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Background: To assess the prophylactic effect of simultaneous placement of mesh and incidence of parastomal hernia (PSH) after abdominoperineal resection of rectal cancer. Methods: This study included real world data of 56 surgically resected colorectal cancer (CRC) patients who were consecutively assigned to 2 groups: control (no mesh, n=32) and experimental (received mesh, n=24). An artificial patch was placed under the tunica vaginalis of rectus abdominis for patients in experimental group whereas the control group received routine sigmoidostomy. The median follow-up time was >20 months. Difference in hazards function was analyzed by cox regression analysis and results were expressed as hazard ratio (HR) and 95% confidence interval (CI). A P-value < 0.05 was considered as significant. Results: Post-operative incidence rate of PSH was lower in experimental (41.7 %) than in control group (71.9 %; P=0.045). PSH postoperative time in experimental group was significantly delayed than control group (48 months vs 10 months; PConclusions: Prophylactic mesh placement significantly prolonged post-operative time to recurrence and incidence rate of PSH. The incidence of recurrence of H2 (severe PSH) requiring secondary surgical repair was reduced.

Accessibility of Electric Vehicle (EV) charging stations is an important factor for adoption of EV, which is an effective green technology for reducing carbon emissions. Recognizing this, many governments are contemplating ideas for achieving EV adoption targets, such as constructing extra EV charging stations directly or offering subsidies to entice automakers to construct more EV charging stations. To achieve these targets, governments need to coordinate with automakers to ensure that the total number of charging stations is planned optimally. We study this coordination problem by considering the interactions among the government, automakers, and consumers, our equilibrium analysis yields three major results. First, both the government and the automaker should build extra EV charging stations when their construction costs are independent. Simultaneously, the government should offer a per-station subsidy to the automaker only when the adoption target and the construction cost are both high. However, when the construction costs are dependent, the government should delegate the construction to the automaker by offering a per-station subsidy. Second, when the government considers consumer purchase subsidy as an extra lever, we find that the purchase subsidy for consumers is more cost-effective than offering a per-station subsidy to the automaker. Third, the structure of the optimal government policy remains the same regardless of whether the government's goal is to improve EV adoption or consumer welfare. Our results can serve as guidelines for governments when contemplating coordination with automakers for the construction of EV charging stations to improve EV adoption as well as consumer welfare further.

To discuss the application of Internet of Things (IoT) in the agriculture, an agricultural product price prediction model is constructed based on the improved Elman neural network (ENN) of deep learning. Simulation experiment of pest prediction is carried out based on MATLAB, and then the agricultural IoT information service system is combined with the improved ENN prediction model to predict and analyze agricultural product prices. The results show that the agricultural product price prediction model based on the improved ENN has high accuracy, which reaches 0.9241. Through this agricultural product price prediction model, the agricultural IoT information service system can predict the price trend of agricultural products better, and it can also predict the shortcomings in the production and sales of agricultural products, so that corresponding management and control measures can be realized. This work can provide an important reference for the application of deep learning methods in agricultural IoT.

Connected and automated vehicle (CAV) technology is providing urban transportation managers tremendous opportunities for better operation of urban mobility systems. However, there are significant challenges in real-time implementation as the computational time of the corresponding operations optimization model increases exponentially with increasing vehicle numbers. Following the companion paper (Chen et al. 2021), which proposes a novel automated traffic control scheme for isolated intersections, this study proposes a network-level, real-time traffic control framework for CAVs on grid networks. The proposed framework integrates a rhythmic control method with an online routing algorithm to realize collision-free control of all CAVs on a network and achieve superior performance in average vehicle delay, network traffic throughput, and computational scalability. Specifically, we construct a preset network rhythm that all CAVs can follow to move on the network and avoid collisions at all intersections. Based on the network rhythm, we then formulate online routing for the CAVs as a mixed integer linear program, which optimizes the entry times of CAVs at all entrances of the network and their time–space routings in real time. We provide a sufficient condition that the linear programming relaxation of the online routing model yields an optimal integer solution. Extensive numerical tests are conducted to show the performance of the proposed operations management framework under various scenarios. It is illustrated that the framework is capable of achieving negligible delays and increased network throughput. Furthermore, the computational time results are also promising. The CPU time for solving a collision-free control optimization problem with 2,000 vehicles is only 0.3 second on an ordinary personal computer.

Many countries and governments consider smart cities a solution to global warming, population growth, and resource depletion. Numerous challenges arise while creating a smart city. Digital twins, along with the Internet of Things, fifth-generation wireless systems, blockchain, collaborative computing, simulation, and artificial intelligence technologies, offer great potential in the transformation of the current urban governance paradigm toward smart cities. In this paper, the concept of a digital twin city (DTC) is proposed. The characteristics, key technologies, and application scenarios of a DTC are elaborated upon. Further, we discuss the theories, research directions, and framework regarding DTCs.

Data center networks provide the physical infrastructure that hosts Internet‐related services and cloud computing. Designing data center networks properly is imperative for Internet‐related service and cloud computing providers to gain competitive edges through cost efficiency and service quality. In this study, we formulate a mathematical programming model to address the data center network design problem, in which the objective is to minimize total operating cost and the service delay penalty by optimizing data center location, footprint allocation, and resource provisioning decisions, while incorporating essential features, such as latency, power, multiple resources, configuration limits, and interdependent footprints. We employ a queueing model to approximate the service latency and provide tractable reformulations. To enhance computational efficiency for large‐scale problems, we further develop Lagrangian relaxation methods and generate strengthening cuts by exploiting the structural properties of the problem. Our numerical studies demonstrate that the proposed model, which jointly optimizes location, allocation, and resource provisioning, can achieve significant cost reductions and improvements in service quality compared with a hierarchical approach that optimizes these decisions sequentially. Moreover, our proposed solution methods outperform state‐of‐the‐art commercial software in terms of computational efficiency. Based on real‐world datasets, the proposed model selects data centers that have been chosen by major cloud computing infrastructure providers. We also draw managerial insights that can be used as design guidelines in practice.

