Yes / This paper presents the development of a new chatter model using measured cutting forces instead of a mathematical model with empirical nature that describes them. The utilisation of measured cutting forces enables the prediction of real-time chatter conditions and stable machining. The chatter model is validated using fast Fourier transform (FFT) analyses for detection of chatter. The key contribution of the developed chatter model is that it can be incorporated in digital twins for process monitoring and control in order to achieve greater material removal rates and improved surface quality in future industrial applications involving machining processes. / Research Development Fund Publication Prize Award winner, Sep 2020.

Yes / Industry 4.0 promotes highly automated mechanisms for setting up and operating flexible manufacturing systems, using distributed control and data-driven machine intelligence. This paper presents an approach to reconfiguring distributed production systems based on complex product requirements, combining the capabilities of the available production resources. A method for both checking the “realisability” of a product by matching required operations and capabilities, and adapting resources is introduced. The reconfiguration is handled by a multi-agent system, which reflects the distributed nature of the production system and provides an intelligent interface to the user. This is all integrated with a self-adaptation technique for learning how to improve the performance of the production system as part of a reconfiguration. This technique is based on a machine learning algorithm that generalises from past experience on adjustments. The mechanisms of the proposed approach have been evaluated on a distributed robotic manufacturing system, demonstrating their efficacy. Nevertheless, the approach is general and it can be applied to other scenarios. / This work was supported by the SURE Research Projects Fund of the University of Bradford and the European Commission (grant agreement no. 314762). / Research Development Fund Publication Prize Award winner, Nov 2022

Yes / A set of shallow-water equations (SWEs) based on a k-epsilon Reynold stress model is established to simulate the turbulent flows over a complex roughness bed. The fundamental equations are discretized by the second-order finite-difference method (FDM), in which spatial and temporal discretization are conducted by staggered-grid and leap-frog schemes, respectively. The turbulent model in this study stems from the standard k-epsilon model, but is enhanced by replacing the conventional vertical production with a more rigorous and precise generation derived from the energy spectrum and turbulence scales. To verify its effectiveness, the model is applied to compute the turbulence in complex flow surroundings (including a rough bed) in an abrupt bend and in a natural waterway. The comparison of the model results against experimental data and other numerical results shows the robustness and accuracy of the present model in describing hydrodynamic characteristics, especially turbulence features on the complex roughness bottom. / National Key Research and Development Program of China (Grant No: 2016YFE0122500, 2013CB036401 and 2013CB036402), China Postdoctoral Science Foundation (Grant No: 2016M591184) and Programme of Introducing Talents of Discipline to Universities (Grant No: BC2018038) / Research Development Fund Publication Prize Award winner, June 2019.

Yes / Surgical smoke removal algorithms can improve the quality of intra-operative imaging and reduce hazards in image-guided surgery, a highly desirable post-process for many clinical applications. These algorithms also enable effective computer vision tasks for future robotic surgery. In this paper, we present a new unsupervised learning framework for high-quality pixel-wise smoke detection and removal. One of the well recognized grand challenges in using convolutional neural networks (CNNs) for medical image processing is to obtain intra-operative medical imaging datasets for network training and validation, but availability and quality of these datasets are scarce. Our novel training framework does not require ground-truth image pairs. Instead, it learns purely from computer-generated simulation images. This approach opens up new avenues and bridges a substantial gap between conventional non-learning based methods and which requiring prior knowledge gained from extensive training datasets. Inspired by the Generative Adversarial Network (GAN), we have developed a novel generative-collaborative learning scheme that decomposes the de-smoke process into two separate tasks: smoke detection and smoke removal. The detection network is used as prior knowledge, and also as a loss function to maximize its support for training of the smoke removal network. Quantitative and qualitative studies show that the proposed training framework outperforms the state-of-the-art de-smoking approaches including the latest GAN framework (such as PIX2PIX). Although trained on synthetic images, experimental results on clinical images have proved the effectiveness of the proposed network for detecting and removing surgical smoke on both simulated and real-world laparoscopic images. / Research Development Fund Publication Prize Award winner, November 2019.

В нашей дипломной работе рассматривается феномен self-publishing как способ самостоятельного издания публикации, отражаются его история в России и в мире, виды self-publishing, принцип работы платформы Amazon. Таким образом, проводится практическое исследование публикаций на Amazon, выделяются основные характеристики наиболее продаваемых книг, что позволило нам сделать выводы о критериях успешности публикации. / The diploma paper examines the phenomenon of self-publishing as a way of independent publishing, reflects its history in Russia and in the world, types of self-publishing, the principle of publishing on Amazon. Thus, a practical study of publications on Amazon is carried out, the main characteristics of the best-selling books are highlighted, which allowed us to draw the conclusions about the criteria for publication success.

