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The conventional customer lifecycle fails to acknowledge the “sleeping” stage between regular patronage and churn, particularly prevalent in the hospitality industry. This study constructs an awakening model to regain “sleeping” guests.

342 questionnaires from Macau using partial least squares-structural equation modeling (PLS-SEM) were analyzed. The model was compared across different membership levels through multigroup analysis.

The results indicate that the point policy can awaken “sleeping” guests by influencing their perceived value, regret, and integrated satisfaction with a shorter “sleeping” period. Two path coefficients showed significant differences among basic and elite members.

Companies with loyalty programs should implement a transitional period before resetting points, leveraging altruistic point policies to awaken “sleeping” guests via direct communication. This strategy mitigates the negative impact of finite point expiration policies, enhancing customer re-engagement and point utilization.

Our study focuses on a crucial facet of hotel marketing—customer regain strategies. By identifying customer segments who have not revisited the hotel group for more than twelve months, we confirm the concept of “sleeping” guests. This term offers a nuanced perspective, distinguishing “sleeping” guests from generic lost customers. The “sleeping” guest segment provides valuable insights for enhancing targeted and effective marketing activities in the highly competitive hotel industry.

A transmission tower usually experiences bolt loosening under long-term alternating cyclic load, which may lead to collapse of the tower in extreme operating conditions. The paper aims to propose a data-driven identification method for bolt looseness of complicated tower structures based on reduced-order models and numerical simulations to perceive and evaluate the health state of a tower in operation.

The equivalent stiffnesses of three types of bolt joints under various loosening scenarios are numerically determined by three-dimensional finite element (FE) simulations. The order of the FE model of a tower structure with bolt loosening is reduced by means of the component modal synthesis method, and the dynamic responses of the reducer-order model under calibration loads are simulated and used to create the dataset. An identification model for bolt looseness of the tower structure based on convolutional neural networks driven by the acceleration sensors is constructed.

An identification model for bolt looseness of the tower structure based on convolutional neural networks driven by the acceleration sensors is constructed and the applicability of the model is investigated. It is shown that the proposed method has a high identification accuracy and strong robustness to data noise and data missing. Meanwhile, the method is less dependent on the number and location of sensors and is easier to apply in real transmission lines.

This paper proposes a data-driven identification method for bolt looseness of a complicated tower structure based on reduced-order models and numerical simulations. Non-linear relationships between equivalent stiffness of bolted joints and bolt preload depicting looseness are obtained and reduced-order model of tower structure with bolt looseness is established. Finally, this paper investigates applicability of identification model for bolt looseness.

This paper aims to introduce a novel structure for the physical internet (PI)–enabled sustainable supplier selection and inventory management problem under uncertain environments.

To address hybrid uncertainty both in the objective function and constraints, a novel interactive hybrid multi-objective optimization solution approach combining Me-based fuzzy possibilistic programming and interval programming approaches is tailored.

Various numerical experiments are introduced to validate the feasibility of the established model and the proposed solution method.

Due to its interconnectedness, the PI has the opportunity to support firms in addressing sustainability challenges and reducing initial impact. The sustainable supplier selection and inventory management have become critical operational challenges in PI-enabled supply chain problems. This is the first attempt on this issue, which uses the presented novel interactive possibilistic programming method.

This article attempts to contribute to medical dispute resolution by examining the adoption of medical judicial expertise opinions in determining medical malpractice responsibility and its coordination with the judge’s legal opinions.

This article examines the legal basis and empirical data to demonstrate the decisive effect of medical judicial experts’ opinions in allocating medical malpractice responsibility and corresponding dispute resolution effectiveness.

High reliance on medical judicial expertise in medical dispute litigation not only unifies the judicial standards but also limits judges’ discretion, which brings the risk of contradiction between factual and legal findings, which currently ends in judges’ compromise.

The current medical malpractice provisions neglect the divergence of medical judicial expertise and judges’ opinions in determining medical malpractice responsibility, which produces difficulties in harmonizing awarded compensations and parties’ expectations, leading to problematic medical dispute litigation in Mainland China.

This paper aims to highlight the factors affecting health and safety (H&S) and the SMART Technologies (ST) used to mitigate them in the construction industry through a range of selected papers to encourage readers and potential audiences to consider the need for intelligent technologies to minimize the risks of injuries, illnesses and severe harm in the construction industry.

This paper adopts a double systematic literature review (SLR) to analyse studies investigating the factors affecting H&S and the ST in the construction industry using databases such as Google Scholar, Scopus, Science Direct and Emerald Insight publication.