In recent years, the frequency, intensity, and severity of wildfires have been on the rise due to various environmental factors. Several studies show that the strategic application of fuel treatments is effective at altering fire behavior and its spread patterns. Effective planning for mitigating future expected losses under wildfire risk is a complex challenge that requires the integration of fire spread, simulation, and optimization models as well as the inclusion of multiple objectives into a unified framework. Previous works simplify the analysis by valuing the landscape regions using a unique objective (e.g., minimize the average expected area burned) or a predefined objective function. However, such an assumption is a simplification of the real system as multiple parts of the landscape have different values based on factors such as the presence of human settlements and infrastructure, availability of environmental services, and forest health. In this work, we expand these previous attempts by providing an integrated framework to naturally include and weight multiple objectives into the optimization model and analyze the trade-off between present objectives and future protection against wildfire risk. We study three key regions based on their recent fire history, landscape diversity, and demographic variety to quantify the impact of multiple objectives in landscape management. We obtain treatment plans using various combinations of these layers reflecting how different priorities of the decision-makers could affect treatment policies.

This study was performed to propose a method, the Feature Ambiguity Mitigate Operator (FAMO) model, to mitigate feature ambiguity in bone fracture detection on radiographs of various body parts. A total of 9040 radiographic studies were extracted. These images were classified into several body part types including 1651 hand, 1302 wrist, 406 elbow, 696 shoulder, 1580 pelvic, 948 knee, 1180 ankle, and 1277 foot images. Instance segmentation was annotated by radiologists. The ResNext-101+FPN was employed as the baseline network structure and the FAMO model for processing. The proposed FAMO model and other ablative models were tested on a test set of 20% total radiographs in a balanced body part distribution. To the per-fracture extent, an AP (average precision) analysis was performed. For per-image and per-case, the sensitivity, specificity, and AUC (area under the receiver operating characteristic curve) were analyzed. At the per-fracture level, the controlled experiment set the baseline AP to 76.8% (95% CI: 76.1%, 77.4%), and the major experiment using FAMO as a preprocessor improved the AP to 77.4% (95% CI: 76.6%, 78.2%). At the per-image level, the sensitivity, specificity, and AUC were 61.9% (95% CI: 58.7%, 65.0%), 91.5% (95% CI: 89.5%, 93.3%), and 74.9% (95% CI: 74.1%, 75.7%), respectively, for the controlled experiment, and 64.5% (95% CI: 61.3%, 67.5%), 92.9% (95% CI: 91.0%, 94.5%), and 77.5% (95% CI: 76.5%, 78.5%), respectively, for the experiment with FAMO. At the per-case level, the sensitivity, specificity, and AUC were 74.9% (95% CI: 70.6%, 78.7%), 91.7%% (95% CI: 88.8%, 93.9%), and 85.7% (95% CI: 84.8%, 86.5%), respectively, for the controlled experiment, and 77.5% (95% CI: 73.3%, 81.1%), 93.4% (95% CI: 90.7%, 95.4%), and 86.5% (95% CI: 85.6%, 87.4%), respectively, for the experiment with FAMO. In conclusion, in bone fracture detection, FAMO is an effective preprocessor to enhance model performance by mitigating feature ambiguity in the network.

The aim of this study is to investigate the clinical features and outcome of interstitial lung disease (ILD)-onset rheumatoid arthritis (RA) and anti-citrullinated protein antibody (ACPA)-positive ILD-only patients. Arthritis-onset and ILD-onset RA-ILD and ACPA-positive ILD-only patients consecutively admitted to Peking Union Medical College Hospital from January 2008 to December 2017 were enrolled and followed-up. Their demographic, clinical, and laboratory features as well as outcome were collected and analyzed. Compared with arthritis-onset RA-ILD (n = 166, median arthritis-to-ILD interval: 60 months), the ILD-onset RA-ILD (n = 75, median ILD-to-arthritis interval: 2 months) had less rheumatoid nodules and higher titer of ACPA, and manifested more stable ILD (median estimated progression-free survival: 120 vs. 100 months, p = 0.019). Elder age (≥ 65 years) at ILD diagnosis and UIP pattern were associated with ILD progression by both univariate and Cox hazards modeling analysis (p

Background Wound infections can lead to high risks of postoperative septic complications. To date, the efficacy of a closed suction irrigation method of wound closure on the rate of postoperative wound infection in patients is poorly understood. Therefore, comparing this new strategy with conventional primary wound closure on the incidence of postoperative wound infection can provide value for clinical treatment. Methods Our study focused on retrospective research for patients with open abdominal wounds. All patients were referred by tertiary hospitals and registered in our hospital from January 2009 to July 2019. According to wound types, patients were divided into two groups, namely patients with wounds primarily closed as the control group and patients with wounds sutured by closed suction irrigation after primary closure as the study group. Results A total of 159 patients were collected in our present research, with 86 as study subjects and 73 as control subjects. The wound infection rate was significantly lower in the study group than the control group (P=0.017). The time interval for diagnosing wound infection from the primary operation was 4.895±0.875 days for the study group and 4.509±0.697 days for the control group. The length of hospital stay and the time taken for the wound to heal completely was shorter in patients in the study group compared to patients in the control group (8.046±2.110 vs. 8.983±2.887 days and 8.092±3.258 vs. 10.328±6.074 days, respectively). Conclusions The closed suction irrigation method significantly reduced postoperative wound infection rates in patients with a high risk of postoperative septic complications. The discoveries of our investigation should be further validated in large scope randomized controlled trials.

A robotic mobile fulfillment system (RMFS) performs the order fulfillment process by bringing inventory to workers at pick-pack-and-ship warehouses. In the RMFS, robots lift and carry shelving units, called inventory pods, from storage locations to picking stations where workers pick items off the pods and put them into shipping cartons. The robots then return the pods to the storage area and transport other pods. In this article, we consider an item assignment problem in the RMFS in order to maximize the sum of similarity values of items in each pod. We especially focus on a reoptimization heuristic to address the situation where the similarity values are altered so that a good assignment solution can be obtained quickly with the changed similarity values. A constructive heuristic algorithm for the item assignment problem is developed, and then, a reoptimization heuristic is proposed based on the constructive heuristic algorithm. Then, computational results for several instances of the problem with 10–500 items are presented. We further analyze the case for which an item type can be placed into two pods. Note to Practitioners —This article proposes an efficient heuristic algorithm for assigning items to pods in a robotic mobile fulfillment system (RMFS) so that items ordered together frequently are put into the same pod. Computational results with 10–500 items show that the gaps from upper bounds are very small on average. For cases where the similarity values between items change or their estimation is not accurate due to the fluctuations in demand, a reoptimization heuristic algorithm that alters the original assignment is developed. The experimental results show that the reoptimization algorithm is robust when perturbation levels are approximately 40%–50% of the original similarity values with much less computation times. We believe that this research work can be very helpful for operating the RMFS efficiently.

