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The purpose of this paper is to consider three factors, namely, intra-week demand fluctuations, interrelationship between the number of robots and order scheduling and conflicting objectives (i.e. cost minimization and customer satisfaction maximization), to optimize the robot logistics system.

The number of robots and the sequence of delivery orders are first optimized using the heuristic algorithm NSGACoDEM, which is designed using genetic algorithm and composite difference evolution. The superiority of this method is then confirmed by a case study of a four-star grade hotel in South Korea and several comparative experiments.

Two performance metrics reveal the superior performance of the proposed approach compared to other baseline approaches. Results of comparative experiments found that the consideration of three influencing factors in the operation design of a robot logistic system can effectively balance cost and customer satisfaction over the course of a week in hotel operation and optimize robot scheduling flexibility.

The results of this study reveal that numerous factors (e.g. intra-week demand fluctuations) can optimize the performance efficiency of robots. The proposed algorithm can be used by hotels to overcome the influence of intra-week demand fluctuations on robot scheduling flexibility effectively and thereby enhance work efficiency.

The design of a novel algorithm in this study entails enhancing the current robot logistics system. This algorithm can successfully manage cost and customer satisfaction during off-seasons and peak seasons in the hotel industry while offering diversified schemes to various types of hotels.

Although building information modeling (BIM) has brought competitive advantages and many new jobs, the BIM-related job market is still confusing in China, which will undermine the adoption of BIM. This paper aims to show what kinds of BIM-related jobs are there in China, what employers require and whether all BIM engineers are the same kind.

A text mining approach, structural topic model, was used to process the job descriptions of 1,221 BIM-related online job advertisements in China, followed by a cluster analysis based on it.

First, 10 topics of requirements with the impact of experience and educational background to them were found, namely, rendering software, international project, design, management, personal quality, experience, modeling, relation and certificate. Then, six types were clustered, namely, BIM modeler, BIM application engineer, BIM consultant, BIM manager, BIM developer and BIM designer. Finally, different kinds of BIM engineers proved this title was an expediency leading to confusion.

This paper can provide a clear and insightful look into the confusing and unheeded BIM-related job market in China and might help to cope with the abuse of job titles. It could also benefit both employers and candidates in their recruitment for better matching.

In September 2019, Booking.com changed from the smiley-based scoring system (2.5–10) to the purely 10-point evaluation system (1–10). The smiley-based service evaluation is based on the multi-dimensional (M-D) system, whereas the purely 10-point service evaluation is based on the single-dimensional (S-D) system. This paper aims to focus on how a change in review posting policies impacts service evaluations regarding review generation and distribution.

The authors exploit the natural experiment using Booking.com when the site changed its scoring system from a multidimensional smiley-based service evaluation system to an S-D scoring system. The authors collected online reviews posted on two travel agencies (Booking.com and Priceline.com) between September 2019 and October 2020. A quasi-experimental approach, Difference-in-Differences, was used to isolate the impacts of the new scoring system from the impacts of the change in the service evaluation environment, i.e. COVID-19.

The change in the scoring system considerably alters review distributions by decreasing the portion of positive reviews but increasing the portion of highly positive reviews. Using the theory of emotion work (Hochschild, 1979, 2001), DID is also the reason that the former M-D smiley-based system could have underrated, highly positive reviews of services. Using the information transfer theory (Belkin, 1984), the authors reason the asymmetric transfer of information when users consume reviews from the older (M-D) system but are required to generate reviews on a newer (S-D) system.

The findings would provide online review platform management with a deeper understanding of the consequences of changes in service evaluations when the scoring system is changed.

Though the change in the scoring system would affect how customers evaluate the services of hotels, the causal impacts of switching to the new S-D scoring system have not yet been thoroughly covered by prior hospitality and service evaluation literature, which this research aspires to do.

Traditional nondestructive failure localization techniques are increasingly difficult to meet the requirements of high density and integration of system in package (SIP) devices in terms of resolution and accuracy. Time domain reflection (TDR) is recognized as a novel positioning analysis technology gradually being used in the electronics industry because of the good compatibility, high accuracy and high efficiency. However, there are limited reports focus on the application of TDR technology to SiP devices.

In this study, the authors used the TDR technique to locate the failure of SiP devices, and the results showed that the TDR technique can accurately locate the cracking of internal solder joints of SiP devices.

