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It has been witnessed that digital technology has the potential to improve the efficiency of emergent healthcare management in COVID-19, which however has not been widely adopted due to unclear definition and configuration. This research aims to propose a proof of concept of digital twins for emergent healthcare management through configuring the cyber and functional interdependencies of healthcare systems at local and city levels. Critical interdependencies of healthcare systems have been firstly identified at both levels, then the information and associated cyber and functional interdependencies embedded in seven critical hospital information systems (HISs) have been identified and mapped. The proposed conceptual digital twin-based approach has been then developed for information coordination amongst these critical HISs at both local and city levels based on permissioned blockchain to (1) integrate and manage the information from seven critical HISs, and further (2) predict the demands of medical resources according to patient trajectory. A case study has been finally conducted at three hospitals in London during the COVID-19 period, and the results showed that the developed framework of blockchain-integrated digital twins is a promising way to provide more accurate and timely procurement information to decision-makers and can effectively support evidence-based decisions on medical resource allocation in the pandemic. © 2023 ICE Publishing: All rights reserved.
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The continuous increase in COVID-19 positive cases in the Philippines might further weaken the local healthcare system. As such, an efficient system must be implemented to further improve the immunization efforts of the country. In this paper, a contactless digital electronic device is proposed to assess the vaccine and booster brand compatibility. Using Logisim 2.7.1, the logic diagrams were designed and simulated with the help of truth tables and Boolean functions. Moreover, the finalized logic circuit design was converted into its equivalent complementary metal-oxide semiconductor (CMOS) and stick diagrams to help contextualize how the integrated circuits will be designed. Results have shown that the proposed device was able to accept three inputs of the top three COVID-19 vaccine brands (Sinovac, AstraZeneca, and Pfizer) and assess the compatibility of heterologous vaccinations. With the successful results of the circuit, it can be concluded that this low-power device can be used to manufacture a physical prototype for use in booster vaccination sites. © 2022 IEEE.
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ABOUT ICMSOA2022Organized by Yaseen Academy, 2022 The 2nd International Conference on Modeling, Simulation, Optimization and Algorithm (ICMSOA 2022), which was planned to be held during 11-13 November, 2022 at Sanya, Hainan Province, China. Due to the travel restrictions caused by covid, the participants joined the conference online via Tencent Meeting at 12 November, 2022. The Conference looks for significant contributions to related fields of Modeling, Simulation, Optimization and Algorithm. The aim of the conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.CALL FOR PAPERSPlease make sure your submission is in related areas of the following general topics. The topics include, but are not limited to:Simulation modeling theory and technology, Computational modeling and simulation, System modeling and simulation, Device/VLSI modeling and simulation, Control theory and applications, Military Technology Simulation, Aerospace technology simulation, Information engineering simulation, Energy Engineering Simulation, Manufacturing Simulation, Intelligent engineering simulation, Building engineering simulation, Electromagnetic field simulation, Material engineering simulation, Visual simulation, Fluid mechanics engineering simulation, Manufacturing simulation technology, Simulation architecture, Simulation software platform and Intelligent Optimization Algorithm, Dynamic Programming, Ant Colony Optimization, Genetic Algorithm, Simulated Annealing Algorithm, Tabu Search Algorithm, Ant Colony System Algorithm, Hybrid Optimization Algorithm in other related areas.The conference was begun at 10:00am, ended at 17:30am, 12 November, 2022. There were 77 participants in total, 2 keynote speakers and 17 invited oral speakers, Assoc. Prof. Jinyang Xu from Shanghai Jiaotong Univeristy in China and Dr. Victor Koledov from Innowledgement GmbH in Germany delivered their keynote speeches, each speech cost about 50 minutes, including the questions&discussion time.On behalf of the conference organizing committee, we'd like to acknowledge the unstinting support from our colleagues at Yaseen Academy, all Technical Program Members, speakers, reviewers, and all the participants for their sincere support.Conference Organizing CommitteeICMSOA 2022List of Conference General Chair, Program Chair, Conference Committee Chair Members, International Technical Committee Members, International Reviewers are available in this Pdf.