We study the energy consumption minimization problems of natural gas transmission in gunbarrel structured networks. In particular, we consider the transient-state dynamics of natural gas and the compressor’s nonlinear working domain and min-up-and-down constraints. We formulate the problem as a two-level dynamic program (DP), where the upper-level DP problem models each compressor station as a decision stage and each station’s optimization problem is further formulated as a lower-level DP by setting each time period as a stage. The upper-level DP faces the curse of high dimensionality. We propose an approximate dynamic programming (ADP) approach for the upper-level DP using appropriate basis functions and an exact approach for the lower-level DP by exploiting the structure of the problem. We validate the superior performance of the proposed ADP approach on both synthetic and real networks compared with the benchmark simulated annealing (SA) heuristic and the commonly used myopic policy and steady-state policy. On the synthetic networks (SNs), the ADP reduces the energy consumption by 5.8%–6.7% from the SA and 12% from the myopic policy. On the test gunbarrel network with 21 compressor stations and 28 pipes calibrated from China National Petroleum Corporation, the ADP saves 4.8%–5.1% (with an average of 5.0%) energy consumption compared with the SA and the currently deployed steady-state policy, which translates to cost savings of millions of dollars a year. Moreover, the proposed ADP algorithm requires 18.4%–61.0% less computation time than the SA. The advantages in both solution quality and computation time strongly support the proposed ADP algorithm in practice.

Liver resection for hepatocellular carcinoma (HCC) patients with portal vein tumor thrombus (PVTT) offers a chance of cure, although survival is often limited. The actual 3-year survival and its associated prognostic factors have not been reported. A nationwide database of HCC patients with PVTT who underwent liver resection with ‘curative’ intent was analyzed. The clinicopathologic characteristics, the perioperative, and survival outcomes for the actual long-term survivors were compared with the non-long-term survivors (patients who died within 3 years of surgery). Univariable and multivariable regression analyses were performed to identify predictive factors associated with long-term survival outcomes. The study included 1590 patients with an actuarial 3-year survival of 16.6%, while the actual 3-year survival rate was 11.7%. There were 171 patients who survived for at least 3 years after surgery and 1290 who died within 3 years of surgery. Multivariable regression analysis revealed that total bilirubin > 17.1 μmol/l, AFP > 400 ng/ml, types of hepatectomy, extent of PVTT, intraoperative blood loss > 400 ml, tumor diameter > 5 cm, tumor encapsulation, R0 resection, liver cirrhosis, adjuvant TACE, postoperative early recurrence (

Innovation is critical to a firm’s success in the current world. Several key features play important roles in the innovation process, including the innovation level, innovation uncertainty and new product press timing. This paper develops a framework where a high-tech firm sequentially introduces new products with incremental innovation over two selling seasons in the presence of strategic consumers and uncertain demand, to address the firm’s optimal stocking and pricing decisions, and investigate how the key innovation features impact the firm’s operational strategy and profit. By characterizing the subgame perfect Nash equilibrium with rational expectations, we find: (1) Strategic consumers can increase the firm’s profit when demand variance is high, for the successive innovation allows the firm to take advantage of consumers’ deferred purchase to better match supply and demand. (2) The firm’s total expected profit always decreases with respect to the new product press time under certain mild condition, but can be non-monotonic with respect to the RD high innovation level can play a role as price commitment when it exceeds one threshold.

Electric vehicles (EVs) are widely considered to be a solution to the problems of increasing carbon emissions and dependence on fossil fuels. However, the adoption of EVs remains sluggish due to range anxiety, long charging times, and inconvenient and insufficient charging infrastructure. Various problems with EV service operations should be addressed to overcome these challenges. This study reviews the state-of-the-art mathematical modeling-based literature on EV operations management. The literature is classified according to recurring themes, such as EV charging infrastructure planning, EV charging operations, and public policy and business models. In each theme, typical optimization models and algorithms proposed in previous studies are surveyed. The review concludes with a discussion of several possible questions for future research on EV service operations management.

With the applications of electric buses (e-buses), potential solutions to problems related to infrastructures for charging e-buses are emerging. This study particularly focused on large-scale fast charging-station planning for e-buses in the public transportation electrification process, according to the characteristics of e-bus operation and plug-in fast charging mode. We conducted an interdisciplinary study to optimize planning jointly under the transportation system and power grid. In addition to capturing the spatiality of the e-bus charging service network, we further considered temporality in order to conduct long-term planning in view of the continuously growing e-bus charging demand. A spatial-temporal model, which determines the sites and sizes of e-bus charging stations, was proposed and the strategies for multistage infrastructure planning were put forward. The model was equivalently transformed into a mixed-integer second-order cone programming with high computational efficiency. The model and the multistage planning strategies were justified through a series of numerical experiments. A case study of Shenzhen, China was implemented and the robustness of the model to plan changes was studied.

Population screening played a substantial role in safely reopening the economy and avoiding new outbreaks of COVID-19. PCR-based pooled screening makes it possible to test the population with limited resources by pooling multiple individual samples. Our study compared different population-wide screening methods as transmission-mitigating interventions, including pooled PCR, individual PCR, and antigen screening. Incorporating testing-isolation process and individual-level viral load trajectories into an epidemic model, we further studied the impacts of testing-isolation on test sensitivities. Results show that the testing-isolation process could maintain a stable test sensitivity during the outbreak by removing most infected individuals, especially during the epidemic decline. Moreover, we compared the efficiency, accuracy, and cost of different screening methods during the pandemic. Our results show that PCR-based pooled screening is cost-effective in reversing the pandemic at low prevalence. When the prevalence is high, PCR-based pooled screening may not stop the outbreak. In contrast, antigen screening with sufficient frequency could reverse the epidemic, despite the high cost and the large numbers of false positives in the screening process.

Idiopathic pulmonary fibrosis (IPF), the prototype of interstitial lung diseases, has the worst prognosis and is the only interstitial lung disease for which approved pharmacological treatments are available. Despite being considered a rare disease, IPF patients pose major challenges to both physicians and healthcare systems. It is estimated that a large number of IPF patients reside in BRIC countries (Brazil, Russia, India, and China) given their overall total population of approximately 3 billion inhabitants. Nevertheless, the limited availability of chest imaging in BRIC countries is considered a chief obstacle to diagnosis, since high-resolution computed tomography of the chest is the key diagnostic test for IPF. Further, obtaining reliable lung function tests and providing treatment access is difficult in the more rural areas of these countries. However, IPF might represent an opportunity for BRIC countries: the exponentially increasing demand for the enrollment of IPF patients in clinical trials of new drugs is predicted to face a shortage of patients - BRIC countries may thus play a crucial role in advancing towards a cure for IPF.

