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This paper aims to present wireless passive temperature sensors by using high-temperature stable polymer-derived silicoaluminum carbonitride (PDC-SiAlCN) ceramic materials.

In this paper, a novel PDC-SiAlCN ceramic was synthesized by using polyvinylsilazne and aluminum-tri-sec-butoxide as precursors. Then, PDC-SiAlCN was used as the sensing material to fabricate sensors. The sensors are based on a cavity resonator and an integrated slot antenna. The resonant frequencies of the sensors are determined by the dielectric constants of PDC-SiAlCN ceramic, which monotonically increase versus temperature.

The effect of sensor dimension on the performance of the sensors was investigated using simulation and experimental methods. The using temperature, reliability and sensing distance of the sensors were studied experimentally. The sensors performed measurement up to 1100°C with excellent reliability and repeatability. The sensing distance varied from 38 to 14 mm when the temperature increasing from 20°C to 1100°C.

PDC-SiAlCN ceramic based wireless passive temperature sensors have the advantage of seamless integration of slot antennas and resonators, which greatly reduces the size of the sensor, reduces the direction of antenna transmission and increases the transmission space. The sensors can be used for many harsh environment applications such as engine monitoring.

This paper aims to examine the day-to-day within-person associations between employees' flow experience and organizational identification within the rarely studied context of construction engineering project organizations.

In this daily diary design, the authors surveyed 204 (M_age = 28.3, SD = 5.69) employees of a state-owned construction engineering project organization in southwest China via the online questionnaires comprising flow and organizational identification scales once daily on each workday for three consecutive weeks, which yielded 3,060 data entries. The authors then tested the temporal directionality between flow and organizational identification with multilevel time-series cross-lagged path analysis using Mplus 8.3.

Daily flow experience was linked positively with same-day organizational identification. Importantly, flow experience on the previous day predicted organizational identification on a subsequent day, but not vice versa.

This study suggests that construction engineering project managers should implement interventions fostering the employees' flow experience to promote organizational identification, with important implications for organizations aiming at flourishing workforces by facilitating organizational identification through implementing flow strategies.

There is a dearth of diary studies on flow and organizational identification specific to construction engineering project employees. The authors’ findings provide concrete evidence of the fluctuant nature of daily flow experience and organizational identification as well as their dynamic predictive pathway relationship.

This study aims to examine farmers' decision to use smartphone agricultural applications (SAAs) and how SAAs adoption impact their land transfer behaviors in terms of the current land transfer-in area (LTA) and the future willingness to renew land transfer-in after it expires (WTR).

This study provides empirical evidence on the relationship between farmers' use of SAAs and land transfer choice, using a field survey data of 752 rural farm households in 2020 from Sichuan province of China. The endogenous switching models are employed to address potential self-selection bias associated with voluntary SAAs use and to quantitatively examine the impacts of SAAs use on land transfer choice.

The empirical results reveal that SAAs significantly improves the probability of transfer-in of more land by 39.10%. We find SAAs use has heterogeneous impacts on land transfer-in choice in the groups of agricultural technology, extension service, marketing and credit. Besides, we also find that SAAs use exerts highly positive and significant impact on farmers with less land area transfer-in. Moreover, SAAs can increase the probability of farmers' willingness to renew the land transfer-in by 30%.

To the best of our knowledge, this study is the first to explore the quantitative relationship between the use of SAAs and farm households' land transfer choice. The findings of this work can provide policy-related insights to help government promote the development of digital applications in the agricultural sector.

The purpose of this paper is to present a K-band modified hairpin bandpass filter on a planar circuit with harmonic suppression and compact size.

The inter-connect transmission lines of conventional hairpin filter are replayed by T-shaped open stub to achieve transmission zero for second harmonic. This filter is simulated and optimized by using electromagnetic simulation software and tested on-chip.

This proposed filter shows the return loss of better than −10dB, the insertion loss of better than 2 dB in pass-band and suppression of more than 40 dB at second harmonic.

The proposed filter can be designed on monolithic microwave integrated circuit, PCB or LTCC and it is useable for microwave and microwave and millimeter-wave systems.

This paper aims to realize an in-situ quality inspection system rapidly for new injection molding (IM) tasks via transfer learning (TL) approach and automation technology.

The proposed in-situ quality inspection system consists of an injection machine, USB camera, programmable logic controller and personal computer, interconnected via OPC or USB communication interfaces. This configuration enables seamless automation of the IM process, real-time quality inspection and automated decision-making. In addition, a MobileNet-based deep learning (DL) model is proposed for quality inspection of injection parts, fine-tuned using the TL approach.

Using the TL approach, the MobileNet-based DL model demonstrates exceptional performance, achieving validation accuracy of 99.1% with the utilization of merely 50 images per category. Its detection speed and accuracy surpass those of DenseNet121-based, VGG16-based, ResNet50-based and Xception-based convolutional neural networks. Further evaluation using a random data set of 120 images, as assessed through the confusion matrix, attests to an accuracy rate of 96.67%.