The measured transmission rate of electromagnetic wave signal was 9.56 × 107 m/s in the experimental SiP devices. In addition, the TDR technique successfully located the failure point, which was mainly caused by the cracking of the solder joint at the edge of the SiP device after 1,500 thermal cycles.

TDR technology is creatively applied to SiP device failure location, and quantitative analysis is realized.

This study aims to explore how the “new normal” induces the dynamics in the asymmetric relationship between service quality attributes and customer satisfaction.

This study analyzes online reviews for hotels in New York City. The authors use multi-attribute models to examine how a situational factor – the COVID-19 outbreak – creates dynamics in the asymmetric effect of service quality attributes on customer satisfaction. Then, the authors examine the change in these dynamics over time after adjusting to the “new normal.”

The COVID-19 pandemic has introduced dynamics into the asymmetrical relationship between hotel service attribute performances and customer satisfaction. The pandemic magnified the asymmetric influences of particular attributes on satisfaction in the hospitality industry. In addition, the findings indicate the changes in such dynamics over time.

The findings emphasize that hotel managers should consider situational factors when understanding customer satisfaction. Particularly, this study suggests developing tailored strategies for responses during the COVID-19 pandemic. Hotel managers need to address changing customer expectations of service attributes to overcome unprecedented difficulties because of the limitations and new needs imposed during the pandemic.

This study contributes to the hospitality literature with an understanding of the significance of situational factors in asymmetric analysis.

The purpose of this study is to use the corrected stress field theory to derive the shear capacity of geopolymer concrete beams (GPC) and consider the shear-span ratio as a major factor affecting the shear capacity. This research aims to provide guidance for studying the shear capacity of GPC and to observe how the failure modes of beams change with the variation of the shear-span ratio, thereby discovering underlying patterns.

Three test beams with shear span ratios of 1.5, 2.0 and 2.5 are investigated in this paper. For GPC beams with shear-span ratios of 1.5, 2.0 and 2.5, ultimate capacities are 337kN, 235kN and 195kN, respectively. Transitioning from 1.5 to 2.0 results in a 30% decrease in capacity, a reduction of 102kN. Moving from 2.0 to 2.5 sees a 17% decrease, with a loss of 40KN in capacity. A shear capacity formula, derived from modified compression field theory and considering concrete shear strength, stirrups and aggregate interlocking force, was validated through finite element modeling. Additionally, models with shear ratios of 1 and 3 were created to observe crack propagation patterns.

For GPC beams with shear-span ratios of 1.5, 2.0 and 2.5, ultimate capacities of 337KN, 235KN and 195KN are achieved, respectively. A reduction in capacity of 102KN occurs when transitioning from 1.5 to 2.0 and a decrease of 40KN is observed when moving from 2.0 to 2.5. The average test-to-theory ratio, at 1.015 with a variance of 0.001, demonstrates strong agreement. ABAQUS models beams with ratios ranging from 1.0 to 3.0, revealing crack trends indicative of reduced crack angles with higher ratios. The failure mode observed in the models aligns with experimental results.

This article provides a reference for the shear bearing capacity formula of geopolymer reinforced concrete (GRC) beams, addressing the limited research in this area. Additionally, an exponential model incorporating the shear-span ratio as a variable was employed to calculate the shear capacity, based on previous studies. Moreover, the analysis of shear capacity results integrated literature from prior research. By fitting previous experimental data to the proposed formula, the accuracy of this study's derived formula was further validated, with theoretical values aligning well with experimental results. Additionally, guidance is offered for utilizing ABAQUS in simulating the failure process of GRC beams.

With the rapid growth of the domestic lending industry, assessing whether the borrower of each loan is at risk of default is a pressing issue for financial institutions. Although there are some models that can handle such problems well, there are still some shortcomings in some aspects. The purpose of this paper is to improve the accuracy of credit assessment models.

In this paper, three different stages are used to improve the classification performance of LSTM, so that financial institutions can more accurately identify borrowers at risk of default. The first approach is to use the K-Means-SMOTE algorithm to eliminate the imbalance within the class. In the second step, ResNet is used for feature extraction, and then two-layer LSTM is used for learning to strengthen the ability of neural networks to mine and utilize deep information. Finally, the model performance is improved by using the IDWPSO algorithm for optimization when debugging the neural network.

On two unbalanced datasets (category ratios of 700:1 and 3:1 respectively), the multi-stage improved model was compared with ten other models using accuracy, precision, specificity, recall, G-measure, F-measure and the nonparametric Wilcoxon test. It was demonstrated that the multi-stage improved model showed a more significant advantage in evaluating the imbalanced credit dataset.