BackgroundOccurrence of portal vein tumor thrombus (PVTT) worsens the outcomes of hepatocellular carcinoma (HCC) and imparts high economic burden on society. Patients with high risks of having hypercoagulation are more likely to experience thrombosis. Herein, we examined how preoperative international normalized ratio (INR) was related to the incidence and extent of PVTT, and associated with survival outcomes in HCC patients following R0 liver resection (LR).MethodsPatients with HCC and PVTT were enrolled from six major hospitals in China. The overall survival (OS) and recurrence-free survival (RFS) rates of individuals with different INR levels were assessed with Cox regression analysis as well as Kaplan-Meier method.ResultsThis study included 2207 HCC patients, among whom 1005 patients had concurrent PVTT. HCC patients in the Low INR group had a significantly higher incidence of PVTT and more extensive PVTT than the Normal and High INR groups (PConclusionPreoperative INR influenced the incidence and extent of PVTT in HCC. Particularly, patients with HCC and PVTT in the Low INR group had worse postoperative prognosis relative to the High and Normal INR groups.

Electrifying transportation in the form of the large-scale development of electric vehicles (EVs) plays a pivotal role in reducing urban atmospheric pollution and alleviating fossil fuel dependence. However, the rising scale of EV deployment is exposing problems that were previously hidden in small-scale EV applications, and the lack of large-scale EV operating data deters relevant explorations. Here, we report several issues related to the battery utilization and energy consumption of urban-scale EVs by connecting three unique datasets of real-world operating states of over 3 million Chinese EVs, operational data, and vehicle feature data. Meanwhile, by incorporating climatic data and EV data outside China, we extend our models to several metropolitan areas worldwide. We find that blindly increasing the battery energy of urban EVs could be detrimental to sustainable development. The impact of changes in the energy consumption of EVs would be exacerbated in large-scale EV utilization, especially during seasonal shifts. For instance, even with a constant monthly driving demand, the average energy consumption of Beijing light-duty EVs would change by up to 21% during winter-spring shifts. Our results may also prove useful for research on battery resources, urban power supply, environmental impacts, and policymaking.

In this paper, we develop an iterative piecewise linear approximation approach with a novel initialization method to solve natural gas pipeline transmission problems with the nonuniform network elevation. Previous approaches, such as energy minimization methods, cannot be applied to solve problems with the nonuniform network elevation because they exclude pressure range constraints, and thus provide solutions far from optimum. We propose a new initialization model that considers pressure range constraints and improves the optimality of the solutions and the computational efficiency. Furthermore, we extend the energy minimization methods and provide the necessary conditions under which the extended methods operate in networks with the nonuniform elevation. We test the performances of the methods with previously reported pipeline networks from the literature, with the open data set GasLib, and with our industrial collaborator. The initialization approach is shown to be more efficient than the method with fixed initial breakpoints. The newly proposed initialization approach generates solutions with a higher accuracy than the extended energy minimization methods, especially in large‐size networks. The proposed method has been applied to natural gas transmission planning by the China National Petroleum Corporation and has brought a direct profit increase of 330 million U.S. dollars in 2015‐2017.

Background The identification of V-raf murine sarcoma viral oncogene homolog B1 (BRAF)V600E mutations has been recommended in patients with Langerhans cell histiocytosis (LCH) with difficult diagnosis and failure of first-line treatment. The reported frequencies of BRAFV600E mutations vary in Chinese patients with LCH. Methods We conducted a retrospective analysis of LCH patients with a definitive pathological diagnosis who were hospitalized between 2013 and 2017. The BRAFV600E mutations were detected with the human BRAFV600E amplification refractory mutation system-PCR (ARMS-PCR) kit from the collected tissue samples. Results This study consisted of 46 male (68.7%) and 21 female (31.3%) patients, with a mean age of 29.1 years (range, 2-76 years). Most were adults (45/67.2%) with the multisysytem-LCH (MS-LCH) disease subtype (49/61.3%). The overall frequency of BRAFV600E mutations was 22.4% (15 of 67 patients), confirmed by PCR analysis. These mutations were not closely correlated with age (nonadults vs. adults = 5/22.7% vs. 10/22.2%, P = 0.54), gender (female vs. male = 9/19.6% vs. 6/28.6%, P = 0.61), LCH classification type (single system: MS-risk organ+ : MS-risk organ- = 3/16.7%: 12:28.6%: 0, P = 0.19) or prognosis (cured: improved/stable: exacerbated: died = 4/44.4%: 19.2%: 20%: 0, P = 0.37). There were 33 patients (49.2%) with lung involvement, and 12 patients (36.3%) underwent lung biopsies; after screening, four patients were diagnosed with solitary pulmonary LCH, all of whom were negative for BRAFV600E mutations. Conclusion The BRAFV600E mutation rate in patients with LCH was lower than those reported in other studies. In addition, BRAFV600E mutations might not be correlated with age, gender, LCH classification type or prognosis for Chinese cases.

When product recalls happen, firms not only have to incur additional logistics costs but also suffer from a damaged reputation. Besides, they may also supply disruption risk, under which a supplier could fail to deliver the quantity ordered at all. In this study, we discuss how to mitigate supply disruption and recall risks in the perspective of (outbound) product distribution strategy in joint with (inbound) sourcing decision. Specifically, we compare the dedicated and uniform distribution strategies as follows: in the dedicated strategy, the firm divides its market into several regions, and ships products from different suppliers based on the regions; whereas, in the uniform strategy, the firm serves the entire market by randomly shipping the products from several suppliers without distinguishing their sources. Through the comparison of both strategies, we show that the role of recall risk is different from that of supply disruption on the sourcing and distribution decision. In particular, single sourcing is optimal when both recall risk and disruption probability are low; dual sourcing with the dedicated strategy is optimal when the disruption probability is moderate and the recall risk is high; dual sourcing with the uniform strategy is optimal when the disruption probability is high. We also find that the positive correlation in recalls improves (undermines) the performance of the uniform (dedicated) strategy. In contrast, the positive correlation in supply disruptions only undermines the performance of the uniform strategy.

