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A key feature of the booming smart home is the integration of a wide assortment of technologies, including various standards, proprietary communication protocols and heterogeneous platforms. Due to customization, unsatisfied assumptions and incompatibility in the integration, critical security vulnerabilities are likely to be introduced by the integration. Hence, this work addresses the security problems in smart home systems from an integration perspective, as a complement to numerous studies that focus on the analysis of individual technologies. We propose HOMESCAN, an approach that examines the security of the implementations of smart home systems. It extracts the abstract specification of application-layer protocols and internal behaviors of entities, so that it is able to conduct an end-to-end security analysis against various attack models. Applying HOMESCAN on three extensively-used smart home systems, we have found twelve non-trivial security vulnerabilities, which may lead to unauthorized remote control and credential leakage.

Introduction Metastasis is the primary cause of lung cancer-related death. Nevertheless, the underlying molecular mechanisms and evolutionary patterns of lung cancer metastases are still elusive. Methods We performed whole-exome sequencing for 40 primary tumors (PTs) and 61 metastases from 47 patients with lung cancer, of which 40 patients had paired PTs and metastases. The PT-metastasis genomic divergence, metastatic drivers, timing of metastatic dissemination, and evolutionary origins were analyzed using appropriate statistical tools and mathematical models. Results There were various degrees of genomic heterogeneity when comparing the paired primary and metastatic lesions or comparing metastases of different sites. Multiple metastasis-selected/enriched genetic alterations were found, such as MYC amplification, NKX2-1 amplification, RICTOR amplification, arm 20p gain, and arm 11p loss, and these results were were also featured in a meta-analysis cross-validated using an independent cohort from Memorial Sloan-Kettering Cancer Center database. To elucidate the metastatic seeding time, we applied a metastatic model and found 61.1% of the tumors were late dissemination, in which the metastatic seeding happened approximately 2.74 years before clinical detection. One exception was lymph node metastases whose dissemination time was relatively early. By analyzing the evolutionary origins, we reported that nonlymph node metastases were mainly seeded by the PT (87.5%) rather than the earlier colonized lymph node metastases. Conclusions Our results shed light on the molecular features that potentially drive lung cancer metastases. The distinct temporospatial pattern of disease progression revealed that lung cancer was susceptible to either late dissemination or indolent early lymph node metastases, leaving a potential time window to minimize metastases by early cancer detection.

To evaluate the utility of a contact-free device in screening for obstructive sleep apnea.Three hundred fifty-nine participants (mean age 46 ± 13 years, body mass index 26.1 ± 4.2 kg/m², 67.7% male) underwent overnight monitoring using a contact-free device, the OrbSense, and polysomnography (PSG) in the sleep laboratory simultaneously. The OrbSense recordings were analyzed automatically, and PSG was scored based on recommended guidelines.The respiratory event index from the OrbSense was lower than the apnea-hypopnea index (AHI) from PSG (25.5 ± 20.7 vs 27.0 ± 25.2 events/h; P = .007) and was significantly correlated with AHI (Pearson coefficient, 0.92; P.0001). Bland-Altman analysis showed a mean difference of 1.5 events/h, and the limit of agreement was -18.6 to 21.5 events/h. Use of the OrbSense resulted in larger underestimates of AHI and lower negative predictive values at higher AHI values (especially when AHI ≥ 30 events/h). When we used a PSG diagnostic criterion of AHI5 events/h, the optimal diagnostic cutoff value from the OrbSense was 8 events/h, with a sensitivity of 90.4%, a specificity of 77.6%, a 94.6% positive predictive value, and a 65% negative predictive value. For patients with moderate to severe obstructive sleep apnea whose AHI was15 events/h, the OrbSense cutoff was 16.6 events/h, with a sensitivity of 87.1% and a specificity of 89.7%. Among the 359 participants, 250 patients (69.6%) had the same obstructive sleep apnea severity division classified by both PSG and the OrbSense.The contact-free device OrbSense can detect respiratory events during sleep and has close agreement with in-laboratory PSG in screening for obstructive sleep apnea. Further studies are warranted to test its utility in community-based settings and at home.

Adversarial Examples threaten to fool deep learning models to output erroneous predictions with high confidence. Optimization-based methods for constructing such samples have been extensively studied. While being effective in terms of aggression, they typically lack clear interpretation and constraint about their underlying generation process, which thus hinders us from leveraging the produced adversarial samples for model protection in the reverse direction. Hence, we expect them to repair bugs in the pre-trained models by produced additional training data equipped with strong attack ability rather than time-consuming full re-training from scratch. To address these issues, we first study the black-box behaviors and the intrinsic deficiency of neighborhood information in previous optimization-based adversarial attacks and defenses, respectively. Then we introduce a new method dubbed FeaCP , which uses correct predicted samples in disjoint classes to guide the generation of more explainable adversarial samples in the ambiguous region around the decision boundary instead of uncontrolled “blind spots”, via convex combination in a feature component-wise manner which takes the individual importance of feature ingredients into account. Our method incorporates the prior fact that for well-separated samples, the path connecting them would go through model’s decision-boundary that lies in a low-density region, however, wherein adversarial examples are spread with high probability, thus having an impact on the ultimate trained model. In our work, the path is constructed by proposed inhomogeneous feature-wise convex interpolation rather than operating on sample-wise level, limiting the search space of FeaCP to obtain an adaptive neighborhood. Finally, we provide detailed insights and extend our method to adversarial fine-tuning using vicinity distribution to optimize the approximated decision boundary, and validate the significance of our FeaCP to model performance. The experimental results show that our method provides competitive performance on various datasets and networks.