The SLR identified “fatal or focus five factors” that include objects Fall from heights (FFH) and trapped between objects; Falls, Trips and slips (FTS); Machinery/Equipment Malfunction and Moving Equipment; Pollutants: Chemicals, Airborne Dust, Asbestos; and Electrocution. The ST includes Safety Boots/SMART Glasses/SMART Helmet/SMART Vests/SMART PPE/SMART Watch, Mobile Apps, Building Information Modelling (BIM), Virtual Reality/Augmented Reality (VR/AR), Drones/Unmanned Aerial Vehicles and Wearable Technology/Mobile Sensors help mitigate the risk posed by “Fatal five”. However, other factors within the scope of ST, such as Weather Conditions, Vibrations, Violence, Disease and illness, Fire and Explosion and Over Exertion, are yet to be adopted in the field.

SLR methodology limitations of not obtaining the most updated field knowledge are critical and are offset by choosing 72% of H&S and 92% of SM review literature post-2017. Limitations to capturing articles because of the restriction of database access: only English language search and journals that are not a part of the databases selected are acknowledged. However, key database search that recognizes rigorous peer-reviewed articles offset these limitations. The researcher’s Bias is acknowledged.

This paper unravels the construction H&S factors and their interlinks with ST, which would aid industry understanding and focus on mitigating associated risks. The paper highlights the Fatal five and trivial 15, which would help better understand the causes of the H&S risks. Further, the paper discusses ST’s connectivity, which would aid the organization’s overall H&S management. The practical and theoretical implications include a better understanding of all factors that affect H&S and ST available to help mitigate concerns. The operating managers could use the ST to reduce H&S risks at every construction process stage. This paper on H&S and ST and relationships can theorize that the construction industry is more likely to identify clear root causes of H&S and ST usage than previously. The theoretical implications include enhanced understanding for academics on H&S factors, ST and gaps in ST concerning H&S, which can be expanded to provide new insights into existing knowledge.

This paper highlights all factors affecting H&S and ST that help mitigate associated risks and identifies the “Fatal five” factors. The paper is the first to highlight the factors affecting H&S combined with ST in use and their interactions. The paper also identified factors within the ST scope that are yet to be explored.

Heterogeneous graphs, composed of diverse nodes and edges, are prevalent in real-world applications and effectively model complex web-based relational networks, such as social media, e-commerce and knowledge graphs. As a crucial data source in heterogeneous networks, Node attribute information plays a vital role in Web data mining. Analyzing and leveraging node attributes is essential in heterogeneous network representation learning. In this context, this paper aims to propose a novel attribute-aware heterogeneous information network representation learning algorithm, AAHIN, which incorporates two key strategies: an attribute information coverage-aware random walk strategy and a node-influence-based attribute aggregation strategy.

First, the transition probability of the next node is determined by comparing the attribute similarity between historical nodes and prewalk nodes in a random walk, and nodes with dissimilar attributes are selected to increase the information coverage of different attributes. Then, the representation is enhanced by aggregating the attribute information of different types of high-order neighbors. Additionally, the neighbor attribute information is aggregated by emphasizing the varying influence of each neighbor node.

This paper conducted comprehensive experiments on three real heterogeneous attribute networks, highlighting the superior performance of the AAHIN model over other baseline methods.

This paper proposes an attribute-aware random walk strategy to enhance attribute coverage and walk randomness, improving the quality of walk sequences. A node-influence-based attribute aggregation method is introduced, aggregating neighboring node attributes while preserving the information from different types of high-order neighbors.

This paper aims to study the elasto-dynamic behavior of additively manufactured metallic lattice implants and compare them with human lower-body bone. This work is a step toward producing implants with high similarity of material properties to bone by developing a dynamic design approach.

A suitable topology was selected and admissible design space was established. Implants were fabricated by selective laser melting. Material dynamics, including elastic modulus, damping and natural frequency, were analyzed with experimental and finite element method methodology.

Generally, porosity improves dynamic properties up to an optimum point, which depends on printability, that is, ∼70%. Regarding elastic modulus and natural frequency, it is possible to achieve enough similarity with bone. But, considering damping, the similarity is <23% and <12% with dry and fresh bone, respectively. Damping and strain rate sensitivity increase with porosity. The natural frequency decreases with porosity. Bone ingrowth into lattice implants improves damping substantially while increasing elastic modulus.

Designers, dominantly had quasi-static approach, which considered only elastic modulus. But, the human body is a dynamic structure and experiences dynamic loads; meanwhile, bone, with its damping and natural frequency, regulates dynamic events like shock absorption and elastic wave filtering. Importantly, bone cells sense no load in quasi-static loading and must receive impact loads near their natural frequencies and special accelerations to conduct optimum mechanotransduction. So, it is necessary to develop a dynamic strategy which is comprehensive and describes bone duties.