The effects of nicotine replacement therapy (NRT)-aided smoking cessation on vascular function are not fully clarified. We investigated 100 healthy smokers who were motivated to quit and received NRT for a 3-month period. Vascular endothelial function (measured by reactive hyperemia-peripheral arterial tonometry [RH-PAT]), arterial stiffness (measured by augmentation index [AI] and brachial-ankle pulse wave velocity [baPWV]), and systemic inflammation markers (including serum soluble intercellular adhesion molecule-1 [sICAM-1] and interleukin-1β [IL-1β]) were assessed at baseline and 3 and 12 months of follow-up. After 3 months of intervention, endothelial function, arterial stiffness, and inflammatory markers significantly improved (RH-PAT increased, AI and baPWV decreased, sICAM-1 and IL-1β decreased, all P < .05) for the participants who abstained from smoking completely, but for those who did not abstained completely, RH-PAT, AI, baPWV, and IL-1β remained unchanged. At 12 months follow-up, endothelial function (RH-PAT), arterial stiffness (AI and baPWV), and inflammatory markers (sICAM-1 and IL-1β) were further improved in participants who abstained from smoking ( P < .001), while the above parameters deteriorated in continued smokers ( P < .05). In conclusion, vascular dysfunction can be reversible after NRT-aided smoking cessation in healthy smokers and vascular function could be further damaged if they continue smoking.

Retail business has been rapidly evolving in the past decades with the boom of internet, mobile technologies and most importantly e-commerce. Supply chain management, as a core part of retail business, has also gone through significant changes with new business scenarios and more advanced technologies in both algorithm design and computation power. In this review, we focus on several core components of supply chain management, i.e. vendor management, demand forecasting, inventory management and order fulfillment. We will discuss the key innovations from both academia and industry and highlight the current trend and future challenges.

Background: Patients with Cushing's disease (CD) with hypercortisolism have an increased risk of opportunistic infection. However, most CD patients exposed to infections are diagnostic latency, leading to a poor prognosis. Methods: Six patients in our hospital and an additional six patients in the literature were included in this study. Clinical information of CD patients with pulmonary Cryptococcus neoformans are reviewed. Results: The average baseline total cortisol and ACTH in serum at 8 am of all the patients was 44.85 μg/dL (normal range 4.0–22.3 μg/dL) and 200.3 pg/mL (normal range 0–46 pg/mL), respectively. Lymphopenia was found in 2 out of 6 patients in our hospital. The pulmonary radiologic findings included nodules (4/12), masses with or without a cavity (5/12), infiltration (5/12), and consolidation (4/12). The diagnosis of C. neoformans was established by lung pathology results (7/12), microorganism culture (3/12), and serum cryptococcal polysaccharide antigen (4/12). Lung lobectomy was performed in two patients who had a nodule in one lung lobe. Antifungal drugs were administered, including amphotericin-B (7/12), fluconazole (4/12), flucytosine (2/12) and liposomal amphotericin (1/12). Additional therapies for CD included trans-sphenoidal pituitary adenoma surgery (9/12), adrenalectomy (1/12) and ketoconazole (2/12). Seven patients survived, and five patients died. Conclusion: Pulmonary C. neoformans is an uncommon but fatal opportunistic infection in CD patients. Pulmonary nodules or masses should be aggressively investigated to exclude the C. neoformans among CD patients. The infiltration lesions in chest CT scan and lymphopenia are associated with poor prognosis. Keywords: Cushing's disease, Lung, Cryptococcus neoformans

China National Petroleum Corporation collaborated with researchers from the University of California and Tsinghua University to develop software for planning its natural gas pipelines. The work resulted in increased profits of $530 million between 2015 and 2017 and a budget reduction of more than $20 billion in construction costs for the subsequent five years.

Objective The present study, for the first time, was designed to examine the antifibrosis effects of Shenge Yangfei Capsules (SGYF) on bleomycin-induced pulmonary injury and fibrosis in rats. Methods Animals were divided into six groups randomly: saline control group, model group (bleomycin 5 mg/kg), and positive control group (Dexamethasone 5 mg/kg), and SGYF (70, 420, and 850 mg/kg/d, ig) group. Effects of SGYF on body weight and lung index were evaluated. For histological evaluation of lungs injury and fibrosis, lung specimens were stained with hematoxylin and eosin (HE) and Masson trichrome. Body weight and lung index from various groups were measured, and the concentrations of monoamine (MAO) and transforming growth factor-β1(TGF-β1) were also detected in lung homogenates. Results SGYF attenuated the bleomycin-induced weight loss and the lung index. Histologica levidence showed the ability of SGYF to decrease bleomycin-induced lung fibrosis and consolidation. SGYF reduced MAO and TGF-β1 activity in lung tissues. Conclusions These findings suggest that SGYF may be a promising candidate to prevent bleomycin-induced lung damage or other interstitial lesions.

We herein investigate the single item, multi-period inventory management problem with fixed ordering cost, holding cost, and lost sales cost. Here, both the demand and vendor lead time (VLT) are uncertain; Additionally, the contextual information that may be useful for predicting the distribution of the demand and VLT is available. We assume that the retailer has access to rich history observations and wishes to decide: (1) when to place an order and (2) how much to order. We solve this problem via an end-to-end (E2E) learning approach, where we develop a deep learning framework that directly outputs the optimal order timing and quantity with the given contextual information as the input. Our numerical experiments, which use the real-world data obtained from an online retailer, demonstrate the advantages of our approach over benchmark methods, such as the (r, Q) policy, predict-then-optimize (PTO) framework, and the E2E method with given order timing. Furthermore, we analyze the convergence of our E2E approach and provide upper bounds for the expected daily average cost of our E2E approach under certain conditions. To compare our approach with the optimal policy, we also provide lower bounds for the cost of the optimal policy under certain assumptions.

Objective To study the changes of white matter tracts in precentral gyrus and postcentral gyrus during normal brain aging process by analyzing fractional anisotropy (FA) values obtained from diffusion tensor imaging (DTI) technology. Methods Magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) were conducted on 120 healthy right‐handed subjects. The subjects were separated into four age groups, namely Young Male/Female (45 years old). Each subject undertakes routine MRI and DTI scans. Left/right precentral and left/right postcentral gyrus are automatically detected on the image. The area for region of interest (ROI) is set to be 18 ± 2 mm2. Results For each age group, the FA values of white matter in precentral gyrus and postcentral gyrus are statistically different (p, The FA values of precentral gyrus and postcentral gyrus are statistically different across genders, age groups, and hemispheres. Additionally, the FA values of both precentral gyrus and postcentral gyrus decrease over time, which is a strong indication of aging.

