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The paper is aimed to avoid the situation that historical relics are encroached, isolated and fragmented because of cities in the rapid urban process. Taking the environment around the Qinglong Temple in Xi'an city as an example and based on the characteristics of urban patterns in the history, this paper explores the spatial connection relationship between historical relics and surrounding villages as well as the connection between metro traffic and commercial bodies. At the end of the paper, the improvement strategy is put forward, namely the design concepts of “stepwise style” and “landscape style”, which can be achieved by the demand of ecological restoration and the relationship between urban axis. To be noted, the research shows, by restructuring new connection space, the city can promote the urban memory to be restored, the urban appearance to be reshaped, and the urban patterns in the history to be respected and displayed.

Textual information about restaurants, such as online reviews and food categories, is essential for consumer purchase decisions. However, previous restaurant recommendation studies have failed to use textual information containing essential information for predicting consumer preferences effectively. This study aims to propose a novel restaurant recommendation model to effectively estimate the assessment behaviors of consumers for multiple restaurant attributes.

The authors collected 1,206,587 reviews from 25,369 consumers of 46,613 restaurants from Yelp.com. Using these data, the authors generated a consumer preference vector by combining consumer identity and online consumer reviews. Thereafter, the authors combined the restaurant identity and food categories to generate a restaurant information vector. Finally, the nonlinear interaction between the consumer preference and restaurant information vectors was learned by considering the restaurant attribute vector.

This study found that the proposed recommendation model exhibited excellent performance compared with state-of-the-art models, suggesting that combining various textual information on consumers and restaurants is a fundamental factor in determining consumer preference predictions.

To the best of the authors’ knowledge, this is the first study to develop a personalized restaurant recommendation model using textual information from real-world online restaurant platforms. This study also presents deep learning mechanisms that outperform the recommendation performance of state-of-the-art models. The results of this study can reduce the cost of exploring consumers and support effective purchasing decisions.

关于餐厅的文本信息, 如在线评论和食品分类, 对于消费者的购买决策产生至关重要。然而, 先前的餐厅推荐研究未能有效利这些文本信息去预测消费者喜好。本研究提出了一种新颖的餐厅推荐模型, 以有效估计消费者对多个餐厅属性的评估行为。

我们从 Yelp.com 收集了来自25,369名消费者对 46,613 家餐厅的 1,206,587 条评论。利用这些数据, 我们通过结合消费者身份和在线消费者评论生成了消费者偏好向量。然后, 我们结合了餐厅身份和食品分类来生成餐厅信息向量。最后, 考虑到餐厅属性向量, 本研究调查了消费者偏好和餐厅信息向量之间的非线性交互关系。

我们发现, 所提出的推荐模型相比于之前最先进的模型表现出更优秀的性能, 这表明结合消费者和餐厅的各种文本信息是预测消费者喜好的基本因素。

据我们所知, 这是第一项利用来自真实在线餐厅平台的文本信息开发个性化餐厅推荐模型的研究。本研究还提出了胜过最先进模型的深度学习机制。本研究的结果可以降低探索消费者行为的成本并支持有效的购买决策。

The objective of this study is to develop an automated tool condition monitoring scheme for PCB drilling.

Vibration signals are used to distinguish micro drill wear stages with proper features extraction and classifier design. Then a tool condition monitoring system is built up through a back propagation neural network (BPNN).

Experimental results show that BPNN is a practical method of modeling tool wear, and with this method a tool condition monitoring system is built up using energy ratio, root mean square (RMS) and kurtosis coefficient that transformed by vibration signals.

In the further investigation, more signal samples should be computed as monitoring features for BPNN modeling. In addition, in order to build the best monitoring model, it is necessary to evaluate the performance of the BPNN model in advance, and optimize the process.

The paper provides a method and a system for PCB drill wear monitoring. The method and system can achieve on‐line monitoring of PCB drill condition.

