Entwicklung einer Vorgehensweise zur Bestimmung und Verifikation von Wärmeübergangsparametern aus CFD Simulationen von Trockenkupplungssystemen = Development of an Estimation and Verification Approach for Heat Transfer Parameters of DryClutch Systems Obtained from CFD Simulations

Die Dimensionierung einer Kupplung für manuelle Schaltgetriebe ist stark von den Temperaturen im Friktionskontakt abhängig. Das richtige Verständnis für die Einstufung der Wärmeübertragungsmechanismen und der Randbedingungen ist eine Schlüsselstelle in der Ermittlung der Kupplungsgröße. Diese Arbeit widmet sich der Charakterisierung des Wärmeübergangs von Trockenkupplungssystemen, dem Prozess der Ergebnisvereinfachung und ?validierung, die von einer CFD Simulation generiert wurden

Serologic and immunohistochemical prognostic biomarkers of cutaneous malignancies

Biomarkers are important tools in clinical diagnosis and prognostic classification of various cutaneous malignancies. Besides clinical and histopathological aspects (e.g. anatomic site and type of the primary tumour, tumour size and invasion depth, ulceration, vascular invasion), an increasing variety of molecular markers have been identified, providing the possibility of a more detailed diagnostic and prognostic subgrouping of tumour entities, up to even changing existing classification systems. Recently published gene expression or proteomic profiling data relate to new marker molecules involved in skin cancer pathogenesis, which may, after validation by suitable studies, represent future prognostic or predictive biomarkers in cutaneous malignancies. We, here, give an overview on currently known serologic and newer immunohistochemical biomarker molecules in the most common cutaneous malignancies, malignant melanoma, squamous cell carcinoma and cutaneous lymphoma, particularly emphasizing their prognostic and predictive significance

Research and Design of a Routing Protocol in Large-Scale Wireless Sensor Networks

无线传感器网络，作为全球未来十大技术之一，集成了传感器技术、嵌入式计算技术、分布式信息处理和自组织网技术，可实时感知、采集、处理、传输网络分布区域内的各种信息数据，在军事国防、生物医疗、环境监测、抢险救灾、防恐反恐、危险区域远程控制等领域具有十分广阔的应用前景。 本文研究分析了无线传感器网络的已有路由协议，并针对大规模的无线传感器网络设计了一种树状路由协议，它根据节点地址信息来形成路由，从而简化了复杂繁冗的路由表查找和维护，节省了不必要的开销，提高了路由效率，实现了快速有效的数据传输。 为支持此路由协议本文提出了一种自适应动态地址分配算——ADAR（AdaptiveDynamicAddre...As one of the ten high technologies in the future, wireless sensor network, which is the integration of micro-sensors, embedded computing, modern network and Ad Hoc technologies, can apperceive, collect, process and transmit various information data within the region. It can be used in military defense, biomedical, environmental monitoring, disaster relief, counter-terrorism, remote control of haz...学位：工学硕士院系专业：信息科学与技术学院通信工程系_通信与信息系统学号：2332007115216

Prediction of melanoma evolution in melanocytic nevi via artificial intelligence: A call for prospective data

Recent research revealed the superiority of artificial intelligence over dermatologists to diagnose melanoma from images. However, 30-50% of all melanomas and more than half of those in young patients evolve from initially benign lesions. Despite its high relevance for melanoma screening, neither clinicians nor computers are yet able to reliably predict a nevus' oncologic transformation. The cause of this lies in the static nature of lesion presentation in the current standard of care, both for clinicians and algorithms. The status quo makes it difficult to train algorithms (and clinicians) to precisely assess the likelihood of a benign skin lesion to transform into melanoma. In addition, it inhibits the precision of current algorithms since 'evolution' image features may not be part of their decision. The current literature reveals certain types of melanocytic nevi (i.e. 'spitzoid' or 'dysplastic' nevi) and criteria (i.e. visible vasculature) that, in general, appear to have a higher chance to transform into melanoma. However, owing to the cumulative nature of oncogenic mutations in melanoma, a more fine-grained early morphologic footprint is likely to be detectable by an algorithm. In this perspective article, the concept of melanoma prediction is further explored by the discussion of the evolution of melanoma, the concept for training of such a nevi classifier and the implications of early melanoma prediction for clinical practice. In conclusion, the authors believe that artificial intelligence trained on prospective image data could be transformative for skin cancer diagnostics by (a) predicting melanoma before it occurs (i.e. pre-in situ) and (b) further enhancing the accuracy of current melanoma classifiers. Necessary prospective images for this research are obtained via free mole-monitoring mobile apps. (C) 2019 The Authors. Published by Elsevier Ltd