Problem definition: In this paper, we study a reliable hub location model, the objective of which is to minimize the associated costs plus the penalty for unserved demands. The model assigns to eac...

Background: Recently, some studies have revealed that microRNAs (miRs; miRNAs) mainly regulate gene expression in the transcription of microRNAs. However, the role of microRNAs in cell development, differentiation, proliferation, and other physiological processes remains unclear. Objectives: The present study aimed to investigate the expression level of microRNA-195 in the pathological tissues of patients with cardiac carcinoma and its clinical effects. Methods: Patients with primary cardiac carcinoma were enrolled as the study subjects. The tumor and adjacent tissue samples for cancer pathology were obtained during the operation. Reverse transcription polymerase chain reaction (RT-PCR) was used to detect the expression of microRNA-195 and its correlation with the clinicopathological features of cardiac carcinoma was analyzed. Results: A total of 64 patients were included in this study. Firstly, the expression rate of miR-195 in cardiac carcinoma tissues showed a significant decreasing trend (3.65 ± 0.42 vs. 2.05 ± 0.33) (P < 0.001) and the actual expression level of miR-195 in pathological tissues of cardiac carcinoma was negatively correlated with the malignant degree of pathological tissues (P = 0.028), invasion (P = 0.037), and lymph node metastasis of cardiac carcinoma (P = 0.023), but miR-195 was not correlated with age (P = 0.615) and gender (P = 0.465). Conclusions: The expression of microRNA-195 is closely correlated to the severity of cardiac carcinoma and the occurrence of lymph node metastasis.

As digitization enables service platforms to access users' information, important questions arise about how digital service platforms should disseminate information to improve platform operations. We contribute to this understanding by examining how providing customer-related information at the beginning of a service encounter affects service capacity and enjoyment. We evaluate this strategy via a field experiment on a live-streaming platform that connects hundreds of millions of individual broadcasters and viewers. When viewers entered shows, we provided viewer-related information to broadcasters who were randomly assigned to the treatment condition (but not to control broadcasters). Our analysis, involving a subsample of 49,998 broadcasters, demonstrates that relative to control broadcasters, treatment broadcasters expanded service capacity by 12.62% by increasing both show frequency (3.31%) and show length (7.10%), thus earning 10.44% more based on our conservative estimate. Moreover, our intervention increased service enjoyment (measured by viewer watch time) by 4.51%. Two surveys and additional analyses provide evidence for two mechanisms: (1) information about viewers presented shortly after their entry enables personalized greetings and facilitates social interaction, which addresses both viewers' and broadcasters’ needs for affiliation and satisfies viewers' preference for personalized treatment; (2) broadcasters receiving viewer-related information have a more vivid depiction of viewers and thus feel that their service is more appreciated and meaningful, which addresses broadcasters’ needs for competence. We also rule out several alternative explanations. Our low-cost, information-based intervention has important implications for digital service platforms that have little control over service providers’ work schedules and service quality.

We investigate a data-driven multiperiod inventory replenishment problem with uncertain demand and vendor lead time (VLT) with accessibility to a large quantity of historical data. Different from the traditional two-step predict-then-optimize (PTO) solution framework, we propose a one-step end-to-end (E2E) framework that uses deep learning models to output the suggested replenishment amount directly from input features without any intermediate step. The E2E model is trained to capture the behavior of the optimal dynamic programming solution under historical observations without any prior assumptions on the distributions of the demand and the VLT. By conducting a series of thorough numerical experiments using real data from one of the leading e-commerce companies, we demonstrate the advantages of the proposed E2E model over conventional PTO frameworks. We also conduct a field experiment with JD.com, and the results show that our new algorithm reduces holding cost, stockout cost, total inventory cost, and turnover rate substantially compared with JD’s current practice. For the supply chain management industry, our E2E model shortens the decision process and provides an automatic inventory management solution with the possibility to generalize and scale. The concept of E2E, which uses the input information directly for the ultimate goal, can also be useful in practice for other supply chain management circumstances. This paper was accepted by Hamid Nazerzadeh, big data analytics. Funding: This research was supported by the National Key Research and Development Program of China [Grant 2018YFB1700600] and National Natural Science Foundation of China [Grants 71991462 and 91746210]. Supplemental Material: The online data are available at https://doi.org/10.1287/mnsc.2022.4564 .

We address a dynamic covering location problem of an unmanned aerial vehicle base station (UAV-BS), in which the location sequence of a single UAV-BS in a wireless communication network is determined to satisfy data demand arising from ground users. This problem is especially relevant in the context of smart grid and disaster relief. The vertical movement ability of the UAV-BS and nonconvex covering functions in wireless communication restrict utilizing classical planar covering location approaches. Therefore, we develop new formulations to this emerging problem for a finite time horizon to maximize the total coverage. In particular, we develop a mixed-integer nonlinear programming formulation that is nonconvex in nature and propose a Lagrangean decomposition algorithm (LDA) to solve this formulation. Because of the high complexity of the problem, the LDA is still unable to find good local solutions to large-scale problems. Therefore, we develop a continuum approximation (CA) model and show that CA would be a promising approach in terms of both computational time and solution accuracy. Our numerical study also shows that the CA model can be a remedy to build efficient initial solutions for exact solution algorithms. Summary of Contribution: This paper addresses a facet of mixed integer nonlinear programming formulations. Dynamic facility location problems (DFLPs) arise in a wide range of applications. However, classical DFLPs typically focus on the two-dimensional spaces. Emerging technologies in wireless communication and some other promising application areas, such as smart grids, have brought new location problems that cannot be solved with classical approaches. For practical reasons, many research attempts to solve this new problem, especially by researchers whose primary research area is not OR, have seemed far from analyzing the characteristics of the formulations. Rather, solution-oriented greedy heuristics have been proposed. This paper has two main objectives: (i) to close the gap between practical and theoretical sides of this new problem with the help of current knowledge that OR possesses to solve facility location problems and (ii) to support the findings with an exhaustive computational study to show how these findings can be applied to practice.