Cui Ying Wei,1,* Zhong Ming He,2,* Han Yan,3 Jing Li,3 Pei An,3 Long Zhao,3 Li Nong Ji,4 Zhan Cheng Gao,3 Xiao Song Dong,3 Fang Han3 1Department of Endocrinology, First Affiliated Hospital, Baotou Medical College, Baotou, Inner Mongolia, People’s Republic of China; 2Department of Pulmonary Medicine, Karamay Central Hospital, Karamay, Xinjiang, People’s Republic of China; 3Department of Respiratory and Critical Care Medicine, Peking University People’s Hospital, Beijing, People’s Republic of China; 4Department of Endocrinology, Peking University People’s Hospital, Beijing, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xiao Song DongDepartment of Respiratory and Critical Care Medicine, Peking University People’s Hospital, Beijing, People’s Republic of ChinaTel/Fax +86-10-88324207Email dongxiaosong@pkuph.edu.cnPurpose: The study aimed at assessing glucose control measured with a continuous glucose monitoring system (CGMS) before and after short-term continuous positive airway pressure (CPAP).Materials and Methods: Twenty-four type 2 diabetic patients (T2DM) with Obstructive sleep apnea syndrome (OSAS) (mean age 55.0 ± 9.0 years; BMI 29.5 ± 5.2 kg/m2) were admitted and kept under diet control for 2 days, then underwent 2 overnight polysomnographies: a diagnostic study and one with CPAP titration. Then they were treated by CPAP during sleep for the following three nights. Participants were divided into subgroup D (only diet control) and subgroup M (with DM medication). CGMS was utilized over the last five days. Glucose control was also assessed with plasma insulin and a clinical measure of insulin resistance (HOMA-IR) index.Results: The mean (±SD) apnea-hypopnea index (AHI) at diagnostic polysomnography was 51.2 ± 22.4 (range 10– 88) events/h. CPAP treatment in the subjects with OSAS resulted in the index of oxygenation desaturations being reduced from 33.3 ± 20.1 to 1.1 ± 1.6 (P =0.00). CGMS showed mean 24-hours glucose values significantly lower after CPAP treatment than at baseline in both subgroups (7.97± 1.31 vs 7.52± 0.94, P=0.033 in subgroup D; and 7.72± 1.51 vs 7.17± 1.21, P=0.05 in subgroup M), as the fasting plasma insulin levels and HOMA-IR were also decreased significantly after CPAP treatment (13.0 ± 7.5μU/mL vs 10.8 ± 5.4μU/mL, P=0.044; and 4.2 ± 2.2 vs 3.1± 1.7, P=0.003, respectively). Standard deviation (SD) and mean amplitude of glucose excursions (MAGE) were also decreased in the subgroup D (1.91 ± 1.10 vs 1.61 ± 1.20, P=0.014; 1.26 ± 1.13 vs 1.01 ± 0.98, P=0.008, respectively) only.Conclusion: Short-term CPAP treatment in OSAS with type 2 diabetic patients is accompanied by a decrease in blood glucose level and improved insulin sensitivity. Glucose variability was reduced but only in the patients with diet control.Keywords: type 2 diabetes mellitus, obstructive sleep apnea syndrome, continuous positive airway pressure, continuous glucose monitoring system, glucose variability, HOMA-IR

Background Localization of small pulmonary nodules is an inevitable challenge for the thoracic surgeon. This study aimed to investigate the accuracy of three‐dimensional (3D) printing technology for localizing small pulmonary nodules, especially ground‐glass nodules (GGNs). Methods This study enrolled patients with peripheral small pulmonary nodules (≤ 2 cm) who required preoperative localization. In the comparison period, patients underwent both computed tomography‐guided (CT‐G) and 3D‐printing template guided (3D‐G) localization to compare the accuracies of the two methods. In the testing period, the 3D‐printing technique was implemented alone. The 3D‐printing physical navigational template was designed based on data from perioperative CT images. Clinical data, imaging data, surgical data, and evaluation index were collected for further analysis. The learning curve of the 3D‐printing localization technique was assessed using cumulative sum (CUSUM) analysis and multiple linear regression analysis. Results In the comparison period (n = 14), the success rates of CT‐G and 3D‐G were 100% and 92.9% (P = 0.31), respectively; in the testing period (n = 23), the success rate of 3D‐G was 95.6%. The localization times of CT‐G, 3D‐G (comparison), and 3D‐G (testing) were 23.6 ± 5.3, 19.3 ± 6.8, and 9.8 ± 4.6 minutes, respectively. The CUSUM learning curve was modeled using the equation: Y = 0.48X2 − 0.013X − 0.454 (R2 = 0.89). The learning curve was composed of two phases, phase 1 (the initial 20 patients) and phase 2 (the remaining 17 patients). Conclusions 3D printing localization has adequate accuracy and is a feasible and accessible strategy for use in localizing small pulmonary nodules, especially in right upper lobe. The use of this technique could facilitate lung nodule localization prior to surgery., Localization of small pulmonary nodules is an inevitable challenge for the thoracic surgeon. This study demonstrated that the 3D printing localization technique is a feasible and accessible strategy to localize non‐palpable pulmonary nodules.

Service composition aims at achieving a business goal by composing existing service-based applications or components. The response time of a service is crucial especially in time critical business environments, which is often stated as a clause in service level agreements between service providers and service users. To meet the guaranteed response time requirement of a composite service, it is important to select a feasible set of component services such that their response time will collectively satisfy the response time requirement of the composite service. In this work, we use the BPEL modeling language, that aims at specifying Web services. We extend it with timing parameters, and equip it with a formal semantics. Then, we propose a fully automated approach to synthesize the response time requirement of component services modeled using BPEL, in the form of a constraint on the local response times. The synthesized requirement will guarantee the satisfaction of the global response time requirement, statically or dynamically. We implemented our work into a tool, Selamat, and performed several experiments to evaluate the validity of our approach., Comment: This is a pre-print of an article published in the International Journal on Software and Systems Modeling (SoSyM)

Inflammatory pain is a complex condition that has a major negative impact on post-injury quality of life. Recently, astaxanthin treatment in mice has been shown to relieve the development and maintenance of inflammatory pain through its effective antioxidant and anti-inflammatory activities. However, the specific mechanisms underlying these effects remain unclear. In this study, we aimed to elucidate the mechanism by which astaxanthin alleviated inflammation using complete Freund's adjuvant (CFA)-induced inflammatory pain mice model. Mechanical allodynia and thermal hyperalgesia were observed on day 1 and up to day 14 post-surgery. The expression of p38 mitogen-activated protein kinases (MAPKs) in the left paw and L4-6 DRG were upregulated in the CFA model, and the expression of nuclear factor erythroid 2-related factor 2 (Nrf2)/heme Oxygenase-1 (HO-1) pathways were increased. Astaxanthin relieved mechanical allodynia and thermal hyperalgesia caused by CFA-induced inflammatory pain and inhibited the inflammatory response (e.g., infiltration of inflammatory cells and production of inflammatory factors) in the paw and DRG. Additionally, astaxanthin inhibited p38 MAPK and enhanced Nrf2/HO-1 content in the left paw and DRG; it also reversed the pain caused by p38 MAPK and Nrf2 inhibitors. These findings suggest that AST exerts anti-inflammatory effects and regulates p38 MAPK and Nrf2/HO-1 to alleviate inflammatory pain.