The Vietnamese science and high education system plays a major role in the country’s social and economical development. Due to a mixed influence of international education systems, the contribution of the Vietnamese research to the international scientific landscape is still modest. Over the past decades, the most scientific programmes focused mostly on rather theoretical sciences and less on applied sciences. The results are reflected by a rather low rate of international publications on experimental science. Together with the country’s efforts on the efficient use of natural resources, there is an urgent demand for strengthening the scientific activity on environmental sciences. The new Journal of Vietnamese Environment was created to respond to the increasing interest in environmental research. The journal was founded as part of an academic network initiated by the Dresden University of Technology in the framework of Vietnamese-German cooperation programs on training and education. With multidisciplinary fields of interest and several types of manuscripts, the journal has a predominant academic character, the submission of manuscripts is open to students, graduates, researchers and staff members of research and academic institutions, as well as to any individual willing to disseminate the knowledge about the management of Vietnamese environment. / Hệ thống khoa học và giáo dục đại học Việt Nam đóng vai trò quan trọng trong sự phát triển kinh tế và xã hội của đất nước. Trong xu hướng giao thoa mạnh mẽ giữa các hệ thống giáo dục quốc tế, đóng góp của các nhà nghiên cứu ở Việt Nam cho cộng đồng khoa học quốc tế còn khiêm tốn. Trong những thập niên qua, hầu hết các hoạt động khoa học tập trung vào khoa học lý thuyết hơn là các lĩnh vực khoa học ứng dụng. Điều này đã được phản ánh qua tỷ lệ khá thấp các ấn phẩm quốc tế về khoa học thực nghiệm. Cùng với những nỗ lực của đất nước để sử dụng hiệu quả các nguồn tài nguyên thiên nhiên, một nhu cầu cấp bách đặt ra là tăng cường các hoạt động nghiên cứu về khoa học môi trường. Tạp chí Môi trường Việt Nam ra đời nhằm hưởng ứng sự quan tâm ngày một gia tăng trong nghiên cứu môi trường. Tạp chí được thành lập như một phần của mạng lưới học thuật được đề xuất bởi Trường Đại học Tổng hợp Kỹ Thuật Dresden trong khuôn khổ chương trình hợp tác Việt Nam - CHLB Đức về đào tạo và giáo dục. Với mối quan tâm đa ngành và đa dạng trong ấn phẩm, tạp chí chủ yếu mang tính học thuật, cơ hội gửi đăng bài viết mở rộng cho cả sinh viên, kỹ sư / cử nhân, nghiên cứu viên và các thành viên của Viện nghiên cứu và giáo dục, các cá nhân có mong muốn phổ biến kiến thức về quản lý môi trường ở Việt Nam.

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Yes / Prediction of engine-out emissions with high fidelity from in-cylinder combustion simulations is still a significant challenge early in the engine development process. This paper contributes to this fast evolving body of knowledge by focusing on the evaluation of NOx emissions predictions capability of a Probability Density Function (PDF) based Stochastic Reactor Engine Models (SRM), for a Diesel engine. The research implements a systematic approach to the study of the SRM engine model performance, based on a detailed space-filling design of experiments based sensitivity analysis of both external and internal parameters, evaluating their effects on the accuracy in matching physical measurements of in-cylinder conditions, and NOx emissions output. The approach proposed in this paper introduces an automatic SRM model calibration methodology across the engine operating envelope, based on a multi-objective optimization approach. This aims to exploit opportunities for internal SRM parameters tuning to achieve good overall modelling performance as a trade-off between physical in-cylinder measurements accuracy and the output NOx emissions predictions error. The results from the case study provide a valuable insight into the effectiveness of the SRM model, showing good capability for NOx emissions prediction and trends, while pointing out the critical sensitivity to the external input parameters and modelling conditions. / 41043/R00836 Jaguar Land Rover funded research “MULTI-PHYSICS ENGINE SIMULATION FRAMEWORK: RESEARCH INTO ADVANCED CAE CAPABILITY FOR MULTI-PHYSICS SIMULATION FRAMEWORK TO GENERATE HIGH FIDELITY PREDICTION OF ENGINE-OUT EMISSIONS”, 2016 – 2019. / Research Development Fund Publication Prize Award winner, March 2019.

Yes / Studies of nanosized forms of bismuth (Bi)-containing materials have recently expanded from optical, chemical, electronic, and engineering fields towards biomedicine, as a result of their safety, cost-effective fabrication processes, large surface area, high stability, and high versatility in terms of shape, size, and porosity. Bi, as a nontoxic and inexpensive diamagnetic heavy metal, has been used for the fabrication of various nanoparticles (NPs) with unique structural, physicochemical, and compositional features to combine various properties, such as a favourably high X-ray attenuation coefficient and near-infrared (NIR) absorbance, excellent light-to-heat conversion efficiency, and a long circulation half-life. These features have rendered bismuth-containing nanoparticles (BiNPs) with desirable performance for combined cancer therapy, photothermal and radiation therapy (RT), multimodal imaging, theranostics, drug delivery, biosensing, and tissue engineering. Bismuth oxyhalides (BiOx, where X is Cl, Br or I) and bismuth chalcogenides, including bismuth oxide, bismuth sulfide, bismuth selenide, and bismuth telluride, have been heavily investigated for therapeutic purposes. The pharmacokinetics of these BiNPs can be easily improved via the facile modification of their surfaces with biocompatible polymers and proteins, resulting in enhanced colloidal stability, extended blood circulation, and reduced toxicity. Desirable antibacterial effects, bone regeneration potential, and tumor growth suppression under NIR laser radiation are the main biomedical research areas involving BiNPs that have opened up a new paradigm for their future clinical translation. This review emphasizes the synthesis and state-of-the-art progress related to the biomedical applications of BiNPs with different structures, sizes, and compositions. Furthermore, a comprehensive discussion focusing on challenges and future opportunities is presented. / M.-A. Shahbazi acknowledges financial support from the Academy of Finland (grant no. 317316). P. Figueiredo acknowledges the Finnish Cultural Foundation for its financial support (decision no. 00190246). H. A. Santos acknowledges financial support from the HiLIFE Research Funds, the Sigrid Juse´lius Foundation, and the Academy of Finland (grant no. 317042). / Research Development Fund Publication Prize Award winner, Jan 2020.