A flow picking system in which the existing picking list is updated in real time has been considered as an effective solution for e-commerce warehouses to increase order fulfillment efficiency. The pivotal issues of performance analysis of flow picking systems, and comparison between batch picking systems and flow picking systems are of great concern, both for academics and practitioners of warehouse operation management. In this study, we first develop analytic models to estimate the critical performance indicators of a flow picking system, including picking density and turnover time of an order. Second, we leverage the proposed models and real warehouse data to compare the performance of batch picking and flow picking systems through simulation. Our results show that a flow picking system requires fewer order pickers and shorter walking distances than a batch picking system in most scenarios, especially those with a higher order arrival rate to achieve the same service level. Our study can provide valuable guidelines to warehouse managers and decision-makers for choosing an order fulfillment solution by comparing a batch picking system and a flow picking system.

With cloud robotics, particularly robotic vision available wi-thin a household, human are able to live a convenient and safer life in an ambient assisted living environment. Recent advances in computational intelligence including neural network improves the computational capability of the robotic vision to better understand the environment. Recently, internet hoaxes that affected the social community greatly have raised strong awareness among public in parental control and the content that the youngster can view. Therefore, this paper focuses on filtering movies or videos that is not suitable for youngster by attempting to identify movie genre. Movie genre classification has been investigated in recent years, but there exist noise in normal videos referred as generic frames, as mentioned in [1], that makes differentiation movies with similar frame difficult. A filtering approach is proposed in this paper in order to identify generic frames within the video and discard them from genre classification process, in order to improve genre classification performance. Experiment shows that the filtering approach are able to improve action genre class, but have difficulties and improving other genre classes.

Problem definition: Nonrenewable energy is typically recognized as ''dirty energy'' with respect to the environment such that most countries reduce their nonrenewable facilities and then improve their renewable energy adoption. In this paper, we study and develop effective hybrid energy generation and charging operations strategies with the aim of exploring and enhancing the environmental benefits of nonrenewable energy integrated electric vehicle (EV) charging service in a planning problem for photovoltaic (PV) charging stations. Academic/Practical relevance: Our research contributes to the literature on and practice of sustainable operations management by developing an integrated stochastic robust planning model that allows us to explore its implications in three areas: (1) the environmental benefits of nonrenewable energy synthesis, (2) integrated modeling with processor-sharing queuing to optimize the EV charging service coupled with renewable energy power scheduling, (3) the performance of proposed demand response (DR) policies that incorporate the features of intermittent energy, such as unstable solar resources. Methodology: We characterize the fleet charging dynamics by adopting a processor-sharing queuing network in a mixed integer programming (MIP) integrated planning model. We model the uncertain solar resources in a worst-case expectation weighted by geometric-like probabilities obtained by processor-sharing queuing modeling. We then develop its linear equivalent form to approximate a tractable optimization model. We also adopt a multinomial logit (MNL) model to estimate customers’ charing utilities and the incentive payment across time slots in our integrated DR policy. Results: In a real-world case study of CAR2GO in Amsterdam, we find that nonrenewable energy integration improves clean energy utilization and provides a reliable and cost-effective EV charging service under current PV charging practice. The results also demonstrate the advantages of the proposed DR policy in incorporating uncertain solar resources on the reliability, profitability and environmental benefits of the EV charging service. Managerial implications: In their efforts to improve renewable energy planning, policy-makers should be more prudent about giving up nonrenewable energy. Certain reasonable features - synthesizing DR operations coupled with efficient energy scheduling - can alleviate the adverse impacts of intermittency and uncertainties that plague renewable energy and, in turn, gradually improve the quality of EV charging services.

Background This study aims to investigate the application value of three-dimensional arterial spin labeling (3DASL) in investigating cerebral blood flow dynamics in full-term neonates. Methods A total of 60 full-term neonates without known intracranial pathology were recruited for 3DASL examination. These neonates were divided into three groups: 1–3 day group, 4–7 day group, and 8–15 day group. On the cerebral blood flow (CBF) images, regions of interest (ROI) were selected from the frontal white matter, parietal white matter, basal ganglia, corona radiata, thalamus and brainstem, and the CBF values of each ROI were recorded. The CBF values of ROIs at bilaterally symmetric locations, the values of each ROI between males and females, and the values of each ROI among these three different age groups were compared. Results The difference in CBF values of the frontal white matter, parietal white matter, basal ganglia, corona radiata and thalamus at the bilateral symmetric positions were not statistically significant. There was no statistical difference in the CBF values of each brain region between the male and female groups. The CBF values at the basal ganglia region, corona radiata and parietal white matter were higher in the 8–15 day group, when compared to the 1–3 day and 4–7 day groups (P P P P Conclusion The 3DASL can quantitatively measure CBF, and be used to evaluate cerebral hemodynamics in neonates. The basal ganglia region and corona radiata CBF increases with the increase in neonatal diurnal age.

We are entering an era of great expectations towards our cities. The vision of “smart city” has been pursued worldwide to transform urban habitats into superior efficiency, quality, and sustainability. This phenomenon prompts us to ponder what role the scholars in operations management (OM) can assume. In this essay, we express our initial thoughts on expanding OM to the smart‐city scope. We review smart‐city initiatives of governments, industry, national laboratories and academia. We argue that the smart‐city movement will transition from the tech‐oriented stage to the decision‐oriented stage. Hence, a smart city can be perceived as a system scope within which planning and operational decisions are orchestrated at the urban scale, reflective of multi‐dimensional needs, and adaptive to massive data and innovation. The benefits of studying smart‐city OM are manifold and significant: contributing to deeper understanding of smart cities by providing advanced analytical frameworks, pushing OM knowledge boundaries (such as data‐driven decision making), and empowering the OM community to deliver much broader impacts than before. We discuss several research opportunities to embody these thoughts, in the interconnected contexts of smart buildings, smart grid, smart mobility and new retail. These opportunities arise from the increasing integration of systems and business models at the urban scale.

There have been rapidly growing applications using machine learning models for predictive analytics in Electronic Health Records (EHR) to improve the quality of hospital services and the efficiency of healthcare resource utilization. A fundamental and crucial step in developing such models is to convert medical codes in EHR to feature vectors. These medical codes are used to represent diagnoses or procedures. Their vector representations have a tremendous impact on the performance of machine learning models. Recently, some researchers have utilized representation learning methods from Natural Language Processing (NLP) to learn vector representations of medical codes. However, most previous approaches are unsupervised, i.e. the generation of medical code vectors is independent from prediction tasks. Thus, the obtained feature vectors may be inappropriate for a specific prediction task. Moreover, unsupervised methods often require a lot of samples to obtain reliable results, but most practical problems have very limited patient samples. In this paper, we develop a new method called Prediction Task Guided Health Record Aggregation (PTGHRA), which aggregates health records guided by prediction tasks, to construct training corpus for various representation learning models. Compared with unsupervised approaches, representation learning models integrated with PTGHRA yield a significant improvement in predictive capability of generated medical code vectors, especially for limited training samples.