The ADJUVANT study reported the comparative superiority of adjuvant gefitinib over chemotherapy in disease-free survival of resected EGFR-mutant stage II–IIIA non-small cell lung cancer (NSCLC). However, not all patients experienced favorable clinical outcomes with tyrosine kinase inhibitors (TKI), raising the necessity for further biomarker assessment. In this work, by comprehensive genomic profiling of 171 tumor tissues from the ADJUVANT trial, five predictive biomarkers are identified (TP53 exon4/5 mutations, RB1 alterations, and copy number gains of NKX2-1, CDK4, and MYC). Then we integrate them into the Multiple-gene INdex to Evaluate the Relative benefit of Various Adjuvant therapies (MINERVA) score, which categorizes patients into three subgroups with relative disease-free survival and overall survival benefits from either adjuvant gefitinib or chemotherapy (Highly TKI-Preferable, TKI-Preferable, and Chemotherapy-Preferable groups). This study demonstrates that predictive genomic signatures could potentially stratify resected EGFR-mutant NSCLC patients and provide precise guidance towards future personalized adjuvant therapy., Adjuvant gefitinib improves outcomes in non-small cell lung cancer (NSCLC) patients compared to chemotherapy, but not in all cases. Here, the authors find genomic biomarkers of response to gefitinib in NSCLC patients from the ADJUVANT trial, and propose a score to stratify them by potential benefit from the treatment.

Introduction: Pulmonary atypical carcinoid (PAC) is a rare subtype of pulmonary neuroendocrine neoplasm. Although EML4-ALK fusion has been detected in PAC, EGFR mutations have not been reported before. Methods: We performed hematoxylin and eosin staining, immunohistochemistry, and next-generation sequencing on tissues at baseline and after surgery. Results: The patient was diagnosed with having advanced PAC harboring the EGFR L858R mutation and then received a combination of icotinib and irinotecan plus cisplatin chemotherapy, achieving a partial response before the operation. Postoperative histology results revealed SCLC harboring the EGFR L858R mutation. Surprisingly, both the KRAS amplification and the RB1 deletion disappeared. Conclusions: EGFR tyrosine inhibitors plus irinotecan plus cisplatin chemotherapy might be a potential treatment option for advanced pulmonary neuroendocrine neoplasms harboring EGFR mutations.

Objective To determine the clinical value of 18F-FDG-PET/CT and dual-energy virtual noncalcium CT to detect and identify single osteolytic metastases (SOM) in participants with non-small cell lung cancer (NSCLC). Materials and Methods Forty-two participants (mean age, 63.5 years ± 10.1; range, 41–81 years) with suspected SOM diagnosed by whole-body 18F-FDG-PET/CT underwent non-enhanced dual-energy CT. All images were visually and quantitatively evaluated by two nuclear medicine physicians (R1 and R2) and two radiologists (R3 and R4) independently. The results of visual and quantitative analysis of 18F-FDG-PET/CT and dual-energy CT were compared with pathological results. Results In the visual analysis, the specificity and positive predictive value of dual-energy CT for reader 1 and reader 2 is larger than the corresponding figures of18F-FDG-PET/CT for reader 3 and reader 4 (94.1% each vs 82.4%/76.5%; 95.2%/95.0% vs 88.9%/86.2%). The sensitivity and negative predictive value of dual-energy CT is relatively lower than the number of 18F-FDG-PET/CT for readers (80.0%/76.0% vs 96.0%/100.0%; 76.2%/72.7% vs 93.3%/100.0%, respectively). ROI-based analysis of SUVmax on PET/CT images and CT numbers on VNCa images showed a significant difference between metastases and non-metastases (P Conclusions Pre-surgical evaluation by combination of whole-body 18F-FDG-PET/CT and dual-energy CT could improve the classification of SOM and may further guide the surgical decision-making in participants with NSCLC.

Background Locally advanced NSCLC is one of the most heterogeneous conditions, with multidimensional treatments involved. Neoadjuvant therapy had been commonly considered an optimal management strategy for patients with operable locally advanced. However, as targeted therapy has been widely applied in advanced NSCLC, neoadjuvant targeted therapy has remained poorly explored in locally advanced disease. Methods We have described 11 ALK receptor tyrosine kinase gene (ALK)-positive patients with pathologically confirmed N2 NSCLC who were treated with neoadjuvant crizotinib. All the patients were treatment naive and received crizotinib at a starting dose of 250 mg twice daily. Patient 3 was provided with dynamic monitoring before and after neoadjuvant therapy through next-generation sequencing of plasma and tissue. In case 4, next-generation sequencing of preoperative tissue was performed. Results Of the 11 patients, 10 had a partial response and one was stable disease after neoadjuvant crizotinib, with one suffering from grade 4 hepatic damage. Of the 11 patients, 10 (91.0%) received an R0 resection and 2 patients achieved a pathological complete response to neoadjuvant crizotinib. Six patients had disease recurrence, with five of them receiving crizotinib as first-line treatment and achieving a long duration of response. Dynamic monitoring of both plasma and tissue simultaneously indicated a decrease in sensitive ALK signaling in patient 3 and a partial response (approximately 50% of partial response), and no ALK-dependent resistance variants were captured. Conclusion Neoadjuvant crizotinib may be feasible and well tolerated in locally advanced disease for complete resection. Crizotinib therapy before surgery may provide thorough elimination of circulating molecular residual disease and not influence the reuse of first-line crizotinib, but ongoing prospective trials are warranted to prove its efficacy in the neoadjuvant setting.

Background Neurolytic splanchnic nerve block is used to manage pancreatic cancer pain. However, its impact on survival and quality of life remains controversial. The authors’ primary hypothesis was that pain relief would be better with a nerve block. Secondarily, they hypothesized that analgesic use, survival, and quality of life might be affected. Methods This randomized, double-blind, parallel-armed trial was conducted in five Chinese centers. Eligible patients suffering from moderate to severe pain conditions were randomly assigned to receive splanchnic nerve block with either absolute alcohol (neurolysis) or normal saline (control). The primary outcome was pain relief measured on a visual analogue scale. Opioid consumption, survival, quality of life, and adverse effects were also documented. Analgesics were managed using a protocol common to all centers. Patients were followed up for 8 months or until death. Results Ninety-six patients (48 for each group) were included in the analysis. Pain relief with neurolysis was greater for the first 3 months (largest at the first month; mean difference, 0.7 [95% CI, 0.3 to 1.0]; adjusted P < 0.001) compared with placebo injection. Opioid consumption with neurolysis was lower for the first 5 months (largest at the first month; mean difference, 95.8 [95% CI, 67.4 to 124.1]; adjusted P < 0.001) compared with placebo injection. There was a significant difference in survival (hazard ratio, 1.56 [95% CI, 1.03 to 2.35]; P = 0.036) between groups. A significant reduction in survival in neurolysis was found for stage IV patients (hazard ratio, 1.94 [95% CI, 1.29 to 2.93]; P = 0.001), but not for stage III patients (hazard ratio, 1.08 [95% CI, 0.59 to 1.97]; P = 0.809). No differences in quality of life were observed. Conclusions Neurolytic splanchnic nerve block appears to be an effective option for controlling pain and reducing opioid requirements in patients with unresectable pancreatic cancer. Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New

