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Industrial parks are regarded as a possible fashion to reduce the manufacturing costs and increase the efficiency by sharing cost-effective infrastructure and communal services. However, the waste of manufacturing resources could impede the development of manufacturing enterprises that locate in an industrial park when market demands fluctuate dynamically. This article aims to use the cloud manufacturing (CMfg) to relieve the temporary shortage of manufacturing resources and capabilities by sharing them among these enterprises in an industrial park. This article focuses on manufacturing cloud service allocation (MCSA), which is one of the critical processes to implement CMfg. Two multiunit double auction mechanisms are proposed to allocate the manufacturing cloud services (MCSs) with reasonable trade price in bilateral markets, and their properties and bidding strategies are proved. Numerical studies are conducted, and the results show the effectiveness and efficiency of two mechanisms for dealing with MCSA and verify the theoretical proofs.

With the development of Internet of Things (IoT), 5 G, and cloud computing technologies, the amount of data from manufacturing systems has been increasing rapidly. With massive industrial data, achievements beyond expectations have been made in the product design, manufacturing, and maintain process. Big data analytics (BDA) have been a core technology to empower intelligent manufacturing systems. In order to fully report BDA for intelligent manufacturing systems, this paper provides a comprehensive review of associated topics such as the concept of big data, model driven and data driven methodologies. The framework, development, key technologies, and applications of BDA for intelligent manufacturing systems are discussed. The challenges and opportunities for future research are highlighted. Through this work, it is hoped to spark new ideas in the effort to realize the BDA for intelligent manufacturing systems.

ObjectivesDiffusion-weighted magnetic resonance imaging (DWI) of the musculoskeletal system has various applications, including visualization of bone tumors. However, DWI acquired with echo-planar imaging is susceptible to distortions due to static magnetic field inhomogeneities. This study aimed to estimate spatial displacements of bone and to examine whether distortion corrected DWI images more accurately reflect underlying anatomy.MethodsWhole-body MRI data from 127 prostate cancer patients were analyzed. The reverse polarity gradient (RPG) technique was applied to DWI data to estimate voxel-level distortions and to produce a distortion corrected DWI dataset. First, an anatomic landmark analysis was conducted, in which corresponding vertebral landmarks on DWI and anatomic T2-weighted images were annotated. Changes in distance between DWI- and T2-defined landmarks (i.e., changes in error) after distortion correction were calculated. In secondary analyses, distortion estimates from RPG were used to assess spatial displacements of bone metastases. Lastly, changes in mutual information between DWI and T2-weighted images of bone metastases after distortion correction were calculated.ResultsDistortion correction reduced anatomic error of vertebral DWI up to 29 mm. Error reductions were consistent across subjects (Wilcoxon signed-rank p−20). On average (±SD), participants’ largest error reduction was 11.8 mm (±3.6). Mean (95% CI) displacement of bone lesions was 6.0 mm (95% CI: 5.1-7.0); maximum displacement was 17.1 mm. Corrected diffusion images were more similar to structural MRI, as evidenced by consistent increases in mutual information (Wilcoxon signed-rank p−12).DiscussionThese findings support the use of distortion correction techniques to improve localization of bone on DWI.Key Points-Diffusion weighted images of bone tissue undergo substantial spatial distortions when acquired with echo-planar imaging.-These distortions can be efficiently corrected with the reverse polarity gradient technique to generate diffusion images that more accurately reflect underlying anatomy.-In the context of bone tumor imaging where precise localization may be required, distortion correction techniques, such as reverse polarity gradient, should be applied.

Object segmentation for 3-D point clouds plays a critical role in autonomous driving, robotic navigation, and other computer version applications. In object segmentation, all points are considered to be equal of importance in the literature. However, unequal cases exist and a segmentation boundary is mainly determined by neighbor points. To investigate point inequivalence, in this article, an unequal learning approach is proposed to integrate gene expression programming (GEP) and a deep neural network (DNN). GEP is designed to discover the inequivalent function, which measures the importance of different points according to the distances to the segmentation boundary. A cost sensitive learning method is improved to guide the DNN to obtain the loss of different points unequally with the discovered inequivalent function during model training. The experimental results reveal that point inequivalence with respect to boundary distance exists and is helpful to improve the accuracy of object segmentation.

PurposeThis paper proposes an evaluation model for prefabricated construction to guide a supply chain with controllable costs. Prefabricated construction is prevalent due to area limitations. Nevertheless, the development is limited by budget control and identifying the factors affecting cost. The degree of close collaboration in the supply chain is closely interconnected with cost performance that includes direct and indirect factors. This paper not only quantizes these factors but also distinguishes the degree of influence of various factors.Design/methodology/approachSystem dynamics is applied to simulate and analyze the construction cost factors through Vensim software. It can also clarify the relationship between cost and other influencing factors. The input data are collected from an Internet of Things (IoT)-enabled system under a Building Information Modeling (BIM) system and Hong Kong government reports.FindingsSimulation results indicate that prefabricated construction cost is mainly influenced by government promotion degree (GPD), working pressure from on-site construction (WPOSC), prefab quality (PQ), load-bearing capacity per vehicle (LBPV) and mold quality (MQ). However, it is more sensitive toward GPD, which indicates that the government should take measures to promote this construction technology. On-site worker management is also essential for the assembly process and indirectly influences the construction cost.Research limitations/implicationsThis paper quantifies indirect influential factors to clarify the specific features for prefabricated construction. The investigated factors are limited.Practical implicationsThe contractor can identify all factors and classify the levels of influence to make decisions under the supply chain system boundary.Social implicationsThe input data are collected from an IoT-enabled system under a BIM system and Hong Kong government reports. Thus, the relationship between construction cost influential factors can be investigated.Originality/valueThis paper quantifies indirect influencing factors and clarifies the specific features in prefabricated construction. The contractor could identify these factors to make decisions and classify the levels of influence under the supply chain system boundary.