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The COVID-19 pandemic exposed the vulnerability of global supply chains of many products. One area that requires improved supply chain resilience and that is of particular importance to electronic designers is the shortage of basic dual in-line package (DIP) electronic components commonly used for prototyping. This anecdotal observation was investigated as a case study of using additive manufacturing to enforce contact between premade, off-the-shelf conductors to allow for electrical continuity between two arbitrary points by examining data relating to the stock quantity of electronic components, extracted from Digi-Key Electronics. This study applies this concept using an open hardware approach for the design, testing, and use of a simple, parametric, 3-D printable invention that allows for small outline integrated circuit (SOIC) components to be used in DIP package circuits (i.e., breadboards, protoboards, etc.). The additive manufacture breakout board (AMBB) design was developed using two different open-source modelers, OpenSCAD and FreeCAD, to provide reliable and consistent electrical contact between the component and the rest of the circuit and was demonstrated with reusable 8-SOIC to DIP breakout adapters. The three-part design was optimized for manufacturing with RepRap-class fused filament 3-D printers, making the AMBB a prime candidate for use in distributed manufacturing models. The AMBB offers increased flexibility during circuit prototyping by allowing arbitrary connections between the component and prototyping interface as well as superior organization through the ability to color-code different component types. The cost of the AMBB is CAD $0.066/unit, which is a 94% saving compared to conventional PCB-based breakout boards. Use of the AMBB device can provide electronics designers with an increased selection of components for through-hole use by more than a factor of seven. Future development of AMBB devices to allow for low-cost conversion between arbitrary package types provides a path towards more accessible and inclusive electronics design as well as faster prototyping and technical innovation.
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The 2022 IEEE Microwave Theory and Technology Society (MTT-S) International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications (IMWS-AMP 2022) was held in Guangzhou, China, 12-14 December 2022 (see Figure 1). Due to the continuous impact of COVID-19 pandemic, small-size, on-site opening and closing ceremonies were organized in Guangzhou, while all the conference sessions were held online. The special Women in Microwaves (WiM) and Wireless session sponsored by the WiM subcommittee under the IEEE Membership and Geographical Activities of the MTT-S AdCom, was held in the afternoon of 13 December. More than 60 people attended this event, including three invited speakers from Austria, Japan, and Mainland China;six panelists from Mainland China, and some other professionals and graduate students from industries and universities (see Figure 2). © 2000-2012 IEEE.
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This paper presents a portable impedimetric biosensor for detecting infectious diseases such as SARS-CoV-2 Infections. A bio-ready sensing electrode functionalized with SARS-CoV-2 nucleocapsid antibody was employed to quantitatively convert the concentration of nucleocapsid protein (N-protein) into impedance changes. In this paper, we proposed a readout system with a dynamic input range of 200 Ωto 1 MΩmagnitude and 0 to 180°phase. The resolution of this device is 1% and 6.5°for measuring the magnitude and phase, respectively. Herein we demonstrate and discuss the proposed system’s functionality, sensitivity, and selectivity using the clinical swab samples. As per these results, this readout system is suitable for the detection of N-protein ranging up to 10,000 pg/mL with a resolution of 56 fg/mL. The proposed impedimetric sensing system can be adopted for the detection of infectious diseases in the future. This low-cost (<$80) device using off-the-shelf is a unique candidate for batch production purposes during urgent pandemic situations. IEEE
