteristics of air‐cooled battery pack

With the depletion of fossil fuels and the aggravation of environmental pollution, the research and development speed of electric vehicles has been accelerating, and the thermal management of battery pack has become increasingly important. This paper selects the electric vehicle battery pack with natural air cooling as the study subject, conducts simulation analysis of the heat dissipation performance of battery packs with and without vents. Then this paper researches on the influence of internal flow field and external flow field. Field synergy principle is used to analyze the effect of velocity field and temperature field amplitude. The results show the following: it is found that the maximum temperature rise and the internal maximum temperature difference of the battery pack with vents are reduced by about 23.1% and 19.9%, raising speed value can improve the heat dissipation performance, and raising temperature value can decrease the heat dissipation performance. Reasonable design of the vents can make the inner and outer flow field work synergistically to achieve the best cooling effect. Then the reference basis for the air cooling heat dissipation performance analysis of electric vehicle, battery pack structure arrangement, and air‐inlet and air‐outlet pattern choosing are offered.     

An economic evaluation of management strategies to mitigate the negative effect of twinning in dairy herds

Our objectives were to develop an economic model to estimate the economic impact of twinning in dairy cows and to evaluate management strategies to mitigate the negative economic impact of twinning in dairy herds. A probabilistic tree considering spontaneous embryo reduction, early pregnancy loss, abortion, metritis, retained placenta, and culling rate at 120 d of the second, at the end of the second, and at the end of the third lactation was developed for a single pregnancy; we also developed 3 management options upon diagnosis of a twin pregnancy: (1) do nothing, (2) induce abortion using PGF_2α, or (3) attempt manual embryo reduction. A value was given to each branch of the tree by simulating cow states on a farm for 1,400 d to encompass 4 consecutive lactations. The incomes considered in the simulations included milk income over feed cost, income from calves, and slaughter value upon culling. The expenses taken into account depending on each branch included additional inseminations and synchronization protocols, embryo reduction, induction of abortion, replacement heifers, and costs due to metritis and retained placenta. The gross value for a singleton pregnancy and the 3 management options upon diagnosis of a twin pregnancy were calculated by adding the value of all braches multiplied by their probability. The costs for the 3 management options were calculated by subtracting its gross value minus the gross value of a singleton pregnancy. The negative economic impact of a twin pregnancy ranged from $97 to $225 depending on the type of twin pregnancy (unilateral vs. bilateral), parity, and DIM when the twin pregnancy occurred. The overall negative economic impact of twinning on dairy farm profitability in the United States was estimated to be $96 million per year. Attempting manual embryo reduction early during gestation upon diagnosis of a twin pregnancy was the optimal management strategy for mitigating the negative economic impact of twinning under a wide variety of scenarios.     

The effect of traffic data source on deterioration rates of heavy-duty flexible pavements

The purpose of this study is to assess the effect of traffic data source (estimated vs. actual) on predicted progression rates of roughness and rutting for heavy-duty flexible pavements of rural freeways. Progression rates are predicted using calibrated HDM-4 models. The assessment is performed in terms of variations in maintenance intervention timing associated with the variations in progression rates. Time series pavement condition data (covering 3–5 years) have been collected for 7 sections of rural freeways for use in calibrating HDM-4 deterioration models. They range in length from 10 to 60.8 km and cover different traffic volumes, climate zones and subgrade soil types. For these sections, estimated annual average daily traffic (AADT), growth factors and assumed loading have been extracted from relevant database. Only six segments of these sections have Weigh-in-Motion (WIM) sites so relevant actual AADT, growth factors and axle load distributions have been extracted from WIM reports. The results of running the calibrated HDM-4 deterioration models using different traffic data show that actual traffic data from WIM sites result in higher rates of deterioration to that of estimated data for four sites, resulting in earlier intervention timing and higher present value agency cost. The other two sites have lower rates with actual data due to lower traffic loading than estimated.