Metformin is the first-line therapy for the treatment of type 2 diabetes (T2D) through mechanism of reduction in hepatic glucose production and fasting plasma glucose. Dorzagliatin is a novel glucokinase activator (GKA) that improves glucose and insulin sensitivity which significantly reduces post meal plasma glucose. Combination of dorzagliatin with metformin would offer benefits through the synergy of two mechanisms. In this randomized, double-blind, placebo-controlled phase 3 trial (NCT03141073), the efficacy and safety of dorzagliatin add-on to metformin in T2D patients were assessed. Eligible T2D patients (n=767) were randomly assigned in a 1:1 ratio to dorzagliatin group or placebo group add-on to metformin (1500 mg/day) for a 24-week double-blind treatment, then followed by a 28-week open-label treatment with dorzagliatin in all patients. The primary efficacy endpoint was the change from baseline in the glycated hemoglobin(HbA1c) level at week 24 and the safety was assessed throughout the trial. At week 24, the HbA1c was reduced from baseline in dorzagliatin group, superior to placebo (-1.02% vs. -0.36%; ETD, -0.66%; 95% CI, -0.79 to -0.53; P

Although deep learning has demonstrated astonishing performance in many applications, there are still concerns about its dependability. One desirable property of deep learning applications with societal impact is fairness (i.e., non-discrimination). Unfortunately, discrimination might be intrinsically embedded into the models due to the discrimination in the training data. As a countermeasure, fairness testing systemically identifies discriminatory samples, which can be used to retrain the model and improve the model's fairness. Existing fairness testing approaches however have two major limitations. Firstly, they only work well on traditional machine learning models and have poor performance (e.g., effectiveness and efficiency) on deep learning models. Secondly, they only work on simple structured (e.g., tabular) data and are not applicable for domains such as text. In this work, we bridge the gap by proposing a scalable and effective approach for systematically searching for discriminatory samples while extending existing fairness testing approaches to address a more challenging domain, i.e., text classification. Compared with state-of-the-art methods, our approach only employs lightweight procedures like gradient computation and clustering, which is significantly more scalable and effective. Experimental results show that on average, our approach explores the search space much more effectively (9.62 and 2.38 times more than the state-of-the-art methods respectively on tabular and text datasets) and generates much more discriminatory samples (24.95 and 2.68 times) within a same reasonable time. Moreover, the retrained models reduce discrimination by 57.2% and 60.2% respectively on average.

With many trigger-action platforms that integrate Internet of Things (IoT) systems and online services, rich functionalities transparently connecting digital and physical worlds become easily accessible for the end users. On the other hand, such facilities incorporate multiple parties whose data control policies may radically differ and even contradict each other, and thus privacy violations may arise throughout the lifecycle (e.g., generation and transmission) of triggers and actions. In this work, we conduct an in-depth study on the privacy issues in multi-party trigger-action integration platforms (TAIPs). We first characterize privacy violations that may arise with the integration of heterogeneous systems and services. Based on this knowledge, we propose Taifu, a dynamic testing approach to identify privacy weaknesses from the TAIP. The key insight of Taifu is that the applets which actually program the trigger-action rules can be used as test cases to explore the behavior of the TAIP. We evaluate the effectiveness of our approach by applying it on the TAIPs that are built around the IFTTT platform. To our great surprise, we find that privacy violations are prevalent among them. Using the automatically generated 407 applets, each from a different TAIP, Taifu detects 194 cases with access policy breaches, 218 access control missing, 90 access revocation missing, 15 unintended flows, and 73 over-privilege access.

Background: The cisterna Intrathecal Drug Delivery system (IDDS) with morphine has proven to be effective in treating refractory cancer pain above the middle thoracic vertebrae level in some countries. However, it has not been fully investigated in others. We designed the current project to investigate the efficacy and safety of cisterna IDDS for pain relief in refractory pain above the middle thoracic vertebrae level in advanced cancer patients. Methods: This study protocol allows for eligible cancer patients to receive the cisterna IDDS operation. Pain intensity (Visual Analogue scale, VAS), quality of life (36-Item Short-Form Health Survey, SF-36), and depression (Self-Rating Depression scale, SDS) are assessed along with side effects in the postoperative follow-up visits. Recent literature suggests a potential role for cisterna IDDS morphine delivery for refractory pain states above the middle thoracic level. Conclusion: The results of this study may provide further evidence that cisterna IDDS of morphine can serve as an effective and safe pain relief strategy for refractory pain above the middle thoracic vertebrae level in advanced cancer patients.

The ADJUVANT study reported the comparative superiority of adjuvant gefitinib over chemotherapy in disease-free survival (DFS) of resected EGFR-mutant stage II-IIIA non-small cell lung cancer (NSCLC). However, not all patients experienced favorable clinical outcomes with TKI, raising the necessity for further biomarker assessment. By comprehensive genomic profiling of 171 tumor tissues from the ADJUVANT trial, five predictive biomarkers were identified (TP53 exon4/5 mutations, RB1 alterations, and copy number gains of NKX2-1, CDK4, and MYC. Then we integrated them into the Multiple-gene INdex to Evaluate the Relative benefit of Various Adjuvant therapies (MINERVA) score, which categorized patients into three subgroups with relative disease-free survival and overall survival benefits from either adjuvant gefitinib or chemotherapy (Highly TKI-Preferable, TKI-Preferable, and Chemotherapy-Preferable groups). This study demonstrates that predictive genomic signatures could potentially stratify resected EGFR-mutant NSCLC patients and provide precise guidance towards future personalized adjuvant therapy.