As the wide use of various smart sensors in the manufacturing environment, traditional factories have been upgraded and transformed into an intelligent level. Smart manufacturing factory thus has been enabled by some advanced technologies, such as Internet of Things (IoT) which could facilitate production operations and decision-makings on the one hand. On the other hand, enormous data will be created by the IoT devices. Manufacturing companies are facing some challenges when attempting to make full use of the huge datasets which are heterogeneous in format, complex in logic, unstructured in storage, and abstract in interpretation. In order to address these challenges, this article proposes a data heterogeneous analytics framework for a radio-frequency identification (RFID) enabled factory. RFID captured data from a real-life company is used for validating the proposed framework. Specifically, the performance of machining processes, logistics operations, and inspection behavior are examined from the RFID captured data.

ObjectiveThis study investigated the characteristics and prognosis of the feeling of ear fullness in patients with unilateral all-frequency sudden sensorineural hearing loss.MethodsOur study included 56 patients with a diagnosis of unilateral all-frequency sudden sensorineural hearing loss accompanied by a feeling of ear fullness and 48 patients without a feeling of ear fullness. The condition of these patients was prospectively observed.ResultsPositive correlations were observed between grading of feeling of ear fullness and hearing loss in patients with a feeling of ear fullness (r = 0.599, p < 0.001). No significant differences were observed in the total effective rate of hearing recovery between patients with and without a feeling of ear fullness after one month of treatment (Z = −0.641, p = 0.521). Eighty-six per cent of patients (48 out of 56) showed complete recovery from the feeling of ear fullness. There was no correlation between feeling of ear fullness recovery and hearing recovery (r = 0.040, p = 0.769).ConclusionThe prognosis of feeling of ear fullness is good. There was no correlation between feeling of ear fullness recovery and hearing recovery for all-frequency sudden sensorineural hearing loss patients.

Companies with manufacturing systems that are more responsive and resilient will be able to survive or even gain market shares in the face of the unpredicted variable of an outbreak similar to the COVID-19 pandemic. Motivated by an industrial company restructuring its manufacturing system with the layout of fixed-position assembly islands (FPAI) during the COVID-19 pandemic, this paper introduces the synchronization-oriented reconfiguration of FPAI under Graduation Intelligent Manufacturing System (GiMS). Inspired by the graduation ceremony, a novel manufacturing mode-Graduation Manufacturing System (GMS) with ticket-based reconfigurable structures, is designed for organizing production operations with simplicity and resilience for the layout of FPAI. The IIoT and digital twin-enabled GiMS is developed for transforming real-time visibility in operations to support the reconfiguration of the manufacturing system. A synchronization-oriented reconfiguration mechanism is proposed to achieve the synchronous interaction among changing customer demand, island configuration, and production activities allocation rapidly and cost-effectively. Cloud services integrating the proposed reconfiguration mechanism are developed for managers and onsite operators for supporting the successful reconfiguration implementation with enhanced operational visibility. Through the case study of an industrial company, the effectiveness of the proposed concept and approach is verified.

Purpose To investigate the association between urinary complement proteins and renal outcome in biopsy-proven diabetic nephropathy (DN). Methods Untargeted proteomic and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional analyses and targeted proteomic analysis using parallel reaction-monitoring (PRM)-mass spectrometry was performed to determine the abundance of urinary complement proteins in healthy controls, type 2 diabetes mellitus (T2DM) patients, and patients with T2DM and biopsy-proven DN. The abundance of each urinary complement protein was individually included in Cox proportional hazards models for predicting progression to end-stage renal disease (ESRD). Results Untargeted proteomic and functional analysis using the KEGG showed that differentially expressed urinary proteins were primarily associated with the complement and coagulation cascades. Subsequent urinary complement proteins quantification using PRM showed that urinary abundances of C3, C9, and complement factor H (CFAH) correlated negatively with annual estimated glomerular filtration rate (eGFR) decline, while urinary abundances of C5, decay-accelerating factor (DAF), and CD59 correlated positively with annual rate of eGFR decline. Furthermore, higher urinary abundance of CFAH and lower urinary abundance of DAF were independently associated with greater risk of progression to ESRD. Urinary abundance of CFAH and DAF had a larger area under the curve (AUC) than that of eGFR, proteinuria, or any pathological parameter. Moreover, the model that included CFAH or DAF had a larger AUC than that with only clinical or pathological parameters. Conclusion Urinary abundance of complement proteins was significantly associated with ESRD in patients with T2DM and biopsy-proven DN, indicating that therapeutically targeting the complement pathway may alleviate progression of DN.

PurposePhysical gatherings at social events have been found as one of the main causes of COVID-19 transmission all over the world. Smartphone has been used for contact tracing by exchanging messages through Bluetooth signals. However, recent confirmed cases found in venues indicated that indirect transmission of the causative virus occurred, resulting from virus contamination of common objects, virus aerosolization in a confined space or spread from inadequate ventilation environment with no indication of human direct or close contact observed.Design/methodology/approachThis paper presents a novel cyber-physical architecture for spatial temporal analytics (iGather for short). Locations with time windows are modeled as digital chromosomes in cyberspace to represent human activity instances in the physical world.FindingsResults show that the high spatial temporal correlated but indirect tracing can be realized through the deployment of physical hardware and spatial temporal analytics including mobility and traceability analytics. iGather is tested and verified in different spatial temporal correlated cases. From a management perspective of mobilizing social capacity, the venue plays not only a promotion role in boosting the utilization rates but also a supervision-assisted role for keeping the venue in a safe and healthy situation.Social implicationsThis research is of particular significance when physical distancing measures are being relaxed with situations gradually become contained. iGather is able to help the general public to ease open questions: Is a venue safe enough? Is there anyone at a gathering at risk? What should one do when someone gets infected without raising privacy issues?Originality/valueThis study contributes to the existing literature by cyber-physical spatial temporal analytics to trace COVID-19 indirect contacts through digital chromosome, a representation of digital twin technology. Also, the authors have proposed a venue-oriented management perspective to resolve privacy-preserving and unitization rate concerns.

