Search results

The study aims to determine the the optimal value of output parameters of a variable compression ratio (CR) diesel engine are investigated at different loads, CR and fuel modes of operation experimentally. The output parameters of a variable compression ratio (CR) diesel engine are investigated at different loads, CR and fuel modes of operation experimentally. The performance parameters like brake thermal efficiency (BTE) and brake specific energy consumption (BSEC), whereas CO emission, HC emission, CO₂ emission, NO_x emission, exhaust gas temperature (EGT) and opacity are the emission parameters measured during the test. Tests are conducted for 2, 6 and 10 kg of load, 16.5 and 17.5 of CR.

In this investigation, the first engine was fueled with 100% diesel and 100% Calophyllum inophyllum oil in single-fuel mode. Then Calophyllum inophyllum oil with producer gas was fed to the engine. Calophyllum inophyllum oil offers lower BTE, CO and HC emissions, opacity and higher EGT, BSEC, CO₂ emission and NO_x emissions compared to diesel fuel in both fuel modes of operation observed. The performance optimization using the Taguchi approach is carried out to determine the optimal input parameters for maximum performance and minimum emissions for the test engine. The optimized value of the input parameters is then fed into the prediction techniques, such as the artificial neural network (ANN).

From multiple response optimization, the minimum emissions of 0.58% of CO, 42% of HC, 191 ppm NO_x and maximum BTE of 21.56% for 16.5 CR, 10 kg load and dual fuel mode of operation are determined. Based on generated errors, the ANN is also ranked for precision. The proposed ANN model provides better prediction with minimum experimental data sets. The values of the R² correlation coefficient are 1, 0.95552, 0.94367 and 0.97789 for training, validation, testing and all, respectively. The said biodiesel may be used as a substitute for conventional diesel fuel.

The blend of Calophyllum inophyllum oil-producer gas is used to run the diesel engine. Performance and emission analysis has been carried out, compared, optimized and validated.

In this study, the authors sought to investigate how the implicit social ties of both project owners and potential backers are associated with crowdfunding project success.

Drawing on social ties theory and factors that affect crowdfunding success, in this research, the authors developed a model to study how project owners' and potential backers' implicit social ties are associated with crowdfunding projects' degrees of success. The proposed model was empirically tested with crowdfunding data collected from Kickstarter and social media data collected from Twitter. The authors performed the test using an ordinary least squares (OLS) regression model with fixed effects.

The authors found that project owners' implicit social ties (specifically, their social media activities, degree centrality and betweenness centrality) are significantly and positively associated with crowdfunding projects' degrees of success. Meanwhile, potential project backers' implicit social ties (their social media activities and degree centrality) are negatively associated with crowdfunding projects' degrees of success. The authors also found that project size moderates the effects of project owners' social media activities on projects' degrees of success.

This work contributes to the literature on crowdfunding by investigating how the implicit social ties of both potential backers and project owners on social media are associated with crowdfunding project success. This study extends the previous research on social ties' roles in explaining crowdfunding project success by including implicit social ties, while the literature explored only explicit social ties.

This paper aims to propose a stable and precise recognition and pose estimation method to deal with the difficulties that industrial parts often present, such as incomplete point cloud data due to surface reflections, lack of color texture features and limited availability of effective three-dimensional geometric information. These challenges lead to less-than-ideal performance of existing object recognition and pose estimation methods based on two-dimensional images or three-dimensional point cloud features.

In this paper, an image-guided depth map completion method is proposed to improve the algorithm's adaptability to noise and incomplete point cloud scenes. Furthermore, this paper also proposes a pose estimation method based on contour feature matching.

Through experimental testing on real-world and virtual scene dataset, it has been verified that the image-guided depth map completion method exhibits higher accuracy in estimating depth values for depth map hole pixels. The pose estimation method proposed in this paper was applied to conduct pose estimation experiments on various parts. The average recognition accuracy in real-world scenes was 88.17%, whereas in virtual scenes, the average recognition accuracy reached 95%.