Deep neural networks are superior to dermatologists in melanoma image classification

Background: Melanoma is the most dangerous type of skin cancer but is curable if detected early. Recent publications demonstrated that artificial intelligence is capable in classifying images of benign nevi and melanoma with dermatologist-level precision. However, a statistically significant improvement compared with dermatologist classification has not been reported to date. Methods: For this comparative study, 4204 biopsy-proven images of melanoma and nevi (1:1) were used for the training of a convolutional neural network (CNN). New techniques of deep learning were integrated. For the experiment, an additional 804 biopsy-proven dermoscopic images of melanoma and nevi (1:1) were randomly presented to dermatologists of nine German university hospitals, who evaluated the quality of each image and stated their recommended treatment (19,296 recommendations in total). Three McNemar's tests comparing the results of the CNN's test runs in terms of sensitivity, specificity and overall correctness were predefined as the main outcomes. Findings: The respective sensitivity and specificity of lesion classification by the dermatologists were 67.2% (95% confidence interval [CI]: 62.6%-71.7%) and 62.2% (95% CI: 57.6%-66.9%). In comparison, the trained CNN achieved a higher sensitivity of 82.3% (95% CI: 78.3%-85.7%) and a higher specificity of 77.9% (95% CI: 73.8%-81.8%). The three McNemar's tests in 2 x 2 tables all reached a significance level of p < 0.001. This significance level was sustained for both subgroups. Interpretation: For the first time, automated dermoscopic melanoma image classification was shown to be significantly superior to both junior and board-certified dermatologists (p < 0.001). (C) 2019 The Authors. Published by Elsevier Ltd

Comparing artificial intelligence algorithms to 157 German dermatologists: the melanoma classification benchmark

Background: Several recent publications have demonstrated the use of convolutional neural networks to classify images of melanoma at par with board-certified dermatologists. However, the non-availability of a public human benchmark restricts the comparability of the performance of these algorithms and thereby the technical progress in this field. Methods: An electronic questionnaire was sent to dermatologists at 12 German university hospitals. Each questionnaire comprised 100 dermoscopic and 100 clinical images (80 nevi images and 20 biopsy-verified melanoma images, each), all open-source. The questionnaire recorded factors such as the years of experience in dermatology, performed skin checks, age, sex and the rank within the university hospital or the status as resident physician. For each image, the dermatologists were asked to provide a management decision (treat/biopsy lesion or reassure the patient). Main outcome measures were sensitivity, specificity and the receiver operating characteristics (ROC). Results: Total 157 dermatologists assessed all 100 dermoscopic images with an overall sensitivity of 74.1%, specificity of 60.0% and an ROC of 0.67 (range = 0.538-0.769); 145 dermatologists assessed all 100 clinical images with an overall sensitivity of 89.4%, specificity of 64.4% and an ROC of 0.769 (range = 0.613-0.9). Results between test-sets were significantly different (P < 0.05) confirming the need for a standardised benchmark. Conclusions: We present the first public melanoma classification benchmark for both non-dermoscopic and dermoscopic images for comparing artificial intelligence algorithms with diagnostic performance of 145 or 157 dermatologists. Melanoma Classification Benchmark should be considered as a reference standard for white-skinned Western populations in the field of binary algorithmic melanoma classification. (c) 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Artificial Intelligence and Its Effect on Dermatologists' Accuracy in Dermoscopic Melanoma Image Classification: Web-Based Survey Study