Grinding may generate high temperature along the arc of grinding zone, especially during the grinding process of difficult‐to‐machine materials. It can cause thermal damage to the ground surface and poor surface integrity. Conventional cooling methods based on large amounts of water‐oil emulsions can be both ineffective and environmentally unacceptable. The purpose of this paper is to offer a new high efficiency cooling method – cryogenic pneumatic mist jet cooling (CPMJ) to enhance heat transfer in the grinding zone during grinding of difficult‐to‐machine materials.

CPMJ equipment is a set up, which can produce water mist of −5°C with jet velocity above 150 m/s and mean particle size below 20 μm at the impingement distance of 10‐40 mm on the symmetry axis. To validate the cooling efficiency of CPMJ equipment, heat transfer experiments were carrying out on it. Finally, CPMJ was applied to the grinding of titanium alloy to verify its cooling effects.

With high penetrative power and water mist of −5°C, CPMJ can greatly improve heat transfer efficiency in the grinding zone. Experimental results, including heat transfer experiments and grinding experiments, indicate that CPMJ has strong cooling ability and can offer better cooling effects compared with cold air jet and traditional flood cooling method. With CPMJ cooling method, grinding zone temperature can be effectively reduced and good surface quality can be achieved during grinding of titanium alloy.

CPMJ cooling method is an effective and pollution‐free way to solve the thermal problems during grinding of difficult‐to‐machine materials.

The purpose of this paper is to investigate the cutting mechanism of drilling printed circuit board (PCB) and to optimize the drilling parameters for decreasing burr size and thrust force.

An experimental study was carried out to investigate the effect of drilling parameters on thrust force and burr formation. The drilling process of PCB was divided by the variation of drilling force signals. Analysis of variance (ANVONA) was carried out for burr size and thrust force. Desirability function method was used in multiple response optimization, to find the best drilling parameters.

Enter burr and exit burr have different morphologies and types. The generation of enter burr is mainly caused by burr bending which can be observed in micrographs, whereas the generation of exit burr is more complicated than enter burr; both burr breakup and burr bending are observed in exit burrs. In the selected area, the optimized spindle speed and feed rate for drilling PCB is 12 krev/min and 6 mm/s, respectively.

In this paper, hole wall roughness and tool wear were not considered in the optimization of drilling parameters. The future research work should consider them.

This paper investigates the mechanism of burr formation and thrust force in drilling PCB and then optimizes the drilling parameters to decrease the burr formation and thrust force.

The purpose of this study was to compare machining performance between chemical vapor deposition (CVD)- and physical vapor deposition (PVD)-coated cutting tools to obtain the optimal cutting parameters based on different types of tools for machining titanium alloy (Ti-6Al-4V).

The design of the experiment was constructed using the response surface methodology (RSM) with the Box–Behnken method. Two types of round-shaped tungsten carbide inserts were used in this experiment, namely, PVD TiAlN/AlCrN insert tool and CVD TiCN/Al₂O₃ insert tool. The titanium alloy (Ti-6Al-4V) material was used throughout this experiment. The tool wear and microstructure analysis were measured using a tool maker microscope, an optical microscope and a scanning electron machine.

The PVD TiAlN/AlCrN insert tool produces the lowest tool wear that significantly prolongs the cutting tool life compared to the CVD TiCN/Al₂O₃ insert tool. In addition, depth of cut was the main factor affecting the tool life, followed by cutting speed and feed rate.

This study was conducted to compare machining performance between CVD- and PVD-coated cutting tools to obtain the optimal cutting parameters based on different types of tools for machining titanium alloy (Ti-6Al-4V). In addition, the information presented in this paper helps reduce the manufacturing cost and setup time for machining titanium alloy. Finally, tool wear comparison between PVD- and CVD-coated titanium alloys was also presented for future improvement for tool manufacturing application.

The purpose of this study is to propose an analytical model with consideration of the permeability of soft-magnetic materials, which can predict the magnetic field distribution more accurately and facilitate the initial design and parameter optimization of the machine.

This paper proposes an analytical model of stator yokeless radial flux dual rotor permanent magnet synchronous machine (SYRFDR-PMSM) with the consideration of magnetic saturation of soft-magnetic material. The analytical model of SYRFDR-PMSM is divided into seven regions along the radial direction according to the different excitation source and magnetic medium, and the iron permeability in each region is considered based on the Maxwell–Fourier method and Cauchy’s product theorem. The magnetic vector potential of each region is obtained by the Laplace’s or Poisson’s equation, and the magnetic field solution is determined using the boundary conditions of adjacent regions.