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Internet is almost a necessary facility and tool to solve daily life problems in every field life. Whether at the individual level or national and international level sale purchase of any kind of object has always been of much importance, especially after Corona Pandemic, when online business is at its peak. Because of the enhancement of online sales and purchases, various businessmen are looking for suitable internet websites for their businesses, and the selection of the most suitable internet websites is one of the multi-attribute decision-making (MADM) dilemmas. Thus, in this script, we take benefits of three various concepts that are Bonferroni mean (BM) operator which is a significant technique to catch the interrelatedness among any number of inputs, Dombi operations which are based on Dombi t-norm and t-conorm and the ability to create an aggregation procedure more flexible because of the parameter, bipolar complex fuzzy set (BCFS) which is an outstanding model for tackling two-dimensional information with negative aspect and interpret bipolar complex fuzzy (BCF) Dombi Bonferroni mean (BCFDBM), BCF weighted Dombi Bonferroni mean (BCFWDBM), BCF Dombi geometric Bonferroni mean (BCFDGBM), and BCF weighted Dombi geometric Bonferroni mean (BCFWDGBM) operators. After ward, in this script, for tackling MADM dilemmas in the setting of BCFS, we investigate a MADM procedure based on the investigated operators and solve a MADM dilemma (selection of a suitable internet website for businessmen). Further, to display the superiority and efficiency of our work, we compare our approach and operators with a few current approaches and operators. Author
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This paper examines the role of Intellectual Capital (IC) and its contribution to Business Sustainability (BS) among Large Manufacturing Firms (LMF) in Malaysia. It seeks to explain the relationship between them under turbulent market conditions. The study used the survey method to collect data from 203 large companies, and the hypotheses were tested using Partial-Least Squares-Structural Equation Modeling. Based on the findings, two dimensions of IC, namely Human capital (HC) and Structural Capital (SC), had a significant effect on business sustainability, but Relational Capital (RC) did not. Also results indicate that Market Turbulence (MT) moderates the relationship between two IC dimensions, HC and RC but not that between SC and BS. The study findings can be used as guidelines by CEOs of LMFs, policy makers and researchers to comprehend positive the influence of MT and IC on BS. © 2022
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Microbial infections remain a grave threat to global health security due to the increase in antibiotic resistance. The ongoing coronavirus pandemic has increased the risk of microbial and fungal infections. New therapeutic agents are needed to combat microbial infections. Two series of new clubbed 1,1'-biphenyl-pyrazole have been synthesized. The newly synthesized pyrazole derivatives were evaluated for in vitro antibacterial activity against E. coli (NCIM 2574), P. mirabilis (NCIM 2388), B. subtilis (NCIM 2063), S. albus (NCIM 2178), and in vitro antifungal activity against A. niger (ATCC 504) and C. albicans (NCIM 3100). Compound 10b has shown good activity against P. mirabilis with MIC of 15.62 µg mL-1. Against C. albicans, eleven pyrazole derivatives 5c, 5e, 5f, 5g, 6a, 6b, 6c, 6e, 10b, 10c, and 11a have shown good antifungal activity with MIC of 62.5-31.25 µg mL-1. Also, against A. niger, seven compounds 5f, 5g, 6e, 10a, 10b, 10c, and 11b, have exhibited good activity with MIC of 62.5-31.25 µg mL-1. Compounds 6e, 10c, and 11b were further evaluated and showed ergosterol biosynthesis inhibition activity. Thus, the significant antimicrobial activity of 1-(3-substituted-4'-alkoxy-3'-(methylsulfonyl)-[1,1'-biphenyl]-4-yl)-5-substituted-3-(trifluoromethyl)-1H-pyrazole, and 1-isobutyl-5-(4'-alkoxy-3'-(methylsulfonyl)-[1,1'-biphenyl]-4-yl)-3-(fluoromethyl)-1H-pyrazole derivatives indicate that these compounds could assist in the development of lead compounds to treat microbial infections. © 2023 The Royal Society of Chemistry.
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West Japan Railway Company (hereinafter referred to as "JR West") introduced the IC card ticket system "JCOCA" and has expanded the use area of the IC card. For the local lines, the onboard type of ticket gate machine for the IC card ticket is introduced to allow to use the IC card. This type of ticket gate machine is also effective for reducing the burden on the train crew. The introduction of this on-board type of IC ticket gate is our effort to allow passengers to smoothly get on/off and to provide safe and secure transport service under the COVID-19 epidemic. © 2022 Japan Railway Engineers' Association. All rights reserved.