Internet of things (IoT) and Radio Frequency Identification (RFID) technologies are gradually adopted in manufacturing recently. With the aid of them, numerous data is generated from daily manufacturing operations. Big data analytics is used in locating deficiencies and thus improving the productivity of a manufacturing shopfloor. Many studies have also examined the effect of “Blue Monday” and “post-lunch slump” on worker’s performance. This paper provides a big data approach on analyzing worker’s performance with the data collected from a manufacturing shopfloor. By evaluating the worker’s performance at different time periods, a better decision can be arranged for improving overall productivity.

Fabric defect detection plays an important role in ensuring quality control in the textile manufacturing industry. This study introduces a fabric defect detection method based on a multi-scale convolutional neural network (MSCNN) to improve accuracy and time efficiency. For detection accuracy, the MSCNN is constructed to obtain different scales of feature maps, which enhance the representation of tiny scale fabric defects. A faster defect locating method is designed with pre-known size information obtained by clustering analysis to reduce the computation time. An experiment is carried out for illustrating that the accuracy of MSCNN for each defect reaches over 92%, and the frames per second (FPS) is more than 29. Further analysis results demonstrate that the proposed MSCNN can accurately detect the fabric defects with a tiny scale, and the speed of detection can reach 30 m/min to satisfy the industrial requirements.

PurposeThe purpose of this paper is to develop an intelligent manufacturing system for transforming production management and operations to an Industry 4.0 manufacturing paradigm.Design/methodology/approachA manufacturing mode-Graduation Manufacturing System is designed for organizing and controlling production operations. An Industrial Internet of Things (IIoT) and digital twin-enabled Graduation Intelligent Manufacturing System (GiMS) with real-time task allocation and execution mechanisms is proposed to achieve real-time information sharing and production planning, scheduling, execution and control with reduced complexity and uncertainty.FindingsThe implementation of GiMS in an industrial company illustrates the potential advantages for real-time production planning, scheduling, execution and control with reduced complexity and uncertainty. For production managers and onsite operators, effective tools, such as cloud services integrates effective production and operations management strategies are needed to facilitate their decision-making and daily operations at the operational level.Originality/valueThis paper presents an Industry 4.0 paradigm-GiMS, which aims to explore Industry 4.0 technologies opportunities on operations and production management, especially on production planning, scheduling, execution and control.

With the wide integration of the Cyber-Physical System (CPS) and Internet of things (IoT), the manufacturing industry has entered into an era of big data. Thus, manufacturing companies are facing challenges when conducting Big Data Analytics, including the high velocity of data generation, the enormous volume, the multifarious formats and types as well as the quality or fidelity. In this paper, a Cyber-Physical Production System (CPPS) using data analytics is proposed to enable production visibility. Firstly, this study uses data stream processing approaches to clean redundant data efficiently. Secondly, a Bayesian inference engine, which is trained by ming the historical data offline, is employed to identify the accuracy of an RFID-captured event online. Then, complex event processing is applied to fuse multi-source heterogeneous data. Finally, production progress visibility is achieved by the Business Process Management. The proposed system demonstrates that it is significant to implement real-time data collection, processing and visibility, as well as to improve production efficiency. A demonstrative case from the machinery industry is presented to validate the CPPS.

Our systematic review and meta-analysis indicated that pro-inflammatory diets, as measured by higher Dietary Inflammatory Index scores, are significantly associated with lower BMD of lumbar spine and total hip as well as elevated risk of osteoporosis and fractures. These findings may contribute to the development of public health strategies. Inflammatory Index (DII) is a method to assess the inflammatory potential of diets; it has been reported to be associated with several diseases. However, the relation between DII and bone health remains controversial for the inconsistent findings from previous studies. This systematic review and meta-analysis aimed to ascertain the underlying relationships between DII and bone mineral density (BMD), osteoporosis risk, and fracture risk. We systematically searched PubMed and Web of Science for all relevant epidemiological studies published up to May 1, 2020. Fixed-effects model or random-effects model was employed to pool the study-specific effect sizes (ESs) and 95% confidence intervals (CIs). Eleven studies with a total of 127,769 participants were included. We found that continuous DII was negatively associated with BMD of lumbar spine (odds ratios [OR]: 0.990; 95% CI: 0.984, 0.995) and total hip (OR: 0.995; 95% CI: 0.990, 0.999), but not femoral neck (OR: 0.998; 95% CI: 0.994, 1.002). Moreover, the highest category of DII displayed significantly associations to increased risk of osteoporosis (ES: 1.31; 95% CI: 1.16, 1.48) and fractures (ES: 1.28; 95% CI: 1.03, 1.59) compared with the lowest category of DII, respectively. Our analysis indicated that diets with high pro-inflammatory components might increase the risk of osteoporosis and fractures and lower BMD of lumbar spine and total hip. More prospective studies involving populations of diverse ages and genders are expected to further verify the universality of the results.

Aiming at the problems existing on the expression of atmospheric pollution dispersion, a data model of state-oriented and object-oriented hybrid modeling is designed. The model supports true three-dimensional grid representation and cellular automata deduction, and can combine the calculation and deduction of atmospheric pollution to improve the accuracy and efficiency of the model. The simulation experiments of single source and multi-source pollutants are carried out. The results show that: the model conforms to the diffusion law of atmospheric pollution. The pollutant concentration at any time, section or space in the whole diffusion process can be calculated and expressed dynamically, which improves the accuracy, rapidity, intuitiveness and analysation of the deduction of atmospheric pollutant diffusion process.

