Enablers for embedding big data solutions in smart factories: an empirical investigation

This study provides insight into the enablers that assist organizations in implementing big data solutions in their smart factory development, as well as the interrelationships between these enablers from an information system (IS) perspective. The research followed an inductive qualitative approach. Twenty-two in-depth semi-structured interviews were conducted with experienced consultants and IT managers from SAP Consultancy Company, and general managers and engineers from Xiamen Intretech Inc., a leading manufacturing company in adopting big data solutions in smart factory. Following thematic analysis approach, three sets of enablers including organization, technology and external environment were identified together with the interrelationships between them. This paper extends the current understanding of smart factory and big data solutions in information system research through offering an empirical investigation of different enablers in this context. The findings also provide recommendations for practitioners to increase the possibilities of success when implementing big data solutions in smart factory context

Value co-creation in industrial AI: The interactive role of B2B supplier, customer and technology provider

This research explores the interactive role of supplier, customer and technology company in business-to-business (B2B) marketing when they develop and use industrial artificial intelligence (AI). From a value co-creation perspective and following a service-dominant logic, this study aims to identify essential value types that are created collaboratively by B2B professionals (namely suppliers, customers and AI providers), and critical capabilities that contribute to their value co-creation practices. Nineteen in-depth semi-structured interviews were conducted with three groups of B2B stakeholders in six companies that involved in an industrial AI development and usage project. The data was then analysed using a thematic analysis approach. The results of this research contain a categorisation of four value types and three sets of capabilities, together with the interrelationships between them. This study contributes to the literature of value co-creation, information system and B2B marketing by bridging these three disciplines within the context of industrial AI development and usage

The value of dual-energy computed tomography (DECT) in the diagnosis of urinary calculi: a systematic review and meta-analysis of retrospective studies

Objective Dual-energy computed tomography (DECT) imaging technology opens a new idea and method for analyzing stone composition, which can obtain several quantitative parameters reflecting tissue-related information and energy images different from traditional images. However, the application of DECT in diagnosing urinary calculi remains unknown. This study aims to evaluate the value of DECT in diagnosing urinary calculi by meta-analysis. Methods PubMed, EMBASE, Web of Science, and the Cochrane Library were searched to articles published from the establishment of the databases to April 18, 2023. We reviewed the articles on the diagnosis of urinary calculi detected by DECT, established standards, screened the articles, and extracted data. Two researchers carried out data extraction and the Cohen’s unweighted kappa was estimated for inter-investigator reliability. The quality of the literature was evaluated by the diagnostic test accuracy quality evaluation tool (QUADAS-2). The heterogeneity and threshold effects were analyzed by Meta-Disc 1.4 software, and the combined sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic ratio were calculated. The combined receiver-operating characteristic (ROC) curve was drawn, and the value of DECT in the diagnosis of urinary calculi was evaluated by the area under the curve (AUC). The meta-analysis was registered at PROSPERO (CRD42023418204). Results One thousand and twenty-seven stones were detected in 1,223 samples from 10 diagnostic tests. The analyzed kappa alternated between 0.78-0.85 for the document’s retrieval and detection procedure. The sensitivity of DECT in the diagnosis of urinary calculi was 0.94 (95% CI [0.92–0.96]). The positive likelihood ratio (PLR) of DECT in the diagnosis of urinary stones was 0.91 (95% CI [0.88–0.94]), and the negative likelihood ratio (NLR) was 0.08 (95% CI [0.05–0.11]). The specificity of DECT for detecting urinary calculi was 0.91 (95% CI [0.88–0.94]). The area under the curve of the summary receiver operator characteristic (SROC) was 0.9875. The sensitivity of dual-energy CT in the diagnosis of urinary calculi diameter <3 mm was 0.94 (95% CI [0.91–0.96]). The PLR of DECT in the diagnosis of urinary stones diameter <3 mm was 10.79 (95% CI [5.25 to 22.17]), and the NLR was 0.08 (95% CI [0.05–0.13]). The specificity of DECT for detecting urinary calculi <3 mm was 0.91 (95% CI [0.87–0.94]). The SROC was 0.9772. Conclusion The DECT has noble application value in detecting urinary calculi

An overview of GeoAI applications in health and healthcare

Abstract The moulding together of artificial intelligence (AI) and the geographic/geographic information systems (GIS) dimension creates GeoAI. There is an emerging role for GeoAI in health and healthcare, as location is an integral part of both population and individual health. This article provides an overview of GeoAI technologies (methods, tools and software), and their current and potential applications in several disciplines within public health, precision medicine, and Internet of Things-powered smart healthy cities. The potential challenges currently facing GeoAI research and applications in health and healthcare are also briefly discussed

The Influence of a CGA-BP Neural-Network-Based Aeration Oxygen Supply Prediction Model on the Maturity of Aerobic Composting

In order to improve the problem of low oxygen supply efficiency during aerobic composting and prolong composting maturity, a genetic algorithm was used to optimize the initial weights and thresholds of the standard BP neural network and obtain the optimal parameters, and then a clonal selection algorithm was used to optimize the mutation operator in the genetic algorithm and duplicate the operator. A CGA-BP neural network based on an aeration oxygen supply prediction model was constructed, and the aeration oxygen supply predicted by the model was used to ferment the compost and accelerate the process of compost maturation. The results show that compared with the standard BP neural network algorithm and the GA-BP neural network algorithm, this model has accurate prediction performance in predicting aeration oxygen supply, with a prediction accuracy of 99.26%. The aeration oxygen supply predicted based on the CGA-BP model can effectively promote the composting maturity process and meet the needs of aeration oxygen supply throughout the entire fermentation process of aerobic compost