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Despite longstanding opposition from mainstream economists to industrial policy, in the period of 2020–2021 the United States, confronted by advanced technology competition from China, the demands of climate change, and the need to respond to a global pandemic, adopted a series of major industrial policy programs. Although the U.S. Defense Department has long practiced industrial policy approaches, and the U.S. has followed industrial economic policies in its agriculture, transportation, electric power and healthcare sectors, the new programs focused on promoting technology innovation, so can be labled “industrial innovation policy.” The large scale of these efforts amounted to a new step for the U.S. in non-defense sectors. There is history behind this step. Contrasting Hamiltonian and Jacksonian economic views anticipated this industrial policy debate. While during World War II the U.S. entered into a highly connected set of industrial innovation policies, linking industry, universities and government for technologies like radar, electronics and nuclear energy, it departed from this approach in the immediate postwar. Vannevar Bush, the architect of postwar science organization, backed a linear model, combining federal support for basic research with a supposition that industry would manage the subsequent technology implementation. This position came under fire in the 1980s from critics like Donald Stokes as U.S. manufacturing declined with the rise of Japan’s quality manufacturing model, which was backed by government industrial coordination and support. Gradually, the U.S. began retreating from a basic research-only approach in non-defense areas through a series of policies. These included, in the 1980s a response to Japan’s quality manufacturing model, then starting in the 2000s a response to climate change through a reorganization of energy programs, and then after 2012 in response to China’s manufacturing advances the adoption of advanced manufacturing policies. Although the definition of industrial policy is debated, with some arguing it should serve social needs versus specific technology advances, this study adopts a more straightforward definition. Industrial innovation policy involves governmental intervention in one or more of the post-research innovation stages, from development to prototyping to production, to further technology innovation. The study reviews in detail six major examples of new U.S. industrial innovation policies adopted between 2020 and 2022: Operation Warp Speed for coronavirus pandemic vaccines;the CHIPS Act to restore U.S. semiconductor leadership;the Infrastructure Act of 2021, with its major support for new energy technology development;the Inflation Reduction Act, with its impetus for implementation of new energy technologies;the Biden Administration’s Assuring Domestic Supply Chains initiative, and the Endless Frontier/CHIPS and Science Act, with its support for applied development of critical technologies and regional innovation. All adopt an industrial innovation policy approach. These take different approaches. Operation Warp Speed, for example was more “top down,” with government selecting then supporting a series of companies to develop four different vaccine platforms. Tesla was an example of a “bottom up” approach, with government creating a range of technology incentives which companies – in this case Tesla – could systematically apply to electric vehicle development. However, there remain major gaps in U.S. industrial innovation efforts in scale-up financing, advanced manufacturing support and cross-agency coordination. The scale of China’s extensive industrial financing policies offers a useful comparison to U.S. scale up efforts. Overall, the study reviews in detail the need for the U.S. to adopt a new kind of infrastructure and accompanying operational mechanisms in order to make its new industrial innovation policies work. These include: rebuilding manufacturing foundations;testing and demonstration capability;mapping supply chains technology certification;better integration between industries, universities and government;technology scale up support;application of government procurement;and use of flexible contracting mechanisms.
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In this research, we quantified semiconductor fabrication capability parameters and test capability parameters and used a digital integrated circuit test model (DITM) to discuss the impact of the test guardband (TGB) on test yield. Furthermore, the DITM can be used to estimate the yield distribution trend of future semiconductor products using data from the IEEE International Roadmap for Devices and Systems (IRDS) 2021 Table. With the COVID-19 pandemic, the global semiconductor industry is facing chip and material shortages. Furthermore, test technology lags behind semiconductor manufacturing technology, and the test yield of production capacity is deteriorating, seriously affecting the entire semiconductor supply chain. Therefore, we proposed the recycling test method, extending the test time, moving TGB, and repeatedly looking for reliable products. These parameters were calculated using the estimated product parameters released by the IRDS 2021. We proved that the proposed recycling test method could improve the high-yield target of semiconductor testing. As long as test methods are used properly, not only can high-yield shipments be made, but also companies’overall profit will be significantly improved, and the problem of chip shortages will be solved. IEEE
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An innovative sonication method has been developed to produce inclusion complexes (ICs) of Oseltamivir (OTV) which is a potentially water-soluble anti-viral agent with lesser cytotoxicity. Proton signals and chemical shifts of OTV without any ambiguity confirm the formation of ICs with β-Cyclodextrin (B-CD) and Hydroxypropyl-β-cyclodextrin (H-CD). ICs are also supported by their atomic percentages as secondary evidence using XPS analysis. Analysis of drug release at three pH levels revealed the slow release of the OTV from ICs and also suitable for viral inactivation. A very less cytotoxic ability on cancer cell lines and enhanced the viral inactivation of OTV after being made into water-soluble ICs. © 2022 Elsevier B.V.
