To address the challenge of efficiently allocating limited medical resources in China, this study proposes a similarity-driven online doctor recommendation model (SimRec) to improve healthcare accessibility and resource utilization. The model was developed using object-oriented methods to analyze the current service mode of online consultation platforms, incorporating the actual needs of doctors and patients into its design. The framework consists of two layers: the object layer, which represents patient and doctor models abstractly, and the function layer, which implements recommendation technology. The function layer divides the process into two stages-department prediction and doctor-patient matching-to guide patients to appropriate departments, recommend suitable doctors, and allocate doctors based on patient needs. Tests on real-world data demonstrate that SimRec achieves better performance compared to baseline models in both department prediction and doctor-patient matching, indicating its effectiveness in optimizing medical resource allocation.

This study tackles the critical issue of medical resource allocation, with a particular focus on China, where the overutilization of top-tier hospitals exacerbates healthcare shortages. Given the challenges of expanding medical resources, optimizing their utilization becomes essential. We introduce a novel physician recommendation approach that estimates physician abilities by analyzing both external features derived from physician-disease interactions and internal characteristics, such as hospital levels and physician titles. By integrating these dimensions, we offer a comprehensive assessment of physician capabilities, considering both the complexity of diseases and the physician's competence. Experimental results demonstrate that our method outperforms both traditional and state-of-the-art models, significantly improving the efficient distribution of limited medical resources. The effectiveness of the physician rankings in optimizing resource allocation is validated through the use of the Kendall Rank Correlation Coefficient (KRCC). Our approach holds considerable promise in enhancing healthcare resource utilization and alleviating resource constraints.

Recent technological advancements have enabled an increasing number of consumers to select services from online platforms and utilize them in offline stores, a model known as online-to-offline (O2O) e-commerce. This emerging model has garnered significant attention from both business and academic communities. However, with the rapid growth of O2O services, consumers face challenges in selecting services that align with their preferences from a vast array of options. To address this issue, this paper proposes a novel O2O service recommendation method based on dynamic similarity estimation (ReDPS). The dynamic similarity is calculated by tracking changes in consumer preferences over time, providing a more accurate and robust measure of consumer relationships. We validate the ReDPS method using both the Dianping dataset and the publicly available Yelp dataset. Experimental results show that: 1) ReDPS significantly outperforms classical and state-of-the-art recommendation methods, with its effectiveness improving over longer time spans of consumer feature data. 2) Consumer preferences are more strongly influenced by variations in service categories and geographical locations over time than by changes in service evaluations, though all factors are important, and consumers of the same gender tend to exhibit similar preferences. 3) Optimal parameter configurations for ReDPS are identified through the experiments.

This special issue deals with research related to applications of and methods to support Big Data analytics in complex social information networks. The digital age and the rise of social media have sped up changes to social systems with unforeseen consequences. However, there are major challenges created.

The chapter begins with consideration of project impact measurement. A number of options are typically available in projects. Selection practice techniques are presented, beginning with simple screening. Quantitative assessment techniques are described, to include cost–benefit analysis, payback, and net present value. Models supporting analysis of cases involving conflicting criteria are presented in the form of the simple multiattribute rating technique (SMART).

The differences between different organizational forms are presented. Functional organizational structure is compared with project organization and the common matrix organizational form often used in organizations that deal with repetitive projects. Specialty forms to include task forces and hybrids are described. Each organizational form has relative advantages and disadvantages. Criteria to consider in selection of project form is presented.

The rapid improvements in communication and self-driving technology in recent years have made connected autonomous cars an essential component of urban road transit. Connected autonomous vehicles excel in eliminating uncertainties arising from human driving behaviors. Consequently, they alleviate the issue of 'phantom congestion', a phenomenon that significantly impacts traffic efficiency, safety and sustainability while simultaneously enhancing overall traffic flow stability and safety. Moreover, the increasing adoption of connected autonomous vehicles has led to improved driving efficiency, resulting in reduced energy emissions and decreased environmental pollution. This paper endeavors to conduct an extensive review concerning the effects of CAVs on mixed traffic flows, with a primary emphasis on their impact on traffic efficiency and congestion. Additionally, secondary aspects such as stability, safety, and environmental repercussions will be addressed. The article begins with a concise historical account of connected autonomous vehicles and their related technologies. Subsequently, an investigation was conducted into their impact on the mixed traffic environment, along with corresponding policy recommendations. Finally, potential avenues for future research were identified.

Projects typically involve high levels of uncertainty, especially with respect to activity durations. The Project Evaluation and Review Technique (PERT) is presented as a tool for modeling this uncertainty. The PERT assumptions are discussed, and the relative superiority of Monte Carlo simulation to PERT is contended. Simple Excel simulation modeling is used for demonstration.

The Critical Path Method (CPM) is presented, to include beginning with the early start schedule. Project networks are described, aiding in identification of late start schedules that enable identification of activity slacks. Resource leveling and resource smoothing are demonstrated. The chapter concludes with review of critical path model assumptions and criticisms.

Since projects involve high levels of uncertainty, means to manage them in terms of catching up when delays are experienced are highly attractive. In the software project field, the concepts of agile project management and SCRUM techniques have been developed. Both agile and SCRUM are described in this chapter. The Project Management Institute view of both is also presented. In conventional critical path management, the logical concept of project crashing is demonstrated with quantitative examples.

The chapter presents an overview of information system project analysis and design methods, to include the standard waterfall approach, and expediting methods such as prototyping and agile development. Three software estimation methods are demonstrated, to include simple lines of code methods, function point analysis, and the constructive cost model. The capability maturity model is described, with more detailed presentation of a systems development approach.

This book presents data mining methods and demonstrates applications and tools of data analytics in the field of healthcare management.

This paper introduces a novel method aimed at enhancing online-to-offline (O2O) services recommendations by utilizing two-layer knowledge networks. The primary objective of this method is to assist consumers in efficiently navigating the myriad of options available when choosing O2O services. Using co-occurrence relationships, we construct a two-layer knowledge network system, comprising a service knowledge network based on service usage information as the first layer and a consumer knowledge network, built on "co-used" behaviors as the second layer. The former is established upon service use data, while the latter is founded on "co-used" behaviors among consumers. The features and information of these two knowledge networks can complement each other to produce precise and effective recommendations. Empirical findings gained from our experiments demonstrate that: (1) the proposed recommendation method outperforms widely-used and state-of-the-art recommendation methods; (2) both the service knowledge network and consumer knowledge network play an equally significant role in O2O service recommendations; (3) the location of O2O services is an essential factor in consumers' choices for services. Notably, this research also identifies the optimal parameter settings for the proposed recommendation method.

We attempt to understand the role of accounts receivable mortgage in a capital-constrained supply chain and to capture the interaction of firms' operations decisions within non-cooperation or cooperation conditions based on different bank credit policies. We consider a short-term loan directly to the retailer as a supplement to accounts receivable financing. Given different bank credit policies based on the retailer's and manufacturer's original operational capacity, we identify optimal decisions in different situations, finding that supply chain efficiency is better attained using bank short-term loans. Generally, firms with high solvency order more compared to low solvency firms. Specifically, we find that the optimal sourcing choices depend on the ratio of the share of the manufacturer with the share of the retailer (RMR). When RMR is low, trade credit would be better for sourcing. Conversely, bank credit would be the better choice for a capital-constrained supply chain. Finally, we present empirical evidence to demonstrate the results of our study.