The proposed recognition and pose estimation method can stably and precisely deal with the difficulties that industrial parts present and improve the algorithm's adaptability to noise and incomplete point cloud scenes.

The purpose of this study is to identify the capacity needs of building professionals to deliver environmentally sustainable buildings (ESBs) globally, from a unique systems approach. Through a review of extant literature, this study contributes to knowledge of the global delivery of ESBs by identifying research trends and gaps that can be tackled in future research, and current hotspots in capacity building (CB) research within the built environment (BE). The adopted systems approach to CB postulates that the construction industry is systemized in nature, and thus, CB solutions within the industry should be approached from a systems approach.

A literature search was conducted using the Scopus search engine, augmented by Google Scholar and Web of Science, to produce 54 relevant articles for analysis. The scientometric analysis was undertaken with the use of VOSViewer to better understand the broad literature on CB in the construction industry which would not have been possible with traditional data analysis. The content analysis allowed, for a systematic review of selected articles, to reveal key themes in this study.

Through a content analysis, four levels of CB were identified within the construction industry; individual level, organizational level, industry level and state level. Nine sub-capacities were subsumed under the four identified levels adopted from Potter and Brough (2004), including but not limited to Performance capacity, Workload capacity, Supervisory capacity, Industry Role capacity and Systems capacity. Under each sub-capacity, key questions were posed to aid identify the capacity needs of BE professionals. A framework for identifying capacity needs in the BE is proposed.

The findings of this study serve as a useful reference for practitioners and policymakers to assess their level of commitment to CB efforts for ESB delivery. The findings of this study have revealed that building the capacities of BE professionals to deliver ESBs should be addressed as part of a broader framework, interdependent on the other levels of CB in the systemic construction industry.

As a review study identifying capacity needs for BE professionals to deliver ESBs, this study enhances knowledge of CB within the construction industry.

This paper tries to retrospect the mounting application of machine learning (ML) and artificial intelligence (AI) in the human resource management area. The document applies bibliometric analysis and uses relational techniques to explore dimensions of documents in the field. The results highlight publication trends, most impactful authors, countries and institutes in the research area. The science mapping along with co-citation and bibliometric coupling analysis revealed major developments in the field. The thematic mapping and trend analysis highlighted the past and emerging trends towards significant and impactful research in the areas of robotics, big data, AI and data analytics. This paper sets the base for future researchers by coordinating and combining various past researches to help in understanding the evolution of ML and AI in human resource management and expansion of knowledgebase.

In response to the challenges posed by the conventional manual flange docking method in the LNG (Liquefied Natural Gas) loading process, such as low positioning accuracy, constraints on production efficiency and safety hazards, this study analyzed the LNG five-axis loading arm’s main functions and structural characteristics.

An automated solution for the joints of the LNG loading arm was designed. The forward kinematic model of the LNG loading arm was established using the Denavit–Hartenberg (D-H) parameter method, and its workspace was analyzed. The Newton–Raphson iteration method was employed to solve the inverse kinematics of the LNG loading arm, facilitating trajectory planning. The relationship between the target position and the joint variables was established to verify the stability of the arm’s motion. Flange center identification was achieved using the Hough transform function. Based on the ROS platform, combined with Gazebo and Rviz, an experimental simulation of automatic docking of the LNG loading arm was conducted.

The docking errors in the XYZ directions were all less than 0.8 mm, meeting the required docking accuracy. Moreover, the motion performance of the loading arm during docking was smooth and free of abrupt changes, validating its capability to accomplish the automatic docking task.

The proposed trajectory planning and automatic docking scheme can be used for the rapid filling of LNG filling arms and LNG tankers to improve the efficiency of LNG transportation. In guiding the docking, the proposed automatic docking scheme is an accurate and efficient way to improve safety.

This paper aims to conduct a bibliometric analysis of studies focusing on green supply chain management (GSCM) within the context of the digital economy.