Based on the extended Huygens–Fresnel integral, the cross-spectral density function of a rectangular multi-Gaussian Schell-model (RMGSM) beam propagating in biological tissues has been derived, and the spreading properties and spectral degree of coherence of an RMGSM beam propagating in biological tissues were studied by using the numerical examples. The results show that an RMGSM beam propagating in biological tissues will first evolve into a rectangular flat-topped beam and then evolve into a Gaussian-like beam due to the influences of the turbulence of biological tissues. It is also found that an RMGSM beam with smaller coherence length can maintain the rectangular flat-topped beam spot at the longer propagation distance.

Beidou high-precision reference station network in Beibu Gulf of Guangxi consists of five parts, namely Beidou reference stations, the data processing system, the operation and service platform, the data broadcast system and user terminals. It is an important infrastructure for the construction of the China-ASEAN Information Harbor. Since its operation, the system has contributed significantly to the industrial development in Guangxi and the mass application of Beidou. This paper focuses on the operation of Beidou high-precision reference station network in Beibu Gulf of Guangxi from the perspectives of performance, navigation and positioning accuracy, application experience, etc. Experimental results demonstrated that: in the area of Beibu Gulf of Guangxi, the application of Beidou Navigation Satellite System (BDS) has significantly increased the number of satellites observed and enhanced the spatial accessibility of the system. The percentage of fixed solutions in the spatial accessibility test of the system was about 87.3%; the network-wide time availability within the effective coverage of the system was 92.7%; the internal accordant accuracy of points in Beibu Gulf BDS was 37.7% and 33.3% higher than the dual constellation system (GPS+GLONASS) in the horizontal and the vertical directions respectively, and the average external accordant accuracy was 20.60% higher than the dual constellation system in the horizontal direction (equivalent in the vertical direction). This paper objective reflected the present construction and utilization situation of Beidou high-precision reference station network in Beibu Gulf of Guangxi, which would be of great significance to the construction and development of the Beidou system.

Inkjet technology is an essential tool for precise and quick delivery of liquids in micro-droplets. A key topic of the technology is to deliver the droplets efficiently by designing the nozzle that is related to the droplet speed and the droplet volume in a stable inkjet process. The ejected droplets are usually too small to determine their physical states through onsite measurement. Complex physical phenomena, such as the coupling effects of surface tension, viscous force and inertial force, make it difficult to optimize the nozzle design by experiments alone. In the paper, we adopt computational fluid dynamics to investigate the inkjet process with the orthogonal test method to arrange the studied cases. The computational results firstly have been verified through measuring a simulated case that could be observed in the experiment. Different nozzle structures then have been examined by numerical simulation. It is found that the Laval-shaped nozzle can improve the droplet speed significantly to deliver the droplets fast, and that the curvilinear-triangle-shaped nozzle can minimize the droplet volume to improve the printing accuracy. It is further revealed that a large ink viscosity and surface tension, as well as a low ink density can improve the process stability. Additionally, a parameter combined by the droplet speed, the droplet volume and the stability level is proposed to evaluate the comprehensive performance of the inkjet nozzle.

The Fourth Industrial Revolution (Industry 4.0) leads to an age of extraordinary changes through digital transformation. High customer demands and market competitions drive almost all business sectors to meet individuals’ requirements with a cost close to mass production. This paper aims to get the best out of Digital Twin capabilities for meeting mass personalization. A cross-sectional study was undertaken to explore the potential relationship between Industry 4.0, Information and communication technologies (ICT), and Digital Twin towards mass personalization. This study identifies cutting-edge technologies for building a Digital Twin reference model. The results reveal that Digital Twin fulfils mass personalization under Industry 4.0. The findings can contribute to a better understanding of new industrial applications for a wide range of Digital Twin integration levels.

The emergence of Internet of Things (IoT) and new manufacturing paradigms have brought greater complexity of massive datasets. Radio frequency identification (RFID), as one of the key IoT technologies, has been used to collect real-time production data to support the manufacturing decision-making in smart factories. The adoption of these technologies results in a large amount of data collection. To extract useful information from this data, this paper utilizes a big data approach to figure out useful insights from RFID-enabled data regarding possible bottlenecks or inefficiencies on the shop floor so as to improve the quality management. Time and quality are the main metrics measured in this paper, where the longest process times, part accuracy percentage, and failure rate are determined for each of the workers (UserIDs) and process types (ProcCodes). Key findings and observations are significant to make advanced decisions in the smart factory by making full use of the RFID captured data. Note to Practitioners — This paper was motivated by a real-life company which has used RFID technology for over ten years. Great myriad of data has been captured from frontline production shop floors where machines, workers, materials, and manufacturing jobs were logically associated. Processing time and quality data are studied in this paper so as to investigate the production performance in the RFID-enabled smart factory.

As a major challenge and opportunity for traditional manufacturing, intelligent manufacturing is facing the needs of sustainable development in future. Sustainability assessment undoubtedly plays a pivotal role for future development of intelligent manufacturing. Aiming at this, the paper presents the digital twin driven information architecture of sustainability assessment oriented for dynamic evolution under the whole life cycle based on the classic digital twin mapping system. The sustainability assessment method segment of the architecture includes indicator system building, indicator value determination, indicator importance degree determination and intelligent manufacturing project assessing. A novel approach for treating the ambiguity of expert judgment in indicator value determination by introducing trapezoidal fuzzy number into analytic hierarchy process is proposed, while the complexity of the influence relationship among the indicators is processed by the integration of complex networks modeling and PROMETHEE II for the indicator importance degree determination. A two-stage evidence combination model based on evidence theory is built for intelligent manufacturing project assessing lastly. The presented digital-twin-driven information architecture and the sustainability assessment method is tested and validated on a study of sustainability assessment of 8 intelligent manufacturing projects of an air conditioning enterprise. The results of the presented method were validated by comparing them with the results of the fuzzy and rough extension of the PROMETHEE II, TOPSIS and VIKOR methods, indicator importance degree determining method by entropy and indicator value determining method by accurate expert scoring. Funding Agencies|National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [51875251, 51765001]; Guangdong Special Support Talent Program-Innovation and Entrepreneurship Leading Team [2019BT02S593]; 2018 Guangzhou Leading Innovation Team Program (China) [201909010006]; Ningxia Natural Science Foundation [2020AAC03202, NZ17111]; Third Batch of Ningxia Youth Talents Supporting Program [TGJC2018048]; Blue Fire Project (Huizhou) Industry-University-Research Joint Innovation Fund of the Ministry of Education (China) [CXZJHZ201722]