In this paper, the parameters of the ResNet-LSTM hybrid neural network, which can fully mine and utilize the deep information, are tuned by an innovative intelligent optimization algorithm to strengthen the classification performance of the model.

A model for calculating the global overpressure time history of a single cloud detonation from overpressure time history of discrete positions in the range of single cloud detonation is to be proposed and verified. The overpressure distribution produced by multiple cloud detonation and the influence of cloud spacing and fuel mass of every cloud on the overpressure distribution are to be studied.

A calculation method is used to obtain the global overpressure field distribution after single cloud detonation from the overpressure time history of discrete distance to detonation center after single cloud detonation. On this basis, the overpressure distribution produced by multi-cloud under different cloud spacing and different fuel mass conditions is obtained.

The results show that for 150 kg fuel, when the spacing of three clouds is 40 m, 50 m, respectively, the overpressure range of larger than 0.1 MPa is 5496.48 mˆ2 and 6235.2 mˆ2, which is 2.89 times and 3.28 times of that of single cloud detonation. The superposition effect can be ignored when the spacing between the three clouds is greater than 60 m. In the case of fixed cloud spacing, once the overpressure forms continuous effective superposition, the marginal utility of fuel decreases.

A model for calculating the global overpressure time history of a single cloud detonation from overpressure time history of discrete positions in the range of single cloud detonation is proposed and verified. Based on this method, the global overpressure field of single cloud detonation is reconstructed, and the superimposed overpressure distribution characteristics of three cloud detonation are calculated and analyzed.

Limited by the types of sensors, the state information available for musculoskeletal robots with highly redundant, nonlinear muscles is often incomplete, which makes the control face a bottleneck problem. The aim of this paper is to design a method to improve the motion performance of musculoskeletal robots in partially observable scenarios, and to leverage the ontology knowledge to enhance the algorithm’s adaptability to musculoskeletal robots that have undergone changes.

A memory and attention-based reinforcement learning method is proposed for musculoskeletal robots with prior knowledge of muscle synergies. First, to deal with partially observed states available to musculoskeletal robots, a memory and attention-based network architecture is proposed for inferring more sufficient and intrinsic states. Second, inspired by muscle synergy hypothesis in neuroscience, prior knowledge of a musculoskeletal robot’s muscle synergies is embedded in network structure and reward shaping.

Based on systematic validation, it is found that the proposed method demonstrates superiority over the traditional twin delayed deep deterministic policy gradients (TD3) algorithm. A musculoskeletal robot with highly redundant, nonlinear muscles is adopted to implement goal-directed tasks. In the case of 21-dimensional states, the learning efficiency and accuracy are significantly improved compared with the traditional TD3 algorithm; in the case of 13-dimensional states without velocities and information from the end effector, the traditional TD3 is unable to complete the reaching tasks, while the proposed method breaks through this bottleneck problem.

In this paper, a novel memory and attention-based reinforcement learning method with prior knowledge of muscle synergies is proposed for musculoskeletal robots to deal with partially observable scenarios. Compared with the existing methods, the proposed method effectively improves the performance. Furthermore, this paper promotes the fusion of neuroscience and robotics.

This paper aims to develop a new method of fuselage drag optimization that can obtain results faster than the conventional methods based on full computational fluid dynamics (CFD) calculations and can be used to improve the efficiency of preliminary design.

An efficient method for helicopter fuselage shape optimization based on surrogate-based optimization is presented. Two numerical simulation methods are applied in different stages of optimization according to their relative advantages. The fast panel method is used to calculate the sample data to save calculation time for a large number of sample points. The initial solution is obtained by combining the Kriging surrogate model and the multi-island genetic algorithm. Then, the accuracy of the solution is determined by using the infill criteria based on CFD corrections. A parametric model of the fuselage is established by several characteristic sections and guiding curves.

It is demonstrated that this method can greatly reduce the calculation time while ensuring a high accuracy in the XH-59A helicopter example. The drag coefficient of the optimized fuselage is reduced by 13.3%. Because of the use of different calculation methods for samples, this novel method reduces the total calculation time by almost fourfold compared with full CFD calculations.

To the best of the authors’ knowledge, this is the first study to provide a novel method of fuselage drag optimization by combining different numerical simulation methods. Some suggestions on fuselage shape optimization are given for the XH-59A example.