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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.

Additive manufacturing is a fabrication technology with flexibility and economy. 18Ni300 is one of maraging steels with ultra-high strength, superior toughness, so it is an excellent candidate of structural material. This paper aims to explore the feasibility of using direct laser metal deposition method to fabricate18Ni300, and the evolution of its microstructure and defects is studied.

The experiments were conceived from single-trace-single-layer (STSL) test to multi-trace-multi-layers (MTML) test via single-trace-multi-layers (STML) test. The microstructure, defects and mechanical properties were analyzed.

The STML results showed that the columnar/equiaxed transformation occurred at the top part and the grain size increased with the layer number increasing, and it was explained by an innovative attempt combining columnar/equiaxed transformation model and the change of grain size. The MTML test with the interlayer orthogonal parallel reciprocating scanning pattern resulted in the grain growing along orthogonal directions; with the increase of overlap rate, the length and the area of the columnar grain decreased. What is more, the later deposition layer had lower micro-hardness value because of heat history.

Direct laser metal deposition method was a novel additive manufacturing method to manufacture 18Ni300 components, as 18Ni300 maraging steel was mainly manufactured by selective laser melting (SLM) method nowadays. It was useful to manufacture maraging steel parts using direct laser deposition method because it could manufacture larger parts than SLM method. Influence of processing parameters on forming quality and microstructure evolution was studied. The findings will be helpful to understand the forming mechanism of laser additive manufacturing of 18Ni300 components.

Despite several efforts during the last years, the web model and semantic web technologies have not yet been successfully applied to empower Ubiquitous Computing architectures in order to create knowledge‐rich environments populated by interconnected smart devices. In this paper we point out some problems of these previous initiatives and introduce SoaM (Smart Objects Awareness and Adaptation Model), an architecture for designing and seamlessly deploying web‐powered context‐aware semantic gadgets. Implementation and evaluation details of SoaM are also provided in order to identify future research challenges.

The density and pattern of urban parks, traffic conditions are the main factors affecting urban park accessibility. To clarify the influence of traffic mode and urban road network on urban park accessibility, we examine downtown area of Nanjing, China, and based on GIS network analysis, analyze urban park accessibility under different traffic modes in the current year (2017) and the Nanjing master planning target year (2030). The results shows: Using automobiles takes the shortest time to get to urban parks in 2017 and 2030 (if the problem of parking is ignored). Comparing the results of 2030 and 2017, by when the ground transportation network in the study area will be further improved, urban park accessibility was improved by a small margin under walking and automobile traffic modes, however, the density of rail traits increased fastest, urban park accessibility is improved most under this mode of transportation, rail transit route development becomes the dominant factor in improving park accessibility in downtown area of Nanjing. To a certain extent, this study reveals the leading factors of improving the accessibility of urban parks on the premise that the system of urban parks tends to be stable, and provides a reference for improving urban park accessibility.

The contradiction of construction land in economically developed regions is becoming more prominent, and the scale of construction land in some large cities is close to the ceiling. Therefore, China implemented the policy of construction land reduction in 2014. The main objective is to optimize the stock of homesteads and then help to realize rural revitalization by transferring land indexes across regions. Shanghai took the lead in implementing the reduction policy in 2014, for which reduction acceptance data are available. Thus, this paper evaluates the impact of homestead reduction on rural economic development based on data from towns in Shanghai.

This paper uses the difference-in-difference (DID) model to analyze the policy effects of homestead reduction on rural residents' income and industrial integration development. Using economic agglomeration (EA) as a mediating variable, the authors explore how homestead reduction (HR) promotes EA to drive rural economic development and analyze the impact of geographic location and government investment.

HR significantly promotes rural economic development and shows a significant cumulative effect. In the long run, HR can improve rural residents' income and promote industrial integration by promoting EA. The positive effect of HR and EA in suburban regions on industrial integration development is gradually increasing. However, the incentive effect on rural residents' income is weakening. The positive mediating effect of EA is significantly higher in regions with low government investment than in regions with high government investment.

This paper contributes to testing the impact of HR policy on rural economic development and can provide a reference for other regions aiming to implement reduction policy.

This work aims to review past and present articles about data-driven quality management (DDQM) in supply chains (SCs). The motive behind the review is to identify associated literature gaps and to provide a future research direction in the field of DDQM in SCs.

A systematic literature review was done in the field of DDQM in SCs. SCOPUS database was chosen to collect articles in the selected field and then an SLR methodology has been followed to review the selected articles. The bibliometric and network analysis has also been conducted to analyze the contributions of various authors, countries and institutions in the field of DDQM in SCs. Network analysis was done by using VOS viewer package to analyze collaboration among researchers.

The findings of the study reveal that the adoption of data-driven technologies and quality management tools can help in strategic decision making. The usage of data-driven technologies such as artificial intelligence and machine learning can significantly enhance the performance of SC operations and network.

