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The purpose of this paper is to study a transportation service procurement bid construction problem from a less than a full truckload perspective. It seeks to establish stochastic mixed integer programming to allow for the proper bundle of loads to be chosen based on price, which could improve the likelihood that carrier can earn its maximum utility.

The authors proposes a bi-level programming that integrates the bid selection and winner determination and a discrete particle swarm optimization (PSO) solution algorithm is then developed, and a numerical simulation is used to make model and algorithm analysis.

The algorithm comparison shows that although GA could find a little more Pareto solutions than PSO, it takes a longer time and the quality of these solutions is not dominant. The model analysis shows that compared with traditional approach, our model could promote the likelihood of winning bids and the decision effectiveness of the whole system because it considers the reaction of the shipper.

The highlights of this paper are considering the likelihood of winning the business and describing the conflicting and cooperative relationship between the carrier and the shipper by using a stochastic mixed integer programming, which has been rarely examined in previous research.

This study aims to profile the government microbloggers and evaluate their roles. The results can help improve the governments' response capability to public emergencies.

This study proposes the user profiling and role evaluation model of government microbloggers in the context of public emergencies. The indicators are designed from the four dimensions of time, content, scale and influence, and the feature labels are identified. Three different public emergencies were investigated, including the West Africa Ebola outbreak, the Middle East respiratory syndrome outbreak and the Shandong vaccine case in China.

The results found that most government microbloggers were follower responders, short-term participants, originators, occasional participants and low influencers. The role distribution of government microbloggers was highly concentrated. However, in terms of individual profiles, the role of a government microblogger varied with events.

The findings can provide a reference for the performance assessment of the government microbloggers in the context of public emergencies and help them improve their ability to communicate with the public and respond to public emergencies.

By analyzing the performance of government microbloggers from the four dimensions of time, content, scale and influence, this paper fills the gap in existing literature on designing the user profiling and role evaluation model of government microbloggers in the context of public emergencies.

The purpose of this paper is to deal with the economic requirements of power system loading dispatch and reduce the fuel cost of generation units. In order to optimize the scheduling of power load, an improved chicken swarm optimization (ICSO) is proposed to be adopted, for solving economic load dispatch (ELD) problem.

The ICSO increased the self-foraging factor to the chicks whose activities were the highest. And the evolutionary operations of chicks capturing the rooster food were increased. Therefore, these helped the ICSO to jump out of the local extreme traps and obtain the global optimal solution. In this study, the generation capacity of the generation unit is regarded as a variable, and the fuel cost is regarded as the objective function. The particle swarm optimization (PSO), chicken swarm optimization (CSO), and ICSO were used to optimize the fuel cost of three different test systems.

The result showed that the convergence speed, global search ability, and total fuel cost of the ICSO were better than those of PSO and CSO under different test systems. The non-linearity of the input and output of the generating unit satisfied the equality constraints; the average ratio of the optimal solution obtained by PSO, CSO, and ICSO was 1:0.999994:0.999988. The result also presented the equality and inequality constraints; the average ratio of the optimal solution was 1:0.997200:0.996033. The third test system took the non-linearity of the input and output of the generating unit that satisfied both equality and inequality constraints; the average ratio was 1:0.995968:0.993564.

This study realizes the whole fuel cost minimization in which various types of intelligent algorithms have been applied to the field of load economic scheduling. With the continuous evolution of intelligent algorithms, they save a lot of fuel cost for the ELD problem.

The ICSO is applied to solve the ELD problem. The quality of the optimal solution and the convergence speed of ICSO are better than that of CSO and PSO. Compared with PSO and CSO, ICSO can dispatch the generator more reasonably, thus saving the fuel cost. This will help the power sector to achieve greater economic benefits. Hence, the ICSO has good performance and significant effectiveness in solving the ELD problem.

Roofing is one of the most dangerous jobs in the construction industry. Due to factors such as lack of planning, training and use of precaution, roofing contractors and workers continuously violate the fall protection standards enforced by the US Occupational Safety and Health Administration. A preferable way to alleviate this situation is automating the process of non-compliance checking of safety standards through measurements conducted in site daily accumulated videos and photos. As a key component, the purpose of this paper is to devise a method to detect roofs in site images that is indispensable for such automation process.

This method represents roof objects through image segmentation and visual feature extraction. The visual features include colour, texture, compactness, contrast and the presence of roof corner. A classification algorithm is selected to use the derived representation for statistical learning and detection.

The experiments led to detection accuracy of 97.50 per cent, with over 15 per cent improvement in comparison to conventional classifiers, signifying the effectiveness of the proposed method.

This study did not test on images of roofs in the following conditions: roofs initially built without apparent appearance (e.g. structural roof framing completed and undergoing the sheathing process) and flat, barrel and dome roofs. From a standpoint of construction safety, while the present work is vital, coupling with semantic representation and analysis is still needed to allow for risk analysis of fall violations on roof sites.

This study is the first to address roof detection in site images. Its findings provide a basis to enable semantic representation of roof site objects of interests (e.g. co-existence and correlation among roof site, roofer, guardrail and personal fall arrest system) that is needed to automate the non-compliance checking of safety standards on roof sites.

This paper studied a bid generation problem in combinatorial transportation auctions that considered in-vehicle consolidations. The purpose of this paper seeks to establish mixed integer programming to the most profitable transportation task packages.

The authors proposes a mathematical model to identify the most profitable transportation task packages under vehicle capacity, flow balance and in-vehicle consolidation operational constraints, after which a two-phase heuristic algorithm was designed to solve the proposed model. In the first phase, a method was defined to compute bundle synergy, which was then combined with particle swarm optimization (PSO) to determine a satisfactory task package, and in the second phase, the PSO was adopted to program vehicle routings that considered in-vehicle consolidation.