We utilize the Web of Science database to search and filter relevant documents spanning the years 2003–2022. This extensive dataset enables us to analyze the growth and cutting-edge developments in research pertaining to GSCM in the digital economy.

The paper finds a significant increase in research interest and output, particularly noticeable from 2016 onwards, indicating the growing relevance of integrating GSCM with digital technologies. It is found that the prominent contribution of countries like China, England and the USA, underscoring a strong geographical diversity in research outputs. China leads in the number of publications, which reflects its significant role in shaping the discourse around GSCM in the digital economy. However, when it comes to citations, the USA leads, suggesting a higher impact or quality of research emanating from this region. Collaborative dynamics outlined in the study demonstrate extensive international cooperation, primarily among leading research countries, which is facilitated by shared digital platforms enhancing the research’s reach and impact. The study also highlights a range of emerging themes such as the adoption of blockchain technology, Internet of Things (IoT) and the circular economy within GSCM, indicating dynamic areas for future research.

The findings of this study hold significant practical implications for researchers, practitioners and policymakers. They shed light on the current state of research in GSCM within the digital economy, highlighting areas where further investigation is needed and pointing to the emerging trends in this field. Understanding the distribution of research and influential authors can guide future collaborative efforts and inform decision-making processes in the pursuit of sustainable supply chain practices in the digital era.

This paper contributes to the existing body of knowledge by providing a comprehensive bibliometric analysis of the evolving landscape of GSCM in the digital economy. It offers valuable insights into the growth patterns, key contributors and geographical distribution of research in this domain. This information is crucial for researchers and stakeholders seeking to stay at the forefront of sustainable supply chain practices in an increasingly digital world.

Ciliated microelectromechanical system (MEMS) vector hydrophones pick up sound signals through Wheatstone bridge in cross beam-ciliated microstructures to achieve information transmission. This paper aims to overcome the complexity and variability of the marine environment and achieve accurate location of targets. In this paper, a new method for ocean noise denoising based on improved complete ensemble empirical mode decomposition with adaptive noise combined with wavelet threshold processing method (CEEMDAN-WT) is proposed.

Based on the CEEMDAN-WT method, the signal is decomposed into different intrinsic mode functions (IMFs), and relevant parameters are selected to obtain IMF denoised signals through WT method for the noisy mode components with low sample entropy. The final pure signal is obtained by reconstructing the unprocessed mode components and the denoising component, effectively separating the signal from the wave interference.

The three methods of empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD) and CEEMDAN are compared and analyzed by simulation. The simulation results show that the CEEMDAN method has higher signal-to-noise ratio and smaller reconstruction error than EMD and EEMD. The feasibility and practicability of the combined denoising method are verified by indoor and outdoor experiments, and the underwater acoustic experiment data after processing are combined beams. The problem of blurry left and right sides is solved, and the high precision orientation of the target is realized.

This algorithm provides a theoretical basis for MEMS hydrophones to achieve accurate target positioning in the ocean, and can be applied to the hardware design of sonobuoys, which is widely used in various underwater acoustic work.

The digital marketing landscape is rapidly evolving, but the integration of visual content still heavily depends on human expertise. Driven by the quest for innovative marketing strategies that resonate with family-oriented consumers, this study seeks to bridge this gap by applying machine learning to analyze visual content in the maternity and baby care product sector.

This study incorporates a range of machine learning techniques – including open science framework feature detection, panoptic segmentation, customized instance segmentation, and face detection calculation methods – to analyze and predict the appeal of images, thereby enhancing user engagement and parent-child intimacy.

The exploration of various ML models, such as DT, LightGBM, RIPPER algorithm, and CNNs, has offered a comparative analysis that addresses a methodological gap in the existing literature, which frequently depends on isolated model evaluations. According to our quadrant analysis with respect to engagement rate and parent-child intimacy, the selection of a model for real-world applications depends on balancing performance and interpretability.

The proposed system offers a series of actionable recommendations designed to enhance customer engagement and foster brand loyalty. This study contributes to image design in maternity and baby care marketing and provides analytical insights for recommendation systems.