This work follow the verification as planning paradigm and propose to use model-checking techniques to solve planning and goal reasoning problems for autonomous systems with high-degree of assurance. It presents a novel modelling framework — Goal Task Network (GTN) that encompass both goal reasoning and planning under a unified formal description that enables the use of assurance tools. The paper provides a systematic method that highlights how an industrial model checker (PAT) can be used to solve goal reasoning and planning problems modelled by GTNs. Further, this paper also introduces the design of an automated system framework for Goal Reasoning And Verification for Independent Trusted Autonomous Systems (GRAVITAS). The proposed framework is demonstrated in an experiment that simulates a survey mission performed by the REMUS-100 autonomous underwater vehicle Refereed/Peer-reviewed

The widespread adoption of Deep Neural Networks (DNNs) in important domains raises questions about the trustworthiness of DNN outputs. Even a highly accurate DNN will make mistakes some of the time, and in settings like self-driving vehicles these mistakes must be quickly detected and properly dealt with in deployment. Just as our community has developed effective techniques and mechanisms to monitor and check programmed components, we believe it is now necessary to do the same for DNNs. In this paper we present DNN self-checking as a process by which internal DNN layer features are used to check DNN predictions. We detail SelfChecker, a self-checking system that monitors DNN outputs and triggers an alarm if the internal layer features of the model are inconsistent with the final prediction. SelfChecker also provides advice in the form of an alternative prediction. We evaluated SelfChecker on four popular image datasets and three DNN models and found that SelfChecker triggers correct alarms on 60.56% of wrong DNN predictions, and false alarms on 2.04% of correct DNN predictions. This is a substantial improvement over prior work (SelfOracle, Dissector, and ConfidNet). In experiments with self-driving car scenarios, SelfChecker triggers more correct alarms than SelfOracle for two DNN models (DAVE-2 and Chauffeur) with comparable false alarms. Our implementation is available as open source.

Background: Neuropathic pain (NP) can be treated effectively using pulsed radiofrequency (PRF). NP development and maintenance involves the essential neurotransmitter chemokine c-c motif ligand 2 (CCL2). The present study aimed to determine whether PRF regulated CCL2 expression in sciatic nerve injury (SNI) model rats.Methods: Sprague-Dawley rats were divided randomly into a sham group, an SNI group, and a PRF group. In the PRF group, L5 dorsal root ganglia received PRF treatment. After paw withdrawal mechanical threshold (PWMT) was examined, the expression levels of CCL2 and nuclear factor kappa B (NF-κB) in spinal dorsal horn were determined.Results: The PWMT in PRF group increased significantly compared with that of the SNI group (P < 0.05). The CCL2 and NF-κB expression levels in the PRF group were significant lower than those in the SNI group (P < 0.05).Conclusion: NP was effectively alleviated by PRF-mediated reductions in CCL2 expression via inhibition of NF-κB activation in the spinal cord of SNI model rats.

We are in the era that sport is increasingly defined by data. Rich data is a powerful enabling foundation for novel insights on the game and on player actions, and consequently used for fan engagement and better decision making. Detailed data that has specifications of each shot is currently missing in tennis, as the game dynamics is fast and it is beyond human ability to manually record all specifications of each and every shot, even using scoring software. In this paper, we present an intelligent system to automatically recognize player actions, ball and player movements, and important game events. The system annotates the video with a suitable set of keywords for fast retrieval in broadcast production. Various techniques of computer vision, alignment, filtering, and pre-trained deep learning models are utilized by our system. The evaluation of our results on multiple broadcast videos show great accuracy and timeliness. The implications of the work presented in this paper are profound in the current workflow in broadcast coverage of a tennis match where normally multiple video operators and judges are needed to identify events and retrieve the related clips from multiple cameras.

Reflection is a notion that naturally emerges from philosophy, mathematics, and sciences. In short, reflection is the ability of an entity to alter its own behaviour. This paper suggests that reflection is crucial to the development of complex and trustworthy software systems. We present RL, a reflective computational model that aims to support the development of a large-scale framework for modelling and manipulating structured data. We give the formal semantics for this computational model. We also share preliminary work on a proof-of-concept implementation of RL and discuss future work.

Deep neural networks (DNN) are increasingly applied in safety-critical systems, e.g., for face recognition, autonomous car control and malware detection. It is also shown that DNNs are subject to attacks such as adversarial perturbation and thus must be properly tested. Many coverage criteria for DNN since have been proposed, inspired by the success of code coverage criteria for software programs. The expectation is that if a DNN is well tested (and retrained) according to such coverage criteria, it is more likely to be robust. In this work, we conduct an empirical study to evaluate the relationship between coverage, robustness and attack/defense metrics for DNN. Our study is the largest to date and systematically done based on 100 DNN models and 25 metrics. One of our findings is that there is limited correlation between coverage and robustness, i.e., improving coverage does not help improve the robustness. Our dataset and implementation have been made available to serve as a benchmark for future studies on testing DNN.

Introduction： Ovarian hyperstimulation syndrome (OHSS) is an iatrogenic serious complication during assisted reproductive technology, the aim of our research is to explore the differences of pregnancy outcome between severe OHSS patients and the non-OHSS IVF patients, and also compare the pregnancy outcome of different ascites drainage way of severe OHSS patients.Design: This is a retrospective cohort analysis carried out in a University-affiliated reproductive center from 2012 to 2019 ,between 359 women with severe OHSS following ascites draining and 345 non-OHSS women (matched by age and retrieved oocyte number).We examined the rates of clinical pregnancy, multiple pregnancies, miscarriage, live birth and preterm delivery between the two groups. Within the OHSS group (central venous catheter group vs paracentesis group), odds ratios (ORs) and 95% confidence intervals (CIs) of measure of clinical pregnancy were also analyzed.Result： The biochemical pregnancy loss rate of non-OHSS patients was significantly higher than that of severe OHSS patients (11.1% vs 0.6%) , the live birth, multiple pregnancy (twin pregnancy ) was much more in severe OHSS group than in control group(88.9% vs 73.5%; 57.6% vs 28.1% respectively), and there was no significant difference between singleton and twin preterm rate and neonatal birth weight between OHSS and control group. Binary logistic regression analysis revealed multiple pregnancy and long-protocol showed relatively high odds ratio, suggest that thelong-protocol of COS and multiple pregnancy are the risk factor of severe OHSS.Among severe OHSS following ascites draining, in comparison with abdominal paracentesis and central venous catheter, the ascites volume in central venous catheter group was much more than abdominal paracentesis group, there was significant difference between the two groups. There was no significant difference between the two groups in comparison of live birth, and the birth weight of singleton and twin between these two groups. but the premature birth rate of singleton in group 1 was 9.3% (10/107), whereas in group 2, it was 3.75%.Conclusion： In conclusion, this preliminary report suggests that for the IVF-ET patients, less basic follicle number, GnRH antagonist cycle, singleton maybe the protective factors for the late-onset OHSS. Paracentesis and central venous catheter are all the effective modality to manage the ascites for the severe OHSS patients. For the patients who have the possibility of repeated paracentesis, catheter is a safe and effective way.