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This paper describes a novel project-oriented system on chip (SoC) design course. The course is taught in the Computer Science and Engineering (CSE) Department at the University of Texas at Arlington and is offered as CSE 4356 System on Chip Design for computer engineering undergraduates, as CSE 5356 for computer engineering graduate students, and as EE 5315 for electrical engineering graduate students. It is taught as one course combining all numbers. All students are given the same lectures, course materials, assignments, and projects. Grading standards and expectations are the same for all students as well. The course in its current form was first offered in fall 2020 and was taught online due to COVID-19 restrictions. The course was offered again in fall 2021 in a traditional on-campus, in-person mode of delivery. Two seasoned educators, with more than eighty years of total teaching experience, combined to team teach the course. One also brought more than thirty years of industrial design experience to the course. SoC FPGA devices have been available for use by designers for more than 10 years and are widely used in applications that require both an embedded microcomputer and FPGA-based logic for real-time computationally-intense solutions. Such solutions require skills in C programming, HDL programming, bus topologies forming the bridge between FPGA fabric and the microprocessor space, Linux operating systems and virtualization, and kernel device driver development. The breadth of the skills that were conveyed to students necessitated a team teaching approach to leverage the diverse background of the instructors. With such a wide range of topics, one of the biggest challenges was developing a course that was approachable for a greatly varied population of students - a mix of Computer Engineering (CpE) and Electrical Engineering (EE) students at both the graduate and undergraduate level. Another, perhaps less obvious, challenge was the inherently application focus of the course, which presents challenges to many graduate students whose undergraduate degree lacked a robust hands-on design experience. Selection of an appropriate project was key to making the course effective and providing a fun learning experience for students. The projects were aligned to relevant industry applications, stressing complex modern intellectual property (IP) work flows, while still being approachable to students. The design of a universal asynchronous receiver transmitter (UART) IP module in 2020 and a serial peripheral interface (SPI) IP module in 2021 were chosen as the projects for the first two offerings of the course. The Terasic/Intel DE1-SoC development board and Intel Quartus Prime 18.1 design software were the technologies chosen for the course. The development board and basic test instruments were provided to each student in a take-home lab kit. The system on chip design course has proven to be a popular but challenging course for our undergraduate and graduate students in computer engineering and electrical engineering. The course has demonstrated that it is possible to successfully teach an advanced design-oriented course to students of varying majors, levels, educational backgrounds, and cultures. © American Society for Engineering Education, 2022.
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This work introduces a simple detector for SARS-CoV-2 (COVID-19) virus. The detector operates in a very simple mechanism. Peripheral circuits to represent the testing result are also simulated. The system can be designed and fabricated in a single integrated circuit (IC) chip. The response time analysis of the device shows the speed of detection of this device. This detector will be highly effective to detect the SARS-CoV-2 virus in the future. © 2022 IEEE.
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The outbreak of COVID-19 has attracted people’s attention to our healthcare system, stimulating the advancement of next-generation health monitoring technologies. IoT attracts extensive attention in this advancement for its advantage in ubiquitous communication and sensing. RFID plays a key role in IoT to tackle the challenges in passive communication and identification and is now emerging as a sensing technology which has the ability to reduce the cost and complexity of data collection. It is advantageous to introduce RFID sensor technologies in health-related sensing and monitoring, as there are many sensors used in health monitoring systems with the potential to be integrated with RFID for smart sensing and monitoring. But due to the unique characteristics of the human body, there are challenges in developing effective RFID sensors for human health monitoring in terms of communication and sensing. For example, in a typical IoT health monitoring application, the main challenges are as follows: (1) energy issues, the efficiency of RF front-end energy harvesting and power conversion is measured;(2) communication issues, the basic technology of RFID sensors shows great heterogeneity in terms of antennas, integrated circuit functions, sensing elements, and data protocols;and (3) performance stability and sensitivity issues, the RFID sensors are mainly attached to the object to be measured to carry out identification and parameter sensing. However, in practical applications, these can also be affected by certain environmental factors. This paper presents the recent advancement in RFID sensor technologies and the challenges for the IoT healthcare system. The current sensors used in health monitoring are also reviewed with regard to integrating possibility with RFID and IoT. The future research direction is pointed out for the emergence of the next-generation healthcare and monitoring system.