Study question To evaluate whether the pregnancy outcomes could be improved in implantation failure patients by endometrial receptivity array, endometrial immune profiling, or a combination of both. Summary answer There was no statistical difference between different endometrial receptivity evaluation and treatment in improving the clinical pregnancy rate. What is known already Both endometrial receptivity array and endometrial immune profiling were promised to improve the endometrial receptivity and subsequent clinical pregnancy. However, less is known about the efficiency between each other and whether the combination could further enhance their clinical value. Study design, size, duration Between November 2019 and September 2020, 143 women with a history of at least two or more consecutive implantation failure in IVF/ICSI treatment in Chengdu Xinan Gynecology Hospital were included. They were divided into three groups: ‘ERA + Immune Profiling’ (n = 70), ‘Immune Profiling’ (n = 41), and ‘ERA’ (n = 32). Participants/materials, setting, methods Inclusion criteria were age ≤ 38, with normal uterus and uterine cavity. All patients were suggested to evaluate endometrial receptivity by ERA test (Igenomix, Valencia, Spain) and endometrial immune profiling based on immunohistochemistry simultaneously, who would be free to choose each or both evaluation approaches. Personal Embryo Transfer and/or personal medical care were adopted according to evaluation results. Clinical pregnancy was confirmed by gestational sacs observed under ultrasonography. Main results and the role of chance The overall prevalence of displaced window of implantation (WOI) is 84.3%, and nearly 74.8% (83/111) patients were diagnosed as endometrial immune dysregulation. Clinical Pregnancy rate and embryonic implantation rate decreased in the ‘Immune Test’ groups, but without a statistical difference (P = 0.311, and 0.158, respectively). Multivariable logistic regression analysis showed that different endometrial receptivity evaluation and treatment was not associated the clinical pregnancy rate, suggesting the performance of different endometrial receptivity evaluation and treatment is similar in improving the clinical pregnancy rate. Neither the immune profiling (CD56, P = 0.591; FOXP3, P = 0.195; CD68, P = 0.820; CD163, P = 0.926; CD1a, P = 0.561; CD57, P = 0.221; CD8, P = 0.427; CD138 CE, P = 0.372) nor histologic endometrial dating defined by Noyes criteria (P = 0.374) were associated with ERA phases. Limitations, reasons for caution Although the selection of evaluation approaches was based on patients’ willingness, the variances of baseline characteristics and immune profiling existed in different groups. The immunological treatment efficacy based on immune profiling was not evaluated before embryo transfer. Wider implications of the findings: To our knowledge, this is the first study comparing the pregnancy outcomes after two typical endometrial receptivity evaluation approaches. The findings highlight the unsubstitutability for each assessment, indicating that both asynchronous and pathological WOI contribute to implantation failure. Trial registration number X2019004

Study question To quantify the effectiveness of endometrial receptivity analysis (ERA)-guided personalized embryo transfer (pET) in Chinese women. Summary answer ERA-guided pET may remarkably improve pregnancy and implantation rates among Chinese women with Recurrent implantation failure (RIF). What is known already RIF is a major cause of infertility, and endometrial receptivity is widely accepted to impact implantation failure. Precision prediction of the WOI, the time when the endometrium is most receptive to the implantation of the embryo, is, therefore, of great significance to improve implantation prospects. Previous studies have shown the effectiveness of ERA for the prediction of the WOI, and how pET, timed by ERA, improves implantation and pregnancy rates; however, the efficacy of ERA-guided pET remains unknown for Chinese women. Study design, size, duration Patients in Chengdu Xi’nan Gynecology Hospital (Chengdu, China) who were undergoing frozen embryo transfer (FET) at the blastocyst stage on day five or day six during the period from November 2019 through September 2020 were recruited for this study. A total of 145 eligible patients were included in the study and assigned to the ERA group (n = 67) or the control group (n = 78). Clinical pregnancy outcomes were compared between the two groups. Participants/materials, setting, methods Endometrial specimens were collected the from ERA group. Total RNA was extracted from endometrial specimens, the transcriptomic sequencing data were processed using RNA-Seq and the endometrial receptivity status was assessed by the ERA predictor. The endometrium was classified as receptive or non-receptive according to the ERA assessment, and pET was done at the time determined by ERA in the ERA group. Subjects in the control group did not receive ERA and underwent blastocyst transfer normally. Main results and the role of chance The demographic and clinical characteristics were comparable between the ERA and control groups (P > 0.05). The ERA test identified 10.45% of samples as receptive and 89.55% of samples as non-receptive in the ERA group, with 70.15% of samples presenting a pre-receptive profile. We observed higher cumulative pregnancy (74.63% vs. 64.10%) and cumulative implantation rate (47.32% vs. 21.68%) rates, and a lower biochemical pregnancy rate (18.00% vs. 34.00%) in the ERA group when compared to the control group (P Limitations, reasons for caution First, this is a retrospective analysis, which is relatively more biased than prospective clinical trials. Second, the study sample is considerably small. Third, only 10.45% of the subjects were identified as presenting a receptive profile, which limits the comparisons of clinical outcomes between patients with receptive and non-receptive endometria. Wider implications of the findings: This study demonstrates that the ERA test helps to determine the optimal timing for embryo transfer, improve pregnancy and implantation rates in patients with RIF, and guides the clinical application of the ERA test. Trial registration number approval No. 2020–018