Although deep neural networks (DNNs) have demonstrated astonishing performance in many applications, there are still concerns on their dependability. One desirable property of DNN for applications with societal impact is fairness (i.e., non-discrimination). In this work, we propose a scalable approach for searching individual discriminatory instances of DNN. Compared with state-of-the-art methods, our approach only employs lightweight procedures like gradient computation and clustering, which makes it significantly more scalable than existing methods. Experimental results show that our approach explores the search space more effectively (9 times) and generates much more individual discriminatory instances (25 times) using much less time (half to 1/7).

Background Since the conception of enhanced recovery after surgery protocols, tubeless strategies have become popular. Herein, we introduce a previously unreported alternative air-extraction strategy for patients who have undergone thoracoscopic wedge resection and explore its feasibility and safety. Methods Between January 2015 and June 2017, 264 consecutive patients underwent thoracoscopic wedge resection with different drainage strategies. Patients were divided according to the postoperative drainage strategies used: routine chest tube drainage (RT group), complete omission of chest tube drainage (OT group), and prophylactic air-extraction catheter insertion procedure (PC group). Using the propensity score matching method, clinical parameters and objective operative qualities were compared among the three groups. Results Optimal 1:1 matching was used to form pairs of RT (n =36) and PC (n =36) groups and balance baseline characteristics among the three groups. The incidence rates of pneumothorax were 5.6% (2/36), 9.8% (5/51), and 19.4% (7/36) in the RT, OT, and PC groups, respectively (P = 0.07). Chest tube reinsertion incidence for postoperative pneumothorax was 19.4% (1/7) in the PC group and 60% (3/5) in the OT group. Other postoperative complications were comparable among these groups. Conclusions The prophylactic air-extraction strategy may be an alternative procedure for selected patients. Remedial air extraction may reduce the occurrence of chest tube reinsertion for pneumothorax patients, but further investigation is required.

Nowadays, protocols often use time to provide better security. For instance, critical credentials are often associated with expiry dates in system designs. However, using time correctly in protocol design is challenging, due to the lack of time related formal specification and verification techniques. Thus, we propose a comprehensive analysis framework to formally specify as well as automatically verify timed security protocols. A parameterized method is introduced in our framework to handle timing parameters whose values cannot be decided in the protocol design stage. In this work, we first propose timed applied $\pi$ -calculus as a formal language for specifying timed security protocols. It supports modeling of continuous time as well as application of cryptographic functions. Then, we define its formal semantics based on timed logic rules , which facilitates efficient verification against various authentication and secrecy properties. Given a parameterized security protocol, our method either produces a constraint on the timing parameters which guarantees the security property satisfied by the protocol, or reports an attack that works for any parameter value. The correctness of our verification algorithm has been formally proved. We evaluate our framework with multiple timed and untimed security protocols and successfully find a previously unknown timing attack in Kerberos V.

As feature-rich Android applications (apps for short) are increasingly popularized in security-sensitive scenarios, methods to verify their security properties are highly desirable. Existing approaches on verifying Android apps often have limited effectiveness. For instance, static analysis often suffers from a high false-positive rate, whereas approaches based on dynamic testing are limited in coverage. In this work, we propose an alternative approach, which is to apply the software model checking technique to verify Android apps. We have built a general framework named DroidPF upon Java PathFinder (JPF), towards model checking Android apps. In the framework, we craft an executable mock-up Android OS which enables JPF to dynamically explore the concrete state spaces of the tested apps; we construct programs to generate user interaction and environmental input so as to drive the dynamic execution of the apps; and we introduce Android specific reduction techniques to help alleviate the state space explosion. DroidPF focuses on common security vulnerabilities in Android apps including sensitive data leakage involving a non-trivial flow- and context-sensitive taint-style analysis. DroidPF has been evaluated with 131 apps, which include real-world apps, third-party libraries, malware samples and benchmarks for evaluating app analysis techniques like ours. DroidPF precisely identifies nearly all of the previously known security issues and nine previously unreported vulnerabilities/bugs. NRF (Natl Research Foundation, S’pore)

To investigate whether nasopharyngeal airway (NPA) intubation could reduce the risk of complications caused by radiofrequency thermocoagulation (RFT) of the trigeminal ganglion.From November 1, 2014 to May 1, 2015, 200 patients treated with sedation (combination of sufentanil and propofol) were randomly divided into two groups, one in which NPA intubation was used (intervention group) and one in which it was not used (control group). The primary outcome was the frequency of hypoxemia, and secondary outcomes were the frequency of hypotension, nasal mucosa damage, corneal numbness, masticatory weakness, palsies of other cranial nerves, and intracranial hemorrhage. Statistical analyses were performed by using the Statistical Package for Social Sciences version 19.0. A P value.05 was considered to reflect statistical significance. Differences in the frequencies of adverse events between the two groups were assessed by using Fisher exact test.Five patients in the intervention group showed minor nasal mucosa injury (P = .027). Hypoxemia (19 vs 3, P.001), corneal numbness (12 vs 4), and masticatory weakness (11 vs 3) occurred more frequently in the control group than in the intervention group (P.05). No significant differences in the incidence of hypotension or palsies of other cranial nerves were observed between the two groups (P.05).NPA intubation can reduce the frequency of hypoxemia and complications related to the thermocoagulation of the trigeminal ganglion with minor risks for nasopharyngeal injury.

This study aims to evaluate the role of chymostatin in paraquat-induced acute lung injury. Institute of Cancer Research mice were randomly distributed into the NS, DMSO, chymostatin, paraquat or chymostatin treatment groups. Six mice from each group were intraperitoneally injected with chloral hydrate at 0, 1, 2, 4, 8, 12, 24 and 48 h after treatment administration. Blood samples were collected through cardiac puncture. Lung tissues were stained with haematoxylin and eosin for the observation of lung histology. The degree of pulmonary oedema was determined on the basis of lung wet-to-dry ratio (W/D). The serum activity of cathepsin G was determined through substrate fluorescence assay. The serum levels of endothelial cell-specific molecule-1 (endocan), tumour necrosis factor-a (TNF-a), interleukin-1β (IL-1β), IL-6 and high-mobility group box protein 1 (HMGB1) were determined through enzyme-linked immunosorbent assay. The expression levels of endocan and nuclear NF-κBp65 in the lung were quantified through Western blot. Chymostatin alleviated the pathological changes associated with acute alveolitis in mice; decreased the lung W/D ratio, the activity of cathepsin G and the serum concentrations of TNF-a, IL-1β, IL-6 and HMGB1; and increased the serum concentration of endocan. Western blot results revealed that chymostatin up-regulated endocan expression and down-regulated nuclear NF-κBp65 expression in the lung. Chymostatin reversed the inflammatory effects of paraquat-induced lung injury by inhibiting cathepsin G activity to up-regulate endocan expression and indirectly inhibit NF-κBp65 activity.