The paper discusses the importance of data-driven techniques enabling quality in SC management systems. The linkage between the data-driven techniques and quality management for improving the SC performance was also elaborated in the presented study.

Soft robotics is currently a rapidly growing new field of robotics whereby the robots are fundamentally soft and elastically deformable. Fabrication of soft robots is currently challenging and highly time- and labor-intensive. Recent advancements in three-dimensional (3D) printing of soft materials and multi-materials have become the key to enable direct manufacturing of soft robots with sophisticated designs and functions. Hence, this paper aims to review the current 3D printing processes and materials for soft robotics applications, as well as the potentials of 3D printing technologies on 3D printed soft robotics.

The paper reviews the polymer 3D printing techniques and materials that have been used for the development of soft robotics. Current challenges to adopting 3D printing for soft robotics are also discussed. Next, the potentials of 3D printing technologies and the future outlooks of 3D printed soft robotics are presented.

This paper reviews five different 3D printing techniques and commonly used materials. The advantages and disadvantages of each technique for the soft robotic application are evaluated. The typical designs and geometries used by each technique are also summarized. There is an increasing trend of printing shape memory polymers, as well as multiple materials simultaneously using direct ink writing and material jetting techniques to produce robotics with varying stiffness values that range from intrinsically soft and highly compliant to rigid polymers. Although the recent work is done is still limited to experimentation and prototyping of 3D printed soft robotics, additive manufacturing could ultimately be used for the end-use and production of soft robotics.

The paper provides the current trend of how 3D printing techniques and materials are used particularly in the soft robotics application. The potentials of 3D printing technology on the soft robotic applications and the future outlooks of 3D printed soft robotics are also presented.

This study aims to develop a four-dimensional (4D) textile composite that self-forms upon thermal stimulation while eliminating thermomechanical programming steps by using fused deposition modeling (FDM) 3D printing technology, and tries to refine the product development path for this composite.

Polylactic acid (PLA) printing filaments were deposited on prestretched Lycra-knitted fabric using desktop-level FDM 3D printing technology to construct a three-layer structure of thermally responsive 4D textiles. Subsequently, the effects of different PLA thicknesses and Lycra knit fabric relative elongation on the permanent shape of thermally responsive 4D textiles were studied. Finally, a simulation program was written, and a case in this study demonstrates the usage of thermally responsive 4D textiles and the simulation program to design a wrist support product.

The constructed three-layer structure of PLA and Lycra knitted fabric can self-form under thermal stimulation. The material can also achieve reversible transformation between a permanent shape and multiple temporary shapes. Thinner PLA deposition and higher relative elongation of the Lycra-knitted fabric result in the greater curvature of the permanent shape of the thermally responsive 4D textile. The simulation program accurately predicted the permanent form of multiple basic shapes.

The proposed method enables 4D textiles to directly self-form upon thermal, which helps to improve the manufacturing efficiency of 4D textiles. The thermal responsiveness of the composite also contributes to building an intelligent human–material–environment interaction system.

The purpose of this paper is to discuss an online sensor-based support system which the authors believe can be useful in such scenarios. Persons with a cognitive impairment, such as those with Alzheimer’s disease, suffer from deficiencies in cognitive skills which reduce their independence; such patients can benefit from the provision of further assistance such as reminders for carrying out instrumental activities of daily living (IADLs).

The system proposed processes data from a network of sensors that have the capability of sensing user interactions and on-going IADLs in the living environment itself. A probabilistic learning model is built that computes joint probability distributions over different activities representing users’ behavioural patterns in performing activities. This probability model can underpin an intervention framework that prompts the user with the next step in the IADL when inactivity is being observed. This prompt for the next step is inferred from the conditional probability taken into consideration the IADL steps that have already been completed, in addition to contextual information relating to the time of day and the amount of time already spent on the activity. The originality of the work lies in combining partially observed sensor sequences and duration data associated with the IADLs. The prediction of the next step is then adjusted as further steps are completed and more time is spent towards the completion of the activity, thus updating the confidence that the prediction is correct. A reminder is only issued when there has been sufficient inactivity on the part of the patient and the confidence is high that the prediction is correct.

The results of this study verify that by including duration information the prediction accuracy of the model is increased and the confidence level for the next step in the IADL is also increased. As such, there is approximately a 10 per cent rise in the prediction performance in the case of single sensor activation in comparison to an alternative approach which did not consider activity durations.

Duration information to a certain extent has been widely ignored by activity recognition researchers and has received a very limited application within smart environments.

This study concludes that incorporating progressive duration information into partially observed sensor sequences of IADLs has the potential to increase performance of a reminder system for patients with a cognitive impairment, such as Alzheimer’s disease.