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The purpose of this study is to process the wettability surface of the ZrO₂ ceramics to improve their surface friction performance.

Microtexture was processed on the surface of ZrO₂ ceramics using a femtosecond laser. The three-dimensional texture morphology, surface contact angle, friction curve and wear morphology were measured by the laser confocal microscope, the contact angle meter, the reciprocating friction and wear tester and the scanning electron microscope, respectively. Based on Wenzel and partial impalement models, a geometric model of micro pits is established to study the influence mechanism of micro pit depth, diameter and distribution density on wettability and to analyze the relationship between surface wettability and tribological properties.

The results show that changing the geometric characteristics of the texture will lead to a change in the solid-liquid contact mode, and then lead to a change of in the surface contact angle. Wettability is an essential factor that affects the reduction of surface friction. The construction of a reasonable texture can enhance the surface hydrophilicity, which is conducive to the formation of a lubricating film on the ceramic surface, thereby reducing abrasive and adhesive wear, and improving the wear resistance of the ZrO₂ ceramic surface.

The results provide a theoretical reference for femtosecond laser surface texture wettability regulation and tribological performance improvement.

Time-cost trade-off is normal conduct in construction projects when projects are expectedly late for delivery. Existing research on time-cost trade-off strategic management mostly focused on the technical calculation towards the optimal combination of activities to be accelerated, while the managerial aspects are mostly neglected. This paper aims to understand the managerial efforts necessary to prepare construction projects ready for an upcoming trade-off implementation.

A preliminary list of critical factors was first identified from the literature and verified by a Delphi survey. Quantitative data was then collected by a questionnaire survey to first shortlist the preliminary factors and quantify the predictive model with different machine learning algorithms, i.e. k-nearest neighbours (kNN), radial basis function (RBF), multiplayer perceptron (MLP), multinomial logistic regression (MLR), naïve Bayes classifier (NBC) and Bayesian belief networks (BBNs).

The model's independent variable importance ranking revealed that the top challenges faced were the realism of contractual obligation, contractor planning and control and client management and monitoring. Among the tested machine learning algorithms, multilayer perceptron was demonstrated to be the most suitable in this case. This model accuracy reached 96.5% with the training dataset and 95.6% with an independent test dataset and could be used as the contingency approach for time-cost trade-offs.

The identified factor list contributed to the theoretical explanation of the failed implementation in general and practical managerial improvement to better avoid such failure. In addition, the established predictive model provided an ad-hoc early warning and diagnostic tool to better ensure time-cost implementation success.

Citation contexts have been found useful in many scenarios. However, existing context-based recommendations ignored the importance of diversity in reducing the redundant issues and thus cannot cover the broad range of user interests. To address this gap, the paper aims to propose a novelty task that can recommend a set of diverse citation contexts extracted from a list of citing articles. This will assist users in understanding how other scholars have cited an article and deciding which articles they should cite in their own writing.

This research combines three semantic distance algorithms and three diversification re-ranking algorithms for the diversifying recommendation based on the CiteSeer^X data set and then evaluates the generated citation context lists by applying a user case study on 30 articles.

Results show that a diversification strategy that combined “word2vec” and “Integer Linear Programming” leads to better reading experience for participants than other diversification strategies, such as CiteSeer^X using a list sorted by citation counts.

This diversifying recommendation task is valuable for developing better systems in information retrieval, automatic academic recommendations and summarization.

The originality of the research lies in the proposal of a novelty task that can recommend a diversification context list describing how other scholars cited an article, thereby making citing decisions easier. A novel mixed approach is explored to generate the most efficient diversifying strategy. Besides, rather than traditional information retrieval evaluation, a user evaluation framework is introduced to reflect user information needs more objectively.