As surgical proficiency and the development of medical techniques have improved, uniportal video-assisted thoracoscopic surgery (VATS) has emerged as a minimally invasive alternative in the surgical management of lung cancer. The spectrum of uniportal VATS indications is now almost equal to that of conventional VATS. Recent decades have witnessed the emergence of numerous uniportal VATS techniques. A significant proportion of these have to be converted into a multiport approach and even open thoracotomy due to the difficulty of managing the upper lobe vein and bronchus, particularly for the technically challenging left upper lobectomy. Although many successfully uniportal VATS left upper lobectomies have been reported, their procedures were modularized without describing refined techniques or operative improvements. This report describes a patient who was clinically diagnosed with stage IB (T2aN0M0) primary lung cancer of the left upper lobe (LUL), and who underwent left upper lobectomy and systematic lymph node dissection. During the operation, the placement of a single incision was readjusted to obtain optimal angulation; the versatile electrocautery hook and curved suction tube were used in conjunction with each other; accessible, cost-effective modified instruments were used; and new operative tricks were created; in addition, the operative sequence was alternated and the nerves were preserved to ensure a smooth procedure, improve efficiency, embody tumor-free operation and ensure safety. These are all good ideas that are worth disseminating.

N-PAT is a new model-checking tool that supports the verification of nested-models, i.e. models whose behaviour depends on the results of verification tasks. In this paper, we describe its operation and discuss mechanisms that are tailored to the efficient verification of nested-models. Further, we motivate the advantages of N-PAT over traditional model-checking tools through a network security case study.

Background: The ADJUVANT study reported the comparative superiority of adjuvant gefitinib over chemotherapy in disease-free survival (DFS) of resected EGFR-mutated stage II-IIIA non-small cell lung cancer (NSCLC). However, inconsistent clinical benefits have necessitated precise genetic categorization to reevaluate the benefit and risk assessment. Methods: Total 171 baseline surgical specimens from the ADJUVANT trial (n=95, gefitinib arm; n=76, VP arm) underwent targeted sequencing of 422 cancer-related genes. Predictive biomarkers of DFS were identified by Cox proportional hazard regression with gene-by-treatment interactions. A multi-gene composite score, MINERVA (Multiple-gene INdex to Evaluate the Relative benefit of Various Adjuvant therapies), that incorporates the predictive value of selected biomarkers, was developed for survival stratification based on the ratio of 2-year DFS rate and the difference in median DFS. The model was internally validated using ten-fold and leave-one-out cross-validation methods. Findings: TP53 exon4/5 mutations, RB1 alterations, and copy number gains of NKX2-1, CDK4, and MYC were identified as significant predictors for DFS and integrated into MINERVA score. Although updated overall OS presented no difference between adjuvant TKI and chemotherapy, MINERVA categorized patients into three subgroups. In Highly TKI-Preferable group (n=60, 35%), gefitinib achieved significantly longer median DFS than chemotherapy (34.5 vs 9.1 months; hazard ratio (HR) 0.21; p

This book covers the conventional and most recent theories and applications in the area of evolutionary algorithms, swarm intelligence, and meta-heuristics. Each chapter offers a comprehensive description of a specific algorithm, from the mathematical model to its practical application. Different kind of optimization problems are solved in this book, including those related to path planning, image processing, hand gesture detection, among others. All in all, the book offers a tutorial on how to design, adapt, and evaluate evolutionary algorithms. Source codes for most of the proposed techniques have been included as supplementary materials on a dedicated webpage.

Feature selection is a preprocessing step that aims to eliminate the features that may negatively influence the performance of the machine learning techniques. The negative influence is due to the possibility of having many irrelevant and/or redundant features. In this chapter, a binary variant of recent Harris hawks optimizer (HHO) is proposed to boost the efficacy of wrapper-based feature selection techniques. HHO is a new fast and efficient swarm-based optimizer with various simple but effective exploratory and exploitative mechanisms (Levy flight, greedy selection, etc.) and a dynamic structure for solving continuous problems. However, it was originally designed for continuous search spaces. To deal with binary feature spaces, we propose a new binary HHO in this chapter. The binary HHO is validated based on special types of feature selection datasets. These hard datasets are high dimensional, which means that there is a huge number of features. Simultaneously, we should deal with a low number of samples. Various experiments and comparisons reveal the improved stability of HHO in dealing with this type of datasets.

This chapter proposes a new efficient moth-flame-embedded multilayer perceptrons (MLP) neuroevolution model to deal with classification problems. Moth-flame optimizer (MFO) is one of the effective swarm-based metaheuristic methods inspired by the natural direction-finding behaviours of moth insects and their well-known entrapment phenomena when they circulate the non-natural lights and flames. MFO is capable of demonstrating a very promising performance in terms of exploration and exploitation inclinations. The proposed MFO-MLP model is extensively substantiated on 16 benchmark datasets, and the results are compared to well-known methods such as particle swarm optimizer (PSO), population-based incremental learning (PBIL), differential evolution (DE), and genetic algorithm (GA). The obtained results indicate the efficacy of the MFO-embedded neuroevolution model as a potential method in dealing with classification cases.

Background Patients undergoing thoracic lung wedge resection could benefit from tubeless strategies. However, postoperative pneumothorax is a primary limiting factor for such strategies. Accordingly, we evaluated the safety and efficacy of the prophylactic use of an air-extraction catheter as an improved drainage strategy and compared the findings with those for chest tube drainage in patients undergoing thoracic wedge resection. Methods Patients undergoing thoracic wedge resection between August 2017 and October 2018 were enrolled in this single-center, randomized, open-label, noninferiority trial. Patients who received an improved drainage strategy involving the use of a prophylactic air-extraction catheter were randomized to the intervention group, whereas those who underwent routine chest tube drainage were assigned to the control group. Analysis was based on the per-protocol population. The primary outcome was the incidence of pneumothorax on postoperative day 1. Secondary outcomes included patient recovery and related complications, including pleural effusion, lung infection, numeric rating scale score for pain, postoperative chest tube or catheter removal, postoperative hospitalization, and chest tube reinsertion. Results A total of 96 patients were randomized. Baseline demographic and clinical characteristics were similar between groups. The incidence of pneumothorax in the intervention and control groups was 10.0% and 9.1%, respectively (noninferiority, P = 1.00). In addition, there were no significant between-group differences in secondary outcomes. A significantly lower pain score was observed in the intervention group (P = .001). Conclusions The improved drainage strategy is not inferior to standard chest tube drainage after thoracic wedge resection and should be popularized.