Wire electrical discharge machining (WEDM), widely used to fabricate micro and precision parts in manufacturing industry, is a nontraditional machining method using discharge energy which is transformed into thermal energy to efficiently remove materials. A great amount of research has been conducted based on pulse characteristics. However, the spark image-based approach has little research reported. This paper proposes a discharge spark image-based approach. A model is introduced to predict the discharge status using spark image features through a synchronous high-speed image and waveform acquisition system. First, the relationship between the spark image features (e.g., area, energy, energy density, distribution, etc.) and discharge status is explored by a set of experiments). Traditional methods have claimed that pulse waveform of “short” status is related to the status of non-machining while through our research, it is concluded that this is not always true by conducting experiments based on the spark images. Second, a deep learning model based on Convolution neural network (CNN) and Gated recurrent unit (GRU) is proposed to predict the discharge status. A time series of spark image features extracted by CNN form a 3D feature space is used to predict the discharge status through GRU. Moreover, a quantitative labeling method of machining state is proposed to improve the stability of the model. Due the effective features and the quantitative labeling method, the proposed approach achieves better predict result comparing with the single GRU model.

Cloud manufacturing (CMfg) has been widely recognized since production resources could be encapsulated into smart manufacturing services and managed by advanced information and manufacturing technologies. As a core part for promoting sustainable production processes, manufacturing service configuration (MSC) aims to optimize the allocation of services for tasks in CMfg. This research studies an MSC problem considering the decision autonomy (DA) and limited production capacities (PC) of service providers (MSC-DA-PC). Augmented Lagrangian coordination (ALC), an emerged distributed optimization method, can support open-structure collaboration and allow participants to maintain decision autonomy. In this paper, ALC is extended to solve the proposed MSC-DA-PC problem by the introduction of a novel coordination element (“CO” element). The working logic and solution strategy of the “CO” element are investigated. Two case studies are employed to verify the effectiveness and efficiency of the extended ALC method. The observation shows the dynamic formation of MSC results along with the changing of task quantity.

Due to the wide usage of digital devices and easy access to the edge items in manufacturing industry, massive industrial data is generated and collected. A data-driven smart production line (SPL), which is a basic cell in a smart factory, is derived primarily. This paper studies the data-driven SPL and its common factors. Firstly, common factors such as integration, data-driven, service collaboration, and proactive service of SPL are investigated. Then, a data-driven method including data self-perception, data understanding, decision-making, and precise control for implementing SPL is proposed. As a reference, the research of the common factors and the data-driven method could offer a systematic standard for both academia and industry. Moreover, in order to validate this method, this paper presents an industrial case by taking an energy consumption forecast and fault diagnosis based on energy consumption data in a prototype of LED epoxy molding compound (EMC) production lines for example.

In this paper, the problem of hybrid flow shop scheduling with common due dates (HFSCDD) is studied, and the objectives are to minimize the total waiting time and the total earliness/tardiness issues that arise. This study was motivated by a real-life shop floor, with the predefined goal of meeting the requirements of the final product in many manufacturing industries. Where the final product is assembled from multiple components and, the assembly is only initiated when all components of the product are complete in number. These interrelated components have common due dates. In this study, we developed a mathematical model of HFSCDD which made up of “n” jobs that were processed in “m” machines, located on “I” stages by taking into consideration the common due dates. This problem is classified as being NP-hard, and so an efficient modified genetic algorithm is developed to solve it. The proposed modify GA is developed based on the NSGA II method for large sized problems. The results of the proposed algorithm have been compared with PSO and GA algorithms and showed that the proposed algorithm achieved better performance than existing solutions, since the waiting time and the earliness/tardiness are significantly reduced. This is facilitated by the simultaneous production of components for the same product.

The era of big data has brought new challenges to chemical enterprises. In order to maximize the benefits, enterprises are considering to implement intelligent service technology into traditional production systems to improve the level of intelligence in business. This paper proposes a service framework based on big data driven prediction, which includes information perception layer, information application layer and big data service layer. In this paper, the composition of big data service layer is described in detail, and a sales predicting method based on neural network is introduced. The salability of products is divided, and the qualitative economic production volume mechanism is finally given. Based on the framework, an intelligent service system for enterprises with the characteristics of mass production is implemented. Experimental results show that the big data service framework can support chemical enterprises to make decisions to reduce costs, and provides an effective method for Smart Product Service System (PSS).

Manufacturing enterprises are attempting to change from product suppliers into product-service providers through product-service integration during product lifecycle. In this context, logistics capability plays a significant role in transformation and upgrading, apart from design and manufacturing capability. To further improve logistics capability, logistics product service system (LPSS) is proposed based on product service system. This paper focuses on provision of idle logistics resource sharing for promoting LPSS. In order to share idle logistics resources, a model for logistics product service allocation is established firstly. After that, a Vickrey-Clarke-Groves-based payment rule is designed to complete transaction. Finally, a demonstrative case with the logistics product service provider and eight manufacturers is presented to illustrate the feasibility and applicability of the proposed method.

Radio frequency identification technology commonly known as RFID technology is now widely used because it can increase productivity, efficiency and convenience. RFID has also been used in the manufacturing industry like shop floors where production data can be collected from production lines and sent to the information center for further analysis. Big data analysis provides a good opportunity, which can help the management of the shop floors. This paper reports on a case study using RFID datasets from a manufacturing shop floor to achieve performance evaluation. The datasets are processed by machine learning algorithms in R language. Some findings and observations have been obtained, which can be used as a reference for the future production.