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Globally, Facial recognition systems have been increasingly adopted, by governments, as a viable means of identification and verification in public spaces such as the airport, train stations, and stadiums. However, in the wake of the COVID-19 outbreak, the World Health Organization (WHO) declared that wearing face masks is an essential safety precaution. As a result, current facial recognition systems have difficulties recognizing faces accurately, which motivated this study. This research aims to implement an embedded masked face recognition system using the HuskyLens SoC module to identify people, even while wearing a face mask. The developed method was actualized using the Kendryte K210 chip embedded in the HuskyLens module. This system-on-chip design was integrated with other peripherals using an Arduino Pro-mini board. The results of testing and evaluating the system's performance show that the system's facial recognition accuracy with masked and without masks faces was 90% and 95%, respectively. Implementing this solution in our environment would enable accurate real-time recognition of masked and unmasked faces © 2022 IEEE.
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Demand forecasting plays a crucial role in a company’s operating costs. Excessive inventory can increase costs and unnecessary waste can be reduced if managers plan for uncertain future demand and determine the most favorable decisions. Managers are demanding increasing accuracy in forecasting as technology advances. Most of the literature discusses forecasting results’ inaccuracy by suspending the model and reloading the data for model retraining and correction, which is extensively employed but causes a bottleneck in practice since users do not have the sufficient ability to correct the model. This study proposes an error compensation mechanism and uses the individuals and moving-range (I-MR) control chart to evaluate the requirement for compensation to solve the current bottleneck using forecasting models. The approach is validated using the case companies’ historical data, and the model is developed using a rolling long short-term memory (LSTM) to output the predicted values;then, five indicators are proposed for screening to determine the prediction statistics to be subsequently employed. Root mean squared error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) compare the LSTM, rolling LSTM combined index, and LSTM-autoregressive moving average (ARMA) models. The results demonstrate that the RMSE, MAPE, and MAE of LSTM-ARMA are smaller than those of the other two models, indicating that the error compensation mechanism that is proposed in this study can enhance the prediction’s accuracy.
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This paper reports an improved deep ultraviolet LED (DUV-LED) packaging based on Si Micro-Electro-Mechanical Systems (MEMS) process technology. The Si package (Si-PKG) consists of a cavity formed by Si crystalline anisotropic wet etching and through-silicon vias (TSVs) filled with electroplated Cu. The Si-PKG is hermetically sealed by laser local heating of screen-printed glass frit. This technology allows for the use of a DUV-transparent glass substrate, which has an unmatched coefficient of thermal expansion (CTE). Using a high-density array of TSV capped with AuSn solder bumps, the cooling performance of the DUV-LED has been greatly improved. And the contribution by the Si (111) side reflection of Si-PKG to the total light output was confirmed 13 %. As a result, an optical output of 114 % (50 mW) and a volumetric light power density of 380 % (14 mW/mm3) were recorded compared with the conventional AlN-packaged device. The developed compact low-cost Si-PKG is promising for wider applications of the DUV-LED including the disinfection of the new coronaviruses. © 2022 Elsevier B.V.
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Novel and high-performance medical image classification pipelines are heavily utilizing ensemble learning strategies. The idea of ensemble learning is to assemble diverse models or multiple predictions and, thus, boost prediction performance. However, it is still an open question to what extent as well as which ensemble learning strategies are beneficial in deep learning based medical image classification pipelines. In this work, we proposed a reproducible medical image classification pipeline for analyzing the performance impact of the following ensemble learning techniques: Augmenting, Stacking, and Bagging. The pipeline consists of state-of-the-art preprocessing and image augmentation methods as well as 9 deep convolution neural network architectures. It was applied on four popular medical imaging datasets with varying complexity. Furthermore, 12 pooling functions for combining multiple predictions were analyzed, ranging from simple statistical functions like unweighted averaging up to more complex learning-based functions like support vector machines. Our results revealed that Stacking achieved the largest performance gain of up to 13% F1-score increase. Augmenting showed consistent improvement capabilities by up to 4% and is also applicable to single model based pipelines. Cross-validation based Bagging demonstrated significant performance gain close to Stacking, which resulted in an F1-score increase up to +11%. Furthermore, we demonstrated that simple statistical pooling functions are equal or often even better than more complex pooling functions. We concluded that the integration of ensemble learning techniques is a powerful method for any medical image classification pipeline to improve robustness and boost performance.