Swarm Intelligence (SI) refers to the collective behaviour of a group of creatures without a centralized unit control. This field was first established in 1989 in a robotic project [1]. Systems built based on SI typically have independent intelligent agents that interact locally to achieve a goal as a team [2]. Most of the algorithms in this field mimic swarm intelligence in nature. For instance, Ant Colony Optimization (ACO) [3] mimics swarm intelligence of ants in an ant colony using stigmergy, which is the communication between individuals in a swarm by modifying environment. It has been proved that ants can find the shortest path between multiple path to a food course from their nest by a depositing and marking the ground using pheromone

Evolutionary Algorithms mimic natural evolutionary process in nature. One of the most well-regarded evolutionary algorithms is Genetic Algorithm (GA) [1]. This algorithm has been inspired from the Drawin’s theory of evolutionary. This theory states that natural organisms develop using the natural selection. Natural selection refers to the process of selecting and propagating the best genes from one generation to the next that helps the new generation to better survive, compete, and reproduce.

Surgical method improvements aim to optimize the patient experience. The problem of healing of the drainage tube hole has not received attention and is of concern because it can plague patient recovery. In this article we report on how we have improved the method of suturing the drainage tube hole and explore the safety and effectiveness of this method. Between December 2017 to August 2018, 102 patients underwent thoracoscopic lung resection (single port or single utility port) using different methods of suturing drainage tube holes. The intervention group received improved methods with subcuticular and intradermal suture and removal-free stitches, whilst the control group received a conventional mattress suture and fixed chest tube. A preset line was left to tie knots and close the hole after the removal of the chest tube. The stitches were removed 7-12 days after surgery. The baseline clinical features of the patients were subsequently analyzed. The objective and subjective conditions of scars were evaluated using the Vancouver Scar Scale (VSS) and the Patient and Observer Scar Assessment Scale (POSAS) at one month after surgery. The intervention group (n = 71) and control group (n = 31) had balanced baseline clinical characteristics. There were no significant differences between the two groups in terms of three-day postoperative pain and postoperative hospital stay. In the intervention group, three patients (4.23%) had wound splitting that required re-suturing, which was better than five patients (16.13%) in the control group (P < 0.05). The incidence of pleural fluid outflow, wound infection, post-removal pneumothorax, chest tube prolapse and incisional hernia were not different between the two groups. We conclude that the objective and subjective evaluation results of scars were significantly different between the two groups (P < 0.05), and the experimental group was superior to the control group. A balanced result between aesthetic appearance and safety as regards video-assisted thoracic surgery can be achieved through the chest tube hole improved suture method. This method also improves the patient's recovery experience.

This paper introduces a new high-performance machine learning tool named Silas, which is built to provide a more transparent, dependable and efficient data analytics service. We discuss the machine learning aspects of Silas and demonstrate the advantage of Silas in its predictive and computational performance. We show that several customised algorithms in Silas yield better predictions in a significantly shorter time compared to the state-of-the-art. Another focus of Silas is on providing a formal foundation of decision trees to support logical analysis and verification of learned prediction models. We illustrate the potential capabilities of the fusion of machine learning and logical reasoning by showcasing applications in three directions: formal verification of the prediction model against user specifications, training correct-by-construction models, and explaining the decision-making of predictions.

OBJECTIVES The management of non-small cell lung cancer (NSCLC) relies on the tumour-node-metastasis (TNM) stage, and the treatment regimen differs based on the N status. Positron emission tomography-computed tomography (PET-CT) has emerged as a powerful imaging tool for the detection of various cancers with a relatively low false-negative rate. We explored predictors to identify false-negative N2 disease in PET-CT. METHODS A total of 284 consecutive cN0 patients with peripheral NSCLC who underwent PET-CT scans followed by curative intent resections were enrolled as a training set to identify predictors of occult N2 metastases by multivariable analysis. The accuracy and cut-off values for the predictors were calculated using a receiver operating characteristic curve. Clinical and pathological data were analysed retrospectively. An additional 151 patients were collected as a test set to validate the results, including the occult N2 rate and accuracy. RESULTS In total, 8.5% (24/284) PET-CT-diagnosed N0 NSCLC cases had pathologically diagnosed N2 metastases. The SUVmax of the primary tumour was a unique independent risk factor for occult N2 NSCLC [P = 0.003, 95% confidence interval = 0.81-0.96, odds ratio (OR) = 0.88]. Occult N2 metastases occurred more frequently in the subcarinal (16/24) and right lower paratracheal lymph nodes (12/24). Accordingly, we divided the patients into two groups by SUVmax: the occult N2 rates in the SUVmax of

Diabetes & Metabolism - In Press.Proof corrected by the author Available online since vendredi 4 novembre 2016

The influence of low-mode-driven asymmetry on yield degradation in shaped-pulse indirect-drive implosions has been investigated at the 100 kJ laser facility. In this work, P2- and P4-driven asymmetries were tuned by varying hohlraum gas-fill density and capsule diameter. The measured neutron yield varied from 2.1 × 109 to 7.6 × 109 and the yield measured by a 1D simulation (YOC1D) was increased from 3% to 16%. Meanwhile, considering the temporal P2- and P4-driven asymmetries, the yields measured by 2D simulations (YOC2D) were from 26% to 81% (the YOC2D of the majority of the shots was higher than 50%). Furthermore, both the ion temperature and neutron bang-time showed good agreement between the measurements and the 2D simulations. The simulations demonstrated that the temporal P2- and P4-driven asymmetries can decrease the efficiency of PdV work and increase the energy loss due to electron thermal conduction. In addition, the internal energy of the deuterium (DD) fuel clearly decreased and the neutron yield was degraded by low-mode asymmetry. In a future work, we will enlarge the cylindrical hohlraum diameter or use an I-hohlraum to improve the low-mode-driven symmetry, along with measurement of the hot-spot shape.