Background: Early detection of melanoma can be lifesaving but this remains a challenge. Recent diagnostic studies have revealed the superiority of artificial intelligence (AI) in classifying dermoscopic images of melanoma and nevi, concluding that these algorithms should assist a dermatologist's diagnoses. Objective: The aim of this study was to investigate whether AI support improves the accuracy and overall diagnostic performance of dermatologists in the dichotomous image-based discrimination between melanoma and nevus. Methods: Twelve board-certified dermatologists were presented disjoint sets of 100 unique dermoscopic images of melanomas and nevi (total of 1200 unique images), and they had to classify the images based on personal experience alone (part I) and with the support of a trained convolutional neural network (CNN, part II). Additionally, dermatologists were asked to rate their confidence in their final decision for each image. Results: While the mean specificity of the dermatologists based on personal experience alone remained almost unchanged (70.6% vs 72.4%; P=.54) with AI support, the mean sensitivity and mean accuracy increased significantly (59.4% vs 74.6%; P=.003 and 65.0% vs 73.6%; P=.002, respectively) with AI support. Out of the 10% (10/94; 95% CI 8.4%-11.8%) of cases where dermatologists were correct and AI was incorrect, dermatologists on average changed to the incorrect answer for 39% (4/10; 95% CI 23.2%-55.6%) of cases. When dermatologists were incorrect and AI was correct (25/94, 27%; 95% CI 24.0%-30.1%), dermatologists changed their answers to the correct answer for 46% (11/25; 95% CI 33.1%-58.4%) of cases. Additionally, the dermatologists' average confidence in their decisions increased when the CNN confirmed their decision and decreased when the CNN disagreed, even when the dermatologists were correct. Reported values are based on the mean of all participants. Whenever absolute values are shown, the denominator and numerator are approximations as every dermatologist ended up rating a varying number of images due to a quality control step. Conclusions: The findings of our study show that AI support can improve the overall accuracy of the dermatologists in the dichotomous image-based discrimination between melanoma and nevus. This supports the argument for AI-based tools to aid clinicians in skin lesion classification and provides a rationale for studies of such classifiers in real-life settings, wherein clinicians can integrate additional information such as patient age and medical history into their decisions

Prognosis of Patients With Primary Melanoma Stage I and II According to American Joint Committee on Cancer Version 8 Validated in Two Independent Cohorts:Implications for Adjuvant Treatment

The first randomized trial of adjuvant treatment with checkpoint inhibitor in stage II melanoma reported a significant reduction in risk of tumor recurrence. This study evaluates two independent data sets to further document survival probabilities for patients with primary stage I and II melanoma. The Central Malignant Melanoma Registry (CMMR) in Germany evaluated 17,544 patients with a primary diagnosis of stage I and II melanoma from 2000 to 2015. The exploratory cohort consisted of 6,725 patients from the Center for Dermato-Oncology at the University of Tübingen, and the confirmatory cohort consisted of 10,819 patients from 11 other German centers. Survival outcomes were compared with published American Joint Committee on Cancer version 8 (AJCCv8) stage I and II survival data. For the two CMMR cohorts in stage IA compared with the AJCCv8 cohort, melanoma-specific survival rates at 10 years were 95.1%-95.6% versus 98%; 89.7%-90.9% versus 94% in stage IB; 80.7%-83.1% versus 88% in stage IIA; 72.0%-79.9% versus 82% in stage IIB; and 57.6%-64.7% versus 75% in stage IIC, respectively. Recurrence rates were approximately twice as high as melanoma-specific mortality rates in stages IA-IIA. The melanoma-specific survival rates in the two CMMR cohorts across stages I and II are less favorable than published in AJCCv8. This has important implications for the consideration of adjuvant treatment in this population