PurposeMultiparametric MRI (mpMRI) improves detection of clinically significant prostate cancer (csPCa), but the qualitative PI-RADS system and quantitative apparent diffusion coefficient (ADC) yield inconsistent results. An advanced Restrictrion Spectrum Imaging (RSI) model may yield a better quantitative marker for csPCa, the RSI restriction score (RSIrs). We evaluated RSIrs for patient-level detection of csPCa.Materials and MethodsRetrospective analysis of men who underwent mpMRI with RSI and prostate biopsy for suspected prostate cancer from 2017-2019. Maximum RSIrs within the prostate was assessed by area under the receiver operating characteristic curve (AUC) for discriminating csPCa (grade group ≥2) from benign or grade group 1 biopsies. Performance of RSIrs was compared to minimum ADC and PI-RADS v2-2.1via bootstrap confidence intervals and bootstrap difference (two-tailed α=0.05). We also tested whether the combination of PI-RADS and RSIrs (PI-RADS+RSIrs) was superior to PI-RADS, alone.Results151 patients met criteria for inclusion. AUC values for ADC, RSIrs, and PI-RADS were 0.50 [95% confidence interval: 0.41, 0.60], 0.76 [0.68, 0.84], and 0.78 [0.71, 0.85], respectively. RSIrs (p=0.0002) and PI-RADS (prs was comparable to that of PI-RADS (p=0.6). AUC for PI-RADS+RSIrs was 0.84 [0.77, 0.90], superior to PI-RADS or RSIrs, alone (p=0.008, p=0.009).ConclusionsRSIrs was superior to conventional ADC and comparable to (routine, clinical) PI-RADS for patient-level detection of csPCa. The combination of PI-RADS and RSIrs was superior to either alone. RSIrs is a promising quantitative marker worthy of prospective study in the setting of csPCa detection.DisclosuresMEH reports honoraria from Multimodal Imaging Services Corporation and research funding from General Electric Healthcare. AMD is a Founder of and holds equity in CorTechs Labs, Inc, and serves on its Scientific Advisory Board. He is a member of the Scientific Advisory Board of Human Longevity, Inc. and receives funding through research agreements with General Electric Healthcare. The terms of these arrangements have been reviewed and approved by the University of California San Diego in accordance with its conflict-of-interest policies. TMS reports honoraria from Multimodal Imaging Services Corporation, Varian Medical Systems, and WebMD; he has an equity interest in CorTechs Labs, Inc. and also serves on its Scientific Advisory Board. These companies might potentially benefit from the research results. The terms of this arrangement have been reviewed and approved by the University of California San Diego in accordance with its conflict-of-interest policies.

With a focus on psychometric assessment, the current study investigated the extent to which spatial orientation ability (SOA), as conceptualized in the spatial cognition and navigation literature, predicted air traffic conflict detection performance in a simulated free route airspace (FRA). Within a FRA, airspace users have the flexibility to plan flights by selecting preferred routes between predefined waypoints. Despite such benefits, FRA implementation can introduce conflicts that are geometrically complex, and of which would require a high level of SOA engagement. Based on a sample of 20 young adults who have the prospect to become air traffic controllers (ATCOs), we found that response time-based performance on a newly developed computerized spatial orientation test (SOT) predicted time to loss of minimum separation (tLMS)-based performance on a conflict detection task to a moderately large extent under scenarios with high air traffic density. We explained these findings in light of similar or overlapping mental processes that were most likely activated optimally under task conditions featuring approximately equal numbers of outcome-relevant stimuli. We also discussed the potential use of the new SOT in relation to the selection of prospective ATCOs who can demonstrate high levels of conflict detection performance in FRA during training simulations.

With the advantages of long-distance contactless identification and data storage capacity, the use of radio frequency identification (RFID) technology in the fields of manufacturing, transportation and logistics has been widely reported. Fatigue of workers plays a critical role in impacting the manufacturing efficiency because it reduces productivity and increases accident rates. Therefore, the workers’ fatigue must be well examined and addressed. This paper thus proposes an analytic approach to use RFID captured production data and builds an effective method for mining the structural insight to predict the fatigue trajectory in workplace from a huge number of RFID data which may be full of inaccurate, incomplete and missing records. In this research, realistic processing time is used to measure the workers’ fatigue. Based on a general framework for the fatigue examination, the proposed approach is able to estimate the employees’ fatigue trajectory within designated period of time using RFID-enabled production data. Different genders and shifts are considered to find the key impact factors on fatigue.

This research is motivated by a real-life case which runs the public transportation services in Hong Kong, China. The mass transit railway (MTR) is contemplating the maintenance by considering the manpower planning in its depot. The main purpose of this research is to improve the efficiency of assembly with adjustable manpower planning based on an integer programming optimization model based on specify assumptions is presented. The strength of this model simplifies practical problems by considering several scenarios in the MTR assembly operations. The numerical study shows that the model of this paper can be effectively used to improve the utilization and quality of the practical applications.

Background: Through great efforts, the outbreak of 2019 novel corona virus disease (COVID-19) has been slowing down in Wuhan This study was to assess the potential errors of established admission procedures from a tertiary cancer center Methods: A multidisciplinary team of eight frontline nurses and oncologists would conduct a failure mode and effects analysis (FMEA) to our established procedures The FMEA consisted of 4 main steps, Including a detailed review of the on-going admission processes and the drawing of the corresponding flow chart, followed by repeated discussions of the possible errors among those processes, and then evaluation of the occurrence (O), detectability (D), and severity of impact (S) of each failure mode according to a scoring criteria (a five-point scale) Finally, the risk of errors were determined through a calculation of risk priority number (RPN=O*D*S) Results: From March 24, 2020 to May 14, 2020, based on the established procedures, our center has screened 1,214 cancer patients in the oncology outpatient department and subsequent buffer wards No nosocomial infection (among doctors or patients, or between patients and doctors) occurred On the scale of RPN from high to low, ten high-risk steps were identified by FMEA, involving a failure of scheduled screening for particularly vulnerable populations, the failure of hand hygiene in outpatient and buffer wards, and the incorrect disposal of clinical waste by cleaning service staff In addition, the psychological burden to cancer patients might increase the risk of buffer ward management failure Conclusions: Self-review and continuous improvement for established procedures can minimized underlying mistakes Increasing the approaches to treatment appointments, reasonably optimizing the working during for outpatient physicians, strengthening the awareness of hand hygiene (both physicians and patients), and setting up oncological psychological counseling groups will likely improve the potential error steps Legal entity responsible for the study: The authors Funding: Has not received any funding Disclosure: All authors have declared no conflicts of interest