The inner and outer air-gap flux density, flux linkage, output torque, etc., of SYRFDR-PMSM are predicted by analytical model, resulting in good agreement with that of finite element model. Additionally, the SYRFDR-PMSM prototype is manufactured and the correctness of analytical model is further verified by experiments on no-load back electromotive force and current–torque curve. Reasonable design of the slot opening width and pole arc coefficient can improve the average output torque and reduce output torque ripple.

The analytical model proposed in this paper assumes that the permeability of soft-magnetic material is a fixed value. However, the actual iron’s permeability varies nonlinearly; thus, the prediction results of the analytical model will have some deviations from the actual machine.

The main contribution of this paper is to propose an accurate magnetic field analytical model of SYRFDR-PMSM. It takes into account the permeability of soft-magnetic material and slot opening, which can quickly and accurately predict the electromagnetic performance of SYRFDR-PMSM. It can provide assistance for the initial design and optimization of SYRFDR-PMSM.

Selective crowdsourcing is an important type of crowdsourcing which has been popularly used in practice. However, because selective crowdsourcing uses a winner-takes-all mechanism, implying that the efforts of most participants except the final winner will be just in vain. The purpose of this paper is to explore why this costly mechanism can become a popularity during the past decade and which type of tasks can fit this mechanism well.

The authors propose a game model between a sponsor and N participants. The sponsor is to determine its reward and the participants are to optimize their effort-spending strategy. In this model, each participant's ability is the private information, and thus, all roles in the system face incomplete information.

The results of this paper demonstrate the following: whether the sponsor can obtain a positive expected payoff are determined by the type of tasks, while the complex tasks with a strong learning effect is more suitable to selective crowdsourcing, as for the other two types of task, the sponsor cannot obtain a positive payoff, or can just gain a rather low payoff; besides the task type, the sponsor's efficiency in using the solutions and the public's marginal cost also influence the result that whether the sponsor can obtain a positive surplus from the winner-takes-all mechanism.

The model presented in this paper is innovative by containing the following characteristics. First, each participant's ability is private information, and thus, all roles in the system face incomplete information. Second, the winner-takes-all mechanism is used, implying that the sponsor's reward will be entirely given to the participant with the highest quality solution. Third, the sponsor's utility from the solutions, as well as the public's cost to complete the task, are both assumed as functions just satisfying general properties.

The current study investigates the impact on perceived review helpfulness of the simultaneous processing of information from multiple cues with various central and peripheral cue combinations based on the elaboration likelihood model (ELM). Thus, the current study develops and tests hypotheses by analyzing real-world review data with a text mining approach in e-commerce to investigate how information consistency (rating inconsistency, review consistency and text similarity) influences perceived helpfulness. Moreover, the role of product type is examined in online consumer reviews of perceived helpfulness.

The current study collected 61,900 online reviews, including 600 products in six categories, from Amazon.com. Additionally, 51,927 reviews were filtered that received helpfulness votes, and then text mining and negative binomial regression were applied.

The current study found that rating inconsistency and text similarity negatively affect perceived helpfulness and that review consistency positively affects perceived helpfulness. Moreover, peripheral cues (rating inconsistency) positively affect perceived helpfulness in reviews of experience goods rather than search goods. However, there is a lack of evidence to demonstrate the hypothesis that product types moderate the effectiveness of central cues (review consistency and text similarity) on perceived helpfulness.

Previous studies have mainly focused on numerical and textual factors to investigate the effect on perceived helpfulness. Additionally, previous studies have independently confirmed the factors that affect perceived helpfulness. The current study investigated how information consistency affects perceived helpfulness and found that various combinations of cues significantly affect perceived helpfulness. This result contributes to the review helpfulness and ELM literature by identifying the impact on perceived helpfulness from a comprehensive perspective of consumer review and information consistency.