The proposed MobileNet-based DL model achieves higher accuracy with less resource consumption using the TL approach. It is integrated with automation technologies to build the in-situ quality inspection system of injection parts, which improves the cost-efficiency by facilitating the acquisition and labeling of task-specific images, enabling automatic defect detection and decision-making online, thus holding profound significance for the IM industry and its pursuit of enhanced quality inspection measures.

This paper aims to disentangle Chinese-English-rich resources linguistic and speaker timbre features, achieving cross-lingual speaker transfer for Cambodian.

This study introduces a novel approach: the construction of a cross-lingual feature disentangler coupled with the integration of time-frequency attention adaptive normalization to proficiently convert Cambodian speaker timbre into Chinese-English without altering the underlying Cambodian speech content.

Considering the limited availability of multi-speaker corpora in Cambodia, conventional methods have demonstrated subpar performance in Cambodian speaker voice transfer.

The originality of this study lies in the effectiveness of the disentanglement process and precise control over speaker timbre feature transfer.

Large digital platforms face intense scrutiny over self-preferencing, which involves a platform provider favoring its own offers over those of competitors. In online marketplaces, also called retail or e-commerce platforms, much of the academic and regulatory debate focuses on determining whether the marketplace provider gives preference to its own private labels, such as “Amazon Basics” or Walmart’s “Great Value” products. However, we outline, both conceptually and empirically, that self-preferencing can also occur through other dimensions of vertical integration – namely, retailing and fulfillment.

This article contributes by conceptualizing three dimensions of vertical integration in online marketplaces – private labels, retailing and fulfillment. Then, two studies empirically assess (1) which of the 20 most-visited global online marketplaces vertically integrates which dimension and (2) which share of 600 m available offers is vertically integrated to which degree in eleven international Amazon marketplaces.

The majority of the leading marketplaces vertically integrate all three dimensions, implying ample opportunities for self-preferencing. Across international Amazon marketplaces, only 0.02% of available offers consist of an Amazon private-label product. However, Amazon is a retailer for around 31% and fulfills around 38% of all available offers in its marketplaces. Hence, self-preferencing on Amazon can occur most frequently through retailing and fulfillment but comparatively infrequently through private-label offers. Still, these shares differ substantially by country – every second offer is vertically integrated in the USA, but only one in ten in India.

Most of the self-preferencing debate often focuses on private-label products. Instead, we present large-scale empirical results showing that self-preferencing on Amazon could occur most often through retailing and fulfillment because these channels affect much larger shares of offers. We also measure the variation of these shares across countries and relate them to regulatory environments.

Modular construction is widely adopted and used in the construction industry to improve construction performance with respect to both efficiency and productivity. The evaluation of design options for modular construction can be iterative, and thus automation is required to develop design alternatives. This research aims to explore the potential of utilizing the generative design approach to automate modular construction for residential building structures in urban areas such as New York City.

The proposed research methodology is investigated for a systematic approach to parametrize design parameters for modular construction layout design as well as incorporate design rules/parameters into modularizing design layouts in a Building Information Modeling (BIM) environment. Based on current building codes and necessary inputs by the user, the proposed approach enables providing recommendations in a generative design method and optimizes construction processes by performing analytical calculations.

The generative design has been found to be efficient in generating layout designs for modular construction based on parametric design. The integration of BIM and generative design can allow industry practitioners to fast generate design layout with evaluations from constructability perspectives.

This paper has proposed a new approach of incorporating generative design with BIM technologies to solve module layout generations by considering design and constructability constraints. The method can be further extended for evaluating modular construction design from manufacturability and assembly perspectives.

Studies have suggested that entrepreneurship is a key mechanism for rejuvenating and facilitating economic growth in deprived areas. To provide further understanding of the persistently low entrepreneurial intentions found in deprived areas this chapter identifies key mechanisms and theoretical frameworks that link the formation of appropriate human capital to the prevailing environment, and that influences may flow in both directions. This contributes to the existing literature to provide a fuller understanding of interest to policy-makers of why past interventions have struggled to boost entrepreneurial intentions and where new interventions may be most effective in generating more positive entrepreneurial intentions in deprived areas.

This paper aims to solve the problem that the changing of groove size and assembly gap would affect the precision of the multi-pass path planning and the welding quality and realize the automatic welding of a thick plate.

First, a double-sided double arc welding (DSAW) system with a self-designed passive vision sensor was established, then the image of the groove was captured and the characteristic parameters of groove were extracted by image processing. According to the welding parameters and the extracted geometry size, multi-pass path planning was executed by the DSAW system.

A DSAW system with a self-designed passive vision sensor was established which can realize the welding thick plate by double-sided double arc by two robots. The clear welding image of the groove was acquired, and an available image processing algorithm was proposed to accurately extract the characteristic parameters of the groove. According to the welding parameters and the extracted geometry size, multi-pass path planning can be executed by the DSAW system automatically.

Gas metal arc welding is used for root welding and filler passes in DSAW. Multi-pass path planning for thick plate by Double-sided Double Arc Welding (DSAW) based on vision sensor was proposed.