Three numerical examples were given to analyze the effects of the proposed model and method, with the first two small-scale examples coming from the same data base and the third being a larger scale example. The results showed that: (1) the proposed model was able to find a satisfactory solution for the three numerical examples; (2) the computation time was significantly shorter than the accurate algorithm and (3) considering in-vehicle consolidations operations could increase the carrier profits.

The highlights of this paper are summarized as following: (1) it considers in-vehicle consolidation when generating bids to maximize profits; (2) it simultaneously identifies the most valuable lane packages and reconstructs vehicle routes and (3) proposes a simple but effective synergy-based method to solve the model.

Blended learning is an emerging trend across many educational settings, adopting the purposeful integration of traditional face-to-face and online teaching to establishing an engaging learning experience for the students. Blended learning provides an ideal platform for the implementation of reusable learning objects (RLOs) as a pedagogical tool to support classroom instruction.

This study had conducted a quasi-experiment followed by semi-structured interviews to determine if a blended learning approach using RLOs can enhance students’ learning in an undergraduate engineering computer-aided design (CAD) module. This study involved learners studying engineering in two different academic years.

Students from the first year were taught using traditional face-to-face teaching approach. The cohort of students from the subsequent year adopted a blended learning approach: face to face and access to a series of RLOs. The analysis revealed statistical evidence that the use of blended learning had a significant impact on the students' end of term exam grades in the CAD module in comparison to the students who undertook traditional face-to-face teaching approach. The qualitative findings highlighted the positive impact of RLOs on students’ learning behaviour, engagement and knowledge retention.

This study provided empirical evidence of the benefits of using RLOs as a blended learning tool in engineering domain. From a theoretical perspective, the findings highlighted the importance of good instructional design and sound theoretical underpinning of the pedagogical strategy. From a practical point of view, this study informed academics on how to improve learner's academic achievement using RLOs.

In this chapter, rephrasing Spivak's question into ‘can subaltern children speak?’, I reorient the research on China's gigantic population of children and youths in rural migrant families towards a critical interpretative approach. Based on life history and longitudinal ethnographic interview gathered with three cases, I unpack the multiple meanings migrants' children attach to mobility in their childhood experiences. First, despite emotional difficulties, children see their parents' out-migration more as a ‘mobility imperative’ than their abandonment of parental responsibilities, which should be contextualized in China's long-term urban-biased social policies and the resultant development gaps in rural and urban societies. Second, the seemingly ‘unstable’ and ‘flexible’ mobility patterns observed in migrant families should be understood in relation to a long-term family social mobility strategy to promote children's educational achievement and future attainment. The combination of absent class politics in an illiberal society with an enduring ideology of education-based meritocracy in Confucianism makes this strategy a culturally legitimate channel of social struggle for recognition and respect for the subaltern. Last, children in migrant families are active contributors to their families' everyday organization amidst mobilities through sharing care and household responsibilities, and developing temporal and mobility strategies to keep alive intergenerational exchanges and family togetherness. The study uncovers coexisting resilience and vulnerabilities of migrants' children in their ‘doing class’ in contemporary China. It also contributes insights into our understanding of the diversity of childhoods in Asian societies at the intersection of familyhood, class dynamics and cultural politics.

Beijing rail transit can actively control the density of rail transit passenger flow, ensure travel facilities and provide a safe and comfortable riding atmosphere for rail transit passengers during the epidemic. The purpose of this paper is to efficiently monitor the flow of rail passengers, the first method is to regulate the flow of passengers by means of a coordinated connection between the stations of the railway line; the second method is to objectively distribute the inbound traffic quotas between stations to achieve the aim of accurate and reasonable control according to the actual number of people entering the station.

This paper analyzes the rules of rail transit passenger flow and updates the passenger flow prediction model in time according to the characteristics of passenger flow during the epidemic to solve the above-mentioned problems. Big data system analysis restores and refines the time and space distribution of the finely expected passenger flow and the train service plan of each route. Get information on the passenger travel chain from arriving, boarding, transferring, getting off and leaving, as well as the full load rate of each train.

A series of digital flow control models, based on the time and space composition of passengers on trains with congested sections, has been designed and developed to scientifically calculate the number of passengers entering the station and provide an operational basis for operating companies to accurately control flow.

This study can analyze the section where the highest full load occurs, the composition of passengers in this section and when and where passengers board the train, based on the measured train full load rate data. Then, this paper combines the full load rate control index to perform reverse deduction to calculate the inbound volume time-sharing indicators of each station and redistribute the time-sharing indicators for each station according to the actual situation of the inbound volume of each line during the epidemic. Finally, form the specified full load rate index digital time-sharing passenger flow control scheme.

The purpose of this paper is to present an exhaustive review of research studies and activities in the inkjet printing of conductive materials.

This paper gives a detailed literature survey of research carried out in inkjet printing of conductive materials.

This article explains the inkjet printing process and the various types of conductive inks. It then examines the various factors that affect the quality of inkjet printed interconnects such as printing parameters, materials and substrate treatments. Methods of characterising both the inkjet printing process and the electrical properties of printed conductive materials are also presented. Finally relevant applications of this technology are described.

Inkjet printing is currently one of the cheapest direct write techniques for manufacturing. The use of this technique in electronic manufacturing, where interconnects and other conductive features are required is an area of increasing relevance to the fields of electronics manufacturing, packaging and assembly. This review paper would therefore be of great value and interest to this community.