Radio frequency identification (RFID) technology has been widely used in various industries due to its object identification and monitoring capability. In recent years, low-frequency (LF) RFID has been increasingly used in hydraulic studies for sediment transport studies and the detection of underground utilities. Nevertheless, prior studies have indicated that the use of LF RFID technology is limited due to its short detection range and high sensitivity of orientation relative to the antenna place. Ultra-high frequency (UHF) RFID (865-928 MHz) technology has much longer reading ranges compared to the LF RFID. However, up to date, the application of UHF RFID technology in aquatic environments is still rare because of its high sensitivity to water. This study aims to investigate the reading range behaviors of passive UHF RFID tags for surface applications in aquatic environments. This research used circular and linear polarization antennae, which have a gain of 8 dBic, 9 dBic, and 12.5 dBi, respectively, to examine six pre-selected UHF RFID tags behaviors. The results of this study showed that a decrease in the performance of maximum reading distances when the tags are placed in a vertical orientation at the center of the antenna compared with the horizontal orientation. The maximum reading ranges varied between 1 to 35% when using the 8 dBic antenna, between 0% to 48% with the 9 dBic antenna, and between 14 to 82% with the 12.5 dBi antenna. Based on key findings and observations from this study, we demonstrated design requirements of tags to overcome sensitivity limitations to design for future surface-water velocity monitoring applications in the natural open water and built water transport environments.

Aiming to improve the adaptability of fabric defect detection, this paper proposes an “edge-cloud” collaborative fabric defect detection architecture that contains edge layer, platform layer, and application layer. In the edge layer, the fabric defect detection machine is able to realize the collection and detection of fabric images data. In the platform layer, the cloud platform that integrates memory computing, parallel storage, and a relational library is designed to realize the efficient storage and analysis of fabric data. In the application layer, a deep learning fabric defect detection algorithm is designed to recognize the defect patterns. The interaction between the cloud platform and the detection device is designed to adaptively adjust the detection algorithm. The closed-loop optimization is achieved by implementing “edge-cloud” architecture that the fabric pictures are captured and analyzed for fast detection algorithm in edge devices. The captured data is stored and monitored by the cloud platform. The cloud platform adjusts the edge detection algorithm by transfer learning, which can adapt to the changing environment. A case study illustrates that the proposed edge-cloud collaborative fabric defect detection can achieve better dynamic adaptability.

This research is motivated by the industrial applications of Cyber-physical System (CPS) such as smart warehouse and intelligent manufacturing which require the support of AGV (Automated Guided Vehicle). One of the key research is locating and navigating approach that should be with much flexibility for dealing with complex industrial applications. This paper takes a passive radio frequency identification (RFID) tag-based locating and navigating approach for AGV to examine the influences of tags, antennas, and environmental aspects. The approach was validated and a prototype system was built up. Several key observations are significant from the implementation and simulation study. Firstly, it is observed that adding angle reflector could eliminate the back lobe and restrain backward reflection. Secondly, RFID antenna center and the tag center should be designed at the same height could reduce the impact area of reflection. Lessons and insights from this study will be significant for industrial practitioners to implement AGVs in smart warehouse and manufacturing management.

Aim To investigate the value of intravoxel incoherent motion (IVIM) in the differentiation of sinonasal small round cell malignant tumours (SRCMTs) from non-SRCMTs and to compare and correlate these results with those of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI). Materials and methods Ninety patients with histologically confirmed sinonasal malignant tumours (53 SRCMTs and 37 non-SRCMTs) who underwent conventional MRI, IVIM, and DCE-MRI before treatment were enrolled. The IVIM and DCE-MRI parameters were measured. Statistical analyses were performed using Student's t-tests, receiver operating characteristic (ROC) curve analyses, and Spearman's correlation coefficients. Results A lower pure diffusion coefficient (D) value and a higher pseudo-diffusion coefficient (D*) value were found in the sinonasal SRCMTs than in the non-SRCMTs (p Conclusions The D and D* values of IVIM and the Ve value of DCE-MRI are helpful in distinguishing sinonasal SRCMTs from non-SRCMTs, with the D values having the best diagnostic efficiency.

Purpose Additive manufacturing (AM) has been increasingly used in various applications in recent years. However, it is still challenge when it comes to selecting a suitable AM process. This is because the outcome may vary due to not only different materials and printers but also different parameters and post-processes. This paper aims to develop an efficient method to help users understand trade-offs and make right decisions. Design/methodology/approach A hybrid method is proposed to help users select appropriate options from a large-scale and discrete option space in an interactive way. First, the design-by-shopping approach is applied to allow users exploring and refining the option space. The analytical hierarchical process method is then used to capture customers’ preferences. After analyzing the results of different normalization methods, a modified Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) approach is proposed to rank solutions and provide suggestions. Findings The usefulness of proposed method is illustrated in a case study. The results show that it can help customers understand performance distributions and find most suitable options accurately. The ranking of the modified TOPSIS method is more reasonable. Originality/value Due to the complexity of AM technologies, the process selection is considered at the parameter level. A new system framework is proposed for decision support. The TOPSIS method is modified to achieve a stable performance.

Manufacturing Execution System (MES) plays an important role in supporting small and medium-sized enterprises (SMEs) for their daily production operations. SMEs’ businesses traditionally rely on paper based system of operations. With the rapid increase in technology, it is almost guaranteed that this will be replaced by more efficient and reliable smart MES. This paper presents a RFID enabled real-time manufacturing system which will incorporate five functions. These five functions will be integrated into an android based interface which reads and writes data to a cloud based database in real time. NFC technology is also utilized to improve the ease of use. This aims to promote communication within a company, improve product quality, optimize data storage and reduce paper waste. A case study demonstrates the feasibility and practicality of the designed and developed system.