Developing a Framework for Smart City Planning in China: an Action Research Approach

China has long been using a planned economy that involves all aspects of economic, social and political development in China. Having been appointed to develop a 5-Year Plan for two important cities in China, and faced with the absence of an adequate approach, the authors developed a smart city development planning framework (SCDP) using an action research approach that comprised two full cycles of research. This paper presents this methodology and discusses the action research process used. The SCDP will interest practitioners and academics and aims to be a first step in a new area of very difficult research and increasing in complexity. This paper reports on lessons learned when preparing and planning for the 13th 5-Year Plan released in 2016. The findings and methodology presented are very topical, as all Chinese cities are now starting to prepare for the next 5-Year planning process that start in 2020

Kinematic Analysis of a Mechanism With Dual Remote Centre of Motion and its Potential Application

Development of a mechanism with dual remote center of motion (dual-RCM mechanism) intended for teleoperated ophthalmic surgery is reported in this paper. First, characteristics of RCM mechanisms are analyzed. Then, a method to synthesize dual-RCM mechanisms is proposed. Further the mechanical design parameters are optimized to synthesize types of mechanisms meeting functional requirements as well as workspace constraints. The dual-RCM mechanism intended for teleoperated ophthalmic surgery includes two end-effectors: one provides the tool insertion, the other tracks eye movement. The superiority is embodied in the self-synchronized motion of double end-effectors, which allows RCM point of the working instrument to track the penetration point real-time, thereby enhancing microsurgical accuracy. In the proposed implementation, a conceptual helmet mechanical architecture integrating surgical tools with triple-parallelogram linkages is introduced to release the surgeon’s hands by enabling more robotic technologies during the procedures. The vision of the research is to help revolutionize the ophthalmic surgical procedures from bimanual fashion to master-slave teleoperation.</jats:p

Preparation of a Novel SERS Platform Based on Mantis Wing with High-Density and Multi-Level “Hot Spots”

The recent development of SERS substrates based on irregular nanostructures for directly molecule recognition has aroused increasing attention. By combining the irregular flake-like nanostructures of mantis wings, high SERS performance of Ag nanofilms, and the chemical stability of Au nanoparticles (NPs), an ultra-sensitive and flexible SERS substrate based on Au NPs functionalized Ag nanofilms-mantis wings (Au-Ag-M.w.) hybrid system is successfully fabricated. When 4-aminothiophenol is selected as the probe molecule, the limit of detection (LOD) is as low as 10&#8722;13 M and the relative standard deviation (RSD) is lower than 7.15%. This novel SERS platform exhibits high SERS performance in terms of sensitivity, reproducibility and practicability mainly because there are high-density and multi-level &#8220;hot spots&#8221; in the appropriate nanogaps. Meanwhile, it also systematically compares the differences of the SERS performance of Cu and Ag decorated M.w. hybrids and how these differences can alter their response. Moreover, the proposed substrate is employed to rapidly detect the pesticide residues on apple peels and the LOD for cypermethrin is estimated at 10&#8722;10 mg/mL. Therefore, this novel SERS substrate has great potential in rapid sampling of pesticide residues on real samples and expands the investigation to other natural materials for fabricating various SERS platforms

Exploration and Comparison of the Effect of Conventional and Advanced Modeling Algorithms on Landslide Susceptibility Prediction: A Case Study from Yadong Country, Tibet

Shallow landslides pose serious threats to human existence and economic development, especially in the Himalayan areas. Landslide susceptibility mapping (LSM) is a proven way for minimizing the hazard and risk of landslides. Modeling as an essential step, various algorithms have been applied to LSM, but no consensus exists on which model is most suitable or best. In this study, information value (IV) and logistic regression (LR) were selected as representatives of the conventional algorithms, categorical boosting (CatBoost), and conventional neural networks (CNN) as the advanced algorithms, for LSM in Yadong County, and their performance was compared. To begin with, 496 historical landslide events were compiled into a landslide inventory map, followed by a list of 11 conditioning factors, forming a data set. Secondly, the data set was randomly divided into two parts, 80% of which was used for modeling and 20% for validation. Finally, the area under the curve (AUC) and statistical metrics were applied to validate and compare the performance of the models. The results showed that the CNN model performed the best (sensitivity = 79.38%, specificity = 91.00%, accuracy = 85.28%, and AUC = 0.908), while the LR model performed the worst (sensitivity = 79.38%, specificity = 76.00%, accuracy = 77.66%, and AUC = 0.838) and the CatBoost model performed better (sensitivity = 76.28%, specificity = 85.00%, accuracy = 80.81%, and AUC = 0.893). Moreover, the LSM constructed by the CNN model did a more reasonable prediction of the distribution of susceptible areas. As for feature selection, a more detailed analysis of conditioning factors was conducted, but the results were uncertain. The result analyzed by GI may be more reliable but fluctuates with the amount of data. The conclusion reveals that the accuracy of LSM can be further improved with the advancement of algorithms, by determining more representative features, which serve as a more effective guide for land use planning in the study area or other highlands where landslides are frequent