ObjectiveThere is little evidence that adjuvant therapy after radical surgical resection of hepatocellular carcinoma (HCC) improves recurrence-free survival (RFS) or overall survival (OS). We conducted a multicentre, randomised, controlled, phase IV trial evaluating the benefit of an aqueous extract of Trametes robinophila Murr (Huaier granule) to address this unmet need.Design and resultsA total of 1044 patients were randomised in 2:1 ratio to receive either Huaier or no further treatment (controls) for a maximum of 96 weeks. The primary endpoint was RFS. Secondary endpoints included OS and tumour extrahepatic recurrence rate (ERR). The Huaier (n=686) and control groups (n=316) had a mean RFS of 75.5 weeks and 68.5 weeks, respectively (HR 0.67; 95% CI 0.55 to 0.81). The difference in the RFS rate between Huaier and control groups was 62.39% and 49.05% (95% CI 6.74 to 19.94; p=0.0001); this led to an OS rate in the Huaier and control groups of 95.19% and 91.46%, respectively (95% CI 0.26 to 7.21; p=0.0207). The tumour ERR between Huaier and control groups was 8.60% and 13.61% (95% CI −12.59 to −2.50; p=0.0018), respectively.ConclusionsThis is the first nationwide multicentre study, involving 39 centres and 1044 patients, to prove the effectiveness of Huaier granule as adjuvant therapy for HCC after curative liver resection. It demonstrated a significant prolongation of RFS and reduced extrahepatic recurrence in Huaier group.Trial registrationNCT01770431; Post-results.

// Chao Xue 1, * , Ling Bian 1, * , Yu Shui Xie 1 , Zhao Fang Yin 1 , Zuo Jun Xu 1 , Qi Zhi Chen 1 , Hui Li Zhang 1 , Yu Qi Fan 1 , Run Du 2 and Chang Qian Wang 1 1 Department of Cardiology, Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, People’s Republic of China 2 Department of Cardiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, People’s Republic of China * These authors have contributed equally to this work Correspondence to: Chang Qian Wang, email: wangchangqianjy@163.com Run Du, email: durun198210@163.com Keywords: health-related quality of life, free triiodothyronine, drug-eluting stent, acute coronary syndrome Received: May 31, 2017 Accepted: September 20, 2017 Published: October 10, 2017 ABSTRACT Acute coronary syndrome (ACS) patients with low triiodothyronine (T3) syndrome characterized by low free T3 (fT3) levels with normal thyroxine (T4) and thyroid-stimulating hormone (TSH) have a higher rate of death. The impact of fT3 on Health related quality of life (HRQOL) in patients with ACS is still unknown. 528 ACS patients treated with drug-eluting stent (DES) were included in this prospective, observational study. Patients were classified into low fT3 group (n=126) and normal fT3 group (n=402) according to serum fT3 level. Every patient was prospectively interviewed at baseline and 1 year following percutaneous coronary intervention (PCI). HRQOL was assessed with the use of the Medical Outcomes Study 36-Item Short-Form Health Survey (SF-36). Low fT3 patients had poorer HRQOL than normal fT3 patients both at baseline and 1-year follow-up (all p

The destructive potential of wildfires has been exacerbated by climate change, causing their frequencies and intensities to continuously increase globally. Generating fire-resilient landscapes via efficient and calculated fuel-treatment plans is critical to protecting native forests, agricultural resources, biodiversity, and human communities. To tackle this challenge, we propose a framework that integrates fire spread, optimization, and simulation models. We introduce the concept of Downstream Protection Value (DPV), a flexible metric that assays and ranks the impact of treating a unit of the landscape, by modeling a forest as a network and the fire propagation as a tree graph. Using our open-source decision support system, custom performance metrics can be optimized to minimize wildfire losses, obtaining effective treatment plans. Experiments with real forests show that our model is able to consistently outperform alternative methods and accurately detect high-risk and potential ignition areas, focusing the treatment on the most critical zones. Results indicate that our methodology is able to decrease the expected area burned and fire propagation rate by more than half in comparison to alternative methods under ignition and weather uncertainty.

High variability interchange times often significantly affect the reliability of metro travels. Fine-grained measurements of interchange times during metro transfers can provide valuable insights on the crowdedness of stations, usage of station facilities and efficiency of metro lines. Measuring interchange times in metro systems is challenging since agent-operated systems like automatic fare collection systems only provide coarse-grained trip information and popular localization services like GPS are often inaccessible underground. In this paper, we propose a smartphone-based interchange time measuring method from the passengers’ perspective. It leverages low-power sensors embedded in modern smartphones to record ambient contextual features, and utilizes a two-tier classifier to infer interchange states during a metro trip, and further distinguishes 10 fine-grained cases during interchanges. Experimental results within 6 months across over 14 subway lines in 3 major cities demonstrate that our approach yields an overall interchange state inference F1-measurement of 91.0% and an average time error of less than 2 min at an inference interval of 20 s, and an average accuracy of 89.3% to distinguish the 10 fine-grained interchange cases. We also conducted a series of case studies using measurements collected from crowdsourced users during 3 months, which reveals findings previously unattainable without fine-grained interchange time measurements, such as portions of waiting time during interchange, interchange directions, usage of facilities (stairs/escalators/lifts), and the root causes of long interchange times.

Most studies of process flexibility design have focused on homogeneous networks, whereas production systems in practice usually differ in many aspects, such as plant efficiency and product profitab...

The non-axisymmetric cone has broad application prospects and is feasible to be manufactured via die-less shear spinning process which however lacks of research. With the roller path equation and other boundary conditions, the finite element model for non-axisymmetric die-less shear spinning process is established and simulated by adopting ABAQUS/explicit software in this study. The FEM results are experimentally verified by comparing the morphology and the HCAs. The distribution of stress and strain in different working conditions and the ellipticity and the elongation of the material in different spinning process during non-axisymmetric die-less shear spinning process are analyzed. It shows that the 0° area of the flange is the position where the wrinkle occurs easily, and the elongation of the material reaches the maximum value; the larger equivalent stress and strain distribute on the conical surface with the smaller HCA. With the movement of the roller, the ellipticity of the cross section increases in the bigger HCA working conditions, but decreases in the smaller HCA working conditions due to the combined effects of the deformation and the springback.